esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, multiple technologies have been proposed with the aim of combining the characteristics of traditional planar and non-planar transmission lines. The first and most popular of these technologies is the Substrate Integrated Waveguide (SIW), where rows of metallic vias are mechanized in a printed circuit board (PCB). These vias, together with the top and bottom metal layers of the PCB, form a channel for the propagation of the electromagnetic fields, similar to that of a rectangular waveguide, but through a dielectric body, which increases the losses. To reduce these losses, the empty substrate integrated waveguide (ESIW) was recently proposed. In the ESIW, the dielectric is removed from the substrate, and this results in better performance (low profile and easy manufacturing as in SIW, but lower losses and better quality factor for resonators). Recently, to increase the operational bandwidth (monomode propagation) of the ESIW, the ridge ESIW (RESIW) and a transition from RESIW to microstrip line was proposed. In this work, a new and improved wideband transition from microstrip line (MS) to RESIW, with a dielectric taper based on the equations of the superellipse, is proposed. The new wideband transition presents simulated return losses in a back-to-back transition greater than 20 dB in an 87% fractional bandwidth, while in the previous transition the fractional bandwidth was 82%. This is an increment of 5%. In addition, the transition presents simulated return losses greater than 26 dB in an 84% fractional bandwidth. For validation purposes, a back-to-back configuration of the new transition was successfully manufactured and measured. The measured return loss is better than 14 dB with an insertion loss lower than 1 dB over the whole band.  
substrate integrated waveguide; ridge waveguide; tapering structure; broadband; microwave devices +
Footnote †: journal: Applied Physics Letters
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 2 The Single Beam Wollaston Interferometer Setup
### Experimental Set-Up for Non-Rotational Measurements  
Figure 7 depicts the experimental setup to obtain tomographic projection data of the solenoid gas jet with a supersonic shock front generated by inserting a razor blade into the gas flow; in order to keep the gas jet and the imaging system fixed, but to position the blade around the gas jet with two degrees of freedom. This is achieved with a rotational piezo stage and a linear piezo positioner, which is attached to the rotational stage. The rotational stage allows one to measure phase projections along different directions with respect to the orientation of the shock front. The resulting density gradients are shown in Section 4.2. The radial positioning can then be used to vary the position of the blade with respect to the center of the gas jet. This allows us to change the properties of the shock front. Furthermore, the radial degree of freedom can be used to correct the eccentricity between the center of the gas jet and the axis of the rotational stage due to mechanical imperfections. The vertical distance between the razor blade and the nozzle of the gas jet is fixed in this setup to 1.6 mm.  
Figure 6: Parker solenoid valve and interference fringes. (**A**) Miniature high speed high vacuum dispensing valve with a conical outlet (Parker 009-0442-900 ); (**B**) typical interference fringes of the Parker solenoid valve at 35 bar backing pressure with argon. Here, the gas flow is directed upwards. The two non-straight areas correspond to positive and negative phase differences \(\Delta\phi\) (see Equation (4)).
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 5 Results
### Network Model Parameters
#### 5.2.1 Effect of LSTM Layers and Parameters on Recognition Rate  
We analyzed the effects of the number of LSTM layers and cells on the recognition rate of the algorithm, as shown in Figures 7 and 8, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Label** & **Number of Signals** & **Number of Samples (2\(\times\)400)** \\ \hline linear block code & 0 & \(2000\times 400\) & 2000 \\ self-synchronous & 1 & \(2000\times 400\) & 2000 \\ scrambler & 2 & \(2000\times 400\) & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear block code scrambling dataset under bit error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Label** & **Number of Signals** & **Number of Samples (2\(\times\)800)** \\ \hline linear block code & 0 & \(2000\times 800\) & 2000 \\ self-synchronous & 1 & \(2000\times 800\) & 2000 \\ scrambler & 2 & \(2000\times 800\) & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Linear block code scrambling dataset with noise.  
In Figures 7 and 8, we can see that the recognition rate is the highest when the number of LSTM layers is 1, and the recognition results are better when the number of cells is 32 or 68. However, the training time of the network increases a lot when the number of cells is 68, so for the number of cells, 32 was chosen, as it has a shorter training time.
## 3 Proposed Method  
### Overview of the Proposed Framework  
Learning good feature representations from limited data is a very challenging task. To address this challenge, we attempt to make full use of the original data. As mentioned previously, remote sensing images are different from natural images in many respects, so it is important to design specific methods consistent with the characteristics of remote sensing images. Indeed, some methods designed for application to natural images can even compromise the performance on RSSC tasks if they are used inappropriately. To this end, the end-to-end GFSC is proposed to help train CNNs on limited data. Figure 3 illustrates the core idea of our proposed framework, GFSC. The goal of our method is to help various backbones achieve results comparable to those of models with ImageNet pretraining using only RSSC datasets.  
As shown in Figure 3, Starting from the overall training process, we consider various elements of the process to address the degradation of RSSC model training without ImageNet pretraining. For the initialization of the weights, we employ SSL to learn the characteristics of the remote sensing images themselves. Data augmentation and regularization strategies are also important when the amount of data is insufficient. For data augmentation, we learn suitable data augmentation strategies specific to RSSC datasets. For regularization, we use Mixup as the regularization strategy.
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 6 Conclusions  
This paper proposed the SDP + CNN method for surge arrester fault recognition. Four common fault types for surge arresters were established in this paper. The PD signals of the surge arrester were transformed by SDP from the time domain waveform into SDP diagrams for use as fault diagnosis samples and imported into the trained CNN model for recognition. The proposed method was compared to conventional HOG + SVM, HOG + BPNN, and HOG + KNN. The HOG + SVM and HOG + KNN methods only spent 1.7 s and 0.3 s on training, respectively, but the test time required 5 s and 3 s, and their respective recognition accuracies were 95% and 91.2%. The training time of HOG + BPNN was 121 s, which was the most time-consuming among the four detection methods, but the test time was only 0.35 s, and the recognition accuracy was 94.6%. The recognition accuracy of the proposed method in this paper was as high as 97.9% and the test required 0.46 s. By comparing the identification accuracy of the HOG + SVM, HOG + BPNN, and HOG + KNN, the proposed (SDP + CNN) method was found to exhibit a good accuracy of up to 97.9%, indicating that it can be applied for effective fault diagnosis of surge arresters. In the future, the proposed approach could also be applied in other high-voltage electric power and energy-related fields, such as generators, power capacitors, power cables, etc.  
Contributions: [PERSON] conceived the presented idea, designed, and experimented with this work; [PERSON] supervised, planned the experiments, and wrote this article; [PERSON] performed and validated the numerical simulations; all authors provided critical feedback and helped shape the research, analysis, and manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology of Taiwan, under contract numbers: MOST 110-2221-E-167-006 and MOST 110-2221-E-167-025. The authors declare no conflict of interest.
## 4 Evaluation
### Time Analysis  
It is critical for our proposed scheme to reduce processing overhead while maintaining secure communication and centralized coordination. To analyze the runtime composition of each subcomponent, Figure 10 presents the execution time breakdown when encrypting a 100 GB file using 16 MB subset size across 2, 4, and 8 nodes. We observe the runtime of five major components: _Encryption, Dcrypption, BB84 Protocol, PQC Generation and Encapsulation_, and _Communication_.  
As shown in the figure, _Encryption_ accounts for the largest portion of runtime in the 2-node case, taking 252.62 s, which corresponds to 54.7% of the total time. As the number of nodes increases to 4 and 8, the encryption time decreases to 153.41 s (39.6%) and 31.17 s (11.8%), respectively. This corresponds to a 39.3% reduction from 2 to 4 nodes, and an 87.7% reduction from 2 to 8 nodes, demonstrating strong scalability through distributed subset encryption. In contrast, _Decryption_ shows limited scalability due to its centralized execution at the master node. The decryption time decreases from 179.52 s at 2 nodes to 169.65 s (5.5% reduction) at 4 nodes and 119.92 s (33.2% reduction) at 8 nodes. Although decryption is centralized and not parallelized across nodes, our scheme mitigates potential bottlenecks by decoupling communication and computation. While encrypted subsets are being received, the master prepares in-memory buffers and schedules decryption tasks, enabling immediate processing after sorting is complete. This structure reduces idle time and improves overall throughput, especially for large files.  
The _BB84 Protocol_ time remains relatively small and does not show a clear correlation with the number of nodes. It varies between 4.23 and 7.23 s across configurations, primarily due to the probabilistic nature of quantum key generation rather than node-level scaling. This range indicates stable runtime behavior under simulated QKD conditions. The _PQC Encapsulation_ time remains stable at approximately 1 s across all configurations. This is because key encapsulation is performed once during the initialization phase on the master  
Figure 10: Time analysis using 100 GB file (subset: 16 MB).  
Figure 9: Communication time comparison of the proposed scheme (PQC + QKD + AES) with varying subset sizes across different node counts.  
node, independent of the number of slave nodes, and introduces negligible overhead within the overall encryption pipeline. Finally, _Communication_ shows minimal variation, measured as 15.49 s at 2 nodes, 16.06 s at 4 nodes, and 15.51 s at 8 nodes. This stability comes from transmitting only pre-encrypted subset data using non-blocking MPI without additional protocol overhead. Since all data is encrypted before transmission and secured through PQC-based key encapsulation and QKD-based masking, no additional secure channel like TLS is required. As a result, communication cost remains low regardless of the number of nodes.
## 4 Experiment Arrangement and Results
### Performance Metrics
#### 4.1.1 Accuracy Estimating Indexes  
Model efficacy is often assessed through prediction accuracy, a practice widely documented in the literature [37; 38; 39; 40]. Following an extensive review, seven mainstream evaluation metrics are employed in this study to assess the predictive performance of the proposed model: Mean Absolute Error (MAE), Theil U statistical 1 (U1), Theil U statistical 2 (U2), root mean square error (RMSE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R\({}^{2}\)), and relative error (RE) are selected to evaluate the prediction performance of the model. Table 5 provides the mathematical expressions for these metrics.
# Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error
###### Abstract  
In large ports, shipyards, and other places of handling operations, gantry cranes are widely used. As a typical slewing crane, its slewing-bearing large gear ring is prone to tooth breakage problems due to the existence of long-term complex alternating load. This paper presents a new fault analysis method based on the gear position accuracy error. Firstly, the relative displacement relationship between the pinion and large gear ring and the large gear ring gear teeth stress nature were analyzed through the establishment of the rotary table structure, slewing bearing, and cylinder structure assembly finite element model. In addition, a dynamic data acquisition instrument and resistance strain gauge data were separately applied to analyze the pinion and large gear ring displacement along with cylinder structure stress for the test position accuracy error. The final results show that under the frontal load condition, the horizontal displacement deviation of the pinion gear and the large gear ring is approximately 2.9 mm, which leads to an increase of 23% in the stress value of the upper tooth root at the engagement of the large gear ring, causing extrusion and accelerating the fatigue and even fracture of the gear teeth.  
slewing bearing; failure analysis; broken tooth; gear position accuracy; finite element model 2022
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 2 AI-Based QoT and Impairment Modeling
### AI-Based Impairment Modeling
#### 2.3.2 Filtering Effect  
In future EON, ROADM can enable optical networks to support the flexible multiplexing and demultiplexing, which is important for build an intelligent network with more capacity and dynamicity. However, in this case, the filtering effect caused by cascaded ROADMs can also influence the QoT much more significantly because of the reduced guard band between channels. In [9], an ANN-aided approach is introduced to estimate the filtering effect. The input features of the neural network are the ROADM number, OSNR, loaded noise distribution and bandwidth distribution. A one-hidden layer ANN can estimate the SNR of the light path induced by the filtering effect with error mostly less than 1 dB. In practical systems, the filtering effect can be more significant when multiple impairments co-exist such as nonlinearity. Besides, the filtering effect is not a kind of additive noise and SNR may not be the best metric for evaluation. Therefore, problems like how to model the filtering effect together with other impairments and how to quantify the filtering effect using a proper metric should be further investigated.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## Appendix A.1.2 Beidou-2 IGSO Navigation Satellite RFI Data
### Radio Frequency Interferences in C Band  
The analyses of RFI in C band have been carried out by exploiting Sentinel-1 data according to the procedure described in [12]. The procedure is general and can be applied worldwide, thanks to the systematic acquisition of S1. The evaluation of RFI EIRP transmitted in the direction of the satellite has been achieved by averaging RFI over a time interval of 10 pulses (6 ms) each 20 km. S1 receiver bandwidth is 40-70 MHz, depending on the swath and the thermal noise is about 800 \({}^{\circ}\)K.  
As example of detection of RFI, achieved by processing 400 datasets, each of them spanning an area \(250\times 250\) km wide and acquired in 2016-2017 is shown in Figure 10. In the figure, all the detected RFI, affecting half of the datasets, are stacked. The vertical axis is the received power spectrum, normalized to S1 noise floor. The spectra show many distributed sources and a few, strong RFI. An evaluation of the RFI probability of power distribution with frequency is in Figure 11. In the figure on the left, each column is an estimate of the cumulated density of probability (CDF) that RFI exceeds a certain power threshold. Notice that in most of the cases, this probability of exceeding a value of around 3, that roughly corresponds to 2400 \({}^{\circ}\)K, is less than 1/100. In the same figure, on the right, the mean and the standard deviations are plotted for the RFI stacked in Figure 10 and accounting for the cases in which no RFI was detected. Here again, one can assume that the RFI level does not exceeds a threshold of 2400 \({}^{\circ}\)K. However, this holds only by assuming random distributed RFI and not--for example, for the very strong values observed in some specific geographic locations.  
Figure 10: GEAO SAR SINR based on the Beidou-2 IGSO navigation satellite data.  
Figure 11: GEAO SAR SINR based on the Beidou-2 IGSO navigation satellite data.  
The case of point-like RFI needs different analyses. Several repeated acquisitions centred in location in Middle East where the strongest RFI are measured, have been exploited leading to the PDF and CDF shown in Figure A8. The plots show that strong RFI repeats systematically in the same bandwidth, that is however only a portion of the entire spectrum available. This suggests mitigation with an agile frequency allocation.  
To evaluate the RFI impact in C band GEO SAR with a 2250 w average transmitted power, we utilize the Sentinel-1 RFI data. Based on the Sentinel-1 RFI data in Beijing area, we integrate the RFI energy within the 400 km swath in the GEO SAR case. According to the previous analysis, the equivalent antenna temperature is about 2400 K (total energy received by the antenna). The GEO SAR SINR map by using the Sentinel-1 data is given in Figure A9. The maximum SINR is more than 24 dB. Under the proposed case, the impact of the C band RFI has no impact on GEO SAR imaging (compared with a 10 dB SINR requirement).
###### Abstract  
We demonstrate a low latency delay of a radio frequency (RF)-linear frequency-modulated (LFM) pulse by modulating it onto optical carriers from a Kerr comb and sending the signal through a concatenation of off-the-shelf linearly chirped fiber Bragg gratings (LC-FBGs) and chirped-and-sampled FBG (CS-FBG). We characterize the frequency response and latency of the LC-FBG and CS-FBG. Then, experimentally, the LFM pulse performance is characterized by measuring the peak sidelobe level (PSL) at the output of the tunable delay system. The experiment, performed with an LFM pulse of 1 GHz bandwidth at a 10 GHz center frequency, shows a PSL better than 34.4 dB, attesting to the high quality of the buffer RF transfer function. Thus, the proposed optical memory buffer architecture, utilizing compact devices based on a Kerr comb and FBGs, offers several benefits for delaying LFM pulses, including (i) a larger tunable delay range, (ii) low latency, (iii) wide bandwidth, and (iv) high PSL.  
Keywords:Kerr comb; LFM pulse; fiber Bragg grating +
Footnote †: journal:
## 6 Application Verification Research of MSCL  
To verify the effectiveness of the MSCL control method, the experimental verification was carried out by the roller staking equipment. The MS14101-9 bearing was selected, and the flanging lip deformation of the V groove under different states was analyzed through experiments. Figure 11 shows the test machine for the roller staking process. The speed-time control was adopted first, and the rotate speed of the roller tool was set to 200 r/min [32], after the feeding speed was gradually increased to 0.1 mm/s, and keep it constant and continue rolling. When the staking load was detected to reach 2000 N, it was kept constant, and then force-time control was adopted, when the feeding displacement reached 0.5\(k\) (i.e., 0.71 mm), feeding was stopped and rolling continued for 3-5 turns for surface quality of the flanging lip.  
Figure 12 shows the cross-sectional view and the state comparison of specimens under MSCL control and displacement-time control. Figure 12a shows the initial deformation state of the flanging lip with MSCL control method. Figure 12b shows the \"State 2\" of MSCL control method, at this time. A fit begins to appear between the flanging lip and the housing chamfer. Figure 12c shows the \"State 3\" of MSCL control method. The fit quality is better, and the requirements and bearing performance are met. Figure 12d shows the \"State 5\" of displacement-time control method. The fit is tighter; however, it is obvious that the flanging lip is excessively squeezed, and material damage occurs.  
Similarly, for the other two process-loading control methods, 50 bearings were selected for the roller staking tests. Figure 13 shows the qualification rate comparison of different quality indexes (starting torque, surface quality, and fit between the flanging lip and the housing chamfer after roller staking) under the three process-loading control methods. It can be seen from the figure that the qualification rate of the bearing can be significantly improved by the MSCL loading control method. Through the test comparison and analysis, it can be seen that when the MSCL control method is adopted, the staking quality can be controlled more easily, and more bearings can be guaranteed to be in \"State 3\" after the roller staking process, and thereby increase the yield rate of the bearing.  
Figure 11: Schematic diagram of the roller swaging apparatus and the structure of the roller tool: (**a**) Schematic diagram of the roller swaging apparatus; (**b**) Real test machine. 1—Electric cylinder; 2—Pressure transducer; 3—Machine tool fixture; 4—Roller tool; 5—Fixed fixture; 6—Frame; 7—Drive motor; 8—Reducer.
## 3 Application Results and Discussion
### Selection of the Input Variables Using the PCA Technique
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Median** & **Kurtosis** & **Skewness** & **Minimum** & **Maximum** \\ \hline
**CA** & 1014.9 & 184.2 & 1095 & \(-\)0.68 & \(-\)0.62 & 585 & 1325 \\
**FA** & 855.87 & 225.7 & 807 & \(-\)0.13 & \(-\)0.22 & 272.5 & 1263 \\
**SCM** & 86.26 & 72.23 & 90 & \(-\)0.7 & 0.44 & 0 & 272.5 \\
**W** & 129.29 & 39.57 & 117 & 7.5 & 2.26 & 78 & 336.25 \\
**B** & 311.6 & 66.44 & 295 & 8.34 & 2.12 & 200 & 672.5 \\
**AS** & 35.54 & 42.55 & 28 & 2.25 & 1.6 & 1 & 180 \\
**CS** & 33.276 & 16.553 & 31.4 & \(-\)0.46 & 0.38 & 1.88 & 83 \\
**TS** & 3.1828 & 1.2761 & 3.2 & \(-\)0.25 & 0.08 & 0.14 & 6.4 \\
**FS** & 4.498 & 1.864 & 4.55 & \(-\)0.47 & 0.07 & 0.4 & 8.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical measures of independent and dependent variables for compressive strength (CS), tensile strength (TS), and flexural strength (FS).