esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 4 Combination of Multisource Data for Road Extraction
### Combination of SAR Images with Other Data for Road Extraction  
Several studies have been published on the fusion of optical imagery and SAR data. [PERSON] [239] proposed road extraction via the fusion of infrared and SAR images. [PERSON] et al. [240] compared multiple remote sensing datasets (Spot5, IKONOS, QuickBird, DMC and airborne SAR datasets) and algorithms. Road trackers were designed for five different road types: national highways, interstate highways, railroads, avenues and lanes. Evidently, fused multiple remote sensing data were more efficient than a single data source. [PERSON] et al. [241] fused TerraSAR-X and QuickBird data for road network extraction at two test sites, and the road extraction accuracy for the fused data was 10-30% higher than those obtained for individual datasets. Multitemporal SAR image stacks (TSX and CSK) were also studied. [PERSON] et al. [242] studied the arctic settlements using Sentinel-2 optical and Sentinel-1 SAR satellite images. Pixel-based classification using a gradient boosting machine and a deep learning approach based on windowed semantic segmentation using U-Net architecture were compared. Asphalt roads were easily detected than gravel roads, and for arctic mapping, both methods and sensors were recommended. [PERSON] et al. [243] studied urban area mapping using Sentinel-1 SAR and Sentinel-2 optical data and proposed the integration of object-based postclassification refinement and CNNs for land cover mapping. Notably, for road mapping, SAR backscattering provided different physical information on roads (low backscatters) than that provided by optical remote sensing; moreover, the roads were identified with higher accuracy by combining the optical data with that of the SAR. [PERSON] et al. [244] extracted impervious surfaces using optical, SAR and LiDAR DSM data. The non-shadow and shadow classes were trained using the combined optical-SAR-LiDAR data. As a result, the shadow effects in the classification results were reduced.
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 3 SCAR-Phase of Spectrum Sensing
### First Sample Processing Period
#### 3.1.1 Training Sequence Synchronization
It can be noticed that for a determined value of SNR, the probability of synchronization error is lower when a greater number of received samples are utilized. It is important to note that in the SNR region of interest, up to \(-20\) dB, the probability of synchronization error approaches zero with a reasonable number of samples \(N\). For example, with \(N=6000\), the probability of synchronization error is \(\approx 2\times 10^{-2}\), and with \(N=9000\) this probability reduces to \(\approx 1.8\times 10^{-3}\). Therefore, Figure 3 shows that it is feasible for the SU to synchronize with the PU's ST transmitted sequence using a relatively low number of samples. Figure 3: Probability of synchronization error against signal-to-noise ratio (SNR).
## 3 Design
### Priority Score Calculation  
The intuition of input scheduling is to choose the inputs that can generate the most new coverage by concolic execution. Therefore, we need more valid different target branches as possible. It would be best if all of these branches will guide to unexplored code. The priority score calculation algorithm is based on the input priority score, which is decided by the number of critical branches.  
We use four steps to calculate the priority score related to each input, with the information generated by the branch scheduling part.  
* Choose the branches with the \(H\) value as zero. Firstly, we consider the \(H\) value, which denotes if a branch is explored or not. We will choose these branches with the \(H\) value as zero for each input, which means they are not explored.
* Choose the branches with the \(S\) value as one. Secondly, we consider the \(S\) value, which denotes if a branch can be solved or not. We will choose these branches with the \(S\) value as one for each input, which means they can be solved.
* Calculate the complexity of the chosen branches. Thirdly, we consider the \(C\) value, which denotes the complexity to solve a branch. We choose the top 50% of branches with the biggest \(C\) values.
* Calculate the priority score for each input. Lastly, we calculate the priority score for each input by the number of critical branches chosen in the previous three steps, as Algorithm 1 shows.  
With the priority score, we can have an ordering input list and choose inputs sequentially from this list, then run concolic execution on each input with related target branches. There is another thing worth mentioning, regarding the sequence that we follow to consider each value. We believe that it is more important to explore untouched paths than solve simple constraints. Therefore, we consider the hit and solvability value before the complexity value. Furthermore, the percentage of critical branches can always be changed for concrete situations. If there are not many branches in one run, we can choose more than half of the branches. We can also define the exact number of branches to choose.
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Optimization of the Fuzzy Logic Controller Based on the Genetic Algorithm
#### 4.2.2 When the Robot Tracks the Other Different Paths  
In order to investigate operating ability of the GA_FLC, it has been used to control the DDWMR to move in several typical different paths. In this case, the DDWMR is controlled by the GA_FLC (optimized and taken from the previous experiment) to track the square path, the sharp turn path, the straightforward path, and the diamond-shaped path sequentially. Figure 15a-d and Tables 12-15 indicate that the performance of the motion control is still good when the robot is controlled by the GA_FLC compared to the others in all given paths. Specifically, 110.2%, 109.7%, 110.3%, and 107.9% reduction of the energy consumption was achieved using the proposed method compared to the best of six alternatives studied when the robot tracks the square path, the sharp turn path, the straightforward path, and the diamond shaped path, respectively. Especially, the limitations of the other controllers such as the vibration and the jerk have been solved by the GA_FLC. So, it can be also concluded that the GA_FLC is the best controller for the differential drive wheeled mobile robot on the different given paths.  
In addition, Figure 15e,f indicate that the robot still tracks the number eight-shaped path and the circle path tightly. In these cases, the energy consumption of the differential drive wheeled mobile robot is also quite low (E = 8.73 J for the number eight-shaped path, and E = 8.75 J for the circle path).
## 3 Background and Theory
### Fractal Dimension Features  
The fifth and final feature extraction method is based on fractal dimension features, which fall under time-frequency analysis characteristics. In this study, we applied several fractal dimension algorithms commonly utilized in EEG analysis, specifically the Katz, Petrosian, and Higuchi methods. Fractal dimensions offer insights into the self-similarity and complexity of the signal.  
The Katz fractal dimension [11] assesses signal complexity by examining how the signal's structure occupies space as it scales. This method evaluates deviations from smoothness by considering both the total length of the signal path and its spatial extent, as expressed in Equation (16):  
\[FD_{\text{Katz}}=\frac{\ln(n)}{\ln(n)+\ln\left(\frac{d}{L}\right)} \tag{16}\]  
where the following apply:  
* In denotes the natural logarithm;
* \(n\) is the total number of observations (data points) in the time series;
* L is the total length of the signal path;
* \(d\) is the maximum distance from the first point to any other point in the signal.  
Petrosian fractal dimension [12] measures the irregularity or self-similarity within a signal by calculating the number of sign changes in its first derivative. It is defined by Equation (17):  
\[FD_{Petrosian}=\frac{\log_{10}(n)}{\log_{10}(n)+\log_{10}\left(\frac{n}{n+0.4\cdot N _{8}}\right)} \tag{17}\]  
where the following apply:  
* \(\log_{10}\) is the base-10 logarithm;
* \(n\) is the total number of observations (data points) in the time series;
* \(N_{8}\) represents the number of sign changes in the derivative, providing insights into the signal's frequency content and waveform complexity.  
The Higuchi fractal dimension [13] assesses the roughness or complexity of a signal by examining its variation as it undergoes successive downsampling, represented by Equations (18)-(20):  
\[HFD=-\text{slope}(\log(x),\log(L)) \tag{18}\]  
\[L_{k}=\frac{1}{k}\sum_{m=0}^{k-1}\left(\frac{N-1}{k(N-m-1)}\sum_{i=1}^{\lfloor \frac{N-m}{k}\rfloor}\left|data[m+ik]-data[m+(i-1)k]\right|\right) \tag{19}\]  
\[x=[1,2,\ldots,k_{\max}] \tag{20}\]  
where the following apply:  
* \(HFD\) is the Higuchi fractal dimension resulting from the computation;
* \(L_{k}\) is the average length over \(k\) sets;
* \(k\) is an integer defining the time interval for calculating \(L_{k}\);
* \(k_{\max}\) is the maximum No. of \(k\), defining different scales at which the time series is analyzed;
* \(N\) is the total No. of data points;
* \(m\) ranges from \(0\) to \(k-1\);
* \(L_{m,\;k}\) denotes the length of the curve for a particular scale \(k\) and starting point \(m\);
* \(i\) indexes the sum over \(N-m\) data points;
* \(data\) refers to the actual EEG time series being analyzed;
* \(x\) is a list of integers from \(1\) to \(k_{\max}\), which are used alongside \(L\) to compute the slope.
## 2 Literature Review
### Integrated Optimization Approaches for Hinterland Connectivity  
This paper [33] uses integrated optimization approaches for hinterland connectivity to address the complexities of port-hinterland freight transportation. By incorporating inter-modal transport and considering uncertain transportation demand, the study integrateseconomic and environmental objectives to develop green flow distribution solutions. A robust optimization model is employed, leveraging chance constraints to handle demand uncertainties and ensure that transportation needs are met even under worst-case scenarios. The approach, applied to the Yangtze River Economic Belt, reveals the benefits of promoting railway-road intermodal transport, balancing network costs, and emissions, thus providing strategic guidance for sustainable port-hinterland connectivity in dynamic and stochastic conditions.  
The study [1] investigates mathematical models for optimizing container transport in port areas by integrating inter-terminal transport (ITT) and rail freight operations. Using an integer linear programming model, it addresses the challenges of container movements within ports and their subsequent transport to hinterland areas. A tabu search algorithm is proposed to solve the complex optimization problem, tested across realistic infrastructure and demand scenarios. The findings demonstrate significant improvements in operational efficiency when ITT and railway processes are integrated, showing a 20% reduction in ITT costs and a 44% decrease in railway operational costs, compared to a 17% ITT cost reduction but a 93% increase in railway costs when ITT is optimized independently.  
Another study [34] explores the use of computer simulations to analyze the correlation effects in tourism space systems, focusing on Mohan port and the Yunnan economic hinterland. Using gray correlation analysis from 2006 to 2020, the study quantifies the relationship between the port and hinterland, highlighting trends influenced by administrative actions, port competition, and other factors. Key findings include a rising and falling correlation trend, significant fluctuations in the port-city relationship, and spatial evolution patterns. Factors such as location, policies, and infrastructure are identified as major drivers. This research contributes to theoretical advancements in economic buildings and supports decision-making in tourism spatial optimization.
## 4 Stress Strength Reliability and Information Measures
### S-S Parameter  
The notion \"S-S reliability\", denoted by \(\Re=P[T_{2}<T_{1}]\), is illustrated by \(T_{1}\) representing component strength and \(T_{2}\) representing stress. If \(T_{2}>T_{1}\) in S-S modeling, the system is a mail function. Let \(T_{1}\sim\text{UEHLD}\ (\delta,\varphi_{1})\) and \(T_{2}\sim\text{UEHLD}\ (\delta,\varphi_{2})\), then \(\Re\) is determined as follows:  
\[\Re=1-\int\limits_{0}^{1}\frac{2\varphi_{1}(\varphi_{1}+\varphi_{2})\delta t^{ \delta-1}}{(\varphi_{1}+\varphi_{2})\big{(}1+t^{\delta}\big{)}^{2}}\bigg{(} \frac{1-t^{\delta}}{1+t^{\delta}}\bigg{)}^{\varphi_{1}+\varphi_{2}-1}dt=\frac{ \varphi_{2}}{(\varphi_{1}+\varphi_{2})}. \tag{12}\]  
The S-S reliability in (12) depends on \(\varphi_{1}\), and \(\varphi_{2}\).
# Root System and Its Relations with Soil Physical and Chemical Attributes in Orange Culture
###### Abstract  
Citrus companies have sought and developed alternative systems of tillage or implanting orchards so as not to significantly alter the physical and chemical attributes of the soil and, consequently, the root development of plants. Therefore, the aim of this study was to identify the physical and chemical attributes of the soil that most influence the root volume of the orange crop in different tillage systems. The experiment was carried out in the region of Avare, state of Sao Paulo, Brazil, in Utissol and Oxisol. For the planting of the orange crop, the following tillages were made: minimum tillage, subsoiler tillage and soil tillage using a triple tillage implement. The physical and chemical attributes evaluated were bulk density, macroporosity, microporosity, total porosity, soil moisture, soil mean weight-diameter, soil resistance to penetration, sum of bases, cation exchange capacity, base saturation, pH, exchangeable cations, potential acidity, available phosphorus, analysis of micr-conutrients such as copper, iron, manganese and zinc, and organic carbon content. The root system was evaluated using SIARCS(r) software. For data classification, data mining techniques were used such as attribute selection and decision tree induction. Regardless of the soil type, the use of the triple operation implement provided greater root volume for orange plants. For the Utisol area, the pH value of 4.2 was the main attribute that provided a high root volume. For the Oxisol, the presence of copper, in levels that did not generate toxicity for the plants, provided a high volume of root for the crop.  
data mining; soil tillage; root volume; _Citrus sinensis_
## 2 Materials and Methods
### Classification Models  
Three different classification models were trained to predict the sentinel lymph node positivity. We used well-known machine learning methods, which were Random Forest (RF), logistic regression, and Naive Bayesian.  
Random Forests is a well-known ensemble machine learning classifier, which generally provides good performance with low over-fitting [28]. RF provides an embedded method for feature selection: it takes advantage of its own feature selection process and performs classification at the same time. There are two measures of importance for each feature: the first one measures how much the accuracy decreases when a feature is excluded to the forest, the second one measures the decrease in Gini impurity when a feature is chosen to split a node of a tree. In our work, we used this second method. A standard configuration of RF was adopted with 100 trees and 20 features (as described in Breiman (24)) randomly selected at each split because more complicated architectures did not give any significant classification improvement. Moreover, in order to control the over-fitting risk, we have fixed a small number of observations per tree leaf, such as five.  
A logistic regression prediction model measures the underlying relationships between features and patient outcomes existing within the data [29]. The accuracy of a logistic regression model is mainly judged by considering discrimination and calibration. Discrimination is the model capability to correctly assign a higher risk of an outcome to the patients who are truly at higher risk, whereas calibration is the model capability to assign the correct average absolute level of risk, i.e., to accurately estimate the probability of the patient outcome.  
The Naive Bayes classifier is a probabilistic machine learning model that is used to fulfill classification tasks based on the Bayes theorem [30]. It requires a small amount of training data to estimate the necessary parameters; despite their apparently over-simplified assumptions, Naive Bayes classifiers worked well in many real-world situations.  
Default parameters were used for logistic regression and Naive Bayes classifiers.  
The considered classifiers are based on a different approach to solve the classification task. A RF classifier is an ensemble technique that combines several decision trees to calculate the predicted class, so that the forecasts made by decision trees, which may be individually inaccurate, aim to improve performance and reduce over-fitting when combined together. The Naive Bayesian classifier is based on a probabilistic approach to solve a classification problem, whereas the logistic regression model exploits a mathematical approach to the problem through the estimation of the classification score performance by means of a linear combination of features.  
Feature importance techniques and classification models were performed using the MATLAB R2018a (Mathworks, Inc., Natick, MA, USA) software.
## 3 Proposed Method
### Model Training
In the prediction, we have \(x_{1}^{p}\), \(y_{1}^{p}\) and \(\left(x_{2}^{p},y_{2}^{p}\right)\), and Equation (5) is used to calculate the \(IOU\) as follows:  
\[loss_{1}=IoU=\frac{Area_{intersection}}{Area_{union}} \tag{5}\]  
where  
\[Area_{intersection}=max\Big{(}0,min\Big{(}x_{2}^{p},x_{2}^{g}\Big{)}-max\Big{(}x _{1}^{p},x_{1}^{g}\Big{)}\Big{)}\times max\Big{(}0,min\Big{(}y_{2}^{p},y_{2} ^{g}\Big{)}-max\Big{(}y_{1}^{p},y_{1}^{g}\Big{)}\Big{)},\]  
and  
\[Area_{union}=Area_{predicted}+Area_{ground-truth}-Area_{intersection}\]  
Smooth L1 loss is given in (6), where \(x\) represents the difference between the predicted and the target bounding box coordinates. The reason behind the employment of this loss function pertains to [55], where it is affirmed that Smooth L1 loss penalizes large errors more gently compared with traditional MSE loss, which makes it suitable for object detection tasks. \[loss_{2}=\text{SmoothL1}(x)=\left\{ \begin{matrix}0.5*x^{2}&if\ |x|<1\\ |x|-0.5\ otherwise\end{matrix} \right. \tag{6}\]  
This study represents a binary classification problem, so the binary cross-entropy loss function shown in (7) is used. \[loss_{3}=L_{binary\_crossentropy}=-\Big{(}y_{true}\text{log}\Big{(}y_{pred} \Big{)}+(1-y_{true})\text{log}\Big{(}1-y_{pred}\Big{)}\Big{)} \tag{7}\]  
where  
\(y_{true}\) is the true class label (0 or 1). \(y_{pred}\) is the predicted probability of the positive class (class 1).
# Hybrid Consensus Algorithm Based on Modified Proof-of-Probability and DPoS
###### Abstract  
As the core of blockchain technology, the consensus algorithm plays an important role in determining the security, data consistency, and efficiency of blockchain systems. The existing mainstream consensus algorithm is experiencing difficulties satisfying the needs of efficiency, security, and decentralization in real-world scenarios. This paper proposes a hybrid consensus algorithm based on modified Proof-of-Probability and Delegated Proof-of-Stake. In this method, the work of block generation and validation is, respectively, completed by the nodes using the modified Proof-of-Probability consensus algorithm and Delegated Proof-of-Stake consensus algorithm. When a transaction occurs, the system sends several target hash values to the whole network. Each modified Proof-of-Probability node has a different sorting algorithm, so they have different mining priorities. Every time a hash is decrypted by a modified Proof-of-Probability node, the modulo operation is done to the value of nonce, which is then compared with the expected value given by the supernode selected by the Delegated Proof-of-Stake nodes. If they are not the same, the Proof-of-Probability node enters the waiting time and the other Proof-of-Probability nodes continue to mine. By adopting two consensus algorithms, the malicious nodes must control more than 51% of the nodes that adopt the two consensus algorithms, at the same time, to effectively attack the system, that is, they must have more than 51% of the computing power and more than 51% of the tokens. This not only increases the cost of malicious attacks, but also reduces waste of computing power. In addition, the efficiency of the DPoS algorithm makes up for the deficiency of the PoP algorithm in system efficiency, and the mining behavior based on probability in the PoP algorithm also significantly weakens the ability of supernodes in the DPoS algorithm to conduct monopoly behavior or other malicious behaviors. In a word, the combination of the two algorithms makes the system perform better in terms of security, system efficiency, and decentralization.  
**Keywords: blockchain; consensus algorithm; hash; transaction; voting**
# Microbes in Health and Disease: Human Gut Microbiota
## 3 Multifunction of the Human Gut Microbiota
### Colonization Resistance  
The gut microbiota suppresses the colonization of pathogens via various mechanisms, such as the production of antimicrobials and nutrient competition (Figure 3). The normal healthy gut microbiota transforms primary bile acids into secondary bile acids, which is regarded as one of important mechanisms in colonization resistance by which the human gut microbiota suppresses the diarrhea-causing gut pathogen _Clostridioides difficile_ because secondary bile acids inhibit the outgrowth of _C. difficile_[61].
## 3 Accelerated Environmental Conditions and Test Methods  
### Repeated Wetting and Drying Cycles  
Hybrid panels react sensitively to moisture conditions. In particular, along with environmental conditions involving continuous contact with moisture, the possibility of fracture in a hybrid panel surface increases when the panel is exposed to repeated wet and dry cycles. Therefore, the influence of the hybrid panel's exposure to an environment of repeated dry-wet cycles was evaluated. Each cycle consisted of drying in an oven at 60 \({}^{\circ}\)C for 24 h and then immersing in water at 60 \({}^{\circ}\)C for 24 h. The panels were tested for 25 cycles over 50 days. Exposure temperatures were based on the results of a previous study in which the durability of FRP composites was evaluated at various environmental temperatures to accelerate deterioration [36, 37, 38]. In the current study, the exposure temperature of 60 \({}^{\circ}\)C corresponds to an artificially harsh environment and serves to accelerate the deterioration processes that would occur naturally in a modified movable weir.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Steel** & **Steel Thickness (mm)** & **GFRP Thickness (mm)** & **Total Thickness (mm)** \\ \hline \multirow{3}{*}{Control} & 3.2 & 6.0 & 9.2 \\ \cline{2-4}  & 3.2 & 9.0 & 12.2 \\ \cline{2-4}  & 3.2 & 12.0 & 15.2 \\ \hline \multirow{3}{*}{Sand blast} & 3.2 & 6.0 & 9.2 \\ \cline{2-4}  & 3.2 & 9.0 & 12.2 \\ \cline{2-4}  & 3.2 & 12.0 & 15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test parameters of steel and GFRP panels.