esa-sceva/satcom-chunk-collection huggingface dataset:
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 3 Controllability of Take-Off in Jumping Animals and Robots
### Take-off Direction
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Year** & **Name** & **Mass** & **Jumping** & **Jumping** & **Atatators and** & **Highlights** & **Deficiences** & **Aut1** \\ \hline
2020 & TAUB [89] & 43–46 g & N/A & 80–120 cm & \multirow{2}{*}{Torsional spring} & Single actuator, perform the flipping task after landing, reorient and jump in required direction & Jumping performance is decreased because of the added mechanism & Y \\ \hline
2017 & Arachnabot [76] & 36 g & N/A & 11.5 cm & \multirow{2}{*}{Fluidic air pressure} & Pneumates and electrically-actuated tendons capable of strong, dynamic, and rapid joint movement & Counter, landing, and re-jumping & N \\ \hline
2017 & Salbo-1P [16,161] & 98 g & \(>\)2 m & 125 cm & \multirow{2}{*}{Torsional spring, thruster} & Light weight, continuous jumping, high agility & Jumping, limited distance of wireless communication & Y \\ \hline
2016 & Salbo [158] & 100 g & N/A & 120 cm & \multirow{2}{*}{Torsional spring} & Light weight, two-stage jump for increased height, high agility & Need of artificial assistance, not able to jump forward & N \\ \hline
2016 & Frohopper-inspired robot [135] & 3.06 g & N/A & N/A & SMA spring and linear spring & Added grasp help realize synchronous jumping and change direction from & Camnot control in air jumping and change direction from & N \\ \hline
2016 & JumpRoACH [148] & 99.4 g & N/A & 1.1–1.62m & \multirow{2}{*}{Lates rubber} & Adjust both crawling speed and jumping-the-off speed, height-adjustable via a novel active trigger & Landing and resetting abilities are poor & N \\ \hline
2015 & Roby-poly Soft Robot [56] & 2.1 kg & N/A & 20 cm & Combustion & Soft material, combustion drive & Hard control on jumping direction, low jumping frequency & Y \\ \hline
2014 & MultiMo-Bat [151] & 115.6 g & 232 cm & 305 cm & SMA wire, cable, clutch, linear spring & High jumping distance and height, components sharing, jumping and wights regulation & Impossibility to change-of-angle, lack of glide control and wings regulation & N \\ \hline
2013 & MSU-tailbot [215] & 26.5 g & 90 cm & 87 cm & \multirow{2}{*}{Spring} & Wheel on the ground, control its body angle using an active tail to dynamically maneuver in midair for safe landings & Impossibility to change take-of angle and direction & Y \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons of existing invertebrate animals inspired jumping robots.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Random Forest  
A combination of multiple decision trees is used to predict more stably. RF is a very adaptable approach to implement in our study. As it handles complex datasets and over fits, it will have better performance in predictions. The crop datasets can have subsets to apply a decision tree, and then based on averaging in a random forest, we can classify the crops. Here, we can obtain better results without tuning its hyper parameters. By lowering the Gini index, we can predict the more appropriate crop for the area.  
In Equation (10), the final prediction \(P(a)\) is shown, and \(T_{i}\) is the prediction for each input \(a\).  
\[P(a)=mode\left\{T_{1}(a),\ T_{2}(a),\ T_{3}(a),\ldots,T_{n}(a)\right\} \tag{10}\]  
where the \(n\) decision trees available are \(T_{1}\), \(T_{2}\), \(T_{3}\), \(\ldots\), \(T_{n}\), and the input supplied to each tree is \(a\). Here, the mode shows the majority voting applied among the predictions.
## 6 Simulation Evaluation
### Comparison and Analysis of Simulation Results
#### 6.2.4 Network Reliability  
Network reliability is defined in Section 3.2.3 as a criterion of QoS for UWSNs. In this section, Figure 12 shows the comparison of the average node degree varying with the number of nodes on NDACS and VBDA, and the influences of the simulation results on the different values of parameter \(\alpha\) to average node degree are analyzed. As shown in Figure 12, compared with VBDA, the NDACS algorithm can maintain a higher average node degree, but the growth of the average node degree of VBDA is gentle, with an increment of the total nodes' number. With this finding, the NDACS algorithm can adjust the communication radius by using the adjustable communication radius model in Section 3.1.3 and considering real time connectivity between the parent node and its child nodes. However, VBDA cannot completely guarantee interlayer connectivity under different values of parameter \(\alpha\) (as shown in Figure 10c. Therefore, the NDACS algorithm has a higher average node degree, which can maintain stronger communication connectivity between the node and the base station on the water surface, thus network operation is more reliable.
## 2 Foundational Concepts and Technologies
### Large Language Models (LLMs)
#### 2.2.3 Retrieval-Augmented Generation (RAG)  
Retrieval-augmented generation represents advancement in improving the precision and reliability of generative AI systems through systematic knowledge integration. This architecture diverges from traditional generative paradigms that rely exclusively on parametric knowledge stored in model weights, instead implementing a sophisticated bi-directional retrieval mechanism that interfaces with external knowledge repositories [12]. The system operates through a multi-stage process, initially converting incoming queries into dense vector representations (embeddings), then performing efficient similarity matching within a vectorized knowledge base, and finally synthesizing the retrieved contextual information with the model's generative capabilities to produce comprehensive responses, as illustrated in Figure 7. This methodology extends beyond mere information retrieval by enabling dynamic knowledge incorporation, which substantially improves response accuracy and significantly mitigates the occurrence of fabricated information or \"hallucinations\"--a common challenge in traditional language models [11].  
One significant advantage of RAG is its ability to update knowledge by simply modifying the external knowledge base without requiring model retraining. This feature is particularly valuable in dynamic domains where information needs to be frequently updated. Additionally, RAG models have demonstrated superior performance in maintaining factual accuracy and reducing hallucinations compared to traditional language models. The effectiveness of RAG is further enhanced through the use of dense vector representations for document retrieval, which allows for more nuanced semantic matching compared to traditional keyword-based retrieval methods. The system can process and incorporate multiple retrieved documents, weighing their relevance and combining information from various sources to generate more comprehensive and accurate responses. This multi-document approach is particularly beneficial when dealing with complex queries that require synthesizing information from multiple sources. The integration of retrieval mechanisms allows RAG to maintain up-to-date knowledge bases and provide more reliable, factual responses compared to static parametric-only models
## 2 Methods
### Statistical Analysis
#### 2.4.1 Model Specifications  
The dependent variable in our analysis was the T-value, representing the standardized pitch across various tonal combinations. The independent variables included two main factors: the subject group (either Polish learners or TM natives) and the tonal positions (coded as Position CodingA for the first syllable and Position CodingB for the second syllable). The reference category for each tonal position was set as T4, commonly considered the baseline in tonal studies of Mandarin.  
The mixture random effects model was chosen for its robustness in handling data with complex hierarchical structures and repeated measures. Each participant provided multiple readings, leading to an inherently nested data arrangement. To appropriatelyaccount for this, we included participant ID as a random effect. This inclusion was critical to control individual variability, which is a common issue when participants are exposed to repeated testing conditions. The fixed effects of the model included the group and tonal positions, allowing us to assess the main effects and interactions systematically. The choice of a mixture random effects model was driven by several considerations: (a) Repeated Measures: Each participant's multiple observations introduced serial correlation, which needed to be addressed to avoid inflated type I error rates. The mixture model, with its capability to include random effects, provided a means to account for this intra-subject correlation effectively. (b) Flexibility: This model accommodates both fixed and random effects, offering a flexible framework suitable for the complexity of linguistic data, where both participant-specific random variability and fixed effects of instructional conditions are of interest.  
In conclusion, the mixture random effects model enabled a nuanced analysis of the tonal acquisition process among Polish learners of Mandarin, yielding insights into both the fixed effects of learning context and the random effects due to individual differences. This methodological approach not only supported our hypotheses about the effects of different teaching methods and learner backgrounds but also underscored the complexity of tonal learning in a second language acquisition context.  
Following the initial analysis using the mixture random effects model, we conducted pairwise comparisons to further dissect the differences in tonal production across the different tones within each syllable. This detailed examination was crucial for understanding the subtleties in tonal variation that may not be fully captured using broader model-based approaches. For a refined analysis of the individual tone differences, pairwise comparisons were performed separately for the tones in the first and second syllables. These comparisons were crucial for identifying specific patterns of tonal production that could inform targeted instructional strategies for language learning.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 3 Robust Matrix Factorization with Robust Adaptive Structure Learning
### Computational Complexity Analysis  
The computational complexity can be calculated in light of the above flowchart of the proposed RMFRASL algorithm. First, the computational complexities of all variables in one iteration are shown in Table 2, where \(d\), \(n\), and k represent the dimensions of the original sample, the number of samples, and the number of selected features, respectively. Since \(k<<d\) and \(k<<n\), we only need to compare the size of \(d\) and \(n\). (1) If \(d>n\), \(S\), \(A\), and \(W\) need \(O(nd^{2})\), \(O(kdn)\), and \(O(nd^{2})\), respectively. Then, the computational complexity of the RMFRASL algorithm is \(O(t\times nd^{2}\) ) where \(t\) is the number of iterations. (2) If \(n\geq d\), \(S\), \(A\), and \(W\) need \(O(dn^{2})\), \(O(kn^{2})\), and \(O(dn^{2})\), respectively. Then, the computational complexity of the RMFRASL algorithm is \(O(t\times dn^{2})\). Finally, we can conclude that the computational complexity of the RMFRASL algorithm is \(O(t\times\max\,(dn^{2},\,nd^{2})+dn^{2})\).  
Meanwhile, the running time of the proposed RMFRASL and other compared feature selection methods was also tested when the number of experimental iterations was set to 10. From the experimental results listed in Table 3, the running time of the proposed method was lower than that of the other compared methods.
## Enhanced Single-Probe Method and Verification
### Sensitivity Analysis for Justification of the Two-Turn Loop Setup
First, when this setup is employed to measure the impedance of an active device like an operating power converter (this application will be presented in the next section), if \(k=N_{probe}/N_{turns}<1\), then an interference-noise current \(I_{n}\) generated by such a converter and flowing in the secondary would result in an increased noise current \(I_{n}\)/\(k\) injected in the VNA port connected to the primary, which could cause measurement errors and even possible instrument damage. Second, the use of too many hand-made wire turns would increase the uncertainty of the geometrical arrangement, which reduces the repeatability of the test setup. Therefore, considering the trade-off between sensitivity and other aspects, the optimal solution appears to be a number of wire turns equal to the number of turns of the probe inner winding, i.e., \(k=N_{probe}/N_{turns}=1\). For the available BCI probe, the optimal choice results to be \(N_{turns}=N_{probe}=\)2.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **IV-VIF0.01** & **IV-VIF0.001** & **AIC** & **BIC** & **Lasso** & **Elastic Net** & **PCA** \\ \hline SVM & 0.7607 & 0.7746 & 0.8732 & 0.9155 & 0.9014 & 0.9014 & 0.8309 \\ DT & 0.8309 & 0.8732 & 0.9718 & 0.9718 & 0.9718 & 0.9859 & 0.8873 \\ KNN & 0.7887 & 0.8592 & 0.9155 & 0.9437 & 0.9155 & 0.9155 & 0.8873 \\ LDA & 0.8028 & 0.8028 & 0.8873 & 0.9014 & 0.8873 & 0.8873 & 0.8592 \\ gcForest & 0.8732 & 0.8591 & 0.9859 & 0.9859 & 0.9577 & 0.9577 & 0.9296 \\ RF & 0.9014 & 0.9014 & 1 & 0.9718 & 0.9859 & 0.9718 & 0.9155 \\ CatBoost & 0.8873 & 0.8169 & 0.9859 & 0.9859 & 0.9859 & 0.9014 \\ RUSBoost & 0.8028 & 0.8308 & 0.9718 & 0.9859 & 0.9577 & 0.9718 & 0.9014 \\ Imbalance-XGBoost & 0.8028 & 0.8028 & 0.9859 & 0.9859 & 0.9437 & 0.9437 & 0.8732 \\ Stacking & 0.8591 & 0.9014 & 0.9859 & 0.9718 & 0.9437 & 0.9437 & 0.8873 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of different feature selection methods on accuracy of single classifiers and ensemble models.
# Single Trace Analysis against HyMES by Exploitation of Joint Distributions of Leakages
###### Abstract  
Beginning with the proposal of the McEliece cryptosystem in 1978, code-based cryptography has positioned itself as one of main categories in post-quantum cryptography (PQC). To date, the algebraic security of certain variants of McEliece cryptosystems has been challenged many times, although some of the variants have remained secure. However, recent studies on code-based cryptography have focused on the side-channel resistance since previous studies have indicated that the existing algorithms were vulnerable to side-channel analysis. In this paper, we propose the first side-channel attack on the Hybrid McEliece Scheme (HyMES) using only a single power consumption trace. HyMES is a variant of the McEliece system that provides smaller keys, along with faster encryption and decryption speed. By exploiting joint distributions of nonlinear functions in the decryption process, we were able to recover the private key of HyMES. To the best of our knowledge, this is the first work proposing a side-channel analysis based on a joint distribution of the leakages on the public-key system.  
McEliece; HyMES; side-channel analysis; code-based cryptosystem; joint distribution; single trace analysis +
Footnote †: journal: Applied Sciences
# Time-Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
## 2 Materials and Methods
### Hilbert Transform  
After introducing the algorithm of CEEMD, we describe the principle of the Hilbert transform.  
For any time series \(X(t)\), we can get its Hilbert transform result:  
\[Y(t)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{X(\tau)}{t-\tau}d\tau. \tag{11}\]  
The Hilbert inverse transformation is  
\[X(t)=H^{-1}[Y(t)]=-\frac{1}{\pi t}*Y(t)=-\frac{1}{\pi}\int_{-\infty}^{\infty} \frac{Y(t-\tau)}{\tau}d\tau=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{Y(t+ \tau)}{\tau}d\tau. \tag{12}\]  
For a real signal \(X(t)\), there are many kinds of complex methods, and it is the simplest and most effective to obtain the analytical signal by the Hilbert transform method. Assuming that the Hilbert transform of \(X(t)\) is \(Y(t)\), the expression of the parsed signal is  
\[Z(t)=X(t)+iY(t)=a(t)e^{i\theta(t)}, \tag{13}\]  
where \(a(t)\) and \(\theta(t)\) are the instantaneous amplitude and phase of the signal, respectively, and they can be written in the following form:  
\[a(t)=\sqrt{X^{2}(t)+Y^{2}(t)}, \tag{14}\]  
\[\theta(t)=\tan^{-1}\frac{X(t)}{Y(t)}. \tag{15}\]  
The signal's instantaneous frequency can be obtained by deriving the instantaneous phase:  
\[\omega(t)=\frac{d\theta(t)}{dt}. \tag{16}\]  
Unlike the Fourier transform, \(a\) and \(\omega\) in the equation are not constant, but a function of time, so the Hilbert spectrum can characterize the temporal variation of a data sequence; the instantaneous frequency is relative to the analytical signal, which is the analytical signal phase.  
The frequency obtained by the signal FT reflects the distribution characteristics of the signal with time, and the frequency of the signal with time cannot be obtained. The instantaneous frequency reflects the frequency of the signal at a certain time, which can better describe the characteristics of the signal. This is also the main difference between them. The Hilbert transform can only deal with the stationary signal of a single frequency, which has great limitations for non-stationary signals with multiple frequencies at a certain time. Therefore, the Hilbert transform must meet some constraints in the application to obtain a physical instantaneous frequency.
## 3 Proposed Cross-Working Condition RUL Prediction Method
### JTMN-Based RUL Prediction
#### 3.2.2 Joint Transfer Metric Network for RUL Prediction  
Traditional RUL prediction methods adopt various advanced feature extraction techniques to mine RUL-related features; however, the domain drift problem has not been solved under cross-working conditions. Fortunately, based on proposed joint domain adaptation loss, a cross-working condition RUL prediction method via a joint transfer metric network (JTMN) consisting of RUL prediction loss and joint domain adaptation loss is built. The mean square error (MSE) is used in RUL prediction loss. This will reflect the relationship between actual and predicted values. The commonly used loss function is \(L_{rul}\). The joint domain adaptation loss \(L_{jda}\) is a combination of \(L_{vdr}\) VDR and RSD \(L_{rsd}\), which operate in the high-dimensional feature layer to calculate the distribution discrepancy of representation ability and feature manifold distribution between the training domain and the testing domain. In addition, maximizing the domain classification error \(L_{c}\) between source and target domains can further promote distribution difference alignment between the source and target domains.  
In order to make JTMN have better generalization performance under a variety of working conditions, the objectives of this model improvement include loss function \(L_{rul}\) from the source domain, condition generalization loss \(L_{jda}\), and domain classification error \(L_{c}\). The final loss function can be expressed as Equation (22):  
\[L_{total}=L_{rul}+\alpha L_{vdr}+\beta L_{rsd}+\gamma L_{c} \tag{22}\]  
\[\gamma=\frac{2}{1+\exp(-i\cdot e)}-1 \tag{23}\]  
where \(i\) is number of current iteration steps, \(e\) is number of stop iteration steps, and \(\alpha\) and \(\beta\) are the compromise parameters trained by back propagation. In this way, the model not only learns RUL mapping information, but also lessens distribution discrepancy. \(\gamma\) is the adaptive parameters [15]. The prediction accuracy is ultimately improved under different operating conditions.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 4 Proposed Method
### Centralized Scheme  
The main challenge in solving problem \((\mathrm{P1})\) lies in the coupling between the transmit power \(p_{k}\) of devices and the denoising factor \(\theta_{m}\) within the expression of the optimality gap \(\mathrm{Gap}_{m}\), which renders the original problem non-convex. To address this, we propose an iterative centralized algorithm. Specifically, we first fix the transmit powers \(p_{k}\) and optimize the denoising factor \(\theta_{m}\) for each cell, then substitute the resulting \(\theta_{m}\) back into the original problem to solve for the optimal transmit powers. This alternating optimization (AO) continues until convergence.  
Figure 2: Pareto boundary.
## 3 Results
### Speed Results  
The analysis of the speed variable (Table 4), based on a five-cluster k-means model, reveals that U14 players perform the majority of their actions at low movement speeds. Specifically, 63.6% of all game actions occur at speeds below 5.42 km/h, with a cluster center of 2.98 km/h, corresponding to approximately 3.73 million data points out of a total of 5.86 million. These values reflect moments when players are either stationary or moving at very low intensity.  
In addition, 21.94% of actions occur at low speed (5.42-10.19 km/h), typically associated with walking, with a cluster center of 7.45 km/h (\(\approx\)1.29 million data points). Moderate-speed actions (10.20-14.63 km/h), corresponding to jogging, represent 10.59% of the total (\(\approx\)621,000), with a cluster center of 12.16 km/h. High-speed actions (14.64-18.59 km/h), associated with running, account for 3.28% (\(\approx\)192,000), with a cluster center of 16.17 km/h. Very high-speed actions in this study were defined as movements exceeding 18.59 km/h, aligning with the general literature threshold of high-speed running (>18.5 km/h). Among U14 male basketball players, such actions were rare, representing only 0.59% of total movements (approximately 34,800 instances), with a cluster center of 20.98 km/h. This value also surpasses the commonly accepted sprint threshold of >20 km/h.  
The analysis of movement velocity (Figure 4) among U14 male basketball players revealed a strong predominance of low-intensity locomotor activity during match play. Specifically, 63.60% of all recorded actions occurred at very low speeds (<5.42 km/h), corresponding to standing or walking intensities. When combined with low-speed walking (5.42-10.19 km/h) and moderate jogging (10.20-14.63 km/h), the proportion of actions performed at low to moderate intensities rises to 96.13% of total game actions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speed (km/h)** & **Very Low/Standing** & **Low/Walking** & **Moderate/Jogging** & **High/Running** & **Very High/Sprinting** \\ \hline Cluster centers & 2.98 & 7.45 & 12.16 & 16.17 & 20.05 \\ Ranges & \textless{}5.42 & 5.42 to 10.19 & 10.20 to 14.63 & 14.64 to 18.59 & \textgreater{}18.59 \\ n & 3,730,195 & 1,286,446 & 621,004 & 192,342 & 34,755 \\ \% & 63.60 & 21.94 & 10.59 & 3.28 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the five-k means cluster of the variable speed.  
Figure 3: Graphical interpretation of all the decelerations made in the men’s under-14 national championship.  
Only 3.28% of actions were classified as high-speed running (14.64-18.59 km/h), with a cluster center of 16.17 km/h. Very high-speed or sprinting actions (>18.59 km/h) were rare, accounting for just 0.59% of total actions (Figure 4). Notably, the sprint cluster center was 20.05 km/h, which exceeds the sprint threshold of 20 km/h.