esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Edge Optimization Embedded System Using Graph Theory with 2-Tiered Security
## 4 Energy-Efficient Enabled Reliable Edge-SDN Protocol with Graph Theory
### System Model  
We model the network system with sensors \(N\), edges, and sink nodes. Furthermore, SDN controllers are considered in the proposed system for efficient management of device constraints. Nodes belong to a particular group and each group is associated with the gateway. All the groups and nodes have unique identities. The received signal strength indicator (RSSI) determines the distance between nodes. The border nodes are considered edge nodes and have sufficient intelligence to make local decisions. All the edges are interconnected with each other to formulate a routing chain and provide the necessary information among network structures to the SDN controller. We assume the following network assumptions:  
1. Nodes are randomly deployed in the targeted area.
2. Nodes are static with mobile gateways.
3. Gateways can perform data aggregation and maintain proximity tables.
4. Tables are not static and frequently change the information when any event incurs.
5. Sensor nodes cannot direct communication with edge devices.
6. If the residual energy of any node is below the threshold, its flag value is zero.
## 4 Discussion
### High-Frequency Studies
It is important to highlight that, of all the devices used in the high-frequency studies, the Vpro5 (Propel Orthodontics, Ossining, New York) (120 Hz) is the only high-frequency device commercially available that specifically offers to accelerate the orthodontic tooth movement, unlike the other devices previously mentioned, which were not specifically designed to accelerate tooth movement during orthodontic treatment. In this sense, [PERSON] et al. evaluated the effectiveness of the vibratory stimulus by using the device 5 min a day in 75 patients divided into five groups treated with Invisalign(r) (Align Technology, Inc., Santa Clara, CA, USA). Under these conditions, they measured the rate of anteroposterior movement of a lower anterior tooth, and the results showed that Vpro5 can reduce the time interval between aligner changes without affecting the treatment efficacy, as equal magnitudes of tooth movement are shown in subjects treated with aligner changes every 14 days or in those who had aligners changed every 7 days (\(p<0.003\)) [30]. Nevertheless, as it presents a different appliance, and a short period of evaluation, it is not possible to discern if this benefit will be obtained during the entire treatment, as well as if it is applicable to all types of orthodontics, or if it only works mechanically, allowing a greater adjustment of the aligners. Finally, [PERSON] et al. designed a split-mouth study in 23 patients with MBT prescription brackets (3M Gemini Series(tm) MBT); as these patients required canine distalization, attachments such as Miniscrew implant (tomas(tm) Dentaurum) were used, and the electric toothbrush Oral B CrossAction(r) Power Dual Clean was used as a supplementary vibration device of 100-105 Hz, for 5 min, three times a day on the experimental side. The three-months results showed no statistically significant difference when the experimental and control sides were compared (\(p=0.70\)) [31]. It should be noted that this study did not mention having checked the frequency of its device, as it was based on the Cochrane Review of manual versus electric toothrushing for oral health. An interesting finding is that the previously mentioned articles were evaluated in this systematic review with a moderate or high risk of bias, and it is even more important that the only two articles that reported a positive effect of the use of vibrations showed a high risk of bias. Nevertheless, despite this finding, the evidence is still limited to conclude whether or not vibrations, particularly those of high frequency, have a biological effect.
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 5 Conclusions  
In this paper, we introduce a BSEGAN-SI method for speaker-independent spectral enhancement for BC speech. By establishing feature mapping between speech, we can better enhance the auditory effect of speech. This method uses spectral distance to constrain the difference between the BC speech spread spectrum and the AC speech spectrum so that the generated enhanced BC speech spectrum is closer to the AC speech spectrum. In addition, the parameters and complexity of the model are low, which can achieve a better enhancement effect through short-time training. The above results show that this method can better recover the lost components of the BC speech spectrum, obtain a more complete spectrum structure similar to the AC speech, improve the auditory perception and articulation quality of BC speech, and help improve the quality of speech communication in poor environments. Furthermore, after the training of the model, the enhancement of BC speech does not need the help of AC speech information. As a future extension, the model can be used as the final system for real-time operation. If you want to obtain a program code for research, please email [EMAIL_ADDRESS].  
Conceptualization, L.C., software, L.C. and H.W.; methodology, L.C. and Y.D.; validation, Y.D. and J.Z.; resources, L.T. and J.Z.; writing--original draft preparation, L.C.; writing--review and editing, L.C., J.Z., Y.D., and H.W.; supervision, L.T. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Joint Fund Key Project (U2003207), National Natural Science Foundation of China (61902064), Natural Science Foundation of Anhui Province (No. 1908085 MF209), Key Projects of Natural Science Foundation of Anhui Province Universities (No. KJ2018A0018).  
Not applicable.  
The authors declare no conflict of interest.
## 3 The Proposed Scheme
### Grid Construction  
In CBDAS, the whole sensor field is partitioned into a logical grid of \(M\times N\) cells, where \(N\) is even. Each cell has an ID, containing sensor nodes. A sensor node can calculate its cell ID [\(C_{x}\), \(C_{Y}\)] from its geographic location (\(x\), \(y\)) as follows:  
\[C_{x}=\left\lfloor\frac{x-x_{0}}{\alpha}\right\rfloor,C_{x}=\left\lfloor\frac {y-y_{0}}{\alpha}\right\rfloor \tag{1}\]  
where (\(x_{0}\), \(y_{0}\)) is the location of the virtual origin set at the network initialization stage, \(\alpha\) is the cell size, and \(\left\lfloor k\right\rfloor\) is the largest integer not greater than \(k\). For simplicity, we assume that all cell IDs are positive.  
Figure 1 illustrates an example sensor field partitioned into a logical grid of 4 \(\times\)4 cells, where the cell IDs are enclosed by square brackets. From left to right, the cell IDs are [0, 0], [1, 0], [2, 0] and [3, 0] in the first row, the cell IDs are [0, 1], [1, 1], [2, 1] and [3, 1] in the second row, and so on and so forth.  
In CBDAS, each cell has a head and ordinary nodes, the other nodes of the cell. The cell head is responsible for aggregating data transmitted from ordinary nodes in the same cell and in turn transmitting it to the neighboring cell head toward the cycle leader.
## 2 Materials and Methods
### Problem Statement  
This work focuses on the estimation of the EMS damage severity levels for the sub-areas of an area hit by a new wildfire. More formally, given a post-fire Sentinel-2 L2A satellite acquisition (an image of 12 spectral bands) of an area that has been hit by a new wildfire the goal is to predict a continuous value in the range \([0,4]\in\Re\) for each pixel of the input post-fire image, in order to approximate the Copernicus EMS damage severity grading map values, whose severity levels are natural numbers within the same range. The problem is configured as a regression task because the target variable is a numerical feature, which is used to represent ordered severity values. A set of training post-wildfire imagery related to past wildfires, for which the value of the target variable is known, is used to train a predictive model that is then applied to perform the prediction for the pixels of the new post-wildfire imagery.
## 3 Results and Discussion
### Quantification of the Four Marker analytes for Quality Control of DGJT Sample Using HPLC  
Using the developed and validated HPLC analysis method, the four marker analytes in the prepared DGJT samples were compared to standard compounds with respect to their UV spectra and retention times and finally confirmed via co-injection. Based on this, hyperoside and querctrin were quantified at 255 nm, and ginseng Rg1 and 6-gingerol were quantified at 203 nm. The content of the four marker analytes in the prepared DGJT sample was found to be 0.95-13.86 mg/g (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analyte** & **Quantification** & **Linear Range** & **Regression Equation 1 ** & **\(r^{2}\)** & **LOD 2 ** & **LOQ 3 ** (ug/mL)** \\ \hline Hyperoside & 255 & 0.78–50.00 & \(y\) = 30,622.39\(x\) + 5809.97 & 1.0000 & 0.17 & 0.51 \\ Querctrin & 255 & 0.31–20.00 & \(y\) = 28,066.51\(x\) + 1978.77 & 1.0000 & 0.08 & 0.24 \\ Ginsenoside Rg1 & 203 & 1.56–100.00 & \(y\) = 4651.00\(x\) + 1806.42 & 0.9999 & 0.46 & 1.41 \\
6-Gingerol & 203 & 1.56–100.00 & \(y\) = 86,371.87\(x\) + 38,306.05 & 0.9999 & 0.10 & 0.29 \\ \hline \hline \end{tabular}  
* \(y\) and \(x\) indicate the peak area and concentration of each analyte, respectively, using the standard solution. 2 LOD = \(3.3\times\sigma/S\) and 1 LOQ  
\end{table}
Table 1: Linear range, regression equation, coefficient of determination (\(r^{2}\)), limit of detection (LOD), and limit of quantification (LOQ) of the four marker analytes for the quantification using HPLC (\(n\) = 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & **Spiked Conc. (ug/mL)** & **Found Conc. (\(\mu\)g/mL)** & **Recovery (\%) 1 ** & **SD** & **RSD (\%) 2 ** \\ \hline Hyperoside & 12.50 & 12.79 & 0.50 & 102.31 & 12.65 & 0.97 & 101.23 \\ Hyperoside & 25.00 & 25.12 & 0.47 & 100.49 & 25.16 & 0.69 & 100.65 \\  & 50.00 & 50.08 & 0.34 & 100.15 & 50.07 & 0.72 & 100.14 \\  & 5.00 & 5.11 & 0.45 & 102.16 & 5.06 & 0.84 & 101.27 \\ Querctrin & 10.00 & 10.04 & 0.49 & 100.45 & 10.07 & 0.71 & 100.68 \\  & 20.00 & 20.03 & 0.31 & 100.14 & 20.03 & 0.75 & 100.17 \\  & 25.00 & 25.72 & 0.20 & 102.88 & 25.53 & 0.84 & 102.11 \\ Ginsenoside Rg1 & 50.00 & 50.64 & 0.47 & 101.28 & 50.62 & 0.71 & 101.24 \\  & 100.00 & 100.34 & 0.41 & 100.34 & 0.73 & 100.34 \\  & 25.00 & 25.60 & 0.15 & 102.41 & 25.47 & 0.62 & 101.87 \\
6-Gingerol & 50.00 & 50.11 & 0.39 & 100.22 & 50.24 & 0.64 & 100.47 \\  & 100.00 & 101.30 & 0.37 & 101.30 & 101.26 & 0.75 & 101.26 \\ \hline \hline \end{tabular}  
* \(\mathbf{{}^{a}}\) Precision (%) = SD/mean \(\times\) 100%.  
\end{table}
Table 3: Verification of the precision of the analysis method developed using the four marker analytes.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Concentrating Systems
#### 2.1.5 Optical Fiber Photoreactors  
Optical fiber photoreceptors have recently been studied for water remediation. The basic principle here is to use optical waveguides, usually in the form of optical fibers, to convey photons into the bulk of a wastewater. An interesting possibility is that of using a photocatalyst directly supported on the waveguides to achieve very high surface area to volume ratios. Some reports show values even higher than for a conventional photoreceptors with suspended photocatalysts [61]. The use of optical fibers as waveguides has been investigated mostly with artificial light [62; 63]. Extending the concept for use with sunlight would be much more complicated, with the need for a very accurate reflectors/tracking system to compensate for the sun's movement.  
The main advantage of this design is that the distance between the light collection system and the photoreactor can be great and non-linear, thanks to the properties of optical fibers. This would make it possible to reach otherwise inaccessible wastewater reservoirs, for example within buildings or subterranean reservoirs [64]. To date however, its complexity and low efficiency makes this design impractical [33].
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 2 Related Work
### Traditional Drivable Road Area Segmentation  
Drivable road areas usually differ from surrounding pixels and have unique local visual features. Therefore, based on image features, traditional image processing drivable road area segmentation methods can be divided into edge-based, texture-classification-based, illuminant-invariance-based, and geometric-vanish-point-based methods [3]. First, edges are a commonly used visual feature for drivable road area segmentation. For example, [PERSON] et al. [9] proposed a color-feature- and edge-image-based algorithm, by obtaining road boundaries and delimiting the area complying with Gaussian distribution, improving accuracy and reducing the computational complexity. Second, texture-classification-based methods are employed for drivable road area segmentation. [PERSON] et al. [10] innovatively divided one road picture into distinguishable regions and subsequently calculated their texture differences based on statistical numerical features. Third, illuminant-invariance-based methods have also been designed for drivable road area segmentation. [PERSON] et al. [11] proposed a novel method based on shadow-invariant features, which took full advantage of RGB-distributed information and camera direction information for road segmentation, achieving more robust and efficient results. Furthermore, geometric-vanish-point-based methods are also applied in this field. In [12], texture directions were extracted using confidence-weighted Gabor filters and clustered for estimating the vanish point, and then road boundaries were obtained through calculation.  
Moreover, machine learning methods based on hand-crafted visual features have also been leveraged for drivable road area segmentation. These methods usually consist of three steps: feature extraction, image classification, and post-processing. For example, [PERSON] et al. [13] extracted both color features and texture features, and then an SVM classifier was employed for classification. In [4], structured SVMs were utilized for learning geometric features based on edges, color, and homography. In [14], [PERSON] et al. used a simple neural network to achieve real-time road area segmentation. Some researchers use a composite of multiple machine learning models [15; 16]. Both of them achieved promising segmentation results.  
However, though both the aforementioned traditional-image-processing-based methods and machine learning methods may work in some simple scenarios, they are vulnerable to various environmental factors, such as lightning and blocking. They tend to fail in knotty but more common real-life scenarios where road shadows, vehicle obscuration, and picture defects exist.
# On the Security of Practical Mail User Agents against Cache Side-Channel Attacks +
## 2 Preliminary  
### Mail User Agents  
An MUA, also referred to as an email client, provides overall functionality for the access and management of a user's email. MUAs are preferred by many users because they can avoid the storage and functionality problems of web mail services and manage multiple email accounts in an integrated, ready-to-use email environment. Also, MUAs offer advantages in terms of reliability and security. For example, the emails stored in a personal system can be accessed whenever the user wants, even when the email server is shut down or network access fails. Many MUAs also provide email encryption functionality for secure email services using third-party crypto libraries such as OpenSSL or GnuPG. To access encrypted email, a corresponding secret key that is associated with the authorized user is required. In addition to basic email service functionalities, MUA programs that support email encryption also provide key management.  
Thus, the protection of secret keys is the most important requirement for secure MUA email services; however, we have observed that many existing MUAs still allow the use of the CRT-RSA algorithm offered by GnuPG 1.4.13 (and earlier versions) to encrypt and decrypt emails by adopting the library as a plugin or add-on without a security check. Even in the presence of the patch for GnuPG (version 1.4.14 and later) introduced to prevent cache side-channel attacks, a flawed MUA configuration can be exploited to avoid the security patch of the underlying crypto layer, as we demonstrate in Section 3.
# Computational Evaluation of Shock Wave Interaction with a Cylindrical Water Column
## 2 Computational Model
### CFD Analysis
#### 2.2.2 CFD Solution  
The mean turbulent flow in the virtual shock tube device was simulated by solving the unsteady compressible RANS equations. The shear-stress transport (SST) \(k\)-\(\omega\) two-equation eddy-viscosity model was used for turbulence modeling, owing to its proven suitability for complex industrial engineering applications [22]. The flow governing equations, which were not reported here for brevity, can be found, for instance, in [23]. The computational evaluation was performed by using the industrial solver Ansys Fluent 19, which has been successfully employed in analogous works, e.g., [16], as well as in previous CFD studies using RANS models by the same research group [24; 25; 26].  
The present pressure-based solver utilizes the FV approach to discretize the governing equations and approximate the unsteady mean flow solution, where the conservation principles are applied over each computational cell, e.g., [27]. The system of integral balance equations for mass, momentum, and energy was completed by employing two equations of state to model the density variations of the two different fluids. Specifically, along with the ideal gas law for the gas phase, the following [PERSON]'s equation of state for the liquid phase was considered, e.g., [28],  
\[\rho_{l}=\left(\frac{p+\mathcal{B}}{p_{0}+\mathcal{B}}\right)^{\frac{1}{k}} \rho_{l0}, \tag{7}\]where \(p_{0}\) and \(\rho_{l0}\) represent the reference pressure and density levels, while \(\mathcal{B}\) and \(\kappa\) are constant parameters. Following [12], the pressure-like parameter \(\mathcal{B}\) and the so-called adiabatic index \(\kappa\) were set to the values of 305 MPa and 6.68, respectively. This way, given the maximum pressure levels that were expected in the simulations, the liquid density can be considered practically constant for the present engineering analysis.  
The FV mesh was suitably refined in the flow region that was expected to be occupied by the deforming water column, including the near wake. Three different meshes with varying resolution were tested, as reported in Table 4, where \(h_{min}\) stands for the minimum mesh size. The total number of FV cells ranged between two and five-hundred thousand, where the highest local resolution resulted in being about 60 and 430 cells per cylinder diameter, respectively. Actually, the computational domain \(\Omega\) that was used corresponded to half the spatial domain sketched in Figure 1, where a symmetry boundary condition at the centerline (\(y=0\)) was imposed. In fact, according to previous experimental visualizations, the flow can be assumed symmetric at the early stages of the interaction process, e.g., [11]. As for the other boundaries, wall boundary conditions were employed for simulating the solid walls of the virtual shock tube device. Note that the present boundary conditions coincided with those adopted in other similar simulations, e.g., [12]. Finally, the constant time-step of \(\Delta t=0.1\) us was used for the explicit transient calculation.
## 3 Data and Methods  
This study is based on 3D seismic interpretation data and well logging. The logging data are derived from 23 wells in the study area, which are mainly distributed in the five structural units of the eastern strike-slip zone, the northern depression, the central structural zone, the southern sub-depression, and the southern gentle slope belt. Logging data include natural gamma ray (GR), spontaneous potential (SP), shallow investigate double-lateral resistivity log (RS), deep investigate double-lateral resistivity log (RD), and lithology information, using the LAS file format. The 3D seismic data mainly covers 560 km\({}^{2}\) and uses the SGY format with a seismic resolution of 10 m.  
The main research methods of this paper are divided into three parts. The first part analyzes the sequence stratigraphy, mainly to interpret the seismic data. The typical seismic lines (line No. 3151, No. 3251, No. 3611) passing through the southern sub-depression and the northern depression are selected for fine seismic interpretation and seismic reflection characteristics identification. Combined with logging information, a well-seismic combination is carried out. The sequence boundaries of the different types of sequences are identified by seismic reflection termination, logging curve shape change, and lithology transformation. The fourth member of the Shahejie Formation is divided into one second-order sequence, and two third-order sequences are divided into the upper fourth member (SQS\({}_{4}\)\({}^{\text{U}}\)) and the lower fourth member (SQS\({}_{4}\)\({}^{\text{L}}\)). Through the division of system tracts and the analysis of the stratigraphic sedimentary processes through the sequence stratigraphic superposition patterns of different types of sequences, a unified third-order sequence stratigraphic framework is finally established and the stratigraphic distribution law is analyzed in combination with the stratigraphic thickness map. The second part establishes the sequence development model and discusses the sedimentary evolution process of the strata through the comprehensive analysis of the stratigraphic superposition pattern, paleoclimate change, volcanic activity and paleogeomorphological characteristics. The third part, on the basis of the first two steps of analysis combined with seismic reflection characteristics and structural characteristics, predicts favorable reservoir targets in the study area and deepens the practical significance of this study.