esa-sceva/satcom-chunk-collection huggingface dataset:
# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
## 4 Experimental Results
### Evaluation Metrics  
The prediction output of the system for an input image is either real or tampered, so it is a binary classification problem. The performance of the system is usually represented in a confusion matrix, which is given in Table 2.  
After the confusion matrix was constructed, accuracy, precision, and recall are computed to investigate the proposed model performance. Accuracy refers to the proportion of correctly classified samples (TP and TN) among the total samples in the test dataset. Accuracy cannot provide a thorough evaluation of the model. Therefore, precision and recall are two widely used additional measurements.  
\[\text{Accuracy}=\ \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{3}\]  
Figure 6: Illustration of data-based approaches, which include under-sampling, over-sampling, and hybrid methods.  
\[\text{Precision}=\ \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{Recall}=\ \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
where TP, FN, FP, and TN are the corresponding true positive, true negative, false positive, and false negative values for one class against the other class, which were taken from the confusion matrix.  
Based on the acquired values from the confusion matrix, true-positive rate (TPR) and false-positive rate (FPR) measurements are computed. The TPR is the same as recall, whereas FPR is shown in the following equation:  
\[\text{FPR}=\ \frac{\text{FP}}{\text{TN}+\text{FP}} \tag{6}\]  
A receiver operating characteristic (ROC) curve [26] is illustrated with TPR against the FPR for separate cut-off points. Moreover, in the multi-class classification, every false prediction is an FP for a class, and every single negative is an FN for a class. Each point on the curve depicts a sensitivity/specificity set that correlates with a particular decision threshold. The area under the ROC curve, or AUC [26], is usually applied to estimate the performance of the proposed classification model. If a ROC curve for class 1 (C1) has a higher AUC value than class 2 (C2), then the proposed classifier C1 is considered to achieve a better performance than C2.
## 3 Nuclear Analyses
### Summary of the WCLL vs. HCPB Comparison
The radiation damage values and related cross-section have been assessed according to the NRT method [55; 56]. In this regard, the displacement cross-sections [54; 55; 57; 58; 59] versus incident neutrons energy for Iron, Copper and Tungsten appear to have two separate regions: lower energies region (\(10^{-9}\)-\(10^{-3}\) MeV) where the cross-section generally increases with decreasing energy and higher energies range (\(10^{-2}\)-\(100\) MeV) where cross-section generally increases (or it shows resonance peaks) with the increasing of incident neutron energy. Although, a more in-depth analysis would be necessary, in the first instance, again in this case the water moderation of the impinging spectrum on the targets, would seem to have an impact on the low energy-displacement cross sections region, resulting in higher fluxes of moderated neutrons, so leading to greater damage levels with WCLL. The variation in He-production between WCLL and HCPB case appears to be negligible in Copper/CuCrZr and Tungsten, and slightly more noticeable in Eurofer steel used in supports and CB first layers. The main reactions of He production in Copper (\(63 Cu(n,\alpha)60C\), \(65 Cu(n,\alpha)62 Co\)) and Tungsten (\(182W(n,\alpha)179 Hf\), \(183W(n,\alpha)180 Hf\)), which are responsible for most of the He produced in armor, interlayer and heat-sink materials, are threshold reactions [53; 54; 60]. Only for neutron energies above the threshold does a reaction become possible, and the threshold energies are: 2 and 3.5 MeV for the \((n,\alpha)\) reactions in \(63 Cu\) and \(65 Cu\) respectively; around 1 MeV for \((n,\alpha)\) reactions in W. From a close examination of the spectra between HCPB and WCLL, it is apparent that there is a trend similarity in the neutron energies above 1 MeV (see Figure 54), which is precisely the range over which the gas production reactions become more likely, thus entailing as much as similar He-production level in Copper as well as in Tungsten components, both with WCLL and HCPB blanket. The main reactions of He production in Iron, as principal element of Eurofer steel, are: first of all, \(56 Fe(n,\alpha)53 Cr\) and as lower of contribution \(57 Fe(n,\alpha)54 Cr\). The first reaction has a threshold energy of 3.7 MeV, the second one has a cross-section peak at 10-20 MeV, and a second region where cross-section values increase while neutron energies are reduced. In principle, He-production levels in Eurofer PFC-CB supports, show slight differences among WCLL and HCPB, and a higher variation on the first layers of Eurofer in the CB. Although \(56 Fe\) shows a cross-section trend with threshold energy, probably the presence of other elements in Eurofer, which could be affected by the different shape of the neutron spectra (Figure 54) regarding the gas transmutation reactions, it may lead to a more substantial dependence of the He-production in steel on the blanket type, compared to Tungsten and Copper, where this effect is not considerable.
## 2 Basics of Gust Load Alleviation
### Gust Load Definition
#### 2.1.1 Discrete Gusts  
'One-minus-cosine' gust is the typical discrete gust defined by the certification specifications of large commercial aircraft covered by the EASA CS-25 [24]. The gust profile is shown in Equation (1) and according to EASA CS-25, the gust shape can be expressed as  
\[w_{g}(x_{g})=\frac{w_{g0}}{2}\bigg{(}1-\cos\bigg{(}\frac{2\pi x_{g}}{L_{g}} \bigg{)}\bigg{)},\ \ 0\leq x_{g}\leq L_{g} \tag{1}\]  
where, \(w_{g0}\) is the magnitude of the peak gust velocity; \(L_{g}\) is the gust wavelength or twice the 'gust gradient' \(H_{g}\). According to EASA CS-25, the gust wavelength is in the range from 9 to 107 m. In practice, the typical value is 12.5 \(\bar{x}\) (\(\bar{c}\) is the mean aerodynamic chord length).  
Figure 2: The sketch of the spanwise load distributions under cruise and gust encountering conditions (from [30]).  
The design gust velocity \(w_{g0}\) changes with gust wavelength and altitude which is expressed in relations of the gust gradient \(H_{g}\) (in m), the reference gust velocity \(w_{ref}\) and the flight profile alleviation factor \(F_{g}\), as  
\[w_{g0}=w_{ref}F_{g}\left(\frac{H_{g}}{106.17}\right)^{\frac{1}{6}} \tag{2}\]  
where, \(w_{ref}\) decreases linearly from 17.07 m/s equivalent airspeed (EAS) at sea level to 13.41 m/s EAS at 4572 m and then again to 6.36 m/s EAS at 18,288 m. The flight profile alleviation factor \(F_{g}\) is related to the aircraft weight and the maximum operating altitude [33].  
\[F_{g}=\frac{1}{2}\big{[}F_{g2}+F_{gm}\big{]}=\frac{1}{2}\Bigg{[}\left(1-\frac{ Z_{m0}}{76,200}\right)+\sqrt{R_{2}\tan\left(\frac{\pi R_{1}}{4}\right)}\Bigg{]} \tag{3}\]  
\[R_{1}=\frac{W_{MLW}}{W_{MTOW}}\,\ \ \ \ \ \ \ \ \ R_{2}=\frac{W_{MZFW}}{W_{MTOW}} \tag{4}\]  
where, \(Z_{m0}\) is the maximum operating altitude, \(W_{MLW}\) is the maximum landing weight, \(W_{MZFW}\) is the maximum zero-fuel weight and \(W_{MTOW}\) is the maximum take-off weight.  
Assuming an aircraft cruising with the speed \(U_{\infty}\) and encountering a one-minus-cosine gust, the gust penetrating distance is \(x_{g}=U_{\infty}t\), and Equation (1) can be rewritten as  
\[w_{g}\big{(}x_{g}\big{)}=\frac{w_{g0}}{2}\bigg{(}1-\cos\bigg{(}\frac{2\pi U_{ \infty}t}{L_{g}}\bigg{)}\bigg{)}=\frac{w_{g0}}{2}(1-\cos(\omega t)) \tag{5}\]  
An equivalent gust frequency can be obtained as \(\omega=\frac{2\pi U_{\infty}}{L_{g}}\) in radians or \(\omega=\frac{U_{\infty}}{L_{g}}\) in Hz.
# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
###### Abstract  
In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existing literature delineates two primary approaches to image manipulation detection: active and passive. Active techniques intervene preemptively, embedding structures into images to facilitate subsequent authenticity verification, whereas passive methods analyze image content for traces of manipulation. This study presents a novel solution to image manipulation detection by leveraging a multi-stream neural network architecture. Our approach harnesses three convolutional neural networks (CNNs) operating on distinct data streams extracted from the original image. We have developed a solution based on two passive detection methodologies. The system utilizes two separate streams to extract specific data subsets, while a third stream processes the unaltered image. Each net independently processes its respective data stream, capturing diverse facets of the image. The outputs from these nets are then fused through concatenation to ascertain whether the image has undergone manipulation, yielding a comprehensive detection framework surpassing the efficacy of its constituent methods. Our work introduces a unique dataset derived from the fusion of four publicly available datasets, featuring organically manipulated images that closely resemble real-world scenarios. This dataset offers a more authentic representation than other state-of-the-art methods that use algorithmically generated datasets based on image patches. By encompassing genuine manipulation scenarios, our dataset enhances the model's ability to generalize across varied manipulation techniques, thereby improving its performance in real-world settings. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. Moreover, our proposed approach exhibits reduced accuracy variance compared to alternative models, underscoring its stability and robustness across diverse datasets. The approach outlined in this work needs to provide information about the specific location or type of tempering, which limits its practical applications.  
digital image forensics; convolutional neural network; deep learning +
Footnote †: journal:  
Article
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 5 BER Performance  
This section investigates the BER performance of the uncoded and coded GDSS systems and compares them with the corresponding DSSS systems.
## 2 Results
### Electronic Nose Response  
The response signals of ten sensors (ratio of conductance, _G_/_G_\({}_{0}\)) were close to 1.0 in the initial period and then changed continuously until they stabilized after approximately 50 s. The response signals of each sensor at 56 to 58 s were used in the subsequent analyses. Principle component analysis (PCA) was performed, and the cross-validation was used to investigate the difference of the two samples. The PCA results are shown in Figure 5. According to electronic nose analysis, PCA allowed the samples to be easily separated in score plots by combining PC1 (98.63%) with PC2 (1.11%). It was obvious that BT was located on the positive of the PC1 dimension and kept apart from the cluster of WT on the negative side. Considering the sensors used in the electronic nose, the eight sensors, W5S, W3C, W6S, W1S, W1W, W2S, W2W and W3S, were more sensitive to BT on the positive side of the PC1 dimension. In contrast, the sensors of W1C and W5C were more sensitive towards WT on the negative side of PC1. It could be seen that different sensors in the array were sensitive to the diverse volatile compounds released from BT and WT. In general, BT and WT could be distinguished according to the PCA analysis of the electronic nose.  
Figure 5: Principle component analysis (PCA) results of electronic nose analysis: (a) BT and WT samples; (b) ten sensors (W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S) of electronic nose.
## 4 Experimental Results and Discussion
### Practical Experiments and Analysis
#### 4.2.1 INS/GPS Integrated Navigation System
Five filtering methods including standard EKF [33], CKF [40], standard UKF [27], existing adaptive UKF (AUKF) [38], and NAUKF were used in the experiment, and these algorithms were evaluated by the estimation error rather than RMSE, which is computed through  
\[\mathbf{E}=\hat{\mathbf{X}}-\mathbf{X} \tag{50}\]  
The comparisons of the longitude and latitude error curves of five filtering algorithms are shown in Figures 14 and 15, and the corresponding statistical data of the mean absolute error are listed in Table 4. We can see that the EKF and CKF have almost the same filtering accuracy, although both EKF and CKF can deal with the nonlinear filtering problems--they only have first order accuracy. The filtering errors are, respectively, \([-18.1025,19.9471]\) m and \([-17.5118,18.4296]\) m of the longitude error ranging, and \([-20.1143,16.2477]\) m and \([-17.4154,14.9953]\) m of the latitude error ranging. The standard UKF has better filtering precision than does EKF and CKF, because UKF can reach third order accuracy, but it still has substantial oscillation and error due to the longitude error ranging \([-11.0549,10.2178]\) m and latitude error ranging \([-9.1766,9.9737]\) m. The AUKF has the adaptive ability of the imprecise noise in the urban complex environment, so it has better filtering accuracy than the UKF with the longitude error ranging \([-5.8142,6.0273]\) m and latitude error ranging \([-8.1184,6.4153]\) m. Ultimately, NAUKF has the best filtering accuracy, with the longitude error ranging \([-4.1109,4.3804]\) m and latitude error ranging \([-6.1096,4.7138]\) m, since it can not only adapt to the noise but also the sensor faults and model mismatches. In summary, UKF has better filtering precision than KF, EKF, and CKF, because UKF can reach third order accuracy. The proposed NAUKF has the best filtering performance in terms of precision and dynamic response for its adaptive ability to both imprecise noise and sensor faults, and it can increase the precision and adaptability of the navigation system effectively.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 5 Handover and Load Balancing in HWLNets
### Hho and Vho in HWLNets  
HWLNets rely on handover between WiFi and LiFi, thus having a longer processing time when using VHO [64] than with HHO and having problems with superfluous user numbers in the RF spectrum that cause low throughput. This makes decision-making a crucial component in HWLNets. Users in the light spectrum that encounter temporary  
Figure 10: Phases in vertical handover model.  
light blockades do not need to be switched to RF, whereas fast moving users would benefit more from RF than from light spectrum. A possible solution to this problem is a handover method based on fuzzy logic [65], which takes into account user's speed, data rate, and channel state information to make decisions. Blockages are a major impact on any network, especially LiFi, so another method was proposed in [66] that uses statistical data to optimize the handover process but has excessive computational complexity. Using artificial neural networks, we can improve a network throughput by 50% when the parameters for the decision are set on the user's speed and the locations of APs.
## 4 Results and Analysis
### Comparison
#### 4.1.1 Comparison with Malware Detection Methods  
We compared 11 advanced malware classification models, utilizing deep learning to better understand their performance. Please note that the existing works on malware detection methods are not publicly available. Therefore, we implemented all of the existing methods for the comparison. We cross-checked the implementation by reproducing their claimed results on their reported dataset. Later, we trained each model with their recommended hyperparameters and trained them until they converged with the given dataset. After applying our pre-processing and augmentation techniques, we evaluated each model's performance in a benchmark dataset. Table 2 illustrates the comparison between proposed network and state-of-the-art malware classification models. Our proposed method outperformed the existing methods in all evaluation metrics, achieving a 1.18% gain in accuracy, 0.0214 gain in precision, 0.0155 gain in recall, and 0.0101 gain in F1-score. It is worth noting that we did not perform any weight balancing, which was included in previous studies, to enhance the scores. We evaluated all methods similarly to how they would be evaluated in real-world scenarios, and our proposed method still outperformed its counterparts by learning salient features through two distinct feature branches.
# Stand Structure Impacts on Forest Modelling
## 1 Introduction  
Forest ecosystems exist over a long timeframe. Their assessment, monitoring and management requires information both quantitative and qualitative [1, 2, 3]. Stand structure has been used to characterise forest stands, as it provides information regarding its dynamics in time and space [4, 5]. It comprises regime (characterised by the type of regeneration: high forest with the regeneration of seed origin and coppice with vegetative regeneration), structure (depicted by the number of cohorts or age classes: even-aged for stands with one cohort and uneven-aged for stands with two or more cohorts) and composition (refereeing to the number of species: pure for stands with one species and mixed with two or more species) [1, 6, 7]. Forest tree species, density, and tree spatial arrangement (both horizontal and vertical) are reflected in stand structure and its variability. The aforementioned derives in a wide range of interactions between individual trees, which in turn originates a wide suite of single tree allometries [8]. As a consequence, modelling is dependent on stand structure. The tree allometry variability has to be accommodated in the models in order to improve accuracy and precision and to minimise the prediction errors and uncertainties. This has resulted in an extensive set of models.  
The focus of this review is the evaluation of the impact of stand structure on modelling at tree level. This study is organised in 5 sections; factors affected by and affecting stand structure (Section 2), reasons for modelling (Section 3), diameter at breast height and basal area (Section 4), height (Section 5) and volume (Section 6).
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 4 Simulation Analysis
### Stimulation Analysis of the Pest and Disease Diagnosis Model
#### 4.2.1 Data Observation Stage  
By varying \(\lambda_{1}\) from 1 to 20 while keeping all other \(\lambda\) value constants, the relationship between \(\lambda_{1}\) and the steady-state probabilities of each system state is uncovered, as illustrated in Figure 5. As \(\lambda_{1}\) increases--indicating a faster data-processing rate in the data observation stage--the probability of the system remaining in the information filtering state P(M\({}_{1}\)) significantly decreases, while the probability of reaching the treatment effectiveness assessment state P(M\({}_{21}\)) increases most rapidly. This suggests that enhancing the data-filtering efficiency during the early stages of pest and disease spread can substantially improve the system's overall processing performance, thereby accelerating progression toward the final stage of treatment assessment. The observed trend reflects the presence of an effective feedback mechanism: speeding up the information filtering and integration phase directly drives higher efficiency in subsequent evaluation, decision-making, and control processes. Such changes highlight the critical role of the information-processing speed in pest and disease diagnosis. In practical applications, it is essential to optimize the information-processing pipeline to improve the system's overall response speed and effectiveness.  
Figure 5: Steady-state probability distribution of the plant pest and disease diagnosis model under variation in \(\lambda_{1}\).
## 3 The Proposed Scheme
### Multi-Bit Data Hiding Scheme for Compressing Secret Messages  
In data hiding schemes, unambiguousness means that the stego image was securely transmitted to the receiver and extracts the same secret message as embedded by the sender. To support this attribute, we need to employ a lossless compression method to allow us access to the original data. Fortunately, RLE is suitable, since it is simple, quick and lossless. There are three phases included in the proposed scheme. The flowchart of three phases (secret image compression phase, MGEMD phase and embedding phase) are shown in Figure 1.  
Figure 1: Flowchart of the proposed scheme.
## 3 Challenge-Based CIDNs
### Robustness  
A line of research studies (e.g., [5; 6; 7]) have shown that challenge-based trust mechanisms can protect CIDNs against common threats such as a sybil attack, a newcomer (re-entry) attack, and a betrayal attack.  
* _Sybil attack_. This kind of attack describes the situation in which a node tries to create many fake identities [25]. These fake identities can be utilized to gain a larger impact on alarm aggregation in a CIDN node. The challenge mechanism mitigates this attack through requesting each IDS node to register via a trusted CA and obtain a unique proof identity.
* _Neuvcomer (re-entry) attack_. This type of attack indicates the situation in which a node tries to re-enter the network as a newcomer, aiming to erase its bad history. The challenge mechanism avoids this attack by allocating a low reputation level to all new joined nodes.
* _Betrayal attack_. This kind of attack indicates the situation in which a trusted node turns out to be an untruthful node unexpectedly. The challenge mechanism mitigates this attack by employing a strategy: that is, a high reputation level can only be achieved after a long time-period of interaction with consistent good behavior, whereas the reputation can be quickly degraded by detecting only a few bad actions. To realize this strategy, a forgetting factor can be used to give more weight to recent behavioral events.  
Overall, challenge-based CIDNs can encourage collaborations among various IDS nodes, as well as identify common insider attacks. However, it is found that challenge-based CIDNs would suffer from advanced insider attacks, because the adopted assumptions are not realistic in real-world implementations [8; 20; 21].