esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents the results of field tests conducted in the project \"Implementation of TV White Spaces (TVWS) for Internet Access in Brazil\". This study evaluates the feasibility and regulatory implications of TVWS in rural and remote areas. TVWS systems are promising for sensor network applications, enabling efficient and long-range connectivity. The experiments assess the coexistence of TVWS signals, applying, for example, the Remote Area Access Network System for the Fifth Generation (5G-RANGE) using the generalized frequency division multiplexing (GFDM) technique, with the Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) system. Laboratory tests determined the protection ratio (PR) between digital television (DTV) signals and interfering signals, with minimum PR values of \(-31.38\) dB on channel \(n-1\) and \(-33.24\) dB on channel \(n+1\) for 5G-RANGE using GFDM, highlighting its low out-of-band emission (OOBE). Field tests confirmed the laboratory results, with the worst recorded PR causing interference being \(-30.2\) dB on channel \(n-1\). The power restriction to \(1\) Wp limited coverage, allowing 96 Mbps in 24 MHz BW at 14.7 km from the base station. These results highlight that regulatory adjustments can be made to support TVWS deployment in Brazil.  
5G-RANGE; DTV; field tests; GFDM; ISDB-T; spectrum coexistence; TVWS Article
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 1 Introduction
Meanwhile, in graph learning, a rank constraint is applied to the Laplacian matrix yielded by the common subspace representation to directly produce the clustering result. Therefore, the proposed method is a one-step multi-view subspace clustering method. The main contributions of the work are summarized as follows:  
* A novel one-step multi-view subspace clustering method is proposed, which fuses the subspace representation (exploring local and global information), multi-view information fusion (constructing a common subspace representation by fusing different view-specific subspace representations), and clustering (imposing rank constraint on the Laplacian matrix from the common subspace representation) as a unified optimization framework to realize the end-to-end clustering. * We develop an effective optimization algorithm to solve the proposed method. Comprehensive experiments on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with some state-of-the-art multi-view clustering methods. The rest of this paper is organized as follows. In Section 2, we review the related works. In Section 3, we introduce the formulation of the proposed LGOMSC method. In Section 4, we provide the optimization algorithm to solve the proposed LGOMSC method, including the analysis of the convergence and computation complexity. In Section 5, we conduct the experiments on nine popular multi-view data sets and analyze the experimental results. Finally, we provide the conclusion in Section 6.
# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones
## 4 Results  
### TC Track Estimation  
When the SMAP radiometer and CYGNSS data were combined to estimate the position of Hurricane Florence, the final fusion image was obtained and this is shown in Figure 9. To maintain data integrity, the observation results were obtained individually by the SMAP satellite or CYGNSS constellation and did not contain any information about the TC; these are also presented below. As shown in Figure 9, the joint use of the SMAP radiometer and CYGNSS data provides better visualization of TCs, and TCs can be observed with a higher temporal resolution compared to the case where only the SMAP radiometer was used to monitor TCs. The final fusion image, which was obtained by only fusing the CYGNSS data according to the movement speed of the hurricane, is presented in Figure 10. A comparison with Figure 9 shows that the number of fusion results decreased and the visualization of TC also became worse due to the narrow tracks of the CYGNSS satellite. Note that the location of the black circle was automatically determined (see Figure 10) in relation to the shape of high wind-speed region and the movement speed of the TC by using the TC center-fixing algorithm described in Section 3.  
According to the processing method described in Section 3, we estimated the track of Hurricane Florence from Figures 9 and 10. Additionally, a comparison between the estimated and NHC best tracks of Hurricane Florence is illustrated in Figure 11. Figure 11 clearly shows that the TC track obtained by fusing the SMAP radiometer data and the CYGNSS data is more consistent with the  
Figure 10: The final fusion image of Hurricane Florence observed from 8 to 12 September 2018 using the CYGNSS data. (**a1**)–(**a3**), (**b1**)–(**b3**), (**c1**)–(**c3**), (**d1**)–(**d3**), and (**e1**)–(**e3**) show the daily fusion results from 8 to 12 September 2018, respectively. A black circle with a radius of 150 km was used to mark the location of Hurricane Florence in the fusion image.  
NHC best track than that obtained by only fusing the CYGNSS data. A more detailed comparison between the estimated and NHC best tracks of Hurricane Florence is given in Table 1. Furthermore, another nine hurricanes were analyzed in the same way as Hurricane Florence, and the results of this analysis are also listed in Table 1. Here, the mean absolute error (MAE) and the standard deviation (SD) were estimated by calculating the distance between the estimated center of hurricane and that obtained from the NHC best track data. Note that the NHC best tracks for all hurricanes observed can be accessed at [[https://www.nhc.noaa.gov/data/tcr/](https://www.nhc.noaa.gov/data/tcr/)]([https://www.nhc.noaa.gov/data/tcr/](https://www.nhc.noaa.gov/data/tcr/)).  
Table 1 shows the comparison between the TC tracks obtained by fusing the SMAP radiometer and CYGNSS data and the NHC best tracks. The MAE values range between 18.4 and 46 km, the SD values vary between 15.1 and 28.2 km, and both of them are smaller than those obtained by only fusing the CYGNSS data.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 3 A Multi-Application Scheduling Formal Definition
### Optimization Objective
\[\mathrm{comm}\left(\mathrm{n}_{\mathrm{i}},\mathrm{n}_{\mathrm{j}}\right)=\max_{ \mathrm{n}_{\mathrm{i}}\in\mathrm{pred}\left(\mathrm{n}_{\mathrm{i}}\right)} \frac{\mathrm{w}_{\mathrm{i},\mathrm{j}}}{\mathsf{B}\Bigl{(}\mathrm{p}_{\mathrm{ m}\left(\mathrm{n}_{\mathrm{i}}\right)}\cdot\mathrm{P}_{\mathrm{n}\left(\mathrm{n}_{ \mathrm{j}}\right)}\Bigr{)}} \tag{1}\]The earliest start time (EST) and earliest finish time (EFT) of task \(\mathrm{n_{i}}\) on processor \(\mathrm{p_{m}}\) are given by Equations (2) and (3), where \(\mathrm{pred}(\mathrm{n_{i}})\) is the parent node of task \(\mathrm{n_{i}}\), avail \(\mathrm{[p_{m}]}\) denotes the earliest time that \(\mathrm{p_{m}}\) can be used to perform the task, and AFT \(\mathrm{(n_{j})}\) is the time that \(\mathrm{n_{j}}\) actually finishes. \[\mathrm{EST}(\mathrm{n_{i}},\mathrm{p_{m}})=\max\Bigg{\{}\mathrm{avail}[ \mathrm{p_{m}}],\max_{\mathrm{n_{j}}\in\mathrm{pred}(\mathrm{n_{i}})}\big{(} \mathrm{AFT}\mathrm{(n_{j})}+\mathrm{comm}(\mathrm{n_{j}},\mathrm{n_{i}})\big{)} \Bigg{\}} \tag{2}\]  
\[\mathrm{EFT}(\mathrm{n_{i}},\mathrm{p_{m}})=\mathrm{comp}(\mathrm{n_{i}}, \mathrm{p_{m}})+\mathrm{EST}(\mathrm{n_{i}},\mathrm{p_{m}}) \tag{3}\]  
The true execution time of a completed application \(\mathrm{j}\in\mathrm{J_{comp}}\) is exec (j), given by Equation (4), where \(\mathrm{n_{exit}}\) is the exit node of application and AFT \(\mathrm{(n_{exit})}\) is the actual completion time of the exit node. \[\mathrm{exec(j)}=\max\{\mathrm{AFT}(\mathrm{n_{exit}})\} \tag{4}\]  
In this paper, the final objective of the scheduling algorithm is to minimize the average execution time of multiple applications (avg.JET) as shown in Equation (5). Where \(\mathrm{J_{comp}}\) is the set of completed job DAGs and \(\mathrm{J_{comp}}\in\mathrm{J}\), \(\mathrm{J_{comp}}\) is the number of completed job DAGs. \[\mathrm{min~{}avg.JET}=\frac{\sum_{\mathrm{j}\in\mathrm{J_{comp}}}\mathrm{exec (j)}}{\left|\mathrm{J_{comp}}\right|} \tag{5}\]
## 4 Validation
### Simulation Input
#### 4.2.4 IB Transfer and Maneuvering Times  
Due to a lack of available real-world data, the time it takes for an available IB to reach a ship in need of assistance, in the following referred to as IB transfer time, is determined based on statistics of simulated transfer times obtained from research project WINMOS II [26], generated based on the mathematical simulation approach by [12]. Following the obtained data, IB transfer times are assumed to be exponentially distributed as per the probability density function (PDF) in Figure 6. Accordingly, the time it takes for an available IB to reach a ship in need of assistance is assumed to be in the range of 0-8 h. The maximum transfer time may correspond to a situation where an IB must cover some 80 NM at an average speed of 10 knots to reach a ship in need of assistance. This distance is roughly equivalent to, for instance, the distance L1-L4 (see Figure 4). The IB maneuvering time is assumed to follow a normal distribution with a mean value of 20 min and a standard deviation of 5 min.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Date** & **L1** & **L2** & **L3** & **L4** & **L5** & **L6** & **L7** & **L8** & **L9** & **L10** & **L11** & **L12** & **L13** & **L14** & **L15** \\ \hline
2 February 2010 & 4 & 5 & 5 & 33 & 5 & 5 & 5 & 38 & 21 & 32 & 38 & 21 & 38 & 38 & 43 \\
4 February 2010 & 4 & 5 & 5 & 33 & 5 & 5 & 5 & 38 & 21 & 32 & 38 & 21 & 38 & 38 & 43 \\
5 February 2010 & 23 & 23 & 23 & 40 & 23 & 23 & 23 & 38 & 23 & 38 & 38 & 39 & 45 & 45 & 45 \\
6 February 2010 & 23 & 23 & 23 & 40 & 23 & 23 & 23 & 38 & 23 & 38 & 38 & 39 & 45 & 45 & 45 \\
7 February 2010 & 23 & 23 & 23 & 40 & 23 & 23 & 23 & 38 & 23 & 38 & 38 & 39 & 45 & 45 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extract of \(H_{\text{eq\_avg}}\) (cm) values.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Date** & **L1** & **L2** & **L3** & **L4** & **L5** & **L6** & **L7** & **L8** & **L9** & **L10** & **L11** & **L12** & **L13** & **L14** & **L15** \\ \hline
2 February 2010 & 5 & 5 & 5 & 40 & 5 & 5 & 5 & 50 & 30 & 30 & 50 & 30 & 50 & 50 & 54 \\
3 February 2010 & 5 & 5 & 5 & 40 & 5 & 5 & 5 & 50 & 30 & 30 & 50 & 30 & 50 & 50 & 54 \\
4 February 2010 & 5 & 5 & 5 & 40 & 5 & 5 & 5 & 50 & 30 & 30 & 50 & 30 & 50 & 50 & 54 \\
5 February 2010 & 36 & 36 & 36 & 50 & 36 & 36 & 50 & 36 & 50 & 36 & 50 & 50 & 50 & 60 & 60 & 59 \\
6 February 2010 & 36 & 36 & 36 & 50 & 36 & 36 & 36 & 50 & 36 & 50 & 50 & 50 & 50 & 60 & 60 & 59 \\
7 February 2010 & 36 & 36 & 36 & 50 & 36 & 36 & 50 & 36 & 50 & 36 & 50 & 50 & 50 & 60 & 60 & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extract of \(H_{\text{eq\_max}}\) values (cm).
# Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
###### Abstract  
There are many pre-trained deep learning-based face recognition models developed in the literature, such as FaceNet, ArcFace, VGG-Face, and DeepFace. However, performing transfer learning of these models for handling face sketch recognition is not applicable due to the challenge of limited sketch datasets (single sketch per subject). One promising solution to mitigate this issue is by using optimization algorithms, which will perform a fine-tuning and fitting of these models for the face sketch problem. Specifically, this research introduces an enhanced optimizer that will evolve these models by performing automatic weightage/fine-tuning of the generated feature vector guided by the recognition accuracy of the training data. The following are the key contributions to this work: (i) this paper introduces a novel Smart Switching Slime Mold Algorithm (S2 SMA), which has been improved by embedding several search operations and control rules; (ii) the proposed S2 SMA aims to fine-tune the pre-trained deep learning models in order to improve the accuracy of the face sketch recognition problem; and (iii) the proposed S2 SMA makes simultaneous fine-tuning of multiple pre-trained deep learning models toward further improving the recognition accuracy of the face sketch problem. The performance of the S2 SMA has been evaluated on two face sketch databases, which are XM2 VTS and CUFSF, and on CEC's 2010 large-scale benchmark. In addition, the outcomes were compared to several variations of the SMA and related optimization techniques. The numerical results demonstrated that the improved optimizer obtained a higher level of fitness value as well as better face sketch recognition accuracy. The statistical data demonstrate that S2 SMA significantly outperforms other optimization techniques with a rapid convergence curve.  
deep face sketch recognition; slime mold algorithm; fine-tuning +
Footnote †: journal: Applied Sciences
# Theoretical Study on a Cable-Bracing Inerter System for Seismic Mitigation
## 2 Theoretical Analysis of a Cable-Bracing Inerter System  
### CBIS Concept  
Cable bracing is the proposed mechanism of translation-to-rotation conversion for an inerter connected to a structure. Figure 1 shows an SDOF structure with a CBIS, which consists of a pair of bracing cables, a pair of conductor plates (flywheels), and a shaft. A pair of cables is pre-tensioned connecting the structural frame and the shaft diagonally. Both ends of the shaft are supported by a pair of shaft bearings mounted on the side plates fixed on the ground floor, making sure that only the shaft rotates. When inter-story drift occurs in structures, one of the cables will shorten and drive the shaft into rotation. The low-speed translational movement of the structure can be converted into a high-speed rotational motion of the conductor plates by cable bracing.  
Conductor plates are fixed on the shaft and rotate together; meanwhile, several magnets with alternating magnetic polarization are allocated on the fixed side plate to generate the electromagnetic field. The rotational conductor plates and shaft function as an inerter. The fixed side plate and conductor plate form one eddy current damper when the rotation conductor plate is cutting through the electromagnetic field, thereby dissipating the vibration energy in the form of heat. As a result,  
Figure 1: A single-degree-of-freedom (SDOF) structure with a CBIS: (**a**) structure with a CBIS and (**b**) detail of a CBIS. Note that the conductor plates serve as flywheels.  
the novel cable-bracing inerter system presented herein can obtain its inertance and enhance the energy dissipation capacity via the additional damping provided by the eddy current damping. Compared to the classic ball-screw mechanism of an inerter, CBIS is cost-effective and very simple.
# Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels
## 2 Materials and Methods
### Micro Gas Turbine (MGT) Engine Fabrication
#### Plasma Unit Selection and External Integration  
The choice of plasma device for this study was influenced by factors such as the required temperature for the plasma torch, the cost implication of different plasma torches on the market, and applications in previous studies. The main advantage of a plasma-based igniter compared to a conventional spark plug is the considerably higher generated plasma plume volume and velocity, as seen in the adopted Plasmatron torch, with a generated temperature range between 5000 and 8000 \({}^{\circ}\)C, allowing for deeper penetration of a highly reactive plasma plume into the combustion zone of an engine, as proposed in [45].  
The developed testbed produced plasma from water vapor at a pressure of 1 bar (14.7 psi). The device consists of a 240 volt, 3.5 kW electrical source power unit; the plasma torch was positioned in the front of the fabricated compressor inlet (after many trials) [46] for hydrogen gas reformation. The hydrogen gas reformed at 5000-8000 \({}^{\circ}\)C is transmitted into the combustion chamber, as seen in Figure 6c.  
The reformed gas is injected into the combustion chamber, and the compressor system increases the combustion efficiency, as previously reported [36]. The Plasmatron devices used in this study also provide ohmic heating of gases to an elevated temperature at which the gas is partially ionized as a requirement to aid in heat conduction. A wide range of applications is possible, from oxidation to steam reforming, boosting the reaction rate that occurred through the creation of a small region of very high temperature from 5000 to 8000 \({}^{\circ}\)C. In this situation, radicals are produced, increasing the average temperature in the surrounding area. Adequate safety precautions were taken in handling the Plasmatron system, including placing the MGT engine testbed in a smaller room inside the laboratory with double thickness and a view window. This will support the complete combustion of the proposed fuels to limit GHG emissions in gas turbine engines.
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 9 Demonstration of Reduction of Integer Divider
### Results  
The primary goal of this work is to introduce techniques for facilitating the reduction in quantum circuit width (required qubit count) through static analysis. In this section, results from testing the full and specialised circuits are presented.  
The toolchain's testing components are used to verify the correctness of the generated dividers using the logic-based circuit simulator. Both the full divider and the specialised versions were tested.  
We start by verifying the functionality of the non-specialised divider demonstrated in Figure 16 (Design 3). The circuit is constructed as described in the previous section by the code in Figure 24. Table 1 shows a sample from the results of running an automatically generated test suite for this circuit. The first column shows the integer division performed, while the second and third columns show the qubit register state after initialization and completion of the circuit simulation, respectively. For clarity, the final column summarizes the 4-qubit output from the long-division performed. For all cases considered, the obtained output from the quantum circuit simulator used in verification matched the intended output.  
We also verify the functionality of a divider with a fully specialised divisor. It is important to note that, unlike the full divider test suite which operates only on one circuit, the specialised dividers require specific quantum circuits for each value of the divisor. Thus, several test suites are constructed (this is automated with list comprehensions, as described in Figure 23), one for each divisor. Table 2 shows the results of testing these circuits, collated from multiple test suites.  
Figure 26: Fully specialised quantum circuit implementation of integer division. Reduction for \(|q4d^{\prime}|q3d^{\prime}|q2d^{\prime}|q1d^{\prime}|q0d^{\prime}\rangle=|10111\rangle\) represents cases where the divisor is 9. First step of long division.  
Figure 27: Fully specialised quantum circuit implementation of integer division. Reduction for \(|q4d^{\prime}|q3d^{\prime}|q2d^{\prime}|q1d^{\prime}|q0d^{\prime}\rangle=|10 101\rangle\) represents cases where the divisor is \(11\). First step of long division.
## 2 Engineering Background
### Surface Buildings and Structures and Control Criterion  
The backfill panel is in the Tiesan area of the Tangshan Coal Mine. Surface buildings and structures in the area mainly include different types of companies, factories, commercial wholesale and retail outlets, and large, densely-populated residential areas newly constructed after the magnitude 7.8 Tangshan Earthquake (1976). The buildings have diverse dimensions, plan layouts, and structures, while they are mainly one-story houses with brick-concrete or masonry-timber structures (Figure 2). According to incomplete statistics, more than 800 enterprises and public institutions are based in the area, covering a building area of 1.8 \(\times\) 10\({}^{6}\) m\({}^{2}\) and having a population of about 9 \(\times\) 10\({}^{4}\). Above the backfill panel is the eastern area of Lunar District of Tangshan City, where South Jianshe Road passes over the panel. Kailuan Coal Mining Administration and Labour Service and Construction Decoration Company are located directly above the backfill panel.  
Figure 1: Layout of the underground backfill panel.  
Figure 2: Distribution of surface buildings and structures in Tangshan Coal Mine.  
According to conditions of surface buildings and structures in the Tiesan area and the severity of damage to brick-concrete-structured buildings, the damage of backfill mining to surface buildings needs to be controlled below class-II damage (slight damage) to ensure normal use of these buildings and structures. In the meanwhile, buildings in the Tiesan area are also characterized by their dense distribution, unequal quality, and complex tenure. Considering these, the compressive and tensile deformation when surface buildings have class-II damage should not exceed \(-2.0\) mm/m and \(1.5\) mm/m, respectively. Considering that excessive subsidence may cause poor drainage in the city, the surface subsidence should not exceed \(500\) mm.
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 2 Single-Element Antenna  
### Design Methodology  
Figure 1 depicts the proposed single-element monopole antenna with a slotted CPW ground plane and a semi-arc stub integrated modified circular radiator. A flexible PET substrate with a dielectric constant of 3.4, thickness of 0.1 mm, and a loss tangent of 0.022 was utilized for this work. Copper was utilized in this study as the conducting material for the radiating parts. The antenna's physical dimension was 40 \(\times\) 30 \(\times\) 0.1 mm\({}^{3}\) (0.609A \(\times\) 0.457A \(\times\) 0.0015A), with the wavelength \(\lambda\) calculated at 4.57 GHz. The optimal dimensions of the single-element antenna are given in Table 1.  
The antenna has evolved through a sequence of evolution stages. A slotted CPW ground plane and a circular monopole antenna make up the first stage. The antenna had a low impedance matching with an operating frequency from 4.3 to 5.1 GHz as in Figure 2. To increase the impedance matching and to further widen the bandwidth, modifications were made in the radiator by etching copper from it. This stage enhances the antenna's impedance matching; however, the frequency of the antenna was shifted to a higher frequency as depicted in Figure 2. A semi-arc-shaped stub was engineered to the radiator  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Ws** & **Ls** & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **L** & **R1** & **R2** & **H** \\ \hline
30 & 40 & 10 & 0.6 & 14 & 0.45 & 9.02 & 8 & 3 & 20 & 11 & 12 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the antenna parameters (in mm).  
Figure 1: Configuration schematic of the single-element antenna: (**a**) front view; (**b**) side view.  
to shift the frequency to a lower range with good impedance matching. With a bandwidth of 4.57-5.50 GHz, this stage offers the desired band of interest. For comparison, the gain over frequency of all the evolution stages as well as the radiation pattern of all stages calculated at the center frequency of 5 GHz is given in Figure 2b,c. From the figure, it is evident that the proposed single-element antenna offers wide bandwidth, good gain and radiation characteristics.