esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Experimental Results
### Analysis of Prediction Results of Missing Soil Heavy-Metal Values
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**SA-FOA-BP Neural Network**} & \multicolumn{4}{c}{**BP Neural Network Prediction Results**} \\ \multirow{2}{*}{\begin{tabular}{c} **Heavy** \\ **Metal** \\ **Elements** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Sample** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Actual** \\ **Value/** \\ **(mg/kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Predicted** \\ **Value/** \\ **(mg/kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Relative** \\ **Error** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Overall** \\ **Mean** \\ **Error** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Predicted** \\ **Mean** \\ **Error** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Relative** \\ **Error** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Predicted** \\ **Value/** \\ **(mg/kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Value/** \\ **Error** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Predicted** \\ **Value/** \\ **Error** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Mean** \\ **Error** \\ \end{tabular} } \\ \hline \multirow{4}{*}{Hg} & 1–10 & 0.191 & 0.208 & 19.46\% & & 0.183 & 27.57\% & \\  & 11–20 & 0.168 & 0.184 & 14.25\% & & 0.159 & 26.86\% & \\  & 21–30 & 0.172 & 0.167 & 18.36\% & & 0.175 & 28.66\% & \\  & 31–40 & 0.171 & 0.175 & 13.28\% & & 0.156 & 27.32\% & \\ \hline \multirow{4}{*}{Cd} & 1–10 & 0.367 & 0.365 & 17.14\% & & 0.332 & 27.31\% & \\  & 11–20 & 0.197 & 0.192 & 15.95\% & & 0.186 & 26.21\% & \\  & 21–30 & 0.274 & 0.257 & 16.44\% & & 0.226 & 25.95\% & \\  & 31–40 & 0.163 & 0.174 & 16.74\% & & 0.175 & 26.83\% & \\ \hline \multirow{4}{*}{Pb} & 1–10 & 23.251 & 24.459 & 15.84\% & & 24.248 & 27.24\% & \\  & 11–20 & 26.658 & 25.909 & 15.16\% & & 26.876 & 26.32\% & \\  & 21–30 & 27.685 & 28.440 & 13.39\% & & 25.855 & 26.81\% & \\  & 31–40 & 24.429 & 24.138 & 14.35\% & & 24.274 & 25.14 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of SA-FOA-BP neural network and traditional BP neural network prediction results. Figure 5: Comparison of measured Hg value and values predicted by different models. Figure 6: Comparison of measured Pb value and values predicted by different models. In Figure 8, the left image represents the iteration graph of the SA-FOA algorithm, while the right image represents the iteration graph of the traditional FOA algorithm. It can be observed that after 200 iterations, the SA-FOA algorithm has already calculated a value of \(-5\). However, under the same number of iterations, the traditional FOA algorithm converges slower than the SA-FOA algorithm. This indicates that the SA-FOA algorithm, compared to the FOA algorithm, has a more noticeable optimization effect for solving general function problems.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 3 Methodology
### Loss Function  
The model employs a composite loss function, which is a weighted sum of the CE loss and Dice loss, designated as the total loss:  
\[\text{L}_{\text{total}}=\lambda\text{L}_{\text{CE}}+(1-\lambda)\text{L}_{ \text{Dice}} \tag{15}\]  
Here, \(L_{total}\) represents the total loss, \(L_{\text{CE}}\) denotes the cross-entropy loss, and \(L_{Dice}\) refers to the Dice loss. For the purposes of our experiment, we set \(\lambda\) to 0.5.
## 5 Limitations and Future Studies  
One limitation of this study was the small sample size. In the future, studies should include more participants. Similarly, this study only focused on four arm movements. More movements, including legs and full-body exercises, should be tested. Additionally, it would be a more thorough investigation of SS if more SS muscles, such as the rotator cuff muscles, teres major, and serratus anterior, could be tested for %EMG peak activation.
## 3 Bi-Level Optimal Model of a Distribution Network Comprising a Micro Energy Grid
### Optimal Model of the Distribution Network
#### 3.1.3 Processing Method for Transforming the Multi-Objective Model into a Single-Objective Model  
A multi-objective problem cannot be visually compared with data values; instead, it must be transformed into a single-objective function. The judgment matrix is a method for the quantitative and qualitative calculation of weights that reflect the objective situation to a certain extent, and consider the importance of different users to each objective. In this paper, the judgment-matrix method [34] was used to transform the multi-objective function into a single-objective function. The original multi-objective function was transformed into the following:  
\[F_{1}=\text{min}(B_{1}F_{2}{}^{\prime}(t)+B_{2}F_{3}{}^{\prime}(t)+B_{3}F_{4} {}^{\prime}(t)]), \tag{8}\]  
where \(F_{2}{}^{\prime}(t)\), \(F_{3}{}^{\prime}(t)\), and \(F_{4}{}^{\prime}(t)\) are the normalized values of the objective function (i.e., transformed into the range [0, 1]). \(B_{1}\), \(B_{2}\), and \(B_{3}\) are the weight factors. The different dimensions of the objective function were eliminated based on the effect of the optimal result.  
The main goal of the judgment-matrix method was to determine the judgment matrix according to the hierarchical relationship between the goals. The criteria were formed as shown in Table 1.  
According to the problems outlined in this study, the goals were graded by importance. The importance of each index was judged by expert experience. According to the actual circumstances of distribution networks in China, the power-supply voltage quality can meet the national standard; therefore, reducing the operating cost is the primary concern of power enterprises. In this paper, the integrated operating cost of the distribution network was the first grade goal. The standard deviation of the voltage deviation in the distribution network was the second grade goal. The network's active power loss was the third grade goal. Combined with the above analysis, the judgment matrix was formed as follows:  
\[J=\left[\begin{array}{ccc}1&2&3\\ 1/2&1&2\\ 1/3&1/2&1\end{array}\right]. \tag{9}\]  
After matrix processing, the weight vector of the objective function was determined to be \(W=(0.5393,0.2974,0.1633)\).
# Automatic Correction of Indonesian Grammatical Errors Based on Transformer
## 6 Result and Discussion
### Impact of the Synthetic Data  
In the beginning, the impact of the semi-supervised method that could generate reliable training data was investigated in different frameworks. Table 3 shows the impact of the synthetic data with a bidirectional recurrent neural network (BRNN), convolutional neural network (CNN), self-attention network (SAN) and BPE without fine-tuning. A BRNN with an attention mechanism was investigated for Indonesian GEC, which was used to localized only on the nearby words, and the attention mechanism was reduced during the backward propagation, reporting 36.22 as the F1 score and 48.18 as the BLEU score. It is worth mentioning that the CNN and SAN have never been investigated with IGEC before. CNN-GEC with attention has the ability of parallel training, in which there is no need for sequential operations. Additionally, the CNN combines feature extraction and classification into one task, which makes the training process and decoding speed faster. This achieved  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Size** \\ \hline Embedding size & 256 \\ Batch size & 128 \\ Head attention & 8 \\ Encoder-decoder layer & 4 \\ Dropout ratio & 0.1 \\ Learning rate & 0.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Size of parameters.  
remarkable improvement in the F1 score (+07.31) and F1 score (+08.58). This result without fine-tuning outperformed a recent work on the IGEC framework, presented in [4]. SAN-GEC was based on a modified version of Transformer, which perceives the entire input sequence simultaneously. SAN-GEC increased the F1 score to 55.28 and F1 score to 59.91. This finding demonstrates the efficiency and reliability of the semi-supervised method for constructing training data for the Indonesian GEC task and addressing the challenge of a lack of training data. Figure 5 shows a comparison of these models.
## 3 Findings
### Tools/Models  
While a majority of these articles have conducted manual coding to analyze the visual and textual contents, it is interesting to note that most of these studies have not used any type of software tool (or algorithm) to assist their analysis. Popular tools that have been prescribed for content analysis in hate speech detection to reduce datasets by classifying them into relevant categories (that are specific to the topic under investigation) is not evident in their usage in selected literature. Rather, we find much use of statistical analysis with IBM SPSS [43] in many research investigations [24; 29; 34; 40] in comparison to R [44] and SAS [45] which have recently emerged as strongholds of statistical measures. In having said this, we also witnessed a collective usage of several emergent modelling tools in five studies [24; 35; 37; 39; 41]. For instance, [PERSON], et al. [39] developed a NLP (natural language processing) model to analyze the sentiments of the data corpus and used DataTurks platform for manual data annotation. LIWC (Linguistic Inquiry of Word Count) is used by [PERSON], et al. [33] to identify health related topics as it assisted in revealing thoughts, feelings, personality and motivations within a given corpus. [PERSON], et al. [35] used Apache Spark and TensorFlow to implement their proposed framework. They further employed VADER (Valence Aware Dictionary and sEntiment Reasoner), CoreNLP and TextBlob for obtaining more accurate sentiment analysis in the corpus. The application of Textometrica on word co-occurrence analysis has been studied by [30]. NodeXL was used by [PERSON] and [PERSON] [36] to construct social network graph for the dataset and further examine the clusters formed as a result of retweets, replies and mentions using Clauset-Newman-Moore clustering algorithm. After experimenting with different machine learning algorithms, [PERSON], et al. [41] have used ULMFiT (Universal Language Model Fine-Tuning), a recurrent neural network-based model, to acquire better contextualized representations of words in a corpus. ULMFiT employs a language model (LSTM: long short-term memory) for processing sequential sequences using iteratively averaged weights (referred as weight-drop) to efficiently analyze large volumes of textual data (e.g., AWD-LSTM). Thus, NLP tools are gathering attention by researchers to enable them to delve into a variety of optimization strategies for language modelling and sequence learning tasks.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques
### Homomorphic Encryption  
Homomorphic encryption is a type of encryption that allows computations to be performed on encrypted data without having to decrypt it [92]. In an FRS, homomorphic encryption allows for data to be encrypted and shared between multiple parties while allowing for the computation of recommendations based on the encrypted data [93]. There are two main types of homomorphic encryption used in FRSs: fully homomorphic encryption (FHE) and partially homomorphic encryption (PHE). FHE allows for the computation of arbitrary functions on encrypted data, while PHE allows only specific computations, such as addition or multiplication [94].  
Several studies have used homomorphic encryption in recommendation systems with promising results. For example, a recent study [95] developed a privacy-preserving system for matrix factorization using FHE. Another study [96] proposed a privacy-preserving user-based recommendation system using homomorphic encryption. The main benefit of homomorphic encryption in FRSs is that it provides a high level of privacy and security for users, as their data are encrypted and remain confidential [97; 98]. However, the main limitation of homomorphic encryption is that it is computationally expensive, which can reduce the system's overall performance.
## Materials and Methods
### Determination of Viscosity  
The viscosity of the different NADES (Bet/Sor/W, Bet/Suc/Pro/W, Gly/Glu/Sorb/W, Pro/Gly/Sorb/W) was determined using a MCR102 Modular Compact Rheometer ([PERSON]) fitted with a parallel plate geometry with 50 mm of diameter (PP50, [PERSON]) and  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{**NADES Composition**} & \multicolumn{1}{c}{**Molar Ratio**} & \multicolumn{1}{c}{**Abbreviation**} & \multicolumn{1}{c}{**Price**} & \multicolumn{1}{c}{**Viscosity (mPa-s)**} \\  & & & \multicolumn{1}{c}{**(EUR kg\({}^{-1}\)) a**} & \multicolumn{1}{c}{**30 \({}^{\circ}\)C**} \\ \hline Betaine/Ethylene glycol & 1:3 & Bet/EG & 95.34 & 48.63 \(\pm\) 0.42 \\ Betaine/Sorbitol/Water & 1:1:3 & Bet/Sor/W & 81.07 & 747.40 \(\pm\) 14.83 \\ Betaine/Sucrose/Proline/Water & 5:2:2:21 & Bet/Suc/Pro/W & 170.09 & 853.70 \(\pm\) 39.37 \\ Betaine/Glycerol/Sucrose/Water & 2:3:1:5 & Bet/Gly/Suc/W & 109.47 & 2246.77 \(\pm\) 45.27 \\ Glycerol/Glucose & 4:1:1 & Gly/Glu & 118.13 & 5258.47 \(\pm\) 322.86 \\ Glycerol/Glucose/Sorbitol/Water & 1:1:1:3 & Gly/Glu/Sorb/W & 68.95 & 1460.03 \(\pm\) 53.23 \\ Glycerol/Proline/Sorbitol/Water & 1:1:1:13 & Pro/Gly/Sorb/W & 190.55 & 23.70 \(\pm\) 1.44 \\ \hline \hline \end{tabular}  
* The price of systems was estimated according to the website of Merck (Germany) for individual components. The price of proline was estimated according to the website of Alfa Aesar (Germany; United States).  
\end{table}
Table 1: Composition, molar ratio, abbreviation, and viscosity of the prepared NADESs.  
1 mm of gap. Measurements were conducted in triplicates. Viscosity of Gly/Glu, Bet/EG, and Bet/Gly/Suc/W was determined previously [12; 13].
# Study on Penetration Sensing Method Based on Pool Oscillation and Arc Voltage during Pulsed GMAW
## 3 Results and Discussion
### The Link between the Voltage Signal and the Weld Pool Behavior
Similarly, researchers have previously reported the non-proportional relationship between arc voltage and arc length [19; 20; 21], while there were also significant positive correlations between arc voltage and arc length. Therefore, it can be inferred that the change of voltage during the base time (\(\Delta\)U) can be used to sense the penetration status which is similar to the amplitude of the weld pool oscillation (described in Section 3.1). Figure 15 shows the curves of \(\Delta\)U with time with different penetration status. \(\Delta\)U was plotted with welding time at different penetration status, as shown in Figure 15a-g. The trend of mean value and the error analysis of \(\Delta\)Us at different penetration status are shown in Figure 15g. With the decrease in \(\Delta\)U, the status transform from the full penetration to the partial penetration, and an abrupt transition occurred of \(\Delta\)U in the transition from full to critical penetration status which was similar to the trend of the oscillation amplitude. \(\Delta\)U of full penetration was all greater than 2 V, while which of partial penetration was less than 1 V. As for critical penetration, the uncertainty of \(\Delta\)U was the maxima relative to which of full penetration and partial penetration as shown in Figure 15h. The range of \(\Delta\)U was from 0.8 V-2 V due to nonconstant weld pool boundary conditions. The fluctuation amplitude of the arc voltage in the base duration (\(\Delta\)U) reflects the penetration status. Figure 15: \(\Delta\)U with arc burning time at different penetration status. Full penetration: (**a**) V = 11 cm/min; (**b**) V = 13.75 cm/min; (**c**) V = 16.5 cm/min. Critical penetration: (**d**) V = 19.25 cm/min. Full penetration: (**e**) V = 24.75 cm/min; (**f**) V = 30.25 cm/min; (**g**) V = 35.75 cm/min; (**h**) the relationship between \(\Delta\)U and geometry of the weld joints. And \(\Delta\)U occurs in every pulse base phase, which indicates that the curve of time and \(\Delta\)U was a good indicator to sense weld penetration status. Under the experimental conditions in the present work, \(\Delta\)U persistently greater than 2 V can be the threshold for full penetration. From the analyses made above, the constant watch of the curve of time and \(\Delta\)U during P-GMAW was an efficient way to sensor the weld pool penetration status.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 4 Discussion  
In many application scenarios, such as a brain-controlled wheelchair and BCI spelling, a higher \(ITR\) can meet the high-level requirements for the response time of controlling and transmission rate of signals in actual control. In this case, it is more practical to consider the \(ITR\) than the accuracy alone; \(ITR\) is similar to the concept of derivative. If the accuracy does not grow with it when the time grows by a certain value, the \(ITR\) will decrease and vice versa.  
As shown in Table 2, when the \(ITR\) exceeds 80 bits/min, the data length is less than or equal to 2 s. Although the accuracy is only about 70%, abnormal data is limited during actual application processes, which means \(ITR\) will further increase, and it can meet the fundamental requirements of BCI wheelchair control.  
As shown in Figure 10, all metrics are time-dependent and most balanced at 2.5 s. It reveals that \(A_{are}\), \(S_{min}\) and \(S_{1}\) are positively correlated with the time length, while \(ITR_{are}\) and \(S_{2}\) are negatively correlated with time length. The performance of \(A_{are}\), \(S_{min}\), \(S_{1}\), \(S_{2}\), which are all related to the accuracy, improves after time length increase, and \(ITR_{are}\), which is related to the efficiency, on the contrary, decreases. Different application scenarios should decide the acquisition time length according to the requirements.  
Two criteria can evaluate the performance of the model, one is the accuracy rate, and the other is the \(ITR\). The accuracy rate is used as a criterion for a trial or application scenario, which needs higher precision but not a quicker acquisition time. The experimental data in this paper shows that the highest accuracy rate can be achieved with a time length  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Acquisition** & \(A_{are}\) **(\%)** & \(\begin{array}{c}ITR_{are}\\ \text{(bits/min)}\end{array}\) & \(S_{min}\) & \(S_{1}\) & \(S_{2}\) \\ \hline
1.5 & 71.5 & 88.64 & 10 & 13 & 6 \\
2.0 & 79.1 & 80.09 & 12 & 17 & 5 \\
2.5 & 83.1 & 75.31 & 30 & 18 & 4 \\
3.0 & 86.8 & 69.62 & 41 & 19 & 4 \\
3.5 & 88.9 & 63.48 & 48 & 21 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different data lengths.  
Figure 10: Comparison of different data lengths.  
of 3.5 s, four harmonics and five subbands, which is 88.9%. \(ITR\) has a relatively important significance for online testing. For the unsupervised FBCCA algorithm, Table 2 above also lists some comparative \(ITR\)s, and then the maximum \(ITR\) can be achieved with a time length of 1.5 s, four harmonics and five subbands, which is 88.64 bits/min.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 5 Results and Evaluation  
### Experimental Set Up  
This paper used a commercial TP-Link wireless router and a Thinkpad x201 laptop equipped with a Wi-Fi network card as the experimental equipment. As shown in Figure 8, the Wi-Fi network card was an Intel Wi-Fi Link 5300 with 3 antennas as the receiver, and the router is a TL-WDR5300 with three antennas as a transmitter for sending CSI signals. To collect CSI measurements, we installed the Linux CSI tool [22] on a laptop. In our experiments, the transmitter operated in the 5 GHz band using a 20 MHz channel bandwidth. To ensure communication quality, the notebook computer was equipped with an extended antenna, and the signal gain was 6 dB. At the same time, the CSI measurements obtained from the Linux CSI tool were processed using MATLAB.  
To evaluate the performance of through-the-wall human body detection in different environments, four wall materials and three experimental environments were designed for verification. The experimental environments were bedroom, meeting room, and hall, respectively. The general structure is shown in Figure 9. The wall materials were concrete, plaster walls, wooden doors, and glass. Laptops with Intel Wi-Fi Link 5300 were deployed on one side and TP-Link wireless routers on the other in each environment.  
Table 2 shows the recognition accuracy under different wall materials. It can be seen that the recognition accuracy rate of the glass wall material was the highest, and the effect was the best. The accuracy rate of the wooden door and gypsum wall material was slightly lower, and the accuracy rate of the concrete wall material was the lowest. However, the recognition accuracy rate was more than 90%, which was a better result.
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 1 Introduction
### Geometric Models of SAR Images
The general polynomial transformation is the prevalent correspondence model, which creates a functional link between ground and image coordinates and is usually established by fitting numerous reference points. For the initial positioning of airborne SAR images, [PERSON] et al. [17] used a primary affine transformation to approximate the translation between image and geographical coordinates, disregarding the impact of terrain elevation on positioning accuracy. In contrast, [PERSON] et al. [18] introduced elevation into a general polynomial model for SAR geolocation. The rational function model (RFM)--now the dominant true replacement model--was originally based on optical satellite imagery [19] and has become the primary geometric processing model for optical satellite images [20]. For SAR image positioning, the RFM has 78 rational polynomial coefficients (RPCs) to be fitted from the R-D model's virtual positioning results [21; 22]. [PERSON] et al. [22] analyzed factors affecting the RPC-fitting accuracy with respect to the R-D model and assessed the impacts of virtual control point grid size, elevation layer number, orbit accuracy, and other aspects on RPC solutions. Their study highlighted the importance of elevation layers in regard to SAR RPC extraction accuracy and suggested that careful adjustment of the terrain conditions should be considered. Typically, the accuracy of RPC fitting with respect to the R-D model exceeds 1% of a pixel. For high-precision sensors such as TerraSAR-X, which have stable motion and excellent imaging capabilities, RPC fitting accuracies better than 10\({}^{-3}\) relative to the R-D model can be achieved [23].