esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 5 Global Performance of the Bottom-Set Gillnet  
### Effects of Current Velocity  
Figure 6 shows the lateral-response envelopes of the net assembly at various current velocities. Waves were not considered in this section to observe the effects of constant-current velocity. Generally, the net assembly stretched well between respective anchors/mooring lines for the given current-velocity range. Except for the current velocity of 0.2 m/s, lateral envelopes were almost the same because mooring lines maintained the net position tightly without any more extension. Figure 7 presents the envelopes of the net heights at diverse current velocities. Current velocity greatly influences net height, and, generally, the higher the current velocity, the lower the net height without wood bars [17,32,33]. However, in the present net system, the net height was maintained well due to the adoption of wood bars, as shown in Figure 7. As mentioned before, wood bars were located in the net assembly at 50 m intervals. Because of these wood bars, the net heights around the wood bars kept their original net height of 2.0 m while reductions were observed between them. The most substantial reduction in net height was observed at the smallest current velocity of 0.2 m/s since too low current velocity results in less drag force to stretch the net assembly fully; however, its reduction was not significant. In addition, net-height reduction at high current velocities was largely improved by the existence of wood bars. In other words, the net assembly was well stretched in the vertical direction at all current velocities, mainly due to the wood bars. In conclusion, the designed bottom-set gillnet showed excellent stretching performance in the lateral and vertical directions across a typical range of current velocity. In addition, as shown in Figure 5, mooring tensions increased with current velocity because of the increased drag forces on the gillnet. Higher mooring tensions were observed in top mooring lines because current velocity decreases with submergence depth.  
Figure 6: Envelopes of the lateral responses (Z = \(-\)23 m) of the net assembly at different current velocities.  
Figure 7: Envelopes of the vertical responses (net-top motion minus net-bottom motion) of the net assembly at different current velocities.
## 4 Results and Discussion
### Degree of Anisotropy  
A common metric for determining the anisotropy of a material with cubic symmetry is the Zener index [52] (or Zener ratio), which is defined by (16).  
\[A=\frac{2G(1+\
u)}{E} \tag{16}\]  
Isotropic materials have Zener index of one. The further from one, the more anisotropic the material. The Zener index has been calculated for the gyroid using the semi-empirical equations developed previously, and the results plotted in Figure 16.  
The figure shows that the gyroid's Zener index varies with varying density, but that it remains close to one (0.95 \(\leq A\leq\) 1.25). By comparison, some other cubically symmetric  
Figure 16: Zener index of gyroid infills.  
Figure 15: FEA predictions vs. semi-empirical equation for Poisson’s ratio.  
infills have been shown to exhibit Zener ratios above two [53]. It is clear, then, that the gyroid is very-nearly isotropic. This near isotropy greatly simplifies implementation of gyroids into design and optimization workflows.
## 3 Case Study
### Real-World Case Study
The important knowledge, however, has been successfully incorporated in the form of interaction noise in the proposed MANN model. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{10}{c}{**Weights**} \\ \cline{2-10}  & **ANN1** & **ANN2** & **ANN3** & **ANN4** & **ANN5** & **ANN6** & **ANN7** & **ANN8** & **ANN9** \\ \hline
**b1->h1** & 0.11 & \(-\)3.74 & \(-\)2.15 & \(-\)0.15 & \(-\)0.11 & 5.61 & \(-\)0.05 & 0.23 & 0.03 \\
**i->h1** & 0.66 & 5.64 & 2.54 & \(-\)6.28 & 2.04 & \(-\)7.38 & 1.97 & \(-\)2.48 & \(-\)0.54 \\
**b1->h2** & 0.03 & 0.65 & \(-\)0.53 & 0.29 & 4.02 & 0.56 & 6.08 & 0.24 & 0.53 \\
**i->h2** & 0.14 & 0.52 & 1.08 & \(-\)1.02 & \(-\)4.93 & 2.65 & \(-\)7.00 & \(-\)2.48 & \(-\)0.42 \\
**b1->h3** & \(-\)5.43 & 0.66 & 1.04 & \(-\)3.16 & 0.60 & 0.09 & 0.28 & 0.24 & 2.73 \\
**i->h3** & 6.00 & 10.09 & \(-\)1.58 & 3.85 & 12.06 & 0.13 & 0.80 & \(-\)2.48 & \(-\)5.02 \\
**b2->o** & \(-\)0.49 & 2.31 & \(-\)0.70 & \(-\)0.21 & 4.87 & 0.91 & 1.42 & 2.33 & 0.55 \\
**h1->o** & \(-\)0.75 & 4.81 & 3.05 & \(-\)4.61 & 4.83 & \(-\)4.12 & 2.18 & \(-\)2.87 & 0.62 \\
**h2->o** & \(-\)0.24 & 1.71 & 1.18 & \(-\)1.24 & \(-\)3.58 & 2.25 & \(-\)4.72 & \(-\)2.87 & 0.60 \\
**h3->o** & 6.76 & \(-\)5.56 & \(-\)2.32 & 4.12 & \(-\)5.68 & 0.47 & 1.30 & \(-\)2.87 & -2.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training weights of MANN with interaction noise. Input node, hidden nodes and output node are represented by i, h1 to h3 and o, respectively; b1 and b2 are the bias nodes in the hidden layers. Figure 6: Maximum correlation coefficients between various transition probabilities. Figure 8: (**a**) Original image obtained from **R** software data set (jura.grid); (**b**) Classification maps generated by MANN with interaction noise. Simulations are conditioned on 359 samples in Jura lithology data set, note the small Quaternary patches (visualized in black) neglected in (a) are successfully recovered in the result of the proposed method (b). Figure 7: Training errors in the iteration process of MANN with interaction noise; different colors and line styles are used to distinguish the nine compositional ANNs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Argovian** & **Kimmeridgian** & **Sequanian** & **Portlandian** & **Quaternary** & **Total** \\ \hline MANN with & 75.78\% & 78.19\% & 64.07\% & 0.00\% & 43.69\% & 65.12\% \\ interaction noise & (898/1185) & (1592/2036) & (1043/1628) & (0/316) & (346/792) & (3879/5957) \\ \hline MCRF & 72.07\% & 74.17\% & 61.61\% & 0.00\% & 47.60\% & 62.85\% \\ (854/1185) & (1510/2036) & (1003/1628) & (0/316) & (377/792) & (3744/5957) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction accuracy comparison of the Jura data set.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 2 Materials and Methods
### Mechanical System  
The microscope mechanical system is constituted by a combination of racks and pinions to achieve the stage motion on the X-Y axes and coarse/fine focus on the Z axis. These motions are manually applied by four knobs. In addition, the rotating nosepiece turret allows changing the four objectives connected to it. In order to accomplish the automatic movement of the three mentioned systems--X-Y stage, Z focus, and nosepiece turret--several elements are employed (see the details in Figure 3):  
* Bipolar stepper motors NEMA 14 (3 uds.) with step angle 0.9\({}^{o}\)/400, 17 oz-in(torque), and 650 mA (max. current) for movements following the X-Y/Z axes. These are coupled to the knobs and stage by customized adapters: (A) a supporting plate attached to the stage, made of thermoplastic PLA (polylactic acid) filament by 3D printing; (B) a pulley-belt for the X-Y stage coordinates; and (C) a direct drive for the focus Z coordinate.
* Bipolar stepper motor NEMA 17 with step angle 1.8\({}^{o}\)/200, 44 oz-in (torque), and 1200 mA (max. current) for the nosepiece turret objective changer. In this case, the customized adapter consists of a cogwheel pair--made by 3D printing.
* Optical limit switch for controlling the motors' homing motions.  
The four stepper motors are controlled by an Arduino Mega 2560 board, with a Ramps 1.4 shield for the POLOLU A4988 current driver, connected by a USB2.0 interface to the main microscope PC controller.  
An important limitation of the mechanical system is derived from backlash effects in the transmission elements (see Figure 4). This effect appears during X-Y and Z motions with pernicious consequences on precision, but inherent to the use of low-cost components. In our system, backlash is more severe in the pulley-belt transmissions (i.e., X-Y stage motion). In the Z focus axis, backlash has less magnitude because a direct transmission is employed. Backlash represents a truly tough problemwith a difficult solution. For mitigating its effect, the more precise controlled movements should be done in the same direction whenever possible to standardize the derived position bias.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 3 An Overview of LoRaWAN and SD-WAN Technologies
### WAN and SD-WAN Technologies
#### 3.2.2 SD-WAN Architecture and Security Options  
SD-WAN was developed to replace the classic WAN. The core job of SD-WAN is to simplify all the functions of a WAN to provide flexibility and scalability over the traditional WAN. The architectural structure of SD-WAN includes three layers [40]. The \"data\" layer consists of hardware and software devices with similar functionalities close to IP networks which can virtualize bandwidth by forwarding data, i.e., to 4G/LTE mobile networks. Sometimes routers are also considered virtual cloud services, such as laas. The \"control\" layer is responsible for network improvement, energy traffic monitoring, and providing a better quality of service for all the transmitted data. It can also perform many managed implementations for debugging, collecting information about the network topology and its  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **GSM** & **LTE-M** & **5G (Targets)** \\ \hline Coverage Area & \textless{}15 km (164 dB) & \textless{}10 km (156 dB) & \textless{}12 km (160 dB) \\ \hline \multirow{3}{*}{Frequency} & Licensed & Licensed & Licensed \\  & 800, 900 MHz & 700, 900, 1.4 MHz shared & 700, 900 MHz shared \\ \hline Data Rate & 10 kbps & \textless{}1 Mbps & \textless{}1 Mbps \\ \hline \multirow{2}{*}{Network Reforming} & Moderate & \multirow{2}{*}{Small} & Requires \\  & (LTE reuse) & & 5G NWs \\ \hline \hline \end{tabular}
\end{table}
Table 3: WAN Comparison Performance.  
status, and managing the number of real-time connected devices. The \"application\" layer allows network administrators and application developers to configure the requirements for an efficient network with a low energy cost and high availability [32; 33; 40; 41].  
The SD-WAN provides secure connections from edge to edge, allowing users to create a new complicated network infrastructure between MPLS and LTE to increase their critical connection experience. The SD-WAN can be traffic manageable by providing priority to specific nodes. Many hardware devices are no longer needed due to the virtualizing of services, and therefore lower costs and better performance, either to bandwidth or to the network, are achieved. Available scalability and agility are flexible services to add any new sites. The QoS for data transition is higher due to load balancing because the network is centralized and controlled [32; 42]. It also provides end-to-end encrypted traffic within the network over the internet. All components are fully certified and safe, and the \"certified keys\" exchanging procedure gives SD-WAN the possibility of seamless communication between headquarters and data centers, as this communication is always safely controlled since it is encrypted [43].
## 2 Materials and Methods
### Determination of the Track Axis and Measurement Uncertainty
After transforming these ECEF coordinates into the local east-north-up (ENU) frame, a rotation matrix is applied to update the variance-covariance matrix, from which the DOP coefficients--HDOP (horizontal), VDOP (vertical), and PDOP (positional)--are derived [30]. Finally, these DOP values are converted into the CQ quality indicators: CQ2D for horizontal position, CQ1D for vertical position, and CQ3D for three-dimensional positioning errors [31]. Trimble R10 GNSS receivers were used to collect the data for the study. The receiver-transmitted satellite data and received RTK/RTN correctional data were processed jointly in the Trimble Access software of the receiver controller. To estimate the single-point error, despite the Dilution-Of-Precision (DOP) coefficients, the signal analysis and filtering were performed by the software. The quality check of the calculated data included the issue of elimination of potentially erroneous satellite signals. A sample measurement record is presented in Table 1.  
The receiver software used in the described position calculated only part of all receiver satellite signals: 6 satellites out of 8 for GPS system (GPS SVs) and 4 out of 5 of the GLONASS system (GLONASS SVs). Hence, the obtained CQ values include the important issue of elimination of false observations. Independent and reliable ground truth data for the analyzed railway track axis were not available. Typically, the coordinates of points describing the railway axis in Poland are obtained through tachymetric measurements, which rely on the national geodetic control network. Given the length of the analyzed track section, these measurements were based on control points from multiple geodetic traverses. As a result, the adjusted coordinates of the control points may have been affected by errors ranging from a few millimeters to several centimeters. This implies that, despite using a consistent reference frame (PL-2000), significant discrepancies may arise in large-scale analyses, such as the one presented in this study.
## 2 Model Description
### Mesh-Sensitive Test and Model Validation  
The geometry of the header is discrete by the hexahedral mesh (Figure 2) and the first layer of the mesh is set in the sub-layer region, in which y+ is larger than 11.225. Moreover, four sets of cases with different mesh numbers are simulated for the mesh-sensitive test. Figure 3a shows that when the mesh number is more than 2.03 million, the liquid drainage rate by the orifice (\(\mu_{\text{L,O}}\)) changes insignificantly. Hence, to balance the calculation consumption and the accuracy of the numerical model, the mesh number of 2.03 million is selected, in which the minimum orthogonal quality, the maximum ortho skew and the aspect ratio are 0.54, 0.43 and 2.53, respectively.  
The numerical model is also validated based on the experiments [15]. It is seen from Figure 3b that when the inlet vapor quality \(x_{\text{in}}\) changes from 0.45 to 0.82, the average deviation between simulation data and experimental results is less than 10%, indicating good validity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Equations**} \\ \hline Volume Fraction & \(\frac{\partial}{\partial t}\left(a_{\text{p}}\rho_{\text{q}}\right)+\
abla \left(a_{\text{q}}\rho_{\text{q}}\overset{\rightarrow}{u_{\text{q}}}\right)=0\) & (1) \\ \hline \(\alpha_{\text{L}}+\alpha_{\text{V}}=1\) & (2) \\ \hline Momentum & \(\frac{\partial}{\partial t}\left(\rho\overset{\rightarrow}{u}\right)+\
abla \cdot\left(\rho\overset{\rightarrow}{u}\overset{\rightarrow}{u}\right)=- \
abla p+\
abla\cdot\left[\mu\left(\
abla\overset{\rightarrow}{u}+\
abla \overset{\rightarrow}{u}^{\text{T}}\right)\right]+\rho\overset{\rightarrow}{g} +\overset{\rightarrow}{F}\sigma\) & (3) \\ \hline Properties & \(\rho=\alpha_{\text{L}}\rho_{\text{L}}+\alpha_{\text{V}}\rho_{\text{V}}\) & (4) \\ \hline \(\mu=\alpha_{\text{L}}\mu_{\text{L}}+\alpha_{\text{V}}\mu_{\text{V}}\) & (5) \\ \hline Turbulence kinetic \(k\) & \(\frac{\partial}{\partial t}\left(\rho k\right)+\
abla\cdot\left(\rho\overset{ \rightarrow}{u}k\right)=\
abla\cdot\left[\left(\mu+\frac{\mu_{\text{u}}}{ \alpha_{\text{L}}}\right)\
abla k\right]+G_{\text{k}}-\rho\epsilon\) & (6) \\ \hline Turbulence kinetic dissipation \(\epsilon\) & \(\frac{\partial}{\partial t}\left(\rho\epsilon\right)+\
abla\cdot\left(\rho \overset{\rightarrow}{u}\epsilon\right)=\
abla\cdot\left[\left(\mu+\frac{\mu_{ \text{u}}}{\epsilon_{\text{L}}}\right)\
abla\epsilon\right]+C_{\text{L}} \epsilon\frac{\epsilon}{k}\left(G_{\text{k}}\right)-C_{2\epsilon}\rho\frac{ \epsilon^{2}}{k}\) & (7) \\ \hline Generation of turbulent kinetic energy \(G_{\text{k}}\) & \(G_{\text{k}}=\frac{\mu_{\text{t}}\left(\
abla u_{\text{t}}\
abla u^{\text{T}} \right)}{\
abla u}\) & (8) \\ \hline Eddy viscosity \(\mu_{\text{t}}\) & \(\mu_{\text{t}}=\rho C_{\text{L}}\frac{k^{2}}{\varepsilon}\) & (9) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Governing equations.  
Figure 3: (**a**) Mesh-sensitive test; (**b**) model validation [15].
## 1 Introduction
Thus, other supplementary validations or comparisons are necessary to fully evaluate the performances of global actual ET products [42; 43]. Inter-comparison across climate zones is an important and indirect approach [29; 30], which can assess the spatial consistency among different ET products. Using ET observations from other regions with the same or similar climate zones is another potential approach to assessing the performance of ET products in areas with few or even no flux sites. The Lancang-Mekong River (LMR) originates from the Tanggula Mountains in China and passes through Myanmar, Laos, Thailand, Cambodia and Vietnam. The river finally flows into the South China Sea from Ho Chi Minh City. As the largest transboundary river in Southeast Asia, LMR has attracted much attention from the world [44; 45; 46]. First of all, competition for water resources in this basin is obvious, especially for countries in its lower reaches. The rapid development of industry and agriculture in Southeast Asia has resulted in an increased demand for water resources. As a result, several dams have been built in the lower reaches of LMR as a step to better manage its water resources [47]. Characterized by diverse climate zones and complex vegetation types, it is challenging to accurately estimate the water budget in the LMR basin (LMRB). LMRB covers major climate types of the globe, including the equatorial climate zone, the warm temperate climate zone, snow and the polar climate zone from the south to the north. Meanwhile, the basin is rich in vegetation types, including tropical rainforests, tropical seasonal forests, subtropical forests, temperate deciduous forests, coniferous forests, savannas, alpine meadows, etc. Overall, the complexity and diversity of climate zones and land cover types makes this basin a representative study area for the evaluation of global ET products. Besides, although quite a lot of studies have explored the performances of global remote-sensing-derived ET products in different regions and basins, there is no such assessment for LMRB, despite its huge economic returns and ecosystem services. Hence, the objectives of this study are to: (1) evaluate the performances of four remote-sensing-derived ET products (i.e., MOD16, PML-V2, BESS and GLASS) based on Eddy Covariance (EC) observations, inter-comparison and comparisons with ET from literature in other regions with the same climate zones; and (2) analyze the potential reasons and uncertainties for the performance differences between the four products.
## 3 Results
### Relationship between GPP and LAI  
The comparison between GPP and LAI is noteworthy. We download GLASS-LAI products with an 8-day temporal resolution and 0.05 spatial resolution from 1982 to 2015 [39; 53; 54] and then pre-processed it to a 34-year time series of mean value of LAI within the growing season (denoted as LAIGS) across the whole NEC (Figure 6a). The detection of LAI trend across the NEC shows a dramatic increase in LAIGS across the whole NEC over the last 30 years (Figure 6b). It is the same as the widely reported worldwide greening [8; 55; 56; 57].  
Figure 5: Dominant factors for GPPGS during 1982–2015 at pixel scale (main map) and at regional scale (the inset map).  
Different from the significant increase in \(\text{LAI}_{\text{GS}}\) across the whole NEC as Figure 6b shows, there is a relatively smaller increment of \(\text{GPP}_{\text{GS}}\) in forested region across the NEC (the slight increment in the GKM and LKM, and even no increments of \(\text{GPP}_{\text{GS}}\) in the CBM); quantitatively, the absolute change ratio (ACR) and relative change ratio (RCR, defined as \(\frac{ACR\ast study\ period}{\frac{multi-year\ mean\ value}}\)) of \(\text{GPP}_{\text{GS}}\) and \(\text{LAI}_{\text{GS}}\) in each part of the NEC are displayed in Table 2. In forested regions, the increments of \(\text{LAI}_{\text{GS}}\) are larger than that of \(\text{GPP}_{\text{GS}}\), especially in CBM, while, in croplands, the RCR of \(\text{GPP}_{\text{GS}}\) is twice that of \(\text{LAI}_{\text{GS}}\). The relationship between \(\text{GPP}_{\text{GS}}\) and \(\text{LAI}_{\text{GS}}\) seems to vary in different regions.  
According to correlation analysis (Figure 7), \(\text{GPP}_{\text{GS}}\) is correlated with \(\text{LAI}_{\text{GS}}\) in relatively high-latitude forests (GKM: R = 0.652, \(p<\) 0.05; LKM: R = 0.691, \(p<\) 0.05) and parts of cropland; however, in mid-latitude CBM, there is non-significant correlation (R = 0.334; \(p>\) 0.05), indicating that GPP is not always changing simultaneously with LAI.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\text{GPP}_{\text{GS}}\)} & \multicolumn{2}{c}{\(\text{LAI}_{\text{GS}}\)} \\ \cline{2-5}  & **ACR** & **RCR** & **ACR** & **RCR** \\ \hline
**GKM** & 2.88 & 11.24 & 0.014 & 22.26 \\
**LKM** & 4.77 & 15.51 & 0.012 & 17.46 \\
**CBM** & 0.79 & 2.33 & 0.012 & 15.84 \\
**Plain** & 7.345 & 29.75 & 0.0057 & 15.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The absolute change ratio (ACR) and relative change ratio (RCR) of \(\text{GPP}_{\text{GS}}\) and mean value of LAI within the growing season (\(\text{LAI}_{\text{GS}}\)) from 1982 to 2015 across the NEC.  
Figure 6: Spatial distribution of (**a**) multi-year average mean value of LAI within the growing season (\(\text{LAI}_{\text{GS}}\)) and (**b**) trend in \(\text{LAI}_{\text{GS}}\) across the NEC from 1982 to 2015.