esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results and Discussions
### Force-Driven Poiseuille Flow
As the Knudsen number further increases, e.g., \(Kn=10\), the moment method doesn't work anymore. However, we can still evaluate the accuracy of \(\overline{f}^{(1)},\overline{f}^{(3)},\overline{f}^{(5)}\) using the moments obtained from the DVM, as shown in Figure 3. From the Figure 3a,b we can see that the'real VDF' \(\overline{f}\) is clearly far away from the equilibrium state. In contrast, all of the VDFs \(\overline{f}^{(1)},\overline{f}^{(3)},\overline{f}^{(5)}\) can only describe the gas that is not far away from the continuum regime. The corresponding errors of \(\overline{f}^{(1)},\overline{f}^{(3)},\overline{f}^{(5)}\) are calculated based on Equation (17). Four Knudsen numbers, i.e., \(Kn=0.01,\;0.1,\;0.5,\;1\) are considered. The errors \(E_{\rm s}^{(1)},\;E_{\rm s}^{(3)},\;E_{\rm s}^{(5)}\) along the channel are shown in Figure 4.  
The overall results in Figure 4 indicate that, as the Knudsen number increases, \(E_{\rm s}^{(1)},\;E_{\rm s}^{(3)},\;E_{\rm s}^{(5)}\) increase subsequently. When \(Kn=0.01\), all of the errors are very close to \(0\), so that all the macroscopic equations are able to predict accurate solutions. Besides, for all the tested Knudsen numbers, we can find that \(E_{\rm s}^{(1)}>E_{\rm s}^{(3)}>E_{\rm s}^{(5)}\), which means \(\overline{f}^{(5)}\) is more accurate than \(\overline{f}^{(3)}\) and \(\overline{f}^{(1)}\). Moreover, from each line of the error profile, it is easy to find that the value of the error in the near wall region is larger than that in the center of the channel, which indicates that the 'non-equilibrium effects' in the near wall region is stronger than that in the center of the channel. Our simulations start from a global equilibrium state, and the convergence criterion for the steady-state is defined by Equation (19). For this case, all tests are done on single processor (IntelCorporation, Santa Clara, CA, USA, i7-8550u). The computational cost, in terms of the computational memory and time cost, are listed in Table 1.  
\[E(n+1)=\frac{\sum\bigl{|}u_{i}^{n+1}-u_{i}^{n}\bigr{|}}{\sum\bigl{|}u_{i}^{n+1} \bigr{|}}<10^{-6}, \tag{19}\]  
Due to the multi-dimensional nature of the VDF, the computational memory cost of DVM is about 2528 times larger than the macroscopic solvers. Besides, in the slip regime, the convergence rate of the DVM is also much slower than the macroscopic solvers, especially when the Knudsen number \(Kn<0.1\). As the Knudsen number increases, e.g., \(Kn=1\), the implicit DVM becomes highly efficient which needs 46 steps to get the steady-state solutions. In contrast, the macroscopic equations, especially the R13 and R26 moment equations, cost more computational time than the DVM. That is because when the value of the Knudsen number is high, the relaxation factors for the R13 and R26 moment equations should be very small to avoid the divergence.
## 2 Circular Cavity Metasurface Antenna with a Diameter of 0.25 \(\lambda_{0}\)
### Simple Models for Describing Small-Cavity Antenna Bandwidth Behavior
#### 2.3.2 Transmission Line Model of a Cavity Metasurface  
Figure 5 represents the model for a cavity with a metasurface at the aperture. This is essentially the same as the model analyzed in [8]. Arranging multiple smaller patches instead of a single large patch leads to a decrease of the series \"patch inductance\", and gives the desired result of only a capacitance \(C_{\alpha\beta}\) at the aperture. This leads to a smaller \(Q\) factor (less energy storage), and hence an increase in BW. By splitting the patch and reducing the sizes of the metallic strips, we are essentially \"cutting\" the current that usually exists in the center of a patch surface into smaller pieces, which reduces the inductive effect.  
The resonance condition from this model is given by:  
\[jY_{Ap}-j\frac{k_{TE}}{k\eta_{0}}\cot(k_{TE}h_{c})+j\omega C_{eq}=0. \tag{2}\]  
Figure 4: (**a**) Patch at the aperture of the cavity; and (**b**) electrical model of the patch in the cavity.  
Figure 5: (**a**) Cavity antenna with parasitic capacitance (realized by a metasurface) at the aperture, and (**b**) its electrical model.  
Once the resonance condition (2) is satisfied, for a set \(\left(h_{c},\ C_{\alpha\beta}\right)\), if \(\varepsilon_{r}\) is taken to be 1 (which turns out to be the best case for BW), one can calculate the \(Q\) factor of such an antenna using:  
\[Q_{Ant.}=\ \frac{\omega_{0}}{2\ G_{A\mu}(\omega_{0})}\left|\frac{\partial Y( \omega_{0})}{\partial\omega}\right| \tag{3}\]  
where \(Y\) is the sum of the aperture and waveguide admittances, and the derivative is evaluated at the resonance frequency \(\omega_{0}\). From the \(Q\) factor, S\({}_{11}<-10\) dB, and fractional bandwidth is given by \(FBW=2/3Q\).
## 3 Results and Discussion
### Energy Levels  
Figure 2 shows the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) levels for two dyes, and Table S1 lists the energy data and energy gap. From Figure 2, it was found that the energy level of the TiO\({}_{2}\) semiconductor (4.0 eV) is much less than the energy level of the LUMO dyes, which is beneficial to the electronic injection (excited dye \(\rightarrow\) TiO\({}_{2}\)). Comparing the LUMO of the two dyes, it was found that D35 is higher than that of XY1, meaning that CT could take place from LUMO of D35 to XY1, producing intermolecular CT. Furthermore, the higher HOMO energy level for XY1 can minimize energy gap (D35 and XY are 2.28 and 1.98 eV, respectively), leading to the red-shifted absorption peak. The smaller band of cosensitized system should reflect the tendency of broadened absorption spectra, which will compensate for the inadequacies of isolated dye absorption. The improvement in spectra for cosensitization will enhance the solar utilization affecting the overall efficiency. For cosensitized systems (D35 + XY1), the energy gap is close to that of the XY1 due to the near HOMO energy level with XY1 that can be chalked up to the contribution of XY1 for cosensitized HOMO (see Table S1). As a whole, energy levels of XY1, D35, and XY1 and D35 have good matching with the semiconductor band.  
Upon dye adsorption on TiO\({}_{2}\), the energy level will be changed, as shown in Figure 2. Adsorption energy (\(E_{ads}\)) was calculated by the strong interaction energy of dye-TiO\({}_{2}\) (\(E_{dye+TiO2}\)) and the isolated dye (\(E_{dye}\)) and TiO\({}_{2}\) (\(E_{TiO2}\)); that is to say, \(E_{ads}=E_{dye+TiO2}-(E_{dye}+E_{TiO2})\), and XY1 (\(-4.04\)) \(<\) D35 (\(-4.03\)), which are almost the same negative values, displaying the stable configuration. The HOMO level of D35 and D35-TiO\({}_{2}\) is \(-5.03\) eV and \(-4.97\) eV, respectively, with very little change. LUMO (\(-2.81\) eV) for D35-TiO\({}_{2}\) is lower than that of isolated dye D35 (\(-2.75\)). Similar trends were found in XY1 and XY1-TiO\({}_{2}\). Given that HOMO levels are closely related to donors and LUMO levels mainly affected by receptors, dye adsorption in semiconductor will result in strong electron coupling and cause the lower LUMO. In addition, energy gap is decreased compared to the isolated dye; however, the decreased value of energy gap is at the same level.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
###### Abstract  
Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss-Seidel (SDGS) joint detection, which directly estimates the user's transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.  
Keywords:5G; massive MIMO; computational efficiency; precoding algorithms; channel estimation +
Footnote †: journal: Electronics
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 2 State of the Art
### Collision Avoidance  
Nowadays, it is common and necessary for operational satellites to take action to avoid collisions in space. Space debris and other objects in orbit around the Earth pose a significant risk to operational satellites. Collisions can cause irreparable damage, rendering a satellite inoperable and generating more debris that can further threaten other space assets.  
The use of CDMs has become a standard practice for operational satellites to avoid collisions with other objects in space. CDMs provide information on potential conjunctionsbetween space objects, including the probability of a collision and the distance between the objects at the time of closest approach. Satellite operators can use this information to plan and execute avoidance maneuvers to reduce the risk of a collision. This may involve adjusting the satellite's orbit or performing a course correction maneuver to avoid a potential collision.  
The use of CDMs has become increasingly important as the number of objects in orbit around the Earth continues to grow. Space agencies and satellite operators must work together to ensure the safe and sustainable use of space resources for future generations. This requires responsible space practices, such as the use of CDMs, to avoid collisions and reduce the risk of generating more debris in orbit. If the risk of a collision is deemed high based on factors such as distance and probability (collision probabilities > \(10^{-4}\) and miss distance < 1 km), an avoidance maneuver will be executed within one day before the closest approach. It is important to investigate potential critical situations beforehand to efficiently manage them and prevent collisions. CDMs are notified daily if the distance between the active satellite and the space object is less than the specific thresholds (1 km in cross and along tracks and 200 m for radial track) [13].  
The CDMs have a standardized message format for exchanging warnings between the originators of Conjunction Assessments (CAs) and authorized parties, such as satellite owners/operators. They are used to notify satellite owners/operators of potential collisions between space objects, which may be detected by different organizations using various CA techniques [14]. This Recommended Standard is applicable to satellite operations where close approaches and collisions are a concern. The data types included in CDMs are Time of Closest Approach (TCA), miss distance (the proximity of the two objects at TCA), probability of collision, and closest approach relative position and velocity.  
Figure 2 presents a timeline of collision avoidance operational activities at Geo-Informatics and Space Technology Development Agency (GISTDA) (started in 2008), describing the operations to be performed during the previous 3 days before the TCA, including the notification, the discussion and decision, the maneuver planning, the telecommand ready, and the execution maneuver.
## 3 The Test Results and Discussion  
### Failure Model and Damage Processes  
Figure 5 shows three stages of cumulative damage of welded specimens under low-cycle cyclic loading. The first stage that can be observed is the initial stage of damage process, which was characterized by the initiation of cracks on the surface of the specimen. As the loading cycles increased, micro cracks could be observed on the surface of the specimen, as shown in Figure 5a. This stage was typically short in duration, and the crack occurred in the slip band of the micro-defect or weak position of the material, which then developed internally along the direction of the maximum shear stress (45\({}^{\circ}\) with the principal stress). As shown in Figure 5b, with the increase of loading cycles, the damage accumulated continually and the specimen gradually entered into the second stable stage, in which the crack began to widen and the direction of the crack propagation gradually turned perpendicular to the principal stress. This stage has the longest duration and the crack developed relatively slow,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimens** & \(f_{y}\)**/MPa** & \(f_{u}\)**/MPa** & \(\epsilon_{y}\)**/\%** & \(\epsilon_{u}\)**/\%** & **E**/**GPa** \\ \hline SA1 & 268.8 & 430.1 & 0.15 & 16.3 & 207.1 \\ SA2 & 268.1 & 429.7 & 0.15 & 16.4 & 195.7 \\ WA1 & 391.7 & 497.6 & 0.16 & 13.0 & 239.7 \\ WA2 & 401.6 & 486.8 & 0.17 & 12.7 & 233.6 \\ WA3 & 402.3 & 497.8 & 0.17 & 11.8 & 213.0 \\ HA1 & 254.7 & 433.5 & 0.13 & 15.8 & 223.3 \\ HA2 & 259.2 & 423.6 & 0.13 & 15.5 & 203.6 \\ HA3 & 255.4 & 422.9 & 0.13 & 15.6 & 204.5 \\ \hline SB1 & 365.6 & 532.6 & 0.16 & 16.0 & 216.1 \\ SB2 & 385.1 & 540.1 & 0.15 & 15.2 & 217.9 \\ WB1 & 420.1 & 498.1 & 0.15 & 12.7 & 235.5 \\ WB2 & 426.3 & 508.5 & 0.17 & 11.7 & 251.0 \\ WB3 & 431.5 & 525.4 & 0.17 & 11.3 & 217.6 \\ HB1 & 365.2 & 531.0 & 0.16 & 16.0 & 235.0 \\ HB2 & 357.8 & 530.5 & 0.15 & 15.3 & 225.1 \\ HB3 & 358.5 & 527.6 & 0.16 & 15.2 & 218.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters under monotonic loading.  
Figure 4: Cyclic loading patterns. (**a**) For all No. (1) specimens; (**b**) For all No. (2) specimens; (**c**) For all No. (3) specimens.  
which is considered to be the main stage of damage accumulation. The last stage is the failure stage, as shown in Figure 5c, where the specimens were fractured at the maximum main crack.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 5 Mesh Networking with IEEE 802.15.4
### Reference Standard: ZigBee Pro & ZigBee 3.0
In detail, a route discovery procedure involves the following steps: (i) the parent of the source end device (\(P_{\text{sed}}\)) sends a broadcast discovery request with the end device's network address; (ii) the parent of the destination end device (\(P_{\text{ded}}\)) receives the broadcast discovery request, and sends back a reply addressed to \(P_{\text{sed}}\); and (iii) when the reply message is forwarded back through the ZigBee Pro network, the number of hops a measure of each hop signal quality are recorded. In this way, each routing node composing the path can store a routing table entry containing the best path to \(P_{\text{ded}}\) which generally, is the one with the smallest number of hops. Thanks to this strategy, unidirectional routes from a source \(S_{\text{ed}}\) to a destination end device \(D_{\text{ed}}\) are built. This means that the reverse routes (from \(D_{\text{ed}}\) to \(S_{\text{ed}}\)), must be discovered starting a new route discovery round. A \"particular\" case related to the route discovery procedure is the \"many-to-one\" routing, which is employed when most of the nodes in the network need to communicate with a single node, denoted as concentrator (e.g., a gateway). In this particular scenario, each node can initiate the route discovery process. After that, each node in the path adds a new routing table entry. Unfortunately, for increasing number of involved nodes, a large number of concurrent route discovery messages are sent, requiring significant memory space for the concentrator neighbour nodes which can occur in memory overflow or traffic congestion problems [59]. Therefore, the concentrator can establish routes more efficiently initiating a \"many-to-one\" route discovery process for routes from all other network nodes to the gateway, in turn broadcasting a route discovery request. In this way, the involved nodes routing tables are updated only when the broadcast propagates through the network and, since no responses are required, the use of storage resources and network traffic congestion are minimized [60].
## 5 Simulation and Experimental Results
### The Electronically Tunable VM Biquad
Figure 20 shows the measured output P1 dB of the IBPF response on the V\({}_{\text{o2}}\) output terminal when only V\({}_{\text{i1}}\) is applied to the input signal, which indicates that the measured output P1 dB point is about \(-7\) dBm. The spectrum of the IBPF response is illustrated in Figure 10:3 The measured angular frequency is about 144.64 kHz, which is very close to the theoretical value of 144.69 kHz. To represent the nonlinearity of the biquad proposed in Figure 2, a two-tone testing of intermodulation distortion (IMD) has been used to characterize the nonlinearity of the IBPF response on the V\({}_{\text{o2}}\) output terminal. Figure 22 shows the spectrum of IBPF responses obtained by applying two-tone signals, f\({}_{1}\) and f\({}_{2}\), near the center frequency of f\({}_{\text{o}}\) = 144.69 kHz for intermodulation characterization. In Figure 22, a low-frequency tone with f\({}_{1}\) = 143.69 kHz and a high-frequency tone with f\({}_{2}\) = 145.69 kHz are used, and the input amplitude is equal to 126 mV\({}_{\text{pp}}\). The measured value of the third-order IMD (IMD3) is about \(-50.83\) dBc, and the measured value of the third-order intercept (TOI) point is about 5.136 dBm. The figure-of-merit (FoM) of the biquad was defined as [14]:  
Figure 10:4  
where f\({}_{\text{o}}\) is the central frequency. According the measured IBPF frequency spectrum in Figure 21, the dynamic range between the fundamental tone and the largest spur of the spurious-free dynamic range is approximately 53.49 dB. Hence, the FoM of the proposed IBPF is approximately calculated as 268.75 \(\times\) 10\({}^{3}\). Figure 10:5 Noise simulation results of BPF against frequency. Figure 10:6 Frequency spectrum of the IBPF at the V\({}_{\alpha 2}\) output terminal. Figure 10:7 The IBPF output spectrum of V\({}_{\alpha 2}\) for a two-tone -IMD test.
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 1 Introduction  
Baby strollers are essential tools in childcare. Using a stroller offers several advantages to parents, including reducing the load of physically holding the baby, offering a cozy and safe resting spot for the child, and being easy to move. However, manual baby strollers require the users to push or pull the stroller for navigation, which is tiring under long-time usage or in the case of climbing a slope and especially problematic for mothers of multiple children, pregnant mothers, and even senior guardians. Also, manual operation restricts the parent's ability to perform other tasks while using the stroller. Lightweight and power-assisted baby strollers are designed to reduce parents' physical load while pushing the standard stroller. However, the situation where parents' hands are occupied after shopping or a mother of multiple children needs to hold another child's hand while pushing a stroller, has not yet been well addressed. A smart stroller with a human-following or a human-guided function can reduce the parents' childcare burden and give them more freedom to move compared to manual operation strollers. Hereafter, we refer to a motorized stroller with a sophisticated control system as a smart stroller and to a motorized stroller with the ability to follow or precede the user as a human-following or -guided smart stroller. Human-following or -guided smart strollers have been investigated in the literature [1, 2]. Theyinvestigated a smart human-following stroller by utilizing computer vision technology to track the target human and recognize human pose to enable the stroller to follow. However, considerations of relative posture between the human and stroller are lacking, and the safety concern of the parents in the baby stroller scenario was not discussed sufficiently, especially for their human tracking systems. Furthermore, the proposed systems' usability was not researched, and the effective hands-free control interfaces for a stroller are yet to be realized. Autonomous smart strollers offer greater freedom to parents while babysitting. The work in [3] proposes an autonomous motion control scheme using predictive control to safely and stably cope with difficult manual situations, such as sharp turns. The proposed method's feasibility was validated in simulation with satisfactory results. However, the system can be affected by sensor noise and uncertain disturbances in the real-life scenario, which cause insecurity. Additionally, some parents feel uncomfortable being too far from the stroller or not being in control, which can lead to a feeling of insecurity. Therefore, considering practicality and addressing parents' psychological concerns, we focus on the human-following or human-guided stroller in this work. We refer to the literature on human-following (or human-guided) robots due to the scarcity of research on human-following smart strollers, and due to the similarity in kinematics and technology between smart strollers and human-following robots.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
###### Abstract
However, such systems need two or more classifiers to address the multiclass classification problem and the computational cost and time increase with such classifiers [13]. The accuracy of 92% was achieved by [11] using four SVM classifiers in a one-vs-all scheme to classify four-class MI data. In contrast, deep learning techniques such as convolutional neural networks (CNNs) can learn high-level features as the deep-stacked layers enhance the feature levels [14]. Many neural network models in the literature, such as [15, 16, 17, 18, 19, 20, 21], classify EEG signals. Further  
Figure 1: The compressive sensing system for EEG signals for BCI applications. In the transmission stage, the signals are represented sparsely. At the receiver end, the sparse signals are reconstructed, and features are extracted for classification and used for neuroprosthesis applications. more, there is no need to stack two or more classifiers for a multiclass classification problem. However, poor classification accuracy was observed in the literature for sparsity-based classification using deep learning models. Therefore, we introduce a novel sparse representation-based multiclass EEG classification method that reduces computational complexity and time while increasing classification accuracy. We extracted wavelet features (energy and entropy) directly from the raw EEG data, eliminating the preprocessing step. The length of the EEG epochs plays an essential role in improving the classification accuracy of the machine learning models. Therefore, we investigated different epoch lengths to identify which yields better classification accuracy. This paper aims to find an optimal epoch length for an MI-based classification while maintaining classification accuracy. In addition, we aimed to develop a framework that works efficiently for all users irrespective of their proficiency in MI-based BCI training. Therefore, we performed a subject-independent classification where we acquired training and testing data from different subjects. Finally, the proposed method retains the following contributions:  
* Works well without preprocessing, such as using bandpass or spatial filters. * Maintains the classification accuracy irrespective of the users' proficiency in performing MI tasks. * The classification accuracy is improved with the selected channels related to the sensory motor cortex region. * The accuracy is optimal with reduced epoch lengths and is therefore suitable for a real-time MI-based BCI. * The proposed method is computationally inexpensive and faster than other machine learning algorithms. The rest of the paper is organized as follows. Section 2 presents an overview of the methodology and the algorithms used in this manuscript, and Section 3 describes the classification results and compares the results with those of state-of-the-art methods. Section 4 presents a discussion and conclusions.