esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Savitzky-Golay Filter  
In this study, the Savitzky-Golay filter is employed to reduce path oscillation. A Savitzky-Golay filter is a data smoothing algorithm based on local least-squares polynomial approximation [20]. The filter is able to preserve the original structure of the signal while removing noise. There are three inputs required for the implementation: noisy signal, the order of polynomial, and frame-size. The best fit of these values is estimated using the trial and error method [21].  
For example, a set of waypoints trajectory \(S~{}=~{}\left\{s_{i}\right\}~{}=~{}\left\{\left(x_{i},~{}y_{i}\right),i=1,2,~ {}...,n\right\}\), is expressed as a set of positive odd integer, \(m\) frames with coefficients, \(\mathrm{C}_{i}\), given as follows:  
\[sf_{i}~{}=~{}\sum_{i~{}=~{}-\frac{m-1}{2}}^{\frac{m-1}{2}}C_{i}\,s_{i} \tag{5}\]  
where \(s_{i}\) and \(sf_{i}\) represent unfiltered and filtered data, respectively. To the author's knowledge, there is no clear definition of the best value suitable to be used, thus, as previously mentioned, the final result will be based on trial and error estimation.
## 2 Deep Learning Models
### Gated Recurrent Units  
Gated Recurrent Units (GRU) is a featured and efficient variant of LSTM. It maintains the effect of LSTM while making the structure simpler, which introduced by [PERSON] et al. [26]. The GRU combines the input gate and the forget gate of LSTM into an update gate \(\Gamma_{u-1}\), and the output gate in LSTM is named a reset gate (\(\Gamma_{r-1}\)) in GRU. \(\Gamma_{u-1}\$\) determines how to combine the new input with the previous memory, and \(\Gamma_{r-1}\) determines how much of the previous memory can pass through. GRU model structure is shown in Figure 3.  
The formulation for updating the cell states of GRU and parameters as follows:  
\[\Gamma_{r-1}=\sigma(W_{r-1}[S_{t-1},x_{t}]+b_{r-1}) \tag{12}\]  
\[\Gamma_{u-1}=\sigma(W_{u-1}[S_{t-1},x_{t}]+b_{u-1}) \tag{13}\]  
Figure 3: Basic gated recurrent units structure.  
Figure 2: Basic long short-term memory structure.  
\[\widetilde{S_{t}}=tanh(W_{s}[\Gamma_{r-1}*S_{t-1},x_{t}]+b_{s}) \tag{14}\]  
\[S_{t}=(1-\Gamma_{u-1})*S_{t-1}+\Gamma_{u-1}*\widetilde{S_{t}} \tag{15}\]  
\[\hat{y_{t}}=\sigma(W_{o}S_{t}). \tag{16}\]  
The symbols share the same meaning as LSTM except for \(\Gamma_{u-1}\) and \(\Gamma_{r-1}\). GRU has fewer parameters and thereby benefit from a faster training speed than LSTM.
# Research on a Service Load Prediction Method Based on VMD-GLRT
### Related Work
#### 1.2.1 Machine Learning  
Machine learning has become increasingly popular among scholars due to its many advantages, such as the ability to train on large datasets and use labeling, prediction, and other operations to obtain a more accurate prediction model.  
The study in [7] proposed an Evolutionary neural Network NN (ENN) that used Particle Swarm Optimization (PSO), Differential Evolution (DE), and the covariance matrix adaptive optimization evolution strategy. This method can predict more accurately in a short time, but in the long run, there is a decrease in accuracy. The study in [8] proposed an Automatic Regression Integrated Moving Average workload forecasting (ARIMA-PERP) model, which determined the best-fitting ARIMA model by minimizing Information Criterion, Enhanced Minimizing Information Criterion, and Bayesian Information Criterion (BIC). It realized the accurate advance of node load in advance, and the accuracy reached 91.11%. The study in [9] proposed a prediction method based on ensemble learning, which used the fast-learning ability of an Extreme Learning Machine (ELM) to reduce the consumption of training time, selected its weight based on a heuristic algorithm to predict the load of the cloud resources and improved the prediction accuracy compared with the traditional model. The study in [10] proposed a novel hybrid wavelet time series decomposition and GMMH-ELM ensemble method (WGE) for NFV workload prediction, which predicted and integrated workloads on different time-frequency scales. Compared with the traditional SVR and LSTM on multiple data sets, the MAPE was improved by about 8%. The study in [11] proposed a prediction method based on support vector regression, which used the historical host utilization of multiple resources to train support vector machines and realize prediction. Compared with the traditional linear regression prediction, Euclidean distance, and regression based on absolute sum, the proposed method performed better in many aspects, such as mean square error and fundamental percentage error. The study in [12] analyzed and compared the prediction results of more mainstream machine learning methods, such as linear regression and ElasticNet, with the Gated Recurrent Unit (GRU). The results show that it can provide superior prediction accuracy with increased data volume, which is contrary to the traditional machine learning methods. Therefore, it was concluded that the deep learning method is excellent for the conventional machine learning regression algorithm in time series modeling and can provide superior prediction accuracy.
###### Abstract  
The quality of a mine's microseismic network layout directly affects the location accuracy of the microseismic network. Introducing the microseismic probability factor \(F_{e}\), the microseismic importance factor \(F_{Q}\), and the effective range factor \(F_{V}\), an improved particle swarm algorithm with bacterial foraging algorithm is proposed to optimize the mine's microseismic network layout and evaluation system based on the \(D\)-value optimization design theory. Through numerical simulation experiments, it is found that the system has the advantages of fast optimization speed and good network layout effect. Combined with the system application at Xiashije Coal Mine in Tongchuan City, Shaanxi Province, the method in this paper successfully optimizes the layout of the 20-channel network, ensuring that the positioning error of key monitoring areas is controlled within 20 m, and the minimum measurable magnitude can reach \(-3.26\). Finally, it is verified by blasting tests that the maximum spatial positioning accuracy of the site is within 12.2 m, and the positioning capability of the site network is more accurately evaluated. The relevant research can provide a reference for the layout of the microseismic monitoring network for similar projects.  
microseismic network layout; the \(D\)-value optimization design theory; improved particle swarm algorithm (IPSO); numerical simulation experiment; blasting test +
Footnote †: journal: Applied Sciences  
19 July 2022  
Accepted: 19 August 2022  
Published: 24 August 2022
# Lightweight and Robust Malware Detection Using Dictionaries of API Calls
## 9 Conclusions and Future Direction  
The objective of this study was to detect malicious software by utilizing malware API calls as classification features. First, the approach involved extracting the initial part of the API calls of length 100. By adopting this method, the study aimed to identify malicious behavior at the earliest possible stage. Nonetheless, this approach conceals a wealth of valuable information, particularly for goodware. To uncover comprehensive insights into both malware and goodware, extracting full-length sequences of API calls proves to be highly beneficial. Furthermore, the reuse rate of API calls offers significant insights into the utilization of identical combinations by malware, thereby uncovering trends followed by malware creators. It is worth noting that the dictionary of API calls used by malware is comparatively limited in comparison to goodware, as attackers frequently resort to employing previously successful tactics.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **SCT** & **CatBoost**[30] & **Model 2 (DGCNN)**[31] & **LSTM**[31] \\ \hline Precision & 0.97 & 0.96 & 0.99 & 0.99 \\ \hline Recall & 1.0 & 0.84 & 0.99 & 0.99 \\ \hline F1 score & 0.98 & 0.89 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with cutting-edge approaches.  
While there has been considerable success in utilizing time series data of API call sequences with neural network models for malware detection, it is important to note that these solutions often demand substantial computational power and resources.  
STC demonstrates shorter training time and lower intrinsic complexity compared to other approaches. The experimental results exhibited a superior performance of STC in detecting malware, indicating its potential for real-time inference on edge devices. Additionally, it has been demonstrated that STC exhibits robustness against model drift.  
For future research, it would be intriguing to train STC using a dataset and assess its performance on a daily basis with collected malware samples. Furthermore, as an extension of this study, the STC results can be utilized as a feature in combination with other static or dynamic features to minimize false positives, false negatives, and enhance overall detection accuracy.  
Conceptualization, A.Y.D. and A.A.-N.; Data curation, A.Y.D.; Formal analysis, A.Y.D.; Funding acquisition, A.A.-N. and J.C.; Investigation, A.Y.D.; Methodology, A.Y.D. and A.A.-N.; Project administration, A.A.-N. and J.C.; Resources, A.Y.D.; Software, A.Y.D.; Validation, A.Y.D.; Writing--original draft, A.Y.D.; Writing--review & editing, A.A.-N. and J.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All data were presented in the main text.  
The authors declare no conflict of interest.
## 2 Problem Statement
\begin{array}{rcl}\dot{x}_{1}(t)&=&-\lambda x_{1}(t)-\theta(t)x_{1}(t)x_{2}(t )+\rho x_{4}(t)+\alpha x_{1}(t)x_{3}(t)+\lambda-\frac{v(t)}{N(t)}\\ \dot{x}_{2}(t)&=&-(\lambda+\gamma_{1}+\sigma)x_{2}(t)+\theta(t)x_{1}(t)x_{2}(t )+\alpha x_{2}(t)x_{3}(t)\\ \dot{x}_{3}(t)&=&-(\lambda+\gamma_{2}+\alpha)x_{3}(t)+\sigma x_{2}(t)+\alpha x _{3}^{2}(t)\\ \dot{x}_{4}(t)&=&-(\lambda+\rho)x_{4}(t)+\gamma_{1}x_{2}(t)+\gamma_{2}x_{3}(t )+\alpha x_{4}(t)x_{3}(t)+\frac{v(t)}{N(t)}\end{array}  \tag{8}\]  
where \(\theta(t)=\beta(t)\). This change in notation (i.e., \(\theta(t)=\beta(t)\)) has been considered for two main reasons: _(i)_ in the background literature, \(\theta(t)\) is usually referred to as the parameter to be estimated; _(ii)_ considering that the estimation \(\hat{\theta}(t)\) is obtained from the model (8), then \(\hat{\beta}(t)\) will be computed from the expression  
\[\hat{\beta}(t)=\text{sat}_{\hat{p}_{m}}^{\hat{\beta}_{M}}(\hat{\theta}(t)), \tag{9}\]  
whose value will be more reliable since the bounds for the transmission rate (see Assumption 2) are being taken into account. Recall that \(\text{sat}()\) is defined in the Notation section, Equation (1). Considering that  
\[x_{1}(t)+x_{2}(t)+x_{3}(t)+x_{4}(t)=1, \tag{10}\]  
one can eliminate one of the equations (e.g., \(\dot{x}_{4}(t)\)) and rewrite system (8) in vector notation, that is,  
\[\dot{x}(t)=f(x(t),\theta(t))+\sum_{i=1}^{2}g_{i}(x(t))u_{i}(t) \tag{11}\]  
where  
\[f(x(t),\theta(t))=\left[ \begin{array}{c}Ax_{1}(t)-\theta(t)x_{1}(t)x_{2}(t )+\alpha x_{1}(t)x_{3}(t)-\rho(x_{2}(t)+x_{3}(t))\\ Bx_{2}(t)+\theta(t)x_{1}(t)x_{2}(t)+\alpha x_{2}(t)x_{3}(t)\\ Cx_{3}(t)+\sigma x_{2}(t)+\alpha x_{3}^{2}(t)\end{array} \right],\quad g_{1} =\left[ \begin{array}{c}1\\ 0\\ 0\end{array} \right],\quad g_{2}=\left[ \begin{array}{c}1\\ 0\\ 0\end{array} \right], \tag{12}\]  
\[\text{and }x(t)=[x_{1}(t)\,x_{2}(t)\,x_{3}(t)]^{T},f:\mathbb{R}^{3}\times \mathbb{R}_{+}\rightarrow\mathbb{R}^{3}\text{, and }g_{i}:\mathbb{R}^{3}\rightarrow\mathbb{R}^{3}\text{ for }i=1,2,\]  
\[u_{1}(t)=-\frac{v(t)}{N(t)},\quad u_{2}=\rho+\lambda, \tag{13}\]  
and  
\[A=-(\lambda+\rho),\,B=-(\lambda+\gamma_{1}+\sigma),\,C=-(\lambda+\gamma_{2}+ \alpha). \tag{14}\]  
Two outputs will be considered from the dynamical system (12), that is,  
\[y_{1}(t)=\sigma x_{2}(t)\quad\text{and}\quad y_{2}(t)=\alpha x_{3}(t), \tag{15}\]  
where \(y_{1}\) and \(y_{2}\) stand for new hospital admissions and new deaths, respectively. For notation convenience, from now on, the time dependency of variables and functions will not be explicitly written in proofs unless it is necessary to avoid any confusion.
## 3 Results  
### Characterization  
FT-IR spectra confirm that Eg-LiCl was loaded onto mGO completely (Figure 1). The FT-IR spectrum of mGO was drawn in the orange spectrum. The stretching O-H, carbonyl C=O, aromatic C=C, bending O-H, aromatic C-C, alkoxy C-O-C, stretching CH\({}_{2}\), and carbonyl C=O bonds are identified according to the peaks of 3380, 1730, 1630, 1360, 1220, 1050, 2880, and 1730 cm\({}^{-1}\), respectively. Peaks lower than 700 cm\({}^{-1}\) characterizebonds of Fe-O [25]. The blue spectrum is related to EgLiCl-mGO. The bands of 3430 and 1640 cm\({}^{-1}\) are associated O-H bonding. Alkane CH\({}_{2}\) stretching, CH\({}_{2}\) scissor, CH\({}_{2}\) wagging, and CH\({}_{2}\) twisting are characterized according to peaks around 2914, 1460, 1350, and 1258 cm\({}^{-1}\), respectively. The sorption peaks of Li-O and Fe-O were seen at the wavelengths of 1300 and 700 cm\({}^{-1}\), respectively [25]. Table S1 summarizes the data.  
The X-ray diffraction (XRD) analysis of EgLiCl-mGO is patterned in Figure 2a, which has good agreement with JCPDS cards No. 01-075-0449, indicating magnetite Fe\({}_{3}\)O\({}_{4}\) and JCPDS cards No. 01-079-1715 confirming the GO structure. Fe\({}_{3}\)O\({}_{4}\) was characterized by peaks at 30.70, 35.97, 37.23, 43.74, 53.94, 57.55, 63.32, and 67.14\({}^{\circ}\). Additionally, peaks at 11.23, 21.64, 30.70, 35.97, 43.74, 53.94, 57.55, 63.32, and 75.08\({}^{\circ}\) are related to GO [26]. The process of loading EgLiCl onto mGO broadened the peaks of mGO. The crystal size of the total product was calculated using the Scherrer formula. It is about 6 nm.  
The AGFM curve of EgLiCl-mGO is presented in Figure 2b. The amount of \(\pm\)25 electromagnetic units (emu g\({}^{-1}\)) is helpful for the rapid gathering of the adsorbent in the sample solution [27]. The remanent magnetization is almost zero, confirming the superparamagnetic property and the presence of Fe\({}_{3}\)O\({}_{4}\) nanoparticles.  
Figure 1: FT-IR spectra of mGO and EgLiCl-mGO.  
Figure 2: (a) The XRD pattern of EgLiCl-mGO; (b) magnetic hysteresis loops image of EgLiCl-mGO.
# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 4 Reputation-Based Collaborative User Recruitment Algorithm
### User Reputation
Although past reputation scores play an important role in users' reliability and credibility, the cloud platform will also consider real-time factors, for instance, distance and time limitations. We assume that \(\sigma_{u_{i}}^{inst}(t)\) denotes the user's instantaneous reputation score, which is limited by some restrictions, such as the locations and sensing radius. So we can attain the instantaneous reputation score \(\sigma_{u_{i}}^{inst}(t)\) of user \(u_{i}\) for task \(\theta_{j}\) as follow:  
\[\sigma_{u_{i}}^{inst}(t)=\beta D_{i}+(1-\beta)\frac{R_{u_{i}}}{R_{\theta_{j}}}, \tag{8}\]  
where \(\beta\) is the weight coefficients ranging from 0 to 1. \(R_{u_{i}}\) is the sensing radius of user \(u_{i}\), and \(R_{\theta_{j}}\) represents the coverage radius of task \(\theta_{j}\). So \(R_{u_{i}}/R_{\theta_{j}}\) indicates the coverage ability of user \(u_{i}\) for task \(\theta_{j}\). \(D_{i}\) is the distance factor, which can be represented by  
\[D_{i}=1-\left(D_{u_{i},\theta_{j}}\Big{/}D_{\text{max}}\right), \tag{9}\]  
where \(D_{u_{i},\theta_{j}}\) represents the distance between user \(u_{i}\) and task \(\theta_{j}\), and \(D_{\text{max}}\) equals to \(R_{u_{i}}+R_{\theta_{j}}\), which is the maximum distance the users can provide the coverage for the task. Through Equation (8), we know that the instantaneous reputation score \(\sigma_{u_{i}}^{inst}(t)\) affects whether a user would complete the task with high coverage and quality. In addition, we consider all the factors to deduce the user \(u_{i}\) reputation score \(\sigma_{u_{i}}(t)\) equation at time \(t\) as follows:  
\[\sigma_{u_{i}}(t)=\omega\sigma_{u_{i}}(t^{-})+(1-\omega)\sigma_{u_{i}}^{inst} (t), \tag{10}\]  
where \(\omega\) represents the balancing parameter ranging from 0 to 1.
# Prototypical Graph Contrastive Learning for Recommendation
## 5 Related Work
### Graph Contrastive Learning for Recommendation  
Contrastive learning presents a solution to the sparsity issue in recommendation by leveraging its self-supervised nature to extract features from vast amounts of unlabeled data [36; 37]. Influenced by research in other domains [38], certain studies construct suitable contrastive views by perturbing the structure of interaction graphs. SGL [5] employs a random strategy to discard nodes and edges from the interaction graph, maximizing consistency among embeddings across different views. LightGCL [12] adopts a strategy of random singular value decomposition to reconstruct the adjacency matrix, effectively preserving crucial graph structural information and offering a low-rank solution for optimizing the contrastive learning paradigm. Despite the effectiveness of graph structure perturbation, it may lead to certain nodes failing to learn useful neighborhood information, thus misguiding feature learning. Therefore, some methods different from structure augmentation have started to emerge. SimGCL [10] adds uniform noise in the embedding space to achieve feature augmentation, reducing the negative effects of graph structure perturbations. AdaGCL [26] employs variational autoencoder to generate a graph during training, mitigating the influence of noisy interaction in the original graph. Nonetheless, frequent graph generation raises concerns regarding complexity.
###### Abstract
The AHSI is capable of collecting data at a 30-m resolution with a 60 km swath. AHSI's radiometric quality was evaluated based on its on-orbit absolute radiometric calibration performance from two aspects: (1) the uncertainty of the on-orbit absolute radiometric calibration, and (2) the error of the absolute radiometric calibration [21]. The former is within 2.59% (VNIR) and 2.68% (SWIR), which is assessed from comparison between the in situ measured and simulated data. The latter is smaller than 5% for the VNIR bands (i.e., 390-1029 nm). Specifically, to determine the error in the absolute radiometric calibration, the radiometric calibration parameters acquired from the Baotou calibration field were applied on another image collected for the Dunhuang calibration field, and the calculated image radiance was then compared to the MODTRAN simulated values based on in situ measured spectra. Compared with the specifications of other instruments (e.g., MODIS, MERIS, and Landsat), AHSI also has the potential for applications of inland water quality monitoring, especially for small- or medium-sized waterbodies. In addition, AHSI's numerous bands may allow its data to retrieve \(Z_{sd}\) with a higher accuracy compared to that of multi-spectral sensors (e.g., Landsat). But corresponding studies have not yet discussed, in detail, on the AHSI data. In this study, we aim to test the space-borne hyperspectral instrument AHSI's ability in terms of \(Z_{sd}\) retrieval and validate the semi-analytical method applied to AHSI imagery. Section 2 provides an introduction to the study area and datasets used here, as well as the necessary preprocessings associated with the AHSI images. We introduce the semi-analytical model with various QAAs in Section 3. Section 4 presents the experiments, including the testing of various \(Z_{sd}\) methods using in situ and image AHSI band \(R_{rs}\), as well as an accuracy analysis of the retrieved \(Z_{sd}\) using the AHSI imagery. We discuss the experiments Section 5 and present our conclusions in Section 6.
# Named Entity Recognition Method Based on Multi-Feature Fusion
## 5 Discussion  
First, the use of Non-negative Matrix Factorization (NMF) technology to extract entity types from online review datasets can more accurately capture the key information in the dataset. NMF is an effective text mining method that identifies the potential semantic structure in a document by decomposing the document-vocabulary matrix into the product of two non-negative matrices. This method is particularly suitable for processing unstructured text data, such as online reviews, because it can reveal the implicit topics or entity types in the comments, thereby helping us better understand and analyze users' real feedback and opinions.  
Second, in the Named Entity Recognition (NER) task of Chinese text, the performance of the BERT model is better than that of ELECTRA and RoBERTa-wmm. As a method of pre-training language representation, BERT can capture rich language features and contextual information by pre-training deep bidirectional representations on large-scale corpora. In the Chinese NER task, BERT can more accurately identify entities in the text, such as names of people and places, thanks to its powerful context modeling capabilities. In contrast, although ELECTRA and RoBERTa-wmm also use similar pre-training strategies, their performance on the Chinese NER task is not as good as BERT.  
Finally, in the feature extraction layer, the method of using BiLSTM and DGCNN at the same time is better than using these two methods in a step-by-step manner. BiLSTM can capture long-distance dependencies in text, while DGCNN is good at capturing local structural features. Combining these two methods can take advantage of their advantages at the same time so as to obtain more comprehensive information during feature extraction. This joint method can improve the model's ability to understand text data, thereby improving the performance of named entity recognition tasks.
## 4 Performance Analysis
### SINR Comparison of All Beamformer with Respect to Disparity Angle  
An SINR performance comparison of the LCA, LCPAA, LRAA, and LCCAA beamformers with respect to \(1^{\circ}\), \(2^{\circ}\), and \(3^{\circ}\) disparity angles (degree) is shown in this section using FDL, ODL, VDL, and NVL techniques.