esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 4 Experimental Evaluation
### Comparative Analysis  
To contextualize our framework's contributions, we conducted a systematic performance evaluation against existing privacy-preserving image-processing approaches using a representative brightness-adjustment task on 64 \(\times\) 64 grayscale images (4096 pixels). We employ CRYSTALS-Kyber-512 for post-quantum key exchange, which adds only \(\sim\)0.5 ms of setup overhead. The reported setup cost of \(285\pm 22\) ms focuses exclusively on the FHE side and includes CKKS parameter initialization (degree = 8192, modulus chain), key-pair generation, Galois (rotation) and relinearization key creation, self-test encryption/decryption, and packaging of public materials. Table 4 summarizes the comparative results, isolating the amortized per-image cost (\(18.6\pm 2.1\) ms) from this one-time FHE initialization.  
As part of classical approaches, traditional TLS-based solutions provide efficient transport but require the server to process plaintext, compromising computational privacy. Even post-quantum TLS variants, which secure transmission against quantum attacks, still expose data to honest-but-curious servers.  
Pixel-wise FHE encrypts each pixel individually, offering strong privacy at a steep computational cost. For example, using SEAL 3.6, a single brightness adjustment on a \(64\times 64\) image entails 4096 separate homomorphic operations--making real-time use impractical.  
Figure 1: Client-side performance vs. image size (SIMD).  
Figure 2: Server-side homomorphic operation performance vs. image size (SIMD).  
SIMD-optimized FHE schemes (e.g., CKKS) pack many values into one ciphertext, drastically reducing the number of operations. However, most implementations rely on classical key exchange, leaving the transport layer vulnerable to future quantum threats.  
Our framework closes these gaps by combining CRYSTALS-Kyber-512 for post-quantum key establishment with SIMD-optimized homomorphic computation. Although the one-time FHE setup cost is relatively high (\(285\pm 22\) ms), the amortized per-image latency (\(18.6\pm 2.1\) ms) supports interactive applications.  
We benchmark brightness adjustment as a linear operation; more complex tasks--such as convolutions or non-linear filters--may exhibit different performance. Note that our timings derive from component-level benchmarks, not full end-to-end deployments. Future work will employ standardized benchmark suites and evaluate advanced image-processing operations beyond element-wise arithmetic.
# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 2 UAV System Model
### UAV-Enabled and -Assisted MEC Architecture
In the third scenario depicted in Figure 4c, the UAV serves as a central relay, assisting mobile users to offload their computing tasks to a specific MEC server. Despite the substantial benefits afforded by the architecture of UAV-enabled MECs to provide low latency and high-reliability services to IoT devices, numerous challenges remain. These challenges are found in intra-UAV communication, UAV security, air data security, data storage, and management. Moreover, the coordination between UAVs and ground-based users requires considerable improvement due to the system's mobility characteristics, which complicate communication. Another significant limitation of UAVs is found in their low onboard battery capacity. In addition to the energy spent in hovering, accelerating/decelerating, and climbing up/down, additional energy is consumed due to onboard computations. Therefore, designing efficient methods for resource and energy management is urgently required. Furthermore, one of the major design problems regarding computation task requirements is the joint trajectory design in UAV-enabled MEC networks. In this regard, research efforts should emphasize how to forecast mobile user movement and follow the trajectory so that computing activities can be offloaded quickly, and computation results may be sent back to users on time. The trajectory design is especially challenging when dealing with multi-UAV-enabled MEC services. Another challenge is found in the integration of the blockchain into UAVs. As standalone technologies, UAVs face many challenges such as privacy issues, air traffic violations, quantum attacks, ML, and algorithmic game-theory-based attacks [13].
# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
## 3 Joint Optimization Algorithm  
In this section, we use an alternating iterative method to solve problem P1. First, we fix the UAV position and introduce slack variables to solve for the optimal transmitting beamforming \(\omega_{k}^{c},\forall k\) and \(\omega_{n}^{r},\forall n\) using the Dinkelbach method. Then, with the transmitting beamforming fixed, we use the SCA algorithm to find the optimal position \(\mathbf{q}\). This process is iteratively repeated until convergence is achieved.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 4 c-Cbba
### Response to Abnormal Situations  
The above description of the algorithm is based on the assumption that when the algorithm starts running, the satellite that is running normally remains normal throughout the entire algorithm running process. However, there may be a situation where a certain satellite malfunctions. This section provides a brief discussion based on this situation.  
1. Fault occurs after the algorithm operation  
After the algorithm runs, each satellite can obtain the task allocation scheme for the entire constellation through its own maintained global highest bid satellite list \(z_{i}\). If a satellite is unable to perform tasks due to a fault at this time, the task set assigned to the faulty satellite will be regarded as a new request and the c-CBBA algorithm will be rerun throughout the entire constellation.
2. Fault occurs during the algorithm operation  
Assuming that a satellite malfunctions during the operation of the algorithm. At this point, for any other satellite within the constellation, if the global highest bid satellite list maintained includes faulty satellites, initialize all task information corresponding to the faulty satellite, and then continue running the algorithm. Specifically, if \(z_{ij}\) refers to the faulty satellite, the following variables need to be updated: \(z_{ij}=\) none, \(y_{ij}=0\), \(h_{ij}=0\), \(u_{ij}=0\). Then, when the algorithm reaches global consistency, the faulty satellite will not be assigned to any tasks.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 4 Results
### Comparison of the UAV Validation and Radar Instrument Measurement Results  
In the actual application of operational radars, the performance index and observation effect of the S-band radar were better than those of the X-band radar system. To objectively evaluate the performance of the X-band radar system and improve its observation effect, this paper refers to the meteorological industry standard of the People's Republic of China, \"S-band Dual-Line Polarimetric Doppler Weather Radar\", which stipulates that the error in radial velocity measurement should not be more than 1 m/s. In addition, the experiment uses the measured radial velocity results from both internal and external instruments as a reference for validation. As shown in Table 8, under single-frequency conditions, the maximum deviation of the external velocity measurement is 0.04 m/s, whereas the maximum deviation of the internal measurements is 0.1 m/s. The optimal absolute accuracy  
Figure 14: The weather radar radial velocity comparison results. (**a**) 7 m/s; (**b**) 10 m/s; (**c**) 8 m/s; (**d**) 10 m/s.  
Figure 15: Target intensity simulation based on the sinc function. The red box represents the sample time and amplitude fluctuations in the high-velocity case, and the green box corresponds to the sample time and amplitude fluctuations in the low-velocity case.  
of the radar radial velocity validation based on the RTK UAV is in close agreement with external instrument measurements, as shown in Table 9. In summary, both the traditional relative and absolute accuracy (the maximum deviation) and the optimal absolute accuracy index can meet the requirements of \(\pm\)1.0 m/s radial velocity measurement, which further verifies the feasibility and effectiveness of the proposed method.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 4 Mechanism of Microbial Decontamination
### Photo-Physical Effect  
The photo-physical effect of PL has also been identified to be a contributory factor for microbial inactivation. The tendency of high energy pulses to disrupt the cell structure was demonstrated by [PERSON] et al. [52]. They showed that _Staphylococcus aureus_ cells suffered cell wall damage, shrinkage of plasma membrane, and leakage of cell organelles. In another study, [PERSON] et al. [53] observed changes in cell shape and cell membrane, and the elusion of intracellular components and proteins in _S. cerevisiae_ due to PL. [PERSON] et al. [54] also observed cytoplasmic damages to _L. innocua_ and _E. coli_ post PL treatment. [PERSON] et al. [55] carried out PL treatment of eggs to study photo-physical changes to _E. coli_ cells and observed structural damage in the form of central depression in cells. These all studies show a contributory effect of photo-physical effect on microbial inactivation exclusive to PL. The structural damage tendency of PL is higher than that of continuous UV [47]. This might be due to application of high energy 'pulses' as compared to a continuous nature of the latter.
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 4 The Proposed HWN Architecture
### Inter-Networked Distribution Substations  
Consider \(N\) distribution substations connected by feeders to a primary substation as depicted in Figure 1b. The large number of these substations mandates the usage of affordable but reliable communication technology. Our study focuses on wireless technologies since they are easy to install with cost-effective equipment, especially in old substations, where extending a wired infrastructure is hard. We assume that all the MCUs in the considered distribution substations can communicate to the MCU in the primary substation in a single-hop fashion as presented in Figure 1b (Only one connection between a distribution substation and the primary substation is shown for easy readability). Each MCU is equipped with a dual-interface wireless transceiver. One interface is a WiFi interface that offers a contention-based access using IEEE 802.11 protocol. The other interface connects to a 3G/4G base station directly. Since distribution substations serve the end user premises, we assume that all the \(N\) distribution substations and the primary substation are covered by a single WiFi network. This mimics a worst case scenario that can happen in densely-populated areas or in case the secondary substation role is similarly performed by a pole-mounted transformer. The assumed scenario also suits the case of the existence of a high capacity backbone network with a WiFi access at the edge. Indeed, this backbone can be represented either by a WiFi mesh network or WiFi-based data concentrators connected through a backbone network of a large capacity. In this case, the secondary substationscompete for sending their data to the nearest mesh router or data concentrator, which forwards their data via the WiFi mesh or the concentrators' backbone network to primary substations. The coverage of the WiFi network overlaps with a 3G/4G cellular network. Each 3G/4G link acts as a dedicated link with no packet contention. The cellular network interfaces for distribution substations are connected to the primary substation via the backbone of the cellular network, which is assumed to be of a high capacity with no limiting congestion.  
The MPTCP protocol is used as the transport layer protocol. A brief overview of the MPTCP protocol is given in the next section.
# ACT-FRCNN: Progress Towards Transformer-Based Object Detection
## 4 Experimental Procedure
### Comparison with the State of Art
#### 4.2.3. Results on NYUDv2 Dataset  
In NYUDv2, we conducted tests on RGB images and evaluated them in comparison to cutting-edge techniques, such as gPb-ucm [10], Silberman [11], gPb+NG, SE [53], SE+NG+CEF, Semi Contour, HED, RCF [46], AMH-Net [57], LPCB [59], BDCN [50], PiDiNet [61], and EDTER [61]. On a single-scale input, all outcomes are based. The numeric outcomes of our approach and those of our rivals are displayed in Table 6. Our approach results in the top ODS, OIS, and AP scores of 81.3%, 82.0%, and 85.1%, respectively. We improved the results in three categories by 3.9%, 3.1%, and 3.2% when compared to the second-best method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **ODS** & **OIS** & **AP** \\ \hline gPb-ucm [10] & 2011 & 0.632 & 0.661 & 0.562 \\ Silberman [11] & 2012 & 0.658 & 0.661 & - \\ gPb+NG [56] & 2013 & 0.687 & 0.716 & 0.629 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative comparisons on NYUDv2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline ResNet101-FRCNN-FPN & 15.9 & 34.1 & 12.9 & 16.1 \\ \hline ResNet50-FRCNN-FPN & 13.9 & 30.8 & 10.2 & 14.1 \\ \hline ViT-B/32*-FRCNN & 17.9 & 36.4 & 15.8 & 18.0 \\ \hline ViT-B/32-FRCNN & 16.4 & 34.5 & 14.0 & 16.5 \\ \hline ViT-B/16*-FRCNN & 22.9 & 46.6 & 20.9 & 23.0 \\ \hline ViT-B/16-FRCNN & 20.2 & 40.5 & 17.8 & 20.4 \\ \hline
**ACT-FRCNN** & **24.7** & **48.5** & **22.6** & **25.1** \\ \hline \hline \end{tabular}
\end{table}
Table 4: ACT-FRCNN outperforms ResNet-FRCNN techniques in terms of out-of-domain performance as measured by average precision (AP) in the COCO dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **ODS** & **OIS** & **AP** \\ \hline SCG [46] & 2012 & 0.739 & 0.758 & 0.773 \\ Sketch Tokens [63] & 2013 & 0.272 & 0.746 & 0.780 \\ SE [64] & 2014 & 0.746 & 0.767 & 0.803 \\ MES [48] & 2015 & 0.756 & 0.776 & 0.756 \\ \hline DeepBoundary [65] & 2015 & 0.813 & 0.831 & 0.866 \\ CED [51] & 2017 & 0.815 & 0.833 & 0.889 \\ LPCB [52] & 2018 & 0.815 & 0.834 &....... \\ BDCN [45] & 2019 & 0.828 & 0.844 & 0.890 \\ DexiNed [53] & 2020 & 0.729 & 0.745 & 0.583 \\ PiDiNet [54] & 2021 & 0.807 & 0.823 &....... \\ EDTER-MS-VOC [55] & 2022 & 0.848 & 0.865 & 0.903 \\ \hline
**ACT-FRCNN (our)** & **.....** & **0.865** & **0.883** & **0.924** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Outcomes on the BSDS500 dataset.
# Performance Analysis of ICA in Sensor Array
## 2 Hypotheses, Definitions and Notations  
### Noisy ICA Model  
Denote \(\mathbf{x}\) a data matrix. We think of it as a \(P\times L\) object collecting \(L\) samples of a \(P\times 1\) vector, the model of independent component analysis postulates a number \(N\) of sources so that the observation matrix \(\mathbf{x}\) can be explained as:  
\[\mathbf{x}=\mathbf{As}+\mathbf{V} \tag{1}\]  
where \(\mathbf{A}\) is the unknown \(P\times N\) mixing matrix, \(\mathbf{s}\) is a \(N\times L\) matrix in which each row is called a \"source signal\" or a \"component\". Matrix \(\mathbf{V}\) represents an additive noise item or some other form of measurement uncertainty [1]. ICA is distinguished from other approaches to source separation in that it requires relatively few assumptions on the sources and on the mixing process. Basic assumptions of ICA include [5]:  
(1) The sources being considered are statistically independent.  
The first assumption is fundamental to ICA. Statistical independence is the key feature that enables estimation of the independent components from the observations.  
(2) The independent components have non-Gaussian distribution.  
The second assumption is necessary because of the close link between Gaussianity and independence. It is impossible to separate Gaussian sources using the ICA framework because the sum of Gaussian sources is indistinguishable from a single Gaussian source, and for this reason Gaussian sources are forbidden. This is not an overly restrictive assumption as in practice most sources of interest are non-Gaussian.  
(3) The mixing matrix is invertible.  
The third assumption is straightforward. If the mixing matrix is not invertible then clearly the demixing matrix we seek to estimate does not even exist.  
Generally speaking, the ICA scheme aims at estimating a demixing matrix \(\mathbf{B}\) based on the output independence that satisfies [3; 4; 5]:  
\[\mathbf{B}=\mathbf{PDA}^{-1} \tag{2}\]  
where \(\mathbf{P}\) is a permutation matrix, \(\mathbf{D}\) is a diagonal matrix, and the superscript '\((\circ)\)' indicates the matrix inversion. The estimation of sources (with intrinsic scale-permutation ambiguity) is obtained with:  
\[\mathbf{\hat{s}}=\mathbf{B}\mathbf{x} \tag{3}\]
## 5 Experimental Analysis and Verification
### Sensitivity of RDCA Model Parameters  
The structure of the model determines the classification performance of the extracted features. In this light, we conducted many experiments to test the performance of diverse layers, sizes of convolution filters, and convergence speeds as is shown in Figure 9. The 20 dB GN is added to the PQDs data set in this test. Moreover, the depth of each layer in the encoder is set to 8, 16, 24, and 32. In addition, we set the equal size of convolution filter per layer in each experiment.  
Figure 9: The classification accuracies and loss curve, where the RDCA-\(i\) denotes the RDCA has \(i\) layers convolution and max-pooling. (**a**) Accuracy with different layers and filter sizes when \(\lambda=0\), (**b**) accuracy with different \(\lambda\), (**c**) verification loss of SCNN with and without BN layer.  
First of all, it is clear from Figure 9a that the classification accuracy increases significantly when the convolution filter size is lower than 5 \(\times\) 5, and the performance of size 7 \(\times\) 7 to 13 \(\times\) 13 maintain at the same level for a certain layer. Secondly, when the layers are lower than 3, it has been witnessed that the precision is the corresponding increase with the rise of the layer number. The RDCA-3 has the best performance compared with the RDCA-4, indicating that the RDCA-4 may be overfitting due to lots of parameters. Thus a three layers RDCA with the 11 \(\times\) 11 convolution filter in the first layer and 9 \(\times\) 9 filter in the next two layers are selected.  
Based on the RDCA-3, we further verify the validity of the weight residual structure as shown in Figure 9b. It shows that the RDCA performs better than the regular AE (\(\lambda\) = 0). We set \(\lambda\) = 0.4 because the RDCA-3 has the highest accuracy. Followed by the RDCA-3, we verify the convergence speed of SCNN. Figure 9c shows that the SCNN with the BN achieves stability when iterations is 15. The loss is also smaller, indicating that the SCNN has learned more information.
# Strain Transfer Analysis of a Clamped Fiber Bragg Grating Sensor
## 3 Theoretical Strain Transfer Analysis
### Strain Transfer Rate Distribution of the \(m\)-Segment of the Optical Fiber  
The origin of the coordinate system is selected as point B to show the mechanical model of the micro unit of the \(m\)-segment from Figure 2, and the following equation can be obtained:  
\[2\pi\sigma_{g}\cdot\tau_{g}(x,r_{g})\cdot\mathrm{dx}+\pi{r_{g}}^{2}\cdot \mathrm{d}\sigma_{g}=0 \tag{3}\]  
Figure 1: Mechanical model of clamped fiber Bragg grating (FBG).  
Figure 3: Mechanical model of interlayer micro unit.  
Figure 2: Mechanical model of \(m\)-segment micro unit.  
\[u_{i}-u_{g}-\frac{u_{g}}{\mathrm{L}_{1}}\cdot\frac{\mathrm{L}_{2}}{2}=-\frac{ \mathrm{E}_{g}}{2\mathrm{G}_{j}}\cdot r_{g}^{2}\cdot\ln(\frac{r_{i}}{r_{g}}) \cdot\frac{\mathrm{d}\epsilon_{g}}{\mathrm{d}\mathrm{x}}=-\frac{1}{k^{2}}\cdot \frac{\mathrm{d}\epsilon_{g}}{\mathrm{d}\mathrm{x}} \tag{15}\]  
where \(u_{g}\), \(\mathrm{L}_{1}\), \(\mathrm{L}_{2}\) are the displacement of the \(m\)-segment of the optical fiber, the interlayer length and the gauge length, respectively. In the tests, fiber is encapsulated in the steel pipe by using adhesive. The inner radius of steel pipe is equal to the radius of interlayer, which can be measured as:  
\[\mathrm{G}_{j}=\frac{\mathrm{E}_{j}}{2(1+\upmu)} \tag{16}\]  
in which \(\upmu\) is Poisson ratio; and,  
\[k^{2}=\frac{1}{(1+\upmu)\cdot\frac{\mathrm{E}_{g}}{\mathrm{E}_{j}}\cdot r_{g} ^{2}\cdot\ln(\frac{r_{i}}{r_{g}})} \tag{17}\]  
where \(a=1+\frac{\mathrm{L}_{2}}{2\mathrm{L}_{1}}\), \(a\) is defined as the gauge ratio parameter. Then we have the following equation from Equation (15):  
\[u_{i}-a\;\cdot u_{g}=-\frac{1}{k^{2}}\cdot\frac{\mathrm{d}\epsilon_{g}}{ \mathrm{d}\mathrm{x}} \tag{18}\]  
Taking the derivation of Equation (18) with respect to \(x\), the second-order differential equation is shown as follows:  
\[\frac{\mathrm{d}\epsilon_{g}^{2}}{\mathrm{d}\mathrm{x}^{2}}-a\;\cdot k^{2} \cdot\epsilon_{g}=-k^{2}\cdot\epsilon_{i} \tag{19}\]  
The general solution of this differential equation is:  
\[\epsilon_{g}(\mathrm{x})=\mathrm{C}_{1}\cdot\mathrm{e}^{\sqrt{2}kx}+\mathrm{C }_{2}\cdot\mathrm{e}^{-\sqrt{2}kx}+\frac{\epsilon_{i}}{a} \tag{20}\]  
where \(\mathrm{C}_{1}\) and \(\mathrm{C}_{2}\) are two integral constants. As point A and point D have no constraints, the boundary conditions can be expressed as:  
\[\epsilon_{g}(\mathrm{L}_{1})=\epsilon_{g}(-\mathrm{L}_{1})=0 \tag{21}\]  
The integral constants are acquired as:  
\[\mathrm{C}_{1}\mathrm{=C}_{2}=-\frac{\epsilon_{i}}{2a\cdot\cosh[-\sqrt{a} \cdot k\cdot\mathrm{L}_{1}]} \tag{22}\]  
The strain transfer rate distribution of the \(m\)-segment of the optical fiber is:  
\[\upalpha_{\mathrm{m}}(\mathrm{x})=\frac{\epsilon_{g}(\mathrm{x})}{\epsilon_{ i}}=\frac{1}{a}\bigg{\{}1-\frac{\cosh(\sqrt{a}\cdot k\cdot x)}{\cosh(\sqrt{a} \cdot k\cdot\mathrm{L}_{1})}\bigg{\}} \tag{23}\]
# Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
## 5 Results
### Analysis of Results for the Proposed Method
Additionally, to evaluate the performance improvements achieved by the feature fusion technique, a comparative analysis was conducted between the proposed model and a CNN model that employed transfer learning from the source domain to the target domain without applying feature fusion (CNN + Attention (TL)). The proposed model in this study  
Figure 6: Graph of loss and accuracy by epoch of the model: (**a**) results of loss graph by epoch of the model; (**b**) results of accuracy by epoch of the model. is designed with an architecture that includes CNN layers, attention layers, and a feature fusion process. The proposed model achieved an accuracy of 94.21% and an F1-score of 94.16%, outperforming all comparison models. Among traditional machine learning models, KNN exhibited the highest performance, with 94.12% accuracy and a 94.05% F1-score, followed by SVM with 92.06% accuracy and a 91.84% F1-score, and logistic regression (LR) with 90.86% accuracy and a 90.77% F1-score. The CNN (NoTL) model recorded 92.70% accuracy and a 92.63% F1-score among deep learning models. The CNN + Attention (NoTL) model, incorporating an attention mechanism, demonstrated improved performance with 93.71% accuracy and a 93.64% F1-score. The CNN + Attention (TL) model, which adopted transfer learning, achieved improved performance with an accuracy of 93.96% and an F1-score of 93.90%. However, the proposed model outperformed all comparison models, confirming that model architecture optimization and learning strategy improvements effectively enhance classification performance.