esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Assimilation and Impact of GNSS RO Data
### Method of Assimilating GNSS RO Data  
For assimilating GNSS RO data, here are several methods. The assimilation systems in forecast models have some generic and popular approaches, including 3 DVAR, 4 DVAR, and ensemble Kalman filters (EnKF), for both RO data and GNSS-R data [61; 62; 63]. 3 DVAR uses observations and background in models to adjust analyses. It has nice stability and high computational efficiency, but due to the ignoration of time, its dynamic constraints are weak. 4 DVAR added the time dimension to capture the rapid changes effectively. However, it is sensitive to background errors and costs many computational resources. ENKF, based on statistics methods, makes the background errors more suitable to the real atmosphere, but the ensemble size is limited by the resources. Here is also a choice to combine the EnKF's background errors and the variational assimilating method to improve the results flexibly [64]. As for the operators [65], firstly, we could use 1 DVAR to retrieve the atmospheric temperature and moisture profiles, and then assimilate the physical information to NWP. In addition, with the application of a forward operator and adjoint operator [66], choosing the refractivity or bending angle to replace moisture and temperature could also affect the results of assimilation. The refractivity includes the local and non-local refractivity [67; 68], and the bending angle [69; 70] could be derived by the ray tracer, a model of simulating the local bending angle [71] under Snell's Law.  
The refractivity local operator relies on a certain position in the atmosphere, which could simplify the computation. However, the non-local operator would consider the whole signal path, and it captures the cumulative refraction effects across various atmospheric layers on the entire signal propagation path. So, it is significant to perform the global vertical structures [17]. Also, the non-local refractivity operator provides a way to derive the bending angle. Different bending angle ray tracers [72] are adapted to different atmosphere assumptions. If there is a single point in the atmosphere or at a certain height, the local bending angle operator is suitable for estimating the bending angle using less computational cost. 2D ray tracer is for a symmetric and uniform atmosphere, where the refractivity, temperature, and moisture are changed with the altitude but not distance horizontally [73]. This means that the signal propagates straightly. The bending angle 3D ray tracer could describe the atmospheric changes in both directions, and it could be used in complex situations, such as the unsymmetric temperature gradient. According to the computing resources and the forecasting problems, we could choose different settings.  
Figure 7: Difference distribution between the cloud top heights and the tropopause altitudes in GPS RO profiles with TC best tracks [58].
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### Algorithm Selection  
So far, a wide range of conventional ML and deep learning (e.g., neural networks) algorithms have been developed for classification and regression purposes. They have been used in various landslide studies, yet there is still no consensus on an \"optimal\" algorithm nor a single \"best\" algorithm [14]. While [PERSON] et al. (2020) [15] considered the use of ML methods in landslide predictions achieve satisfactory performance in general, the use of ensemble learners constructed from a set of base learners was recommended. [PERSON] et al. (2020) [19] reviewed various ML methods for landslide susceptibility studies and suggested that tree-based ensemble algorithms achieve excellent results compared to others. Among the tree-based ensemble algorithms, the Random Forest algorithm was identified to offer robust performance for accurate landslide susceptibility mapping with only a small number of adjustments required before training the model. On the other hand, conventional ML algorithms are preferable to deep learning algorithms for structured data as the latter often work as black-box models which jeopardize the interpretability of the models. In this pilot study, the ML algorithms were selected based on the following key factors. 1. Interpretability of the algorithms: how easy is it to explain the results from the input data, or to understand the patterns that models use to link to the training datasets ([PERSON] et al., 2021). This factor is essential for detecting bias and debugging the models. The model predictions should also be explainable using our professional knowledge (domain knowledge) on landslides. 2. Balance between bias and variances: whether the algorithm can form a predictive model that is generalized enough to give consistent yet accurate forward predictions. Algorithms which are prone to overfitting should be avoided as a result. 3. Suitability for handling correlated features: the ability of an algorithm to handle correlated features provides additional flexibility in the selection of features and is thus preferable. 4. Computational efficiency: the time and computational effort spent on the study. With reference to the above considerations, three tree-based ML algorithms, namely: Decision Tree [20], Random Forest [21], and XGBoost [22] were identified and adopted in this study. Table 2 provides a brief description of these ML algorithms.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 4 Results and Discussion
### Experimental Results and Discussion  
In the experiment, Canny, Sobel, and Log, edge detectors are applied in five LSB-cleared images to identify edge and non-edge pixels. Canny-, Sobel-, and Log-based edge detector functions of MATLAB return an edge image for a given input image. The resultant edge image is a binary image. Next, those edge images are hybridized using the logical ORoperator. To increase the number of edge pixels, a morphological dilation operation was also performed. Only edge pixels are selected from the cover image using a hybridized image; it is an edge image. The edge image is divided into different \(3\times 3\) block sizes and generates LBP code for specific blocks and implant data according to embedding rules. The payload also calculated and analyzed the performance in embedding capacity with respect to tempered pixels. Embedding capacity is graphically shown in Figure 6. Figure 6 shows that our proposed scheme has the lowest embedding capacity with respect to [PERSON]'s [44] and [PERSON]'s [25] since secret messages are implanted in edge pixels only, and others are embedded in all the pixel values of images. Considering edge pixels, the proposed method demonstrates dominant performance.  
The visual quality and structural originality of stego images are also analyzed. Visual quality is measured by PSNR values and is sketched in Figure 7. It is clear from the diagram that the proposed scheme has a higher PSNR value than the competing schemes. The structural similarity index value, SSIM is also shown in Figures 8 and 9. Both figures show that the proposed scheme has  
Figure 8: Comparing SSIM of different images in different schemes. The figure states that the proposed scheme has the highest SSIM value [24,25,27,44].  
Figure 7: PSNR of different images in different schemes. The figure states that the proposed scheme has the highest PSNR of maximum images [24,25,27,44].
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Optimization and Design of IRSs
#### 3.3.4 Multiuser MISO/SIMO Scenarios
In [82] a downlink multiuser IRS-aided MISO system is considered, where the IRS only presents a finite number of phase shifts at each element to assist the communication. A transmit power minimization approach is developed by jointly optimizing the transmit precoding at the BS and the discrete phase shifts at the IRS, subject to a given set of minimum SINR constraints at the user receivers. To solve the problem, the authors first study the case where only one user is assisted by the IRS and propose both optimal and suboptimal algorithms for solving it. It is also shown that the IRS with discrete phase shifts achieves the same squared power gain in terms of an asymptotically large number of reflecting elements, while a constant proportional power loss is incurred depending on the number of phase-shift quantization levels. The proposed designs for the single-user case are also extended for the multiuser scenario where some of them are aided by the IRS. In the simulation results, comparisons with several benchmark schemes regarding the quantization in the IRS elements are performed. The authors in [83] consider the passive beamforming and information transfer (PBIT) technique for an uplink multiuser IRS-assisted SIMO systems. The IRS is assumed to follow a passive beamforming (phase shifting) and an on-off reflecting modulation. The authors formulate the problem as a two-step stochastic problem and aim to maximize the achievable user sum rate of the system. An sample average approximation (SAA) based iterative algorithm and a simplified algorithm by approximating the stochastic program as a deterministic AO problem is proposed for the IRS phase-shift matrix design. The solutions are extensible for multi-IRS system models. The simulation results exhibit the large gains offered by the proposed approach over a random phase-shift strategy at the IRS. Table 4 summarizes the review about multiuser IRS-assisted MISO/SIMO approaches. The considered system models, the metrics to evaluate the performance, and the main results are included. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **System Model** & **Performance Metric** & **Main Results** \\ \hline \multirow{3}{*}{[24]} & LOS, AO, & & Tx power \\  & practical & Tx power & reduction for \\  & phase shifters & & a practical model \\ \hline \multirow{3}{*}{[61]} & Downlink, multi-IRS & \multirow{3}{*}{Ergodic rate,  SER} & \multirow{3}{*}{Asymptotic  optimality} \\  & LOS, IRS-aided users & & \\  & and no IRS-aided users & & \\ \hline \multirow{3}{*}{[62]} & \multirow{3}{*}{Uplink, LOS, ZF} & \multirow{3}{*}{SIR} & SIR maximization \\  & & & at an IRS used \\ \cline{1-1}  & & & as receiver \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multiuser MISO/SIMO approaches.
## 2 Materials and Methods
### Preliminary Analysis and Optimisation of the Extraction Method
#### 2.1.1 GPP Data and Study Areas  
Daily GPP data (g C m\({}^{-2}\) d\({}^{-1}\)), used as observed values of grassland growing seasons, were collected at study sites of the FLUXNET ([[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)), accessed on 5 July 2022 [39]) and European Fluxes Database Cluster ([[http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)]([http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)), accessed on 7 July 2022) networks. At these sites, CO\({}_{2}\) flux of grasslands is regularly measured from in situ towers by means of the eddy covariance method, and then further partitioned into ecosystem respiration and GPP [40]. From these two networks, 9 study areas in Europe with different altitudinal and botanical conditions were selected in order to obtain a representative European dataset. The list of study areas with the relative information is reported in Table 1. From the GPP patterns elaborated from this dataset, SOS, POS and EOS were extracted with the different approaches that are explained in Section 2.1.3.  
Figure 1: Workflow of the methodology, divided into “Approach to estimate SOS, POS, EOS” (2.1) and “SOS, POS, EOS trend analysis” (2.2).
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 5 Discussion  
Compared to the centralized approach of VECNets, our presented model using consortium blockchain performed adequately in security aspects, as shown in Section 4.1. Our system aims to avoid SPoF attacks as a bottleneck in a centralized framework that might expose users' private data due to compromised central data server. Contrary, the proposed system is controlled by multiple authorized RSUs, which will execute a consensus mechanism to reach an agreement before storing the data to the blockchain network. Thus,the user's private information cannot be exposed throughout the data sharing process. Moreover, the cryptography algorithm guarantees the vehicle identity anonymity and communication security. In order to evade the malicious vehicle that may transmit false messages to the system (spoofing attacks), we offer a message credibility evaluation conducted by neighboring vehicles. The neighboring vehicle can comprehensively evaluate the transmitted message and generate ratings for the message's trustworthiness. Nevertheless, the malicious vehicle may indeed act as a neighboring vehicle and generate a false assessment by upload an unfair rating (false rating attacks) to RSU. Fortunately, due to the limited number of attackers and the guarantee of cryptography encryption on vehicle authentication, the unfair ratings might hardly alter the aggregated trust values in authorized RSU. Furthermore, a consortium blockchain with multiple preselected RSUs is utilized to prevent the compromised RSUs that might be creating and broadcasting modified blocks (data modification attacks) in the consensus process. Therefore, based on the discussion above, our proposed system can reach decentralized trust management in VECNets.  
However, further discussion will be required in order to form a prevention approach rather than only a detection approach [38], such as to avoid attacker interference in transaction propagation time [39; 40]. One of the solutions is by employing a robust message authentication mechanism to facilitate the vehicles data sharing process securely while preventing attackers from hijacking transactions, identifying the message's identity, and stopping the transaction's propagation of the system [41]. As pointed out in [14], there are several techniques can be leveraged to enhance the security and privacy of blockchain-based VECNets, such as group signature, homomorphic encryption, attribute-based encryption, and ring signature. In the context of the consortium blockchain framework, a group signature is more suitable since the signer's identity can be hidden among multiple users [14]. This algorithm allows all messages to be encrypted with the sending vehicle's private key before sending. The vehicle's anonymity is protected because no one knows to whom the message is sent. Thus, the attackers cannot expose transactions without cracking the sender's private key. Therefore, the more robust data authentication technique makes the system more secure from malicious vehicles.
## 5 Nonlinear Observer
### Nonlinear Observer Design
For linear systems, the observer theory is almost complete and mature. Research on the nonlinear system state observer began in the 1970s, and various types of nonlinear observers have been developed  
Figure 8: Observer structure. There exists a positive definite symmetric matrix \(P\) and a positive definite matrix \(Q\), satisfying [PERSON]'s equation as follows:  
\[A_{0}^{T}P+PA_{0}=-Q \tag{51}\]  
The stability of the nonlinear observer as shown in (49) is proven as follows:  
Let [PERSON]'s function \(V(e)=e^{T}Pe\); then,  
\[ \begin{array}{c}\dot{V}\\ =(A_{0}e-\Gamma+\Delta(e)+B\sigma+\omega_{1})^{T}Pe+e^{T}P(A_{0}e-\Gamma+ \Delta(e)+B\sigma+\omega_{1})\\ =e^{T}(A_{0}^{T}P+PA_{0})e+2e^{T}P(\Delta(e)+B\sigma+\omega_{1}-\Gamma)\\ =-e^{T}Qe+2e^{T}P(\Delta(e)+B\sigma+\omega_{1}-\rho\text{sgn}(\vec{y}))\end{array}  \tag{52}\]  
Let \(\lambda_{\text{min}}(Q)\) and \(\lambda_{\text{max}}(P)\) be the minimum characteristic root of \(Q\) and the maximum characteristic root of \(P\), respectively; then,  
\[\dot{V}\leq-\lambda_{\text{min}}(Q)\|e\|^{2}+2\|e\|\lambda_{\text{max}}(P)( \beta_{1}+\beta_{2}+\beta_{3}+\rho) \tag{53}\]  
As \(\dot{V}\leq 0\) and \(\|e\|\geq 0\),  
\[\|e\|\geq R_{e} \tag{54}\]  
where \(R_{e}=2\frac{\lambda_{\text{max}}(P)}{\lambda_{\text{min}}(Q)}(\beta_{1}+ \beta_{2}+\beta_{3}+\rho)\). Therefore, when (54) exists, the observer is stable and the observation error decreases gradually until it enters the spherical domain with a radius of \(R_{e}\) centered on the origin in an \(n\)-dimensional space. After entering the spherical domain, the state estimation error reduces to a certain degree and oscillates, meeting the following condition:  
\[\|e\|\leq R_{e} \tag{55}\]  
From (55), we infer that the observer error is uniformly bounded. To reduce the observer error, when given \(P,Q,\beta_{2},\beta_{3}\), an effective method is to model the disturbing force and the disturbance compensation to reduce \(\beta_{1}\). Even though the disturbance model cannot fully represent the actual disturbance, it can effectively reduce the upper bound of the observation error and considerably weaken the chatter of the observer.
## 4 The Proposed Approach
### System Design
#### 4.1.2 Foraging Task Model
: _During the search, the robots use a search probability \(\rho\in\) (0; 1) to decide whether they should travel based on the selected locations (\(H_{t}\) or \(D_{t}\)) or switch to a uniform random search at each time step. This probability controls the distance and time that each robot travels in a straight line and allows flexibility in searching the area based on a uniform random distribution._  
At the beginning of the task, the resources are randomly distributed around the area, and the DD_Model is used to randomly change the resource states to infected and set their initial deadlines. The robots in this task have limited communication ranges and sensing capabilities, yet they need to work together to collect as many resources as possible before their deadlines. The resource locations are unknown to the robots; hence, they blindly search while collecting information about their surroundings and report the information when delivering resources to the closest zone they find. All types of robots can move globally around the area but searching robots \(R_{s}\) return to the closest depot when they collect a resource. When a resource is collected by an \(R_{s}\), the \(D_{t}\) and \(H_{t}\) of its surroundings are computed and reported to the closest collection zone. This information can be used by other robots to give priority to intense spots. Robots that detect a high density of resources \(D_{t}\) in a certain region have a high probability of returning to that region. This probability is defined by a Poisson cumulative distribution function (CDF) POIS (\(D_{i}\); \(p\)), as shown in (9), where p is the reliability parameter set by the user. The robot returns to its previous location when the parameterized Poisson CDF exceeds a uniform random value, i.e., POIS (\(D_{t}\); \(p\)) \(>\)\(\rho\in\) (0; 1); otherwise, the robot returns to one ofthe hotspots in its region, if any exist. If there are no hotspots, then the robot switches to a uniform random search. \[POIS(k;p)=e^{-p}*\Sigma_{i=0}^{Dt}\frac{p^{i}}{i!} \tag{9}\]  
The main objective of this task is to reduce the number of expired deadlines by observing the environment and updating \(D_{t}\) and \(H_{t}\) accordingly to collect resources as quickly as possible to give priority to hotspots.
# Recent Development of Aptamer Sensors for the Quantification of Aflatoxin B1
## 1 Introduction
As shown in Figure 1, this review focuses on the application of nucleic acid aptamer sensors in AFB1 detection, based on colorimetric, electrochemical, fluorescent, and surface-enhanced Raman scattering aptamer sensors, in recent decades. Figure 1: Schematic illustration of detection strategies for aflatoxin B1 (AFB1) by utilizing AFB1 aptamer sensors.
## 6 Experimental Results
### Comparative Analysis
#### 6.2.3 Analysis of End-to-End Delay  
The end-to-end delay is defined as the amount of time taken by the packets released from the WSN nodes to the destination. The mathematical formulation of end-to-end delay is defined as:  
\[\text{E2E}=\text{Ov}^{\text{del}}-\text{Tx}^{\text{del}}+\text{Rx}^{\text{del} }+\text{Pro}^{\text{del}} \tag{41}\]  
where Ovdel, Txdel, Rxdel, and Prodel denote the overall, transmission, reception, and processing delay, respectively.  
Figure 8 represents the comparison of E2E with respect to SDWSN nodes for the proposed MTS-SDWSN and existing works SEMRP-WSN and EDASS works, respectively. From the figure, it is shown that the E2E of both the proposed and existing models increases with an increase in the number of SDWSN nodes. From the inference, it is shown that the proposed model gains less E2E than any of the existing works. The main reason for such lesser E2E is that we effectively construct the network with 2D hexagonal grids with the aid of ensuring robust and resilient connectivity, so that the packets sent are easily transmitted, received, and processed in less time, thereby achieving less E2E. In contrast, the existing works SEMRP-WSN and EDASS lacks with network construction; rather, they place their WSN nodes in a random manner, which leads to more time for transmission, reception, and processing results in higher E2E than the proposed work.  
The quantitative analysis from the graph shows that the proposed MTS-SDWSN gains less E2E of 17 s when the SDWSN nodes reach 100, whereas for the same SDWSN nodes the E2E rate of SEMRP-WSN and EDASS gains with 23 s and 29 s, respectively, which is 6 s-12 s more than our proposed model.  
Figure 7: Speed of nodes vs. Packet Delivery Ratio.
# Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems
## 5 Application and Experiment
### Image Acquisition Experiment  
After completing the construction of the system, we carried out the system acquisition image experiment; Figure 13 shows the experimental results. Figure 13a is the low-resolution thermal microscope image collected by the system. This is resized using bilinear interpolation, as shown in Figure 13b. Figure 13c is the micro-scan reconstructed image collected by the system, which is obtained by interpolating the four low-resolution images collected by the system according to the 2 \(\times\) 2 micro-scan [37]. Since the ideal high-resolution image cannot be obtained in this experiment, instead we provide the image information entropy (SNT) for assessment [38; 39].  
Figure 11: High-resolution thermal microscope imaging system.  
Figure 12: High-resolution thermal microscope imaging system block diagram.  
According to the experimental results, the information entropy of the original image is 5.4627, the information entropy of the image after bilinear interpolation is 5.6503, and the information entropy of the micro-scan reconstructed image is 5.7148. The micro-scan reconstructed image can distinguish more detailed information, and, at the same time, the spatial resolution of the original low-resolution image is significantly improved. Compared with the two-line interpolation image, the image after system reconstruction has clearer details and has the advantage of zooming in and not distorting the details, which proves the effectiveness of the system design. It also proves that the theory provided in this article can provide a theoretical basis for the design of TMISs.