esa-sceva/satcom-chunk-collection huggingface dataset:
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 3 Methodology
### Vibration-Data Acquisition Technique  
#### 3.2.1 Vibration Monitoring Method  
Figure 2 illustrates a vibration monitoring setup on a submerged caisson-foundation system. This system has three main components: an above-water concrete cap, a submerged caisson, and a foundation. The foundation is constructed with a sand mound and gravel armor. The caisson is oriented perpendicular to the direction of wave propagation and is subjected to impulsive breaking wave forces. The vibration test for in situ submerged caisson-foundation systems uses wave forces as the excitation source. Alternatively, a tugboat with adjustable weight and speed can be used to generate controlled artificial excitation for the vibration tests.  
Figure 1: Scheme of CNN deep learning model for a caisson-foundation system using vibration data.  
Figure 3 illustrates vibration monitoring for a caisson-foundation system. Several accelerometers are strategically installed on the above-water concrete cap of the caisson to obtain vibration data. These accelerometers are oriented in three directions (x, y, and z) to capture all caisson motions, including heave, sway, and roll. In this study, the time-history responses are acquired by five accelerometers (i.e., ACC 1z, ACC 2z, ACC 2x, ACC 3x, and ACC 2y) for 15 s at a sampling rate of 1 kHz. Only 1000 data points that contain the most important information (i.e., 100 data points before the impact load and 900 data points after) are chosen. These time-history responses from the five sensors are then concatenated to form one continuous response, as shown in Figure 3b. The concatenating technique transforms the data into a 1-D array which is well-suited for the 1-D CNN model. The concatenated signal contains the information of all five accelerations with inherent order. The 1-D CNN model can simultaneously learn the characteristics of time-history responses from all sensors. In addition, concatenating allows the 1-D CNN model to capture the relation movements of five sensors.
# Prevent Workers from Injuries in the Brewing Company via Using Digital Human Modelling Technology
## 1 Introduction
JACK Siemens software has been widely implemented in different research areas [32; 33] due to its adequate database of human performance models and population anthropometric data. Hence, its force outputs have been extensively validated [34; 35]. However, limitations also exist within DHM technology. There is no clear understanding of the principles behind human movement and posture in many software [36], and predicted human movement cannot represent real human motion due to the limited human anthropometry and posture algorithm in the model, which accounts for a different distribution of forces at the modeled joints [25; 37]. Additionally, most of the studies only focused on a sequence of static poses, which cannot imitate realistic full-body human kinematic movement. Hence, some inappropriate postures during the entire task performance could be easily negligible. However, these awkward postures are the main factors that directly lead to a high risk of injuries on the lower back occurring in workplaces [24; 33]. Additionally, it is a time-consuming process to generate a full-body simulation. Each anatomical joint should be manually manipulated [38; 39]. Moreover, a small difference of joint angles in DHM posturing can cause a statistical difference of the spinal forces exerted on the lower back [25; 37; 25]. Currently, there are many studies assessing the risk of injuries by analyzing recorded motion data [40; 41; 42]. However, only a few integrated advanced human motion tracking systems into ergonomic biomechanics software models [34]. In ref [34], the camera-based system was used to drive the classic JACK animation for the lower back load analysis. Due to the reflective markers, which were easily blocked by the surrounding objects or the body segments, the trajectory of blocked markers needs to be predicted by the nearby markers. To overcome this limitation, we decided to use the wearable inertial sensors to record full-body human movement. Each marker is easily tracked in the acceptable wireless range. Accordingly, to effectively analyze the worker's lower back load by using a common task that beer workers repeatedly must undergo and to overcome the aforementioned limitations in the current DHM technology, this study focused on integrating an advanced human motion tracking system with ergonomic software to overcome the issue of posture sensitivity as individual postures do not have to be manually estimated and later merged to form one dynamic simulation [43]. This integrated approach was implemented to evaluate the postures at which the risk of injury is the highest and can provide an estimate of the maximum load of each worker's hand, leading to the lower back force reaching an unsafe threshold. This study provides a real-time physical training opportunity on how brewery workers can avoid awkward postures in their daily handling tasks. Additionally, this study can allow for the selection of appropriate assistive devices that should be involved when the spinal force reaches the threshold limit.
## 3 Simulation Studies
### One Target  
The first simulation is conducted to test the cooperative hunting process without obstacles. It is assumed that there are four hunting AUVs with only one target. Figure 3a shows the initial locations and stage of hunting condition. At the beginning of the hunting task, AUVs search for targets in different directions based on the proposed algorithm. Targets move at random before being discovered. After a while, the target \(T1\) is found by the AUV \(R2\). Figure 3b shows AUVs' search trajectory for the target. Because the target has the same intelligence of AUV except the cooperation, target \(T1\) will escape. The AUV \(R2\) will track the target \(T1\) and send the location information of the target to other AUVs. According to the location of the target \(T1\), the proposed algorithm automatically plans a collision-free pursuing path for each hunter. Figure 3c shows \(R1\), \(R2\), \(R3\), and \(R4\) hunting trajectory for the target \(T1\). Obviously, the simulation result shows that the proposed algorithm realizes cooperative hunting in surface-water experiments with obstacles.  
Figure 2: Target is hunted by AUVs.  
In order to further validate the performance of the proposed algorithm, the Simulation of hunting process with dynamic obstacle is provided. Figure 4 shows this process of four AUVs hunting target and avoid dynamic obstacles clearly. The results show in the Figure 4, it validates that it is available to apply the proposed algorithm to the multi-AUV hunting task, and it can effectively avoid dynamic obstacle in path planning.  
Figure 4: _Cont._  
Figure 3: Simulation of hunting process with one target. (**a**) The initial state; (**b**) AUVs’ search trajectory for the target; (**c**) Final trajectories of the AUVs.
## 4 Performance Analysis
### Average Bep/SEP  
ABEP/ASEP is a conventional important measure to reveal the wireless communications system behaviors over fading environments. In the previous technical literature, two classical methods are often used to evaluate the ABEP/ASEP of various digital modulation schemes. One is the PDF-based approach, and the other is the MGF-based approach. In this paper, the former is considered and the unified conditional BEP/SEP formulas for several classical modulation schemes, such as DPSK, BPSK (Binary Phase Shift Keying), MPSK (M-ary PSK), and MQAM (Multiple Quadrature Amplitude Modulation), are summarized in terms of the exponential function form and the complementary error function form in Table 2, where \(erfc(*)\) is the complementary error function defined in [37] (Equation (8.250.4)).  
As is well-known, the ABEP/ASEP in fading environments can be obtained by averaging the conditional BEP/SEP (\(P_{e}(\varepsilon|\gamma)\) in Table 2) over the PDF (\(f_{\gamma}(\gamma)\)) of the instantaneous SNR (\(\gamma\)), namely  
\[P_{e}(\varepsilon)=\int_{0}^{\infty}P_{e}(\varepsilon|\gamma)f_{\gamma}( \gamma)d\gamma \tag{37}\]  
Consequently, the ABEP/ASEP of the single-link and MRC systems in Sections 2 and 3 can be deduced by substituting their corresponding PDFs into (37).  
\begin{table}
\begin{tabular}{c c c} \hline
**BEP/SEP (\(P_{e}(\varepsilon|\gamma)\))** & **Modulation Scheme** & **Parameter Values** \\ \hline \(Aexp(-B\gamma)\) & DPSK & \(A=0.5,\ B=1\) \\ \hline \multirow{4}{*}{\(Cerfc(\sqrt{E\gamma})-Derfc^{2}(\sqrt{E\gamma})\)} & Coherent BPSK & \(C=0.5,\ D=0,\ E=1\) \\  & MPSK & \(C=1,\ D=0,\ E=\sin^{2}(\pi/\mathrm{M}),\mathrm{M}>4\) \\ \cline{1-1}  & Square MQAM & \(C=2\left(1-1/\sqrt{\mathrm{M}}\right),\ D=\left(1-1/\sqrt{\mathrm{M}}\right)^{2},\ E=1.5/(\mathrm{M}-1)\) \\ \hline \end{tabular}
\end{table}
Table 2: Theoretical conditional BEP/SEP for several classical modulation schemes.
## 3 Results and Discussions
### Optimization Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(Geo_{i}\) & **Healthcare** & **Water** & **Cellular** & **Shelter** & **Total** \\ \hline
9 & 0 & 3285 & 15 & 0 & 3300 \\
8 & 0 & 0 & 45 & 0 & 45 \\
7 & 27,333.3 & 0 & 0 & 0 & 27,333 \\
6 & 94,416.7 & 0 & 95 & 44,467 & 138,979 \\
5 & 189,000 & 0 & 90 & 88,336 & 277,426 \\
4 & 0 & 0 & 0 & 0 & 0 \\
3 & 20,750 & 98,280 & 75 & 0 & 119,105 \\
2 & 0 & 0 & 0 & 389 & 389 \\
1 & 33,000 & 55,949 & 0 & 22,400 & 111,349 \\ \hline Total & 364,500 & 157,514 & 320 & 155,592 & 677,926 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Total power demand for every zone (kWh). Figure 4: Critical node heat map for each individual infrastructure. Figure 5: Heat map of critical nodes across all infrastructures. The solution of optimization model \(P2\) was studied next. As noted earlier, \(P2\) allows a microgrid to supply important power nodes outside of the \(Geo_{i}\) it is located in, as long as the distance to the node is less than a given maximum (e.g., 6 km). The results of solving \(P2\), using the same cost values \(F_{i}\) as discussed above, to place three microgrids sequentially are shown in Figure 6. In Figure 6, the squares identify the microgrid locations, and the circles denote the nodes connected to the microgrid. Notice that the microgrid in \(Geo_{5}\) powers two nodes (cellular base stations) in \(Geo_{1}\). Similarly, the microgrid in \(Geo_{6}\) connects to nodes in \(Geo_{5}\) and \(Geo_{3}\) as well as nodes in \(Geo_{6}\). Table 12 shows how the power created by each microgrid is shared among the four infrastructures for each microgrid. Notice that it varies with location. Comparing the solution of \(P2\) with the results of problem \(P1\), the total cost of three microgrids will be less than \(P2\) (29.9 vs. 33.5 million). Furthermore, the effects of varying the capacity of the microgrids \(S_{i}\) in the optimization models were considered. Specifically, we varied the microgrid capacity over \(3,4,5\), and 10 MW and determined each case's corresponding cost \(F_{i}\). The Pittsburgh case study's solution to both optimization algorithms does not change. For the solution to problem \(P2\), the location of three microgrids is \(Geo_{5}\), \(Geo_{6}\), and \(Geo_{3}\), regardless of the microgrid capacity. Similarly, the solution to \(P1\) is \(Geo_{5}\), \(Geo_{5}\), and \(Geo_{6}\) for the location of three microgrids for all microgrid capacities considered. For this paper, which focuses on showing the applicability of shared Microgrid among interdependent infrastructure, we did not consider the existence of emergency generation assets required by regulation at critical infrastructure. However, such work can be extended in future work by addressing all the possible scenarios and applying all required policies.
# New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
## 4 Experimental Results and Discussion
### Analysis of the Experimental Results  
In addition, this study tried to identify whether there was a significant difference in the prediction errors between the FGM (1,1) and BP-FGM (1,1) models. Three datasets were used and 11 th fractional orders were performed in the experiment. Additionally, the absolute percentage error (\(APE\)) is used as the error index for each order. The formula is shown in (14). After calculating the prediction errors of all the data, we were able to use the \(MAPE\) as the error measurement, as shown in (15).  
\[APE=\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right|\times 100\% \tag{14}\]  
\[MAPE=\left|\frac{1}{N-4}\right|\sum_{i=5}^{N}APE_{i} \tag{15}\]  
The experiment results from the data of the company and MOEA are summarized in Table 3, where the errors are MAPEs (mean absolute percentage error) calculated by Equation (15). The detailed results are shown in Figures 7-9, where the errors are APEs (absolute percentage error) commutated by Equation (14) for 11 fractional orders that start from 1.1 to 2.1. The lower the APE values, the higher the indication of prediction accuracy.  
\begin{table}
\begin{tabular}{c c} \hline
**Month of 2021** & **Company A** \\ \hline August & 39 \\ September & 37 \\ October & 42 \\ November & 40 \\ December & 41 \\ \hline \end{tabular}
\end{table}
Table 1: The dataset collected from Company A.  
\begin{table}
\begin{tabular}{c c c} \hline
**Month of 2021** & **Company B** & **Company C** \\ \hline January & 1072 & 1051 \\ February & 1011 & 1006 \\ March & 1262 & 1190 \\ April & 1051 & 1067 \\ May & 1121 & 1085 \\ June & 1151 & 1103 \\ July & 1111 & 1111 \\ August & 1162 & 1143 \\ September & 1212 & 1143 \\ October & 1191 & 1124 \\ November & 1234 & 1120 \\ Dec & 1279 & 1175 \\ \hline \end{tabular}
\end{table}
Table 2: The dataset collected from MOEA, Company B and C.  
Figure 8: Representing the absolute percentage error for Case B by using the FGM and BP-FGM model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case/MAPE (\%)** & **FGM (1,1)** & **BP-FGM (1,1)** \\ \hline Company A & 31.303\% & 17.152\% \\ Company B & 27.062\% & 19.195\% \\ Company C & 27.641\% & 20.775\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The summary of experiment errors by using the FGM and BP-FGM models.  
Figure 7: Representing the absolute percentage error for Case A by using the FGM and BP-FGM model.
## 2 Distributed Optical Fibre Sensors
### Brillouin-Based Distributed Sensing
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Type**} & **Spatial** & \multirow{2}{*}{**Sensing Range**} & **Temperature** & **Strain** & **Acquisition** \\  & **Resolution** & & **Repeability** & **Repeability** & **Repeability** & **Time**\({}^{\ddagger}\) \\ \hline  & 5 m & 45 km & 0.1 \({}^{\circ}\)C/3 \({}^{\circ}\)C & 2 \(\mu\)/60 \(\mu\) & 1800 s \\  & 80 m & 70 km & 0.005 \({}^{\circ}\)C/2 \({}^{\circ}\)C & 0.1 \(\mu\)/40 \(\mu\) & n/a \\ BOTDR & 22 m & 80 km & n/a & 1 \(\mu\)/30 \(\mu\) & n/a \\  & 110 m & 80 km & n/a & 10 \(\mu\)/100 \(\mu\) & n/a \\  & 11 m & 55 km & n/a & 1 \(\mu\)/50 \(\mu\) & n/a \\ \hline  & 4 m & 60 km (120 km loop) & 0.1 \({}^{\circ}\)C/1 \({}^{\circ}\)C & 2 \(\mu\)/20 \(\mu\) & 600 s \\ BOTDA & 1.5 m & 10 km (20 km loop) & 0.1 \({}^{\circ}\)C/1 \({}^{\circ}\)C & 2 \(\mu\)/20 \(\mu\) & 4 s \\  & 20 m & 30 km (60 km loop) & 0.1 \({}^{\circ}\)C/n/a & 2 \(\mu\)/n/a & 600 s \\ \hline BOTDA (\({}^{\star}\)) & 50 m & 50 km (100 km loop) & 0.005 \({}^{\circ}\)C/0.1 \({}^{\circ}\)C & 0.1 \(\mu\)/2 \(\mu\) & 100 s \\ \hline \hline \end{tabular}  
* Time to reach the indicated temperature resolution; \({}^{\ddagger}\) Resolution at the maximum specified range;
* This device can measure both temperature and strain separately.  
\end{table} 
Table 3: Performances of some commercial BOTDR/BOTDA systems (sources: producers’ datasheets, technical notes, and scientific papers).
## 5 Research Method
The lesser time taken to build the model, the better the classifier. **Sensitivity** is another basic measurement to assess the performance of any algorithm. It is known as a true positive rate that is determined correctly and can be calculated using the following formula: Sensitivity = TP/TP + FN. **Specificity** is a measure of the proportion of an actual negative rate recognized correctly by a learning algorithm and can be measured using the following formula: _Specificity = TN/FP + TN._  
**Precision** is one of the primary performance indicators. It indicates the fact of being accurate and correct. It gives the impression of correctly predicted instances. It is calculated as _TP/TP + FP._The **F-measure** is the weighted average of precision and sensitivity and can be calculated by 2 TP/2 TP + FP + FN. It indicates the accuracy of a test by numerating the balance between precision and recall. It is the harmonic mean of sensitivity and precision. The **Receiver Operating Characteristics (ROC)** is a graphical means of evaluating ML classifiers and visualizing the relationship between the TP and FP rates of IDSs. Essentially, it describes the performance of ML classifiers. It is used to effectively compare ML classifiers in terms of accuracy. ML classifiers having more area under the curve have high performance. The **TP rate** indicates the possibility of an ML classifier predicting positive instances as correct and normal. Also called sensitivity, the TP rate is the probability that an actual positive will test positive. A high TP is preferable. The TP rate can be calculated using the following formula: _TP rate = TP/TP + FN_. The **FP rate**, also called the false alarm rate, implies the possibility of an ML algorithm forecasting a normal instance as an attack. A consistent increase in the FP rate might mislead the network manager to deliberately ignore alerts from the network system. A low FP is therefore desirable. The FP rate can be measured using the following formula: _FP rate = FP/FP + TN_.
## 3 Analysis of Encrypted Traffic Features  
This section provides a comprehensive analysis of encrypted traffic features, aiming to uncover the underlying patterns in encrypted communication behavior. By rigorously examining a dataset of real malware communications, this study seeks to establish a robust theoretical foundation for developing an effective and accurate model for representing encrypted traffic features.
# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 1 Introduction
The results obtained indicate that the suggested thermal management system is capable of tuning the battery temperature and cabin temperature in various conditions, complying with the protective rules required in real EVs. Finally, some works deal with the coupling between the dynamic analysis and the external environment. [PERSON] et al. [12] analyze differences in spatial structure and environmental characteristics of urban roads to design environmental load spectrum tests for various road types, including open roads, boulevards and underground parking lots. The tests are conducted in a southern Chinese city to acquire characteristic parameters such as solar radiation, temperature and humidity for different urban microclimates in road spaces. Then, the effects on the thermal response of the cabin and the passengers' cooling requirements are analyzed. Finally, cooling schemes are developed to match the specific urban microclimates of the road space, with the goal of thermal comfort and low energy consumption. The aim of this work is to model the transient behavior of a vapor compression refrigerating system for automotive use through a Matlab/Simulink code. The code is applied to a refrigeration system built for automobiles, working with R134a, and inserted into a simplified model of an electric car. The proposed innovative control strategy offers several advantages. It includes a comprehensive control-oriented model developed for simulating the HVAC system, capable of accurately capturing system dynamic performance using generalized correlations for the evaporator and condenser, which allows for the incorporation of additional control variables. The accuracy of this model is validated through experiments performed on a real device built for an electric vehicle. Furthermore, when applied within the developed model, the proposed control strategy demonstrates significant improvements in energy savings for the thermal management system compared to the PI control reference under the same temperature control standards. The model is applied to constant-velocity scenarios and the regulated ECE 15 driving cycle [13]. In this case, it is shown that the HVAC system considered increases the total energy consumption of the electric vehicle by about 40% of the total energy required to drive. The contribution of the HVAC system has been shown to be very important in the overall energy consumption of an electric vehicle. For this reason, a computational model that allows one to evaluate the electrical consumption due to the HVAC system is very important to improve the efficiency of electric mobility.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results  
The performance of the OFSMA scheme and the proposed C-OFSMA are evaluated for intra-vehicular communication. To achieve the standard of the 5G URLLC network, we calculate the minimum subcarrier channels. The simulation result is generated in the MATLAB simulator. Table 2 shows the parameter used in the simulation. The used vehicular network considers the 90 general sensors, specialized 4-audio, and 6-video sensors. These general sensors can transmit 3-, 5-, and 7-packet duplication at different arrival rates using a different number of subcarrier channels. The selection of random subcarrier channels helps to maintain the latency limit. The packet generation and transmission follow the slotted-Aloha system using the poison arrival process. The slotted-Aloha provides 0.2 ms for each slot. For general assessment, different clusters are considered for C-OFSMA in Sections 4.1, 4.2 and 4.4. Section 4.3 calculates the optimum number of clusters for sensors deployment to achieve the best transmission reliability.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.4 Problem Formulation  
The formulation of the problem of a multi-user, multi-UAV-aided MEC system is presented in this subsection. Additionally, load balancing, task offloading, and resource allocation are all taken into account for each mobile user. Moreover, both the energy and time overhead are jointly optimized in the objective. The problem can be formulated as follows:  
\[\begin{array}{ll}\min_{a}&\Big{[}\sum_{i=1}^{K}\Omega_{i}\Big{]}\\ \text{s.t}&\Big{[}T_{i}^{comm}+T_{i}^{Comp}\Big{]}\leq\Gamma_{i},&C1\\ &\sum_{a=0}^{+1}y_{i,j}=1,&C2\\ &y_{i,j}\in\{0,1\},&C3\end{array} \tag{11}\]  
where decreasing the total system cost is the main objective. In addition, the first constraint handles the delay requirements for each user, whereas constraint C2 ensures that each task will only be executed once and constraint C3 assures the binarization of the task-offloading variable.  
The problem's solution is derived through determining the best values for the offloading. Nevertheless, it is not possible to solve this problem using a convex feasible set since \(y\) is a binary variable. In addition, the problem is NP-hard, since the objective is not convex [44]. Moreover, solving it in polynomial time is difficult, particularly with a large number of device users, since the problem size grows exponentially with the increase in devices. As result, reinforcement learning can be used as an alternative to conventional optimization methods to solve these problems and obtain close-optimal solutions.