esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 4 Experiments  
### Implementation Details  
All experiments were conducted using the PyTorch [48] on a single NVIDIA RTX 3090 GPU. We resized all training and testing images to the size of \(128\times 128\) (\(H=W=128\)), and set the watermark length \(L\) to 32. Based on this setting, the comparison of parameter quantities is shown in Table 1. The network was trained using the Adam [49] optimizer, with a learning rate of 0.0001 and a batch size of 8 for 100K iterations. We empirically set the weights of different loss terms as \(\lambda_{1}:\lambda_{2}:\lambda_{3}:\lambda_{4}:\lambda_{5}=1.5:2:3:0.5:2.5\).  
For comprehensive evaluation of model robustness, we implemented various noise augmentation techniques during both training and testing phases, as shown in Table 2. During training, we applied fixed parameter settings for each noise type. We selected moderate noise levels for training to ensure effective model learning while maintaining reasonable image quality: dropout (\(p\)) and dropout (\(p\)) with 30%, resize (\(r\)) with ratio 50%, Gaussian noise (\(\sigma\)) with standard deviation 0.05, rotation (\(\theta\)) angle of 30\({}^{\circ}\), and affine (\(s\)) transformation with shear angle of 10\({}^{\circ}\). For JPEG compression in the training phase, we utilized the JPEG-Mask approach instead of a fixed quality parameter. For testing, we evaluated model performance across three different intensity levels (low, medium, and high) for each noise type. The testing parameters were systematically varied from mild to severe perturbations, allowing us to assess the model's robustness under different noise conditions. For instance, Gaussian noise testing ranged from \(\sigma=0.01\) to 0.05, and rotation angles varied from \(\theta=10^{\circ}\) to \(30^{\circ}\).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithm** & **\#Params (M)** \\ \hline HiDDeN [10] & 4.1 \\ IGA [50] & 4.2 \\ StegaStamp [13] & 25.17 \\ MBRS [14] & 20.92 \\ SSLW [34] & 21.92 \\ De-END [15] & 6.84 \\ ARWGAN [35] & 30.92 \\ FlexMark [36] & 123.68 \\ Our Model & 40.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of computational parameter counts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Type** & **Training** & \multicolumn{3}{c}{**Testing**} \\ \cline{3-5}  & **Low** & **Medium** & **High** \\ \hline Cropout (\(p\)) & 30\% & 10\% & 20\% & 30\% \\ \hline Dropout (\(p\)) & 30\% & 10\% & 20\% & 30\% \\ \hline Resize (\(r\)) & 50\% & 90\% & 70\% & 50\% \\ \hline JPEG (\(q\)) & - & 60 & 50 & 40 \\ \hline Gaussian (\(\sigma\)) & 0.05 & 0.01 & 0.03 & 0.05 \\ \hline Rotation (\(\theta\)) & 30\({}^{\circ}\) & 10\({}^{\circ}\) & 20\({}^{\circ}\) & 30\({}^{\circ}\) \\ \hline Affine (\(s\)) & 10\({}^{\circ}\) & 6\({}^{\circ}\) & 8\({}^{\circ}\) & 10\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Noise augmentation parameters for training and testing phases. ‘-’ indicates that JPEG-Mask is used for compression during training instead of a fixed quality parameter.
## 4 Experimental Results and Analysis
### WVE-UDI Algorithm Ablation Experiments
As can be seen from Figure 15, when two unknown IoT devices coexist in the open environment, the WVE-UDI algorithm is usually able to achieve more than 90% recognition accuracy, showing relatively good recognition results, as follows: (1) In the open environment where Belkin Wemo Switch and Canary Camera devices coexist, a single clustering algorithm often only achieves high accuracy for the Belkin Wemo Switch devices to achieve high accuracy recognition, while the recognition effect on Canary Camera devices is poorer, both reaching 80%. With the integration of the WVE-UDI algorithm, while the recognition accuracy of the Belkin Wemo Switch device decreased slightly to 93.8%, a decrease of 4.1 percentage points, the recognition accuracy of the Canary Camera device increased significantly to 88%, a maximum improvement of 78 percentage points. This shows that the WVE-UDI algorithm is able to balance the recognition effect among different IoT devices to some extent when integrating the clustering capabilities of multiple clustering algorithms; (2) Both the Agglomerative and Birch algorithms are unable to recognize Insteon Camera devices when Belkin Wemo Switch devices and Insteon Camera devices exist in the open environment. However, the WVE-UDI algorithm is able to achieve 100% accurate recognition of Insteon Camera devices, which is mainly attributed to the fact that the WVE-UDI algorithm successfully integrates the Gaussian Mixture algorithm's ability to recognize Insteon Camera devices, which significantly improves the final recognition results; (3) When both Canary Camera devices and Insteon Camera devices are present in the open environment, the Birch algorithm still fails to recognize the Insteon Camera devices, while the Canary Camera devices are recognized with 90% accuracy. Meanwhile, the remaining two single clustering algorithms perform opposite to the Birch algorithm, with low recognition accuracy for Canary Camera devices but high recognition accuracy for Insteon Camera devices. On the one hand, this is due to the fact that both Canary Camera devices and Insteon Camera devices are video cameras, and there are similarities in their data features, which leads to the single clustering algorithms facing difficulties in distinguishing between the two; on the other hand, the data features of the Insteon Camera devices may not be fully matched with the clustering patterns relied on by the Birch algorithm, which further affects the recognition effect of the algorithm. However, through the integration of the WVE-UDI algorithm, the recognition accuracy of the Canary Camera device reaches 78%, and the recognition accuracy of the Insteon Camera device jumps from 0% to 100%, successfully realizing the effective recognition of two unknown IoT devices. This proves the advantage of the WVE-UDI algorithm in integrating the recognition capability of multiple clustering algorithms, and achieves better recognition effect in the open environment where two unknown IoT devices coexist.
# Food Allergies: Immunosensors and Management
## 2 Milk Allergy
### Immunosensor  
Biosensors are an alternative offering comparable sensitivity and selectivity enabling on-line and real-time measurements. Great efforts have proven the use of biosensors in detecting milk protein. For example, [PERSON] et al., have developed a biosensor based on the optical property of surface plasmon resonance (SPR) for the simultaneous measurements of various milk proteins in dairy goods showing different measurement scopes [22]. Recently, an SPR sensor was designed to detect the casein protein as a means for detecting allergens in laundry samples from the on-site cleaning system (CIP) of a food preparing process, providing an LOD of 58 ng mL\({}^{-1}\) and adequate sampling. However, whey protein cannot be measured by the uses of casein-related sensors. Therefore, the fabrication of a lactoglobulin sensor is significant for the detection of whey protein allergens. Currently, there exists an immunoassay-based SPR sensor that is optimized to detect lactoglobulin for future application in on-site cleaning final rinse (CIP) water samples. SPR sensors have provided great advantages for these applications as they are fully automatic, flexible, fast, and real-time analyses. The sensors also offer the promise of on-line or at-line analysis to operate samples. Lactoglobulin is an essential milk protein and the measurement of lactoglobulin can be a reliable biomarker in the evaluation of milk content and milk whey fraction during the CIP process, in combination with with other acquainted allergen sampling methods such as surface swabs. Recent biosensors have sensitivities at sub-ppm levels, as well as superior selectivity, and are capable of measuring lactoglobulin at sub-ppm concentrations. The SPR biosensors work alongside a casein sensor for simultaneous measurements of milk and whey fraction allergens when applied individually in the food manufacturing process [22]. These immunosensors have been investigated for the trustworthy measurement of IgG from unclean milk with colostrum or milk from various animals, including goat, cow, and sheep.
## 2 Design of Path Tracking Controller with Constraints on Tire Slip Angles
### Design of MPC with Constraints on Tire Slip Angles
 \tag{23}\]  
\[\mathbf{\overline{Q}}=diag(\mathbf{Q},\cdots,\mathbf{Q}),\ \mathbf{\overline{R}}=diag( \mathbf{R},\cdots,\mathbf{R})\]  
\[\mathbf{X}=\mathbf{\overline{F}}\mathbf{x}_{0}+\mathbf{\overline{H}}\mathbf{U} \tag{24}\]  
\[J_{MPC}=\mathbf{x}_{0}^{T}\mathbf{Q}\mathbf{x}_{0}+\mathbf{X}^{T}\mathbf{ \overline{Q}}\mathbf{X}+\mathbf{U}^{T}\mathbf{\overline{R}}\mathbf{U} \tag{25}\]  
\[J_{MPC}=\mathbf{U}^{T}\left(\mathbf{\overline{H}}^{T}\mathbf{\overline{Q}} \mathbf{H}+\mathbf{\overline{R}}\right)\mathbf{U}+2\mathbf{x}_{0}^{T}\mathbf{ \overline{F}}^{T}\mathbf{\overline{Q}}\mathbf{H}\mathbf{U} \tag{26}\]  
\[-\delta_{\max}\leq\mathbf{U}\leq\delta_{\max} \tag{27}\]  
\[\left\{ \begin{array}{l}-\alpha_{m}+\beta_{0}+\frac{l_{f}\gamma_{0}}{v_{x}} \leq\mathbf{U}_{f}\leq\alpha_{m}+\beta_{0}+\frac{l_{f}\gamma_{0}}{v_{x}}\\ -\alpha_{m}+\beta_{0}-\frac{l_{f}\gamma_{0}}{v_{x}}\leq\mathbf{U}_{r}\leq \alpha_{m}+\beta_{0}-\frac{l_{f}\gamma_{0}}{v_{x}}\end{array} \right. \tag{28}\]  
Different from LQR, MPC can directly handle the constraints on the control inputs, i.e., \(\delta_{f}\) and \(\delta_{r}\), as given in Equation (27) and Equation (28). However, MPC is to solve the optimization problem every time step, which requires a large amount of computations, compared to LQR with clipping. For the reason, there are hard limitations in implementing MPC on real vehicles. Let this denote MPC without and with the CTSA as MPCN and MPCC, respectively.
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 4 Experiments and Analysis
### Analysis  
Experimental results show that our DWPIS with two branches can obtain stronger railway skeletons than the base architecture of the skeleton-detection network. Upon careful analysis, these enhancements come from three main sources: the instance-segmentation branch, fused loss function, and inference function.  
Firstly, the instance segmentation locates the railway target. Through training the instance-segmentation branch, the network extracts the useful features of the railways containing the skeleton and the parameters of the shared backbone are optimized.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network** & **F-Score** \\ \hline Fixed AdaLSN (only skeleton) & 48.8 \\ Ours (only skeleton) & 47.9 \\ Ours (two branches) & 51.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of with and without instance branch.  
Figure 9: Comparison of the results of our network with and without instance-segmentation branch. The first row: input images. The second row: results of our network without instance-segmentation branch, in which the dashed ellipses represent the misidentified or undetected skeletons. The third row: results of our network with instance-segmentation branch.  
Secondly, the fused loss function with dynamic weight decided, using training-epoch changes, the dominance of the training. Through controlling the dominant branch, the network is mainly trained for instance segmentation first and then mainly for skeleton detection for a long time. For the skeleton-detection branch, the Dice loss function is more suitable for the serious imbalance between positive and negative samples and increases the convergence speed of training.  
Finally, the threshold function, the subject of the skeleton-detection branch in the inference procedure, optimizes the skeleton results. By adding a threshold function after the original sigmoid function, better results can be obtained by filtering noise after fewer training epochs.
## 3 Proposed Solution  
This paper proposes a joint protection and error recovery mechanism for compressed videos against transmission losses and unauthorized access to the content while preserving the video quality intact of the original video and utilizing a minimum of bandwidth. The proposed scheme is implemented in following three phases; the details are discussed in the subsequent subsections.  
* In the _first phase_, video content is compressed and protected at the same time. The privacy protection is implemented using a two-round secure process. First, data diffusion is achieved by applying permutation on selected residuals data of compressed H.264/AVC bitstreams, and later, the XOR encryption algorithm is applied to the permuted data. The compressed selectively encrypted video bitstreams are produced as an output of this phase.
* In the _second phase_, channel modeling is performed through the Markov-Chain based Gilbert-Elliot model, which introduces bit errors inside the selectively encrypted videos (output of _Phase 1_) and enables simulations of the burst error effects of communications links.
* In the _last phase_, an FEC mechanism is applied (on both the encoder and decoder side) to detect and correct bit errors from the H.264/AVC selectively encrypted bitstreams (output of _Phase 2_) for their error-free transmission.
## 4 Discussion and Results
characterized by a radius \(a=\lambda_{0}/2\) and operating frequency \(f_{0}=2.6\) GHz is considered. For clarity and readability, we present one representative case for each main lobe direction in Figure 3b. The directivity pattern of the GA manipulation is compared to the radiation pattern coming from the design of an HMS structure obtained with the full-wave simulations. A very good matching between the two curves can be observed, particularly regarding the maximum and minimum positions. The GA model accurately predicts the shape of the radiation pattern, the beamwidth, and the sidelobe levels. Thanks to this approach, it is possible to calculate the maximum directivity value (i.e., the ratio of the maximum radiation intensity in a given direction to the average radiation intensity over all directions), which is 8.19 dB, in a very fast and easy way. The value of 8.19 dB was obtained from the optimized radiation pattern computed through the genetic algorithm. This process involved maximizing the radiation intensity in the desired direction based on the excitation phases and amplitudes of the array. The results obtained by implementing the GA demonstrated significantly greater precision in controlling the position of the main beam and nulls compared to conventional methods. To further corroborate the results, in Figure 4, we report the 3D radiation pattern of the antenna coated by the HMS designed through the circular array approach (Figure 4a) and after the GA optimization (Figure 4b). By effectively utilizing GA optimization, improvements in both beam-steering and null-steering can be observed in the final radiation pattern of the structure. Finally, it is worth noting that despite the introduction of the HMS coating, the matching performance of the dipole antenna remains quite good, as demonstrated in Figure 4c. The figure compares the reflection coefficient magnitude at the antenna input port with that of the same structure but different values of HMS cell's reactances corresponding to the cases of the circular synthesis approach and with the GA optimization procedure. While the proposed metasurface design demonstrates promising simulation results, practical implementation involves several challenges that must be addressed. First, the selection of suitable materials with precise electromagnetic properties is critical to achieving the desired performance. Second, manufacturing tolerances, particularly for subwavelength-scale features of the metasurface, can introduce deviations from the ideal design. Third, integration of the metasurface with existing antenna systems requires careful mechanical and electrical considerations. Future work will explore these aspects to ensure the feasibility of the proposed design in real-world applications. Figure 4: A 3D directivity radiation pattern of the HMS structure (**a**) before and (**b**) after the GA optimization. (**c**) Magnitude of the reflection coefficient at the antenna input port for the scenarios w/o (blue line) and w/red and yellow lines) the optimization for the HMS coating.
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 2 Background
### SCA Generalities
* **Required access:** First, the attacker can have local access to the system in _software-only attacks_, where only an adversary application co-located with the victim on the same hardware resources is required (e.g., attacks observing the access patterns/times on shared resources such as cache memories). Second, the attacker can be in physical proximity to the system in order to monitor power consumption or EM emissions (i.e., this is a hardware attack). A third category called _remote attacks_ is distinguished. This latter defines hardware attacks performed from software, for instance, attacks observing power consumption from embedded sensors information accessed by software. Note that attacker access on the target system is irrespective to the exploited property. For instance, an adversary application co-located with the victim and sharing hardware resources can exploit timing physical properties by observing timing through changes on the state of the shared cache. In this work physical and remote access-based attacks are covered. * **Profiling phase:** A profiling phase prior to the actual attack might be necessary for certain attacks. During this phase, the attacker characterises the device behaviour and side-channel information leakage in an ideal environment where the adversary has full control and knowledge of the victim. This includes the access to an exact copy of the system, full knowledge of the neural network implementation on the target device and full control of the inputs and access to the output. The adversary is therefore capable of extensively characterising the device according to the observed side-channel information in order to distinguish different _profiles_ (also called _templates_. In the second phase, which corresponds to the actual attack, the adversary has limited knowledge and/or control on the victim (e.g., limited knowledge on the neural network implementation details or limited control on the inputs) and compares the observed side-channel information to the prior characterisation in order to determine the most probable profile. Attacks requiring a profiling phase are also known as _template attacks_. Finally, different SCA main techniques and variants exist depending on the exploited physical properties. We focus on the exploitation of power consumption and EM that are introduced in the following subsection.
## 2 Description of the Method through a 1D Example
### Reconstruction of an In-Depth Point Source  
To illustrate the use of the Bayesian inference, let us consider the case of a unique point source whose intensity is assumed to be known located at \(z=z_{0}\), as illustrated in Figure 2.  
The surface temperature is built by noising the analytical solution with a white noise \(\varepsilon\):  
\[\theta_{meas}(t)=\theta_{1D}(t)+\varepsilon(t) \tag{6}\]  
The intensity of \(\varepsilon\) will be modulated to test the sensitivity of the method.  
The Bayesian inference is used to determine the vector probability \(P_{\Omega}=[P_{1}\ P_{2}\...P_{n}]\), where \(P_{i}\) is the probability of finding a source at position \(i\), knowing the measured surface temperature \(\theta_{meas}\). At each time step \(t_{j}\) and for each possible thickness position \(i\), the likelihood \(L_{ij}\) is computed by the following Equation (7):  
\[L_{ij}=\exp\left(-\frac{1}{2\Gamma^{2}}||\theta_{meas}(t_{j})-\mathbf{A} \Omega_{i}||_{2}^{2}\right) \tag{7}\]  
Figure 2: One source problem.  
and a vector \(P_{\Omega}\) is determined as follows:  
\[P_{\Omega}(t_{j})=\prod_{k=1}^{j-1}P_{\Omega}(t_{k}). \tag{8}\]  
As Equation (8) shows, the final probability takes into account the information in the whole time interval \([0,t_{f}]\). Figure 3 illustrates the evolution of (a) the likelihood \(L_{ij}\) and (b) the cumulative probability \(P_{\Omega}(t_{j})\) at each time step \(j\) for \(\text{SNR}=100\).  
One can then note that for early time steps, the deepest sources are still possible (likelihood\(=1\)). This is coherent with the fact that at an early time, only the closest sources can be detected. This explains the fact that for these sources, the probability switches to 0 quickly. With the evolution of time, the deepest sources can be detected.  
Then, Figure 4 enables the obtained probability to be determined for three different signal-to-noise ratios: \(\text{SNR}=10\) (a), \(\text{SNR}=50\) (b), and \(\text{SNR}=100\) (c). One can note that the method is not so sensitive to the noise compared to literature methods [8; 12; 15]: for \(\text{SNR}=50\) and \(100\), the probability at the source position is superior to 0.95, while for \(\text{SNR}=10\), the probability is 0.30, which can be questionable in absolute terms but not in the case where the other source position probabilities are equal to 0.  
Figure 4: Source position probability \(P_{\Omega}(t_{f})\) for (a) Signal to Noise Ratio (\(\text{SNR})=10\), (b) \(\text{SNR}=50\) and (c) \(\text{SNR}=100\).  
Figure 3: Probability repartition at each time step: (a) likelihoods \(L_{ij}\) and (b) cumulative probability \(P_{\Omega}(t_{j})\).
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 4 Experimental Results
### Environmental Setup and Evaluation Metrics  
We accomplished the overall experiments in the Linux-20.04 environment. The hardware devices including CPU-Intel (R) Core (TM) i7 and GPU-GeForce GTX 1080 are used to perform the experiments. We used Python-3.8 and Matlab-202a as the programming languages. We trained the deep learning model until 100 epochs. The batch size and learning rate were set to 16 and 0.001, respectively. The learning rate drops 10% after every 20 epochs. For optimization, we conducted stochastic gradient descent (SGD) optimizer with a momentum of 0.9. We reported the recognition results by calculating the accuracy, defined as follows:  
\[Accuracy(\%)=\frac{Correctly\ Predicted\ Samples}{Total\ Number\ of\ Samples}\times 100 \tag{4}\]  
Owing to the smaller number of video sequences in action recognition datasets, we have used transfer learning in which, first, we have trained the model with the jester dataset [12]. Then, the human action datasets are trained with the pre-trained weight of the ResNet101 model.
## 3 Results and Discussion
### Effect of AITC on the Microbiological Properties of Chicken Meat
In addition, AITC showed antimicrobial activity against yeasts and molds in cottage cheese [69], against _Listeria innocua_ in frozen ready-to-eat shrimps [70], and against _Salmonella enterica_ in liquid egg albumen [71]. In accordance with the current study, [PERSON] et al. [39] found that the diffusion of the AITC from the packaging film into shredded, cooked chicken meat reduced the microbial contamination, controlled oxidation, and reduced the color changes. Additionally, the mechanism of action of the antibacterial activity of AITC has been found to occur through several pathways, including alteration in cell membrane permeability, disruption of energy production, breakage of disulfide bonds, and inactivation of oxidative induction [29]. Eventually, it causes cell membrane damage and leakage of cellular metabolites at all growth stages, and this action is thought to be due to its chemical group [30; 72]. In this study, due to the effect of AITC on AMCs, a cell count below 7 log CFU/g was only observed in samples treated with four times the MIC concentration of AITC, indicating that the shelf life of meat in this group was extended to 2 weeks of storage time. Regarding the safety of AITC-treated chicken breasts, further presence/absence detection studies are needed to determine the survival of _L. monocytogenes_ and _S._ Typhimuriumium.