esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study: AQM Implementation
### Verification and Assessment for the CNS  
The RVQA is a process model developed for the verification and assessment phase of the AQM framework to perform run-time data confidentiality checking and assessment processes. The processes are carried out within the agent-based CNS model. The RVQA process in the CNS starts when the user agents are created [51]. The run-time verification is implemented to identify when the defined critical  
Figure 8: Contributing factors toward data confidentiality disclosure conditions in the CNS.  
states are reached. The critical states are the states where the violations can occur. At the critical states, the quality assessment is performed. The assessment is performed to indicate the disclosure status of the agent identity, location, social information, and contents by considering the contextual information. The contextual information, i.e., agent activities and decisions, determine the agent data confidentiality status as disclosed or protected. Finally, the identified data confidentiality disclosures are classified and reported as detected data confidentiality violations.  
The RVQA assesses the agent data confidentiality disclosure status based on the defined checking rules for each confidentiality metric [51]. As the verification and assessment processes are part of the AQM framework and the AQM phases are performed as a cycle, the RVQA can be performed at one level at a time starting from the individual level, followed by the social and content levels, to improve the effectiveness of violation detection. To perform the detection process, the confidentiality requirements and specified checking rules are used to design the algorithms to detect the data confidentiality disclosures. The detection includes the process of checking the reachability of critical states, assessing disclosure status at the critical states, and finally, reporting the number of detected confidentiality violations. The following subsections explain the RVQA process at the agent's individual, social, and content levels in detail.
# Development of a Power and Communication Bus Using HIL and Computational Intelligence
## 1 Introduction
Voltage on the line is 12 V, and baud rate is low. Transfer is not expressed in bits per second. AS-I can check the status of 62 nodes in 20 ms. One of the oldest commonly used interfaces that uses only ground and one wire to deliver power and data is \"1 wire\", introduced by Dallas Semiconductor, now known as Maxim. The authors developed an interface to transfer power and data on the same line. The idea of this solution is to determine time frames for power transmission and another one for data transmission. The prototype of the proposed solution was built, and it was used in order to carry out several tests in a laboratory environment as well as in real conditions (Figure 1). It was possible to achieve a data link up to 2 Mbps, a power link up to 48 V and 10 A, hence the possibility of transmitting 480 W of power was accomplished. These experiments showed the high potential of the proposed solution. The next step presented in this study deals with solving the auto-addressing problem. The next step of this solution is to design auto-addressing.
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 2 Methods
### Fusion Positioning Algorithm  
In this study, a fusion positioning algorithm was designed by combining the DOP numerical analysis model, WLS algorithm and KF algorithm. The system state equation was constructed using Taylor expansion. The coordinate solution of the WLS positioning algorithm was used as the observation value of KF. DOP was introduced into the measurement noise covariance parameter of the KF observation equation to suppress the positioning error in the narrow space. The specific algorithm procedure is as follows:  
1. Building the system model:  
The prediction equation and observation equation of the system are constructed according to Equations (9) and (10). Considering that the position of the moving object will not change exponentially, the second-order Taylor expansion of \(X_{k}\) at \(X_{k-1}\) can be carried out to complete the construction of the prediction equation. In this way, the transfer matrix \(F\) in Equation (9) can be acquired. In addition, since this study regards the output coordinates of WLS as the observed values, which is close to the position state value, we replace the observation transition matrix \(H\) in Equation (10) with the unit matrix \(I\) in the experiment. Consequently, Equations (15) and (16) are, respectively, the prediction equation and observation equation:  
\[X_{k}=X_{k-1}+X_{k-1}^{\prime}\times dt+\frac{1}{2}\times X_{k-1}^{\prime \prime}\times dt^{2}+Q_{k}. \tag{15}\]  
\[Y_{k}=IX_{k}+R_{k}. \tag{16}\]  
2. Measuring system observation value \(X\):  
Based on the transceiver information between UWB devices, the distance measurement from the tag to the base station is completed, and the ranging information is used as the input of the WLS positioning algorithm to preliminarily calculate the position coordinates of the node to be measured. The position coordinate information can be used as the observation value of KF.  
3. Calculating system noise covariance \(R\):  
First, the PDOP values of the narrow space is calculated for each coordinate position within the space by Equation (8). Then, the noise covariance \(R\) can be acquired by Equation (14).  
4. Calculating the Kalman gain \(K\):  
Based on the output of Step 2 and Step 3, the Kalman gain is calculated by Equation (12).  
5. Predicting the position coordinate:  
Based on the output of Step 2, Step 3 and Step 4, the position coordinate is predicted by Equation (13).  
6. Updating measurement noise covariance \(R\):  
The measurement noise covariance matrix \(R\) is updated by the one corresponding to the predicted coordinate position (See Step 3). Then, Step 4 and Step 5 are repeated to update the position coordinate of the tag. The measurement noise covariance is updated continuously in this way until the tag moves to other locations.  
The flow chart of the algorithm is shown in Figure 2.
## 1 Introduction
### Sound Coding Strategies  
In practice, a wide variation of outcomes is observed amongst implanted patients, which is probably linked to the duration of deafness, the age at implantation, the age at onset of deafness, the duration of implant use and the patient's social environment [4].  
Some studies showed a superiority of an FC strategy over a CP strategy particularly in noise [5]. Others like [PERSON] et al. and [PERSON] et al. [6, 7] showed a positive difference, for speech recognition, in favour of the Advanced Combination Encoder (ACE) (CP strategy) over Continuous Interleaved Sampling (CIS) (FC strategy) and Spectral Peak Picking (SPEAK) (CP strategy) [6]. [PERSON] et al. [8] compared the musical activities and perception of cochlear implant users and concluded that CIS, SPEAK and ACE did not differ significantly. No clear advantage for a particular coding scheme has been identified yet.  
The number of spectral channels required for speech recognition depends on the difficulty of the listening situation for both FC and CP strategies [9, 10]. For FC strategies, all channels are transmitted to the corresponding electrodes, usually between 12 and 16, leading to a relatively large amount of spectral information that may be blurred by the current spread in the cochlea. When the stimulation rate is high, some results suggest that this rate may be beneficial to speech perception [11]. Another feature of the strategies lies in the non-overlapping (interleaved) pulse delivery; pulses are brief with a minimum delay between them and rapid variations in speech can be tracked [12].
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
###### Abstract  
The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computational complexity and low acquisition accuracy under high dynamic conditions. In this paper, a GNSS signal acquisition algorithm based on two-stage compression of the code-frequency domain is proposed. The algorithm maps the incoming signal of the same interval to multiple carrier frequency bins and overlaps the mapped signal that belongs to the same code phase. Meanwhile, the code domain compression is introduced to the preprocessed signal, replacing circular correlation with compressed reconstruction to obtain Doppler frequency and code phase. Theoretical analyses and simulation results show that the proposed algorithm can improve the frequency search accuracy and reduce the computational complexity by about 50% in high dynamics.  
GNSS; signal acquisition; sparse; compressed sensing; high dynamic; complexity
## 4 Discussion
### Lag and Cumulative Effects between Drought and Ecosystem Function  
In this study, we found that the time-lag and cumulative relationships between SPEI and ecosystem function indicators varied among species and geographical distribution (Figures 8 and 9). In the Songnen Plain, SPEI and LAI were both positively improved within 12 months after mutual impact, namely becoming wetter or having increased vegetation cover. The rising wetness in the study area was followed by a predominantly increasing trend in ET. By contrast, as an increase in ET increased, SPEI of more than half of the pixels increased. Meanwhile, more areas showed a trend toward drought. In the northeast areas dominated by forests, the pixels with negative impulse response coefficients were distributed concentratedly. The increased humidity will hinder the rate of water vapor movement in evapotranspiration. Woody plants with greater evapotranspiration have the potential to cause regional drought when moisture conditions are constant [56]. In the last 21 years, the Songnen Plain has become more humid, showing an increasing GPP almost in the entire study area. In the opposite direction, the increase in GPP was followed by a drought trend in about half of the total area, mainly in the northeast and in forests along the southwest border. Generally, the composition, structure, and function of forests are closely related to water. Forests have essential adaptive effects and feedback on water factors at various scales [46]. In some areas, the problem of excessive water consumption in forests affects regional water movement and cycle [57].  
Figure 9: The proportion of pixels of cumulative pulse coefficient among SPEI and ecosystem functional indicators.  
Our experiments showed that there existed a certain lag between moisture conditions and vegetation in the Songnen Plain. The greatest response were in 6-8 th months later, and the mutual impact tended to disappear after one year. Some studies indicated that the most sensitive period between vegetation and moisture in arid and semi-arid regions was from April to July [58; 59]. Compared with woody plants, herbaceous plants were more sensitive to external conditions. Due to their shallow root systems and lower xylem hydraulic resistance, grasslands prone to the drought stress [11; 12]. The relationship between ecosystem function and moisture conditions in the area dominated by the same vegetation type varied depending on the geographical location, which might be related to spatial differences in climate [25]. In summary, GPP was most affected by water conditions in the Songnen Plain, followed by vegetation cover (LAI) with the second broadest coverage; ET is the smallest but similar to LAI. As a previous study indicated, the correlation between LAI and biomass-related variables was high in semi-arid regions globally [27].
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 3 System Modeling Method Construction
### Support Vector Machine SVM Model Method
It incorporates most of the core concepts of SVMs, but unlike SVMs used for classification, the goal of SVR is to find a function that closely approximates all training samples within a predetermined tolerance range. SVR attempts to learn a function, as shown in Formula (6):  
\[f(x)=w\cdot x+b \tag{6}\]  
where '\(w\)' is the weight vector and '\(b\)' is the bias term. This function should be able to predict a result as close as possible to the true value '\(y\)' for each input. SVR has an important parameter, '\(\epsilon\)', known as the epsilon-insensitive loss, which means that when the absolute difference between the predicted value and the true value is within the range of '\(\epsilon\)', and it is not considered an error. This setting creates a tube (or boundary) centered around the prediction function with a width of '\(2\epsilon\)', where prediction errors within this interval are treated as zero. The loss function is defined as in Formula (7):  
\[L_{\epsilon}(y,f(x))=\max(0,|y-f(x)|-\epsilon) \tag{7}\]To find the optimal 'w' and 'b', SVR solves the minimization problem:  
\[\frac{1}{2}|\text{w}|^{2}+\text{C}\sum_{i=1}^{n}\delta_{i}+\delta_{i}^{*} \tag{8}\]  
Subject to the constraint conditions as shown in Formula (9):  
\[\left\{ \begin{aligned} & y_{i}-w\cdot x_{i}-b\leq\varepsilon+ \delta_{i}\\ & w\cdot x_{i}+b-y_{i}\leq\varepsilon+\delta_{i}^{*}\end{aligned} \right. \tag{9}\]  
where '\(\delta_{i}\)' and '\(\delta_{i}^{*}\)' are slack variables, both greater than or equal to zero, used to handle data points that are not within the tube. 'C' is a regularization parameter that determines the model's tolerance for errors. SVR is a powerful regression tool that can handle linear and nonlinear relationships, and by selecting appropriate parameters, overfitting can be avoided. It is suitable for many practical regression tasks.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 3 Results and Discussion
### Dataset Description  
In this paper, nine different datasets of varying complexity and domain are considered, as shown in Table 1. For the datasets, the number of features ranges between 8 and 4000, whereas the number of data points or instances ranges between 62 to 1000. Thus, it is evident that both low and high feature dimensions as well as low- and high-scale datasets are considered here. The optimization objective of this paper is already described in Equation (1), i.e., an optimal combination of a lower number of features and higher classification accuracy (or low classification error) is desired.
## 2 Overview of Current Methods for Connecting Modular Units
### Bolt Connection Using Connector Plates  
Inserting a connector plate between modules and connecting them with bolts is the most widely used method for connecting upper and lower units and left and right units. Depending on the layout of the units, four or eight upper-lower and left-right modules may meet at a single point.  
In the connection system for Toyota Home [7], a hash-shaped (#) connector plate is used to connect eight modules. The protruding guide pin enables coordination with the upper module. At least one of the four modules to be positioned above must have an opening that can act as a service opening or an access hole for bolting. As these openings must be finished on site, the building is obviously affected by the site finishing work when using this modular method.  
In the Sekisui Heim system [8] for connecting modules, the upper and lower modules are connected by friction bolts using small steel blocks at the boundary, while a steel plate is added for bolting the left and right modules. Similarly, to connect units, part of the ceiling of the lower module and part of the corner floor of the upper module must be finished on site after connection.  
The VectorBloc system [9] is for high-rise buildings developed in the USA by Vector Praxis. Blocks produced by casting are welded to the ends of the floor and ceiling beams of the module, and a connector plate is inserted between the upper and lower modules and connected by tightening the bolt from the floor of the upper module. The block welded to the ceiling beam also functions as a nut, and fastening is performed from top to bottom from the inside, and thus safety and constructabilityare guaranteed. Additionally, the module units and connection system are precisely manufactured, with an error of approximately 2 mm per 100 m.  
For methods where units are connected from inside the module using a connector plate, the connection approach determines the factory finish of the module and significantly impacts the efficiency of the connections. In particular, when a space or gap is left for connection of a module to a concrete slab, the production rate decreases. Thus, whether an opening is left for connection of a module in the ceiling needs to be considered. In addition, when a flat surface is designed to enable connection between modules using a duct or shaft, the constraints imposed by the concrete floor can be minimized.
# GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification
## 3 Preliminary
### The Classical Graph Neural Networks  
The classical graph neural networks are formulated as follows:  
\[h_{i}^{(k+1)}=\sigma(\sum_{j\in\mathcal{N}_{i}}e_{ij}^{(k)}h_{j}^{(k)}W^{(k)}) \tag{1}\]  
where \(h_{j}^{(k)}\in R^{p_{k}}\) is the hidden features of the \(j\)-th node resulting from the \(k\)-th graph neural hidden layer, \(W^{(k)}\in R^{p_{k}\times p_{k+1}}\) is the learnable weight matrix of \(k\)-th graph neural hidden layer, \(\mathcal{N}_{i}\) is the node set including the first-order neighbor nodes of the \(i\)-th node and the \(i\)-th node itself, \(e_{ij}^{(k)}\) is the weight between the \(i\)-th node and the \(j\)-th node of the \(k\)-th graph neural hidden layer, and \(\sigma(\centerdot)\) is a nonlinear activation function (e.g., \(Relu(\centerdot)=max(\centerdot,0)\)). GCN uses \(e_{ij}^{(k)}=1/\sqrt{d_{i}d_{j}}\) to obtain the weight between the \(i\)-th node and the \(j\)-th node, where \(d_{i}(d_{j})\) is the degree of the \(i\)-\(th\)(\(j\)-\(th\)) node.
# A Review of Time-Scale Modification of Music Signals +
## 5 TSM Based on the Phase Vocoder (PV-TSM)
### Artifacts  
By design, PV-TSM can achieve phase continuity for all sinusoidal components contributing to the output signal. This property, also known as _horizontal phase coherence_, ensures that there are no artifacts related to phase jumps. However, the _vertical phase coherence_, _i.e._, the phase relationships of sinusoidal components within one frame, is usually destroyed in the phase propagation process. The loss of vertical phase coherence affects the time localization of events such as transients. As a result, transients are often _smeared_ in signals modified with PV-TSM (Figure 10). Furthermore, output signals of PV-TSM commonly have a very distinct sound coloration known as _phasiness_[18], an artifact also caused by the loss of vertical phase coherence. Signals suffering from phasiness are described as being reverberant, having \"less bite,\" or a \"loss of presence\" [6].  
Figure 9: The principle of TSM Based on the Phase Vocoder (PV-TSM). (**a**) STFT \(X\) (time-frequency bins are visualized by sinusoidals); (**b**) Using the original phase of \(X(m+1,k)\) leads to phase jumps when overlapping the frames at the synthesis hopsize \(H_{\mathrm{s}}\) (blue oval); (**c**) Update of the sinusoids’ phases via phase propagation. Phase jumps are reduced (blue oval); (**d**) Signal reconstruction from the modified STFT \(X^{\mathrm{Mod}}\).
## 7 Conclusions  
We presented a novel MIMO precoding scheme based on RL, jointly optimizing both the precoding and combining matrix of the transceiver and receiver, respectively. The simulation results indicate that the proposed RL-based MIMO precoding scheme outperforms existing methods under a wide range of operating conditions, delivering significant gains in terms of BER performance. The proposed system exhibits strong robustness and can achieve the BER of less than \(10^{-4}\) even when the CSI is imperfect. The proposed solution offers a highly effective approach for jointly optimizing both the precoding and detection matrix of the transceiver and receiver.  
The dead time in photon counting systems refers to the minimum time delay that must occur between the detection of two consecutive photons by a detector. During this dead time period, the detector is unable to register or detect any subsequent photons. This study does not consider the impact of dead time. In the future, we can analyze the system throughput with precoding and the impact of dead time. In addition, the system considered in this article is a single user system, which can also be extended to multi-user systems in the future. It is worth exploring how to eliminate the inter-user interference in multi-user systems by designing precoding and detection matrices.  
Methodology, Z.L., C.W. and X.Z.; writing--original draft, Z.L. and C.W.; and writing--review and editing, X.Z., G.D. and Y.W. 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 under Grant No. 62231010 and the Innovation Program of Shanghai Municipal Science and Technology Commission under Grant No. 21 XD1400300.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Performance comparison under different \(n_{b}\) in strong turbulence.
## 4 Algorithm Analysis
### Feature Selection
Therefore, after ranking the importance of the input features using the RF algorithm, the top 14 features were selected as the model's input, which are as follows: Daily planned output (t), Coal seam thickness (m), Maximum mining height of coal seam (m), Immediate roof type, Minimum mining height of coal seam (m), Shearer slope cutting entry time (min), Coal seam cutting specific energy consumption (kW\(\cdot\)h/t), Maximum roof control distance (m), Main roof type, Floor type, Fully mechanized mining face length (m), Coal bulk density (t/m\({}^{3}\)), Equipment utilization rate, Gas content (m\({}^{3}\)/t). The selection of these features fully reflects the combined impact of geological conditions and mining face conditions. For example, the daily production plan and coal seam thickness are variables directly related to the equipment workload, significantly affecting equipment selection and operational efficiency. The types of immediate roof and basic roof reflect the restrictive requirements of geological structure on equipment parameters. Features such as the startup rate and gas content directly relate to the operational stability and safety of the equipment in the actual mining face. In this context, by using only 14 key features, the model significantly reduces the complexity of input variables while ensuring prediction accuracy. This not only reduces the difficulty of data collection and processing but also enhances the model's applicability and computational efficiency under dynamic conditions. This provides important technical support for subsequent practical applications. Figure 3.1 The impact of selected feature count on training accuracy.