esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characteristics of Spoke Type IPM Model and Flared Type IPM Model  
### Characteristics at No-Load Condition  
To examine the validity of the proposed model, the no-load analysis and the load analysis were performed, and the performance improvement was confirmed by comparing the results. Figure 10 shows the back EMF waveform at the no-load condition. Compared to the basic spoke type IPM model, the flared-shape structure of ferrite magnets has the advantage that magnetic flux is more concentrated in the central part of the rotor core as a result of the arrangement of C-shaped ferrite magnets. Therefore, the magnitude of back EMF of the proposed flared type IPM model in the no-load condition increased by 4.96% compared to that of the basic spoke type IPM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Unit** & **Basic Model** & **Proposed Model** \\ \hline Material & - & YBM-9 BE (Ferrite) & YBM-9 BE (Ferrite) \\ Magnet shape & - & Bar-type & C-type \\ Dimensions & mm & \(16.5\times 7.4\)t & \(70\text{deg}\times 3.4\)t \\ Cross sectional area & mm\({}^{2}\) & 122.1 & 30.6 \\ Height (z-axis) & mm & 27.5 & 27.5 \\ Total quantity & Ea & 8 & 32 \\ Total weight & g & 134.3 & 134.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Magnet specifications of the conventional spoke model and the proposed flared model.  
Figure 9: The magnetization direction of the proposed flared ferrite IPM model: (**a**) radial magnetization pattern; (**b**) parallel magnetization pattern; (**c**) flux density of radial magnetization pattern; (**d**) flux density of parallel magnetization pattern.  
Figure 11 shows THD analysis of the back EMF waveforms. The THD of the back EMF is 1.47% which is higher than in the basic spoke type IPM model (0.69%). In general, the limit of THD is under 3.0% in the electrical vehicle and domestic appliance industries.  
Figure 12 shows the cogging torque waveforms at no-load. The proposed flared type IPM model has increased effective magnetic flux due to the characteristic of magnetic flux concentration, and the cogging torque ripple is 17% higher compared to the basic spoke type IPM model. Although cogging torque ripple increased, the magnitude of the back EMF also increased; therefore, improved efficiency is expected because the current is reduced at the rated load.  
Figure 11: Characteristics of total harmonic distortion (THD) of the back EMF waveforms.  
Figure 10: Characteristics of the back EMF waveform.  
Table 5 shows the 2D FEM analysis results of the proposed flared type IPM model in comparison with the basic spoke type IPM PMSM model.
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 5 Experimental Results and Comparative Analysis
### Image Data Preprocessing  
This study carried out two images pre-processing for training the GAN and object detection models. For training, the GAN model complied with the requirement of SG2-ADA model by converting the sizes of the raw data into a uniform scale of 256 \(\times\) 256 pixels. We used the Python Imaging Library (PIL) to implement the resizing tasks [62]. While for the object detection models, we labeled the datasets and annotated them based on bounding boxes using LabelImg [63]. Then, we converted the dataset to tf-record formats which were divided into 70% for training, 15% for evaluation, and 15% for testing the performances of the model.
# Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
## 2 Hybrid Algorithm in TTF Diagnosis System
### Negative Sequence Directional Algorithm  
The second sub algorithm is the negative-sequence directional algorithm. This algorithm uses an asymmetrical component from a 3-phase current principle to detect a fault, especially a TTF. When there is a fault, either internal or external, the current phase will not be symmetric or phasors become unbalanced. An unbalanced phasor generates positive (I\({}_{1}\)), negative (I\({}_{2}\)), and zero (I\({}_{0}\)) sequence components, which are members of the balance set. The 3-phase currents from the primary and secondary side are extracted by the discrete Fourier transform algorithm. The symmetrical components determined by applying the Fortescue transformation are presented in Equation (3). The unbalance current can be expressed as the sum of their components, as shown in Equation (2).  
\[\begin{array}{lcl}\text{I}_{\text{a}}=&\text{I}_{0}+\text{I}_{1}+\text{I}_{2 }\\ \text{I}_{\text{b}}=&\text{I}_{0}+\text{a}^{2}\text{I}_{1}+\text{a}\text{I}_{2 }\\ \text{I}_{\text{c}}=&\text{I}_{0}+\text{a}\text{I}_{1}+\text{a}^{2}\text{I}_{2 }\end{array} \tag{2}\]A phase rotation operator (a) is defined to rotate a phasor vector. Phases are rotated forward by 120\({}^{\circ}\). Conversely, the phase currents' sequence components can be written as Equation (2) [22].  
\[\begin{array}{rcl}\mathrm{I}_{0}&=&\frac{1}{3}(\mathrm{I}_{\mathrm{a}}+ \mathrm{I}_{\mathrm{b}}+\mathrm{I}_{\mathrm{c}})\\ \mathrm{I}_{1}&=&\frac{1}{3}(\mathrm{I}_{\mathrm{a}}+\mathrm{a}\mathrm{I}_{ \mathrm{b}}+\mathrm{a}^{2}\mathrm{I}_{\mathrm{c}})\\ \mathrm{I}_{2}&=&\frac{1}{3}(\mathrm{I}_{\mathrm{a}}+\mathrm{a}^{2}\mathrm{I}_ {\mathrm{b}}+\mathrm{a}\mathrm{I}_{\mathrm{c}})\end{array} \tag{3}\]  
The negative sequence current (I\({}_{2}\)) will increase if there is an unbalanced phasor or fault. If the value exceeds a predefined threshold with a typical default of 4% of the rated current, the angle between them is compared. When a different angle (\(\Delta_{\phi 2\mathrm{TH}}\)) is between a predefined directional limit, typically from \(\pm 60^{\circ}\) to \(\pm 85^{\circ}\)[10], the internal fault is detected. Otherwise, it is an external fault (Figure 2). The second subalgorithm is used for distinguishing between internal and external faults and normal condition.
# Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers
## 5 Numerical Results and Simulations
The choice of the number of users is 5 and 10, and the number of BS receiving antennas is from 15 to 180.
## 2 Fatigue Constitutive Model of Materials  
### Fatigue Constitutive Model of Concrete  
#### 2.1.1 Fatigue Stiffness Degradation  
Research has found [11] that the stiffness of concrete degrades in three stages as the number of fatigue loadings increases. In the first stage, the elastic modulus of concrete decays rapidly, accounting for about 10% of the total fatigue life. In the second stage, the elastic modulus of concrete degrades steadily and linearly, accounting for about 80% of the total fatigue life. In the third stage, the elastic modulus decreases rapidly, accounting for about 10% of the total fatigue life. [PERSON] [12] proposed the degradation equation for the fatigue elastic modulus of concrete, as shown in Equation (1):  
\[E_{\text{N}}=(1-0.33N/N_{t})E_{0} \tag{1}\]  
where \(E_{\text{N}}\) is the residual elastic modulus of concrete after \(N\) cycles of fatigue loading, \(E_{0}\) is the initial elastic modulus of concrete and \(N_{t}\) is the fatigue life of concrete.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 6 Open Challenges and Future Research Directions
### Energy-Efficient  
The achievement of energy-efficient data transmission while maintaining scalability and security is another major challenge facing the NDN [58]. Previous methodologies and strategies often lead to excessive energy consumption and vulnerability to attacks such as APTs and side-channel exploits. This challenge was addressed by proposing, for example, the RLEAFS (reinforcement learning-based energy-aware forwarding strategy) protocol, which intelligently selects energy-aware paths and incorporates adaptive sleep modes [58]. The RLEAFS approach reduces energy consumption and prolongs network lifetime, and also enhances security by making the network more resilient to attacks through optimizedrouting and resource management [59]. The protocol incorporates adaptive sleep modes, where non-essential nodes estimate their hop distance to the sink and enter sleep states to conserve energy, waking periodically to check for ongoing transmissions.  
By combining reinforcement learning, energy-sensitive routing, and sleep mechanisms, RLEAFS offers a robust, efficient, and secure framework for the dissemination of IoT data, ensuring sustainable operation in resource-constrained environments while maintaining high performance and data integrity [59].
## 2 Design of the Antenna for RF Sensing and That for Communication
(**a**) Geometry; (**b**) S\({}_{11}\) as the reflection coefficient of the antenna; (**c**) far-field pattern of the one-dimensional antenna expressed in from red (strongest) through green (middle) to blue (weakest); (**d**) measured beam-pattern. and unequal. This phenomenon deteriorates the impedance matching of the input port. To solve these problems, the power divider here has been designed well, which is proven by the S\({}_{11}\)-curve in Figure 4c. Using 24 GHz as the target frequency, S\({}_{11}\) is below \(-10\) dB in the frequency band. From the one and only input port to the many branches and radiating patches in the power-dividing circuit, it is apparent that the impedance is matched. The RF signal divided through the feeding circuit reaches the rectangular patches and makes them radiate the electromagnetic fields to the free-space. This can be verified by the far-field pattern. Figure 4d,e has the three-dimensional beam-pattern plotted just over the front side of the structure for the sake of convenience. The far-field pattern has a narrow beam resulting in high directivity that is adequate for building a wireless link with RF sensors, such as radar and microwave imaging apparatus. When there is an object to be detected and it is located very far away from the source of radiation, a highly directive beam makes the returning signal stronger than other cases. Figure 4: _Cont_.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 4 Proposed Protocol
### Device Discovery Scheme  
In order to undertake D2D communication, the devices must execute a device discovery to detect and identify devices in proximity [13]. The 3 GPP technical specification TS 23303 [20] describes two models of devices discovered with no permission necessary from the User Equipment (UE) to be discovered or with authorization required. The first model is Model A \"I am here\", in which devices broadcast some information to announce their existence and monitor if interested devices also shared their information. In the second model, Model B \"Who is there?\", devices work as discoverers by broadcasting the characteristics which it expects to find in the nearby devices and waiting for the response of those which are eligible to fulfill its expectations.  
In this work we adopt Model A. The device discovery is executed following the solution presented in [PERSON] et al. [13] and the technical specification TS 23303 [20], which is described as follows.  
First, as mentioned by [13], each device must prove to be authentic to the HSS, which checks if the International Mobile Subscriber Identity (_IMSI_) of the device matches the identity of the device registered in the database and if that device is eligible to perform D2D. If the verification passes, the devices are enabled to perform D2D communication. The authorization is stored in the eNB and refreshed at the expiration of a validity timer.  
Next, it adopts a model with direct discovery among devices through a dedicated ProSe server, one of the solutions presented by [13] and based on the specifications of TS 23303 [20]. The devices detect and identify each other using Evolved Terrestrial Radio Access Network (E-UTRAN) or Wireless Local Area Network (WLAN) direct radio signals to share their identities.
## 3 Experimental Methodology  
### Test Bench  
An ad hoc test rig was set up and used for the high-speed lubrication tests of various wheels including disc, cylindrical gear, bevel gear, and orthogonal face gear (see Figure 2). The specific test bench consists of a high-speed motor whose rated rotating speed is 12,000 rpm, a torque transducer whose accuracy is 0.1 Nm, and the full-range value is 10 Nm, a 3D printed testing wheel, and a rectangular gearbox housing (380 mm \(\times\) 266 mm \(\times\) 100 mm) made of Plexiglas, which is convenient for observation by a high-speed camera. It is generally acknowledged that the PIV technique should be used to obtain the velocity information for the flow field and further determine the pressure field, vorticity field, and other physical information for quantitative research prior to the flow field images captured by a high-speed camera for qualitative research (2013). Subject to the experimental conditions, a high-speed camera is adopted in this paper. We used 3D-printed gears instead of metal gears to investigate the no-load power loss, for the following reasons. Firstly, it is accepted that the no-load power loss, whether windage or churning loss, is caused by the relative motion between the rotating wheel and the surrounding fluid medium. Secondly, the net resistance concerning windage or churning loss is extracted from the gear system by subtracting the no-gear system. Therefore, these power losses have no connection with the gear materials. Similarly, 3D-printed gear was used to conduct splash lubrication for an orthogonal face gear, and the experiments agreed well with theoretical analysis according to [PERSON] and [PERSON] (2013). The rotary speed and resistance torque were measured and recorded by a torque transducer (DYN-200, accuracy = 0.1%, measuring range = 0-10 Nm) and each test reached relative stability as the torque variation rate did not exceed 8%. It takes about 1-2 min for each case to reach relative stability. The net drag torque concerning windage or churning loss is determined by subtracting the no-gear resisting torque from the gear resisting torque. Additionally, the churning power loss generated by different wheels in this paper is not very large and raises the oil temperature very little in such short a time; therefore, the oil density or viscosity is regarded to be nearly constant over all cases.  
The main parameters of different kinds of wheels, including outside diameter (\(D_{0}=163.5\) mm), teeth width (\(b_{t}=17.83\) mm), and teeth number (\(Z=51\)), remain the same to explore the influence of the wheel types. The typical geometric structure is shown in Figure 3, with the pitch angle of the bevel gear being \(45^{\circ}\). The test speeds of revolutions in the counterclockwise direction were changed from 0 to 6000 rpm. Aviation lubricating oil was replaced by purified water for cleaning purposes. The initial dimensionless oil immersion depth was 0.3 and 0.5.
# Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods
## 3 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Data Source** & **Methods** & **Summary** \\ \hline
[25] & Trained with 2007–2008 data & & Predicted time/network-related delays 2–24 h in advance, 19\% test error when classifying flight either above or below 60 min \\ \hline
[26] & U.S. domestic traffic & & Supervised machine learning algorithms to predict individual flight delays. Highest 83.07\% accuracy with Adaboost \\  & & & \\ \hline
[27] & 10 U.S. airports & RNN Long Short-Term Memory—LSTM & The highest accuracy was achieved at Atlanta Airport, reaching 90.95\%, while the lowest accuracy was observed for Phoenix Airport, which was 71.34\% \\ \hline
[28] & U.S. airports & DT & Used MapReduce program to analyze airline flight and weather datasets. With a 15 min delay threshold, the accuracy was 74.2\% \\ \hline
[29] & April–October 2013 U.S. & & Study focused on Gradient Boosted Decision Trees analyzing departure delay statistics with MAE, RMSE, and R\({}^{2}\) \\ \hline
[30] & Actual operation data from Tokyo Airport from 42 days in 2012 & ANN & Evaluated the performance of neural networks compared to traditional queueing analysis \\ \hline
[31] & U.S. & RF, GB, ANN, K-Nearest Neighbors (KNN), Extra Trees & The model firstly performed a binary classification to predict whether a flight would be delayed and, in the second stage, conducted a regression analysis to estimate the duration of the delay in minutes \\  & & Extra Trees & The highest departure delay classification was gathered with Gradient Boosting, with accuracy of 86.48\% \\ \hline
[32] & Kaggle.com & ANN, Deep Belief Network & Deep neural networks attained an accuracy rate of 77\%, whereas ANN achieved an accuracy rate of 89\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flight delay prediction studies.
## 3 The Proposed Solution  
As well-known from the literature [68; 69], GIS systems are widely used to store and to correlate information regarding several aspect of urban infrastructure, including the state of the pavements alongside the roads, the electrical grid and much more information. GIS can be used not only to visualize the data in thematic maps but, by using properly queries, it is possible to correlate spatially and temporally the data about \"road accidents\" with \"luminosity data\", especially if the luminosity data are periodically updated.  
A method for the collection, processing and representation of the light intensity data through the use of IoT sensors and GIS has been developed and presented in the following section. This method is part of a wider research framework described in Figure 4.  
The idea concerns the development of a location-based decision support system (DSS), able to support decisions on operations maintenance and repair at the urban level. Data are collected from  
Figure 4: The workflow of the proposed approach.  
Figure 3: (**a**) (Global Warming Potential (GWP) emission reduction in tons of CO\({}_{2}\) for the Austrian projects in the European renovation project Streetlight EPC [66]; (**b**) number of lamps before and after the renovation and luminaires points of light before in the same cities. The luminaires points after the renovation are the same value reported for the new number of lamps.  
different sources: hard key performance indicators (KPIs) concerning technical and measurable data providing information directly related to the safety in the urban environment, and soft KPIs calculated from open data and the crowdsourcing campaign [70], allowing us to capture information impacting on safety issues, even though in an indirect manner. Since the described phenomena belong to the built environment and urban domain, information is collected and integrated in a GIS through which data processing and visualization can be carried out and relations unveiled. These tools allow quantitative and qualitative analysis, data mapping and representations, are include in a location-based DSS which can be related to policies at the municipality level, to obtain informed decisions on prioritization of operations, maintenance and repair (OM&R) procedures. This process allows OM&R to be optimized when the resources (time, budget and people) are scarce and have to be optimized and prioritized.  
In this article only one part of the wider research schema depicted in Figure 4 has been addressed, since the implementation of the procedures and tools employed for the development of the location-based DSS can be carried out gradually. This article focuses on the collection of data related to the technical performances of the urban component of lighting, data processing through GIS and the development of data analysis and visualization through GIS, integrating databases from different open sources.
# Feasibility of a 3D Surgical Guide Technique for
###### Abstract  
This study aimed to evaluate the feasibility of a 3-dimensional (3D) planned surgical guide technique designed for impacted supernumerary teeth (STs) extraction using 3D printed simulation models. In total, 17 participants from two university hospitals were recruited. Each participant performed surgery with both the conventional and surgical guide techniques. The following parameters were evaluated: (1) The time required for ST extraction, (2) the area of the window opening, and (3) the volume of the material removed. Time required for ST extraction was \(213.65\pm 167.45\) sec and \(114.76\pm 42.87\) sec in the conventional and surgical guide techniques, respectively, with significant differences (\(p=0.028\)). The required area of the window opening was \(48.10\pm 9.44\) mm\({}^{2}\) and \(45.90\pm 8.17\) mm\({}^{2}\), respectively, in the conventional and surgical guide techniques, with no significant differences (\(p>0.05\)). The required volume of the material removed was \(121.65\pm 43.69\) mm\({}^{3}\) and \(99.12\pm 17.88\) mm\({}^{3}\) in the conventional and surgical guide techniques, respectively, with significant differences (\(p=0.031\)). The 3D planned surgical guide technique was effective for impacted ST extraction. Within the limits of this study, the surgical guide technique allows us to accomplish minimally invasive surgery within a shorter duration.  
supremurary tooth; surgical guide; CAD/CAM; 3D printing; mesiodens Article
## 2 Materials and Methods
### Electronic Nose  
The Agrinose device designed and constructed at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin was used in the study [17]. It has a matrix of eight MOS sensors (TGS2600--general air contaminants, hydrogen and carbon monoxide; TGS2602--ammonia, hydrogen sulfide, high sensitivity to VOC, and odorous gases; TGS2603--odors generated from spoiled foods; TGS2610--LP gas, butane; TGS2611--natural gas, methane; TGS2612--methane, propane, and butane; TGS2620--solvent vapors, volatile vapors, alcohol; AS-MLV-P2--CO, butane, methane, ethanol, hydrogen. Specifically designed for volatile organic compounds). Agrinose was previously used to evaluate bread spoilage, rancidity of edible oils, and seed spoilage and for control of baking. In comparison with the previous studies, the device was modified by replacing sensor TGS2611 E00 (sensitivity to natural gas) with sensor TGS2603 for detection of food spoilage, which significantly improved the sensitivity of the device in identification of VOCs in biological materials [16].  
In contrast to common descriptions of the odor profile, a new three-parameter method for generation of the smellprint was used in the present study (Figure 1). To date, the maximum sensor response \(\Delta R/R_{max}\), i.e., the maximum value of the change in e.g., the resistance of MOS or conducting polymer (CP) sensors, has been most commonly used for generation of smellprints. The authors developed a new method for generation of smellprints based on two additional parameters [17; 15]: the time until achievement of the maximum response--the response time \(T_{R}\) and the cleaning time \(T_{CL}\), which is the time of removal of molecules from the sensor's active surface, i.e., the time from achievement of the maximum response \(\Delta R/R_{max}\) to half of its value. The parameters established in the study were dependent on the type of volatile substances contained in the odor profile and the intensity of emissions of compounds. A new odor intensity factor was used, i.e., the ratio of the reaction times \(T_{ratio}\) (Figure 1). Its physical interpretation is based on the assumption that the greater the value is than zero, the more intense the interaction of volatile molecules with the active surface is.