esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Voltammetric Responses of DA and UA at Various Electrodes  
CV responses of 10 uM DA and UA (1:1) were measured at different electrodes in 0.1 M PBS (pH 3.93) (Figure 4). When the potentials were scanned from 0 to 0.8 V, two anodic peaks were observed at all electrodes, which are closely related to the oxidation of DA and UA. However, only one peak belonging to the reduction of DA occurred at reverse scanning. These phenomena indicate that the electrooxidation of UA is totally irreversible. On the bare GCE, two very weak anodic peaks appeared (\(i_{\mathrm{pa}}\)(DA) = 1.126 uA, \(i_{\mathrm{pa}}\)(UA) = 0.385 uA), demonstrating sluggish kinetics for the electrooxidation of DA and UA. After modification of the GCE by MnO\({}_{2}\)NFs, the \(i_{\mathrm{pa}}\)(DA) increased a little (\(i_{\mathrm{pa}}\)(DA) = 3.766 uA) while the \(i_{\mathrm{pa}}\)(UA) was enhanced significantly (\(i_{\mathrm{pa}}\)(UA) = 12.73 uA), showing MnO\({}_{2}\)NFs have good electrocatalytic toward the oxidation of UA because of the presence of Mn\({}^{4+}\)/Mn\({}^{3+}\) as an electron mediator. Moreover, the high-specific surface area also contributed to the obvious enhancement on the \(i_{\mathrm{pa}}\)(UA). When GCE was modified with NrGO, the \(i_{\mathrm{pa}}\)(DA) and \(i_{\mathrm{pa}}\)(UA) increased to 7.029 uA and 16.43 uA respectively, suggesting superb electrocatalytic activity toward the oxidation of DA and UA. The superb electrocatalytic activity of NrGO can explain the following facts. Nitrogen atoms  
Figure 3: Cyclic voltammetry (CVs) (**A**) and Nyquist plots (**B**) for various electrodes in 0.5 mM [Fe(CN)\({}_{6}\)]\({}^{3-\
icefrac{{4}}{{4}}}\)- containing 0.1 M KCl solution. The inset in Figure 3B represents the magnification of Nyquist at a higher frequency.  
in NrGO sheets may interact with target biomolecules via hydrogen bond, which can activate the amine and hydroxy groups and expedite the charge transfer process. Meanwhile, the \(\pi\)-\(\pi\) interactions between NrGO and target biomolecules can also facilitate the charge transfer process [43]. Two sharp anodic peaks occurred at MnO2 NFs/NrGO/GCE, and the anodic peak currents enhanced remarkably (\(i_{\text{pa}}\)(DA) = 14.8 \(\mu\)A, \(i_{\text{pa}}\)(UA) = 36.3 \(\mu\)A). The synergistic effect between MnO2 NFs and NrGO sheets was mainly responsible for the enhanced response peak currents. Specially, MnO2 NFs had higher catalytic activity toward the oxidation of DA and UA when the electrical conductivity was improved by coupling with NrGO sheets. Meanwhile, the hydrogen bond and \(\pi\)-\(\pi\) interactions between NrGO sheets and target biomolecules can also facilitate the charge transfer process. It is worth noting the biggest peak potential separation (about 150 mV) at MnO2 NFs/NrGO/GCE, rendering this composite more selective for the simultaneous detection of UA and DA. Besides, the largest background current was also obtained at the MnO2 NFs/NrGO nanocomposites, due to the high-specific capacitance of the MnO2 NFs/NrGO nanocomposites [44].
# Design and Experiment of Variable-Spray System Based on Deep Learning
## 3 Results and Discussion
### Analysis of Segmentation Models
#### 3.1.2 Analysis of Segmentation Results from Different Models  
The comparative segmentation results of the improved DeepLab V3+ model with different models are shown in Figure 9. In the figure, the red area represents weeds, the green area represents wheat, and the yellow boxed area indicates regions incorrectly segmented by the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**IoU/\%**} & \multirow{2}{*}{**MIoU/\%**} & \multirow{2}{*}{**MPA**} & \multirow{2}{*}{**Segmentation**} \\ \cline{2-2} \cline{5-6}  & **Background** & & & & & **Time/s** \\ \hline SegNet & 80.56 & 70.23 & 41.36 & 64.05 & 71.53 & 0.15 \\ UNet & 81.24 & 71.41 & 50.36 & 67.57 & 74.32 & 0.13 \\ DeepLab V3+ & 83.38 & 73.44 & 53.12 & 69.98 & 77.34 & 0.12 \\ Proposed & 84.87 & 77.28 & 57.87 & 73.34 & 80.76 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of performance parameters of different semantic segmentation models.  
Figure 9: Comparison of segmentation results of different models. (**a**) Original figure; (**b**) SegNet; (**c**) UNet; (**d**) DeepLab V3+; (**e**) Proposed.  
As shown in Figure 9b, the SegNet model is inaccurate in weed recognition, and the images of weeds cannot be accurately segmented under conditions of obstruction. As in Figure 9c, the UNet model shows inaccurate segmentation at the intersection of wheat and weeds, and it also fails to completely segment the image of weeds at the intersection. In Figure 9d, the DeepLab V3+ model accurately classifies wheat and weeds but does not entirely segment the image of the unobstructed weeds areas. From Figure 9e, we can observe that the improved DeepLabV3+ model proposed in this paper can segment weeds completely and accurately, and the segmentation effect is more refined in the details of the weed edges. It indicated that the improved model is accurate and superior in weed segmentation and has the characteristics of strong robustness and good generalization performance.
## 2 Tunnel Numerical Simulation
### Principles of Numerical Simulation  
The numerical model is established according to the size of the engineering simplified model, and AUTODYN finite difference software is used for numerical simulation. The 3D model was established and divided into 600,237 tetrahedral mesh cells. Four sizes, 1.5 mm, 1 mm, 0.5 mm, and 0.25 mm, were used in the convergence study, and the size 0.5 mm was used in this study. The mesh at the blasting source and the tunnel side was densified and refined. The model cell division is shown in Figure 5.  
Figure 4: Boundary conditions of the tunnel numerical model.  
Figure 5: Establishment of numerical model and meshing.  
The constitutive model relationship of the propagation medium of the numerical model must meet the requirements of its strength and fracture parameters. The surrounding rock material adopts a linear equation of state (EOS), and the strength model adopts an elastic state. Since no stemming and no coupling were applied to the borehole, the blast-induced gas products will leak out; this means that the gas products do not play any role in the crack propagation. Only the shock wave action in this numerical study was simulated. In this study, the modified maximum principal stress failure criterion (MMTS) was used to simulate the fracture criterion of the rock under impact load, that is, when the maximum principal stress of a material element exceeds the maximum tensile strength of the material, the material element will fail.  
Because the pressure and the deformation are relatively small, a linear EOS was applied to the rock and models; and it can be written as  
\[P(\rho)=\kappa\left(\frac{\rho}{\rho_{o}}-1\right) \tag{1}\]  
where \(\kappa\) is the bulk modulus, and \(\rho_{o}\) and \(\rho\) are the density in the initial state and in the current state, respectively.  
For the explosive Hexogen of the detonator, a JWL equation of state is applied, and it can be expressed as  
\[P=A\cdot\left(1-\frac{\omega}{R_{1}V}\right)\cdot e^{-R_{1}v}+B\cdot\left(1- \frac{\omega}{R_{2}V}\right)\cdot e^{-R_{2}v}+\frac{\omega E_{0}}{V} \tag{2}\]  
where \(P\) is the pressure, \(E_{0}\) is the total initial energy, \(V\) is the specific volume of detonation products, \(A\), \(B\), \(R_{1}\), \(R_{2}\) and \(\omega\) are constants, and \(A\) = 778.3 GPa, \(B\) = 7.071 GPa, \(R_{1}\) = 7.0, \(R_{2}\) = 2.0, \(\omega\) = 0.24.  
For the rock specimen, the major principal stress and the maximum shear stress failure criteria were applied in describing the material status, which means that when the major principal stress \(\sigma_{1}\) of an element reaches the dynamic tensile strength \(\sigma_{T}\), or the maximum shear stress \(\tau_{max}\) reaches the dynamic shear strength of the material \(\tau_{c}\), the element is failed, i.e.,  
\[\sigma_{1}\leq\sigma_{T}\text{ or }\tau_{max}\leq\tau_{c} \tag{3}\]
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 5 Multiple Input Multiple Output Technology
### Spatial Multiplexing  
Spatial multiplexing involves the transmission of independent parallel information streams to boast the data rate and enhance spectrum efficiency. Spatial multiplexing allows the system designer to use a different modulation code to modulate information bits and send them to various antenna ports [143; 144]. The two most notable spatial multiplexing receiver algorithms are zero forcing (ZF) [145; 146] and minimum mean-square error (MMSE) [147; 148]. A schematic diagram of spatial multiplexing is depicted in Figure 15.  
Spatial multiplexing in MIMO are of two variants namely, single-user MIMO (SU-MIMO) and multi-user MIMO (MU-MIMO). In SU-MIMO design configuration, all the elements of the signal vector \(\overrightarrow{x}\) belong to a single user in this case, and perhaps for one UAV receiver array antenna. While, in MU-MIMO, the data streams of different users are multiplexed on different antenna and transmitted over the wireless channel coefficient \(h\). In the context of UAV tracking antenna, this might involve transmitting the tracking parameters and information signals simultaneously.
## 2 LoRaWAN Network Downlink Communication
### Downlink Routing Modeling Based on the SDN  
The downlink routing model is developed to analyze the data packets downlink routing process of LoRaWAN based on the SDN framework. We assume the downlink  
Figure 1: Gateway and server downlink communication protocol.  
routing model is represented as \(G=(V,L)\), where \(V=\{v_{1},v_{2},\ldots,v_{i}\}\) is the switch node sets in the LoRaWAN downlink routing and \(i\) denotes the different SDN switch numbers. The downlink route set is represented as \(L=\{l_{1},l_{2},\ldots,l_{j}\}\), where \(j\) refers to the number of each downlink route. A downlink routing model of LoRaWAN network based on the SDN framework is developed and the modeling topology is shown in Figure 2. The routing of the downlink communication is set up by the SDN controller to monitor the network operation status. The OpenFlow protocol is adopted, and the flow entries are developed for the data transmitting. The downlink communication data can only be sent and received on the port connected with the corresponding SDN switch.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### High Frequency (HF) Section
#### 2.3.3 Dipole Antenna  
Figure 10 shows the shape of the inductive coupling dipole antenna used by the proposed passive tag temperature sensor. The antenna impedance \(Z_{a}\) is expressed in Equation (37) where \(R_{a}\) and \(X_{a}\) are the real and imaginary part of the antenna impedance, \(f_{o}\) is the central operation frequency, \(R_{rb_{o}}\) is the resistance of the radiation body, \(L_{loop}\) is the inductance of the loop, and \(M\) is the coupling factor between the radiation body and the loop. Because of the nature of \(Z_{a}\), the electrical model of the antenna can be split as shown in Figure 9.  
\[Z_{a}=R_{a}+jX_{a}=\frac{(2\pi f_{o}M)^{2}}{R_{rb_{o}}}+j2\pi f_{o}L_{loop} \tag{37}\]  
It can be noted in Equation (37) that the real part of the antenna impedance can be easily controlled by decreasing the coupling factor \(M\). Hence, the separation distance between the loop and the radiating body may be increased. Furthermore, the size of the loop governs the imaginary part.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 2 Materials and Methods
### The Improved Dijkstra Algorithm
#### 2.1.2 Introducing the Real-Time Node Occupancy into [PERSON]'s Algorithm  
The steps are as follows:  
1. The traditional Dijkstra algorithm is used to plan the initial distribution path for the robot, and the initial distribution path is decomposed in real time. The driving frequency \(K_{V_{i}}\) of each robot on each node is recorded, and the real-time occupancy of this node is calculated according to \[f(V_{i})=K_{V_{i}}/\sum\limits^{k=0}K_{V_{k}}\] (7)
2. The real-time node occupancy is introduced into the weight function of the traditional Dijkstra algorithm, and the impact factor and real-time node occupancy of the traditional Dijkstra algorithm are considered at the same time. In this case, the weight function of [PERSON]'s algorithm is as follows: \[L^{\prime}\big{(}V_{0},V_{p}\big{)}=\sum\limits^{i=0}_{p}(1+f(V_{i}))\cdot L \big{(}V_{0},V_{p}\big{)}\] (8) where the initial factor of the path node is \(1\), \(L\big{(}V_{0},V_{p}\big{)}\) is the original length of the path, and \(L^{\prime}\big{(}V_{0},V_{p}\big{)}\) is the weight after considering the real-time occupancy of the node.  
The process of [PERSON]'s algorithm considering the real-time occupation of the nodes is shown in Figure 1:
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 2 EMERGE Onboard System Architecture
### EMERGE Navigation System Implementation
#### 2.1.5 Monitoring  
The Monitoring component consists of a Frontend, a graphical interface that displays the data of interest, and a Backend responsible for manipulating and storing internal and external information flows. This project's Monitoring layer is based on the Node-RED [29] tool or framework. Node-RED is a flow-based development tool developed to connect devices, APIs and online services as part of the IoT. In addition, it provides a web browser-based flow editor, which can be used to create JavaScript functions. Application elements can be saved or shared for reuse. The runtime uses Node.js, and the streams created are stored using JSON. The decision to use this technology is based on simplicity and support for the MQTT protocol and sensor information. In addition, the built-in WEB service allows remote monitoring of the system.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Other New TD-NIRS Systems
#### 3.3.6 TD-NIRS Systems Using ICCD  
Time-domain near-infrared spectroscopy systems using a time-gated intensified charge coupled device (ICCD) instead of using the TCSPC technique have been developed. For the purpose of measuring TOF distributions in several different time-gates, [PERSON] et al. [65] developed a system employing optical fibers with different lengths leading to time differences for the time-gates. [PERSON] et al. [66] developed a TD-NIRS system employing a time-gated ICCD enabling non-contact measurement with a wide dynamic range of detected signals. They reported the possibility of imaging absorbers deep in media with liquid phantom experiments.  
[PERSON] et al. [67] developed a broadband multichannel TD-NIRS system using a supercontinuum laser for a light source and an ICCD camera coupled with an imaging spectrometer for a detector. The spectral range was from 600 nm to 900 nm and the IRF was about 660 ps. The performances of the system were demonstrated by in vivo experiments to monitor the hemodynamic responses in adult arms during a cuff occlusion and in adult brains during a cognitive task.
## 8 Archiving of Surveyed Data and Management of Survey Results
### Management of the Raw Field-Detected Data  
With regard to the raw data detected in the field, the surveyors, once the post-processing activity of the data detected in the field for a building complex was completed, were required to share the raw data detected by Heron(r), the files produced by the SLAM (Heron Desktop(r) post-processing software, and the point cloud model and images of the complex in the project format of the Reconstructor(r) point cloud management software. In this way, the quality test staff was enabled to navigate the project within the Reconstructor(r) platform, verifying the correct alignment of the point cloud model and compliance with all design specifications. As mentioned in 7.1, for 5 percent of the surveying, the tester group would have to repeat the entire data post-processing process from raw data to the final model. Moreover, within the Reconstructor platform, the tester provided quality verification of all photographic captures acquired with the mobile system. After the testing operations were completed and only after the delivery of the data in the E57 format with images and subsequent uploading to the Cintoo(r) platform, the raw data of the Heron(r) survey (consisting of all acquired images, the data files taken from the LiDAR sensors, and the inertial sensor file), the Heron Desktop(r) software management files, and the Reconstructor(r) project files could be removed from the server. Such data are not maintained in the archive.
## 3 Experimental Program  
### Description of the Test Specim  
#### 3.1.1 Specimen Details  
According to the proposed PRW-TSD system, the precast concrete wall panels were expected to exhibit little to no damage after earthquake actions. Therefore, two precast concrete wall panels, described in Section 3.1.2, were designed and produced to make one rocking wall specimen, as shown in Figure 2. The two panels were located on the foundation beam with a 10-mm wide vertical gap. Two bundles of prestressing strands, each of which consisted of four 15.2 mm diameter strands, were employed to connect the foundation. A post-tension force equal to 480.4 kN was applied through the strands for each panel. To investigate the influence of the steel energy dissipaters on the seismic performance of the whole system, three different combinations of MBSFs and SEDPs were mounted onto the rocking wall specimen for three quasistatic reversed-cyclic lateral loading tests. The three test cases were recorded as RW1-1, RW1-2, and RW1-3. SEDPs of the same configuration were applied to all three loading test cases, while the usage of the MBSFs varied.  
The rocking wall specimen was first loaded (referred to as Test RW1-1) with only one type of energy dissipater, i.e., four SEDPs installed on the two sides of the specimens. Then, new SEDPs were replaced for the second test (referred to as Test RW1-2), and four MBSFs with Belleville springs were mounted at the bottom corners of the test specimen. The Belleville springs could retain a certain gap between the bolted nuts and the fixed support to control the function time of the MBSFs, which could perform as a hierarchical energy dissipation (HED) mechanism [32]. To study the possibility of achieving the HED mechanism for the PRW-TSD system, 7 mm wide gaps, described in detail in Section 3.1.4, were set at the connection of the MBSFs and the wall in Test RW1-2, which made the installed MBSFs begin to function when the rocking wall specimen experienced a 1% drift deformation. The last test (referred to as Test RW1-3) was conducted with the new SEDPs and the MBSFs (without Belleville springs). The test matrix is summarized in Table 1.  
Figure 2: Details of the specimen.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
###### Abstract  
Wireless Sensors Networks (WSNs) are an essential element of the Internet of Things (IoT), and are the main producers of big data. Collecting a huge amount of data produced by a resource-constrained network is a very difficult task, presenting several challenges. Big data gathering involves not only periodic data sensing, but also the forwarding of queries and commands to the network. Conventional network protocols present unfeasible strategies for large-scale networks and may not be directly applicable to IoT environments. Information-Centric Networking is a revolutionary paradigm that can overcome such big data gathering challenges. In this work, we propose a soft-state information-centric protocol, ICENET (Information Centric protocol for sEnsor NETworks), for big data gathering in large-scale WSNs. ICENET can efficiently propagate user queries in a wireless network by using a soft-state recovery mechanism for lossy links. The scalability of our solution is evaluated in different network scenarios. Results show that the proposed protocol presents approximately 84% less overhead and a higher data delivery rate than the CoAP (Constrained Application Protocol), which is a popular protocol for IoT environments.  
Wireless Sensor Networks; Information Centric; big data; Internet of Things +
Footnote †: journal: Accepted: 18 February 2019; Published: 22 February 2019
## 2 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compounds** & **LRI a** & **\% Area b** \\ \hline
**Monoterpenes (Total)** & & 1.55 \\ (\(-\))\(\alpha\)-Pinene & 944 & 0.12 \\ Camphene & 948 & 0.17 \\ (\(\beta\)-Myrcene & 989 & 0.84 \\ (\(+\))-3-Carene & 1014 & 0.07 \\ \(\alpha\)-Phellandrene & 1014 & 0.01 \\ \(\o\)-Cymene & 1034 & 0.01 \\ \(p\)-Cymene & 1034 & 0.01 \\ p-Limonene & 1039 & 0.15 \\ \(\beta\)-Ocimene & 1041 & 0.15 \\ \(\delta\)-Terpinene & 1067 & 0.01 \\ Borryl acetate & 1300 & 0.01 \\
**Oxygenate monoterpenes (Total)** & & 0.40 \\ Eucalyptus & 1041 & 0.01 \\ \(\iota\)-Fenchone & 1101 & 0.01 \\ Camphor & 1163 & 0.12 \\ Pinocarvone & 1163 & 0.15 \\ endo-Borneol & 1184 & 0.04 \\ Torpinen-4-ol & 1193 & 0.03 \\ \(\alpha\)-Terpineol & 1204 & 0.04 \\
**Phenylproponoids (Total)** & & 97.90 \\ Methyl chavicol (Estragole) & 1210 & 19.36 \\ _trans_-Anethole & 1300 & 78.54 \\
**Sesquierpenes (Total)** & & 0.09 \\ \(\alpha\)-Copene & 1402 & 0.01 \\ Allaormodendrene & 1496 & 0.08 \\ Total & & 99.94 \\ \hline \hline \end{tabular} a
[FOOTNOTE:a]Footnote a: footnotetext: Linear Retention Indices (LRI) on DB-5 MS capillary column. b  
\end{table} 
Table 1: Volatile compounds of EOEP analyzed by HS-SPME-GC/MS.
## 6 Experimental Results and Discussion  
To effectively analyze the image transmission characteristics of VLRC in the pipe, a series of experiments have been carried out in a pipe with two elbows (total length 3.5 m and 80 mm in diameter). The camera was mounted at a position 0.5 m from the end of the 1.5 m length straight branch pipe. The two VLRC nodes have been installed at the elbows of the pipe. Three convex lenses were placed at a distance of 150 mm from the receiver of each node. Moreover, we have designed two types of test environments. Usually, in order to safely enter the gas pipeline to detect damage, the pipe has to be opened in advance to release the gas to avoid accidents. In this research, we considered the gas pipeline as the empty pipe. For the water pipe, before being inspected by the mobile inspection system, valves were also cut off and even drained. However, some water was still inside the pipe. As depicted in Figure 3e,f, this research designed three 70 mm diameter 0.8 m length transparent sealed inner tubes which were filled with half of the water (nearly 1.5 L ). Then we placed them inside the experimental pipe. We considered them as the water pipe environment. In these two scenarios, waveform tests on two signal transmission methods AISRT and DIFRT have been conducted. Four important coefficients MAE, MSE, PSNR, and SSIM which can describe the transmission efficiency, image transmission quality, and error were also recorded through the experiments. Furthermore, for DIFRT, the relationship between the BER and image quality has also been compared and analyzed. In particular, to guarantee the accuracy of experiments, the interference from ambient light has been strictly avoided [38; 39; 40].