esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Clustering
### Confusion Matrix and Evaluation Metrics  
The confusion matrix shows the success of the model. Each cell represents an evaluation criterion. The criteria are the numerical equivalents of the actual values and the values to be predicted. Four different values are presented as true positive (TP), true negative, false positive (FP) and false negative (FN).  
If a positive result is obtained for an actually positive situation, it is called TP; if a negative result is obtained for an actually positive situation, it is called FN; if a negative result is obtained for an actually negative situation, it is called TN; and if a positive result is obtained for an actually negative situation, it is called FP [41].  
The performance metrics calculated with these values obtained from the confusion matrix are given in Equations (4)-(7) [41].  
Accuracy is the ratio of correctly predicted values to the total dataset.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{4}\]  
Precision is the ratio of true positives to total positives.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
Recall is the ratio of true positives to the sum of true positives and false negatives.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
F1 score is the harmonic meaning value of recall and precision values.  
\[\text{F1 score}=2*\frac{\text{Recall}*\text{Precision}}{\text{Recall}+ \text{Precision}} \tag{7}\]  
Figure 4: Clustering process representation.  
In the Materials and Methods Section, the method of the proposed AAIA algorithm is presented. For this purpose, the calculations related to visual angle and perceptual size are given in the adapted versions of the AAIA algorithm. It is explained how the concepts of visual angle and perceptual size are used in the proposed method. The pseudocodes of the AAIA algorithm and the general working principle of the method are presented.  
In the Experimental Results Section, after the data properties of the three datasets are presented, the confusion matrices are obtained according to the clustering results produced by the AAIA algorithm that are given. Then, the obtained clustering intervals are presented, and their evaluations are made. The clustering results are visualized with the help of the presented graphics. In addition, in this section, the results of other studies conducted in the literature with three datasets are given. Thus, the advantages of the AAIA algorithm compared to the literature are also presented.  
In the Comparison of AAIA with Methods in Literature Section, the AAIA algorithm is compared with other heuristic methods in the literature in terms of clustering. The studies compared in the literature are selected especially from the studies using the three datasets in this study.  
In the Discussion Section, the distributions of the datasets are analyzed and the effects of the distribution on clustering in the distance-based clustering method are discussed. The related graphics are presented.
## 2 Materials and Methods
### Crop Yield Statistics  
Annual crop yield, harvest area and production data for the Canadian Prairies from 2000 to 2013 were obtained from Statistics Canada's socioeconomic database CANSIM ([[http://www5.statcan.gc.ca/cansim/](http://www5.statcan.gc.ca/cansim/)]([http://www5.statcan.gc.ca/cansim/](http://www5.statcan.gc.ca/cansim/))). The crop data were collected through an annual farm sample survey program and are reported at the Census Agricultural Region (CAR) level for each crop type. CARs without complete yield records in each year were excluded from this study. The yield of barely, wheat, canola and oats in each CAR were then converted into net primary production (NPP, gC\(\cdot\) m\({}^{-2}\cdot\) yr\({}^{-1}\)) based on the method provided by [PERSON], _et al._[50] as follows,  
\[NPP=\left(\left(Yield\times f_{mass}\right)/HI\times\left(1+RS\right)\right) \times f_{dry}\times f_{carbon} \tag{1}\]  
where _Yield_ is the crop yield obtained from the statistical data, \(HI\) is the harvest index, \(RS\) is the root to shoot ratio used to estimate the total biomass of the crop, \(f_{mass}\) is a factor to convert the yield in reporting units to a standard unit, \(f_{dry}\) is a factor to convert the mass to dry biomass, and \(f_{carbon}\) is a factor to convert the dry biomass to carbon (450 g\(\cdot\) kg\({}^{-1}\)). The conversion factors in different crop types were referenced from the study of [PERSON], _et al._[50].  
The final NPP integrated with different crop in each CAR was calculated on a unit per area basis (\(NPP_{\text{CAR}}\), g\(\cdot\) C\(\cdot\) m\({}^{-2}\)) with the following equation:  
\[NPP_{\text{CAR}}=\sum_{i=0}^{n}NPP\left(i\right)\times Area\left(i\right) \Big{/}\sum_{i=0}^{n}Area\left(i\right) \tag{2}\]  
At the same time, the derived CFAPAR was averaged into FAPAR at the CAR level (\(\text{FAPAR}_{\text{CAR}}\)) based on the area of the cropland mask of the CAR. The correlation analysis between \(\mathrm{NPP_{CAR}}\) and FAPAR\({}_{\mathrm{CAR}}\) was then conducted to evaluate the representativeness of CFAPAR for cropland productivity.
# Impact of Aerodynamic Interactions on Aeroelastic Stability of Wing-Propeller Systems +
## 4 Results
### Preliminary Flutter Prediction  
To establish an initial velocity range for flutter analysis, SDBox was employed to predict critical speeds for the wing-propeller system, with the propeller positioned at 50% of the semispan wing. The mounting stiffness of the propeller is set to 40,000 N m rad\({}^{-1}\). The relatively low mounting stiffness is intentionally chosen to induce wing flutter within a realistic velocity range. In general, such low stiffness values can be considered a failure case of the pylon, where structural integrity is reduced. The propeller aerodynamics are modeled with precalculated unsteady propeller derivatives in windmilling conditions [11]. The resulting flutter curves are depicted in Figure 8. As shown by the red marker, flutter occurs at 133 m s\({}^{-1}\) triggered by mode 3. The corresponding flutter mode shape, presented in Figure 9, shows a strong coupling between the out-of-plane bending and the torsional motion of the wing. The propeller exhibits an elliptical backward whirl motion due to gyroscopic precession. This flutter mechanism is referred to as wing-dominant flutter [11] since the modal participation of the wing dynamics is dominant compared to the propeller motion.  
Figure 7: Coupled simulation results of DUST for the wing-propeller system at 5\({}^{\circ}\) angle of attack. Iso-surface of Q-Criterion for flow filed. Pressure coefficient \(C_{p}\) contours for the surfaces.  
It should be highlighted that whirl flutter is not present for the configuration investigated, even though the mounting stiffness is smaller than the identified required stiffness for the stability of the standalone propeller (see Figure 5). This behavior is attributed to the stabilizing influence of the elastic wing on whirl flutter [31]. In the case of a rigid wing, whirl flutter would indeed occur.  
The predicted flutter speed was used to define the velocity range of interest for the time-marching analyses and the propeller trimming process. The required collective pitch angle for the propeller blades as a function of the free stream velocity was calculated based on performance curves of the standalone propeller (hybrid approach) and the wing-propeller system (fully coupled approach). The trim results are shown in Figure 10. The solid curves represent the windmilling condition, and the dashed curves are the thrusting condition. As expected, with increasing free stream velocity, the pitch angle to maintain the trim condition increases as well. The collective pitch angles for the thrusting case are higher compared to the windmilling condition. It is interesting to note that the trim angles for the fully coupled simulation (blue lines) are constantly smaller compared to the hybrid approach (red lines). The offset on the collective pitch is due to the aerodynamic interaction between the propeller and wing.  
Figure 8: Flutter curves (v-\(\xi\): **left** and v-f: **right**) for propeller at 50% span with 40,000 N m rad\({}^{-1}\) mounting stiffness.  
Figure 9: Predicted flutter mode shape (mode 3) at 133 m s\({}^{-1}\).
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 4 Face Subsystem
### Tracking and Identity Assignment Scoring  
Once all the above information was obtained, we developed an algorithm to carry out the tracking and identity assignment process, which is depicted in Figure 2 and follows a similar philosophy to the one developed in [27]. In this algorithm, the tracking process was developed by shot, so a change of shot restarts the tracking. Therefore, while the shot is the same, the algorithm checks, frame by frame, the clustering information and the correspondence between the coordinates of the current frame and the previous frame to establish links to perform the tracking process. When a relationship exists between both frames and has a high confidence term, the identity assignment of the previous frame is used to select the enrollment model and obtain the score. This score is compared with a confidence threshold to determine whether the assigned identity is correct or not. However, when there is no relationship between the coordinates of the current and previous frame, or the confidence term is low, the frame embedding is compared to all enrollment models  
Figure 3: (**a**) Left: Example of Embedding Extraction and Training Enrollment Model ID1, where the dashed line indicates the two steps of the process. (**b**) Right: Example of Embedding Extraction and Training Enrollment Model ID2, where the dashed line indicates the two steps of the process.  
to obtain a score and determine whether it is a new identity to assign. Once the identity assignment is performed on the current frame, the score is stored, the coordinates are updated, and the algorithm checks whether the shot changes.  
Tracking is carried out with the above steps, but the identity assignment process performed is only an initial assignment. When a shot change is detected, the system checks the identities and scores stored in the shot to remove inconsistent segment assignments. After that, the final segments with their identity assignments are written to the rich transcription time marked (RTTM) file. In addition, score confidence values are stored when a final identity assignment is made. If these values are greater than a more restrictive threshold which is set with the development set, we augment the enrollment models with the current face embedding. The whole process is repeated with all detected shots.
###### Abstract  
International policies mainly that are focused on energy-dependence reduction and climate change objectives have been widely proposed by most developed countries over the last years. These actions aim to promote the integration of renewables and the reduction of emissions in all sectors. Among the different sectors, agriculture emerges as a remarkable opportunity to integrate these proposals. Indeed, this sector accounts for 10% of the total greenhouse gas (GHG) emissions in the EU, representing 1.5% of gross domestic product (GDP) in 2016. Within the agriculture sector, current solutions for groundwater pumping purposes are mainly based on diesel technologies, leading to a remarkable fossil fuel dependence and emissions that must be reduced to fulfill both energy and environmental requirements. Relevant actions must be proposed that are focused on sustainable strategies and initiatives. Under this scenario, the integration of photovoltaic (PV) power plants into groundwater pumping installations has recently been considered as a suitable solution. However, this approach requires a more extended analysis, including different risks and impacts related to sustainability from the economic and energy points of view, and by considering other relevant aspects such as environmental consequences. In addition, PV solar power systems connected to the grid for groundwater pumping purposes provide a relevant opportunity to optimize the power supplied by these installations in terms of self-consumption and net-metering advantages. Actually, the excess PV power might be injected to the grid, with potential profits and benefits for the agriculture sector. Under this scenario, the present paper gives a multidimensional analysis of PV solar power systems connected to the grid for groundwater pumping solutions, including net-metering conditions and benefit estimations that are focused on a Spanish case study. Extensive results based on a real aquifer (Aquirer 23) located in Castilla La Mancha (Spain) are included and discussed in detail.  
economical-energy-environment (3E) analysis; solar pumping; renewable energy source (RES) integration; net-metering; sustainable rural development
## 2 Theoretical Background
### Material Intrinsic Nonlinearity  
In this article, material intrinsic nonlinearity specifically refers to the global material nonlinearity (i.e., classical nonlinearity), which differs from the instrumental nonlinearity and the local Hertzian nonlinearity. The MIN theory is based on a high-order expansion of the classical [PERSON]'s law, and the MIN is determined by the material atomic properties (e.g., Lame constants) and is able to introduce the nonlinear features without interfaces.  
[PERSON]'s law is based on the second-order expansion of free energy in the power series as [18]:  
\[G=G_{0}+\frac{1}{2}\lambda u_{\rm ii}^{2}+\mu u_{\rm ik}^{2}, \tag{13}\]  
where \(G_{0}\) is the initial value, \(\lambda\) and \(\mu\) are the Lame constants and \(u_{ik}\) is the deformation tensor, which can be described as:  
\[u_{ik}=\frac{1}{2}\bigg{(}\frac{\partial u_{i}}{\partial x_{k}}+\frac{\partial u _{k}}{\partial x_{i}}\bigg{)}. \tag{14}\]Considering a third-order series of \(u_{ik}\), we have:  
\[u_{ik}=\frac{1}{2}\bigg{(}\frac{\partial u_{i}}{\partial x_{k}}+\frac{\partial u _{k}}{\partial x_{i}}+\frac{\partial u_{l}}{\partial x_{i}}\frac{\partial u_{l} }{\partial x_{k}}\bigg{)}\,, \tag{15}\]  
which can cause a nonlinear quadratic waveform (i.e., SOH) in the solution of the motion equation. The SB (at the frequencies of \(\omega_{1}\pm\omega_{2}\)) can be generated when LF and its SOH interact due to the intrinsic material nonlinearity.  
Even though MIN has been reported as a weaker contribution to structural nonlinear responses, compared to CAN [18; 29] in a structure with imperfect interfaces, it is worth noting that MIN still can become a dominant nonlinear source when the CAN is relatively weak (e.g., strongly bonded or tightening interfaces).
# A Review of Long-Term Skid Resistance of Asphalt Pavement
## 4 Decay and Prediction of Long-Term Skid Resistance of
### Summary  
Factors influencing the attenuation of skid resistance include material properties, environmental factors, traffic loads, micro- and macro-textures, etc. Among them, the hardness, shape, and distribution of aggregates have a significant impact on the skid resistance of asphalt pavements. Adding polymers or modifiers to asphalt mixtures can improve their skid resistance. The macro-texture of the pavement surface (such as roughness) contributes greatly to friction. However, as the service time increases, the macro-texture gradually wears out, leading to a decline in skid resistance. The micro-texture mainly affects the friction mechanism through the micro-protrusions.  
At present, a variety of prediction models have been proposed for the attenuation of the skid resistance of asphalt pavements. For example, through accelerated loading tests, the effects of different aggregate types and sand densities on the skid resistance of asphalt mixtures are studied, and corresponding prediction models are established. Statistical methods (such as gray relational analysis) are used to study the relationship between aggregate characteristics and skid resistance, and empirical formulas are proposed. By combining material mechanical properties (such as hardness, density, etc.) and environmental factors (such as temperature and humidity), comprehensive prediction models are established. However, the accuracy of the models is constrained by the quality of data. If there are missing values, errors, or unrepresentative samples in the data, the predicted results will deviate from the actual situation. On the other hand, most models do not fully consider the coupled effects of multiple factors. For example, the complex interactions between traffic and climate, as well as between materials and the environment, are difficult to fully elucidate in the models, resulting in deviations between the predicted long-term skid resistance and the actual situation. In addition, the models lack the ability to update in real time. Facing the dynamic changes in actual road conditions, it is difficult to adjust the parameters in a timely manner to adapt to new situations, which limits the practical effectiveness of the models.
## 4 Discussion and Conclusions
The sound pressure of BS recorded using noncontact microphones is lower than that of BS recorded using electronic stethoscopes applied to the surface of the human body, and there are fewer perceptible BS. As such, using sound data recorded without contact requires an automatic BS extraction method that is resistant to extraneous noise. Even so, the results suggest that the system proposed herein--which uses PNCC and has excellent noise resistance--is able to automatically extract BS with approximately 90% accuracy if the SNR threshold is 0 dB. Furthermore, even when the SNR threshold drops to 0 dB, results suggest that bowel motility can be evaluated using the acoustic features other than those from the BS length time domain, such as BS detected per minute, SNR, and SS interval. The proposed method could extract more sound by decreasing the SNR threshold used in the STE method, further extending segment length to increase the information provided to the system for BS/non-BS differentiation. We believe that as a result of this extension, we could improve the performance of automatic BS extraction. However, this also suggests that proper BS length cannot be obtained because of the extension in BS segment length caused by the decrease in the SNR threshold used in the STE method. Compared to the results of the performance evaluation based on random sampling, the results based on leave-one-out cross validation tended to have a larger standard deviation and decreased sensitivity in the proposed method, particularly before the consumption of carbonated water by participants. The cause of this was thought to be the small number of participants, meaning that sufficient BS segments were not available for use in leave-one-out cross validation. As such, we expect an improvement with increase in the number of subjects. To further improve system performance, a combination of the following two measures would likely be useful: (1) replacing the STE method with another method for detecting segments having sound; and (2) selecting acoustic features with excellent resistance to extraneous noise. In this study, we have provided new knowledge for noncontact automatic evaluation of bowel motility. It is hoped that the foundations of the system developed in this study can assist in the further development of the evaluation of bowel motility using noncontact microphones and research related to diagnostic support for bowel disorders. [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed materials/analysis tools; [PERSON] and [PERSON] wrote the paper. This study was partly supported by the Ono Charitable Trust for acoustics. The authors declare no conflict of interest.
## 3 Multi-Objective Optimization (MOO) Algorithm for RRBCS of R-EEV  
### Description of MOO Problem for RRBCS  
Compared with the general optimization problem, any decision variable of the MOO problem may influence any optimization target. Obviously, the essential MOO problem is to solve the mapping relationship of the function. The mapping relationship between the decision space and objective space belongs to a complex many-to-many relationship [21; 22]. As we know, the solution set of the multi-objective problem belongs to vector optimization in nature. Among the solution sets, such as the absolute optimal solution, strong efficient solution, fuzzy efficient solution, Pareto optimal solutions, and others, the Pareto optimal solution is the best solution of the multi-objective mapping relation. The Pareto optimal solutions are a set of optimal solutions (Pareto front), among which one cannot be claimed to be better than the other without more information [23; 24].  
There are many parameters that affect the efficiency of the RRBCS. It is difficult to optimize all parameters due to its complexity and diversity. The vehicle's structural parameters cannot be changed, but the control parameters of RRBCS can be optimized and changed in reality. From the previous section, the allocation rules of work-point are decided by the four shutdown threshold parameters, namely, \(\mu_{\varphi\_I}\), \(\mu_{\varphi\_h}\), \(Z_{a\_1}\), and \(Z_{b\_h}\). The tire-road adhesion factor \(W\) is a function of \(\mu_{w\_l}\) and \(\mu_{w\_h}\), which determines the value of \(Z_{a\_1}\) and \(Z_{b\_h}\).  
The problem described in this section is optimization with the objective of \(I_{bp\_cyc}\), \(I_{rble\_cyc}\), \(I_{bclr\_cyc}\), and \(I_{crbp\_cyc}\) under multidimensional constraints. Therefore, the overall expression of the objective is expressed as follows:  
\[J_{\min}[\mu_{\varphi\_I},\ \ \mu_{\varphi\_h},\mu_{w\_l},\mu_{w\_h}\ ]=\arg \!\int_{0}^{T}\omega_{b}I_{bp\_cyc}\!+\!\omega_{r}I_{r\_cyc}+\omega_{l}I_{ bclr\_cyc} \tag{9}\]  
In the expression, \(J_{min}\) represents the minimum total evaluation indicator of control system, and \(\mu_{\varphi\_I}\), \(\mu_{\varphi\_h}\), \(\mu_{w\_l}\), and \(\mu_{w\_h}\) represent a set of optimization variables that make the objective function obtain its minimum \(I_{bp\_cyc}\), \(I_{rb\_cyc}\), and \(I_{bclr\_cyc}\), respectively. \(I_{bp\_cyc}\) represents the evaluation indicator of braking performance, \(I_{rble\_cyc}\) represents the evaluation indicator of regenerative braking loss efficiency, and \(I_{bclr\_cyc}\) represents the battery capacity loss rate.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### System Model
#### 3.1.1 Geometric Model-IoT Devices  
We assume that the ground user IoT devices are randomly distributed over Earth's surface, represented by the homogeneous Binomial point process (BPP), with an average density \(\lambda=\
u\lambda_{\rm o}\), where \(\
u\) is the spatial duty cycle of the active IoT devices and \(\lambda_{\rm o}\) is the density of all users including active and inactive IoT devices. We also assume that all active IoT devices are sharing the same channel frequency. Using BPP for the spatial distribution aims at providing a theoretical lower bound of the performance and not to replicate case-specific implementation scenarios. However, the emulator can also incorporate practical distributions to increase the authenticity of the results.