esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Fuzzy Vector Signature with Reusability
###### Abstract  
Fuzzy vector signature (FVS) is a new primitive where a fuzzy (biometric) data \(w\) is used to generate a verification key (\(\mathsf{VK}_{w}\)), and, later, a distinct fuzzy (biometric) data \(w^{\prime}\) (as well as a message) is used to generate a signature (\(\sigma_{w^{\prime}}\)). The primary feature of FVS is that the signature (\(\sigma_{w^{\prime}}\)) can be verified under the verification key (\(\mathsf{VK}_{w}\)) only if \(w\) is close to \(w^{\prime}\) in a certain predefined distance. Recently, [PERSON] et al. proposed an FVS scheme that was constructed (loosely) using a subset-based sampling method to reduce the size of helper data. However, their construction fails to provide the reusability property that requires that no adversary gains the information on fuzzy (biometric) data even if multiple verification keys and relevant signatures of a single user, which are all generated with correlated fuzzy (biometric) data, are exposed to the adversary. In this paper, we propose an improved FVS scheme which is proven to be reusable with respect to arbitrary correlated fuzzy (biometric) inputs. Our efficiency improvement is achieved by strictly applying the subset-based sampling method used before to build a fuzzy extractor by [PERSON] et al. and by slightly modifying the structure of the verification key. Our FVS scheme can still tolerate sub-linear error rates of input sources and also reduce the signing cost of a user by about half of the original FVS scheme. Finally, we present authentication protocols based on fuzzy extractor and FVS scheme and give performance comparison between them in terms of computation and transmission costs.  
fuzzy vector signature; reusability; biometric authentication +
Footnote †: journal: Applied Sciences
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 3 Results and Analysis
### Reconstruction Quality Analysis and Comparative Experiment  
To better characterize the results of the deep learning method proposed in this paper in autocorrelated image recovery. In this paper, peak signal-to-noise ratio (\(PSNR\)), structural similarity index (\(SSIM\)), and Mutual Information (\(MI\)) are used as the quality evaluation of the self-correlated images recovered by deep learning.  
Figure 11: Results obtained using 600-mesh frosted-glass collection data as the training set and testing on 220-mesh frosted-glass collection data.  
The \(PSNR\) is expressed as:  
\[PSNR(x,y)=10\log_{10}\frac{MAX^{2}}{\frac{1}{m\times n}\sum_{i=1}^{m\times n}(x_{ i}-y_{i})^{2}} \tag{10}\]  
where \((m\times n)\) is the number of pixels of the image, \(x_{i}\) is the pixel value of each pixel of the reconstructed image, \(y_{i}\) is the pixel value of each pixel of the reference image, and \(MAX\) is the maximum value of the image. The \(SSIM\) is expressed as:  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+c_{2}\right)} \tag{11}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the average values of the total pixels in the reconstructed image and the reference image, respectively. \(\sigma_{x}\) and \(\sigma_{y}\) are the variances of the reconstructed image and the reference image. \(\sigma_{xy}\) is the covariance of the reconstructed image and the reference image. \(c_{1}\) and \(c_{2}\) are two small positive numbers used to avoid being divided by 0. The \(MI\) is expressed as:  
\[MI(x,y)=\sum_{i=0}^{L-1}\sum_{j=0}^{L-1}h_{xy}(i,j)\log_{2}\frac{h_{xy}(i,j)}{ h_{x}(i,j)h_{y}(j)} \tag{12}\]  
_MI_ reflects the same amount of information in two images. Where \(h_{x}(i,j)\) and \(h_{y}(j)\) represent histograms of \(x\) and \(y\), and \(h_{xy}(i,j)\) represents the joint histogram of these two images.  
Combined with the evaluation metrics, the effectiveness of this paper's method is first verified, while the results are analyzed and compared with those of mainstream target image recovery methods. The results are shown in Table 1 below.  
Analysis of the data in the table shows that the method in this paper performs well in the recovery of autocorrelated images, as indicated by the values marked in bold. The results of _MI_ evaluation show that the method effectively recovers the target information.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 4 Resource Management Algorithm
### Outer Loop Procedure  
Once the AL sub-problems are approximately solved, the dual variables and the penalty parameters are updated in the outer loop. We introduce the constraint violation indicator function \(h(Z^{n})\), which measures the violation of equality constraints in the \(n\)th iteration.  
\[h(Z_{i}) =[t_{k,j}-\frac{D_{i}\cdot\alpha_{k,i}}{sC_{0}}-\frac{D_{i}\cdot W _{k,i}}{B},\] \[t_{0,j}-\frac{D_{i}\cdot\alpha_{0,i}s}{C_{0}},\] \[P_{k,i}\cdot\kappa_{k,i}\cdot\alpha_{k,i}-V_{k,i},\] \[\alpha_{k,i}\cdot\kappa_{k,i}-W_{k,i},\] \[\kappa_{k,i}\cdot\log_{2}(1+\frac{P_{k,i}\mu_{0}}{\sigma^{2}d^{2} })-1] \tag{48}\]If the indicator satisfies \(\|h(Z^{n})\|\geq\eta_{k}\) or the loop number is less than the maximum loop number \(N\), the AL multipliers and penalty factors \(\lambda_{k,i},\rho_{k,i}\) are updated by the following formulas:  
\[\lambda^{n}_{k,i}=\lambda^{n-1}_{k,i}+\frac{1}{\rho_{k}}\left(h(Z_{ i})\right) \forall k,i \tag{49a}\] \[\rho^{n}_{i}=\gamma\rho^{n-1}_{i} \forall i \tag{49b}\]  
where \(\gamma\) is a constant between 0 and 1. It is easy to see that, when \(h(Z^{n})=0\), the problem is solved to a Karush-Kuhn-Tucker (KKT) stationary solution. After \(n\) iterations of the algorithm, the violation indicator will approach some threshold, \(\zeta\), and if \(\zeta\leq\eta\) (where \(\eta\) is defined accuracy threshold), we obtain the approximate KKT solution of the problem. The flowchart and pseudocode of the proposed DB-IPDD algorithm are shown in Figure 2 and Algorithm 1, respectively.  
```
Input: Initialization: \(Z^{0}=\{a^{0},P^{0},x^{0},W^{0},V^{0}\},\lambda^{0},\rho^{0}\); Output:\(Z^{*}\)
1 for\(i\gets 1\to N\)do
2 for\(j\gets 1\to Iteration\) Numberdo
3 for\(k\gets 1\to M\)do
4 update \(W_{k,i}\) via (42a)
5 update \(\alpha_{k,i}\) via (43a)
6 update \(P_{k,i}\) via (44a)
7 update \(V_{k,i}\) via (45a)
8 update \(\alpha_{k,i}\) via (46a)
9 update \(\alpha_{k,i}\), \(P_{k,i}\) via (47)
10 update \(\rho^{n}_{i}\), \(\lambda^{n}_{k,i}\) via (49)
11 end for
12
13 end for return the KKT point \(Z^{*}=\{\alpha^{*},P^{*},\kappa^{*},W^{*},V^{*}\}\)
```  
**Algorithm 1** DB-IPDD Algorithm
# Automated Design of Salient Object Detection Algorithms with Brain Programming
## 3 Methodology
### Evaluation Measures  
Evolutionary algorithms usually apply a previously defined fitness function to evaluate the individuals' performance. BP designs algorithms using the generated \(EVOs\) to extract features from input images through the ADS hierarchical structure depicted in Figure 1. Experts agree on evaluating the various proposals for solutions to the problem of salient object detection. In this work, we follow the protocol detailed with source code in [29] and apply two main evaluation measures: precision-recall and F-measure. Researchers represent the first through Equation (5):  
\[Precision=\frac{|BM\cap G|}{|BM|},\quad Recall=\frac{|BM\cap G|}{|G|}. \tag{5}\]  
To compute a saliency map \(S\), we convert it to a binary mask \(BM\), and compute \(Precision\) and \(Recall\) by comparing \(BM\) with ground-truth \(G\). In this definition, binarization is a critical step in the evaluation. The benchmark offers a method based on thresholds to generate a precision-recall curve. The second measure (Equation (6)) is made with this information to obtain a figure of merit:  
\[F_{\beta}=\frac{(1+\beta^{2})Precision\times Recall}{\beta^{2}Precision+Recall}. \tag{6}\]  
This expression comprehensively evaluates the quality of a saliency map. The F-measure is the weighted harmonic mean of precision and recall. In the benchmark, \(\beta^{2}\) is set to 0.3 to increase the importance of the precision value.  
We calculate both evaluations with two variants. In our first approach, we obtain the maximum F-measure considering different thresholds for each image during the binarization process. Then, we calculate the average of all photos in the training or testing set; see [5]. The benchmark uses the second variant, which consists of calculating the average that results from varying the thresholds and then reporting the maximum resulting from evaluating all the images. We will use both approaches during the experiments to ensure that such evaluations do not change the rankings.
## 1 Introduction  
Mild cognitive impairment (MCI) is a mild neurocognitive disorder characterized by a subjective and objective decline from a previous level of functioning in one or more of the six cognitive domains that does not substantially interfere with the instrumental activities of daily living and does not occur in the context of delirium or other psychological disorders [1]. The key criteria that distinguish MCI from dementia are preservation of independence in functional abilities and lack of significant impairment in social or occupational functioning. In addition, MCI subtypes are sometimes defined on the basis of the presence or absence of memory difficulties and the number of affected cognitive domains [2]. The estimated prevalence of MCI ranges from 18.8% to 28.3% in people over 60 years of age, with people affected by this condition being at greater risk of developing dementia than the general population and with annual progression rates between 10% and 15% [3; 4; 5].  
There are several risk factors that increase the probability of developing MCI. While age is the strongest risk factor, other well-established factors include male sex; the presence of the apolipoprotein E allele; a family history of cognitive impairment; and vascular risk factors such as hypertension, hyperlipidemia, coronary artery disease, stroke, and other chronic medical conditions, such as chronic obstructive pulmonary disease, depression, and diabetes mellitus [6; 7; 8]. Currently, there are no available disease-modifying therapies for MCI, although symptomatic and supportive treatments have proven to be valuable [9]. In fact, among the risk factors, sedentary behavior and low physical activity might play a role in the transition from MCI to dementia. Physical activity and exercise have been shown to improve not only global cognition but also several noncognitive outcomes, such as disability, falls, and neuropsychiatric symptoms, in participants affected by dementia [10; 11].  
In this context, COGNIVITRA is an information and communications technology (ICT)-based solution that targets people \(\geq\)50 years old at risk for cognitive impairment, at risk of worsening cognitive impairment or with MCI [12; 13]. Its aim is to support cognitive vitality training at home, extending cognitive and physical stimulation programs performed in hospital or clinical environments to a community setting (e.g., home care center or patient home). It integrates both supporting cognitive and physical exercises (dual-task training) [14] through web-based tools and movement sensors, building a centralized platform that facilitates the interface and communication between patients and care providers. The objective of the present study was to determine the implementation of this dual-task training system and analyze its usability, the impact of its implementation strategy on the population's access to specialized cognitive care, and its economic impact.
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 5 Discussion
The thick oil layer with a dark black tone is likely heavy oil, such as bunker C oil, while the thin oil layers with bright silver and rainbow tones are likely diesel oil. Distinguishing oil types through color characteristics and histogram analysis offers substantial practical value for environmental monitoring and response. By categorizing oil spills based on color characteristics, such as dark black tones for heavy oils and silver or rainbow tones for lighter oils, responders can quickly identify the type of oil involved. This distinction is crucial, as different oil types require tailored cleanup approaches. Heavy  
Figure 11: Thin oil layers with a bright silver tone: histogram distribution graph and box plot of oil spill pixels extracted from the labels, DeepLabV3+, Swin-UPerNet, and Mask2 Former. The \(x\)-axis values represent the digital numbers (DNs) from PlanetScope images. (**a**) Oil mask, (**b**) histogram, and (**c**) box plot. oils, often persistent in marine environments, require robust removal strategies [28; 29], while lighter, volatile oils may disperse more readily but pose acute toxicity risks [28; 30]. Additionally, color analysis enables the detection of oil spill thickness and condition over time, aiding in the monitoring of oil dispersion or degradation. Utilizing color analysis enhances the ability to rapidly assess and respond to oil spill incidents, supporting more effective and responsive environmental management. Accurate extraction of oil spill pixels is also essential for oil spill color classification, as the inclusion of non-oil pixels can distort the results. Transformer-based models, such as Swin-UPerNet and Mask2 Former, excel at precisely delineating oil spill edges, making them more effective at isolating only the oil spill pixels, leading to reliable histogram analyses of oil characteristics and clearer classification of oil types based on color properties. Figure 12: Thin oil layers with a bright rainbow tone: histogram distribution graph and box plot of oil spill pixels extracted from the labels, DeepLabV3+, Swin-UPerNet, and Mask2 Former. The \(x\)-axis values represent the digital numbers (DNs) from PlanetScope images. (**a**) Oil mask, (**b**) histogram, and (**c**) box plot.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 4 Chronological Review of Retrospective Approaches
### [2006-2008]  
The establishment of the SD-OCT allowed to acquire faster and larger OCT volumes, which required the introduction of new techniques for correcting misalignments between cross-sectional images (slow axis), as shown in Figure 6.  
**[PERSON] et al. [19] (2007)** proposed new methods for correcting eye motion and scanning beam distortions. For the motion artefacts, a rigid pairwise registration (only translation) based on auto-correlation was used to correct distortions between consecutive B-scans. The concept of complementary data appears here for the first time, as additional B-scans were acquired along the slow axis (with vertical scanners scanning fast) and used as a reference for axial shifts of consecutive B-scans. The procedure was tested in two volumes of the same subject, and validated with visual inspection.
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 3 Connection-Aware DT for 5G-MANETs
### Connection-Aware Synchronization of the DT
Once a path is established between offline nodes and online entities, devices are able to transmit new events from their local reports to the MQTT interface using intermediate nodes. This technique is dynamic in response to changes in topology, as it can detect disconnections and abrupt changes in links. ```
1:\(I\) as 5G interface, \(B\) as local wireless communication interface. 2:while\(I.internetDown()\) is true do
3:\(connection\gets B.receiveConnectionData()\)
4:\(newDistance\leftarrow connection.getDistance()+1\)
5:if newDistance is 1 then
6:\(newDistance\leftarrow connection.get\)
7:else
8:if newDistance < currentDistance then
9:\(currentDistance\gets newDistance\)
10:endif
11:\(B.broadcastConnectionData(B.connections()-connection)\)
12:endif
13:endwhile
```  
**Algorithm 1** Routing strategy to provide network information in partially connected 5G-MANETs  
Alternatively, there may be contexts where all nodes in the topology become disconnected from 5G. In these cases, it is possible to route information toward the last online node; however, its eventual permanent disconnection would lead to harmful energy consumption. As a result, fully offline topologies become impractical for providing a reliable virtual representation in the DT. For such cases, it would be possible to estimate reports based on the simulation core, though this approach assumes the possibility of deviations. Considering the description of the proposal, its architectural components, and the context-aware strategy, the next section presents the experimental results obtained from a proof-of-concept implementation of the proposal.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Modelling Optimisation Analysis  
#### 2.4.1 Optimization Principles and Processes  
The cleaning robot traveling mechanism moves along the upper and lower edges of the photovoltaic panels and has high requirements for stability during the passage through the non-coplanar orbit. Therefore, the curvature change in the trajectory should be as small as possible, and the designed track should tend to be smooth. In order to determine an appropriate scale parameter, this paper proposes using the average distance as the objective function. It identifies a non-coplanar trajectory that satisfies the requirements by incorporating additional constraints. The specific optimization process is illustrated in Figure 5.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
###### Abstract  
This paper presents a thorough comparison of the Transport Layer Security (TLS) v1.2 and Datagram TLS (DTLS) v1.2 handshake in 6 TiSCH networks. TLS and DTLS play a crucial role in protecting daily Internet traffic, while 6 TiSCH is a major low-power link layer technology for the IoT. In recent years, DTLS has been the de-facto security protocol to protect IoT application traffic, mainly because it runs over lightweight, unreliable transport protocols, i.e., UDP. However, unlike the DTLS record layer, the handshake requires reliable message delivery. It, therefore, incorporates sequence numbers, a retransmission timer, and a fragmentation algorithm. Our goal is to study how well these mechanisms perform, in the constrained setting of 6 TiSCH, compared to TCP's reliability algorithms, relied upon by TLS. We port the mbedTLS library to OpenWSN, a 6 TiSCH reference implementation, and deploy the code on the state-of-the-art OpenMote platform. We show that, when the peers use an ideal channel, the DTLS handshake uses up to 800 B less and completes 0.6 s faster. Nonetheless, using an unreliable communication link, the DTLS handshake duration suffers a performance penalty of roughly 45%, while TLS' handshake duration degrades by merely 15%. Similarly, the number of exchanged bytes doubles for DTLS while for TLS the increase is limited to 15%. The results indicate that IoT product developers should account for network characteristics when selecting a security protocol. Neglecting to do so can negatively impact the battery lifetime of the entire constrained network.  
Keywords: internet of things; TLS; DTLS; 6 TiSCH; TSCH; OpenWSN +
Footnote †: journal: Sensors
## 5 Performance Study
### Controllable Mobility Simulator  
It is obvious that algorithms using controllable mobility need to be simulated and tested for various scenarios before they are actually applied for real world situations. Unfortunately, existing network simulators do not support controllable mobility. In order to evaluate the performance of RoCoMAR, we developed ConMoSim, a controllable mobility simulation architecture that can operate with a network simulator. ConMoSim is designed to co-operate with a well-known network simulator, QualNet.  
Figure 5 shows the structure of ConMoSim and how the information and data are exchanged. As shown in Figure 5, ConMoSim consists of a mobility manager, an event based mobility controller and interfaces to communicate with other modules of QualNet.  
The mobility manager determines whether or not the node moves and, if it decides to move, the speed and direction based on given objectives of the network and the collected information from the network protocol stack _i.e_., the current network situation. In our protocol, the mobility manager can send/receive  
Figure 4: Link quality gain.  
information to/from network layer of protocol stack including the position information to control the nodes' movements for routing purpose. When the mobility manager decides to move, it generates a new move event and sends the event to the mobility controller. The mobility controller handles the move event and controls the movement of the node using the QualNet's API functions related to node mobility as shown in Figure 5.
## 2 Results and Discussion
These differences may be appropriate for tracking specific strawberry varieties. This is an important issue for future research including other fruits. Figure 3 shows a diagram of the possible metabolic pathways involved in the synthesis of the nine main VOCs obtained from the analysis in Figure 3. These nine VOCs belong to three chemical families: esters, terpenes and alkanes. The ester chemical family results from the lipoxygenase metabolic pathway, which involves the metabolism of fatty acids; this allows us to deduce that esters are secondary metabolites derived from the methylesterification of some fatty acids [9]. We speculate that the metabolites from the terpene chemical family are derived from the catalysis of some phenolic compounds from the biosynthesis in the mexulonic acid pathway. This explains why terpenes have been cataloged as compounds with antioxidant activity [36]. Finally, the alkane group compounds are the endpoint of the hydrolysis of some fatty acids. It is worth pointing out that these have been classified as characteristic compounds of herbaceous aromas and are considered key compounds in immature strawberry fruits [37]. The metabolic pathways for volatile biosynthesis, including esters, terpenes, alkanes, amino acids, fatty acids, and carotenoids, are diverse and often highly integrated with other parts of the primary and secondary metabolism. These results therefore need to be interpreted with caution. More research is needed to identify the key substrates and enzymes involved in order to point to a more extensive explanation of the possible metabolic pathways during fruit maturation. However, this study helps us to better understand the maturation process of non-climacter fruits. It also shows evidence of an effect of variety on aroma development. The times of harvest and the ripening stage are very important in each variety to preserve or enhance the desirable aromatic qualities of strawberry fruits. This work lays the foundations for future studies related to the harvest of non-climacteric fruits and studies to optimize the application of agronomic techniques to increase the quality of strawberry fruits. Additionally, the information presented could influence the planning and management of strawberry crops, including integrated pest and disease control.