esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 6 Conclusions and Perspectives  
### Conclusions  
This new practical work using a classic spreadsheet has enabled students eager to learn how to model an induction motor to not only create the model, but also gain a deeper understanding of its equations. They have also improved their proficiency with the software and learned to integrate electrical formulas from their lessons into their spreadsheets, as discussed in the first part of this paper. Industrial practitioners will find this work valuable for quickly and accurately modeling the main operating values of their industrial motors, allowing for more efficient calculation of characteristics.  
The spreadsheets provided in this work model an induction motor and plot the characteristic function against the rotational speed axis. With the practical experience described in the final section of this paper, readers will be able to input the correct formulas and calculate all the power values in the power balance shown in Figure 4.  
By the end of this work, readers will have a digital model of an induction motor in their toolkit. This model will be useful for addressing various electrical engineering challenges related to the motor's use under different mechanical constraints and for selecting a motor from various available models.  
We chose to present the induction machine in a manner that is accessible to students who are new to the subject. While contemporary applications of slip ring machines primarily involve wind energy as doubly fed machines, our goal is to provide a foundational understanding without overwhelming students with complex concepts at the outset. In terms of educational focus, our approach is designed to ease students into the topic by focusing on the basic principles and applications of slip ring motors. This method ensures that students build a solid foundation before progressing to more advanced topics.
## 4 Model Design and Implementation
### MRI Image Sample Preprocessing Method  
MRI (Magnetic Resonance Imaging) images provide doctors with clear, high-resolution images of the internal tissues and organs of the body. These images can reveal abnormal structures and help doctors accurately diagnose and develop treatment plans. To further improve the accuracy and reliability of the model, we designed a method to preprocess MRI image samples. The specific process is summarized as shown in Figure 4.  
Since each MRI image is not entirely aligned, the MRI image sample preprocessing method we designed is shown in Figure 4. The image is first corrected for head motion. Since MRI images contain some non-brain structures (such as skulls), we delete non-brain structures in the images to avoid increasing the computational load and improving model efficiency. To further improve the training efficiency of the model, we denoise the image and  
Figure 4: MRI image sample preprocessing method flow.  
use median filtering to denoise the spatial domain of the image. To extract more detailed features in the image, we equalize the image. Since MRI images are three-dimensional data, the model designed in this article inputs two-dimensional images. Therefore, in the MRI image sample preprocessing method we designed, only the coronal, sagittal, and transverse planes in the MRI images are extracted. To prevent the model from overfitting and improve the generalization ability of the model, we scale and rotate the MRI images. Finally, the MRI image sample preprocessing method we designed normalizes the data by traversing each pixel in the MRI image matrix. The normalization operation formula is summarized in Formula (1) during the processing process.  
\[\hat{I}=\log 10(\hat{I})/\log 10(\max(\hat{I})) \tag{1}\]  
\(\hat{I}\) in Formula (1) represents the original image, \(\hat{I}\) represents the normalized image, \(\log 10(\hat{I})\) represents the logarithmic transformation function with base 10, and \(\max(\hat{I})\) represents the maximum value in the image data.
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 5 Experimental Application Results
### Evaluation of the Control through Smartphone  
The evaluation of the control of the elevator through a smartphone was tested using the web interface (Figure 9c) and by defining two experiments: calling the elevator (accessing the IoT device to control the elevator from one floor, outside the car) and using the elevator (accessing the IoT device to control the elevator from inside the car). These experiments required the smartphone and the IoT device controlling the elevator to be connected to the wireless network of the building. In general, some connection problems can be expected when the elevator doors are closed. These experiments analyze and quantify such connection problems.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 5 Biofilms Enhance the Transmission of Virulence and Antibiotic Resistance
### Bacterial Communication  
Communication amongst bacteria is essential for many cellular functions, such as biofilm formation, sporulation, competence, antibiotic resistance, and regulation of a variety of other virulence factors [182; 183; 184]. Bacteria communicate either through (1) quorum sensing and (2) two component systems, which are directed and deliberate forms of communication of bacteria.  
Quorum sensing cell-to-cell communication coordinates cellular actions based on bacterial density. In quorum sensing mechanisms, individual bacteria secrete chemical signal molecules, autoinducers (AI). As bacterial density increases, the concentration of AI in the immediate environment also increases and interacts with cell signal receptors on surrounding bacteria [57; 185; 186]. Acylated homoserine lactone autoinducers mediate communication between gram-negative bacteria. Alternatively, two additional autoinducers; autoinducer 1 (AI-1) and autoinducer 2 (AI-2), mediate species-specific communication molecules and interspecies communication molecules, respectively [187; 188; 189]. Other secreted peptides, also known as autoinducing peptides, facilitate gram-positive bacteria quorum sensing [191]. In addition to using quorum sensing as a means to communicate for biofilm formation, bacteria can also use quorum sensing to increase virulence. In _Acidovorax citrulli_, quorum sensing molecules enhanced virulence of the strain by influencing genes that control cellular attachment and biofilm formation [192]. Enhancing virulence genes that increase biofilm formation can facilitate the exchange of antibiotic resistance genes.  
Virulence can also be enhanced via two-component bacterial communication systems [193]. Two-component systems work by binding an external signaling molecule to a histidine kinase receptor protein that causes a phosphorylation cascade event [194; 195]. WalKR, a two-component system in _S. aureus_ that controls multiple cellular functions, is essential to virulence and is being investigated as a potential target to combat staph infections [196]. Regardless of the specific bacterial communication mechanism, it is clear that communication enhances virulence and may provide new druggable targets with little chance for bacterial resistance to arise due to (1) lack of selective life or death pressure and (2) few fitness advantages.
## 2 Related Work  
### Augmented Reality and Real Walking with VR  
Augmented reality (AR) enhances the sense of reality by superimposing virtual objects on the real world in real time [4]. With AR, walking is easy because reality is mainly displayed as it is. However, it gets more difficult when part of the information in the real environment is replaced with virtual information [1]. In addition, there are many technical problems, due to device restrictions [4; 5; 6].  
Mechanisms for avoiding collisions with real objects during the VR experience include displaying a bounding box around a real object, displaying a real object as a 3D model, and overlaying an image of a real object on the virtual world using a camera. According to [PERSON] et al., displaying a 3D model is the best of these three methods, in terms of adjusting the movement speed around obstacles and ensuring the desired impression of the user. In addition, the display of a bounding box is excellent, in terms of safety, such as for collision avoidance [7]. In our proposed system, real objects are displayed as point clouds, which is similar to the mechanism of displaying a 3D model.  
Generally, for avoiding collisions with real objects in VR, VR spaces that reflect a real environment are created in advance. For example, Oasis [8] utilizes a mechanism for creating a VR space and enabling real walking that uses a pre-scanned real environment. As a mechanism for expanding the play area, [PERSON] et al. proposed a system that enables real walking by dynamically acquiring the environment around the user and integrating it with the virtual space [9]. In these examples, a VR space is created in advance, in order to enable real walking. When creating a VR space in advance, it is necessary to recreate the space as the environment changes, such as when an object moves.  
An additional example that enables real walking is DreamWalker [10], where object collision, when walking outdoors, is prevented by using a technique called \"redirected walking\".  
In our system, real walking is made possible by displaying a real environment, in VR space, in real time. With real-time reflection of changes, it is not necessary to recreate the VR space every time there is a change to the environment. Further, by not displaying real objects as they are and not using techniques such as redirected walking, it is possible to not only avoid real objects but also to move them in VR.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 4 Experiments and Analyses
### Parameters Selection Experiments
#### 4.1.2 Threshold Parameter \(\sigma\)  
Based on information measures, the threshold parameter \(\sigma\) has a great influence on the dimensionality of HSIs. In the experiment, three bands (\(n=1\)) were selected and the window size was set to \(m=11\). Using the dataset of Indian Pines, the influences of the threshold \(\sigma\) on UE, BR, ASA are shown in Table 1.  
It is important to choose the appropriate window size \(m\) and threshold \(\sigma\). According to Table 1, when \(\sigma=0.15\), the UE had the lowest. However, BR and ASA were relatively low. When \(m=11\), \(\sigma\geq 0.2\) (\(\sigma=0.2\), \(\sigma=0.3\), \(\sigma=0.4\)), the values of ASA, UE, and BR were the same. The results indicated when \(\sigma\) is large enough, the impact of different thresholds on the segmentation result is almost the same. When \(\sigma=0.05\), the values of BR and ASA reached the maximum simultaneously. The three indicators were considered comprehensively to evaluate the performance of segmentation. Thus, when BR and ASA were simultaneously maximal, even if UE was a bit high, it still can be considered that the segmentation performance at \(\sigma=0.05\) was more satisfactory. In the following experiments, the value of \(\sigma\) was set to 0.05.
# Energy of Pythagorean Fuzzy Graphs with Applications
## 5 Applications of the Energy of PFGs in Decision-Making
### Evaluation of the Schemes of Reservoir Operation
We present our proposed method in the following Algorithm 2. **INPUT**: A discrete set of schemes (alternatives) \(Z=\{z_{1},z_{2},\ldots,z_{n}\}\), a set of experts \(e=\{e_{1},e_{2},\ldots,e_{s}\}\) and construction of PFPR \(R_{k}=(p_{ij}^{(k)})_{n\times n}\) for each expert. **OUTPUT**: The selection of the optimal scheme. 1. **begin**
2. Calculate the energy and Laplacian energy of each PFDG \(\mathcal{D}_{k}(k=1,2,\ldots,s)\). 3. Determine the wight vector for experts based on the energy of PFDGs by utilizing \(w_{k}=\left(\frac{E((\mathcal{D}_{p})_{k})}{\sum\limits_{l=1}^{s}E((\mathcal{ D}_{p})_{l})},\frac{E((\mathcal{D}_{v})_{k})}{\sum\limits_{l=1}^{s}E(( \mathcal{D}_{v})_{l})}\right),\) and then, based on the Laplacian energy of PFDGs by utilizing \(w_{k}=\left(\frac{LE((\mathcal{D}_{p})_{k})}{\sum\limits_{l=1}^{s}LE(( \mathcal{D}_{p})_{l})},\frac{LE((\mathcal{D}_{v})_{k})}{\sum\limits_{l=1}^{s} LE((\mathcal{D}_{p})_{l})}\right),k=1,2,\ldots,s\). 4. Utilize the PFWA operator to fuse all the individual PFPRs \(R_{k}=(r_{ij}^{(k)})_{n\times n}\)\((k=1,2,\ldots,s)\) into the collective PFPR \(R=(r_{ij})_{n\times n}\). 5. Calculate their scores using the score function \(s_{ij}=\mu_{ij}^{2}-\
u_{ij}^{2}\). 6. Determine the net degree of preference of scheme \(z_{i}\) over the other schemes by utilizing: \[\phi(z_{i})=\sum\limits_{k=1}^{s}w_{k}\left(\sum\limits_{j=1}^{n}\mu_{ij}^{(k )}-r_{ji}^{(k)})\right),\ i=1,2,\ldots,n.\]
7. Rank all the schemes \(z_{i}(i=1,2,\ldots,n)\) according to the net flows \(\phi(z_{i})(i=1,2,\ldots,n)\). 8. Output the best scheme. 9. **end**  
**6. Conclusions**  
A Pythagorean fuzzy set model is suitable for modeling problems with uncertainty, indeterminacy and inconsistent information in which human knowledge is necessary and human evaluation needed. Pythagorean fuzzy models give more precision, flexibility and compatibility to the system as compared to the classical, fuzzy and intuitionistic fuzzy models. A PFG can describe the uncertainty of all kinds of networks well. In this paper, we have introduced the concepts of energy and Laplacian energy of graphs in Pythagorean fuzzy circumstances and investigated their interesting properties. We have derived the lower and upper bounds for the energy and Laplacian energy of a PFG. We have also introduced the concept of the energy and Laplacian energy of a PFG along with its applications in decision making problems. We are planing to extend our research work to: (1) interval-valued Pythagorean fuzzy graphs; (2) simplified interval-valued Pythagorean fuzzy graphs; and (3) hesitant Pythagorean fuzzy graphs. [PERSON] and [PERSON] conceived of the presented idea. [PERSON] developed the theory and performed the computations. [PERSON] verified the analytical methods. This research received no external funding. The authors declare no conflict of interest.
## 5 Conclusions  
Concerning the laboratory results, the optimal combination, with an initial organic load of 10 gSV/L and s/x of 1, demonstrated a biogas yield of 89,420 mL/gSVi and AGVs production equivalent to 1128 mg COD/L. Comparing this combination to the other eight evaluated, it is evident that, for all loads, s/x values directly influenced biogas production. As the values increased, so did the yield, indicating that higher substrate-to-inoculum ratios improved biogas performance. This behavior can be explained by considering the composition percentages of the used substrate, OFMSW waste, which, compared to other organic wastes, exhibited a lower volatile solids content and, consequently, lower organic matter content. This directly impacted the inoculum's metabolism, especially under s/x conditions where the inoculum proportion was greater than that of the substrate, limiting the fermentation process.  
Regarding the scaling of the energy valorization process, a positive energy balance was maintained, delivering a net electrical energy flow of 2,368,358.72 kWh/day, suggesting the process's sustainability from an energy perspective. However, it is advisable to explore and scale the process using higher s/x ratios, as the ones studied limited hydrogen production. Additionally, operating at low s/x ratios presents challenges in terms of the requiredinoculum quantities. Water consumption was identified as the main environmental aspect to address, emphasizing the need for strategies to save and responsibly use this resource.  
From the perspective of integrated solid waste management (ISWM), the proposed valorization strategy aligns with waste utilization, offering an alternative to landfill disposal and promoting the lifespan of these facilities. Hydrogen production and the potential integration of byproducts, such as unstabilized digestate, into value chains like AGV production, bring the strategy closer to a biorefinery approach, meeting the guidelines set by CONPES 3874 in 2016.  
Conceptualization, A.-P.-Q. and S.-A.-R.-M.; methodology, S.-A.-R.-M., A.-P.B.-Q. and P.-A.-A.-P.; software, A.-P.B.-Q. and S.-A.R.-M.; validation, A.-P.B.-Q. and P.-A.-P.; formal analysis, A.-P.B.-Q., P.-A.A.-P., J.R.-I. and M.-E.-R.-C.; investigation, A.-P.B.-Q. and P.-A.A.-P.; resources, A.-P.B.-Q., P.-A.-P. and J.R.-I.; data curation, S.-A.R.-M., A.-P.B.-Q. and P.-A.-P.; writing--original draft preparation, A.-P.B.-Q., P.-A.-A.-P., J.R.-I. and M.-E.R.-C.; writing--review and editing, J.R.-I. and M.-E.R.-C.; visualization, A.-P.B.-Q., J.R.-I. and M.-E.R.-C.; supervision, A.-P.B.-Q., J.R.-I. and M.-E.R.-C.; project administration, A.-P.B.-Q. and J.R.-I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Vis/NIR Spectroscopy Instrumentation  
The equipment for collecting spectral signals is shown in Figure 2. It included a portable NIR spectrometer, a portable Vis spectrometer, and a halogen lamp. The NIR spectrometer (SW2520, Wuling Optics Co., Ltd., Shanghai, China) has a measurement wavelength range of 950-1700 nm, and the highest optical resolution is 8.0 nm. The Vis spectrometer (EE2063, Wuling Optics Co., Ltd., Shanghai, China) has a measurement wavelength range of 180-1100 nm, and the highest optical resolution is 0.2 nm. The two spectrometers and the halogen lamp were connected to each other through a three-in-one optical fiber. The halogen light source was emitted from the end of the optical fiber to the probe detection cavity. After the light was reflected on the sample surface, it entered the NIR/Vis spectrometers through the optical fiber. The two spectrometers collected spectral signals synchronously, and both were set to automatically save the average value of 5 consecutive spectral signals. Both spectrometers were set to save the spectrum signal with 0.5 nm accuracy in order to unify the data processing. For the case where the highest optical resolution of the spectrometer was lower than 0.5 nm, the software automatically performed interpolation operations.  
Figure 1: Distribution of N, P\({}_{2}\)O\({}_{5}\), K\({}_{2}\)O content in samples.  
Figure 2: Spectral acquisition instrument.
## 3 Proposed Method
### Data Acquisition
#### 3.1.1 Process of Acquiring Environmental Sensor Data  
Figure 7 illustrates the process of acquiring environmental sensor data, which operates through RS-485 communication between the main board and the three sensor boards for data transmission and reception. The first sensor board is responsible for acquiring four types of data: air temperature, humidity, CO\({}_{2}\), and insolation. The second sensor board is dedicated to acquiring three types of data: soil temperature, soil moisture, and soil EC. Lastly, the third sensor board is responsible for acquiring two types of data: drainage EC and drainage pH.
## 2 An Optimization Model for Wind Farms with an ESS
### Objective Function  
Revenue and expense models were defined for describing the behaviors of the wind farm operator. The revenue models were composed of revenues in off-peak periods, \(R_{t}^{opk}\), and peak periods, \(R_{t}^{pk}\). In  
Figure 1: A coordinated model of a wind farm with an energy storage system (ESS).  
the expense model, \(C_{t}^{ESS}\), could vary with the total amount of charged and discharged power. Hence, the objective function is given by:  
\[\textit{Maximize}\sum_{t\subset Off\textit{ peak}}R_{t}^{\textit{opk}}+\sum_{t \subset peak}R_{t}^{\textit{pk}}-\sum_{\forall t}C_{t}^{ESS}\ \ \forall t, \tag{1}\]  
and, considering revenues in off-peak and peak periods and expense  
\[R_{t}^{\textit{opk}}=P_{t}^{\textit{PCC}}\times smp_{t}+\left(P_{t}^{\textit{ unfund}}-P_{t}^{ch}\right)\times REC_{t}\ \forall t, \tag{2}\]  
\[R_{t}^{\textit{pk}}=P_{t}^{\textit{PCC}}\times smp_{t}+\left(P_{t}^{\textit{ unfund}}-P_{t}^{ch}\right)\times REC_{t}+\alpha\times P_{t}^{\textit{dch}} \times REC_{t}\ \forall t, \tag{3}\]  
\[C_{t}^{\textit{ESS}}=\left(P_{t}^{\textit{ch}}+P_{t}^{\textit{dch}}\right) \times\omega^{VO\&M}\ \forall t, \tag{4}\]  
where the power at the PCC, \(P_{t}^{\textit{PCC}}\), is described as  
\[P_{t}^{\textit{PCC}}=P_{t}^{\textit{wind}}-P_{t}^{ch}+P_{t}^{\textit{dch}}\ \forall t. \tag{5}\]  
The power at the PCC and the net power of the wind farm are paid out at the SMP and the REC price, respectively, regardless of the time period, as shown by the first and second variables of the right side of the Equations (2) and (3). The ESS discharged power during the peak period is paid out at the REC price by multiplying the REC multiplier, \(\alpha\), as depicted by the third variable from the right side of Equation (3). The variable costs of operating the ESS is composed of variable operation and maintenance costs and the total amount of charging and discharging as shown in Equation (5).  
Using this optimization problem, wind farm operators can maximize revenue by controlling the amount of ESS charged and discharged power.
## 2 Methodology  
### Design Structure and Fabrication  
The structure of the proposed CPW antenna is shown in Figure 1. It is comprised of a rectangular patch with a dimension of \(15.2\times 6.5\) mm\({}^{2}\) sandwiched between the OSC and substrate, where a gap of 1.5 mm is left between the antenna and the OSC. The solar cells are connected in a series and parallel combinations of a \(3\times 4\) array in order to yield an overall open circuit voltage of about \(V_{\infty}=1.2\) V and a short circuit current density of about \(I_{sc}=20\) mA\(\cdot\)cm\({}^{-2}\). Since the solar array backside is mounted onto the backside of the antenna, the antenna performance would not interfere with the solar cell transparency. The proposed configuration was purposely chosen to allow 100% insolation of the OSC, thereby improving the performance of the antenna. The patch geometry was purposely chosen in order to be coupled with step load structure in order to provide a wide bandwidth while keeping the antenna gain as high as possible. Thus, we placed the antenna at the backside of OSC, where the antenna face was directed towards the earth. When it comes to space applications, the CPW-fed monopole radiated more in the opposite direction of the OSC, which was towards the earth. Furthermore, the aluminum electrode of the OSC at the back could act as a reflector to redirect the wave propagation density, which helped the antenna provide higher directivity and gain.  
In the numerical simulation, the metal layer of the antenna was chosen to be copper metal with a thickness of 0.035 mm and a conductivity of \(5.96\times 107\) S/m. The utilized substrate was made of IS680 with a thickness of 0.762 mm, dielectric constant of 2.8, and loss tangent of 0.025. The rectangular patch was coupled to the feeding port via a center step-loaded structure with a 50 \(\Omega\) SMA connector. The outer part of the SMA connector was connected to the ground plane and the inner part was extended through the substrate in order to be connected to the extended strip. The numerical simulation was performed by using the finite integration technique (FIT) based on the high-frequency electromagnetic Computer Simulation Technology (CST) microwave studio. The optimum required dimensions of the proposed structure have been determined by the genetic algorithm approach, in which the antenna was operated in the Ku band frequency range. The other dimensional parameters of the antenna are given in Table 1. The area of the utilized OSC was considered to be the same size as the antenna size.