esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Scheme Using Optical-Electronic Joint Phase Compensation
With the coupler and circulator, part of light is returned to the transmitter through the same fiber link. Since the light has the same optical path in the same fiber and the RF signal will suffer the same phase delay \(\varphi_{\text{p}}\), therefore, the phase delay of the laser signal returning to the transmitter is \(\varphi_{\text{r}}=\varphi_{0}+\varphi_{\text{cl}}+\varphi_{\text{cl}}+2 \varphi_{\text{p}}\). The returning light passes through the fiber stretcher again, thus adding another phase delay \(\varphi_{\text{cl}2}\). The light is then converted to an RF signal by a photodiode (PD1), which is then phase shifted by another shifter (PS2). The PS1 and PS2 used in our experiment are exactly the same, and they provide the same phase delay. Therefore, the recovered RF signal has a final phase delay \(\varphi_{\text{final}}=\varphi_{0}+2\varphi_{\text{cl}1}+2\varphi_{\text{cl}2}+ 2\varphi_{\text{p}}\), as shown in Figure 3. We mix the recovered RF signal with the reference source in the mixer to obtain an error signal. Here, the initial phase \(\varphi_{0}\) in the error signal is eliminated, and only the phase information \(2(\varphi_{\text{c1}}+\varphi_{\text{c2}}+\varphi_{\text{p}})\) is remained. The error signal is divided into two parts, which are fed back to the fiber stretcher and phase shifter, respectively via the API controller and DPI controller. In this case, the phase delay \(\varphi_{\text{p}}\) has been compensated, as equation \(2(\varphi_{\text{c1}}+\varphi_{\text{c2}}+\varphi_{\text{p}})=0\). Compared to previous transfer schemes with a single optical compensator, the proposed frequency transfer system has been greatly improved in terms of resolution and compensation range, thanks to the combined optical and electrical compensation scheme. To verify the feasibility of the proposed scheme, we designed and implemented an actual frequency transfer experiment.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Method
#### 3.2.1 Improvement in TWSC Uncertainty
This involved introducing the mean-squared value of the sum of squares of the off-diagonal elements in the upper-right corner of \(R\), defined as:\[\left[G(r_{iN})\right]^{2}=\frac{1}{N}\underset{i<k}{\overset{N}{\sum}}r_{ij}^{2} \tag{7}\]  
Substituting Equation (6) into Equation (7), we can obtain:  
\[\left[G(r_{iN})\right]^{2}=\frac{1}{N}\underset{i<k}{\overset{N}{\sum}}r_{ij}^{2 }=\frac{1}{N}\underset{i<k}{\overset{N}{\sum}}r_{iN}^{2} \tag{8}\]  
Considering the constraint conditions, it is necessary to define an appropriate objective function and minimize it to determine the \(N\) free parameters. The objective function is as follows:  
\[F(r_{1N},\dots r_{NN})=\frac{1}{K^{2}}\underset{i<k}{\overset{N}{\sum}}r_{ij}^ {2} \tag{9}\]  
To satisfy the constraint that \(R\) is a positive definite matrix, the initial values for iterative calculation are set as:  
\[\left\{ \begin{array}{l}r_{iN}^{0}=0,i<N\\ r_{NN}^{0}=\frac{1}{2^{s_{i}}}s^{\star}=[1,\dots,1]S^{-1}[1,\dots,1]^{T}\end{array} \right. \tag{10}\]  
Minimizing Equation (9) under the constraint conditions (Equation (8)) yields a set of solutions for the free parameters, which are the variances in uncertainties for different observation series. 2. TWSC fusion  
Based on the uncertainty of each TWSC series calculated by GTCH, their weights were assigned to the TWSC series. Then, these TWSC series were fused according to their weights:  
\[TWSC_{fuse}=\underset{i=1}{\overset{F}{\sum}}p_{i}\times TWSC_{i} \tag{11}\]  
where \(TWSC_{i}\) and \(p_{i}\) denote the TWSC value and its corresponding weight, \(F\), denotes the number of TWSC series. The weights were determined according to the following expression:  
\[p_{i}=\frac{1/r_{ii}}{\underset{n=1}{\overset{F}{\sum}}1/r_{nn}} \tag{12}\]  
where \(r_{ii}\) denotes the variance of the \(i\)th TWSC series estimated by the GTCH method. The above process was performed grid by grid until we had fused the six datasets on all the grid nodes.
## 6 Discussion  
The classical LSM method is based on a predefined probabilistic model of an underlying asset price. The LSM samples future paths of the underlying asset price from the predefined probabilistic model, estimates conditional expected values using regression, and derives the option value through backward induction.  
Conversely, the Reinforcement Learning (RL) approach is model free, eliminating the need for a predefined probabilistic price model to value an option. Instead, RL can utilize historical underlying asset price data to train for the optimal decision policy, exemplifying a data-driven approach to pricing American Options. Our study demonstrates that the RL  
Figure 14: Frequencies of exercise time for LSM and RL with the calibrated EGARCH model.  
method is effective for valuing American Options and yields results comparable to those obtained using the BOPM and LSM methods.  
A limitation of the RL method needs to be mentioned. The stability of the training process can be an issue in finding the optimal policy. Careful attention is required when designing the RL algorithm with the appropriate number of batches, iterations, and learning rates to avoid overfitting and underfitting.  
Future work could explore the potential of different feature functions (in this study only Laguerre was considered) in improving policy and valuation of the American Option. Additionally, investigating other types of underlying price models, such as jump models or two-factor models, could provide further insight.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 1 Introduction
### Experimental Research
#### 1.2.2 Measurement Techniques  
In the experimental setups, the most commonly measured characteristic is the height of levitation. The measurement can be easily completed with the use of optical displacement sensors or eddy current sensors [16]. Researchers trying to measure other properties are rare. [PERSON] [17] described using the pressure sensor in the setup of a sliding bearing. The measurement showed the average dynamic pressure of the film. [PERSON] et al. [18] used a force sensor to determine the load-carrying force in the proposed journal bearing. In addition, for studies dealing with the forced motion of the sample, inertial sensors [11] and shaft speed sensors [7] were used.
# Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit
## 3 Target Selection of SSO Space Debris
### Selection of SSO Target Debris  
Next, let us build up the target debris database for this multiple-SSO-object flyby mission. Based on the distribution characteristics of the orbital location, type, source, and size of SSO space cataloged objects, this paper selects space cataloged objects that satisfy the following conditions to construct the target debris collection for the flyby mission:  
1. The types of target debris are debris or rocket body.
2. The perigee altitude has not yet begun to decrease.
3. The target debris is generated by Changzheng rockets.
4. The radar cross-section area of the target is as follows: \(S_{\text{min}}>1\text{ m}^{2}\).
5. The mass of the target is as follows: \(m>1\text{ kg}\).
6. \(h_{P}\in[600,900]\text{ km}\), \(h_{a}\in[650,1050]\text{ km}\), and \(i\in[96.3,99.3]^{\circ}\).  
The second condition means that the orbit of space debris remains stable. In the fourth condition, \(S_{\text{min}}>1\text{ m}^{2}\) means that the target debris is large space debris. The last condition means that the target debris is in the area with the highest density of debris distribution.  
A total of 22 pieces of debris were selected that satisfied the above conditions. Their orbits in the J2000 ECI coordinate are shown in Figure 4. The physical and orbital parameters of these target debris are shown in Tables A1 and A2 in Appendix A.
# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 3 Method
### Statistical Analysis  
To compare the image quality scores and NIIRS values for nSight-1 and Landsat 8 images we performed a statistical analysis in GraphPad Prism(r) software. The Shapiro-Wilk test was first used to test the normal distribution of the visual scores from the blind assessment image quality scores and NIIRS scores while a Student's \(t\)-test was used to compare the score from the two datasets using a significance level of 0.05.
## 2 Related Work
### PSO Algorithm
#### 2.4.1 Basic PSO Solution Process  
In the PSO algorithm, each particle is represented as a potential solution, and several particles form a swarm. Let \(\mathbb{R}^{D}\) denote the \(D\)-dimensional problem space. In the \(t\)th generation, the position and velocity of the \(i\)th particle are expressed as \(\mathbf{x}_{i}^{t}\ =\ (x_{i1},x_{i2},\ldots,x_{iD})\in\mathbb{R}^{D}\) and \(\mathbf{v}_{i}^{t}\ =\ (v_{i1},v_{i2},\ldots,v_{iD})\in\mathbb{R}^{D}\). Let \(\mathbf{pbest}^{t}\) be the individual best position of this particle and \(\mathbf{gbest}^{t}\) be the swarm best position. In the \((t+1)\)th generation, the velocity and position of the particle are updated as follows:  
\[\mathbf{v}_{i}^{t+1}(j)\ =\ \omega*\mathbf{v}_{i}^{t}(j)+c_{1}*r_{1}*\left(\mathbf{pbest}^{t }(j)-\mathbf{x}_{ij}^{t}\right)+c_{2}*r_{2}*\left(\mathbf{gbest}^{t}(j)-\mathbf{x}_{ij}^{t }\right),j\ =\ 1,2,\ldots D \tag{10}\]  
\[\mathbf{x}_{i}^{t+1}\ =\ \mathbf{v}_{i}^{t+1}+\mathbf{x}_{i}^{t} \tag{11}\]  
where \(c_{1}\) is the social learning factor, \(c_{2}\) is the cognition learning factor, \(c_{1}\) and \(c_{2}\) determine the magnitude of the random force in the direction of particles with the previous best-visited position and the best particle, \(r_{1}\) and \(r_{2}\) are two independent uniform random numbers between [0,1], and \(\omega\) is the inertia weight used to control the balance between exploration and exploitation. A large \(\omega\) is useful for jumping out of the local optimal solution and improves the global search ability. In contrast, a small \(\omega\) is suitable for algorithm convergence and enhancing the local search ability. Since inertia weight is one of the essential parameters in PSO, in this study, a dynamic inertia weight [53] is used to improve PSO performance through a linearly decreasing mechanism, as shown in Equation (12). This scenario requires emphasizing the particle's exploration ability in the early period and the particle's exploitation ability in the later period.  
\[\omega\ =\ \omega_{max}-\frac{t*(\omega_{max}-\omega_{min})}{T_{max}} \tag{12}\]  
where \(\omega_{max}\) and \(\omega_{min}\) are the bounds on the inertia weight, \(t\) is the number of iterations, and \(T_{max}\) is the maximum number of iterations. Generally, \(\omega_{max}\ =\ 0.9\) and \(\omega_{min}\ =\ 0.4\) are fixed values.  
In addition, a maximum velocity \(\mathbf{v_{max}}\) serves as a constraint to control the positions of swarms within the solution search space.
## 3 Results
### Temporal Distribution of GPP  
#### 3.3.1 Overall Temporal Variation in GPP  
The GPP derived from the Landsat data using the CASA model, with a 16-day temporal resolution, was averaged across the annual and intra-annual time series and visualized as a time series line plot, as shown in Figure 5. Interannually, the annual GPP exhibits a slight upward trend from 2001 to 2020. The GPP shows noticeable interannual fluctuations, particularly in the middle years (i.e., 2009-2012), where the values are relatively stable but do not exhibit significant dips, which is likely due to the balanced climatic conditions during this period. Intra-annually, GPP follows a characteristic \"low-high-low\" pattern, aligning with the seasonal vegetation growth cycles. At certain intervals, GPP displays a sequence of peaking, declining, and subsequently increasing again, which can be attributed to the interactions between vegetation phenology and climatic drivers, which is a common phenomenon. Peak GPP values typically range from 100 to 140 gC-m\({}^{-2}\)-16 d\({}^{-1}\).  
Figure 4: Distribution of GPP with elevation in the study area (the \(y\)-axis represents the years, ranging from 2001 to 2020; the \(x\)-axis represents the elevation in northwestern Hubei; and the \(z\)-axis represents the mean GPP value).
## 3 Assessment of the Developed Algorithm Efficiency in Some Case Studies
### North Sea
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{2}{*}{_In situ_ measurements} & \multicolumn{5}{c|}{**Remote sensing data**} \\ \cline{2-6}  & \multicolumn{3}{c|}{**LM - algorithm**} & \multicolumn{3}{c|}{**ESA standard algorithms**} \\ \hline _chl_, **ug/L** & _tsm_, **mg/L** & _chl_, **ug/L** & _sm_, **mg/L** & _algal 1_, **mg/C/L** & _algal 2_, **mg/C/L** \\ \hline  
MSE** & & 0.95 & 0.46 & 1.27 & 2.10 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of WQP concentrations measured _in situ_ and retrieved from MERIS data for the North Sea, Skagerrak, 2007 with the LM-algorithm and standard ESA algorithms. factor between _chl_ concentration and _chl_ fluorescence varies due to a number of factors. However, _chl\({}_{F}\)_ provides important information about the variation and gradients of phytoplankton activity along the ferry transect. The relative agreement between the satellite product and the _in situ_ observations along the transect can thus be studied. Our comparison shows (Figure 5) that _chl_ concentrations retrieved with the LM algorithm are rather close to the _chl\({}_{F}\)_ values derived from the night-time fluorescence signals (r = 0.83, RMSE = 2.7 \(\mu\)g/L) and compare worse with the _chl\({}_{F}\)_ values derived from the day-time fluorescence signals (r = 0.48, RMSE 8.85 \(\mu\)g/L).
# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 4 Experimental Results and Comparisons
### Visualization of the Feature Distribution  
Given a sequence \(\mathcal{I}\) as input, our VAE outputs the feature map in the size of \(h_{l}\times w_{l}\times c_{l}\), which means we extract a \(c\)-dimension feature vector at each location \((i,j)\). To observe the distribution of the feature vectors, we set the feature dimension \(c\) to 3 as an example and select a fixed location \((i_{*},j_{*})\). Then we can scatter the feature vectors \([f(i_{*},j_{*},1),f(i_{*},j_{*},2),f(i_{*},j_{*},3)]^{\top}\), which are extracted from the same receptive field of both training and testing sequence sets, in a three-dimensional grid as follows.  
As shown in Figure 3, all the feature vectors \([f(i_{*},j_{*},1),f(i_{*},j_{*},2),f(i_{*},j_{*},3)]^{\top}\) extracted from the same receptive field of the input series are plotted as scatter points in the 3-dimensional coordinate system. The distribution of both training and testing scatter points is almost a 3D fusiform shape, which is a typical shape of the multivariate Gaussian distribution. Moreover, the contours of the projection on the three planes also indicate that the distribution of the scatter points is Gaussian. Then we examine the relationship between the receptive patches and the feature points and take three from testing samples as examples. The feature vectors that near the center come from the normal samples (the pedestrian and background) and some similar samples of anomalies (the cyclist). The points that far from the center come from the anomaly samples (the car). This distribution trend indicates that the fitting the feature vectors by Gaussian is feasible.
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
###### Abstract  
This paper presents a Deep Learning (DL) and Image-Processing (IP) pipeline that addresses exposure recovery in challenging lighting conditions for enhancing First Responders' (FRs) Situational Awareness (SA) during rescue operations. The method aims to improve the quality of images captured by FRs, particularly in overexposed and underexposed environments while providing a response time suitable for rescue scenarios. The paper describes the technical details of the pipeline, including exposure correction, segmentation, and fusion techniques. Our results demonstrate that the pipeline effectively recovers details in challenging lighting conditions, improves object detection, and is efficient in high-stress, fast-paced rescue situations.  
deep learning; image processing; exposure recovery; situational awareness; first responders 1
## 3 Methodology
### Data Standardization
#### 3.3.5 Optimization
The next step in applying PCA, is to observe the results by plotting the components against the total variance that they represent, as shown in Figure 12a and also the entire dataset, after the transformation, against the target class to inspect how easily the two classes could be distinguished as shown in Figure 12b. One of the most applied methods for hyperparameter optimization (HPO) is grid search. From a computational perspective, it is a costly operation since it essentially is a brute force black-box task. However, it allows us to find the optimal values for the parameters of multiple algorithms without human interaction. According to the literature, one can find a few alternatives to grid search, such as the population-based methods of random search, genetic algorithms, particle swarm optimization, the Bayesian optimization methods and others that are less computationally expensive [51]. However, for this work, since the dataset is of relatively small size, the grid search method was preferred.
###### Abstract  
Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in present cryptographic algorithms and therefore, Symmetric Random Function Generator (SRFG) has been developed. In this paper, we have analysed SRFG based on propagation feature and immunity. Moreover, NIST recommended statistical suite has been tested on SRFG outputs. The test values show that SRFG possess some of the useful randomness properties for cryptographic applications such as individual frequency in a sequence and block-based frequency, long run of sequences, oscillations from 0 to 1 or vice-versa, patterns of bits, gap bits between two patterns, and overlapping block bits. We also analyze the comparison of SRFG and some existing random number generators. We observe that SRFG is efficient for cryptographic operations in terms of propagation and immunity features.  
cryptography; random; propagation; symmetric; immunity; function; boolean +
Footnote †: journal: Applied sciences  
Article  
A Secure Random Number Generator with Immunity and Propagation Characteristics for Cryptography Functions
# Review of Convective Heat Transfer Modelling in CFD Simulations of Fire-Driven Flows
## 4 Specific Treatment of Burning Surfaces
### Special Topic: Influence of Grid Size  
It is worth noting that in the case of turbulent wall fires (i.e., involving both inert and/or flammable surfaces), the use of smaller grid sizes will result in higher convective heat fluxes at the fuel surface due to better resolution of the flame temperature in the near-wall region and due to the fact that heat is distributed over smaller cell volumes. The way pyrolysis is usually modelled in CFD codes (i.e., there's no fuel mass fraction boundary condition at the wall surface) does not affect the reaction rate in the gas phase.  
This is not the case for liquid pool fires where a fuel mass fraction boundary condition is imposed and used when solving the gas phase equations. Decreasing the grid size results in higher fuel mass fraction values in the first cells above the fuel surface (i.e., development of a fuel rich region typically on the centreline and on the burner edges). This aspect decreases the reaction rate and the gas temperature on the first grid cells, which in turn decreases the convective heat fluxes as well. This is primarily a numerical issue that can occur when the calculation of the convective heat fluxes in pool fires is based on the first grid cell values and the surface temperature. In such cases, a decrease in the convective heat fluxes with decreasing grid size will occur around the centre of the pool fire and can potentially affect the numerical predictions if liquid evaporation is modelled. This aspect has been reported/discussed in the past by [43; 67; 73] in the context of pool fire modelling. A suggested guideline would be to avoid the calculation of the convective heat fluxes based on the first grid cell values as these approaches will be highly grid sensitive. Rather the use of an average gas phase temperature (i.e., determined a-priori or dynamically during the simulation) could be a good alternative.
## 6 Conclusions  
A thermal and mechanical analysis of the switched reluctance motor (SRM) for mining applications was discussed in this paper. Several case studies of water jacket configurations were carried out to determine an appropriate method for ensuring uniform temperature distribution in the proposed model. A 2D finite element model for the SRM was developed to obtain the losses and radial force. The cooling jacket configurations with 17 channels at a flow rate of 0.6 L/s were found to be optimal for a 75 kW, 72/48 SRM. The simulation results showed that the highest temperature point was 77.5 \({}^{\circ}\)C, located inside the SRM stator winding ends. The natural frequencies were calculated with the developed finite element mechanical, structural model. The 3D geometry of the SRM was modeled to obtain the vibration characteristics of the motor under free vibration for modal analysis, as well as the forced vibration response as radial forces for harmonic analysis. The structure of the shaft with a spoke was proposed for a 72/48 SRM; the advantages of the structure caused the natural frequency to rise and reduced the weight and temperature of the SRM prototype. The experimental and computational fluid dynamics (CFD) results were compared and analyzed. The simulation results were in close agreement with the experimental results and the accuracy of the simulation results was proved.  
Conceptualization, E.E., Z.L. and G.L. Methodology, E.E. and Z.L.; Software, E.E.; Validation, E.E. and Z.L.; Formal analysis, E.E. and Z.L.; Investigation, Z.L. and E.E.; Resources, E.E. and Z.L.; Data curation, E.E. and Z.L.; Writing--original draft preparation, E.E.; Writing--review and editing, G.L. and Z.L.; Supervision, G.L. and Z.L.; Project administration, G.L. Funding acquisition, G.L.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode Shape**} & **Experimental Modes (Hz)** & **FEM Modes** & **Error** \\  & **Modes (Hz)** & **of Spoke Shaft (Hz)** & **(\%)** \\ \hline M-1 & 490.3 & 508.64 & 3.7 \\ M-2 & 501.4 & 516.56 & 3.02 \\ M-3 & 729.7 & 748.64 & 2.59 \\ M-4 & 780.5 & 800.23 & 2.52 \\ M-5 & 842.2 & 859.18 & 2.02 \\ M-6 & 848.1 & 872.78 & 2.91 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of the FEM and experimental natural frequencies of the SRM with spoke shaft.  
Figure 23: The diagram of impact hammer test bench for the SRM.