esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technologies of Monitoring in Montado
### Precision Agriculture
#### 3.2.1 Global Navigation Satellite Systems (GNSS)  
Knowledge of the characteristics of the plots is essential for developing any engineering project involving agro-silvo-pastoral activities [95]. In this sense, the positioning based on GNSS has become an essential tool for surveying areas, mapping, PA, engineering and construction, aerial images, and sensors and management of public services, presenting greater precision and positioning reliability, if compared to the GPS [96]. The GNSS technology allows for terrestrial mapping, by collecting georeferenced data, which provides the area and perimeter of the plots [97]. Altimetric surveys aim to obtain unevenness of selected points [95]. The variation of altimetric values can be correlated with several soil characteristics, such as texture, water retention capacity and nutrient content [98]. Thus, using GNSS RTK (Real Time Kinematic) technologies has become an alternative for obtaining altimetry data for agriculture, combining high accuracy and good operational performance [99]. [PERSON] et al. (2010), cited by [99], compared the data obtained by GPS RTK, in static mode, obtaining correlations of 99%, with topographic surveys carried out conventionally. These data show the potential of GPS RTK for surveying the altimetry of plots. Additionally, according to [PERSON] et al. [100], the accuracy and precision of the altimetry data obtained through the GNSS RTK technologies are visible, even on board vehicles that travel through the plots. However, according to [PERSON] et al. [99], when using all-terrain vehicles, the collection of altimetric data should only be carried out in parallel lines to the crop lines, with a speed of \(\pm 2.2\) m/s. The use of these new technologies, combined with the use of specific computer programs, facilitating field and office work, also significantly improves the accuracy of measurements since they are less subject to interference from errors caused by environmental conditions and also from errors caused by human interference [95]. In PA, GNSS-based positioning includes topographic mapping, crop production maps, and machine driving [101]. However, the altimetric survey carried out with GNSS receivers in the open field is more accurate than that in plots with trees, since the tree canopy leads to signal loss, which affects the accuracy and precision of the results [95].
# Spatial Image-Based Walkability Evaluation Using Regression Model
## 2 Proposed Method
### Data Acquisition  
#### 2.1.1 Image Collection  
Road images for the walkability evaluation were exclusively collected in Jeonju-si, Republic of Korea, using Kakao Maps. Data was extracted from a total of 49,156 points by dividing the road network of Jeonju-si into 30 m intervals. The panoramic images of each point were divided into left, front, right, and back, as shown in Figure 2, resulting in 196,624 images. Thereafter, images containing large portions of tunnels, buildings, and trees were manually removed, as it is difficult to determine whether these environments are suitable for walking. Stratified extraction was performed based on road widths (daero, ro, gil) and land types (industrial, commercial, high-rise housing, detached house, etc.). \"Daero\" are roads with eight or more lanes, \"ro\" are roads with two-seven lanes, and \"gil\" are roads that are narrower than \"ro.\" Land types were classified according to their use cases. After stratified extraction, 10,590 points out of 49,156 points were extracted, and 42,360 images were used for the walkability evaluation [25; 26].
# CBMDB: A Database for Accessing, Analyzing, and Mining CBM Information
## 2 Materials and Methods
### Integration of Web Tools  
For the analysis of CBM sequences, various tools were set up in the CBMDB. Users can click \"upload\" on the home page to use these tools. Brief descriptions of these tools are listed below.  
The information search tool on the home page was implemented by connecting to the database through PHP and using MySQL query statements. Wildcards and regular expressions added to the MySQL statement make this search tool capable of fuzzy queries.  
BLAST [14]: Users can align their sequences with CBM sequences in our database and can perform pairwise alignment. Users are permitted to input query sequences or to submit files in FASTA format. The output is shown in the normal BLAST output. Users can use the above two functions in the \"BLAST\" and \"Global Align\" sections.  
Clustal Omega [15]: Clustal Omega is a popular kit for performing multiple sequence alignment. It is swift enough to make very large alignments, and the precision of the protein alignments is high when compared to alternative packages. This tool can be found in the \"Multiple sequence alignment\" section.  
HMMER [13]: HMMER is a package based on profile hidden Markov models, which are computational algorithms for predicting probabilistic protein models and functions. Relying on the strength of probability models, HMMER searches for homologous sequences in sequence databases and performs either single sequences or multiple sequence alignments.  
Phylogeny tree [16]: This tool provides a view of the phylogenetic tree and appearance modifications including layout, hint labels, node labels, node shapes, node bars, branch labels, and scale bars.  
Application program interface (API) [17]: RCSB PDB Data API supports the HTTP GET method in accessing PDB data through a set of endpoints (or URLs). The API can quickly search the PDB number of proteins on the basis of the query sequence to view and analyze the three-dimensional structure of proteins. This function and sequence similarity alignment was integrated into the web page using BLAST for alignment.  
Jmol [18]: Jmol was integrated into the website's tools so that users can directly view interactive images of the protein structures on the web page. Jmol ([[http://www.jmol.org/](http://www.jmol.org/)]([http://www.jmol.org/](http://www.jmol.org/))) is an open-source software for visualizing chemical structures in 3D. The Jmol applet can be inserted into our web page to generate three-dimensional protein structure visualizations without local 3D model files by using the following website: [[https://chemapps.stolaf.edu/jmol/jmol.php](https://chemapps.stolaf.edu/jmol/jmol.php)]([https://chemapps.stolaf.edu/jmol/jmol.php](https://chemapps.stolaf.edu/jmol/jmol.php)) (accessed on 1 August 2022). The 3D models can be obtained either from the RCSB using a PDB ID or from the NIH CACTVS server using a chemical identifier.
## 5 Numerical Results  
In this section, we provide both the solution method used to solve model (21) and also the numerical results. The results that are obtained are used to demonstrate the effect of joint optimization of transmission power, transmission rate and transmission delay in a CRWSN. We consider a network in with 3 PUs and 10 CRSN nodes. The distance between any two CRWSN nodes in the system is 9 m whereas the CRSN node to PU distance shall be 80 m. In our network model, we allow for fading due to shadowing, with a path-loss exponent of 3.5 [4]. The received PU power at a CRSN node receiver, power spectral density, antenna gains of receiver and transmitter of CRSN node are all provided in Table 1. The maximum power, maximum delay and maximum BER for each channel are also 100 mW, 160 ms and 0.01 respectively. The rest of the parameters used in the simulations for our network model are given in Table 1.  
In order to solve the problem, we employ a branch-and-cut approach that has been developed and extensively studied in the literature, for example [38]. This technique is an exact global optimization method for solving non-convex and non-linear optimization problems. The technique involves developing polyhedral cutting planes and relaxations at each branch. The main idea of this technique is to recognize convexity in the functions of the optimization problem. Once a function is a known and recognizable convex function, then better relaxations and approximations of it can easily be made. For those functions that are not convex, either prior studied convex function compositions of it are used or even a user is allowed to supply their own better estimators. It has been proved in [38] and other works that the polyhedral cutting planes generated by this method are much tighter than any outer approximations of the original functions. This makes this technique much faster than other outer approximation methods. These polyhedral relaxations are efficiently handled by most state-of-the-art linear programming solvers. For our case, we use CPLEX 12.5 as the linear programming solver. This technique has been developed into a solver called BARON which is accessible from [[https://www.minlp.com](https://www.minlp.com)]([https://www.minlp.com](https://www.minlp.com)). In order to run simulations of our model, we use MATLAB together with the BARON solver. We also explore further the effect of using different scalarization factors \(\lambda_{1},\lambda_{2}\) and \(\lambda_{3}\) in problem (21). It should be noted that the size of the weights \(\lambda_{1},\lambda_{2}\) and \(\lambda_{3}\) is an indication of the relative weight or importance put on total rate, total power and total transmission delay, respectively, during optimization. We thus come up with four different scenarios or cases explained in terms of the relative importance or weight put on all the three objective Equations (18)-(20) of the optimization problem (17). The first is when equal weight (\(\lambda_{1}=\lambda_{2}=\lambda_{3}=\frac{1}{3}\)), is put on all the three objective
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 2 Fuzzy Methodologies
### Fuzzy Inductive Reasoning (FIR)
#### 2.1.2 Qualitative Model Identification  
A FIR model is composed by a structure, called mask, and a pattern rule base, named behaviour matrix. A mask denotes a dynamic relationship among qualitative variables. An example of a mask is presented in Figure 2.  
Each negative element in the mask is called m-input (mask input). It denotes a causal relation with the output, that is, it influences the output up to a certain degree. The enumeration of the m-inputs is immaterial and has no relevance. The single positive value denotes the m-output. The mask of Figure 2 contains four m-inputs. In this example, the first and second m-inputs, \(\mathrm{i_{1}}\) and \(\mathrm{i_{2}}\), correspond to the input variables \(\mathrm{u_{1}}\) and \(\mathrm{u_{4}}\) two sampling intervals back in time, \(\mathrm{u_{1}}\)(t-26t) and \(\mathrm{u_{4}}\)(t-26t). The third m-input, \(\mathrm{i_{3}}\), refers to the output variable \(\mathrm{y_{1}}\) one sampling interval into the past, \(\mathrm{y_{1}}\)(t-8t), etc.  
Finding a mask that optimizes the predicivness of the model can be done either by a mechanism of exhaustive search of exponential complexity or by one among a number of suboptimal search strategies of polynomial complexity. The optimality of the mask is evaluated with respect to the maximisation of its forecasting power using the Shannon entropy measure [27]. The m-inputs of the optimal mask are the features that will be used in the prediction process. Therefore, the mask identification can be viewed as a feature selection algorithm.  
Once the best mask is identified it can be used to obtain the pattern rule base. This process is illustrated in Figure 3.  
The mask is used to 'flatten' dynamic relationships into pseudo-static relationships. The left side of Figure 3 shows an excerpt of the qualitative data matrix that stores the class values. It shows the numerical rather than the symbolic class values. The box symbolizes the optimal mask that is shifted downwards along the qualitative data matrix. The round \"holes\" in the mask denote the positions of the m-inputs, whereas the square \"hole\" indicates the position of the m-output. The class values are read out from the class value matrix through the \"holes\" of the mask, and are placed next to each other in the behaviour matrix that is shown on the right side of Figure 3. Each row of the behaviour matrix represents one pseudo-static qualitative state or qualitative rule (also called pattern rule). For example, the framed rule of Figure 3 can be read as follows, \"If all m-inputs (\(i_{1}\), \(i_{2}\), \(i_{3}\), \(i_{4}\)) have a value of 2 (corresponding to _medium_) then the m-output, \(\mathrm{O_{1}}\), assumes a value of 1 (corresponding to _low_)\".  
Figure 1: Fuzzy inductive reasoning (FIR) fuzzification process of human age variable.  
Figure 2: Example of a mask in FIR methodology.
# Reliability-Based Design of Driven Piles Considering Setup Effects
## 4 Parametric Analysis
### Effect of \(\rho_{\text{R0,Rsetup}}\) on Resistance Factor and Factor of Safety  
In order to illustrate the effect of the correlation coefficient between \(R_{0}\) and \(R_{\text{setup}}\), \(\rho_{\text{R0,Rsetup}}\) on resistance factor and factor of safety for driven piles in sand and clay, Equations (16) and (18) are used to calculate the resistance factor and factor of safety. Meanwhile, in order to further analyze the influence of \(\rho_{\text{R0,Rsetup}}\) on the resistance factor and the factor of safety, some parameters are determined, for example \(\beta_{\text{T}}\) = 2.33, \(M_{\text{setup}}\) = 1.0, \(\rho\) = 3.69. The variations of the calculated resistance factor and factor of safety with \(\rho_{\text{R0,Rsetup}}\) for driven piles in clay and sand are shown in Figure 4.  
Figure 4: Resistance factor and factor of safety with \(\rho_{\text{R0,Rsetup}}\) for driven piles in clay and sand.  
Figure 3: Resistance factor and factor of safety with \(M_{\text{setup}}\) for driven piles in clay and sand.  
It can be seen that the resistance factor of driven piles in clay and sand both decrease with increasing \(\rho_{\text{R0,Rsetup}}\), but the decreasing rate of resistance factor with \(\rho_{\text{R0,Rsetup}}\) between \(-1.0\) and \(0\) is obviously greater than that with \(\rho_{\text{R0,Rsetup}}\) between \(0\) and \(1.0\) in clay and sand. This indicates that the negative correlation between \(R_{0}\) and \(R_{\text{setup}}\) has a significant influence on the resistance factor of driven piles, while the positive correlation between \(R_{0}\) and \(R_{\text{setup}}\) has a slight influence on the resistance factor of driven piles. It is obvious from Figure 4 that the resistance factor of driven piles in clay and the resistance factor of driven piles in sandy are quite different, and the difference is about \(20\%\) under the same correlation coefficient between \(R_{0}\) and \(R_{\text{setup}}\) levels. Therefore, the value selection of \(\rho_{\text{R0,Rsetup}}\) is crucial in geotechnical engineering.  
In the distribution curve of FOS versus \(\rho_{\text{R0,Rsetup}}\), the value of FOS increases obviously when \(\rho_{\text{R0,Rsetup}}\) increases from \(-1\) to \(1\). The increasing rate of driven piles in sand increases gradually, but the decreasing rate of driven piles in clay decreases gradually. This indicates that the positive correlation between \(R_{0}\) and \(R_{\text{setup}}\) has a significant influence on the safety of driven piles, while the negative correlation between \(R_{0}\) and \(R_{\text{setup}}\) has a little influence on the safety of driven piles.
## 1 Introduction  
This paper proposes a hybrid energy storage system, combining supercapacitors with lead-acid accumulators, as a further step towards higher demands on efficient and adaptive energy storage. This hybrid approach [1; 2] uses the complementary benefits of these two technologies [3]: supercapacitors provide very fast charging/discharging capabilities with high power density, suitable for sudden surges in power demand. At the same time, accumulators ensure higher energy capacity for stable power supply over longer periods [4; 5; 6]. Together, these form a very robust storage solution capable of handling steady-state and fluctuating power demands that are highly suitable for applications whose energy needs vary.  
The novelty of this work is the design, implementation, and practical evaluation of a hybrid energy storage system [7] that can optimally combine SC modules with lead-acid accumulators. Unlike previously used approaches, the system described herein includes an interconnect board that links the SC module with a bidirectional DC/DC converter [8; 9; 10] and an accumulator to enable smooth transitions in power delivery [11; 12] and real-time monitoring of system variables. The measurement circuitries are disposed on the interconnect board for precision monitoring of voltage and current, both for the SC module, accumulator, and converter for reliable systems management under diversified operating conditions. The originality of this paper is a practical implementation of HESS with a small power output and a bidirectional operation. Adding the SC module to the circuit with an accumulator significantly improves the current waveform from the accumulator regarding the lower ripple. This will dramatically extend the battery lifetime.  
In Section 2, the specific requirements of the SC module are detailed, together with the overall design of the hybrid system. Section 3 extensively discusses the calculations and design of the SC module and the layout and features of the interconnect board. Section 4 presents a control algorithm specifically designed for hybrid energy management applications that optimize the usage of SC modules for supplying high-current surges. In contrast, the accumulator can ensure continuous power delivery. Section 5 is devoted to the experimental evaluation of the system with the proposed control algorithm from the resultsobtained in terms of current and voltage measurements at defined operating conditions. The introduced system proposes a new hybrid storage solution for energy management in applications demanding fast response and stable power delivery.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 4 Results and Discussions
### Accuracy for Determined Principal Routes
#### 4.1.2 Route around the Side Accuracy  
As discussed earlier, the routes around both sides of a building are calculated by the discussed algorithm. The desired route of one side is shown by the blue color line; the created route follows the XZ plane cutting technique to calculate the intersection point on the sidewalls of the building shown below in Figure 15a. The value at point 1, just next to the source, has (x, y, z) coordinate to check the accuracy. Make deviations in the z value of the 1 st point from the desired z value in (\(\pm\)1, \(\pm\)2, \(\pm\)3) m and develop the route for modified z. In deviations, the (+) indicates the positive changes from desired point and (-) indicates the negative changes. The changes in route length with deviation in positive and negative  
Figure 14: (**a**) The blue route is the desired shortest route, and the rest of the yellow routes are the deviated routes. (**b**) Graph for the accuracy of route over the top.  
direction of the desired point are shown in Figure 15b. It is clear from the calculation that the route calculated by \"Routes around the side algorithm\" is the shortest route.  
In a complex building scenario, between any pair of the source (primary or secondary) and destination, the routing algorithm works and finds the best routes, which are the shortest routes.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 3 The Proposed Algorithm
### Calculating the Mean of the Local Slopes  
In this step, the mean of the local slopes needs to be calculated. This mean is always accounted for by considering some of the previous points until the current point, as shown in Equation (4).  
\[Mean(Point_{i})=\frac{\sum_{x=i-n}^{x=i}Slope(Point_{x})}{n}, \tag{4}\]  
where \(n\) is the size of the window. The value of \(n\) is important to produce a mean that can show the mean of the fluctuations in a note. If \(n\) is too big, it may include some old-time fluctuations that make an incorrectly local mean. In contrast, if \(n\) is too small, there would not be enough fluctuations to calculate the correct local mean. The \(n\) should be selected based on the singing technique, duration, and intervals. In this study, the selected values of \(n\) were chosen to be 230 ms for the Erkomaishvili dataset and 46 ms for the SVNote1 dataset. These selections for \(n\) were made according to a trial-and-error method of adjusting the \(n\) value to have the best result for one of the files of each dataset.  
As shown in Figure 6, although the median duration of the notes in both datasets is almost similar, roughly 0.42 s, the duration of most of the notes in the Erkomaishvili dataset is longer than the median. In contrast, the duration of the notes in the SVNote1 dataset is distributed approximately uniformly below and above the average. Therefore, the variance of notes' duration in the Erkomaishvili dataset is greater than in the SVNote1 dataset. In addition, the variance of the intervals between notes in the Erkomaishvili dataset is smaller than in the SVNote1 dataset. Thus, two different \(n\) values for each dataset were selected.
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 5 Conclusions  
* The large number of different models used to calculate chlorophyll a concentrations and turbidity in water means that there is no one universal model for use with different bodies of water.
* Models developed as the product of the reflectance powers (after logarithm) undergo statistical analysis to eliminate irrelevant components. This procedure simplifies the model. The final model may take into account the reflectances of many wavelengths, which are then eliminated by the statistical test at the significance level, e.g., 0.05 (Student's t-distribution).
* The specific physic-chemical and biological composition of the water in a given reservoir may result in significant differences between the spectral curves for individual water components.
* Shallow reservoirs require corrections for the bottom reflectance. It is impossible to predict in advance at what depth and for which wavelengths the bottom reflectance is already negligible. The analysis of satellite images helps to determine whether the bottom reflectance at a given wavelength is insignificant (poor bottom visibility); this simplifies the model.
* It is difficult or even impossible to use a combination of physical equations (type (7)-(9)) to calculate the concentration of chlorophyll a based on the reflectance recorded by the satellite; such reflectance is a mix of the chlorophyll a reflectance and the reflectance of many other substances present in the water. The authors could not find parameters of the model types (7)-(9) for just one wavelength and the known turbidity and maintain a satisfactory accuracy. Therefore, they proposed model (14) that took into account the reflectance for different wavelengths.
* Reflectances corresponding to the middle wave range 665, 705, 740, and 842 nm have been used in the model of chlorophyll concentration, while the effect of lake bottom interaction is associated with wavelengths 665 and 705 nm.
* Reflectances corresponding to the middle wave range 705, 740, and 783 nm have been used in the model of turbidity, while the effect of lake bottom interaction is associated with wavelength 705 nm.
* The described models use the reflectances normalized to the conditions prevailing at a specific moment over the reference surface.  
Conceptualization, A.B.; Formal analysis, A.B.; Software, C.T.; Validation, A.B.; Visualization, C.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors thank the Waterworks of the City of Krakow for providing assistance in the physic-chemical analyses of water samples and for sharing the data regarding the water quality of Lake Dobczyce.  
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