esa-sceva/satcom-chunk-collection huggingface dataset:
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 3 Edge Computing and Deep Learning-Based Malware Detection Method
### Main Functions of the Edge Computing Deep Learning-Based Malware Detection System
#### 3.4.3 Model Inference  
The edge device extracts (preprocessing) the characteristics of the collected data to be analyzed and performs inference (attack detection) with the distributed deep learning model. If malware is suspected as a result of detection, the system administrator is notified or measures are taken to automatically block the packet. The deep learning detection should be performed in real time using the limited hardware resources of each edge device (G/W).
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### Implementation Framework for Multi-Regional Load Forecasting Integration
#### 2.5.3 Provincial Forecast Generation  
Our framework preserves the autonomy of provincial forecasting systems while implementing standardized interfaces for forecast collection and evaluation. Rather than imposing a single forecasting methodology, our framework accommodates diverse provincial approaches based on regional characteristics:  
* Regions with stable industrial profiles: Statistical methods like ARIMA or exponential smoothing.
* Regions with complex seasonal patterns: Recurrent neural networks (LSTM, GRU).
* Regions with multiple influential external factors: Attention-based architectures.
## 5 Experimental Results
### Security and Privacy Analysis  
FlexiChain Technology is built with CPS applications in mind; therefore, security measures are built in at both the hardware and software levels. Researchers may assess the efficacy of the technique by simulating a variety of security threats [16]. Such attacks include  
Figure 8: The 64 FlexiChain Nodes Registered.  
Figure 9: Nodes Containers on Docker.  
corrupting the exchanged data between nodes, implanting incorrect data during communication, and full malicious control over the network authority. The feasibility that each attack can take place will be calculated and compared to each other for each scenario [16]. Three scenarios are proposed: traditional central authority, blockchain technology and FlexiChain technology for the listed attacks below:  
1. Attack-1: Data Corruption: For this attack, the digital assets exchanged among participants will be corrupted.
2. Attack-2 Implant Incorrect Data: For this attack, malicious activity by implanting incorrect data takes place while transacting data.
3. Attack-3: Central Authority Full Malicious Control: For this attack, maliciously expose the authority database.
## 6 Conclusions and Future Direction  
### Conclusions  
This paper has explored the critical role of perceptual constraints in information theory and their impact on practical generative modeling scenarios. We have demonstrated that incorporating perceptual constraints into the RD framework, resulting in the RDP framework, offers a novel and comprehensive perspective for evaluating the performance of AI-empowered systems in compression and communication. We have discussed the implications of the perceptual constraint on the design and deployment of generative models, which is particularly relevant in intelligent communication systems, where downstream tasks demand high perceptual fidelity, such as interactive AI agents and visually pleasing content. By highlighting the importance of perceptual constraints and demonstrating their impact on system design and performance, we project an increase in expected traffic in intelligent communication networks with consideration of perceptual quality. While our theoretical analysis suggests that incorporating perceptual constraints generally requires additional rate, we acknowledge that intelligent compression techniques can significantly mitigate this traffic growth.
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 5 Conclusions
_arXiv_**2023**, arXiv:2304.04421. * [10] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Video Stabilization via Robust Homography Estimation. _IEEE Signal Process. Lett._**2023**, _30_, 1602-1606. [CrossRef]
* [11] [PERSON]; [PERSON]; [PERSON] Out-of-boundary view synthesis towards full-frame video stabilization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 11-17 October 2021; pp. 4842-4851. * [12] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-scale progressive fusion network for single image deraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13-19 June 2020; pp. 8346-8355. * [13] [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Satellite video super-resolution via multiscale deformable convolution alignment and temporal grouping projection. _IEEE Trans. Geosci. Remote. Sens._**2021**, _60_, 1-19. [CrossRef]
* [14] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Real-time visual tracking of moving targets using a low-cost unmanned aerial vehicle with a 3-axis stabilized gimbal system. _Appl. Sci._**2020**, _10_, 5064. [CrossRef]
* [15] [PERSON]; [PERSON] Long short-term memory. _Neural Comput._**1997**, \(9\), 1735-1780. [CrossRef] [PubMed]
* [16] [PERSON]; [PERSON] Gyroflow Project. Available online: [[https://gyroflow.xyz](https://gyroflow.xyz)]([https://gyroflow.xyz](https://gyroflow.xyz)) (accessed on 22 April 2023). * [17] [PERSON] Converting video formats with FFmpeg. _Linux J._**2006**, _2006_, 10. * [18] [PERSON]; [PERSON] Deep iterative frame interpolation for full-frame video stabilization. _ACM Trans. Graph. (TOG)_**2020**, _39_, 1-9. [CrossRef]
* [19] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Minimum latency deep online video stabilization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 2-6 October 2023; pp. 23030-23039. * [20] [PERSON]; [PERSON]; [PERSON]; [PERSON] Image quality assessment: from error visibility to structural similarity. _IEEE Trans. Image Process._**2004**, _13_, 600-612. [CrossRef] [PubMed]
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# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 6 Effects of Coil Misalignments  
The evaluation of coil misalignment effects is important in the design of inductive links for implantable sensor devices. In our intraocular sensor device, the implant coil could also be moved due to the movement of the eyeball and the displacement of the glasses.  
Figure 9 shows the calculated coupling coefficients (\(k_{12}\) and \(k_{23}\)) versus the misalignments of the implant coil along the X and Y axes when the coil separation (\(d\)) is 20 mm. The directions of the X axis and Y axis are illustrated in Figure 3. It can be seen that both \(k_{12}\) and \(k_{23}\) are insensitive to the misalignments along the X axis. \(k_{12}\) and \(k_{23}\) are sensitive to the misalignments along the Y axis. Along with the misalignments in the Y+ axis, \(k_{12}\) decreases while \(k_{23}\) increases. Along with the misalignments in the Y axis, \(k_{12}\) increases while \(k_{23}\) decreases. This asymmetry phenomenon is due to the small offset (\(d_{12}\)) of the implant coil in the Y axis. In the worst case of our application, the misalignment of 6 mm in the Y axis could lead to about a \(\frac{1}{3}\)-times decrease of \(k_{23}\). The decrease of \(k_{23}\) could lead to about a \(-9.5\) dB decrease of the received uplink signal power, which is acceptable to our uplink system because that leaves a 10-dB reliable margin for the uplink receiving as is mentioned in Section 5.  
Figure 8: Simulated received uplink signal power and the loaded quality factor \(Q_{\text{L\_Proposed}}\) versus \(R_{\text{isolate}}\), when the coil separation \(d\) = 40 mm.  
Figure 9: Calculated coupling coefficient \(k_{12}\) and \(k_{23}\) versus three different misalignments of the implant coil (Y+’ in the notation means misalignments in the positive direction of the Y axis, and ‘Y-’ means in the negative direction. The directions of the X axis and the Y axis are illustrated in Figure 3).
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology
### RC and Video Jamming
However, this interference was negligible and did not compromise the control or functionality of the devices. The interference gradually became more noticeable as the distance between the operator and the drone increased. At around 85 m, the interference started to affect the connection, causing video transmission interruptions and introducing a slight delay in executing the maneuvers commanded by the operator. This interference intensified progressively until the connection was lost at approximately 120 m. At this point, the drone stopped responding to the operator's commands, and the video transmission froze, displaying the last frame in black and white on the remote control, along with the message \"Remote Controller signal lost\", as depicted in Figure 14.  
Another drone tested was the Autel Evo Nano+. With this drone, successful jamming was attained at much shorter distances, with effective jamming observed starting from 50 m. Beyond this distance, the drone ceased responding to the operator's commands, and the video feed froze on the last received frame. The Husban Zino Mini Pro was the only drone for which jamming did not interfere in any way. The FCC ID showed that it only used the 2.4 GHz band, and no reaction to the jamming was observed. Further research revealed that this drone was a \"Refined\" version, designed to switch to 4G communication, and was possibly the only one in its weight class [36]. Finally, the ZLL SG 108, a very simple and rudimentary drone compared to the others, which lacked a visual interface, proved to be the most affected by jamming. At 30 m, the drone became unresponsive and landed automatically.
## 2 Measurement Principles of Four-Sensor Probe
### Innovations of the Present Probe Algorithm  
In the pioneering literature related to four-sensor probe, the interfacial velocity can only be obtained for the component that is vertical to the interface itself. If the full interfacial velocity vector is intended to be obtained, assumptions must be made. For instance, bubbles formed in the flow field in [51] were so small that they were assumed to be spherical. In [53], the authors made an assumption that the interface is very large and it moves only in its normal direction. In [56], the flattened bubbles were regarded as symmetrical to a center plane. Although these assumptions help ascertain the full components of the interfacial velocity, they only apply in special cases since the interface in practical gas-liquid flow is usually quite complicated.  
Considering the fact that the explicit expression of bubble or interfacial velocity for four-sensor probe has not been completely developed, this paper proposes that, if the moving direction of interface can be ascertained prior to probe measurement, then the above assumptions will be unnecessary and the full interfacial velocity can be acquired. Fortunately, the fields of averaged moving directions of the interface, for so many quasi-steady two-phase flows, are actually the flow fields for the two-phase mixture and are easy to make certain through methods of prior measurement, simulation or even legitimate assumptions. These are the primary innovations of the present algorithm for four-sensor probe, as shown in Section 2.4.  
Besides, the signal deduction process is all vector-based, which is different from the complex matrix, trigonometric functions and tensors. Although the basic rules are the same in essence and the results are expected to be same, no matter vector-based or trigonometric function-based algorithms are used, the method proposed in this paper has the merits of easy to read, clear and efficient to modify or improve.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Application Layer Transmission Requirements
#### 2.3.3 CoAP Protocol  
CoAP [41] is a data transfer protocol between resource-limited devices. It provides four methods for resource data operation: PUT, DELETE, GET, and POST. It is based on the UDP protocol and uses its own lightweight protocol to ensure the reliability of packet transmission. Currently, it is standardized in draft RFC 7252. There are many open-source implementations of this protocol that are based on languages such as Java, C, Go, Node.js and can run on Arduino and other micro controllers.
# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 2 Related Background
### Deep Compressed Sensing  
In 2019, [PERSON] et al. proposed deep compressed sensing (DCS) based on CSGM. They introduced the model-agnostic meta-learning (MAML) algorithm [28] to solve the problem of thousands of gradient descent required for CSGM reconstruction. The process of the inner loop is shown in Formula (8):  
\[\theta_{i}^{\prime}=\theta-\alpha\
abla_{\theta}\mathcal{L}_{\mathcal{T}_{i}}( f_{\theta}) \tag{8}\]\(f_{\theta}\) is a parameterized function, which is represented as a network model. \(\mathcal{T}_{i}\) is the task, which is sampled from the task distribution \(p(\mathcal{T})\). \(\alpha\) is the inner loop learning rate. In the inner loop process, the model parameter \(\theta\) is optimized to \(\theta_{i}^{\prime}\) in order to fit task \(\mathcal{T}_{i}\).  
\[\underset{\theta}{min}\sum_{\mathcal{T}_{i}\sim p(\mathcal{T})}\mathcal{L}_{ \mathcal{T}_{i}}\Big{(}f_{\theta_{i}^{\prime}}\Big{)}=\sum_{\mathcal{T}_{i} \sim p(\mathcal{T})}\mathcal{L}_{\mathcal{T}_{i}}\Big{(}f_{\theta-\alpha} \
abla_{\theta}\mathcal{T}_{i}(f_{\theta})\Big{)} \tag{9}\]  
The optimization goal of parameter \(\theta\) is to find a \(\theta_{i}^{\prime}\) that adapts task \(\mathcal{T}_{i}\) to minimize the loss of current task \(\mathcal{T}_{i}\).  
Finding the \(\theta_{i}^{\prime}\) of the adaptation task is equivalent to finding the exact direction of gradient descent of the model parameter \(\theta\) during the outer loop process. The parameter \(\theta\) of the model \(f_{\theta}\) is updated as:  
\[\theta\leftarrow\theta-\beta\
abla_{\theta}\sum_{\mathcal{T}_{i}\sim p( \mathcal{T})}\mathcal{L}_{\mathcal{T}_{i}}\Big{(}f_{\theta_{i}^{\prime}} \Big{)} \tag{10}\]  
\(\beta\) is the outer loop learning rate. The alternating optimization of the inner and outer loops to the same objective accelerates the convergence of the network model in training. Unlike CSGM, DCS introduced MAML so that the generative model did not have to pre-train. Moreover, the observation matrix was set from the untrainable random matrix \(\mathbf{\Phi}\) to the trainable observation model \(\mathbf{\Phi}_{DCS}\). The RIP loss function for the observation model \(\mathbf{\Phi}_{DCS}\) was proposed, and the generation loss was combined to update the generative and observation models.  
The most significant feature of DCS is the introduction of MAML, the observation and reconstruction can be deep neural networks, and the reconstruction and optimization efficiency is significantly better than CSGM.
# Coal-Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT
## 1 Introduction
To address these challenges, one work from the literature [21] proposed a CNN-based coal-rock identification method utilizing hyperspectral data. By preprocessing the reflectance spectral data of coal and rock, the suggested one-dimensional convolutional neural network model achieved a recognition accuracy of 94.6%. In 2022, [PERSON] et al. [22] proposed a coal identification model combining a convolutional neural network and extreme learning machine. It provides a low-cost, convenient, and effective method for the rapid identification of on-site coal. In 2022, [PERSON] et al. [23] proposed a deep learning target detection algorithm to replace artificial coal-gangue preparation. The problem of the vanishing generator gradient was overcome. The image generated by the improved Deep Convolutional Generative Adversarial Networks (DCGANs) can improve the recognition accuracy of coal-gangue by 5.87%. [PERSON] et al. [24] proposed a primary process of underground raw coal with mechanical sieve jig as the main washing equipment. The main structures of the jig drive mechanism and waste discharge mechanism are optimized and improved. The process requirements and narrow space environment of the underground mine are satisfied, and the effective separation of coal and gangue is realized. [PERSON] et al. [25] proposed a threshold sorting method based on dual-energy X-ray technology. The recognition rates of 150-300 mm materials were 96.99% and 92.32%, respectively. This study aimed to identify coal-gangue, using near-infrared spectroscopy. Although previous studies investigated recognition and classification algorithms, they did not achieve the desired degree of accuracy. Thus, this study converted collected coal and gangue hyperspectral data into RGB images and used them as input for various neural network models (CBAM-VIT, VIT [26], AlexNet [27], and VGG-16 [28]). The study further trained and tested these models on the collected datasets. The t-distributed Stochastic Neighbor Embedding (t-SNE) technique [29] was utilized to reduce the dimensionality of the near-infrared spec trum data. Finally, comparative experiments were conducted to comprehensively evaluate the proposed network model. The main work of this paper is as follows:  
1. The features of the three preprocessing methods are fused and converted to RGB fusion images by signal image conversion. 2. A novel CNN-based ViT algorithm is introduced, aiming to comprehensively incorporate the information from both local and global features of coal and gangue data, thereby significantly enhancing the recognition performance.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 5 Experiment Design
### Training
#### 5.1.2 Data Augmentation  
We utilized data augmentation techniques during training to improve the model's generalisability and increase the segmentation accuracy. Thanks to this, the number of the image examples expanded artificially with each technique while maintaining the data characteristics that may naturally occur in the data. The following transformations performed in 2D for each cross-section were used:  
* Horizontal Flip--Allows obtaining a mirrored image which coincides with having a scan of the other eye (left for right and right for left), preserving the morphology of retinal structures (such as vessels and layers topology).
* Rotation--Slight variations in retina orientation are natural when acquiring an OCT scan. Thus, augmenting images in this way allows training the model to anticipate any future examples in a wide variety of retina orientation angles. In order to determine the range of random orientations to apply, we performed a statistical analysis of a retina orientation distribution within the CAVRI dataset. As can be seen in Figure 5, the obtained results for all subsets have similar distribution and are within \(\pm 25\) degrees. Thus, a rotation with a randomly chosen angle in the range of \(\pm 20\) degrees was performed for each image.
* Vertical Translation--Automatic acquisition protocol in an OCT device aims at focusing the device's optics on the retina. Notably, the thickness of the retina tissues extends to an average of 200 \(\upmu\)m within a 2 mm depth of the scan. Therefore, we performed a statistical analysis of the retina position within the image (across all cross-sections in the database) to determine the retina vertical position distribution. For that purpose, we estimated the center of mass in each image and plotted the obtained positions within the image dimensions range as illustrates Figure 6. It can also be noted that each subset maintains a similar distribution, confirming appropriate dissemination of samples between the subsets. Based on the gathered information, we set the range of vertical translation of the image to \(\pm 10\%\) of the image height, equal to \(\pm 64\) px.
* Random Crop--The wide variety of OCT acquisition devices allow for an even greater number of scanning protocols, with various image sizes and scanning widths. Thus, performing an augmentation technique of random cropping, we train the network to perform well on any input image regardless of its size or fovea width to image width ratio. In our experiment, we employed a crop with randomly selected values for both width and height (within the range of 80-100% of the original values).  
\begin{table}
\begin{tabular}{c c c c} \hline
**0: Vitreous** & **1: Preretinal Space** & **2: Retina** & **3: Region below Retina** \\ \hline
0.1 & 0.5 & 0.29 & 0.11 \\ \hline \end{tabular}
\end{table}
Table 2: Weights associated with each of the segmented class in the CAVRI dataset.  
Utilizing such data augmentation techniques allowed to increase the number of training examples, as shown in Table 3.
## 2 Materials and Methods
### Characterization  
The produced N-TiO\({}_{2}\) powder was physicochemically and structurally analyzed by various techniques, including FTIR, DLS, BET, SEM/EDS, and TEM analysis. Each FTIR (model Spectrum 100, U-ATR, ZnSe Ri 2.4 crystal, PerkinElmer, Waltham, MA, USA) spectrum was obtained by performing 16 scans, with a 4 cm\({}^{-1}\) resolution. The size distribution and zeta potential of the NPs were estimated with a dynamic light scattering apparatus (Zeta Sizer nano S, Malvern Inst., Malvern, UK). All of the measurements were performed at 25 \({}^{\circ}\)C and pH 6, on aqueous suspensions of the powder. The Brunauer-Emmett-Teller (BET) method (ChemBET 3000 Instrument, Yumpu, Diepoldsau, Switzerland) was applied to analyze the surface area, the average pore diameter, and the pore volume of the N-TiO\({}_{2}\) NPs. The micropore surface area was calculated via t-plot analysis, according to the Harkins-Jura model; the cumulative volume of pores between 1.7 and 300 nm was calculated from N\({}_{2}\)-sorption data and the BJH desorption method, and the average pore diameter was calculated by the 4 V/\(\sigma\) method, where V was set to be equal to the maximum volume of N\({}_{2}\) adsorbed along the isotherm as P/Po\(\rightarrow\)1.0. The morphological characteristics and surface of the N-TiO\({}_{2}\) powder were examined using a scanning electron microscope with energy-dispersive X-ray spectroscopy (SEM/EDS, JSM-6390, JEOL, Tokyo, Japan), along with a transmission electron microscope (TEM, CM20, Philips, Amsterdam, The Netherlands).
## 5 Results and Discussion
### Average E2E Delay  
Figure 16a shows that the average E2E delay for a malicious network is much higher than the NTRUEncrypt-secured and healthy network of 20 nodes. This is because packet drops increase in the malicious network because an attacking node is added to this networkwithout any precautionary measures, and data take time to reach the sink/destination node. The other reason is the node's unavailability, so the source node selects another alternative path. By doing so, time consumption occurs, and it causes delays. The average E2E delay of the NTRUEncrypt-secured network slightly increases compared to the healthy network because of the associated overhead of the cryptographic algorithm. However, NTRUEncrypt achieves significant improvement when compared to the malicious network, resulting in a decrease in delay in the presence of an attacking node.