esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 3 Designing MEC Model for MFL System
### Calculation of the MEC Model Permeances  
In the MEC solution, iterative methods are needed to calculate magnetic permeances [23]. This is because the permeabilities of the magnetic permeances vary nonlinearly with flux density. The \(B\)-\(H\) curve, which describes this variation, is used as a reference for calculating such permeances. The permeabilities of the air permeances, on the other hand, do not change with flux density and, therefore, remain fixed. Hence, these permeances can be calculated in a single step in the MEC solution. Additionally, both air and magnetic permeances vary depending on the system dimensions, directly affecting the model flux distributions. Therefore, reliable system design necessitates accurate calculation of all MEC model permeance elements.
# Shape Optimization of an Open Photoacoustic Resonator
## 3 Results and Discussion
### Parameter Sweep
#### 3.2.2 Cavity Cylinder  
A cavity cylinder sweep is performed by changing its length \(L_{c}\) and radius \(R_{c}\). The radius is changed from 2 mm to 5 mm with 0.5 mm step size while the length is changed from 12 mm to 24 mm in 1 mm step size. Similar to the resonance cylinder sweep, the PA signal is calculated for all combinations of \(L_{c}\) and \(R_{c}\) with the other resonator dimensions kept constant.  
Figure 7 shows the relative PA signal \(\zeta\) for the cavity cylinder sweep. The \(R_{c}\) values between 3.5 mm and 4.5 mm have \(\zeta\) values of more than 1 with the highest value if \(R_{c}\) equals 4 mm. Outside this range, the \(\zeta\) value is less than 1. At \(R_{c}\) = 4 mm, the \(\zeta\) value is highest if \(L_{c}\) is between 15 mm and 17 mm. The highest \(\zeta\) value of 2.2 is observed if \(L_{c}\) is 17 mm and \(R_{c}\) is 4 mm (highlighted in Figure 7). The PA signal is affected considerably by \(L_{c}\). \(R_{c}\) of the reference resonator is already close to its optimum value.  
Based on the results of the parameter sweep, we conclude that the PA signal is most affected by \(L_{c}\) and \(D_{r}\). A finer sweep is performed by changing \(L_{c}\) between 16.6 mm and 17.3 mm with steps of 0.1 mm and \(D_{r}\) between 5.5 mm and 7.0 mm with steps of 0.5 mm. The results of the sweep are shown in Figure 8. The combined sweep increased the \(\zeta\) value from 2.2 to 3.5 when \(L_{c}\) is 17.1 mm and \(D_{r}\) is 6.5 mm.  
Figure 6: Relative PA signal of the finer resonance cylinder sweep (**a**) along with a contour plot (**b**). The color scale gives the values of \(\zeta\).  
Figure 7: Relative PA signal of the coarse cavity cylinder sweep (**a**) along with a contour plot (**b**). The color scale gives the values of \(\zeta\). The peak highlighted in the left plot is the region with the highest relative PA signal.
## 3 Price Predictions Using Deep Learning Models
### Training Prediction Models
A comparison of model predictions vs. the actual data is given in Table 2.  
\[\text{MAE}=\frac{1}{\text{N}}\sum_{\text{t=1}}^{\text{N}}\lvert y_{\text{t}}- \hat{y}_{\text{t}}\rvert \tag{1}\]  
\[\text{MSE}=\frac{1}{\text{N}}\sum_{\text{t=1}}^{\text{N}}\left(y_{\text{t}}- \hat{y}_{\text{t}}\right)^{2} \tag{2}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\sum_{\text{t=1}}^{\text{N}}\frac{\left( y_{\text{t}}-\hat{y}_{\text{t}}\right)^{2}}{\text{N}}} \tag{3}\]  
\[\text{COEV}=100.RMSE(y_{t},\hat{y}_{t})/\;\overline{y_{t}} \tag{4}\] \begin{table}
\begin{tabular}{l l} \hline Model Architecture &
\begin{tabular}{l} 3 stacks with 1 block each that has 2 fully connected layers with \\ 512 neurons each \\ \end{tabular} \\ \hline Dropout & 0.1 \\ \hline Loss Function & MSE \\ \hline Activation Function & ReLU \\ \hline Batch Size & 32 \\ \hline Epochs & 50 \\ \hline Training Environment & NVIDIA RTX 3090 GPU, Python 3.11, PyTorch 2.1.1 and CUDA 11.8 \\ \hline \end{tabular}
\end{table} 
Table 1: The hyperparameters and training details of the trained N-HiTS model. \[\text{MAPE}=\frac{100}{n}\sum_{t=1}^{n}\left|\frac{y_{t}-\dot{y}_{t}}{y_{t}}\right| \tag{5}\]  
Predictions of the selected model (N-HiTS) vs. the other top two performing models according to the MSE (GRU and LSTM) and the actual values are compared in Figure 2. \begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Model/Metric**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{4}{c}{**MSE**} & \multicolumn{4}{c}{**RMSE**} \\ \cline{2-13}  & **February** & **April** & **June** & **October** & **February** & **April** & **June** & **October** & **February** & **April** & **June** & **October** \\ \hline \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{24.74} & \multirow{2}{*}{50.31} & \multirow{2}{*}{11.56} & \multirow{2}{*}{12.75} & \multirow{2}{*}{926.2} & \multirow{2}{*}{3143.63} & \multirow{2}{*}{236.72} & \multirow{2}{*}{305.37} & \multirow{2}{*}{30.43} & \multirow{2}{*}{56.07} & \multirow{2}{*}{15.39} & \multirow{2}{*}{17.47} \\  & & & & & & & & & & & \\ \hline
**Simple RNN** & 15.97 & 40.94 & 12.62 & 7.85 & 403.18 & 2427.15 & 256.52 & 149.23 & 20.08 & 49.29 & 16.02 & 12.22 \\ \hline
**LSTM** & 16.07 & 38.88 & 12.41 & 8.1 & 381.88 & 2166.13 & 210.99 & 136.86 & 19.54 & 46.54 & 14.53 & 11.7 \\ \hline
**GRU** & 16.46 & 32.57 & 10.81 & 8.72 & 458.87 & 1667.21 & 167.29 & 120.56 & 21.42 & 40.83 & 12.93 & 10.98 \\ \hline
**N-BEATS** & 20.1 & 30.22 & 12.52 & 12.87 & 746.58 & 1206.96 & 252.78 & 283.46 & 27.32 & 34.74 & 15.9 & 16.84 \\ \hline
**N-HITS** & 13.73 & 24.46 & 10.06 & 9.01 & 309.61 & 893.18 & 159.26 & 136.4 & 17.6 & 29.89 & 12.62 & 11.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of prediction accuracy using common metrics. Figure 2: Comparison of our predictions vs. actual values.
## 2 Background and Related Work
### WSN Deployment
#### 2.4.1 Deterministic Deployment  
Deterministic deployment predetermines the location of sensor nodes and is usually pursued in indoor applications [35]. This approach requires a detailed understanding of the monitored area, including terrain, physical characteristics, and specific monitoring requirements. Nodes are then strategically placed where they can provide the most effective coverage and connectivity. Deterministic deployments often use resources more efficiently because they can control where each node is placed. However, implementing it can be expensive and inaccurate, especially if the deployment area still needs to be determined.
## 4 Conclusions  
A new explicit analytical method for equivalent circuit parameter calculation is presented. This method allows calculating the equivalent circuit by knowing the short-circuit point and slope, the open-circuit point, and any other experimental point with its slope of the \(I\)-\(V\) curve.  
Apart from its simplicity, the principal advantage of the method is that, contrary to other analytical methods, it is not necessary to know the maximum power point. Since this point is not easily obtainable experimentally, the proposed method can be very useful in reducing the data necessary for parameter determination. Furthermore, the accuracy of the results seems to be high, with a difference between experimental results of less than 1% in terms of current. The accuracy increases when the third point used for the calculation is nearby the maximum power point.  
From the authors' point of view, the simplicity and accuracy of these methods would make it interesting for end users with little calculation and testing resources. Also, the procedure described in the present work seems to be very appropriate for analyses that imply profuse calculations of equivalent circuits or determine initial values for numerical methods.  
Finally, a MPPT method based in the analytical models has been proposed. Using the experimental data set of a solar panel, each measurement has been considered as an instantaneous operation point to be optimized. The value of this point, together with the value of the slope of the \(I\)-\(V\) curve at each point, has been used to predict the position of the maximum power point with quite high accuracy. The implementation of this methodology on the space power systems design works at IDR/UPM Institute is preliminary at present. Nevertheless, it represents a solid line of work as the accurate prediction of the maximum power point, with a small amount of information regarding the solar panel performance (short-circuit current and slope, open-circuit voltage, and operating point and slope), has  
Figure 8: Normalized RMSE, \(\xi\), between the calculated and experimental \(I\)-\(V\) curves as a function of the voltage level, \(V_{i}\), at the operating point (\(V_{i}\), \(I_{i}\)) used in the estimation.  
been proven. Finally, it should be emphasized that an advantage of this method is the combination of the precision and feedback of online MPPT with the instantaneous reaction of offline methods.  
The authors are indebted to [PERSON], from CIEMAT, for his kind help and support regarding the UPMSat-2 solar panels' testing activities. The authors are grateful to the Reviewers for their wise comments, which helped us to improve this work.  
[PERSON] and [PERSON] developed the idea behind the present research, carrying also out the literature review and manuscript preparation at early stages. The methodology and analytic procedures were carried out by [PERSON]. Final review, including final manuscript corrections, was done by [PERSON] and [PERSON]. All authors were in charge of data generation/analysis and post-processing of the results.  
The authors declare no conflict of interest.
## 2 Optical Layout  
A prototype of the optical bench was built to help in the optimization of the hydroxide-catalysis bonding and in determining the precision of the optical assembly. As shown in Figure 1 (this figure was created using the component library by [PERSON], Albert Einstein Institution, Hannover, Germany, 2017), the optical layout of the interferometer includes a simple Mach-Zehnder interferometry system. A laser beam from a frequency-stabilized laser (wavelength: 1064 nm, linear polarized, and single mode) is split by a 50/50 BS (Beam Splitter). The transmitted beam directly accesses an AOM (Acoustic-Optical Modulator), whereas the reflected beam accesses another AOM after a reflector changes its direction of propagation. Both beams are frequency shifted by the AOMs, and the difference between the modulated values is the frequency of the heterodyne interferometric signal. Next, these two beams are coupled into a single-mode polarization-maintaining fiber and sent into the interferometry system. The interference system is located in the vacuum chamber to reduce the influence of air disturbance on the measurement precision. The interferometer contains two FCPSs (Fiber Collimation Package Subassemblies) for beam collimating, two BSs and two BCs (Beam Combiners), which perform the interferometry. Two PDs (Photo-Detectors) convert the optical signals into electric signals. Finally, the phase information of the electric signals from the PDs are obtained using a phasemeter.
## 1 Introduction
The strength of the LUE model is related to the possibility of deriving its input values from remote sensing observations [24; 38; 39]. Several vegetation indices (VIs) have been used to estimate \(f\mathrm{APAR}_{\mathrm{green}}\), most notably the normalized difference vegetation index (NDVI) [40]. When combined with PAR irradiance, either as a modeled or directly measured value (using PAR sensors), \(f\mathrm{APAR}_{\mathrm{green}}\) readily provides a reliable measure of \(\mathrm{APAR}_{\mathrm{green}}\), the primary term in the LUE model (Equation (2)). The rising interest in linking carbon flux measurements, proximal (_in situ_) spectral measurements and remote sensing data has motivated the establishment of a global network of experimental sites in various ecosystems that simultaneously collect optical and flux datasets [26; 27]. A key issue in combining optical and flux measurements is the evaluation of temporal scale, as optical and flux measurements vary on different time scales [26]. To our knowledge, few studies explicitly consider the effect of temporal aggregation when comparing remote sensing and flux data, in part because many satellite sensors (e.g., the MODIS Aqua and Terra sensors) are in sun-synchronous orbit that limit the number of daily overpasses. In this study, we took advantage of the continuous nature of flux and optical data to enable an explicit consideration of temporal aggregation, as recommended by the SpecNet community [27]. The objectives of this study were to:  
* assess the seasonal patterns of CO\({}_{2}\) exchange, above-ground biomass accumulation, and optical properties of a prairie grassland ecosystem comparing different optical sampling techniques (NDVI\({}_{680,800}\) from field spectrometer measurements, proxy NDVI measurements from automated sensors on an \"optical phenology station,\" and MODIS NDVI measurements);
* compare midday data aggregation periods for accurate prediction of fluxes from optical data;
* test different NDVI inputs and related biophysical parameters (\(f\)APAR\({}_{\text{green}}\) and APAR\({}_{\text{green}}\)) from both satellite and ground measurements as proxies for flux measurements using the LUE model;
* test the hypothesis of constant \(\varepsilon\) over the entire season in grassland ecosystem; and
* illustrate the potential of optical data to gap-fill eddy covariance measurements.
## 3 Probabilistic Analysis
### Reliability Analysis  
Reliability analysis takes advantage of the capabilities of the metamodeling technique to optimize computational resources [29]. First, a set of random variables (see Table 2) is assessed and introduced to UQlab. Then, by using the Latin Hypercube Sampling method, an experimental design (i.e., collection of known input-output pairs) is obtained. Before performing a sensitivity analysis, a surrogate model is introduced that represents the structural behavior of the case study by using a small experimental design [29]. Then, after the sensitivity analysis is performed, it is possible to identify the variables with the highest impact on the load-carrying capacity of the structure, and a new experimental design is obtained based on the relevant variables, which will later be used to update the surrogate model [24]. Subsequently, the capacity curve of the structure for a given discharge value is obtained and compared with the loading curve to compute the reliability index by using the subset simulation technique [44]. Additionally, model uncertainties are being considered  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & **Description** & **Nominal Values** & **Units** & **CV (\%)** & **Type** & **Reference** \\ \hline B & River width & 15.00 & m & 5 & Gaussian & [40] \\ D & Pier width & 2.00 & m & 5 & Gaussian & [40] \\ L & Pier length & 10.50 & m & 5 & Gaussian & [40] \\ n & Manning coeff & 0.0020 & - & 28 & Lognormal & [40] \\ s & Longitudinal slope & 0.0750 & - & 5 & Lognormal & [40] \\ \(K_{3}\) & Bed condition coeff & 1.10 & - & 5 & Uniform & [40] \\ \(\lambda\) (MU[XE “MU”]) & Model uncertainty & 0.82 & - & 23 & Gaussian & [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Random variables for the scour analysis.  
Figure 5: Fitted Gumbel distributions for the scour depth for each discharge value (Q1–Q8) for the cases with the value of the flow angle of attack of: (**a**) 0\({}^{\circ}\) (symmetric scour); (**b**) 20\({}^{\circ}\) (asymmetric scour).  
in the process to obtain the failure probability of the structure [45]. Finally, the process is repeated for each value of flow discharge, and the angle of attack herein is considered to build the fragility curve.  
To better determine the influence of the soil's mechanical properties when performing a reliability analysis, their interaction should be included. The cross-correlation coefficient (\(\rho\)) and different values of the correlation coefficient for different combinations of soil parameters (e.g., unit weight, cohesion, and angle of friction) have been identified and implemented for the case study [49; 50].
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 1 Introduction  
With the advantages of fast development cycle, economic and flexible characteristics, small and micro satellites are attracting more attention from commercial companies, institutes and universities [1]. Satellite fleet or satellite constellation which incorporates dozens or hundreds of small and micro satellites helps to improve the revisit cycle, and in turn makes the fleet or constellation more practical and useful nowadays [2; 3; 4; 5; 6]. Nevertheless, owing to the features of being small in volume, lightweight and with low heat inertia, coupled with their complicated working modes and limited satellite battery storage, it's a great challenge to achieve a well performing satellite TCS [7; 8; 9].  
Satellite TCS design is a critical part of the satellite. It helps to control the heat exchange between the satellite and the outside environment in order to maintain the temperature level of all the subsystems and instruments of the satellite [10]. Due to the limited onboard battery storage, low heat inertia and various work modes of Luojia 1-01 satellite, the mass and power allocated tothe TCS is inadequate. As a result, it is unattainable to have as much active thermal control as it expects. It's significant to present a quasi-all-passive TCS design of the satellite which characterized by light-weighted and low power consumption. A properly controlled temperature condition is critical for the satellite to perform its imaging tasks at nightlight time and daytime on-orbit.  
This paper aims to present a quasi-all-passive TCS design and to verify the effectiveness of the design by ground thermal balanced test and on-orbit performance which can be gathered from the telemetry data according to the characteristics of Luojia 1-01 satellite. In the end the thermal model was improved and updated referring to the data of thermal balanced test and on orbit data. Normally, the subsystem design of satellites is always about getting more resources like mass and power consumption to have excessive safety margins, but sometimes this might end up causing redundancy and lead to an increase in cost. Compared with traditional TCS proposals, this paper comes up with a low-cost, low-energy thermal control system named quasi-all-passive TCS by adopting an isothermal cabin design method. The experiments and in-orbit data indicate is has good performance and effectiveness. In the end, this quasi-all-passive TCS provides an instance of reference for other thermal design of small and micro satellites which have limited mass and power for thermal control.
## 6 Numerical Results
### Analysis of Network Performance
#### 6.4.1 URLLC Traffic Ratio  
Figure 9 shows the impact of the URLLC traffic ratio (\(\gamma\)) on the number of active PPs (\(z\)) for different numerologies (\(\mu\)) and \(R=30\) RUs in networks MESH-20 and MESH-38.  
In general, the increase of the URRLC traffic ratio results in decreased eMBB traffic, as both utilize limited radio resources, which turns into a higher volume of more latency-sensitive FH traffic (with the 50 us limit) in the network. Since the buffering latencies of the URLLC FH packets increase, this FH traffic cannot be routed to distant PP locations due to latency constraints. Therefore, more PPs in the proximity of RUs have to be activated. The effect is more prominent for a low numerology (\(\mu=1\)) as buffering latencies are higher here.
## 6 Conclusions  
In this paper, we have introduced data-driven channel prediction strategies for multi-antenna frequency-selective channels that aim at reducing the number of pilots by integrating transfer and meta-learning with a novel parametrization of linear predictors. The methods leverage the underlying structure of the wireless channels, which can be expressed in terms of a long short-term decomposition (LSTD) into long-term space-time features and fading amplitudes. To enable transfer and meta-learning under an LSTD-based model, we have proposed an optimization strategy based on equilibrium propagation (EP) and alternating least squares (ALS). Numerical experiments have shown that the proposed LSTD-based transfer and meta-learning methods far outperform conventional prediction methods, especially in the few-pilots regime. For instance, under a standard 3 GPP SCM channel model, assuming four transmit antennas and two receive antennas, using only one pilot meta-learning with LSTD can reduce the normalized prediction MSE by 3 dB as compared to standard learning techniques. Future work may consider the joint use of deep neural networks, in lieu of linear prediction filters, although related results for multi-antenna frequency-flat channels have not reported any significant advantage to date [14; 15; 16; 19].  
Conceptualization, software, formal analysis, writing, S.P.; conceptualization, supervision, writing, project administration, funding acquisition, O.S. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] and O.S. was partly supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 725731).  
Figure 10: Multi-antenna frequency-selective channel prediction performance as a function of the number of available previous frames \(F\) under 19-clustered, two taps (\(W=2\)), multi-antenna (\(N_{T}=4,N_{R}=2\)) 3 GPP SCM channel model for a fast-varying environment (top) and slow-varying environment (bottom) with number of training samples \(L^{\text{new}}=1\).  
Data Availability Statement: Code is available at [[https://github.com/kclip/channel-prediction-meta-learning](https://github.com/kclip/channel-prediction-meta-learning)]([https://github.com/kclip/channel-prediction-meta-learning](https://github.com/kclip/channel-prediction-meta-learning)) (accessed on 22 September 2022).
## 4 Antennas and Antenna Related Technologies for Future Internet
### Antennas for Centralized Solutions
Additionally, the highly integrated Q/V-band multibeam phased-array antenna [277], designed for LEO satellite applications, leverages an advanced beamformer chip and hybrid construction for efficient power management and thermal control, achieving an effective gain of 24.4 dBi. The reconfigurable multibeam holographic antenna [278] employs liquid-crystal technology to achieve low power consumption, independent beam control, and flexible beam steering, making it suitable for Ka-band LEO satellite communication. In [279, 280, 281], direct radiating array solutions for MEO satellites, combined with digital or hybrid beamforming, are analyzed and studied. Figure 16: Additive manufactured 3D discrete lenses antenna, with beam scanning \(\pm\)70\({}^{\circ}\) and gain of 5.93 dBi [276]. Figure 15: Base station antenna with 288 elements [244]. The development of 6G networks will introduce several critical challenges for base station antenna systems. Higher frequency operation at THz bands will require improved materials and designs to manage increased signal attenuation and path loss. Extreme beamforming requirements will necessitate advanced phased-array and metasurface technologies. Energy efficiency will become increasingly important to ensure sustainable deployments as data rates and frequency ranges grow. Finally, the demand for compact and lightweight designs will require innovative engineering to accommodate denser urban environments while maintaining optimal performance.