esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Ablation Experiment
#### 4.4.3 Ablation Experiment on Depthwise Separable Convolution  
The advantage of DWConv is the reduction in the convolution parameters, which was the main reason for adopting it in the proposed model. To verify the feasibility of DWConv in the model, a series of comparative experiments were performed, especially compared with the current mainstream lightweight convolution. The detailed experimental results are shown in Table 4.  
The experimental results indicated that adopting DWConv significantly improved the performance. Although there was a slight sacrifice in the FPS, there were substantial  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & \multirow{2}{*}{**FPS**} & **Parameters** & **FLOPs** & **Weights** \\  & **(\%)** & **(\%)** & & **(M)** & **(G)** & **(MB)** \\ \hline Conv & 98.4 & 69.0 & 47.02 & 7.84 & 20.1 & 15.9 \\ GhostConv [65] & 98.5 & 68.7 & 44.63 & 6.70 & 17.9 & 13.7 \\ GSConv [66] & 98.2 & 68.9 & 41.28 & 6.70 & 17.9 & 13.7 \\ SPConv [67] & 98.2 & 68.3 & 36.44 & 25.84 & 16.2 & 12.0 \\ AKConv [68] & 98.4 & 69.0 & 34.15 & 6.35 & 17.2 & 13.0 \\ DWConv & 98.4 & 68.6 & 44.85 & 5.54 & 15.5 & 11.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different lightweight convolution experimental results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & \multirow{2}{*}{**Parameters**} & **FLOPs** & **Weights** \\  & **(\%)** & **(\%)** & **(M)** & **(G)** & **(MB)** \\ \hline YOLOv8n & 98.2 & 67.7 & 3.01 & 8.1 & 6.2 \\ YOLOv8s & 98.3 & 69.0 & 11.13 & 28.4 & 22.5 \\ YOLOv8m & 98.2 & 69.4 & 25.84 & 78.7 & 52.0 \\ YOLOv8l & 98.1 & 69.2 & 43.61 & 164.8 & 87.6 \\ YOLOv8x & 98.2 & 69.4 & 68.13 & 257.4 & 136.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline model selection.  
reductions in the number of parameters, FLOPs, and weight, decreasing by 29.34%, 22.89%, and 28.93%, respectively. This indicates that DWConv effectively enhances the model performance while also significantly reducing the computational and storage overheads. In practical applications, this allows for a more lightweight and efficient model without sacrificing too much real-time performance, providing superior solutions for specific scenarios.
## 3 Results
### Spatial Distribution of the Soil Properties  
The spatial distribution of the SOC, STP, and SAP in the study area was mapped based on the RF and XGBoost models without and with variable selection (Figure 5). No distinct spatial patterns were observed for any soil properties predicted by the all-variable models (RF_all and XG_all). The overall trends of the soil properties predicted by the different methods barely changed after variable selection. High values of the SOC were distributed in the northwestern and southeastern parts of the sub-watershed, with low values in the central part. The location of the high SOC areas was highly consistent with the distribution of water areas within the sub-watershed. The high SAP areas were concentrated in the central part of the sub-watershed, with a cloud-like distribution. All three soil properties showed a high spatial heterogeneity.  
The relative importance of the variables in the RF model was calculated. Among all types of variables, the significant variables for the SOC were mainly related to remote sensing (36.51%) and human activity (29.29%; Figure 6). PCA2 (31.65%) was the most important variable for the SOC, followed by PM2.5 (19.31%). The STP was prominently influenced by TDLY (62.27%) related to human activity. This affirms a high consistency between the spatial distributions of STP and the land use types in the study area. The organism covariates (66.58%) followed by the relief (24.06%) were important for the SAP, with PET accounting for the highest percentage (37.05%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Property** & **Model \({}^{\star}\)** & _RMSE_ & _MAE_ & \(R^{2}\) \\ \hline \multirow{4}{*}{SOC} & RF\_all & 0.41 & 0.33 & 0.45 \\  & RF\_sel & 0.25 & 0.17 & 0.79 \\  & XG\_all & 0.44 & 0.36 & 0.37 \\  & XG\_sel & 0.22 & 0.12 & 0.84 \\  & RF\_all & 171.91 & 136.82 & 0.20 \\ \multirow{4}{*}{STP} & RF\_sel & 71.21 & 50.01 & 0.86 \\  & XG\_all & 163.76 & 128.10 & 0.27 \\  & XG\_sel & 62.67 & 37.86 & 0.89 \\  & RF\_all & 14.22 & 12.85 & 0.27 \\ \multirow{4}{*}{SAP} & RF\_sel & 6.54 & 3.94 & 0.70 \\  & XG\_all & 11.81 & 9.02 & 0.15 \\ \cline{1-1}  & XG\_sel & 5.69 & 3.05 & 0.77 \\ \hline \hline \end{tabular}  
* RF_all and RF_sel represent the random forest models with all variables and selected variables, respectively; and XG_all and XG_sel represent the extreme gradient boosting models with all variables and selected variables, respectively. The models’ prediction accuracy was evaluated in terms of the mean absolute error (_MAE_), root mean square error (_RMSE_), and coefficient of determination (\(R^{2}\)).  
\end{table}
Table 4: Model prediction accuracy for the soil properties in the study area.
## 2 Materials and Methods
### Datasets
#### 2.2.2 Rain Gauge Data  
Based on the 25-km distance of the barycenter of the central area, the investigated area could be classified into three categories: urban area (Ur), outskirt of urban area (OUr) and outer suburb area (OS) (see Figure 3). Daily rain gauge-based precipitation observations from January 1979 to December 2010 were obtained from the Shanghai Meteorological Bureau ([[http://sh.cma.gov.cn/](http://sh.cma.gov.cn/)]([http://sh.cma.gov.cn/](http://sh.cma.gov.cn/))). As shown in Figure 3, Xujiahui gauge (red dot) is located in the Ur region. Minhang, Baoshan, Jiading and Songjiang gauges (orange dots) are distributed in the OUr region (the distance of the barycenter of the central area is less than 25 km). In addition, other gauges (e.g., Qingpu, Jinshan, Nanhui and Chongming gauges) are positioned in the OS region, which are far from the city center (distance of the barycenter of central area is more than 25 km).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Period** & **Date** & **Sensors** \\ \hline
1979 & 4 August & Landsat Multispectral Scanner (MSS) \\
1990 & 4 December & Landsat-5 Thematic Mapper (TM) \\
2000 & 29 June & Landsat-7 Enhanced TM (ETM+) \\
2010 & 22 May & Landsat-7 Enhanced TM (ETM+) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the four-periods for the Landsat satellite images.  
Figure 2: Spatiotemporal distributions of built-up and non-built up areas in Shanghai.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Types of Land Use** & **LULC Descriptions** \\ \hline Built-up area & / & Urban land, settlements, other construction land (industrial land, transportation land, airport, special land, etc.) \\  & Farmland & Crop fields, vegetable field, fallow and alternate land, etc.) \\ Non-built-up area & Green land & Land used for growing forest, arbors, shrubs, bamboos, coastal \\  & Bare land & Land to be built on, unused land, rock, beaches, etc. \\  & Waters & Rivers, lakes, reservoirs, ponds, mud flat, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of each land use type.  
In addition, we selected five rain stations located in coastal areas around Shanghai including one in Jiangsu province (Lvsi site) and four in Zhejiang province (Pinghu, Cixi, Dinghai and Shengsi sites) (see Figure 4). Given that these stations are mainly distributed on islands and in coastal regions, their precipitation trend changes are strongly affected by climatic factors, especially for the oceans.  
Figure 4: Locations of Xujiahui and five coastal stations.  
Figure 3: Location of the selected 10 rain gauges in Shanghai and the two surrounding rain gauges.
## 4 Comprehensive Review
### A Comprehensive Overview of Bioactive Compounds in Different Food Drying Systems
#### 4.1.4 Ultrasound
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Pretreatment** & **Drying Method** & **Material** & **Main Findings** & **Reference** \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{Ultrasound} & \multirow{4}{*}{Spray drying} & \multirow{4}{*}{Oregon} & The ultrasound-extracted oregano extract was more active and selected for microencapsulation, showing higher phenolic (29\%) and protein (4.7\%) content. Its IC\({}_{50}\) was 0.27 g/L, lower than that of the sequential treatment (0.49 g/L). Ethanol and ultrasound pretreatments accelerated drying and reduced energy consumption. Bananas treated with ethanol + ultrasound showed better spherical quality, attractive color, and greater preservation of biouctives due to shorter drying time. & [94] \\  & & & & Vacuum microwave drying & \\ \multirow{4}{*}{9} & \multirow{4}{*}{Ethanol and ultrasound} & \multirow{4}{*}{Convective drying} & \multirow{4}{*}{Cranberries (_Vaccinium_ _oxyoccus_)} & \multirow{4}{*}{Cranberries (_Vaccinium_ _oxyoccus_)} & \multirow{4}{*}{Cranberries (_Vaccinium_ _oxyoccus_)} & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._Overall, while US offers a promising approach for improving drying efficiency and preserving bioactive compounds, the precise control of operational parameters, such as the amplitude, frequency, and treatment duration, is crucial to maximize its benefits and prevent potential losses due to excessive degradation or oxidation.
# Output Force Density Saturation in COMSOL Simulations of Biomimetic Artificial Muscles
###### Abstract  
Many modern applications, such as undersea drones, exoskeletal suits, all-terrain walker drones, prosthetics, and medical augments, would greatly benefit from artificial muscles. Such may be built through 3D-printed microfluidic devices that mimic biological muscles and actuate electrostatically. Our preliminary results from COMSOL simulations of individual devices and small arrays (\(2\times 2\times 1\)) established the basic feasibility of this approach. Herein, we report on the extension of this work to N \(\times\) N \(\times\) 10 arrays where N\({}_{\text{max}}\) = 13. For each N, parameter sweeps were performed to determine the maximal output force density, which, when plotted vs. N, exhibited saturation behavior for N \(\geq\) 10. This indicates that COMSOL simulations of a \(10\times 10\times 10\) array of this type are sufficient to predict the behavior of far larger arrays. Also, the saturation force density was \(\sim\)9 kPa for the 100 \(\upmu\)m scale. Both results are very important for the development of 3D-printable artificial muscles and their applications, as they indicate that computationally accessible simulation sizes would provide sufficiently accurate quantitative predictions of the force density output and overall performance of macro-scale arrays of artificial muscle fibers. Hence, simulations of new geometries can be done rapidly and with quantitative results that are directly extendable to full-scale prototypes, thereby accelerating the pace of research and development in the field of actuators.  
acutator; muscle; biomimetic; electrostatic; architecture; microfluidic; COMSOL; simulations 2023
## 3 Methodology
### Principles of the PSO-LSSVM Model  
#### 3.2.1 Least Squares Support Vector Machine (LSSVM)  
The LSSVM is a supervised learning method that is not only used to analyze data and identify patterns, but is also used for classification and regression analyses. The LSSVM applies equality constraints to inequality constraints of the SVM, thereby converting the quadratic optimization problem into a system of linear equations that is based on the least squares criterion.  
\((x_{i},y_{i})_{i=1}^{l}\) is a dataset of \(l\) samples where the input data, \(x_{i}\), are a D-dimensional input vector and \(y_{i}\) denotes the target data. The regression estimation function of the LSSVM is as follows:  
\[f(x)=w^{T}\varphi(x)+b \tag{8}\]  
where \(f(x)\) denotes the predictive values, \(\varphi(x)\) is the nonlinear mapping from the input space to the output space, and \(w\) and \(b\) are the coefficients to be adjusted.  
Transforming the regression estimation function into a function minimization problem using the slack variable \(e\) is performed by the following:  
\[\text{Minimize }J(w,e)=\frac{1}{2}\|w\|^{2}+\frac{1}{2}\gamma\sum\limits_{i=1}^{l}e _{i}^{2} \tag{9}\]  
\[\text{Subject to }y_{i}=w^{T}\varphi(x_{i})+b+e_{i},i=1,\dots,l \tag{10}\]  
where \(\gamma\) is a regularization parameter.  
To solve the optimization problem of the LSSVM, the Lagrangian function is constructed as follows:  
\[L(w,b,e,\alpha)=J(w,e)-\sum\limits_{i=1}^{l}\alpha_{i}\Big{(}w^{T}\varphi(x_{ i})+b+e_{i}-y_{i}\Big{)} \tag{11}\]  
where \(\alpha_{i}\) is the Lagrange multiplier. The conditions for optimality are given by:  
\[\left\{\begin{array}{l}\frac{\partial L}{\partial w}=0\to w=\sum \limits_{i=1}^{l}\alpha_{i}\varphi(x_{i})\\ \frac{\partial L}{\partial b}=0\rightarrow\sum\limits_{i=1}^{l}\alpha_{i}=0 \\ \frac{\partial L}{\partial e_{i}}=0\rightarrow\alpha_{i}=\gamma e_{i}\\ \frac{\partial L}{\partial a_{i}}=0\to w^{T}\varphi(x_{i})+b+e_{i}=y_{ i}\end{array}\right. \tag{12}\]  
According to the Karush-Kuhn-Tucker optimum condition, the following linear equation set is obtained:  
\[\left[\begin{array}{cc}0&I_{l}^{T}\\ I_{l}&K+\gamma^{-1}I_{l}\end{array}\right]\left[\begin{array}{c}b\\ \alpha\end{array}\right]=\left[\begin{array}{c}0\\ y\end{array}\right] \tag{13}\]  
where \(I_{l}=[1,1,\dots,1]^{T}\), \(\alpha=[\alpha_{1},\alpha_{2},\dots,\alpha_{l}]^{T}\),\(y=[y_{1},y_{2},\dots,y_{l}]^{T}\), \(I_{l}\) is an \(l\times l\) identity matrix, \(K\) is a kernel function with \(l\times l\) symmetric matrix, and \(K_{ij}=\varphi(x_{i})^{T}\varphi(x_{i})=K(x_{i},x_{j})\).  
There are three commonly used kernel functions available: the linear kernel, polynomial kernel, and radial basis kernel function (RBF). In this paper, the radial basic function (RBF) is used as the kernel function of the LSSVM model because of its wide convergence domain and excellent nonlinear mapping performance [44]. Finally, the regression function of the LSSVM prediction model can be obtained by:  
\[f(x)=w^{T}\varphi(x)+b=\sum\limits_{i=1}^{l}\alpha_{i}K(x,x_{i})+b \tag{14}\]
# TDM-PON PAM Downstream Transmission for 25 Gbit/s and Beyond
## 3 Simulation Results
### 37.5 Gbit/s PAM8  
PAM8 is generated from three PRBS15 data streams. The amplitudes of two of the streams are attenuated, the former by half and the latter being divided by 4. A different timing delay equal to an integer number of bits is applied on each PRBS15 in order to decorrelate the streams. Natural labeling PAM8 decoding is based on seven decision thresholds and eight exclusive-OR operations (Figure 5).  
We observe that a 37.5 Gbit/s PAM8 transmission cannot be correctly performed using the simulation parameters issued from our 25 Gbit/s PAM4 calibration. This is mainly due to the noise added to emulate our homemade decoder. The amplitude levels are too close from one another to be correctly distinguished from each other; thus, the BER never falls below the FEC threshold, whatever the OB (see Figure 6a and \"500\" a.a.u. curve in Figure 6d).  
In order to evaluate how a better-quality decoder device would mitigate the observed drawback and allow a 37.5 Gbit/s system, the standard deviation of the decoder noise was decreased progressively (see eye diagrams in Figure 6a-c). The first OB curve that does not exhibit a pronounced error floor is obtained for a Gaussian noise standard deviation equal to 140 a.a.u. (which represents a noise amplitude around \(\sim\)6 mVPP on each level for a 120 mVPP PAM8 signal). In this configuration, PAM8 still does not reach a 28 dB (see Figures 6d and 7) and is not compliant with any of the  
Figure 4: (a) Calibration of the simulation with experiment for PAM4 transmission in optical back to back at 25 Gbit/s with a PRBS15; (b) Eye diagram with rectangular decision masks.  
Figure 5: Natural labeling PAM8 decoder.  
budget classes depicted in the standard. We can thus fairly say that realizing PAM8 IMDD transmission compliant with ODN class N1 would only be possible with a far better decoder or at least with DSP or equalization procedures, which should be avoided to allow cost-efficient CPEs.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 1 Introduction  
### Research Background  
The Internet of Things (IoT) market is expected to grow at an average annual rate of 30% [1]. In particular, the narrowband Internet of Things (NB-IoT) has excellent potential for development as a 5G IoT standard technology. The NB-IoT currently connects 100,000 devices and must improve its performance to meet the requirement for 5G communication [2, 3]. Improvements in satellite communications are currently attracting attention. Satellites offer extensive coverage and are not affected by terrain obstacles [4]. The 3 rd Generation Partnership Project (3 GPP) is considering new radio (NR)-based non-terrestrial networks (NTN) for 5G activation and expansion. The 3 GPP conducted a study on this topic in Release 15 [5], and a further study regarding IoT and enhanced machine type communication (eMTC) support for NTN was conducted in Release 17 [6, 7]. The satellites discussed by the 3 GPP include geostationary earth orbit (GEO) and low earth orbit (LEO) satellites. Satellites in LEO have the advantage of a shorter propagation delay than those in GEO; therefore, research on LEO satellites is being conducted actively. In particular, interest in LEO satellites has increased with the advent of Falcon 9, a reusable space launcher developed by SpaceX [8, 9]. In conventional satellite communications, ground stations useparabolic antennas. However, since a parabolic antenna has a large volume, research on beamforming through a phased array antenna is being conducted [10; 11; 12].  
Two problems must be addressed for LEO satellites to communicate with the NB-IoT. The first is a high Doppler shift. Unlike GEO satellites, LEO satellites orbit the Earth at high speeds when viewed from the Earth, causing high Doppler shifts to occur when communicating with them. This increases the carrier frequency offset, making it difficult for the receiver's channel estimation [13; 14; 15]. Traditional LEO satellite communications involve a single communication link with the gateway. The global navigation satellite system (GNSS) determines the satellite's position and calculates the Doppler shift, making compensation possible [16; 17; 18].  
However, it is not easy to apply these methods to NB-IoT uplinks that use single carrier frequency division multiple access (SC-FDMA) [15; 19]. SC-FDMA is a technology that bundles multiple carriers together. Because the bundled carrier has a single frequency but combines signals with different Doppler shifts, compensating for the individual Doppler shifts is challenging.  
The second problem in data rate reduction is due to the long round-trip time (\(RTT\)). Among current NTN target performance scenarios, IoT connectivity requires a data rate of 10 kbps or more [20]. Still, it is challenging to meet the requirement because of the current long \(RTT\). In 3 GPP Release 17 [7], there is no consensus on a solution to improve throughput due to the battery life problem caused by GNSS. Therefore, this study shows the achievable throughput in the scenarios specified.
## 3 Results and Discussion
### Experimental Campaign  
The goal of the field trials campaign is to assess the camera contribution to the improvement of a PVT solution computation and to validate the fusing algorithm. The experimental configuration is illustrated in the right-hand part of Figure 5. The data collecting system consists of:  
* Fisheye camera;
* GNSS Data Acquisition System (GDAS-2) used to capture and record GNSS raw signals;
* Thales Alenia Space Software (SW) receiver GEMS [7] fully instrumented with a MC discriminator [28] used to simulate the proposed camera aiding solution and to provide the corrected trajectory;
* The live-sky testing platform, including the car carrying the equipment, was provided by GNSS Usage Innovation and Development of Excellence (GUIDE), a Toulousian test laboratory. It embeds a GBOX that provides the reference trajectory based on high-grade INS and GNSS differential corrections from the International GNSS Service (IGS).  
The reference trajectory is then compared to the corrected trajectory obtained by computing the car's positions with the proposed algorithm, to calculate the positioning error and evaluate the integrity of the proposed navigation algorithm.  
The campaign was organized on January 2016 in Toulouse on the itinerary shown in the left-hand part of Figure 5. The main trajectory lasted 1800 s in an urban environment and involved 18 satellites from three constellations (8 GPS; 2 Galileo; 8 GLONASS), among which a maximum of 12 satellites were used simultaneously in the PVT.  
Figure 5: Experimental configuration.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 1 Introduction  
Glycosylation is the process by which a carbohydrate is attached to a target macromolecule, typically proteins and lipids. In respect to proteins, this process is called \"protein glycosylation\" and the newly formed molecule is called a \"glycoprotein\" [1]. Protein glycosylation is a widespread post-translational modification that is conserved among various organisms and most cells of a specific individual [2]. Such a process plays a significant role in protein folding, targeted transport, cellular localisation and activity [3]. In an organism, glycoproteins are involved in multiple biological processes: cell recognition, differentiation, development, signal transduction, and immunity response, etc. For pathogen-host interactions, the pathogenicity, on the one hand, and host resistance (susceptibility) on the other, can be attributed to the glycosylation, for instance, pathogen-secreted proteins, host cell-surface, and organelle-resident proteins [4]. Glycoproteins are found in various forms, e.g., enzymes (nuclease, protease, glycosidase) [5; 6], peptide hormones (chorionic gonadotropin, luteinizing hormone, thyrotropin, erythropoietin) [7], antibodies [8], lectins [9], membrane bond proteins [10], collagen [11], and fibronectin [12], some of which have great significance for clinical medicine. Those glycoproteins discussed in this review, with roles in host-pathogen interactions, are listed in Table 1.  
Due to the structural variability of carbohydrate side chains, glycosylation increases the diversity of the proteome to a level unmatched by any other post-translational modifications [2]. While carbohydrate is universal in nature, only a few monosaccharides can serve as a donor in glycosylation. The main substrates in the protein glycosylation are hexose and its derivatives, including p-glucose [43], p-mannose [44], p-galactose [45], fucose [46], \(N\)-acetyl-glucosamine [47], and galactosamine [48]. In some proteins, sialic acid (SA) or xylose are also the substrates of protein glycosylation [49; 50]. During glycosylation, straight or branched oligosaccharides consisting of 2-20 monosaccharide units are covalently bonded with amino acid side chains in proteins to form glycoproteins, catalyzed by glycosyltransferases which located in the endoplasmic reticulum (ER) and Golgi complex [51].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
According to the amino acid atom to which the carbohydrate chain is linked, the glycoproteins can be divided into \(N\)-linked and \(O\)-linked glycoprotein subtypes [2] (Figure 1). Protein modification by structurally varying glycosylation can either be favourable or disadvantageous for the infected organism, as it affects both, the host resistance and the pathogen virulence. In this review, we summarize the roles of protein glycosylation in pathogen-host interactions.