esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sampled Apodized Grating Waveguide Structure and Design
### Waveguide Material  
To realize the sampled apodized grating, low-loss fluorinated photopolymers are used as core waveguide materials. The photopolymers are composed of fluorinated epoxy resin (FSU-8) and fluorinated epoxy-terminated polycarbonates (FBPA-PC EP). The molecular structure of FSU-8 and FBPA-PC EP are shown in Figure 1a and the near-infrared absorption spectrum of the photopolymer film is presented in Figure 1b. Compared with common waveguide materials, the optical absorption loss between 1310 nm and 1550 nm can be reduced by replacing C-H bonds with C-F bonds in the SU-8 material. It can be explained that the atom of hydrogen is replaced with larger atomic mass atom, the resonant frequency decreases and the wavelength of low loss has a blue shift. The refractive index can be changed from 1.495 to 1.565 at 1550-nm wavelength when the content of FSU-8 is changed from 10 mol% to 75 mol%. Table 1 shows the glass transition temperature (T\({}_{\text{g}}\)), the temperature for 5% weight loss of uncross-linked polymers (Td\({}^{\text{C}}\)) and the temperature for 5% weight loss of cross-linked polymers (Td\({}^{\text{D}}\)). The temperature Td\({}^{\text{D}}\) for 5% weight loss of the cross-linked polymer is obtained to be 303 \({}^{\circ}\)C by thermo gravity analysis (TGA). The 25% FSU-8 doped fluorinated photopolymers have better characteristics of low-loss and good thermal stability. The 25% FSU-8 doped photopolymer is adopted in the simulation and experiment.
## 3 Results and Discussion
### Performance Metrics Evaluation  
The CNN model was developed on a system that was configured with the AMD Ryzen processor consisting of SSD and 24 GB RAM with a 2 GB graphics card [10]. The training algorithm ran with TensorFlow and Keras backend [62; 63]. Each epoch approximately took an average of 5 to 10 s. Tables 4 and 5 show the results of the single-lead and multi-lead ECG/TMT-ECG for the single-, two-, and three-layer convolutional neural networks, respectively (precision, recall, F1-score, accuracy, and validation loss), depending on the classification of CAD and normal ECG segments based on the single-lead ECG and 12-lead ECG. In Tables 4 and 5, one could see that very high diagnostic accuracy was obtained with a defined number of training samples.  
Figure 12: Data flow of the single-lead with the comparison of derived (extracted) data and manual (original) data: (**a**) data flow and (**b**) data correlation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Extracted Data vs. Original Data** & **Mean (\(\Delta\))** & **Median (\(\Delta\))** & **Correlation (\(\Delta\))** \\ \hline Mean & \(-0.0311\) & \(0.02102\) & \(0.98\) \\ \hline Standard Deviation & \(0.02423\) & \(0.03541\) & \(0.9\) \\ \hline Minimum & \(-0.06378\) & \(0.06784\) & \(0.89\) \\ \hline Maximum & \(0.02785\) & \(0.02516\) & \(0.97\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Study of the correlation of 100 randomly selected samples.  
The data were of two types (i) single-lead signal and (ii) 12-lead signal data. These data were used for system training; the detailed evaluation [64] of the model was performed based on testing and validation data using a standard protocol. The commonly used performance metrics were accuracy (Equation (7)), sensitivity (Equation (8)), f1 score (Equation (9)), specificity (Equation (10)), and precision (Equation (11)). [7; 8; 56].  
\[\text{Accuracy}= \frac{TP+TN}{TP+FP+TN+FN} \tag{7}\] \[\text{Sensitivity}= \frac{TP}{TP+FN}\] (8) \[\text{F}_{1}\text{ Score}= \frac{2\times TP}{2\times TP+FP+FN}\] (9) \[\text{Specificity}=\frac{TN}{FP+TN}\] (10) \[\text{Precision}=\frac{TP}{TP+FP} \tag{11}\]  
These are related to the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) rates of the confusion matrix (Figures 13 and 14).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Single-Layer CNN**} & \multicolumn{3}{c}{**Two-Layer CNN**} & \multicolumn{3}{c}{**Three-Layer CNN**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Network (N)**} & & & & & \\ \hline  & **N-1** & **N-2** & **N-3** & **N-4** & **N-5** & **N-6** & **N-7** & **N-8** & **N-9** & **N-10** \\ \hline Precision & 72.50\%
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Land Use Data  
A land use database consisting of 1 km resolution data for China in 2000 and 2015 has been developed from Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM) satellite data by interactive interpretation; the database has a comprehensive accuracy of more than 90% [26; 27].  
The land use types were classified into the following 6 classes: Cropland, woodland, grassland, water bodies, built-up land, and unused land. The unused land refers to land that humans have not used. Most of the unused land exists because it is hard to utilize, i.e., it consists of sand, desert, saline land, marshes, barren land, and so on. We assumed that land use changes only occurred between the 6 classes and not in the sub-classes. For example, we took the change from woodland into cropland as one type of land use change. However, we recorded the change from paddy fields into dry land as unchanged land use because the paddy fields and dry land were subclasses of cropland. \"Cropland  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Zone Name**} & \(\geq\)**10 \({}^{\circ}\)C Accumulated** & \multirow{2}{*}{**Drought Index**} & \multirow{2}{*}{**Frost-Free Season (Day)**} \\  & **Temperature (\({}^{\circ}\)C)** & & \\ \hline NEC & 1400–3200 & 0.5–1.2 & \textless{}145 \\ IM & 2000–3000 & 1.2–4.0 & \textless{}180 \\ NWC & 3200–4500 & \textgreater{}4.0 & \textless{}200 \\ NC & 3200–4500 & 0.5–1.5 & 150–200 1 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Data from [25].  
\end{table}
Table 1: Main climatic indicators of the major zones in northern China.  
Figure 1: Location of the study area in China.  
expansion\" was defined as the conversion of the other 5 land use classes into cropland. Because of the small area of cropland expansion from water bodies and built-up land, and also the uncertainty and complexity of the radiation change due to cropland expansion from unused land, we only focused on cropland expansion from woodland and grassland in the remaining analyses of this study.
## 2 Study Area  
The study area was chosen to be in the Taranaki basin as this area is still considered a virgin and a major resource for hydrocarbon accumulation. The total area investigated in this study was approximately 214 km\({}^{2}\). The significant geological formation was found to be a series of adjacent and interlocked Cretaceous-Cenozoic sedimentary basins that outspread along the western boundary of New Zealand [18]. Primarily, the basin subsurface configuration lies offshore underneath the continental shelf, but it is also comprised of the Taranaki Peninsula at the northern coastal areas and in the northwestern south part. Several tectonic events have been occurred which reformed and modified the basin. Thus, it is challenging to define and classify the basin style or the geographic area individually. The Taranaki Basin remained open towards the sea from the west and north parts, whereas the fluctuated depositional area distinguished the east and south parts under the influence of eustatic and tectonic events. Besides, the Taranaki basin was separated into two structural regions; the steady western platform and the eastern movable belt as shown in Figure 1.  
The movement of the eastern movable region of the basin started to slow down since the Cretaceous times and was categorized as a layered cake.  
This was followed by a degradation deposition on an un-faulted, sub-horizontal, and locally subsided seafloor [21]. The eastern movable belt also showed a complex morphology due to the tectonics associated with the Neogene Kaikoura Orogeny [17]. Figure 1 also shows the study area of the Taranaki basin, the most important structural features, and the well location. To obtain a complete geological interpretation, several stages of analysis must be carried out such as analysis of seismic sequence, seismic facies, and relative changes of sea-level investigation.
# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 3 Results
### Input Length Analysis and Fusion Policy  
The variations in input length can lead to changes in the PM2.5 patterns shown in each window, potentially impacting the forecast model's accuracy. In the experimental setup, four distinct combinations of lengths were utilized as inputs for forecasting PM2.5 levels on the second, third, and seventh days. Subsequently, RMSE values were compared to identify the optimal input length.  
In Table 3, there is a comparison of RMSEs for forecast results of the next day, three days, and seven days based on various input lengths. For projects with 'forecast day = 1', the RMSEs for the total input lengths of 4, 8, and 12 were less than the RMSEs for the remaining three combinations. Hence, this particular input mix is appropriate for forecasting PM2.5 levels for the next day. The RMSE for the third combination, where the predicted days were set to 3, was 0.4225, indicating the superior performance among the four length combinations.  
Hence, a mix of 12, 15, and 18 has been chosen to forecast PM2.5 levels for the upcoming three days. The lowest prediction error for PM2.5 over the next week was achieved by combining time intervals of 28, 35, and 42 days out of four different combinations. Hence, the mixture is utilized in forecasting PM2.5 levels for the upcoming week.
## 3 Results and Discussion
### Sensory Analysis  
The acceptability of the different cookies to consumers was determined by a sensory evaluation according to a five-point hedonic scale. The average scores for the various consumer sensory properties: appearance (the physical aspect of the cookie: whether it is firm or not), color (influence of the addition of baobab on the color of the cookie), flavor, texture, aroma, and overall acceptability of the cookies are presented in the figure below.  
Partial substitution of BWF by BF of up to 20% significantly increased scores for all attributes (Figure 3). For each of the attributes, BBC2 was the sample with the highest scores across all samples, including the control sample (CC). It scored 4.53 \(\pm\) 0.51, 4.65 \(\pm\) 0.49,4.53 \(\pm\) 0.51, 4.41 \(\pm\) 0.71, 4.47 \(\pm\) 0.72, and 4.41 \(\pm\) 0.62, respectively, for appearance, color, texture, taste, flavor, and overall acceptability. Having obtained scores between 4.5 and 5 for appearance, color, and texture, BBC2 is very acceptable for these different criteria. On the other hand, it is acceptable for the rest of the criteria, since its scores for these criteria (taste, flavor, and overall acceptability) were between 3.5 and 4.49. Sample BBC3 had the lowest scores among the samples with different proportions of BF (BBC1, BBC2, and BBC3). It should be noted that for some attributes BBC3 scored higher than CC. These were appearance (4.29 \(\pm\) 0.69 vs. 3.41 \(\pm\) 1.33), color (4.24 \(\pm\) 0.66 vs. 4.12 \(\pm\) 1.36), and texture (3.94 \(\pm\) 0.75 vs. 3.71 \(\pm\) 1.31). From all this information, it should be noted that BF improved all the sensory qualities of buckwheat gluten-free cookies by up to 20%. It should also be noted that up to 30% BF continues to improve the appearance, color, and texture of cookies. However, at 30% BF and above, consumers were less and less appreciative of the taste and flavor of the cookies, probably due to the tangy, lemony flavor that baobab brings to the cookies, which was not necessarily to consumers' taste. In conclusion, the limit of acceptability of baobab in cookies is 20%. It was also reported in [34] that replacing 20% BF in the cookie improved sensory qualities. A comparison of the acceptable limit level of BF substitution in bakery products from several studies [25; 26; 34], including the present study, allows us to say that Baobab is more suitable for cookie production than cake and bread production from a sensory point of view. Indeed, the substitution limit for BFs was 10% in bread [25] and 15% in cakes [26] but can be as high as 20% for cookies (Figure 3; [34]).  
Figure 3: Overall sensory-evaluation values (consumer acceptance). Column values represent the mean of three determinations \(\pm\) standard deviation (SD). Within each characteristic category, statistically significant differences (\(p\) < 0.05) are indicated by different letters (a–d) in the columns. CC—control cookie with 100% buckwheat flour; BBC1—cookie with 10% baobab flour and 90% buckwheat flour; BBC2—cookie with 20% baobab flour and 80% buckwheat flour; and BBC3—cookie with 30% baobab flour and 70% buckwheat flour.
# A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall
## 3 Evaluation of the Performance of the Subject Section
### Evaluation of Soil Conditions in MSE Wall
#### 3.3.1 Soil Modulus from DCP Test and CPT
Due to the lack of a well-established model for the relationship between the CPT cone resistance and resilient modulus, a correlation study between the resilient moduli estimated from DCP results and cone resistances from CPTs was carried out for the project site to empirically determine the resilient moduli using the CPT results. At locations where both DCP tests and CPTs were performed (i.e., locations 3, 5, 6, 7, and 8), the resilient moduli from DCP tests were compared to the average cone resistance from CPTs for the corresponding soil layers. Then, regression analysis, as shown in Figure 17, was performed to establish a site-specific correlation between them for the subject section. The resilient moduli of backfill soils were then determined using the established correlation from the cone resistance measurements for the entire CPT investigation depths. Figure 18 presents the resilient modulus estimated via CPTs vs. depth, and the bottom of the MSE wall at each CPT location is indicated in the figure (note that CPT8 is a control location). Figure 18 shows that resilient moduli of backfill soils in the MSE wall from the non-control CPTs are, overall, less than 10 ksi (69 MPa) for the entire MSE wall height, except for near the wall base, suggesting the poor conditions of backfill soils in the MSE wall. In particular, CPT6 and CPT7, locations where severe distresses were observed in the outside lane, showed resilient moduli even smaller than 8 ksi (55.2 MPa) (note that the weighted average values of resilient moduli estimated from DCP at DCP6 OL LWP and DCP7 OL LWP were also smaller than 8 ksi (55.2 MPa), 5.3 ksi (36.5 MPa), and 7.0 ksi (48.3 MPa), respectively, as presented in Table 1). Figure 17: Correlation between resilient moduli from DCP tests and average cone resistances from CPTs for the corresponding layers.
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 6 Conclusions  
In this paper, a gear fault diagnosis method based on kurtosis criterion VMD and SOM neural network is proposed, and the following conclusions are obtained:  
(1) The VMD is selected to decompose the gear acceleration signal, which can effectively improve the mode mixing phenomenon of EMD, LMD, and other decomposition methods. Then, the kurtosis normalized value of components is selected as the feature vectors, which can not only retain the effective fault information in the signal, but also effectively eliminate noise interference.  
(2) The instantaneous frequency mean is selected as the basis of VMD decomposition number, which can effectively select the decomposition number in the VMD process. Through the above process, more representative components can be selected after VMD decomposition.  
(3) The SOM neural network has the ability of dimensionality reduction of multidimensional spatial data, which can be trained with fewer samples to obtain the spatial topological relationship of classified data and obtain higher accuracy. At the same time, SOM neural network can also conduct fewer training steps and obtain higher accuracy.  
Conceptualization, D.X. and J.D.; methodology, J.D.; writing--original draft preparation, D.X. and J.D.; writing--review and editing, J.D., D.X., X.L., and L.H.; funding acquisition, D.X. and X.L.  
This research was supported by National Natural Science Foundation of China (No. 51875195, 51875196).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Decomposition Algorithm** & **Classification Algorithm** & **Fault Diagnosis Identification Rate** \\ \hline VMD & SOM & 100\% \\ VMD & SVM & 25\% \\ EMD & SOM & 75\% \\ EMD & SVM & 14.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: The accuracy of gear fault diagnosis by different decomposition algorithms and classification algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Gear Fault Type** & \begin{tabular}{c} **Training** \\ **Steps** \\ \end{tabular} & **Normal Gear** & \begin{tabular}{c} **Gear with** \\ **Tooth Wear** \\ \end{tabular} & \begin{tabular}{c} **Gear with** \\ **Tooth Crack** \\ \end{tabular} & \begin{tabular}{c} **Gear with** \\ **Tooth Break** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Sample number \\ \end{tabular} & 100 & 3 & 4 & 7 & 8 & 11 & 12 & 15 & 16 \\ \hline
\begin{tabular}{c} Classification \\ results \\ \end{tabular} & 7 & 22 & 26 & 27 & 8 & 32 & 6 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 15: The classification results of the test samples at 1000 training times by EMD.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 1 Introduction
### Motivation  
Long-duration activities may require hot replacement of drones due to battery exhaustion. In this work, we would like to deploy aerial APs to provide VoWiFi service to ground users in an outdoor long-duration activity (e.g., concert or similar longer 20-25 min) that will likely require hot replacement of drones due to battery exhaustion. In this context, the following considerations should be taken into account:  
* Besides coverage, the multi-UAV 3D optimal placement has to consider VoWiFi speech quality for which the mac-sublayer of the communication protocol is crucial. To the best of our knowledge, the only work that has considered this factor [12] sets the focus only in minimizing the number of UAVs deployed (among solutions with a similar number of drones, the one that extended the battery life of served users was chosen) disregarding energy efficiency in UAVs.
* The number of UAVs required for the mission depends not only on the UAVs initially deployed but also on the number of UAVs launched to replace those in battery exhaustion. Thus, besides minimizing the number of UAV initially deployed, we should also seek the placement that maximizes the UAVs operation lifespan, considering UAV communication and mechanical energy.
* Long-duration events should consider users' mobility. So besides the initial 3D placement, there should be a method in place so that UAVs dynamically relocate themselves for service continuity.
###### Abstract
[12] adopted a 1-D modelling approximation for the coolant side, and the model was coupled with the turbulent reacting flow solver for the hot gas side. They analyzed different channel configurations of engines with kerosene, and reported good agreement with experimental coolant pressure and temperature measurements. A recent study by [PERSON] et al. [13] on a CO\({}_{2}\)/Mg engine, which is regeneratively cooled by CO\({}_{2}\), included a 1-D model, in which the thermal effects of the channel geometry were examined. Heat transfer characteristics in different phase regimes of CO\({}_{2}\) were analyzed, and the results of the model were compared with the data of an experimental work. Liquid hydrocarbons have been among the most widely studied fuels in rocket propulsion, mainly due to the fact that they possess a higher density compared to hydrogen  
Figure 1: Schematics of a regeneratively cooled liquid rocket engine and its cooling channels. and provide a higher specific impulse compared to other fuels. There has been a recent tendency to use green fuels in rocket engines. Methane and propane are two hydrocarbon fuels that are studied and used as rocket fuels and are also considered green propellants [14]. Especially with the advent of commercial rockets utilizing methane, LOX/liquid methane (LCH4) engines have been studied extensively over the last two decades [15; 16; 17; 18; 19; 20]. In some of these studies, 1-D regenerative cooling analyses of such engines were conducted. For example, [PERSON] [21] analyzed a LOX/LCH4 engine comparing copper and aluminum as engine materials. In that study, a 1-D tool was developed and shared for the preliminary analyses and parametric studies of only LOX/LCH4 engines. In a separate study, [PERSON] et al. [22] adopted a 1-D modelling approach for the variable thrust subcritical LOX/LCH4 engines, and analyzed the cooling performances of the coolant methane depending on the changing rated power level. On the other hand, although it has recently been utilized as a commercial rocket fuel and could be obtained as a biofuel [23], liquid propane (LC3H8) has not been studied to the same extent except for a few studies [24; 25; 26]. Furthermore, no recent study has compared these two hydrocarbon fuels, methane and propane, as a regenerative coolant. In this study, LOX/LCH4 and LOX/LC3H8 bipropellant liquid rocket engines were compared in terms of heat transfer characteristics using a simple 1-D modelling tool. Herewith, ODREC, a One-Dimensional Regenerative Cooling modelling tool, is introduced as an open-source, free access tool [27]. ODREC is intended to present a reliable approximation to the problem by avoiding the complexity and the computational cost of the coupled CFD and conjugate heat transfer analyses. In the current study, the cooling performances of fuel and oxidizer were compared for both engines under the effect of phase change using ODREC.
## 3 Results and Discussion
### Analysis of Water Vapor Flux Divergence and Water Vapor Flux
The eastern low-pressure center was moving toward Tonga after the eruption (Figure 10d), and the influence of water vapor on CKIS was weakened (Figure 4i), so the PWV of CKIS returned to normal after 17 January [48]. The formation of the cyclone in eastern Tonga may have been related to the eruption of the HTHH volcano and may have affected precipitation in the South Pacific [49; 50; 51]. It is worth noting that the volcanic activity in Tonga volcano was also intense on the 15 th. Therefore, we can conclude that volcanoes have a certain correlation with water vapor activity. Figure 9: The 850 hPa water vapor flux divergence (color shaded areas, unit: g\(\cdot\)cm\({}^{-2}\)\(\cdot\)h\(\cdot\)Pa\({}^{-1}\)\(\cdot\)s\({}^{-1}\)) and 850 hPa water vapor flux (arrows, Unit: g\(\cdot\)cm\({}^{-1}\)\(\cdot\)hPa\({}^{-1}\)\(\cdot\)s\({}^{-1}\)) from 14 to 17 January 2022 at 23:00 (UT). The red and green boxes indicate the main areas of water vapor flux concentration. All red and green boxes indicate variations in the same water vapor flux concentration area.