esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Effect of Wind Speed on Hail Impacts
## 2 Experimental Overview
### Parameters and Fabrication of Simulated Hail  
According to the available data, approximately 70% of hailstones during hailstorms have diameters smaller than 4 cm [28]. In this study, simulated hailstones with diameters of 2.4 cm, 2.8 cm, and 3.2 cm were selected based on the Chinese National Standard GB/T 27957-2011 (Grade of Hail) [29]. During the fabrication process, the partial surface melting of the hailstones was intentionally induced to facilitate demolding, resulting in a slight reduction in their actual mass compared to theoretical values. The theoretical mass of the simulated hail was calculated using sphere mass Formula (1). The actual mass was weighed by precision electronic scales. The measured parameters of the simulated hailstones are summarized in Table 1.  
\[m=\rho_{i}\cdot\frac{4}{3}\pi r^{3} \tag{1}\]  
Figure 1: Wind–hail coupling test platform.  
Figure 2: The force measurement system consists of two parts: (**a**) force-measuring balance and (**b**) custom-designed force measurement bracket.  
In the equation, \(\rho_{i}\): the density of the hailstone, taken as \(\rho_{i}\) = 0.9 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\); \(\pi\): the circular ratio; and \(r\): the radius of the hailstone (unit: m).  
In this study, simulated hailstones were fabricated using aluminum molds. Each hailstone mold consisted of two aluminum alloy hemispherical shells. A small hole was drilled on the top surface of the mold to facilitate water injection and the internal positioning of the hailstone core. The interface between the two hemispherical molds was sealed with silicone sealant and secured via bolted fastening. However, due to technical limitations in replicating natural hailstones in laboratory settings, pure water ice was employed as a substitute for the hailstone material [30; 31]. Pure water was injected into the aluminum molds using a syringe, followed by freezing in a \(-\)23 \({}^{\circ}\)C refrigeration chamber for over 24 h [32; 33] to ensure complete solidification. During demolding, the volume expansion effect of ice [34] was accounted for: after removal from the mold, the hailstones were allowed to rest at room temperature until partial surface melting occurred, enabling natural demolding. This step also alleviated the internal stresses caused by volumetric expansion, preventing additional cracks in the hailstones. The demolded hailstones were resealed and stored in the \(-\)23 \({}^{\circ}\)C refrigeration chamber until testing. The physical mold is illustrated in Figure 3.
## 4 Evaluation
### Numerical Simulation
#### 4.1.1 Path Following with Different Parameters  
In this scenario, the velocity of the UAV is limited to 20 m/s. The heading rate is limited within \([-0.2,0.2]\) rad/s. Different \(k_{v}\) and \(c_{v}\) are considered in the simulation to evaluate the VFGL method. The results are given in Figure 5.  
Figure 5a shows the simulation results with constant \(k_{v}=0.5\) and changing \(c_{v}\). When \(c_{v}=\sqrt{k_{v}}\), there are oscillations before the trajectory of the UAV converges to the reference. When \(c_{v}=2\sqrt{k_{v}}\) and \(c_{v}=3\sqrt{k_{v}}\), the trajectory of the UAV converges to the reference without overshoot or oscillation. This is consistent with the theoretical results that, if the parameters satisfy \(c_{v}\geq 2\sqrt{k_{v}}\), \(d\) converges to 0 without any overshoot or oscillation. We also find that the convergence is slower with the increase of \(c_{v}\).  
Figure 5b is the simulation results with different \(k_{v}\) and \(c_{v}\). It shows that the convergence obtains faster with the increase of \(k_{v}\) (\(c_{v}=2\sqrt{k_{v}}\)). When \(k_{v}\) increases to 2, there is an overshoot of more than 10 m before the UAV converges to the reference. That is because the control input (course rate and the velocity) of UAV is constrained. The overshoot can be reduced by adding \(c_{v}\). The result shows that when \(c_{v}\) increases to \(3\sqrt{k_{v}}\), the overshoot is suppressed to 0.
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
### Underwater Acoustic Communications for SONs  
Underwater acoustic communication (UAC) schemes for several existing cableless SONs [2; 5; 6] are listed in Table 1, where both coherent and noncoherent ones can be found. Coherent schemes [2; 5; 7] have a high spectral efficiency, thus the potential to obtain a high data rate, e.g., 2.5-10 kbit/s was achieved with a bandwidth of 5-20 kHz [8]. However, they are vulnerable to harsh channel conditions and demand advanced receiver techniques and multichannel combining to guarantee transmission performance. By comparison, noncoherent schemes are much more robust despite the channel conditions [9] at the cost of degraded spectral efficiency. In the Deep-Ocean Assessment and Reporting of Tsunamis (DART) warning system [6], a noncoherent UAC using the multiple frequency-shift keying (MFSK) modulation was adopted. With only a single hydrophone on the receiver side, robust transmission was achieved at a low power consumption. It is noted by [8] that, in most SON applications, only short packets need to be transmitted periodically and the resulting data rate is no more than 1200 bit/s. Therefore, a low-power and high-reliability noncoherent UAC scheme is more desirable for a long-term cableless SON [10].
# A Forward\(-\)Backward Splitting Equivalent Source Method Based on S\(-\)Difference
## 4 Experimental Verifications  
### Experimental Procedure  
In this section, experiments were conducted to verify the sound field reconstruction effect of the four near-field acoustic holography algorithms mentioned above. This will facilitate further improvement of near-field acoustic holography technology. The improved sparse equivalent source method is more accurate for the identification and location of automobile noise sources, and also provides a reference for suppressing the noise of automobile parts. In the experiment, an 18-channel wheel array with a diameter of 0.38 m from HBK was used for measurement. Data collector and PLUSE Lab Shop Version acquisition software were used to process the time domain sound pressure signal. The microphone position distribution is shown in Figure 9.  
The experimental conditions are shown in Figure 10. A single loudspeaker was placed at the origin of the coordinates, taking the center axis of the measurement surface of the microphone array as the reference. The single loudspeaker is arranged on the center axis, with the corresponding coordinates of s0 (0, 0, 0) m, and the distance from the loudspeaker to the measurement surface of the microphone array is 0.1m; the spatial coordinates of the coherent sound sources are s1 (0.2, 0, 0) m and s2 (-0.2, 0, 0) m, respectively. During the experiment, the first step was to ensure that the two speakers were of the same model. The  
Figure 8: Coherent source reconstruction errors between Tikhonov, WBH, ISTA, and FBS.  
Figure 9: Microphone array coordinates.  
loudspeakers were firstly used to emit sounds with frequencies of 200 Hz and 2500 Hz, respectively, based on the assurance that the two loudspeakers were of the same model, and 2500 Hz, respectively. The sampling frequency was set to 16,384 Hz, and the sampling time was 5s to ensure that the measurement data were stable and valid, and we added 20 dB of random noise during the experiment to ensure the measurement conditions. Through the holographic surface, we collected the time domain signal and exported it to a table data file, and then used the software to process the data of acoustic information. The sound pressure level was used as the output parameter and the display range of the cloud was set to 15 dB with a gradient of 3 dB.
## 3 Design of the Proposed Solution
### Critical Switch Detection and Expected Load
Therefore, the study obtains the summation \(\sum\ N_{\Psi,\ flag}\) in each list of \(k\) paths, which in turn served as critical switch frequency score (\(\mathrm{CWF_{Score}}\)). In this way, for each switch \(Si\) the value of \(C_{SCBR}(N\,)\) and \(C_{load}\) are retrieved and append the corresponding \(S_{i}\) flag as shown in Algorithm 2 line (12-16). Similarly, for each \(P_{i}\) in P, such that \(P_{i}\in\mathrm{P}\), the summation of \(S_{i}\) value is appended to the corresponded \(P_{i}\). This way, three case scenarios are derived, Algorithm 2 lines (17-19) signifies the procedure. If \(\mathrm{CWF_{Score}}\) in \(P_{i}\) is less than \(T\), then \(Pi\) is considered, and the status is updated to \(\mathrm{P_{low}}\). However, the \(\mathrm{CWF_{Score}}\) is greater than T \(Pi\) is set to \(\mathrm{P_{high}}\). Interestingly, there may be some variation, or more than one \(Pi\) may have the same score. In this case, the \(Pi\) with a minimum number of hop count or distance is considered as the final path, Algorithm 2 demonstrates the procedure line (20-32). In practice, shorter paths typically tend to consume fewer network resources compare to longer paths, and hence the path which best minimizes the CWF\({}_{\text{score}}\) is one among the \(k\) shortest candidate path. SCBR performs route path selection using online (reactive). ```
1 Get distance
2 Get set of \(k\left\{\text{P}_{i},\text{P}_{2},\text{P}_{n}\right\}\)
3//Compute NBC
4
5\(\text{S}_{i}\gets C_{SCBR}(N)=\sum_{(s,t)\in N,s\
eq t}\frac{k\left(s,t \mid N\right)}{k\left(s,t\mid N\right)}\), \(\sum R\left(N\right)\)
5\(\text{S}_{i}\gets C_{load}=\sum(\sigma(N_{\text{w}})\,,\sigma(N_{\text{o}}))\)
6 If\(\text{S}_{SCBR}>\text{N}\ \&\ C_{load}>\text{N}\), then
7\(\text{Set}\ S_{i}\gets N_{\text{w}}\)
8 else
9\(\text{Set}\ S_{i}\gets N_{\text{o}}\)
10
11 end if
12 For\(\text{S}\) in \(\text{P}_{i}\)
13 Get\(\ C_{SCBR}\)
14 Get\(\ C_{load}\)
15 Set\(\text{Sing}\leftarrow\left(C_{SCBR}\text{U}\ C_{load}\right)\)
16 end if
17 For each P\({}_{i}\) in P:
18 Get\(\text{CWF}_{\text{score}}\)
19 Append P(\(\text{CWF}_{\text{score}}\))
20 If\(\text{CWF}_{\text{score}}\) in \(\text{P}\) i \(<\text{T}\), then
21 Pi\(\_\)status = low
22 Set\(\text{Pi}\leftarrow\text{P}_{\text{low}}\)
23 end if
24 If\(\text{CWF}_{\text{score}}\) in \(\text{P}\) i \(>\text{T}\), then
25 Pi\(\_\)status = high
26 Set\(\text{Pi}\leftarrow\text{P}_{\text{high}}\)
27 End
28 If\(\text{CWF}_{\text{score}}\) in \(\text{P}\) i \(<\text{T}\) & \(\text{P}\) i \(>\text{I}\), then
29 Select\(\text{min}(\text{P})\)
30 End
31 end
```  
**Algorithm 2**Critical switch detection and load
# A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
AP Access point ASK Amplitude-shift keying AWGN Additive white Gaussian noise BEE Blind energy estimation BER Bit-error-rate CSI Channel state information CSIT Channel state information is available only at transmitter EM Expectation-maximization GMM Gaussian mixture model I-Q In-phase and quadrature i.i.d. independent and identically distributed MIMO Multiple-input multiple-output ML Maximum-likelihood NOMA Non-orthogonal multiple access PAM Pulse amplitude modulation PSK Phase-shift keying SNR Signal-to-noise ratio STA Station (IoT device) STLC Space-time line code STLC-NOMA Space-time line coded non-orthogonal multiple access
# On the Local and String Stability Analysis of Traffic Collision Risk
## 4 Stability Analysis on Traffic Collision Risk
### Risk Disturbance Modeling  
In the initial conditions, traffic is assumed to be in an equilibrium state, which in turn implies that \(r_{n}\) starts from an equilibrium state as well:  
\[r_{n}^{eq}=\frac{v^{eq}}{d^{eq}} \tag{15}\]  
where \(v^{eq}=\dot{x}_{n}^{eq}\) and \(d^{eq}=x_{n-1}^{eq}-x_{n}^{eq}\) denote the velocity and desired spacing in the equilibrium state.  
At a certain point, the equilibrium state of traffic is disrupted by thedisturbance. The disturbance of vehicle \(n-1\) is denoted by \(y_{n-1}(t)\), and the disturbance of vehicle \(n\) is \(y_{n}(t)\). Then, the collision risk for vehicle \(n\) can be rewritten as  
\[r_{n}(t)=\frac{v^{eq}+\dot{y}_{n}(t)}{d^{eq}+y_{n-1}(t)-y_{n}(t)} \tag{16}\]  
Since the relationship between \(Y_{n}(s)\) and \(Y_{n-1}(s)\) can be obtained from the transfer function in Equation (3), the time-domain \(y_{n}(t)\) can be obtained through convolution:  
\[y_{n}=\mathscr{L}^{-1}(Y_{n-1}(s)Q_{n}(s)) \tag{17}\]  
where \(\mathscr{L}^{-1}(\cdot)\) denotes the inverse Laplace transform operator.  
Similarly, \(\dot{y}_{n}(t)\) can be calculated as  
\[\dot{y}_{n}=\mathscr{L}^{-1}(sY_{n-1}(s)Q_{n}(s)) \tag{18}\]  
Substituting Equations (17) and (18) into Equation (16), \(r_{n}(t)\) is formulated as:  
\[r_{n}(t)=\frac{v^{eq}+\mathscr{L}^{-1}(sY_{n-1}(s)Q_{n}(s))}{d^{eq}+y_{n-1}- \mathscr{L}^{-1}(Y_{n-1}(s)Q_{n}(s))} \tag{19}\]  
Accordingly, the collision risk for vehicle \(n+1\) is represented by  
\[r_{n+1}(t)=\frac{v^{eq}+\mathscr{L}^{-1}(sY_{n-1}(s)Q_{n}^{2}(s))}{d^{eq}+ \mathscr{L}^{-1}(Y_{n-1}(s)Q_{n}(s))-\mathscr{L}^{-1}(Y_{n-1}(s)Q_{n}^{2}(s))} \tag{20}\]  
The risk disturbances for vehicle \(n\) (denoted by \(\Delta r_{n}(t)\)) and vehicle \(n+1\) (denoted by \(\Delta r_{n+1}(t)\)), deviated from equilibrium risk (\(r_{n}^{eq}\)), can be formulated as  
\[\Delta r_{n}(t)=\frac{v^{eq}+\mathscr{L}^{-1}(sY_{n-1}(s)Q_{n}(s))}{d^{eq}+y _{n-1}-\mathscr{L}^{-1}(Y_{n-1}(s)Q_{n}(s))}-\frac{v^{eq}}{d^{eq}} \tag{21}\]  
\[\Delta r_{n+1}(t)=\frac{v^{eq}+\mathscr{L}^{-1}(sY_{n-1}(s)Q_{n}^{2}(s))}{d^{ eq}+\mathscr{L}^{-1}(Y_{n-1}(s)Q_{n}(s))-\mathscr{L}^{-1}(Y_{n-1}(s)Q_{n}^{2}(s))}- \frac{v^{eq}}{d^{eq}} \tag{22}\]
###### Abstract  
This paper introduces a novel approach to frequency regulation in stand-alone synchronous generators by combining particle swarm optimization (PSO) with a Fuzzy PID controller. This study compares three control methods: a programmable logic controller (PLC)-based PID, a Fuzzy PID, and a PSO-Fuzzy PID controller. An experimental setup is implemented using real physical equipment, including an asynchronous motor, a synchronous generator, and various power and control components. The system is monitored and controlled in real-time via an S7-1215 PLC with the TIA Portal V17 interface, and the controllers are designed using MATLAB/Simulink. PLC-MATLAB communication is implemented using the KEPServerEX interface and the OPC UA protocol. The PSO-Fuzzy PID controller demonstrates superior performance, reducing overshoot, undershoot, and settling time compared to the other methods. These results highlight the effectiveness and real-time applicability of the PSO-Fuzzy PID controller for industrial frequency control, especially under varying load conditions and the nonlinear characteristics of the synchronous generator.  
particle swarm optimization; fuzzy logic; PID; frequency control; synchronous generator; PLC 2025153634
## 4 Results and Analysis
### Evaluating the 'Assembleability' of Design Schemes  
A product design is considered not good enough if it cannot be manufactured and assembled in an efficient way. The implementation of the design for assembly (DFA) approach during product design is to improve the productivity. The application of DFA presents several advantages as follows:  
1. Excessive components in a product can be reduced.
2. The assembly efficiency can be improved.
3. The assembly time of a product can be reduced.
4. Members in a design team can spend less time on communication.
5. The number of components that are used in a product can be reduced after a redesign.  
During the first stage, the main task is to determine the minimum number of components. The calculation of the minimum number of components is determined by considering whether the components can deliver the required functions for the product. It is advised to carry out a discussion on the four questions as follows:  
1. Is the component required to take relative motion to other components during operation?
2. Is the component separate to other components that are already assembled or does it require different types of materials?
3. When assembling a component or carrying out maintenance, is it required separating a component from other components that are already assembled?
4. Is the component the only one that can deliver the desired function?  
If the answer to any of the above questions is yes, the component should exist in a standalone way. During the second stage, the main task is to analyze and measure the manual handling and manual insertion of each component:  
1. Manual handling: This deals with the motions including grabbing, transporting and positioning components and their direction.  
Figure 17: Membership values of emotional factors.  
Figure 18: Membership values of satisfaction factors.  
11 Manual insertion: This involves the motions of placing a component and adding it to the tooling to form an assembly.
* Aiming at manual assembly, find a two-digit manual code and a two-digit placing motion code.
* Carrying out the evaluation according to the manual handling and manual insertion tables that were proposed by [PERSON] and [PERSON] [17] as follows.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Fiberglass Industry  
Kaolin plays a vital role in the fiberglass industry, in which it is a key raw material in the production of various fiberglass products [37]. Fiberglass is widely used for applications such as thermal and acoustic insulation, reinforcement in plastics, textile yams, electronic circuit boards, and even paper, cloth, and roofing shingles. The production of fiberglass requires a blend of several components, with kaolin being a significant part of this mix. The primary materials for fiberglass include silica, kaolin, and limestone, which are complemented by smaller amounts of boric acid, soda ash, and sodium sulfate. Kaolin's contribution is crucial due to its specific properties, which help in achieving the desired characteristics of fiberglass. For instance, kaolin must meet the following stringent chemical specifications: it should contain approximately 38.5 \(\pm\) 0.6% alumina (Al2O3), 45.0 \(\pm\) 0.5% silica (SiO2), no more than 1.5 \(\pm\) 0.3% titanium dioxide (TiO2), and a maximum of 0.6% iron oxide (Fe2O3) [119]. These specifications ensure that the kaolin integrates well with other materials and contributes to the strength, durability, and overall quality of the final product. The extensive use of kaolin in fiberglass production is reflected in the substantial quantities required annually. The kaolin used is typically processed to a dry form to meet the specific demands of fiberglass production. This large-scale use underscores kaolin's importance in the fiberglass sector, in which its properties enhance the performance and reliability of various fiberglass applications. Its role in providing stability, improving mechanical strength, and contributing to the structural integrity of fiberglass products makes it an indispensable component in this industry.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 5 Discussion
### Gait Efficiency and Dual-Task Performance (Secondary Outcome)  
The study also addressed secondary gait-related outcomes, such as step variability, targeted foot placement, and dual-task execution. These parameters improved markedly in the Exergame and Agility groups, suggesting that cognitive-motor integration tasks--particularly those involving rapid visual feedback--are effective in mitigating gait instability and freezing episodes. The Exergame group, which combined audiovisual cues with obstacle negotiation, outperformed others in both gait precision and adaptability, indicating a strong link between cognitive load and motor correction in PD rehabilitation.
# Study on Disturbance Mechanism of Squeezed and Non-Squeezed Soil Piles on Soft Soil Foundation
## 2 Materials
### Numerical Modelling  
Due to the substantial impact of boundary conditions and grid division differences on the evaluation results of various pile types, a standardized numerical model construction method for pile disturbance evaluation is proposed in this paper. This method generalizes the disturbance dynamics process of static pressure piles, bag piles, and drilled piles into a circular hole expansion/unloading mechanical model and achieves parameterized automatic modeling of a universal hexagonal structured grid through Python- FLAC\({}^{\text{3D}}\)-based secondary development. Starting from the hemispherical body at the end of the soil compaction pile, reasonable topological segmentation is achieved through auxiliary surfaces to establish a structured mesh of the sphere (Figure 2a), then achieve structured segmentation of adjacent soil and soil between piles (Figure 2b,c), and finally stretch the top surface of the pile end as the reference point to form the pile and the surrounding soil (Figure 2d). Consequently, a universal method for developing pile foundation disturbance evaluation models for various pile types is implemented.  
Further, FLAC\({}^{\text{3D}}\) was used to simulate the squeezing effects of hydrostatic piles and bag grouting piles, as well as the unloading effects of bored piles. For simplifying the computation, the soil foundation was considered a homogeneous layer in the simulation. Figure 3a-c show the model grids for the three types of piles, all adopting a quarter symmetric model method with the grids centered on the cylindrical pile axis (Figure 3d). The grids' density gradually increased from the periphery of the pile to the pile position, in  
Figure 2: Universal meshing techniques for squeezed and non-squeezed soil piles. (**a**) establishing a structured mesh of the sphere; (**b**) pile end changed to close soil cube; (**c**) structured segmentation of adjacent soil; (**d**) formation of the pile and the surrounding soil.  
order to effectively simulate the three-dimensional stress state of the soil and significantly reduce the computational workload. The geometric parameters of the numerical model are presented in Table 2. Based on the aforementioned special properties of soft soil, this study employed an undrained numerical model that ignores the pore water pressure.  
This simulation will focus on the displacement field, stress field, and disturbance range of the soil caused by the pile driving process, as these three indicators are representative and can substantially demonstrate the disturbance effects of piling on the soil.