esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Study Steps  
A systematic methodology was utilized in this study. First, the data collected were prepared by removing all outliers and then were partitioned into two sets (training set and testing set) and normalized into the interval [-1,1]. Then the various models were built by selecting the model's hyperparameter. The models were then trained using the training dataset. Finally, the model's results were assessed based on the test dataset by some performance indicators. The performance results were then analyzed to either finetune the model's hyperparameter or select the model as optimum. Figure 5 shows the flowchart applied in this study.  
Figure 4: Portable ground vibration monitoring station in realistic conditions (at Shree Cement Ras, Limestone Mine in India).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **AD** & **B** & **S** & **D** & **PF** & **SL** & **MC** & **PPV** \\ \hline AD & \(1\) & & & & & & & \\ B & \(0.1697\) & \(1\) & & & & & & \\ S & \(0.3347\) & \(0.8330\) & \(1\) & & & & & \\ D & \(0.1407\) & \(-0.0467\) & \(-0.0201\) & \(1\) & & & & \\ PF & \(-0.1046\) & \(0.5294\) & \(0.4905\) & \(-0.1069\) & \(1\) & & & \\ SL & \(0.7702\) & \(-0.1507\) & \(-0.0235\) & \(0.0595\) & \(-0.1329\) & \(1\) & & \\ MC & \(0.9301\) & \(0.3514\) & \(0.4996\) & \(0.1617\) & \(-0.2019\) & \(0.6145\) & \(1\) & \\ PPV & \(-0.0016\) & \(0.1492\) & \(0.2160\) & \(-0.7503\) & \(0.0789\) & \(-0.0837\) & \(0.0293\) & \(1\) \\ \hline \end{tabular}
\end{table}
Table 5: Matrix of Correlation Coefficients Between Input Parameters and PPV Measured.
# An Architecture for Biometric Electronic Identification Document System Based on Blockchain +
## 6 System Performance Results  
Some tests have been made in network to verify the efficiency of the consensus algorithm proposed (tournament consensus algorithm) measuring the time for mining and broadcasting it, cache memory usage, CPU percentage. In these tests, a transaction has described in Table 1.  
The common characteristics of the lab computers used to perform several tests are as follows:  
* Processor: Intel Core i5 750, 2.66 GHz of 4 Cores.
* RAM Memory: 4 GB.
* HDD: 512 GB.
* Network Card: Intel 82574L Gigabit Ethernet NIC.
* OS: Linux Ubuntu 18.04.  
Figure 6 shows the calculated time for the mining process of one block and the posterior broadcasting process of this block to every node in the Blockchain system. In this test was used a lab computer, varying the number of transactions, in order to obtain an estimated time. According to the figure, for mining one block containing one thousand (1000) transactions, the spent time was approximately five hundred and thirty (530) milliseconds.  
In order to estimate the cache memory needed when several transactions must be kept in memory until the corresponding block is closed, a test was made and with one thousand (1000) transactions the RAM used is six hundred (600) megabytes  
\begin{table}
\begin{tabular}{l c} \hline \hline
**ID Number** & 101 \\ \hline
**Serial number** & 1 \\ \hline
**Class** & Notary \\ \hline
**Type** & Civil status \\ \hline
**Information** & Married \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transaction example.  
Figure 6: Time for mining and broadcasting one block versus the number of transactions.  
Figure 8 shows the CPU usage by a miner computer in the broadcasting process. At first, between one hundred and two hundred (100-200) transactions, the memory consumes rise quickly but after two hundred ten transactions, it continues to rise but to a lesser extent. In this way, for broadcasting one thousand (1000) transactions, the CPU usage amounts to approximately 43%.  
Although the CPU usage percentage is considerable, could be applied to different strategies, for example,  
* If the number of users is not so high, adjusting the number of transactions setting a limit of CPU usage. For example with 30%, and according to Figure 8, could be managed four hundred fifty (450) transactions.
* In special dates, where thousands of transactions could be made, (for example popular elections) it could be possible to maintain the number of transactions (1000 transactions), but dedicating some special machines with better characteristics for creating and broadcasting transactions when a block is mined.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Orbit Design
#### 2.1.1 Orbit Analysis  
An analysis is performed concerning the orbital requirements for the payload, especially the laser ranging reflector and GNSS experiment, and the thermal environment of the satellite.  
The laser ranging experiment can be operated at an altitude ranging from 400 km to 600 km with no prerequisites regarding inclination. Thus, the limiting constraint is the accessibility to the ground station in Braunschweig.  
The GNSS payload can provide continuous availability from a large number of receiveable satellites due to low interference and good reception geometry at all times on a low earth orbit below 3000 km ([3], see Figure 1 left). The L1 band shared by the Global Positioning System (GPS) and Galileo can provide a large number of receivable satellites in the nominal carrier frequency due to is wide main lobe [4].  
A minimum inclination is necessary to access the ground station in Braunschweig, which is at a latitude of 52.26\({}^{\circ}\) North. Apart from financial considerations, a maximization of access time is the key consideration.  
The different thermal conditions arising from different orbits are negligible, as eclipse durations are very similar in low altitude orbits. No subsystem of the satellite needs to operate in specified temperature ranges. Passive methods are being implemented to keep the satellite in safe temperature and power levels.
###### Abstract  
To meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft/unmanned aerial vehicles (UAVs) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide path by integrating computational fluid dynamics (CFD), backpropagation (BP) neural network, and Doppler wind lidar (DWL). Firstly, taking the conceptual design aircraft carrier model as the research object, CFD numerical simulations of the ship airwake within the glide path region are carried out using the Poly-Hexcore grid and the detached eddy simulation (DES)/the Reynolds-averaged Navier-Stokes (RANS) turbulence models. Then, using the high spatial resolution ship airwake along the glide path obtained from steady RANS computations under different inflow conditions as a sample dataset, the BP neural network prediction models were trained and optimized. Along the ideal glide path within 200 m behind the stern, the correlation coefficients between the predicted results of the BP neural network and the headwind, crosswind, and vertical wind of the testing samples exceeded 0.95, 0.91, and 0.82, respectively. Finally, using the inflow speed and direction with high temporal resolution from the bow direction obtained by the shipborne DWL as input, the BP prediction models can achieve accurate prediction of the 3D ship airwake along the glide path with high spatiotemporal resolution (3 m, 3 Hz).  
ship airwake; high spatiotemporal resolution; BP neural network; CFD; DWL licenses/by/4.0/. 2020
## 2 Three-dimensional Object Detection Techniques  
Three-dimensional object detection requires inferring height and depth information along with object location. To acquire this knowledge about objects, researchers have used several modalities of data from different kinds of device or sensors. Different benchmark datasets of 3D object detection techniques incorporate different modalities, and they exhibit different scopes and constraints. Table 2 briefly discusses the modalities, annotated object category and constraints of the benchmark datasets.  
In singular modality only one type of device is used, whereas multimodal methods combine more than one device. These modalities are reviewed in this section. Sections 2.1 and 2.2, respectively, describe singular modalities and multimodal methods practiced by researchers.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Modalities**} & \multicolumn{1}{c}{\multirow{2}{*}{**Sereo**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RADAR**}} & \multicolumn{1}{c}{\multirow{2}{*}{**RGB-D**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Subject Category**}} & \multirow{2}{*}{**Constraint**} \\ \cline{2-2} \cline{6-7}  & \multicolumn{1}{c}{**LiDAR**} & & & & & & & \\ \cline{2-2} \cline{6-7}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & & & & & \\ \hline KITTI [9] & ✓ & ✓ & \multicolumn{1}{c}{(Greyscale)} & \multicolumn{1}{c}{\multirow{2}{*}{**✗**}} & \multicolumn{1}{c}{\multirow{2}{*}{11 classes}} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{l} Focuses on road scene. \\ Not suitable for indoor \\ robotic vision research. \\ \end{tabular} }} \\ nuScenes [12] & ✓ & ✓ & ✗ & ✓ & \multicolumn{1}{c}{\multirow{2}{*}{23 classes}} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{l} Focuses on autonomous \\ driving scenario. \\ Exclusively built for \\ autonomous driving. \\ \end{tabular} }} \\ Waymo [13] & ✓ & ✓ & ✗ & ✓ & \multicolumn{1}{c}{\multirow{2}{*}{23 classes}} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{l} Focuses on autonomous \\ footnotemarks \\ \end{tabular} }} \\ SUN RGB-D [10] & ✗ & ✓ & ✗ & ✗ & ✓ & 700 classes & \multicolumn{1}{c}{\
\begin{tabular}{l} Focuses on \\ indoor scene. \\ Focused on \\ indoor scenes. \\ \end{tabular} } \\ ScanNet [11] & ✗ & ✓ & ✗ & ✗ & ✓ & 20 classes & \multicolumn{1}{c}{
\begin{tabular}{l} Focuses on \\ indoor scenes. \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table}
Table 2: Benchmark datasets of 3D object detection.
## 4 New Tools for Digital Pathology in the Era of Artificial Intelligence
### QuPath  
QuPath is one of the most widely used open-source bioimage analysis software. It was developed to visualize and analyze whole slide images comprehensively, offer pathologists a useful tool for the identification of human tumors, and provide histopathological with batch-processing and scripting functionalities. Moreover, QuPath is an extensible platform with which pathologists can share algorithms to analyze complex images. According to this original flexible design, to date, QuPath appears to be a suitable tool for a wide range of additional image analysis applications over multiple biomedical research fields [34]. The impact of QuPath in delivering highly impactful research is summarized in a recent review [35] specifically dedicated to this software. Probably the most relevant key point of QuPath is linked with its inherent flexibility, which is demonstrated by its application to a wide range of human pathology research and clinical areas. QuPath has been successfully utilized for computer-assisted tumor grading, PD-L1 scoring, and quantification of CD8-positive immune cell density in urothelial carcinoma [36] and to detect and measure cell morphology. Another study [37] used QuPath to identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers from tissue microarray. In this case, the software demonstrated high sensitivity and specificity in detecting MLH1 expression and improving diagnostic accuracy (100% sensitive and 98.25% specific). QuPath was also used to analyze whole slide images for the identification of islets of Langerhans [38] and define their composition and essential morphological characteristics, showing that QuPath can identify immune cell populations and accurately quantify immune infiltrates in the pancreas.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods  
The methodology used in the present research was a simulation using the NS2 simulator. It was started by designing a simulation. The simulation design was carried out to determine the examples of cases in the real world that would be simulated to determine the simulation parameters. Next, the process continued, with the scenarios' test design relating to the routing protocol's performance test, designing the node positions, and the network traffic. AODV and OLSR routing protocol settings were developed to set the AODV routing parameters and install the OLSR routing protocols. Next, the simulation was run, and it produced a trace file used to analyze the values of PDR, throughput, packet loss, delay, energy consumption, and routing overhead. A general description of the research methodology is shown in Figure 1.
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
## 2 Kinematic Model and Position Accuracy Analysis  
### The \(n\)-DOF Planar CDSM  
The CDSM is usually a hyper-redundant DOF system; its DOF in the joint space is more than that in the task space. For forward kinematics, a set of joint angles corresponds to an end pose. However, the redundant character leads to multiple solutions in inverse kinematics.  
In this paper, we studied a CDSM with parallel joints, whose spin axes of the joints were parallel to each other. It could realize planar motion with the movement along X and Y axes and the rotation around the Z axis.  
The structure sketch of the \(n\)-DOF planar CDSM driven by m cables is shown in Figure 1: Where the colorful thick lines represent rigid links; the red small circles represent joints; the colorful thin lines represent the driving cables; and the ellipses represent supporting disks, on which holes distribute to guide the driving cables. As seen from Figure 1, the manipulator could be divided into a fixed link and n movable links, and these links were connected by \(n\) joints. Moreover, each movable link was fixed with a disk, and each disk was controlled by two driving cables to produce a rotational motion.  
To further describe modular unit, the relationship regarding the three adjacent units of the CDSM are presented in Figure 2. As shown in the picture, for the \(i^{\text{th}}\) linkage (i.e., linkage_i), it is composed of two links (link\({}_{j\_i,i}\) and link\({}_{i,\_j,i+1}\)) and one disk (disk_i) with the thickness of B. Link\({}_{i-1,\_i,\_i}\) and link\({}_{j\_i,i}\) are connected by the \(i^{\text{th}}\) joint (i.e., joint_i); similarly, link\({}_{i,\_l,i+1}\) and link\({}_{j\_i+1,\_i+1}\) are connected by the \((i+1)^{\text{th}}\) joint (i.e., joint_i+1). The cables pass through the holes in the disks to pull the disks, making the joints rotate. For the sake of describing geometric relationship, we set frames on joints and disks: \(R_{\_l,j}\big{\{}O_{\_i,j},x_{\_i,j},y_{\_j,j}\big{\}}\) and \(R_{\_l,j+1}\big{\{}O_{\_i,j+1},x_{\_i,j+1},y_{\_j,j+1}\big{\}}\), respectively, represent the frames on the joint_i and joint_i+1; \(R_{\_i,\alpha}\big{\{}O_{\_i,\alpha},x_{\_i,\alpha},y_{\_i,\alpha}\big{\}}\) and \(R_{\_i,\beta}\big{\{}O_{\_i,\beta},x_{\_i,\beta},y_{\_i,\beta}\big{\}}\), respectively, represent the frames on the two sides of the disk_i; \(R_{\_i-1,\_b}\big{\{}O_{\_i-1,\_b},x_{\_i-1,\_b},y_{\_i-1,\_b}\big{\}}\) and \(R_{\_i+1,\alpha}\big{\{}O_{\_i+1,\alpha},x_{\_i+1,\alpha},y_{\_i+1,\alpha}\big{\}}\), respectively, represent the frames on the disk_i - 1 the disk_i + 1.
## 4 Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Location Category** & **Learning Model** & **XGB** & \
\begin{tabular}{c} **+Feature Selection** \\ \end{tabular} &
\begin{tabular}{c} **+Hyperparameter Optimization** \\ \end{tabular} \\ \hline Foreign Institutions & 1.56 & 2.72 & 3.88 & 3.88 & 3.88 \\ \hline Retail Business & 47.16 & 43.76 & 49.20 & 50.78 & 46.25 \\ \hline Service Industry & 52.50 & 61.50 & 61.55 & 61.50 & 61.50 \\ \hline Restaurant & 76.44 & 76.15 & 78.70 & 78.94 & 78.76 \\ \hline Pub & 61.90 & 58.84 & 63.19 & 65.30 & 63.86 \\ \hline Beverage Store & 60.97 & 52.30 & 74.25 & 74.25 & 74.25 \\ \hline Theater and Concert Hall & 74.99 & 69.39 & 68.32 & 70.49 & 69.64 \\ \hline Institutions of Education & 72.49 & 67.14 & 77.45 & 77.45 & 77.45 \\ \hline Hospital & 12.30 & 15.15 & 16.97 & 16.97 & 16.11 \\ \hline Museum, Gallery, Historical site, Tourist spots & 43.06 & 43.57 & 50.74 & 50.72 & 48.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy of XGBoost. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Location Category** & **Learning Model** & **RF** & \
\begin{tabular}{c} **+Feature Selection** \\ \end{tabular} &
\begin{tabular}{c} **+Hyperparameter Optimization** \\ \end{tabular} \\ \hline Foreign Institutions & 2.96 & 3.07 & 3.98 & 2.9 & 4.03 \\ \hline Retail Business & 48.98 & 50.89 & 51.67 & 52.57 & 55.78 \\ \hline Service Industry & 60.98 & 58.16 & 59.59 & 59.18 & 59.09 \\ \hline Restaurant & 78.09 & 75.6 & 77.5 & 77.45 & 77.95 \\ \hline Pub & 65.32 & 63.62 & 68.83 & 69.09 & 69.82 \\ \hline Beverage Store & 69.66 & 69.59 & 66.02 & 69.66 & 70.4 \\ \hline Theater and Concert Hall & 64.43 & 63.62 & 64.76 & 66.6 & 62.26 \\ \hline Institutions of Education & 76.66 & 75.79 & 77.05 & 76.2 & 76.22 \\ \hline Hospital & 12.63 & 10.44 & 10.62 & 11.84 & 14.13 \\ \hline Museum, Gallery, Historical site, Tourist spots & 44.57 & 41.18 & 42.38 & 42.68 & 42.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy of Random Forest. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Location Category** & **Learning Model** & **Stacking** & **+Feature Selection** \\ \hline Foreign Institutions & 2.88 & 1.77 \\ \hline Retail Business & 49.65 & 58.50 \\ \hline Service Industry & 55.51 & 57.19 \\ \hline Restaurant & 82.03 & 84.19 \\ \hline Pub & 63.20 & 66.43 \\ \hline Beverage Store & 72.52 & 78.75 \\ \hline Theater and Concert Hall & 55.01 & 57.25 \\ \hline Institutions of Education & 72.40 & 80.90 \\ \hline Hospital & 23.40 & 14.48 \\ \hline Museum, Gallery, Historical site, Tourist spots & 43.76 & 45.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Accuracy of Stacking. Figure 6: _Cont._Figure 6: Cont. Figure 6: _Cont._
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 2 Quality Control Algorithm and Quality Measures  
The quality of information from a monitoring scheme is typically characterized by its precision, reliability, sensitivity, and economy [1]. In this investigation, monitoring analysis is implemented through a GNSS positioning solution. Accordingly, the precision and reliability of this solution, which can directly affect the monitoring system's performance, must be discussed. The question to be addressed is whether all GNSS measurements can be converged as a normal distribution by applying least squares estimation to the GNSS measurements. If not, there is a significant possibility that any existing outliers are within a normal distribution range. To assess the precision and reliability of the final GNSS positioning solution, errors should be screened, or other prior-variance calculated, with the remaining measurements; moreover, these errors should be assigned to the adjustment procedure. To estimate the GNSS positioning solution, the two mathematical models mentioned previously are considered. However, outliers cannot be simply considered through functional and stochastic models, because the position accuracy should be assessed with regard to the true value or most probable value (MPV). In processing the GNSS data, systematic errors that were not considered in the functional model and outliers should be isolated before conducting the final least squares estimation to maximize the positioning accuracy and to secure the reliability of the positioning solution. Such a series of inquiries and answers are typically analysed through statistical testing and the answers can thereby be determined. This process is required because of the high sensitivity of the least squares estimation to outliers, which can dangerously reduce the quality of the positioning solution. At this stage, the monitoring analysis quality is vaguely associated with the concepts of precision and accuracy from the GNSS positioning solutions. Therefore, by optimal estimation, an accurate positioning solution can be achieved when proper quality control exists, thereby ensuring that the outliers are excluded.
## 4 Preliminaries  
Notation \(P_{0},P_{1},P_{2}\) are used to represent the parties involved in the protocol, which denote the compute server, model server and data server, respectively. In addition, the algorithm needs to know that \(P_{0}\) is the next party of \(P_{2}\) and the previous party of \(P_{1}\) is \(P_{0}\). To ensure protocol inheritance, the algorithm uses the notation described in FALCON. Use prefix \(\Pi\) to denote a function with participation of \(P_{0},P_{1}\) and \(P_{2}\). This paper uses \([\![x]\!]^{m}\) to denote the replicated secret sharing (RSS) of modulo \(m\) for a general modulus \(m\), such as the arithmetic share \([\![x]\!]^{L}\) or the boolean share value \([\![x]\!]^{2}\), and uses \(x[i]\) to denote the \(i\) th component of vector \(x\). All of the protocols are based on three specific rings which are \(\mathbb{Z}_{L}\), \(\mathbb{Z}_{p}\) and \(\mathbb{Z}_{2}\), where \((L=2^{\ell})\), \(\ell=64\), \(p=67\). This work uses a fixed-point parameter \(f\) with \(13\), which is for scaling shares \(x^{\prime}\) forming a real value \(x\), where \(x=x^{\prime}\cdot 2^{-f}\). The fixed-point parameter will be used in multiplication for truncating the result.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 4 Simulation Results  
In this section, we evaluate the performance of the proposed deep learning-based channel estimations over the 5G channel profile and compare it with the traditional methods; i.e., LS and LMMSE. We also provide an explanation for each obtained result. First, the settings for the simulation are described, and then the simulation results for three different aspects are presented and analyzed.
## 6 Discussion
### Discussion for the Difference between IF-ANP and F-ANP  
As shown in Figure 5, if the experts were directly asked about the quantitative value of interrelations among risks, the survey data calculated from the experts contained much subjectivity. Since the F-ANP method cannot directly reduce the subjectivity of the survey data itself, the credibility of its conclusion has much room for improvement. With the IF-ANP method, by comparing two sets of indirect survey data, subjectivity in the expert survey data cancel each other out, thus improving the consistency of the risk evaluation values given by the experts. Therefore, expert survey data calculated by the IF-ANP has higher credibility.  
Even though IF-ANP has the advantage of higher accuracy, IF-ANP has disadvantages compared to F-ANP. The most important limitation of the IF-ANP method is that the research process is complex. When IF-ANP and DEMATEL methods are combined, each expert has to fill in large numbers of comparison matrixes. This requires researchers to invest much time and money in the process of collecting and calculating data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**IF-ANP**} & \multicolumn{6}{c}{**Direct Evaluation**} \\ \hline \(k\) & **1–6** & **7–12** & **13–18** & **19–24** & **25–30** & \(k\) & **1–6** & **7–12** & **13–18** & **19–24** & **25–30** \\ \hline  & 0.703 & 0.806 & 0.806 & 0.674 & 0.682 & & 0.600 & 1.000 & 1.000 & 1.000 & 0.400 \\  & 0.641 & 0.664 & 0.632 & 0.723 & 0.751 & & 0.200 & 0.800 & 0.200 & 0.600 & 0.200 \\  & 0.723 & 0.658 & 0.712 & 0.812 & 0.764 & & 0.200 & 0.400 & 0.200 & 0.600 & 0.400 \\  & 0.812 & 0.765 & 0.756 & 0.807 & 0.999 & & 0.600 & 0.600 & 0.800 & 0.800 & 1.000 \\  & 0.802 & 0.698 & 0.704 & 0.663 & 0.773 & & 0.200 & 0.200 & 1.000 & 1.000 & 0.200 \\  & 0.812 & 0.715 & 0.714 & 0.655 & 0.694 & & 0.600 & 1.000 & 0.400 & 0.600 & 0.200 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Normalized \(I^{(k)}\) (\(D\)\(\rightarrow\)\(C\)) values given by the IF-ANP and by direct evaluation.