esa-sceva/satcom-chunk-collection huggingface dataset:
# Reverse Engineering Used to Profile a Gerotor Pump Rotor
## 3 Results
### Study of the Enwrapping Profiles of Z\({}_{\mu}\)/Z\({}_{5}\) Gerotor Pump Rotors  
The determination of the sections where the fluid accumulates during its transport from suction to discharge can be defined from the analysis of the hatched active section in Figure 9, which is delimited by two hypocyclodes \(I\) and \(II\)[46].  
Figure 8: The map of deviations between the scanned model and the CAD model for the inner rotor (**a**) and the outer rotor (**b**).  
Figure 9: Active section in the gerotor pump delimited by two hypocyclodes [46].  
In addition to the established hypocycloidal profile models, circular arc models are also used for rotor profiles [10; 15; 47].  
Analyzing the dimensions obtained by measurement, the following values were identified (see also Figure 5): \(A_{12}=3.8\) mm; \(r=16\) mm; \(R_{0}=32.26\) mm.  
These values are established in the reference systems shown in Figure 10 and whose meaning is: \(x_{1}O_{1}y_{1}\) is the fixed reference system, having its origin in the rotation center of the \(Z_{5}\) rotor, \(O_{1}\) point; \(x_{2}O_{2}y_{2}\) is the fixed reference system, having its origin in the rotation center of the \(Z_{4}\) rotor, \(O_{2}\) point; \(X_{1}O_{1}Y_{1}\) is a mobile reference system, joined with the \(Z_{5}\) rotor. Initially, it has the axes superimposed on the \(x_{1}O_{1}y_{1}\) system. The \(X_{2}O_{2}Y_{2}\) system is the mobile reference system, joined with the \(Z_{4}\) rotor, and has the axes superimposed on the \(x_{2}O_{2}y_{2}\) system at the initial moment.  
Assuming that the two profiles of the rotors are reciprocally enwrapping curves, which would ensure perfect contact between them during operation and therefore maximum efficiency of the gerotor pump, it follows that the profiles can be associated with a couple of rolling centrodes.  
The two centrodes are circles, with centers at \(O_{1}\) and \(O_{2}\) points and radii \(R_{r1}\) and \(R_{r2}\).  
From the condition that the distance between the centers of the rolling circles is \(A_{12}=R_{r1}-R_{r2}\) and from the condition that the ratio of the rolling radii is equal to the ratio of the lobe numbers of the two rotors, the values of the rolling radii can be determined:  
\[\left.\begin{array}{l}R_{r1}-R_{r2}=A_{12}\\ \frac{R_{r1}}{R_{r2}}=\frac{5}{4}\end{array}\right\}\Rightarrow R_{r1}=5\cdot A _{12};\,R_{r2}=\frac{4}{5}\cdot R_{r1}. \tag{1}\]  
The values are obtained: \(R_{r1}=19\) mm, \(R_{r2}=15.2\) mm.
# Botanicals in Functional Foods and Food Supplements: Tradition, Efficacy and Regulatory Aspects
## 6 Safety Aspects  
This chapter deals with food, the safety of which should be able to be taken for granted. Actually, when botanicals are food supplement ingredients, some distinction has to be made. Compared to common or functional food, food supplements containing botanicals have no or little role from the nutritional point of view, as these do not contribute significantly to the intake of calories and nutrients. In fact, normally these products do not contain caloric nutrients (sugars, fats and proteins) and the presence of vitamins and minerals (not mandatory) covers, with a few exceptions, very limited percentages of the recommended daily dose. On the other hands, food supplements with botanicals are frequently promoted for their possible healthy properties, leading the consumer to prolonged use over time. Furthermore, as mentioned above, the growing popularity among consumers of \"natural products\" has led to an increasing number of producers and thousands of food supplements on the international market, without certainties relating their composition, activity and safety. While biological activity may be at least partly based on the \"tradition of use\", the risk/benefit assessment still remains orphaned of sufficiently reliable scientific data.  
Botanicals, especially if in their extract form, can have biological effects which, in relation to the dose and quality of the ingredient, can be functional, therapeutic and also toxic [32]. Different factors could be involved in the incidence of adverse effects such as the quality of the raw botanical material, the presence of environmental contaminants, the specific technological process applied (extraction and concentration), misidentifications of the plant ingredients, adulterations, counterfeits. The phytochemical fingerprint of a botanical can vary significantly, when the biological variability or the different processes applied are considered. Different factors (extract solvent, the temperature and time, the time from harvesting, etc.) can modulate the quality of botanical ingredients, with consequent changes in the relative abundance of active molecules present in the final product. Consumer's age and gender, unsuitable use of botanical food supplements, genetic factors and specific pathological or physiological conditions are further possible sources of unexpected adverse reactions.  
Confirming the particularity of the supplements with respect to usual food, there are cases in which limits on the content of molecules naturally present in botanicals are regulated by national and international directives. The established values must also be indicated on the label, such as in the case of bitter orange (_Citrus aurantium_ L.) for which it is necessary to declare the content of synephrine and other active amines with the daily dose (Figure 2). Limits are established for molecules that can lead to unwanted side effects at high levels and therefore for which safe daily doses for consumers need to be \"standardized\".
## 4 Power Management for the Energy Router  
### Design of the Fuzzy Logic Controller  
As the \"energy buffer\" of the HEMS, the self-energy storage system works to suppress the power fluctuation generated by the DGs when they get access to the HEMS through the energy router. Hence one of the key issues of the HEMS is to regulate the charging or discharging power of the battery in real time. On the one hand, the charging or discharging power is determined based on the gap between DG outputs and load demand; On the other hand, the SOC should also be considered to extend the service life of the battery unit. In order to maintain balance between these two aspects, the fuzzy logic controller [24] is designed to determine the charging and discharging power of the battery.
## 5 Interoperability, Standardization and Security
### Integration with Existing Irrigation Infrastructure  
Transitioning from traditional irrigation methods to automated systems presents practical challenges, particularly in integrating the legacy infrastructure with modern technologies. One approach is to retrofit existing irrigation components with IoT sensors and control devices. [PERSON] et al. [142] presented a smart irrigation system that enhances traditional time-based control methods by incorporating soil moisture and raindrop sensors connected to ESP32 and Arduino Uno microcontrollers. The system enables both manual and automatic irrigation modes through a user-friendly Thinger.io interface, demonstrating improved water conservation compared to conventional methods. This case study illustrates how legacy systems can be augmented with real-time data collection and wireless communication capabilities to optimize irrigation efficiency.  
Another strategy involves developing low-cost frameworks that facilitate the integration of traditional irrigation practices with IoT-enabled water management solutions. [PERSON] et al. [143] proposed a two-part system consisting of a watering system and a mobile app interface. In automatic mode, the water supply is connected to a pump that is activated based on soil moisture, humidity, and temperature data collected by sensors. The system has the potential to save 25% to 40% of water and energy resources compared to traditional irrigation methods. This example highlights how smart technologies can be designed to work alongside existing infrastructure, promoting compatibility and sustainable resource management.
# Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information
## 2 Approach Overview  
Figure 1 is the flowchart of the approach. Feature lines in the image pyramid are extracted by the EDLines [23]. For the same line in different octave images, a vector is used to represent them. Based on this, the line support regions are determined. Using the intensity histogram of the line support region, the sub-regions are obtained by adaptive partitioning. The pixels in each sub-region are grouped and sampled in a way that the corresponding index of the sampling points could be obtained through their local gradient order. Then, corresponding index position of the histogram is voted, and the histograms of different partitions and groups are cascaded together to get the local gradient order histogram. As for anchor points, they are calculated by utilizing the global image. Thereafter, the histogram of non-local structural information is obtained by encoding the structural information between the sampled points and the anchor points. Next, these two histograms are normalized separately and then cascaded to obtain the final line descriptor. The nearest neighbor distance ratio (NNDR) algorithm is implemented to get the candidate matching line pairs. Finally, the adjacency matrix [9; 24] is constructed by making use of the geometric properties and descriptor of the line segment. Then the greedy algorithm is used [25] and final match results will be available.  
Figure 1: The proposed approach flowchart.
# Full-Duplex MAC Protocol for CSMA/CA-Based Single-Hop Wireless Networks
## 4 Numerical Simulation and Analysis
Note that the value of \(p\) needs to be given in advance and it is difficult for each node to adjust this value adaptively. If the value of \(p\) is too small, then the channel cannot be fully utilized. If it is set at a too large value, then the number of collisions among the nodes will increase, resulting in reduced system throughput. As shown in Figure 10, when we set the value of \(p\) to 0.1, 0.2, and 0.3, respectively, ([5] sets this value to 0.2), \(Th_{FD(basic)}\) is reduced when compared with that of our protocol. Moreover, as \(n\) increased, the decrease of \(Th_{FD(basic)}\) will accelerate with the increase of the \(p\) value. When \(p\) is set to 0.2 or 0.3, as \(n\) increases to a certain extent (35 or 20 in Figure 10), \(Th_{FD(basic)}\) is even lower than \(Th_{HD(RTS)}\). We believe that the question of how to set an appropriate value of \(p\) is a key issue for this kind of work.
## 3 Real-time Capable Robust Maximum Power Point Tracking
### Numerical Validation of the Maximum Power Point Tracking Procedure  
After the capability of the Kalman filter with respect to the accurate online identification of locally quadratic approximations for the current-power characteristic of SOFCs was confirmed, the complete algorithm from Figure 7 was executed. Here, the _first phase_ was based on linearly increasing currents with the upper bound 17 A up to the point where both threshold relations \(\sigma_{\text{PEL},k}\leq\sigma_{\text{PEL},\text{min}}\), and \(\tau_{k}\leq\tau_{\text{min}}\) were satisfied.  
According to Figure 10a, this first phase takes approximately 100 discretization steps, if the same initialization as in the previous subsection is used for the Kalman filter. However, to validate the maximum power point tracking capabilities, measured data were now replaced with randomly disturbed values taken from the analytic representation of the current-power map in Figure 6b with a slowly increasing hydrogen mass flow. Both the hydrogen mass flow and the power data were corrupted by zero-mean Gaussian noise with the same standard deviations that appear at the available test rig before they are provided to the estimator and optimization procedure.  
Figure 10: Validation of the Kalman filter-based maximum power point tracking procedure; relative deviations in Figure 10d,e are only reasonable for the phase in which the maximum power point tracking (MPPT) is active. In inactive phases, they are purposefully set to zero.  
Figure 9: Quantification of the accuracy of the estimated electric fuel cell power. (**a**) Standard deviation of the estimated power. (**b**) Deviation of the estimated power in percent.  
As soon as the _second phase_ started (indicated also by arrows in Figures 10b,c, the proposed algorithm allowed for a simultaneous online identification of the power characteristic and for an almost perfect tracking of the optimal operating state. These excellent tracking capabilities are visualized in terms of both the absolute power in Figure 10c and the relative deviations in percent in Figure 10d,e. These figures depict the absolute values of the deviations in percent between the _true_ optima and the corresponding estimates for the electric current and power. Despite the use of noisy data during the maximum power point tracking, the estimation accuracy remained unchanged in comparison with Figure 9, where the pure filter-based identification task was considered.  
In this case study, robust overshoot prevention over the maximum power point despite noisy data was achieved by selecting the safety margin \(\eta=0.25\) in the optimality condition in Equation (34).
## 5 Conclusions  
In this article, a new XAITO-DRGC technique was projected for the detection and classification of DR. The presented XAITO-DRGC model utilizes OphthoAI IoMT headsets to enable remote monitoring of DR disease. Initially, the XAITO-DRGC model applies MF and contrast enhancement as a pre-processing step. In addition, the XAITO-DRGC model applies U-Net-based image segmentation and SqueezeNet-based feature extractor. Finally, AOA with BGRCU is exploited for DR detection and classification where the AOA assist in optimal hyperparameter tuning of the BGRCU model. The experimental validation of the XAITO-DRGC technique can be tested using a benchmark dataset and the outcomes were assessed under distinct prospects. Extensive comparison studies stated the enhancements of the XAITO-DRGC model over recent approaches with maximum accuracy of 98.24% whereas the existing CNN512-dropout model has attained reduced accuracy of 84.10% on the DDR dataset. In the future, the presented method is extended to the design of fusion-based DL models.  
Conceptualization, M.O.; Data curation, N.N.; Formal analysis, N.N.; Funding acquisition, M.A.D.; Investigation, M.K.N.; Methodology, M.O.; Project administration, M.K.N. and M.A.D.; Resources, H.M.; Software, H.M. and M.R.; Supervision, M.R. and A.S.Z.; Validation, A.S.Z. and A.M.; Visualization, A.M.; Writing--original draft, M.O.; Writing--review & editing, M.A.D. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Large Groups Project under grant number (71/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R203), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: 22 UQU4310373 DSR35.  
Not applicable.  
Data Availability StatementData sharing is not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript was written with the contributions of all authors. All authors have given approval for the final version of the manuscript.
## 2 Materials and Methods
### Data
#### 2.1.4 Lsa-Saf Metop/Avhfrn Endvi10 Version 2  
The European Organisation for the Exploitation of Meteorological Satellites (EUMET-SAT) Polar System (EPS) consists of a series of polar-orbiting meteorological satellites known as METOP. The AVHRR instruments onboard METOP are used to generate the 10-day Enhanced NDVI (ENDVI10) by the Land Surface Analysis Satellite Application Facility (LSA-SAF). ENDVI10 version 2 is processed in a similar way to the S10-TOC of PROBA-V, with the same water vapor and ozone inputs and similar atmospheric correction and compositing method [26]. The ENDVI10 compositing method is similar to the SYN V10 temporal compositing scheme, and the products are also provided on the same regular latitude/longitude grid as Sentinel-3 SYN VGT products. Although there are differences in spectral response, calibration, cloud detection and overpass time stability that will influence the intercomparison, the ENDVI10 is a relevant external reference since no spatial or temporal remapping is needed, which would introduce additional uncertainties. Global ENDVI10 version 2 products derived from METOP-A (launched 19 October 2006) for the period of January 2009-April 2013 and METOP-B (launched 17 September 2012) for the period of May 2013-July 2024 are used in the evaluation as an external reference.
## 4 Results
### Multilevel Analysis  
Multilevel analysis is a method to answer research questions when students are nested in schools or classrooms. All students are nested within classrooms, and all classrooms are nested within schools. Students represent level 1, classrooms represent level 2 and schools represent level 3.  
Different models are run in the multilevel analysis using the Mixed Models procedure of IBM SPSS. The \"empty model\" consists only of the dependent variable: Internet safety (Table 4). The proportions of variance in Internet safety accounted for by schools and classrooms are calculated. An interclass correlation is run. The analysis demonstrates that 16.93% of the variance is attributed to differences between schools, 14.87% of the variance can be attributed to differences between classrooms, and the residual accounts for 68.19% of the variance.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Model 0** & **Model 1** & **Model 2** \\  & **Coefficient (SE)** & **Coefficient (SE)** \\ \hline FIXED EFFECTS & & & \\ \hline Constant & 14.77 ** (0.34) & 15.14 ** (0.29) & 14.69 ** (0.24) \\ Gender (boys = 0, girls = 1) & 0.27 ** (0.10) & 0.26 * (0.10) \\ Languages other than Norwegian at home & & \(-\)1.68 ** (0.19) & \(-\)1.68 ** (0.20) \\ Norwegian and other languages at home & & \(-\)1.60 ** (0.11) & \(-\)1.61 ** (0.11) \\ Mainly Norwegian language at home & & - & - \\ Cultural capital & & 0.26 ** (0.04) & 0.24 ** (0.04) \\ Academic aspirations & & & 0.94 ** (0.23) \\ Reading proficiency & & & 0.06 * (0.01) \\ \hline COVARIANCE ESTIMATES & & & \\ \hline Residual & 9.25 ** (0.21) & 8.57 ** (0.19) & 8.57 ** (0.19) \\ Intercept (classroom level) & 2.02 * (0.27) & 1.55 ** (0.22) & 1.40 ** (0.20) \\ Intercept (school level) & 2.30 * (0.77) & 1.22 ** (0.44) & 0.14 ** (0.28) \\ Model fit: \(-\)2 LL & 21 754 & 21 387 & 21 337 \\ Decrease in \(-\)2 LL & & 48 ** & 50 ** \\ \hline \end{tabular}  
* \(p<0.05\); ** \(p<0.01\)  
\end{table}
Table 4: Results from Multilevel Analysis with Internet Safety as Dependent Variables and Cultural Capital, Language Integration at Home, Gender (Control Variable), Academic Aspirations and Reading Proficiency as Independent Variables.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Variables** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1. Internet safety & 1 & & & & & \\
2. Language at home & 0.35 ** & 1 & & & & \\
3. Cultural capital & 0.35 ** & 0.44 ** & 1 & & & \\
4. Academic aspirations & 0.27 ** & 0.09 ** & 0.29 ** & 1 & & \\
5. Gender & 0.09 ** & 0.06 ** & 0.08 ** & 0.09 ** & 1 & \\
6. Reading proficiency & 0.40 ** & 0.30 ** & 0.41 ** & 0.52 ** & 0.06 ** & 1 \\ \hline  & & & & & & \\ \end{tabular}  
* \(p<0.01\).  
\end{table}
Table 3: Bivariate Correlation between Internet Safety, Cultural Capital, Language at home, Gender (Control Variable), Academic Aspirations and Reading Proficiency (measured with regression analysis).
## 4 Results and Discussions
### Applications in Occupational Health and Safety in Manufacturing  
Immersive and digital technologies play a significant role in modern safety training and incident management. The metaverse and other technological advancements are enabling innovative training methods with immersive technologies, offering a compelling alternative to traditional training [40]. The convergence of DT and IM technologies is reshaping the management of OHS in manufacturing. By integrating real-time data, virtual environments, and intelligent analytics, these technologies enable continuous monitoring, dynamic risk assessment, and immersive safety intervention. These technologies support the transition from reactive to predictive and proactive safety strategies, addressing ergonomic risks, hazard recognition, training limitations, incident management, and decision-making inefficiencies. This section explores how their combined use enhances workplace safety and provides examples of their implementation in real manufacturing contexts. Furthermore, this thematic synthesis reflects the bibliometric results (Figures 6 and 7), linked to predictive monitoring and ergonomics, immersive safety training, and human-robot collaboration, providing a structured perspective on how these technologies are being applied across key manufacturing safety challenges.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 4 Results and Discussion  
### Single-View Consistency Evaluation  
An objective and uniform criterion for evaluating the results is still an unresolved problem. Color consistency has a strong correlation with the subjectivity of human vision. A good result needs high resolution, high definition, moderate contrast, and correct average brightness, while keeping as much of the original information as possible [9]. We adopted both subjective and quantitative evaluations to prove the validity of the proposed method.
## 5 Experimental Results
### Experimental Results for CBA Algorithm
#### 5.1.1 Experimental Baselines  
As discussed in Section 2, the approaches which considered the joint optimization of value and energy required prior information collected at design-time of jobs [17; 34]. Similar approaches considered indirect energy optimization by minimizing CPU idle time [32] which was not a solution to the problem addressed by this paper. We compared results of experiments using the CBA approach to a simple heuristic approach. As no prior information was available, the heuristic used was the value obtained by the job if completion was instantaneous, i.e., heuristic was a value optimizing approach [15; 28]. This approach, further referred to as the Heuristic-Based Approach (HBA), was implemented by replacing lines 22-24 with \(u\Leftarrow j\cdot getValueAtTime(curDelay)\).