esa-sceva/satcom-chunk-collection huggingface dataset:
# Freeform Wide Field-of-View Spaceborne Imaging Telescope: From Design to Demonstrator
## 2 Methods: Optical Design
### Tolerance Analysis  
To evaluate the required manufacturing and alignment accuracy, tolerance analysis is performed on the optical telescope design. This analysis contains two types of tolerances: (1) surface tolerances including manufacturing defects of the shapes of the two freeform mirrors (Table 1), and (2) element tolerances, including possible misalignments of the mirrors, stop and detector plane (Table 2). The evaluation is performed at a wavelength of 1.7 \(\upmu\)m, the upper limit of the considered spectral range, and using focus compensation.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Surface Tolerances**} \\ \hline Radius & 0.1\% (precision) \\ Thickness & 0.01 mm \\ Decenter X & 0.01 mm \\ Decenter Y & 0.01 mm \\ Tilt X & 0.0167\({}^{\circ}\) \\ Tilt Y & 0.0167\({}^{\circ}\) \\ Irregularity & 0.2 fringes \\ \hline \end{tabular}
\end{table}
Table 1: Surface tolerances using the _High Precision_ tolerances from Edmund Optics. Note that we use _precision_ for the Radius, as _high precision_ would mean 0%, which would be unfeasible.  
Figure 4: Relative position of the spots on the image plane, considering the simulated fields of Figure 3.  
Figure 5: MTF evaluation of the telescope, where the different colors correspond to the different fields shown in Figure 3 and the black line corresponds to the diffraction limit. The full and dashed lines correspond to the tangential and sagittal planes, respectively.  
The tolerance analysis is performed using a statistical Monte Carlo analysis considering 1000 iterations on the optical design while aiming to evaluate if the decrease in optical performance is still acceptable when taking the (surface and element) tolerances into account. For this, the RMS spot sizes and MTF were considered figures of merit during the evaluation. A worst-case evaluation indicates that the mean (averaged over all fields) RMS spot value equals less than 11.3 \(\upmu\)m at the detector plane and that the mean MTF exceeds 0.44 at 17 cycles/mm, for more than 98% of the traceable Monte Carlo files. More than 80% of our simulated designs show less than a 15% deviation for the RMS spot size and MTF. As a result, this tolerance analysis shows a robust optical design, giving us high confidence to achieve a tolerant and efficient telescope.
## 2 System Model and Bayesian Filter  
### System Model  
The nonlinear discrete estimation system model can be expressed as follows:  
\[\left\{\begin{array}{l}\mathbf{x}_{k}=f(\mathbf{x}_{k-1})+\mathbf{v}_{k-1}\\ \mathbf{z}_{k}=h(\mathbf{x}_{k})+\mathbf{e}_{k}\end{array}\right., \tag{1}\]  
where \(\mathbf{x}_{k}\in\mathbb{R}^{n_{x}}\) is the state vector at time index \(k\) with \(n_{x}\) dimensions, and \(\mathbf{z}_{k}\in\mathbb{R}^{n_{z}}\) is the measurement vector with \(n_{z}\) dimensions. The values of \(n_{x}\) and \(n_{z}\) are dependent on specific applications. \(f(\cdot)\) and \(h(\cdot)\) are the nonlinear function, here \(k=1,2,\cdots\). The process noise \(\mathbf{v}_{k-1}\) and the measurement noise \(\mathbf{e}_{k}\) are generally assumed to be independent and identically distributed zero mean Gaussian noise with the covariance matrices \(\mathbf{Q}_{k-1}\) and \(\mathbf{R}_{k}\), respectively.
# Investigation into the Effect of Atmospheric
## 4 Results, Analysis and Discussion
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set 1** & **Day 1 ZTD (m)** & **Day 2 ZTD (m)** & **\(\Delta\)ZTD (m)** \\ \hline
19:00 & 2.460 & 2.459 & 0.0008 \\
20:00 & 2.466 & 2.460 & 0.0063 \\
21:00 & 2.468 & 2.462 & 0.0059 \\
22:00 & 2.470 & 2.460 & 0.0103 \\
23:00 & 2.471 & 2.471 & \(-\)0.0007 \\
00:00 & 2.468 & 2.470 & \(-\)0.0027 \\
01:00 & 2.466 & 2.475 & \(-\)0.0090 \\
02:00 & 2.464 & 2.482 & \(-\)0.0179 \\  & & mean & \(-\)0.0009 \\ \hline
**Data set 2** & **Day 1 ZTD (m)** & **Day 2 ZTD (m)** & **\(\Delta\)ZTD (m)** \\ \hline
16:00 & 2.455 & 2.492 & \(-\)0.0364 \\
17:00 & 2.457 & 2.493 & \(-\)0.0356 \\
18:00 & 2.459 & 2.492 & \(-\)0.0332 \\
19:00 & 2.459 & 2.493 & \(-\)0.0335 \\
20:00 & 2.460 & 2.492 & \(-\)0.0322 \\
21:00 & 2.462 & 2.491 & \(-\)0.0281 \\
22:00 & 2.460 & 2.489 & \(-\)0.0294 \\
23:00 & 2.471 & 2.484 & \(-\)0.0122 \\
00:00 & 2.470 & 2.482 & \(-\)0.0112 \\  & & mean & \(-\)0.0280 \\ \hline
**Data set 3** & **Day 1 ZTD (m)** & **Day 2 ZTD (m)** & **\(\Delta\)ZTD (m)** \\ \hline
21:00 & 2.417 & 2.388 & 0.0284 \\
22:00 & 2.419 & 2.389 & 0.0296 \\
23:00 & 2.419 & 2.386 & 0.0329 \\
00:00 & 2.418 & 2.385 & 0.0324 \\
01:00 & 2.418 & 2.389 & 0.0286 \\  & & mean & \(0.0304\) \\ \hline
**Data set 4** & **Day 1 ZTD (m)** & **Day 2 ZTD (m)** & **\(\Delta\)ZTD (m)** \\ \hline
19:00 & 2.440 & 2.448 & \(-\)0.0085 \\
20:00 & 2.439 & 2.446 & \(-\)0.0070 \\
21:00 & 2.442 & 2.448 & \(-\)0.0052 \\
22:00 & 2.444 & 2.448 & \(-\)0.0041 \\
23:00 & 2.443 & 2.445 & \(-\)0.0017 \\
00:00 & 2.444 & 2.444 & 0.0002 \\  & & mean & \(-\)0.0044 \\ \hline
**Data set 5** & **Day 1 ZTD (m)** & **Day 2 ZTD (m)** & **\(\Delta\)ZTD (m)** \\ \hline
15:00 & 2.437 & 2.451 & \(-\)0.0145 \\
16:00 & 2.441 & 2.452 & \(-\)0.0114 \\
17:00 & 2.443 & 2.450 & \(-\)0.0072 \\
18:00 & 2.442 & 2.451 & \(-\)0.0088 \\  & & mean & \(-\)0.0105 \\ \hline
**Data set 6** & **Day 1 ZTD (m)** & **Day 2 ZTD (m)** & **\(\Delta\)ZTD (m)** \\ \hline
20:00 & 2.450 & 2.424 & 0.0260 \\
21:00 & 2.446 & 2.420 & 0.0265 \\
22:00 & 2.446 & 2.422 & 0.0239 \\
23:00 & 2.442 & 2.422 & 0.0203 \\
00:00 & 2.442 & 2.416 & 0.0260 \\
01:00 & 2.442 & 2.415 & 0.0270 \\  & & mean & 0.0250 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: [PERSON] modelled ZTD in Day 1 and Day 2 and their differences of the six data sets. are about 0.2 and 0.4 mm, respectively, if 1 km is assumed as the PM\({}_{2.5}\)/PM\({}_{10}\) layer height. When a simple mapping function of \(1/\cos(zenith\ angle\ of\ satellite)\) is applied, the path delays of PM\({}_{2.5}\) and PM\({}_{10}\) particles become about 1.3 and 2.4 mm, respectively, when the zenith angle of a satellite is 80\({}^{\circ}\) (i.e., the elevation angle is 10\({}^{\circ}\)). Therefore, based on (12), the path delays induced by PM\({}_{2.5}\)/PM\({}_{10}\) particles are in the similar range of GPS carrier-phase measurement noise or slightly larger when compared with the GNSS carrier-phase measurement noise levels described in [29].
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Time-Series Clustering  
Given a set \(\mathcal{X}=\{X_{1},\ldots,X_{N}\}\) of \(N\) time series, a \(k\)-_clustering_ of \(\mathcal{X}\) is a partitioning of \(\mathcal{X}\) into \(k\) sets (or _clusters_) \(\mathcal{X}_{1},\ldots,\mathcal{X}_{k}\subseteq\mathcal{X}\) such that similar time series are assigned to the same set (see also ([2], Definition 1)).  
In this work, we mostly focus on time series with the same number \(T\) of data points and on _shape-based clustering_, where we aim to maximize the similarity of time series in the same cluster (or to maximize the dissimilarity of time series in different clusters). Shape-based clustering is defined with respect to a shape-based _dissimilarity_ (or _distance_) function \(d:\mathcal{X}\times\mathcal{X}\rightarrow\mathbb{R}_{\geq 0}\), which is symmetric, i.e., \(d(X,Y)=d(Y,X)\) for all \(X,Y\in\mathcal{X}\), and satisfies \(d(X,X)=0\) for all \(X\in\mathcal{X}\), but in the context of our work, may not satisfy the triangle inequality. We say that a dissimilarity function \(d\) satisfies the _triangle inequality_ if for all \(X,Y,Z\in\mathcal{X}\), \(d(X,Z)\leq d(X,Y)+d(Y,Z)\). If a dissimilarity function is symmetric, has \(d(X,X)=0\) for all \(X\in\mathcal{X}\), and satisfies the triangle inequality, we say that \(d\) is a distance function. For simplicity and clarity, we abuse the terminology and refer to dissimilarity functions \(d\) that may not satisfy the triangle inequality as distance functions. We focus on the widely used Dynamic Time Warping (DTW) distance (cf. Section 2.2), which is symmetric but does not satisfy the triangle inequality.  
Given a distance function \(d:\mathcal{X}\times\mathcal{X}\to\mathbb{R}_{\geq 0}\), a \(k\)-shape-based clustering (or \(k\)-clustering, for brevity) is a partitioning \(\mathcal{X}\) into \(k\) clusters \(\mathcal{X}_{1},\ldots,\mathcal{X}_{k}\subseteq\mathcal{X}\), where each cluster \(\mathcal{X}_{j}\) is associated with a representative time series \(C_{j}\) (which may or may not belong to \(\mathcal{X}_{j}\)), with the closest representative of each time series \(X\in\mathcal{X}_{j}\) being \(C_{j}\), i.e.,  
\[\mathcal{X}_{j}=\left\{X\in\mathcal{X}:d(X,C_{j})=\min_{i\in[k]}\{d(X,C_{i}) \}\right\}, \tag{1}\]  
such that the total clustering cost, defined as  
\[\text{Cost}(\mathcal{X},k)=\sum_{j=1}^{k}\sum_{X\in\mathcal{X}_{j}}d(X,C_{j})\,, \tag{2}\]  
is minimized.
## 3 Results and Discussion
### Microfluidic-Sensing Studies for Amyloid-Beta Analysis
#### 3.2.2 Binding Effects of CRANAD-2 with Amyloid-Beta  
Following the confirmation of CRANAD-2's fluorescence efficiency under miniaturized conditions, its binding interactions with \(\Delta\beta\) were evaluated in the microfluidic platform. Figure 4c,d presents the fluorescence results for both oligomeric and fibrillar A\(\beta\) across concentrations ranging from 100 nM to 1000 nM, when bonded with 25 mM CRANAD-2, mirroring the macroscopic assays (Figure 4a,b) to establish a basis for comparison between both experimental scales. Once again, it can be seen that oligomeric conformation (Figure 4c) attains a lower fluorescence intensity than fibrillar A\(\beta\) (Figure 4d). These findings are consistent with macroscale results (Figure 4i), confirming CRANAD-2's higher affinity for fibrillar structures.  
Figure 4: Fluorescence intensity spectra and microfluidic sensing data for A\(\beta\) oligomers and fibrils in the presence of 25 mM CRANAD-2, measured at different concentrations. The results are presented for both macroscale and microfluidic sensing methods: (**i**) fluorescence intensity spectrum at 590 nm excitation (c.p.s) derived from the macroscale results of oligomeric and fibrillar forms of A\(\beta\) at different concentrations (100–1000 nM), in the presence of 25 mM CRANAD-2 (n = 3 for each concentration): (**a**) oligomers; (**b**) fibrils; (**ii**) microfluid-sensing results of oligomeric and fibrillar forms of A\(\beta\) at different concentrations (100–1000 nM), in the presence of 25 mM CRANAD-2. (**c**) oligomers; (**d**) fibrils; (**iii**) microfluid-sensing results of oligomeric and fibrillar forms of A\(\beta\) at different concentrations (5–100 nM), in the presence of 25 mM CRANAD-2: (**e**) oligomers; (**f**) fibrils.  
As expected, the intensity range in the microscale system is reduced compared to macroscale results (following Section S4 of Supplementary Materials), which does not hinder effective differentiation. Due to the optical phenomena and prototype's miniaturized design, the SNR in the microfluidic environment was notably lower than in the macroscale setup. Specifically, this reduction stems from lower absolute fluorescence intensities coupled with inherent background noise. Nonetheless, the fluorescence response was still sufficient to enable reliable quantitative analysis and so this limitation does not compromise the detection of distinct intensity gradients, as the probe's fluorescence behavior remains concentration-dependent and differentiable for both protein forms.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 2 Theory of Clay Deformation under Cyclic Loading
### A Variable-Order Fractional Abel Dashpot  
#### 3.1. Mathematical Preliminaries  
**Definition 1**.: _Let \(L_{1}=L_{1}(I)\) be the class of Lebsgue integral functions on the interval \(I=(0,+\infty)\), and \(f(t)\in L_{1}\), \(\alpha\in(0,+\infty)\), for \(t>0\), \(Re(\alpha)>0\), the Riemann-Liouville integration with the order \(\alpha\) is defined [42]._  
\[D_{t}^{-\alpha}f(t)=\frac{1}{\Gamma(\alpha)}\int_{0}^{t}(t-\tau)^{\alpha-1}f( \tau)d\tau \tag{9}\]  
Figure 4: (a) Representative accumulative strain-time curve; (b) Analysis of critical dynamic stress.  
Figure 5: Total dynamic strain-time curve of clay.  
The fractional derivative is defined as  
\[D_{t}^{\alpha}f(t)=\frac{\mathrm{d}^{\alpha}}{\mathrm{d}t^{\alpha}}\left(\int_{0} ^{t}(t-\tau)^{\sigma-1}f(\tau)d\tau\right) \tag{10}\]  
In the above formula, \(D_{t}^{-\alpha}f(t)\) and \(D_{t}^{\alpha}f(t)\) are the Riemann-Liouville fractional integral operator and derivative operator, respectively; \(t>0\), \(\alpha\geq 0\) ; \(n\) is the smallest positive integer that exceeds \(\alpha\); the order \(\sigma=n-\alpha\); and \(\Gamma(\alpha)\) is the gamma function \(\Gamma(\alpha)=\int_{0}^{\infty}\mathrm{e}^{-t}t^{\alpha-1}d\tau\).  
It can be determined from Equation (9) that the Riemann-Liouville integration with the order \(\alpha\) is actually a weighted integral. It is worth noting that, for \(\alpha=1\), the equation is transformed into a common integral, and the weight value is equal to 1; for \(\alpha>1\), the farther the distance between variable t and integral upper limit \(\tau\), the larger the weighted value; for \(\alpha<1\), the further the distance between variable t and integral upper limit \(\tau\), the smaller the weighted value [43]. The convolution structure fully reflects the temporal dependence of system function development [44]. The Riemann-Liouville fractional calculus is therefore an essential tool for describing the creep behavior of clay.
# A Regret-Enhanced DEA Approach to Mapping Renewable Energy Efficiency in Asia's Growth Economies
## 3 Results
Data for countries corresponding to inputs and outputs are collected from open data sources of non-governmental organizations such as the International Renewable Energy Agency (IRENA) [9; 66], the International Energy Agency (IEA) [15; 16], Central Intelligence Agency (CIA) [67], ASIAN development bank (ADB) [68], and ASEAN Centre for Energy (ACE) [69]. The evaluation data are summarized in Table 3 below. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Input**} & \multicolumn{3}{c}{**Output**} \\ \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Public Flows**} & \multirow{2}{*}{**Energy**} & \multirow{2}{*}{**Transmission**} & \multirow{2}{*}{**RE Supply**} & \multirow{2}{*}{**Renewable**} & \multirow{2}{*}{**Renewable**} & \multirow{2}{*}{**Installed**} & \multirow{2}{*}{**Non-renewable**} \\  & & & & & & **Energy** & **Electricity** & **Electricity** & **Emissions** \\  & & & & & & **Capacity** & **Replaced** \\ \hline AF & 49.26 & 2.40 & 61.6 & 36.777 & 24,982 & 79.5 & 387.7 & 0.9 \\ BG & 23.13 & 2.36 & 9537.0 & 536,582 & 540,860 & 1.8 & 744.0 & 0.7 \\ BT & 0.75 & 7.91 & 60.0 & 57,149 & 131,124 & 100.0 & 2335.4 & 9.6 \\ KH & 27.94 & 4.68 & 1187.0 & 171,746 & 180,963 & 59.0 & 1748.6 & 4.2 \\ IN & 1677.24 & 4.28 & 270,701.0 & 907,449 & 10,196,470 & 18.9 & 147,107.6 & 281.8 \\ ID & 418.11 & 3.16 & 25,080.0 & 2,221,260 & 2,317,278 & 18.1 & 11,537.3 & 46.5 \\ LA & 0.44 & 4.35 & 2262.0 & 90,504 & 252,824 & 69.3 & 8934.3 & 29.9 \\ MY & 3.87 & 4.25 & 12,124.0 & 166,883 & 281,917 & 17.8 & 8898.2 & 21.5 \\ MY & 22.06 & 2.68 & 21.0 & 336 & 422 & 5.8 & 32.5 & 0.0 \\ MM & 63.26 & 3.58 & 21.0 & 496,449 & 488,910 & 46.4 & 3443.6 & 6.3 \\ NP & 99.38 & 5.23 & 1183.0 & 480,625 & 493,064 & 100.0 & 2096.2 & 6.4 \\ PK & 870.53 & 4.59 & 17,389.0 & 874,721 & 1,020,571 & 29.8 & 12,887.4 & 34.2 \\ PH & 1.72 & 2.68 & 9994.0 & 799,202 & 879,527 & 21.9 & 7312.3 & 18.2 \\ LK & 0.58 & 1.78 & 1337.0 & 162,464 & 183,001 & 51.3 & 2720.4 & 5.4 \\ TH & 54.82 & 4.52 & 1328.0 & 705,237 & 810,786 & 18.8 & 12,197.2 & 25.9 \\ VN & 166.91 & 4.92 & 15,479.0 & 862,519 & 1,163,843 & 42.8 & 42,728.5 & 83.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation data.
## 2 Mathematical Model of the Hybrid MG System
### Centralized Voltage Source Inverter (CVSI)  
The systematic diagram of the CVSI is presented in Figure 2. In the analysis of this system, the microgrid frequency, denoted as \(\omega\), determines the dynamic behavior of the system. By utilizing the synchronous \(d\)-\(q\) reference frame, which simplifies the analysis of three-phase systems, the governing equations for both the inductor current and the output voltages of the CVSI are derived in (12) and (13). These equations are essential for understanding the interaction between the CVSI and the rest of the microgrid, particularly in how they regulate power flow and maintain voltage stability.  
\[\frac{d}{dt}\begin{bmatrix}\dot{t}^{d}_{p}\\ \dot{t}^{d}_{q}\end{bmatrix}=\begin{bmatrix}-\frac{r^{g}_{r}}{t^{g}_{r}}& \omega\\ -\omega&\frac{-r^{g}_{r}}{t^{g}_{r}}\end{bmatrix}\begin{bmatrix}\dot{t}^{d}_{ q}\\ \dot{t}^{d}_{q}\end{bmatrix}+\frac{1}{l^{w}_{f}}\begin{bmatrix}V^{d}_{B1}-V^{d}_{ ipw}\end{bmatrix} \tag{12}\]  
\[\frac{d}{dt}\begin{bmatrix}V^{d}_{B1}\\ V^{d}_{B1}\end{bmatrix}=\frac{1}{c^{g}_{r}}\begin{bmatrix}\dot{t}^{d}_{inv}- \dot{t}^{d}_{B1}\\ \dot{t}^{d}_{inv}-\dot{t}^{d}_{B1}\end{bmatrix}+\omega\begin{bmatrix}V^{d}_{B1 }\\ -V^{d}_{B1}\end{bmatrix} \tag{13}\]
## 3 Results and Discussion  
### Technical and Economic Evaluation by Process Stage  
The technologies considered for each process stage were simulated with the respective models in the SuperPro Designer v10(r) simulator (Intelligen, Inc., Scotch Plains, NJ, USA) and with the conditions considered according to the reported literature. In the first stage, the technologies were evaluated individually, considering the same feeding conditions. Subsequently, the individual impact of the technologies considered in each stage of the process was analyzed and compared. In the second stage, the models of all the process stages were integrated to form the various production scenarios and estimate the energy consumption and the cost associated with the operation of the process. Additionally, fresh water consumption was determined to compare the production scenarios, with respect to this criterion.
## 1 Introduction
In the case of DAM, the bids are performed considering day-ahead forecasting. Therefore, a highly accurate prediction for PV is essential. In [19], the analysis for day-ahead forecasting for PVs in the Spanish electricity market is provided in order to see the economic performance for various forecasting models. Similarly, in [20], the significance of accurate forecasting for solar energy is shown in the context of German electricity market conditions. Their analysis concludes that the improved forecast accuracy reduces the financial risks however, it is also related to the market prices. DAM operation considering over-forecast and under-forecast of the PV power with the effect of locational marginal prices (LMP) and the real-time market is analyzed for California [21]. Figure 1: Forecasting applications and their associated time scales and forecast horizons. In the proposed study, an improved forecasting model is presented using a hybrid approach with a deep recurrent neural network based on long short-term memory (LSTM-RNN). A clustering method is first used to classify the daily weather condition as either sunny or cloudy. Then, the LSTM-RNN model is used to predict the SI for both types of days. Clustering enables the identification of similar patterns for SI of each day type that leads to the advantage of realizing better accuracy. As the LSTM-RNN model learn the uncertainty and variability for each type of cluster separately, it allows fitting the curve with more accuracy. The remainder of this paper is organized as follows. The related work is presented in Section 2; the methodology of the proposed hybrid LSTM-RNN approach for SI is explained in Section 3; implementation details for the proposed method are presented in Section 4; the results are presented and discussed in Section 5, and the conclusions are summarized in Section 6.
## 4 Antennas for Beam Steering and Beamforming  
Beam steering and beamforming are related concepts in antenna systems, but their meanings and applications differ slightly. Beamforming is a signal-processing technique used to shape the radiation pattern of an antenna array to focus energy in a desired direction while minimizing interference in other directions. It involves adjusting the amplitude and phase of signals fed to or received from individual elements of an antenna array to create constructive interference in the desired direction and destructive interference elsewhere. Beamforming enhances signal strength and quality in a specific direction, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Size (cm2)** & **f\({}_{\text{f}}\) (GHz)** & **BW (\%)** & **Gain (dBi)** &
\begin{tabular}{c} **CubeSat** \\ **Type** \\ \end{tabular} & **Deployable** & **Polarization** & **Application** \\ \hline
[1] & N/A & 7.4 & 16.21 & 7.2 & 1U & No & CP &
\begin{tabular}{c} Ground \\ Communication \\ Interatellite \\ \end{tabular} \\
[2] & \(1.41\times 1.17\) & 5.1 & 10.2 & 5.9 & 1U & No & CP &
\begin{tabular}{c} Ground \\ Communication \\ \end{tabular} \\
[9] & \(0.78\times 0.78\) & 2.45 & 44.9 & 8.22 & 3U & No & CP &
\begin{tabular}{c} Earth Observation \\ Ground \\ Communication \\ \end{tabular} \\
[10] & \(0.23\times 0.35\) & 2.43 & 1.65 & 5.3 & \(\text{1U,2U,3U}\) & no & N/A &
\begin{tabular}{c} Ground \\ Communication \\ \end{tabular} \\
[11] & \(0.19\times 0.20\) & 2.45 & 2.45 & 4.8 & 1U & No & CP &
\begin{tabular}{c} Ground/Intersatellite \\ Communication \\ \end{tabular} \\
[12] & \(1.93\times 1.93\) & 5.8 & 1.2 & 6.98 & 1U & No & CP &
\begin{tabular}{c} Communication \\ Educational \\ \end{tabular} \\
[13] & \(2.13\times 2.13\) & 8,11.2 & 2.39,3.22 & 6.45, 5.34 & 3U & No & Dual, CP &
\begin{tabular}{c} Ground \\ Communication \\ \end{tabular} \\
[14] & \(0.89\times 0.69\) & 5.03 (C-band) & 1.99 & 4.98 & 1U & No & LHCP &
\begin{tabular}{c} Communication \\ Communication \\ \end{tabular} \\
[15] & \(0.96\times 0.4\) & 0.485/0.5 (UHF) & N/A & 4 & 1.5U & No & CP &
\begin{tabular}{c} TT\&C \\ Ground \\ \end{tabular} \\
[17] & \(0.76\times 0\) & 2.45 (S-band) & 4.45 & 8.62 & 2U & No & CP &
\begin{tabular}{c} Communication \\ Ground/Intersatellite \\ Communication \\ \end{tabular} \\
[18] & \(0.4\times 0.4\) & 2.45 (S-band) & 2.05 & 5.8 & 1U & No & CP &
\begin{tabular}{c} Ground/Intersatellite \\ Communication \\ \end{tabular} \\
[43] & \(0.67\times 1.51\) & 2.52 & 11.11 & 5.2 & 3U & No & CP &
\begin{tabular}{c} Ground/Intersatellite \\ Communication \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proposed patch antennas designed for the CubeSat.