esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 2 MPC Path Tracking Controller
### Model Linearization and Prediction Equation
\tag{4}\]  
where \(\mathbf{A}(k)=\mathbf{I}+T\mathbf{A}(t)\), \(\mathbf{B}(k)=T\mathbf{B}(t)\), \(\mathbf{I}\) denote the identity matrix, and \(T\) represents the sampling period. Formulate a prediction equation to calculate the current state vector \(\xi(k)\) and the control input sequence \(\Delta\mathbf{U}(k)\) over the control time horizon, thereby obtaining the future system output:  
\[\mathbf{\xi}(k\mid t)= \begin{bmatrix}\mathbf{\xi}(k\mid t)\\ u(k-1\mid t)\end{bmatrix}  \tag{5}\]  
By integrating Formulae (4) and (5), a novel state-space expression is derived:  
\[\left\{ \begin{array}{l}\mathbf{\xi}(k+1\mid t)=\widetilde{A}(k)\mathbf{\xi}(k\mid t )+\widetilde{B}(k)\Delta\mathbf{u}(k\mid t)\\ \mathbf{y}(k\mid t)=\widetilde{C}(k)\xi(k\mid t)\end{array} \right.\] (6  
The primary objective of the linearization approach is to enable real-time computation capabilities for model predictive control (MPC) implementation, which is a prevalent practice in automotive control systems. This necessity arises from several critical factors: the stringent timing requirements that sampling rates must adhere to, the constrained computational resources of controller hardware and the imperative for predictions to be completed within fixed control cycles. The linearized model in this paper adheres to well-established procedures within the vehicle dynamics control literature, as evidenced by the references cited in the work. The reference trajectory is initially generated by the path planning module, which provides a series of waypoints (X_ref_, Y_ref_) based on the vehicle's global positioning and road geometry information. These waypoints encompass the desired lateral positions, heading angles and curvature details. Based on the vehicle's current speed and the sampling period, the waypoints in global coordinates are discretized into time-series data \(y_{\text{ref}}(k)\), ensuring that the reference trajectory can be dynamically updated to align with the vehicle's motion state. The curvature of the reference path is calculated and integrated into Equation (6) to account for variable curvature conditions. This enables the MPC controller to anticipate upcoming changes in the road geometry and adjust the control inputs accordingly.
# Development of Rockburst Research: A Comprehensive Review
## 5 Rockburst Intensity Classification and Prediction
### Mathematical Model Approaches for Rockburst Classification and Prediction
#### 5.3.2 Machine Learning
The fracture-induced electromagnetic radiation (FEMR) signal recognition model based on bidirectional long short-term memory recurrent neural networks (bi-directional LSTM RNN) had a good response to the occurrence of rockburst and can capture rockburst information in advance in order to realize the automatic/intelligent discrimination of rockburst precursory [156]. Ensemble learning is a method which can improve the learning effect via gathering several weak classifiers of machine learning. The strong classifier of ensemble learning can overcome the defects of each weak classifier (such as the difference of algorithm and the application scope of classifier), divide a large problem into thousands of small problems and then train the corresponding weak classifier to break these small problems one by one. [PERSON] et al. integrated seven classifiers (BP neural network, SVM, DT, KNN, logistic regression, multiple linear regression and naive Bayes) and combined them with nine data interpolation methods to classify rockburst severity. Compared with the single classifier, which had the best classification effect, the accuracy was improved by 15.4% [157]. Based on the tunnel microseismic data of the Jinping II hydropower station, [PERSON] et al. used six microseismic monitoring indicators as training sets and test sets via five ensemble learning methods (random forest RF, adaptive boosting, gradient boosting decision tree GBDT, extreme gradient boosting and light gradient boosting machine) to predict the short-term rockburst risk. The results showed that RF and GBDT had better comprehensive prediction performance [158]. By the stacking technique of ensemble learning, based on KNN, SVM, deep neural network (DNN) and RNN, four ensemble learning models KNN-RNN, SVM-RNN, DNN-RNN and KNN-SVM-DNN-RNN were built to predict the occurrence of rockburst. The results showed that the stacking technique of ensemble learning had unique superiority when using unbalanced data for rockburst prediction [159]. Five ensemble classifiers based on logistic regression, naive Bayes, Gaussian process (GP), multilayer perceptron (MLP), SVM and DT were used to estimate the occurrence probability of each risk level for short-term rockburst prediction. The results showed that the comprehensive performance of ensemble classifiers was better than each basic classifier individually [160]. Due to the characteristics of the reinforcement learning algorithm and rockburst, there is no research on rockburst classification and prediction using reinforcement learning. Many machine learning methods have been used to study classification and prediction of rockburst, but there are still some problems in engineering application, such as insufficient data, inability to provide a simple model and computational overhead. Figure 7: Network structure of ConvNetQuake model (from [80]).
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 6 Experimental Validation and Discussion
### Q-Factor of Planar Array Configurations  
The Q-factor, a crucial parameter in planar array configurations, is measured using monostatic and collocated bistatic configurations. The linear trend line variation of the Q-factor is plotted as shown in Figure 25. However, there is a noticeable difference between the measured Q-factor and the simulated results. This difference in the Q-factor could be due to various factors, such as the quality of the measurement setup components, the alignment of the tag with the reader antenna, the reader and antenna's efficiency, electromagnetic interference, or the properties of the material used to create the tags.  
As shown in Figure 25a, the collocated bistatic measured Q-factor of unit cell tag shows the highest Q-factor, and then it is reduced when the configurations of the array increase from 2 \(\times\) 2 to 4 \(\times\) 4. As the number of elements in the array increases, there is a greater chance of electromagnetic coupling between adjacent elements, and it can result in a lower Q-factor. Additionally, when a plane wave interrogates the chipless RFID array, the arrays may experience more significant radiation losses, leading to a further decrease in the Q-factor. A similar behaviour can also be found in the monostatic measured Q-factors as shown in Figure 25b. Furthermore, the results of the bistatic measurements show a higher Q-factor than those of the monostatic measurements. In a monostatic setup, a single antenna is used to transmit and receive signals. While the antenna excites the tag and receives the backscattered signal, it has to handle both the transmitted and weak backscattered signals. This can result in signal degradation, reduced sensitivity, and a lower measured Q-factor due to increased losses and noise.  
On the other hand, separate antennas transmit and receive signals in a collocated bistatic configuration. Each antenna can be optimised for its specific function, leading to better performance than a single antenna used in the monostatic configuration. This can result in a higher measured Q-factor due to reduced losses and improved sensitivity.  
Figure 25: Measured Q-factor of planar array configurations. (**a**) Collocated bistatic configuration. (**b**) Monostatic configuration.
## 4 Evaluation Methods
### Threshold Optimization  
The proposed model incorporates a threshold to recognize uncertainty. If the model's assessed uncertainty is below the threshold, the result is accepted; if it exceeds the threshold, the result is rejected. This characteristic introduces a trade-off between performance and the rejection rate based on the threshold setting. For example, Figure 4 depicts the preliminary results according to the threshold value. As shown in Figure 4a, a high threshold accepts most results and allows most poor predictions. Conversely, as shown in Figure 4b, a low threshold rejects most results including many poor predictions. However, too frequent rejections of results can hinder the practical usability. Therefore, determining an appropriate threshold is crucial for accurately evaluating the model's performance. The optimal values is the one that maximizes performance while minimizing rejections.  
To address this issue, we approached the problem statistically. Figure 5b depicts the \(p\)-value representing the difference between \(Cal\) and \(CPF\) predictions relative to the rejection threshold. At high thresholds, the distributions of the two predictions are nearly identical, but as the threshold decreases, a statistical difference emerges. When the \(p\)-value falls below a certain level (\(<\)0.05), it supports the alternative hypothesis that a significant difference exists. This implies that using Equation (3) to calculate uncertainty is ineffective at these levels. To balance acceptance and reliability, we set the significance level at \(p\)-value (=0.05) to determine the threshold, thereby supporting the null hypothesis that there is no statistically significant difference between the distributions. This approach ensures that uncertainty is assessed using Equation (3) at a st  
Figure 4: Preliminary results with different uncertainty thresholds. (**a**) High threshold: accepts most predictions, including poor ones. (**b**) Low threshold: rejects poor predictions. This highlights the trade-off between performance and rejection rate.  
When setting the threshold to a \(p\)-value of \(5\%\), a value of \(0.36\) can be obtained, and when applied to Figure 5a, it shows that the model accepts the prediction results at a \(72\%\) level.
## 2 Methodologies
### Overview of the Proposed MDRN Approach  
Having addressed the constituents separately, the present approach for the manufacturing quality prediction can be summarized as follows, and is illustrated by Figure 2.  
Step 1. Collect multi-parameter \(\mathbf{X}\) and the corresponding quality from each manufacturing batch.  
Figure 1: Structures of the autoencoder (AE)-based deep regression network (DRN) model. \(\mathbf{w}^{l}\) and \(\mathbf{b}^{l}\) represent the connection weight matrix and bias values between neighboring layers ((\(l\)-1)-th and \(l\)-th layers), respectively.  
Figure 2: Flow chart of the proposed manifold learning-based deep regression network (MDRN) approach for multi-parameter manufacturing quality prediction.  
Step 2. Perform the \(K\)-Isomap algorithm and get the low-dimensional information \(\mathbf{Y}\).  
Step 3. Construct the DRN model and get the quality prediction.  
(a) Construct the AE-based deep framework for the feature learning of \(\mathbf{Y}\), and get the parameter sets (\(\mathbf{w}\), \(\mathbf{b}\)).  
(b) Initialize the regression network with the parameter sets (\(\mathbf{w}\), \(\mathbf{b}\)) pre-trained by (a), and model the relationships between \(\mathbf{Y}\) and the corresponding quality.
## 2 Methods
### Definitions and Data Collection
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Risk Factors** & \
\begin{tabular}{c} **Mean** \\ **or n** \\ \end{tabular} &
\begin{tabular}{c} **SD or** \\ **\%** \\ \end{tabular} & \multicolumn{3}{c}{**Univariate Analysis**} & \multicolumn{3}{c}{**Multivariable Analysis**} \\ \hline  & & & \(OR\) & 95\% _CI_ & \(p\) & \(OR\) & 95\% _CI_ & \(p\) \\ \hline
**DEMOGRAPHIC** & & & & & & & \\ \hline
**Age** & 14.1 & 2.5 & 0.99 & 0.82 to 1.19 & 0.891 & & \\ \hline
**GYMASTICS** & & & & & & & \\
**ACTIVITY** & & & & & & & \\
**BEFORE 2020/2021** & & & & & & & \\ \hline
**Years of practice** & 9.66 & 2.86 & 1.04 & 0.88 to 1.22 & 0.673 & & \\ \hline
**Hours of training before** & & & & & & & \\
**2020/2021** & 21.20 & 6.57 & 1.00 & 0.94 to 1.08 & 0.934 & & \\ \hline
**Performance level during** & & & & & & & \\
**2019-2020 season** & & & & & & & \\ \hline Local & 0 & 0\% & - & - & - & - & \\ \hline Regional & 9 & 10.2\% & 0.59 & 0.13 to 2.89 & 0.503 & & \\ \hline National & 51 & 58.0\% & 1.38 & 0.49 to 3.80 & 0.529 & & \\ \hline International & 28 & 31.8\% & - & - & - & - & \\ \hline
**LIFESTYLE** & & & & & & & \\ \hline
**School level** & & & & & & \\ \hline Primary school & 3 & 3.4\% & 1.00 & \begin{tabular}{c} 2.31 \(\times\) 10\({}^{-91}\) to \\ \(8.82\times\) 10\({}^{-86}\) \\ \end{tabular} & 1.000 & \\ \hline Secondary school & 47 & 53.4\% & \(3.46\times 10^{-8}\) & 0.00 to NA & 0.997 & \\ \hline High school & 32 & 36.4\% & \(4.04\times 10^{-8}\) & 0.00 to NA & 0.997 & \\ \hline College & 4 & 4.5\% & \(1.33\times 10^{-16}\) & \
\begin{tabular}{c} 1.45 \(\times\) 10\({}^{-236}\) \\ to NA \\ \end{tabular} & 0.996 & \\ \hline Other & 2 & 2.3\% & 1.00 &
\begin{tabular}{c} 1.37 \(\times\) 10\({}^{-86}\) \\ \(3.77\times\) 10\({}^{-89}\) \\ \end{tabular} & 1.000 & \\ \hline
**School adaptation** & & & & & & \\ (i.e., flexible schedule) & 77 & 87.5\% & 1.43 & 0.35 to 5.23 & 0.598 & \\ \hline
**Hours of school** & 4.35 & 1.4 & 0.93 & 0.66 to 1.29 & 0.651 & \\ \hline
**Time in transport per day (min)** & 49.5 & 28.5 & 1.00 & 0.99 to 1.02 & 0.805 & \\ \hline
**Sedentary per day (min)** & 283.7 & 112.4 & 1.00 & 0.99 to 1.01 & 0.261 & \\ \hline
\begin{tabular}{c} 5.21 \(\times\) 10\({}^{-7}\) \\ \(1.21\times\) 10\({}^{-122}\) \\ \end{tabular} & 0.992 & \\ \hline
**SLEEP** & & & & & \\ \hline
**Quantity (hours)** & 8.24 & 1.08 & 1.28 & 0.83 to 1.98 & 0.262 & \\ \hline
**Difficulty falling asleep** & & & & & \\ \hline Never & 23 & 26.1\% & - & - & - \\ \hline Sometimes & 45 & 51.3\% & 0.62 & 0.18 to 1.91 & 0.417 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the 88 participating gymnasts and univariate and multivariable analyses. For the univariate analysis, there is no model adjustment (each variable was analysed alone with the dependent variable). In the multivariable analysis, we kept only significant variables (_p_ \(<\) 0.05) from the univariate analysis and they were all included in the model of the multivariable analysis.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 4 Experiments
### Evaluation Metrics  
In our work, developers write and submit their text requirements for mashup creation, and the recommender system (RS) returns top-N most relevant services. Therefore, it can be regarded as an information retrieval (IR) problem. We adopt two widely used metrics in IR and RS to evaluate the recommendation accuracy: Recall@K and NDCG@K [18; 34]. Intuitively, Recall measures whether the component service is in the recommendation list, and NDCG gives more weight to correct predictions at the start of the recommendation list and discounts correct predictions farther from the beginning of the list.  
Recall@K is the ratio of the number of actual component services in the top-K recommendation list to the number of services composed by the mashup. It is defined as:  
\[\text{Recall@K}=\frac{1}{|MT|}\sum_{m\in MT}\frac{|\text{rec}(m)\cap\text{ truth}(m)|}{|\text{truth}(m)|}, \tag{5}\]  
where \(MT\) is the set of mashups in the test set, \(\text{rec}(m)\) is the recommendation list of services of size K for mashup \(m\). \(\text{truth}(m)\) is the ground truth list of services that are composed by the mashup \(m\) in the test set.  
Normalized Discounted Cumulative Gain (NDCG) [35] considers the ranking order of the recommended services, and assigns different weights to each service in the top-K recommendation list. it is defined as:  
\[\text{NDCG@K}=\frac{1}{|MT|}\sum_{m\in MT}\frac{\sum_{i=1}^{K}\frac{2^{I(i)}- 1}{\log_{2}(i+1)}}{\text{IDCG@K}}, \tag{6}\]  
where \(I(i)\) indicates whether the service at position \(i\) of the ranking list is in \(\text{truth}(m)\). IDCG@K is the ideal DCG score of the top K services that can be achieved.
## 7 Discussion and Limitation
### Assumptions about Attacker Capabilities  
Another limitation is the assumption about the capabilities of the attacker. In our experiments, we only assumed that the attacker could monitor the connection between one of the client and entry nodes. However, the attacker's success rate gradually increases if the attacker becomes an ISP that can control the user by starting more entry ORs in Tor. Tor protects relays by periodically changing the relay list [51]. Although it is difficult to control one or a few malicious nodes to successfully implement a WFP attack, or as a more powerful adversary who controls all the relays, it does not mean that the user's anonymity will not be vulnerable due to this situation, so we need to do further research experiments to prove this issue.
## 3 Infrastructure Inspection and Monitoring Carried Out  
### Infrastructure Description  
The so-called Nudo de Colmenar Bridge (Madrid) connects the M-607 road (Madrid Way) and the M-40 ring road (A-1 Way). The structure is located in the Nudo de Colmenar detailed in Figure 1.  
The structure runs over several infrastructures, including the aforementioned highways M-40 ring road, M-607 road and connecting branches, as well as railway lines. It is a hyperstatic overpass whose typology is a continuous post-tensioning concrete slab. Its ground plan is curved. The structure has a total length of 561 m distributed in 17 spans with the following lengths: 20.5-32.2-35.6-37.9-56.4-32.9-36.7-39.0-28.8-29.6-36.5-28.6-30.3-32.9-33.1-28.4-21.6 m. These lengths were taken from the topographic survey carried out during the repair works made on the structure.  
The cross-section is constant along the entire length of the deck. The slab has curved geometry with a maximum thickness of 1.40 m at the deck axis that is reduced to 0.20 m at the edge of the deck using a circular segment. The section is composed of a set of six circular lightning elements of variable diameter between 0.65 m and 1.00 m, as can be seen in Figure 2.  
The prestressing in the spans is not constant due to their different lengths, changing from 9 tendons of 15 \(\mathcal{O}\) 0.6\({}^{\prime\prime}\) in the short spans to 16 tendons of 19 \(\mathcal{O}\) 0.6\({}^{\prime\prime}\) in the longer spans.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 4 Performance Evaluation  
In this section, we evaluate and compare the performance of the proposed distributed method (denoted by DM) with that of the centralized method (denoted by CM) in terms of the communication cost and the server computation cost because existing work for range monitoring queries in the road network mostly adopts CM [1]. CM uses the same basic data structures (i.e., EI, ET, and QT) as DM. The communication cost of CM was measured by the total number of location-update messages transmitted from moving objects, while that of DM was measured by the total number of messages transmitted between the server and moving objects, i.e., the sum of (i) the number of point-to-point messages (RequestVR, UpdateResult, and AssignVR) and (ii) the number of broadcast messages (InsertQS, SplitRegion, DeleteQS, and MergeRegion). On the other hand, the server computation cost of CM and DM was measured by the amount of CPU-time the server takes for query processing. Because we are interested in the advantages of maintaining the full list at each SSP-tree node, we implemented two versions of the SSP-tree; one is the proposed SSP-tree, where each node maintains the full list, and the other is the naive SSP-tree, where each node does not maintain the full list. The simulationswere coded in Java on Intel Xeon E5-2620 6-core Processor with 8 GB RAM running Linux Ubuntu 12.04 (64-bit) operating system.
## 3 Results and Discussion
### Microstructural Analysis of the Heat-Treated Specimens
Inverse FFT of the FFT pattern masked at the extra spots confirms that the moire fringes in Figure 7b are the nanometric domains responsible for the extra frequencies. The analysis performed on the HT5-treated specimen, which was subjected only to direct aging up to 100 h, revealed again the presence of only Nb carbides. The STEM-HAADF image reported in Figure 8 highlights the presence of NbC particles, similar to the ones previously observed in the HT4 specimen. Indeed, Figure 8 shows that the carbides were finer than the ones observed in the HT4 condition. These carbides were dispersed in the thin lamella with an average distance of about 2.7 \(\upmu\)m, and they measured about 43 nm in equivalent diameter. This evidence agrees with other experimental findings, as reported in [21], with the authors affirming that the size of these carbides continued to increase until 120 h at 480 \({}^{\circ}\)C. In [17], the authors confirmed that prolonged aging times at 480 \({}^{\circ}\)C induced the coarsening in size of the Nb carbides, even if this process was slower in case of direct aging. Other authors [55] suggested that Nb carbides and Cu-rich particles may appear over an extended period of up to around 9000 h, but at a lower aging temperature of 350 \({}^{\circ}\)C.  
Figure 7: **(a)** STEM-HAADF images of HT4-treated specimen with a dark particle identified as Nb carbide (white arrow), **(b)** HRTEM view of the martensitic crystal domain with Cu-rich nanometric precipitates (black arrows), and **(c)** FFT of the image (**b**) with white triangles (**c**) pointing to extra spatial frequencies ascribed to Cu-rich precipitates. Conversely, considering the previous HT4-treated specimen, the HREM image and the corresponding FFT in the inset do not reveal the presence of nanometric domains within the Fe matrix which could possibly be attributed to Cu-based precipitates. In the HREM image, no evidence of moire fringes arising from the superposition of crystal lattices was observed, and the analysis of the FFT of the HREM image did not reveal the presence of spatial frequencies belonging to a second phase other than the Fe matrix.
## 3 The Cross Time Slot Partial Interference Alignment
### Partial Interference Alignment for the CEU  
According to (6), the signal transmitted to UE[2,1] will be interfered with by the intra-cell interference contained in the combined signal and the inter-cell interference caused by Relay 2 in Cell 2. Note that the inter-cell interference contains signals to both the CCU and CEU (i.e., \(\mathbf{s}_{t1}^{[1,2]}\) and \(\mathbf{s}_{t1}^{[2,2]}\)) in sub-time slot 1. These two signals are carried by different transmit beamforming matrices, which means they cannot be aligned to the same subspace. Hence, we choose to align one of them to the intra-cell interference subspace. For example, we align the intra-cell interference \(\mathbf{s}_{t1}^{[1,1]}\) and inter-cell interference \(\mathbf{s}_{t1}^{[2,2]}\) to the same subspace such that  
\[span\big{(}\mathbf{H}_{R1}^{[2,1]}\mathbf{G}_{1}\mathbf{H}_{1}^{R1}\mathbf{V} ^{[1,1]}\big{)}{=}span\big{(}\mathbf{H}_{R2}^{[2,1]}\mathbf{G}_{2}\mathbf{H}_{2 }^{R2}\mathbf{V}^{[2,2]}\big{)}. \tag{11}\]  
As in Cell 2, by aligning the intra-cell end inter-cell interference at UE[2,2], we can obtain the transmit beamforming matrix to UE[2,1], i.e.,  
\[span\big{(}\mathbf{H}_{R1}^{[2,2]}\mathbf{G}_{1}\mathbf{H}_{1}^{R1}\mathbf{V} ^{[2,1]}\big{)}{=}span\big{(}\mathbf{H}_{R2}^{[2,2]}\mathbf{G}_{2}\mathbf{H}_{ 2}^{R2}\mathbf{V}^{[1,2]}\big{)}. \tag{12}\]