esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Image feature extraction
#### Texture Features  
Normally texture analysis can be grouped into four categories: model-based, statistical-based, structural-based, and transform-based methods. Structural approaches seek to understand the hierarchal structure of the image, while statistical methods describe the image using pure numerical analysis of pixel intensity values. Transform approaches generally perform some kind of modification to the image, obtaining a new \"response\" image that is then analyzed as a representative proxy for the original image. Model-based methods are based on the concept of predicting pixel values based on a mathematical model. In this research we focus on three well-known texture analysis techniques: _co-occurrence matrices_ (a statistical-based method), _Gabor filters_ (a transform-based method), and _Markov Random Fields_ (a model based method).  
Co-occurrence matrices focus on the distributions and relationships of the gray-level intensity of pixels in the image. They are calculated along four directions (0\({}^{\rm o}\), 45\({}^{\rm o}\), 90\({}^{\rm o}\), and 135\({}^{\rm o}\)) and five distances (1, 2, 3, 4 and 5 pixels) producing 20 co-occurrence matrices. Once the co-occurrence matrices are calculated, eleven Haralick texture descriptors are then calculated from each co-occurrence matrix. Although each Haralick texture descriptor is calculated from each co-occurrence matrix, we averaged the features across all distance/direction pairs resulting in 11 (instead of \(11\times 4\times 5\)) Haralick features per image.  
Gabor filtering is a transform based method which extracts texture information from an image in the form of a response image. A Gabor filter is a sinusoid function modulated by a Gaussian and discretized over orientation and frequency. We convolve the image with 12 Gabor filters: four orientations (0\({}^{\rm o}\), 45\({}^{\rm o}\), 90\({}^{\rm o}\), and 135\({}^{\rm o}\)) and three frequencies (0.3, 0.4, and 0.5), where frequency is the inverse of wavelength. We then calculate means and standard deviations from the 12 response images resulting in 24 Gabor features per image.  
Markov Random Fields (MRFs) is a model based method which captures the local contextual information of an image. We calculate five features corresponding to four orientations (0\({}^{\rm o}\), 45\({}^{\rm o}\), 90\({}^{\rm o}\), 135\({}^{\rm o}\)) along with the variance. We calculate feature vectors for each pixel by using a 9 estimation window. The mean of four different response images and the variance response image are used as our five MRF features.
# Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains
## 4 Experimental Results and Discussion  
### Datasets  
We evaluated our method on the Market1501, DukeMTMC-reID and MSMT17 datasets. Following previous works, we used rank-1 accuracy of the cumulative matching characteristics and the mean average precision (mAP) accuracy for evaluation on all datasets.  
**Market1501**[3], a large public benchmark dataset on the ReID task, was collected using six different outdoor cameras in the campus of Tsinghua University. The datasetincludes training and test sets. The training set contains 12,936 images and 751 pedestrian IDs, whereas the test set contains 19,732 gallery images and 3386 query images from 750 other pedestrian IDs. Images of each pedestrian were captured by at least two cameras.  
**DukeMTMC-reID**[4], a ReID subset of the Duke-MTMC dataset, was collected simultaneously using eight cameras at Duke University. The training set contains 16,522 images from 702 people, and the test set contains 2228 query images and 17,661 gallery images from 702 other pedestrians.  
**MSMT17**[13] was collected with 15 cameras. The dataset was randomly divided according to the training-test ratio of 1:3. Finally, the training set contains 1,041 pedestrians with a total of 32,621 bounding boxes, whereas the test set includes 3060 pedestrians with a total of 93,820 bounding boxes. From the test set, 11,659 bounding boxes were randomly selected as query images, and the other 82,161 bounding boxes were used as gallery images.
## 1 Introduction
In theory, Synthetic Aperture Radar (SAR) systems should provide the best data for capturing the hydrodynamics of pothole wetlands as SAR can detect open water and flooded vegetation with proper polarimetric configurations [26; 27; 28], and can provide a dense time-series data stack to capture all temporary and seasonal wetlands. Operationally, SAR does provide a few challenges. For instance, Sentinel-1 SAR provides weekly data for everywhere in the PPR,but prior studies from [PERSON] et al. [29] have shown poor performance of Sentinel-1 to distinguish seasonally flooded wetlands from native grasslands in the PPR of Alberta, Canada. More recent work by [PERSON] et al. [30] applying Sentinel-1 SAR to multi-temporal water body detection in North Dakota, United States showed more promise, however. Other studies have used RADARSAT-2 to capture pothole/wetland hydrodynamics [31; 32], but scaling up RADARSAT-2 workflows for large-scale inventory generation remains expensive and challenging. RADARSAT Constellation Mission (RCM) may be the next technological advancement in operational prairie pothole wetland mapping and monitoring, but a larger time-series stack is probably needed which is not currently available. Knowing the limitations of traditional wetland classifications in the PPR and success of prior studies using multi-temporal EO data to capture hydrodynamics in the region, this study investigates the application of multi-temporal S2 data and GEE to map pothole wetland dynamics. We assessed how these time-series data can be used to produce a wetland inventory for areas in the PPR of Alberta, Canada. Specifically, we tested how hydroperoid data plus ancillary data could be used to generate an inventory at the CWCS class level and be further enhanced with water permanence from the AWCS wetland type level (Table 1). We also assessed a realistic minimum mapping unit (MMU) of these wetlands knowing that these features are typically very small. Our goal is to present a framework for operational mapping of PPR wetlands achieved with open-access EO data and no requirement of prior training and validation data.
## 3 Method Design
### Data Preprocessing  
To evaluate the effectiveness of the proposed method, this paper selects the publicly available Web API Dataset [25] from Github for experimentation. This dataset contains information on various Web APIs, including their descriptions, parameters, requests, and responses, totaling 1123 entries. Among them, there are 624 sensitive data entries and 499 non-sensitive data entries. This dataset can be used to train models to understand API functionality, input/output types, semantic relationships, and more. The dataset sources include public API documentation and online API platforms.The raw dataset may suffer from issues such as missing values, outliers, noise, or data imbalance. Data preprocessing steps are essential to enhance sample quality. Given the specificity of API sensitive data samples, the main preprocessing techniques applied include removing duplicate samples, eliminating empty samples, URL decoding, and removing redundant white spaces. The experimental data are cleaned and formatted, and manual annotation is performed to  
Figure 3: The training process of LoRA based on federated large models.  
indicate whether each data point contains sensitive information, aligning with the model's input requirements.
###### Abstract  
Globally, there is a significant trend in the loss of native forests, including those of the _Polylepis_ genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Stigterras project topographic data. The scenarios were categorized into two groups: SCI-SC6, combining 5 m resolution data, and SC7-SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing _Polylepis_ stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing _Polylepis_ from non-_Polylepis_). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the _Polylepis_ class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the _Polylepis_ stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.  
Andes Mountains; Google Earth Engine; NICFI; random forest; RFE Article
# Slipping Trend Prediction Based on Improved Informer
## 4 Experiment
### Analysis of Rotation Velocity and Axial Force Before and After Slipping  
In the train operation data, the percentage of axial force can well represent the traction force of a single axle of the locomotive, and the calculation formula is as follows:  
\[f_{0}=\frac{F}{100}\times 100\% \tag{29}\]  
where \(f_{0}\) is the percentage of axial force and \(F\) is the uniaxial traction force. The percentage of single-axle axial force in a certain period is shown in Figure 7. After the locomotive reaches the maximum torque (50 s), multiple slipping phenomena occur after 150 s, resulting in a significant decrease in axial force. The improved Informer proposed in this paper can well extract relevant characteristics and classify and predict the slipping state. ROC is often used to evaluate the classifier, which can compare the detection ability of different methods. The dashed diagonal line in the ROC plot represents the performance of a random classifier. The closer the ROC curve is to the top-left corner and the farther above the diagonal line it lies, the better the classifier performs. The closer to the upper left corner of the curve, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predict** & **Accuracy \%** & **Recall \%** & **F1 \%** \\ \hline
1 s & 96.55 & 94.96 & 95.75 \\
2 s & 94.53 & 91.01 & 92.74 \\
3 s & 89.67 & 81.23 & 85.24 \\
4 s & 84.63 & 77.66 & 81.00 \\
5 s & 76.41 & 71.82 & 74.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation index table for single-axle slipping prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predict** & **Accuracy \%** & **Recall \%** & **F1 \%** \\ \hline
1 s & 94.75 & 91.23 & 92.67 \\
2 s & 92.45 & 85.36 & 88.76 \\
3 s & 89.34 & 79.63 & 84.21 \\
4 s & 88.61 & 71.81 & 79.33 \\
5 s & 86.73 & 67.41 & 75.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation index table for multi-axle slipping state prediction.  
better the method. The area under the curve is called the AUC (Area Under Curve), and the size of the AUC can reflect the detection capability more clearly. As can be seen from Figure 7, the ACU with axle traction is 0.992, and the AUC without axle traction is 0.973, which indicates that the parameter works.  
In order to make the slipping more intuitive, we counted the proportion of slipping at each stage of velocity from 0 to 2100 rpm. The percentage of slipping in each speed range is shown in Figure 8. From the data analysis, it can be seen that the slipping rate is as high as 1.73% at 1800 to 2100 rpm, which shows that slipping phenomena are more likely to occur at high speeds. In the model proposed in this paper, the weights of neural networks can be affected by these auxiliary parameters, so as to improve the performance.
# Surface-Modified Ceramic Boron Carbide as a Platform for Specific Targeting in Tumour Environments
## 1 Introduction
One approach to achieve this goal involves the functionalisation of boron carbide nanoparticles with reactive functional groups, such as -COOH or -NH2, which may facilitate the attachment of a dye or peptide. In a recent study, commercial boron carbide was functionalised using the fluorescent dyes triethylalanine and lysamine, resulting in the attachment of reactive amine groups. The modified nanoparticles were shown to translocate into mice with malignant skin tumour and thymoma cells, inhibiting the growth of neighbouring cells after neutron irradiation. While this approach improves therapeutic efficacy, it also leads to increased particle agglomeration, potentially impeding the penetration of the carrier into tumour cells [12; 13; 14]. An alternative method for modifying boron carbide surfaces to improve therapeutic efficacy involves coating B4C particles with positively charged poly-L-lysine and negatively charged polyglutamic acid followed by conjugation with transferring. In vitro tests on the cervical cancer cell line HeLa and healthy cells derived from rat kidney NRK demonstrated that the modified B4C was taken up by cancer cells via the transferrin receptor, but not by the NRK cells. Furthermore, when the conjugate was injected intravenously into mice, it was transported to the tumour and internalised by the cancer cells near the nucleus via endocytosis. These findings suggest that the modified B4C conjugate is promising as a potential boron delivery agent for BNCT [15]. In the next approach, using a pulsed laser irradiation method, boron carbide particles approximately 200 nm in size were obtained. The obtained particles were encapsulated in a graphite layer. This modification makes it possible to obtain particles characterised by easier conjugation with a given anticancer drug or to enable a specific tissue recognition ability. The graphite-coated B4C modified in this way was tested in vitro to investigate the utility of boron neutron capture therapy in a human squamous cell carcinoma (SCC) cell line and in nude SAS heterotransplanted mice. In this model, boron carbide particles are directly injected into tumours [16]. The primary objective of this study was to optimise the surfaces of boron carbide nanoparticles through modification with compounds such as dextrin, dextran T70, sorbitol, lysine, and arginine. Surface modification in subsequent stages of the research will facilitate the functionalisation of nanoparticles to target the tumour environment.
# Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter
## 4 Pattern Generation
### Semantic Similarity as a Semantic Filter
#### 4.2.2 Similarity at Word Embedding Space  
The semantic relatedness between words can be measured by comparing their contexts. In general, the context of a word is defined as its neighbor words, and a distributed word representation represents words as high-dimensional real-valued vectors by encoding the context information. Many previous studies proposed diverse methods for distributed word representation. Among them, neural-network-based word embeddings achieve state-of-the-art performance in word semantics tasks such as word analogy and word composition [31]. They use a simple but efficient model so that they can process a huge number of documents to encode context information. Therefore, we apply the word embedding proposed by [PERSON] et al. [31] to computing word similarity.  
Let a pattern \(p\) consist of \(n\) words of \(\mathbf{p}=\{pw_{1},\,\ldots,\,pw_{n}\}\) except entity words. Since each word \(pw_{i}\) can be expressed as a vector \(\mathbf{pw}_{i}\) at a word embedding space, the pattern \(p\) is expressed as the mean vector of \(\mathbf{pw}_{i}\)'s. That is, \(\mathbf{pw}\), the average word vector of \(p\) is \(\mathbf{pw}=\frac{1}{n}\sum_{i=1}^{n}\mathbf{pw}_{i}\). In a similar way, when a relation \(r\) is expressed with \(m\) words of \(\mathbf{r}=\{rw_{1},\,\ldots,\,rw_{m}\}\), each word \(rw_{i}\) is expressed as a vector \(\mathbf{rw}_{i}\) at the word embedding space. Then, the relation \(r\) is also expressed as the mean vector of \(\mathbf{rw}_{i}\)'s. That is, the relation \(r\) is represented by a vector \(\mathbf{rw}\) defined as \(\mathbf{rw}=\frac{1}{m}\sum_{i=1}^{m}\mathbf{rw}_{i}\). Since both \(p\) and \(r\) are expressed as vectors at the same space, their similarity can be computed using cosine similarity. That is,  
\[sim_{WE}(p,r)=\frac{\mathbf{pw}\cdot\mathbf{rw}}{||\mathbf{pw}||||\mathbf{rw }||}.\]
## 4 Modeling of the System Components
### Refrigerant Pump
#### 4.2.3 Electromechanical Efficiency  
The electric power consumption of the pump is higher than its thermodynamic power due to mechanical losses in the shaft coupling and bearings and the electric losses in the electric motor. Considering the sum of the mechanical and electrical losses, the electromechanical efficiency is defined as the following:  
\[\eta_{el-mech}=\frac{\dot{m}(h_{ex}-h_{su})}{\dot{W}_{el,meas}}\times 100 \tag{28}\]  
where the denominator is the measured pump electric power consumption (active power). In the model, the electric power consumption is correlated to the pump reversible work using Equation (29):  
\[\dot{W}_{el,cal}=a\cdot\dot{W}_{rev}{}^{b} \tag{29}\]  
\[\dot{W}_{rev}=\dot{m}\frac{\Delta P}{\rho_{su}}=\dot{m}\frac{P_{ex}-P_{su}}{ \rho_{su}} \tag{30}\]  
where \"\(a\)\" and \"\(b\)\" are the empirical coefficients found by minimization of the objective function for electromechanical efficiency as in Equation (21). Therefore, the pump electric power consumption was calculated using Equation (31):  
\[\dot{W}_{el}=30.6\dot{W}_{rev}{}^{0.53} \tag{31}\]  
The pump electric power consumption and its electromechanical efficiency calculated using the presented empirical models are presented in Figure 15, showing good agreement between the suggested model and the experimental data.
## 3 Results and Discussion
### Comparison of Escape Time for Vtical Form Removal  
Figure 12 shows the variation in concrete strength according to the average temperature after 12 h. Based on the relationship between the maturity and concrete strength, concrete strength development after 12 h of curing time was calculated and reviewed by referring to the codes of each country [36]. This was conducted to derive the optimal mixture by establishing an objective criterion for the formwork removal time of concrete.  
Figure 11: Relation between SO3 contents of cement and compressive strength development.  
Figure 12: Variation in the concrete the strength according to the average temperature after 12 h.  
The results of this study showed that it would be difficult to apply the 10 \({}^{\circ}\)C and 12 h demolding conditions suggested by American Concrete Institute (ACI) [20] because compressive strength development was not observed in all the mixtures. Further, the application of British Standards (BS) EN [22] is difficult except for 330 EP and 330 HFS_ePC.  
The Asian (South Korea and Japan) criterion of 5 MPa [23; 24] was found to be applicable only to 330 EP and 330 HFS_ePC at 12 h. As the accurate value for demolding strength was presented, however, it is expected that the formwork removal time can be predicted by examining the compressive strength and applying the maturity. Therefore, in this study, the demolding strength of each mixture was analyzed using the Asian criterion, as aforementioned.  
Figure 13 shows the variation in the elapsed time at the strength of 5 MPa for concrete. The 5 MPa development time was examined according to the 10 \({}^{\circ}\)C condition suggested by ACI and the 20 \({}^{\circ}\)C condition, which is the curing criterion managed in the field. An order of 330P (68 h) \(>\) 350P (55 h) \(<\) 380P (41 h) \(<\) 330 HFS (35 h) \(<\) 330 HFS_ePC (24 h) \(<\) 330 EP (20 h) was found at 10 \({}^{\circ}\)C. At 20 \({}^{\circ}\)C, the following order was found: 330P (46 h) \(>\) 350P (37 h) \(<\) 380P (28 h) \(<\) 330 HFS (24 h) \(<\) 330 HFS_ePC (16 h) \(<\) 330 EP (14 h). The formwork removal time was shortened as the temperature increased, and 330 HFS_ePC and 330 EP exhibited almost similar formwork removal times.  
Further, future studies should be conducted on securing the demolding time of the formwork using environmentally friendly materials [37; 38] and the corrosion resistance according to the SO\({}_{3}\) content [39].
## 2 Methodology
### Spectral Clustering  
In graph theory, there are many clustering algorithms that assign objects into clusters with respect to their pairwise similarity measure. The success of spectral clustering is based on the fact that it does not make any assumptions about the data structure. Spectral clustering can handle complex and unknown cluster shapes; in these cases, the commonly used methods, such as K-means or mixture models, may fail.  
The idea of spectral clustering is to cluster based on the eigenvectors of a similarity matrix \(W\) defined on the set of nodes of a graph \(G(V,E)\) in which \(V=\{v_{1},\ldots,v_{N}\}\in\mathbb{R}^{N}\) is the set of nodes, \(N\) is the number of nodes in the network, and \(E\) is the set of edges. The goal is to bring similar nodes to the same cluster, thus, identifying the set of nodes that share similar characteristics. Thus, to partition the graph appropriately, one can minimize the objective function, which can be of the normalized cut type.  
For a set of clustering results \(C=\{C_{1},\ldots,C_{K}\}\) such that \(V=\cup_{k=1}^{K}C_{k}\), and \(C_{i}\cap C_{j}=\varnothing\) for \(i\
eq j\); the normalized cut is formulated as follows [26]:  
\[NC=\sum_{k=1}^{K}\frac{cut(C_{k},\overline{C_{k}})}{cut(C_{k},V)}, \tag{5}\]  
where \(K\) is the number of clusters, \(\overline{C_{k}}\subset V\) is defined as the complementary set of \(C_{k}\). The cut-weight between two subsets \(C_{k}\) and \(\overline{C_{k}}\) is defined as:  
\[cut(C_{k},\overline{C_{k}})=\sum_{v_{j}\in C_{k}}\sum_{v_{j}\in C_{k}}w_{ij}. \tag{6}\]  
A partition can be expressed as an \(N\)-by-\(K\) cluster indicator matrix \(Z\) whose elements are in \(\{0,1\}\), with \(Z(i,j)=1\) if and only if node \(i\) belongs to cluster \(j\). Let \(W\) denote the  
Figure 5: Snake computed on the network of the sixth district of Paris. The number of links in the snake is equal to 64.  
graph similarity matrix and \(D\) the diagonal degree matrix with \(D(i,j)=\sum_{j=1}^{N}w_{ij}\). The normalized cut can be equivalently written as:  
\[NC=K-Tr\Big{[}X^{T}\Big{(}D^{-1/2}WD^{-1/2}\Big{)}X\Big{]}, \tag{7}\]  
where \(X\in\mathbb{R}^{N\times\mathbb{K}}\) is the relaxed continuous-value of \(Z\) with \(X^{T}X=I_{K}\). This problem can be converted to a trace maximization, where a solution is the matrix \(X_{t}\) whose columns are the K-eigenvectors associated with the top K-eigenvalues of the Laplacian matrix defined by:  
\[L_{p}=D^{-1/2}WD^{-1/2}. \tag{8}\]  
In our work, we compute \(X\) for each time period \(t\) and we project the data points into \(span(X)\), which is the subspace spanned by the columns of \(X\), and then we apply the K-means algorithm to the projected data in order to obtain the clustering results.