esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modeling Process of the Pump Turbine Based on AE-PLS-ELM  
The proposed AE-PLS-ELM model is used to model the pump turbine characteristics of a PSU. The flow and moment characteristic curves of the pump turbine are preprocessed using an improve Suter transformation method. Two independent AE-PLS-ELM models are used to model the preprocessed complete curves. The preprocessed complete curves are then converted into target variables to construct a neural network model that can be used for real-time simulation. The input of the AE-PLS-ELM model is the relative flow angle \(x\) and the relative vane opening \(y\). The full characteristic curves based on improved Suter transformation (Figure 2) are employed as data samples.  
The specific steps of the modeling process of the pump turbine characteristics based on AE-PLS-ELM are as follows.  
Step 1: Apply the improved Suter transformation to the complete characteristic curves of the pump turbine provided by the power station and the corresponding preprocessed WH and WM curves are obtained.  
Step 2: Extract data points from the curves. Convert the relative flow angle \(x\), the relative vane opening \(y\), and the extracted data points into input and output sample pairs.  
Step 3: Divide the above sample data into training data and test data. Since the dimensions and magnitudes of the data samples are different, the input and output data are normalized to facilitate the modeling and calculation process.  
Step 4: Set the Sigmoid function as the activation function of the hidden layer and determine the range of the number of hidden layer nodes, according to the Kolmogorov empirical formula. The optimal number of hidden nodes is selected using a trial calculation modeling error.  
Step 5: Import the normalized training data to the AE-PLS-ELM model for training, and a well-trained AE-PLS-ELM model is obtained.  
Step 6: Import the normalized test data to the well-trained AE-PLS-ELM model and de-normalize the output data to obtain the test output.
## 4 Experiments
### Comparisons with Other Methods  
We use Caltech as a benchmark. Figure 6 shows the comparisons with other methods under the \(IoU=0.5\) setting, Figure 7 shows the comparisons with other methods under \(IoU=0.75\) setting, and more methods are compared in Table 3. AFFDet, our proposed method, achieves \(3.7\%\)\(MR^{-2}\) under the \(IoU=0.5\) setting, which is \(0.8\%\) better than the closest CSP's \(4.5\%\). Compared with the stricter \(IoU=0.75\) setting, we are even more ahead, and have achieved \(21.0\%\)\(MR^{-2}\), which is \(4.8\%\) better than the closest CSP's \(25.8\%\). And in terms of speed, our AFFDet achieved \(32.3 FPS\), which is faster than the officially implemented CSP's \(23.6 FPS\). For the case of heavy occlusion, we also achieved \(43.2\%\), which has a better effect of more than \(4\%\)\(MR^{-2}\) compared with \(47.9\%\) of RepLoss detector specially designed for occlusion problem. This shows that our adaptive multi-level and multi-scale feature fusion method is very effective in pedestrian detection, and our performance is ahead of other methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**level=**} & \multicolumn{2}{c}{\(MR^{-2}\)**(\%)**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**Params (M)**} \\ \cline{3-3} \cline{5-6}  & & **IoU = 0.5** & & & \\ \hline (a) & & 4.12 & 21.67 & 35.9 & 36.6 \\ \hline (b) & 3 & 4.35 & 22.56 & 35.1 & 36.6 \\ \hline (c) & & **3.65** & **21.01** & 32.3 & 40.2 \\ \hline (a) & & 4.07 & 22.46 & 30.2 & 43.7 \\ \hline (b) & 6 & 4.34 & 23.20 & 28.6 & 50.8 \\ \hline (c) & & 3.71 & 22.70 & 28.0 & 50.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of different external structures of MFFUs on Caltech. In which, The number under level= represents the number of MFFUs in MFFM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **IoU = 0.5 (\(MR^{-2}\)(\%))** & **IoU = 0.75 (\(MR^{-2}\)(\%))** \\ \hline Faster R-CNN & 8.7 & - \\ FR-CNN+ATT [32] & 8.1 & 64.1 \\ RPN+BF & 7.3 & 57.8 \\ ALFNet & 6.1 & - \\ HyperLearner [33] & 5.5 & - \\ AR-Ped [34] & 5.1 & 58.9 \\ RepLoss & 5.0 & - \\ CSP & 4.5 & 25.8 \\ AFFDet(Ours) & **3.7** & **21.0** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons with other methods on Caltech.
## 4 Improved Bat Algorithm for Resolving DRT
### Heuristic Algorithm for Generating Initial Population  
Many complicated factors influence the DRT model, which creates difficulty in randomly generating a feasible solution to this problem. Therefore, a heuristic algorithm is designed to generate the initial population with the specific following steps:  
1. Read input data for the DRT model, including: \(I\), \(M\), \(Q\), \(D_{\max}\) and \(T_{\min}\).
2. Randomly choose a dispatch center \(m\) and let \(i=m\) and \(N^{\prime}=I\). For each vehicle \(k\) located at the dispatch center \(m\), turn to Step 3 to build the route.
3. According to the constraints, such as \(\mathfrak{q}_{i}^{k}\leq Q_{\forall i,j\in I\cup M_{s}}x_{ij}^{k}d_{ij}\leq D _{\max}\), and \(\sum\limits_{\forall i,j_{i}\in I\cup M_{s}}x_{ij}^{k}t_{ij}\geq T_{\max}\), find the feasible set \(N^{\prime\prime}\) of next vehicular nodes in \(N^{\prime}\) after the vehicle visiting the current vehicular node \(i\), before randomly selecting the vehicular node \(j\in N^{\prime\prime}\) as the next visiting point (i.e., \(x_{ij}^{k}=1\)). If \(N^{\prime\prime}=\varnothing\), let \(j\in M\) and turn to Step 2. Otherwise, return to Step 3.
4. Let \(N^{\prime}=N^{\prime}-\{j\}\). If \(N^{\prime}=\varnothing\), output the result. Otherwise, turn to Step 3.
## 3 Results
### Crop Coefficients (Kc) Derived from NDVI, NDWI, and SAVI  
The calculation of Kc based on the NDVI provided valuable insights into the seasonal and spatial variability of water requirements for the avocado orchard. The relationship between Kc and the NDVI was used to derive seasonal trends in crop water dynamics, directly linking changes in vegetation health and canopy development to the crop's water needs.  
Figure 9 illustrates the seasonal evolution of Kc, derived from the NDVI, for both the 2021 and 2022 growing seasons. The graphs depict the Kc values calculated from the NDVI, NDWI, and SAVI across both years, providing a comprehensive understanding of crop water dynamics. Each index reveals distinct patterns of temporal variability corresponding to changes in vegetative growth, water availability, and phenological stages.  
The Kc-NDVI relationship shows a clear seasonal trend, with Kc values increasing during the early stages of crop growth as the canopy develops and the vegetation becomes more vigorous. This increase is followed by a peak during the mid-growing season, aligning with the period of maximum canopy cover and vegetative health. Subsequently, Kc values decline towards the end of the growing season as the crop experiences senescence or harvest periods. This pattern is consistent across both 2021 and 2022, with some variations in the magnitude of Kc due to seasonal differences in rainfall, temperature, and other climatic factors.  
The Kc values derived from the NDVI reflect the dynamic relationship between vegetation growth and water availability. Higher Kc values during active growth indicate increased water demand, while lower values correspond to reduced crop water requirements during the later stages of the growing season. These findings underscore the importance of NDVI-based crop coefficients in assessing water stress and optimizing irrigation schedules in avocado orchards, particularly in regions with limited access to ground-based monitoring data.  
The spatial variability of Kc values across different areas of the orchard further highlights the heterogeneity of water requirements within the study area. This variability was influenced by the soil texture, vegetation density, and local microclimatic conditions, which affect how water is distributed and retained within the orchard. Integrating Kc values with satellite-derived soil moisture and precipitation data provides a more robust and comprehensive understanding of the orchard's water dynamics, facilitating better water management practices in regions with limited technological infrastructure.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 3 Perturbation Observer-Based Nonlinear Adaptive Controller Design
### NAC Design of PMSG
#### 3.2.4 Design of Nonlinear Adaptive Controller  
The estimated perturbations are used for compensating the real perturbation, and control laws of subsystems \(S_{2}\) and \(S_{3}\) can be obtained as follows:  
\[\left[\begin{array}{c}u_{2}\\ u_{3}\end{array}\right]=B2(0)^{-1}\left[\left[\begin{array}{c}-\hat{\mathbf{ \varepsilon}}_{22}\\ -\hat{\mathbf{\varepsilon}}_{32}\end{array}\right]+\left[\begin{array}{c}v_{2} \\ v_{3}\end{array}\right]\right] \tag{40}\]  
where \(v_{2,3}\) is defined as  
\[\left\{\begin{array}{rcl}v_{2}&=&k_{21}(z_{21\text{r}}-\hat{\mathbf{\varepsilon} }_{21})+\hat{\mathbf{\varepsilon}}_{21\text{r}}\\ v_{3}&=&k_{31}(z_{31\text{r}}-\hat{\mathbf{\varepsilon}}_{31})+\hat{\mathbf{ \varepsilon}}_{31\text{r}}\end{array}\right. \tag{41}\]  
The final control law represented by currents and inductances, are expressed as follows:  
\[\left\{\begin{array}{c}u_{2}=L_{\text{md0}}[k_{21}(i_{\text{mdr}}-i_{\text{ md}})+i_{\text{mdr}}-\hat{\Psi}_{2}]\\ u_{3}=L_{\text{mq0}}[k_{31}(i_{\text{mqr}}-i_{\text{mq}})+i_{\text{mqr}}-\hat{ \Psi}_{3}]\end{array}\right. \tag{42}\]  
Please note that only the nominal values of \(L_{\text{md0}}\), \(L_{\text{mq0}}\), and measurements of \(i_{\text{md}}\) and \(i_{\text{mq}}\) are required in the NAC design.  
To clearly illustrate its principle, Figure 4 shows the block diagram of the NAC.  
The following assumptions are made in [19; 21; 36; 37; 38; 39].  
**Assumption 1**.: _Input gain \(B(x)\) and its derivative are bounded by \(0<M_{1}\leq B(x)\leq M_{2}\), \(|\dot{B}(x)|\leq M_{3}\), where \(M_{i}\), \(i=1,2,3\) are finite constants [for convenience we assume that \(B(x)>0\)]. \(B(0)\) is chosen to satisfy: \(|B(x)/B(0)-1|\leq\theta<1\), where \(\theta\) is a positive constant. The control \(u\) is assumed to be bounded but big enough for the purpose of perturbation cancellation._  
**Assumption 2**.: _The perturbation \(\Psi_{i}(x,t)\) and its derivative \(\dot{\Psi}_{i}(x,t)\) are locally Lipschitz in their arguments and bounded over the domain of interest._
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Normal**} & \multicolumn{1}{c}{**Low MM**} & \multicolumn{1}{c}{**Poor PP**} & \multicolumn{1}{c}{**Sarcopeniaia**} & \multicolumn{1}{c}{_p_ **Value**} \\  & **(_n_ = 53_)** & **(_n_ = 69_)** & **(_n_ = 64)** & **(_n_ = 56)** & **\(p\)** **Value** \\ \hline WC (cm) & 102.3 \(\pm\) 12.1 b,d & 95.2 \(\pm\) 14.3 a ** & 98.2 \(\pm\) 13.5 & 95.4 \(\pm\) 12.5 a  & 0.016 \\ SBP (mmHg) & 130.3 \(\pm\) 15.9 & 131.1 \(\pm\) 12.7 & 130.8 \(\pm\) 14.2 & 130.5 \(\pm\) 14.9 & 0.991 \\ DBP (mmHg) & 70.2 \(\pm\) 9.1 & 71.3 \(\pm\) 8.7 & 71.1 \(\pm\) 9.3 & 70.8 \(\pm\) 8.2 & 0.914 \\ AMMI (kg/m\({}^{2}\)) & 6.4 \(\pm\) 0.7 b,d & 5.6 \(\pm\) 0.7 a,c  & 6.3 \(\pm\) 0.8 b,d & 5.4 \(\pm\) 0.8 a,c  & \(<\)0.001 \\ SPPB (score) & 10.7 \(\pm\) 0.7 c,d & 10.7 \(\pm\) 0.6 c,d & 8.3 \(\pm\) 0.9 a,b  & 8.1 \(\pm\) 1.0 a,b [FOOTNOTE:c]Footnote b: difference vs. poor PP, d difference vs. sarcopenia. \(p\) < 0.001 \\ Smoking status, \(n\) (%) & & & & \\ Non/past smoking & 53 (100) & 67 (97.1) & 61 (95.3) & 54 (96.4) & 0.184 \\ Current smoking & 0 & 2 (2.9) & 3 (4.7) & 2 (3.6) & \\ Alcohol consumption, \(n\) & & & & \\ (\%) & & & & \\ Non-drinker & 36 & 54 & 46 & 45 & 0.291 \\ \(\leq\)1 time/month & 6 & 5 & 2 & 5 & \\ \(\geq\)2 times/month & 11 & 10 & 16 & 6 & \\ Comorbidity, \(n\) (\%) & & & & \\
0 & 2 (3.8) & 12 (17.4) & 11 (17.2) & 9 (16.1) & 0.276 \\
1 & 23 (43.4) & 32 (46.4) & 26 (40.6) & 19 (33.9) & \\ \(\geq\)2 & 28 (42.8) & 25 (36.2) & 27 (42.2) & 28 (50.0) & \\ FES (score) & 94.5 \(\pm\) 6.2 c,d & 92.8 \(\pm\) 10.6 c,d & 85.9 \(\pm\) 14.1 a,b  & 89.3 \(\pm\) 11.7 a,b  & \(<\)0.001 \\ ABC (score) & 87.8 \(\pm\) 7.5 c,d & 85.3 \(\pm\) 13.9 c,d  & 76.7 \(\pm\) 17.1 a,b  & 75.5 \(\pm\) 16.8 a,b  & \(<\)0.001 \\ \hline  & BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; ADMI, appendicular skeletal muscle mass index; SPPB, short physical performance battery; FES, fall efficacy scale; ABC, activity-specific balance confidence scale; MM, low muscle mass; poor PP, poor physical performance. Statistically significant differences were observed in the following comparisons: d difference vs. normal, b difference vs. low MM, c difference vs. poor PP, d difference vs. scorep. \(p\) < 0.05.[ENDFOOTNOTE] & & \\ \end{tabular}
\end{table} 
Table 2: _Cont._  
\(p<0.001\)), with a sensitivity of 78.9% and a specificity of 76.3%, indicating improved predictive performance (Table 6).
## 3 Design of the New Sand Control Screen
### Design Philosophy
#### 3.1.2 Design Concept of Protection Pipe  
The protective pipe of the screen mainly guides the flow direction of the sand-containing fluid and reduces the erosion damage of the sand to the internal sand control filter layer, so as to protect the screen. The main erosion resistance theoretical research models are as follows:  
(1) Micro-cutting theory  
According to the micro-cutting theory [22; 23], the amount of erosion wear can be studied and analyzed based on the following expression.  
\[V=K\frac{mv^{2}}{p}f(\alpha) \tag{7}\]  
\[f(\alpha)=\left\{\begin{array}{l}\sin 2\alpha-3\sin^{2}\alpha\ \left(\alpha\leq 18.5 ^{\lx@math@degree}\right)\\ \frac{\cos^{2}\alpha}{3}\ \ \ \ \ \left(\alpha\geq 18.5^{\lx@math@degree} \right)\end{array}\right. \tag{8}\]  
In Equations (7) and (8), \(V\) is the amount of erosion wear, \(\alpha\) is the erosion angle, \(K\) is a constant of the erosion rate or the erosion sensitivity constant of materials, \(p\) is the material flow-stress. The micro-cutting model is suitable for the erosion wear rule of plastic materials under the erosion condition of \(\alpha\leq 18.5^{\lx@math@degree}\), and when the erosion angle reaches \(90^{\lx@math@degree}\), there will be a large error.  
(2) Deformation and wear theory  
According to the deformation wear theory [24; 25; 26], combined with the possible elastic deformation and plastic deformation, the deformation wear quantity \(W_{D}\) and cutting wear quantity \(W_{C}\) are studied and analyzed, and the erosion wear amount of the protective tube is obtained as the sum of these two.  
\[W_{D}=\frac{m(v\sin\alpha-K)^{2}}{2\epsilon} \tag{9}\]  
\[W_{c}=\left\{\begin{array}{l}W_{c1}=\frac{2 mC(v\sin\alpha-K)^{2}}{\left(v \sin\alpha\right)^{2}}\left(v\sin\alpha-\frac{C(v\sin\alpha-K)^{2}}{\left(v \sin\alpha\right)^{2}}Q\right)\ \left(\alpha\leq\alpha_{0}\right)\\ W_{c2}=\frac{m}{2Q}\left(v^{2}\cos^{2}\alpha-K_{1}(v\sin\alpha-K)^{\frac{2}{2 }}\right)\ \ \ \ \ \ \ \left(\alpha\geq\alpha_{0}\right)\end{array}\right. \tag{10}\]  
where, in Equations (9) and (10), \(v\) is the rate of erosion of sand; \(m\) is the quality of the sand, \(\alpha\) is the erosion angle, \(Q\) is the cutting wear coefficient; \(\epsilon\) is the deformation wear coefficient; \(\alpha_{0}\) is the erosion angle at different erosion angles when \(W_{c1}=W_{c2}\); and the others are constants.  
The main influencing factors of erosion wear include fluid factors, sand factors (such as size, shape, hardness and concentration), the material of the protective tube and environmental factors, which in turn affect erosion parameters such as erosion angle, erosion speed and erosion amount. Therefore, when we designing a new type of sand screen, the micro-cutting theory and deformation wear theory should be take into account comprehensively. Especially in highly curved wellholes, the deformation wear theory should also be considered, and the protection structure design of the new screen should be continuously optimized to reduce the erosion of the sand screens.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 2 Related Work
On the other hand, the use of OAuth 2.0 protocol to create access tokens that represent the devices delegates the authentication to a external service and saves load in the devices. We adopt both ideas for designing the solution we propose. However, all the approaches exposed above consider the device as the object of the operations. The other entity (a client or an external service) is the subject that sends a request to the device in order to perform an action (in the case of actuators) or to get information (in the case of sensors). If we change this paradigm by making the device the subject that checks or updates information in a publish/subscribe service, we can manage authorization and authentication externally. In the model we propose, the IoT device acts as resource owner (following the OAuth 2.0 terminology), and it uses a token to perform authentication and authorization in an externalized system. Since these processes are delegated and the keys are not shared, what we seek and get is a lighter and safer scenario. After checking the token validity, the request is sent to the publish/subscribe service. As outlined in [6], centralization is a common problem with AC models. Centralized AC approaches are responsible for authorization, attributes and access policies as a central entity. However, when a distributed scheme is followed, validating the correctness of the authorization tokens is the responsibility of the IoT device. As outlined in the next section, one of the critical requirements of the IoT environments is to remove from the device as many operations as possible. Thereby, delegating authorization to a centralized component is a good design decision. Furthermore, a centralizedsolution is always compatible with scalable deployments that adapt the resources availability to the system demand.
## 3 Results and Discussion  
### Effect of Hydraulic Retention Time on Biogas Production and COD Removal  
The effect of the HRT on the natural anaerobic digestion (AD) of municipal wastewater with low organic content without additives was monitored. During the AD process, microorganisms require time to adapt to a new environment before they can produce methane. In this study, the first five days had no production of biogas and COD degradation, as shown in Figure 2a, and 2b. The microorganisms responsible for the biodegradation process were probably still adapting to the environment. After five days, a gradual increase in biogas production and COD reduction was achieved, with an exponential increase in both biogas and COD reduction between days 9-18. A slow performance towards the end  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Containments** & **Stream 1** & **Stream 2** \\ \hline COD (mg COD/L) & 4320 \(\pm\) 12 & 9590 \(\pm\) 58 \\ TOC (mg TOC/L) & 18.2 \(\pm\) 2 & 200.6 \(\pm\) 2 \\ TDS (ppt) & 3.07 \(\pm\) 2 & 15.69 \(\pm\) 0.7 \\ Turbidity (NTU) & 1480 \(\pm\) 16 & 3590 \(\pm\) 14 \\ Color (Pt-Co) & 2725 \(\pm\) 24 & 221.00 \(\pm\) 60 \\ Phosphorus (mg PO\({}_{4}\)/L) & 3.2 \(\pm\) 0.2 & 52.1 \(\pm\) 0.2 \\ Nitrates (mg NO\({}_{3}\)/L) & 65 \(\pm\) 1.1 & 512 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of two local wastewater streams.  
of digestion was achieved between days 19-21 and thereafter, no AD performance was observed between days 22-30. These findings are in agreement with the study by [26], which reported that the best performance in anaerobic digestion of municipal sewage waste was achieved between days 10-20 under mesophilic AD conditions.
# CFD Analysis on the Heat Dissipation of a Dry-Lubricated Gear Stage
## 2 Methods and Materials
### Numerical Modeling and Calculation
#### 2.3.7 Simulation Procedure  
Figure 4 summarizes the simulation procedure pursued in this work with the modeling and calculation steps, as well as the boundary conditions. First, geometry models of the gearboxes are created that consider the requirements of the dynamic meshing approach, see Section 2.3.1. Second, the mesh models Mesh\({}_{\text{gear-C}}\) and Mesh\({}_{\text{gear-LL}}\) are developed, as described in Section 2.3.2. Third, the CFD models CFD\({}_{\text{gear-C}}\) and CFD\({}_{\text{gear-LL}}\) are derived based on the numerical modeling approach presented in Sections 2.3.2-2.3.6. A Newtonian fluid behavior is considered.  
[PERSON] et al. [22] showed that a consecutive calculation methodology enables the efficient calculation of several simulation cases that are building up on each other. Therefore, a resilient simulation state serves as a starting point of the consecutive simulation of a suitable varied operating condition. A consecutive simulation methodology is also applied in this work to increase the calculation efficiency. In the first simulation step, flow fields for the different gears and circumferential speeds are calculated based on a steady-state and isothermal CFD simulation. For steady-state simulations, the solver does not contain time-dependent terms. Numerical convergence is reached within approx. 5000 iterations. In the second simulation step, transient and thermal CFD simulations are performed based on the steady-state simulation results. The thermal boundary conditions presented in Section 2.2 are considered. In the equations presented in Section 2.3.6, the wall temperature \(T_{wall}\) refers to the temperatures \(T_{MI}\) or, rather, \(T_{M2}\) and the reference temperature \(T_{ref}\) refers to the air temperature \(T_{air}\).  
A time step that results in a change in rotation angle of the pinion of 0.1\({}^{\circ}\) is chosen ([PERSON] et al. [3; 31]). The local convergence criterion is set to 10\({}^{-5}\) and used for all equations. All calculations are carried out at the Leibnitz Rechenzentrum (LRZ) using 112 cores (Intel Xeon E5-2690 v3 (Intel Corporation, Santa Clara, CA, USA), clock rate 2.6 GHz, and working memory 64 GB). Within a simulated physical time of \(\mathrm{t}_{\mathrm{sim}}\) = 0.5 s, a quasi-stationary state is reached with respect to the convergence of the calculated HTC within the transient CFD simulations.