esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Myoelectric Control for Finger Prostheses for Multiple Finger Loss
## 7 Future Perspectives in Myoelectric Prosthesis  
Currently, although myoelectrical prosthetic fingers/hands in the market feature advanced mechanics, the control methods are still rudimentary in most cases. Most myoelectrical hand prostheses lack somatosensory feedback and 100% control robustness,cannot provide sufficient control speed, and are incapable of reproducing various hand movements [20; 28]. Hence, they are not sufficient for many real-life applications. The main reason for the lacking robustness is the motion artifacts which typically occur in the low-frequency range [63]. Real-time motion artifact suppression algorithms can be used on an ultra-low-power microcontroller to achieve high robustness.  
All myoelectrical control-based prosthetic hands may not have similar operations and exhibit variations in sensing input, deciphering signals, and actuating prosthetic hands [64; 65]. The factors that influence myoelectrical fingers/hand prostheses are the shape of the first layer of the network, initial weights of the layers, data augmentation procedures, and the learning rate [20]. The EMG architecture can be made more complex and can be trained with EMG data to produce higher accuracy. Further research and development are necessary for precise control of various prosthetic hand movements with sufficient speed and robust control.
# Adaptive Semi-Supervised Algorithm for Intrusion Detection and Unknown Attack Identification
## 4 Experiments
### Dataset and Evaluation Metrics  
**Dataset:** We used the CIC-IDS2017 and Kitsune datasets for model training and evaluation. The following table presents the network traffic types and data volume after preprocessing (Table 2):  
\begin{table}
\begin{tabular}{c c c c c} \hline
**CPU** & **GPU** & **OS** & **CUDA** & **Pytorch** \\ \hline Intel i5-12400 & NVIDIA-A4500 & Ubuntu-22.04 & 11.8 & 2.0.0 \\ \hline \end{tabular}
\end{table}
Table 1: Hardware and software configuration for training and testing the algorithm model.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Attack Type** & **Data Volume** \\ \hline \multirow{6}{*}{CIC-IDS2017} & Benign & 2,271,285 \\  & DoS & 251,701 \\  & PortScan & 158,804 \\  & DDoS & 128,019 \\  & BruteForce & 13,832 \\  & WebAttack & 2180 \\ \hline \multirow{6}{*}{Kitsune} & OS Scan & 1,697,851 \\  & Fuzzing & 2,244,139 \\  & Video Injection & 2,472,401 \\  & ARP MiM & 2,504,267 \\ \cline{1-1}  & Active Wiretap & 4,554,925 \\ \cline{1-1}  & SSDP Flood & 4,077,266 \\ \cline{1-1}  & SYN DoS & 2,771,276 \\ \cline{1-1}  & SSL Renegotiation & 6,084,492 \\ \cline{1-1}  & Mirai & 764,137 \\ \hline \end{tabular}
\end{table}
Table 2: Description and data volume of network traffic types after preprocessing for CIC-IDS2017 and Kitsune datasets.  
Our model is trained using only positive (benign) samples, so there is no need to split the attack traffic data into training and validation sets. For the Benign class, we randomly select two million samples as the training set. During validation, we randomly select a number of Benign samples equal to the number of attack samples for each attack type from the remaining Benign data. For example, in the CIC-IDS2017 dataset, since the BruteForce attack has 13,832 samples, we randomly select 13,832 samples from the remaining 271,285 Benign samples to form the validation set along with the BruteForce data. This process is repeated for each attack type.  
**Evaluation Metrics:** We used the following evaluation metrics to assess the performance of the designed model (Figure 7):  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{9}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{10}\]  
\[F1_{\text{score}}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{11}\]  
\[FOR=\frac{\text{FN}}{\text{FN}+\text{TN}} \tag{12}\]  
TP (true positive) represents the number of attacks correctly detected by the model, while TN (true negative) refers to the number of normal traffic instances correctly identified by the model. FP (false positive) is the number of normal traffic instances incorrectly classified as attacks, and FN (false negative) is the number of attacks that the model failed to identify [25].
## Appendix A  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Indices** & **Abbreviation** & **Formula** & **Reference** \\ \hline Normalized Difference & NDVI & NDVI = \(\frac{\text{NIR}-\text{red}}{\text{NIR}+\text{red}}\) & [PERSON] [58] \\ Vegetation Index & maxNDVI & maxNDVI = \(\max\{\text{NDVI}(i,\text{i})\}_{1}\), NDVI(i,\(\text{i}))\({}_{2}\), \(\dots\), NDVI(i,\(\text{i}))\({}_{n}\) & [PERSON], et al. [59] \\ Minimum NDVI & minNDVI & minNDVI = \(\min\{\text{NDVI}(i,\text{i})\}_{1}\), NDVI(i,\(\text{i}))\({}_{2}\), \(\dots\), NDVI(i,\(\text{i}))\({}_{n}\) & \\ \hline Enhance Vegetation Index & EVI & EVI = G \(\times\)\(\frac{\text{NIR}-\text{red}}{\text{NIR}+(\text{C}_{1}\times\text{red})-( \text{C}_{2}\times\text{B})+\text{L}}\) & [PERSON], et al. [60] \\ \hline Normalized Difference & NDWI & NDWI = \(\frac{\text{GREREN}-\text{NIR}}{\text{GREN}+\text{NIR}}\) & [PERSON] [61] \\ Water Index & SAVI & SAVI = \(\frac{\text{NIR}-\text{red}}{\text{NIR}+\text{red}+\text{L}}(1+\text{L})\) & [PERSON] [62] \\ \hline Ratio Vegetation Index & RVI & RVI = \(\frac{\text{NIR}}{\text{red}}\) & [PERSON] [63] \\ \hline Normalized Difference & NDII & NDII = \(\frac{(\text{NIR}-\text{SWIR})}{(\text{NIR}+\text{SWIR}1)}\) & [PERSON] [64] \\ Infrared Index & NDMI & NDMI = \(\frac{(\text{NIR}-\text{SWIR}2)}{(\text{NIR}+\text{SWIR}2)}\) & [PERSON] [65] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nine vegetation indices (VIs) for crop type and LC classification for five base years.
## 3 Construction of Measurement Matrix by Cyclic Direct Product and QR Decomposition  
### Definition of Direct Product  
Let \(A=\left(\alpha_{ij}\right)_{m\times n}\) and \(B=\left(\beta_{ij}\right)_{p\times q}\); then, the \(mp\times nq\) matrix will be given by \[\left[\begin{array}{ccccccccc}\alpha_{11}B&\alpha_{12}B&\cdots&\cdots&\cdots& \alpha_{1n}B\\ \alpha_{21}B&\alpha_{22}B&\cdots&\cdots&\cdots&\alpha_{2n}B\\ &&\cdots&\cdots&&&\\ \alpha_{m1}B&\alpha_{m2}B&\cdots&\cdots&\cdots&\alpha_{mn}B\end{array}\right].\]  
This matrix is called the Kronecker product or direct product [30] of \(A\) and \(B\), and is denoted as \(A\otimes B\).  
An important property of the direct product is as follows: let \(\chi_{1}\), \(\chi_{2}\),..., \(\chi_{n}\) be \(N\) numbers of linearly independent \(m\)-dimensional column vectors, and \(y_{1}\), \(y_{2}\),..., \(y_{q}\) be \(q\) numbers of linearly independent \(p\)-dimensional column vectors; it then follows that the \(nq\) numbers of \(mp\)-dimensional column vectors \(\chi_{i}\otimes y_{j}\) (\(i\) = 1, 2,..., \(n\); \(j\) = 1, 2,..., \(n\)) are linearly independent, and vice versa.  
From the properties of the direct product, it is possible to generate higher-dimension matrices from lower-dimension ones, and make the resulting column vectors linearly independent. The key to this method is to select a set of well-behaving lower-dimension orthogonal column vectors as the \"seed sequence\" to ensure mutual orthogonality in the set of higher-dimension column vectors constructed by the cyclic direct product, and the maximum non-correlation possible among the row vectors. From the definition of the direct product, the number of cyclic direct products performed is related to the dimension of the \"seed\" vectors \(n\) and the dimension of the higher-dimension column vectors \(N\), such that the number of cyclic computations \(k\) = log\({}_{n}\)\(N\), as in the case of \(k\) = log\({}_{n}\)\(N\) = log\({}_{2}\)1024 = 10. Compared with conventional higher-dimension matrices and their construction, the cyclic direct product is easier to perform and greatly saves matrix construction time.
## 2 Materials and Methods
### Microorganisms  
The antimicrobial activity of tested EOs was evaluated against different standard strains of microorganisms: (1) three Gram (+) bacteria (_Staphylococcus aureus_ ATCC 25923, _Enterococcus faecalis_ ATCC 29212, and _Bacillus subtilis_ ATCC 6633), (2) four Gram (-) bacteria (_Escherichia coli_ ATCC 25922, _Klebsiella pneumoniae_ ATCC 13883, _Salmonella_ Abony NCTC 6017, and _Pseudomonas aeruginosa_ ATCC 27853); and one strain of the yeast _Candidida albicans_ ATCC 10231. The selected bacteria used in this study are common causes of various human infections (skin, urinary, and digestive tract infections). The minimal inhibitory concentrations (MICs) of EOs were determined by the broth microdilution method (CLSI M07, 2018). In our study, we used Muller-Hinton Agar (MHA, Torlak, Serbia) to cultivate and maintain bacteria and Sabouraud dextrose agar (SDA, Torlak, Serbia) for _C. albicans_.
# A Novel Carrier Tracking Approach for GPS Signals Based on Gauss-Hermite Kalman Filter
###### Abstract
Recently, other approaches based on statistical processing theory have been proposed to deal with nonlinear systems. A typical example of these novel techniques is the particle filter (PF). PF is a sequential Monte Carlo method based on the Bayesian estimation principle. The PF uses a series of weighted random samples, which are also called particles, to approximate the posterior probability density, so that many kinds of statistical estimates such as the mean and variance can be computed, which makes it capable of dealing with any nonlinear models [6]. In [21; 22], PF-based carrier tracking approaches were proposed for GNSS receivers. However, the main drawback of the PF approach is the lack of computational efficiency [6]. To overcome the problems of low UKF quality and high PF complicity, this paper proposes a new approach to the nonlinear tracking system, which is based on the Gauss-Hermite Kalman filter (GHKF). This approach, which belongs to the family of SPKF, ensures an additional justification of the UKF development and offers further options to improve the accuracy of the approximation. Furthermore, GHKF can provide estimation accuracy comparable to that of a much more computationally intensive simulation-based PF [23]. As a direct quadrature formula, the proposed filter does not need the careful selection of tuning parameters like the UKF [6]. The proposed tracking system was used to estimate the following RNPs of the received GPS signal: amplitude \(\hat{A}\), carrier phase \(\phi\), Doppler frequency \(\hat{f}_{d}\), and its change rate \(\hat{f}^{\prime}_{d}\). In the present paper, RNP estimates were compared to those of the EKF- and UKF-based tracking approaches. It should be noted that, in the available literature, there is no performance comparison between nonlinear KF-based tracking loop architectures operating under the same conditions. The remainder of this paper is organized as follows: in Section 2, the problem description is presented. Then, Section 3 describes the problem solution. In Section 4, the proposed GHKF-based tracking architecture is described in detail. In Section 5, simulations are performed to evaluate the tracking performance and weak dynamic signal performance. Lastly, conclusions are presented in Section 6.
## 7 Defenses against Privacy Attacks
### Trusted Execution Environments (TEEs)  
In FL, Trusted Execution Environments (TEEs) provide secure enclaves within client devices or servers, ensuring that sensitive model training processes are protected from tampering or unauthorized access [168]. This security measure is vital in preventing privacy breaches where adversaries might attempt to intercept or manipulate model updates exchanged between clients and the central server. By securely executing computations within TEEs, FL systems safeguard the confidentiality and integrity of participant data, thereby fostering trust among clients and encouraging greater participation in collaborative learning tasks [181]. TEEs in FL play a critical role in maintaining the privacy of individual data contributions while enabling effective and secure collective model training across distributed networks. The schematic representation of this process is shown in Figure 21.  
Further, ref. [182] introduces a privacy-preserving FL scheme to defend against causative attacks using TEEs like Intel SGX. The participants and the aggregation server run their algorithms within secure TEEs, ensuring the integrity of the local training and global aggregation processes. The framework includes a training-integrity protocol, implementing verified programs within TEEs, and employs remote attestation to verify the integrity of the enclave execution. This setup detects and excludes manipulated gradients, preventing tampering with the global model and ensuring secure, reliable FL.  
Another paper [183] presents Privacy-preserving Federated Learning (PPFL), a defense framework designed to protect against privacy attacks in FL systems. PPFL utilizes TEEs on both server and client devices to conceal model and gradient updates, as well as trained layers, from adversaries, thereby preventing attacks aimed at reconstructing private data or inferring properties and membership from trained models or gradients. Training is conducted in a greedy layer-wise manner, with each layer independently trained within TEEs until convergence, overcoming the memory constraints of TEEs. To defend against membership inference attacks on the final model, the last layer and its outputs remain within the client's TEE after training. Secure communication channels are established between the server and client TEEs, with data transmission encrypted using keys known only within the TEEs, ensuring data confidentiality and integrity. Evaluation shows that, by maintaining the privacy of the training process within TEEs, PPFL effectively mitigates known attacks like data reconstruction and property inference.  
All the above-described privacy frameworks are summarized in Table 3.  
Figure 21: Visual representation of Trusted Execution Environments (TEEs).
## 3 Cyclic Depressurization Simulation Study
### Results of Production Time Case during Secondary depressurization Stage  
Simulations were conducted with production days during the secondary depressurization stage changed by 1, 2 and 4 days. As shown in Figure 15a, the production rates during the primary depressurization stage were similar for all cases. In the case of 1 day, maximum gas production rate was about 126 m\({}^{3}\)/day and there was little gas production after 100 days during secondary depressurization stage. The gas-production rate of the 4-day case during the secondary depressurization stage was high at the initial production time, and the maximum gas production rate was 214 m\({}^{3}\)/day; however, it reduced after 96 days. Each cumulative gas production was between 1.81 \(\times\) 10\({}^{5}\) m\({}^{3}\) and 2.27 \(\times\) 10\({}^{5}\) m  
Figure 14: Results of vertical displacement by use of different bottomhole pressure during secondary depressurization stage.  
Figure 13: Results of gas production by use of different bottomhole pressure during secondary depressurization stage: (a) gas production rate and (b) cumulative gas rate.  
(Figure 15b). Specially, cumulative gas production of the 1-day case was very similar to the production of the non-cyclic case, and their difference was only 0.5%.  
The vertical subsidence results are represented in Figure 16. The final vertical displacement values during primary depressurization stage were between \(-1.87\) m (in the case of 4 days) and \(-1.92\) m (in the case of 1 day), and the difference was very small. However, the final vertical displacement during the secondary depressurization stage was between \(-1.19\) m (in the case of 4 days) and \(-1.65\) m (in the case of 1 day), and the difference was large. The reason is that pore pressure was restored consistently because the low gas production time was longer. Therefore, additional production time in the secondary stage did not increase stability, because the final vertical displacement values were similar during primary depressurization stage.
# Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification
## 2 FCN Ensemble Built by Random Subspace Method
In the ensemble phase, Random Subspace Method is deployed on FCNs (Figure 3), which keeps the same structure with that found in another paper [5]. FCNs are trained by different combinations selected from top-K discriminative subsequences. Finally, the soft classification scores given by each individual classifier will be combined by soft voting. Figure 2: Explanatory diagrams of the four hyperparameters. Based on above settings and descriptions, the details of RSE-FCN can be presented in Algorithm 2. First, by **GetTopKBBestSubsequences** method (Algorithm 1), **KBestId**, the set of ordinal number corresponding to K optimal intervals, can be obtained. The sets of N intervals _Ds_ and _Ts_, which the train set D and test set T are divided into, should be reserved. Second, M ordinal numbers **RsID** are randomly selected out from **KBestId** every time and this operation should be repeated for E times. Next, according to _Ds_, _Ts_ and current **RsID**, new training set _D'_ and test set _T'_ can be generated. To preserves the features which stretch over division nodes, the subsequences combining order should be in accordance with the number-ascending order rather than the selection order. Or reversely, we can get rid of the subsequences whose ordinal number not in the selected M ones from the interval sets of whole time series. Then, FCN is trained by current dataset _D'_, to yield the judgment for the test set _T'_. Finally, the classification results of E individual FCN classifiers are integrated by soft voting, thus the ensemble decision for test set T can be obtained. ```
1:Input: Training set \(D\) and its label set _Yd_, Test set \(T\) and its label set \(Y\),and \(N\), \(K\), \(M\), \(E\)
2:Output: The classification result \(Y\) of _T'_
3:\(k\)_BestID_, _D\({}_{S}\), _T\({}_{S}\)_ = GetTopKBBestSubsequences\((D\), \(T\), _Yd_, _Yt_, \(N\), _K_)
4:U = {1, 2, 3,..., N} // The full set of ordinal numbers
5:IndividualResults = \(\emptyset\)
6:for\(i\) in range(I,E)do
7:\(R\)_sID_ = RandomSelectFrom(\(k\)_BestID_,M_)
8:Drop = U-_RsID_ // Subtraction of set, getting the ordinal numbers to be eliminated
9:D' = combineExcept(D\({}_{S}\), Drop),T' = combineExcept (\(T_{S}\), Drop)
10:FCN_train(D', _Yd_)
11:\(r_{i}\) = FCN_test(T')
12:IndividualResults.append(\(r_{i}\))
13:endfor
14:Y \(\leftarrow\) IndividualResults // Vote
15:return Y
```  
**Algorithm 2** Random Subspace Ensembles of FCN.
# A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
## 5 Conclusions  
In this work, we propose HTGQE, which improves retrieval efficiency and effectiveness by combining generation models and PRF techniques to generate various query expansion contexts. The pseudo-relevant passages served as reference material for the generation model, which increases the model's input while reducing the pressure on generative models and improving the quality of generated terms. Various query expansion contexts complement each other, reducing noise during query context generation and improving the final retrieval accuracy. Notably, compared to the closed-book text generation method GAR, when configured with extractive readers, HTGQE achieves EM score improvements of 4.57% and 6.35% on NQ and Trivia, respectively. When equipped with generative readers, HTGQE achieves EM score improvements of 14.34% and 17.09% on NQ and Trivia datasets, respectively. In addition, HTGQE is a plug-and-play approach that can be applied to existing R2 systems under specific datasets, independent of the specific structure of the retriever and reader models, which allows easy and quick integration into existing R2 systems. However, our method still has its limitations. Adding expansion terms to the initial query can improve the retrieval efficiency of the QA system compared to the standard R2 system, but also slow down the inference speed within an acceptable range.  
Conceptualization, X.Z. In addition, W.Z.; methodology, X.Z. In addition, W.Z.; software, X.Z. In addition, W.Z.; validation, Q.Z. In addition, C.L.; formal analysis, X.Z. In addition, W.Z.; investigation, Q.Z.; resources, C.L.; data curation, X.Z. In addition, Q.Z.; writing--original draft preparation, X.Z., W.Z. In addition, Q.Z.; writing--review and editing, C.L.; visualization, X.Z.; supervision, C.L.; project administration, C.L.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Natural Science Foundation of China (No. 61572434) and Shanghai Science and Technology Committee (No. 19 DZ2204800).  
The data used in the experiment are all presented in the Github Repository: [[https://github.com/XY2323819551/TGQE_code](https://github.com/XY2323819551/TGQE_code)]([https://github.com/XY2323819551/TGQE_code](https://github.com/XY2323819551/TGQE_code)). All the data is public, accessed on 27 April 2023.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} OpenQA & Open-Domain Question Answering \\ PRF & Pseudo-Relevance Feedback \\ HTGQE & Hybrid Text Generation-based Query Expansion \\ QE & Query Expansion \\ NQ & Natural Questions \\ Trivia & TriviaQA \\ \end{tabular}