esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Results and Discussions
#### 4.2.1 Comparison of Prediction Performance
However, it can be seen from Figures 5 and 6 that the RMSE, MAE, and SMAPE obtained by the proposed PIANet are lower than those obtained by the comparison methods at all time intervals in BJSubway2021 and BJSubway2022, which further verifies the superiority of the proposed PIANet in short-term OD prediction performance. In addition, the comparisons of convergence curves of best validation RMSE among the proposed PIANet and the comparison methods in BJSubway2021 and BJSubway2022 are shown in Figure 7. According to Figure 7a, CASCNN and STResNet stop converging to the lower minima at the 7 th epoch and the 15 th epoch, respectively. The convergence of ConvLSTM is relatively smooth; however, the convergence speed is slow. Compared with the three comparison methods, the proposed PIANet can converge faster and can stably converge to a lower best RMSE. According to Figure 7b, the proposed PIANet and the three comparison methods can all converge smoothly. Notably, compared with the three comparison methods, the proposed PIANet can still converge faster and still stably converge to a lower best RMSE. Figure 5: Comparisons of the RMSE, MAE, and SMAPE at each time interval during the morning peak hours (i.e., 7:30–9:10) among the proposed PIANet and the comparison methods in BJSubway2021. Figure 6: Comparisons of the RMSE, MAE, and SMAPE at each time interval during the morning peak hours (i.e., 7:30–9:10) among the proposed PIANet and the comparison methods in BJSubway2022. The possible reasons for the better short-term OD prediction performance of the proposed PIANet compared to ConvLSTM, STResNet, and CASCNN are as follows: In terms of feature extraction, ConvLSTM can capture the temporal correlation well, but it cannot capture the spatial correlation. STResNet only uses a stack of convolutional layers for short-term OD prediction, and thus it is difficult to extract effective non-local feature information. The architecture of CASCNN is relatively simple; therefore, it is difficult to extract effective feature information for complex spatio-temporal correlations. In the proposed PIANet, the proposed OAMs are used to improve the representational power of the network by calculating the feature weights in the channel-spatial dimension, which can extract effective feature information for complex spatio-temporal correlations. In terms of feature utilization, none of the comparison methods have feature interactions between two network branches, which makes the feature utilization between two network branches low. However, the feature interaction between two network branches is used in the proposed PIANet, which further improves feature utilization between two network branches. In short, compared with the comparison methods, the proposed PIANet can stably converge to a lower best validation RMSE during training; thus, the proposed PIANet shows better short-time OD prediction performance during testing.
[7] compared the basic version of SMO with eleven other SI algorithms for UCAV path planning in a complex 2D environment. The results showed that SMO had better performance in finding a safe path compared to the other algorithms. However, there is always room for improvement, and to enhance the performance of the SMO algorithm, an improved version called SMOBHC has been proposed. The main contributions of this study are threefold. Firstly, the Beta-Hill Climbing Optimizer (BHC), a new exploratory local search algorithm, has been integrated into the three main phases of the SMO algorithm to create SMOBHC. This new algorithm has the advantage of being better than the SMO in terms of intensification, exploration, avoidance of local minima, and speed of convergence. The BHC is implemented in the SMO algorithm for three main purposes: to improve each new Spider Monkey solution generated in the Local Leader Phase (LLP), to enhance each new Spider Monkey solution produced in the Global Leader Phase (GLP), and finally to update the positions of all Local Leader members under a specific condition in the Local Leader Decision (LLD) phase. Secondly, the SMOBHC algorithm has been adapted and applied to generate an optimal path for the UCAV in 2D space in complex battlefields under different conditions. Finally, to demonstrate the effectiveness and efficiency of the proposed algorithm, SMOBHC has been compared with the SMO and a large set of powerful and recent algorithms. The results showed that SMOBHC outperformed SMO and other algorithms in terms of finding an optimal path for the UCAV in a complex 2D environment. Overall, it can be seen that the SMO algorithm and its improved version, SMOBHC, are highly effective in solving optimization problems, particularly in the area of UCAV path planning. The successful integration of the BHC algorithm into the SMO algorithm has resulted in significant improvements in its performance, which is demonstrated through extensive experimentation and comparison with other algorithms in this study. A list of the meanings of variables, abbreviations, and adjustable control variables utilized in this paper can be found in Table 11, which is located in Appendix A. The rest of this paper is organized as follows: Section two presents a review of related works. Section three discusses the 2D mathematical model of the UCAV path planning problem. Section four provides details on the SMO, the Beta-Hill Climbing Optimizer (BHC), and the SMOBHC. Section five presents the experimental results. Finally, Section six contains our concluding remarks.
## 2 Nonequilibrium Dispersion Force on a Nanoparticle on the Source Side of Gapped Graphene
They are expressed via the components of the polarization tensor of graphene \(\Pi_{ij}(\omega,k;\Delta,T_{g})\)[51; 52; 72]  
\[R_{\text{TM}}(\omega,k;\Delta,T_{g})=\frac{q(\omega,k)\Pi_{00}( \omega,k;\Delta,T_{g})}{2\hbar k^{2}+q(\omega,k)\Pi_{00}(\omega,k;\Delta,T_{g })},\] \[R_{\text{TE}}(\omega,k;\Delta,T_{g})=-\frac{\Pi(\omega,k;\Delta, T_{g})}{2\hbar k^{2}q(\omega,k)+\Pi(\omega,k;\Delta,T_{g})}, \tag{4}\]  
where \(q^{2}(\omega,k)=k^{2}-\omega^{2}/c^{2}\), and the quantity \(\Pi\) is defined as  
\[\Pi(\omega,k;\Delta,T_{g})=k^{2}\Pi_{i}^{i}(\omega,k;\Delta,T_{g})-q^{2}( \omega,k)\Pi_{00}(\omega,k;\Delta,T_{g}) \tag{5}\]  
with the summation over the repeated index \(i=0,\,1,\,2\). The explicit expressions for the polarization tensor in the required frequency regions are given in Section 3. Note that the polarization tensor \(\Pi_{ij}\), describing the response of graphene to quantum and thermal fluctuations of the electromagnetic field, strongly depends on temperature \(T_{g}\) as a parameter. When applied to the nonequilibrium dispersion forces, a similar situation was previously considered for the phase-change [63] and metallic [64] materials. The second term on the right-hand side of (2), according to the division accepted in [67], takes the form [58]  
\[F_{r}(a,\Delta,T_{E},T_{g})=\frac{2\hbar\alpha(0)}{\pi c^{2}}\int\limits_{0}^{ \infty}\!\!d\omega\Theta(\omega,T_{E},T_{g})\!\!\int\limits_{\omega/c}^{\infty} \!k\,dke^{-2 aq(\omega,k)}\text{Im}\Big{\{}\Big{[}2q^{2}(\omega,k)c^{2}+\omega^ {2}\Big{]}R_{\text{TM}}(\omega,k;\Delta,T_{g})+\omega^{2}R_{\text{TE}}(\omega,k ;\Delta,T_{g})\Big{\}}. \tag{6}\]  
Here, the quantity \(\Theta(\omega,T_{E},T_{g})\) is defined as  
\[\Theta(\omega,T_{E},T_{g})=\frac{1}{\exp\Big{(}\frac{\hbar\omega}{k_{B}T_{E}} \Big{)}-1}-\frac{1}{\exp\Big{(}\frac{\hbar\omega}{k_{B}T_{g}}\Big{)}-1}. \tag{7}\]  
The important property of the division (2) accepted in [67] is that the quantity \(F_{r}\), expressed in terms of real frequencies, is determined by the contribution of only the evanescent waves for which \(k>\omega/c\). As a result, the exponent in (6) has the real power. This is advantageous as compared to the standard Lifshitz formula for equilibrium Casimir and Casimir-Polder forces written in terms of real frequencies, containing the contributions of both the evanescent and propagating (\(k<\omega/c\)) waves [71]. For the latter contribution, the quantity \(q(\omega,k)\) is purely imaginary, resulting in the integral of the quickly oscillating function, which makes integration difficult.
## 2 Materials and Methods  
### Equipment  
Combining a pressure control device with a LIBS system allows for experiments under varying pressures. The pressure control device comprises components like a pump and a pressure gauge, while the LIBS system includes a laser, optical elements, a spectrometer, and a delay generator. The schematic of the setup is shown in Figure 2.  
The laser emits a high-energy pulsed laser light, which enters the chamber through a window in the vacuum box. The laser is reflected by a medium film mirror to a long focal length focusing lens, and is focused onto the surface of the sample, ablating the sample to generate plasma. The plasma emits characteristic spectra as it cools, and part of the spectral signal is collected by a fiber optic probe after being focused by a short focal length lens, ultimately transmitted to a computer for processing via a spectrometer. A timer is used to adjust the triggering sequence between the laser and the spectrometer, thereby avoiding interference from background signals. A vacuum pump is used to extract gas from the chamber, and a pressure gauge is used to monitor the gas pressure inside the chamber.  
The laser selected is the Nd:YAG laser from Lei Bao Optoelectronics Company (Beijing, China), model Nimma-900, which operates at a wavelength of 1064 nm. This wavelength of laser exhibits good energy stability and a low air attenuation rate, with a maximum single pulse energy of 900 mJ, pulse width of \(\leq\) 9 ns, a laser beam diameter of 9 mm, and a working frequency of 1-10 Hz, utilizing an external trigger mode. The spectrometer selected is the AVS-RACKMOUNT-USB2 from Beijing Avantes Co., Ltd. (Beijing, China), with a wavelength range of 196 nm to 641 nm and a resolution of 0.01-0.15 nm. The timer chosen is the eight-channel digital delay generator DG645 form Stanford Research Systems (CA, USA), with a delay time set to 2.5 us. The delay resolution for all channels of the signal delay generator DG645 is 5 ps, with jitter between channels being less than 25 ps, and a delay control accuracy of 1 ns. This signal generator is used to set the delays for the laser and spectrometer, emitting the Q-switch signal for the laser and the external trigger signal for the spectrometer to control the timing sequence.  
The pressure vessel shell is made of stainless steel to prevent deformation of the device due to the pressure difference between the inside and outside of the chamber at low air pressure. The windows used are optical lenses, allowing lasers to enter the pressure control device through the optical lens window. The configured devices, such as air pumps and pressure gauges, can regulate and measure the air pressure within the pressure control device, which can be as low as 45 kPa. Additionally, the confocal microscope used is the VT6100 laser confocal microscope from Zhongtu Instruments Co., Ltd. (Shenzhen, China), with a height measurement resolution of 0.5 nm and a width measurement resolution of 1 nm. The light source is a white LED, and the travel range in the xyz directions is 100 mm.  
Figure 2: Schematic diagram of the experimental setup.
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 3 Uncertainty Estimation Approach  
This section describes a proposed approach to obtain a reliability estimator for heart rate measurement.  
Reliability \(R\), the probability that the measurement is correct, is a function of the measurement device, the perturbations represented by its distribution function and its correlation, in addition to the number of data used in the estimation.  
To estimate the reliability and the uncertainty in the measurement, the following procedure is proposed:  
1. System model: device model and perturbation model.
2. Distribution function estimation of the perturbations and correlation function.
3. Estimators properties analysis using the Monte-Carlo method.
4. Correction factors calculation to compensate estimators.
5. Confidence intervals for the \(R\) estimation.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 2 Materials and Methods
### ROCOV Algorithm  
The ROCOV (rate of change of voltage) algorithm is based on the direct-current voltage (\(\text{V}_{\text{DC}}\)) measurement and the calculation of its derivative with respect to time (5).  
\[\text{Fault detection}=1\text{ if ROCOV}>\text{ROCOV}_{\text{THR}} \tag{5}\]  
\[\text{ROCOV}=\Delta\text{V}_{\text{DC}}/\Delta\text{t}=(\text{V}_{2}-\text{ V}_{1})/(\text{t}_{2}-\text{t}_{1}) \tag{6}\]  
where ROCOV is the calculation defined in Equation (5) and \(\text{ROCOV}_{\text{THR}}\) is the predefined threshold for the algorithm.  
The method involves measuring two consecutive samples of \(\text{V}_{\text{DC}}\), \(\text{V}_{1}\) and \(\text{V}_{2}\), recorded at times \(\text{t}_{1}\) and \(\text{t}_{2}\), and applying Equation (5). During normal operation of the system, the result of the ROCOV should be zero, but due to system fluctuations and measurement errors, it is not zero. To detect a fault condition, the calculated ROCOV value is compared against a predefined threshold, as shown in Equation (6). One of the most outstanding features is the ability to swiftly detect faults because of the used detection magnitude.  
The ROCOV is effective in detecting faults that cause rapid voltage fluctuations, particularly in systems where the voltage dips or surges are more pronounced, like short circuits. However, in high-resistance faults, where the voltage deviations are smaller, the ROCOV's effectiveness may be reduced. This method is best suited for situations with significant voltage instability.  
In [20], both a new directional logic using the ROCOV to improve the fault discrimination and a methodology to set the protection thresholds are proposed. Reference [21] analyzes an MMC-based MTDC system where the ROCOV is used to protect the DC links, in combination with a current-limiting control. Additionally, [22] presents a method for high-impedance faults, in which the maximum ROCOV value is used to determine an adaptive threshold.
# Using Tablets to Teach Basic Literacy Skills to Kindergarten Students: A Case Study
## 2 Background
### Statement of the Problem, Issues in Current Research  
Despite the growing integration of technology in early childhood education, particularly the use of tablets to enhance literacy skills among kindergarten students, significant research gaps remain. In detail:  
* While systematic reviews have identified the positive impact of tablets and mobile devices on literacy and numeracy outcomes, these findings are tempered by methodological limitations, including small sample sizes and potential biases [39]. This underscores the need for robust empirical research to delineate the conditions and groups that benefit most from tablet integration in early education.
* Existing studies have highlighted the potential of tablets as effective tools for improving emergent literacy skills, such as letter name identification and letter sound recognition. However, the body of literature on these applications remains fragmented and insufficiently comprehensive [36; 39].
* Additionally, the effectiveness of ICT interventions often hinges on adult participation and pedagogical scaffolding, as indicated by studies that demonstrate the necessity of teacher guidance in tablet-assisted literacy instruction [36]. Nonetheless, the extent to which such guidance can mitigate disparities in outcomes remains underexplored, particularly regarding variances tied to demographic and instructional factors [38].
* Moreover, while the tactile and interactive nature of tablets is associated with enhanced engagement and creativity (e.g., [33; 34]), research has yet to fully address how these devices can be systematically integrated into the curricula for phonological awareness and phonics [36].
* Attention must also be paid to the quality of educational applications, as not all are developed with pedagogical efficacy in mind, leading to potential gaps in learning outcomes [35]. While the utilization of applications should adhere to phonological awareness and phonics instructional recommendations, current applications may fail to meet these criteria [45].
## 2 Literature Review
### Mathematical Models for Lidar Applications
\[P_{r}(r)=\frac{c\eta A}{2}\int_{0}^{rc/2}P_{0}\sin^{2}\left(\frac{\pi}{\text{ n}_{\text{H}}}t\right)\frac{\exp(-2\int_{0}^{r}\alpha_{\text{ext},}(r^{\prime}) dr^{\prime})}{r^{2}}\xi(r)\beta_{0}\delta(r-\frac{ct^{\prime}}{2}-R_{0})dt^{\prime} \tag{5}\]  
While structurally similar to (4), Equation (5) includes some additional factors: the squared sine function describes the pulsed laser wave, \(\xi(r)\) is the illumination factor of the bistatic beam configuration, \(\beta_{0}\) is the reflection coefficient of the object surface, which depends on the color and surface structure of the object, e.g., cars' refelctivity varies between a high of 80% (for white cars) and a low of 4% for black cars [14; 15; 16]. The delta function \(\delta(r-\frac{ct^{\prime}}{2}-R_{0})\) at the end stands for the spatial response of reflected laser power of a so-called hard target. A hard target in this context is a solid object that cannot be penetrated by the laser beam. By contrast, a soft target, such as rain, fog, or even a glass window, is considered to be penetrable by the laser beam. For soft targets, Equation (5) hasto be adapted, e.g., [10] have proposed a model for soft targets (Equation (6)), derived from the model proposed by [9]. \[ \begin{split} P_{r}^{\text{soft}}(r)=\frac{c\eta A}{2}\int_{0}^{rc/2} P_{0}\sin^{2}\biggl{(}\frac{\pi}{\tau_{\text{H}}}t\biggr{)}\\ \cdot\frac{\exp(-2\int_{0}^{r}\alpha_{\text{ext},}(r^{\prime}- \frac{ct^{\prime}}{2})dr^{\prime})}{(r-\frac{ct^{\prime}}{2})^{2}}\xi(r-\frac{ ct^{\prime}}{2})\beta\\ \cdot U(R_{0}-r+\frac{ct^{\prime}}{2}R_{0})dt^{\prime}\end{split}  \tag{6}\]  
As soft targets are placed in between the lidar and a hard target of interest (e.g., rain droplets in the path of the laser beam), Equation (6) calculates the potential reflected power based on the backscattering coefficient \(\beta\) of the scattering particles. Equation (6) also considers the fact that as soon as the beam reaches the hard target at position \(R_{0}\), backscattering of the particle ceases to exist, so a Heaviside step function \(U\) replaces the delta function of a hard target. Equation (6) shows that in adverse weather conditions, the extinction and backscattering coefficients can have a major impact on the lidar's perception. There are two different ways to calculate these coefficients: (i) calculating them directly using Mie Scattering and specific particle density functions, or (ii) by using empirical models. Both approaches are discussed briefly in the next sections.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 5 Conclusions  
This paper has addressed the parameter uncertainty along with its compensation in the discrete-time sliding mode controller (DSMC) system. Theoretical analysis of the DSMC system stability under the influence of parameter uncertainty has been reconsidered and reevaluated. The results show that even if the control parameters are adjusted so that the eigenvalues are within the unit circle, the DSMC system can still be unstable subjected to parameter uncertainty. Then, a detailed comparison of six existing disturbance compensators for the DSMC system with parameter uncertainty was presented. The considered disturbance compensators were the following: the N-steps delay estimation (NSDE), the one-step delay estimation (OSDE), the two-step delay estimation (TSDE), the SMC disturbance compensator (SDC), the decoupled disturbance compensator (DDC),and the discrete-time disturbance observer (DTDO). The theoretical bases of the compensators were presented and compared in detail. Moreover, simulation was carried out using seven different DSMC controllers. Considering the simulation results, they show that the parameter uncertainty in a DSMC system cannot be effectively compensated by all these compensators. The issue of extending the proposed method to practical systems will be studied in the future.  
**Author Contributions:** Conceptualization, Z.G. and H.M.; methodology, Q.S.; validation, Z.G., H.M. and Q.S.; formal analysis, H.M.; investigation, Z.G.; resources, H.M.; data curation, Q.S.; writing--original draft preparation, Z.G.; writing--review and editing, H.M.; visualization, Q.S.; supervision, H.M.; project administration, H.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was supported by the National Natural Science Foundation of China (51805327).  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.  
**Nomenclature**  
\begin{tabular}{l l} Symbols & Description \\ \(\Phi\), \(\Gamma\) & System matrix and vector. \\ \(\Delta\Phi\) & Parameter uncertainty. \\ \(k\) & \(k\)-th step in the discrete-time system. \\ \(d(k)\) & Disturbance. \\ \(x(k)\) & System state. \\ \(u(k)\) & Control input. \\ \(f(k)\) & Lumped disturbance. \\ \(z(k)\) & System state after transformation. \\ \(s(k)\) & Switching function. \\ \(C\) & Gain vector of switching function. \\ \(\
abla\) & Difference operator. \\ \(q\), \(\xi\), \(g\) & Control parameters. \\ \(\lambda\) & Eigenvalue. \\ \(\Omega_{i}\), \(i=1\), \(2\), \(3\) & Upper bound. \\ \(\Lambda\) & Diagonal matrix. \\ \(\delta(k)\) & Finite number function. \\ \end{tabular}
## 2 Modelling Degradation  
Degradation is not simple to model as a random process. If one takes degradation as loss--which is quite reasonable--it has to be modelled as something that cannot change its sign. In other words, degradation is monotone, and in the context of this study, it cannot be reversed.  
There are two immediate choices available. The first one is to model the process as a lognormal one. Second, a random field such as Karhunen-Loeve (KL) expansion can be squared. This latter approach is the one adopted here. Some realisations of KL-expansions are shown in Figure 2. Every initial configuration represents a nominal domain. The realisations by definition will have a smaller volume (or area, depending on the model), and the difference is the loss.