esa-sceva/satcom-chunk-collection huggingface dataset:
# Studies regarding the Use of Pneumatic Muscles in Precise Positioning Systems
## 2 Materials and Methods
### Pneumatic Muscle Principle
For the first experiment (Figure 3a), the mass (\(M\)) is constant and the initial supply pressure is zero. At zero pressure, the volume of the muscle is minimal (\(V_{min}\)) and its length is maximal (\(l_{max}\)). If the muscle is fed to the pressure (\(p_{1}\)), it will begin to expand in diameter and, at the same time, will develop a pulling force, lifting the load (\(M\)). The muscle volume will reach the value \(V_{1}\) and its length will become \(l_{1}\). Increasing the pressure to \(p_{2}\) will continue this process. From this experiment, two basic rules can be deduced, according to [32]:  
(1) A pneumatic muscle is shortened by increasing its radial dimensions;  
(2) The length of the muscle will decrease as the pressure increases, the applied load being constant. From the second experiment, presented in Figure 3b, two other rules arise. The pressure is maintained at a constant value (\(p\)), while the mass attached to the free end is diminished. In this case, the muscle will expand in the radial direction and shorten. If the load is completely removed, the expanding in the radial direction is at its maximum and the length will reach its minimum value, \(l_{min}\). As result, it means that:  
Figure 3: Operating mode at (**a**) constant load (\(M\)); (**b**) constant pressure (\(p\)). (3) A muscle at a constant pressure will shorten if its load is reduced;  
(4) Its axial contraction has an upper limit over which no force develops. From both experiments presented above, another rule can be deduced:  
(5) For each pair of pressure-load, a pneumatic muscle has an equilibrium length. This behavior differs from that of a pneumatic cylinder; a cylinder develops a force that depends only on the pressure and the surface of the piston. Therefore, at a constant pressure, the force will be constant, regardless of the displacement performed. The presented research applied the first operating mode (Figure 3a) with a constant load and increased the pressure from 0 to 6 bar. There are three situations considered for the experiment: without a load at the free end of the muscle, with a 2.55 kg load, and with a 4.55 kg load.
## 3 System Identification for Position Model
### Model Structure Selection  
When choosing a parametric model based on the applicability of the desired system, the auto-regressive with exogenous input (ARX), auto-regressive moving average with exogenous input (ARMAX), output-error (OE), and Box-Jenkins (BJ) models are just a few of the many options. The parametric ARX model is one of the popular selections for obtaining a linear mathematical model of soft actuators [49; 50; 51]. A High degree of precision has been shown in these studies. Therefore, the ARX model was selected for this study. The ARX model is represented in Equations (1) and (2), where Equation (1) is derived in the  
Figure 3: Cover to attach flex sensor (spectra symbol FS-L-0095-103-ST) to FBBA.  
Figure 2: The experimental setup for the FBBA system.  
time domain and Equation (2) is derived in the z domain. All these derivations assume that the noise is zero.  
\[\mathrm{y(k)}+\ \mathrm{a_{1}y(k-1)}+\cdots+\mathrm{a_{na}y(k-na)}=\mathrm{b_{1}u(k -d)}+\mathrm{b_{2}u(k-d-1)}+\cdots+\mathrm{b_{nb}u(k-d-nb+1)} \tag{1}\]  
\[\frac{\mathrm{Y(z^{-1})}}{\mathrm{U(z^{-1})}}=\mathrm{z^{-d}}\frac{\mathrm{B(z ^{-1})}}{\mathrm{A(z^{-1})}} \tag{2}\]  
where, k : discrete time step; d : time delay; na : number of poles; nb : number of zeros; u(k) : input; y(k) : output; and z\({}^{-1}\) : delay operator.  
The ARX's structure can be determined by zeros, poles, and delay values. Different structural configurations produce different results and have a significant impact on the mathematical model's performance. ARX is represented as na - nb - nk, where na represents polynomial A order, nb represents polynomial B + 1 order, and nk represents the delay of input-output. In each system, the order can be different. Lower-order structures are commonly used because higher-order structures produce more unstable outputs and add complexity to the model.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
###### Abstract  
With the evolution of fifth-generation (5G) to sixth-generation (6G) communication systems, the utilization of spectrum resources faces incremental challenges. Integrated sensing and communication (ISAC) technology, as a crucial element in 6G technology, is expected to enhance energy efficiency and spectrum utilization efficiency by integrating radar and communication signals, achieving environmental awareness, and enabling scene interconnection. Simultaneously, to realize the vision of seamless coverage in 6G, research on integrated satellite-terrestrial communication has been prioritized. To integrate the advantages, ISAC for integrated satellite-terrestrial networks (ISTNs) in 6G has emerged as a potential research direction. This paper offers an extensive overview of the present state of key technologies for ISAC and the development of ISTNs. Meanwhile, with a focus on the ISTN-oriented 6G ISAC system, several hotspot topics, including future application scenarios and key technological developments, are outlined and demonstrated.  
integrated sensing and communication (ISAC); integrated satellite-terrestrial networks (ISTNs); waveform design; beamforming +
Footnote †: journal: Electronics  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Secure Routing Scheme Based on ECC
#### 3.2.1 Digital Signature Scheme Based on Elliptic Curve
It can be seen from the above signature scheme that the public key of the system CA is used in the signature verification process, so that the signature verification process and the validity verification of the public key are completed in one step, thereby avoiding the introduction of certificates to verify the validity of the public key. There is no need to pass certificates during the routing process, which reduces communication, calculation, and storage costs. ```
1:for each \(U_{i}\)do
2:\(A\) randomly selects an integer variable \(k,k\in[2,n-2]\)
3: Calculate \(R=k\times G\), \(r=X(R)(\bmod q)\), \(s=k+S_{a}\times h(M||r)(\bmod n)\)
4:\(A\) transmits signature (\(r\), \(s\)) and \(M\) to \(B\)
5:\(B\) calculates \(V_{a}\): \(V_{a}=P_{a}+h(ID_{a})\times G+[(X(P_{a})+h(ID_{a}))\bmod n]\times P_{SA}\)
6:\(s\times G-h(M||r)\times V_{a}\) \(=k\times G+S_{a}\times h(M||r)\times G-h(M||r)\times(S_{a}\times G)\)
7:\(=k\times G=(x_{1},y_{1})\)
8:if\(x_{1}=r(\bmod q)\)then
9: the signature is valid
10:else
11: the signature is invalid
12:endif
13:endfor
```  
**Algorithm 3** The Process of Signature and Verification
## 2 Materials and Methods
### Performance Evaluation by Simulation of the UAV Network
#### 2.3.2 Simulation of the UAV Network
The graphical interface presenting the results (calculated delivery rate and latency) is shown in Figure 13.  
Figure 12: One—triangular network. The delivery rate and latency metrics are used to measure the performance of all six routing protocols analyzed in this paper. The delivery rate is determined as a ratio between the number of successfully delivered messages and the number of created ones. The latency is the average time needed for a message to reach the destination starting from the source (departure node).
## 5 Conclusions  
This study demonstrated that trypsin/PAR2 signaling regulates ICR murine small intestinal motility by modulating the pacemaker activity of ICCs. Trypsin and AC55541 (PAR2 agonists) dose-dependently depolarized the pacemaker potentials and decreased their amplitude through tachykinin NK1 and NK2 receptors. The trypsin/PAR2 signaling-regulated pacemaker potential depolarization is dependent on extracellular Ca\({}^{2+}\) and the G protein, PLC, PKC, and IP\({}_{3}\) signaling pathways but not on the TRPV4 signalingpathways (Figure 8). Thus, trypsin/PAR2 signaling regulates intestinal motility by modulating the activity of small intestinal ICCs. In general, trypsin is a digestive enzyme that is critical for good health. In addition, the results of this paper indicate that trypsin may play a role in regulating gastrointestinal motility. Therefore, it is considered as one of the good proteases that can treat abnormalities of the digestive system by controlling the concentration of trypsin.  
**Author Contributions:** Conceptualization, B.J.K.; methodology, N.R.C. and J.N.K.; software, J.N.K.; validation, N.R.C.; formal analysis, N.R.C.; investigation, N.R.C. and J.N.K.; resources, N.R.C. and J.N.K.; data curation, N.R.C. and J.N.K.; writing--original draft preparation, N.R.C.; writing--review and editing, B.J.K.; supervision, B.J.K.; project administration, B.J.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R11A3042479).  
**Institutional Review Board Statement:** All animal care and experiments were conducted in accordance with the guidelines issued by the Institutional Animal Care and Use Committee (IACUC) at Pusan National University (Busan, Republic of Korea; approval no. PNU-2020-2831).  
**Informed Consent Statement:** No applicable.  
**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare that they have no competing interest.  
Figure 8: Schematic representation of the signaling pathway of trypsin-induced ICC depolarization. Trypsin-induced depolarization may be mediated by NK1 and NK2 receptors, G-protein, PLC, PKC, IP\({}_{3}\), and external Ca\({}^{2+}\)-dependent pathways. ICC: Interstitial cells of Cajal.
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## 9 Experimental Results and Validation  
To validate the theoretical results, including clock phase-noise effects, experimental data are collected using a signal generator and receiver front-end, and processed with a software defined receiver (SDR).  
A Spectracom GSG-6 series signal generator with a SpectraTime LPFRS-01/AV1 rubidium reference oscillator is used to simulate GPS satellite signals. A USRP N200 with DBSRX2 daughterboard and separate LPFRS reference oscillator is used as the GPS receiver front end. The front-end outputs an IF signal at 5 MHz with a sampling frequency of 20 MHz which is saved for post-processing. Finally, a software defined receiver (SDR) based on that provided with [14] is used to acquire and track the signal. The signal is acquired with a 45.5 dB-Hz carrier-to-noise ratio, which is decreased to the desired level once PLL tracking is initiated to simulate a broadband interference event.  
Figure 13 shows experimental tracking error results for varying carrier-to-noise ratios compared to theory which now includes the effect of receiver clock phase-noise. The phase-noise PSD corresponds to the LPFRS oscillator actually used. The theory is represented using solid curves while experimental results are plotted using markers. There is very good agreement between theory and experiment for PLL bandwidths greater than about 1 Hz. (Note that the AWGN and clock phase-noise add directly in the new linear model. Thus, generally for high PLL bandwidths, the the additive white noise dominates.) However, at lower bandwidths where the clock phase-noise model dominates, there is disagreement. The PSD power-law model for the LPFRS oscillator computed using the method described in Appendix A appears to be overly conservative.  
Figure 14 shows the same experimental results compared to theory utilizing the OCXO clock model PSD from Table 1. This phase-noise model is a much better approximation to that of the actual oscillator. There is still a mismatch at low PLL bandwidths for \(C/N_{0}=45\) dB-Hz, but the correspondence is better for lower carrier-to-noise ratios. At 30.5 dB-Hz, the AWGN essentially dominates over the entire range of PLL bandwidths.  
Figure 14: Experimental results with theory using OCXO PSD model.  
Figure 13: Experimental results with theory using Rb PSD model.  
Accurately modeling oscillator phase-noise is necessary to determine the extent to which the PLL bandwidth can be tightened. As shown in Section 7, this improves the PLL output quality, and therefore will improve the quality of carrier-phase measurements. A low phase-noise clock is desirable because it does not intensify the standard deviation of tracking error as much as a clock with higher phase-noise, and can tolerate more interference at lower PLL bandwidths before the tracking error threshold is reached.
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 2 Machine Learning Algorithms in Healthcare
### Application in Predicting Hospital Length of Stay  
ML algorithms are used by almost 60% of companies in the healthcare industry [13]. These algorithms play a critical role in predicting hospital LoS with the most-used types being classification algorithms, regression algorithms, and neural network algorithms.  
* Classification algorithms can be employed to predict whether a patient will be admitted to the hospital (binary classification). These algorithms analyze various patient data to determine the likelihood of admission or the hospital LoS. Popular classification algorithms include decision trees, random forest, and logistic regression. For example, logistic regression can effectively handle binary outcomes, making it suitable for predicting hospital admissions based on patient health indicators and history;Regression algorithms can be used to forecast the number of days a patient will remain hospitalized: the hospital LoS. Unlike classification, regression aims to predict a continuous numeric output. Common regression algorithms include logistic regression, ElasticNet regression, and support vector regression. These models consider a multitude of variables, such as patient demographics, clinical conditions, and treatment plans, to provide accurate hospital LoS predictions. Such predictions are crucial for optimizing bed management and resource allocation in hospitals.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 3 Methods
### Model Hyperparameter Optimization
Additionally, the Artificial Bee Colony algorithm, Sparrow Search Optimization algorithm, and Whale Optimization algorithm were used to optimize the CNN-RGRU-Attention model. The training iteration processes for the CNN-RGRU-Attention model optimized by the Particle Swarm Optimization algorithm, Artificial Bee Colony algorithm, Sparrow Search Optimization algorithm, and Whale Optimization algorithm are shown in Figure 8.  
The training graphs for each optimized network model are shown in Figures 9 and 10. From Figures 9 and 10, it can be observed that the loss function curves for each neural network model optimized by the Particle Swarm Optimization algorithm, as well as the CNN-RGRU-Attention model optimized by the aforementioned optimization algorithms, all reached convergence within the preset number of iterations.
# Bee Venom Acupuncture in Traditional Korean Medicine: A Review of Clinical Practice Guidelines
## 4 Level of Evidence for BVA Therapy in TKM-CPGs  
Many CPGs recommended BVA based on RCTs, implying an effort to incorporate higher-quality evidence. However, there were CPGs recommending BVA based on non-randomized studies (NRSs), such as degenerative arthritis of the hip joint [32], gout [41], and postoperative syndrome after lumbar surgery [34]. The reliance on NRS may reflect a lack of RCTs. Meanwhile, the recommendations for degenerative arthritis of the phalangeal joint [32], postoperative syndrome of total knee arthroplasty and rotator cuff surgery [34], prostatic hypertrophy [40], and traffic accident injury [35] did not present any supporting clinical studies due to a lack of appropriate research. As a result, the level of evidence for these recommendations was either not assessed [32; 40] or rated at the lowest level (\"Very low\") [34; 35]. There were no recommendations with high-level evidence (Table 1). Table 3 summarizes the level of evidence and its relevant information on BVA recommendations presented in the CPGs with solely RCTs. Among the recommendations for standalone BVA therapy, the level of evidence was found to be either moderate or low. For chronic low back pain [24] and temporomandibular joint disorder [33], the level was moderate, while for facial nerve palsy [37], knee osteoarthritis [27], and rheumatoid arthritis [31], it was low. For combined therapy recommendations, the level of evidence ranged from moderate to very low. Moderate levels of evidence were found in lumbar herniated intervertebral disc [28], traffic accident injury [35], shoulder pain [26], and cervical pain [25], while low levels were found in cancer-accompanying symptoms [39], shoulder pain or motor disability due to stroke [36], rheumatoid arthritis [31], ankle sprain [29], and idiopathic facial palsy with post-auricular pain [37]. The sample size was also a notable concern. While some CPGs, such as lumbar herniated intervertebral disc [28] and knee osteoarthritis [27], included RCTs with relatively large sample sizes (\(n=382\) and \(n=367\), respectively), many other recommendations relied on RCTs with smaller sample sizes. These smaller sample sizes could limit the statistical power to detect true effects and may contribute to imprecision in the effect estimates. The GRADE assessment criteria revealed common factors that led to downgrading the level of evidence. Imprecision, related to small sample sizes and wide confidence intervals, was the most frequent factor [25; 26; 27; 28; 29; 30; 31; 33; 35; 36; 37; 38]. RoB also contributed to downgrading [24; 27; 29; 30; 31; 36; 37; 38], indicating methodological limitations of the primary RCTs. Inconsistency was noted in some cases [29; 37; 39], suggesting heterogeneity among study findings. Indirectness was only cited as a reason for downgrading in tension-type headache [38]. Publication bias was not mentioned in any of the guidelines.
## 4 Simulation and Analysis  
### Simulation Models with Ideal and Nonideal Devices  
To verify the novel single-channel CTS structure, two simulation models with two sets of parameters are built in Advanced Design System (ADS). One of the parameters is based on ideal devices and another is based on nonideal devices. Two models built for the classical two-channel CTS system and the novel one-channel system are based on the schematics shown in Figures 2 and 5 individually. The matching network in the novel one-channel system is ignored as the dispersion characteristics of the introduced mixer is quite small compared to the pulse width in time domain. The measured signals are set to be some discrete sinusoidal signals with identical power so as to verify the spectrum accuracy and amplitude consistency. The modulation chirp signal is generated by the voltage source of ADS. An S2P file with fixed bandwidth, time duration, and chirp rate is created to represent the SAW filter for final pulse compression. The total simulation time is set to be five times of the compression period, and the max time step is smaller than the time width of output pulses.  
In the model with ideal devices, the frequencies of the input measured signals are 1.9 GHz, 1.95 GHz, 2.05 GHz, and 2.2 GHz, in which the frequency interval increases linearly. Compared to the classical two-channel CTS system, the novel single-channel system only adds a source, a mixer, a splitter, and a combiner. So, we set another parameter with conversion loss for the nonideal mixer and download S3P files for the splitter and combiner. The frequencies of the input measured signal are 1.8 GHz, 1.9 GHz, 2.0 GHz, and 2.1 GHz, with identical frequency interval in the model with nonideal devices.
# Flexible DEM Model Development and Parameter Calibration for Rape Stem
## 3 Results and Discussion
### Flexible DEM Model
#### 3.2.1 Bonding Parameter Box-Behnken Simulation Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Coefficient Estimate (Coded Factors)**} & \multirow{2}{*}{**Sum of Squares**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\ \hline Model & \(\backslash\) & 6184.30 & 9 & 687.14 & 40.93 & 0.0004 ** \\ \(G\) & 15.77 & 1990.80 & 1 & 1990.80 & 118.59 & 0.0001 ** \\ \(H\) & 2.28 & 41.68 & 1 & 41.68 & 2.48 & 0.1759 \\ \(I\) & 20.68 & 3422.13 & 1 & 3422.13 & 203.86 & \textless{}0.0001 ** \\ \(GH\) & 0.5475 & 1.20 & 1 & 1.20 & 0.0714 & 0.7999 \\ \(GI\) & 10.24 & 419.23 & 1 & 419.23 & 24.97 & 0.0041 ** \\ \(HI\) & 1.34 & 7.16 & 1 & 7.16 & 0.4263 & 0.5426 \\ \(G^{2}\) & \(-\)0.0354 & 0.0046 & 1 & 0.0046 & 0.0003 & 0.9874 \\ \(H^{2}\) & \(-\)2.19 & 17.63 & 1 & 17.63 & 1.05 & 0.3524 \\ \(I^{2}\) & 8.57 & 271.15 & 1 & 271.15 & 16.15 & 0.0101 * \\ Intercept & 26.93 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ Residual & \(\backslash\) & 83.93 & 5 & 16.79 & & \\ Lack of Fit & \(\backslash\) & 79.89 & 3 & 26.63 & 13.16 & 0.0714 \\ Pure Error & \(\backslash\) & 4.05 & 2 & 2.02 & \(\backslash\) & \(\backslash\) \\ Cor Total & \(\backslash\) & 6268.24 & 14 & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ \hline \hline \end{tabular} Note: ** means highly significant (\(p<0.01\)), * means significant (\(0.01\leq p<0.05\)).  
\end{table} 
Table 8: ANOVA for bonding parameter Box-Behnken simulation result. Figure 12: The effect of interaction factors on maximum damage force. \(y_{2}=f\) (\(G\), \(5.50\times 10^{8}\), \(I\)). Using the physical test measurement of the maximum damage force, 16.20 N, as the optimization target, the combination of the bonding parameters' optimization model is:  
\[\left\{ \begin{array}{c}y_{1}\!=16.2\ \mathrm{N}\\ \mathrm{s.t.}\left\{\begin{array}{c}1.0\times 10^{9}\leq G\leq 1.0\times 10^{10} \\ 1.0\times 10^{8}\leq H\leq 1.0\times 10^{9}\\ 1.0\ \mathrm{mm}\leq I\leq 2.0\ \mathrm{mm}\end{array}\right.\end{array} \right. \tag{14}\]  
Based on Equations (13) and (14), the solution for the optimal combination of parameters was solved as \(G=4.4\times 10^{9}\ \mathrm{N}\ \mathrm{m}^{-3}\), \(H=7.73\times 10^{8}\ \mathrm{N}\ \mathrm{m}^{-3}\), and \(I=1.26\ \mathrm{mm}\).