esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
It is difficult to adapt traditional screw motors with rubber stators to the high-temperature and high-pressure conditions in ultra-deep wells, where rubber stators age, deform and carbonize, resulting in motor failure. In this study, the goal is to develop volumetric power drilling tools that can be used to drill at depths of 10,000 m. To meet this goal, an all-metal screw motor that can be applied in ultra-deep wells is designed, then its deformation and structural design are studied. Through numerical simulation, the change in clearance of the motor after expansion in high-temperature environments and the characteristics of deformation in ultra-deep well environments are analyzed. The results show that the metal screw motor has good performance and stability. The maximum deformation is less than 0.3 mm at depths of 9000-15,000 m in ultra-deep wells. The minimum design clearance should be greater than 0.2 mm to ensure that the stator and rotor remain engaged. The results of this research are expected to provide theoretical guidance for the design of all-metal screw motors for applications in ultra-deep well drilling projects to meet the demand for deep earth resource development.  
ultra-deep well; all-metal screw motor; numerical analysis; mating clearance; structural design +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Preprocessing  
After data preprocessing of ECG and PPG signals, paired ECG and PPG signal segments are obtained. Data preprocessing mainly includes four steps: filtering, normalization, segmentation, and data splitting.  
* **Filtering.** A fourth-order Chebyshev bandpass filter is applied to the PPG and ECG signals in the range of 0.5-10 Hz and 0.5-20 Hz, respectively.
* **Normalization.** The PPG signal is normalized. After data filtering, the PPG signal was scaled to the range of [0, 1]. Note that since the amplitude of the ECG signal is meaningful, the amplitude of the PPG signal is meaningless. Therefore, only the PPG signal is normalized here, and the ECG signal is not normalized.
* **Segmentation.** The ECG and PPG signals were segmented. The obtained ECG and PPG signals were divided into 8 s segments. In [26], the length of signal segmentation is 1000 samples. Therefore, in this study, the signal is segmented into 8 s (1000 samples) signal segments.
* **Data splitting.** The ECG and PPG signals were divided into completely subject-independence models. Specifically, 60% of the total records were used as a training set, 20% as a validation set, and 20% as a test set.
_Applied_  
_sciences_  
**Impact of Microwave Plasma Torch on the Yeast**  
_Candida glabata_  
**[PERSON]\({}^{\copyright}\), [PERSON] \({}^{\copyright}\), [PERSON]\({}^{\copyright}\) and [PERSON] \({}^{\ast}\)**  
Faculty of Chemistry, Brno University of Technology, Purkynova 118, 61200 Brno, Czech Republic;  
[EMAIL_ADDRESS] (K.T.J.; [EMAIL_ADDRESS] (Z.K.)  
\({}^{\ast}\) Correspondence: [PERSON]L_ADDRESS] (F.K.); [EMAIL_ADDRESS] (P.M.)  
Received: 30 June 2020; Accepted: 6 August 2020; Published: 11 August 2020  
**Featured Application: A cold gaseous plasma torch generated in argon at low powers and atmospheric pressure is applicable in medicinal treatment where it could significantly decrease microbial skin or tissue contamination in a short time and with low operating costs.**  
**Abstract:** Recently, various cold plasma sources have been tested for their bactericidal and fungicidal effects with respect to their application in medicine and agriculture. The purpose of this work is to study the effects of a 2.45 GHz microwave generated plasma torch on a model yeast example _Candida glabata_. The microwave plasma was generated by a surfatron resonator, and pure argon at a constant flow rate of 5\(\;\)Slim was used as a working gas. Thanks to a high number of active particles generated in low-temperature plasma, this type of plasma has become highly popular, especially thanks to its bactericidal effects. However, its antimycotic effects and mechanisms of fungal inactivation are still not fully understood. Therefore, this study focuses on the antifungal effects of the microwave discharge on _Candida glabata_. The main focus is on the measurement and evaluation of changes in inactivation effects caused by varying initial concentration of _Candida glabata_ cells, applied microwave power and exposure time. The discharge was applied on freshly inoculated colonies of _Candida glabata_ spread on the agar plates and its inhibitory effects were observed in the form of inhibition zones formed after the subsequent cultivation.  
**Keywords:** microwave discharge; surface wave sustained discharge; atmospheric pressure plasma torch; low-temperature plasma; plasma sterilization; yeast inactivation; _Candida glabata_
## 4 Discussion  
### Reliability Analysis of the Detection Results  
To assess the reliability of the research results, we conducted a comparative analysis between the SBAS-InSAR and GNSS methods, focusing on surface subsidence at the GNSS observation point as an example. By adopting the mutually corroborating method, we quantitatively examined the monitoring results of both methods. From 31 December 2020 to 7 February 2021, as depicted in Figure 10, the cumulative surface settlement measured using GNSS was 1.2 mm, while Figure 7 illustrates that the cumulative surface settlement detected using SBAS-InSAR was 2 mm. The difference between the two measurements was less than 1 mm, indicating a high level of reliability within our detection results. Furthermore, we also conducted a field survey in the area experiencing significant deformation. Through discussions with the channel manager, we verified the severity of the left bank uplift in area C, as shown in Figure 7. It was confirmed that repair work had indeed been carried out on the channel slope in this area, as depicted in Figure 11. This alignment between our research findings and the actual site conditions provides further evidence of the reliability of our results. It underscores the valuable support our scientific data can offer in ensuring the safety and protection of the channel slope.  
In addition, to validate the accuracy of the measurement results obtained using the SBAS-InSAR technology in this study, we conducted a comparative analysis between the 10-phase SBAS-InSAR measurements and leveling measurements at three points within the severely uplifted deformation area (Figure 11). The results are statistically presented and compared in Table 3. From the table, it can be seen that (1) the maximum subsidence value observed at the monitoring point was 15 mm, which is within the range of 19 mm. This can be attributed to the location of the three leveling monitoring points on the cemented surface of the channel walkway, which is hardened by the cement material, and due to the influence of the hardened surface, the uplift deformation of the walkway is obviously not the largest. (2) The SBAS-InSAR monitoring results consistently show larger values  
Figure 11: The channel’s severe deformation repair area and measurement point location.  
compared to the leveling results. This can be attributed to the fact that the SBAS-InSAR monitoring results average multiple nearby SBAS-InSAR points around the leveling points. The surface of these surrounding points was not hardened, making them more prone to expansion compared to the hardened surface. (3) The comparison of the two monitoring methods revealed that the maximum error between them was merely 2 mm in absolute value. This further confirms the high reliability of the findings obtained in this study.
## 4 Experimental Results and Discussion
### Effect of SE and BiGRU Modules  
We achieved additional investigations to explore SE modules and BiGRU modules in the SEResidual blocks of the proposed model. As shown in Table 8, both the SE module and BiGRU module can enhance the effectiveness of the recognition on the two standard datasets.  
Due to the SE and BiGRU modules, the proposed SEResNet-BiGRU model has superior overall effectiveness. The complexity of a model is compared to the mean prediction time to determine the model's efficiency. A collection of test data samples is fed into the Tensorflow. Lite models, and the mean prediction time is calculated from this data due to this process. Table 9 compares the mean prediction times.  
Table 9 shows the results of this experiment with the mean prediction time in seconds to process one window of the deep learning models performing on the two datasets (HAPT and MobiAct v2.0). The comparative results show that the mean prediction times vary by added SE and BiGRU modules, and it was noticed that the BiGRU modules need more computational time than the SE module. The proposed SEResNet-BiGRU model shows the mean prediction time is 1.3208 ms. and 2.0047 ms. for HAPT and MobiAct v2.0, respectively.
## 3 Methods
### Accuracy Assessment  
This study evaluated crop distribution at both the pixel and county levels. At the pixel level, accuracy assessment was performed using a confusion matrix, with evaluation metrics, including User Accuracy, Producer Accuracy, overall accuracy, Kappa coefficient, and F1 score [60; 61].  
Figure 4: Time series of vegetation indices for four crop types in 2023. The curves represent 15-day interval mean values of vegetation indices across the growing season.  
At the county level, the extracted areas were compared with statistical data. The evaluation was performed using the coefficient of determination (R2) and root mean square error (RMSE), with the following formulas:  
\[\mathrm{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i =1}^{n}\left(y_{i}-\overline{y}_{i}\right)^{2}} \tag{1}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{n}} \tag{2}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) represent the crop planting area statistical data and the extracted crop area for the i county, respectively. \(\overline{y}_{i}\) is the average statistical area, and n is the total number of counties.
## 4 Design and Electrical Performance Characterization
### Configuration Set-Up, and Identification of Materials and Their Electrical Performance
#### 4.2.1 Setup of Analysis  
The resonator is housed within a cavity on a perforated substrate, with the hole diameter ensuring proper centering and alignment while minimizing mechanical design complexity. Both dielectric and conductor losses have been incorporated in the simulation setup. The eigen-mode analysis was initialized at 4 GHz--a frequency sufficiently separated from the X-band lower boundary to avoid influencing potential nearby modes.  
The Device Under Simulation (DUS) is shown in Figure 8, depicting both lateral and cross-sectional views.
## 2 Background and Significance
One of the strong points of the research discussed in [29], in which a platform with a range of functions for motor and social cognitive rehabilitation for hospitalized children is discussed, is that these contents can be edited by means of high-level graphic tools. Likewise, controlling emotions appropriately and feedback provided to patients is also dealt with in research that focuses on designing rehabilitation frameworks [30]. Plus, the process for creating contents within the exergames themselves (such as, for example, the dynamic appearance of game elements according to how patients are performing [31]) has also been addressed in the literature, mainly for the dynamic adaptation of the level of difficulty in the game to patient performance [32]. Ontologies have also been devised for describing exergames. The work discussed in [33] is a recent example, in which OWL Web Ontology Language is used for such a description and for defining the key components of exergames. Within this context, the authors also developed a framework in order to apply the created ontology to online exergames. On the other hand, grammars may also play an essential role to simplify the process of creating rehabilitation exercises. Thus, it is possible to define specific languages for specifying body postures and movements [34], which can be augmented thanks to editors that facilitate therapists' work. In this contribution, an exercise-specification grammar is employed to guide the virtual avatar and provide quality feedback by comparing the user's gestures against those performed by the coaching avatar. Related to these works, there are more general approaches, such as those discussed in [35; 36], where the models may be used for guiding game designers to specify game design elements. The work proposed in this article aims at providing the therapists with tools that can be used to personalize how patients employ rehabilitation systems and how therapists run the rehabilitation process. However, instead of designing and developing ad-hoc tools, we have devised a language that can work as the core component in multiple rehabilitation tools. This language provides sentences that can be used to design personalized exergames, and these sentences can be processed in order to automatically generate exergames for specific platforms and game engines. This is the major contribution of this work, which opens the door to deepening the idea of precision physical rehabilitation. On this basis, high-level authoring tools can be built that allow therapists to visually create exergames. Assuming that these tools export such a creation to a set of PEL sentences, then it is possible to cover the whole cycle of design and automatic generation of exergames. On the other hand, the definition of a language, and the capacity to process its sentences by a computer program, would facilitate the automatic adaptation of the rehabilitation routines that are defined by therapists.
###### Abstract  
Space Electrostatic Discharge Effect (SESD) and Single Event Effect (SEE) are two major space environmental factors that cause spacecraft failure. Previous studies have established that both can lead to soft errors such as upset of memory cells. An ESD generator and a pulsed laser experimental facility were used to test a low-power asynchronous timing monolithic SRAM. The characteristics of soft error number, single/multi-bit upsets, and supply current values were compared for similarities and differences. The test revealed that SEE-induced soft errors were mainly single-bit upsets (SBU), whereas SESD-induced soft errors were predominantly multi-bit upsets (MBU). Additionally, when soft errors occur in the circuits, the current of the power supply drops, which enables the device to be evaluated by monitoring the current value. This study provides experimental support for distinguishing device errors caused by these two effects, as well as references for further accurate identification of in-orbit faults and corresponding protection design.  
SRAM; SEE; SESD; soft errors; failure identification +
Footnote †: journal: Applied sciences
## 6 Discussion
### Sensitivity Analysis  
A sensitivity analysis is conducted at the optimal design from GA, \(x^{*}\) = (15/1, \(-\)54.7, 5, 2, 47.1), where the local gradient is obtained at the optimal point followed by normalization. As shown in Equation (18), the local gradient vector of fitness, \(\
abla J\), is obtained by incrementing one design variable at a time and dividing fitness difference by that increment. The local gradient vector is normalized through entrywise multiplication (\(\circ\)) with a scaled vector, \(x^{*}\) divided by fitness at \(x^{*}\)[9].  
\[\
abla\overline{J}=\frac{x^{*}}{J(x^{*})}\circ\
abla J=\frac{1}{J(x^{*})} \left[\begin{array}{c}N\\ A\\ P\\ S\\ R\end{array}\right]^{*}\circ\left[\begin{array}{c}\partial J/\partial N\\ \partial J/\partial A\\ \partial J/\partial P\\ \partial J/\partial S\\ \partial J/\partial R\end{array}\right]^{*}=\frac{1}{J(x^{*})}\left[\begin{array} []{c}N\partial J/\partial N\\ A\partial J/\partial A\\ P\partial J/\partial P\\ S\partial J/\partial S\\ R\end{array}\right]^{*} \tag{18}\]  
Table 20 and Figure 17 show that the RGT ratio has the largest impact (highest sensitivity), followed by the number of planes. Because both variables have positive values, increasing these variables will reduce the optimality of a ReCon. In fact, increasing the RGT ratio by 0.5 decreases the RGT altitude by 100 km approximately, which increases fuel consumption to compensate for atmospheric drag and to keep satellites in place. Due to a similar reason, it makes intuitive sense that  
Figure 16: Time taken by each method and solution optimality.  
the number of satellites in the constellation would drive the objective function. The sensitivity of the altitude difference was negative at this design point, which means that increasing this variable will improve the optimality.  
Figure 18 shows a relationship between the GA problem size and computation time. The problem size equals the number of generations times the number of individuals within a population. It can be inferred that computation time linearly increases with the problem size. As for parameter setting, the mutation rate of 0.001 was increased to 0.002 and 0.004, leading to increased average / scores of 1.73 and 2.30 with a statistically significant \(p\)-value at 0.0 (significance level 0.05).
## 2 Introduction to Digital Watermarking: Trajectory, Techniques, and Applications
### Hybrid Techniques:  
Contemporary watermarking frequently combines multiple approaches. Multi-domain techniques embed watermarks across different transform domains simultaneously [19; 24; 25]. The cross-modal approaches enable the transfer of information between different types of media [19]. Optimisation-based methods use mathematical optimisation to balance imperceptibility and robustness [38]. Model-guided techniques use predictive models to improve embedding, while multitask and data-adaptive approaches optimise for multiple objectives simultaneously [18; 39].
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Physical Layer Security
As a result, the achieved level of security will not be compromised; even if the unauthorized devices in the wireless network are provided with powerful computational capabilities, secure and safe communications can still be performed. * Second, physical layer security techniques have high scalability [3; 43]. Wireless devices always join or exit the network at any time; due to the decentralized nature of the network, the PLS technique can provide secure data communication in the network.
## 4 Numerical Experimental Simulation and Analysis  
After developing the G-Pre preconditioning algorithm, numerical experimental simulations were conducted to evaluate its performance by comparing it with the ILU factorization solution process. In this section, we present the findings in detail. Firstly, the effectiveness of the G-Pre algorithm was verified and analyzed using asymmetric matrices. Subsequently, the G-Pre algorithm was integrated into the finite element simulation process of a real model and was tested using a realistic mesh model under static analysis conditions. The G-Pre algorithm was implemented in C++. All experiments were performed on a system equipped with a 12-core Intel Core i7-12700 processor and 32 GB of RAM (Intel, Santa Clara, CA, USA). The results, as shown in the related graphs, illustrate the running times of the corresponding experiments. Unless otherwise stated, the relative tolerance convergence bounds for all iterative algorithms in the numerical experiments were set to \(10^{-5}\).
###### Abstract  
This paper presents a model-based approach for 3D pose estimation of a single RGB image to keep the 3D scene model up-to-date using a low-cost camera. A prelearned image model of the target scene is first reconstructed using a training RGB-D video. Next, the model is analyzed using the proposed multiple principal analysis to label the viewpoint class of each training RGB image and construct a training dataset for training a deep learning viewpoint classification neural network (DVCNN). For all training images in a viewpoint class, the DVCNN estimates their membership probabilities and defines the template of the class as the one of the highest probability. To achieve the goal of scene reconstruction in a 3D space using a camera, using the information of templates, a pose estimation algorithm follows to estimate the pose parameters and depth map of a single RGB image captured by navigating the camera to a specific viewpoint. Obviously, the pose estimation algorithm is the key to success for updating the status of the 3D scene. To compare with conventional pose estimation algorithms which use sparse features for pose estimation, our approach enhances the quality of reconstructing the 3D scene point cloud using the template-to-frame registration. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets and compare it with the state-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods in terms of the accuracy of pose estimation.  
image-based 3D model; pose estimation; viewpoint classification; deep learning; template-to-frame registration; multiple principal plane analysis +
Footnote †: journal: Medical-Based 3D Pose Estimation of a Single RGB Image Using a Deep Viewpoint Classification
# Design and Application of Logical Range Framework Based on Digital Twin
## 3 Key Technologies
### Virtual-Real Synchronous Mapping Method  
The mapping between virtual space and physical space is the basis for the realization of virtual-real interactions. In order to ensure the accurate characterization of the test process and to realize the effective collaboration between test resources, it is necessary to realize the virtual and real synchronization mapping between the physical and virtual test resources.  
The virtual-real mapping process is mainly divided into four parts--data acquisition, data integration, data matching, and data visualization--as shown in Figure 7. The main steps are detailed in the following:  
1. _Data Acquisition_ The first step of data acquisition is to accurately collect the original data from the physical test field in real-time. The collected data will be used as the data source for the data processing and mapping phases. In order to be compatible with a variety of data transfer protocols, proxies are developed for all kinds of equipment. The proxy collects raw data through serial ports, Bluetooth, local area networks, and so on.
2. _Data Integration_ With the real-time massive data collected during the data acquisition stage, data integration mainly involves pre-processing operations (e.g., data cleaning and con  
Figure 6: Data communication architecture.  
version), followed by the unified storage and management of data, ensuring that the value of the data is maximized.
3. _Data Matching_ In order to realize virtual-real synchronous data mapping, a subscribe/publish data transmission mode was designed based on search tree. For each piece of physical equipment, its proxy will create a topic in the message middleware during the initialization process for the transmission of equipment data, and the topic name is added to the search tree. Models in the virtual space will search the search tree for topic names before receiving data. As the topic names satisfy certain generation rules, at least one result will be retrieved and topic matching can be completed.
4. _Data Visualization_ Once the data matching is completed, the twin model in the virtual space receives real-time data from the physical space. These real-time data will drive the virtual equipment to update its state, according to the time sequence, thus completing the state update of the whole twin scene.