esa-sceva/satcom-chunk-collection huggingface dataset:
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 5 Challenges and Limitations
### Implementation Challenges and Solutions
#### 5.1.2 Proposed Solutions  
To overcome the data management and security challenges, the following solutions are proposed:  
* Data Encryption and End-to-End Security Protocols:
* Data encryption and end-to-end security protocols are crucial for safeguarding Digital Twins (DTs) in various applications. The integration of advanced encryption methods, such as AES and Homomorphic Encryption, enhances data protection during transmission and storage. For instance, a proposed architecture utilizes AES encryption alongside a keystore to secure data interactions, while identity authentication ensures data integrity [43]. Additionally, TwinCrypt, a novel algorithm, combines Homomorphic Encryption with Secure Multi-Party Computation to achieve efficient data synchronization in Fog-Edge Cloud environments [57]. Furthermore, the incorporation of blockchain technology with Digital Twins provides a tamper-proof environment for data exchange, enhancing security and transparency across sectors [58].
* Cloud and Edge Computing for Data Processing:  
Cloud and edge computing are integral to optimizing data processing in Digital Twin systems, which handle large volumes of real-time data. Cloud platforms offer scalable storage and computational power, while edge computing reduces latency by processing data closer to the source. This hybrid approach enhances both the efficiency and security. Cloud computing ensures scalability and cost-efficiency, reducing operational costs by up to 30% through dynamic resource allocation [101], while edge computing significantly decreases latency, with average delays dropping as low as 3.03 milliseconds [102]. Additionally, localized data processing improves security by minimizing transmission risks, making this combination vital for industries like healthcare and finance [103].  
Automated Data Integrity Checks and Synchronization Protocols:  
Automated data integrity checks and synchronization protocols are essential for maintaining alignment between physical and Digital Twins. These checks enable real-time synchronization, alerting users to discrepancies, and are further enhanced by predictive algorithms that anticipate changes in physical systems, minimizing data lags. Automated checks ensure continuous data flow between twins, reducing misalignment risks [44], while systems leveraging cloud services and in-house servers enhance scalability. Predictive algorithms forecast changes in physical assets, allowing timely updates to Digital Twins, mitigating risks from delayed data updates [45]. Additionally, integrity-preserving schemes, such as homomorphic fingerprinting, ensure data security during aggregation [46], fostering trust in Digital Twin applications.
## 3 Results
### Between-Group Comparison for QE Time  
Average QE times were longer for professional players when compared to U16 (587.1 ms vs. 546.3 ms). They were also longer in both age groups when converted vs. non-converted shots were compared (U16: 563.21 ms vs. 536.18 ms; Pro: 594.27 vs. 574.17 ms).  
Between-group analysis only showed statistically significant differences for positions 8 and 9 and at shot angles 135\({}^{\circ}\) and 180\({}^{\circ}\) (Table 2).  
\begin{table}
\begin{tabular}{r r r r r r r} \hline  & & \multicolumn{3}{c}{\(\widetilde{\mathbf{x}}\)\(\pm\)**SD**} & \multicolumn{1}{c}{\(\mathbf{t}\)} & \multicolumn{1}{c}{\(\boldsymbol{p}\)} & \multicolumn{1}{c}{\(\mathbf{d}\)} \\  & \multicolumn{1}{c}{**U16**} & \multicolumn{1}{c}{**Pro**} & & & & \\ \hline \multirow{8}{*}{positions} & 1 & 363.50 \(\pm\) 221.54  & 483.50 \(\pm\) 252.54 & \(-\)2.526 & 0.013  & 0.505 \\  & 2 & 463.50 \(\pm\) 251.73 & 553.50 \(\pm\) 242.44 & \(-\)1.821 & 0.072 & 0.364 \\  & 3 & 413.50 \(\pm\) 242.44  & 523.50 \(\pm\) 249.26 & \(-\)2.237 & 0.028  & 0.447 \\  & 4 & 473.50 \(\pm\) 252.34 & 613.50 \(\pm\) 215.71 & \(-\)2.982 & 0.004  & 0.596 \\  & 5 & 393.50 \(\pm\) 235.61  & 583.50 \(\pm\) 231.46 & \(-\)4.068 & 0.001  & 0.814 \\  & 6 & 573.50 \(\pm\) 235.61  & 593.50 \(\pm\) 226.78 & \(-\)0.432 & 0.666 & 0.087 \\  & 7 & 413.50 \(\pm\) 242.44  & 523.50 \(\pm\) 249.29 & \(-\)2.237 & 0.028  & 0.447 \\  & 8 & 433.50 \(\pm\) 247.44 & 593.50 \(\pm\) 226.78 & \(-\)3.371 & 0.001  & 0.674 \\  & 9 & 423.50 \(\pm\) 245.16 & 523.50 \(\pm\) 249.29 & \(-\)2.022 & 0.046  & 0.404 \\  & 10 & 523.50 \(\pm\) 249.29  & 543.50 \(\pm\) 245.16 & \(-\)0.404 & 0.687 & 0.081 \\ \hline \multirow{8}{*}{angles} & 0\({}^{\circ}
# Innovative Ultrasonic Spray Methods for Indoor Disinfection
## 2 Development of Ultrasonic and Combined Spraying Devices for Disinfectant Liquids  
An analysis of existing data on the disinfection of various objects allows for the categorization of disinfection approaches based on the characteristic scales, the distance from the sprayer to the object, and the required droplet size [15]:  
* For disinfecting people in factories, offices, hospitals, public places (including land, underground, and air transportation), and restaurants, the effective droplet size is 10-35 \(\upmu\)m;
* For disinfecting indoor spaces and vehicles, droplet sizes between 25-70 \(\upmu\)m are ideal;
* For disinfecting open spaces (e.g., roads, buildings, pavilions, and recreational areas), droplet sizes of 150-300 \(\upmu\)m are required.  
Thus, the spraying capacity must vary significantly to ensure the correct number of droplets for even surface coverage in a single layer, where the layer thickness does not exceed the droplet size.  
The versatility of ultrasonic atomization, combined with the wide range of disinfection tasks, requires the development and use of specialized equipment. Recent research and development efforts [16] demonstrate that the authors' ultrasonic atomizers effectively address these challenges.
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions
### Discussion
#### 5.6.2 Program Run Time  
All test procedures are implemented in MATLAB on a desktop personal computer with a 3.4-GHz CPU and 8 GB RAM. From the perspective of the execution time of the program, our method is not optimal, so we should do corresponding optimization in the next work to further improve the execution efficiency of the program. As for our current work scenario, we will give a brief introduction. We mainly extract CFPN through the algorithm proposed in this paper, and then write the extracted results into the embedded system to deduct the CFPN from the camera in real time. In general, CFPN in sCMOS camera will not change much in a few hours. Although we take a little more time to extract CFPN at a time, the result can be used for several hours, so we are not too sensitive to the running time. The running time of each method is shown in Table 6.
## 4 Experiments
### Data Description  
The data used in this research paper fits within the framework of the Topo Iberia Project [43], which was operated by the Scientific Institute of Rabat and the Jaume Almera Institute of Barcelona in 2007. The ubiquitous seismic network throughout the Rif territory (Figure 6) consists of 15 broadband stations implemented in 2014. In this study, we used microtremor measurement as an input to assess liquefaction using AI, as depicted in Figures 7 and 8. The ground noise is modeled as a numeric microtremor signal registered by a Nanometrics Trillium 120P seismometer, which samples data at rate of 100 samples per second.  
The dataset used for liquefaction identification comprised geophysical data obtained from the Rif region of Morocco, including various features--both qualitative and quantitative. Among the features, it is relevant to include meta-information about acquisition settings and filtering steps, namely Station, Days, e_filtered, n_filtered, and z_filtered.  
The violin plot of amplitude (_Af_) versus liquefaction label compares two groups of data, non-liquefaction and liquefaction instances, with the distribution of amplitudes regarding liquefied and not-liquefied soil. As a result, non-liquefaction appears taller, with a broader mean, amplitudes that are more spread out, and a principal bulk ranging between 1.2 and 3.8. This indicates that the non-liquefied sites have a considerable amplitude range, and more of their data include points within this range. However, the liquefaction group is narrower and shorter because the distribution is more compact, lying between 2.5 and 2.9. this suggests that liquefaction-prone sites have a smaller, more consistent range of amplitudes, more closely clustered around the lower end of the scale. The plot showshow the amplitude behaves in sites that either liquefy or do not, providing information on possible seismic behaviors related to liquefaction risks (Figure 9).  
The Seismic Vulnerability Index (\(K_{g}\)) is heavily concentrated at low values, indicating a dominance of stability and a landscape immune to seismic stress. But its long tail towards high values indicates sporadic anomalies, i.e., outliers that may represent fragile zones susceptible to collapse. The kernel density estimate subtly reflects the unpredictability of seismic behavior and shows fluctuations not evident with simple frequency counts. The spiky nature of the histogram bars at places shows localized instabilities, as if certain structural configurations make specific values of \(K_{g}\) more likely to emerge. This suggests a hidden interplay of resilience, anomalies, and environmental stress distributions (Figure 10).  
Figure 8: A day plot of the East component at M410 station within a 15 min frame.
## 3 Antennas with General Aspects Utilized for CubeSat  
This section depicts all recent antennas designed and used for CubeSat communications and applications. All relevant aspects of antennas designed for this purpose, such as the working band, polarization, dimensions, and materials, were considered in the analysis of each work.
## 4 Experiments
### Experimental Environment
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sequence Length**} & \multicolumn{3}{c}{**No. of Sequences**} \\ \cline{2-3}  & **Training Set (80\%)** & **Testing Set (20\%)** \\ \hline
3 & 1,266,123 & 316,531 \\
5 & 759,674 & 189,918 \\
10 & 379,837 & 94,959 \\
15 & 253,224 & 63,306 \\
20 & 189,918 & 47,480 \\
30 & 126,612 & 31,653 \\
50 & 75,967 & 18,992 \\
100 & 37,983 & 9496 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Impact of sequence length on dataset size. dictionary, based on syntactic similarity. By setting the maximum length of character \(n\)-grams to 0, we configured the fastText algorithm not to use this functionality. In our model, log templates are replaced by their indexes in the dictionary, which would cause the above functionality to falsely correlate the syntactic similarity of template indexes--for example, templates with indexes 200 and 201, despite their syntactic similarity, may not necessarily show semantic similarity at all. Extracting features from log templates is a task based solely on detecting semantic dependencies. Furthermore, word embedding based on character \(n\)-grams, according to experiments, contributes to the deterioration of the accuracy of semantic dependency detection [46]. As a consequence of such a configuration, when an unknown word is encountered, the model is unable to compute its vector representation, so it replaces it with a vector of zeros. The final anomaly detection process was performed by binary classifiers configured as follows:  
* GNB--with default configuration;
* DT--with Gini impurity as a measure of the quality of a split;
* RF--with 100 trees in the forest and the Gini impurity as a measure of the quality of a split;
* AB--with 100 estimators, SAMME.R real boosting algorithm, and learning rate of 1.0;
* XB--with logistic regression for binary classification, negative log-likelihood evaluation metrics for validation data, and configured weight scaling as the sum of negative instances divided by the sum of positive instances;
* MLP--with 100 neurons in the hidden layer, the Adam optimizer, and rectified linear unit (ReLU) as the activation function.
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 2 Methodology
### Computational Performance
Specifically, (i) the computational cost of our method (blue line in Figure 2) is approximately eight orders of  
Figure 3: Graph comparing the runtime versus the number of observation points for the classical equivalent layer technique (black line), standard CGLS (red line), and our method (blue line) with \(it=50\) iterations and \(L=5\) components, both selected arbitrarily. The runtime of our method remains negligible as the number of observations increases, in contrast to the rapidly growing runtime of the classical and standard CGLS methods. Figure 2: Graph comparing the number of _flops_ versus the number of observation points for the classical equivalent layer technique (black line, Equation (20)), standard CGLS (red line, Equation (21)), and our method (blue line, Equation (22)), using \(it=50\) iterations and \(L=5\) components, both selected arbitrarily. The computational cost of our method is approximately eight orders of magnitude lower than that of the classical approach. magnitude lower than that of the classical approach (black line) and (ii) the runtime of both the classical (black line) and standard CGLS (red line) methods in Figure 3 increases rapidly with the number of observations, while the runtime of our method (blue line) remains negligible.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Handover Challenges  
HetNets has significantly enhanced the network throughput, spectral efficiency and QoS, but they also lead to several technical challenges. Due to the dense deployment of cells and overlapping coverage areas, a UE will observe frequent HO. That will create challenges related to interference from adjacent cells and Inter-Cells, scheduling of neighbour cells available for target cells, load management between cells and mobility management.
## 2 Geological Setting  
The Sichuan Basin, located in southwestern China, covers an area of approximately \(18\times 10^{4}\) km\({}^{2}\) (Figure 1a). It is a large, superimposed petroleum basin developed on a craton and has undergone multiple tectonic movements [18; 19; 20; 21; 22]. The basin can be divided into six primary tectonic units (Figure 1b). The Sichuan Basin is rich in petroleum resources, with a proven geological resource reserve of 38.18 \(\times\) 10\({}^{12}\) m\({}^{3}\) of natural gas. Among them, conventional gas accounts for approximately 12.47 \(\times\) 10\({}^{12}\) m\({}^{3}\), tight gas accounts for approximately 3.98 \(\times\) 10\({}^{12}\) m\({}^{3}\), and shale gas at depths shallower than 4500 m accounts for approximately 21.74 \(\times\) 10\({}^{12}\) m\({}^{3}\)[23; 24]. It is the basin with the richest natural gas resources in China. The Zizhong area is located in the central part of the Sichuan Basin (Figure 1b), and the main body is located in the Deyang-Anyue rift trough. It is currently the primary block for shale gas exploration and development in the Qiongzhusi Formation [25].  
The target formation of this study is the Qiongzhusi Formation shale, which has a thickness of 50-750 m in the research area. The shale in the Qiongzhusi Formation exhibits the characteristic of being thicker in the middle and thinner on the sides when viewed from east to west (Figure 1c). The Qiongzhusi Formation can be divided into two sections, namely Qiong 1 and Qiong 2. The lithology of the formation mainly consists of shale, mudstone, silt shale, and siltstone (Figure 1d).
## 9 Discussion  
In addition to the permanent coseismic uplift and subsidence identified at the studied sites, the primary earthquake environmental effects also included coseismic surface ruptures located in the north-central part of Samos. The observed ruptures could be interpreted as evidence of coseismic surface faulting. Based on the provided focal mechanisms and the spatial distribution of the aftershock sequence [1] and on results from preliminary modeling of earthquake slip by inversion of GNSS and InSAR data [2], it was concluded that the causative fault is the E-W-striking and N-dipping normal fault offshore northern Samos. Based on its dimensions and the empirical equations of magnitude and rupture length from [48], it was concluded that this fault has the potential for the generation of an Mw = 7.0 earthquake.  
Taking into account that the northern and northwestern parts of Samos Island have been uplifted and affected by coseismic surface ruptures and slope movements, it is suggested that this area constitutes an almost 30-km-long coseismic deformation zone extending from the Ayios Isidoros area to the west to the Kokkari area to the east. This coseismic deformation zone is characterized by permanent uplift and permanent subsidence. The permanent uplift can be attributed to the observed deformation of the western part of this zone and the shallow slope movements to the subsidence observed in its eastern part.  
Moreover, the surface projection of the E-W-striking and N-dipping fault offshore northern Samos points to this coseismic deformation zone indicating a depth-surface connection. Taking also into account the spatial distribution of the aftershock sequence, it can be suggested that this zone played a significant role in stopping the rupture propagation.  
From the aforementioned, it could be suggested that the 30 October 2020, Mw = 7.0, Samos earthquake ruptured multiple geometric or structural segments located not only offshore but also onshore northern Samos. The observed and presented primary and secondary earthquake environmental effects triggered in the northern part of Samos could be attributed to the failure of these segments.  
Figure 32: Map of Environmental Seismic Intensity (ESI-07) scale intensities for sites affected by primary and secondary earthquake environmental effects triggered by the 30 October 2020, Mw = 7.0, Samos earthquake. * Tsunami sites from [16] and ** uplift sites with the dark blue symbol from [15].
# Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
## 3 Experimental Design
### Data Pre-Processing  
Missing values are removed directly as most of them exist in the first five records and the missing rate is less than 1%. The boxplots and violin plots for the remaining 358 samples are displayed in Figure 6.  
After removing the outlier in Flow, the wastewater treatment dataset contains 357 samples, which are split into the training and test sets at a ratio of 7:3 in this work. For the integrity and continuity of the time series data, the first 250 samples are considered as the training set, and the remaining 107 measurements are used as the test set. Among all violin plots, SS, BOD, COD, and TN exhibit symmetrical shapes around their median lines, with high-density areas aligning closely with the median, indicating that these variables approximately follow a normal distribution. However, the data points of TP and COD-E are significantly clustered in the smaller value areas, and it can be inferred that these two variables are supposed to show a negative skew distribution. Thus, it is necessary to perform exponential transformation on them before training to eliminate the skewness.  
Together with the results of the time lag analysis in Figure 7 and the spearman correlation analysis in Figure 8, there are strong interactions between variables of the experimental data, while each series has a complex temporal pattern within itself. As observed in Figure 7, there are still rather strong dependencies between the data point at time \(t\) and that before 30 timesteps in terms of BOD, COD, and COD-E. These factors mentioned above are both crucial for accuracy when forecasting with the multivariate time series. Therefore, a multi-scale model can better deal with the dynamic characteristics, and the introduction of the adjacency matrix allows the model to flexibly capture diverse relationships in data, enhancing its predictive performance and interpretability.  
Figure 5: The modeling process of MSTCN.  
Figure 6: The boxplot and violin plot of each variable: (**a**) Flow; (**b**) SS; (**c**) BOD; (**d**) COD; (**e**) TN; (**f**) TP; and (**g**) COD-E.  
Figure 7: The temporal correlation of variables: (**a**) flow; (**b**) SS; (**c**) BOD; (**d**) COD; (**e**) TN; (**f**) TP; and (**g**) COD-E.
## 4 Rate Optimization  
For a given IRS configuration \(\mathbf{\theta}\), the achievable rate [30; 31; 32] is as follows:  
\[\mathbf{R}=\frac{B}{K+M-1}\sum_{i=0}^{K-1}\log_{2}\left(1+\frac{P|\mathbf{H_{d_{i}}}+ \mathbf{V_{i}}\mathbf{\theta}|^{2}}{BN_{0}}\right) \tag{14}\]  
where \(\mathbf{H_{d_{i}}}\) is the \(i\)th row of \(\mathbf{H_{d}}\), \(\mathbf{V_{i}}\) is the \(i\)th row of \(\mathbf{V}\), \(B\) represents the bandwidth (symbol rate), \(P\) is the signal power, and \(N_{0}\) represents the noise power spectral density. Maximizing the rate equation will lead to overall enhancement in the IRS system. There are two stages in maximizing the rate equation.