esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 3 Proposed Classifier
### Optimization of Gabor Parameters and Weights  
The next stage is to pick algorithms/classifiers to reliably identify the type of intercepted signal. During classifier combination, several classifiers are merged at the feature level, the data level, and the classifier or division level. A novel classification technique based on the GA has been presented for dealing with datasets with an imbalanced number of classes. The hybridized GA is intended to address the problems regarding sampling, feature subset selection, the fine tuning of traditional learning techniques, and cost matrix optimization [45]. There are several advantages to combining classifiers, and selecting a classifier with a hybrid function is the most important task, since a classifier that excels at categorizing one class may not be appropriate for another class [46].  
The genetic algorithm (GA) was chosen as the optimization algorithm to minimize the cost function defined in (8) by adjusting four parameters, i.e., \(\mathbf{c}\), \(\sigma\), \(\mathbf{f}\), and \(\wp\). The Gabor features and weights are adjusted until the cost function \(\longrightarrow\mathbf{0}\). Initially, the linear filter of the GFN obtains the value of its fitness function. Next, this value is input into an optimization tool utilizing one of the most well-known heuristic evolutionary computing approaches, the GA. This study developed an optimization procedure based on threshold criteria and parameter lists to identify the optimal signal utilizing global and local search techniques. Using the hybrid function effectively resolves optimization concerns. After the GA ends, the hybrid function begins operating, with the GA result as the beginning point; the hybrid function consists of the GA, GA-Fminsearch, GA-Pattern Search, GA-Fminunc, and GA-Fmincon [47; 48; 49]. The GA-based Gabor filter parameter and weight optimization algorithm is presented in Algorithm 2.  
```
1 Initialization (No. of chromosomes and genes, % of offspring and parents for making a new generation, Gabor parameters and weights)
2 GeneratePopulation Matrix \(\mathbb{Y}_{k}\in[0,1]\). whileFitness \(<\)Thresholddo
3 evaluate fitness\(\rightarrow\) (8)
4 apply\(\rightarrow\)Sorting (Descending Order)
5 generate\(\rightarrow\)New Population (Multi-Point Crossover)
6 for\(j=1\):\(K\)do
7 for\(k=1\):\(K\)\(-\)\(1\)do
8 Best-Fitted Population (Parents and Offspring)
9 if\(FF\)\(\longrightarrow\)0, or No. of iterations \(\longrightarrow\)\(max_{Value}\)then
10 terminate
11 else
12 Repeat
13
14 returnUpdate Gabor parameters and weights (\(\mathbf{c}\), \(\sigma\), \(\mathbf{f}\), \(\wp\))
```  
**Algorithm 2**GA-based optimal Gabor parameter and weight finder.
## 1 Introduction
Spread-spectrum technology refers to a technology that uses pseudo-random sequences (PRNs) to expand the bandwidth of transmitted information and uses the same PRNs at the receiving end to perform the despreading of correlation and recover communication data [18; 19; 20]. Because noise is uncorrelated with the PRN, once the correlation operation has been performed, the spectrum of noise is spread, and the spectral density is reduced. Therefore, the power of the interference signals falling into the communication signal frequency band is reduced, thereby achieving anti-interference and improving the SNR at the receiver output [21; 22; 23]. Spread-spectrum technology is applied to beacon light detection. At the transmitter, beacon communication data are spread-spectrum-modulated with the PRN. The spread-spectrum signal is used to modulate the transmitted beacon light power. At the receiver end, the QD receives the beacon light and generates photocurrent signals. These signals are then correlated with the same PRN that was used at the transmitter to suppress background light and detector noise, resulting in high-SNR output signals. We utilize high-SNR signals for beacon communication and to estimate the centroid position of the light spot, enhancing the precision of QD position detection and the sensitivity of the system detector. This article aims to investigate the influence of beacon spread-spectrum modulation on the precision and sensitivity of QD position detection. The detection principle of the QD under beacon spread-spectrum technology is analyzed, and a mathematical model is formulated to characterize the precision of position detection under spread-spectrum modulation in terms of the spread-spectrum gain, system SNR, Gaussian beam waist radius, and spot centroid position. Theoretical derivations are conducted, and simulations are performed to assess the improvements achieved by spread-spectrum technology in the sensitivity of the detector and precision of position detection. Subsequently, a beacon spread-spectrum-modulated QD system is constructed to validate the effectiveness of this method and evaluate the enhancement in system sensitivity and the precision of position detection under varying spread-spectrum gains. The experimental results are consistent with the theoretical analysis. This technology is therefore anticipated to facilitate the high-precision detection of beacon light in complex optical backgrounds over long distances.
## 3 Methods
### Classification Methods  
ML and DL methods, non-neural and neural, were used to classify the severity of accidents. The non-neural methods tested were logistic regression (LR), support vector machine (SVM), and tree structure models such as decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Tree structure models are well accepted due to their effectiveness in processing multifaceted data, flexibility, and ease of interpretation [55], in addition to working satisfactorily with high-dimensional data (due to automatic variable selection) and having an integrated interaction detector [56].  
The neural methods tested were multilayer perceptron (MLP), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and 1D convolutional neural networks (1D-CNN).
## 3 Sink-Based Centralized Transmission Scheduling
### Dealing with Frame Loss
#### 3.4.2 Addition of Retransmission Slots for Data  
If a sender node retransmits a data frame when it fails to receive an ACK frame, there will be a shortage in the communication possible time. This is because the number of slots assigned as the communication possible time is equal to the number of data frames owned (and to be forwarded) by the sender without considering retransmissions. Hence, the sender node cannot finish transmitting all data frames whenever retransmissions occur. To solve this problem, we add retransmission slots to the default communication possible time. An example of assigning retransmission slots is shown in Figure 5.  
Figure 4: Data collection procedure in the proposed method.  
The number of retransmission slots is determined by the sink node according to the following algorithm.  
1. Calculate the average SNR based on the distance between the sender and the receiver of a hop.
2. Determine the slot error rate (SER) from the average SNR using the SNR-BER table and the property of channel fading.
3. Calculate the minimal number of retransmission slots \(n\) so that all data frames at a sender node, sharing \(n\) retransmission slots, are transmitted successfully with a probability no less than 99.75%, in other words, the frame error rate under retransmissions is no more than 0.25%.
# Searching Open-Source Vulnerability Function Based on Software Modularization
## 3 Methodology
### Open-Source Vulnerability Detection  
#### 3.2.1 Open-Source Component Vulnerability Function Detection  
Source code preprocessing based on normalization. Most software developers reuse open-source components with code or structural changes, and open-source components are constantly updated to provide better functionality. However, internal and external open-source component changes can lead to syntactic diversity of vulnerable code. We can address the syntactic diversity problem of vulnerability code clones by using the signature database in Movery [35]. Consequently, this paper uses the vulnerability signature database to perform vulnerability function detection for open-source components.  
The signature database is generated by the key techniques of function collision and core code line extraction, including vulnerability signature and patch signature. During the generation of the signature database, essential and dependent vulnerable code lines are extracted to generate extensible vulnerability signatures for addressing syntax diversity caused by internal open-source component modifications. Then, critical lines of code, dependent lines of code, and control flow lines of code are extracted from vulnerability and patch functions to address the syntax diversity caused by external open-source component changes. Finally, the vulnerability signature and patch signature are generated based on extracted contents.  
Before vulnerability detection, we generate the signature for the target component through function hash and code normalization. Hash values and the path information of functions will be stored in the function hash file. Then we remove white spaces and comments and convert upper-case characters to lower-case to parse all function code lines. Unnormalized and normalized code line forms are stored in the function signature.  
Vulnerability function detection. We compare the target open-source component signature with the vulnerability signature database to detect the vulnerability code clone in the component to determine the vulnerability function. As is shown in Equation (5), when all codes of the target function are included in the vulnerability signature, we calculate the similarity of syntax between the target function and the function in the vulnerability signature through the Jaccard similarity coefficient. If it reaches the threshold (0.5), the target function code is the vulnerability code clone. We have  
\[sim(f,f_{v})=\frac{|f\cap f_{v}|}{|f\cup f_{v}|}. \tag{5}\]
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 2 Methods
### Inter-Satellite Differential Validation Method  
The inter-satellite differential validation is a method that uses carrier-phase data of two GPS satellites that are not participating in LEO POD and the precise ephemeris of GPS to calculate the difference between the carrier-phase distances of the adjacent epochs of the two satellites. The orbit determined of LEO and the precise ephemeris of GPS are then used to calculate the geometrical distance between the HY-2D and the GPS satellite to obtain the difference between the carrier-phase distance and the geometrical distance. Finally, the result is differentiated to eliminate the influence of other errors and determine the validation residuals.  
Equation (2) is used to obtain Ionosphere-Free combination observations for carrier-phase data, and the carrier distance after eliminating the influence of the ionosphere is \(LC_{j}\).  
The difference between the carrier-phase distance of GPS satellite \(a\) and that of GPS satellite \(b\) at the same epoch:  
\[\Delta LC_{i}=LC_{ai}-LC_{bi} \tag{8}\]  
where \(LC_{ai}\) represents the carrier-phase distance between GPS \(a\) and HY-2D, and \(LC_{bi}\) represents the carrier-phase distance between GPS \(b\) and HY-2D; \(\Delta LC_{i}\) is the difference of carrier-phase distance between GPS \(a\) and GPS \(b\). The geometrical distances \(\rho_{ai}\) and \(\rho_{bi}\) are calculated using the coordinates of GPS and the coordinates of HY-2D. The difference is found to obtain \(\Delta\rho_{i}\):  
\[\Delta\rho_{i}=\rho_{ai}-\rho_{bi} \tag{9}\]  
where \(\rho_{ai}\) represents the geometrical distance between GPS \(a\) and HY-2D, and \(\rho_{bi}\) represents the geometrical distance between GPS \(b\) and HY-2D. The carrier-phase distance difference and geometrical distance difference between the two GPS satellites and HY-2D are made differences to obtain the residual \(e_{i}\):  
\[e_{i}=\Delta LC_{i}-\Delta\rho_{i} \tag{10}\]  
Since the two GPS satellites that participate in the validations are moving in different directions and at different speeds, the \(e_{i}\) values of the adjacent epochs are differenced to obtain the final residual \(\Delta e_{i}\):  
\[\Delta e_{i}=e_{i+1}-e_{i} \tag{11}\]  
The core idea of inter-satellite differential validation is to use the carrier-phase data of two GPS satellites to calculate the relative variations between the two satellites and HY-2D in adjacent epochs and compare them with the relative geometrical distance variations calculated using the coordinates. Therefore, when selecting data for experiments, it is necessary to ensure that two GPS satellites can be observed simultaneously and continuously during this period.
# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 3 Model of a Drone-Assisted Mobile Edge Computing System  
### System Model  
We consider a UAV-supported MEC network consisting of multiple edge servers (ECSs), unmanned aerial vehicles (UAVs), and users (UEs). The set of ECSs is denoted by E, E = [1, 2,..., E], and the set of UAVs served by ECS m is denoted by U m, U m = {1, 2,..., U m}, as shown in Figure 1. The entire network can be divided into three layers: the ECS layer, the UAV layer, and the UE layer. In the ECS layer, there are multiple ECSs that are responsible for allocating edge computing resources to UAVs. They can control the transaction price of the allocated edge computing resources. In the UAV layer, each UAV can provide computing services to mobile users. To improve the quality of service (QoS) provided to mobile users, UAVs with limited on-board resources should apply to ECSs for edge computing resources. At the user layer, each mobile user will upload service requests to UAVs. The service requests include computation tasks, communication requests, and so on. It is assumed that each UAV accesses its nearest ECS, and each mobile user accesses its nearest UAV. Then, all the service requests of the mobile users can be transmitted to the drones, and all the drones can use the edge computing resources obtained from the ECS to provide satisfactory services to the mobile users. This paper introduces a multi-layer architecture and key parameter settings in the simulated system model, as shown in Table 1, to fully reproduce the core features of the actual deployment. Specifically, we simulate the dynamic movement of the drone, in which the drone flies according to a preset trajectory, combined with random path deviations to simulate changes in mobility in complex environments. For resource and communication constraints, the computing power and bandwidth of the edge server (ECS) and the drone are set to finite values. The relevant parameters are based on the existing literature and actual systems to ensure the reliability and reference value of the simulation results. In terms of task characteristics, user service requests include two types: computation-intensive and communication-intensive. Task requirements are generated randomly, following the distribution characteristics of user requests in the real environment. In addition, it is assumed that each drone and user accesses its nearest ECS and drone, simulating the access mechanism in the real environment. Although this paper has verified the effectiveness of the proposed algorithm by simulating a detailed environment to restore the operational characteristics of the real UAV-assisted MEC system as much as possible, we also recognize that there are still complex factors in real deployment, such as multipath interference, resource heterogeneity, and dynamic changes in the environment. Future work will further promote the implementation and testing of the algorithm in actual UAV networks, evaluate its applicability and performance in complex dynamic environments, and enhance the practical value of the research for engineering.
# A Broadband Ultra High Frequency (UHF) Fat-Dipole Antenna for Digital TV Applications
## 1 Introduction
Among the various DTV standards, the Advanced Television Systems Committee (ATSC 3.0) proposes MIMO as a way to enhance robustness by transmitting two independent data signals over a single RF channel, thereby enabling spatial multiplexing [15]. The additional capacity provided by MIMO facilitates DTV features that allow for the transmission of multiple channels within the same bandwidth or a return path for user interaction. Figure 1 illustrates the antenna with a generic Digital TV receiver block diagram. The relevant design constraints are presented, along with implementation and 3D electromagnetic simulation details, which are compared with measurements. Specific MIMO parameters are discussed and applied to the antenna design. Figure 1: Inclusion of the antennas in the block diagram of a DTV receiver. LNA: low-noise amplifier; ADC: analog-to-digital converter; OFDM: Orthogonal Frequency-Division Multiplex; T/C/D/F-dl: Time/Code/Frequency-Domain Interference Cancellation; ML: Maximum Likelihood; BICM: Bit-Interleaved coded modulation; and PLP: Physical Layer Pipe. Adapted from [15].
## 2 Materials and Methods
### Methods
#### 2.2.3 Comprehensive Gain Calculation  
Using satellite data with different integration stages acquired on different dates and the corresponding RadCalNet ground measurement data, several groups of \(N_{stages}^{k}\), \(DN_{i}^{k}\), \(L_{i}^{k*}\) could be obtained and then used to solve the comprehensive gain \(T_{i}\) by using the adaptive weighted least squares (WLS) method. The cost function is shown as Equation (6):  
\[e^{2}=\sum_{k=1}^{N}\frac{\left(L_{i}^{k*}-(T_{i}\times\frac{DN_{i}^{k}}{N_{ stages}^{k}}+B_{i})\right)^{2}}{w_{k}} \tag{6}\]  
Figure 8: Flow chart of the proposed calibration method.  
where \(w_{n}\) is the given weight factor of the \(k\)-th sample, \(N\) is the number of samples, and \(e^{2}\) is the fitting mean square error, which reflects the fitting effect of the calibration model on the observed values. The smaller the value, the better the fitting effect.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 5 Results
### Symbol Error Rate Characterization  
Based on the results of the received power and cross-talk interference, we estimated the SER with respect to the total luminous flux emitted by the RGB-LED at three photodetector positions. The Python package includes a module that performs a sweep of the luminous flux emitted by the LED source and then calculates the corresponding SER values. A simulation was conducted with a dark current of 1 pA for each color detector. In order to ensure that the received symbols fell within an appropriate operational range for analog-digital converters, the gain factor defined in the photodetector noise modeling (Equation (22)) was set to a value of \(2\times 10^{5}\). The bandwidth was defined as 3.5 MHz under the rise time reported by the photodetector manufacturer. The estimation of the SER was conducted for CSK with sixteen symbols based on the IEEE 802.15.7 standard. Figure 6 shows the SER performance of the CSK-based VLC system in the indoor environment.
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 2 Principle of FPM
### Fourier Ptychographic Reconstruction Process
The participating pupil function means that, for each LED, only a part of the spectrum is updated, which is equivalent to the constraint of the update range introduced by the pupil function in the frequency domain. This algorithm replaces and projects the collected low-resolution images and pupil functions as spatial and frequency domain constraints, so this method is also called the AP algorithm (alternating projection). When \(\alpha=1\), the iterative update process of the above formula is the G-S algorithm commonly used in traditional FPM. In each iteration, the frequency domain constraint is updated once. Therefore, due to the updated frequency domain constraints, the spectral radius reconstructed by FPM technology only depends on the numerical aperture of the objective lens \(NA_{obj}\) and the maximum illumination of the LED array NA (\(NA_{ill}^{max}\)): \(\frac{NA_{obj}+NA_{ill}^{max}}{\lambda}\). Therefore, the reconstructed complex amplitude resolution is:  
\[\epsilon_{rec}=\frac{\lambda}{2\Big{(}NA_{obj}+NA_{ill}^{max}\Big{)}} \tag{10}\]  
The traditional G-S algorithm only considers the low-pass nature of the pupil function and cannot deal with the aberration problem. Therefore, the process of spectrum updates and the update process of the pupil function of the system must be synchronized, as shown in Equation (11):  
\[P_{new}\big{(}k_{x},k_{y}\big{)}=P\big{(}k_{x},k_{y}\big{)} \tag{11}\]  
In Formula (11), \(\beta\) is the step size for updating the pupil function. Combined with Equation (9), the FPM optimization algorithm with aberration correction can be obtained. It is worth noting that \(\beta<\alpha\). The above update formulas are several commonly used aberration-free reconstruction algorithms (EPRY).
## 5 Parametric Studies
### Effect of Thickness of FRP Layers (nt)  
The range of thickness of FRP layers was 0.15 mm to 1.05 mm at an increment of 0.10 mm. The effect of variations of thickness of FRP layers is shown in Figure 13c,e,f. When \(nt\) increases from 0.15 mm to 1.05 mm and \(E_{f}\) increases from 110 GPa to 245 GPa, the increment in \(\varepsilon_{cc}\) was up to 133.80%. When \(nt\) increases from 0.15 mm to 1.05 mm with an increase of 50 MPa in \(f^{\prime}_{co}\), 28.48% increase in the \(\varepsilon_{cc}\) occurred showing that the effect of increasing the \(nt\) is more significant at higher values of \(f^{\prime}_{co}\). Similarly, the effect of increasing the \(nt\) at larger diameters of concrete was negligible, but at lower values of diameter, the effect of increment of \(nt\) on \(\varepsilon_{cc}\) was significant as presented in Figure 13f.  
In general, by increasing the unconfined concrete strength (\(f^{\prime}_{co}\)), the confined concrete strain (\(\varepsilon_{cc}\)) increases slightly and by increasing the thickness of FRP (\(nt\)), there is a significant increase in \(\varepsilon_{cc}\). Similarly, the effect of the increase of elastic modulus of FRP (\(E_{f}\)) on \(\varepsilon_{cc}\) is significant. However, the effect of increasing the diameter of concrete (\(d\)) was opposite. At lower values of diameter, the value of \(\varepsilon_{cc}\) was higher.
## 2 Materials and Methods
### Learning Algorithms
#### 2.3.5 Recurrent Neural Network (RNN)  
RNN can work with both time series and sequential data, with an internal memory that can implement neuron feedback itself. This allows the model to have short-term memory, making it possible for time series forecasting [39]. RNNs represent a favorable choice for time series-based forecasting, and a standard RNN has simple functions for input data and forecasting with minimal errors and simple mechanisms. Some drawbacks of RNNs include gradient exploding/disappearing and data morphing [5; 47; 48; 49]. The advantages of RNNs include their effectiveness in sequence modeling tasks, their explicit design for processing sequential data, and their ability to process sequences of variable length. In contrast, the disadvantages of RNNs include limited memory, being problematic when dealing with long sequences, and being biased toward recent data in the sequence.