esa-sceva/satcom-chunk-collection huggingface dataset:
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 3 A Topology Change Aware Routing Protocol Choosing Scheme (TARCS)  
A topology change aware routing protocols choosing scheme (TARCS) is proposed based on the results of the periodic topology change awareness (PTCA) and the adaptive route choosing scheme (ARCS). The purpose of the PTCA is to accurately measure topology changes and determine the movement mode. The purpose of the ARCS is to select an appropriate routing protocol for the current motion mode and to ensure that network performance is affected as little as possible. The periodic topology change perception is based on the topological change degree (TCD), a mobility indicator proposed by the previous study, which could measure the topology changes of nodes and distinguish the Random Waypoint model (RWP [7]), the Reference Point Group Mobility Model (RPGM [7; 21]) with different group numbers and the Pursue model [7; 21].
# Research on Optimal Control of Treadmill Shock Absorption Based on Ground Reaction Force Constraint
## 2 Establishment of the Mathematical Model
### Establishing a System Stability Model
#### 2.4.3 Stability Analysis  
To conduct a stability analysis, it is necessary to consider the linearized state of the system near the operating point and analyze the eigenvalues. The real part of the eigenvalues can indicate the stability of the system; a negative eigenvalue indicates that the system is stable near that operating point. The linearized dynamics Equation (20) can be written in matrix form as  
\[\frac{d}{dt}\begin{bmatrix}P\\ v\end{bmatrix}=\begin{bmatrix}-k_{2}&-\dot{k_{1}}\\ 0&0\end{bmatrix}\begin{bmatrix}P\\ v\end{bmatrix}+\begin{bmatrix}k_{1}\cdot v_{0}-k_{1}\cdot v_{ref}\\ 0\end{bmatrix} \tag{21}\]  
The system can be expressed as \(\dot{x}=Ax+Bu\), where \(x\) is the state variable vector, \(A\) is the state transition matrix, \(B\) is the input matrix, and \(u\) is the control variable vector. \(A\) is the coefficient matrix of the linearized dynamics equation. The eigenvalues \(\lambda\) are the roots that satisfy the equation \(\det(A-\lambda I)=0\), where \(I\) is the identity matrix. According to the quadratic formula, \(\lambda\) can be solved as  
\[\lambda=\frac{-k_{2}\pm\sqrt{k_{2}^{2}}}{2} \tag{22}\]
# Data Gathering Techniques in WSN: A Cross-Layer View
## 6 Wearables and Wireless Body Area Networks (WBAN)
### Transmission Protocols  
We note that while many of the works mentioned above employ a smartphone device with an application that is presumably tailored to receive data from the wearables, having a dedicated hardware device that would offload some of the networking burden (e.g., potential simplification of the packetizing process) of the sensors would be more effective and reliable. Indeed, in [PERSON] et al. (2018), a central processing hub allows one to circumvent the cumbersome processes of sensor identification, sensor joining, and reconfiguration by providing a designated interface. The authors provide real hardware implementation, explore various performance metrics, and provide energy measurements.  
Several available protocols for data transmission are suitable for WBAN. We mention here the low Bluetooth energy (BLE; see, e.g., [PERSON] et al. (2019) for a detailed protocol stack description and [PERSON] et al. (2010) for performance evaluation and comparison with ordinary Bluetooth and other protocols). For distances of several centimeters, near-field communication (NFC) protocol (see [PERSON] et al. (2011) for the theory and [PERSON] et al. (2012) for a description of possible device designs and applications). Details of the IEEE 802.15.6 standard, which covers WBAN, can be found in, for example, [PERSON] et al. (2013). One sees therein how the human body communications PHY layer in particular is defined.
## 4 SAE Levels of Autonomy and V2X
One of the critical challenges in LoA 3 automation is the handover of control between the automated system and the human operator. It ensures that the human operator is always sufficiently aware and engaged to take over control is crucial to avoid potential hazards or delays in critical situations. Human-machine interfaces, clear communication, and proper training are paramount to facilitate effective control transitions and maintain the overall safety of the driving experience. * _LoA 4: High automation_ It represents a significant advancement in autonomous driving. At this level, the dynamic driving task is jointly performed by the vehicle's automated system, encompassing control, decision making, and environment recognition tasks. This joint performance is limited to a specific Operational Design Domain (ODD) defined by certain boundaries and conditions. In the event of a system malfunction or encountering tasks that exceed the system's capabilities, the vehicle is equipped with mechanisms to transit to a safe state. This ensures that the vehicle can mitigate risks and respond appropriately in challenging or unpredictable situations. * _LoA 5: Full automation_ It signifies the highest level of autonomous driving capability, where the automated system autonomously manages all dynamic driving tasks across any operational design domain without the need for human intervention. At this level, the vehicle is fully self-driving, equipped with robust decision-making capabilities. It can navigate and adapt to a wide range of driving scenarios, including complex urban environments, highways, and challenging weather conditions.
## 2 Background and Key Features
### UAV Communication
#### 2.2.2 Satellite Communication  
Satellite communication (SATCOM) is primarily used for remotely controlling and monitoring large-scale UAV activities, such as military or rescue drones. SATCOM provides global communication coverage and can meet the demands for long distance and high bandwidth, but the high-speed mobility of UAVs poses challenges for data transmission with satellites. In [33], the authors analyze the issue of rapidly changing channel characteristics due to the mobility of UAVs and propose a new three-dimensional channel modeling and tracking method to achieve a stable connection between UAVs and satellites. In [34], the authors explore the advantages of applying optical communication technology to the data transmission between UAVs and satellites and propose several solutions aimed at achieving a more efficient and stable drone-satellite optical communication link.
## 1 Introduction
The non-adaptability of droop-based voltage control has also been identified in many studies [25; 26]. Most of these literature works only use a linear droop function and do not investigate some other droop function apart from linear droop. Moreover, unlike conventional droop techniques, adjusting droop values based on irradiance and/or loading conditions can limit the curtailment compared to constant droop-based techniques. In this work, we propose a machine learning-based method named supplementary adaptive dynamic programming (ADP) controller to adjust the droop gains. The ADP is utilized for generating a supplementary signal that adjusts the droop value to minimize curtailment. In addition, the droop value is changed by small values based on the supplementary signal so that the system remains relatively stable and does not deviate from its normal operation. The control framework designed with ADP assists in establishing a robust and optimal control concurrently by solving the Hamilton-Jacobi-Bellman equations of optimal control iteratively using the reinforcement learning concept [27]. Similar techniques have been endorsed to tackle different power systems problems [28; 29; 30; 31; 32; 33]. A preliminary study on implementation of APC method with constant droop and ADP control on a simplified distribution model has been presented in [34; 35]. In this paper, we extend the ADP controller to quadratic and exponential droops on a typical suburban network in North America. The test system has been modeled for low-voltage PV clusters with solar irradiance profile as input. The controller will be tested in more complex benchmarks with a greater number of houses in different configurations as part of our future work. The main contributions of the paper are twofold: First, we aim to study the impacts of different droop functions in APC-based inverter control for LV distribution networks with PV. Second, we aim to design the APC-based smart inverter control with different droop models and ADP as a supplementary controller for overvoltage prevention for reduced curtailment in the LV distribution network. The control methods are further compared on its outcome on overvoltage prevention, curtailed energy, and power curtailment. The paper is organized as follows: the control formulation of inverter based APC control and the proposed method with supplementary control are presented in Section 2. The description of the test system and base case scenario are given in Section 3. Section 4 presents the results and the conclusions are described in Section 6.
## 4 Discussion
These findings highlight the context-dependent nature of EUR and caution against its use as a standalone performance metric without consideration of underlying biomechanical factors. Furthermore, in male athletes, RSI showed significant associations with CMJ, 10-20 m, and 0-30 m sprint performances (\(p<0.05\)), underscoring the role of reactive strength capacity in sprint performance. While several previous studies support the association between RSI and sprint outcomes [42, 43], some have reported conflicting findings [24, 44]. The finding that CONMP was only associated with 0-10 m sprint performance further emphasizes the role of concentric strength in early-phase acceleration. Additionally, the observation that stiffness was significantly related only to COD performance suggests that the elastic properties of the muscle-tendon complex may be particularly important in rapid directional changes. Overall, it appears that in male athletes, absolute strength and power output parameters provide stronger explanatory power for performance than ratio-based indicators such as EUR. This study has several limitations. First, the sample consisted solely of a limited number of elite or professional-level athletes, which restricts the generalizability of the findings. Additionally, EUR and other performance parameters were evaluated exclusively based on mechanical outputs; physiological determinants such as muscle-tendon architecture, muscle fiber type, and neuromuscular control were not assessed. Due to the cross-sectional design of the study, changes in EUR over time or in response to training interventions could not be examined. Therefore, future research should investigate EUR and related variables using longitudinal designs and larger, more diverse samples of athletes at different performance levels. Moreover, participants came from various sports disciplines, including basketball, volleyball, handball, and soccer. Sport-specific physiological and technical demands may have influenced individual responses to the performance tests. Additionally, participants' familiarity with the test procedures varied; some had previously undergone the tests multiple times, while others had limited exposure. These differences may have affected test outcomes due to varying degrees of neural, metabolic, and structural adaptation.
# Trends and Challenges in Network Covert Channels Countermeasures
## 4 Covert Channel Detection
### IPv6  
The advent of IPv6 opens to many potential covert channels. Specifically, the attacker can exploit new features offered by the protocol or the various transitional and tunneling mechanisms designed to allow IPv4 and IPv6 to coexist. Despite the rich set of features offered by IPv6, real network deployments scale back the different types of covert channels that can be used to launch a real attack [30]. In fact, the amount of IPv6 traffic is still limited (compared to IPv4) and its statistical variability can make some channels highly detectable. For instance, when using the Traffic Class field to contain secrets, real traces are characterized only by three different values, thus limiting the bandwidth of the covert channel to few bit/s. A further constraint is due to the Explicit Congestion Notification feature, which is rarely used in real-world nodes, thus causing the alteration of the field as highly suspicious.  
As a consequence, the development of detection techniques targeting IPv6 traffic seldom necessitates of additional hardware/software and can be implemented on top of normal traffic inspection frameworks. Similar to many other threats, another emerging approach leverages machine learning. As an example, the work in [35] demonstrates the use of fuzzy logic and genetic algorithms to achieve high detection rates of covert channels targeting the IPv6 header. We point out that both methods require to employ deep packet inspection principles, thus impacting on the scalability of the approach.  
Transitional mechanisms can be also abused to create network covert channels or exfiltrate information. According to [36], modern intrusion detection systems are affected by major drawbacks when handling IPv6 traffic. For instance, covert channels targeting transition methods using both IPv4 and IPv6 traffic are difficult to detect compared to other attacks exploiting DNS or SSH tunneling.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Relationship Between Evaluation Indicators and Forecasting Performance
#### 3.5.2 Time-Varying Weight Dynamics
December (Winter Conditions)  
The December weight distribution reveals a notable shift, with significantly higher weights assigned to Anhui (0.30-0.35) when measured against summer months, while Jiangsu's weights decrease substantially (0.20-0.25). This seasonal redistribution of weights reflects changing regional load characteristics during winter, where provinces with higher heating demands (such as Anhui) gain greater influence in the integrated forecast. Interestingly, the MAPE distribution during December shows a distinctive pattern, with higher errors occurring during early morning hours (3:00-6:00) rather than during daytime peaks. The weighted fusion approach achieves particularly impressive performance during these early morning hours, reducing MAPE by over 5% when examined against direct aggregation. Cross-Seasonal Comparative Analysis  
Comparing the three seasonal periods reveals important patterns in how provincial weights influence forecasting accuracy:  
1. Seasonal Weight Redistribution: The substantial shift in weights from Jiangsu's dominance in summer months to the increased Anhui contribution in winter demonstrates our methodology's ability to recognize and adapt to seasonal changes in provincial load characteristics. This redistribution is essential for maintaining forecasting accuracy across different operational conditions. 2. Temporal Granularity Impact: The increased temporal variations in weights during August, particularly for Jiangsu and Zhejiang, coincide with higher overall MAPE values but also greater performance improvements from weighted fusion. This suggests that more dynamic weight adjustments are both necessary and effective during high-volatility periods. 3. Error Profile Transformation: The shift in error profiles from afternoon-peaked in summer months to morning-peaked in winter illustrates how seasonal load characteristics fundamentally alter forecasting challenges. The weighted fusion methodology successfully adapts to these changing error profiles by dynamically redistributing provincial weights. These observations confirm that the superior performance of our weighted fusion approach stems from its ability to dynamically adjust to both seasonal variations in provincial load characteristics and temporal changes in forecasting reliability. By continuously rebalancing provincial contributions based on real-time evaluation metrics, the methodology effectively mitigates the forecasting challenges associated with different seasonal conditions, particularly during periods of high load volatility.
## 1 Introduction
[32] identified a rapid fluctuation change in ground temperature of 0.001-0.002 \({}^{\circ}\)C approximately 80 days before the Lushan \(M_{8}7.0\) earthquake. Changes in bedrock temperature indicate the state of the rock during stress adjustment, which is part of earthquake formation. These precursory anomalies in ground temperature provide a reference for understanding the development of strong earthquakes. Earth is a dynamic system, and changes in the lithosphere will probably cause atmospheric resonance. The coupling of multiple layers before strong earthquakes has attracted attention. [PERSON] et al. [33] integrated observational methods for atmospheric pressure, vertical electric field, atmospheric radon concentration, groundwater level, and precipitation to construct the MVP-LAI (monitoring vibrations and perturbations in the lithosphere, atmosphere, and ionosphere) system, which they used to observe the surface vibration, atmospheric pressure, and electron concentration before the 2021 Luxian \(M_{8}6.0\) earthquake synchronous coupling effect. Using ground electromagnetic data, [PERSON] et al. [34] analyzed the electromagnetic coupling effect three days before the 2008 Wenchuan \(M_{8}8.0\) earthquake. [PERSON] et al. [35] constructed a coupling model for the period before strong earthquakes using ground motion and ionospheric data. Notable changes in ionospheric data before strong earthquakes have been extensively studied. They were likely caused by the acoustic-gravity wave or electric field generated in the lithosphere before the earthquake [33; 36], but further research may be needed [37]. The communication of energy and matter exists between the lithosphere, atmosphere, and ionosphere. If the coupling relationship between multiple layers can be further understood, it will enrich our study of earthquakes. Before the \(M_{8}6.4\) Jiashi earthquake in Xinjiang in 2020, there was an anomaly in bedrock temperature [38; 39], caused by changes in crustal stress and secondary fluid effects. In this work, using observational data on rock temperature and the ionosphere, the relationship between the various characteristics of rock temperature before earthquakes and the sub-instability process are studied using cross-layer coupling. It is found that the stress release of the fault area accelerates and changes from quasi-static to dynamic, which is similar to the variation of faults before instability studied by [PERSON] et al. [40].
# Interval-Based LDA Algorithm for Electrocardiograms for Individual Verification
## 2 Materials and Methods
### Feature Extraction
#### 2.2.1 Calculating the Reference Profile, \(\overline{\Phi}(m)\)  
As mentioned in the previous section, during the enrollment mode, 2 min long ECG signals received from the ECG sensor are considered to be the training set \(x(n)\). The training set \(x(n)\) is then divided into several classes with a length of \(N\). Each class of \(x(n)\) is labeled \(x_{c}(n)\) with the class \"\(c\)\", as follows  
\[x_{c}(n)=\ x(n)\cdot R_{N}(n-N\cdot c)\ \ \text{for a given class}\ c=0,1,\cdots,C-1\] (1a) where \[R_{N}(n)=\left\{\begin{array}{cc}1\,,&0\leq n\leq N-1\\ 0\,,&\text{otherwise}\end{array}\right. \tag{1b}\]  
Subsequently, the ACP \(\Phi_{c}(m)\) of \(x_{c}(n)\) can be computed by the following Equation (2a).  
\[\Phi_{c}(m)=\sum_{n=0}^{N-1}x_{c}(n)\cdot x_{c}(n+m)\ \text{for}\ 0\leq m\leq Q -1\ \text{and a given class}\ c\] (2a) where \[x(n+m)\] is the time-shifted version of ECG with a time lag \[m\] and \[Q\] is the length of the QRS complex. Figure 4 shows how the signals \[x_{c}(n)\] and \[x_{c}(n+m)\] are defined to obtain the ACP, that is, \[\Phi_{c}(m)\].  
Based on the previous research, the QRS complex is considered to be the most prominent waveform of ECG for individual verification [41; 45; 46]. Hence, the time shift parameter m of \(\Phi_{c}(m)\) was chosen between 0 to \(Q-1\). In this paper, the length \(Q\), which depends on the individual, was calculated using the QRS detection method based on the Pan-Tompkins algorithm [47]. Depending on the individual, the different length of \(Q\) helps in achieving a high identification accuracy and robustness of the proposed algorithm.  
Figure 4: The two-minute-long training set \(x(n)\) and its time-shifted version \(x(n+m)\). The training set \(x(n)\) is divided into \(C\) classes, each of which is \(N\) samples long. For example, \(x_{1}(n)\) and \(x_{1}(n+m)\) are the 1 st class of \(x(n)\) and \(x(n+m)\), respectively.  
Based on the assumption that the training set \(x(n)\) includes C classes, we define a set of ACPs for C classes as follows:  
\[\Phi_{0}(m),\Phi_{1}(m),\cdots,\Phi_{c}(m),\cdots,\Phi_{\text{C-1}}(m) \tag{2b}\]  
Subsequently, the reference profile \(\overline{\Phi}(m)\) is defined as the average of all ACPs contained in the training set \(x(n)\), given by:  
\[\overline{\Phi}(m)=\frac{1}{C}\sum_{c=0}^{C-1}\Phi_{c}(m) \tag{3}\]  
where \(C\) is the total number of classes in the training set \(x(n)\) for 2 min.
## 3 Results  
The TP computed from the available dataset during stoop and squat lifting tasks (number of datapoints = 94) ranged from 5\({}^{\circ}\) to 44\({}^{\circ}\). No heteroscedasticity was suspected from a visual inspection of the Bland-Altman plot (Figure 4), and [PERSON]'s \(\tau\) was found to be \(<\)0.1. A strong association was found between TP and \(\varphi\) (r = 0.970, \(p<\) 0.0001) (Figure 5), and no statistically significant differences were revealed by [PERSON]'s t-test between TP and \(\varphi\) (\(p<\) 0.0001).  
Figure 4: Bland–Altman plot for visually inspecting the presence of heteroscedasticity (i.e., linear trend) in the dataset. The plot depicts the differences (error) between the angle of the thorax relative to the pelvis (TP) and \(\varphi\) against the respective means.  
Figure 5: Linear trend of \(\varphi\) with respect to TP depicting the strong association between these two variables.  
The prediction of L by using \(\varphi\) resulted in an average absolute error of 0.004 \(\pm\) 0.002 m (8%). Figure 6 shows the behavior of this error as a function of \(\varphi\).  
According to [PERSON]'s model, the variation of L with TP averages 0.001 m (Figure 1a). Since an error of 0.004 m is higher than this variation and by considering the linear behavior of L\({}_{\text{e}}\) as a function of \(\varphi\) (\(r^{2}\) = 0.931), L\({}_{\varphi}\) was adjusted as follows:  
\[\text{L}^{\prime}_{\varphi}=\text{L}_{\varphi}-(0.0002\cdot\varphi)-0.0018 \tag{8}\]  
where 0.0002 and 0.0018 are the coefficients of the linear model shown in Figure 6.  
When this correction was applied, L\({}_{\text{e}}\) decreased to 0.001 \(\pm\) 0.001 m. [PERSON]'s correlation analysis confirmed a strong association between L\({}_{\text{TP}}\) and L\({}_{\varphi}\)\({}^{\prime}\) (r = 0.965, \(p\) < 0.0001), while Bland-Altman plot analysis revealed a high agreement between methods with a bias <0.001 m, upper and lower limits of agreement (uLOA) of 0.002 m and a lower limit of agreement (ILOA) < 0.001 m (Figure 7a).  
Figure 6: Linear trend of the error in predicting the back extensor muscle moment arm by using \(\varphi\) with respect to the degree of trunk flexion (\(\varphi\)).  
Figure 7: Bland–Altman plot analysis for repeated measurements depicting the absolute agreement between the back extensor muscle moment arm (**a**) and line of action (**b**) as predicted using TP and \(\varphi\). Horizontal lines represent the bias (solid) and upper and lower limits of agreement (dotted). SD = standard deviation.  
Finally, the prediction of \(\delta\) by using \(\varphi\) resulted in an average absolute error of \(2.7^{\circ}\pm 1.3^{\circ}\) (2%), which corresponds, approximately, to the variation of \(\delta\) with TP according to [PERSON]'s model (Figure 1b). For this reason, no correction was considered as needed for \(\delta_{\varphi}\). A strong correlation (r = 0.970, \(p<0.0001\)) as well as high agreement (bias = 2.8\({}^{\circ}\), uLOA = 5.3\({}^{\circ}\) and ILOA = 0.2\({}^{\circ}\)) were found between \(\delta_{\rm TP}\) and \(\delta_{\varphi}\) (Figure 7b).