esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Architecture and Implementation
### Technical Implementation of the System  
To ensure robust and secure communication within the vehicular network, the proposed framework integrates blockchain technology with cryptographic methods, consensus algorithms, and smart contracts. Below are the detailed components of the system implementation.  
* Initialization and node configuration. Each vehicle and RSU acts as a blockchain node and is equipped with the following:
* Public-private key pairs-each vehicle and RSU is assigned a unique identity represented by a public-private key pair using elliptic curve cryptography (ECC). The public key acts as an identifier, while the private key is used to sign transactions securely.
* Blockchain client-vehicles and RSUs are equipped with lightweight blockchain clients to minimize resource usage while ensuring compatibility with the decentralized network.
* IoT sensors-vehicles are equipped with various sensors (e.g., GPS, LIDAR, and cameras) to collect data for secure communication and decision making. These sensor data are signed and securely transmitted to RSUs and other vehicles for validation.
* Secure data generation and signing. When a vehicle generates data (e.g., location, speed, and hazard warnings), the system ensures data integrity through cryptographic signing:
* The vehicle's on-board unit (OBU) signs the data using its private key.
* The signed data are packaged into a blockchain transaction and propagated across the network to nearby RSUs and vehicles.
Data validation via the blockchain. * Transaction propagation-the transaction (signed data) is propagated through the network and received via nearby RSUs (edge nodes) and vehicles. * Consensus mechanism-RSUs use the Practical Byzantine Fault Tolerance (PBFT) protocol to validate transactions: * Verify the signature using the sender's public key. * Ensure the data's integrity and authenticity. * Transaction recording-once the transaction is validated, it is bundled into a block. Each RSU maintains a local blockchain ledger, and validated blocks are propagated to other RSUs in the network to ensure consistency. * Smart contract deployment. Smart contracts play a critical role in automating network operations: * Authentication-when a vehicle or RSU joins the network, the smart contract verifies its identity using its public-private key pair. * Role-based access control-smart contracts enforce access restrictions to sensitive data. For example, hazard alerts are broadcast to all nearby vehicles, but personal vehicle information remains encrypted and accessible only to authorized nodes. * Tools and platforms. * Hyperledger fabric-used as the blockchain platform due to its support for permissioned networks and modular architecture. * Python-utilized for simulations and integration with Hyperledger Fabric. * Key libraries-cryptography is used for ECC key generation and signature verification, while SimPy is employed for network latency and mobility modeling.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 3 User-Based Integrity with LEO-PNT Formulation (Option 2)
### Performance Assessment  
#### 3.5.1 Methodology  
A Service Volume Simulator, SVS, has been developed in Python 3.11.6 to analyze the performance of the integrity monitor.  
The SVS's process is shown in Figure 2.
## 6 Conclusions  
In this paper, a new structurally and thermally stable design of resonant mode-matched electrostatic \(z\)-axis MEMS gyroscope that utilizes two separate masses for the drive and sense axis has been presented. The device, with an overall size of \(1557\times 1816\)\(\upmu\)m\({}^{2}\), is designed following the foundry process limitations of relatively low cost and commercially available Silicon-on-Insulator (SOI)-based SOIMUMPs microfabrication process. The performance of the MEMS gyroscope design has been verified through detailed coupled-field electric-structural-thermal FEM-based analysis. The results have shown operating temper  
Figure 16: Mechanical sensitivity percentage change of the proposed dual mass MEMS gyroscope in the gyroscope operational temperature values between \(-40\)\({}^{\circ}\)C and \(100\)\({}^{\circ}\)C, with and without the electrostatic tuning application.  
ature variations and microfabrication process tolerances may cause a frequency mismatch between modes, which can cause the performance of the proposed MEMS gyroscope to reduce drastically since even a slight mismatch can reduce the amplitude response of the resonant gyroscope significantly. For the compensation of the frequency mismatch between modes, comb-drive-based electrostatic tuning has been implemented in the proposed design, which allows tuning the sense-mode frequency to approximate the drive-mode one, guaranteeing the maximum possible mechanical sensitivity. For a linear relation between the input angular velocity and the displacement in the sense direction and between the input angular velocity and the net capacitance change, in the case of a perfect match between drive and sense resonant frequency values, the mechanical sensitivity and the capacitance sensitivity values are 4.6433 \(\times 10^{-4}\)\(\upmu\)m/deg/s and 0.0560 fF/deg/s, respectively. The compensation of the initial frequency mismatch of 497 Hz due to the electrostatic tuning-voltage application of 14.903 V has resulted in an increase in the mechanical sensitivity of 238.9%.  
Conceptualization, A.S.; methodology, F.P., M.M.S.; software, F.P.; validation, A.S., M.M.S.; resources, A.S.; writing--original draft preparation, F.P., M.M.S., A.S.; supervision, A.S.; All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Methods
### Virtual Image Generation  
#### 3.2.1 Conditional Generative Adversarial Network (CGAN)  
As a base model of a CGAN, a generative adversarial network (GAN) is a CNN-based architecture that consists of a generator and a discriminator [35], where the former generates virtual images after learning from the input images, and the latter distinguishes the virtual images from real images. The virtual output images can be generated through such an adversarial learning procedure.  
A CGAN was proposed to overcome the limitations of the GAN, including the generation of unconditional or uncontrolled random images [34; 36]. As implied from its name, the CGAN is an extended model of GANs, where both the generator and discriminator are conditioned on additional data [62]. More specifically, the generator combines the prior input noise (\(\mathbf{z}\)) and an additional condition variable (\(\mathbf{y}\)) in joint hidden representation, and  
Figure 5: Workflow for the preparation of paired training patches employed in this study. Not all training and validation patches are shown in this figure.  
input data (\(\mathbf{x}\)) and \(\mathbf{y}\) are utilized as inputs for the discriminator [36]. The objective function of CGAN is defined as  
\[L_{CGAN}(G,D)=E_{\mathbf{x},\,\mathbf{y}}[\log D(\mathbf{x},\mathbf{y})]+E_{ \mathbf{z},\mathbf{y}}[\log(1-D(G(\mathbf{z},\mathbf{y}),\mathbf{y}))], \tag{1}\]  
where \(G\) and \(D\) are the generator and the discriminator, respectively. \(E[\cdot]\) is an expectation operator. \(G\) tries to minimize the above objective function, whereas \(D\) tries to maximize it through the two-player min-max game [34].  
The L1 loss function for training the generator is also introduced to alleviate blurring as  
\[L_{L1}=E_{\mathbf{x},\mathbf{y},\mathbf{z}}[\big{|}|\mathbf{x}-G(\mathbf{z}, \mathbf{y})|\big{|}|_{1}]. \tag{2}\]  
Finally, both the objective function in Equation (1) and the L1 loss function in Equation (2) are simultaneously optimized [29] as  
\[\underset{G}{\text{minmax}}L_{CGAN}(G,D)+\lambda L_{L1}, \tag{3}\]  
where \(\lambda\) is the hyperparameter that controls the weights of the two functions.
## 2 Materials and Methods
### Data
#### 2.2.2 Climate Time-Series Data  
Daily climate data were generated from 1901 to 2011 with quarter-degree spatial resolution by merging reanalysis-based products and observation-based products based on a similar approach used with the CRUNCEP data [44]. For example, daily air temperature data from 1901 to 2011 were generated based on the Climate Research Unit Time Series 3.2 (CRU TS3.2) data (available period: 1901-2011) [45] and National Centers for Environmental Prediction (NCEP)/the National Center for Atmospheric Research (NCAR) reanalysis (NCEP/NCAR reanalysis) data (available period: 1948 to present) [46]. The daily variations in air temperature were based on NCEP/NCAR reanalysis data; the monthly averages were adjusted to fit the CRU TS3.2 data by adding an offset. Because NCEP/NCAR reanalysis data are not available for the 1901-1947 period, the reanalysis data for 1948 were used instead. Similarly, precipitation data were generated by merging daily NCEP/NCAR reanalysis data and CRU TS3.2 monthly precipitation data [45] by multiplying by an adjustment factor. In addition, we found that the frequency of rainy days in the NCEP/NCAR reanalysis data was too high compared with the CRU TS3.2 wet-day frequency data. Therefore, the frequency of wet days was also adjusted using the CRU TS3.2 wet-day frequency data by redistributing the rain. Incoming surface shortwave radiation data were generated by merging daily shortwave radiation created by the Mountain Climate Simulator (MTCLIM) [47] (available period: 1901-2011) and monthly averages of Japan Aerospace Exploration Agency (JAXA) NASA/MODIS global 5-km irradiance (JAXA radiation) data [30,48] (available period: 2000-2010) by multiplying by an adjustment factor. The MTCLIM-based shortwave radiation relies on the assumption that variations in diurnal temperature ranges are related to incoming solar radiation. Because JAXA radiation data are not available for 1901-1999 and 2011, the average of the data for 2000-2010 was used instead. Daily vapor pressure deficit data were generated using MTCLIM. For longwave radiation and wind speed, NCEP/NCAR reanalysis data were directly used; the data from 1948 were used for the 1901-1947 period.
## 4 Methodology
### Candidate Points Extraction Based on Image Semantic Information
#### 4.1.2 Candidate Points Extraction  
For each input image \(I\), the corresponding semantic image \(I_{sem}\) containing dynamic objects information can be get according to Section 4.1.1. For candidate points extraction from keyframes, the image is split into \(d\times d\) blocks firstly. Then, we calculate a region adaptive threshold for each block, and select the points with the largest gradient and exceeding the gradient threshold as the candidate points. In order to obtain information from weak intensity changes, such as smooth changes in lighting on white walls, some points with smaller gradients from areas without high gradient points are selected. We follow the approach as [PERSON] [11] described and select pixels with a lower gradient threshold and an increased \(d\).  
For the candidate point \(p\) extracted from image \(I\), we map it to the semantic image \(I_{sem}\). If the mapping position is in the dynamic region, then the point \(p\) will be discarded, otherwise we reserve it. Since the edge of image segmentation results maybe not very accurate, we not only consider the position of point \(p\), but also judge the four adjacent points of \(p\). Only when these five points are not in the dynamic region, \(p\) is reserved as a candidate point. A summary of the whole extraction method is given in Algorithm 1.  
Figure 3: The upper line is the RGB images in TUM dynamic dataset [29], and the bottom line is the indoor pedestrians segmentation results by Mask R-CNN, where the black parts are dynamic regions. The pixel value of black regions (dynamic regions) is 0. The pixel value of white regions (static regions) is 1.  
Figure 2: Based on life experience, the dynamic degree of common objects is scored. Only a few common objects are listed. The threshold is set to 5. If the detection score of an object is higher than the threshold, it is considered as a dynamic object, otherwise we think it as a static object.  
```
Input: The original image \(I\), the semantic image \(I_{sem}\), the number of points required: \(N\); Output: The set \(S\) of candidate points, the number of selected points \(N_{sel}\); Initialize: S =[\(\varnothing\)], \(N_{sel}\) = 0, \(k\) = 1; while\(N_{sel}\) < \(N\)do for\(k\) <= 4 do  Split image \(I\) into \(kd\) \(\times\)\(kd\) blocks; for each blockdo  Select a point \(p\) with the highest gradient which surpass the gradient threshold; ifno selected point in this blockthen  Select a point \(p\) with the highest gradient which surpass the lower gradient threshold; endif if\(I_{sem}\) [\(p\)]!= 0 then  Find the points \(p_{1}\), \(p_{2}\), \(p_{3}\), \(p_{4}\), where they are distributed above, below, left and right at \(p\) with the pixel interval \(D\); if(\(I_{sem}\) [\(p_{1}\)]!= 0 && \(I_{sem}\) [\(p_{2}\)]!= 0 && \(I_{sem}\) [\(p_{3}\)]!= 0 && \(I_{sem}\) [\(p_{4}\)]!= 0 ) then  Append point \(p\) to set \(S\); endif endif endfor \(k\) = 2 \(\times\)\(k\); endfor \(N_{sel}\) = \(N_{sel}\) + the number of selected points; endwhile
```  
**Algorithm 1**Candidate points extraction based on image semantic information
# High-Precision Tracking of Free-Space Optical Communication System on Mobile Platforms
## 2 Design of FSO Communication System for Mobile Platform
The rest of the beam passes through the planar reflector to change the direction again, is reflected to the FSM2 mirror in the dichroic mirror, and finally enters the collimator at the receiving end after adjusting the direction by FSM2. The transmitting optical path also adopts the same optical path structure. However, the direction of the transmit beam is not changed by the dichroic mirror, and it is not divided by the beam splitter. Therefore, the transmitting light diverges through the RC telescope system after changing the light path direction twice by the planar reflector and FSM1.
## 3 Datasets and Methods
### Pixel-based Classification  
To attempt to identify the two types of _Spartina_ and differentiate them from each other, we tested pixel classification algorithms, using both unsupervised and supervised approaches.  
Unsupervised methods provide the advantage of being more flexible [52], and this approach was considered with a view to extending the vegetation mapping to the entire Bay. Unsupervised approaches recognize patterns of spectral response using cluster analysis techniques. The resulting classification must be analyzed to regroup and label the classes using ground truth information. Even if there is a perspective of the long-term monitoring of salt marshes of the entire Bay, in this work, we will only focus on the results for the study zone.  
Pixel-based unsupervised classification was performed using the Simulated Annealing Algorithm [53], a probabilistic method to find the class centers, which minimizes the cumulated distance from every pixel to their center of class in the spectral space. This is a non-linear algorithm that, with the appropriate parameters, provides a converged solution to the global optimized minimum. To improve the search for the best solution, a start temperature (a control parameter that determines the range of the initial search for the solution) is given, and its decrease, with the number of iterations, corresponds to a decrease in the probability of the acceptance of a worse solution than that previously found.  
For the supervised classification approach, we considered different supervised classifiers, where preliminary tests showed that all techniques provided similar results. For this reason, we further proceeded with the Random Forests (RF) classifier [54], which is indicated in the literature as a suitable algorithm, when using a high number of variables and small training sets [4; 10]. When applying the RF classifier, the classification map is accompanied by an accuracy map, which contains the indicator of the confidence degree of the classification. More precisely, the RF classifier is composed of a set of different individual base classifiers (decision trees). Each tree makes a decision, and the final decision corresponds to the majority vote. The degree of confidence is obtained as the percentage of the individual classifiers that provide a decision equivalent to the final one.  
Pixel classification was performed on the three-band MS images. Supervised classification was performed using MATLAB(r) on a standard personal computer, an Intel(r) Core(tm) i5-4690 processor at 3.50 GHz, with 8 Go memory. For images with dimensions of 281 \(\times\) 300 at a 2 m resolution, the algorithm's computation time was under 5 minutes. As for the unsupervised classification, the simulated annealing algorithm was written in Fortran. It ran on a bi-socket server using AMD Opteronprocessors at 2.30 GHz, and it was parallelized on 24 cores using the OpenMP library. The longest computation was approximately 10 minutes.  
All image processing steps are summarized in Figure 3.
# Simulation of Surface Topography Considering Cut-in Impact and Tool Flank Wear
## 7 Results
Meanwhile, it is found that the impact vibration has little effect on the texture structure of the surface morphology but affects the scallop-height of the machined surface. The reasons for the difference in surface morphology between the simulated and experimental are as follows: (1) the cut-in phase angle is random in the experiment process; and (2) the surface topography model does not take into account the tool deformation. When the dynamic characteristics of cutting system are not considered, the simulation error of surface roughness is 19.05%. When only the vibration caused by a nonlinear change of chip thickness is considered, the surface roughness error is 12.09%. When the vibration of cutting system with cutting impact vibration is considered, the error of surface roughness is 9.52%. The simulation and experimental results of surface roughness at different tool wear for cutting parameters (Test 1) are shown in Figure 13.  
It can be seen from Figure 13 that the surface roughness increases with the increase of tool wear. When the tool wear amount is less than 0.164 mm, the surface roughness increases more slowly. When tool wear increases from 0.164 mm to 0.21 mm, the increase rate of surface roughness is close to 50%. When the tool wear is less than 0.164 mm, the maximum error between the surface roughness simulation and the experimental measurement is 12.3%, which is within the acceptable range. When the tool wear is 0.21 mm, the maximum error between the surface roughness simulation and the experimental measurement is 21.9%. The reason is that the cutting edge near the tool tip induces micro-chipping. The simulation and experimental results of surface morphology at different tool wear for cutting parameters (Test 1) are shown in Figure 14.  
Figure 13: Simulation and experiment of surface roughness at different tool wear. VB—the tool wear. (**a**) effect of tool wear on surface roughness; (**b**) error. It can be seen from Figure 14 that, with the increase of tool wear, the texture structure of the workpiece surface changes and the maximum scallop height on the surface increases accordingly. Meanwhile, the texture asperities grow and the grooves deepen with tool wear in the experiments. The worn tool causes scratches on the machined surface, which leads to deterioration of the machined surface. It is found that the scratch width of simulation is larger than that of the experiment. The reason is that the tool wear is assumed to be the same in the wear area in the simulation process, which is inconsistent with the actual situation.
## 4 Testbed Design
### ML Datasets for Experimentation  
Driven by our motivating example of a healthcare provider in the field of imaging diagnostics (i.e., MRI-based research), we have based our evaluation on two widely recognized databases in computer vision: MNIST [18] and CIFAR-10 [19].  
The fundamental difference between them lies in the complexity and characteristics of the images they contain. MNIST is a simple database that consists of grayscale images of handwritten digits. It includes 60,000 training images and 10,000 test images, each measuring 28 \(\times\) 28 pixels. The dataset is divided into 10 different classes representing digits from 0 to 9. CIFAR10 is more complex and contains colored images of everyday objects like airplanes, cars, and animals. These images are larger, measuring 32 \(\times\) 32 pixels, and contain three color channels (RGB). CIFAR-10 consists of 50,000 training images and 10,000 test images, spanning 10 distinct object classes.  
Albeit both databases are common in the benchmarking of computer vision algorithms, the choice between MNIST and CIFAR-10 depends on the complexity of the task and the specific application scope. MNIST, with its simplicity and small image size, serves as a foundational dataset for evaluating and prototyping machine learning algorithms. It is commonly used to explore different classification techniques and benchmark the performance of new models. On the other hand, CIFAR-10 presents a more challenging task for algorithms due to its higher complexity and inclusion of color images. It serves as a stepping stone to more advanced computer vision tasks and is often used to assess the performance of deep learning architectures. Working with CIFAR-10 allows researchers to tackle the challenges posed by color information and gain insights into developing more robust and accurate models for image classification.
# Effects of Equatorial Plasma Bubbles on Multi-GNSS Signals: A Case Study over South China
## 3 Comparison of GNSS and Airglow Data
This might be because the irregularity scale sizes of the EPB changed during different life  
periods, and the L-band sensitive scale size of 300-400 m might not be dominant near the end of the EPB lifetime. Benefiting from the almost fixed position of the GEO satellite, joint observations reveal some detailed relationships between EPBs and scintillation. The center of the EPB depletion often corresponds to stronger scintillation, while the edge of the bubble, which is considered to have the largest density gradient, corresponds to relatively smaller scintillation instead. From the perspective of the dVTEC obtained from phase observations, the sharp fluctuations also correspond to the center of the airglow depletion. Figure 2: Comparison of the S\({}_{4}\) index (Galileo: L1C, GPS: L1C), airglow depletion, and deviation VTEC (Galileo: L1C-L5Q, GPS: L1C-L2W) for (**a,c**) Galileo and (**b,d**) GPS during 12–20 UT on 3 April 2022. The signal path with the highest elevation is selected among all the satellites at a certain epoch. (**a,b**) Distributions of the selected ionospheric pierce points (IPPs, color dots) at an effective height of 300 km. The selected area is at 19\({}^{\circ}\)–23\({}^{\circ}\)N, which is covered by the airglow image. The black stars represent the location of the GNSS receiver and airglow imager at Zhuhaai, China. (**c,d**) Variations of the S\({}_{4}\) index (orange bar), airglow depletion (blue bar), and deviation VTEC (colored line) as a function of UT (LT \(\approx\) UT + 7.5). The airglow values are the post-processed airglow values divided by a constant value (it is 200 in this work). The specific PRNs are also marked in the panels, and the colors used for specific PRNs are the same in panels (**a,c**).