esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation
### Comparison of Experimental and Numerical Results  
The yield force, peak load, and ultimate displacement of the five specimens are compared in Table 2. The numerical results generally overestimated the experimental loads a bit, and the average values for the ratios of the numerical results to the experimental ones for the yield strength, the peak load, and the ultimate displacement were 1.07, 1.04, and 1.01. Values for the correlation were 0.50, 0.90, and 0.28, respectively. The relative errors of the numerical results of the yield strength and the peak load ranged from 5% to 9% and 0% to 6%, respectively; values for the corresponding coefficient of variation (CoV) were \(1.84\times 10^{-4}\) and \(4.96\times 10^{-4}\); The ultimate displacement of the specimens was also predicted with good accuracy, with an minimum average value of 1.01 for the ratio of the FE value to corresponding experimental value, where the corresponding relative error ranged from \(-7\) to 12%. The corresponding CoV was \(4.60\times 10^{-3}\), implying that the ductile fracture model could effectively predict failure of the steel specimens. The load-displacement curves of the experimental results are compared with the corresponding numerical results in Figure 7. Based on the comparison results, it was found that loads and failure displacements were predicted with good accuracy. Cracking and final rupture of the specimens were also successfully captured by the FE analyses. Comparison of the rupture modes is given in Figure 8, indicating that the fracture model could generally reproduce the whole cracking processes. Due to the randomness of cracking in nature and the high degree of symmetry of configuration of the specimens, the cracking paths of the FE results were a bit different from the experimental ones, especially for Specimens H40A80R0.5, H40A85R0, and H40A85R1. Many factors can affect the crack path, such as manufacturing deviations and loading eccentricity. Ideally, the crack paths should be symmetrical since the loading conditions and the specimens were symmetrical. However, none of the specimens cracked symmetrically in the experimental or numerical results. This indicates randomness of the crack paths of the specimens. In addition, all the fracture modes were ductile fracture, and the random crack paths had no significant effect on the global seismic performance of the specimens.  
Figure 7: Comparisons of load-displacement curves between experimental and numerical results with (**a**) H40A85R0.5, **(b)** H50A85R0.5, **(c)** H40A80R0.5, **(d)** H40A85R0 and **(e)** H40A85R1.
## 4 Experiments and Results  
To validate the performance of our proposed AV-SSLT in cocktail party environments, we performed experiments to test its ability to accurately identify the target speaker in a noisy environment with several speakers and its accuracy of speech recognition when the robot is at the end of tracking. The experimental setup and the experimental results are as follows.
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
###### Abstract  
Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.  
p 2024 14 3628 1
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 4 Test Data Processing of the Vibration Signal
### Feature Filtering by PCA  
Too high feature dimensions lead to over fitting and some of the features are highly correlated, which will increase the unnecessary calculation burden [51]. Therefore, the PCA algorithm is used in this paper to obtain new low-dimensional features, remove redundant features, and retain the main information of original feature vectors while reducing the complexity of classification and recognition model [52]. The results are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency Domain** & **Expression** & **Frequency Domain** & **Expression** \\
**Characteristic** & & **Characteristic** & **Expression** \\ \hline \(F_{10}\) & \(\frac{1}{N}\sum\limits_{i=1}^{N}{s\left(i\right)}\) & \(F_{11}\) & \(\sum\limits_{i=1}^{N}{\left(\mathrm{s}(i)-F_{10}\right)^{2}}\) \\ \hline \(F_{12}\) & \(\frac{\sum\limits_{i=1}^{N}{f_{i}s(i)}}{\sum\limits_{i=1}^{N}{s(i)}}\) & \(F_{13}\) & \(\sqrt{\frac{  
the whole feature set can not be expressed, and if different degradation states have different numbers of principal components, the calculation will become more complex and reduce the accuracy of model prediction. Therefore, the first six principal components are used to replace the original 20-dimensional degenerate feature vector, which reduces the data dimension and calculation cost. The first six principal components are used as new feature vectors to construct the training set and test set of XGBoost model respectively.
## 3 Results and Discussion
The monoterpenes \(\beta\)-pinene (30.4%, 18.7%) and 1,8-cineole (13.3%, 18.5%) were the most abundant compounds in the white and deep pink morphotypes, respectively, and camphene (20.9%) and 1,8-cineole (23.8%) in the pink sample collected in point D (Table 4). \(\gamma\)-Cadinene was the major sesquiterpene in the white (13.1%) and pink (12.9%) morphotypes and \(\beta\)-caryophyllene (16.4%) in the deep pink. Some compounds such as sabinene (4.7%), \(\alpha\)-longipinene (9.0%), eudesma-1,4(15), 11-triene (1.3%), \(\alpha\)-gurjunene (1.3%), \(\delta\)-cadinene (1.8%), and other minors were present only in the white sample, and 1-hexanol (0.3%), lavandul (0.7%), isopinocampone (0.2%), lavandulyl acetate (0.3%), and bornyl acetate (1.1%) were only found in the deep pink sample. The pink morphotype was instead characterized by compounds also detected in the other two. In this study, a multivariate metabolomics data analysis was also performed to better understand which volatile compounds best characterize _A. millefolium_ L. both in relation to morphotype and collection point. The complete list of compounds detected in all investigated samples was reported in a heatmap (Figure 2) where each heatmap describes the point of collection. Next to the heatmap, a hierarchical clustering (HCA) technique based on Euclidean distance method was employed to analyze the similarities of metabolite trends in the different morphotypeswith a repetitive process that associates/dissociates (agglomerative/divisive methods) object by object until all are equally and completely processed [30; 31]. From this first explorative analysis, it appeared that in points C and D, there was a cluster of compounds characterized by a high percentage involving \(\delta\)-cadinene, 1,8-cineole, and \(\beta\)-pinene. These last two volatile compounds were also strongly represented in point B in the deep pink and pink (not in white) morphotypes, jointly with \(\beta\)-caryophyllene. Indeed, in point B, the white morphotype was mainly characterized by germacrene D, sabinene, and \(p\)-cymene. In point A, high levels of \(\alpha\)-pinene and \(\gamma\)-muurolene were identified, especially in the deep pink and white morphotypes, whereas the pink morphotype was mostly characterized by \(\beta\)-myrcene and trans-\(p\)-menth-2-en-7-ol. In both points A and C, the main sample cluster included the pink and white morphotypes, followed by the deep pink morphotype (Figure 2A,C). In contrast, in point B, pink and deep pink were included in the same first cluster followed by white (Figure 2B), while in point D, white was clustered with deep pink (Figure 2D).
# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 1 Introduction
Inspired by this, we incorporate macro impact factors into the DA-RNN model to predict Bus Stop Dwelling Time and Stop-to-Stop Travel Time, resulting in an enhanced model termed macro impact factor DA-RNN (MDARNN). On the other hand, the Long Short-Term Memory (LSTM) model is chosen for Current Driving Segment Travel Time and Transfer Point Waiting Time prediction. Therefore, in this paper, a parallel structure consisting of four sub-models is proposed, which employs both LSTM and MDARNN to predict four different time periods based on their characteristics. The main contributions of this work are outlined below:  
* A travel chain is constructed using the original bus datasets, which addresses the existing constraint of inadequate historical data concerning individual passengers' bus journeys, and provides a reference for the analysis of transit transfer time under the condition of a single GPS data source. * A novel enhanced network named MDARNN is introduced, building upon the DA-RNN model. This enhancement involves the classification of input features into two distinct components: macro impact factors and local impact factors, which allows for a more detailed and accurate analysis of the various elements influencing bus travel time. * A partition and combination framework referred to as PMLNet, is presented. Both MDARNN and LSTM are selected to individually predict four time periods, which are corrected and combined using four traffic impact factors. This not only enhances prediction accuracy by considering diverse traffic impact factors but also showcases a novel approach to integrating and correcting. * A real-time bus travel time prediction system is designed. The results affirm the system's effectiveness in delivering accurate travel time predictions, underscoring the potential impact of our contributions on improving public transportation efficiency and user experience. This paper is organized as follows: Section 2 gives an overview of the relevant literature related to bus travel time prediction. Section 3 introduces the system architecture of bus travel time prediction system; data processing and the core part of the system, PMLNet, are also presented in Section 3. Experimental results analysis and system validation are presented in Section 4. Finally, Section 5 offers a conclusion to this paper and future research directions.
## 2 Negative-Stiffness Device and Its Mechanical Model  
### Negative-Stiffness Device  
Negative stiffness means that, in the force-displacement curve, the force decreases with the increase of displacement, making the slope of this section of the curve negative, as graphically illustrated in Reference [10]. Combined with the definition of stiffness, the concept of negative stiffness comes into being. In the traditional isolation layer, the sum of the horizontal stiffness of all rubber bearings is positive. If a negative-stiffness device is added to the isolation layer, the stiffness of the isolation layer can be further reduced (the total stiffness is still positive), so that its natural vibration frequency can be further reduced, thus effectively reducing the seismic response of the structure under long-period earthquake. Based on the principle of variable stiffness and the idea of track type, a negative-stiffness device with damping is proposed which is mainly composed of a track plate, roller, preloaded Belleville spring and viscous damper in Belleville spring guide sleeve with the curve of plate surface as a function, as shown in Figure 1. Changing the function of the track surface curve can realize the flexible adjustment of the isolation performance of the device.
## 4 NAEEA for Clustering Optimization of AUSCNs
### Adaptive Operation  
For better reducing the energy consumption by using the NAEEA to optimize the cluster of AUSCNs, the NAEEA employs a novel adaptive operator to adjust the mutation rate in a dynamic way. In the original evolutionary algorithm, the probability of mutation is fixed. Although the constant mutation probability simplifies the algorithm process, it also reduces the diversity in the process of population evolution. In the NAEEA, adaptive operations adjust the mutation probability according to the individuals' fitness. It is defined as follows:  
\[P_{mut}(I_{m})=\begin{cases}1,Fitness(I_{m})>\frac{1}{N}\sum\limits_{n=1}^{N} Fitness(I_{n})\\ 0,Fitness(I_{m})<\frac{1}{N}\sum\limits_{n=1}^{N}Fitness(I_{n})\end{cases} \tag{13}\]  
The lower the energy consumption, the lower the fitness and the probability of mutation. On the contrary, the higher the energy consumption, the greater the fitness and the probability of mutation. By dynamically adjusting the mutation probability, the probability of individuals with low energy consumption entering the next generation is improved.  
Figure 8: Mutation example.
elements. [PERSON] and [PERSON] [8] presented a method to determine the element reliability based on the ductility of the used material. [PERSON] and [PERSON] [9] performed the reliability analysis based on the flexural failure modes. Unluckily, these structural reliability analyses do not consider the generated internal stresses in their probabilistic approach. On the other hand, [PERSON] [10] developed a strength stochastic model for both Normal or Weibull distributions. However, the constants of their differential equation do not represent the material's time-dependent strength behavior nor the strength ratio used to determine the Weibull parameters (See [PERSON] [11]). As the equivalent stress (\(\sigma_{eq}\)) that causes the failure [12] is determined by the instantaneous strength, which due to the fatigue phenomenon is higher than the applied stress, a strength behavior analysis through time is needed [12; 13; 14; 15]. Therefore, in this paper, the \(\sigma_{eq}\) value is determined based on bending beams and the changes that the elasticity modulus experiences through time, and on the viscoelastic behavior theory.  
As a result, in the proposed method, at any time the minimal required strength is related to its corresponding instantaneous time-dependent strain (\(\varepsilon_{i}\)). Because our proposed method is based on the stresses, by using the \(\varepsilon_{i}\) value in the corresponding strain-life curve (\(\varepsilon N\)), we estimate the equivalent stress \(\sigma_{eq}\) value that we finally use in the stress-strength proposed method to determine the reliability of the beam. Moreover, because of this and the hysteresis analysis, both the \(\varepsilon N\) curve and the corresponding equivalent stress relation always exist. Therefore, the proposed method is sufficiently flexible to be applied in any structural element lifetime analysis. In contrast with the flexural members methodology [6] (Chapter F), the proposed method, using the optimal W beam selection, is performed based on the time-dependent stress (\(\sigma_{i}\)) and its corresponding reliability index.  
The structure of the paper is as follows: Section 2 presents the theoretical background of the equivalent stress that is used to determine the reliability of the beam. In Section 3, the proposed stress-strength method is given. Finally, in Section 4, the general conclusions are given.
# The Effect of Pitch Surface on Match Running Performance in Women's Soccer
## 2 Materials and Methods  
### Match Data  
In this observational study, the MRP data were collected using a 10 Hz global positioning system (GPS) (Apex, STATSports, Newry, Northern Ireland, firmware 4.30). The system includes a 3-axis accelerometer, gyroscope, and magnetometer. Previous studies have confirmed the system's validity and reliability in reporting distance and speed data [30]. To prevent inter-unit variability, players consistently used the same device for each match. In compliance with the manufacturer's guidelines, all devices were activated approximately 30 min before the pre-match warm-up to ensure adequate satellite signal acquisition. Following each match, raw data files were exported using software specific to the system (STATSports Sonra version 3.0, Newry, Northern Ireland). These files were individually trimmed to retain only the data relevant to each specific match for further analysis. The MRP variables included total distance covered (TD) (m), high-intensity running (HIR) (\(\geq\)5.5 m/s) (m), number of high-intensity accelerations (HIA) (\(>\)3 m/s\({}^{2}\)), and number of high-intensity decelerations (HID) (less than \(-\)3 m/s\({}^{2}\)) [31].
# Federated Learning: A Comparative Study of Defenses Against Poisoning Attacks
## 5 Defense Methods
### ConTRA  
CONTRA, presented by [PERSON] et al. in [8], is a distinctive defense mechanism in federated learning that takes a novel approach to defending against potential malicious clients. Instead of outright identification and exclusion, CONTRA implements a reputation system. Each client is assigned a reputation score, and is penalized for suspicious behavior detected by assessing the similarity of updates to historical data. The algorithm aims to limit the impact of malicious agents and reduce their chances of being chosen in subsequent training rounds [8].  
The algorithm for the federated learning system using CONTRA as the aggregation model works as follows (Algorithm 3):  
```
Input: numberOfAgents, T (number of iterations) Output: globalWeights // Step 1: Initialization and Configuration  Initialize agents with indices [1, 2,..., numberOfAgents]  Initialize globalWeights  Set t = 0 // Step 2: Main Training Loop whilet < T do // Step 2a: Calculate probabilities for agent selection probabilities = CalculateSelectionProbabilities(agents) // Step 2b: Start parallel training and wait for completion  selectedAgents = SelectAgents (probabilities)  updates = ParallelTraining(selectedAgents) // Step 2c: Calculate Offsets  for each agent in selectedAgents do  offset[agent] = CalculateOffset(agent.update, referenceUpdate) end for // Step 2d: Similarity Measurement for each agent in selectedAgents do  similarityScore[agent] = MeasureSimilarity(offset[agent], referenceUpdate) endfor // Step 2e: Weighted Aggregation  globalUpdate = 0  totalWeight = 0 for each agent in selectedAgents do if similarityScore[agent] is High then  weight = AssignHigherWeight(similarityScore[agent])  globalUpdate = weight * agent.update  totalWeight += weight  endif  globalUpdate = globalUpdate/totalWeight // Step 3: Evaluation and Updating  globalWeights = UpdateGlobalWeights(globalWeights, globalUpdate)  EvaluateModel(globalWeights)
```  
**Algorithm 3**Identifying malicious model updates using CONTRA
###### Abstract  
By leveraging amplitude differences between reflected and diffracted signals in Ground Penetrating Radar (GPR) data, multiple singular spectrum analysis (MSSA) is considered an attractive approach to separate diffraction, which has identified great potential in their detectability of small-scale geological structures. However, conventional MSSA encounters difficulties in pinpointing the singular value threshold that corresponds to reflection, diffraction, and noise within the singular spectrum, leading to a resolution loss of the extracted diffraction profile. To address this issue, this paper develops a new technique that incorporates multilevel wavelet transform (MWT) and MSSA to separate GPR diffraction. By first implementing the MWT on GPR data decompose, the strategy can obtain various approximate detailed coefficients of multiple transformation levels for the subsequent inverse MWT to construct the corresponding coefficient profile. The issue of coefficient profiles that depict reflections often contains residual diffractions is also addressed by performing multiple singular spectrum SVDs based on the Hankel matrix within the dominant frequency domain. Building upon this, the \(k\)-means clustering algorithm is introduced to perform MSSA for classifying singular values into \(k\) categories. The diffraction wavefield is rebuilt by combining these outcomes with the coefficient profiles that depict diffractions at various transformation levels. Numerical tests showcase that the biorthogonal wavelet basis function bior4.4 provides remarkably efficient GPR diffraction separation performance, and the number of clusters in the k-means clustering algorithm typically ranges from 9 to 15, accounting for the complexity of the wave components. Compared to plane wave deconstruction (PWD), the proposed MWT-MSSA approach reduces energy loss at the diffraction vertex, decreases residual diffraction energy within the reflection profile, and enhances computational efficiency by approximately 70-80% to facilitate the subsequent subtle imaging.  
Keywords:ground penetrating radar (GPR); multilevel wavelet transform (MWT); multiple singular spectrum analysis (MSSA); \(k\)-means clustering algorithm; diffraction separation +
Footnote †: journal: Applied sciences
## 3 Results and Discussion  
ML has been increasingly utilized for SM estimation through RS since 2010 using various approaches and algorithms (Figure 4). We collected 144 papers related to SM estimation based on ML and RS from 2010 to 2024. Over that period, publications with ML utilization increased with the most studies from Asia (68 studies, concentrated in China, Iran, and India), followed by North America (24 studies), Europe (21), Australia (5), Africa (3), and South America (1) (Supplementary Figure S1). Since 2019, there has been a growing emphasis on root zone SM (RZSM) estimation.  
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**Criterion** & **Exclusion Criteria** \\
**Number** & \\ \hline
1 & Publications not written in English \\
2 & Study outside the scope of research questions (not related to soil moisture estimation using remote sensing and machine learning) \\
3 & Publication is a book, e-book, poster, survey \\
4 & Published before 2010 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exclusion criteria for paper selection.  
Figure 3: Methodology details followed in the review process.