extfieldsofstudy:
Environmental Science
Physics

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Factors affecting the enforcement of environmental regulation: the case of erosion and sediment control plan

Construction activities significantly increase the amount of sediment loss from sites due to erosion. Hence, governments are implementing environmental regulations, including the Erosion and Sediment Control Plan (ESCP), to reduce the construction industry’s impact on the environment. Prior studies suggest that effective enforcement by government bodies is crucial to ensure proper ESCP implementation. Therefore, this study aims to identify the success factors and challenges of enforcing ESCP in practice. To achieve that objective, this study employs a qualitative research technique that compares ESCP guidelines between countries. The significant results include: (a) the challenges are in the aspect of the hierarchy of authorities, lack of information in the guidelines, and enforcement that are lenient; and (b) the success factors include the elements of having detailed information in guidelines, a suitable period is given to all parties, and frequent inspections. These findings can assist researchers and government bodies to develop strategies to enforce ESCP better. The development of better strategies would help improve the success of ESCP implementation. This paper contributes to the body of knowledge in analyzing enforcement strategies in existing guidelines and standards for ESCP.

Introduction
Erosion and Sediment Control Plan (ESCP) is a plan that can be used for every construction project to control soil erosion. Soil erosion and sediment will bring a negative impact on construction and community. It can be differentiated into two types of effects, which are on-site impact and off-site impact. Hence, ESCP should be implemented and enforced by authorities to make sure that the soil erosion can be controlled to minimize the impact on construction and community. For the enforcement of ESCP, it is done by the Department of Irrigation and Drainage (DID) and Department of Environment (DoE). Construction players are required to submit ESCP before the construction activities started. It is hard to identify whether the enforcement of ESCP is functioning successfully. However, with some news and tragedies that happened in construction projects due to erosion and sedimentation, it can be said that ESCP did not operate efficiently in Malaysia. After the spot check, the authority found out that at least 85% of contractors do not comply with ESCP. In 2017, 77 construction sites violated ESCP, while in 2019, 55 out of 66 sites did not comply with ESCP. A tragedy happened at a construction site in Bukit Kukus in 2016, which caused nine (9) deaths and four (4) injuries. After the investigation, the authority found out that the contractor failed to fulfill the guideline of ESCP. These results show that there are some problems with the enforcement of ESCP in construction projects. Research related to the degree of erosion and sediment control meets the required standards and regulations [1]. From 85 construction sites and 147 sediment ponds and traps, 62% of sediment ponds 2 and traps are installed incorrectly. Another research shows that one-third of the respondents mentioned that enforcement is the biggest challenge in implementing erosion and sediment control [2]. Besides, in another case study, most respondents perceived that environmental laws and policies inadequately implemented due to a lack of enforcement and effective mechanisms to ensure compliance [3]. A study also suggests that stormwater violations by public agencies and lack of permits in Minnesota are caused by a lack of best management practices (BMPs) [4]. Hence, the enforcement of ESCP should be investigated to ensure it is suitable for every party involved. This study aims to identify the factors affecting the enforcement of ESCP in Malaysia. The factors are including challenges and success factors in enforcing ESCP in construction projects. The problems and success reasons affect the enforcement of ESCP, including human factors, environmental issues, and weather conditions. Government authorities can act based on the list, including eliminating the challenges and improving the success factors that already have in the enforcement.

Methodology
This research is done by using a qualitative research technique with the document analysis method. Document analysis is one of the qualitative research methods involving the interpretation of meaning from documents. The method reviews documents to give meaning to the recorded words without altering the original context [5]. There are three types of suitable materials and will be used in document analysis, including public records, personal documents, and physical evidence [6]. Qualitative comparative analysis is another method that was used in this research. The qualitative comparative analysis is the method that can be used when the study is related to the phenomenon in the formation of two or more countries [7]. From the analysis, it can compare the similarities and differences between countries. It helps in generate better understanding and awareness by comparing it with different countries. It also helps in explaining some complex and changes in some cases but not others. The number of sample sizes is not defined specifically. However, according to the research done by other researchers, the guidelines from Malaysia with the guidelines from other countries which including Australia, Britain, and the United States of America (USA), are compared [8]. Hence, at least three documents from other countries are needed to consider as sufficient sources. In this research, seven (7) guidelines are collected and compared. The guidelines are: The text of your paper should be formatted as follows:

Results and discussion
This research found that there are thirteen (13) factors that will affect the enforcement of ESCP. Table  1 shows the scoring of comparable items between the guidelines of each country. The details will be discussed in the upcoming subsections.

Inspection report
The inspection report is the report that is used to record the inspection details for site conditions. It is an essential material for every party involved in construction projects. It can provide evidence that soil erosion and sediment control devices are inspected and can be used effectively. The guidelines for erosion and sediment control in Malaysia provide a sample of guidelines that clearly describe the condition of devices required by authorities. The information to be recorded in the inspection check sheet includes project name, developer name, inspection date, time and weather, inspection type, and construction stage. Meanwhile, in the guideline from Canada, New Jersey, and Oregon, the inspection checklist is not provided in detail. They only offer some examples of questions in guidelines for construction projects, and no checklist is provided. It means that there are no examples of inspection checklist provided in the guidelines, resulting in decision-makers not following the authorities' requirements. The detailed description of the inspection checklist might improve and benefit the enforcement of ESCP in Malaysia. It is because the comprehensive inspection checklist provides more information from authority to construction management. Besides, the inspection checklist can be a reminder for contractors [9]. The contractor can review the critical items to be done in the checklist and recheck it once the work is done. It can be made as a record for reference in the future. It also can be used in some legal actions in the future.

Site inspection period
Site Inspection is an activity that needs to be done when there is a new construction product, activities, and any event that will cause devices damaged. To make the inspection effective, the authority needs to identify a specified period for every construction project. For erosion and sediment control guidelines in Malaysia, the site inspection should be done periodically as a schedule planned by developers or contractors. The inspection should be done after every storm event to ensure that the devices can function efficiently. The site inspection should also be done for the installation and removal of BMPs. This is also to make sure that the devices still can function well after installing and removing.
For guidelines in Canada, there are least accurately describing the period of the site inspection. In the guideline, it is only mentioned that the inspection should be done periodically with schedules. It does not indicate any inspection under a condition, which means the inspection will only be done by following the schedule. Meanwhile, in Oregon, the guideline shows a different site inspection period with others where the site inspection will be obtained once a week for active sites and once every two weeks for inactive sites. Besides, the inspection also needs to be done within 24 hours after the event of rain. For Georgia, the guideline mentioned that the site inspection should be done periodically with schedules. However, the inspection also can be done randomly to have a spot check for the construction project. The scheduled inspection will bring benefits to enforcement in construction projects in Malaysia. When the inspection period is scheduled, a specified target needs to be inspected, and it is notified to contractors beforehand. This can lead to contractors, or the person in charge will have plenty of time to prepare [10]. However, a random inspection provides a more "real" result for erosion and sediment control devices because it is an immediate checking for the construction site. The contractors and person in charge might not have enough time to repair and prepare devices for inspection; hence, it will be a usual situation. Some enforcement will benefit Malaysia's guidelines where the inspection would need to be done after every storm event. It is a safer action for every construction project to check their devices' condition after the event of a storm.

Time for maintenance after inspection
There are some maintenances needed for erosion and sediment control devices to ensure that it can function properly to reduce any impact on the environment. The inspection should be done after the maintenance of the devices. Hence, the maintenance time after inspection should be determined and enforced to ensure that the devices can be fixed in a short time and minimize the impact and effect on the environment. For guidelines in Malaysia, the time for maintenance after inspection should be 48 hours after the storm event. It means that within 48 hours after a storm event, the inspection and maintenance need to be done to ensure the requirement from authority can be achieved. The authority might aim to minimize the impact on the environment. Besides, guidelines for Malaysia also mentioned that the maintenance should be done regularly. It is to make sure the devices can maintain in good condition from time to time. In Oregon's guideline, there are no times mentioned to maintenance after inspection. It is only indicating that the devices need to be fixed immediately after damage. There are no specific times provided for contractors to undergo maintenance. However, the other countries' guidelines do not mention the maintenance after inspection. Georgia's guidelines in the year 2016 require contractors to execute the maintenance after inspection within 24 hours after a storm event. By comparing the time for maintenance after inspection between Malaysia, Oregon, and Georgia, Georgia has a shorter time for maintenance after inspection than Oregon and Malaysia. The frequency of maintenance is constant for every week. It can be adopted by Malaysia to improve the quality of enforcement. The shorter time for maintenance can minimize any environmental impact, and weekly maintenance can provide a better quality of erosion and sediment control devices.

Time for keeping records of inspection
The inspection will be done by authority and project management on sites and erosion and sediment control devices. The inspector will record the state of devices and sites in the inspection form as evidence for future legal purposes. According to the Guideline for Erosion and Sediment Control in Malaysia, the period to keep records of inspection should be at least three years by developer or owner. Besides, other relevant documents and records for incidents should also be kept for maintenance and better quality of BMPs. For the guideline in other countries, Canada's only guideline shows that the document for inspection and maintenance should be kept at least one year after the development is completed. By comparing the time to keep records after inspection between Malaysia and other countries, Malaysia provides a specific time, at least three years, and detailed descriptions are given while other countries are not providing it. Compared with Canada, which is at least one year, Malaysia is enforcing a longer time than Canada, which may benefit Malaysia. More prolonged periods for keeping the records will protect the liability of construction. The period for retaining records should be the period of liability for the claim, plus one year [11].

ESCP checklist
ESCP is a plan that detailed the measures that control the impact of erosion and sediment. Different devices were obtained in ESCP, including sediment basins, sediment traps, and temporary stormwater diversion. Besides, the minimum requirement of ESCP in the checklist will also be necessary for the enforcement. In Malaysia, the ESCP checklists are described in detail and grouped into categories that include a general requirement, site plans minimum requirement, erosion and sediment control, final erosion and sediment control plans, and minimum requirements. The checklist for ESCP also provided a detailed description of the required part, which helps the management and contractors to follow the checklist. For guidelines in Canada, the checklist of ESCP does not group as Malaysia's guideline, where it only provides a simple description of the items required in ESCP. However, a detailed description of erosion and sediment control required by authority is provided in the appendix part together with pictures and details. Comparing the ESCP checklist condition of Malaysia and other countries shows a benefit and advantages to the success of enforcement of ESCP in Malaysia. With the detailed checklist for construction management, the management shall make things organized to make sure there is no process skipped and forgot [12]. The checklist can be a to-do list for the management to follow up on what they have done and what they have not done. It is a quick and easier review for the management, and it helps to improve productivity. Hence, when there is an ESCP checklist in Malaysia, and other countries do not obtain the checklist in their guideline, Malaysia will have a bigger advantage than the others. For the information in the checklist, it should be in detail to ensure that the contractors clearly understand authority requirements. Hence, Malaysia is having a more detailed checklist when compared with Canada and Oregon.

Submission time
In Malaysia, the submission of ESCP to local authority should be at least two months or more extended periods or according to the local authority requirement. Malaysia provides more time for the authority to review and evaluate ESCP from the developer and owner to ensure that the plan is suitable and valid to be used. The periods of 2 months' submission times should be counted before the beginning of construction activity. For guidelines in Canada and Georgia show no specific time for the submission of ESCP but mentioned during the construction project's planning phases. For New Jersey, the guideline mentioned that the authority needs 30 days for approval, and the district has the right to have 30 additional days for review. This means the construction project would also need to submit their plan at least 60 days, which is approximately two months before the construction activities begin so that the progress can be continued smoothly. For Oregon, Maryland, and New Zealand, no specific time is shown in the guideline. The submission of the plan should be made for months before the beginning of construction projects. This can increase the period of evaluation for the plan to make sure that it can work efficiently. By comparing guidelines in Malaysia and other countries, Malaysia obtains the most extended and fastest submission period. Besides, Malaysia's guidelines also clearly defined the specific time for submission, while others do not mention submission time. For guidelines in New Jersey, authority remains the right to extend an extra 30 days for the approval period, which shows uncertainty to management. Hence, with a specific time and longest submission time, Malaysia shows a better advantage to others.

Approval from authorities
The party involved as the authority is the local authority for each area in Malaysia. The permit given by local authorities should be at least 14 days before the construction activities begin. For guidelines in other countries, more parties are involved as the authority-however, no specific time is provided to approve the plan before the construction activities begin. For the authority involved in the approval process, more parties involved will have a better quality of plan because there will be more opinions from different experts in different fields. The subcommittee of Maryland with various parties' members shows good practice in evaluating the plan. It is a cross-functional team collaboration that  [13]. This can be adopted by Malaysia to improve the enforcement of ESCP since more aspects will be taking care of to make sure the plan can be completed.

ESCP valid time
The guideline for Erosion and Sediment Control in Malaysia states that the valid time for ESCP should be two years from the date of acceptance by authorities. After a valid time, the plan should be rereview and re-acceptance by the authorities, as stated in the guideline. The plan will be a review based on the current condition to ensure the plan is suitable to be used. For guidelines in New Jersey, the plan will be valid for the duration of initial project approval granted. This means that the plan will always be used for the whole project. This can benefit developers and contractors where the plan will no need to be revised and changed anything which will increase their work. For guidelines in other countries, the valid time for the plan is not mentioned in it. It should be stated and enforced for a valid time to ensure that the plan can be updated to fit with the current situation. Hence, by comparing Malaysia's guideline with other countries, Malaysia shows advantages since its guideline states a valid time for plans clearly. In contrast, others do not mention it. When compared with guidelines in New Jersey, the plan will be valid for the duration of projects. In other words, the plan will not be rereviewed unless some problems occur. With this, Malaysia obtained a valid two-year time for the plan to ensure that the plan is suitable for the construction through the review process. This action is more beneficial to the enforcement of authority to ESCP where the plan must be re-reviewed in a certain time; otherwise, the construction activities cannot be continued.

Wet weather plan requirement
All seven guidelines do not require wet weather plans, except Oregon. Oregon's wet weather plan provides a checklist to ensure the erosion and sediment control plan can sustain wet weather. To do that, the wet weather plan requires the establishment of native vegetation, temporary vegetation, slope stabilization, and permanent ground cover. In a nutshell, the amount of rain in Malaysia (79 inches/year) is higher than Oregon (43 inches/year) [14]. Therefore, Malaysia should also consider developing a wet weather plan requirement for its erosion and sedimentation control plan. It is always an excellent action to prepare for a backup plan during wet weather, minimizing any environmental impact when problems occurred. It is found that the non-linear effect of high-intensity storms on runoff and erosion processes. Hence, it can be concluded that high intensity and short duration storms lead to higher soil losses [15]. With this, a wet weather plan will be needed to ensure that all devices can function well while minimizing the environmental impact by applying a wet weather plan. This can be adopted by Malaysia so that the enforcement of ESCP can be improved since 79 inches of rains are obtained annually, which is more than Oregon.

Project size requirement
For guidelines in Malaysia and Canada, the management should submit ESCP when the size of projects greater than 1 hectare. However, in Oregon, all construction projects should submit ESCP, including the project, which less than 1 hectare. This means that all construction will need to install BMPs to ensure that any environmental impact can be minimized. The enforcement in Oregon where all size of construction project needs to submit ESCP should be adopted by Malaysia to improve on the enforcement of ESCP. It helps minimize the impact of erosion and sediment on the environment and prevents any party from violating the rules.

Number of rules referred
Thirty-two legislations, eight municipal rules, and six guidelines are referred in a guideline from Malaysia to come out with the guidelines of erosion and sediment control. The regulations and legislation are listed out which including Water Act, Land Conservation Act, Sewerage Services Act. The legislation also included the enactment and ordinance in Sabah and Sarawak. Besides, for local


Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

: Accurate real-time water quality prediction is of great signiﬁcance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially signiﬁcant for non-periodic parameters of TN.

Introduction
As the most important water source for human life and industry production, surface water is extremely vulnerable to being polluted. By quantifying different types of parameters, water quality monitoring can help us to develop best management practices to protect water source safety and improve aquatic habitats [1]. With the development of the social economy, many regions of China are facing severe water pressure due to increasing water demand and decreasing available water resources as the result of surface water pollution [2]. Therefore, precisely and timely monitoring and prediction of surface water quality is particularly critical to local environmental managers for designing pollution reduction strategies and responding to environmental emergencies. In recent years, many studies have estimated water quality parameters through different methods based on artificial intelligence tools. For example, Fijani et al. designed a hybrid two-layer decomposition performances; and (3) compare the results of different prediction steps and analyze the impact of prediction steps on different parameters and different models.

Study Area and Data Description
The case study area in this paper is Xin'anjiang River, which is a transboundary river that crosses Anhui Province and Zhejiang Province. The Xin'anjiang River originated in Huangshan City, Anhui Province, and flows into Thousand Island Lake through Chun'an County, Zhejiang Province ( Figure 1). As the most important water source flowing into the Thousand Island Lake, the water quality of Xin'anjiang River is concerned with the health of 115.91 million people in both Anhui and Zhejiang provinces [19]. The Jiekou monitoring station is located at the junction of these two provinces, which is mainly used to monitor the water quality of Xin'anjiang River (Figure 1). Jiekou station automatically analyzes water samples and records data every 4 h to realize real-time monitoring of water quality. We collected water quality data including dissolved oxygen (DO) and total nitrogen (TN) from 0:00 on 6 May 2015 to 20:00 on 20 January 2020. The DO and TN datasets both had 10,326 records, which were used to conduct the predictive models. To perform the model development and evaluation, the full dataset was split into training, validating, and testing subsets. The data division criterion in this study was shown in Figure 2. The training, validating, and testing sub-sets included 6609 records (nearly 64% of the dataset, from 0:00 on 6 May 2015 to 8:00 on 11 May 2018), 1652 records (nearly 16% of the dataset, from 12:00 on 11 May 2018 to 20:00 on 10 February 2019), and 2065 records (nearly 20% of the dataset, from 0:00 on 11 February 2019 to 20:00 on 20 January 2020), respectively. The descriptive statistics of the training set, validating set, and testing set for these two parameters were summarized in Table 1.

Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise
CEEMDAN is an improved adaptive signal time-frequency analysis method [20], which is developed following the empirical mode decomposition (EMD) [21] and ensemble empirical mode decomposition (EEMD) [22]. EMD could decompose complex signal into intrinsic mode functions (IMFs) and a residue, so as to better understand the linear and nonlinear characteristics of the signal. However, since either a single IMF is composed of very different scales, or similar scales reside in different IMF components, the original EMD has the disadvantage of mode mixing [3]. The EEMD method is proposed to solve the problem of mode mixing by adding white noise [22]. However, the added white noise could not be completely eliminated during the decomposition process, so the interaction between the signal and the noise may produce different modes [23]. The CEEMD algorithm completely eliminates the residual white noise by adding positive and negative auxiliary white noise pairs to the original signal, and further improves the performance of the decomposition algorithm [24]. By introducing the adaptive noise component, the CEEMDAN algorithm not only maintains the ability to eliminate mode mixing and residual noise, but also has fewer iterations and higher convergence performance [20]. In this study, CEEMDAN was used to decompose the original DO and TN data series to reduce their complexities.

Convolutional Neural Network
The convolutional neural network (CNN) is a multi-layer feedforward neural network that can be used to process data of multiple dimensions, such as time series data (which can be considered as a 1-D grid sampled at regular intervals) and image data (which can be considered as pixel 2-D grid) [25]. With the application of CNNs, many variants of convolutional network structure have appeared. However, the basic structures of most networks are similar, including input layer, convolution layer, pooling layer, fully connected layer, and output layer [16,26]. The input data needs to enter the convolutional neural network through the input layer. According to the observed data and study objectives, the input layer has a variety of formats, including 1-D, 2-D, and even n-D, and these data usually contain 1 to n channels. In this study, the original time series could be viewed as 1-D grid data to build a 1-D convolutional network. Moreover, the original data preprocessed by CEEMDAN were decomposed into multi-frequency IMFs, and all of them were put into CNN as input data in the form of image. These images had only one channel, and the CNN built based on them could be considered a 2-D model.
Through a series of comparative experiments, the basic structure of the CNN models used in this study was determined. After the input layer, the second layer was a convolutional layer with a number of learnable convolution kernels. The application of these kernels was to obtain multiple feature maps by learning different features of the input data. The third layer was the activation layer that used rectified linear unit (ReLU) as the activation function. The fourth layer was a pooling layer using the average function. The pooling layer reduced the dimensionality and processing time by sub-sampling the feature maps extracted from the convolutional layer. The fully connected layer, which was the last part before the output layer, connected the output of the pooling layer to a one-dimensional row vector. Finally, for the regression problem of this study, the regression output layer was used as the last layer of CNN. In addition to these basic structures, in order to reduce the problem of overfitting, dropout and batch-normalization methods were used in this study [27]. Dropout randomly set the input elements to zero with a given probability in each training case, so that a large number of different networks could be trained in a reasonable time to effectively reduce the problem of overfitting [28]. Batch normalization can mitigate the internal covariate shift and the dependence of the gradient on the parameter or its initial value, so as to achieve the purpose of accelerating training and producing more reliable models [29].

Long Short-Term Memory Neural Network
The long short-term memory (LSTM) network is a special variant of the recurrent neural network (RNN). In traditional feedforward neural networks, information can only flow from the input layer to the hidden layer and finally to the output layer in one direction. The biggest difference between RNN and the feedforward neural network is that RNN has a recurrent hidden unit to implicitly maintain historical states about all past elements in the sequence [30,31]. That is to say, in RNN, the final output is based on the feedback of the input layer and the historical states of all hidden units. However, when the RNN is trained using the gradient descent method, the gradient may show an exponential increase or decay, which will cause the gradient to explode or disappear [31,32]. LSTM introduces the concept of input and output gates to improve RNN. LSTM applies memory cells, which could filter and process historical states and information instead of the recurrent hidden units of the RNN, and the basic structure of three consecutive memory cells is shown in Figure 3. Each memory cell contains an input gate (i t ), a forget gate (f t ), and an output gate (O t ) to control the flow of information. The input gate (i t ) is used to determine how much input data at the current moment (t) needs to be stored in the cell state, and an intermediate value Ct is used to update the state in this process.
Water 2021, 13, 1547 6 of 20 ting, the dropout layer was inserted before the fully connected layer. All of the algorithms were performed by programming codes in MATLAB R2020b, and a function developed by Torres et al. [20] was used to design the CEEMDAN model. The Deep Learning Toolbox including the basic framework of multiple deep neural networks was applied to design the CNN, LSTM, and CNN-LSTM models.  Figure 3. The architecture of long short-term memory (LSTM) neural network.
The forget gate (f t ) determines how much of the cell state from the previous moment (t−1) needs to be retained to the current moment.
By deleting part of the old information and adding the filtered intermediate value, the cell state is updated from C t−1 to C t .
The output gate controls how much of the current cell state needs to be output to the new hidden state.
In the above formula, W * and b * , respectively, represent the relevant weight matrices and bias vectors, while σ(·) and tanh(·) are the sigmoid function and hyperbolic tangent function. The basic structure of LSTM models used in this study included the input layer, LSTM layer, fully connected layer, and regression output layer. To prevent overfitting, the dropout layer was inserted before the fully connected layer.
All of the algorithms were performed by programming codes in MATLAB R2020b, and a function developed by Torres et al. [20] was used to design the CEEMDAN model. The Deep Learning Toolbox including the basic framework of multiple deep neural networks was applied to design the CNN, LSTM, and CNN-LSTM models.

The Hybrid Forecasting Models Development
Before constructing the forecasting models, the Lyapunov exponents were calculated first to determine whether there were nonlinear and dynamic characteristics in the DO and TN data series. When the maximum Lyapunov exponent is positive, it is a powerful indicator of the chaotic features of the data series. The values of the Lyapunov exponent for DO and TN were 0.36 and 0.44, respectively, indicating that the monitoring data of these two parameters were both chaotic time series. For chaotic nonlinear dynamic systems, the data may be corrupted by noise or their internal features may be completely unknown, so deep learning methods are very suitable for prediction and feature extraction of such data. In this study, we investigated the potential uses of single models and hybrid models for multi-step ahead forecasting of DO and TN. The procedures of the water quality prediction models based on CNN and LSTM are described as follows.
Step 1: The explanatory variables and target variable were decided. Although some commonly used analysis methods (such as autocorrelation function and partial autocorrelation function) have been tried to determine the input variables, no significant number of lags has been found. Taking into account the monitoring frequency and effectiveness of the data, twelve records in two days were selected as input variables, and six records in the following day were used as output variables. Taking DO as an example, the input and output variables are shown in Table 2.
Step 2: DO and TN data series were decomposed into several different IMFs and a residual component. These components can provide detailed information from high frequency to low frequency contained in the input data series. Previous studies indicated that the added noise level and the number of realizations in the CEEMDAN modeling process can be adjusted according to the application [33]. According to some empirical algorithms in the previous studies (for example, the recommended white noise amplitude is about 20% of the standard deviation, etc.) and the results of trial and error, a noise level of 0.1, a realization of 100, and a maximum of 2000 sifting iterations were set [3,22,34].
Step 3: The data series were divided into training, validating, and testing sets, respectively. It is worth noting that although the testing subset is completely independent of the training process and is used to compare the forecasting performances of the models, the decomposition process of CEEMDAN needs to be applied to the entire dataset. If the training subset and the testing subset are decomposed separately, the obtained IMFs and residual component are inconsistent and the trained model becomes invalid for the testing data.
Step 4: The training subset was used to train each model for multi-step ahead forecast. This study contained two types of input data: the original one-dimensional time series and the two-dimensional gray-scale image based on CEEMDAN decomposition. The original one-dimensional input was a set of 12 × 1 data sequences, while the CEEMDAN-based input was a set of 12 × n two-dimensional images (the value of n was decided by the results of decomposition process). The one-dimensional and two-dimensional images are shown in Figure 4, respectively. Each type of data was used to train three deep neural networks: CNN, LSTM, and CNN-LSTM.
Step 5: The testing subset was used to obtain multi-step ahead forecasting results and compare the performances of different models. Since the testing data were not involved in the training process at all, the forecasting results of testing data set could well reflect the generalization ability of the models. At the same time, the results of different ahead steps can be used to evaluate the effective forecasting duration of the models.

Evaluation Criteria
To quantitatively evaluate the performances of the aforementioned models, three statistical evaluation criteria were selected for comparison, including the coefficient of efficiency (CE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The CE was also known as the Nash-Sutcliffe coefficient and defined as follows: The RMSE was calculated as: The MAPE was defined as: where n was the number of input samples, O i and P i were observed and predicted value of sample i, respectively. O was the mean value of observed data. Generally, the larger the CE value and the smaller the RMSE and MAPE values, the smaller the difference between the predicted value and the actual value, that is, the higher the prediction accuracy of the model.

Results of DO Data Series
Based on the CEEMDAN preprocessing method, the time series of the DO data set were decomposed into 13 IMFs and one residual term, shown in Figure 5. These IMFs reflected the oscillating modes of DO in different periods, and the residual term indicated the trend of the data. In this study, the input data had two forms, one is the original time series, and the other is the gray image based on the CEEMDAN results ( Figure 4). The original input was a set of 12 × 1 data sequences, while the CEEMDAN-based input was a set of 12 × 14 two-dimensional images. Obviously, compared to a single time series, the CEEMDAN-based input data could reflect more information. Moreover, increasing the Water 2021, 13, 1547 9 of 20 dimensionality of the input data was beneficial to the deep learning neural networks to extract data features, thereby improving the prediction accuracy. The CE, RMSE, and MAPE statistics of single and hybrid deep learning neural networks in the testing period for the DO dataset are shown in Table 3. For targets with different ahead steps, the best error measures were highlighted in red. According to the results, it can be found that the CEEMDAN-CNN-LSTM model had the best prediction accuracy for all targets. Whether for the separate CNN and LSTM model or the CNN-LSTM hybrid model, the prediction accuracies of CEEMDAN-based input data were better than the original input data. As the forecasting step increased, the accuracy difference between these two sets of input data gradually increased. For example, the differences of CE, RMSE, and MAPE between DO t and DO t+5 for CNN-LSTM and CEEMDAN-CNN-LSTM were 0.09 (|0.97-0.88|), 0.30 (|0.35-0.65|), and 2.61 (|3.06-5.67|) and 0.04 (|0.98-0.94|), 0.22 (|0.26-0.48|), and 2.01 (|2.55-4.56|), respectively. In other words, as the forecasting step increased, the decrease of the prediction accuracy of CEEMDAN-based input data was slower than the original input data. The six models, especially the CEEMDAN-CNN-LSTM model, could capture the general trend of the testing data series across different forecasting steps. However, it was necessary to evaluate the model accuracy based on the forecasts of the peak and lowest values. In this study, the MAPE between observations and predictions of the 10% lowest and 10% highest values of the testing data was analyzed. Figure 6a,c showed the MAPE of lowest values from Step 1 to 6 based on original and CEEMDAN-based input. The MAPEs of the six models increased as the forecasting step became longer, and the MAPEs based on the original input data were larger than the CEEMDAN-based input. The MAPEs of the CNN models were the largest, LSTM models were the second, and CNN-LSTM models were the smallest, which indicated that the CNN-LSTM model was superior to the separate models in forecasting the lowest values. For the prediction of peak values (Figure 6b,d), the MAPEs of CEEMDAN-based inputs were smaller than the original inputs, and the MAPEs increased as the forecasting step became longer. Being different from the lowest values prediction, LSTM was the model with the largest MAPEs for the peak values prediction, followed by the CNN model, and CNN-LSTM was the smallest. Therefore, the CEEMDAN-CNN-LSTM was the best forecasting model for both the lowest and highest values.

Results of TN Data Series
The decomposition results of the TN data series using the CEEMDAN method are shown in Figure 7. The TN data set was also decomposed into 13 IMFs and one residual term. The CE, RMSE, and MAPE statistics of single and hybrid deep learning neural networks in the testing period for the TN dataset are shown in Table 4. The results indicated that the CEEMDAN-CNN-LSTM model had the best performances across different forecasting steps. However, for the prediction of TN, the differences between the results obtained from the original input and the CEEMDAN-based input were very significant. Especially with the increase of the time steps, the performance of the original input deteriorated rapidly, while the CEEMDAN-based input showed much better stability for TN forecasting. For CNN, LSTM, and CNN-LSTM models, the CEs of TN n+3 , TN n+4 , and TN n+5 were all less than 0.5, which was not within the range of satisfactory model simulations [35]. In other words, when the time step exceeded 3, no model could reliably predict the TN concentration based on the time series data alone.   The MAPEs of different models for the 10% peak values and 10% lowest values of the TN testing data set are shown in Figure 8. The prediction of the lowest values showed that the CNN-LSTM was better than the CNN, while the LSTM performed the worst among the three models (Figure 8a,c). For the original input and CEEMDAN-based input, the average MAPEs of the three models across different time steps were 39.20, 41.71, and 51.72 and 26.07, 28.12, and 31.30, respectively. Obviously, the prediction of the lowest values based on the original input data had a greater deviation from the observed values. According to the prediction results of the peak values, LSTM was the worst performing model, followed by the CNN model, and the CNN-LSTM model performed best (Figure 8b,d). In addition, as the time step increased, the MAPEs gradually became larger, that is, the deviation between the predicted values and the observed values became larger. The change curves of MAPE were nearly the same; however, the MAPEs obtained from the original input were about twice the MAPEs obtained from the CEEMDAN-based input. In general, CEEMDAN-CNN-LSTM was the best model for forecasting TN extreme values, while LSTM was the least accurate model for extreme values prediction.

Discussion
Numerous studies have applied single and hybrid deep learning models to predict hydrological and water quality data [15,16,36]. Among them, the most commonly used method was to decompose the data series at first, then make predictions for each IMF separately, and finally add the results to get the reconstructed time series. However, data decomposition was a data-dependent method, so it needed to be repeated as the data was updated. Generally, huge calculation cost was accompanied by long calculation time, so the commonly used method was too time-consuming for real-time data prediction. In this study, we tried to input the decomposed data series as two-dimensional images for multi-step water quality prediction. The results indicated that compared with the original one-dimensional time series input data, the two-dimensional input data decomposed by the CEEMDAN method could improve the prediction accuracies. However, for water quality parameters with different fluctuation characteristics, the two-dimensional input data improved the prediction accuracies differently. It can be found from Figure 4 that the DO data had very obvious seasonal changes. The DO concentrations in spring and summer were higher than those in autumn and winter. Due to the strong periodicity of DO data, the standalone model and hybrid model with two types of input data had satisfactory performances. However, the performances of the hybrid models were slightly better than that of the standalone models, so the results were compared and discussed based on the hybrid models.  Figure 7 showed that the TN data, which was mainly affected by human activities, had no periodic fluctuation characteristics. The results based on the CEEMDAN input data were significantly better than the results based on the original input data. For TN t , the CE, RMSE, and MAPE of CNN-LSTM and CEEMDAN-CNN-LSTM were 0.76 vs. 0.92, 0.18 vs. 0.10, and 10.06 vs. 6.63, respectively. The CEEMDAN-CNN-LSTM model performed significantly better than the CNN-LSTM, which indicated that decomposing the input data was more beneficial to improve the prediction accuracy of non-periodic time series. The CEEMDAN method added adaptive white noise in each decomposition to smooth the impulse interference, so that the decomposition of the data was very complete [37]. For non-periodic parameters, the complete decomposition could better separate the noise component and periodic components of the data. Among them, the periodic components could be much better predicted, so the prediction accuracies of the data series were also improved.
Increasing the forecasting steps of each round of prediction may help to reduce computational consumption, while relevant previous studies rarely discussed the impact of the increase in forecasting steps on model performances [14,15]. In this study, all of the models were tested for one-step-ahead (4 h) to six-step-ahead (1 day) forecasting, which were aimed to analyze the impact of the forecasting steps on the performances of the models. As shown in Figures 9 and 10, with the increase of prediction steps, the CE of all models decreased, while the RMSE and MAPE gradually increased. Some previous studies on hydrological data prediction have also obtained similar results on a monthly scale. For example, in a wavelet analysis-ANN-model-based multi-scale monthly groundwater level prediction study, the prediction results of 2 and 3 months ahead were worse than the results of 1 month ahead [38]. In the study using a two-phase hybrid model to predict the runoff in Yingluoxia watershed, the model's accuracy for shorter lead times (1 month) were better than the 3-and 6-month horizon [34]. However, in addition to the conclusion that the prediction accuracy would decrease with the increase of prediction steps, we found that the accuracies' attenuation rates of different water quality parameters were different. Taking the indicator CE as an example, the CE attenuation rates of the DO models were much lower than those of the TN models as the prediction steps increased. Based on the CE at time t (i.e., one-step-ahead), the CE attenuation rates of CNN-LSTM and CEEMDAN-CNN-LSTM models at time t+1, t+2, t+3, t+4, and t+5 were calculated, and the results are shown in Figure 11. For DO, the attenuation rates of CNN-LSTM models and CEEMDAN-CNN-LSTM models were 3.09~9.28 and 0~4.08, respectively. For TN, the attenuation rates of CNN-LSTM models and CEEMDAN-CNN-LSTM models were 13.16~48.68 and 2.17~14.13, respectively. Obviously, the attenuation rates of the periodic parameter DO were lower than those of the non-periodic parameter TN, and the attenuation rates based on two-dimensional input data were lower than those of one-dimensional input data. It was interesting to note that the prediction accuracy decreased as the number of prediction steps increased. With the continuous flow of the river, the waters that have experienced different influences gradually flowed through the monitoring station. As the time interval became longer, the differences of water quality became greater. Therefore, the prediction accuracies based on the same set of input data gradually decreased. Moreover, the TN concentrations were mainly affected by human activities. The TN emission sources were always changing, as the prediction steps increased, the sources may appear or disappear. Therefore, the attenuation rates of TN were faster than DO.

Conclusions
In this study, the performances of the deep neural networks CNN, LSTM, and CNN-LSTM with and without the preprocessing method CEEMDAN were compared for the water quality parameters' forecasting. A real-time water quality monitoring dataset with 10,326 records from 2015 to 2020 was collected as the input data. According to the completeness of the data and the fluctuation characteristics of the parameters, DO and TN were selected as prediction targets. The DO and TN data series were decomposed by the CEEMDAN method at first, and two decomposed datasets with 13 IMFs and one residual term were obtained. Taking into account the monitoring frequency (every 4 h) of the data, twelve records in two days were selected as input variables, and six records in the following day were used as output variables. Then, a set of 12 × 1 data sequences was prepared as the original input data, while the CEEMDAN-based input data were a set of 12 × 14 two-dimensional grey images. The two types of input data were used to train and test CNN, LSTM, and CNN-LSTM models, respectively. The results indicated that the performances of hybrid model CNN-LSTM were superior to the standalone model CNN and LSTM. The models used CEEMDAN-based two-dimensional input data performed much better than the models used the original input data. Moreover, the improvements of CEEMDAN-based data for non-periodic parameter TN were much greater than that for periodic parameter DO. In addition, the impacts of the prediction steps on the forecasting accuracies of the models were analyzed in this study. The results showed that the model forecasting accuracies gradually decreased with the increase of prediction steps. The attenuation rates presented the features that the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. In general, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep neural networks, and this improvement was especially obvious for non-periodic parameter. Overall, compared with a separate monitoring system, the incorporated monitoring and modeling framework may provide better knowledge, which could help decision-making departments respond to upcoming events and outbreaks.


extfieldsofstudy:
Computer Science
Engineering
Mathematics

text:
Evolving Intelligent Reflector Surface towards 6G for Public Health: Application in Airborne Virus Detection

While metasurface based intelligent reflecting surfaces (IRS) are an important emerging technology for future generations of wireless connectivity in its own right, the plans for the mass deployment of these surfaces motivate the question of their integration with other new and emerging technologies that would require mass proliferation. This question of integration and the vision of future communication systems as an invaluable component for public health motivated our new concept of Intelligent Reflector-Viral Detectors (IR-VD). In this novel scheme, we propose deployment of intelligent reflectors with strips of receptor-based viral detectors placed between the reflective surface tiles. Our proposed approach encodes information of the virus by flicking the angle of the reflected beams, using time variations between the beam deviations to represent the messages. This information includes the presence of the virus, its location and load size. The paper presents simulation to demonstrate the encoding process based on varying quantity of virus that have bound onto the IR-VD.

I. INTRODUCTION
T HE recent COVID-19 pandemic has put the world on its heels with numerous countries around the world facing lockdowns that have affected the global economy. The virus itself posed significant challenging questions to the scientific community, and this includes, (i) how does the virus affect the human body, (ii) how does it spread in indoor and outdoor spaces and (iii) how to provide rapid and efficient virus sensing mechanisms.
Since this is a global challenge, a collaborative effort is required from scientists that goes beyond virologists, immunologists and public health experts. Researchers in information and communications technology (ICT) have recently begun working towards solutions that can help curb the spread of the virus, through contact tracing applications on mobile devices. While 6G networks are expected to chiefly focus on high capacity and latency & reliability requirements for HŠ and NM are with CONNECT Centre, Trinity College Dublin, Ireland; NA, DPM, BB and SB are with the Telecommunication Software & Systems Group, Waterford Institute of Technology, Ireland; MTB is with the University of Essex, United Kingdom; AF is with Maynooth University, Ireland. This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland under Grant Number [16/RC/3835] -VistaMilk, and is co-funded under the European Regional Development Fund for the CONNECT research centre (13/RC/2077). Corresponding author (HŠ): harun.siljak@tcd.ie applications as diverse as pervasive connectivity and Industry 4.0, here we join the growing community [1] of voices calling for its paramount importance in enabling novel and efficient approaches to public health. In this paper, we propose a new solution where wireless communication systems can play a role in detecting the virus shed by infected individuals. This is based on the use of Intelligent Reflective Surfaces (IRS) that have been proposed for reflecting, refracting and diffracting electromagnetic waves in the mm-wave and terahertz spectrum. Such electromagnetic waves suffer from numerous effects that lead to unreliable communication between the transmitter and receiver. Firstly, the challenge of high spreading loss means that high-gain, highly directive antennas are needed to extend range. Consequently, the signals appear as pencil-thin beams that are highly directive, requiring Lineof-Sight (LoS) visibility (hence no obstacles) between the communication points. This is closely tied with the effect of atmospheric conditions (e.g., raindrops and fog (water vapour)) that absorb the incident energy at those frequencies and so can significantly attenuate the signals, thereby further reducing their range. IRS [2], [3] have been proposed to counter these challenges, by redirecting and reflecting beams to the mobile devices, to ensure non-LoS signal coverage. The introduction of IRS will require infrastructure changes, where this could come in the form of wallpapers that integrate electronics and nanotechnology to ensure compactness, flexibility and scalability in the installed reflectors. With this pervasive infrastructural intervention, it is reasonable to plan other generally useful features that could be symbiotically tied to IRS. An IRS can co-exist with very dense sensors which uses the reflector for coding spatiotemporal information about measurements, updating information in real time and, if needed, integrate with the smart building functionalities.
Our proposed solution is a symbiotic integration of viral detectors with the IRS. In such an Intelligent Reflector -Viral Detectors (IR-VD), adhesive strips with chemical receptors capture virus particles that bind to the strips to perform detection. Upon sensing virus particles that have bound to the strips, the unit cells will reflect signals with an embedded code to provide information about the virus, and comes in the form of phase manipulation that results in beam flicking encoding a binary string, which is detected by the receiver by sensing the power deviation of the signals. Patches that are not adjacent to such virus-affected areas will not have this embedded code. Once detected by the mobile device, the embedded code and its location will indicate the zone of the wall that is contaminated with the virus. The flexibility of the adhesive strips applicable to most virus (e.g., SARS-CoV-2) that can have engineered receptors. This in turn will result in a different code that is embedded into the signals reflected from the IR-VD.
In this paper, we present the design of IR-VD, motivation for its development, and simulation of its performance which both verifies the principle of operation and explains it practically. We give a contextual overview of viral detection, as well as the convergence of different technologies and means of communications that have enabled development of the concept, and discuss potential challenges and future work.

II. AIRBORNE VIRUS BACKGROUND
Indoor environments can expose humans to a variety of airborne viruses, some of which may cause respiratory diseases. These pathogens can be transmitted through air in the form of liquid droplets or airborne particles [4]. Due to their small sizes (1 − 40 µm when coughing) and low settling velocity (up to 3.1×10 −3 m/s for 10 µm particles), these viruses can remain in the air, increasing their likelihood of infecting other people within an indoor environment [4], [5]. The virus survivability in the air is also related to environmental factors, such as temperature, relative humidity (RH) and surrounding biological materials that can absorb the virus [6]. From these factors, higher temperatures decrease the virus survivability while the surrounding biological material increases it. In this case, the higher survivability is related to the physical protection against environmental changes provided by the biological material (i.e., mucus) that encapsulates the viruses [6]. On the other hand, RH values affect the survivability of each virus family differently. For example, most respiratory viruses, including influenza, survive longer in environments with RH between 20% and 30%. However, non-enveloped viruses (e.g., polio virus) show higher survivability at higher RH that is between 70% and 90% [6]. Viral and droplet/particle sizes are also related to the different infected areas of the respiratory tract. For example, when inhaled, larger particles (greater than 5 µm) deposit on the upper respiratory tract while smaller particles deposit in the lower part of the lungs and can cause severe pneumonia [4]. The infected people will then become the new source of viral aerosols and can spread them through coughing, sneezing or speaking when are close to other people. In addition to these, there are other sources of viral aerosols in the home environments, such as flushing toilets (liquid droplets coming from the toilet can carry viral particles excreted from the human body) and changing diapers (by manipulating diapers some viral particles can be thrown in the air), and in laboratory settings, e.g. shaking, homogenisation and sonication of materials that contain viral particles [4]. The campaign around social distancing can reduce viral aerosols and reduce the propagation of virus to infect other people [4] [5]. Therefore, the air quality has become a public health and safety concern due to the annual numbers of respiratory infections, specially in the recent years with the SARS and COVID-19 pandemics, which has driven investigation into new methods to improve airborne virus detection and identification [6]. In the next section, we introduce a new solution to monitor the presence of airborne viruses within an indoor environment, which can be applied for commercial or domestic purposes to help detect airborne as well as viral droplets.

III. INTELLIGENT REFLECTORS FOR VIRAL DETECTION
As described in the introduction, the future wireless communication systems envisions a new form of IRS infrastructure placed on the wall that are used to reflect high frequency spectrum signals to devices. Our proposed approach is to make use of this new infrastructure to detect viral particles, enabling it to have dual function: high-speed wireless communication and sensing airborne viral and communicating to mobile devices. This makes viral sensing and IRS a good match, since virus sensing can be integrated with other prominent wireless communication systems of the future, which is appealing from a deployment perspective as well as a future sensing technology: pervasive, reliable, high-tech entities that not only  coexist, but support one another. In the context of proliferation incentives, they also form a symbiotic relationship: the push for better connectivity would bring health protection, and vice versa, contributing to wide market penetration.

A. Viral Detection Beam Steering Protocol
IRS functionalities can be further extended to detect airborne virus within an indoor environments based on people coughing or sneezing by adding viral detector strips, illustrated in Fig. 2. The strips, combined with the components found in most IRS which includes patch electromagnetic antennas, a dielectric substrate, a ground metallic material, and a chip that controls the phase changes of the reflected signals constitute the IR-VD. The adhesive viral detector strips will be placed in the spaces between the patch antenna tiles and contain functionalized receptors that correspond to the proteins on the virus membrane. These receptors can detect single or multiple families of viruses. For example, human α-2,6-linked sialosaccharides have been reported to bind influenza virus family, making these sugars suitable candidates for the receptors [7]. In terms of integrating with the conductance change that interfaces to the chip, we can foresee the usage of electrostaticbased detection using protein-ligand response that changes the conductance in a substrate with response time around 2 minutes for viruses such as Influenza, A/HN1, H5N1 AND SARS [5]. This modular design will enable the providers to customise the reflectors to their needs, such as focusing on the detection of the most dangerous families of viruses, in case of a viral outbreak or pandemic. The process of viral detection and monitoring can be described as follows: upon viral contact with the adhesive receptor strips, the conductance along the tubes containing the receptors will change, indicating its presence on a particular section of the IR-VD. The change in conductance will be detected by the chip that controls the beam steering, and will encode information by flicking the phase change of the signals, as depicted in Fig. 3. The encoded information will depend on the duration between the phase changes (e.g., a digital 1 bit will result in a longer durations between the steered beams compared to digital 0 bit) [3]. Therefore, the flicking of the beams will need to be detected by the receiver and will also need to detect the timing between the power changes as the beams are flicked. The encoding of the information can include the type of virus, as well as the density on the wall, and possibly the location on the IR-VD.

B. Viral Detectors Strips for IRS
Since the introduction of IRS, a number of different solutions and configurations have been proposed. In Table I we describe three IRS solutions based on their frequency of operation, tile size material used and steering granularity. Moreover, we describe the free spacing on the tile (i.e., space that is not occupied by the patch antenna or other electronic device), and the density of viral detectors that can be placed on the tile depending on this free spacing. In [8], a novel antenna structure is proposed to individually control the phase of the reflected signal by configuring different RF paths. Each tile is composed of three reflective paths arranged in a T-shaped configuration with a fourth dummy path just to maintain a symmetric structure. For this solution, the antenna patch is placed in the center of the IRS tile, and the following spaces are free and can be used to placed the viral detectors: 13 × 50 mm 2 for the left and right sides, and 13 × 37 mm 2 for A similar structure was proposed in [9], where an IRS was designed to recognize human gesture and respiration in real-time by transmitting and receiving electromagnetic waves and processing them using an artificial neural network. This solution has plenty of open areas around the antenna patch where the viral detectors can be placed at: 17 × 54 mm 2 on the left side, 17 × 37 mm 2 the upper side, 17 × 36 mm 2 on the lower side (due to the presence of an inductor on the IRS), 17×17.4 mm 2 and 17×26.6 mm 2 on the right side (above and below the resistor present on the IRS, respectively). Given the spacing on each tile, this model is able to fill a higher quantity of viral detectors, reaching up to 726 per tile.
Other designs can be considered as well, however they might not posses enough free spacing to place a great number of viral detectors as the squared shape with the centered patched antenna described in [8], [9]. For example, we also reviewed the design proposed by [10]. where small tubes filled with water are used to reflect electromagnetic waves. This IRS achieves different beam phases through a combination of empty and water-filled tubes for the reflectors. This design have small areas between the tubes to allow us to place the adhesive viral detector strips. In this case, the free spacing are two small areas of 16 × 6 mm 2 , which enables to place 48 viral detectors.

IV. SIMULATIONS A. Simulation Scenario
In the scenario shown in Fig. 1, the people inside the room disperse droplets with viral particles (e.g., by sneezing), and the droplets that hit the portion of the wall occupied by our IR-VD is of interest to us. This scenario setting can be interpreted through the concept of an breath molecular communication model [11].
In a breath molecular communication system, we observe the dispersion of aerosol from the respiratory tract of the people present in the space. The droplets with viral content that reaches the IR-VD and binds onto the surface will trigger a message, which is created and encoded into the reflected beam that will flick depending on the data to be transmitted. If observed as a molecular MIMO (multiple input, multiple output) system, where people in the room are the viral sources, and different strips on the reflectors are the receivers, known results about signal detection in such configurations [12] show that it is possible to reconstruct which viral sources (i.e. person) transmitted which "message" even if the sources are not spatially distanced. While such an option opens the question of anonymity if identification is done at the level of an individual, it is not hard to devise a use-case in which such information is used through aggregation. For example, if the patches are placed within an air conditioning network serving several rooms at a workplace, knowing which room contains stream that has high probability of containing virus could help in providing a safe working environment.

B. Viral Molecular Communication and Binding Model
For the simulated effects of molecular communication sneezing (quick burst transmission in the atmospheric channel towards a dense array of receivers on the wall), the effects of the channel can be modelled in a relatively simple manner. A range of droplets of relatively large mass and volume originating from the same point in space under a range of horizontal and vertical angles and initial velocities travel as projectiles with air drag effect towards the wall. The particular parameters used for this simulation are listed in Table II. With breathing or speaking, the effects of the dynamic molecular communication channel become more emphasised, with the air movement dictating the motion of small particles with low initial velocities. Once the particles reach the IR-VD, the density of the receptors per tile dictates the sensing capabilities and the resolution of measurement.
The simulated virus binding process is based on the model previously reported in [13]. A chemical reaction occurs whenever the virus proteins, i.e. ligands, have physical contact with the host receptors, which in this case they will be the viral detectors, resulting in the binding of these proteins. The strength of this attachment is a design requirement to ensure that the virus will remain bound to the viral detector, and it will be dependent on the concentration of viral proteins and host receptors on the adhesive strips, which depends on the type of IRS discussed in Section III (b). These concentrations are estimated using the ligands, receptors, virus and viral detector dimensions found in [13].

C. IRS Simulator
We have performed simulations on Matlab to demonstrate the working of our proposed method. Our simulator comprises of a feedback-based beam steering mechanism. More specifically, we have used an Extremum Seeking Control (ESC) algorithm [3] to steer the beam towards receiver. In this scheme, a dither signal (of 0.5 amplitude and 50 rad/s frequency in our case as shown in Table II) is used to excite the periodic response of the received signal power and corresponding change in the received power is used to drive and maintain the angle of reflection. For more details, readers are referred to [3]. Moreover, the parameters for these simulations are listed in Table II. Once the virus and its concentration have been detected by viral detection strips, this information is conveyed to the receiver by deviating the beam from its desired angle while at the same time beam steering mechanism tends to bring the beam back to its desired angle.

D. Simulation Results
As indicated in Fig. 3, the information about virus detected via distributed sensors on IR-VD, is encoded into its reflection properties. This results in the deviation of the beam at the receiver and gets notified about the presence of virus by decoding the pattern given by the changes occurring to its received signal power. Fig. 4 shows two simple simulated examples of the particle dispersion over a 1 × 2 m IR-VD placed on the wall with a varying distance of the sneezing person standing in front of the surface. In the figure, the heat map colour represents the density of droplets on the surface as the number of positive detections per tile, from low density (blue) to high (yellow).
When the virus information encoding process is integrated with our beam steering scheme [3], we enable communicating information about viral contamination. In the figure we observe that a beam after reflecting through the IR-VD was initially targeting towards 48 degrees but the receiver was located at 50 degrees (this 50 degrees serves as a reference angle) with respect to the wall. Our beam steering algorithm works based on a feedback principle and tends to steer the beam towards 50 degrees at receiver location with short transient flicking behavior of around 1 second. It can be observed that once the beam tracks the receiver at 50 degrees, at the time instant (1 s), the received signal power is at its maximum value of 1 due to normalization. At 2 sec time instant, the beam flicking process starts due to the viral detection. The flicking results in a small angle deviation based on the phase gradient change. This disturbance causes the angle to deviate by 1 degree and the beam reflects towards 49 degrees, and there is a corresponding decrease in the received power at the same instant. The pattern of change in the received signal power and

V. CHALLENGES AND FUTURE DIRECTIONS
While the introduction of IRS will transform the management of wireless signals going forward into the future, the integration of viral detector and conveying that information to mobile devices will increase the power and need of this new infrastructure through the IR-VD. However, there are a number of challenges that will need to be overcome for the IR-VD to have its dual function. This section will review some of these challenges and future directions.

A. Viral Detection Uncertainties
Airborne virus detection is particularly challenging since it aims to obtain a sample of the viral particles to detect. Existing literature [5] shows that these techniques need to consider a few fundamental system characteristics including particle random motion propagation, sample quantity variations on the reflector that can be due to low viral particle density in the aerosol particles, dust interference, and the affinity of virus binding to receptor structures on the adhesive strips. These challenges currently inhibit developments in airborne virus detector, which is reflected in the number of existing products available in the market today. At the same time, the environments will also require restricted disruptive air-flow to ensure that the virus can bind successfully onto the receptors and protect the sample for the duration of the detection. The usage of air trapping mechanisms can provide a protection to the air samples in the vicinity of the biosensing technology so that disruptive air-flow do not interfere with the binding process between virus and the sensing receptors. However, the design of the intelligent reflectors, whether they have different sizes and shapes of tiles, can possibly overcome these challenges, and further mathematical and testing work is most needed.

B. Linking to Contact Tracing
One way of countering the spread of airborne viruses is by social distancing. As introduced in Section II, the recommended distance for people to stay apart from each other should be approximately two meters. Based on this, solutions that monitor the social distancing and identify the close contacts of known infected individuals are being proposed using the current wireless technologies, such as Bluetooth, although there are concerns in the accuracy of distance measurements between the devices. Our proposed viral detectors can complement such apps because our virus detection strips are integrated with a communication system that provides near real-time alerts to those in the vicinity of the virus particles.
This can serve as another sensor information point for the contact tracing app that can propagate between the devices to avoid certain areas that may contain droplets.

C. Virus Ultraviolet Treatment
While the detectors can enable the virus to bind to provide information on hazardous regions, there also needs to be a solution to treat the virus to minimize physical contacts between people and the wall. Recent research has found that using ultraviolet light can inactivate airborne viruses [14]. A possible addition to the adhesive strips are local ultraviolet sources that are able to inactivate the virus once they have bound onto the receptors. The frequency range of the ultraviolet light are chosen in such way that it only affects the viral proteins and not the receptors on the strip. This ensures that the strips can be reused again to allow the receptors to become free, and at the same time ensure that the droplets do not infect any people within the vicinity. However, this can increase the complexity and cost of the IR-VD in order to ensure that the ultraviolet source will emit sufficient rays to treat the bound virus.

D. Integration into the Environment
A limitation of the proposed solution is that it will only detect the virus emitted through droplets in the vicinity of the reflectors. A next step would be the application of the viral detector strips to infrastructures within domestic and commercial environments, such as tables, floors and shopping store shelves. For that we need higher complexity beam control techniques that can overcome also signal losses from different object materials, such as the one proposed in [15] for example. This will ensure very accurate detection of any virus droplets in areas of high density of people. However, a number of issues needs to be considered. This includes the task of transmitting signals from the strips to a device within the vicinity, which may require reflection of signals from a base station. Another challenge is the increase in cost of designing furniture, cabinets, and shelves that contain integrated circuits to connect to the adhesive strips containing the virus detectors as well as reflection communication system.

VI. CONCLUSION
Novel viruses easily shared by air can bring the modern civilisation to an abrupt halt, as shown by the course of 2020. In indoor environments, viral load is often observed on surfaces, easily landing there through virus carriers breathing, sneezing, coughing or speaking. An indication of existence of such load in the room or its parts would be an important information for health and safety protocols in the building, an important health information for the people in the shared common space, and for general public health. Our design of IR-VD enables such pervasive sensing of viral presence, coupled with the roll-out of next generation wireless infrastructure, the IRS. The paper present a design that integrates such viral detectors through adhesive strips and how they can be integrated into conventional IRS. The communication of the viral load is achieved by manipulating the reflected signals through beam flicking that encodes the information about the virus bound on the call, and not compromising the orignal reflected signals. We have shown how such a system could operate, why is the convergence of technologies in this case natural and necessary, and what the future directions might look like for this synergy of communications and health technology very much in spirit of the sixth wireless generation.


The Effectiveness of Image Preprocessing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract

Optical Character Recognition (OCR) has been widely discussed in various topics in the rise of robotics, artificial intelligence and computer vision. OCR has become a solution in extracting characters from the image into machine-encoded text. This research aims to discuss character recognition from digital handwritten image. However, characters recognition problems using OCR has been more or less solved. OCR mainly implemented in reading characters from scanned of printed documents. In this research, image preprocessing including convert to grayscale, morphological operations and noise removal has been successfully boost the accuracy score of OCR performance. The average success outcome resulted to 79.26% in reading characters from the image.

INTRODUCTION
Optical Character Recognition (OCR) is the process of reading text from printed or scanned-handwritten document. Nowadays, OCR already implemented in extracting filled forms, passports, ID cards recognition, license plate, etc. [1] [2]. Not only in desktop application, by uploading or capturing the image, OCR has been widely implemented in smartphones. The quality of image-input become a major factors of recognition performance and affects the accuracy rate of OCR [3] [4].
Even though the data in digitization format, there are challenging issues such as the image noise, different handwriting style, different type and font sizes, not aligned, etc. is become the major problems in recognizing characters. There are many OCR engine available and consistently updated newer version such as Tesseract, Google OCR, ABBYY FineReader, FreeOCR, etc., however, Tesseract stands out of all OCR engines [5]. Tesseract supports in Windows, Linux and MacOS. Tesseract provides 120+ models data trained language data files which already applied the new LSTM neural net-based engine. Tesseract also can process right-to-left text such as Arabic or Hebrew [6].
Tesseract is an open-source OCR engine that was developed by HP between 1984 and 1994. Tesseract was first started as a PhD research project. In the year 2005, Tesseract was released by Hewlett Packard and University of Nevada, Las Vegas. Now, partially Tesseract engine funded by Google and released under the Apache license, version 2.0. The lastest version, Tesseract 4.x is released in December, 2019. Tesseract engine implemented the key functional modules such as: This paper is organized as follows: Section 1 explained briefly about Optical Character Recognition (OCR). In the Section 2 we discussed a brief overview of related work and also we explained the proposed method of our research in Section 3, the experiments of the research are described and discussed in Section 4 before we conclude in Section 5.

RELATED WORK
Many researchers implemented various techniques of combination preprocessing for different types of character. For non-latin characters, in 2020, Anuradha et al. consists three main stages of recognizing Sinhala characters: preprocessing, recognition process and postprocessing. Their preprocessing proposed the techniques such as image binarization, noise removing, skew detection and correction, normalization and segmentation. The accuracy rate of reading Sinhala characters including the complex characters has achieved between range 80% -90% [10]. In 2016, Qi Li et al. using five steps: trainned chinese characters data, dilation, erotion, image enlargement and reduction in the image. Their proposed methods using scanned handwritten chinese characters that reach the highest accuracy of all samples 64%. Meanwhile, the recognition accuracy rate of each chinese characters is over 92% [11]. In 2011, Kumar et al. implemented digitization the indian scripts as their input being converted into scanned document. The preprocessing using 3 steps: skew detection/correction, skeletonization, and noise removal/reduction. And also, the important steps such as: segmentation, feature extraction and classification applied [12].
Moreover for latin characters, in [4] use captured label of counterfeited products as their dataset. The preprocessed methods they used are : image binarization, noise reduction, image sharpening and image cropping. They calculate the accuracy for each methods, noise reduction: 78%, sharpening image: 76%, image cropping: 73% and binarization: 82%. In different methods, Sandip et al. applied labeling training data and segmentation in scanned Roman scripts. They test for total 1844 training sets and reach highest accuracy 83.50% [13].
Furthermore, character recognition problems using OCR has been more or less solved. The reserachers combining different type of technique preprocessing considering can produce high quality input for OCR prosess. This research aims to process in recognizing handwritting latin scripts with the combination of preprocessing technique such as: convert into grayscale, morphological operations, and noise removal. The details of proposed methods will be discussed in the next section. Figure 2. Illustrates the proposed method of this research was implemented in the following stages. The method is divided into three phases, i.e., input, process and output.

DATASET PREPARATION
This research developed a canvas in order to make a dataset of digital handwritten text. This feature capable of doing free-flow writing in website. This feature is developed because the author facing limitation in finding digital handwritten text dataset collection. In the previous study, the researchers used scanned of handwriting image as their dataset [3][12][14] [15]. With total of 50 sets of image format digital handwritten image including the total of 323 characters as the dataset of the research. The image contains pixels, fuzzy, grainy in the edge, to achieve the high qualities input for OCR process, we implemented the preprocessing step that described in Section 3.2.

PREPROCESSING
Preprocessing is subjected as the initial stage of image processing before sent to the OCR process. We investigated the preprocessing techniques including convert into grayscale, basic morphological operations: erode and dilate, and noise removal need to be performed.

CONVERT INTO GRAYSCALE
First, converting the image into grayscale from the blue color text on the image. By setting the gray-level in averaging the intensity level of three colors pixel.
This process aims to differentiate the pixel noise in the image. The grayscale image is easier to work in morphological operations than color image [16].

MORPHOLOGICAL OPERATION
Morphological operation is to filter the image by replacing the convolution operation by the logical operation. The basic set of morphological operations are erosion and dilation that processed the images based on shapes. This process aims to isolate the individual elements and joining disparate elements in the image, finding intensity bumps and holes in the characters, smoothing contours, thin characters, and extracting the boundaries [17]. Moreover, morphological operations can be successfully implemented in handwritten text due to erratic hand and movement and different handwriting style [18].  In details, Figure 4. the words of 'O' rounded by holes in the edge of the word. After the process of erode and dilate ( Figure 5. and Figure 6.), the holes and bumps are reduced. The output of morphological operations made the word into an accurate shape than before preprocessing.

NOISE REMOVAL
The last step of preprocessing is noise removal. The output of morphological operations still contains noise, so that the pixels that are not needed can be eliminated. Using median blur operations, the central element of image is replaced by the median of all pixels in the kernel area. In details of Figure 7. The character of 'M' shows a bump at the bottom. Performing noise removal operations, the bumps eliminated and produce an accurate shape of 'M'. Furthermore, the usability of image preprocessing improves the character level which has an important role in OCR process recognizing in word level. Tesseract engines provide additional language regarding the language used. This research implemented the tessdata/eng.trainned data trained model as the dictionary of existing word in English. Figure 8 shows the process of Tesseract engine [5]. First, page layout analysis is for detecting the text area in the image. Second step, dividing the detected text area into a series of blobs. The blob is categorized as a classifiable unit, which may contain several characters or may contain a part of some character. The third step is to define the text lines and to combine the blobs into a series of words according to the blank space [19]. These three steps are prepared for the word recognition. The next step is to recognize each word in two passes. On each pass, Tesseract conducts various splitting and merging operations into blobs in one word and form a series of character outlines to be recognized [20]. On the first pass, Tesseract recognizes the characters outlines with static classifier based on the features library. If the recognition's result is high confidence, the process will be continue to the adaptive classifier as training data. On the second pass, Tesseract tries in recognizes again those words with low confidence from the first pass, in order to omprove the recognition accuracy. The last step, Tesseract chooses only one result with the highest confidence as the output [5] [19].

RESULT AND DISCUSSION
A total 50 sets digital handwritten image and resulting in 323 characters. As the initial, the image contains pixels, fuzzy, and grainy. Preprocessing has been applied for each image including convert into grayscale in defining noise at the characters, basic morphological operations such as erode and dilate for joining disparate elements and finding intensity bumps and holes in the characters. The last step of preprocessing is noise removal, using median filter, unnecessary elements in the image can be omitted. The image preprocessing has been executed using OpenCV library which support in Java programming languages for this research. The comparation several results of OCR between original image and preprocessing presented in Table 1. OCR result with preprocessing shows significantly return nearly correct output compared with original image. Similarity characters (data no. 3) occurred between "t" and "+", in the other results returning into numbers e.g. "Z" to '2', "g" into "9".

Lily Rojabiyati Mursari, Antoni Wibowo The Effectiveness of Image Processing on Digital Handwritten Scripts Recognition with The Implementation of OCR Tesseract
The calculation of accuracy rate of OCR on each image is determined on the number of character extraction results. In 1995, Rice et al. presented The 4 th Annual Test of OCR Accuracy rate [21].
OCR cannot produce a 100% accuracy rate for characters recognition, it depends on the applied technique approach for the OCR [20]. The test has been done and calculated the accuracy using Equals 2. for each image. Table 2 presents the average success of reading each character from handwritten image between original and preprocessing. From a total 50 sets images, an average of 30.03% of the characters on the original image can be extracted using Tesseract 4.x. Using the same OCR engine, an average of 79.26% can be read on the preprocessing image.

CONCLUSION
The research results in demonstrate recognizing characters from the digital handwritten image with preprocessing steps including grayscale, morphological operations and noise removal implemented before sent to OCR engine has improved the OCR performance. Using OCR engine Tesseract 4.x, original image reached the percentage of success to 30.03% in reading characters from the image. This is because Tesseract already applied the static character classifier, linguistic analysis and adaptive classifier in recognizing characters and words [8]. Meanwhile, preprocessing reach the percentage of success to 79.26% and successfully boost the OCR performance. Therefore, further studies need to explore in implementing more advance methods such as normalization, segmentation and skeletonization of preprocessing in order to get higher accuracy in recognizing digital handwritten both for latin and non-latin text.