esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Analysis of Enhancing Question Diversity
#### 4.2.2 Multivariate Analysis of Variance (MANOVA)  
To analyze in depth the impact of generation methods on the diversity, challenge, and higher-order thinking of the question sets, we conducted a Multivariate Analysis of Variance (MANOVA). Before performing the analysis, we checked the assumptions of MANOVA, including multivariate normality (using [PERSON]'s test) and homogeneity of covariance matrices (using [PERSON]'s M test). The results indicated that the data generally met these assumptions (\(p>0.05\)).  
Table 7 presents the results of the MANOVA, with the independent variable being the generation method and the dependent variables being the scores for diversity, challenge, and higher-order thinking. The MANOVA results showed that the generation method has a significant overall effect on the diversity, challenge, and higher-order thinking of the question sets (Wilks' \(\lambda=0.523\), F(6, 170) = 11.258, \(p<0.001\), partial \(\eta^{2}=0.284\)). According to [PERSON] (1988), this represents a large effect size, indicating that the generation method has a substantial impact on the overall quality of the question sets.
## 3 Results and Discussion  
### Basic Composition of Wheat Flour, Mushroom Powders, and Shorthead Cookies  
Table 1 presents the results for the basic composition of raw materials (mushroom powders and wheat flour) and shorthead cookies. Compared with wheat flour, mushroom lyophilistates contained significantly (_p_ < 0.05) higher protein content (from 26.89% to 33.61%) and fat content (from 2.75% to 3.20%), 16-20 times more ash, and 10-15 times more fiber. The basic composition of mushroom powders used as a substitute for base flour translates into the composition of baked goods. The increasing content of mushroom flour from 0% to 6% contributed to a significant (_p_ < 0.05) increase in the amount of protein, ash, and fiber in the cookies. The highest protein content was recorded in cookies with 6% button mushroom (7.20%) compared with the control cookies (6.21%). With 6% replacement of wheat flour, the amount of ash compared with the control cookies increased to more than double in the case of cookies supplemented with oyster mushrooms (0.5%) and to more than three times in the case of cookies with _A. bisporus_ (0.64%). Cookies enriched with mushroom lyophilistates had a higher fiber content of from 5.67% (Ab2 cookies) to 8.02% (Po6 cookies) compared with control cookies (2.8%). A slight decrease in the amount of carbohydrates in products with mushrooms was recorded, and no significant differences were noticed in the amount of fat between the different versions of the cookies. The results presented in this work show that the highest moisture content was recorded in cookies with 100% wheat flour (5.34%) and cookies with 2% button mushroom powder (5.09%). The remaining versions of the cookies had significantly lower moisture content, from 4.10% to 4.57%. The mushroom powders used for this research can be considered high-protein and high-fiber foods. According to European Union law [60], food that contains at least 3 g of fiber per 100 g or at least 1.5 g of fiber per 100 kcal may be called a \"source of fiber\", while food containing at least 6 g of fiber per 100 g or at least at least 3 g of fiber per 100 kcal can be considered \"high in fiber\". The same document specifies the possibility of using the claims \"source of protein\" and \"high protein\" in the cases of foods where at least 12% or 20% of the energy value of the food comes from protein. Mushrooms are a raw material with a significant amount of fiber, minerals, and protein containing all essential amino acids, while being low in fat [43]. The amount of protein reported in _A. bisporus_ ranges from 7% to 40.5% of dry weight, while in _Pleurotus_ spp. it ranges from 7.6% to 37.04% [61, 62]. The main component of mushrooms, apart from water, is carbohydrates, which include mono- and disaccharides, polyols, polysaccharides
## 2 Data and Their Processing
### The Impact of Data Preprocessing on Model Performance  
EWMAS and EWMS are applied to smooth the time-series data, which likely reduces high-frequency noise inherent in sensor measurements. They can help the regression model focus on underlying trends rather than random fluctuations. Over-smoothing could obscure abrupt changes in rotor temperature if they occur (e.g., during rapid thermal transitions). However, the dataset's description notes that rotor temperature changes slowly [36]. Thus, the risk is mitigated.  
The original sampling rate of 2 Hz may lead to highly correlated consecutive samples. Down sampling (e.g., retaining every fifth sample for Group A) reduces redundancy while preserving the temporal structure of slower thermal dynamics. This aligns with the dataset's purpose of predicting rotor temperature, which evolves gradually.  
The data partitioning strategy evaluates model performance across two groups with distinct challenges. Group A employs a 500/2503 training/test split. The small training set pressures the model to generalize from limited data, while the large test set ensures the robust evaluation of real-world \"unknown data\" scenarios, as intended. Group B uses an extreme 109/109 split. This tests the model's ability to generalize with minimal training  
Figure 2: Flowchart of data processing and utilization.  
data, reflecting practical constraints like scarce high-temperature data. Yet, the tiny training set heightens overfitting risks, as the model may over memorize noise.
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## Principle of PRG
### Coarse Range Finding Method  
Through the above analysis, we can get the need to provide better than \(5\lambda/12\) wavelength ranging precision of coarse range finding method can eliminate the carrier phase integer ambiguity, and then gets the fractional part of carrier phase measurement by the carrier loop measuring phase difference between master and slave node, to can achieve high precision range.We assume that the second-order carrier wavelength of the range finding signal is 30 m, and then the coarse range finding method with accuracy of 12.5 m is required in PRG method. Existing media transmissions such as Wi-Fi, BLE are commonly used for indoor positioning, and can provide the range finding between two nodes. The accuracy of range finding using BLE is less than 1.5 m [6] and Wi-Fi is less than 2.9 m [15]. Range finding accuracy using Wi-Fi and BLE meets the requirements of the above coarse range finding. Wi-Fi signals are common in modern buildings, so there need not deploy any equipment. We can through the fingerprint positioning algorithm based on Wi-Fi get the coarse position of the current node, and then calculate the Euclidean distance of coordinates points of two nodes could satisfy the requirement of the PRG algorithm. We adopted Wi-Fi fingerprint positioning based on Gradient Boost Decision Tree(GBDT), and the algorithm flow is shown in Figure 10.  
Figure 9: The relationship between coherent integral loss and frequency error.  
The proposed fingerprint algorithm consists of two phase: an offline phase and an online phase. During the offline phase, RSS readings and its orientation that the mobile device are facing are collected on grid of reference points(RP), and then these are stored in fingerprint database, GBDT algorithm was used to train the fingerprint database, and the relationship model between signal strength vector \(\mathbf{x}\) and position coordinate point \(\mathbf{p}\) was obtained, \(f_{M}(\mathbf{x})\). The online phase consists of the mobile devices measuring RSS, and positioning estimation by model \(f_{M}(\mathbf{x})\).
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 4 Results
### Results Obtained with Feature Selection: Data Set 1
#### 4.2.1 Ann  
The hidden layer is set to 16 with GridSearchCV; kernel_initializer is set to glorot_uniform; optimizer is Adam; the epochs are 150; the batch_size is 10, and the confusion matrixes for fivefold cross-validations of ANN are shown in Table 9. The average accuracy rate is 92.06%. Take the confusion matrix with CV = 1 as an example; among the outcomes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Prediction Results**} \\ \hline  & **CV** & \multicolumn{2}{c}{**Win**} & **Lose** & **Accuracy** \\ \hline \multirow{6}{*}{Actual results} & \multirow{2}{*}{1} & Win & 446 & 66 & \multirow{2}{*}{91.15\%} \\  & & Lose & 20 & 440 & \\ \cline{2-5}  & & Win & 440 & 72 & \multirow{2}{*}{90.64\%} \\  & & Lose & 19 & 441 & \\ \cline{2-5}  & & Win & 481 & 31 & \multirow{2}{*}{92.49\%} \\  & & Lose & 42 & 418 & \\ \cline{2-5}  & & Win & 485 & 27 & \multirow{2}{*}{92.59\%} \\  & & Lose & 45 & 415 & \\ \cline{2-5}  & & Win & 473 & 39 & \multirow{2}{*}{93.21\%} \\  & & Lose & 27 & 433 & \\ \hline \multicolumn{5}{c}{Average} & \multicolumn{6}{c}{92.02\%} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix of ANN without feature selection (dataset 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Prediction Results**} \\ \hline  & **CV** & \multicolumn{2}{c}{**Win**} & **Lose** & **Accuracy** \\ \hline \multirow{6}{*}{Actual results} & \multirow{2}{*}{1} & Win & 432 & 51 & \multirow{2}{*}{91.26\%} \\  & & Lose & 34 & 455 & \\ \cline{2-5}  & & Win & 447 & 52 & \multirow{2}{*}{90.65\%} \\  & & Lose & 36 & 437 & \\ \cline{2-5}  & & Win & 439 & 52 & \multirow{2}{*}{91.56\%} \\  & & Lose & 36 & 437 & \\ \cline{2-5}  & & Win & 439 & 47 & \multirow{2}{*}{92.08\%} \\  & & Lose & 35 & 451 & \\ \cline{2-5}  & & Win & 459 & 43 & \\ \cline{2-5}  & & Lose & 34 & 436 & \\ \hline \multicolumn{5}{c}{Average} & \multicolumn{6}{c}{91.36\%} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix of SVM without feature selection (dataset 1).  
of the 972 matches, 475 matches were correctly predicted wins, 37 matches were incorrectly predicted losses, 420 matches were correctly predicted losses, and 40 matches were incorrectly predicted wins. A total of 895 matches were predicted correctly and 77 matches were predicted incorrectly, yielding an accuracy rate of 92.08%.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison of the Proposed PDTR to the State-of-the-Art Routing Protocols
This can be explained by the fact that more source nodes means more generated packets, and more generated packets need more energy consumption, hence leading to a shorter lifetime. From Figure 13, we can conclude that there is a decreasing relationship between the lifetime and the number of generated packets; as explained above, more generated packets need more energy consumption, thus leading to shorter lifetime.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Mobile Networks
#### 4.5.2 3G/WCDMA/UMTS  
Third-generation technology (3G), often known as Universal Mobile Telecommunication Service, came onboard under the 3 rd Generation Partnership Project (3 GPP). It started with Code Division Multiple Access 2000 (CDMA 2000) and later Wideband Code Division Multiple Access (WCDMA), and provided high quality audio, video, Multimedia Services (MMS), and data. WCDMA was first launched commercially in 2001 in Japan. Another technology used in China for 3G wireless telecommunication is Time Division Synchronous Code Division Multiple Access (TD-SCDMA).  
This technology advanced from 3G to 3.75G [115]. High-Speed Downlink Data Access (HSDPA) is commonly known as 3.5G, and can offer a data rate of 14.4 Mbps, while 3.75G refers to High Speed Uplink Data Access.
## 2 Model and Methods
### Power Shifting to Access Attractors or Windows
#### 2.3.3 Power Shift with Noise and Ramped Injected Signal  
The power-shift method can be altered theoretically by applying noise to the injected signal frequency at regular intervals in time with an amplitude range simulating experimental conditions more accurately. We add noise with amplitude _0.015_ to the injected signal frequency every _0.4_\(\tau\) using the RandomReal function in Mathematica. The addition of noise in the injected signal frequency does modify slightly the system parameters \(\bar{\Delta}\) and \(\Phi\) to comfort with the model. The change is insignificant.  
The second modification of the power-shift method's experiment is to apply a linear ramp to the power-shift amplitude instead of the Heaviside function initially used. In experiment, an actual laser does not instantaneously reach its new power. To simulate the lag effect, we select power shifts of the type \(Y(\tau,TAP)=Y_{0}+\textit{slope}(\tau\text{-}TAP)\), \(\textit{slope}=(Y_{PS}-Y_{0})/T_{\textit{lag}}\). See Section 3.2.1 for the results of the data collection.
###### Abstract
Moreover, the scheme does not need to eliminate the time synchronization offset but can achieve the same performance as the ideal synchronization. The rest of the paper is organized as follows. Section 2 introduces the related works. Section 3 introduces the system model of CP-SC-FDE in UAV LOS communication. Section 4 presents the NLOS communication scheme based on CP/CS-SC-FDE for UAV PC. Numerical results are given in Section 5, and conclusions are given in Section 6.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 7 Conclusions  
This article provides an extensive evaluation of two cell-balancing topologies for their use in space applications. DSSCE and the SFCBE architectures were designed, developed, and tested, enabling a comparison of the balancing speed, design complexity, and fault tolerance.  
With the information obtained in this work, the DSSCE topology was selected as the most appropriate solution for its integration into a BMS of large CubeSats intended for deep space missions. In those environments, fault tolerance circuits are crucial due to the increased risk of radiation failures. In contrast, the SFCBE topology may be a more appropriate choice for smaller CubeSats operating in LEO, where the environment conditions are less severe, the available space is more limited, and solar irradiance is high.  
Conceptualization, P.C. and C.T.; methodology, P.C. and C.T.; software, P.C. and C.T.; validation, P.C., C.T. and D.M.; formal analysis, D.M.; investigation, P.C. and C.T.; writing--original draft preparation, P.C. and C.T.; writing--review and editing, J.M.B. and A.G.; visualization, J.M.B. and A.G.; supervision, J.M.B. and A.G.; project administration, J.M.B. and A.G.; funding acquisition, J.M.B. and A.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union NextGenerationEU and Generalitat Valenciana under grant ASFAE/2022/21.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AC2 AC Any Cell to Any Cell AC2C Adjacent Cell-to-Cell BMS Battery Management System C2P Cell-to-Pack C2R Cell-to-Resistor  
Figure 10: Photo of the final design implemented for use in a 6U CubeSat.
## 4 Solution of the Fiber Optical Repeater
### System Infrastructure and Link Budget  
In the real-life deployment of Taiwan Railway Administration (TRA) mobile communication systems, leakage cables (Andrew Company) were used. The performance of a leakage cable is quantified by the attenuation loss and coupling loss.  
The attenuation loss is determined in a similar manner to that of a traditional coaxial cable, while the coupling loss is defined as the difference in power between the transmission power inside the leakage cable and the received power with a standard dipole antenna positioned at a distance of 1 m from the leakage cable. The attenuation loss usually ranges from 2.1 to 4.87 dB/100 m, while the coupling loss ranges from 68 to 71 dB for the leakage cables at NBiToT/LTE-A and 5G frequency bands, as indicated in Table 5.  
The TRA underground environment includes multi-layer services stations and dual parallel tunnels. The coverage of stations is realized using a hybrid antenna system approach. Hybrid solutions are used in two areas: 1. Tunnel coverage with active fiber optic distribution systems and passive leakage cables. 2. Station coverage with passive distributed antenna systems, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Leaking Cable** & **Diameter** & \multicolumn{2}{c}{**Longitudinal Loss (dB/100 m)**} & \multicolumn{2}{c}{**Coupling Loss (dB)**} \\ \cline{3-6}
**Type** & **(mm)** & **700 MHz** & **2200 MHz** & **700 MHz** & **2200 MHz** \\ \hline
**1/4\({}^{\prime\prime}\)** & 7.5 & 23–32 & 32–52 & 69 & 71 \\ \hline
3/8\({}^{\prime\prime}\) & 9.5 & 12–14 & 18–21 & 68 & 74 \\ \hline
1/2\({}^{\prime\prime}\) & 13.7 & 9.5–11 & 13–18 & 68 & 73 \\ \hline
7/8\({}^{\prime\prime}\) & 24.9 & 5.5–6 & 8–11 & 69 & 72 \\ \hline
1–1/4\({}^{\prime\prime}\) & 36.0 & 2.86–4.5 & 8.15–7.24 & 68 & 71 \\ \hline
1–7/8\({}^{\prime\prime}\) & 46.5 & 2.1 & 4.80–4.87 & 71 & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Radiated-mode leakage coaxial cables at NBiToT/LTE-A and 5G.
## 3 Optimization Design
### Pseudo-Rigid-Body Model Optimization  
In this section, the optimization problem is formulated using the PRBM of the mechanism in Figure 3, and the displacement amplification ratio in Equation (4). Other precision-positioning CMs often choose displacement amplification maximization as an objective. However, the trade-off would be a worse positioning resolution and a nonlinear input/output displacement relation if \(A_{amp}\) is excessively large. In this design, an adequate displacement gain of \(A_{amp}=3\) is chosen as a compromise, considering the limited maximum stroke of the PZT actuator and its unexpectedly increased resolution. The DVs and their limits, the objective function, and the necessary constraint functions are summarized, as shown in Table 1. In the optimization problem of the PRBM structure in this study, the  
Figure 7: Reliability-based design optimization using NSGA-II.  
\"fmincon\" function in MATLAB is used to optimize Equation (35). Table 2 presents the optimal results for the DVs.  
For the simple mechanism configuration in Figure 2, PRBM optimization can be unnecessary. One can also quickly and straightforwardly solve it using the provided system of equations. However, this paper proposes a general optimization procedure that could work for more complicated CMs. As mentioned in Section 2.2, the PRBM aims to determine the length of rigid links, quickly fulfilling the preliminary requirements. This optimization step helps reduce the number of design parameters in the next section.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 1 Introduction
The contributions of our work include:  
* A new synchronization method for distributed scheduling was developed, whose basic idea involves scheduling using a deterministic algorithm. Because deterministic algorithms can generate the same results independently when given identical initial conditions, the synchronization for distributed scheduling can be achieved via a two-way handshake, which is the simplest form of the handshaking protocol and has the least overhead. * The influence of the sending sequence of the source nodes on the concurrent transmission efficiency was discussed. A scheduling method based on dynamic programming for the joint optimization of the node's sending sequence and time is proposed. By considering the sending sequence of the source nodes, the transmission efficiency can be further improved. * Based on the above two methods, we propose a new distributed scheduling MAC protocol for underwater acoustic networks that supports multiple handshakes and concurrent transmissions in one transmission cycle for one-hop clusters. By supporting more concurrent transmission in a transmission cycle, the proposed method outperforms several conventional underwater MAC protocols in the simulations. The rest of this paper is organized as follows: in Section 2, the works related to underwater MAC protocols are reviewed. In Section 3, the new distributed scheduling method for underwater acoustic networks is proposed. Sections 4 and 5 present and discuss the simulation results, respectively. Finally, Section 6 concludes the paper.
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 4 Simulation Results and Analysis
### Verification of Tracking Performance
#### 4.2.2 Scenario 2: Turn Left with Deceleration at Crossing  
In urban traffic, the intersection is a common scene. The vehicle is required to slow down through a right-angle bend with a radius of 50 m. On a dry road with a road adhesion coefficient of 0.8, the vehicle speed decelerates to 9 m/s from 12 m/s within 5 s.  
Figure 8 shows that there are two big pulses in the target \(\hat{w}_{r}\) which are caused by the noncontinuous curvature. As the pulses with large rates of change exceeded the range of the training set, the target \(\hat{w}_{r}\) was difficult to track and resulted in a chain reaction in the longitudinal and lateral directions. However, the longitudinal and lateral fluctuations accompanied by yaw pulses do not mean decoupling failure, because besides the fluctuating part, the other targets in the three motion directions are accurately tracked without interferences. This gives us two inspirations; first, the target vehicle motion states should be smooth and remain within the range of the training set; the other is that the curvature of the target position curve designed by the planning level should be as continuous as possible.  
Figure 9 shows that the proposed method realizes the velocity, position and yaw tracking simultaneously. The fluctuations in \(a_{x}\) and \(a_{y}\) are amplified and accumulated, resulting in a stable velocity and lateral error of 0.193 km/h and 0.36 m after a 150 m trip. As the lateral error is seriously related to driving safety, the tracking performance of pure-pursuit is the best in this scenario. The MPC failed to reduce the error in the X direction of the Cartesian coordinate system without coordinate transformation, which leads to a larger lateral error in the vehicle coordinate system.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Unit** & **Decoupling Control** & **MPC** & **PP** \\ \hline \(\max(e_{Y})\) & m & 0.1054 & 0.0113 & 0.0296 \\ \(\min(e_{Y})\) & m & \(-\)0.0090 & \(-\)0.1795 & \(-\)0.0399 \\ \(MSE_{V}\) & m\({}^{2}\)/s\({}^{2}\) & 0.0033 & 0.0110 & 0.1164 \\ \(MSE_{Y}\) & m\({}^{2}\) & 0.0011 & 0.0036 & 0.0002 \\ \(MSE_{y\text{airp}}\) & \(\circ^{2}\) & 0.0382 & 0.0127 & 0.0386 \\ \hline \end{tabular}
\end{table}
Table 1: The tracking performance statistics.  
Figure 7: Trajectory tracking results in scenario 1.