esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Alloy and Fiber Preparation  
The alloy Ti\({}_{20}\)Zr\({}_{20}\)V\({}_{20}\)Nb\({}_{20}\)Ta\({}_{20}\) was chosen based on previously published data [43]. Zr (purity 99.5%), Ti (purity 99.5%), V (purity 99.7%), Nb (purity 99.95%) and Ta (purity 99.95%) metal chips were used to prepare powder mixtures and ingots. Ti-Zr-V-Nb-Ta alloy ingots (in equiatomic elemental composition) were produced by the arc melting of mixtures of pure metals in an argon atmosphere purified with a Ti getter. The ingots were turned over and remelted five times to ensure the homogeneity of the composition after melting.  
The method of EBM-PDME was used to obtain micro-sized fibers from the arc-melted ingot [43; 44]. The formation of fibers was carried out in a vacuum installation with an electron beam melting of the previously melting material synthesized by conventional arc. According to this method, the lower end of the vertically positioned workpiece melts to form a hanging drop of melt. The resulting drop is fed to a rotating heat absorber and, after solidification, is cut into an isosceles triangle. As the heat sink rotates, the solidified material is removed from the melt in the form of fibers under the action of centrifugal force.
## 2 Detection Model and Existing Problems  
### Detection Model  
The detection model of multiple GNSS-R signals for multiple targets was firstly outlined. The BDS signals, which are transmitted by MEO (medium earth orbit) satellites in the Beidou No. 3 system fixed at 2.1528 \(\times\) 10\({}^{4}\) km above the Earth's surface, were used as detection sources. The detection simulated maritime multiple moving target detection under space-based observation. The geometry of the model is given in Figure 1, where a rectangular coordinate system is established and the XOY level coincides with the sea level. The satellites, targets, and receiver positions are marked in red.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 2 Materials and Methods
### GNSS-R-Like System
#### 2.1.4 Reflection Geometry
Figure 3: Sketch of the elevation antennas geometry (side view). Black dots represent the antenna positions, and red dots the specular reflection points (see Figure 1). placed inside this area. In the other tests, the plastic litter entered and left the Fresnel zone pushed by the waves, so it was only visible for a small fraction of the time. Note that for the lateral antennas (azimuth angles of \(30^{\circ}\), \(45^{\circ}\), and \(60^{\circ}\)) the number of events of plastic litter entering in their respective Fresnel zones was too small to allow to conduct the study.
# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 5 Experimental Results
### Additional Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**\# Shots**} \\ \cline{2-11}
**Type** & \multicolumn{2}{c}{**0**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**16**} \\ \cline{2-11}  & **BLEU** & **GLEU** & **BLEU** & **BLEU** & **BLEU** & **GLEU** & **BLEU** & **BLEU** & **BLEU** & **BLEU** & **BLEU** \\ \hline remove & 45.05 & 44.33 & 53.10 & 51.39 & 57.24 & 54.85 & 64.98 & 61.93 & 67.74 & 64.98 \\ \hline addition & 48.01 & 47.37 & 59.14 & 56.99 & 68.10 & 65.59 & 76.28 & 73.18 & 76.31 & 73.33 \\ word\_replace & 46.93 & 45.63 & 52.22 & 50.53 & 67.94 & 65.59 & 69.76 & 68.33 & 70.18 & 68.33 \\ \hline rotation\_replace & 51.43 & 50.93 & 58.72 & 55.92 & 67.19 & 64.21 & 77.97 & 74.20 & 76.61 & 73.66 \\ \hline separation & 57.01 & 58.17 & 75.31 & 74.58 & 58.58 & 85.57 & 87.55 & 87.22 & 85.06 & 84.89 \\ \hline typing\_language & 44.16 & 39.80 & 27.12 & 29.72 & - & - & - & - & - & - \\ \hline foreign\_and\_conversion & 47.68 & 45.16 & 58.23 & 56.03 & 75.20 & 71.59 & 77.18 & 72.88 & 80.34 & 76.43 \\ \hline element & 38.59 & 38.85 & 47.83 & 46.45 & 56.84 & 56.39 & 63.99 & 63.11 & 64.13 & 63.52 \\ \hline consonant\_vowel\_conversion & 52.55 & 50.96 & 60.50 & 57.91 & 70.98 & 68.88 & 73.87 & 71.69 & 78.15 & 75.53 \\ \hline G2P & 47.40 & 45.44 & 59.93 & 57.17 & 71.93 & 69.98 & 75.36 & 73.12 & 77.64 & 74.77 \\ \hline tense & 45.42 & 39.39 & 50.66 & 44.04 & 60.24 & 49.33 & 71.31 & 61.02 & 66.75 & 58.96 \\ \hline postposition & 49.44 & 47.25 & 62.86 & 59.10 & 67.93 & 65.32 & 73.09 & 71.01 & 77.49 & 73.85 \\ \hline suffix & 48.69 & 48.04 & 62.86 & 62.26 & 73.79 & 73.65 & 74.94 & 75.05 & 76.76 & 76.86 \\ \hline auxiliary\_predicate & 51.64 & 52.07 & 52.18 & 53.04 & 66.73 & 67.38 & 72.29 & 74.05 & 70.37 & 71.11 \\ \hline dialect & 47.49 & 51.42 & 57.52 & 60.91 & 69.90 & 74.32 & 73.31 & 78.36 & 75.13 & 80.33 \\ \hline polite\_speech & 47.67 & 47.90 & 52.83 & 51.93 & 56.41 & 57.09 & 65.09 & 65.94 & 65.65 & 65.80 \\ \hline behavior & 38.90 & 35.31 & 51.19 & 45.74 & 53.03 & 47.33 & 57.51 & 51.32 & 57.54 & 51.81 \\ \hline neologism & 39.32 & 39.12 & 47.32 & 46.42 & 61.56 & 59.54 & 66.68 & 64.15 & 64.53 & 62.06 \\ \hline Avg. & 46.91 & 45.77 & 55.65 & 53.77 & 67.60 & 65.39 & 72.66 & 70.47 & 73.53 & 71.34 \\ \hline \hline \end{tabular}
\end{table}
## 2 Materials and Methods
### Preliminaries
#### 2.2.6 Security Model  
Negligible function: For a security parameter \(\lambda\), we call a function \(negl(\lambda)\) negligible if it satisfies the following condition: \(negl(\lambda)<\frac{1}{p(\lambda)}\) for all possible polynomials \(p\) and sufficiently large \(\lambda\).  
Computational indistinguishability: For a sufficiently large \(\lambda\), \(X=\{X_{\lambda}\}_{\lambda\in N}\) and \(Y=\{Y_{\lambda}\}_{\lambda\in N}\) represent two distributions of length \(\lambda\). \(X\) and \(Y\) are computationally indistinguishable if, for any possible polynomial-time algorithm \(T\), the following condition is satisfied:  
\[|Pr[T(X_{\lambda})\to 1]-Pr[T(Y_{\lambda})\to 1]|\leq negl(\lambda) \tag{13}\]  
where \(Pr[E]\) represents the probability of the event \(E\) occurring.  
We call it \(X\simeq Y\).  
Semi-honest model: Each participant strictly adheres to the normal operation of the protocol, including the inputs, outputs, and intermediate processes. In the semi-honest adversary model, however, an adversary attempts to obtain any information about other participants.  
Security model under semi-honest adversaries: In a general multiparty computation, one of the participants is called the server \(P_{t}\), and the others are called the clients\((P_{1},\ldots,P_{t-1})\). Let \(S=S_{1},\ldots,S_{t}\) represent the set of inputs from the clients and the server and \(f(S)=(\bot,\cap)\) represent a function in which the server \(P_{t}\) obtains the intersection and the clients obtain nothing. Assume a protocol \(\prod_{t}\) with \(t\) participants to compute the function \(f(S)\). During the execution of the protocol \(\prod_{t}\), the server's view is \(View_{P_{t}}^{\prod_{t}}=(S_{t},r_{t},\ell_{t},out_{t})\), and the clients' views are \(View_{P_{i}}^{\prod_{t}}=(S_{i},r_{i},\ell_{i},out_{i})\), in which \(i\in\{1,\ldots,t-1\}\), \(r_{i}\) and \(r_{t}\) represent the random numbers generated by each of the clients \(P_{i}\) and server \(P_{t}\), \(\ell_{i}\) and \(\ell_{t}\) are the messages received by each of the clients \(P_{i}\) and server \(P_{t}\), respectively, and \(out_{i}\) and \(out_{t}\) represent the outputs of each of the clients \(P_{i}\) and server \(P_{t}\).  
If \(\prod_{t}\) satisfies the following conditions: there exist polynomial-time simulators \(Sim_{t}(S_{t},\cap)\) and \(Sim_{i}(S_{i},\bot)\) such that \(Sim_{t}(S_{t},\cap)\) and \(View_{P_{t}}^{\prod_{t}}\) and \(Sim_{i}(S_{i},\bot)\) and \(View_{P_{t}}^{\prod_{t}}\) are computationally indistinguishable, where \(i\in\{1,\ldots,t-1\}\), \(\prod_{t}\) securely computes the function \(f(S)\).  
\[Sim_{t}(S_{t},\cap)\simeq View_{P_{t}}^{\prod_{t}} \tag{14}\] \[Sim_{i}(S_{i},\bot)\simeq View_{P_{t}}^{\prod_{t}}\]  
From Equation (14), it is clear that such a secure multiparty computation protocol \(\prod_{t}\) is secure in the presence of a semi-honest adversary.
## 3 Conventional Frequency Control Support Techniques
### Technique II Employing the Variable Gain  
To increase the performance of the frequency regulation of a WPG with stable operation, Technique II suggests an adaptable gain that is established on the WPG's rotor speed [14]. Technique I using fixed gain, which is enclosed in the blue dotted line as shown in Figure 6, is replaced by Technique II using variable gain. Furthermore, Technique II adopts the frequency deviation loop to compensate for the ROCOF loop. In Technique II, the variable gain \(AG(\omega_{r})\) is relational to the releasable kinetic energy (\(\Delta E\)) delivered by the WPG, illustrated as follows:  
\[AG(\omega_{r})\propto\Delta E \tag{9}\]  
\[\Delta E=\frac{1}{2}J\Big{(}\omega_{r}^{2}-\omega_{\min}^{2}\Big{)} \tag{10}\]  
Figure 6: Technique I using the fixed gain [23].  
\[AG(\omega_{r})=\left\{\begin{array}{l}C\big{(}\omega_{r}^{2}-\omega_{\text{min}}^ {2}\big{)}\text{ for }\omega_{r}<\omega_{\text{min}}\\ C\big{(}\omega_{\text{max}}^{2}-\omega_{r}^{2}\big{)}\text{ for }\omega_{r}\geq\omega_{\text{max}} \end{array}\right. \tag{11}\]  
where \(J\) represents the rotor moment of inertia and \(C\) is a proportional constant chosen to be 200. \(C\) can be determined by the droop at the maximum rotor speed, as seen below:  
\[C=\frac{1}{R_{\text{max}}\big{(}\omega_{\text{max}}^{2}-\omega_{\text{min}}^{2 }\big{)}} \tag{12}\]  
where \(R_{\text{max}}\) denotes the droop at the maximum rotor speed of DFIG (\(\omega_{\text{max}}\)). To apply \(AG(\omega_{r})\), the power system can be enhanced to consider the aspects of the frequency nadir when a disturbance occurs.  
In Equation (10), \(AG(\omega_{r})\) obtains the bigger gain when the WPG has enough kinetic energy (\(K_{e}\)); on the other hand, \(AG(\omega_{r})\) has zero value at the bottom of the rotor speed to keep the operation of the WPG stable. Technique II, however, can only help the frequency control within the bounds dictated by the releasable kinetic energy. Figure 7 shows the illustration of Technique II using the variable gain in which \(K_{e}\) and \(R_{o}\) are adjusted depending on the releasable kinetic energy. In the ROCOF loop, a first-order low-pass filter is used to remove the noise features in the measured frequency. Additionally, the rate limiter and the maximum limiter are utilized to obtain accurate results, where the rate limiter which is set to 0.45 pu/s [14] takes into consideration the mechanical stress on the DFIG's drivetrain, and the maximum limiter is set to the DFIG's minimum value of the power and torque limits.
# Reliability and Validity of the Portable KForce Plates for Measuring Countermovement Jump (CMJ)
## 2 Materials and Methods
### Procedures  
Before testing, all participants were made familiar with the CMJ. An experienced sports physiotherapist briefed them one week in advance with a live presentation and explanation of the optimal technique. Prior to the testing procedure, they performed a standard 10 min warm-up consisting of 8 min of jogging on a treadmill (40-50% of maximal heart rate) and 2 min of VJs with moderate intensity (continuously at their own pace). Their body mass was measured to the nearest 0.1 kg using the electronic scale Mi Body Composition Scale 2 (Xiaomi Inc., Beijing, China), and their height was measured on a portable stationer SECA 213 (SECA Instruments Ltd., Hamburg, Germany) to the nearest 1 cm [16]. During each session, participants performed 3 CMJs, and they were instructed to perform the highest possible jump. The highest CMJ was then taken into statistical analysis, and there was a one-minute rest between trials. CMJs were recorded with the validated My Jump 2 application [16; 21] through an iPhone 13 (Apple Inc., Cupertino, CA, USA) and also using the KForce Plates system application (Kinvent, Montpellier, France) on a Samsung tablet (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) concurrently. In order to avoid any errors during recordings, the experienced sports physiotherapist used a portable photo stand in order to secure the stability of the iPhone 13, and, simultaneously, he also used the default automated procedure for measuring CMJ with the KForce plates application. To initiate the recording, KForce plates need to detect the CMJ performance of the athlete, whereas in the My Jump 2 app, the recording begins by pressing the recording button. The testing procedure took place on the 1 st and 7 th days for all participants at the Laboratory of Advanced Physiotherapy of the University of West Attica in order to evaluate the test-retest reliability of the assessments (jump height and flight time). The timeframe was determined based on previous research in order to ensure that neuromuscular fatigue could not alter movement strategies [26].
# Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs
## 1 Introduction
It is worth noting that these aforementioned routing mechanisms have all or part of the following problems: (1) the decision-making method is central rather than distributed, thus the decision-making process may consume large communication resources and bring large time delay; (2) the proposed method does not take the mobile characteristics of nodes in FANETs into account, so the method may not be applicable; (3) information of only one-hop neighboring nodes is obtained, which leads to the method being merely able to obtain a local optimal solution with limited performance; (4) Q-learning is adopted to record and update the routing table, but the table becomes cumbersome and inefficient due to the topology dynamics of FANETs. This paper focuses on exploring and proposing an intelligent decentralized routing method that mainly uses the state information of nodes in two hops. Moreover, the location, moving speed, load degree, and link quality of the nodes are utilized flexibly to build the supporting elements of the method. The proposed method does not need to predict the state of links/nodes or set classification thresholds. The remainder of this paper is organized as follows. The system model is introduced in Section 2. Section 3 gives an introduction to reinforcement learning. The proposed intelligent routing method is detailed in Section 4. Section 5 and Section 6 separately cover the performance verification for the proposed method and the conclusion. _Notation:_ Throughout the paper, scalars are denoted by a nonboldface type, while vectors and matrices are denoted by a boldface type. \((\cdot)^{T}\) and \(E\{\cdot\}\), respectively, signify matrix transpose and statistical expectation. Furthermore, \(\omega_{i}\) represents the \(i\)th entry of the vector \(\mathbf{\omega}\).
## 4 Numerical Experiment
### Numerical Analysis Method
#### 4.1.2 Dynamic Model of the Track  
Figure 11 shows the dynamic model of the track. The track and structure were modeled using the finite element method. Assembling all elements in the model, the equation of motion of the track and structure was obtained in a standard matrix form as:  
\[M^{B}\ddot{X}^{B}+C^{B}\dot{X}^{B}+K^{B}X^{B}=F_{L}^{B}+F_{\Gamma}^{B}\left(X^ {V},X^{B}\right)+F_{N}^{B}\left(X^{B}\right) \tag{2}\]  
where \(X^{B}\) is a displacement vector of the track and structure; \(M^{B}\), \(C^{B}\), and \(K^{B}\) are the mass, damping, and stiffness matrices, respectively; \(F_{L}^{B}\) represents the load vectors; \(F_{L}^{B}\left(X^{V},X^{B}\right)\)  
Figure 9: Comparison of the response magnification of the ladder sleeper and PC sleeper.  
is the interaction force between the rail and the car; and \(F_{N}^{B}(X^{B})\) represents the load vectors of the nonlinear spring force in the structure.  
In this study, as shown in Figure 11, not only was the ballasted ladder track analysis model but also the conventional PC sleeper track analysis model was constructed for comparison. The only difference between the models is the shape of the sleeper; all other conditions are the same.  
The rails were represented by beam elements, the rail pads by spring elements, the sleepers by solid elements, and the roadbed by spring elements. In the section adjacent to the ladder sleepers, the rails were modeled with beam elements, rail pads, ballast, and the roadbed with composite springs. In this study, we mainly focus on the transmission of forces and vibrations in the vertical direction, so the degrees of freedom in the \(Y\) direction of the analysis model and the degrees of freedom in the \(X\) direction of the end in the longitudinal direction of the model are constrained. Furthermore, the degree of freedom of rotation around the \(Z\)-axis was constrained.  
The space between rails and sleepers was divided into five elements for rails and sleepers, and the entire ballast layer was divided into five layers for ballast. If the actual phenomenon is to be accurately reproduced, a nonlinear element or the like should be provided at the boundary between the sleeper and the ballast to reproduce the contact state and the noncontact state of the sleeper and the ballast. However, because this is not the main point of this study, the nonlinear element is not included in this analysis model, and the sleepers and ballast are connected by a rigid spring. The total number of nodes in the ballasted ladder track analysis model was 5430, and the total number of elements was 5728.  
Figure 11: Dynamic model of the track: (**a**) ballasted ladder track; (**b**) PC sleeper track.  
Figure 10: Dynamic model of the vehicle.  
The total number of nodes in the PC sleeper track analysis model was 5310, and the total number of elements was \(5584\).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & **SJ** & **CMJ** & **COD** & **COD0\%** & **0–10 m** & **10–20 m** & **0–20 m** & **20–30 m** & **0–30 m** \\ \hline \multirow{4}{*}{**ECCPF/BM**} & \(\beta\) & 0.482 & 0.471 & \(-\)0.290 & \(-\)0.316 & 0.325 & 0.421 & 0.412 & 0.107 & 0.335 \\ \cline{2-11}  & \(p\) & **0.005** & **0.007** & 0.107 & 0.079 & 0.070 & **0.016** & **0.019** & 0.561 & 0.061 \\ \cline{2-11}  & \(\mathbf{R}^{2}\) & 0.232 & 0.221 & 0.084 & 0.100 & 0.106 & 0.178 & 0.170 & **0.011** & 0.112 \\ \hline \multirow{4}{*}{**PP**} & \(\beta\) & 0.762 & 0.774 & \(-\)0.712 & \(-\)0.629 & 0.503 & 0.603 & 0.578 & 0.581 & 0.681 \\ \cline{2-11}  & \(p\) & **0.001** & **0.001** & **0.001** & **0.001** & 0.003 & **0.001** & **0.001** & **0.001** & 0.001 \\ \cline{2-11}  & \(\mathbf{R}^{2}\) & 0.581 & 0.599 & 0.507 & 0.396 & 0.253 & 0.363 & 0.334 & 0.338 & 0.463 \\ \hline \multirow{4}{*}{**RSI**} & \(\beta\) & 0.204 & 0.244 & \(-\)0.252 & \(-\)0.412 & 0.318 & \(-\)0.055 & 0.238 & 0.085 & 0.203 \\ \cline{2-11}  & \(p\) & 0.262 & 0.178 & 0.165 & **0.019** & 0.076 & 0.765 & 0.190 & 0.645 & 0.264 \\ \cline{2-11}  & \(\mathbf{R}^{2}\) & 0.042 & 0.060 & 0.063 & 0.170 & 0.101 & 0.003 & 0.056 & 0.007 & 0.041 \\ \hline \multirow{4}{*}{**STIFNESS**} & \(\beta\) & 0.300 & 0.222 & 0.117 & \(-\)0.294 & 0.350 & \(-\)0.143 & 0.205 & 0.149 & 0.224 \\ \cline{2-11}  & \(p\) & 0.095 & 0.223 & 0.523 & 0.102 & **0.049** & 0.434 & 0.261 & 0.417 & 0.218 \\ \cline{2-11}  & \(\mathbf{R}^{2}\) & 0.090 & 0.049 & 0.014 & 0.086 & 0.123 & 0.021 & 0.042 & 0.022 & 0.050 \\ \hline \multirow{4}{*}{**PSAP**} & \(\beta\) & \(-\)0.120 & 0.073 & \(-\)0.056 & \(-\)0.195 & 0.120 & \(-\)0.025 & 0.118 & 0.080 & 0.092 \\ \cline{2-11}  & \(p\) & 0.512 & 0.691 & 0.759 & 0.284 & 0.513 & 0.894 & 0.520 & 0.663 & 0.615 \\ \cline{1-1} \cline{2-11}  & \(\mathbf{R}^{2}\) & 0.014 & 0.005 & 0.003 & 0.038 & 0.014 & 0.001 & 0.014 & 0.006 & 0.009 \\ \hline \end{tabular}  
* **Note:** Each predictor was analyzed in a separate simple linear regression model to examine its individual association with each performance variable. **EUR**: Eccentric Utilization Ratio; **PP**: Peak Power; **CONPF**: Concentric Peak Force; **ECCPP**: Eccentric Peak Power; **ECCPF**: Eccentric Peak Force; **RSI**: Reactive Strength Index; **PSAP**: Pre-stretch Augmentation Percentage.  
\end{table} 
Table 2: _Cont._The CONMP variable was significantly associated only with 0-10 m sprint performance (\(p=0.034\), R\({}^{2}=0.106\)). The stiffness variable showed a significant association solely with COD performance (\(p=0.001\), R\({}^{2}=0.542\)). Peak power (PP) was significantly related only to 0-10 m sprint (\(p=0.008\), R\({}^{2}=0.405\)). Other parameters including CONPF/BM, ECCMP, ECCPP, ECCPF/BM, COD%, and PSAP did not show any statistically significant associations with the performance variables (\(p>0.05\)). Notably, although some associations were statistically significant, several R\({}^{2}\) values (e.g., for CONMP and RSI in sprint segments) indicated a relatively low explanatory power, suggesting that these variables account for only a modest portion of performance variance.
## 3 Stability Based Coordinated Control of Active Suspension and DYC
### Active Suspension Roll Controller  
Active suspension can produce an anti-roll torque by adjusting the active forces of the four suspensions. It can modify the body posture to avoid rollover accidents. For example, when the vehicle turns to the left, there will be a rightward roll tendency. What the active suspension does is to generate a reverse torque to suppress this tendency. The left and right suspensions, respectively, produce an active force with the same magnitude but the opposite direction. In Section 3.1, the active forces generated by the front and rear suspensions are the same. Then, the genetic fuzzy PI algorithm is used to design the roll controller. The flow chart is shown in Figure 1.  
The roll controller takes the error \(e\) between the body roll angle \(y(t)\) and its ideal value \(r(t)\) which is zero as the input and the suspension active forces as the output. The initial parameters of the PI controller are optimized by the genetic algorithm. Additionally, fuzzy control is used to deal with the changes in the working conditions. The inputs are the error \(e\) and its first derivative \(ec\). So, the parameters are adaptively modified. The expression is:  
\[\left\{\begin{array}{l}K_{p}=K_{GP}+\Delta K_{p}\\ K_{i}=K_{Gi}+\Delta K_{i}\end{array}\right. \tag{20}\]  
where \(K_{GP}\) and \(K_{Gi}\) are the initial proportional and integral coefficient optimized by the genetic algorithm and \(\Delta K_{p}\) and \(\Delta K_{i}\) are the correction value of \(K_{GP}\) and \(K_{Gi}\) obtained by fuzzy control.