esa-sceva/satcom-chunk-collection huggingface dataset:
# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 4 The Detection Model of Kilometer Post Based on an Improved SSD Algorithm  
### SSD Algorithm  
SSD is an end-to-end detection model for object detection and recognition proposed by [PERSON] et al. On one hand, it is similar to the YOLO algorithm. The input data are processed by a convolutional neural network, and finally, the object coordinates and category information can be output. On the other hand, in order to improve the detection accuracy, the SSD model adds the anchor mechanism used by R-CNN algorithm, which is the equivalent to combining the region proposal network (RPN) function with the regression mechanism. SSD uses deep features around each object for detection and recognition, and extracts object features from different feature maps of the deep neural network, which adds more scale information and improves the detection accuracy without affecting the speed. SSD uses a Visual Geometry Group 16 (VGG-16) [38] convolutional neural network as the basic network. A number of auxiliary convolutional layers whose sizes decrease in turn are connected. Finally, a multi-scale pyramid structure feature map is generated to detect the objects of different sizes, as shown in Figure 5. The basic network is mainly used for image classification, and the multi-scale convolutional layer is mainly used to extract and detect object features. For a single input image, SSD will generate multiple bounding boxes and score the object categories. Then, non-maximum suppression (NMS) operation is performed to obtain the final prediction results, which significantly improves the detection accuracy and speed.
## 3 Results and Discussion
### Solids Content  
Total solids present in the feed and output/permeate of different treatments are depicted in Figure 10. The DAF (A) produced a 37.69% reduction in TS from 3932 ppm to 2424 ppm. In part B of Figure 10, the permeate of the RO membrane had the lowest TS, 311 ppm. The feed to this treatment method and DAF are the same. The feed to the treatment method C post-DAF membrane filtration is same as the output of the DAF. In this method, the permeate of the RO membrane had the lowest TS, i.e., 145 ppm. This TS is lower than that of the RO membrane in treatment B, since the feed to the latter treatment is higher. The feed to the membrane filtration experiment (B) has solids that are greater than 200 kDa, and the feed to the post-DAF membrane filtration (C) has solids that are greater than 8 kDa, but smaller than 20 kDa. So, the DAF is removing the solids which are bigger than 20 kDa, but is not effective at removing solids smaller than that.
## 4 Results and Discussion
However, as the baud rate increases, ISI becomes the dominant impairment, 4-D constellation with SVM displays a larger performance gain. At the operating condition of 412.5 MBaud, the BER of LMS without SVM and LMS with 2-D SVM has exceeded the 7% FEC threshold (3.8\(\times\) 10\({}^{3}\)), while the BER of LMS with 4-D SVM still satisfies the threshold. The available baud rate is increased by 12.5 MBaud, corresponding to the data rate improvement of 50 Mbps. For fair comparison, we also add the results employing artificial neural network (ANN) for nonlinear equalization as the dashed lines in Figure 9. The ANN is a feedforward complex-valued network with a dual branch structure [6]. The input layer has 21 nodes, sharing the linear and nonlinear branches. The nonlinear branch contains one hidden layer with 10 nodes. The activation function is complex-ReLU. The Adam optimizer is used for training. We can observe that the ANN-based nonlinear equalization performs better than the LMS-based linear equalization. With the joint effort of 4-D SVM soft decision, the BER can keep under the threshold at 425 MBaud. It should be noted that the training size is 26,214, whereas after data preprocessing the training size is decreased by 56%. As the complexity of the training phase scales between O(\(dN_{1}\)\({}^{2}\)) and O(\(dN_{1}\)\({}^{3}\)) for SVM with \(d\)-dimensional feature vectors and \(N_{\text{t}}\) training points, the required computational resource has been significantly reduced.
## 6 Conclusions and Further Studies  
The path generation from the start node to the target node in a complex network is a representative problem that has been steadily investigated in the field of network optimization and machine learning. While most algorithms, including ACO, use a unidirectional search in a serial manner, the computation takes a long time as the complexity increases, with the drawback that it fails to search the route effectively in a dynamic environment.  
In order to overcome these issues, the MPACO algorithm is proposed to search for an effective path by updating the pheromone through a Bayesian-type GPR process at the node where ant agents search multi-directionally. The numerical studies show the greater effectiveness of the MPACO algorithm than the existing ACO and the greater the complexity of the network, the more efficient it is. However, while the MPACO algorithm generates the route faster than the ACO algorithm in a static environment, the renewal process of the pheromone fails to recognize the neighboring nodes' topology changes in the network.  
This issue is overcome using the proposed CMPACO framework, which updates the pheromone by interpolating discrete Gaussian distributions at the collision node. In order to verify the effectiveness of the proposed algorithm, it is compared to the ACO algorithm and the MPACO algorithm. The experimental results show that the presented CMPACO algorithm offers better performance than the ACO algorithm and MPACO algorithm, with respect to the computation speed and the optimized cost function.  
The proposed framework can be applied to various network route problems with complex and dynamic topology changes and to real road networks. As further studies, more effective meta-heuristic algorithms and pheromone diffusion algorithms can be considered for much quicker path generation.  
[PERSON] and [PERSON] conceptualized the framework and developed the methodologies. [PERSON] implemented and validated the framework. [PERSON] supervised the overall research processes. [PERSON] wrote the manuscript. And [PERSON] reviewed and edited it.  
This research was supported by The Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, S. Korea (grant number: NRF-2018R1D1A3B07047113).  
The authors declare no conflicts of interest.  
Figure 14: Comparisons of the cost function values using ACO, MPACO and CMPACO with the test network 1.
# Error-Function-Based Penalized Quantile Regression in the Linear Mixed Model
## 6 Conclusions  
In this paper, we established the DERF-QR method for both parameter estimation and variable selection in the linear mixed-effects model. We studied a non-convex and smooth error function regularization, which is quite different from traditional regularization methods for variable selection in quantile regression for this model. We derived the asymptotic behavior of the penalty, and viewed the penalty as a close approximation to \(L_{0}\) regularization as \(\sigma\to 0^{+}\).  
We conducted simulations under various scenarios. DERF-QR yielded lower PE across various signal-to-noise ratios and random-effect structures. Even under the challenging setting of \(\upsilon=2\) and \(D=D_{3}\), it retained low PE and successfully eliminated 80% irrelevant variables. When \(n=50\) and \(\rho=0.8\), PE remained lower than under \(n=30\) and \(\rho=0.5\), indicating strong stability in the presence of larger sample sizes and high correlation. Compared with other penalized quantile regression methods, DERF-QR consistently achieved the lowest PE and the highest TN in both sparse and dense settings, demonstrating robust performance in coefficient estimation and variable selection. We also developed this method for financial data and showed that it is practically effective.  
Our proposed DERF-QR method effectively extends penalized quantile regression for linear mixed-effects models, demonstrating excellent performance in both parameter estimation and variable selection. There are still some open issues for future research in this field. For instance, the IRW-pADMM algorithm involves parameter selection, and selecting inappropriate parameters could result in slow convergence during the iteration process. Therefore, improving the efficiency of the algorithm remains an important topic for future work. Additionally, this approach can be further applied and studied in other models, such as spatial mixed-effects models or generalized linear mixed models.  
Figure 6: Correlation coefficients and sample size.  
**Author Contributions:** Methodology, Z.H.; Writing--original draft, Z.H.; Writing--review & editing, X.H.; Project administration, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Double First Class Special Project, Jiangsu, China] grant number [1014/B22017010224] And The APC was funded by [Hohai University].  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 1 Introduction
The numerical simulations in this study take place in the Central Yunnan Water Delivery Project in China. This project starts at Shigu and ends at Mengzi. A schematic diagram of the project layout is shown in Figure 1. Its total length is equal to 664 km. The DLII34+481-34+768 section of the Shizishan Tunnel is considered in this paper, as shown in the enlarged view at the bottom left of Figure 1. The length of the tunnel is equal to 287 m. The burial depth ranges from 460 to 490 m, as shown in Figure 2. According to the geological data, the DLII34+481-34+768 section of the Shizishan Tunnel passes through the Chenghai-Binchuan fault zone (F16), also known as the Q3 active fault zone, which affects the seismic and shear resistance of the transacting tunnel. This tunnel's red bed mudstone has low strength and relatively poor stability. The strength-to-stress ratio of the rock amounts to 0.26. Hence, this tunnel has high in situ stress and may experience potentially significant deformation problems during excavation. The rock mass properties of the red bed stone of this tunnel are summarized in Table 1.  
The rest of the paper is organized as follows: In Section 2, the visco-elastic-plastic model is presented. Section 3 is devoted to the setup of the numerical model. The focus of Section 4 is on the sensitivity analysis concerning the mechanical responses of the rock bolts and the rock mass after the tunnel's excavation. Finally, the conclusions are presented in Section 5. Figure 1: Schematic layout diagram of the Central Yunnan Water Diversion Project (Dali Section II). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Tunnel Depth (m)**} & \multirow{2}{*}{**Strata Type**} & \multirow{2}{*}{**Lithology**} & \multirow{2}{*}{**Elasticity Modulus (GFa)**} & \multicolumn{2}{c}{**Triaxial Test**} & \multicolumn{2}{c}{**Direct Shear Stress Test**} & \multicolumn{1}{c}{**Uniaxial Compressive Strength**} \\  & & & & **Moolus** & **Crision** & **Anglie (\(\lx@sectionsign\))** & **Cohesion** & **Internal (MPa)** & **Internal (MPa)** & **Compressive Strength** \\ \hline
460–490 & Mudstone & J1f & 7.44 & 13.93 & 20.84 & 3.81 & 33.18 & 4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mechanical properties of the rock mass of the Shizishan Tunnel. Figure 2: The profile of physical and mechanical indicators of the rock mass in section, DLI34+481–34+768.
Addressing issues such as multi-actuator coupling, nonlinearity, uncertainty, and actuator faults in ASR systems, a multi-agent system (MAS) based on an adaptive non-singular terminal sliding mode (NTSM) fault-tolerant control method was proposed by [PERSON]. This method employs an adaptive estimation mechanism to select the gain for the controller's switching term, ensuring the smoothness of the actual control signal and reducing chattering and energy consumption [29]. Research indicates that sliding mode control has the advantages of strong disturbance rejection and low dependency on system parameters. Nevertheless, it inevitably encounters issues such as chattering and steady-state errors. Therefore, under conditions of low and varying friction coefficients, it remains challenging to minimize errors and chattering, achieve minimal overshoot and rapid response during the startup process, and ensure quick reaction and smooth transitions during switching. The main contributions of this paper are as follows:In terms of road friction coefficient estimation, to reduce the impact of road surface random noise, the Sage-Husa method was combined with the UKF method and adaptively improved. Additionally, the longitudinal force derived from the tire model and the longitudinal force estimated based on wheel dynamics were integrated, which mitigated the impact of estimation errors and was used to derive the road friction coefficient and the optimal slip ratio. 2. In terms of slip ratio control algorithms, a dynamic adaptive compensation sliding mode control (DASMC) algorithm based on wheel slip ratio was proposed. This algorithm introduces a dynamically weighted integral function into the control law, adaptively adjusting the integral action based on the error to achieve low overshoot, rapid response, and smooth transitions under varying road friction conditions. 3. Joint simulations under two typical conditions were conducted to verify the effectiveness of the algorithm. Discussion of the simulation results indicates that the improved estimation algorithm can mitigate the impact of noise errors, while the DASMC outperforms traditional sliding mode control algorithms in terms of chattering resistance and steady-state response. The remaining content of this paper is organized as follows: Section 2 introduces the vehicle dynamics model, tire dynamics model, and motor model. In Section 3, an adaptive UKF road estimator is established to estimate the adhesion coefficient of the road surface under working conditions and obtain the optimal slip rate. Section 4 designs a dynamic adaptive compensation control strategy for slip rate to stabilize the wheel slip rate at the target value. The overall structure of the anti-slip system is shown in Figure 1. Section 5 conducts simulation verification and discusses the simulation results based on performance indicators. Finally, the conclusions are given in Section 6.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods
### Processing and Evaluation of the Acquired Data
#### 2.6.2 Transformation of the Clouds to Spherical Targets  
In general, point clouds from the tested scanning systems were georeferenced using the spatial transformation (scale = 1) to the local reference cloud coordinate system based on the spherical targets (only targets No. 5001, 5002, 5005, and 5006 located at the extreme ends of the trajectory were used to allow subsequent determination of the errors arising along the route). The clouds were transformed using the function Align two clouds by picking (at least 4) equivalent point pairs. Subsequently, each cloud was cropped to contain only the area captured by the reference scanner. Ideally, the cloud transformed in this way should be identical to the reference cloud.  
Prior to the dilution, spherical targets serving as reference points were always carefully cut out, their centers determined and used to acquire transformation parameters (three angles of rotation along the X, Y, and Z axes and three displacements in the directions of the X, Y, and Z axes). Using these parameters, the cloud was transformed into the reference cloud system. The root mean square error of the initial transformation to the spherical targets (i.e., the distance between the centers of the spherical targets of the tested and reference cloud acquired after this basic transformation to spherical targets--RMSE\({}_{\text{SPHER}}\): Equation (1)) served as the first metric used to evaluate the scanner performance.  
\[\text{RMSE}_{\text{SPHER}}=\sqrt{\frac{\sum_{1}^{n}\text{d}_{1}^{2}}{n}} \tag{1}\]  
where d is the distance describing the difference between the reference points (the centers of the four spherical targets) of the tested and reference cloud and n is the number of these distances (in this case, n = 4).  
This parameter reflects, in particular, the error in the distance between the initial and furthest points of the trajectory. To a smaller degree, it also reflects the quality of the determination of the centers of the spherical targets in the cloud. It is important to point out that the shape of the cloud is not altered during this or any other transformation used in this paper--only displacements and rotations of the entire cloud are applied here.
The Effect of \"Directness\" of the Distance Metric to Produce Compact and Accurate Associative Classification Models  
[PERSON]  
1 Information Technologies Department, Urgench State University, [PERSON] 14, Urgench 20100, Uzbekistan Engineering and Computer Science Department, California State University, Fullerton, 800 N State College Blvd, Fullerton, CA 92831, USA 1  
[PERSON]  
2 Email: 2 Email:  
the case of hierarchical clustering [24], rules are grouped based on a nested sequence of partitions. Combining clustering and association rule mining methods [25; 26] enables users to analyze accurately, explore, and identify hidden patterns in the dataset, and build compact classification models.  
Our main goals and contributions in this work are as follows:  
* To explore how the amount of directness and indirectness in the distance metrics affects the accuracy and size of the associative classifier;
* To build new explainable compact associative classifiers and experimentally show the advantages of these models on real-life datasets.  
To achieve the above-mentioned goals, new distance (similarity) metrics (WCB: Weighted Combined and Balanced, WCI: Weighted Combined and Indirect, WCD: Weighted Combined and Direct) were developed based on \"direct\" (depending only the content of rules) and \"indirect\" (depending on rule metrics, such as coverage, support, and so on) measures to compute the similarity of two class association rules (CAR) and show the compact influence of those metrics to produce compact and accurate models.  
More precisely, a new explainable cluster-based AC method was proposed by utilizing the newly developed distance metrics and showing the effects of those similarity measures on the performance of AC models. AC models are produced in three steps: firstly, class association rules (class association rule is an association rule where antecedent is itemset and the consequent is a class label) are generated; CARs are clustered based on a hierarchical agglomerative fashion in the second step; and finally, the representative rule for each cluster is extracted to build the final classifier.  
The experiments were conducted on 12 real-life datasets from the UCI ML Repository [27]. The performance of our proposed methods was compared with 6 associative classifiers (CBA [9], Simple Associative Classifier (SA) [28], J&B [29], Cluster-based associative classifiers DC [30], DDC, and CDC [31]).  
The structure of the rest of the paper is as follows: Section 2 presents an overview of existing research and the relatedness of those methods to our algorithm. A detailed description of our proposed method is provided in Section 3. Section 4 outlines the main results obtained by the proposed models and presents a comprehensive experimental evaluation. Section 5 focuses on the discussions and views of the obtained results, followed by Section 6, which concludes the paper and highlights our future plans.
# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 3 Methods
### Removing Unimportant Factors by Mutual Information  
Mutual Information (MI) is a widely used method to measure the correlation between two variables. A higher value of MI represents a higher correlation between two variables. MI can effectively measure the correlation between variables with nonlinear relationships, which has been extensively adopted for feature selection.  
Using \(\mu_{X,Y}\) to refer to the joint probability density function of two random variables, the marginal density functions of these two random variables can be represented respectively as:  
\[\mu_{X}(x)=\int\mu_{X,Y}(x,y)dy;\mu_{Y}(y)=\int\mu_{X,Y}(x,y)dx \tag{3}\]  
Then  
The measure of uncertainty of variable \(Y\) obtained after observing another variable \(X\) is called its conditional entropy, denoted as \(H(Y|X)\). Specifically, \(H(Y|X)\) can be defined as follows:  
\[H(Y|X)=-\int\mu_{X}(x)\int\mu_{Y}(y|X=x)log\mu_{Y}(y|X=x)dydx \tag{5}\]  
Then, the MI between \(X\) and \(Y\) is calculated as:  
\[M(X,Y)=H(Y)-H(Y|X) \tag{6}\]  
The correlation of various external variables with load was examined using the MI method. The results are presented in the form of a horizontal bar graph in Figure 3.  
Figure 2: Framework of the proposed Causal–Informer model.  
The degree of correlation between external variables and load is proportional to the value of the MI, which was used in the present study to obtain the correlation ranking. After confirming with energy professionals, the correlation ranking of the external variables in Figure 3 was found to be generally consistent with empirical judgments. On the basis of the correlation ranking and empirical judgment, the proportion was excluded from the input to the model. As shown in Figure 3, temperature and servicing were the most relevant to load. Servicing is a binary variable, wherein 1 indicates that there are units in the plant under maintenance, while 0 indicates that all units in the plant are operating normally. Temperature is a continuous variable, and the causal effect of temperature on load was investigated subsequently. The remaining external variables were transformed into binary variables by setting certain conditions in order to calculate their causal effects. The specific transformation conditions are presented in Section 4.2.1.