esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Criteria of Response Function Selection
### Step One--Accuracy of the Coefficients
Figure 4: The maximum relative error of the base polynomial coefficients \(\max\Delta_{\text{rel},c}\) for various \(POb\) and the number of significant digits of rounding \(D1\): (a) considering only the polynomial dependences, (b) considering the polynomials and all the other groups of functions. Regardless of the choice of the functions group, with the exception of the initial perturbations, in the case of reference polynomials of higher degrees, clearly greater precision of discrete data was required to obtain a specific error rate of the coefficients. It is worth noting that the not too high critical a posteriori error value of 5% was reached approximately for the second-order base polynomial and seven significant digits of discrete data rounding (Table 3, Figure 4). The standard accuracy of the FEM results is much lower, so if they are used to obtain the structure response function, even for the base polynomials more complex than linear, there is a risk of exceeding the above critical value of the relative error of the coefficients \(\max\Delta_{\text{rel},\text{c}}\). It is also worth emphasizing that thus far, it had been assumed that discrete data were described by a specific group of functions based on a polynomial \(w\) with a known degree, while in most practical cases, the form of the response function is unknown. Therefore, the response function with satisfactory accuracy of the coefficients can be obtained on the basis of the FEM results only in a very limited number of simple cases in which we know that the state function dependence is linear--in some cases it may be enough to reduce to it. This situation is a serious problem that could prevent further effective application of this method in practice. However, when the RFM is used for reliability analysis, more important than the accuracy of the function coefficients is the one connected with the probabilistic moments because they can be used in further calculations (Figure 1).
# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 7 Discussion
### Statistics, Benchmarks, and Comparisons  
Human perception studies found that participants could only discriminate fake from real images about 50% of the time [67; 68]. In deep learning approaches, notable successes include an 80% detection rate using VGG-Face [69] and a 99.4% rate using CNNs for deepfake identification [70]. Further, 80% detection success was achieved for the detection of fake images produced by ProGAN and 90% and above for CycleGAN and StarGAN [71]. The use of a Deep Convolution GAN improves detection accuracy, demonstrating that even with limited datasets, effective results can be achieved through diverse data inputs [32].  
Figure 8: Accuracy vs. time for StyleGAN3 deepfake face image detection for various classifiers.  
In our study, we found a marked improvement in detection ability across datasets from different GAN models, across various frequency transformations as well as across various classifiers [72]. The closest comparison we can make to our work is with [23]. In comparison, our study is wider in scope in methods and metrics measured and uses different datasets. We used Fourier, wavelet, and HOG transforms in the frequency domain while they used the wavelet transform. We also used self-created datasets for StyleGAN3, ProGAN and InterfaceGAN. We measured accuracy, recall, precision and F1 score, as well as computation time, while they only reported accuracy. While [23] reported impressive detection accuracy percentages in the eighties and nineties, for all datasets, we were able to achieve 100% accuracy in at least one combination of transform and classifier.  
The RealForensics detector [11], which has state-of-the-art generalization performance on unseen datasets, achieves a high accuracy of 83% and 75% for real and fake images. In comparison, our proposed method uniformly outperforms this for all the tested GANs.
## 1 Introduction
Through large-scale data training, they can efficiently distinguish between gravity anomaly signals and various types of complex noise, significantly improving denoising performance and processing efficiency. These methods can automatically construct nonlinear mappings from noisy gravity data to denoised data without manual intervention for feature extraction and integration. After model training and optimization, they can rapidly and stably process new observation data, providing high-quality basic data for geophysical exploration, geological surveys, and seismic monitoring. Against this background, this study addresses the denoising of gravity and gravity gradient data by proposing a U-Net architecture incorporating multi-scale feature fusion modules and attention mechanisms. Additionally, we introduce a physical consistency constraint during model training by embedding the Laplace equation--which governs the geophysical gravity potential--into the loss function as a Laplace term. This approach effectively enhances the physical interpretability and generalization capacity of the net work's denoising results, addressing existing issues of poor denoising performance and low processing efficiency for gravity data. The proposed network structure, which integrates multi-scale feature fusion and attention mechanisms, is better able to capture spatial features at different scales and recognize key spatial information within complex geological signals. The main innovations and contributions of this work are as follows:  
1. We propose a U-Net architecture integrating multi-scale features and attention mechanisms and introduce a physical consistency constraint for the gravity potential field in the loss function. This achieves an effective combination of physical priors and deep learning, enhancing the modeling and discrimination of complex geological spatial features. 2. A physics-driven data augmentation strategy is employed, introducing a large number of random walk-based subsurface material distribution simulations during dataset construction. This approach ensures that the training samples better reflect real-world subsurface environments, thus improving the model's generalization performance. 3. In addition to denoising gravity data, we systematically investigate denoising methods for the three components of gravity gradient data (T\({}_{\text{xx}}\), T\({}_{\text{yy}}\), and T\({}_{\text{zz}}\)), thereby expanding the practical applications of the proposed model.
## 4 Precoding Schemes
### Introduction to Precoding Schemes
#### 4.1.3 Capacity of Mimo VLC Systems  
The capacity of the additive white Gaussian noise (AWGN) channel was derived by [PERSON] in [36; 37]. For MIMO systems based on RF transmission, the capacity has been studied in several works [148; 149; 150]. In this sense, non-linear transmission schemes such as dirty paper coding (DPC) are capacity achieving, although subject to a high computational complexity [151]. In this context, a common oversight is to consider the Shannon capacity straightforwardly for analyzing the performance of precoding schemes in VLC.  
In [40], the capacity of the single-input single-output (SISO) free-space optical intensity channel is upper and lower bounded. Similarly, the upper and lower bounds of the optical channel considering IM/DD with PAM are derived in [38]. This work is extended in [39], considering non-uniform input distribution for PAM transmission. These works provide tight bounds for a single optical link at low and high SNRs. Assuming parallel optical channels, i.e., non-interfering channels between the same pair transmitter-receiver employing multiplexing techniques such as TDMA, the resulting lower and upper bounds of the capacity are derived in [152]. For precoding schemes applied to VLC, a lower bound of the capacity is derived in [153]. Specifically, the lower bound of the capacity for user \(k\) is \[C^{[k]}\geq\frac{1}{2}\log\left(1+\frac{2|\mathbf{h}^{[k]}{}^{T}\mathbf{w}^{[k]}|^{ 2}}{\pi e\left(\frac{1}{3}\sum_{j\
eq k}|\mathbf{h}^{[k]}{}^{T}\mathbf{w}^{[j]}| ^{2}+\sigma_{n}^{2}\right)}\right). \tag{23}\]  
Considering exclusively ZF precoding, i.e., \(\mathbf{h}^{[k]}{}^{T}\mathbf{w}^{[j]}=0\), \(\forall k\
eq j\), the specific lower bounds and an alternative methodology for obtaining the ZF precoding vectors based on solving a specific optimization problem are derived in [34].  
For illustrative purposes, the exact, lower and upper bounds of the capacity assuming ZF precoding versus the peak SNR of an amplitude constrained Gaussian channels are shown in Figure 18. It can be seen that the lower bound of the capacity provides a good approximation of the exact capacity. Moreover, mathematically, the lower bound results in being easier to manage than the upper bound derived in [40; 153], which requires one to solve an optimization problem for satisfying the amplitude constraint.
## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Damage Identification
#### 3.2.1 Overview
These methods which rely on structural properties formed the basis of vibration-based damage detection and have acquired extensive applications in past decades. Time-frequency features can describe local details of measurements both in the time and frequency domain, and thus are able to detect damage-induced variations in a timely manner, even with nonstationary signals. This category of features is commonly extracted by short-time Fourier transform, Hilbert-Huangtransform, and wavelet transform, among others. Time-series approaches fit the structural response with autoregressive (AR) models or their variants such as autoregressive with exogenous input (ARX) using the model coefficients as features to detect damage. Afterwards, the implementation of comprehensive machine learning methodologies, which include not only the extraction of features but also their normalization, fusion, and classification, is discussed in Section 3.2.3.
## 4 Experiments and Results
### Comparative Analysis of Underwater Environments and Terrestrial Indoor Environments  
By comparing the results, we can analyze the differences in signal degradation when a signal propagates in terrestrial indoor environments and when a signal propagates in underwater  
Figure 4: Estimating the path loss exponent underwater environment.  
Figure 5: RSS at different distances in underwater environment.  
environments. From the results, the path loss exponent for indoor environments is 1.8, and the path loss exponent calculated for underwater environments is 3.4. This shows that when the signal propagates in an indoor environment, it degrades with the order of 1.8 and when the signal propagates underwater, the signal degrades with the order of 3.4. Underwater signal degradation is far greater than terrestrial degradation due to the channel characteristics.  
Figure 6 shows the overall path loss in underwater environments. The path loss calculated is 26.5 dB from 0.1 m to 0.5 m. Thus, when EM signal propagate underwater, specifically in freshwater, they undergo a signal loss of around 26.5 dBs with a path loss exponent of 3.4.
###### Abstract  
LoRaWAN networks might be a technology that could facilitate extreme energy-efficient operation while offering great capacity for suburban and rural area deployment, but this can be a challenging task for a network administrator. Constraints that deform the trade-off triangle of coverage, scalability and energy efficiency need to be overcome. The scope of this study is to review the limitations of the LoRaWAN protocol in order to summarize and assess the crucial factors that affect communication performance, related to data rate allocation, bidirectional traffic and radio spectrum utilization. Based on the literature, these factors correspond mostly to configurable payload transmission parameters, including transmission interval, data rate allocation, requirement for acknowledgements and retransmission. In this work, with simulation experiments, we find that collision occurrences greatly affect channel occupancy. In particular, it was evaluated that collision occurrence is increasingly affected by transmission intervals, which have the most significant negative impact on packet delivery rate (PDR). We then validated that clustering of end nodes in the vicinity of a gateway, taking into account distance and transmission settings, can improve network scalability. This can assure distribution of the total transmission time to end nodes with respect to application-related QoS requirements. Following this clustering approach, we achieved a PDR greater than 0.90 in a simulation setting with 6000 end nodes in a 10 km coverage.  
LoRaWAN; LPWAN; network planning; network administration; performance; scalability; clustering
## 5 Simulation and Numerical Analysis  
The aim of numerical and simulation experiments is to determine the limits of applicability of the obtained Gaussian approximation of the distribution function.  
Let us consider as an input an MMPP with the following parameters:  
\[\mathbf{\Lambda}=\begin{bmatrix}0.5&0&0\\ 0&1&0\\ 0&0&1.5\end{bmatrix},\ \ \ \mathbf{\mathrm{Q}}=\begin{bmatrix}-0.8&0.4&0.4\\ 0.3&-0.6&0.3\\ 0.4&0.4&-0.8\end{bmatrix}.\]  
For the renewal arrival process, the intervals between the arrivals have a uniform distribution over the interval \([0.5,1.5]\).  
The type of an incoming customer is defined as \(i\)-type with probabilities of \(p_{1}=0.5\), \(p_{2}=0.3\), \(p_{3}=0.2\). Table 1 shows the distribution laws of the random variables which characterize the amount of occupied resources and the time required to service the customers.  
By means of a simulation, we obtained the marginal empirical probability distribution functions for the total numbers of resources occupied by each type of customer. Our goal was to compare the empirical and asymptotic distribution laws. To this end, we used the Kolmogorov distance: \(\Delta_{i}=\sup_{x}\lvert F_{em}(x)-F_{as}(x)\rvert\), \(i=1,\ldots,3\), where the subscript \"\(em\)\" denotes the empirical distribution function built on the simulation results and \"\(as\)\" denotes the asymptotic distribution function based on the theorem.  
Tables 2 and 3 show values of the Kolmogorov distances for the total volumes of the occupied resources of each type. The accuracy of the approximation increases with an increase in the mean of the system load, so, even with \(N=30\) for MMPP and \(N=20\) for renewal arrival process, the value of \(\Delta_{i}\), \(i=1,2,3\), does not exceed \(0.05\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of System** & **Volume** & **Service Time** \\ \hline First & Geometric \((0.2)\) & Gamma \((0.5,0.5)\) \\ Second & Poisson \((3)\) & Exponential \((1)\) \\ Third & Binomial \((0.5,12)\) & Uniform \((0.1,0.9)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The laws of parameter distributions for systems of various types.  
We can use the results presented in Section 4 in the case of a system with limited resources. For example, for the task of choosing the optimal values of resources provided in each channel. These limit values should provide a given probability of losing requests due to a lack of resources to service them. Denote by \(V_{k}^{opt}\) the optimal volume of the \(k\)-th channel. The estimation of these values is based on the Gaussian distribution (Theorem 1) and the method described in [40] and has the form:  
\[V_{k}^{opt}=a_{k}+r\sqrt{K_{\text{k}k}}. \tag{47}\]  
Here \(r\) is the so-called hyper-ellipsoid radius, which depends on \(p_{loss}\) and which can be evaluated using the method described in [40].
## 3 Materials and Methods
### Evaluation Metrics
#### 3.5.1 Mean Absolute Error (MAE)  
MAE measures the average magnitude of absolute errors between actual and predicted values. Lower MAE values indicate better accuracy. It is widely used in forecasting tasks because it provides an intuitive measure of prediction error in the same unit as the data.  
Mean Absolute Error, is given by [37]:  
\[MAE=\frac{1}{n}\,\sum_{i=1}^{n}\lvert F_{i}-A_{i}\rvert\]  
where n is the number of observations, F\({}_{i}\) is the forecasted value for observation i, and A\({}_{i}\) is the actual value for observation i.
## 4 Empirical Modeling  
Variations of the thrust force and moment due to the rotational motion of the azimuth thruster were modeled based on the steady-state thrust force. Longitudinal thrust force is generated by a thruster, as shown in Figure 7a. The steady-state thrust force is proportional to the duty ratio of the PWM from the motor drive, as shown in Figure 7b. The empirical model expression of the steady-state thrust force for PWM is as follows:  
\[f_{\rm ss}=0.55\rm PWM-7.10. \tag{4}\]  
The steps of empirical modeling are as follows: (1) Repeat the rotational motion experiment, increasing the PWM by 5% from 55% to 95%. (2) Measure the force and moment by entering the velocity profile as shown in Figure 6b on the 1 st and 2 nd axes, respectively. (3) Calculate the force of the moving frame reference according to the angle of each axis using the adjoint matrix. (4) Draw the Bode plot of the pseudo-impulse response through the signal compression. (5) Derive the model through the curve fitting with the numerical method to maximize the correlation number.  
As a result of the repeated experiments, waveforms for \(x\)-direction attenuation and \(y\)- and \(z\)-direction generation of propulsion were similar, and the reaction moments showed different forms of waveforms. The models can be derived by the analysis of the Bode plot with the corner frequency and the slope of asymptotes [25], as shown in Figure 8. As the  
Figure 7: The longitudinal thrust force according to pulse-width modulation (PWM). (**a**) Thrust force of a fixed thruster in time domain for PWM; (**b**) steady-state thrust force with PWM variation.  
phase plot is noisy, we focused on the magnitude plot. The model expressions of the force and moment are defined as follows:  
\[\frac{F(s)}{\dot{\theta}(s)}=\frac{K\big{(}s^{2}+as+b\big{)}}{(s^{2}+cs+d)(s^{2}+ es+g)} \tag{5}\]  
\[\frac{M(s)}{\dot{\theta}(s)}=\frac{Ks^{2}}{s^{4}+as^{3}+bs^{2}+cs+d} \tag{6}\]  
The force and moment models have the same system order but slightly different equations. The coefficients of the model expression were obtained through numerical optimization that maximizes the correlation coefficients. The results of experiments with significantly different tendencies through repeated experiments were eliminated. The objective function of modeling is defined as the maximum correlation coefficient with repeated experimental results, and uncertainty in the data is handled.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 2 System Model and Routing Evaluation Parameter  
### System Model and Relevant Definitions  
The wireless sensor network considered in this paper contains \(N\) isomorphic sensor nodes with the same initial state. Each node except the sink can receive and forward a packet and generate packets at a specific rate \(r\). The sink node is responsible for aggregating and processing the data. Each node's maximum battery capacity is \(E_{o}\), and the maximum communication radius is \(R\). The sensor node will be dead if its battery energy is lower than 0. The initial load value of all nodes is 0, and their cache queue is a fixed value, so packets need to be processed in a first-in-first-out manner. In a square \(S\), a sensor node is randomly deployed. The center of this square is the sink node. The entire sensor network forms an undirected graph \(G(P,E)\). \(P\) denotes the set of all nodes in the WSNs. \(E\) denotes the set of edges. An edge \(e_{ij}\in E\) implies that the data from node \(i\) can be successfully received by node \(j\); that is, node \(j\) is located in the wireless signal coverage of node \(i\).  
The WSN involved in this article is primarily deployed in the data acquisition area. Senor nodes percept the surrounding environment or monitor particular objects. These nodes periodically produce and transmit data to the sink in wireless radio. To explain the MPFDR algorithm, we make the following definitions:  
**Definition 1**.: _According to the wireless signal converge area, the neighbor set \(NB(i)\) of a node \(i\) is expressed by the following:_  
\[NB(i)=\left\{j\big{|}d_{i,j}\leq R,i,j\in P\right\} \tag{1}\]  
_where \(d_{i,j}\) is the geometrical distance between nodes \(i\) and \(j\)._  
**Definition 2**.: _Considering a packet located at node \(i\), to transmit data against the sink without backhaul, the packet should be sent to a forward neighbor of node \(i\). Based on the model in [35; 36], the forward neighbor set \(FN(i)\) of node \(i\) can be expressed as follows:_  
\[FN(i)=\left\{j\big{|}j\in NB(i),d_{j,sink}\leq d_{i,sink}\right\} \tag{2}\]  
_where \(d_{i,sink}\) and \(d_{j,sink}\) represent the distances from nodes \(i\) or \(j\) to sink. Here, the forward neighbor is one of the next hop candidates, which will be used hereafter._  
**Definition 3**.: \(\Theta\)_sinki is a circular region, and its center is the sink. It is radius is \(d_{i,sink}\). \(\Theta i\_R\) is another circular region with center node \(i\) and radius \(R\). Therefore, the forward neighbor area \(FNA(i)\) of the sensor node \(i\) is as follows:_  
\[FNA(i)=\Theta\emph{sinki}\cap\ \Theta i\_R \tag{3}\]
## 4 Results and Discussion
### Results of Learning
This result suggests that the optimal parameters also depend on the shape of the reconstruction target. Figure 10: _Cont._  
Figure 11 shows the contour maps of the evaluation function values of the reconstructed image \(z(K)\) for changes in the parameters \(\gamma\) and \(\alpha\) according to the projection \(y^{\ell}\). On all the test data, the optimizer \(\Lambda\) could estimate parameters that reduce the evaluation function value more than the MLEM algorithm. In the case of \(\ell=1\) and \(\ell=3\), where significant improvements were observed, the region of suitable parameters is located far from \((\gamma,\alpha)=(1,1)\) corresponding to the parameter for MLEM, and the optimizer \(\Lambda\) succeeds in estimating the values around it. On the other hand, for \(\ell=2,4\), and \(5\), where the differences in the evaluation functions are slight, the optimal parameters are relatively close to \((1,1)\). In other words, these cases are problem settings in which the MLEM algorithm can achieve high performance. Even in such cases, our optimizer can estimate the parameters better than it can. The previous study suggested the possibility of a rough parameter estimation by using the noise level. Indeed, the results for \(\ell=1,4\), and \(5\) show similar trends in the contour plots for the same noise level, and the results for \(\ell=1,2\) and \(3\) show a significant change in trend with the noise level. However, the optimal parameter values and contour maps depend not only on the noise level but also on the shape of the reconstruction target. Figure 10: Evaluation function values of \(z(k)\) with projection \(y^{\ell}\) for any \(k\). Blue and red points are for parameters \(\lambda=\hat{\lambda}^{\ell}\) and \((1,1)\), respectively.