esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
## 1 Introduction  
In the European Union, the extensive railway network spans approximately 216,000 km [1]. To ensure proper availability, ongoing maintenance and servicing are essential. Currently, manual track inspections form a significant part of these maintenance activities. For instance, in the \"Deutsche Bahn\" network (approximately 38,400 km), track inspections are scheduled at least every two years. In addition to manual inspections, there are also advanced maintenance and servicing systems equipped with specialized sensors. These systems employ sensors mounted on traction units to detect and identify railway-related objects within the track network.  
These comprehensive systems provide technicians and managers with a holistic overview of track conditions. This system is instrumental in planning the necessary actions and interventions. It combines various sensory inputs to offer valuable insights into track conditions, aiding in decision-making processes. The majority of railway tracks installed worldwide consist of Vignole rails, which can be categorized into three different parts--the rail head, rail web, and rail foot--as depicted in Figure 1.  
Typically, the rolling mark is positioned on the rail web at consistent intervals along the rail line, as illustrated in Figure 2. This mark serves as an identifier and contains essential information pertaining to the rail. It comprises a combination of letters, numbers, and special characters, often including horizontal lines as well. These characters collectively convey specific details about the rail, allowing for identification and data interpretation.  
In this paper, we introduce a novel camera-based system designed for automatically extracting information from rolling marks. The paper is organized as follows.  
Section 2 provides an in-depth look into rolling marks. Section 3 describes the method for detecting rolling marks, including the physical principle of measurement and the basic principles of the evaluation software. In Section 4, we demonstrate an exemplary evaluation of the presented method. We use test data to highlight the strengths and weaknesses of the method. Section 5 summarizes the paper, and Section 6 provides an outlook on potential further applications of the system.
## 2 Models and Definitions
### Node Energy Consumption Model  
Underwater sensor network nodes communicate with one another by acoustic signals. Accordingly, this study uses an energy consumption model of underwater sensor network data  
Figure 1: Underwater wireless sensor network (UWSN) system model.  
Figure 2: 3D Boolean perception model.  
communication with the sound wave as the medium [36]. The underwater acoustic signal attenuation model \(A(d)\) is given as follows:  
\[A\left(d\left(s_{se},s_{rec}\right)\right)=d^{\lambda}\left(s_{se},s_{rec}\right) \alpha^{d\left(s_{se},s_{rec}\right)} \tag{1}\]  
Equation (1) describes the energy attenuation when the data packet transmitting distance from the source node \(s_{se}\) to the destination node \(s_{rec}\) is \(d(s_{se},s_{rec})\), where \(\lambda\) is the energy diffusion factor (the cylindrical diffusion is one; the actual situation is 1.5; and the spherical diffusion is two). The parameter \(\alpha=10^{a\left(F_{r}\right)/10}\), where the absorption coefficient \(a(F_{r})\) is shown as follows:  
\[a(F_{r})=0.11\frac{10^{-3}F_{r}^{2}}{1+F_{r}^{2}}+44\frac{10^{-3}F_{r}^{2}}{41 00+F_{r}^{2}}+2.75\times 10^{-7}F_{r}^{2}+3\times 10^{-6} \tag{2}\]  
where \(F_{r}\) is the carrier frequency, with the unit of kHz. The unit of the absorption coefficient is dB/m.  
The energy for the nodes to send data \(E_{tx}(d(s_{se},s_{rec}))\) is expressed as follows:  
\[E_{tx}\left(d\left(s_{se},s_{rec}\right)\right)=P_{r}\times T_{p}\times A\left( d\left(s_{se},s_{rec}\right)\right) \tag{3}\]  
where \(T_{p}\) is the data transmission time and \(P_{r}\) is the minimum power packets that can be received.
## 5 Conclusions  
In this paper, a rail defect signal extraction method based on a time-frequency separation neural network is proposed. Firstly, a superior time-frequency analysis, namely SPWVD, is combined as a pre-processing technique to retain a high-resolution spectrogram. Then, a specific structure including a 1D-CNN feature extractor and a weight estimator was utilized to avoid oscillation and provide a faster convergence and higher accuracy. In this way, the proposed TFSNN is expected to greatly improve separation efficiency and recovery details. Compared with the separation effect of three classical BSS algorithms, the simulation and experimental results show that the separation effect of the improved algorithm is superior to the others in most cases, which verifies the necessity and advantages of integrating TF analysis and adaptive self-organizing neural networks into BSS problems. In its results, this research has the potential to offer valuable insights for similar safety and health monitoring applications. Although TFSNN offers promising solutions to address key  
Figure 13: Averaged SISNRs for different methods under a series of Gaussian noise levels.  
challenges in blind source separation (BSS) tasks, such as handling non-stationary signals and improving stability, the noise data utilized in the simulation experiment presented in this paper were collected from a passenger vehicle operating at a maximum speed of approximately 55 km/h. The current experimental validations have not yet accounted for the impact of high-speed railway noise, which typically exhibits broader bandwidth and higher amplitude. To enhance TFSNN's effectiveness in high-speed and complex noisy environments, further research should focus on integrating joint optimization with flexible separation approaches, such as spectrogram masking, permutation invariant training (PIT), and scale-invariant signal-to-distortion ratios. Also, it is important to modify the TFSNN to be lightweight and scalable, allowing it to handle varying numbers of sources and changing environmental conditions without significant reconfiguration. In this way, it will be more effective and reliable for real-time applications and could be extended to a wider range of real-world scenarios in the future.  
Conceptualization, K.W.; methodology, K.W. and M.Z.; software, M.Z.; validation, M.Z., Y.Y. ([PERSON]) and Y.C.; writing--original draft preparation, M.Z.; writing--review and editing, K.W., Y.Y. ([PERSON]) and Y.C.; supervision, K.W.; funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 62401383), the China Postdoctoral Science Foundation (grant number 2023M732539), and the State Key Laboratory of Advanced Rail Autonomous Operation (grant number RAO2025K10), Beijing Jiaotong University.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Review of Magnetoelectric Effects on Coaxial Fibers of Ferrites and Ferroelectrics
## 2 Theory
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \begin{tabular}{c} \(4\pi\text{M}_{\text{s}}\) \\ **(kG)** \\ \end{tabular} & \begin{tabular}{c} \(\lambda\) \\ **(10\({}^{-6}\))** \\ \end{tabular} & \begin{tabular}{c} \(\text{q}_{33}\) \\ **(10\({}^{-12}\) m/A)** \\ \end{tabular} & \begin{tabular}{c} \(\text{d}_{33}\) \\ **(10\({}^{-12}\) m/V)** \\ \end{tabular} & \
\begin{tabular}{c} \(\text{d}_{33}\) \\ **(10\({}^{-12}\) m/V)** \\ \end{tabular} &
\begin{tabular}{c} \(\text{e}_{33}/\text{e}_{0}\) \\ \end{tabular} \\ \hline \hline PZT & - & & & 400 & 1750 \\ \hline BTO & - & & & 260 & 2500 \\ \hline NFO & 3.3 & \(-\)30 & \(-\)680 & & 10 \\ \hline Co\({}_{2}\)W & 2.3 & \(-\)74 & \(-\)170 & - & 10 \\ \hline Zn\({}_{2}\)W & 1.5 & \(-\)15 & \(-\)20 & - & 10 \\ \hline Zn\({}_{2}\)Y & 2.58 & \(-\)10 & \(-\)7 & - & 10 \\ \hline Ni\({}_{2}\)Y & 7.51 & \(-\)19 & \(-\)625 & - & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Room temperature piezoelectric coupling coefficient \(\text{d}_{33}\) and dielectric permittivity \(\varepsilon_{33}/\varepsilon_{0}\) for PZT and BTO and saturation magnetization \(4\pi\text{M}_{\text{s}}\), magnetostriction \(\lambda\), and piezomagnetic coefficient \(\text{q}_{33}\) for NFO, and Y- and W-type hexagonal ferrites. The parameters are the highest measured values for magnetic or electric fields along the length of the fiber (direction 3) and magnetostriction or piezoelectric coupling coefficients measured along the same direction. These values are used in the models in [41; 47]. Figure 1: Estimated maximum value of ME voltage coefficient (MEVC) as a function of (**a**) the ratio of the radius of BTO core and NFO shell and (**b**) radius of NFO core and BTO shell. [41]. the largest for fibers with Ni\({}_{2}\)Y and the smallest for fibers withZn\({}_{2}\)Y. A maximum HEVC is expected when the ratio of the core-to-shell radius is \(\approx\)0.3. Fibers with Zn\({}_{2}\)W or Zn\({}_{2}\)Y have much lower HEVC compared with their counterparts due to a weak magnetostriction and q-value. Estimates for the hexaferrite core-PZT shell are provided in [47]. The results in Figures 1 and 2 are for a single isolated fiber. A very significant reduction in the measured values of HEVC is expected, in particular, for assemblies of the fibers due to several assumptions in the theory. The theory does not consider the following: (i) The results in Figures 1 and 2 are for material parameters measured in polycrystalline ferroelectric and magnetic phases. For nanomaterials, however, one anticipates much smaller ferroic order parameters. (ii) The theory did not take into account magnetic or electric dipole-dipole interactions. Interactions between ferrite cores or shells will lead to a demagnetizing field that will result in a reduction in the strengths of both DC and AC magnetic fields and a decrease in HEVC. (iii) Porosity or airgaps in the fiber assemblies will also have a similar effect on HEVC. Influences of these factors are considered in detail later in the discussion part (Section 6).
## 3 Polyunsaturated Fatty Acids (PUFA) Exploitation from Autotrophic Microalgae
### Microalgae--PUFA Enhancement Strategies
In this study, maximal EPA productivities were achieved at 60 umol photons m\({}^{-2}\) s\({}^{-1}\) and at a photoperiod regime of 18 h:6 h (light:dark); this observation was made for 1L cultures. Two-stage cultivation and combined nutrient and abiotic stresses are novel approaches used to enhance the microalgal biochemical composition. In the two-stage cultivation, microalgae are first grown to gain higher biomass and are then exposed to different cultivation conditions to trigger the accumulation of desired product content. For _Nannochloropsis gaditana_ IMTE1, a two-stage cultivation was studied by [PERSON] et al. [74]. This microalgal strain was firstly grown in batch culture for 6 days, washed, and then transferred for a chemostat culture with a fixed dilution rate and adjusted nitrate concentrations. In these cultivation conditions, the highest biomass (897.10 mg dw L\({}^{-1}\); dw--dry weight) and EPA content (2.62% dw) were obtained at high nitrogen supplementation. When combining nutrient and abiotic stresses, it is important to know the importance of each factor for the desired product accumulation, as well as its synergistic effects [67]. With the purpose of optimizing \(\omega\)3-fatty acid production by _Pavlova lutheri_, [PERSON] and [PERSON] [75] studied the combined effects of the dilution rate, light intensity, and CO\({}_{2}\) concentration under continuous mode. The optimum conditions for EPA and DHAproduction were found in cultures supplied with 0.5% CO\({}_{2}\), at a dilution rate of 0.297 d\({}^{-1}\) and a light intensity of 120 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\)[75]. Other combined nutrient and abiotic stresses are summarized in Table 1 for the following strains: _Chlamydomonas reinhardtii_ CC124, _Nannochloropsis gaditana_ CCNM1032, _Phaeodactyllum tricornutum_ CS-29C, and _Chaetoceros muelleri_ CS-176. The major disadvantage of the enounced novel approaches is that large-scale trials are required.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 4 Utility Optimization
### Property of the Problem
Lemma 1: _The optimal receive threshold \(\theta_{k}^{*}\) in any tier decreases with the user association probability \(A_{k}\)._  
Proof: This lemma can be proved by exploiting the implicit function theorem which can be found in Appendix A. It should be noted that when the user association probability \(A_{k}\) increases, more nodes will be associated with the BSs. In this case, the average transmit rate will decrease due to the bandwidth allocated to each node reducing. Therefore, the decreasing of optimal receive threshold will increase the energy efficiency proportional fairness utility function. Theorem 2: _The relaxed energy efficiency proportional fairness utility function is concave for association probability in any tier._  
Proof: This theorem can be proved by exploiting the implicit function theorem which can be found in Appendix B.  
Figure 2: The relationship between energy efficiency proportional fairness utility function and the receive threshold for different transmit powers and densities of BSs in two-tier heterogeneous WPCNs.
## 4 Description of the Proposed Topology Construction
### Design Issues of the Proposed Logical Topology
#### 4.5.6 Number of Layers  
Although we describe the proposed multiple chain oriented logical topology using a two-layer model, the number of layers can be extended based on the number of sensor nodes in the target field. For example, Figure 16 depicts a model of multiple chain oriented logical topology with three hierarchical layers. In this figure, the black nodes are the member nodes of layer-1 green-coloured chains. In each layer-1 chain, a node is elected as a leader and marked in green. All green-coloured layer-1 leaders construct several layer-2 blue coloured chains. Similarly, in each layer-2 chain, a node is elected as a leader marked in blue. All the blue-coloured layer-2 leaders further construct a layer-3 chain, and one of its members is elected as a leader. This leader is the highest level leader, and is marked in red. A black node sends the data to its leader (green) via the green chain, a green node sends the accumulated data to its leader (blue) via the blue chain, and finally, a blue node sends its accumulated data to its leader (red). The highest level leader (red) then sends the data to the BS.  
For a large scaled sensor network, a two-layer model may not be suitable, because layer-2 chain in that can be excessively long. On the other hand, for a WSN with several hundred nodes, a three-layer design would be wasting more energy and time, because sensor data would have to be propagated through various hierarchical chains. To find the threshold point, we run a simulation varying the number of sensors from hundred nodes to more than three thousand nodes and measured total energy spent by the system and amount of time required. Figure 17 shows the simulation results and comparison between 2-layered and 3-layered chains with respect to the time required for 100 rounds. The figure demonstrates that 2-layered chains take less time to reach 100 rounds than 3-layered chains until the number of sensors is greater than 1600, when the reverse becomes true. The same situation arises for total energy consumption depicted in Figure 18. The 2-layered architecture saves more energy than the 3-layered architecture up until the number of sensor nodes exceeds 1500, when the reverse becomes true. Thus, it is concluded that, if the number of sensor nodes in the target field is less than 1500, two-layeredarchitecture is used, while if the number of sensor nodes is equal to or greater than 1500, three-layered architecture is more suitable.  
Figure 16: Three-layered hierarchical multiple chain oriented topology.  
Figure 17: Timing differences between two-layered and three-layered chains.
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 3 Methodology
### SCN Training Targets and Loss Function  
As mentioned above, in SCN, we regress the two sets of prediction key points, PSD prediction for subcarrier FC positions and the corresponding BW prediction.  
In Figure 4, the PSD targets are an example of the input power spectrum. Let \(I\in S^{L}\) be an input power spectrum of length \(L\). Therefore, we aim to produce the PSD outputs \(P\in[0,\,1]^{\frac{L}{4}}\) for the set output stride to a fixed value of 4 in SCN. Let \(P_{i}\) be the score at the \(i_{th}\) point in the predicted PSD, and let \(Y_{i}\) be the ground-truth PSD, where the subcarrier center denotes \(P_{i}=1\), corresponding to a detected keypoint, while the others are all background and \(P_{i}=0\). In accordance with [24; 25], a 1D Gaussian kernel \(e^{-\frac{(x-P_{i})^{2}}{2\pi^{2}}}\) is used to produce the ground-truth at the frequency center of the PSD, where \(\sigma\) is \(1/3\) of the corresponding BW. If the two Gaussians overlap, we consider the element-wise maximum [32].  
In the training phase, we utilize a modified focal loss [33] to optimize the PSD outputs:  
\[L_{psd}=-\frac{1}{N}\sum_{i=1}^{L}\begin{cases}(1-P_{i})^{\alpha}\log(P_{i})&if \ Y_{i}=1\\ (1-Y_{i})^{\beta}(P_{i})^{\alpha}\log(1-P_{i})&otherwise\end{cases} \tag{2}\]  
where \(\alpha\) and \(\beta\) are hyper-parameters of the modified focal loss and \(N\) is the number of all subcarriers in the power spectrum. We set \(\alpha\) to 2 and \(\beta\) to 4 in all our experiments. The loss reduces the penalty around the ground-truth locations.  
Let \(BSW\) be the broadband power spectrum bandwidth, and let \(BW^{k}\) be the \(k_{th}\) subcarrier bandwidth. Therefore, we can use an embedding vector to predict the BW regression. Here, the \(k_{th}\) subcarrier center is the ground-truth embedding value set to \(\hat{W}^{k}=\frac{L_{\times}BW^{k}}{BSW}\). As a result, we can predict a set of BW regression embedding and directly use the raw spectrum coordinates without normalizing the scale, so-called \(W^{k}\). To train the BW regression, we utilize a L1 loss at the center point of the subcarrier. All the other locations are not considered and the formula is as follows:  
\[L_{bw}=-\frac{1}{N}\sum_{k=1}^{N}\left|\hat{W}^{k}-W^{k}\right| \tag{3}\]  
To balance the PSD and BW losses, we use a constant \(\lambda_{bw}\) to scale the BW loss. The overall training loss is as follows:  
\[L_{det}=L_{psd}+\lambda_{bw}L_{bw} \tag{4}\]  
In all our experiments, \(\lambda_{bw}=0.1\).
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Raff Culture Zone Fusion Evaluation
#### 3.2.2 GF-2 Fusion Evaluation  
As shown in Table 6, the standard deviation, information entropy, and the average gradient of B1, B2, and B3 bands in the NNDiffuse results are higher than in other fused images (Figure 9). This indicates the three bands include the highest number of gray levels, the most spatial information, and the most detailed texture. The maximum standard deviation, information entropy, and average gradient occurred in B4 of the GS fusion results. The correlation coefficients in the fused bands were higher than in the original multispectral bands, indicating the information to be more uniform. Information inclusion was highest with the NNDiffuse algorithm, which produced the lowest spectral distortion and deviation index in each band. It also produced the highest levels of spectrum preservation, as the gray mean value in each band of the fused images was closest to the original multispectral data. These results suggest NNDiffuse is the most capable of maintaining spectral characteristics, particularly for GF-2 data, followed by GS fusion. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**GF-2**} & \multirow{2}{*}{**Band**} & **Gray Mean** & **Standard** & **Information** & **Average** & **Correlation** & **Spectral** & **Bias Index** \\  & & **Value** & **Deviation** & **Entropy** & **Gradient** & **Coefficient** & **Distortion** & **Bias Index** \\ \hline \multirow{4}{*}{MS} & Band1 & 365.22 & 4.65 & 3.99 & 1.38 & 0 & 0 & 0 \\  & Band2 & 281.02 & 5.77 & 4.25 & 1.38 & 0 & 0 & 0 \\  & Band3 & 157.28 & 9.09 & 4.32 & 1.26 & 0 & 0 & 0 \\  & Band4 & 58.15 & 10.60 & 3.75 & 0.28 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{GS} & Band1 & 279.78 & 12.89 & 4.59 & 5.28 & 0.04 & 85.56 & 0.23 \\  & Band2 & 213.61 & 9.97 & 4.47 & 4.07 & 0.03 & 67.49 & 0.24 \\  & Band3 & 103.82 & 8.70 & 3.96 & 3.21 & 0.08 & 53.56 & 0.34 \\  & Band4 & 81.63 & 8.80 & 4.01 & 2.05 & 0.03 & 9.78 & 0.16 \\ \hline \multirow{4}{*}{NNNDiffuse} & Band1 & **361.97** & **23.59** & **5.58** & **9.93** & 0.04 & 13.87 & 0.04 \\  & Band2 & **277.39** & **17.97** & **5.29** & **7.57** & 0.03 & 11.67 & 0.04 \\  & Band3 & **150.20** & **10.68** & **4.28** & **4.27** & 0.03 & 8.73 & 0.05 \\  & Band4 & **56.26** & **5.78** & **3.64** & **1.17** & 0.03 & 2.05 & 0.03 \\ \hline \multirow{4}{*}{PCA} & Band1 & 261.14 & 13.14 & 4.56 & 5.30 & 0.01 & 104.13 & 0.29 \\  & Band2 & 199.04 & 10.17 & 4.49 & 4.16 & 0.03 & 82.02 & 0.29 \\ \cline{1-1}  & Band3 & 93.10 & 8.65 & 4.04 & 3.24 & 0.09 & 64.22 & 0.41 \\ \cline{1-1}  & Band4 & 25.89 & 7.75 & 1.92 & 1.89 & 0.03 & 11.56 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative evaluation results for GF-2 data from a floating raft culture area.