esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 3 PQ Mitigation Techniques
### Traditional Methods
#### Three-Bridge Four Wire (TBFW) Inverter  
It performs as an active filter to mitigate PQ disturbances by providing compensation for current imbalance and current harmonics [28]. It also provides active and reactive power support. A bridge rectifier and a closed-loop boost converter are used to convert the output of the WTG to constant DC voltage. This constant voltage is then inverted to AC using TBFW inverter. It uses a hysteresis current controller to function as an APF and as a conventional inverter in TBFW [31].
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 4 MGBD Scheme and Adaptive MGBD Scheme
### Modified GBD Precoder
#### 4.1.1 Case 1  
When \(r\) is equal to rank of \(\mathbf{H}\)\((L=Kr_{i})\), the MGBD precoder operates like RBD precoder. In this case, all ranks of \(\mathbf{H}\) are consumed to increase data rate. Therefore, the system does not obtain additional diversity gain.
## 2 Discrete Element Modeling and Energy Consumption Calculation
### Discrete Element Modeling of Soil-Rock Mixtures
In this study, the effective contact modulus of soil particles Ecb is 1.24 \(\times\) 10\({}^{8}\) Pa, and the effective contact modulus of loaded plate particles \(E_{cl}\) is 3.31 \(\times\) 10\({}^{9}\) Pa. Tangential stiffness \(k_{\rm s}\) used a normal-to-tangential stiffness ratio \(k^{*}\) = \(k_{\rm n}/k_{\rm s}\) between 1.0 and 1.5 for realistic materials [20; 21]. Here k\({}^{*}\) = 4.0/3.0 was applied. Density was set as 2650 kg/m\({}^{3}\), similar to actual granular geo-materials. Dashpot was utilized to dissipate energy within the particle assembly during preparation and loading processes. Common forms of dashpot include local dashpot, which directly decreases particle velocities, and viscous dashpot, which introduces contact dashpot forces. The local dashpot coefficient, \(D_{\rm p}\), regulates the unbalanced forces, as demonstrated in Equation (4):  
\[F_{l}^{\rm d}=D_{\rm p}\Big{(}\Big{|}F_{l}^{\rm d}\Big{|}\Big{)}\text{sign}(v_ {i}) \tag{4}\]  
where \(D_{\rm p}\) is the local dashpot coefficient; sign is the sign function; and \(v_{i}\) (\(i\) = 1-6) is the linear and angular velocities. Viscous dashpot introduces normal and tangential contact dashpot forces, as shown in Equation (5):  
\[F_{j}^{\rm d}=\Big{(}2\beta_{j}\sqrt{\overline{m}k_{j}}\Big{)}\dot{\delta}_{j} \tag{5}\]  
where \(j\) = n, and s represents the normal and tangential directions of particle contact, respectively; \(\beta_{\rm n}\) = \(\beta_{\rm s}\) = 0.7; and \(\overline{m}\) is the equivalent value of contacting particle mass (\(m_{1}\) and \(m_{2}\)), \(\overline{m}\) = \(m_{1}m_{2}\)/(\(m_{1}\)+\(m_{2}\)). The contact between particles follows [PERSON]'s law of friction, that is \(f_{\rm s}\leq\mu_{\rm b}f_{\rm n}\), where \(f_{\rm n}\) and \(f_{\rm s}\) are normal and tangential contact forces. The static friction coefficient \(\mu_{\rm b}\) = 0.6. Particle shapes were incorporated via a rolling resistance model giving rotational moments, as shown in Equation (6):  
\[M\leq\mu_{\rm r}\overline{R}f_{\rm n} \tag{6}\]  
where \(\mu_{\rm r}\) = 0.25 is the rolling friction and \(\overline{R}\) is the effective particle radius. Key meso-parameters are listed in Figure 2.0 Coarse to fine particle diameter ratio \(d_{\rm c}/d_{\rm f}\) was 5.0. Previous studies have indicated that mechanical behavior remains relatively unchanged when the ratio exceeds 4.44, which corresponds to the situation where fine particles fill the voids between coarse particles [17; 22]. Therefore, variations in particle size scales did not significantly influence the objectives of the current research.
## 2 Study Area and Data  
### Study Area  
The study was carried in south-central Chinese province in Hunan, which is characterized by hilly topography with the elevation ranging from 0 m to 2100 m above the WGS84 ellipsoid, and bounded between 24 deg38' N and 30 deg08' N latitudes and 108 deg47\" E and 114 deg15' E longitudes. The total area of Hunan province is approximately 2,118,000 km2. Figure 1a,b display the corresponding topography and land cover map over the study area, respectively. In addition, this study area contains a highly diverse land cover types and ecosystems due to the complex terrains and weather. Under the continental subtropical monsoon climate, dense vegetation significantly decreases the DEM accuracy [3; 30].
## 1 Indoor Positioning for Smartphones Using Asynchronous Ultrasound Trilateration  
**[PERSON] *, [PERSON] and [PERSON]**  
Digital Media Centre, Dublin Institute of Technology, Dublin 2, Ireland;  
E-Mails: [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (J.D.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +353-1-402-3264; Fax: +353-1-402-3269.  
_Received: 3 May 2013; in revised form: 6 June 2013 / Accepted: 20 June 2013 / Published: 27 June 2013_  
**Abstract:** Modern smartphones are a great platform for Location Based Services (LBS). While outdoor LBS for smartphones has proven to be very successful, indoor LBS for smartphones has not yet fully developed due to the lack of an accurate positioning technology. In this paper we present an accurate indoor positioning approach for commercial off-the-shelf (COTS) smartphones that uses the innate ability of mobile phones to produce ultrasound, combined with Time-Difference-of-Arrival (TDOA) asynchronous trilateration. We evaluate our indoor positioning approach by describing its strengths and weaknesses, and determine its absolute accuracy. This is accomplished through a range of experiments that involve variables such as position of control point microphones, position of phone within the room, direction speaker is facing and presence of user in the signal path. Test results show that our Lok8 (locate) mobile positioning system can achieve accuracies better than 10 cm in a real-world environment.  
**Keywords:** mobile indoor positioning; ultrasound; LBS; least squares trilateration; TDOA
###### Abstract  
This paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues and whose constraints involve technical limitations of both network and distributed resources. Emphasis is given to the power quality issue with particular attention to unbalance reduction and voltage profile improvement. Technological issues, such as those related to the preservation of batteries' lifetime, were also taken into account. The planning problem is solved by means of a genetic algorithm which includes an inner algorithm based on sequential quadratic programming. In order to limit the processing time while maintaining reasonable accuracy, the genetic algorithm search space is significantly reduced identifying a subset of candidate buses for the siting of the storage resources. The Inherent Structure Theory of Networks and the Loading Constraints Criterion were used to identify the candidate buses. The proposed method has been applied to a low voltage test network demonstrating the effectiveness of the procedure in terms of computational burden while also preserving the accuracy of the solution.  
distributed energy storage systems; optimization method; genetic algorithm; distribution networks +
Footnote †: journal: Applied Sciences
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 3 Experiments and Results
### Positioning Quality Degradation over Time
#### 3.3.1 Accuracy of the Real-Time PPP-B2b Positioning
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **Height (km)** \\ \hline BJ01 & 40.07 & 116.27 & 0.09 \\ JFNG & 30.52 & 114.49 & 0.07 \\ HKSC & 22.32 & 114.14 & 0.02 \\ KSKT & 39.50 & 75.93 & 1.27 \\ MSSA & 36.14 & 138.35 & 1.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Locations of the five stations selected in the simulated kinematic PPP-B2b experiment.
###### Abstract  
With the high availability of low-cost and energy-efficient LEDs and cameras, there is increased interest in optical camera communication (OCC) to provide nonradio-frequency-based communication solutions in the domains of advertisement, vehicular communication, and the Internet of Things (IoT). As per the IEEE 802.15.7-2018 standard, new physical-layer clauses support low-frame-rate camera communication with allowable flickering. This paper proposes an OCC system that can provide user-centric multiple-input multiple-output (MIMO) loosely based on quantum-chromodynamics (QCD) concepts. A QCD-OCC simulator and prototype are proposed, implemented, and evaluated on the basis of the pixel intensity profile, peak signal-to-noise ratio (PSNR), the success of reception (%), bit-error rate (BER), and throughput under different ambient lighting conditions and distances. We observed 100% and 84% success of reception using the proposed prototype and simulator, respectively, for the data rate of 720 bps. The maximal tolerable BER of \(1.13\times 10^{-2}\) for IoT applications was observed at a maximal distance of 200 cm and a maximal data rate of 3600 bps. The proposed system was also compared with other existing OCC systems with similar hardware and implementation requirements. The proposed QCD-OCC system provided rotation support up to 90 degrees and throughput of 4.32 kbps for a 30 fps camera.  
visible light communication; optical camera communication; multiple-input multiple-output; quantum chromodynamics; Internet of Things; light-emitting diodes Article  
Quantum-Chromodynamics-Inspired 2D Multicolor LED Matrix to Camera  
MIMO. _Appl. Sci._**2022**, 12, 10204. [[https://doi.org/10.3390/app122010204](https://doi.org/10.3390/app122010204)]([https://doi.org/10.3390/app122010204](https://doi.org/10.3390/app122010204))  
Academic Editor: [PERSON]  
Received: 10 August 2022  
Accepted: 5 October 2022  
Published: 11 October 2022
## 3 Method
### Types of Analyses
#### 3.3.1 Linear Static Analysis  
Linear static analysis is a common solver in Strand7, which analyses the structure based on the following two key assumptions:  
1. The response of the structure is linear.
2. The loading on the structure is static.  
This implies that the structure's mechanical properties must follow Hooke's law. Moreover, the deformation of the structure must be significantly small and proportionate to its undeformed state [45]. The linear static solver is performed by considering the stiffness, nodal displacement, and force for each element in the discretised structure. Through an assembly process, the individual element matrices are then assembled to form the global matrix of the system (Equation (1)).  
\[[K]\{d\}=\{F\} \tag{1}\]  
The global matrix links the global nodal force vector, \(\{F\}\), to the product of the global stiffness matrix, \([K]\), and unknown nodal displacement vectors, \(\{d\}\). This enables finding the stresses, strains and reactions on the nodes and elements.  
Linear static solver is a widely used solution to analyse the structures and was also used in this paper. However, the assumption of analysing the undeformed state and having a perfectly linear relationship between stress and strain is not always valid. This introduces another type of analysis called the non-linear static analysis, which is out of the scope of this paper due to its negligible difference from the linear static analysis.
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 4 Models for Differentiating T1D from T2D  
Differentiating T1D from T2D remains a challenging task, as the two conditions share numerous overlapping characteristics. Traits such as obesity, insulin resistance, and metabolic syndrome are strongly associated with T2D; however, evidence suggests that beta-cell dysfunction may begin even before impaired glucose tolerance is observed [55]. Patients with T2D typically present with elevated C-peptide levels, the absence of autoantibodies, a strong family history of diabetes, obesity, and markers of insulin resistance, such as hypertension, acanthosis nigricans, or other related features. Understanding the underlying mechanisms of diabetes and testing for its causes are crucial in guiding treatment strategies tailored to the individual needs of patients.  
To address this diagnostic challenge, [PERSON] et al. [77] developed a method for classifying insulin-treated diabetes types using two datasets: the UK Biobank (UKBB) and the Diabetes Alliance for Research in England (DARE). In the UKBB, the proportions of T1D were estimated using a genetic risk score specifically designed for T1D, though it is important to note that genetic predisposition does not entirely rule out the possibility of T2D. In contrast, the DARE dataset defined T1D as severe insulin deficiency and T2D as insulin-treated diabetes. The combination of these datasets resulted in a method that demonstrated higher accuracy, particularly in patients under 20 years of age. While this approach showed promise, further research and optimization are recommended, particularly through the inclusion of additional biomarkers to improve diagnostic precision.  
[PERSON] et al. [78] also contributed to the effort of differentiating between T1D and T2D by developing an algorithm that uses key characteristics of both types of diabetes. These characteristics include diagnostic parameters, age of onset, and prescribed medications. The algorithm facilitates the classification of diabetes types, while enabling the analysis of important epidemiological indicators and the prevalence of comorbidities. The authors recommended integrating this algorithm into future diabetes surveillance programs to enhance the understanding, monitoring, and management of this condition.  
Together, these studies highlight the importance of precise classification methods in diabetes care. Accurate differentiation between T1D and T2D is essential for ensuring appropriate treatment plans and for advancing research into diabetes-related complications [79]. Further development of predictive models, incorporation of novel biomarkers, and refinement of diagnostic tools are necessary to improve differentiation strategies and ultimately enhance clinical outcomes for patients with diabetes.
# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
## 6 Conclusions and Further Work  
This work presented a passive human counting architecture based on Bluetooth Low Energy (BLE) and a deep neural network model capable of analyzing signal deformations caused by human presence to detect and count occupants in closed indoor environments. Unlike other approaches in the literature, the proposed model is pre-trained on a large dataset and then fine-tuned using a significantly smaller and potentially incomplete dataset acquired directly in the target environment. Experimental results demonstrate that this solution is not only more accurate than state-of-the-art techniques, obtaining a remarkable 99.13% accuracy value with a fine-tuning on only 350 samples, but also easier to deploy and calibrate, requiring only a few minutes for setup. We believe that this approach can be especially valuable in critical and privacy-sensitive environments such as laboratories, clinics, and hospitals, where camera-based monitoring may be inappropriate or restricted.  
The main limitation of the proposed approach is the relatively poor ability to generalize to unseen environments. The principal cause for the need for fine-tuning is the lack of explicit information regarding the structure and geometry of the monitored area, so the model has to learn such information from data. When the monitored area changes, the model needs some form of retraining (for example, fine-tuning) to re-learn such structure.  
Future developments of this research will focus on building a generalizable model that eliminates the need for fine-tuning, enabling immediate, privacy-preserving deployment across a wide range of indoor contexts.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15116142/s1](https://www.mdpi.com/article/10.3390/app15116142/s1)]([https://www.mdpi.com/article/10.3390/app15116142/s1](https://www.mdpi.com/article/10.3390/app15116142/s1)).  
Conceptualization, methodology, investigation, formal analysis, writing--original draft preparation, writing--review and editing, G.I. and L.L.B; investigation, data curation, writing--review and editing, A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AAS & Atomic absorption spectroscopy \\ AES & Atomic emission spectroscopy \\ Bipy & 4.4\({}^{\prime}\)-Bipyridine \\ CD & Cyclodextrin \\ \end{tabular}  
CNT carbon nano-tubes CP-MASS Cross-polarization magic angle spinning spectroscopy CRGO Chemically-reduced graphene oxide CS Chitosan CuAAC Copper-catalyzed azide-alkyne cycloaddition CuNPs Copper nanoparticles DMF \(N\),\(N\)-Dimethylformamide DMSO Dimethylsulfoxide DVB divinylbenzene EDACell Ethylenediamine-modified cellulose EDS Energy-dispersive (X-ray) spectroscopy FT-IR Fourier-transform infra-red spectroscopy GO Graphene oxide Himdc 4,5-Imidazole-ide-ide-carboxylate HPA Heteropolyacid ICP Inductively-coupled plasma LDH Layered double hydroxide M-dots Melanine nanoparticles MOF Metal-organic framework MS Mass spectrometry MW Microwave, molecular weight NHC \(N\)-Heterocyclic carbene OES Optical emission spectroscopy pABA Poly(2-aminobenzcoic acid) PANI Polyaniline PIB Poly(isobutylene) PEG Poly(ethylene glycol), poly(ethylene oxide) Pim Poly(vinyl imidazole) p[C4R-NH2] amine-modified calix[4]resorcinarene-formaldehyde resin P[im/IL] Imidazole-ionic liquid copolymer PMMA Poly(methyl methacrylate) POM Polyoxometalate ppm, ppb parts per million, parts per bilion PTFE Poly(tetrafluoroethylene) PVA Poly(vinyl alcohol) PVP Poly(N-vinyl pyrrolidone) PVP Ph Poly(4-vinylphenol) rat Room temperature SEM Scanning electron microscopy SMI Poly(styrene-co-maleic anhydride) imide SNW Schiff-base network polymer TBTA Tris[(1-benzyl-1,2,3-triazole-4-yl)methyl]amine TEM Transmission electron microscopy TGA Thermogravimetric analysis THF Tetrahydrofuran TOF Turnover frequency TON Turnover number UV Ultraviolet spectroscopy XPS X-ray photoelectron spectroscopy XRD X-ray (powder) diffraction analysis XRF X-ray fluorescence analysis
# Surface Quality of Wood Plastic Composites as Function of Water Exposure
## 1 Introduction
Having different types of adhesives and hydrophobic chemicals in the manufacture of wood-based composites creates a barrier against moisture and this is more enhanced in the case of WPCs. It is expected that WPCs should have an excellent resistance against moisture exposure, which keeps the surface quality and dimensional stability in the initial condition. However, having almost 50% wood in a typical WPC could still create a certain level of hygroscopicity. Any kind of moisture absorption could easily cause problems in the form of deterioration of the surface of the unit, resulting in irregularities. Such roughness will not only reduce the quality of the appearance of the material, but the restoration and maintenance of the surface will also be more difficult and costly. Therefore, if the surface quality of WPCs is numerically quantified as a function of moisture content or outdoor exposure, any product made from WPC can be used more efficiently by extending its service life. The quantification of the surface quality of wood-based panels has been studied for many years but there is still no well-accepted method [22]. The stylus method is relatively accurate, practical, and easy compared to other approaches, such as pneumatic, light sectioning, and laser line methods [22; 23]. The stylus method was employed to determine the surface quality of different wood-based panels. In one of those studies, it was found that the stylus method can distinguish any irregularities on flooring material manufactured from high-density fiberboard as a function of water exposure [24]. The surface roughness of both commercially and experimentally manufactured particleboard and medium-density fiberboard were evaluated using the stylus technique [25]. Additionally, the surface roughness of overlaid particleboard samples exposed to relative humidity cycles was investigated [22]. Overall, it was found that stylus-type equipment, resulting in the accepted standard roughness parameters, namely average roughness (R\({}_{\text{a}}\)) and mean peak-to-valley (R\({}_{\text{z}}\)), can be employed to determine any irregularities on the surface of such products. Although the mechanical properties of WPCs manufactured from eastern redcedar have been studied, there is a very little information on the surface quality of such experimental samples as a function of water exposure. Therefore, the objective of this work was to quantify the surface roughness of WPC samples made from eastern redcedar as a function of water exposure by employing a stylus method as a quality control tool. The data found in this work could be beneficial for WPC manufacturers to understand better the overall hygroscopic behavior of WPCs.
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
## 3 Backstepping-Based Quadrotor Formation Control
### Leader-Follower Formation Control  
The formation control strategy used in this paper is as follows: first, the leader tracks the predefined trajectory, and the followers follow the leader with a certain distance from the leader. In the altitude controller of the leader, an altitude control input is created to track the reference altitude. Next, in the \(x,y\) position controller, using the designed altitude control input, the reference roll and pitch angles are calculated for the leader to track the predefined \(x,y\) position trajectory. The leader can track the predefined trajectory through the attitude tracking control with the previously calculated reference roll and pitch angles. On the other hand, the followers have a control scheme similar to the leader's trajectory tracking control. Instead of the predefined trajectory given to the leader, the \(x,y\) and \(z\) positions of the leader and the set relative distance \(d_{i,(x,y,z)}\) between the followers and leader are used for the formation control of the followers. The overall proposed formation control system block diagram is shown in Figure 2. In this paper, the same backstepping-based control strategy can be used for both the leader and the followers.  
A leader-follower structure example consisting of three quadrotors is shown in Figure 3. Here, the position and attitude angles of each quadrotor are represented by \((x,y,z,\phi,\theta,\psi)\), where the subscript \(l\) denotes the leader and \(f\) denotes a follower. The formation configuration is specified by the desired vector \(d_{i,(x,y,z)}\) between the leader and a follower. As shown in Figure 3, each follower maintains the formation separated by the relative distance \(d_{1,(x,y,z)}\), \(d_{2,(x,y,z)}\) from the leader quadrotor.  
Figure 2: The proposed formation control system block diagram.