esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Allowable Pitch Angle (APA) Region  
In order to calculate the allowable pitch angle (APA) region (i.e., ultimate load reduction area) where IPC could be applied, we should find the pitch angle region where the individual pitch movement ultimate loads are smaller than the CPC ultimate load, as shown in Equations (5) and (6).  
\[F_{a}=\left(F_{R\_{ipa}}\right)_{n^{\prime}}\left(F_{R\_{ipa}}\right)_{n}\leq F _{Rc} \tag{5}\]  
\[M_{a}=\left(M_{R\_{ipa}}\right)_{n^{\prime}}\left(M_{R\_{ipa}}\right)_{n}\leq M _{Rc} \tag{6}\]  
Here, \(M_{a}\) and \(F_{a}\) refer to the allowable resultant moment and resultant force, respectively, and \(n\) refers to the pitch angle of \(0^{\circ}\), \(2^{\circ}\), \(\cdots\), \(30^{\circ}\).  
In this study, the APA region was analyzed for the resultant moment \(M_{a}\). This is because the influence of the resultant moment is dominant when aerodynamic imbalance loads occur, as described in Section 3.2. Figures 7-12 show the \(M_{a}\)-based APA region (blue circle) obtained through the process according to the wind speed described in Equation (6), and summarized in Table 5. In these figures, the APA region shows the shape that spreads like a cone as the pitch angle increases. This occurs because the pitch angle of each blade increases with the growth in the APA, thus leading to the aerodynamic stall effect, which results in the load reduction of the wind turbine. In addition, the pitch angle increases with the increase in wind speed, and the relative pitch angle between the blades can only be within in a relatively small region.  
Figure 6: Aerodynamic imbalance loads of \(F_{R\_{ipa}}\).  
Figure 8: APA and ANN data for \(M_{a}\) of 14 m/s.  
Figure 7: APA and ANN data for \(M_{a}\) of 11 m/s.  
Figure 9: APA and ANN data for \(M_{a}\) of 17 m/s.  
Figure 11: APA and ANN data for \(M_{a}\) of 22 m/s.  
Figure 12: APA and ANN data for \(M_{a}\) of 24 m/s.  
Figure 10: APA and ANN data for \(M_{a}\) of 20 m/s.
# A Survey of CNN-Based Network Intrusion Detection
## 5 CNN-Based IDS Approaches
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Architecture** & **Input Shape** & **Feature Extraction** & **Classifier** \\ \hline [PERSON] et al. [38] & 4 Conv, 3 MaxPool, 1 AvgPooling, 3 LSTM, 1 FC & 1D & CNN, LSTM & SoftMax \\ [PERSON] et al. [39] & 4 Conv, 2 MaxPool, 2 FC,1 LSTM & 2D (10 \(\times\) 10) & LSTM, CNN & SoftMax \\ [PERSON] et al. [40] & 1 Conv, 1 MaxPool, 1 LSTM, 1 FC & 1D (41) & CNN & SoftMax \\ [PERSON] et al. [41] & 2 Conv, 2 Pooling, 1 FC & 1D & CNN, LSTM & SoftMax \\ [PERSON] et al. [42] & 2 Conv, 1 MaxPool, 1 FC & 2D & CNN-WDLSTM & SoftMax \\ [PERSON] et al. [43] & 2 Conv, 2 Pool, 2 LSTM, 2 FC & 1D & CNN, LSTM & SoftMax \\ [PERSON] et al. [44] & 3 Conv, 3 MaxPool, 3 LSTM, 1 FC & 2D (11 \(\times\) 11) & CNN, LSTM & SoftMax \\ [PERSON] et al. [45] & 2 Conv, 2 MaxPool, 1 LSTM, 9 FC & 1D (1000) & CNN, LSTM & DNN \\ [PERSON] et al. [46] & 2 Conv, 2 MaxPool, 2 FC,2 LSTM & 2D & CNN & SoftMax \\ [PERSON] et al. [47] & 2 Conv, 2 MaxPool, 2 LSTM, 1 FC & 2D (11 \(\times\) 11) & CNN, LSTM & SoftMax \\ [PERSON] et al. [48] & 1 CNN, 1 MaxPool, 1 LSTM & 1D (81) & CNN, NSGA-II & SoftMax \\ [PERSON] et al. [49] & 2 Conv, 2 Pooling, 2 BiLSTM, 1 FC & 2D (11 \(\times\) 11) & CNN, BiLSTM & SoftMax \\ [PERSON] et al. [50] & 2 Conv, 3 MaxPool, 1 FC & 1D (41) & CNN, GRU & Decision maker \\ [PERSON], M.A. [51] & 2 Conv, 2 MaxPool, 2 RNN, 1 Flatten, 1 FC & 2D & CNN, RN & SoftMax \\ [PERSON] et al. [52] & Defined by the MECNN algorithm & 1D (74) & MOEA/D, CNN & SoftMax \\ [PERSON] et al. [53] & 6 Conv, 6 Dropout, 1 Flatten, 1 FC & 2D (64 \(\times\) 19) & CNN, LSTM & SoftMax \\ [PERSON] et al. [54] & — & 1D & CNN, LSTM & SoftMax \\ [PERSON] et al. [55] & 2 Conv, 2 MaxPool, 1 Dropout, 2 FC & 2D (128 \(\times\) 128) & CNN, LSTM & SoftMax \\ [PERSON] et al. [56] & 2 Conv, 2 MaxPool, 1 Dropout, 1 LSTM, 1 FC & 2D (8 \(\times\) 6) & CNN, LSTM & SoftMax \\ [PERSON] et al. [57] & 1 Conv, 1 BatchNo, 1 MaxPool, 2 Dropout, 1 LSTM, 1 Flatten, 2 FC & 1D (128) & CNN, LSTM & SoftMax \\ [PERSON] et al. [58] & 1 Conv, 1 MaxPool, 1 BiLSTM, 1 Attention, 1 FC & 2D & CNN-BiLSTM + \\ [PERSON] et al. [59] & 2 Conv, 2 LSTM, 1 FCC & 2D & stacked autoencoder & CNN-LSTM \\ [PERSON] et al. [60] & 1 BatchNo, 1 AvgPool, 2 BiLSTM, 1 & 1D (128) & BiLSTM, CNN & SoftMax \\  & & & & \\ \hline \hline \end{tabular} 
\begin{tabular}{c} **1**D** (128) \\ **Accuracy
# Modeling and Resolving Uncertainty in DIKWP Model
## 2 Problem Description
### Regulatory Uncertainty
#### 2.3.1 Changes in Regulatory Interpretations  
The shifting interpretations of regulations pose a significant source of uncertainty for financial markets, particularly for trading strategies reliant on precise legal interpretations.  
* **Increased compliance costs (\(e_{16}\)):** Regulatory agencies' new interpretations of existing rules may escalate compliance costs for enterprises. Companies may need to allocate additional resources to comprehend new interpretations, adjust their business processes, update compliance strategies, or even redesign products or services. For instance, financial regulatory bodies may reinterpret rules regarding algorithmic trading, necessitating entities employing algorithms in trading to engage in more frequent self-assessment and reporting. For HFT firms, this could entail investment in advanced compliance monitoring systems, thereby escalating operational costs.
* **Adjustment of business models (\(e_{17}\)):** When regulatory interpretations change, businesses may need to modify their business models, especially if the new interpretations impact their core revenue streams. For example, if regulatory bodies decide to classify a widely adopted HFT strategy as market manipulation, trading firms relying on this strategy may have to completely revamp their trading models, potentially affecting their profitability and business continuity.
# Analysis and Assessment of a Brushless DC Outrunner Motor for Agriculture Drones Using JMAG
## 2 Propulsion System Design in Agriculture Multi-Rotor UAVs
### Propeller
#### 2.1.1 Cfd  
The essence of Computational Fluid Dynamics (CFD) in multi-rotors revolves around numerically resolving the Navier-Stokes equations for viscous and incompressible fluids in a three-dimensional space:  
\[\
abla\cdot\vec{U}=0 \tag{3}\]  
\[\rho\left(\vec{U}\cdot\
abla\right)\vec{U}=-\
abla P+\mu\
abla^{2}\vec{U}+\vec {F} \tag{4}\]  
where \(\mu\) is the fluid viscosity, \(p\) is the pressure, \(U\) is the velocity field, and \(F\) denotes the source terms encompassing the mass forces acting on the body.  
Propeller performance can be assessed through spatially averaged solutions using Moving Reference Frames (MRFs) and Reynolds-Averaged Navier-Stokes (RANS) equations [11]:  
\[\rho\frac{\partial}{\partial x_{j}}(u_{i}u_{j})=-\frac{\partial P}{\partial x_ {i}}+\frac{\partial}{\partial x_{j}}\left[\mu\left(\frac{\partial u_{i}}{ \partial x_{j}}-\frac{2}{3}\delta_{ij}\frac{\partial u_{i}}{\partial x_{i}} \right)\right]+\frac{\partial}{\partial x_{j}}(-\rho u_{i}^{\prime}u_{j}^{ \prime}) \tag{5}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \(\rho\) & 1.225 & kg/m\({}^{3}\) \\ \(T_{max}\) & 98.1 & N \\ \(N_{max}\) & 3200 & rpm \\ \(\sigma_{p}\) & 0.1054 & - \\ \(\tilde{\beta}_{p}\) & 2 & - \\ \(k_{t0}\) & 0.3462 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Propeller sizing variables [2].  
where \((-\rho u_{i}^{\prime}u_{j}^{\prime})\) denotes the Reynolds stresses, accounting for turbulent effects.  
MRFs allow for flow modeling around moving parts in steady-state conditions by including acceleration terms in the RANS momentum equations [12]. For an absolute velocity formulation, the additional term takes the following form:  
\[S_{acc}=\vec{\omega}\times\vec{u} \tag{6}\]  
where \(\omega\) and \(\vec{u}\) are the angular and absolute velocity, respectively.  
The low-to-medium Reynolds numbers characteristic of agriculture multi-rotors require turbulence models capable of appropriately solving viscous effects (adverse pressure gradient flows) near the propeller and free shear flows away from it [13; 14]. For this, the shear-stress transport (SST) \(k-\omega\) turbulence model is employed in addition to the RANS equations. This hybrid model utilizes the empirical \(k-\omega\) formulation near the boundary layer for its numerical stability while switching to the robust \(k-\epsilon\) model in the free-stream region. The superior accuracy and good convergence of this model in multi-rotor applications had made it widely used in the literature [15].
## 3 Modeling Analysis of Bowden Cable-Fabric Force Transfer
### Force Transfer Modeling and Friction Compensation of Fabric  
The fabric, as the end of the force-transmitting device, is connected with the Bowden cable and the human lower limb. When the knee joint of the human lower limb is bent, the contact part of the fabric will have friction and extrusion with the knee joint, which is called the contact section of the fabric bending stage. To this end, it is necessary to conduct a micro-element force analysis on the fabric contact section. In order to better analyze the fabric contact section, the following assumptions were made:  
1. The weight, compressibility, and Poisson's ratio of the fabric are ignored;
2. The movement direction of the fabric is along the tangential direction of the column;
3. It is assumed that the fabric sample is a homogeneous flexible material with flexural stiffness and linear elasticity.  
The element force analysis of the fabric contact section is shown in Figure 8. According to the classical friction law and the Capstan equation [21,22], the tension relationship between the two ends of the fabric is as follows:  
\[T_{2}=T_{1}e^{\mu_{2}\varphi} \tag{6}\]  
where \(T_{1}\) and \(T_{2}\) are the tension at both ends of the fabric, \(\varphi\) is the angle of the AB segment of the fabric, and \(\mu_{2}\) is the friction coefficient between the fabric and the leg binding.  
Through Formula (6) combined with the tension \(T_{2}=T_{1}\) when the fabric is at rest, the tension control model of the input and output ends can be obtained as follows:  
\[T_{out}=\left\{\begin{array}{ll}T_{in}e^{\mu_{2}\varphi(t)}&\dot{\varepsilon }>0\\ T_{in}&\dot{\varepsilon}=0\\ T_{in}e^{-\mu_{2}\varphi(t)}&\dot{\varepsilon}<0\end{array}\right. \tag{7}\]  
Figure 7: The static force analysis of Bowden cable: (**a**) force analysis of Bowden cable element; (**b**) Bowden cable of variable curvature.  
where \(T_{in}\) and \(T_{out}\) are the tension at the input and output ends of the fabric, \(\varphi(t)\) is the function of the fabric angle with time, and \(\hat{\epsilon}\) is the strain rate of the fabric.  
According to the difference between \(T_{in}\) and \(T_{out}\), the friction force when the output force is \(T_{out}\) can be obtained; then, the friction force under the expected pulling force of the fabric when the output force is \(F_{d}\) can be expressed as follows:  
\[f_{T}=\frac{T_{in}-T_{out}}{T_{out}}F_{d}=\left\{\begin{array}{cc}F_{d}\left[ e^{-\mu_{2}\varphi(t)}-1\right]\hat{\epsilon}>0\\ 0&\hat{\epsilon}=0\\ F_{d}\left[e^{\mu_{2}\varphi(t)}-1\right]&\hat{\epsilon}<0\end{array}\right. \tag{8}\]  
where \(f_{T}\) is the friction force of the fabric under the expected pulling force, and \(F_{d}\) is the expected pulling force.  
In order to compensate the nonlinear friction loss of the fabric in the process of passing around the column, the corresponding friction compensation is added to the control model as follows:  
\[T_{A}=F_{d}+f_{T} \tag{9}\]  
where \(T_{A}\) is the actual given tension of the fabric.
## 3 Results
### Prediction Results for LSTM  
With the LSTM models trained using the data from 1981 to 2019, we predicted the number of marine accidents for the marine accidents in 2020 for testing the models. The prediction results for each model are shown in Figure 6 and Table 7. We also calculated the MAPE for each model summarized in Table 7 and then compared the results to find the best model for predicting the frequency of marine accidents by navigator's watch duty time for 2020. We found that the LSTM3 performed best among the four models (MAPEs for: LSTM3 (0.059) > LSTM4 (0.065) > LSTM1 (0.066) > LSTM2 (0.076).  
Figure 5: Prediction results of ARIMA (2,1,3) model (MAPE = 0.089). The blue line represents training data, and the orange and green lines represent actual and forecasted values, respectively.  
Figure 6: Prediction results with test data (marine accidents by watch duty time in 2020) for each LSTM: (**a**) LSTM1 (MAPE = 0.087), (**b**) LSTM2 (MAPE = 0.107), (**c**) LSTM3 (MAPE = 0.076), and (**d**) LSTM4 (MAPE = 0.133). The numbers 0 to 5 in the time axis represent the navigator’s watch duty time 00–04, 04–08, 08–12, 12–16, 16–20, and 20–24, respectively. Blue and orange lines represent actual and predicted values, respectively.
## 2 Related Works
### Simulation Testing  
There has been extensive research on the use of simulation techniques for V2X technology, primarily focusing on combining network simulators with traffic simulators to investigate different communication stacks and assess the feasibility of communication performance in supporting the implementation of applications. [PERSON] et al. [9] developed an open-source, scalable joint simulation platform specifically for Long-Term Evolution-V2X (LTE-V2X) PC5, validating the effectiveness of geography-based resource selection algorithms in various traffic scenarios. Additionally, [PERSON] et al. [10] proposed a joint simulation framework that supports multiple communication stacks, embedding the Institute of Electrical and Electronics Engineers (IEEE) 802.11p, 3 rd Generation Partnership Project (3 GPP) C-V2X Mode 4, and LTE communication stacks, as well as the European Telecommunications Standards Institute (ETSI)-compliant vehicular messaging stacks. [PERSON] et al. [11] provide a detailed summary of such simulation-based works, categorising them into four types based on their simulation scale and joint simulation coupling degree. They also present a large-scale joint simulation framework for direct C-V2X, which simulates a large-scale scenario with up to 145,000 vehicles in downtown Los Angeles through the use of analytical model network simulators and the dynamic interval coupling of models. This work signifies the advancement of ITS research towards large-scale traffic flow applications.
## 2 Materials and Methods
### Sampling Protocol  
Water quality assessment occurred at each of the 7 watershed outlets (Figure 1). Physicochemical parameters, including specific conductance, temperature, dissolved oxygen, and oxidation reduction potential, were measured in the field using a _YSI-556_ MultiProbe meter. A _Hach_ turbidimeter was used to measure turbidity. Stream discharge was determined by multiplying the cross-sectional area of a transect by the mean velocity. Stream velocity was measured using a _Global Water_ FP101 flow meter or the floating object method. The floating object method was used in scenarios when stream velocity was too low or the water not deep enough to engage the flow meter propeller [30]. Water samples were collected from watershed outlets monthly beginning in April 2015 until March 2016 (\(n=84\), 12 samples per watershed). Sterile 100-mL bottles were immersed in streams until bottles reached 100 mL. Samples were stored in an iced cooler and transported to East Carolina University where they were analyzed for FIB concentrations within 6 hours of sampling. Additionally, 250-mL bottles were used to collect water samples for chloride analyses. These samples were also stored on an iced cooler until they were vacuum filtered and analyzed using a _Unity_ SmartChem 200 autoanalyzer.  
_E. coli_ and enterococci concentrations in streams were quantified using QuantiTray2000(r) trays via the IDEXYM method (IDEXX Laboratories, Inc., Westbrook, Maine). Some samples were diluted to allow for enumeration of FIB beyond maximum concentrations of 2419.6 MPN 100 mL\({}^{-1}\). Dilution factors generally ranged from 2 to 10. FIB concentrations were determined based on the MPN 100 mL\({}^{-1}\) associated with the number of fluorescent wells using a chart provided by IDEXX.
## 2 Materials and Methods
### Classification of the Reference Patch-Types at the US-LA3 Site  
Following [PERSON] and [PERSON] [10], we used a WorldView-3 multi-spectral image for day 18 August 2018 to develop a reference for the ground truth of the patch-type among the coarser HLS pixel locations. The WorldView-3 multi-spectral image was requested through the Commercial Smallsat Data Acquisition (CSDA) Program as Pansharpened (level 2A). The Image ID is 10400100408C8100. The image resolution was 1.24 m, and it fitted the quality-check requirements for most pixels included within the image, which overlap with the wetland area. The supervised classification of the high-resolution WorldView image during peak growing season was used as reference for the ground truth patch-type observation. We used reflectance data in several spectral bands at once to group pixels with similar values. Red, green, blue, and red edge and NIR bands were used as input data for cluster analysis, applying the K-means approach and using Euclidean distance as the metric. The image pixels were classified using a predetermined number of classes, which for US-LA3 were three: _Juncus_-dominated, _Spartina_-dominated, and open water (Figure 2a). This classified image was rasterized and upscaled to match the HLS resolution. Each HLS-matching pixel in this upscaled WorldView image was reclassified based of the dominant (>50%) type among all pixels of the original high-resolution WorldView image within that upscaled pixel. We identified 10 \"pure pixels\" of _Spartina_ and 5 of _Juncus_ (_Juncus_ is much less present than _Spartina_). \"Pure pixels\" are defined such that at least 80% of the original WorldView high-resolution pixels within a single upscaled HLS-matching pixel belonged to the same patch-type (Figure 2b). Satellite data was processed using GDAL 3.6.4 and the corresponding calculations for the site classification were performed using Python 3.11 code relying on open-source packages. In particular, tlslearn 0.6.3 package was applied for cluster analysis. Site classification was visualized using QGIS 3.22.
## 4 Case Studies: Impact on Pavement Asset Value Modeling
### Rehabilitation Programs
characteristics of common pavement materials, which are essential for determining bearing capacity and predicting structural life expectancy. In relation to future research aimed at improving deterioration models, it is necessary to consider the ongoing evolution of pavement materials and technologies. As these advancements continue, models must be updated to reflect new forms of degradation. The increasing use of recycled constituents--such as reclaimed asphalt pavement, crumb rubber, plastic waste, and used cooking oil--alters the viscoelastic properties and aging characteristics of pavement materials, thereby influencing their long-term performance [67; 68]. At the same time, the integration of autonomous repair technologies, including induction heating systems, microencapsulated rejuvenators, and vascular healing networks, is actively changing the progression of damage by enabling in-situ healing processes [69; 70]. These developments introduce new material behaviors and damage mechanisms that conventional phenomenological models are not equipped to capture. Consequently, there is a growing need for next-generation deterioration frameworks that incorporate micromechanical interactions and account for the time-dependent evolution of pavement properties under realistic service conditions.
## 4 Modified Interfine Void Ratio
As shown in Figure 11a, for all tested specimens with different carbonate sand contents, there is a clear linear relationship between the deviator stress and the mean effective stress at the critical state. The CSL in the \(p^{\prime}\)-\(q\) plane is a straight line, and all the critical state data are precisely fitted by only one straight line passing through the zero point, indicating the uniqueness of the CSL for carbonate sand-fine mixtures in the \(p^{\prime}\)-\(q\) plane. According to Equation (8), there is a linear relationship between the modified interfine void ratio \(e_{fc}\) and void ratio \(e\). Given the established linear relationship of the critical state data in the \(e\)-\(\ln p^{\prime}\) plane, it follows that the relationship of the critical state data in the \(e_{fc}\)-\(\ln p^{\prime}\) plane should also exhibit linearity. Figure 11b shows that although the data are somewhat discrete, for all tested specimens with different carbonate sand content, a clear linear relationship is observed between the modified interfine void ratio and the logarithm of the mean effective stress at the critical state. The CSL in the \(e_{fc}\)-\(\ln p^{\prime}\) plane is depicted using only one straight line, implying that the CSL of carbonate sand-fine mixtures in the \(e_{fc}\)-\(\ln p^{\prime}\) plane is also unique. The above fitting and analysis results show that for carbonate sand-fine mixtures with a carbonate sand content of no more than 40%, the critical state line in the variable space represented by (\(p^{\prime}\), \(q\), \(e_{fc}\)) is unique and not affected by the carbonate sand content. This further proves the rationality of the modified interfine void ratio as a state variable to describe the density of carbonate sand-fine mixtures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Modified**} & \multicolumn{2}{c}{**Critical State**} \\ \cline{3-5}
**Specimen** & **Interfine Void** & **Mean** & **Deviator** & **Modified** \\
**Identity** & **Ratio Affer** & **Effective** & **Stress (MPa)** & **Stress (MPa)** & **Interface Void** \\  & **Consolidation** & **Stress (MPa)** & **Ratio** \\ \hline C5\_V0 & 0.581 & 0.789 & 0.895 & 0.597 \\ C5\_V20 & 0.625 & 0.809 & 0.926 & 0.606 \\ C5\_V40 & 0.685 & 0.804 & 0.913 & 0.618 \\ C10\_V0 & 0.587 & 1.564 & 1.693 & 0.577 \\ C10\_V20 & 0.620 & 1.657 & 1.972 & 0.576 \\ C10\_V40 & 0.654 & 1.624 & 1.873 & 0.564 \\ C30\_V0 & 0.587 & 4.609 & 4.828 & 0.509 \\ C30\_V20 & 0.601 & 4.741 & 5.223 & 0.482 \\ C30\_V40 & 0.649 & 4.682 & 5.046 & 0.482 \\ C5\_I0 & 0.663 & 0.807 & 0.922 & 0.620 \\ C5\_I40 & 0.530 & 0.897 & 1.190 & 0.570 \\ C10\_I0 & 0.670 & 1.611 & 1.833 & 0.589 \\ C10\_I40 & 0.532 & 1.682 & 2.046 & 0.551 \\ C30\_I0 & 0.655 & 4.829 & 5.487 & 0.529 \\ C30\_I40 & 0.533 & 4.837 & 5.511 & 0.479 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Critical state data of triaxial tests in terms of the modified interfine void ratio.