esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Testbed Development
### Characterization of Starlink
#### 4.1.1 Mega-Constellation Infrastructure  
The constant evolution of Starlink's space infrastructure is reflected in an uninterrupted growth in the number of active satellites and in the re-configuration of orbital parameters. This dynamism usually leads to outdated and inaccurate information regarding the mega-constellation's configuration.  
The Starlink project comprises various satellite versions, which are classified into the first generation and the second generation, officially known as Gen2 [43]. Each spacecraft design enhances operational capabilities and physical features, such as the implementation of laser Inter-Satellite Links (ISL) in v1.5 [44] or the introduction of dielectric mirror films in Gen2 to reduce satellite reflection [45]. The latest versions implement cutting-edge broadband technologies such as direct to cell, an NTN approach to communicate unmodified 3 GPP terminals directly with satellites. These advances show the continuous evolution of the mega-constellation and its adaptation to emerging industry demands.  
The latest filings indicate that SpaceX has successfully obtained approval for its mega-constellation proposal, which entails the launch of 41,943 satellites as outlined in Table 1. The initial expectations regarding their orbital configuration have been dynamically read-justed throughout the development of the project. Nevertheless, the near-term scope has been recently updated, leading to the configuration delineated in Table 2[46; 47]. These data represent the orbital shell configuration that has been authorized by the corresponding regulatory entities at this time. It does not describe neither the current state nor the final state, but rather an intermediate step of the development roadmap. In order to provide a visual perspective of this mega-constellation, Figure 2 shows a simulation of the appearance that it would have at present [48]. Satellites are particularly concentrated over the most populated latitudes while extending more sparsely over the polar regions, enabling global coverage. Note that this visualization tool ([[https://satellitemap.space](https://satellitemap.space)]([https://satellitemap.space](https://satellitemap.space)), accessed on 28 October 2024) highlights the position of the International Space Station (ISS) as a green dot and the calculated areas in which a satellite will soon perform a re-entry as red dots.  
Figure 2: Visual simulation of the Starlink satellites orbiting Earth in mid-2024 ([[https://satellitemap.space](https://satellitemap.space)]([https://satellitemap.space](https://satellitemap.space)), accessed on 28 October 2024). Note that the white dots represent satellites in orbit, the green dot indicates the position of the International Space Station (ISS), and the red dots correspond to potential satellite re-entry points.
## 3 Materials and Methods
### Features
#### 3.3.9 MAC--IP Mapping  
**Feature feasibility:** MAC to IP mapping can be used in any LAN to detect changes in hardware components. The appearance of new MAC indicates that new hardware has appeared in the network. While MAC addresses can be forged, they are still usable for impersonation detection. It also helps the ICS network operator to be aware of all legitimate hardware in the system.  
**Our data:** No MAC to IP mapping anomalies were detected in the traffic.
## 2 Materials and Methods
### Cell Culture and Treatment  
#### 2.2.1 Osteoblasts  
MC3T3-E1 murine osteoblast cells were purchased from the Korean Cell Line Bank (KCLB, Seoul, Republic of Korea) and maintained in alpha minimum essential medium (\(\alpha\)-MEM) with 10% fetal bovine serum (FBS, Welgene, Daegu, Republic of Korea), 100 unit/mL penicillin (Welgene, Republic of Korea), and 100 ug/mL streptomycin (Welgene, Republic of Korea). The cells were seeded in 96-well plates at 1 \(\times\) 10\({}^{4}\) cells/well. After 24 h, the cells were differentiated by replacing fresh \(\alpha\)-MEM medium with 10 mM \(\beta\)-glycerol phosphate, 50 mg/mL ascorbic acid, and GG or GAM at the indicated doses.
## 3 Results and Discussion
### Effect of the Crosslinking Reaction on the Thermo-Mechanical Properties
Figure 4a shows the MSD curves obtained for the initial 200 ps in the NPT ensemble. The MSD of the uncrosslinked BC model was especially large over the entire simulation time, and the profiles graduallydecreased with the crosslinking ratio. The results suggest that as the MMA liquid became covalently connected to the adjacent MMA and/or PMMA, the translational freedom of the molecular networks became highly suppressed. Figure 4: Thermal properties of the PMMA-based BC models. (**a**) MSD and (**b**) LCTE of BCs at 300 K, according to the crosslinking density. Figure 3: Mechanical tests of PMMA-based BC at different temperatures. (**a**) Elastic stiffness according to the crosslinking ratio. (**b**) Visualization of the BC-50 model with Connolly surface and (**c**) the corresponding free volume fraction derived at each stage of temperature. The linear coefficient of thermal expansion (LCTE) was also calculated for each BC model. Each BC model was equilibrated at 390 K and 1 atm using the NPT dynamics for 1 ns, and then was gradually cooled down at a constant rate of 0.02 K/ps. For each level of temperature, the change in the specific volume of the unit cell was monitored, and the LCTE was approximated using the following linear regression [30]:  
\[3\alpha\cong\gamma=\frac{1}{V_{0}}\frac{\partial V}{\partial T}\cong\frac{1}{V _{0}}\frac{\Delta V}{\Delta T}=\rho_{0}\frac{\Delta v}{\Delta T} \tag{4}\]  
where \(\alpha\) is the LCTE, while \(\gamma\) is the volumetric coefficient. In addition, \(V\) and \(v\) are the volume and the specific volume, respectively, while \(V_{0}\) and \(\rho_{0}\) are the volume and the density of the unit cell at room temperature, respectively. Similar to the results for the MSD curves, the LCTE value of the fully crosslinked BC decreased by approximately 20%, compared with the uncrosslinked BC, as shown in Figure 4b. In particular, the LCTE value for BC-80 (80.5 ppm/K) agreed well with the experimentally reported value (approximately 98 ppm/K, for the highest molecular weight) [31]. The strong covalent bonds among the BC materials improved the thermal stability by limiting the free movement of the molecular chains. In conclusion, the change in the crosslinking density with curing time clearly affected both the structural configuration and the thermal stability of the microstructures. Such significant changes in the thermomechanical properties of BC materials are particularly important for dealing with interactions with the bone scaffold (HAp), as will be discussed in the next section.
## 1 Introduction  
High-voltage power cables often exhibit significant skin and proximity effects when carrying alternating current (AC). The skin effect concentrates current near the conductor surface, effectively reducing the cross-sectional area for current flow and thus raising the AC resistance [1]. The proximity effect further distorts current distribution in the presence of neighboring conductors, potentially increasing losses [2]. These effects become especially pronounced in large-diameter conductors and at elevated frequencies [3].  
To mitigate AC losses, segmented cables (Milliken designs) are subdivided into multiple lobes or bundles, often separated by thin insulation or air gaps [4, 5]. Accurate simulation of such designs typically involves finite element (FE) methods, which are flexible enough to capture complex geometries [6].  
However, 3D finite-element analysis is computationally expensive, especially for cables with hundreds of strands [7]. Under certain conditions--most notably, an infinite lay length or purely parallel conductor strands--2D cross-sectional simulations can approximate 3D results well. Various studies [8] confirm that when no helical twist is present, 2D and 3D solutions for skin and proximity losses vary only a few percent.  
All conductors in this study are considered to have a hexagonal cross-section, as this shape is typical under full compression [9]. This design minimizes voids and optimizes packing efficiency in cables, ensuring reduced AC resistance. Figure 1 illustrates the difference between uncompressed and fully compressed cross-sectional configurations.  
Hexagonal segments are particularly relevant for evaluating skin and proximity effects due to their uniformity and predictability.  
High-voltage power cables often carry currents with a dominant fundamental frequency of 50 Hz [10]. However, higher-order harmonics, particularly the 3 rd (150 Hz) and 5 th (250 Hz) harmonics, frequently arise in power systems due to nonlinear loads and switching devices [11]. These harmonics contribute disproportionately to additional losses because their higher frequencies exacerbate the skin and proximity effects. Hence, the 3 rd and 5 th harmonics are also selected for simulation to capture their impact on \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\).  
This paper systematically compares 2D vs. 3D FE for single-, three-, and five-segment cables under frequencies 0, 50, 150, and 250 Hz. We expand upon the earlier validation in [12], employing hexagonal sub-conductors to simulate full compression. The principal questions are as follows:  
* How accurately does 2D match 3D for each model?
* How do additional segments affect \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\) across various frequencies?
* What computational savings arise from 2D modeling?  
Results indicate that 2D runs provide accurate predictions (<2% deviation) at all tested frequencies for parallel-lay cables.
## 2 Measurement Principle
By using the error \(\varepsilon_{2}\) in the alignment of \(\theta_{2}\) in measurement of the negative first-order diffracted beam, \(\theta_{2}\) can be represented as \(\theta_{2}=-\theta/2+\varepsilon_{2}\). According to Equations (3) and (4), the following equation can be obtained:  
\[e_{X}(x/\alpha_{1},y)+e_{X}(x/\alpha_{2},y)=\frac{g}{2\pi}[I_{X+1}(x,y)-I_{X-1 }(x,y)]-\frac{2g}{\lambda}[\alpha_{1}e_{Z}(x/\alpha_{1},y)-\alpha_{2}e_{Z}(x/ \alpha_{2},y)] \tag{5}\]  
In the same manner, the \(Y\)-directional pitch deviation \(e_{Y}(x,y)\) of the planar scale grating can be obtained as follows by measuring the \(Y\)-directional positive and negative first-order phase outputs \(I_{Y+1}(x,y)\) and \(I_{Y-1}(x,y)\), respectively:  
\[I_{Y+1}(x,y)=2\pi\frac{e_{Y}(x,y/\alpha_{1}^{\prime})}{g}+\frac{4\pi}{\lambda} [\alpha_{1}^{\prime}e_{Z}(x,y/\alpha_{1}^{\prime})-e_{R}(x,y)], \tag{6}\]  
\[I_{Y-1}(x,y)=-2\pi\frac{e_{Y}(x,y/\alpha_{2}^{\prime})}{g}+\frac{4\pi}{\lambda }[\alpha_{2}e_{Z}(x,y/\alpha_{2}^{\prime})-e_{R}(x,y)], \tag{7}\]  
where \(\alpha_{1}^{\prime}=\cos\theta_{1}^{\prime}=\cos(\theta/2+\varepsilon^{ \prime}{}_{1})\) and \(\alpha^{\prime}{}_{2}=\cos\theta_{2}^{\prime}=\cos(\theta/2+\varepsilon^{ \prime}{}_{2})\). \(\varepsilon^{\prime}{}_{1}\) and \(\varepsilon^{\prime}{}_{2}\) represent the misalignments of \(\theta\) in the measurement of the wavefronts of the \(Y\)-directional positive and negative first-order diffracted beams, respectively. Note here that the nominal pitches of the planar scale grating  
Figure 2: Measurement of \(X\)-directional positive first-order diffracted wavefront with inclination error (negative first-order diffracted wavefront is not shown for the sake of simplicity). along the \(X\)- and \(Y\)-directions are assumed to be the same for the sake of simplicity. According to Equations (6) and (7), the following equation can be obtained:  
\[e_{Y}(x,y/\alpha_{1}^{\prime})+e_{Y}(x,y/\alpha_{2}^{\prime})=\frac{g}{2\pi}[I_{ Y+1}(x,y)-I_{Y-1}(x,y)]-\frac{2g}{\lambda}[\alpha_{1}^{\prime}e_{Z}(x,y/\alpha_{1}^{ \prime})-\alpha_{2}^{\prime}e_{Z}(x,y/\alpha_{2}^{\prime})] \tag{8}\]  
In the following section of this paper, how to further obtain the mean values and uncertainties of \(e_{Z}(x,y)\), \(e_{X}(x,y)\), and \(e_{Y}(x,y)\) according to Equations (2), (5), and (8) are presented. Since the evaluations of the mean value and uncertainty of \(e_{Y}(x,y)\) are similar to those of \(e_{X}(x,y)\), only the latter is focused in the following for the sake of clarity.
# Complete Subhedge Projection for Stepwise Hedge Automata +
## 8 Earliest Membership with Subhedge Projection
### In-Memory Complexity  
We next discuss the complexity of earliest membership testing with complete subhedge projection by an in-memory evaluator for \(A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}\).  
Lemma 19: _The number of states in \(\mathcal{Q}^{cgr}(\mathbf{S})_{e(\mathbf{S})}\) is in \(O(2^{n^{2}+2n+log(n)})\), where \(n=|\mathcal{Q}|\)._  
Proof: Using Lemma 18, the number of states of \(A^{cqgr}(\mathbf{S})\) is in \(O(2^{n^{2}+n})\), where \(n=|\mathcal{Q}|\). The number of states of \(A_{e(\mathbf{S})}\) is in \(O(n2^{n})\), and thus, in \(O(2^{n+log(n)})\). The number of states of \(Q^{cqgr}(\mathbf{S})_{e(\mathbf{S})}\) is thus in \(O(2^{n^{2}+n}\ 2^{n+log(n)})\), which is in \(O(2^{n^{2}+2n+log(n)})\)  
Corollary 2: _Let the signature \(\Sigma\) be fixed. For any complete \(d\textsc{Gha}\)A with schema \(\mathbf{S}\subseteq\mathcal{H}_{\Sigma}\), earliest membership on a input hedge \(h\in\mathbf{S}\) can be tested in memory in time \(O(|1|)\) per nonprojected node of \(h\) after a preprocessing time of \(O(|A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}|+n^{3}d+n\ s)\), where \(d\) is the number of difference relations and \(s\) is the number of subset of safe states in \(\mathcal{Q}^{cgr}(\mathbf{S})_{e(\mathbf{S})}\). The preprocessing time is also in \(O(2^{2n^{2}+4n+2 log(n)})\), where \(n\) is the number of states of \(A\)._  
Proof: Since \(A\) is complete, Thereom 4 shows that \(A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}\) is the earliest, sound, and complete for subhedge projection for schema \(\mathbf{S}\). The in-memory evaluator of \(A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}\) can be run on any input hedge \(h\in\mathbf{S}\) in time \(O(|1|)\) per nonprojected node of \(h\) after a preprocessing time of \(O(|A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}|+n^{3}d+n\ s)\). \(\Sigma\) is fixed to the size \(|A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}|\) bounded by \(O(|Q^{cqgr}(\mathbf{S})_{e(\mathbf{S})}|^{2})\). Lemma 19 shows that \(|Q^{cqgr}(\mathbf{S})_{e(\mathbf{S})}|\) is in \(O(2^{n^{2}+2n+log(n)})\), so \(|A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}|\) is in \(O(2^{2n^{2}+4n+2 log(n)})\).  
As before, instead of precomputing \(A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}\) from the input \(d\textsc{Gha}\)A and \(F_{\mathbf{S}}\), we can compute only the part needed for evaluating the input hedge \(h\) on-the-fly. If the needed part of \(A^{cqgr}(\mathbf{S})_{e(\mathbf{S})}\) for evaluating \(h\) is small, the overall time and space go down considerably.
## 2 Materials and Methods
### Soil Sampling and Laboratory Analysis  
Firstly, a total of 1000 sample points were uniformly deployed according to the pattern of the grid distribution, and then samples of 0-20 cm tillage layer were collected. In the actual sampling process, the number of samples in each grid was determined according to the main crop types in the grid. When a crop type was in the grid, we sampled at the center of the plot. When there were multiple crop types in the grid, we selected the plot with the larger type for sampling. At the same time, some sample points were slightly adjusted by referencing the land use and road traffic situation. The central plot of the sampling site was sampled by \"S shape\", and 5-10 points were collected from each site and fully mixed. 1 kg soil samples were retained by quartering method and loaded into the sample bag for soil  
Figure 1: Location and elevation of Kenli County in Yellow River Delta.  
analysis. Global Positioning System (GPS) was used to precisely locate each sampling site. The eastern area was not sampled because of conservation policy from the Yellow River Delta Nature Reserve. Finally, a total of 926 soil samples were collected in fall 2009 before and after crop harvest, and the sampling area was approximately 1400 km\({}^{2}\).  
The collected soil samples were taken back to the laboratory for air drying, grinding and passing through a 0.25 mm nylon sieve for analysis. The soil organic carbon (SOC) was determined by the K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) oxidation-titration method [20]. The content of SOM was obtained by multiplying the content of SOC by the van Benmmelen coefficient (1.724).
## 5 Evaluation and Discussion
### Routing and Sink Placement Optimization  
The dominating set and facility location problems provide a reliable theoretical frame for our sink node placement optimization problem. Nevertheless, our approach is to combine the toolset, restrictions, and different objective functions of the given theoretical frame in the area of infection models. The advantage of using infection models (with appropriate modifications) is that it can guarantee proper theoretical bounds regarding the solution of the optimization problem as well as a reliable theoretical environment for our problem. An example for the modification of the infection models can be found in [80]. In our previous work, we introduced an infection model based on a simulation environment using the greedy method with different spreading strategies and objectives for Wireless Sensor Networks [81]. Due to the restrictions discussed in the given paper, our initial work provided a solution for the centralized version of a sensor network. Regarding the methodology used, it is important to note that, due to the submodularity of the problem we introduced, the method in the area of infection models performs within a constant factor of \(1-1/e\) as good as the optimal solution. The basic problem was further investigated in our latest research, which is, however, a generalized solution that can be used in a decentralized solution [82]. The main problem of the greedy algorithm is that it provides an insufficient running time in the case of big networks, and it is not efficiently scalable. In our preliminary results, we were aiming to reduce the running time of the method while, at the same time, keeping the quality of the solution. Using structural properties of the given network, we were able to improve the runtime as well as provide an environment to benchmark and evaluate different community detection methods. Our solution places the seed nodes (or, in a sink placement problem, the sink nodes), in places that are somehow in a central position between dense subnetworks identifying the critical positions that can endanger the wireless sensor network from a connectivity point of view. In the paper, we have compared our solution using eight different methods with centrality-based approaches, as well as with the original greedy method. As a final result, we can say that our methodology is efficient from two points of view. First, it is suitable to reduce the search space of the general optimization problem (as well as the sink node placement optimization), and second, it provides an environment to test and compare the effectiveness of different methods in reducing the search space of the sink placement optimization problem. The research gives us a good basic model with which we are able to evaluate the different solutions regarding to the structural properties of such networks.
# Recursive Optimal Finite Impulse Response Filter and Its Application to Adaptive Estimation
## 1 Introduction
However, even though the horizon initial state can be ignored, the state at time \(k-N_{opt}+K\) in Figure 1b considered as an actual initial state is required at each estimation horizon, and they are obtained by calculating the batch form of the filter equation. In addition to the aforementioned computational inefficiency, these approaches have a common disadvantage due to the fact that heavy computational load is also required to specify the optimal horizon length, because it must be obtained for each horizon in time-varying systems. Moreover, the optimality of KUFIR filters is not guaranteed and the horizon length cannot be adjusted. Therefore, in this paper, a new recursive optimal FIR (ROFIR) filter is proposed for linear time-varying systems in order to overcome the disadvantages of previous methods  
Figure 1: (**a**) The concept of RHK filter. (**b**) The concept of KUFIR filter. for recursive FIR filtering. The ROFIR filter is derived by employing the finite horizon Kalman filter and the optimal and unbiased initial state estimation. The initial state and its corresponding error covariance on the estimation horizon are obtained by solving the maximum likelihood estimation problem, then they initiate the finite horizon Kalman filter. Since the initial state is estimated from the measurements at each finite estimation horizon, the ROFIR filter does not require any priori initial information. In addition, the proposed ROFIR filter is derived without assumption on a nonsingular state-transition matrix and has less computational burden than the KUFIR filters for time-varying systems. Furthermore, the ROFIR filter provides the best linear unbiased estimate (BLUE) of the state on the finite estimation horizon. In addition, since AFIR filters in previous studies were designed in batch form, they were mostly focused on how to adjust the horizon length [17; 18; 19]. To the author's best knowledge, there are no results on the adaptive FIR (AFIR) filters which consider noise statistics, thus, we propose a new adaptive FIR filtering algorithm by employing a sequential noise statistics estimation technique as an application of the proposed ROFIR filter. This paper is organized as follows: In Section 2, the ROFIR filter is proposed for linear time-varying state-space models and its optimality and unbiasedness are proved. Moreover, the AFIR filter is also proposed by applying the modified sequential noise statistics estimation method to the proposed ROFIR filter. In Section 3, the performance and effectiveness of the proposed ROFIR and AFIR filters are shown and discussed via computer simulations. Finally, our conclusions are presented in Section 4.
###### Abstract  
In this paper, a machine learning-based tunable optical-digital signal processor is demonstrated for a short-reach optical communication system. The effect of fiber chromatic dispersion after square-law detection is mitigated using a hybrid structure, which shares the complexity between the optical and the digital domain. The optical part mitigates the chromatic dispersion by slicing the signal into small sub-bands and delaying them accordingly, before regrouping the signal again. The optimal delay is calculated in each scenario to minimize the bit error rate. The digital part is a nonlinear equalizer based on a neural network. The results are analyzed in terms of signal-to-noise penalty at the KP4 forward error correction threshold. The penalty is calculated with respect to a back-to-back transmission without equalization. Considering 32 GBd transmission and 0 dB penalty, the proposed hybrid solution shows chromatic dispersion mitigation up to 200 ps/nm (12 km of equivalent standard single-mode fiber length) for stage 1 of the hybrid module and roughly double for the second stage. A simplified version of the optical module is demonstrated with an approximated 1.5 dB penalty compared to the complete two-stage hybrid module. Chromatic dispersion tolerance for a fixed optical structure and a simpler configuration of the nonlinear equalizer is also investigated.  
chromatic dispersion; short-reach communication; neural network; hybrid signal processing Article