esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 2 Materials and Methods
### Experimental Protocol  
This experiment involved one single visit to the field hockey pitch at the National Institute of Education, Nanyang Technological University, Singapore. GNSS data were collected outdoors without high surrounding buildings to enhance satellite reception [34]. After sufficient warm-up and putting on the strap with 8 GNSS units, participants were asked to perform one straight-line sprint across the hockey pitch with maximum effort (Figure 2). A sprint distance of 45.72 m was chosen as it is exactly half of the hockey pitch  
Figure 1: Each participant wore eight GNSS units at once in the maximal sprint test.  
with a clear marked line. This distance also allowed sufficient time for participants to reach their peak speed. To help participants discern the start and end points, the sprint area was marked out using colored cones. Participants were asked to run pass the 45.72 m end-line before they could slow down. The sprint test was recorded using two video cameras at 60 Hz for subsequent determination of sprint times and peak speeds. To minimize the effect of camera lens distortion, we used two smartphone cameras to cover the entire 45.72 m range (Figure 2). The midline distance of 22.86 m was used to calibrate each camera (0 to 22.86 m, 22.86 to 45.72 m) as the midline can be clearly seen from both camera views.  
GNSS units were switched on at least 5 min before the units were strapped on the participants. After strapping on all units, participants were verbally briefed and then asked to familiar themselves with the task. The GNSS units were switched on for more than 15 min to receive the complete almanac before the commencement of the test. Participants were also instructed to stay still for 30 s, before the start of the sprint. This was to enable subsequent determination of the start time for each trial when the speed increased sharply from zero.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 3 Material and Methods
### Methods  
The proposed approach comprises four parts: (i) handcrafted feature extraction, (ii) deep features extraction, (iii) feature fusion and selection of discriminant features by PCA, and (iv) LDA-based classification. The benchmark dataset was split into 75%:25% for training and testing. The 75% data used for model training. During model development, 5-fold cross validation was used on training data, where 4 folds were used for training and a fifth was used for validation. This process continued for several iterations. Once model training is finished, the unseen 25% data were fed to the trained model for evaluation. The schematic block diagram of the proposed approach is shown in Figure 2. Subsequently, the methodology of the proposed technique is explained in detail.  
Figure 1: Sample images of potato leaf dataset: (**a,d**) Healthy, (**b,e**) Late Blight, and (**c,f**) Early Blight.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 1 Introduction
In this method, a residual nodal load vector is derived based on the computed EEP solution from the conventional FE analysis and then, without changing the FE meshes and the global stiffness matrices, a simple back-substitution process generates a correction term for nodal displacements, which enables an improved EEP solution to gain the fourth-order super-convergence for nodal displacements and third-order super-convergent interior displacements for all 1D to 3D linear elements. It is noted that, among the 1D to 3D problems, it is more difficult for the 2D and 3D linear elements focused on in this paper to achieve such super-convergent displacements. With the improved EEP solution obtained as a reliable error estimator, an effective and reliable adaptive FE solution strategy, which requires the maximum error of the FE displacements to satisfy user-specified error tolerances, is proposed for linear elements. This strategy changes the unfavorable situation that linear elements have long been excluded from the EEP-based adaptive finite element analysis, whose efficiency turns out to be even superior to cubic elements for singularity problems. The remainder of this paper is organized as follows: Section 2 briefly describes some mature theories which will be used in this paper. Section 3 proposes the theory of the nodal accuracy improvement technique for multidimensional problems. In Section 4, the adaptivity strategy based on EEP is further improved to make it suitable for linear elements. In Section 5, the theories proposed in Sections 3 and 4 are verified by representative numerical results. Some conclusions are given in Section 6.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 4 High-Order QAM and APSK Modulation and Demodulation
### APSK Modulation  
APSK modulation constellation diagram consists of \(k\) concentric circles with different radii, with multiple PSK signal points uniformly distributed on each circle. The number of constellation points on the outer circle is greater than that on the inner circle. The signal set \(S_{k}\) formed by the constellation points on the \(k\)-th concentric circle can be expressed as:  
\[S_{k}=R_{k}\text{exp}\left[j\left(\frac{2\pi}{m_{k}}i_{k}+\theta_{k}\right)\right] \tag{31}\]  
where \(R_{k}\) is the radius of the \(k\)-th concentric circle, \(m_{k}\) is the number of constellation points on the \(k\)-th circle, \(i_{k}(i_{k}=0,1,\ldots,m_{k-1})\) is one of the constellation points on the \(k\)-th circle, and \(\theta_{k}\) is the initial phase of the constellation points on the \(k\)-th circle.  
The constellation diagram of 16 APSK consists of two concentric circles, with multiple signal points evenly distributed on each circle. The signal set formed by these points can be represented as:  
\[\begin{cases}S_{1}=R_{1}exp\left[j\cdot\left(\frac{\pi}{2}i_{k}+\frac{\pi}{4} \right)\right],(i_{k}=0,1,2,3)\\ S_{2}=R_{2}exp\left[j\cdot\left(\frac{\pi}{2}i_{k}+\frac{\pi}{12}\right)\right],(i_{k}=0,1,\ldots,11)\end{cases} \tag{32}\]  
where \(S_{1}\) represents the signal set formed by the four points on the inner circle, \(S_{2}\) represents the signal set formed by the twelve points on the outer circle, \(\frac{\pi}{4}\) and \(\frac{\pi}{12}\) are the initial phases of the inner and outer circle signals respectively, and the inner and outer circle radii are \(R_{1}\) and \(R_{2}\) respectively. The ratio \(\gamma=\frac{R_{2}}{R_{1}}\) is determined by the code rate of the encoding, and the smaller the code rate, the smaller the corresponding value of \(\gamma\). As shown in Figure 5, each constellation point in 16 APSK can carry 4 bits of information, and Gray coding is used for constellation mapping, so that adjacent signal points have only a 1-bit difference in their encoding.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 2 Domain Adaptation Techniques
### Overview of Domain Adaptation Techniques
#### 2.1.1 Scholarly Landscape Visualization
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**References** & **Title** & **Last Author** & **Year** & **Citations** & **Graph** \\ \hline
[40] & Deep learning for unsupervised domain & & & & \\ adaptation in medical imaging: Recent & & & & \\ advancements and future perspectives & & & & & \\ \hline
[41] & A Comprehensive Survey on Test Time & & & & \\ Adaptation under Distribution Shifts & & & & \\ \hline
[42] & Towards Better Stability and Adaptability: & & & & \\ Improve Online Self-Training for Model & & & & \\ Adaptation in Semantic Segmentation & & & & \\ \hline
[43] & Source-Free Unsupervised Domain Adaptation: & & & & \\ A Survey & & & & \\ \hline
[44] & Source Data-Free Cross-Domain Semantic & & & & \\ Segmentation: Align, Teach and Propagate & & & & \\ \hline
[45] & Unsupervised Adaptation of Semantic & & & & \\ Segmentation Models without Source Data & & & & \\ \hline
[46] & Domain Adaptive Semantic Segmentation & & & & \\ without Source Data: Align, Teach & & & & \\ and Propagate & & & & \\ \hline
[47] & Unsupervised Domain Adaptation for Semantic & & & \\ Image Segmentation: a Comprehensive Survey & & & & \\ \hline
[48] & Self-training via Metric Learning for Source-Free & & & \\ Domain Adaptation of Semantic Segmentation & & & & \\ \hline
[49] & Semantic Image Segmentation: Two Decades & & & \\ of Research & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Derivative works on domain adaptation for image segmentation. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**References** & **Title** & **Last Author** & **Year** & **Citations** & **Graph Citations** \\ \hline
[50] & Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks & [PERSON] & 2015 & 46,871 & 40 \\ \hline
[55] & You Only Look Once: Unified, Real-Time Object Detection & [PERSON] & 2015 & 25,552 & 35 \\ \hline
[56] & Focal Loss for Dense Object Detection & [PERSON] & 2017 & 15,883 & 35 \\ \hline
[51] & SSD: Single Shot MultiBox Detector & [PERSON] & 2015 & 21,997 & 34 \\ \hline
[52] & Deep Residual Learning for Image Recognition & [PERSON] & 2015 & 141,511 & 33 \\ \hline
[53] & Microsoft COCO: Common Objects in Context & [PERSON] & 2014 & 30,078 & 33 \\ \hline
[33] & Feature Pyramid Networks for Object Detection & [PERSON] & 2016 & 15,509 & 33 \\ \hline
[57] & YOLOv4: Optimal Speed and Accuracy of Object Detection & [PERSON] & 2020 & 7287 & 31 \\ \hline
[58] & YOLOv3: An Incremental Improvement & [PERSON] & 2018 & 14,615 & 29 \\ \hline
[59] & YOLO9000: Better, Faster, Stronger & [PERSON] & 2016 & 11,721 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prior works on domain adaptation for object identification.
## 2 Related Work
### Edge-Board Computing in Autonomous Driving  
On-board Edge Computing performs a critical role in real-time decision-making and processing in off-road environments, particularly in autonomous driving [36]. Autonomous vehicles must process vast quantities of sensor data quickly and handle these data directly within the vehicle rather than sending them to the cloud, which is essential [37; 38; 39]. The need for rapid response, particularly in complex terrain, further emphasizes the importance of edge computing. Edge computing platforms, such as the NVIDIA Jetson series, offer compact yet powerful performance that can handle deep learning models in real time. Research has shown that the Jetson AGX Xavier supports real-time 3D object detection and enables autonomous vehicles to avoid obstacles [40]. Furthermore, optimizing deep learning models using TensorRT can accelerate inference speed while reducing resource consumption [41]. Edge computing enables autonomous vehicles to operate reliably under unstable network conditions, thereby allowing them to function in offline environments without cloud-based processing.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 2 Attacks on VSS
### Malware Attack  
Malware comes in different forms and types such as viruses, Trojans, ransomware and spyware. In a smartphone, users download mobile applications, and malicious code embedded into the application program can gain access to personal information which the attackers can then exploit for financial gain [36]. The Mirai malware that occurred in 2016 takes advantage of the IP addresses of the devices that are vulnerable in the Internet of Things (IoT), and then turns these devices into bots that can be used as part of botnets for large-scale network attacks. Another malware, BrickerBot, like Mirai malware, infects its target and gains access to the device and destroys it--\"bricks it\", i.e., the device is longer operable. In a surveillance network, no one wants to wake up and realize that their surveillance camera no longer works because it was intentionally destroyed by someone trying to \"protect\" it. Nor does anyone want a picture or video of their device or application storage that went viral on social media due to their camera (which can be a surveillance camera or smartphone camera) being hacked. There are significant security threats with a malware attack which enables an attacker to execute any command on a target machine or within a target process. Due to this, the attacker can have a different way to invade by performing malicious code injection, data leaks and also performing privilege escalation. Access control entry vulnerabilities have been discovered on IP cameras, DVRs, and VPN routers which are publicly listed in [[https://cve.mitre.org](https://cve.mitre.org)]([https://cve.mitre.org](https://cve.mitre.org)). Here, CVE stands for Common Vulnerability Exposure. (For example: CVE-2021-1131, CVE-2020-7848, CVE-2020-11624, CVE-2018-6414, CVE-2018-9156).
## 3 Results and Discussions
### Effect of Initial Concentration  
The ratio between accessible active sites of MnFe\({}_{2}\)O\({}_{4}\)/graphene oxide nanocomposite adsorbent and the concentration of adsorbate (Pb\({}^{2+}\) and NR dye) is the adsorption efficiency (%) that also associates adsorbent surface coverage. The concentrations range from 10 to 300 and 20 to 200 mg/L were used to study the adsorption of Pb\({}^{2+}\) and NR over MnFe\({}_{2}\)O\({}_{4}\)/graphene oxide nanocomposite, respectively as shown in Figure 4. According to Figure 4, the increased concentration of the adsorbate led to a decrease of removal efficiency from 99% to 33% for Pb\({}^{2+}\) ions and from 92.8 to 89% for NR dye. Nevertheless, the increase of concentration led to the increase of adsorption capacity from 63 to 625 mg/g for Pb\({}^{2+}\) ions and from 20 to 90 mg/g for NR dye due to the accelerated mass transfer of pollutants toward the surface of nanocomposite that attributed to high pollutants concentrations [50]. The removal efficiency reached its maximum value of Pb\({}^{2+}\) ions and NR adsorption at lower adsorbate concentrations due to high available adsorption active sites. This inverse relationship between adsorption efficiency and adsorption capacity during initial concentration increase was observed in many similar reports.  
Figure 3: Wide scan XPS (**a**), room temperature magnetization curve (**b**), and Raman spectra (**c**) of MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite.
# Data-Aided Frequency Offset Estimation for CE-OFDM Broadband Satellite Systems
## 4 Pilot Symbol Structure  
In order to obtain the frequency domain signal, we performed the DFT transform on Equation (3). The result is  
\[Y_{\theta}(k)=2\pi hS(k)+\frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}\big{(}\frac{2\pi n \Delta f}{N}+\theta\big{)}e^{\frac{-2\pi nk}{N}}+\overline{W}_{\theta}(k) \tag{4}\]  
where \(Y_{\theta}(k)\) and \(\overline{W}_{\theta}(k)\) are the DFT of \(y_{\theta}(n)\) and \(\overline{w}_{\theta}(n)\), respectively.  
From Equation (4), it can be seen that there is an additive relationship between the \(\Delta f\), the \(\theta\), and the modulation symbols \(S(k)\). Therefore, if the pilot symbols are inserted into the frequency domain subcarriers at the transmitter, these symbols can be used to estimate the \(\Delta f\) through the PSCE. In this section, we propose the optimal pilot symbol structure.  
Similar to the LTE system, the resources in the CE-OFDM system can be divided into time-frequency resource blocks, which are shown in Figure 12. It can be seen from Figure 12 that for each time domain symbol \(x(n)\), the frequency domain symbols \(\left.S(k)\right|_{k=1}^{N_{sym}}\) are placed symmetrically concerning the direct current (DC) subcarrier, and the zero-filling high-frequency subcarriers are used to achieve oversampling gain (like LTE).  
Figure 3: Phase diagram of the received signal.  
The workflow of the proposed optimal pilot symbol structure is illustrated as follows.  
**Transmitter processing flow:**  
1: Calculate the carrier frequency offset in the communication scenario based on the speed of motion of the satellite and the terminal.  
2: Determine the pilot symbol overhead and the pilot symbol placement interval according to the accuracy requirements of the frequency offset estimation.  
3: Place the frequency domain pilot symbols \(S_{p}(k)|_{k=1}^{N_{p}}\) symmetrically on both sides of the DC subcarrier. It should be noted that the pilot symbols should be placed on the low-frequency subcarriers.  
4: Place the useful symbols symmetrically on the remaining symbols, and then the \(S(k)|_{k=0}^{N-1}\) is obtained.  
5: Get the \(s(n)|_{n=0}^{N-1}\) by doing IDFT on the \(S(k)|_{k=0}^{N-1}\).  
6: After the phase modulation of \(s(n)|_{n=0}^{N-1}\), the \(x(n)=Ae^{2\pi\hbar s(n)}\) is obtained.  
**Receiver processing flow:**  
1: The received signal \(y(n)\) is sent to the PSCE module. The \(\overline{Y}_{\theta}(k)=Y_{\theta}(k)-2\pi\hbar S_{p}(k)\) is obtained by doing the phase demodulation and the DFT operations.  
2: Calculate the \(\mathbf{V}\) and \(\mathbf{Q}\) according to the method in Section??.  
3: The estimation of CFO is \(\Delta\hat{f}=((V^{T}V)^{-1}V^{T}\mathbf{Q})_{1,1}\).  
4: Use the \(\Delta\hat{f}\) to compensate the received signal as \(\hat{y}(n)=y(n)e^{-\frac{2\pi\hbar t}{N}}\).  
5: The compensated signal is sent to the DFT module to obtain useful symbols.  
In the next section, we will present the PSCE and prove that the proposed pilot symbol structure has the optimal estimation accuracy through the analysis of CRB.
## 2 Measurement Principle
The digital signal sequence is obtained and input in the FPGA, and the data processing is also completed in the FPGA. According to references [24; 26], the DC and harmonics terms are extracted by employing the digital phase-locked technology. The first and second harmonics terms \(V_{1f}=I_{0}\sin\delta_{(\gamma,\lambda,V)}I_{1}(A_{0})\) and \(V_{2f}=I_{0}\cos\delta_{(\gamma,\lambda,V)}I_{2}(A_{0})\) are used to calculate the ratio. Finally, the phase retardation of the EO sample can be concluded as follows. \[\delta_{(\gamma,\lambda,V)}=\arctan\frac{V_{1f}J_{2}(A_{0})}{V_{2f}J_{1}(A_{0})} \tag{3}\]  
From the measured phase retardation, the EO coefficients can be further estimated once the geometric size parameters of the EO sample, the wavelength of the incident light, and the value of the external electric field voltage are known. Figure 1: Schematic diagram of the dispersion measurement system of the electro-optic (EO) coefficients based on the photoelastic modulation.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 5 The Proposed Secure Deduplication Protocol
### Ownership Update
* For existing owner \(i\):
* The CS generates \(CRGK_{i}\) by performing an XOR operation on the random value \(r_{i}\) of client \(i\) and the \(RGK_{other}\). * The CS sends \(CRGK_{i}\) to client \(i\). * The client \(i\) recovers \(GK_{j}\) from the value received. * \(CRGK_{i}\bigoplus GK_{l}\bigoplus r_{i}=GK_{l}\). * Both clients store the group key \(GK_{l}\) for session \(J\).
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 3 FLATSIM Architecture
### Architecture of the FLATSIM Delivery Module  
The role of the delivery module (see Figure 9) is to diffuse the FLATSIM products to the scientists and public community. The products are first made available to scientific experts for validation before being distributed to end users.  
The FLATSIM delivery module is composed of web services and websites. The web services and the expertise website are packed in an Apache HTTP server hosted in the CNES infrastructure in a virtual machine (VM) provided by the CNES web-site hosting mutualized services called Web-NG. In the CNES infrastructure, the delivery module uses a shared PostgreSQL DBMS and the mutualized datalake to store the data. Depending on their types, the data are stored on different technical means:  
* Downloadable products on the GPFS part of the datalake;
* Visualization data on the network attached storage (NAS) part of the datalake;
* Metadata in the PostgreSQL database.  
Figure 9: Delivery module for user access.
## 4 Constant Power Speed Range Performance and Anti-Demagnetization Performance
### Comparison of Anti-Demagnetization Performance  
Permanent magnets with smaller size are usually more prone to demagnetization. Since the proposed and optimized models in this paper use multilayer and smaller-sized permanent magnets, it is necessary to analyze the anti-demagnetization performance of the three models. The demagnetization of permanent magnets is analyzed by examining the changes in remanence of permanent magnets before and after applying high input currents. The definition formula for demagnetization rate [32] is shown in Equation (12).  
\[\delta= \left(1-\frac{B_{r}^{\prime}}{B_{r}}\right)\times 100\% \tag{12}\]  
where \(B_{r}^{\prime}\) is the remanence of the permanent magnet after a high input current, and \(B_{r}\) is the remanence of permanent magnets in the initial state.  
When the control temperature is 170 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, the maximum controlled input current is nine times the rated current. The maximum demagnetization rates of the permanent magnets for the three models under different input current conditions are shown in Figure 17. Figure 17a compares the maximum demagnetization rate of the three models with different input currents at 170 \({}^{\circ}\)C. The maximum demagnetization rate of all three models is less than 1%, indicating minimal demagnetization. It is shown that the three models have good anti-demagnetization performance under extreme operating conditions. Figure 17b shows the comparison of the maximum demagnetization rate of the three models with different input currents at 200 \({}^{\circ}\)C. At four times the rated current, the permanent magnets of the proposed and optimized models start to demagnetize. Compared to the proposed model and the optimized model, the permanent magnet of the conventional model has a larger size and a stronger anti-demagnetization performance. In Table 6, it can be seen that the maximum demagnetization rate of the optimized model is reduced by 13.84% compared to the proposed model at a temperature of 200 \({}^{\circ}\)C and nine times the rated current operating condition. This indicates that the anti-demagnetization performance of the optimized model is improved compared to the proposed model. Figure 18 shows the demagnetization rate distributions of the three models at a temperature of 200 \({}^{\circ}\)C and an input current of nine times the rated current.  
Figure 16: Speed–efficiency comparison of the three models. (**a**) Conventional model. (**b**) Proposed model. (**c**) Optimized model.  
Figure 17: Maximum demagnetization rates for the three models with different input currents. **(a)** For input of 170 \({}^{\circ}\)C. **(b)** For input of 200 \({}^{\circ}\)C.  
Figure 18: Demagnetization rate distribution plots for the three models at 200 \({}^{\circ}\)C and nine times the input current. **(a)** Conventional model. **(b)** Proposed model. **(c)** Optimized model.