esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Contextual Relation Model for Improving Response Generation
## 3 Context Relations Analysis
### Problem Statement  
As mentioned in the introduction, in this paper, we aim to tackle a dialogue generation problem. Given the dialogue history of the two speakers, the goal is to predict the most appropriate reply based on a pre-trained model. A simple and widely used method is to predict responses based solely on history, but since the connection between responses and history is often loose in the open domain, it is often necessary to introduce more information to obtain a suitable reply, such as conversational future information. However, in the case of low-conversational sentences, more information is often less helpful, and in the case of high relevance, more information is more effective. Therefore, we must first clarify the correlation between dialogue sentences. In order to obtain the relationship of conversational sentences, we first consider the basic unit composition of sentence correlation and the types of correlation corresponding to the correlation combination.  
In our context relations analysis, considering that (1) selecting multi-sentence contexts introduces analytical and computational complexity, and (2) predicting multiple future sentences by the dialogue model increases the inaccuracy and easily introduces noise, we choose three dialogue sentences to form a concise unit that is relatively enough to express a basic context without making the analysis too complicated.  
In addition, although the correlation between two sentences is divided into strong and weak, the specific numerical value is difficult to obtain because (1) it is difficult to grade the correlation, (2) different people have different definitions of the strength of sentence relevance, and (3) it is difficult to train annotators to have a unified understanding of complex annotations. Moreover, since our goal is to find relevant and irrelevant aids for computational models, we believe that simple labels that ensure high accuracy are most appropriate. Therefore, we use binary relevance to analyze two dialogue utterances, this not only reduces the difficulty of annotating but also makes the modeling of the utterance relations more straightforward.  
Based on the above analysis, we define the contextual relationship in the three sentences as follows.  
For the \(T\) turns dialogue sequence \(u_{1},u_{2},u_{3},\ldots,u_{T}\), note any three consecutive turns \(u_{t-1},u_{t},u_{t+1}\) as \(H,R,F\), denoting dialogue history, response, and future, respectively. We analyze the binary relation of \(H\)-\(R\) and the binary relation of \(R\)-\(F\). Based on the relation between \(H\)-\(R\) and \(R\)-\(F\), we can draw three response-centered context relations:  
1. relation1, \(H\) and \(R\) are related, \(R\) and \(F\) are unrelated;
2. relation2, \(H\) and \(R\) are unrelated, \(R\) and \(F\) are related;
3. relation3, \(H\) and \(R\) are related, \(R\) and \(F\) are related.  
Note that there are very few cases where \(H\)-\(R\) is irrelevant and \(R\)-\(F\) is irrelevant, so we do not consider it.
# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 2 Materials and Methods
### Classification Algorithms  
In machine learning, classification is a process for categorizing data into classes. The objective is to predict the class of given data points or instances. For this study, we implemented the following algorithms using the scikit-learn framework:  
* Decision Tree: This is supervised machine learning algorithm where the data are divided into several levels to obtain an outcome (class). For the evaluation, the algorithm's parameters were tested to evaluate the best performance for the classification. However, the best results were achieved when the entropy of the value and random were assigned to the parameters criterion and splitter.
* Logistic Regression: This is a machine learning algorithm for binary classification. This method measures the relationship between the variable that we want to predict and the features by estimating probabilities. One of the parameters established in the configuration was class_weight to define if the dataset was balanced or not. Besides, the solver used was liblinear to minimize the multivariate function by solving the univariate optimization problem in a loop.
* Random Forest: This is a machine learning algorithm based on the ensemble of decision trees. The configuration of this algorithm that achieved the best performance of the model included 35 estimators, entropy as a function to measure the quality of a split (criterion), and bootstrap option.
* Support Vector Machine: This is a popular supervised learning algorithm; its goal is to create the best line (decision) boundary to segregate \(n\)-dimensional space into classes. We used a kernel polynomial with 3 degrees and without a limit of iterations for building the SVM classifier. All settings were made according to the scikit-learn configurations.
# Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition
## 5 Experimental Results
### Experiment on AR Dataset  
The AR dataset contains over 4000 pictures of 126 objects. Figure 5 shows a diagram of some examples from the AR face database. The dimension of each face image is \(165\times 120\). Each person had 26 photographs taken in two separate sessions, except 17 people had fewer than 26 images. Here, the nonoccluded image training set from session 1 and all images of the 17 people are selected for training. There are two cases in the gallery set. One gallery sample is equal to the training samples, which means each theme has multiple samples, and the other gallery set has only one sample per subject, which is a collection of neutral samples for each subject in session 1. The alternative training samples are generated by mirroring, with added noise and random square block occlusion of the original training samples. Finally, all images in session 2 are chosen as the testing samples, the test samples are divided into three subsets: expression, lighting, occlusion. The proposed KRDD method learned from the real occlusion variations in the 17 subjects and the stimulated disturbance from the alternative training samples.  
Table 1 shows the recognition results and classification time of the KRDD algorithm and the six comparison methods using the AR dataset. Each person in the gallery set used in this experiment has multiple gallery samples. From Table 1, it can be noted that KRDD achieved the highest recognition rate. The KED is the second-best algorithm. In addition, the KRDD recognition rate is 1.3% higher than that of the KED in the occlusion subset. All these results show that the KRDD performs better than KED in both disturbance and nondisturbance samples. The fastest classification method is MOST because it is not based on the sparse method. KRDD is much faster than the other methods. Table 2 differs from Table 1 because each person in the gallery set uses a single gallery sample. Based on the experimental results, it is obvious that the performance of KRDD is virtually unchanged, and the classification speed is faster, which proves the robustness of KRDD. From Tables 1 and 2 we can conclude that the recognition rate of KRDD in the three subsets is higher than those of KED. Especially in the occlusion subset, KRDD has more obvious improvement. This is because the KRDD model has two different types of disturbance dictionary, which have one more disturbance dictionary than the KED model has, to cope with more noisy data.  
Figure 5: Example Images of one Subject on the AR Dataset.
## 4 Test Results and Analysis
### Additional Confinement Effect of Stirrup Rebars (A12H-1 and -3, A12H-8 --11)
#### 4.3.2 Ultimate Bearing Strength  
Figure 10 compares the load-displacement curves and ultimate bearing strengths with respect to the stirrup rebar design. The A12H-1, -8, -9 specimens showed almost identical load-displacement curves, ultimate strengths, and sectional efficiency despite their differences in stirrup rebars, as shown in Figures 5 and 10. This is because they were fully confined, with the spiral and stirrup exceeding the maximum effective lateral confinement.  
In contrast to the A12H-1, -8, -9 specimens, the load-displacement curves and ultimate strengths of A12H-3, -10, -11 reveal that the additional stirrup rebar enhanced the ultimate performance. This means that the concrete between the spiral and stirrup played a role, and its effect increased as the stirrup rebar size increased. In these specimens, the ultimate strength increased by 8% between \(\varnothing\)10 and \(\varnothing\)13 and 16% between \(\varnothing\)10 and \(\varnothing\)16. However, a comparison between A12H-6 (\(D\) = 745 mm, \(P_{u}\) = 6022 kN) and A12H-3 (\(D\) = 295 mm, \(P_{u}\) = 5,228 kN) reveals that confining of a larger concrete area with a large spiral and stirrup is more effective than confining two separate areas where the core is partially confined by the spiral and stirrup, and the outer part is confined only by the stirrup.
## 3 The Proposed Algorithm  
### Temporal-Spatial Correlation Analysis  
The mobility model of the users is shown in Figure 1. The LEO satellite used the SFC coverage mode. The user mobility was simplified to a linear motion. Compared with the satellite movement speed, the speed of the terminal users had the same value and the opposite direction [38].  
The high-speed movement of LEO satellites makes calls frequently hand over between cells or satellites. The departure call of the current cell and the new call of the adjacent cell occur at the same time. A handoff call occurs in the only neighbor cell that is in the opposite direction to the satellite movement. Therefore, a handoff call has the characteristic of a temporal-spatial correlation in LEO satellite networks.  
A call of the current cell hands over at a certain time because of the high-speed movement of the LEO satellite. The call of the current cell departs; meanwhile, a new call of the adjacent cell occurs. The adjacent cell is the only neighbor cell where the call of the current cell hands over. As shown in Figure 1, cell 19 is the adjacent cell of cell 18. In other words, the call of cell 18 will hand over to cell 19.  
When a handoff call occurs, the channel allocation algorithm not only releases the occupied channel for the departure call, but also allocates the channel for the handoff call of the adjacent cell. Generally speaking, one channel resource is usually allocated for one call in LEO satellite networks. The allocated channel must also meet any electromagnetic compatibility constraints to avoid co-channel interference [39].  
We assumed that the LEO satellite network had \(N\) cells = {1, 2, 3,...... \(N\)} and \(K\) channels 'K = {1, 2, 3,...... \(K\)}. The conflicting cell \(I(n)\) of cell \(n\) referred to the cells whose distances from themselves to cell \(n\) were less than the minimal reuse distance. This could be expressed by:  
\[I(n)=\{n\in,dist(n,m)<d\} \tag{3}\]  
where \(dist(n,m)\) represents the distance between cell \(n\) and cell \(m\) and \(d\) represents the minimal reuse distance. As shown in Figure 1, the 18 cells in gray were conflicting the cell of cell 25 if \(d=3\).  
The same channel cannot simultaneously be allocated to the calls of the current cell and its conflicting cell. The eligible channel \(\tilde{A}(n)\) of cell \(n\) refers to the idle channel in both cell \(n\) itself and its conflicting cell and can be expressed by:  
\[\tilde{A}(n)=\{k\in\mathbb{K},\sum_{m\in\mathbb{I}(n)}x(m,k)=0\} \tag{4}\]  
where \(x(m,k)\) represents the status of channel k of cell m. The value 0 means idle and 1 means occupied.
## 5 Security Analysis
### Heuristic Analysis
#### 5.2.7 Man-in-the-Middle Attacks  
Consider a scenario where \(\mathcal{A}\) successfully intercepts both the login request \(\{M_{2},K_{1},K_{2}\}\) and challenge messages \(\{K_{3},M_{3}\}\), acquiring all parameters of the smart card \(SC_{i}\) as outlined in the protocol. To launch an effective man-in-the-middle attack, \(\mathcal{A}\) must either fabricate new message sequences \(\{M_{2},K_{1},K_{2}\}\), \(\left\{M_{3}^{*},K_{3}^{*}\right\}\) or replay previously intercepted ones. However, as established, the proposed protocol effectively mitigates replay and impersonation attacks, rendering both the aggregated server \(S_{j}\) and the user \(U_{i}\) incapable of authenticating \(\mathcal{A}\). Consequently, the protocol is adeptly fortified against man-in-the-middle attacks.
# Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework
## 7 Evaluation of IDBA: The Experiments
### Experiment 1: Evaluating the Performance of IDBA  
The objective of this experiment was to evaluate the performance of IDBA. For this purpose, we applied IDBA to the six currency pairs sampled minute-by-minute: AUD/CAD, AUD/USD, GBPCAD, GBPNZD, NZDUSD, and EUR/NZD. In particularly, we considered the six sets of rolling windows: _AUDCAD_RW_, _AUDUSD_RW_, _GBPCAD_RW_, _GBPNZD_RW_, _NZDUSD_RW_, and _EURNZD_RW_ (previously composed in Section 6.2). For each of these six sets, the training period of each rolling window (12 months) was used as a training dataset to find the values of: (a) the trading parameters: _best_theta_ and _best_down_ind_ (Section 5.1); (b) the order size parameters \(PPV\) and \(FNR\) (Section 5.2.3); and (c) the risk threshold \(MDD_{Training}\) (Section 5.4).  
IDBA then employed the values of these parameters to trade over the trading period. The overall trading period of each set was twelve months in length: from 1 January 2017 to 31 July 2017 (Section 6.2). Note that, although our initial datasets in this experiment (i.e., the six exchange rates) were sampled as a time series (with an interval of one minute), the IDBA's trading rules (presented in Section 4) are based on variables (e.g., \(P_{\text{DCC}\uparrow}\)) which originate from the DC concept. To compute the Sharpe ratio, we considered a risk-free rate of 3% based on the 10-year US treasury bonds (Source: [[https://www.cnbc.com/quotes/?symbol=US10Y](https://www.cnbc.com/quotes/?symbol=US10Y)]([https://www.cnbc.com/quotes/?symbol=US10Y](https://www.cnbc.com/quotes/?symbol=US10Y)). Actually, the highest rate for US10Y recorded during 2017 was 2.630 on 13 March 2017). We also used the results of this experiment to analyze the impact of the two presented modules: (a) order size (Section 5.2); and (b) risk management (Section 5.3).  
We also want to emphasize that we considered the instantaneous bid and ask prices in all experiments. Most existing DC-based trading strategies do not consider the transaction costs in their experiments (e.g., [11; 12; 13; 14]). Therefore, in our experiments, we do not account for the transaction costs either. In this paper, while there is no direct transaction fee, we considered the bid-ask spread as a kind of indirect charge, as in [10; 12; 30]. We should also point out that we ignored the effect of \"slippage\" in our trading simulations. In trading, \"slippage\" refers to the difference between what a trader expects to pay for a trade and the actual price at which the trade is executed. Normally, slippage happens because there might be a slight time delay between the trader initiating the trade and the time the broker receives the order. During this time delay, the price may have changed. It can either work in favor of, or against, the trader [31].
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Propellant Manufacturing  
To manufacture the propellant required to complete a mission under nominal conditions, multiple resources must be obtained from the Mars environment. In a single launch, Charon will use up to 38 tons of methane and 143 tons of oxidiser, including boil-off and thrusters. Water mining facilities and a carbon dioxide capturing system must be available to acquire the necessary reactants to create the propellant. First, water must be mined in order to produce H\({}_{2}\) and O\({}_{2}\) through water electrolysis. The hydrogen is used in combination with the captured carbon dioxide in the Sabatier reaction, to produce methane and water, as mentioned by [PERSON] et al. [44] (p. 6). For the first launch propellant production, 170 tons of water must be mined, and 104 tons of carbon dioxide must be captured from the atmosphere to produce enough propellant. Because of the production of water after the Sabatier reaction, the amount of water to be drilled for later propellant production is reduced to approximately 76 tons.
## 4 Discussion
### ITS Methods with or without Seed Points
The parameter eps in DBSCAN has some impact on the final results of ITS during the refining process. This parameter was not set to a fixed value for all data due to variation in point cloud density among sample plots. Our method iterates different eps values to improve the capacity of the refining process (Figure 12). The size and interval of eps among iterations could be adjusted, although the preset eps values (0.4, 0.55, 0.7) can essentially meet the refining process in all plots. In addition, the coefficient of \(Ni\) and \(Ai\) for the core cluster of initial individual tree points selection (Equation (1)) were derived by trial and error. Specifically, after conducting multiple experiments with coefficients ranging from 0.2 to 0.8, we selected a value of 0.3 based on a comprehensive visual analysis of the sampling sites. This selected coefficient was then uniformly applied to all point cloud data. In theory, it is possible to adjust this parameter individually for each site based on the initial segmentation outcomes. However, it is generally recommended to choose smaller coefficients, as larger ones may lead to under-segmentation errors by reducing the detection rate of the initial tree. In the process of relabeling point clouds for non-core clustering, it was observed that when the value of k is small (less than 10), the value of k seems to have little effect on the resulting classification results; but when k is taken too large, it will increase the possibility of misclassification. Therefore, in this study, the default value of k has been selected as 5 for the sake of consistency and practicality; more details of k value selection can be found in Supplementary Materials A. The whole processing time of the ITS methods based on 3D point clouds is significantly higher than that based on CHM raster or point clouds in 2D plane (Table 6). Indeed, the processing time of ITS methods based on 3D point clouds increases along with the increasing of point cloud density because they are more sensitive to point cloud density. Therefore, the commonly used ITS methods based on 3D point clouds from airborne LiDAR need to be adjusted when applied to UAV-LiDAR point clouds with much higher density. Voxelization, or down-sampling of 3D point clouds, is one approach to reduce the processing time in this situation [43]. The proposed ITDS framework we used suggests that the seed points based ITS methods significantly reduced the processing time of the direct ITS methods based on 3D point clouds (Table 6). This improves our ability to apply the method on point clouds with high density or large regions in future studies. Figure 11: Errors for the seed points based ITS methods in the sample plot Y16-2. **(a)** The positions of the center seeds, top seeds and the RTK references of tree locations. **(b)** Errors for the Seeds + Kmeans method. The red rectangle is the segmentation error caused by iterative center seed points. **(c)** Errors for the Seeds + Dalpnote2016 method. The red rectangle is the segmentation error caused by wrong top seed points.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 3 APTs Faced by IIoT and Its Defense Methods  
In the previous section, we describe the definition and development of APTs. In this section, we describe the APTs faced by IIoT and ways to defend against them from the perspective of IIoT architecture. The IIoT architecture is a higher-level abstract description of IIoT that helps identify the problems and challenges in different application scenarios. The design of IIoT architecture needs to emphasize the scalability, modularity, interoperability, and scalability of heterogeneous equipment using different technologies. Several reference architecture frameworks originated from different application environments of IoT and IIoT in the past [41]. In reference to studies [42; 43; 44; 45], this paper introduces a widely accepted 4-layer IIoT model as a reference architecture, as shown in Figure 1. This architecture includes the perception layer, transport layer, platform layer, and application layer according to different functions. The perception layer consists of various types of basic chips, sensors, RFID scanners, actuators, and other equipment. In an industrial environment, all this equipment may be accompanied by equipment such as conveyor systems and industrial robots. Equipment is responsible for capturing data and gathering  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Time (Year)** & **Target** & **Description** \\ \hline Industroyer [35] & 2016 & Ukrainian power grid & Power outages in some areas last for several hours \\ \hline TRITON [36] & 2017 & Saudi oil factory & Destroy its safety control system \\ \hline VPNFilter [37] & 2018 & The whole world & Infected router equipment \\ \hline LockerGoga [38] & 2019 & Norwegian aluminum company & The factory was forced to shut down for several days \\ \hline Winnti [39] & 2020 & Organizations in North America, Europe, and Asia & Stealing sensitive information and intellectual property rights \\ \hline DarkSide [40] & 2021 & American Colonial Oil Pipeline Company & The oil pipeline was paralyzed for a week \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attack cases of APTs in recent years.  
information. The transport layer can use Wi-Fi, Bluetooth, Nb-IoT, LoRa, etc., to transmit information to the processing system at the next layer. The platform layer is composed of a server and database, which is responsible for performing many tasks, such as making decisions, calculating optimization algorithms, and storing a large amount of data. Finally, the application layer is responsible for meeting the special needs of end users' applications. For example, intelligent factories and intelligent robots are considered to be applications of IIoT. Next, we will introduce these layers one by one, and the APTs and defense methods that may exist in each layer will be highlighted.