esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Future Research
### Limitations  
The most significant limitation of our algorithm is its reliance on the performance of the semantic keypoint detector. If no keypoints are detected, our method does not provide a pose estimate. This warrants further research on 2D semantic keypoint estimation techniques. Additionally, the re-identification of vehicles still poses a challenge, although make and model recognition could mitigate this. We plan on performing experiments to verify this; however, there are no currently available datasets containing sequence annotations, LiDAR point clouds and camera images, and most importantly vehicle-type annotations. This presents a research gap.  
Our method struggles with cases of severe occlusion compared to deep learning methods such as BAAM [13]. In a parking garage environment, this can be mitigated by adequate camera coverage, meaning there will be no occlusion in the scene of a setup like the BME experiment. Nevertheless, this warrants further research, as no smart parking garage dataset has been proposed to date. Additionally, the implementation algorithms taking advantage of target motion and other contextual constraints such as road planarity (when available) presents a future research opportunity to handle these cases.
# Analytical Modeling of the Maximum Power Point with Series Resistance
## 2 Background  
Assuming nondegenerate conditions, the total current density, \(i\), produced by a solar cell is given by [PERSON]'s diode equation [11],  
\[i=i_{\text{G}}-i_{0}\exp\left[\frac{V}{V_{\text{t}}}\right], \tag{1}\]  
where \(V\) is the voltage and \(i_{\text{G}}\) and \(i_{0}\) are the _generation_ and _thermal recombination_[12] currents, respectively. Here, the thermal voltage, \(V_{\text{t}}\), given by \(qV_{\text{t}}=kT\) with \(T\), \(k\), and \(q\) being the cell temperature, [PERSON]'s constant, and the electron charge, respectively, is introduced. The total power density, \(P\), is given by the product \(P=Vi\)[13]. At the maximum power point, it holds that:  
\[\frac{\text{d}}{\text{d}V}P=i+V\frac{\text{d}}{\text{d}V}i=0. \tag{2}\]  
[PERSON] et al. found that Equation (2) is solved by [14; 15]:  
\[V_{\text{mpp}}=V_{\text{t}}\left(\text{W}\left[\frac{i_{\text{G}}}{i_{0}}e \right]-1\right), \tag{3}\]  
where \(\text{W}(x)\), defined by \(x=\text{W}(xe^{x})\), is [PERSON]'s W function [16]. An expression for the maximum power point current, \(i_{\text{mpp}}\), can be obtained by inserting Equation (3) into Equation (1), and an expression for the maximum power, \(P_{\text{mpp}}\), is obtained from the product \(P_{\text{mpp}}=V_{\text{mpp}}i_{\text{mpp}}\). This yields [14; 15]:  
\[i_{\text{mpp}}=i_{\text{G}}\left(1-\frac{1}{\text{W}\left[\frac{i_{\text{G}}} {i_{0}}e\right]}\right), \tag{4}\]  
\[P_{\text{mpp}}=i_{\text{G}}V_{\text{t}}\left(\text{W}\left[\frac{i_{\text{G}}} {i_{0}}e\right]-2+\frac{1}{\text{W}\left[\frac{i_{\text{G}}}{i_{0}}e\right]} \right). \tag{5}\]When series resistance is accounted for, Equation (1) becomes [13]:  
\[i=i_{\rm G}-i_{0}\exp\left[\frac{V+ir}{V_{\rm t}}\right], \tag{6}\]  
where \(r\) is the cell series resistance \(([r]=\Omega\cdot\rm cm^{2})\). [PERSON] et al. proved in [2] that Lambert's W function allows for an explicit expression for \(i\). Defining the voltages \(V_{\rm G}\) and \(V_{0}\) as \(V_{\rm G}=i_{\rm G}r\) and \(V_{0}=i_{0}r\), Equation (6) becomes [2]:  
\[i=i_{\rm G}-\frac{V_{\rm t}}{r}\rm W\bigg{[}\frac{V_{0}}{V_{\rm t}}\exp\bigg{[} \frac{V_{\rm G}}{V_{\rm t}}+\frac{V}{V_{\rm t}}\bigg{]}\bigg{]}, \tag{7}\]  
from which it can be seen that in the limit \(r\to 0\), Equation (1) is recovered.
## 2 Setup of Experiment  
### Instrumentation  
The experimental study was conducted using the combined wave-current-wind tank, which is 50 m in length, 1 m in width and 1.2 m in height, in the hydrodynamics laboratory at the Ocean University of China. The testing instrumentation (Figure 1) consists of the following parts: (a) current generation system; (b) velocity monitoring system; (c) end condition system, and (d) data acquisition system. More specifically, the current generation system is utilized to create a uniform flow in the water tank, and the velocity of the current that can be generated ranges from 0 m/s-1.4 m/s, with an accuracy of 5% for the velocity generated. In this study, current velocities of 0.2 m/s, 0.6 m/s and 1.0 m/s are employed. Here we have to note that the riser model is located at the middle part of the water tank in order to ensure that the current velocity is steady at this location. Therefore, the current velocity generated might be different from that at the riser location due to friction. To avoid this, the velocity of the current at the riser location is measured by Nortek Vectrino profiler, and the data is used as feedback to adjust the uniform velocity. The Nortek Vectrino profiler is placed 1 m ahead of the riser model to avoid its influence. The velocity range that can be measured by the Nortek Vectrino profiler is \(\pm\)0.03 m/s to 4 m/s, and the accuracy is \(\pm\)0.5% of the measured value of \(\pm\)1 mm/s. The end condition system is employed to apply top-tension force (ALYIOI digital force gauge, ALX bracket and special fixture) using a simple support at top and simple/fixed support at bottom (self-made holders). The ALYIOI digital force gauge is connected to the riser model with its special fixture, and it measures up to 200 N with a resolution of 0.1 N. The accuracy of this force gauge is \(\pm\)0.5% of the measured value. To provide a simple support at the top and a simple/fixed support at bottom, self-made holders are utilized. For the simple support, a hollow metal ball outside and a solid metal ball inside with a connector to the riser are manufactured; for the fixed support, the connector to the riser is welded to the base plate directly. The data acquisition system is composed of electric resistance strain gauges on the riser model: a SDY2102E dynamic strain indicator and a WS-5921/U60532 supervisory control and data acquisition (SCADA). The resistance value, sensitivity factor and strain limit of the electric resistance strain gauges used are 120 \(\pm\) 1 \(\Omega\), 2.0 + 1% and 20,000 um/m. SDY2102E dynamic strain indicator has 16 channels with bridge resistance of 60 \(\Omega\) to 1 k\(\Omega\). The frequency response of SDY2102E is from DC to 10 kHz (200 Hz is selected in this study) and the sensitivity is 1 V/100 \(\mu\epsilon\). WS-5921/U60532 supervisory control and data acquisition has 32 channels with a sampling frequency of 500 kHz, which is utilized to transfer the dynamic strain response of riser model to the Vib'SYS software for further analysis. In this study, 40 s is set as the time range.  
Figure 1: _Cont._
## 2 Model
### Basic Assumptions  
The first basic assumption of our novel approach is to partition the cross-section of a multi-chamber actuator with the initial length \(L_{0}\) into areas whose extensional stiffness and dimensions are known or can be determined experimentally. Figure 2 illustrates the partitioning, where the cross-section shown has three chambers and is oriented on typical multi-chamber actuators such as the _STIFF-FLOP_[4], and the designs used by [PERSON] et al. [12] and [PERSON] [9]. In the case of three chambers, the cross-section is partitioned in seven sections: one central, one for each chamber and one between each two chambers. More generally, with this scheme, the number of sections is \(2j+1\) for an actuator with \(j\) chambers.  
Each section \(k\) has an extensional stiffness  
\[E_{k}=\Big{\{}E^{I},E^{II},E^{I},\ldots,E^{I},E^{II},E^{III}\Big{\}}, \tag{13}\]  
which is similar for sections with similar cross-sections. Internal forces \(F_{k}\) can act on the actuator in the axial direction, being introduced to represent pressurization of the chambers, but could be used otherwise as well. In case they represent pressurization, the internal forces of the sections without a chamber are zero. Lastly, we assign a \"phase angle\" \(\varphi_{k}\) to each of the sections and a circle around the centerline of radius \(r_{k}\) on which the center points of the sections are arranged  
\[\varphi_{k} =\Big{\{}0,\frac{\pi}{n},\frac{2\pi}{n},\frac{3\pi}{n},\ldots, \Big{(}\pi-\frac{\pi}{n}\Big{)},0\Big{\}}, \tag{14}\] \[r_{k} =\Big{\{}r^{I},r^{II},r^{I},\ldots,r^{I},r^{II},0\Big{\}}. \tag{15}\]  
In all vectors, the last entry describes the central section.  
In the following, we examine an actuator with no external loads applied that deforms with a constant curvature \(\kappa\). Constant curvature is valid in this case, since it is well known that the accuracy of constant curvature models [17] only suffers from external loads. Importantly, and in opposition to these models, assuming constant curvature is only necessary in order to derive the parameters of the beam but not when they are applied in the beam model.  
The second basic assumption is that the bending stiffness of the individual sections of a multi-chamber actuator is negligible compared to their extensional stiffness. This can be shown by a minor extension of the single-chamber model from a preliminary work [18] to include not only axial stretch but also bending.
# Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
## 2 Relation Work  
### Graph Embedding  
Graph embedding (GE) [16] revealed certain geometrical features in the data through the spectrogram theory. It used the Laplacian operator to process the constructed graph, which retained useful information and suppressed useless information of the graph in low-dimensional embedding. In GE, the construction of an intrinsic graph was required to describe the geometric characteristics of similar data, and a penalty graph was also constructed to represent the geometric characteristics, which should be avoided. The intrinsic graph \(G=\left\{X,W\right\}\) and the penalty graph \(G^{p}=\left\{X,W^{p}\right\}\) are both undirected graphs, where \(X\) is the vertex set and \(W\in R^{n\times n}\) and \(W^{p}\in R^{n\times n}\) represent the weight matrices. In graph \(G\), the weight \(w_{ij}\) denotes the edge weights between vertices \(x_{i}\) and \(x_{j}\), which needed to maintain the similarity characteristics of similar data. In graph \(G^{p}\), the weight \(w_{ij}^{p}\) suppressed the similarity characteristics of heterogeneous data in low-dimensional embedding. According to the principle of the graph embedding relationship, the objective function can be written as:  
\[\min_{y^{\mathrm{T}}By=c}\frac{1}{2}\sum_{i,j}\|y_{i}-y_{j}\|^{2}w_{ij}=\min_{ y^{\mathrm{T}}By=c}Y^{\mathrm{T}}LY, \tag{1}\]  
where c is a constant. In order to avoid degeneracy, an extra constraint matrix, \(B\), was added. \(L\) was a Laplacian matrix of the intrinsic graph \(G\). \(B\) was a typical normalized diagonal matrix and can be set as the Laplacian matrix of the penalty graph \(G^{p}\), that is, \(B=L^{p}\). Thus, the Laplacian matrix \(L\) and \(L^{p}\) were defined as:  
\[L=D-W,\qquad D_{ii}=\sum_{j\
eq i}w_{ij},\quad\forall i\]  
\[L^{p}=D^{p}-W^{p},\qquad D_{ii}^{p}=\sum_{j\
eq i}w_{ii}^{p},\quad\forall i \tag{2}\]where \(D\) and \(D^{p}\) are both diagonal matrices. If the relationship is linear, that is, \(Y=V^{T}X\), the graph embedded objective function can be converted to:  
\[\min_{\text{tr}(V^{T}XBX^{T}V)}\text{tr}(V^{T}XBX^{T}V). \tag{3}\]
## 4 Optimisation Approach  
Our optimisation algorithm is a hybrid evolutionary algorithm (HEA) whose block diagram is shown in Figure 4. It consists of a first stage where a genetic algorithm (GA) uses the first 95% of evaluations (950 in our study) to explore the solution space, converging to competitive solutions [30]. After that, a local search (LS) explores the neighbourhood of the best solution found by the GA to improve the algorithm's result by using a high-level relay hybridisation (HRH) approach [31].  
Genetic algorithms mimic processes present in evolution such as natural selection, gene recombination after reproduction, gene mutation, and the dominance of fittest individuals over the weaker ones. Our proposed GA follows a steady-state design, where an offspring of \(\lambda=10\) individuals is obtained from the population \(\mu=100\), so that the auxiliary population \(Q\) contains a subset of individuals from the population \(pop\).  
Following the HEA diagram, first of all, the _Initialisation_ function fills the population \(pop(0)\) with \(\mu\) random individuals. Secondly, the main loop is executed until the termination condition is fulfilled (950 evaluations). Binary Tournament [32] was used as the selection operator, Uniform Crossover [33] was used as the recombination operator (\(P_{c}=0.9\)), and Integer Polynomial Mutation [34] was used as the mutation operator (\(P_{m}=1/L=0.25\)), where \(L\) is the length of the solution vector. After each generation, an elistist replacement was used to update the algorithm population. Note that for the initial population and each 10 generations, ARGoS simulations were used to evaluate the individuals in order to update their fitness value if needed. Otherwise, the faster predictions provided by a surrogate model were used.  
After the GA stage, the best solution obtained becomes the starting point of the hill climbing algorithm [35] (HC). It explores the best solution neighbourhood during the last 50 evaluations following the gradient of the solution with the best fitness, improving the solution found by the GA. At each iteration of the HC algorithm evaluates the solutions next to the current best one, keeping it in case of finding a better fitness. Therefore, once we first explored the search space using the GA, we exploit the best solution found usingthe proposed HC. The HC algorithm does not require any parameterisation other than the maximum number of evaluations.
# Dual-Band Shared-Aperture Antenna with Pattern and Polarization Diversity
## 2 Antenna Design and Analysis
### Bowl-Shape Monopole Antenna Design  
A metal cone antenna Design 1 is first proposed, as shown in Figure 2, which consists of the metal bowl structure, reflective ground, and a coaxial cable. In order to achieve a lightweight, the inner part of the cone monopole is removed without affecting the radiation performance. The thickness of the metal to make the bowl structure is 0.5 mm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(L_{1}\) & 6 mm \\ \(L_{2}\) & 12 mm \\ \(L_{3}\) & 5 mm \\ \(W\) & 110 mm \\ \(W_{1}\) & 42 mm \\ \(W_{2}\) & 3.2 mm \\ \(W_{3}\) & 2.2 mm \\ \(w_{1}\) & 54 mm \\ \(w_{2}\) & 2 mm \\ \(w_{3}\) & 1.3 mm \\ \(S\) & 7.31 mm \\ \(H_{1}\) & 27.5 mm \\ \(H_{2}\) & 26.5 mm \\ \(\alpha\) & 45\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimized parameter values for the proposed antenna.  
Figure 1: Geometry of the proposed antenna: (**a**) side view, and (**b**) top view.  
To further analyze the working mechanism of the monopole antenna, Figure 3 shows the current distribution of Design 1 at 2.4 GHz, 6 GHz, and 11 GHz. It can be seen that the current is mainly distributed on the outer surface of the hollow cone, so the internal structure of the monopole has little influence on the reflection coefficient and radiation. The reflection coefficients of the solid conical and hollow conical structures are also compared in Figure 4a to prove this. The uniformly distributed current along the bowl structure at the same height leads to an omnidirectional radiation pattern.  
Figure 3: Current distribution of monopole antenna Design 1 of (**a**) side view of 2.4 GHz, (**b**) top view of 2.4 GHz, (**c**) side view of 6 GHz, (**d**) top view of 6 GHz, (**e**) side view of 7.5 GHz, (**f**) top view of 7.5 GHz.  
Figure 2: (**a**) Side view, and (**b**) top view of monopole antenna Design 1.  
The IMBW for S11 \(<-10\) dB of the monopole antenna is 2.08-12.43 GHz, which is mainly affected by the bowl structure of the monopole. The study of the angle \(\alpha\) is shown in Figure 4. With the variation of \(\alpha\), the structure of the antenna changes, resulting in the change of the antenna impedance, which affects the impedance bandwidth. The IMBWs of the three angles are 2.26-13, 2.06-13, and 2.06-12.67 GHz. When the \(\alpha\) is close to \(45^{\circ}\), a wider IMBW and lower reflection coefficient can be obtained.  
With the increase of \(\alpha\), it is obvious that the IMBW between the 1.89 and 10 GHz frequency band improves, while the impedance matching of 10-12 GHz becomes worse. To achieve a wide IMBW, \(\alpha=45^{\circ}\) is selected.
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### PHY Technologies for WBANs
#### 4.3.1 NB Technologies  
NB transceiver technologies have been traditionally employed in short and medium range technologies (e.g., Zigbee, Bluetooth, IEEE 802.11 WLANs) and are widely used in commercially available products. In WBANs, NB systems can operate at different frequency bands (Table 2) and are suitable for both on-body and in-body communications. Most existing works on NB transceiver design are focused on the implementation of energy-efficient modulation and demodulation schemes, and their contributions are summarized in the rest of this section.  
Two main classes of NB receivers for on-body WBANs may be found in the literature: (i) ZigBee receivers, equipped with quadrature mixers that support Quadrature Phase Shift Keying (QPSK) modulation. The power expensive Phase-Locked Loops (PLLs) and the quadrature oscillators decrease the energy efficiency of NB receivers with respect to UWB or HBC receivers; (ii) Receivers that use regenerative positive feedback to achieve gain, and non-coherent detection to improve energy efficiency. The modulation schemes applied in this case are simple amplitude-based signaling schemes such as On-Off-Keying (OOK). The main drawback of these receivers is the poor sensitivity to RF signals, that can cause oscillation. When oscillating, not only is it impossible to receive any signal, but those oscillations are driven against the antenna and retransmitted into the air, causing interference to nearby receivers.  
In the case of in-body communications, given that low-power consumption is one of the key requirements, energy-efficient modulations with small envelope variations are favorable. However, when high data rates must be employed, it is of great importance to choose a high-sensitivity modulation and demodulation technique in order to overcome the strong attenuation caused by the human body. The modulation schemes that have prevailed in NB systems for mHealth applications are \(\pi/4\)-shift QPSK, offset QPSK, FSK and MSK (Minimum Shift Keying). [PERSON] _et al._[52] propose a new type of PSK modulation for high data rates, called Phase Silence Shift Keying (PSSK), which is more bandwidth-efficient than orthogonal modulation schemes such as OOK. Under an Additive White Gaussian Noise (AWGN) channel model with path loss for the human body, the authors in [52] show that the BER of an 8-PSSK is lower than that of various sinusoidal carrier-based modulation schemes. The authors in [53] propose a simple ultra-low power hardware design for an FSK/MSK direct modulation transmitter in medical implant communications. Finally, an extended protocol that uses a rateless code with FSK modulation in order to overcome the reliability and power cost concerns in implanted sensors has been proposed in [54].
# Robustness Analysis of the Collective Nonlinear Dynamics of a Periodic Coupled Pendulums Chain
## 2 Mechanical Model
### Equations of Motion  
Applying the Lagrange approach leads to the equation of motion of the \(n\)th pendulum:  
\[\ddot{\phi}_{n}+c_{n}\dot{\phi}_{n}+\omega_{n}^{2}\phi_{n}+k_{n}\mathcal{L}_{c }(\phi,\widetilde{\phi})+\alpha_{n}\phi_{n}{}^{3}=f_{n}\cos(\Omega t), \tag{1}\]  
where \(c_{n}=\frac{c}{m}\), \(k_{n}=\frac{k}{m!^{2}}\), \(\alpha_{n}=-\frac{g}{\widetilde{\phi}l}\), \(f_{n}=\frac{f}{ml^{2}}\) if the \(n^{th}\) pendulum is deterministic and \(c_{n}=\frac{c}{m}\), \(k_{n}=\frac{k}{ml^{2}}\), \(\alpha_{n}=-\frac{g}{\widetilde{\phi}_{n}}\), \(f_{n}=\frac{f}{ml_{s}^{2}}\) if the length of the \(n^{th}\) pendulum, of stochastic displacement \(\widetilde{\phi}_{n}\), is uncertain.  
Since linear coupling between pendulums is very weak and small imperfections are considered, each angular frequency \(\omega_{n}\) is supposed to be equal to the eigenfrequency \(\omega_{0}\) (\(\omega_{n}=\omega_{0}=\sqrt{g/l}\)).  
The linear coupling term \(\mathcal{L}_{c}(\phi,\widetilde{\phi})\) depends on the positions of the stochastic pendulums in the chain. If stochastic pendulums are not adjacent, four different configurations are distinguished:  
**a.**: If the concerned pendulum is deterministic as well as its neighbors, \(\mathcal{L}_{c}(\phi,\widetilde{\phi})=2\phi_{n}-\phi_{n-1}-\phi_{n+1}\);
**b.**: If the concerned pendulum is deterministic but the previous one is stochastic, \(\mathcal{L}_{c}(\phi,\widetilde{\phi})=2\phi_{n}-\widetilde{\phi}_{n-1}-\phi_{ n+1}\);
**c.**: If the concerned pendulum is deterministic but the following is stochastic, \(\mathcal{L}_{c}(\phi,\widetilde{\phi})=2\phi_{n}-\phi_{n-1}-\widetilde{\phi}_{ n+1}\);
**d.**: If the stochastic pendulum is concerned, the deterministic displacement \(\phi_{n}\) is replaced by the stochastic one, \(\widetilde{\phi}_{n}\), in Equation (1) such as  
\[\ddot{\widetilde{\phi}}_{n}+c_{n}\dot{\widetilde{\phi}}_{n}+\omega_{0}^{2} \widetilde{\phi}_{n}+k_{n}\mathcal{L}_{c}(\phi,\widetilde{\phi})+\alpha_{n} \widetilde{\phi}_{n}^{3}=f_{n}\cos(\Omega t), \tag{2}\]  
and \(\mathcal{L}_{c}(\phi,\widetilde{\phi})=2\widetilde{\phi}_{n}-\phi_{n-1}-\phi_{ n+1}\), in this case.  
Figure 1: Periodic nonlinear coupled-pendulums chain with imperfection.  
The displacement \(\phi_{n}\) can be expressed as a sum of standing wave modes with slowly varying amplitudes [12; 15; 28; 29]. Taking into account the boundary conditions \(\phi_{0}=\phi_{N+1}=0\), the standing wave modes are:  
\[u_{n}=sin(nq_{m})\text{ with }q_{m}=\frac{m\pi}{N+1},\ m=1\ldots N. \tag{3}\]  
The displacement \(\phi_{n}\) of the \(n^{th}\) pendulum is thus expressed as  
\[\phi_{n}=\underbrace{\sum_{m=1}^{N}A_{m}sin(nq_{m})exp(i\omega_{0}t)+c.c.}_{ \phi_{n0}}+\varepsilon\,\phi_{n1}, \tag{4}\]  
if it is deterministic, and as  
\[\widetilde{\phi}_{n}=\underbrace{\sum_{m=1}^{N}\widetilde{A}_{m}sin(nq_{m}) exp(i\omega_{0}t)+c.c.}_{\widetilde{\phi}_{0n}}+\varepsilon\,\widetilde{\phi}_{1n}, \tag{5}\]  
if it is affected by uncertainties.