esa-sceva/satcom-chunk-collection huggingface dataset:
# Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise
###### Abstract  
Recovering three-dimensional (3D) shape of an object from two-dimensional (2D) information is one of the major domains of computer vision applications. Shape from Focus (SFF) is a passive optical technique that reconstructs 3D shape of an object using 2D images with different focus settings. When a 2D image sequence is obtained with constant step size in SFF, mechanical vibrations, referred as jitter noise, occur in each step. Since the jitter noise changes the focus values of 2D images, it causes erroneous recovery of 3D shape. In this paper, a new filtering method for estimating optimal image positions is proposed. First, jitter noise is modeled as Gaussian or speckle function, secondly, the focus curves acquired by one of the focus measure operators are modeled as a quadratic function for application of the filter. Finally, Kalman filter as the proposed method is designed and applied for removing jitter noise. The proposed method is experimented by using image sequences of synthetic and real objects. The performance is evaluated through various metrics to show the effectiveness of the proposed method in terms of reconstruction accuracy and computational complexity. Root Mean Square Error (RMSE), correlation, Peak Signal-to-Noise Ratio (PSNR), and computational time of the proposed method are improved on average by about 48%, 11%, 15%, and 5691%, respectively, compared with conventional filtering methods.  
Keywords:shape from focus (SFF); jitter noise; focus curve; Kalman filter +
Footnote †: journal: Applied
# Education and Training in Applied Remote Sensing in Africa: The ARCSSTE-E Experience
## 4 Best Practices
Mapping activities on the university campus with a hand-held Global Positioning System (GPS) and Google Earth are also used to establish the links between remote sensing and Geographic Information Systems (RS/GIS) concepts. Although the exercise of mapping the location of sites on a university campus appears to be a straightforward activity, when combined with Google Earth activities, it helps users to learn the basic skills of data collection, management, integration, and visualization [18]. These basic skills are important for users to have mastered prior to moving on to more complex geospatial activities. (v) _Regular Review of Course Content_: ARCSSTE-E conducts a periodic survey of its alumni to enquire about their individual and institutional needs. ARCSSTE-E also keeps tab on global trends, so as to review its courses and prepare its participants to be part of the future workforce. For example, ARCSSTE-E is developing an online course, titled 'Spatial Data Analytics'. The course draws on the 'big data' paradigm. (vi) _Funding_: Funding is one of the major challenges facing educational institutions. With regards to all of the Regional Centres, including ARCSSTE-E, the Member States are supposed to make statutory contributions to the operation of the Centres; however this has not always been so. In ARCSSTE-E, Nigeria bears all the burden of running the Centre, including staff salaries, utility bills and subsistence allowances for international students. The UNOOSA caters for only the travel cost of the international students. Over the years, the required financing from the host country Nigeria through its National Space Research and Development Agency (NASRDA) and from UNOOSA is most often inadequate. Rather than rely on these bodies, ARCSSTE-E devised an innovative funding mechanism, which included tuition fees from private-funded students, organisation of workshops, and research grants. (vii) _Management_: As mentioned earlier in this paper, host countries of regional educational institutions often bear a large portion of the burden of management and funding. The UN Regional Centres have Governing Boards (GB), which have the responsibility of giving strategic oversight to the management team. For ARCSSTE-E, the participation of Member States in GB meetings has been poor Up until 2012, the cost of bringing GB members to Nigeria was borne by the host country (Nigeria), with dwindling funds being accessible to the Centre. However, it has been unable to continue with this sponsorship. Therefore, ARCSSTE-E is making efforts to rotate the meeting venues of the GB among the Member States, so that they all have a sense of belonging and responsibility. When funds are available, ARCSSTE-E also invites experts from Member States to teach in the PGD programme.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 3 Proposed Scheme
### Registration Phase
#### 3.2.1 User Registration  
User \(U_{i}\) selects their own id \(ID_{i}\), password \(PW_{i}\), random number \(n_{i}\). He/she sends \(ID_{i}\) to \(CS\) by the secure channel. When \(CS\) receives \(ID_{i}\), \(CS\) checks it for its validity. If it is invalid, \(CS\) will stop this phase; otherwise, \(CS\) selects a pseudo-identity \(PID_{i}\) for \(U_{i}\) and uses the secret key \(x\) to compute \(A_{i}=h(PID_{i}||ID_{CS}||x)\) and \(B_{i}=h(ID_{i}||x)\). \(CS\) stores \(ID_{i}\) in its database and sends (\(A_{i}\), \(B_{i}\), \(PID_{i}\), \(ID_{cs}\)) to \(U_{i}\) by the secure channel. Once \(U_{i}\) obtains these parameters, \(U_{i}\) calculates \(C_{1}=A_{i}\phi h(ID_{i}||n_{i})\), \(C_{2}=B_{i}\phi h(PW_{i}||n_{i})\), \(C_{3}=n_{i}\phi h(ID_{i}||PW_{i})\), and \(C_{4}=h(ID_{i}||PW_{i}||n_{i})\) and then stores (\(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(PID_{i}\), \(ID_{cs}\)) in a smart card. The flowchart for user registration is shown in Figure 1.
## 6 ArTIC-MS Use Case Specification
### Development and Provisioning of a M&S Service  
This use case, whose execution flow is outlined in Figure 10, involves two users owning different skills: the TOSCA expert (TE) and the simulation service provider (SSP).  
As discussed in Section 5.4, a _service template_ is composed of _typed_ elements (e.g., nodes and relationships) and might include other existing (and abstract) templates. Thus, the SSP makes use of existing types and templates that are refined, extended, and composed in order to develop the needed M&S service. Differently, the TE is responsible for the specification of the _TOSCA core MSAaS templates_ and also supports the SPP to specify the various YAML-based TOSCA templates needed to make available the provided M&S services.  
In this respect, Figure 10 also provides a more detailed view of the following ArTIC-MS building blocks:  
* **TOSCA engine:** Provides the implementation of _TOSCA normative types_ which constitutes the building blocks of other templates;
* **Services repository:** Includes the _TOSCA MSAaS core types repository_, which stores the abstract and concrete templates for the _core_ MSAaS types upon which other service templates are built, and the _TOSCA MG&S services repository_, which hosts the CSAR packages wrapping the provided _M&S services_, respectively.  
The use case is specified as follows:  
1. The TOSCA Expert (TE) logs into ArTIC-MS to create the required templates for specifying _MSaaS core types_ used to build the M&S services;
2. The YAML templates are stored in the _TOSCA MSAaS core template repository_ and corresponding entries are added to the services registry;
3. The simulation service provider (SSP) develops the executable artifacts implementing the provided M&S services;
4. The TE and SSP cooperate to specify an appropriate YAML template for each provided M&S services;
5. The related CSAR package is built and stored in the _TOSCA MG&S service repository_;
6. The SSP provides a metadata description for each M&S service that is stored in the services registry.  
Figure 10: Use case for building a TOSCA-based M&S Service.
## 3 Measurement and Result Analysis
### Bending Analysis for the Proposed Design
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Design of the** & **Operating** & **Unit Cell 2D** & **Type of Dielectric** & **Resonant** & \\  & **Radiating Patch** & **Frequency Band** & **Size (mm\({}^{2}\))** & **Material** & **Frequency (GHz)** & **Absorption** \\ \hline
[3] & Square type SRR & Ku & \(9\times 9\) & FR4 & 14.62, 16.30 & 99.99\% \\  & with multi splits & & & & \\
[6] & Circular type SRR & X & \(12\times 12\) & FR4 & 9.828 & 99.99996\% \\
[7] & E-Shape & X and Ku & \(9\times 9\) & FR4 & 8.14 and 14.01 & - \\
[12] & Oval shape & C and X & \(9\times 9\) & Rogers & 7.33 and 9.06 & \\
[13] & Hexagonal SRR & C and X & \(10\times 7.29\) & FR4 & 5.3, 10.5 & - \\
[14] & Square type SRR & X and Ku & \(12.5\times 10\) & Ni Al\({}_{2}\) O\({}_{4}\) & 8.83, 13.18 and & - \\
[15] & Circular type SRR & C and Ku & \(8\times 8\) & FR4 & 6.46 and 16.92 & - \\
[16] & Z-shape & C and X & \(10\times 10\) & Epoxy resin & 9.12 and 5.83 & - \\
[18] & Circle with splits & X & \(8.8\times 8.8\) & FR4 & 10.45 & 91\% \\
[20] & Square shape & S, C, X and Ku & \(14\times 14\) & Rogers RT6002 & 3.384, 5.436, 7.002, & - \\
[21] & Square & S, C, X and Ku & \(9\times 9\) & Rogers RT5880 & 2.5, 4.7, 8.8, 11.3, & - \\
[22] & Parallel LC shape & C And X & \(10\times 13\) & FR4 & 5 and 10.84 & - \\  & Circular SRRs & & & & \\  & and square split & X & \(10\times 10\) & Polyimide & 9.84 and 11.46 & - \\ This work & & ring resonators & & & & \\  & combination & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison table with existing work. Figure 21: (**a**) E-field distribution, (**b**) H-field distribution and (**c**) surface current distributions of the proposed structure at 9.84 GHz and 11.47 GHz with 15\({}^{\circ}\) vertical bending. Figure 22: (**a**) E-field distribution, (**b**) H-field distribution and (**c**) surface current distributions of the proposed structure at 9.81 GHz and 11.42 GHz with 30\({}^{\circ}\) vertical bending. Figure 23: (**a**) E-field distribution, (**b**) H-field distribution and (**c**) surface current distributions of the proposed structure at 9.83 GHz and 11.40 GHz with 45\({}^{\circ}\) vertical bending.
# The Effect of Processing Methods on Phytochemical Composition in Bergamot Juice
## 4 Discussion  
The objectives of this study were to evaluate the effects of different juice extraction systems on proximate constituents, bioactive components composition, and antioxidant activity of bergamot juice.  
Beneficial activities of the components of citrus juices for human health were primarily ascribed to its antioxidant activity mainly due to L-ascorbic acids and polyphenols, like flavanone glucosides ([PERSON], 2016). Despite these beneficial effects, the unprocessed fresh bergamot juice showed sour taste due to a high citric acid content (Table 1) and a bitter taste due to limonoids and flavanone-7-O-neohesperidosides which were also the most dominant bitter principles (Table 2).  
Flavanones usually occurred as O-glycosl derivatives, with the sugar moiety bound to the aglycone hydroxyl group at either C7 or C3. Among these compounds, the O-diglycosides were a dominant category and their structures were usually characterized by the linkage of either neohesperidose or rutinose to the flavonoid skeleton. The bitterness caused by flavanone-7-O-neohesperidosides was often referred to as 'primary' bitterness, while flavanone-7-O- rutinosides were tasteless ([PERSON], 2016).  
The amount of the flavanone glycosides depends on the species and of the productive processes used (time and intensity of extraction, line production technology, and quality of the fruit used). It is known that a higher content of flavonoids and limonoids were found in albedo and membranes than in juice sacs ([PERSON], 2016). Since there were several methods of extraction juice, each processing method may have its own characteristics in terms of the concentration of bioactive compounds as well as juice quality (proximate constituents).  
Hand squeezing (Ep-1) was compared with two industrial squeezing methods: a \"soft\" juice extraction process (FMC single-strength extraction method, Ep-2) and a \"hard\" juice extraction process (Ep-3) operated by a screw press of peeled fruits, to evaluate their influences on health components of BJ.
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 3 Directed Graphs and Distributed Low-Density Parity-Check Codes for Data Collection
### Directed Graphs for Multi-Source Sparse Cooperation
#### 3.1.1 One-Round Cooperation Graph  
In this line-like cooperation model, each source node except \(s_{1}\) and \(s_{N}\) is aided by one source node and also assists another node. In this paper, we propose a directed graph to represent the cooperation relation. Figure 4a illustrates the directed graph for the cooperative model in Figure 2. If there is a directed edge \(e_{ij}=(s_{i},s_{j})\) in the graph, it means that the source node \(s_{j}\) can successfully receive the data from \(s_{i}\) and the node \(s_{j}\) volunteers to jointly encode and forward the data from \(s_{i}\) and its own data. In Figure 4a, there is a loop for each node \(s_{i},1\leq i\leq N\), for each node participates in the transmission of its own data. Therefore, we can devise the cooperation topology on a directed graph \(D=(S,E)\), where the set \(S\) consists of all the nodes in the cooperative link and the edge set \(E\) defines the cooperative relation among all the nodes.  
Then, let define a \(N\times N\) cooperation matrix \(G_{1}\) given by  
\[G_{1}(D)=[g_{j,i}^{1}]_{N\times N}, \tag{1}\]  
where \(N\) is the number of the nodes involved in the cooperation, and \(g_{j,i}^{1}=1\) denotes a directed edge from \(s_{i}\) to \(s_{j}\). If there is a directed edge from \(s_{i}\) to \(s_{j}\), the source \(s_{j}\) helps the source \(s_{i}\) forward the data to the destination. There is a directed edge from the source \(s_{i}\) to \(s_{i}\), i.e., the element \(g_{j,i}^{1}=1\), which means that \(s_{i}\) participates its own data transmission. Figure 4b illustrates the cooperation matrix \(G_{1}\) for the directed graph in Figure 4a. It is obvious that the bidiagonal matrix \(G_{1}\) is a sparse matrix for the large \(N\) in the one-round cooperation model. Thus, we call it the multi-source sparse cooperation in this paper. In the following, the matrix \(G_{1}\) is used to design the basic matrix for the distributed LDPC code.  
Figure 4: _Cont._
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
###### Abstract
As a result, the adaptive fuzzy weighted mean filter (AFWMF) model is proposed to be used in this study for reducing and overcoming this problem, as it does not rely primarily on the median operation to replace noise. The AFWMF model uses a directional median to preserve detail and calculate the weights of pixels in the filtering window with fuzzy rules. Each weight is represented as a pixel, which should provide useful information for restoration to enhance filtering performance. Fuzzy inference membership functions can change dynamically depending on the distribution of the filter window to manage random-valued impulse noise. As a result, it is expected that the AFWMF model will enhance filtering performance. In addition, the detection of the switching framework will affect the filter stage of the same framework directly. Therefore, this study modifies the ROR (robust outlyingness ratio) detection algorithm, using fuzzy rules to make the ROR algorithm more precise and powerful. This study is structured in the following order. Section 2 reviews the ROR detection algorithm and filter order. Section 3 introduces the AFWMF model in detail. Section 4 summarizes the proposed algorithm of the AFWMF model. Section 5 contains the empirical results obtained, illustrating the accuracy of the novel method using multiple testing photos. Finally, conclusions are drawn in Section 5.
## 4 Results and Discussion
### Mechanical Properties
#### 4.1.3 Toughness Tests via Charpy Method
For the materials based on vinylester resin, the best toughness was observed for the samples produced with an addition of chopped fibre (VE/BF) because the KZ value in that case exceeded 60%. The epoxy materials with an addition of powder (EP/BP) and chopped fibre (EP/BF) were characterised by values of more than 80%.
# Motion Simulation and Human-Computer Interaction System for Lunar Exploration
## 2 Materials and Methods
### Modeling of Lunar Environment and Rover
#### 2.2.4 Virtual Sensors and Driving Warning  
We installed virtual sensors on the lunar rover to collect the terrain data of the surrounding environment. The sensor model was based on the VLP-32 three-dimensional lidar developed by Velodyne and implemented using the ray detection function in Unity.  
The VLP-32 lidar scanned the lunar terrain with a fixed angular resolution. After scanning for a week, the angle and distance data of a set of points were obtained and converted into coordinates in the global coordinate system. We generated squares of different colors according to the difficulty of driving on the terrain, and used this as a marker. The rendering effect of the virtual sensor is shown in Figure 9.  
We generated mark points according to actual operation requirements, and the classification method is shown in Table 1. The effect of driving warnings is shown in Figure 10.  
With the completion of the detection, the system carried out path planning according to the terrain data obtained by lidar and calculated the best scheme to reach the target point.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Angle** & **Color** & **Explain** \\ \hline Less than \(-25^{\circ}\) & Purple & Dangerous slope, no passage \\ From \(-25^{\circ}\) to \(110^{\circ}\) & Blue & The brakes need to be applied \\ From \(-10^{\circ}\) to \(10^{\circ}\) & Green & Safe passage \\ From \(10^{\circ}\) to \(25^{\circ}\) & Yellow & A certain pre-acceleration is required to pass \\ Greater than \(25^{\circ}\) & Red & Dangerous slope, no passage \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mark point classification.  
Figure 10: Virtual environment rendering.  
Figure 9: Virtual sensor development rendering.  
We used the A* algorithm for path planning [21]. The algorithm considers the distance cost and the slope cost calculated from radar data, and constantly searches for unknown nodes according to the heuristic function \(f(n)\). The formula is as follows:  
\[f(n)=g(n)+h(n) \tag{18}\]  
\[g(n)=x(n)+\alpha\times d(n) \tag{19}\]  
where \(g(n)\) represents the exact path from the starting point to node \(n\), \(h(n)\) is the estimated remaining path from node \(n\) to the target point, \(x(n)\) is the horizontal distance from the starting point to node \(n\), \(d(n)\) is the vertical distance from the starting point to node \(n\), and \(\alpha\) is the slope parameter. The results of the path planning are shown in Figure 11.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 1 Introduction
Specifically, they consist of introducing more delays in the transmission control protocol (TCP) and UDP packet transmission, where attackers can scramble packet orders before forwarding them to the destination node. To deal with Jellyfish attacks and other attacks of a similar nature, a variety of security mechanisms have been proposed; however, these security mechanisms are not effective against all three types of Jellyfish attacks, such as Jellyfish reorder attacks, Jellyfish periodic drop attacks, and Jellyfish delay variance attacks, because they do not consider their impact. To guarantee the successful prevention of various Jellyfish attack categories, we need to choose the appropriate metric for every Jellyfish category based on their impact. In this paper, we design a new security protocol to deal with Jellyfish attacks on top of the greedy perimeter stateless protocol (GPSR) as a routing protocol [14]. Thus, we propose a new secure and efficient routing protocol for preventing Jellyfish attacks in V2X, called SecE-V2X. In order to defend networks against different Jellyfish attack categories, our proposed protocol selects the trusted paths for routing packets to their destinations by calculating the Honesty criterion. This latter factor is determined based on three metrics: the packet delay, packet loss rate, and packet reordering. A node's Honesty value is dependent upon each node's properties and endorsements from its neighbors. The remainder of this paper is organized as follows: Section 0 describes Jellyfish attacks and their variants. Related works and attack protection mechanisms are discussed in Section 0. Section 0 describes the proposed SecE-V2X routing protocol. The simulation results are provided in Section 0. Section 0 provides conclusions and some perspectives. Finally, Section 0 describes the adopted abbreviations.
# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 4 Proposed Solution
### Obtaining Hint Images Matching Real-Time Locations  
To provide hint information using the existing street view services (e.g., Google Street View ([[https://www.google.com/streetview/](https://www.google.com/streetview/)]([https://www.google.com/streetview/](https://www.google.com/streetview/)), accessed on 24 February 2021), Naver Road View ([[https://map.naver.com/](https://map.naver.com/)]([https://map.naver.com/](https://map.naver.com/)), accessed on 24 February 2021), a street view image matching the current location and orientation is required. We can compare GPS coordinates to find a matching image for the current location. However, the street view image that exactly matches the current location may not exist since street view images are captured at regular intervals. Thus, it is necessary to obtain hint information to be supplemented to a nighttime image using the street view image closest to the current location.  
Figure 6 shows an example of image taken by a camera on a car driving on a road at night (Figure 6a) and the corresponding street view image for the matching location (Figure 6b). As we can see in Figure 6b, the street view image is a 360-degree image and it has relatively wide field of view. Thus, only a part of the image that matches the current nighttime image should be used to provide hint information. Accordingly, it is necessary to find and crop such apart from an entire street view image.  
Figure 5: A flowchart to show detailed processing steps of Daydriex processing pipeline.  
Figure 6: An example nighttime image captured from a car (**a**) and the corresponding street view image from the same location (**b**)—source: NAVER MAPS Road View ([[https://map.naver.com/](https://map.naver.com/)]([https://map.naver.com/](https://map.naver.com/)), accessed on 24 February 2021).  
We devise a method to pinpoint a part of street view image that matches an image captured at the vehicle location. The matching procedure is as follows: (1) Using the vehicle's GPS coordinates, the street view image closest to the vehicle location is obtained. An example is specified in Figure 7. (2) The distance between the GPS coordinates of the vehicle (1) and the coordinates where the street view image was taken (2) is calculated (see Figure 7). The physical distance between the two coordinates is transformed to the pixel distance (3). (3) A window to crop the hint image (4) is moved from the middle of the street view image by the distance. (4) the part of image inside the window (4) is cropped.