esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Simulating Radiative Transfer in Complex Environments
## 2 Model Description
### Proof-of-Concept Simulation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Layer** & **Name** & **Albedo** & **Absorptivity** & **Emissivity** & **Thickness [m]** & **Conductivity** & **Density** & **Specific Heat** & **(log K)-1** \\ \hline \multirow{4}{*}{**Concrete** **Wall**} & Outside & Concrete & 0.2 & 0.8 & 0.9 & 0.2 & 1.311 & 2240 & 836.8 \\  & Middle & \
\begin{tabular}{c} Steel frame \\ Insulation \\ \end{tabular} & 0.2 & 0.8 & 0.9 & 0.08 & 0.049 & 265 & 837 \\ \cline{2-9}  & Inside &
\begin{tabular}{c} 1/2N \\ Gypsum \\ \end{tabular} & 0.2 & 0.8 & 0.9 & 0.01 & 0.16 & 785 & 830 \\ \hline
**Glass Wall** & All & Glass & 0.25 & 0.05 & 0.94 & \(3\times 0.01\) & 0.029 & 830 & 840 \\ \hline \multirow{2}{*}{**Brick Wall**} & All & Brick & 0.15 & 0.85 & 0.9 & \(3\times 0.08\) & 0.66 & 1500 & 1000 \\ \cline{2-9}  & Outside & \begin{tabular}{c} Default \\ membrane \\ \end{tabular} & 0.2 & 0.8 & 0.9 & 0.01 & 0.16 & 1121 & 1460 \\ \cline{2-9}  & Middle & \
\begin{tabular}{c} Insulation \\ \end{tabular} & 0.2 & 0.8 & 0.9 & 0.14 & 0.049 & 265 & 837 \\ \cline{2-9}  & Inside & Metal Decking & 0.2 & 0.8 & 0.9 & 0.01 & 45 & 7680 & 418 \\ \hline \multirow{4}{*}{**Coal Roof**} & Outside & Cool material & 0.65 & 0.35 & 0.9 & 0.02 & 0.85 & 1200 & 1200 \\ \cline{2-9}  & Middle &
\begin{tabular}{c} Insulation \\ \end{tabular} & 0.2 & 0.8 & 0.9 & 0.14 & 0.049 & 265 & 837 \\ \cline{1-1} \cline{2-9}  & Inside & Metal Decking & 0.2 & 0.8 & 0.9 & 0.01 & 45 & 7680 & 418 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of material properties in the proof-of-concept simulation [51]. General simulation and model area properties are described in Table 2. Air temperature within buildings is held constant at 20 \({}^{\circ}\)C to account for the strong air conditioning cooling used in almost every building in NYC. Radiation scheme settings for the IVS simulations feature medium resolutions for lower cells at pedestrian level and low resolutions for higher cells above 8 m. Due to the large size of the model area featuring high-rise buildings and covering multiple different urban morphologies and typologies in a high spatial resolution, IVS settings could not feature higher view facet resolutions due to memory limitations. With these settings, the simulation yields around 50 GB of RAM for the IVS simulation.
## 3 Method
### BTDM Encoder  
The BTDM encoder takes as input a sentence \(S\) with \(n\) tokens. To obtain a sentence contextual representation \(\mathbf{h}_{i}\) for each token, we generated an initial representation with a large-scale pre-trained language model (PLM). We utilized the BERT [20] encoder so that our comparisons were consistent, but alternative encoders, such as Glove [21] or RoBERTa [22], would work just as well. The detailed operations are as follows:  
\[\mathbf{h} =\Big{\{}\mathbf{h}_{1},\mathbf{h}_{2},...,\mathbf{h}_{n}|\mathbf{h}_{i}\in \mathbb{R}^{d\times 1}\Big{\}} \tag{1}\] \[=\text{PLM}(\{x_{1},x_{2},...,x_{n}\})\]  
where \(d\) is the embedding dimension, \(\mathbf{h}_{i}\) is the representation of \(i\)-th token in sentence \(S\), and \(\mathbf{h}\) is the contextual representation. Previous excellent works, such as CasRel [17] or SPN [23] PRGC [19], all used the single text feature \(h\) to identify triples. However, we believe that each of the three elements of the triple should have a unique set of features.  
We obtained the relational embedding and added it to the state of subject-object alignment, which also helps to strengthen the interaction between entity pairs and relations. For this reason, we further extracted subject features, object features, and relation features on the basis of text features \(h\). Notably, \(h\) is fed into three different feed-forward networks(FFN) to generate subject features, object features, and relation features (denoted as _hs_, _ho_, _hr_, respectively). The detailed operations are as follows:  
\[\begin{split}&\textbf{{hs}}=\text{FFN\_S}(\textbf{{h}})\\ &\textbf{{hr}}=\text{FFN\_R}(\textbf{{h}})\\ &\textbf{{ho}}=\text{FFN\_O}(\textbf{{h}})\end{split} \tag{2}\]
## 3 Results and Discussion
### Analysis of Particle Morphology Characteristics  
Scanning electron microscopy (SEM) can be directly used to obtain the morphology of dustfall and subsequently analyze the mineral composition of a single particle, which can provide not only information on its source, but also an important method to understand atmospheric chemical reactions [57].  
Figure 5 shows the electronic images of typical dustfall samples in 2019 and 2020. It can be found that there were significant differences in the dustfall samples before and after the epidemic's occurrence. The particle shapes can be generally classified as spherical and massive particulates. The morphology of the typical dustfall samples in 2019 is shown in Figure 5a. The particles of the YP district are mainly spherical and massive, most likely due to industrial activity products of high temperature processes such as metal smelting and cement [58]. The particle appearance of the dustfall sample in the JA district (a typical residential district) is mainly elongated shapes. This phenomenon is most likely caused by frequent local building construction [59]. The particle appearance in the dustfall sample of the XH district (a typical industrial district) shows blocky and irregular shapes, which may be due to the interference of road dust [52]. The dustfall sample in the BS district (a typical industrial district) has a mostly columnar morphology, further indicating the significant impact of industrial activities. In addition, the SEM characteristics of typical dustfall samples in 2020, sharing the same locations with the samples in 2019, are shown in Figure 5b. The particle appearances of the dustfall samples in different districts are spherical, massive, and have a few irregularities as a whole. The spherical shape is the most prominent, indicating the significant impact from human activities, such as building dust and automobile exhaust emissions. In summary, the morphological characteristics of the dustfall samples in 2019 and 2020 are mainly dominated by road dust, intensive traffic, and the impact of nearby construction, and part of the pollution comes from natural pollution sources such as soil or rock weathering dust. Significantly, compared with 2020, the morphological types of dustfall samples in 2019 are more extensive and characteristic, indicating that the forms of human activities were more diverse before the epidemic.  
Figure 5: Morphology characteristics of typical dustfall samples.
## 2 Background
### Weather Data
#### 2.1.2 Hazardous Weather Alerts  
Hazardous weather conditions can be detected through the analysis of weather information, in accordance with governmental regulations. This process involves a meticulous evaluation of meteorological data and climatic patterns, aligning with established guidelines and protocols. Once identified, hazardous weather alerts serve as a critical early-warning system, notifying individuals and authorities about imminent threats such as hurricanes, tornadoes, severe storms, flooding, wildfires, and extreme temperatures. They provide essential information that can help people take proactive measures to protect their lives and properties.  
Examples of hazard alerts in the literature encompass a broad spectrum of weather-related phenomena. Some alerts do not depend significantly on the location. This is the case for the fire and wind hazards. Nevertheless, other weather conditions can significantly depend on this parameter. For example, coastal areas are more susceptible to hurricanes, storm surges, and coastal flooding, while regions prone to tectonic activity may face seismic hazards. In contrast, areas in the vicinity of forests or grasslands may be at risk of wildfires during dry seasons. Similarly, regions in colder climates may face challenges related to heavy snowfall and ice accumulation, while areas near rivers may be vulnerable to flooding. For instance, in recent years coastal settlements in the central area of Chile have been affected by extreme events such as swells [51]. This phenomenon is due to long-wavelength ocean waves generated by a combination of factors such as distant storms, atmospheric pressure changes, fetch, and wind speed and duration, with wind being the most predominant factor [52]. In the same area, in both coastal and offshore sectors, a sequence of large-scale wildfires have occurred, even requiring international assistance to mitigate their effects [53]. These events can be human-induced as well due to a combination of weather-environmental factors like drought, high temperatures, low humidity, winds, and the accumulation of dry vegetation [54].  
It is in light of these location-specific considerations that the information embedded in the BIM model, encompassing topography and geographical characteristics, plays an important role in discerning the precise hazardous weather conditions that a structure may encounter.
# Plant Disease Diagnosis in the Visible Spectrum
## 3 Materials and Methods
### Plantvillage Dataset and Benchmarking  
In order to compare the performance of our method, we used a set of 4954 images selected from the PlantVillage dataset analyzed in [1], which employed a deep learning approach specifically trained on the dataset, which is considered the benchmark.  
This dataset is composed of images of 256 \(\times\) 256 dimension that belong to different classes, which are labeled as either healthy or as having only one specific disease; therefore, those images that belong to a class associated with a disease presenting colors similar to any of the three diseases considered in this work were selected.  
As the proposed methods generate a diagnosis of the portion of the plant that has a specific disease, different levels of thresholds were used to transform the original portion diagnosis into a binary diagnosis. If the portion of the plant that has colors associated with a specific disease is larger or equal to a threshold, the plant is classified as having that disease.  
Since the dataset is composed of classes of both healthy and diseased plants, we propose two metrics to evaluate the accuracy of the diagnosis.  
* Diseased class. In the case of having a class associated to a disease, the accuracy is calculated as follows, \[ACCD=\frac{DI}{TI},\] (24) where \(ACCD\) is the accuracy; \(DI\) is the number of images classified as diseased; \(TI\) is the total number of images belonging to that class.
* Healthy class. In the case of having a class associated with healthy plants, the accuracy is calculated as follows, \[ACCH=1-\frac{DI}{TI},\] (25) where, \(ACCH\) is accuracy,_DI_ is number of images classified as diseased,  
_TI_ is the total number of images belonging to that class.  
Table 1 presents the results achieved with different levels of thresholds; the following is necessary to be specified.  
* Class is the name of the class.
* SZ is the number of images belonging to that class.
* T is the average processing time in milliseconds recorded using a machine with Intel i7 @ 1.90 GHz 2.40 GHz and 8 GB RAM.
* When using a threshold of 1%, the accuracy achieved is 1%. If the portion of the plant that has colors associated to a disease is larger or equal to 1%, the plant is considered diseased.
* When using a threshold of 2%, the accuracy achieved is 2%. If the portion of the plant that has colors associated to a disease is larger or equal to 2%, the plant is considered diseased.
* When using a threshold of 5%, the accuracy achieved is 5%. If the portion of the plant that has colors associated to a disease is larger or equal to 5%, the plant is considered diseased.
* When using a threshold of 20%, the accuracy achieved is 20%. If the portion of the plant that has colors associated to a disease is larger or equal to 20%, the plant is considered diseased.
* MAX is the maximum accuracy achieved per class.
* BM is the accuracy achieved by the benchmark.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 4 Results and Discussions
### Path Planning Optimization
As can be seen, the execution time of PSO and GA algorithms is almost the same, while PSO-GA executes far faster than the other two algorithms, about two times in complex scenarios. This superior performance becomes even more critical when the algorithms run on low-power computing devices, such as onboard processors or embedded systems.
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 4 Experiment and Analysis
the total number, correct number, and accuracy rate of the finally matched line segments is much higher. This is because although the original images have many LJL structures to try to match, due to serious distortions, the LJL structure descriptors to be matched are relatively different. This leads to large differences in descriptor vector distances, resulting in greater difficulty in matching and an increase in the mismatching rate. 4. Comparing results of the first and second image pairs, it is found that both of these image pairs are down-looking and back-looking; however, for the first image pair, the correct matching rate is 62.9% under LJL processing, and 79.1% under Perspective-LJL; this is lower than the rate of 78.3% and 93.2% for the second image pair. Meanwhile, for the increase in the number of matched lines (both total number and the correct number), the effect of the second image pair is significantly better than that of the first image pair. In other words, the matching effect of the first image pair and the improving effect of Perspective-LJL on the first image pair is weaker than those observed in the second image pair. By observing the buildings' distribution and topography in these two surveying areas, it can be seen that the elevation difference of buildings in the first image pair is large. There are many spatial planes with obvious differences, and occlusions are more serious. In the second image pair, most buildings are flatly distributed, and there are uniform deformations. It is appropriate to use a plane to fit the surfaces of these buildings, and the occlusions are not obvious. When the perspective transformation model is used to rectify buildings to a new spatial plane, the deformations can be well rectified. 5. Compared to the first and second image pairs, the Perspective-LJL method has more obvious effects on the extra-wide-baseline images of the third and fourth pairs, which fully prove the effectiveness of the proposed method. 6. By using the Perspective-LJL method, the matching accuracy rate is improved to 79.1%, 93.2%, 86.9%, and 88.8%, respectively; however, there still exists a gap of improving the matching rate up to 97% of the short-baseline images. This indicates that the perspective transformation model can only eliminate some of the perspective effects, and cannot completely solve the problems caused by large viewing angles.
## 2 Materials and Methods
### Machine Learning
#### 2.2.2 Support Vector Machines  
Support Vector Machines (SVMs) constitute a supervised machine learning algorithm that can be used for classification or regression problems. The basic idea of the SVM regression method is to find the linear discriminant function that reflects the characteristics of the available training data as closely as possible and that fits the statistical learning theory. Similar to classification, kernel functions are used in regression to handle nonlinear cases. Two situations that can be encountered in SVMs are that the data are in a structure that can be separated linearly or in a structure that cannot be separated linearly. In case the samples can be separated linearly, the aim is to find the most suitable separator plane dividing both classes equidistantly [32].  
\[(\text{x}_{i},\text{y}_{\text{i}}),\text{x}_{\text{i}}\in\mathbb{R}^{\text{n }},\text{y}_{\text{i}}\in\{1,-1\},\text{i}=1,\dots,1 \tag{3}\]  
The SVM model for Equation (3) is given in Equation (4).  
\[\min\text{w},\text{b}\frac{1}{2}\text{w}^{\text{t}}\text{w}+C\sum_{\text{i}= 1}^{1}\delta(\text{w},\text{b};\text{x}_{\text{i}},\text{y}_{\text{i}}) \tag{4}\]  
\(\delta(\text{w},\text{b};\text{x}_{\text{i}},\text{y}_{\text{i}})\) is the loss function, and \(C\geq 0\) is the penalty parameter for the training error. Two loss functions that are widely used are given in Equations (5) and (6).  
\[\max\Bigl{(}1-\text{y}_{\text{i}}\Bigl{(}\text{w}^{\text{T}}\sigma(\text{x}_{ \text{i}})+\text{b}\Bigr{)},0\Bigr{)} \tag{5}\]  
\[\max\Bigl{(}1-\text{y}_{\text{i}}\Bigl{(}\text{w}^{\text{T}}\sigma(\text{x}_{ \text{i}})+\text{b}\Bigr{)},0\Bigr{)}^{2} \tag{6}\]  
Here, \(\sigma\) represents the function used to move the training data to a higher dimensional space. The decision function for each x test data is given in Equation (7).  
\[\text{f}(\text{x})=\text{sgn}\Bigl{(}\text{w}^{\text{T}}\sigma(\text{x})+ \text{b}\Bigr{)} \tag{7}\]For SVM training, the RBF kernel function was used, which gives better results than linear, polynomial, and RBF (Radial Basis Function) functions. The formula is given in Equation (8):  
\[\mathrm{K}\big{(}\mathrm{x}_{i},\mathrm{x}_{j}\big{)}=\exp\!\left(-\gamma\big{|} \big{|}\mathrm{x}_{i}-\mathrm{x}_{j}\big{|}\big{|}^{2},\gamma\right)>0 \tag{8}\]  
\[\gamma=\frac{1}{2\sigma^{2}} \tag{9}\]  
In the equation, \(\big{|}\big{|}\mathrm{x}_{i}-\mathrm{x}_{j}\big{|}\big{|}^{2}\) shows vectorial distance. The \(\gamma\) parameter is a free variable, and it determines the width of function. As its value increases, the width of the bell-shaped function becomes narrower, and, as its value decreases, the width increases [33].  
The Support Vector Machine (SVM) algorithm predicts outcomes by identifying the optimal hyperplane that separates data points. During model training, k-fold cross-validation was employed to optimize the C and \(\epsilon\) hyperparameters, ensuring improved model performance.
## 2 Materials and Methods
### HPLC Analysis  
The tested phenolic acids, i.e., gallic acid (GA), 3,4-dihydroxybenzoic acid (34 DHBA), 2,5-dihydroxybenzoic acid (25 DHBA), chlorogenic acid (ChA), and 3-hydroxybenzoic acids (3 HBA), were separated on a 125 \(\times\) 4 mm column Hypersil ODS using the HPLC system from Knauer (Berlin, Germany) along with the WellChrom UV K-2600 detector. The mobile phase included 1% acetic acid and methanol (80:20), and the flow rate was 1 mL/min. Twenty uL of the sample were injected into the column. The compounds were identified according to their retention time (RT) at a wavelength of 280 nm. The calibration curve exhibited correlation coefficients of 0.9989 for GA (y = 31740x \(-\) 3.7628), 0.9993 for 34 DHBA (y = 21523x \(-\) 1.8144), 0.9997 for 25 DHBA (y = 32115x \(-\) 0.9831), 0.9998 for ChA (y = 39852x \(-\) 2.2859), and 0.9991 for 3 HBA (y = 15820x \(+\) 1.8199). Each sample was examined three times.
# One-Dimensional Nanostructure Engineering of Conducting Polymers for Thermoelectric Applications
###### Abstract  
The past few decades have witnessed considerable progress of conducting polymer-based organic thermoelectric materials due to their significant advantages over the traditional inorganic materials. The nanostructure engineering and performance investigation of these conducting polymers for thermoelectric applications have received considerable interest but have not been well documented. This review gives an outline of the synthesis of various one-dimensional (1D) structured conducting polymers as well as the strategies for hybridization with other nanomaterials or polymers. The thermoelectric performance enhancement of these materials in association with the unique morphologies and structures are discussed. Finally, perspectives and suggestions for the future research based on these interesting nanostructuring methodologies for improvement of thermoelectric materials are also presented.  
thermoelectric; conducting polymer; one dimensional; nanostructure; nanocomposites +
Footnote †: journal: Applied Physics Letters
# Optimization and Experimental Verification of Lower
## 4 Conclusions  
In this study, an optimization was conducted on the lower articulation, a critical component of the low-floor tram, and experimentally validated through structural testing. The key findings from this research are as follows:  
1. Eight factors affecting the structural safety of the lower articulation were identified, and their correlations with stress and weight were established.
2. The ISCD-II technique was employed to minimize the number of analyses to 16. Sensitivity analyses were used to identify the primary design factors significantly influencing stress and weight.
3. The structural safety of the optimized lower articulation was evaluated through both a structural analysis and experimental validation.
4. A comparison between the structural analysis and test results revealed an average error within 10% for each load direction, thereby validating the accuracy of the analysis.
5. Non-destructive testing conducted after completing the fatigue tests confirmed the absence of fatigue cracks, indicating that adequate durability was achieved.
6. The performance and durability of the lower articulation can be tested and verified through real vehicle tests by installing it on a low-floor tram.  
Data curation, [PERSON], J.-W.J. and J.C.; software, W.-J.H.; writing--review and editing, J.-W.J. and H.J. All authors have read and agreed to the published version of the manuscript.  
This R&D is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2021-KA163484).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy of this research.  
Author [PERSON] was employed by the company RND Department, THEJST. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Survey on Fault Diagnosis of Rolling Bearings
## 3 Rolling Bearing Fault Detection
### Decomposition-Based Fault Detection Methods
First, the end effect and the mode confusion; Second, the recursive procedure lacks error feedback and correction; Third, the decomposition results are easily affected by noise and abnormal components and have no physical meaning. [PERSON] et al. (2019) transformed signal decomposition into a constrained variational problem and proposed variational mode decomposition (VMD), in which the central frequency and bandwidth of each mode depend on the optimal solution variational model found iteratively, avoiding mode aliasing and improving the decomposition accuracy. The decomposition effect of VMD is affected by the number of decomposed modes K and the penalty factor \(\alpha\). The particle swarm optimization (PSO) and GA were applied to search the parameter values to enhance the performance of VMD for fault detection ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020). [PERSON] et al. (2020) proposed the singular spectrum decomposition (SSD), which can adaptively determine the embedding dimension required for each singular value decomposition process and decompose the original signal in narrow-banded components. SSD has the advantages of small end effect, weak mode aliasing and no parameter selection. EMD does not require parameter selection either, but SSD is more effective in decomposing nonlinear and nonstationary time series. There was the development of SSD methods that could improve the decomposition accuracy and the detection ability of weak fault signals, which could be applied more effectively for the fault detection of mechanical equipment ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020).