esa-sceva/satcom-chunk-collection huggingface dataset:
# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 3 Experiment
### Experimental Results
#### 3.3.3 Effect of Kernel Size (k) on Our Model  
According to the analysis in Section 2.1 of this paper, \(k\) in Formula (1) and (2) is the key parameter. In practice, \(k\) is also the size of 1D convolution kernel. In this section, its effects on our person ReID model are evaluated. We train our model by setting \(k\) be 3 to 9. The results of the experiment can be obtained from Figure 8. First of all, we fix the value of \(k\) over all the convolution blocks, and our model achieves the best results when k = 9. Additionally, when we adopt the adaptive strategy of Formula (2), the results outperform the fixed ones. The above analyses show the effectiveness of the adaptive strategy in Formula (2) in obtaining stable and better results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Method** & **Rank-1** & **mAP** \\ \hline \multirow{2}{*}{Traditional methods} & XQDA [4] & 43.0 & 21.7 \\  & NPD [3] & 55.4 & 30.0 \\ \hline \multirow{2}{*}{Deep metric learning} & Quad [33] & 80.0 & 61.1 \\  & TriNet [17] & 84.9 & 69.1 \\  & MSML [19] & 85.2 & 69.6 \\ \hline \multirow{4}{*}{Local feature} & PCB [34] & 92.3 & 77.4 \\  & PAN [35] & 81.0 & 63.4 \\  & DSA [10] & 95.7 & 87.6 \\  & HPM [11] & 94.2 & 82.7 \\ \hline \multirow{2}{*}{Global-local feature} & MSCAN [14] & 80.3 & 57.5 \\  & HAN [16] & 93.1 & 89.6 \\ \hline \multirow{2}{*}{Auxiliary feature} & AACN [36] & 85.9 & 66.9 \\  & AANet [12] & 93.9 & 83.4 \\ \hline \multirow{4}{*}{Global feature} & IDE [6] & 81.9 & 61.0 \\  & Mancs [37] & 93.1 & 82.3 \\ \cline{1-1}  & BagTricks [7] & 94.5 & 85.9 \\ \cline{1-1}  & ADMS [9] & 95.5 & 89.0 \\ \cline{1-1}  & Ours & 95.2 & 87.1 \\ \cline{1-1}  & Ours + Re-ranking & 95.9 & 94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results of different methods under the Market1501 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Method** & **Rank-1** & **mAP** \\ \hline \multirow{2}{*}{Traditional approaches} & XQDA [4] & 31.2 & 17.2 \\  & NPD [3] & 46.7 & 27.3 \\ \hline \multirow{2}{*}{Deep metric learning} & Quad [33] & 73.4 & 58.0 \\ \hline \multirow{2}{*}{Local feature} & PCB [34] & 81.7 & 66.1 \\  & PAN [35] & 71.6 & 51.5 \\  & DSA [10] & 86.2 & 74.3 \\  & HPM [11] & 86.6 & 74.3 \\ \hline \multirow{2}{*}{Global-local feature} & HAN [16] & 84.6 & 81.3 \\ \hline \multirow{2}{*}{Auxiliary feature} & AACN [36] & 76.8 & 59.3 \\  & AANet [12] & 87.6 & 74.2 \\ \hline \multirow{4}{*}{Global feature} & Mancs [37] & 84.9 & 71.8 \\  & BagTricks [7] & 86.4 & 76.4 \\ \cline{1-1}  & ADMS [9] & 89.4 & 79.2 \\ \cline{1-1}  & Ours & 88.9 & 78.5 \\ \cline{1-1}  & Ours + Re-ranking & 90.1 & 89.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the results of different methods under the DukeMTMC-reID dataset.
## 4 Results and Discussion
### Analysis of the Influence of Time Interval
#### 4.3.3 DM  
The transition status of DM for different time interval groups is shown in Figure 13.  
It can be found that the three groups of data are not particularly different in terms of the average transfer time or the percentage of transfer. This is mainly because the number of new cases of diabetes in the cohort is relatively stable, resulting in the calculated transfer probability under different groups reflects the trend of stable metastasis. However, in a nuance, the longer the interval, the average transfer time still becomes smaller.
# An Audio-Based Motor-Fault Diagnosis System with SOM-LSTM
## 5 Conclusions  
This paper has proposed the advantages of multi-model combination. By combining multiple models, such as FFT, SOM, and LSTM, and using the experimental design of the Taguchi method, the parameters of SOM-LSTM are optimized. The experimental design of the Taguchi method makes SOM-LSTM allow the full exhibition of the model. The execution efficiency of classification and monitoring is improved, providing a comprehensive and effective solution for the identification of motor operating sounds. This method covers many aspects, such as data processing, feature extraction, and model building; thus, a comprehensive solution is provided. The preliminary processing and extraction of data have been considered, and the deep learning technology was fully used for model building, thus ensuring a comprehensive analysis and the monitoring of motor operating sounds and improving the accuracy and reliability of classification and monitoring. SOM-LSTM constructs a sound-based motor-fault diagnosis system to solve the problem of motor-abnormality detection. This paper is expected to help in the preventive maintenance of motor equipment, making this work more efficient, saving time and costs, and enhancing the capability of industrial circles to monitor motor operation information.  
[PERSON] is the first author. He generalized the novel algorithms and designed system-planning projects. [PERSON] contributed material tools and the experiments and conducted simulations. [PERSON] assisted in the performance of the project, modeled the theory, conducted formal analysis, and prepared the manuscript as the corresponding author. All authors were involved in exploring system validation. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the National Science and Technology Council 2021 research and innovation programm, grant number NSC 110-2221-E-230-003.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Exp. 9**} & \multicolumn{3}{c}{**Identification Rates (\%)**} \\ \cline{2-4}  & **Training Sample** & **Test Sample** & **Average** \\ \hline Best & 100.00 & 92.19 & 96.09 \\ \hline Worst & 92.19 & 78.13 & 85.16 \\ \hline Average & 98.44 & 87.50 & 92.97 \\ \hline Execution time & 1.231 s & 1.231 s & 1.231 s \\ \hline \end{tabular}
\end{table}
Table 8: The robust test of Exp. 9 of SOM-LSTM.  
Figure 7: The RMSE training error of SOM-LSTM.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are unavailable due to privacy restrictions.  
**Acknowledgments:** We would like to thank the National Science and Technology Council, Taiwan, for its financial support (Grant Number: NSC 110-2221-E-230-003).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Stolen Verifier Attack  
Stolen verifier attacks [21; 22] occur when an attacker \(A\) obtains some precious information that is stored on a server's end. In our scheme, the only information about \(ID_{i}\) is stored in a database. \(H_{i}\) and \(L_{i}\) are enciphered, and \(R_{c}\) is protected by \(k_{s}\). Thus, \(A\) cannot utilize \(ID_{i}\) to obtain other values. Therefore, our proposed scheme resists stolen verifier attacks.
# Protection of Buried Pipelines from High-Speed Train Vibrations with Expanded Polystyrene Geofoam
## 2 Materials and Methods
### Description of the HST Moving Loads and Buried Pipeline Numerical Model
Initially, a commonly used transferring natural gas steel pipeline was examined; it has an outside diameter and thickness equal to 914 mm and 12.7 mm, respectively. The properties of X-65 steel material are as follows: tensile yield stress and ultimate strength 450 MPa and 560 MPa, while Young's modulus is 210 GPa. Figure 5(a) depicts the 3D sketch of the pipeline, while Figure 5(b) illustrates the corresponding mesh for the soil/track part and the embedded pipe. The burial depth of the pipeline was chosen to be equal to double the pipe's diameter, as is usually done in practice [28]. Furthermore, \"hard contact\" conditions have been selected for the interface, which allows for the separation of the pipeline from the subsoil, while the soil-pipeline interface friction parameter has been assumed to be equal to \(\mu\) = 0.3 since this value is commonly used in relevant investigations [29; 30]. Figure 7 presents the examined mitigation measure using a limited number of EPS blocks above the pipe. This approach is based on the so-called \"imperfect trench method\", which was proposed by the Norwegian Public Roads Administration (NPRA) [31; 32]. According to the NPRA, a horizontal EPS-filled trench, with a 1.5D width, is placed at 0.2D above the pipe, where D is the diameter of the pipe. Herein, the examined steel pipe diameter is equal to 914 mm; thus, the EPS layer width and the distance from the pipe were set equal to 1371 mm and 182.8 mm, respectively. The recommended height of the EPS block according to the NPRA is equal to 50 cm. In the present investigation, the effectiveness of the EPS layer with several thicknesses between 12.5 cm and 50 cm has been examined in order to achieve an optimal balance between efficiency and cost. As mentioned in the Introduction, EPS geofoam is a commonly used material in geotechnical applications; hence, there are various EPS materials with wide-ranging mechanical properties. According to ASTM D6817 [33], EPS geofoam is classified into seven categories (EPS12, EPS15, EPS19, EPS22, EPS29, EPS39, and EPS46). In the present study, EPS19, a relatively low-density geofoam, was selected as the trench fill material, with a Young modulus and Poisson's ratio of 4000 kPa and 0.05, respectively, whereas material damping is equal to 2%. Figure 6: Finite-element discretization of the: (**a**) steel pipe; (**b**) pipe and soil cross-section. Figure 7: Recommended EPS layout above the pipe using the imperfect trench method.
## 4 Antenna Broadcast Signal Propagation Model
### Eastward Transmission  
During the eastward propagation of the eLoran signal, the field strength of the signal will be affected by the terrain and change accordingly. Simulation Figure 11 shows the change of geographical environment during propagation, while Figure 12 shows that the signal field strength changes with the increase in propagation distance under the influence of geographical environment.
## 4 Results
### Success Rates  
In addition to the statistical analysis conducted on **Test 1**, we also examined the success rates for both **Test 1** and **Test 2** only considering the 12 unimpaired subjects. Figure 7(a) shows the success rate histogram for **Test 1**, in which two bars for each target represent the percentage of successful touches before and after training, respectively. The success rate analysis further supports the positive impact of training. For instance, all 12 participants were able to reach the first target both before and after training. However, the second  
Figure 7: Box-plots of the Accuracy, \(A\), before and after training for the third target calculated based on data of the 12 unimpaired subjects. (**a**) Considers the distance between the target and the robot base, while (**b**) the distance between the target and the robot’s end-effector. The red line indicates the median values of the metrics across unimpaired subjects, while the dots represent the two SCI subjects excluded from the statistics.  
and third targets presented greater challenges initially, with pre-training success rates of 41.67% and 16.67%, respectively. Post-training, the success rate increased significantly to 100% for the second target and 75% for the third target, indicating an improvement in user proficiency over time. As introduced in the previous subsection with the identification of an outlier, it is evident from this analysis that the outlier is part of the 25% who failed to reach the third target after training.  
For **Test 2**, we evaluated the success rate of the 12 unimpaired subjects in reaching the red glass on the table. As depicted in Figure 7(b), the percentage of successful touches improved from 42% to 83%, further emphasizing the effectiveness of the training sessions. For what concerns the two SCI participants, due to the limited sample size, we do not report any statistical analysis, but we can highlight that, in **Test 1**, SCI 1 successfully reached all three targets both before and after training. On the other hand, SCI 2 successfully reached only the first target both before and after training, while the second and third targets were never reached. For **Test 2**, SCI 1 failed to reach the glass both before and after training, whereas SCI 2 did not reach the glass before training but successfully reached it after training.
## 2 Related Work
The author used ultrasound, MRI, and histopathology images, and among these, ultrasound images were selected for cancer detection. For the classification of prostate cancer, feature extraction was carried out using the ultrasound echo radio-frequency (RF) signals, B-scan images, and Doppler images. [PERSON] et al. [44] proposed a deep CNN (DCNN) model for breast cancer classification. The model was developed based on the three powerful CNN architecture by combining the strength of the inception network (Inception-v4), the residual network (ResNet), and the recurrent convolutional neural network (RCNN). Thus, their proposed model was named as inception recurrent residual convolution neural network (IRRCNN). They used two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. The test results were compared against the existing state-of-art models for image-based, patch-based, image-level, and patient-level classification. [PERSON] et al. [45] proposed a novel method for the classification of colorectal cancer histopathological images. The author developed a novel bilinear convolutional neural network (BCNN) model that consists of two CNNs, and the outputs of the CNN layers are multiplied with the outer product at each spatial domain. Color deconvolution was performed to separate the tissue components (hematoxylin and eosin) for BCNN classification. Their proposed model performed better than the traditional CNN by classifying colorectal cancer images into eight different classes. [PERSON] et al. [20] compared the combination effect of six different colour pre-processing methods and 12 colour texture features on the patch-based classification of H&E stained images. They found that classification performance was poor using the generated colour descriptors. However, they achieved promising results using some pre-processing methods such as co-occurrence matrices, Gabor filters, and Local Binary Patterns. [PERSON] et al. [31] investigated the usefulness of image texture features, pre-trained convolutional networks against variants of local binary patterns for classifying different types of tissue sub-regions, namely stroma, epithelium, necrosis, and lymphocytes. They used seven different datasets of histological images for classifying the handcrafted and non-handcrafted features using standard classifiers (e.g., support vector machines) to obtain overall accuracy between 95% and 99%.
## 2 Contribution and Comparison with Related Surveys  
This section aims to highlight the contributions of this survey in comparison to other related surveys in the field of handover management and load balancing in ultra-dense 5G/6G cellular networks. By reviewing previous surveys, we identify gaps and outline how our work addresses these gaps, providing comprehensive and up-to-date analysis, as shown in Table 1.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Focus** & **Challenges Addressed** & **Limitations** \\ \hline
2022 [17] & Integration of 5G networks with IoT in UDNs. & Latency, scalability, interoperability, security vulnerabilities, computational costs, limited AI training data. & Primarily focuses on IoT integration and does not extensively cover handover management and load balancing. \\ \hline
2024 [5] & Role of UDNs in high-capacity and low-latency communication. & Interference management, resource allocation, energy efficiency. & Focuses on UDNs but does not provide a detailed analysis of AI-driven handover and load balancing techniques. \\ \hline
2024 [18] & Evolution of UDNs and small-cell integration. & Interference management, resource allocation, energy consumption. & Provides a broad overview but lacks in-depth analysis of specific AI techniques for handover and load balancing. \\ \hline
2020 [19] & Future research directions in UDNs. & Scalability, energy efficiency, security. & Focuses on future directions rather than current state-of-the-art techniques. \\ \hline
2025 [20] & Adaptive handover management in high-mobility networks for smart cities. & Service continuity, latency, network resource optimization. & Focuses on smart city applications but does not cover load balancing in detail. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Related Surveys and Contributions of This Survey.
# Effect of Injecting Epoxy Resin Adhesive into Cement Mortar on Tile Adhesion Performance
## 3 Results and Discussion
### Shrinkage Characteristics  
Figure 11 depicts the drying shrinkage strain of the actual mortar. In the case of DM, the continuous hydration reaction and evaporation of moisture resulted in the largest drying shrinkage strain. The drying shrinkage strain of AM, which is a mixture of mortar cement and a polymer-type ingredient, was smaller than that of DM because of the reduced bleeding and improved cement cohesion produced by the addition of the polymer [15; 16; 17]. Meanwhile, ERM exhibited a smaller drying shrinkage strain than DM and AM. This is attributed to reduced moisture evaporation from the mortar with the epoxy resin adhesive and the suppressed shrinkage as the epoxy resin reinforcement penetrated and cured inside the mortar matrix.  
Figure 12 shows the shrinkage strain of the tile surface as a function of the type of tile. The shrinkage strain was lowest for all tiles when the epoxy resin adhesive was used; this was particularly evident with the ceramic tile. Furthermore, compared to the other specimens, the shrinkage stabilized more quickly when the epoxy resin reinforcement was used, regardless of the type of tile. The ceramic tile was most impacted by shrinkage because the tile itself exhibited high porosity and absorption compared to the porcelain and polished tiles. The low porosity and absorption rate of the porcelain and polished tiles resulted in less shrinkage, which was even more pronounced when ERM was used. Previous studies have reported that the occurrence of shear stress due to the drying and hydration shrinkages of the mortar is an important factor in reducing the adhesion of the tiles and mortar [9; 10; 18]. The smaller shrinkage observed with the use of ERM is expected to effectively enhance the adhesion strength.  
Figure 11: Drying shrinkage of mortar.  
Figure 12: Shrinkage of tile: (**a**) ceramic, (**b**) porcelain, and (**c**) polished.
# ADAfinder Tool Applied to EGMS Data for the Structural Health Monitoring of Urban Settlements
###### Abstract  
The new European Ground Motion Service (EGMS) opens a new prospect in the study of the ground deformation phenomena influencing structures and infrastructures, at regional scale, exploiting the huge archives of Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 satellites. The research is currently oriented toward developing new methodologies to exploit this great volume of data, the management of which is difficult and onerous in terms of time. A new methodology for the monitoring of the deformations of urban settlements, based on the application of the ADAfinder tool to EGMS measure points, is proposed in this work. It targets the semi-automatic extraction of active deformation areas (ADA), given in the form of maps, with the goal to identify the buildings affected by displacements above a given threshold among all the buildings included in the investigated area. This allows a smart selection of the buildings needing insights about their condition through on-site monitoring or inspections, providing real support for the management of the urban areas. The proposed methodology is applied to two different case study areas in the city of Barcelona (Spain): the _Example_, in the heart of the city, and the _Zona Franca_, an industrial area near to the harbor.  
Structural Health Monitoring; European Ground Motion Service; A-DInSAR; ADAfinder; deformation maps +
Footnote †: journal: _Remote Sensing_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] ADAfinder Tool Applied to EGMS Data for the Structural Health Monitoring of Urban Settlements. _Remote Sensing_**2023**, _15_, 324. [[https://doi.org/10.3390/rs15020324](https://doi.org/10.3390/rs15020324)]([https://doi.org/10.3390/rs15020324](https://doi.org/10.3390/rs15020324))
# Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table
## 4 Optimization of Robotic Deburring
### Tracking the Toolpath  
The second optimization step is to ensure that the robot tracks the deburring path with the highest possible overall structural stiffness, starting from an initial workpiece placement determined by the GA. A rotary table is used to position the workpiece during deburring to assist the robot in maximizing its stiffness. One table rotation is allowed for each successive segment of the toolpath. For each rotation, the workpiece is repositioned, and the robot must adopt a new posture to reach it. For cutting motions, a limited rotation of the rotary table is allowed to avoid collisions due to the relative motion between the tool and the workpiece. For non-cutting motions, the rotary table is used to reposition the workpiece with unrestricted rotation.  
The optimization problem to determine the stiffest robot posture for the next segment of the toolpath is formulated as a one-dimensional maximization problem with fixed constraints. The inverse of the structural stiffness of the robot, as given in Equation (10), was used as the objective function.  
For computational efficiency, every tenth segment of the toolpath was considered in the optimization; therefore, the optimized toolpath consisted of 155 segments. The optimization problem was solved using the MATLAB function _fminbnd_.