esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Analysis on Access Point Density  
As shown in Figure 5, we studied the effect of the AP density \(\rho_{A}\) on the average system throughput. The four curves have the same trend, with the average system throughput decreasing as \(\rho_{A}\) increases. The proposed algorithm outperforms the RDR-PA method and the uniform method. On one hand, an increase in the AP density increases the total amount of network resources. On the other hand, an increase in the AP density worsens the interference in overlapping areas. In general, the negative effect of interference is greater than the positive effect of the increase resource amount. Therefore, a higher \(\rho_{A}\) results in a lower average system throughput. From the perspective of energy saving, the deployment density of APs should be appropriately reduced. Compared with the RDR-PA method, the improvement amount and improvement ratio of average system throughput decreases as \(\rho_{A}\) increases. When the AP density is 0.11, the proposed algorithm achieves the highest throughput improvement of 1.46 Gbps with an improvement ratio of 57.8%.  
Figure 4: The average ratio of satisfied terminals versus the density of terminals.  
Figure 3: The average throughput per terminal versus the density of terminals.  
In Figure 6, the average ratio of satisfied terminals is shown as a function of \(\rho_{A}\). The performance of the proposed algorithm first increases and then decreases as \(\rho_{A}\) increases. When \(\rho_{A}\) is 0.25, the peak value of the proposed algorithm occurs. However, the performance of the RDR-PA method decreases as \(\rho_{A}\) increases. This is because the proposed algorithm has a higher resource efficiency. When \(\rho_{A}\) is low, the positive effect of an increased resource amount is greater than the negative effect of interference. When \(\rho_{A}\) is high, the opposite occurs. When \(\rho_{A}\) is 0.22, the average ratio of satisfied terminals reaches 0.352. Furthermore, when \(\rho_{A}\) is 0.28, the proposed algorithm achieves the highest performance improvement of 0.222 compared to the RDR-PA method with a performance improvement ratio of 172.8%. In summary, the proposed algorithm has the ability to overcome the inter-cell interference in terms of the average ratio of satisfied terminals.
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 2 Principles and Methods  
Seismic communication systems mainly include four parts: the mode of seismic communication, facilities employed in the experiment, modulation of transmitted signal and the method of signal extraction, as described in the following sections.
# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
## 2 Materials and Methods
### Local Hue Variance in HSI Color Space  
Since the shadow area may not be subject to the model of (3), after recognizing the main part of the water region, another method to recognize the water area covered by the shadow is needed to increase the recall rate of the water region's segmentation. In shadow, the lighting conditions are difficult to estimate, and the reflection law reflected by (8) is not available. However, \(H\) values keep uniformity within neighbor pixels as shown in Figure 4.  
The calculation of the local hue variance is as follows: firstly, convert the original RGB input image block into an HSI image. Then the extracted \(H\) layer is divided into 9 blocks of the same size, as shown in Figure 6. Finally, calculate the mean value of \(H\) denoted as \(H_{k}(k=1,2,3\dots,9)\) of each block and obtain the variance of \(H_{k}\):  
\[V_{H}=\frac{\sum_{k=1}^{9}\left(H_{k}-\overline{H}\right)^{2}}{9}. \tag{15}\]  
An appropriate threshold \(T_{2}\) is then adopted to compare with the obtained \(V_{H}\) to identify the shadow area. The image patches that have bigger \(V_{H}\) than the designed threshold are labeled as part of water, which is express as:  
\[\left\{\begin{array}{l}\text{water, if }V_{H}<T_{2}\\ \text{not water, if }V_{H}\geq T_{2}\end{array}\right. \tag{16}\]  
Since \(H_{k}\) are normalized values in the calculation, the same \(T_{2}\) can be used for different images. Empirically, \(T_{2}\) can be set within \([1.5,1.8]\) to get satisfactory performance in most scenes.
## 4 Simulation and Discussion
### Simulation 1  
This simulation aims to verify the effectiveness of the proposed approach using SCARA and UR5. The specific values of the target joint variables (\(\mathbf{\Theta_{\text{goal}}}\)) and the initial joint variables (\(\mathbf{\Theta_{\text{in1}}}\),\(\mathbf{\Theta_{\text{in2}}}\)) with respect to SCARA and UR5 are shown in Table 3. According to Equation (1), the goal configurations of the end-effector frames of the two robot manipulators relative to \(\mathbf{\Theta_{\text{goal}}}\) are described as  
\[\mathbf{T_{\text{goal}}}=\begin{bmatrix}-0.8479&-0.5301&0&160.1408\\ 0.5301&-0.8479&0&383.1681\\ 0&0&1.0000&-520.0000\\ 0&0&0&1.0000\\ \end{bmatrix},(\text{SCARA}), \tag{76}\] \[\mathbf{T_{\text{goal}}}=\begin{bmatrix}-0.9592&-0.0838&0.2699&-93.119 1\\ 0.2823&-0.3247&0.9027&-20.4293\\ 0.0120&0.9421&0.3351&-716.5883\\ 0&0&0&1.0000\\ \end{bmatrix},(\text{UR5}).\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**i**} & \multicolumn{3}{c}{**3R**} & \multicolumn{3}{c}{**SCARA**} & \multicolumn{3}{c}{**3P**} \\ \cline{2-7}  & \(\mathbf{\omega_{i}^{T}}\) & \(\mathbf{v_{i}  
The inverse kinematics are calculated using the NICCD and the NR method, with \(\mathbf{\Theta_{\text{in1}}}\) and \(\mathbf{\Theta_{\text{in2}}}\) as initial values. In order to illustrate the effectiveness of the proposed method, the angle error, and position error are defined as  
\[\begin{split}\text{Err}_{R}&=\sqrt{\text{trace}\left( (\mathbf{R_{\text{goal}}}-\mathbf{R_{\text{out}}})^{T}(\mathbf{R_{\text{goal}}}-\mathbf{R_{ \text{out}}})\right)},\\ \text{Err}_{P}&=\sqrt{(\mathbf{P_{\text{goal}}}-\mathbf{P_{ \text{out}}})^{T}(\mathbf{P_{\text{goal}}}-\mathbf{P_{\text{out}}})},\end{split} \tag{77}\]  
where \(\mathbf{R_{\text{goal}}}\) and \(\mathbf{P_{\text{goal}}}\) are the elements in \(\mathbf{T_{\text{goal}}}\), and \(\mathbf{R_{\text{out}}}\) and \(\mathbf{P_{\text{out}}}\) are the elements in \(\mathbf{T_{\text{out}}}\), which are obtained by the forward kinematics calculated by either the NICCD or the NR method with respect to \(\mathbf{\Theta_{\text{out}}}\). The position and angle errors of the initial selected joint angles of the SCARA and the UR5 versus the convergence times of the NICCD and the NR methods are illustrated in Figure 2.  
From Figure 2, when the initial value is \(\mathbf{\Theta_{\text{in1}}}\), the NICCD method converges more rapidly than the NR method with respect to SCARA. However, it is slower with respect to UR5. When the initial value is \(\mathbf{\Theta_{\text{in2}}}\), the NICCD method still converges, but the NR method diverges with respect to the inverse kinematics of SCARA and UR5. In addition, the NICCD method has less fluctuation in both the angle error and the position error during the iteration process. Therefore, it can be concluded that the proposed approach is more stable and effective than the NR method.
## 4 Experimental Setup, Results, and Discussion
### Experiments on Conv-TasNet with the VoiceBank-DEMAND Task  
We start with the VoiceBank-DEMAND task to evaluate the Conv-TasNet method using different features in the encoder. These features are created by integrating time-domain features with either STFT-domain, one-level DWT-domain or two-level DWT-domain features, as described in Sections 2.1 and 2.2. The corresponding PESQ, STOI, and SI-SNR results for the test set are listed in Table 1. From this table, we note the following:  
1. Compared with the unprocessed baseline, all Conv-TasNet variants used here give significantly superior PESQ and SI-SNR scores, reflecting their excellent capability of SE. By contrast, the brought STOI improvement is moderate, probably because the baseline STOI score has been as high as 0.921.  
2. The cross-domain Conv-TasNet [31] with time and STFT features outperforms the original Conv-TasNet with time-domain features only in PESQ and SI-SNR, and it provides the best possible SI-SNR among all of the variants. These results reveal that the addition of STFT features benefits Conv-TasNet significantly.
3. When the STFT is replaced by one-level DWT, the corresponding three Conv-TasNet variants exhibit similar or better PESQ results. It indicates that DWT features can complement time-domain features to provide Conv-TasNet with better speech quality. Among the three one-level DWT-based Conv-TasNets, addition-wise integration behaves the best in PESQ, outperforming the more complicated method of implementation with one BPF and concatenation. However, when it comes to SI-SNR, the one-level DWT-wise methods behave worse than the method with STFT.
4. As for the cases with which the two-level DWT features are involved, the achieved PESQ scores are close to or moderately better than that obtained by the method with STFT, but not necessarily superior to those with one-level DWT. However, the optimal PESQ score is obtained by concatenating time features and two-level DWT features that use one MPF and inter-channel softmax. This result might indicate that further weighting the different channel values in the used encoding features is a promising direction to improve the SE performance.
5. To further examine if the presented fusion feature sets provide statistically significant improvement in PESQ relative to the pure time-domain feature, we perform a one-tailed two-sample \(t\)-test, the details of which are shown in Appendix A. Referring to the results shown in Table 11, we see that the four types of fusion \"addition\", \"concatenation\", \"one BPF and concatenation\", and \"one MPF with inter-channel softmax\" provide Conv-TasNet with significant improvement in PESQ when compared with Conv-TasNet using the time-domain feature only. The other two fusion types (\"two BPFs\" and \"one MPF with intra-channel softmax\") do not improve PESQ significantly.
# Application of Dynamic Time Warping (DTW) in Comparing MRT Signals of Steel Ropes
## 3 Results
### Active Experiment
It is also possible to synchronize hybrid time series from the measurements in TIMER mode and ENCODER mode, which reduces the risk of misdiagnosis caused by wheel slippage in ENCODER mode and enables the detection of faulty operation of the LF signal compensation system in the flaw detector. Along with the increase in the difference in the sampling resolution of the signal \(LF(k)\), the level of residual noise associated with quantization noise and the dynamics of signal changes in magnetic anomaly zones increases, which in the future may be used as a new diagnostic symptom of the MRT method. The residual signal from DTW significantly improves the signal-to-noise ratio--a criterion for objective assessment of the quality of the MRT diagnostic symptom according to EN 12927:2019--and its quantitative and qualitative analysis can be used to verify the correctness of time-series synchronization. The results of the active experiment confirm the accuracy of the DTW-based time-series synchronization approach under controlled laboratory conditions using known artificial defects, and thus, serve as a preliminary validation of the model.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 2 Current-Voltage Curves
### Short-Circuit Faults  
Short-circuit faults in solar modules occur when the current in the solar panel forms a low-resistance path on an unexpected route, causing the current to flow directly without passing through the load. This can be due to faults in the connecting wires or circuit components within the solar panel or due to external factors such as damage, overheating, or physical damage causing contact between two or more electrodes. Short-circuit faults lead to the solar panel's current losing its normal path, potentially causing system overheating, damage to the battery components, or even severe consequences like fires.  
As shown in Figure 12, two solar cell modules are connected in series, each paralleled with a diode, and one module is short-circuited to simulate varying degrees of short-circuit faults as shown in Figure 13.  
Figure 11: PID fault current–voltage curve.  
Figure 12: Short-circuit fault current–voltage curve simulation circuit diagram.  
Figure 10: PID fault current–voltage curve simulation circuit diagram.
###### Abstract  
Volatile organic compounds (VOCs) reduce indoor air quality. They are associated with negative effects on human health and wellbeing. In terms of legislation requirements and consumer pressure, VOCs from engineered wood materials are reduced due to use of water based additives and adhesives in their formulation. Therefore, the main source of VOCs remains the raw material--the wood itself. Alternatives to wood strands, annual plant materials, are tested nowadays due to their advantages: The short cycle; the raw material is sourced naturally and can be produced more sustainably; and faster sequestering atmospheric carbon. The aim of this work was to investigate volatile organic compounds emitted from untreated and chemically treated hemp shive and compare the emission characteristics to soft wood strands. Simple, yet effective chemical treatments, like tartaric acid, citric acid and sodium bicarbonate were used in order to reduce VOC emissions. Gas chromatography-mass spectrometry (GC-MS) combined with headspace solid-phase microextraction (HS-SPME) was used to analyse the volatile compounds emissions. Specific VOCs like acetic acid; Benzaldehyde; hexanal, \(\alpha\)-, \(\beta\)-pinene; limonene and camphene were monitored before and after the treatments. Non-target screening was performed to identify the most responsible compound for differentiation of samples according to their treatments. Comparing untreated samples, spruce strands showed highest amounts of total VOCs, while untreated hemp shive showed the lowest. Further, due to the chemical modification of hemp woody core components, such as hemicellulose, lignin, and extractives, the key VOCs showed significant changes leading to an increase in the amount of total emissions.  
g +
Footnote †: journal: materials  
_Article_  
VOC Emissions from Spruce Strands and Hemp  
Shive: In Search for a Low Emission Raw Material for Bio-Based Construction Materials
## 4 Example Analysis
### The Effect of Increased Penetration on Economic Performance  
As shown in Figure 5, the number of new energy installations are expanded to increase the new energy penetration rate (total new energy installed units' capacity/total installed units' capacity) to 36.8%.  
First, the optimal uncertainty set is rediscovered. It can be seen from Figure 6 that the proportion of the optimal uncertainty set is between 25% and 35% when the new energy penetration increases. The optimal uncertainty set, as shown in Table 3, is determined by binary search.  
Figure 5: Modified load and new energy forecast curve.  
Figure 6: Comprehensive costs under different uncertainty sets.  
Taking 27% as the optimal uncertainty set, the benefits of the multi-energy coordination strategy are calculated and compared according to the steps above.  
Table 4 shows the comparison of robust reserve rate and various benefits under different penetrance, and illustrates the following issues:  
(a) Increased penetration, greater fluctuations in new energy, and higher system reserve requirements.  
(b) The cost-effectiveness of risk increases significantly with increased penetration. This is because the fluctuation of new energy is stronger with the increase of penetration. Since the traditional method does not consider the fluctuation of new energy, its wind/light curtailment volume and cutting load increase greatly, so the risk cost increases sharply, and the risk-benefit of the corresponding robust method also increases sharply.  
(c) The reduction of environmental benefit and operation benefit is caused by the reason that the increase of new energy penetration results in the increase of new energy output, on the other hand, the load is constant. Consequently, all this leads to the decrease of conventional unit output and the synchronous decrease of coal and gas consumption.  
(d) The comprehensive benefit increases significantly with the increase of the penetration, which is caused by the sharp increase of the risk-benefit, which is the core benefit of the robust method. It can be shown that the comprehensive benefit of the robust method increases rapidly with the increase of penetrance.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 1 Introduction
In addition, it can be easily deployed that it can achieve a satisfying performance even using a single pair of transceivers, and needs no re-calibration in different scenarios. We prototyped RDFID in three typical home and office scenarios with commodity WiFi devices composing only one wireless link. We evaluate the system and compare the performance with Fine-grained Real-time passive human motion Detection (FRID), device-free Passive Detection of moving humans with dynamic Speed (PADS) and Fine-grained Indoor Motion Detection (FIMD). The results show that the detection precision of RDFID can achieve over 97% under different moving patterns. Consequently, it makes intrusion detection systems a step closer to practical use. In summary, the contributions of our work are as follows:  
* We propose RDFID, a novel device-free WiFi-based intrusion detection approach, which can detect intruders with different moving patterns at a high accuracy, and needs no re-calibration in different scenarios. It can be deployed in smart home scenarios to ensure security. * We extract real-time features from CSIs in frequency domain, which is more sensitive to human motion of various moving patterns. * We use the Gaussian Mixture Model (GMM) as the classifier based on the observation that the feature values under different moving patterns and different environments can be seen to be generated by different Gaussian Models. In the rest of this paper, the related works about WiFi-based human detection are reviewed in Section 2. Some preliminaries are introduced in Section 3. Section 4 presents the design details of our proposed intrusion detection system, while the performance evaluation is provided in Section 5. In Section 6, the potentials and limitations are discussed and we conclude this work in Section 7.
## 6 Conclusions  
Based on the improvement engineering of soft soil ground in Zhuhai City on the Pearl River Delta, a case comparative study on vacuum preloading and surcharge preloading was performed. The conclusions are drawn below:  
1. Compared to the surcharge preloading method, the vacuum preloading method is more efficient in terms of consolidation time and settlement reduction. However, surcharge preloading results in smaller secondary consolidation settlements than vacuum preloading and should be given priority in order to reduce the settlement foundation in the service stage.
2. The primary consolidation settlement (\(S_{pt}\)) quickly increases up to 90% of the total primary consolidation settlement (\(S_{p}\)), with an increase in the average excess pore water pressures (\(u_{t}\)) of less than \(-40\) kPa in vacuum preloading, while it also quickly increases, with the average excess pore water pressures (\(u_{t}\)) hovering between 20 kPa and 25 kPa in surcharge preloading.
3. The sharp increase in strata permeability coefficient will induce the increase in strata consolidation degrees and has little effect on the ground consolidation degrees.
4. The vacuum preloading is more beneficial to reduce uneven settlement, and the surcharge preloading is more beneficial to reduce ground settlement during its service.  
Conceptualization, L.F. and S.P.; methodology, L.F.; software, Z.X.; validation, Z.X.; formal analysis, Z.X.; investigation, L.F.; resources, L.F.; data curation, Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, L.F. and S.P.; visualization, L.F.; supervision, S.P.; project administration, L.F.; funding acquisition, L.F. and S.P. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Science Foundation of China (Grant No. 52174100 and No. 51674287), and the National Science Foundation of Hunan Province, China (Grant No. 2021 JJ30834).  
Not applicable.  
The data that support the findings of this study are available upon request from the authors.  
This study was supported by t National Natural Science Foundation of China (Grant No. 52174100 and No. 51674287), and the National Science Foundation of Hunan Province, China (Grant No. 2021 JJ30834). We would also like to sincerely thank [PERSON], [PERSON], [PERSON], and [PERSON], who gave valuable comments on both the pre-conceptualization and post-revision of the manuscript, which greatly improved the manuscript.  
The authors declare no conflict of interest.
## 4 Efficiency and Fairness Signal Optimization Model  
### Feasibility Analysis  
Considering equity will have an impact on delays and will require sacrificing delays for fairness, but is the sacrifice worth it? This section discusses this issue by analyzing the delay-to-fairness conversion rate.  
The delay-to-fairness conversion rate represents the ratio between the gain in fairness and the delay sacrifice. Here, the fairness gain is measured by the proportion of fairness improvement, while the delay sacrifice is quantified by the proportion of the increase in delay. This is specified in Equation (18) below:  
\[I_{cr}=\frac{\left(H-H^{\prime}\right)/H^{\prime}}{\left(D-D^{\prime}\right)/D ^{\prime}} \tag{18}\]  
In Equation (18), \(D^{\prime}\) and \(H^{\prime}\) denote the average intersection vehicle delays calculated using the Webster model and the corresponding fairness evaluation indexes; \(D\) and \(H\) denote the average intersection vehicle delays and fairness indexes, respectively.