esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Stress Field Prediction of Umbilical Based on PyEf-U-Net Convolutional Neural Network
## 2 Mathematical Modeling Solutions
The DE algorithm mainly consists of four steps:  
(1) Initialize the Population  
The size of the population size in the DE algorithm is denoted by \(NP\), while the size of the dimension of the individuals is denoted by \(D\), and the initialized individual is expressed as  
\[x_{ij}(0)=x_{ij}^{L}+r\times\left(x_{ij}^{L}-x_{ij}^{L}\right) \tag{14}\]in which, \(i=1,\,2,\,\cdots,\,NP,\,j=1,\,2,\,\cdots,\,D\). \(x_{ij}\) represents the number of dimensions of the population individuals, while the upper and lower limits are represented by \(U\) and \(L\), respectively. \(r\) refers to the random number between [0,1]. (2) Variant Operation  
To maintain the diversity of the population, the DE/rand/1/bin difference strategy is used to make the population variable. The expression is  
\[v_{ij}(n+1)=x_{r1}(n)+Q\cdot\left(x_{r2j}(n)-x_{r3j}(n)\right) \tag{15}\]  
where the scaling factor \(Q\) is a constant, and \(r_{1}\), \(r_{2}\), and \(r_{3}\) represent the individual number. (3) Crossover Operation  
Crossover operation is introduced to maintain the diversity of the population. The crossover operation is  
\[u_{ij}(n+1)=\left\{ \begin{array}{l}w_{ij}(n+1),rand(j)\leq CR\;\;\;or\;\;j=k\\ x_{ij}(n),otherwise\end{array} \right. \tag{16}\]  
in which \(CR\) denotes the crossover probability and \(rand(j)\in[0,1]\). (4) Selection Operation  
The DE algorithm ensures the convergence of the computation according to the adaptation evaluation mechanism, which is operated as follows:  
\[x_{ij}(n+1)=\left\{ \begin{array}{l}u_{i}(n+1),f(u_{i}(n+1))<f(x_{i}(n))\\ x_{i}(n),\;\;\;\;\;\;f(u_{i}(n+1))\geq f(x_{i}(n))\end{array} \right. \tag{17}\]  
where \(u_{i}(n+1)\) represents the experimental individual and \(x_{i}(n)\) denotes the baseline individual. Penalty functions [27] can be used with the DE algorithm to solve constrained optimization problems. The general form of the penalty function is given as  
\[P(X)=f(X)+\sum_{i=1}^{n}r_{i}G_{i}(X) \tag{18}\]  
\[G_{i}(X)=\max[0,g_{i}(X)] \tag{19}\]  
where \(f(X)\) denotes the objective function, \(r_{i}\) stands for the penalty coefficient, \(G_{i}(X)\) represents the constraint violation function, and \(g_{i}(X)\) denotes the constraint function. GLM [28] combines the Lagrange multiplier method and the outer point penalty function method for solving the optimal value, which is one of the ways to solve constrained optimization problems. After obtaining the feasible solution \(X^{1}\) with the DE algorithm, the exact solution \(X^{2}\) is then rapidly acquired via the GLM algorithm, which can be simplified as follows:  
\[\left\{ \begin{array}{l}\min f(X^{1})\\ s.t.:g_{i}(X^{1})\leq 0\;\;\;\;\;i=1,2,\cdots,n\end{array} \right. \tag{20}\]  
in which \(f(X^{1})\) represents the objective function and \(g_{i}(X^{1})\) refers to the inequality-constrained equation. Four different layout cases of the umbilical are presented in Figure 4.
## 3 Method
### Data Processing and Contourlet Transform
Initially, the image's high frequencies are computed. Next, the image undergoes a subtraction process to remove the lower frequencies. The intermediate frequencies are isolated, and directional operators are subsequently applied. When comparing the wavelet transform to the desired number of directions, we observe the extraction of several directions instead of just three. Each of these instructions assists us in identifying distinct boundaries of the image. Similarly, the method is replicated in the low-frequency region using sample reduction. The intermediate frequencies are extracted using the DFB operator, and the procedure is repeated as often as required. Contourlet transformation involves utilizing the Laplacian pyramid with varying sizes and the DFB filter in different orientations. Our goal in this transformation was to classify and label the pixels of the brain MRI image into two categories. In this way, the ground truth is obtained for each MR image using image processing techniques and contourlet transformation. We can apply the contourlet transformation in our MRI to find the boundaries and edges of the image in different levels and directions. Due to the two-dimensionality of the images, we performed the contourlet transformation in the second level in the four main directions, and the resulting images from this decomposition level are shown in Figure 6. To find the borders of the MR image, for simplicity and to obtain the desired results, we limited ourselves to  
Figure 4: The structure of the filters that make up the contourlet transform. analysis at two levels and the finding of the borders in four directions. Moreover, if the images are more complex or three-dimensional, we can carry out this conversion at the third level by analyzing them in eight directions. Combining the original image's decomposition coefficients in four directions was performed to find the image's borders. After multiplying the coefficients of these four directions, the edges of the original image were obtained. By setting the binary threshold limit, we obtained the desired edges of the image and, finally, the mask that results from the ground truth is shown in Figure 7.  
Figure 5: The frequency structure of the contourlet transform. Figure 6: Decomposition of the second level in four directions in sample MRI. Figure 7: (**a**) The edges of the images; (**b**) ground truth related to the sample MRI.
## 3 Materials and Methods
### Pollutants Removal
#### 3.2.2 CECs Degradation  
The degradation tests on benzotriazole, bisphenol A, diclofenac and sulfamethoxazole were performed at natural pH (pH = 7.1) in the presence of the catalyst at 0.2 g/L. The concentration of each compound was 4 mg/L. All the experiments were performed in Pyrex glass cells kept under magnetic stirring and filled with 5 mL of sample and catalyst suspension. The cells were irradiated in a Solarbox equipped with a 340 nm cut-off filter. The samples were filtered through a 0.45 \(\upmu\)m PTFE membrane and the photodegradation process was followed over time (sampling times: 0, 1, 3, 5, 7, 10, 15, 30, 60, and 120 min). All the synthetized materials were tested and Evonik P25 titanium dioxide was used as benchmark. Other pollutants namely phenol, carbamazepine, tolytriazole, and ofloxacin were preliminarily tested under the same conditions, and employing phenol as a model pollutant, the possibility of reusing the photocatalysts was verified by recovering them from the filter after utilization and using them for a second and third cycle of degradation. Analyses were performed with a Merck-Hitachi HPLC system equipped with a L-6200A Intelligent Pump, a L-4200 UV-VIS Detector and a six-way Rheodyne valve injection system. The detection wavelength was set at 220 nm and a gradient elution was performed with a mixture of phosphoric acid solution at pH 2.8 (A) and acetonitrile (B) at a flow rate of 1 mL/min in accordance with the following program: 75:25 A/B for 3 min; 75:25 to 65:35 over 4 min; 65:35 to 40:60 over 8 min; 40:60 for 5 min; 40:60 to 75:25 over 0.5 min; 75:25 for 5 min.  
The retention times were 2.96, 5.35, 14.08 and 18.20 min for benzotriazole, sulfamethoxazole, bisphenol A and diclofenac, respectively.  
Moreover, in order to further comprehend the mechanism of degradation of CECs by synthetized photocatalysts, experiments were repeated using two scavengers of ROS. Specifically, tert-Butyl alcohol in concentration equal to 10 mM was added as a scavenger of hydroxyl radicals and p-Benzoquinone 10 mM as a scavenger of superoxide radicals.
## 3 Background  
### Automatic Program Repair  
Automatic Program Repair (APR) is a technique that automatically generates patches to fix the buggy program. Figure 1 shows an overview of APR in detail. Generally, APR tools have three main phases: Fault Localization, Program Modification, and Program Validation. In Astor, the GZoltar [24] fault localization tool with the Ochiai [25] algorithm is used. Initially, the Fault localization tool takes Faulty Program and Repair Tests as inputs and automatically finds suspicious statements in the program. Each suspicious statement is given a suspicious score and selected randomly to undergo modification in the Program Modification phase. It uses a genetic algorithm to generate candidate solutions, subsequently, by applying atomic operators. Candidate solutions are generated based on the given number of generations or time limit. These generated candidate solutions are then applied to the buggy program to produce patches. These candidate solutions are also known as program variants.  
Soon after the generation, the program variants will be validated in the Program Validation phase against the Repair Tests (patch validation test suite) used during Fault Localization. If all the test cases get passed, it outputs a test suite adequate patch(es), also known as Repaired Programs. Otherwise, it is considered an invalid patch, and it is dropped or sent for modification once again based on its fitness score.  
The Generate and Validate approach is an important technique in automatic program repair. This approach executes an iterative process that contains two main components, generate and validate. GenProg is one of the first APR tools introduced using the Generate and Validate approach that is based on genetic programming [10]. Based on the higher suspicious score, the generate component selects the locations where the modification should take place, and it uses change operators to modify the buggy program and produces candidate solutions (program variants). In some previous techniques, change operators not only applied to the buggy program but also to the candidate solutions [7,10]. Searching of a candidate solution ends when all the possible elements in the search space have been considered or when the time allocated to the repairing process is expired. The validation component checks the correctness of the program variants (candidate solutions) that have been generated. Currently, most of the available generate and validate techniques evaluate the candidate solution correctness by running the available test suite. During validation, some or all candidate solutions that fail many test cases are usually discarded. However, if the candidate solution passes against all the available test cases, it is considered a test suite adequate patch and is delivered as a possible fix to the developer.
# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 3 Results and Discussion
### Analysis of Embedding Models Using Cosine Similarity  
This subsection presents a detailed analysis based on the performance of various embedding models using the cosine similarity metric, as summarized in Table 5. In this table, all values represent percentages, indicating the accuracy of each model in matching candidates to job descriptions for the top 1, top 50, up to top 500 candidates.  
The 'all-MiniLM-L6-v2' model, with a vector length of 512, consistently shows the highest Top-1 accuracy across all models. This indicates its superior ability to accurately match the most relevant candidate to a job description on the first try. Notably, this model also maintains its lead in performance as the number of top candidates increases, demonstrating robustness in identifying suitable candidates within a broader pool. The high accuracy rates persist up to the top 500 candidates, peaking at 81.11%, which is significantly higher than the other models.  
On the other end of the spectrum, the model with the lowest performance in the Top-1 category is the 'paraphrase-multilingual-MiniLM-L12-v2' with a vector length of 1024, showing an accuracy of only 1.92%. However, it is important to note that the performance of all models generally improves as the number of top candidates considered increases. Even the lower-performing models show a substantial increase in accuracy at higher K values, though they do not surpass the leading models.  
In summary, the 'all-MiniLM-L6-v2' model with a vector length of 512 emerges as the most effective model for job-candidate matching using the cosine similarity metric. This model not only excels at identifying the most relevant candidate but also maintains superior performance across a wider range of top candidates. The results emphasize the importance of choosing the right model and vector length for efficient and accurate job-candidate matching in recruitment processes.
## 2 Problem Description  
Two cylinders arranged symmetrically on a frame has become a major form of steering mechanisms for AORVs. The steering process of an AORV can be taken as a circular motion with a certain center and radius; the certain center is the hinge-point of the front and rear frames, and the certain radius depends on the installation position of the cylinders. As shown in Figures 1 and 2, one of two cylinders is stretched out, and the other is compressed after turning. However, the stroke of the two cylinders is different.  
As shown in Figure 3, the stroke difference and arm difference can be analyzed from the perspective of mathematical geometry. To facilitate calculation, \(r\) and \(h\) are taken as the AO and CO. According to the triangle cosine theorem, it is easy to determine the stroke difference and arm difference. As seen in Figure 3, if the values of AO and CO are \(r\) and \(h\), then \(\text{AO}=\text{BO}=\text{A}^{\prime}\text{O}=\text{B}^{\prime}\text{O}=r\), \(\text{CO}=\text{DO}=h\). The geometrical relationship in the picture is shown as follows.  
\[\begin{array}{l}AC=BD=\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos a}\\ A^{\prime}C=\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos(a+b)}\\ B^{\prime}D=\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos(a-b)}\end{array} \tag{1}\]  
\[\begin{array}{l}\Delta l=(A^{\prime}C-AC)-(BD-B^{\prime}D)\\ =\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos(a+b)}+\\ \sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos(a-b)}-\\ 2\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos a}\end{array} \tag{2}\]  
Figure 1: The straight form of steering mechanism for an articulated off-road vehicle (AORV).  
Figure 2: The turning form of steering mechanism for an AORV.  
\[\begin{array}{l}d1=d2=\frac{AO\cdot CQ\cdot\sin a}{\Lambda_{C}}\\ d1^{\prime}=\frac{A^{\prime}O\cdot\mathrm{CO}\sin(a+b)}{A^{\prime}C}\\ d2^{\prime}=\frac{B^{\prime}O\cdot\mathrm{DO}\cdot\sin(a-b)}{B^{\prime}D}\\ \end{array} \tag{3}\]  
\[\begin{array}{l}\Delta d=(d1-d1^{\prime})-(d2^{\prime}-d2)\\ =2d1-d1^{\prime}-d2^{\prime}\\ =\frac{2 rh\sin a}{\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos a}}\\ \frac{rh\sin(a+b)}{\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos(a+b)}}\\ -\frac{rh\sin a-b}{\sqrt{r^{2}+h^{2}+2r\cdot h\cdot\cos(a-b)}}\\ \end{array} \tag{4}\]  
A, B: The installation position of the two cylinders on the front frame;  
C, D: The installation position of the two cylinders on the rear frame;  
O: The hinge point of the front and rear frame.  
\(\Delta l\): Stroke difference;  
\(\Delta d\): Arm difference;  
\(b\): The steering angle.  
From the formula above, as long as the value of \(b\) is not equal to 0, it is easy to prove that \(\Delta l\) and \(\Delta d\) are never equal to 0. In other words, the differences of the stroke and arm are all present. As a result, the stroke difference and arm difference cause the non-constant flow of hydraulic oil, which can lead to a pressure pulsation in the hydraulic system, as well as energy loss [4; 11]. Thus, based on GA, it is essential to reduce the two differences.
# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Food Analysis
It is obvious that privacy does not have to be observed in this application scenario, as the food samples do not have any privacy rights. However, with the help of these analytical methods, it is possible not only to detect allergens in food products but also to determine which ingredients are present in the product. That is, deep insights into the product and manufacturing process can be gained with today's sensors and data processing technology [91]. As a result, it is possible to identify a manufacturer's secret ingredients, which represents a competitive advantage in the food market. Therefore, maintaining confidential business information is a necessary tool in the food chemical sector [92]. The inherent opportunities and privacy threats of IoT-supported food analysis can be broken down as follows:  
Opportunities:  
With IoT-supported food analysis, food samples can be analyzed much more efficiently and effectively. Due to the increasing number of people suffering from a food allergy, it is important that food products are correctly labeled and that this labeling is also thoroughly verifiable. Figure 5: Schematic Representation of Food Analysis to Detect Allergens in a Food Sample. Due to a predominantly automated processing of food products, a thorough inspection of these products is required in order to detect any foreign substances or contaminants at an early stage. Privacy Threats:  
In this application scenario, there are no privacy threats, but there are confidentiality threats, as food analysis can provide deep insights into the food product, revealing specific ingredients or preparation methods, possibly leading to a loss of competitive advantage.
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 5 Problem Formulation
Therefore, this paper proposes that the value of the capacitor should be selected to be optimized to reduce the THD using the HHO and ASO algorithm. The objective function to achieve optimal capacitor values leading to the minimum quadratic error of the THD can be calculated as follows:  
\[F_{1}=\int\limits_{0}^{\infty}t\cdot THD^{2}dt \tag{28}\]  
Moreover, to achieve the optimal values of both \(K_{p}\) and \(K_{i}\) for each PI controller, which lead to a minimum quadratic error, the objective function can be calculated as follows:  
\[F_{2}=\int\limits_{0}^{\infty}t\cdot e^{2}dt \tag{29}\]  
where _e._ is the error signal between the desired and the typical signals and is fed to the PI controller. The objective function to minimize F can be determined as follows:  
\[\text{Minimize}\ \{F(C,(K_{\text{p}},K_{i})_{\text{VDC}},(K_{\text{p}},K_{i})_{ \text{phase balancing}}\} \tag{30}\]  
where \(F\) is the summation of the \(F\)1 and \(F\)2.
# An Established Theory of Digital Twin Model for Tunnel Construction Safety Assessment
## 5 Case Study
### Predictive Performance Analysis  
In this paper, a double-hole one-way tunnel is selected as an example. Taking a section of the tunnel as the research object, the building boundary of the tunnel is 10.25 m \(\times\) 5 m, and the surrounding rock level of the monitoring section is Grade V. As described in Section 5.1, this article carries out structural safety assessment by analyzing the settlement value. During monitoring, measuring points are arranged at the arch crown, left arch and right arch respectively, and the average value of the three measuring points is used as the arch crown settlement value. According to the actual project overview and relevant specifications, the design limit displacement of the tunnel is 120 mm [44]. The displacement management level can be divided into three levels, and the specific rules are shown in Table 1 below:  
The collected settlement data is divided into a training set and a test set according to the proportion of 9:1. LSTM parameters are as follows: The max epoch is 300, the learning rate is 0.005, the gradient threshold is 1, the learning rate decline cycle is 150, and the learning rate decline factor is 0.2.  
The effect of the test set is shown in the Figure 8. It can be seen from the figure that its RMES is 0.092924, the absolute value of the maximum error is about 0.135 mm, and its prediction effect and accuracy meet the requirements for judging the construction safety state.  
By combining the prediction results with the relevant standards in the knowledge model, it is judged that the tunnel construction structure safety is always at level II, and no special operation other than reinforcement is required.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Displacement Relation/mm** & **Security Status** \\ \hline
**I** & \(U<U_{0}/3\) & normal \\
**II** & \(U_{0}/3\leq U\leq 2U_{0}/3\) & reinforcement \\
**III** & \(U>2U_{0}/3\) & special treatment \\ \hline \hline \end{tabular} Note: \(U\) is the measured value of vault settlement; \(U_{0}\) is the design limit displacement value of the settlement.  
\end{table}
Table 1: Displacement Management Level.  
Figure 8: The LSTM prediction effect analysis chart.
## 2 Proposed Method
### Insulator Detection
#### 2.1.2 Training Preparation  
To obtain the accurate locations of different scales of the insulators in an image, the k-means cluster algorithm is applied to automatically find a good bounding box prior instead of hand-picked prior, and the result is shown in Figure 3. Based on the observation of Figure 3, it is found that \(k=12\) can be treated as a compromise that has a good IoU (Intersection over Union) and moderate model complexity. Therefore, we choose 12 clusters corresponding to IoU = 64.73, and then divide the 12 clusters into three categories for different detectors, which is given as follows:  
1. First branch (Large): (322 103), (256 158), (253 289), (363 202).
2. Second branch (Middle): (45 225), (214 82), (135 132), (68 319).
3. Third branch (Small): (31 26), (66 48), (32 126), (123 58).  
The proposed method adopts the loss function that is proposed in [44], which is given in Formula (2).  
\[\begin{array}{ll}\text{\it Loss}&=\lambda_{\text{\it{card}}}\sum\limits_{i= 1}^{\tilde{S}^{2}}\sum\limits_{j=1}^{k}1^{obj}_{ij}(x_{i}-\tilde{x}_{i})^{2}+( y_{i}-\tilde{y}_{i})^{2}\\ &+\lambda_{\text{\it{card}}}\sum\limits_{i=1}^{\tilde{S}^{2}}\sum\limits_{j=1 }^{k}1^{obj}_{ij}\big{(}\sqrt{w_{i}}-\sqrt{w_{i}}\big{)}^{2}+\left(\sqrt{h_{i} }-\sqrt{h_{i}}\right)^{2}\\ &+\sum\limits_{i=1}^{\tilde{S}^{2}}\sum\limits_{j=1}^{k}1^{obj}_{ij}\big{(}C_ {i}-\hat{C}_{i}\big{)}^{2}\\ &+\lambda_{\text{\it{noeb}}}\sum\limits_{i=1}^{\tilde{S}^{2}}\sum\limits_{j=1 }^{k}1^{noeb}_{ij}\big{(}C_{i}-\hat{C}_{i}\big{)}^{2}\\ &+\sum\limits_{i=1}^{\tilde{S}^{2}}1^{obj}_{i}\sum\limits_{\text{\it{ecclass} }}(p_{i}(c)-\tilde{p}_{i}(c))^{2}\end{array} \tag{2}\]Figure 2: The architecture of the proposed network.  
In Formula (2), \(x_{i}\) is the \(x\) coordinate of the prediction while the \(x\) with symbol '\(\wedge\)' is the coordinate of the ground-truth. The definitions of the other parts in Formula (2) are similar to the above definition. Moreover, the first line and the second line of Formula (2) indicates the coordinates loss and distance loss between the prediction and the ground-truth. The third line represents the confidence loss of the predicted bounding box containing the insulators, while the fourth line gives the confidence loss of the predicted bounding box does not contain the insulators. The fifth line denotes the category prediction loss. Unlike the work of [44], the k is set to be 4 in the proposed network.