esa-sceva/satcom-chunk-collection huggingface dataset:
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 2 Literature Review
### Phishing Scam-Related Studies in Blockchain Transactions  
On the Ethereum blockchain, [PERSON] et al. [75] developed a technique for detecting fraudulent accounts. Malicious and non-malicious addresses were included in the data collection. A string comparison was utilized to filter out duplicate addresses regardless of case sensitivity. Addresses that had null transactions were also removed from the data set. Their method compared KNN, decision tree, RF, and XGBoost supervised learning algorithms. [PERSON] and [PERSON] [76] suggested a multilayer perceptron design for detecting cryptocurrency dishonesty. The data for their assessment was gathered from the CMC website and classified as either lawful or fraudulent. Linear regression, Softmax regression, SVM, and MLP were used in the analysis, while MLP was shown to be the most accurate. [PERSON] et al. [77] suggested an improved graph classification technique (Graph2 Vec) for phishing detection on the Ethereum blockchain. They took phishing addresses from etherscan.io and added the same number of legitimate addresses to generate data collection. They gathered the transactions for each lesson, deleted the duplicate data, and excluded addresses with fewer than ten transactions and more than 300 transactions. They compared the results to node2 vec, WL-kernel, and Graph2 Vec, among other approaches. [PERSON] et al. [78] introduced a phishing scam detection method for the Ethereum blockchain. They employed a convolutional graph network and an autoencoder to detect phishing accounts. They utilized the Ethereum transaction history for data collection. They compared the performance of their GCN approach, DeepWalk, Node2 Vec, and LINE methods. [PERSON] et al. [79] employed ML methods to identify Ponzi schemes on the Ethereum blockchain. They gathered smart contract source code from etherscan.io and manually evaluated whether they were Ponzi scheme contracts to produce data collection. The characteristics were extracted without the course code, along with all linked and failedtransactions. The contracts were then translated from bytecode to opcode, and the features were categorized before feature extraction. XGBoost was utilized by [PERSON] et al. [80] to find Ponzi schemes on the Ethereum blockchain. They used etherscan.io to gather smart contracts to test their technology. The frequency of the bytecodes was determined after they were translated into opcodes. Ponzi and non-Ponzi contracts were labeled.
## GPS & GLONASS Mass-Market Receivers: Positioning Performances and Peculiarities  
**[PERSON] * and [PERSON]**  
Politecnico di Torino--DIATI Department, Corso Duca degli Abruzzi 24, Turin 10129, Italy;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-011-090-7662; Fax: +39-011-090-7699.  
External Editor: [PERSON]  
_Received: 14 July 2014; in revised form: 12 November 2014 / Accepted: 17 November 2014 / Published: 25 November 2014_  
**Abstract:** Over the last twenty years, positioning with low cost Global Navigation Satellite System (GNSS) sensors have rapidly developed around the world at both a commercial and academic research level. For many years these instruments have only acquired the GPS constellation but are now able to track the Global'naja Navigacionnaja Sputnikovaja Sistema (GLONASS) constellation. This characteristic is very interesting, especially if used in hard-urban environments or in hard conditions where satellite visibility is low. The goal of this research is to investigate the contribution of the GLONASS constellation for mass-market receivers in order to analyse the performance in real time (Network Real Time Kinematic--NRTK positioning) with post-processing approaches. Under these conditions, it is possible to confirm that mass-market sensors could be a valid alternative to a more expensive receiver for a large number of surveying applications, but with low cost hardware the contribution of the GLONASS constellation for fixing ambiguities is useless, if not dangerous.  
**Keywords:** GNSS; GLONASS; mass-market receivers; quality position; accuracy
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
## 4 Numerical Results and Discussion
### Sobol Indices  
Sobol Index is a value that varies from 0 to 1 which indicates the sensitivity of the output against each parameter. More the index is close to 1, more influence this parameter has on the result. With the value of Sobol indices, it is easy to figure out the parameter that should be paid more attention and which one less attention. Each Sobol index of the output \(\text{Gain}=M\) (bone radius \(B\), reflector distance \(R\), fat thickness \(F\), muscle thickness \(M\), implantation depth \(I\)) is defined as  
\[Soboll Index=\frac{Var[E[\text{Gain}[P]]]}{Var[Gain]} \tag{14}\]  
where \(P\) is one of the variables (\(B\), \(R\), \(F\), \(M\), \(I\)) that affect the output.  
The Sobol indices are easy to evaluate with a surrogate PCE model by using Matlab. Figure 5 shows the first order Sobol Index relevant to the different uncertainty parameters.  
Figure 4: Leave-One-Out (LOO) Error in log VS Number of samples.  
Figure 5: The Sobol indices VS different parameters under different number of samples.  
As shown in Figure 5, the implantation depth is the most important factor among the five different parameters. It is worth pointing out that the implantation depth refers to the distance between the muscle-fat interface and the antenna center. This conclusion could be deduced even with only 30 samples. Therefore, the implantation depth should always be considered during the design and analyze for a wireless power transmission system.
# Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot
## 5 Conclusions  
In this paper we proposed a remote-controlled method with force and visual assists for a mobile robot. The visual assist was used to improve operability, and the force assist was used for safety performance. The force and visual assists could help the operator avoid collisions and maintain remote-controlled operability. The force assist was generated based on the TTC of the mobile robot against an obstacle. For collision avoidance, this force assist was applied to the operator via a control device. The predicted trajectory of the mobile robot was generated based on the TTC as a visual assist. The predicted trajectory with color gradation was provided on the monitor to improve operability. Ten subjects participated in the experiments to evaluate operability and safety performance. In summary, the proposed method, which comprised force and visual assists for a mobile robot with a low presence of the force assist, was evaluated experimentally and its validity was confirmed.  
Conceptualization, R.M.; methodology, R.M.; software, R.M.; validation, R.M.; formal analysis, R.M.; investigation, R.M.; resources, R.M.; data curation, R.M.; writing--original draft preparation, R.M. and M.K.; writing--review and editing, R.M. and M.K.; visualization, R.M.; supervision, N.M.; project administration, N.M.; funding acquisition, N.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by JSPS KAKENHI (19K04454), and International Affairs Special Funding from the Graduate School of Maritime Sciences, Kobe University.  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TTC & Time to collision \\ LRF & Laser range finder \\ UDP & User datagram protocol \\ DOB & Disturbance observer \\ RFOB & Reaction force observer \\ LPF & Low-pass filter \\ RGB & Red, green, and blue \\ \end{tabular}
## 2 Study Area and Data
### Data Description and Processing
#### 2.2.3 Land Use Data  
The land use data (Globeland30, [[http://www.globallandcover.com/GLC30](http://www.globallandcover.com/GLC30) Download/index.aspx]([http://www.globallandcover.com/GLC30](http://www.globallandcover.com/GLC30) Download/index.aspx)) of spatial 30 m resolution in 2010 were employed to modify the extracted area of summer maize in the reference area. The cultivated land (code 10) dataset in the land use data was overlaid with the spatial distribution of summer maize from ETM+ images to exclude the influence of other land use types on the extraction results in the reference area.
## 2 Literature Review
### Research on Topic Identification  
Research on topic identification mainly focuses on the overall research and the summary of methods adopted. Generally speaking, [PERSON] summarized topic discovery and evolution research outlined in Chinese scientific and technological documents based on the topic model, using the document analysis method [10]. [PERSON] and [PERSON] analyzed the research status of disruptive technology identification from the three perspectives of data sources, technical characteristics and identification methods [11]. [PERSON] and [PERSON] analyzed the existing index system for subversive technology identification from the perspectives of technical characteristics, market characteristics and the macro environment [12]. [PERSON] et al. sorted out the existing subversive technology identification methods from the perspective of technology, the market, and a combination of technology and the market [13]. [PERSON] et al. proposed a text mining framework to extract the main topics from speeches [14]. [PERSON] et al. identified around 20 topics related to service robotics by applying13 topic modeling [15].  
As regards the application methods, [PERSON] summarized emerging topic identification methodology systems, including an index and trend analysis method based on external feature statistics, a citation analysis method based on document citation patterns, a network analysis method based on co-occurrence relationships and a text mining method based on semantic analyses [2]. [PERSON] and [PERSON] summarized topic discovery technology based on co-word analyses, and improved the partition clustering algorithm, hierarchical clustering algorithm and other clustering methods [16]. [PERSON] and [PERSON] focused on quantitative research methods based on literature assessments and data mining, and classified them into three categories--subject word or literature statistics methods, citation network clustering methods, and text mining analysis methods--and carried out comparative analyses of these methods [17]. [PERSON] et al. reviewed urban flood numerical simulations by summarizing the calculation methods of surface runoff, drainage systems, and coupled models [18]. [PERSON] et al. provided an overview of the methods of data collection, sampling, and analysis in qualitative research [19]. [PERSON] et al. compared different research methods related to the soil cracking field [20].  
In general, these studies give an overview of topic identification research and the methods applied, but they do not subject the models and algorithms separately to analysis and study, and these studies have not taken a quantitative approach to assessing the topic identification methods either.
## 3 Results
### Relation to Demographic, Clinical, Neuropsychological and Neuroanatomical Variables
#### 3.2.2 Neuropsychological Measures  
We fitted three different models that tested the relations between the correct performance and ACE-R, the comprehension part of the BDAE and the expression part of the BDAE. The best-fitting model included the scores of the ACE-R and the total of the comprehension subtests of the BDAE. This model suggested that the BDAE comprehension score predicts the percentage of the correct responses, but this was not the case for the ACE-R score (see Table 5 for estimates, x\({}^{2}\) and R\({}^{2}\)). The performance in the expressive subtests of the BDAE was not a predictor; i.e., the model that included the score in the expressive subtests was not significantly better than the one with the intercept, either, although the language task is a production task. This suggests that general language abilities are predictors of correct performance, while general cognitive abilities are not related to the performance in this type of sentence.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictor** & **Estimate (b)** & **Standard Error** & **z-Value** & \(p\)**-Value** \\ \hline Intercept & \(-18\) & \(4.16\) & \(-4.34\) & \(<\)0.001 \\ \hline Age & \(0.24\) & \(0.054\) & \(4.41\) & \(0.00001\) \\ \hline Years of Education & \(0.22\) & \(0.09\) & \(2.49\) & \(0.013\) \\ \hline \hline \end{tabular} Note: R\({}^{2}\) = 0.26 (Hosmer and Lemeshow), 0.29 (Cox and Snell), 0.39 ([PERSON]), Model x\({}^{2}\) (1) = 7.6, \(p\) = 0.006.  
\end{table}
Table 4: Estimates, x\({}^{2}\) and R\({}^{2}\) of the best-fitted model for demographic characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictor** & **Estimate (b)** & **Standard Error** & **z-Value** & \(p\)**-Value** \\ \hline Intercept & \(-6\) & \(1.9\) & \(-3.1\) & \(0.0017\) \\ \hline ACE-R & \(0.0137\) & \(0.0216\) & \(0.64\) & \(0.5\) \\ \hline BDAE Comprehension & \(0.22\) & \(0.09\) & \(2.5\) & \(0.011\) \\ \hline \hline \end{tabular} Note: R\({}^{2}\) = 0.14 ([PERSON] and [PERSON]), 0.17 ([PERSON] and [PERSON]), 0.23 ([PERSON]), Model x\({}^{2}\) (1) = 6.7, \(p\) = 0.006.  
\end{table}
Table 5: Estimates, x\({}^{2}\) and R\({}^{2}\) of the best-fitted model for neuropsychological tests.
## 4 Experiments and Results  
In this section, we evaluated the discrimination ability of IACS values between adversarial examples and normal examples and tested these features on the MNIST, SVHN, and CIFAR10 datasets. We conducted a comparison with the state-of-art methods including kernel density estimates (KD)-based method [21], local intrinsic dimensionality (LID)-based method [13] and natural scene statistics (NSS)-based method [18].
# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
###### Abstract
[PERSON] [19] provided a universal solution for cross-domain information fusion scenarios in body sensor networks and a feasible solution for quantitatively evaluating the domain weights; they conducted experiments to verify the adaptability and promising performance of fusion frameworks and dynamic domain evaluation for cross-domain information fusion in BSNs. In terms of new systems, [PERSON] et al. [20] presented an EEG signal-based automated cross-subject emotion recognition framework that decomposed the EEG signals into four sub-band signals; the test results showed that the presented framework had advantages for classifying human emotions compared to other state-of-the-art emotion classification models. [PERSON] et al. [21] proposed a novel platform of physiological signal acquisition for multi-sensory emotion detection to record and analyze different physiological signals, and the most important features of the proposed platform were compared with those of a proven wearable device. [PERSON] et al. [22] built a system to assess the emotional response in drivers while they were driving on a track with different car handling setups. The experimental results based on the system indicated that the base car setup appeared to be the least stressful, and that the presented system enabled one to effectively recognize stress while the subjects were driving in the different car configurations. [PERSON] et al. [23] constructed a hybrid deep neural network to evaluate automobile interior acceleration sounds fused with physiological signals, and the results showed that the constructed network contributed to achieving accurate evaluations of automobile sound quality with EEG signals. A review of research on emotion recognition and judgment based on physiological signals can be carried out from different aspects. In the study of this field, the most common approach is to establish a system or model based on physiological signals as the comparison standard to recognize and judge emotions, so psychological signals are a basic element. People's psychological signals are mainly composed of EEG, EDA, ECG, EMG, pulse waves, etc. Pulse waves have obvious mechanical characteristics of blood flow and are employed less often in studies based on emotion recognition and judgment. Therefore, this paper mainly offers a review of the research work and application of emotion recognition and judgment based on EEG, EDA, ECG and EMG in recent years. It aims to provide readers with a further understanding of the role and characteristics of different psychological signals in emotional recognition and judgment. The rest of the paper is structured as follows. In Section 2, we introduce the research and application of EEG. Section 3 introduces research and application of EDA, ECG and EMG. The research and application of multimodal physiological signals are presented in Section 4. Section 5 presents ethical and privacy concerns related to the use of physiological signals for emotion recognition. Finally, a brief conclusion with future prospects is presented in Section 6.
## 3 Results
### Adsorption Equilibria
#### 3.2.3 Effect of pH  
The initial pH of the adsorption medium is one of the most influential parameters in the process of adsorption, it affects both the surface charges of the adsorbent and the degree of ionization of the adsorbate. In the present study, the adsorption experiments were performed in the initial solution pH range from 2 to 12 (Figure 5). The results show a significant increase in the adsorption efficiency and uptake from 19.83% and 4.42 mg/g to 88.33% and 21.26 mg/g when the pH values increased from 2 to 4, respectively, followed by a slight increase for pH 12 (95.97%, 22.75 mg/g). The increase in adsorption efficiency may also be due to the protonation of functional groups on the adsorbent surface. As already shown, the pH\({}_{\text{ZFC}}\) of SSS is 7.14; therefore, the surface of the adsorbent will be positively charged at a pH below pH\({}_{\text{ZFC}}\) and negatively at a pH above pH\({}_{\text{ZFC}}\). Thus, adsorption of MC (cationic substance) is favorable in basic medium. On the other hand, at acidic pHs, the surface of the SSS is excessively protonated, thus, the overall load of the adsorbent is positive. The cationic molecule MC releases positive ions in solution. Consequently, the electrostatic attraction of the cationic pollutant decreases and its adsorption becomes weaker. As the pH increases, the concentration of H ions decreases and more MC molecules occupy the active sites of the SSS until saturation [7,8].  
Figure 4: Effect of adsorbent dose on the adsorption efficiency (**a**) and uptake (**b**) of MC onto SSS (conditions: initial MC concentration 60 mg/L, pH 6, flask shaking at 300 rpm at 25 \({}^{\circ}\)C).  
Figure 5: Effect of pH on the adsorption efficiency (**a**) and uptake (**b**) of MC onto SSS (conditions: initial MC concentration 60 mg/L, adsorbent dose: 2.5 g/L, flask shaking at 300 rpm at 25 \({}^{\circ}\)C).
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 4 Experimental Results and Analysis
### Experimental Platform  
Job traces from CTC-SP2, HPC2N, Lublin-256, and SDSC-BLUE are used to evaluate our algorithm. Figure 5 illustrates key characteristics of the Lublin-256 trace, including requested execution times and processor counts. The selected workloads fall into two categories: synthetic and real. Lublin-256 is a synthetic workload, generated from a widely used workload model [40], while the remaining workloads come from real traces in the Standard Workload Format archive. These traces were chosen for two reasons. First, they are widely used in scheduling studies [1; 2; 11; 13; 22; 23], making them a popular choice for benchmarking. Second, they are diverse and represent common resource scenarios in large-scale HPC systems.  
We collect data and evaluate our method using a simulated HPC environment. Specifically, we extend the RL-compatible simulator Sched  
Figure 5: Lublin-256 trace characteristics.  
the collection of trajectories from various scheduling methods. In the evaluations, we compare our scheduling algorithm against others, using real traces to generate resource requests in the same order and with the same submission times. When a scheduling point is reached (e.g., upon new job generation or resource release), the scheduler invokes our algorithm to make scheduling decisions, allocating jobs to the HPC system based on available resources. If insufficient resources are available, scheduled jobs enter a waiting state until more resources are freed. This ensures the scheduling process realistically reflects actual workload submission and system behavior.  
Our algorithm is trained in epochs. During each epoch, multiple trajectories are sampled from a mixed dataset, with each trajectory consisting of interactions between the agent and the environment. The collected rewards are used to update the agent. Each epoch samples 10 trajectories, with each trajectory containing scheduling decisions for 256 consecutive jobs. Between epochs, 100 iterations are run to update both the action advantage and state-value networks. The target Q-network is updated every 10 epochs.
# On-Board Decentralized Observation Planning for LEO Satellite Constellations
## 2 Problem Description
### Communication Network Topology  
In MSOOP, the communication links are used to carry out data routing related to the planning process, such as broadcast of task sets, interaction of consensus information, etc. The size of these data is usually small, which is different from bulk data such as images. Therefore, there is no strict demand for the bandwidth of the communication links. The availability of the communication links is mainly discussed here. For any two nodes at a certain time, if a communication link can be established between them, they are defined to be neighbors to each other, and this relationship is bi-directional.  
* Communication-related variables: Let \(\mathcal{L}_{ii^{\prime}}=\left\{1,\,2,\,\ldots,P_{ii^{\prime}}\right\}\) be an index set of \(P_{ii^{\prime}}\)\(sat_{i}\)-to-\(sat_{i^{\prime}}\) communication availability masks. The \(l\)th mask \(cam_{ii^{\prime}l}\) is associated with the start time \(st_{ii^{\prime}l}^{\text{cam}}\) and the end time \(et_{ii^{\prime}l^{\prime}}^{\text{cam}}\). If \(\exists l\in\mathcal{L}_{ii^{\prime}}\) satisfies \(st_{ii^{\prime}l^{\prime}}^{\text{cam}}\leq t\leq et_{ii^{\prime}l^{\prime}}^ {\text{cam}}\) at time \(t\), \(sat_{i^{\prime}}\) is called the neighbor of \(sat_{i}\) at time \(t\). The index set of the neighbors of \(sat_{i}\) at time \(t\) is denoted as \(\mathcal{N}_{i}^{t}=\left\{1,\,2,\,\ldots,Q_{i}^{t}\right\}\), where \(Q_{i}^{t}\) is the number of the neighbors of \(sat_{i}\) at time \(t\). Due to the bi-directional characteristic, \(\mathcal{L}_{ii^{\prime}}=\mathcal{L}_{ii^{\prime}}\) and \(i^{\prime}\in\mathcal{N}_{i}^{t}\equiv i\in\mathcal{N}_{i^{\prime}}^{t}\).  
The crosslink between satellites is realized by establishing ISLs. ISLs are divided into intra-plane ISLs and inter-plane ISLs according to whether the sender and the receiver belong to the same plane. Each satellite can establish a stable intra-plane ISL with satellites on both sides of the same plane at any time, which makes the intra-plane ISLs form a closed loop. The availability of inter-plane ISLs is time-varying due to the relative motion of satellites on different planes. In this paper, we make a reasonable simplified availability standard that an ISL is available whenever the line-of-sight vector between two satellites passes above the surface of the earth. The communication network topology of Walker-\(\delta\) (30/3/1, 600 km, 60\({}^{\circ}\)) at time \(t=0\) is shown in Figure 2. For instance, \(sat_{1}\) has two intra-plane neighbors (\(sat_{2}\), \(sat_{10}\)). \(sat_{2}\) has two intra-plane neighbors (\(sat_{1}\), \(sat_{3}\)) and three inter-plane neighbors (\(sat_{23}\), \(sat_{24}\), \(sat_{25}\)).