esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spectral Collection  
Raman spectra information were collected using a Qepro6500 laser Raman spectrometer (Ocean Optics, Delray Beach, FL, USA) equipped with a 785 nm semiconductor laser. The spectral acquisition range was 0-2000 cm\({}^{-1}\), and the laser power was 300 mW. Prior to each sample acquisition, background spectra were acquired using a clean, empty cuvette. The resolution was 4 cm\({}^{-1}\) and the ambient was 20 \({}^{\circ}\)C during the acquisition. Measurements were repeated 5 times for each sample, and the mean spectra were taken to build the model.
## 5 Conclusions  
This paper presents experimental and theoretical analyses of the eccentric compression of SCGC with different reinforcement ratios and eccentricity, leading to the following key conclusions:  
(1) The failure patterns of SCGC under eccentric compression are similar to those of ORC columns, and its lateral displacement curve resembles a sine wave. However, SCGC exhibits greater lateral deformation compared to ORC.  
(2) CAC, OAC, LAC, and CAGC exhibit a similar ascending branch. However, in the descending branch, the CAGC curve is steeper than that of the OAC and LAC curves, while flatter than that of the CAC curve. Through a systematic analysis of the stress-strain response characteristics specific to CAGC, this paper proposes a novel constitutive model for CAGC.  
(3) When \(e_{0}/h\geq 0.7\), SCGC experiences large eccentric failure; otherwise, it undergoes small eccentric failure. Under large eccentric compression, the tensioned SSR yields while the compressed SSR does not, which differs from that of reinforced OAC columns. Conversely, under small eccentric compression, the yielding behavior of the SSR is exactly opposite.  
(4) This study proposes a novel load-bearing capacity prediction model for SCGC columns through the introduction of the CAGC constitutive model. The refined model demonstrates a strong agreement with the experimental results, indicating that it has promising applications.  
(5) An improved crack width calculation model based on the bond-slip theory is developed, taking into account the different bonding characteristics between CAGC and SSR compared to OAC and carbon steel bars and the characteristic of SSRs lacking a yield plateau. The modified model is able to predict the crack width accurately.  
H.X.: Methodology, Visualization, and Supervision. J.J.: Writing--original draft and Formal analysis. C.Y.: Investigation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Characterisation of Coastal Sediment Properties from Spectral Reflectance Data
###### Abstract  
Remote sensing of coastal sediments for the purpose of automated mapping of their physical properties (grain size, mineralogy and carbonate content) across space has not been widely applied globally or in South Africa. This paper describes a baseline study towards achieving this aim by examining the spectral reflectance signatures of field sediment samples from a beach-dune system at Oyster Bay, Eastern Cape, South Africa. Laboratory measurements of grain size and carbonate content of field samples (\(n=134\)) were compared to laboratory measurements of the spectral signature of these samples using an analytical spectral device (ASD), and the results interrogated using different statistical methods. These results show that the proportion of fine sand, CaCO\({}_{3}\) content and the distributional range of sediment grain sizes within a sample (here termed _spun_) are the parameters with greatest statistical significance--and thus greatest potential interpretive value--with respect to their spectral signatures measured by the ASD. These parameters are also statistically associated with specific wavebands in the visible and near infrared, and the shortwave infrared parts of the spectrum. These results show the potential of spectral reflectance data for discriminating elements of grain size properties of coastal sediments, and thus can provide the baseline towards achieving automated spatial mapping of sediment properties across coastal beach-dune environments using hyperspectral remote sensing techniques.  
g 2022
# An MMIC LNA for Millimeter-Wave Radar and 5G Applications with GaN-on-SiC Technology
## 2 Analysis and Design of MMIC LNA  
In the field of low NF circuit design, the selection of an optimal topology for the GaN HEMT LNA holds significant importance. The effectiveness and performance of the LNA greatly depend on the chosen topology. In this particular study, the MMIC LNA's topology is explicitly shown in Figure 1, providing a visual representation of its design. In order to fulfill the requirement for small signal gain, the MMIC LNA is built with an architecture consisting of a three-stage common-source (CS) amplifier [30]. By utilizing a multi-stage approach, the LNA can achieve higher gain levels while maintaining low noise performance. One critical factor that needs to be taken into account when designing the LNA is the Miller effect caused by the gate-to-drain capacitance (C\({}_{\text{gd}}\)). To counteract this effect and minimize its impact on amplifier performance, an RC series network is introduced. This network is connected in parallel to the drain of the GaN HEMT, accompanied by a series \(\lambda\)/4 microstrip line. This combination effectively reduces the undesirable consequences of C\({}_{\text{gd}}\), thus enabling better overall performance of the LNA. Another aspect to consider is the detrimental impact of excessive input power on the LNA. When the input power exceeds certain limits, it can lead to a direct current (dc) harmonic surpassing the breakdown current. To mitigate this potential issue, a resistor with a kilo-ohm range has been integrated into the gate bias feed. By incorporating this resistor, the DC gate current can be limited effectively, providing additional protection to the LNA, and will help against the high-input-power-induced failures. Additionally, the equivalent model of the MNs is also depicted in the illustrated topology. These networks play a crucial role in ensuring appropriate impedance matching between different components of the LNA, optimizing power transfer, and maximizing efficiency. A comprehensive explanation of the various structures incorporated within the topology will be provided in subsequent sections, offering a detailed understanding and analysis of the design choices made in this study.  
Figure 1: Simplified schematic of proposed LNA.
## 3 Experimental Data Set and Analysis
### The Effect of Interpolation Method on Ionosphere-constrained GPS Single-frequency PPP
at the 95% level is improved from 3.87 to 3.14 min (18.9%) in horizontal (H(95%)) and by from 19.44 to 15.18 min (21.9%) in the vertical components (V(95%)) in the static mode, while, in the kinematic mode, it increased by 15.3% in the horizontal component and 13.2% in the vertical component. At the 68% level, the average convergence time in the horizontal component (H(68%)) significantly reduced by 17.9% from 17.55 to 14.40 min and by 23.6% from 42.92 to 32.81 min in the vertical component (V(68%)) in the static mode, and in the kinematic mode, lessened by 6.8% and 2.3% in the horizontal component and vertical component, respectively. The calculation time of these 18720 tests was normalized to FLRS station (Table 4). We used a Lenovo computer, which is configured with Windows 8.1 professional computer with an i5-3470 Central Processing Unit (CPU) and 4 GB of installed Random Access Memory (RAM). From Table 4, it can be concluded that the BILI interpolation method calculation time is almost the same as the Junkins weighted interpolation method, which takes the shortest time. According to all of the above analysis, the interpolation method that is adopted for GIM products in the following ionosphere-constrained model is bilinear interpolation.
## 2 Materials and Methods
### Mesoscale Numerical Weather Modelling  
The Weather Research and Forecasting (WRF) mesoscale model was developed by the National Center for Atmospheric Research (NCAR) with users and contributions from several meteorological institutions around the world. The version used in this work is v3.9.1, and the simulations are set up as a one-way nested run with 4 domains, D01-D04, as shown in Figure 2. The horizontal resolutions are 27, 9, 3 and 1 km, respectively, all domains with 70 vertical levels, using the Global Multi-resolution Terrain Elevation Data (GMTED) at a 30-arcsecond resolution.  
The planetary boundary layer (PBL) is modelled using Mellor Yamada Nakanishi and Niino (option 5) to provide sub-grid TKE terms [33]. The following options were used for the additional physics options: microphysics using the Thompson scheme (option 8) [34], long- and shortwave radiation using the fast Rapid Radiative Transfer Model (option 24) [35], Noah Land Surface Model (option 2) [36], surface layer using the revised MM4 scheme (option 1) [37], and cumulus parameterisation using Kain-Fritsch scheme (option 1) [38].  
Figure 2: WRF domain locations with colours showing model height data from 0 to 2000 m. White rectangles show borders between the nested domains.  
The ERA interim reanalysis data with 6 h temporal sampling from the European Centre for Medium-Range Weather Forecasts (ECMWF) was used as initial and boundary inputs to the WRF model, and daily simulations were set up with a 12 h spin-up time. Model results were saved every 10 th minute, and a full year of time series was created using the daily simulations with a linear interpolation for the last 6 h of the spin-up time for all relevant parameters. Vertical linear interpolation was used to get from the WRF model pressure-based eta level to the 80 magl height, and horizontal bilinear interpolation was used to obtain the latitudes and longitudes of the location of the measurement mast and wind turbines.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Processing Module
#### 2.1.1 Microcontroller  
Our tag design includes an STM32L071C8 (M0+ cortex), a low-cost microcontroller, which has low current consumption in its maximum workload as well as built-in low power consumption modes. The maximum working frequency of 32 MHz, 192 KB for flash, 6 KB for EEPROM, and 20 KB for RAM (Random Access Memory) memory of the chip is enough to perform the tasks described in previous sections, and to accommodate the operating system.  
The microcontroller is connected to the transceiver and the memory via SPI (Serial Peripheral Interface) bus, where it is the master and the others are slaves. This chip is responsible for every data transaction, for turning off and for waking up both the transceiver and the memory. Accordingly, it commands the other components and executes the process to follow.  
The mode in which normal operation is developed is the active mode (default mode). Their working frequency can be configured. The microcontroller integrates low consumption modes which we will use in our tag in order to decrease the energy consumption and extend the lifetime. We use the stop mode with RTC (Real-Time Clock), which works at 32 KHz, which retains the RAM, the register contents and the real time clock.  
The plan is to use the stop mode with RTC, one of the modes with lower power consumption, the majority of the implemented duty cycle, which depends on the application, except for the moments in which it is woken up by the transceiver or it needs to process and save data that have been received.  
The working mode changes to active mode (default mode) working at 16 MHz to reduce consumption without losing functionality because higher frequencies usually involve a higher consumption. This last mode is the one used for processing receptions and transmissions.  
There are also connections via a button and a LED for programmer control, and a UART (Universal Asynchronous Receiver-Transmitter) for debugging purposes. The LED will not be assembled in the final device because it would increase the power consumption of the tag.
## 3 Decoupled Control [23]  
In this control scheme, the controller was applied after adding a decoupler element between the ADRC and the nonlinear MIMO system. In recall Equations (2) and (3), the simplified model is expressed as,  
\[\left\{\begin{array}{l}\xi_{i}^{(\gamma_{i})}=F_{i}+\sum\limits_{q=1}^{p}b_{ i,q}u_{q}\\ y_{i}=\xi_{i}\,,\;i\in\{1,2,..,p\}\end{array}\right. \tag{4}\]  
The matrix form of Equation (4) is given as,  
\[\left(\begin{array}{c}\xi_{1}^{(\gamma_{i})}\\ \xi_{2}^{(\gamma_{2})}\\ \vdots\\ \xi_{p}^{(\gamma_{p})}\end{array}\right)=\left(\begin{array}{c}F_{1}\\ F_{2}\\ \vdots\\ F_{p}\end{array}\right)+B\left(\begin{array}{c}u_{1}\\ u_{2}\\ \vdots\\ u_{p}\end{array}\right) \tag{5}\]  
where \(B=\left(\begin{array}{cccc}b_{11}&b_{12}&\ldots&b_{1p}\\ b_{21}&b_{22}&\ldots&b_{2p}\\ \vdots&\vdots&\ddots&\vdots\\ b_{p1}&b_{p2}&\ldots&b_{pp}\end{array}\right)\) is a non-singular input gain matrix and its inverse matrix \(B^{*}\) is expressed as:  
\[B^{-1}=\left(\begin{array}{cccc}b_{11}^{*}&\cdots&b_{1p}^{*}\\ \vdots&\ddots&\vdots\\ b_{p1}^{*}&\ldots&b_{pp}^{*}\end{array}\right)=\left(\begin{array}{cccc}b_{ 11}&\cdots&b_{1p}\\ \vdots&\ddots&\vdots\\ b_{p1}&\ldots&b_{pp}\end{array}\right)^{-1} \tag{6}\]  
Figure 2: Generalized disturbance representation for the MIMO nonlinear system.  
The proposed decoupler element used in this scheme is given as [23]:  
\[\left(\begin{array}{c}u_{1}\\ u_{2}\\ \vdots\\ u_{p}\end{array}\right)=B^{-1}\left(\begin{array}{c}u_{1}^{*}\\ u_{2}^{*}\\ \vdots\\ u_{p}^{*}\end{array}\right) \tag{7}\]  
By substituting the decoupler (Equation (7)) element in the system model (Equation (5)), we get  
\(\left(\begin{array}{c}\xi_{1}^{(\gamma_{1})}\\ \xi_{2}^{(\gamma_{2})}\\ \vdots\\ \xi_{p}^{(\gamma_{p})}\end{array}\right)=\left(\begin{array}{c}F_{1}\\ F_{2}\\ \vdots\\ F_{p}\end{array}\right)+BB^{-1}\left(\begin{array}{c}u_{1}^{*}\\ u_{2}^{*}\\ \vdots\\ u_{p}^{*}\end{array}\right)\)  
This leads to the following decoupled model,  
\[\left(\begin{array}{c}\xi_{1}^{(\gamma_{1})}\\ \xi_{2}^{(\gamma_{2})}\\ \vdots\\ \xi_{p}^{(\gamma_{p})}\end{array}\right)=\left(\begin{array}{c}F_{1}\\ F_{2}\\ \vdots\\ F_{p}\end{array}\right)+\left(\begin{
## 3 Control of Thermal Deformation Optimization Strategy
### Thermal Deformation of Satellite Bus  
Typically, the satellite's structure is placed with the communication load and the star sensor load separately. Using this design strategy, we compute the impact of the satellite's thermal deformation on the angle variation between the laser and the star sensor load.  
With the maximum temperature difference obtained by simulation in high and low temperature conditions as input, the satellite finite element simulation model is used to calculate the thermal deformation of the satellite, as shown in Figure 12. The maximum displacement of the satellite during low- and high-temperature fluctuations is 0.689 mm. Both the laser communication load and the star sensor are directly mounted on the +X honeycomb structure. The flatness of the +X honeycomb structure will change under the coupling effect of the whole satellite thermal deformation, and the normal vector of the mounting surface will change due to the change in the flatness of the +X honeycomb structure, resulting in changes in the laser communication load and the pointing of the star sensor.  
The coordinates of the laser communication load and star sensor installation point after deformation are extracted from the finite element model. The plane corresponding to the point coordinates fitted by the least square method is the loading surface of the star sensor and laser communication. The included angle variation between the fitted plane vectors is the one between the star sensor and the laser communication load caused by temperature change. The variation of the normal vector angle variation between the star  
Figure 11: Temperature distribution under high-temperature and low-temperature conditions: (**a**) \(-\)X\(\backslash-\)Y\(\backslash-\)Z structural plate at high-temperature condition; (**b**) +X\(\backslash-\)Y\(\backslash+\)Z structural plate at high-temperature condition; (**c**) \(-\)X\(\backslash-\)Y\(\backslash-\)Z structural plate at low-temperature condition; (**d**) +X\(\backslash-\)Y\(\backslash+\)Z structural plate at low-temperature condition.  
sensor and the laser communication load mounting surface obtained by fitting here is 113.58\".
## 4 On-Site Monitoring Results Analysis
### Depth and Breadth of Blasting Effects  
Because the early monitoring was not automatic, it was impossible to monitor the influence of blasting on the displacement of surrounding rock in this interval before the primary support was applied to the secondary lining. In addition, only the existing data were analyzed. From the depth point of view, the main influence range of blasting was the surrounding rock at a depth of 0-5 m. The displacement fluctuation caused by blasting was negatively correlated with the tunnel face, and the displacement fluctuation caused by blasting was about 25 m away from the tunnel face. Combined with the comprehensive analysis of Figure 26, it was concluded that the blasting shock wave caused transient damage to the surrounding rock, and the NPR anchor cable had good blast resistance and reliability. Due to the suspension effect of the NPR anchor cable, the integrity of the surrounding rock was not seriously affected. Under the combined action of the NPR anchor cable and steel arch, the surrounding rock had a tendency to continue to deform and then stabilize.  
Figure 25: Displacement rate of unit depth surrounding rock with time. (**a**) Left side; (**b**) right side.
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### PHY Technologies for WBANs
#### 4.3.3 HBC Technologies  
Unlike the other two WBAN PHY layer wireless technologies, HBC uses the human body as the communication medium. HBC transceivers enable low-power communication at low frequencies. Previous studies have shown that the optimal operation frequency range for body coupled communications ranges between 10-100 MHz [68; 69]. The lower bound is imposed by propagation losses while the upper bound is determined by the fact that the RF energy is no longer confined to the body but is radiated into the environment. Therefore, HBC signals must be modulated to a carrier lower than 100 MHz, before being capacitively coupled to the body via electrodes on or near the surface of the skin. Recently published works on HBC transceivers [70; 71] have shown energy efficiency in the order of 1 nJ/bit, data rates of 1 Mbps and receiver sensitivity of \(-60\) to \(-70\) dBm.  
The low amount of power radiated outside the body by an HBC transceiver provides a layer of inherent security to the user, allowing spectrum reuse between adjacent users without requiring an explicit cellular structure. A major concern, however, is the interference cancellation due to the presence of high-powered television and radio stations that coexist in the same bandwidth. The authors in [70] adopt a cognitive FSK modulation to avoid frequencies with high interference, whereas the authors in [71] employ a high-input resistance at the receiver to reject offending spectrum above 30 MHz, and a correlation-based scheme to reject signals with low correlation with respect to the expected received signals.  
An overview of the presented related work on PHY layer transmission techniques has been provided in Table 3.
###### Abstract  
The vigorous development of Low Earth Orbit (LEO) satellite constellation programs imposes higher requirements for the accuracy of satellite orbit determination. Significant variations in atmospheric density within the operational region of LEO satellites are primary factors influencing their orbital decay and operational lifespan. This article first summarizes the research advancements in atmospheric density inversion utilizing LEO satellites, comparing and analyzing the principles of various algorithms, factors affecting accuracy, as well as the advantages and disadvantages associated with different acquisition methods. Subsequently, we introduce recent progress in enhancing atmospheric density inversion algorithms and data analysis applications based on LEO satellites. The SWARM-A satellite, equipped with a high-precision GPS receiver and accelerometer, was employed to invert atmospheric density using both semi-long axis attenuation and accelerometer methodologies. The inversion results were compared against empirical models to validate their reliability; specifically, the correlation coefficient between the semi-long axis attenuation method and nrlmsise00 reached 0.9158, while that between the accelerometer method and nrlmsise00 attained 0.9204. Notably, the inversion accuracy achieved by the accelerometer slightly surpasses that of the semi-long axis attenuation method. These findings provide valuable support for predicting large air tightness based on LEO satellite orbit data inversions and for adjusting operational orbits to ensure successful execution of satellite missions.  
Low Earth Orbit (LEO); satellites; atmospheric density; SWARM-A
###### Abstract
However, introducing extra components, including inductors, capacitors, switching devices, and logic gate drivers, results in a more complex hardware circuit, diminishing the overall system's transmission efficiency to 42% at a data rate of 1 Mbps. In an attempt to address these challenges, reference [29] adjusts the resonance capacitance of the Class-E PA to achieve ASK modulation, attaining a power transmission efficiency of 64.6%. Nevertheless, this adjustment of resonance capacitance may elevate current harmonic levels, leading to increased reactive loss. Yet, further power transmission efficiency and data rate improvement are desired and an current research challenge. This paper endeavors to contribute by enhancing the power transmission efficiency and data rates. To do so, first of all, a comprehensive theoretical which highlights low- and high-efficiency regions analysis is presented, providing design insights. Then, leveraging  
Figure 1: Implantable medical devices. (**a**) Cochlear implant. (**b**) Pacemaker. (**c**) Deep brain stimulator. (**d**) Retinal prosthesis. the insights of efficiency regions, an innovative WPDT circuit for IMD is proposed, which achieves ASK modulation with the help of additional MOSFETs and an inductor. The proposed circuit switches between load networks to maintain high transfer efficiency for ASK modulation, enhancing the power transmission efficiency and data rates. The subsequent sections of this paper are structured as follows: In Section 2, the efficiency analysis and derivation of WPT are depicted. Section 3 demonstrates the proposed architecture, including its analysis, calculation, and design procedure. Simulation and experimental results are shown in Section 4. Finally, Section 5 presents the conclusion.