esa-sceva/satcom-chunk-collection huggingface dataset:
# CFD and Experimental Comparison for Micro-Pump Performance in Space Applications: A Case Study
## 3 Results
### CFD and Experimental Data Comparison
Despite variations in inlet turbulence intensity (up to 10%), pump performance remains largely unaffected, indicating a low sensitivity to this parameter. Measurements of total pressure at the rotor and pipe outlets confirm that losses in the passive components (volute and outlet pipe) increase with mass flow. The observed linear relationship between mass flow and pressure losses suggests that these losses are primarily kinematic in nature, rather than dynamic. Figure 23: Speedline map 93% from the nominal speed for (**a**) total pressure; (**b**) torque. Figure 24: Speedline map at nominal speed for (**a**) total pressure; (**b**) torque. It is important to emphasize that these scaling factors are purely empirical and were never intended--or presented--as universal corrections. They are not derived from a physical model and do not constitute a validated adjustment method. Nonetheless, the fact that a single linear factor per axis can align the entire performance map suggests the existence of a systematic, possibly numerical, underlying cause. These adjustments are intended solely to illustrate the nature of the observed mismatch between simulation and experiment--not to imply physical accuracy or general applicability. Future work will focus on investigating the origin of this behavior and developing physically grounded methods to address the discrepancy at its source.
## 4 Results
### Evaluation Result for Complexity Analysis  
The complexity of both satellite design concepts is computed using Equation (1). Figure 16 shows the comparative value of each complexity variable of both 3U designs. The complexity analysis reveals that the overall complexity of the slot-based design is reduced by 40.43%. All the sub-complexity variables show an improvement in comparison with the conventional-type structural design. The reduction in the component complexity by 23.98% shows a reduction in the level of difficulty when handling individual components in the case of the slot-based design. The improvement in liaison complexity is rather noticeable, being approximately 61.03%. Due to the small number of structural screws and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structure** & **Structural** & **Rails and** & **Stacking** & & **Total No. of** \\
**Type** & **Screws** & **Frames** & **Rods** & & **Parts** \\ \hline Conventional & 76 & 14 & 4 & 48 & 66 \\ Slot & 72 & 8 & 0 & 30 & 38 \\ Improvement & 5\% & 43\% & 100\% & 38\% & 42\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Part count of 3U structure.  
Figure 15: Modal survey before and after random vibration on Y-axis.  
components, as well as the way they are brought together, without the need for long screws, it is possible to reduce the liaison complexity. From a global perspective, the intricateness of the overall integration is also improved, but by a small amount.
## 5 Methodology
### Encoding and Representation  
The selection of the encoding of the chromosomes is of great importance, as it affects the performance of the algorithm in terms of the speed and quality of solutions. The main encoding schemes are the direct and indirect encoding, also referred to as permutation and value encoding, respectively. In direct encoding, the chromosome is an array of integers with a size equal to the number of examinations, so that each cell corresponds to the respective examination, and the values of each cell of the array being the time slot in which the corresponding examination is scheduled (Figure 1). In indirect encoding, the chromosome is an array of lists of a size equal to the number of time slots. Thus, there exists one cell for every time slot, and each cell of the array contains the examinations that are assigned to the particular time slot (Figure 2). [PERSON] _et al._[57] argue about the importance of the right chromosome encoding used in the algorithm and propose the cataclysmic adaptive mutation as a solution to the problems that direct representation introduces in the search process. In this work, the direct encoding scheme was selected, because the arrays are more suitable for the GPU vector architecture than the lists needed in the indirect encoding. In addition, the crossover operator in indirect encoding sometimes results in infeasible solutions, because some examinations may disappear or appear twice, as Figure 2 shows (in bold red color, the examinations that appear twice are shown), while in direct encoding, this cannot happen (Figure 1). In indirect encoding, if after the crossover operator, an examination is listed twice or not at all, usually a repair method is applied to find a similar legal solution.  
Figure 1: Direct encoding: single-point crossover between two chromosomes after the second gene. E1–E5: examinations; T1–T3: timeslots.
# The Research of Complex Product Design Process Model under the Concept of Self-Recovery
## 3 The Principle of Functional Self-Recovery
### The Principle of Complex Product Function Category Classification  
Compared with parameters and structures, functions have unique classification criteria. Using functional value as a criterion, only one category of function units obtained fromfunction decomposition is the main function [21], which is the purpose of the product's existence. The second category is the additional function [21]. The additional function is the function that supports the main function of the product. Problems with different categories have different impacts on the operation of the product. Problems with the main function will directly cause the product to stop running, while problems with additional functions will affect the efficiency of the product. For example, with the development of electronic devices, additional functions of the electric water heater such as \"reducing energy consumption\" and \"timing shutdown\" have emerged. However, for users, the main function of the electric water heater is always \"improve water temperature\". When the \"improve water temperature\" function fails, the product will stop running directly. When the \"timing shutdown\" or \"reduce energy consumption\" function fails, the energy consumption of the electric water heater will increase, but the product will still run. Therefore, if the self-recovery concept is applied to the design process of the electric water heater, the first target is to ensure the realization of the function \"improve water temperature\".  
Self-recovery regulation for all functions in the product is the ideal result in the design stage but adding self-diagnosis and self-regulation functions to all functions in the product will significantly increase the design cost and complexity in the later manufacturing stage. Therefore, it is necessary to classify the functions in the product, prioritize the resources allocated for the main functions of the product, and arrange a reasonable self-recovery priority.
## 1 Introduction  
Nanocellulose is a biological material that shows tensile strength that is similar to that of steel or Kevlar (100 to 160 GPa) along with low density (0.8 to 1.5 g/cm\({}^{3}\)), high specific surface area, and good biodegradability [1]. Given these advantages, it is being explored for use in various devices and fields, including packaging materials, biomedical devices, adhesives, and electronic and electrical materials [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15].  
Recently, [PERSON] et al. [16] explored novel cellulose cement composites with improved mechanical performance based on cellulose nanocrystals (CNCs). CNCs are environmentally friendly because they can be produced from abundantly available natural sources and are biodegradable in nature. Scanning electron microscopy (SEM) analysis has revealed that CNCs dispersed in an unhydrated cement core act as water channels and enhance hydration in the core, thus improving the strength of the cement.  
[PERSON] [17] studied the [PERSON]'s modulus of nanocellulose with the density of crystalline cellulose of approximately 1.5-1.6 g/cm\({}^{3}\). Further, it is known that CNCs are stronger than steel (Young'smodulus of 200-220 GPa, density of approximately 8 g/cm\({}^{3}\).) While the process for producing CNCs is a complex one, the final product suggests improvements in the cement composites by improving mechanical properties. Calcium silicate hydrate (CSH) gel formation was improved in CNC cement mortar, with the compressive strength of the mortar being 42-45% higher than that of conventional cement mortar. The formation of the CSH gel improved the strength of the cement by improving its hydration. In order to quantify the performance of cement composites based on CNCs, their degree of hydration was measured using an isothermal calorimeter and a thermogravimetric analyzer [18; 19].  
Studies on the application of cement to conventional CNCs have been carried out only in some strength studies using optimal mixing conditions. Ultrasonic methods are primarily used to disperse CNCs [16; 18; 19; 20; 21]. However, ultrasonic dispersion techniques have certain limitations with respect to the production of cement paste and mortar, because they can only produce small amounts of CNC suspensions (10-250 mL by Sonics and Processor USA). When attempting to produce large amounts of CNC suspensions, as is the case when making concrete mixtures, the ultrasonic dispersion equipment may exhibit problems caused by prolonged use.  
Therefore, methods should be developed that allow one to prepare CNC-based concrete on a large scale while causing fewer equipment problems. In this study, we attempted to determine the optimal CNC dispersion conditions based on an evaluation of the previously reported dispersion data with the aim of using CNCs as a construction material.
## 4 Simulation Results
### Simulation Results of the Smart Inverter Control  
The architecture of a three-phase photovoltaic smart inverter system built with PLECS software split the tested mains into three types of voltage range: (1) \(V_{grid}\) = 1 p.u.; (2) 1.03 p.u. \(>V_{grid}\geq\) 1 p.u.; and (3) 1 p.u. \(>V_{grid}>\) 0.97 p.u. The smart inverter control was utilized for providing or absorbing reactive power, which further regulated the mains voltage.
# Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma
## 2 Methods  
The proposed approach to the recognition of images of fundi is based on the use of the knowledge of specialists in the field of ophthalmology and consists of creating an expert system for the diagnosis of glaucoma. The basis of the system is a reference sample of digital images, the description of which is stored in the knowledge base. An important component of the system is the analytical subsystem, which includes many rules by which decisions are made (Figure 1) [19; 20; 21; 22].  
There are factors that have a dominant influence on the correctness of image recognition. As a result of the research conducted here, several factors that had the most significant impacts on the accuracy of the measurement results were identified, and they were divided into three groups based on the type of source (instrumental, methodical, subjective user errors).  
For the considered system, instrumental factors can be classified into two groups; the first is that of factors that are due to physical processes in the equipment used, while the second is that of factors that are due to the influence of external conditions. The first group of instrumental factors includes the noise of the image sensor, color distortions of the camera, brightness distortions, diffraction effects of the optical system, uneven spectral characteristics of the illuminator in the ophthalmoscope, uneven illumination of the drug in the field of view of the camera, etc. The factors of the second group include factors determined by the external conditions of the system's application [23; 24; 25].  
So, for example, an image registered in the system can be affected by factors such as the presence of bright external lighting (sunlight), which can decrease the contrast of the image. In addition, the external factors include surface vibration (this can lead to image distortion when using large exposures) [26; 27].  
First, the methodological factors include the measurement model and mathematical methods of image processing that are implemented in the system software, as well as the discretization and quantization operations that are carried out when forming a digital image.  
A group of factors that depend on the user is related to the setting of the ophthalmoscope (the choice of the lens, the position of the condenser, field, and aperture diaphragm,  
Figure 1: The analytical subsystem, which includes many rules, according to which decision-making is carried out.  
the voltage of the lamp, and light flux correction filters, position of the lens focus). Along with these, this group includes factors related to the selection of the field for research and the positioning of the research object in the field of view of the camera, as well as factors that depend on the user in the interactive mode of image processing (when the processing parameters are specified by the user when implementing the processing in the application software).
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 7 Conclusions  
A new approach for the exploitation of ST PUs' systems in the context of spectrum sensing for CR was presented. The proposed SCAR-Phase sensing took advantage of the ST information to carry out the synchronization of an SU with the ST PU's sequence and also to enhance its detection performance. This was possible by splitting the spectrum sensing phase into one sample collection and two sample processing periods. In the first one, the synchronization with the ST sequence was achieved by means of a robust algorithm with a small probability of error in the low SNR region. Then, in the second one, an enhanced ST-based detector (i.e., ST-Det) was designed to perform the spectrum sensing with a small number of samples and a high probability of detection. This approach is different from the two-stage spectrum sensing scheme, which consists of a short first sample collection plus a large second sample collection with its processing period each. Furthermore, a simplified version of the SCAR-Phase sensing with a reduced computational complexity was introduced by exploiting the high detection performance of the proposed ST-Det. The results showed that, in the low SNR, the proposed approaches exhibit a significantly higher overall probability of detection than the energy detector. Moreover, it was shown that the required number of samples to achieve a target probability of detection is significantly lower for the proposed schemes. Furthermore, the maximum SU's achievable throughput was quantified and showed to be higher for the SCAR-Phase sensing and the simplified version than that for the energy detector. Further studies need to be carried out in order to assess the performance of the proposed methods in the presence of noise power uncertainty.  
Conceptualization: L.L.-L., M.C.-J. and E.S.-N.; Formal analysis: L.L.-L., [PERSON]-J. and A.G.O.-L.; Investigation: [PERSON]-L., [PERSON]-J. and A.G.O.-L.; Software: L.L.-L. and M.C.[PERSON]; Supervision: [PERSON]; Validation: [PERSON]-N., U.P.-R. and A.A.; Visualization: E.S.-N., U.P.-R. and A.A.; Writing--original draft: [PERSON], [PERSON] and [PERSON]; Writing--review & editing: [PERSON], [PERSON], A.A. and [PERSON].  
This research was funded by the Mexican National Council for Science and Technology (CONACYT) grant number 633737/331743.  
Figure 11: Achievable throughput for the secondary network.  
**Acknowledgments:** This work was partially supported by the Mexican National Council for Science and Technology (CONACYT).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 System Model and Framework
### System Architecture
R is the communication radius of the relay nodes and \(r\) is the communication radius of the local nodes where \(R\geq r>\)_0_. The data transmission or communication policy between two nodes in this network can be summarized as below:
## 1 Introduction
[8,23] proposed an analytical approach to predict the cutting-edge temperature during the trochoidal milling process and verified that the thermal shock in trochoidal milling is much smaller than that in side milling. [PERSON] et al. [24,25] analyzed the influence of machining parameters and trochoidal toolpath parameters on cutting forces and tool flank wear and found that the nutational rate and rotational rate have the largest interactions. [PERSON] et al. [26] established a mathematical-statistical model for the cutting-force prediction, which allows setting up the trochoidal parameters to optimize the cutting load and potentially prolong the tool life. [PERSON] et al. [27] presented an uncut chip thickness model for trochoidal milling based on the numerical intersection-finding algorithm. Subsequently, they investigated the interaction of trochoidal toolpath parameters with stability behavior [28]. [PERSON] and [PERSON] [29] presented the dynamic model of the circular milling process by considering the time-varying engagement angles. [PERSON] et al. [30] predicted the stability lobe diagram of the trochoidal milling process to select the trochoidal step and spindle speed without chatter. [PERSON] et al. [31] studied the effect of the cutting speed and trochoidal step modification on the cutting force and milling vibration. The advantages of trochoidal milling in rough machining can be further enhanced by optimizing the toolpath, such as improving the toolpath continuity [15,16], shortening the toolpath length [2,14,17], balancing the engagement angle and the cutting load [32,33,34,35]. The true trochoidal toolpath has \(C^{2}\) continuity and can avoid a sharp change in engagement angle and cutting force during the milling process, and this helps to improve the smoothness of machine tool movements [2,36]. Based on the true trochoidal toolpath, this paper presents an ellipse-based trochoidal toolpath model and investigates the effects of toolpath parameters on the engagement angle and the cutting force. In Section 2, an ellipse-based trochoidal toolpath model is established. In Section 3, the analytical calculation method of the cutter-workpiece engagement angle is proposed. In Section 4, a mechanistic cutting-force model for the trochoidal milling process is developed. In Section 5, according to the slots with given dimensions, the trochoidal toolpaths and engagement angles are first calculated. The influence of toolpath parameters on the engagement angle is analyzed. The cutting forces under different toolpath parameters are predicted and analyzed. Severalmilling experiments are conducted to verify the accuracy and reliability of the proposed method. Finally, some conclusions are presented.
## 1 Introduction
### Related Works
Furthermore, in [29], the system performance was examined in terms of the average capacity and OP, when the signal-of-interest and interferer were experiencing the \(\kappa-\mu\) shadowed fading. In case of cooperative systems, the system performance of a fixed-gain AF relaying system with beamforming was analyzed, in [30], in terms of the OP and error rate. The authors in [4] studied a variable-gain AF relaying system with beamforming, and they derived the exact analytical results for the OP and average capacity. In [31], an AF relaying system with beamforming in a satellite communication system was studied, and the analytical expressions for the SER were obtained. The performance of a DF satellite relaying system was evaluated in [32], and the OP, BER, and average capacity expressions were derived. In [33], a dual-hop DF energy harvesting relaying system was studied in the presence of hardware impairments in \(\kappa-\mu\) shadowed fading environments. A non-linear energy harvester was accounted at the relay node, which restricts the harvested power level, with a saturation threshold. Using the received radio-frequency (RF) signals from the source node, the relay harvests the energy and uses it to send the information to the destination.
## 2 Ground Motion Database
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Computed**} & \multirow{2}{*}{**Estimated**} & \multicolumn{2}{c}{**Site Class**} & \multirow{2}{*}{**Local**} & \multirow{2}{*}{**Estimated Depth**} & \multirow{2}{*}{**Available Ground**} \\  & & & & & **Eurocode 8 Draft** & & **Geology** & **to Engineering** \\  & & & & & **Revision [1]** & & **Bedrock (m)** & **Recordings** \\ \hline Adjud & 295 & 232 & F & Quaternary & >100 & Vrancea \\ Bacau & 370 & 318 & F & Quaternary & >100 & Vrancea \\ Baia & 550 & 501 & B & Cretaceous & 15 & Vrancea \\ Banloc & 290 & 281 & F & Quaternary & >100 & Banat \\ Bucharest & 285 & 255 & F & Quaternary & >100 & Vrancea \\ Calarasi & 365 & 273 & C & Quaternary & <100 & Vrancea \\ Campina & 397 & 400 & B & Miocene & <100 & Vrancea \\ Carcaliu & 410 & 461 & B & Pliocene & 25 & Vrancea \\ Cernavoda & 465 & 434 & B & Miocene & <100 & Vrancea \\ Chisinau & 320 & 500 & C & Miocene & <100 & Vrancea \\ Giurgiu & 342 & 247 & C & Miocene & <100 & Vrancea \\ Iasi & 320 & 350 & C & Quaternary & <100 & Vrancea \\ Petresti & 290 & 248 & F & Quaternary & >100 & Vrancea \\ Tulcea & 500 & 450 & B & Mesozoic & 20 & Vrancea \\ Valenii de & 496 & 456 & B & Miocene & <100 & Vrancea \\ Munte & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: The database of sites used in the analyses, the computed and estimated Vs30 using the topographic slope method [17], the local geological conditions, the estimated depth to engineering bedrock and the seismic source for which the ground motion recordings are available.
## 2 Preliminaries
### System Model
Thus, for channel state \(h_{k,t}\), we adopt the widely used Jakes model to depict the correlation of channel states between frames:  
\[h_{k,t}=\rho h_{k,t-1}+\sqrt{1-\rho^{2}}\Delta h_{k,t} \tag{4}\]  
where \(h_{k,t}\) is the channel state of frame \(t\) between user \(k\) and the edge server, \(h_{k,t-1}\) is the channel state of frame \(t-1\) between user \(k\) and the edge server, \(\rho\in[0,1]\) is the channel correlation coefficient, and \(\Delta h_{k,t}\sim\mathcal{CN}(0,1)\) is a complex Gaussian random variable. The channel state \(h_{k,t}\) is modeled as influenced by the previous state \(h_{k,t-1}\) through the correlation coefficient \(\rho\) and a random component \(\sqrt{1-\rho^{2}}\Delta h_{k,t}\), which represents the time correlation and randomness of the wireless channel. Hence, we have the received signal-to-noise (SNR) given by  
\[\gamma_{k,t}=\overline{\gamma}_{k}h_{k,t}^{2} \tag{5}\]  
where \(\overline{\gamma}_{k}\) is the average SNR of channel \(k\). We consider instantaneous perfect Channel State Information (CSI) is available.