esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.3 Geographic Detector  
A geographic detector is used to detect the spatial heterogeneity of geographic zones and explore driving forces that can detect the interaction between two factors and the dependent variable, as well as the strength and direction of the interaction. This study used a geographic detector to detect the spatial heterogeneity and driving forces of WUE in China from 2001 to 2020. The main steps of the method are as follows [48; 49]:  
(1) Divergence and factor detection: this involves the detection of the spatial dissimilarity of Y, as well as the detection of the extent to which a factor X explains the spatial dissimilarity of attribute Y, and can be measured in terms of q values [48; 49]:  
\[q=1-\frac{1}{N\sigma^{2}}{\sum}_{h=1}^{L}N_{h}\sigma_{h}^{2} \tag{6}\]  
where q is the explanatory power of a certain factor, L represents the stratification of the variable (Y) or the factor (X), and N\({}_{\text{h}}\) and N represent the number of cells in stratum h and the whole region, respectively, \(\sigma_{h}^{2}\) and \(\sigma^{2}\) are the variances of stratum h and the whole region, respectively. The q value ranges from [0, 1], and a larger value indicates that the factor (X) is more explanatory of the variable (Y), and q = 0 indicates no relation.  
(2) Interaction detection: This step involves the identification of interactions among diverse risk factors X\({}_{\text{i}}\). Specifically, it evaluates whether the concurrent presence of factors X1 and X2 enhances or diminishes the predictive capacity of the dependent variable Y. Fur thermore, it examines whether the impacts of these factors on Y are mutually independent. This evaluation process begins by determining the individual q values of X1 and X2 with respect to Y, denoted as q(X1) and q(X2), respectively. The q value is subsequently calculated when these factors interact, which is represented by the novel polygonal distributions arising from the tangent of the superimposed variables X1 and X2, designated q(X1X2). Finally, a comparative analysis is conducted between q(X1), q(X2), and q(X1X2) to ascertain the nature of the interaction. There are five types of interactions between two independent variables and the dependent variable, as shown in Table 1.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 3 Experiment and Result Analysis
### Experimental Results and Analysis  
#### 3.3.1 Comparison of Mimo-Net and Y-Net Training Results  
Under the same software and hardware environment and dataset conditions, using \(256\times 256\), \(128\times 128\), and \(64\times 64\) scale datasets to train the Mimo-Net network model, the Mimo-Net test set loss is obtained. Y-Net network models were trained on datasets of \(256\times 256\), \(128\times 128\), and \(64\times 64\) scales, respectively, and the average test set losses of the three models were calculated. The comparative curve of test set losses for both network models is shown in Figure 5, where the y-axis represents the \(L_{IP}\) and the x-axis represents the epochs, ranging from 0 to 100. When the models reached an epoch value of 20, the Mimo-Net network showed a relatively stable trend, while the Y-Net network still showed more significant fluctuations. On the right side of the figure, with epoch values ranging from 90 to 100, the loss values of both networks are amplified for comparison. The Mimo-Net network has better overall convergence, accuracy, and stability, indicating successful training. The final loss value for Mimo-Net tends to be closer to 0.15, while for Y-Net it tends to be closer to 0.23. The Mimo-Net network shows an improvement in accuracy of 34.78%.  
Record training time for Y-Net and Mimo-Net. Y-Net requires three training rounds to reconstruct intensity and phase information at three different scales. Mimo-Net, on the other hand, only needs to be trained once to reconstruct intensity and phase information simultaneously at scales of \(256\times 256\), \(128\times 128\), and \(64\times 64\). The recorded times are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Net** & **Scales** & **Times/s** & **Total Times/s** \\ \hline \multirow{3}{*}{Y-Net} & \(256\times 256\) & \(1687\) & \multirow{3}{*}{2713} \\  & \(128\times 128\) & \(582\) & \\ \cline{1-1}  & \(64\times 64\) & \(444\) & \\ \cline{1-1}   Mimo-Net & \(256\times 256\), \(128\times 128\), \(64\times 64\) & \(1008\) & \(1008\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Y-Net and Mimo-Net reconstruct the training time of intensity and phase information at three scales.  
Figure 5: Comparison image of test set loss values between Y-Net and Mimo-Net networks.  
Table 2 shows that training Y-Net to reconstruct intensity and phase information at three different scales took 2713 s, while Mimo-Net reconstruction took 1008 s. Therefore, the reconstruction time of the Mimo-Net network was reduced by 62%, leading to an improvement in reconstruction efficiency.
## 4 Discussion
### Distances Covered  
Results showed that TD (3188.84 \(\pm\) 808.37 m) is less than distances covered reported by previous studies that applied TMA (from 4404 to 7558 m) [4; 5]. The results are slightly lower to a study to that applied UWB technology, where distances reported were from 2000 to 4000 m [8]. Although, it is important to take into consideration that the devices the manufacturer used and the gender were different. As we know, TD will be determined by contextual factors during the game (such as quality rival, competition, tactical aspects, etc.), methodological factors, technology applied, and mainly, the duration that each player is on the court [44; 45]. If we compare m-min (83.95 \(\pm\) 7.24 m) with previous studies that have used LPS [8; 45], the outcomes gave contradictory results. This could be due to differences in sample and methodology factors such as gender, age category, level competition, and technology applied. Consequently, these are difficult to discuss. In turn, these results should be considered by physical trainers and basketball coaches in order to prepare specific exercises during training sessions.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 5 Implementation Problems
### Modulation Method
The results show that with a significance level of 5%, the probability of distinguishing between the samples from 12-bit quantization and the samples from the true Gaussian distribution is as small as 6.24%. It can be noted that a $1000 software-defined radio device includes a 12-bit digital-to-analog converter [40]. Thus, we can conclude that the quantized Gaussian signal is sufficiently close to the real Gaussian signal despite the hardware limitation.
# Study on the Population Balance Dynamics Simulation of Grinding under Impact Crushing
## 3 Results and Discussion  
The Population Balance Dynamics Simulation of Grinding under Impact Crushing of Quartz 3.1.1. Calculate the Cumulative Particle Size Distribution under Sieve \(t_{xx}\) for Any Sieve Size Based on the Drop Weight Test Results  
Based on the data obtained from the drop weight test in Section 2.2.1, the calculated \(xx\) value is used as a subscript for the cumulative particle size distribution under the sieve, and the results shown in Table 3 are obtained. According to Section 2.2.1, based on the quartz drop weight test data, we used the interpolation method to obtain points of the cumulative particle size distribution curve under the sieve and calculated the \(t_{xx}\) values listed in Table 3. On this basis, we drew a scatter relationship diagram between the \(t_{xx}\) value and the corresponding groups of \(t_{10}\) values, as shown in Figure 1. Using the Origin polynomial-fitting method, the functional relationship between \(t_{10}\)-\(t_{xx}\) of the quartz under impact is obtained, as shown in Table 4. According to this, given any value of \(t_{10}\), the corresponding series of \(t_{xx}\) values can be calculated, that is, the cumulative particle size distribution under the sieve for different particle sizes. Table 4 reveals that the \(R^{2}\) values of all \(t_{xx}\) are close to 1, higher than the \(R^{2}\) value of Deniz [47], and similar to the \(R^{2}\) values of Genc [48; 49], Deniz [50], and Mulenga [51], indicating that the data in this study have a high level of reliability. The above \(t_{10}\)-\(t_{xx}\) function relationship is obtained under the impact of the drop weight test and is only related to the properties of mineral samples. When the quartz is broken in the mill, \(E_{cs}\) can be calculated based on Section 2.2.3. Based on Equation (2), the corresponding \(t_{10}\) value for each particle size in the mill can be calculated. Combining the \(t_{10}\)-\(t_{xx}\) function relationship in Table 4, a series of \(t_{xx}\) values under any \(t_{10}\) can be obtained. Therefore, a complete particle size distribution can be obtained. According to Sections 2.2.1 and 2.2.3, the calculated results of the \(E_{cs}\) and \(t_{10}\) values for each particle size are shown in Table 5.  
From Table 5, it can be seen that the \(E_{cs}\) of the 25 mm steel ball media for 2.8118 mm quartz particles in a cylindrical ball mill reaches 1.39 kWh/t, which can lead to its \(t_{10}\) reaching 46.35%. When the quartz particle is 1.0015 mm, its \(E_{cs}\) is 30.97 kWh/t, which can lead to its \(t_{10}\) reaching the limit value of 67.33%. As the particle size continues to decrease, the \(E_{cs}\) of the particles increases, but the degree of fragmentation does not increase. This is because finer particle sizes demand greater energy inputs for comminution and there is a smaller probability of contact with the steel ball, so its \(t_{10}\) no longer increases with the increase in the \(E_{cs}\) received.
## 5 Discussion
In addition, they applied data augmentation on their dataset and used a deep learning model to attain the high classification performance [58], which increased the model's computational complexity. In contrast, our ExMPLPQ algorithm achieved high classification performance with low computational complexity. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Pre-Trained Model**} & **Axial** & **Sagittal** & **Hybrid** \\  & & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline  
fficient b0 [57] & 93.69 \(\pm\) 0.45 & 90.26 \(\pm\) 0.39 & 93.22 \(\pm\) 0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Means \(\pm\) standard deviations of accuracy results attained by the19 comparator pre-trained models after 100 classification runs using Fine kNN classifier with 10-fold cross-validation. Table 6 tabulated that our model attained superior classification results than other previously presented state-of-art methods. The salient points of the proposed ExMPLPQ algorithm are discussed in below. Benefits:  
* A new brain MRI dataset comprising three study data subsets was prospectively acquired to train and test the model. This dataset has been made publicly available. * By design, the ExMPLPQ model exploited the advantages of both exemplar and multiple parameters for feature extraction. * The best features were automatically selected for each of the three binary classification problems using INCA. * ExMPLPQ attained over 97% accuracies for all study data subsets. * ExMPLPQ attained better classification performance compared with 19-pre-trained CNNs. Of note, more than a million parameters were required to be assigned/optimized using the deep learning models, which increased their time complexity considerably. * The ExMPLPQ algorithm has a low time complexity of approximately \(O(n\log n)\). * The base architecture of ExMPLPQ is parametric and is amenable to modification and optimization to create new models using variable patch sizes and updating of feature extractors, feature selectors, and classifiers. Limitations:  
* The dataset was new, which precluded direct comparison with extant methods in the literature. Nevertheless, the common dataset was used to test the ExMPLPQ model and 19 comparator deep learning techniques, which demonstrated superior results for our model. * Only MS patients admitted to one hospital during one year (2021) were included in the study. * Patients with less than 9 MS lesions on brain MRIs were excluded from the study. * Patients with poor MRI image quality and motion artifacts were excluded from the study.
## 3 Results and Discussion
### Operational Amplifier (OPA) as Current to Voltage Converter  
To convert the current from PMT into voltage, four transimpedance amplifiers are required as shown in Figure 2. The proposed OPA is configured as transimpedance amplifier as shown in Figure 6a. Resistor R1 is used as feedback resistor and the output voltage of the circuit is given by:  
\[Vout=11\cdot R1 \tag{12}\]  
Figure 5: (**a**) Slew rate simulation model. (**b**) Simulation output.  
Figure 6: (**a**) Operational amplifier (OPA) as transimpedance amplifier (**b**) Simulation result (**c**) Simulation results showing the Linearity of TIA for 1 \(\mu\)A to 1 mA input current.  
Figure 6b is the simulation result of the transimpedance amplifier, and it shows that, for the applied input current of 1 \(\upmu\)A the corresponding output voltage is 1 mV, according to that given by Equation (9). The current from the PSPMT is in the range of tens of \(\upmu\)A and Figure 6c shows the linearity of the TIA for the current range from 1 \(\upmu\)A to 1 mA.
## 4 Experiment
### Ablation Experiments  
In this section, we analyze the effects of the bilateral filter and the proposed SSIM loss. The results of each section are shown in Figure 11. Where GAN indicates that the removal of bilateral filter and \(L_{\text{SSIM}}\) based on our proposed network; GAN + \(L_{\text{SSIM}}\) indicates that only \(L_{\text{SSIM}}\) is used; and GAN + Filter indicates that only bilateral filter is used.  
We zoomed in on the two localized parts of the figure, and GAN removed some evident bright white streaks. After adding \(L_{\text{SSIM}}\), the detailed information of the image is preserved, as shown in Figure 11d, but there is still a small amount of rain streaks, which can be a comprehensive measure of the similarity of the image, and retains as much detail information in the image as possible; after adding the bilateral filter, the rain streaks are all removed, but some detail information is lost. As shown in Figure 11e, it allows our network to exclude the background information and focus more on the removal of rain streaks to the maximum extent. By adding \(L_{\text{SSIM}}\) and a bilateral filter at the same time, the rain streak removal effect is better and closest to the ground truth image, as shown in Figure 11e. PSNR and SSIM also reached optimal values. The ablation experiments show  
Figure 9: An example of the filtering results of different low-pass filters. The first line is the original image with rain, and the remaining lines are filtered from top to bottom to means filter, median filter, Gaussian filter, NLM filter, and bilateral filter.
## 3 Data Analysis
### Time Lag Effect  
The lag load or loads from the previous day of the same hour can be highly related to future loads. Studies on lag load relation correlation [8; 32] showed that first-day lag impact has a high contribution to the demand. Likewise, one study [7] discussed the repletion of weekdays such that the 7-day lag had a high contribution because of the same day and same hour relating similar effects each day. The correlation plot in Figure 6 shows a high correlation between the electricity demand and the previous time lags, which suggests that the previous day's load of the dependent variable (i.e., electricity demand) could be a useful variable for the model's development.  
From Figure 6, we can observe correlations of 0.928, 0.917, 0.912, 0.907, 0.901, and 0.904 with the lag loads from days 1, 2, 3, 4, 5, and 6. The correlations tended to decrease when moving further back in time, which indicates that the load had a similar daily load pattern throughout the week, as short load lag relation could also give satisfying forecasting results. Similarly, Figure 7 shows the correlation plot of the load with the previous 30 lags, in which the correlation value decreased when moving back in time. However, a small spike in the lag on days 7, 14, 21, and 28 can be seen, as these days addressed the similar daily effect such that each weekday repeated after 7 days, with the seventh day's lag being the same for the repeated day of the week. The load showed a 0.9095 correlation value with the 7-day lag load, which was higher than the correlation with the lags for days 4, 5, and 6.  
Figure 5: (**a**) Joint plot of the electric load vs. temperature. (**b**) Monthly mean plot of the electric load and temperature. (**c**) Mean plot of the temperature vs. electric load.
## 2 Materials and Methods
### Data Processing and Calculations  
CO\({}_{2}\) eddy covariance data were processed with REddyProc 1.2.1 [46], which filters low turbulence data using the methodology from [47] (with the 50-percentile criterion) and then fills all the gaps produced by the filtering technique or by instrument failure with a lookup table. The soil temperature at the lowest depth was chosen as the input variable for REddyProc along with the above canopy air temperature (\(T_{air}\)), the vapor pressure deficit and the photosynthetic photon flux density divided by 0.47 as global radiation.  
Ecosystem respiration (\(R_{eco}\)), its photosynthesis (gross primary productivity; \(GPP\)) and \(NEE\) are related according to the equation:  
\[NEE=R_{eco}-GPP \tag{1}\]  
In this study, \(R_{eco}\) was estimated with REddyProc based on the nighttime approach [21; 22], which fits the [PERSON] [48] model for respiration (Equation (2)) using only nighttime data, because \(NEE=R_{eco}\) at night, and then extrapolating the parameters \(R_{ref}\) and \(E_{0}\) found in the regression to calculate daytime \(R_{eco}\) (\(T_{ref}\) and \(T_{0}\) are fixed). Then, \(GPP\) is calculated with Equation (1) [49].  
\[R_{eco}=R_{ref}~{}e^{\frac{E0}{T_{ref}-10}-\frac{E0}{T_{air}-10}} \tag{2}\]  
Afterward, yearly \(NEE\), \(R_{eco}\) and \(GPP\) sums were calculated in Python 3.7.7. In the literature, \(NEE\) can also be expressed as net ecosystem production (\(NEP\)), where \(NEP\) = \(NEE\)[50].
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 4 Discussion  
The results presented in the \"Results\" Section signify the following observations: First, the environment in which the activities were performed affects the achieved recognition accuracy. This is illustrated by the difference in the accuracies obtained for the office and hallway environments. More specifically, in the office environment, the distance between the transmitter and the receiver was 3.6 m, while the distance between the transmitter and receiver in the hallway environment was 7.6 m. This difference in the distance between the transmitter and receiver in the two environments resulted in a decrease of 4.74% in the accuracy achieved using the data recorded in the hallway environment. Second, more data do not necessarily mean better performance. Specifically, the results indicate that using only six principal components instead of using all the principal components achieves better performance, which is shown in Figure 15. In addition, using all the features does not mean better performance. Particularly, Figure 17 shows that using a subset of selected features achieves better performance than using all the features.  
Third, the confusion matrices clearly show that for both environments the walking activity achieved the highest accuracy with 99.5% TPR in the office environment and 96.5% TPR in the hallway environment. This can be attributed to the fact that the pattern of walking is highly different from the pattern of falling, sitting on a chair, or standing still. Specifically, when the subjects perform the walking activity, they cover the entire distance between the transmitter and the receiver. On the other hand, when the subjects perform the falling, sitting on a chair, and picking up a pen from the ground, they remain in the same place, limiting their movement pattern. Thus, activities such as falling, picking up a pen from the ground, and sitting on a chair have more incorrect predictions due to the relatively high similarity among these activities, while the walking activity has less incorrect predictions, which is translated to higher recognition accuracy due to their distinct movement pattern.
## 4 Identification of LncRNAs as Major Regulators in Human Disease  
LncRNAs have been implicated in the pathogenesis of disease by interacting with non-coding and coding genes to regulate a diverse network of cellular processes [30]. For example, an lncRNA named cardiac apoptosis-related lncRNA (CARL) inhibits miR-539 by acting as an endogenous sponge of miR-539. Because miR-539 induces mitochondrial fission and apoptosis in the heart by targeting the gene _PHB2_ and downregulating its expression, CARL was shown to increase _PHB2_ gene expression and inhibit mitochondrial fission or apoptosis in cardiomyocytes [31]. In a study conducted by [PERSON] _et al._, the T allele of Ex9b-single-nucleotide polymorphism (SNP)10 in a lncRNA was discovered to increase the risk of an individual for autoimmune thyroid disease, which is caused by abnormal immune responses [32]. Ex9b-SNP10 was found in the intron 9 of _zinc finger and AT hook domain containing gene_ (_ZFAT_) and an antisense lncRNA called small antisense transcript of ZFAT (SAS-ZFAT) is located in the promoter region. It was found that in the presence of Ex9b-SNP10, SAS-ZFAT was upregulated and inhibited its sense counterpart. The significance of the study is that that SAS-ZFAT was only found in CD19+ B cells, indicating that SAS-ZFAT is a major regulator of B cell function and susceptibility to autoimmune thyroid disease [32].  
Chromatin-modifying proteins like Sin3A and coREST are being used to target stroke-induced lncRNAs following ischemic stroke. A recent study showed that the levels of lncRNAs were increased after transient focal ischemia. It was shown that 177 of a pool of 2497 lncRNAs bind to chromatin-modifying proteins. Of the 177, only 26 were bound to Sin3A while 11 were associated with coREST following ischemic attack. Further analysis revealed that these lncRNAs share common locus and may interact with each other through an epigenetic signaling [33].  
Among multiple human diseases, we focus on lncRNAs' roles in cardiovascular development and disease in the following sections.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 6 Efficiency
### Packet Fragmentation  
Taking into account that we use the 6 LoWPAN protocol, packets with a length of 70 bytes or less are never fragmented. When a packet is larger and fragmentation occurs, the number of fragments required is given by \(1+(length-64)/72\), since the first fragment is maximally 64 bytes long and all subsequent fragments are maximally 72 bytes.  
Only the packets which are sent in the key construction phase (depending on the number of members in the group) possibly require fragmentation. These are the packet sent by the NMM to the publisher and the packet sent from the publisher to all the other nodes. Please note that using the method without LI will not result in fragmentation for the packet from the publisher, since it is always 32 bytes long, resulting in less LL frames being sent.  
Table 2 indicates the size of these packets, depending on the number of group members, and the corresponding number of fragments. These numbers are confirmed by simulations.