esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Trends
### Novel Contributions  
This paper presents a novel integration of Visible Light Communication (VLC) and Artificial Intelligence (AI) for adaptive traffic management. Unlike prior studies that primarily focus on VLC-based communication systems or traffic control methods [5; 22; 24; 25; 26], this study proposes a unified framework that combines VLC and AI. The AI-based approach leverages Deep Reinforcement Learning (DRL) to dynamically optimize traffic flow and enhance safety.  
A key contribution of this paper is the design of customized reward functions to handle rerouting scenarios effectively. These reward functions prevent queue spillovers and mitigate cascading congestion while ensuring safe and timely pedestrian crossings--a feature not addressed in previous studies.  
Another significant advancement is the implementation of global coordination for traffic management using multi-agent systems. Unlike previous work that focused on single-intersection optimization, this study employs a global agent coordination mechanism to manage traffic signals across multi-intersection networks. This approach facilitates indirect communication between local agents, enhancing scalability and adaptability.
# Numerical Modeling of Non-Uniformly Reinforced Carbon Concrete Lightweight Ceiling Elements
## 4 Resolved Model of a Cross Section
### Verification of the Resolved Approach for Bending  
As mentioned in Section 3.3, a correct prediction of the bending response is one of the requirements that leads to the usage of resolved, layered representation of the shell cross section. To test the ability of the resolved model to correctly represent the development of an effective lever arm within a cross section, a parametric study of a three-point bending test with the span of 500 mm and width of 100 mm was performed with three different positions of the fabric within the cross sections. A discrete load was applied at the mid-span of the beam. The cross section with the height of 10 mm was reinforced with one layer of carbon fabric with 3300 tex. The behavior of the fabrics was assumed isotropic within the reinforcement layer with the stress-strain curve \(\sigma_{\rm f}^{3300+800}(\epsilon_{\rm c})\) and a layer thickness adjusted such that it matches the area of the fabric \(a_{\rm f}^{3300+800}\) within the cross section. The studied configurations (i), (ii) and (iii) are shown in Figure 14 together with the corresponding load-deflection curves obtained from the simulation. The deflection was recorded using the midspan section of the girder.  
Figure 14: Load displacement curves of the simulation of carbon concrete beam with variation of the reinforcement position in the cross section subjected to bending action.  
Figure 13: Damage functions describing the matrix cracking calibrated using the matrix stress–strain curve in Figure 11 extracted from the composite tensile tests with (**a**) 800 tex and (**b**) 3300 tex.  
As apparent from the figure, the effect of the placement of the fabric is reproduced plausibly. The placement of the fabric at the bottom of the cross section in the tensile zone leads to a large lever arm and, thus, to an activation of a higher amount of compressive stresses within the cross section. As a result, this study shows the structural response with high stiffness and ductility (iii). The qualitative effect of reduced lever arm for configurations (i) and (ii) is reproduced correctly. For illustration, the development of the stress over the cross-sectional thickness for three selected levels of load is shown in Figure 15.  
Let us note that, in case of a smeared cross-sectional model, the effect of fabric placement within the cross section cannot be captured. Considering the case of the studied girder with the fabric placed in the middle of the cross section, the smeared approach necessarily leads to an overestimation of the lever arm as can be recognized by comparing the stress profiles in a smeared and resolved cross section models depicted in Figure 10.
## 2 System and Signal Characterization
### Channel Estimation  
This article assumes that training sequences (pilots) are utilized to perform the channel estimation. The channel frequency response is defined as [21]:  
\[\widetilde{H}_{k}^{(t,r)}=\frac{Y_{k}^{(r)}X_{k}^{(t)TS*}}{2\sigma_{TS}^{2}} \tag{6}\]  
where \(X_{k}^{(t)TS}\) denotes the training sequence transmitted by the \(t\)th transmitting antenna \((t=1,2,\ldots,T)\), \(Y_{k}^{(r)}\) stands for the signal at the \(r\)th receiving antenna \((r=1,2,\ldots,R)\) (\(TS\) means training sequence), and \(\sigma_{TS}^{2}\) stands for the power (variance) of the training sequences. In this estimation, it is assumed that that the training sequences associated with different transmitting antennas are orthogonal, leading to \(X_{k}^{TS(m)}X_{k}^{TS(q)*}=0\)\(m\
eq q\). This leads to \(\widetilde{H}_{k}=H_{k}+\varepsilon_{k}\), where \(\varepsilon_{k}\) stands for the channel estimation error, being Gaussian-distributed, with zero-mean, defined as  
\[\text{E}\left\{\left|\varepsilon_{k}\right|^{2}\right\}=\frac{\sigma_{N}^{2}} {\sigma_{TS}^{2}} \tag{7}\]  
with \(\sigma_{N}^{2}=\text{E}\left\{\left|N_{k}\right|^{2}\right\}\) and with \(\sigma_{TS}^{2}=\left|X_{k}^{TS}\right|^{2}/2\), as defined in [21].  
In order to improve the channel estimates, the following enhancement can be employed [22; 23]:  
\[\widehat{H}_{k}^{(t,r)}=\text{DFT}\left\{\widetilde{h}_{n}^{(t,r)}w_{n}\right\}, \tag{8}\]  
where \(w_{n}=1\) if the \(n\)th time-domain sample is inside the cyclic prefix, and 0 otherwise.
## 4 Discussion
If the flexible state and the strong restraint are distinguished through the ROM, the stress applied to the screw and BIP in the flexible state simultaneously decreases. In the case of the strong restraint, more load was placed on the PIB than on the screw. Finally, the stress applied to the IVD was low, and there was no significant difference in the magnitude of the stress based on the fixation method. Thus, it is difficult to determine the advantages and disadvantages of the fixation method solely with the stress values of the IVD. However, if an efficient fixation method is selected numerical data regarding the motion average stress, T11-L1-L2 fixation applies the least stress to the IVD, and it has a slight advantage over other fixation methods depending on the screw type. As a result, the T10-T11-L1-L2 fixation method using a CS on the fractured spine places the least burden on the screw and vertebra. However, studies have shown that using fixation of a segment as short as possible is more desirable than fixing at the expense of unfractured segments for ROM optimization. Therefore, it may be reasonable to choose T10-T11-L1 fixation using a PS, which applies relatively high stress (2.5 MPa) to the PIB, or T11-L1 fixation using a CS, which applies high stress (4.2 MPa) to the PIB. However, since minimization of spinal fixation segments requires a comprehensive judgment according to the patient's condition, further studies are needed to determine to what extent it is necessary to minimize the fixation segments while withstanding the increase in pressure applied to the PIB. The present study has several limitations. First, the muscles were not included in the model. Spine motion simulation involving the muscles can be used to analyze biomechanical behavior for additional external forces in addition to the basic four motions. Second, this study analyzed several surgical methods using a single FE model. Therefore, it is unreasonable to uniformly apply the results of this study to models of different shapes.
## 4 Illustrative Example
### Turn Radius Influence on Maximal Error  
This example deals with an analysis of the turn radius influence on the maximal lateral deviation allowed by the controller. Therefore, a 180\({}^{\circ}\) turn is considered, while the aircraft velocity is set to \(V_{K}=22\,\mathrm{m/s}\), which corresponds to a typical flight velocity of a small unmanned aerial vehicle. Initially, the aircraft's position and the desired trajectory match.  
First, Figure 2 shows the maximum lateral deviation of the 180\({}^{\circ}\)-turn for different turn radii. It is clear that for a smaller turn radius, i.e., a tighter, more aggressive maneuver, the lateral deviation error is increased. Still, the maximum error is only around 0.1 m at a turn radius of approximately 5 m, which shows the capabilities of the proposed controller for highly accurate trajectory control. It is furthermore seen that for gentle maneuvers, i.e., an increased turn radius, the lateral deviation asymptotically approaches zero. The remaining magnitude of the error is hereby mainly related to the model lags introduced in Equation (12). It should be further noted that it is expected that the maximum lateral deviation decreases further when not only the specific force is commanded to the aircraft but also its derivative. Then, the reaction to deviations is improved even further and the introduced lags in Equation (12) can be directly accounted for.  
Continuing, Figure 3 displays the maximum absolute lateral specific force along the 180\({}^{\circ}\)-turn. As expected, a higher lateral force is required to follow a tighter, i.e., more aggressive, turn maneuver. This strengthens the argumentation used before in Figure 2 that a more aggressive maneuver, yielding a larger lateral specific force requirement, results in a greater deviation. Still, as also already seen in Figure 2, even a considerable lateral specific force of around 9 m/s\({}^{2}\) only yields to a tolerable lateral deviation of 0.1 m.  
Concluding, this application example shows the applicability of the proposed trajectory controller in scenarios with high accuracy demand. Specifically, both gentle as well as aggressive maneuvers are handled by the proposed scheme.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name** & **Symbol** & **Value** & **Unit** \\ \hline Actuator natural frequency & \(\omega_{0}\) & 20 & \(\frac{\mathrm{rad}}{\mathrm{s}}\) \\ Actuator damping & \(\zeta\) & 1 & \(-\) \\ Gravitational acceleration & \(g\) & 9.81 & \(\frac{\mathrm{m}}{\mathrm{s}}\) \\ Linear force derivative & \(Z_{\mathrm{x}}\) & \(-3\) & \(\frac{\mathrm{s}}{\mathrm{s}}\) \\ Position error gain matrix & \(\mathbf{K}_{\mathrm{P}}\) & \(\mathbf{I}_{3\times 3}\) & \(-\) \\ Velocity error gain matrix & \(\mathbf{K}_{\mathrm{d}}\) & \(3\cdot\mathbf{I}_{3\times 3}\) & \(-\) \\ Acceleration error gain matrix & \(\mathbf{K}_{\mathrm{d}\mathrm{d}}\) & \(9\cdot\mathbf{I}_{3\times 3}\) & \(-\) \\ Aircraft mass & \(m\) & 100 & kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constants used in the simulation model.
# Hashcash Tree, a Data Structure to Mitigate Denial-of-Service Attacks
## 3 Hashcash Trees and Their Application to Client Puzzle Protocols
(As an optimization, witness integers of labels not in the path from \(i\) to the root can be discarded.) 7. \(V\) checks all the following conditions: \(s=h(mk||Req||n||t)\); \(t\) is in the future; \(I=h(mk||Req||sol||i)\), where \(sol\) is \(\ell_{|1|}\) in \(S\) (i.e., the hash value associated with the root of the partial hashcash tree sent by \(P\)); \(S\) is valid; \(Req=h(req)\). If all conditions are met, \(req\) is processed. The validation of \(S\) at step 7 amounts to check that each label \(\ell_{j}\) in the path from \(i\) to the root is actually obtained according to (2); Algorithm 4 is used. Figure 10: A hashcash tree of size 4 for the string \(s\). The path from the first leaf (node with order 4) to the root is shown with dashed lines. Siblings of nodes in that path are connected to their parents by dotted lines. **Example 8**. : _Let us run the CPP for \(\mathit{req}=``\mathbf{req}\"\), using the hash function SHA-\(256\) and a prefix length \(k=4\). \(V\) generates the master key \(mk=41416d572 ab944 bab514 eb6 ab388c434\). \(P\) sends \(\mathit{Req}=c37f9 kdf537c46724392c4428e47e04c148c56966190c3c9 ed9211480059f35 bb\) to \(V\). \(V\) determines \(n=4\) (encoded as \(0400\)), \(t=1691442051.890254\) (\(10\)\(s\) in the future, encoded as \(ecf9f8c05634d941\)), \(s=\mathit{SHA}256(mk||\mathit{Req}||n||t)=4b4107 ccb397a1 fb3a1f9d278e8\)\(edaba350530291 bcc0c7e211 acaf1042 dcled6\), and sends \(\langle s,n,t\rangle\) to \(P\). \(P\) computes the hashcash tree shown in Figure 12, and sends \(\mathit{sol}=002 eb1f4d23d95984 fa2 be728044 ebfa4 fd65c2f944757\)\(3b31a4266885b115a9e6\) (with \(s,n,t\), and \(\mathit{Req}\)) to \(V\) checks that \(s\) and \(t\) are valid, randomly selects \(i=4\) (the first leaf), computes \(I=8125f\sigma ab943890 cd3d3f5 ec0031/8a5 fb3 fe392b47a59\)\(19d4a06c74c371 da20\), and sends \(\langle I,i\rangle\) to \(P\). \(P\) sends the labels \(\langle sol,3\rangle\), \(\langle h_{2},13\rangle\), \(\langle h_{3},3\rangle\), \(\langle h_{4},15\rangle\) and \(\langle\epsilon,0\rangle\) to \(V\) (with the other required data). \(V\) runs Algorithm 4 on a tree \(T\) constructed with the received labels (other labels are irrelevant). Since the algorithm returns \(\mathbf{true}\), and all other conditions are met, the request req is processed._
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 5 Discussion
### Computational Complexity Comparison  
By segmenting the data and using the Teager-Kaiser energy method, the model training time was significantly optimized. For example, using the same data, when segmented into 2400 data points, the six features processed using traditional methods resulted in a data size of 854 \(\times\) 6. When segmented into 12,000 data points, the six features processed using traditional methods resulted in a data size of 171 \(\times\) 6. With an older GPU, training time typically remained under 1 min; using a newer graphics card, training time can be reduced to just a few seconds.  
However, the computational complexity comparison of different methods cannot be quantified by a single indicator. Although time complexity, space complexity, the number of parameters, running time, memory usage, and other dimensions can be considered separately, it is difficult to use a unified quantitative standard to directly compare methods, due to differences in algorithmic structure, preprocessing requirements, hardware dependence, etc. This situation requires combining specific experimental design and application scenarios and comprehensively evaluating indicators from multiple angles to obtain a more reasonable comparison.  
However, it is still possible to discuss comparison of computational complexity under similar conditions. For example, if we go from 4 to 6 or 11 features, the impact on complexitydepends on the type of SVC; with a nonlinear kernel (RBF), computational complexity increases from \(O\left(n^{2}\times 4\right)\) to \(O\left(n^{2}\times 6\right)\) or \(O\left(n^{2}\times 11\right)\), where \(n\) is the number of samples.  
Therefore, all else being equal, with 6 or 11 features, the algorithm takes about 1.5 or 2.75 times longer than with 4 features. If the number of samples is large, the impact of going from 4 to 11 features can become significant, especially for SVMs with nonlinear kernels.  
RFC training time increased linearly with the amount of data, especially when the dataset exceeded 50,000 records, at which point the training time increased significantly. In contrast, SVCs are faster to train on smaller datasets, but as the data size and feature count grew, the computational complexity increased substantially.
# Rapid Tracking Satellite Servo Control for Three-Axis Satcom-on-the-Move Antenna
## 4 Experiment Analysis
### Experiment Setting  
Experiment Address: SATPRO M&C Tech Co., Ltd., Weiyang District, Xi'an, China  
Experiment Date: 17 November 2023  
Experiment Weather: Sunny, \(-3\)\({}^{\circ}\)C  
Coordinates of the Swing Platform: \(108.8479^{\circ}\) E, \(34.3679^{\circ}\) N  
Target Communication Satellite: YATAI 6D Geosynchronous Satellite  
Antenna Manufacture: SATPRO M&C Tech Co., Ltd. (Xi'an, China)  
Antenna Working Band: Ku Band  
Antenna Receiving Frequency: \(10.7\)-\(12.75\) GHz  
Antenna Transmitting Frequency: \(13.75\)-\(14.5\) GHz  
Azimuth range of the Swing Platform: \(-15^{\circ}\)-\(15^{\circ}\)  
Pitch range of the Swing Platform: \(-15^{\circ}\)-\(15^{\circ}\)  
Roll range of the Swing Platform: \(-15^{\circ}\)-\(15^{\circ}\)  
Antenna Azimuth Scanning Range: \(0^{\circ}\)-\(360^{\circ}\)  
Antenna Pitch Scanning Range: \(0^{\circ}\)-\(90^{\circ}\)  
Antenna Roll Scanning Range: \(-15^{\circ}\)-\(15^{\circ}\)  
The experimental site environment is shown in Figure 7.  
Figure 7: Experiment site.
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 2 Problem Description
### Problem of Real-Time Routing on OBCs with Limited Resources  
The LEO satellite network requires real-time routing because of its rapidly changing link state and high mobility. However, the OBC mounted on the existing LEO satellite has limited computational power and memory [17]. This may not fully reflect the real-time performance that requires many and fast operations. To address this, we used an FPGA and verified the possibility of performing EAR and KSP algorithms in the PS area of the FPGA. After that, the PS-PL collaboration structure was implemented and the real-time routing possibility was increased by reducing the computational time through pipelining in PL.
## 3 Results  
### The Responses of GPP and SIF to the Environment  
#### 3.1.1 The Responses of GPP and SIF to Radiation under Different EF and Ta Conditions at the DM Site  
We analyzed the responses of GPP and SIF to the environment based on half-hourly and daily data at the DM site using a binning method (all variables were standardized). Figure 2 shows the half-hourly light response curves of GPP and SIF under different EF bins. In general, GPP and SIF had similar light responses which increased with increasing PAR in each EF bin. However, SIF increased faster than GPP especially in lower EF bins, and the difference between the increase speeds of GPP and SIF was gradually reduced with increasing EF. As shown in Figure S1, there were divergences in the slopes of the SIF and GPP light response curves within each EF bin. The slopes of the SIF light response curves were higher than those of GPP at the lower EF bins (EF \(\in[-2,\ -1.5\)), EF \(\in[-1.5,\ -1.0\))) which indicated that SIF increased faster than GPP when the water availability was not high. With the increase in EF, the difference between the slopes of SIF and GPP light response curves gradually weakened (EF \(\in[-1,\ 0.5)\)); until EF was higher (EF \(\in[0.5,\ 1.5]\)), the slopes of GPP light response curves were higher than that of SIF before the light saturation point. Figure S2 showed the daily GPP and SIF light responses at DM site under different EF bins which was similar to the half-hourly GPP and SIF light responses. SIF increased faster than GPP in lower EF bins and the difference between the response speeds of GPP and SIF gradually reduced with increasing EF. In addition, EF had more influence on the GPP light response than on SIF at both half-hourly and daily scales.  
Figure 3 showed the half-hourly GPP and SIF light responses under different Ta bins at DM site. The overall trends of GPP and SIF were similar which both increased with increasing PAR. While the response speeds of SIF and GPP to the light energy also had difference. As shown in Figure S3, when temperature was lower, the slopes of SIF light response curves were higher than that of GPP which indicated that SIF increased faster than GPP (Ta \(\in[-2,\ -0.5)\)). With increase in temperature, the difference between the slope values of SIF and GPP light response curves gradually reduced until temperature was higher in which GPP increased faster than SIF before light saturation point. Figure S4 showed the daily GPP and SIF light responses at DM site under different Ta bins which were similar to the half-hourly GPP and SIF light responses. Similar to EF, the GPP light response was more dependent on Ta than on SIF at both half-hourly and daily scales.
## 3 Results and Discussion
### Improved Detection Limits through Design Optimization
#### 3.1.3 Optimization Results  
As can be seen in Figure 2, the full width at half maximum (FWHM) could be significantly decreased to about 5 s compared to 22 s with the original measurement system, reported previously [31]. Starting point was the rise of the detector signal. At both measurements, the data acquisition was not triggered by laser shots and absolute delay was not regarded due to the lack of influence to the LOD. The transferline length differed from 1.5 m for the original system to 0.25 m for the novel one. As a result, the length of the transferline contributed to the peak shape and subsequently to the system response time. A higher signal at shorter timescale was obtained that, in addition, should contribute to a lower limit of detection. Depending on the exact application and scenario, the system allows a flexible change of the transferline length to adjust for a more sensitive or more flexible analysis (reaching surfaces in narrow places). Therefore, it is a matter of priorities which option to choose. On the one hand, better peak widths of just a few seconds and faster analytical results can be obtained. On the other hand, more flexibility at surface sampling is achievable by using a longer transferline.  
These changes lead to significant improvements in the limits of detection, and the obtained results are shown in Table 1. It summarizes different individually measured nitro aromatic and peroxide explosives, drugs, drug precursors, and their obtained LOD. All LOD are real measured signals and were determined with the same procedure (described in the materials and methods section) at the same mass spectrometer for both systems. As expected, the limit of detection was lowered by one order of magnitude, for e.g., down to 2 ng tetryl in relation to 30 ng from previous results. In addition, for other explosives, like dinitrotoluene (DNT) and trinitrotoluene (TNT), the detectable amounts could be lowered significantly: for TNT down into the picogram range.