esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Study Results
### Differential Speed-Limit (DSL) Results  
#### 4.2.1 Simulated Mobility Results  
Figures 5 and 6 and Tables 11 and 12 show the average delay and the ATS results for the differential speed-limit scenarios, for speed limits of 80 and 90 km/h for PCs and 60 km/h for HVs for different traffic volumes and different percentages of HVs, for both of the conventional TLTW, and the 2+1 that prohibits overtaking in the opposite direction designs. It is worth noting that the 2+1 design that allows overtaking in the opposite direction was not further investigated, as the USL scenarios showed that this design compromised safety compared to the TLTW design.  
In contrast to the USL strategy, the DSL strategy shows that by increasing the portion of heavy vehicles in the traffic stream, there is a significant decrease in delay and a significant increase in the ATS for all speed limits and all traffic volumes. In addition, for traffic volumes greater than 500 vehicle/h/direction, the results of the DSL scenarios show that the 2+1 design would significantly decrease the average network delay compared to the TLTW design. The reduction in the average delay is between 26% and 48% and between 26% and 51% for speed limits of 80 km/h and 90 km/h, respectively. Furthermore, the results of the DSL scenarios show that the 2+1 design would significantly increase the ATS compared to the TLTW design. The increase in the ATS is from 1.5% to 12.5% and from 1.4% to 16.9% for speed limits of 80 km/h and 90 km/h, respectively, with approximately the same rate of increase for all percentages of HVs. However, for a traffic volume of 250 vehicle/h/direction (i.e., free-flow condition), there is no statistically significant difference in the ATS for both the TLTW and the 2+1 designs.  
Figure 5: Average network delay (second/vehicle) for differential speed-limit scenarios: (**a**) speed limit = 80 km/h for PCs and 60 km/h for HVs, (**b**) speed limit = 90 km/h for PCs and 60 km/h for HVs.  
It is worth noting that, for all the USL scenarios (Figure 4) and all DSL scenarios (Figure 6), there is a noticeable reduction in the ATS with the increase in traffic volume. In addition, the results show that, by converting the 6.0-km segment from TLTW to 2+1, the ATS would increase for all traffic volumes. This proves the mobility efficiency of the 2+1 design compared to the TLTW design.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**V**} & \multirow{2}{*}{**\%HV**} & \multicolumn{2}{c}{**TLTW**} & \multicolumn{2}{c}{**2+1 (No Pass)**} \\ \cline{3-6}  & & **Speed (Km/h)** & **Delay (S/veh)** & **Speed (Km/h)** & **Delay (S/veh)** \\ \hline
250 & & & 76.54 \(\pm\) 0.31 & 25.54 \(\pm\) 1.97 & 77.68 \(\pm\) 0.33 (1.5) & 18.96 \(\pm\) 1.41 (\(-\)26) \\
500 & & & 68.78
# Coastal Sargassum Level Estimation from Smartphone Pictures
## 4 Experiments and Discussion
### Predictions Analysis  
In this section, we make a deeper analysis on the predictions provided by the model with the highest validation accuracy observed (VGG16 trained with fine tuning). From Figure 3, we can observe that predictions are near to the true label. This phenomenon is better observed in the normalized matrix (Figure 2(b)), where the majority of predictions are in the diagonal (true positives) and the remaining ones are distributed among the contiguous classes. This reflects the fact that there is no a hard rule for this classification, but it is based on manual labeling which is prone to the point of view of the human labeler. See Figure 4 for prediction examples. In conclusion, the predictions provided by tested models can be useful as a good approximation of the Sargassum amount.  
Figure 4: _Cont_.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Experiments**} & \multicolumn{3}{c}{**Orthogonal**} & \multicolumn{3}{c}{**Metrics**} \\ \cline{2-10}
**Optimizer** & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & **Epochs** & \
\begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} & **Accuray** & **Precision** & **Recall** & **F1-Score** &
\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} \\ \hline SGD & \(1\times 10^{-3}\) & 500 & 100 & 0.60 & 0.59 & 0.60 & 0.60 & **112 min 27 s** \\ \hline SGD & \(1\times 10^{-4}\) & 500 & 100 & 0.59 & 0.58 & 0.59 & 0.58 & 156 min 49 s \\ \hline SGD & \(1\times 10^{-5}\) & 500 & 100 & 0.56 & 0.56 & 0.56 & 0.56 & 113 min 39 s \\ \hline Adam & \(1\times 10^{-3}\) & 500 & 100 & 0.36 & - & - & - & 119 min 37 s \\ \hline Adam & \(1\times 10^{-4}\) & 500 & 100 & **0.64** & **0.65** & **0.64** & **0.64** & 152 min 44 s \\ \hline Adam & \(1\times 10^{-5}\) & 500 & 100 & 0.59 & 0.60 & 0.59 & 0.58 & 120 min 24 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of results obtained by the exploitation grid search. Time is displayed in minutes (min) plus seconds (s).  
Figure 3: Single and normalized confusion matrices for the classification of Sargassum. Translated labels are: _nada_ (nothing), _bajo_ (low), _moderado_ (fair), _abundante_ (plenty), and _excessive_ (excessive).
# Prediction of Compressive Strength Loss of Normal Concrete after Exposure to High Temperature
## 2 Modeling
### Parameter Determination  
Compressive strength loss of normal concrete after high temperatures is mainly influenced by material parameters, such as water binder ratio (W/B) and high-temperature operating mechanisms, such as heating temperature (T), heating velocity (V), maintaining duration at target temperature (MD), cooling method (C) and resting duration after cooling (RD). Therefore, in this paper, the W/B, T, V, MD, C and RD were used as input parameters to establish the models. The cooling method (water cooling/natural cooling) was converted into a digital signal (0/1). To eliminate the influence of initial strength at room temperature (i.e., before exposure to high temperature), strength loss percentage after high temperatures (P) was selected as the output parameter in this paper. The P is determined according to Equation (1).  
\[\mathrm{P=\frac{f_{cu,\;high\;temperature}-f_{cu,\;room\;temperature}}{f_{cu,\; room\;temperature}}\times 100\%,} \tag{1}\]  
where \(f_{cu,\;high\;temperature}\) is the compressive strength of concrete after high temperature and \(f_{cu,\;room\;temperature}\) is the compressive strength of concrete at room temperature.
###### Abstract  
Advancements in semiconductor packaging toward higher integration and interconnect density have increased the risk of structural defects--such as missing solder balls, pad delamination, and bridging--that can disrupt thermal conduction paths, leading to localized overheating and potential chip failure. To address the limitations of traditional non-destructive testing methods in detecting micron-scale defects, this study introduces a multimodal detection approach combining finite-element thermal simulation, infrared thermography, and the YOLO11 deep learning network. A comprehensive 3D finite-element model of a ball grid array (BGA) package was developed to analyze the impact of typical defects on both steady-state and transient thermal distributions, providing a solid physical foundation for modeling defect-induced thermal characteristics. An infrared thermal imaging platform was established to capture real thermal images, which were then compared with simulation results to verify physical consistency. An integrated dataset of simulated and infrared images was constructed to enhance the robustness of the detection model. Leveraging the YOLO11 network's capabilities in end-to-end training, dataset small-object detection, and rapid inference, the system achieved accurate and rapid localization of defect regions. Experimental results show a mean average precision (mAP) of 99.5% at an intersection over union (IoU) threshold of 0.5 and an inference speed of 556 frames per second on the simulation dataset. Training with the hybrid dataset improved detection accuracy on real images from 41.7% to 91.7%, significantly outperforming models trained on a single data source. Furthermore, the maximum temperature discrepancy between simulation and experimental measurements was less than 5%, validating the reliability of the proposed method. This research offers a high-precision, real-time solution for semiconductor packaging defect detection, with substantial potential for industrial application.  
microelectronics reliability; finite-element simulation; infrared thermal imaging; YOLO11; packaging defect detection; artificial intelligence; deep learning; neural network; AI-assisted detection +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Clusters Analysis and Candidates  
The military police (MP) perform their activities via regions and shifts (dawn, morning, afternoon, or night) using vehicles to patrol a certain coverage area from an initial facility location. For this reason, MP facility locations are crucial to minimizing the vehicle response time to criminal occurrences. Thus, prior to carrying out an optimized location assessment for the police facilities, initial studies are necessary to define candidate zones to allocate the facilities and small stations. The proposed MIS and the described database were used to group crimes into clusters based on the regions' similarity for crimes with a high incidence. Figure 7 illustrates ten regions where crimes occur with greater frequency and similarity in the city of Recife.  
Figure 6: Preprint reports screen.  
The centroid of each region delimited by the clusters is found using the proposed MIS. Each cluster's centroid is a potential candidate location for a police facility. Table 1 presents the centroids' information as geographic coordinates, with all ten candidate locations.  
As there is no specific and exact coverage for the facilities in Recife, this assessment explores, in the next section, several potential scenarios through one to five kilometers of radius coverage. In addition, we analyze the sensitivity of this parameter, investigating the behavior of the optimized allocation of facilities for each coverage radius.
# Incremental Canonical Correlation Analysis
## 2 Related Work  
Owing to the problem of CCA described above, a large number of methods were proposed to solve this problem. Most studies were along the specific application of this main line to improve CCA. Based on this consideration, we mainly review in this section those optimization algorithms about CCA that are most related to our method.  
To accelerate the efficiency of CCA, a randomized CCA algorithm for a pair of tall and thin matrices was proposed in [36], which first obtained the matrices by randomly reducing the dimensionality of the matrices. The newly generated matrices, as a different dataset, were applied in existing algorithms. As a result, [PERSON] et al. [33] pinpointed that the randomized CCA algorithm still cannot thoroughly solve the original high complexity problems. [PERSON] et al. [31] obtained some suboptimal results by viewing this problem as a sequence of iterative least-squares with imprecise approximation. An iterative algorithm achieved excellent results with a low cost. In [30], an alternating least-squares algorithm was proposed and the performance is better than [33]. [PERSON] et al. [29] found that the objective of CCA is not stochastic convex programming. [PERSON] et al. [33] gave much attention to globally convergent stochastic optimization of CCA. [PERSON] et al. [39] made a breakthrough to this issue. However, the problem of sample complexity still exists. [PERSON] et al. [40] proposed truly alternating least-squares (TALS) for efficient CCA with momentum. The offline algorithm was not suitable for real-time scenes that resultedin high memory storage, so some scholars tried to design fast algorithms to solve the problem of high complexity. [PERSON] et al. [29] proposed a first-order stochastic approximation algorithm for canonical correlation analysis (CCA), in which the convex relaxation was introduced. Different from these methods, which all approximated the objective of plain CCA, the proposed ICCA directly accelerates original CCA. Note that the sequential Karhunen-Loeve or SVD used in ICCA was originally proposed by [PERSON] [41], which accelerated SVD rather than matrix inversion. Moreover, early on, it was applied in visual tracking [42], which puts forward learning the eigenbasis of PCA. It is obvious that PCA is not CCA and their motivations are very different. More importantly, the proposed ICCA greatly expands the applicability of sequential SVD in multiview tasks.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 5 Antenna Poses Estimated from Captured Images
### Pose Refinement
#### 5.2.2 Refinement via Constraint-Based Optimization  
To summarize, the optimization defined in Equation (7) is now rewritten as:  
\[\underset{\Delta\pm,\Delta\beta,\Delta\gamma,\Delta T}{\text{min}} \sum_{i=1}^{P}d_{c}^{c}(\Phi_{i}^{\pi}(R_{\parallel}^{\prime}\left(\gamma^{ \prime}+\Delta\gamma\right)R_{\perp}^{\prime}\left(\alpha^{\prime}+\Delta \alpha,\beta^{\prime}+\Delta\beta\right),T^{\prime}+\Delta T),\Phi_{i}) \tag{9}\] \[\text{s.t.}\quad\begin{cases}-(\varepsilon,\varepsilon,e)<\left( \Delta\alpha,\Delta\beta,\Delta\gamma\right)<\left(w_{1}\varepsilon,\ w_{1} \varepsilon,e\right)\\ \\ -R^{\prime}\left(D,w_{2}H,D\right)^{T}<\Delta T<R^{\prime}\left(D,w_{2}H,D \right)^{T}.\end{cases}\]  
The constraints expressed in Equation (9) are simple box-shaped boundary constraints, which enable us to search for the refined pose in a reduced space by seeking global convergence using algorithms such as the dividing rectangles (DIRECT) algorithm [60]. Typically, another round of local optimization (we use constrained optimization by linear approximations (COBYLA) [61]) is then performed to also ensure local optimality.
## 4 Validation and Discussion
### \(D\) Tunnel Stability Analysis: \(L/d\geq 0.5\)
#### 4.2.2 Comparison of Predicted Stability Numbers with Centrifuge Model Tests Results  
Figure 4 shows the comparison of stability numbers with various \(L/D\) derived from cavity contraction solution (Equations (18) and (19)) and centrifuge model tests of [PERSON] [5]. The model tests were conducted in 3D semi-circular apparatus (Main Series II in [5]) at an average acceleration of 118g. The tunnel excavation was modelled by gradually reducing the internal supporting pressure, which was provided by an airbag was recorded during the whole unloading process. The saturated Speswhite clay is used in the tests of Main Series II, and the undrained shear strength is kept constant in this series. It can be seen again the predicted (Equation (18)) and measured values (model tests of [PERSON] [5]) match well with each other, which shows the reasonability of the cavity contraction method in predicting 3D tunnel stability.
## 1 Introduction
Typically, estimates of the performance of real-size EAHXs are derived from small-scale models or commercial computational tools [8; 18; 19; 20]. These computational tools may not be available to HVAC designers willing to consider EAHX solutions, and the procedure used in system design/sizing, focusing on the analysis of specific and demanding operating conditions, is distinct from a performance analysis based on climate data or on monitored outdoor conditions for a particular period of time. Simplified analytical and empirical methods are also available and are sufficiently accurate in predicting the temperature of the air exiting the EAHX, as concluded by [PERSON] and [PERSON] [21], who analyzed the sensitivity of different methods to predict EAHX performance. Consequently, simplified methods remain adequate for EAHX design. Simplified methods available to HVAC designers are presented as analytical expressions [22], as rules derived from parametric studies [1; 13; 23; 24], and as simplified computational tools [25; 26; 27]. Still, concerning large-diameter EAHXs used for room load removal in hot and dry climates, a research gap persists, and studies dedicated to the design of these EAHXs using post-occupancy monitoring data are rare. This paper contributes to the reduction of this research gap, presenting a graph-based method to assist the design of a large-diameter EAHX. Moreover, with post-occupancy data, the performance of a large-diameter EAHX designed for standalone cooling is assessed, and the challenges facing designers considering using EAHXs to cool office buildings located in hot and dry climates are discussed. Using the experience gathered from a five-year-long project consisting of the design, construction, and monitoring of a large-diameter EAHX, this paper starts by presenting characteristics and limitations of EAHXs for room load removal and the graph-based method used to determine EAHX performance under design conditions. Next, a detailed description of the built EAHX, relevant specifications of the building (being cooled by the large-diameter EAHX), and climate/soil characteristics are provided. Using post-occupancy monitoring data and considering an adaptive indoor thermal environment standard, the performance of the built EAHX is assessed. Post-occupancy data are also used to evaluate the graph-based design methodology. Useful recommendations for the design of large-diameter EAHX capable of standalone room cooling are, finally, presented.
## 2 ML Methodologies
### Supervised Learning
#### 2.1.1 Classification
In nonlinear classification, the decision boundary is not a straight line, plane, or hyperplane. Instead, it can have curves, twists, or other complex shapes [13]. The steps that can be applied to create a classification model are as follows:  
1. Data collection and preprocessing: Collect the dataset and perform data preprocessing tasks, such as data cleaning, handling missing values, and transforming variables if necessary. This step ensures that the dataset is in a suitable format for the classification model and that the data are harmonized. 2. Model initialization: Choose an appropriate classification algorithm or model for the task at hand. Select from options such as logistic regression, decision trees, random forests, or support vector machines based on the problem and data characteristics. 3. Cross-validation and dataset separation: Split the dataset into training and testing subsets using cross-validation techniques. This helps evaluate the model's performance by training on a portion of the data and testing on unseen data, allowing the detection of issues such as overfitting or underfitting. 4. Training the model: Feed the training data into the classifier model. The model learns from the labeled training data and adjusts its internal parameters to discover the best decision boundaries or rules for classification. Iteratively update the model based on the training data until satisfactory performance is achieved. 5. Evaluating the model performance: Once the model is trained, evaluate it as a newly created classifier on the evaluation dataset. Apply the learned decision boundaries or rules to classify attributes with unknown labels into predefined classes, providing insights and aiding decision making. Below, Figure 1 serves as an informative illustration depicting the crucial phases in the development of a classification model. This flowchart offers a comprehensive overview of the essential steps required to establish a classifier with the capability to address a wide range of classification tasks. By following these numbered steps, a classification model can be developed and utilized to classify new observations based on their features, enabling various applications in fields such as healthcare, finance, and customer behavior analysis [17]. Beneath, Table 1, provides a comprehensive overview of various linear classification techniques. These algorithms assume that the decision boundary separating different classes is a linear function of the input features. Using the table above, we have now linked all the aforementioned water management methodologies, mapping them to specific linear classification approaches, and provided the relevant applications along with supported publication references for verification. We depict this linkage in the following Table 2.
## 2 Materials and Methods
### Workflow for GNSS-Based Strain Field Generation and Seismic Hazard Zonation  
To systematically assess crustal deformation and its implications for seismic hazard, this study developed a multi-step analytical framework that integrates geodetic data processing, strain tensor computation, and hazard mapping. The approach leverages high  
Figure 3: Geodetic baselines exhibiting strain rates exceeding 2 ppm/year during two observation periods: 2018–2020 (**left**) and 2019–2020 (**right**). Red lines correspond to high-strain-rate baselines that predominantly trace the Yangsan and Ulsan Fault systems. Green triangles highlight specific geodetic subnetworks (Triangles 8, 16, 19, and 22) where elevated strain was detected during the 2018–2020 interval. Among these, only the U0745–U0752 baseline within Triangle 8 continued to exhibit strain rates exceeding 2 ppm/year during the 2019–2020 interval. The network includes 23 geodetic control points (blue squares) and 10 CORS (black circles).  
resolution GNSS observations to quantify spatial patterns of tectonic stress and identify structurally sensitive regions.  
Figure 4 illustrates the comprehensive workflow adopted in this study to assess seismic hazards through GNSS-derived strain field analysis. The process begins with three discrete geodetic campaign surveys (1 st-, 2 nd-, and 3 rd-order observations) and continuous CORS (Continuously Operating Reference Stations) data collection. These datasets are used to compute horizontal velocity vectors, which serve as the basis for constructing an optimized triangulated geodetic network.  
Subsequently, strain tensor parameters are calculated for each triangular element, yielding key deformation metrics, including principal strain components (\(\epsilon_{1}\), \(\epsilon_{2}\)), maximum shear strain (\(\gamma_{\max}\)), areal dilation (\(\Delta\)), and individual stress components (\(\sigma_{\rm xx}\), \(\sigma_{\rm yy}\), \(\sigma_{\rm xy}\)). These outputs are then spatially mapped to visualize regional variations in strain and stress accumulation.  
The final step involves integrating these strain-derived parameters into a seismic hazard donation framework, enabling the identification of structurally sensitive zones with  
Figure 4: Workflow diagram of the crustal deformation analysis. The process includes data collection from geodetic point observations and CORSs, velocity estimation, optimal triangular network design, and deformation parameter calculation. Derived parameters include principal strain, maximum shear strain, dilatation, and stress components. The final outputs include spatial visualization of strain and stress fields and seismic hazard donation based on strain field interpretation.  
elevated tectonic stress. This systematic approach establishes a reproducible framework for applying high-density GNSS monitoring and elastic strain modeling to generate input parameters for regional seismic hazard assessment in intraplate tectonic environments such as the Korean Peninsula.