esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study on Load Monitoring Approaches
## 2 Load Monitoring  
Load monitoring is the concept that refers to the process of identifying and acquiring the consumption measurement of the load in an electrical system [14]. It is essential for effective management and accurate control of the electrical energy consumed [15; 16], as it provides detailed information to electricity end-users about their consumption patterns and power consumption habits. In households, load monitoring is assessed by directly monitoring each device or by breaking down the total power signal [14; 17]. Through these actions, consumers can understand how each device works and contributes to daily power consumption.  
Over time, many research works have contributed to the evolution of this concept, resulting in two distinct types of load monitoring approaches: intrusive and non-intrusive. In practice, the choice of approach for obtaining satisfactory results depends on several criteria that the researcher must first evaluate. In the literature, the non-intrusive approach to load monitoring is by far the most developed, probably due to the rise of artificial intelligence and machine learning.  
When it comes to artificial intelligence, it has the merit of revolutionizing load monitoring by improving the accuracy of load predictions, detecting anomalies in energy consumption, and optimizing load management in real time. Here are some examples of AI techniques that have been applied to load monitoring:  
Deep Neural Networks (DNNs): Deep neural networks have been used to predict short-term load demand with high accuracy. To this end, a study by [18] showed that a DNN architecture, in this case Recurrent Deep Neural Network (Deep-RNN) with a tanh activation function, performs better than other cases in terms of MAPE metric values.  
Convolutional Neural Networks (CNNs): Convolutional neural networks have been used for feature extraction and classification of energy consumption data. A study by [19] showed that a combination of a convolutional neural network (CNN) and long-term memory (LSTM) could extract complex features from energy consumption. Exploiting this elaborate combination, it was possible to make a near perfect prediction of electricity consumption, which was once difficult. The results indicate that the CNN-LSTM model predicts residential electricity demand in an efficient and stable manner, and has the best performance compared to other existing methods, such as linear regression model (LR), random forest regression (RF), decision tree regression (DT), and multilayer perceptron (MLP).
# Improved Training of CAE-Based Defect Detectors Using Structural Noise
## 4 Experiments
### Experimental Results
#### 4.2.1 Models Trained without Noise  
Two models resulted from the trainings with unaltered pictures.  
Firstly, a model was trained on pictures without spots. The Figure 11 gives an overview of an ideal input, its structure, and a perfect output.  
The differences between pieces with no defects and defective pieces were discriminated as in Figure 12.  
Figure 11: Classic CAE training on spotless pieces.  
Figure 12: Differences expected versus differences detected.  
The error rates of differences were calculated to evaluate the executable of the detections. Because differences are closely related to defects: the smaller the error rate, the more accurate the defect detection (and conversely). The formula used to compute the error rates is shown in Equation (1):  
\[\text{Error rate}[\%]=\left|1-\frac{\text{total area of differences detected}\left[\text{px}\right]}{\text{total area of differences expected}\left[\text{px}\right]}\right|*100 \tag{1}\]  
The Figure 13 shows the differences between the output and the input, after the restoration process. The bluer a pixel is within \"Difference\", the blacker this pixel is within \"Output\" compared to \"Input\", conversely, the redder a pixel is within \"Difference\", the white this pixel is within \"Output\" compared to \"Input\". Therefore, the white a pixel is within \"Difference\", the more alike the pixels are within \"Output\" and \"Input\". No particular discrepancies were observed when the inputs were similar to what the CAE is familiar with (spotless pieces), while the dissimilarities of spotted pieces were clearly highlighted. The blue disc corresponds to the spot of the spotted input that was not restored, as the NN was not trained on data including this feature. Nonetheless, a few subtle irregularities occurred in both reconstructions because the autoencoder cannot compress/decompress data without a loss of information.  
Secondly, another model was trained on pictures with spots. The Figure 14 provides an overview of an ideal input, its structure, and a perfect output.  
The Figure 15 also shows the differences between the output and the input, after the restoration process. The color pattern is the same as the previous input/output comparisons.No particular discrepancies were observed when the inputs were similar to what the CAE is familiar with (spotted pieces), while the dissimilarities of spotless pieces were clearly highlighted. The red disc corresponds to the \"missing\" spot of the spotless input  
Figure 14: Classic CAE training on spotted pieces.  
Figure 13: Reconstruction of samples after training.  
that was created, as the NN was not trained on data excluding this feature. Once again, some subtle differences were noticed for the same reasons mentioned in the previous case.  
Since the main purpose of this study was to detect flaws (such as scratches, spots, wrong colors or sizes), the errors caused by the restorations of the edges were dismissed from the \"defective area\" candidates.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme
### Handover Latency in Non-Location-Based RSS Extensions
#### 3.3.3 Buffering Cost  
Buffering is required for the packet-loss problem because, during the handover process, the MN is disconnected and the packets may be lost. For such a problem's solution, the incoming packets are stored until the MN is connected to the target MAG. However, long handover latency and the additional latency due to RSS error incur additional buffering. Although, handover efficiency contributes to the efficiency of buffering. However, several schemes keep multiple copies of the packets to ensure that the packet loss that consumes additional resources is required. Therefore, an efficient PMIPv6 extension avoids unnecessary buffering during MN's handover. The following is the detail of the buffering mechanism in the existing and proposed scheme.
## 2 Materials and Methods
### Characterizations  
The particle diameter (d) of the nanoparticles was measured using a high-resolution transmission electron microscope (HRTEM) (Titan G2 Chemi STEM CS Probe, FEI, Hillsboro, OR, USA) operated at 200 kV acceleration voltage. The copper grid (PELCO No.160, TED PELLA, Inc., Redding, CA, USA) covered with carbon film was used to support nanoparticles for imaging. The hydrodynamic diameter (a) and zeta potential (\(\xi\)) were measured using a dynamic light scattering (DLS) particle size and zeta potential analyzer (Zetasizer Nano ZS, Malvern, Malkern, UK) using a 0.01 mM Gd solution sample. The crystal structural analyses of the nanoparticles before and after thermogravimetric analysis (TGA) were conducted using a multi-purpose powder X-ray diffraction (XRD) spectrometer (X'PERT PRO MRD, Philips, The Netherlands) with unfiltered CuK\(\alpha\) radiation (\(\lambda\) = 1.54184 A). The Gd-concentration of the aqueous nanoparticle solution sample was measured using an inductively coupled plasma-atomic emission spectrometer (ICP-AES) (IRIS/AP, Thermo Jarrell Ash Co., Waltham, MA, USA). A Fourier transform-infrared (FT-IR) absorption spectrometer (Galaxy 7020A, Mattson Instrument, Inc., Madison, WI, USA) was used to study the surface coating of the nanoparticles. To record FT-IR absorption spectra, pellets of the powder samples in KBr were prepared and the spectra were recorded between 400 and 4000 cm\({}^{-1}\). A TGA instrument (SDT-Q600, TA Instrument, New Castle, DE, USA) was used to record the TGA curve of the powder sample between room temperature and 900 \({}^{\circ}\)C while air flowed over the sample. The average amount of PASA polymers coating a nanoparticle was quantified from the mass drop in the TGA curve after considering the initial mass drop from room temperature to \(\sim\)110 \({}^{\circ}\)C due to water desorption. A vibrating sample magnetometer (VSM) (7407-S, Lake Shore Cryotronics Inc., Westerville, OH, USA) was used to obtain the magnetic properties of the powder sample by recording a magnetization (M) versus applied field (H) (or M\(-\)H) curve (\(-\)2.0 T \(\leq\) H \(\leq\) 2.0 T) at 300 K. The net M value of the sample (i.e., only the Gd\({}_{2}\)O\({}_{3}\) nanoparticles without PASA coating) was estimated using the net mass of Gd\({}_{2}\)O\({}_{3}\) nanoparticles extracted from the TGA curve.
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 4 Mathematical Models and Algorithms for Emergency Escape Routes
### Path Optimization Strategy  
The above algorithm yields a Pareto optimal set, in which any path is not dominant in terms of the two attributes of equivalent length and time spent walking in the smoke relative to other paths. In order to achieve real-time path-assisted decision making, it is also necessary to consider parameters such as the choice of disaster escape exits and the time to escape the disaster area. The target of the escape exit is to prioritize surface exits such as the auxiliary shaft and the main shaft. When no solution can be found for all surface exits, the refuge chamber is used as the escape exit.  
In addition, in order to ensure that the underground personnel escape from the potentially affected area as soon as possible, not only should the route with the least time of exposure to the disaster be chosen but also the route with the fastest access to the roadway that is not attacked by the smoke flow should be chosen. Taking a feasible solution of an emergency escape route \(<v_{0},v_{1},\ldots v_{n}>\) as an example, if all the edges after the path reaches node \(i\) are safe edges, the time to reach point \(i\) is defined as a temporary escape time. For paths arriving at the same end point, the path with shorter temporary de-risking time is taken as the better path. The path optimization strategy is shown in Figure 4.  
Figure 2: Case of the shortest path problem with constraints on two resources.  
Figure 4: Path optimization strategy.  
Figure 3: The flow chart of the RCSPP algorithm.
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Approaches
#### 3.1.5 Visual Inspection/Manual Editing Versus Automated Process  
As discussed above, SAR-derived inundation results are affected by many error sources, which can hardly be eliminated by most algorithms. Visual inspection and manual editing could help in drawing training ROIs [42] and reducing false positives/negatives. As manual editing requires an overwhelmingly large workforce, it however, cannot be applied to rapid response to flood disasters, especially during back-to-back flood events. Studies aiming to improve automation include [PERSON] et al. [47], [PERSON] et al. [49], [PERSON] et al. [51], [PERSON] et al. [53], [PERSON] [60]; [PERSON] et al. [61], [PERSON] et al. [63], [PERSON] et al. [68]; and [PERSON] et al. [69].
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 4 A Proposed New Secured Unclonable Medical Watermarking Scheme
### The Proposed Embedding and Extraction of Clone-Resistant Watermarking
#### 4.2.1 Generating, Signing, and Embedding Watermarks (One-Time Watermark Signature)  
Pertinent features namely skewness, entropy, and median are extracted from the original image [35]. The patient name is extracted from the header of the DICOM image and the corresponding initials (the first letter of the given name and family name) are transformed into a binary matrix of size \(16\times 16\)  
Figure 8: An example of a patient’s record in the database (DB).  
A matrix of size \(16\times 16\) is then generated from the original image by a cumulative subtraction process. All this information is used to build a meaningful watermark based on the Jacobian model [10].  
The embedding process of the watermark is illustrated in Figure 9. A standard cipher \(E\) is deployed to sign the extracted watermark by using a one-time ticket (\(x_{A}\), \(y_{A}\)) offered by TA for SUC\({}_{A}\) of the imaging device. Here, the chosen standard cipher \(E\) can be perceived as a tool for the signature mechanism and \(E\) should be secure in terms of indistinguishability [36]. The resulting signed watermark can be considered as a one-time clone-resistant watermark signature Z. After that, Z is embedded in the original image using the difference expansion technique to obtain the clone resistant watermarked image (WMI).
## 3 The Hierarchical Steel Surface Detection Methodology
### Image Segmentation for the Scratch Defect Detection  
Image segmentation is a process in which the image is broken down into small segments. Out of all the segments, the segment that contains the important information is processed in the image processing algorithm. The pixels with related features are accumulated together through image segmentation. Along with the classification and localization, it also provides us with the exact shape of an object in an image. The image segmentation techniques are broadly classified into the following two types: semantic segmentation and instance segmentation. Keeping in mind the constraint of this paper, we focus only on semantic segmentation. Semantic segmentation works on every pixel of an image. It assigns a label to every pixel of the image. It denotes the same labels to pixels of multiple objects but belongs to the same class. It reduces the inference time for detection, as it only processes the particular region instead of the whole image for detection.  
Figure 2: Block diagram of feature pyramid network (FPN).  
One of the semantic segmentation models is UNet [49]. UNet is a special type of architecture for semantic segmentation. It was designed for biomedical image applications, but now it is widely used in different fields. UNet, as the simple and efficient segmentation model, has inspired many researchers to implement it for defect segmentation on any surface. The architecture shown in Figure 3 is the combination of convolutional and max-pooling layers arranged in a particular form for processing the image. It contains two paths, one is the down sampling, encoder, or contraction path and the other path is the up sampling, decoder, or expansion path. The concatenation process between the encoder and decoder path provides us with the localization information of the objects. During concatenation, cropping of the encoder output is eliminated in the proposed model, although it is carried out in the original model. In the model, the upper layers provide the information about the classification of objects, which means that it answers the 'what' question. As we go to deeper layers, it provides the localization information, which means the answer to the 'where' question. The advantage of this model is that it can be trained efficiently with fewer datasets. The UNet model for scratch detection is adopted. The visibility of some scratches is very low in the dataset, due to which it was difficult to detect the scratch properly on an image. The UNet model, which is simple with less inference time, generated better results by precisely locating the defect and its shape. We use the binary focal loss to calculate the loss to reduce the imbalance issue.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries  
To ensure the development of a robust and effective model capable of predicting customer churn, we conducted several experiments exploring different classification methods and embedding techniques. This section covers the necessary background for understanding the remainder of the paper, including large language models (LLMs) embeddings, embedding techniques, and the classification algorithms employed in this paper.
## 6 V-DPAP Solution Methods
### Approximate Leximin Allocation (a-lex)  
The previous model implements an exact leximin rule, and thus enforces fairness in the resulting allocation. However, it may not scale well when increasing the number of agents and edges. This is why we provide an approximate version of the computation of the leximin based on an iterated maximin scheme. This approach considers at each step a minimum utility \(\Delta_{a}\geq 0\) for some agents and maximizes the worst utility among the remaining agents, for which we arbitrarily assume \(\Delta_{a}=-1\). The problem to solve, referred to as \(P_{\text{a-lex}}(\langle\mathcal{A},\mathcal{G},\mu,\mathcal{C}\rangle,\Delta)\), is the following one:  
\[\text{maximize} \delta \tag{18}\] \[\text{such that}\quad(1),(2),(3),(4),(5),(6),(7)\] \[\delta\leq\sum\limits_{g\in\mathcal{G}_{a}}\sum\limits_{e\in \mathbb{E}_{g}}u_{g}(e)x_{e},\quad\forall a\in\mathcal{A}\mid\Delta_{a}=-1\] (19) \[\sum\limits_{g\in\mathcal{G}_{a}}\sum\limits_{e\in\mathbb{E}_{g} }u_{g}(e)x_{e}\geq\Delta_{a},\quad\forall a\in\mathcal{A}\mid\Delta_{a}\
eq-1\] (20) \[\delta\in\mathbb{R}^{+} \tag{21}\]  
The solution method then consists in optimizing in an iterative manner, as for leximin. As sketched in Algorithm 2, at each iteration (one per agent), \(P_{\text{a-lex}}\) is solved, one worst agent \(\hat{a}\) is determined, and its minimum utility \(\Delta_{\hat{a}}\) is fixed. The main difference with \(P_{\text{lex}}\), is that at each iteration in \(P_{\text{a-lex}}\) the position of an agent in the order is implicitly determined once for the whole algorithm, while in \(P_{\text{lex}}\) the order can be revised at each iteration. Moreover, if any equality occurs at line 5 to determine the worst agent (case \(|S|>1\)), one may rely on some heuristic or arbitrary choice. Thus, \(P_{\text{a-lex}}\) is an approximation of \(P_{\text{lex}}\) that contains fewer variables and constraints.  
```
Data: A V-DPAP \(\langle\mathcal{A},\mathcal{G},\mu,\mathcal{C}\rangle\) Result: A leximin-optimal path allocation \(\pi\)
1 for\(K=1\)to\(|\mathcal{A}|\)do
2\((\lambda^{*},\text{sol})\leftarrow\) solve \(P_{\text{lex}}(\langle\mathcal{A},\mathcal{G},\mu,\mathcal{C}\rangle,K,[ \Lambda_{1},\ldots,\Lambda_{K-1}])\)
3\(\Lambda_{K}\leftarrow\lambda^{*}\)for\(g\in\mathcal{G}\)do\(\pi(g)\leftarrow\{v\in V_{g}\mid\text{sol}(g_{v})=1\}\) return\(\pi\)
```  
**Algorithm 2**Leximin allocation.  
**Example 9**.: _The approximate leximin allocation for the example in Figure 1 is \(\pi_{\text{a-lex}}=\{g_{\text{a}}\mapsto\{s_{\text{a}},a_{1},a_{2},t_{\text{a}} \},g_{\text{b}}\mapsto\{s_{\text{b}},b_{3},b_{4},t_{\text{b}}\}\}\), with utility vector \((u_{\text{A}}(\pi_{\text{lex}}),u_{\text{B}}(\pi_{\text{lex}}))=(0.7,0.7)\). This is the same as \(\pi_{\text{lex}}\), but in the general case, \(\pi_{\text{a-lex}}\) and \(\pi_{\text{lex}}\) can differ._
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 4 Topological Characteristics of the CRN  
Based on the real data from the CR Express website, Railway 12306 website, and Belt and Road Initiative portal website, the Space L method is used to construct the topology model of the CRN. The nodes represent cities, and, if there is a railway connection between two cities, there exists a link between the two stations. The length of each link is the actual railway distance between the cities. Due to the bidirectional traffic characteristics of railway transport routes, the topology model is considered as an undirected graph \(\mathrm{G}=<\mathrm{V},\mathrm{E}>\), where \(\mathrm{V}\) is the set of cities, and \(E\) represents the edges between elements in V. Furthermore, the adjacency matrix of the network is \(A=\left(a_{ij}\right)_{n\times n^{\prime}}\) representing the connection between city \(i\) and \(j\), defined as \(a_{ij}=\begin{cases}1,\ (i,j)\in E\\ 0,\ (i,j)\
otin E\end{cases}\). Similarly, the distance  
Figure 2: Schematic diagram of cargo flow allocation.  
matrix can also be represented as \(d_{ij}=\left\{\begin{array}{ll}d_{ij},\,(i,j)\in E\\ 0,\,(i,j)\
otin E\end{array}\right.\), where \(d_{ij}\) represents the actual railway distance between cities. The established network topology of the CR Express is shown in Figure 3.  
The statistical results show that there are 20 nodes with degree value of 3 in the CRN, and Figure 4a confirms that the CRN is a scale-free network in the L space. In addition, the node degree and cumulative degree distributions of the CRN are fitted using a double logarithmic coordinate system, as shown in Figure 4b. The cumulative degree distribution of the CRN follows the power-law distribution \(p(k)\sim 0.2400x^{-0.4343}\). The power-law exponent is \(-0.4343\), indicating that the CRN is a scale-free network with heterogeneity. Several sites are called \"deadly\" nodes, which play a dominant role in the whole network. This heterogeneity makes scale-free networks more vulnerable to deliberate attacks.  
The node degree, betweenness, and strength of each station in the CRN are calculated, and the top 10 main stations are selected as shown in Table 1. It can be seen that the city with the highest ranking in all three categories is Xi'an. In order to distinguish the impact of these three indicators on the network, subsequent intentional attacks will target all stations  
Figure 4: CRN topology feature analysis: (**a**) all node degrees; and (**b**) degree and degree distribution.  
Figure 3: Schematic diagram of CRN topology.  
ranked within the top 10 for each indicator to observe the influence of different stations on the network under different indicators. Then, the betweenness and weight of each edge in the CRN are calculated, as shown in Table 2. Unlike cities, since the interruption of an edge may not have a serious impact on the network compared to nodes, it is chosen to simulate one or more edge failures to determine whether edge interruptions in the network will cause cascading failures and the scale of cascading failures.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 5 Joint Resource Allocation Optimization Scheme  
Due to the application of NOMA and complicated objective function, the original problem in (11) is complex; therefore, we propose a power control factor optimization and power allocation scheme to obtain the optimal solutions.