esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations
## 2 Literature Review  
There is a relatively small number of literature that is concerned with integrating blockchain technology with space industry applications. [PERSON] et al. [26] proposed a blockchain-based reputation system for developing a decentralized and secure system for satellite relay networks. The authors investigated and validated the network performance using average latency, computational complexity, and storage considerations for a variety of use cases. [PERSON] and [PERSON] [16] studied the security problem for mobile satellite communication networks (MSNET). The authors proposed a new security framework for securing mobile satellite communication networks based on reformulating satellite communication networks as delay-tolerance networks (DTN). The blockchain is used with DTN to (1) secure data transactions and (2) resist the unexpected cyber-attacks that target mobile satellite networks by integrating blockchain with the practical satellite constellation management algorithm. [PERSON] et al. [27] proposed a fast and efficient access verification protocol called Blockchain-based Access Verification Protocol (BAVP) that depends on integrating identity-based encryption and blockchain technology for authenticating LEO-satellite constellations. The BAVP represents a good alternative instead of the traditional centralized authentication protocols that work for MEO/GEO satellite networks. The simulation results on the OPNET platform clarified good results of the reliability, effectiveness, and fast-switching efficiency of the proposed protocol. Also, [PERSON] et al. [28] proposed a blockchain-based access control mechanism for addressing both access authorization issues and identity authentication in the distributed space network environment. The author implemented the proposed mechanism on both resource-constrained edge devices and more powerful devices and deployed it on a local private Ethereum blockchain network for evaluating the computational and timeliness performance of the proposed access control mechanism. Satellite data broadcasting is another problem that has been investigated in blockchain adoption in the space industry. [PERSON] and [PERSON] in Zhang and Liu (2019) introduced a new blockchain protocol that works based on satellite broadcasting communications instead of the traditional Internet for data dissemination. Simulation results clarified that the proposed technique achieved a lower communication cost and can improve the throughput of the blockchain system to 6,000,000 TPS with a 20 Gbps satellite bandwidth. From the literature, it can be noticed that there is no solution aiming to manage and authenticate satellite communications using blockchain. There is no work addressing the management of transactions between two satellites in the same constellation (or swarm) or transactions between two/more satellites in different constellations. These will be addressed in this paper.
## 4 Flow Field Characteristics behind the WGB  
### Mean Wind Characteristics  
#### 4.1.1 Streamwise Velocity (\(u\)) Distributions  
To investigate the effect of WGB on the flow field of bridge deck wake, this paper presents the contour lines of the three-direction wind velocity components in various cases. It can be seen in Figure 13 that the shielding effect of WGB is worse due to vertical wind barriers. Due to the flow around the WGB, a shear layer is formed above the barrier, and its height increases gradually from upstream to downstream. Moreover, the airflow passing through the gap at the bottom of the WGB forms a jet, which creates a high-speed area with a wind velocity ratio of over 0.6 at the bottom of the barrier. This jet may cause an increase in negative pressure at the bottom and windward sides of the train, leading to a decrease in \(C_{V}\) and an increase in \(C_{H}\). The increased shielding area in the upper part of the WGB, coupled with the backflow effect behind the barrier, results in relatively lower wind velocity in that area.  
Figure 12: Effect of WGB parameters on bridge wind pressure distribution: (**a**) angle of the main deflector plate \(\theta_{1}\); (**b**) width of the WGB root \(B_{1}\); (**c**) pressure distribution zones on the bridge; (**d**) size of the upper deflector angle \(h_{1}\).  
Regarding the effect of \(\theta_{1}\), the test results of Cases 3 and 4 show that increasing \(\theta_{1}\) will reduce the shielding efficiency of WGB, and the flow velocity in the backflow area near the top of the barrier gradually decreases. Regarding the effect of \(h_{1}\), the test results of Cases 3 and 5 show that when \(h_{1}\) increases, the projected windward area of the barrier decreases, and the bluff body characteristics weaken. Therefore, the total wind velocity within the height range of the barrier increases, and the airflow passing through the bottom of the barrier to the bridge deck enhances, resulting in an increase in wind velocity near the bridge deck. However, the wind velocity at the 1.65 m position on the windward side of the track is locally reduced due to the backflow effect behind the barrier. Regarding the effect of \(B_{1}\), the test results of Cases 4, 6, and 7 show that as \(B_{1}\) increases, the blocking effect of the WGB increases, and the jet at the bottom gradually weakens, effectively reducing the wind velocity in the flow direction near the bridge deck. When \(B_{1}\) increases from 250 mm to 300 mm, the influence on the external flow field above the shear layer is more obvious, and when \(B_{1}\) increases from 300 mm to 350 mm, the effect on the flow field near the top of the WGB is more obvious.
###### Abstract  
Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models' outcomes.  
Keywords:explainable AI; XAI; SHAP; Shapley; machine learning; rolling bearings; machine fault diagnosis; intelligent fault diagnosis; condition monitoring; rotating machinery +
Footnote †: journal: Applied sciences
## 2 Material and Methods
### Focused Question and Eligibility Criteria  
The following question was set: \"Is there scientific evidence that the use of probiotics confers benefits to human oral health?\" The PICOS criteria that allowed the elaboration of the research question are presented in Table S2.  
The eligibility criteria of the studies to be included in the review were:  
1. Types of study: randomized clinical trials, without any information of blinding, blind (single, double, or triple), placebo controlled, or non-placebo controlled (compared to another intervention), including cross-over studies.
2. Type of participants: of any age (adults, children, the elderly), without gender restriction, healthy or not.
3. Type of intervention: use of any probiotic (alone or in combination).
4. Considering any dosage regimen, vehicle of delivery or frequency of intervention. Comparators may consist of placebo or other active intervention without probiotics (with or without prebiotic/synbiotic vs. placebo/other intervention). Prebiotics are a group of nutrients that are degraded by gut microbiota. Synbiotic is a mixture of pre- and probiotics. Studies including an auxiliary to the active treatment were analyzed.
5. Primary outcomes: clinical, microbiological, immunological, and biochemical parameters.
6. Secondary outcomes: any adverse effects, rate of adherence, quality of life.
## 1 Introduction  
Visual object tracking is a fundamental task in the field of computer vision. It has a wide range of application prospects in autopilot, robot navigation, drone cruise, intelligent security, human-computer interaction, etc. In the literature, researchers have proposed various visual object tracking methods. The visual tracking techniques are based on correlation filter-based methods, deep learning-based methods, and transformer-based methods. Recently, the methods based on deep learning have attracted the attention of the research community due to their advantage of end-to-end learning fashion. In particular, the object tracking methods based on Siamese convolutional neural networks have been developed rapidly due to their simple structures and efficiency. The Siamese convolutional neural network-based object tracking method uses deep convolutional features to describe objects. The deep convolutional features are extracted using multiple convolutional and activation layers. The feature maps of different layers have different characteristics. The use of features obtained using different convolution kernels to realize the effective distinction between the object and the background is a key issue for improving the effectiveness of features.  
Based on the aforementioned analysis, in this work, we propose an adaptive convolution kernel attention mechanism for visual object tracking tasks. The main contributions of this paper include:* The paper analyzes the effects of different convolution kernels on object tracking tasks and proves the necessity of convolution kernel attention.
* The paper analyzes the problems with the offline training-based attention mechanism in the tracking task and finds out the reason why this attention method is not suitable for object tracking.
* The paper presents an object-aware convolution kernel attention module. The proposed module improves the specificity of object features for different individuals based on the difference between each convolution kernel feature.  
The proposed attention mechanism can be used as a general convolution kernel attention module and has good versatility. It can be used as a \"plug and play\" module by embedding it in various object tracking methods based on convolution feature matching, thus significantly improving the tracking performance of the original method. Finally, we combine the proposed convolution kernel attention module with classic object tracking methods. We perform experiments using OTB and VOT benchmark datasets to verify that the proposed technique significantly improves the object tracking performance.
## 3 Basic System Operation
### Metric-Based Searching  
Metric space \(M\) can be defined as a pair \(M=(D,\,d)\), where \(D\) is the domain of objects and \(d\) is the _distance function--\(d\):_\(D\)\(\times D\rightarrow\mathbf{R}\) satisfying the following constraints for all objects \(a\), \(b\), \(c\)\(\in\)\(D\):  
\[\begin{array}{c}d(a,\,b)\geq 0\ (\text{non-negativity})\\ d(a,\,b)=0\ \text{if}\ a=b\ (\text{identity})\\ d(a,\,b)=d(b,\,a)\ (\text{symmetry})\\ d(a,\,c)\leq d(a,\,b)+d(b,\,c)\ (\text{triangle inequality})\end{array} \tag{1}\]  
In this metric space, two types of similarity queries can be defined including range query \(Range(q,\,r)\) and \(K\)-nearest neighbor search \(KNN(q,\,k)\) by the resultant set \(X\), considering \(\,I\subseteq D\) to be a finite set of indexed objects:  
\[\begin{array}{c}\mathbf{Range(q,r)}\text{:}\\ X=\{a\in I|d(q,\,a)\leq r\}\end{array} \tag{2}\]  
\[\begin{array}{c}\mathbf{KNN(q,k)}\text{:}\\ X\subseteq I:\big{|}X\big{|}=k,\bigtriangledown_{a}\in X,\bigtriangledown_{b} \in I\setminus X:d(q,a)\leq d(q,b)\end{array} \tag{3}\]  
The data space can be divided into \(S\) segments (\(S\) is the total number of sectors) with a pivot point, denoted by \(Pi\), for each sector \(S\). The _iDistance_ key for an object \(x\)\(\in\)\(D\) can be defined as (Figure 2a):  
\[\begin{array}{c}iDist(x)=d(Pi,\,x)+i\ c\end{array} \tag{4}\]  
In Equation (4), \(c\) is the separating constant for individual sectors. Given \(q\)\(\in\)\(D\), the range query for \(q\) with the range of \(r\) can be defined as (Figure 2b):  
Figure 1: (**a**) DBS mapping; (**b**) inter-sector communication; and (**c**) intra-sector member node to head node communication.  
As shown in Table 3, weights have been assigned to different attributes based on their importance in the event description. Hence, an attribute with higher weight has greater influence on the similarity among events.
# Tree-Structured Regression Model Using a Projection Pursuit Approach
## 4 Results
### Application: White and Red Wine Data
Alcohol, density, volatile acidity, and free sulfur dioxide are the four most important variables in randomForest. The roles of residual sugar and free sulfur dioxide are quite different in the two methods. Residual sugar is a very important variable in the projection pursuit regression tree but not in randomForest, and free sulfur dioxide is important in randomForest but not in the projection pursuit regression tree. Table 6 shows the importance measures for red wine. The most important variable is alcohol in both methods. However, the other patterns are quite different. Random forest shows only two more important variables: sulphates and volatile acidity. However, the importance measures in the projection pursuit regression tree change smoothly up to total sulfur dioxide. This is also a quite different pattern to that of the importance measures of white wine. From this result, it is found that the quality of red wine is determined by more complex tastes than the quality of white wine. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable Name** & \multicolumn{4}{c}{**White Wine**} & \multicolumn{4}{c}{**Red Wine**} \\ \hline  & **Node 1** & **Node 2** & **Node 3** & **Node 4** & **Node 1** & **Node 2** & **Node 3** & **Node 4** \\ \hline fixed acidity & \(1.56\) & \(-0.24\) & \(1.78\) & \(-0.04\) & \(4.34\) & \(1.04\) & \(-4.32\) & \(-5.05\) \\ volatile acidity & \(-1.24\) & \(-4.71\) & \(0.31\) & \(-2.71\) & \(-2.34\) & \(-4.49\) & \(3.71\) & \(-4.84\) \\ citric acid & \(-0.17\) & \(0.14\) & \(0.51\) & \(0.33\) & \(1.24\) & \(-1.83\) & \(1.92\) & \(-1.36\) \\ residual sugar & \(6.30\) & \(5.08\) & \(7.20\) & \(5.62\) & \(2.61\) & \(-0.02\) & \(-1.00\) & \(-2.91\) \\ chlorides & \(-0.23\) & \(0.12\) & \(2.78\) & \(0.14\) & \(-2.26\) & \(-1.47\) & \(-3.76\) & \(-0.96\) \\ free sulfur dioxide & \(0.98\) & \(1.48\) & \(2.69\) & \(-0.037\) & \(-0.42\) & \(2.28\) & \(0.50\) & \(0.52\) \\ total sulfur dioxide & \(-0.41\) & \(-0.74\) & \(-1.07\) & \(1.63\) & \(-1.61\) & \(-4.79\) & \(-2.02\) & \(2.36\) \\ density & \(-7.80\) & \(-4.18\) & \(-6.62\) & \(-7.70\) & \(-5.08\) & \(0.04\) & \(0.43\) & \(5.51\) \\ pH & \(2.03\) & \(0.96\) & \(2.24\) & \(0.25\) & \(0.20\) & \(-0.61\) & \(-6.10\) & \(-4.69\) \\ sulphates & \(1.26\) & \(1.48\) & \(-0.68\) & \(0.35\) & \(4.37\) & \(3.70\) & \(2.83\) & \(-0.76\) \\ alcohol & \(3.10\) & \(7.04\) & \(0.45\) & \(-4.45\) & \(5.96\) & \(7.22\) & \(4.45\) & \(1.41\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Projection pursuit coefficients in each node for the white and red wine.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 3 Performance Evaluation
### Two Detectors  
Herein, two detectors, monitoring the correlator outputs and consistency of Doppler, are given. They aim at different features of GNSS signal and provide different evidence of the authenticity of the signal.
# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 1 Introduction
Secondly, using the trained entity-relationship extraction model, we extract the entity information and the relationship information between entities in the document, use them together with the syntactic dependency information as the edges of the graph neural network, and compute and backpropagate the document representation of BERT together with the document vector of GCN. To further enhance the learning ability of the model for semantic dependencies between words, the graph structure mask is used to learn edge relations and edge types. Finally, a softmax function is connected to obtain the classification probability to finally achieve domain-length text classification. The method further improves the accuracy of long text classification by fusing knowledge features and data features. Overall, our contributions to this paper are listed as follows:  
* There is a problem of lack of domain knowledge in existing Chinese long text classification techniques, and we consider that knowledge graphs can solve this problem well. The entity-entity-relationship in the knowledge graph fully reflects the text features and semantic relationships, which can be used as the edge of the graph neural network together with the dependency information in the dependency syntax tree, and the introduction of knowledge can better improve the model performance;
* The problem of word ambiguity generated by static word vectors can be solved by using BERT to encode text to learn dynamic word vectors. BERT and GCN jointly calculate the loss function, which can solve the problem of lack of word sense information in graph convolutional neural networks. However, the BERT and GCN models have too many parameters and are prone to the problem of overfitting or difficult convergence. We splice the vectors of the last four layers of BERT to prevent the model from using only the output of the last layer of BERT, which generates the problem of over-computation of parameters;
* In order to improve the learning ability of the model for semantic dependencies between words and make the model more adaptable to the fusion of the knowledge graph and GCN, we propose graph structure masking, i.e., randomly masking the edge connection between two tokens, and then let the model predict whether there is a connection relationship between two tokens and the type of connection.
## 3 More Recent Technology: Non-Thermal Plasma
### Advantages and Disadvantages of Plasma Purification  
Plasma shows significant potential in pollutant removal. It is capable of quickly oxidising organics, inorganics and pathogens with reasonable power consumption [8]. Since plasma only requires an energy source with no additional chemicals, it has the potential to be far more affordable than some of the mature counterparts such as chlorination. On a qualitative level, the research suggests that plasma is a suitable competitor to more established processes.  
Plasma is most notable for its ability to remove persistent organic pollutants. Globally, concerns are rising over these compounds because our current purification systems are not highly effective at removing them. This creates the ideal opportunity for the mainstream adoption of plasma. If retrofitted to older plants, it could serve to eliminate these concerns.  
Plasma purification is also likely to be more effective than ozone purification. This is because when plasma is generated, all of the radicals produced enter the water to provide a wide range of disinfection abilities. In contrast, during ozone production, when air is energised to create ozone, only the ozone molecules are applied to the water [40].  
Furthermore, while plasma produces bromate (a DBP similar to that of ozonation) in treatment solutions containing bromide [41; 42], it is also capable of removing chloroform, one of the major DBPs linked with chlorination [43].  
Unfortunately, one of the major concerns associated with plasma is its high energy consumption in some cases, although significant progress has been made based on the use of pulsed and nanoseconds pulsed power supply units. The generation of non-thermal plasma with an efficient reactor type and power supply sources can substantially reduce the energy requirements [38].
# Neither Denied nor Exposed: Fixing WebRTC Privacy Leaks
## 4 IP Disclosure
### Problem Statement
Precisely, WebRTC can leak the existence of any microphone and camera that the user may utilize. After obtaining an array of the available \"MediaDeviceInfo\" objects, one per media device, the browser can collect information for input and output devices though the object's properties. For instance, the \"kind\" property will return an enumerated value that is either \"videoinput\", \"audioinput\" or \"audiooutput\", while the \"label\" property will return a DOMString describing the device. By default, the \"label\" is an empty string; however, if one or more media streams are active, or if the user granted persistent permissions to the browser, then the DOMString will contain information about any employed peripheral device, including its name and type, e.g., \"External USB Webcam\". For example, the enumeration of \"MediaDeviceInfo\" array may produce an output similar to that in Listing 5. Thus, if two audio and one video input were attached to the client's machine, the kind of each input along with its ID would be outputted. ```
1 videoinput:id=56430b9613 fcb1 ac822 fd53a6c25
2 audioinput:id=321668 ae7 aebb94d0d2b90 bee995
3 audioinput:id=0 fd5b84 ae87a3420486e2e2c4d9f
```  
Listing 5: Example of MediaDeviceInfo output. On the other hand, each item of Listing 5 would contain all the available information in case one or more media streams were active or permissions were granted by the user, as presented in Listing 6. That is, for each media input, apart from device's ID and its type, the \"label\" describing the device and a \"groupID\" would also be returned. The \"groupID\" represents the group of the device and two devices have the same group identifier if they are part of the same physical device, for example, if both a camera and a microphone belong to the same monitor. ```
1 kind:videinput
2 label:IntegratedWebcam(lbcf:28b0)
3 deviceId:56430b9613 fcb1 ac822 fd53a6c25
4 groupId:85632d755 cfb1 dfb30228124124 ec
5
6 kind:audioinput
7 label:Microphone(RealtekAudio)
8 deviceId:Of45b84 ae87e3420486e2e2c4d9f
9 groupId:40756e2116 ee3d4d75183136 bd03e
10
11 kind:audioinput
12 label:Headset(HD4.40 BTHands-FreeAGAudio)
13 deviceId:321668 ae7 aebb94d0d2b90 bee995
14 groupId:b0d25385669f2a63 bfe9d556 ee46
```  
Listing 6: MediaDeviceInfo output with granted permissions.