esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Dimensional Variables on the Behavior of Trees for Biomechanical Studies
## 3 Results and Discussion
### Normal Stresses
The authors' results showed that although the bending moments increased from the top to the bottom of the tree and the maximum moment was located near the base, the normal stresses varied along the stem following a third-degree polynomial. The results obtained by [33] show that the maximum normal stresses occur between 19% and 63% of the total height of the tree. The same study also shows that shortest trees had the highest maximum stress position (63% \(H\)), and the stress distribution along the trunk generally followed a fourth-degree polynomial. In ref. [46] found that uniformly tapered stems (i.e., those of Cryptomeria japonica trees) subjected to wind and snow loads had breaks located between 50% and 70% of the tree's height, while In ref. [43] showed trunk break at 13.5% \(H\). The distribution of stresses along the stem is influenced not only by the horizontal load considering the wind, but also by the moments applied considering the branches and by the horizontal components of the self-weight, which appear due to the deflection of the trunk. Furthermore, stresses are affected by diameter variation, which impacts the moment of inertia of the transversal section of the trunk. These loads are composed of the horizontal load, changing the behavior of the stresses along the trunk [47]. In scenario 2, to have slenderness variation keeping the height (7.5 m) and the taper (0.030) fixed, it was necessary to vary the DBH from 0.19 m to 0.94 m, reducing the maximum stresses in smaller l conditions that correspond to the highest DBH. A compilation of the results of pulling tests with more than 4500 trees carried out in ten different countries shows that, as expected, taller trees are subjected to higher wind loads ([PERSON], 2004 cited by [11]), with an increase in the resulting moment in the base. However, the stem diameter strongly influences the resulting stresses ([PERSON], 2004 cited by [11]). The maximum normal stresses (positive and negative) in this scenario range from 0% H to 63% H (Tables 4-6). Studies by [33] and [10] attribute the variation of stress along the stem to the slenderness, with a deflection from the base (where the maximum moments occur due to the action of wind forces) for the upper regions of the stem. These authors also reject the hypothesis of constant stress distribution along the stem, and conclude that such distribution is strongly dependent on the relationship between height and diameter. In scenario 3, with defined height (7.5 m) and slenderness [22] and varied taper, normal stresses (positive and negative) grow as the tapering increases, and the position of these maximums varies from 23% \(H\) to 63% \(H\) (Tables 4-6).
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 2 Materials and Methods
### Potential Solutions to Improve Scalability in Agricultural Similarity-Based Method  
In the similarity-based method, the approach is to calculate the distances between farmers to determine the most similar users. This requires calculating the distance between the selected farmer and all the other users, as discussed above. This may be challenging to implement promptly when applied to real-time data. For this, the best approach is using ANN algorithms, which offer a solution by trading off some accuracy for speed, making them ideal for real-time applications where quick decisions are essential [37]. ANN algorithms can quickly identify neighbours by focusing on approximate similarities rather than on exact distances. One of the most popular ANN methods is Locality Sensitive Hashing (LSH), which is widely used in high-dimensional data to identify similar items more efficiently [38; 39; 40; 41; 42].  
LSH creates hash functions that map similar data points into the same \"bucket\" or hash space. The algorithm assigns each data point a hash value based on its features, so that points close in the feature space (similar users) are likely to fall into the same bucket. Table 3 provides an overview of how the number of hash functions and bands impact the balance between speed and accuracy in our dataset, helping to adjust the performance of the LSH algorithm.  
Instead of comparing every single farmer, LSH allows the algorithm to search only within the bucket where the target farmer is located. This drastically reduces the search space, speeding up the recommendation process.  
The key idea behind LSH is that it preserves locality: similar data points end up in the same or nearby buckets with high probability.  
This approach, applied in our similarity-based algorithm, will conclude to a quicker farmer grouping sharing the same environmental conditions, for example, without needing to compare one by one. This grouping allows the system to deliver recommendations faster, even as the dataset grows.
_Article_  
A Temperature Sensor Clustering Method for  
Thermal Error Modeling of Heavy Milling  
Machine Tools  
[PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 3 and [PERSON] 1,2  
1 Institute of Manufacturing Engineering, Department of Mechanical Engineering, Tsinghua University,  
Beijing 100084, China; [EMAIL_ADDRESS] (T.L.); [EMAIL_ADDRESS] (Y.J.)  
2 Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipment and Control, Tsinghua University,  
Beijing 100084, China  
3 School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100084, China; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +86-10-6279-2792  
Academic Editor: [PERSON]  
Received: 14 November 2016; Accepted: 10 January 2017; Published: 16 January 2017  
**Abstract:** A clustering method is an effective way to select the proper temperature sensor location for thermal error modeling of machine tools. In this paper, a new temperature sensor clustering method is proposed. By analyzing the characteristics of the temperature of the sensors in a heavy floor-type milling machine tool, an indicator involving both the Euclidean distance and the correlation coefficient was proposed to reflect the differences between temperature sensors, and the indicator was expressed by a distance matrix to be used for hierarchical clustering. Then, the weight coefficient in the distance matrix and the number of the clusters (groups) were optimized by a genetic algorithm (GA), and the fitness function of the GA was also rebuilt by establishing the thermal error model at one rotation speed, then deriving its accuracy at two different rotation speeds with a temperature disturbance. Thus, the parameters for clustering, as well as the final selection of the temperature sensors, were derived. Finally, the method proposed in this paper was verified on a machine tool. According to the selected temperature sensors, a thermal error model of the machine tool was established and used to predict the thermal error. The results indicate that the selected temperature sensors can accurately predict thermal error at different rotation speeds, and the proposed temperature sensor clustering method for sensor selection is expected to be used for the thermal error modeling for other machine tools.  
**Keywords:** thermal error; temperature sensor selection; clustering method; genetic algorithm; machine tools
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 2 Materials and Methods
### TCP Sockets
#### 2.3.5 Receiving NACK Notifications  
There are two possible paths for the received packets treatment, \"Fast Path\" and \"Slow Path\". All the packets with a raised NACK flag use the \"Slow Path\". First the packet integrity is checked by a checksum to consequently verify the NACK flag state, if this is positive, the _tcp_retransmit_skb_ function is called for an immediate retransmission of the requested packet, which is determined by the _tcp_write_queue_head_ function.
# Fuzzy Neural Networks--A Review with Case Study
## 1 Introduction  
Practical and Methodological Aims of the Study; Motivation for Conducting Research; The contributions of the study; Organization of the Paper.  
In the era of data explosion and the growing need for instance controlling, as well as parameter optimization, the choice of appropriate machine learning methods plays a key role. One of the algorithm's tasks is, i.a., to solve a variety of classification problems. Classic algorithms such as k-Nearest Neighbors (KNN), decision trees (Decision Tree Classifier), and Random Forest are the foundation of data analysis systems. They are characterized by their simplicity and efficiency. However, in recent years, fuzzy networks have been attracting growing attention, combining the advantages of fuzzy logic and machine learning methods. Fuzzy networks such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Falcon, or approaches using Ordered Fuzzy Numbers offer the unique capability of modeling uncertainty and imprecision in the data. This allows them to be used in situations where data is subject to measurement errors or is imprecise in its nature.  
The aim of this publication is to review and compare selected fuzzy networks with classical classification algorithms in the context of their effectiveness in the task of classifying the Iris set. The analyzed models include ANFIS networks, two implementations using Ordered Fuzzy Numbers, as well as KNN, Decision Tree Classifier, and Random Forest implemented in the TensorFlow library. The comparison will be carried out in terms of the use of fuzzy networks and the results they produce for the aforementioned algorithms. Thisresearch aims to evaluate the potential of fuzzy networks as an alternative or complement to traditional algorithms in modern analytical systems.  
The motivation of this review was to check where fuzzy neural networks are used, and which of them could be easy to use according to their configuration and implementation-code accessibility.  
The structure of the paper is as follows:  
Section 2 presents the fuzzy neural network description with the review criteria and summary of the reviewed papers.  
Section 3 provides more information on selected networks: FALCON, ANFIS, and fuzzy neural networks with Ordered Fuzzy Numbers.  
Section 4 provides the methodology.  
Section 5 provides the results achieved during a test.  
Section 6 includes the discussion.  
Finally, Section 7 presents the conclusions.
## 3 Background
### Current Challenges with Conventional Authentication Methods
#### 3.5.2. Phishing  
Phishing is a social engineering attack where an attacker manipulates a victim to reveal sensitive information, such as login credentials and payment card information, to download and run malicious software or allow the attacker to gain unauthorized access to information or systems [35,36].  
Phishing can be done in various settings such as SMS-based phishing (known as smishing) and voice call-based phishing (through voice calls, known as vishing). Additionally, there are more specific terms for phishing attacks, such as spear phishing, where the target is carefully selected based on reconnaissance, often using Open-Source Intelligence (OSINT) techniques. Another variant is whaling, which refers to spear phishing attacks targeting individuals with additional responsibilities, such as C-level executives in a corporation [35,36].  
Phishing is the most common cyberattack and is often considered the origin of more severe cybersecurity incidents, such as ransomware or data leaks [37].  
This is because most widespread phishing attacks are made using phishkits. This tool quickly resembles a widely-known, trusted website with data submissions being handed over to the attacker instead of the intended receiver, e.g., the widely known website. These phishkits allow even people with low technical skills to deploy a phishing website. Since finding information about people is easy, using leaked or public databases of phone numbers or emails, phishing attacks are widespread and relatively cheap to conduct [38].
## 2 Bridge Model Design
### Test Cases  
Since Chinese specifications require two natural seismic waves and one artificial seismic wave to be used for testing, the El Centro and Imperial Valley waves are natural, while the Lanzhou wave is artificial. The Imperial Valley is a seismic wave of site I, while the El Centro and Lanzhou are seismic waves of site II. However, it should be understood that the dynamic responses of structures and/or geotechnical systems are closely related to time-frequency characteristics of the input ground motions, which should be considered in the analysis [25]. In addition, the structure of the scaled bridge, the adequacy and the uniformity of the additional mass, and the cumulative damage of the structure after multiple seismic inputs should be considered, which affect the frequency of the test model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **Product Type** & **Sensitivity** & **Measuring Range** \\ \hline Accelerometer sensor & 1A202E & 10 mV/m\(\cdot\)s\({}^{-2}\) & 50 m\(\cdot\)s\({}^{-2}\) \\ Displacement sensor & 5G203 & 0.0195 mV/mm@2V & 750 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensor specifications.  
Figure 3: Bridge model on the shaking table.  
The possible frequencies of the test model and the prototype are not equal, and there is a certain proportional relationship between them. Therefore, the response of the structure is also a coupling of several factors. Currently, this test considers some reasons for the research under current conditions. It is believed that this test will provide experimental support for further exploration and study on selecting the records of ground motions.  
El Centro, Imperial Valley, and Lanzhou earthquakes were used as ground motion inputs [1,22]. The records of earthquakes were normalized so that the maximum acceleration peak value of the original seismic wave was uniformly treated as 1, and the other values were simultaneously changed. Subsequently, when different acceleration inputs were required, the input acceleration of waves required for the experiment could be obtained by multiplying a coefficient, which was convenient. The seismic waves used for the shaking-table tests are shown in Figure 4. Three types of input were adopted: unidirectional (Z), bidirectional (X + Y), and tridirectional (X + Y + Z). Seismic waves in the north and south directions were used to excite the structure in the X- and Z-directions, and waves in the east and west directions were used to excite the structure in the Y-direction. The ratio of the peak X-, Y-, and Z-directional seismic waves was set to 1:0.85:0.65 for the multidirectional input [1,22,23]. The seismic response of the structure was studied for a magnitude 9 earthquake (0.4 \(g\)), according to JTG/T B02-01 [26].  
Based on the calculated similarity ratio, the input for the shaking table should be 2.5 times the original value. Thus, the peak value for the seismic input was set to 1.00 \(g\). Table 3 lists the input excitations.  
Figure 4: Seismic waves used for the shaking-table tests. (**a**) El Centro; (**b**) Imperial Valley; (**c**) Lanzhou.
# Mechanical Behavior of a Mine Tailing Stabilized with a Sustainable Binder
## 3 Experimental Program
### Mechanical Behavior of the Mine Tailing before Stabilization
#### 3.1.2 Triaxial Compression Test  
Triaxial compression tests were performed to determine the shear strength parameters of the mine tailings, including cohesion and friction angle. The tests were carried out based on the BS 1377-7 (1990) standard, considering three distinct test phases: saturation, consolidation, and shear.  
During the saturation phase, the Skempton B parameter was calculated and had to be higher than 0.95 to make the specimens considered saturated. After this stage, the consolidation phase was followed by an increase in the confining stress (\(\sigma_{3}\)). The specimens were tested for confining stresses of 50 kPa, 100 kPa and 200 kPa in NC and OC conditions. The isotropic consolidation curve of the mine tailings was also obtained by increasing and decreasing the confining stress, starting by increasing \(\sigma_{3}\) up to 500 kPa, gradually decreasing until it becomes null, and finally, increasing \(\sigma_{3}\) to 700 kPa. These loading and unloading cycles made it possible to determine the compressibility, expansibility and recompressibility of the material under analysis.  
After consolidating the specimens, the shear phase was carried out in undrained conditions with the pore pressure (\(\Delta\)u) (CU) measurement. The shear strength parameters were determined in terms of total (i.e., c and \(\phi\)) and effective (i.e., c\({}^{\prime}\) and \(\phi^{\prime}\)) stresses. This phase was carried out through loading and unloading cycles for values of axial strain (\(\varepsilon_{\text{a}}\)) of 0.2%, 0.5%, 2% and 5%, respectively.
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 1 Introduction
The ESA openly and freely provides Sentinel-2 with an improved number of multispectral bands, a more frequent revisit time, and a higher spatial resolution that should be of great interest to the agricultural community and to international agricultural development researchers worldwide. This review article focuses on the field of precision agriculture and monitoring, with a particular emphasis on the recent advances provided by the ESA Sentinel-2 constellation, as part of the European Union (EU) Copernicus Program. Our goal is to frame remote sensing principles and Sentinel-2 A + B constellation features in the context of the state-of-the-art in precision agriculture. In order to better understand the improved features of Sentinel-2, its characteristics were compared to other satellites with potential use for agricultural monitoring. We also present an updated review of scientific literature on Sentinel-2 applications and advancements in precision agriculture. Furthermore, we explore its applications in biotic and abiotic stress monitoring, as well as at different management scales for agriculture.
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 5 Conclusions  
In this work, we proposed a robust underwater image enhancement method based on the novel Retinex transmission map estimation and adaptive color correction. By taking advantage of white balance and Retinex operations, Retinex transmission map estimation is exempted from the unknown backscattered light. Adaptive color correction chooses suitable stretching parameters in order to avoid channel stretching imbalance. Quantitative evaluation demonstrates that our method outperforms other SOTA methods in terms of full-reference metrics. In addition, it also achieves superior performance in the non-reference evaluation. In visual comparison, the proposed method produces results with higher visibility and less artifacts.  
Despite the superiority of our method, it may fail in some rare cases. Since we only use a simple gray world white balance algorithm, the proposed method may produce poor enhancement if white balance fails due to some reasons. In future work, we will investigate more robust white balance algorithms to improve the enhancement stability.  
Conceptualization, E.C. and T.Y.; methodology, E.C. and T.Y.; software, T.Y. and Q.C.; validation, T.Y. and Q.C.; formal analysis, B.H.; investigation, B.H.; resources, T.Y.; data curation, Q.C.; writing--original draft preparation, T.Y.; writing--review and editing, E.C. and Y.H.; project administration, Y.H.; funding acquisition, E.C. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Fujian Province of China under Grant (2021J01867), Education Department of Fujian Province under Grant (JAT190301), and the Foundation of Jimei University under Grant (ZP2020034).  
Figure 4: Visual comparison of the enhanced outputs produced by two MSR-based methods and the proposed method. Original images and their corresponding ground truth images are from UIEB test dataset.
## 2 Materials and Methods
### Overall Methodology
#### 2.5.3 Streamflow Simulation  
The SbPPs and GbGPPs for this study were selected based on their performance in previous applications over the Southeast Asian Region [2; 36; 41]. Initially, the SWAT model developed for the HBS watershed was run with RG measured rainfall. Next, the HBS watershed was modelled with different meteorological inputs of SbPPs and GbGPPs for a time period of 11 years from 2004 to 2014. The streamflow was simulated at Station kh.92 for different precipitation inputs and compared against the observed flow data. The same parameters calibrated with RG measured rainfall were used to simulate the SWAT model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rank** & **Parameter** & **Description** & **Initial Values** & **Fitted Value** \\ \hline \multirow{8}{*}{1} & & & SCS-CN & 73–92 & \multirow{8}{*}{1} \\  & & & Deciduous forest & 77 & 73 \\  & & & Cassava & 85 & 83 \\  & & & Sugarcane & 85 & 83 \\  & & & Rice & 81 & 81 \\  & & & Rubber & 77 & 77 \\  & & & Rangeland & 79 & 79 \\  & & & Water & 92 & 92 \\  & & & Urban & 90 & 90 \\
2 & ESCO & Soil evaporation compensation factor & 0.95 & 0.70–0.95 \\  & & & Available soil water capacity & \multirow{4}{*}{3} \\  & & & Hang Chat/Loamy sand & 0.14 & 0.1 \\  & & & Slope Complex/Loamy sand & 0.14 & 0.1 \\  & & & San Sai/Sandy loamy & 0.1 & 0.13 \\  & & & Phon Phisai/Sandy loamy & 0.1 & 0.14 \\  & & & San Patong/Loamy sand & 0.1 & 0.15 \\
4 & ALPHA\_BF & & Base-flow alpha factor & 0.048 & 0.99 \\
5 & GW\_DELAY & & Ground water delay & 31 & 2 \\
6 & GW\_REVAP & & Groundwater “revap” coefficient & 0.02 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adjusted range of parameters for streamflow calibration and their final values in the HBS [58].  
with other precipitation products as well. The hydrologic utility of different precipitation products was analysed based on these results.