esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results Discussion  
### Base Cases  
As mentioned in the introduction, solar DHW systems are traditionally controlled using the temperature difference between the fluid in the collectors and in the thermal storage. More evolved systems take into account a minimum radiation at which the system is switched on, and as reportedin Ref. [29], the control signal of the pump can be a function of the radiation itself. Therefore, four different control strategies were applied to this system in order to form a solid base of reference cases:  
* Control of the primary pump based on the temperature difference between the collectors and the thermal storage (DT) using a hysteresis with fixed values (2-7 \({}^{\circ}\)C);
* Control of the primary pump based on the global irradiance using a hysteresis with fixed values (100-150 W/m\({}^{2}\));
* A combination of controls A and B. The A controller is qualified by the output of the hysteresis of the B controller;
* Control C where the primary pump modulates the mass flow as a function of the radiation (linear modulation with the maximum at 600 W/m\({}^{2}\)).  
In all the above cases, a further control on the maximum temperature allowed in the storage has been implemented to avoid overheating and stagnation problems in the solar circuit during simulations.  
Table 2 compares the yearly performance of the four control strategies using the performance figures introduced above, while Figures 6 and 7 graph the monthly performance. The SPF\({}_{\text{coll}}\) increases from case B, to cases A, C, and D mainly for the use of a control strategy based on two parameters (radiation and temperature) that allows a reduction of the thermal losses and the electrical consumption of the pump. The best SPF\({}_{\text{coll}}\) performance was achieved in case \"D\" where the control of temperature, radiation, and the modulation of the pump (as a function of the radiation) were adopted. From the system point of view, the best performance in terms of SPF\({}_{\text{DHW}}\) was achieved when the losses (reversed energy flow) were minimized using the DT control and temperatures in the storage were maximized, reducing the usage of electrical backup (case  
Increasing the hysteresis controlling the temperature difference in the cases A, C, and D (from 2-7 \({}^{\circ}\)C to 2-14 \({}^{\circ}\)C) allows decreasing the number of on/off cycles with a limited impact on the performance. A slight increase of SPF\({}_{\text{coll}}\) can be observed in case C and D and a slight decrease in case A, all between 2.5% and 7.5%. The situation was opposite for SPF\({}_{\text{dhw}}\), where a slight increase in case A and a slight decrease in cases C and D was observed. The number of on/off cycles, however, remained elevated, between 5200 and 7200 per year.
## 4 Numerical Simulation of the Model
### Numerical Procedure  
The finite volume method implemented in Ansys Fluent(r) 13.0 [33] was used as the numerical discretization method. A segregated scheme using a second-order centered scheme for the diffusion terms was used when solving the equations of the mathematical models. A second-order upwind scheme was employed to discretize the convection terms of the RANS equations and the transport equations for the turbulence quantities k and epsilon. The SIMPLE pressure-velocity-coupling algorithm was employed to solve the continuity equation by using a momentum interpolation method to calculate velocities at cell faces.
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 2 Numerical Experiments
### Computational Domain and Meshing  
To avoid blocking effects [31] caused by setting the computational domain too small, set the right-hand reference coordinate system and arrange the computational domain as shown in Figure 3. The outer boundary of the flow field, namely the water channel, is a cylinder with a diameter of \(7D\) and a height of \(10D\). The center of propeller blades is at the origin and the flow direction is in the positive direction of the \(x\)-axis. The computational domain spans between \(2D\) upstream and \(8D\) downstream of the propeller. The channel blocking factor for this arrangement is about \(0.021\) (less than \(0.1\)), where the blocking factor of the channel is defined as \(\epsilon=A/A_{c}\) with \(A\) the disc area of propeller and \(A_{c}\) the area of water tunnel cross-section. The simulations are performed using unstructured grids with about \(8.5\) million hexahedral cells for LES-LDKSSG and RANS-kEpsilon (also with different refinements). Views of overall mesh distribution are shown in Figure 4. Grid refinement is performed in the wake zone of the propeller to capture the small-scale structures of interest to us. The grid scale size in different regions in Figure 3 is shown in Table 2. The time step for LES numerical simulation is set to \(10^{-5}\)s, and the time taken for the propeller to rotate one cycle at the designed advance ratio \(J=0.889\) is equivalent to \(3956\) calculation time steps, and at \(J=313\),\(350\) calculation time steps.  
Considering the computational cost, LES-LDKSGS simulation was only performed on approximately 8.5 million grids with \(J=0.889\) and \(J=3\). In order to compare and illustrate, we also intentionally performed simulations on about 8.5 million, 3.07 million, 1.15 million and 38,000 grids for RANS-kEpsilon at different advance ratios, although there is no need for such fine mesh (such as 8.5 million) in RANS-kEpsilon simulation with a good grid strategy. All 42 simulations included in this paper are listed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Region** & **Cell Scales: (with about Total 8.5 Million Cells)** \\ \hline Region 0 (double-dashed line) & 0.125D \\ Region 1 (dashed line) & 0.015625\(D\) \\ Region 2 (dotted line) & 0.0078125\(D\) \\ \hline \end{tabular}
\end{table}
Table 2: Grid size in different regions of the computational domain.  
Figure 4: Close-up view of original surface mesh of propeller.  
Figure 3: Computational domain.
## 1 Introduction
Most importantly, the data rate in the cited work is not sufficiently high to fulfill the demands of applications for the next-generation access network. OpEx and CapEx constraints and the development of metro network traffic have led to a search for architectures with reduced energy consumption, smaller footprints, and cheaper costs. For this reason, a centralized OLT in the OMAN using WDM technology is configured in a ring architecture at the feeder fibers. At the same time, star topology is set up at the user end. Deploying OFCs in an OMAN is important for enhancing data transmission by providing high bandwidth to each user with high-frequency spacing. This could ensure a high data rate transmission efficiently with superb scalability, provide low latency, and fulfil the urban network demands. An OMCG-based OLT, which can efficiently transmit 20 Gbps duo-binary modulated data at a frequency spacing of 20 GHz, is presented in this study. At each OMAN remote node (RN), optical switches (OSws) can support downlink and uplink transmission. Our OMC spectrum distribution divides the entire wavelengths into six RNs, with ten wavelengths each. Depending on the end-user's location, this system can increase or decrease the number of RNs and ONUs. At each RN, the ONUs are connected through star-topology distribution cables. Overall, 60 ONUs are connected to the central office, enabling a broadcast of 1200 Gpbs, with 20 Gpbs being allocated to each user at varying distances. WDM-PON assigns each subscriber a distinct wavelength. By eliminating ROADMs, OADMs, and various light sources, a cost-effective and efficient OMAN is presented and supported by the results. The main contributions of this paper are summarized here for easy understanding by the readers:  
* This study proposes a new central office for an OMAN based on an OMC generator, focusing on affordability and an efficient switching system. * The OMCG is based on a cascaded configuration of a phase modulator (PM) and two Mach Zehnder modulators (MZMs), driven by an RF signal controlling the carriers' frequency spacing. Over sixty frequencies are generated with a high tone-to-noise ratio (TNR) of 40 dB and minimal amplitude difference among the generated carriers. * An efficient switching mechanism is introduced in the proposed OMAN to handle the transmissions during the fiber failures in the main core fiber in the ring architecture. This failure could be a single fiber failure or a multistage fiber failure, which are also considered and presented for proper maintenance/management of the transmission. The proposed CO design can cope with different scenario-based problems without compromising the transmission quality.
# Path Loss Model for 3.5 GHz and 5.6 GHz Bands in Cascaded Tunnel Environments
## 3 Channel Measurement and RT Parameters Calibration in a Subway Tunnel
### RT Parameters Calibration  
In Figure 8, we reconstructed the 3D model of the actual metro tunnel and then imported it into Wireless Insite (RT-based channel simulation software) [40]. During the RT simulation, the antenna type, frequency, and locations are consistent with the actual measured scenario.  
To ensure that the simulation is consistent with the actual measurement, the parameters of material property in the 3D tunnel model need to be set properly, as shown in Table 2. The permittivity and conductivity of the materials employ the values recommended by ITU-R P.2040 [41]. The roughness of concrete is set as 0.075, and the surface of the metal is smooth enough that its roughness could be ignored in RT simulation [42]. In order to  
Figure 8: The 3D model of the straight tunnel.  
Figure 7: The configuration of the measurement system.  
generate channel characteristics close to the actual measurement, the parameters in the RT simulation are calibrated according to the path loss of the field measurement [43,44]. After several simulation experiments, the number of reflections is set as 23, and the fine-tuned spacing of rays is set as 0.12\({}^{\circ}\).  
As shown in Figure 9, we use the classic floating intercept path-loss model [45] to fit the RT simulation and the measurement. The equation is described as follows:  
\[\mathrm{PL}(d)=\beta+10\alpha log\;(\frac{d}{d_{0}})+X_{\sigma}(\mathrm{dB}) \tag{24}\]  
where \(\alpha\) is the slope of the PL model, and \(d_{0}\) is the reference distance, which is 1 m in this study. \(\beta\) is the model intercept which represents the path loss when the transceiver distance is the reference distance, and \(X_{\sigma}\) is a Gaussian random variable with a standard deviation of \(\sigma\).  
As shown in Table 3, it can be seen that the PL model for the actual measurement campaign and the RT simulation match very well, and the difference in parameters (\(\alpha\), \(\beta\), and \(X_{\sigma}\)) between the actual measurement campaign and RT simulation is quite small. The comparison results confirm that the RT simulation parameters are very close to the values in the actual environment, indicating that the corrected material parameters can be used to simulate in different subway tunnel environments [32].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Material** & **Roughness (m)** & **Conductivity (S/m)** & **Permitivity** \\ \hline Tunnel Wall & Concrete & 0.075 & 0.09 (3.5 GHz)/0.15 (5.6 GHz) & 5.31 \\ Rail & Metal & - & \(10^{7}\) & 5.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters after calibration.  
Figure 9: Comparison of path-loss results between measurement and RT simulation (**a**) 3.5 GHz (**b**) 5.6 GHz.
## 7 Performance Evaluations
### Results and Discussion
#### 7.2.2 Effectiveness of Data Scheduling at the Network Edge  
To evaluate data scheduling at the network edge, we first illustrate the information loss and marginal utility of non-IID HAR data samples in Figure 7.  
As class concentration parameter \(\alpha\) increases, local data distributions become more homogeneous. As mini-batch SGD performs well on IID datasets, data quality \(Q_{n,r}\) increases in Figure 7a. Furthermore, the diminishing effect of data marginal utility is shown in Figure 7b. An interesting observation is that the prediction accuracy curves in Section 7.2.1 reflect the marginal utility. Therefore, (19) as derived can approximate the real diminishing formula of the data marginal utility effectively.  
Figure 8 shows that our proposed data scheduling can achieve slightly better performance than SOTA Curricula when all three methods use FedAvg as the model aggregation protocol. Algorithm 1 and Curricula achieve 90% test accuracy on CIFAR-10 images with similar times, while Anti-Curricula fails to reach the desired 90% accuracy threshold.  
Figure 6: Model utility enhancement: proposed approach shows more than 10% improvement in test accuracy on CIFAR-100 dataset.  
Figure 7: Information loss and marginal utility of heterogeneous HAR datasets: (**a**) information loss of label skews; (**b**) marginal utility as a function of data quantity.  
Although Curricula achieves similar performance to our design, it uses a significantly greater amount of data samples per round; thereby, training latency is increased. Figure 9 shows the average training latency of each smartphone during DML. Algorithm 1 is observed to reduce the overall training latency by 30% and achieve approximately 50% latency reduction in later rounds. Figures 8 and 9 jointly show the superiority of our data scheduling algorithm.
# Comparative Analysis of Measurement Tools in the Cognex D900 Vision System
## 1 Introduction  
In industrial automation, vision systems play a key role in increasing the efficiency, precision, and quality of production processes [1]. They are used, among other things, for quality control, where they detect product defects, measure dimensions, and check assembly completeness [2]. Additionally, vision systems assist robots in manipulation tasks, enabling the precise positioning and assembly of components. They also enable the automation of inspection processes, significantly reducing production time and operational costs [3].  
The integration of vision systems with other automation components, such as robots, PLC controllers, and SCADA systems, allows for the creation of advanced and intelligent production lines that can quickly respond to changing conditions and market demands. By applying vision technologies, the industry can achieve higher levels of efficiency, waste reduction, and overall product quality improvement [4].  
In the era of rapid technological development and increasing demands for quality and production efficiency, including the elimination of human factors, vision systems are gaining importance as key tools supporting the automation of industrial processes. This work is dedicated to the analysis and discussion of vision systems and their applications in various industry sectors. It provides a detailed description of the tools of the Cognex In-Sight D900 vision system (Cognex Corporation, Natick, MA, USA), one of the leaders in the vision technology market.  
Vision systems utilizing advanced image processing algorithms and machine learning enable the automatic analysis of the visual features of production elements for control, identification, measurement, and sorting. The wide range of applications covers various industries such as the automotive industry, pharmaceuticals, food production, and many others where the execution, precision and reliability of control are crucial factors.  
This work will thoroughly discuss the measurement tools of the Cognex D900 vision system (Cognex Corporation, Natick, MA, USA), which, thanks to its advanced technology and ease of integration with industrial systems, provides a solution adaptable to the needs of modern industry.  
The aim of this work is not only to present the theoretical foundations of the operation of vision systems but also to showcase their practical applications and benefits, as well as to compare their strengths and weaknesses.  
The structure of the paper is as follows. Sections 2 and 3 are devoted to an analysis of the literature in the field of industrial vision systems and image analysis. Section 2 discusses the development of vision systems over the years and describes the general method of image recognition. Section 3 discusses the practical applications of Cognex vision systems in industry. Next, in Section 4, a comparative analysis of the available tools for the selected vision system is presented. Section 5 is devoted to a discussion of the research results achieved. Finally, Section 6 concludes the paper, summarizing the main objectives.
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 4 Photodiode Photonic-Integrated Applications
### Photodiodes Integrated with Antennas
(**a**) Band diagram of MUTC–PD; (**b**) MUTC–PD integrated with bowtie antenna; (**c**) incident light coupling to MUTC–PD by a refracting–facet plane and \(45^{\circ}\) mirror; (**d**) frequency response of MUTC–PD with three quasi–field values; (**e**) comparisons of the measured THz power (open circles) and calculated values of the integrated device using different electron effective velocity, \(v_{eff}\). Note: The inset is the packaged device module (i.e., photomixer) with Si–lens and fiber pigtail.
###### Abstract  
Nanoporous structures have attracted great attention in electronics, sensor and storage devices, and photonics because of their large surface area, large volume to surface ratio, and potential for high-sensitivity sensor applications. Normally, electron or ion beam patterning can be used for nanopores fabrication by direct writing. However, direct writing is a rather expensive and time-consuming method due to its serial nature. Therefore, it may not translate to a preferred manufacturing process. In this research, a perfectly ordered large-area periodic pattern in an area of approximately 1 cm\({}^{2}\) has been successfully fabricated on various substrates including glass, silicon, and polydimethylsiloxane, using a two-step process comprising visible light-based multibeam interference lithography and subsequent pattern transfer processes of reactive ion etching and nanomolding. Additionally, the multibeam interference lithography templated anodized aluminum oxide process has been described. Since the fabrication area in multibeam interference lithography can be extended by using a larger beam size, it is highly cost effective and manufacturable. Furthermore, although not described here, an electrodeposition process can be utilized as a pattern transfer process. This large-area perfectly ordered nanopore array will be very useful for high-density electronic memory and photonic bandgap and metamaterial applications.  
multibeam interference lithography (MIL); reactive ion etching (RIE); large area; nanopore; transfer; pattern +
Footnote †: journal: Applied Sciences
They also provided an initial guess strategy later and recommended that the S/P ratio should be smaller than a constant value of about 18.1834, which is better for convergence when the value of the S/P ratio is too large [18; 19]. An alternative approach of iteration method was established based on a Rational Taylor function for modeling with a small average error of about 0.28% compared with the results utilizing iteration [20]. From the study of [PERSON], a white LED consisting of four-hump quantum dot light-emitting diodes could improve the luminous and the visual performance with the S/P ratio of 2.52, the luminous efficacy of 460.78 lm/W, the CCT of 5454 K, and the CRI of 90.3 [21]. [PERSON] adopted the Monte Carlo and genetic algorithm to find an optimal white LED spectrum on the basis of the S/P ratio and CRI for the mesopic vision, where the S/P ratio, CCT, and CRI were 2.064, 4981 K, and 93.9, respectively [22]. [PERSON] optimized the white LED spectra with the combinations of blue, green, and orange components to reach the ideal luminous efficacy and the \(R_{f}\) values of IES (Illuminaing Engineering Society) color fidelity over 70, 80, and 90 simultaneously [23]. A functional model based on the chromaticity-guided was built for lighting applications of the trichromatic light source in both photonic and mesopic conditions [24]. [PERSON] developed a measurement technology by use of a calibrated digital camera to acquire pixel-wise S/P ratio and further calculated the mesopic luminance [25]. [PERSON] proposed a mesopic color appearance model, which could be used to simulate color appearance in the mesopic range as well as photopic and scotopic conditions by adding rod intrusion to the two-stage model [26; 27]. Most night-time outdoor and traffic lighting scenarios are in the mesopic range. However, the majority of the white LED spectrum is optimized based on luminous efficiency and color rendering index mostly in the photopic vision that shows a wider color gamut than in mesopic vision. As the color gamut in the mesopic conditions are relatively small, the general color rendering index is not applicable to evaluate color fidelity. Therefore, an optimization of the white LED spectrum based on color gamut is essential to raise the ability of color recognition under constant contrast conditions for lighting application. The present study includes two major parts: (1) optimization of white LED spectra, (2) building an evaluation model under dim lighting conditions for lighting applications. Different peak wavelength positions and density ratios are tested to obtain optimal spectra and see whether the use of a neural network can successfully predict the mesopic luminance, color gamut volume, and S/P ratio.