esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
On the other hand, the integration of the people-counting system into our support tool provides critical real-time data that can be used to monitor and manage situations more effectively during emergencies. The discussion encompassed its potential contributions to handling various emergencies, including fires, earthquakes, and floods. Additionally, the paper delved into its role in managing pandemics, exemplified by the COVID-19 outbreak. Lastly, critical issues pertaining to the existing people-counting system were presented, highlighting the need for addressing or mitigating these challenges for the system's effective implementation in emergency management. In this paper, starting from the scenarios presented in [25], we provide a technical contribution regarding the system architecture and the User Interface (UI). The proposed system architecture is tailored specifically for disaster management within a smart campus environment and has the main aim of increasing the reliability of communication, maintaining the low-cost aspect of the system. With regard to the UIs, we propose a brand-new UI, tailored to support emergency management. These interfaces have been designed and evaluated with a focus on usability, and have been tested through a study conducted to validate their effectiveness. The rest of the paper goes as follows. Section 2 discusses some related works focused on emergency management using IoT from a general perspective and in the smart city and in smart campuses contexts. Then, Section 3 details the previous architecture of the people-counting system, together with the requirements that drove this study. Then, Section 4 illustrates the proposed architecture and the new UIs designed as an emergency management support tool. Finally, Section 5 wraps up the paper, offering final remarks and outlining potential directions for future work.
## 4 Experiments
### Ablation Study  
#### 4.6.1 Effectiveness of the Uncertainty-Guided Adaptation Loss  
To evaluate the efficacy of the proposed uncertainty loss (\(\mathcal{L}_{ua}\)), we conducted supplementary experiments where we removed the uncertainty loss and retained only the asymmetric consistency loss for evaluating model performance, referred to as CDA (Figure 6). Specifically, using the PCam dataset with 1% labeled data, we compared the overall performance of UCDA with and without the uncertainty loss.  
The outcomes presented in Figure 6 demonstrate that when incorporating the uncertainty loss in the proposed approach, UCDA outperforms the model without the uncertainty loss in terms of accuracy and F1-score, achieving improvements of 3.82% and 4.13%, respectively. These findings validate the effectiveness and indispensability of the uncertainty loss, indicating its ability to enhance the accuracy of pseudo-labels. Consequently, UCDA becomes more cautious in differentiating between lesion features within medical images. The inclusion of the uncertainty loss contributes value by refining model predictions and  
Figure 6: Performance change in UCDA after removing uncertainty loss.  
improving the quality of generated pseudo-labels, which collectively contribute to the overall enhanced performance of UCDA.
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
## 2 Materials and Methods
### Matlab-CST API  
A novel Matlab-CST wrapper was designed by the authors, allowing the manipulation of CST antenna designs, using the BeSD Matlab script code. This API enables the optimization process to take place, in which the varying values of the degrees of freedom are provided as input by the algorithm, in an effort to reach the optimal design, and the evaluation of the corresponding antenna is obtained by its cost function. In this minimization problem, the cost function is defined by the \(S_{11}\) response graph of the antenna and then returned to the algorithm. Each simulated antenna design corresponds to an agent of a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Design Type** & **Frequency Bands** \\ \hline
[4] & square, two layer, coupled antenna & 2.1 GHz, 2.4–2.48 GHz, \\  & slotted fractal patch antenna with & 3.3–3.8 GHz \\
[5] & partial grounding & 2.15–2.9 GHz \\
[6] & cross-dipole slotted antenna & 1.8–2.5 GHz \\
[7] & 1 \(\times\) 4 quasi-Yagi-Uda antenna array & 1.8–2.2 GHz \\
[8] & 16-port dual-polarized patch antenna & 1.74–2.57 GHz \\
[9] & compact slotted patch antenna & 2.1–3.5 GHz \\
[1] & wideband patch antenna with slots, stubs, and & 1.7–2.7 GHz \\  & partial grounding & 1.7–2.7 GHz \\
[2] & wideband patch antenna with stubs and partial & 1.7–2.7 GHz \\  & grounding, optimized with BES [10] & 1.7–2.7 GHz \\ proposed patch & ultra-wideband patch antenna with a stub and & \\  & partial grounding, optimized with BeSD [3] & 1.4–3.9 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative table of antennas operating in target frequency bandgiven iteration, with algorithm-defined parameters, in an effort to find the optimal result (minimum cost function).
## 2 Materials and Methods
### Principal Component Analysis  
Principal component analysis (PCA) is a data-dimensionally reduced method. PCA is widely used for data analysis, and the variable interacting with information from the dimensionally reduced dataset can persist [43; 44]. It embeds samples in high-dimensional space into low-dimensional space, and the dimensionally reduced data represents the original data as closely as possible. The PCA of a data matrix determines the main information from a matrix according to a complementary group of scores and loading diagrams.  
Furthermore, PCA converts primitive variables into a linear combination set, the principal components (PCs), which catch the data variables, are linearly independent, and are weighted indecreasing order of variance coverage [45]. This can reduce the data dimension directly by discarding low variability characteristic elements. In this way, all original \(\alpha\)-dimensional datasets can be optimally implanted in a feature space with lower dimension.  
The concept and calculation of PCA technology is simple. It can be expressed as follows: Given \(\mathrm{M}=A_{ij}\ (i=1,2,\cdots,\alpha;\ j=1,2,\cdots,\beta)\), where \(A_{ij}\) denotes the feature value of the \(j\)-th sample with the \(i\)-th feature. Firstly, \(\alpha\)-dimensional means that the full datasets with vector \(\mu_{j}\) and \(\alpha\times\alpha\) covariance matrix are calculated. Secondly, the feature vectors and feature values are calculated and sorted according to decreasing feature values. These feature vectors can be expressed as \(F_{1}\) with feature value \(\lambda_{1}\), as \(F_{2}\) with feature value \(\lambda_{2}\), and so on. The largest k feature vectors can then be obtained. This can be done by observing the frequency content of feature vectors. The maximum feature values are equivalent to the dimensions which is the large variance of the dataset. Finally, we can construct an \(\alpha\times k\) matrix **X**, whose rows denote the number of samples, and \(k\) makes up the feature vectors. Afterwards, the lower dimensional feature space's number of \(k\) (\(k<\alpha\)) was transformed by **N** = **X\({}^{\mathrm{T}}\)M(**A**). It is thus shown that the representation can minimize the square error criterion.
## 3 Results
### Multi-Channel EMG and Muscle Fatigue
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**PREE**} & \multicolumn{8}{c}{**POST**} \\ \hline  & **Vastus Lat.** & **Rectus** & **Vastus** & **Vastus Lat.** & **Rectus** & **Total** & **Vastus Lat.** & **Rectus** & **Vastus Lat.** & **Rectus** & **Vastus Lat.** & **Rectus** \\  & **(prox)** & **Fem. (prox)** & **Med.** & **(dis0)** & **Fem. (dis0)** & **Total** & **(prox)** & **Fem. (prox)** & **Med.** & **(dis0)** & **Fem. (dis0)** & **Total** \\ \hline S1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 1 & 1 & 0 & 2 \\ S2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S3 & 0 & 1 & 1 & 0 & 1 & 3 & 0 & 1 & 1 & 0 & 0 & 2 \\ S4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S6 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S7 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S8 & 0 & 1 & 1 & 1 & 1 & 4 & 0 & 0 & 1 & 0 & 1 \\ S9 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 \\ S10 & 1 & 1 & 1 & 1 & 1 & 5 & 1 & 1 & 1 & 1 & 1 & 5 \\ S11 & 0 & 1 & 1 & 1 & 1 & 4 & 1 & 1 & 1 & 1 & 1 & 5 \\ S12 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S13 & 1 & 1 & 0 & 1 & 0 & 3 & 0 & 0 & 0 & 1 & 0 & 1 \\ S14 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S15 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S16 & 0 & 0 & 1 & 0 & 1 & 2 & 0 & 0 & 1 & 1 & 3 \\ S17 & 1 & 0 & 0 & 1 & 1 & 3 & 0 & 0 & 0 & 0 & 0 \\ S18 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 0 & 1 \\ S19 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ S20 & 0 & 0 & 0 & 1 & 1 & 2 & 0 & 0 & 0 & 1 & 1 & 2 \\ \hline Mean & & & & & & 1.40 & & & & & & 1.15 \\ Sd & & & & & & 1.70 & & & & & & 1.59 \\ \(p\) & & & & & & & & & & & 0.175 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Muscles in fatigue condition and number of fatigued muscle heads for each of the subjects in PRE and POST therapy. Fatigued muscles are identified with 1, muscles with no fatigue with 0.
## 4 A Case Study in Milan--Italy
### Project Technical Specifications  
Taking into account the provided details, a list of survey criteria was created to be accomplished:  
* To identify all necessary assets and categorize them into several classifications such as rooms, staircases, public areas, external pathways, and technical rooms;
* To facilitate centimeter-level local measurements;
* To disseminate the survey results on the cloud as a virtual tour, enabling MM technical staff to identify the assets and populate the database;
* Point clouds and RGB data must be stored on a server without utilizing proprietary formats, but using open formats such as E57, JPG, TIFF, or LAS;
* A quality assurance mechanism must be established to facilitate real time quality monitoring of the survey;
* To provide a 3D point cloud model in a way that allows running innovative autolocalization systems based on the matching between point clouds and images taken from a camera carried by the operators  
Characteristics of the Instrument and of the Surveying Methodology for the 3D Survey of the Buildings  
To carry out the building's 3D capture activity and complete the task outlined in the previous paragraphs, an indoor mapping system based on SLAM technology and multi-beam LiDAR sensors was chosen. This system can provide a 3D model of the surroundings, along with synchronized and calibrated high resolution panoramic images. The selection process focused on systems that also possessed high-quality photographic acquisition capabilities. This was essential to facilitate the identification of assets and items in the common areas of the buildings through the examination of the spherical images captured by the system.
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 2 Literature Review
### Research Novelty
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Frequency**} & \multirow{2}{*}{**Reference**} \\ \cline{3-6}  & & **NS 1** & & **RS 2** & & **PS 3** & **FS 4** \\ \hline Single & DNN & - & 1 & - & - & [19] \\  & MLP & 1 & - & 2 & - & [6; 56; 57] \\  & CNN & - & - & 4 & - & [7; 8; 25; 27] \\  & LSTM & - & 1 & 1 & - & [44; 45] \\  & BiLSTM & 1 & - & - & - & [58] \\  & BiGRU & - & - & 1 & - & [48] \\  & CNN, CNN & - & - & 1 & - & [37] \\  & CNN, LSTM & 1 & - & 4 & - & [30; 31; 33; 59; 60] \\  & CNN, BiLSTM & 3 & - & 1 & - & [10; 36; 40; 61] \\  & CNN, GRU & - & - & 1 & - & [34] \\  & CNN, RNN, MLP & - & - & 1 & - & [11] \\  & DNN, DBM, SAE & 1 & - & - & - & [21] \\  & DNN, CNN, LSTM & - & - & 1 & - & [20] \\  & LSTM, GRU, BiLSTM, BiGRU & - & - & 1 & - & [41] \\  & DNN, CNN, LSTM, GRU & - & - & - & 1 & Our study \\  & Total & 7 & 2 & 18 & 1 & \\ \hline \hline \end{tabular}  
* \({}^{1}\) Not Specified. \({}^{2}\) Rarely Specified. \({}^{3}\) Partly Specified. \({}^{4}\) Fully Specified.  
\end{table} 
Table 1: Frequency of parameter settings for DL architectures. per second, detection cost, etc., were seldom used. Unlike previous studies that measured only some of the performance metrics, our study covered most of them. In addition to the four common measurements (ACC, PR, RC, F1), other metrics, including FPR, FNR, and AUC, are important indicators of the DL model's effectiveness in detecting phishing attacks. Furthermore, time complexity (training and testing time) and memory constraints (parameter size and GPU storage) are also crucial factors that need to be considered when assessing the feasibility of DL phishing detection models. As a result, all of these metrics were included in the evaluation process of this empirical study.
###### Abstract  
Climate change continues to have adverse effects on crop yields in Africa and globally. In Morocco, rising temperatures and declining precipitation are having daunting effects on the vulnerability of crops. This study examines the vulnerability of barley, maize, and wheat to variations in growing season precipitation and socio-economic proxies of adaptive capacity such as literacy and poverty rates at both national and sub-national scales in Morocco. The methodology is based on a composite vulnerability index (vulnerability is a function of exposure, sensitivity, and adaptive capacity). National and sub-national crop yield data used to compute the sensitivity index were downloaded from FAOSTAT and the global crop yield gaps Atlas. The mean annual growing season precipitation data at both the national and sub-national scales used to compute the exposure index were downloaded from the world bank climate portal. Proxy data for adaptive capacity in the form of literacy and poverty rates were downloaded from the world bank, figshare, and MPR archives. The CANESM model was used to validate the crop yield observations. The results show that wheat shows the lowest vulnerability and the highest adaptive capacity, while maize has the highest vulnerability and lowest adaptive capacity. Sub-nationally, vulnerability indexes decrease northwards while adaptive capacity and normalized growing season precipitation increase northwards. Wheat also shows the lowest vulnerability and highest adaptive capacity and normalized growing season precipitation at each latitude northward. Model validation shows that the models used here reproduce most of the spatial patterns of the crops concerned. These findings have implications for climate change adaptation and climate policy in Morocco, as it becomes evident which of these most cultivated crops are more vulnerable nationally and spatially. These results have implications for future research, as it might be important to understand how these crops perform under growing season temperature as well as what future projections and yield gaps can be observed.  
vulnerability; exposure; sensitivity; adaptive capacity; precipitation; poverty; literacy; Morocco +
Footnote †: journal: Applied Sciences
# Site-Specific Spectra for the City of Mexicali, Mexico, Obtained from April 2010 Earthquake Records
## 2 Determination of the Site-Specific Spectra  
### Regional Tectonics and Seismicity  
The main tectonic feature of the area is the boundary between the Pacific and North American plates. This boundary is formed by a series of seismically active faults from the northern shores of the Gulf of California, beginning with the Cerro Prieto fault to the south, crossing sedimentary basins such as the Mexicali Valley, the Altar Basin, and the Salton Sea, and connecting with the Elsinore, San Jacinto, and San Andreas faults to the north (Figure 2).  
There are also fault systems adjacent to this boundary. The rupture area of the 2010 earthquake occurred in one of these zones between the El Mayor and Cucapah mountain ranges on the southwestern boundary of the Pescadores fault [23].  
The western side of the Sierra Cucapah is controlled by an essential active dextral fault with a small normal component called the Laguna Salada fault [24].  
According to [23], \"The background seismicity shows prominent geographical trends in the northeast and northwest directions\". Notably, the authors of this manuscript [23] describe the region's seismicity in terms of bands. They mentioned that the longest bands of seismicity reach more than 20 km long and that faulting features produce two types of focal mechanisms: right-lateral strike-slip and normal focal [25].  
During the decade from 2001 to 2010, the background seismicity in the Baja California region was high and complex. A significant number of swarms and mainshock-aftershock sequences occurred, and in some cases, the swarms were associated with geothermal areas [23; 26].
## 5 Results and Discussion
### Summary of Lamb-Wave Attenuation
As summarized above, peak displacements were down to 10 to 15 pm levels. Thus, a laser vibrometer must \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Test**} & \multicolumn{1}{c}{**Thickness**} & \multicolumn{2}{c}{**Symmetric**} & \multicolumn{2}{c}{**Asymmetric**} \\ \cline{3-6} \multicolumn{1}{c}{**No.**} & \multirow{2}{*}{**Material**} & \multicolumn{1}{c}{**mm**} & \multicolumn{1}{c}{**Transmitter**} & \multicolumn{1}{c}{**nm**} & \multicolumn{1}{c}{**Transmitter**} & \multicolumn{1}{c}{**Peak Amplitude**} \\ \hline G1 & PS & 1.38 & NDT C16 & 0.08 & NDT C16 & 0.117 \\ G2a & Al-1100-H18 & 2.06 & PAC Pico & 1.31 & NDT C16 & 0.39 \\ G2b & Al-1100-H18 & 6.38 & Oly. V101 & 1.46 & NDT C16 & 1 \\ G3 & Al-6061-T6 & 12.7 & PAC Pico & 0.125 & PAC Pico & 0.114 \\ G4 & Al-7475 & 2.72 & Oly. V101 & 0.62 & KRN-BB-PCP & 0.63 \\ G5 & 41085 & 3.16 & Oly. V101 & 0.36 & NDT C16 & 0.51 \\ G6a & Soda-lime glass & 1.98 & Oly. V103 & 1.31 & NDT C16 & 0.073 \\ G6b & Soda-lime glass & 4.72 & Oly. V101 & 0.43 & NDT C16 & 1.24 \\ G7a & PMMA & 1.76 & Oly. V101 & 0.01 & NDT C16 & 0.037 \\ G7b & PMMA & 6.3 & Oly. V101 & 0.047 & Oly. V103 & 0.034 \\ G8 & PVC & 4.3 & NDT C16 & 0.018 & NDT C16 & 0.014 \\ G9 & HDPE & 7.09 & Oly. V101 & 0.015 & NDT C16 & 0.017 \\ G10 & XP-GFRP & 2.3 & Oly. V101 & 0.26 & Oly. V103 & 0.036 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Peak displacement of detected Lamb-wave signals using a point contact sensor (KRN-BB-PC). be selected with sufficient resolution to obtain the needed SN ratio. Note that 1-pm displacement detection with KRN-BB-PC sensor was achieved with 40-dB SN ratio, as described above. Thus, in order to eliminate measurement errors of a point-contact sensor emanating at the plate surface, proper selection and set-up of a laser vibrometer is required. For example, [PERSON] at al. [52] reported 13.8 nm peak-to-peak displacement of A\({}_{0}\) Lamb wave (converted from 6.5 mm/s peak-to-peak velocity at 75 kHz). This was measured at 15 mm away from a transmitter (8-mm diameter piezo wafer) on a 2-mm thick Al sheet, excited by 5 V peak-to-peak sinewave tone burst. This value is 6.2-times larger than the displacement produced by a similar piezo wafer, directly coupled to a KRN sensor, calibrated to normally incident waves. It appears that the vibrometer used (Polytec PSV-300) was not correctly operated, showing that even with a modern instrument, rigorous training is essential.
## 5 Discussion and Conclusions  
In this work, we first investigated the outage capacity for the performance of TOW links with slow-fading skinchannels along with the presence of varying stochastic spatial jitter. In this respect, an outage capacity analysis was performed, while novel analytical expressions were derived for this crucial performance metric which incorporate the most significant parameters and effects that play a key role in TOW performance and availability. Their analytical results which were validated by proper simulations reveal that the achievable TOW outage capacity largely depends on the average electrical SNR at the receiver's side, as well as on stochastic pointing errors' strength, along with transdermal propagating distance for the information-bearing light which penetrates into the skin channel. In this context, the feasibility of establishing high-speed TOW links was demonstrated for typical transdermal link lengths, even in very harsh pointing mismatch conditions. Toward the development of future higher-speed TOW links of enhanced robustness, the analysis proposed can be added to the engineer's quiver as a useful tool for the design of such effective transdermal wireless systems.  
In view of the above, the key contribution of this work was to develop a theoretical framework for the outage capacity estimation of typical TOW links quantifying the impact for weak to strong stochastic pointing errors.It should be noted, however, that, for a real human skin channel, human body temperature, in addition to other specific skin and tissue particularities, should be taken into consideration. Nevertheless, the performance comparison ofour findings in TOW area withcorresponding outage capacity results in  
Figure 3: Outage capacity versus average electrical SNR for \(\delta=4\) mm and \(r=0.01\) under the presence of varying, weak to strong stochastic pointing errors.  
traditional free-air communication systems evaluated in [42; 43] reveal the feasibility of our findings and suggestions.  
Conceptualization, G.K.V., K.A. and H.E.N.; methodology, G.K.V., K.A. and H.E.N.; software, G.K.V. and K.A.; validation, G.K.V., H.E.N. and K.A.; investigation, G.K.V., H.E.N. and K.A.; resources, G.K.V., K.A. and H.E.N.; writing--original draft preparation, G.K.V.; writing--review and editing, G.K.V. and H.E.N.; supervision, G.K.V., K.A. and H.E.N.; funding acquisition, G.K.V., K.A. and H.E.N. All authors read and agreed to the published version of the manuscript.  
This research was funded by Ajman University, grant number 2020-IRG-ENIT-02.  
Not applicable.  
The authors acknowledge funding from Ajman University under grant agreement 2020-IRG-ENIT-02.  
The authors declare no conflict of interest.
# Cooperative Admission Control with Network Coding in 5G Underlying D2D-Satellite Communication
## Problem Formulation
### Multiple Access Network Coding (Lossless Second Stage)
#### 3.2.1 MANC Batch  
The upper bound of the packets completion time T when transmitted by the MANC-batch is bounded mathematically by  
\[T\leq\Bigg{[}\max\Big{(}|\mathcal{X}_{c}|,\frac{1}{3}\big{(}\max_{n\in \mathcal{R}}|\mathcal{D}_{n}|+\min_{n\in\mathcal{R}}|\mathcal{D}_{n}|\big{)},\frac{1}{2}\max_{n\in\mathcal{R}}|\mathcal{D}_{n}|\big{)}\Bigg{]}. \tag{17}\]  
The proof is provided in Appendix A.