esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Progress on Truing and Sharpening Process of Diamond Abrasive Grinding Tools
## 1 Introduction  
With the continuous development of science and technology, super-hard abrasive grinding tools have gradually replaced ordinary abrasive grinding tools and become the main means of high-quality, efficient, and precise machining of difficult-to-machine materials. Super-hard abrasive grinding wheels often employ synthetic diamond and cubic boron nitride (CBN) as abrasives and use ceramics, metals, and resins as the bonds, which have high processing durability and can improve the surface quality of workpieces, making them more suitable for automated production. Super-hard abrasive grinding wheels are the basis for high-speed and ultra-high-speed grinding, and high-efficiency, ultra-precision grinding [1].  
Diamond abrasive grinding tools are the most widely used super-hard abrasive grinding tools. During the grinding process, the abrasive grains will be gradually rounded and blunted under the action of friction and extrusion force. During grinding, the grains will slip with the workpiece and may cause vibration and noise, which will affect the shape accuracy and roughness of the workpiece surface and reduce grinding efficiency and grinding performance. The dressing technology of diamond abrasive grinding tools, which is often restricted by a series of factors, such as dressing efficiency, dressing accuracy, dressing tools, and complex surface dressing, is presently an urgent problem to be solved.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 4 U-Convolutional Blocks Enhanced with Conformer Layer  
The work in [15] proposes a mask estimation network topology with consecutive subsampling and supersampling layers close to the U-Convolutional block used in SuDoRM-RF++. However, conformer layers are applied at the output of the final subsampling layer. The flowchart of a conformer layer is depicted in Figure 5, showing that it is composed of a feed-forward module, a convolution module, a multihead self-attention (MHSA) module, and another feed-forward module. As stated in [15], the conformer layer operates as a dual-path network, sequentially processing both local and global contexts while being less computationally demanding. In this study, we suggest incorporating the conformer layer into the U-Convolutonal blocks of the separator for ESC-MASD-Net to investigate its potential additive nature. We propose the following three locations for the conformer layer:These three arrangements are depicted in Figures 6-8 for clarity. In these figures, the ordinary U-convolutional block and revised U-convolutional block (with conformer at the bottom of the ordinary U-Convolutional block) are depicted in Figures 9 and 10. Options (a) and (b) utilize just a single conformer layer, but option (c) employs \(B\) conformer layers. Nevertheless, the conformer layer employed in options (b) and (c) exhibits a significantly reduced number of parameters compared to the one utilized in option (a). This disparity arises from the fact that the data processed in options (b) and (c) undergo temporal downsampling, but the data in (a) does not.  
Figure 5: Flowchart of a conformer layer [15].  
Figure 6: Option (a): the conformer layer is right before the \(B\) U-Convolutional blocks.  
Figure 7: Option (b): the conformer layer is at the bottom of the **first** U-Convolutional block, and thus there is one revised U-Convolutional block in the beginning, concatenated with \((B-1)\) ordinary U-Convolutional blocks.  
Figure 8: Option (c): the conformer layer is at the bottom of **all** U-Convolutional blocks, and thus, there are \(B\) revised U-Convolutional blocks in concatenation.  
Figure 9: Flowchart of an ordinary U-Convolutional block.
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 6 Experimental Results
### The Number of Patients Is 20  
The initial location points of the 20 patients are shown in Figure 4. The maximum number of iterations is specified at 100. The BSO algorithm, BSO+LNS algorithm, and HBSO algorithm are applied to solve the model, respectively. After running three algorithms, roadmaps of the optimal relief solutions of the three algorithms are obtained, as shown in Figures 5-7, respectively. The final results are shown in Table 3.  
The experimental data show that the solution from the HBSO algorithm outperforms the other two methods in terms of overall performance when the number of patients is 20. The values of BSO+LNS and HBSO are significantly smaller than those of the original BSO in terms of the mean value of the objective function and the mean value of the total distance traveled. This shows that the LNS helps the algorithm to improve its ability to find the local optimal solution. Meanwhile, the variance of BSO+LNS and HBSO decreased more than 80% compared to the original BSO in terms of the variance of the objective function and the variance of the total distance traveled. It can be seen that LNS significantly improves the stability of the algorithm in finding the optimal solution. Comparing the metrics of HBSO and BSO+LNS, HBSO gets better results except for the value of the deprivation cost. It can be seen that the addition of SA also effectively improves the overall performance of the algorithm. In addition, in the variance of the objective function and the variance of the total distance traveled, HBSO is more than 30% more optimized than BSO+LNS, which can indicate that SA further helps the algorithm to improve its ability to jump out of the local optimum. For the mean value of the final deprivation cost, the results obtained by the three algorithms are not significantly different, indicating that the objective function is effectively established to prioritize relief for patients with more severe conditions.  
Figure 4: The location of patients (20).  
Figure 5: The optimal relief route from the BSO algorithm (20).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BSO** & **BSO+LNS** & **HBSO** \\ \hline Mean value of the objective function & 40,107.8964 & 37,211.6867 & **37**,069.2484 \\ Variance of the objective function & 1,000,988.112 & 160,159.8646 & **108**,**116.0327** \\ Mean value of total travel distance & 11,313.426 & 8455.6771 & **8313**.2388** \\ Variance of the total travel distance & 942,419.6876 & 160,159.8646 & **108**,**116.0327** \\ Final value of the deprivation cost & 1894.5709 & **1892.0069** & **1892.0069** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The final results from three algorithms.
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 5 Integration in CROSS-CPP  
As was mentioned in Section 1, the CROSS-CPP is an IT environment for the integration and analytics of data streams coming from high volume (mass) products with cyber physical features. Consequently, in this section, we will firstly review the process that has to be followed to harvest data and then make available for the data analytics toolbox to show how the proposed clustering algorithm has been integrated in Cross-CPP. Once we describe the integration on the CROSS-CPP framework, we will analyze how to run and the results of different executions using real datasets.  
In what follows, the process performed to calculate clustering of trajectories of the data available at the platform is analyzed. The process is divided into two phases: Obtaining access to the data (data harvesting) and performing the desired analysis--clustering in this case.
## 5 Discussion
### Limitations and Future Work  
This study is subject to several limitations that should be taken into consideration when interpreting the findings.  
First, the participant sample comprised second-year undergraduate students with varying degrees of prior programming experience. Although this population is appropriate for evaluating the educational utility of LLMs, the generalizability of the results to more experienced developers or professional settings remains limited. Future studies should consider a broader demographic, including advanced students and industry practitioners, to assess whether similar effects are observed across different experience levels.  
Second, the programming tasks used in the study were intentionally simplified to fit the academic context and time constraints. These assignments were designed to reinforce specific technical concepts rather than replicate the complexity of real-world software development. Consequently, they did not incorporate aspects such as ambiguous or evolving requirements, team-based collaboration, scalability considerations, or deployment workflows. Follow-up research should employ more realistic and open-ended programming scenarios to examine the robustness of LLM assistance in production-like environments.  
Third, the investigation focused exclusively on ChatGPT as the LLM used by participants. While ChatGPT is one of the most widely adopted models, its behaviour and performance may not be representative of other generative systems. Alternative LLMs, such as Claude, Gemini, Copilot, or open-source models, differ in terms of architecture, training data, and interaction style. Future research should include comparative analyses across multiple LLM platforms to evaluate whether the observed effects related to query type hold consistently.  
Finally, the analysis was limited to the categorization of query types and their relationship to performance outcomes. The study did not include a systematic evaluation of the correctness, completeness, or pedagogical quality of the LLM-generated responses. Future work should examine the content of these responses using expert evaluation criteria to better understand how different types of queries influence the educational value and functional reliability of LLM outputs.  
Collectively, these limitations suggest several directions for future research, including extending participant diversity, increasing task realism, comparing multiple LLMs, formalizing annotation reliability, and evaluating AI response quality. Addressing these areas would contribute to a more comprehensive understanding of how conversational LLMs can best support programming education and software development workflows.
## 3 Results and Discussion  
### Seismic Performance Analysis for Different Damping Ratios  
The damping ratio is one of the important characteristics of the earthquake ground motion, which is one of the parameters used in structural analyses. This ratio can be expressed as the decrease in the magnitude of the vibration motion over time. Structural dynamic parameters (natural frequency, period, and damping ratio) can be obtained using theoretical and experimental methods [72,73]. Although the damping ratio is difficult and complex to define, it is very important in terms of realistically determining the nonlinear behavior of the structure [74, 75, 76]. In this paper, structural analyses were applied by considering four different damping ratios such as 2%, 5%, 7%, and 10%. The comparison of the target displacements attained for various damping ratios as variables is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Limit State** & **Description** & **Return Period (Year)** & **Probability of Exceedance (in 50 Years)** \\ \hline \multirow{4}{*}{Limit state of significant damage (SD)} & Significantly damaged, some residual strength and stiffness, non-structural components damaged, uneconomic to repair. & 475 & 0.10 \\ \hline \multirow{2}{*}{Limit state of near collapse (NC)} & Heavily damaged, very low residual strength and stiffness, large permanent drift but still standing. & 2475 & 0.02 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 6: Limit states on the typical pushover curve.  
As the soil properties weakened, the target displacements increased to larger values from a structural safety perspective. Additionally, as the damping ratio increased, the target displacements decreased due to the reduction in earthquake effects. To better understand all the results, the comparisons are shown in Figure 7.
## 4 Results and Discussion
### Assessment of Hg Delivery to the Augusta Harbor: Direct Inputs or Resuspension?
The concentration of PCDD/Fs measured in a Kieselrot slag was extremely high (5,899,700 ng kg\({}^{-1}\), Figure 4b), comparable to the highest values found in sediments of the Brentella Canal (Figure 5). The relative signatures were the same, with OCDF as the most abundant congener, followed by heavy furans and a small amount of OCDD (Figure 4a,b). In addition, As was measured in Kieselrot slag with a good correlation with PCDD/Fs [65], as observed also in the industrial canals Nord and Brentella of the 1 st Industrial Area of Porto Marghera (Figure 5). No further evidence was needed, and the PCDD/F contamination found in the Brentella Canal was then ascribed entirely to sources \begin{table}
\begin{tabular}{c} \hline 1. Double exchange between iron sulfate or copper sulfate with sodium chloride \\ FeSO\({}_{4}\) + NaCl \(\rightarrow\) Na\({}_{2}\)SO\({}_{4}\) + FeCl\({}_{2}\), \\ CuSO\({}_{4}\) + NaCl \(\rightarrow\) Na\({}_{2}\)SO\({}_{4}\) + CuCl\({}_{2}\), \\ \hline 2. Oxidation of iron (II) chloride to iron (III) chloride \\ 12 FeCl\({}_{2}\) + 3 O\({}_{2}\) \(\rightarrow\) 8 FeCl\({}_{3}\) + 2 Fe\({}_{2}\)O\({}_{3}\) \\ \hline 3. Reaction of iron (III) chloride with Cu sulfide \\ 4 FeCl\({}_{3}\) + 3 Cu\({}_{2}\)S + 6 O\({}_{2}\) \(\rightarrow\) 6 CuCl\({}_{2}\) + 2 Fe\({}_{2}\)O\({}_{3}\) + 3 SO\({}_{2}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Main chemical reactions occurring during the roasting of pyrite ashes [67]. Figure 5: Association between As peaks (ug g\({}^{-1}\)) and PCDD/Fs (ug kg\({}^{-1}\)) in sediments of the industrial canals Nord and Brentella. Data are from [67]. linked to Cu extraction from pyrite ashes. This industrial process had been active in the 1 st Industrial Area since 1927 and, until the decommissioning in the early 1970s, produced about 130,000 units year of tiles and iron minerals [72]. The answer to the second question was more straightforward: the Lusore-Brentelle Canal was under the direct influence of a VCM stripping plant and the comparison of PCDD/F congener composition measured at site C9 with that observed in sediments from the Gulf of Finland under the direct influence of VCM stripping discharges (sample PU4043, [66], Figure 4b) left no doubt about the origin of dioxins and furans in the two areas. Therefore, it was concluded that VCM production in the 2 nd Industrial Area affected only a restricted nearby area.
# A Review of Deep Learning Applications for Railway Safety
## 6 Operation Safety
### Railway Detection  
Quickly detecting the front rail area can help prevent train accidents, such as derailments. However, railway detection in outdoor environments suffers from light-related issues, such as shadows, reflections, tunnels, and low contrast to the ground. In addition, railway detection becomes challenging in areas of overlapping multiple rails. [PERSON] et al. [81] proposed a CNN-based deep learning model trained by the BH-rail dataset that contains railway images captured at various times. [PERSON] et al. [82] proposed RailNet, a railway detection deep learning-based algorithm that processes video from front-view on-board cameras. RailNet consists of two networks: a network for feature extraction and another for railway segmentation. The feature extraction network has a pyramid structure to allow features to have top-to-bottom propagation. The railway segmentation network combines a ResNet50 backbone network with a fully convolutional network to generate the segmentation map.
## 1 Introduction
The authors also hypothesized that the pinch and pulling force measurements of the right hands are significantly higher than pinch and pulling force measurements of left hands at the slip onset for right-handed volunteers. To address the first question, an automated data acquisition system was developed. The automated system must be able to measure and acquire pinch and pulling forces as well as slip displacement accurately and automatically. The automated system uses linear electric actuator applying automatic pull, customized pinch force load cell measuring the pinch force, pulling force load cell measuring the pulling force, and Linear Variable Differential Transformer (LVDT) displacement sensor detecting the slip onset. The sensors and actuator were calibrated and then integrated together with Arduino(r) Due board. Subsequently, the data from 50 right-handed male volunteers within the age of 18-24 years were collected according to the UE-FMA protocol to establish the relationship between pulling and pinch forces in order to observe the slip onset. The paired \(t\)-test was performed on collected data to address the second question.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 5 The Calibration of Extrinsic Parameters
### Translation Vector Solution
#### 5.2.1 Rotation Radius Vector Solution  
As shown in Figure 6, for the camera whose optical center does not coincide with the rotation axis, we need to introduce the concept of a rotation radius vector. For instance, by taking the horizontal rotation into account and referring to the rotation plane as \(\gamma\), which is perpendicular to the rotation axis (\(\mathbf{v}\)), the optical center can rotate around a circle with a  
Figure 5: Schematic diagram of horizontal rotation and pitch rotation. The optical center rotates from \(O_{1}\) to \(O_{2}\) around \(\mathbf{v}\).  
rotation radius of \(\|\mathbf{m}\|\) on plane \(\gamma\). The intersection of \(\gamma\) and the vector (\(\mathbf{v}\)) is point c; the vector from the circle center, c, to the optical center in the camera's initial coordinate system, \(O_{1}-xyz\), is the radius vector (\(\mathbf{m}\)).  
Using early calibration, we can obtain the translation vector between the camera's coordinate system at \(O_{1}\) and the corner coordinate system at \(\mathbf{T}_{1}\); the translation vector at \(O_{2}\) is \(\mathbf{T}_{2}\), which is explained in Section 5.1.1. It is worth noting that the translation vector is under the initial camera coordinate, \(O_{1}-xyz\). According to the relationship between \(\mathbf{T}_{1}\) and \(\mathbf{T}_{2}\), the translation vector of the optical center moving from \(O_{1}\) to \(O_{2}\) is:  
\[\mathbf{T}_{v-\alpha}^{\prime}=\mathbf{T}_{1}-\mathbf{R}_{v-\alpha}\mathbf{T} _{2} \tag{15}\]  
When rotation radius vector \(\mathbf{m}\) rotates \(\alpha\) degrees around the axis, \(\mathbf{v}\), the translation vector can also be obtained:  
\[\mathbf{T}_{v-\alpha}=\mathbf{R}_{v-\alpha}\mathbf{m}-\mathbf{m} \tag{16}\]  
The calculation results of (15) and (16) shall be consistent with each other:  
\[\mathbf{T}_{1}-\mathbf{R}_{v-\alpha}\mathbf{T}_{2}=\mathbf{R}_{v-\alpha} \mathbf{m}-\mathbf{m} \tag{17}\]  
Figure 6: Schematic diagram of the change in the optical center caused by zooming.  
Therefore, we can obtain the rotation radius vector (**m**) of the camera's horizontal rotation:  
\[\textbf{m}^{v}=(\textbf{R}_{v-\alpha}-\textbf{I})^{-1}(\textbf{T}_{1}-\textbf{R} _{v-\alpha}\textbf{T}_{2}) \tag{18}\]  
where **I** is the third-order unit matrix.  
Using the method above, we can obtain the horizontal and pitch rotation radius vectors, \(\textbf{m}_{l}^{b}\textbf{m}_{l}^{b}\textbf{m}_{r}^{v}\) and \(\textbf{m}_{r}^{b}\), of left and right cameras, respectively.
# Flexural Behavior of Self-Prestressed RC Slabs with Fe-Based Shape Memory Alloy Rebar
## 3 Experiment Results and Discussion
### Failure Mode  
At the beginning of loading, the initial cracks occurred in the middle zone of all specimens. As the deflection of the specimen increased with the increment of the applied load, the initial crack propagated toward the compression zone. Finally, yielding of the Fe-SMA rebar was followed by crushing of the compressive fiber concrete, and the applied load to the specimen was decreased.  
Figure 8 shows the shape of the specimen after the experiment was completed, and Table 3 presents the summarized experimental results. As shown in Figure 8a, four flexural cracks were visually observed in SL-2N, the non-activated test specimen. On the other hand, three flexural cracks were observed in SL-2A, which had the same amount of rebar as SL-2N, and the Fe-SMA rebar was activated. Additionally, by comparing Figure 8a,b, it can be confirmed that the crack distribution width of SL-2N was wider than that of SL-2A. A reduction in both the number of flexural cracks and the crack distribution width according to the activation of the Fe-SMA rebar was also observed in the specimens with different amounts of Fe-SMA rebar. This reduction was considered to be the result of the recovery stress generated in the Fe-SMA rebar by activation, acting as a compressive force on the specimens.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Initial Crack**} & \multicolumn{2}{c}{**Ultimate**} & \\ \cline{2-5}
**Specimen** & **Deflection** & **Load** & **Deflection** & **Load** & **Failure Mode** \\ \hline SL-2N & 1.45 & 25.59 & 87.69 & 58.66 & \\ SL-2A & 1.42 & 35.88 & 89.34 & 59.05 & \\ SL-3N & 1.2 & 27.73 & 74.73 & 92.04 & \\ SL-3A & 1.99 & 46.92 & 72.59 & 93.23 & \\ SL-3R & 2.35 & 45.66 & 79.77 & 94.87 & Flexural \\ SL-4N & 0.88 & 29.06 & 75.52 & 117.01 & \\ SL-4A & 3.24 & 52.81 & 71.53 & 120.82 & \\ SL-5N & 1.00 & 29.28 & 96.95 & 147.91 & \\ SL-5A & 2.33 & 59.03 & 59.03 & 148.09 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of test results.  
Figure 7: Time–displacement relationship according to amount of Fe-SMA rebar. (**a**) Heating region. (**b**) Cooling region.