esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### HCV and Cellular Glycoslation Pathways  
A number of host genes involved in protein glycosylation were significantly increased and not previously reported to be changed during acute HCV infection. In particular, a dramatic 21 to 25-fold increase in fucosyltransferase 1 mRNA was observed at 72 hours after infection. Although, an increase in _FUT1_ expression has been reported in previous studies its effect on HCV replication remained unknown. _FUT1_ has been noted to be upregulated, most likely by a viral induced transcriptional change, during acute CMV infection and was suggested to play a role in virus spreading among cells or possibly viral escape from immune responses [64]. In this study we show that silencing _FUT1_ in Huh 7.5 cells inhibited HCV RNA replication by up to 80% of controls and infectious virion production was inhibited by 50%. These results provide direct evidence that the dramatic induction of _FUT1_ does have a biological role during acute HCV infection and encourage follow up studies regarding the mechanism of this _FUT1_ induction and its function during acute infection.  
In addition to a role in early viral replication, changes in protein glycosylation also play an important role in the pathogenesis and progression of some liver diseases [65; 66]. Altered glycosylation is recognized as a core characteristic of many cancer cells and changes in glycan structures have been used in the development of biomarkers for liver damage [65; 66]. Fuosylation is one of the most important glycosylation events in cancer cells; this pathway includes eleven known fucosyltransferase, as well as GDP-fucose synthetic enzymes and a GDP-fucose transporter [67]. Our study revealed increased expression of the _FUT1_ (21 to 25-fold) (\(\alpha\)1,2 fucosyltransferaseases), _FUT5_ (5-fold) (\(\alpha\)1,3/\(\alpha\)1,4), and _FUT8_ (2-fold) (\(\alpha\)1,6 or core fucose). In addition, genes involved in _de novo_ GDP-fucose synthesis pathway as well as a salvage pathway showed increased expression (_FUK_ and _TSTA3_) at 48 hours. Well-known fucosyltransferase products are sialyl selectin receptors (Lewis glycol-epitopes) that play a critical role in leukocyte extravasation, lymphocyte maturation, and cell signaling and development [67]. HIV, CMV, VZV and HSV-1 infections induce expression of Lewis X and Lewis Y epitopes by activating transcription of FUT genes, which play a vital role in the spread of these viruses [64; 68]. A similar strategy of hijacking the selectin-dependent mechanism (binding of sLeX on tumor cells to P-selectin on endothelial cells) was reported during metastasis of several tumors [69]. Recent studies provide evidence that HSV-1 induction of specific FUTs (_FUT3_, _FUT5_, and _FUT6_) was initiated by interactions between viral dsRNA and PKR and RIG-I. It was also reported that the herpesviruses induced increases in Lewis antigens and the sialylation of other glycoproteins (CD45) regulate TCR signaling, adhesion and apoptosis in thymocytes [70; 71; 72].
# Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
## 4 Experiment  
### Experimental Settings  
**Dataset.** This paper uses the labeled mechanical fault relevant text as the training data of the NER model, with a total of 900 sample sentences and a total of 5860 labeled entities. For entity labeling, the BIO labeling method is used. In addition, this paper also needs to combine relational information to predict entities. This paper uses four relation types to label the relation between event argument entities, as follows: Appear, Lead To, Consist Of and Has Attributes.  
**Evaluation index.** In this paper, the accuracy rate P (Precision), the recall rate R (Recall), and the F1 score are used as indicators to evaluate the performance of the model.  
**Model hyperparameter settings and environment configuration.** The model proposed in this paper is implemented based on the Tensorflow framework, and the experiment will perform 5-fold cross-validation. Adam optimizer is used during training. The dropout rate is set to 0.9 to prevent overfitting. The experiments performed in this paper all use the same configuration.
## 3 Material and Methods
### Segmentation  
Segmentation is an approach in which some portion of data is extracted from the signal that is used for further evaluation purposes. In the case of MIT-BIH arrhythmia, some ECG signals higher T-peaks than R-peaks. After removing baseline the drift as well as low- and high-frequency noise, the signals are in a form to detect the R peaks. A well-known Pan-Tompkins [49] method is used for R Peaks detection. In the literature [19], Pan-Tompkins is already applied for R-Peaks detection for the ECG-ID, MIT-BIH and NSRDB databases. The electrocardiogram (ECG) is an interpretation of the QRS complex. The R point is the local maximum point shown in Figure 3, the Q point is on their left side and the S point is on their right side. One of the most important sections of this complex is the R wave. A total of 94 samples are extracted from the signal using the R-peaks point. These samples are fed to a convolutional recurrent neural network (1D-CRNN) and discrete wavelet transform (DWT) for analysis. The QRS complex samples shown in Figure 4 and the equation for segmentation are given below [1];  
\[S_{r}=[S(R_{p}-45):S(R_{p}-49)] \tag{6}\]  
where \(S_{r}\) saves the segmentation results from 49 samples after the peak and 45 samples before the R-peak and \(S(R_{p})\) represent the peak points in the signal. Given the fact that Equation (4) is repeated for each signal sample,  
Figure 2: Signal preprocessing steps: (**a**) Raw ECG signal; (**b**) After applying high-pass filter and powerline interference; and (**c**) After normalization of signal.
## 2 Preliminaries  
### Stream Ciphers  
Stream ciphers are symmetric ciphers that generate pseudorandom sequences of bits from a given secret key. These pseudorandom bits are later used for encrypting plaintext using the XOR operation. Stream ciphers are widely and effectively used in securing TLS, Bluetooth and 4G connections [19].  
Stream ciphers take two input values: a secret key (K) and an initial vector (IV). The length of the secret key is usually between 128 and 256 bits. The IV can be either a secret or known value, but keeping it secret adds an additional level of security. The general structure of stream ciphers is illustrated in Figure 1, where KS represents a keystream, and PT and CT represent the plaintext and ciphertext, respectively.  
The keystream is computed as a function KsG such that Ks = KsG (K, IV). The plaintext is encrypted as CT = PT \(\oplus\) Ks, and the ciphertext is decrypted as PT = CT \(\oplus\) Ks. Note that the encryption and decryption operations are the same, as they perform the same XOR operation. This justifies the absence of decrypt() functions in certain cryptographic libraries; the encrypt() function can be used for both encryption and decryption [19].
## 3 Results and Discussion  
### Mechanical Properties  
Dynamic mechanical analysis (DMA) was used to study the mechanical strength of the PEMs upon adding the PSLM nanoparticles. Figure 2a,b shows the temperature evolution of the storage modulus and the dumping factor (tan \(\delta\)), respectively, for the sPEEK-based membranes. Commercial Nafion 212 membrane was also measured and the data are reported in the graphs for comparison. We can observe how the addition of PSLM platelets in the polymer matrix resulted in a marked increase of the storage modulus and in an appreciable extension of the thermal resistance. For instance, the storage modulus of the sPEEK-based membranes remained quite constant, at least up to 200 \({}^{\circ}\)C, and with values of one order of magnitude higher than Nafion 212 (Figure 2a). The addition of the silica nanofiller improved the robustness and, for example, the sPEEK-PSLM\({}_{\mathrm{3}}\) sample displayed a storage modulus of about 280 MPa, which is four-fold higher than pristine sPEEK. Furthermore, the introduction of the silica-based nanoplatelets also has a beneficial effect on the thermal properties of the membrane. This can be clearly seen from the temperature evolution of \(\tan\delta\), illustrated in Figure 2b. For each membrane, the dumping factor plot exhibits a single peak, which can be assigned to the \(\alpha\) transition (T\({}_{\mathrm{g}}\)) of the ionic clusters of the polymer. While Nafion [36] exhibits a T\({}_{\mathrm{g}}\) of ca. 120 \({}^{\circ}\)C, the \(\alpha\)-transition for SPEEK is shifted at remarkably higher temperatures, i.e., above 200 \({}^{\circ}\)C. This indicates this polyaromatic polymer can definitively withstand higher operating temperatures with respect to the Nafion benchmark. For the SPEEK-based nanocomposites, the increasing filler content moves the peak further toward a higher temperature, reaching an impressive value of 245 \({}^{\circ}\)C at 3 wt% of filler loading. De facto, the nanocomposite membrane conjugate had outstanding mechanical strength with impressive thermal stability, and can be potentially applied in PEMFCs able to effectively operate under harsh conditions, which are high temperature and severe mechanical solicitations.
## 7 Conclusions
### Summary of Contributions  
Our research advances the state-of-the-art through four principal contributions:  
First, we introduced the first comprehensive integration of adaptive timing correction mechanisms within ESC communication protocols, achieving sub-10 us execution latency with 31.01% timing correction success rate under adversarial conditions.  
Second, our multi-algorithm framework provides robust performance across heterogeneous error modalities, with HATC meta-learning enabling 26.5% improvement over baseline approaches (\(p<0.001\), [PERSON]'s \(d=0.82\)).  
Third, the complete implementation on PolarFire SoC FPGA confirms practical feasibility with minimal resource overhead (2.16% LUT utilization, 16.6 mW per channel).  
Fourth, rigorous security analysis demonstrates 88.3% attack detection across six threat scenarios, reducing CVSS severity from High (7.3) to Low (3.1).  
Security Paradigm Achievement: SMART DShot fundamentally transforms UAV security from passive defense to active, intelligent protection. The 88.3% attack detection rate across six threat categories, combined with 2.3% false positive rate, establishes a new security baseline for autonomous systems. The reduction of CVSS scores from High (7.3) to Low (3.1) severity represents a categorical advancement in cyber-physical security capabilities. This achievement demonstrates that embedded ML can provide enterprise-grade security within microsecond-scale constraints, opening new possibilities for securing critical infrastructure.
# MCF-Net: Fusion Network of Facial and Scene Features for Expression Recognition in the Wild
###### Abstract  
Nowadays, the facial expression recognition (FER) task has transitioned from a laboratory-controlled scenario to in-the-wild conditions. However, recognizing facial expressions in the wild is challenging due to factors such as variant backgrounds, low-quality facial images, and the subjectiveness of annotators. Therefore, deep neural networks have increasingly been leveraged to learn discriminative representations for FER. In this work, we propose the Multi-cues Fusion Net (MCF-Net), a novel deep learning model with a two-stream structure for FER. Our model first proposes a two-stream coding network to extract face and scene representations. Then, an adaptive fusion module is employed to fuse the two different representations for final recognition. In the face coding stream, a Sparse Mask Attention Learning (SMAL) module is utilized to adaptively generate the corresponding sparse face mask according to the input image. Meanwhile, we employ a Multi-scale Attention (MSA) module for extracting fine-grained feature subsets, which can obtain richer multi-scale interaction information. In the scene coding stream, a Relational Attention (RA) module is applied to construct the hidden relationship between the face and contextual features of non-facial regions by capturing the pairwise similarity. In order to verify the effectiveness and accuracy of our model, a large number of experiments are carried out on two public large-scale static facial expression image datasets, CAER-S and NCAER-S. By comparing the performance of our MCF-Net with other methods, the proposed model achieves superior results on two in-the-wild FER benchmarks: CAER-S with an accuracy of 81.82% and NCAER-S with an accuracy of 45.59%.  
Keywords:expression recognition; sparse mask attention learning; multi-scale attention; Relation Attention; feature fusion +
Footnote †: journal: Medical Image and Image and Vision
## 2 Related Work or Literature Survey
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Application** & **Temporal** & **Forecast** & **Performed** & **Benchmark** & **Performance** \\  & **Type** & **Granularity** & **Horizon** & **Model** & **Models** & **Results** \\ \hline
[34] & Natl. Grid & Daily & Daily & Mobi-MTL & NN, Mobi, & 1.590\% \\  & (Vars. & & & & Retrained NN & (MAPE) \\  & Countries) & & & & LR, RF, GM, & 0.034\% \\
[16] & Natl. Grid & Monthly & Monthly & SVM & LR, RF, GM, & 0.034\% \\  & (Vars. & & & & NGBM, & (MAPE) \\  & Countries) & & & & FANGBM, & \\  & & & & & SGM, & \\  & & & & & SFANGBM, & \\  & & & & & FDGGM & \\
[11] & Natl. Grid & Daily & Monthly & LSTM ANN & NARX ANN & 1.107\% \\  & (MAPE) & & & & (MAPE) \\
[12] & Natl. Grid & Daily & Monthly & SMOReg & GPR, XNV, & 3.685\% \\  & & & & & M5P, REPTree & (MAPE) \\
[13] & Natl. Grid & Hourly & Hourly & NARX ANN & NARX ANN & N/A \\
[14] & Natl. Grid & Daily & Monthly & LSTM & ARIMA, & 3.914\% \\  & & & & & NARNN & (MAPE) \\
[15] & Natl. Grid & Daily & Monthly & Winters’ & Seasonal, & 2.482\% \\  & & & & Multiplicative & Winters’ & \\  & & & & & Additive & (MAPE) \\
[18] & Natl. Grid & 1-h & Hourly & CNN & GoogleNet, & 0.064\% \\  & & & & & ResNet18, & \\  & & & & & AlexNet, & \\
[20] & Natl. Grid & 30-min & Daily & N/A & N/A & \(\approx\)1.900\% \\
[21] & Natl. Grid & Daily & Daily & Bi-LSTM & Bi-LSTM & (MAPE) \\
[22] & Natl. Grid & Daily & Monthly & FBP & FBP & 3.000 \\  & & & & & (ME) \\
[23] & Natl. Grid & Monthly & Monthly & ARDL & ANN & N/A \\
[25] & Natl. Grid & Monthly & Monthly & ARIMA-BP & BP, ARIMA & 4.980\% \\  & & & & & (MAPE) \\
[27] & Natl. Grid & Monthly & Monthly & N/A & Prophet, & 0.001\% \\  & & & & XGBoost, & LSTM, SVR & (MAPE) \\
[31] & Natl. Grid & 30-min & Monthly & ARIMAX & ARIMAX, & 4.100\% \\  & & & & ANN & & (MAPE) \\
[33] & Natl. Grid & 15-min & Daily & ANN Net05 & ANN & 0.531 \\  & & & & Net01-10 & & 0.531 \\  & & & & & (MAE) \\
[35] & Natl. Grid & Monthly & Monthly & RF & XGBoost, & 0.198\% \\  & & & & SVM & (MAPE) \\ \hline \end{tabular} N/A stand for not applicable. See the list of abbreviations for the remaining terms.  
\end{table} 
Table 1: _Cont._hourly and daily, which is shorter term. It uses DNN and GEP methods and provides their benchmarking. It is also the first time they have been used in COVID-19-based forecasting studies. DNN and GEP methods offer several advantages, which are explained in detail in Section 3.2. Their most significant advantage is that their model equations are comprehensible, allowing easier implementation while trying to find the relationships between the variables. The variables used are given in Table 2, and it is the first time electrical, meteorological and COVID-19 variables are incorporated in such a way.
# Risk Assessment of Bisphenol A in the Korean General Population
## 5 Further Study for BPA  
BPA has been used in food packaging since the 1960s. Many studies on BPA have been reported to evaluate potential risks to human health. Although a wealth of data show that BPA is an endocrine-disrupting chemical, controversy on whether exposure poses a risk to human health remains. In Republic of Korea, risk assessment for BPA has been focused on relying on traditional toxicology animal studies rather than academic studies whose endpoints are more sensitive and considered appropriate. As mentioned above, traditional animal toxicity tests have limitations in identifying causality with human health, especially for low-dose exposure. The method for hazard identification and characterization is also evolving, and it will be necessary to incorporate this method in the risk assessment. This study identified the need for further studies to reinforce BPA risk assessment. First, the biological hazard characterization of BPA is limited when applying the traditional risk assessment approach. Unlike safety management based on TDI for many hazardous chemicals, a new concept is required for BPA. If TDI continues to be lowered with an uncertainty factor without a new toxicity dose through animal testing, it will be difficult to judge whether it is safe. In the case of non-monotonic BPA, it is difficult to develop a new dose-response evaluation. It may be necessary to regulate the major exposure source with an MOE approach based on a non-threshold concept. In addition, human exposure to BPA occurs via various routes, primarily orally. It is possible that as mixtures are exposed to BPA analogs because of restrictions on the use of BPA in various consumer products, that they have been replaced with other bisphenol analogs such as bisphenol S (BPS) and bisphenol F (BPF). These analogs have similar chemical properties to BPA, and have high stability and long biological half-life in animals and humans [65; 66]. Dose-response assessment for mixtures of chemical exposure requires further investigation. Second, biomonitoring is an alternative approach for refining traditional risk assessment that will provide more information for understanding the low-dose health effects of BPA. A method to obtain repeated BPA measurements under real exposure conditions is needed in future studies. A single measurement of BPA in a spot urine sample may not be representative of overall BPA exposure. The measurement of BPA in 12 h collected urine samples from Koreans was higher than that of spot urine samples. Biomonitoring and extrapolation approaches for exposure assessment using the PBPK model or statistical approaches for epidemiological correlations are needed to understand the new low-dose health effects of BPA. Third, epidemiologic dose-response analyses are useful for causal inference and risk assessment. The possibility of performing a dose-response assessment based on
## 5 Discussion
### Changing Correlations between LOS and GSI  
This study shows widespread changes in the strength of correlations between LOS and GSI (70.90% of the study area) during the past three decades in the NH (Figure 7). Regions that are covered by significant positive correlation between LOS and GSI are becoming more dominant over time, as compared to areas of decreasing correlations. However, by comparing Figures 5 and 7, it can be observed that grasslands in the mid-latitude arid/semi-arid areas with low/negative correlations (e.g., central NA, central Asia, Mongolia, and north China) show significant decreasing strength in the correlation between LOS and GSI, whereas high-latitude tundra regions with low/negative correlations (e.g., arctic area of NA and EA) exhibit a significant increasing correlation. Since both regions are less affected by direct human influence, this difference may reflect latitude- and/or biome-dependent changes in ecosystem functioning in response to long-term climate change or climate extremes/natural disturbance. In contrast to significant changes in these non-forest biomes, both positive/negative trends in correlations between LOS and GSI are rarely found in forest biomes (Figure 8A). This is also confirmed by [PERSON] et al. (2017) who revealed non-significant correlation between LOS and plant productivity in most of southern Canadian Arctic tundra and further suggests biome-specific changes in long-term correlations between LOS and GSI.
# GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images
## 3 GPU Parallel Implementation Based on CUDA
### Svd  
In some other parallel processing of the hyperspectral image, GPU acceleration is mostly performed only for matrix operations [29] or using the CPU computing library for feature decomposition [30]. In our algorithm, we will perform feature decomposition in GPU based on the cuSolver library and reduce data transfer between the host and the device as much as possible.  
The basic use of cuSolver to implement the SVD computation is reported in the official library documentation [31]. Different from the steps in the official documentation, we don't need to create all the parameter pointers and their corresponding memory here, because they have been declared or completed in the previous context. Besides, the input data and results of the calculation don't have to be  
Figure 1: Compute unified device architecture (CUDA) codes for matrix multiplication.  
Figure 2: Schematic diagram of our threads organization for performing noise estimation.  
transferred between host and device, and we only need to use part of the calculated value. What more, encapsulating SVD into a class helps us to call it multiple times, and we can dynamically choose whether to return the host or continue the calculation in the device without having to reconstruct the context of the complete calculation function multiple times, thereby reducing time consumption. In addition, this approach is also very conducive to the migration of our algorithm.  
We show the simplified SVD parallel code in Algorithm 2. The first step is to set up the SVDT class and declare its constructor, destructor, calculation function, and private parameters. When a new object of the SVDT class is created, the constructor is executed. As shown in step 2, the parameters m and n are the dimensions of the matrix to be decomposed. We only need to initialize the cuSolverDN library and calculate the size of the work buffers needed here. Step 3 is the execution of the calculation function, and no data transfer operation is required here. The fourth step is the execution of the destructor, which is used to release temporary memory and useless pointers. Function _get_p_ is used to call the function pointer to facilitate debugging and verify the results.  
```
1:set class \(SVDT\){
2 public:\(SVDcompute(args)\);
3\(\sim SVDT()\);
4\(SVDT(\textit{const int m, const int n})\);
5\(float*get\_p(void)\);
6 private:\(cublas\_status\);......float*\(d\_work=NULL\);......
7}
8:\(SVDT::SVDT(\textit{const int m, const int n})\){
9\(cusolverDnCreate(\&cusolverH)\);
10\(cusolverDnSgesvdbufferSize(cusolverH,m,n,\&lwork)\);
11}
12:\(SVDT::SVD\){
13\(cudaEventCreate(\&startEvent)\);
14\(cusolverDnSgesvd(args)\);
15\(cudaEventRecord(stopEvent)\);
16\(cusolverDnDestroy(cusolverH)\);
17}
18:\(SVDT::\ \sim SVDT()\);
```  
**Algorithm 2**SVD parallelization.