esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Aim of the Article  
The aim of this article is to introduce a hybrid CNN-LSTM-based method for removing muscle artifacts from EEG signals and to compare its effectiveness with other widely used approaches, such as linear regression and independent component analysis. A key aspect of this approach involves simultaneously recording additional EMG signals--representing muscle activity--alongside the EEG signals. The research focused on cleaning EEG signals containing steady-state visually evoked potentials. To that end, EEG and EMG data were recorded from 24 participants during SSVEP stimulation and muscle activity (specifically, jaw clenching). The proposed novel approach utilizes a CNN-LSTM for denoising the EEG signals. To train the model effectively, we developed a method for generating augmented EEG and EMG recordings, which resulted in a diverse training dataset.  
Our approach is novel in that it uses changes in the signal-to-noise ratio (SNR) as a method for evaluating artifact removal effectiveness, based on the SSVEP response. Specifically, we analyze whether the SNR increases after signal cleaning, which indicates successful noise reduction while preserving the relevant neural response evoked by visual stimulation. To the best of our knowledge, this use of SNR to quantitatively assess what has been removed versus what has been preserved during signal processing has not been reported in the SSVEP literature, making our proposed metric both original and useful. The analysis included assessing the strengths and weaknesses of each tested method in terms of removing muscle artifacts. Next, we compared the results obtained using the CNN-LSTM against traditional techniques. The quality of the cleaned signals was evaluated in both the time and frequency domains. A key focus was determining how well each method not only eliminates unwanted interference but also preserves crucial EEG components, particularly SSVEP.  
Key highlights and contributions:  
* The introduction of a novel CNN-LSTM-based approach for removing muscle artifacts from EEG signals while retaining essential components such as SSVEP.
* The introduction of SNR variation as a quantitative metric for evaluating artifact removal performance in the context of SSVEP signals.
* The evaluation of cleaned signal quality in both the time and frequency domains, with emphasis on preserving critical signal information.
* The comparison of CNN-LSTM performance with commonly used methods, including linear regression and independent component analysis.
* The development of an innovative strategy for generating augmented EEG and EMG data, enabling the creation of diverse training datasets.
* The validation of the proposed method using raw EEG and EMG signals recorded during controlled experiments with 24 participants.
* Practical relevance for brain-computer interface applications, medical diagnostics, and brain activity research.
* The identification of future research directions, including the integration of hybrid methods and real-time system implementation.
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 4 Experimental Results Analysis
### Comparison of Recognition Performance under Different Parameters
#### 4.2.2 Comparison of Recognition Accuracy for Different \(r\) Values  
Before fitting the Weibull model for each class, the distances between the correctly classified samples and the corresponding mean features during the training process needed to be sorted, and the Weibull model was fitted using the \(r\) largest distances of the samples after sorting. The \(\lambda\) value was set to 0.005, and the comparison of the effect of \(r\) on the recognition performance is shown in Figure 7.  
The experimental results showed that the recognition accuracy was optimal when \(r\) was 10-20, and gradually decreased when \(r\) was too large. This also verified the previous analysis of \(r\). Fitting the Weibull model with a large number of samples of known classes increased the probability that the known classes were recognized as unknown ones.  
Figure 6: Feature distribution under different values of \(\lambda\): (**a**) training features when \(\lambda\) was 0; (**b**) testing features when \(\lambda\) was 0; (**c**) training features when \(\lambda\) was 0.005; (**d**) testing features when \(\lambda\) was 0.005.  
Figure 7: Comparison of recognition accuracy for different \(r\) values.
## 3 Results
### Correlation Between Landscape Patterns and Surface Water Quality
#### 3.3.2 Landscape Configuration and Surface Water Quality
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Radius (m)**} & \multirow{2}{*}{**Indicator**} & \multicolumn{2}{c}{**Farmland**} & \multicolumn{2}{c}{**Forest Land**} & \multicolumn{2}{c}{**Grass Land**} & \multicolumn{2}{c}{**Urban Area**} \\ \cline{3-10}  & & **PLAND** & **LPI** & **PLAND** & **LPI** & **PLAND** & **LPI** & **PLAND** & **LPI** \\ \hline \multirow{4}{*}{\(1500\)} & COD & 0.09 & 0.13 & \(-\)0.35 & \(-\)0.27 & \(-\)0.46 & \(-\)0.50 & 0.29 & 0.22 \\  & NH\({}_{3}\)-N & 0.04 & 0.11 & \(-\)0.30 & \(-\)0.32 & 0.10 & 0.09 & 0.25 & 0.35 \\  & TP & \(-\)0.46 * & \(-\)0.33 & \(-\)0.20 & \(-\)0.15 & 0.25 & 0.18 & 0.41 & 0.49 * \\  & DO & 0.64 ** & 0.70 ** & \(-\)0.33 & \(-\)0.24 & \(-\)0.38 & \(-\)0.49 & \(-\)0.25 & \(-\)0.20 \\  & EC & \(-\)0.10 & \(-\)0.06 & \(-\)0.41 & \(-\)0.37 & \(-\)0.44 & \(-\)0.31 & 0.44 & 0.38 \\  & Score & \(-\)0.10 & \(-\)0.02 & \(-\)0.33 & \(-\)0.31 & \(-\)0.14 & \(-\)0.20 & 0.39 & 0.42 \\ \hline \multirow{4}{*}{\(2000\)} & COD & 0.06 & 0.04 & \(-\)0.32 & \(-\)0.28 & \(-\)0.50 & \(-\)0.48 & 0.46 * & 0.37 \\  & NH\({}_{3}\)-N & \(-\)0.01 & 0.03 & \(-\)0.33 & \(-\)0.21 & \(-\)0.01 & 0.09 & 0.35 & 0.51 * \\ \cline{1-1}  & TP & \(-\)0.45 * & \(-\)0.40 & \(-\)0.15 & \(-\)0.06 & 0.11 & 0.10 & 0.47 * & 0.62 ** \\ \cline{1-1}  & DO & 0.56 * & 0.61 ** & \(-\)0.33 & \(-\)0.26 & \(-\)0.37 & \(-\)0.58 & \(-\)0.01 & \(-\)0.08 \\ \cline{1-1}  & EC & \(-\)0.12 & \(-\)0.10 & \(-\)0.34 & \(-\)0.27 & \(-\)0.38 & \(-\)0.19 & 0.48 * & 0.48 * \\ \cline{1-1}  & Score & \(-\)0.14 & \(-\)0.11 & \(-\)0.31 & \(-\)0.22 & \(-\)0.25 & \(-\)0.27 & 0.51 * & 0.60 ** \\ \hline \hline \end{tabular}  
* Score is the comprehensive principal components* score. ** the correlation is significant at the 0.01 level;
* the correlation is significant at the 0.05 level.  
\end{table} 
Table 3: _Cont._
## 4 Discussions  
_Limitations of the Techniques: EDX, XRF, and LIBS_  
EDX connected with SEM is usually employed for the qualitative analysis of a material. The EDX depth profile is normally around 5 microns and its detection limit is \(\sim\)0.1 wt.%. Therefore, only those elements which are present in concentrations \(>\)0.1% can be detected in the EDX analysis. The major elements with concentrations \(>\)10% such as Si, Mg, K, Cu, Fe, and Ca are determined as major in all the analytical techniques as detected by EDX.  
XRF was used here to analyze the same samples. The XRF technique has a depth profile of about 100 microns and the detection limit is \(\sim\) 0.01 wt.%. Therefore, the results again indicate that when the ingredient weight% in the sample \(>\)0.01% and the sample size is greater than a few millimeters, then elements or constituents can be analyzed with better precision and would be quantified. The elements including Li, C and Na remained undetected due to the lower concentration of the element in the sample and also an XRF limitation. However, major elements with good concentration such as \(>\)10% were detected successfully.  
The LIBS qualitative/quantitative analysis of any sample, can be conducted without damaging the surface of the material. The LIBS depth profile is approximately hundreds of microns (\(\sim\)100 microns) with a detection limit of about 10-50 ppm. In the present study, the LIBS results are reliably showing a good detection limit. Interestingly, LIBS results are in reasonable agreement with that of the XRF and EDX, in the case of major-elements. However, EDX and XRF are quite deviating in the case of light elements such as Li, Na, and Mg due to their as-mentioned detection limits
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 3 Hardware Architecture  
### Modular Arithmetic-Logic Unit  
#### 3.1.1 Modular Addition/Subtraction  
The base of ECC algorithms is modular operations. Figure 3 shows the combinational architecture of modular addition/subtraction. This module could perform both addition and subtraction with the modularity of a prime number \(p\). The architecture is split into two branches. The left branch performs the operation without \(p\), and the right branch performs the operation with \(p\). When the \(sub\) signal is activated, the multiplexer at the left-side selects \(-b\), then \(<a-b>\) is performed, while the multiplexer at the right-side selects \(p\) and then the addition circuit performs \(<a-b+p>\). When the \(sub\) signal is inactivated, \(<a+b>\) is calculated on the left circuit, and \(<a+b-p>\) is calculated on the right circuit. The final value is selected depending on the carry out of the right branch. In this architecture, the carry save adders (CSAs) are used to reduce the logic delay of the three-operands additions.  
Figure 4 illustrates the architecture CSAs that are used in Figure 3. A three-operands CSA includes two layers. In the first layer, the corresponding bits from each operand are simultaneously added together using full adders (FAs). The results of the first layer is the sum bits [\(5_{0}...s_{n}\)] and the carry bits [\(c_{0}...c_{n}\)]. In the second layer, a ripple-carry addition is performed between the generated sum and carry from the first layer. The result of this  
Figure 3: (a) Modular addition/subtraction architecture. (b) Processing element architecture.  
Figure 2: ECDSA/EddSA hierarchy.  
layer is the final result of the three-operands addition. The delay of CSA is the total delay of two layers. The delay of the first layer is equal to the delay of a 1-bit FA. The delay of the second layer is the delay of the two-operands adder. Figure 5 illustrates the architecture of a conventional three-operands adder. Two operands are added in the first layer, and then the result is added with the third operands in the second layer. Figures 4 and 5 reveal that the conventional three-operand adder suffers more ripple carry delay from two consecutive adders while using the same resource as CSA.
## 2 Study Area and Data  
### Study Area  
The study area is located within the geographic coordinates of 35\({}^{\circ}\)28\({}^{\prime}\)N-37\({}^{\circ}\)31\({}^{\prime}\)N and 102\({}^{\circ}\)25\({}^{\prime}\)E-104\({}^{\circ}\)57\({}^{\prime}\)E, covering an area of 48,400 km\({}^{2}\) (Figure 1). It is located in the west of the Loess Plateau, at the junction of China's arid and semi-arid regions, with an elevation ranging from 1245 to 4622 m (Figure 1b). Lanzhou, Baiyin, Linxia, and Dingxi are major cities in this region. The Yellow River runs from southwest to northeast across the study area, and the Liujiaxia Reservoir is in the southwest of the region, just north of Linxia City. The climate is generally semi-arid, and rainfall mainly falls in the months from June to September. In the northeastern part of the study area, precipitation is relatively low compared to other parts of the study area (Figure 2). The annual average temperature in this region is 7.63 \({}^{\circ}\)C.  
The climate in this region is characterized by very steep gradients. In the northeastern part, there is a temperate continental arid climate, with annual precipitation of less than 200 mm. This climate is accompanied by low vegetation coverage, mostly barren land with some grassland and cropland patches scattered within it. Very sparse short bushes grow on this barren land. Crops are cultivated along the river valleys, where water conditions are favorable (Figure 1a). Besides barren land and cropland, grasslands are also very common in this region.  
The grasslands in the study area mainly consist of the following species: Stipa bungeana, _Stipa breeviflora_ [PERSON], and _Stipa grandis_ P. [PERSON]. The forests mainly include _Picea crassifolia_ Kom and _Sabine prezewalskii_ Kom. The shrubland mainly contains _Reaumuria_ _soongorica_, _Caragana licentiana_, and _C. opulens_. On the cropland, there are mainly _Zea mays_ L, _Triticum aestivum_ L, and _Solanum tuberosum_ L.  
In the northwest of the study area is Wushaoling Mountain, with its main peak at 3562 m above mean sea level. It belongs to the Qilian Mountains and represents the boundary between the arid and semi-arid regions in China. Jishi Mountain is in the southeastern part of the study area, with its highest elevation at more than 4000 m above mean sea level, and it is the border between the Qinghai-Tibet Plateau and the Loess Plateau.  
Figure 1: Land cover types and elevation of the study region. (**a**) Several land cover types are distributed in this arid and semi-arid region. The land cover data were obtained from the Finer Resolution Observation and Monitoring—Global Land Cover (FROM-GLC10) dataset, which provides land cover types of the whole globe at 10 m resolution. (**b**) The elevation of the study area ranges from 1245 to 4622 m. Nine meteorological stations are distributed at various elevations and land cover types in this region, two of which are located at elevations of more than 3300 m above mean sea level.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 3 Simulation Results
### Tests under Steady-State Conditions
#### 3.2.3 Unbalance
ROCOF measurement is fairly good (second only to the SV-F method), but it is worth highlighting that significantly lower \(\mid\)FE\(\mid\) and \(\mid\)RFE\(\mid\) can be obtained by adopting, as mentioned before, Hann weighting in filter design; values of 0.06 mHz and 0.01 Hz/s at 52 Hz are achieved in a worst-case scenario. Similar results, confirming the above discussion, can be found also in case of phase unbalance, thus reducing the angle of phase \(a\) by \(10^{\circ}\) with respect to purely sinusoidal positive sequence conditions. Since the unbalance level is higher in this case (5.6%), the errors are expected to be higher too. TVE increases but it is still very small (below \(1.8\cdot 10^{-2}\)%); Figures 11 and 12 show the \(\mid\)FE\(\mid\) and \(\mid\)RFE\(\mid\) values. It is worth highlighting that results show that the magnitudes of all the errors increase proportionally to the unbalance level; this has been extensively studied in in [34] for IEC-P, SV-F, and TF-based algorithms and analytical expression to predict their values are also available. This consideration applies with excellent accuracy also for the SV-IPDFT estimator, even though it is intrinsically nonlinear. Once again, the performance of SV-TF can be enhanced if the filters are obtained with the WLS approach (weights corresponding to the squares of the Hann window coefficients) and \(\mid\)FE\(\mid\) and \(\mid\)RFE\(\mid\) thus become lower than 0.12 Hz and 0.03 Hz/s, respectively, again reflecting the stronger impact of the disturbance with respect to the previous unbalance test case. Figure 10: Maximum \(|\)RFE\(|\) results under off-nominal frequency conditions in the presence of magnitude unbalance. Figure 9: Maximum \(|\)FE\(|\) results under off-nominal frequency conditions in the presence of magnitude unbalance.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 3 Results
### Performance Metrics and Evaluation
By examining these deviations, we can assess the effectiveness of our algorithms and identify areas where the implementation aligns closely with theoretical models or deviates from expected performance, represented in Table 3. This comparison not only validates the theoretical models but also helps in fine-tuning the algorithms for practical applications. The efficiency of key operations, including key generation, insertion, and verification, is crucial for the practical deployment of CONIKS. Table 4 summarizes the average time required for these operations under different key sizes. The results demonstrate that key generation and insertion operations are handled efficiently, with the time complexity remaining manageable for moderate key sizes. However, as the key size increases, there is a noticeable increase in the time required for insertion and verification. Scalability analysis evaluates how CONIKS performs with increasing numbers of keys. Table 5 presents the system's performance with different volumes of keys. The results indicate that, while the system scales well for small to medium volumes, there is a gradual \begin{table}
\begin{tabular}{l l l} \hline \hline
**Operation** & **Key Size (bits)** & **Time (ms)** \\ \hline Key Generation & 2048 & 1500 \\ Insertion & 2048 & 12 \\ Verification & 2048 & 15 \\ Key Generation & 4096 & 4000 \\ Insertion & 4096 & 20 \\ Verification & 4096 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The efficiency of key operations. Figure 3: Signing and verification time vs. key size. increase in processing time as the number of keys grows. The performance degradation is more pronounced with larger datasets. The performance of CONIKS is influenced by various system parameters such as hash function efficiency and Merkle tree depth. Table 6 provides an analysis of how different parameters affect operational performance. For example, increasing the hash function output size impacts hash computation time, while deeper Merkle trees affect verification times. To enhance system performance, various optimization techniques were applied. Table 7 summarizes the impact of these optimizations, including faster hash functions and parallel processing strategies. The results show significant improvements in processing times and overall system efficiency.
# Program Code Generation with Generative AIs
## 2 Related Work
_HumanEval_+ is 80 times larger than _HumanEval_ which enables more comprehensive testing and analysis of AI-generated code. With _HumanEval_+, ref. [15] evaluated the _functional correctness_ of program code generated by 26 different AI models which are based on GPT-4 [20], Phind-CodeLlama [21], WizardCoder-CodeLlama [22], ChatGPT [23], Code Llama [24], StarCoder [25], CodeGen [26], CODETS+ [27], MISTRAL [28], CodeGen2 [29], VICUNA [30], SantaCoder [31], INCODER [32], GPT-J [33], GPT-NEO [34], PolyCoder [35], and StableLM [36] with _pass@k_. Looking at _pass@1_, the top five performers were GPT-4 (76.2%), Phind-CodeLlama (67.1%), WizardCoder-CodeLlama (64.6%), ChatGPT (63.4%), and Code Llama (42.7%). DeepMind developed an AI model for code generation named _AlphaCode_[37]. On the coding competition website _codeforces.com_, the AI model achieved an average ranking in the top 54.3% of more than 5000 participants for Python and C++ tasks. The ranking takes _runtime_ and _memory usage_ into account. Ref. [38] evaluated GH Copilot's ability to produce solutions for 33 LeetCode problems using Python, Java, JavaScript, and C. They calculated the _correctness_ by dividing the passed test cases by the total number of test cases per problem. The numbers for _understandability_, _cyclomatic_ and _cognitive complexity_ were retrieved using SonarQube, but due to configuration problems, they were not able to analyze the _understandability_ of the codes generated in C. Concerning _correctness_, GH Copilot performed best in Java (57%) and worst in JavaScript (27%). In terms of _understandability_, GH Copilot's Python, Java and JavaScript program codes had a _cognitive complexity_ of 6 and a _cyclomatic complexity_ of 5 on average, with no statistically significant differences between the programming languages. In comparison to the aforementioned related work, our focus is to evaluate the Java, Python and C++ code produced by Codex (GPT-3.0), CodeWhisperer, BingAI Chat (GPT-4.0), ChatGPT (GPT-3.5), Code Llama (Llama 2), StarCoder, and InstructCodeT5+. For comparison, we also assess human-generated program code. We obtain the _correctness_, _efficiency_ and _maintainability_ for our program codes by measuring _time_ and _space complexity_, _runtime_ and _memory usage_, _lines of code_, _cyclomatic complexity_, _Halstead complexity_ and _maintainability index_. As a user usually does not generate code for the same problem several times, we evaluate _pass@k_ with \(k=1\), which is our metric for _correctness_. Finally, we analyze which of the _incorrect_ and the _not executable_ program codes have the potential to be easily modified manually and then used quickly and without much effort to solve the corresponding coding problems.
# Balanced Parallel Exploration of Orthogonal Regions
## 5 Analysis
### Combination of Work  
In this section, we will combine Lemmas 4 and 14 to get the total work for all movements in the algorithm. We will also combine the total amount of work completed by an agent to get the total work done by each agent using Lemmas 6 and 15.  
For the combination of these Lemmas and to use Lemma 15 for work of agents in room scan we assume \(N_{i}\geq 2D_{\max}k\). As \(C_{\max}\) is along a single dimension and \(D_{\max}\) is along two dimensions, \(D_{\max}\geq 2C_{\max}\). From Lemma 4, we get that the total work during the perimeter is \(O(kP_{i}+N_{i})\), and from Lemma 14 we get that the total work during all room scans combined is \(O(N_{i})\). Therefore,  
Corollary 2: _The total work done by all agents at the end of scanning room \(R_{i}\) is \(O(kP_{i}+N_{i})\)._From Lemma 6 agents perform work \(O(P_{i}+N_{i}/k)\) and from Lemma 15 each agent performs work \(O(N_{i}/k)\). Therefore,  
Corollary 3: _At the end of scanning room \(R_{i}\), each agent performs work \(O(P_{i}+N_{i}/k)\)._  
Theorem 4.2 (Work Balancing): _If \(P_{i}=(N_{i}/k)\), then at the end of scanning room \(R_{i}\), the total work of all agents is \(O(N_{i})\) and the work of each agent is \(O(N_{i}/k)\)._  
Proof: Since from Corollary 2 the total work is \(O(kP_{i}+N_{i})\), if \(P_{i}\leq c_{1}(N_{i}/k)\) for some constant \(c_{1}\), then \(O(kP_{i}+N_{i})=O(N_{i})\). From Corollary 3, the total work performed by each individual agent is \(O(P_{i}+N_{i}/k)\), with the above value of \(P_{i}\) this is \(O(N_{i}/k)\).