esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks
## 3 ANN Architectures and Training Algorithms
### Layer-Recurrent Networks  
In layer-recurrent networks, each hidden layer has a recurrent connection with additional tap delays. The circles in the hidden layer of Figure 3c depict these delays. This feature allows such networks to have a dynamic response to time series data. In our work, we have used a tap delay of two in each hidden layer.  
In all of our simulations, we have made use of _tan-sigmoid_ (a hyperbolic tangent sigmoid function in the hidden layers) and _purelin_ (a linear function in the output layer) as the activation functions. As far as training the network is concerned, several algorithms have been proposed in literature. The most widely adopted among those--which we also consider in this work--include Levenberg-Marquardt (LM) [27], Bayesian Regularization (BR) [28], Resilient Backpropogation (NR), Conjugate Gradient (CGF) [29; 30; 31; 32; 33], Quasi-Newton Backpropagation (BFGS) [34], and Variable Learning Rate Back Propagation with Momentum (GDX) [35].
# SITAEI HC1 Low-Current Hollow Cathode
## 4 Results
### Coupled Tests with HT100  
The typical ignition sequence of the HC1 in coupled configuration with HT100 is reported in Figure 9, in terms of the current and voltage trends of the heater, keeper, and thruster anode. With the gas flowing in both the cathode and anode units, a keeper voltage of 100 V is applied, together with an anode voltage of 300 V, then the heater is switched on, supplying it with a current of 6.8 A. Correspondingly, the heater voltage increases, reaching a steady-state value of about 7 V, resulting in slightly less than 50 W being supplied to the heater. After about 5 min, the discharge ignites and a keeper current of 1 A (limited value set on the power supply) is reached, along with an anode current of about 0.5 A (the anode being in voltage control mode). As soon as the cathode is ignited, the heater is switched off and the self-mode operation is maintained by the plasma heating mechanisms [13]. The keeper voltage during the starting transient drops from the applied value of 100 V to a steady-state value of about 14 V, corresponding to the characteristic voltage drop of the cathode at the operating point with a 1 A keeper current and 0.2 mg/s Xe. After switching the heater off, the keeper is also switched off and the discharge is maintained between the anode and cathode tube.  
Within the framework of the uHETSat program, during the qualification test campaign for the 100 W thruster unit (TU) composed of the HT100 anode unit and two HC1 cathodes, the cathode met the TU requirements for thrust, specific impulse, and power consumption. The performance values at the nominal operating point are about 7 mN of thrust, 1020 s of specific impulse, and 140 W of power consumption. Shock and vibration tests were successfully completed, as described in [8], proving the cathode with mechanical robustness in withstanding the typical launch loads and vibrations required by the final application. Figure 10 shows the TU mounted on the thrust balance before a coupling test (left) and during operation (right).  
Figure 8: Electrical characteristics comparison between the model and experiment at 0.1 mg/s Xe.  
Figure 9: Thruster ignition sequence after the heating transient: currents (left) and voltages (right).
## 1 Introduction
However, by combining the GBP and the dead reckoning (DR) methods, the experimental results corroborate that with the LHO scheme a handover can be performed right before the robot loses its LoS connection with respect to (w.r.t.) the mmWave AP. Hence, the LHO scheme guarantees higher throughput for the robot than the conventional received signal strength indicator (RSSI)-based handover approach. The key contributions of our paper are summarized as follows:  
* we formulate and analyze a positioning algorithm, GBP, based on the angle measurements from one mmWave AP enabling 2D positioning in the 3D environment;
* we propose an efficient LHO handover scheme that utilizes both the GBP positioning algorithm as well as the DR method;
* we study and test the proposed handover scheme using a multi-RAT prototype robot developed by our group, and we assess the statistics of RSSI, latency, and estimated robot locations. The rest of the paper is organized as follows. In Section 2, we systematically review the state-of-the-art in LHO/LNS, which is followed by the introduction of the problem formulation. Section 3 outlines the utilized multi-RAT robotic platform as well as the scenario of interest for conducting the LHO. In Section 4, we describe the constructed 3D map for our simulation campaign together with the proposed positioning algorithm and handover scheme. Section 5 provides a discussion on the simulation results of the theoretical positioning accuracy and the experimental results related to the handover schemes. Finally, Section 6 wraps up this paper with a conclusion.
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 5 Design of the TLECD  
### Influence of Physical Parameters on the Force-Velocity Relationship of the TLECD  
From [PERSON]'s model in Section 2, it is clear that the force-velocity relationship of the TLECD is determined by the maximum damping force and the critical relative velocity, and they also have been proven to have a great influence on the peak responses of the SDOF system in Section 4.5. As a result, the design of the TLECD-HA for these two mechanical parameters is crucial for shock vibration control of the structure with TLECDs. The array type and size of the PMs, their remanence, the air gap, the conductivity of the conductor, and conductor thickness are just a few physical design parameters of the TLECD that affect these two mechanical parameters in a practical model [31; 32; 41]. Some of these physical  
Figure 11: Maximum responses of the SDOF system with various ECDs under half-cycle sine shock excitation: (**a**) \(\gamma=0.5\); and (**b**) \(\gamma=1\).  
design parameters have a significant impact on both mechanical parameters, while others mainly affect one of them.  
From the electromagnetic finite element analysis for the TLECD using the COMSOL Multiphysics 5.6 software, the influence of some physical design parameters on the force-velocity relationship of the TLECD can be seen in Figure 12. It can be seen that the radial height of the air gap, the number of PM rings, the radial height of PM rings, and the radial inner diameter of PM rings only affect the maximum damping force, whereas the conductivity of the conductor just influences the critical relative velocity. In addition, the radial height of the conductor determines both mechanical parameters. As a result, the design of a practical ECD can be mainly divided into two parts: one design part for one mechanical parameter, and another design part for another mechanical parameter. Different from the idealized influence in Figure 12, more realistic factors are expected to be taken into account. For example, the size of the permanent magnet and the conductor should be within an appropriate range, and there is a bottom limit on the height of the air gap due to possible contact. Moreover, under different processing methods and heat treatment conditions, the conductivity of copper alloy or aluminum alloy can be adjusted from 6%IACS (International Annealed Copper Standard) to 95%IACS, but the conductivity of the commonly used metals or metal alloys is fixed. More practical considerations are not mentioned here.  
Figure 12: Influence of physical design parameters of the ECD on its constitutive behavior: (**a**) Damping force versus the radial height of air gap; (**b**) Damping force versus the number of PM rings; (**c**) Damping force versus the radial height of PM rings; (**d**) Damping force versus the radial inner diameter of PM rings; (**e**) Damping force versus the conductivity of conductor; and (**f**) Damping force versus the radial height of conductor.
## 4 Discussion
ET estimations are important to observe plants' response to the climate change of the last decade. Climate change is manifesting as prolonged drought periods and more frequent and intense erosive precipitation events [35]. ET estimation can help determine the magnitude of a drought, as low ET is expected under drought conditions [36]. Higher temperatures usually indicate higher rates of ET because of plant stomata, and in extreme scenarios, a decrease in ET because of a lack of water. In this case, ET happened at a higher rate, which promoted an increase in GPP. Again, LST was not the main contributing factor to the ET increase in 2015. This can be observed in Figure 9, where actual ET and GPP are positively correlated. [PERSON] [37] reported on the relationship between actual ET and productivity, where actual ET is a significant indicator of productivity in mature terrestrial plant communities. This relationship happens because actual ET measures the most important part of plant productivity/GPP: water and solar energy used for photosynthesis. We must acknowledge that even though ET was at its highest in 2015, the GPP rates were not. It has been reported that stressed soil moisture levels are the most crucial driving parameter regarding the productivity of an ecosystem [38]. Due to a lack of precipitation in 2015, soil moisture was low and consequently impacted GPP. In regards to the spatial distribution of canopy (tree branches and leaves), imperviousness (nonpenetrable surfaces) (Figure 2), and the datasets created for NDVI, GPP, ET, and LST, the areas most affected were either low-canopy areas or areas with a higher concentration of anthropogenic activity. As hypothesized, areas closer to mountains or further away from the coast showed higher resilience. In contrast, plains closer to the coast presented lower resilience. Vegetation was reduced on a greater scale after Hurricanes Irma and Maria. Due to the type of vegetation in urban areas and its density in coastal urban areas,the impact of an atmospheric event such as a hurricane can cause more deterioration to the ecosystem than what is observed closer to mountain ranges. Consequently, the ecosystem takes longer to recover. Long-term degradation of the ecosystem can be mitigated by using native vegetation and maintaining the mangrove areas in the area of study. [PERSON] and [PERSON] [39] proposed that resilience in urban areas is a product of human patterns and is controlled by socioeconomic and biophysical processes. This also means that urban areas are vulnerable to changes in structure due to changes to the natural ecosystem such as land cover, rate of water infiltration, hard covers, and other parameters that are an effect of human activity.
## 5 Conclusions  
In the present study, two quasi-MMIC two-stage PAs were developed for the 5G NR n77 and n78 bands using WIN(tm Semiconductors(\lx@sectionsign\) 0.25 \(\upupupupup\)m GaN/SiC HEMT process and GaAs IPD process. Given thermal considerations, the CoB wire-bond assembly method was chosen, and the interconnect design was carefully executed using three bond wires with lengths of 300-400 \(\upupupup\)m. To demonstrate the concept's potential, two assembly methods were implemented: the split and stack designs. The split design consists of GaN chips with an area of 2.87 mm\({}^{2}\) along with GaAs IPD chips measuring 0.64 mm\({}^{2}\) and 2.19 mm\({}^{2}\) (excluding the cutting street). The split design was found to be the more cost-effective option. On the other hand, the stack design involves GaN chips with an area of 2.1 mm\({}^{2}\) and GaAs IPD chips with a much larger area of 12.11 mm\({}^{2}\). The stack design optimizes the GaN chip sizes while providing a larger GaAs IPD area. This larger GaAs IPD chip area presents the potential for integrating an SiP, enabling further development with advanced functionalities. The continuous Class-J mode design was employed for both spilt and stack design. The CoB wire-bond assembly was carefully considered in the IMN and OMN designs. The fabricated split design PA achieved a maximum \(P_{sat}\) value of 40.3 dBm (10.3 W), an \(OP_{LAB}\) value of 36.4 dBm, a bandwidth of 44.5%, and a peak PAE of 39.3%. On the other hand, the stack CoB wire-bond assembly PA achieved a maximum \(P_{sat}\) value of 40 dBm (10 W), an \(OP_{LAB}\) value of 36.4 dBm, a bandwidth of 44.6%, and a peak PAE of 35.6%. Both the split and stack design PAs demonstrated \(P_{sat}\) values exceeding 37.4 dBm and 37.9 dBm within the frequency range of 2.7 to 4.5 GHz. The fabricated PAs exhibited excellent in-band and out-of-band performance under a 5G NR FR1 100 MHz 256-QAM signal. The overall measurement results indicate that the proposed two continuous Class-J mode quasi-MMIC PAs can be used for 5G NR FR1 microcell applications. Conceptualization, H.-C.L. and H.-K.C. ; methodology, H.-C.L. ; software, H.-C.L. ; validation, H.-C.L. ; formal analysis, H.-C.L. ; investigation, H.-C.L. ; resources, H.-C.L. ; data curation, H.-C.L. ; writing--original draft preparation, H.-C.L. ; writing--review and editing, H.-C.L. ; visualization, H.-C.L. ; supervision, D.-C.C. and H.-K.C. ; project administration, D.-C.C. and H.-K.C. ; funding acquisition, D.-C.C. and H.-K.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology of Taiwan under contract number MOST 109-2221-E-008-065-MY3 and MOST 112-2218-E492-005. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors thank the Taiwan Semiconductor Research Institute (TSRI) and the National Chung-Shan Institute of Science & Technology (NCSIST) for their support with chip fabrication and measurement.
# CMAG: A Mission to Study and Monitor the Inner Corona Magnetic Field
## 1 Introduction
### Solar Corona Polarimetry
Remarkably, no existing or planned mission is able to tackle these challenging measurements, despite the efforts of the solar community over the last few decades. Solar missions with specific instrumentation for performing spectropolarimetry in the corona, such as the solar magnetism explorer (Solmex) [39] or the Magnetic Imaging of the Outer Solar Atmosphere (MImOSA) [40] missions, have already been proposed. Certainly, the CRYO-NIRSP instrument [18] will likely provide excellent data on the inner corona, although it is expected that the observations will be much more limited in terms of time and have very restricted fields of view compared to CMAG, which aims to observe the entire inner corona. Currently, there are ground-based spectropolarimetric observations of the inner corona, such as those from uCoMP, at a spatial resolution of 6\({}^{\prime\prime}\) and a field of view covering from 1.03 to 2 solar radii. Linear polarization information has already been retrieved although circular polarization still remains elusive so far. It is worth mentioning that on 2 September 2023, the Indian Space Research Organisation launched the Aditya-L1 mission ([[https://www.isro.gov.in/Aditya_L1-MissionDetails.html](https://www.isro.gov.in/Aditya_L1-MissionDetails.html)]([https://www.isro.gov.in/Aditya_L1-MissionDetails.html](https://www.isro.gov.in/Aditya_L1-MissionDetails.html)) accessed on 15 November 2023). The mission features the Visible Emission Line Coronagraph (VELC), developed by the Indian Institute of Astrophysics in Bangalore. The primary objective of VELC is to measure the coronal magnetic field through spectropolarimetric observations of the Fe XIII 1074 nm line.
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
###### Abstract  
In this paper, a linearly approximate signal detection scheme is proposed in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. The huge MIMO-OFDM system, which uses many transmit antennas and high order modulation, requires a detection scheme at the receiver with very low complexity for practical implementation. In the proposed detection scheme, one \(N\times N\) MIMO-OFDM system is divided into \(N/2\)\(2\times 2\) MIMO-OFDM systems for linear increase of complexity. After the partial zero-forcing (ZF), decision feedback equalizer (DFE) and QR decomposition-\(M\) algorithm (QRD-\(M\)) are applied to each \(2\times 2\) MIMO-OFDM system. Despite nonlinear detection schemes, the overall complexity of the proposed algorithm increases almost linearly because the DFE and the QRD-\(M\) are applied to \(2\times 2\) MIMO-OFDM systems. Also, the value of \(M\) in the QRD-\(M\) is fixed according to position of the center point in constellation for efficient signal detection. In simulation results, the proposed detection scheme has higher error performance and lower complexity than the conventional ZF. Also, the proposed detection scheme has very lower complexity than the conventional DFE, with slight loss of error performance.  
MIMO-OFDM; partial ZF; DFE; QRD-\(M\) Article
# Low-Rank Approximation of Frequency Response Analysis of Perforated Cylinders under Uncertainty
## 5 Numerical Experiments: Trommel Screen
### Asymptotics  
As stated above, there are two different interesting asymptotics related to the most energetic response that follow directly from this study. These are the observed frequency and the total energy as functions of the thickness. Two features of the experimental setup are central. First, the energy contributions of the regular grid part and the triangular pattern panels are parameter-dependent; second, for a given loading, there exists a critical thickness \(t_{c}\) at which the solution is aligned with the regular grid so that maximal amplitudes are exactly in between the perforation rows leading to the energetic response.  
Figure 6: Low-rank approximation. Convergence of the low-rank approximation as the number of the Krylov basis vectors grows. Solid line: full, dotted: rank = 2,, dot-dashed: rank = 3, dashed: rank = 4. The \(\theta\)-components explain the difference in the observed energies at \(f=70\).  
Figure 7: Concentrated load. Low-rank approximation results in a spurious peak at \(f=25\). Interestingly, at \(M=6\) for the dominant peak at \(f=15\), one standard deviation compensates for the difference between the full and low approximation.  
For thicknesses \(\ll t_{c}\), local bending occurs at the scale of the smallest perforations and thus total energy growth saturates.  
In Figures 8 and 10, both sets of asymptotics are shown. Let us first concentrate on the observed frequencies. Both \(K=5\) and the concentrated load have one dominant parameter-dependent response over the whole range, with respective rates of \(\sim\)1 and \(\sim\)1/2. However, for \(K=10\) and \(K=20\), there are two competing ones, with the transition to the higher-frequency one occurring at some critical thickness \(t_{c}\). For \(K=5\), this transition would also eventually occur, but with a critical thickness below the range considered here. Moreover, for \(K=10\), this transition is sharp; for \(K=20\), it is more gradual; in fact, in Figure 8c, around \(t=3/1000\), the low-rank approximation picks higher frequencies already before the transition. This is explained with graphs in Figure 9.  
The energy asymptotics of Figure 10 tell a similar story. For \(K=10\), the sharpness of the transition leads to an almost monotone increase in the observed total energy. In the case of \(K=5\), the large holes dominate first, leading to a higher initial rate of energy increase. A similar change in rate is also seen for \(K=20\), but here, the reason is saturation in the higher frequencies with local features dominating.  
Figure 8: Most energetic response. Observed frequency as a function of the thickness. Red colour indicates low-rank approximation if there is a discrepancy. All trend lines for Fourier loads have a rate \(\sim\)1; for the concentrated load, the rate is \(\sim\)1/2.  
Figure 9: Fourier \(K=20\): Comparison of the full (solid line) and low-rank approximation (dashed line) over a range of frequencies. Low-rank approximation picks a higher frequency.
# Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures
## 4 Methodological Extension
### Explosion Response of Water and Layered Geological Structures Models
#### 4.1.3 Numerical Results of Layered Geological Structures Models  
Figure 11 provides insights into the stress propagation within the underwater granite and basalt geology. One crucial observation is the impact of the interface between granite and basalt. When the stress wave encounters this boundary, a portion of it reflects, while the rest transmits into the granite medium. The magnitude of reflection here is lesser compared to that at the fluid-solid interface. This can be attributed to the relatively minimal wave impedance difference between granite and basalt. Consequently, the energy of the stress wave transitions more smoothly between these two solid mediums. As the stress wave encounters the boundary between the water and granite, it splits: a portion transmits into the adjacent medium, while the remainder reflects into the original one. Given the juxtaposition of solid basalt and fluidic water, only compressive stress moves into the  
Figure 10: (**a**) Pressure-time relationship in water at various distances. (**b**) Variation of peak pressure at different distances from the hypocenter in the water.  
Figure 9: Pressure distributions in water from underwater explosions.  
water as the wave progresses from granite. A significant reflection can be observed at the interface, primarily because of the stark contrast between the wave impedance of water and that of granite.  
Figure 12 depicts the interaction between stress and time at varying distances from the epicenter. An intriguing pattern emerges at the 1 km distance, where a minor stress-wave fluctuation is evident around 0.75 s. This phenomenon can be attributed to the reflective action occurring at the granite-basal interface. However, due to the minimal impedance gap between the two solid media, the reflection remains relatively subdued. It is important to note that the difference in wave impedance between granite and basalt affects the behavior of reflected and refracted energy.  
At a greater depth of 7 km, the waveform undergoes a distinct evolution. The initial stress wave peaks at approximately 1.05 s, with a peak value of 8.93 MPa. Subsequently, a reflected wave is observed at 1.35 s, reaching a peak value of 6.19 MPa. This pronounced reflection is a direct consequence of the considerable wave impedance disparity between granite and water, resulting in the majority of the stress wave rebounding at the interface.