esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Methodology
### Description of the Experiments  
For the two scenarios mentioned above, three experimental evaluations were carried out. Scenario 1 was used to evaluate the impact of distance and STANAG 5066 ARQ configuration on PLR. On the other hand, Scenario 2 was subject to a long simulation in which a detailed log file was obtained in order to estimate PLR and latency respect to PIR.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator time slots & 25 ms \\ Simulated modem & CML Microcircuits FX614 \\ Modulation & 2-FSK \\ Data rate & 1200 bps \\ Output power & 33 dBm \\ Channel model & [36] \\ Receiver sensitivity & \(-120\) dBm \\ ARQ link break time & 30 minutes \\ Initial ARQ back-off time & 0 to 4 s (Uniform distribution) \\ ARQ back-off range increments & 2\(\times\) \\ STANAG 5066 internal buffer & 20 packets \\ STANAG 5066 maximum transmission time & 60 s \\ STANAG 5066 wait time between transmissions & 0 to 4 s (Uniform distribution) \\ STANAG 5066 maximum chunk size & 100 bytes \\ STANAG 5066 maximum packet size (including overhead) & 116 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 8: Topology of Scenario 2. The scenario corresponds to a linear topology with a single directed routing scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Node** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline Distance (meters) & 19.5 & 20.0 & 20.1 & 20.2 & 20.3 & 20.4 & 20.5 & 20.6 & 20.7 & 20.8 & 20.9 & 21.0 & 21.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distances of each node of Scenario 1.
## 3 Results
SL (Table 1) that SL depicted  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SL** & **DL** & \(p\) \\ \hline \multirow{4}{*}{Ankle} & Peak Ankle Plantarflexion Angle (°) & \(-19.88\pm 8.80\)* (initial contact) & \(-8.58\pm 8.28\)* (initial contact) & 0.001 \\ \cline{2-4}  & Peak Ankle Dorsiflexion Angle (°) & \(25.93\pm 7.94\) & \(34.31\pm 14.49\) (193.6 \(\pm\) 29.8 ms) & 0.058 \\ \cline{2-4}  & Ankle ROM (°) & \(45.81\pm 6.38\)* & \(42.89\pm 7.98\)* & 0.023 \\ \cline{2-4}  & Peak Ankle Moment (Nm/kg) & \(-3.26\pm 0.55\)* (24.2 \(\pm\) 3.2 ms) & (18.5\(\pm\) 2.3 ms) & 0.001 \\ \cline{2-4}  & Peak Ankle Power (W/kg) & \(-37.97\pm 5.44\)* (16.7 \(\pm\) 2.8 ms) & (18.7\(\pm\) 3.3 ms) & 0.001 \\ \cline{2-4}  & Peak Ankle Angular Velocity (°/s) & \(844.91\pm 95.22\)* (8.6 \(\pm\) 1.7 ms) & 559.49\(\pm\) 96.93* (8.4 \(\pm\) 1.3 ms) & 0.001 \\ \hline \multirow{4}{*}{Knee} & Peak Knee Flexion Angle (°) & \(-74.26\pm 15.18\)* (193.6 \(\pm\) 29.8 ms) & \(-99.83\pm 17.58\)* (197.1 \(\pm\) 33.4 ms) & 0.001 \\ \cline{2-4}  & Peak Knee Extension Angle (°) & \(-6.96\pm 6.39\)* (initial contact) & \(-20.73\pm 6.84\)* (initial contact) & 0.003 \\ \cline{2-4}  & Knee ROM (°) & \(67.30\pm 10.99\) & \(79.10\pm 17.33\) & 0.056 \\ \cline{2-4}  & Peak Knee Moment (Nm/kg) & \(5.52\pm 1.79\)* (9.7 \(\pm\) 3.5 ms) & \(3.88\pm 1.50\)* (8.5 \(\pm\) 1.9 ms) & 0.005 \\ \cline{2-4}  & Peak Knee Power (W/kg) & \(-42.77\pm 5.77\)* (26.3 \(\pm\) 4.1 ms) & \(-29.44\pm 5.57\)(27.8 \(\pm\) 4.4 ms) & 0.002 \\ \cline{2-4}  & Peak Knee Angular Velocity (°/s) & \(-464.22\pm 80.49\)* (20.6 \(\pm\) 3.6 ms) & \(-671.56\pm 77.83\)* (24.3 \(\pm\) 2.8 ms) & 0.001 \\ \hline \multirow{4}{*}{Hip} & Peak Hip Flexion Angle (°) & \(-43.01\pm 12.82\)* (193.6 \(\pm\) 29.8 ms) & \(-74.31\pm 10.87\)* (197.1 \(\pm\) 33.4 ms) & 0.001 \\ \cline{2-4}  & Peak Hip Extension Angle (°) & \(-9.21\pm 4.09\) (initial contact) & \(-20.04\pm 8.61\)(initial contact) & 0.021 \\ \cline{2-4}  & Peak Hip ROM (°) & \(33.80\pm 9.52\)* (30.80 \(\pm\) 9.  
a smaller peak flexion angle (\(p\) = 0.001) and ROM (\(p\) = 0.001) and peak angular velocity (\(p\) = 0.001), greater peak moment (\(p\) = 0.007) and peak power (\(p\) = 0.001). For the results of VGRF. SPM analysis revealed that SL depicted a significantly greater VGRF than DL during the 5.87-8.25% (SL: 1.62-1.43 BW, DL: 1.43-1.09 BW, \(p\) = 0.029), 19.75-24.14% (SL: 3.43-4.71 BW, DL: 2.28-2.94 BW, \(p\) = 0.003) and 47.09-52.46% (SL: 1.79-1.93 BW, DL: 1.15 -1.18 BW, \(p\) = 0.004) landing phase (Figure 5). Table 1 shows that SL depicted greater peak VGRF (\(p\) = 0.001) and loading rate of VGRF (\(p\) = 0.001) than DL.
## 2 Modeling and Optimization Methodology
### System Modeling  
The simulation is accomplished with the development of all component models and their subsequent integration to a total energy system model. The thermoeconomic modeling and optimization is developed in the software Engineering Equation Solver (EES)--Professional version. The component models include the PV and SOFC subsystems. The latter includes the Steam Methane Reformer (SMR) reactor, the fuel cell stack, and auxiliary components (air blower, fuel compressor, water pumps, catalytic combustor, and heat exchangers). All the aforementioned component model equations (except the SOFC stack, which is described below), the overall system balance equations, and cost model equations are identical to those already presented by the authors in a previous publication [15]. In the end of this section, the applied optimization strategy is described in detail.
## 1 Introduction
This has motivatedthe work of this paper. A recent field experiment was conducted to collect data over a range of 7.4 km, when three modulation schemes were used. Four data rates with a maximal data rate of 4 kilo-bits/s have been achieved. Using the same real data, we compare the performance of three receiver structures: McDFE, PPC-DFE, and PPC-McDFE. These structures are frequently discussed in the literature, and in the future we may extend the discussion to other structures and modulation schemes. As required, information of the channel characteristics for PPC processing can be obtained by a channel probe signal or estimated using training symbols. For example, using a linear frequency modulation pulse (LFM) chirp as a channel probe signal, when the chirp is also used as a shaping pulse at the transmitter, the received LFM is immediately used for PPC processing. Alternatively, the channel is estimated using training symbols, when a root-raised-cosine pulse (RRC) is used as a shaping pulse. In this paper, we have also tested the scenario using the two shaping pulses. The contributions of this paper include: (1) experimental assessment of the difference between two shaping pulses--LFM and RRC; (2) performance comparison of the McDFE, PPC-DFE, and PPC-McDFE structures; (3) evaluation of the block-based approach for PPC-McDFE; and (4) assessment of the MP algorithm for both PPC-DFE and PPC-McDFE, in which PPC processing is implemented in two modes--one block and multi-block. This paper is organized as follows: Section 2 introduces the field experiment conducted in Trondheim harbor on 7 September 2011. Section 3 shows the receiver structures: (1) McDFE; (2) PPC-DFE; and (3) PPC-McDFE. Section 4 briefly introduces channel estimations for PPC processing, the LS method and the MP algorithm. In Section 5, the results are presented and discussed, and performance of the three structures is shown. Finally, Section 6 summarizes the work.
# Statistical Model for Mobile User Positioning Based on Social Information
## 4 Model Assessment
### Computational Complexity  
As a preliminary process, previous to the statistical positioning methods, firstly, input data collection and preprocessing are required. The execution time of trace preprocessing grows linearly with the number of connections and data fields, while map construction  
Figure 8: Results of \(M_{\text{U}E}\) with the Social model in the sections presented in Figure 4.  
grows linearly with the number of map tiles. In this preliminary stage, the task demanding the highest computational load is the kriging process. Its time complexity is \(\mathcal{O}(N^{3})\)[38] to solve \(N+1\) linear equations for \(N\) source points.  
Once the input data are available, the computational complexity of the social method is given by the algorithm constructing the spatial connection distribution. The algorithm distributes \(N_{conn}(r,c)\) connections originating in the area \(R_{ta}(c)\) covered by an \(r\) ring out of the \(N_{r}\) rings in the scenario. Thus, the worst-case time complexity is \(\mathcal{O}(N_{r}*R_{ta}*N_{conn})\).  
Trace processing is performed by complex event processing with Esper routines, land uses are processed with Matlab, and social network data is obtained with the Twitter Streaming API using Java (Twitter4j library) and the Flickr API using Python [39; 40; 41], both processed with Matlab. Finally, the social model has been implemented using the image processing toolbox in Matlab.  
All these processes have been tested on a server with a 2.4 GHz octa-core processor and 64 GB of RAM. To decode and process the connection traces (400 cells, 2 h of traces, 166,561 connections), the time required is 282 s. Likewise, the times to construct \(M_{lu}(x,y)\) and \(M^{t}_{p}(x,y)\) (125 km\({}^{2}\), 1,222,787 tiles) are 50,444 and 57,743 s, respectively. To conclude, the time spent in positioning connections is 159.46 s.
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
###### Abstract  
Advances in visual sensor devices and computing power are revolutionising the interaction of robots with their environment. Cameras that capture depth information along with a common colour image play a significant role. These devices are cheap, small, and fairly precise. The information provided, particularly point clouds, can be generated in a virtual computing environment, providing complete 3D information for applications. However, off-the-shelf cameras often have a limited field of view, both on the horizontal and vertical axis. In larger environments, it is therefore often necessary to combine information from several cameras or positions. To concatenate multiple point clouds and generate the complete environment information, the pose of each camera must be known in the outer scene, i.e., they must reference a common coordinate system. To achieve this, a coordinate system must be defined, and then every device must be positioned according to this coordinate system. For cameras, a calibration can be performed to find its pose in relation to this coordinate system. Several calibration methods have been proposed to solve this challenge, ranging from structured objects such as chessboards to features in the environment. In this study, we investigate how three different pose estimation methods for multi-camera perspectives perform when reconstructing a scene in 3D. We evaluate the usage of a charuco cube, a double-sided charuco board, and a robot's tool centre point (TCP) position in a real usage case, where precision is a key point for the system. We define a methodology to identify the points in the 3D space and measure the root-mean-square error (RMSE) based on the Euclidean distance of the actual point to a generated ground-truth point. The reconstruction carried out using the robot's TCP position produced the best result, followed by the charuco cuboid; the double-sided angled charuco board exhibited the worst performance.  
pose estimation; robotics; 3D reconstruction; charuco cuboid +
Footnote †: journal: _Applied_  
_aciences_
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 4 Analysis
### Sample Data Ranges  
Further, we used the following ranges for each parameter:  
* Size of XML data (\(size_{xml}\)): 5 MB, 10 MB, 15 MB
* Complexity of XML data (\(complexity_{xml}\)): Low (1), Medium (2), High (3)
* Number of partitions: 3, 5, 7
* Bandwidth of each channel (\(bandwidth_{channel}\)): 1 MBps, 2 MBps
* Number of channels (\(number_{channels}\)): 3, 5, 7
* Complexity of routing (\(complexity_{routing}\)): Low (1), Medium (2), High (3)* Complexity of query (_complexityquery_): Low (1), Medium (2), High (3)
* Efficiency of processing (_efficiencyprocessing_): Low (0.5 s per unit), Medium (1 s per unit), High (1.5 s per unit) We assumed a fixed routing time based on the complexity level:
* Low: 0.5 s
* Medium: 1.0 s
* High: 1.5 s Table 1 depicts the access time of the proposed indexing scheme. Each row illustrates how changing the parameters affects the total access time. This demonstrates how different XML sizes, partition numbers, bandwidths, routing complexities, and query complexities affect access time when querying XML over wireless broadcast channels.
# Loss Research and Thermal Analysis of BLDC Hollow-Cup Motor Under Reactor Suppression
## 5 Experiments and Comparative Analysis
### Efficiency Test Experiment  
In this section, the motor efficiency is measured and calculated for the motor with no series reactor between the motor and the controller and with a 15 \(\upmu\)H reactor in series. The results are compared and analyzed to verify the reasonableness of the optimization method.  
Measurements of electromagnetic torque (\(T\)), input voltage (\(U\)), and input current (\(I\)) at different rotational speeds (\(n\)) are recorded, and the efficiencies are calculated using a WT310 dynamometer. Efficiency calculations are carried out based on the MTPA (maximum torque per ampere) control method to measure the motor's [22] success in achieving maximum torque output with minimum stator current for a given load condition. See Tables A1 and A2 of results. The efficiency is calculated using Equation (19), and the comparison of efficiency is shown in Figure 16.  
\[\eta=\frac{P_{2}}{P_{1}}=\left(\frac{T\times n}{9.55}\right)/(U\times I). \tag{19}\]  
The measured values of motor efficiency are analyzed, and under two conditions with the same input power, the motor efficiency with the 15 \(\upmu\)H reactor in series has a significant upward trend compared to that without the reactor in series. When the input current is  
Figure 16: Comparison of efficiency.  
Figure 15: Comparison of the setup before and after adding a series reactor to the motor system: (**a**) before adding the system series reactor; (**b**) after adding the system series reactor.  
the rated current of the motor, the efficiency of the motor with the 15 \(\upmu\)H reactor in series is about 3.77% higher than the efficiency of the motor without the reactor in series. This measurement shows that the motor losses are significantly reduced when the 15 \(\upmu\)H reactor is connected in series, which is consistent with the FE simulation results.
## 5 Implementation Environment
### Experimental Setup  
The experimental setup is summarized in Table 2. The core system components are Ubuntu 18.04.1 LTS as an operating system, with 23 Gb memory, GPU is Nvidia GForce 1080. The implementation is done in Python language along with Tensorflow API. For all experiments, we have divided the data into 70% training data and 30% testing data. By training the 70% of the data, we were able to have ground-truth data ready for predicting the topics and trends of discussions in the remaining testing data. The primary objective is to discover the trends in the comments and recommend investors accordingly.  
Figure 5: Proposed system architecture for topic class prediction and optimized recommendations.
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 3 Basic Solid Polymer Electrolytes
### Strategies to Improve Ionic Conductivity of SPEs
#### 3.2.6 Composite Solid Polymer Electrolytes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrolyte System** & **Preparation** & **Conductivity, \(\sigma\)** & **Important Findings** & **Reference** \\  & **Method** & **(S cm\({}^{-1}\))** & & \\ \hline PEO-LiCF3 SO3-TiO2 [EO]/[Li] = 9:1 & Solution casting & \(4.90\times 10^{-5}\) (30 \({}^{\circ}\)C) & \(E_{A}=0.82\) eV; \(T_{g}=-46\)\({}^{\circ}\)C; & [43] \\  & & & \(T_{m}=60\)\({}^{\circ}\)C & \\ \hline PEO-LiN(CF3 SO2)2-  SiO2  & Solution casting & \(1.40\times 10^{-4}\) & \begin{tabular}{c} \(E_{A}=0.54\) eV; \(T_{g}=-43\)\({}^{\circ}\)C \\ Without filler: \\ \(\sigma=1.50\times 10^{-5}\) S cm\({}^{-1}\) (27 \({}^{\circ}\)C); \\ \(E_{A}=0.60\) eV; \(T_{g}=-49\)\({}^{\circ}\)C \\ \end{tabular} & [177] \\  & & & \(E_{A}=0.55\) eV; \(T_{g}=-44\)\({}^{\circ}\)C & \\ \hline PEO-LiClO4-SiO2 [EO]/[Li] = 8:1 & Solution casting & \(2.50\times 10^{-5}\) & \begin{tabular}{c} \(E_{A}=0.55\) eV; \(T_{g}=-44\)\({}^{\circ}\)C \\ Without filler: \\ \(\sigma=9.70\times 10^{-7}\) S cm\({}^{-1}\); \(T_{g}=-46\)\({}^{\circ}\)C \\ \end{tabular} & [177] \\ \hline PEO-LiClO4-LLTO & Solution casting & \(7.99\times 10^{-4}\) (70 \({}^{\circ}\)C) & \begin{tabular}{c} LLTO nanoparticles was synthesized \\ via Pechini precursor method. \\ PCTB/SPE/Li cell (60 mA g\({}^{-1}\); 70 \({}^{\circ}\)C); \\ Discharge capacity = 104 mA g\({}^{-1}\) \\ 90\% capacity retention after \\ 300 th cycle \\ \end{tabular} & [178] \\ \hline PEO-LiPF6-EC-aCCNT & Solution casting & \(1.30\times 10^{-3}\) (25 \({}^{\circ}\)C) & \begin{tabular}{c} \(\alpha\)CNT was synthesized via \\ chemical route \\ Bandgap energy = 4.42 eV (indirect) \\ and 4.60 eV (direct) \\ \end{tabular} & [103] \\ \hline Chitosan-LiClO4-  glycerol-Ag  nanowires & Solution casting & \(-2.07\times 10^{-5}\) (25 \({}^{\circ}\)C) & \
\begin{tabular}{c} Diameter of Ag nanowires = 20 nm \\ Elastic modulus = \(\sim\)3.5 MPa \\ \end{tabular} & [107] \\ \hline Chitosan-LiClO4-  glycerol-Ag  nanospheres & Solution casting & \(\sim\)1.00 \(\times 10^{-4}\) (25 \({}^{\circ}\)C) &
\begin{tabular}{c} Diameter of Ag nanospheres = 100 nm \\ Elastic modulus = \(\sim\)1.0 MPa \\ \end{tabular} & [107] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Various SPEs systems with fillers added.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 3 Approach
### Blockchain-Based Network Architecture
According to the consensus mechanism between server nodes, the token transactions cannot be revised arbitrarily by malicious nodes, to some extent, the token accurately represents the packet passed between the routing nodes. Compared with traditional routing architectures, our system differs in that each routing node is registered on the registration contract after entering the blockchain-based routing network. The routing node will forward the packets to its next-hop routing node off the blockchain. Then they must confirm the routing information on the blockchain including the address of the next-hop routing node, the number of packets sent to the next node, and the timestamp. Then the routing information will be confirmed by the server nodes through the blockchain consensus mechanism and updated on the blockchain. The learning model of each routing node will pull this information from the blockchain and feed back the subsequent routing policy to the routing node. In the following subsections, we will introduce the specific blockchain network implementation and the routing policy generation method in detail.
## 2 Advanced Instruments in Remote Sensing of EBC
### Thermal Remote Sensing  
Thermal remote sensing detects the energy emitted from the Earth's surface in the thermal infrared (TIR, 3 \(\upmu\)m to 15 \(\upmu\)m), which can be radiated by all bodies above absolute zero. Theoretically, TIR sensors measure the surface temperature and thermal properties of targets [36], which are essential for developing a better understanding, and more robust models, of land-surface energy balance interactions [37]. Moreover, TIR remote sensing is capable of uncovering the principles of ecological patterns of structure and function due to the development of ecological thermodynamics [37]. A thermal grey level image is generated based on relative radiant temperatures (a thermogram), and light tones correspond to warmer temperatures and dark tones to cooler temperatures [36]. TIR remote sensing plays an important role in observation of Earth surface characteristics, and is very useful for research regarding analysis of biophysical Earth processes, in particular landscape characterization and measurement of land surface processes [37]. The well-known sensors with TIR bands include the Advanced Very High Resolution Radiometer (AVHRR) onboard the Polar Orbiting Environmental Satellites (POES), the Landsat Thematic Mapper (TM) and ETM+, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra Earth observing satellite platform, _etc._[37].  
TIR remote sensing has been developing since 1880, and has proven to be an integral part of understanding landscape characteristics [37], although it is relatively rarely used by ecologists [2]. However, interests have increasingly focused on the use of TIR remote sensing in EBC. For instance, biophysical variables were derived from thermal and multispectral remote sensing data and coupled with a Soil-Vegetation-Atmosphere-Transfer (SVAT) model [38]. [PERSON] _et al._ pointed out the TIR region is an important source of information to study environmental disturbance because of the negative relationship between vegetation density and land surface temperatures [13]. [PERSON] _et al._ proposed a disturbance detection index using Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day Enhanced Vegetation Index (EVI) and 8-day Land Surface Temperature (LST), and it was successfully applied to detect continental-scale disturbance events such as wildfire, irrigated vegetation, precipitation variability, and the incremental process of recovery of disturbed landscapes [39]. Another good use of TIR remote sensing data is to measure evapotranspiration, evaporation, and soil moisture. For example, [PERSON] and [PERSON] analyzed the continental-scale performance of surface soil moisture retrieval algorithms depending on satellite passive microwave, scatterometer, and thermal remote sensing observations [40]. [PERSON] _et al._ reviewed Ts/VI (surface temperature/vegetation index) remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture, and suggested one piece of future work should evaluate the accuracy of these methods under diverse environments [41].
## 3 Results
### Selecting the Optimal Probability Threshold  
The output of the LDA model is whether a pixel is contaminated with crude oil or whether it is uncontaminated. The final decision in such a model is based on the probability of a pixel to fit one of the classes, where the default probability is 50%. However, this default value does not necessarily give the ideal outcome. Figure 5 exhibits, per sensor, three accuracy metrics scores for each probability threshold, where the goal is to find the threshold values that minimize the differences between the metrics (i.e., the intersection point between the lines).