esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
In this section, the formal description and the mathematical formulation of the eLCVRP-CS-PDS are presented. Given a network conformed by customers and electrical nodes scattered along the space, the eLCVRP-CS-PDS intends to obtain a set of routes traveled by a fixed number of homogeneous light commercial EVs that depart from and arrive to a common depot. As the EVs travel the corresponding route, customer demand is met, considering that, in the intermediate of the route, if necessary, the EV is requiredto visit a charging station located at an electrical node, before the battery is depleted. In agreement with the current technology, we assume a fast charging process that does not affect the duration of the route, and that the state of charge of the battery is reduced in a linear proportion with the distance traveled. Additionally, another group of EVs with a lower battery capacity is used to execute the last mile delivery approach. The vehicles depart from and arrive to known origin and destination points, traveling the shortest path between both points, and considering that a charging station must be visited if required. Throughout the mathematical formulation, there are a set of constraints able to control whether a charging station should be installed at an electrical node, depending on the battery capacity. In addition, the intended charging station can receive multiple visits from the same or different EVs.  
The eLCVRP-CS-PDS proposed in this work can be described as a graph theory problem. Let \(G=(V,A)\) be a complete graph with a set of vertices \(V=N\cup C\) and a set of arcs \(A\) with the interconnection of all vertices. Set \(N=\{1,\ldots,n\}\) represents the customer vertices where index 1 is the depot, and set \(C=\{n+1,\ldots,n+c\}\) describes the charging station candidate points corresponding to the power distribution network. Each arc and customer are associated with distance \(d_{ij}\) and merchandise demand \(D_{i}\), respectively. Due to the homogeneous nature of the fleet, all of the vehicles are featured by a constant capacity for cargo and batteries. It is necessary to point out that there are two patterns identified for the EVs running into this problem: a group of EVs follow the CVRP approach, with a battery capacity larger than that for those EVs designated for the last mile delivery problem, which corresponds to the other group of EVs. Thus, the constraints of the mathematical model are split into several components: CVRP approach, last mile delivery problem, charging stations location, and a linear proposal of power flow in electrical distribution systems. For better understanding, we first describe the nomenclature, followed by the constraints of the model and the objective function.
# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
###### Abstract  
Accurately classifying targets and clutter plots is crucial in radar data processing. It is beneficial for filtering out a large amount of clutters and improving the track initiation speed and tracking accuracy of real targets. However, in practical applications, this problem becomes difficult due to complex electromagnetic environments such as cloud and rain clutter, sea clutter, and strong ground clutter. This has led to poor performance of some commonly used radar plot classification algorithms. In order to solve this problem and further improve classification accuracy, the radar plot classification algorithm based on evidence adaptive updating (RPC-EAU) is proposed in this paper. Firstly, the multi-dimensional recognition features of radar plots used for classification are established. Secondly, the construction and combination of mass functions based on feature sample distribution are designed. Then, a confidence network classifier containing an uncertain class was designed, and an iterative update strategy for it was provided. Finally, several experiments based on synthetic and real radar plots were presented. The results show that RPC-EAU can effectively improve the radar plot classification performance, achieving a classification accuracy of about 0.96 and a clutter removal rate of 0.95. Compared with some traditional radar pattern recognition algorithms, it can improve by 1 to 10 percentage points. The target loss rate of RPC-EAU is also the lowest, only about 0.02, which is about one third to one half of the comparison algorithms. In addition, RPC-EAU avoids clustering all radar points in each update, greatly saving the computational time. The proposed algorithm has the characteristics of high classification accuracy, low target loss rate, and less computational time. Therefore, it is suitable for radar data processing with high timeliness requirements and multiple radar plots.  
radar plot; belief functions; adaptive updating; data classification; evidence combination Article  
[PERSON], [PERSON], [PERSON] RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating  
[[https://doi.org/10.3390/app1410260](https://doi.org/10.3390/app1410260)]([https://doi.org/10.3390/app1410260](https://doi.org/10.3390/app1410260))
# Proxy Signature-Based Management Model of Sharing Energy Storage in Blockchain Environment
## 3 Blockchains and Authentication Technology
### Blockchain Framework of SES
The smart contract is applied in transaction matching to improve the matching efficiency and maintain the fairness of matching results. A matching result consists of the names of a supplier and a demander, the number and price of energy storage resources as well as the usage period. S3: Transaction checking. In this stage, the matching results are submitted to the checking nodes which belong to a power dispatching organization or an exchange center. Security checking is completed by the nodes, which modify or cancel the matching results which violate line flow. The final matching results are recorded in electronic contracts. This stage can be integrated into the first two stages for a highly decentralized market. S4: Transaction executing. In this stage, the transaction is executed by participants according to the matching results. The demanders remotely utilize the purchased energy storage resources within a prescribed period. Meanwhile, the suppliers provide the energy storage services that meet the demands. During execution, the power data of demanders and suppliers are submitted to the blockchain by smart meters, which is credible, tamper proof and traceable. S5: Transaction settlement. In this stage, the transaction is settled in the light of the power data stored in the blockchain. The indicators for evaluating the service effect include the error between  
Figure 3: The blockchain framework of SES. supply and demand curves, the average response time of the supplier for the instructions from demanders and the contract performance by participants. Finally, the settlement amount is computed and sent to participants by using smart contracts, representing the end of an SES transaction.
## 3 Formability Classification and Prediction Models
### AI Learning  
In this study, classification and regression models were used to predict the formability according to drawbacks. Random Forest, SVR, and GBM were the machine learning models used. ANN was also applied as a deep learning model to compare accuracy. In this instance, the hyperparameters of each algorithm were optimized by exhaustively exploring all combinations of possible values through the grid search technique to determine the optimal settings. Among the 256 data obtained through the analysis, 80% were used as training data and 20% as test data. Of the training data, 10% was applied as validation data. The mean square error (MSE) was applied to obtain the accuracy of the AI prediction results. It is an indicator used to evaluate the predictive performance of models in machine learning and statistics and given by Equation (3) [26], where \(n\) is the number of data points, \(y_{i}\) is the actual value, and \(\hat{y}_{i}\) is the predicted value. MSE squares the difference between the predicted and actual values and then obtains the average of the values. As the prediction error increases, MSE becomes larger, making it possible to intuitively identify the significance of the model error.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{3}\]  
Figure 7 shows the process of the model that predicts the formability of drawbacks. After the positions and forces of drawbacks are input, the occurrence of cracks is examined. When cracks have occurred, the number of cracks is predicted using a prediction algorithm. The occurrence or non-occurrence of wrinkles was not classified in this instance, as there is no case where wrinkles do not occur in the forming of renders, which are the target product. Stepwise approaches, such as the classification of the occurrence or non-occurrence of cracks, can improve the accuracy and efficiency of the prediction model. When the number of cracks is evaluated with their occurrence or non-occurrence clearly classified, the error of the model is reduced, and the reliability of the results can be improved because the analysis is performed only for the case where cracks actually occurred. Furthermore, predicting wrinkles and cracks at the same time increases the complexity of the model and the possibility of errors in the presence of two mechanisms. Therefore, the characteristics of each phenomenon can be reflected more clearly by evaluating them independently. Because wrinkles or cracks do not occur in all the forming analysis results, we determine their numbers after examining whether their occurrence can reduce unnecessary calculations, which results in the efficient use of computational resources and reduced noise in data. As such, the performance of the prediction models was improved in this study by analyzing the formability results in more detail by predicting the numbers after determining the occurrence of cracks.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Bead 1–12 [N] & \(7.75\leq\text{x}\leq 251.40\) \\ \hline \end{tabular}
\end{table}
Table 2: Bead force according to drawbead position.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### Machine Learning Preparation Phase
#### 4.3.4 Data Versioning  
In order to build robust and reproducible models, special attention must be payed to the versioning of every data source involved in the project. The prevention of data leakage [11], e.g., the strict separation of test, training, and validation data sets, is vital to the success of ML models When the characteristic of input data changes, the re-training of existing models, the fine-tuning of hyperparameters applied respectively may be necessary. By associating the specific circumstances, e.g., a new data version or iteration, the foundation of 'audit trails' for input data is created. In contrast to simple ML-based approaches, where a finite set of samples trains a model, specific data versions may be extended over time with new samples or features. The advantages of versioning every piece of data involved in ML projects can be summarized as tracking the evolution of data over time. As there is often a massive amount of frequently changing input data involved in ML projects, local data repositories (e.g., edge devices) often hold the actual data set. At the same time, the remote storage solutions only persist a hash of the current version [18].
## 3 Materials and Methods
### Gravity Prospection
#### 3.1.4 Euler Deconvolution  
The Euler deconvolution technique, initially proposed by [PERSON] [66], is a filtering method that facilitates the characterization of geological structures by enabling the detection of their location, determining their depth, and identifying their degree of rooting (Figure 4a) [17; 18; 67]. Numerous research studies have extensively validated this technique [11; 13; 29; 30; 68; 69; 70]. The \"structural index\" parameter is a crucial factor for selecting the type of structures to be emphasized, with integer values ranging from 0 to 3. The other essential parameter is the size of the moving window, which depends on the wavelength of the anomalies under investigation [34; 71]. Using a small window may not allow for proper interpretation of long wavelength anomalies, whereas selecting a large window can introduce the effects of multiple sources, leading to a cloud of ill-defined solutions that obscure the best solutions [15; 26; 70; 72]. For example, given a source S located at point M with coordinates (\(X_{0}\), \(Y_{0}\), \(Z_{0}\)), the gravimetric field intensity F at the observation point can be computed using Equation (3) [15; 26; 29; 30; 33].  
\[\mathrm{F(X,\ Y)=f[(X-X_{0}),(Y-Y_{0}),(Z-Z_{0})]} \tag{3}\]  
[PERSON] [66] showed that the Euler homogeneity equation can be written as (Equation (4)):  
\[(X-X_{0})\frac{\partial\mathrm{F}}{\partial X}+(Y-Y_{0})\frac{\partial\mathrm{ F}}{\partial y}+(Z-Z_{0})\frac{\partial\mathrm{F}}{\partial z}=-\mathrm{NT(B-F)} \tag{4}\]  
(\(X_{0}\), \(Y_{0}\), \(Z_{0}\)): Position of the gravimetric anomaly source,  
(\(X\), \(Y\), \(Z\)): Position of the observation point,  
F: Intensity of the gravity field measured at the point (\(X\), \(Y\), \(Z\)),  
B: Regional value of the gravity field,  
N: Degree of homogeneity or structural index (SI) that characterizes the type of source and rate of change of the field as a function of distance.  
The principle of the method is based on solving a system of equations with four unknowns: \(X_{0}\), \(Y_{0}\), \(Z_{0}\), and B.  
The present study used the Euler deconvolution technique with a structural index of IS = 0, a moving window size equivalent to nine times the dimension of the unit cell of the interpolation grid, and a tolerance level of 15%. After applying mathematical treatment to the gravity data, a field of lineaments with varying lengths and extensions was identified. These lineaments were then superimposed on a geological map that included structures and lithology, producing a new structural map for the study area. Additionally, data on flow rates and permeability acquired from hydrogeological reconnaissance boreholes and wells were integrated into the map. As a result, a hydro-structural map of the Ifrii inlier was developed. A summary of the methodological approach utilized in this study is presented in Figure 4b.
# Collision Avoidance Resource Allocation for LoRaWAN
## 5 Experimentation and Performance Results
### Performance Evaluation of the Proposed Solution
In this case the SF distribution is more uniform and, thus, this effect is negligible and the improvement for using CARA is higher. In light of the obtained results some conclusions can be highlighted. The first one is that with CARA it is obtained a great overall network throughput up to an area of 8 \(\times\) 8 km\({}^{2}\) with 1000 devices. For longer areas, due to the less proximity of the end devices to the gateway, the slowest and more robust data rates are more frequently used and the probability of collisions increases. On the contrary, with the same number of end devices but smaller areas, CARA contributes to the increase of the throughput and presents a great improvement compared to the throughput obtained with the ADR algorithm. Another result to highlight is that, although our solution slightly increases the overall transmission time and may lead to a little increase in energy consumption compared to the ADR algorithm proposed in the LoRaWAN specification, it achieves a fairer resource sharing. For example, in a coverage area of 2 \(\times\) 2 km\({}^{2}\) the ADR algorithm assigns shortest SFs due to the proximity of the end devices to the gateway, thus allowing shortest transmission times. However, this leads to more collisions due to the saturation of the fastest RBs. With the CARA algorithm, owing to the set of SFs each device is able to use, it leads to a more uniform RBs utilization, causing less collisions and therefore improving the overall network throughput.
###### Abstract  
Bubbly liquids are widely present in the natural environment and industrial fields, such as seawater near the ocean bottom, the multiphase flow in petroleum reservoirs, and the blood with bubbles resulting in decompression sickness. Therefore, accurate measurement of the gas content is of great significance for hydroacoustic physics, oil and gas resources exploration, and disease prevention and diagnosis. Trace bubbles in liquids can lead to considerable changes in the acoustic properties of gas-liquid two-phase media. Acoustic measurements can therefore be applied for trace bubble detection. This study derived the reflection coefficient of acoustic waves propagating in a sandwich layering model with liquid, bubbly liquid, and liquid. The influences of gas contents on the reflection coefficient at the layer interface were analyzed based on theoretical calculations. It was revealed that the magnitude of the reflection coefficient and the frequency interval between its valleys have a quantitative correlation with the gas contents. Thus, a novel means to detect the contents of trace bubbles was proposed by evaluating the reflection coefficients. The reflection features of a thin layer with bubbly liquid were then studied through experiments. It was validated by acoustical measurements and theories that the reflection coefficient is considerably sensitive to the change of gas contents as long as the gas content is tiny. With the increasing gas content, the maximum value of the reflection coefficient increases; meanwhile, the frequency intervals between the valleys become smaller. However, when the gas content is extensive enough, e.g., greater than 1%, the effect of the change of gas content on the reflection coefficient becomes inapparent. In that case, it is not easy to measure the gas content by the acoustic reflection signals with satisfying precision. This proposed method has potential applications for the detection of trace gas bubble content in several scenarios, e.g., decompression illness prevention and diagnosis.  
bubbly liquid; acoustic reflection coefficient; bubble detection; experimental measurements 2022220221 State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China School of Physics Sciences, University of Chinese Academy of Sciences, Beijing 100049, China Beijing Engineering Research Center of Sea Deep Drilling and Exploration, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China Correspondence: [EMAIL_ADDRESS]
## 5 Toxicity Profile of _C. asiatica_ Constituents  
There is a scarcity of literature relating to the adverse effects and toxicity of _C. asiatica_. In a recommended dosage in humans _C. asiatica_ is not toxic and side effects are rare, although it may cause skin allergy and burning sensations with external use, and in high doses, headache, upset stomach, nausea, dizziness and drowsiness were observed [59]. Contact dermatitis cases were reported on a few occasions from using topical preparations that contained _C. asiatica_[60; 61; 62; 63; 64; 65]. The median lethal dose of dried powder of _C. asiatica_ given orally to mice was greater than 8 g/kg, attesting to the lack of acute toxicity of the dried plant [66]. In a chronic toxicity study, 20, 200, 600 and 1200 mg/kg/day of _C. asiatica_ were given to Wistar rats for six months. At the end of the study, treated animals showed no significant changes in body weight, blood chemistry, clinical chemistry and histopathology when compared to the control group [67]. However, in contrast, oral administration of dried _C. asiatica_ at a dose of 1000 mg/kg/day for 30 days to albino rats produced hepatic damage [68]. Furthermore, there is also a report on the risk of hepatotoxicity in humans treated with _C. asiatica_ for 20-60 days [69]. Although _C. asiatica_ was rarely reported to cause any adverse effects, it is advised not to be consumed by women during pregnancy and lactation, as it may have emmenagogue effects [70]. Extended treatment with high doses appears to reduce the metabolism of active constituents, which may increase the risk of toxicity; this should be considered during pharmacotherapy [55].  
There are few studies on the toxicity of individual constituents of _C. asiatica_. One study reported that asiaticoside did not exhibit any signs of toxicity after oral administration of 1 mg/kg. However, intramuscular injection of asiaticoside caused toxicity in mice and rabbits at 40-50 mg/kg [71]. Asiaticoside was also implicated as a weak carcinogen after repeated topical application on hairless mice [72]. Drug-drug interactions are possible with asiaticoside and/or macdecassoside, as they were shown to inhibit human cytochrome P450 enzymes that are responsible for drug metabolism [73].
# Crack Detection of Concrete Images Using Dilatation and Crack Detection Algorithms
## 2 Crack Detection Procedure  
In this study, the location and size of cracks were detected in concrete images, and the analysis was essentially performed using the Image Processing Toolbox in Matlab [16], and two user algorithms were added to improve the accuracy of crack detection.  
For concrete images, datasets collected from METU campus buildings were used [14; 15]. The collected datasets consist of 20,000 crack images and 20,000 non-crack images. However, in this study, 1000 images from each category, a total of 2000 images, were selected and analyzed. The size of each color image is 227 \(\times\) 227 \(\times\) 3, and the resolution is 51,529 pixels, a very low resolution. The 227 \(\times\) 227 size represents the width and height of the image, and the unit is pixels. The number \"3\" at the end indicates a color image, which consists of three channels: Red, Green, and Blue. Twenty crack images and twenty non-crack images are randomly shown in Figures 1 and 2.  
The crack detection procedure consists of 10 steps, as shown in Figure 3 and detailed in Tables 1 and 2. Two user algorithms are needed in steps 5 and 9, and these are explained in detail in Section 3. In Table 1, two crack images ('00330.jpg' and '00797.jpg') are shown, and in Table 2, two non-crack images ('00283.jpg' and '00318.jpg') are shown as examples.  
Figure 1: Sample crack images.  
Figure 2: Sample non-crack images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Step 1: Read the image (Original image) & Step 2: Enhance the image \\ \hline Step 3: Filtering & Step 4: Binarize and complement the image \\ \hline Step 5: Dilate the image (User Algorithm I) & Step 6: Open the image \\ \hline Step 7: Erode the image & Step 8: Labeling connected components \\ \hline Step 9: Detect the cracks (User Algorithm II) & Step 10: Overlay the image \\ \hline \hline \end{tabular}
\end{table}
Table 2: The analysis procedures for non-crack images (’00283.jpg’ and ’00318.jpg’).