esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Effect of Consolidation Time  
In this single drainage condition, SD, the effect of consolidation time on the NSF and axial load of pile is illustrated in Figure 6. In this analysis process, the consolidation pressure, p, is 100 kPa. With the increase of consolidation time, t, pore water discharges and pore water pressure dissipate gradually. The friction coefficient was constant, so the slope of fully mobilized skin friction versus depth was constant, as expected in the ESM above. It should be noted that the slope is relatively big when the consolidation time is short. It is believed that the friction coefficient of the pile-soil interface is reduced by water-bearing high pressure in pore. With an increase in consolidation time, the magnitude of skin friction  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Properties** & **Value** \\ \hline \multirow{5}{*}{Pile} & Length (m) & 20 \\  & Diameter (m) & 1 \\  & Weight (kN/m\({}^{3}\)) & 18 \\  & Elastic modulus (MPa) & 30,000 \\  & Poisson’s ratio & 0.15 \\ \hline \multirow{5}{*}{Consolidating layer} & Weight (kN/m\({}^{3}\)) & 18 \\  & Poisson’s ratio & 0.3 \\  & Elastic modulus (MPa) & 5 \\ \cline{1-1}  & Cohesion (kPa) & 5 \\ \cline{1-1}  & Friction angle (\({}^{\circ}\)) & 20 \\ \cline{1-1}  & Void ratio & 1 \\ \cline{1-1}  & Coefficient of permeability (\(\times 10^{-4}\) m/d) & 6.8 \\ \hline \multirow{5}{*}{Bearing layer} & Weight (kN/m\({}^{3}\)) & 18 \\  & Poisson’s ratio & 0.3 \\ \cline{1-1}  & Elastic modulus (MPa) & 30 \\ \cline{1-1}  & Cohesion (kPa) & 5 \\ \cline{1-1}  & Friction angle (\({}^{\circ}\)) & 30 \\ \cline{1-1}  & Void ratio & 1 \\ \cline{1-1}  & Coefficient of permeability (\(\times 10^{-4}\) m/d) & 6.8 \\ \hline \multirow{2}{*}{Interface} & Elastic critical value of relative settlement & \\ \cline{1-1}  & of pile soil (mm) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of the pile–soil system used in FEM model.  
Figure 5: 2D axisymmetric model used in the analysis.  
and position of neutral point increased continuously, as shown in Figure 6a. The axial load of pile shaft versus the normalized depth is plotted in Figure 6b. With an increase in consolidation time, the drag-load caused by the settlement of soil increased. The location of the maximum axial load was the position of the neutral point, which is consistent with the theoretical analysis results.
## 2 Methodology  
In this research work, a three-dimensional model of the mandible jaw was generated from DICOM files obtained with the courtesy of Oral and Maxillofacial surgeon Dr. [PERSON], Jabalpur Research Centre. That patient had an injury on his right condylar head during cricket practice. To perform this study, a CT scan was performed on the injured patient's skull. CT scan machine gives DICOM (Digital Imaging and Communications in Medicine) file output that is converted into Nrrd (Nearly raw raster data) file. Nrrd file extension plays an important role in processing images from CT scans. Afterward, Nrrd files were converted into STL file format using Democratiz3D. STL files format contains all data in 3D vector form where 3D geometry was formed by joining small triangles, which have coordinated location from origin. STL file acts as a bridge between scanned file and an editable CAD file, and the whole design was carried out with the reference of STL file. After obtaining STL files of the skull-mandibular region, extra bones and parts of the skull were removed from geometry as we only needed jaw, condylar part, and fossa part of skull bone. After removing extra geometry, geometry refinement was performed to obtain the better finish of geometry in the Meshmixer tool since the obtained 3D model is in a rough condition that cannot be used for further analysis; thereafter STL files were imported in SOLIDWORKS 2018, which is a mechanical CAD tool. In SOLIDWORKS, we repaired the injured right condylar head by mirroring the left condylar as shown in Figure 1a,b and then the geometry file was saved as an IGS file. Again, the STL file was imported to SOLIDWORKS to design a patient-specific TMJ Implant. References of condylar surface and fossa were taken to design an implant for an exact fit to the patient. CAD file of the custom implant was saved to IGES file format. Linear static analysis was performed on both designs, one is naturally repaired condylar and TMJ and the other is patient-specific TMJ implant.  
Afterward, for the generation of the mandible jaw of the patient, the file in IGES format was imported into the ANSYS workbench. Main steps concerning the design and numerical simulation of patient-specific TMJ Implant are represented in Figure 2.  
Figure 1: (**a**) The mandible jaw with damaged condyle, and (**b**) Later on rebuild model.
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis
### Deep Neural Network
#### 8.3.4 LSTM Model Architecture of Experiment 1.1 and Experiment 1.2  
For the implementation of LSTM for the CSI data classification, an eight layer network was designed. It involves the following:  
1. One input layer.  
Figure 10: LSTM data matrix selection for Experiment 1.1 and Experiment 1.2.  
Two hidden LSTM layers of 125 and 100 hidden units simultaneously to learn long-term dependencies.
* Two dropout layers with 20% neurons dropout for every 10 epochs to avoid the gradient explosion problem and stable learning without any glitches.
* Four fully connected network layers, each activity followed by a softmax layer for transforming the weights between 0 to 1.
* A classification layer for the activity classifications.  
The adaptive moment estimation (ADAM) optimizer uses the lower order moments and gradients of the first order for effective stochastic optimization in the training options [47] because is computationally efficient, has little memory requirement, is invariant to diagonal rescaling of gradients, and is well suited for problems that are large in terms of data/parameters. As described in Section 8.2, higher-order moments were not useful for better machine learning and improving the classification. The max epochs size between 40 to 45, a minibatch size of 32, and a validation factor for every 130 to 135 iterations are configured for the best outcome. A gradient threshold set as one and piecewise dropping rate methodology avoids the exploding gradient problem and overfitting. The LSTM network architecture is illustrated in Figure 11.
###### Abstract  
This paper proposes a modified polarization converter for generating radially and azimuthally polarized beams. Based on a Mach-Zehnder-like interferometric structure, the device consists of a pair of lateral displacement beamsplitters (LDBs) and two half-wave plates that manipulate the states of polarization of incident linearly polarized light. Through the coherent superposition of two orthogonal Hermite-Gaussian modes in the far field, radially and azimuthally polarized beams can be obtained simultaneously. A prototype was assembled to demonstrate the feasibility of the design. The proposed design has the advantage of having a simple, symmetric, compact, and robust structure. In addition, the introduction of LDBs substantially reduces the cost of this device. Moreover, the design can be applied to a broadband pulsed laser with considerable potential in high-power applications.  
beam splitters; waveplates; coherence; interference; polarizations Article
# Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning
## 4 Numerical Data Experiment and Simulation Analysis
### Analysis of the Parameter Setting Experiment
The iteration comparison curve is shown in Figure 12. It can be seen from the figure that the algorithm proposed in this paper is not only considerably better than the other three optimisation algorithms in terms of search breadth but also has the ability to jump out of the local optimum. The open-loop response of the motor model is shown in Figure 13 It can be observed that the dynamic response performance under the open-loop is not ideal; the overshoot is too large, and the adjustment time is too long. Figure 14 shows the four parameter-tuning methods and open-loop Bode diagram of the system. It can be seen from the figure that for the servomotor model selected in this study, the phase margin under the four controllers is positive, and the phase near the cut-off frequency (10 rad/s) is smooth, indicating that the system is robust when the control gain fluctuates within a certain range. Figure 11: GWO and CSO_IGWO iteration comparison curve. Figure 12: Iteration comparison curve of four optimisation algorithms. Figure 13: Open-loop step response curve of the system. To reflect the superiority of the CSO_IGWO algorithm in tuning PID control parameters, the parameter tuning method proposed in this paper is compared with the traditional Z-N method, the improved particle swarm optimisation algorithm, and the improved genetic optimisation algorithm, as shown in Figure 15.  
The performance indicators are presented in Table 4. It can be seen from the table that the traditional Ziegler-Nichols (Z-N) method for tuning the PID controller parameters can improve the dynamic performance of the controlled system, but compared with the CSO_IGWO algorithm for optimising PID control parameters, the CSO_IGWO algorithm for optimising dynamic performance is more obvious. However, the improved particle swarm optimisation (IPSO) algorithm and the improved genetic optimisation (IGA) algorithm clearly show their defects and easily fall into local optima. These fall into different local optimum solutions. The PID parameter overshoot under the improved genetic algorithm is large, whereas the PID parameter overshoot under the improved particle swarm optimisation algorithm is small but very slow. Through comparison, the parameter tuning method proposed in this study can help the system achieve satisfactory performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tuning Method** & \(\mathbf{T_{r}/s}\) & \(\mathbf{Mp/\%}\) & \(\mathbf{T_{s}/s}\) & \(\mathbf{E_{ss}/\%}\) \\ \hline None & 1.75 & 52.451 & 30.67 & 2 \\ Z–N method & 2.0724 & 25.374 & 8.289 & 0 \\ IPSO method & 1.381 & 14.1 & 7.726 & 0 \\ IGA method & 4.483 & 4.6 & 7.291 & 0 \\ CSO_IGWO method & 2.1316 & 4.5911 & 4.021 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental results. Figure 14: Bode diagram of system response. Figure 15: Comparison curves of system step responses.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 3 Description of the INSPIRE-SAT 7 Mission and Mission Operations
### The Space Segment
#### 3.2.6 The INSPIRE-SAT 7 IMU  
The IMU sensor (\(50\times 25\times 10\) mm, 30 g, 60 mW OAP) is based on an inertial measurement unit. It uses a 3-axis accelerometer, a 3-axis gyroscope, and a 3-axis compass that will measure the magnetic fields in three directions. IMU is an improved 3-axis accelerometer/gyroscope/compass, which will be used for determining the attitude of the INSPIRE-SAT 7 CubeSat. Main expected IMU sensor technical requirements are in Table 6.
# Influence of Joint Stiffness and Motion Time on the Trajectories of Underactuated Robots
## 5 Optimal Combination of Stiffness and Motion Time  
In this section, the combined effect of joint stiffness and motion time is considered. Since the moments of inertia are constants, Equation (4) shows that the variation in joint stiffness corresponds to a variation in the natural period \(T\) defined as:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Motion Time (s)** & **2-DOF Area (m2)** & **3-DOF Area (m2)** & **Reduction (\%)** \\ \hline
0.45 & 0.2065 & 0.1329 & 35.64 \\
0.55 & 0.2065 & 0.1379 & 33.22 \\
0.7 & 0.2065 & 0.1801 & 12.78 \\
0.8 & 0.2065 & 0.1958 & 5.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Area reduction using the 3-DOF underactuated robot considering the same stiffness value (\(k_{3}=2.6\cdot 10^{-3}\,\mathrm{Nm}/\mathrm{rad}\)).  
Figure 11: Areas swept by the robots considering four different motion times: (**a**) \(t_{f}=0.45\,\mathrm{s}\), (**b**) \(t_{f}=0.55\,\mathrm{s}\), (**c**) \(t_{f}=0.70\,\mathrm{s}\), (**d**) \(t_{f}=0.80\,\mathrm{s}\) with the same stiffness value (\(k_{3}=2.6\cdot 10^{-3}\,\mathrm{Nm}/\mathrm{rad}\)).  
\[T=\frac{1}{f_{n3}}=2\pi\sqrt{\frac{l_{3}^{s}(l_{2}^{s}-l_{3}^{s})}{k_{3}l_{2}^{s}}} \tag{24}\]  
The main advantage of using \(T\) instead of \(k_{3}\) is that \(T\) includes the ratio between the moments of inertia and joint stiffness; thus, it can be used with underactuated systems of any size. Moreover, the behavior of the robot is described by means of two time variables, the first related to the natural oscillation of the system, and the second related to the imposed motion.  
It is worth noticing that a decrease in \(k_{3}\) leads an increase in the natural period \(T\).  
Two figures of merit are considered. The first one is the sum of the motor torques. To take into account that the motor torques change sign during the motion, each motor torque is squared and the integral over motion time is calculated, then the two integrals are added. This figure of merit is related to the energy consumed by the robot to perform the assigned task.  
The second figure of merit is the area swept by the links of the robot.  
Results are depicted in Figure 12 as contour plots; lighter colors represent the larger values. The first graph (Figure 12a) shows the sum of the squared actuator torques, while the second graph (Figure 12b) shows the area swept by the 3-DOF robot.
# Flutter Reliability Analysis of Xiangshan Harbor Highway Cable-Stayed Bridges in Service
## 2 The Flutter Analysis Framework for Flexible Bridge
### Influence of Cable Breakage Position on Flutter Stability  
During the service of the bridge, some special situations or replacing of cables will lead to the overall loss of the cable (or cable breakage). In order to explore the influence of cable breakage position on flutter instability wind speed, the cable breaks at the middle position of the main span, the quarter position of the main span and the bridge tower position, which were termed as Condition 1, Condition 2 and Condition 3, respectively, are shown in Figure 16. The results of the flutter critical wind speed of the bridge are listed in Table 5.  
As can be seen from the above table, the cable in the middle of the main span had the greatest influence on the flutter critical wind speed. When the cable in the middle of the main span broke, the flutter instability wind speed dropped to 53 m/s, which is 17.2% less than the initial normal-use state. Meanwhile. the cable breakage close to the bridge tower had almost no influence on the flutter instability wind speed. Therefore, the wind environment should be paid close attention when the cable is replaced near the middle of the main span, so as to avoid wind-induced flutter of the bridge.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 1 Introduction  
On 31 May 2021, the QUIC protocol was standardized as RFC 9000 [1] by the Internet Engineering Task Force (IETF). It has attracted interest along with HTTP/3, which is a new application protocol based on QUIC. According to w3 tech usage statistics [2], approximately 25% of web sites use HTTP/3 as an element, and over 75% of Internet traffic on Meta's own services is HTTP/3 and QUIC. Thus, QUIC has received support from vendors with large customer bases on the real Internet and is expected to expand further in the future. For HTTP/3 connectivity checks, RFC9113 [3] already stipulates the use of the HTTP Alternative Services (Alt-Svc) [4] method. This method is designed within the context of guaranteed communication over old HTTP and the fact that HTTP/3 penetration is not necessarily dominant; it is considered as effective in the early stages of transition. However, when HTTP/3 is well established and the transitional period has passed, the uncertainty and redundancy of the Alt-Svc procedure are detrimental. In Alt-Svc, to use HTTP/3, it is necessary to first complete the old HTTP connection, and then migrate to HTTP/3 if possible. As HTTP/3 is a protocol that eliminates the waste of the old HTTP handshake (TCP handshake followed by TLS handshake), the current Alt-Svc does notfully benefit from the rapid connection establishment of HTTP/3. Alt-Svc, which connects using the old HTTP and then uses HTTP/3 if possible, is a suitable method during the transition period when the old HTTP is the mainstream. However, even if HTTP/3 is used extensively in the future, web servers must always respond to requests for the old HTTP because the connection must first be completed using the old HTTP. Moreover, because the HTTP/3 connection is initiated once the old HTTP connection has been completed, Alt-Svc is an inefficient method. Therefore, a method that attempts HTTP/3 connections from the beginning is necessary for the latter part of the transition period to HTTP/3. In this study, we propose a connectivity check for the latter half of the HTTP/3 transition period, with the aim of aiding the transition to HTTP/3.
## 2 Related Works
### Positioning Techniques Based on Common Measurements  
Indoor positioning is a challenging problem that has been extensively investigated, resulting in the development of various technologies such as WiFi, Bluetooth, Ultra-Wideband (UWB), geomagnetism, sound/ultrasound, or Pedestrian Dead Reckoning (PDR) [8; 17].  
Though a number of facilities can be used for positioning, in traditional positioning schemes for both cellular and non-cellular positioning systems, some universal signal measurements are used. In this section, we introduce positioning methods based on common measurements such as RSS, AOA, and TDOA.  
The RSS-based approach [18] is a simple and commonly used method for indoor positioning, which involves measuring the strength of the received signal. By using signal propagation models with the knowledge of the transmission power or power at a reference point, it is possible to estimate the absolute distance between the two devices based on the RSS value. In the device-based positioning, RSS positioning requires the use of trilateration or N-point lateration [19]. This involves using the RSS at the UE to estimate the precise distance between a UE and three or more signal sources. Subsequently, basic geometry and trigonometry are applied to determine the location of the device relative to the reference points.  
The AOA-based methods [20] make use of antenna arrays on the receiver side to determine the angle that the transmitted signal arrives at the receiver. This is achieved by calculating the TDOA at each element of the antenna array. While AOA can provide an accurate estimation for short distances between the transmitter and receiver, it requires more complex hardware and precise calibration compared with RSS techniques. Additionally, the accuracy of the AOA-based positioning decreases as the distance between the transmitter and receiver increases, as even a small error in the angle calculation can result in a significant error in the actual location estimation. Furthermore, in an indoor environment with multipath effects, obtaining the LOS condition for AOA-based positioning can be challenging.  
The TDOA-based methods [21] use the differences in signal propagation time measured at the receivers from different transmitters. To accurately determine the location of the receiver, the TDOAs from at least three transmitters are required. This allows for the calculation of the receiver's position as the intersection of three or more hyperboloids [21]. Solving the system of hyperbola equations can be achieved through methods such as linear regression or by linearizing the equation using Taylor-series expansion.  
However, all of the aforementioned existing methods heavily depend on LOS scenarios, and in other scenarios, such as urban streets and indoor scenarios, the complex signal propagation paths will lead to unreliable positioning results.
## 3 Results and Discussion
### C-T Network Construction and Analysis  
A total of 234 proteins were retrieved as the putative targets of the twelve selected cannabinoidss from PharmMapper (Table S2), and the C-T network was constructed which consisted of 247 nodes and 688 edges with a median degree centrality (DC) of 17 (Figure 3). The CBD had the highest DC and connecting to 126 protein targets, the second one was CBDA with a DC of 115, and the third one was \(\Delta^{9}\)-THC with a DC of 82, followed by CBN (73), CBC (55), CBGA (44), \(\Delta^{8}\)-THC (42), CBG (40), \(\Delta^{9}\)-THCA (37), CBDV (27), 11-OH-\(\Delta^{9}\)-THC (25), and \(\Delta^{9}\)-THCV (22).
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 2 MPC for Current Control of MPS  
In this section, the basic elements of FSMPC are reviewed. In the description that follows, a five-phase induction motor is used as a reference and to provide a concrete example. As discussed in the Introduction, MPC concepts have been widely applied in the field of variable-speed drives, with predictive stator current control being one of the most relevant approaches for MPS. The relevant components of this control scheme are depicted in Figure 1 and are detailed below. The reference example is based on the work presented in [16].
## 3 Influence of Inertia on Frequency Regulation
### Inertia Coefficient Analysis
#### 3.2.1 \(\zeta<1\) Case
It can be concluded from the above analysis that a large \(H\) helps to reduce the ROCOF, frequency deviation, and overshoot at the expense of increased settling time for the case of \(\zeta<1\).
# Deep Representation of a Normal Map for Screen-Space Fluid Rendering
###### Abstract
There is a significant difference between the proposed method and existing particle rendering methods. Because the target normal map is generated using a surface reconstruction method, our method is more robust than the existing screen-space methods [1,2] with respect to preserving features in the rendering results. In addition, because the cGAN model generates a refined normal map through a convolution operation, the computation time is related neither to the number of particles nor to the resolution of the surface reconstruction method [7]. Owing to this characteristic, the rendering quality tends to be improved in some cases. The contributions of the present study can be summarized as follows. * We propose a cGAN-based filter to improve the results of screen-space fluid rendering effectively. * We propose a novel loss term to encourage clear refinement of the normal map. Figure 1: (**a**) Rendering via a bilateral Gaussian filter [1]. (**b**) Rendering via our approach. * Because we constructed a normal map dataset for different types of fluid simulation, the experimental results generated by the deep normal map representation demonstrated the generality of our method and its efficient applicability to arbitrary fluid scenes.
## 4 Experimental Verification
### Validation of Measured Datasets
#### 4.2.2 Inner Ring Fault
The SDE parameter optimization process involves multiple VMD decompositions, which may increase computational burden and hinder real-time online monitoring. Future research can be expanded in the following directions:  
1. Combining intelligent algorithms (e.g., particle swarm optimization, reinforcement learning) to further improve parameter optimization efficiency;
2. Investigating the applicability of the method in fault diagnosis for other rotating machinery, such as gearboxes and rotor systems. Figure 16: Inner ring reconstructed signal envelope spectrum. Figure 15: Quantitative comparison (inner ring).