esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Design of a Novel Vibration Energy Absorbing Mechanism for Cables
## 4 Numerical Simulation
### Hydraulic Damper  
Under normal conditions, the stay cable vibration has a small amplitude. However, under special environmental impacts, such as earthquakes and typhoons, the amplitude of the stay cable vibration can become large. The VEAM incorporates hydraulic viscous dampers in its design to dampen such large amplitude vibrations by exploiting their high damping performance. This section explores the effects of damping and spring stiffness on the performance of this damper module. In this section, the excitation amplitude \(\mathrm{Y_{c}}\) is set as 0.005 m.  
In practical applications, the damping ratio of the hydraulic damper used in VEAM can be adjusted by changing the size of the orifice in the flow path. To make performance comparisons, we set the damping ratios of the hydraulic cylinders at 0.2, 0.6, and 0.8. Figure 13 shows the simulated transmissibility results of the VEAM sub-system. It can be seen that the higher the damping ratio of the hydraulic cylinder the lower the peak value of the transmissibility. However, although the curve with the lowest damping ratio of 0.2 has a higher transmissibility in the low-frequency region (region of amplification) within a characteristic frequency, the frequency region after this frequency, which is called the \"isolation region\", shows better vibration isolation performance.  
Figure 14 shows a comparison of the simulated transmissibility with varying values of \(\mathrm{k_{s}}\). Similar to Figure 9, the frequency domain of the peak wave of transmissibility curve becomes wider as the  
Figure 12: Transmissibility of the non-linear stiffness isolator module of VEAM with varying values of \(\zeta\).  
Figure 13: Transmissibility of the hydraulic viscous damper module of VEAM with varying values of \(\zeta\).  
value of k\({}_{\text{s}}\) increases, and the maximum transmissibility occurs at a higher frequency. However, the difference between the two figures is that the peak value of the transmissibility is not affected by the change in the value of k\({}_{\text{s}}\). This is because, when the hydraulic shock absorber is in operation, the magnetic spring stops working, and the equivalent stiffness of the entire system no longer changes due to the nonlinear stiffness. Thus, for the damping module, the spring with a lower k\({}_{\text{s}}\) value shows better isolation performance in the high frequency region.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 1 Introduction
(4) Voxels can be systematically updated when new sensing data becomes available, supporting real-time tracking and productivity assessment at an operational level. Despite these advantages, the practical implementation of voxel-based models for earthwork digital twins, particularly their efficient creation, visualization, updating, and management in a constantly changing environment, remains a significant research challenge [4; 26]. This article proposes a voxel-based digital twin framework for earthwork construction that addresses the critical gaps. The framework is designed to integrate the earthwork information (including terrain, geotechnical, design, and construction data) and uses voxels for the as-planned and as-built modeling, enabling volumetric representation and allowing for real-time simulation and progress tracking. The proposed framework has been validated through real-world case studies, demonstrating the system's effectiveness in providing a high-fidelity, operationally insightful digital twin for earthwork. Moreover, the scope of this study is primarily focused on the virtual twin creation for earthwork, the development of graphical simulations that facilitate model-equipment interaction, and the monitoringof progress through the dynamic integration of construction data from the physical twin to the digital twin. The primary objective of the proposed study is to achieve a voxel-based digital twin framework that overcomes the limitations of the existing representation schemes. The particular objectives are as follows: (1) to integrate terrain, geotechnical, and construction data, with an update function, into a unified voxel-based environment; (2) to represent heterogeneous earthwork semantics through attribute-rich voxels; (3) to enable volumetric modeling of as-planned and as-built earthwork conditions; (4) to facilitate earthwork model-equipment interaction and progress monitoring with semantic tracking during construction. By achieving these particular goals, the proposed system bridges the gap between static modeling practices and dynamic earthwork management needs, contributing to improved automation, visualization, and decision-making in a complex construction environment.
## 3 Effect of Coil Width during Forming
### Width Effect on Deformation  
Figures 12 and 13 show the plate deformations caused by line heating with coils of various sizes. In the diagrams, \(\delta_{B}\) represents the maximum vertical deflection of the \(S_{B1}\)-\(S_{B1}\) section in Figure 5, and \(\delta_{L}\) represents the maximum vertical deflection of the \(S_{L1}\)-\(S_{L1}\) section in Figure 5. Figures 12 and 13 show that when the coil travel speed and highest temperature are fixed, within a large coil width variation range, the plate's maximum transversal and longitudinal deflections increase with the coil dimension. Thus, the plate deformation increases with the coil dimension.  
Figure 11: Comparison of plate longitudinal bending deformation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Coil Speed**} & \multicolumn{3}{c}{**Highest Temperature**} \\ \hline \multicolumn{2}{c}{10 mm/s} & \multicolumn{3}{c}{700 \({}^{\circ}\)C} \\ \hline \multicolumn{5}{c}{Calculation Schemes} \\ \hline Case 1 & Case 2 & Case 3 & Case 4 & Case 5 \\ \(L_{C}/B=0.34\) & \(L_{C}/B=0.28\) & \(L_{C}/B=0.22\) & \(L_{C}/B=0.16\) & \(L_{C}/B=0.10\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation schemes.  
For plates with different thicknesses, the calculation results show similar patterns. The diagrams also show that when the coil dimension increases to a certain level, the increasing trend of transversal deflection gradually slows. For longitudinal deflection, when \(L_{\text{C}}\)/\(B\) > 0.3 and as the coil dimension continuously increases, the bending deflection demonstrates a declining trend. This occurs because for the transversal deflection, the distance between the heating surface of the coil and the upper surface of the deformed plate increases as the coil width increases. Hence, although the temperature at the heating line of the plate is fixed in the calculation, the high-temperature effective range decreases as this distance increases. Therefore, the deflection increase rate slows. For longitudinal deflection, as shown in Figure 4, the distance from section \(T_{L}\) to the \(B\)-direction section centroid decreases, which reduces the bending moment \(M_{L}\) and thus reduces the deflection in the \(L\) direction. The result shows that when coil size increases to a certain level, it is possible that heating gradually causes changes in the local characteristics, which gradually weakens the local deformation characteristics of the plate.  
The calculated effects of the coil width on the plate curvature are shown in Figure 14. The diagrams show that as coil size increases, the transversal maximum curvatures and curvature ranges increase accordingly.  
Figure 12: Effect of coil width on the transversal bending deflection.  
Figure 13: Effect of coil width on the longitudinal bending deflection.
# Sleep Apnea Classification Using the Mean Euler-Poincare Characteristic and AI Techniques
## 3 Data Used  
The Apnea-ECG database [42] was used in this study. It contains 70 records, divided into two groups: a training set of 35 records, including 20 severe cases, 5 mild cases, and 10 non-apnea cases, and a validation set consisting of the remaining 35 records. All ECG signals in the dataset are annotated with QRS complexes. The data are derived from a population aged between 27 and 60 years, with all records digitized at 100 samples per second. The length of the records ranges from 7 to 10 h.
# Towards Hyper-Dimensional Variography Using the Product-Sum Covariance Model
## 2 Theory
### Original Product-Sum Model and Modeling Procedure  
The product-sum variogram (covariance) model [22; 35; 36] was initially developed to model spatio-temporal covariance structures. [PERSON] et al. [3] provides an exhaustive definition of the model and provides the reader with several examples. The following class of valid product-sum covariance models was introduced in [PERSON] et al. [35], and further developed in [PERSON] et al. [22]:  
\[C_{s,t}(h_{s},h_{t})=a_{1}C_{s}(h_{s})C_{t}(h_{t})+a_{2}C_{s}(h_{s})+a_{3}C_{t }(h_{t}) \tag{2}\]  
where \(C_{s}\) and \(C_{t}\) are valid spatial and temporal covariance models, respectively. [PERSON] et al. [22] proved that for positive definiteness, it is sufficient that a\({}_{1}>0\), a\({}_{2}\geq 0\) and a\({}_{3}\geq 0\).  
The model in Equation (2) corresponds to the spatio-temporal variogram shown in Equation (3). In the original procedure, [PERSON] et al. [22] estimated separate V\({}^{(1)}\) spatial (\(h_{s}=0\)) and temporal (\(h_{t}=0\)) variograms using the data, and then combined these models to obtain the final spatio-temporal variogram model:  
\[\gamma_{s,t}\ (h_{s},h_{t})=\gamma_{s,t}\ (h_{s},0)+\gamma_{s,t}\ (0,h_{t}) \ \text{-}\ k\gamma_{s,t}\ (h_{s},0)\gamma_{s,t}\ (0,h_{t}) \tag{3}\]  
where \(\gamma_{s,t}(h_{s},0)\) and \(\gamma_{s,t}(0,h_{t})\) are spatio-temporal variograms for \(h_{s}=0\) and \(h_{t}=0\), respectively.  
Parameter \(k\) is estimated from the data, which makes the model easy to apply:  
\[k\ =\ \frac{k_{s}C_{s}(0)+k_{t}C_{t}(0)-C_{s,t}(0,0)}{k_{s}C_{s}(0)k_{t}C_{t}(0)} \tag{4}\]  
where \(k_{s}C_{s}(0)\) and \(k_{t}C_{t}(0)\) are spatial and temporal sills (variances) obtained in modeling of separate spatial and temporal variograms (both V\({}^{(1)}\)). The only condition \(k\) has to fulfill to create an admissible covariance model is:  
\[0<k\leq\frac{1}{max\{sill(\gamma_{s,t}(h_{s},0));\ sill(\gamma_{s,t}(0,h_{t}))\}} \tag{5}\]  
The original modeling procedure [22] assumed separate modeling of the spatial and temporal covariance (variograms) and their later unification into a spatio-temporal product-sum model. Apart from the apparent simplicity, such an approach posed one problem. Namely, in cases where sparse data are located on an irregular grid, there are often insufficient data to model separate variograms, thus the authors suggested using a tolerance along each dimension, or, in other words, an arbitrarily predetermined maximal allowed distance between data points in order to consider them collocated or coincident. However, if the tolerance is large, it degrades the quality of the V\({}^{(1)}\) models, and the existence of tolerance per se makes the procedure somewhat subjective. In what follows, we present a new approach that does not require defining tolerances.
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 3 System Model
### Network Model  
The network model used in this paper assumes that all sensor nodes are distributed in region \(\Omega\) randomly and independently, and each node has a unique ID with radio range \(r\). In addition, all nodes adopt asynchronous working-sleeping cycle strategy for sensing and communication, and the working time \(\alpha\) and the sleeping time \(\gamma\) for each node can be anisochronous. Therefore, sensor nodes can communicate with each other only when they are in their working statuses. Moreover, this network supports multiple mobile sinks to collect data and each mobile sink has a unique sink ID (SID) to distinguish from each other, however, we only use single mobile sink to describe the design principle in this work for simplicity. The mobile sink can move randomly and collect data actively anywhere in network at any time, it will always be in the working status for having sufficient energy and has two adjustable radio range \(R_{l}\) and \(R_{s}\), of which \(R_{l}\) is the long radio range that is fixed in each data collection period but adjustable in different periods used to broadcast the tag message to launch a data collection task, and \(R_{s}\) is the short radio range that is always equal to \(r\) used to transfer data. The symbols and parameters used in network are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Description** \\ \hline \(r\) & Radio range for sensor nodes \\ \(R_{l}\) & Long radio range for the mobile sink \\ \(R_{s}\) & Short radio range for the mobile sink \\ \(v_{s}\) & Source node \\ \(v_{\sin k}\) & Mobile sink \\ N & Number of nodes in a data collection scope \\ \(\emptyset\) & Empty set \\ \hline \hline \end{tabular}
\end{table}
Table 1: The symbols and parameters.  
Figure 2 depicts the asynchronous working-sleeping cycle strategy for sensor nodes. Three given nodes \(v_{1}\), \(v_{2}\), \(v_{3}\) are within the radio range of each other, but their operating statuses are asynchronous and their working time and sleeping time are anisochronous. Therefore, they can communicate with each other only when they are all in their working status, such as [t\({}_{1}\),t\({}_{2}\)] or [t\({}_{3}\),t\({}_{4}\)].
# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
###### Abstract  
Anorexia is a mental disorder that involves serious abnormalities in nutritional intake behavior. This behavior leads to significant weight loss, which can lead to severe malnutrition. Specifically, eating disorders exhibit the highest mortality rate of any mental illness. Early identification of anorexia, along with appropriate treatment, improves the speed of recovery in patients. Presently there is a strong and consistent association between social media use and eating concerns. Natural Language Processing, a branch of artificial intelligence, has the potential to contribute towards early anorexia detection in textual data. Currently, there is still a long way to go in the identification of anorexia on social media due to the low number of texts available and in fact, most of these are focused on the treatment of English texts. The main contribution of this paper is the application of transfer learning techniques using Transformer-based models for detecting anorexia in tweets written in Spanish. In particular, we compare the performance between already available multilingual and monolingual models, and we conduct an error analysis to understand the capabilities of these models for Spanish.  
anorexia detection; transfer learning; BERT; text classification; Natural Language Processing Article  
How Successful Is Transfer Learning for Detecting Anorexia on Social Media?  
[PERSON], [PERSON].  
2. We employ state-of-the-art systems based on transfer learning - including Transformer-based models such as BERT and XLM (see Section 4.2). To the best of our knowledge, this type of method has not yet been applied to detecting anorexia in informal texts written in Spanish.
3. We compare the results obtained with deep learning systems - including LSTMs and CNNs with Transformer-based models to observe the behavior of this type of system in tweets written in Spanish (see Section 5).
4. We compare the monolingual Transformer-based model (BETO) with two multilingual ones (M-BERT and XLM) to explore whether BETO trained on Spanish texts outperform multilingual models in the Spanish anorexia task.
5. We study the behavior of the algorithms explored in Spanish. Most of the studies for the detection of anorexia focused on English. However, anorexia detection is a global concern involving all languages. In particular, Spanish is the second most spoken language in the world and the third most used in social networks, which shows the need to develop algorithms capable of detecting psychological disorders in social media.
6. We perform an error analysis to identify the weaknesses and capabilities of the systems.  
The rest of the paper is structured as follows: In Section 2 some previous related studies are described. The data we used to evaluate our experiments are described in Section 3. The experimental methodology is laid out in Section 4. The evaluation of the results is presented in Section 5. Finally, the analysis of errors is conducted in Section 6, and conclusions are presented in Section 7.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
###### Abstract  
Over the past decade, the development of computer-aided detection tools for medical image analysis has seen significant advancements. However, tasks such as the automatic differentiation of tissues or regions in medical images remain challenging. Magnetic resonance imaging (MRI) has proven valuable for early diagnosis, particularly in conditions like prostate cancer, yet it often struggles to produce high-resolution images with clearly defined boundaries. In this article, we propose a novel segmentation approach based on minimum cross-entropy thresholding using the equilibrium optimizer (MCE-EO) to enhance the visual differentiation of tissues in prostate MRI scans. To validate our method, we conducted two experiments. The first evaluated the overall performance of MCE-EO using standard grayscale benchmark images, while the second focused on a set of transaxial-cut prostate MRI scans. MCE-EO's performance was compared against six stochastic optimization techniques. Statistical analysis of the results demonstrates that MCE-EO offers superior performance for prostate MRI segmentation, providing a more effective tool for distinguishing between various tissue types.  
multilevel thresholding; minimum cross-entropy; magnetic resonance images +
Footnote †: journal: Medical Image Analysis
## 2 Materials and Methods  
Field trials were carried out from March 2014 to September 2014 (220 days) in 22 apiaries distributed in the Viterbo province (Central Italy), in an area of approximately 2700 km\({}^{2}\) (Figure 1).  
_L. plantarum_, stable stabulogen autochthonous strain (LMG P-21806), isolated from healthy bees and brood in northern Italy (Piacenza), was applied in water solution trickled on the bees. Then, 20 g containing 2 \(\times\) 10\({}^{11}\) CFU of _L. plantarum_ were mixed in one litve of water to obtain a suspension of 2 \(\times\) 10\({}^{8}\) CFU/mL. The amount of solution administered per colony (dosage) was of 5 mL for each intercomb space covered by bees. Trials involved 979 colonies, kept in 10 frame Dadant-Blatt hives, free of any other symptomatic disease at the beginning of the trial. These colonies were randomly divided into two groups (Table 1):  
* _L. plantarum_ group, consisted of 488 colonies treated once a week, for a total of 4 administrations.
* Control group, consisted of 491 colonies that received only water in the amount of 5 mL per hive, dropped onto the inter comb space covered by bees, for a total of 4 administrations.  
All colonies were carefully checked at least once a month by visual inspection for the presence of clinical symptoms of disease. The on-field diagnosis was performed by the Public Veterinary Services and the confirmation was provided by the official laboratory Istituto Zooprofilatticeo Sperimentale del Lazio e della Toscana (IZSLT) following the methods reported in the OIE Manual of diagnostic tests and vaccines for terrestrial animals. Suspicious larval samples collected were cultured for the isolation and characterization of _P. larvae_ or _M. plutoniumis_ isolates, subsequently processed by RT-PCR [59; 60].  
All hives that resulted positive to EFB or AFB were recorded and immediately destroyed by the Public Veterinary Services.  
Kaplan-Meier estimator of the survival function, followed by Log-Rank test was used to evaluate the efficacy of _L. plantarum_ administration in preventing disease outbreaks in tested colonies. Data were processed by means of statistical software XLSTAT(tm) (Addinsoft, Paris, France, 2020).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Apiary ID** & \
\begin{tabular}{c} **Number of Hives Belonging** \\ **to L.**_plantarum_ **Group** \\ \end{tabular} &
\begin{tabular}{c} **Number of Hives Belonging** \\ **to Control Group** \\ \end{tabular} \\ \hline  
Total & 488 & 491 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of colonies randomly assigned to the treatment and control groups in the 22 apiaries.
# Dynamic Functional Principal Components for Testing Causality
## 2 Linear Causality with Two Functional Time Series
### An Example  
Let \(\mathbb{X}_{t}=(Y_{t},X_{t})^{\prime}\) be an autoregressive process valued at \(\mathcal{H}:=L^{2}[0,1]\times L^{2}[0,1]\) associated with \((\rho,\varepsilon)\), where \(\rho\) is a linear operator of \(\mathcal{H}\) and \(\varepsilon\) is a \(\mathcal{H}\)-strong white noise. For more information on the definition of autoregressive process valued in separable Hilbert space, see [3]. We have  
\[\mathbb{X}_{t}=\rho(\mathbb{X}_{t-1})+\varepsilon_{t}. \tag{2}\]  
We can rewrite the model as  
\[\left\{\begin{array}{l}Y_{t}=\rho_{11}(Y_{t-1})+\rho_{12}(X_{t-1})+ \varepsilon_{1t},\\ X_{t}=\rho_{21}(Y_{t-1})+\rho_{22}(X_{t-1})+\varepsilon_{2t},\end{array}\right.\]  
with \(\rho_{11}:=\left(\rho\upharpoonright_{L^{2}[0,1]\times 0}\right)^{(1)}\), \(\rho_{12}:=\left(\rho\upharpoonright_{0\times L^{2}[0,1]}\right)^{(1)}\) and the same idea for \(\rho_{21}\) and \(\rho_{22}\). The symbol \(f\upharpoonright_{A}\) stands for the restriction of the function \(f\) on the subset \(A\) of a definition set. The notation \(F^{(1)}\) for an operator \(F\), whose image is on a product space, means we take only the first component of the operator. According to the Definition 1, we say that \((X_{t})\) does not linearly cause \((Y_{t})\) if and only if \(\rho_{12}=0\). In fact, if \(\rho_{12}=0\), the two covariance operators of the definition are equals, and then the difference between the operators cannot be positively defined.
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 2 Materials and Methods
### GAs
#### 2.2.1 Population Initialization  
The first step in this phase is to define individual representations as candidate solutions for the source parameter estimation problem. As mentioned previously, in this study the GA only estimates four parameters through the inversion, i.e., length, width, rake, and slip. Hence, in this study, each individual (\(I\)) consists of four chromosomes, \(I~{}=~{}[c_{1},~{}c_{2},c_{3},c_{4}]\), where \(c_{i}\) is the chromosome representing the candidate solution for the \(i\)th estimated variable, i.e., length, width, rake, and slip, respectively.  
Each chromosome is represented as a number of bits. Therefore, the number of bits parameter (\(n\_bits\)) is tuned in this experiment. The number of bits will determine the number of possible values generated for each variable. Tuning of the number of bits parameter is described in the scenarios and settings section in Section 3.1. Each chromosome has a predetermined range of values that depends on the estimated variable it represents. According to the range of values for the Okada model parameters previously described in Section 2.1 and the parameter values of fault models tested, the search value for the length parameter ranges from 25 to 750 km, the width parameter from 10 to 300 km, the rake parameter from 60\({}^{\circ}\) to 120\({}^{\circ}\), and the slip parameter from 0.1 to 25 m. The parameters of length, width, and rake are specified as integer data type, while the slip parameter is of real data type. The fault models tested in this study are described in detail in Section 3.1.1.  
In addition to individual representation, another hyperparameter that needs to be determined in this phase is the size of the population, which denotes the number of individuals generated in one population. The size of the population is also included in the hyperparameter tuning of the experiment.