esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Testbed for Power System Cyber-Physical Operations Training
## 3 CPS Testbed Architecture
### Communication Layer  
The EXata CPS software from Scalable Technology provides a communication network emulation platform to simulate and predict the behavior of networked environments based on various operational scenarios, including different cyberattacks. The emulation runs in real time and models connections, computers, protocols, firewalls, and other network nodes [47]. The software runs within OPAL-RT and is integrated with HYPERSIM software to offer a complete real-time cyber-physical situation for developing, testing, and assessing electrical grids with communication networks [44]. This solution offers low-latency communications to analyze cyber threats that can be injected into the network layer. Besides that, as soon as all communication nodes and connections are mapped, it allows users to create controlled cyberattacks on the network.
# Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer
## 3 Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer (DHSMEO)
### Dynamic Dual-Subpopulation Adaptive Grouping Strategy  
The optimization process of the standard EO only relies on the equilibrium optimization strategy guided by equilibrium candidates, which leads to a relatively single search mode. When the EO becomes stuck into local optima, such search mode restricts the particle search efficiency across the optimization space and makes it difficult to extract more useful information for further performance enhancement. Therefore, a dynamic dual-subpopulation adaptive grouping strategy is constructed to promote the increase in population diversity and provide an effective information-exchange structure for the different search strategies. Given that distance effectively reflects the diversity of population particles, the proposed dynamic dual-subpopulation-adaptive grouping strategy divides the entire population into a kernel subpopulation and an auxiliary subpopulation according to distances between search particles and the currently obtained best particle. The kernel subpopulation consists of search particles with a farther distance from the current best particle. These particles are still guided by equilibrium candidates for optimization search, which is utilized to maintain the kernel search capability from the standard algorithm. The auxiliary subpopulation consists of search particles with a closer distance to the current optimal solution. These particles are guided solely by the optimal equilibrium candidate, which is used to assist the kernel subpopulation in further mining valuable information from the optimization space to enhance the optimization performance. Meanwhile, the entire iterative optimization process of the algorithm in the traditional population-grouping strategies typically employs the grouping method of fixed-size subpopulations to improve the population diversity. However, the grouping method of fixed-size subpopulations limits the information-mining capability of subpopulations with different search strategies to a certain extent during the whole algorithm execution process. And the subpopulation with a larger number of particles can further enhance their optimization performance. Therefore, the proposed dynamic dual-subpopulation adaptive grouping strategy introduces a grouping method with an adaptive change in subpopulation size throughout the iterative process to meet the optimization performance requirements at different stages. As the algorithm is executed iteratively, the population particles progressively approximate the current optimal solution. To further improve the search accuracy, this process requires an increasing number of auxiliary subpopulation particles to assist the kernel subpopulation particles in improving the search performance. Therefore, the size of the kernel subpopulation should gradually decrease as the algorithm iterates, while the size of the auxiliary subpopulation should progressively increase.
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
###### Abstract  
High-precision orbits of Low Earth Orbit (LEO) satellites are essential for many scientific applications, such as assessing the change in current global mean sea level, estimating the coefficients of gravity field, and so on. How to determinate the high-precision orbits for LEO satellites has gradually become an important research focus. HY2D is a new altimetry satellite of China, which is equipped with a Global Positioning System (GPS) and the third generations of the BeiDou Global Navigation Satellite System (BDS-3) in order to guarantee the reliability of orbital precision in radar altimetry mission. Therefore, this study adopts one month of spaceborne data to conduct the research of precise orbit determination (POD) for the HY2D satellite. Our analysis results are: (1) The standard deviation of residuals for the HY2D satellite based on spaceborne BDS and GPS data are 9.12 mm and 8.53 mm, respectively, and there are no significant systematic errors in these residuals. (2) The comparison results with Doppler Orbitography and Radio-positioning Integrated by Satellite (DORIS)-derived orbits indicate that the HY2D satellite, using spaceborne BDS and GPS data, can achieve the radial accuracy of 1.4-1.5 cm, and the mean three-dimensional (3D) accuracy are 5.3 cm and 4.3 cm, respectively, which can satisfy high-precision altimetry applications. (3) By means of satellite laser ranging (SLR), the accuracy of Global Navigation Satellite System (GNSS)-derived orbits of HY2D is approximately 3.3 cm, which reflects that the model strategies are reliable.  
high-precision orbits; HY2D; spaceborne BDS and GPS data; radial accuracy; model strategies 4.0.1
## 5 Conclusions  
MRP regularization can be used to suppress local non-monotonic structures and eliminate the influence of isolated noise. WTDM regularization not only considers the sparsity constraint of the image gradient, but also considers the continuity constraint of the image gradient, which can effectively protect the gradient information in different directions of the image. Therefore, we combine MRP and WTDM as the regularization constraint of the SIRT-MWTDM algorithm.  
The quality of the reconstructed image is often affected by noise in the actual acquired data. To simulate the real image more realistically, Poisson noise was added to the simulated sinogram. Different numbers of projections were used to reconstruct images. We used subjective evaluation and three objective indicators (PSNR, SSIM, and RMSE) to evaluate and analyze the reconstructed results obtained by using five algorithms (FBP, SIRT, SIRT-TV, SIRT-WTDM, and SIRT-MWTDM). The SIRT-MWTDM algorithm shows advantages compared with the other algorithms both in subjective vision and objective evaluation. Moreover, the profiles from the interesting positions of the reconstructed image also show that the modified algorithm in this paper can better retain the details and suppress noise. The profile of the image reconstructed by the modified algorithm is closer to the reference image. Finally, it can be concluded that the SIRT-MWTDM algorithm shows great advantages both in simulated data and real neutron projection data when compared to the other algorithms in this paper.  
Conceptualization, Y.W., M.Y., and L.H.; methodology, Y.W. and Q.L.; software, Y.W. and Q.L.; validation, Y.W. and L.H.; formal analysis, Y.W.; investigation, Y.W.; resources, L.H.; data curation, L.H. and M.W.; neutron experiment, Z.L. and L.H.; raw data reduction, M.W. and X.L.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W., L.H., and M.Y.; visualization, Y.L.; project administration, M.Y. and L.H.; funding acquisition, M.Y. and L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (No. 2017 YFA0403701, No. 2016 YFA0401502), the National Natural Science Foundation of China (NSFC) (No. 11675012, No. U1730116).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors would like to thank the fellow apprentices in the research group for their help.  
Figure 15: The RMSE and SSIM change curves of reconstructed images with different algorithms for 30 projections of the circular phantom. **(a)** RMSE; **(b)** SSIM.
## 5 Surface Subsidence Prediction Method for UCG
### Surface Subsidence Prediction Method Based on the Roof Subsidence Space  
In the prediction of surface subsidence caused by coal mining, the probability integral method based on random medium theory is one of the most mature and effective methods, which is widely used in the prediction of surface subsidence under various mining technologies. From the basic theory of stochastic media, the number of resources mined in volume per unit at underground coordinates \(s\) causes subsidence at any point \(x\) on the ground surface as [43]:  
\[w_{e}(x)=\frac{1}{r}e^{-\frac{\pi(x-s)^{2}}{r^{2}}} \tag{21}\]  
\(w_{e}\) is the surface subsidence at \(x\) caused by unit mining at coordinate \(s\), and \(r\) is the main influence radius.  
If the mining thickness here is \(m\) and the subsidence coefficient is \(q\), then the subsidence at \(x\) of the surface is as follows:  
\[w_{e}=mq\frac{1}{r}e^{-\frac{\pi(x-s)^{2}}{r^{2}}} \tag{22}\]  
Through the above analysis, we obtain the calculation method of the roof subsidence space of UCG mining, and the roof subsidence space can be regarded as mining space. Meanwhile, under similar geological and mining conditions, the ratio of main influence radius \(r\) to mining depth \(H\) is constant. We call it the tangent of the major influence angle, and the calculation method is \(\tan\beta=\frac{H}{r}\). At this time, the surface subsidence prediction model of UCG is as follows:  
\[W(x)=q\frac{\tan\beta}{H}\int_{0}^{l}w_{g}(x)e^{-\frac{\pi H^{2}(x-m)^{2}}{ \tan^{2}\beta}}dm \tag{23}\]  
where \(W_{(x)}\) is the subsidence at the surface coordinate \(x\), and \(w_{g(x)}\) is the roof subsidence space at the coordinate \(m\).  
Equation (23) is the formula for predicting surface subsidence caused by UCG.
# Collaborative Networks: A Pillar of Digital Transformation
## 5 Relevant Collaboration Aspects in Industry 4.0
### Collaboration in Digitalization of Products and Services  
Leveraging the full potential of the \"smart product\" concept requires effective collaboration among the organizations that are part of the corresponding value chain. The availability of data \"next to the product\" depends not only on the capabilities offered by the technical infrastructure but also on the effective collaboration among all entities with a role in the product history (Figure 9). To keep updated history records associated to the product and support tracing functionalities, it is necessary to rely on collaboration. Furthermore, the smart product itself can be used as the vehicle to mediate collaboration, implementing a kind of stigmergy [104]. In stigmergic collaboration, the communication and coordination among participants are not performed through direct interactions but rather indirectly by modifying their environment.  
The addition of user assistance, self-diagnostic, self-configurability, maintenance, and other value-added business services, which materialize the smartness of the product, naturally requires contributions from various stakeholders. On the other hand, the inherent technological features of smart products are likely to inspire the emergence of new business services that add value to those products. As such, there is an opportunity for new players, and the creation of \"collaboration communities\" around the product (\"product-related digital ecosystems\").  
What Can CNs Contribute?  
Although CN developments specifically for smart products are still not very noticeable, various other developments can have a potential application in this dimension, including:  
* Various cases of \"stigmergic collaboration\" can be identified in the area of mass collaboration whereby \"agents communicate with one another indirectly through traces left in the shared environment\" [104]. Originated in the study of termites in the 1950s, this notion is now being used in the case of collaboration among large groups. Wikipedia, Digg, SETI@home, Scratch, Galaxyzoo, etc. are some of those examples [105].
* Provision of integrated business services through collaboration of multiple stakeholders has been an emphasized topic in several research works [89; 106; 80]. This includes issues such as composition of integrated services, service evolution, service discovery, etc. within collaborative environments.
* On a more general perspective, the contribution of CNs to the transition to service-enhanced products/product-service systems has been widely addressed [107; 91; 108].
* Furthermore, various works have also addressed the (potential) contribution of CNs to innovation ecosystems and open innovation [109; 110; 111].
## 3 FCS-MPC Algorithm for Boost Converters  
The FCS-MPC algorithm simplifies the optimization problem by limiting the controller outputs to a finite set, significantly reducing computational complexity. This characteristic makes it highly suitable for applications requiring fast dynamic responses, such as DC-DC boost converters. The core of FCS-MPC lies in predicting the future behavior of the converter based on a discrete model and optimizing control actions to minimize a cost function that typically includes terms for tracking the reference voltage and minimizing inductor current deviation. The short-horizon FCS-MPC implementation discussed in this paper focuses on predicting future states and evaluating potential switching actions within a single timeframe, enhancing the controller's responsiveness to dynamic changes. However, this approach introduces challenges, particularly in managing the non-minimum phase behavior characteristic of boost converters.  
The general block diagram for the short-horizon FCS-MPC implementation is demonstrated in Figure 2. It consists of a capacitor voltage predictor and an inductor current predictor that forecast the future states of these variables based on the discrete Equations (1) and (2). A cost function calculator evaluates the performance of each possible switching state, and an optimum state finder identifies the control action that minimizes the cost function. Finally, a pulse generator translates the optimal control action into switching signals for the power converter without involving any PWM modulator. For each possible switching state, the predicted inductor current (\(i_{LP}\)) and capacitor voltage (\(v_{CP}\)  
desired setpoint, providing stability and efficiency in the power supply. Meanwhile, the current control prevents excessive current, which could lead to component stress, thermal issues, and potential damage. By balancing these two aspects, the MPC can achieve a harmonious operation, maintaining precise voltage regulation while protecting the system from overcurrent conditions, thereby enhancing the overall reliability and lifespan of the boost converter system.  
In (5), the references can be constant or variable over time. For this study, the output voltage reference is assumed to be constant, while the reference current is dynamically updated by an external circuit that includes a PI controller and a load detector, as detailed in the next section. The discrete model of the converter and the FCS-MPC algorithm are implemented on an FPGA using MATLAB HDL Coder (MATLAB R2024a). This setup enables real-time emulation within a single FPGA, simplifying the system and avoiding clock mismatch issues between the controller and the converter model simulated in real time. Thanks to HDL Coder, the entire system is translated into a VHDL code for FPGA programming via the MATLAB tool without requiring deep knowledge of VHDL programming.
## Appendix A Fractional Order Calculus  
Fractional-order calculus is a generalization of the integration and differentiation from integer to non-integer order. This appendix introduces only definitions which are widely used in the area of control system.  
First, the gamma function \(\Gamma(z)\) which is the extension of the factorial for non-integer number \(z\) is introduced  
\[\Gamma(z)=\int_{0}^{\infty}e^{-t}t^{z-1}dt \tag{10}\]  
The most important property of the gamma function is  
\[z\Gamma(z)=\Gamma(z+1) \tag{11}\]  
Then, the definition of derivative of order \(\alpha\in\mathbb{R}\) is presented. In continuous-time domain, the most often used one is the _Riemann-Liouville_ definition  
\[t_{0}D_{t}^{\alpha}f(t)=\frac{1}{\Gamma(n-\alpha)}\frac{d^{n}}{dt^{n}}\int \limits_{t_{0}}^{t}\frac{f(\tau)}{(t-\tau)^{r-n-1}}d\tau \tag{12}\]  
where \(t_{0}\) and \(t\) are the limits and \(n\) is an integer number satisfying \(n-1<\alpha<n\). In practical applications where computer-based control devices are used, the following _Grunwald-Letnikov_ definition with _short memory principle_ is preferred:  
\[t_{0}D_{t}^{\alpha}f(t)=T_{s}^{-\alpha}\sum_{j=0}^{\left[\frac{t-t_{0}}{T_{s}} \right]}(-1)^{j}\binom{\alpha}{j}f(t-jT_{s}) \tag{13}\]  
in which \([.]\) means the integer part, \(T_{s}\) is the sampling time and \(\binom{\alpha}{j}\) is the binomial coefficient defined by  
\[\binom{\alpha}{j}=\frac{\Gamma(\alpha+1)}{\Gamma(j+1)\Gamma(\alpha-j+1)} \tag{14}\]
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 3 Heuristic Methods on a Simple Thermal Model Reduction and Correlation  
### Reduction and Correlation Process  
The application of a heuristic method for global optimization in the thermal model reduction and correlation process is structured in two distinct phases, as illustrated in Figure 3: Phase 1--Build Thermal Models and Phase 2--Global Optimization Loop.  
In Phase 1, the process begins with the development of both the detailed thermal mathematical model (DTMM) and the reduced thermal mathematical model (RTMM). These models are created and arranged to enable comparison and correlation. An initialrevision of the RTMM is conducted to prepare it for reduction and alignment with the DTMM. Phase 2 encompasses an iterative global optimization loop aimed at minimizing the discrepancy between the DTMM and RTMM outputs. A global heuristic optimization method is applied to guide adjustments to the RTMM parameters. Each iteration involves updating the RTMM, validating its performance through a comparison against the DTMM using a fitness function, and checking whether the predefined convergence criterion is met. This criterion--often referred to as a \"fitness function\" [2; 4; 5; 6; 14; 17; 20; 59], \"objective function\" [16; 21; 40], or \"error function\" [22]--quantifies the accuracy of the RTMM relative to the DTMM. The loop continues until the fitness function indicates that the model meets the correlation acceptance criteria. Once this condition is satisfied, the process concludes with a validated and reduced thermal model.  
Generally, this \"fitness function\" is defined as something similar to the root sum square of all selected nodes between DTMM and RTMM. The equations are generally written as follows [2; 17; 21]:  
\[F\mathrm{F_{example\;1}}=\sum_{\mathrm{nodes}}(T_{\mathrm{DTMM}}-T_{\mathrm{ RTMM}})^{2},\text{ by [PERSON]} \tag{1}\]  
\[F\mathrm{F_{example\;2}}=\sqrt{\sum_{\mathrm{nodes}}(T_{\mathrm{DTMM}}-T_{ \mathrm{RTMM}})^{2}},\text{ by [PERSON]} \tag{2}\]  
\[F\mathrm{F_{example\;3}}=\frac{1}{N_{\mathrm{TEMP}}}\sum_{i=1}^{N_{\mathrm{ TEMP}}}|T_{i,\mathrm{DTMM}}-T_{i,\mathrm{RTMM}}|,\text{ by [PERSON]} \tag{3}\]  
where \(N_{\mathrm{TEMP}}=N_{\mathrm{nodes}}\times N_{\mathrm{time\;steps}}\).  
These equations demonstrate that typical fitness functions evaluate root sum square of differences between corresponding DTMM and RTMM nodes.  
Once the fitness function between the DTMM and RTMM is defined, it implements the heuristic loop method to iteratively optimize the adjustable parameters of the RTMM. The process operates in a loop:  
1. Adjust the parameters by updating the RTMM;
2. Run thermal simulations for RTMM;
3. Evaluate the fitness function.  
This optimization is carried out using a heuristic method, such as a Genetic Algorithm, simulated annealing, or other optimization techniques, as shown in Table 5. The goal is to minimize the fitness function, ideally reducing it to zero, thereby achieving a high degree of correlation between the DTMM and RTMM.
###### Abstract  
A magnetometer based on Hall effect sensors was developed using a 1020 steel base, two magnets (K&J Magnetics, Pipersville, PA, USA), and two Hall effect sensors: MLX-90215 (Melexis Inc., San Jose, CA, USA) and AD-AD22151 (Analog Devices, Inc., Wilmington, MA, USA). Both sensors were assembled on an acrylic support and powered with a 5-volt battery. The readings can be taken using bench multimeters (HP 34401A or Iming ET-2042D). The magnetometer records magnetic hysteresis loop and saturation by applying a field of up to 0.33 T at room temperature. The device was tested using magnetic hysteresis data from samples of 99% pure nickel sphere and magnetite microparticles. A dipole magnetic model was used to obtain the moment of the samples. The sensor used for reading the induced magnetic field of the sample AD-AD22151 has a sensitivity can be adjusted from to 110 mV/mT. This device is cheap, versatile, and easy to build, and despite its low accuracy, when compared to commercial equipment, it can be useful in low-budget teaching and research laboratories.  
Keywords:hall magnetometer; magnetic properties; magnetite microparticles +
Footnote †: journal: Applied
## 4 Verification
### Simulation Validation Utilizing Authentic PMU Data
#### 4.2.1 The Recognition Outcomes of the Fundamental Elements  
Figure 2(a) depicts the parameter recognition outcomes of the fundamental elements obtained by three approaches. The figure clearly illustrates that the recognition outcomes of \(f_{0}\) from the fitting-based ERA were between 49.94 Hz and 50.03 Hz, and the identification results of \(x_{0}\) were between 0.0430 p.u. and 0.0506 p.u. The difference in identification results compared with the existing ERA and interpolated DFT algorithm was insignificant. Theoretically, as the length of the data window increases, a greater amount of information  
Figure 3: Identified results of frequency and amplitude for actual measured PMU data case. (**a**). Fundamental component frequency (**b**). Subsynchronous oscillation component frequency.  
is extracted, leading to more precise identification results, but the real-time performance will be worse. The existing ERA and interpolated DFT algorithm employ data window durations of 1 s and 2 s, respectively. In contrast, the fitting-based ERA can obtain identification results with similar accuracy using only a 200 ms data window. Using a 200 ms ultra-short data window, the ERA approach, rooted in fitting techniques, facilitates the dynamic observation and analysis of SSO activity in an electrical grid, ensuring continuous monitoring capabilities.