esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 3 Discussion
### Future Research and Expectations  
Our ongoing project is currently researching the solid-content detection of concrete wastewater in mixing stations. Based on the comprehensive investigations into deep learning applications for tiny object detection, solution inspection, and water surface monitoring, the experimental setup can focus on the wastewater sample extraction using test-tubes, where the distribution of the particles represents the particle situation within the wastewater source in the target mixing station. The particle sedimentation in the test tube stops at a certain time, and the solid-liquid separation surface can be marked on the tube scale. Then, images can be tracked to show the amount of solid content available for deep learning models to learn and recognize. Therefore, the specific solid content at a certain time can be monitored if the particle sedimentation, distribution, and the solid-liquid separation surface can be captured using deep learning. Some possible techniques can be selected to improve the model accuracy, speed, and efficiency:  
* The data augmentation technique is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data, providing the model with a more extensive data range in variations and increasing the model's accuracy [38].  
* Hybrid approaches can mix different imaging techniques, which are combined with additional information and the improved accuracy from the deep learning models, such as by using hyperspectral imaging or X-ray imaging followed by deep learning algorithms [39] to improve the model performance.
* Transfer learning utilizes pre-trained models from other image recognition tasks, such as object detection or segmentation, to fine tune the target model with a small amount of image data, leading to faster and in-time model deployment [40].
* Multi-sensor integration is the process of combining data from multiple sensors, which can enhance the model generalization to improve the accuracy and reliability of the data [41]. Multi-sensor integration includes integrating different sensing technologies that capture different aspects of solid particles in concrete wastewater.  
In summary, future research into the solid detection of concrete wastewater using an experimental setup with deep learning assistance can improve the accuracy, efficiency, and speed of the process, leading to better wastewater management and environmental protection. The YOLO (You Only Look Once) algorithm is a real-time object detection algorithm used in computer vision, whose basic principle is to take an image as input and divide it into a grid. Each grid cell predicts multiple bounding boxes with associated class probabilities, which are refined using regression to provide accurate object locations [42]. YOLO possesses superior features due to its real-time performance, simplicity, and reliable accuracy. Overall, YOLO is widely used in various domains, such as surveillance systems and object recognition tasks.
## 6 Experimental Results
### Comparison with RC Snubber  
Finally, the proposed technique was compared against the use of a RC snubber in terms of common-mode emissions. More precisely, the frequency spectra of the corresponding CM EMI are shown in Figure 18a in case of a 3 A load current. The source inductance was fixed to 6.6 nH, which corresponds to the optimal value for a 3 A output current. In such a case, the source inductance and the snubber solution both remove the turn on peak altogether. As far as power circuits are concerned, the efficiency should be the highest as  
Figure 16: Sensitivity of the CM EMI with respect to (**a**) different load current (1 A, 2 A, 3 A, 4 A, and 5 A), and (**b**) different input voltage (36 V, 42 V, 48 V, 54 V) with a 6.6 nH source inductance.  
Figure 17: Spectra of the DM conducted current obtained from the time-domain voltages at the line impedance stabilization networks (LISNs) measurement port with no damping and with the proposed technique.  
possible. Since the power dissipated by active devices is strongly affected the switching power, i.e., during the turn-on and the turn-off transients, also the efficiency will be affected by the switching transients. The conversion efficiency (\(\eta\)) is considered as a figure of merit in what follows, and it can be evaluated as the ratio between the output and the input power of the converter. More precisely, it is  
\[\eta=\frac{V_{\text{LOAD}}\cdot I_{\text{LOAD}}}{V_{\text{PS}}\cdot I_{ \text{PS}}}, \tag{11}\]  
where \(V_{\text{PS}}\) (\(I_{\text{PS}}\)) is the average voltage (current) provided by the input supply voltage to the EUT, whether \(V_{\text{LOAD}}\) (\(I_{\text{LOAD}}\)) the average voltage (current) provided by the EUT to the load. Such quantities can be evaluated by means of DC voltmeters and ammeters placed in parallel and in series to the input and output ports of the DC-DC converter. The results are plotted in Figure 18b for different setups. It is worth noticing that the no damping solution (circle marker) results in the higher efficiency (85.5%), which is reasonable as the transients are the fastest. The snubber solution, which effectively damps the oscillation and reduces the CM EMI, result in a 84.77% efficiency, meaning that the frequency peak at \(f_{\text{r,on}}\) is cancelled at the cost of 0.8% lower efficiency. The proposed solution, on the other hand, results in an efficiency of 85.2%. Such a result validates a better trade-off between EMI reduction and efficiency of the proposed solution with respect to the snubber one.
## 3 Results and Discussion
### Superiority Verification of the Optimized DDARF Program
#### 3.2.4 Computing Efficiency Comparison  
In the calculation of the DDARF, screen recording software is used to record the whole process, and the time spent in the simulation experiment of each case can be recorded, as shown in Table 4.  
According to Table 4, in the Case 1 and Case 2 models, the preset joints initiate in more than one minute, and before nine minutes, both casemodels are damaged. However, in the Case 3 model, the preset joint initiates at 2:27, and it takes as long as 17:46 for the model to be damaged. Therefore, the computational efficiencies of Case 1 and Case 2 are higher than that of Case 3, which indicates that for the same model, when the model grids are denser, the computational efficiency is lower. Compared with Case 2 and Case 3 with the same accuracy, the calculation time of Case 2 is about 50% shorter than that of Case 3; therefore, Case 2 can not only ensure the accuracy of the simulation calculation but also improve the efficiency of the calculation.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 8 Advanced BGP attack Detection Candidates
### Multidimensional and Leader-Follower MP  
Multidimensional variants of the MP technique represent areas for future research on MVP BGP anomaly detection. Unlike a majority of time series detection algorithms, MP is unfazed by large, sparse datasets. It allows for anytime computation whilst being extremely scalable and storage-efficient; massive datasets can be processed in its main memory, for example, and MP is extremely parallelizable. Due to an exceptionally low parameter scope, MP discords minimize overfitting and are also free of data assumptions. MP has also shown that it can discover anomalies in datasets with missing data, with no FNs [246; 57; 247].  
A variant of MP for motif discovery (repeated patterns) in multidimensional data was described in [248], though further research would be required to ascertain whether there is any utility to its multidimensional discord discovery. Additionally, DAMP provides some evidence that it can work with high-dimensional data, such as that from group-level AS information [219]. For example, it appears that this modified form could accommodate multidimensional data from multiple sources. Although there was no evidence in the literature that this technique can capture information on the group-level interactions and dynamics of multidimensional systems, this is an avenue for further work to explore.  
Interesting MP advancements in leader-follower dynamics for the purposes of understanding collective behaviors also represent an avenue for MP as a possible MVP BGP anomaly detection technique [220]. This approach leverages MP to identify leader and follower patterns within time series data, offering a potential candidate for capturing the dynamics of collective behaviors across ASs at the group level.  
Preliminary results from applying this technique to the Telekom Malaysia incident suggest its effectiveness in capturing leader-follower motif patterns among ASs (Figure 10 illustrates the application of MP leader-follower dynamics in the Telekom Malaysia incident). By identifying motifs and discords within AS interactions, this method holds promise for unveiling the intricate dynamics that underpin collective anomalies in BGP data. However, these initial findings necessitate further validation through comprehensive analyses to determine if leader-follower discords are possible and to validate MP's utility as a collective AS BGP detection technique.
The Kalantari dataset [10] is used for training the model and for the experiments conducted. In addition to the Kalantari dataset, the HDR-Eye [25], Sen [26], Tursun [27], and Tel [28] datasets are also utilized in the experiments. To evaluate the proposed model, quantitative analyses are performed using the metrics PSNR\(-\mu\), PSNR\(-l\), PSNR\(-PU\), SSIM\(-\mu\), SSIM\(-l\), SSIM\(-PU\), HDR-VDP2, HDR-VDP3, and IL-NIQE, and the computational cost is assessed based on the number of parameters and FLOPs. Additionally, a qualitative analysis is conducted with selected  
Figure 2: Reconstruction of image 010 from [PERSON] et al.’s dataset [10] using HDR-GAN [16]. samples, performing visual comparisons regarding HDR image reconstruction, verifying the fusion and alignment capabilities. In summary, the main contributions of this paper are as follows:  
* Optimization of the HDR-GAN architecture using depthwise separable convolution, significantly reducing computational cost while maintaining high-quality HDR reconstruction. * Thorough quantitative and qualitative evaluation of the proposed model, demonstrating the efficiency and effectiveness of the optimization on multiple datasets. * Extensive comparative experiments with other methods, showing the ability of the proposed model to handle motion and alignment challenges in multi-exposure HDR reconstruction, while achieving a balance between performance and computational efficiency. This paper is organized as follows: The literature review is presented in Section 2. The methods used, as well as the proposed approach, are described in Section 2.2. The dataset, metrics, and results of the conducted experiments are highlighted in Section 3.3. The conclusion and future work are outlined in Section 5.
## 5 Performance Analysis and Simulation
### Comparison of the Performance Simulation  
In order to verify the performance of our proposed schemes, we perform simulations with Matlab platform on the safety period and communication overhead. We compare our proposed schemes with RPBMP [16], EPUSBRF [18] and the shortest path algorithm. In order to facilitate the comparison of the performance of each scheme, we follow the simulation scenario of [18].  
Suppose that 10,000 nodes are randomly and evenly distributed in the area of 6000 m*6000 m, and the communication radius of each node is 110 m. The entire network is divided into 3600 grids, each with a unit length of 100 m. The SN is fixed at the center of the network, and the SoN is randomly selected from the nodes in the network which corresponds to the scenario that the panda pops up at a random location. The attacker's hearing radius is the same as the sensor node's communication radius, and the visible area radius is set to be 110 m. In the \(h\)-hop directed routing phase of EPUSBRF and RPBMP, \(h\) is set to be 10 hops, and the threshold of RPBMP in the same hop routing phase is also \(h\).
# A Graph-Cut-Based Approach to Community Detection in Networks
## 3 Methods
### Minimum Cut  
Since the minimum cut in graph theory is employed in the proposed method, a brief review is provided in this section.  
**Definition 3**.: _A cut \(C=(S,T)\) is a partition of the set of vertices \(V\) of a graph \(G=(V,E)\) into two subsets \(S\) and \(T\). The cut-set of a cut \(C=(S,T)\) is the set \(\{\{u,v\}\in E\,|\,u\in S,v\in T\}\) of edges that have one endpoint in \(S\) and the other endpoint in \(T\). If \(s\) and \(t\) are specified vertices of the graph \(G\), then an s-t cut is a cut \(C=(S,T)\) such that \(s\in S\) and \(t\in T\)._  
In this paper, we consider networks whose edges have no weight values assigned, i.e., unweighted and undirected graphs. However, graphs may have edge weights in general, and the _weight_ of a cut in an undirected graph is defined as follows.  
**Definition 4**.: _In an unweighted and undirected graph, the size or weight of a cut is defined to be the number of edges that belong to the cut-set of the cut. In a weighted graph, the value or weight of a cut is defined to be the sum of the weights of the edges in the cut-set._  
Given the definitions of a _cut_ and the _weight_ of a cut, a minimum cut of a network is defined as follows.  
**Definition 5**.: _A minimum cut of an undirected graph is a cut whose weight is not larger than the weight of any other cut._  
It is well known in network science that the maximum flow of a flow network from a source vertex \(s\) to a sink vertex \(t\) is equal to the capacity of a minimum cut that separates the two vertices \(s\) and \(t\). This result is called the max-flow min-cut theorem and was introduced by [PERSON] and [PERSON] [58]. The Ford-Fulkerson method can be used to find a minimum cut and a maximum flow, and the Edmonds-Karp algorithm is an implementation of the method that runs in polynomial time [59; 60].  
Even though the max-flow min-cut theorem deals with a directed graph with capacity values assigned on edges, it is straightforward to extend the theorem to undirected and weighted graphs. Besides the Ford-Fulkerson method, there are other algorithms for finding a minimum cut of undirected edge-weighted graphs such as the Stoer-Wagner algorithm [61; 62]. The Stoer-Wagner algorithm has the time complexity of \(O(|V||E|+|V|^{2}\log|V|)\)[62].  
Since the max-flow min-cut theorem is well known and there are many algorithms for finding a minimum cut of undirected graphs, we denote such an algorithm as MINIMUM-CUT without a detailed description of the procedure.  
**Definition 6**.: _Given a connected graph \(G=(V,E)\), a capacity function \(c\), and source and sink vertices \(s\) and \(t\), the procedure MINIMUM-CUT\((G,c,s,t)\) is an implementation of a method that finds a minimum cut of the graph \(G\) with \(c\), \(s\), and \(t\)._  
The capacity function \(c\) in Definition 6 gives the weight value of each edge of a weighted graph, i.e., a capacity is used interchangeably with a weight in this paper. The MINIMUM-CUT procedure is employed in our proposed algorithm, and more details are described later.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### Objective Function  
The total energy cost \(C_{total}\) takes into consideration the cost of the power supplied from the public grid, the profit from the power injected into the public grid, the cost of the stationary storage, the penalty cost in case the EV has not reached the desired SOC at departure, and the cost associated with shedding power from the PV sources. To prevent excessive switching during changing or discharging, a switching penalty \(C_{SW}\) is introduced to ensure that the action is completed in the fewest cycles possible. Therefore, the objective function minimizes \(C_{total}\), as given by Equations (36)-(41):  
\[\begin{split}& min\,\,C_{total}=C_{G}+C_{S}+C_{PVS}+C_{EV\_ penalty}+C_{SW},\end{split} \tag{36}\]  
\[\begin{split}& C_{G}=\sum\limits_{t_{i}=t_{0}}^{t_{F}}\,[c_{G}(t_{i })\cdot\Delta t\cdot(-p_{G\_I}(t_{i})+p_{G\_S}(t_{i}))]\\ & c_{G}(t_{i})=\left\{\begin{matrix}c_{G\_NH}\,for\,t\in normal \,\,hours\\ c_{G\_PH}\,for\,t\in normal\,\,hours\end{matrix}\right.\\ & C_{S}=\sum\limits_{t_{i}=t_{0}}^{t_{F}}\,[c_{S}(t_{i})\cdot \Delta t\cdot(p_{S\_C}(t_{i})+p_{S\_D}(t_{i}))],\end{split} \tag{38}\]  
\[\begin{split}& C_{PVS}=\sum\limits_{t_{i}=t_{0}}^{t_{F}}\,[c_{ PVS}(t_{i})\cdot\Delta t\cdot p_{PVS}(t_{i})],\end{split} \tag{39}\]  
\[\begin{split}& C_{EV\_penalty}=\sum\limits_{v}^{\rm N_{v}}\,[c_{ EV\_p}\cdot(SOC_{EV\_des_{v}}-SOC_{EV\_dep_{v}})\cdot E],\end{split} \tag{40}\]\[C_{SW}=\sum_{t_{i}=t_{0}}^{t_{F}}\sum_{v}^{N_{v}}{[c_{SW}(t_{i})\cdot(V2G_{SW_{v}}(t _{i})+G2V_{SW_{v}}(t_{i}))]}, \tag{41}\]  
where \(C_{G}\), \(C_{S}\), \(C_{PVS}\), and \(C_{EV\_penalty}\) are the public grid, stationary storage, PV shedding, and EV penalty costs, respectively; and \(c_{G}\), \(c_{S}\), \(c_{PVS}\), \(c_{EV\_p}\), and \(c_{SW}\) are the public grid, stationary storage, PV shedding, EV penalty, and switching penalty tariffs, respectively. The energy cost optimization problem is formulated to minimize the objective function in (36) with respect to Constraints (8)-(35), determining the decision variables: \(p_{G}\), \(p_{S}\), \(p_{PV\_S}\), \(p_{EV_{V}}\), \(V2G_{Shu_{v}}\), \(G2V_{Shu_{v}}\), \(V2G_{SW_{v}}\), and \(G2V_{SW_{v}}\).
# An Overview of Machine Learning and 5G for People with Disabilities
## 4 Application Scenarios and Benefits of Machine Learning and 5G for People with Disabilities
### At Work  
American Sign Language (ASL) is a natural predominant sign language of deaf and hard hearing communities in the United States and most of anglophone Canada. It is  
Figure 4: Different application scenarios for the integration of machine learning and 5G for people with disabilities.  
expressed by movements of the hands and face. ASL can be recognized using deep learning and computer vision [52] to allow natural communication between sign and non-sign language speakers. With 5G networks up to 100 times faster than 4G, the latency problem is eliminated so that all users benefit from videoconferencing for a better communications experience; consequently, employment opportunities for the hearing impaired are increased.  
Smart assistance systems help the visually impaired by converting visual data using video and image processing. A lightweight Convolutional Neural Network (CNN)-based 2.5D object-recognition module [53] provides a depth image-based object-detection method to classify obstacles and give location and orientation information; furthermore, optical character recognition (OCR) is used to detect and read text from images captured by the camera.
## 2 Materials and Methods
### Architecture of 6 mAPred-MSFF
#### 2.2.2 Feature Extraction Module
The stacked architecture of MS-CAM is shown in Figure 1. \(L(X)\in R^{C\times H\times W}\) is computed as follows:  
\[L(X)=\beta(PWConv_{2}(\beta(PWConv_{1}(X)))) \tag{12}\]  
where \(\beta\) denotes the Batch Normalization (BN) [54]. The pointwise convolution (PWConv) is used as the local channel context aggregator, which only exploits pointwise channel interactions for each spatial position. The expansion ratios of \(PWConv_{1}\) and \(PWConv_{2}\) are \(r\) and \(1/r\), respectively. Different from the original MS-CAM, we remove the nonlinear activation function between the convolution layer to prevent the destruction of feature information. \(G(X)\in R^{C}\) is computed as follows:  
\[G(X)=\beta(PWConv_{2}(\beta(PWConv_{1}(g(X))))) \tag{13}\]  
where \(g(X)\) is global average pooling (GAP) and computed as follows:  
\[g(X)=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}X_{[:i,j]} \tag{14}\]  
The expansion ratios of \(PWConv_{1}\) and \(PWConv_{2}\) are \(r\) and \(1/r\), respectively. \(\beta\) denotes the Batch Normalization (BN). The refined \(X^{\prime}\in R^{H\times W\times C}\) can be obtained as follows:  
\[X^{\prime}=X\otimes\sigma(M(X))=X\otimes\sigma(L(X)\oplus G(X)) \tag{15}\]  
where \(\sigma\) is the sigmoid function. \(\oplus\) denotes the broadcasting addition and \(\otimes\) denotes the element-wise multiplication. Bi-directional Long Short Term Memory Network. Recurrent Neural Network (RNN) is a powerful neural network for processing sequential data [55]. As a special type of RNN, Long Short Term Memory Network (LSTM) is not only designed to capture the long dependent information in sequence but also overcome the training difficulty of RNN due to gradient explosion and disappearance [56]; thus, it is the most widely used RNN in real applications. In LSTM, a storage mechanism is used to replace the hidden function in traditional RNN, with a purpose to enhance the learning ability of LSTM for long-distance dependency. Bi-directional LSTM (BLSTM), compared with unidirectional LSTM, better captures the information of sequence context. The LSTM components are updated by the following formulations:  
\[ \begin{gathered} f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f }\Big{)}\\ i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i})\\ C^{\prime}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C})\\ C_{t}=i_{t}C^{\prime}+f_{t}C_{t-1}\\ o_{t}=\sigma(W_{X}h_{t-1}+W_{h}x_{t}+W_{c}C_{t}+b_{0})\\ h_{t}=o_{t}\text{tanh}(C_{t})\end{gathered}  \tag{16}\]  
where \(i_{t}\),\(f_{t}\), and \(o_{t}\) represent the input, forget, and output gate, respectively; \(C^{\prime}_{t}\) is temp value for calculating \(C_{t}\); \(t\) denotes the recurrent time step; \(W_{f}\), \(W_{i}\), \(W_{C}\) and \(b\) are the weights of each equation. \(h_{t}\) is the output of LSTM at the \(t\) time step. The bi-directional LSTM consists of two direction LSTM networks. Therefore, the \(h_{t}\) is computed as follows:  
\[h_{t}=\left[ \begin{array}{c}\rightarrow\\ h_{t}\oplus h_{t}\end{array} \right] \tag{17}\]