esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Techniques for Protecting the Privacy of Web Users
## 6 Discussion  
In particular, this study investigates how users of the most common Saudi Arabia websites are tracked, how their privacy is exposed, and how to protect users' privacy according to their needs by evaluating which publicly available privacy protection methods that can reduce tracking with low impact on users' performance. In order to achieve this, we automated browsing the top 50 Saudi Arabia websites according to Alexa.com and categorized them into nine categories. The results of the long-term crawling process identified 1578 unique JavaScript elements on the most visited websites in Saudi Arabia. On average, there were 45 tracking elements per site was found within the top 50 websites. This indicates that the scale of the problem we are addressing is significant. Misclassifying a small portion could negatively affect users' experience. Another scientific contribution is the evaluation of the existing privacy protection methods in order to guide users toward a privacy tool that is suitable for their needs. In order to achieve this, we conducted a systematic analysis of the most common PPTs based on their accuracy, performance, and effectiveness in detecting different forms of obfuscated JavaScript programs by analyzing a new dataset of 1578 JavaScript elements generated by different tools. The results indicated that Ghostery had the highest sensitivity rate of permitting functional scripts with the lowest error rate, as well as better browser speed. While NoScript achieved the highest specificity rate of blocking tracking scripts since it is the highest blocker of third-party services. Further results indicated that the majority of the privacy protection tools are ineffective in detecting JavaScript programs that are obfuscated. The reason behind this is  
Figure 8: The ratio of obfuscated JavaScript programs presents on the top 50 websites.  
Figure 7: The average page load time, or PLT.  
that all existing privacy solutions heavily rely on the use of a static blacklist to filter out unwanted content. Therefore, when the JavaScript program becomes more complicated with multiple forms of obfuscation it can easily overcome the detection. Recent work has implemented machine learning techniques with blacklisting into PPTs to automatically detect tracking and functional JavaScript programs [1; 9]. However, existing approaches have not adequately addressed the obfuscation problem [1; 27].To the best of our knowledge, no previous research has been conducted to detect obfuscated tracking and obfuscated functional JavaScript programs. Finally, as future work, we are planning to characterize JavaScript programs' obfuscation techniques to enhance the resiliency of the PPTs by implementing different machine learning techniques.
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
###### Abstract  
Given a directed graph, a \(k\)-hop reachability query, \(u\stackrel{{\text{\tiny{\text  
approaches utilize an index that covers a part of the reachability information, rather than all of it, to answer kHRQs. However, when the index cannot answer the query, they still need to perform a graph traversal operation, which can lead to poor query performance in practice.  
In this paper, our main focus is on developing an efficient approach for answering kHRQs on general directed graphs with _SCCs_. We propose to first find a vertex cover, \(S\), based on which we construct a two-hop index [15; 16], maintaining the distance between each pair of vertices. Compared with _kReach_ and _ESTI_, our approach avoids the costly graph traversal operation. We propose a linear-time algorithm, which computes a smaller vertex cover, \(S\), satisfying that the vertices in \(S\) always have a larger degree. Based on the vertex cover, we propose to construct a degree-constrained two-hop label to efficiently answer kHRQs. Our contributions are as follows:  
1. We propose constructing a compact two-hop index that utilizes a vertex cover and suggest a linear-time algorithm for computing a smaller vertex cover. We prove that it correctly answers kHRQs by utilizing the two-hop index constructed from the vertex cover and introduce efficient algorithms for constructing the index.
2. We propose a degree-constrained two-hop label to accelerate query answering. By this labeling scheme, we take the vertices of the cover, set as hop vertices, and perform _BFS_ from these vertices. During _BFS_, only the vertices with a degree \(>d\) are assigned with the two-hop label, where \(d\) is a small constant. In this way, we guarantee that we can answer a kHRQ by comparing at most \(d^{2}\) labels.
3. We verify the benefits of our approach by conducting rich experiments. The experimental results show that our approach is much more efficient when answering kHRQs with a reasonable index size and index construction time.  
The remainder of this paper is structured as follows: Section 2 provides an overview of the related work in the field. In Section 3, we introduce the fundamental concept of our approach, which relies on utilizing a vertex cover. A detailed description of the index's construction is then presented in Section 3.2. Section 4 presents the experimental results, and Section 5 concludes the paper.
## 5 Evaluation and Translation Results  
### Experiment Settings  
We followed the NMT architecture by [PERSON] et al. (2018) and implemented the NMT architecture using OpenNMT ([PERSON] et al., 2017). The LSTM model had one layer, each with 512 cells, with embedding size of 512. The parameters were uniformly initialized in \((-0.1,0.1)\), using plain SGD, starting with a learning rate of 1.0 until Epoch 6, and subsequently 0.5 times for each epoch. The max-batch size was 100. The normalized gradient was rescaled whenever its norm exceeded one. Because of the amounts of training data (150k and 300k as the baseline) were relatively small, the dropout probability was set as 0.5 to avoid overfitting. Decoding was performed by beam search with a beam size of five. The maximum length of a sentence was 250 by default, but it was set to 500 because it became much longer in character-level MT.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Original/Generated Sentences** & **Input Japanese Sentence** & **English Translation of the Input Japanese Sentence** \\ \hline Source sentence & \(\times\), / /Because sentences in Japanese and Chinese are written without spaces, we tokenized them with MeCab [[http://taku910.github.io/mecab](http://taku910.github.io/mecab)]([http://taku910.github.io/mecab](http://taku910.github.io/mecab)) for Japanese and Jieba [[http://github.com/fxsjy/jieba](http://github.com/fxsjy/jieba)]([http://github.com/fxsjy/jieba](http://github.com/fxsjy/jieba)) for Chinese. We employed fast_align to obtain word alignment information, which was symmetrized using the included tool command [[http://github.com/clab/fast_align](http://github.com/clab/fast_align)]([http://github.com/clab/fast_align](http://github.com/clab/fast_align)).  
BiLingual Evaluation Understudy (BLEU) is an algorithm for evaluating the quality of text that has been machine-translated from one natural language to another [27]. Translation Error Rate (TER) is an error metric for machine translation that measures the number of edits required to change a system output into one of the references [28]. The BLEU and TER scores were calculated on the same test data (2109 sentence pairs) and the same development-test data (2148 sentence pairs) of the ASPEC-JC corpus for each method, using the \"tools/score.lua\" of OpenNMT after word segmentation. In other words, we took the word-level evaluation. The validation perplexity (i.e., perplexity with the same development data for each method) usually stopped declining around Epoch 10 in the settings above. The average of BLEU and TER scores from that point to Epoch 16 was taken as the BLEU and TER values.
# Fatigue Characteristics of Fe-Based Shape-Memory Alloys
## Materials
### Mechanical Properties of Fe-SMAs  
A direct tensile test was performed to determine the mechanical properties of the Fe-SMAs used in this study. Specimens were prepared according to ASTM A370 [13] standards and tested with a 100 kN universal testing machine. Figure 1 and Table 2 show the stress-strain curves and mechanical properties of each Fe-SMA, respectively. The elastic modulus of the A-type and B-type Fe-SMAs was similar, i.e., 124.57 GPa and 123.26 GPa, respectively. As shown in Figure 1, the Fe-SMAs do not exhibit an exact yielding point, unlike ordinary rebars. Deriving the yield point of the Fe-SMAs using the 0.2% offset method, the yield strengths of A-type and B-type alloys were determined as 599 MPa and 410 MPa, respectively. The ultimate strength of the A-type alloy was 1140 MPa, whereas the ultimate strength of the B-type alloy was found to be 1080 MPa--5% smaller than that of the A-type alloy. On the other hand, the ultimate strains of the A-type and B-type alloys were 0.22556 and 0.39510, respectively, indicating that the B-type alloy has more ductile behavior than the A-type alloy. The ultimate strain and ultimate strength of the Fe-17 Mn-10 Cr-4 Ni-1(V,C) alloy having a chemical composition similar to that of the B-type alloy were reported as 0.28 and 868 MPa, respectively [9]. These values are relatively small compared to those of the B-type alloy. In addition, Fe-17 Mn-10 Cr-4 Ni-1(V,C) and B-type did not contain titanium carbide (TiC), so the ultimate strength was lower than that of the A-type alloy [14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of SMA** & **Chemical Composition (weight\%)** & **Heat Treatment** \\ \hline A-type & Fe–17 Mn–5 Si–5 Cr–0.3C–1 Ti & Non-heat treatment \\ B-type & Fe–17 Mn–5 Si–5 Cr–4 Ni–0.1C & Heat treatment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of Fe-SMA [12].
# Compound Heuristic Information Guided Policy Improvement for Robot Motor Skill Acquisition
## 3 Path Integral Policy Improvement with Covariance Matrix Adaption  
PI\({}^{2}\) is derived from the first principles of stochastic optimal control. What sets PI\({}^{2}\) apart from other direct policy improvement algorithms is its use of probability-weighted averaging to perform a parameter update, rather than using an estimate of the gradient. In Section 2, we obtain the weight vector \(\mathbf{\omega}\) by parameterizing the demonstration trajectory. The idea of PI\({}^{2}\) is to add stochastic exploration noise \(\mathbf{\varepsilon}_{d}\) (\(\mathbf{\varepsilon}_{d}\sim\mathcal{N}(\mathbf{0},\mathbf{\sigma}^{2})\)) to \(\mathbf{\omega}_{d}\) and generate \(K\) roll-outs with different costs by executing parameterized policy. As for a robot with \(D\) DOF, the cost function of the \(k\)th roll-out at time \(i\) is  
\[J(\tau_{,i,k})=\sum_{d=1}^{D}\Biggl{[}\varphi_{d,N,k}+\sum_{j=i}^{N-1}q_{d,j,k }+\frac{1}{2}\sum_{j=i}^{N-1}(\mathbf{\omega}_{d}+\mathbf{M}_{d,j,k}\mathbf{\varepsilon}_{ d,j,k})^{\mathrm{T}}\mathbf{R}(\mathbf{\omega}_{d}+\mathbf{M}_{d,j,k}\mathbf{\varepsilon}_{ d,j,k})\Biggr{]}\, \tag{3}\]  
where \(\tau_{,i,k}\tau\) is a sample path (or trajectory piece). \(\cdot\) indicates all DOF. \(\varphi_{d,N,k}\) represents the terminal reward of the \(k\)th trajectory of the \(d\)th DOF. \(q_{d,j,k}\) is the immediate reward of the \(k\)th trajectory of the \(d\)th DOF at time \(j\). Specifically, it is expressed as \(\tilde{y}_{t}\) (i.e., the square of joint's acceleration). \(\mathbf{M}_{d,j,k}\) is the mapping matrix of the \(k\)th trajectory of the \(d\)th DOF at time \(j\). \(\mathbf{R}\) is the positive semi-definite weight matrix of the quadratic control cost. \(N\) is the maximum value of the time index. Next, the exploration is evaluated. First, the cost of obtained trajectories is sorted, then \(K_{e}\) elite samples are selected, and finally perform probability-weighted averaging to obtain \(\delta\mathbf{\omega}_{d,i}\) with th DOF at time \(i\):  
\[\delta\mathbf{\omega}_{d,i}=\sum_{k=1}^{K_{e}}\left[P(\tau_{,i,k})\mathbf{M}_{d,j,k} \mathbf{\varepsilon}_{d,i,k}\right]\, \tag{4}\]  
where \(P(\tau_{,i,k})\) is the probability-weighted value of the \(k\)th trajectory of the \(d\)th DOF at time \(i\). It is obtained by softmax transformation of the cost function:
## 2 Materials and Methods
### Model Training
#### 2.6.1 Traditional Machine Learning Classifiers  
We employed four widely adopted traditional machine learning classifiers to evaluate their effectiveness in predicting FRPs:  
**Support Vector Machine (SVM):** A robust supervised learning algorithm that seeks to find the optimal hyperplane that best separates data into distinct classes [34]. By maximizing the margin between classes, SVM is effective in high-dimensional spaces. We employed the radial basis function (RBF) kernel, renowned for its ability to model non-linear relationships within the data.  
**Random Forest (RF):** Random Forest (RF) is a popular ensemble learning method for classification and regression tasks in machine learning. It extends the decision trees by combining multiple trees to make predictions [35]. For classification, RF predicts the class label via majority voting among individual trees, where each tree's prediction contributes to the final class with the most votes.  
**Naive Bayes (NB):** A probabilistic classifier based on Bayes' theorem, which operates under the assumption of conditional independence between features given the class label [36]. Despite its simplicity, Naive Bayes classifiers have demonstrated strong performance in various classification tasks, particularly with high-dimensional data.  
**eXtreme Gradient Boosting (XGBoost):** A scalable and efficient implementation of gradient boosting machines, celebrated for its high accuracy and efficiency in handling structured data [37; 38]. XGBoost builds an ensemble of weak predictive models, typically decision trees, in a stage-wise manner and optimizes a differentiable loss function.  
These classifiers were implemented using the scikit-learn library (v0.24.2) in Python 3.10 [39]. Hyperparameters were optimized through grid search and cross-validation to ensure optimal performance for each classifier.
## 4 Experiments and Results
### Closed Loop Control Results  
We further performed experiments of moving the magnetic carrier along the predesigned trajectory path under closed loop control. Figure 10 shows the moving sequence at different time instants, viewed from both top and the side of the workspace. Figure 11 shows 3D motion trajectory. As shown in Figure 12, the average errors of trajectory tracking were 0.44 mm.  
Figure 10: Demonstration of visual feedback control and the magnetic carrier completes a “mountain climbing” movement. Average trajectory-completion time: 23.43 s. The figure indicates the captured image in real-time that was processed gradation conversion, Gaussian filtering, binarization, expansion processing, finding contours and locating the contour center point. The blue line represents the predesigned trajectory, the red line represents the actual trajectory, and the green arrow represents the direction of movement of the magnetic carrier.  
Figure 11: (**a**) Motion trajectory of the controlled magnetic carrier. (**b**) Projection of the trajectory in the _XOY_ plane. (**c**) Projection of the trajectory in the _XOZ_ plane.  
Figure 12: Position tracking error in the _X_-axis, _Y_-axis, _Z_-axis direction and total position error.
## 3 The Proposed Method
### The DQN with the VDN  
Section 3.2 details the optimization of the network from the channel and spatial aspects, and this section looks at improving the system performance in terms of the reward mechanism. Due to partial observability, the spurious rewards problem exists in both the fully centralized and decentralized mechanisms. To avoid the spurious reward data produced by the naive independent agents, a VDN is introduced while training the individual agents. The operation is to obtain agent-wise value functions by decomposing the team value function. Based on team rewards, the system can acquire optimal linear decomposition learning. The total Q-gradient is propagated backward by the deep neural network (DNN) representing the function of each agent-wise value.  
For the multi-UAV formation network, distributed across \(n\) UAVs, the observations and actions are labeled as n-dimensional tuples of observations in \(O\) and actions in \(A\).  
The additive decomposition can be expressed by the following equation.  
\[Q((h^{1},h^{2},\cdots,h^{n}),(a^{1},a^{2},\cdots,a^{n}))\approx\sum_{i=1}^{n} \tilde{Q}_{i}(h^{i},a^{i}), \tag{22}\]  
where the \(\tilde{Q}_{i}\) represent every agent's own observations and is learned by back propagating gradients according to the joint summation reward, rather than the specific reward of agent \(i\), \((h^{1},h^{2};\cdots,h^{n})\) is the history tuple of one agent. For the improved summation reward, \(\tilde{Q}_{i}\) are action-value functions that consist of all particular rewards of the UAVs, without any other constraints. The overall architecture is shown in Figure 1.
## 5 Result and Analysis  
This study aimed to analyze the experimental data through quantitative and qualitative methods in order to assess the performance of different interaction techniques in terms of task completion time, cognitive load, and user satisfaction. A series of statistical methods were employed to analyze the performance of different interaction techniques in the above aspects. Initially, an ANOVA test was conducted on all subjective and objective quantitative outcomes. In cases of heteroscedasticity, the Welch test was utilized. Post-hoc pairwise comparisons were performed using the Bonferroni correction method to control the overall error rate of multiple comparisons. For data failing to meet the assumption of homogeneity of variances, the Games-Howell test was deployed to investigate specific differences among interaction methods.
# Optimal Base Station Density of Dense Network:
## 1 Introduction  
We are embracing a booming era of information technology, where all people and industries are powered to reach their full potential. In the coming few years, smart devices of an increasingly powerful scope will connect to wireless networks, allowing the opportunity to use extremely diverse multimedia services. The technical reports from Ericsson and Qualcomm [1, 2] predicted that the number of devices connected to wireless networks would soar to 28 billion by 2021 and the mobile data traffic in 2020 will be 1000 times greater than that of the 2010 baseline. These trends show a high traffic density in the near future. To triumph over this formidable challenge, International Telecommunication Union-Radiocommunication Sector (ITU-R) has defined Enhance Mobile Broadband (eMBB) as one of three typical scenarios of 5G (5 th generation wireless systems) [3]. According to the report from IMT-2020 promotion group of China, the 5G network is expected to provide tens of Tbps/km\({}^{2}\) traffic volume density [4].  
As a key solution to high traffic density, network densification is proposed to deploy ultra-dense heterogeneous networks (HetNets) [5]. Network densification has many advantages. It improves the area spectral efficiency by reusing the spectrum extensively over geographic areas, which effectively deals with the spectrum shortage. Moreover, it can also offload traffic from congested macro base stations (MaBSs) to small base stations (SmBSs). It reduces the number of users competing for communication resources and improves the service quality. Acknowledging these advantages, researchers are studying the multi-tier heterogeneous networks (HetNets) to implement network densification, where different radio access technologies (RATs) working in both licensed and unlicensed spectrums coexist.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 4 Automated and Transparent Encryption and Decryption
### Access to Decrypted Data  
In our proposed solution, the original encrypted Large Objects (LOBs) containing the spatial files **are never replaced** with a decrypted version. During decryption, instead of replacing the original encrypted LOBs in the database's permanent tablespace, the LOBs are decrypted and stored in the temporary tablespace. Oracle DBMS supports \"_temporary tablespaces that contain data that persists only for the duration of a user's session_\" [71, 72]. This permits temporary LOB datatype storage in such a way that data are not permanently stored in the database. Thus, we replace the LOB locator pointing to the original spatial file with the temporary locator that is stored in a separate ECMSDK table within the temporary tablespace. After decryption, users access, use, and update this copy of the LOB that contains the file content.  
After users log off, these spatial files stored as temporary LOB content are encrypted back, replacing the original LOB in the permanent tablespace. For authorised users, read and write access to encrypted and decrypted files within ECMSDK is controlled through setting its _access control list_ (see Section 3.1). The decrypted files within the temporary LOBs will be freed from the temporary tablespace as soon as the database crashes. **This way, an adversary with access to the disk will always have access to only the encrypted spatial files.** Using temporary LOBs in the decryption process does not require any additional overhead, as instead of data being written into the same file's LOB object, it is written into the temporary LOB object.