esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Capability of Machine Learning Models and Their Comparison
A potential reason for the difference can be accounted for by the fact that the generalization capability of ANFIS is hugely influenced by FIS with various identification algorithms [45; 58], indicating that other identification algorithms for generating a FIS, such as fuzzy c-means, mountain, and grid partitioning, may enhance the modeling performance of ANFIS method for the current research. Moreover, in order to improve the predictive ability of data-driven techniques, packaging heuristic algorithms into data-driven models with the intention of optimizing the model structures and/or parameters is broadly adopted as a preferred solution. A large number of novel nature-inspired optimization algorithms (e.g., particle swarm optimization [59], artificial bee colony [60] and cuckoo search optimization [61]) have been developed over the last two decades [62]. It should be particularly stressed that the models developed in this study have been successfully optimized by various heuristic algorithms in diverse research fields (e.g., hydrological prediction, wind speed and solar radiation forecasting). For instance, [PERSON] et al. [63] used genetic algorithm to tune, optimize, and determine the respective key parameters of ANFIS and SVM methods for predicting the dew point temperature of moist air and obtained excellent results; [PERSON] et al. [64] utilized particle swarm optimization algorithm in conjunction with ANFIS and SVM approaches to estimate the surface soil moisture and respectively improved the predictive performance of original ANFIS and SVM models without using optimization algorithm. Consequently, to improve the modeling accuracy of carbon fluxes in the present work, further studies can be devoted to optimizing the machine learning models by the aid of different meta-heuristic algorithms.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 4 Algorithm Description
### Motivation  
In an ICN, the replica-selection algorithm will directly affect the user experience. A better replica-selection algorithm can effectively increase the user's download rate and ensure a relatively stable rate, which is conducive to improving the overall throughput of the network.  
Figure 3 is a schematic diagram of replica selection. Assuming that the user to R1 node is not the bottleneck, if RN1 and RN2 can be used reasonably, the speed of users' downloading NDC can be improved. In addition, users and replica nodes (RN1, RN2) are all in the same ENRS domain, so it will not increase the traffic in the core network. Considering the replica-selection algorithm based on the estimation of transmission completion time, if the estimated value of transmission completion time of the replica node of RN2 is lower than that of RN1, RN2 will be selected to provide services. After RN2 is selected once, when the replica selection is performed again, because some resources of RN2 are occupied, the estimated value of the transmission completion time may be higher than that of RN1, and RN1 will be selected at this time. By continuously selecting replica nodes with smaller transmission completion time estimates, a reasonable distribution of traffic among different replica nodes can be achieved. Therefore, we will propose a replica-selection algorithm based on transmission completion time estimation, called TCTE. TCTE enables better replica selection based on transmission completion time estimates of replica nodes in the domain.
## 7 Analysis of Working Fluids for Low-Temperature ORC
The investigation evaluates the fluids based on several technical criteria, such as critical temperature, boiling point, and heat transfer coefficient. The results show that the choice of working fluid significantly impacts the ORC performance and that the most influencing parameter for assessing fluid performance is the critical temperature. The study concludes that R245 fa, R245 ca, R123, R141b, and benzene, in that order, are the best-performing fluids for a temperature range of 197-380 \({}^{\circ}\)C.  
[PERSON] et al. [96] review the thermal stability of several working fluids and discuss three methods for measuring thermal stability: differential scanning calorimetry (DSC), pressure changes, and rapid experimental methods. The results reveal that the thermal stability of working fluids mainly depends on their chemical properties, not their physical properties. R245 fa and R1336 mzz-Z are recommended as working fluids with good thermal stability. The study by [97] applies the technique for order preference by similarity to an ideal solution to determine properties. The Pearson correlation coefficient (R) is calculated to identify the statistical relation between the physical properties. The investigation illustrates pentane as a clean and thermally superior fluid, followed by butane, cyclopropane, and isobutane. However, chlorofluorocarbons (CFCs) and hydrofluorocarbons (HFCs) acquire good ranks but pose serious environmental threats. The study also concludes that a combination of thermal and environmental properties is necessary for selecting suitable organic fluids for ORC. The study by [98] explores the choice of suitable working fluids through exergy-economic analyses, considering a heat source temperature of 120 \({}^{\circ}\)C and a pressure ratio of approximately 3. Objective functions for system optimization include exergy efficiency and the cost rate of electricity, evaluating each fluid based on optimal operating conditions. Exergy efficiency ranges from 15% to 22%, while thermal efficiency ranges from 4.4% to 6.2%. R134a and isobutane demonstrate the highest exergy efficiencies and represent the most appropriate working fluids for investigated ORC systems. The methodology entails simulating the thermodynamic performance of a regenerative ORC that harnesses low-temperature heat sources. The results indicate that the optimal working fluid for ORC, considering parameters such as expander size, system efficiency, and pressure, lies between R290 and R134a or R600a.
## 2 Experimental Section  
The 100-nm-thick silicon-containing films were deposited onto the silicon and PET substrates using a planar-type capacitively coupled radio-frequency (13.56 MHz) discharge PECVD system. The PET substrate was cleaned with isopropyl alcohol (IPA) in an ultrasonic washer prior to the film deposition. Vaporized source from liquid tetramethylsilane (TMS, Si(CH\({}_{3}\))\({}_{4}\)) was employed as the silicon precursor and was mixed with the reactant gases of oxygen and ammonia. The organosilicon monomer was reservoired in a steel vessel and directly introduced into the deposition chamber without heating. The gas flow rate of the TMS monomer was maintained at 60 sccm, while the gas flow ratios (\(R\) = NH\({}_{3}\)/(O\({}_{2}\) + NH\({}_{3}\))) were controlled at 0, 0.25, 0.5, 0.75, and 1, respectively, with a total gas flow rate (O\({}_{2}\) + NH\({}_{3}\)) of 120 sccm. The TMS-O\({}_{2}\)-NH\({}_{3}\) gas mixture was dispersed through a shower head array. The upper anodized electrode was 200-mm-diameter and spaced 50 mm from the lower electrode (grounded) in a nearly symmetrical configuration. The deposition pressure, radio frequency power, and substrate temperature were controlled at 27 Pa and 70 W, and 120 \({}^{\circ}\)C (controlled by a thermal heater system), respectively.  
The optical emission spectra (OES) afforded by the dissociated species in the TMS-O\({}_{2}\)-NH\({}_{3}\) glow discharge were monitored using optical emission spectroscopy by the Plasus Emicon system (ranged from 200 to 1100 nm). The thickness of the deposited films was measured using a surface profile system (Dektak 6M, Veeco, New York, NY, USA). The chemical bonding states and compositions of these films were examined using a Fourier transform infrared (FTIR) spectrometry (FT/IR-4100, JASCO, Halifax, NS, Canada) and an X-ray photoelectron (XPS) spectroscope (PHI Quantera SXM\({}^{\mathrm{TM}}\), ULVAC-PHI, Kanagawa, Japan). Root-mean-square surface roughness (\(R_{q}\)) and surface morphologies of these films were measured and observed by an atomic force microscope (AFM; DI-3100, Vecco, New York, NY, USA) using the tapping-mode and a field emission scanning electron microscope (FE-SEM, JSM-6700F, JEOL, Tokyo, Japan) operated at 15 kV. The internal stress, \(\sigma_{f}\), resided in the films with a thickness, \(d_{f}\), was measured by the beam bending method using a thin film stress measurement instrument (FLX-2320, Tencor, Chicago, IL, USA), and derived from the Stoney formula expressed as [14]:  
\[\sigma_{f}=\frac{E_{s}d_{s}^{2}}{6(1-\
u_{s})d_{f}}\left(\frac{1}{R_{f}}-\frac{ 1}{R_{0}}\right) \tag{1}\]  
where, \(E_{s}\), \(\
u_{s}\), and \(d_{s}\) are [PERSON]'s modulus, Poisson ratio, and thickness of the substrate, respectively. \(R_{0}\) and \(R_{f}\) are the radii of the sample curvature before and after the barrier film deposition. The water vapor permeation of the bare and coated PET substrates was measured using a WVTR measurement system (PERMATRAN-W 3/61, MOCON Inc., Minneapolis, MN, USA) at a temperature of 40 \({}^{\circ}\)C with 95% relative humidity (RH).
# Adjustable Capacity Evaluation Method Based on Step-by-Step Power Mapping of Offshore Wind Farms
## 5 Conclusions  
The power generated by the wind turbines in the offshore wind farm generates various losses during the transmission to the onshore grid connection point. Therefore, it needs to be corrected based on the maximum theoretical power of the wind turbine and then reported as an adjustable capacity. The cable loss and other losses are used to realize the step-by-step mapping of the wind farm power, and a mapping model is established based on the actual offshore wind farm.  
Based on the wind farm power step-by-step mapping model, an adjustable capacity estimation method is established. In practical applications, the adjustable capacity of the offshore wind farm can be obtained by taking the real-time maximum theoretical power of each wind turbine provided by the wind turbine SCADA system as the input. An example analysis is carried out using the actual data of offshore wind farms. Compared with the direct power mapping model, the step-by-step mapping method of wind farm power proposed in this paper considers the losses in the power transmission process of the wind farm more efficiently. The calculation result is closer to the actual value, and the error is significantly reduced. The effectiveness and superiority of this method are verified. However, there is still room for further improvement in the calculation effect of submarine cable loss and the modeling of other losses can be more refined, which will be the direction of our future work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & _MAE_ **(MW)** & _RMSE_ **(MW)** \\ \hline Segmented Mapping Model & 1.59 & 2.08 \\ Direct Mapping Model & 2.55 & 3.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error comparison of grid connection power calculation results.  
**Author Contributions:** Conceptualization, X.D.; Data curation, J.Z. ([PERSON]); Formal analysis, J.Z. ([PERSON]), Z.L., J.Z. ([PERSON]) and Z.W.; Investigation, X.D.; Methodology, J.Z. ([PERSON]), Z.L., S.Z. and J.Z. ([PERSON]); Resources, X.D. and S.Z.; Software, S.Z.; Supervision, Z.W.; Writing--original draft, Z.L.; Writing--review & editing, X.D., J.Z. ([PERSON]) and Z.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data is provided by third parties and is not shared.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
## 4 Experimental Validation
### Discussion of the Experimental Results  
According to the experimental results in Figures 7-9, one can see that both the value of \(\theta\) and the spectrum of primary noise affect the performance of an adaptive ANC system. We define the average noise reduction (ANR) as the average of the difference in power spectra between ANC Off and ANC On within a specified frequency range. The ANR values for different positions and methods are displayed in Table 2.  
For \"type a\" and \"type b\" primary noise, the spectrum distribution was within half of the sampling rate but the bandwidths were different. [PERSON]'s method had the best ANRs at (192, 106) and (112, 166) within the ranges of 100 Hz-500 Hz and 100 Hz-2000 Hz, respectively, but the mirror noise caused by [PERSON]'s method was above -66 dB and -52 dB, respectively, which would directly affect the listener's experience. The ANR values of the proposed method were slightly lower than those of [PERSON]'s method at positions (192, 106) and (112, 166), but they were much better than those of the single-rate method, and the mirror noise was under -89 dB and -80 dB, respectively, which meant that the group delay was better compromised with the mirror noise cancellation.  
For \"type c\" and \"type d\" primary noise, there were components above half of the sampling rate, but the ratios of components above and below the sampling rate were different. The aliasing noise is an important factor in ANC systems. The ANR values of [PERSON]'s method decreased by more than 5 dB in comparison with that of \"type b\" in positions (112, 166) and (0, 250). The proposed method had better anti-aliasing noise reduction properties than those of [PERSON]'s method as the ANR values at position (0, 250) were at least 6.5 dB better. At the same time, the proposed method had better space robustness than that of the single-rate method as the ANR values in position (112, 166) were better than 6.7 dB and 8.5 dB, respectively, meaning that the group delay was better compromised with the aliasing noise cancellation.  
The experimental results fully demonstrate that the proposed method for dynamic matching of reconstruction and anti-aliasing filters can effectively improve the spatial robustness of a system while controlling the mirror noise and aliasing noise.
# Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage
## 4 Accessible Cultural Heritage through Explainable Artificial Intelligence
### Explaining Art through Language
#### 4.2.2 Visual Question Answering Models  
One of the advances in the NLP models brings also the possibility to produce explanations about an image though visual question answering [125], which in our view can be especially useful for the blind. To enrich the experience of a user when observing art, an advisable interactive introspection explanation could be: _Pay attention to where the light is set in this painting. What is the center of focus the author is highlighting as such? Why?_  
* Could art explanations be generated interactively on request, i.e., using visual question answering (VQA)?
* Could advisable explanations increase the engagement and interest in artwork?
* Should only objective or also subjective information be part of an artistic explanation?
## 3 Overview of Video Delivery in 4G Mobile Cellular Networks  
This section reviews mobile video delivery in 4G LTE mobile cellular networks. We first provide background information on standard video content formats, unicast, and multicast/broadcast services in LTE. Then we provide specific details on LTE CN and RAN support for mobile video delivery.
# Numerical Analysis of Laser Pattern Effects to Residual Stress on Metal 3D Printing
## 2 Composition of FEM Model  
### Composition of Thermal-Structure-Linked Numerical Analysis Model  
Using the FEM analysis model, the mechanisms that occur during 3D printing were simulated by confirming the changes in thermal expansion and residual stress occurring along the heat input path of the laser. Problems associated with residual stress and the resulting distortion and destruction should be considered for heat input in adjacent areas after melting and solidification. Hence, a tool was configured to confirm the complex heat gradient occurring in the 3D printing process by continuously calculating multiple laser paths and stacked layers. Subsequently, the suitability of the analysis model was verified by comparing it with the actual process of the configured analysis model.  
Unlike in previous studies, in this study, heat transfer and deformation between powder and solid were considered, and analysis conditions were designed considering the differences and changes in physical properties due to temperature and phase changes. In this regard, a thermal-structure-coupled FEM model was constructed for the SLM process. Subsequently, an analysis was performed while considering the thermal-structure physical system, and the associated effects were considered when calculating the convergence. The associated coupling equation is shown in Equation (1) [8].  
\[\left[\begin{array}{cc}[M]&[0]\\ [0]&[0]\end{array}\right]\left\{\begin{array}{c}\left\{\begin{array}{c} \left\{i\right\}\\ \left\{i\right\}\end{array}\right\}\\ \left\{i\right\}\end{array}\right\}+\left[\begin{array}{cc}[C]&[0]\\ [C^{u}]&[C^{v}]\end{array}\right]\left\{\begin{array}{c}\left\{i\right\}\\ \left\{i\right\}\end{array}\right\}+\left[\begin{array}{cc}[K]&[K^{u}]\\ [0]&[K^{v}]\end{array}\right]\left\{\begin{array}{c}\left\{i\right\}\\ \left\{T\right\}\end{array}\right\}=\left\{\begin{array}{c}\left\{F\right\} +[K^{u}]\left\{T_{ord}\right\}\\ \left\{Q\right\}\end{array}\right\} \tag{1}\]  
In this equation, \([K^{u}]\) is the thermoelastic stiffness matrix of the element, and \([C^{u}]\) is the attenuation matrix owing to the thermoelastic effect. \([K^{l}]\) is a heat conduction matrix and can be calculated by summing the conductivity to material diffusion (\([K^{lb}]\)) and conduction based on the material convection surface (\([K^{lc}]\)).
# Surface Passivation of Crystalline Silicon Wafer Using H\({}_{2}\)[PERSON]
## 1 Introduction  
In recent years, photovoltaic systems are becoming very popular because of their enormous amount of clean and unlimited solar energy, which can be harvested through photovoltaic solar cells [1; 2; 3]. Among the various technologies that increase solar cells' efficiency, surface passivation treatment plays a key role in the silicon-based solar cell fabrication process. The dangling bonds on the silicon wafer surface are known to work as recombination centers and cause the loss of photon-generated carriers. Many advanced passivation materials have been developed and are widely used in commercial production, including SiN\({}_{\chi}\):H, SiO\({}_{\chi}\), and AlOx [4; 5; 6; 7; 8; 9]. These materials have been shown to have good passivation effects on the silicon wafer surface. However, these materials are produced through complex fabrication systems that necessitate precise control of gaseous reactants [10].  
According to [PERSON] and [PERSON], the high density of the semiconductor/metal interface will pin the interfacial Fermi level, making the barrier height less sensitive to the metalwork function [11]. Experimentally, [PERSON] found that sulfur-passivated silicon substrates show greater sensitivity to the metalwork function in terms of Schottky barrier height [12]. This was attributed to the reduction in surface density through passivation of dangling silicon bonds by S. Previous results show that S atoms are absorbed on the Si wafer surface in the form of Si-S-Si bridge bonds [13; 14]. The significant minority carrier lifetime gains of multicrystalline Si recently achieved by sulfur treatment further prove that sulfur treatment can effectively passivate dangling silicon bonds on the surface [15].  
In this study, we report the effect of sulfur passivation on crystalline silicon wafers using H\({}_{2}\)S annealing treatment at various temperatures. The results show that a significant improvement in passivation can be obtained after H\({}_{2}\)S annealing treatment. Moreover, 600 \({}^{\circ}\)C was found to be the best temperature condition for crystalline silicon wafers. Under this condition, the highest minority carrier lifetime gain of up to 2030% can be observed using the Sinton WCT-120 measurement system.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 3 The Improved Clustering Scheme
### Design Issues of the ICV-MIMO Scheme
In addition, in the process of power adjustment, reducing transmission power might increase channel error, which results in the increase of retransmission and power consumption. A threshold of retransmission count may be employed to control the power decreasing process. When the number of retransmissions reaches this threshold (which can be preliminarily determined by the actual measurements), the transmission power is no longer reduced. In addition, in order to prevent the frequent adjustment of transmission power due to the channel quality fluctuation, we may increase a little margin to the resulting transmission power obtained by Algorithms 5 and 6.
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
## 3 Orbit Determination under Different Scenarios
### Antenna Attitude
Figure 11: Angle differences between the east, north, and up directions in the antenna reference frame (ARF) and the opposite along-track direction, the cross-track direction, and the radial direction. The data for GRACE FO-1 was used for the plot. Note that the blue line is almost overwritten by the green dashed line. Figure 10: The antenna reference frame (ARF, shown in red), the satellite reference frame (SRF, shown in yellow), and the radial, along-track, and cross-track (RSW, shown in green) system. \(t_{1}\) and \(t_{2}\) represent two conservative time epochs for the same satellite. The figure is scaled for a better presentation. Benefiting from the fact that the angle differences between the actual and the expected antenna orientation are small, even without having the attitude information available, differences less than 10 mm can mostly be observed in the final orbits provided that the observation data is complete and continuous (see Figure 12), i.e., with a 3D RMSE amounting to 8 mm. Applying the IGS final products, for other cases with reduced duty-cycles and mean satellite numbers, the differences in the RMSE (referenced to the best-case scenario) with and without applying the attitude file are also mostly at several millimeters. This applies also to the cases when using the IGR and IGC products. This suggests that good attitude control can maintain the POD accuracy even when the attitudes are not properly determined or transferred back to the GPC.