esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ensemble precipitation forecasts (EPFs) derived from single numerical weather predictions (NWPs) often miss extreme events, and individual hydrological models (HMs) often fail to accurately capture all types of flows, including flood peaks. To address these shortcomings, this study introduced four \"EPF + HM\" schemes for ensemble flood forecasting (EFF) by combining two EPFs and two HMs. A generator-based post-processing (GPP) method was applied to correct biases and undersigversion within the raw EPF data. The effectiveness of these schemes in delivering high-quality flood forecasts was assessed using both deterministic and probabilistic metrics. The results indicate that, once post-processed by GPP, all proposed schemes show improvements in both deterministic and probabilistic performances, with skillful tool forecasts for 1-7 lead days. The deterioration in forecast performance with extended lead times is also lessened. Notably, the results indicate that uncertainty within hydrological models has a more pronounced impact on capturing flood peaks than uncertainty in precipitation inputs. This study recommends combining individual EPF with multiple hydrological models for reliable flood forecasting. In conclusion, effective flood forecasting necessitates employing post-processing techniques to correct EPFs and accounting for the uncertainty inherent in hydrological models, rather than relying solely on the uncertainty of the input data.  
ensemble flood forecasting; grand ensemble; post-processing; multiple hydrological models; evaluation +
Footnote †: journal: Article
# Investigation of Roller Press Surface and Stud Based on FEM Simulation
## 2 Mathematical Model of Roller Press
### Compression and Rebound Characteristics of Material Layer  
To predict the powder behavior between rolls, [PERSON] [29] introduced a continuous model. Which introduced the concept of the nip angle that defines two contiguous zones. Before the nip angle, the material granular is transported by relative sliding on the roll, and once the nip angle is achieved, the powder is assumed to be drawn by sticking to the rolls surface and to be densified by deforming under the high roll pressure. The compression and rebound characteristic of the material layer is the basis of studying the dynamics of the roller press. Many factors affect the compression and rebound characteristics of the material layer, such as the type, particle size and water content of the material. The compressibility and rebound characteristics of materials can be described by the relationship between the relative density of materials (\(\delta\)) and the pressure (\(p\)) acting on them. Figure 2 is the schematic diagram of the device for measuring the material curve. The material sample is filled in a cylindrical container. The inner diameter of the container is D. The sample volume is continuously compressed by applying pressure to the material through the piston. At the same time, the piston moving distance and the pressure exerted by the piston are recorded. The experiment is carried out on the premise that the sample quality remains unchanged. The starting point of curve \(p\)-\(\delta\) represents the loose state of the material. At this time, the relative density is \(\delta_{0}\), the initial height of the sample is h\({}_{0}\). It is assumed that the compression speed is very small, so that the effect of the speed on the pressure can be ignored. According to the experimental data of material compression, the formula describing the compression curve can be determined as follows:  
\[p_{p}=p_{c}\left[\ln\left(\frac{1-\delta_{0}}{1-\delta}\right)\right]^{1/n} \tag{1}\]  
where: \(p_{p}\)--The pressure acting on the material in the compression stage;  
\(p_{c}\)--Specific pressure of material;  
\(\delta_{0}\)--Initial relative density, \(\delta_{0}=\rho_{0}/\rho\);  
\(\rho_{0}\)--Initial density;  
\(\rho\)--Density of any material layer;  
\(n\)--Compression curve factor.  
After compression, when the pressure on the material disappears, the compressed material will rebound. According to the experimental data, the rebound process of the material can be described by the following Equation (2).  
\[p_{e}=p_{c}\bigg{[}\ln\bigg{(}\frac{1-\delta_{0}}{1-\delta_{a}}\bigg{)}\bigg{]}^{ 1/n}\cdot\bigg{(}\frac{\delta-\delta_{r}}{\delta_{s}-\delta_{r}}\bigg{)}^{k} \tag{2}\]  
\(p_{e}\)--The pressure acting on the material during the rebound stage;  
\(\delta_{s}\)--Relative density at the end of the compression stage;  
\(\delta_{r}\)--Relative density at the end of the rebound stage, \(\delta_{r}=k_{e}\cdot\delta_{s}\);  
\(k_{e}\)--Material rebound rate;  
\(k\)--rebound factor.
# Robust Speed Tracking of Induction Motors: An Arduino-Implemented Intelligent Control Approach +
## 4 Experimental Study
### Experimental Results  
In the sequel, the proposed modified Z-N PID+Fuzzy, the modified Z-N PID and the Z-N PID are abbreviated as Modified Z-N+Fuzzy, Modified Z-N and Z-N, respectively.  
In the experimental tests, we use a cycle of the reference speed command. The reference speed is increased from 0 rpm to 900 rpm at 4.25 s and then starts decreasing from 900 rpm at 8.25 s to \(-900\) rpm at 16.25 s and subsequently, starts increasing from \(-900\) rpm at 20.25 s to 0 rpm at 24.25 s. In addition, test cases with and without sudden load change are as follows:  
* Case A. No external load torque is applied to the shaft;
* Case B. An external load torque of 1.1 Nm is suddenly applied to the shaft at 5 s;
* Case C. An external load torque of 1.1 Nm is suddenly applied to the shaft at 18 s.  
For the PC-based and the Arduino-based experiments, we obtain the instantaneous velocity tracking performance of the V/f controlled IM using Modified Z-N+Fuzzy, Modified Z-N and Z-N. We also obtain the root-mean-square-errors (RMSE) of each method based on the average over 100 runs for the PC-based experiments. In addition, numerical comparisons of robust tracking performance with and without sudden load changes can be acquired based on maximum speed error (%) between 4.2 s and 8.25 s as well as that between 16.25 s and 20.25 s). The settling time \(t_{s}\) is also taken into account (i.e., when the reference speed 900 rpm/or \(-900\) rpm, after the motor speed reaches 900 rpm/or \(-900\) rpm, the last time that the motor speed lies between 855 rpm and 945 rpm/or \(-855\) rpm). No steady state error is considered since the command speed is time-varying.  
The experimental results consistently confirm that the proposed Modified Z-N+Fuzzy is the best among controllers tested. The Arduino-implemented Modified Z-N+Fuzzy for the V/f controlled IM speed tracking generally yields higher tracking errors (e.g., larger maximum speed error) and longer settling time than its PC-based counterpart. Even so, it is a favorable choice when the tradeoff between price, size and robust tracking performance is taken into account.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 4 Experimental Setup
### Receiver and Algorithm Implementation
#### 4.1.1 \(C/n_{0}\)-Based DSKF Configuration  
The time of response of the \(C/N_{0}\) estimation determines the agility of the measurement's covariance update. This parameter changes according to the accumulated correlation samples \(N\) (see Equation (39)). This study evaluates this method's tracking and system performance with \(N=100\) samples and \(N=500\) samples. Using an integration time \(\tau_{\text{int}}\) of \(20\,\text{ms}\), the response time of the \(C/N_{0}\)'s estimate is \(2\,\text{s}\) and \(10\,\text{s}\), respectively. The steady-state process variance \(q\) is set to a constant value. Different values of \(q\) are evaluated since this parameter directly impacts on the robustness to signal dynamics.
## 3 Results  
### Seasonal Variations in GPP, ET and Ecosystem-Level WUE  
The seasonal dynamics of GPP, ET, and WUE at the two stations, Arou and Daman, from 2013 and 2014 are shown in Figure 2. Distinct seasonal cycles are observed during the two-year measurements. Meanwhile, the changing trends in GPP, \(\text{R}_{\text{e}}\), and WUE at eight-day periods for both grassland and cropland are quite consistent in spite of the magnitudes. GPP generally increased quickly with plant growth, and reached the peak value at about WOY 25 (mid-to-late July). The maximum GPP values (averaged in 2013 and 2014) were 8.87 g m\({}^{-2}\) d\({}^{-1}\) and 17.57 g m\({}^{-2}\) d\({}^{-1}\) for the Arou grassland site and Daman cropland site, respectively. The seasonal variations in ET fluctuated relatively more than GPP variability, whereas the peak ET value exhibited one week later than the maximum GPP in the summertime with 4.45 mm d\({}^{-1}\) (Arou site) and 5.84 mm d\({}^{-1}\) (Daman site), respectively. EcosystemWUE also had a similar seasonal variation pattern as GPP. However, there was an obvious trough during the peak growing season (at the end of July and start of August). The values of GPP, ET and WUE at the two sites were almost zero beyond the vegetative season, especially in winter owing to the frozen soil and bare land. Ecosystem WUE at the Arou grassland site during 2013 and 2014 were 0.63 g kg\({}^{-1}\) and 0.73 g kg\({}^{-1}\), respectively, while it is slightly higher at the Daman cropland station with approximately 0.84 g kg\({}^{-1}\) and 0.90 g kg\({}^{-1}\) across the two years.
## 4 Results and Discussion
### Periodic Distortion  
Global uniformity is studied with full field illumination. The images shown in Figure 11 are obtained with G3S, G5S, and MCoG centroiding respectively. There is an obvious periodic fixed pattern in the image, which is related to the XS anode periodic structure.  
Figure 10: Image obtained with (**a**) G3S, (**b**) G5S, and (**c**) MCoG algorithm, respectively. The insets in the figure show that histograms through the vertical and horizontal lines of minimum element can be distinguished. The resulting image obtained with MCoG centroiding takes into five largest strips above a threshold Th fixed at 3% of total per-axis charge.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Centroidings** & **Formula** \\ \hline G3S & \(i_{0}=\frac{\ln\Omega_{i0}-\ln\Omega_{i-1}}{2(2\ln\Omega_{i}-\ln\Omega_{i-1}- \ln\Omega_{i-1})}+\mathrm{i}\,^{1}\) \\ G5S & Levenberg-Marquardt \\ MCoG & \(i_{0}=\frac{\frac{\ln\Omega_{i0}(i_{0}-T_{0})}{\ln\sum\limits_{i=1}^{N}\mathrm{ max}[\Omega_{i0}(i_{0}-T_{0})]}^{2}}{\sum\limits_{i=1}^{N}\mathrm{max}[\Omega_{i0}(i_{0}-T_{0})]}\) \\ \hline \hline \end{tabular} \({}^{\dagger}\)Where \(i\) is strip number, \(Q_{i}\) is charge value of \(i\)-th strip, \(Q_{total}\) is the total charge, and \(i_{0}\) is the centroid in \(X\)-axis or \(Y\)-axis. \({}^{2}\)_Th_ is a threshold, which, when used to reduce the influence of readout noise and minimize the fix pattern, can be a fixed value or a floating value.  
\end{table}
Table 3: Different centroiding algorithms.  
To characterize the fixed pattern of different centroiding algorithms, the photon distribution of a four-anode periods region in the image is counted. It is clear that G3S centroiding has severe periodic distortion. G5S and MCoG centroiding have less periodic distortion. The contrast of the fixed pattern can be obtained by:  
\[D=\frac{N_{\text{max}}-N_{\text{min}}}{N_{\text{max}}+N_{\text{min}}}\times 100\% \tag{8}\]  
where \(N_{\text{max}}\) and \(N_{\text{min}}\) are the number of photons in the peak and valley region of an anode period, respectively. Three centroiding algorithms are compared in terms of resolution, periodic distortion, complexity, and computation speed, and the results are shown in Table 4. It is clear that MCoG has the best performance.
# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 4 Experiments and Analysis
### Results and Analysis
Conversely, the DBN model shows slightly better performance in managing dynamically complex faults like speed-regulation malfunction, indicating its strength in learning from raw data. However, PCA-LSTM demonstrates robust performance across most metrics for signal-system error, showcasing its effectiveness in dealing with complex and irregular data patterns. This detailed comparison underscores the importance of selecting the appropriate model based on specific fault characteristics to optimize elevator fault diagnosis systems. Figure 13: Performance comparison of PCA-LSTM and IAO-XGBoost models across various elevator fault types. Although the PCA-LSTM model demonstrates superior performance in certain aspects compared to the DBN model, the evolution of deep learning architectures has led to the introduction of advanced models such as GANs and GNNs [18] in the field of fault diagnosis. The incorporation of these models holds great potential for advancing elevator fault diagnosis, as they offer new ways to handle complex and dynamic data patterns, thereby improving diagnostic accuracy and reliability. Figures 15 and 16 showcase the PCA-LSTM model's effectiveness in fault diagnosis, with the bar graph displaying F1 scores for various fault types and the heatmap illustrating the confusion matrix detailing correct predictions and misclassifications. To assess the fault diagnosis capability of the model more accurately, we extracted 25 samples from the remaining data for a separate fault diagnosis of each type of operating state, and we generated a confusion matrix. As shown in Figure 17, the diagnostic accuracies of the model for various faults in actual operation were quite high, and there were almost no cases of misclassifying normal states as faults. However, there is still room for improvement in the classification accuracy for door-opening, -closing, and braking faults. Although we did not misclassify the normal state as a fault, there are still cases of missed faults that need to be further improved. Figure 16: F1 score plot. Figure 17: Distributed model diagnosis effect diagram.
## 4 Discussion
These findings are consistent with the elevated concentrations of bioactive compounds in MN70, as indicated in the phytochemical profile (Table 1). MN70 demonstrated significantly higher levels of flavonoids such as rutin (5009.22 vs. 4625.30 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), quercetin (1416.42 vs. 1217.03 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), and naringin (596.21 vs. 475.70 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), all of which are known to exert antimicrobial effects via membrane disruption and inhibition of microbial enzymatic systems [77; 78]. Moreover, the content of hydroxycinnamic acids, including _p_-coumaric (810.55 vs. 696.42 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) and _trans_-cinnamic acid (524.36 vs. 399.40 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), may further contribute to the observed activity through oxidative stress induction and microbial metabolic interference [79; 80]. The increased total polyphenol content in MN70 (6.77% vs. 4.73%) also suggests a broader spectrum of synergistic phytochemicals enhancing antimicrobial potency. Overall, the more pronounced biological activity of MN70 is likely attributable to its richer and more diverse composition of phenolic and flavonoid compounds, reinforcing its potential for further development as a phytotherapeutic agent. Importantly, the extracts retained antimicrobial activity in many cases against strains of micro-organisms resistant to commonly used antibiotics, highlighting their potential in addressing antibiotic resistance. Limitations and Future Perspectives. While the current study offers encouraging evidence of pharmacological activity, several limitations warrant acknowledgement. The pharmacological investigations conducted were primarily of a preliminary, screening nature. Crucially, no histological analyses were performed and chronic or long-term exposure models were not included. Additionally, the biological effects of the extract have yet to be evaluated in human-derived cells, which restricts translational relevance. Considering the promising results, future research should aim to establish dose-dependent relationships and elucidate the underlying mechanisms of action across more advanced experimental systems.
# Long-Term Prosthetic Aftercare of mandibular Implant-Supported Overdenture
## 3 Results  
Forty-five consecutive patients (23 women and 22 men; mean age 65 \(\pm\) 8 years, range 47-80 years) were included. The demographic characteristics showed insignificant differences between groups (mean age--63 vs. 65 years, \(p\) = 0.573; gender (male/female)--10/12 vs. 12/11).  
All individuals were healthy or with mild controlled systemic disease. Individuals with severe systemic uncontrolled disease (e.g., smokers, diabetics, obese, on medications) were excluded.  
A total of 90 (44 group A vs. 46 group B) implants were inserted. Two implant types were used: MIS Seven \({}^{\mathrm{TM}}\) implants (Implant Technologies, Bar Lev Industry, Israel)--23 patients; Zimmer TSV implants (Zimmer Dental, Carlsbad, CA, USA)--22 patients. The implant lengths used were 10, 11.5, or 13 mm, depending on the height of the mandible. Implant diameter was standard platform--3.75 mm. All abutments used were MIS (Implant Technologies, Bar Lev Industry, and Israel) due to university financial policy.  
The mean follow-up of the entire study population was 84 \(\pm\) 21 months, range of 39-120 months; group A mean follow-up was 88 \(\pm\) 25, range of 39-120 months; group B mean follow-up was 80 \(\pm\) 19, range of 45-110 months.  
Statistical analysis revealed a greater need for prosthetic and oral hygiene interventions in group B (locator) vs. group A (ball). The mean number of aftercare visits was 11.69 \(\pm\) 1.04 vs. 5.12 \(\pm\) 0.4, \(p\) < 0.001. The mean number of visits due to pressure sores relief was 6.09 \(\pm\) 1.04 vs. 3.03 \(\pm\) 0.77, \(p\) < 0.001 (Figure 4), also for group B vs. group A.  
Liner (silicone insert) exchange due to loss of retention (5.6 \(\pm\) 1.03 vs. 2.09 \(\pm\) 1.04, \(p\) < 0.001), was significantly higher in group B (Figure 5).  
Debris (food/calculus) accumulation inside the attachment was noted only for the locator's group in 75% of the MISODs (\(p\) < 0.001). Debris was checked only at the intraoral attachment level, not in the denture part of the attachment for both systems. No statistically significant differences between the groups were noted for the denture repair aftercare treatments (8/23 vs. 7/22) nor for dentures in the opposing arch (\(p\) = 0.318). The mean Gingival index also exhibited statistically significant differences (2.3 \(\pm\) 0.3 vs. 1.03 \(\pm\) 0.2, \(p\) < 0.001) (Figure 6).  
The independent variables including age, gender, implant length, and type did not significantly affect the need for prosthetic aftercare.  
Figure 4: The mean number of visits dedicated to pressure sores relief.
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 4 Problem Solution  
Optimization involves both continuous and discrete variables, which is a mixed-integer programming problem. The constraints are nonlinear, so this problem can be classified as a mixed integer nonlinear programming problem. For the above type of complex optimization problems, this paper divides them into two sub-problems:  
Figure 2: Business satellite load aggregation diagram.  
* Sub-problem 1: Determining the set of sleeping satellites
* Sub-problem 2: aggregating businesses from sleeping satellites to working satellites  
This paper proposes an optimal integration strategy for optimizing business efficiency (OCSBE), which aims to aggregate business onto service satellites. The specific process is shown in Figure 3.  
* Step 1: Initialization. The control satellite initializes the process by collecting information on service distribution and satellite parameters.
* Step 2: Determine the number of working satellites. Based on the current service volume, determine the number of satellites that will remain active.
* Step 3: Determine the set of active satellites and the set of dormant satellites. Classify satellites into active and dormant sets based on service demand.
* Step 4: Model the \(\mathbb{B}_{uj}\) transfer problem using MDP. Use a Markov Decision Process (MDP) to model the transfer problem for \(\mathbb{B}_{uj}\).
* Step 5: Perform transfer analysis of \(\mathbb{B}_{uj}\) using double DQN. Conduct transfer analysis for \(\mathbb{B}_{uj}\) using a Double Deep Q-Network (Double DQN).
* Step 6: Analyze indicator values, record the number of working satellites and business aggregation plans. Analyze indicator values, and record the number of active satellites and the business aggregation scheme.
* Step 7: Check if all dormant satellite services are considered. Verify if all services for dormant satellites have been considered. If not, return to continue the process; if yes, end the process.
## 2 Materials and Methods
### Noise Removal  
The vectorization process results in consecutive points containing both voltage and time data, which are stored in a linked list data structure. One benefit of the vectorization process is that it reduces noise based on the slope fluctuation. After vectorization, noise is further removed in a process that eliminates all small fluctuations that can be considered noise based on their amplitude and duration. All spikes of amplitude below \(0.06 Vpp\) and duration below \(25\) ms are eliminated by deleting all the corresponding data points within each spike period. Figure 4 presents the combined result of vectorization and noise removal.
## 2 Materials and Methods  
The use of microstrips as transmission lines has been well studied for almost half a century, making them useful for new applications. The goal of this work was to design a single microstrip arrangement that is capable of operating at four frequencies. The initial idea was to arrange the four strips on top of a dielectric material; however, given that the strip lines would be in close proximity, high levels of coupling could be expected. Further, the tuning of the strip lines would become complicated, as multiple resonance modes would occur. Therefore, we chose to find a way to isolate each line as a function of the frequency. A gap was introduced between the source and the strip line; the gap, in combination with the dielectric constant and the loading capacitance, made it possible to run the current at the specified frequency. In order to reduce the coupling between the lines, we placed the strip lines with similar frequencies far from one another. From left to right, the operation frequencies were 300, 121, 280, and 78 MHz. The dielectric material was also split into four areas. The two dielectrics with the lower relative permittivity wereplaced under the strip lines that operated at higher frequencies, while the two with high relative permittivity were located under the strip lines that operated at low frequencies. By using this arrangement, we were able to reduce the coupling and obtain a uniform field distribution, or propagation constant along the strip lines.