esa-sceva/satcom-chunk-collection huggingface dataset:
# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 3 Validation on Synthetic GPR Data  
### Data Preparation and Network Training  
Currently, the available real GPR image data are limited and inadequate for model training. This study used the Finite-Difference Time-Domain (FDTD) method for forward numerical simulation to generate simulated GPR data of void defects [28]. According to the different characteristics of various media, through the interaction between electromagnetic waves and different media, two sets of GPR simulated images were obtained: one with rebar clutter and the other without. It is important to note that these two sets of images are unpaired. The relative permittivity and conductivity of various media are shown in Table 1. The model grid size was set to 2.08 m (width) \(\times\) 1.08 m (depth) with a grid spacing of 0.004 m. The central frequency of the transmit antenna was 800 MHz, with the receive antenna located 0.1 m from the transmit antenna. The time window was 20 ns. During the simulation process, the antenna moved in steps of 0.008 m, performing 240 scans per B-scan image. To reduce electromagnetic losses and improve numerical simulation accuracy, Perfectly Matched Layers (PMLs) were placed around the model boundaries for absorbing conditions [29].  
To enhance the authenticity of the synthetic data and align with the actual tunnel construction lining standards, this study's simulation model followed standard tunnel lining design specifications for rebar placement. By reasonably adjusting the spacing and positioning of the rebars, it aimed to match the common rebar arrangement patterns observed in real-world scenarios.  
We placed rebars layer 0.1 m below the air layer, with a spacing of 0.2 m between the rebars, and randomly generated positions and shapes of defects beneath the rebars. The defects were filled with air or water, with concrete serving as the background fill. Direct waves were removed from the GPR images generated after simulation, as shown in Figure 4. Ultimately, we obtained a dataset of 600 pairs of non-paired GPR images, which included GPR images containing echoes of void defects and rebar clutter, as well as images containing only echoes of void defects. These images were split into training and testing datasets in an 8:2 ratio, with each image sized 256 \(\times\) 256.  
The model was trained on a Linux server equipped with an NVIDIA RTX 3090 (24 GB) GPU (NVIDIA Corporation, Santa Clara, CA, USA). The framework for implementing is Pytorch. The total number of iterations for model training was set to \(10,000\), utilizing the Adam optimizer [30]. Due to the limited training dataset, we did not use randomly initialized parameters to train the network initially. In the first 3000 iterations, we pre-trained the network using the simulated dataset from [PERSON] et al. [19]. Subsequently, we trained the network with our dataset based on this pre-training.
# _In Silico_ Development of SARS-CoV-2 Non-Covalent Mpro Inhibitors: A Review
## 2 Mpro Inhibitors for SARS-CoV-2  
### Overview of Mpro Inhibitors  
Over the past few years, various classes of inhibitors have been explored, ranging from covalent and non-covalent small molecules to natural-product-derived inhibitors. While some have shown promise in preclinical and clinical trials, challenges related to resistance, pharmacokinetics, and selectivity persist [34]. Each class comes with distinct mechanisms of action, advantages, and challenges. This review focuses on non-covalent Mpro inhibitors due to their reversibility, reduced risk of off-target toxicity, and increasing evidence of potent antiviral efficacy.  
Covalent inhibitors form a covalent bond with the target protein, often at a nucleophilic residue within the active site [35]. These inhibitors can be further categorised as reversible and irreversible inhibitors, such as irrmatrelvir, an FDA-approved drug, which forms reversible bonds with the active site of Mpro via reactive functional groups like nitriles or Michael acceptors [35].  
Several Mpro covalent inhibitors have shown promising potential against SARS-CoV-2, which has been identified through both experimental assays and _in silico_ studies (Figure 4). Among these, MG-101, a calpain inhibitor, exhibited robust inhibition of SARS-CoV-2 replication with an IC\({}_{50}\) of 2.89 \(\pm\) 0.86 \(\upmu\)m using the FRET assay [36] and was shown to impact both Mpro activity and viral entry, with an EC\({}_{50}\) of 0.038 \(\upmu\)m, suggesting its potential as a dual inhibitor [37]. Lycorine HCl, a plant-derived alkaloid, demonstrated potent antiviral activity with an EC\({}_{50}\) of 0.01 \(\upmu\)m and a high selectivity index [38]. Additionally, the HIV protease inhibitors Atazanavir and Nelfinavir mesylate displayed significant binding affinity to Mpro, contributing to their antiviral efficacy [39]. The effectiveness of drug combination therapy was demonstrated when MG-101 was paired with other Mpro inhibitors like Lycorine HCl or Nelfinavir, achieving substantial reductions in viral titer [37].  
This combination approach, targeting both Mpro and PLpro, presents a promising strategy for COVID-19 treatment [37].  
Dual inhibitors, like oligotrelvir, combine Mpro inhibition with the targeting of additional pathways, such as host proteases, to enhance antiviral potency and reduce resistance [40]. While covalent inhibitors like irrmatrelvir have demonstrated significant clinical success, non-covalent inhibitors and dual-acting compounds hold promise for addressing challenges such as resistance and toxicity [35]. Although covalent inhibitors have shown significant promise, non-covalent Mpro inhibitors are gaining attention for their potential for reversible binding [41].
## 3 The Proposed Approach
### Prediction Model Training  
The training of the RUL prediction model passes by two different steps: Training of the SAEs and OS-ELM. In this paper, both unsupervised and supervised learning are performed based on the proposed algorithm. Therefore, before going any further, first, we will explain the fundamentals of basic OS-ELM. Then, the proposed OS-ELM will be explained in detail for both unsupervised and supervised learning strategies.
## 1 Introduction
Moreover, an increased consumer awareness about the negative effects of chemicals on human health results in the search for new methods allowing the elimination of bacteria in the production environment [9,21]. A reasonable alternative for a chemical disinfection might be the application of different types of radiation [22]. One of them is a technology defined as radiant catalytic ionization (RCI) [8,23, 24, 25, 26, 27, 28]. The RCI technique was initially applied in hospitals and offices to purify air, which in consequence reduced also the level of indoor surface contamination [8]. The effect of RCI on microorganism cells results from the generation of various reactive oxygen species (ROS), among which hydroxyl radical (\({}^{\bullet}\)OH) and singlet oxygen (\({}^{1}\)O\({}_{2}\)) are extremely toxic [29]. ROS effectively inhibit growth of Gram-positive and Gram-negative bacteria, fungi, and viruses. Their preventive action against biofilm formation has also been proven [22,30]. The main mechanisms by which ROS affect pathogens activity are as follows: damage of various cellular sites, destruction of proteins and genetic material, inactivation of cellular enzymes, or disturbance of metabolic pathways [28,29]. Since the contamination of food contact surfaces contributes to the recontamination of food products, increasing the risk of human infections, it is extremely important to find methods allowing the elimination of both planktonic forms of bacteria and biofilms from the food production environment. RCI is a cutting-edge technology that has no direct contact with the food and does not leave any chemicals on the treated surface. According to the manufacturer, this method has no negative impact on the food and humans. This is the first study to determine its antibacterial efficacy under refrigeration conditions. As glass is the most popular material used in refrigerators, we decided to use this type of surface in our experiments. In addition, in previous studies, the effectiveness of RCI against bacteria on a glass surface was not determined. The aim of this study was to assess the effect of the RCI method on the reduction of _Campylobacter jejuni_, _Salmonella_ Enteritidis, and _Listeria monocytogenes_ from the biofilm formed on a glass surface under refrigeration conditions.
# Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
## 3 System Model
### Node Energy Model  
In the edge-side WSN, the sensor cannot be charged after the power is exhausted, so battery consumption is a crucial issue. Specifically, energy consumption in a WSN at the edge mainly comes from data sending and receiving. The energy consumption of the node data transmission process is mainly related to the size of the transmitted data packet and the transmission distance. The node data sending and receiving process satisfies the multipath fading channel and free-space model. A free-space model is typically used when the threshold distance exceeds the calculated distance. A multipath fading model is used in another case where the threshold is smaller [32]. The threshold distance \(d_{0}\) is defined as  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{1}\]  
where \(\varepsilon_{fs}\) is the energy required when using the free-space model, and \(\varepsilon_{mp}\) is the energy of the power amplifier. Then its specific data transmission energy consumption formula is as follows:  
\[E_{tx}(k,d)=\left\{\begin{array}{l}k\cdot E_{n}+k\,\varepsilon_{fs}\cdot d^{2 },d<d_{0}\\ k\cdot E_{n}+k\,\varepsilon_{mp}\cdot d^{4},d\geq d_{0}\end{array}\right. \tag{2}\]  
where \(E_{tx}(k,d)\) is the energy consumed by transmitting \(k\)-bit data for \(d\) meters, and \(E_{n}\) is the energy consumption per unit of data sent or received by the sensor node. Different from sending data, the energy consumption when a node receives data is mainly related to the size of the data packet. Then the received data energy consumption \(E_{r}x\) is defined as  
\[E_{rx}=k\times E_{n} \tag{3}\]  
Therefore, the network communication energy consumption model of sensor nodes at the edge can be defined as  
\[E_{tot}=E_{tx}+E_{rx}+E_{0} \tag{4}\]  
where \(E_{tot}\) represents the total communication energy consumption of the node, and \(E_{0}\) is the energy consumption of the node when it is dormant.
## 4 Experiments
### Quantitative Results
#### 4.2.3 Evaluation on Multi-Trajectory Datasets  
In addition to the ETH and UCY datasets, we evaluated the miniADE and minFDE metrics on the simulated multi-trajectory datasets. In this experiment, we utilized two models, SSA-GAN and S-GAN, to generate 25 predicted trajectories for each pedestrian. Next, we calculated the minADE and minFDE metrics. These metrics require identifying the trajectory with the smallest average ADE/FDE among the 25 trajectories generated by the models pertaining to multiple labeled trajectories in the simulated dataset. The benchmark used for this metric was the multiple feasible future trajectories. The computation involved determining the average error between the predicted trajectories and the multiple benchmark trajectories and identifying the minimum value in the process. Table 3 shows the results of both metrics for S-GAN and the proposed SSA-GAN. It can be seen that the SSA-GAN performed better on diverse trajectories, despite both methods having similar diversity loss functions. This can be attributed to the social attention and spatial attention mechanisms that we introduced into our model, which facilitate the exploration of more feasible trajectories.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Linear**} & \multirow{2}{*}{**S-LSTM**} & \multirow{2}{*}{**S-GAN**} & \multirow{2}{*}{**SoPhie**} & \multicolumn{2}{c}{**SSA-GAN**} \\  & & & & & & **(Ours)** \\ \hline \multirow{8}{*}{ADE} & ETH-Eth & 1.33 & 1.09 & 0.81 & 0.70 & **0.68** \\  & ETH-Hotel & **0.39** & 0.79 & 0.72 & 0.76 & 0.69 \\  & UCY-Univ & 0.82 & 0.67 & 0.60 & **0.54** & 0.55 \\  & UCY-Zara1 & 0.62 & 0.47 & 0.34 & 0.30 & **0.30** \\  & UCY-Zara2 & 0.77 & 0.56 & 0.42 & 0.38 & **0.36** \\  & AVG & 0.79 & 0.72 & 0.58 & 0.54 & **0.52** \\ \hline \multirow{8}{*}{FDE} & ETH-Eth & 2.94 & 2.35 & 1.52 & **1.43** & 1.44 \\  & ETH-Hotel & **0.72** & 1.76 & 1.61 & 1.67 & 1.55 \\ \cline{1-1}  & UCY-Univ & 1.59 & 1.40 & 1.26 & 1.24 & **1.22** \\ \cline{1-1}  & UCY-Zara1 & 1.21 & 1.00 & 0.69 & 0.63 & **0.63** \\ \cline{1-1}  & UCY-Zara2 & 1.48 & 1.17 & 0.84 & 0.78 & **0.75** \\ \cline{1-1}  & AVG & 1.59 & 1.54 & 1.18 & 1.15 & **1.12** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of our model with the four baselines on the ETH and UCY datasets. The prediction error metrics are the ADE and FDE in meters.
## 3 Results
### AR Application Evaluation  
The AR application was positively evaluated by the participants. The distribution of the satisfaction levels of the different visitors can be observed in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Learning**} \\ \hline  & & **No** & **Yes** & **Total** \\ \hline
**Visit** & **Traditional** & 6 & 99 & 105 \\  & **AR** & 1 & 137 & 138 \\  & **Total** & 7 & 236 & 243 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contingency table. Distribution of learning experience by kind of visit. The table shows the number of participants who reported learning and who did not report learning for each type of visit (traditional/AR).  
Figure 8: Satisfaction level with the AR application histogram (from 1—poorly satisfied to 5—very satisfied).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Preferred Museum Section**} \\ \hline  & & **Radio** & **Telephony** & **Sound and Image** & **Laboratory Instruments** & **Telegraphy** & **Total** \\ \hline
**Visit** & **Trad.** & 15 & 26 & 44 & 6 & 8 & 99 \\  & **AR** & 34 & 34 & 45 & 16 & 8 & 137 \\  & **Total** & 49 & 60 & 89 & 22 & 16 & 236 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contingency table. Distribution of preferred museum section by kind of visit. The table shows the number of participants who reported radio, telephony, sound and image, laboratory instruments and telegraphy as their preferred museum section for each type of visit (traditional/AR).  
The open-answer question was included to detect improvable aspects in the application. Although approximately one-third of the participants did not give any suggestion, it has to be highlighted that an important percentage of the visitors that participated in the AR visit evaluated the experience as a completely positive one (27%), even if the question only asked them to identify issues to be improved in the application.  
The answers identified four main technical issues to be improved in future versions. Firstly, approximately 25% of the participants indicated factors related to the images and videos' quality (resolution, size, or realism). Secondly, around 9% of the participants remarked that the audio volume should be increased, especially during the videos' visualization, because in some cases, they were not able to hear the contents. Thirdly, approximately 7% of the visitors remarked that more interactivity options should be added. Finally, around 7% of the participants suggested that the application should be available for different kinds of mobile operating systems, not only for Android.  
There were other comments that were made by a small number of participants (less than 3%). These other suggestions included adding other kinds of animations at some points of the visit, reinforcing the stability, and complementing the exhibit with more multimedia content. The possibility of including animations and additional multimedia content can be easily afforded in future versions of the application.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 2 Related Work  
Indoor navigation systems have been developed for many years now. In the early days of technology, developers focused on positioning technology. Nowadays, an increasing amount of research is being conducted on indoor navigation guidance--how to create appropriate navigation directions that intuitively guide users to their destinations.  
The aim of the LIFT project was to create an indoor navigation system for people with special needs. The system was designed to work in buildings at rail transport stations, with particular emphasis on metro stations. In order to build the system, a number of problems had to be addressed in various areas: the development of urban commuter planners, navigation guidance in indoor navigation systems in general, indoor navigation systems for buildings with irregular architecture, and indoor navigation systems for people with special needs.
## 2 Literature Review
The authors of [30] have come up with a novel embedded platform for the analysis of ECG signals of patients when being treated remotely. However, their analysis takes place locally, and improvement is possible by implementing cloud processing and storage. In the proposed work, [PERSON]. [31; 32] propose a smart health-based solution for remote monitoring of patients. The work addresses important aspects such as accessibility and affordability along with availability. However, the proposed system does not consider remote monitoring of patients who are currently in transit in a mobile vehicle. As observed in the research papers discussed above, the lack of healthcare access to many portions of the population affected timely assistance during the COVID-19 pandemic. Even though multiple implementations and research regarding the monitoring of patients can be found in the literature, very little research is found towards the continuous monitoring of patients' vital signs and criticality levels during transit in an ambulance. Moreover, most existing medical risk scoring mechanisms require a pathological lab setup and are challenging to implement on a moving ambulance. Some literature work addresses the major issue of telemedicine and the solutions with IoT [33]. IoT-based edge traffic offloading is proposed with a secured cloud environment [34]. Each data packet with patients' vital signs has its own QoS requirement, and the telemedicine system needs to adaptively select the communication protocol for reliable data transmission from the ambulance to the remote hospitals or doctors based on the available communication infrastructure. However, the same has not been explored in the existing literature. This paper proposes a telemedicine system that continuously monitors a patient's vital signs and presents OFEHS, a novel risk-scoring mechanism that can be implemented in moving ambulances. Furthermore, the paper's algorithm, AQPT-Fog, adaptively selects the routing mode based on required QoS parameters and available infrastructure. We have assumed that the healthcare workers present in the ambulance, as well as the hospitals, are trained for information collection in real time and interpret the same as required.
# RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management
###### Abstract  
In post-disaster scenarios, such as after floods, earthquakes, and in war zones, the cellular communication infrastructure may be destroyed or seriously disrupted. In such emergency scenarios, it becomes very important for first aid responders to communicate with other rescue teams in order to provide feedback to both the central office and the disaster survivors. To address this issue, rapidly deployable systems are required to re-establish connectivity and assist users and first responders in the region of incident. In this work, we describe the design, implementation, and evaluation of a rapidly deployable system for first response applications in post-disaster situations, named RDSP. The proposed system helps early rescue responders and victims by sharing their location information to remotely located servers by utilizing a novel routing scheme. This novel routing scheme consists of the Dynamic ID Assignment (DIA) algorithm and the Minimum Maximum Neighbor (MMN) algorithm. The DIA algorithm is used by relay devices to dynamically select their IDs on the basis of all the available IDs of networks. Whereas, the MMN algorithm is used by the client and relay devices to dynamically select their next neighbor relays for the transmission of messages. The RDSP contains three devices; the client device sends the victim's location information to the server, the relay device relays information between client and server device, the server device receives messages from the client device to alert the rescue team. We deployed and evaluated our system in the outdoor environment of the university campus. The experimental results show that the RDSP system reduces the message delivery delay and improves the message delivery ratio with lower communication overhead.  
disaster management services; device-to-device communication; multi-hop relaying; WiFi; ad hoc network; GPS 202020548 202
## 2 Materials and Methods
### Materials
#### 2.1.2 GOME-2 SIF Data  
GOME-2 is a scanning spectrometer carried aboard the MetOp series of polar-orbiting satellites, which was designed for monitoring atmospheric ozone and other trace gases. It was launched in October 2006 and has been in operation since early 2007, accumulating more than a decade of observational data. Due to its spectral coverage extending into the near-infrared region (around 740 nm), it has also been used for retrieving SIF data [28]. Before 2013, its observational footprint was 40 \(\times\) 80 km, which was improved to 40 \(\times\) 40 km after 2013, with a revisit time of approximately 1.5 days. Using a data-driven approach, [PERSON] et al. retrieved GOME-2 SIF data at 740 nm and compiled them into a daily observation dataset spanning 2007-2018 with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). The GOME-2 SIF product provides relatively high temporal resolution, making it valuable for studying long-term ecosystem changes. However, its coarse spatial resolution limits  
Figure 1: Global land use/land cover (LULC) map and the six regions where HRSIF was compared to the original 8-day synthetic GOME-2 SIF (red boxes). The LULC types and their abbreviations represented by the numbers in the IGBP classification of the MCD12C1 product: 0 water bodies (WAT), 1 evergreen needleleaf forests (ENF), 2 evergreen broadleaf forests (EBF), 3 deciduous needleleaf forests (DNF), 4 deciduous broadleaf forests (EBF), 5 mixed forests (MF), 6 closed shrulands (CSH), 7 open shrulands (OSH), 8 woody savannas (WSA), 9 savannas (SAV), 10 grasslands (GRA), 11 wetlands (WET), 12 croplands (CRO), 13 urban and built-up (URB), 14 cropland/nature vegetation mosaic (CVM), 15 snow and ice (SNO), 16 barren sparse vegetation (BSV).  
its range of applications. Therefore, to enhance its usability and value, it is necessary to improve its spatial resolution. Since GOME-2 collects spectral data at approximately 9:30 a.m. daily, a correction factor must be applied to convert instantaneous observations into daily values. To ensure input data quality, only observations with a cloud fraction of less than 50% were retained through a quality control process [29]. The data used to construct the model are listed in Table 1.