esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tracking Control Laws
### Rudder Control to Stabilize the Roll  
To make the roll of the vehicle converge to zero in disturbed environments, the rudder command is set as \(Q\times\text{sign}(\phi)\) where \(\phi\) is the roll of the vehicle and \(Q>0\) is a constant. Also, \(\text{sign}(\phi)\) is the sign of \(\phi\). Under our depth control laws, the vehicle moves straight using (6) or changes its pitch using (5) or (7).  
Consider the case where the vehicle moves straight using (6). In the case where we deflect two rudders by the identical angle, the vehicle rotates in one direction using Theorem 2. Thus, by setting the rudder command as \(Q\times\text{sign}(\phi)\), the roll of the vehicle converges to zero.  
Next, consider the case where the vehicle changes its pitch using (5) or (7). Recall that under our depth control laws, the pitch rate of the vehicle is small to satisfy that \(\theta\approx 0\). This implies that the vehicle moves almost straight. Thus, by setting the rudder command as \(Q\times\text{sign}(\phi)\), the roll of the vehicle converges to zero.  
Sliding mode controls (SMCs) are well-known for their robustness to uncertain models [20]. This robustness comes from the fact that the SMC switches between two distinct modes to converge to desired states. \(Q\times\text{sign}(\phi)\) is similar to the SMC in that it switches between two distinct modes to converge to desired states. The robustness of our control laws is verified using MATLAB simulations.
# Mechanical Modeling and Dynamic Characteristics of a Three-Directional Vibration Absorber
## 3 Results and Discussion
### Experimental Validation  
#### 3.5.1 Static Experimental Validation  
Figure 16 characterizes the static mechanical properties of the three-directional vibration isolator, and the quasi-static loading-unloading tests were conducted using a WDW-T200 electronic universal testing machine (Jinan Tianchen, Jinan, China). This testing machine features a vertical lifting platform with two working surfaces: an upper movable platform equipped with a force sensor and a lower fixed platform for support. The isolator's mounting interface contacts the upper platen to receive compressive loading, while the reaction force is recorded by the integrated load cell. The standardized testing protocol consists of the following:  
1. The isolator was secured to the lower platen. After initializing the test software, the upper platen was manually lowered at reduced speed (0.6 mm/min) until contact with the isolator was established, at which point displacement readings were zeroed.
2. Based on specific test requirements, displacement-controlled loading was performed at a constant rate of 0.6 mm/min with a maximum displacement of 8 mm to ensure measurement accuracy.  
Quasi-static loading and unloading tests were performed on the vibration isolator. As shown in Figure 17, the experimental hysteresis curves were compared with theoretical predictions, where the equivalent curve was derived by averaging the loading and  
Figure 16: Static loading test device for the vibration isolator.  
Figure 15: Variation in force transmissibility of the vibration isolator with excitation frequency ratio \(\Omega\) under different equivalent Coulomb friction damping factors \(\xi_{c}\).  
unloading test data. Although minor deviations were observed, the experimental results demonstrated good agreement with the theoretical analysis overall. Compared with the theoretical curve, the experimental results show hysteresis between the loading and unloading curves, and the actual stiffness is slightly higher than the theoretical analysis value. This is caused by Coulomb friction generated in the spherical hinges of the isolator's internal hinge assembly during movement.
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### LSTM Neural Network Prediction Experiment across Seasons  
For modeling the satellite and its sensors to evaluate performance in real time, we used the Systems Tool Kit(r) (STK), used by engineers, mission analysts, and operators. The tool is limited to a 1 s prediction. The experiment setup comprised an LSTM neural network system, a Matlab environment loaded with the code described in Section 3.5, and 98 sample records described in Section 3.5. We built the network using the LSTM method and created a tool that predicts SNR values for a future timeframe. The model succeeded in predicting the SNR sample for the next one second based on the data with a deviation of 1%, which is still reliable. We collected data from satellites, researched and explored optimal ways to build the deep neural network architecture of the LSTM, and chose the parameters which reduced the function loss to a minimum. The LSTM neural network training process receives the 98 training samples and outputs the predicted value for the next timeframe. The method comprises a three-stage process, starting with data collection from the satellite, building, training, and testing the RNN using the LSTM method and then iteratively compiling a prediction of the signal intensity for the next timeframe.  
The conceptual LSTM process, starting with all 98 samples, was reduced to 20 and, finally, to the resulting SNR value. We defined a hyper-parameters function and, with many empirical experiments, we found that the optimal parameters converged after four epochs. The parameters are: (a) Loss function: mean absolute error, (b) Optimizer: \"Adam\", (c) Activation function: \"tanh\", (d) Several epochs to saturation 4, 3 layers: 2 LSTM, 1 dense--First layer: 98 neurons, Second layer: 20 neurons, Last layer: 1 neuron. Figure 4 depicts a chart of a one-month sampling experiment using a 10 GHz frequency. It compares the predicted signal graph generated by the LSTM neural network (left) and the actual signal graph (right). We can see that the two charts are very similar, with an average matching of 93%. We revealed similar results for the next four consecutive months, proving that the prediction accuracy depends on various behaviors across seasons.
Several studies have investigated TMJ involvement in JIA patients with instrumental equipment such as stereophogrammetrically with 3D photographs [19], power Doppler ultrasound (US) [20], 3D surface scans [21], CBCT [22] and orthodontic orthopantomograms [23]. Other studies defined clinical protocols for orofacial examination in JIA children [15; 24]. Recent systematic reviews concluded that there are current low levels of evidence, and that the heterogeneity of the studies does not allow identifying clinical findings that could accurately predict TMJ arthritis [25; 26]. The objective of the current study is to test clinical and instrumental methods as reliable, non-invasive functional prognostic indicators of possible TMJ involvement in patients with JIA. The outcomes investigated are the following: muscle pain, the ratio of masseter and temporal muscle activity, mouth opening width and jaw deviation during mouth opening. The methods tested are as follows: (a) palpation of head and neck muscles, (b) electromyography (EMG) and (c) kinesisography. The null hypothesis to be tested is that there are no significant differences observed in pain during muscle palpation, in mouth opening width and deviation on mouth opening during kinesigraphy and in the frequency of the pathologic ratio during electromyography between case and control groups.
# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 5 Experimental Results
This indicates that, when the enrollment speech duration reaches 3 s, the speaker identity information can be accurately captured for our text-dependent ASV task. This may also due to the quiet speech recording environment and the limited number of speakers in our tasks (only 20 speakers). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Task** & **Gender** & **Reading** & **Singing** \\ \hline \multirow{4}{*}{GMM-based} & \multirow{3}{*}{1 s-1 s-GD} & Male & 1.0 & 1.0 \\  & & Female & 4.3 & 3.3 \\  & & All & 2.5 & 2.2 \\ \cline{2-5}  & 1 s-1 s-GI & All & 2.3 & 1.7 \\ \hline \multirow{4}{*}{DTW-based} & \multirow{3}{*}{1 s-1 s-GD} & Male & 1.0 & 1.0 \\  & & Female & 4.3 & 3.3 \\  & & All & 2.5 & **2.2** \\ \cline{2-5}  & 1 s-1 s-GI & All & 2.3 & **1.8** \\ \hline \multirow{4}{*}{X-vector based} & \multirow{3}{*}{1 s-1 s-GD} & Male & 1.7 & 1.7 \\  & & Female & 0.0 & 0.0 \\ \cline{1-1}  & & All & 0.5 & 0.5 \\ \cline{1-1} \cline{2-5}  & 1 s-1 s-GI & All & 0.8 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: EER% comparison on 1 s-1 s text-dependent ASV tasks, using different systems. From the extraordinary results on 3 s-3 s tasks, we may think that, in a real-world system with limited speakers, when the enrollment speech duration is long enough, e.g., 3 s, the speaker identity information can be accurately captured, even we use very simple classifiers such as GMM and DTW. However, in most real-world applications, the background noise or speech recording and transmission channel mismatch between training and testing will degrade the ASV system performance significantly. There are many research works focused on the speech enhancement and channel compensation issues in ASV [1; 37]. However, our motivation is to see the difference between singing and normal reading speech in ASV, thus we recorded the RSS corpus under a very quiet office environment to eliminate other interference such as background noise, etc. It is normal for us to obtain zero EER on a text-dependent ASV task under our speech recording setups. Therefore, the extraordinary results on our 3 s-3 s tasks does not indicate that we can obtain exactly the same good results under most real-world ASV applications, except for under the same data recording and setups with our RSS corpus. In X-vector based systems, the other interesting thing we found was that the NCCF feature brought no performance gains; we obtained the same EERs on both 1 s-1 s and 3 s-3 s tasks from the 60-dimensional MFCCs and the same 60-dimensional MFCCs plus one-dimensional NCCF feature. This observation may also be affected by the fact that the X-vector extractor was trained from out-of-domain corpus. Anyway, all of these observations will be further validated in our future works when many singing and reading speech samples are recorded.
## 2 Experiments  
### Materials, Solution and Preprocessing  
N80 steel was chosen for the immersion experiments and electrochemical tests in this study. The composition of N80 steel (wt.%) is shown in Table 1. The dimensions of the steel samples used in the immersion experiments were 50 \(\times\) 10 \(\times\) 3 mm. The sampleswere prepared for the studies by grinding them with 8000# silicon carbide sandpaper, washing them with deionized water, cleaning them with ethanol, and then drying them in a desiccator. Similarly, the electrochemical test samples had the same dimensions of 50 \(\times\) 10 \(\times\) 3 mm and were polished using silicon carbide sandpaper ranging from 1000# to 2000#. The samples were then cleaned using ethanol and deionized water and were allowed to air dry. Once dry, they were then embedded in 704 silicone rubber, leaving an area of 0.35 cm\({}^{2}\) which was exposed to the test solution. The test solution used was a laboratory-simulated formation water, which was formulated to mimic the composition of the formation water found in the Sudong Block of the Sulige Gas Field. It had the following composition (g/L): 5.99 Na\({}^{+}\), 5.22 K\({}^{+}\), 7.14 Ca\({}^{2+}\), 0.29 Mg\({}^{2+}\), 26.96 Cl\({}^{-}\), 0.56 SO\({}_{4}\)\({}^{2-}\), and 0.14 HCO\({}_{3}\)\({}^{-}\).
# Image-Based Malicious Network Traffic Detection Framework: Data-Centric Approach
## 3 Proposed Detection Framework
### Feature Engineering Overview  
The data preprocessing and feature engineering module converts raw data into image data. This module is structured into two primary components: data preprocessing and feature engineering. In data preprocessing, first, data transformation converts categorical features (non-numeric features) into numeric feature vectors using label encoding as a method. Then, in data normalization, all numeric feature vectors are normalized by applying feature scaling, and all the feature values are converted to values between 0 and 255. If the range of values of a feature varies significantly, biases towards these features can emerge. To address this issue, the logarithmic function is applied for feature scaling. In the feature engineering component, information gain is used to measure the importance of each feature and to reduce the dimensionality complexity of the data. Features with lower importance based on the measured information gain values are discarded, while the remaining features with higher importance are retained. Additionally, feature correlation analysis examines the relationships between features to determine the optimal pixel mapping strategy for image generation. In particular, we tested whether arranging highly correlated features to adjacent pixels in the image improves classification accuracy. However, the experimental results showed no significant performance difference compared to simple sequential pixel mapping; thus, the final feature vectors were arranged in sequential pixel order.  
The network traffic image dataset generation module creates an image dataset based on the finalized numeric feature vectors produced by the data preprocessing and feature engineering module. Through sequential pixel mapping, each feature value is assigned to a specific position in a 6 \(\times\) 6-pixel grid, creating a two-dimensional grayscale image representation of each network traffic sample. Through this process, attack and normal image datasets are generated, respectively.  
Finally, the CNN-based detection module utilizes a CNN model designed with parameters optimized through experiments. This module trains a model with the network traffic image dataset generated in the previous processes and measures the classification accuracy of malicious network traffic using a test set. The CNN architecture processes the spatial patterns present in the traffic images, effectively learning to differentiate between normal and malicious network behaviors based on their visual signatures.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 6 Conclusions  
We present an ABE scheme that is based on the SM9 IBE scheme, which supports the generalized access structure. The proposed scheme complies with the Chinese commercial cryptographic standard and the forthcoming ISO standard. In terms of implementation, we first implement and embed the R-ate pairing on BN-curves, which is required by SM9, into the JPBC library; and, then implement SM9 as well as the proposed ABE. Moreover, we also develop a hybrid ABE for achieving lower ciphertext expansion rate when the size of access structure or plaintext is large. In general, the proposed schemes' performance is reasonably well on PCs, and is also acceptable on smart phones and embedded devices, according to our experimental results.  
Figure 14: Hybrid ABE algorithms’ energy consumption on Raspberry Pi 3 and OnePlus A6000 smartphone (1 KB plaintext).  
Figure 13: Hybrid ABE’s ciphertext’s size with respect to plaintext’s length (the size of access structure is 10).  
**Author Contributions:** Conceptualization, Y.S. and X.W.; methodology, Y.S.; software, Z.M., Y.S. and W.W.; validation, R.Q.; writing--original draft preparation, Z.M.; writing--review and editing, H.F.  
**Funding:** The National Natural Science Foundation of China (grant numbers 61772371, 61702374, and 61672128), the National Key Research and Development Program of China (grant number 2018 YFC0830406), the National Critical Science and Technology Infrastructure Program (China National Seafloor Scientific Observatory, Tongji University), the Shanghai Sailing Program (grant number 17 YF1420500), and the Fundamental Research Funds for the Central Universities.  
**Acknowledgments:** The authors are grateful to the editors and anonymous reviewers for their suggestions and comments for improvement of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract
[21] carried out a fundamental study on distributed strain measurements based on the Rayleigh and Brillouin frequency shifts of two different sandstone samples (Berea sandstone and Tako sandstone) under controlled hydrostatic confining and pore pressure in the laboratory. By comparing with strain data obtained from conventional strain gauges, they showed the potential use of deformation monitoring with the DFOS technology in complex lithology reservoirs. As a sequel to the study of fiber optic sensing for geomechanical monitoring by [PERSON] et al. [21], this paper presents a field study of distributed strain measurement in a pumping test and also discusses the roles of distributed strain data and a combined analytical and numerical approach in providing the scaled-up formation properties for geomechanical modeling and analysis. Section 2 introduces a general modeling framework and the numerical simulation method used in the coupled thermal, hydrological, and mechanical (THM) analysis. Section 3 describes the field case study results from the pumping test. Finally, Section 4 summarizes the future practical uses of fiber optic sensing in geomechanical monitoring at the CO\({}_{2}\) storage and oil and gas fields.
## 5 RIBE and RIAE Studies at the AS and ESRF Synchrotron Facilities
### Studies at the AS
#### 5.1.2 In Vivo RIAE Studies at the AS
Thus, CCL2 was essential in the tumor-induced systemic genotoxic response in vivo. Little or no change in DNA damage and apoptosis was observed in out-of-field tissues of the immune-deficient mice after local MRT exposure, proving that RIAE relies on the functional immune response [105]. No change in DNA damage and apoptosis was observed between CCL2 KO mice and WT mice injected with anti-CSF1R neutralizing antibody, which renders mice macrophage-depleted, indicating that macrophages and CCL2 play key roles in generation and propagation of RIAE. We speculate that CCL2, induced at the irradiated site, attracts macrophages that secrete more CCL2 and TGF8, that bind to their receptors, CCR2 and TGF\(\beta\)R1, in out-of-field tissues. CCL2 then creates a site of local inflammation by activating tissue-associated macrophages (TAM). ROS at the abscopal site can be generated directly by TAM, or via TGF\(\beta\) production. CCR2 also up-regulates TGF\(\beta\). Subsequently, TGF\(\beta\) promotes RIAE, including DNA damage and apoptosis. Therefore, targeting the innate immunity via CSF1R in macrophages and/or blocking TGF\(\beta\) and CCL2 could potentially protect out-of-field tissues from non-targeted systemic effects of IR [105; 110]. Finally, out-of-field (i.e., 35 mm from the irradiated site) skin samples were collected in both aforementioned studies for gene expression analysis [111]. The influence of irradiated target size, dose, and beam modality (MRT and BB) on gene expression was investigated in both C57 BL6/J WT and CCL KO mice. Gene expression changed in six genes, Tg\(\beta\), Tp53, Tnf, Ccl2, Cd22, and Mdm2, with a degree of independence from dose and radiation modality. Particularly significant were an increase in Tnf expression and a decrease in Mdm2 expression, genes associated with inflammation, including macrophage activation [112], and DNA damage. In CCL2 KO mice, gene expression profile exhibited an early increase in Mdm2, Tgf\(\beta\)1, Tnf and Cd22 expression that was not observed in the immune-proficient mice. Therefore, the innate immune system is involved in out-of-field tissue responses and alterations in the immune status change the tissue response to RIAE.
# An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching
## 4 Experimental Results
### Comparison the Proposed Method with Previous Methods
#### 4.3.2 Feature Comparison  
In addition to performance comparison, a feature comparison in terms of reversibility, high capacity, image expansion and high security is shown in Table 4. The proposed method is reversible, whereas the recovered image is only similar to the secret image in [PERSON] et al.'s method and [PERSON] et al.'s method. In other words, their methods are not reversible. Although [PERSON] et al.'s method is reversible, it can not ensure a relatively large payload (more than 1 bit per pixel), while the proposed method can achieve high capacity. Compared with [PERSON] et al.'s method, the stego-image in the proposed method is not expanded because it has the same size as the secret image. Moreover, the secret image of the proposed method has high security because it is hard to recover the secret image only by the stego-image which looks like the freely-selected target image.
## 2 BDS-3 SAIM Monitoring Method
### Enhanced Ephemeris Monitoring Methods
#### 2.2.1 Extrapolated Ephemeris  
In BDS-3, the ground control system injects the broadcast ephemeris to the satellite once every hour. After receiving the latest broadcast ephemeris, the satellite can use the navigation ephemeris message posted from the ground station in the previous hour to calculate the position predicted by the two groups of ephemeris messages for the same time and to calculate the satellite's position difference calculated from the two ephemerides. If the position difference exceeds the threshold, an alarm will be given through SAIM to monitor the orbit of the navigation message and prevent the wrong ephemeris from being received by the users, even when the satellite receives the wrong ephemeris. The method of extrapolating satellite position using ephemeris can refer to BeiDou Navigation Satellite System Signal in Space Interface Control Document Open Service Signal B1C (Version 1.0) [20].  
Figure 3: The new SAIM design with additional ephemeris message integrity monitoring.  
Figure 2: SAIM ephemeris integrity monitoring process.  
Usually, the forecast error of the satellite's broadcast ephemeris for 1 h is at the decimeter level, which can be used as a reference for monitoring the newly posted broadcast ephemeris. The monitoring method is simple, effective and reliable.
### Contributions  
The main contributions of this paper are enumerated as follows.  
* We consider the downlink transmission in a cache-enabled UAV-assisted wireless communication system, which consists of UAVs, users, and an MBS. The design of UAV deployment, caching and beamforming is investigated. As far as we are concerned,this is a positive attempt to jointly optimize UAV location, caching and beamforming in a UAV-assisted network.
* We decompose the formulated problem into two subproblems and solve them iteratively. Inspired by [21], we use the DC-based method to convert the original problem into a convex form. Specifically, we bring out a method to apply linear approximation in the DC-based algorithm, which is particularly suitable to the problems involving complicated summations.
* Finally, via numerical results, we show that the proposed DC-based algorithm can efficiently reduce transmission latency. The DC-based algorithm outperforms the benchmark algorithms, especially in the case of the UAV caching capacity being small or the Zipf parameter of content popularity being large.  
The rest of this paper is organized as follows. Section 2 introduces the system model. Then, in Section 3, the problem formulation is presented and a DC-based joint optimization algorithm is proposed. Section 4 presents and analyzes the numerical results. Finally, the conclusion of this paper is drawn in Section 5.