esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Engineering Application Analysis
### Observation Results of Mine Pressure  
In the process of excavation and mining, adopts the cross-point method to monitor the deformation of the roadway's surrounding rock, and the monitoring equipment is a tower ruler and a laser range finder. The measurement frequency is once every five days. The core features of the cross-point method are simple structure and intuitive data.  
The results obtained during the roadway excavation are presented in Figure 22. These results indicate that the deformation stabilized approximately 30 days after excavation, with a maximum roof subsidence of 159 mm. In addition, maximum convergence values of 219 mm and 160 mm were recorded for the pillar side and solid coal side, respectively. After mining the coal seam, the maximum subsidence of the roadway roof was recorded at 235 mm, with maximum convergence values of 306 mm and 281 mm observed on the coal pillar side and solid coal side, respectively. Notably, during the entire retreat mining period, there were no instances of severe mining pressure phenomena such as monolithic pillar compression or anchor cable failure in the roadway.  
Figure 22: Roadway surrounding rock deformation during excavation.  
The aforementioned data and observations indicate that, in the context of an extra-thick coal seam where the MRFL is positioned above the gob-side roadway, utilizing a five-hole tray in conjunction with high-strength prestressed anchor cables for support yields notable outcomes in managing roof deformation.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 4 The Sparse Time-Series Clustering Framework
### Modeling through Sparse Gaussian Process Regression  
In this stage, we apply the framework of Sparse Gaussian Process Regression, as outlined in Section 3, in order to obtain a sparse approximation \(X^{\prime}=(x_{1}^{\prime},\tau_{1}),\ldots,(x_{m}^{\prime},\tau_{m}))\) of each time series \(X=((x_{1},t_{1}),\ldots,(x_{T},t_{T}))\) in the original dataset \(\mathcal{X}\) (see also Algorithm 2).  
We use the Variational Free Energy (VFE) (a.k.a. Sparse Gaussian Process Regression (SGPR)) approach, outlined in Section 3.4, using the Matern kernel with parameter \(\
u=3/2\). Each time series \(X=((x_{1},t_{1}),\ldots,(x_{T},t_{T}))\) is approximated by a sparse time series \(X^{\prime}=((x_{1}^{\prime},\tau_{1}),\ldots,(x_{m}^{\prime},\tau_{m}))\) with \(m\ll T\) inducing data points.  
A logarithmic (in length \(T\) of the original time series) number of inducing points suffices for a reasonably good approximation of the original time series. More specifically, we use \(m=\gamma\log_{2}T\), for \(\gamma\in\{1,2,3,4,5\}\) (and with the resulting number rounded to the closest integer), in our experimental evaluation. Thus, the time complexity of this step is \(O(T\log^{2}T)\), and the space required is \(O(T+\log^{2}T)\).
## 4 Materials and Methods  
### Materials  
LPS purified from _Escherichia coli_ O111:B4, sodium dodecyl sulfate (SDS), radioimmunoprecipitation assay buffer (RIPA), acrylamide, calcein, Hematoxylin and Eosin (H&E), polyvinylidene difluoride (PVDF) membrane, and horseradish peroxidase (HRP)-conjugated anti-rabbit IgG or anti-mouse IgG were purchased from Sigma-Aldrich (St. Louis, MO, USA). Primary antibodies toward TLR4 (ab13556), phospho-NF-\(\kappa\)B (#3031), Total NF-\(\kappa\)B (#8242), phospho-ERK (#9106), Total-ERK (#9107), phospho-JNK (#4671), Total-JNK (#9252), and \(\beta\)-actin (sc-47778) were purchased from Abcam (Cambridge, MA, USA), Cell Signaling Technology (Danvers, MA, USA) and Santa-Cruz Biotechnology (Dallas, TX, USA). Perdeuterated dodecylphosphocholine (DPC), egg yolk L-a-phosphatidylcholine (PC), and egg yolk L-a-phosphatidyl-DL-glycerol (PG) were purchased from Avanti Polar Lipids, Inc. (Albaster, AL, USA). All other reagents and chemicals used were of high analytical grade.
## 3 Dataset and Preprocessing
### Image Preprocessing Methods  
Before the model is trained, the size of the input image is reduced to 32 \(\times\) 32 \(\times\) 3. Since traffic signs do not have different meanings due to different sign colors, this paper ignores the color differences of images in the data when the model is trained for traffic sign recognition, and only the brightness differences of different parts of the image are considered. In this paper, the HSV and YUV color spaces are used: the V channel in the HSV space and the Y channel in the YUV space are taken. To enhance the brightness of the image, the Y channel and the V channel are respectively equalized in the histogram as follows: for an image with n pixels and pixel values ranging from [0, L\(-\)1], let \(r_{k}\) represent the \(k\)-th pixel level and nk represent the number of pixels in the image, and then the histogram probability density \(p(r_{k})\) of \(r_{k}\) is \(p(rk)=\frac{nk}{n}\),\(k\)= 0,1,2 \(\ldots\), \(L-1\). The cumulative distribution function is shown in Formula (22).  
\[Sk=\left(\sum_{j=0}^{k}p(rj)\right)^{2}\ \times\ (1+2\ \times(1-\sum_{j=0}^{k}p( rj))) \tag{22}\]  
where, \(rj\) represents the \(j\)-th pixel level, \(p(rj)\) represents the probability density of the \(j\)-th pixel level, and the final image is M = 255 \(\times\)\(S_{k}\).  
As shown in Figure 8, Figure 8a shows the original image of the traffic sign, Figure 8b shows the image of the V channel in HSV through histogram equalization, and Figure 8c shows the image of the Y channel in YUV through histogram equalization. It can be seen from Figure 8b,c that after histogram equalization of the image, the image data is converted into a range of [\(-\)1, 1], and the poorly lit image has a better graphic outline. Through this preprocessing method, the contrast of parts of the image is enhanced without affecting the overall contrast.  
Figure 7: Some images of the GTSRB dataset.  
Due to the complex environment of traffic signs outdoors, the image of traffic signs will produce different changes. To eliminate the influence of external factors on the image, during model training, the maximum range of image rotation angle is set to 10 degrees, the maximum random magnification is set to 1.2, the maximum random horizontal offset is set to 0.08, and the range of up and down offset is set to 0.08. According to the characteristics of the deep learning model, images with a small size can be enhanced by geometric transformations (such as translation, rotation, scale stretching, contrast adjustment, and color transformation). For images with a large size, mean reduction is used to reduce the image size.
## 1 Introduction
### Layers  
Below each layer of the model is briefly described in order to provide a general understanding for the reader.
# A Frame-to-Frame Scan Matching Algorithm for 2D Lidar Based on Attention
## 7 Conclusions  
This paper introduced an attention mechanism and proposed a new frame-to-frame matching algorithm based on attention. The introduction of the attention mechanism  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **PP-ICP (cm)** & **PL-ICP (cm)** & **Icsm (cm)** & **IMLS-ICP (cm)** & **ASM (cm)** \\ \hline Office & 97.78 & 58.04 & 21.53 & 8.96 & **2.39** \\ \hline Office building & 38.93 & 31.14 & 27.72 & 21.67 & **5.85** \\ \hline Library & 120.03 & 72.05 & 52.67 & 27.43 & **11.71** \\ \hline Shopping mall & 207.50 & 161.79 & 145.48 & 127.10 & **50.15** \\ \hline \hline \end{tabular}
\end{table}
Table 1: End-to-end error statistics table.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **PP-ICP (ms)** & **PL-ICP (ms)** & **iCSM (ms)** & **IMLS-ICP (ms)** & **ASM (ms)** \\ \hline Office & 43.1 & 45.7 & 62.7 & 100.5 & **10.3** \\ \hline Office building & 42.7 & 44.3 & 63.1 & 101.8 & **9.4** \\ \hline Library & 44.3 & 45.0 & 63.5 & 102.3 & **9.71** \\ \hline Shopping mall & 43.8 & 44.5 & 61.2 & 102.7 & **10.0** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computation time statistics table.  
dramatically reduced the accumulated error rate and greatly improved the computational efficiency. An attention area selection and updated algorithm was proposed that successfully realized adaptive reference frame selection and the switching of different reference landmarks. To verify the effectiveness of the proposed algorithm, we conducted extensive experiments using real robots. Thanks to the attention mechanism, the accumulation error of the ASM method is very small, and the amount of the data involved in the calculation is also very small, which makes the ASM method is far superior to the other four methods discussed in this paper in terms of accuracy and computational efficiency in the four selected test environments. Additionally, the ASM method was able to achieve better results in a large-scale environment. The experimental results demonstrated that the robot could significantly improve navigation performance by simulating the way of human navigation, which can be regarded as a new exploration direction. In future work, semantic features will be introduced to define the attention area and to explore the attention area selection, update methods and improve the reliability in highly dynamic environments [33; 34; 35; 36].  
Conceptualization, S.H. and H.-Z.H.; data curation, S.H.; methodology, S.H.; formal analysis, S.H. and H.-Z.H.; formal analysis, S.H. and H.-Z.H.; writing--original draft preparation, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the National Key R&D Program of Chinal grant number [2017 YFB1301300] and The APC was funded by [hong-zhong Huang].  
Not applicable.  
Thanks to [PERSON] for his help in the experiments.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Hexinwu Building Complex Fire Risk Situation  
The composite average derived from the data in Figure 4 and the expert scoring can be used to calculate the fire risk score for the new sub-indicator (shown as dots in Figure 5). The total score resulting from all these scores is 5.797. According to Table 3, it can be concluded that the safety level of the Hexinwu Building Complex is Level 4, signifying that it is relatively dangerous. This corresponds to the preliminary determination made by the expert group. Therefore, measures must be taken to ensure fire safety protection for the cultural sustainability of this architectural heritage. The area is in a relatively remote, mostly rural location, making it difficult to achieve timely fire rescue capabilities. Furthermore, buildings here typically feature brick-timber materials, which are prone to catching fire. Most of these buildings are also in disrepair, and due to villagers' lack of awareness regarding fire safety, unsafe practices such as improper use of electricity, smoking, playing with fire, and lightning strikes during dry winter months, all contribute significantly to the risk of a potential fire accident.  
Figure 4: Sub-index factor weights.  
From Figure 5, it is clear that the greatest current danger is the ability to fight initial fires (A3), the fire resistance of building materials (C3), fire rescue capacity (D5), building fire load (C1), and the electricity situation of residents (A1). This danger is mainly caused by the architectural features and the environment of the Hexinwu Building Complex and most ancient vernacular residences in ancient villages. These vernacular residences are often located in remote areas, leading to delayed response times for fire rescues. In addition to limited awareness of fire safety and nonstandard use of electricity, these buildings are highly flammable and densely packed, making them much more susceptible to fires compared to high-rise buildings. Therefore, for the conservation of ancient villages, it is necessary to use new materials to establish adequate fireproof layers, improve the fire resistance rating of buildings, and implement cost-effective automatic fire protection facilities. For example, automatic fire alarms, fire extinguishing systems, and electrical monitoring can reduce the risk of heritage loss. The use of technologies such as the Internet of Things, artificial intelligence, and information from cell phone apps can enhance the firewall protection of heritage buildings.
## 2 Experimental Setup and Details
### Original Test Model Details  
Figure 1 displays the sketch of the SBG model that was used in the current investigation. The Great Belt East Bridge, whose geometric scale of the main girder is 31 m in width and 4.34 m in height, possesses a main span of 1624 m. The SBG model was derived from the Great Belt East Bridge (Denmark) due to this bridge frequently occurring VIV at low airflow speed [1; 20]. The SBG model was produced using a high-precision three-dimensional printer with a scale ratio of 1:125. Therefore, the SBG model had a width (B) of 248 mm, a height (H) of 34.72 mm, and a length (L) of 480 mm in the span-wise direction, as shown in Figure 1a,c. The slope surface on the carriageway was considered. The secondary structures on the surface of the bridge girder were neglected in this investigation. The Reynolds number was calculated as \(Re=2.08\times 10^{4}\) according to the incoming flow velocity of 9 m/s and the height of the SBG model. It should be declared that despite the \(Re\) of the present experiment being much less than that of the real bridge in a natural wind environment, this investigation aimed to study the control effectiveness of the control scheme of the trailing edge jets. There were two iron bars that were used to firmly fix the SBG model in the test channel to prevent displacement and vibration during the experiment. The coordinate system was set as shown in Figure 1a. The positive X and Y are defined as the airflow and upward directions, respectively. The positive Z meets the right-hand rule.
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 4 Evaluation  
### Dataset  
Almost all of the other works in vulnerability detection use the dataset processed by themselves and their source code is not being published. However, to better evaluate our work, we still try to reproduce the whole project of V-Fuzz and make a comparison with it. Hence, we use the Juliet Test Suite (v1.3) [18] for our model training and testing, which has been widely utilized in previous vulnerability-related studies [5; 12; 41]. This dataset was released by the National Institute of Standards and Technology (NIST) in 2017 and involves 118 different common weakness enumeration (CWE) entities and 64,099 cases. All testcases are written in C/C++, and each function is labeled with \"good\" or \"bad\", where \"good\" mean \"secure\" and \"bad\" means \"vulnerable\". Among the 118 CWE entities in the Juliet Test Suite, we select 12 memory-related entities to better compare our model with V-Fuzz, which uses the same CWEs. The Juliet Test Suite differentiates functions as \"source\" and \"sink\". Whereas source functions generate the data, sink functions utilize the data and are more closely related to program crashes. Thus, we select the sink functions and construct our dataset. The specific types of CWE and the number of labeled functions are recorded in Table 3. To train the model for each type of vulnerability equally, we attempt to include the same number of positive and negative cases in each CWE. If the total number is less than the target number, we sample from the overall legacy data to complete the dataset. Table 4 shows the data volume of each dataset. We extract a total of 22,000 positive cases and 22,000 negative cases; the training set contains 18,000 of each, and the test and development sets contain 2000 of each.
###### Abstract  
To overcome the limitations of gravity inversion methods in fault inversion, this paper proposed a constrained adaptive weighted particle swarm optimization algorithm. Simulation experiments demonstrate that the proposed method exhibits stronger noise resistance compared to traditional optimization methods. In practical cases, the inversion accuracy of this method is improved by at least 64.4%, and the predicted gravity anomaly curve is closer to the observed data. The research findings are as follows: (1) The linearly decreasing inertia weight strategy performs best in terms of convergence efficiency and global search capability; (2) among the fault parameters, the top-layer center depth \(z\) and bottom-layer center depth \(w\) show higher sensitivity, and the inversion results for these parameters are more stable, which is beneficial for determining the depth information of faults; (3) introducing L2 regularization and penalty terms as constraints significantly improves the inversion stability, and among these, \(z\) and \(w\) have a particularly notable impact on the error.  
gravity inversion; fault parameters; Constrained Adaptive Weighted Particle Swarm Optimization (C-AWPSO) Article
# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
## 2 Method
### Hierarchical Fault Diagnosis Strategy
Using the training process detailed in Section 2.2, 5 kinds of training samples are studied, including the bearing state training sample, the fault position training sample, the inner race fault degree training sample, the ball fault degree training sample and the outer race fault degree training sample. A hierarchical decision fusion network of rolling bearings is established capable of fault detection, isolation and fault degree estimation. (2) Diagnosis Stage  
In the application stage, the flow of the hierarchical decision fusion fault diagnosis method is shown in Figure 5.  
**Datasets collection and feature extraction**  
**Fault selection layer**  
**BPRNN_FI**  
**BPRNN_FI**  
**Fault selection layer**  
**BPRNN_FI**  
**BPRNN_FI**  
**BPRNN_FI**  
Step 1: Database collection and feature extraction  
The vibration signals of the rolling bearing are collected in real time. \(N\) samples of the same length are randomly sampled from the raw vibration signal. Wavelet packet decomposition and signal reconstruction of data samples are carried out to extract \(m\) kinds of features of reconstructed signals and form feature vectors \([F_{1},F_{2},\ldots,F_{m}]\). Finally, the feature vector is normalized. Step 2: Fault detection  
\(m\) normalized feature vectors are input into \(m\) fault detection neural networks (BPNN_FD), and the current state of the bearing is judged by decision fusion. Step 2 and Step 3 are entered into only when the bearing is in a faulty state. Because most of the bearings are in the normal state during use, such a processing method will reduce the operational burden of the algorithm. Step 3: Fault isolation  
When a bearing is detected as being in a faulty state, \(m\) normalized feature vectors are input into \(m\) fault isolation neural networks (BPNN_FI). After decision fusion, it can be determined whether the bearing fault has occurred in the inner race, ball or outer race. Step 4: Fault severity estimation  
After identifying the location where the bearing fault has occurred, \(m\) normalized feature vectors are input to \(m\) fault severity estimation neural networks corresponding to the fault location. After decision fusion, the severity of the fault location (degree I, degree II, degree III) is determined. In this paper, there are 11 inner race fault severity estimation neural networks (BPNN_BA) and 11 outer race fault severity estimation neural networks (BPNN_OR).
## 4 Discussion
[17] reported that a slight deficit irrigation (stem water potential slightly below \(-1.3\) MPa during stage II) increased the concentration of volatile compounds in pistachio nuts; [PERSON] and [PERSON] [21] reported that irrigated conditions over the season had lowered the concentration of volatile compounds. The different trend found in our experiment could be explained as a result of a combination of (1) different degrees of stress and (2) the period of the season when the stress was applied, considering the high influence that the phenological stage of fruit development hason pistachio capability to physiologically deal with water stress [25] and the strong seasonal pattern of terpenes associated with kernel maturation [48]. The identified fatty acids in this study shown in Table 4 confirm what has been previously reported by several authors in their studies on different cultivars from numerous countries [14; 15; 16; 17]. In details, the high value of oleic acid found in our samples (70%) is slightly higher than values reported by [PERSON] [8] for cultivar Cerasola from Italy (67.86%) and cultivar Pontikis from Greece (68.33%). [PERSON] et al. [10], in their fatty acids characterization of pistachio from different origins, reported that the amount of oleic acid is 70-72% in pistachios from Sicily (very similar to the values that we found) and 68-70% in those from Turkey and Greece, while Iranian pistachios were found to have the lowest amount (55%). Finally, we found no effect of water status on fatty acid composition as shown in Table 4, and as previously demonstrated in pistachio [17], as well as in other crops [49; 50]. Changes in fatty acid composition are generally function of genotype [51] or environmental factors (mostly temperature) [52]--both parameters were not variable in our experimental conditions.
## 3 Challenges and Issues in Hybrid AC/DC Microgrid
### Power Quality Issues  
Power quality challenges are more dominant in the future in hybrid microgrids due to the integration of single-phase/unbalanced load, unbalanced distributed generations, and non-linear loads. The penetration of Distributed generators in the network produces many quality issues such as voltage fluctuations, harmonic distortion, voltage surges, and frequency deviations. Moreover, the variable output of distributed generators produces a reverse power flow problem. These issues of power quality amplify in the case of hybrid microgrids. The system power quality is the most critical issue regarding the operation and falls in the categories of harmonics, reactive power compensation, and standardization of technical and commercial protocols [15].  
Owing to the intense integration of distributed generation units, in the power system due to nonlinear power electronics devices, harmonics are introduced. The three-phase unbalanced loads cause a large negative sequence current that increases the losses of the system, reducing the overall efficiency of the entire system. Hence, in order to address this problem, some filtering techniques should be applied to make a pollution-free system. In [16], the authors present harmonic compensation using interfacing and interlinking converters, where the harmonic control is performed by converter at different frequencies, power rating, and filter design. Bidirectional AC/DC converters of hybrid grid normally are voltage sources and have smart functions of self-commutation and the ability to provide reactive power. It avoids voltage sag in a system [17].  
In a hybrid microgrid, the DC side has the edge over the AC but still faces several problems because it is in the growing stage. Therefore, the DC side requires further work to standardize the DC grid and its voltage level. One of the key issues is the lack of adequate equipment and standards for DC power distribution.