esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 3 Model Construction
### Feature Selection Based on Minimal Redundancy Maximal Relevance (mRMR)
#### 3.1.2 Maximal Relevance  
The maximal relevance could be calculated as shown in Equation (3).  
\[maxD(S,y),D=\frac{1}{|S|}\sum_{x_{i}\in S}I(x_{i},y), \tag{3}\]  
where, \(S\) is the set of influencing factors \(\{x_{i}\}\), \(|S|=m\) is the number of key influencing factors to be selected, and \(D\) is the calculation result of correlation degree between m influencing factors selected and load value. Through this process, we selected the set of \(m\) key influencing factors, which has the greatest correlation with the load value.
## 2 Methods  
### Polysommograms and Audio Data Collection and Storage  
The presented study was based on the collection of polysommographic data from 239 patients undergoing polysommography (PSG) in the Sleep Study Unit of Sismanoglio General Hospital of Athens and diagnosed with \"severe\" or \"moderate\" SAS. In the supervised sleep study, the standard protocol for split PSG was followed [2]. According to this protocol, if a patient exhibits severe apnea within the first approx. 4 h of sleep, then the second part of the (typically 8-h long) PSG study is used for titration to the optimal pressure level that should be used by a continuous positive airway pressure (CPAP) device. Eventually, the second part of the PSG study was excluded from the herein proposed algorithmic process. All the PSG recorded signals, comprising an electrocardiogram, an electroocologram, an electromyogram, a flow rate signal recorded through pressure and thermal sensors, the respiratory effort signals monitoring the thoracabdominal movement, and a low-quality tracheal contact microphone were stored in EDF files through the PSG monitoring software \"Sleepware G3\".  
Along with the PSG system, a separate system of dual-channel audio recording was installed (Figure 1). A portable multitrack recorder (Tascam DR-680 MK II) was used to acquire and store audio signals from (a) a contact electret microphone (Clockaudio CTH100, 900 \(\Omega\) input impedance) placed on the trachea of the patient and (b) an ambient, omnidirectional, condenser microphone (Behringer ECM8000, electret, 900 \(\Omega\) input impedance) located approx. 1 m over the position of the patient's head. The contact tracheal microphone exhibits a spectral response in the range of 350 Hz-8 kHz while the ambient microphone has a flat frequency response in the range of 15 Hz-20 kHz. The breathing sound signals by each one of these microphones were sampled at 48 kHz and were stored in dual-channel uncompressed WAV files. The maximum time length of each WAV file was approx. 2 h and the entire night recording was stored in consecutive files of the same duration.  
The data collection process was in full compliance with the European regulations for personal data protection [31]. The local ethics committee of Sismanoglio General Hospital of Athens approved the described data acquisition process, provided signed consent was  
Figure 1: Setup of polysommography records and dual audio recordings: (**a**) Schematic indicating the signals acquisition; (**b**) Photograph of the room of sleep study unit where the position of the ambient microphone can be observed.  
given by the participating subjects. The patients participating in this study were asked to provide signed consent for the use of the recordings--both PSG and audio--during their sleep study, provided the removal of any data that might lead to their identification. All relevant information was removed by the clinical staff prior to any processing of the data for the purposes of this research study.
# RF Energy Harvesting Wireless Communications:
## 4 Power Management in RF-EHWC
### Practical Issues in Power Management
#### 4.2.2 Battery Imperfection  
Battery imperfections include energy leakage, capacity degradation over time and imperfect knowledge of battery status. Next, we investigate the impact of battery imperfection on power management.  
The energy stored in a battery always gradually leak with time due to the off current in a circuit and the self-discharge characteristic of the battery [64]. Depending on the material used in a battery, the self-discharging rate can range from 2% to 50% per month (e.g., Lithium-ion: 2%; Nickel-cadmium: 15%; NiMH: 30%; and supercapacitor: 50%) [65; 66]. The impact of energy leakage on power management can be solved by alternatively interpreting the leakage as the operation power of the circuit [64], which is discussed in Section 4.2.3.  
The capacity degradation of the battery modifies the shape of feasible energy tunnel, i.e., the tunnel width monotonously decreases reflecting the time-varying battery capacity, as shown in Figure 18. According to the piecewise linear feature of the optimal transmission strategy, the transmission power changes only when new energy is harvested. This implies that the time-varying battery capacity that modifies the tunnel width will not affect the optimal power management in between two adjacent energy replenishment. Therefore, \(b(t)\) can be modeled as discrete values, \(b_{t_{i}}\), where \(t_{i}\) is the time of the \(i\)th energy harvest [64]. Conventional solutions to piecewise linear optimization problem can be used to solve the optimal power management with the consideration of battery capacity degradation.  
Another type of battery imperfection comes from the imperfect knowledge of the instant battery level (e.g., the node may only know if the battery is low or high). Most of power management policies introduced in Section 4 may fail to manage the transmission power efficiently without the exact knowledge of the battery state. The authors of [67] investigated the features of limited knowledge of battery level and proposed a partially observable Markov decision process (POMDP) model. Based on the historical observation of energy harvest in the past, it maintains a probability distribution of the  
Figure 18: Power management with the time-varying battery capacity, where \(b(t)\) is the time-varying battery capacity. \(e_{i}\) and \(t_{i}\) are the amount of harvested energy and the arrival time of energy packet \(i\), respectively. \(p(t)\) is the instant transmission power of an RF-EHWC device at time \(t\).  
residual energy over a set of possible states. To maximize a reward, which is the long-term throughput, the system makes the best decision on the data transmission based on the limited knowledge.
## 4 Analyzing Experimental Results
### Ablation Experiment  
In our research, we modified the model to prove that our modifications helped in the training of the model and the prediction of cracks, as well as the deployment of the model. We performed a series of ablation experiments for validation. We not only conducted experiments for each module with individual modifications, but also combined multiple experimental scenarios. After each experiment was completed, we recorded the experimental results of each model, including its main contrast parameters. The five main comparison values are shown in Table 3. After training, the accuracy, recall, number of parameters, and mAP scores of our proposed crack detection model were 0.896, 0.914, 5,267,826, and 0.947, respectively.  
Although the accuracy and recall were reduced after replacing the conventional convolution, the combination of Transformer and DWConv still achieves the detection requirements with a significant reduction in the number of parameters and the addition of the SimAM parametric-free attention mechanism also made a small improvement in the model effect. Compared to YOLOv5s, our proposed BT-YOLO crack detection model improved by 4.5% in accuracy, 8.3% in recall, and 8% in mAP, and produced 24.9% parameter reduction in the primary model. The detection veracity requirement was achieved, while satisfying the edge deployment.
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 3 System Architecture
### YOLOv8 Model
The sum-squared error is aggregated for both the \(x\) and \(y\) coordinates in the \(n^{th}\) cells. The square root for the width (denoted as \(w\)) and the height (denoted as \(h\)) in the \(n^{th}\) cells are aggregated to consider the equality of small and large bounding boxes to ensure that the error is handled uniformly across both large and small boxes. The second two lines represent the confidence loss function, where \(\Theta\) denotes the confidence score and \(\widehat{\Theta}\) the predicted confidence score (i.e., the first line in the confidence loss function represents the confidence error when an ambulance is detected in the cell). \(\lambda_{\Delta}\) denotes the coefficient that multiplies the ambulance losses for each grid cell in the \(S\times S\) grid (i.e., the second line in the confidence loss function represents the confidence error when an ambulance is not detected in the cell). The last line represents the classification loss function where the conditional probability denoted as \(\rho\) is aggregated for the ambulance class \(c\), which is \(\in\) the set classes denoted as \(\gamma\). We defined two different classes including an ambulance and background.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 5 Numerical Experiments with Practical Sound Fields
### Standard Implicit TD-FEM and Dispersion Reducing Implicit TD-FEM  
Standard implicit TD-FEM solves the second-order ODE of Equation (3) with a time integration method called constant averaged acceleration method: CAA. Actually, CAA is known to be an unconditionally stable Newmark method with parameter \(\beta=1/4\). With the space discretization using linear quadrilateral FEs, the resulting implicit time marching scheme has second-order accuracy in both space and time. The implicit time marching scheme is expressed as  
\[(M+\frac{c_{0}^{2}\Delta t^{2}}{4}\mathbf{K}+\frac{c_{0}\Delta t}{2}\mathbf{C})\mathbf{\dot {p}}^{n+1}=\mathbf{f}^{n+1}-c_{0}\mathbf{C}\mathbf{P}_{1}-c_{0}^{2}\mathbf{K}\mathbf{P}_{2}, \tag{45}\]  
\[\mathbf{p}^{n+1}=\mathbf{p}^{n}+\Delta t\mathbf{\dot{p}}^{n}+\frac{\Delta t^{2}}{4}\mathbf{ \dot{p}}^{n}+\frac{\Delta t^{2}}{4}\mathbf{\ddot{p}}^{n+1}, \tag{46}\]  
\[\mathbf{\dot{p}}^{n+1}=\mathbf{\dot{p}}^{n}+\frac{\Delta t}{2}\mathbf{\ddot{p}}^{n}+\frac {\Delta t^{2}}{2}\mathbf{\ddot{p}}^{n+1}, \tag{47}\]  
where  
\[\begin{array}{l}\mathbf{P}_{1}=\mathbf{\dot{p}}^{n}+\frac{\Delta t}{2}\mathbf{\ddot{p}}^ {n},\\ \mathbf{P}_{2}=\mathbf{p}^{n}+\Delta t\mathbf{\dot{p}}^{n}+\frac{\Delta t^{2}}{2}\mathbf{\ddot{p }}^{n}.\end{array} \tag{48}\]  
A Conjugate Gradient (CG) iterative solver is useful to solve a linear system of equations of Equation (45) easily with convergence tolerance of \(10^{-4}\). The simplest diagonal scaling is used as a preconditioning technique to facilitate the convergence of an iterative solver. The main operation of standard implicit TD-FEM at each time step comprises sparse MVPs at each iteration process of CG solver and an additional two MVPs. Fourth-order accurate implicit TD-FEM [19; 31] for calculating the reference solution also solves the second-order ODE of Equation (3). However, it uses linear quadrilateral FEs with modified integration rules and a highly accurate Newmark method called Fox-Goodwin method. The main operation is the same as standard implicit TD-FEM, but the convergence of iterative solver becomes much better because of the additional effect of modified integration rules. The theoretically estimated dispersion error presented in earlier reports [19; 31]  
Figure 7: Theoretical and numerical dissipation errors: (**a**) \(e_{\text{reference}}\) vs. \(e_{\text{n}}\) at 2 kHz and 3 kHz and (**b**) absolute errors from \(e_{\text{reference}}\) at 2 kHz and 3 kHz.  
shows that this dispersion-reducing implicit TD-FEM has a lower error magnitude than the proposed explicit TD-FEM.
## 3 Parameter Optimization and Performance Test
### Optimization Results and Verification Test of Drive Cam Installation Angle
#### 3.2.3 Test Method  
Before the test, the motor speed was adjusted to \(120^{\circ}\)/s using a photoelectric speed sensor (model: TA8146A, minimum measurement speed: \(2.5\) r/min, resolution: \(0.1\) r/min) and the built-in controller of the motor. The initial state of the drive cam was the rigid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Text No.** & \multicolumn{3}{c}{**Net Seeding Dropping Duration of Each Pot Seeding Position \(t\)/s**} \\  & **No. 1** & **No. 2** & **No. 3** & **No. 4** & **No. 5** \\ \hline
1 & 0.34 & 0.36 & 0.33 & 0.40 & 0.34 \\
2 & 0.32 & 0.36 & 0.31 & 0.38 & 0.36 \\
3 & 0.42 & 0.35 & 0.31 & 0.36 & 0.39 \\
4 & 0.36 & 0.41 & 0.32 & 0.34 & 0.33 \\
5 & 0.33 & 0.42 & 0.32 & 0.34 & 0.32 \\
6 & 0.34 & 0.38 & 0.30 & 0.33 & 0.40 \\
7 & 0.38 & 0.41 & 0.32 & 0.32 & 0.40 \\
8 & 0.41 & 0.36 & 0.29 & 0.38 & 0.40 \\
9 & 0.38 & 0.32 & 0.32 & 0.36 & 0.36 \\
10 & 0.34 & 0.38 & 0.34 & 0.38 & 0.42 \\ \hline Average value/s & 0.36 & 0.38 & 0.32 & 0.36 & 0.37 \\ Standard deviation/\% & 3.43 & 3.14 & 1.43 & 2.60 & 3.46 \\ Variation coefficient/\% & 9.46 & 8.36 & 4.52 & 7.25 & 9.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Net seedling dropping duration test results.  
contact position between the cam at the No. 1 position and the seedling plate, and all the seedling plates were horizontal. The pot seedlings were weighed to obtain the mass \(m_{bi}\) before dropping the seedlings. To accurately measure the matrix loss caused by the collision between the pot seedlings and the inner wall of the seedling cylinder during the seedling dropping process, the height of the seedling outlet from the ground should be as small as possible to reduce the matrix loss caused by the secondary collision with the ground. The average height of the bot seedlings was set to 200 mm.  
When the test began, the motor was started, the motor was activated to drive the cams to rotate at a uniform speed, and then the potted seedlings started to fall. High-speed photography equipment was used to shoot the seedling dropping process at a frame rate of 240 frames/s.  
After the test was completed, the generated 30 frames/s of slow-motion video were processed to obtain the duration \(t_{1}\). When the pot seedlings at the No. 1 position reached the designated seedling dropping position, and the time interval between the pot seedlings at the No. 2-5 positions and the previous plant when they reached the designated position was \(T_{i,i+1}\) (\(i\) = 1, 2, 3, 4), the pot seedlings after dropping were weighed to obtain the mass \(m_{ai}\).
# UAV Formation Shape Control via Decentralized Markov Decision Processes
###### Abstract
The authors of [26] used the decentralized partially observable Markov decision process (Dec-POMDP) to formulate and solve a target tracking problem with a swarm of decentralized UAVs. As solving decentralized POMDP is very difficult (as is the case with solving any decision-theoretic methods), the authors introduced an approximate dynamic programming method called _nominal belief-state optimization_ (NBO) to solve the control problem. The authors in [30] developed a UAV formation control approach using decentralized Model Predictive Control (MPC). In their work, the UAVs were able to avoid collisions with multiple obstacles in a decentralized manner. They used a figure of eight as a reference trajectory; their results show that the UAVs were able to avoid collision with obstacles and among themselves. Several recent papers describe the formation control of different geometric shapes, e.g., multi-agent circular shape with a leader [9]. The authors of [9] propose centralized formation control, which is not suitable for swarm control when the number of UAVs in the swarm is large. Although decentralized control methods exist in the literature, our method is novel in the sense that each UAV in the swarm optimizes its own control commands and its nearest neighbor's controls over time. Then, each UAV implements its own optimized controls, and discards the neighbor's controls. We anticipate, from this decentralized control optimization approach,a global cooperative behavior among the UAVs emerges mimicking a centralized control approach. The authors of [31] demonstrated a successful use of a distributed UAV control framework for wildfire monitoring while avoiding in-flight collisions. The authors of [32] introduced path tracking and desired formation for networked mobile vehicles using nonlinear control theory to maintain the formation in the network. They have showed that path tracking error of each vehicle is reduced to zero and formation is achieved asymptotically. As centralized control strategies suffer from exponential computational complexity and high memory usage, the decentralized control methods are being actively pursued in the context of swarm control, especially when the size of the swarm is large. A survey of these decentralized control strategies can be found in [29]. In this paper, we develop a novel decentralized UAV swarm formation control approach using Dec-MDP formation. In this problem, the goal is to optimize the UAV control decisions (e.g., waypoints) in a decentralized manner, such that the swarm forms a certain geometrical shape while avoiding collisions. We use dynamic programming principles to solve the decentralized swarm motion control problem. As most dynamic programming problems suffer from the curse of dimensionality, we adapt a fast heuristic approach called _nominal belief-state optimization_ (NBO) [10; 33] to approximately solve the formation control problem. We perform simulation studies to validate our control algorithms and compare their performance with centralized approaches for bench marking the performance.
# A Framework for Proactive Resource Provisioning in IaaS Clouds
###### Abstract  
Cloud computing is an emerging technology for rapidly provisioning and releasing resources on-demand from a shared resource pool. When big data is analyzed/mined on the cloud platform, the efficiency of resource provisioning would affect the system performance. This work proposes a framework for proactive resource provisioning in IaaS (Infrastructure as a Service) clouds to improve system performance. The proposed framework consists of the virtual cluster computing system, the profiling system, the resource management system, and the monitoring system. In this framework, the over-commit mechanism is applied to improve resource utilization. Furthermore, a proactive task scheduling approach is also present to prevent the postponement of tasks in critical stages, especially when the amount of aggregated resources requested by virtual machines exceeds that of available resources on the over-committed physical machines. Experimental results show that the over-commit approach indeed improves the resource utilization. However, when the degree of applying the over-commit approach increases, the burden of this proposed approach also conceivably increases. Therefore, the proposed framework further applies the proactive task scheduling approach to execute the time-critical tasks earlier to shorten the processing time. A small-scale cloud system including 3 servers is built for experiments. Preliminary experimental results show the performance improvement of our proposed framework in IaaS clouds.  
**Keywords:** over-commit; resource provisioning; IaaS; cloud computing; critical path; task scheduling; data stream computing +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion  
### Experimental Results  
During the experimental program, a dataset containing 30 various mixing proportions was achieved, which was utilized to build and verify the proposed stress-strain model of hybrid fiber concrete. Figure 7 graphically depicts the results of the compressive strength of 30 mixtures at 28 days of curing, the red dashed line representing the best fit changing in the maximum compressive strength of the tested concrete samples. At the same ratio of fiber content and aggregate (A) to binder (B) ratio (A/B), the results show that increasing the replacement ratio of FA induces a loss in compressive strength. Due to the higher particle size of FA compared to conventional Portland cement (NPC), this may be the cause. Cement particles dissolving in an alkaline liquid provide a smaller surface area than FA, which contributes to a delayed hydration process [43].  
Each concrete specimen's maximum strain can be determined from its stress-strain curves, based on the results of the experiments. The maximum strain of all samples can be shown to vary depending on the variance of the FA, fiber, and A/B ratios of the samples. The results show that using cement type \(II\) causing a reduction in the maximum strain values. Moreover, increasing the FA and A/B ratios has opposite effects where FA has a positive trend in increasing the strain value while the A/B leads to a decrease in the strain, as shown in Figure 8, the red dashed line representing the best fit changing in the maximum strain values of the tested concrete samples. The stress-strain compressive behavior with increasing strain of the tested hybrid concrete specimens has been investigated through displacement-controlled tests, as illustrated in Figure 9.
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 4 Results and Discussion
### Case A  
In this case, C\({}_{2}\)H\({}_{2}\)%, C\({}_{2}\)H\({}_{4}\)%, and CH\({}_{4}\)% were placed on the three sides of the triangle in the DTM, which was used to diagnose all faults. Their concentrations were used as the input dataset for the algorithms. The MLMs' performance evaluation results with the DTM (case A) for training and testing are shown in Tables 5 and 6.  
Figures 2 and 3 illustrate the results chart of the six metrics for MLMs on the case A dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** & **MCC** \\ \hline RF & 95.2\% & 95.8\% & 95.2\% & 99.3\% & 94.9\% & 90.1\% \\ \hline BPNN & 69.8\% & 48.8\% & 69.8\% & 30.2\% & 57.4\% & 0\% \\ \hline KNN & 81.7\% & 79.7\% & 81.7\% & 71.4\% & 80\% & 58.6\% \\ \hline SVM & 78.6\% & 79.2\% & 78.6\% & 60.5\% & 74.3\% & 48.1\% \\ \hline Decision Tree & 86.5\% & 87.5\% & 86.5\% & 89.9\% & 84.9\% & 72.2\% \\ \hline Naive Bayes & 74.6\% & 77.1\% & 74.6\% & 86\% & 75.6\% & 50.5\% \\ \hline \end{tabular}
\end{table}
Table 6: Machine learning methods’ performance evaluation results: case A testing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** & **MCC** \\ \hline RF & 95\% & 94.6\%  
Figure 4: Confusion matrix of case A dataset for RF—testing stage.  
Figure 5: Confusion matrix of case A dataset for BPNN—testing stage.  
Figure 6: Confusion matrix of case A dataset for KNN—testing stage.  
Figure 7: Confusion matrix of case A dataset for SVM—testing stage.  
As mentioned above, RF obtained the best accuracy. The confusion matrix of RF in Figure 2 shows that the OH fault has the highest accuracy detection with Normal detection.