esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### Experimental Setup  
The experiments have been performed on a computer with an Intel Core i5 CPU of 1.00 GHz and 8 GB of RAM. A dApp has been developed in React ([[https://react.dev/](https://react.dev/)]([https://react.dev/](https://react.dev/)), accessed on 12 March 2024) using the TypeScript language ([[https://www.typescriptlang.org/](https://www.typescriptlang.org/)]([https://www.typescriptlang.org/](https://www.typescriptlang.org/)), accessed on 12 March 2024), CryptoJS (version 4.2.0) for cryptographic functionalities, and the ipfs-http-client (version 60.0.1) for IPFS decentralized storage interactions. Additionally, we employ the AES algorithm for encryption and decryption with a key length of 256 bits derived from the secret. To ensure the integrity of the data, we leverage the SHA256 hashing algorithm. The proposed solution uses IPFS for data storage and retrieval, with Infura as the IPFS node provider and Ethereum blockchain for storing the IPFS CID to ensure robust and decentralized storage. Table 1 presents a detailed description of the experimental setup. The source code of the solutions has been made available through a GitHub repository ([[https://github.com/[PERSON]/Efficient-and-Secure-Distributed-Data-Storage-and-Retrieval-Using-IPFS-and-Blockchain](https://github.com/[PERSON]/Efficient-and-Secure-Distributed-Data-Storage-and-Retrieval-Using-IPFS-and-Blockchain)]([https://github.com/[PERSON]/Efficient-and-Secure-Distributed-Data-Storage-and-Retrieval-Using-IPFS-and-Blockchain](https://github.com/[PERSON]/Efficient-and-Secure-Distributed-Data-Storage-and-Retrieval-Using-IPFS-and-Blockchain)), accessed on 12 March 2024).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Specification** & **Details** \\ \hline Front-end Framework & React 18.2.0 \\ Programming Language & TypeScript \\ Encryption Library & CryptoJS 4.2.0 \\ IPFS Client & ipfs-http-client 60.0.1 \\ Encryption Algorithm & AES \\ Key Generation Method & PBKDF2 \\ Hashing Algorithm & SHA256 \\ IPFS Node Provider & Infura \\ Blockchain & Ethereum \\ Processor & Intel Core i5 CPU 1.00 GHz \\ RAM & 8.00 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Setup Specifications.
# Face Recognition and Tracking Framework for Human-Robot Interaction
## 5 Experiments and Analysis
### Face Detection  
For the face detection, the RetinaFace is trained on the WIDER FACE dataset [52]. It contains 32,203 images and 393,703 face bounding boxes with a high degree of variability in scale, pose, expression, occlusion, and illumination. The evaluation is performed on the WIDER FACE validation set, with Average Precision (AP) of 0.83 for the hard subset.
## 2 The Proposed Method
### GSC Method with Sidelobe Neutralization  
#### 2.3.1 Beamforming Pattern  
In this study, the microphone array was assumed to be a uniform circle array (UCA), with \(M\) microphones deployed uniformly on a circle with a radius of \(r\). The sound source and the microphone array were assumed to be on the same horizontal plane for convenience. That is, the incident elevation angle of the desired signal and the noise signal both were \(0^{\circ}\). The sound source direction vector \(v\) was obtained as shown Equation (8), based on the angle interval \(\alpha_{m}\) and relative distance \(x_{r}\) between microphones.  
\[\alpha_{m}=2\pi(m-1)/M \tag{6}\]  
\[x_{r}=r\cdot\cos(\phi-\alpha_{m}) \tag{7}\]  
\[\
u=\left[e^{i(kx_{1})},e^{i(kx_{2})},\ldots,e^{i(kx_{M})}\right]^{T} \tag{8}\]  
Figure 4: Comparison of the energy of the estimated noise frame with different methods: (**a**) all frames; (**b**) 50 th to 100 th frame.  
Thus, the beamforming pattern could be derived using the following equations [35]:  
\[B(\phi_{s},\phi_{i})=\sum_{m=1}^{M}\omega_{(s,m)}^{*}\
u_{(i,m)}=\frac{1}{M}\sum_ {m=1}^{M}e^{j2 kr\sin(\frac{\phi_{s}-\phi_{i}}{2})\sin(\frac{\phi_{s}+\phi_{i}}{2 }-\alpha_{m})}=\frac{1}{M}\sum_{m=1}^{M}\exp(-jz\sin(\beta-\alpha_{m})), \tag{9}\]  
where  
\[z=2 kr\sin(\frac{\phi_{s}-\phi_{i}}{2}),\ \beta=\frac{\phi_{s}+\phi_{i}}{2}, \tag{10}\]  
and \(\omega_{(s,m)}\) is the weight of the microphone channel to adjust the look direction \(\Phi_{s}\) of the microphone array. \(\Phi_{i}\) represents the actual incident direction of the sound source.  
Based the Jacobi-Anger identity,  
\[\exp(jz\sin(\alpha))=\sum_{n=-\infty}^{\infty}J_{n}(z)\exp(jn\alpha) \tag{11}\]  
where \(J_{n}\) is the \(n\)th-order Bessel function; let \(b=n/M\),  
\[\text{Final}:\ B(\phi_{s},\phi_{i})=\sum_{b=-\infty}^{\infty}J_{bM}(z)\exp(- jbM\beta), \tag{12}\]  
\[\text{If $M$ is even}:\ B(\phi_{s},\phi_{i})=J_{0}(z)+2\sum_{b=1}^{\infty}J_{bM}(z) \cos(bM\beta), \tag{13}\]  
Consequently, the beamforming pattern could be formed by the Bessel functions. Considering the property of the Bessel function, the value of the second item of Equation (13) will be close to zero and can be neglected with an increasing number of microphones. Thus, the beamforming pattern could be approximated as a zero-order Bessel function.
## 4 Antenna Geometry and Design  
The circular patch antenna in Figure 1 is loaded with the CDNG structure on the ground plane. Loading of CDNG structure perturbs the radiating field and introduces additional capacitive and inductive effect on the basic patch antenna. This increased capacitive and inductive effect reduces the resonance frequency. Thus, the electrical size of the antenna is reduced and the ka value gets reduced to 0.72. Since the CDNG structure loaded antenna has \(ka<1\), it can be termed as electrically small antenna. Top and bottom view of the proposed antenna are shown in Figure 6. Design parameters of the circular patch antenna and the loaded CDNG-MTM are given in Table 1. Figure 7a,b show the current distribution of the proposed antenna at 2.43 GHz and 5.2 GHz respectively. At the lower band, current is distributed more in CDNG-MTM and less in patch and at the higher band current is distributed equally in both patch and CDNG-MTM in the ground plane. This shows that the lower band resonance is mainly due to the CDNG structure and higher band resonance is due to the combined effect of patch and CDNG-MTM. Gain plot of the proposed antenna is shown in Figure 8. It can be observed that peak gains of 3.8 dBi at 2.43 GHz and 2.9 dBi at 5.2 GHz are obtained for the proposed antenna.  
Figure 4: \(S_{11}\) and \(S_{21}\) (**a**) magnitude and (**b**) phase plots of the CDNG structure.  
Figure 5: Retrieved results for: (**a**) relative permittivity \(E\) and relative permeability \(\mu\); (**b**) refractive index \(n\) and impedance \(z\).  
In conventional antennas, miniaturisation can be achieved by increasing the substrate permittivity. Without changing the substrate material, effective permittivity can be increased by incorporating an MTM or an MTM effect within the antenna design. This in turn would reduce the size of the antenna. Thus, MTMs can function as a material substitute in achieving miniaturisation of antennas. In the proposed design also the change of effective permittivity using MTM is utilised for achieving miniaturisation.  
Figure 8: Gain plot of the proposed antenna with and without CDNG-MTM.  
Figure 6: Structure of the proposed antenna. (**a**) Top view. (**b**) Bottom view.  
Figure 7: Current distribution of the proposed antenna. (**a**) 2.43 GHz. (**b**) 5.2 GHz.
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 5 Experiment and Result Analysis
### Filter Performance Analysis at Different Target Speeds  
In order to further investigate the effect of different target velocities on the filter performance, three sets of experiments are conducted in this section to simulate clutter with different clutter center frequencies, varying the velocities added to the simulated targets. The experimental parameters and simulated clutter parameters are shown in Tables 5 and 6. The SCNR comparisons of the three methods at different speeds are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **Type of Amplitude** & **Clutter** & **Spectral Characteristics of Clutter 1/Hz** & **Spectral Characteristics of Clutter 2/Hz** \\ \cline{3-5}
**Number** & **Type of Amplitude** & **Power/dB** & **Spectral Center** & **Spectral Width** & **Spectral Center** & **Spectral Width** \\ \hline
1 & K distribution & 55 & 0 & 30 & - & - \\
2 & K distribution & 55 & \(150\) & 30 & - & - \\
3 & K distribution & 55 & \(-150\) & 30 & \(150\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulated clutter parameters.  
Figure 6: Three methods of filter frequency response comparison plot: (**a**) Three method filter response curves for the 23 rd range cell. (**b**) Three method filter response curves for the 68 th range cell.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Range cell of target & 23 & 35 & 55 & 68 \\ MTI method output SCNR & 12.0202 & 12.0267 & 13.3109 & 13.2589 \\ Traditional eigenvector method output SCNR & 26.1501 & 25.2207 & 20.1637 & 21.2227 \\ Proposed method output SCNR & 27.6286 & 26.2508 & 22.1673 & 22.9845 \\ Proposed method output SCNR gain & 37.6286 & 36.2508 & 32.1673 & 32.9845 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of clutter suppression when the target is located at different range cells.
###### Abstract  
This paper proposes a virtual infrastructure-based hierarchical information transmission scheme consisting of two phases, macroscopic transmission and local transmission controls. The macroscopic transmission control builds a hub-node-based virtual infrastructure and then finds the optimal hub route that satisfies the stochastic end-to-end delay constraint. Each node determines whether it belongs to a hub node by itself in a distributed manner. The local transmission control builds a robust local path between hub nodes by using the synchronized redundancy concept; this minimizes the effects of transmission failure by reducing the control traffic overhead and time to reconfigure transmission paths without rediscovery of the transmission path from the source node. Simulations confirmed that, as the network size increased, the performance of the proposed transmission scheme increased in terms of packet delivery ratio and control packet overhead. The scheme can be applied to mobile ad hoc cloud computing systems based on self-organizing vehicular networks or drone networks as a key control.  
delay-constrained control; distributed and localized control; hierarchical forwarding; on-demand transmission control +
Footnote †: journal: Applied Softwares
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 3 Numerical Example
### Comparison of the Performance Between the Proposed Method and the Traditional Approach
#### 3.3.4 Robust Performance of the Proposed Method  
The determination of the damage feature with the proposed method depends on the generated Hankel matrix; hence, the form of the Hankel matrix influences the robustness and accuracy of damage detection. To discuss the rules followed to generate the Hankel matrix, we use the following numerical example. Nine sensors are placed along the main beam of the bridge and a total of 36 elements are supplied to establish the Hankel matrix. If row number \(p\) and column number \(q\) are equal to 1 and 36, respectively, the Hankel matrix takes the row matrix form. For this situation, there are 35 null space matrices in the Hankel matrix, as described in Equation (8). With this Hankel matrix, the damage detection results are shown in Figure 9a, in which 35 damage detection results are obtained using 35 different null space matrices. These results demonstrate a substantial discrepancy and some results  
Figure 8: Comparison of the damage detection performance between the traditional method and the proposed method before and after damage with a 1.6% noise level (with vehicle weights of 100 kN and 50 kN before and after damage, respectively): (**a**) displacement ILs; (**b**) difference between two displacement ILs obtained by the traditional method; (**c**) metric of the damage feature obtained by the proposed method.  
even provide the wrong determination. If \(p\) and \(q\) take values of 7 and 30, respectively, the Hankel matrix contains 23 null space matrices. All damage detection results are presented in Figure 9b, which shows that the robustness of the proposed method is significantly improved. We repeat this calculation until \(p\) and \(q\) take values of 17 and 20, respectively; the corresponding damage detection results are shown in Figure 9c. For this situation, the robustness and accuracy of the proposed method are evidently superior. Therefore, it is recommended that the form of the Hankel matrix be approximately square, that is, the value of \(p\) is equal to either \(q-1\) or \(q-2\).
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Hydrogen Diffusion at Temperatures between 900 and 1400 \({}^{\circ}\)C
#### 3.1.2 Results  
Figure 6 gives examples of the images taken at 900 \({}^{\circ}\)C at various times after starting the hydrogen injection into the furnace. A video about this process is available online as supplement S1. The progress of the hydrogen penetration into the solid cylinder is clearly visible.  
The transmission distribution can be determined for each image. The macroscopic total neutron cross section was calculated from these transmissions. As an example, Figure 7a plots these distributions for several times of the test at 1200 \({}^{\circ}\)C. These curves were fitted by the solution of [PERSON]'s law for constant concentration at the surface through which the hydrogen penetrates.  
\[c(x,t)=c_{0}\bigg{(}1-erf\bigg{(}\frac{x}{2\sqrt{D\ t}}\bigg{)}\bigg{)} \tag{1}\]  
Figure 6: Image sequence of the hydrogen diffusion into Zry-4 at 1173 K.  
\(c(x,t)\) is the actual concentration, \(c_{0}\) the constant concentration at the surface, \(x\) the distance to the surface, \(D\) the diffusivity, and \(t\) the time. This equation is valid as long as the hydrogen does not reach the opposite end of the sample. Because the total macroscopic neutron cross section depends linearly on the number density of hydrogen, no calibration of this dependence is needed to determine the diffusivity from the width of the distribution given by \(erf\left(\frac{x}{2\sqrt{D}\,t}\right)\). An example for the fits is given in Figure 7b.
# Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks
## 4 Simulation Results and Discussions  
In this section we demonstrate the simulation results for star topology UWB based wireless sensor network. In this network each sensor node communicate with the sink node with a specific pseudo random sequence, i.e., the network employ the DS-UWB multiple access strategy. The performance of five multiuser detection strategies are presented here, including matched filter (MF), decorrlating detector (DEC), minimum mean square error (MMSE), optimal detector (OMUD) and the proposed iterative detector based on differential characteristics.  
During the simulation in this section the signal is transmitted over 3-6 GHz and the chip transmission rate is 2 GHz. Two types of channel are simulated in this section, one is the AWGN channel, the other is a multipath channel, the IEEE 802.15.4a channel (without loss of generality, IEEE 802.15.4a CM1 channel is studied for simulation). For the AWGN channel the signal is received with simple matched filter, while for the multipath IEEE 82.15.4a channel we choose the selective RAKE (S-RAKE) receiver. The S-RAKE receiver chooses 16 branches and uses the maximum ratio combining (MRC) rule. Our main object in this section is to check the BER performance of the proposed iterative multiuser detector based on differential characteristics. Monte Carlo simulations are utilized to examine the proposed iterative multiuser detection algorithm in this section. Major simulation parameters employed in this section are listed in Table 1.
## 4 Discussion
### Charging Efficiency Performance in the Real Case of Each Sensor  
In this section, numbers are assigned to the sensors. The closer a sensor is to the data sink node, the smaller the number that is assigned to it. In Figure 12, the effect of the charging efficiency of each sensor in the different algorithms is shown. We limit the number of deployed chargers to 10. The efficiency  
Figure 11: Effects of the angle \(\theta\).  
Figure 10: Effects of the charging demand adjustment coefficient \(\mu\).  
of the RPRO charging method is very unbalanced; this is because the deployment method randomly picked the interested points. The strategy used by the LMD approach is to find the best charging efficiency for the sensors. In contrast, our method considered the charging demand instead of charging efficiency. The aim of our approach is to maintain the operation of the sensors instead of going after the best charging efficiency. In a realistic environment, our approach can effectively reduce the number of required chargers.
## 3 Results  
In order to evaluate the positioning performance of real-time PPP and the accuracy of PPP ZTD during rainfall and nonrainfall periods, observation data from 18 GNSS observation stations in Hong Kong, China, during June 2021 (DOY 173-DOY 179, during rainfall) and August 2021 (DOY 229-DOY 235, during nonrainfall) were selected. The real-time PPP data processing was carried out by using the real-time orbit and real-time clock products provided by CNES and the RTKLIB open-source software platform. The positioning accuracy of real-time PPP was evaluated by comparing it with the final product of IGS. Then, the real-time PPP PWV of the HKSC station was obtained by using the \(Tm\) model established by the ERA5 dataset, and the PWV of the radiosonde station was used as a reference to evaluate the accuracy of real-time PPP PWV.
## 4 Mechanical Design and Thermal Modelling of 6U CubeSat
### Thermal Analysis  
_Heat Load Parameters_  
Thermal analyses were performed for the given mission parameters and heat loads for both extreme temperature conditions. The 6U satellite with all the major heat dissipating elements such as a mini cryocooler, battery pack, avionics and payload are considered in the thermal model. Passive thermal control methods include heat pipes, thermal straps, and body-mount radiator are analyzed initially. Later, radiator design for the same 6U satellite configuration is analyzed for various design parameters.
# PCB Network Analysis for Circuit Partitioning
## 6 Discussion  
We also analyze the three rules proposed in this paper, and report results on Stage 1, Stage 2, and Stage 3 of the algorithm in terms of ACC, Dice, IOU, SE, and PR in Table 3. The scattered components in \(\mathbf{X}_{S*}\) are allocated by Rule (1) in the first stage, and the rest of the components in \(\mathbf{X}_{S*}\) are allocated by Rule (3). The scattered components in \(\mathbf{X}_{P*}\) are allocated by Rule (2). Rule (1) is useful for the components allocated between signal net and  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**ACC**} & \multicolumn{2}{c}{**Dice**} & \multicolumn{2}{c}{**IOU**} & \multicolumn{2}{c}{**SE**} & \multicolumn{2}{c}{**PR**} \\ \cline{2-11}  & **MaxFlow** & **Ours** & **MaxFlow** & **Ours** & **MaxFlow** & **Ours** & **MaxFlow** & **Ours** & **MaxFlow** & **Ours** \\ \hline ANDZ & 11.4 & **96.8** & 22.4 & **91.4** & 15.6 & **87.7** & 28.7 & **89.0** & 18.4 & **93.9** \\ N1 & 37.1 & **86.9** & 26.3 & **85.8** & 17.7 & **77.1** & 35.3 & **87.2** & 21.5 & **84.4** \\ N2 & 39.2 & **84.9** & 53.2 & **88.4** & 40.5 & **80.8** & 55.8 & **88.9** & 50.8 & **87.9** \\ N3 & 34.3 & **91.6** & 44.8 & **88.7** & 32.9 & **82.7** & 50.1 & **86.8** & 40.5 & **90.7** \\ N4 & 34.5 & **84.4** & 32.9 & **80.2** & 23.1 & **72.9** & 35.7 & **80.6** & 30.5 & **79.8** \\ N5 & 35.7 & **86.6** & 41.8 & **81.1** & 31.6 & **73.3** & 41.4 & **80.6** & 42.2 & **81.6** \\ N6 & 30.0 & **89.3** & 33.9 & **84.9** & 23.5 & **77.7** & 35.0 & **85.0** & 32.9 & **84.8** \\ N7 & 17.6 & **88.8** & 15.2 & **93.0** & 10.4 & **89.5** & 19.4 & **94.2** & 12.5 & **91.8** \\ N8 & 22.9 & **84.9** & 38.6 & **82.7** & 29.2 & **76.2** & 43.4 & **84.3** & 34.8 & **81.2** \\ N9 & 53.6 & **82.8** & 68.5 & **91.1** & 59.4 & **85.8** & 74.2 & **91.2** & 63.6 & **91.0** \\ N10 & 38.0 & **89.8** & 55.1 & **92.8** & 44.6 & **88.4** & 54.6 & **93.1** & 55.6 & **92.5** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results. Results are compared with MaxFlow [10] in terms of ACC, Dice, IOU, SE, and PR.  
Figure 8: How to compute TP, FN, FP, and TN. Ground truth set \(A\) is in the green block, partition set \(B\) from the proposed method is in the red block, and components set in the gray block are the whole candidate set \(C\). Better viewed in color. \(TP=A\cap B\), \(FN=A-A\cap B\), \(FP=B-A\cap B\), \(TN=C-A\cup B\).  
power net, and between signal nets. Rule (2) is useful to allocate the scattered components between power nets and GND net, which are usually repetitive modules. It can be seen that the results are improved one by one from Stage 1 to Stage 3, which means that the three rules summarized from our observation are meaningful. Rule (2) significantly improves the results from Stage 2 to Stage 3. It shows that a lot of repetitive modules exist in netlists. Even with a simple pattern discovered, the partition results are improved a lot.
# Impact of COVID-19 Mobility Changes on Air Quality in Warsaw
## 2 Materials and Methods  
### Mobility Data  
In order to analyse the impact of changes in passenger mobility on air quality, a specific database was created. The database was created using mobile phone operator data on travel/movement of active SIM cards. Such data show the traffic potential of each zone in the form of a set of numbers of SIM cards, which users did not travel or travelled only within the zone. These data enable the analysis of zonal trip-end and movement data over any period, e.g., for consecutive hours in a day.  
Mobile phone operator data on movements were acquired for 3 pandemic phases in 2020, for selected, representative working days in the periods 17-31 March 2020 (phase 1--when there were strong mobility restrictions mainly from concern for one's health), 8-19 April 2020 (phase 2--when there were strong administrative mobility restrictions), 19 May-11 June 2020 (phase 3--when the gradual reducing of restrictions began). Data were obtained for weekdays (Tuesday, Wednesday, Thursday) because the impact of mobility restrictions was analysed in relation to the entire transport system (car traffic and public transport journeys) and in relation to do-called obligatory trips (to work, to school), which are most typically on these days. The data is purchased in the form of a completely anonymized (by the data supplier) trip matrix, containing information on the number of trips generated in each zone and the spatial distribution of these trips, i.e., the number of trips between each pair of zones. Before use, the data is checked. This applies, for example to size of the matrix, individual values of trips between pairs of zones, average trip distances, distribution of trip distances. In addition, the trip matrices provided for 2019 were compared with the trip matrices developed for Warsaw for the same year on the basis of comprehensive, origin-destination travel studies.  
Estimated actual trip numbers were obtained in an augmented system, i.e., expanded to the whole population by using the so-called SIM multiplier. The multiplier is determined by the data supplier for each movement (trip) made between a pair of zones, taking into account each of the following zones: population (residents with more than 10 years of age), the operator's share in the SIM card market, number of people owning more than one mobile phone and the number of SIM card users who provided their marketing consent. In this case, the average SIM card multiplier of 8.65 was applied.