esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cnn
Throughout the nine training iterations, the accuracy values obtained ranged between 84.38% and 88.82%, with individual training accuracies recorded as 86.51%, 88.82%, 87.34%, 84.38%, 85.03%, 87.99%, 87.01%, 87.99%, and 86.68%. Upon completing all training cycles, the final model was constructed by combining the nine trained models, resulting in a final accuracy of 100%. The accuracy and loss trends observed throughout the training process are visually represented in Figure 19. At the same time, the confusion matrix, which provides a detailed breakdown of the test results, is presented in Figure 20.  
As demonstrated in Figure 19, the data were retrieved from the MATLAB Deep Learning platform. Figure 19 is divided into two sections: the upper section displays the accuracy values, while the lower section presents the loss values. The horizontal axis (\(x\)-axis) denotes the number of iterations in the training process, while the vertical axis (\(y\)-axis) shows the accuracy and loss values. In the accuracy graph, the blue color represents the accuracy rates in the training dataset, while the black curve represents the accuracy rates in the validation dataset. The graph illustrates a rapid escalation from the commencement of training, attaining over 90% accuracy following approximately 4000 iterations and stabilizing at the subsequent stages of training, nearing 100% accuracy. The proximity of the training and validation curves suggests that overfitting issues do not afflict the model and that it possesses a high degree of generalization capability. Figure 19: Final model accuracy and loss graphics. Figure 20: CNN confusion matrix. In the loss graph, the orange color indicates the loss values in the training data, while the black curve represents the loss values in the validation data. The loss values, which are elevated at the commencement of training, rapidly decline to a low level after approximately 2000 iterations and stabilize at the minimum value in the subsequent iterations. This outcome demonstrates that the model exhibits an adequate learning capacity and that the parameters are effectively updated during training. Consequently, to enhance the clarity of the graphs, the accuracy and loss curves are presented in a larger format with more precise details; axis labels, legends, and visual explanations are incorporated. In the CNN k-fold training and test results, i.e., accuracy, specificity, and sensitivity, are given in Table 5.  
Based on the confusion matrix presented in Figure 20, the F1-score, precision, and recall values are detailed in Table 6.
## 3 Results and Discussion
### Phytochemical Characterization
#### 3.2.3 GC-MS Analysis-Essential Oil Profile  
Essential oil analysis involved 34 accessions, for two years of analysis (2018-2019). 18 aroma metabolites were identified (Table S2); essential oil composition is reported in Figure 2, with volatile metabolites grouped into six different classes (Monoterpenoids, Sesquiterpenoids hydrocarbons, Oxygenated monoterreponoids, Esters, Ketones and others, where unidentified molecules are also included). It is possible to observe a high variability of the essential oil composition within the studied population, with the majority of compounds belonging to the sesquiterpene fraction for all the accessions, except for L5 and L45 (Figure 2). L3 showed the highest sesquiterpene and esters percentages (69.40% and 25.20%, respectively); instead, L5 and L45 are characterized by other compounds that do not belong to any of the considered group. In the sesquiterpene fraction, \(\alpha\)-Humulene and \(\alpha\)- and \(\beta\)-Selinene are the compounds more present and they contribute to confer to Italian wild samples fresh and spicy characterization of the Italian wild samples, in line with a previous study on European cultivars [38; 39]. Moreover, the chemical composition of several of the Italian genotypes analyzed in this study appeared to have relevant similarity to cultivars known as \"noble hops\", characterized by the presence of spicy/herbal notes, due to the presence of Trans-\(\beta\)-famesene and the oxidation of sesquiterpenes like \(\alpha\)  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ID** & **\(\alpha\)-Acids \% (DM)** & **\(\beta\)-Acids \% (DM)** & **COH\% in Tot \(\alpha\)-Acids** & **Oil Yield \%** \\ \hline  
}-\text{l}}\) & 3.473 \(\pm\) 0.455 \({}^{\text{b}-\text{e}}\) & 16.106 \(\pm\) 0.013 \({}^{\text{g}-\text{l}  
Humuluene [40; 41; 42] (Table S2). The monoterpene fraction is mainly composed by Myrcene, with little contribution of \(\beta\)-Pinene, Limonene and Bergamotene when present. Data on Myrcene presence are variable, in line with previous studies [21; 32], and in the studied genotypes, the content do not exceed 31.48%. The presence of Esters, conferred marked floral and fruity notes to the wild hop samples, and L3 accessions is characterized by the highest amount (9.07%) (Figure 2). \(\alpha\)-Humulene is instead characteristic of different wild genotypes, covering the 34.75% of the total aromatic profile in L50 and showing interesting aromatic profile. L3 and L43 showed instead the highest ketone percentage, with 7.25 and 6.19%, respectively, thus possess fruity characters. The high variability showed in the volatile composition is a demonstration of the importance of the biodiversity preservation, which is a source of characters of ecological and commercial importance. In the population studied in this research, accessions showed aromatic profiles, different for the type and amount of volatile molecules detected; genotypes characterized by spicy notes and accessions with fruity and citrusy characters were observed.
## 3 Results and Discussion  
### Experimental Results of the Measurement of Sound Velocity in Human Crus  
The transducer array was located along the central line between two rows formed by MR-visible fiducial markers. Thus, the cross section of an MRI image corresponding to a cross section of an ultrasound image was estimated as the plane through the central line and perpendicular to the lines formed by the pairs of markers. The resultant cross section of the MRI image is shown in Figure 5.  
The estimated values of the sound velocity in human muscle and fat tissue in the crus for three subjects are shown in Table 5. The sound velocities in fat and muscle were measured as 1450 \(\pm\) 20 m/s and 1560 \(\pm\) 20 m/s, respectively. The sound velocities in human fat and muscle measured in vitro are typically in the ranges of 1459-1479 m/s and 1540-1566 m/s, respectively [1]. Those values are in approximate agreement with those obtained in this study, i.e., falling within the standard deviation.  
Figure 5: An example of an MRI image of a human crus. The fat layer corresponds to the upper high-brightness region. The sound velocity in fat was measured in the central fat layer. The sound velocity in muscle was measured in the central muscle region between the bottom of the fat layer and the top of the fascia layer (lower-brightness horizontal stripe region).
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 4 Results and Discussion
### Accuracy  
* Pattern Matching: This achieved high accuracy in object detection, with straightforward configuration by adjusting a single threshold and angle.
* SIFT: This demonstrated efficiency in finding key points, especially effective in rotation scenarios, contributing to its versatility across various applications.
* ORB: This maintained reliable detection accuracy for the front side of objects under certain conditions but showed limitations in recognizing the back part of matchboxes.
* Haar cascade: Despite a time-intensive training process, this exhibited only acceptable accuracy.
# Dynamic Analytical Solution of a Piezoelectric Stack Utilized in an Actuator and a Generator
## 2 Basic Equations
### Model B: Piezoelectric Stack Subjected to a Uniformly Distributed Load  
Figure 1b shows the configuration of a piezoelectric stack subjected to a uniformly distributed load. It should be noted that Equations (8), (9), (10), and (12)-(21) for Model A are still valid for Model B.  
The mechanical and electrical boundary conditions in Model B can be given as:  
\[\left.\sigma_{zPN}\right|_{z=h_{N}}=-q(t) \tag{25}\]\[V(t)=jR\omega S|e_{33}|\sum\limits_{i=1}^{N}C_{1i}e^{i\omega t}=V_{0}e^{i\omega t} \tag{26}\]  
where  
\[V_{0}=jR\omega S|e_{33}|\sum\limits_{i=1}^{N}C_{1i} \tag{27}\]  
Combining Equations (14), (15), and (27), C\({}_{1i}\) can be obtained:  
\[C_{1i}=c_{i}A_{P1} \tag{28}\]  
where \(c_{i}\) can be determined using the following set of equations:  
\[c_{i}-(a_{i}^{1}\lambda_{i}^{1}+b_{i}^{1}\lambda_{i}^{2}+V_{i}^{1})jR\omega S| e_{33}|\sum\limits_{i=1}^{N}c_{i}=a_{i}^{1}\delta_{i}^{1}+b_{i}^{1}\delta_{i}^ {2}\ \ (i=1,2,3,\cdots,N) \tag{29}\]  
Substituting Equations (7), (14), (27), and (28) into Equation (25), \(A_{P1}\) can be determined to be:  
\[A_{P1}=-\frac{q_{0}}{d_{1}} \tag{30}\]  
where  
\[\begin{array}{l}d_{1}=p_{P}[(\delta_{N}^{1}+\lambda_{N}^{1}jR\omega S|e_{33 }|\sum\limits_{i=1}^{N}c_{i})\cos k_{P}h_{N}\\ -(\delta_{N}^{2}+\lambda_{N}^{2}jR\omega S|e_{33}|\sum\limits_{i=1}^{N}c_{i}) \sin k_{P}h_{N}]+\frac{e_{33}^{2}}{k_{33}^{2}}c_{N}\end{array} \tag{31}\]
# Forecasting the Distortion in Solar Radiation during Midday
## 2 Hypothesis and Methodology
### Methodology  
The global solar radiation of one complete year for two sites Saudi Arabia was considered in this research. These two sites are Riyadh city and Al-Kharj city. The per-minute readings of global solar radiation for the year 2019 were obtained from King Abdullah City for Atomic and Renewable Energy (KACARE), the leading authorized agency that is responsible for collecting and monitoring the data on renewable energy resources in Saudi Arabia. It should be noted that there are many published papers in various journals depending on the data of KACARE [42; 43]. The mathematical difference between every two readings during the hours 7 a.m. and 8 a.m. (early morning hours) was calculated. The following information was deduced from the difference between the readings of the early hours:  
1--Sunny day (normal situation): if the difference is positive, the radiation is increasing which is a normal situation.  
2--Cloudy day (abnormal situation): if the difference is negative, the radiation is decreasing which is an abnormal situation that implies that there are clouds in the sky.  
3--If the difference is zero, the radiation is fixed, which means that the clouds still exist.  
These information are utilized to obtain a distortion index (_DI_) for solar radiation during the midday hours to consequently forecast the fluctuation in the power generation from PV plants.
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 3 Description of the Proposed SDN-Based Service for Rail and Road Coexistence
### Tools Used for Service Implementation and Evaluation  
To develop an SDN application designed to meet the objectives described in Section 3.1 and evaluate its performance in a realistic scenario, the following tools were used:  
* **Open Network Operating System (ONOS) SDN controller:** An open-source project aimed at creating a software-defined networking operating system for communications service providers that is designed for scalability, high performance, and high availability. This tool is widely used in both academic and industrial environments [31].
* **Mininet-WiFi network emulator:** A software-defined network emulator tool that has the capability to define different network topologies, where nodes/hosts can be configured with multiple wireless interfaces [32]. It can, therefore, reproduce complex rail and road environments by directly injecting real traffic data.
* **Scapy tool:** A packet manipulation tool for computer networks that can forge or decode packets, send them on the wire, capture them, and match requests and replies. It will be used in the implemented system to generate common/emergency messages [33].
* **Matt's traceroute (MTR) tool:** A commonly used tool to measure the latency of the developed network. It uses the ping and traceroute to calculate the latency and jitter in the network [34]. In our context, it will be used to demonstrate the real-life applicability of the proposed solution. Detailed information about these tools is available in [27].
## 5 Experiments and Results  
### Experimental Setup  
In the experiment, a performance evaluation, including an analysis of the time delay, packet loss rate, and throughput, was conducted in Wuhan, China, and the test progress was completed in accordance with the interior standards designed by Xingyun Satellite Co., Ltd., including the communication protocol between the XY-2 satellite \(\alpha\)-stage low-mobility communication module and terminal. In the experiments, we evaluated the performance by transmitting data cached in the developed ground IoT terminals to the data center via the two Xingyun satellites. Notably, to observe the functionality and performance of the system, data of different sizes, including 54, 108, 162, 200, 324, and 400 B, were selected and cached in the ground IoT terminal in advance. Throughout the transmission process, the cached data in the ground IoT terminal were transmitted to the satellite via an uplink, transmitted to the ground gateway station located in Wuhan via a downlink, and finally transmitted to the data center via the Internet. The time delay of the system was evaluated to verify the data upload and download efficiency via satellites under a real-world scenario. The time delay was defined as the time consumption of data transferral from the ground IoT terminal to the remote data center via satellites. Because the satellite communication data bandwidth is very narrow with a maximum bandwidth of up to 200 B, it was necessary to convert the data into subdata for transmission through the satellite-enabled IoT network. Thus, the packet creation mode was needed in the data transmission process to guarantee the success of the mission. The packet structure of the data transmission is shown in Figure 7.
## 3 Datasets
### Additional Data  
#### 3.3.1 Wave Buoys  
In the German Bight, sensors measuring the SWH were deployed by federal and scientific institutions, whereby the larger part was installed by the German Federal Maritime and Hydrographic Agency (Bundesamt fur Seeschifffahrt und Hydrographie (BSH)). These sensors are either wave buoys or radar sensors. In this work, data from two BSH wave buoys, to the northwest of Heligoland (HelgolandNorthWR) and in the Outer Elbe (ElbeWR) were used. Even if other sensors have higher sampling rates and better resolution properties they were too far away to represent the SWH at the measuring sites. The wave buoys use acceleration data from a 30 min interval to calculate the SWH with a resolution of 0.10 m. As no accuracy information is provided, a standard deviation of 0.05 m had to be assumed.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments
### Implementation Details
#### 4.2.4 Configuration of Attention Mechanisms  
To extract meaningful features, we adopted three kinds of attention mechanisms: channel-wise, spatial-wise, and mixed attention mechanisms. Firstly, for the channel-wise attention mechanism, we utilized a squeeze-and-excitation (SE) block [44], which performed global average pooling on the feature maps and used a fully connected layer to output the channel-wise attention maps. In this way, our model could learn global information on feature maps and extract the features of large-scale structures. Secondly, for the spatial-wise attention mechanism, we utilized an attention module (AM) [43], which performed a convolution operation on the feature maps and used the sigmoid activation function to obtain the planar attention maps. This attention mechanism allowed our model to establish the relationships between pixels in the images. Thirdly, for the mixed attention mechanism, we utilized a convolutional block attention module (CBAM) [45], which generated channel-wise attention maps and spatial-wise attention maps as feature maps. This method combined the characteristics of the other two attention mechanisms to adaptively fine-tune the weight of each channel and planar map. In our experiments, we attempted to select the suitable attention mechanisms for the foreground and background depth prediction models to extract meaningful features.  
Figure 4: Distribution mapA of the areas of image segments.
## 5 Numerical Analysis Results and Discussion
### PLAXIS Analysis
#### 5.3.2 Case Simulation of Site 2  
Table 10 displays the soil properties for Site 2, Tables 11 and 12 display the material parameters of Site 2's slope protection structures, and Figure 22 displays Site 2's analysis model. The road load is indicated by the blue arrow, the free segment of the ground anchor is indicated by the black line, and the anchor segment is indicated by the red line. From top to bottom, the soil layer is separated into worn sandstone and colluvium.  
The displacement of Site 2 in each scenario is shown in Table 13. The contour line diagrams of the slope displacement in various scenarios are displayed in Figures 23-25. Figure 23 shows that the forces operating on the slope were concentrated on the sliding surface and a portion of the slope top because Site 2 was at the normal groundwater level. With the high groundwater level, Figure 24 shows that the force acting on the slope moved  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Layer**} & \multicolumn{6}{c}{**Parameters**} \\ \cline{2-7}  & \(\gamma_{\text{unsat}}\) & \(\gamma_{\text{sat}}\) & \(E\) & \(\
u\) & \(c\) & \(\varphi\) & \(\psi\) \\  & **(kN/m\({}^{3}\))** & **(kN/m\({}^{3}\))** & **(kN/m\({}^{2}\))** & **(kN/m\({}^{2}\))** & **(kN/m\({}^{2}\))** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** \\ \hline Colluvium & 19.6 & 20.1 & \(7\times 10^{4}\) & 0.3 & 10 & 28 & 0 \\ Weathered sandstone & 23.5 & 24.0 & \(3\times 10^{5}\) & 0.3 & 50 & 30 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Soil parameters of Site 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Simulation Elements**} & **Young’s** & **Unit** & **Diameter** & **Horizontal** & **Side** & **Side** \\  & **Modulus** & **Weight** & **(cm)** & **Spacing** & **Friction** & **Friction** \\  & **(kN/m\({}^{2}\))** & **(kN/m\({}^{3}\))** & & **(m)** & **Resistance** & **Resistance** \\ \hline Embedded beam row element & 2.5 \(\times\)\(10^{7}\) & 10 & 20 & 3 & 95.5 & 95.5 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Material parameters of anchor section of ground anchor of Site 2.  
Figure 21: Site 1’s \(\Sigma\)M\(\underline{s}\) in various scenarios.  
Figure 30 shows Site 2's \(\Sigma\)_Msf_ curve in different scenarios. The SF was determined from the ordinate value after the curve in the figure reached the stable stage. As Table 14  
Figure 28: The potential sliding surface based on safety analysis in Scenario 3 at Site 2.  
Figure 29: The shading distribution diagram of the potential sliding surface based on safety analysis in Scenario 3 at Site 2.  
shows, the SFs of Scenarios 1, 2, and 3 were 1.265, 1.105, and 1.009, respectively. According to the analysis results, in three different scenarios, the SF value of the slope of Site 2 was lower than the design specification value [11]. Moreover, according to [PERSON] safety factor standard [46], the slope of Site 2 in Scenarios 1, 2, and 3 was at a stable, critical, and labile level, respectively. This indicates that the effect of the slope protection project at Site 2 needs to be improved.
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 5 Performance Assessment  
This section evaluates the impact of 3 GPP LBT priorities on service performance in coexistence for indoor dense scenarios. The environment defined by 3 GPP for this kind of analysis [22] is used. Thus, among all the deployment scenarios for 5G (rural, dense, urban and urban macro outdoor), the one chosen is proposed in TR38.889 [15] as a baseline on coexistence as indoor Hotspot scenario and specified as the most suitable to evaluate the coexistence with 802.11 ac WiFi networks whose power and technical characteristics defined by ETSI do not allow exceeding 23 dBm.This criterion is followedby some of the key articles on coexistence of 3 GPP-WiFi technologies in unlicensed band [13; 16; 35; 36]. Both LBT and WiFi nodes share the same 20-MHz channel (number 36 within 5-GHz band).
## 3 Results  
### Decrease in the Percentage of ChAT+ Neurons in the MP of Early- and Late-Stage EAE Mice  
The neurochemical signature of both the SP and MP was studied in EAE, which is the common mouse model of MS. Mice were studied during early- and late-stage disease, and results were compared with a control-immunized group that had received HEL as the immunizing antigen. In HEL-immunized control mice, 181 ganglia were analyzed in the SP and 54 in the MP; in early-stage EAE mice, 194 ganglia were analyzed in the SP and 72 in the MP; in late-stage EAE mice, 186 ganglia were analyzed in the SP and 81 in the MP. The number of HU+ neuronal perikarya was investigated in both the SP (Figure 1) and the MP (Figure 2).  
The percentages of neurons containing nNOS, VIP, or ChAT were calculated in each group. In the SP of HEL-immunized mice, ganglia contained a median number of 7.58 (min-max: 5.93-8.95) neurons, whereas 6.48 (4.41-10.83) neurons per ganglion were observed in the early-stage EAE group and 8.00 (6.58-10.34) in the late-stage EAE group (Figure 1b). The median percentage of VIP-positive (VIP+) neurons barely varied among the groups (HEL: 51.81% (min-max: 25.78-82.73%); early-stage EAE: 47.97% (32.84-58.45%); late-stage EAE: 55.28% (40.19-73.73%)) (Figure 1c). The same observation applied to nNOS-positive (nNOS+) neurons (HEL: 46.46% (min-max: 12.55-68.72%); early-stage EAE: 31.63% (8.7-46.22%); late-stage EAE: 32.97% (3.25-58.65%)) (Figure 1d). The number of submucosal neurons staining positively for ChAT (ChAT+) was consistently low (HEL: 11.91% (min-max: 6.34-14.77%); early-stage EAE: 8.05% (3.88-17.11%); late-stage EAE: 6.20% (3.13-10.93%)) (Figure 1e). Overall, no notable differences were observed in the SP between HEL-immunized mice, early-stage, and late-stage EAE.  
In the MP, the median number of neurons per ganglion was slightly higher in HEL-immunized (39.17 (min-max: 25.67-44.1)) and early-stage EAE mice (38.85 (25.71-48.44)) compared with late-stage EAE (27.55 (18.93-30.56)) (Figure 2b). VIP+ neurons were scarce (HEL 5.30% (min-max: 2.47-5.78%); early-stage EAE 6.61% (4.65-10.48%); late-stage EAE: 4.31% (3.25-6.76%)) (Figure 2c). The percentage of nNOS+ neurons was similar in all three groups (HEL 28.64% (min-max: 16.25-38.37%); early-stage EAE 32.00% (27.86-46.47%); late-stage EAE 25.65% (16.79-28.85%)) (Figure 2d). Thus, there were no statistically significant alterations in the total number of neurons per ganglion and in the percentages of VIP+ and nNOS+ neurons in the MP. However, a statistically significant lower percentage of ChAT+ neurons was found in early-stage EAE (9.96% (min-max: 6.73-10.28%)) compared with the HEL control group (14.04% (11.46-32.36%)) (\(p\)\(<\) 0.05; Figure 2e). A strong trend toward a decrease was observed in late-stage EAE (10.15% (min-max: 4.96-11.85%)).