esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 3 Impact of Thermal Stress on Perovskites  
Typically, device temperatures can be over 45 \({}^{\circ}\)C higher than ambient temperatures when solar cells operate under direct sunlight. According to International Standards (IEC 61646 climatic chamber tests), long-term stability at 85 \({}^{\circ}\)C is required to compete with other solar cell technologies [38]. Therefore, the study of the thermal stability of perovskite solar devices has attracted world-wide research attention. Different degradation pathways have been proposed and the topic remains under discussion, but the material MAPbI\({}_{3}\) (CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\)) clearly loses its excellent light harvesting properties over time because it evolves into PbI\({}_{2}\) after a nominal loss of CH\({}_{3}\)NH\({}_{3}\)I. The degradation from CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) to PbI\({}_{2}\) is most likely accompanied by a release of gases via simple sublimation or assisted chemical reaction. It is proposed that the first mass loss step during the thermal degradation of CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) and CH\({}_{3}\)NH\({}_{3}\)I under an inert atmosphere proceeds as [39]:  
\[\mathrm{CH}_{3}\mathrm{NH}_{3}\mathrm{PbI}_{3}\xrightarrow{\Delta}\mathrm{NH}_ {3}+\mathrm{CH}_{3}\mathrm{I}+\mathrm{PbI}_{2} \tag{2}\]  
\[\mathrm{CH}_{3}\mathrm{NH}_{3}\mathrm{I}\xrightarrow{\Delta}\mathrm{NH}_{3}+ \mathrm{CH}_{3}\mathrm{I} \tag{3}\]  
Therefore, the determination of the composition of these released gases during the controlled thermal degradation of CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) is expected to pinpoint the degradation pathways. Different interpretations can be found in the literature regarding the chemical nature of these released gases during thermal degradation and several studies have analyzed the effect of intrinsic degradation and thermal stress on the perovskites. Herein, an overview of the impact of thermal stress on perovskites, based on different aspects such as chemical, crystallographic, morphological and optical degradation, is described.
# A Proposal for Rapid Assessment of Long-Distance Oil and Gas Pipelines After Earthquakes
## 2 Analysis of Pipeline Safety Assessment Elements Under Seismic Impacts
### Pipeline Engineering Project Attribute Sorting  
According to the different transport media, oil and gas pipelines can be divided into crude oil pipelines, refined oil pipelines, and natural gas pipelines, and there are differences in the diameters, thicknesses, materials, and other elements of pipelines for different transport media [22; 23]. In addition, unlike the buried laying in the plains, the pipeline laying project in the mountainous river valley section is more complicated, considering the spatial relationship between the river valley and the pipeline direction, the pipeline laying method in the river valley area can be divided into two categories: parallel river valley direction laying and cross-cutting river valley direction laying (Figure 3). The detailed standards and schematics of the division can be referred to [5; 24]. It is worth noting that there is a significant difference in the susceptibility to geological hazards considering different pipeline laying methods, where the method of pipeline cross-slope laying is the most prone to slope stability issues, whereas the method of tunnel traversing and crossing is generally free of adverse geological issues. Therefore, the pipeline engineering project situation needs to be properly considered in the pipeline seismic safety evaluation.  
Figure 3: Classification of pipelaying methods. (Modified from [PERSON] et al. [5; 24]).
# Multi-Feature Guided Low-Light Image Enhancement
## 3 Materials and Methods
### Enhancement Network
#### 3.3.4 Enhancement Loss  
In order to better evaluate the effect of the enhancement network, we define the loss function from the three aspects: image structure similarity, color, and area.  
Structural loss: We evaluate the similarity index between the generated image and the input image by introducing structural similarity loss. We use the SSIM algorithm to calculate, specifically expressed as:  
\[\mathcal{L}_{\text{SSIM}}(\text{x},\text{y})=-\log\left(\frac{1}{2|\text{P}|} \sum_{\text{p}\in\text{P}(\text{x},\text{y})}(1+\text{SSIM}(\text{p}))\right) \tag{5}\]  
where P is the element set of the SSIM index in the input image (x, y).  
Color loss: The structural loss is implicitly calculated for the color difference, but it is only limited to the numerical value and cannot guarantee the same direction of the color vector, which will also cause color shift, so we use color loss to correct it. The specific performance is:  
\[\mathcal{L}_{\text{color}}(\text{x},\text{y})=\sum_{\text{p}\in\text{P}(\text {x},\text{y})}\angle\left(\text{x}_{\text{p}},\text{y}_{\text{p}}\right) \tag{6}\]  
where \(\angle\) is the operator for calculating the color angle, and P is the element set of the input image (x, y).  
Area loss: In order to suppress the underexposed or overexposed areas, we construct the area loss according to the distance between the average local exposure level and the expected exposure level. The specific form is:  
\[\mathcal{L}_{\text{region}}=\frac{1}{\text{N}}\sum_{\text{l}}|\text{Y}_{\text {l}}-\text{E}| \tag{7}\]  
where N represents the number of nonoverlapping blocks of size 16 \(\times\) 16, Y is the average intensity value of the local enhancement area, and E is the expected intensity value. We empirically set E to 0.6.  
Therefore, the loss function of the enhancement network can be expressed as:  
\[\mathcal{L}_{\text{e}}=\omega_{\text{a}}\mathcal{L}_{\text{SSIM}}+\omega_{ \text{b}}\mathcal{L}_{\text{color}}+\omega_{\text{c}}\mathcal{L}_{\text{region}} \tag{8}\]  
where \(\omega_{\text{a}}\), \(\omega_{\text{b}}\), and \(\omega_{\text{c}}\) are the proportional coefficients corresponding to the three loss functions.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 2 Materials and Methods
### Keypoint Extraction Techniques
#### 2.2.2. _Keypoint-Based Pose Estimation_  
Unlike direct pose prediction methods, keypoint-based approaches [15; 16; 17; 18] enhance robustness by leveraging projection equations. These methods define predetermined keypoints on the object's surface and predict their locations in the image frame or camera coordinate system. The object's pose is then computed based on these correspondences using PnP-RANSAC or least-squares fitting [35; 36].  
PVN3D [15] extends PVNet [14] to predict keypoints in 3D space, addressing the issue where small projection errors can lead to significant real-world deviations. FFB6D [16] further improves upon PVN3D [15] by incorporating a bidirectional fusion module to share information between the two modalities at an early stage. RCVPose [17] introduces a novel keypoint voting scheme using 1D radial voting to reduce cumulative errors in each channel, thereby significantly improving the accuracy of keypoint localization. DFTr [18] employs Transformer blocks to fuse cross-modal features and transmit global information between the two modalities. Additionally, DFTr [18] proposes a non-iterative weighted vector voting scheme to reduce computational costs, replacing the MeanShift [37] algorithm for keypoint voting.  
Overall, keypoint-based methods offer greater robustness and accuracy compared to direct pose prediction methods, making them suitable for applications where precision is critical.
## 3 Results
### SESTM Inhibits Lipid Accumulation and ROS Production  
To investigate the effect of ESETM on lipid accumulation, histological changes in the WAT were visualized using H&E staining and the adipocyte size in each group was measured. In comparison with the mice from the ND group, those fed with HFD had increased adipocyte size. In contrast, the mice administered ESETM showed a dose-dependent reduction in the adipocyte size, consistent with the reduction in the WAT weight, \begin{table}
\begin{tabular}{c c c c c c} \hline  & **CON** & **HFD** & **HFD + GC** & **HFD + ESETML** & **HFD + ESETMM** & **HFD + ESETMM** \\ \hline TG (mg/dL) & \(55.33\pm 6.75\) d & \(137.90\pm 20.97\) a & \(74.92\pm 24.42\) bc & \(105.26\pm 16.57\) b & \(89.99\pm 10.07\) b & \(74.93\pm 8.92\) bc \\ TC (mg/dL) & \(157.73\pm 19.48\) d & \(211.67\pm 7.65\) a & \(191.10\pm 1.56\) b & \(182.64\pm 18.51\) b & \(182.99\pm 12.16\) b & \(170.77\pm 9.49\) bc \\ LDL-C (mg/dL) & \(23.48\pm 3.10\) b & \(35.19\pm 1.78\) a & \(29.19\pm 1.53\) b & \(29.87\pm 5.90\) a & \(29.24\pm 4.07\) a & \(25.34\pm 3.14\) b \\ HDL-C (mg/dL) & \(196.46\pm 2.42\) a & \(13.76\pm 11.94\) c & \(186.35\pm 10.78\) a & \(170.79\pm 6.41\) b & \(180.56\pm 8.90\) b & \(184.45\pm 3.76\) ab \\ AST (IU/L) & \(75.52\pm 2.34\) c & \(118.49\pm 5.67\) a & \(78.50\pm 4.32\) b & \(89.58\pm 3.33\) b & \(83.99\pm 1.42\) bc & \(75.70\pm 6.31\) b \\ ALT (IU/L) & \(43.86\pm 2.75\) c & \(75.91\pm 15.39\) a & \(42.17\pm 7.41\) c & \(67.00\pm 6.43\) ab & \(59.86\pm 7.03\) b & \(54.73\pm 4.16\) bc \\ \hline \end{tabular}  
* The results are expressed as mean \(\pm\) SD (\(n=5\)). Different low case letters above columns indicate statistical differences among groups at \(p<0.05\) by one-way ANOVA. AST, aspartate aminotransferase; ALT, alanine aminotransferase; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride.  
\end{table} 
Table 3: Effects of ESETM serum biochemical analysis in HFD-fed obese ICR mice. Figure 2: Effects of ESETM supplementation for 7 weeks on body mass (**a**), body mass gain (**b**), daily food intake (**c**), and food efficiency ratio (**d**) in ICR mice fed high-fat diet (HFD). Data are shown as the means \(\pm\) standard deviation (SD) (\(n=5\)). Different lower case letters above columns indicate statistical differences at \(p<0.05\). as compared to the mice fed with only HFD (Figure 3a). We first evaluated the cytotoxicity of PTM, ESE, ESETM, and GC on adipocytes. As shown in Figure 3b, no cytotoxicity was observed in any treatment group or at any tested concentration. The degree of lipid accumulation was measured by treatment with ORO, which stains only triglycerides in red.
## 3 Results
### Inter-Individual Variability  
Effects of CPRSs on measures of RE showed considerable inter-individual variability (cf. Figure 4a). Mean effects of CPRSs on ECT ranged from \(-1.7\) to \(6.5\%\) for HOK, \(-2.5\) to \(9.4\%\) for MIZ, \(-2.2\) to \(4.9\%\) for PUM, and \(-4.5\) to \(5.8\%\) for SAU among individuals. In summary, \(21\%\) of all subjects reached their individually best relative reduction in ECT wearing HOK, \(32\%\) wearing MIZ, \(16\%\) wearing PUM, and \(32\%\) wearing SAU, respectively.  
Figure 5 displays the percentage of subjects experiencing mean metabolic savings between \(0\) and \(10\%\) in the four CPRS conditions studied. In summary, ECT was reduced by at least \(1\%\) in \(89.5\%\) of all subjects wearing SAU, by \(73.7\%\) of subjects wearing HOK, by \(63.2\%\) of subjects wearing MIZ, and by \(52.6\%\) of subjects wearing PUM shoes. Reductions in ECT of \(4\%\) were exceeded by \(36.8\%\) of subjects wearing MIZ, \(31.6\%\) HOK, \(15.8\%\) SAU, and \(10.5\%\) PUM. Notably, \(10.5\%\) of subjects wearing SAU, \(15.8\%\) wearing HOK, \(36.8\%\) wearing MIZ, and also \(36.8\%\) wearing PUM experienced a _detrimental_ effect on ECT instead of an improvement.  
Figure 5: Percentage of subjects that experienced average metabolic savings between \(0\) and \(10\%\) in CPRS conditions. HOK = Hoka Rocket X2, MIZ = Mizuno Wave Rebellion Pro, PUM = Puma Fast-R Nitro Elite, and SAU = Saucony Endorphin Pro 3. The dashed lines depict the thresholds of savings of \(1\%\) and \(4\%\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Change Compared to OWN**} \\ \hline \multirow{2}{*}{**Footwear**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{\(\mathbf{\Delta}\)} & \multicolumn{2}{c}{**Energy Cost of Transport (kcal\(\cdot\)kg\({}^{-1}\cdot\)km\({}^{-1}\))**} & \multirow{2}{*}{**Effect Size**} \\  & & & & **\%** & & **\(\mathbf{P_{\text{[PERSON]}}}\)** & & **\(\mathbf{d_{\text{[PERSON]}}}\)** \\ \hline MIZ & 19 & \(v_{1}\) & 0.02 & 2.38 & 0.555 & & 0.43 & small \\  & 19 & \(v_{2}\) & 0.02 & 2.10 & 0.230 & & 0.53 & moderate \\  & 19 & \(v_{3}\) & 0.03 & 3.23 & \(<\)0.001 & * & 0.97 & large \\  & 57 & \(\mathcal{O}\) & 0.03 & 2.57 & \(<\)0.001 & * & 0.60 & moderate \\ \hline PUM & 19 & \(v_{1}\) & 0.01 & 0.89 & 1.000 & & 0.20 & small \\  & 19 & \(v_{2}\) & 0.02 & 1.80 & 0.230 & & 0.55 & moderate \\  & 19 & \(v_{3}\) & 0.02 & 1.86 & 0.015 & * & 0.61 & moderate \\  & 57 & \(\mathcal{O}\) & 0.02 & 1.52 & 0.016 & * & 0.42 & small \\ \hline SAU & 19 & \(v_{1}\) & 0.02 & 1.68 & 0.311 & & 0.53 & moderate \\  & 19 & \(v_{2}\) & 0.02 & 2.40 & 0.023 & * & 0.81 & large \\  & 19 & \(v_{3}\) & 0.03 & 2.83 & \(<\)0.001 & * & 1.17 & large \\  & 57 & \(\mathcal{O}\) & 0.02 & 2.31 & \(<\)0.001 & * & 0.80 & large \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 5 Performance Evaluation
### Discussion and Implications  
The results presented in this study demonstrate the effectiveness and robustness of the proposed FLDQN algorithm in solving travel time minimization problems within MARL scenarios. This work addresses the limitations of traditional single-agent RL by introducing a cooperative, FL-based multi-agent approach. In FLDQN, agents are able to share their experiences through a federated server in an efficient manner. This allows agents to influence each other's policies and make optimal decisions while maintaining data privacy, as only model weights are exchanged rather than raw data. This characteristic makes FLDQN particularly suitable for scenarios requiring collaboration and knowledge sharing, especially where data privacy is crucial. The obtained results indicate that with more agents involved, the better and more robust the overall system becomes. This is because agents can collectively explore the environment more effectively, accelerating the exploration process for other agents. Such cooperation fosters a faster convergence towards optimal decision-making and enhances the system's ability to adapt to dynamic conditions.  
To validate the proposed algorithm, we employ the SUMO simulation environment, which has been widely validated in the literature [22, 37, 40, 41] for its ability to simulate scenarios closely resembling real-world traffic conditions. Based on these studies, SUMO is selected as an appropriate tool to demonstrate the feasibility and effectiveness of our methodology. It is worth noting that FLDQN has the potential to be generalized and applied to other real-world scenarios beyond traffic congestion. Any setting where multiple agents require cooperative decision-making, secure data handling, and shared learning could benefit from this approach. Furthermore, this study paves the way for future research to explore the applicability of FLDQN in domains such as smart fleet management, supply chain management, and collaborative robotics, where the principles of FL can be extended.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Data-driven Intelligent Diagnosis Algorithm
#### 3.3.2 Support Vector Machine  
The support vector machine (SVM), a commonly used machine learning theory, was proposed by [PERSON] in 1999 [89], whose main idea was to maximize the distance between training data and a decision boundary in the feature space. The decision boundary can be formed by separating the hyperplane and the training vectors closest to the decision boundary, which are called the support vectors, in order determine its position. Suppose the input of training samples is \(x_{i}\in R^{d}\) and the output is \(y_{i}\in\{-1,1\}\), the purpose of SVM is to solve the following problem:  
\[\min_{w,b}\frac{\|x\|^{2}}{2},s.t.y_{i}\Big{(}w^{T}x_{i}+b\Big{)}\geq 1 \tag{13}\]  
Compared with NN, SVM is more suitable for small sample classification. However, the basic model of SVM can only be used to solve the two-class problem. To meet practical needs, [PERSON] et al. used one-versus-all SVMs for the PMSM inter-turn short circuit fault classification [90]. Furthermore, wavelet SVM is considered to be well suited for complex mechanical fault signals [91]. And [PERSON] et al. used support vector regression (SVR) to map the feature vectors from current and torque into the different demagnetization levels of PMSM [92]. Furthermore, since the performance of SVM depends largely on the choice of SVM parameters, parameter optimization algorithms, such as particle swarm optimization (PSO) [93], should also be noted.
## Materials and Methods
### General Methodology
#### 2.3.6 Seasonal Monitoring  
To provide a qualitative validation of LAI and pigment estimations, estimations using the best inversion strategies (Sections 3.3.2 and 3.3.3) were computed, and seasonal variations were monitored and compared to expected variations. The seasonal monitoring involved LAI and foliar C\({}_{ab}\) and Car. Estimation maps were built using AVIRIS-C acquisitions done in the spring (DOY 122 and 119), summer (DOY 155 and 159), and fall (DOY 262 and 286) of 2013 and 2015. Each estimation's uncertainty is the STDB of the variables' estimations (STDB are presented in Sections 3.3.2 and 3.3.3). Changes in the site's mean LAI and foliar C\({}_{ab}\) and Car were considered significant if they were greater than the STDB of their respective variables.
# Study of the Direct Metal Deposition of AA2024 by ElectroSpark for Coating and Reparation Scopes
## 3 Results and Discussion  
### Microstructure of AA2024 Al-Cu-Mg Alloy  
The microstructure of the substrate cross-section is shown in Figure 2a,b. The optical micrograph (OM) after chemical etchant (Figure 2a) and SEM/EDS analyses (Figure 2b) reveal that the intermetallic particles are mainly roughly spherical Al\({}_{2}\)CuMg (n\({}^{\circ}\)1 Figure 2a,b) of approximate size 5-10 \(\upmu\)m, and light grey CuAl\({}_{2}\) particles (n\({}^{\circ}\)2 Figure 2a,b). Moreover (Mn,Fe)\({}_{3}\)SiAl\({}_{12}\) (light brown n\({}^{\circ}\)3 Figure 2a,b) and Al(Cu,Fe,Mn) phase (dark brown n\({}^{\circ}\)4 Figure 2a,b) have been detected. These last particles are as big as 10 \(\upmu\)m and with an irregular shape. Furthermore, according to literature [6,30], the fine dispersoids observed in the Al matrix could be both Cu\({}_{2}\)Mn\({}_{3}\)Al\({}_{20}\) and/or Al\({}_{2}\)Cu. Figure 2c displays the as-cast microstructure of the electrode. The electrode microstructure is homogeneous and consisting of a matrix of Al studded by the same intermetallic particles observed in the substrate but having a smaller size.  
Figure 3 shows the substrate microstructure after anodizing both in the ST-LT (Figure 3a) and in the ST-RD plane (Figure 3b). As expected, the grain structure is elongated, especially in the rolling direction (Figure 3b). Both the planes exhibit the same average micro-hardness (HV\({}_{0.05/15}\)) equal to 141 \(\pm\) 2.2. The average micro-hardness (HV\({}_{0.05/15}\)) of AA2024 electrode is 124 \(\pm\) 1.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 4 Model Development and Training
### Model Optimization Methods and Hyperparameter Tuning  
In this study, hyperparameter tuning and training stabilization techniques were systematically applied to improve the performance of retriever and generator models.  
In the retriever learning stage, learning rate (\(1\times 10^{-5}\), \(2\times 10^{-5}\), \(5\times 10^{-5}\)), batch size (8, 16, 32), and temperature parameter (0.01, 0.05, 0.1) were set as variables, and optimal combinations were determined through grid search. Average search similarity and Top-k accuracy were used as evaluation criteria, with experimental results showing that the combination of learning rate \(2\times 10^{-5}\), batch size 16, and temperature 0.05 had the best convergence speed and performance.  
In generator learning, we quantitatively analyzed performance changes across different epoch numbers during Instruction Tuning-based fine-tuning. The LoRA-related parameters (rank, alpha, dropout) were empirically optimized through exploratory experiments, and the final configuration (rank = 64, alpha = 128, dropout = 0.1) was adopted for subsequent training. These settings align with prior recommendations for large-scale parameter-efficient fine-tuning [20], where the alpha-to-rank ratio of 2:1 is commonly used to maintain training stability, as further supported by QLoRA guidelines [21]. To prevent overfitting, validation loss was continuously monitored with early stopping conditions, terminating training when the loss failed to improve for three consecutive iterations. As a result, the generator model showed the best performance at three epochs, although training was performed up to five epochs for comparative analysis.  
Additionally, Mixed Precision Training, effective large-scale batch learning through gradient accumulation, and memory occupancy reduction strategies through GradientCheckpointing were applied in parallel to ensure stability during long-term training and minimize resource consumption.