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IEEE Committee on Man and Radiation—COMAR Technical Information Statement: Health and Safety Issues Concerning Exposure of the General Public to Electromagnetic Energy from 5G Wireless Communications Networks

Abstract This COMAR Technical Information Statement (TIS) addresses health and safety issues concerning exposure of the general public to radiofrequency (RF) fields from 5G wireless communications networks, the expansion of which started on a large scale in 2018 to 2019. 5G technology can transmit much greater amounts of data at much higher speeds for a vastly expanded array of applications compared with preceding 2-4G systems; this is due, in part, to using the greater bandwidth available at much higher frequencies than those used by most existing networks. Although the 5G engineering standard may be deployed for operating networks currently using frequencies extending from 100s to 1,000s of MHz, it can also operate in the 10s of GHz where the wavelengths are 10 mm or less, the so-called millimeter wave (MMW) band. Until now, such fields were found in a limited number of applications (e.g., airport scanners, automotive collision avoidance systems, perimeter surveillance radar), but the rapid expansion of 5G will produce a more ubiquitous presence of MMW in the environment. While some 5G signals will originate from small antennas placed on existing base stations, most will be deployed with some key differences relative to typical transmissions from 2-4G base stations. Because MMW do not penetrate foliage and building materials as well as signals at lower frequencies, the networks will require “densification,” the installation of many lower power transmitters (often called “small cells” located mainly on buildings and utility poles) to provide for effective indoor coverage. Also, “beamforming” antennas on some 5G systems will transmit one or more signals directed to individual users as they move about, thus limiting exposures to non-users. In this paper, COMAR notes the following perspectives to address concerns expressed about possible health effects of RF field exposure from 5G technology. First, unlike lower frequency fields, MMW do not penetrate beyond the outer skin layers and thus do not expose inner tissues to MMW. Second, current research indicates that overall levels of exposure to RF are unlikely to be significantly altered by 5G, and exposure will continue to originate mostly from the “uplink” signals from one’s own device (as they do now). Third, exposure levels in publicly accessible spaces will remain well below exposure limits established by international guideline and standard setting organizations, including ICNIRP and IEEE. Finally, so long as exposures remain below established guidelines, the research results to date do not support a determination that adverse health effects are associated with RF exposures, including those from 5G systems. While it is acknowledged that the scientific literature on MMW biological effect research is more limited than that for lower frequencies, we also note that it is of mixed quality and stress that future research should use appropriate precautions to enhance validity. The authorship of this paper includes a physician/biologist, epidemiologist, engineers, and physical scientists working voluntarily and collaboratively on a consensus basis.

INTRODUCTION
FIFTH GENERATION, or 5G, refers to a set of engineering standards 2 for operating mobile wireless networks. It handles data significantly faster than 2G/3G/4G technologies, whose capabilities are approaching their limits and falling short of expected future demands. 5G has significantly greater data transfer rates (bandwidth) and much shorter delays (latency) at the base station in responding to incoming signals. 5G will also be needed to manage the data traffic on present wireless networks, which is increasing by 90% per year. As of early 2019, data traffic stood at about 29 exabytes (29 Â 10 18 bytes) per month (Table 1). For perspective, one exabyte of data could store 100,000 times the information in all printed material in the U.S. Library of Congress. 3 5G began its first commercial launches in several countries in Europe, Asia, and North America in 2018-2019, with widespread commercial adoption planned for the early to mid-2020s (Pujol et al. 2019). Fig. 1 depicts the expansion of 5G in the coming years.
In June 2019, a workshop was convened by the Government-University-Industry Research Roundtable (GUIRR) to discuss the cutting-edge issues related to 5G technology and its deployment. According to the workshop proceedings, Nada Golmie of the National Institute of Standards and Technology (NIST), "...indicated that 5G will substantially improve communications capabilities through innovations in connectivity, adaptability, and capacity because the use cases and applications of 5G technologies are wide-reaching and transformational in areas including-but not limited to-agriculture, transportation, and energy." (NAS 2019).
Initial applications of 5G include transmission of multiple streams of high-resolution entertainment content to multiple users simultaneously (e.g., videos and gaming applications) at a capacity large enough to relieve traffic congestion on wireless networks. Real-time 5G communications will enable humans and machines to control or operate remote equipment and devices to accomplish tasks such as remote surgery, repair of equipment in hostile environments, and remote control of aircraft and other vehicles. Other advanced applications might include control of medical devices on the surface of the body or implanted within it (e.g., insulin pumps or cardiac devices) or augmented reality applications on cell phones. 5G wireless networks, and their predecessors (2-4G), are sometimes called "cellular" networks because of the way the network base stations create "cells" of coverage to mobile subscribers. As subscribers move through the cells, the network "hands off " the connection with the subscriber to the next cell. This enables the same frequency bands to be used repeatedly across a large geographic area with minimal interference. As these networks continue to mature, they are often referred to as mobile-wireless or simply wireless networks, which is the term used here.

Technical background
5G is not specific to frequency and will operate across the RF spectrum: a "low band" below 1 GHz for voice and support of many IoT (Internet of Things) 4 applications; a "middle band" in the 1-6 GHz range-already in use and in many cases nearing capacity-in which the 5G protocols will enable faster data transfer compared to 2-4G; and a new "high band" of~30-300 GHz in the millimeter wave (MMW) portion of the spectrum corresponding to wavelengths from 10 mm to 1 mm that can support extremely high data transfer rates. MMWs are not entirely novel to the environment, as they are found in applications such as airport scanners, automotive collision avoidance systems, and perimeter surveillance radar security systems. The specific band used for 5G will vary by country; for example, in several nations in Europe, 5G was principally introduced in the 3.6 GHz band.
To provide adequate spectrum for 5G, the US Federal Communications Commission (US FCC) has already auctioned spectrum at 24 and 28 GHz and continues to auction frequencies in the 37 GHz, 39 GHz, and 47 GHz bands, with other countries taking similar measures. While the use of MMW allows transmissions across very large bandwidths at higher frequencies, they are readily absorbed outdoors by flora (e.g., wet trees and their foliage) and by building materials, such that a receiving/transmitting device deployed on the building exterior is necessary to receive and transmit the signal to the intended recipient within. Although 5G will operate across a wide spectrum (see above), discussions among the public concerning health and safety issues focus generally on 5G's MMW transmissions.
Sources of RF exposure from cellular telephone networks. Exposure to RF fields from a cellular network originates from two possible sources: one is the "downlink" signal from the base station, while the other is the "uplink" signal from one's own or a nearby person's handset. Handsets can transmit at power levels of up to about 1 watt, but their actual output is set by the network to the lowest level 3 Wikipedia https://en.m.wikipedia.org/wiki/Exabyte. Accessed 6 April 2020. 4 The Internet of Things represents a network of Internet connected devices capable of collecting and exchanging data without requiring human-to-human or human-to-computer interaction. that communicates effectively with the base station, in some cases as low as a few milliwatts. The power 5 transmitted by a base station varies with the area it serves, ranging from several hundred watts in a "microcell" base station mounted on a tower or on the top of a building, to typically less than 10 W for a "small cell" mounted on a 10-m utility pole, to mW for 'microcells, picocells or femtocells' 6 commonly mounted in the celling or on the inside wall of a building and to serve very limited areas. The signal's frequency depends on the wireless provider and on which of several different services (voice vs. data) the provider supports in a particular region.
When cellular networks were first introduced in the 1990s, there were few subscribers, and cells covered large areas (several km in radius). Their base stations and antennas (macrocells) were mounted as high as 30-50 m above the ground on new towers or on other existing tall structures. However, those base stations only supported a very limited number of simultaneous mobile phone and data traffic transmissions. Even with improvements in bandwidth and capacity, the expanding demand for data transmission has generally outpaced capacity with each new generation of wireless technology. Over time, the addition of small cells within a region increased network capacities while alleviating the load on the existing macrocell site. As each cell then covered a smaller area, they operated at lower power and the antennas were mounted at lower heights. One industry source 7 reports that by 2025, more than 70 million small cells will be installed worldwide, one third of which will be for 5G networks. The increase in numbers of small cells is referred to as densification.
Influence of small cells on population exposure to RF signals. Adding small cells to a network has mixed results on overall RF exposures. Their addition will increase the downlink signal levels intended to improve quality of service. Secondarily, these higher signal strengths will cause the network to reduce the power output of handsets (the uplink). Since in most cases the stronger exposure to cellphone users is from uplink signals from their own devices, the presence of small cells will generally reduce overall RF exposure to a user or a bystander (Stephan et al. 2014;Mazloum et al. 2017). However, many variables determine personal exposure to RF fields, and simple generalizations of this sort do not always apply (Lonn et al. 2004;Boursianis et al. 2012;Durrenberger et al. 2014;Plets et al. 2015;Krayni et al. 2017;Huang 2018;Zeleke et al. 2018).

RF exposure limits
For recent reviews of RF exposure limits, see Wood and Karapidis (2017) and Foster et al. (2018). In most countries, RF exposure limits are based generally on either the International Commission on Nonionizing Radiation Protection (ICNIRP) guidelines (ICNIRP 1998(ICNIRP , 2020 or the Institute of Electrical and Electronics Engineers (IEEE) Standard C95.1-2019 (IEEE 2019). In the US, RF exposures are regulated by the Federal Communications Commission (FCC). IEEE and ICNIRP limits were last updated in 2019 and 2020, respectively. The current version of the FCC limit was approved in 1996 (FCC 1997), but in August of 2019, 5 Power (typically in watts or dBm) can be expressed as power delivered to the antenna from the transmitter or the effective radiated power (ERP) which includes the concentration of power in a particular direction (gain) by the antenna. In the macrocell example above, 200 watts delivered to the antenna could result in an ERP of 5 kW. 6 Terms commonly used by wireless carriers to describe very low power base stations. 7 https://www.smallcellforum.org/press-releases/market-status-apac-northamerica-lead-network-densification-2021/. Accessed 26 September 2019. the FCC issued a press release stating that it intends to maintain its current RF exposure safety standards, citing a statement from the Director of the US Food and Drug Administration Center for Devices and Radiological Health that "the available scientific evidence to date does not support adverse health effects in humans due to exposures at or under the current limits." 8 Both IEEE and ICNIRP RF limits were set to protect against the possibility of a significant rise in tissue temperature (even in warm work environments) due to exhaustion of the body's normal thermoregulatory capacity (Laakso et al. 2017;Foster et al. 2018;Hirata et al. 2019). The IEEE standard sets an exposure limit, formally called the "Exposure Reference Level" (ERL); for RF fields exceeding 2 GHz, the whole-body ERL for the general public is 10 W m −2 . The scientific basis of the limits is supported by reviews of the scientific literature by expert panels convened by health agencies or other official entities. For example, Section C1 of IEEE C95.1-2019 cites 20 reviews through 2017 that found no evidence of health hazards from exposures to RF energy at levels below current limits and identified "...no accepted theoretical mechanisms exist that would suggest the existence of such effects." The US Food and Drug Administration (FDA) published an in-depth review of epidemiology and laboratory studies on RF relevant to cancer published between 2008 and 2018 with selected updates through August 2019; the frequencies used in these studies were almost all between~800-2,500 MHz, which cover 2-4G cellular frequencies. The FDA concluded (US FDA 2020): "Based on the studies that are described in detail in this report, there is insufficient evidence to support a causal association between RFR exposure and tumorigenesis. There is a lack of clear dose response relationship, a lack of consistent findings or specificity, and a lack of biological mechanistic plausibility." Well-established hazards of millimeter waves include thermal pain in the skin (with a threshold temperature of about 44°C) and, if the temperature is maintained at high levels (generally above 43°C) for minutes or more, burns or other thermal damage to the skin and cornea could occur (Foster and Morrissey 2011;Sienkiewicz et al. 2016). Such hazards require exposures far above IEEE and ICNIRP exposure limits.
While most countries have adopted limits based on ICNIRP or IEEE, several cities (and even a few countries) have established their own "precautionary" limits based on a philosophy of minimizing exposure to avoid as-yet unproven hazards and are motivated in part by social concerns about RF technology. This contrasts with ICES and ICNIRP exposure limits (versions of which are adopted in most countries) that are designed to protect against known hazards (Foster et al. 2018a). Compliance of 5G base stations with regulatory limits may be further complicated in some countries that have adopted such lower "precautionary" limits (Foster et al. 2018). For example, exposure limits in Switzerland and Italy at frequencies relevant to current cellular base stations are 100 times lower than ICNIRP limits, while in India they are 10 times lower. The implications of these very restrictive limits on the deployment of new 5G base stations is under review (Chiaraviglio et al. 2018;ITU 2018).
Public exposure to RF signals from cell phone (wireless) networks There are two distinct issues related to the public's exposure to RF fields from cellular networks. One is compliance of transmitting facilities with regulatory limits, which may be an issue in the immediate vicinity of base station antennas. The second concerns the distribution of exposure across the general population, which is relevant to addressing potential public health impacts. . This is not likely to change when 5G systems are installed; however, exposures may be higher near base station antennas, but wireless carriers are still obligated legally to ensure that transmitting facilities comply with regulatory limits. Issues related to compliance are quite possible in countries that have adopted "precautionary" limits that are considerably lower than those in internationally accepted guidelines and standards (Foster et al. 2018).
A recent analysis indicates that the proliferation of base stations, independent of other factors, will lower exposure levels because, in an expanded network of base stations, each cell will require lower fields in its territory to still function properly (Chiaraviglio et al. 2019). However, a key difference between 2G/3G/4G and 5G systems is that RF signals from present and older-generation systems remain in a fixed (or static) spatial pattern, whereas 5G antennas can be configured to transmit multiple beams that are steered toward individual users as they move about within a base station's coverage area. Such antennas are often referred to as beamforming or "smart" antennas. Importantly, the term 5G may encompass either static antenna beams or beamforming antennas.
Thus, even if a 5G base station that uses beamforming technology were to transmit more total power than a comparable 4G station, that power could be divided among 8 Press release, Chairman Pai proposes to maintain current radiofrequency exposure safety standards, 8 August 2019 https://docs.fcc.gov/ public/attachments/DOC-358968A1.pdf. Accessed 14 June 2020. multiple narrowly focused beams, each propagating in a different direction. The net effect is that 5G beamforming systems will have the capacity to provide high-quality communications through the more efficient use of transmitter power that can steer signals toward specific users. Field levels within other (off-beam) areas having no users would be sharply lower than those from a typical 4G base station; and since the 5G beam will exist only while communicating with a user, the longterm time-averaged exposure levels will also be lower.
Exposure levels from 5G base stations are under study by several groups, mainly to improve methods to assure their compliance with health and safety guidelines. Assessments have included exposures from stations employing beamforming antennas, as well as exposures from small cells that use stationary beams (Degirmenci et al. 2016;IEC 2017;Mazloum et al. 2017;Baracca et al. 2018;Kopacz and Heberling 2019).
Compliance distance is the distance from an antenna beyond which RF exposures cannot exceed applicable exposure limits. Wireless technology manufacturer Ericsson is one of the few firms presently selling 5G base station equipment. At a recent conference, an Ericsson RF safety expert presented calculations for two example cases (in terms of a site's total radiated energy) for deployment of 5G base stations (Törnevik 2017). One was a small cell radio with eight beams transmitting collectively less than 1 W at 28 GHz, which had a calculated public exposure compliance distance of 1.5 m in the boresight direction. The second was a macrocell site with a 200-W, 3.5-GHz 5G transmitter colocated with a group of 2G, 3G, and 4G antennas. In this case, the public exposure compliance distance when combined with the output of the 2G, 3G, and 4G emissions was 25 m. This evaluation was conducted assuming that all the antennas were transmitting at maximum power simultaneously, which is an extremely conservative assumption. Had the 200-W 5G macro cell radio existed in isolation, and had the actual maximum time-averaged power been considered, the compliance distance would have been substantially less, or about 11 m [see IEC TR 62669 (2019), clause 15]. For such a macrocell antenna array mounted on a tower or above a rooftop, this compliance distance is achievable, as the general public cannot, or in the case of a rooftop would not, be allowed (by the use signage and physical or indicative barriers) to approach within that distance.
Environmental exposures to RF. In urban areas in the US and Europe, environmental RF exposures come mainly from three sources: downlink signals from cellular base stations, uplink signals from cell phones operated near the body, and the signals from broadcast transmitters in the area (e.g., AM and FM radio and TV broadcasting facilities). Other sources such as Wi-Fi, other communications systems, and some household appliances contribute greater or lesser amounts to the total exposure depending on local circumstances. Dürrenberger et al. noted that the dramatic increase in data traffic in the coming decades added to the emergence of smart electrical grids will alter exposure characteristics throughout the population (Dürrenberger et al. 2014). Thus, it seems apparent that extrapolating from the current exposure patterns to those in the future is not possible with any certainty. (Note: The smart grid has been defined as, "At its core, smart grid represents the transformational application of information and communications technology to a more efficient and effective electric system" (Widergren and Pratt 2016).
A recent meta-analysis of RF exposure levels in European cities found RF exposure levels over a broad frequency range to be generally below 1 V m −1 (< 3 mW m −2 ), with the highest mean exposures found in environments associated with electric rail transportation (~10 mW m −2 ), i.e., inside rail cars, chiefly due to cell phone use by passengers. These investigators reported "no obvious temporal trend" in the public's overall RF exposure between 2005 and 2013 and suggested that increasing efficiency in transmitting data and reductions in output power of wireless devices may have offset a possible increase in exposure due to the rapid increase in data traffic (Sagar et al. 2018).
Evidence regarding the relative contributions of uplink and downlink wireless signals to population exposure has evolved over time and depends on many factors, including density of wireless facilities typically indicated by whether the exposures occur in an urban or rural environment. Birks et al. surveyed 529 children aged 8-18 y in five European countries and reported a median RF exposure level from all sources of 75 mW m −2 , with downlink exposures (from base stations) somewhat higher on the average than uplink exposures (from by-stander cell phones and excluding exposure from one's own cell phone) (Birks et al. 2018). Another recent survey in five urban areas in Belgium also found generally higher downlink than uplink signals (Velghe et al. 2019).
The surveys performed with subjects carrying RF monitors measured uplink exposures from nearby cell phones but were unable to evaluate exposure from one's own cell phone placed close to the body. While the results of these and other surveys show considerable variability in RF exposures from diverse sources, they also demonstrate that in virtually all cases the measured exposures were a small fraction of accepted international limits. None of the surveys cited above considered occupational exposures, whose assessments would require different approaches than those used in studies among the general public.
Recently, an exposure model was developed to assess personal exposures associated with the newly installed 3.6 GHz cellular system in Switzerland (Kuehn et al. 2019). This system operates under a 5G protocol and served in this exposure analysis as a surrogate for future 5G networks, expected to operate at higher millimeter-wave frequencies.
The study considered various conditions, such as the amount of one's use of a cell phone, location (urban, suburban, or rural), a base station's area of coverage, and the extent of indoor coverage. The key finding was that "Except for non-users...total exposure is dominated by the person's own mobile device." In other words, exposure from one's own uplink, which will increase with the amounts of data transmitted, is expected to significantly exceed exposures from downlinks and base stations. Exposures for non-users would increase marginally under the scenarios examined.
So far, no comprehensive surveys of environmental 5G signals have been conducted; few 5G networks are presently in operation, and those are largely demonstration projects transmitting at less than full capacity. Nevertheless, the designs of 5G networks are constrained by the same requirements that apply to previous generations of cellular systems: to provide a signal that is strong enough to be useful within a given cell but not so strong as to cause interference to users in nearby cells. Consequently, on this basis alone, one can expect that exposures from 5G networks will not differ greatly from those associated with present generation networks. In fact, most 5G systems transmitting millimeter waves will operate with only a few watts of power.
The advent of new technologies operating under the 5G standard will alter the mix of RF exposure sources and their respective frequencies. In fact, such trends are not strictly specific to 5G and have been underway with present generations of wireless networks. Contributions to exposure will also arise from other noncellular wireless networking technologies. Nevertheless, cumulative exposure levels from wireless technologies are expected to remain well below internationally accepted exposure limits.
Assessing potential biological and health effects from exposure to millimeter waves While many bioeffects studies have been performed using RF fields in cellular bands currently in use, comparatively few studies have been done in the millimeter wave band. As shown in Fig. 2, millimeter waves (30-300 GHz) occupy the higher end of the RF frequency spectrum (3 kHz-300 GHz) just below the infrared. Like all RF energy, it is nonionizing in that the photons have insufficient energy to eject an orbital electron, break chemical bonds, and form free radicals, a potent source of damage to biological molecules including DNA.
Since the 1960s, thousands of studies have been conducted to address possible biological and health effects from exposure to RF fields, including frequencies used by wireless communications (Fig. 3). The studies range from epidemiologic investigations of potential risks among the public and occupational populations to experimental studies aimed at uncovering biological effects of RF energy and their biophysical basis. The number of bioeffect studies using millimeter waves is a small but growing fraction of this literature. For example, roughly 100 experimental studies have been reported concerning biological effects of millimeter waves.
The penetration of RF energy into a body decreases with increasing frequency. Beyond about~3-4 GHz, RF waves have a penetration depth into the skin of <10 mm, and beyond~20-30 GHz the depth is <1 mm. Consequently, whole-body heating is not a concern for millimeter wave exposure because the deposition of RF energy is confined to the outermost layers of the body. The report cited above (Kuehn et al. 2019) of an exposure model to estimate future exposures from 5G in Switzerland also reported that, due to a smaller penetration depth at 3.6 GHz, the rate of RF energy deposition within the brain would decrease by >6fold compared to frequencies <1 GHz. For frequencies ≥6 GHz, IEEE Std C95.1 expresses the ERL exclusively in terms of the time-averaged power density incident upon "any 4 cm 2 of body surface." At 30 GHz, the local IEEE public ERL is~30 W m −2 . Accordingly, the obvious potential hazards of exposure to millimeter waves are thermal damage to skin and cornea.
The most extensive studies that were designed explicitly to assess potential hazards of millimeter waves were carried out in the late 1990s to early 2000s by a group at Brooks Air Force Base, TX, and their collaborators at several universities. Many of those studies involved shortterm exposure (seconds to minutes) at levels far above current exposure limits and examined effects such as thresholds for thermal pain in humans (Walters et al. 2000) or corneal burns in rhesus monkeys (Chalfin et al. 2002). The Air Force supported one long-term cancer study using a well-established skin tumor model in mice with negative results (Mason et al. 2001). More recently, extensive studies on ocular damage from millimeter wave exposure at high levels have been conducted by a group at Kanazawa (Japan) Medical Center (Kojima et al. 2009;Kojima et al. 2020).
Skin and ocular injury. These and other studies demonstrate without question that exposure to millimeter waves far in excess of national and international exposure limits can be hazardous. This evidence is reviewed in IEEE C95.1-2019 Annex C.8. Both IEEE and ICNIRP limits for occupational exposures above 6 GHz were designed to limit increases in localized skin temperature to about 2-3°C in a continually exposed person. Across the whole RF spectrum, questions about possible nonthermal (not heat related) hazards have long been discussed, but neither IEEE, ICNIRP, nor health agencies have considered the evidence for these persuasive at exposure levels below current limits (ICNIRP 2009;HC 2015;IEEE 2019;SSM 2019;US FDA 2020).
At sufficiently high intensities, millimeter waves may be particularly injurious to the lens of the eye and cornea because they are avascular with limited ability to dissipate thermal energy. This together with the limited volume of tissue in which millimeter wave energy is deposited can result in a rapid temperature increase and high peak corneal temperatures. However, these effects occur at power densities far in excess of current safety standards. For example, Kues et al. reported on chronic exposures of rabbits and rhesus monkeys to 60 GHz millimeter waves at an incident power density of 100 W m −2 (Kues et al. 1999). A variety of diagnostic tests was used before and after exposure to examine for damage in each subject. After single 8-h or five separate 4-h exposures, damage to the exposed eyes of rabbits and monkeys was not observed. Light and transmission electron microscopy of eye tissues failed to reveal any damage from the exposures. If damage is limited to the lenticular or corneal epithelium, it will undergo a normal repair process. However, damage to the underlying stromal layer can result in the development of clinically significant opacities (Lipshy et al. 1996). For the same incident millimeter wave power density that might result in repairable damage to the epithelium of the eye, the probability of severe stromal layer damage increases at lower frequencies because of deeper penetration of RF energy (Rosenthal et al. 1977). Note that the eye damage threshold levels reported are conservative as they were determined under anesthesia, depriving the animals of their normal convective cooling as no blinking occurred during irradiation.
Medical and bioeffects literature. The medical literature of the former Soviet Union and its former Warsaw Pact allies contains several hundred papers on various aspects of medical applications of millimeter waves, which generally involve exposure levels above existing safety limits but do not cause perceptible heating or adverse health effects (Pakhomov et al. 1998). However, this literature is difficult to access by Western readers, and such treatments have not generally been accepted by Western medicine. Moreover, these studies as a group do not meet current scientific standards of study quality. The therapeutic use of millimeter waves remains experimental in Western countries with limited research literature on the topic, and the prior Soviet results do not amount to persuasive evidence for hazards of millimeter wave exposures at levels below current limits.
Recent narrative (as opposed to more in-depth, critical systematic) reviews of millimeter wave bioeffects research vary widely in viewpoint, quality, and conclusions (Anton et al. 2015;Romanenko et al. 2017;Alekseev and Ziskin 2018;Di Ciaula 2018). COMAR was not able to identify any systematic reviews of the RF bioeffects literature for millimeter waves, either by individual authors or by expert committees under official auspices. One recent "pragmatic review" (an adaptation of a systematic review) of 94 in vivo or in vitro bioeffects studies covering the frequency range 6-100 GHz by Simkó and Mattsson (2019). About threequarters of those studies used exposure levels above the current ICNIRP and IEEE exposure limits for the general public (10 W m −2 ), and more than one-third used exposure levels 10 times or more above these limits.
Approximately two-thirds of the studies reviewed by Simkó and Mattsson reported statistically significant effects of some sort. However, only a small fraction of the studies (3 out of 45 in vivo studies, and 1 out of 53 in vitro studies) met all of the quality criteria that the investigators established (blinded study design, adequate exposure assessment, use of sham controls, adequate thermal control, use of positive controls where appropriate), and thus the reliability of the findings can be questioned. Moreover, there were few if any replication studies to confirm previously reported effects, and the health implications of the reported effects are unclear.
Simkó and Mattson concluded "there does not seem to be a consistent relationship between intensity (power density), exposure time, or frequency, and the effects of expo-sure… higher power densities do not cause more frequent responses…" Their review provided "no clear evidence" for health effects of RF exposure in the frequency range they considered, and "no clear explanation" for effects other than due to heating. However, there were "too few studies [that fulfilled] the minimal quality criteria to allow any further conclusions." A recent review by the Swedish Radiation Safety Authority concluded, "Despite the lack of established mechanism for affecting health with weak radio wave exposure, there is however need for more research covering the novel frequency domains, used for 5G" (SSM 2019). Recently the Australian Radiation Protection and Nuclear Safety Agency, in response to the expression of public concern in regard to 5G networks, stated, "There is no established evidence that low level radio wave exposure from 5G and other wireless telecommunications can affect the immune system or cause any other long term or short term health effects." 9 Gaps in knowledge COMAR offers these suggestions for further research on possible health and safety issues associated with MMW: 1. There is a paucity of well-done studies on health-related effects of millimeter waves. The results of most studies in the literature are of uncertain relevance to health. Many have small samples of subjects, and many lack elementary precautions to ensure reliability. Well-done studies to identify biological effects of millimeter waves of potential health significance are warranted. For example, since some future 5G systems will transmit at millimeter wave frequencies, which until now have not been prevalent in the public domain (with limited exceptions), research should examine whether skin heating from such exposures at sufficiently high levels results in adverse biological effects that differ from those due to infrared heating. Such studies should be designed with appropriate precautions to ensure validity (e.g., criteria proposed by Simkó and Mattsson) and, where appropriate, use of an "open science" model with preregistered study design (Nosek and Lakens 2014); 2. The thermal response of the body to millimeter wave exposures for extended times (10s of minutes or longer) needs further study. Above 6 GHz, present IEEE and ICNIRP exposure limits rely on theoretical/numerical models to predict transient and steady-state increases in tissue temperature. Better experimental validation of these models is needed, as well as an assessment of inter-and intra-subject variability in responses. This is particularly needed for occupational exposure limits, where extended high-level exposures of several 10s of minutes or more might result in unacceptable temperature increases in the skin under worst-case conditions (Foster et al. 2017). In terms of power density, nonoccupational exposure limits are one-fifth of occupational limits and are well below thermally significant (or detectable) heating thresholds (Blick et al. 1997;Wu et al. 2015); and 3. Further refinements in methodology are needed to assess exposure from 5G transmitters, both environmental exposures from base stations and local exposures from transmitters used close to the body. Accurate exposure assessment of the narrow and often moving beams of transmitted signals is needed both to assess compliance with exposure limits and for health and safety research. Substantial efforts are underway on these issues by working groups of the International Committee on Electromagnetic Safety (ICES) and the International Electrotechnical Commission (IEC 2017).

CONCLUSION
The emergence of 5G cellular networks into widespread usage has attracted public attention. This development is due in part to the necessary reliance of 5G sources of MMW on many small cells installed near subscribers (densification), as well as to the introduction into the environment of RF fields from a part of the spectrum to which the public has not previously been exposed to any significant extent. Though research efforts have begun, the effect of 5G networks on population exposures to RF signals has not been as thoroughly researched as have RF exposures at lower frequencies. However, we anticipate that in all cases, exposure levels will remain well below major international exposure limits and that network operators will be aware of their obligation to maintain their systems within compliant operating parameters. When exposure levels are maintained below current exposure limits, neither health agencies nor guideline/standards setting organizations have identified hazards from exposure to millimeter waves or RF signals in lower frequency bands used in previous generation technologies. Given the limited bioeffects literature on millimeter wave exposure, however, COMAR recommends more high-quality research on MMW, together with ongoing surveillance by health agencies of relevant scientific developments. This effort should result in systematic reviews of the literature done under established protocols, with appropriate selection and evaluation criteria for research papers. Such efforts will serve the public interest and assist our society's adaptation to 5G with minimal, if any, disruption.
Finally, COMAR is comprised of career professionals who deal with environmental and health issues associated with electromagnetic exposures from across the non-ionizing spectrum, including power delivery, RF broadcast, and wireless technologies. The advent of 5G technologies has been accompanied by a steady stream of media pieces expressing various opinions on 5G ranging from the ominous to the exculpatory. Given the background and commitment of its members, we feel a unique responsibility to provide an objective assessment of where 5G technologies stand with respect to health and safety issues. COMAR concludes that while we acknowledge gaps in the scientific literature, particularly for exposures at millimeter wave frequencies, the likelihood of yet unknown health hazards at exposure levels within current exposure limits is considered to be very low, if they exist at all.


Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions

Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri. In this paper, we present an in-depth analytical study on the main features relevant to the task of definition extraction. Our main goal is to study whether linguistic structures from canonical (the Aristotelian or genus et differentia model) can be leveraged to retrieve definitions from corpora in different domains of knowledge and textual genres alike. To this end, we develop a simple linear classifier and analyze the contribution of several (sets of) linguistic features. Finally, as a result of our experiments, we also shed light on the particularities of existing benchmarks as well as the most challenging aspects of the task.

Introduction
Definition Extraction (DE) is the task to extract textual definitions from naturally occurring texts . The development of models able to identify definitions in freely occurring text has many applications such as the automatic generation of dictionaries, thesauri and glossaries, as well as e-learning materials and lexical taxonomies (Westerhout, 2009;Del Gaudio et al., 2014;Jurgens and Pilehvar, 2015;Espinosa-Anke et al., 2016). Moreover, definitional knowledge has proven to be a useful signal for improving language models in downstream NLP tasks (Joshi et al., 2020). The task of DE is currently approached almost unanimously as a supervised classification problem, and the latest methods have demonstrated an outstanding performance, to the point of reducing the error rate to less than 2% in some datasets (Veyseh et al., 2019). However, the high performance of these models could be mainly due to artifacts in the data, and thus they may not generalize to different domains.
The main aim of this paper is to analyze to what extent is possible to learn a universal definition extraction system from canonical definitions, and to understand the core differences that currently exist in standard evaluation testbeds. In particular, we propose experiments where we develop a machine learning model able to distinguish definitions with high accuracy in a corpus of canonical definitions, and later evaluate such model in different (pertaining to different domains and genres) datasets. Our evaluation datasets are two, namely: the Word-Class Lattices (WCL) dataset from , and DEFT, from the SemEval 2020 Task 6 -Subtask 1 (Spala et al., 2019). The former provides an annotated set of definitions and non-definitions with syntactic patterns similar to those of definition sentences from Wikipedia (what the authors call syntactically plausible false definitions). The latter presents a robust English corpus that explores the less straightforward cases of term-definition structures in free and semistructured text from different domains (i.e., biology, history and government), and which is not limited to well-defined, structured, and narrow conditions.
We include a detailed descriptive analysis of both corpora that identifies similarities and differences between definitions and non-definitions, later used for feature selection and analysis. We come to conclusions regarding the discriminative power of certain linguistic features. Interestingly, these features alone do not have a strong effect on the results, but, the combining feature sets of different nature can improve performance, even in target corpora having heterogeneous domains and non-canonical definitions.
To train and first evaluate the model, we use the annotated WCL dataset. This dataset contains sentences from a sample of the WCL corpus that includes both definitions and non-definitions with syntactic patterns very similar to those found in definitions (e.g. "Snowcap is unmistakable"). The syntactic patterns are simple and represent what we could refer to as canonical definitions. We will test the performance of a model trained on this dataset, and evaluate on the DEFT dataset, which contains a set of definitions and non-definitions from various topics such as biology, history and government.

Related Work
Over the last years, DE has received notorious attention for its applications in Natural Language Processing, Computational Linguistics and Computational Lexicography (Espinosa-Anke and Saggion, 2014), as it has been proven to be applicable to glossary generation (Muresan and Klavans, 2002;Park et al., 2002), terminological databases (Nakamura and Nagao, 1988) or question answering systems (Saggion and Gaizauskas, 2004;Cui et al., 2005), among many others.
Research on DE has seen contributions where the task is typically proposed as a binary classification problem (whether a sentence is a definition or not), although with exceptions (Jin et al., 2013). DE has also been studied in languages other than English, e.g., Slavic languages (Przepiórkowski et al., 2007), Spanish (Sierra et al., 2008) or Portuguese (Del Gaudio et al., 2014). Many of these approaches use symbolic methods depending on manually crafted or semi-automatically learned lexico-syntactic patterns (Hovy et al., 2003;Westerhout and Monachesi, 2007) such as 'refers to' or 'is a'.
A notable contribution to DE is the Word Class Lattices model , which explores DE on the WCL dataset, a set of encyclopedic definitions and distractors, and which we use in this paper. In a subsequent contribution, Espinosa-Anke and Saggion (2014) present a supervised approach in which only syntactic features derived from dependency relations are used, and whose results are reported higher to the WCL method. For identifying definitions with higher linguistic variability, a weakly supervised approach is presented in Espinosa-Anke et al. (2015). And finally, models based on neural networks have been leveraged for exploiting both long and short-range dependencies, either combining CNNs and LSTMS (Espinosa-Anke and Schockaert, 2018) or BERT (Veyseh et al., 2019), and which are currently the highest performing models on WCL.

Data
In this section we present the datasets utilized for our analysis, namely WCL (Section 3.1) and DEFT (Section 3.2), and provide a descriptive analysis comparing both datasets (Section 3.3).

WCL dataset
The WCL dataset  contains 1,772 definitions and 2,847 non-definitions. Each instance is extracted from Wikipedia, and definitions follow a canonical structure following the genus et differentia model (i.e., 'X is a Y which Z'). A preliminary (and shallow) analysis that can be performed without any linguistic detail revolves around comparing the length of definitions vs non definitions. Specifically, definitions have 27.5 words on average, while non-definitions have an average length of 27.2 words. The median for definitions and non-definitions, respectively, is 25 and 24. Although the difference is quite small, it seems that encyclopedic definitions are in general slightly longer.
A particular feature of the WCL dataset is that each candidate is composed of a sentence with part-ofspeech and phrase chunking annotation. For definitional sentences, an additional set of tags is provided, which identify core components in definitions such as DEFINIENDUM (term defined), DEFINITOR (definition trigger), DEFINIENS (cluster of words that define the definiendum) and REST (rest of the sentence).
Let us look now at the average length of each of these definition components (see Table 1). The DEFINIENS is typically the most important part of definition sentences (where the definition actually happens), however, it is also the shortest one, followed by the DEFINIENDUM. Moreover, REST is generally the longest but also the one with the highest variance, which fits in with the fact that it is a non-essential part of the definition that can contain varying amounts of information. These results seem to suggest that the part of the sentence that actually makes it a definition (definiens and definiendum) is, in many occasions, quite short compared to the overall length of the sentence.  Table 1: Summary statistics of the length of definiendum, definiens and rest.
The original annotation of the WCL dataset also identifies the main verb of the definition, i.e. that are not in the REST part (Table 1(a) lists the frequent ones). As expected, the verb "to be" tops the list, with four different conjugations taking up the top 5 verbs. Note that these 5 verbs together appear in 1,670 of the 1,772 definitions in the WCL corpus, which could be a sign that the appearance of one of these verbs is a relevant feature to identify definitions. We can also find the most common hypernyms in Table Table 2: 5 most common main verbs and hypernyms in definitions in the WCL dataset.

DEFT dataset
The DEFT dataset (Spala et al., 2019) contains 853 sentences, of which 279 are definitions and 574 are non-definitions. It presents a corpus of natural language term-definition pairs embracing different topics such as biology, history, physics, psychology, economics, sociology and government. Sentences have been classified following a new schema that explores how explicit in-text definitions and glosses work in free and semi-structured text, especially those whose term-definition pairs span crosses a sentence boundary and those lacking explicit definition phrases. Thus, they identify as definitions sentences where the relation between a term and a definition requires more deduction than finding a definition verb phrase. Their focus is to identify terms and definitions, but not necessarily the verb that may or may not connect them two, which identifies as definitions a broather variety of structures. In this case, the average length of definitions is 27.38 and non-definitions have an average length of 23.84. The median length for definitions and non-definitions is 26 and 22 respectively.

Descriptive analysis
In this section we perform a short descriptive analysis comparing the two datasets. Continuing with the instance length analysis, Table 3 shows statistics for both datasets, this time comparing length of positive (definition) and negative (non definition) sentences. As can be observed, definitions generally tend to be longer than non-definitions, although the main part of the definition is quite short compared to its overall length. Moreover, while the distribution of definitions/non-definitions is similar, the number of instances is considerably larger in the WCL corpus, which is improtant to note, as we will use it as our training set in our experiments (cf. Section 4.1.) Regarding frequency of specific POS tags, in Section 3.1 we have seen how some verbs are extremely abundant in definitions in the WCL corpus. However, these are quite common verbs in general in these datasets, as Figures 1(a) and 1(b) show. Note that, for instance, 'is' is more frequent in definitions in both datasets, with an average frequency greater than 1 in both datasets (1.4% and 1.1% in WCL and DEFT, respectively). However, 'was' is actually the opposite and is more frequent in non-definitions while the others are much less common and do not seem to be as present in both types of sentences.    Concerning hypernyms (a.k.a genus in Aristotelian definitions), although the counts are much lower for hypernyms than for verbs (Table 2), in Figure 2 we illustrate how the hypernyms that appear at least 5 times in the WCL dataset are usually more common in definitions in both datasets. The presence of such hypernyms is likely to be more related to the topics defined than the structure of the sentence, but having any kind of hypernym is probably a relevant feature of definitions, as canonical or lexicographic definitions have (or should have) at least one.
We observed that definitions and non-definitions present different frequencies of POS and chunk patterns. In the WCL dataset it seems that definitions have a higher frequency of noun phrases (denoted as 'NP' or 'NP NN', for instance), while non-definitions have more prepositional phrases ('PP' or 'PP IN'). However, we do not observe these similarities in the DEFT dataset.
Finally, we computed the most PoS-based patterns structures 1 (occurring at least 5 times) in the main part of definitions from the WCL dataset. We have observed that these structures are much more common in definitions than in non-definitions in both corpora, which seems to indicate definitions tend to use a particular morphosyntactic set of structures which can be strong indicators of definitional knowledge.

Evaluation
In this section we explain our experiments in definition extraction. In particular, we train a supervised model on the WCL corpus of canonical definitions, and tested on the same corpus (via cross-validation) and the DEFT corpus. With this experiment we aim at understanding relevant features for definition extraction and whether features from canonical definitions can be extrapolated to other domains.
Section 4.1 describes the experimental settings and Section 4.2 presents the main results.

Experimental setting
In the following we explain the experimental setting for our definition extraction experiments. In Section 4.1.1 we explain our supervised definition extraction model and its features inspired by our descriptive analysis. Then, we explain the data preprocessing (Section 4.1.2) and training details (Section 4.1.3).

Model and features
As supervised model we made use of a Support Vector Machine (SVM) given its efficiency and effectiveness in handling a large set of linguistic features. The model uses an RBF kernel and a combination of different features. The main one is based on n-grams of range 1 to 3 from the tagged sentences, i.e. each word contains chunk tag, PoS tag and word separated by an underscore.  The other features are based on the findings from Section 3.3. For each training set, the model computes the 5 most common definition verbs, i.e. in the main part of the definition, the 20 most common hypernyms, the 10 most common composition of chunk and PoS tags, the 6 most common chunk tags 2 , the 10 most common structures of chunk and PoS tags combined, the 10 most common structures of chunk tags and the maximum length of definitions. Using this, we obtain the following new features: • VERB: Count of common verbs present in the sentence.
• HYP: Count of common hypernyms present in the sentence.
• CT-Ch, CT-Ch&PoS: For each of the 6 most common chunk tags and the 10 most common combinations of chunk and PoS, number of occurrences divided by total number of tags in the sentence.
• STR-Ch, STR-Ch&PoS: For each of the 10 most common structures (chunk and combination of chunk and PoS respectively), a binary variable indicating if the structure is present in the sentence.
• LEN: The length of the sentence divided by the maximum length of a definition.

Data preprocessing
As each corpus contains different information and has a different structure, their preprocessing is slightly different, although the output has the same format: a matrix where the features are obtained from. As the WCL dataset contains all the definitions' annotations, we classify each part in a different column and also the verbs and hypernyms annotated. We later retag the sentences with PoS tags and chunk, using, respectively, the NLTK 3 pos tag function and the RegexpParser with the following grammar: It distinguishes 3 phrases: verb phrases (contain a verb sometimes preceded by a modal or 'to', with possible adverbs and another verb after a coordinating conjunction), prepositional phrases (starting with a preposition and followed by determinants, cardinal numbers, nouns or pronouns) and noun phrases (including a noun or pronoun sometimes preceded by determiners, cardinal numbers or adjectives).
The output is a matrix where each row corresponds to a sentence and each column has different information such as the sentence (tagged and not), the term being defined, the hypernyms annotated in the sentence, the main verb of the definition, the label and different columns that contain the tags (both PoS tags and chunk or only chunk) for the whole sentence and for the main part of the definition (definiendum and definiens). For non-definitions, some columns such as the verb, hypernym and tags of the main part of the definition contain NaN values, as they only exist for definitions.
The preprocessing for the DEFT corpus is simpler: we tag the sentences using the same rules and save the sentences, tags and label in different columns. Numbers at the beginning of sentences have removed.

Training procedure
As mentioned earlier, the model was trained on the WCL dataset. We used sklearn 4 for training and evaluating the SVM model. For the experiments, the SVM hyperparameters were chosen after testing the following values: [0.0001, 0.001, 0.1, 1, 5, 10, 50, 100] for C, and [0.0001, 0.001, 0.1, 1, 5, 10, 50, 100] for gamma, both in a validation set. Finally, the evaluation on the WCL dataset is performed through 10-fold cross-validation, with 10% of the corpus used for validation in each fold. Then, the model is trained on the whole WCL corpus and evaluated on the DEFT corpus. The final hyperparameters of the SVM were C = 5 and gamma= 0.1. In addition to the SVM model, as a baseline we trained a Naive Bayes with the same features. This model was trained with its standard implementation in sklearn.

Results
The results on the WCL dataset are displayed in Table 4. As a naive baseline we include the results of a system that would identify all sentences as definitions (referred to as Naive(all defs) in the table).
As can be observed, all metrics are above 0.97 and the average metrics are all close to 0.98. This proves the reliability of the SVM model with all our proposed linguistic features, which attains the highest performance of any non-linear model in the task. As a point of comparison, recent works have reported slightly worse results using highly parametrized models such as convolutional and recurrent neural networks (Espinosa-Anke and Schockaert, 2018  When testing the model on the DEFT corpus, the results are not close to being as satisfactory as they are in the WCL dataset, as we can see in Table 5. The model trained on the WCL dataset performs significantly worse than other recent models (Spala et al., 2020), which could be expected given the different nature of the definitions. In the following section we provide a more extensive analysis that also attempts at explaining the performance difference between the two datasets.

Model
Accuracy Precision Recall F1-Score SVM 0.7011 0.6573 0.5900 0.5872 Naive Bayes 0.5909 0.5626 0.5689 0.5611 Naive(all defs) 0.3271 0.1635 0.5000 0.2465 Table 5: DEFT results of the SVM and baselines trained on the WCL corpus. Figure 5 shows the features of the model with highest χ 2 . Some of them are compositions extremely common in definitions such as 'is a', 'is an' or 'refers', but we also find others more topic related such as 'mythology' or 'greek', which would probably be artifacts from the WCL dataset. For a detailed view of each additional feature's significance, we ran the model removing one or more features at a time. Morever, we also ran the model using the n-gram features only, with different combinations of words and tags. We can find this feature analysis in Table 6. Although the accuracy in the 10-fold cross-validation setting does not change significantly when removing only one feature, and even improves slightly in the case of hypernyms, they do show changes when evaluating on the DEFT corpus. We observe significantly lower accuracy when removing more than one feature at a time (last two rows), decreasing regularly when removing more features and obtaining between 0.93 − 0.94 using only n-gram features, which indicates that these features rely on and interact with each other to improve accuracy. The differences are more significant when evaluating the model on the DEFT corpus, the accuracy goes from around 0.70 when using all features to 0.55 when removing some of them. This proves that the additional features are relevant to identify definitions and improve the metrics significantly, especially in unseen corpora. In fact, the performance of using n-grams features only achieves a performance of 0.934 F1 in the in-domain WCL corpus, and a significantly lower 0.575 performance on the DEFT corpus.  Furthermore, we can see in Table 7 how the n-gram model is significantly more accurate when using both PoS and chunk tags and words rather than only some of them, which indicates that both words and structure of the sentence determine whether it is a definition or not.

Error analysis
In Table 8 we can see some examples of predictions from the model that provide a more in-depth view. We observe how the model is successful in correctly predicting sentences with unorthodox structures, such as non-definitions using the verb "is", and syntactically complex definitions. Moreover, some of sentences that have been predicted wrongly as definitions could be considered as definitions, but they are not defining the target word. The false negatives present complex structures probably unseen for the model. Thus, evidence suggests the model succeeds most of the times at identifying definitions and nondefinitions, and has incorporated satisfactorily the distinctive characteristics of each kind of sentence.
As for the DEFT dataset, as expected from the obtained accuracy, the model makes numerous mistakes.  It has a large number of false negatives (23.9 %), making its predictions less reliable in this setting. The model does a good job at detecting true negatives (91.1% of all negative instances), also due to the fact that most sentences are predicted as non-definitions. However, some false negatives do not seem to contain definitional information. Something similar happens with false positives, as some of them would most likely be considered definitions under more flexible criteria. Thus, although the performance of the model on this data set seems to be relatively low overall, this is probably because of the different tagging criteria, as many sentences that appeared as incorrectly predicted, could be labelled correctly under the annotation criteria used in the WCL dataset. For instance, the sentence "Elimination blackjack is a tournament format of blackjack." could be considered a definition with the criteria used in the DEFT dataset as it presents a direct-defines relation, while "It carries the correct amino acid to the site of protein synthesis" would not be considered a definition in the WCL corpus as it is not an actual textual definition.

WCL nodef
His death is deeply mourned by Alleycats fans as seen in the press and media. The term "carbonate" is also commonly used to refer to one of these salts or carbonate minerals. Covering the head is respectful in Sikhism and if a man is not wearing a turban, then a rumāl must be worn before entering the Gurdwara.
Elimination blackjack is a tournament format of blackjack.
The following are links to pictures of Myddfai taken by the club. Balderton Old Boys also are a local football team. def The Callitrichinae form one of the four families of New World monkeys now recognised The Aurochs or urus (Bos primigenius) was a very large type of cattle that was prevalent in Europe until its extinction in 1627. In everyday usage, risk is often used synonymously with the probability of a known loss.
In the 19th century the term anglicanism was coined to describe the common religious tradition of these churches. Both equivocation and amphiboly are fallacies arising from ambiguity.
The term biotic refers to the condition of living organisms.

DEFT nodef
Living things are highly organized and structured , following a hierarchy that can be examined on a scale from small to large.
Transfer RNA ( tRNA ) is one of the smallest of the four types of RNA , usually 70 -90 nucleotides long. At its most fundamental level , life is made up of matter.
A microphyll is small and has a simple vascular system. It consists of a nucleus surrounded by electrons.
An individual with dyslexia exhibits an inability to correctly process letters.
def It carries the correct amino acid to the site of protein synthesis. The atom is the smallest and most fundamental unit of matter.
The rays themselves are called nuclear radiation. A prokaryote is a simple, mostly single-celled ( unicellular ) organism that lacks a nucleus, or any other membrane-bound organelle. Herbivores eat plant material , and planktivores eat plankton.
Matter is any substance that occupies space and has mass. Table 8: Definition (def * ) and non-definition (nodef * ) predictions on both WCL and DEFT ground truth (for def and nodef classes).

Conclusion and Future Work
In conclusion, extracting definitions from texts is a challenging research task, which is highly dependant on the distribution and scope of the application. Nonetheless, in this paper we have shown that a simple SVM model trained on a dataset with canonical definitions using linguistic features can provide high performance while helping us understand the task better. This model has also been evaluated on a corpus with heterogeneous domains, which also provided us with insights on the qualitative difference among definitions in each setting. Our descriptive analysis discovered interesting differences and similarities between definitions and non-definitions that can be used to differentiate them automatically. The inclusion of linguistic features based on our analysis improved significantly the performance of the model. As future work it would be interesting to extend the analysis to corpora of different characteristics and languages. As an straightforward application, a model with accurate performance across corpora would allow the automatic creation of dictionaries from general or specialized domains, as well as to better understand certain topics.


Introducing Aspect-Oriented Programming in Improving the Modularity of Middleware for Internet of Things

Internet of Things (IoT) has become the buzzword for the development of Smart City and its applications. In this context, development of supporting software forms the core part of the IoT infrastructure. A Middleware sits in between the IoT devices and interacts between them to exchange data among the components of the automated architecture. The Middleware services include hand shaking, data transfer and security among its core set of functionalities. It also includes cross-cutting functional services such as authentication, logging and caching. A software that can run these Middleware services requires a careful choice of a good software modelling technique. Aspect-Oriented Programming (AOP) is a software development methodology that can be used to independently encapsulate the core and cross-cutting functionalities of the Middleware services of the IoT infrastructure. In this paper, an attempt has been made using a simulation environment to independently model the two orthogonal functionalities of the Middleware with the focus to improve its modularity. Further, a quantitative measurement of the core design property of cohesion has been done to infer on the improvement in the reusability of the modules encapsulated in the Middleware of IoT. Based on the measurement, it was found that the modularity and reusability of functionalities in the Middleware software has improved in the AspectJ version compared to its equivalent Java version.

I. INTRODUCTION
In today's highly connected world of sensory and interconnected components, efficient communication plays a major role in improving the quality of networked devices, operating in tandem with each other. Wireless network communication between the disparate components have become popular today, because of the growth in high speed communication equipments and related technologies. Due to the pervasive use of Internet in communication between diverse, identifiable and network connected components, Internet of Things (IoT) is the buzzword of choice for such an environment.
IoT [1] is a framework of connected computing devices such as electronic equipments, vehicles, machines and human assisted devices, operating and communicating with each other to offer a seamless set of services. In fact, the significant use of IoT could exponentially increase due to its application in the development of Smart Cities [2]. The technology could be prevalently used in the communication of various interacting devices of such cities. These devices need to provide important composed services and are required to inter-exchange stateful information. The devices could be uniquely identified in an Internet environment and they also use various communication technologies to share and interact with each other.
The field of Aspect-Oriented Software Development (AOSD) [3] is popularly known as Aspect-Oriented Programming (AOP). It allows the development of software to clearly and independently encapsulate the core and crosscutting concerns as individual of functionality. The unique method of improvement in modularity was proposed by Gregor Kiczales [2] in the Palo Alto Research Center. Quite a number of researchers [5]- [7] have been working on inferring the quality of its application with a new set of metrics for measuring the core design properties of the software design. But, the acceptance of the methodology requires a thorough study of its application in various instances, where there is a need for improvement in modularity.
Any modern software inherently requires modules to connect itself with the network for inter-exchange of information. In such an environment, a Middleware becomes an essential component to manage a set of services. A Middleware was born when the common network services were refactored from the application logic and encapsulated as an independent set of services. These intermediate services are necessary to improve the modularity of the software and thereby increasing the quality of service in a networked environment.
In an IoT based environment, different network components such as sensors have to exchange their measured data with other components. Such exchange requires a communication component on both the ends with embedded set of important services. Some of these embedded services are security, handshaking, data transfer, etc. These services in the communication component are modelled as Middleware services and are vital in the successful functioning of the networked devices. Additionally, there are also services that are tangled and scattered across the above mentioned services of the Middleware.
As mentioned above, the Middleware of the IoT communication environment contains both core and crosscutting functionalities. Hence, this is an ideal environment for refactoring these different concerns into independent units of functionalities. Since AOSD provides constructs and methods for the clear separation of the two concerns, this methodology can be applied in refactoring the Middleware services. The refactored services can be further modelled into classes and aspects of AOP. The most prevalently used and supported AspectJ programming language [8] was considered for the implementation of refactored concerns in a simulated environment of the Middleware services.
Cohesion is one of the important software design property reflecting in the attainment of modularity level for a software. Considering this property, it is always preferred to have a higher cohesion value for its components. In this work, the design property of cohesion is measured for the different design components and compared with equivalent Java and AspectJ versions of the Middleware software. A similar implementation and measurement has not been attempted by others. This work will signify the application of the methodology in the architecture of IoT. Based on the comparison, it was found that the cohesion of the classes have improved in the AspectJ version compared to the equivalent Java version.
The rest of the paper is organized as follows: Section II expands the existing work in the literature about the Middleware services and application of AOP in IoT. Section III expands the primary objectives of this research. The concept of modularity of Middleware in IoT is explained in Section IV. Section V explains the proposed measurement model for this research. The new set of proposed metrics for this research work is defined in Section VI. The metrics values obtained by applying them on to the simulated environment is tabulated and explained in Section VII. Section VIII discusses the inferences on applying the proposed metrics. Finally, Section IX concludes and provides pointers for the future scope of this research work.

II. EXISTING WORK
Applying the concepts of AOP is an important area that requires a considerable amount of investigation. This will help in understanding the overall effect of applying the technique to the mainstream programming. Even though a number of authors have attempted to measure the effect of aspectizing a non-AO based software, work done on applying this unique technique on the IoT platform is very few in the literature.
In a work done by Maingret et. al., [8], event based handlers have been used to interconnect the dynamic external behaviors with the decision process of an IoT environment. In such a kind of environment, the dynamic external behaviors can be modeled as an aspect of AOP thereby decoupling them from the decision making process. Such a solution was not attempted by the authors to improve the overall modularity of the framework.
Razzaque et. al., [9] have done a survey on the work done by researchers on the Middleware of IoT. A number of authors specified in the paper have reported their findings on identifying the methods and technologies in the environment. But, there is no work done on improving the modularity of the Middleware software by incorporating techniques to neatly encapsulate the different concerns.
In a work done by Cerny [10], the author has suggested a typical roadmap in using AOP to effectively address with the cross-cutting concerns in the design of applications and integration of systems for the SOA and IoT environments. But no implementation and measurement attempts have been made to quantitatively assess the effect of such application in the Middleware of the IoT environment.
Wang et. al., [11] have used Spring AOP framework for the application of IoT and fog computing in community safety. According to the author this was done in a 5G environment, but the Middleware was not considered for the application of AOP. The framework used is similar to the AspectJ programming language but does not include the constructs in the level of the programming language. Also no quantitative measurements have been attempted to infer on the effect of software quality.

III. FOCUS OF THE RESEARCH PROBLEM
Aspect-Oriented Software Development had focused on improving the modularity of software for the clear and neat encapsulation of core and crossing concerns as independent units of functionalities. But, the application of this methodology requires various implementations and measurements across different domain of software. Implementation of software using this methodology and development of metrics to quantify the modular constructs is the first step in evaluating this unique methodology.
In this paper, the objective is to remodel the software that has been designed using object oriented methodology into a higher modular Aspect-Oriented software. Since, the focus is on the Middleware of IoT, the constructs defined in the Java version of the IoT Middleware have been refactored and remodeled into its equivalent AspectJ version. New set of metrics have been defined to measure the effect of aspectization on the core design property of cohesion for the respective version. The design property of cohesion has been chosen since it is one of the most important property affecting the modularity of software.

IV. MODULARITY OF MIDDLEWARE IN IOT
Two communicating entities in an IoT environment require a Middleware for the communication and exchange of acquired data through its sensors. Hence, a Middleware can be considered as the backbone for the design and development of IoT infrastructure. Fig. 1 depicts the typical role played by the Middleware in such an infrastructure. The Middleware being an intermediary between two IoT devices consists of different types of functionalities embedded inside its framework. These functionalities provide necessary and vital services to the IoT infrastructure. A few of its cohesive set of functionalities are handshaking, data transfer and security. These are the core set of functionalities built as part of the Middleware services. There are also other set of functionalities that constitute the Middleware services defined in its framework.
The functionalities built as the Middleware services of IoT framework are independently designed and encapsulated into software modules. Such an independent modularization is necessary to improve the cohesiveness of the modules in the software. In spite of doing so, there are still set of functionalities that cut across the functionalities embedded in these modules. These functionalities are the scattered and tangled set of concerns that cannot be neatly encapsulated into independent units and thereby improving the modularity of the Middleware software. Hence, there is a need to introduce a software development methodology to enable the modularization of cross-cutting concerns into independent units of functionality.

Fig. 2. Framework of AO in IoT
The significance in the need for modularization can be clearly visualized from the block diagram shown in Fig. 2. The core and the cross-cutting services are shown as separate units inside the rectangular boxes on both ends of the diagram. The core services considered are handshaking, data transfer and security functionalities. These are the important set of core services considered in this study, since they are the necessary and primary services of the IoT Middleware.
Similarly, the services shown in oval boxes are the composition of cross-cutting Middleware services. These tangled and scattered services are woven on the core Middleware services shown as rectangular boxes. The crosscutting services considered are the modularized functionalities of synchronization, logging and caching. Again, these are the most important set of functionalities modeled as cross-cutting Middleware services.

V. PROPOSED MEASUREMENT MODEL AND METRICS
Understanding the changes in software modeling needs the definition of a systematic and predictable quantitative measurement model. The measurement needs to be defined in such a way that it can accurately capture the software design properties in focus. The current study is about the effect of changing the design in the modelling of Middleware software. Hence, the proposed change in modelling of concerns using AOP needs to independently capture both the constructs that define the core and cross-cutting concerns.
The two different cohesion measurement models to measure the Object Oriented (OO) and Aspect Oriented (AO) versions of the IoT Middleware software is shown in Fig 3. The Java version of the software will map the functionalities using classes. Whereas the equivalent AspectJ version is able to independently model the core and cross-cutting concerns into separate set of functionalities. As specified in Fig. 2 the core functionalities considered in the design are handshaking, data transfer and security. In the AO version the functionalities of synchronization, logging and caching are considered and modeled as aspect in AspectJ language.
It is well known that any study on the effect of using a different software design model cannot be measured directly. Hence, there is a need for a well-defined and comprehensive quality model for its evaluation. The quality model can include the measurement of design property from which the effect on the quality can be inferred in a systematic manner. The design property of cohesion is considered in this study to measure and infer on the improvement in quality. The measurement models shown Fig. 3 considers cohesion for the measurement.

VI. MEASURMENT OF COHESION
Cohesion is one of the most important design property to be considered during the design of software. It is a core software design property that quantifies the ability of a software module to focus only on a single functionality. The spectrum for the cohesion metric value ranges from low to high or from 0 to 1. A software module that exhibits higher cohesion will have a value closer to 1 and if it exhibits lower cohesion, then the value will be close to 0. For example, if a software module is defined with two embedded functionalities, then the value of its cohesion is 0.5. Similarly, if three functionalities are embedded then its cohesion value is 0.33.
In the literature, various metrics have been proposed by researchers [12]- [15] in order to quantify cohesion of the given AO software design. These metrics were able to consider the different constructs defined in the AO design and implementation. For example, in AspectJ programming the classes and aspects are the two encapsulated modular entities considered for the measurement of cohesion. These constructs have to be individually measured to find the net cohesion value of an AspecJ version of software. The equivalent Java version will only contain the classes as the encapsulated entities and will be counted during the measurement.
The metrics considered for the measurement of Cohesion Index (CoI) in this study is shown in Equations 1 and 2.
Consider the following units before specifying the metrics: Cohesion Index (AspectJ aspects), The metric CoI(J) for the Java classes and CoI(AJ) for the AspectJ aspect of the IoT Middleware have been normalized and can range between the values 0 to 1. This method enables to compare the two equivalent, but different model based versions to be compared to evaluate the effect of aspectizing an OO based software.

VII. MEASURED VALUES OF PROPOSED METRICS
In this paper, a new set of metrics have been proposed to measure the cohesion of Java and AspectJ versions of a software. Further, the software that simulates the Middleware of the IoT environment has been developed using Java and AspectJ programming languages. The functionalities embedded in the versions have been explained in Section II of this paper.
The first metric, cohesion of the Java classes CoI(J), is applied on both the Java and AspectJ versions of the IoT Middleware software. Cohesion of the AspectJ aspects, CoI(AJ) metric, has been applied only on the AspectJ version of the IoT Middleware software. The respective measured values of the two proposed metrics, CoI(J) and CoI(AJ) are tabulated in Table I. During the measurement, the first metric, CoI(J) has been applied on both the versions of the IoT Middleware because, the AspectJ version consists of both classes and aspects. The classes were used to encapsulate the core concerns and the aspects were used to encapsulate the cross-cutting concerns of the software. The vice versa is not possible as the Java version modularizes the core and cross-cutting concerns in a tangled and scattered form of software code.

VIII. DISCUSSION ON MEASUREMENTS
Understanding the effect of aspectizing an OO based IoT Middleware software requires careful definition and application of measurements. As discussed in the previous sections, a new set of metrics have been defined to the measure the core design property of cohesion for the Java and AspectJ versions of the IoT Middleware. These metrics have been applied to the respective versions and the value are tabulated in Table I. In order to visually understand the effect of aspectization the measured values of the proposed metrics have been statistically represented using a bar graph shown in Fig. 4.
The following are the set of inferences derived from this quantitative assessment which provides insights on the effect of aspectizing IoT Middleware towards the higher level software quality attributes: • Value of the CoI(J) metric has increased in the AspectJ version of the Middleware software compared to the Java version. This implies that the modularity of functional components modeled as classes in the AspectJ version is better than the Java version of the Middleware. The reason for such a change in the values is because the tangled and scattered functionalities modelled in the classes of the Java version have now been refactored into more modular aspects and woven at the respective join points in the classes.
• Notably, CoI(AJ) of the AspectJ version has shown a high cohesion with the maximum value of 1. This has happened because the tangled and scattered concerns present in the Java version of the Middleware is now refactored as independent units of functionality. In our implementation, the ability of the AspectJ implementation to independently encapsulate the functionalities of synchronization, logging and caching has helped to aspects to attain the highest values of cohesion metric. This value in turn also suggests that aspects are components with high degree of modularity and reusability.
• The average value of the CoI(J) and CoI(AJ), has naturally increased in the AspectJ version of the Middleware compared to its equivalent Java version. This increase reflects the ability of the AOP technique to improve the overall cohesive strength of the modules modelled with the required functionalities of the Middleware. Such a kind of modeling will help in the overall quality of the software thereby, decreasing the maintainability of the modules encapsulated as classes and aspects.
To summarize, aspectization of the Java version of the IoT Middleware which resulted into the equivalent ApsectJ version has shown measured sign of increase in reusability, maintainability and modularity, which are some of the core higher level quality attributes of a software. The powerful and flexible AOSD methodology, which was introduced in the late 90s, has sustained and grown in spite of other introducing other similar methodologies. The proponents of AOSD have done a considerable amount of research work to evaluate and learn about the positive effects and difficulties in adopting this methodology. In the last 20 years, a large number of researchers have attempted to apply and quantitatively evaluate using new set of metrics. Due to this a significant amount of research content is available in the literature related to aspectization.
Based on the need to quantitatively assess the effects of aspectizing a non-AO (OO) software, this research has focused on applying the technique in the infrastructure of IoT. In particular, the Middleware of the IoT infrastructure has been considered for the application of aspectization. The core and cross-cutting concerns of the functionalities embedded in the Middleware have been considered the application. Both Java and AspectJ versions of the IoT Middleware were developed using a simple simulation environment.
Further, to evaluate the effect of aspectizing an OO based software, the design property of cohesion has been considered for the measurement. A new set of metrics have been defined to measure the effect of aspectizing an OO based Middleware software. The first metric was used to measure the functionalities embedded in the constructs in the Java classes and the second metric was used to measure the cross-cutting functionalities defined in the aspects of AOP. The first metric, CoI(J) was applied to both the Java and AspectJ versions of the IoT Middleware, whereas the second metric, CoI(AJ) was applied only to the AspectJ version of the Middleware.
Based on the measurement it was found that the modularity and reusability of the functionalities has improved in the AspectJ version of IoT Middleware due to the increase in the cohesion values. This work can be further extended by increasing the number of functionalities considered in the IoT Middleware for aspectization. Further, the core software defined in the IoT devices of the infrastructure can also be considered understand the overall effect of applying AOSD methodology.