esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Field Tests and Discussion on Results
### Field Test of NB-PLC-Based SACs on Conventional and Inverter Based Air Conditioners
#### 5.2.2 Response of Variable Frequency Air Conditioners  
Similarly, DSM control technique showed energy-efficient and cost-saving performance when applied on variable frequency SACs by setting the thermostat values to 25 \({}^{\circ}\)C and then to 27 \({}^{\circ}\)C. The hourly power consumption profile is shown in Figure 10c,d where the temperature change from 25 \({}^{\circ}\)C to 27 \({}^{\circ}\)C decreases the average power consumption from 315 watts to 180 watts with 42.85% power saving.
# Launch Bar Dynamics Character Analysis of Carrier-Based Aircraft Catapult Launch
## 2 Mathematical Model of Catapult Launch
### Mathematical Model of the Launch Bar  
#### 2.2.1 The Mathematical Model of the Launch Bar  
The launch bar is connected with the nose gear and shuttle before the launch bar automatically disengages, as presented in Figure 2.  
In Figure 2, \(Sx_{s}y_{s}z_{s}\) is the carrier-body coordinate system which is fixed with the carrier. The origin \(S\) is placed at the center of gravity of the carrier. Axis \(x_{s}\) is parallel with the deck surface of the carrier and points to the prow. Axis \(z_{s}\) is in the carrier symmetry plane and points to the bottom, and axis \(y_{s}\) is perpendicular to the carrier symmetry plane and points to the starboard. The point \(O\) is the pivot  
Figure 2: The engagement system of the launch bar, nose gear, and shuttle.  
point of the launch bar. \(Ox_{o}y_{o}z_{o}\) is the pivot point coordinate system which is fixed with the nose gear. Axis \(x_{o}\) is perpendicular to the nose gear and points to the front. Axis \(z_{o}\) is parallel with the nose gear and points downward, and axis \(y_{o}\) follows the right-hand rule. Point \(O_{n}\) is the center of gravity of the tire of the nose gear. Point \(D_{n}\) is the contact point of the deck and the tire of the nose gear.  
According to the geometrical relationship between the launch bar and nose gear, we can get the equation of the center of gravity (cg) position of the launch bar as follows:  
\[\left\{\begin{array}{l}x_{cg}=x_{O}+K_{lunch}L_{lunch}\cos\theta_{c}\\ z_{cg}=z_{O}+K_{launch}L_{launch}\sin\theta_{c}\end{array}\right., \tag{1}\]  
where \((x_{O},z_{O})\) is the coordinate of the pivot point, \(K_{launch}\) is a proportionality factor which is defined as the distance from the cg position of the launch bar to the pivot point divided by the length of the launch bar, \(L_{launch}\) is the length of the launch bar, and \(\theta_{c}\) is the angle between the launch bar axis and the deck.  
According to the geometrical relationship between the launch bar and nose gear, we can get the equation of the end of the launch bar as follows:  
\[\left\{\begin{array}{l}x_{end}=x_{O}+L_{launch}\cos\theta_{c}\\ z_{end}=z_{O}+L_{launch}\sin\theta_{c}\end{array}\right.. \tag{2}\]  
The steam catapult transmits the load to the nose gear via the launch bar; thus, the axial load of the launch bar can be divided into two parts: the catapult force \(F_{launch1}\) which is parallel with the deck, and the force \(F_{launch1}\) which is vertical with respect to the deck; they are given by  
\[\left\{\begin{array}{l}F_{launch1}=F_{launch}\cos\theta_{c}\\ F_{launch2}=F_{launch1}\tan\theta_{c}\end{array}\right., \tag{3}\]  
where \(F_{launch}\) is the axial load of the launch bar.
## 4 Analysis of Experimental Results  
In order to proceed with the test smoothly, relevant numerical simulation for the control system was carried out prior to executing the experiments. Test results were also analyzed for the control system carried out by using HSA approach with two control strategies, namely, the continuity mode (CM) and switch mode (SM).
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 3 System Model
### Data Freshness Metric  
We adopt the AoI of the FC as the freshness metric of the system. It is defined as the difference between the current time and the time when the most recently received observation is generated. Formally, at a given time t, AoI is expressed as:  
\[\Delta_{t}:=t-u_{t}, \tag{2}\]  
where \(u_{t}\) represents the generating time of the most recently received observation. Accordingly, as is described in [7], the average AoI in the interval of \((0,T)\) is given as:  
\[\overline{\Delta}=\lim_{T\to\infty}\frac{1}{T}\int_{0}^{T}\Delta_{t}\,dt. \tag{3}\]  
Figure 2a gives a typical example of the AoI evolution curve where \(t^{(1)}\) represents the generation time of the first update, and \(t^{(1)^{\prime}}\) is the time it arrives at the FC. The AoI curve keeps growing until a new update is received by the FC, thus it exhibits a sawtooth shape. However, recall that in our proposed model, the FC works in rounds (round index shown in the form of superscript), where, in each round, there are \(N\) sensors(shown in the form of subscript) observing the source, and a new round of data collecting begins at the exact moment when the previous round is finished, i.e., \(t^{(1)^{\prime}}_{N}=t^{(2)}_{N}\), etc. As shown in Figure 2b, the average AoI can be obtained through calculating the difference between the isosceles triangle expressed as \(\frac{1}{2}\big{[}\big{(}R^{(j)}_{N}+R^{(j+1)}_{N}\big{)}^{2}-\big{(}R^{(j+1) }_{N}\big{)}^{2}\big{]}\). Specifically, considering \(I\) rounds of sensing in total, the time average AoI is:  
\[\overline{\Delta}=\lim_{I\to\infty}\frac{\sum_{j=1}^{I}\!\left[\frac{1}{2} \big{[}R^{(j)}_{N}\big{]}^{2}+R^{(j)}_{N}R^{(j+1)}_{N}\right]}{\sum_{j=1}^{I} R^{(j)}_{N}}=\frac{\mathbb{E}\big{[}\big{(}R^{(j)}_{N}\big{)}^{2}\big{]}+2 \mathbb{E}[R^{(j)}_{N}]\mathbb{E}[R^{(j+1)}_{N}]}{2\mathbb{E}[R^{(j)}_{N}]}.\]  
where \(\mathbb{E}[\cdot]\) represents the mathematical expectation operator. Note that \(\mathbb{E}[R^{(j)}_{N}]\) and \(\mathbb{E}[R^{(j+1)}_{N}]\) are independent from each other as round index approaches infinity, we have \(\mathbb{E}[R^{(j)}_{N}]\mathbb{E}[R^{(j+1)}_{N}]=\mathbb{E}[R^{(j)}_{N}]^{2}\). Notice that the statistical characteristics of \(R^{(j)}_{N}\) are the same, and \(R^{(j)}_{N}\) is actually related to the number of sensor nodes \(N\), so we remove the superscript \(j\) and specify the number of sensor nodes \(N\) as a subscript of the interval random variable, i.e., \(R_{N}\). Then, the average AoI is  
\[\overline{\Delta}(N)=\mathbb{E}[R_{N}]+\frac{\mathbb{E}[R^{2}_{N}]}{2\mathbb{ E}[R_{N}]}. \tag{4}\]  
Figure 2: (**a**) Typical AoI evolution. (**b**) AoI evolution of our proposed system.
## 5 Performance Analysis
Further, the proposed feature selection method is analyzed in the terms of the reduction in the selected features. Figure 4 illustrates a bar chart for the percentage of the reduced features by considered method on the considered datasets. It can be observed that the proposed method, DFG-A-DFC, has attained a maximum reduction of 40% on the WDBC dataset. While the DFG-A-DFC method is competitive on other datasets. Therefore, it can be claimed that the proposed method is an efficient feature selection method. Moreover, this paper presents an ablation study on the two-phase architecture of the proposed method. It focuses on studying the performance of the proposed method after the second phase. Table 4 highlights the classification accuracy (ACC) of the proposed method after the first and second phases with different classifiers for each considered dataset. It is notable that the proposed method has shown significant improvement in classification accuracy for each dataset after the second phase. Therefore, it can be concluded that the inclusion of the second phase has strengthened the proposed method.
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 3 Materials and Methods
### Estimated CO\({}_{2}\) Emissions Baseline (Pre-AV)  
To estimate CO\({}_{2}\) emissions under traditional (non-AV) traffic conditions, we employed an empirical factor-based approach that integrates vehicle-specific emission coefficients with real-world trip distance data from the SUMO simulation. Emission factors were obtained from Gulf-region transport reports and IPCC Tier 1 guidelines, representing average tailpipe CO\({}_{2}\) emissions for various vehicle classes operating under urban driving conditions. These factors were multiplied by the corresponding route lengths simulated in SUMO to yield trip-level emissions. The total CO\({}_{2}\) emissions were computed using the following formula:  
\[\text{CO}_{2}\ \text{(g)}=\text{Emission Factor (g/km)}\times\text{Rout Length (km)}. \tag{1}\]  
The average simulated route length was 13.2 km, with a standard deviation of 0.6 km. Vehicle category shares were assigned based on regional fleet statistics for Saudi Arabia: 55% gasoline sedans, 20% diesel SUVs, 15% pickup trucks, 5% hybrids, and 5% electric vehicles. These proportions were consistently used across all baseline simulation runs. The resulting CO\({}_{2}\) emissions per trip as stratified by vehicle type are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Vehicle ID**} & \multirow{2}{*}{**Vehicle Type**} & \multicolumn{2}{c}{**Depature Time**} & \multirow{2}{*}{**Arrival Time (s)**} & \multirow{2}{*}{**Trip Duration (s)**} & **Rout Length** \\  & & **(s)** & & & **(km)** \\ \hline veh\_001 & HV & 100 & 710 & 610 & 12.5 \\ veh\_002 & AV & 120 & 735 & 615 & 13.2 \\ veh\_003 & HV & 135 & 755 & 620 & 11.8 \\ veh\_004 & AV & 145 & 760 & 615 & 13.5 \\ veh\_005 & HV & 160 & 785 & 625 & 12.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample SUMO trip data with variable route lengths.  
This pre-AV emission baseline serves as a critical benchmark for evaluating the environmental benefits of autonomous vehicles, allowing for quantification of potential CO\({}_{2}\) savings under the alternative mobility scenarios introduced in subsequent sections.
## 5 Experimental Data Analysis
### Analysis of Test and Simulation Results
#### 5.3.2 Error Analysis of Test Simulation Experiment Results
The spectrum shows that the components of 'Signal a' were concentrated in the low frequency and high frequency parts, while 'Signal b' was concentrated in a small number of high frequency parts, which is the third reason for miscalculation.
## 5 Experiments and Results
### Detailed Experimentation and Results Analysis with Pix2 PixHD  
Given the exceptional performance Pix2 PixHD displayed when given mask inputs, it became necessary to explore additional input variations in order to learn more about the model's capabilities. Therefore, we conducted several experiments using various data inputs to fully understand Pix2 PixHD's ability to enhance X-ray image generation. These inputs comprised different combinations of edges and masks, allowing us to explore the model's potential comprehensively.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 6 Discussion and Conclusions  
The experimental results validate the effectiveness of the proposed fall detection system in real-world scenarios, combining YOLOv11-based pose estimation with interactive voice assistance to deliver accurate and responsive monitoring. Beyond performance metrics, the system introduces a context-aware approach by integrating user intent verification, which helps reduce false alarms and ensures meaningful alerts. This section discusses the system's current limitations and proposes future directions for enhancing its robustness, usability, and applicability in diverse environments.
## 2 PA Drive Methods and Computational Cases  
The plasma actuator is installed at 5% chord length from the leading edge. This position is close to the separation point of the flow at \(\alpha=12\) and is effective for flow separation control [8]. Two PA drive methods are considered in the present study.  
The continuous actuation is a standard drive method of PA. The AC voltage, which is based on a base frequency \(f_{\text{base}}\) is continuously applied to the PA. The burst actuation is the other method of driving PA. The AC voltage modulated with the duty cycle is applied to PA during the burst actuation. Figure 2 shows a schematic diagram of the AC voltage waveform for the burst actuation. Parameters of the waveform are defined as in Equation (1). \(f_{\text{base}}\) is a base frequency of the AC voltage waveform. \(t_{\text{on}}\) and \(t_{\text{burst}}\) are the driving time and burst period, respectively. \(f_{\text{burst}}\) denotes the burst frequency. \(F^{+}\), \(F_{\text{base}}\), and \(BR\) are non-dimensional \(f_{\text{burst}}\), non-dimensional \(f_{\text{base}}\), and burst ratio, respectively. Previous studies [30,33] have shown that the burst actuation controls flow separations more efficiently than the continuous actuation at low Reynolds numbers (\(Re\simeq 10^{4}\)-\(10^{5}\)) at post-stall angles of attack.  
The studies show that the burst actuation with \(F^{+}=6\) and \(BR=0.1\), which promotes a turbulent transition, is effective for suppressing a flow separation at \(\alpha=12\). In the present study, the burst actuation with those burst parameters was employed to confirm a flow control authority of PA at pre-stall angles of attack.  
Hereafter, we call a computational case of the continuous drive a \"Continuous\" case and the burst drive a \"Burst\" case. In both cases, \(F_{\rm base}\) is set to 60 according to the previous experimental and computational studies [8; 31]. In addition to the computational cases with PA, Flows without PA are considered, called \"PA-OFF\" cases, as baseline flows. Four angles of attack \(\alpha=4\), 6, 8, and 10 are considered. Therefore, the total number of computational cases is 12. In addition, each case at \(\alpha=12\) is used to discuss the difference in the phenomena at pre-stall and post-stall angles.  
\[F^{+}=\frac{f_{\rm burst}c}{U_{\infty}},\ \ \ \ \ \ F_{\rm base}=\frac{f_{\rm base }c}{U_{\infty}},\ \ \ \ \ \ BR=\frac{t_{\rm on}}{t_{\rm burst}}. \tag{1}\]
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 3 Idle/Busy Time Segmentation
### Integrated Network Indicator Classification
\(\omega_{A}\) and \(\omega_{P}\) are the sum of the subjective and objective effect weights of all influencing factors; \(\omega_{AP}(i)\) is the weight value of each index combined with subjective and objective influence. The MATLAB R2020b software is used to simulate the conflict network generated by the airspace in Figure 3, which contains 75 nodes. After visualization, the depth of the node color is set according to the degree value. The larger the degree value is, the deeper the color is, indicating that the conflict is more serious. The thickness of the edge represents the urgency of the conflict. The thicker the edge is, the more urgent the conflict is. The computational complexity metrics are shown in Table 1.  
The multi-factor mutual matrix of the six indicators calculated by this method is shown in Figure 4. The blue part is the expert scoring weight, and the green part is the maximum information coefficient weight. According to the MFIM method, the final weights and the order of importance of the six indicators were \(\overline{C}\) (2.86) \(>\)\(\overline{k}\) (2.71) \(>ND\) (2.63) \(>k_{sum}\) (2.30) \(>NE\) (2.16) \(>\)\(\overline{s}\) (1.85). Each evaluation indicator was normalized to obtain a constant number of weights. The weighting of the combined six indicators constitutes the comprehensive network metrics (CNM) for evaluating the flight posture, and the CNM can be expressed as:  
\[\text{CNM}=0.158\overline{C}+0.187\overline{k}+0.128 ND+0.197k_{sum}+0.181 NE+ 0.149\overline{s} \tag{23}\]  
This index can comprehensively describe the flight conflict situation and flight posture in the airspace, using MATLAB software to simulate the airspace to generate 100 conflict networks that reflect the air operation situation. The conflict network setting rules are as follows: randomly generate 10-100 aircraft nodes, use the above aircraft protection area model and three-dimensional velocity obstacle method to judge the conflict relationship between aircraft, calculate the indicators in different conflict networks as sample sets, and calculate the comprehensive network indicator values of each conflict network. At the same time, three experts in related fields are invited to evaluate the flight situation and divide it into two different periods: busy/idle. The indicators and expert scoring are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(k_{sum}\) & \(\overline{k}\) & \(\overline{s}\) & \(\overline{C}\) & \(\mathbf{ND}\) & \(\mathbf{NE}\) \\ \hline
494 & 6.59 & 0.328 & 0.154 & 0.078 & 0.069 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network complexity index. Figure 3: Conflict network diagram. The comprehensive network index is corresponding to the time-period division. The specific numerical division of the evaluation index is bounded by the value of 4. The comprehensive network index is greater than 4 and is defined as the busy period, and less than 4 is defined as the idle period.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 1 Introduction
[27] and [PERSON] [28] proposed parameter estimation method for the reliability of a load-sharing system. No matter what areas the load-sharing model has been used in, the most important element of the load-sharing model is the rule that decides how the loads redistribute after some components in the system fail [29]. In this regard, three important load-sharing rules have been considered, consisting of equal, local, and monotone rules [30; 31; 32]. However, many researchers only focused on the equal load-sharing rule rather than the remaining two, because it is easy to give a fixed expression of the system reliability while considering the equal load-sharing rule. In view of the above considerations, the present study defines a unified load-sharing rule structure and analyzes LTD system reliability based on a load-sharing mechanism. Except for the equal load-sharing rule, different failure sequences will lead to different load redistribution results. Considering the randomness of the module failure sequences, a self-adaptive Monte Carlo flow is proposed to simulate the statistic effects. The rest of the paper is organized as follows. Section 2 describes the LTD system considered and established a system reliability model roughly. In Section 3, we define a unified load-sharing rule structure and discuss four typical rules based on the structure in detail. Subsequently, numerical experiments results are analyzed in Section 4. Finally, Section 5 ends the paper by drawing concluding remarks and summarizing the research paths that deserve most efforts from the community in the near future.
## 4 Systematic Performances Analysis of UWSNs
### Systematic Quar-Performance Calculation Model of UWSN
#### 4.2.4 Measurement Formula of Rapid-Reactivity  
Rapid-reactivity is denoted as \(\Omega\), which is determined by the network delay and network redundancy. Whether due to the network initialization or abnormal nodes adding or exiting, the delay is determined mainly by the communication delay and waiting mechanism. In UWSNs, the maximum delay occurs in the process of communication between nodes of the network diameter ends, where \(V\) is the acoustic speed in water, and \(L\) is the network diameter. Therefore, the maximum delay of the network diameter direction is \(\tau=\frac{l}{V}\). Assume that the error rate is \(\eta\) for each jump through acoustic communication, then the success rate of communication in network diameter direction is \(\varphi=(1-\eta)^{\frac{L}{p_{b}}}\).  
In the specific network protocol, assume that the standard waiting time for retransmission is \(s\) times of the round-trip time in network diameter direction, and the number of retransmission waiting periods is \(l\), then the average waiting time for the network diameter direction is represented by Equation (11):  
\[t=\sum_{k=0}^{\infty}(1-\varphi)^{k}\varphi\cdot(\tau+k\cdot\tau\cdot s\cdot l )=\tau\cdot(1+\frac{s\cdot l}{\varphi}) \tag{11}\]  
If we set \(k_{\text{b}}\) as the network redundancy, rapid-reactivity can be measured by Equation (12).  
\[\Omega=\frac{\alpha\cdot k_{b}\cdot b_{b}}{\frac{L}{V}\cdot(1+\frac{s\cdot l}{\varphi})} \tag{12}\]  
In the equation, \(\varphi=(1-\eta)^{\frac{L}{b_{b}}}\), \(\alpha>0\) is the network redundancy influence factor.
## 3 Results
### Orbit Quality
#### 3.2.2 SLR Residuals  
To further investigate the orbit accuracy of BDS-3 POD solutions, SLR tracking data has been used to assess the orbits. Two BDS-3 MEOs (C20/M2, C21/M3) manufactured by CAST and two BDS-3 MEOs (C29/M9, C30/M10) manufactured by SECM are regularly observed by the International Laser Ranging Service (ILRS) [44]. The system eccentricities and station coordinates are updated and fixed to SLRF2014, and the retro-reflector offsets of the MEOs are given by CCSNO [45]. SLR observations from 1 July 2022 to 31 July 2022 collected by ILRS have been processed. The statistics of the SLR validation is demonstrated in Table 5 and shown in Figure 8.  
The SLR validation results basically coincide with the orbit-only SISRE investigation. For CODE, GFZ, B1 CB2a and B1 CB2b solutions, the mean RMS of SLR residuals are 3-4 cm and the mean values are approximately 2 cm and \(-4\) cm for CAST and SECM satellites, respectively. Meanwhile, the mean RMS of B1 IB3I solution is a few millimeters worse, which is approximately 4-5 cm. Moreover, mean values of SLR residuals for BDS-3 CAST and SECM satellites point to remaining deficiencies in BDS-3 satellite empirical solar radiation pressure modelling.  
\begin{table}
\begin{tabular}{c c c c} \hline
**PRN of BDS-3** & **Solutions** & **Mean Value (cm)** & **Mean RMS (cm)** \\ \hline \multirow{5}{*}{C20, M2 (CAST)} & CODE & 2.24 & 3.50 \\  & GFZ & 2.55 & 3.90 \\  & B1 CB2a & 2.34 & 4.13 \\  & B1 CB2b & 2.54 & 4.20 \\  & B1 IB3I & 2.94 & 4.83 \\  & CODE & 2.27 & 3.58 \\  & GFZ & 1.82 & 3.54 \\ C21, M3 (CAST) & B1 CB2a & 2.44 & 4.02 \\  & B1 CB2b & 2.47 & 4.12 \\  & B1 IB3I & 3.01 & 5.25 \\  & CODE & \(-3.68\) & 4.03 \\  & GFZ & \(-3.19\) & 3.87 \\ C29, M9 (SECM) & B1 CB2a & \(-3.74\) & 4.01 \\  & B1 CB2b & \(-4.07\) & 4.35 \\  & B1 IB3I & \(-4.64\) & 5.03 \\  & CODE & \(-4.37\) & 4.68 \\  & GFZ & \(-3.36\) & 3.61 \\ C30, M10 (SECM) & B1 CB2a & \(-3.89\) & 4.05 \\  & B1 CB2b & \(-4.26\) & 4.43 \\  & B1 IB3I & \(-4.48\) & 4.86 \\ \hline \end{tabular}
\end{table}
Table 5: Mean value and mean RMS of SLR residuals of BDS-3 MEOs.