esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Discussion
### Force-Charge Signal Characterization
#### 3.3.2 Frequency-Amplitude Characteristics and Impedance Measurement  
In order to further explore the relationship between the sensitivity of the sensor and the frequency of the excitation signal, the frequency-amplitude characteristics of the sensor were measured by using the same experimental system and similar methods.  
Figure 8 shows the relationship between the output response of the sensor and the frequency of the excitation signal, with the voltage amplitude of the excitation signal remaining constant. The transverse coordinate is the excitation signal frequency (in Hz) and the longitudinal coordinate is the sensor's output response (in mV). With the increase of the excitation signal frequency, the output response of the sensor increases gradually until it reaches its maximum at 15 Hz. The slope of the curve indicates the growth rate of the sensitivity, which is low at a low frequency, and gets higher as the frequency rises.  
To study the reason for the low sensitivity of the sensor at low frequency, the admittance circles of four sensors prepared in the same batch were tested, and the output impedance values of the sensors were obtained. The measuring circuit and the admittance circles are shown in Figure 9, where E is the function signal generator, P is the measured sensor, and R is the non-inductive resistance. The average diameter of the four admittance circles is \(2.16\ \times\ 10^{-6}\ \mathrm{m}\). According to this, the output impedance is reciprocal to the diameter of the admittance circle, and the output impedance of the sensor is \(462\ \mathrm{K\Omega}\).  
The reason for the low sensitivity of the sensor at a low frequency is that the lower charge produced by the film in units of time, and the low internal resistance of PVDF film (\(462\ \mathrm{K\Omega}\)), result in the poor low frequency that is characteristic of the sensor and obvious charges leakage, which is shown as the low sensitivity in low frequency. The necessity of adding a signal preprocessing circuit board near the sensor to improve the signal-to-noise ratio was also proven.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Frequency (Hz)** & 1 & 2 & 5 & 10 & 15 & 20 \\ \hline
**Sensitivity (pC/N)** & 1.54 & 2.01 & 2.34 & 2.81 & 3.01 & 3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity of wrist PVDF piezoelectric sensor under different excitation frequencies.  
Figure 7: **(a)** The output response of the PVDF sensor from the pressures of A: 0.48 N, B: 0.99 N, C: 1.45 N, D: 1.96 N, E: 2.45 N, and F: 2.95 N at 5 Hz of excitation frequency; **(b)** the force-charge curve of the PVDF Sensor at 5 Hz of excitation frequency.
## 3 Case Validation: Typhoon Haikui
### Impact Awareness
#### 3.2.3 Water and Electricity Impact  
This study found a significant spatial correlation between water and power outages and geographic location. Analysis of Weibo data in Figure 13 shows that prolonged outages were primarily concentrated in the Cangshan District of Fuzhou, which may be related to the area's weaker infrastructure capacity or lower elevation. Additionally, the spatial distribution of water outages was mainly concentrated in the Gulou District, indicating the vulnerability of the water supply system in this area when facing floods. Further spatial comparison analysis revealed a high degree of overlap between the locations of water and power outages and the areas that experienced the highest rainfall on 6 September. This suggests that rainfall distribution directly influenced the occurrence of these outages. This spatial consistency indicates that rainfall is likely a key factor contributing to the water and power outages in these areas, especially in Gulou and Cangshan Districts. These findings provide important spatial guidance for future urban flood control and disaster management. Strengthening infrastructure and emergency response plans in these high-risk areas could effectively reduce the impact of similar disasters on residents' lives.  
Figure 13: Total water and electricity impact map.
## 5 Numerical Simulations
### Simulation Results with Disturbances and Noises  
To imitate the measurement noises of encoders equipped in the FJM, Gaussian white noise with zero mean and standard deviation of 0.1, which can be generated by the \"randn\" function in MATLAB, is added for all the state measurements. The simulation results are depicted in Figures 4-9.  
Figure 4 displays the trajectories of the system output \(y\) and the given reference signal \(y_{\rm d}\). Figure 5 shows the curve of tracking error. From these figures, we can see that the proposed approach achieves a satisfactory tracking control performance despite the presence of mismatched and matched disturbances as well as noises affecting measurements. The estimates of the mismatched and matched disturbances are respectively illustrated in Figures 6 and 7, from which we can see that the designed RESOs can estimate the disturbances quickly and precisely.  
Figure 4: Tracking performance of the system output \(y\) under the proposed approach with noises.  
Figure 5: Tracking error \(e_{1}\) under the proposed approach with noises.  
Figure 8 depicts the intermediate virtual control law \(\alpha_{1}\) and the corresponding filtered control signal \(z_{1}\) from the SCF. It is clearly seen that the virtual control law \(\alpha_{1}\) is corrupted by the white noises, but the filtered signal \(z_{1}\) through the SCF is smooth. In addition, Figure 9 shows the comparison results between the direct time derivative of \(\alpha_{1}\) and the output \(z_{1\text{d}}\) of the SCF in the presence of white noises. It is noticed that the chattering amplitude of \(\dot{\alpha}_{1}\) is much bigger than that of \(z_{1\text{d}}\), which indicates that the noises in \(\dot{\alpha}_{1}\) are amplified. The reason is that the derivative of \(\alpha_{1}\) is approximated by the SCF through integration rather than differentiation (as shown in Figure 2). These results demonstrate that the SCF employed for FJMs with measurement noises can not only filter the noises for the control signal to some extent, but also avoid the amplification of the noises in the  
Figure 8: Filtering performance of the SCF with \(\alpha_{1}\) being the input and \(z_{1}\) being the output.  
Figure 6: Estimation performance of \(d_{1}\) via RESO.  
Figure 7: Estimation performance of \(d_{2}\) via RESO.  
recursive backstepping design. Similar results are obtained for \(\alpha_{i}\ (i=2,3)\) and \(z_{\rm id}\), which are omitted for the space saving.
## 4 Experimental Results and Discussion
### Comparison Analysis of Results
Finally, while our proposed study evaluates the anticipated predictive models using diverse evaluation metrics, some related works failed to evaluate all metrics since they did not consider the choice of different features. Indeed, access to the best features with a minimum of features is extremely important to improve accuracy and conserve resources and reduce training time complexity.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 4 Simulation Results
### Simulation Results for Asymmetric Cases  
In asymmetric cases, no M-sequence-based algorithms have been proposed yet. Therefore, only a comparison between the JS algorithm and the reinforcement learning-based algorithm is conducted. In asymmetric scenarios, only a subset of the channels each SU possesses are shared channels, while the remaining channels are either occupied by the PU or unsuitable for communication. Each SU attempts rendezvous without knowing whether a channel is shared, which typically leads to a significantly higher ETTR compared to symmetric cases. Similarly to the symmetric scenario, in this simulation, we independently conducted 15,000 tests (episodes) for each case and compared the methodologies based  
Figure 7: ETTR for symmetric and asymmetric scenarios.  
on the average performance of each case. However, the difference is that 30% of the total channels, \(M\), were set as shared channels, while the rest were assumed to be either for the PU or unavailable. Naturally, each SU is not aware of which channels are shared, and only after a successful rendezvous can they identify if the channel is shared. The probability of co-channel occurrence was set equally across all channels, and the simulation was conducted accordingly. Figure 6(b) shows a comparison of rendezvous algorithms in asymmetric environments with varying total numbers of channels and shared channels. It can be observed that the reinforcement learning-based algorithm achieves lower ETTR compared to the JS algorithm.  
Analyzing not only the ETTR but also the cumulative density graph of TTR can provide valuable insights into the comparison of different methodologies. Figure 8 illustrates the TTR distribution for each rendezvous approach in asymmetric scenarios. The reinforcement learning-based algorithm demonstrates relative superiority over the JS algorithm because its TTR is distributed in a lower range compared to the JS algorithm. The failure probability can be inferred by setting a threshold for the number of trials and measuring the frequency of episodes exceeding that threshold. However, since the probability varies depending on the chosen threshold, it was excluded from the analysis of this experiment.  
Figure 8: _Cont_.
## 3 Guiding Technologies
### Trusted Platform Module
#### 3.1.1 Platform Configuration Register  
The TPM has 24 Platform Configuration Registers (PCRs). According to the TCG, PCRs are protected regions used to validate the content of a measurement log [50]. In other words, PCRs are designed to preserve critical measurements, derived through the application of hashing algorithms to various types of content, which can range from an executable's binary to configuration files.  
It is important to note that each measurement is not directly associated with a specific PCR. To incorporate a measurement, the TPM performs an extended hash operation on the current value of the corresponding PCR. For example, if PCR 10 is extended with a measurement \(M\), then the new value of PCR 10 is calculated as follows:  
\[PCR10_{novo}=hash(PCR10_{atual}\mid M)\]  
where \(||\) represents a concatenation operation between the current PCR value and the measurement to be extended [4]. After the concatenation, the new PCR value is obtained by applying one of the hash algorithms provided by the TPM [30].
# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 5 Case Studies
### PV Output Forecast Results  
In the small-sample scenarios of the summer, winter, and transition seasons, data from a typical day in each season are selected as a test. The remaining data are used for training and validation, with an 8:2 ratio between the training set and the validation set. Data expansion methods, including those without data expansion, with data expansion, and with oversampling, are compared. For the XGBoost PV output prediction model, a grid search method is employed to determine the optimal parameters for training and prediction. The search range and step size are set, and the possible parameter combinations are explored and tested. The parameter combinations with the smallest error are then selected as the optimal parameters of the model. RMSE and MAE were used as indicators to evaluate the accuracy of the prediction. Table 3 shows the prediction errors in different seasons.  
Figure 7 compares the predicted results and the actual values of the photovoltaic power station without dataset expansion. The relevant accuracy indicators are shown in Figure 7. Due to the severe lack of historical output data of the newly built microgrid, the data used in the training set by traditional methods are too small, which are prone to underfitting and lead to poor prediction accuracy. Based on the RMSE index for further analysis, the prediction error of the prediction method in this paper has decreased by 49.36%, 14.75%, and 30.49%, respectively, in the summer, winter, and transitional seasons compared to the data-free expansion method. Among them, during the transition season, the prediction error decreased by 25.53% after adding data expansion and by an additional 6.66% after further increasing oversampling. After adding data expansion in the summer scene, the prediction error decreased by 34.03%. After adding oversampling, the error decreased by another 23.24%. After adding data expansion in the winter scenario, the prediction error decreased by 9.29%. After adding oversampling, the error further decreased by 6.02%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station No.** & **Longitude** & **Latitude** \\ \hline Target photovoltaics & \(-\)94.0427 & 44.7910 \\ \hline No. 1 Photovoltaic & \(-\)94.7264 & 44.3268 \\ \hline No. 2 Photovoltaic & \(-\)93.8454 & 44.5473 \\ \hline No. 3 Photovoltaic & \(-\)94.3427 & 45.0465 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The geographical locations of photovoltaic power stations.  
Figure 7a-c show the comparison of photovoltaic prediction curves for typical days in the summer, winter, and transitional seasons. Due to the lack of sufficient training data, the prediction effect is poor in the data-driven prediction model. However, this paper can effectively alleviate this problem through data expansion and oversampling techniques. It is proven that the method proposed in this paper can effectively improve the accuracy of renewable energy output prediction under the condition of small samples.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 6 Conclusions  
The SOC provides vital state information for EVs and is strongly nonlinear and time-varying. For the purpose of the accurate online estimation of the SOC, the parameters of a second-order RC equivalent circuit model are identified by combining circuit principles and optimized rationally.  
Figure 16: The comparison of the SOC estimation between the simulated value and the theoretical value.  
Figure 17: The comparison of the estimation values in the first 400 s.  
The AUKF with optimized model parameters is used to estimate the SOC. The conclusions of this work can be summarized as follows:  
1. The second-order RC model is proven to be the most suitable equivalent circuit model compared with the Thevenin and multi-order models.
2. The covariances of the process noise and the measurement noise of the UKF are updated in real time to improve the accuracy of SOC estimation.
3. A reasonable optimization method is adopted to weaken model parameter mutation. Optimized parameters can significantly improve the online estimation accuracy of the AUKF based on the original data and have little effect on the convergence of the algorithm.  
In the future, to further improve the accuracy of SOC estimation, the influence of temperature and SOH on SOC estimation must be considered.  
Conceptualization, X.G. and X.X.; methodology, X.G.; software, X.G.; validation, J.G., Y.G. and X.X.; formal analysis, Z.L.; investigation, X.G.; resources, X.H.; data curation, X.H.; writing--original draft preparation, X.G.; writing--review and editing, X.X. and J.G.; visualization, X.X.; supervision, X.G.; project administration, X.G. and X.X.; funding acquisition, X.G. and X.X.  
This research was funded by the National Natural Science Foundation of China, grant number 61703145. The Key Scientific Research Projects of Higher Education Institutions in Henan Province, grant number 19A470001, 18A470014. The Key Scientific and Technological Research Projects in Henan Province, grant number 192102210073. The APC was funded by 192102210073.  
I would like to express my deepest gratitude to my supervisor, [PERSON], who has provided me with valuable guidance at every stage of writing this paper. I would also like to thank the anonymous reviewers for dedicating the time to review my paper despite their busy schedules.  
The authors declare no conflict of interest.
## 6 Discussion and Future Directions
### Challenges and Considerations
#### 6.3.2 Scalability Issues in Blockchain-Based IoV  
The problem of scalability arises as IoV system expands and the number of vehicles begin to increase, leading to increased number of blockchain nodes as well [8]. Other factors that affect scalability include block size, the number of transactions, and the consensus protocols employed [8; 169]. Latency also experiences increment as vehicles are added into the IoV system. Protocols that seek to address scalability issues in IoV systems often make tradeoffs between solutions, which leads to an increase in latency at the expense of high scalability. Finding optimal solutions would involve developing efficient consensus algorithms designed towards improving scalability in the IoV system.
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 3 Case Studies
### Correlation Regression
#### 3.2.4 Results
The times reported for our method include times for creating a 2D histogram, transferring data to GPU, and running the CNNs. If dCor is implemented directly according to its definition [4], the running times are shorter than 1 s when \(n<2^{12}\) in our experiments. In the case of the fast implementation of [29], the times are less than 1 sec when \(n<2^{14}\). Our method, however, only takes less than 1 s until \(n\) reaches \(2^{14}\) on both of CPU and GPU. This is due to the fixed running time of our CNNs regardless of the sample size \(n\), since the 2D histogram size is always equal no matter what the sample size is. The increasing time according to \(n\) results from running times for creating the 2D histogram. Note that our method is faster even on CPU when \(n>2^{13}\). According to the result, when a sample size is large, our method is more efficient than other methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Size** & **dCor**[4] & **dCor**[29] & **CorrCNN (CPU)** & **CorrCNN (GPU)** \\ \hline \(2^{7}\) & **0.0010** & 0.0070 & 0.0543 & 0.0159 \\ \(2^{8}\) & **0.0019** & 0.0024 & 0.0550 & 0.0155 \\ \(2^{9}\) & 0.0078 & **0.0045** & 0.0527
## Materials and Methods
### Setup  
#### 2.2.1 Accelerated Carbonation Apparatus  
The machine (Figure 1) was comprised of a series of pressure valves and a pump to inject CO\({}_{2}\) into a steel chamber, which contained 15 samples. Carbon dioxide with approximately 99.5% purity was injected into the chamber. The CO\({}_{2}\) used in the experiments was supplied by the Linde Thailand Company (Samutprakarn, Thailand). The pressure was regulated between 1 to 3 bars using a system of seals and gaskets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Properties** & \multicolumn{2}{c}{**Elastic Modulus**} & **Tensile Index** & **Tear Index** & **Freeness** \\ \cline{2-7}  & **(MPa)** & **(N m/g)** & **(mN m2/g)** & **(-)** \\ \hline Cellulose fiber & 10837 & 58.18 & 11.17 & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphological properties of the cellulose fiber.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**\% Oxide** & **CaO** & **MgO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **K\({}_{2}\)O** & **SO\({}_{3}\)** & **P\({}_{2}\)O\({}_{5}\)** & **TiO\({}_{2}\)** \\ \hline
\begin{tabular}{c} Portland \\ cement \\ \end{tabular} & 62.70 & 0.85 & 20.01 & 4.41 & 3.48 & 0.37 & 2.73 & 0.07 & 0.25 \\ Limestone & 91.12 & 2.42 & 1.52 & 0.56 & 0.24 & 0.16 & - & - & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of cement and limestone (% by mass of oxide).
# Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode
## 3 The Receiving Method of the Integrated Signal in the TDD Mode
### Motion Compensation Method in TDD Mode
However, the acceleration direction measured by the two methods is different from the direction of the two spacecraft, and we need to project the acceleration vector to the direction between the spacecraft. With the above extrapolation method, first, the times of the distance and velocity measurements by nodes A and B are synchronized. Thus, DOWR can be used to eliminate the distance error caused by the clock difference, and the integrated signal can provide high-precision measurements [43]. Second, the extrapolation ensures that the tracking loop remains locked during the TDD mode signal transmission period, which enables the loop to converge faster when receiving the signal input.
# Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors
## 3 Second-Order Motion Descriptor  
Many descriptors have been successfully applied to human behavior such as trajectory shape, HOG, HOF, and MBH. They describe trajectory shape characteristics, static behavior characteristics, motion velocity characteristics, and motion boundary characteristics from different aspects. Among them, both HOF and MBH are descriptors based on motion features, and they have better recognition effects than static descriptors [17]. Motion feature is an important part of human behavior recognition. Therefore, in order to better describe motion information contained in videos, the motion differential descriptor (MDD) was obtained by calculating temporal variation in optical flow along the trajectory, which was used to represent the relative motion information of the behavior.  
For a trajectory \(\{(x_{t+l},y_{t+l})|l=0,1,2,\ldots,L-1\}\) in frame t, \(L\) is the length of the trajectory, and MDD is defined as the time derivative of the optical flow displacement along the trajectory direction. Assuming that point \((\widetilde{x}_{t+l},\widetilde{y}_{t+l})\) is any point in the neighborhood of point \((x_{t+l},y_{t+l})\) on the trajectory, and point \((\widetilde{x}_{t+l+1},\widetilde{y}_{t+l+1})\) is the corresponding point at the same position in the spatial neighborhood of point \((x_{t+l+1},y_{t+l+1})\) on the trajectory in the next frame, the time derivative of the optical flow along the trajectory of point \((\widetilde{x}_{t+l},\widetilde{y}_{t+l})\) is defined as:  
\[\Delta u_{t+l}\Big{|}(\widetilde{x}_{t+l},\widetilde{y}_{t+l}) =u_{t+l+1}\Big{|}(\widetilde{x}_{t+l+1},\widetilde{y}_{t+l+1})-u_ {t+l}\Big{|}(\widetilde{x}_{t+l},\widetilde{y}_{t+l}); \tag{4}\] \[\Delta v_{t+l}\Big{|}(\widetilde{x}_{t+l},\widetilde{y}_{t+l}) =v_{t+l+1}\Big{|}(\widetilde{x}_{t+l+1},\widetilde{y}_{t+l+1})-v_ {t+l}\Big{|}(\widetilde{x}_{t+l},\widetilde{y}_{t+l}). \tag{5}\]  
Firstly, the optical flow derivatives of each point in the trajectory and its neighborhood are calculated. Then, the optical flow derivatives are accumulated into an 8-bit directional histogram according to the magnitude of the optical flow. We can get the motion difference descriptor \(f_{l}\) for a point in the trajectory. Based on this, the optical flow time derivative of the machine neighborhood of the trajectory point is calculated and quantified according to the trajectory length \(L\). Finally, the motion difference descriptor of the trajectory is obtained as follows:  
\[MDD=\frac{1}{L}\sum_{0}^{L-1}f_{l}. \tag{6}\]
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### IPT Batteries Charging in EVs in SCs  
A typical case of decarbonized energy applications is related to the substitution of internal combustion engine vehicles with EVs provided with battery energy storage. This solution was thought of in an ecological background for reducing air pollution and protecting global biodiversity and ecosystems, which are presently crucial. The energy storage batteries of these EVs, at last, will be wirelessly charged by IPTs in stationary and/or running modes. When replacing a long-established mode of transport to protect biodiversity and the ecosystem, it is necessary to ensure that the replacement solutions are consistent with such protection. Moreover, the building of these IPTs must expand what is ecologically considered to be best associated with a clean energy economy. In such a framework, both the RA and OH approaches will find their place in the design and control of IPT tools involved in the SC environment.