esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Results
### Sphere and Plate  
In this example, as shown in the Figure 4, the scatterer is a composite object including a PEC sphere above a PEC plate residing in free space.  
The PEC sphere radius \(r_{s}\) is 1 m; the PEC plate's sides are \(l_{px}=l_{py}=10\)m; and the distance \(D\) between the sphere's center and the PEC plate's surface is 5 m. For the first simulation, the incident wave was a plane wave excited by a Gaussian pulse modulation. During the simulation, the transient scattered field of the composite object was computed by DGTDPO. All the MoM results were obtained by ANSYS HFSS-IE solver. In addition, HFSS and Mie are defined as the references; the results computed by various methods of normalized root mean square deviation (NRMSD) were used to estimate the deviation. NRMSD is defined as follows:  
\[\text{NRMSD}=\frac{\sqrt{\frac{\sum_{m=1}^{m}\left(C(m)-R(m)\right)^{2}}{n}}}{R (\text{max})-R(\text{min})} \tag{20}\]  
where \(m\) represents the value of the discrete result and \(C(m)\) is one of results of other methods, and \(R(m)\) is the result of HFSS.  
As shown in Figure 5a, the scattering field of a PEC sphere computed by DGTD agrees well with Mie which is an analytic method and the NRMSD is equal to 0.0398. Figure 5b is computed by the Fourier-transformed and the NRMSD is 0.0288. Figure 5b illustrates that DGTD can calculate well for various electrical sizes. As shown in Figure 6a,b, these are the bistatic RCS results of the PEC sphere-plate composite object at 0.3004 GHz and 0.5 GHz, as well as the NRMSD is 0.0685 and 0.0632 respectively. The results are similar to these calculated by MoM. Although there are some deviations between 150\({}^{\circ}\) and 230\({}^{\circ}\), the part below the PEC plate is not considered in the electromagnetic scattering calculation. Therefore, the accuracy of the hybrid algorithm can be guaranteed.  
As shown in Table 2, because the DGTDPO hybrid method uses the TDPO method for large-scale background and the shadow region is automatically disregarded, the calculation speed and memory consumption are improved by 99.55% and 70.27% respectively, compared with DGTD.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DGTD** & **DGTDPO** & **Decreasing Rate** \\ \hline CPU Time & 60.87 h & 0.33 h & 99.46\% \\ Memory & 1.79 G & 545 M & 70.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The CPU time and memory of DGTD and DGTDPO for a PEC sphere-plate composite object.  
Figure 4: PEC sphere-plate composite object.
## 2 Materials and Methods
### Instruments, Procedure, and Variables Analysed
#### 2.3.3 Variables  
The players' demands during matches were monitored using an IMU technology-based data collection instrument. Smart motion devices were directly mounted on the soccer players' boots to quantify technical and player load variables (Table 1).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Technical Variables** & & **Value** \\
**Abbreviature** & **Explanation** & \\ PPT & Number of minutes in which the player participated in the session & min \\ TT & Number of contacts with left and right foot & n \\ TPM & Number of times the ball hits the player’s foot in a period of 1 min & n/min \\ R & Releases or number of times in which the player executes (hits—passes or throws) & n \\ RPM & Releases per minute or Number of times in which the player pass, hits or throws with his foot in & n/min \\ a period of 1 min & RVA & Release Velocity Max (m/s)—Maximum intensity hit performed & m/s \\ RVM & Release Velocity Avg (m/s)—Average intensity of all hits performed & m/s \\ RIB & Release Index (Striking Index) & a.u. \\ RHI & Number releases of high intensity (\textgreater{}15\) m/s) & n \\ TP & Number of possessions that the player has control of the ball (1 touch, short possession, and long possession) & n \\ TTOB & Total time in seconds that the player kept the ball in his possession during the match or session & s \\ \hline \multicolumn{3}{l}{**Physical load variables**} \\ \hline TS & Maximum speed reached or Highest peak reached by a player (m/s). & m/s \\ DC & Total distance covered during the session. & m \\ WR & Average distance covered or work rate. & m/min \\ HIDC & Distance covered at high intensity (\textgreater{}5.83\) m/s—21 km/h) & m \\ HID & Distance covered at high intensity per minute of play & m/min \\ SDC & Distance covered sprinting (\textgreater{}6.66\) m/s—24 km/h) & m \\ SD & Distance covered sprinting per minute of play (m/min). Range of speed \textgreater{}5.5 m/s between the record time of a (1) min & m/min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical and physical load variables.  
Figure 1: Playermaker® devices and iPad linked. Posaositioning device below the lateral malleolus and above the boot and connected via Bluetooth through the Playermaker® app on an iPad.
## 2 Security Protocols
### Application-Layer Security  
Providing security at IP layer (through IPSec) or transport layer (through TLS or DTLS) has several advantages. The main ones are: first, the same standard mechanism and the same implementation can be shared by all applications, resulting in code reuse and reduced code size; second, programmers do not have to deal with the implementation of any security mechanism; this significantly simplifies the development of applications, also in presence of secure communications. Unfortunately, as described in the previous sections, both IPSec and (D)TLS have their own drawbacks. Probably the main issue that is common to both IP and transport approaches is due to the impossibility to assure complete end-to-end security when application communications are relayed by intermediate nodes that work at application level (e.g., proxies). In this case end-to-end security can be still provided with transport or IP level mechanisms, but only in the presence of very trusted intermediate systems. However, in this case, the overall security is complicated by the handling of such hop-by-hop trust management.  
A different approach aiming at providing complete end-to-end security is to enforce security directly at application level. This of course simplifies the requirements for underlying layers, and probably reduces the cost, in term of packet size and data processing, since only application data have to be secured and per-data and not per-packet overhead is introduced. Moreover, multicast communications, and in-network data aggregation in encrypted domains (for example through homomorphic cryptography) is easier to be implemented at application level.  
The main disadvantages of providing security at application level are the complications introduced for application development and the overall code size due to poor reuse of software codes. This is mainly due to the lack of well defined and adopted secure protocols at application level. Examples of standards that can be used for this purpose are S/MIME and SRTP. S/MIME (Secure/Multipurpose Internet Mail Extensions) [20] is a standard for providing authentication, message integrity, non-repudiation of origin, and confidentiality for application data. Although S/MIME has been originally developed for securing MIME data between mail user agents, it is not restricted to mail and can be used for securing any application data and encapsulated within any application and transport protocols. SRTP (Secure Real-time Transport Protocol) [21] is another secure communication protocol that provides confidentiality, message authentication, and replay protection to application data. It is an extension of the Real-time Transport Protocol (RTP) specifically developed for handling real-time data communications (e.g., voice or video communication), but can be re-used also in other application scenarios. It works in a per-packet fashion and is usually encapsulated in UDP. However, more investigation is required to state which is the standard protocol most suitable for securing data at application level in network and node constrained scenarios such as for IoT.
# Numerical Implementation of the Barcelona Basic Model Based on Return-Mapping Integration
## 4 Results  
Based on the implicit integration algorithm of return mapping, the numerical integration expression of the BBM is established, and implement the BBM into the OGS platform for numerical modeling of the coupled HM behavior of unsaturated soil. The numerical basis for studying the elastic-plastic mechanical behavior of bentonite/quartz sand mixture in the coupled process of seepage and stress is provided. The corresponding experimental research is conducted and compared the results with the numerical simulation, the results show that:  
1. Numerical calculation of void ratio along with the change of axial compression stress results can fit the experimental results well, showing that the numerical calculation described the elastic-plastic mechanics response of bentonite/quartz sand mixture parameters with rationality, and the unsaturated soil elastic-plastic model of the numerical integration algorithm can well reflect the bentonite/elastic-plastic mechanical behavior of the quartz sand mixture. 2. In the initial stage of the compression process, the strain distribution of the numerical model is uniform, showing an obvious elastic stage, and then the local strain increases, showing an obvious plastic process. Figure 10: Vertical strain contour of SO-03 at **(a)** 0.5 MPa, **(b)** 2.5 MPa, **(c)** 10 MPa, and **(d)** 50 MPa in compression, and **(e)** 25 MPa and **(f)** 0.5 MPa in recovery. Figure 9: Vertical strain contour of SO-02 at **(a)** 0.5 MPa, **(b)** 2.5 MPa, **(c)** 10 MPa, and **(d)** 50 MPa in compression, and **(e)** 25 MPa and **(f)** 0.5 MPa in recovery. (3) Both numerical calculation and experimental results show that the water absorption process can reduce the compression stiffness of bentonite/quartz sand mixture and increase the recovery stiffness. Conceptualization, H.Y. and Z.J. ; methodology, G.L. ; software, G.L. ; formal analysis, H.Y. ; investigation, G.L. and Z.J. ; data curation, G.L. ; writing--original draft preparation, H.Y. and Z.J. ; writing--review and editing, H.Y. and B.L. ; visualization, H.Y. ; supervision, H.Y. and Z.W. ; funding acquisition, H.Y. and Z.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 (Grant No.52004090), Hebei Natural Science Foundation (Grant No.E2020508025), Project of Basic Research and Strategic Reserve Technology Research Fund of Directly affiliated Institutes of petrochina Co., LTD. \"Research on the Technology of Rebuilding Underground Gas Storage from Abandoned Coal Mines\" (Grant No. 2019D-500811), Scientific Research and Technology Development Project of China National Petroleum Corporation Limited, Operation and Management Research of Key Laboratory of Underground Oil and Gas Storage (Grant No. 2022 DQ010107-18). Not applicable. The data supporting this study are included in the paper. On behalf of all authors, the corresponding author states that there is no conflict of interest.
## 4 Results  
### Model Training and Evaluation Process  
The study methodology included two types of labels: positive, which indicated the presence of a history of falls, and negative, which indicated the absence of such a history. To obtain the results presented in Figure 5 and Table 2, we followed a two-step process, which involved training neural networks for each individual task in the Mini-BESTest, as well as a combined model that utilized data from all tasks. Training of individual models for each task: Each task in the Mini-BESTest (e.g., \"Sitting to standing\", \"Gait speed change\") was treated independently during the model training phase. For each task, a dedicated neural network was trained using only the sensor data corresponding to that specific gesture. This approach allowed the model to focus on identifying fall-risk patterns derived from the unique characteristics of each movement. After the training process, each task-specific model was evaluated based on several performance metrics, including precision, recall, specificity, and F1-score. The results for each task are presented in Table 2, reflecting the model's ability to classify fallers and non-fallers based on the individual task data.  
Model trained with all tasks combined: In addition to the individual models, a separate model was trained using data from all tasks combined. The sensor data for each task were aggregated into a single dataset, allowing the neural network to learn from a wider range of movement patterns. This approach aimed to capture a more comprehensive view of the subject's balance and gait characteristics by integrating multiple functional movements  
Figure 5: Radar chart comparing the classification performance metrics across Mini-BESTest tasks grouped by type. The radial axis represents values for six metrics: Accuracy, Precision, Recall, F1 Score, Specificity, and Balanced Accuracy. Each axis ranges from 0 to 1, where 1 represents the highest possible value. Tasks are grouped by category (Postural Control, Reactive Control, Sensory Orientation, Dynamic Gait, and All Combined), and within each category, different line styles distinguish individual tasks. Larger areas on the chart correspond to stronger performance across all metrics, while smaller areas indicate weaknesses in certain metrics.  
into a single model. The combined model was evaluated using the same performance metrics as the individual models, and its results are presented in Table 2 under the title \"All Combined\". This method provides a more holistic assessment of fall risk, potentially improving the model's predictive power by leveraging information from multiple tasks. The primary objective of training both individual models and a combined model was to compare their performance in predicting fall risk. This comparison allowed us to evaluate whether the integration of multiple functional movements offers advantages over task-specific assessments in terms of predictive accuracy and generalization to broader contexts.
## 3 Proposed Method
### Performance Matrix of the System  
* Area throughput  
The data transmission rate per square meter in a wireless network is known as area throughput. Ordinarily, it is expressed in bits per second per square meter (\(\mathrm{bps/m^{2}}\)). It serves as a gauge for how many users a network can accommodate at once in a specific location.  
\[Throughput=B*SE*D \tag{19}\]  
where SE is the amount of data that can be sent per unit of the spectrum, measured in bits per second per Hertz (\(\mathrm{bps/Hz}\)), which is evaluated by Equation (16); B is the total amount of spectrum available for transmission, measured in Hertz (Hz); and D is the number of APs deployed per unit area, measured as the number of APs per square meter (\(\mathrm{APs/m^{2}}\)).  
* Computational Complexity  
The conventional PA-MMSE estimator achieves high performance but requires the inversion of large matrices, leading to high computational complexity, especially as the system size (number of antennas, subcarriers, or users) increases. The matrix inversion operation typically has a complexity of \(\mathrm{O(N^{3})}\), where N is the matrix dimension.  
The proposed estimator leverages QR decomposition to avoid direct matrix inversion. QR decomposition reduces the computational burden, as it decomposes the matrix into orthogonal and triangular matrices, which are easier to handle. The complexity of QR decomposition is generally \(\mathrm{O(N^{2})}\), which is significantly lower than direct inversion for large matrices. Additionally, the use of a precoding vector at the user side further simplifies receiver processing.  
* Energy Efficiency  
EE is the capacity of the system to send and receive wireless signals at high data rates while consuming the least amount of energy possible. EE is a crucial factor to take into account when designing wireless communication systems, particularly in the case of M-MIMO, which deploys a lot of antennas to serve several user devices. It can be evaluated as  
\[EE=B*SE/P \tag{20}\]  
where EE denotes energy efficiency (bit/joule), B is the bandwidth of the system, SE denotes spectral efficiency (\(\mathrm{bit/s/Hz}\)), and P is the power required to transmit the signal (W). The EE is evaluated from the SE discussed in Equation (16). Also, the spectral efficiency defined in Equation (16) is another performance metric.  
The flowchart shown in Figure 4 shows the EW MMSE with QR decomposition and a precoding matrix at the UE side channel estimation process in a CF M-MIMO system. This flowchart outlines the EW-MMSE channel estimation process in a CF M-MIMO system with multi-antenna UE under Rician fading channel conditions. It includes the steps of transmitting pilot signals, receiving and measuring pilot signals, initializing the channel estimation variables, performing QR decomposition on the channel matrix, computing the precoding matrix at the UE side, calculating MMSE weights, performing channel estimation for each element in the channel estimate matrix, and finally sending the estimated channel coefficients back to the central processor.
## 2 DC-DC Synchronous Buck Converter  
The synchronous buck converter is distinguished by its simplicity and efficiency, making it an advantageous choice for power conversion [25]. Characterized by high efficiency and low power dissipation, synchronous buck converters are particularly beneficial in systems where power efficiency is crucial. In this work, a three-channel synchronous buck converter is designed and analyzed, whose key advantage compared to a single-channel, is its ability to distribute the power load across three channels, thereby reducing the stress on individual components and improving overall system reliability [26]. This multi-channel approach enhances the converter performance by reducing ripple, distributing thermal loads more evenly, and allowing for higher output current and frequency. Additionally, synchronous buck converters are known for reduced switching losses and improved thermal performance, which are essential for maintaining high efficiency [27].  
The topology shown in Figure 1 illustrates a three-channel DC-DC synchronous buck converter, with the power transistors and the gate drivers being fully integrated within the chip. The integration of power circuits offers numerous benefits that enhance performance and reliability in high-efficiency applications. Reducing the size of the integrated power device and minimizing interconnection parasitics allows for more compact converter designs. It also ensures reliable high-current operation and improved thermal performance by the reduced gate loop inductance, which minimizes gate voltage stress and the risk of false turn-on. In fact, reducing interconnections in the power loop directly impacts parasitic inductance [28]. Additionally, integrated signal conditioning circuits, such as adaptive dead time, occupy minimal area, reducing the overall converter size. These advantages make power circuit integration a compelling solution for applications demanding high efficiency, high power density, and reliable operation [17].  
Figure 1: DC-DC synchronous buck converter topology.  
The passive elements and the input modulation signals are sourced externally from the system due to the limited available area for integration. The decision not to integrate the passive components, such as inductors and capacitors, enables the use of external components that meet the specific requirements of various applications. Additionally, the control signals were not integrated within the system to provide greater operational flexibility, and to permit a better testing of the circuit. This approach allows for the definition and adjustment of these signals externally, ensuring that the converter can be precisely tuned to meet the performance demands of different operational scenarios.
## 6 Moving Cell Based on C-RAN  
The wireless access methods researched or deployed in the current HSR are based on fixed cells or segments [44]. Thus, when the train antenna exceeds the cell boundary of the BS, it needs to disconnect with the old BS and then establish a new connection with the next BS, that is, handover. Considering the leaky waveguide as a solution to provide wireless broadband access for the vactrain,and each segment leaky waveguide corresponds to a BS, with a length of, for example, 100 m. As is mentioned above, when the cell size is reduced and trains' speed is increased, the handover rate will drastically increase. It is extremely important to keep the handover times as short as possible: the typical handover times of HSR in the order of, for example, 0.1 s to 1s are absolutely intolerable to vactrain. Thus, minimizing the handover times will be the most important challenge. To remedy this situation, the moving cell scheme based on the Centralized, cooperative, cloud radio access network (CRAN) is adopted to reduce the handover time.
# Design and Material Optimization of Oil Plant Piping Structure for Mitigating Erosion Wear
## 3 Piping Structure Design and Case Formation
### Summary of Simulation Cases  
The FEA simulation cases for all affecting variables are summarized in Table 2, which illustrates the Case ID and design variable details, according to the piping material, piping thickness, elbow angle, and fluid velocity. The standard piping structure set is Case ID C90. For the Case ID's, C is the initial of the material, which is carbon steel and 90 means elbow angle. In cases of material variables, AISI 304 stainless steel and Inconel 617 alloy were used instead of carbon steel, and Case IDs are set to A90 and I90 based on the initials of the materials. For the piping thickness variable, piping structures with the thickness/diameter (t/D) of 5.16/69.3 mm, which is thinner than C90, are set to C90-tn, and piping structures with the t/D of 14.02/87.02, which is thicker than C90, are set to C90-tk. For the elbow angle variable, 45\({}^{\circ}\) and 25\({}^{\circ}\), which are less than 90\({}^{\circ}\), Case IDs are set as C45 and C25. For the fluid velocity variable, a fluid velocity of 1.38 m/s, which is higher than the velocity of C90, was set as a Case ID of C90-hv, while a fluid velocity of 0.344 m/s, which is lower than the velocity of C90, is set to C90-lv.  
Throughout the various simulation case analyses, the overall length of the piping structure, the inner diameter, and the radius of curvature were maintained as constants. Only the factors discussed previously were altered in each case, and each case was analyzed separately.