esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 4 Physical SCA Attacks on DNNs  
Since the last couple of years, research has shown that embedded DNNs are vulnerable to physical SCA attacks. The first very recent endeavors on classifying existing SCA attacks on DNNs have only very recently been published. Considering a different perspective from this paper, these first efforts are complementary and show the extent of this emerging research field and the necessity to have a broad overview of current attacks. However, to the best of our knowledge, no previous work has focused on physical power and EM-based SCA against embedded DNNs.  
[PERSON] et al. [22] present SCA on CNNs aimed at extracting the model architecture. The attack considered scenario is limited to DNN models on mobile phones as well as hosted by a cloud provider in the context of ML as a service (MLaaS). Contrary to this work, we do not consider MLaaS but we focus on the IoT context and on pre-trained models deployed on low-cost processors or FPGAs that are physically accessible by the attacker. Specifically, the authors of this work [22] provide an overview on existing timing and local access-based attacks (refer to the taxonomy proposed in Section 2.2 and Figure 2) in which the adversary application is co-located on the same hardware machine as the victim, thus sharing hardware resources and particularly cache memories. In this scenario, physical proximity to gain access to physical measurements is not required. In contrast to this work, we focus on physical access-based SCA (note that they might be performed remotely as presented further in this section). Therefore, SCA based on local access and exploitation of logical properties are out of the scope of this work (see Figure 2).  
[PERSON] et al. [23] focus on active attacks that are out of the scope of our work. The authors particularly consider hardware Trojan insertion and fault injection. They gave an overview on attacks aimed at extracting models as well as corrupting models and outputs. Contrary to this work, we focus on passive attacks exploiting side-channel information.  
Finally, a very recent white paper is available [24] that provides an overview of different literature endeavors on the extraction of training data and DNN models. Reviewed works included some SCA attacks, as well as purely software attacks (e.g., adversarial example-based attacks also referenced as application programming interfaced-based attacks). Similarly, recent work [25] considers, among others, physical SCA in its review. Contrary to this paper, the authors did not specially focus on power and EM SCA and did not consider recent physical SCAs that are remotely performed. However, all these endeavors are complementary to our work. In this paper the aim is to provide an understandable yet detailed view and classification, focusing on power and EM SCA attacks that are not limited to the extraction of training data set and models.
## 6 Simulation Analyses
### Flying Base Station Channel Capacity: \(\eta\) and SNR\({}_{Su}\)  
We analyze the flying base station channel capacity per bandwidth or \(\frac{C_{FUI}}{R}\) in bits-per-second over Hz (bps/Hz), because the channel capacity increases proportionally with the transmission bandwidth; as shown in Equation (1), given the bandwidth resource allocated for the channel (\(W\)), the channel capacity is the product between \(W\) and \(\frac{C_{FUI}}{W}\).  
Figure 2 varies \(\eta\) (the flying base station's power relative to the stationary base station's) and plots the channel capacity given SNR\({}_{SU}=10\) dB and the ideal positioning. The flying base station channel capacity increases as \(\eta\) increases (more transmission power by the flying base station). Figure 1(a) corresponds to the case when the flying base station has the half-duplex communication capability. When the line-of-sight path dominates (\(\gamma=2\)), \(C_{FUI}\) per bandwidth ranges from 1.16 bps/Hz to 4.82 bps/Hz. For example, if \(\eta=1\), \(\frac{C_{FUI}}{W}=2.68\) bps/Hz and if \(\eta=10\), \(\frac{C_{FUI}}{W}=4.32\) bps/Hz. Furthermore, the flying base station channel capacity increases as \(\gamma\) increases (i.e., channel greater attenuation) given fixed \(\eta\); the flying base station is even more relevant providing greater channel capacity when the channel experiences greater path attenuation loss. Figure 1(b) corresponds to the case when the flying base station has full-duplex communication capability. The capacity performance doubles the capacity performance from the half-duplex case in Figure 1(a) because the flying base station does not need to interleave its transmission and receiving if it has the full-duplex capability.  
When varying the stationary base station's SNR given \(\eta=1\) and half-duplex capability, the flying base station channel capacity increases, as shown in Figure 3, because the flying base station acts as a relay between the user and the stationary base station and its channel performance depends on the stationary base station's. The full-duplex capability (Figure 2(b)) yields twice as much flying base station channel capacity performance as the half-duplex case (Figure 2(a)).  
In both analyses varying \(\eta\) (Figure 2) and varying SNR\({}_{SU}\) (Figure 3), the flying base station channel capacity increases as \(\gamma\) increases (less dominant line-of-sight path and more fading). While greater \(\gamma\) attenuates the signal quicker as the signal propagates in distance, the flying base station channel increases because the stationary base station's SNR is given (i.e., SNR varies in the x-axis in Figure 3). The SNR itself decreases as \(\gamma\) increases (and thus the stationary base station's channel capacity decreases as expressed in Equation (4)) so greater \(\gamma\) requires either greater transmission power from the stationary base station or greater proximity to the user to achieve the same SNR.  
Figure 2: Flying base station channel capacity per bandwidth with respect to the transmission power ratio \(\eta\).
# GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation
## 5 Ablation Study  
### Mixing Strategies  
There are several ways to integrate the source patch with the target image. In this section, we analyze the impact of different mixing strategies on the proposed method. We conducted experiments using GaussianMix with the ResNet-18 [1] architecture on the CIFAR-100 [21] dataset and the ResNet-50 [1] architecture on the Tiny-ImageNet [22] dataset. For both experiments, training proceeded for 200 epochs. We evaluated four mixing strategies: (i) **Corresponding position**, which refers to the placement of the source patch at the same spatial location within the target image that it originally occupied in the source image; (ii) **Center position**, where the source patch is placed at the image center;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Baseline** & **CutMix** & **SaliencyMix** & **GaussianMix** \\ \hline
23.71 & 41.83 & 40.49 & **41.85** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of adversarial attack robustness. The table shows the top-1 accuracy of the ResNet-101 architecture trained using various data augmentation methods, with results reported on the ImageNet validation dataset. GaussianMix achieves the highest performance, with a top-1 accuracy of 41.85%. (Bold depicts best performance).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Backbone Network** & **ImageNet Classification Error** & **Object Detection Performance Faster-RCNN (mAP)** \\ \hline ResNet-101 (Baseline) & 21.87 & 77.53 (+0.00) \\ CutMix-Trained & 20.17 & 80.04 (+2.51) \\ SaliencyMix-Trained & 20.09 & 79.91 (+2.38) \\ GaussianMix-Trained & 19.80 & 80.00 (+2.47) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of GaussianMix on transfer learning with a pretrained object detection model.  
(iii) **Random (Uniform) position**, where the source patch is placed at a random location; and (iv) **Random (Gaussian) position**, where the source patch is positioned based on a Gaussian distribution over the target image. Tables 5 and 6 present the results of our comparative analysis of different mixing strategies. Using the Center position for mixing consistently occludes the central region of the target image, which typically contains the most salient information.  
This can result in reduced diversity, potentially limiting the regularization effect. In contrast, the Random (Uniform, Gaussian) position strategies introduce greater diversity, which can enhance performance. However, excessive diversity may lead to over-regularization, ultimately degrading model performance. Among the evaluated strategies, Corresponding position mixing demonstrates the best performance, striking a balance between diversity and regularization. Notably, SaliencyMix [15] exhibits a similar trend, with the Corresponding position strategy yielding the best results. Based on these findings, we adopted the Corresponding position as the default mixing strategy for GaussianMix data augmentation.
# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 4 Results
### Research Question 1.  
_What effect does the quality of training have on the performance of an immersive assistance system?_  
**Answer 1.** In short, QoT has a significant impact on performance. For the dependent variables, statistically significant differences between QoT could be analysed using ANOVAs OP F(2, 49) = 5.28; \(p\) = 0.008; \(\eta^{2}\)p = 0.177; and power = 0.84 and TOTP F(2, 28.8) = 21.67; \(p\) < 0.001; \(\eta^{2}\)p = 0.51; and power = 0.99, as well as the index performance F(2, 29.6) = 12.83; \(p\) < 0.001; \(\eta^{2}\)p = 0.40; and power = 0.99. Within the sample, a performance of 1.61%/min was achieved for the operational tasks with QoT corresponding to the MT scenario, i.e., _insufficient_ training. A performance of 2.20%/min was achieved in the DT scenario, which corresponds to detailed, personalised training. In contrast to the OT scenario, the subject was able to perform their task at 4.09%/min, which corresponds to an improvement by a factor of approximately 2.5 in relation to the erroneous training.
## 1 Introduction  
Various studies have been actively underway on underwater acoustic communication with a low probability of interception (LPI) to promote reliable communication [1; 2; 3]. The covert underwater acoustic communication system satisfies both the LPI characteristic, in which the transmitted signal is not intercepted by receivers other than the intended receiver, and the low probability of detection (LPD) characteristic, which reduces the probability of being detected by transmitting signals at a low signal to noise ratio (SNR) below the surrounding background noise in order to maintain covertness. A number of methods have been proposed, and the most well-known communication method is a spread spectrum communication technique [4; 5]. The typical methods used in the spread spectrum communication technique are direct sequence spread spectrum (DSSS) and frequency-hopping spread spectrum (FHSS). In this paper, a direct sequence spread spectrum that ensures covertness is applied by multiplying the pseudo noise (PN) code sequence that has P chips by the transmission signals to be transmitted. The spread spectrum technique can obtain diffusion gain in the multi-sensor communication and a frequency diversity effect in underwater acoustic communication, where selective frequency fading occurs even when data transmission efficiency is sacrificed [6].  
In this paper, the Bahl-Cocke-Jelinek-Raviv (BCJR)-based convolutional code, which is a coding technique that shows excellent performance even in a low SNR using the spread spectrum technique, is applied [7]. From the receive side, a RAKE receiver that sums the power of signals received through multipath is applied by delaying the reception of signals by as much as the chip length of PN code to compensate for interference between signals due to multipath. In the RAKE processing process, a method that sets a threshold value and multiplies the received signal that is over the threshold by a weighting value was proposed. To compensate distorted data, a receive structure using the iterative turbo equalization structure that exchanges and updates external information of the decision feedback equalizer (DFE) and the decoder's external information is proposed [8; 9; 10].  
Furthermore, in recent years much attention has been paid not only to information detection via single sensor but also to the information detection and acquisition problem via multiple sensors, such as simultaneous information collection at multiple places, environment monitoring, search, etc. in underwater acoustic communication. In the area of underwater communication, few studies have been conducted on multiple sensor accesses compared to studies on single sensor [11; 12]. Thus, this paper proposes a transceiver for covert multiple accesses using the characteristics of the spread spectrum technique based on studies on covert underwater acoustic communication. The data from experiments in the underwater environment were collected, and the efficiency of the proposed structure was proven by changing the number of chips.
## 2 Related Works
The results point to an average energy saving of up to 21% compared to two previous approaches pointed out in the study, without compromising the applications' requirements. [PERSON] et al. [47] report in their study that considering the thermal effects of server workloads in conjunction with the control parameters of the cooling unit saves more energy than optimizing each one separately. For this, a low complexity holistic data center model is used that provides control decisions with refined control variables based on the thermal interactions between IT and the refrigeration unit entities. The methodology resulting in savings of 11% combining cooling and workload management. [PERSON] et al. [48] propose the PACT model to reduce energy consumption in data centers with high demands, which combines the use of two mechanisms, Turbo Control and CPU Jailing. In experiments with Google data centers, the authors showed an average of 9% energy savings regardless of workload. In addition, there was a 4% improvement in performance. Table 1 presents the summary of related works, emphasizing the approach used and the energy-saving rate. Different from the previously mentioned works, this work aimed to report the experience of optimizing energy efficiency in a data center through the reuse of legacy equipment and the alteration of the layout.
## 4 Current Status and Identified Needs
### Level 1 TOA Radiometry and Geometry
#### 4.1.8 SI-Traceability in Space  
Our ability to accurately assess TOA radiometry will make an unprecedented leap forward in the future with the launch of the Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS) mission [113]. This UK-lead ESA mission will allow ultimately to attain the required accuracy (<1% at surface reflectance level), which is needed for climate and solar studies [114]. The TRUTHS platform will carry on board a hyperspectral imager measuring spectrally resolved incoming and reflected solar radiation (in the 320 nm to 2350 nm range) at high spatial resolution. TRUTHS and CLARREO Pathfinder [115; 116] will be the first missions to be calibrated directly traceable to SI units, in TRUTHS's case, via a primary standard in orbit, therefore providing, for the first time, an absolute high-accuracy traceable calibration reference to anchor any optical sensor to a common radiometric scale. TRUTHS will be the final missing element to complete ESA's Cal/Val strategy at Level 1, complementing the existing elements (vicarious targets, ground-based network, on-board devices, pre-flight calibrations) and allowing to reach the final goal of SI-traceability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Status and Needs** & **Recommendations** \\ \hline \multirow{4}{*}{IDN-01} & Pre-launch calibration is critical for ensuring accurate satellite observations, meeting the requirements of the science community. Though additional effort should be spent in fostering the adoption of metrological practices during pre-launch characterisation activities. & Space Agencies should embrace metrological practices in the early phase of the mission design by including relevant requirements in Mission and System Requirement Documents (MRD, SRD) and allocating appropriate budget to pre-launch calibration activities. \\ \hline \multirow{4}{*}{IDN-02} & Pre-launch calibration database is most of the time undisclosed to users or protected by property rights with resulting difficulty in applying a full traceability tree in the Level 1 processing. & Space Agencies should ease traceability of calibration processing by: requiring instrument providers to identify measurement equation and develop traceability chain, archiving, maintaining and making accessible to users the relevant pre-launch characterisation database. \\ \hline \multirow{4}{*}{IDN-03} & While radiometric uncertainty is well characterised, theoretical advances needs to be made for fully characterising geometric errors and spatial correlation induced by interpolation and regridding. & Space Agencies should foster advances of theoretical approaches for fully characterising geometric and spatially correlated errors in Level 1 products, such as those induced by orthorectification, regridding, projection. \\ \cline{1-1}
# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 3 UAV-Enabled and Assisted MEC State of the Art
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Year** & **Evaluated Performance Metrics** & **Summary** \\ \hline \multirow{4}{*}{[77]} & \multirow{4}{*}{2021} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Communication security issue in the dual-UAV-MEC system} & A secure communication mechanism was created, dedicated to the dual-UAV-MEC system. The main objective of this task was to maximize the user’s security and computing capacity by optimizing the resources and trajectory of the UAV server. The authors relied on mathematical techniques, including the sequential convex approximation (SCA) and block coordinate descent (BCD) algorithms to enhance UAV-enabled MEC security and computing capacity. \\ \hline \multirow{4}{*}{[37]} & \multirow{4}{*}{2020} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Computation offloading} & A hybrid DL and fuzzy c-means clustering-based algorithms were proposed to predict the positions of ground-based users and UAVs in a hybrid MEC (H-MEC) network. As a result, IoT devices can efficiently offload their intensive tasks to the UAV servers. \\ \hline \multirow{4}{*}{[78]} & \multirow{4}{*}{2020} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Computation offloading Latency} & The authors proposed an intelligent task-offloading algorithm (iTOA) for UAV-enabled MEC services. The proposed approach intelligently perceives the network’s environment and decides the offloading action using the deep Monte Carlo tree algorithm. This method outperforms game theory and greedy search-based methods in latency performance. \\ \hline \multirow{4}{*}{[79]} & \multirow{4}{*}{2021} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Computation offloading Latency} & UAVs were adopted in 5G mobile networks to reduce the end-to-end latency and improve communication reliability. Using UAVs with MEC has provided effective traffic management, resulting in decreased latency and better offloading operations. \\ \hline \multirow{4}{*}{[80]} & \multirow{4}{*}{2021} & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Energy} & A novel system called GESE was proposed to provide computation services on the network’s edge by integrating cloudlets on multiple aerial UAVs. The system performance has been investigated to understand the relationship between energy efficiency and computation task offloading. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: UAV-enabled MEC and assisted MEC research activities.
## 3 Materials and Methods
### Model Fusion
\(\omega_{l}^{i}\) and \(b_{l}^{i}\) represent the weights and biases of layer \(l\) in model \(i\), while \(\omega_{l}^{M}\) and \(b_{l}^{M}\) denote the weights and biases of layer \(l\) in the new model \(M\). Finally, these parameters from each layer are integrated and aggregated to obtain the final model \(M^{\prime\prime}\mathit{ave}\):  
\[M^{\prime\prime}\mathit{ave}=\mathit{swish}(X_{l}\cdot\omega_{l}^{M}+b_{l}^{M}) \tag{10}\]  
where _swish_ is an activation function used within the network. In the weighted averaging fusion method, each model's contribution is weighted based on its performance on validation dataset, ensuring that models with better performance and greater generalization ability have a larger influence on the final outcome. This approach not only effectively mitigates the impact of overfitting but also better addresses the issue of multiple models potentially converging to different local optima, thereby guiding the ensemble model towards a more effective global optimum. Through this weighted mechanism, the stability and generalization performance of the model can be significantly enhanced, leading to more reliable and consistent results across different datasets and tasks. The detailed model fusion method is provided in Algorithm 1. ```
1:Initialize Weighted_Sum as an empty dictionary
2:Initialize Total_Weight as an empty dictionary
3:for each model \(Mi\) in set of models \(M\) do
4: Compute weight \(Wi\) = 1/(Loss of \(Mi+\varepsilon\))
5: for each parameter k in the intersection of keys in base model \(Mb\) and \(Mi\) do
6: If k is not in Weighted_Sum
7: Initialize Weighted_Sum[k] = 0
8: Initialize Total_Weight[k] = 0
9: Weighted_Sum[k] += \(Wi\) * value of parameter k in \(Mi\)
10: Total_Weight[k] += \(Wi\)
11:end for
12:for each parameter k in Weighted_Sum do
13: Y_weighted[k] = Weighted_Sum[k]/Total_Weight[k]
14:end for
15:Return Y_weighted
```  
**Algorithm 1** Weighted Average Prediction  
In our experiments, we applied the above-mentioned weighted averaging fusion method to combine three networks: the Conformer and its two variants. The final result is a new hybrid model, Mixformer. The specific fusion process is illustrated in Figure 6. This fusion approach allows us to effectively integrate the strengths of the three models, further improving the overall performance and generalization ability of the model.
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 1 Introduction
The latter is indeed widely recognized as very effective for lossy image compression. This paper is organized as follows: Section 2 describes the dataset used and the methodology by defining the AANNs, the DWT and the working flow. This section also provides a description of the dimensionality reduction process, comparing the results obtained with the two considered techniques. The change detection results are presented in Section 3 and discussed in Section 4. Finally, Section 5 describes the conclusion and future developments.