esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cylinder  
The sample has the following parameters. Height: 0.075 m, Diameter: 0.065 m, minimum frequency 0.025 m, and maximum 0.017 m that can be referred to a satellite of 2.25 m in height at 12 GHz and to one of 3.3 m at 18 GHz. Three configurations were considered. The first, cylinder in a vertical position; the second, cylinder in a horizontal position and rotating around the vertical axis; the third, cylinder in precession, according to  
Figure 3: Cubesat signal reflection: TE polarization reflection vs. frequency.  
Figure 2: Cubesat positioning in respect to wave incidence direction.  
a cone of 45\({}^{\circ}\) with respect to the vertical axis. In this configuration, the 0\({}^{\circ}\) angle is the one in which the satellite is aligned with the incident wave. The positions 45\({}^{\circ}\), 90\({}^{\circ}\), 315\({}^{\circ}\) have been considered. In Figure 4, the positioning of the samples is shown, while in Figure 5, the reflection plots are given.  
In this case, too, the greatest reflection occurs in the vertical configuration, followed by the three horizontal configurations. The object is clearly visible throughout the frequency range. The precession configurations, on the other hand, are, at these frequencies, completely invisible to the radar, as their reflection does not occur in the direction of the receiving antenna.  
Regarding the scaled objects, the result also leads to the consideration that satellites with the dimensions range aforementioned are visible with a 400 MHz radar system when in a vertical or horizontal attitude, while the precession configurations are completely invisible to the radar.  
Figure 4: Cylinder positioning in respect to wave incidence direction. **(a)** Horizontal and vertical positions; **(b)** precession positions.  
Figure 5: Cylinder signal reflection: TE polarization reflection vs. frequency.
## 3 Optimal Service Provisioning Framework
### Customized Mist-Edge-Cloud Framework
#### 3.1.4. Central Cloud Layer  
The central cloud manages long-term analytics, large-scale training of predictive models, and population-level trend analysis. It stores historical patient data and supports computationally intensive operations such as ML-based risk stratification, disease progression modeling, and cross-site healthcare analytics. It also ensures system-wide fault tolerance and service continuity.
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 2 Related Works
### Three-Dimensional Radar Perception  
Due to the lack of height information, conventional 3D radar methods (sometimes referred to as 2D+1D) usually take a bird's-eye-view (BEV) approach. The 2D-BEV approach by [PERSON] et al. [14] combines a classification network with a segmentation network for semantic masking to provide input for a box regression network for detection. [PERSON] et al. [15] instead used a graph convolution network with a 2D-CNN, processing range-azimuth-Doppler information for object detection.  
Sensor fusion is a method that has proven to counteract the sparsity of LiDAR data [16] and is a natural approach to complement radars' missing height information. Image data are dense and include height information but lack depth information, which radar can provide. LiDAR sensors provide 3D data, and while denser than radar data, these data are sparser compared to image data. Additionally, a large part of the rotating LiDAR data is not used during fusion, as the horizontal field of view of radar sensors only covers a part of the 360\({}^{\circ}\) data.  
RODNet [17] uses an image-radar teacher network to teach a temporal range-azimuth radar network for object localization. RADDET [18] additionally includes Doppler information and uses an image-radar network to perform pseudo-labeled instance annotations. RAMP-CNN [19] utilizes three different temporal views and networks, which are fused and supervised by an image network. CenterFusion [20] uses an image network for monocular object detection, refining the results through frustum association with radar data. [PERSON] et al. [21] combined image and BEV networks, classical filtering, temporal radar information, and fusion in an ROI-based approach for object detection. RadarNet [22] combines LiDAR and radar in a voxel-based approach for bird's-eye-view detection. Other researchers have combined image, radar, and LiDAR modalities [23; 24], even including temporal information [25], to obtain dense data.
## 3 Simulation Setup  
Numerical simulations were carried out with a bi-directional Raman amplified system, as shown in Figure 3. In the transmitter, a laser with a linewidth of 100 kHz and an ideal IQ modulator were assumed to generate the signal. A 32 Gbaud 16 QAM signal was generated with a pseudo-random binary sequence of length 215-1. The signal was sampled at eight samples/symbol and filtered by a Gaussian low-pass filter for pulse shaping. Two EDFAs, each with a noise figure of 4 dB, were deployed as the booster amplifier and pre-amplifier before the transmission link and before the coherent receiver. The transmission link was a 300-km long fiber, with attenuation coefficients of 0.2 dB/km for the signal and 0.26 dB/km for the pump, dispersion coefficient 17 ps\(\cdot\)nm\({}^{-1}\cdot\)km\({}^{-1}\) and nonlinear coefficient 1.1 \(W^{-1}\cdot\)km\({}^{-1}\). The signal propagation in the fiber was simulated by solving the modified nonlinear Schrodinger equation using the split-step Fourier method (SSFM) [17].  
Figure 3: Simulation setup: LD: laser diode; IQ mod.: IQ modulator; OBPF: optical band-pass filter; Co. Rx: coherent receiver.  
Figure 2: NSNI in a bi-directional Raman amplified system with split-DBP.  
The wavelengths of the signal and pump were 1550 nm and 1455 nm, and the distributed Raman gain was calculated by solving the power coupled equations of the distributed Raman amplifier ([17], Equations (8.1.2)-(8.1.3)). In the simulation, the forward Raman pump power was set at 450 mW, and the backward Raman pump power was set at 600 mW. The relative intensity noise (RIN) transfer from the Raman pump to the signal was simulated by a time variation of the distributed Raman gain. The time variation was produced by multiplying the RIN of the pump laser and the RIN transfer function (TF) from the pump to the signal [18]. The RIN of the pump laser was set at \(-120\) dBc-Hz\({}^{-1}\). The step size of the SSFM was 0.01 km and in each step spontaneous Raman scattering was added to the signal. After transmission, an optical band-pass filter (OBPF) was deployed with a central wavelength of 1550 nm and a bandwidth of 100 GHz. In the coherent receiver a laser with the same parameters as those in the transmitter was used as local oscillator. The photodetectors in the receiver were modeled with ideal response properties, and thermal noise and shot noise were considered. The thermal noise was modeled as white noise in the spectrum with \(10^{-12}\) A-Hz\({}^{-1/2}\). Then the received signal was down-sampled to two samples/symbol for subsequent DSP.  
In the receiver, DSP carrier recovery was applied to compensate for the phase shift of the laser. Two nonlinear compensation methods were applied in the simulation: receiver-side DBP and split-DBP. To fairly compare the performance of the transmitter-side DBP and split-DBP, in the split-DBP scheme, the signal was processed by the transmitter-side DBP at two samples/symbol, and then the processed signal was up-sampled to eight samples/symbol for fiber transmission. The electrical SNR of the signal was calculated after the DSP.
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 2 Materials and Methods  
### VIGS Vectors  
The pEuMV-YP:Krt18 vector was constructed with a fragment of the _M. musculus_ Krt18 gene (GenBank: NM_010664) that was amplified using the primers forward 5'ACTGCCTCCACCACCTTCTC3' and reverse 5'AGACAGAATCGAGGCACTCAAGG3', generating a 589 bp amplicon. This amplicon was digested with _Pst_ I and _EcoR_ V enzymes, generating a 432 pb fragment. This allows the Krt18 fragment to be ligated to pEuMV-YP:\(\Delta\)AV1, which contains these two enzyme cutting sites (_Pst_ I and _EcoR_ V) in its multiple cloning site (MCS) [22], which had been digested previously with the same pair of enzymes (Figure 1). To verify that similar sequences of Krt18 are not present in the _N. benthamiana_ genome, BLAST was used as a search engine in GenBank. pEuMV-YP:\(\Delta\)AV1:chII, which contains a 403 bp DNA fragment from the _Nicotiana tabucum_ (_N. tabucum_) Magnesium-chelatase subunit I (ChII) gene [22], was used as a negative control for the silencing assay.
## 3 Results and Discussion
### Analysis of the Mechanism of Curing Soft Soil  
#### 3.4.1 XRD Analysis  
Figure 10 shows the XRD patterns of soft soil and seawater-mixed GC, G4, and G7 after curing for 28 days. The main crystalline phases of the undisturbed soft soil are quartz (SiO\({}_{2}\)), kaolinite (Al4[Si4O10](OH)8), muscovite (KAl2(AlSi3O10)(OH)2), and montmorillonite ((Na,Ca)0.33(Al,Mg)2[Si4O10](OH)2\(\cdot\)nH2O). The hydration products observed include C-S-H, C-A-S-H, and ettringite (AFt). The diffraction peaks for Friedel salt and other salts are also present in this figure.  
By comparing the Friedel salt peak heights of cement soil GC and stabilized soil samples G4 and G7, it can be observed that the peak strength of GC is higher than that of G4 and G7. This indicates that cement produces more Friedel salt in the soft soil within the seawater system. Friedel salt is a hydration product with low gel strength that consumes a large amount of Ca(OH)2, inhibits the formation of other gel-strengthening hydration products, and covers the surface of hydration products to prevent further reaction, resulting in the lower strength of cement soil. This figure shows that dispersion peaks for C-A-S-H related to Cl\({}^{-}\) and SO4\({}^{2-}\) adsorption appear in stabilized soils G4 and G7. The reduction in Cl\({}^{-}\) and SO4\({}^{2-}\) content in the solution can effectively mitigate their impact on the hydration reaction [23]. This figure also reveals that, due to the low content of solidified materials in soft soil and the high overlap of diffraction peaks between minerals and hydration products, phases with low crystallinity cannot be accurately identified [24]. Therefore, additional characterization methods are needed to study stabilized soft soil more comprehensively.  
Figure 10: XRD of soft soil, cement soil, and stabilized soil.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview  
The satellites avionics (see Figure 8) is composed of modules, that are common to most satellites. These include an onboard computer (OBC), an ADCS, a communication interface for up-/downlink and a PCDU. Most of the modules are dual modular redundant (DMR).  
The ADCS is replicated twice. Aside from the common SKITH interface implementing the control algorithms, there are additional components e.g., two micro electro mechanical systems (MEMS) gyroscopes, two magnetometer and front-ends for torquer control. Complementary, an interface to the sun-sensors and the reaction wheels is integrated. Both ADCS modules are assembled on a single board, which is mounted in a self-contained unit, holding the torquer and wheels. Further details are given in the corresponding subsection of the ADCS system.  
The up-/down link transceivers for communication with the ground station are replicated to avoid a single point of failure. Additionally, a satellite modem is implemented, building a backup communication channel. The modular concept is not limited to the avionics system, complementing a modular structure, which is explained in the following chapter.
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 2 Relate Work
### Vision Transformer  
Motivated by the excellent performance of Transformer in miscellaneous NLP tasks, increasing the number of methods based on Transformer was proposed in various computer vision tasks. In the recent research on vision Transformer, a novel model based on pure Transformer was introduced by ViT [17]. The model achieved the SOTA performance on the image classification task. However, such performance must depend on pre-training on large datasets, such as JFT-300M and ImageNet-22K. In order to solve the above problem, knowledge distillation was applied in DeiT [28]. The model with an effective training strategy allows ViT to promote performance on smaller datasets, such as ImageNet-1K. Swin Transformer [31], a hierarchical architecture model, has linear computational complexity relative to the image size. This model, through limited self-attention to non-overlapping local windows, proposed a shifted window scheme to achieve higher efficiency. Furthermore, CSwin Transformer [32] effectively strengthened the information interaction between patches. This model obtained SOTA performance in various CV tasks and achieved lower computational complexity than Swin Transformer. Motivated by these models, we introduced an UperNet-like structure that used CSwin Transformer blocks to feature extraction.
## 5 The Hardware Implementation of the Dual-Axis-Based Solar Tracking System
### Proposed ASPCB Design with Self-Testing Facilities
The compression and signature analysis section represents the final layer of the self-test design and consists of several D-type flip-flops (labeled U39A to U43A) interconnected via XOR gates (listed U44A to U47A). Although the RBILBO adopts a similar structure to the PRPG circuits, the modified variant of MISR provides a series of additional circuits for adjusting the characteristic generator polynomial. Therefore, we notice the presence of four AND gates (SN74 LVC1G08 DBVR) marked from U35A to U38A, used to capture the data received from the outputs of the Atmega2560 microcontroller by means of a validation line. We can also distinguish four additional XOR gates labeled U48A through U51A inserted on the feedback paths of the MISR circuit. The feedback path is selected using a multiplexer circuit U52 (SN74 LS253 DR) with four input channels and one output channel. The 2-bit select line and multiplexer enable signal are directly controlled by the Atmega2560 microcontroller. The 5-bit signatures generated during each clock cycle are routed to the final signal analysis block, which consists of a 4-bit dual-channel comparator (U53) (CD74 HC85 MT) and a 128-bit EPROM unit (U54) (24 AA00T-I/OT). The EPROM stores the fully working device's golden signatures, and the comparator circuit compares the standard signatures to the bit vectors received from the MISR. To convert the 4-bit comparator to an extended 5-bit form, the input and output corresponding to the operation (A = B) were canceled, and the remaining pins were grouped to obtain the appropriate number of bits for the two 5-bit inputs. An XOR gate (U55) and an inverting gate (U56) were added to the comparator circuit's output to ensure that the signatures were identical. Potential errors in the system are notified by diodes DS1 and DS2 (TLMS1100-GS08), mounted on outputs (A > B) and (A < B). Thus, Sa0 and Sa1 faults are indicated by the red color of the relevant LEDs. When no faults are discovered in the system operation mode,diode D3 (LTST-C190 GKT) turns green. The resistors R4-R6 (CRCW0603200 RFKEA) were used to limit current at the anode of the reference diodes.
## Problem Statement  
### Problem Description  
Without a loss of generality, let us assume that the DDR has to move from the starting point to the given destination (the goal) through all the given waypoints (\(W_{1}\), \(W_{2}\), \(W_{3}\), and \(W_{4}\)) to fulfill its task as shown in Figure 1. Thus, a controller is needed for the DDR to navigate the robot from starting point to the given destination effectively.  
In this study, an FLC is designed based on the genetic algorithm to help the robot solve its task effectively. Specifically, the FLC is designed for the DDR to drive the robot from the starting point to the given destination through all the given waypoints with the minimum kinetic energy loss criterion while the other performances of the motion control are still good.  
The relationship between the current robot position and the next waypoint is needed for the design process and it is indicated in Figure 2[10].  
In Figure 2, the distance (\(D\)) and the angle (\(\Delta\theta\)) are calculated as follows [10]:  
\[D=\sqrt{\left(x_{W}-x_{Q}\right)^{2}+\left(y_{W}-y_{Q}\right)^{2}} \tag{1}\]  
\[\Delta\theta=\theta_{Q}-\theta_{W} \tag{2}\]  
\[\tan\theta_{W}=\frac{y_{W}-y_{Q}}{x_{W}-x_{Q}} \tag{3}\]
# Prediction of the Load-Bearing Behavior of SPSW with Rectangular Opening by RBF Network
## 4 Results  
The network was trained using the input data and was tested by the remaining data. The regression diagrams, as a tool to evaluate the performance of the network, for the real and predicted values of the proposed model are depicted in Figure 7. As it can be seen, predicted values by RBF network are shown by the circle symbols and the real data are shown by solid lines. According to Figure 7, the proposed model has precisely modeled the rectangular opening effects in the SPSW with different plate thickness. The MSE results of the model output parameters versus the various numbers of epochs are illustrated in Figure 8. As can be seen, the MSE results of the model output parameters decrease with increasing the epochs. In Figure 8, MSE is corresponding to the normalized data values.  
As mentioned earlier, the training and testing procedures of the calculated data are performed by the proposed RBF network. The MAE and MSE results of the proposed model for training and testing procedures are listed in Table 5. As mentioned, 110 data were used in the presented model, in which 77 data were considered for training and 33 were assumed for the testing of the model. This data are normalized and randomly selected for test and train. However, the MAE and MSE results in Table 5 are calculated for the denormalized predicted data.  
In Figure 9, the real and predicted normalized load (PN) values are compared with different rectangular opening surfaces to the overall SPSW plate surface ratio (A\({}_{0}\)/A\({}_{\mathrm{s}}\)). As can be seen, an increase in the opening area may lead to a decrease in normalize load-bearing capacity and the rate of decrease is higher for the thicker infill plates. In this figure, predicted values by the RBF network are shown by the star symbols and the real data are shown by the circle symbols and solid lines.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Input parameters & 3 \\ Outputs parameters & 2 \\ hidden layer functions & Radial basis \\ Output layer functions & Linear \\ Maximum number of epochs & 70 \\ Train data number & 77 \\ Test data number & 33 \\ Total data number & 110 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the proposed RBF model.  
Figure 8: The MSE results of the model output parameters versus different number of epochs.  
Figure 7: Regression diagrams for the real and predicted values of the proposed model.
## 1 Introduction
The sea surface temperature data come from the Copernicus Marine Service ([[https://data.marine](https://data.marine)]([https://data.marine](https://data.marine)). copernicus.eu), which is the marine component of the Copernicus Programme of the European Union. dataset: VIIRS Boat Detection (VBD) [[[https://eogdata.mines.edu/products/vbd/](https://eogdata.mines.edu/products/vbd/)]([https://eogdata.mines.edu/products/vbd/](https://eogdata.mines.edu/products/vbd/))]. However, the improved algorithm's effectiveness in extracting lit fishing vessels diminishes significantly under complex weather conditions [18; 19]. During the central-east stock fishing season, which overlaps with summer, the VIIRS DNB nighttime imagery is significantly impacted by strong solar illumination and cloud cover (Figure 1). This affects the reliability of VBD products in the region, necessitating further comparison and validation. To achieve more accurate monitoring and advance related research, there is an urgent need not only to develop more suitable extraction algorithms but also to supplement long-term monitoring data. In recent years, advances in computer technology have enabled computer vision algorithms to excel in detecting small, obscured, or blurred targets, demonstrating great potential for vessel detection in nightly imagery impacted by clouds [20; 21; 22; 23]. These methods automatically learn target features and have been successfully applied across various scenarios. Therefore, leveraging computer vision technology could significantly enhance the ability to extract and monitor lit fishing vessels in complex and cloudy weather conditions, which is crucial for advancing relevant research. In this study, we initially established a dataset of nighttime remote sensing images of fishing vessels operating in the central-east stock, spanning various meteorological conditions from 2012 to 2022, along with corresponding labeled vessel targets. By leveraging this image database and the You Only Look Once (YOLO) architecture, we refined the network's sensitivity to small objects and developed improved methods for extracting fishing vessels under varying cloud conditions [20; 21; 22; 23; 24]. The effectiveness of these methods was evaluated by comparing visual inspection, VBD threshold extraction, and YOLO-based models using data from the 2020 fishing season. We also extracted dynamic information on fishing vessels in the study area over the past decade, focusing on peak activity in June and July. Finally, a discussion is provided to explore the advantages, limitations, necessity, and future development directions of nighttime remote sensing-based fishing vessel extraction and monitoring methods.
## 4 Trust-Driven Incentive and Aggregation Mechanism
### Trust-Guided Soft Aggregation Mechanism  
Traditional robust aggregation methods often adopt rigid techniques such as outlier removal or hard thresholds, which may harm diversity and inclusiveness. To address this, we propose a trust-guided non-linear soft suppression strategy to attenuate the impact of low-trust updates via a continuous weighting function.  
We define a sigmoid-based suppression function as follows:  
\[\sigma(\tau)=\frac{1}{1+e^{-k(\tau-\mu)}}, \tag{11}\]  
where \(\mu\) controls the suppression threshold and \(k\) controls the steepness. The final aggregation weight is determined by the trust score and corrected contribution as follows:  
\[\alpha_{i}^{t}=\frac{\sigma(\tau_{i}^{t})\cdot\hat{\sigma}_{i}^{t}}{\sum_{j} \sigma(\tau_{j}^{t})\cdot\hat{\sigma}_{j}^{t}}. \tag{12}\]  
The global model update becomes the following:  
\[w_{t+1}=w_{t}+\sum_{i\in\mathcal{S}_{t}}\alpha_{i}^{t}\cdot\Delta w_{i}^{t}, \tag{13}\]  
This aggregation scheme suppresses the influence of malicious updates while allowing low-trust clients to be re-evaluated and regain weight, enhancing long-term fairness and convergence.  
To ensure practical deployability, the sigmoid-based suppression function is implemented using precomputed lookup tables or approximate activation functions to avoid runtime overhead. Similarly, trust score updates are server-side vector operations with minimal cost. These design choices ensure that the trust-aware mechanism does not introduce significant delays compared to standard aggregation.