esa-sceva/satcom-chunk-collection huggingface dataset:
# Proving Properties of [PERSON]'s Algorithm for Mutual Exclusion of N Processes
## 1 Introduction
The adopted approach was successfully exploited to check exhaustively, under atomic and non-atomic registers, the properties of [PERSON]'s basic algorithm and some of its variants in [10; 22], also when the solution for two processes is used as the arbitration unit in a general and standard tournament binary tree (TT) organization [16; 23; 24]. The approach was also successfully applied to prove properties of several algorithms for \(N\geq 2\)[25; 26]. Three specific techniques are considered and compared in [7] for modeling non-atomic registers. In a recent paper [1], [PERSON] proposed a generalization of his basic algorithm for two processes to cover \(N\geq 2\) processes in a fair way. The new algorithm will be referred to as Dekker-N. To the best of our knowledge, Dekker-N was only studied by the author using informal reasoning and atomic registers. An original contribution of this paper is a formal specification of Dekker-N by TA and its thorough model checking when atomic registers or non-atomic registers are used. This paper demonstrates that the Dekker-N is effectively a correct algorithm under atomic registers. It fulfills all the [PERSON] stated mutual exclusion requirements and guarantees fair behavior of processes through a linear overtaking factor. The paper also shows, though, that Dekker-N becomes incorrect when not-atomic registers are used. A specific contribution of this paper is to prove that through fencing. Specifically, making a single shared communication variable safe under read/write, Dekker-N becomes correct and fair also under non-atomic registers. The paper is structured in two parts. In the first part (Section 2), the abstract structure of a mutual exclusion protocol and the modeling approach based on Uppaal are presented. The Dekker-2 algorithm is used as a working example. The modeling issues based on atomic and non-atomic registers are discussed. After that, the verification of all the properties of a mutual exclusion model using the Uppaal model checker with a few Timed Computational Tree Logic (TCTL) queries is detailed. In the second part (Section 3), [PERSON]'s algorithm for \(N\geq 2\) is thoroughly investigated. First, a Uppaal model is derived under the hypothesis of atomic registers, and its properties are verified. Then, a model for Dekker-N with non-atomic registers is presented and deeply model checked. It is shown that Dekker-N is correct for two processes with non-atomic registers, but it is incorrect for \(N>2\). The effectiveness of the Uppaal-based approach is then demonstrated by showing how the general Dekker-N can be made correct for any number of processes and non-atomic registers. Finally, conclusions are drawn with an indication of future work.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 3 Materials and Methods  
### Dataset Preparation  
The customer churn dataset is an open-source dataset [17] that contains 21 features and 3333 observations. The feature 'Churn' shows customer churn or non-churn based on existing conditions. Approximately 14.5% of the 'Churn' is 'T' label, and 84.5% of 'non-churn' is 'F' label. Table 1 shows the data features. In this experiment, 80% (2666 instances) and 20% (667 instances) of the dataset are used for training and test datasets, respectively.
## 3 Construction of Recognition Model
### ACmix Module  
The ACmix module [35], proposed by researchers at Tsinghua University, introduces a new deep-learning architecture specifically designed to enhance the ability of convolutional neural networks to process complex features. This module combines convolution and self-attention operations, as illustrated in Figure 8. Initially, feature maps are processed through three \(1\times 1\) kernel convolutional layers, producing 3 \(\times\) N intermediate feature maps. Subsequently, based on the initial feature conversion, two operations are executed: a convolution operation via a lightweight fully-connected layer to transform feature maps and generate k\({}^{2}\) new feature representations; and the introduction of a self-attention mechanism to compute attention weights for the query, key, and value, followed by their fusion. Finally, the outcomes of the convolution and self-attention processes are integrated with varying weights at the ACmix module's conclusion using the following formula:  
\[F_{out}=\alpha F_{att}+\beta F_{conv} \tag{4}\]  
where \(\alpha\) and \(\beta\) are learnable parameters; \(F_{att}\) is the weight obtained by self-attention operation; \(F_{conv}\) is the feature obtained by convolution operation; and \(F_{out}\) is the fusion output.  
Figure 8: ACmix module.  
In this research, ACmix is integrated into the ResNet50 model to enhance the traditional convolutional network's ability to process high-level features. The ACmix model introduces a self-attention mechanism at the network's early stages, enabling the capture of more comprehensive information from sEMG signals while maintaining sensitivity to local features. The integration of the ACmix module into ResNet50 occurs as follows: The ACmix module is inserted immediately after the first convolutional layer (conv2d) within the first residual block (layer1) of the ResNet50 network, as shown in Figure 6. Initially, the original feature maps are processed by ResNet50's initial convolutional layer, then undergo deep feature extraction and transformation within the ACmix module. The output from the Acmix module serves as the input for the subsequent residual block, facilitating close collaboration between ACmix and ResNet50's other modules. Through this strategy, a more robust and adaptable model is developed, achieving an enhanced performance across various complex grayscale image vision tasks.
# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 3 Results
### Performance Evaluation Using Hydrological Model
For the monthly time step (Figure 11), the CHIRPS generally generates higher correlation (\(R^{2}\_TQ\) = 0.50 CHIRPS, 0.43 ERA5), lower mean error (_MAE_TQ_ = 4.0 mm 10y\({}^{-1}\) CHIRPS, 5.0 mm 10y\({}^{-1}\) ERA5) and a lower amount of missed trend significance (8% misses for CHIRPS, 11% misses for ERA5) than ERA5. The general monthly _TP_ performance (lower ERA5 _MAE_TP_) is not followed by the general monthly _TQ_ performance (lower CHIRPS _MAE_TQ_). A relatively strong negative _TQ_ in July is generated by both GPPs. The observed statistically significant positive runoff trend in April was also generated by both products, but this trend is dominantly influenced by the air temperature increase and not the precipitation increase. The GPP forcing can outperform the runoff trends with measured precipitation forcing. Runoff trends from simulations show that CHIRPS slightly outperforms ERA5 with lower bias and deviation in annual, seasonal and monthly runoff trends, with less missed monthly trend significance. CHIRPS performs better in catchment b1, while ERA5 in catchment b3. ERA5 generates inconsistent bias between _TQ_ and _TP_ in catchment b1. Both GPPs perform worse in the warm season, reflecting higher _TP_ uncertainty. A relatively strong negative _TQ_ in July is generated by both GPPs. Compared to the model forced with measured precipitation (MEAS), the best-performing GPPs can generate lower _TQ_ uncertainty at annual and seasonal scales and can outperform MEAS in capturing monthly runoff trends.
## Appendix A Adaptive Dimension Reduction  
The ADR procedure which maps into the relevant space endowed with a reduced dimension \(n\) adopts centered features and starts by implementing a PCA, that is by keeping just the eigenvectors associated with the \(n\) largest eigenvalues of the covariance matrix \(X^{T}X\), where \(X\) is a \(M\times N\) matrix representing the data set with each row corresponding to a patient \(\mathbf{x}_{i}\). The aforementioned diagonalization is implemented through a \(N\times N\) rotation matrix \(R\), whose columns correspond to eigenvectors \(r_{j}\) of the covariance matrix \(X^{T}X\). Denoting by \(Q^{(1)}\) the \(N\times n\) matrix composed by the selected eigenvectors, we obtain a PCA dimensional reduction represented by the \(M\times n\) matrix \(Y^{(1)}=XQ^{(1)}\). The first iteration implements a k-means clustering concerning the points coordinate \(\mathbf{y}_{i}\) in \(Y^{(1)}\), whose output is expressed by the \(M\times k\) matrix \(H^{(1)}\), where \(k=n+1\) is an optimal value, with elements \(H^{l}_{i}=1\) if \(\mathbf{y}_{i}\varepsilon C^{(1)}_{l}\), \(l=1,\ \ldots,\ k\), otherwise \(H^{l}_{i}=0\). An important role is played by clusters centroids in the original full dimension space, whose coordinates are expressed by \(\mu^{(1)}=X^{T}H^{(1)}{(H^{(1)T}H^{(1)})}^{-1}\), thus yielding elements \(\mu^{l}_{j}=\sum_{i\ \varepsilon C^{(1)}_{l}}x^{j}_{i}/|C^{(1)}_{l}|\), with \(|C^{(1)}_{l}|\) standing for the cardinality of patients' cluster \(C^{(1)}_{l}\). The last step looks at the covariance matrix of clusters centroids, weighted by their cardinality, thus reading \(S^{(1)}=\mu^{(1)}{(H^{(1)T}H^{(1)})}\mu^{(1)T}\), whose PCA leads to the new iteration by using \(Q^{(2)}\). We will follow the iteration cycle until the Frobenius norm \(||Q^{(a)}-Q^{(a+1)}||_{F}<\varepsilon\), with \(\varepsilon\) a small preset parameter equal to 0.001 in the Rdimtools package when it is not specified by the user, as in our case motivated by our use of scaled features.  
The preferred embedding in the two-dimensional plane is caused by the new weight attributed just to the in situ component in the three-dimensional case shown in Figure A1. Indeed, it is possible to recognize the embedding presented in Figure 2 in the plane \(\mathbf{y}=\left(y^{1},y^{2}\right)\), while patients accumulate approximately along two parallel planes in the third dimension because of the values assumed by the new feature listed in Table 1, where just a few cases are referred to G1, G2 and G3. Moreover, the in situ component does not influence the therapy choice, even if the variance explained is 61.43% compared with 45.24% obtained in the two-dimensional embedding, thus confirming its noise content for our aims.
## 3 Physicochemical Study of Particles and Coated Samples
### Electrochemical Studies  
Electrochemical impedance spectroscopy (EIS) measurements were carried out using a Gamry 600+ Potentiostat in a Faraday cage to avoid electromagnetic interference. The experiments were performed in triplicate, in the frequency range of 50 kHz to 5 mHz in 0.05 M NaCl. The spectra were obtained under open circuit potential using a sinusoidal perturbation of 10 mV (rms) in a 3-electrodes electrochemical cell. It consisted of a Pt spiral serving as counter electrode-, a saturated calomel electrode (SCE) as reference electrode-, and the working electrode (coated steel samples). Studies were carried out on intact coatings and previously damaged coatings. The damage was a round defect of approximately 200 \(\upmu\)m, created with a sharp knife. The size of the defect was controlled by optical microscopy. The aim was to achieve a well-controlled fast-track for corrosion propagation and its inhibition.
## 5 Evaluation and Discussion
### Experimental Setup and Datasets  
The TKIFRPM and MTKPP approaches are implemented in Python 3.0 to perform the comparative evaluation. The three benchmark datasets, namely mushroom, accident, and chess, are used for both algorithms. The detailed characteristics of the used datasets are presented in Table 7. The comparative evaluation is carried out using \(K\), \(\theta_{r}\), and \(T_{b}\), where \(K\) presents the topmost patterns, \(\theta_{r}\) represents is periodicity, and \(T_{b}\) represents the transaction buffer size. The different \(K\) = {100, 200, 300, 400, 500} values are used in the experimental setup to discover the topmost patterns. The fixed value of periodicity \(\theta_{r}=15\) is used in every experiment for each of the datasets to compare the runtime on each iteration. The transaction buffer \(T_{b}\) = {3K for mushroom, 30K for accident, and 1.5K for chess} sizes (i.e., size and number of transactions of the dataset) for recording online committed transactionsis used to simulate an online dataset environment. The experiments are performed in three iterations on every dataset. The variable size of the transaction buffer is used to conclude every experiment in each of the three iterations due to the size of the datasets, which varies in terms of the number of transactions. The transaction buffer \(T_{b}\) size is kept at 3K transactions for the mushroom dataset, 30K transactions for the accident dataset, and 1.5K transactions for the chess dataset. An individual transaction is read from the dataset and recorded in the transaction buffer in every transaction, and when the transaction buffer is found full it is copied to the ISI-tables.
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
## 6 Conclusions  
A novel filtering method, the proposed filter, for Synthetic Aperture Radar (SAR) interferograms with the shearlet threshold filtering based on the coherence and look number of the interference phase was proposed in this paper. This method analyzes the statistical characteristics of Interferometric Synthetic Aperture Radar (InSAR) interference phase, and combines the phase noise standard deviation with shearlet filter, which relates the filtering threshold to the coherence and look number of the interference phase. Verification experiments show that the proposed filter offers more accurate results and clearer interference fringes, and can improve the filtering precision. The proposed filter achieves very good filtering effects even in low coherence areas, and it has important application value for InSAR phase unwrapping and InSAR data processing. This will benefit the InSAR applications in seismic deformation, volcanic activity, glacial drift, urban settlement and landslides.  
The work presented in the paper was supported by the Nature Science Foundation of China (No. 41531068, 41371335, 41274010, and 41671356), Nature Science Foundation of Hunan (No. 14J)2131), Science research project of Hunan Provincial Department of Education (No. 13C345); Hunan Provincial Department of Education Science Research Key Project (No. 15A074); Yongzhou guiding science and technology projects application of InSAR in Ground Subsideso Observation.  
[PERSON] conceived the idea, designed the experiments, and wrote and revised the paper; [PERSON] performed the shearlet experiments and revised the paper; [PERSON] analyzed the experimental results and discussed the paper; [PERSON] analyzed the experimental results and revised the paper; [PERSON] performed the simulated experiments; [PERSON] discussed the paper and drawn some of the figures.  
The authors declare no conflict of interest.
# Intelligent Monitoring of Data Center Physical Infrastructure
## 1 Introduction
This paper presents a new intelligent monitoring method for data center physical infrastructure parameters, based on multilayer node and event entities processing. The method highly improves information about the events in a specific node. Its major advantage is that in the case of a large number of nodes, it considerably reduces the number of identified events (alarms/alerts) that might be the main cause. The importance of this research lies in the fast and more detailed intelligent analysis of all parameters of the physical infrastructure, including events that may affect energy overconsumption. When designing a computer data center it is vital to ensure the necessary main and backup energy supplies, an uninterruptible power supply (UPS), energy support for the system and communication cabinets, and cooling device power supplies, because costs are an important factor when designing the support infrastructure [11; 12]. Compared to other approaches [15; 16], the proposed method quickly provides filtered information about individual events in nodes and fast main event identification, as well as comprehensive monitoring of the IT data center infrastructure [17; 18].
## Case Study
### Strategic Verification
#### 5.2.2 Description of the Case  
As Figure 12 shows, an Institute of Electrical and Electronics Engineers (IEEE) system including four generations and two areas was taken as the power system model. The total load of the power system was 1450 MW. The system was separated into two zones (A and B) after the tie lines 6-11 and 7-12 were disconnected. Then, 70-MW responsible loads were set at bus 10 as the reserve for load shedding in the frequency control. Both traditional under-frequency load shedding (UFLS) and the proposed ALS approach were applied on the bus 10 loads.  
For the ALS approach, the frequency threshold was transmitted to the corresponding smart terminals in advance. The simulation platform was hooked to five smart terminals; thus, the loads at bus 10 were divided into five grades with different thresholds, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Load Type** & **Threshold (Hz)** & **Quantity for Action (MW)** \\ \hline Electro-thermal loads 1 (air conditioners, & & \\ refrigerators, and water heaters) & 49.7 & 10 \\ Electro-thermal loads 2 & 49.5 & 10 \\ Electro-chemical loads (electric cars) & 49.3 & 10 \\ Fundamental loads 1 (lighters, motors, and others & & \\ with no energy storage) & 49.0 & 20 \\ Fundamental loads 2 & 48.8 & 20 \\ \hline \end{tabular}
\end{table}
Table 2: Frequency thresholds and quantity of under-frequency load shedding (UFLS) via DR approach.  
Figure 11: Control scheme of active load shedding (ALS) in the proposed DR system.  
Figure 12: Simulation scenario of the power system.  
For the traditional UFLS approach, the parameter setting method referred to the standards of the China State Grid. The first grade should be set at 49.0 Hz and, after UFLS, the frequency should recover to a value between 49.5 and 51.5 Hz. For the tie lines cut off, this would lead to approximately 50 MW of power loss in zone B, where the quantities of UFLS were set as 30 MW and 40 MW to decrease the influences from the parameter setting strategy. Relevant settings are shown in Table 3.  
The delay for the utility from detecting the frequency exceeding the threshold to cut off loads via UFLS was assumed as 0.2 s, which is better than the actual performance of UFLS.
## 4 Angle Selection and Ray Categorization
### Neighbor Discovery  
Once oriented, a node announces its presence to neighbors using the selected channels (picked using the procedure in Section 4.1). To do so, a node first represents each beam in the set \(\kappa\) using the spherical coordinate system \((r,\theta_{i},\phi_{i})\), as shown in Figure 7, where \(r\) is the radius of TS, \(i=1,2,\ldots,|\kappa|\), and \(\theta\) and \(\phi\) are the angles measured from the transmitter's \(z\)-axis and \(x\)-axis, respectively. Moreover, we will use \(n_{T},i\) to refer to beam \(i\) that is transmitted by node \(n_{T}\). To announce its presence and establish communication links, a \"_hello_\" message is composed where different headers for each beam \(k_{i}\in\kappa\) are composed that contains: (1) the node depth \((z_{T})\), (2) the corresponding beam width \((\psi)\), (3) the transmission direction \(\left(\theta_{n_{T},i},\phi_{n_{T},i}\right)\), and (4) the local measured SS \((c_{T})\). Then for each angle, the corresponding \"_hello_\" message is transmitted announcing the presence of node \(n_{T}\) to its neighbors and supplying the receiving nodes with data to infer the type of signal, i.e., refracted or reflected.  
Figure 6: Conceptual 2D representation of the z-axis refinement process where bisecting beam 1 containing the surface reflected ray orients towards \(z_{i}\). Then, by transmitting Beams 2 and 3, and detecting their reflections, the node adjusts the direction of the z-axis to \(z_{f}\) that bisects the angle between the furthest edge rays of beams 1 and 2.  
Upon hearing a discovery message, a receiver \(n_{R}\) notes the values provided by the sender. In addition, node \(n_{R}\) notes the angle at which it received the signal relative to its own local coordinate system \(\left(\theta_{n_{R},i},\phi_{n_{R},i}\right)\), its measured depth \((z_{R})\) and the measured SS at the receiver \((c_{R})\). Such information is linked to each \"_hello_\" message to enable determining if a ray faced reflection at a later stage. Once all information is collected, node \(n_{R}\) projects the 3D environment into an equivalent 2D one, detailed next.
## 9 Stokes Space Polarization and Field Recovery Principle
### Polarization Recovery Techniques in the Direct Detection Stokes Vector Receiver
#### 9.1.3. Stoke Space Signal-Distribution-Based Polarization Demultiplexing
For instance, a 16-QAM implementation with three modules and the SVs being partitioned into three groups is depicted in Figure 22c. Furthermore, when the CSPR is set to 0 dB, each of the Stokes vector groups will produce a plane in the Stokes space at the transmitter, as illustrated in Figure 22d. Consequently, by following the aforementioned processes for the 4-QAM, any group of points can be selected at the receiver to determine the least-squares plane and, subsequently, the rotation matrix. Moreover, it should be noted that within each of the planes, the Stokes vector power (i.e., \(S_{0}\)) is maintained to be the same [11].
## 5 Experimental Setup
### Evaluation of Indicators  
RMSE and MAE. We evaluate recommendation performance using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Smaller RMSE and MAE values indicate closer predicted ratings to actual ratings. The mathematical expressions for RMSE and MAE are as follows:  
\[\begin{split}\text{RMSE}=\sqrt{\frac{1}{N}\sum_{u,j}(r_{u,i}-r_{ u,i})^{2}},\\ \text{MAE}=\frac{1}{N}\sum_{u,j}\lvert{\hat{r}_{u,i}-r_{u,i}}\rvert \end{split} \tag{18}\]Here, \(N\) is the number of ratings between users and items. \(\hat{p}_{u,i}\) is the predicted rating of item \(i\) by user \(u\), and \(r_{u,i}\) is the actual rating.  
BLEU and ROUGE. We use BLEU [41] (Bilingual Evaluation Understudy) and ROUGE [42] (Recall-Oriented Understudy for Gisting Evaluation) to evaluate interpretation performance. BLEU measures text accuracy: BLEU-1 for word-level accuracy and BLEU-4 for sentence fluency. ROUGE evaluates text quality, with ROUGE-1 and ROUGE-2 scores computing Precision, Recall and F-score for our model's reason generation task. Higher BLEU and ROUGE scores indicate better text quality. The mathematical expressions for ROUGE are as follows:  
\[ROUGE\_n/p =\frac{\sum_{u,i}\text{Count}(N)}{\sum_{u,i}\text{Count}(N_{S})} \tag{19}\] \[ROUGE\_n/r =\frac{\sum_{u,i}\text{Count}(N)}{\sum_{u,i}\text{Count}(N_{S})}\] \[ROUGE\_n/f =\frac{2\cdot ROUGE\_n/p\cdot ROUGE\_n/r}{ROUGE\_n/p+ROUGE\_n/r}\]  
where \(N\) denotes the \(n-gram\), i.e., the matching of \(n\) consecutive words in the generated text \(\hat{S}\) with the real text \(S\), where \(\hat{S}\) represents the generated text and \(S\) represents the real text. The mathematical expressions for BLEU are as follows:  
\[\text{BLEU-n}=BP\cdot\exp\left(\sum_{u,i}\omega_{n}\log p_{n}\right) \tag{20}\]  
where \(BP=\exp\left(1-\frac{L_{\hat{S}}}{L_{S}}\right)\), \(\omega_{n}\) is the weight, and \(p_{n}\) represents the accuracy of the generated text versus the real text at the \(n-gram\) level.