esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 2 Literature Review
### Word Embeddings
#### 2.4.3 Sentence Transformer (SBERT)  
SBERT is a word-embedding model that complements the topic modeling process by enabling semantic understanding and similarity computations between textual data points. It leverages transformer architectures (i.e., bidirectional encoder representations from transformers (BERTs) [20]) to generate dense vector embeddings for textual data, enhancing topic modeling by capturing contextual and semantic features.  
By embedding text into a high-dimensional vector space, the framework facilitates grouping semantically similar sentences, overcoming the limitations of traditional methods like LDA, which rely heavily on surface-level word co-occurrence. This semantic approach significantly improves the clustering and interpretability of complex datasets, as well as unstructured and varied log messages, in topic modeling applications.  
Although algorithms such as LDA, LSA, and NMF have demonstrated utility in log anomaly detection, they exhibit notable limitations. These methods predominantly rely on bag-of-words representations, which are insufficient for capturing the contextual and sequential information inherent to log messages. Additionally, their generalizability to unseen data is constrained, particularly when the underlying log distribution evolves over time. These challenges underscore the need for advancements in topic modeling techniques. In this context, approaches leveraging deep learning or contextual embeddings offer a promising avenue for addressing the shortcomings of traditional algorithms. In the following section, we introduce our proposed log anomaly detection framework, which utilizes sentence transformer embeddings (SBERTs) and the BERTopic model to overcome these limitations effectively.
## 5 Conclusions and Future Work  
An SCE model with area weight was established in this study to provide a more representative estimation of PAR during electroporation. It can be concluded that the proposed SCE model, which includes the concept of the cumulative effect, can dynamically and quantitatively describe the relationship between pulse-setting parameters and the evolution of electroporation. This information is helpful in the design of H-FIRE protocols for tumor ablation in the clinical setting.  
To the best of our knowledge, our work represents the first description of the relationship between the H-FIRE protocols and the dynamics of electroporation (i.e., PAR). However, there are some limitations to our work. The results achieved in this study rely heavily on the accuracy of the SCE model, which must be further validated by experiments. Second, the commonly used values for cell physical properties, such as the conductivity of the solution filling the pore, the extracellular conductivity, and the cytoplasm conductivity, are adopted from the literature, and a spherical shape of a cell membrane is used in this study. Therefore, the results and conclusions may not be applicable to other shapes of cell membranes. Adjustments in the cell's physical parameters are highly recommended when the proposed SCE model is used to predict pore evolution in other cell scenarios.  
Conceptualization, L.D. and B.Z.; methodology, L.D.; software, L.D. and Z.F.; data curation, L.D. and Z.F.; writing--original draft preparation, L.D.; writing--review and editing, B.Z., W.Z. and M.A.J.M.; supervision, B.Z., M.A.J.M. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (Grant No. 81801795), and the first author would like to acknowledge the financial support from the Chinese Scholarship Council (CSC, Grant No. 202006740029) and the NSERC (Natural Sciences and Engineering Research Council of Canada) CREATE (Collaborative Research and Training Experience) program (Grant No. 565429-2022).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Preliminaries  
In this section, we provide three definitions that constitute the basis for subsequent discussion.  
**Definition 1**.: _Nonadditive measure [32]._  
_If X is a finite nonempty set, then a set function \(\mu\): \(\Gamma(X)\)\(\rightarrow\) [0, 1] can be defined as a nonadditive measure or fuzzy measure if the set function satisfies the following conditions:_  
_(i)_ \(\mu(\varnothing)=0,\ \mu(X)=1\)__(ii)_ \(\mu(M)\leq\mu(N),\ \forall M\subseteq N\subseteq X\)__  
_where \(\mu\) is a regular fuzzy measure on X._  
**Definition 2**.: _Choquet integral (CI) [31]._  
_Let \(\mu\): \(\Gamma(X)\)\(\rightarrow\) [0, 1] be a nonadditive measure of X; then, the Choquet integral of function F(x) can be defined as_  
\[CI_{\mu}=\sum_{i=1}^{n}F\Big{(}x_{\rho(i)}\Big{)}\big{(}\mu(A_{i})-\mu(A_{i+1 })\big{)} \tag{1}\]  
_where \(A_{i}=\Big{\{}x_{\rho(i)},\ \ldots,\ x_{\rho(n)}\Big{\}}\). \(A_{i}\) is a permutation on set \(A\) that satisfies \(x_{\rho(1)}\leq x_{\rho(2)}\leq\cdots\leq x_{\rho(n)}\). Note that \(A_{i+1}=\varnothing\)._  
**Definition 3**.: _Attitudinal Choquet integral (ACI) [19]._  
_Let \(\mu\): \(\Gamma(X)\)\(\rightarrow\)[0, 1] be a nonadditive measure on X; then, the attitudinal Choquet integral can be defined as_  
\[ACI_{\mu,\lambda}=\text{log}_{\lambda}\Bigg{(}\sum_{i=1}^{n}[\mu(A_{i})-\mu(A_ {i+1})]^{\lambda F(x_{\rho(i)})}\Bigg{)} \tag{2}\]  
_where \(\lambda\) indicates the decision maker's attitude satisfying \(\lambda\in(0,\infty)\) and \(\lambda\
eq 1\). A higher \(\lambda\) indicates a more optimistic decision maker. Typically, the value of \(\lambda\) is set to 10\({}^{n}\)[19]. \(\mu\) refers to the importance of individual attributes and their interactions._
## 2 Problem Definition  
Two types of data sources are used in this study to predict the PV power, including cloud images processed from the satellite remote sensing data and in situ data from real PV stations. The in situ data contain historical records of several meteorological factors and PV power. The detailed data description is provided in Section 4.1. We denote the historical steps of input data as \(L_{\text{in}}\) and the prediction steps as \(L_{\text{out}}\). The number of in situ data attributes and remote-sensing data channels used is denoted as \(D_{\text{LS}}\) and \(C_{\text{RS}}\), respectively. In summary, this PV power forecasting problem can be formulated as  
\[f(\mathbf{X}_{\text{RS}};\mathbf{X}_{\text{LS}})\longrightarrow\mathbf{\hat{y}} \tag{1}\]  
where \(f(\cdot)\) is the mapping function, i.e., the forecasting model; \(\mathbf{X}_{\text{RS}}\in\mathbb{R}^{L_{\text{in}}\times H\times W\times C_{ \text{RS}}},\mathbf{X}_{\text{LS}}\in\mathbb{R}^{L_{\text{in}}\times D_{ \text{LS}}}\) are the input remote-sensing data and local sequences (in situ data), respectively; \(\mathbf{\hat{y}}\in\mathbb{R}^{L_{\text{out}}}\) is the PV power prediction. The hidden dimension of the model is denoted as \(D\).
# Notch Effect on the Fatigue Behavior of a TC21 Titanium Alloy in Very High Cycle Regime
###### Abstract  
The very high cycle fatigue (VHCF) property of TC21 titanium alloy blunt-notched specimens were investigated by using an ultrasonic fatigue test machine with a frequency of 20 kHz. S-N of blunt-notched specimens illustrated a continuous decrease characteristic with a horizontal line over the \(10^{5}\)-\(10^{9}\) cycle regimes. However, the fatigue life showed a large scatter for blunt-notched specimens. Blunt-notch significantly reduced the fatigue property in the high cycle and very high cycle regimes compared with that of smooth specimens. The crack initiation modes for blunt-notched specimens in the very high cycle regime can be divided into three types: (i) surface initiation, (ii) subsurface with flat facet, and (iii) subsurface with \"facet + fine granular area\". The crack initiation mechanism of blunt-notched specimens is discussed in view of the interaction of notch stress gradient distribution and heterogeneous microstructure. Furthermore, the fatigue limit model based on the theory of critical distance (TCD) was modified for the very high cycle regime, and the scatter of the fatigue property of the blunt-notched specimens were well predicted by using this model.  
**Keywords:** very high cycle fatigue; notch effect; titanium alloy; fatigue limit
# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
## 2 Material and Methods
### Breast Cancer Diagnosis Framework  
The BCD framework localizes the suspected lesion regions in a mammogram image by generating Bounding Boxes (BBs) around the lesions and classifying them as mass or calcification. It processes the mammogram by dividing it into a grid S \(\times\) S (i.e., 13 \(\times\) 13). If the center coordinates (C\({}_{\rm x}\), C\({}_{\rm y}\)) of a lesion's BB fall into a Grid Cell (GC), that GC is accountable for identifying that lesion. Each GC (S\({}_{\rm i}\), S\({}_{\rm j}\)) calculates confidence scores for BBs overlapping it. The scores indicate the model's certainty that the BB surrounds a lesion. If no lesion is present in a GC, the scores should be zero, and no BB is produced. Otherwise, the score equals the Intersection Over Union (IOU) between the Ground Truth BB (GT\({}_{\rm BB}\) ) and Predicted BB (P\({}_{\rm BB}\) ), as expressed in Equation (1). Furthermore, each GC predicts four BBs in this case.  
\[\text{Conf}_{\rm BB}=\text{P}_{\rm r}(\text{Lesion})*\text{IOU}_{\rm PBB}^{\text {GT}_{\rm BB}} \tag{1}\]  
For each BB, the proposed CNN predicts five crucial information components: the Confidence Score, which indicates the likelihood of a lesion within the BB; the width and height of the BB, providing spatial dimensions; the two center coordinates (x, y), pinpointing the lesion's location; and the conditional class probability P\({}_{\rm r}\) (Class i | Lesion ), which estimates the probability of the lesion belonging to a specific class. This probability is conditioned on the presence of a lesion within the respective grid cell of the mammogram. Notably, the model is designed to predict only one set of class probabilities per GC, regardless of the number of BBs detected within that cell, ensuring focused and specific lesion characterization.  
Figure 2 presents the block diagram of the proposed framework, illustrating the comprehensive workflow of the CAD system for breast cancer detection. It encapsulates the end-to-end process, from image preprocessing to the final diagnostic output, showcasing the integration of advanced techniques and architectures to achieve superior performance in BCD.  
Figure 2: Proposed YOLO-based BCD Framework.
# Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data
The researchers used algorithms such as random forest (RF), support vector machine (SVR), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), combined with auxiliary data such as Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Land Cover (LC), and Digital Elevation Model (DEM), and established a functional relationship between auxiliary data and soil moisture through regression analysis, achieving good results in multiple study areas [4; 10; 21; 22; 23; 24]. The above methods for soil moisture downscaling have their own advantages and disadvantages. Although there have been some studies on soil moisture downscaling, most of them rely on optical remote sensing data. The dependence of optical data on weather conditions leads to its non-negligible missing values, which may cause inaccurate operating results of the model [25]. And a few studies have combined SAR backscattering coefficients with optical remote sensing data. SAR, as an active sensor, which is not limited by time and climate, can penetrate clouds, vegetation, and the ground surface and has good sensitivity to soil moisture, which provides favorable conditions for soil moisture downscaling research. In addition, a few studies have increased the spatial resolution to 100 m, while Landsat 8 has a high spatial resolution, which provides important auxiliary data for the experiment. In this study, active and passive remote sensing data were combined to study the downscaling of soil moisture products. Separate models were established in the Shandian River and Wudaoliang, and the original soil moisture with a resolution of 10 km was downscaled to 1 km and 100 m. The verification of downscaling results includes the relationship between the original soil moisture and the downscaled soil moisture, as well as the relationship between them and in situ observation data. The specific steps are as follows: (1) the auxiliary data were resampled to the original soil moisture scale and the target product scale; (2) RF and extreme gradient boosting (XGB) algorithms were used to establish downscaling models before and after adding SAR backscattering coefficients; and (3) the results of different downscaling models were verified to evaluate the impact of the fused data on soil moisture downscaling. By establishing a downscaling model, we hope to provide higher-resolution soil moisture data to support research and applications in related fields.
## 4 Conclusions  
In this paper, aiming to solve the problems of insufficient utilization of time-frequency features in bearing FD and the weak interaction ability of dynamic features in traditional models, a deep dynamic learning network based on dual-channel cross-attention of time-frequency signals is proposed. By establishing the faulty bearing's dynamic model, the simulated vibration signal is obtained and decomposed into dual-channel features in the  
Figure 16: (**a**) The confusion matrix of the simulation data; (**b**) the t-SNE visualization classification diagram.  
Figure 15: (**a**) CNN confusion matrix; (**b**)visualization graph.  
time domain and frequency domain through the FFT. Combined with the CNN, local and global features are extracted, and a cross-attention mechanism is designed to achieve the dynamic interaction and weight distribution of features in the time domain and frequency domain. It significantly enhances the model's sensitivity to weak fault characteristics and robustness under noise interference. The experimental results show that in the simulation dataset and the data of the self-built bearing test platform, the proposed method is superior to ResNet, BiLSTM and the traditional CNN model in terms of accuracy (above 95%) and classification stability. Especially under non-stationary working conditions, it effectively reduces the misjudgment rate through the complementarity of time-frequency features. Furthermore, the introduction of domain adaptive classifiers further optimizes the feature fusion process and reduces the interference of redundant information. This study validates the effectiveness of the cross-attention mechanism for cross-modal feature fusion. The findings offer a novel approach to IFD in complex industrial environments.  
[PERSON] oversaw the project as the team leader, provided strategic guidance on research direction, and supervised the progress of the study. [PERSON] built the whole constructive and wrote the manuscript. [PERSON] conducted in-depth analysis with data investigation and optimized the methodology. [PERSON] contributed to data processing, indicator calculations, and visualization. [PERSON] and [PERSON] proposed the research framework and technical roadmap. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant 52205117); Department of Science and Technology of Liaoning province (Grant 2024-MSLH-401 and 2024-MS-114); Education Department of Liaoning Provincial Program [Grant Number: JYTQN2023394 and LJ212410153039]; Shenyang bureau of science and technology) (Grant 24-213-3-03).  
The data are available from the corresponding authors on reasonable request.  
Author [PERSON] was employed by the company North Huajin Chemical Industries Group Corporation. 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.
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Methods and Approaches for Scheduling in TSN
#### 4.1.5 Network Health  
The works in this category concern the overall functioning of the network with less focus on the achievable QoS. For example, studies [14; 97; 98; 99; 100] only aimed to find a feasible schedule that satisfies constraints. Other works focused on generating feasible solutions that are tolerant to network faults. Fault tolerance could be enabled using different techniques. Refs. [101; 102] used redundant transmissions as a fault tolerance technique. The authors of [101] based their work on frame replication, while [102] adopted a proactive approach by calculating previously the number of transmissions per HTS stream instance. They also provided some fault tolerance for LTS and BE traffic in case of failures. Ref. [103] also used time redundancy. Although their technique increases the latency of the streams, it could be beneficial in case it is combined with space redundancy. Time redundancy also suffers from the overprovisioning of timing resources in the network. Ref. [104] aimed at maximizing the resilience of control applications as well as the minimum security level. Ref. [105] discussed the loss-of-synchronization and introduced a set of constraints to mitigate the effect of an out-of-sync event for a maximum resynchronization interval. Reliability awareness was discussed in [105; 106]. Ref. [106] used an SMT formulation based on path redundancy. Ref. [39] suggested a network model correction approach based on automatic inference of TSN rules and constraints. Ref. [29] tried to guarantee the worst-case stability of the control application by considering the effect of delay and jitter on QoC. Ref. [107] discussed the trade-off between frame loss during network updates and suggested a scheduler that allows offline and online configuration without extra overhead. Finally, [62] suggested a flow fragmentation approach based on SMT to generate feasible solutions for multicast streams.
# Engineering Vehicle Detection Based on Improved YOLOv6
## 2 Related Work
Within the domain of vehicle detection, numerous references and methods are available, with some vehicle detection approaches still holding reference significance in the context of engineering vehicle detection. Traditional vehicle detection algorithms still depend on the manual extraction of features. [PERSON] et al. applied HOG features to identify engineering vehicles within high-definition images [23]. [PERSON] et al. employed Haar-like features based on Haar feature-based cascade classifiers to swiftly and efficiently detect vehicles in real-time closed-circuit television footages [24]. [PERSON] et al. implemented a Haar cascade classifier and optical flow technique for the purpose of identifying and following vehicles [25]. As deep learning technology has advanced, it has been extensively utilized for vehicle and engineering vehicle detection across various applications. [PERSON] et al. enhanced the region proposal network structure of the Faster R-CNN to achieve the detection of three common types of vehicles encountered in traffic scenarios [26]. [PERSON] et al. enhanced the anchor boxes selection method for YOLOv2 and employed a multi-layer feature fusion strategy [27]. [PERSON] et al. introduced a novel feature fusion module alongside an orientation-aware bounding box proposal module. Combined with the SSD algorithm, this approach achieved the detection of dense engineering vehicles in drone images [28]. [PERSON] et al. implemented an advanced version of YOLOv5 for vehicle detection across diverse traffic contexts, leveraging the flip-mosaic to boost detection of small objects and diminish the rate of false vehicle detections [29]. [PERSON] et al. improved the YOLOv4-Tiny model by incorporating a split-attention module and a dynamic ReLU function to optimize detection in raw material warehouse scenarios. Despite these improvements, the model's generalizability is restricted due to the specialized warehouse environment in which the data was collected [4]. While existing studies offer valuable contributions, most focus on general vehicle detection or are limited by specific environmental conditions, such as camera angles or scene settings. Our research addresses these limitations by developing a more versatile and adaptable detection method, aimed at fulfilling the urgent need for intelligent detection of engineering vehicles in diverse scenarios within intelligent transportation systems.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 4 Results and Discussion
### Strengths and Limitations  
In this paper, we propose a novel index for detecting bare coal using Landsat OLI data and construct a mathematical model of the BCI with respect to the month. The performance of this method was thoroughly evaluated. The method's recognition ability and its applicability of the index were verified by comparing them with other methods, across different months, and in large-scale regional applications. To leverage free satellite images such as Landsat for accurate bare coal identification, the proposed method is relatively efficient and provides a new approach to addressing the high commission rate and severe time constraints associated with existing bare coal detection methods. In addition, we believe that combining multispectral LiDAR-derived intensity data holds significant potential for fine-grained monitoring of mine information in future studies. Specifically, this data fusion approach can provide more accurate and comprehensive support, offering substantial application value in surface change monitoring, geohazard early warning, and resource management and optimization.  
However, the method has certain limitations. Firstly, to improve the accuracy of bare coal identification, the minimum detectable BC fraction is set to 90%, aligning with our original goal of ensuring high accuracy in bare coal detection. This threshold, however, means that some bare coal will remain undetected, with an omission rate of about 17% in the three sample zones. Secondly, variations in water area composition (such as chlorophyll concentration and the content of nitrogen, phosphorus, and potassium) can alter the reflectance curve of some water areas. This can result in the inclusion of water pixels in the bare coal detection results, significantly affecting the performance of the BCI. Therefore, water areas should be excluded before applying the BCI for bare coal detection. While  
Figure 15: Distribution map of open pit mining sites in Inner Mongolia Autonomous Region.  
many effective methods for water removal are available, this additional step increases the workload of bare coal detection.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 3 Performance Analysis  
In this section, the closed-form expressions for the OP of the proposed network for PS-D2D and TS-D2D schemes are obtained, respectively. Specifically, after deriving the OP of the satellite-terrestrial direct link and the terrestrial relay link, respectively, maximal ratio combining (MRC) is employed at the UT and IoT device to obtain their corresponding OP.