esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 4 FEA Results
### Influence Factors of Load Capacity
The related values for factors \(L\) and \(f_{c}\) can be calculated in the same way. Range \(R\) is adopted to reflect the influence degree of the factors, which is the difference between the maximum and the minimum of the average value. According to Equations (9)-(11), if \(R\) is large, a change in the factor will cause an obvious change, so that the factor has a significant influence on the bearing capacity. \[R_{D}=\overline{T_{13}}-\overline{T_{11}} \tag{9}\]  
\[R_{L}=\overline{T_{22}}-\overline{T_{11}} \tag{10}\]  
\[R_{f_{c}}=\overline{T_{32}}-\overline{T_{13}} \tag{11}\]  
For the sake of convenience, the calculation values of the load capacity with three different levels of the factors are plotted in Figure 12. We can see from the curves that the stud diameter \(D\) is the most important factor, while the shear capacity increases with the increase in \(D\), the lateral space \(L\) and concrete strength \(f_{c}\) have a slight influence on the shear capacity, while the shear capacity does not always increase with the increase in \(L\) and \(f_{c}\). During the three levels of the factors in the modeling test, the maximum shear capacity appears when \(D\) = 19 mm, \(L\) = 40 mm, and \(f_{c}\) = 60 MPa. According to the outcomes of the numerical simulation, it is found that the larger the diameter of the stud, the higher the bearing capacity. The optimal value of the lateral space of the vertical steel plate is \(L\) = 40 mm, and for the strength of the concrete, C60 is the optimal value. When using it, it can be selected based on the economic cost of the project and the layout position.
# Research on Inertial Force Attenuation Structure and Semi-Active Control of Regenerative Suspension
## 2 The Structure and Working Principle of Energy-Regenerative Shock Absorbers
In the second frequency range, compared to the suspension without equivalent inertial mass, the equivalent inertial mass causes a significant increase in the acceleration of the sprung mass, which greatly affects the comfort of vehicle driving. From Figure 4b, it can be seen that when the excitation frequency \(f\) is less than the frequency \(f_{sy}\), the amplitude of the relative dynamic load decreases with the increase of the equivalent inertial mass ratio. When the excitation frequency \(f\) is greater than the frequency \(f_{sy}\), the larger the equivalent inertial mass ratio, the greater the relative dynamic load. In the second frequency range, compared to the suspension without equivalent inertial mass, the equivalent inertial mass causes a significant increase in relative dynamic load, which greatly affects the vehicle's handling stability. From Figure 4c, it can be seen that when the excitation frequency \(f\) is less than the frequency, the amplitude of the suspension dynamic deflection decreases with the increase of the equivalent inertial mass ratio. When the excitation frequency \(f\) is greater than the frequency but less than the frequency \(f_{uy}\), the equivalent inertial mass causes a significant increase in relative dynamic deflection. When the excitation frequency \(f\) is greater than the frequency \(f_{uy}\), the equivalent inertial mass reduces the relative dynamic deflection. Figure 4: (**a**) Characteristics of sprung mass acceleration; (**b**) characteristics of relative dynamic load; (**c**) characteristics of suspension dynamic deflection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\xi\) & 0.25 & \(\mu\) & 0.14 \\ \(\varepsilon\) & 0.043 & \(\gamma\) & 9 \\ \(\beta\) & 1.35 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of simulation. From the above analysis, it can be seen that the main problem of energy-regenerative suspension is how to reduce the adverse effects of equivalent inertial mass above the frequency \(f_{sy}\) band, and improve the driving comfort and handling stability of the vehicle.
# Urban Biorefinery Demonstration: Production of Polyhydroxyalkanoates from a Municipal Solid Waste
## 4 Discussion
### Extraction of scl-PHA from the Produced Biomass at Demo Plant  
The scl-PHA extraction yield obtained was 92.78%, demonstrating the technical feasibility of the extraction process developed at demonstrative scale. As observed in Table 6, successive solvent extractions increased the total amount of scl-PHA recovered from the biomass; however, the scl-PHA content in each individual extraction progressively decreased. Consequently, while increasing the number of extraction steps may enhance the overall yield, it could also lead to process inefficiencies by increasing solvent and energy consumption.  
A major limitation of the extraction process is the presence of impurities in the final scl-PHA product. While pure PHA is naturally off-white, the extracted material exhibited a yellow-brownish appearance, indicating contamination (see Figure 9). These impurities may originate from the raw feedstock (OFMSW-Derived Contaminants), bacterial debris [77], or residual solvents. Given that the OFMSW serves as the primary carbon source, residual organic compounds, pigments, and heavy metals could also remain in the polymer. The presence of proteins, endotoxins, and lipids generated during the microbial fermentation process may not have been completely removed during the ethanol washing steps. While DMC was selected as an alternative to hazardous solvents such as chloroform and dichloromethane, incomplete solvent removal could also impact negatively on polymer purity. Additionally, the need for multiple extraction steps highlights a trade-off between extraction yield and efficiency. Although additional solvent cycles improve overall polymer recovery, they increase the operational costs of the final bio-product and may not be economically viable at an industrial scale. The energy-intensive nature of solvent evaporation and recovery further raises concerns about the scalability of the process.  
To sum up, this study demonstrates that scl-PHA can be successfully extracted at demonstrative scale using dimethyl carbonate (DMC) as a green solvent. However, impurity content, solvent consumption, and process scalability remain key challenges to further overtake. Future research on the improvement of solid-liquid separation steps,solvent recovery, and polymer purification strategies will be essential to enhance both the technical and economic viability of scl-PHA production from OFMSW on industrial scale. Additionally, evaluating biodegradable solvents could improve sustainability and cost-effectiveness. Finally, exploring alternative extraction methods such as mechanical disruption, aqueous phase extraction, or biopolymer affinity purification may offer more efficient and eco-friendly solutions [51].
###### Abstract
The first part of these features assesses the contribution of the tested muscles to controlling: (1) a displacement of the upper limb center of mass (COM) (describing the relation between dynamics of the limb and muscle activity); (2) a displacement of the upper-limb wrist joint (WJ) (describing the relation between the kinematics of the limb and muscle activity). The results of the contributions are assessed as: (a) distributions of the synergistic/antagonistic participation of the tested muscles at the time of the motion; (b) the accumulated synergistic/antagonistic participation of the tested muscles in the tested motion. The second part of the features defines whether: (1) the passive manipulator changes the muscle activity in the functional movement and the kinematics of this functional movement; (2) the muscle activities of the four tested muscles depend on an external weight which is moved in a horizontal plane (horizontal motion) at the level of the waist. The motivation of this study was to answer the following questions: (1) How are the muscles of DMD patients activated in comparison to those of healthy subjects? (2) What movement strategies do DMD patients use to perform ADL functional tasks? (3) How does an external passive device influence the kinematics of the movements of DMD patients versus healthy ones in ADL functional tasks? The purpose of this study was: (1) to propose a new approach to assess the quality of motion in ADL functional tasks under clinical conditions by revealing whether muscle activity is correlated more to the displacement of the upper limb COM or to the displacement of the upper-limb WJ; (2) to investigate whether different levels of external mass influence the performance of the dominant right upper limbs of DMD patients in natural conditions and using a passive lightweight manipulator that is a low-friction (non-resistant) device. In this study, the upper limb performance was examined by recording horizontal movementsat the level of the waist and the muscle activity of four chosen superficial muscles and then comparing these results to those of a reference subject (a healthy adolescent). The results of the assessment of the quality of motion are presented in two main parts. The first part involves the results of the application of a piecewise linear multi-regression to define the contribution of the tested muscles to controlling the displacement of the upper limb COM and the displacement of the upper-limb WJ. The second part involves the results of the chosen tests of significance aimed at identifying whether the use of a passive manipulator evoked changes in the muscle activity and changes in the kinematics of functional movements.
## 6 ML and the Security of Blockchain
### Smart Contract Security  
Smart contracts are self-executing agreements whose terms are directly embedded in code. Despite their power in automating transactions, they are vulnerable to a range of security issues, including re-entrancy attacks, integer overflows, and faulty logic. ML techniques--particularly deep learning and natural language processing (NLP)--have increasingly been used to detect such vulnerabilities before contracts are deployed.  
For instance, deep learning models can be trained on large datasets of known smart contract vulnerabilities to classify and identify problematic code. NLP techniques, on the other hand, analyze the semantic structure of the contract source code to detect patterns indicative of security flaws. As reported in [111], bidirectional encoder representations from transformers (BERT) is leveraged to capture semantic features from smart contract opcodes. When combined with a bidirectional LSTM (BiLSTM), the model effectively handles long-term dependencies in the contract code, while an attention mechanism prioritizes critical segments. These innovations enable blockchain developers to automate vulnerability detection and reduce the risk of deploying insecure contracts.
## 6 Application: Identification of the PFS
In contrast, point C displays an opposite behavior: it shows the ability to withstand stress without developing critical deformations, making it less prone to imminent failure. Being located in an area with lower stress concentrations along the PFS, point C exhibits a more stable and controlled response, generating limited deformations even with increasing deviatoric stress. Additionally, a \"M-stage vs. excess pore pressure\" graph was created to analyze how pore pressure evolves in the soil during the progression of loading stages, as shown in Figure 22.  
Interpretation of this graph reveals that points A and B in both constitutive models exhibit dilative soil behavior. This is reflected in the development of negative pore pressures under shear stresses, which increase effective stress and reduce the likelihood of failure. For point C, the initial increase in pore pressure, a peak, and subsequent dissipation toward values near zero reflect a soil that initially exhibits contractive behavior but stabilizes through the dissipation of excess pressure. In Figure 23, the deviatoric stress vs. mean effective stress graph can be observed. The curves from the HSS constitutive model display a steeper slope compared to those of the NS model, indicating a stiffer soil response. At point A, located near the base, the curves suggest that the soil experiences higher resistance to deviatoric stress, which implies that the base of the tailings deposit is less susceptible to static liquefaction due to higher confining stress. Conversely, point C suggests a higher likelihood of static liquefaction due to lower confinement. Figure 20: Control points along the failure surface. (**a**) NS constitutive model. (**b**) HSS constitutive model. The HSS model predicts a stiffer and less liquefiable behavior, making it more suitable for deeper layers. The NS model, in contrast, is better suited for evaluating liquefaction near the surface, where confining stresses are lower and dilative behavior is more pronounced. Figure 21: Deviatoric stress (q) vs. shear strain (\(\gamma_{\rm xy}\)) graph. Figure 23: Deviatoric stress (q) vs. mean effective stress (p’) graph. Figure 22: \(\Sigma\)Mstage vs. excess pore pressure (\(\Delta\)u) graph.
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 2 Method  
We evaluated the potential of spatial data processing techniques in determining the yield gain of corn crops after applying _A. brasilense_. At first, in a high-sample-density scenario, we explored the possibility that generating yield gain was evident using an interpolator based on Kriging. Then, we presented a solution to estimate the yield gain from spectral prediction models based on machine learning in conditions of lower sample density.  
Notably, the two proposals were developed for use in different real-life agricultural production conditions. In general, we assume that the spatialization of the locations of yield gain can be helpful in decision making for management in specific crop locations based on regions of higher and lower yield gain. Specifically, the Kriging data reveal that the simulation of the yield gain surfaces can be applied in cultures with high degrees of mechanization and density of yield data because the technique allows surface simulation with a high degree of detailing from the interpolation of a large sample set of variables. With the image-based yield prediction models (spectral models), the objective is to define a methodology for mapping the areas of gain and loss in a totally remote way that applies to the majority of crop areas, since the models are built with few georeferenced yield samples. This condition represents a large portion of the crops in the southern hemisphere, where information about crop yield is often limited to the average yield of the plot and stratified samples of the area.  
We defined the methodology of this research considering the following steps: (1) acquisition of yield data; (2) generation of yield prediction models from Kriging and spectral models based on orbital-image (PlanetScope) and machine-learning algorithms, considering two treatments (control and application of _A. brasilense_); and (3) generation of yield gain surfaces from differences between the two treatments.
# Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
## 2 Methods  
### Literature Selection Standard  
This review aims to provide an overview of policy learning methods for learning strategies from biological behavior. The keywords \"behavioral strategy/strategic learning\" cover the concept of policy learning while emphasizing the behavioral domain and accurately reflect the core focus of this review. Therefore, using the main keywords \"behavior strategy/policy learning\", we searched three databases: Web of Science, IEEE Xplore, and Scopus. The results were screened for relevant computational methods in the most recent decade to ensure that the investigated techniques were updated. We systematically screened the relevant literature, and the review process was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2022 Statement [7], as shown in Figure 1.  
The literature search was completed in January 2023, and 1855 records were obtained from all databases. The search records were exported to the reference management software, Zotero (version 6.0.18), for integration. We excluded 34 non-English literature items and 558 duplicate items automatically detected by Zotero. The preliminary literature identification resulted in 1263 records.  
Figure 1: Literature screening process via the PRISMA statement.  
In the literature screening phase, 872 results were removed based on titles and abstracts. The main topics of these studies were RL, action recognition, and algorithm optimization, which are not discussed in this review. Among these, 12 records could not be retrieved and were therefore excluded. In the full-text eligibility assessment phase, we preferred strategy learning approaches based on behavioral data-driven methods rather than manually designed reward functions, neural network-based predictions, or model predictive control. Additionally, this review focused on learning single-agent behavioral strategies and did not discuss multi-agent interactive behavioral strategies. In addition, the topics in this review did not engage in active learning, meta-learning, or lifelong learning; thus, 302 records were excluded. In total, 77 articles were used for the systematic evaluation. The overall framework of this review is shown in Table 1. In addition to the literature under review, some of the early classic works are also mentioned in the text.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 4 DDoS Detection Model
### Convolutional Neural Network  
A convolutional neural network is a form of neural network that is commonly used for image identification and classification. CNNs have a variety of applications, including object detection, image processing, computer vision, and face recognition. Images are used to provide input to convolutional neural networks. Convolutional neural networks, as opposed to manually developing features, are used to automatically learn a hierarchy of features that may subsequently be used for categorization. Algorithms are able to adjust to the changing nature of network traffic in SDN, making them appropriate for environments where attack patterns might emerge. They often require an enormous amount of labeled data to achieve excellent performance, which might not always be available in the case of detecting DDoS attacks [25].
###### Abstract  
Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to certain suboptimality from the interaction among different pieces. In other cases, links between different parts may show up in the running time and/or network communications cost, hence the desire to have small cut size. We study a distributed balanced-partitioning problem where the goal is to partition the vertices of a given graph into \(k\) pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks such as MapReduce. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the _linear embedding_ order. We examine various ways to find the first embedding, for example, via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, and minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, for example, a label-propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: For instance, compared to the label-propagation algorithm, we report an improvement of 15-25% in the cut value. We also observe that our algorithms admit scalable distributed implementation for any number of partitions. Finally, we explain three applications of this work at Google: (1) Balanced partitioning is used to route multi-term queries to different replicas in Google Search backend in a way that reduces the cache miss rates by \(\approx\)0.5%, which leads to a double-digit gain in throughput of production clusters. (2) Applied to the Google Maps Driving Directions, balanced partitioning minimizes the number of cross-_shard_ queries with the goal of saving in CPU usage. This system achieves load balancing by dividing the _world graph_ into several \"shards\". Live experiments demonstrate an \(\approx\)40% drop in the number of cross-shard queries when compared to a standard geography-based method. (3) In a job scheduling problem for our data centers, we use balanced partitioning to evenly distribute the work while minimizing the amount of communication across geographically distant servers. In fact, the hierarchical nature of our solution goes well with the layering of data center servers, where certain machines are closer to each other and have faster links to one another.  
cut minimization; embedding to line; imbalance; local improvement; MapReduce; maps; partitioning; social networks +
Footnote †: journal:  
_Algorithms_
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 2 Related Work
### Mobile Agent-Based Routing Protocols  
In Reference [15], the authors discuss the data gathering problem in order to maximize the volume of collected information in networks with self-charging sensors. By means of convex optimization, they present a distributed data collecting schema to approximate the optimal solution. In Reference [16], the authors adopt two mobile agents to gather information collaboratively. The moving trajectory of mobile agents is characterized by a Fermat spiral route, and it can be predicted according to the constant velocity and the direction of the mobile agents. The sojourn times of the clusters are different for further energy balancing.