esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Development
### Lithium-Ion Battery RUL Prognostics  
Capacity and internal resistance are the two most commonly used health indicators for lithium-ion battery RUL prognostics. The cost of measuring internal resistance online is too high, but the capacity can be calculated easily by integrating the current over time. In order to represent the SOH of a lithium-ion battery for the RUL prognostics in a unified form, capacity is typically converted as follows:  
\[SOH_{i}=\frac{C_{i}}{C_{0}}\times 100\%, \tag{21}\]where \(C_{i}\) and \(C_{0}\) are the capacity of the cycle \(i\) and the rated capacity, respectively. When \(C_{i}\) is less than 70% of \(C_{0}\), the battery reaches the end-of-life.  
It is assumed that \(\alpha\) is the current charge-discharge cycle and \(\beta\) is the cycle at the end-of-life. By predicting the end-of-life cycle number \(\beta\), we can get the battery RUL at the \(\alpha\) cycle as follows:  
\[RUL_{\alpha}=\beta-\alpha, \tag{22}\]  
Figure 1 shows the degradation curve of the #5 lithium-ion battery's SOH with the cycle number and calendar time. The test data were released by the NASA Prognostics Center of Excellence (PCoE) at the Ames Research Center [33]. In Figure 1, DT indicates the rest time of two adjacent discharge cycles. The red triangle shows the capacity regeneration phenomenon. From Figure 1, the capacity regeneration phenomenon affects the state and rate of degradation. When the rest time is long enough, the battery capacity will be regenerated, and the battery's SOH will be increased. Moreover, after a visible regeneration appearance, the SOH degradation rate of the next several cycles is much faster than before. Therefore, it is beneficial to take into account the effect of rest time on the prediction which improves the accuracy of the RUL prediction. Similar to some of the concepts [PERSON] et al. [30] defined, in this paper, the cycle with a significant capacity increase caused by a long rest time is called the \"regeneration cycle\", the capacity regeneration cycles is called the \"regeneration region\", the number of cycles in each regeneration region is called the \"regeneration cycle number\", and the cycle except regeneration regions is called the \"normal degradation\".
# Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning
## 3 Materials and Methods
### Dataset  
Unlike other languages, Arabic content on social media (tweets, posts, comments, etc.) are mostly written in local dialects instead the standard Arabic, considered to be the official language in all Arab countries. This comes from the fact that standard Arabic is used for formal, administrative, educational, and religious purposes. Arabs tend to use their local dialects as a means of communication when attending their usual day-to-day tasks, and thus, in social media. For this reason, any efficient attempt to detect offensive and hateful speech in Arabic social media needs to be able to work with Arabic dialects. This urged many researchers, as we have seen in the previous section, to manually collect and annotate their own datasets, serving specific needs.  
However, there are some publicly available datasets in the literature, though rare, that can provide good performance with proper training. [PERSON] et al. [40] created a dataset with 5846 tweets in Levantine dialects that were annotated to either (\"Hate\", \"Abusive\", or \"Normal\"). Another interesting dataset was presented by [PERSON] et al. [41] who collected 15050 YouTube comments in different dialects, namely Gulf, Egyptian, and Iraqi. These comments were labeled as either \"offensive\" or \"inoffensive\". To the best of our knowledge,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Range of Possible Values** \\ \hline Batch Size & \(16,32\) \\ Learning Rate (Adam) & \(5\times 10^{-5},3\times 10^{-5},2\times 10^{-5}\) \\ Number of epochs & \(2,3,4\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: BERT fine-tuning configuration.  
this dataset may be among the largest and most diverse one available so far in the literature, and as such, we will use it for our approach.  
In their annotation process, [PERSON] et al. used the help of three annotators, two of them were from Iraq and Egypt, two countries that were highly represented in the dataset, whereas the third was from Libya, a country that was almost absent from the dataset. For their final annotation, [PERSON] et al. provided two datasets with two different scenarios based on which they would consider if a comment was considered hateful or not. In the first scenario, the annotation is based on a unanimous vote, whereas in the second scenario, it is based on a majority vote.  
For training our BERT-based models, we chose the dataset of the second scenario, which has a better proportion of the two classes. Since BERT is trained in English, and the comments our dataset are in Arabic, we used Google API to translate them into English.
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 6 Structure, Function and Regulation of Pkc1
### Activation Loop Phosphorylation by Pkh1 and Pkh2  
The first suggestive evidence that Pkc1 was a downstream target of Pkh1 and Pkh2 was the observation that the phenotypes of a \(pkh1(D398G)^{ls}\)\(pkh2\) strain resembled mutants defective in Pkc1 function (or in the function of downstream components of the cell wall integrity signaling pathway that it initiates) [69]. Initial biochemical studies showed that Pkh2 was able to phosphorylate Pkc1 in vitro, that the site of phosphorylation (Thr983) (Figure 2) lies in its activation loop within a highly conserved motif very similar to that phosphorylated by mammalian PDK1 in its targets (Figure 4), that Pkc1 activity was markedly reduced in \(pkh1(D398G)^{ls}\)\(pkh2\) cells shifted to the restrictive temperature, and that phosphorylation of Pkc1 at Thr983 is essential for its function [69]. These observations were amply corroborated subsequently [47; 99]. Furthermore, as mentioned earlier, hyperactive alleles of Ypk1 and Ypk2, namely Ybk1(D242A) or Ybk2(D239A), are able to rescue the inviability of cells deficient in TORC2 function [22; 67; 68]. However, such alleles do not rescue the temperature-sensitive lethality of \(pkh1(D398G)^{ls}\)\(pkh2\) cells [67], consistent with the fact that Pkc1 is also an essential protein that requires activation by Pkh1- and Pkh2-mediated phosphorylation.
# Bounce: A High Performance Satellite-Based Blockchain System
## 6 Experiments, Results, and Analysis
### Slot Protocol Measurements
#### 6.3.1 Throughput and Response Time of the One Root per Slot Protocol  
In our protocol, (i) several Merkle Trees can be constructed in parallel and (ii) Merkle Tree construction is decoupled from the Sending Station-Satellite-Broadcast Ground Station flow within a slot. That is, a Sending Station begins a slot with the multisigned root of a Merkle Tree and the multisigned Commit Record of the previous slot.  
Assume we set the slot time to be one second and \(f=18\) (i.e., 18 Broadcast Ground Stations could be traitorous), so the Merkle tree and transactions will need to be spread to 39 Broadcast Ground Stations and receive a multisignature with at least 19 signatures from different Broadcast Ground Stations for the Merkle Tree. This means the Sending Station sends a Signed Merkle Tree Request with one million transactions to the Broadcast Ground Stations.  
Given the timing in Figure 4, constructing the Merkle Tree on at least 19 nodes and forming the multisignature will take under four seconds. Given these numbers, we can support a sustained throughput of one million transactions per second by initiating one Merkle Tree construction every second. Because the multisignatures require 4 s, the response time will be 5 s.  
Figure 6 illustrates how this pipelining works. For example, the Sending Station sends the Sign Merkle Tree Request for tree \(T1\) with one million transactions at time 1. At time 5, the Sending Station receives the signatures from 20 Broadcast Ground Stations and creates the multisigned signature for \(T1\), and then at time 6 (slot 6), the satellite commits \(T1\).
## 3 Signal Acquisition and Preprocessing
### Signal Preprocessing
Assuming that \(x_{short}(iL+n)\) is the \(n\)th sampling point of the \(i\)th ideal local short training symbol, \(y_{short}(iL+n)\) is the \(n\)th value of the \(i\)th received signal of the actual short training symbol, and the frequency deviation of the transmitter and receiver is \(\Delta f\). By calculating the conjugate correlation value between the two adjacent short training symbols, the phase offset of the corresponding point between the \(i\)th and the \((i+1)\)th short training symbol can be expressed as  
\[ \begin{array}{rl}\theta_{y_{i}}(n)&=angle\left[y_{short}(iL+n+L)y_{short}^{* }(iL+n)\right]\\ &=angle\left[x_{short}(iL+n+L)e^{j2\pi\Delta f(iL+n+L)T_{s}}\left(x_{short}( iL+n)e^{j2\pi\Delta f(iL+n)T_{s}}\right)^{*}\right]\\ &=angle\left[x_{short}(iL+n+L)x_{short}^{*}(iL+n)e^{j2\pi\Delta f(iL+n+L)T_{ s}}e^{-j2\pi\Delta f(iL+n)T_{s}}\right]\\ &=angle\left[|x_{short}(iL+n)|^{2}e^{j2\pi\Delta f/LT_{s}}\right]\\ &=2\pi\Delta fLT_{s}\end{array}  \tag{11}\]  
where \(i=0,1,\ldots,8\), \(n=0,1,\ldots,L-1\), \(L\) denotes the sampling points of a short training symbol, \(angle(\bullet)\) denotes calculating phase angle of complex number, and \(T_{s}\) denotes the sampling interval. Then, the average of the phase offset \(\Delta\theta\) of corresponding points between the adjacent short training symbol is calculated. Eventually, the average frequency offset produced by a sampling interval can be calculated by  
\[\Delta f=f_{s}\frac{\Delta\theta}{2\pi L} \tag{12}\]where \(f_{s}\) represents the sampling frequency of the receiver. The frequency offset compensation process is performed on the overall preamble field signal after synchronization, and is implemented by  
\[y_{freq}(n)=y_{sym}(n)e^{-2\pi\Delta f/nT_{s}} \tag{13}\]  
where \(y_{sym}(n)\) is the signal after synchronization and \(y_{freq}(n)\) is the signal after frequency offset compensation. This frequency offset estimation and compensation method can well remove the signal phase deviation caused by the frequency offset, and better serve the subsequent fingerprint extraction.
# A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark
###### Abstract
Aiming at the two computational challenges of FSM, we conduct the subgraph extension and support evaluation across all the distributed cluster worker nodes. For subgraph extension, our approach generates all subgraphs in parallel through FFSM-Join (Fast Frequent Subgraph Mining) and FFSM-Extend proposed by [PERSON] et al. [23], which is an efficient solution for candidate subgraphs enumeration. When computing subgraph support, we adopt the constraint satisfaction problem (CSP) model proposed in [12] as the support evaluation method. The CSP support satisfies the downward closure property (DCP), also known as anti-monotonic (or Apriori property), which means that a subgraph \(g\) is frequent if and only if all of its subgraphs are frequent. As a result, we employ a breadth first search (BFS) strategy in our SSiGraM algorithm. At each iteration, the generated subgraphs are distributed to every executor across the Spark cluster for solving the CSP. Then, the infrequent subgraphs are removed while the remaining frequent subgraphs are passed to the next iteration for candidate subgraph generation. In practice, the support evaluation is more complicated than subgraph extension and will cost most time during the mining process. As a result, besides parallel mining, our SSiGraM algorithm also employs a heuristic search strategy and three novel optimizations: load balancing, pre-search pruning and top-down pruning in the support counting process, which significantly improve the performance. Noteworthily, SSiGraM can also be applied to directed graphs, weighted subgraph mining or uncertain graph mining with slight modifications introduced in [24; 25]. In summary, our main contributions to the frequent subgraph mining in a single large graph are three-pronged:* First, we propose SSiGraM, a novel parallel frequent subgraph mining algorithm in a single large graph using Spark, which is different from the Hadoop MapReduce based and MPI based parallel algorithms. SSiGraM can also easily combine with the bottom Hadoop distributed storage data and other machine learning algorithms. * Second, we conduct in parallel subgraph extension and support counting, respectively, aiming at the two core steps with high computational complexity in frequent subgraph mining. In addition, we provide a heuristic search strategy and three optimizations for the support computing operation. * Third, extensive experimental performance evaluations are conducted with four real-world graphs. The proposed SSiGraM algorithm outperforms the GraMi method by at least one order of magnitude with the same memory allocated. The paper is organized as follows. The problem formalization is provided in Section 2. Our SSiGraM algorithm and its optimizations are presented in Section 3. In Section 4, extensive experiments to evaluate the performance of the proposed algorithm are conducted and analyzed. The work is summarized and conclusions are drawn in Section 5.
## 5 Conclusions  
This study proposes a novel scheme for increasing the data rate of a 2D-DFC system by introducing a reference frame estimation method. The proposed scheme involves computing the estimates at the pilot pixels, followed by the interpolation of the estimates at the information pixels using piecewise cubic interpolation. The scheme then selects virtual pilots based on specific criteria among the data symbols, and uses them as pilot pixels in the next iteration. This iterative process was repeated, and the reference image was re-estimated each time. Simulation results show that the proposed scheme improves the data rate of the 2D-DFC system by almost two-fold at the cost of a slightly reduced PSNR; thus, the proposed scheme provides a way to eliminate the use of reference image frames that typically limit the data rate of 2D-DFC systems. Overall, this study presented a promising approach for enhancing the performance of D2C communication systems.  
Conceptualization, S.-Y.J.; methodology, B.W.K., P.S. and S.-Y.J.; software, P.S.; validation, B.W.K., P.S. and S.-Y.J.; formal analysis, B.W.K. and P.S.; investigation, B.W.K. and P.S.; resources, P.S. and S.-Y.J.; data curation, P.S.; writing--original draft preparation, P.S.; writing--review and editing, B.W.K., P.S. and S.-Y.J.; visualization, B.W.K., P.S. and S.-Y.J.; supervision, B.W.K. and S.-Y.J.; project administration, B.W.K. and S.-Y.J.; funding acquisition, B.W.K. and P.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2B5B01001543 and 2022R1G1A1004799). # MSIT: Ministry of Science and ICT.  
Not applicable.  
The authors declare no conflict of interest.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 5 Conclusions  
To investigate the feasibility of using dielectric properties for detecting the moisture condition of compost, a practical, rapid, and accurate method was proposed for detecting the moisture content of compost based on dielectric properties. In this study, the compost was subjected to frequencies of 1-100 kHz, a moisture content of 5-35%, a temperature of 25-65 \({}^{\circ}\)C, and a bulk density of 665.6-874.3 kg/m\({}^{3}\). The dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\) of straw compost decreased with the increase in the frequency but increased with the increase in moisture content and temperature. When the moisture content was less than 25%, the effect of bulk density on the dielectric properties of samples was not apparent. However, when the moisture content was between 25% and 35%, both the dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\) of the samples increased with the increase in the bulk density. The prediction performance of the model was analyzed by comparing the full-frequency variables and the selected characteristic variables using PCA. The SVR model, which combined the dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\), exhibited the best ability for predicting the moisture content. PCA was used to select characteristic variables. The prediction set coefficient of determination and the root mean square error were 0.9877 and 0.0026, respectively. The results indicated that the dielectric properties of compost exhibited excellent application potential in predicting the moisture content of compost.  
Conceptualization, R.W. and T.R.; methodology, T.R.; software, L.F.; validation, R.W., T.R. and L.F.; formal analysis, T.R.; resources, T.W. ([PERSON]); data curation, T.W. ([PERSON]); writing--original draft preparation, T.R.; writing--review and editing, L.F.; visualization, T.R.; supervision, R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Liaoning Province (2022-NLTS-19-05), and the \"Jie Bang Gua Shuai\" Science and Technology Program Major Project of Liaoning Province (2022 JH1/10400017).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank the Natural Science Foundation of Liaoning Province (2022-NLTS-19-05), and the \"Jie Bang Gua Shuai\" Science and Technology Program Major Project of Liaoning Province (2022 JH1/10400017). We appreciate Shenyang Agricultural University for providing the test instruments and equipment as well as the test site. The authors are grateful to the editor and the anonymous reviewers for their comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 2 Materials and Methods
### Evaluating Indicators of Generated Data
#### 2.4.4 Optimizing the Training Iterations of DCGAN by SVD  
Singular value decomposition (SVD), an effective dimensionality reduction technique, can map high-dimensional data into a lower-dimensional subspace, effectively reducing the E1 waveform to a single point [21]. SVD rapidly and reliably compresses EPG data from hundreds of features into a few composite metrics, facilitating the visualization and differentiation of data variations.  
Generally, the quality of the generated data in a generative model is evaluated based on two criteria: the similarity to real data and the diversity of the generated samples. The Frechet Inception Distance (FID) was commonly used to assess both aspects simultaneously [28]. However, to deal with one-dimensional data, there is a higher risk of producing overly similar data with insufficient diversity during training, in contrast to two-dimensional data. To address this, in this study we used MMD to ensure the generated one-dimensional data closely resembled the real data, while SVD was applied to guarantee the diversity of the generated samples [29; 30]
## 3 Experiments
### Experimental Results  
#### 3.5.1 Experimental Results Under Other Metrics  
To more comprehensively evaluate the classification performance of HoRFNet under various magnifications, this study introduces precision, recall, and F1-score as supplement  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Comparison**} & \multicolumn{2}{c}{**40\(\times\)**} & \multicolumn{2}{c}{**100\(\times\)**} & \multicolumn{2}{c}{**200\(\times\)**} & \multicolumn{2}{c}{**400\(\times\)**} \\ \cline{2-9}  & **Mean**\(\uparrow\) & \(\mathbf{p}\downarrow\) & **Mean**\(\uparrow\) & \(\mathbf{p}\downarrow\) & **Mean**\(\uparrow\) & \(\mathbf{p}\downarrow\) & **Mean**\(\uparrow\) & \(\mathbf{p}\downarrow\) \\ \hline SeONet & 5.095 & 0.097 & 3.840 & 0.028 & 3.970 & 0.022 & 2.015 & 0.049 \\ RFNet & 5.675 & 0.074 & 2.560 & 0.060 & 3.805 & 0.068 & 0.365 & 0.836 \\ HoRFNet & **7.515** & **0.044** & **6.560** & **0.010** & **5.710** & **0.005** & **4.395** & **0.011** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical comparison (mean improvement and \(p\)-value) of SeONet, RFNet, and HoRFNet versus the Backbone across different magnifications using \(t\)-test.  
tary evaluation metrics. The corresponding experimental results are presented in Table 3, and the confusion matrices for each magnification are shown in Figure 8.  
As shown in Table 3, HoRFNet achieves high classification performance across all magnification levels. The average precision reaches 98.47%, the average recall is 99.05%, and the average F1-score is 98.88%. Among the four magnifications, the model achieves the best performance at 200\(\times\), with precision and F1-score reaching 99.53% and 99.64%, respectively. At 100\(\times\) and 200\(\times\), recall reaches 99.76%, indicating that the model demonstrates strong sensitivity in detecting malignant samples.  
Figure 8 further illustrates the model's classification performance under different magnification conditions. For example, on the 40\(\times\) dataset (Figure 8a