esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology
## 3 System Model
### Overview of System Model  
The proposed heterogeneous BS model consists of three basic network entities, in which BSS are fixed deployed by considering a city scenario which is illustrated in Figure 1. By considering the position of EV, if Status Of Charge (SOC) of EV is below the minimum threshold, the EV moves toward the BSS with the help of GC. The basic network entities involved in the system model are described below.  
* EV: Each \(V_{i}\in U_{1}\) has predetermined minimum threshold where \(U=\{V_{1},V_{2},V_{3},\ldots,V_{n}\}\). When SOC of EV is below this threshold, a EV sends the request message to GC for BS. The message contains vehicle ID, location of EV, arrival time, and battery type of EV. Then, the GC checks each BSS in the network according to its battery type and confirm the request to the \(R_{V}\) (requested vehicle) by sending a reply message. When a reply message is delivered to the EV, a driver confirms the recommendation and tried to move EV toward the BSS to switch the battery.
* BSS: Each BSS maintains a stock of a limited number of fully charged batteries. These batteries are grouped into different categories according to heterogeneous EV type.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Papers** & & & & & & & & & & \\ \hline
**Author** & **DC Fast-** & **Charging** & **Charging** & **Data Driven** & **AC Charging** & **Scalar PV** & **NOL-** & **Charging** & **Locally and** & **Route** \\
**Blim** & **Omur** & **Yes** & **No** & **No** & **No** & **No** & **No** & **No** & **Yes** \\ \hline
**Shamsdin** & **Yes** & **Yes** & **No** & **No** & **No** & **No** & **No** & **No** & **Yes** \\
**Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** \\ \hline
**Blim** & **Omur** & **Yes** & **No** & **No** & **No** & **No** & **No** & **No** & **Yes** \\ \hline
**Shamsdin** & **Omur** & **Yes** & **No** & **No** & **No** & **No** & **No** & **No** & **Yes** \\
**Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** & **Fu** \\
**Fu** & **Fu**Furthermore, they are defined as a symbolic set \(X=\{\)type-I; type-II; type-III;...; type-X\(\}\). The depleted battery is replaced by a fully charged battery in BSS within 3-5 min. With such operations, these batteries are cycled from BSS to the charging factory for battery recharging to provide efficient service to EV.
* GC: A GC monitors each BSS in the network and manages the switching reservation of \(R_{V}\). Therefore, GC caches the update information (number of fully charged batteries and available type of ones) periodically to efficiently select the BSS for \(R_{V}\).  
The above network entities communicate with each other via wireless communication such as ubiquitous cellular network 3G/4G allowing for real-time exchange of information. The network entities are equipped with a wireless device for communication and the exchange of information.
## 5 Design Procedure and Application Example
Deriving the previous expression for \(i_{r}\), and replacing \(v_{pv}-v_{r}=\Delta v_{po}\) (change on the reference imposed by the P&O) and \(i_{pv}-(i_{1}+i_{2})=2\cdot\delta i_{2}\) (sum of the current ripples in \(i_{1}\) and \(i_{2}\)), leads to the dynamic restrictions on the voltage reference to ensure the stability of the SMC as follows:  
\[\frac{d{v_{r}}}{dt}=\frac{2\cdot\delta i_{2}}{C_{pv}}-\frac{1}{k_{p}}\cdot \left(\frac{d{i_{r}}}{dt}-k_{i}\cdot\Delta v_{po}\right) \tag{65}\]  
Evaluating the previous expression results in the dynamic restriction \(\left|\frac{d{v_{r}}}{dt}\right|<0.061\) V/\(\upmu\)s, which must be imposed into the reference generated by the P&O algorithm. Such a limitation can be implemented in analog form using operational amplifiers, e.g., using the circuit reported in [36], or by embedding it into the digital processor used to implement the P&O algorithm. This application uses the second option since the digital processor is already needed to execute the P&O algorithm, thus avoiding the use of additional hardware. Such a digital limitation is implemented using expression (66), which generates a ramp with slope \(\max\left(\left|\frac{d{v_{p}}}{dt}\right|\right)\) to impose the desired perturbation magnitude \(\Delta v_{po}\); this expression accounts for both positive and negative perturbations. \[v_{r(k)}=v_{r(k-1)}+\Delta v_{po}\quad\Rightarrow\quad\Delta v_{po}=\pm\max \left(\left|\frac{d{v_{r}}}{dt}\right|\right)\cdot t\quad,\quad\forall\ t\in \left[0,\frac{\Delta v_{po}}{\max\left(\left|\frac{d{v_{po}}}{dt}\right| \right)}\right] \tag{66}\]  
Finally, the designed parameters for both the NEC boost converter and SMC are reported in Table 3, and the design process of those parameters is summarized in the flowchart of Figure 13: the PV panel used in the installation and the maximum switching frequency supported by the MOSFETs must be defined; then, the PV model parameters are calculated using the procedure reported in [35]. Using the previous information, the values for the inductors of the NEC boost converter are calculated using expressions (18)-(24). The next step is to calculate the \(C_{cb}\) capacitor using expression (25), and the input capacitor \(C_{pv}\) is designed from Equation (26). The voltage and current that must be supported by both semiconductors are calculated from (27) to (30), which are needed to select the MOSFET and diode. The parameters of the P&O algorithm are calculated using the procedure reported in [34], and the settling time \(t_{s}\) of the PV voltage must be defined to ensure the P&O stability (\(t_{s}<T_{a}\)). Using expression (54), the hysteresis width \(H\) of the SMC is calculated, and the voltage controller parameters \(k_{p}\) and \(k_{i}\) are calculated using expressions (60) and (64). The control system design is finalized with the calculation of the dynamic limits for the voltage reference using Equations (47), (48), and (65), which is implemented using the difference equation given in (66).
## 3 Attenuation, Phase Shift, and Time Delay Due to Rainfall in Zenith Paths
Now, from \(A(t)\) and \(\Phi(t)\), the transfer function can be calculated at any time \(t\), as we will do in Section 4. Notice that, as \(R(t)\) is averaged in 1 min, \(A(t)\) and \(\Phi(t)\) are also \"averaged\" in 1 min so that the transfer function refers to 1 min and therefore is changing very slowly compared to any digital signal. In other words, every minute the channel can be considered affected by the same in-band rain attenuation and phase delay. Moreover, we assume that at any time \(t\) the channel SNR is larger than the minimum required by the probability of bit error constraint; therefore, the rain attenuation to be compensated with power control, or with other techniques, is that measured at the highest frequency of the upper side-band, i.e., at 85 GHz in our exercise. Because our purpose is to study transfer functions, we assume the channel is still working as required; in other words, outages are not considered as they are not directly related to transfer functions.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 6 Numerical Results
### Accuracy Assessment  
Unfortunately, at the moment of writing the authors do not have access to the operational deployment of mmWave NR technology in an industrial environment. As a result, we cannot benchmark the model against real data. However, understanding the importance of benchmarking, we offer the following simulation-based solution. Recall that our model is characterized by a two-staged decomposed structure, where we first utilize photogrammetry to derive the transparency probabilities and then apply the tools of stochastic geometry to derive the blockage probability. Here, the first part is already a simulation model itself that can only be tested using realistic industrial machines. However, the second part--stochastic geometry assessment--can indeed be tested to ensure that we do not allow for technical errors in the analysis.  
To this aim, we utilized Matlab to create a geometry-based simulation model to assess accuracy of the proposed stochastic geometry analysis. The results are shown in Figure 5 as a function of mean machine width for UE-UE and BS-UE with blockers random heights communications scenarios. The rest of the parameters are: site occupation probability \(\
u=1\), and transparency probability \(\kappa=0.9\), UE coordinates \((5,5)\), \(E[H_{B}]=1\) m, \(h_{U}=1\) m. As one may observe, the developed model allows to precisely capture geometry of the considered scenario returning coinciding LoS blockage probabilities. Similar observations are made for other input parameters.
###### Abstract  
Recently, Telehealth connects patients to vital healthcare services via remote monitoring, wireless communications, videoconferencing, and electronic consults. By increasing access to specialists and physicians, telehealth assists in ensuring patients receive the proper care at the right time and right place. Teleophthalmology is a study of telemedicine that provides services for eye care using digital medical equipment and telecommunication technologies. Multimedia computing with Explainable Artificial Intelligence (XAI) for telehealth has the potential to revolutionize various aspects of our society, but several technical challenges should be resolved before this potential can be realized. Advances in artificial intelligence methods and tools reduce waste and wait times, provide service efficiency and better insights, and increase speed, the level of accuracy, and productivity in medicine and telehealth. Therefore, this study develops an XAI-enabled teleophthalmology for diabetic retinopathy grading and classification (XATTO-DRGC) model. The proposed XATTO-DRGC model utilizes OphthAI IoMT headsets to enable remote monitoring of diabetic retinopathy (DR) disease. To accomplish this, the XATTO-DRGC model applies median filtering (MF) and contrast enhancement as a pre-processing step. In addition, the XATTO-DRGC model applies U-Net-based image segmentation and SqueezeNet-based feature extractor. Moreover, Archimedes optimization algorithm (AOA) with a bidirectional gated recurrent convolutional unit (BGRCU) is exploited for DR detection and classification. The experimental validation of the XATTO-DRGC method can be tested using a benchmark dataset and the outcomes are assessed under distinct prospects. Extensive comparison studies stated the enhancements of the XATTO-DRGC model over recent approaches.  
Keywords:telemedicine; diabetic retinopathy; fundus images; deep learning; teleophthalmology +
Footnote †: journal: Medical Imaging
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 4 The Proposed Approach
### System Architecture and Flow
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Representative** & \multirow{2}{*}{**Pros**} & \multirow{2}{*}{**Cons**} \\  & **Works** & & \\ \hline
2D CNN/ & [55; 56; 57; 58] & Reuse image classification networks, Low & Tend to ignore temporal structure, large training temporal footprint \\ CNN + LSTM & [55; 56; 57; 58] & no. of parameters & (expensive), may not capture \\
3D CNNs & [28; 29; 59; 60] & Directly represent spatiotemporal data & Very high no. of parameters \\ Original & [27; 61] & Use of optical flow along with RGB & Optical flow frames add \\ Two-Stream Nets & [27; 61] & frames gives high performance, low no. of parameters & computational load, high memory \\
3D-Fused & [26; 37; 62; 63] & Less test time augmentation & requirement \\ Two-Stream Nets & [26; 37; 62; 63] & due to snapshot & High no. of parameters \\ Two-Stream Inflated & & Enhanced use of both RGB and flow & Computation load, high memory \\
3D Nets & [31] & inputs, & required low no. of parameters & requirement \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Video Representation Architecture Models. Figure 4: (**a**) Inception module; (**b**) Inflated Inception v1 architecture [35]. block), as proposed in the original architecture [31]. Similarly, the concatenation of layers is carried out followed by each inception module. As evident from the dataset details discussed previously, the UFLV-DS is a small-sized dataset. Even though the study adopted several methods during experiments to avoid over-fitting due to the small dataset, the initial results for training and testing on these datasets alone were still not satisfactory. Therefore, to make up for the disadvantage of an insufficient dataset, the training model was initialized with the i3D model pre-trained on the Kinetics dataset, which is considered as a de-facto standard for video action recognition. Indeed, a 2-step approach to applying transfer learning was followed. The approach is described in the following section.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 3 Sensors
### Laser Sensors  
Distance can be a useful factor in human-agent interaction. Especially for humanoid robots, getting close is a first gesture of intent to engage. [PERSON] et al. [89] used a laser sensor aboard a companion robot to measure distance. The UE-HRI dataset [78] took a distance measure of a laser aboard a Pepper robot as precursor of interaction. Going beyond simple distance measures, Light Detection and Ranging (LiDAR) sensors provide a dense 3D mapping of human agents and their surroundings as point clouds. Researchers have successfully obtained gesture and body poses using LiDAR [90] and even head pose [91]. Nevertheless, LiDAR can at most support engagement estimation, due to its incapacity to detect facial details and emotional cues. Furthermore, powerful LiDARs can be expensive.
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 4 Detectability of GDSS Signals
### Detection Using High-Order Moments  
Moments are statistical parameters that can be used to measure Gaussian distribution as discussed in the previous section. In this section, the 20 th-order moment is used to assess the detectability of the GDSS and DSSS signals operating under the noise floor. The 20 th-order moment of the zero-mean unit-variance Gaussian distribution is 654,729,075. The moments of the GDSS and DSSS signal components are estimated at various SNR and then compared with the theoretical moment value using the absolute deviation between the estimated and theoretical values. The results are generated by averaging results from five tests and expressing the average deviation as a percentage of the theoretical value. Each test result is conducted using a spreading factor of 256 on \(1\times 10^{6}\) message bits. The signal values are first standardised using Equation (5) to have a mean of 0 and a standard deviation of 1 before estimating the moment:  
\[z_{i}=\frac{r_{i}-\overline{r}}{\sigma} \tag{5}\]  
where \(\overline{r}\) and \(\sigma\) denote the mean and standard deviation of the signal values, respectively. The average deviation measures are plotted in Figure 8.  
This figure shows that the average deviation of the DSSS signals are significantly large compared to GDSS values, indicating non-Gaussian features of the DSSS signal even in the presence of high noise levels. Unlike DSSS signals, the measures obtained for the GDSS signals show deviation values very close to zero across all SNR values.  
Assuming an average deviation threshold of 10% (to achieve detection with high confidence), the moment-based detector will fail to detect the presence of a GDSS signal, whereas the same detector can easily detect the DSSS signals for SNR \(>-12\) dB. It should be noted that the standard DSSS signal can be Gaussian distributed if the system operates at a sufficiently low SNR. However, this would require an increase in the spreading factor, resulting in a reduction of data rate.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Moment** & **SGD (Theory)** & **GDSS** & **DSSS** \\ \hline
2 nd & 1 & 1 & 1 \\
4 th & 3 & 3 & 1 \\
6 th & 15 & 15 & 1 \\
8 th & 105 & 105 & 1 \\
10 th & 945 & 947 & 1 \\
12 th & 10,395 & 10,428 & 1 \\
14 th & 135,135 & 135,524 & 1 \\
16 th & 2,027,025 & 2,025,880 & 1 \\
18 th & 34,459,425 & 34,102,797 & 1 \\
20 th & 654,729,075 & 634,790,833 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Moments of GDSS noise-free signal for \(N=256\).
# Towards Explainable Deep Neural Networks for the Automatic Detection of Diabetic Retinopathy
## 3 Results
### Model Performance on the Test Set  
#### 3.1.1 Binary Classification  
Table 2 presents the three models' performance evaluation on the test and train sets for the binary classification of retinal images, i.e., whether normal or contains DR-related signs. As shown, VGG-16 resulted in the highest accuracy on the test set with the least variance between train and test set accuracies. On the other hand, Dense-Net121 clearly overfits, resulting in much lower test accuracy than training accuracy.  
Figure 5 shows the three models' confusion matrices on the test and train sets also for the binary classification of retinal images. As can be seen, VGG-16 resulted in the lowest number of false positives and the highest number of true positives, while ResNet-18 has the highest number of false positives and the lowest number of true positives.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision 1** & **Recall 1** & **F1-Score 1** & **Train Accuracy** & **Test Accuracy** \\ \hline VGG16 & **0.87** & 0.52 & 0.65 & 78.07\% & **73.04\%** \\ ResNet-18 & 0.67 & 0.68 & 0.67 & 78.44\% & 67.14\% \\ DenseNet-121 & 0.74 & **0.71** & **0.73** & **91.11\%** & 72.95\% \\ \hline \hline \end{tabular} \({}^{\dagger}\)Calculated for the test set.  
\end{table}
Table 2: Performance evaluation on the test and train sets for DR detection.
## 3 Results and Discussion
### Physico-Chemical Characterization of the Hydrolysates
#### 3.1.7 Antioxidant Activity  
DPPH estimated the scavenging activity of protein hydrolysates and the results are presented in Figure 3A. The results herein showed that BKH (800 ug/mL) significantly scavenged a high level of DPPH' radical (95%). The concentration dependence of this phenomenon confirms the role of the phenolic compounds in this activity. The antioxidant activity of WKH, RKH, and BKH as determined by \(\beta\)-Carotene/linoleic acid bleaching, indicates that the inhibition in linoleic acid oxidation was increased with by the hydrolysate in a concentration-dependent manner. Figure 3B shows that BKH (800 ug/mL) has the highest values with 88% while RKH and WKH came in the second-order with 82 and 79% inhibition, respectively. The higher scavenging and antioxidant activity of this BKH hydrolysate may be attributed to the relatively high total polyphenols content in this protein hydrolysate. It was recently found that white kidney bean popsin hydrolysate exhibited antioxidant activity with 85%, and white kidney bean popsin hydrolysate exhibited 89%, respectively [21; 75]. Furthermore, the dark red bean protein hydrolysate exhibited stronger antioxidant activity than the protein isolate and higher than ascorbic acid (AA), which is commercially used in the food industry [77]. The antioxidant mechanism of tested peptides depends on their content of aromatic amino acids donating an electron or transferring hydrogen to the free radicals for stability. Whereas, the acidic amino acids maintain the stability of the free radicals by giving a proton through the NH2 and COOH side chains [81]. Antioxidant peptides have critical importance in the food industry, where they keep the product quality by preventing the oxidation of proteins, lipids, and nucleic acids [82].
## 6 Limitations  
The mutation-based ICS time-series anomaly generation proposed in this paper has been demonstrated to be effective through various experiments in terms of visual/statistical plausibility and dataset augmentation. This technique is novel, particularly in its application to time-series data, and can be utilized to address imbalances in various time-series datasets beyond ICS datasets. Despite these advantages, the proposed anomaly generation technique has several clear limitations.  
* Currently, this study is limited to anomaly generation for numerical time-series data. From a broader perspective, sequential data encompass various forms of data, including text. Although there is potential for this technique to be applied to other types of sequential data, further investigation is required.
* The quality of the synthetic anomaly heavily depends on the performance of the generation model. In this study, AVRAE was adopted as the generation model. While AVRAE is a robust generation model, it still falls short of perfectly capturing the temporal dynamics inherent in time-series datasets.
* The most significant limitation of this technique is its inability to generate scenario-based anomalies. The mutation operation fundamentally relies on randomness. Inother words, the generated ICS anomalies do not reflect the causes and consequences of specific cyberattacks. Nevertheless, the proposed technique has sufficient utility because it generates anomalies that consider the correlations between variables in the data through AVRAE.
## 4 Results and Discussions
### Grid Independence Study  
Before any simulation research was being done, a grid independence study was carried out in order to verify the outcome obtained is not being affected by the grid size applied. In this research, the grid independence study was conducted in 4 different minimum grid sizes ranging from 0.006 m (20,990 elements), 0.004 m (39,168 elements), 0.002 m (104,721 elements), 0.001 m (325,010 elements) and 0.0005 m (375,598 elements) shown in Figure 12. The grid independence study was conducted using a transient computational fluid dynamics method with 7 m/s inlet flow velocity while domain and turbulence model settings remained the same. By comparing the root mean square value (RMS) of fluctuating lift force acting on the PVEH plate under the effect of VIV, Figure 12 showed that the force value increases from 0.000176 N with 0.006 m mesh size until it reaches 0.00162 N with 0.002 m of mesh size. The results obtained from then onwards had a constant value of around 0.0016 N and 0.0017 N, which is in an acceptable range. From this, the mesh size of 0.001 m in between 0.002 and 0.0005 m was chosen for the reason of higher computational accuracy but less computational cost induced when conducting simulation.  
Figure 11: Natural frequency from simulation (unit: m); (**a**) without piezo properties = 75.4 Hz; (**b**) with piezo properties = 76.9 Hz.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Description of the Architectural Models
#### 5.1.2 Three-Layered Architecture  
A simpler, traditional architectural model commonly divides a web application into three primary layers. As shown in Table 2, these layers are the Data Access Layer, Application Logic Layer, and Presentation Layer. While effective in separating core concerns, this conventional model does not inherently integrate accessibility or security throughout its layers.  
The three-layered model represents a traditional approach to web application architecture, with a straightforward separation of concerns. However, it does not explicitly integrate security and accessibility within its core layers, potentially leading to gaps in these critical areas.