esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation into Failure Modes of Low-Yield-Point Steel Plate Shear Walls
###### Abstract  
Two specimens of low-yield-point steel plate shear walls used for the experimental investigation into different failure modes of the structure were respectively carried out by the horizontal and vertical loading tests in this paper. The results derived from the horizontal loading test for specimen 1 demonstrated that both the infill panel and the frame column were seriously damaged as the loading level increased with continuous and steady strain growth, in which maximum out-of-plane displacement and strain of the infill panel reached 31.8 mm and 11,735 \(\mu\epsilon\) respectively, whereas the strain of the frame column reached 13,117 \(\mu\epsilon\). Moreover, the structural bearing capacity decreased extremely slowly and the specimen still had a high strength when the controlled displacement of the specimen exceeded 3.28 times the yield displacement, which indicated that the ductility of the structure was excellent. The results of a vertical loading experiment for specimen 2 indicated that the maximum deformation of both the infill panel and frame column occurred in the middle-upper part of the members, with a maximum out-of-plane displacement of 95 mm and 70 mm, respectively. The dramatic out-of-plane bulging phenomenon along the width of the infill plate under the vertical load and the seriously torsional deformation of the frame column were observed.  
low-yield-point steel; bearing capacity; horizontal low cyclic test; vertical loading; failure modes; strain +
Footnote †: journal: Applied sciences
## 7 Results and Discussion
### Comparative Analysis of Client Selection Strategies
#### 7.3.1 Accuracy Performance over 300 Rounds  
The accuracy-only strategy achieved the highest performance in the initial training rounds, particularly on MNIST Figure 18 and EMNIST Figure 19, due to its tendency to consistently select clients with the best local model accuracy, typically smartphones. However, by ignoring battery status and privacy sensitivity, it overuses a small subset of devices, which leads to battery depletion and privacy exhaustion over time. As a result, some high-performing clients may become unavailable in later rounds, either due to simulated battery loss or privacy constraints. This causes the accuracy curve to flatten prematurely, especially in more demanding datasets like Fashion-MNIST Figure 20 and CIFAR-10 Figure 21, where generalization requires broader device participation. Random selection performed inconsistently, with modest accuracy in MNIST but weak convergence in EMNIST and CIFAR-10 due to the absence of any utility-driven prioritization. Battery-only and privacy-only strategies underperformed across all datasets, as they selected clients with minimal training cost or risk but often low contribution to learning progress. RBPS delivered competitive accuracy in simple datasets (e.g., 91% on MNIST; 87% on EMNIST) and outperformed all other methods on Fashion-MNIST (88%) and CIFAR-10 (80%), demonstrating its ability to sustain training quality by balancing accuracy potential with battery and privacy constraints throughout the learning process.  
Figure 19: Accuracy over 300 rounds (EMNIST).  
Figure 18: Accuracy over 300 rounds (MNIST).
###### Abstract  
Microwave ablation at 2.45 GHz is gaining popularity as an alternative therapy to hepatic resection with a higher overall survival rate than external beam radiation therapy and proton beam therapy. It also offers better long-term recurrence-free overall survival when compared with radiofrequency ablation. To improve the design and optimization of microwave ablation procedures, numerical models can provide crucial information. A three-dimensional model of the antenna and targeted tissue without homogeneity assumptions are the most realistic representation of the physical problem. Due to complexity and computational resources consumption, most of the existing numerical studies are based on using two-dimensional axisymmetric models to emulate actual three-dimensional cancers and surrounding tissue, which is often far from reality. The main goal of this study is to develop a fully three-dimensional model of a multislot microwave antenna immersed into liver tissue affected by early-stage hepatocellular carcinoma. The geometry of the tumor is taken from the 3D-IRCAD-01 liver tumors database. Simulations were performed involving the temperature dependence of the blood perfusion, dielectric and thermal properties of both healthy and tumoral liver tissues. The water content changes during the ablation process are also included. The optimal values of the input power and the ablation time are determined to ensure complete treatment of the tumor with minimal damage to the healthy tissue. It was found that a multislot antenna is designed to create predictable, large, spherical zones of the ablation that are not influenced by varying tissue environments. The obtained results may be useful for determining optimal conditions necessary for microwave ablation to be as effective as possible for treating early-stage hepatocellular carcinoma, with minimized invasiveness and collateral damages.  
b ARTICAD-01 2021 13 5784 1 [1][[https://www.mdpi.com/joumal/cancers](https://www.mdpi.com/joumal/cancers)]([https://www.mdpi.com/joumal/cancers](https://www.mdpi.com/joumal/cancers))  
[PERSON]  
1 Institute of Physics, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia; [EMAIL_ADDRESS]  
2 Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava,  
Ilkovicova 2, 84216 Bratislava, Slovakia; [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (L.S.); [EMAIL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (P.C.)
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 4 Tests and Discussion of Results
### Scheduling Solution Inspection
#### 4.1.3 Example 3  
The constraints that describe this last example are listed below, and are depicted in Figure 11:  
* Five-node network, where Node 0 connects the other nodes, but several nodes have direct connections to each other.
* Four streams, two of them made up of three frames; the other two have only one frame. The first two streams have periods of 2000 ms, while the other two have periods of 5000 ms, making the hyper-period 10,000 ms, as in the previous example.
* The stream distribution is as follows:
* Stream 0: Origin at Node 2 and destination at Node 3, passing through Node 0.
* Stream 1: origin at Node 3 and destination at Node 1.
* Stream 2: origin at Node 4 and destination at Node 2.
* Stream 3: origin at Node 0 and destination at Node 3.  
In this example, we have increased the number of frames and used a topology with a higher number of links to explore whether the solutions delivered by our solver meet the constraints for a TSN scheduling system. We can notice that in none of the links, at any time, the location of one frame overlaps with another, even considering the repetitions that the frames may have within the hyper-period.  
Figure 11: Third scheduler problem.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 2 Problem Description
### Motivation of TAID Algorithm  
At the receiver, the task of reconstructing the original OFDM symbol involves a linear combination of the three signal frames. However, it is essential to note that the additive noise becomes intertwined across all three frames during this process, leading to an amplification of the noise power. Furthermore, the utilization of three signal frames to represent a single original OFDM symbol necessitates additional channel bandwidth, exacerbating the presence of noise with increased power levels.  
Hence, this article introduces a novel tolerance-aided interference degradation algorithm to diminish the effective additive noise and facilitate the compensation of clipped signals during the demodulation process. The algorithm is designed to operate by establishing a decision tolerance, thereby enabling the examination of signals received within frames 1 and 2, as illustrated in Figure 2, to determine the necessity of incorporating signals from frame 3. Additionally, it is noteworthy that the signals within frames 1 and 2 can also undergo compensation while contending with the influence of additive noise.
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
###### Abstract  
This article describes a novel experimental study considering a multiband fibre-wireless system for constructing the transport network for fifth-generation (5G) networks. This study describes the development and testing of a 5G new radio (NR) multi-input multi-output (MIMO) hybrid fibre-wireless (FiWi) system for enhanced mobile broadband (eMBB) using digital pre-distortion (DPD). Analog radio over fibre (A-RoF) technology was used to create the optical fronthaul (OFH) that includes a 3 GHz supercell in a long-range scenario as well as a femtocell scenario using the 20 GHz band. As a proof of concept, a Mach Zehnder modulator with two independent radio frequency waveforms modifies a 1310 nm optical carrier using a distributed feedback laser across 10 km of conventional standard single-mode fibre. It may be inferred that a hybrid FiWi-based MIMO-enabled 5G NR system based on OFH could be a strong competitor for future mobile haul applications. Moreover, a convolutional neural network (CNN)-based DPD is used to improve the performance of the link. The error vector magnitude (EVM) performance for 5G NR bands is predicted to fulfil the Third Generation Partnership Project's (3 GPP) Release 17 standards.  
radio over fibre; digital pre-distortion; fibre-wireless system; convolutional neural network; error vector magnitude; power spectral density +
Footnote †: journal: _etlecom_
# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 4 Methodology
### Cluster Analysis Techniques  
In cluster analysis, many various algorithms are available that are characterized with different principles of operation and suitable for different scenarios. We test several popular methods using implementations prepared for Matlab. Finally, we have decided to use three algorithms providing the most promising results. Here, we recall three of the most suitable in our context.  
* K-means clustering is a method of vector quantification that is originally derived from signal processing, and it is famous approach for clustering in data mining [55; 56; 57; 58]. K-means clustering is used to decompose the n observations into k clusters whose observations belong to a cluster with its closest mean. According to the set of observations \(x_{1},x_{2},\...,x_{N}\) where each observation is a \(M\) dimension vector. The target of K-means clustering is to divide \(N\) observation to \(k<=N\) collection \(S=(S_{1},S_{2},\...,S_{k})\) such that the sum of the squares of the difference from the mean (i.e., variance) is minimized for each cluster.
* BIRCH (balanced iterative reducing and clustering using hierarchies) is one of the fastest clustering algorithms, introduced in refs. [59; 60; 61]. The main advantage of BIRCH is that it clusters incrementally and dynamically, attempting to produce the best quality given the time and memory constraints, with the requirement of only a single scan of the data set. However, it needs to specify the cluster count as an input variable. Additionally, BIRCH clustering is used in engineering applications. [PERSON] et al. [62] introduced automatic fault detection based on BIRCH.
* Gaussian mixture modeling (GMM) is one of the popular methods used for unbalanced data clustering. The GMM is a probabilistic model that is based on the assumption that \(M\)-dimensional data \(X\) are arranged as a number of spatially-distributed Gaussian distribution modes with unknown parameters \(\mu\) (list of \(M\)-dimensional means) and \(\Sigma\) (list of \(M\times M\) covariance matrices). The expectation-maximization (EM) algorithm is used to estimate the parameters of GMM, thus allowing to cluster the data [63; 64; 65; 66; 67; 68]. This method can be divided into two parts. At first, the expectation step (E-step) is used to estimate the probabilities for every point to be assigned to every cluster. Then the maximization step (M-step) is utilized to estimate the distributions based on the probabilities from E-step. Those two steps are iterated for a given number of iterations or until convergence.
## 3 The CPN Model
### Complexity Analysis: Execution Timing of the CPN Model  
In this subsection, we formally analyze the execution time of the proposed model. To compute the complexity, we simply consider that each queue system has \(m\) queues and there are at most \(m\) queue systems executing for a resource allocation problem. This means that the tokens are transferred among the model's places in parallel for every queue system. In total, we have \(m^{2}\) queues and due to parallelism (effectively, \(m\) queues are executed in parallel, thus tokens are moving in parallel  
Figure 5: Transition firing time example.  
within \(m\) cores). Thus, the time required to complete the scheduling is \(O(mN_{max})\), where \(N_{max}\) is the maximum number of jobs (or tokens) generated during the allocation process. During the model execution, there is a flow control policy. However, the solution for the system of Equation (4) also depends on \(m\). Since \(m\) is generally limited compared to the number of jobs that can be generated, the overall model is executed in \(O(N)\) time, that is, linear to the number of jobs generated.
# Research on the Decoupling of the Parallel Vehicle Tilting and Steering Mechanism
###### Abstract
The purpose of this present study is to (a) decouple the parallel tilting and steering mechanism of the vehicle and (b) make the wheels on both sides of the vehicle satisfy the ASC, simultaneously. The main works of this study are as follows:  
* A parallel mechanism is proposed. It consists of a spatial steering mechanism and a tilting mechanism in parallel. * A mathematical model of the parallel mechanism including the wheel alignment parameters has been established. This model is a kinematic model. The decoupling conditions of the parallel mechanism (DCPM) have been solved. A decoupling method is proposed that is based on the model. * The parameters of the parallel mechanism have been obtained by the use of the mathematical model. These parameters consider both the DCPM and the ASC. * A prototype has been produced according to the parameters for an experiment. It has been proven by the experiment that the method can reduce the influence of the vehicle's tilting on the steering angle of the wheels. The prototype is able to ensure that the steering angles of the wheels on both sides satisfy the ASC effectively.
## 3 Data and Methods
### Data Preprocessing  
The max-min method in the standardized method was used to process 48 datasets to prevent excessively large data dimensions, which can affect the prediction results. The max-min method can be expressed as Equation (3):  
\[x_{i}^{\prime}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}} \tag{3}\]  
Overall, 40 groups were randomly selected from the processed data and set as training data; the remaining 8 groups were used as prediction data. To verify the accuracy of the model, the root-mean-square error (\(RMSE\)) and average relative error (\(MRE\)) for the prediction data were calculated. The calculation formulas for \(RMSE\) and \(MRE\) are shown in Equation (4) and Equation (5), respectively:  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(p_{i}-o_{i}\right)^{2}}{n}} \tag{4}\]  
\[MRE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|o_{i}-p_{i}\right|}{\text{o}_{i}} \tag{5}\]  
Figure 1: Optimal hyperplane diagram [(23)].  
where \(O_{i}\) denotes the true value of cementing quality, \(p_{i}\) denotes the prediction value of the cementing quality, and \(n\) denotes the number of the predicted data group.
## 2 Review of Issue Contents
Lastly, the fault mode was classified using the VPMCD method. The experimental results demonstrated that the proposed method can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but also be used for fault diagnosis under variable operation conditions. The authors of [10] dealt with the condition monitoring of rotating machinery, in an effort to avoid disastrous failure and guarantee safe operation. Vibration-based fault diagnosis showed the most attractive properties for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) was investigated as an effective tool for FDRM. However, LZC only performs single-scale analysis, which is not suitable for extracting the fault features embedded in a vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results showed that the proposed HLZC method had the best diagnosing performance compared with LZC and multiscale Lempel-Ziv complexity methods. Last, but not least, the authors of [11] researched problems arising in the bearings and shafts of machines. Bearing preload significantly affects the running performance of a shaft-bearing system including the fatigue life, wear, and stiffness. Due to the mounting error, the bearing rings are often angularly misaligned. The effects of the combined bearing preload and angular misalignment on the fatigue life of ball bearings and a shaft-bearing system were analyzed in this paper. The contact force distribution of the angular contact ball bearings in the shaft-bearing system was investigated using the system model. The system model included the bearing model, whereas the shaft model was verified by comparing it with the manufacturer's manual and the results from other theoretical models, revealing a difference within 3% between the results from the present bearing model and the manufacturer manual. The global optimization method was used to solve the ball element displacements and friction coefficients, which improved the computation efficiency of the model. The fatigue life results showed that the system life at the optimal angular misalignment was more than 1.5 times that without angular misalignment at the low preload value, and this ratio decreased as the preload value increased.
# Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic--[PERSON]
## 2 Materials and Methods
### TwinMic Imaging Modes
#### 2.1.4 Compressive Sensing  
Originally introduced in the astronomy field, compressive sensing [68; 69] is an emerging method where image reconstruction is performed from a limited (ideally small) number of datasets without compromising the final quality. It is based on the assumption that the \"most useful\" datasets are smartly acquired, while the less significant ones can be easily extrapolated by a fast or small dataset. Inspired by this idea, we have recently developed in TwinMic a set of approaches [70; 71] that allow us to perform dynamic scans where it is possible to skip points (sparse) and perform acquisitions with variable parameters (i.e., acquisition time). Such an approach leads to a substantial reduction in the required time, besides reducing the size of stored datasets. This reduction in terms of time, coupled with other technological and scientific advances such as new detectors or new sources, can produce maps of very large areas (one or two orders of magnitude higher than the standard ones), which could not be acquired with the traditional ways. Briefly, we proposed three different methods for optimizing the XRF data acquisition, where only the most significant XRF data are collected, by: (i) applying a mask based on a previously acquired absorption image (and therefore based on the sample morphology); (ii) applying an absorption threshold, and therefore acquiring the XRF signal only on specific regions, which represent the real sample (and not the support or the embedding medium); (iii) by applying a threshold on the XRF signal and therefore acquiring a longer XRF spectrum in the regions where a specific element of interest is present in higher concentrations. Naturally, such methods can also be applied as a combination, allowing an even smarter acquisition strategy.  
Of course, these approaches can be extended to other kinds of microscopies or analytical techniques, as suggested or explored by other groups [72; 73; 74; 75].  
Compressive sensing is thus an imaging strategy that can represent a TwinMic imaging modality.
## 2 The FCS and WFCS Algorithms  
In this section, the FCS algorithm is reviewed and data points on the crisp boundary are discussed. Then we present the WFCS algorithm, demonstrate the formulas of the membership and feature weight and give the adjustment strategy of these formulas.  
\(X=\left\{x_{1},x_{2},...,x_{n}\right\}\) is a dataset in an s-dimensional Euclidean space \(\mathbf{R}^{s}\), and \(\overline{X}\) denotes the grand mean of \(X\).  
\[\mu_{y}=\frac{\Big{(}\|x_{j}-a_{i}\|^{2}-\eta_{i}\|a_{i}-\overline{X}\|^{2}\Big{)}^ {\frac{-1}{m-1}}}{\sum\limits_{t=1}^{C}\Big{(}\|x_{j}-a_{i}\|^{2}-\eta_{i}\|a_{ t}-\overline{X}\|^{2}\Big{)}^{\frac{-1}{m-1}}} \tag{6}\]  
According to Equations (5) and (6), dataset \(X\) can be partitioned into \(c\) clusters by iteratively updating cluster centers and membership value.  
The data point in the \(i\)th crisp kernel belongs to the \(i\)th cluster strictly, which is called hard clustering. However, if a data point falls on the crisp boundary (see Figure 1), membership value \(\mu_{y}\) will be infinite. Hence, according to Equation (6) the FCS algorithm fails.