esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Finite Element Analysis
### Cogging Torque  
Cogging torque is produced by the interaction between harmonics of airgap permeance and PM of rotor. Harmonics in airgap permeance is formed from slotted stator and magnet cavities of rotor. Remament flux distributed by interior PM in rotor stores the energy into airgap in form of magnetic field as in Equation (17). The stored energy is position dependent and its derivative is directly associated with cogging torque. Equation (17) formulates the stored energy in the whole airgap and the association between its derivative and cogging torque [26].  
\[W=\frac{1}{2\mu_{0}}\int_{0}^{2\pi}{{B_{r}}^{2}(\theta)\left(\frac{l_{c}}{l_{ c}+l_{g}(\theta,\alpha)}\right)^{2}}d\theta \tag{17}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Requirement** & **Value** \\ \hline Phase & 3 & Stack length & 47 mm & Base torque & 6 Nm \\ Pole & 8 & Diameter of stator & 94 mm & Base speed & 6000 r/min \\ Slot & 12 & Diameter of rotor & 48.8 mm & Max. speed & 8600 r/min \\ Gap & 0.7 mm & Serial turns/phase & 148 turns & Efficiency & 94\% \\ Resistance & 0.36 \(\Omega\) & PM flux density & 1.36 T & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of stator and rotor and requirements.  
Figure 6: The geometric shapes of each model: (**a**) 70 degrees, (**b**) 80 degrees, (**c**) 90 degrees, (**d**) 100 degrees, (**e**) 110 degrees.  
\[T_{cogging}=-\frac{\partial W}{\partial\alpha}\]  
where \(W\): magnetic field energy stored in gap and cavities, \(\theta\): mechanical angular position, \(\alpha\): electrical angular position, \(T_{cogging}\): cogging torque. Figure 7a shows the curves of cogging torque generated in each model during one cycle of electrical rotation. In Figure 7b, peak and RMS values are given for each model. The decreasing rate in peak and RMS is the steepest between 70 and 80 degrees. Every 10 degrees of variation reduce peak values as much as 42.9%, 26.3%, 24.9% and 1.2% in order from 70 to 110 degree. This decreasing trend continues up to 100 degrees and disappears when change from 100 to 110 degrees. The reductions in PM volumes and the area that stores magnetic field energy are expected as the main reasons.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Performance Evaluation  
The model were evaluated using the test dataset. Performance assessment included analyzing the confusion matrix and classification report for each model. For multi-class classification, this study evaluated standard metrics such as accuracy, F1-score, recall, and precision. The formulas for each metric are provided below.  
\[Precision=T_{pos}/T_{pos}+F_{pos} \tag{13}\]  
\[Recall/Sensitivity=T_{pos}/T_{pos}+F_{pos} \tag{14}\]  
\[Accuracy=T_{pos}+T_{pos}/T_{pos}+T_{agg}+F_{pos}+F_{pos} \tag{15}\]  
\[F1-score=2*T_{pos}/(2*T_{pos})+F_{pos}+F_{pos} \tag{16}\]  
A classification is considered accurate when a verse is correctly categorized into its respective meter (true positive, Tpos). For instance, if a verse written in the saree meter is correctly predicted as saree, it constitutes a true positive. A verse deviating from a specified meter but is accurately recognized as not conforming to that meter is a true negative (Treg). Conversely, if a verse from one meter is misclassified as belonging to another, this results in a false positive (Fpos) for the predicted meter and a false negative (Fneg) for the actual meter. For instance, if a kamel meter verse is classified as madeed, it counts as an Fpos for madeed and an Fneg for kamel.
## 6 Results and Discussion  
The performance of the system developed within this study was tested and validated using the following three scenarios listed below:  
1. Scenario 1 (constant speed cruise with a constant power supply)
2. Scenario 2 (charging of the SC during regenerative mode)
3. Scenario 3 (acceleration and deceleration of the vehicle during variable speed)  
These scenarios are explained one by one below.
## 4 Review: Slant Links Rain Fade Models
### Advantages  
The model can predict rain attenuation at the receiving ground station end by finding four coefficients (\(a\), \(b\), \(c\), and \(d\)), which makes it possible to apply the model anywhere in the world.
## 2 Materials and Methods
### Experimental Validation  
To experimentally investigate the performance of the coded MLT schemes, the imaging schemes defined in Section 2.3 were implemented on the ultrasound advanced open platform (ULA-OP) [25,26] equipped with the above mentioned phased array transducer. The pulse repetition frequency (PRF) was 4750 Hz and the prebeamformed radio-frequency (RF) channel data were acquired at a sampling frequency of 50 MHz using the ULA-OP acquisition board [27] and beamformed offline through MATLAB R2015a (The MathWorks, Natick, MA, USA). The beamforming parameters were the same as those applied in the simulations.  
Using these setups, a general-purpose tissue mimicking phantom (CIRS Model 040 GSE, Norfolk, VA, USA) and the left ventricle of a healthy volunteer from a parasternal long axis view were imaged. To quantify the image quality, the contrast-to-noise (CNR), the contrast ratio (CR) of the cystic regions, and the signal-to-noise ratio (SNR) were calculated for the static phantom images:  
\[CNR = \frac{\mu_{b}-\mu_{c}}{\sqrt{\sigma^{2}{{}_{b}}+\sigma^{2}{{}_{c}}}}, \tag{2}\] \[CR = 20 log_{10}\frac{\mu_{b}}{\mu_{c}}\] (3) \[SNR = 10 log_{10}\frac{\mu_{b}}{\mu_{n}} \tag{4}\]  
where \(\mu_{b}\) and \(\mu_{c}\), \(\mu_{n}\), are the mean gray-level in the background, the cystic regions and in the noise region, respectively, and \(\sigma_{b}\) and \(\sigma_{c}\) are their corresponding standard deviations. Finally, the quality of the in-vivo images was visually examined.
# MGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases
###### Abstract  
This paper addresses the problem of missing data in food composition databases (FCDBs). The missing data can be either for selected foods or for specific components only. Most often, the problem is solved by human experts subjectively borrowing data from other FCDBs, for data estimation or imputation. Such an approach is not only time-consuming but may also lead to wrong decisions as the value of certain components in certain foods may vary from database to database due to differences in analytical methods. To ease missing-data borrowing and increase the quality of missing-data selection, we propose a new computer-based methodology, named MGHT - Missing Nutrient Value Imputation UsinG Null Hypothesis Testing, that enables optimal selection of missing data from different FCDBs. The evaluation on a subset of European FCDBs, available through EuroFIR and complied with the Food data structure and format standard BS EN 16104 published in 2012, proves that, in more than 80% of selected cases, MGHT gives more accurate results than techniques currently applied for missing value imputation in FCDBs. MGHT deals with missing data in FCDBs by introducing rules for missing data imputation based on the idea that proper statistical analysis can decrease the error of data borrowing.  
food composition databases; nutrient values; missing data; missing-data imputation; data borrowing; null hypothesis testing Article
## 3 Results and Discussion
### Measurements
#### 3.2.4 Infrared Thermographic Analysis of Undamaged Modules
More detailed conclusions regarding the state of the remaining modules can be made only by applying electroluminescence in accordance with IEC TS 60904-13:2008 and detailed I-V curve analyses [30], which are also the subject of another study to be published in the near future.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Packet Size
#### 4.1.4 Simulation Results of Rician Propagation Model regarding Packet Size  
Because Rician belongs to a multi-path propagation channel, the throughput of the mRSU is better here as well. Additionally, the average latency of the mRSU is reduced in the Rician propagation model as compared to the sRSU. Because the mRSU is in motion with the nodes, the distance between them is reduced, and therefore the latency is reduced. Similarly, the throughput increases owing to the multi-path process since messages can be received from any hop.
# Characterization of Flax and Hemp Using Spectrometric Methods
### General Aspects about Hemp (Cannabis sativa)  
Hemp (_C. sativa_) is one of the oldest plants cultivated in Europe and China (for thousands of years), used for preparing natural remedies, and as a source of food, dietetic oil and cellulose. The content of oil in cannabis sativa seeds amounts to approx. 35%. Hempseed oil is an untraditional oil, which, as a source of essential unsaturated fatty acids and natural antioxidants, may be treated as a product for disease prevention and as a functional food [22].  
Another nonconventional food which is being focused on by medicine today is _Cannabis sativa_ or hemp. This famous plant is found in the wild flora of Central Asia [23]. Different cannabis varieties with a reduced number of psychoactive cannabinoids and flax seeds are used for the production of fiber and oilseed [24]. _C. sativa_ seeds consist of protein (20-25%), carbohydrates (20-30%), fiber (10-15%), oil (25-35%), minerals such as phosphorus, magnesium, potassium, calcium and sulfur, iron and zinc and also a vitamin A precursor caroten [25; 26]. Furthermore, the _C. sativa_ seed oil structure is mostly based on polyunsaturated fatty acids (<70%). It is considered important among other oils mainly for its optimal linoleic and linolenic acid ratio (3:1), which is considered beneficial for human health. Additionally, with the high concentrations of \(\gamma\)-linolenic acid (GLA) and steasteradionic acid (SDA), _C. sativa_ seed oil is found to be significant throughout the other industrial crops [27; 28]. These fatty acids and other components from the oil were found to be efficient against inflammation, ischemic heart diseases, psoriasis, atopic dermatitis and mastalgia, and also regulate biochemical blood parameters, providing stimulation of the general metabolism [29; 30; 26; 31; 26].  
Hemp seeds are high in calories, fat and protein. Hemp seeds contain all 21 fatty amino acids, including those eight amino acids that the human body cannot produce, which makes hemp a complete protein. These substances are very beneficial for heart health, help the brain develop in children, reduce inflammation in the body, regulate blood pressure, reduce cholesterol and reduce the risk of developing many associated diseases [33].
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 3 Architecture Exploration for Backprojection Based Video SAR
### Architectures for BP-VISAR with Full-Synthetic-Aperture Processing
All the transferred data for the BP-VISAR can be allocated as the page-locked memory because it can fit within the size of the RAM in the host; for example, the minimum page-locked memories required for 4096\(\times\)4096 frame resolution are 153 MB and 135 MB for Arch. F2 and F3, respectively. Although the host-to-device bandwidth required for range profile data transfer in Arch. F2 is not affected by the overlap mode, it depends on the overlap ratio \(\alpha\) in Arch. F3. If \(\alpha\) equals zero, these architectures operate in a non-overlapped mode. For the given target frame rate, the device-to-host bandwidth for output frame transfer is the same for both Arch. F2 and F3 modes. All the bandwidths required for the continuous operation of the BP-VISAR algorithm with the target frame rate can be calculated with actual data. For example, for a frame resolution of 4096 \(\times\) 4096 and a target frame rate of 10, the required H2D and D2H bandwidths for Arch. F2 and Arch. F3 in the non-overlapped mode are 1.53 GB/s and 1.36 GB/s, respectively. Suppose we use the overlapped mode with an overlap ratio of 80%, the bandwidth for Arch. F3 is reduced to 1.35 GB/s, but the bandwidth for Arch. F2 is the same as that in the non-overlapped mode. The PCIe 4.0x16 is used for the H2D channel and provides a bandwidth of 31.5 GB/s. Thus, the data transfer channel between the host and the device can provide sufficient bandwidth for the BP-VISAR systems.
## 2 Virtual Surgical Planning Protocol Between Surgeons and Bioengineers
### Reconstruction Planning  
Based on the established resection margin, a plan is developed to reconstruct the defect area. This process requires close collaboration between the surgeon and bioengineer, led by the surgeon. The surgeon can formulate a surgical plan considering the length, width, height, and shape of the defect area, as well as potential donor sites such as fibular or iliac crest free flaps. A partial osteotomy of the bone flap may be necessary given the curvature of the defect area, and the characteristics of the soft tissue flap and the position of the donor site's supply vessels should also be considered.  
The surgeon provides the overall direction for reconstruction based on clinical experience and anatomical knowledge, while the bioengineer implements and fine-tunes this in 3D software. The final reconstruction plan is determined through several rounds of feedback and modifications. After the surgeon approves the final reconstruction plan, the template model is printed, and the bioengineer begins designing the guide.  
Surgeon's role: Select donor site and determine overall direction of reconstruction and give final approval.  
Bioengineer's role: Fine-tune and optimize the reconstruction plan.
## 3 Results  
The FE analysis results showed that the maximum von Mises stresses for the implant, implant screw, bone, locking plate and locking plate screw all decreased significantly from stage 1 to stage 2. Bone stress continued to decrease from stage 2 to stage 3 but the implant and implant screw increased slightly. The maximum implant stress value reached 1318 MPa, which exceeded the fracture strength of the titanium alloy material (about 1000 MPa) in stage 1 (Figure 4). The von Mises stress distribution shows that the maximum stress value of the stage 1 implant occurred at the surface lattice mesh structure with apex region/implant stem for the implant, at the junction with the bone for bone, and at the implant screw and locking plate screw, while the locking plate's occurred at the proximal end and locking plate screw junction (Figure 5).  
The overall displacement results can be found at the distal condyle of about 4.8 mm in stage 1, reduced to 2.5 mm with osseointegration and assisted by the locking plate in stage 2, and reduced to 2.1 mm after taking out the locking plate in stage 3 (Figure 6). Most of the strain values were less than 4000 at the proximal and distal implant-bone contact interfaces but only a small amount were more than 4000 around the proximal stem and the distal bone nail in stage 1 (Figure 7). While the corresponding strain value dropped significantly in stage 2, there was a slight increase at the proximal lateral site in stage 3.  
Figure 4: The result of FE analysis for the von Mises stresses for implant, implant screw, locking plate and locking screw at different simulated stages. The value decreased by half after we introduced the rounded angle design into the implant.  
Figure 5: The maximum stress distribution area in the stage 1 occurred at the surface lattice mesh structure with apex region/implant stem for the implant, at the bone-implant interface for the bone, the implant screws, the locking plate and the locking plate screws. Significant decrease of stress can be observed with progress into the second and third stage.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 2 Materials and Methods
### Controller Design  
The control of the tank level is performed using a digital computer. Therefore, two approaches to digital control design are used: The application of the Dahlin algorithm and the design of a digital PID controller, where the PID parameters are adjusted according to the Takahashi PID tuning method.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Trend Analysis Method  
In both continuous production and batch processes, process variables show a dynamic and gradual trend in transition mode. The library trend is qualitative, indicating the change trend in process variables under normal operation, which can be obtained from the historical data of the transition mode. The real-time trend of process variables is obtained from online process data. When a fault occurs, the process variables show a trend in thefault characteristics. Anomalies are identified by qualitatively comparing real-time trends with library trends. Based on a traditional qualitative trend analysis method, [PERSON]. [77] selected the shape of the trend as the qualitative process variable and added quantitative information, such as the amplitude and duration of the trend, and put forward a dynamic feature synchronization algorithm. This method has been applied to the testing of semi-industrial distillation towers and simulated fluid catalytic cracking units to accurately detect faults.
## 3 Control Objectives and Optimal Control
### Linear Quadratic Gaussian Control  
A linear quadratic Gaussian is a combination of a linear quadratic regulator (LQR) and Kalman filter [20; 21]. LQR regulates the plant using the states, which can be estimated from the Kalman filter. Notably, the system (plant) is affected by process and measurement noises. LQG control provides the optimal control input to the system (plant) by diminishing the effect of process and measurement noises. LQG is summarized as follows.  
LQR solves the optimization problem offline:  
\[J=\left\|\hat{\mathbf{x}}^{T}(t)\lambda\hat{\mathbf{x}}(t)\right\|_{2}^{2}+\left\| \hat{u}^{T}(t)R\hat{u}(t)\right\|_{2}^{2} \tag{24}\]  
where \(R\) is an \(m\times m\) symmetric positive semi-definite matrix, and \(\lambda\) is an \(n\times n\) symmetric positive semi-definite matrix. Both matrices are tuned by trial and error. The first and second parts in the optimization function are the minimization of error between the reference and measured value and the minimization of the control effort, respectively.  
The solution to this LQR controller can be written in terms of the simple state feedback law by minimizing the performance index, \(J\), in Equation (24) as a simple matrix gain of the following form:  
\[u(t)=-K\hat{\mathbf{x}}(t) \tag{25}\]  
where \(\hat{\mathbf{x}}(t)\) is the state estimates from the Kalman filter, and \(K\) is a \(p\times q\) matrix as follows:  
\[K=R^{-1}B^{T}X \tag{26}\]  
where \(X\) is the unique positive-definite matrix obtained from solving the continuous algebraic Riccati equation as follows.  
\[A^{T}X+XA-XBR^{-1}B^{T}X+Q=0 \tag{27}\]  
Kalman Filter  
Plant dynamics are corrupted by disturbance signals (process noise) and measurement noise. Thus, we have a plant model of the following:  
\[\hat{\mathbf{x}}(t) =Ax(t)+B_{u}\Delta u(t)+w_{d}(t)\] \[y(t) =Cx(t)+D_{u,g}u(t)+v_{n}(t) \tag{28}\]  
where \(w_{d}(t)\) and \(v_{n}(t)\) are the disturbance (process noise) and measurement noise inputs, respectively, i.e., \(w_{d}(t)\) and \(v_{n}(t)\) are white noise processes with covariances. The inferences can be summarized as follows.  
\[E(w_{d}(t))=E(v_{n}(t)) =0 \tag{29}\] \[E(v_{n}(t)v_{n}^{T}(t)) =R_{N}\] (30) \[E(w_{d}(t)v_{n}^{T}(t)) =N_{N}\] (31) \[E(w_{d}(t)w_{d}^{T}(t)) =Q_{N} \tag{32}\]  
The Kalman filter has the layout of a standard state estimator or observer, as follows.  
\[\hat{\mathbf{x}}(t)=A\hat{\mathbf{x}}(t)+Bu(t)+K_{f}(y(t)-\hat{y}(t)) \tag{33}\]  
The estimator or observer uses the output, \(y(t)\), and inputs, \(u(t)\) to produce the estimated output, \(\hat{y}(t)\), and estimated state, \(\hat{\mathbf{x}}(t)\).  
The optimal solution of \(K_{f}\) is obtained by minimizing the \(J\) as follows:  
\[J=\lim_{t\rightarrow\infty}E((\mathbf{x}(t)-\hat{\mathbf{x}}(t))^{T}(\mathbf{ x}(t)-\hat{\mathbf{x}}(t))) \tag{34}\]  
\[K_{f}=PC^{T}R_{N}^{-1} \tag{35}\]  
where P = \(P^{T}\geq 0\) is a steady-state error covariance matrix obtained by solving the continuous algebraic Riccati equation as follows.  
\[A^{T}P+PA-PC^{T}R_{N}^{-1}BX+Q_{N}=0 \tag{36}\]
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing
### GNSS and Meteorological Data Processing  
#### 4.2.1 GNSS Data Processing  
A database table (Table 2) was established for storing the GNSS monitoring data. The reference station receives satellite signals and calculates the positions, which are compared with the signals received by the monitoring station to calculate residuals. After error correction, the monitoring accuracy reaches the millimeter level. The monitoring data format is set to RTCM 3.2, the data acquisition interval is 10 Hz, the baseline solution time period length is 30 min, the smoothing duration is 1h, the cutoff altitude angle of the participating baseline solution satellites is 20\({}^{\circ}\), the resulting time format is set to H:M:S.SS, and the folder storage time frequency is set to 24 h.  
Figure 7: Image projection results: (**a**) row projection, (**b**) column projection.  
The GNSS receiver used in this study achieves a horizontal accuracy of \(\sigma=\pm\big{(}8+1\times 10^{-6}\times D\big{)}\) mm and a vertical accuracy of \(\sigma=\pm\big{(}15+1\times 10^{-6}\times D\big{)}\) mm, where D denotes the baseline distance. Verification was conducted with baselines of \(D\leq 5\) km. The number of effective GNSS satellites in a single system was \(\geq\)8, and the satellite elevation cutoff angle was set to \(\leq\)10\({}^{\circ}\). The GNSS receiver conducted observations at points with known coordinates through 10 independent sessions. Each session collected 100 RTK measurements. To mitigate correlation errors, the receiver re-initialized by restarting between sessions. The RTK measurement accuracy was calculated using Equations (3) and (4), and it should be better than \(\sigma\).  
\[m_{hk}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\big{[}\left(N_{i}-N _{0}\right)^{2}+\left(E_{i}-E_{0}\right)^{2}\big{]}} \tag{3}\]  
\[m_{\text{{\tiny{}}}k}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left( U_{i}-U_{0}\right)^{2}} \tag{4}\]  
where \(m_{hk}\) and \(m_{\text{{\tiny{}}}k}\) represent the horizontal and vertical accuracy of RTK measurements, respectively. \(N_{0},~{}E_{0}\), and \(U_{0}\) represent the north, east, and height coordinates of the known point in the local topocentric coordinate system, respectively. \(N_{i},~{}E_{i}\), and \(U_{i}\) represent the north, east, and height coordinates of the \(i\)-th positioning result of the tested device in the local topocentric coordinate system, respectively. The unit of these eight parameters is mm. \(i\) represents the index of the RTK measurement result; \(n\) represents the number of RTK measurement results.  
Finally, when \(D=4.5\) km, the measurements showed that \(m_{hk}=7.45\) mm and \(m_{\text{{\tiny{}}}k}=13.28\) mm, which met the engineering accuracy requirements.