esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-AGV Path Planning Based on Time Window
### Multi-AGV Conflict Determination Based on Time Window  
Through the above analysis, it was found that in an environment with multiple AGVs running at the same time, the improved A* algorithm can first be used to determine the best path for each AGV and then to compare it with the path of other AGVs. If two or more AGVs pass through a certain grid cell at a given time, it can be judged that AGV conflict will occur in the grid. By combining the position of each AGV before and after passing through the grid cell, it is possible to determine the type of AGV conflict.  
The time window method is used to determine whether a grid cell has an AGV passing through it. The time window refers to the time information of an AGV passing through a certain path [30], which is used to record the beginning time of an AGV entering the grid cell and the end time of an AGV leaving the grid cell. The time window of the grid cell is divided into idle time windows and occupied time windows. An idle time window is for a grid cell without an AGV driving in it; at this time, any AGV can enter the cell. The occupation time window means that an AGV is occupying the grid cell; therefore, other AGVs cannot enter the path. When the occupancy time window of the grid cell corresponds to multiple AGVs at the same time, there is a conflict between the AGVs here. By setting a time window for each grid cell, the specific time window can determine the order in which an AGV passes through each grid cell. If AGV conflicts occur, the time windows of AGVs passing through the grid will coincide. In this case, different AGV priority rules are set according to different conflict types so that AGVs with lower priorities wait or replan paths to delay or rearrange the time windows. The AGV with a higher priority is driven according to the planned path so that no time window conflict occurs in the driving process of multiple AGVs; finally, AGV conflict-free path planning is realized.
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 5 Learning with Errors (LWE)
### An Efficient Variant of LWE
In the first step, the simulator \(X\) makes \(m\) queries to \(\mathcal{O}\) with outcome \((a_{j},b_{j})\), where \(j\in[m]\), and furthermore, for each \(i\in[m]\), \(X\) chooses \(s_{ij}\leftarrow\mathbb{Z}_{q}^{n}\) and errors \(x_{ij}\leftarrow\chi\). The simulator \(X\) outputs \((A,P)\) as follows: the \(j\)th column of matrix \(A\) is vector \(a_{j}\), and the entries of matrix \(P\) are \(p_{ij}=d_{ij}+x_{ij}\), where \(d_{ij}\) is the entries of the \(D\) matrix. The entries of \(D\) result from the multiplication of \(AS\) and the transformation matrix defined in each case. Hence, for all columns \(j<k\) and for all \(i\in[m]\), \(X\) selects independent error terms \(x_{ij}\) from the distribution \(\chi\) and for all columns \(j>k\) and for all \(i\in[m]\), \(X\) chooses uniform independent error terms from \(\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\). It is obvious that if the oracle \(\mathcal{O}\) generates samples from \(A_{s,\chi}\), the output of the simulator is distributed according to the hybrid distribution \(H_{k-1}\), while if the oracle generates samples from the uniform distribution, the output is distributed according to \(H_{k}\). Thus, under the assumption that LWE with parameters \(q,\chi\) is hard, the above distributions are indistinguishable, and consequently, the distributions \(H_{k-1}\), \(H_{k}\) are indistinguishable.
## 2 Materials and Methods
### Preparation and Characterization of the Polymeric Conjugates
#### 2.3.3 DPPH Assay  
In order to evaluate the antioxidant activity of PLG-GRAPE conjugates and the grape pomace extract, their ability to scavenge a stable free radical, 2,2-diphenyl-1-picrylhydrazyl (DPPH), was analyzed based on the protocol as described [29; 30].  
For this aim, one milliliter of each sample was subsequently mixed with 4 mL of a 188 \(\upmu\)M ethanolic DPPH solution and 5 mL of absolute ethanol. The resulting solutions were incubated in darkness, and after 15 min, the radical scavenging capacity was assessed by measuring the absorbance at 517 nm through a UV-Visible Evolution 201 spectrophotometer (Thermo Fisher Scientific, MA, USA).  
A control sample (Blank), consisting of a 20% w/w aqueous dextran solution, was subjected to the same experimental conditions to investigate its potential interference in the DPPH assay.  
The DPPH assay is a widely used method for evaluating antioxidant capacity, based on the ability of antioxidants to scavenge 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals.  
In the adopted method, the antioxidant molecule converts the radical into a yellow-colored compound, diphenylpicrylhydrazine. The reaction extent depends on the hydrogen-donating capacity of the antioxidant.  
The scavenging activity of the tested samples was assessed based on DPPH reduction and expressed as the percentage of inhibition, calculated using the following Equation (1):  
\[\text{Inhibition}(\%)=\frac{\text{A}_{0}-\text{A}_{1}}{\text{A}_{0}}\times 100 \tag{1}\]  
where A\({}_{0}\) is the absorbance of a control sample prepared under the same conditions without any test compound, and A\({}_{1}\) is the absorbance of the tested sample.  
The assay was repeated in triplicate.
# Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control
## 5 Results and Discussion
Therefore, further restrictions (online education in secondary schools, closure of certain public spaces, banning of most gatherings, and curfew from 8 p.m. to 5 a.m.) had to be introduced in the first half of November 2020. These measures had the planned effect in terms of the significant reduction of the transmission rate, as is visible in Plot 2 of Figure 3. Then, from January 2021, \(R_{t}\) began to increase again due to the appearance of the more contagious alpha (B.1.1.7) variant, although all of the former restrictions remained in effect. The alpha variant caused the largest peak of the epidemic so far in the spring of 2021 with a maximum of 12,553 hospitalized people on 30 March 2021. Further restrictions had to be introduced on 8 March 2021, where the main component was the closing of all schools. Together with the intensive vaccination in the first half of 2021, this made the decrease in the number of infected people definitely fast. The ratio of the peaks of the estimated \(\beta\) in February 2021 and December 2020 was approximately 1.62, which matches well with literature reports (1.4-1.8) in the U.K. [75]. We note that we can compare these data since they were estimated under the same restriction level. Plot 12 in Figure 3 shows the estimated number of people gaining immunity by infection and/or vaccination. According to this estimation, more than 30% of the population might have gone through the COVID infection until the end of June 2021. This suggests an approximately 26% detection rate. This is significantly lower than the value of certain European countries such as Germany, Italy, or Spain, reaching or sometimes exceeding 50%, but the number of performed tests per population has also been much lower in Hungary than in the mentioned countries [76].
###### Abstract  
In order to accurately analyze the load supply capability of power systems with high penetration of renewable energy generation, this paper proposes a probabilistic available load supply capability (ALSC) forecasting method. Firstly, the optimal input features are selected by calculating the maximal information coefficient (MIC) between the input features and the target output. Based on this, a stacking ensemble learning model is applied for the prediction of wind power, photovoltaic power and load power. Secondly, the distributions of the forecasting objects are obtained based on forecasting errors and the error statistics method. Finally, the forecasting distributions of wind power, photovoltaic power and load are set as the parameters of a power system, and then probabilistic ALSC is calculated using Latin hypercube sampling (LHS) and repeated power flow (RPF). In order to simulate a more realistic power system, multiple slack buses are introduced to conduct two types of power imbalance allocations with novel allocation principles during the RPF calculation, which makes the ALSC evaluation results more reasonable and accurate. The results of probabilistic ALSC forecasting can provide a reference for the load power supply capacity of a power system in the future, and they can also provide an early warning for the risk of ALSC threshold overlimit. Case studies carried out on the modified IEEE 39-bus system verify the feasibility and effectiveness of the proposed methods.  
stacking ensemble learning model; probabilistic forecasting; repeated power flow; multi-slack buses; Latin hypercube sampling +
Footnote †: journal: Applied Sciences
## 1 Introduction
For this, we applied techniques used in other research fields, such as image and video classification, to create a useful representation of the reasoning behind the classification of network packets. To make our methodology applicable to all contexts, we used an algorithm that is universally applicable to all CNNs in order to further understand them, and developed several modifications in the model in order to see how the behavior may change depending on the added or removed features. As a result, we focused on understanding important factors for model performance. First, we analyzed characteristics of the data that might be relevant. A good example of this is that data leakage leads to overfitting when packets in the same flow are in both the training and test set. We also analyzed the different parameters of the model that impact performance. Finally, the decision was analyzed using explainable Artificial Intelligence (XAI) techniques, so that we could try to understand how the features were built and what they focused on. The rest of this paper is structured as follows. Next, Section 2 shows the related work, in order to contextualize our research. Then, Section 3 explains how the available datasets were processed, trying to avoid the bias that is present in many prior network classification works. In Section 4, we present the different components of a CNN architecture to classify network packets. After that, Section 5 presents gradient analysis as a way to explain the convolutional models. Section 6 applies this technique to real data, so we can understand what the neural network generalizes from the encrypted traffic it trains with. Finally, Section 7 concludes the paper, providing a summary of the main results and contributions.
# An Omnidirectional Near-Field Comprehensive Damage Detection Method for Composite Structures
###### Abstract  
As one of the active structural health monitoring methods based on the Lamb wave, the ultrasonic phased-array damage detection method can provide information such as damage location and range more intuitively, which is why this method is a research hotspot in the field of Lamb wave-based damage monitoring. However, the ultrasonic phased-array damage detection method intended for the far field is not applicable to near-field damage monitoring. In addition, the traditional one-dimensional piezoelectric phased-array damage imaging method suffers from a blind area in the near field, and the data collection time of its angle scanning is relatively long. In view of these problems, this paper proposes an omnidirectional damage imaging monitoring method, combining the near-field sampling phased-array damage monitoring algorithm and the two-dimensional phased-array. The proposed method is verified by experiments using complex composite materials, and the results obtained show that the proposed omnidirectional near-field sampling phased-array damage imaging method is suitable for real-time damage detection in complex composite materials.  
structural health monitoring; ultrasonic phased array; composite structure; complex structures +
Footnote †: journal: Applied Sciences
## 3 Performance Evaluation of F-SAMI Specimens Using MMLS3
### MMLS3 Test Results  
Figure 4 shows the occurrence and progress of reflective cracking in each specimen as the MMLS3 test proceeds. Figure 4a is the specimen top view before the MMLS3 load is applied, and Figure 4b shows that small cracks that occurred on the surface of specimen on the left side. Figure 4c shows that in the specimen on the left side, reflective cracks have occurred entirely on the surface, while in Figure 4d, the cracks occurred gradually in the specimen on the right side.  
Figure 4: Reflective crack occurrence and propagation. (**a**) specimen top view before the MMLS3 load is applied; (**b**) small cracks that occurred on the surface of specimen on the left side; (**c**) specimen on the left side, reflective cracks have occurred entirely on the surface; (**d**) the cracks occurred gradually in the specimen on the right side.  
Figure 5 shows the side view of the specimen after the completion of the experiment. The MMLS3 experiment was conducted until the specimen was deflected due to reflection cracking and additional loading was impossible, as seen in specimen 5. From Figure 5, it was found that specimens 5 and 6, to which general tack coating was applied, were completely split due to the reflective cracking.  
Table 3 summarizes the number of loadings up to the point of reflective cracking in the upper part of the specimen, according to each specimen type, measured through the MMLS3 test. The number of loadings when reflective cracks completely occurred in the wheel path, using visual inspection, was taken as the failure criterion.  
Regardless of the type of mixture in the lower layer, the specimens applied with the F-SAMI method showed significantly superior resistance to reflection cracking, compared to the specimens applied with the tack coating. In addition, in the F-SAMI method, the specimens containing aggregate showed greater resistance to reflection cracking than specimens containing no aggregate. As a result of comparing the effect of the asphalt content of the F-SAMI method, it was found that the effect of the contents did not show a specific trend. This is judged to be a result of the asphalt content applied in this test, being applied above a certain level that does not degrade the performance of the F-SAMI method.
# A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection
## 3 Preliminaries
### Bagging Framework in Ensemble Learning  
Ensemble learning methods have been proved to be an efficient way to deal with class imbalance and bagging is one of those ensemble learning methods that is widely used and easy to realize. By training a set of weak classifiers based on different sub-datasets, a strong classifier can be obtained by combining their predictions together. Figure 2 illustrates the main process of bagging. Specific information about the bagging framework can be described as follows.  
Generating sub-datasets. The first stage of building a bagging classifier is to generate a set of sub-datasets by random sampling. In this process, it should be confirmed how many samples should be taken out of the base datasets and how many sub-datasets should be generated. Usually, these two parameters can be confirmed by expert knowledge or by cross validation.  
Figure 2: Bagging framework in ensemble learning.  
Training weak classifiers. After generating the sub-datasets, the training process can be started. Each sub-dataset corresponds to a weak classifier and each weak learner is built by corresponding sub-datasets. Because every sub-dataset is generated by random sampling, the weak learners are independent theoretically.  
Combining output results. Once the weak learners have completed their classification results, respectively, it is necessary to combine these outputs to obtain a final result. There are several strategies to achieve combination. (i) Voting. By counting all the classification results, the most voted class is the final result. (ii) Weighted averaging. Differently from the voting method, this method sets a weight for each weak learner. With a greater weight, a weak learner is more important compared to others. In this paper, the voting strategy is used as the combination method.  
Ensemble learning is an excellent approach for improving model performance. However, when implementing ensemble methods, two key factors must be considered, diversity among weak classifiers and a reasonable level of individual accuracy.  
As shown in Figure 3, when each weak classifier has a reasonable performance and the classification results differ across samples, the final ensemble effect can be highly effective. However, as illustrated in Figure 4, when each weak classifier performs poorly, the final results of the ensemble model can be worse.  
Figure 4: Example of ensemble learning with bad performance.  
Figure 3: Example of ensemble learning with good performance.
## 2 Related Work
### IPTs-Based Segmentation  
The IPTs-based segmentation methods, including edge detection, seed-growing, and thresholding methods, have been extensively studied for crack segmentation [43]. For the edge detection method, the Sobel operator and Canny operator have been widely used for crack edge identification based on gradient characteristics. [PERSON] and [PERSON] [44] combined the Bi-dimensional Empirical Mode Decomposition (BEMD) with the Sobel operator to improve the performance of crack edge detection. [PERSON] et al. [45] compared the performance of the Fast Fourier Transform (FFT), Fast Haar Transform (FHT), Sobel operator, and Canny operator, and the results showed that FHT is more accurate and reliable for detecting crack edges. The major problem of the edge detection method lies in its difficulty in detecting complete crack edges, leading to the low performance of crack segmentation.  
The seed-growing method [46; 47] has drawn much attention for its capability of segmenting cracks at high accuracy, in which, to obtain the complete crack, potential crack seeds are first assigned, and the seeds are linked together via the path-searching algorithm. [PERSON] et al. [48] employed the multiple directional non-minimum suppression technique to obtain the crack seeds and then connected these seeds by utilizing the minimum distance cost map. To select crack seeds effectively, [PERSON] et al. [49] further constructed a crack probability map by employing the tensor voting technique. Nevertheless, the seed-growing method needs much calculation for seed selection and path searching, resulting in high computational costs when applied to engineering practice.  
The thresholding method, in which local or global brightness thresholds are used to identify crack pixels, is known to be fast and simple. [PERSON] et al. [50] employed the histogram thresholding method to obtain the approximate locations of cracks and then refine these locations utilizing the snake model. Following that, [PERSON] and [PERSON] [51] proposed a Neighboring Difference Histogram Model (NDHM) considering the fact that crack pixels are darker than their surroundings. [PERSON] and [PERSON] [52] further proposed a dynamic thresholding method in consideration of the standard deviation of all pixel intensities and the entropy of each image block. In addition, the computer vision function library OpenCV also provided an adaptive thresholding method for segmentation, in which the adaptive threshold is calculated for each input pixel.  
Even though there have been a variety of IPTs-based segmentation methods, the background interferences contained in crack regions will greatly hinder the performance of crack segmentation. To solve this problem, a hybrid approach is proposed for crack segmentation by combining the novel DO-YOLOv4 with the effective adaptive thresholding method, which will be presented in detail in Section 3.
## 3 Methodology  
### Objectives  
To address the previously mentioned gaps, this study seeks to answer the following research questions:  
* **RQ1:** How do region-specific prompts influence the linguistic diversity of synthetic datasets?
* **RQ2:** What are the regional differences in language use for expressing emotions in synthetic datasets?
* **RQ3:** How do region-specific synthetic datasets affect the performance of emotion detection models compared to baseline datasets? Based on these questions, the following hypotheses are proposed:
* **H1:** Prior research has shown that prompt engineering can guide language models to generate domain-specific content [19]. By incorporating region-specific cues, prompts are expected to elicit greater linguistic diversity, reflecting local idioms, word choices, and syntactic structures.
* **H2:** Studies on regional language variations suggest that people in different regions use distinct expressions and lexical choices to convey emotions [16; 22]. This hypothesis assumes that these differences will be evident in synthetic datasets when region-specific prompts are used.
* **H3:** Empirical findings indicate that training NLP models on more diverse and contextually relevant data improves performance [15]. Since region-specific datasets are expected to better capture local linguistic nuances, models trained on them should outperform those trained on baseline datasets.
# Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
## 5 Discussion
As discussed, the quality of reconstruction varies significantly depending on the choice of distance function, with EPD and WEPD providing higher quality than the conventional Euclidean distance and KL-divergence. The experiments conducted in this study were designed under conditions of high projection noise and fewer projection directions than the number of pixels. This setting was motivated by the aim of proposing methods for reducing radiation exposure. It is known that with low noise levels, providing a high value of \(\gamma\) to PDEM yields higher-quality images than MLEM [14]. Moreover, the advantages of using iterative methods diminish when noise is low. It should be noted that, underlow noise conditions, the outcomes produced by the proposed method, depending on the distance function, may vary from the results presented in this paper.
## 4 Analysis of Influencing Factors
### Effect of the Support Structure Insertion Ratio
#### 4.4.3 Tunnel Structural Stress  
Figure 17 illustrates the maximum tensile and compressive stresses experienced by the tunnel structure under each insertion ratio. It is evident from the diagram that the tensile stress and compressive stress of the tunnel structure decrease exponentially as the insertion ratio increases. When the insertion ratio increases from 0.2 to 1.0, the tensile stress decreases by 0.67%, 0.99%, 1.13% and 1.18%, respectively, and the compressive stress decreases by 2.37%, 3.71%, 4.30% and 4.49%, respectively. The change in the insertion ratio exerts a larger influence on the tensile stress of the tunnel structure compared to the compressive stress, and when the insertion ratio exceeds 0.8, there is almost no further change in the stress of the tunnel structure.