esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 3 Numerical Investigations of the Methodology
### Ablation Study  
To assess the influence of individual components on the proposed method's damage detection performance, the ablation study is conducted by sequentially removing specific components. The contribution of each component to the method can be thoroughly analyzed by examining the effect of these alterations on the accuracy of damage detection. Removing the multi-scale analysis component and the entropy analysis component degrades the proposed method to LIE and multi-scale mode shape curvature (MS-MSC), while removing both degrades the proposed method to the original mode shape curvature (MSC). Monte Carlo experiments are performed. Firstly, a broad range of SNRs (50 dB-100 dB) is considered to investigate the noise robustness in the ablation study, with the damage degree maintained at a constant level (\(\alpha=0.2\)). Two damage cases are studied: single damage and multiple damages (with 2 to 4 damages at random), with the results presented in Figure 14.  
At high SNRs, all four methods achieve high detection accuracy due to less noise interference. Incorporating entropy analysis enhances the sensitivity of MS-LIE and LIE to damage, leading to marginally higher accuracy compared to MS-MSC and MSC. With the  
Figure 14: Monte Carlo experiments for ablation study on different SNRs: (**a**) single damage case, (**b**) multiple damage case.  
Figure 13: Monte Carlo experiments for comparison with other methods on different SNRs: (**a**) single damage case, (**b**) multiple damage case.  
rise in noise level, the accuracy of LIE and MSC decreases significantly, with LIE experiencing an especially steep drop. This indicates that LIE and MSC are less robust against noise interference. The LIE, with entropy analysis, is more sensitive to damage; however, this sensitivity also extends to noise. In contrast, MS-LIE and MS-MSC consistently retain higher detection accuracy, implying that the multi-scale analysis component is crucial for enhancing the noise immunity of the proposed method.  
Damage degrees over a wide range (0.1-0.3) are also considered to assess the detection sensitivity in the ablation study, while maintaining a constant SNR (SNR = 90 dB). Two damage cases are investigated, including a single damage case and a multiple damage case (with 2 to 4 damages at random), whose results are shown in Figure 15. With noise conditions maintained at a favorable level, the detection accuracy of MS-LIE and LIE is consistently superior to that of MS-MSC and MSC, and this gap increases significantly as the damage degree decreases. This demonstrates that the entropy analysis component renders the proposed method markedly more sensitive to damage. The proposed method integrates the multi-scale analysis component and the entropy analysis component, accounting for both the detection sensitivity and noise immunity, thus exhibiting superior performance.
# A Minimal GBT Model for Distortional-Twist Elastic Analysis of Box-Girder Bridges
## 3 A Minimal GBT Model
### Torsional Mode  
The in-plane displacement field is expressed as a rotation \(\theta=1\) around the centroid, entailing (Figure 3a,b)  
\[U_{t_{i}}=\begin{cases}\frac{h}{2}&i=1,3\\ \frac{h}{2}&i=2,4\end{cases},\qquad V_{t_{i}}=s_{i}\qquad i=1,\ldots 4 \tag{13}\]  
where \(s_{i}\)\((i=1,\ldots,4)\) are local abscissas with origins at the midpoint of each element \(i\). The associated warping is evaluated by Equation (12), applied to each element, which leads to the following:  
\[W_{t_{i}}=\begin{cases}\left(-\frac{h}{2}+\frac{Q_{t}}{t_{i}}\right)s_{i}+C_{i }&i=1,3\\ \left(-\frac{b}{2}+\frac{Q_{t}}{t_{i}}\right)s_{i}+C_{i}&i=2,4\end{cases} \tag{14}\]  
where \(C_{i}\) are integration constants. By enforcing continuity at the four corners and the condition \(\oint_{T}W(s)ds=0\) (for equilibrium along \(z\)), the five unknown are determined as \(C_{i}=0\) and \(Q_{t}=\frac{bht_{i}t_{2}}{bt_{2}+ht_{1}}\). Therefore,  
\[W_{t_{i}}=\begin{cases}\frac{2w_{t}}{b}s_{i}&i=1,3\\ -\frac{2w_{t}}{h}s_{i}&i=2,4\end{cases} \tag{15}\]  
where \(w_{t}:=\frac{bh}{4}\left(\frac{bt_{2}-ht_{1}}{bt_{2}+ht_{1}}\right)\) is the modulus of warping at the corners (Figure 3c). This expression coincides with that in Equation (A1) of Appendix A, obtained by the Vlasov theory; therefore, \(W_{t_{i}}\equiv\omega(s)\). It should be noticed that the torsional warping vanishes when \(\frac{b}{h}=\frac{t_{1}}{t_{2}}\).  
Figure 2: Decomposition of the cross-sectional transformation: (**a**) current configuration, (**b**) skew-symmetric part (twist), and (**c**) symmetric part (distortion). Compatibility at corners to be accounted for later.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 4 Experimental Result Analysis  
### Datasets of Chest X-ray Images  
In this paper, we utilized three datasets containing chest X-ray images to conduct our study. These datasets were sourced from publicly available medical archives and reviewed in collaboration with renowned institutes. The datasets consist of soft copies of X-ray  
Figure 5: Confusion matrix and summary of detector model.  
images, specifically the posteroanterior (PA) chest view, which provides crucial visual information for accurate diagnosis.  
Although CT scans offer better accuracy and efficiency in certain cases [24; 26; 30; 31], the potential long-term effects of exposure to ionizing radiation, such as an increased risk of cancer, particularly for young individuals, led us to focus on X-ray scans for our study. The combined dataset consists of a total of 3474 2D X-ray scans [21]. Among these scans, 415 were confirmed COVID-19 cases, 2880 were standard (healthy) cases, and 179 showed signs of pneumonia.  
To ensure consistency and ease of processing, all images in the dataset were resized to a standardized dimension of \(100\times 100\) pixels. To assess the performance of the proposed COVID-19 detection model, we randomly split the dataset, using 75% of the data for training and the remaining 25% for validation.  
Figure 6 presents representative X-ray chest images from the dataset, illustrating both healthy (standard) cases and cases affected by COVID-19 and pneumonia.  
The utilization of multiple datasets enhances the diversity and generalizability of our model, enabling it to make accurate predictions across different scenarios. The resized images provide a consistent input size for the deep learning model, facilitating efficient training and evaluation. By randomly splitting the dataset into training and validation sets, we ensure an unbiased assessment of the model's performance during the evaluation phase.
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
###### Abstract  
Fly ash consists of significant amounts of lithium, which is an essential resource for developing batteries. This study proposed an efficient method for extracting lithium from fly ash. First, we explored the parameters affecting the activation effect of sodium carbonate roasting and the leaching efficiency of lithium using acid leaching. Additionally, ultrasonic pre-treatment was applied to enhance activation. A further mechanism for the roasting extraction of lithium was symmetrically analyzed. The results showed that ultrasonic treatment at 200 W for 1 h under alkaline leaching conditions (sodium hydroxide solution 4 mol/L, reaction temperature 80 \({}^{\circ}\)C, leaching time 2 h, solid-liquid ratio 1 g:30 mL) achieved a lithium leaching rate of 90.74%, surpassing the 84.72% with traditional roasting-alkaline leaching. Under optimal acid leaching conditions (850 \({}^{\circ}\)C for reaction of 2.5 h, fly ash-to-sodium carbonate ratio (R\({}_{\text{fs}}\)) 1:2, sulfuric acid 2 mol/L, reaction temperature 80 \({}^{\circ}\)C, solid-liquid ratio 1 g:30 mL, and leaching time 1.5 h), the leaching rate reached 96.62%. With ultrasonic pre-treatment and acid leaching, the highest leaching rate of 98.68% achieved under optimal conditions: reaction temperature 850 \({}^{\circ}\)C for 2.5 h, mass R\({}_{\text{fs}}\) at 1:1.5, sulfuric acid 2 mol/L, reaction temperature 80 \({}^{\circ}\)C, solid-liquid ratio 1 g:35 mL, and leaching time 120 min. The study demonstrated that ultrasonic pre-treatment outperforms the traditional method, achieving a higher leaching rate with fewer roasting additives and lower energy consumption.  
f +
Footnote †: journal: Applied sciences
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 2 Related Work
### Tools in Arabic Learning  
Many previous studies have developed tools for recitation assessment; in this regard, the HAFSS system [24] took user recitation of some Holy Quran phrases (in Arabic) as aninput and then assessed the quality of the users' recitation. It provides feedback messages to help users know their pronunciation errors and improve their recitation. This system included a speech recognizer to detect errors in user's recitation. For each decision from the speech recognizer, there is a confidence score that is used to choose the suitable feedback. One of the main components in the system is the automatic generation of the pronunciation hypotheses model, which is used to generate pronunciation errors in user's recitation and detect the pronunciation patterns. The system has been tested in a school with two student groups, and the results showed improvement in students' recitation when using the HAFSS system. The performance of the students has been increased from 38% to 77% while getting lessons from a teacher and using the HAFSS system to practice recitation. In [25], a tool was developed that detects pronunciation errors in young Algerian students. The idea was to differentiate between young Algerian students who have difficulties in pronunciation from those who have standard pronunciation. Since the native language of Algerian is a delicate Arabic language, it is very difficult for Algerians to formulate the equivalent sound in standard Arabic. The researchers proposed a system based on a decision tree that provides a decision for the pronunciation of Arabic if it is correct. Moreover, the system provides feedback if the pronunciation contains articulations problems to enhance the pronunciation skills of the learners--the system trained with the acoustical model built on MFCC representation and HMM models. Three scores were used for the decision tree, first the GLL (global average log-likelihood). The second score was the TDS (Time Duration of the Speech), the total time to produce the sound, and the total number of phonemes of each pronounced word. All the scores are input to the decision tree to accept or reject the pronunciation sound. The system was trained on correct Arabic pronounced words and tested with eight young Algerian students who read 16 Arabic words to test the system. The result showed a 95.8% TPR (true positive rate) for good pronunciation and 88.4% for bad pronunciation. Their dataset was too specific and smaller in size. So, more data with further experiments would be needed to validate their approach. [PERSON] et al. [9] developed a system for teaching Arabic phonemes employing ASR (Automatic speech recognition) by detecting mispronunciation and giving feedback to the learner. In the experimental study, the authors recorded Arabic phonemes 10 times from 89 elementary school children, which resulted in 890 recordings for each Arabic phoneme. The previous studies supported the fact the automated tools are beneficial in support the CA learning process and improve the students' performance.
# Three-Dimensional Geological Modeling Method Based on Potential Vector Fields
## 3 Results
### GPU Acceleration Verification  
To evaluate the acceleration effect of the GPU on modeling, the time required to solve the linear system of equations using the LU decomposition algorithm was tested under both CPU and GPU environments for different data point scales. The results are shown in Table 2. It can be observed that when the number of data points reaches several thousand, the CPU takes several hours, or even tens of hours, to solve the linear system. Thus, it becomes the bottleneck of the entire model generation process, which is unacceptable in practical applications. In contrast, the GPU can complete the solution in just a few seconds, achieving a solution speed more than 1000 times faster than the CPU. This significantly improves computational efficiency and enhances the adaptability of the proposed method when handling large-scale datasets.
## 2 Materials and Methods  
### Generation of Constructs to Test Synechocystis Z-ISO Activity in Escherichia coli  
To test the isomerase activity of Slr1599 in _E. coli_, the slr1599 gene was PCR-amplified from the _Synechocystis_ genome and cloned into the NdeI and Xhol sites of MCS2 of pCDFUet\({}^{\text{Tm}}\)-1 (Novagen). The resulting sequence-verified construct (pAH592) was introduced into _E. coli_ BL21(DE3) along with the pAC-ZETApi plasmid [27] (purchased from Addgene, plasmid #53284) with selection on LB agar containing streptomycin (50 \(\upmu\)m mL\({}^{-1}\)) and chloramphenicol (34 \(\upmu\)g mL\({}^{-1}\)). pCDFDuetTm-1 constructs containing the maize Z-ISO gene (synthesized as a gBLOCK by Integrated DNA Technologies) at MCS2 (NdeI/XhoI sites; pH595), _Synechocystis zds_ (_crtQ_; slr0940) at MCS1 (NcoI/Sall sites; pH585), _zds_ at MCS1 and slr1599 at MCS2 (pAH587), and _zds_ at MCS1 and the maize _Z-ISO_ gene at MSC2 (pAH590) were also generated. Further details of plasmids and primers used in this study are provided in Supplementary Table S1 and Table S2, respectively. PCRs were performed using QS\({}^{\copyright\textregistered}\) Hot Start High-Fidelity 2x Master Mix (New England Biolabs (UK) Ltd.) and restriction digests using Thermo ScientificTM FastDigest enzymes. Ligation reactions were conducted using T4 DNA ligase (New England Biolabs (UK) Ltd.) and all cloning steps used _E. coli_ JM109 competent cells (Promega). DNA was purified using the FastGene Gel/PCR Extraction Kit or Plasmid Mini Kit (both from Nippon Genetics), as appropriate, and automated Sanger sequencing was performed by Eurofins.
# Cyclic Elasto-Plastic Behaviour of 51 CrV4 Steel for Railway Parabolic Leaf Spring Design
## 2 Cyclic Elasto-Plasticity for Steels  
### Elasto-Plasticity Modelling  
Cyclic plasticity behaviour is analysed for components subjected to loads exceeding the material's yield strength. The Chaboche hardening model is usually used to characterise this cyclic behaviour of steels [41]. This model is based on the yield von Mises criterion, as outlined below:  
\[F(\mathbf{\sigma}-\mathbf{\alpha},\sigma_{y})=\sqrt{(\mathbf{\sigma}^{\prime}-\mathbf{\alpha} ^{\prime})\colon(\mathbf{\sigma}^{\prime}-\mathbf{\alpha}^{\prime})}-(\sigma_{y}+R)=0, \tag{1}\]  
with \(\sigma_{y}\) and \(R\) denoting, respectively, the initial size of the elasticity limit surface, \(F\), and the change in the size of the elasticity limit surface (expansion for \(R>0\) and contraction for \(R<0\)). Concerning the stress tensors, \(\mathbf{\sigma}^{\prime}\) and \(\mathbf{\alpha}^{\prime}\) denote, respectively, the deviatoric part of the stress tensor, \(\mathbf{\sigma}\), and the back stress tensor, \(\mathbf{\alpha}\).  
The direction of plastic flow is determined by the associative flow rule, which follows the principle of normality defined as  
\[\dot{\epsilon}^{p}=\dot{\lambda}\frac{\partial F}{\partial\mathbf{\sigma}}, \tag{2}\]  
where \(\dot{\epsilon}^{p}\) denotes the time rate of the plastic strain tensor. The consistency condition, \(\sum[\partial f/\partial\theta]\dot{\theta}=0\), serves as the condition for updating the stress and strain state variables (\(\theta\) assuming \(\mathbf{\sigma}\), \(\mathbf{\alpha}\), or \(R\)) and ensures the Kuhn-Tucker conditions. \(\dot{\lambda}\) denotes the the plastic multiplier, which is determined from the consistency condition.
## 3 The Reasons of the Unbalanced Energy Consumption  
We analyze the energy consumption of the linear network underwater in this section.  
We assume sensor nodes as \(S=\{S_{i}|i\in\{1,......,n\}\}\) form the linear sensor network, just as Figure 2 shows. For analytical simplicity, we assume the internode distance between two adjacent nodes is \(r\). As to \(\forall S_{i}\in S(i=3,......,n)\), \(S_{i-2}\), \(S_{i-1}\) and \(S_{i}\) are adjacent nodes in the linear network where \(S_{i-2}\) is closer to the sink. \(S_{i-1}\) has two neighbors \(S_{i}\), \(S_{i-2}\), in which we define \(S_{i}\) as the upstream neighbor of \(S_{i-1}\) and \(S_{i-2}\) as the downstream neighbor of \(S_{i-1}\).  
Specifically, let's consider a common scenario: each node in the chain has a packet with \(k\) bits message, such as temperature, pressure, salinity, etc., to send to the sink in each round. Data aggregation techniques may reduce packet transmissions, especially in large sensor networks where data generated from neighboring sensors are always highly redundant and correlated [23]. However, in sparsely deployed UWA-SNs, not much data redundancy can be exploited and the data may not be correlated. For example, the physical or chemical monitored parameters such as temperature, conductivity, salinity, pressure, and current speed of ocean are not correlated. Thus, we consider applications that data aggregation cannot reduce much of the data traffic.  
We first consider each sensor sends the packet hop by hop to the sink via the linear sensor network which we denoted as HBH. The energy consumption for the nodes in the chain is different. Considering two adjacent nodes: \(S_{i}\) and \(S_{i-1}\), \(S_{i-1}\) not only transmits its own sensed data towards the sink, but also relays the data it receives from \(S_{i}\). Thus during the same period of time, \(S_{i-1}\) has more packets to send than \(S_{i}\), which leads to uneven energy consumption. The energy consumption ratio of \(S_{1}\) to \(S_{n}\) is \(N\) in each round. As a result, the nodes near the sink will deplete energy more quickly and eventually die out first.  
Now, we consider each node in the linear network sends sensed data directly to the sink, which we denoted as DIRECT. Equations (2) and (3) show that transmission losses in both shallow water and deep sea are related to distance, which means that the nodes far from the sink deplete energy more quickly and eventually die out first.  
Summarizing hop by hop and direct transmission, the unbalanced energy consumption problem is caused mainly by the different distances to the sink and the way of data transmission. To solve the uneven energy consumption problem, we propose two different strategies to achieve balanced energy consumption between nodes, and at the same time maximize the lifetime of such networks both in shallow water and deep sea.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 3 Energy Consumption Based Selfish Nodes
### Energy Consumption based Dynamic Selfish Nodes  
In order to acquire more realistic evaluation results, energy consumption based dynamic selfishness is defined in this paper, and its impact on the network performance is evaluated by comparison with static one as a baseline.  
A node functions altruistically if its remaining energy is always higher than a threshold during the entire procedure. In contrast, a node functions as a static selfish node if its remaining energy is always lower than the threshold. However, an energy consumption based dynamic selfish node alters its states between selfish and altruistic depending on its remaining energy. Most nodes in MANETs are battery-powered. The remaining energy is the precious resource of the nodes in MANETs, which has the highest priority to preserve. Therefore, it is reasonable to assume that nodes behave selfishly only when its remaining energy is lower than a certain value. In other words, the remaining energy of a node represents the integration of all resources it possesses. These definitions make the evaluation more realistic and its results more reliable.  
A switching functionality is implemented at Network Layer in our experiments to simulate the energy consumption based dynamic selfishness. Specifically speaking, whenever the routing protocol checks the routing table to forward a packet, the switching functionality is triggered to determine whether to forward it by calculating the remaining energy capacity. The packet is forwarded if the remaining energy is greater than the selfish threshold. Otherwise, the packet is simply dropped by the routing protocol.  
In this work, the node states are determined by the residual energy capacity (\(REC\)). A node shuts down when its \(REC\) is lower than \(D\)th (power off threshold), restarts it when its energy storage charge reaches \(U\)th (power on threshold), and becomes selfish when its remaining charge is lower than \(S\)th (selfish threshold) as shown in Equation (1).  
\[\text{Node State}:=\begin{cases}up,&\text{if }REC\geq U_{th}\\ selfish,&\text{if }D_{th}<REC\leq S_{th}\\ down,&\text{if }REC\leq D_{th}\end{cases} \tag{1}\]
## 3 Loading Modes and Exposure Conditions  
The load factors in below-grade construction environments have not yet been clearly defined in current international standards and test methods. The current test methods cannot provide clear evaluation criteria, and relying on manufacturer specifications for quality control often leads to poor waterproofing workmanship. It is important to clearly define the loading modes as they can have a significant effect on waterproofing membrane durability. This study proposes three main loading modes and exposure conditions in Sections 3.1-3.3. Section 3.4 compares existing international standards to disclose a lack of connection between manufacturer specifications and performance evaluation criteria. Section 3.5 explains in detail the proposed evaluation criteria, which are used while demonstrating the test method.  
Effects of Zero-Span Tensile Stress, Displacement Range, and Speed of Concrete Joint on Waterproofing Membrane  
Most tensile strength tests today are conducted with free-film specimens. A more realistic testing method would require studying the effects of zero-span tensile stress on waterproofing membranes applied on moving cracks. The JSCE Standard (JSCE 2002) lists zero-span tensile evaluation as one of the requirements for determining waterproofing membrane performance. Studies in Japan have revealed that the localization of waterproofing membrane cracking occurs directly over the concrete joint displacement area [4].