esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Scheme
### Power Control Scheme
#### 3.2.1 Closed-Loop Power Control  
As shown in Figure 5, the transmission power of satellite 1 and satellite 2 should be initialized at first. The initialization transmission power of satellite 1 and satellite 2 can be expressed as  
\[\begin{split} P_{sat1}^{1}&=\min(P_{sat1}^{\max},P_{ sat1}^{0}+\alpha\cdot PL_{sat1}+10\text{log}_{10}M)\\ P_{sat2}^{1}&=\min(P_{sat2}^{\max},P_{sat2}^{0}+ \alpha\cdot PL_{sat2}+10\text{log}_{10}M)\end{split} \tag{29}\]  
where \(P_{sat1}^{\max},P_{sat2}^{\max}\) denote the maximum power that satellite 1 and satellite 2 can transmit. \(P_{sat1}^{0},P_{sat2}^{\max}\) represent the path loss (PL) of satellite 1 and satellite 2. \(\alpha\) denotes the path loss compensation factor. \(M\) is the number of resource blocks (RB) allocated. \(P^{0}\) can be expressed as  
\[\begin{split} P^{0}_{sat1}&=\alpha\cdot(SINR^{0}_{ sat1}+P_{n})+(1-\alpha)\cdot(P^{\max}_{sat1}-10 log_{10}M_{0})\\ P^{0}_{sat2}&=\alpha\cdot(SINR^{0}_{sat2}+P_{n})+(1- \alpha)\cdot(P^{\max}_{sat2}-10 log_{10}M_{0})\end{split} \tag{30}\]  
where \(SINR^{0}_{sat1},SINR^{0}_{sat2}\) represent the target signal-to-interference-noise ratio (SINR). \(P_{n}\) is the noise power of each RB. When \(\alpha=1\), (30) can be rewritten as  
\[\begin{split} P^{0}_{sat1}&=(SINR^{0}_{sat1}+P_{n}) \\ P^{0}_{sat2}&=(SINR^{0}_{sat2}+P_{n})\end{split} \tag{31}\]  
Assuming the initial time is \(t_{0}\), and the power control adjustment cycle is \(T\). At time \(t_{0}+T\), the transmission power of satellite \(1\) and satellite \(2\)\(P^{2}_{sat1},P^{2}_{sat2}\) can be represented as  
\[\begin{split} P^{2}_{sat1}&=P^{1}_{sat1}-d^{1}_{ sat1}\\ P^{2}_{sat2}&=P^{1}_{sat2}-d^{1}_{sat2}\end{split} \tag{32}\]  
where \(d^{1}_{sat1},d^{1}_{sat2}\) denote the power variation value of satellite \(1\) and satellite \(2\), which is given by  
\[\begin{split} d^{1}_{sat1}&=SINR^{ref1}_{sat1}-SINR^{0 }_{sat1}\\ d^{1}_{sat2}&=SINR^{ref1}_{sat2}-SINR^{0}_{sat2} \end{split} \tag{33}\]  
\(SINR^{ref1}_{sat1},SINR^{ref1}_{sat2}\) denote the reference SINR of satellite \(1\) and satellite \(2\) through the feedback information of UT at time \(t_{0}+T\). \(SINR^{0}_{sat1},SINR^{0}_{sat2}\) represent the target SINR of satellite \(2\), respectively.  
Figure 4: Diagram of open-loop power control.  
The closed-loop power control process is as follows:  
Step1: Initialize the transmission power of satellite 1 and satellite 2.  
Step2: Obtain reference SINR of satellite 1 and satellite 2 through feedback information of UT at time \(t_{0}+T\). Calculate the power variation value of satellite 1 and satellite 2 according to (33).  
Step3: Calculate transmission power of satellite 1 and satellite 2 at time \(t_{0}+T\) according to (32).  
Step4: For each subsequent adjustment cycle \(T\), according to step 2 and step 3, update the power change value and transmission power.
## 2 Data and Method
### Methods
#### 2.3.5 Validation  
In this study, six categorical statistics, including the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), equitable threat score (ETS), Hanssen and Kuiper discriminant (HK) and Heidke skill score (HSS), were introduced to evaluate the performance of the rain area delineation methods [35]. The POD and FAR were used to measure the misdetected and false alarm precipitation of the rain area delineated by satellite observations, respectively. Higher values of POD and lower FAR indicate less misclassified rain/no rain areas. CSI, ETS, HK and HSS were used to evaluate the strength of the correlation between the rain areas measured by rain gauges and those delineated by satellite observations. More accurate rain area delineation will produce higher values of CSI, ETS, HK and HSS. These categorical statistics are calculated by Equations (5)-(10):  
\[POD=\frac{h}{m+h} \tag{5}\]  
\[FAR=\frac{f}{f+h} \tag{6}\]  
\[CSI=\frac{h}{m+h+f} \tag{7}\]  
\[ETS=\frac{h-\left((h+m)*(h+f)/\left(z+f+m+h\right)\right)}{m+h+f-\left((h+m)*( h+f)/\left(z+f+m+h\right)\right)} \tag{8}\]  
\[HK=\frac{h}{m+h}-\frac{f}{f+z} \tag{9}\]  
\[HSS=\frac{2*(z*h-f*m)}{\left(z+f\right)*(f+h)+(m+h)*(z+m)} \tag{10}\]  
In these equations, \(h\) and \(z\) represent the number of the correctly-identified rain pixels and no rain pixels, respectively;\(f\) is the number of false alarm pixels that are under no raining conditions, as detected by rain gauges, but still misclassified as rain by satellite observations; \(m\) is the number of misdetected pixels, which are rain pixels detected by rain gauges, but misclassified as no rain by satellite observations. The meanings of these variables are presented in Table 1.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 7 Evaluation  
### Research Questions  
To evaluate the effectiveness of ErrorExplainer in providing meaningful error descriptions for real-world cases, we investigate the following research questions:  
* **RQ1 (Identification accuracy):** How thoroughly does ErrorExplainer locate and extract all transaction-reverting statements from the source code of Solidity smart contracts?
* **RQ2 (Information completeness):** How rich is each extracted error context, and does it supply all data fields required for a useful description?
* **RQ3 (Matching fitness):** How unambiguously can ErrorExplainer map user-side error information to the correct error context?  
RQ1 measures the soundness of the static analysis in identifying and extracting error contexts from the source code of Solidity smart contracts. RQ2 and RQ3 evaluate the practical utility of the resulting error contexts for diagnosing user-reported failures.
# Improving Decodability of Polar Codes by Adding Noise
## 4 Proposed Decoding Algorithms
### Complexity Analysis  
The additional complexities required by the proposed three decoding algorithms for polar codes are discussed in this sub-section. Note that the complexity required for CRC check is ignored in the following analysis. Table 1 compares the computational complexity of the proposed algorithms and other decoding schemes, where \(C_{P}\) and \(C_{g}\) are the unit calculations required in the perturbation operation and genetic operation, as shown in Table 2.  
When the standard polar decoding fails, the Online-PDNE or PDNE decoding algorithm is activated, in which multiple perturbation noises are generated for the first T  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Decoding Algorithms** & **Computational Complexity** \\ \hline SSC [14] & O(NlogN) \\ PB-SSC [33] & O(\(C_{P}\)NlogN) \\ PP-SSC [35] & O(\(C_{P}\)NlogN) + O(\(C_{g}\)NlogN) \\ Proposed Online-PDNE & O(\(C_{P}\)NlogN) + O(\(C_{g}\)NlogN) \\ Proposed PDNE & O(\(C_{P}\)NlogN) \\ Proposed SNE & O(\(C_{P}^{+}\)NlogN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity of different decoding algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Perturbation Operation Calculations (\(C_{P}\))** & **Genetic Operation Calculations (\(C_{g}\))** \\ \hline Multiplication \(C_{P}^{\times}\) & Division, square root \\ (generation of perturbation noise) & (fitness value evaluation) \\ Addition \(C_{P}^{+}\) & Summation, division \\ (perturbing the received signal) & (fitness score evaluation) \\  & Comparison (selection) \\  & Comparison, addition (crossover) \\  & Comparison, addition (mutation) \\  & Addition (perturbing the received signal) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unit calculations in GA process.  
attempts. Given the code length N, the number of computations required to generate multiple candidate codewords by perturbing the output signal in the worst case is C\({}_{\text{p}}\)T\({}_{1}\)N. The number of computations required for the worst-case genetic operation process can be estimated as C\({}_{\text{g}}\)T\({}_{2}\)N. Therefore, the overall additional complexity brought by the proposed Online-PDNE decoder is C\({}_{\text{p}}\)T\({}_{1}\)N+C\({}_{\text{g}}\)T\({}_{2}\)N, which is the same as the PP-SSC decoder.  
In the PDNE decoding algorithm, the genetic process runs in an offline manner, so the extra complexity required is only C\({}_{\text{p}}\)T\({}_{1}\)N, which is due to perturbation operations.  
As for the SNE decoding algorithm, the pre-trained noise set S\({}_{n}^{*}\) is directly called for secondary decoding when the SSC decoding fails. Compared with the PDNE decoding algorithm, the SNE decoding algorithm only needs to perform the addition operation C\({}_{\text{p}}^{+}\) in the perturbation operation.
## 2 Methodology
### Quaternions and Rotation  
The quaternion is a structure with four values, one in the real part and three in the imaginary part, as in Equation (1). In this equation, the values \(q_{0}\), \(q_{1}\), \(q_{2}\), and \(q_{3}\) are coefficients changing through time [18].  
\[q(t)=q_{0}(t)+q_{1}(t)i+q_{2}(t)j+q_{3}(t)k \tag{1}\]  
The displacement of \(q(t)\) is obtained from the sample in \(\Delta t\) time, as is presented in Equation (2), where \(q_{d}(t)\) is the delayed quaternion with respect to \(q(t)\). The values \(q_{d0}\), \(q_{d1}\), \(q_{d2}\), and \(q_{d3}\) are coefficients changing through time.  
\[q_{d}(t)=q(t+\Delta t)=q_{d0}(t)+q_{d1}(t)i+q_{d2}(t)j+q_{d3}(t)k \tag{2}\]  
The quaternion signals \(q(t)\) and \(q_{d}(t)\) are related through the rotation quaternion signal \(q_{r}(t)\), i.e., \(q_{r}(t)\) is the quaternion signal that describes how to move from \(q(t)\) to \(q_{d}(t)\) as shown in Equation (3) [19]:  
\[q_{d}(t)=q_{rot}(t)\cdot q(t). \tag{3}\]  
The aforementioned equation implies that one sample of the rotated quaternion \(q_{r}\) can be determined by \(q\) and \(q_{d}\), as is shown in Equation (4) [18]. Each sample of the rotation is a model that describes the behavior among samples of the signal.  
\[q_{r}=\left[\begin{array}{cc}1-2q_{d2}^{2}-2q_{d3}^{2}&2(q_{d1}q_{d2}+q_{d 0}q_{d3})&2(q_{d1}q_{d3}-q_{d0}q_{d2})\\ 2(q_{d1}q_{d2}-q_{d0}q_{d3})&1-2q_{d1}^{2}-2q_{d3}^{2}&2(q_{d2}q_{d3}+q_{d0}q_ {d1})\\ 2(q_{d1}q_{d3}+q_{d0}q_{d2})&2(q_{d2}q_{d3}-q_{d0}q_{d1})&1-2q_{d1}^{2}-2q_{d2 }^{2}\\ \end{array}\right]\left[\begin{array}{c}q_{1}\\ q_{2}\\ q_{3}\\ \end{array}\right] \tag{4}\]  
The rotation signal \(q_{r}(t)\) is presented as natural quaternions with coefficients \(q_{r0}\) equal to zero because the rotation is developed in three dimensions as shown in Equation (5).  
\[q_{r}(t)=q_{r0}(t)+q_{r1}(t)i+q_{r2}(t)j+q_{r3}(t)k \tag{5}\]
## 6 Conclusions  
Using the concept of smart industrial control services, a new type of industrial control was developed and tested that allows for the complete detachment of control function and associated equipment to globally distributed, cloud-based software control services. A SICS controller is operated by a classic IEC61131-3 control program, thus ensuring the interoperability and industrial compatibility of the control system. The application of the service paradigm for industrial control functions significantly increases flexibility, meets industry 4.0 requirements such as changeability, reconfiguration and autonomy, and enables new business models to lease control functions.  
Examples are presented that make clear the distinctions between general and methodological issues of a SICS.  
For testing and evaluation, prototypical implementations were deployed for a purely server-based SICS controller and for a mixed client/server-based SICS controller. Both SICS controller types were successfully tested in the context of application scenarios from production automation. An evaluation of the SICS applications showed that simple technical processes with process times of greater than 150 ms can already be controlled reliably over the Internet.  
With SICS, previous hardware-oriented and centralized procedures for the control of automated devices, machines and systems (e.g., PLC controllers) can be distributed and used transparently for uncritical real-time conditions (e.g., environmental processes, logistics processes, energy processes, simple assembly processes) through IP-network-distributed software functions.  
Among other things, further research work in the project will focus on increased data security in the system through consistent encryption of communication for each SICS component; reduction of the latencies for process data communication between the SICS controller; the automation devices through optimized web protocols; the use of TSN as a deterministic IP network; examination of the practicability (long-term testing) of a SICS control system for climate control and lighting control of an office building in everyday life; investigating the application of block chain and distributed ledger technologies for the identification management in a SISC control system; and last but not least, testing the business model for the leasing of control functions by an IoT platform with integrated smart SICS services.  
**Author Contributions:** [PERSON] authored the main text and [PERSON] added some parts (e.g., testing a SICS-SMM controller), gave recommendations and suggestions and re-edited the text.  
**Funding:** A part of this research work was funded by Federal Ministry for Economic Affairs and Energy, grant number IGF 18354N.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### RNA-Sequence  
Libraries were generated from 1 ug of total RNA using an Illumina TruSeq stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Each library was sequenced on a Novaseq 6000 (Illumina) with a NovaSeq 6000 S4 Reagent Kit v1.5 (Illumina, San Diego, CA, USA) using 101 base pair end. The reads were mapped on the mouse genome (mm10) with HISTAT2 and read numbers were counted by Stringtie. Differentially expressed genes were determined by exactTest using edgeR after normalization. RNA sequencing data were deposited in the Gene Expression Omnibus under accession no. GSE183159. Pathway analysis was performed using DAVID Bioinformatics Resources [23; 24] and Ingenuity Pathway Analysis (IPA).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gene Name** & **Forward (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Reverse (5\({}^{\prime}\)-3\({}^{\prime}\))** \\ \hline _Actb_ & GGCTGATTTCCCCCTCCATCG & CCAGTTGGAACAATGCCATGT \\ _Myh7_ & AGGGCGACCTCCAACGAT & CAGCAGACTCTGAGGCCTCT \\ _Myh2_ & ACTTTGGCACTACGGGGGAAC & CAGCACACATTCTGATCAGCTC \\ _Myh4_ & GCTTGAAAAGGAGGAGGAGG & CCTCCTCAGCAGCTCTCTCTCTG \\ _Myh1_ & TACTACGCGCACCTGAACG & TGCCTCTCTCAGCTCTCTCTCAT \\ _Irs2_ & CTGCGCCTCTCTCTCCAAAGTG & GGGGCATGGGCATGGC \\ _Nr4a1_ & TTGAGTTCGCAAGCCTACC & GTGTGATCCCATGAAGGTG \\ _Nr4a2_ & GTGTTGACGGCCGAGATG & TGGCAGAATTCTGTTGGGT \\ _Nr4a3_ & TCAGCCCTTTTTGAGCTGTT & TGAAGTCGCATGGC
# Survivability Prediction of Open Source Software with Polynomial Regression
## 4 Results and Analysis  
### Target Application  
To assess our proposed method, we utilized OSS employed in KakaoTalk. It is a mobile messenger app in South Korea, with approximately 50 million users, corresponding to a market share of 94.4%. We selected OSSs used in this application to focus on it commonly used in real software products. The iOS version of KakaoTalk employs a total of 121 OSS projects. However, we restricted our selection to those with recent open source activity within the last 2 years, resulting in the selection of 24 OSS projects meeting this criteria. Other environmental factors were set, as shown in Table 3.  
As shown in Table 4, after constructing and evaluating models with polynomial degrees of 2 to 5, it was found that all degrees except 2 exhibited very high RMSE values, indicating a high level of error. Furthermore, the degree of 2 showed the lowest RSE value, which means that the model represents the variability between features and the target well. Therefore, we set the model degree to 2.
## 5 Simulation Results
### AC Load Step-Change  
The second considered event is an AC load step-change from 25 kW to 50 kW on port 3, occurring at 0.4 s. Since a small, few-milliseconds-long transient on three phase variables can be hardly appreciated, the port 3 voltage module is shown in Figure 7. On the contrary, to appreciate the current tracking effectiveness during fast transients, phase currents are shown in Figure 8a,b. The same considerations reported for the DC load step-change apply, with particular reference to voltage tracking dynamics, momentary loss of the sliding condition, reaching phase, and re-enforcement of sliding condition. In addition, it can be noted that, since the difference between AC voltage and internal DC link voltage is larger than the difference between DC port voltages and internal DC link voltage, the maximum current derivative that can be impressed by the converter on the AC port currents is higher, resulting in a shorter reaching phase (Figure 8b).
# Leveraging User Comments for Recommendation in E-Commerce
## 5 Experimental Results
### Experiment 4--Impact of Imputation  
In this experiment, we show how imputation has an impact on the recommended results. Several strategies have been proposed to deal with missing values in the user-item rating matrix. Among them, four popular ones are:  
* No imputation. If user \(u_{i}\) has not provided a rating for product \(p_{j}\), \(r_{ij}=\)0. That is, a missing value is treated as 0.
* Imputation by least. Each missing value is replaced with the least positive score. For the case of scores being 1-5, a missing value is replaced with 1.
* Imputation by mid. Each missing value is replaced with the middle of positive scores. For the case of scores being 1-5, a missing value is replaced with 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **CFUCC** & **SCC** & **ICRRS** & **CFCB** & **BiFu** \\ \hline MAE & **1.024** & 1.141 & 1.169 & 1.218 & 1.126 \\ RMSE & **1.331** & 1.555 & 1.650 & 1.641 & 1.516 \\ Time(s) & **194.2** & 6154.9 & 7507.9 & 18826.3 & 24075.3 \\ \hline \end{tabular}
\end{table}
Table 6: Performance comparisons among different methods for Experiment 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **CFUCC** & **SCC** & **ICRRS** & **CFCB** & **BiFu** \\ \hline MAE & **0.709** & 0.835 & 0.998 & 0.830 & 0.837 \\ RMSE & **0.996** & 1.168 & 1.419 & 1.235 & 1.202 \\ Time(s) & **51.9** & 945.3 & 358.5 & 373.7 & 660.2 \\ \hline \end{tabular}
\end{table}
Table 4: Performance comparisons among different methods for Experiment 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **CFUCC** & **SCC** & **ICRRS** & **CFCB** & **BiFu** \\ \hline MAE & **0.791** & 0.957 & 0.954 & 1.757 & 1.789 \\ RMSE & **1.224** & 1.323 & 1.374 & 1.948 & 2.041 \\ Time(s) & **111.7** & 2495.5 & 4187.9 & 8348.9 & 13492.2 \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparisons among different methods for Experiment 2.  
* Imputation by mean. Each missing value is replaced with the mean value of all the positive ratings in the given user-item rating matrix.  
Table 7 shows the performance comparison of these four strategies for the Amazon Digital Music dataset. As can be seen, CFUCC is hardly affected by imputation. The reason is that CFUCC uses user comments, instead of rating scores in the user-item rating matrix, to generate and cluster item vectors. Therefore, it is more resistant to the noise caused by imputation strategies. For the other methods, the recommended results depend on the rating scores in the matrix, and thus, they behave differently with different imputation strategies.
## 4 Implementation and Results
### Qualitative Results  
In this subsection, we present the experimental results through visualizations that highlight the performance of our defect detection model. However, due to confidentiality concerns, we cannot showcase images from the industrial dataset in our visualizations. We instead focus on the GDXray dataset to demonstrate the performance of our defect detection model. Specifically, we highlight the effectiveness of the ResNet backbone pretrained with X-ray images using the SimSiam approach. Through the visualization in Figure 4, we aim to provide insights into the practical application and effectiveness of our model in identifying defects in industrial scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Backbone** & **Pretraining** & **(m)AP** & **AP-Critical** \\ \hline \multirow{4}{*}{Industrial} & \multirow{2}{*}{ResNet} & ImageNet & 88.6 & 94.4 \\  & & X-ray SimSiam & 89.6 & 94.8 \\ \cline{3-5}  & & \multirow{2}{*}{Swin-T} & ImageNet & **91.3** & 94.5 \\  & & X-ray SimMIM & **91.3** & **95.5** \\ \hline \multirow{4}{*}{GDXray} & \multirow{2}{*}{ResNet} & ImageNet & 95.7 & - \\  & & X-ray SimSiam & **96.0** & - \\ \cline{1-1} \cline{3-5}  & & ImageNet & 94.0 & - \\ \cline{1-1} \cline{3-5}  & & X-ray SimMIM & 94.6 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fine-Tuning results on defect detection for manufacturing datasets. The fine-tuning performance of the Faster R-CNN model was evaluated on two datasets, the industrial target dataset and the GDXray dataset, using ResNet and Swin-T backbones with different initial weights (ImageNet, X-ray SimSiam, and X-ray SimMIM). Mean Average Precision (mAP) is reported for the industrial dataset and Average Precision (AP) for the GDXray dataset, designated as (m)AP. This notation is used because the industrial dataset contains two categories of defects, while the GDXray dataset does not categorize defects. Additionally, AP-Critical is reported for the industrial dataset, indicating the accuracy of the model in detecting critical defects.  
Figure 4: Results of defect detection on the GDXray dataset. This figure illustrates the original X-ray image, ground-truth annotations, and the predictions of the model.