esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Constant Wind Speed and Constant Wind Direction  
First, the optimization results of a constant 12 m/s wind speed blowing from 0\({}^{\circ}\) wind direction were discussed. Figure 4 presents the total wind farm power output and the total wind farm power losses as a function of wind turbine number from 40 to 46. As the number of wind turbines increased, the total power output showed an almost linear increase. However, the power losses increase was relatively small with turbine numbers from 40 to 44. When the number of turbines further increased, large power losses were identified. Figure 5 shows the optimal wind farm layout with 40 wind turbines optimized. The wind direction was 0\({}^{\circ}\) which is from the left-hand side to the right-hand side of the figure. As can be seen, most of the wind turbines were distributed along the wind farm boundary. The rest of the wind turbines were spread near to the wind farm boundary, while the center area of the wind farm was free of turbines. Along the wind direction, the distribution of wind turbines showed a staggered pattern in order to avoid the upstream wind turbine wake as much as possible. The minimum distance apart for any two wind turbines was 7 turbine diameters and the average distance of all wind turbines was 37.3 turbine diameters.
## 4 Results and Discussion
### Experimental Validation of Current Injection Models
#### 4.2.3 Gaussian Function
\begin{split} a_{0}&=\frac{1}{b}\int_{0}^{b}-f(y)dy=- \frac{1}{b}\cdot\frac{l}{\sigma}\cdot\frac{1}{\sigma_{s}\sqrt{2\pi}}\int_{0}^ {b}e^{-0.5\left(\frac{l-p_{1}}{\sigma_{s}}\right)^{2}}dy=-\frac{1}{b}\cdot \left(\frac{l}{\sigma}\right)\\ b_{0}&=-\frac{a_{0}}{2}\end{split}  \tag{45}\]  
The electric potential equation \(u(x,\,y)\) obtained from Equations (33), (44) and (45) can be used to calculate the potential field in the rectangular domain. The fitted results showed that the electrical conductivity was 16.337 mS\(\cdot\)m\({}^{-1}\) and the RAE was 4.023%. The RAE rate was thus improved compared to the Dirac delta and Heaviside step functions. The improvement over the Dirac delta function was 8.0%. Table 8 shows a comparison between the measured and calculated voltages. An improvement was observed in all  
Figure 19: Boundary illustration of the current injection model using the Gaussian function for Case 2. Figure 21: Electric potential maps created based on calculations using the Gaussian function for Case 2: (**a**) Set #1, (**b**) Set #2, (**c**) Set #3, (**d**) Set #4, (**e**) Set #5, (**f**) Set #6, (**g**) Set #7, and (**h**) Set #8 (\(x\) and \(y\) axes indicate 50 \(\times\) 50 mm corresponding to the size of the sample; the red and blue colors mean positive and negative voltage, respectively).
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 4 FAPEC Tailoring for the KMALL Data Format
### Lossy Compression  
Lossy data compression can be selected by the user through the FAPEC command line options or its API parameters, allowing several levels of quality degradation. Lossy compression has only been implemented for MWC amplitude and phase samples. The level of losses can be different in amplitude or phase samples, as decided by the user. In the case of a high-resolution water column phase, samples may be up to 16 bits. For this reason, we provide 16 levels of losses, leading to divisors from 2 to 256. The difference between levels increases exponentially, and hence, we have more granularity for low levels of losses. Furthermore, note that the last level is only intended for high-resolution phase samples, as it eliminates the 8 Least Significant Bits (LSB). It could also be used to completely suppress the low-resolution phase or water column amplitude if the user is not interested in it. This may be useful, for example, in the case of combined KMALL files (with MRZ and MWC datagrams), allowing the generation of compressed files without MWC samples.  
Losses are introduced by quantizing each of the original raw samples, that is, dividing the sample value by a given factor, which depends on the quality level given by the user. The resulting value is rounded to the nearest integer. Rounding instead of truncating is significant in terms of quantization error as it reduces its bias. For instance, a white pixel (+128) would never be recovered as white if truncation were applied.  
This approach translates into a reduction in the number of gray shades of the water column image, from the original 256 shades (8-bit samples) to 128 (level 1) or even just 2 shades (level 15). However, to avoid severe degradation, it is not recommended to use levels above 10 (11 gray shades).
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 5 Conclusions  
This research outlined our MAUI implementation of the MLS version 12 (using the Cisco library) for UxS within virtual and physical environments. MLS is of particular interest due to its security properties and adaptability in supporting long-lived sessions. The latter, in particular, makes it resistant to link dropping that can occur in UxS use cases and alleviates the need for regular session reestablishment.  
The testing of our MLS MAUI ROS and MLS MAUI ROS Live applications in the virtual environment provides evidence that the use of MLS can support encryption and decryption requirements to exchange data between disparate UxS systems operating in multiple domains. Tests of our MLS MAUI ROS Live application on a combination of virtual and physical devices demonstrate that MLS can serve as a multi-device security protocol that optimizes interoperability, agnostic to IP network and platform type in a multi-domain ad-hoc network configuration. Our use case testing results show that the MLS protocol is a viable solution for our multi-domain proof of concept for secure data exchange and command and control of distributed and dissimilar UxS agnostic to IP network architecture.  
Figure 11: Summary of analysis.  
Achieving these results is a significant step towards improving UxS security hand-in-hand with interoperability.  
Conceptualization, A.L. and C.B. and B.H.; methodology, A.L. and C.B. and B.H.; software, A.L. and C.B.; validation, A.L. and C.B.; investigation, A.L. and C.B.; resources, B.H., writing--original draft preparation, A.L. and C.B. and B.H.; writing--review and editing, A.L. and B.H.; visualization, A.L. and C.B. and B.H.; supervision, B.H.; project administration, B.H.; funding acquisition, B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CRUSER grant number 4019.00.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We would like to thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their input and testing access support at various points in this project.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} API & Application Programming Interface \\ C2 & Command and Control \\ CASSMIR & Collaborative Autonomous Systems for Standoff Maritime Inspection and Response \\ FS & Forward Secrecy dichroism \\ IETF & Internet Engineering Task Force \\ MAUI & MLS API for UxS Integration \\ MDPI & Multidisciplinary Digital Publishing Institute \\ MLS & Messaging Layer Security \\ PCS & Post Compromise Security \\ ROS & Robotic Operating System \\ TCP & Transmission Control Protocol \\ UAS & Unmanned Aerial System \\ UDP & User Datagram Protocol \\ USV & Unmanned Surface Vehicle \\ UxS & Unmanned System \\ VM & Virtual Machine \\ \end{tabular}
## 1 Introduction  
Nonlinear effects in long haul optical transmission systems (OTSs) are the major limitations on the high capacity, long haul and ultra-reliable transmission [1]. The performance of a simple OTS is severly affected by the non-linear effects, which are developed when high capacity signals are communicated over extensive distances [2; 3]. As a result, the performance is degraded, which leads to errors and data integrity issues. These nonlinear effects can be categorized into two types. The first is refractive index-related nonlinearities (RIrNLs), such as cross phase modulation (XPM) [4], self phase modulation (SPM) [5], and four wave mixing (FWM) [6]. The second type of non-linearities are scattering-related, which includes stimulated Raman scattering (SRS) [7] and stimulated berillouin scattering (SBS) [8]. The refractive index-related nonlinearities (RIrNLs) generate unwanted frequencies inside the optical medium, due to mixing of transmitted optical signals with each other or with the carrier wavelength during their propagation. Instead of using multiple fibers [9], the transmission of multiple data channels [10] is always desirable but the extra signals inside the single mode fiber (SMF) cause power penalty problems in long-haul cases because of the erbium doped fiber amplifier (EDFA) saturation in each transmission span and channel filtering at the receiver in a wavelength division multiplexing (WDM) system.
## 4 Discussion
### Data Partition Characteristics
#### 4.2.3 Federated Transfer Learning (FTL)  
Unlike the data configurations in HFL and VFL, data partition in federated transfer learning (FTL) considers the situation of multiple nodes shared neither the same users' profile nor features information, as shown in Figure 8. The main issue in this data partition configuration is that one node lacks labeled data. The nodes could be different health institutions or health data application providers located in different regions. Furthermore, each node shared different patients' features \(\mathcal{X}\), labels \(\mathcal{Y}\), and sample data index \(\mathcal{I}\)[26]. FTL can be denoted as:  
\[\mathcal{X}_{j}\
eq\mathcal{X}_{k},\ \ \mathcal{Y}_{j}\
eq\mathcal{Y}_{k},\ \ \mathcal{I}_{j}\
eq\mathcal{I}_{k},\ \ \forall\mathcal{D}_{j},\ \mathcal{D}_{k},\ \ j\
eq k \tag{5}\]  
Figure 7: The typical medical data partitions scenario for vertical federated learning (VFL). Each node can be a different medical institution and application. They share the same patients’ index \(\mathcal{I}_{j}=\mathcal{I}_{k}\) but have different features of medical diagnosis \(\mathcal{X}_{j}\
eq\mathcal{X}_{k}\).  
For example, there are two distinct healthcare entities: one is a hospital in Taiwan, while the other is in the United States. Due to the geographical limitations, the two healthcare entities' user groups have little overlap, and the data features of the two entities datasets may slightly overlap. FTL addresses limited data sets and label samples in this scenario, thus increasing the model's performance while protecting user privacy.  
The research in FTL is still in the early stages, and there is plenty of room for improvement. [PERSON] et al. (2020) proposed FedHealth assuming FTL data partition. FedHealth method collects data from several users/organizations using FL then offers a personalized model for each user/organization using transfer learning. First, the model learns to classify human activity and then extends the task to [PERSON]'s disease classification with transfer learning. In this case, FTL developed a global model for disease prediction in one task and then could be transferred to another task.
## 3 Simulation-Based System Architecture
### Requests Queue Scheduler
#### 3.4.1 Requests Scheduling of Trips Including a Last Leg of RP  
When requests can be resolved with a trip that considers exclusively the public transport or the ride-pooling service on the first leg (with the possibility of transferring later to PT), their assignment can be accurately completed with the current state of the system and can be resolved in a single batch period or postponed to a later one if no feasible solution was found. However, when 2-leg trips (PT + RP) or 3-leg trips (RP + PT + RP) appear, it may be complex to resolve the last leg of RP that will take place ahead in time. Thus, these trips are divided into two separate periods: in a first period, the first part of the trip until the PT leg is dispatched, an estimation of the last leg of RP is made, and a new, artificial RP request is placed ahead in the list of scheduled events, being processed when the time of that last leg of RP approaches. This makes it necessary to consider the so-called \(LL\) requests (referring to last-leg requests) in the list of events. A time-representation example of this approach is shown in Figure 4. Considering a batch period or cycle \(c\), it may contain: (a) requests from the current period (request \(R_{1}\)), (b) from a previous one if it was not yet dispatched (request \(R_{2}\)) and (c) with LL-type requests (request \(LL_{3}\) from \(R_{3}\)) which was arranged to be processed with some anticipation so that the client could be picked up readily by a vehicle very close to the time that the public transportation leg is finished.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements  
In this section, we discuss two types of noise: source noise and measurement noise.
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
## 3 Materials and Methods  
### Materials  
The dataset employed in the current experiment is derived from the Unreal Rendered Spacecrafts On-Orbit Datasets (URSOs). An example of the Soyuz satellite is presented in Figure 1. This dataset consists of synthetic data from the Unreal Engine that allows the addition of realistic lighting, shadows, and other features for image generation purposes. Figure 1b explains the axis notation for a moving spacecraft frame; the green color represents the \(Y\)-axis, the red color is the \(X\)-axis, and the \(Z\)-axis is the blue one. The URSO is not only realistic but also involves a variety of data, which includes a range of spacecraft, such as space probes and satellites. The implemented dataset has a total of 5000 RGB images with resolutions of 1280 \(\times\) 960 pixels and ground truths in the \([x\ y\ z\ q_{1}\ q_{2}\ q_{3}\ q_{0}]\) format for all images. While \(x,y,z\) are the magnitude of the relative distance in the X, Y, and \(Z\)-axes, respectively. The orientation is presented in quaternions by \(q_{0}\) represents the real part of the quaternions and \(q_{1}\ q_{2}\ q_{3}\) are the components in the vector part. The PE process also uses the quaternion as an input. However, the final results need to be converted to Euler angles for ease of understanding, and the rotation sequence is an important part of such a calculation.  
The dataset possesses various positions and orientation ranges with several background images. It is important for a deep learning method to learn features under various conditions. The Soyuz_easy data package is separated into 4000 training images, 500 testing images, and 500 evaluation images.
# LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
## 3 Materials and Methods
### Model Quantization
#### 3.3.1 Floating Point Quantization  
Floating Point 16 (FP16) quantization involves storing the numerical values using 16-bit floating point format, as opposed to the default 32-bit format. This simple procedure reduces the memory storage usage and accelerates the computation while having a minimal impact on the overall model's performance, and enables deployment on edge devices such as a mobile phone.
## 4 Simulation and Analysis  
To evaluate the effectiveness of the adopted control strategy for achieving the fault ride through of the DFIG and improving the frequency offset of the fault current, a detailed simulation model of the topology shown in Figure 1 was built using the RTDS platform. The RTDS, developed and manufactured by the RTDS Corporation in Manitoba, Canada, is a device specifically designed to study electromagnetic transient phenomena in power systems. Version 1.4.0 of software RSCAD was adopted, and the simulation was performed using a discrete sampling period. The adopted simulation sampling time step of software was 50 us. As shown in Figure 7, the simulated model of the DFIG with a DVR-based SMES system was first established on the principal computer, and the principal computer communicated with the hardware RTDS via an optical fiber. When the whole simulation system started to run, the simulation data in the principal computer was transferred to the RTDS through the optical fiber for calculation, and the RTDS transferred the calculation results to the principal computer through the optical fiber to obtain the simulation results. The specific parameters of the simulation model are shown in Tables 1 and 2. Additionally, the PI parameters of the VSC converter control in the DVR are shown in Table 3.  
The performance of the DVR-based SMES in achieving fault ride through and improving the frequency offset of fault current were verified under the multiple-type faults as follows.  
\begin{table}
\begin{tabular}{c c} \hline
**DFIG Parameters** & **Value** \\ \hline Rated power & 2.5 MW \\ Rated voltage & 0.69 kV \\ Rated wind speed & 12 m/s \\ Rated frequency & 50 Hz \\ Stator resistance & 0.01 pu \\ Rotor resistance & 0.006 pu \\ Stator leakage reactance & 0.102 pu \\ Rotor leakage reactance & 0.08596 pu \\ Mutual inductance resistance & 4.348 pu \\ Turns ratio & 2.637 \\ Inertia constant & 1.5 \\ DC link voltage of DFIG & 1.2 kV \\ \hline
**DVR Parameters** & **Value** \\ \hline Transformer voltage & 35/6 kV \\ Rated power & 2.5 MW \\ DC link voltage of DVR & 1.2 kV \\ DC link capacitor & 10,000 uF \\ \hline \end{tabular}
\end{table}
Table 1: Specific parameters of the simulated model.  
Figure 7: The model of the DFIG with a DVR-based SMES and built-in RTDS platform.
## 2 Materials and Methods
### Method
#### 2.3.2 Data Preprocessing  
Due to the large scope of the study area, the images of each month were captured by at least two dove satellites. So, we mosaicked multiple adjacent images. However, there are certain differences in the spectral information of images obtained by differently numbered satellites. In the process of seamless mosaic, color matching, boundary feathering, and other operations need to be carried out according to the overlapping area of adjacent images to achieve image uniformity. After obtaining the seamless mosaic image, the image should be clipped according to the boundary data of the study area. The premise of clipping was to unify the coordinate system of satellite images and boundary vectors. In this research, processing was in WGS84/UTM Zone 51 N coordinate system. Resampling was required when clipping was complete. We chose the nearest-pixel interpolation method to retain the original image information to the maximum extent. The size of mosaic images was 20,105 \(\times\) 14,671 pixels.  
NDVI data were produced based on the above images. The step of cloud removal was omitted because the cloud coverage of PlanetScope satellite images that were screened was low, as has been mentioned previously. The red band (590-670 nm) and the near-infrared band (780-860 nm) of these processed images were applied to generate NDVI images. After the raster processing of multi-band synthesis, all NDVI datasets were stacked into a time-series NDVI product with 12 bands in chronological order. Through band math from time-series NDVI, the mean NDVI data were generated as a reference. Additionally, the single NDVI in May 2022 was selected as data for subsequent comparison. Both will be the input data to compare with the time-series NDVI.
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 5 Simulation Results
### LDPC and Polar Codes Comparison
Moreover, it is observed from both Tables 3 and 4 that LDPC code latency depends completely on code rate related to the parity check matrix. Furthermore, it is shown that the decoding latency decreases as the code rate increases, while polar code latency is independent of code rate. Decoding latency is the main factor, which dominates in the overall system latency \(T\), which is defined as the total sum of all measured latencies for all modules in the full communication chain. The overall latency \(T\) is related to system throughput \(\delta\) as follows:  
\[\delta=\frac{K_{\mathit{inf}_{0}}\times S}{T} \tag{19}\]  
where \(S\) is the number of simulated frames. Figures 10 and 11 show the comparison between simulation throughput of the LDPC code used in DVB-S2 and optimized polar code with different code rates for normal frame and short frame, respectively. As can be seen, the throughput of optimized polar code is higher than that for LDPC code for the same SNR values, which indicates the effectiveness of replacing LDPC code by optimized polar code. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frame type** & \multicolumn{2}{c}{**Normal Frame (64,800) @ QEF**} & \multicolumn{2}{c}{**Short Frame (16,200) @ QEF**} \\ \hline
**Code Rate R** & **LDPC Latency (ms)** & **Polar Latency (ms)** & **LDPC Latency (ms)** & **Polar Latency (ms)** \\ \hline
1/4 & 71.023 & 0.519 & 13.184 & 0.108 \\
1/3 & 65.079 & 0.587 & 11.223 & 0.111 \\
2/5 & 56.459 & 0.540 & 10.468 & 0.120 \\
1/2 & 45.587 & 0.484 & 8.129 & 0.108 \\
3/5 & 44.816 & 0.506 & 8.803 & 0.118 \\
2/3 & 39.091 & 0.546 & 6.636 & 0.121 \\
3/4 & 33.115 & 0.521 & 5.129 & 0.114 \\
4/5 & 28.185 & 0.505 & 3.742 & 0.114 \\
5/6 & 26.395 & 0.540 & 3.913 & 0.118 \\
8/9 & 16.411 & 0.501 & 2.533 & 0.105 \\
9/10 & 15.383 & 0.474 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Decoding average latency comparisons between LDPC code and optimized Polar code for normal frame and short frame. (bold cells represent the best values). Figure 10: Throughput versus \(E_{b}/N_{0}\) for LDPC and optimized polar codes using normal frame with \(N=64,800\) and different code rates \(R\) in DVB-S2. Finally, Table 5 summarizes all simulation qualitative results of a comparison between LDPC code and optimized Polar code. The point of decoder latency is really significant for all interactive communication systems, especially for satellite systems which are already affected by the high propagation delay of more than 250 milliseconds (ms) for one-way transmission channel or 500 ms for a round trip with respect to geostationary satellites at about 36,000 kilometers above the equator.