esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Case 1: Study with a Random Friction Coefficient  
In the first case study, only the friction coefficient is considered as a random variable. The eigenvalue results and their solution characteristics have already been discussed in Section 2.2. The purpose of this simple case study is to test the stationary kriging in nonstationary environment and validate our proposed kernel with length-scale function. Using eight equally-spaced samples within the interval \([0,1]\), the stationary ordinary kriging (i.e., constant trend function) is applied. The first three unstable eigenvalue pairs are presented in Figure 7. In obtaining the reference results, 300 equally-spaced samples are considered.  
As seen in the results, the samples are far away from the bifurcation point. Therefore, the transition region near this point is not reflected, and except for that region, the obtained results show quite good agreement with reference. In order to investigate the effect of the trend functions, the polynomial trend functions from 0 th to 3 rd order are applied; the error results are given in Table 2. In evaluating the error, the following 2-norm error is considered.  
\[\varepsilon_{\text{2-norm}}=\sqrt{\sum_{j=1}^{2}\sum_{i=1}^{N}\Bigl{(}\lambda_{ \text{kriging}}^{i,j}-\lambda_{\text{ref}}^{i,j}\Bigr{)}^{2}}/\sqrt{\sum_{i= 1}^{2}\sum_{j=1}^{N}\Bigl{(}\lambda_{\text{ref}}^{i,j}\Bigr{)}^{2}} \tag{21}\]  
where superscripts \(i\) and \(j\) denote the \(j\)-th part of the eigenvalue pair in the \(i\)-th sample. The results indicate that the 0 th order trend function shows the best performance under the given samples.  
Figure 7: Evolution of the three unstable eigenvalue pairs using stationary ordinary kriging. Eight samples are considered. (**a**) Real part; (**b**) Imaginary part.  
Next, near each bifurcation point of unstable eigenvalues, an additional sample is introduced. The first three unstable eigenvalue pairs using ordinary kriging are shown in Figure 8. The obtained results show that although the total number of samples is increased, their accuracy is severely degraded. Reflecting the nonsmooth characteristic at the bifurcation point results in inaccurate oscillations over the input range. This implies that the response prediction can be highly sensitive to the sample near the bifurcation point. In order to compare the error, metamodels for different trend functions are computed; the error results are presented in Table 3. Comparing Tables 2 and 3, the results under given samples indicate that applying a high-order trend function may increase the metamodel performance. However, the accuracy is not always guaranteed, and depending on the response characteristics, their optimal polynomial orders differ of samples for each region may be insufficient. Therefore, additional sampling may be introduced to increase accuracy.
# A Strong Noise Reduction Network for Seismic Records
## 1 Introduction
Building upon the U-Net [43], we have integrated an inception module and an attention mechanism, which collectively enhance the network's feature extraction in the time-frequency domain and its ability to differentiate between signals and noise. To assess our model's denoising efficacy, we utilized the STEAD dataset [44] for training and testing. The experimental outcomes confirm that our model can effectively eliminate diverse noise types and maintain the denoising performance even with signal-to-noise ratios below 0 dB. When compared to the U-Net, DeepDenoiser, and DnRDB networks, our model demonstrates superior denoising effects on seismic signals with very low signal-to-noise ratios and significantly improves the accuracy of the initial phase pickup in denoised signals. Lastly, the \"Di Ting\" dataset [45] was employed to validate our model's generalization capability. Our main contributions are summarized as follows. (1) An improved U-Net model is proposed for denoising noisy signals with low signal-to-noise ratios. This model effectively suppresses various types of noise, achieving successful denoising for seismic signals with signal-to-noise ratios below 0 dB. The denoised results significantly enhance the accuracy of the pickup of the phase arrival. (2) Considering that seismic signals and noise may coexist across different frequency channels or within specific frequency bands, the CA mechanism is introduced during feature extraction. This mechanism directs the network's attention to key channels or frequency bands, enabling better differentiation between localized signal features and noise features. Consequently, the model more effectively removes noise that shares the same frequency band as the signal. (3) To mitigate the information loss during down-sampling, we incorporate an inception module in the down-sampling stage. This module enables the network to capture features of different sizes from the seismic signal during feature extraction, ensuring the diversity of the features extracted by the network.
# Analysis and Modeling of Mueller-Muller Clock and Data Recovery Circuits
###### Abstract  
In this paper, an accurate linear model of the Mueller-Muller phase detector (MMPD)-based clock and data recovery circuit (MM-CDR) is proposed, which analyzes several critical points of the MM-CDR including the linearization of the MMPD and the gain of the voter. Using our technique, the jitter between the recovery clock and the input data can be estimated with a sub-picosecond accuracy, as demonstrated in the simulation results of a 56 Gb/s quarter-rate MM-CDR implemented in 28 nm CMOS.  
clock and data recovery; Mueller-Muller phase detector; jitter; jitter tolerance
## 1 Introduction
### Analysis of the Latest Research and Publications
Such hybrid architectures allow for the compensation of the inaccuracies of mathematical models caused by magnetic circuit saturation, temperature effects, or material degradation. Such models demonstrate an increase in the accuracy and adaptability of automated control performance, without a significant increase in computational load. Therefore, based on the logical generalisation of the above-mentioned known scientific works in the subject area, it is worth emphasising the fact that, taking into account the significant progress in the development and application of predictive control methods, including asynchronous electric drives, the vast majority of approaches display limitations associated with insufficient consideration of destabilising factors (iron losses, saturation, etc.) or the lack of practical solutions for hardware and software implementation of models due to high real-time computation power requirements. Consequently, the known research results require further development to create models for the formation of control influences for asynchronous electric motors, taking into account multifactorial constraints in real time.
## 5 Conclusions  
This paper has presented a proposed DSM based on queuing theory for controlling the EVs charging load in the existence of wind power generation in power distribution networks. To represent the uncontrolled demand of the EV, a statistical model was used based on a statistical survey. QSTS was used to solve the 24 h power flow and study the impact of the external load presented by the EV charging process. The proposed DSM-based queueing model uses the owner preferences signal which includes the EPTD and OPR to manage the charging process by operating the EV in one of two states (charge or idle). The proposed DSM takes advantage of integrating wind power generation in the distribution system to help the DSM in case of more generation power available in the system. The IEEE 123 nodes radial test feeder was used to check the performance of the proposed DSM through the owner's satisfaction based on the cost-saving and the overall time in the system. The results showed, using the proposed DSM, a significant cost saving was achieved for the EVs owner. In addition, shaving in the demand was obtained in the case of with and without wind power integration.  
Conceptualization, M.A.-A.; Data curation, S.K.; Formal analysis, S.A.A.; Funding acquisition, S.A.A.; Resources, F.J.; Software, A.S.; Writing--original draft, A.S.; Writing--review & editing, M.A.-A., S.K., F.J. and S.A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding publication of this project.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Case 1** & **Case 2** & **Case 3** \\ \hline \multirow{3}{*}{EV\#i} & Overall connected time (h) & 04:04 & 06:42 & 06:42 \\  & Idle state time (h) & 00:00 & 02:38 & 02:38 \\  & Cost-saving & - & 7.6\% & 34.2\% \\ \hline \multirow{3}{*}{EV\#j} & Overall connected time (h) & 04:04 & 06:39 & 05:05 \\  & Idle state time (h) & 00:00 & 02:35 & 01:01 \\  & Cost-saving & - & 10.6\% & 42.1\% \\ \hline \multirow{3}{*}{EV\#k} & Overall connected time (h) & 04:04 & 06:36 & 04:04 \\  & Idle state time (h) & 00:00 & 02:32 & 00:00 \\ \cline{1-1}  & Cost-saving & - & 29.8\% & 55.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Obtained results for the arbitrary EVs.
###### Abstract  
In today's network intrusion detection systems (NIDS), certain types of network attack packets are sparse compared to regular network packets, making them challenging to collect, and resulting in significant data imbalances in public NIDS datasets. With respect to attack types with rare data, it is difficult to classify them, even by using various algorithms such as machine learning and deep learning. To address this issue, this study proposes a data augmentation technique based on the WGAN-GP model to enhance the recognition accuracy of sparse attacks in network intrusion detection. The enhanced performance of the WGAN-GP model on sparse attack classes is validated by evaluating three sparse data generation methods, namely Gaussian noise, WGAN-GP, and SMOTE, using the NSL-KDD dataset. Additionally, machine learning algorithms, including KNN, SVM, random forest, and XGBoost, as well as neural network models such as multilayer perceptual neural networks (MLP) and convolutional neural networks (CNN), are applied to classify the enhanced NSL-KDD dataset. Experimental results revealed that the WGAN-GP generation model is the most effective for detecting sparse data probes. Furthermore, a two-stage fine-tuning algorithm based on the WGAN-GP model is developed, fine-tuning the classification algorithms and model parameters to optimize the recognition accuracy of the sparse data probes. The final experimental results demonstrate that the MLP classifier significantly increases the accuracy rate from 74% to 80% after fine tuning, surpassing all other classifiers. The proposed method exhibits a 10%, 7%, and 13% improvement over untuned Gaussian noise enhancement, untuned SMOTE enhancement, and no enhancement.  
network intrusion detection; imbalanced data; machine learning; convolutional neural networks; generative adversarial networks +
Footnote †: journal: Medical Image and Image Processing  
1
# Levitating Control System of Maglev Ruler Based on Active Disturbance Rejection Controller
## 4 Experimental Testing of the Levitating Control System  
To confirm the effectiveness of the MR's levitating control system, this paper conducted experimental testing of the levitating control using the hardware platform shown in Figure 14.  
The hardware platform comprised an MR [1], levitating control coil, DC/DC converter, sensors, and a data collector. The levitating height sensor was securely mounted on the stator yoke and utilized a clamping mechanism. \(z_{a}\) in the Figure 15 was captured by levitating height sensor. The angle sensor is affixed to the mover core using an adhesive. \(\psi\) in Figure 15 was captured by an angle sensor. The gathered data was transmitted to the data collector via the bus. The data collector employed the Kalman filtering technique to eliminate invalid data.  
Figure 14: The measurement platform of the levitating system.  
Figure 15: System response curve during startup. (**a**) Position variation curve. (**b**) Angle variation curve.  
Figure 15 is the measured start-up response curve of the MC.  
As shown in Figure 15a, the levitating position setting value of the MC was adjusted to increase by 0.1 mm upward at \(t=1\) s. The levitating position of the MC reached the set value within 0.75 s, with no significant overshoot. After 0.75 s, the levitating position of the MC stabilizes at the set levitating position value; the error is within \(\pm 2\) um. It can be seen that the independent control of the four air gap magnetic fields makes the levitating control system of the MR have characteristics of small overshoot, fast following of the given value, good stability, and few oscillation times, with good dynamic and static characteristics.  
As shown in Figure 15b, the angle of the MC deflected at \(t=1\) s. At this moment, the set value of the levitating position of the MC also underwent a sudden change, and the levitating control system began to adjust the levitating position of the MC. At \(t=1.5\) s, the deflection angle attained its maximum, and during the interval from \(t=1\) s to \(t=1.5\) s, the slope of the curve depicting the levitating position of the MC exhibited its steepest incline. After \(t=1.5\) s, the levitating position of the MC approached and reached the set levitating position, and the deflection angle also continuously decreased. Before \(t=2.5\) s, the deflection angle returned to zero degrees, and the transition process of the levitating position was also completed, with the MC stabilized at the new levitating position. It is evident that there exists a strong coupling in controlling the levitating positions of the left and right ends of the MC. The transitional process of the levitating position introduces disturbances to the angle of the MC. Nevertheless, the levitating control system promptly mitigates these disturbances. Consequently, the levitating control system effectively achieves decoupling control and possesses excellent robustness.
## 2 Framework Design of Gateway
### Gateway: Functional Requirements Analysis
#### 2.1.3 Ability to Communicate with the ZigBee Coordinator through a UART  
Wireless sensing networks submit data to the monitoring server via a UART. We therefore designed this communicative function between the gateway and WSN, enabling data packets to be sent to the server.
## 3 Preliminaries  
**Definition 1** (_Traffic Network_).: _We used a directed graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\) to represent the spatial topological structure of the traffic road network. \(\mathcal{V}\) is the set of \(|\mathcal{V}|=N\) nodes, and each node relates to each traffic sensor that records traffic information. \(\mathcal{E}\) is the set of \(|\mathcal{E}|=M\) edges. \(\mathcal{A}\in R^{N\times N}\) represents the adjacency matrix, where each element signifies the connection strength between nodes._  
**Definition 2** (_Traffic State_).: _A traffic state vector \(X_{t}\in R^{N\times c}\) represents the observed values of all traffic sensors in the traffic network \(\mathcal{G}\) at time step \(t\), such as traffic speed or flow. Here, \(c\) represents the number of features._  
**Problem** (Traffic Prediction).: Given a road network \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\) and its observed \(P\) step traffic states \(\mathbf{\mathcal{X}}=(X_{t-P+1},\,X_{t-P+2},\,\ldots,\,X_{t-P})\in R^{P\times N\times c}\), traffic prediction aims to predict the subsequent \(Q\) step traffic states \(\mathbf{\mathcal{Y}}=\left(\hat{X}_{t+1},\hat{X}_{t+2},\ldots,\hat{X}_{t+Q}\right) \in R^{Q\times N\times c}\) by learning the function \(\mathcal{F}\), represented as follows:  
\[(X_{t-P+1},\,X_{t-P+2},\ldots,X_{t-P};\mathcal{G})\overset{\mathcal{F}}{ \rightarrow}\left(\hat{X}_{t+1},\,\hat{X}_{t+2},\ldots,\hat{X}_{t+Q}\right) \tag{1}\]
###### Abstract  
The Sanjiangyuan National Park is the first Chinese national park system, and the ecological environment is inherently fragile and sensitive. Therefore, for environmental protection, it is imperative to understand the spatiotemporal variation characteristics of the ecological quality of vegetation and its climate influence factors. We used the MODIS normalized difference vegetation index (NDVI) dataset, meteorological dataset, and Carnegie-Ames-Stanford Approach (CASA) model to investigate the spatiotemporal patterns and change trends of the NDVI and the net primary productivity (NPP) of the vegetation in the Sanjiangyuan National Park from 2000 to 2016. A linear regression model was used to explore the influence of the ecological quality of vegetation and climatic factors. The results showed that (1) the NDVI and NPP were high in the southeast area and low in the northwest area. The Yangtze River headwater region had the lowest NDVI (0-0.3) and NPP (0-100 gC/m\({}^{2}\)). The Lancang River had the highest NDVI (0.4-0.8) and NPP (100-250 gC/m\({}^{2}\)). (2) From 2000-2016, approximately 23.46% of the area showed a significant positive trend of the NDVI that was mainly distributed in the prairie areas in the midlands and the north of the Yangtze River headwater region, and was scattered in the midlands and the north of Yellow River headwater region. Furthermore, 24.32% of the NPP was determined to have increased significantly, which was mainly distributed in the midlands and the north of the Yangtze River headwater region, as well as the midlands and the east of the Yellow River headwater region. (3) The vegetation growth in the Sanjiangyuan National Park was regulated by both water and heat conditions. The NDVI was significantly affected by precipitation during the growing season and by the annual precipitation. In addition, the NPP was significantly affected by temperature during the growing season and by the annual average temperature of the study area.  
CAS; climate influence; MODIS NDVI; NPP; Sanjiangyuan national park; spatiotemporal variation; vegetation type +
Footnote †: journal:  
_Article_
# SumMER: Structural Summarization for RDF/S KGs
## 5 Evaluation
### Evaluating Summary Quality  
Although measures like MAE, SMSE, and MedAE can effectively evaluate the regression models, they cannot completely evaluate the generated summaries overall. Also, these measures fail to identify the added value of a semantic summarization system when the results are not the same as the ones in the \"golden standard\".  
As such we exploit coverage, calculating for each query the percentage of the classes and properties that are included in the summary. Again, a class/property appears within a query either directly or indirectly. Directly when the specific class/property appears within a triple pattern of the query. Indirectly for a class is when the said class is the type of an instance or the domain/range of a property that appears in a triple pattern of the query. Indirectly for a property is when the said property is the type of an instance. Having the percentages of the classes and properties included in the summary, the query coverage is the weighted sum of these percentages. As our summaries are node-based we give 0.8 weight to the percentage of the classes and 0.2 weight to the percentage of the properties.  
**Definition 12**.: _(Coverage). Let \((V_{\text{S}},E_{\text{S}})\) be a KG \(G_{\text{S}}\), \(Q\) a query workload \(Q=\{q_{1},\ldots,q_{n}\}\), and two weights for nodes and edges, i.e., \(w_{\text{nodes}}\) and \(w_{\text{props}}\). Query coverage is defined as follows:_  
\[Coverage(G_{\text{s}},Q)=AVG_{q=i}^{n}\bigg{(}W_{nodes}\frac{success_{nodes(q_{ i})}}{nodes(q_{i})}+w_{\text{props}}\frac{success_{properties}(q_{i})}{ properties(q_{i})}\bigg{)}\]  
The results on summary quality based on coverage are shown in Figure 7 for DBpedia v3.8, and in Figure 8 for SWDF. We compare our results with the state-of-the-art system in structural semantic summaries RDFDigest+ [6; 7]. RDFDigest+ employees an adaptation of the betweenness centrality for selecting the most important schema nodes, considering also the number of triples available for each class.  
As shown, in both datasets SumMER can achieve a higher coverage than RDFDigest+ in all cases, always improving the coverage of the result summary.  
Looking at the partial confusion matrix presented in Table 8, we can identify that SumMER is able to make the best predictions on the TPs for all summary sizes, outperforming the RDFDigest+ in all cases. The improvement on the prediction of the top-k nodes is also depicted to the subsequent calculation of the query coverage for both DBpedia v3.8 and SWDF.  
Figure 7: Coverage for testing on DBpedia v3.8.  
Overall, SumMER shows a stable performance, both in selecting the schema nodes and in the overall quality of the generated summary. As shown in our experiments SumMER generates better summaries, that can answer more query fragments than previous works. This happens not only for a different version of the same KG KG (DBpedia), but also for a completely different KG (SWDF), showing that our approach generalizes beyond the boundaries of the same KG.
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 2 Materials and Methods
### Methods
#### 2.3.3 Statistical Analysis  
Statistical methods provide tools for making quantitative decisions about a process or processes [54]. Regression is a statistical technique used for prediction and causal inference to determine the linear relationship between two or more variables [55]. Here, regression analysis was carried out to determine the impact of oil spill volume, the time gap between oil spills and image acquisition date on vegetation health (i.e., NDVI), and the nature of any relationship between the variables and NDVI. Regression analysis was used by [PERSON] et al. [32] to determine the impact of oil spills on the vegetation. To analyse the effect of time on vegetation health recovery following exposure to oil, paired \(t\)-tests were used to compare \(\Delta\)NDVI values from spill sites with those from non-spill sites [1,31,44] for a period of several years. A paired \(t\)-test is used to compare the two population samples; observations in one sample are paired with observations in the other sample [56] to determine the level of significance difference between any two observations.