esa-sceva/satcom-chunk-collection huggingface dataset:
# iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
### Research Contributions  
In this work, a new deepfake detection model (iCaps-Dfake) is introduced to support the fight against this destructive phenomenon. The contribution of this work could be stated as follows:  
* A capsule network (CapsNet) is integrated with an enhanced concurrent routing technique for classification providing superior capabilities in feature abstraction and representation with no need for large amounts of data and an average number of parameters.
* Two different feature extraction concepts are combined, one is texture-based using Local Binary Patterns (LBP) pointing out the differences between textures of real and forged part of the image. The second is a convolutional neural network (CNN) based method with an introduced modification to the High-Resolution Network (HRNet) which previously achieved high results in many applications such as pose estimation, semantics segmentation, object detection and image classification. Our modification of the HRNet managed to output informative representation of features with strong semantics that preserves the CapsNet concept of not losing any spatial information.
* For face detection You Only Look Once (YOLO v3) is utilized, resulting in very few false positives when compared to other approaches thus enhancing the data quality.  
Data preprocessing is performed to minimize the noise in faces loaded to the HRNet for further data quality enhancement.  
The rest of this paper is organized as follows. A literature review of deepfake detection methods is presented in section two. A detailed explanation of the proposed model is explained in Section 3. Section 4 demonstrates the experimental results and finally Section 5 presents a discussion of the proposed work.
# Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
## 2 Materials and Methods
### Side-Fire Fiber Fabrication  
The optical fiber's buffer coating was stripped using a fiber stripping tool, keeping the cladding intact, and the fiber was secured on a glass slide. To address the challenge of focusing the ablation laser on the fiber's curved surface, one side was flattened using a dicing saw (DAD3240, DISCO Corporation, Tokyo, Japan). A 200 um thick, 500-grit diamond blade was used to make 150-um-deep cuts perpendicular to the fiber axis, spaced 100 um apart, creating overlapping passes that flattened the surface by removing the cladding and exposing the core (Figure 1a,b). Additionally, a 45\({}^{\circ}\) level was made at the distal end with the same blade to reflect light back into the fiber core and to reduce forward emissions.  
The fiber was then secured on a glass slide and mounted on a four-axis servomotors-operated stage. The diced surface was patterned using an ArF excimer laser (wavelength = 193 nm; IX-255, IPCG Photonics, Oxford, MA, USA), emitting 10 ns pulses at a 100 Hz repetition rate. We focused a 90 um square laser beam spot onto the fiber, setting the ablation energy at 6.42 J/cm\({}^{2}\). The laser focus was set to the mean depth of the ablated surface, which was found by adjusting the focus of the inspection camera of the laser micromachining system at three positions along the window. As the four-axis stage moved the fiber along the \(x\)-axis in 3 um steps, each pulse overlapping the previous one, continuous ablation lines were created along the fiber's length. We patterned seven parallel lines across the fiber's width (\(y\)-axis) to achieve a 0.8 mm window width (Figure 1c). This process was repeated twice to ablate deeper into the fiber (Figure 1d). For each successive ablation, the width of the cut (along the \(y\)-axis) was reduced by 180 um, giving the ablated region a u-shape as shown in Figure 1e.  
After laser ablation, we cleaned the fibers with isopropyl alcohol, masked the window area with masking tape, and wrapped the proximal end of the fiber in aluminum foil,leaving only the distal end exposed for silver coating. A 3000 A thick layer of silver was deposited using a magnetron sputtering system (Orion3, AJA International, Scituate, MA, USA). Fibers, between 12 cm and 19 cm long to fit within the sputtering system, were placed horizontally on the carrier plate, and sputtering was performed twice to coat the entire fiber circumference. After sputtering, the tape covering the window was removed, and the distal end of the fiber, including the ablated window, was encapsulated in a clear fused quartz tubing (Technical Glass Products Inc., Painesville, OH, USA) with 2.2 mm (inner diameter) \(\times\) 3 mm (outer diameter) dimensions. The final outer diameter of the enclosed fiber was 3 mm.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
###### Abstract  
Electroencephalogram (EEG) analysis is essential for diagnosing neurological disorders but typically requires expert interpretation and significant time. Purpose: This study aims to automate the classification of normal and abnormal EEG recordings to support clinical diagnosis and reduce manual workload. Automating the initial screening of EEGs can help clinicians quickly identify potential neurological abnormalities, enabling timely intervention and guiding further diagnostic and treatment strategies. Methodology: We utilized the Temple University Hospital EEG dataset to develop a DenseNet-based deep learning model. To enable a fair comparison of different EEG representations, we used three input types: signal images, spectrograms, and scalograms. To reduce dimensionality and simplify computation, we focused on two channels: T5 and O1. For interpretability, we applied Local Interpretable Model-agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the EEG regions influencing the model's predictions. Key Findings: Among the input types, spectrogram-based representations achieved the highest classification accuracy, indicating that time-frequency features are especially effective for this task. The model demonstrated strong performance overall, and the integration of LIME and Grad-CAM provided transparent explanations of its decisions, enhancing interpretability. This approach offers a practical and interpretable solution for automated EEG screening, contributing to more efficient clinical workflows and better understanding of complex neurological conditions.  
EEG; spectrogram; DenseNet121; explainable; automated classification +
Footnote †: journal: _Algorithms_
## 4 Features Selection and Analysis
### Features Representation of Physiological and Physical Structure Differences  
After extracting an extensive set of 40 features, a preliminary study was carried out to determine whether certain features, such as Respiratory Rate (RR) and Length (L) (features 15 and 1, respectively), contain information that can distinguish between different populations. The literature suggests that differences in respiration between genders can be particularly pronounced in RR and L values. To investigate this, scatter plots were created (shown in Figure 8) for data visualization, and a basic statistical analysis was conducted, the results of which are presented in Table 3. According to this table, men have, on average, a lower RR than women. Conversely, men tend to have a higher L value than women.  
Figure 7: Normalized PSD curve of respiratory signals for a subject.  
The RR and L features also exhibit distinct distributions in relation to other physical characteristics, such as BMI and CWP. These variations are illustrated in Figure 7(a),b for BMI, and Figure 7(c),d for CWP. It can be inferred that individuals with higher BMI values tend to have lower L values, as shown in Figure 7(a), and also the lowest RR values, as depicted in Figure 7(b). Upon analyzing Figure 7(d), it is evident that individuals with a smaller CWP have a higher RR. Conversely, the highest L values are associated with individuals having a medium CWP, as demonstrated in Figure 7(c). When correlating L with RR, a pattern emerges where higher L values correspond to lower RR values, which is corroborated by the data presented in Figure 7(e).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Gender**} & **RR Mean \(\pm\) STD** & **RR Median** & \multirow{2}{*}{**L Mean \(\pm\) STD**} & **L Median** \\  & **[BrPM]** & **[BrPm]** & & \\ \hline Male & 14.6252 \(\pm\) 4.0037 & 14.7602 & 0.0064 \(\pm\) 0.0034 & 0.0056 \\ Female & 16.5741 \(\pm\) 3.8281 & 16.6840 & 0.0040 \(\pm\) 0.0023 & 0.0034 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical study of the RR and L representation according to gender.  
Figure 8: Overview of the statistical results.
# Privacy Protection Based on Special Identifiers of Intersection Base Computing Technology
## 3 Protocol Process and Proof of Security
### Correctness of the Protocol  
According to the protocol, for each element \(x_{i}\) of Alice, the result of encryption with the Paillier system shown in Section 2.2 is:  
\[A_{i}=E(-x_{i})=g^{-x_{i}}r_{i}^{N}\text{mod}N^{2}\]  
[PERSON] then sends the encrypted data, along with the corresponding data bits \(\lambda_{i}\), to [PERSON]. [PERSON] encrypts \(y_{j}\) using the public key given by [PERSON] (as shown in the encryption process in Section 2.2 above) and obtains  
\[B_{j}=E(y_{j})=g^{y_{j}}r_{j}^{N}\text{mod}N^{2}\]  
If \(\lambda_{i}\
eq\pi_{j}\), that means that the number of bits in the same encoding is different: it means that the two data must not be the same and must not be common elements of the two sets.  
If \(\lambda_{i}=\pi_{j}\), the two elements are likely to be the same, so then \(A_{i}\) and \(B_{j}\) are added homomorphically (as shown in Section 2.2), i.e.,  
\[v_{i}{}^{\prime}=A_{i}\times B_{j}=E(-x_{i})\times E(y_{j})=E(-x_{i}+y_{j})\]  
[PERSON] randomly picks a random number \(r_{s}\in N^{*}\) for calculation:  
\[v_{t}=(v_{t}{}^{\prime})^{r_{s}}=g^{r_{s}\times(-x_{i}+y_{j})}(r_{i}r_{j})^{r_ {s}N}\text{mod}N^{2}\]  
And [PERSON] sends V to [PERSON], who decrypts it and obtains \(E(x_{i}{}^{\prime})=g^{x_{i}^{\prime}}r_{i}^{N}\text{mod}N^{2}\)  
\[D(v_{t}{}^{\prime})=\frac{L((v_{t}{}^{\prime})\text{mod}N^{2})}{L(g^{\lambda} \text{mod}N^{2})}\text{mod}N=r_{s}(-x_{i}+y_{j})\]  
If \(x_{i}\) and \(y_{j}\) are equal, then \(D(v_{i}{}^{\prime})=0\).  
If \(x_{i}\) and \(y_{j}\) are not equal, then \(D(v_{t}{}^{\prime})\
eq 0\).  
Therefore, [PERSON] only needs to calculate the value equal to zero in V, that is, the number of the same elements on both sides, and [PERSON] obtains the base number of the intersection of the two sides, so the agreement is correct.
## 6 Simulation Results
### Sensitivity Tests
#### 6.2.1 Varying the Proportions of PIRs  
Figure 4 shows the sensitivity of the market penetration (at each step) in varying the proportions of PIRs from 30% to 50% to 70% within 180 steps. In general, the change in market penetration in each step increases with the decrease in the proportion of PIRs, and we observe that the increase in the proportion of PIRs results in a decrease in final market penetration (from 43.17% to 36.17% to 25.18%). This suggests that higher proportions of PIRs will cause more obstacles to platform adoption.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 6 Conclusions  
An identification algorithm based on dynamic distance ratios is proposed, transforming previous distance features into ratio-based features to enhance robustness against image distortion and focal length variations. This approach demonstrates strong resilience, especially in the presence of positional noise and false stars. Some pattern matching methods use the nearest neighbor star as a starting point; however, incorrect selection of the starting star can lead to identification failures in cases of significant star extraction errors. To address this issue, the algorithm employs the angles between neighbor stars and the distance ratios between neighbor stars and the observed star as dynamic angular features. These features serve as the starting point for subsequent matching, calculating the matching degree with each navigation star through cumulative angle, and distance verification methods, combined with dynamic ratio calculations. Ultimately, the star with the highest matching degree is identified as the correct navigation star. The algorithm exhibits greater adaptability to distortion, particularly in response to variations in focal length, thereby enhancing its overall robustness.  
**Author Contributions:** Conceptualization, Y.D., R.Z. and S.W.; Investigation, Y.D., C.S., R.Z., S.S. and Y.X.; Methodology, Y.D., C.S., H.Z., R.Z. and S.W.; Project administration, Y.D., C.S., R.Z. and S.W.; Supervision, Y.D., C.S., L.B. and S.W.; Writing--review and editing, Y.D., C.S., L.B. and W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** We gratefully acknowledge the support of Shanghai Jiao Tong University, Shanghai Satellite Network Research Institute Co., Ltd., Shanghai Key Laboratory of Satellite Network and State Key Laboratory of Satellite Network. We also thank the engineers who helped us set up the experimental equipment.  
**Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company Shanghai Satellite Network Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 14: Ideal star spot position. The numbers of (**a**) is [star id, magnitude].
# Role of Ion-Exchange Resins in Hydrogen Reactions
## 3 Ion-Exchange Resins Application to Hydrogenation Reactions
### Hydrogenation of Carbonyl Compounds
Examples of C=O hydrogenation reactions and relevant data are shown in Scheme 4 and Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Resin** & & & & & & & & & \\ \hline
**Type** & **Polymer** & **Functional Group** & **Model** & **Reduction Protocol** & **Tested Reaction** & **Reaction Conditions** & **Conservation (Cs)** & **Scalicity** (Cs)** & **Ref. ** \\ \hline \multirow{2}{*}{Gel-type} & \multirow{2}{*}{DMA, MMA, SEMA, SEMA} & \multirow{2}{*}{-50 GH} & \multirow{2}{*}{Pd (n.t.5)} & \multirow{2}{*}{NNHH\({}_{\text{L}}\)} & \multirow{2}{*}{T \(-20\)C, P =  
Interestingly, Pd/IER catalysts have been mostly used in the revised literature to hydrogenate C=O bonds, despite Pd being more often related to C=C hydrogations [86]. On the contrary, even though Ru-based catalysts are typically considered among the most favored ones for the hydrogenation of C=O bonds [93], scarce references report on the use of Ru/IER for this type of reaction. To date, the only references available correspond to works conducted by [PERSON] and coworkers (2014, 2016), which will be further addressed in Section 3.7, since they are focused on multistep processes involving sequential C=O hydrogenation, followed by dehydration or lactonian reactions to obtain biomass-derived compounds [94; 95]. Consequently, further investigation into the potential use of Ru-doped IER for C=O hydrogations is advised.
## 3 Electron-Atom Bremsstrahlung and Coulomb Focusing
The Coulomb correction in the Hamiltonian transition matrix element can be well approximated by the ratio of the absolute square of the final Coulomb \(s\)-wave function [22]\(\big{|}\,\Psi_{f}(0)\,\big{|}^{2}\big{[}=\,\pi\eta_{f}e^{\pi\eta_{f}}/\sinh( \pi\eta_{f})\big{]}\) to the initial Coulomb \(s\)-wave wave function \(\big{|}\,\Psi_{i}(0)\,\big{|}^{2}\big{[}=\,\pi\eta_{i}e^{\pi\eta_{i}}/\sinh( \pi\eta_{i})\big{]}\) at the origin, since \(l\,=\,0\) survives for \(r\,\rightarrow\,0\) and the mutual Coulomb interaction between the electron and the target ion is quite effective for the small separation due to the Coulomb focusing effect, where \(\eta_{f}\,=\,Ze^{2}/\hbar v_{f}\) and \(\eta_{i}\,=\,Ze^{2}/\hbar v_{0}\). Therefore, the square of the Coulomb wave function at the origin, \(r\,=\,0\), when the incident amplitude of the wave normalized to unity at infinity, \(r\,\rightarrow\,\infty\), is given by \(\big{|}\,\Psi(0)\,\big{|}^{2}\big{[}=\,\pi\eta e^{\pi\eta_{i}}/\sinh(\pi\eta) \big{]}\). It is quite obvious that the maincontribution to emission due to the bremsstrahlung process comes from the wave near the center of the scattering system since the acceleration of the projectile electron is largest near the scattering center. Hence, the Coulomb correction known as the Elwert-Sommerfeld Coulomb focusing factor [3; 14; 23] is represented by:  
\[f_{CF}(\eta_{i},\,\eta_{f})\,=\,\frac{\left|\,\Psi_{f}(0)\,\right|^{2}}{\left|\, \Psi_{i}(0)\,\right|^{2}}\,=\,\frac{\eta_{f}}{\eta_{i}}\frac{1\,-\,\exp(-2\pi \eta_{i})}{1\,-\,\exp(-2\pi\eta_{f})}\,, \tag{16}\]  
and \(f_{CF}(\eta_{i},\,\eta_{f})\to 1\) in the Born limit, i.e., \(\eta_{i}>>1\) and \(\eta_{f}>>1\). It has been also shown that the Elwert-Sommerfeld factor can correctly modified the electron-impact excitation cross section near the threshold domain [23]. As we see in Equation (15), the Coulomb focusing factor diverges at the spectral cutoff. However, this divergence compensates for the vanishing of the nonrelativistic Bethe-Heitler cross section at the cutoff, correctly resulting in a finite Bethe-Heitler cross section at the cutoff spectrum. The detailed discussion of the Coulomb correction using the Elwert-Sommerfeld Coulomb focusing factor is given in a recent work of [PERSON] [14]. In the nonrelativistic electron-atom bremsstrahlung process, the charge number \(Z\) can be replaced by the effective charge number \(Z_{eff}\) [Equation (10)], including the influence of screening by bound electrons.
###### Abstract
However, if the cost function of MLE algorithm has high dimensionality and nonlinearity, this will lead to low computational efficiency and no closed form of solution. A gradient method, such as steepest descent method, Levenberg-Marquardt method, etc., can be used to minimize the cost function through an iterative process. In [21], a maximum likelihood estimation method based on the Levenberg-Marquardt algorithm and message tree transmission idea was proposed. This algorithm not only has high positioning accuracy but can also allow convergence to the same solution as the centralized algorithm. A maximum likelihood estimation method for cases where the parameters are matrices and vectors are not measurable was proposed in [22]. Compared with the multivariable recursive extended least squares algorithm, the multivariable maximum likelihood iterative least squares algorithm has higher estimation accuracy and lower computational complexity. The maximum likelihood method is very important for parameter estimation and system modeling. In [23], the maximum likelihood principle was combined with data filtering technology to estimate the parameters of a class of bilinear systems, with the proposal of a maximum likelihood iterative least squares algorithm based on filtering, which was compared with an iterative algorithm based on least squares. The simulation results showed that the proposed algorithm had higher accuracy and efficiency under different noise variances. The content in this paper is arranged as follows: Section 2 gives the operation mechanism of the proposed VFLL-assisted PLL carrier tracking loop and uses the jerk model to model this loop under high dynamics. The state equation and observation equation of the loop are also provided. Section 3 represents the implementation process of the maximum likelihood estimation method and its corresponding optimization algorithm, the Levenberg-Marquardt algorithm, and the specific operation process of the VFAPLL based on the MLE-LM algorithm. The intermediate frequency data are repeatedly correlated until maximum likelihood convergence is achieved. Section 4 employs a software receiver in MATLAB to verify the proposed loop. Section 5 summarizes the paper.
# Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis
## 3 Data Processing
### SSI/TLS Encryption Protocol and Communication Process
#### 3.1.1 Establish Communication  
The client sends a ClientHello Message to the server. After receiving the request, the server sends a ServerHello Message. Both the ClientHello Message and ServerHello Message contain the information necessary to establish communication.
## 2 Background
### Operational Power System Use Cases  
IFP capability is assessed through three use cases: (i) CVT failure, (ii) loose connection, and (iii) DFR sampling error. All results are based on the utility-provided OSG files generated from deployed DFRs monitoring a real-world power transmission system. For each of the three cases, line recordings are provided for a total of twenty-four hours preceding the last reported event at a sampling rate of 960 Hz and a precision of 16-bits (a.k.a. half-precision) For all three cases, only voltage lines are used for analysis.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
### Key Contributions  
The focus of this paper is the development of an automatic image informatics approach for the segmentation of the preretinal space in both healthy and pathological cases. The major contributions of this study are:  
* Evaluation of state-of-the-art neural network methods employed for the new task of preretinal space segmentation to the best of our knowledge has not been previously attempted. The conducted experiments demonstrate that the deep learning approach is better suited for segmenting preretinal space than a standard graph-search method.
* Analysis of the influence of kernel shape and size on convolutional network performance for OCT images. With the experiments described in Section 6, we show that  
Figure 1: Examples of OCT retina images with posterior vitreous detachment in VMA and VMT. (**a**) Vitreomacular adhesion (VMA). (**b**) Vitreomacular traction (VMT).  
by changing the shape and size of a convolutional kernel, it is possible to overcome topology errors in pixel-wise retina layer segmentation.
* Analysis of relative distance map calculation to improve retina layers topology preservation using various network architectures. We propose two methods of obtaining a distance map for any given image. They do not require any given knowledge about the retina layers (as is the case in the compared methods), are less computationally expensive, and give similar (or in some cases better) results.
* Collection of a unique dataset of 3D OCT images from 50 VMA and VMT subjects (7050 2D B-scans) with manual segmentation data of preretinal and retinal volumes [16]. The gathered data was statistically analyzed with respect to image quality and features distribution.  
The rest of the article is organized as follows. First, the recent state-of-the-art approaches for retina layers segmentation as well as the methods selected for preretinal space segmentation are described in Section 2. Section 3 contains the characterization of the data used in the study. Section 4 presents the methods implemented in this study, while Section 5 describes the experiment setup. The results of the designed experiments are presented in Section 6. Finally, Section 7 summarizes the conducted research, discusses its advantages and limitation, and provides insights into problems worth investigating in future studies.