esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 4 Mesh in Bluetooth/BLE Networks
### Issues  
As of today, among different issues not entirely deeply covered and considered for BLE-oriented mesh networks, the most interesting one is represented by the need for interoperability among the nodes composing this kind of mesh network. This is highlighted more clearly by the fact that the SIG Bluetooth Mesh protocol has been presented as an \"update\" (not requiring additional hardware insertions) to be installed on the billions of different BLE devices already available on the market. In [51], the authors attempt to highlight the interoperability difficulties when a new software supporting Bluetooth Mesh has to be defined and designed, considering three different System-on-Chips (SoCs), namely: CC2650, BGM121, and nRF52832, made by Texas Instruments, Silicon Labs, and Nordic Semiconductors, respectively. The results show that, as can be easily expected, specific Real-Time Operating Systems (RTOS) and memory management core features must be defined when an interoperable solution has to be created, as well as that it is really hard to obtain a complete interoperability, for already functional Bluetooth devices, when the insertion of Bluetooth Mesh functionalities is tried. Another issue, affecting BLE-oriented mesh networks, to be handled is given by security, and this is due to the fact that BLE data streams are generally secured considering the single hop, while complete end-to-end authentication and encryption are still not natively provided. Moreover, in order to avoid location tracking threats or exploiting vendor-specific vulnerabilities, another aspect to be taken into account refers to the possibility to use private addresses (to be frequently updated) by BLE nodes. The main negative impact affecting this approach is related to the need for frequently updating the routing tables, thus leading to increased packet overhead. Moreover, handling a multicast communication in BLE mesh networks translates in supporting a sequence of unicast transmissions and, if the set of multicast endpoints becomes larger than the network size, then it is possible that flooding-based mechanisms are required, in order to better fit to this situation.
# RNA-Peptide Conjugation through an Efficient Covalent Bond Formation
## 3 Results  
### Effect of Solution pH for the Formation of Covalent Linkage  
The effect of solution pH for the formation of covalent linkage between the 3'-dialdehydedebyde group of the oxidized RNA subunit and the hydrazide group at the C-terminal of peptide subunit of the Rev-RRE complex was investigated by carrying out the reaction at various pH levels. The RNA subunit of ATP-binding of the RNP receptor, An16, was oxidized by using sodium periodate in a sodium acetate buffer at pH 5. The reaction was performed at 37 \({}^{\circ}\)C for 1 h. After isolation of the oxidized RNA by ethanol precipitation, acetylated Rev peptide, modified with a C-terminal hydrazide group through a flexible linker (GGS)\({}_{3}\)G (Ac-Rev-(GGS)\({}_{3}\)G-HZ), was mixed with the oxidized RNA in sodium acetate buffer at pH 4 or 5 or in a sodium phosphate buffer at pH 6 or 7. The reaction mixtures were incubated for 3 h at 37 \({}^{\circ}\)C, then the reactions were stopped by the addition of phenol/chloroform and vigorously mixed. The water layer was condensed by ethanol precipitation. The reaction yield of c-RNP in each solution was evaluated by quantitation of the band intensity, corresponding to free RNA and conjugated c-RNP in a denaturing polyacrylamide gel electrophoresis (PAGE) (Figure 2). Over 80% of RNA was reacted with peptide in all conditions. The yields of reaction were 92 \(\pm\) 4% at pH 4, 92 \(\pm\) 5% at pH 5, 86 \(\pm\) 3% at pH 6, and 82 \(\pm\) 3% at pH 7 (Figure S1A). These results indicated that the solution pH ranging from 4 to 7 did not affect much to the yield of RNA-peptide conjugate. Prolonged incubation over 15 h at pH 5 did not show any difference in the ratio of the band intensity for c-RNP and unreacted RNA (data not shown).  
Figure 2: A denaturing polyacrylamide gel electrophoresis (PAGE) analysis (8 M Urea) of the conjugation reaction products of c-RNPs at different pH conditions. The yield was calculated from the ratio of intensities of the bands corresponding to c-RNP and RNA. Lane 1: An16 RNA only; Lane 2: An16 and Ac-Rev-(GGS)\({}_{3}\)G-HZ reacted at pH 4; Lane 3: An16 and Ac-Rev-(GGS)\({}_{3}\)G-HZ reacted at pH 5; Lane 4: An16 and Ac-Rev-(GGS)\({}_{3}\)G-HZ reacted at pH 6; Lane 5: An16 and Ac-Rev-(GGS)\({}_{3}\)G-HZ reacted at pH 7.
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
###### Abstract  
The increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety of applications for Earth observation and monitoring. At the same time, it requires new techniques that are able to manage the amount of data stored and transmitted to the ground. Advanced techniques for on-board data processing answer this problem, offering the possibility to select only the data of interest for a specific application or to extract specific information from data. However, the computational resources that exist on-board are limited compared to the ground segment availability. Alternatively, in applications such as change detection, only images containing changes are useful and worth being stored and sent to the ground. In this paper, we propose a change detection scheme that could be run on-board. It relies on a feature-based representation of the acquired images which is obtained by means of an auto-associative neural network (AANN). Once the AANN is trained, the dissimilarity between two images is evaluated in terms of the extracted features. This information can be subsequently turned into a change detection result. This study, which presents one of the first techniques for on-board change detection, yielded encouraging results on a set of Sentinel-2 images, even in light of comparison with a benchmark technique.  
on-board processing; change detection; auto-associative neural networks; Sentinel-2 Article
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 2 Materials and Methods  
### Search Strategy and Scope  
A systematic bibliometric search was based on the Scopus database for gathering scholarly articles on in-vehicle communication protocols, such as CAN, LIN, FlexRay, MOST, and Automotive Ethernet. To expand the scope of the research, the search using the search word construction with the help of keywords, plus other terms such as \"vehicular communication\", \"automotive networking\", and \"in-vehicle systems\" was utilized. The pool was restricted to academic papers, conference publications, and peer-reviewed articles in English. Using this strategy, the research obtained authoritative and relevant academic work discussing the evolution of vehicular network technologies.  
The search string used was \"Controller Area Network\" OR \"CAN Bus\" OR \"Local Interconnect Network\" OR \"LIN Bus\" OR \"FlexRay\" OR \"Media Oriented Systems Transport\" OR \"MOST protocol\" OR \"Automotive Ethernet\" OR \"Time-Sensitive Networking\" OR \"TSN\" OR \"CAN FD\" OR \"in-vehicle network\" OR \"vehicular communication protocol\".
## 1 Introduction
### Problem Statement  
The result of a measurement is only meaningful if, besides the estimate of the value of the measurand, a statement about the _uncertainty_ of the estimate is given. Uncertainty is defined here as \"the parameter which characterizes the dispersion of the values that could reasonably be attributed to the measurand\" [7]. Stating uncertainties is the prerequisite for measurement results which are traceable to national standards. Metrological traceability is necessary for the comparability of measurement results, the foundation for quantitative scientific and commercial applications.  
The final, combined measurement uncertainty results from several uncertainty contributions, and the calibration uncertainty is one of them. If a SAR system is absolutely calibrated, the uncertainty with which the radar reflectivity of the reference point target is known has a direct impact on the combined uncertainty. Another source of uncertainty is the estimation of the calibration factor from external calibration measurements. Specifically, this second source of uncertainty is addressed in this paper.  
Currently, the derivation of measurement uncertainties in radiometric SAR system calibration is not systematically conducted. To the best of our knowledge, no operator of a previous or current space-borne SAR system claims to provide traceable radiometric calibration, and the quoted measurement uncertainties in the respective data product specifications or other publications [5,6,8,9,10,11] remain questionable. At the moment, a comprehensive uncertainty analysis, preferably in accordance with the ISO _Guide to the expression of uncertainty in measurement_ (GUM) [7], is lacking. This diminishes the scientific integrity of the data products and hinders further adoption of radiometricmeasurement data for advanced quantitative analysis and physical parameter inversion modeling, within and across SAR missions.
## 5 Results
### Performance Analysis  
We compared the performance of different DGGSs by measuring two general properties: area and shape distortions. We used the perimeter and area as the two direct measurements of partition cells. Since the edges of the partition cells are not always great circle arcs in all methods, we used an approximation method to compute the perimeter and area of the partition cells. At all levels of subdivision, we calculated the resolution of representation as not instantly level but level \(2^{(16-\text{subdivision level})}\) of the partition cells. A large number of flat triangular facets approximate a spherical triangle. We calculated the perimeters as the sum of the lengths of straight lines, and we calculated the area as the sum of the area of small planar triangles. For each comparison, we started with a set of partition cells on the sphere that were generated from one of the three polyhedrons, one of the two density ratios, one of four methods (including our approach) for mapping to the sphere, and one of the eight or five levels of recursion, depending on the density ratio.  
For a more natural interpretation of the comparisons, we used standardized measures. For area measurements, we converted the actual areas to regulated areas for each level of recursion by dividing the exact areas by the values listed in Table 2 to obtain regions relative to a unit value of 1. For shape measurements, we used a standardized perimeter-to-area ratio and called this the '_Zone Standardized Compactness_' (ZSC) [20] using the equation below.  
\[ZSC=\frac{\sqrt{4\pi a-\frac{a^{2}}{r^{2}}}}{p}, \tag{15}\]  
where \(a\) is the cell area, \(p\) is the cell perimeter, and \(r\) is the radius of the sphere. The resulting values were then dimensionless numbers between 0 and 1.  
In this subsection, we compare the distortion performance of our approach with that of some classical DGGSs. We implemented two recursive partitioning methods (4-fold, 9-fold) and three methods of mapping the partition from the polyhedron to the surface of the sphere. These three methods are the Snyder projection method, the geo bisector (geodesic bisector) method, and the Dymaxion projection. All these global partitions are based on the tetrahedron, octahedron, and icosahedron.  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline \multirow{2}{*}{**Level**} & \multirow{2}{*}{**Number of Cells**} & \multicolumn{3}{c}{**Area per Cell**} \\ \cline{3-5}  & & **Tetrahedron** & **Octahedron** & **Icosahedron** \\ \hline \multicolumn{5}{l}{(a) 4-fold} \\ \hline
1 & 4 & \(\pi/4\) & \(\pi/8\) & \(\pi/20\) \\
2 & 16 & \(\pi/16\) & \(\pi/32\) & \(\pi/80\) \\
3 & 64 & \(\pi/64\) & \(\pi/128\) & \(\pi/320\) \\
4 & 256 & \(\pi/256\) & \(\pi/512\) & \(\pi/1280\) \\
5 & 1024 & \(\pi/1024\) & \(\pi/2048\) & \(\pi/
## 8 Concluding Remarks  
The innate insect immune system shares many similarities with its mammalian counterpart, including PAMP-sensing PPRs, signal transduction pathways, immune gene expression transcription regulation, phagocytic mechanisms and the production of humoral immune effectors, such as AMP and ROS. The majority of studies reviewed herein examined _G. mellonella_ immune responses as a secondary consideration to their focus on developing anti-microbial infection models or screening for novel bio-insecticides. However, the work with corticosteroids, probiotics and cyclospor demonstrates the emerging recognition and feasibility of _G. mellonella_ as a novel pre-clinical approach to discover and characterise new immunomodulating compounds. _G. mellonella_ has attributes that lend it to the preliminary research of drug effects on the innate immune system with an extensive array of data outputs (see Table 2). It is compatible with the concept of the 3 Rs in that it is a suitable replacement for animals in screening experiments for NP immunomodulators. Despite _G. mellonella_ lacking antigen-specific, memory-based adaptive immunity, there is emerging evidence that larvae immune responses are greatly enhanced in response to re-infection. Furthermore, this immune \"memory\" is epigenetically inherited by subsequent insect generations [87]. Due to the specificity and ability to form \"memory\" this phenomenon, termed immune priming, has been suggested as being analogous to the mammalian adaptive immune system. However recent studies suggest both systems are functionally distinct [83] and that immune priming more closely resembles the phenomenon of \"trained immunity\" of vertebrate innate cells [88] rather than adaptive immunity _per se._  
One principal limitation was the lack of whole genomic sequence data, which is essential to carry out inter-species homology studies; however, this was alleviated by the recent sequencing of the entire _G. mellonella_ genome [27]. Another limitation that has yet to
## 4 Performance Evaluation and Analysis
### Results and Discussion
#### 4.3.1 Impact of Preprocessing on Model Performance
In the context of defect detection in wind turbine blades, it is crucial to predict defects accurately without missing them. The performance metrics associated with this requirement are precision and recall. The application of the transformation module to the synthetic thermographic image dataset impacted the performance of all models, demonstrating significant improvements, particularly in precision and recall for defect detection. Precision, which represents the probability that a prediction of a defect corresponds to an actual defect, improved by \(5.87\%\) in ResNet-50, \(9.71\%\) in DenseNet-121, and \(37.23\%\) in ViT. Recall, which indicates the proportion of actual defective data correctly identified by the model, improved by \(0.42\%\) in ResNet-50, \(7.71\%\) in DenseNet-121, and \(11.43\%\) in ViT. These results suggest that the transformation module enhances the physical characteristics of defect regions to more closely resemble those in real thermographic image data, thereby improving the reliability of deep learning models for defect detection. Figure 11: Comparison of Real Thermographic Images and Feature Maps Generated by Pre-DenseNet-121 and Post-DenseNet-121.
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 3 Technological Background
### Characteristics of CVs and CAVs  
The ever-increasing connectivity around the world has inevitably affected vehicles. Thanks to improvements in connectivity and computing power, CVs and CAVs are becoming the new platform for the development of new applications and services. Equipped with advanced sensing technology, CVs and CAVs generate rich traffic data and affect the human-vehicle dynamic, as well as change the impact of traffic on the environment and the economy [19]. CV technologies have already been used to improve fleet productivity and safety. Although the need for CVs and the technology that supports it has advanced significantly in recent years, there are still difficulties in deploying linked and collaborative vehicle applications before CVs' full potential may be realized. Applications for CVs are developed with an emphasis on solving transportation-related issues. The primary study topics are real-time data capture and management, V2V, and V2I communications for safety [15]. Being a rich data source on the microscopic level, the development of CVs created research potential for various applications, and the CV features also found their applications in intersection control systems. Providing real-time speed, heading, and position is valuable data in traffic control systems, allowing additional enhancements in terms of traffic efficiency and safety.  
The Society of Automotive Engineers (SAE) in [20] defined six levels of automation, where level 0 is without any automation, and level 5 implies full autonomy. The first three levels are vehicles equipped with support features for drivers. Level 3 automation means that the driver must take over control of the vehicle when requested. Levels 4 and 5 are capable of autonomous driving without requesting the driver to overtake control [20]. Although CAVs have a higher SAE level of automation, they can be considered as a subgroup of CVs since they employ V2X communication. Since CAVs are equipped with sensors, which can detect adjacent objects and the ability to communicate with other CVs and CAVs, their driving characteristics will be different from those of conventional HDVs.  
In contrast to the delay in human reaction time or the negative impacts of human mistakes, CAVs are able to obtain more precise driving-condition characteristics than human perception, enabling them to respond to changes in driving circumstances more quickly. The distance between two successive CAVs is much smaller than that between two conventional HDVs because the CAVs can be driven closely together using Adaptive Cruise Control (ACC) technology [21]. It is important to note that the ACC technology is not reserved for CAVs only; it is present in conventional HDVs too, but without collaboration capability with other vehicles.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 5 Discussion
### Scalability of the Proposed Method  
Our proposed denoising method exhibits strong scalability. The full-scale skip connections in our approach effectively integrate multi-scale information, while the SW-MSA, GFRN, and GCEN modules within the Transformer architecture excel at capturing complex noise patterns. This not only makes the method well-suited for the low-light conditions of the lunar environment but also highlights its potential applicability to denoising tasks in low-light terrestrial conditions or heavily degraded images. By adjusting the noise characteristics of the training dataset, this method holds promise for broader applications, such as processing extraterrestrial imagery or addressing image denoising tasks under various noise conditions.
## 2 Technical Backgrounds
### Cloud Computing and Edge Computing  
Edge computing and fog computing are concepts that process data from numerous devices connected to the network using the middle layer between the end device where the data is generated and the cloud [11]. Edge computing is a technology that efficiently operates a network by processing data in and around the device where the data is generated [12]. Edge computing can reduce traffic to the cloud or data centers by processing data locally without having to move the data to the cloud. Edge computing technology ensures that networks of heterogeneous devices have adequate security capabilities [13]. Fog computing is a technology that efficiently manages networks by placing nodes in the area of the local environment to process data [14]. The fog layer connects the terminal nodes of the edge layer and services of the cloud layer. Knowledge information from the data collected at the edge layer is used to efficiently manage the network and improve the capacity of the entire network [15]. These hierarchical network architectures are key technologies to meet the enhanced Mobile Broadband(eMBB), Ultra-Reliable Low Latency Communications(URLLC), and massive Machine Type Communications(mMTC) requirements of 5G networks [11].
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 3 Solution
### Image Segmentation  
The input image sets always have the same number of images (otherwise both sets can immediately be declared as invalid). The Stanford Cars dataset [25] was used in experiments. It is a well-recognized car dataset providing model labels (annotation) and is cited in many research papers. It is freely available to the public and the attached annotation reduces the effort needed for training data preparation. The order of the images is not relevant (the solution must be invariant with respect to index permutation). We assume that each set has images of four views: the front, side, front-side and back-side view.  
Figure 3: The architecture of the image set comparison approach.  
Although in general our approach is view-independent--the user can decide to include any other sets of views--in our opinion, and for the given dataset, these views are the best selection. Our dataset does not have a good representative of back view images--this is the reason why we do not consider such a view. Every image is labeled by one of 4 views, 9 car types, and 197 car models. A class label \"other\" is also given to images which do not match any basic class.  
Image segmentation follows the semantic segmentation approach based on a Mask-RCNN model [6]. To train the Mask-RCNN model, we used the COCO dataset (2017 version) [20]. COCO contains 81 categories of objects sufficient for training and testing the entire model. Semantic segmentation removes unnecessary parts of images and only keeps the parts that are consistent with the created model (application context). In particular, we focus on \"Car\" and \"Truck\" categories from the COCO dataset. The Mask-RCNN implementation uses a ResNet 101 [21] neural network architecture as the backbone.  
In Figure 4, an example of car mask detection results from two steps:  
1. the rectangular boundary box for car region extraction comes from the annotations given in the Stanford Car dataset, and
2. the Mask RCNN generates the proper envelope of the car image region within the boundary box (from previous point).
## 2 Signal Processing Principles
### Frequency Correlation Detection  
To distinguish the photon pulses corresponding to different carrier frequencies, this study employs the cross-correlation detection method for frequency detection. The specific calculations are as follows:  
\[P_{i}(t)=\sum S_{i}(t)\cdot\alpha_{i} \tag{5}\]  
where \(S_{i}(t)\) denotes the sequence of photon pulse counts in the \(i\)-th frequency slice at moment \(t\), and \(\alpha_{i}\) represents the pre-defined frequency feature set of the system. By analyzing the four frequency window output curves derived from cross-correlation calculations, the transmission frequency carrier at the current moment can be determined. \(P_{i}(t)\) represents the frequency window output sequence for the \(i\)-th carrier, which is the result of cross-correlating the frequency slice pulse count at time \(t\) with the frequency characteristic value.