text:
\section{Conclusions}
\label{sec:conc}

We carry out an extensive set of channel measurements in the millimeter-wave band for the particular case of industrial scenarios such as factories, production lines, and server rooms. To this end, we obtain link characteristics both at the physical layer using high-precision hardware to record the raw channel impulse response, as well as at the application layer using COTS devices to record TCP throughput. We collect about 70 gigabytes of raw traces and analyze them in depth to obtain a statistical channel model. Our model along with our data enables the research community to generate arbitrary channels that represent the typical propagation characteristics of the above industrial scenarios. In addition, we discuss in detail our measurement observations for each scenario. We conclude that millimeter-wave communication in industrial environments is not only feasible but occasionally even easier than in typical home or office settings. Thus, our measurement campaign is highly encouraging regarding the use of such very high frequency wireless networks as part of the upcoming Industry 4.0.
\section{Discussion}
\label{sec:disc}

Here goes the discussion
\section{Introduction}\label{sec:introduction}}

The upcoming industry 4.0 revolution with the integration of wireless technologies in the industrial setting seems unquestionable today.
The common design goal in the industrial wireless solutions available today (such as WirelessHART and 6TiSCH) is to trade off performance for gains in energy efficiency and device cost.
However, some sectors, such as the manufacturing industry, require high performance communications in terms of reliability and latency and are not so concerned with the cost and energy consumption of the wireless devices \cite{martinez2018square}.
 We believe this can be a barrier for wireless adoption in the manufacturing industry and that mmWave communication is the way forward as it better fits industrial requirements\cite{Engelhard2018sigcom, Cheffena2016commag}.
The large available bandwidth in mmWave bands ($30$-$300$ GHz) allows for supporting Gigabit-per-second (Gbps) links while maintaining low latency. In addition, the short communication range in mmWave is suitable for industrial applications, where communication is mostly local. Furthermore, the reliance of mmWave communication on directional beamforming can significantly reduce interference.

{\bf Motivation.} Understanding the behavior of the wireless medium is key prior to the deployment of new wireless technologies in a given environment, in particular when operating at very high frequencies. For example, related work in the area of mmWave includes numerous studies and measurement campaigns that analyze channel characteristics for mmWave communication in urban scenarios~\cite{geng2009millimeter,soma2000modeling, elrefaie1997propagation, samimi20163, maccartney2016millimeter, maccartney2017rural, meinila2009winner, Hur:jv, Fan:2016dg, zhang2017millimeter, zhao2017channel}. These studies were a crucial step in the process of standardizing the use of mmWave bands in 5G cellular networking. The outcome of such studies is critical to evaluate whether such bands are suitable for the intended use cases. Also, the results are the foundation of the design of algorithms, protocols, and deployment plans (e.g., networking planning). Unfortunately, work on characterizing wireless channels in industrial settings is limited~\cite{rappaport1991statistical, zaaimia201660}. This is to a great extend due to the difficulty in accessing industrial facilities, thus making such measurement datasets of high value.

Before deploying mmWave in industrial scenarios, it is crucial to build an understanding of channel propagation characteristics in such peculiar environments. Urban and industrial scenarios differ significantly due to: $( i )$ the dominant material used for construction (e.g., concrete) and for the machinery (e.g., metal) in industrial setups; and $( ii )$ space planning, which is aimed at fitting the maximum number of machinery with minimal regards to beauty or other factors that are relevant in urban planning. Since accessing industrial facilities is often tedious due to regulatory reasons, it is important to pursue such measurements campaigns in order to formulate an empirical model that can be widely used within the community.

{\bf Contribution.} In this work, we perform the first extensive industrial mmWave measurement using both specialized measurement hardware and commercial off-the-shelf (COTS) 60 GHz hardware. Our measurements took place at the ALBA Synchrotron, which is a particle accelerator facility in the city of Barcelona. We chose this facility because it provides a unique set of representative industrial environments: $( i )$ server rooms representing data centers, $( ii )$ the experimental hall resembling large production plants, $ (iii) $ underground tunnels along with the particle accelerator ring which resemble the environments within a constrained space such as subway tunnels, and $(iv)$ the cooling facility, which is a widespread environment in many industries, including large pipes and reflecting surfaces.

Our measurement campaign consists of high accuracy measurements across a wide range of locations within the above scenarios. Specifically, the outcome of our campaign is 70 gigabytes of data, which includes both raw physical layer IEEE 802.11ad traces as well as upper layer metrics such as throughput and packet error rates. While the latter provides a broad view on the potential performance of mmWave networks in industrial settings, the former allows us to obtain detailed insights into the operation of the physical layer. We use the above raw traces to fit the parameters of a widely accepted mmWave channel model for the particular case of an industrial environment. We obtain the empirical distributions of each parameter for each of the aforementioned scenarios. This is a highly valuable contribution to the community, since it enables researchers in this field to generate arbitrary channels that are representative of industrial scenarios. Our work opens the door to an accurate understanding of the mmWave channel in such scenarios and enables first-of-its-kind mmWave systems for industry 4.0. In particular, our contributions are as follows:

\begin{itemize}
\item We collect a large dataset of mmWave traces at the ALBA Synchrotron in the city of Barcelona using both COTS IEEE 802.11ad devices as well as high accuracy measurement hardware.
\item While related work focuses on individual mmWave performance measurements in particular scenarios, we generalize our practical insights to obtain a highly accurate channel model. Such a channel model is of much higher relevance to the community than individual traces.
\item We share both our dataset and our model with the community, enabling other researchers to build on our measurements in order to use them as a foundation for the evaluation of arbitrary mmWave systems for industrial scenarios.
\item We carry out our measurement campaign at a unique location which encompasses a wide variety of scenarios that fit many typical industrial settings.
\item We find statistical distributions that fit each model parameter across all of our scenarios, thus obtaining a broad and universal model.
\end{itemize}

The remainder of this paper is structured as follows. In Section~\ref{sec:related_work} we survey related work in the area of mmWave channel studies. After that, we present our measurement methodology in Section~\ref{sec:methodology}, which includes a detailed description of our hardware setup. We then describe each of the scenarios that we analyze at the ALBA Synchrotron in Section~\ref{sec:setup}. In Section~\ref{sec:model} we introduce the channel model that we use as a basis for our study. Next, we present our measurements and fit the parameters of the model in Section~\ref{sec:results}. Finally, we conclude the paper in Section~\ref{sec:conc}.

\section{Methodology}
\label{sec:methodology}

To measure the performance of mmWave communications in the industrial scenarios described in Section~\ref{sec:setup}, we use both a commercial off-the-shelf (COTS) setup as well as specialized measurement hardware. The former reveals how commodity devices would perform in such environments, whereas the latter allows us to gain deep insights into propagation characteristics. For the bulk of our experiments, we use both setups such that we can relate the performance of COTS devices to the actual channel conditions. In the following, we describe each of the setups in detail.

\subsection{Commercial Off-The-Shelf Setup}
\label{subsec:cots_setup}

In our COTS setup, we use commodity hardware that implements the IEEE 802.11ad amendment. This amendment enables WiFi networks to operate in the mmWave band. Specifically, IEEE 802.11ad focuses on the 60 GHz band. At the time of writing, IEEE 802.11ad is the most promising candidate for mmWave indoor communications, which motivates our choice of the corresponding COTS hardware. In particular, we use TP-Link Talon AD7200 routers, which incorporate a tri-band Qualcomm QCA9500 chip that supports IEEE 802.11ad. Figure~\ref{fig:cots_setup} depicts our setup, in which we configure one router as a 60 GHz access point (AP) and a second router as a 60 GHz client. The above chip consists of a baseband module and an antenna module. Both modules are physically separated to allow the router manufacturer to place the antenna at a convenient location within the device case. The antenna module consists of an electronically steerable phased antenna array with 32 radiating elements. This allows the module to use analog beam-forming, which is crucial to overcome the high path loss in the mmWave band.

\begin{figure}[t!]
\centering
 \includegraphics[width=\columnwidth]{./figs/OffTheShelfSetup}
 \caption{Commercial Off-The-Shelf Setup.}
 \label{fig:cots_setup}
\end{figure}

We install a customized LEDE/OpenWRT \cite{lede} system on the routers that enables us to use the ``Talon Tools'' \cite{talon-tools:project}. These tools are a framework for practical IEEE 802.11ad research based on the TP-Link Talon routers. The framework gives us full access to the network interfaces of the router and enables us to configure it both as an access point as well as a client. Moreover, the framework uses open-source drivers for the QCA9500 chip which allow us to gain detailed information about its operation in the 60 GHz band.

Our experiment methodology with the above COTS hardware is as follows. We place one device as an access point at a designated location in each of our scenarios, as discussed in Section~\ref{sec:setup}. We then place a second device as a client at a number of different positions within the scenario. At each position, we establish a 60 GHz connection and generate traffic on the link using iperf.
During this data communication, we record a number of metrics both at the access point and at the client at regular intervals. Specifically, we record the TCP throughput, the identifiers of the beam-patterns chosen at each side of the link, the Modulation and Coding Scheme (MCS), the number of transmitted packets, the Packet Error Rate (PER), and a Signal Quality Indicator (SQI) which reflects changes in the Signal-to-Noise Ratio (SNR). Our measurements allow us to depict the above metrics for each measurement location in each scenario, which provides crucial insights regarding how a particular environment influences the performance of communications in the mmWave band.

\subsection{Measurement Hardware Setup}
\label{subsec:measurement_setup}

While the above COTS setup allows us to understand the performance of mmWave networks in industrial settings at the link layer, it barely reveals any information about the operation of the physical layer. The latter is key to characterize such industrial settings and develop a representative channel model. Next, we present the details of the specialized measurement hardware that we use to extract physical layer channel parameters and formulate such a model (c.f. Section~\ref{sec:model}).

As discussed in Section~\ref{subsec:cots_setup}, the IEEE 802.11ad standard is a promising candidate for indoor mmWave communication. Thus, we focus our physical layer analysis on the specifics of full-bandwidth 802.11ad channels. To this end, we transmit frames using the Talon devices introduced in Section~\ref{subsec:cots_setup} and analyze them using our measurement hardware. Figure~\ref{fig:hw_setup} depicts our measurement setup. We configure a Talon device as an access point and place it at a designated location in each scenario (c.f. Section~\ref{sec:setup}). Since we do not associate any client to this access point, the device simply transmits periodic beacon frames to announce its presence. We capture those beacons at each location at which we placed a client device in Section~\ref{subsec:cots_setup}. Specifically, we use a Sivers IMA FC2221V/01 V-band down-converter attached to a horn antenna to receive the signal in the 60 GHz band and convert it to a baseband signal. We then capture the output of the down-converter using a Keysight DSOS254A oscilloscope. Since this oscilloscope has a bandwidth of 2.5 GHz, we can record the full signal. We then feed the recording to the Keysight Wideband Waveform Center, which is a software that implements a full IEEE 802.11ad decoder. This software detects, demodulates, and decodes each of the beacons, providing full insight into the physical layer parameters of the channel.

\begin{figure}[t!]
\centering
 \includegraphics[width=\columnwidth]{./figs/ExperimentalSetup}
 \caption{Measurement Hardware Setup.}
 \label{fig:hw_setup}
\end{figure}

Our experiment methodology with the above measurement hardware setup is as follows. At each client location, we capture a full beacon sequence of the access point. The access point transmits such sequences at a fixed interval of 102.4 milliseconds \cite{Nitsche:2015vq} to announce its presence. For the case of the Talon router, each sequence contains 32 individual beacons since the access point implements 32 different beam-patterns. Each of the beam-patterns covers a certain azimuthal region. The periodicity of this burst of beacons allows us to easily synchronize the oscilloscope to the signal and record stable traces. In order to obtain averages of each channel parameter in our later analysis, we capture a number of beacon bursts at each client location. Each individual beacon results in different Channel State Information (CSI) since the transmit beam-pattern is different. Thus, after capturing the trace with the oscilloscope, we split it into individual beacons using traditional signal processing techniques in Matlab. We then decode each beacon of each trace individually using the Keysight Wideband Waveform Center. From the decoded data, we extract the beam-pattern identifier which is embedded in each beacon. Finally, at each location we average the channel parameters corresponding to each identifier separately.

In addition to the beam-pattern identifier, we obtain full CSI in terms of amplitude and phase. This includes the channel impulse response, the channel frequency response, the SNR, and the Error Vector Magnitude (EVM) for each beacon. To build our channel model, we extract the amplitude and delay of each channel tap, along with the number of taps in the channel as well as the potential clustering of the taps due to the scarce multi-path propagation in the mmWave band. For a detailed description of our channel model, we refer the reader to Section~\ref{sec:model}.
\section{Channel Model}
\label{sec:model}

In the following, we introduce the channel model that we use to characterize the propagation environment in industrial settings. We base our analysis on the channel model defined as part of IEEE 802.11ad \cite{intel_model} because we focus our study on this widely used standard (c.f. Section~\ref{sec:methodology}). Specifically, our model is a well-known and widely accepted geometrical channel model with some extensions that account for the particularities of mmWave propagation.

\subsection{IEEE 802.11ad Model}
\label{subsec:ad_model}
We first introduce the channel model as defined in \cite{intel_model}. Equation~\ref{equ:overall_cir} shows the overall Channel Impulse Response (CIR), which is the sum of $i$ path clusters $C^{(i)}$ with amplitude $A^{(i)}$. Each cluster $C^{(i)}$ is defined as in Equation~\ref{equ:cluster_cir}.

\begin{figure*}[b]ç
\begin{equation}
\begin{aligned}
\label{equ:overall_cir}
h\left( t, \phi_{tx}, \theta_{tx}, \phi_{rx}, \theta_{rx} \right) = \displaystyle\sum_{i} A^{(i)} C^{(i)} \left(t-T^{(i)}, \phi_{tx}-\Phi_{tx}^{(i)}, \theta_{tx}-\Theta_{tx}^{(i)}, \phi_{rx}-\Phi_{rx}^{(i)}, \theta_{rx}-\Theta_{rx}^{(i)} \right)
\end{aligned}
\end{equation}

\begin{equation}
\begin{aligned}
\label{equ:cluster_cir}
C^{(i)}\left(t, \phi_{tx}, \theta_{tx}, \phi_{rx}, \theta_{rx} \right) = \displaystyle\sum_{k} \alpha^{(i,k)} \delta\left( t - \tau^{(i,k)} \right)
\delta\left( \phi_{tx} - \phi_{tx}^{(i,k)} \right) \delta\left( \theta_{tx} - \theta_{tx}^{(i,k)} \right)
\delta\left( \phi_{rx} - \phi_{rx}^{(i,k)} \right)
\delta\left( \theta_{rx} - \theta_{rx}^{(i,k)} \right)
\end{aligned}
\end{equation}
\end{figure*}

A cluster is the sum of $k$ paths of amplitude $\alpha^{(i,k)}$ and spaced in time $\tau^{(i,k)}$ seconds. The remaining deltas in Equation~\ref{equ:cluster_cir} account for the azimuth and elevation of the path. Basically, a path is only part of the cluster if the antenna points towards the corresponding angle. Otherwise, the deltas in Equation~\ref{equ:cluster_cir} cancel out and the path is not part of $C^{(i)}$. Both the angle-dependency as well as the the cluster structure account for the particularities of mmWave propagation. The former is a direct result of the use of directional antenna beam-patterns. The latter is due to the scarce multi-path propagation environment in the mmWave band. Due to the high path loss and material absorption, most reflections fall below the noise floor at the receiver. In most cases, only the Line-of-Sight (LOS) path and first-order reflections are detectable at the receiver. While related work shows that second-order reflections are feasible as well \cite{Nitsche:2015vq}, they typically occur only in environments with excellent reflectors such as glass. Path clusters are a result of reflections on uneven surfaces such as rugged concrete. The incoming ray scatters on the surface, resulting in one main reflected ray and multiple secondary reflected rays with similar travel direction but with less power each. At the receiver, the main reflection appears as a strong tap in the CIR followed by a number of weaker taps that result of the secondary rays. This bundle of rays forms a path cluster. While similar effects occur also at lower frequencies, the rich multi-path environment at such frequencies masks the clusters in the CIR.

\subsection{Angle-Agnostic Model}
\label{sec:angle_agnostic_model}

The above channel model is strongly dependent on the steering of the particular beam-pattern in use. This ties the channel model to a specific antenna model. To avoid this limitation, we generalize the model in Section~\ref{subsec:ad_model} to be angle-agnostic. As a result, the model in Equations~\ref{equ:overall_cir} and \ref{equ:cluster_cir} are generalized as depicted in Equations~\ref{equ:overall_cir_noangles} and \ref{equ:cluster_cir_noangles}.

\begin{equation}
\label{equ:overall_cir_noangles}
h\left( t \right) = \displaystyle\sum_{i} A^{(i)} C^{(i)} \left(t-T^{(i)} \right)
\end{equation}

\begin{equation}
\label{equ:cluster_cir_noangles}
C^{(i)}\left( t \right) = \displaystyle\sum_{k} \alpha^{(i,k)} \delta\left( t - \tau^{(i,k)} \right)
\end{equation}

This angle-agnostic model still includes all of the cluster and path information, but does not eliminate paths based on antenna steering. Since we aim at computing a statistical channel model which is not limited to a specific location or antenna model, this is a suitable approach. To characterize the channels in our industrial setting, we need the distribution of the following parameters:

\begin{itemize}

	\item The number of clusters in the channel $i$
	\item The amplitude of each cluster $A^{(i)}$
	\item The delay of each cluster $T^{(i)}$
	\item The number of paths within a cluster $k$
	\item The amplitude of each individual path in a cluster $\alpha^{(i,k)}$
	\item The delay of each individual path in a cluster $\tau^{(i,k)}$

\end{itemize}

In Section~\ref{sec:results}, we compute the empirical CDFs of each of the above parameters for each of our scenarios. To this end, we use the data of our exhaustive channel measurements that we carry out as described in Section~\ref{subsec:measurement_setup}. Based on the above statistical distributions, we obtain analytical expressions that describe the behavior of each channel parameter for each of our scenarios.
\section*{Acknowledgment}

We would like to thank ALBA Synchrotron for allowing us performing these experiments in their facilities and for the kind support of their computing and management sections. This work has been partially supported by the European Regional Development Fund under grant TEC2015-71303-R (MINECO/FEDER).

\bibliographystyle{IEEEtran}

\section{Related Work}
\label{sec:related_work}

Many works aim at characterizing and modeling the propagation characteristics of mmWave channels empirically. Most of them are related to this article concerning their methodology and measurement hardware. To this aim, we first provide an overview on seminal works on mmWave channel characteristics and their methodology~\cite{geng2009millimeter,soma2000modeling, elrefaie1997propagation, samimi20163,maccartney2016millimeter,maccartney2017rural,meinila2009winner,Hur:jv, Fan:2016dg,zhang2017millimeter,zhao2017channel} and then we focus on the related literature characterizing wireless propagations in industrial environments~\cite{rappaport1991statistical, zaaimia201660}.

\subsection{mmWave Channel Characterization}
Although mmWave communication has drawn much attention within the past few years, it was initially studied for LOS communication in indoor~\cite{geng2009millimeter} and outdoor~\cite{soma2000modeling, elrefaie1997propagation} environments. In the following, we summarize the latest developments in mmWave channel modeling.

In~\cite{samimi20163}, Samimi {\it et al.} leverage the data from the measurement campaigns of the NYU WIRELESS \cite{nyuwireless} team within the past five years~\cite{maccartney2016millimeter,maccartney2017rural} to derive a statistical 3D channel model for mmWave links. Their measurements are performed in the 28, 38, and 78 GHz bands, which are the candidate frequencies for mmWave cellular networking. The authors provide a detailed description of the methodology they use and the assumptions they make in order to identify path clusters in time and space. In particular, time-delayed versions of the transmitted signal should be partitioned into {\it time clusters} which help identifying the effective multi-path components and compute the delay spread. The same applies to the spatial lobes which characterize the angle of arrival and angle of departure (i.e., angular spread). This characterization of the signal in time and space plays a significant role in the accuracy of the channel model. Indeed, Samimi {\it et al.} elaborate on the fact that the bin size for each cluster should be decided based on the environment.

In parallel, further work in this area has modeled the mmWave channel leveraging a slightly different methodology. The main differences are: $ ( i ) $ combining ray-tracing simulations with actual measurement to reduce the overhead of measurement, and $ ( ii ) $ simplifying the parameterization of the models. This work includes the mmWave spatial channel models proposed in 3GPP~\cite{3GPP25.996} and WINNER II~\cite{meinila2009winner}. Both models characterize the channel based on the delay spread, azimuth spread, shadow fading, and spatial autocorrelation. However, each of the models defines some of these parameters in a slightly different manner. Also, 5G Public Private Partnership (5GPPP) projects such as mmMAGIC \cite{mmmagic} have studied channel models for the mmWave band. Specifically, mmMAGIC covered the frequency range from 6 GHz to 100 GHz and focused on scenarios such as street canyons, open squares, indoor offices, shopping malls, airports, stadiums, and subway stations. Our work stands apart from this model since we consider industrial settings and focus on the characteristics of the 60 GHz band.

In \cite{Hur:jv}, the authors focus on deriving radio propagation parameters at 28 GHz from measurements in urban environments such as New York, USA and Daejeon, Korea. Specifically, they obtain the value of the delay spread and the angular spread using both practical measurements and ray tracing techniques. In~\cite{Fan:2016dg}, Fan {\it et al.} perform indoor channel measurements in 2-4 GHz, 14-16 GHz, and 28-30 GHz. Their study focuses on characterizing the angular and delay spreads for the aforementioned frequency bands. Their measurement results confirm that the number of multipath components decreases at higher frequencies. A similar study in~\cite{zhang2017millimeter} characterizes the mmWave channel at 25.5 GHz, 28 GHz, 37.5 Ghz, and 39.5 GHz in indoor scenarios. The authors claim that their measurements are more accurate than earlier work in the field because they consider a number of different elevations.
In~\cite{zhao2017channel}, the authors perform outdoor channel measurements at 32 GHz. They compare their findings with the values reported by NYU WIRELESS, mmMAGIC, and 3GPP. Their results match the models of NYU WIRELESS and mmMAGIC but deviate from the results reported by 3GPP. The authors explain that this difference is due to the fact that the 3GPP model was derived from limited measurements on a small subset of carrier frequencies.

\subsection{Industrial Channel Characterization}

The characteristics of the wireless channel differ significantly in industrial environments compared to typical indoor scenarios such as office or home environments. The main differences are structural (e.g., ceiling height) and environmental (e.g., wall/floor material and
metallic surfaces)~\cite{zaaimia201660,rappaport1991statistical,shuangchannel}. As a result, channel measurement and modeling in industrial environments is significantly less explored than urban outdoor/indoor scenarios. The limited related work in this field includes the seminal paper by Rappaport {\it et al.} \cite{rappaport1991statistical}, which models the wireless channel for factory settings but is limited to a carrier frequency of 1.3 GHz. Similarly, \cite{hawbaker1990indoor} also considers an industrial scenario but only for communication up to 4 GHz. Recent work extends this analysis to the mmWave band. For instance, the authors of \cite{zaaimia201660} focus on modeling mmWave channels at 60 GHz within a data center. The result of their measurement in terms of path loss and delay spread indicates that the wireless channel in data centers does not match other well-known scenarios due to the aforementioned differences. While we consider data centers a type of industrial environment, our work stands apart from the above study because we consider a much broader set of scenarios that are representative for factories. To the best of our knowledge, no further earlier work considers such scenarios. This highlights the relevance of our contribution in this paper.

\section{Results}
\label{sec:results}

In this section, we present the results of our extensive measurement campaign with both the COTS setup described in \sref{subsec:cots_setup} and the measurement hardware setup discussed in \sref{subsec:measurement_setup}. We collect about 70 gigabytes of raw channel traces from which we derive statistical channel parameter distributions. We then correlate our lower layer insights with the performance in terms of throughput that we observe at higher layers. Our analysis reveals that reflectors play a fundamental role---our Side Tunnel{} scenario, which is a unique location built entirely out of concrete and thus lacking any reflective surfaces, clearly shows a different behavior compared to all of our scenarios. We conclude that the particular characteristics of industrial scenarios are not a hurdle for millimeter-wave communication but rather a benefit in most cases. The vast amplitude of the Experimental Hall{}, the reflective behavior of the pipes along the Tunnel{}, and the curvature of the Particle Accelerator Ring{} are beneficial for signal propagation and thus result in increased coverage compared to a home or office scenario.

\subsection{Parameter Fitting}
\label{subsec:param_fitting}

First, we present an experimental evaluation of the different parameters of the model in \sref{sec:model} for the scenarios described in \sref{sec:setup}. We have obtained these results using the measurement hardware setup described in \sref{subsec:cots_setup}.
Due to safety restrictions in the Particle Accelerator Ring{} and the UPS Room{}, we could not perform this analysis in those scenarios. We thus exclude them in the foregoing evaluation. However, we still present upper-layer throughput results for those scenarios in \sref{subsec:throughput}.
For each parameter of the model, we compute its empirical Cumulative Distribution Function (CDF) and perform a goodness of fit analysis.
The parameterized fitted CDFs are used to draw conclusions on the observed data and can later be exploited to generate synthetic industrial mmWave channels. The research community can use such synthetic channels to evaluate, for instance, the performance of physical and medium access techniques designed for industrial mmWave. Our channel model in \sref{sec:model} is not limited to a specific antenna, which means that it suits the data collected with any of the horn antennas that we use along with our measurement equipment described in \sref{subsec:measurement_setup}. Instead of modeling the decay of the amplitude of each path as transceivers move out of the boresight of each other, we provide the overall picture given a certain location and steering. This approach is the one that fits our goal best, since it allows us to obtain a statistical model that aggregates our measurement insights across locations and steerings for a given antenna beamwidth. Modelling the specific beamwidth of the antenna would be suitable to capture the channel at a particular location. However, that approach would not be suitable to capture the general propagation environment in industrial scenarios.

\subsubsection{Number of Clusters}
\label{subsubsec:num_clusters}

\fref{fig:npaths} shows the CDFs of the number of clusters ($i$) for the Tunnel{}, Experimental Hall{}, Mechanical Room{}, and Side Tunnel{}. As discussed in \sref{sec:model}, each cluster contains the scattered rays that propagate along one geometrical path. The cluster of the LOS path typically consists of a single ray since it does not reflect on any surface, but clusters from reflected paths often include a bundle of rays. \fref{fig:npaths} depicts the aggregated data at the different positions described in \sref{sec:setup}. We aggregate the data for each of the available beam widths (i.e., $7$, $20$ and $80$ degrees) across all locations to generalize our results. As expected, \fref{fig:npaths} shows that the amount of observable clusters is larger the wider the beam width since wider beams capture more propagation paths. For the Mechanical Room{} and the Side Tunnel{}, we only collect measurements with the $20$-degree antenna because the $7$- and $80$-degree antennas do not reveal further paths. In the case of the Mechanical Room{}, this is due to the large amount of obstacles. When using the $20$-degree antenna, communication is not limited due to range but due to blockage. Thus, switching to the narrow $7$-degree antenna does not provide additional path information. While the $80$-degree antenna could capture additional paths, its gain is too limited at most locations in the Mechanical Room{}. In the Side Tunnel{}, the geometry of the environment allows us to receive a strong signal even for large link lengths. The $7$-degree antenna does not provide further information. The gain of the $80$-degree antenna is again too limited.

\begin{figure*}[hhht!]
\centering
\subfigure[Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/tunnel_nopath_fitting}}
\subfigure[Experimental Hall{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/exphall_nopath_fitting}\label{fig:npaths_hall}}
\subfigure[Mechanical Room{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/pipe_nopath_fitting}}
\subfigure[Side Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/side_nopath_fitting}\label{fig:npaths_side}}
\caption{Empirical and best goodness of fit CDFs of the number of clusters ($i$).}
\label{fig:npaths}
\end{figure*}

\begin{table}[tb!]
\centering
    \caption{Quartiles of the number of clusters ($i$).\label{tbl:npaths}\vspace{-3.5mm}}{%
\begin{tabular}{|c|c|c|c|c|} \hline
Scenario & Degree & 1st Quartile & Median & 3rd Quartile\\ \hline
Tunnel & 7 & 2 & 2 & 2 \\ \hline
Tunnel & 20 & 1 & 2 & 2 \\ \hline
Tunnel & 80 & 1 & 2 & 3\\ \hline
Exp. Hall & 7 & 2 & 4 & 6 \\ \hline
Exp. Hall & 20 & 2 & 2 & 3 \\ \hline
Exp. Hall & 80 & 2 & 5 & 8 \\ \hline
Pipe Room & 20 & 2 & 2 & 3 \\ \hline
Side Tunnel & 20 & 1 & 1 & 1 \\ \hline
\end{tabular}}
\end{table}

In \fref{fig:npaths}, we observe that the Experimental Hall{} is the scenario with the highest number of clusters. Specifically, among $20$-$40\%$ of the samples show more than $5$ clusters and among $10$-$20\%$ of the samples show more than $10$ clusters. That is, we observe a large number of reflective propagation paths. While the Experimental Hall{} is large, the beamlines and other machinery located next to the Particle Accelerator Ring{} offer a large number of reflective surfaces that result in the high number of clusters in \fref{fig:npaths_hall}.

In the Tunnel{} and Mechanical Room{} instead, we find that among $5$-$20\%$ of the samples show more than $5$ paths and less than $10\%$ show 10 or more clusters. In the Mechanical Room{}, this is due to the large number of blockages. While the machinery in the room allows for a large number of reflections, during our measurements we observed that most of them were shadowed by the machinery itself. This is an interesting difference to the Experimental Hall{}---while both environments are highly reflective, the contrast in terms of size and thus likelihood of blockage results in very different channels. In the case of the Tunnel{}, the limited number of clusters is due to the elongated geometry of the environment. While the pipes on one side of the Tunnel{} (c.f. \sref{subsec:serv_tunnel}) allow for reflections, geometrically we only receive a handful of them at each measurement location. This is in contrast to the Experimental Hall{} or the Mechanical Room{}, where reflectors are distributed homogeneously. This effect is further exacerbated in the Side Tunnel{}, since it does not feature reflective pipes but only concrete walls. Hence, the Side Tunnel{} is the scenario with the smallest number of paths. As depicted in \fref{fig:npaths_side}, we have not found any instance of more than five paths. \tref{tbl:npaths} depicts numerical values of the 1st quartile, median, and 3rd quartile of the CDFs in \fref{fig:npaths} to enable computational use of the data.

In \fref{fig:npaths} we observe that, with the only exception of the $80$-degree antenna in the Tunnel{}, all empirical distributions of the number of clusters can be well described by a Generalized Extreme Value distribution with parameters $0.31 \leq k \leq 0.93$, $0.9 \leq \sigma \leq 3.43$, $1.42 \leq \mu \leq 2.91$.
The data also suggests that, with the exception of the Side Tunnel{}, the distributions for this parameter are quite similar.
As discussed above, the propagation environment of the Side Tunnel{} is indeed peculiar and does not resemble the characteristics of the other rooms.
Thus, the dissimilarity in the data from the Side Tunnel{} compared to the other rooms is inherit to the specific propagation environments.

\subsubsection{Inter-cluster Delays}
\label{subsubsec:inter-cluser-delays}

Similar to \fref{fig:npaths}, \fref{fig:delays} shows the empirical and best goodness of fit CDFs of the inter-cluster delays ($T^{(i)}$). The inter-cluster delay is essentially the time between the arrival of two subsequent clusters in the CIR (c.f. \sref{sec:model}).
In this case, we observe rather long inter-cluster delays in the Tunnel{}, Experimental Hall{}, and Mechanical Room. Specifically, $20$-$60\%$ of the cases lie in the range of $5$ to $30$~ns. This means that the propagation paths on which the clusters travel differ about 1.5 to 9 meters in terms of length. As expected, \fref{fig:delays_hall} shows that the latter is particularly frequent in the Experimental Hall{} when using the 80-degree antenna. The large dimensions of the Experimental Hall{} and the wide beam at the receiver allow us to detect clusters that travel along very different paths. This is very beneficial to avoid transient blockage, since the probability that all of the paths are blocked simultaneously is low. Transceivers do not need to use a wide 80-degree antenna for communication to benefit from this resilience to blockage. Instead, they may use narrow beam widths---which result in higher gain and thus higher throughput---and simply re-steer to a different path whenever the current one is blocked.

For both the Tunnel{} and the Experimental Hall{}, we observe that narrower beam-widths tend to result in smaller inter-cluster delays. This is expected, since spatial selectivity increases. However, the effect is less apparent in the Experimental Hall{} than in the Tunnel{}. From this, we conclude that the paths are particularly diverse in the Experimental Hall{}. As depicted in \fref{fig:ring}, reflectors are located far and at a significant angle from the link. Thus, the difference among the 7-degree and the 20-degree antennas is limited---we need to switch to the 80-degree beam pattern in order to capture some of the reflections. In other words, the angular spread of this particular propagation environment is high. Conversely, the narrow and elongated nature of the Tunnel{} results in a clear difference among the three antennas in \fref{fig:delays_tunnel}. The angular spread is limited and thus switching from 7 to 20 and from 20 to 80 degrees makes a clear difference in terms of the length of the paths that we observe. The length differences are still significant in spite of the Tunnel{} being narrow due to its sheer length. That is, even if paths are roughly parallel due to the limited angular spread, a slight angle difference over up to 115 meters makes a difference.

While we observe an equivalent behavior in the Mechanical Room{}, the CDF reveals that difference in terms of length of the paths is clearly smaller than in the Experimental Hall{}, rather resembling the Tunnel{}. This is due to the high number of obstacles---although the dimensions of the room would allow for diverse propagation paths, most of them are blocked. In contrast, the Side Tunnel{} exhibits a distinctly different behavior. The length of almost all of the paths differs by at most 60 centimeters. This is due to the lack of reflectors in the Side Tunnel{}. Since barely any reflections exist, we observe a single LOS cluster in most cases. Additional clusters, if at all, travel on similar paths.

In \tref{tbl:delays}, we provide again numerical values of the 1st quartile, median, and 3rd quartile to allow for computational processing of our data. From \fref{fig:delays} we observe that for all rooms except for the Side Tunnel{}, the inter-cluster delays can be characterized by a Generalized Pareto distribution with parameters $-0.71 \leq k \leq 0.93$, $1.63\cdot 10^{-9} \leq \sigma \leq 22.37\cdot 10^{-9}$ and $-214.29\cdot 10^{-11} \leq \theta \leq -2.22\cdot 10^{-15}$. Again we observe that the distributions of this parameter in the Tunnel{}, Experimental Hall{}, and Mechanical Room{} are quite similar. The distribution of the inter-cluster delay for the Side Tunnel{} differs considerably and can be better described by a Generalized Extreme Value distribution.

\begin{figure*}[hhh!]
\centering
\subfigure[Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/tunnel_delays_fitting}\label{fig:delays_tunnel}}
\subfigure[Experimental Hall{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/exphall_delays_fitting}\label{fig:delays_hall}}
\subfigure[Mechanical Room{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/pipe_delays_fitting}}
\subfigure[Side Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/side_delays_fitting}}
\caption{Empirical and best goodness of fit CDFs of the inter-cluster delay ($T^{(i)}$).}
\label{fig:delays}
\end{figure*}

\begin{table}[tb!]
\centering
\caption{Quartiles of the inter-cluster delay ($T^{(i)}$).\label{tbl:delays}\vspace{-3.5mm}}{%
\begin{tabular}{|c|c|c|c|c|} \hline
Scenario & Degree & 1st Quartile & Median & 3rd Quartile\\ \hline
Tunnel & 7 & $2.4\cdot10^{-10}$ & $1.3\cdot10^{-9}$ & $5.8\cdot10^{-9}$ \\ \hline
Tunnel & 20 & $3.6\cdot10^{-10}$ & $1.7\cdot10^{-9}$ & $1.4\cdot10^{-8}$ \\ \hline
Tunnel & 80 & $1.1\cdot10^{-9}$ & $5.1\cdot10^{-9}$ & $2.0\cdot10^{-8}$\\ \hline
Exp. Hall & 7 & $1.3\cdot10^{-9}$ & $6.4\cdot10^{-9}$ & $1.6\cdot10^{-8}$ \\ \hline
Exp. Hall & 20 & $1.1\cdot10^{-9}$ & $3.1\cdot10^{-9}$ & $1.6\cdot10^{-8}$  \\ \hline
Exp. Hall & 80 & $2.4\cdot10^{-9}$ & $8.7\cdot10^{-9}$ & $2.1\cdot10^{-8}$  \\ \hline
Pipe Room & 20 & $3.6\cdot10^{-10}$ & $1.7\cdot10^{-9}$ & $1.4\cdot10^{-8}$ \\ \hline
Side Tunnel & 20 & $2.4\cdot10^{-10}$ & $2.4\cdot10^{-10}$ & $3.6\cdot10^{-10}$  \\ \hline
\end{tabular}}
\end{table}

\subsubsection{Amplitude of Each Cluster}
\label{subsubsec:cluster_amplitude}

\fref{fig:mpower} shows the CDFs of the amplitude of each cluster ($A^{(i)}$). Large amplitude values result from a strong signal at the receiver. We expect the CDF to shift to the left for wide beam patterns since wide patterns capture more reflected paths, which are longer and thus inherently weak due to the high propagation loss. While a wide antenna pattern still captures the comparatively strong LOS path, this strong path plays a minor role in the overall distribution of all the reflected paths. \fref{fig:mpower} confirms this behavior for the two scenarios for which we compare different antennas. In both the Tunnel{} and the Experimental Hall{}, the distributions tend to the left as we increase the beam width. For the same reasons as in \sref{subsubsec:inter-cluser-delays}, the 7 and 20 degree antenna behave similarly in the Experimental Hall{}. However, in this case both antennas also behave roughly similar in the Tunnel{}. That is, the strength of the paths that we observe with the 7-degree antenna is to some extent similar to the one we observe with the 20-degree antenna. Still, the results in \sref{subsubsec:inter-cluser-delays} show that the paths for both antennas are indeed different, unlike in the Experimental Hall{}. While the data does not allow for a clear explanation of this behaviour, the underlying reason is likely related to the number of reflections within each path. Due to the elongated nature of the Tunnel{}, the paths that we observe with the 7- and 20-degree antennas probably reflect only once on the metallic pipes along the tunnel. In contrast, the 80-degree antenna is capable of capturing paths that reflect twice (or more) on the pipes, thus arriving at the receiver with a smaller amplitude.

Overall, \fref{fig:mpower} shows larger amplitudes for the Experimental Hall{} than for the Tunnel{}. This is due to the link distance, which is much shorter in the Experimental Hall{}. In the Mechanical Room{}, cluster amplitudes are similar to the behavior in the Experimental Hall{} for the 20-degree antenna because the length of most of the unobstructed links is in the same order of magnitude. Still, we observe a higher fraction of strong clusters in the Experimental Hall{} than in the Mechanical Room{}. This is expected since the Experimental Hall{} features a clear and strong LOS path, whereas in the Mechanical Room{} more paths are NLOS. In the Side Tunnel{}, the cluster amplitudes are distributed similarly to the vast majority of our measurements in the other three scenarios. However, in contrast to the Experimental Hall{}, Tunnel{}, and Mechanical Room{}, we do not observe any particularly strong clusters. We conjecture that such strong clusters are the result of constructive interference. Since the Side Tunnel{} lacks reflective elements, constructive interference does not occur and the maximum amplitude of clusters is limited.

Numerical values of the 1st quartile, median, and 3rd quartile are provided in \tref{tbl:mpower}. Regarding the modelling of the distributions, in \fref{fig:mpower} we see that the amplitude of each cluster can be accurately described by a Generalized Extreme Value Distribution for the Tunnel{}, Experimental Hall{}, and Mechanical Room{} with parameters ranging among $0.27 \leq k \leq 0.96$, $0.01 \leq \sigma \leq 0.03$, $0.02 \leq \mu \leq 0.04$.
As before, we observe that these distributions are quite similar for the different rooms.
In contrast, the unique behavior of the Side Tunnel{} in terms of the cluster amplitude can be described as a Generalized Pareto Distribution.

\begin{figure*}[hhh!]
\centering
\subfigure[Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/tunnel_mpower_fitting}}
\subfigure[Experimental Hall{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/exphall_mpower_fitting}}
\subfigure[Mechanical Room{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/pipe_mpower_fitting}}
\subfigure[Side Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/side_mpower_fitting}}
\caption{Empirical and best goodness of fit CDFs of the amplitude of each cluster ($A^{(i)}$).}
\label{fig:mpower}
\end{figure*}

\begin{table}[tb!]
\centering
\caption{Quartiles of the amplitude of each cluster ($A^{(i)}$).\label{tbl:mpower}\vspace{-3.5mm}}{%
\begin{tabular}{|c|c|c|c|c|} \hline
Scenario & Degree & 1st Quartile & Median & 3rd Quartile\\ \hline
Tunnel & 7 & 0.030 & 0.044 & 0.083 \\ \hline
Tunnel & 20 & 0.031 & 0.041 & 0.075 \\ \hline
Tunnel & 80 & 0.031 & 0.034 & 0.046\\ \hline
Exp. Hall & 7 & 0.014 & 0.022 & 0.054 \\ \hline
Exp. Hall & 20 & 0.016 & 0.024 & 0.063 \\ \hline
Exp. Hall & 80 & 0.020 & 0.027 & 0.035 \\ \hline
Pipe Room & 20 & 0.028 & 0.038 & 0.070 \\ \hline
Side Tunnel & 20 & 0.047 & 0.065 & 0.083 \\ \hline
\end{tabular}}
\end{table}

\subsubsection{Number of Paths Within a Cluster}

\fref{fig:intra_elem} shows the CDFs of the number of paths within a cluster ($k$). This number is typically related to the type of materials in a certain propagation environment. Specifically, rough surfaces such as raw concrete result in diffuse reflections which spread the incoming ray of a certain path onto a number of approximately parallel rays (c.f. \sref{sec:model}). At the receiver, we observe this as multiple paths in each cluster. In the industrial scenarios that we consider, most of the reflections occur on the metallic surfaces of different types of machinery. These surfaces are typically even and thus result in almost specular reflections of the paths. However, the walls in industrial settings are very often made of rough concrete. Hence, we expect to observe a mixture of both types of reflections. The specific ratio of that mixture should depend on the particular characteristics of each scenario.

\fref{fig:intra_elem} confirms the above discussion. For all of our scenarios in which a significant amount of machinery and other metallic elements are located (i.e., the Tunnel{}, Experimental Hall{}, and Mechanical Room{}), the majority of measurements exhibits a limited number of paths within each cluster. Specifically, we observe barely any instances showing more than $8$ paths, and $50\%$ of the cases show between $1-3$ paths. While reflections on rough concrete walls are possible in all of the three scenarios, the fraction of such instances is negligible. Also, diffuse reflections spread the energy in many directions, which means that the strength of paths reflected on rough walls is limited compared to specular reflections on metal. This difference in terms of signal power makes the former hard to observe at the receiver when it occurs along with the latter. For the scenarios for which we compare different beam widths (i.e., the Tunnel{} and Experimental Hall{}), we observe only little difference among the 7-, 20-, and 80-degree antennas. This means that all of the paths that we capture with each of the antennas experience the same type of reflections, that is, predominantly specular. Still, wider beam widths tend to result in a slightly smaller number of paths within each cluster. The underlying reason is most probably related to the lengths of the paths on which the clusters travel. Wider antennas capture longer paths (c.f. \sref{subsubsec:inter-cluser-delays}) on which the overall propagation losses are higher. Hence, some of the paths within each cluster may fall below the noise level, which results in a smaller number of observed paths at the receiver.

The Mechanical Room{} exhibits a slightly higher maximum number of paths in each cluster compared to the Experimental Hall{} and the Tunnel{}. In particular, \fref{fig:intra_elem_pipe} depicts a fraction of about $2$\% of clusters with more than $8$ paths. This behavior is likely due to the larger number of pipes in the Mechanical Room{}. Since the pipes are made out of curved metal, the angular spread resulting of the limited diffusion on such a surface is higher than in the Experimental Hall{} and Tunnel{}. Still, our results show that this effect has a limited impact, and thus our above discussion is also valid for the Mechanical Room{}. In contrast, the Side Tunnel{} clearly exhibits a different behavior when compared to the Tunnel{}, Experimental Hall{}, and Mechanical Room{}. The CDF in \fref{fig:intra_elem_side} reveals barely any instance with less than three paths and $60\%$ of the samples show more than $8$ paths. This is as expected due to the limited number of reflectors in this scenario. Further, the walls of the Side Tunnel{} are made of rough concrete, which means that the vast majority of the limited number of reflections (c.f. \sref{subsubsec:num_clusters}) is strongly diffuse. This again highlights the impact of metallic surfaces in industrial scenarios on the propagation environment.

\tref{tbl:intra_elem} shows the values of the 1st quartile, median, and 3rd quartile of our results. In \fref{fig:intra_elem} we also observe that the number of paths within a cluster for the Tunnel{}, Experimental Hall{}, and Mechanical Room{} can be described as a Generalized Pareto Distribution with parameters $-0.36 \leq k \leq -0.12$, $2.32 \leq \sigma \leq 3.37$ and $\theta = 1$.
Again we note the similarity of the distributions of this parameter for each scenario. As expected from our above analysis, the number of paths within a cluster for the Side Tunnel{} can be better described as a Gamma distribution instead.

\begin{figure*}[hhh!]
\centering
\subfigure[Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/tunnel_intra_elem_fitting}}
\subfigure[Experimental Hall{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/exphall_intra_elem_fitting}}
\subfigure[Mechanical Room{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/pipe_intra_elem_fitting}\label{fig:intra_elem_pipe}}
\subfigure[Side Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/side_intra_elem_fitting}\label{fig:intra_elem_side}}
\caption{Empirical and best goodness of fit CDFs of the number of paths within a cluster ($k$).}
\label{fig:intra_elem}
\end{figure*}

\begin{table}[tb!]
\centering
\caption{Quartiles of the number of paths within a cluster ($k$).\label{tbl:intra_elem}\vspace{-3.5mm}}{%
\begin{tabular}{|c|c|c|c|c|} \hline
Scenario & Degree & 1st Quartile & Median & 3rd Quartile\\ \hline
Tunnel & 7 & 2 & 3 & 4 \\ \hline
Tunnel & 20 & 2 & 3 & 4 \\ \hline
Tunnel & 80 & 2 & 3 & 4 \\ \hline
Exp. Hall & 7 & 2 & 3 & 4 \\ \hline
Exp. Hall & 20 & 2 & 3 & 4  \\ \hline
Exp. Hall & 80 & 1 & 2 & 3 \\ \hline
Pipe Room & 20 & 2 & 3 & 4 \\ \hline
Side Tunnel & 20 & 7 & 9 & 10 \\ \hline
\end{tabular}}
\end{table}

\subsubsection{Amplitude of Each Path in the Cluster}

\fref{fig:intra_ampl} shows the amplitude of each path in the cluster ($\alpha^{(i,k)}$). For all of our scenarios, we observe a very similar behavior than for the average amplitude of the clusters as a whole $A^{(i)}$ in \fref{fig:mpower}. This is expected since the CDFs show the aggregated distribution of all of the paths in all of the observed clusters. However, while in \sref{subsubsec:cluster_amplitude} we averaged the amplitudes of the paths within each cluster, in \fref{fig:intra_ampl} we compute the distributions of the amplitudes of each individual path. As a result, we observe again similar effects, such as a shift to the left of the CDFs for wide beam patterns. As discussed in \sref{subsubsec:cluster_amplitude}, this occurs because such beam patterns capture more weaker paths. \fref{fig:intra_ampl} also shows the maximum amplitude of the main path in each cluster, which is significantly larger than the average value of the cluster depicted in \fref{fig:mpower}. This means that the main path is much stronger than the diffused paths that conform the rest of the cluster. This is expected since the propagation direction of the diffused paths is not exactly the same than the main path, which means that the receiver only captures part of their energy when aligned with the main path.

From the above, we conclude that the comparison of \fref{fig:mpower} and \fref{fig:intra_ampl} essentially reveals the peak to average ratio of the amplitudes of the paths within the clusters. For the Tunnel{}, we observe that this ratio is close to one for the 7- and 20-degree antennas. That is, the average amplitude distribution of the cluster in \fref{fig:mpower} is similar to the one in \fref{fig:intra_ampl}. In contrast, for the 80-degree antenna, we observe that the peak value in \fref{fig:intra_ampl} is about double as large than in \fref{fig:mpower}. The underlying reason is that in the 80-degree case, the antenna captures more diffused paths for each cluster, thus resulting in a smaller average amplitude. For the aforementioned narrower antennas, the main path is still the predominant component of the cluster. We also observe this effect in the Experimental Hall{}. However, in this case, the peak to average ratio of the path amplitudes in a cluster is relatively large for all of the antennas. This is probably due to the larger number of reflectors in the Experimental Hall{} compared to the Tunnel{}---even when using the narrowest beam width, the receiver still observes a relatively large number of diffused paths. This suggests that the frequency selectivity of the channel may be on average larger in the Experimental Hall{} than in the Tunnel{}. This effect is further exacerbated in the Mechanical Room{}, for which the largest observed amplitudes in \fref{fig:intra_ampl} doubles the value in \fref{fig:mpower} even for the 20-degree antenna. As expected, this effect is limited for the Side Tunnel{} due to the fewer reflections.

The 1st quartile, median, and 3rd quartile of our data are shown in \tref{tbl:intra_ampl}. The distributions of the amplitude of each path in a cluster can be well described by a Generalized Extreme Value Distribution for the Tunnel{}, Experimental Hall{}, and Mechanical Room{} with parameters $0.37 \leq k \leq 0.95$, $0.01 \leq \sigma \leq 0.03$ and $0.02 \leq \mu \leq 0.04$.
We observe again similar distributions of the parameter for the different environments. On the contrary, in the Side Tunnel{} this parameter is better described by an Inverse Gaussian distribution due to the limited number of reflections.

\begin{figure*}[hhh!]
\centering
\subfigure[Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/tunnel_intra_ampl_fitting}}
\subfigure[Experimental Hall{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/exphall_intra_ampl_fitting}}
\subfigure[Mechanical Room{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/pipe_intra_ampl_fitting}}
\subfigure[Tunnel{}]{\includegraphics[width=0.5\columnwidth]{./figs/exp_results/side_intra_ampl_fitting}}
\caption{Empirical and best goodness of fit CDFs of the amplitude of each path in the cluster ($\alpha^{(i,k)}$).}
\label{fig:intra_ampl}
\end{figure*}

\begin{table}[tb!]
\centering
\caption{Quartiles of the the amplitude of each path in the cluster ($\alpha^{(i,k)}$).\label{tbl:intra_ampl}\vspace{-3.5mm}}{%
\begin{tabular}{|c|c|c|c|c|} \hline
Scenario & Degree & 1st Quartile & Median & 3rd Quartile\\ \hline
Tunnel & 7 & 0.029 & 0.041 & 0.090 \\ \hline
Tunnel & 20 & 0.030 & 0.041 & 0.090 \\ \hline
Tunnel & 80 & 0.031 & 0.036 & 0.050\\ \hline
Exp. Hall & 7 & 0.014 & 0.025 & 0.054 \\ \hline
Exp. Hall & 20 & 0.015 & 0.027 & 0.065 \\ \hline
Exp. Hall & 80 & 0.020 & 0.027 & 0.038 \\ \hline
Pipe Room & 20 & 0.027 & 0.037 & 0.079 \\ \hline
Side Tunnel & 20 & 0.034 & 0.055 & 0.095 \\ \hline
\end{tabular}}
\end{table}

\subsection{Throughput Measurements}
\label{subsec:throughput}

In this section we evaluate how our previous insights on the specific propagation environments of industrial settings translate into practical throughput values. In addition to the four scenarios discussed in \sref{subsec:param_fitting}, we also provide results for the Particle Accelerator Ring{} and UPS Room{}. To measure the aforementioned practical throughput, we use the COTS setup described in \sref{subsec:cots_setup}. As discussed in \sref{sec:setup}, we place our COTS transceivers at a large number of locations in each scenario, establish a connection, and exchange data in both directions.

\subsubsection{Mechanical Room{}}

\fref{fig:tp_pipe} shows a heat-map of the throughput (in Gbps) in the Mechanical Room{} measured according to the methodology described in \sref{subsec:cots_setup}. That is, we place a transmitter at a fixed location in the room and measure the throughput for different positions of the receiver. Further, at each position we consider four different orientations for the receiving node: \emph{i)} south, pointing at the transmitter, \emph{ii)} east, \emph{iii)} north, pointing in the opposite direction of the transmitter, and \emph{iv)} west. Note that the antenna module of the TP-Link Talon AD7200 router that we use in this setup does support beam steering. Thus, even if the receiver device itself is not pointing towards the transmitter, it can adjust its beam in order to receive data. However, beam steering on practical devices typically works best in the direction towards which the device is physically pointing, while steering to the side---or backwards, if possible at all---results in strong side-lobes and low antenna gain. This allows us to capture the limitations of millimeter-wave antennas in industrial scenarios.

The heat-map in \fref{fig:tp_pipe} depicts the throughput at each of the positions showed in the room layout in \fref{fig:pipe}. As expected, we see that the throughput is highest when the receiver is pointing south (\fref{fig:tp_pipe_s}). Additionally, we observe that even in the cases where the receiver is pointing towards other directions (\fref{fig:tp_pipe_e}-\fref{fig:tp_pipe_w}), the received throughput is significant for the positions in the middle of the room. This is also the case for the positions close to the transmitter at its left side. These outcomes show that, while practical beam-steering is sub-optimal in terms of antenna gain, current commercial devices are able to successfully establish a link even if strongly misaligned. Most interestingly, \fref{fig:tp_pipe_n} shows that communication is possible even when the receiver points in the opposite direction of the transmitter. This is significant because the antenna module at the receiver is not designed to receive in that direction, and actually the antenna circuitry partially blocks the signal. Beam pattern measurements of the device reveal that partial backwards reception is feasible, but with significantly lower antenna gain. Further, the throughput in \fref{fig:tp_pipe_n} fluctuates significantly as we move away from the transmitter on the positions $20$ to $11$ in \fref{fig:pipe}. In contrast, for the case in \fref{fig:tp_pipe_s} facing towards the transmitter, throughput is much more stable. Along with the limited backwards reception of our device, this suggests that in \fref{fig:tp_pipe_n} the receiver is receiving the signal not only from the LOS path but also from NLOS paths which are available at a subset of the aforementioned positions $20$ to $11$.

For the cases in which the receiver points to the side (\fref{fig:tp_pipe_e} and \fref{fig:tp_pipe_w}), we conclude that reception takes place both via strong side-lobes and reflections. Indeed, a close inspection of the beam patterns that our devices use in this experiment confirms that the transceivers tend to use beams with significant side-lobes that point in each other's direction. Further, we observe that the positions on the right side of the room are hardly reachable for any of the four device orientations that we measure. This is due to the very high degree of blockage in that area, as depicted in \fref{fig:pipe}.

\begin{figure*}[hhht!]
\centering
\subfigure[South.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/pipe_tp_heatmap_1}\label{fig:tp_pipe_s}}
\subfigure[East.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/pipe_tp_heatmap_2}\label{fig:tp_pipe_e}}
\subfigure[North.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/pipe_tp_heatmap_3}\label{fig:tp_pipe_n}}
\subfigure[West.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/pipe_tp_heatmap_4}\label{fig:tp_pipe_w}}\\
\caption{Throughput results in the Mechanical Room{}.}
\label{fig:tp_pipe}
\end{figure*}

\subsubsection{Experimental Hall{}}

\fref{fig:tp_exphall} shows the throughput results in the Experimental Hall{} on the floor level at the different distances shown in \fref{fig:ring}. In this case, we performed the experiments with the receiver pointing towards the transmitter. Given this alignment, our devices are known to use rather directional beam patterns with a strong main lobe and negligible side lobes \cite{conextDaniel}. Further, as discussed in \sref{subsec:param_fitting}, the Experimental Hall{} does feature a significant number of reflectors but many of them only play a role for wide antenna beam patterns due to the size and openness of the area. Thus, in this experiment we expect the Talon router to receive primarily the LOS path and only limited reflections, if at all. This would result in a channel with very low frequency selectivity and thus benevolent for the single carrier modulation used in IEEE 802.11ad. While we cannot measure the channel at the Talon device itself, our results in \fref{fig:tp_exphall} show a relatively stable behaviour with throughput close to 1 Gbps at all locations. The standard allows for higher throughput values but given the hardware constraints of the Talon devices, 1 Gbps is close to the highest achievable throughput in practice at the application layer. As discussed in \sref{subsec:hall_setup}, we also measure throughput for links connecting the lower and the upper level of the experimental hall. If we align the receiver with the transmitter but place the former at the upper floor, we achieve similar throughput ($\sim0.93$~Gbps). However, if we move the receiver at an angle as shown in \fref{fig:ring}, we observe a drop to $\sim0.13$~Gbps. This suggests that the impact of elevation itself is limited but that misalignment---and the resulting signal strength loss along with the frequency selectivity due to reflections captured by side-lobes---plays a significant role. This observation matches the design of the Talon antenna array, which offers limited vertical beam forming capabilities \cite{conextDaniel}. That is, the antenna gain is similar for different elevation angles.

\begin{figure}[hhht!]
\centering
\subfigure[]{\includegraphics[width=0.49\columnwidth]{./figs/router_results/exp_hall_tp_floor}\label{fig:tp_exphall}}
\subfigure[]{\includegraphics[width=0.49\columnwidth]{./figs/router_results/ring_tp}\label{fig:tp_ring}}
\caption{Throughput results in (a) the Experimental Hall{} (floor level) and in (b) the Particle Accelerator Ring{}.}
\label{fig:tp_exphall_ring}
\end{figure}

\subsubsection{Particle Accelerator Ring{}}

The Particle Accelerator Ring{} is a unique environment due to its curvature and the large amount of reflectors that it contains. While we cannot carry out our full channel measurements (c.f. \sref{subsec:param_fitting}) in this scenario due to access restrictions, we are able to perform measurements as depicted in \fref{fig:tp_ring}. Essentially, we place the transmitter at a fixed location in the ring and move the receiver along the ring to increase the link distance. Our goal is to analyze whether the elongated shape of the ring allows us to exploit the many reflectors in order to set up a long link along the curve of the ring even if the transceivers are not in LOS. In other words, we study whether the effects that we observed for the Tunnel{} in \sref{subsec:param_fitting} hold for curved environments. In \fref{fig:tp_ring}, we clearly observe the distance at which the link becomes NLOS---at $20$ meters, the throughput drops from about $0.9$~Gbps to values that fluctuate between $0.2$~Gbps to $0.8$~Gbps for a link distance of up to $36$ meters. This high throughput values in the NLOS half of the graph are highly encouraging, since the peaks at $\sim0.8$~Gbps suggest that strong reflections allow communication along curved narrow structures such as the Particle Accelerator Ring{}, thus increasing the coverage of each individual AP beyond its LOS range. The high fluctuations in \fref{fig:tp_ring} are most likely because reflectors are scattered along the Particle Accelerator Ring{} (c.f. \fref{fig:ring}) and thus reflections may or may not be available. If homogeneous reflectors are available along the curved environment, such as the pipes in the Tunnel{}, we expect a more stable throughput.

\subsubsection{Tunnel{} and Side Tunnel{}}

\fref{fig:tp_tunnel} shows the results of the Tunnel{} for increasing link lengths. We observe that the throughput remains high and reasonably stable until the transmitter and receiver are $30$~m apart. Beyond that point, throughput becomes sensitive to the specific link length, which suggests a high impact of constructive and destructive reflections. While our COTS hardware only provides limited insights regarding the underlying physical layer effects, our full channel measurements in this environment (c.f. \sref{subsec:param_fitting}) suggest a number of reasons for this behavior. First, in \sref{subsubsec:inter-cluser-delays} we observed a particularly high inter-cluster delay. Due to the length of the tunnel, we expect this delay to increase with the link distance as the difference in terms of length between the LOS and the reflected paths increases. This results in a large delay spread at the receiver, which makes channel equalization more challenging for long links. Second, in \sref{subsubsec:cluster_amplitude} we concluded that most reflected paths in the Tunnel{} were likely to reflect only once before reaching the receiver. Thus, we expect reflections to be relatively strong at the receiver when compared to the LOS, which results in higher frequency diversity and hence challenging equalization due to the single-carrier modulation used in IEEE 802.11ad. While \fref{fig:tp_tunnel} shows that the above physical layer effects have a strong impact, existing mechanisms such as Orthogonal Frequency-Division Multiplexing (OFDM) with a long cyclic prefix can easily tackle them. Hence, our results in \fref{fig:tp_tunnel} are encouraging. Remarkably, we achieve $>0.6$~Gbps at $110$~m despite the use of single-carrier modulation. To the best of our knowledge, this is the largest 802.11ad link distance reported in literature \cite{Nitsche:2015vq, fastninfuriating, 7179366, 7568477, SahaVGK15}.

In \fref{fig:tp_side_tunnel} we depict the equivalent results for the Side Tunnel{}. In this case, we observe that the throughput is much more stable than in \fref{fig:tp_tunnel} even for distances beyond $30$~m. This matches our above reasoning. As we concluded from our full channel measurements in \sref{subsec:param_fitting}, the Side Tunnel{} exhibits a very different behavior at the physical layer compared to all of our other scenarios due to the lack of reflectors. This translates into shorter delay spreads and less frequency selectivity, which in turn makes channel equalization easier. As a result, the fluctuations in \fref{fig:tp_side_tunnel} are small compared to \fref{fig:tp_tunnel}.

\begin{figure}[hhht!]
\centering
\subfigure[]{\includegraphics[width=0.49\columnwidth]{./figs/router_results/tunel_tp}\label{fig:tp_tunnel}}
\subfigure[]{\includegraphics[width=0.49\columnwidth]{./figs/router_results/side_tp}\label{fig:tp_side_tunnel}}
\caption{Throughput results in (a) the Tunnel{} and in (b) the Side Tunnel{}.}
\label{fig:tp_tunnel_side_tunnel}
\end{figure}

\subsubsection{UPS Room{}}

Similarly to the Mechanical Room{}, in \fref{fig:tp_ups} we show the heatmap of the throughput in the UPS Room{} for different orientations of the receiver. As depicted in \fref{fig:ups}, the UPS Room{} contains racks of hardware enclosed in massive metal cabinets which are impossible to traverse for the IEEE 802.11ad signal. As a result, we essentially obtain coverage in the LOS locations only. At those locations, the impact of the receiver orientation is as expected---when the receiver points south (\fref{fig:tp_ups_s}) and thus towards the transmitter, we obtain the best results. Other orientations result in degradation of the throughput but still achieve reasonable coverage (e.g., \fref{fig:tp_ups_w}). In the bulk of the NLOS locations, our devices are not able to establish a connection. While the metal cabinets are potentially good reflectors, the intricate geometry of the UPS Room{} requires devices to be located at very specific locations to benefit from such reflections. This is in clear contrast to the Tunnel{} and the Particle Accelerator Ring{}. Further, most locations would only be reachable via second or third order reflections, which inherently results in high losses and thus a weak signal at the receiver. Still, for the first corridor---measurement locations 7 to 10---in \fref{fig:ups}, \fref{fig:tp_ups} reveals some coverage. Most interestingly, the throughput is highest for the north (\fref{fig:tp_ups_n}) and south (\fref{fig:tp_ups_s}) orientations, although the orientation of the corridor is east to west. This indicates that the connection is established via one of the above reflections. We conclude that for those locations path loss is limited due to being close to the transmitter and thus the receiver can still decode the signal despite the above high reflection losses. This shows that intricate environments and blockage are important challenges for millimeter-wave in industrial scenarios.

\begin{figure*}[hhht!]
\centering
\subfigure[South.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/ups_tp_heatmap_1}\label{fig:tp_ups_s}}
\subfigure[East.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/ups_tp_heatmap_2}\label{fig:tp_ups_e}}
\subfigure[North.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/ups_tp_heatmap_3}\label{fig:tp_ups_n}}
\subfigure[West.]{\includegraphics[width=0.5\columnwidth]{./figs/router_results/ups_tp_heatmap_4}\label{fig:tp_ups_w}}\\
\caption{Throughput results in the UPS Room{}.}
\label{fig:tp_ups}
\end{figure*}

\section{Experimental Setup}
\label{sec:setup}

In the following, we describe the layout of the physical environments in which we collect measurement data. We consider five distinct environments: a Mechanical Room{}, an Experimental Hall{}, a Particle Accelerator Ring{}, a Tunnel{}, and a UPS Room{}. We select these areas because they effectively represent the scenarios found in a typical industrial site. For instance, the Mechanical Room{} contains a large number of pipes and other reflective infrastructure that is present in most industrial scenarios. The Particle Accelerator Ring{} resembles a production line and the Tunnel{} resembles a hallway interconnecting different production areas. Last but not least, the UPS Room{} could represent a data center or any environment with large machinery. For each environment, we collect data both using our COTS setup as well as our setup based on measurement hardware (c.f. Section~\ref{sec:methodology}).

\subsection{Mechanical Room{}}
\fref{fig:pipe} shows the layout of the Mechanical Room{}. This room consists of the following main structures: (i) metallic and painted circular pipes with variable sizes and (ii) large and small poles. These structures have a strong impact on several radio propagation effects such as diffraction, reflection, and multi-path transmission.
In addition, they also act as blockages for some paths between the transceivers. However, in contrast to the UPS Room{}, blockage is typically not full but partial. In order to evaluate the effects of these structures on the communication, we place the receiver at 28 different positions as shown in \fref{fig:pipe}. Still, communication is not viable at locations 11 and 29 since permanent infrastructure fully blocks the link. The Mechanical Room{} also provides environments with both sparse and dense infrastructure on the left and right side of the layout, respectively.

\begin{figure}[t!]
\centering
 \includegraphics[width=\columnwidth]{./figs/pipe_A0}
 \caption{Layout of the Mechanical Room{}.}
 \label{fig:pipe}
\end{figure}

\subsection{Experimental Hall{}}
\label{subsec:hall_setup}
\fref{fig:ring} depicts the layout of both the Particle Accelerator Ring{} as well as the Experimental Hall{}. The Experimental Hall{} is a large double-height empty circular space that encloses the Particle Accelerator Ring{} as well as surrounding beamlines that receive the synchrotron light. In \fref{fig:ring}, we show the upper half of the ring as well as a fraction of the hall. The ring is located at the lower level of the hall. The upper level of the hall consists of a walkway that encircles the entire space and provides access to other areas within the particle accelerator plant. It features metal staircases, multiple metal platforms and doors, as well as concrete walls. We collect measurements both in the open space within the Experimental Hall{} as well as at the second level. This allows us to measure links which are even with the ground as well as links with non-zero elevation that connect both levels.
At the ground level, we take measurements at intervals of $1$m up to a total link length of $10$m to characterize signal propagation for increasing distances. For links connecting the upper and lower levels, we consider an elevation angle of about 12$^{\circ}$ and a link distance of $20$m as depicted in \fref{fig:ring}. This measurement allows us to study the performance of industrial mmWave links for cases where the transmitter and the receiver are placed at significantly different heights. Unlike residential or office indoor environments, such height differences are common in industrial settings.

\begin{figure}[tbh!]
\centering
 \includegraphics[width=\columnwidth]{./figs/ring_A0}
 \caption{Experimental Hall{} and Particle Accelerator Ring{}.}
 \label{fig:ring}
\end{figure}

\subsection{Particle Accelerator Ring{}}
The layout of the Particle Accelerator Ring{} is depicted in the lower part of \fref{fig:ring}. The walkway inside the ring is surrounded with particle accelerator equipment. This equipment typically features a reflective metal structure as well as a protective polycarbonate plastic plate in some cases. Within the ring, we focus on measuring the range of a mmWave transmission. Since the above metal structures reflect the mmWave signal, we analyze whether transceivers can still communicate even when they are in non-line-of-sight (NLOS) of each other due to the curvature of the ring.

\begin{figure}[tb!]
\centering
 \includegraphics[width=\columnwidth]{./figs/tunnel_A0}
 \caption{Side Tunnel{} and Service Tunnel.}
 \label{fig:tunnel}
\end{figure}

\subsection{Tunnel{}}

We collect measurements from two tunnels at the particle accelerator plant. One is a narrow concrete \emph{side} tunnel, whereas the other one is a longer and wider \emph{service} tunnel.

\subsubsection{Side Tunnel}
\label{subsec:side_tunnel}
The side tunnel has concrete walls and a length of 80 meters, as shown at the upper part of \fref{fig:tunnel}. The width of the tunnel is one meter. It is empty except for light fixtures attached at regular intervals to the ceiling. One side of the tunnel is a plain concrete wall, whereas the other features a metallic door at about half of the length of the tunnel. We place one transceiver at one end of the tunnel and perform measurements for increasing link lengths. In particular, we collect data at five meter intervals. The lack of metallic elements in the side tunnel allows us to understand signal propagation in a narrow enclosed space with very low reflectiveness.

\subsubsection{Service Tunnel}
\label{subsec:serv_tunnel}
The service tunnel is similar to the side tunnel but features a number of metallic objects which increase the likelihood of reflections. In particular, one side of the tunnel is covered with pipes of different sizes attached to a metal support structure that runs along the whole length of the tunnel (200 meters). Further, fire extinguishers are placed at regular intervals along the tunnel. \fref{fig:tunnel} depicts the layout of the service tunnel. Note that the side tunnel described in Section~\ref{subsec:side_tunnel} lies within the service tunnel but only covers a fraction of its length. We again collect measurements at a regular interval of five meters.

\subsection{UPS Room{}}

As a last scenario, we consider a UPS Room{} with multiple rows of racks that hold the UPS devices. The racks are massive metallic enclosures that fully block any signal in the mmWave band. The goal of the measurement at this location is to study mmWave coverage via reflections in NLOS industrial scenarios. Beyond a UPS Room{}, similar environments are present in data centers or machinery rooms. As shown in \fref{fig:ups}, we place the transmitter at a fixed location in the upper left corner of the room and move the receiver along the grid of locations depicted in the diagram.

\begin{figure}[tbh!]
\centering
 \includegraphics[width=\columnwidth]{./figs/ups_A0}
 \caption{Measurement locations in the UPS Room{}.}
 \label{fig:ups}
\end{figure}

redpajama_set_name: RedPajamaArXiv


text:
Browning Ends Hi-Power Handgun Production
by Guy J. Sagi - Wednesday, February 7, 2018
In February 2018, Browning announced its Hi-Power pistols are no longer in production. "Current dealer inventories will be the last available from Browning for the foreseeable future," the website explains. Supply is scarce, however, because availability to dealers ended sometime in 2017. New Mark IIIs or Standards are easiest to find because they were the last models to come out of the factory.
John Moses Browning's 1911 handgun design and its subsequent 100 years of popularity cast a long and unearned shadow over his subsequent Hi-Power work. When Browning was commissioned by Belgium's FN to create a new military sidearm, the design required an all new approach because he'd sold the 1911 patents to Colt. In 1925, he'd completed the work, and a year later he was dead—making this his last gun design. U.S. patent inspectors approved it in 1927, but FN relied on the expertise of Dieudonné Saive to improve on the locked-breech recoil version. He's credited with completing the project, rightfully so, and the final product included his staggered-cartridge magazine that provided double the capacity of contemporary handguns—a lucky 13.
The single action wasn't without faults, though. In 1928, patents for the Colt Model 1911 expired and some of its features were integrated into the Hi-Power, including the barrel bushing. It was officially adopted by the Belgian military in 1935.
The pistol was battle-tough and fielded by the Allies during World War II. It also came into the hands of Axis forces after the FN factory was taken over by Germany. Canada, in turn, started to produce the pistols to keep supply lines filled during the conflict. It was fielded by the British airborne, the SAS and even the OSS, the forerunner of the modern-day CIA. Some of the firearm's variants are still being used in the global war on terrorism.
Subsequent modifications to some variants included ambidextrous safety, spurred hammer and double-action versions. A 9 mm chambering is most common, but it was also made in .40 S&W.
There's no shortage of modern aftermarket parts or modifications, either. Shooting Illustrated's Richard Mann walked readers through the changes he made to his Hi-Power and why he finally added one to his collection in 2015.
Some sources claim more than a million have been made. The odds are good the figure is greatly higher than that, considering the number of genuine versions that've come out of the FN factory and clones from different countries.
At press time, there was no word if the Belgium manufacturer is still producing the classic handgun. We do know, however, U.S. enthusiasts are going to have a harder time than ever finding one.
browning hi power 9mm handgun pistol john browning fn guy sagi
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redpajama_set_name: RedPajamaCommonCrawl


text:
Role Recall
Are the Kids Alright?
The Never-Weres
Hanson Talk Full-Circle SXSW Return, 25 Years of MMMBopping
On Hanson's first hit single, "MMMBop" — which went to No. 1 in 27 countries, was nominated for two Grammys, and helped the brotherly trio's major-label debut Middle of Nowhere sell 4 million copies in the U.S. alone — the teen-idol siblings harmonized the surprisingly existential line, "In an mmmbop they're gone/In an mmmbop they're not there." But 20 years after that single's breakthrough, the Hanson brothers, now music business veterans in their thirties, are still very much here, celebrating an incredible quarter-century as a band. And this past weekend, their career came full circle as they returned to Austin, Texas's South by Southwest festival, where they were discovered on a softball field way back in 1994.
Returning to the baseball diamond here at @sxsw in #austin 23 years after our first visit #Hanson25 pic.twitter.com/5KLPuyVTP2
— HANSON (@hansonmusic) March 18, 2017
Sitting in a loft overlooking Austin's bustling Sixth Street, Zac Hanson, 32, recalls that famous, life-changing SXSW moment — sort of pop music's equivalent of the old Hollywood "Lana Turner was discovered at Schwab's drugstore" urban legend, and definitely the sort of fairy tale that hardly ever happens at SXSW anymore.
Related: The 6 Best Things We Saw Thursday at SXSW 2017: Hanson's Leon Russell Tribute, Grandmaster Flash, The Drums & More
"We weren't invited to play a showcase, nor did we know anyone in Austin," Zac says. "We basically just showed up and started busking in the streets. Pretty much just anyone we could find, we would say, 'Excuse me, sir, do you work for a record company? We want to sing you a song — can we sing for you?' It really wouldn't matter, so we did it in the streets; we did it at the record-label industry softball games."
"We were building a fanbase, and we couldn't play in bars, of course, but we would play wherever people would listen," explains middle brother Taylor, now 34.
Zac admits that many of the jaded showbiz types that Hanson encountered at SXSW weren't impressed — or were just taken aback to see youngsters roaming the "Live Music Capital of the World," in search of a record deal. "It was a wild kind of experience: 'Kids, singing in the streets, alone? Didn't someone call Child Services on you?' I don't know, it wasn't like that. It was just real," he shrugs.
Related: The Roots, Hanson Lead Chuck Berry Tributes at SXSW 2017 Saturday
And of course, the brothers' mom and dad were totally on board. "If you met our parents, it would make sense," Taylor chuckles. "There was a lot of trust, and there wasn't any fear of [the music business]. It wasn't like, 'Hey, if we walk down to this festival, all of a sudden drugs are going to be there, and you're going to start doing coke at 10 years old!' I think even in the most liberal, free-spirited families, people [have] this idea that you're just going to be corrupted by starting young, by working young at things."
"I actually think people need to start working younger!" laughs eldest brother Isaac, 36.
"There was always a cautiousness about it, like, 'OK, that one guy's shady, and that guy's shady,'" Taylor continues, "but yeah, [our parents] were behind it."
Whatever the risk might have been when the Hanson family journeyed from Tulsa, Oklahoma, to Austin 23 years ago, the trip clearly paid off. The boys ended up at at a SXSW softball game, serenading a young lawyer, Christopher Sabec, accompanied by a boom box — and the rest was pop history.
"[Sabec] said, 'I love what you're doing; I want to be your attorney!' We said, 'We already have an attorney,'" Zac recalls. "He said, 'Well, then, what do you need?' And we said, 'We need a manager.' And he said, 'OK. … I love what you're doing so much, I'm going to figure out how to be a manager.'" Sabec quit his day job, moved to California, and "became the guy who helped us get signed. It was kind of a crazy thing."
Taylor stresses, "The thing about milestones is, you don't know until you've gone past them what things will stand out or what will be important. … You never would've thought that, going into the baseball diamond that day when it was hot as hell, that that would be anything to remember. But I do think that especially now, with technology and as we tweet out and share content and Facebook posts to our whole world, it's important for young artists to have the experience of not just sitting in front of a YouTube camera, where no one can tell them anything they don't want to hear and you can just delete the comments you don't like."
"Life doesn't work that way," notes Isaac.
"Just having to just stretch those muscles," Taylor elaborates. "I mean, yes, you can be great and talented, and, yes, the world has changed and you can make a record on a laptop, but the exercise of hitting the streets and being in front of people and getting positive and negative feedback, and actually figuring out how to push past it, that would be a good lesson for artists. … We as artists need to be tough enough to not just wither when somebody says, 'Eh, I don't know if I get that,' and still push past it. We learned that pretty early on."
Taylor certainly knows what he's talking about. Although Hanson came up in a pre-social media age, the band's early career did coincide with the rise of the Internet, and online detractors were vicious, ridiculing the brothers' pretty blond looks and dismissing them as a manufactured boy band or novelty act. They caught plenty of flak in real life, too.
"Hanson haters showed up [at our concerts] with their signs that said, 'I hate you, you girls!' — or whatever the heck they said," laughs Zac. "We'd say, 'Wow, this guy just drove down, spent the time to make a sign, so that he could spend six hours waiting in line just to show us a sign. … That moron just wasted his whole day, basically, dedicating his time to us.'"
"'Thanks for buying a ticket! That's sad.' We felt bad for him," says Isaac.
Back on the subject of the Internet, ironically, despite all the online hate Hanson received in the '90s, it was ultimately the Web that turned them into teen superstars. "When we first broke, the whole industry essentially was almost laughing off the Internet," Taylor recalls. "You had MTV.com and Hanson — those were the two top music sites on the Internet, period. So our fans were among those early [adopters], and so when we needed to transition and start our label, having the connection with the audience directly through the Internet was one of the ways that we built that."
And now, Hanson's placement in a post-digital music industry is secure. In fact, they're an industry unto themselves, with their own label (3CG Records); their own craft beer (the amusingly named MMMHops); their own music festival (Hop Jam, taking place May 21 in Tulsa); a new single, "I Was Born," which they debuted at SXSW; and an upcoming 25th anniversary tour. Says Taylor, looking back on the band's humble SXSW beginnings: "I'm definitely grateful that we learned about work ethic early on, just the idea that you've got to work at things. … And part of what's really cool about having some history is, if you survive long enough — in this business especially, because it really is survival — you get to help frame the past, and frame the truth of the past and what's happened. Sort of like whoever wins the war writes the history books."
And as for how the brothers have maintained their famous teen-heartthrob looks after 25 years? "We have a special elixir," Taylor jokes. "It's something that we keep to ourselves. You have to pay in blood."
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Big Three. Ang Lee and James Schamus spoke in Zellerbach Hall on Monday as 'Ice Storm' screened in the background. Professor Jeffrey Knapp moderated the conversation.
Speaking at Zellerbach Hall Monday night, Ang Lee and James Schamus brought their passion for cinema to the Berkeley campus. As scenes from "The Ice Storm" played overhead, the two luminaries discussed everything from cinematic subtexts to work habits and future collaborations, treating the audience to a rich discourse on the nuances of the filmmaking process. For Schamus, the experience has added significance: The producer-screenwriter earned his bachelor's, master's and PhD degrees, all in English, here at UC Berkeley.
Over his prolific career, Schamus has collaborated with Lee on 11 feature films, writing nine and producing the other two. Their works have transcended genres and cultural boundaries, winning prestigious awards everywhere from the Academy and BAFTA to film festivals in Berlin and Venice. Their success on the international stage speaks volumes about the consummate nature of their works. "People say that globalization has become a reality, and it really has," Schamus said in an interview before the lecture. "When individuals get together and do small things, their words and actions become essentially universal."
From intimate dramas ( "Eat Drink Man Woman") to rich period pieces ("Sense and Sensibility") and romantic epics ("Brokeback Mountain"), Lee and Schamus have created a body of work as varied as anything the cinema world has seen. Yet unlike many specific-minded auteurs of yesteryear, the two men avoid one-dimensionality with admirable acumen. Their films don't defy genre conventions as much as blend and unite them. Switch the films in the aforementioned categories around, and the characterizations remain palpably apt.
As for their filmmaking philosophy, exploration is key. Asked how they choose projects, Schamus explained, "A lot of it is opportunity, impulse, trying to keep things fresh. Going from one kind of a film to another allows Ang and I to explore things we haven't explored before." In this respect, 2003's "Hulk" is their boldest experiment: Delving into the comic book genre, Lee and Schamus crafted a mature tale of Oedipal proportions that was as misunderstood as the tormented character himself. With their upcoming film, "Taking Woodstock," they plan to venture further into the possibilities of cinema by presenting a candid portrait of a bygone era along with the individuals that made it a reality.
Collaborations between directors and screenwriters have produced no shortage of seminal film works. Alfred Hitchcock often relied on his writers to create scenarios before they envisioned them cinematically; Michel Gondry and Charlie Kaufman deserve equal credit for the masterpiece "Eternal Sunshine of the Spotless Mind." But for Lee and Schamus to collaborate as frequently as they have, it's no stretch to say that their symbiosis is a growing rarity. Schamus' take: "We strive for variety. Every film should say something different; making films with Ang Lee, you get to really learn different things, new things."
Through a dazzling array of films documenting diverse aspects of humanity, Lee and Schamus have expanded the possibility of cross-cultural cinema in a global film culture. Their films combine artistic depth and narrative pathos, a sensitivity toward human connections as only the cinematic medium can capture. Forged from a thriving creative dynamic, their films aim at creating a panorama of the human experience in its entirety: fleeting, tragic and often profoundly moving.


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The EU funded SWIM-H2020 SM Project organizes in Beirut a five-day regional capacity building activity from 25-29 June 2018 that includes a Study Tour of Leachate Treatment Facilities in Lebanon and the launching of an experience exchange process among Mediterranean peers working on leachate management.
This activity is implemented under the Horizon2020 component of the Project in an effort to address the acute problem of hazardous substances disposal such as leachate. It is combined with a National SWIM-H2020 SM Training of Lebanese stakeholders on the same topic. So far, the project has implemented a number of workshops and consultations on the involvement of the private sector in water infrastructures, while more activities are to follow on Integrated Water Resources Management, the abatement of industrial pollution and on the cost of environmental degradation.
The aim is to reinforce the capacities of decision-makers and technical staff on the integrated approach to the sustainable management of leachate based on state-of-the-art technologies and best practices and lessons learned including planning, finance and procurement options. The trainees are supported to better evaluate the existing situation at legal, institutional and technical levels in their countries/regions and upscale them. Visiting the challenging case studies of Lebanon (existing, under construction and new infrastructures) offers the opportunity to the Peers from the other Mediterranean countries, which are facing very similar situations, to benefit from the lessons learned and the state-of-the-art technologies and options that are realistic and feasible.
40 decision-makers and technical staff participated from Algeria, Egypt, Jordan, Lebanon, Morocco, Palestine and Tunisia, from National Ministries or Public Agencies responsible for the design of leachate treatment plants and regional or local authorities in charge of the monitoring or operation of solid waste infrastructures and leachate management plants. The trainees from Lebanon include decision-makers and technical staff from the Ministry of Environment, Ministry of Energy and Water, Municipalities, the Council for Development and Reconstruction (CDR), OMSAR, Operators, Researchers, Consultants, Non-Governmental Organisations and others.
The environmental risks of leachate generation in landfills arise from its escaping into the surrounding environment, particularly to watercourses and groundwater. Ultimately leachate poses serious threats to public health and environmental health. These risks can however be mitigated by properly designed and engineered landfill sites and sustainable leachate treatment. As the MENA countries are beginning to adopt modern solid waste management practices either through creating new facilities or by upgrading existing dumpsites and unsanitary landfills, leachate management requires special attention dealing with treatment technologies, operation procedures and procurement options.
This activity will support Lebanon to better design and choose proven and appropriate technologies for the treatment of leachate within the proposed rehabilitation plans.