esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### RF Coil Design and Construction  
A modified version of an elliptical birdcage (named split-type elliptical birdcage) with 16 legs was constructed for the 1.5 T (63.87 MHz) MRI system with the following dimensions: major axis, 62 cm; minor axis, 58 cm; length, 60 cm. The legs and the end-rings were prepared using copper strips with a thickness of 0.15 mm and widths of 3.5 cm and 5  cm, respectively. Four split legs were placed such that the distance between the neighboring leg and the gap between the leg splitting were identical, and the width of the split legs was 1.75 cm. Acryl was used to fabricate an RF coil supporting frame, which comprised a 7.5 mm-thick elliptical surface (major axis, 62 cm; minor axis, 58 cm; length, 100 cm) to place the RF coil. High quality factor (Q) and high RF power capacitors (DLC70E, Dalicap Tech. Corporation, Dalian, China) were attached at the end-ring to tune the RF coil to the desired resonance mode. Instead of using a single-valued capacitor, the three parallel capacitors were used to ensure no capacitor breakdown due to high RF power. The tuning and the detuning of the RF coil operating frequency was controlled using an MRI controller by supplying forward and reverse DC bias voltages in the detuning circuit [38].  
The detuning circuit utilized at each leg comprised a high-voltage pin-diode (MA4P4000 Series, MACOM Technology Solutions, Lowell, MA, USA) with a parallel resistor to ensure equal bias currents (4.9 k\(\Omega\) CMF65, Vishay Intertechnology, Malvern, PA, USA) in a series network with RF chokes (4611-RC, BOURNS, Riverside, CA, USA) to obstruct high RF power. To prevent the flow of common-mode currents on the shield of the coaxial cable [39], shielded coaxial cable traps or ground breakers (design process is described in Supplementary Materials Section C) were attached after the matching circuit followed by a high-power coaxial cable (RG-214). The scattering (S) parameters and the Q measurement (at 3 dB bandwidth) using two weakly coupled sniffer probes placed across the RF coil were measured for those unloaded and loaded with light (1 g NaCl per 1000 mL of distilled water) and heavy (6 g NaCl per 1000 mL of distilled water) phantom loads inside the MRI bore using a network analyzer (E5063A/N9913A, Keysight Technologies, Santa Rosa, CA, USA). After tuning and matching, they were connected to the two-port hybrid (90\({}^{\circ}\)) coupler through a splitter/combiner, as shown in the schematic illustration of the RF coil (Figure 2). Figure 2 shows the asymmetric leg distribution in the elliptic circumference, the unfolded view of the proposed split-type elliptical birdcage, the constructed prototype, and the schematic illustration of transmission and reception (TX-RX) implementation of a four-port birdcage RF coil through a two-port hybrid coupler TX/RX system.  
Figure 1: The simulated design for different circular and elliptical birdcage RF coils in perspective view. The diameters of each coil are written in the bracket. All the RF coils had identical lengths of 60 cm and identical RF shields with diameters of 64 cm and lengths of 100 cm.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 4 UTM System Architecture
### U-Space Service Manager  
The U-space Service Manager is a key module in the UTM system, as it provides an interface with the rest of the actors in the U-space ecosystem, i.e., the UAS operators and auxiliary stakeholders like the airspace authorities, the fire-fighters or the police.  
Figure 4: Overview of the proposed UTM system architecture. This system would be running in a remote server named U-space Service Provider Platform. The red arrows indicate remote communication links with other machines in the ecosystem.  
First, the USM receives positioning measurements from the control station of each UAS, which is transmitted by their onboard telemetry and ADS-B transceivers (if available). This information is forwarded to the Tracking module in order to keep updated a list of tracks for all the operational UAS. Second, the USM receives warning information that may be relevant for the UTM system, coming from external stakeholders (e.g., a declaration of a wildfire by the fire-fighters) or from the UAS (e.g., the detection of a jamming attack or a technical failure due to a lack of power). A jamming attack consists of an attempt to jeopardize the GNSS (_Global Navigation Satellite System_) signal of a UAS. These previous events are treated as possible threats by the system and are forwarded to the EM, which is in charge of processing them. Last, the USM communicates back to the UAS operators any action determined by the EM (e.g., an immediate landing or an alternative flight plan). Due to regulatory restrictions, the actions involving the variation of a UAS flight plan are just recommendations that must be confirmed or rejected by the corresponding pilot. In case of acceptance, the USM would notify the DB to update the state of that operation and its flight plan.
## 3 Method  
In this section, we present a novel permutation-based method for group feature ranking and selection. The method involves several key steps. First, the datasets that were used for this study have high dimensionality. In each dataset, many features were correlated or relevant to each other and had a common effect on the target variable. Therefore, the relevant features formed groups or clusters. We used the \"agglomerative hierarchical clustering\" technique to find these groups in the high-dimensional datasets. This method helped us to find similar features, so that we could observe the patterns more clearly. Despite splitting datasets into groups, the dimensionality within each group remained high. Hence, we applied the lasso algorithm to eliminate irrelevant and redundant individual features, ensuring that only the most informative features were retained for further analysis.  
Next, we proposed a new method for calculating the permutation importance of each feature group. We obtained an importance score for each group feature by permuting the values of each group feature and measuring the resulting impact on model performance. Based on these permutation importance scores, we ranked the group features in terms of their significance and selected a subset of highly ranked group features for subsequent analyses. Figure 2 provides a visual representation of the overall procedure, illustrating the sequential steps involved in our permutation-based method for group feature ranking and selection.  
Figure 2: The framework of this study.  
The existing group feature selection methods do not provide the relative importance of all group features. However, we are interested in ranking the group features according to their importance. Therefore, we proposed a new permutation importance method to determine the relative importance of all the group features. The permutation-based method allowed us to assess the significance and relative importance of each group feature. A few highly ranked group features were selected for further analysis after assessing the relative importance of all the group features. This approach enhances the interpretability of the results and improves the efficiency of data analysis, particularly for HDLSS datasets, where exhaustive analysis may be computationally expensive. Permutation-based methods are well known for their robustness and consistency [45]. We obtained a more reliable estimate of the group feature importance by considering multiple permutations of the group feature values, thereby reducing the influence of random fluctuations and improving the ranking robustness.
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 5 Experimental Results
### Experiments with Sudden Changes in Irradiance  
Next, the performance of the P&O and SMC controllers when under sudden changes in irradiance was evaluated. In principle, the time step limitation of the LoRa technology will reduce the responsiveness of the MPPT. We conducted experiments on partly cloudy days to analyze this issue.  
The graphical results of the experiment with the P&O MPPT controller are shown in Figure 22. The temperature remained constant at 62 \({}^{\circ}\)C. However, the PV module was shaded by a cloud in the time interval from 15 s to 35 s, changing the irradiance value from 1100 W/m\({}^{2}\) to 500 W/m\({}^{2}\) and then back to 1100 W/m\({}^{2}\). It can be observed that the decrease in irradiance had an immediate effect on the power by decreasing it. However, after the cloud was gone and the irradiance went up, between approximately 25 s and 35 s, the P&O controller did not respond quickly to operate at the MPP. On the contrary, it misinterpreted the irradiance and power increase and it kept decreasing the duty cycle, such that the PV module worked out of the MPP for the new conditions. This occurred because the P&O algorithm detected an increment in the PV power that was not caused by the controller's previous action but rather by the irradiance increment itself, which caused the controller to take the wrong action and keep reducing the duty cycle, thus confirming the MPP drifting problem explained in Section 2.1. In summary, the limitation in the time step caused a wrong performance during a brief period of time, reducing the power extraction from the PV panel. The rest of the experiment proceeded normally and the MPP was reached again, as expected.  
The results of the experiment for the SMC controller under abrupt variations in irradiance are depicted in Figure 23. In this case, a clear interval took place amidst an otherwise cloudy and cooler day. The temperature stayed constant at 41 \({}^{\circ}\)C throughout the duration of the experiment. The irradiance started at 500 W/m\({}^{2}\) and it then rapidly  
Figure 22: The experiment results of the sudden irradiance change conditions for the P&O controller. **(a)** Cell temperature. **(b)** Solar irradiance. **(c)** PV power. **(d)** Duty cycle.  
increased to 1100 W/m\({}^{2}\) and later to 1200 W/m\({}^{2}\) in just a few seconds. From that moment on, the irradiance decreased rather abruptly at first and then smoothly to 500 W/m\({}^{2}\) again. During the first part of the rise, the controller was able to adjust correctly, as seen in the smooth rise and fall intervals. Later, the same effect took place as had happened in the P&O since, in this case, the measured voltage did not change due to the actions of the controller but due to the change in the irradiance itself. Therefore, the controller was not able to take the correct control action until after a delay of about 8 s.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 2 Background and Algorithm Overview  
### Brief History of GNSS-R Remote Sensing  
Using GNSS signals in a bistatic scatterometer-like configuration was first proposed by [PERSON] and [PERSON] in 1988 [3]. Subsequently, the PARIS altimetry concept was put forward by [PERSON] in 1993 [4]. Several years later, the first demonstration of wind sensing using GPS reflections was carried out by [PERSON], [PERSON] and [PERSON] in 1998 from an aircraft instrument [5]. Additionally, several other researchers have performed experiments from near Earth platforms performing wind or wave sensing: [6; 7; 8; 9; 10; 11]. Together, this group of researchers established that GPS signals could be used to sense the near surface ocean wind conditions. In parallel, several researchers were performing GNSS altimetry studies. Importantly, [PERSON] et al. [12] detected a reflected signal from the SIR-C experiment carried on the Space Shuttle. This was followed by the GNSS experiment carried on the UK-DMC satellite which repeatedly detected signals from ocean, land and ice surfaces [13]. The UK-DMC experiment was used to demonstrate the feasibility of estimating ocean surface wind speed from space [14; 15]. This was followed by the TechDemoSat (TDS) on-orbit demonstration [16] mission, and subsequently the NASA CYGNSS eight satellite constellation [17]. These results demonstrated that GNSS signals are easily detectable from a space platform, with the signals clearly responding to surface conditions. A summary of the GNSS-R technique and its applications can be found in [18; 19; 20].
# Low Temperature Microcalorimeters for Decay Energy Spectroscopy
## 3 History
### Low Energy DES Measurements
For NuMECS, the \({}^{163}\)Ho in a HCl solution, was dried onto a gold foil or within a nanoporous gold layer [35]. The former underwent the same mechanical kneading process as had prior samples. Despite this, poor resolution and different pulse shape from the electroplated \({}^{55}\)Fe sample within the same absorber prompted the team to pursue a Au nanofoam absorber. The nanofoam's small pore sizes (50-100 nm) limit the size of crystalline structures formed during the deposition of material. A second step of annealing in forming gas to remove impurities was used to improve the source-absorber matrix. These two steps greatly improved the observed spectral resolution to 35 eV FWHM at 2 keV (resolving power of 57), but the resolution still appeared to be limited by energy thermalization differences within the absorber. HOLMES is also pursuing the implantation route using a custom ion implanter with mass separator. This has yet to be demonstrated, but the idea is that with the capability of simultaneous gold evaporation, both initial and final layers of gold to encapsulate the \({}^{163}\)Ho can be laid down within the same instrument and gold can be evaporated simultaneously as the ion implantation to dilute the \({}^{163}\)Ho concentration [36]. ECHo is pursuing laser ion implantation within a Au absorber layer and then final encapsulation with a Au layer [37; 38]. While stating that energy resolution was not degraded from the ion implantation technique, ECHo still reports energy resolutions worse than those of external \({}^{55}\)Fe X-ray measurements [39; 40]. This is potentially due to lattice damage effects, increase in heat capacity due to the implantation process, or unequal operating parameters described in [39]. Subsequent measurements show that the spectral resolution is more than a factor of two greater than that expected from the detector's thermodynamic properties and the readout noise [41]. In coincidence with the neutrino mass measurement themes, the importance of understanding the weak decays and the atomic and molecular effects on these transitions was realized. As a result measurements were made on \({}^{14}\)C, \({}^{151}\)Sm, \({}^{99}\)Tc, and \({}^{36}\)Cl as part of the MetroBeta project exploring the beta shape [42; 43]. At Los Alamos National Laboratory, measurements of \({}^{193}\)Pt were done to understand the atomic effects on electron capture [44]. The European collaboration, MetroMMC was created to explore electron capture [45]. At Lawrence Livermore National Laboratory, the \({}^{7}\)Be measurements first performed in 2002 with Si:P:B thermistors [17] were revisited in 2020 with Superconducting Tunnel Junctions [46; 47], adding to the body of literature on atomic effects on nuclear weak decay.
## 3 Context-Aware Positioning Model
### Context-Aware Positioning  
The third stage of the model performs the positioning estimation, which is conducted based on the recommendations assembled during the management of contextual information stage. When required, the model will attempt to perform positioning using the recommended candidate strategies, according to the contextual information and priorities included in the summary. Two tasks are performed during this stage: (1) request self-positioning, and (2) perform collaborative positioning. These tasks are explained in the next subsections.
## 3 Void Avoidance Algorithm (VAA)
### Tag Distance Upgrading Algorithm  
If the node is still a concave node after upgrading its VD and/or cannot upgrade its VD further (the condition of VD(n) = MaxVD(F(n)) -1), it upgrades its TD\({}_{1}\) to MaxTD\({}_{1}\)(F(n))-1, i.e it chooses the value just under that of maximum TD\({}_{1}\) of neighbors. If no neighbor has a TD\({}_{1}\) value greater than  
Figure 9: Upgrading virtual distance by concave node.  
NULL, it changes its TD\({}_{1}\) value from NULL to 0 and reverses the link (remember that NULL is smaller than any numerical value in our algorithm).  
Figure 10 illustrates the TD upgrading methods. In Figure 10(a), node n is a D(n) node so TD\({}_{1}\) sets its value from NULL to 0. Node n\({}_{2}\) is a V(n) in Figure 10 (c), so it upgrades its VD to just under that of node n\({}_{1}\)'s VD, i.e. 69. After upgrading VD from 50 to 69, node n\({}_{2}\) is still V(n), so it upgrades its TD\({}_{1}\) as the maximum TD\({}_{1}\) of neighbors TD\({}_{1}-1\); i.e. TD\({}_{1}\) of node n -1 and reverses the link direction. Finally, node n and n\({}_{2}\) are no longer stuck nodes.  
If the node is still a stuck node after upgrading its TD\({}_{1}\), it upgrades its TD\({}_{2}\) to MaxTD\({}_{2}\)(F(n))-1, i.e. it chooses the value just under that of the maximum TD\({}_{2}\) of neighbors. If no neighbor has a TD\({}_{2}\) value greater than NULL, it changes its TD\({}_{2}\) value from NULL to 0 and reverses the link. Whenever a node  
Figure 11: Route by GPSR.  
Figure 12: Route after implementing VAA.  
n finds itself as a dead end node, it changes its TD value from NULL to 0, reverses links and sends update information to neighbors. As shown in Figure 11, GPSR starts perimeter routing after encountering a stuck node and follows the long route towards BS using the right hand rule. VAA replaces the right hand rule and forms the network topology shown in Figure 12.
## 2 Detailed Procedure for Fault Diagnosis
### Diagnosis System Definition and Sensor Data Acquisition  
For fault diagnostics of the plant equipment, its sensor data are required that show the characteristics of fault conditions and normal operation conditions occurring in the plant equipment. In order to analyze the data generated by the target equipment, a unit group of the sensors should be set, a list of sensors to be used for analysis should be set, and signals of each sensor should be acquired. This is a very important concept when building a diagnostic system (i.e., defining a diagnosis system) because it is directly related to the maintenance strategy. For example, let us consider an example of building a fault diagnosis system for two pieces of equipment with pressure (P), temperature (T), and flow (F) sensors attached in parallel, as shown in Figure 3. In such a case, it may be difficult to select a sensor list to determine the type of failure, and it may be necessary to manage unnecessary data that are not related to the failure diagnostics. In this case, it may be efficient to configure and manage one unit according to the operating conditions. Therefore, a list of sensors that can represent the status of the target system should be appropriately selected, excluding the unnecessary ones among the sensors included in the equipment. To this end, in this study, the Pearson correlation coefficient between each sensor signal is calculated by Equation (1) to classify the sensor list having a significant correlation; only signals of the target sensors are acquired.  
\[\rho_{x,y}=\frac{cov(x,y)}{\sigma_{x}\sigma_{y}} \tag{1}\]  
Figure 2: The procedure of fault diagnostics.  
\[cov(x,y)=E\big{[}(x-\mu_{x})\big{(}y-\mu_{y})\big{]} \tag{2}\]  
where, \(\rho_{x,y}\) is the Pearson correlation coefficient of the \(x,y\) signal matrix, and \(\sigma\) and \(\mu\) are the standard deviation and the mean value, respectively. Moreover, \(\text{cov}(x,y)\) is a covariance matrix as shown in Equation (2).
## 1 Introduction
It is believed that by using vibration data alone (which is the focus of this study), it is possible to achieve Levels 1 to 3; however, some authors have stated that vibration-based methodologies alone are inadequate for predicting the remaining service life of a structure (Level 4) without the incorporation of a sophisticated numerical model [30]. The structure of the paper is thus divided into two main sections, with the first section presenting the development of modal-based damage detection methodologies through the years and discussing their challenges and limitations. The second section is further sub-divided in order to deal with each identified issue individually and to discuss potential remedies that have been recently proposed in the literature.
## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Resilience  
Ecosystem resilience means an ecosystem's ability to remain in its current state and return to said state from stress [98], mainly consisting of natural climate change, wildfire, and anthropological activities, such as grazing and prescribed fire. Resilience at a given time may be assessed based on a ratio of a given ecosystem health indicator, such as aboveground biomass, measured between the post- and the pre-disturbance [125]. Remote sensing with a capability for ecosystem health indicator retrieval also provides an opportunity to estimate ecosystem resilience to disturbances. However, such remote sensing data should be able to be frequently acquired in a long time series to cover the regeneration time.  
NDVI data have been widely used to evaluate ecosystem resilience to climate change (e.g., [126, 127]), fire (e.g., [128, 129, 130]), and grazing (e.g., [131, 132, 133]), although other vegetation indices (e.g., Adjusted Transformed Soil-Adjusted Vegetation Index, ATSAVI) were also frequently used. Considering the requirement on time-series data, Landsat MSS, TM, ETM+, and OLI, MODIS, AVHRR, and SPOT-VEG NDVI data are normally options for resilience estimation. Nevertheless, using those NDVI data for evaluating resilience, precautions should be made to minimize the effects of seasonal and inter-annual variations of phenology and climate [128]. To minimize such effects, the quotient NDVI, the average NDVI measurements in the disturbed area divided by the average NDVI measurements in the surrounding undisturbed area, was calculated for resilience evaluation [128]. However, the surrounding undisturbed area should have similar vegetation, topography, and geology to the disturbed area [128]. In certain instances, although the surrounding undisturbed area can be a good reference, it is difficult to find suitable benchmarks [134]. Thus, a dynamic reference-cover method was proposed to separate grazing and rainfall effects in rangelands using remote sensing imagery [134].
## Nomenclature  
### Abbreviations  
A Absorber  
AHE Absorption heat exchanger  
AHT Absorption heat transformer  
AHP Absorption heat pump  
C Condenser  
COP Coefficient of Performance (Heating)  
CT Cooling tower  
DA Double absorption  
DC District Cooling  
DE Double effect  
DH District Heating  
DL Double lift  
E Evaporator  
EER Energy Efficiency Ratio (Cooling)  
EqES Equivalent Electricity Saving  
HE Heat exchanger  
G Generator/desorber  
SDS Step-Down Substation  
SE Single effect  
S Solution heat exchanger  
SUS Step-Up Substation  
_Variables_  
COP Coefficient of Performance (heating)  
Cp Heat Capacity, kJ/kgK  
EER Energy Efficiency Ratio (cooling)  
h Enthalpy, kJ/kg
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 3 Result
### Training ANN Model to Evaluate BCS in the Industry
Additionally, the data in the boundary regions is relatively sparse among the 395 data points used to train the ANN model. This limited data availability restricts the model's ability to effectively learn these patterns, thereby contributing to increased prediction errors. Figure 13 shows the residual distribution histogram for the test data from the ANN model used to predict BCS. The distribution is approximately bell-shaped and centered around zero, suggesting that the model's prediction errors are symmetrically distributed and generally unbiased, though with a slight positive skew. Figure 12: Predicted and actual BCS value distribution of ANN model prediction for the extracted real dataset with 395 data points.