esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Wavelength Digital-Phase-Shifting Moire Based on Moire Wavelength
## 2 Method
### Computation of Moire Wavelength in Moire System Calibration  
Calibration of the moire measurement system essentially involves the computation of the moire wavelength \(\lambda\), the distance between two consecutive bright or dark fringes of a moire pattern on a flat plate, for each phase map (each grid pitch) over the full working depth [17]. A flat plate is mounted on a translation stage and moved toward the camera-projector plane to several known positions (depths or heights). At each position, the same grid pattern used during object measurement is projected onto the plate and an image is captured. For each plate position, a computer-generated grid pattern of the same pitch as in the captured image, is digitally superimposed onto the captured image to generate a moire pattern. The generated images for all positions are filtered using stationary-wavelet Fourier-transform grid-noise removal [19] to extract pure moire patterns. The intensity at a single pixel is tracked across all plate positions (over depth). For every pixel, the moire fringe intensities across plate positions follow a near-sinusoidal function of the plate translation (Figure 1). The moire wavelength is determined by the distance between two consecutive maxima (bright fringes) or two consecutive minima (dark fringes), respectively. It is common to assume that \(\lambda\) is constant between consecutive moire fringes; however, the moire wavelength decreases as calibration depth increases. To determine the moire wavelength as a function of height (depth), the moire wavelength is first computed at each pair of consecutive peaks from the tracked intensities over plate positions. The average moire wavelength over all pixels is then computed at the different peak pairs corresponding to different plate positions (depths or heights). Finally, the moire wavelength as a function of height (depth) is approximated by a line fit to all average moire wavelengths computed at different peak pairs for different values of height. The capture of multiple images at different depth positions is only required during system calibration, and not during object surface measurement.  
To perform heterodyne temporal phase unwrapping, multiple phase maps are required. Since a single value of wavelength is required for each phase map, and since the object surface depth (height) \(h(x,y)\) would not be known a priori during object measurement, the wavelength corresponding to the middle calibration depth, which would tend to minimize phase unwrapping errors, is used for each phase map. The moire-wavelength to height relationship would later be used in pixelwise refinement of wavelength and height during 3D reconstruction [17] (Section 2.4).  
Figure 1: Moiré wavelength \(\lambda\) shown as the distance between two consecutive fringe-plane intensity maxima corresponding to bright fringes on a flat plate, for a single image pixel. The wavelength varies over depth (across different peak pairs).
## 6 Security and Efficiency Analysis
### Efficiency Analysis

Table 1: _Cont._In the realized scenario, 13 WiSeN sensor nodes were used. There are five SNs and one CN in each cluster. There are 10 SNs, 2 CNs, and 1 BS in total. The SNs released into the environment are to detect and transmit the temperature and humidity values. In the scenario, two clusters were created, each consisting of six nodes. The most suitable location among the nodes in the cluster was chosen as the CN. The WiSeN+ SIM900 GSM/GRS node is also used as the BS. The WiSeN sensor nodes are programmed with Code Composer Studio (CCS) Integrated Development Environment (IDE), open source and provided by Texas Instruments, on a personal computer with an Intel i5 processor, 8 GB RAM, and Windows operating system. The representation of the WSN used in the scenario is given in Figure 7.  
There are not enough studies in the literature on blockchain-based authentication protocol research for WSNs yet. In order to make an experimental comparison, studies [50; 51] that make blockchain-based authentication in the WSN are discussed. Only the message sizes transmitted during blockchain transactions are given for energy analysis in these studies. The message sizes in the second study are the same as in the first study. At the same time, the given energy, graphs, etc., are not convincing in the second study. The references compared are not included in the reference list. No comments were made on the graphics. Therefore, only the message sizes transmitted during blockchain transactions were compared with other studies. Figure 6: WiSeN sensor node. Figure 7: The WSN used in this study. In addition, in the created WSN, traditionally, normal results were obtained without any additions, and then the results were obtained by implementing the recommended protocol. Then, the normal results and the obtained results were compared. Finally, the additional load of the proposed protocol to the WSN was analyzed. The IEEE 802.15.4 protocol runs on the WiSeN sensor node, as in TelosB and MicaZ nodes. This protocol carries out network participation, message sending, and authentication. By default, this protocol is available on all nodes. The traditional situation means that the WiSeN sensor node typically works without any add-on, that is, over the 802.15.4 protocol. Blockchain-based refers to the current WiSeN sensor node, after the proposed solution is coded.
# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
###### Abstract
[29] constructed a cotton yield prediction model based on the LSTM network using soil characteristic data, climate variable data, and EVI time-series data, and interpreted the model a posteriori using the SHAP interpretability analysis method. The results show that the method can effectively resolve the influence mechanism of each earth observation feature on cotton yield and reveal the quantitative relationship between different environmental factors and yield fluctuations. Agricultural production is influenced by a complex interplay of climatic, soil, and anthropogenic factors. Within large agricultural areas, the physicochemical properties of the soil and meteorological conditions exhibit significant spatial variability. This heterogeneity leads to considerable variations in soil nutrients and crop growth, even within the same field [30], thereby reducing the accuracy of carbon flux estimations when extrapolated to larger scales. Previous research on carbon flux monitoring in agricultural settings has faced challenges related to scale, often failing to address the unique characteristics of agricultural ecosystems with complex terrain. Furthermore, many existing studies that estimate carbon flux using satellite remote sensing technologies tend to rely on single data products or overlook other critical factors affecting crop growth. In the domain of remote sensing, spatial heterogeneity and nonlinear relationships between variables present additional difficulties, as scaling issues often necessitate adjustments despite apparent similarities across scales. To overcome these limitations, this study integrates MODIS data with TerraClimate meteorological data to extract key multimodal features--encompassing both remote sensing and meteorological variables--that influence wheat crop growth. Building on the cumulative GPP data from MODIS, this study focuses on wheat carbon flux estimation, with an emphasis on the carbon cycling mechanisms within farmland ecosystems. A multimodal carbon flux estimation model for wheat is developed by combining CNN with GRU. Additionally, feature importance analysis is conducted, providing valuable insights into the contributions of various factors. This research offers a novel approach to applying deep learning algorithms in crop carbon flux estimation, thereby advancing the accuracy and applicability of carbon flux monitoring in agricultural systems.
# Application of Artificial Neural Network to Somatotype Determination
## 2 Materials and Methods
### Study Design
#### 2.1.3 Body Composition Analysis  
Before the body composition measurement participant was asked to refrain from vigorous physical activity at least 12 h prior, intake no caffeine and alcohol for 24 h prior to testing, fast, and empty the bladder 30 min prior to testing. Hydration status was tested in urine sample by specific gravity test and colors in each person. To assess individual body composition, multi-frequency BIA (MF-BIA) was used, at 5, 50, 100 and 200 kHz electrical current frequencies (Maltron BioScan 920-2, Maltron International, UK). MF-BIA was performed under standardized conditions according to the manufacturer's protocol. Body composition tests were performed in the supine position with a leg opening of 45 degrees and upper limbs inclined by 30 degrees from the trunk, after 15 min resting. After cleaning the skin with alcohol, two electrode patches were placed on the non-dominate hand and two electrodes on the corresponding foot. The impedance measurements were performed in a single-channel, tetrapolar system. Displaying parameters such as extracellular and intracellular fluids, total body water (TBW, L, %), fat mass (FM, kg, %), fat-free mass (FFM, kg, %), resting metabolic rate (RMR, kcal), dry weight and many others including mineral composition, as well as raw data such as resistance (Res) and reactance (Reac)were analyzed. For further analysis, TBW (%), FFM (%), RMR, Res (\(\Omega\)), and Reac (\(\Omega\)) were selected.  
The descriptive statistics of anthropometric, body composition, bioelectrical and somatotype components are presented in Table 1.
## 3 Results and Discussion
### Biosynthetic Gene Composition in Twenty-Three Annotated Fungal Genomes
This evidence is in contrast to previous results reporting that R-PKS gene numbers exceed the number of NR-PKS genes [6]. As such, a larger data set is necessary to confirm whether R-PKS or NR-PKSs are more numerous in the genomes of lichen-forming fungi. The total number of TI-PKSs identified across all studied genomes was 478. Of those, 44.35% were of the non-reducing (NR), 53.35% of reducing (R), and 2.3% of partly reducing (PR) PKSs. The highest number of PKS genes was found in _E. prunastri_ (36 TI-PKS clusters) and _C. rangiferina_ (34 clusters) and the lowest numbers were in _B. rubella_ (10 TI-PKS clusters), _B. gigantensis_ (11 clusters), and _Cyanodermella asteris_ (10 clusters) (Table 2). Our broad genomic sampling provides phylogenetic context for the NR-PKS genes of _B. rubella_ and _B. gigantensis_. The six TI-PKS genes from _B. rubella_ are NR-PKSs (three in Subclade I and three in Subclade II), while for _B. gigantensis_ seven TI-PKS genes are NR-PKSs (all are in Subclade I). The discrepancy between the large number of recovered TI-PKS sequences and the few experimentally verified secondary metabolites (Table 2) raises questions about the role these genes play in the secondary metabolism, and about the products they produce (e.g., [9; 24; 25; 29]. However, results linking PKS genes to lichen secondary metabolites beyond in silico methods are still scarce.
# Heat Transfer Enhancement of Falling Film Evaporation on a Horizontal Tube by Thermal Spray Coating
## 2 Experimental Apparatus and Methods  
### Experimental Apparatus  
A schematic of the experimental apparatus is shown in Figure 1. The apparatus consisted of a pressure vessel, a refrigerant circulation loop, and a condenser. The tested tube was placed horizontally in the pressure vessel. The pressure vessel was first well evacuated and then filled with the working fluid. Falling film evaporation and pool boiling experiments could be carried out in a pressure vessel by changing the liquid level. In the falling film experiment, the liquid level was set to below the test section. The refrigerant liquid was circulated by a gear pump and supplied to three needle nozzles with an inner diameter of 1.48 mm. The nozzles were fixed at the bottom of a horizontal copper tube above the test tube. As the flow resistance through the nozzles was sufficiently high enough, a uniform distribution could be obtained. The mass flow rate was controlled by the rotational speed of the gear pump and the valve opening in the bypass line and was measured by a Coriolis mass flow meter. The inlet temperature was maintained by a sub-cooler. To supply liquid to the test tube with an axially homogeneous distribution, liquid from the nozzles was supplied via a dummy tube. The dummy tube was a boiling heat transfer enhancement tube with a mechanically processed surface (GEWA-B5H produced by Wieland). After flowing down from the surface of the dummy tube, the liquid spread well in the axial direction and then flowed to the test section immediately below the dummy tube. The gap between the dummy tube and the test tube was set 1 mm to form a stable liquid film. Both tubes were carefully placed in a horizontal position to prevent maldistribution caused by gravity. The refrigerant temperature was measured by K-type thermocouples at the inlet of the nozzles and placed 65 mm above and 35 mm below the test section in the pressure vessel. The refrigerant vapor was condensed in a double tube heat exchanger connected to the upper part of the pressure vessel, and the condensed liquid returned to the test section owing to gravity. The vapor pressure was maintained by the temperature and flow rate of the cooling water and was measured by silicon on sapphire (SOS) pressure transducer in the condensing loop. In the pool boiling experiments, the nozzles and the dummy tube were removed, and the test section was submerged into the liquid pool.
## 4 Results and Discussion  
This section presents and analyzes the results from the receptance analysis of two case studies, Figures 2 and 6, with a focus on validating the GRCFBS method against FEA. This comparative simulation is conducted in MATLAB (2023b) using the theoretical approaches using the parameters discussed here. The findings provide insights into the dynamic behavior of coupled systems and highlight the accuracy and limitations of the proposed method under different structural configurations. Table 1 summarizes the parameters for the modeling of the subsystems, including elastic properties, geometric dimensions, and mass for each substructure used in the case studies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Unit**} & \multicolumn{4}{c}{**Substructure**} \\ \cline{4-6}  & & & **A** & **B** & **C** \\ \cline{3-6}  & & & **Fixed-Free Beam** & **Free-Free Beam** & **Rigid Wheel** \\ \hline E & Elastic modulus & \(\mathrm{N/m^{2}}\) & 200\(\times\) 10\({}^{8}\) & 200\(\times\) 10\({}^{9}\) & \\ \hline I & Moment of inertia & kgm\({}^{2}\) & 3.4\(\times\) 10\({}^{-7}\) & 3.4\(\times\) 10\({}^{-7}\) & 1.9\(\times\) 10\({}^{-8}\) \\ \hline EI & Flexural rigidity & \(\mathrm{Nm^{2}}\) & 7.5\(\times\) 10\({}^{8}\) & 3.8\(\times\) 10\({}^{3}\) & \\ \hline L & Length & m & 0.25 & 0.15 & \\ \hline A & Cross-section area & m\({}^{2}\) & 2\(\times\) 10\({}^{-3}\) & 5\(\times\) 10\({}^{-4}\) & \\ \hline \(\rho\) & Density & kg/m\({}^{3}\) & 7800 & 7800 & \\ \hline m & Mass & kg & 3.90 & 0.57 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subsystem parameters for modeling purposes.  
Figure 7: Excitation loads (force and moment) applied to substructure C.  
For each case study, the synthesis of the receptance matrix components derived from GRCFBS using EBT is compared to FEA results for both direct and cross-receptance.
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 4 Simulation Results
When transmitting with full power, with \(P_{max}=5\) mW, the worst UE obtains 1.76 bit/s/Hz and the network is capable of supporting 27.3 bit/s/Hz. However, this operation point can be optimized in terms of fairness, where for the same total throughput the worst UE is able to transmit with 3.62 bit/s/Hz, denoting a gain of about 106%. It should be noted that all operation points obtained with the bi-objective optimization approach allows for the worst UE to transmit with SE \(\geq\) 2.36 bit/s/Hz, which means gains \(\geq\) 34.1% in terms of max-min fairness. Table 3 summarizes this discussion, where the points of the bi-objective rows are presented with respect to the full power point.
# Spectrum Modeling of Out-of-Band Intermodulation for Dual-Band RF Amplifiers in OFDM Modulation
## 3 Experimental Setup and Verification  
The measurement setup is described in Figure 3, which consists of the Tektronix AWG 70002A arbitrary waveform generator as frequency up-conversion units and DACs, the Tektronix RSA5126B real-time spectrum analyzer for capturing the signal from the output of the RF amplifier for further algorithm verification, the Mini-Circuit LNA ZFL-1000 LN+ as the device under test, and a PC running MATLAB to verify the analytical prediction with the experimental measurements.  
Based on the standards of LTE operating bands [21], a 7 MHz bandwidth with an 864 MHz center frequency was chosen for the left band, and a 7 MHz bandwidth with an 886 MHz center frequency was chosen for the left band. The number of subcarriers for each band is 470. In Figure 4, it can be clearly observed that the theoretically predicted PSD (the red line) matches the experimental results (the blue line).  
Figure 3: Experiment setup.  
Figure 2: \(P_{y}(f)\) with and without CM and out-of-band intermodulation.  
Since only third-order nonlinearity is considered in this manuscript, there are some visible misalignments in the shoulder areas between theoretical calculation and experimental measurement shown in Figure 4. These misalignments are generated by high-order nonlinearities and memory effects.  
In order to further improve the accuracy of this model, higher order nonlinearities and memory effects will be considered in our future study.
# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 3 Material and Method
### Deep Learning Architectures
#### 3.3.4 Ensemble Learning  
One of the most widely used machine learning methods in recent years is ensemble learning. The idea of ensemble methodology, which is a predictive model created by integrating multiple models, is known as a method used to improve prediction performance [47]. Commonly used ensemble learning methods are bagging, boosting and stacking [48]. In bagging, the predictions of different trained models are processed using different methods such as averaging the predictions if it is classification, or averaging the predictions if it is majority voting or regression. In the boosting method, the models focus on making up for each other's shortcomings. In this way, a strong model can be created and difficult problems can be focused on. In the stacking method, the prediction of each trained model is used to train the meta-model. Since the basic models of machine learning are also involved in the stacking method, it can achieve better performance by combining the strengths of these models.  
The basic philosophy underlying the methods used in ensemble learning is to use more models. As the variety of models trained from ensemble learning increases, the classification performance of the system also increases. This study aims to improve the performance of the system by using ensemble learning methodology.
###### Abstract
It has been used for the measurement of gas concentrations for various studies, e.g., soot oxidation [35], or SCR (selective catalytic reduction for NOx) [36; 37] and catalyst evaluation [38; 39; 40] with synthetic gases. It has also been used in engine test beds to assess ethanol [41; 42], biodiesel [43] such as Jatropha [44], dimethyl ether (DME) [45], or hydro-treated vegetable oil (HVO) [46], homogeneous charge compression ignition (HCCI) engines [47], gasoline compression ignition engine [48], post injection effect on emissions [49], NH\({}_{3}\) sensors [50], or even modeling of emissions [51]. FTIR instruments have also been used on chassis dynamometers: Small gasoline engines [52] or even diesel trucks [53; 54]. For example, for exhaust gas recirculation (EGR) [55], alternative fuels [56; 57], reactive nitrogen compounds [58; 59], impact of low temperature on non-regulated pollutants [60], and retrofit evaluation [61; 62] of diesel vehicles. Similarly, chassis dynamometer studies with gasoline vehicles [63] focused on unregulated emissions [64; 65; 66; 67; 68], NH\({}_{3}\)[69; 70; 71; 72; 73], effect of exhaust gas reforming on emissions [74], low temperature [60; 75; 76], alternative fuels [65; 77], and hybrids [78; 79; 80]. Motorcycles' non-regulated pollutants emissions have also been assessed with FTIR [81; 82; 83; 84; 85]. The on-road application started in 2000 [86]. Since then other researchers measured emissions on the road [87; 88; 89], greenhouse gases (GHG) [90], nitrogen species [91], cold start emissions [92; 93; 94; 95; 96] of gasoline vehicles and the impact of ambient temperature [97]. A few also studied compressed natural gas (CNG) [98], diesel fueled vehicles [99; 100; 101] and their non-regulated pollutants [102]. Currently in the European Union (EU), FTIR is allowed for tailpipe (undiluted) NH\({}_{3}\) measurements for heavy duty engines (Commission Regulation (EU) 582/2011). The same applies globally with UNECE (United Nations Economic Commission for Europe) Regulation 49. It is also prescribed in the UNECE light-duty vehicles GTR 15 (Global Technical Regulation) for ethanol, formaldehyde, acetaldehyde and N\({}_{2}\)O from the dilution tunnel. The future Euro 7 regulation on light-duty and heavy-duty vehicles moves in a direction that these pollutants should be tested on the road [103]. Thus, using the FTIR for simultaneous analysis of various pollutants sampling from the tailpipe (undiluted exhaust) is an attractive option [104; 105] that needs to be assessed. The reviews on the topic are limited and 20 years old [31]. Furthermore, assessing FTIR for regulatory purposes has not been discussed before. The objective of this paper is to review studies that have evaluated FTIR systems for automotive exhaust gas applications. Special emphasis is given to portable applications and low emission levels to assess the suitability for future regulations.
## 4 Adaptive OFDM-IM Method
### Scenario Model  
In practical applications, vehicular network technology needs to adapt to different environments, such as urban traffic scenarios and dense building areas, to meet the user requirements in various mobile contexts. Furthermore, as users move through these scenarios, variations in Doppler shifts, the proportion of direct data components, and the reflection coefficients of roadside reflectors impact the channel quality.