esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Fluorophores possess numerous biomedical applications where they are used as tracers and markers. For instance, they can be linked to DNA branes in devices aimed at detecting the presence of viruses in blood samples. Lab-on-a-chip applications of such techniques present promising applications [1], especially in the times of pandemic, where it offers possibilities for the fast detection of viruses in airports or other public places. They could also be used as markers for authenticating the origin of certain manufactured products and avoiding copies and counterfeits as we propose here. The most significant advantages of fluorimetry are its extremely fast response time, the sensitivity of the excited states to the local environment, and the possibility of incorporating fluorophore in a chip/device and simultaneously measuring many samples for a short time [2].  
This article proposes a new fluorescence technique that exploits the strong light-matter interaction of an embedded nanoprobe within a plasmonic cavity. The signature of strong light-matter interactions between nanoprobes and cavity modes is typically detected through changes in the excitation spectrum (electronic or vibrational) of the coupled system [3, 4, 5, 6, 7, 8].  
When strong-coupling occurs, the isolated excited emitter is no longer an eigenstate of the system but corresponds to a superposition of the higher and lower polaritons, which evolve at different frequencies, leading to coherent oscillations of the excitation between the emitter and the cavity mode. On account of these vacuum Rabi oscillations, the emitter cycles through absorption and emission instead of exhibiting the more familiar exponential decay process. Amplification of the signal in a resonant cavity therefore plays an essential role and is accompanied by a specific quantum effect (frequency shift). It presents significant advantages regarding tracing and marking applications:  
* Frequency shifts make it possible to improve the signal-to-noise ratio;
* Frequency splitting constitutes a signature of strong-coupling which is difficult to counterfeit.  
The paper is structured as follows. In Section 2, we outline the basic principles of our proposal, aimed at applying the properties of strongly coupled emitters for authentication and tracing. In Section 3, we summarize the theory of strong-coupling, and we explain the transition from the weak to the strong-coupling regimes in terms of Poincare recurrences. In Section 4, we describe our experimental set-up. In Section 5, we discuss its specific advantages regarding tracing and marking. The Supplementary Material contains details concerning the implementation of our experimental set-up and a theoretical derivation of the spectrum of emission of the cavity in the strong-coupling regime.
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 3 Results
### Visible Cell Stability
However, to gain confidence in the relative synchronization results, it was decided to run additional tests using multiple USRP radios to monitor pairs of cells that were operated by two network service providers simultaneously to test whether the instabilities evidenced above were representative and common in mode to all local cells on the same network or whether they differed between them. The measurements presented in Figure 2b show a discontinuous rate with clear corners in the slope of the curve of phase measurement, which indicates that the clock in the cell was changing or being steered aggressively with long pauses between updates. This behavior represents a clear challenge to the exploitation of the signal for PNT purposes as time-varying error characteristics can frustrate online estimation. However, even if the  
Figure 2: (**a**) This station demonstrates a peak-to-peak one hour deviation of less than 1.25 m, which is equivalent to a time delta of 4.2 nanoseconds; (**b**) this station demonstrates a much higher level of deviation equivalent to more than 1200 ns. other cells operated by the same network service provider are as unstable, they could still be well disciplined to a common clock, thus allowing for relatively low-noise TDOA or pseudo time of arrival (TOA) observable production and navigation techniques, such as those discussed in [25; 26; 27; 28; 29], to be applied with acceptable performance.
## 3 Results  
### Repeatability Study  
A total of 3600 peak forces were recorded across subjects in the three sessions. The median intra-session CV in the dominant and non-dominant leg was approximately 5% and 8% for dorsiflexion and plantarflexion, respectively, in both knee positions (Figure 2, left). The inter-session CV was generally 3-5% higher than that for intra-session (Figure 2, right). The intra- and inter-session CVs calculated for each subject in each condition are reported in the Supplementary Materials.  
The median forces measured by the AFT in the healthy population ranged from 167 to 218 N across dominant and non-dominant legs in both knee postures. The ankle of the dominant leg exhibited significantly greater strength compared to the non-dominant leg regardless of the knee posture or force direction (\(p\) < 0.001) (Table 1). In total, 106 out of 360 comparisons showed larger ankle forces in the dominant leg by more than 10% than in the non-dominant one. Ankle forces were significantly greater in the fully extended knee posture with respect to the flexed knee (\(p\) < 0.001). Overall, dorsiflexion forces were significantly larger across all conditions (\(p\) < 0.001), with the exclusion of the dominant side with a fully extended knee (\(p\) = 0.69).  
Figure 2: Repeatability of ankle force measurements. Box plots showing the 10 th, 25 th, 50 th, 75 th, and 90 th percentiles of the CVs for intra-session (**left**) and inter-session (**right**) repeatability of ankle force data. DL = dominant leg; N-DL = non-dominant leg.
## 4 Experiments and Results
### Results and Analysis
#### 4.2.1 Weather-Influence Coefficient
low cloud base and high echo intensity, high visibility and low echo intensity, moderate visibility and echo intensity, moderate visibility and high echo intensity, low visibility and high echo intensity, and low precipitation and low echo intensity. There were 12 types of bad weather with high precipitation and high echo intensity, for which the weather-influence coefficient could be obtained by calculating the ratio of the numbers of affected flights to planned flights, as listed in Table 2.  
Our approach for optimizing the weather-influence coefficient in the WITI model has overcome the deficiency of only being able to express the influence of thunderstorm weather on aircraft flights. The severity of bad weather has been graded and quantified according to the echo intensity, and so that the weather-influence coefficient can more accurately and clearly reflect the type and influence of the severity of bad weather in the study area.
## 4 Results
### Environmental Pollution Mitigation Analysis  
For its part, Figure 9 shows how the use of passive and active technologies reduces CO\({}_{2}\) equivalent emissions. The monthly results of Lot A are exhibited in Figure 9a. In the case of commercial thermal insulator (Case A-I polyurethane foam) or green roof (Case A-IV), both options are characterized by being mainly thermal barriers that increase resistance to heat flow from radiation. Therefore, their mitigation capacity depends on thickness, which is reflected in the figure where they express an intermediate mitigation level concerning the other cases of thermal restoration. The combinations with reflective paint on the roof (Case A-II and a-III) present the best performance in reducing emissions, with almost identical mitigation potential. In quantitative terms, implementing technologies based on the reflection of solar radiation incidents on the roof can represent mitigations above half a ton of CO\({}_{2}\) equivalent annually, between 719 kg and 725 kg, compared to the baseline.  
Figure 8: Monthly and annual electricity key performance index for the scenarios: (**a**) Lot A: exclusively passive modifications on the envelope; (**b**) Lot B: envelope modification combined with inverter air conditioning systems; (**c**) Lot C: active and passive modifications combined with a reflective coating on the facade.  
Figure 9: Monthly and annual equivalent CO\({}_{2}\) emission index for the scenarios: (**a**) Lot A: exclusively passive modifications on the envelope; (**b**) Lot B: envelope modification combined with inverter air conditioning systems; (**c**) Lot C: active and passive modifications combined with a reflective coating on the facade.  
Regarding Lot B (Figure 9b), implementing the inverter air conditioning dramatically reduces the CEI. In the worst scenario (Case B-I), only inverter technology can mitigate nearly a ton and a half of CO2. Under this same analysis, the integration of reflective paint on the facade or the green roof barely improves the effect achieved by the simple replacement of the active system, where although improvements in the reduction of the CEI are always desirable, it may not be economically justified to invest. For its part, by using the reflective coating on the roof with and without thermal insulation (Case B-III and B-IV, respectively), the mitigation potential can be improved by up to 26%, reaching close to two tons of reduction in CO2 equivalent per year. This implies the impact that selective coating has on buildings in the study area. Finally, the results of Lot C (Figure 9c) show that integrating the facade with selective coating would barely reduce between 169 and 180 kg of CO2 equivalent per year compared to its simile, presented in Figure 9b, which would not represent an attractive option from an investment perspective.
## 2 Materials and Methods
### Methods
#### 2.3.4 Geographical Detector Method  
Geographical detectors are grounded in the measurement of \"factor force\" indicators, integrating spatial overlay techniques and set theory within Geographic Information Systems (GIS) [35]. Factor and interaction detection are widely employed to identify spatial differentiation patterns and the driving forces of variables, serving as effective approaches for spatial heterogeneity analysis [36]. This study employs geographical detectors to examine the factors influencing the spatial heterogeneity of GPP across the Qinghai-Tibet Plateau, while quantitatively assessing the effects of various drivers on GPP variation through factor and interaction detection analyses [37]. By employing the q statistic in geographical detectors, this approach enables robust assessment of spatial heterogeneity, precise identification of key influencing factors, and comprehensive analysis of variable interaction mechanisms [38].  
The differentiation factor serves to quantify both the spatial heterogeneity of independent variables and their respective explanatory power in relation to the spatial variation of the dependent variable. These results are expressed through q-values, with the corresponding calculation formula presented in Equation (5):  
\[q=1-\frac{\sum\limits_{h=1}^{L}N_{h}\sigma_{h}^{2}}{N\sigma^{2}}=1-\frac{W}{T} \tag{5}\]  
\[W=\sum\limits_{h=1}^{L}N_{h}\sigma_{h}^{2},\ T=N\sigma^{2} \tag{6}\]  
In the equation, \(h=1\), \(2\),..., \(L\) denotes the stratification of variable Y or factor X, representing classification or partitioning; N\({}_{\text{h}}\) and N correspond to the number of units in the h-th stratum and the total sample size, respectively. \(\sigma_{h}^{2}\) and \(\sigma^{2}\) represent the variances of Y-values in stratum h and the entire study area, respectively [39]. The q-value ranges between 0 and 1, with higher values indicating greater spatial heterogeneity significance.  
Interaction detection examines the relationships between driving factors, X\({}_{1}\): ET, X\({}_{2}\): LAI, X\({}_{3}\): Precipitation, X\({}_{4}\): Radiation, X\({}_{5}\): Temperature, and X\({}_{6}\): VPD, assessing whether their combined effects on the dependent variable Y (GPP) demonstrate synergistic enhancement, antagonistic weakening, or mutual independence in explanatory power [35; 37].  
Interaction probes are used to explore the interactions between independent variables, i.e., to assess whether the explanatory power for the variable Y is enhanced or diminished when two factors act together, or whether the independent variables are independent of each other.
# Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review
## 3 Resonant Frequency-Based Sensors
Resonant transmission lines in microstrip [127] or coplanar [128; 129] technology have also been proposed, as well as other resonant approaches such as epsilon negative unit-cell [130], planar whispering gallery mode [131] substrate-integrated waveguide [119] or corona shape [132]. The compilation of these approaches along with their significant data and sensitivities is shown in Table 1, ordered by operating resonant frequency and type of sample (aqueous solution or blood). The sensitivities in KHz per mg/dL are given only for the cases in which measurements of physiological concentrations were shown. Data for some references had to be taken from the published plots and must be consequently considered as approximate. The \(RS(G)\) value was computed from the available data for all the references in the table, considering the lowest \(\rho_{\rm g}\) involved in each study for the computation of \(\Delta\rho_{\rm g}\). The \(RS(G)\) column will later enable further analysis and comparisons between various sensing parameter-based sensors. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Sensor** & **Sample** & \multirow{2}{*}{\(\rho_{\rm g}\)**Range** (Volume)**} & \multirow{2}{*}{\(\rho_{\rm g}\)**Range** (wt\%)**} & **Bare\(f_{\rm r}\)** & **Operating\(f_{\rm r}\)** & **S\(f_{\rm r}\)** (GHz per (GHz) & **(kHz per (MHz/dL))** & **RS(G) (\%/\%)** & **Remarks** \\ \hline
[124] & IDC-based resonator & AG (2 \(\mu\)L) & 0–8 & 2.46 & 0.64 & — & 0.556 & Biodegradable flexible substrate \\
[108] & Single & AG (MF) & 0–10 & 1.40 & 1.16 & — & 0.408 & MF channel \\
[125] & IDC SRR & AG (MF) & 0–5 & 4.18 & 1.32 & — & 0.622 & MF channel \\
[122] & Differential & & 0.05–0.3 & 1.74 & 1.44 & 8.9 & 0.510 & Gold-on-glass substrate, MF \\
[91] & Single SRR & AG (90 \(\mu\)L) & 0–50 & 1.83 & 1.49 & — & 0.072 & Enzyme-coated \\
[96] & Single SRR & AG (90 \(\mu\)L) & 0–40 & 1.82 & 1.59 & — & 0.099 & Enzyme-coated \\
[41] & Triple & & & & & & & \\  & CSRR & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Resonant frequency-based sensors. AG = aqueous glucose solutions; (C) SRR = (complementary) split-ring resonator; (C) ELC = (complementary) electric \(LC\) resonator; ENG = epsilon negative; IDC = interdigital capacitor; MLIN = microstrip line; MF = microfluidic; SIW = substrate-integrated waveguide; TX = transmission; WGM = whispering gallery mode. Figure 3: Examples of \(f_{\rm r}\)-based sensors: (a) Single SRR sensor with RF tag sensor concept, reprinted from [121]; (b) Complementary \(LC\) resonator sensor integrating a microfluidic tube, reprinted from [126].
# Acceleration Techniques for Teeth Movements in Extractive Orthodontic Therapy
## 6 Conclusions  
Most orthodontic therapies discussed in the literature seem promising and effective:  
* Techniques like corticotomies and micro-osteo perforations (MOPs) exhibit 1.5 to 2 times faster acceleration than traditional methods.
* Piezoincisions are effective with variable success rates, offering time benefits but potential costs and discomfort.
* Vibrational therapy's impact on tooth movement is debated.
* Pulsed electromagnetic field significantly shortens treatment times.
* Low-level laser therapy speeds up tooth movement and offers analgesic benefits.
* PRF, PRP, and Vitamin D treatments increase movement speed.
* Dentoalveolar distraction aids shorter treatment, particularly in patients with vertical skeletal dimensions, minimizing anchoring loss.  
These strategies expedite treatment, enhance patient experience, and minimise issues like cavities and enamel demineralisation. Research is needed to assess their efficacy, considering compliance, complications, risks, benefits, and efficacy in specific clinical contexts. Larger studies with standardised protocols can illuminate their impact on orthodontic care.  
Conceptualization, G.M.; I.F., F.V., A.M.C., A.D.I. and F.I.; methodology, A.P. ([PERSON]), I.F., A.N., G.D. and A.M.I.; software, A.P. ([PERSON]), D.A., F.I. and G.D.; validation, G.M., S.C., A.D.I. and D.A.; formal analysis, G.M., A.N., D.A., A.M.C. and A.M.I.; investigation, A.D.I., A.P. ([PERSON]), I.F., F.I. and G.D.; resources, A.D.I., F.V, I.F., A.N. and S.C.; data curation, I.F., A.N., D.A., A.M.C. and A.M.I.; writing--original draft preparation, F.V., A.P. ([PERSON]), F.I. and A.M.I.; writing--review and editing, F.V., G.M., S.C., A.M.C. and G.D.; visualisation, A.D.I., A.N., S.C., D.A. and A.M.I.; supervision, I.F., F.V., A.N., A.P. ([PERSON]), S.C. and F.I.; project administration, G.M., A.P. ([PERSON]), F.V., A.M.C. and G.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 5 Simulation
### CRT Map Performance  
In this section, the performance of the proposed CRT Map is studied. First, we intend to comprehend the difference between the traffic densities in the real world and in the CRT Map. By varying the CV, we can measure the average deviation of the number of vehicles per road between the real world and the CRT Map in low and high vehicular density networks. The results are shown in Figure 15a. It is obvious that the deviation increases as the CV increases, especially for a low density network. A small CV means a lower exchange frequency. As a result, the speed of information update does not catch up with the speed of the network change. However, almost all the deviations are lower  
Figure 14: Routing Overhead (per route) vs. Number of vehicles.  
Figure 13: End-to-end delay vs. different value of CV: (**a**) 100 vehicles; and (**b**) 300 vehicles.  
than 4, which indicates that the proposed CRT Map is a good approximation of the real world. Next, we study the convergence time of the CRT Map. CV is set to 1 and the results are shown in Figure 15b. The deviation decreases to 1 and reaches a balance in about three minutes after the network begins, which indicates that the CRT Map can converge at a fast rate. Besides, the deviation in a high density environment decreases more rapidly than a low density environment since vehicles have more chances to acquire road information from encountered vehicles.  
Figure 15: Deviations of the number of vehicles: (**a**) in different exchange frequencies; and (**b**) at different instances of time.
## 4 Experimentation, Performance Evaluation, and Discussion  
This section discusses the experimental configuration of the proposed enhanced HFA-K-means with the parameter settings for its performance evaluation. The benchmark datasets used in validating the HFA-K-means are also described. The simulation results and discussion on the proposed algorithm are presented alongside its comparison with other results found in the literature.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 1 Introduction  
The design of 5G is to fundamentally fulfil the emerging requirements of wireless communication, such as high spectral and energy efficiency, low latency, massive device connectivity, high data rate and ultrahigh reliability [1, 2, 3]. Therefore, this requires a multiple access technology that meets the network capacity within the limited radio spectrum [4, 5, 6].  
NOMA technique is considered for 5G networks to improve system performance, high reliability, increased system capacity and coverage area [7; 8; 9]. NOMA, unlike orthogonal multiple access (OMA), uses the same time, code and or radio frequency resources to provide services to multiple users. This is done by introducing controlled interference through non-orthogonal resource allocation [10]. Most importantly, it supports large numbers of connections and high-congestion transmission [11]. The basic operation of NOMA is to superimpose multiple signals at the transmitter in the power domain. As shown in Figure 1, at the Base Station (BS), data from various users are superimposed on top of one another over the same time/frequency/code resource block. The BS uses a power allocation generator to assign different power levels to each user within resource block. The superimposed signal, \(s(t)\), is transmitted through a fading channel with additive white Gaussian noise (AGWN) to the receiving user equipment (UE). The Gaussian channel's output is the sum of the input signal and an independent identically distributed noise signal from a Gaussian distribution of variance \(N\), that may be produced by the receiver electronics [12]. In addition, at the UE, NOMA employs interference cancellation (SIC) method to improve network performance, to minimize interference and separate multiplexed users [13]. In NOMA, the weak user treats a strong user's signals as noise and directly decodes its signal [14]. The strong user on the other hand uses SIC to detect and decode their signals [7]. Additionally, weak users receive more power than strong users due to interference and poorer channel gains [15]. This is intended to maximize weak user transmission rate, experience and fairness [14].  
The concept of CR-NOMA was established in [16] to increase the performance of cell edge users. In CR-NOMA several network coding schemes are introduced to secure the quality of service to weak users in the communication network [15]. This is to minimize interference and enhance performance. These schemes are decode-and-forward, compress-and-forward, amplify-and-forward, and compute-and-forward.
## 2 Materials and Methods
### Analysis of Volatile Organic Compounds  
Solid-phase microextraction (SPME) was followed by gas chromatography-mass spectrometry (GC-MS) to analyse volatile organic compounds (VOCs) in bread with or without MPP addition. Each sample was chopped, and an amount of 5 g of each sample was exposed to an SPME fibre [30 \(\upmu\)m divinylbenzene (DVB)/carbowax (CAR)/polydimethylsiloxane (PDMS)] (Supelco, Bellefonte, PA, USA) at 25 \({}^{\circ}\)C for 60 min. The SPME fibre was subjected to 250 \({}^{\circ}\)C for 5 min inside the GC injection port after adsorption. A DB-624 capillary column (Agilent Technologies, Santa Clara, CA, USA, 60 m, 60 m, 0.25 mm, 1.40 \(\upmu\)m) was used for chromatographic separation. The GC oven temperature ramped up from 40 to 230 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, remained isothermal for 40 min, and the final temperature was held for 2 min. Mass acquisition was performed in full scan mode in a range from 40 to 400 \(m/z\), with the interface temperature at 230 \({}^{\circ}\)C. VOCs were identified by comparing their mass spectra with the NIST05 library. Results were expressed as percentages, obtained by normalizing peak areas with the total area of significant peaks. Each bread sample was analysed in triplicate.