esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Review Methodology
### Review Protocol  
The research protocol will be based on the following steps: (1) a specific phase will be used in several databases including Scopus, PubMed, ProQuest, and Google Scholar, (2) title screening will determine the included records followed by abstract screening, (3) full-text scanning will be used to choose the correct records that follow the research criteria, (4) the selected literature will be analyzed to extract the results, and (5) the literature review will be interoperated while discussing the limitations and opportunities.  
According to [23], each primary study included in the systematic review must be assigned a quality value. The quality value will be determined based on the following five quality assessment questions (QAs):  
1. QA1: Is the solution proposed using either augmented reality or virtual reality to treat the specific phobia?
2. QA2: Is the research methodology defined in the article clearly?
3. QA3: Is evaluation defined in the article clearly?
4. QA4: Is the proposed solution tested on specific phobia patients aged over 18?QA5: Does the evaluation include a follow-up?  
Table 2 presents our findings regarding specific phobia using augmented and virtual reality, and Table 3 provides existing paper types.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Title** & **Paper** & **Review Type** & **\# of Studies** & **VR** & **AR** & & **Area of Interest** \\ \hline \multirow{4}{*}{Recent developments in the intervention of specific phobia among adults: A rapid review} & \multirow{4}{*}{[5]} & Rapid & \multirow{4}{*}{33} & \multirow{4}{*}{\(\check{\check{\vee}}\)} & \multirow{4}{*}{\(\check{\check{\vee}}\)} & 1. & Psychotherapy \\  & & & Review & & & & 2. & Technology-assisted \\  & & & Review & & & & & therapy \\  & & & & & & 3. & Pharmacotherapy \\  & & & & & & 1. & Specific Phobias \\  & & & & & & 2. & Post-Traumatic Stress \\  & & & & & & & Disorder and Acute \\  & & & & & & 3. & Anxiety \\  & & & & & & 4. & Paranoid \\  & & & & & & & Ideations \\  & & & & & & & 1. & Specific Phobia for Small \\  & & & & & & & Animals \\  & & & & & & 2. & Agoraphobia \\  & & & & & & 3. & Flying phobia \\  & & & & & & 1. & Specific Phobias \\  & & & & & & 2. & Post-Traumatic stress \\  & & & & & & & disorder \\  & & & & & & 1. & Specific Phobias \\  & & & & & & 1. & Specific Phobias \\  & & & & & & 2. & Exposure therapy towards \\  & & & & & & &  Phobia Rehabilitation \\  & & & & & & 1. & Specific Phobias \\  & & & & & & 2. & Agoraphobia \\  & & & & & & 3. & Social Phobia \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing review papers on anxiety disorder exposure therapy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Paper** & **QA1** & **QA2** & **QA3** & **QA4** & **QA5** & **Total** \\ \hline
[24] & 1 & 1 & 0 & 1 & 0 & 3 \\
[32] & 1 & 1 & 1 & 1 & 0 & 4 \\
[33] & 1 & 1 & 1 & 1 & 1 & 5 \\
[34] & 1 & 1 & 1 & 1 & 0 & 4 \\
[35] & 1 & 1 & 1 & 1 & 0 &
# Influence of Acoustic Streams on the Efficiency of Ultrasonic Particle Agglomeration
## 3 Results
### Increasing the Productivity of Gas-Dispersed Flow Processing  
The considered design of the agglomeration chamber can significantly increase the efficiency of coagulation, which subsequently increases the efficiency of capturing dispersed particles.  
However, the processing capacity of the gas-dispersed flow of such a chamber (with a disk diameter of 320 mm) is no more than 10 m\({}^{3}\)/h. To increase productivity, the authors proposed the design of an agglomeration chamber with several parallel oscillating plates, which are installed at a resonant distance from each other and implement the identified optimal conditions and modes of ultrasonic exposure. A sketch of the proposed agglomeration chamber is shown in Figure 12.  
Figure 12: Design of the ultrasonic agglomeration chamber: 1—housing; 2—inlet pipe; 3—outlet pipe; 4—radiator; 5—ultrasonic piezoelectric transducer; 6—reflector; 7—flow distributors; 8—gas flow; N = 1... 3; \(\lambda\)—wavelength.  
The module is based on an ultrasonic oscillatory system (No. 5, Figure 12) with a plate radiator (No. 4, Figure 12), which are installed in the housing (No. 1, Figure 12). A reflector is installed opposite the radiator (No. 6, Figure 12). The sketch and vibration shape of the plate radiator are shown in Figure 13.  
The radiator of the oscillatory system is made in the form of parallel plates (No. 1, Figure 13). The plates undergo bending vibrations (Figure 13B). When installing an oscillating system and a reflector in the agglomeration chamber, the distance between the plates of the radiator and reflector is equal to the wavelength of ultrasonic vibrations in the air.  
With a resonant distance between the plates of the radiator and reflector in the air gap, conditions are created for the emergence of intense acoustic flows and vortices that promote the coagulation of dispersed particles and the deposition of their aggregates.  
In general, the module works as follows. A gas flow containing highly dispersed particles enters the inlet pipe (No. 2, Figure 12). Further, along the direction of gas movement, flow distributors are installed (No. 7, Figure 12), which evenly distribute the gas-dispersed flow throughout the chamber volume.  
Next, highly dispersed particles enter the area of ultrasonic influence--the gaps between the plates of the ultrasonic radiator (No. 4, Figure 12) and reflector (No. 6, Figure 12). In this area, high-intensity ultrasonic action occurs on particles and liquid droplets in a thin gas gap.  
This design of the agglomeration chamber makes it possible to increase the productivity of processing a gas-dispersed flow in proportion to the area of the radiator.
## 4 Validation and Analysis of Results
### Kinetic Analysis of Sand Particle Size Distribution in Embarnment Flow Field  
The purpose of this study is to investigate the particle size distribution changes in the wind and flow field after setting up the windbreak at the microscopic scale, so the particle radius is set to be 0.05 mm, 0.10 mm, 0.15 mm with three kinds of particle sizes, and their proportions are 50%, 30%, and 20%, respectively.  
As shown in Figure 20, sand particle size distribution around the windbreak. After installing the windbreak, the proportion of 0.05 mm sand decreased in front of the windbreak, and the proportions of 0.10 mm and 0.15 mm sand increased, but the proportions fluctuated less. However, the percentage of particles fluctuated significantly behind the windbreak, with a significant decrease in the rate of 0.05 mm sand particles and a substantial increase in the rate of 0.10 mm versus 0.15 mm sand particles. This is because the airflow in the windbreak around the formation of a vortex and movement to the windbreak before the speed began to change. The larger the size of the particles transported by the kinetic energy required to transport, the larger the vortex area, and the more pronounced the influence of the larger size of the sand particles is more likely to be here to form the accumulation of sand.  
As shown in Figure 21, sand particle size distribution at the foot of the leeward slope. Before the installation of the windbreak, the airflow was disturbed by the embankment, and the sand particle size distribution was slightly altered at the foot of the leeward slope, but the overall change was not significant. After setting up the windbreak, the disturbance  
Figure 20: Sand particle size distribution around windbreaks.  
of the flow field becomes more extensive, the proportion of 0.05 mm particles decreases, and the proportion of 0.10 mm and 0.15 mm particles increases significantly. This indicates that the vortex region formed after the installation of the windbreak has a more significant impact on larger grain-size sand particles and that large-grain-size sand particles are more likely to accumulate at the foot of the slope.
# RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks
## 4 Experimentation
### Comparative Study
\begin{table}
\begin{tabular}{r r r r r r r r r r r r r} \hline  & RDTIDS & [28] & [29] & [30] & [31] & [32] & **Random Forest** & **RF Tree** & **MLP** & **Naive Bayes** & **Jrip** & **Jds** \\ \hline
**TNR (BENIGN)** & 98.880\% & 97.135\% & 96.450\% & 93.350\% & 94.870\% & 96.835\% & 98.120\% & 95.160\% & 92.650\% & 96.530\% & 95.330\% & 94.960\% \\
**DR DDoS** & 99.870\% & 94.607\% & 99.818\% & 93.212\% & 55.970\% & 99.758\% & 99.818\% & 99.988\% & 99.781\% & 93.579\% & 99.667\% & 99.788\% \\
**DR DDoS** & 97.758\% & 78.90\% & 92.848\% & 95.030\% & **18.182\%** & 93.758\% & 93.758\% & 99.277\% & 78.485\% & 86.267\% & 93.333\% & 93.879\% \\
**DR DDoS** & 93.841\% & 23.383\% & 86.820\% & 83.832\% & 76.541\% & 78.385\% & 81.352\% & 75.564\% & 88.537\% & 70.060\% & 85.543\% & 80.325\% \\
**DR DDoS** & 96.782\% & 66.000\% & 93.945\% & 98.911\% & 73.707\% & 98.668\% & 95.164\% & 92.128\% & 86.981\% & 97.137\% & 97.264\% & 93.600\% \\
**DR DDoS** & 97.571\% & 48.214\% & 75.671\% & 74.134\% & 75.717\% & 65.434\% & 67.718\% & 64.249\% & 64.213\% & 63.857\% & 67.286\% \\
**DR Heartbleed** & 100\% & 80.000\% & 100\% & 80.000\% & 0.0000\% & 40.000\% & 100\% & 100\% & 100\% & 100\% & 80.000\% & 80.000\% & 100\% \\
**DR PortScan** & 98.981\% & 51.407\% & 99.944\% & 90.460\% & 82.851\% & 87.118\% & 99.881\% & 45.215\% & 99.499\% & 98.596\% \\
**DR Rot** & 46.474\% & 1.442\% & 48.718\% & 52.083\% & 0.000\% & 48.07\% & 49.679\% & 47.756\% & 51.282\% & 29.969\% & 46.474\% & 47.576\% \\
**FF-P** & 99.630\% & 0.000\% & 99.727\% & 100.00\% & 99.680\% & 99.727\% & 99.187\% & 99.090\% & 99.455\% & 99.545\% & 99.545\% \\
**DR SSH-P** & 99.990\% & 1000\% & 100\% & 97.270\% & 1000\% & 99.818\% & 100\% & 97.27\% & 99.
###### Abstract  
This paper presents the results of a comprehensive study of the structure, phase composition, thermal corrosion, and tribological properties of multilayer gradient coatings based on YSZ/NiCrAlY obtained using detonation spraying. X-ray phase analysis showed that the coatings consist entirely of metastable tetragonal zirconium dioxide (t'-ZrO\({}_{2}\)) phase stabilized by high temperature and rapid cooling during spraying. SEM analysis confirmed the multilayer gradient phase distribution and high density of the structure. Wear resistance, optical profilometry, wear quantification, and coefficient of friction measurements were used to evaluate the operational stability. The results confirm that the structural parameters of the coating, such as porosity and phase gradient, play a key role in improving its resistance to thermal corrosion and CMAS melt, which makes such coatings promising for use in high-temperature applications. It is shown that a dense and thick coating effectively prevents the penetration of aggressive media, providing a high barrier effect and minimal structural damage. Tribological tests in the temperature range from 21 \({}^{\circ}\)C to 650 \({}^{\circ}\)C revealed that the best characteristics are observed at 550 \({}^{\circ}\)C: minimum coefficient of friction (0.63) and high stability in the stage of stable wear. At room temperature and at 650 \({}^{\circ}\)C, there is an increase in wear due to the absence or destabilization of the protective layer.  
detonation spraying; YSZ/NiCrAlY coatings; tribological properties; coefficient of friction; coating porosity; CMAS corrosion Article
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 6 Perspectives
### Smart Prediction and Assessment  
At present, massive remote sensing and meteorological data pose new challenges to existing empirical and theoretical methods. These methods need to be corrected or adjusted according to specific regions and crop species, thereby limiting the accuracy of monitoring agricultural drought on a large scale. In this context, the deep learning method and cloud computing on Google Earth Engine (GEE) [292] or Amazon Web Services (AWS) [293] is increasing in popularity because of its ability to process, analyze, and effectively mine information in a large area promptly. Some researchers have applied deep learning methods to agricultural drought prediction and assessment successfully [294; 295; 296; 297]. However, deep learning has its limitations. At present, the mainstream models require a huge amount of labeled training data, and the workload of marking remote sensing images is very heavy [298]. A possible way to make up for this efficiency is to integrate knowledge-driven drought expert systems [299], which can structurize expert knowledge and experience flexibly with the support of a cloud computing ecosystem. The combination of deep learning and expert systems within cloud computing can make the intelligence of agricultural drought prediction and assessment more adaptive and promising from field to global scales.  
**Author Contributions:** Conceptualization, Q.Q.; investigation, Z.W., T.Z., Z.Z., Y.Z., Y.S., W.X. and C.Z. ([PERSON]); resources, Z.W., T.Z., Z.Z., Y.Z., Y.S., W.X. and C.Z. ([PERSON]); writing--original draft preparation, Q.Q., Z.W., T.Z., V.S., Z.Z., Y.Z., C.Z. ([PERSON]) and Y.S.; writing--review and editing, Z.W., T.Z. and V.S.; visualization, Z.W. and Y.S.; supervision, Q.Q. and H.R.; project administration, Q.Q.; funding acquisition, Q.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 41771371) and Major Projects of High Resolution Earth Observation (Grant No. 30-H30C01-9004-19/21).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 6 Trusted Execution Environment (TEE) Support and Feasibility
### Summary  
In conclusion, AMD SEV provides the best balance of hardware features and virtualization transparency to evaluate the feasibility and impact of huge-page-aware prefetch-based covert channels in a real-world TEE deployment. Its support for native huge pages and x86 cache coherence behavior--including observable effects from instructions like PREFETCHW--makes it an ideal platform for exploring covert communication across cores within an encrypted VM.  
We implement and evaluate our covert channel design on AMD SEV in the following section, demonstrating both the feasibility and performance characteristics in a realistic threat model setting.
# Design of Autonomous Mobile Robot for Cleaning in the Environment with Obstacles
## 3 Control Algorithm
: _If the value of the linear control signal is less than the considered threshold value \(v_{t}\), i.e., \(|v|<v_{t}\) (\(v_{t}\)--positive constant), it is replaced by a new scalar function \(\tilde{v}=S(v)v_{t}\), where the following holds:_  
\[S(v)=\left\{ \begin{array}{cc}-1&\text{for}&v<0\\ 1&\text{for}&v\geq 0\end{array} \right.. \tag{12}\]Substituting (11) into (6), the error dynamics is given by the following equations:  
\[ \begin{array}{c}\dot{e}_{x}=e_{y}\omega-c_{2}E_{x}+v_{d}(\cos e_{\theta}-1)\\ \dot{e}_{y}=-e_{x}\omega+v_{d}\sin e_{\theta}\\ \dot{e}_{\theta}=-h(t,E_{y})-c_{1}e_{\theta}\end{array} . \tag{13}\]  
Transforming (13) using (11) and taking into account Assumption 2 (when the robot gets into the collision avoidance area, velocities \(v_{d}\) and \(\omega_{d}\) are substituted as 0) error dynamics can be expressed in the following form:  
\[ \begin{array}{c}\dot{e}_{x}=h(t,E_{y})e_{y}+c_{1}e_{y}e_{\theta}-c_{2}E_{x}\\ \dot{e}_{y}=-h(t,E_{y})e_{x}-c_{1}e_{\theta}e_{x}\\ \dot{e}_{\theta}=-h(t,E_{y})-c_{1}e_{\theta}\end{array} . \tag{14}\]  
A stability analysis based on Lyapunov-like function was given in [8]. The control algorithm generates the linear and angular velocity for the real robot. The equations which convert these control velocities into wheel velocities are as follows:  
\[ \begin{array}{c}v_{r}=v+(\omega L)/2\\ v_{l}=v-(\omega L)/2\end{array} . \tag{15}\]  
where \(v_{r}\) is the robot right side velocities for both wheels, and \(v_{l}\) is the robot left side velocities for both wheels. \(v\) and \(\omega\) are the linear and angular velocities respectively. \(L\) is the distance between the left- and right-side wheels. Now, the velocities for each wheels can be written using (15) as follows:  
\[ \begin{array}{c}v_{rf}=v_{rb}=v_{r}\\ v_{lf}=v_{lb}=v_{l}\end{array} . \tag{16}\]  
where \(v_{rf}\) represents the linear speed for the right-hand side front wheel, \(v_{rb}\) represents the linear speed for the right-hand side back wheel, \(v_{lf}\) represents the linear speed for the left-hand side front wheel, and \(v_{lb}\) represents the linear speed for the left-hand side back wheel. Wheel velocities are desired signals of the motor's PID controllers.
## 2 Related Work
### Steganalysis Resistance  
The field of steganalysis has evolved rapidly in response to advances in steganographic techniques. Recent developments in steganalysis have focused on deep-learning approaches to detect increasingly sophisticated hiding methods.  
[PERSON] et al. [16] introduced R-BILSTM-C, a hybrid approach combining bidirectional LSTM and CNN architectures. Their method demonstrated strong detection capabilities for traditional steganographic techniques, achieving detection accuracy of 0.970 for conventional methods. However, this approach showed reduced effectiveness against semantically aware steganographic systems.  
[PERSON] et al. [17] developed a CNN-based steganalysis framework that automatically learns feature representations from texts. Their approach achieved significant detection rates (0.838-0.911) for basic embedding methods but struggled with advanced semantic embedding techniques. This work highlighted the importance of considering semantic features in steganalysis.  
[PERSON] et al. [18] proposed a densely connected LSTM with feature pyramid architecture for linguistic steganalysis. Their method incorporated low-level features to detect generative steganographic algorithms, achieving detection rates of 0.783-0.917 across different datasets. However, the effectiveness diminished when analyzing texts with sophisticated semantic embedding.  
BERT-LSTM-Att, introduced by [PERSON] et al. [14], represented a significant advancement in steganalysis. This approach leveraged contextualized word associations and attention mechanisms to capture local discordances. While effective against traditional methods (0.972-0.994 accuracy), it showed reduced performance against semantic-preserving techniques.
## 4 Experimental Setup and Results
### Main Results Analysis
#### 4.5.1 Evaluation Performance Comparison  
The experimental scenario demonstrates that words not aligned with TF-IDF features were extracted from the raw text, ensuring that the feature set (vocabulary) for each class and theme in the dataset remained consistent, particularly in tests involving DL methods. Transformers were employed to explore how propaganda functions within COVID-19-related text corpora. Both models utilized dynamic masking strategies alongside fixed batch sizes to prevent the model from excessively memorizing the training corpus. The evaluation focused on our ML-MC-RoBERTa model incorporating CRF and LSTM networks, chosen for its lower computational complexity. However, larger versions were also employed as robustness checks, with no significant improvements observed, as indicated in Table 9. The study further revealed that the fine-tuned RoBERTa model, combined with CRF and LSTM, demonstrated superior performance, achieving the highest evaluation performance. The \"Roberta-base\" model [67], an integral part of the BERTBase architecture with 125 million parameters and additional training on extensive text corpora, played a pivotal role in RoBERTa's effectiveness.  
Figure 11: [PERSON]–Meier estimates propaganda theme scores on ProText data based on (**a**) Facebook and (**b**) Twitter.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc** & **P** & **R** & **F1** & **MCC** & **GM** & **NMI** \\ \hline RoBERTa [67] & 36.69\% & 0.3472 & 0.3069 & 0.3258 & 0.0892 & 0.3264 & 0.5504 \\ BERT [21] & 35.87\% & 0.3011 & 0.3367 & 0.3179 & 0.0153 & 0.3184 & 0.3179 \\ MT-DNN [54] & 39.74\% & 0.3685 & 0.3456 & 0.3567 & 0.1046 & 0.3568 & 0.5645 \\ XLNET [56] & 44.95\% & 0.4108 & 0.4088 & 0.4098 & 0.1335 & 0.6676 & 0.5864 \\ ALBERT [55] & 47.79\% & 0.4453 & 0.4284 & 0.4367 & 0.1491 & 0.6879 & 0.5985 \\ RoBERTa-LSTM [12] & 70.87\% & 0.6591 & 0.6764 & 0.6678 & 0.2970 & 0.8207 & 0.7344 \\ RoBERTa-CRF [57] & 64.52\% & 0.6185 & 0.5907 & 0.6043 & 0.2550 & 0.7844 & 0.7020 \\ H-GIN [52] & 86.9\% & 0.816 & 0.809 & 0.812 & - & - & - \\ ML-MC-RoBERTa-LSTM (proposed model) & 82.46\% & 0.7992 & 0.7734 & 0.7861 & 0.3752 & 0.8899 & 0.8080 \\ ML-MC-RoBERTa-CRF (proposed model) & **88.71\%** & **0.8319** & **0.8275** & **0.8363** & 0.7572 & 0.9131 & 0.5864 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Propaganda detection evaluation performance on ProText dataset.  
H-GIN enhances local word co-occurrence features by incorporating three channel layers--syntactic, semantic, and sequential--which have shown improved effectiveness in experimental results with an accuracy of almost 87% [52].
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot
### Space Mission Description
#### 2.1.2 Position Changes between System Modules  
When performing space missions, the modular space robot can realize structural transformations according to the different requirements of specific missions on the installation location, and transform the payload to the mission location to adapt to complex mission environments.  
As shown in Figure 2, it is assumed that the robot system consists of multiple modules and each module is equipped with different mission loads, which can be used for different missions. For example, when module 4 is used as a working module, the working surface of module 4 is located outside the platform axis. Then, to achieve the requirement that the working surface of module 3 be exposed to the outside, modules 4 and 3 undergo a configuration transformation as a whole. Through allostery, the working interface of module 3 is exposed to the axial outside of the system and enters the working mode.  
Through appropriate modular design and planned allosteric rules, the positions of modules can be interchanged and the functions of the entire reconfigurable system can be expanded.