esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Governing Software
### Simulation Environments  
Simulation environments are essential in the development and testing of robotic applications, especially for rovers and UGVs used in agriculture. In fact, developing robotic systems for farm operations relies heavily on simulation as a vital and dependable method for evaluating different control algorithms for robots or robotic arms. Unfortunately, most existing simulation software primarily focuses on urban, traffic, or industrial settings. The simulation of off-road scenarios is relatively rare, and there is a noticeable absence of a dedicated simulator for agricultural robotics research [52]. The most well-known simulation environments are as follows:  
* Gazebo allows for the simulation of robots in both indoor and outdoor settings, including sensors and actuators, with a high degree of realism thanks to precise physical models. This makes it useful for testing navigation and monitoring in complex agricultural environments.
* CoppeliaSim (formerly V-REP) offers detailed control over simulations, supporting various physical engines. This means that it is able to deal with complex interactions between rovers and various types of terrain or crops.
* Webots is the development environment for the simulation of autonomous mobile robots. It is known for its ease of use and extensive support of robot models. It is used to prototype and test navigation and terrain mapping algorithms.
* Nvidia's Isaac Sim is an advanced simulation environment that utilizes GPU acceleration for realistic and detailed simulations, optimized for robotics and AI. This makes it particularly useful for simulating agricultural rovers that use AI for the visual analysis of crops.  
The most important supported features of such environments, with relevant bibliography references, are presented in Table 3.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Global Single Transformation
#### 3.1.3 FAST  
The Features from Accelerated Segment Test (FAST) [63; 70] is a corner detection method which can be used to extract feature points and later used to track and map objects in many computer vision tasks. A corner detector should satisfy the following criteria: consistent, insensitive to the variation of noise, detected as close as possible to the correct positions (accuracy), and fast enough (speed) [69]. The segment test criteria operate by considering a circle of sixteen pixels around the corner candidate feature \(p\). The original detector classifies \(p\) as a corner if there is a set of n contiguous pixels in the circle which are all brighter than the intensity of the candidate pixel \(p\) plus a threshold t or all darker than \(I_{p}\) minus t [71].
## 2 Conventional SB_DSICED3_W/OF Scheme
### 5G QC-LDPC Coding, Encoder, and Decoder Processes  
In 1962, [PERSON] introduced the first classical LDPC codes [41], and it was rediscovered again by [PERSON] in the late 1990s [42]. In 2018, the 3 rd Generation Partnership Project (3 GPP) chose QC-LDPC codes as the standard codes for 5G networks and applications based on the properties and characteristics of classical LDPC codes, especially for a short-length frame with lower processing throughput for uRLLC and the mMTC or massive Machine-to-Machine (mM2M) communications [32; 34; 43; 44; 45]. Moreover, the authors in [46; 47; 48] showed that 5G QC-LDPC codes are also an optimised design for short-packet transmission because of the low error floor and high-speed transmission. The construction of the encoder and decoder processes are described in Figure 2. At the transmitting side, the binary input signal \(x_{SI}[k]\), where \(k\in[1,K]\), is encoded by using the \((N,K)\) 5G LDPC encoding process to form a codeword with length \(N\). The encoding technique between the exponent parity check matrix \(\mathbf{H}\) and the information bit sequence is based on the Gauss-Jordan elimination algorithm [49], where \(N\) and \(K\) denote the codeword length and information length, respectively. Then, the obtained codeword is interleaved and modulated by using the Quadrature Phase Shift Keysing (QPSK) modulator with the modulation order \(M=4\), in order to form the modulated signal \(x_{SI}[n]\), where \(n\in[1,E]\) and \(E=N/log_{2}(M)\). Then, the \(\beta E\) known pilot symbols, where \(\beta\) is the pilot symbol coefficient and \((0<\beta\leq 1)\), are added to the message sequence after the modulation process in order to form the transmitted signal \(x_{SI}^{\prime}[n]\) with a length of \(E^{\prime}=(1+\beta)E\). Finally, this signal is passed to the DAC process to convert to the continuous time signal \(x_{SI}^{\prime}(t)\). The encoder process for the signal of interest \(x_{B}[k]\) to form transmitted intended signal \(x_{B}^{\prime}(t)\) is similar to that of the SI signal \(x_{SI}[k]\).  
At the receiver side, the residual signal after the DSIC process \(\tilde{y}[n]\) is passed through the equalisation process with the RLS algorithm to estimate the intended channel and obtain the equalised signal \(\tilde{y}^{\prime}[n]\). Then, the pilot symbols are removed from this signal before going to the QPSK demodulator and de-interleaver processes to form the Log Likelihood Ratio (LLR) belief sequence. Then, this LLR sequence is used for decoding and decisions. To obtain the estimated binary input signal \(\hat{x}_{Sol}[k]\) of Node B, the Sum Product Algorithm (SPA) [50; 51; 52] is applied at Node A, which is the message passing between the check nodesand the symbol nodes for guessing the transmitted bits from each other at each iteration \(j\) until it reaches the maximum number of decoding interactions \(j_{max}\).
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Physical Methods
#### 2.1.1 Lithography  
This versatile technique has been employed for the synthesis of a wide range of nanoparticles including MNPs. Lithography is part of the micropatterning technique being based on a controlled transfer of patterns from a mask or template to a substrate, allowing for the precise positioning and arrangement of nano-sized features. Over time, the technique has been optimized and new methods have been implemented: electron beam, scanning, nanosphere, and colloidal lithography, together with soft nanoimprinting [6].
## 5 Results and Discussion
### Results from Integrated Consideration and Footfall Model
#### 5.2.3 Mediating Role of the Consideration Propensity on Footfall  
The results show that the instrumental variable, namely, consideration probability, is statistically significant with a large coefficient (5.44). Hence, increasing the consideration propensity tends to increase ridership by making people more familiar with train travel or its attributes. Consideration propensity has a significant interaction effect with personal mobility resource availability and gender, as well. Females and users in the medium personal mobility segment with larger consideration propensity are more likely to contribute to increased footfall. The significance of these variables demonstrates that non-user response (or self-selection bias in aggregate data) has a significant effect on footfall.  
The joint footfall model is used to disentangle which factors affect only consideration and distinguish them from those that affect only footfall. The results indicate that qualitative LOS variables, such as comfort, security, and ease of purchasing train tickets, affect only the consideration dimension. These represent the set of fully mediated variables \(X_{1}\). Interventions with these fully mediated variables can be effective in low-consideration areas.  
However, variables such as station and terminal facilities (such as an airport near the metro station, transfer station, cycle facilities near the station, etc.), and quantitative LOS factors such as headway and cost of metro trains, only affect footfall directly, but are not considered. These comprise unmediated variables that affect only footfall but not consideration propensity (\(X_{3}\)). Thus, policies targeting these unmediated variables are appropriate in places with high consideration and low ridership. On the other hand, the usability of footpaths and out-of-vehicle travel times affect both dimensions and hence reflect a partially mediated set of explanatory factors \(X_{2}\). Policies based on such partly mediated variables can be beneficial in locations with moderate levels of consideration and ridership.
# Prospects of Structural Similarity Index for Medical Image Analysis
## 4 SSIM in Medical Imaging
### Ultrasonography  
We review SSIM applications for ultrasonography in this section. We found that the first implemented SSIM for US was in 2007. SSIM along with the SNR, coefficient of correlation (CoC), edge preservation index (EPI), and QI were used to quantify the image enhancement when a versatile wavelet domain algorithm was utilized [106]. In the same year, US with two other medical images, CT and X-ray images, were measured using SSIM, MSE, PSNR, CC, and PRD to identify the effectiveness of a novel algorithm for compressing images in the field of teleraliology using an adaptive threshold value of variance [107]. In 2008, an algorithm was developed to reduce the effects of speckle, and the developed algorithm was measured with the Michelson contrast measure (CM), the Beghdadi and Le Negrate contrast measure (CBN), PSNR, and SSIM [108]. Their results showed that SSIM has potential effectiveness as an IQM for the US, although, at the time, SSIM had existed for only 3 or 4 years. The reduction in speckle in US images has brought several types of studies to this issue. In 2016, the least-squares Bayesian [60], adaptive non-local means [109], local statistic, and non-local mean filter [110] algorithm estimations were established to reduce the speckle in US images, and several IQAs including SSIM, SNR, MSE, and a sum of the variance (SV) were designated to evaluate the improved algorithms. An uncommon SSIM implementation was conducted using CW-SSIM as a contour extractor of a tongue by [PERSON] et al. [61; 111]. They compared the performance of MSSIM and CW-SSIM with the normalized PSNR (NPSNR).
## 4 Results
### Spatial Variation of the EE from 2002-2022
#### 4.2.1 Local Spatial Variation in the EE in the Study Area
After 2007, especially during 2009-2017, the EE quality of the direct impact area deteriorated significantly, indicating that underground coal mining had a significant negative impact on the surface EE in the mining area. The impact was mainly limited to the subsidence region, and the impact on the surrounding buffer area was relatively small. In Figure 8, the urban region (A) and the typical GGP region (B1) were selected to analyze the EE variation of the above local region from 2002 to 2022, as shown in Figure 12.  
Figure 9: Location of Dafosi coal mine in China Loess Plateau (CLP) and Shaanxi province. From 2002 to 2022, the EE of the urban region continued to deteriorate as the area of Guanzhong plain urban agglomeration centered on Xi'an increased, and there was an obvious negative correlation between the two. Urbanization only affects the local EE and has a smaller impact on the EE of the entire study area because the area of the urban region makes up a small portion of the entire study area, similar to the mining region. Figure 11: Changes in RSEI in coal mine area, buffer area, and comparison area from 2005 to 2022. Figure 10: Locations of the coal mine area, buffer area, and comparison area. From 2002 to 2022, with GGP implemented, the area of forest and grassland continued to increase, and the EE in the typical GGP region (B1) became significantly better, and there was a high correlation between the two. The same is true for the other typical areas, B2 and B3 in Figure 8.  
From the above analysis, it can be seen that two human projects, underground coal mining, and urbanization have significant negative impacts on the local EE, but the scope of such impact is relatively small and the impact on the ecological variation trend is relatively small. However, the GGP is the main factor driving the improvement in EE.
## 5 Measurement Details and Results
### CP Service  
The CPM--Collective Perception Message--is a message type that can drive so-called Day2--or perception sharing--use cases. The CPMs and CAMs are both sent by V-ITSS nodes and are also sent quite regularly, in many cases with 10 Hz--unlike, for example, the DENM. This means that CAM+CPM messages are responsible for the majority of channel usage. We can assume that if an ITSS supports CPM, it also supports CAM. The measurement of the standalone CP service still makes sense, because ITSG5 supports multiple channels, and it is not certain whether CA and CP services will be deployed on the same channel in the future. We have to emphasize that in our CPM model--thanks to the Artery framework's sensor and environment architecture--the generation of the perception messages perfectly complies with the characteristics of the realistic, traffic-given  
Figure 6: The average CBR value on all R-ITS-S devices when only CA service is running.  
Figure 7: The average CBR value on all V-ITS-S devices when only CA service is running.  
environment. This means that the size of every CPM message is in line with the actually sensed number of objects (other vehicles in the ego sensor range) for every V-ITS-S during the simulation.  
The simulation results show that the CP service is responsible for a similar CBR value compared to the CA service considering the applied vehicle traffic parameters. This is visible in Figure 8. The maximum CBR value was around 6% in each scenario.  
We can see in the Figure 9 diagram that the measured values are slightly higher than the ones measured on the R-ITS-S nodes.  
We can observe in Figures 8 and 9 that if we add the values measured in the CAM and CPM scenarios, the result will be about the same as in the combined CAM + CPM scenario alone. This is due to the relatively low channel usage. We can also see that the traffic measurement intervals with low traffic  
We can also see that the CBR values are characterized mainly by three different clusters, one representing the minimum values, one representing the average values of January and March, and one representing the maximal intervals and the average of September. From the diagram, we can see that September is quite a busy month from a traffic perspective.
## 2 Theoretical Background
### SVM Theory  
Based on the structural risk minimization principle and statistical learning theory, SVM was presented to deal with regression analysis and regression analysis issues. This algorithm can achieve the best combination of learning precision and identification accuracy with only a few samples [38]. SVM is mainly to construct a classification hyperplane to separate the data points of two classes and to maximize the margin of separation hyperplane between these two data sets. When SVM is used as a classifier, it firstly projects input data into the high dimension space by the way of nonlinear transformation, and then an optimal hyperplane is found to classify input data sets. Therefore, the construction of the SVM-based classifier can be treated as a quadratic optimization issue. The generalization ability of SVM is stronger than traditional nonlinear function approximation algorithms. At the same time, SVM can overcome the difficulty in determining the network structure and local minima of the neural network. Therefore, SVM has advantages in the classification of small sample sets and pattern recognition of high dimensional data sets. The principles of SVM are briefly described below [35; 36; 37; 38; 39].  
Set the training sample dataset to (\(x_{i,}y_{i}\)), \(i=1,2\ldots n\), \(x_{i}\in R^{m}\), \(y_{i}\in\)(1, \(-\)1). These samples can be classified by a hyperplane which is described as  
\[g(t)=wx+b=0, \tag{10}\]  
where \(w\) denotes the \(n\) dimensional weight vector which is perpendicular to the hyperplane, and \(b\) is a scalar. If we get a perpendicular, it can be classified by the sample dataset with  
\[\begin{cases}g(x_{i})\geq 1,when&y_{i}=1\\ g(x_{i})<1,when&y_{i}<-1\end{cases}. \tag{11}\]  
To reduce the classification error of the SVM model, the optimization values of \(w\) and \(b\) should be found to make the separating margin maximum. That is to say, the SVM-based classification finally become a quadratic optimization problem. In practical applications of SVM, the sample data set is usual a nonlinearly separable, and the samples should be mapped to high dimensional space with the kernel function before doing the classification.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 2 The TinyJambu Cryptosystem: An AEAD Scheme
### A Detailed Description of the TinyJambu Family
The subscripts of the key \(k\) are always computed modulo \(\left\lvert k\right\rvert\); that is, \(ki=ki\bmod\left\lvert k\right\rvert\). Remark that for permutation \(P_{1024}\), the key is repeated eight times while for \(P_{384}\), the key is repeated three times. Due to the fact that this structure is a classical sponge for the 128-bit state and keyed permutation [6], TinyJambu is an extremely fast cryptosystem. Indeed, it was the fastest among all the candidates submitted to the NIST call [2]. According to the designers, the tapping bit positions were chosen in such a way that during the algorithm execution, 32 rounds of the permutation can be computed in parallel on 32-bit CPUs.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 3 Commonly Utilized Methods  
### Learning Indicators and Available Data  
Learning analytics collects, analyzes, and reports data about learners and their environments. The goal is to better understand and improve education [36]. Since its formal introduction in 2011, this field has grown rapidly and is now used at different educational levels [37]. Its potential to improve education is clear, but its use in daily classroom activities is still slow [38].  
In education, learning indicators fall into five main categories: behavior, cognition, emotion, engagement, and collaboration (Table 1).
# Two-Dimensional Metal-Organic Frameworks and Their Derivative Electrocatalysts for Water Splitting
## 3 Application of 2D MOFs and Their Derivatives for Water Splitting  
Due to their tunable structure, rich porosity, and high specific surface area, 2D MOFs and their derivatives have significant promising potential in many areas. Especially in electrocatalysis, their rich porosity facilitates electron transport, and their tunable morphology through metals and ligands makes them effective in water splitting. In particular, 2D MOF derivatives, with their higher conductivity, have excellent performance in electrocatalysis. In the following section, we mainly introduce the application of 2D MOFs and derivatives in water splitting, including HER and OER.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### High-Resolution Imaging Technology
#### 2.1.3 Application Status and Prospects  
High-resolution images usually contain feature-rich information such as spectral characteristics, geometric features, and texture features, and hence, a significant amount of useful information can be extracted from such images. High-resolution imaging has been widely used in forest management [108], urban mapping [109], farmland management [110], disaster and security mapping, public information service and environmental monitoring. Numerous high-resolution satellites have been developed and launched in recent years. These satellites can form satellite networks to obtain image data with a wide coverage. However, the huge amounts of data also bring new challenges to data transmission and processing [80]. Such high-resolution images have been extensively used for road extraction [34; 71]. Moreover, several commercial products such as Google Maps based on high-resolution images have been successfully developed and applied in many fields in recent years.
## 1 Introduction
Then, a new network DIC-Net on this dataset is trained that can successfully predict the strain field. [39] applies the image-segmentation technique DeepLabv3+ to achieve the pixel level classification of DIC strain field images, thus realizing damage detection in carbon-fiber-reinforced plastic (CFRP). However, the above investigations have not been applied in laser beam welding. In this paper, we first collect a real welding dataset for training a good model. The controlled tensile weldability (CTW) test is a technique to crack the specimen through the external load; it is used to simulate the occurrence of cracks during the data generation. Then, the strain fields are calculated through a strain calculation method applying the OpenCV library [18; 19]. The mechanisms of cracking during laser beam welding have been investigated in previous studies using the mentioned test and the strain measurement method based on an optical flow algorithm [18; 40]. On the other hand, these existing models contain a large number of parameters, reaching up to tens of millions, which occupy a lot of memory. Moreover, there is the risk of overfitting our welding dataset. Two end-to-end models based on the U-Net [41] structure are developed. They adopt ResNet [42] and DenseNet [43] as the backbone for feature extraction and are named StrainNetR and StrainNetD. Finally, the performances of the two models are systematically analyzed; the results show that they achieve near-real-time strain prediction with a low error during the welding process, and the location of the identified cracks matches the real data. The contributions of this paper are as follows:  
* A video of strained welding using CTW was generated;
* Two deep neural networks called StrainNetR and StrainNetD for strain prediction were proposed;
* The two models were trained and evaluated on the real generated dataset.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
###### Abstract  
In large-area wireless sensor networks with hierarchical cluster-based routing protocols, the average number of clusters, \(k\), and the transmission range for the control messages, \(R\), significantly affect the network lifespan. We analyze energy consumption in depth as a function of \((k,R)\), taking into account the energy dissipation of cluster head nodes and the member nodes, separately. To achieve joint optimization of \((k_{opt},R_{opt})\), we adopt derivative-free Nelder-Mead Simplex method. Computer simulations have shown that our approach effectively reduces energy consumption of sensor nodes in the process of clustering and data transmission in large-area sensor fields. Our optimization can be applied to existing cluster-based routing schemes to maximize their energy efficiency.  
internet of things; wireless sensor network; hierarchical routing; optimization; transmission radius; clustering +
Footnote †: journal:  
_Article_  
Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks