esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Waveforms  
As mentioned above, the selection of an optimal non-reciprocal motion is a key factor in low Re environments, since the net displacement only depends on the geometrical sequence of movements [14; 48]. In particular, planar waveforms describe a non-reciprocal motion. From a nature perspective, different kinds of waveforms can be observed in biological swimmers. The first kind is a planar (traveling) wave based on flagella of eukaryotic cells, which is described by a traveling harmonic wave [49]. Linear and quadratic traveling waves define the Carangiform fishes motion, i.e., the bending of their bodies in a propulsive wave that extends from head to tail end [50]. These three waveforms can be described by:  
\[y(x,t)=(c_{0}+c_{1}x+c_{2}x^{2})\sin\left(\frac{2\pi}{\lambda}(x-V_{p}t)\right), \tag{2}\]  
Figure 1: Graphical description of reciprocal (green-red arrow path, A-B-C-B-A) and non-reciprocal (green-yellow arrow path, A-B-C-D-A) motion with a [PERSON]’s swimming robot.  
where \(c_{0}\), \(c_{1}\), and \(c_{2}\) are the coefficients that govern the amplitude growth, \(x\) is the displacement along the propulsion axis, \(V_{p}\) refers to the propagation speed of the wave, and \(\lambda\) is the wavelength. For planar waves, the amplitude is only given by coefficient \(c_{0}\) (the others are zero). For Carangiform swimmers, \(c_{0}=0\). By choosing adequate values for coefficients \(c_{1}\) and \(c_{2}\), two important properties for propulsion can be preserved: (1) the flagellum head is always maintained at zero amplitude (boundary condition, i.e., \(y(0,t)=0\)), and (2) the wave amplitude along the flagellum can be modulated.  
An alternative way for preserving such properties, which has been demonstrated to increase the forward propulsion velocity, is the use of a fractional growth for variable \(x\) as follows [28]  
\[y(x,t)=(cx^{\alpha})\sin\biggl{(}\frac{2\pi}{\lambda}(x-V_{p}t)\biggr{)}, \tag{3}\]  
where \(c\) defines the amplitude at the end of the flagellum, and \(\alpha\in\mathbb{R}^{+}\) (\(0<\alpha<1\)) is the coefficient that determines the wave growth shape. In particular, when \(\alpha=0\), the resulting waveform is harmonic, whereas \(\alpha=1\) results in the Carangiform waveform. Thus, this waveform can be also viewed as a generalization of (2), merging the features from harmonic to Carangiform waveforms. Figure 2 depicts the four traveling waveforms described. Figure 2a illustrates the harmonic waveform, the linear waveform and the Carangiform waveform are depicted in Figure 2b, and the waveform with fractional growth for different values of \(\alpha\) is shown in Figure 2c.
# The Evolution of Service Ecosystems Based on the Lotka-Volterra Model
## 5 Service Co-Opetition Evolution Experimental Analysis
### Numerical Analysis  
To study the competitive and cooperative relationships between services in the service ecosystem, we first conduct a numerical analysis on the improved ternary Lotka-Volterra model proposed in Equation (7) to verify the model's effectiveness and partially study the theoretical service-evolution laws.  
The evolution process of the service ecosystem requires multiple iterations to uncover its co-opetition evolution laws. Therefore, this paper uses the evolution timestamp as the horizontal axis and the scale of various services as the vertical axis. We use Matlab R2018a software to perform a numerical analysis of the improved ternary Lotka-Volterra model. The evolution period is set to t = 100, and the basic assignment of specific model parameters is shown in the following Table 4:  
In this, \(s_{1}\), \(s_{1}\) are regarded as the initial scales of the two original atomic services \(A\) and \(B\), and \(s_{3}\) is regarded as the initial scale of the new type of composite service \(C\).  
From the simulation of the seven stable local equilibrium points of the model, it can be seen that the equilibrium state of service co-opetition is affected by the interaction coefficients between services.  
Analysis from Figure 7a-c shows that when any service type faces fierce competition from other service types, its market share may be significantly eroded by competitors, potentially leading to the decline or extinction of the service type. Meanwhile, the service type that occupies the market share will expand, and its scale growth will be enhanced. Specifically, when one service faces asymmetric competition, such as \(\alpha_{12}=\alpha_{13}=1.5\), it is driven to extinction from Figure 7a, while dominant services thrive.  
Analysis from Figure 7d-f shows that when a service type exhibits a significant advantage in market competition, it may squeeze the survival space of other service types, causing them to gradually decline or withdraw from the market. The dominant service type, by suppressing competitors, often expands its market share, thereby consolidating or enhancing its dominant position in the market. For example, when \(\alpha_{21}=\alpha_{31}=1.5\) in Figure 7d, strong competition suppresses others but allows two services to persist.  
Analysis from Figure 7g shows that balanced competition enables all services to stabilize in the basic parameters. In a word, when services maintain a certain level of co-opetition, the market can support the coexistence of various services. Although theoretically this seems intuitive, the robust operation of the market does not solely rely on natural market forces but also requires appropriate regulatory measures to ensure that services maintain a suitable level of competition and cooperation under different market and social environment changes.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Energy-Efficient Task Scheduling
#### 5.3.3 TDM of Hardware Components  
Another approach is to share the hardware components in a time-division multiplexing fashion with other tasks instead of putting them to sleep. By sharing hardware resources among multiple tasks in a time-sliced manner, the utilization efficiency of those resources can be improved. This leads to reduced energy usage, as it avoids the need to power multiple components simultaneously when a single one can be time-shared.  
The authors of [67] classified ECG signals by converting them first into a binary image using a quantized multi-layer perceptron (qMLP) and then classifying them using a binary convolutional neural network (bCNN) to reduce resource and power consumption. The authors orchestrated their algorithm such that IP MACs (multiply-accumulate) were shared between the three dense layers of the qMLP, which not only reduced the resource consumption but also enabled authors to implement their design on the ultra-low power Lattice iCE40 FPGA, which resulted in an overall power reduction.  
Although this approach reduces power and resource consumption, it can impact latency due to time-sharing. This means there may be delays in task execution as each task waits for its turn to access the hardware. Therefore, this approach is suitable for scenarios where strict real-time monitoring is not required, such as heart rate monitoring.  
The benefits and associated constraints of these optimization techniques are summarized in Table 5 along with their descriptions.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 2 Materials and Methods
### Overall Design of the System
#### 2.2.1 IMU Solution  
The IMU solution is carried out under the east-north-sky navigation coordinate system, denoted by \(n\); the spraying robot coordinate system is denoted by \(b\), the geocentric geostationary coordinate system is denoted by \(e\), and the geocentric inertial coordinate system is denoted by \(i\). The specific solution process is shown in Figure 3, and the navigation differential equation can be expressed as follows:  
\[\begin{bmatrix}\dot{v}_{eb}^{n}\\ \dot{C}_{\text{m}}^{e}\\ \dot{C}_{b}^{n}\\ \dot{h}\end{bmatrix}=\begin{bmatrix}C_{b}^{n}f_{ib}^{b}-\left(2\omega_{ie}^{n} \times+\omega_{en}^{n}\times\right)\bar{v}_{eb}^{n}+g_{l}^{n}\\ C_{n}^{n}(\omega_{en}^{n}\times)\\ C_{b}^{n}\left(\omega_{ib}^{b}\times\right)-\left(\omega_{in}^{n}\times\right) C_{b}^{n}\\ \
u_{U}\end{bmatrix} \tag{1}\]  
where, \(\omega_{ie}^{n}\times\) is the rotation vector of e frame reference \(i\) frame under \(n\) frame; \(\omega_{en}^{n}\times\) is the rotation vector of \(n\) frame reference e frame under \(n\) frame; \(\omega_{ip}^{b}\times\) is the rotation vector of \(b\) frame reference \(i\) frame under b frame; \(g_{l}^{n}\) is the local gravitational acceleration under \(n\) frame, m/s\({}^{2}\); \(\
u_{U}\) is the vertical velocity of download body under \(n\) frame, m/s.  
The specific force equation and attitude information matrix calculation equation can be expressed as follows:  
\[\left\{\begin{array}{l}\dot{v}_{eb}^{n}=f_{ib}^{n}-\left(\omega_{en}^{n} \times+2\omega_{ie}^{n}\times\right)v_{eb}^{n}+g_{l}^{n}\\ f_{ib}^{n}=C_{b}^{n}f_{ib}^{n}\end{array}\right. \tag{2}\]  
\[\dot{C}_{b}^{n}=C_{b}^{n}\left(\omega_{ib}^{b}\times\right)-\left(\omega_{in} ^{n}\times\right)C_{b}^{n} \tag{3}\]  
The position and attitude information of the spraying robot in the east-north-sky coordinate system is obtained by the above IMU solution.  
Figure 2: Physical construction of the orchard spraying robot. Note: 1 is sprinkler; 2 is medicine tank; 3 is BDS receiver antenna; 4 is track; 5 is upper guide wheel; 6 is lower guide wheel; 7 is trust wheel; 8 is suspension tension spring; 9 is suspension; 10 is supporting wheel. 11 is driving wheel.
# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
## 4 Operating Speed of the Link Adaptation
### Execution Time Optimization  
When the main time-consuming functions, snr2 mi and interpolation functions, were compared with the other functions in the firmware, little difference was found in terms of the number of additions and multiplications but the number of conditional statements and repetitive statements was much larger with the snr2 mi and interpolation functions.  
In case of the snr2 mi function, this function was executed once per subcarrier and the conditional statement execution regarding modulation scheme selection also occurred every subcarrier. Therefore, the firmware has been modified and optimized such that the conditional statement execution is not reiterated by the number of subcarriers but occurs for only the MCS indices, leading to a much smaller number of conditional statements executed. To achieve this, the snr2 mi_interp function is made to exist for each of the four modulation schemes and within each modulation scheme, the function execution is reiterated by the number of subcarriers to obtain the MI.  
The snr2 mi_interp function as well has repetitive statements (the _for_ loops) other than conditional statements. The repetitive statement execution is reiterated by the size of the table and hence the reiteration count is a fixed number. Therefore, the repetitive statements are removed and only the conditional statements are left for the sake of execution time saving. Whenever the aforementioned two optimized functions that contribute to reducing the execution time are executed, the interp functions are called, and hence in proportion to the number of executions of the interp function, the execution time is reduced by a large amount.  
The execution time and ratio (in percentage) of each internal function and the total execution time before and after the optimization are listed in Figure 15. It is shown that the ratio of the execution time of each function is not altered a lot but the total execution time after optimization shrunk to less than a half of the execution time before optimization.
## 3 System Model  
### Reference System Model and Formulation  
In this scenario, terminal devices (i.e., CCTV camera, drones equipped camera, and vehicle black-box) are wirelessly connected to an edge server which is located nearby as shown in Figure 2. When the terminal device collects video continuously/seamlessly, there exists the limits of battery and storage capacity depending on the size of devices. Based on this limitation, it is not possible to store or compute all of the data on the device itself in any time. Furthermore, even if all data can be processed, a delay which is caused by the processing limit of the device occurs. This can make serious problems in environments where real-time communications is essentially necessary, e.g., mission-critical communications. For this reason, it is necessary to use virtual servers that have better performance than terminal devices. Moreover, using the edge server can be a way to overcome these problems related to the limitation of the terminal devices. However, using edge computing technique (i.e., data offloading) is always not the best solution. We have to consider the delay time of wireless data transmission between the terminal device and the edge server.  
For this reason, we propose a novel offloading decision algorithm that minimizes the combination of energy consumption and delay at each terminal device. The algorithm we proposed in this paper is based on DQN, which is one of reinforcement learning algorithms that maximizes cumulative summation of rewards. Using this DQN-based algorithm, each terminal device can decide whether it will do offload or not. For this purpose, the corresponding actions and rewards values in DQN-based formulation are as follows:  
* _State_\(S\triangleq\{\)TD capacity, StandBy Q capacity, ES capacity\(\}\)
* _Action_\(A\triangleq\{0,1\}\)
* _Reward_\(R_{combi}\triangleq\beta\times(\gamma\times R_{delay}+(1-\gamma)\times R_{energy})+(1-\beta) \times R_{capacity}\) (Refer to (6), in Section 4)  
where TD capacity, StandBy Q capacity, and ES capacity stand for the capacity of terminal device, stand-by Q capacity, and the capacity of edge server, respectively. Here, the standByQ means the waiting buffer of each terminal device. The data/information that is decided to be offloaded is stored the standByQ. Next, each action means whether it needs to be offloaded or not. Here, the value 1 means offloading happens at time step \(t\), whereas the value 0 means processing the data locally at time step \(t\) (i.e., offloading does not happen). The details of the proposed algorithm are explained in Section 4.
###### Abstract  
Spacecraft formation flying (SFF) in highly elliptical orbit (HEO) has attracted a great deal of attention in many space exploration applications, while precise guidance, navigation, and control (GNC) technology--especially precise ranging--are the basis of success for such SFF missions. In this paper, we introduce a novel K-band microwave ranging (MWR) equipment for the on-orbit verification of submillimeter-level precise ranging technology in future HEO SFF missions. The ranging technique is a synchronous dual one-way ranging (DOWR) microwave phase accumulation system, which achieved a ranging accuracy of tens of microns in the laboratory environment. The detailed design and development process of the MWR equipment are provided, ranging error sources are analyzed, and relative orbit dynamic models for HEO formation scenes are given with real perturbations considered. Moreover, an adaptive Kalman filter algorithm is introduced for SFF relative navigation design, incorporating process noise uncertainty. The performance of SFF relative navigation while using MWR is tested in a hardware-in-the-loop (HIL) simulation system within a high-precision six degrees of freedom (6-DOF) moving platform. The final range estimation errors from MWR using the adaptive filter were less than 35 \(\upmu\)m and 8.5 \(\upmu\)m/s for range rate, demonstrating the promising accuracy for future HEO formation mission applications.  
spacecraft formation flying; highly elliptical orbit; microwave ranging; relative navigation 2020
## 5 Results and Analysis
### Comparison with Other Similar Approaches
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **\begin{tabular}{c} **Averaged** \\ **Accuracy** \\ \end{tabular} & **\begin{tabular}{c} **Averaged** \\ **Precision** \\ \end{tabular} & **\begin{tabular}{c} **Averaged** \\ **Recall** \\ \end{tabular} & **\begin{tabular}{c} **Averaged** \\ **Specificity** \\ \end{tabular} & **\
\begin{tabular}{c} **Averaged** \\ **F-Measure** \\ \end{tabular} & **
\begin{tabular}{c} **Averaged** \\ **AUC** \\ \end{tabular} & **Cost** \\ \hline
[8] & 83.29 \(\pm\) 5.07\% & 82.65 \(\pm\) 5.82\% & 97.20 \(\pm\) 3.34\% & 60.04 \(\pm\) 12.11\% & 88.55 \(\pm\) 3.75\% & 0.934 \(\pm\) 0.50 & 167.31 \\
[15] & 92.64 \(\pm\) 2.74\% & 93.5 \(\pm\) 2.45\% & 97.38 \(\pm\) 3.02\% & 84.68 \(\pm\) 5.44\% & 94.41 \(\pm\) 2.15\% & 0.987 \(\pm\) 0.01 & 141.10 \\
[17] & 90.0 \(\pm\) 2.80\% & 90.71 \(\pm\) 2.57\% & 97.22 \(\pm\) 2.60\% & 78.25 \(\pm\) 4.31\% & 93.07 \(\pm\) 2.22\% & 0.979 \(\pm\) 0.02 & 193.70 \\
[18] & 85.74 \(\pm\) 2.44\% & 84.92 \(\pm\) 2.91\% & **99.04 \(\pm\) 1.0\%** & 62.94 \(\pm\) 4.84\% & 90.75 \(\pm\) 1.82\% & 0.918 \(\pm\) 0.02 & 236.11 \\
[22] & 93.41 \(\pm\) 3.27\% & 94.24 \(\pm\) 3.01\% & 96.61 \(\pm\) 3.34\% & 88.20 \(\pm\) 6.21\% & 95.90 \(\pm\) 2.64\% & 0.981 \(\pm\) 0.02 & 272.20 \\
[32] & 87.37 \(\pm\) 3.40\% & 86.84 \(\pm\) 3.47\% & 98.62 \(\pm\) 1.50\% & 68.87 \(\pm\) 9.18\% & 92.95 \(\pm\) 2.42\% & 0.976 \(\pm\) 0.03 & 136.72 \\
[40] & 90.51 \(\pm\) 3.24\% & 89.54 \(\pm\) 4.18\% & 98.6 \(\pm\) 1.80\% & 80.60 \(\pm\) 8.20\% & 93.31 \(\pm\) 2.42\% & 0.994 \(\pm\) 0.02 & 91 \\ Ensemble-1 & 96.26 \(\pm\) 2.92\% & 95.72 \(\pm\) 3.81\% & 98.76 \(\pm\) 1.64\% & 91.94 \(\pm\) 7.55\% & 97.14 \(\pm\) 2.21\% & **0.95 \(\pm\) 0.006** & 95.05 \\ Ensemble-2 & **97.27 \(\pm\) 2.70\%** & **99.44 \(\pm\) 0.82\%** & 96.25 \(\pm\) 3.88\% & **99.01 \(\pm\) 1.64\%** & **97.68 \(\pm\) 2.31\%** & 0.986 \(\pm\) 0.01 & **64.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Multiple combinations of scoring functions and their respective results. ensemble-2 resulted in the lowest recall value among all the competing approaches, which effectively placed it at 9 th position. Therefore, both the proposed approaches have their strengths and weaknesses. Ensemble-1 performed consistently over both performance and incurred-cost evaluation factors. On the other hand, ensemble-2 was able to outperform ensemble-1 in terms of incurred cost and on general accuracy metrics over the CKD dataset. It is also noted that, in terms of the recall factor, ensemble-2 lagged behind the other techniques mentioned in the comparative analysis. It can be seen that the cost difference between ensemble-1 and ensemble-2 is $31, while the recall difference is around 2.5%. In this regard, it might be worth spending an extra $31 to timely diagnosis patients. In this regard, both ensemble-1 and ensemble-2 can be treated as non-dominating solutions, i.e., the merit of one approach cannot be undermined by that of the another. In such cases, the final selection is left to the discretion of the decision maker.
# Effects of Equatorial Plasma Bubbles on Multi-GNSS Signals: A Case Study over South China
## 4 Signal Quality Assessment
The occurrence rate is defined as the total epoch of cycle slips and loss of phase observations divided by the total epoch in which the signal passes through the EPB in 1 min.
## 2 Materials and Methods
### Data Analyses
#### 2.4.7 Estimating the Power Spectral Density (PSD) for Each Frequency Band  
We estimated the power spectrum density (PSD) for each data segment within the broadband range of 1-45 Hz at the Fz electrode using a modified version of the Welch method [46] (window = 1 s; 0% overlap; resolution = 1 Hz). To minimize autocorrelation between contiguous windows, our modification only considered a single 1 s window every three 1 s windows (i.e., 1 s every 3 s, as computed in [47]). Each condition had a total of 45 windows of 1 s. Power values were log-transformed and converted to the unit 10 deg10[uV2]. For each frequency, the spectral power was detrended for all 1 s spectral estimates throughout the full recording before comparing conditions. This approach minimized the impact of spectral power drifts in the signal, preventing artificial differences between conditions. The resulting spectral power values were computed using [PERSON]'s rule (_band-power_ function in MATLAB) in the theta (4-7.9 Hz), alpha (8-11.9 Hz), beta (12-29.9 Hz), and gamma (30-45 Hz) frequency bands. We excluded the delta band (1-3.9 Hz) from the analysis due to its susceptibility to blinking and movement artifacts [48].
# Experimental Study and Parameter Optimization of a Magnetic Coupled Piezoelectric Energy Harvester
## 4 Results and Discussion
### Experimental Results
#### 4.1.3 Key Parameters Analysis  
In this section, the key nonlinear parameters that affect the harvesting efficiency will be analyzed. The main feature of the magnetic coupled piezoelectric energy harvester is the introduction of vertical nonlinear magnetic forces. It can be seen from Equation (5) that the magnet flux density \(B_{r}\) and the magnets spacing \(d\) (affected by beams spacing \(D\)) directly affect the magnitude of the magnetic force and are the key parameters in the structural design. In the following, we will explore whether there is an optimum value of \(B_{r}\) and \(D\) within a certain range through simulation and experiment.  
Figure 8 shows the output power maximum of the energy harvester at different beams spacing \(D\) (10-25 mm) and different magnet flux density \(B_{r}\) (0.02-0.12 T). Figure 8a is based on simulation, scanning at intervals of 1 mm and 0.01 T. Limited to the experimental conditions, only parts of the simulation parameters are tested. The results of the experiment are shown in Figure 8b with intervals of 2.5 mm and 0.02 T. It can be seen from the results that:  
* An optimal spacing \(D\) for each \(B_{r}\) exists.
* As \(B_{r}\) increases, the optimal spacing increases.
* The values of the respective optimized power in simulation are similar, both around 3.65 mW.
* Similar trends and conclusions can be obtained from experiments and simulations.
* The optimum value of the experiment is between 3.1 and 3.3 mW. Since the spacing between the scan is not short enough, the optimal value of the experiment is significantly smaller than the simulation, and the absolute optimal value may not be produced in the experiment.  
Based on the above analysis, 0.12 T is selected for further study. In addition to the spacing of the above experiments, an experiment of 23 mm is added. Results are compared with the maximum output voltage of linear harvester, as shown in Figure 9. The results show that the performance of the  
Figure 8: Output power maximum at different \(D\) and \(B_{r}\): (**a**) Simulation; (**b**) Experiment.  
Figure 7: Determination of the optimal load resistance.  
nonlinear magnetic coupled piezoelectric energy harvester is significantly better than linear. Under the configuration, the maximum is obtained at 23 mm in the simulation and experiment. A more detailed study will be conducted below for the settings of \(B_{r}=0.12\) T and \(D=23\) mm.