esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Semantic Parameter Discovery  
Our proposed _disentangling flows_ can steer the organization of selected latent dimensions so that they provide a separation of given tags. As this audio space is mapped to parameters through \(p(\mathbf{v}|\mathbf{z})\), this turns the selected dimensions into _macro-parameters_ with a defined semantic meaning. To evaluate this, we analyze the behavior of corresponding latent dimensions, as depicted in Figure 6.  
First, we can see the effect of disentangling flows on the latent space (left), which provide a separation of semantic pairs. We study the traversal of semantic dimensions while keeping all other fixed at \(\mathbf{0}\) and infer parameters through \(p(\mathbf{v}|\mathbf{z})\). We display the 6 parameters with highest variance and the resulting synthesized audio. As previously observed for unsupervised dimensions, the semantic latent dimensions also seem to provide a very smooth evolution in terms of both parameters and synthesized audio. Regarding the precise effect of different semantic dimensions, it appears that the [_'Constant'_, _'Moving'_] pair provides a very intuitive result. Indeed, the synthesized sounds are mostly stationary in extreme negative values, but gradually incorporate clearly marked temporal modulations. Hence, our proposal appears successful to uncover _semantic macro-parameters_ for a given synthesizer. However, the corresponding parameters are quite harder to interpret. The [_'Calm'_, _'Aggressive'_] dimension also provides an intuitive control starting from a sparse sound and increasingly adding modulation, resonance and noise. However, we note that the notion of _'Aggressive'_ is highly subjective and requires finer analyses to be conclusive.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 4 Discussion
### Data Limitations  
The presented method also has limitations with respect to the input data, as it is highly dependent on the resolution of the VHR image data and the density of the LiDAR point cloud. If the resolution of the VHR image is too small, the individual modules cannot be effectively detected, while a low LiDAR point cloud density would yield an insufficient number of points to detect the tilt and aspect angles accurately. Moreover, the proposed method requires orthorectified VHR image data, which might not always be available, as well as georeferencing of VHR and LiDAR in the same geographic coordinate system. Although VHR imagery is now accessible for most urban and suburban areas through various commercial providers (e.g., Maxar, Airbus), licensing and data costs may vary by region. For regions lacking extensive LiDAR scans, alternative 3D acquisition methods may be used, such as photogrammetry, structure from motion from UAVs [29], or publicly available coarse-resolution digital surface models (e.g., derived from multiview satellite imagery). Although these alternatives often have lower spatial resolution compared with airborne LiDAR, they can still be utilized to obtain approximate surface geometric details, which are sufficient in many practical use cases of solar energy estimations such as that considered in this work. It is also reasonable to expect that the amount of publicly available georeferenced VHR and LiDAR data will increase substantially over the next decade; hence, this will provide better opportunities to utilize such a method on a larger scale.  
From a solar energy modeling perspective, region-specific meteorological data, especially horizontal global and diffuse irradiance measurements from pyranometer devices, remain central. In regions where there are no high-frequency local measurements, reanalysis datasets or satellite-based global products (e.g., NASA POWER use [57]) can provide the necessary direct and diffuse irradiance input to construct TMY for a given region.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
###### Abstract  
Three-dimensional (3D), city models have been applied in a variety of fields. One of the main problems in 3D city model utilization, however, is the large volume of data. In this paper, a method is proposed to generalize the 3D building objects in 3D city models at different levels of detail, and to combine multiple Levels of Detail (LODs) for a progressive distribution and visualization of the city models. First, an extended structure for multiple LODs of building objects, BuildingTree, is introduced that supports both single buildings and building groups; second, constructive solid geometry (CSG) representations of buildings are created and generalized. Finally, the BuildingTree is stored in the NoSQL database MongoDB for dynamic visualization requests. The experimental results indicate that the proposed progressive method can efficiently visualize 3D city models, especially for large areas.  
3D city models; symbolization; visualization; CSG representation; X3D; NoSQL +
Footnote †: journal: Geo-Informatics
## 3 Results  
### Polarization Curves Measurements  
The anodic and cathodic polarization curves of the API 5L X65 carbon steel electrode in the NaCl solution in the presence and absence of a magnetic field at various flow concentrations are given in Figure 4. The anodic current increases gradually as the flow velocity increases. However, the presence of a magnetic field in various flow conditions increases the corrosion effect exponentially. From Figure 4a-c, it can be observed that the Tafel curves show a negative shift, indicating that corrosion increases with respect to the effect of a magnetic field. Table 2 presents the fitting parameters of the API 5L X65 carbon steel in various given conditions. Both \(\mathrm{I_{corr}}\) and \(\mathrm{E_{corr}}\) increase with the increase in the flow velocity. As for the Tafel constants, they are higher for the anodic polarization curve than for the cathodic polarization curve, indicating that the anodic reaction is more dominant than the cathodic reaction in the half cell reaction process. The polarization resistance values show a decreasing trend, which corresponds with the corrosion rate that increases from A1 to C3. Corrosion rates, shown in Table 2 in the last column, have a maximum standard deviation of \(\pm\)2%.  
Figure 4: _Cont._  
Figure 4: Potentiodynamic polarization curve (a) zero Velocity, (b) 1 m/s, (c) 2 m/s.  
For A1 at the zero flow, no magnet condition, the CR is observed as 0.28 mmpy (mm/yr), which then increases to 0.33 mmpy at A2 for the N-S magnetic field and 1.00 mmpy at A3 when the N-N magnetic field is applied. The corrosion rate then increases again when both the magnetic field and a velocity of the fluid inside the pipeline is increased. This can be observed from B1 to C3. At B1, the corrosion rate is observed as 0.70 mmpy, which then increases to 1.11 mmpy at B2 and 1.54 mmpy at B3. The maximum CR is observed when the flow velocity is maximal, that is at 2 m/s. At C1, the CR is observed as 0.78, mmpy, which then increases at C2 where the CR is 1.47 mmpy, and at C3 the CR is 2.39 mmpy.
###### Abstract  
In knowledge graph construction, missing facts often lead to incomplete structures, thereby limiting the performance of downstream applications. Although recent knowledge graph completion (KGC) methods based on representation learning have achieved notable progress, they still suffer from two fundamental limitations, namely the lack of structured reasoning capabilities and the inability to assess the confidence of their predictions, which often results in unreliable outputs. We propose the GLR framework, which integrates Graph Chain-of-Thought (Graph-CoT) reasoning, LoRA fine-tuning, and the P(True)-based confidence evaluation mechanism. In the KGC task, this approach effectively enhances the reasoning ability and prediction reliability of large language models (LLMs). Specifically, Graph-CoT introduces local subgraph structures to guide LLMs in performing graph-constrained, step-wise reasoning, improving their ability to model multi-hop relational patterns. Complementing this,LoRA-based fine-tuning enables efficient adaptation of LLMs to the KGC scenario with minimal computational overhead, further enhancing the model's capability for graph-structured reasoning. Moreover, the P(True) mechanism quantifies the reliability of candidate entities, improving the robustness of ranking and the controllability of outputs, thereby enhancing the credibility and interpretability of model predictions in knowledge reasoning tasks. We conducted systematic experiments on the standard KGC datasets FB15K-237, WN18 RR, and UMLS, which demonstrate the effectiveness and robustness of the GLR framework. Notably, GLR achieves a Mean Reciprocal Rank (MRR) of 0.507 on FB15K-237, marking a 6.8% improvement over the best recent instruction-tuned method, DIFT combined with CoLE (MRR = 0.439). GLR also maintains significant performance advantages on WN18 RR and UMLS, verifying its effectiveness in enhancing both the structured reasoning capabilities and the prediction reliability of LLMs for KGC tasks. These results indicate that GLR offers a unified and scalable solution to enhance structure-aware reasoning and output reliability of LLMs in KGC.  
knowledge graph completion; large language models; graph chain-of-thought; LoRA fine-tuning; confidence evaluation 2025157282  
1
# Evaluation of Structural Behavior of Hysteretic Steel Dampers under Cyclic Loading
###### Abstract  
This study proposes a relatively simple steel damper with high energy dissipation capacity. Three types of steel dampers were evaluated for structural performance. The first damper with U-shape had two vertical members and a semicircular connecting member for energy dissipation. The second damper with an angled U-shape replaced the connecting member with a horizontal steel member. The last damper with D-shape had a horizontal member added to the U-shaped damper. All the dampers were designed with steel plates on both sides that transmitted external shear force to the energy-dissipating members. To evaluate the structural performance of the dampers, an in-plane cyclic shear force was applied to the specimens. The D-shaped damper showed ductile behavior with excellent energy dissipation capacity after yielding without decreasing in strength during cyclic load. In other words, the D-shaped specimen showed excellent performance, with about 3.5 times the strength of the U-shaped specimen and about 3.8 times the energy dissipation capacity due to the additional horizontal member. Furthermore, the efficient energy dissipation of the proposed D-shaped steel damper was confirmed from the finite element (FE) analytical and experimental results.  
steel damper; ductility; energy dissipation; damping ratio +
Footnote †: journal: Applied Sciences
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 5 Problems and Solutions  
### Unbalanced Sample Identification Problem  
In industrial products, surface defects can also be detected with deep learning using unbalanced sample sets [137; 138]. To train the deep learning model, it is usually necessary to have a balanced sample set of samples of different categories. This ideal situation, however, almost never occurs in the real world. More often than not, the majority of data in the dataset comes from \"normal\" samples, while \"defective\" or \"abnormal\" samples only make up a small portion. Supervised learning is one of the main tasks that suffers from unbalanced sample identification. The algorithm will therefore pay more attention to categories with larger data volumes and underestimate categories with smaller data volumes, affecting the model's generalization and prediction abilities. The data-level process methods aim to maintain a consistent number of samples for all types within the training set. Resolving the unbalanced sample identification issue at the data level can be broken down into five categories: data resampling, data augmentation, class equalization sampling, data source, and synthetic sampling. It is necessary to collect more samples in fewer categories from the data source. By horizontally or vertically flipping, rotating, zooming, cropping, and other operations, we can purposefully increase the number of sample data in each category.  
Regarding data resampling [139; 140], it is good to resample a sample set to change the proportion of samples in each category, including oversampling and undersampling. Class equalization sampling groups samples by categories and generates sample lists for each category. To ensure that each category has an equal chance of participating in training, a random category is selected during training, and samples are randomly selected from the corresponding sample list. Synthetic samples [141] are generated by combining various characteristics of an existing sample to create a new sample. Using this method, you can create a new sample by randomly selecting a value from the feature.
# Enhancing Sensor Data Imputation: OWA-Based Model Aggregation for Missing Values
## 2 OWA Methodology  
In this section, we explain the concept of ordered weighted averaging and the learning algorithm that is the heart of our proposed method for missing value imputation.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 1 Introduction
During the recent COVID-19 outbreak, a graph may represent the interaction between infected subjects in a certain area. By splitting them into clusters with equal or similar densities, it would be possible to evaluate each cluster relative to the entire network. This can also identify clusters with fewer patients (i.e., nodes) but a higher number of interactions (i.e., edges) that may require special attention. In this case, an overlapping clustering has a unique characteristic. Members in overlapping areas may be considered as superspreaders. Also, these members can be viewed as sources for further communication as they interact with more than one cluster. The above scenarios can be directly transformed into clustering a well-connected graph so that the densities of the resultant clusters are identical or very similar. To date, no algorithm that is capable of partitioning and overlapping graph clustering that permits users to specify any kind of density has been developed. Therefore, this research represents the first attempt at partitioning and overlapping clustering of an undirected, unweighted graph whose required density can be specified by users. The density adopted in this work is the'relative density'; this is explained in Section 3. An assumption is made that clusters within the graph are not easily identifiable or evident. It may be arguable that the implementation of a graph clustering algorithm may not be essential under such conditions. The organization of the manuscript is as follows. Section 2 reviews the existing graph clustering algorithms in terms of their parameters. This is followed by Section 3, where the fundamental concepts adopted in this work are described. The key section of this study is Section 4, which describes in full the edge-based partitioning and overlapping clustering approach used in this work. Section 5 reveals the results of eight real-world networks. The results of two particular networks are graphically illustrated in this section. Section 6 describes the evaluation of results using the existing metrics and points out their inadequacy for this work. The section also reveals the evaluation using the appropriate metric. Relevant and significant findings are discussed in Section 7. The work is concluded in Section 8, and Section 9 discusses possible aspects for future work.
# Strength Characteristics and Slope Stability Analysis of Expansive Soil with Filled Fissures
###### Abstract  
Fissured expansive soils were widely distributed in the South-to-North Water Transfer Project. Most of the fissures were filled with clay, which controlled the stability of the slope. With the method of layered filling--level cutting--refilling and a modular design idea, the sample with a filled fissure preparation device for triaxial test was designed. After setting the filled fissures of gray-green clay in the expansive soil, triaxial tests were carried out for the samples with no filled fissures and with filled fissures with inclination angles of 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\). Then, considering the spatial distribution and the strength of the filled fissures in the slope, the stability analysis method for the expansive soil slope with filled fissures was proposed. The stability of a typical slope in Nanyang was analyzed. The results show that the \(c\) of expansive soil with filled fissures was about 12 to 15 kPa and the \(\varphi\) was 3\({}^{\circ}\) to 6\({}^{\circ}\). Filled fissures had an attenuation effect on the strength of the expansive soil. The larger the inclination of filled fissures, the more significant the effect of soil strength attenuation. The fissured slope stability was controlled by the filled fissures. The sliding surface was affected by the vertical fissures on the top of the slope and the slow-inclined long-large fissures in the slope, and the shape of the sliding surface was a broken line, which was basically consistent with the actual landslide.  
Keywords:expansive soil; filled fissure; triaxial test; sample preparation device; strength characteristics; slope stability; sliding surface +
Footnote †: journal: Applied Sciences
## 3 Proposed Method
### Sensor Feature Selection  
Different types of sensor features play varying roles in recognizing different actions. Using unimportant sensor features may significantly impact recognition due to noise [44]. To capture the contribution weights and potential importance of different types of sensor features, we perform the SFS operation based on the attention mechanism on the sensor input data. Not all sensor features contribute equally when performing action classification. For example, the sensor at the subject's ankle may not contribute much when performing the \"Open Drawer\" action. In addition, this weight not only assigns importance to sensor input features, but also demonstrates the effectiveness of feature selection by visualizing how much attention is paid to specific features for a particular action.  
The SFS operation uses a two-dimensional convolution across sensor feature values and timesteps to extract dependencies between them. First, it takes as input the sensor's feature vectors \([v_{1}^{t},v_{2}^{t},\ldots,v_{i}^{t},\ldots,v_{K}^{t}]\) and reshapes them into a single-channel vector, which is then processed using k convolutional filters to output a k-channel image. This is then converted back to a single channel using a \(1\times 1\) convolutional kernel and the attention weights of the individual sensor feature values are obtained by the softmax operation defined in (4). The whole process can be formalized as  
\[q_{i}^{t}=\tanh\left(W_{1}v_{i}^{t}+b_{1}\right) \tag{3}\]  
\[s_{i}^{t}=\frac{\exp\left((q_{i}^{t})^{T}w_{1}\right)}{\sum_{K}\exp\left((q_{ i}^{t})^{T}w_{1}\right)} \tag{4}\]  
\[c^{t}=\sum_{K}s_{i}^{t}v_{i}^{t} \tag{5}\]where \(i\) denotes the i-th sensor feature value, and \(K\) denotes the number of features of a single timestep sensor. We first obtain the hidden representation of \(v_{k}^{t}\) as \(q_{i}^{t}\) from the convolutional layer, then compute the similarity between \(q_{i}^{t}\) and the context vector \(w_{1}\), and obtain the normalized attention weight \(s_{i}^{t}\) by a softmax operation. \(\{W_{1},w_{1},b_{1}\}\) is the trainable parameter of the attention network, and \(c^{t}\) is the unified feature representation of all \(K\) sensor features obtained after the weighted vector.
###### Abstract
Modeling and analysis are conducted using a system state-space model for optimal efficiency transmission and efficiency detection. Furthermore, the feasibility and effectiveness of the proposed optimal efficiency tracking method and detection method were validated through simulation experiments conducted in MATLAB. The contributions of this study can be summarized as follows: Firstly, a wireless power transfer efficiency optimization tracking method based on full current mode impedance matching is proposed, enabling optimal efficiency transmission in the WPT system. Secondly, the method extended the transmission efficiency of the detection mode in the wireless power transfer system. Finally, simulation experiments conducted in MATLAB validated the feasibility and effectiveness of the proposed method. This study's research outcomes are significant for the development and application of wireless power transfer technology.
# A Review of Oil Spill Remote Sensing
## 1 Introduction  
Oil spill response now includes remote sensing as an essential component. The public expectation is that oil spill extent and location are precisely mapped [1]. Response personnel can use this location information to implement countermeasures to minimize the effect of pollution. Remote sensing is used to check for illegal discharges from ships. This is important, in view of unacceptable seabird mortality resulting from illegal discharges [2].  
The end use of remote sensing data is an important consideration and depends on the oil spill remote sensing equipment utilized. For a given function, several types of systems may be needed. The data end use, such as spill location, enforcement or cleanup support, may require a specific resolution or other characteristics of the data.  
There are general uses of oil spill sensed data:  
1. oil spill mapping for both tactical and strategic countermeasures;
2. slick detection and surveillance;
3. gathering of legal evidence;
4. law enforcement such as regarding ship discharge;
5. direct support for oil spill countermeasures;
6. slick trajectory determination.  
This review will review the information on oil spill remote sensing and show the areas of improvement and areas where further development is needed.
# Multiellipsoidal Mapping Algorithm
## 5 Discussion of Other Mapping Algorithms
### Compactness and Heuristics  
As can be seen, function approximation results in the best approximation; however, because functions with the domain \((-\infty,\infty)\) are considered, such functions have to be bounded, and a heuristic is needed to determine the plane position (and the points transformation) where the 3D function is defined. This problem implies that more parameters for the approximation must be used. In contrast, using geometric entities of GA, self-bounded entities such as spheres and ellipsoids and infinite entities such as pseudocylinders, planes, and pairs of planes are obtained. Additionally, because all the entities belong to the same algebra and the geometric locus is defined by their null space, heuristics to fix the representation are not required. Hence, we conclude that geometric entities are useful for easy implementation and compactness. We can also argue that the best information compression can be obtained as shown in Tables 2-4.