esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 2 Augmentation of Trust Concept in the SIoT
### Trust Characteristics  
Some key characteristics that further interpret the trust concept are summarized as follows:  
* _Trust is subjective_: With the same trustee and trust context, trust might be different from trustors. In other word, trust is dependent on trustor's perspective. For example, [PERSON] (highly) trusts [PERSON] but [PERSON] does not (for fulfilling a trust goal).
* _Trust is asymmetric_: Trust is a non-mutual reciprocal in nature although in some special cases, trust may be symmetric. For example, if [PERSON] (highly) trusts [PERSON] (in fulfilling a trust goal) it does not mean that [PERSON] will (highly) trust [PERSON] (in fulfilling such trust goal).
* _Trust is context-dependent_: With the same trustor and trustee, trust might be different depending on context including: (i) task goal, (ii) period of time, and (iii) environment. For instance, (i) [PERSON]  
Figure 1: (**a**) Trust concept in the relation with dependability and social capital; (**b**) Three main aspects of trust in the SIoT environment.  
(highly) trusts [PERSON] to provide a cloud storage service but not for a real-time streaming service; (ii) [PERSON] (highly) trusted [PERSON] to provide a cloud storage service two years ago but not for now; and (iii) [PERSON] (highly) trusts [PERSON] to provide a cloud storage service in the United Kingdom but not in the United States.
* _Trust is not necessarily transitive but propagative_: If [PERSON] (highly) trusts [PERSON], and [PERSON] (highly) trusts [PERSON] then it is not necessarily true that [PERSON] will (highly) trust [PERSON]. However there are some evidences from the trust relationship between [PERSON] and [PERSON] that [PERSON] can rely on in order to judge the trust in [PERSON]. More details about trust characteristics can be found in [18].
## 4 Discussion
The selection and exploration of new key bands is an important technology in the field of vegetation remote sensing and has been applied in several cases [31]. We selected several widely used vegetation indices covering the whole wavelength range, established a correlation model between leaf chlorophyll and spectra and determined that the VIS and NIR bands provide the best sensitivity in identifying chlorophyll. Based on this optimal sensitivity range, we refined the sampling interval and constructed the spectral vegetation index most sensitive to peanut chlorophyll. Finally, the regression model was established, and the accuracy evaluated. The results confirmed that the vegetation index constructed in this paper is a good predictor of chlorophyll. Non-destructive monitoring of physiological parameters, such as chlorophyll, in peanut plants is of great significance for monitoring crop growth in agricultural production systems and in the accurate diagnosis and management of nutrient indicators, such as nitrogen.
## 3 Results and Discussion
### Optimization of DSPE Conditions  
Several factors can influence the performance of zeolite H-Beta as a DSPE material. The analyte should be absorbed as much as possible by the sorbent and completely desorbed. The DSPE conditions, including the amount of sorbent, adsorption time, solution pH, and two desorption parameters (solvent and mode), were studied and optimized in our work. The blank sample solution was spiked with eight neonicotinoids at a concentration of 0.5 ng/mL for the optimizations. This concentration was around the middle range for the instrument analysis.  
Figure 2: XRD pattern (**A**), N\({}_{2}\) adsorption–desorption isotherm (**B**), and SEM images with different magnifications (**C,D**) of the prepared zeolite H-Beta.
## 3 Materials and Methods
### Sentinel and Landsat Sensors  
Satellite imagery is the core technology used as a base for conducting this review. Table 3 provides a comparative overview of the main satellite missions, with the core technologies mainly being Sentinel-2, Landsat, and Sentinel-1 across the reviewed literature. These missions form the foundation of coastal remote sensing due to their consistent global availability, spectral variety, and high revisit frequency. Sentinel-2 offers high spatial and temporal resolutions, which makes it ideal for tracking shoreline changes with a higher temporal resolution and short timeframes, while Landsat offers long-term historical continuity [70]. Sentinel-1 provides synthetic aperture radar (SAR) data, enabling cloud-independent monitoring, which is crucial in tropical and storm-prone coastal zones [71]. However, Sentinel-1 was not highlighted in the review as it was not among the studies that fulfilled the criteria selection in the workflow, in comparison with Sentinel-2 and Landsat.
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
###### Abstract
The relationships between the elliptical parameters and the radar raw measurements are established with multiple steps of coordinate transformation. Interestingly, a single data point mapping into the parameter space represents a quartic curve. 2. The EHT detection algorithm is systematically designed, and the planarity of the track is also taken into account to reduce the cumulative effect of noise or clutter. 3. The estimation performances related to the primary and secondary thresholds, measurement error, and SNR are analyzed and verified both by simulations and field data. Thus, a guideline is obtained as to what parameters should be used in similar scenarios. Specifically, we use the real radar tracking data set of a scientific satellite on the day 28 May 2017 to investigate the efficiency of the method. By adding some imaginary (artificial) clutter to imitate a very low SNR environment, the results indicate that it is very effective in detecting the satellite trajectory in heavy clutter. The remainder of this paper is organized as follows. Section 2 opens with the elliptical characteristics of exoatmospheric targets. Section 3 establishes the analytical relationship between the raw radar measurements and the defined elliptical parameters. Section 4 presents the EHT algorithm steps in detail. This is followed in Section 5 by simulations and field data analysis. Concluding remarks and recommendations for follow-on studies are provided in the last section.
_Appl. Sci._**2015**, \(5\), 973-988; doi:10.3390/app5040973  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Using a Random Secret Pre-Distribution Scheme to Implement Message Authentication in VANETs**  
**[PERSON] 1, [PERSON] 2,3, [PERSON] 4,*, [PERSON] 2,4, [PERSON] 2 and [PERSON] 2**  
1 Department of Information Management, Shu-Te University, Kaohsiung 82445, Taiwan; E-Mail: [EMAIL_ADDRESS]  
2 Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; E-Mails: [EMAIL_ADDRESS] (Y.-H.H.); [EMAIL_ADDRESS] (W.-S.H.); [EMAIL_ADDRESS] (C.-N.L.); [EMAIL_ADDRESS] (Z.-T.L.)  
3 Department of Computer Science and Information Engineering, Cheng-Shiu University, Kaohsiung 83347, Taiwan  
4 Department of Computer and Communication, Shu-Te University, Kaohsiung 82425, Taiwan  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-7-615-8000 (ext. 4800).  
Academic Editor: [PERSON]  
_Received: 30 July 2015 / Accepted: 16 October 2015 / Published: 30 October 2015_  
**Abstract:** In recent years, the development of the Intelligent Transportation System (ITS) has increased the popularity of vehicular _ad hoc_ networks (VANET). A VANET is designed to enable vehicles to exchange information about traffic or vehicle conditions to help other vehicles avoid traffic accidents or traffic jams. To resist malicious attacks, all vehicles must be anonymous and their routings must be untraceable, but still verifiable. The vehicles must trust each other and communicate confidentially. In a VANET, Road Side Units (RSU) are installed on traffic signs or streetlights to help vehicles maintain anonymity, to authenticate messages, or to support confidentiality. However, the coverage of an RSU is limited and the cost of widespread installation is high. RSU installations are incremental, so messages must be authenticated using dense RSUs or sparse RSUs. In this paper, the concept of random key pre-distribution that is used in Wireless Sensor Networks (WSN) is modified to random secret pre-distribution (RSP), which integrates identity-based cryptography (IBC) to produce a message authentication scheme for VANETs in a sparse RSU environment. In the proposed scheme, vehicles follow a process to determine a common secret, allowing them to authenticate each other and obtain the pairing value as a key for use in message authentication and private communication. Evaluation results show that the proposed scheme outperforms related schemes.  
**Keywords:** VANET; RSP; IBC; anonymity; message authentication; private communication
## 2 Materials, Methods and Equipment
### Multiple Regression Analysis  
A modeling technique that is one step beyond simple regression is multiple regression analysis. In multiple regression analysis, the key distinction from simple regression lies in the inclusion of two or more independent variables (also known as predictor variables) in the model, as opposed to just one [40]. There are two primary forms of regression involving multiple independent variables:  
* Single step or standard: All predictors enter the regression together.
* Hierarchical or sequential: Each block defines a single step, and all predictors are incorporated in the blocks.  
This statistical method is versatile in its ability to analyze various types of data, such as categorical, ordinal, or interval data, and it offers estimations of the importance and intensity of connections between variables. For making predictions about surface roughness using predictor variables like feed rate, depth of cut, and spindle speed, multiple regression analysis can be highly beneficial [41]. Second-order Equation (3), for predicting surface roughness, can be expressed in the following way:  
\[R_{a}=\begin{bmatrix}\Theta_{0}\\ \Theta_{1}\\ \Theta_{2}\\.\\.\\.\\ \Theta_{9}\end{bmatrix}\times\left[\begin{array}{  
The machine used for machining was a Haas VF-3 SS vertical milling machine with three CNC-controlled axes (Figure 4), located at the Faculty of Engineering, Kragujevac.  
This vertical milling machine has an axis of rotation in the \(Z\) direction, while the X axis represents the length of the worktable, and the Y axis is aligned with the movement of the table forward and back. All axis movements are provided by spiral spindles with a nut, and as a measuring device the system uses a linear encoder. The control unit of the HAAS VF-3 SS is HAAS 2. The features of the Haas VF-3 SS vertical milling machine are as follows: X-Y-Z axis range: 1016-508-635 mm; maximum number of revolutions: 12,000 rpm; maximum speed of auxiliary movement: 21.1 m/min; total power: 22.4 kW; capacity of tools in the magazine: 24 + 1. The tool used for machining was a spindle cutter with 3 teeth and a 6 mm diameter made of hard metal (VHM), as given in Figure 5.  
Figure 4: HAAS VF-3 SS milling machine.  
Figure 3: Workpiece: (**a**) CAD software; (**b**) Ultimaker Cura software.  
The measuring of surface roughness was performed with a TALYSURF-6 measuring system (Figure 6) connected to a computer.
# Comparison of Topologies Generated by Evolutionary Neural Architecture Search
## 2 Neural Architecture Search
### Neural Architecture Search (NAS)  
NAS consists of three critical stages: search space, search strategy, and performance estimation strategy [11; 12]. In the search space stage, experts must define the architecture that the search process will explore. Since the search space can be massive, incorporating prior knowledge is essential in order to simplify the search space. However, this stage is highly dependent on human expertise. In the search strategy stage, the method used to explore the search space is defined, which is the most important process in NAS. Finally, the performance estimation strategy stage evaluates the performance of the neural networks in order to identify the architecture with the best performance, which is the main objective of NAS.  
Various search strategies can be employed in order to explore the search space of artificial neural networks, including Bayesian Optimization (BO), Reinforcement Learning (RL), and Evolutionary Algorithms (EAs). While Hyper Parameter Optimization (HPO) methods are successful with BO, they are not suitable for generating neural network topologies [13; 14]. To design neural network topologies, RL can be considered, as it rewards agents for efficient actions [15]. On the other hand, EAs are commonly used by researchers to evolve neural networks, and they generate the topology of the neural network architecture through their evolutionary processes.
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 4 Electrochemical Biofilm Control
### Electrochemical Communication during Biofilm Formation
#### 4.2.1 Electrochemical Communication between Microbial Cells and Conductive Surfaces  
Only some bacteria from family _Shewanellaceae_ and phylum _Proteobacteria_, especially _Geobacter_, are characterized by their capacity of extracellular electron transfer (EET) to the electrode. They are known as exoelectrogens and have aroused great interest in biotechnology, namely in bioelectrochemical systems (BES), for the generation of energy in microbial fuel cells (MFCs). _Geobacter sulfurudencus_[162], _Geobacter metallieducus_[163], _Shewanella oneidensis_[164], _Klebsiella quasipneumoniaie_ sp. 203 [165] and _Desulfovibrio desulfuricans_[166] are able to utilize polarized electrodes as the final electron acceptor of their respiratory chains. For biofilm of _Geobacter sulfuruducens_, a combination of pilli (nanowires) action and cytochromes, referred to as a \"stepping stone\" mechanism, was proposed for long range electron transport (>50 um) through the biofilm [167; 168]. In addition to the direct electron transfer, such a process can be mediated using endogenous redox mediators, as shown in Figure 4. For example, Os(III)/Osmium(V), Fe(II)/Fe(III), and quinoid redox couples can be considered as typical examples of endogenous redox mediators [169; 170; 171; 172].  
In addition to direct electron transfer, microbial cells can indirectly communicate with conductive surfaces via the electroactive products of their metabolism and/or extracellular polymeric substances (EPS) [168]. Herein, it is necessary to accomplish that the microbial cells inside biofilm do not utilize only one type of electron transfer, but their different combinations. The whole system is more complicated than it may seem at first glance, because of the additional ability of microbial cells to communicate with each other via diffusible intermediates, direct electron exchange between physically connected cells, and conductive particles [173].
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 2 System Model
### Network Model  
We consider a scenario where images are transmitted from satellite to ground, as shown in Figure 1. The components involved are as follows:  
* LEO satellite: The satellite revolves around the Earth, constantly moving and covering different regions. The satellite uses the images generated by observing the ground data as the source, and encodes these data, including compression and semantic information extraction, to improve transmission efficiency and data quality. It is necessary to consider the dynamic adjustment of the change in track and coverage areas to ensure the stability of data transmission;
* Transmission link: Links between satellites and ground equipment via wireless channels can be affected by environmental factors such as clouds and electromagnetic interference, which can degrade signal quality. In addition, due to the movement of LEO satellites, the transmission path and conditions will change with the position  
Figure 1: The system model of satellite-terrestrial transmission network.  
of the satellite, so it is necessary to dynamically adjust the transmission strategy to maintain the stability and efficient transmission of signals;
* Ground terminal: Terminal equipment includes ground gateway station and personal terminal. The terminal device receives the signal transmitted by the satellite and decodes it to restore the original image or semantic information, and the user can view or use the received information here.
## 3 Description of Datasets
### The SUSU Dataset  
This dataset was collected in the research laboratory of technical self-diagnostics and self-control of devices and systems at South Ural State University (Figure 13). A wireless acceleration sensor was used for data acquisition, which recorded two orthogonal linear accelerations and angular acceleration. The SUSU dataset contains signals for healthy bearings as well as bearings with inner race, ball, outer race, and combined defects. The specifications of the dataset are as follows:  
* The motor rotation speed was constant at 1200 RPM.
* Data were obtained without load.
* The sampling frequency was 31175 Hz.
* The recorded signal length for each bearing condition was 64.1540 s.  
Table 3 describes the signal classes and the number of frames in each class for the SUSU dataset. The signal segmentation technique used for the CWRU dataset was repeated with a frame length of 6494 samples, which is equivalent to 0.208 s.  
Note that the number of training samples here is significantly larger than for CWRU (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Number** & **Fault Type** & \
\begin{tabular}{c} **Number of** \\ **Training Frames** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Testing Frames** \\ \end{tabular} \\ \hline
0 (Normal) & None & 2155 & 920 \\
1 & Ball & 2155 & 920 \\
2 & Inner & 2155 & 920 \\
3 & Outer & 2155 & 920 \\
4 & Combined & 2155 & 920 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of frames for each sub-dataset in SUSU data.  
Figure 13: SUSU experimental rig.
# Hybrid Consensus Algorithm Based on Modified Proof-of-Probability and DPoS
## 2 Background and Related Work
### Delegated Proof-of-Stake  
DPoS (Delegated Proof of Stake) is the fastest, most effective, most decentralized, and most flexible consensus mechanism among all consensus algorithms. The algorithm flow is shown in Figure 3. DPoS uses the right of stakeholders to approve votes to solve consensus problems in a democratic and fair way. All network parameters, from cost estimation to block spacing and transaction size, can be adjusted by selected representatives. The principle is to let each holder vote, resulting in a certain number of Representatives, which are verified and accounted for by these supernodes on behalf of the holder; the rights of these supernodes are equal. DPoS is like the board of directors voting. The coin holders cast a certain number of supernodes. The selected nodes generate blocks in turns, according to the established schedule. If a supernode fails to exercise its power properly (such as generating blocks), it will be removed, and the network will select a new supernode to replace it. All representatives will receive 10% of the transaction fee contained in the average level block for reward. The deterministic choice of block producers allows transactions to be confirmed in an average of one second. By protecting all participants from unnecessary logical checks, DPoS greatly reduces the number of nodes participating in verification and bookkeeping compared with the PoW and PoS algorithms. DPoS algorithm greatly improves the efficiency and can reach the consensus verification at the second level [16]. It does not have complete decentralization, but has weak centralization. However, the design of the DPoS algorithm does not guarantee that there must be sufficient real block producers. Due to a person or an entity may control multiple nodes, the whole system may be substantially monopolized by one entity. For example, in LBTC (lightning bitcoin), half of the nodes were controlled by the f2 pool. At the same time, the governance power and economic interests of supernodes are too centralized. If they collude, they will further form a giant monopoly, which is at odds with the blockchain idea. In addition, there are many difficulties for the system to deal with the nodes. Community election cannot effectively prevent the emergence of some destructive nodes in time, which causes security risks to the network. At the same time, in the case of a small number of network nodes, the supernodes elected are not representative.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Integrated Silicon Waveguides
#### 4.2.1 Waveguides: Total-Ionizing Dose Effects  
The silicon waveguide core is surrounded by an insulator, which makes it potentially vulnerable to TID. Generating trap states on the interfaces could negatively impact the optical transmission properties. The lack of an electric field in the waveguides, however, does reduce the charge yield, and thus the resulting radiation damage [77]. Two studies that exposed silicon waveguides to a high-dose gamma radiation confirm that the waveguide maintains its transmission properties after irradiation [78; 79]. These results suggest that the charge yield from radiation is low in these components, and that the optical mode within the waveguide is negligibly affected by this type of damage.