esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### PD Classification Performance  
To compare the proposed method against when only the four last layers were fine-tuned (Appendix A) and a prior DL model on the same data set [8], we evaluated it by feeding in randomly selected test data.  
We tested the model 10 times (on randomly chosen train/test partitions). The training accuracy was constantly 100%, but the test accuracy varied in the range of 91 to 100%.  
The average values of performance metrics are reported in Table 5. We extracted the last layer's scores assigned to each class (HC/PD) for 10-fold repetition as predicted classes, compared them to the actual classes, and computed the True Negative, True Positive, False Negative, and False Positive declared in Figure 4.  
In [15], a CNN model is suggested comprised of four consecutive blocks which are comprised of a 1D convolutional layer, max pooling layer, and activation layer, and the activation function is Relu, finally followed by three fully connected layers with Softmax activation function. The learning rate equals to 0.001, and the optimizer is Adam.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Proposed** & **CRNN [8]** & **SVM-RBF [8]** & [15] \\ \hline Accuracy (\%) & 99.6 & 99.2 & 95.4 & 95.4 \\ \hline Precision (\%) & 100 & 98.9 & 96.3 & 95.2 \\ \hline Recall (\%) & 99.17 & 99.4 & 94.3 & 95.5 \\ \hline F1 score & 0.995 & 0.992 & 0.953 & 0.953 \\ \hline AUC (area under the ROC curve) & 0.995 & 0.992 & 0.954 & 0.954 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of models.  
Previous work on the same data set included applications on a variety of machine learning models, including \"KNN\", \"SVM-L\", \"SVM-P2\", \"SVM-P3\", \"SVM-RBF\", and \"RF\" for 779 extracted time-series features, including autocorrelation, quantiles, and number of zero-crossings [8]. In addition, they used univariate statistical analyses (ANOVA) and identified significant features, with a significance level of 0.05. Then, using these significant features, they trained the models. The best performance for these models is reported as \"SVM-RBF\" in Table 5. The proposed approach achieved 99.62% accuracy, 100% precision, 99.17% recall, 0.995 F1 score, and 0.995 AUC.
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 3 Measurement Campaign
### Test Cases  
The harmonic propagation study includes two distinct packages. In Package A, an MG set is used to energize the network with ideal sinusoidal voltage supply. Every harmonic load is connected separately at its designated position. The individual current waveforms and synchronized voltage waveforms are measured at various points of the network. This test concludes with putting all the loads into the operation simultaneously and recording all the electrical parameters synchronously.  
In test package B, the network is energized by the public grid and at the first stages the same scenarios as in package A are tested. Furthermore, an equal share of resistive load banks is connected to the substations G and A. At the next stage, the topology of the network was changed at the MV level by bypassing the impedance section A through section B. Finally, the last change in the configuration involved replacement of the Triphase-emulated harmonic load from Substation A to Substation D. The test mapping with change variables is shown in Table 1.
## 4 Discussion and Conclusions  
In this work, a 1064 nm laser pumping, tunable BGSe-OPO was demonstrated. Using a side-pumped electro-optical Q-switched Nd:YAG laser as the fundamental pump source, a tunable mid-infrared wave with high repetition rate up to 250 Hz was generated from  
Figure 6: Comparison of the temporal pulse profiles of the pump wave and the mid–infrared wave.  
Figure 7: The signal wave spectrum at normal incidence.  
BGSe-OPO. DP-SRO configuration was utilized to improve the conversion efficiency and reduce the OPO threshold. The maximum average power of 250 mW was achieved at 4.06 \(\upmu\)m, with the OPO cavity length of 30 mm. The influences of the repetition rate and the cavity length were studied experimentally. By rotating the BGSe crystal, tunable mid-infrared output from 3.42-4.73 \(\upmu\)m was achieved. The pulse width of the generated mid-infrared wave was measured to be 13.6 ns and the linewidth at 4061 nm was estimated to be 3.92 nm.  
The thermal effect of BGSe crystal was observed to be significant, especially in the high repetition rate OPO. Additionally, the accumulation of the thermal effect would result in the decrease of output power and LIDT with the increase of repetition rate, which was harmful to the parametric conversion process. Further optimizations in the pump beam quality, cooling method and cavity structure of DP-OPO would improve the output characteristics of BGSe-OPO.  
The BGSe-OPO has advantages in generating the mid-infrared wave with widely tuning range, high power and high repetition rate. Furthermore, BGSe-OPO can be pumped with a 1064 nm laser which is low-cost and usually has a simple structure. BGSe-OPO has the advantages of both the tuning range of ZGP-OPO and the simple structure of PPLN-OPO, which are commonly used in commercial products. The system in this work will show great commercialization potential with the utilization of a commercial 1064 nm laser with a compact size.  
Conceptualization, R.Y. and J.Y. ([PERSON]); methodology, D.X.; validation, C.Y, K.C. and Y.H.; formal analysis, Y.W.; investigation, K.Z.; resources, J.Y. ([PERSON]); data curation, C.Y.; writing--original draft preparation, K.C.; writing--review and editing, Y.H.; visualization, K.C.; supervision, J.L.; project administration, D.X.; funding acquisition, D.X. and J.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC), grant numbers U1837202, 62175182 and 62011540006.  
Not applicable.  
The authors declare no conflict of interest.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 5 Analysis
### Application of Machine Learning to LiDAR Data
#### 5.3.2 The Difference between Left and Right LiDAR Sensor Measurements
This is because the Decision Tree algorithm does not rely on the number of data points, which can vary at  
Figure 16: Accuracy of models from the Decision Tree algorithm for various window sizes trained on the differences between the left and right LiDAR sensors’ measurements. Figure 15: An example of the difference between the left and right LiDAR sensor measurements used for training the ML models for tie/ballast differentiation. higher speeds due to a constant data acquisition rate, resulting in fewer recorded data points. Logistic Regression, which previously performed poorly, now exhibits behavior similar to the Decision Tree algorithm, demonstrating improved and consistent accuracy across different window sizes. Training the machine learning model using the Decision Tree algorithm on the standard deviation of the difference between the left and right LiDAR sensor measurements yields the highest possible accuracy, regardless of the window size. The final model, identified as Model 6 in Figure 11, with a window size of 35, was selected for its excellent performance in tie/ballast differentiation compared to the others. Figure 19a shows the model's predictions over an unseen section of the data in a blue line, with 1 indicating a tie and 0 indicating ballast. The high accuracy and precision of the model in distinguishing ties and ballasts are visually evident in this figure. In the entire sampled section, all the ties and ballasts are successfully identified, and the boundaries of each are marked with high precision. The two ties marked with black stars are the same ties shown in Figure 14. The model now performs significantly better than the previous version. Figure 19b presents the confusion matrix for the model, where 95.2% of the ballast data points are classified as ballast, and 92.4% of the ties are classified as ties. The high and closely matched percentages demonstrate the model's effectiveness in accurately classifying both ties and ballasts. The total accuracy of this model is approximately 93.8%. Note that all the accuracies and percentages are calculated for the test dataset. Figure 17: A sample standard deviation of left and right difference LiDAR measurements used in training the ML models for tie/ballast differentiation. Figure 18: Accuracy of models from Decision Tree and Logistic Regression algorithms for various window sizes trained on the standard deviation of the differences between the left and right LiDAR sensors’ measurements.
## 3 Test Results and Discussion
### Results of Repeated Impact Tests
#### 3.2.3. Sandstone Damage Model under Repeated Impacts  
The cumulative damage of the sandstone presented the form of 'rapid rise-steady development-rapid rise' under repeated impacts. The curve form of the Goperz function is'slow increase-rapid development-mature development'. The characteristics of the three stages of the cumulative damage of sandstone can be expressed via an inverse transformation of the Goperz function. Based on this function, the damage evolution model of sandstone under repeated impacts is \(D=\delta-\alpha\cdot\ln(\ln\beta/n)\), where \(\alpha\), \(\beta\) and \(\delta\) are undetermined parameters and \(n\) is the number of repeated impacts. The model fitting curve is shown in Figure 10. The cumulative damage evolution model of sandstone under repeated impacts using the above model is:  
\[D=0.265-0.328\cdot\ln(\ln 13.989/n),\ R^{2}=0.85 \tag{16}\]
# Numerical Technique for Study of Noise Grating Dynamics in Holographic Photopolymers
## 3 Validation
To further validate that the code correctly implements volume diffraction of off the numerically calculated index structure, a Bragg detuning curve found from both methods is compared. In the theoretical case, the calculated first harmonic of the refractive index is used in the beta value method [13] to predict the efficiency as a function of incident beam angle relative to the initial reference beam angle. In the numerical case, the three-dimensional space is illuminated with a tilted reference beam and the electric fields at the far side of the material are calculated via the FBPM method. The diffracted power is found by integrating the intensity in a range of spatial frequencies centered on the diffracted object beam. This is repeated for a series of reference angles. To compare the beta value method prediction of plane-wave diffraction off of an infinite grating to the numerical calculation of a Gaussian  
Figure 5: Comparison of \(1+1\)D theoretical and \(3+1\)D numerical predictions of grating growth versus time. The holographic diffraction efficiency shown in (**a**) exhibits the saturated response expected for an exposure time sufficient to consume all but \(e^{-2.5}\approx 8\%\) of the monomer. Increased efficiency after dark diffusion and flood cure is consistent with slow monomer diffusion. A slice through the 3D numerical space shown in (**b**) is Fourier transformed in the transverse x coordinate to show the coupling of the incident light into the Bragg-matched \(+1\) order and phase-mismatched coupling into the \(-1\) order, illustrating that the numerical solution includes the complete physical description of the problem. In (**c**), a cross-section through the center of the numerical space is used to expand the index in a harmonic series at each time step, shown as circles. The closed-form theoretical prediction, rendered as lines, is in extremely good agreement. The dotted line shows the first-order kinetics expected for the zero and first harmonic in the case of perfect fidelity (\(R_{m}\to 0\)) reaction limited (\(R_{m}\to\infty\)) recording. The index contrast measured between the peak and null of the intensity is compared for the numerical (circles) and theoretical (line) cases in (**d**), again showing excellent agreement. shaped hologram read with a Gaussian shaped reference beam, the theoretical efficiency is divided by \(\left(1+2(w_{0}^{rand}/w_{0}^{write})^{2}\right)\) where \(w_{0}\) is the 1/\(e\) radius of the electric field [14]. Figure 6 shows that the two approaches agree extremely well.
## 3 A Few Notes on the Mathematical Model
### Spacecraft Heliocentric Dynamics  
Following the typical procedure used to complete the preliminary design of the transfer trajectory of an interplanetary spacecraft ([PERSON], 2016), we consider only the effects related to the gravitational attraction of the Sun and the thrust provided by the solar electric propulsion system. Keeping in mind Equation (5) and observing that the general expression of the thrust vector \(\mathbf{T}\) induced by BIT-3 is given by Equation (3), while both the thrust magnitude \(T\) and propellant mass flow rate \(\dot{m}_{p}\) depend on the operating level Id, the CubeSat's equations of motion are given by the classical relations  
\[\frac{\mathrm{d}\mathbf{r}}{\mathrm{d}t}=\mathbf{v}\qquad,\qquad\frac{\mathrm{d}\mathbf{v} }{\mathrm{d}t}=-\frac{\mu_{\odot}}{r^{3}}\mathbf{r}+\frac{\mathbf{T}}{m}\qquad,\qquad \frac{\mathrm{d}m}{\mathrm{d}t}=-\dot{m}_{p} \tag{6}\]  
where \(\mu_{\odot}\) is the gravitational parameter of the Sun. The spacecraft dynamics, that is, the seven first-order scalar differential equations which can be derived from Equation (6), can be more conveniently studied by using the set of modified equinoctial elements proposed by [PERSON] (1951). In this case, the equations of motion of the spacecraft become more complex in terms of their writing, but their use in a trajectory calculation routine is simpler than using the Cartesian equations given by the previous relation. The details regarding the use of the modified equinoctial elements ([PERSON], 2016) and the mathematical form of the spacecraft equations of motion are described in the companion paper ([PERSON], 2016) to this Special Issue ([PERSON], 2016), to which the interested reader is referred for further information. In particular, the last of Equation (6) is used, together with Equations (9)-(14) of Ref. ([PERSON], 2016), in which the three components (radial, transverse, and normal) of the propulsive acceleration vector in RTN are given by the ratios \(T_{R}/m\), \(T_{T}/m\), and \(T_{N}/m\); see also Equation (4).  
The initial conditions (i.e., the conditions at time \(t_{0}\)) that complete the system of differential Equation (6) model the CubeSat which initially travels in a heliocentric parking orbit that coincides with the orbit of the Earth around the Sun. In this situation, the initial value of \(m\) is \(m_{0}\), which is equal to \(13\,\mathrm{kg}\) in the case of a single engine unit or equal to \(19\,\mathrm{kg}\) when there are two engine units. The initial value of the spacecraft position and velocity vectors are instead derived from the orbital elements of the Earth's heliocentric orbit as retrieved from the JPL Horizons On-Line Ephemeris System on the date of 10 April 2025. In particular, the angular position of the CubeSat along the parking orbit is unconstrained and is obtained as the output of the optimization process that provides the heliocentric trajectory of the spacecraft. This optimization process will be briefly described in the next subsection.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 2 System Overview
### Implementation in GNU Radio
#### 2.2.1. The Transmitter  
The structure of the transmitter is a constituent of _gr-ieee_ 802.11. The difference between the recent implementation of [PERSON] et al. is the new block called \"RC Encoder + WiFi Mapper\", otherwise the rest of the blocks remains the same.  
RC Encoder + WiFi Mapper implementation: The novelty in this structure is the new block that is created for RC encoding and CC encoding. First, we recover the old implementation of _gr-ieee_ 802.11. Following the steps of the building, which can be found in [27], we create a new block by using _gr-modtool add_ and the different arguments linked to the blocks as depicted in Figure 2.  
The various parameters associated with this block are also identified:  
* \(ninput\) corresponds to the data from the Medium Access Layer (MAC). They can be in a message or a bit format.
* \(noutput\) represents the encoded data after convolutional encoding.
* \(coderate\) is the coding rate of the convolutional encoder and \(=1/2\).
* \(gfpoly\) corresponds to the primitive polynomial of the rank metric encoder. Since we use \(N=16\), the generator is as follows: \[p(\alpha)=\alpha^{16}+\alpha^{12}+\alpha^{3}+\alpha+1\] (5)
* \(N\) is the size of the Galois field.
* \(t\) represents the power of correction.  
After this block of error correction code, we have the other blocks such as packet header generator, which generates the header of the frame, including the signal and service fields. The header and the remaining frame are both BPSK modulated using chunks to symbols. After that, an OFDM carrier allocation is performed and responsible for the aggregation of the pilot sub-carriers, and the Fast Fourier Transform (FFT) block is responsible for the inverse FFT, i.e., for the transition from the frequency to the time domain. Since the OFDM symbols will be sent to a multi-path channel, the signal at the receiver side will be distorted. One way to combat the effect of the multi-path channel such as the Inter-Symbol Interference (ISI) is to add a cyclic prefix.
## 2 Weather Data Super-Resolution Algorithm Based on CBAM-SRResNet
### Mixed Attention Mechanism  
The Convolutional Block Attention Module (CBAM) integrates multiple attention mechanisms [37], consisting of two main components: the channel attention module and the spatial attention module. In the weather data reconstruction task, the CBAM guides the network to focus on the most representative physical variables in the multivariate field by explicitly modeling the channel and spatial attention mechanism, while strengthening the spatial response of extreme weather regions, thereby improving the fidelity of key regions in the reconstructed image. Compared with the model without an attention mechanism,  
Figure 3: Sub-pixel convolution structure.  
CBAM can effectively reduce the problems of boundary blurring and feature erasing. The structure of the CBAM is shown in Figure 4.  
As shown in Figure 4, CBAM consists of two key sub-modules: the channel attention module and the spatial attention module. The module effectively enhances the feature expression ability by jointly modeling the attention weight of the feature map in the channel dimension and the spatial dimension. The structural design, information flow process, and its mechanism in the feature extraction of these two sub-modules is systematically described below.
## 3 Orthogonal Experiments and Grey Relational Analysis
### Selection of Process Parameters
#### 3.1.3 Friction Coefficient  
In numerical simulations, the friction coefficient is used to control the lubrication effect of the drawing oil, and it is assumed to be consistent across all parts of the component [13]. If the friction coefficient is too high, the sheet material can hardly flow, leading to cracking; if too low, it can lead to wrinkling. Based on theoretical references and actual production, the friction coefficient is set at four levels: 0.050, 0.075, 0.100, and 0.125.
###### Abstract  
To address the fact that changes in network topology can have an impact on the performance of routing, this paper proposes an Elastic Routing Algorithm based on Multi-Agent Deep Deterministic Policy Gradient (ERA-MADDPG), which is implemented within the framework of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in deep reinforcement learning. The algorithm first builds a three-layer architecture based on Software-Defined Networking (SDN). The top-down layers are the multi-agent layer, the controller layer, and the data layer. The architecture's processing flow, including real-time data layer information collection and dynamic policy generation, enables the ERA-MADDPG algorithm to exhibit strong elasticity by quickly adjusting routing decisions in response to topology changes. The actor-critic framework combined with Convolutional Neural Networks (CNN) to implement the ERA-MADDPG routing algorithm effectively improves training efficiency, enhances learning stability, facilitates collaboration, and improves algorithm generalization and applicability. Finally, simulation experiments demonstrate that the convergence speed of the ERA-MADDPG routing algorithm outperforms that of the Multi-Agent Deep Q-Network (MADQN) algorithm and the Smart Routing based on Deep Reinforcement Learning (SR-DRL) algorithm, and the training speed in the initial phase is improved by approximately 20.9% and 39.1% compared to the MADQN algorithm and SR-DRL algorithm, respectively. The elasticity performance of ERA-MADDPG is quantified by re-convergence speed: under 5-15% topology node/link changes, its re-convergence speed is over 25% faster than that of MADQN and SR-DRL, demonstrating superior capability to maintain routing efficiency in dynamic environments.  
DDPG; multi-agent; network topology; routing algorithm; SDN; actor-critic; DRL [[https://doi.org/10.3390/617070291](https://doi.org/10.3390/617070291)]([https://doi.org/10.3390/617070291](https://doi.org/10.3390/617070291))
# A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
## 5 Simulation and Analysis
It can be seen from the figure that the proposed algorithm in this paper has significantly higher system fairness. Figure 5 shows the change trend of system throughput with \(\varphi_{c-n}\) and \(\varphi_{c}\). The value range of \(\varphi_{c-n}\) and \(\varphi_{c}\) is \([0.05,\,0.9]\). As can be seen from Figure 5, the throughput decreases with the increase of the minimum fairness intra and inter clusters, and the downward trend gradually slows down. When \(\varphi_{c}\) is small, the throughput can be increased by reducing \(\varphi_{c-n}\). When \(\varphi_{c}\) is large, the reduction of \(\varphi_{c-n}\) cannot obtain significant throughput gain. In practical applications, the system throughput can be improved by reasonably adjusting the minimum fairness constraints of one aspect according to the different needs of inter and intra cluster user fairness in different scenarios. Figure 3: Throughput varies with the number of users.
## 3 Five-Tier Architecture of Smart Grid Based on Blockchain
### Initialization and Registration Phase  
The initialization and registration phase consists of three phases: system setup, partial key extraction, and user key generation.  
(1) System Setup  
When the system is initialized, the entities at the management layer and the power layer jointly build the consortium blockchain. The dispatching center as a super node has the right to account. The dispatching center as KGC randomly generates the base point:\(G\) and the elliptic curve \(E_{p}(a,b)\) on the prime field \(F_{p}\). Then, the dispatching center generates the long-term master key \(SK_{DC}\) and the long-term public key \(PK_{DC}=SK_{DC}\cdot G\). The \((G,P,a,b,PK_{DC})\) will be uploaded to the blockchain by a smart contract and disclosed in the system.  
(2) Partial Key Extraction  
The dispatching center will give intelligent gateway \(IG\) an identity \(ID_{IG}\). The dispatching center chooses a random value \(r_{IG}\in\mathbb{Z}_{P}^{*}\) and computes \(R_{IG}=r_{IG}G\), \(h=H_{1}(ID_{IG}||R_{IG})\), and \(s_{IG}=(r_{IG}+hSK_{DC})^{-1}\). Then, the dispatching center secretly transmits \(Sig_{DC}(s_{IG},R_{IG})\) to the intelligent gateway \(IG\) and uploads this to the blockchain by the smart contract. The intelligent gateway \(ID_{IG}\) can validate whether \(s_{IG}(R_{IG}+H_{1}(ID_{IG}||R_{IG})PK_{DC})=G\) receives its partial private key.  
(3) User Key Generation  
The intelligent gateway \(IG\) randomly chooses a secret value \(x_{IG}\in\mathbb{Z}_{P}^{*}\) and computes \(upk_{IG}=x_{IG}s_{IG}(R_{IG}+H_{1}(ID_{IG}||R_{IG})PK_{DC})\). Then, the intelligent gateway revives its private keys \((s_{IG},x_{IG})\) and public key \((upk_{IG},R_{IG})\).