esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 1 Introduction
Some of the benefits of this paper are addressing the above limitations and improving the proposed routing approach behavior, in particular the average throughput, the round trip time delay (\(RTT_{Delay}\)) and the end-to-end link quality estimation (\(E2E_{LQE}\)). This work provides the following main contributions:  
* The Critical and Rescue Operations using Wearable Wireless sensors networks (CROW\({}^{2}\)) system is one of the rare proof-of-concept implementations of emergency ad hoc autonomous systems. Unlike the detailed works in Section 2, as an application layer payload, we use real-time human vital signs (motion data, heartbeat, magnetometer, etc.) collected from the on-body sensors and pushed to the IoT platform. The evaluation of the proposed system is realized on a real indoor test-bed under realistic conditions. Indeed, we experimentally investigate the mobile devices' behavior with regards to key performance metrics, such as throughput, end-to-end delay, end-to-end link quality estimation during sensing and disseminating data onto the IoT platform. * The implemented scenario is a reference experiment for researchers and professionals to evaluate concretely the trade-off between the \(\left\{E2E_{LQE},throughput\right\}\) and \(\left\{RTT_{Delay},jitter\right\}\) in the disaster relief context [9]. Additionally, this work emphasizes the difference between UDP and TCP transport mode performances in disaster relief applications and may be considered as a reference for upcoming application implementations with regards to the bandwidth limitations. Moreover, the realized experiment advises also on the optimal hop count to guarantee the required throughput for the critical applications (medical support, military, firefighters, press and media, etc.). * The proposed system is back-ended by an IoT platform. Indeed, at the IoT platform level, we prove the system's capability for mobility pattern recognition and prediction. Then, we discuss the overall interference affecting the mobile devices behavior for the considered indoor scenario. The proposed system shows also a global connectivity status of all of the deployed devices during the disaster relief operations. The remainder of this paper is organized as follows: In Section 2, we present research works proposed recently related to the emergency relief operation systems. In Section 3, we introduce the CROW2 system generic architecture. In Section 4, we focus on the implementation technologies in accordance with the on-body and body-to-body communications. In the last section, we describe the experimentation scenario, and we discuss the obtained results. Finally, we conclude by summarizing the paper's contributions, and we present some promising directions for future research.
# Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
## 3 Results
### Visualization Analysis  
Qualitative results on the infant set and COCO set are provided in Figures 7 and 8. Despite the difference in body proportions, the infant dataset is labeled in the COCO format, so the training process of the proposed method is quite favorable and shows the relationships between the main points of people well. The proposed method can be used for multi-person recognition in 2D.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Input Size** & **GFLOPs** & \(AP\) & \(AR\) \\ \hline AggPose-S [14] & 256 \(\times\)192 & 9.0 & 81.3 & 82.1 \\ \hline AggPose-L [14] & 256 \(\times\) 192 & 15.0 & 83.2 & 83.9 \\ \hline TokenPose-L/D24 [30] & 256 \(\times\) 192 & 11.0 & 81.4 & 82.3 \\ \hline OpenPose-2016 [18] & 368 \(\times\) 368 & 136.1 & 79.0 & 79.8 \\ \hline
**Pretrained model** & **368 \(\times\) 368** & **8.97** & **41.8** & **46.5** \\ \hline
**Proposed model** & **368 \(\times\)368** & **8.97** & **82.7** & **85.3** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with the infant test set. Comparison of methods with object detection and non-object detection (OpenPose CMU, proposed model). The methods compared in this table are transfer-learning (all weights) and the self-collected infant dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Input Size** & **Backbone** & **GFLOPs** & \(AP\) & \(AP_{\textbf{0.5}}\) & \(AP_{\textbf{0.75}}\) & \(AP_{\textbf{M}}\) & \(AP_{\textbf{L}}\) & \(AR\) \\ \hline SimpleBaseline-Res152 [31] & 256 \(\times\) 192 & – & 15.7 & 61.9 & 74.7 & 68.1 & 59.1 & 67.9 & 66.9 \\ \hline HRNet-W48 [27] & 256 \(\times\) 192 & – & 16.0 & 64.6 & 77.9 & 70.7 & 61.5 & 70.3 & 69.1 \\ \hline HRNet-W32 [27] & 256 \(\times\) 192 & – & 7.1 & 64.0 & 77.8 & 70.4 & 60.9 & 69.7 & 68.6 \\ \hline TransPose [28] & 256 \(\times\) 192 & HRNET & 21.8 & 65.2 & 77.5 & 70.6 & 61.8 & 71.2 & 69.5 \\ \hline HRFormer-B [29] & 256 \(\times\) 192 & HRNET & 12.2 & 65.0 & 78.1 & 71.2 & 61.7 & 71.0 & 69.5 \\ \hline TokenPose-L/D24 [30] & 256 \(\times\) 192 & HRNET & 11.0 & 65.2 & 77.7 & 71.0 & 62.2 & 71.1 & 69.5 \\ \hline Openpose-2016 [18] & 368 \(\times\) 368 & VGG19 & 136.1 & 62.6 & 88.2 & 69.5 & 68.3 & 72.2 & – \\ \hline Openpose-2018 [13] & 368 \(\times\) 368 & VGG19 & – & 65.3 & 85.2 & 71.3 & 62.2 & 70.7 & – \\ \hline
**Proposed model (12 first layers)** & **368 \(\times\) 368** & **MobileNet** & **8.97** & **64.4** & **81.6** & **72.3** & **61.1** & **71.2** & **69.3** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons with SOTA methods on the COCO validation set.
## 3 Results and Discussion  
### FTIR Analysis  
Figure 2a displays the FTIR spectra of TiO\({}_{2}\) nanoparticles, POA, PPA, POA@TiO\({}_{2}\), and PPA@TiO\({}_{2}\). The locations of characteristic bands associated with the respective chemical bonds are summarized in Table 1.  
Figure 2: (a) FTIR analysis and (b) XRD powder diffraction patterns of TiO\({}_{2}\) nanoparticles and PPA, POA, POA@TiO\({}_{2}\), and PPA@TiO\({}_{2}\) nanocomposites.  
The bands of TiO\({}_{2}\) nanoparticles are associated as follows: the band at 3449 cm\({}^{-1}\) is ascribed to stretching vibrations of the surface OH\({}^{-}\) or adsorbed H\({}_{2}\)O molecules; a band at 1634 cm\({}^{-1}\) can be assigned to a vibration of -OH on the surface of TiO\({}_{2}\), and the bands at 825 cm\({}^{-1}\) and 661 cm\({}^{-1}\) are due to Ti-O stretching modes [21].  
The results in Table 1 demonstrate some differences between PPA and POA. The IR spectrum of pure POA exhibited a characteristic absorption band at 3185 cm\({}^{-1}\) due to N-H stretching vibrations [22, 23, 24]. The bands at 2933 cm\({}^{-1}\) are indicative of C-H stretching vibrations [25]. The two bands at 1591 cm\({}^{-1}\) and 1499 cm\({}^{-1}\) correspond to the quinoid and benzenoid groups, respectively [26, 27]. The band at 1116 cm\({}^{-1}\) is associated with C-O aromatics [12]. Moreover, a band appearing at 1418 cm\({}^{-1}\) may be due to the presence of a CH\({}_{3}\) group or aromatic characteristics [28]. The band observed at 1283 cm\({}^{-1}\) can be attributed to the C-O-C vibrations of the ether group [12, 29]. Additionally, the bands observed at 1146 cm\({}^{-1}\) and 1338 cm\({}^{-1}\) may be related to C-N and C=N stretching modes [22, 30]. The band at 1018 cm\({}^{-1}\) corresponds to the 1-4 substitution on the benzene ring [26, 27] and the bands at 925 cm\({}^{-1}\) and 809 cm\({}^{-1}\) may be due to the C-H out-plane bending vibrations of 1,2,4 trisubstituted aromatic rings [23]. Therefore, these distinctive spectral features confirmed successful POA formation by FTIR spectroscopic analysis.  
The findings presented in Table 1 highlight notable disparities between PPA and POA, notably in the position and intensity of their respective bands. IR spectra reveal that POA@TiO\({}_{2}\) and PPA@TiO\({}_{2}\) nanocomposites exhibit the principal characteristic bands found in pure polymers (POA and PPA). However, the discernible shift in all bands suggests an interaction between pure polymers and TiO\({}_{2}\). Moreover, the presence of Ti-O bands between 716 cm\({}^{-1}\) and 556 cm\({}^{-1}\) in the FTIR spectra of nanocomposites serves as definitive evidence of the successful integration of TiO\({}_{2}\) into the polymer matrix. This finding substantiates the notion of a chemical interaction occurring between TiO\({}_{2}\) nanoparticles and the polymer structure, influencing spectral characteristics and affirming their amalgamation within composite materials [28, 31].
###### Abstract  
As the world shifts towards a low-carbon economy, the strategic deployment of renewable energy sources (RESs) is critical for maximizing energy output and ensuring sustainability. This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). A key innovation of GREENIA is its ability to provide natural language explanations (NLEs), enabling transparent and interpretable insights for both technical and non-technical stakeholders. Applied in Greece, the framework addresses the challenges posed by the interplay of meteorological factors from 10 different meteorological stations across the country. Validation against real-world data demonstrates improved prediction accuracy using metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHAP analysis enhances transparency by identifying key meteorological influences, such as temperature and humidity, while NLE translates these insights into actionable recommendations in natural language, improving accessibility for energy planners and policymakers. The resulting strategic plan offers precise, intelligent, and interpretable recommendations for deploying RES technologies, ensuring maximum efficiency and sustainability. This approach not only advances renewable energy optimization but also equips stakeholders with practical tools for guiding the strategic deployment of RES across diverse regions, contributing to sustainable energy management.  
Keywords:renewable energy sources; machine learning techniques; evolutionary algorithms; RES placement; meteorological data analysis; explainable artificial intelligence +
Footnote †: journal: applied sciences
# Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis of this study was performed using SPSS 25.0 software (IBM Corp., Armonk, NY, USA). All outcome variables were expressed as the mean and standard deviation of four repeated measurements for each condition, and the Shapiro-Wilk test was used to test the normality of the raw data. To determine whether there was a significant difference between the repeatedly measured data, all variables that followed normality were compared using the repeated measures analysis of variance (RM ANOVA). The degree of correlation between the data measured by the acceleration sensor built into the smartphone and the GAITRite data was expressed as the intraclass correlation coefficients (ICC), and the degree of agreement between the two systems was visualized using Bland-Altman plots. All statistical significance levels were set at \(\alpha\) = 0.05.
## 4 Discussion
The compression of the lesion may cause an underestimation of the lesion dimensions, especially when evaluating TT; however, we recognize that further evaluation is mandatory to further assess the reliability of UHFUS measurements. An initial characterization of OSCC lesions involved the evaluation of the UHFUS parameters of echogenicity and vascularization. Previous research evaluating the echogenicity of both normal mucosa and oral lesions highlighted that the grey scale distribution varied depending on the anatomical site and lesion typology [16; 17]. In our sample, the mean grey values of endophytic and exophytic lesions did not show significant difference. However, a trend was observed for exophytic lesions to present higher mean grey values. Interestingly, the localization did not influence the echogenicity of the lesion. The semiquantitative score employed on the color Doppler UHFUS images highlighted the presence of more intense vascularization in correspondence with endophytic lesions, suggesting that this type of growth pattern could be associated with increased angiogenesis and inflammation. However, further evaluation is needed to better correlate vascularization evaluated by means of color Doppler and histology. To the best of our knowledge, this is the first study to report the application of UHFUS to the evaluation of OSCC, and in particular for TT and DOI estimation. Although a good correlation between UHFUS measurements and histology was observed, the present study has some limitations. First of all, the study was performed on a limited number of subjects and mostly involved early-stage tumors of reduced dimensions. Secondly, no comparison with conventional US was provided, as histology was set as benchmark. Therefore, we recognize that the superiority of UHFUS compared to conventional US is still to be demonstrated. Finally, the evaluation of echogenicity and vascularization needs further correlation with histology and clinical parameters. Nevertheless, these preliminary results suggest that UHFUS has the potential to become a valuable technique for the evaluation of OSCC lesions, and give insight into its application in OSCC diagnostic work-up and surgical management. Conceptualization, R.I., M.N. ; methodology, R.I.; software, R.I., S.G.; validation, L.C., D.C., F.G.; formal analysis, R.I., M.N., S.G.; investigation, R.I.; resources, T.O. ; data curation, R.I., M.N., S.G.; writing--original draft preparation, R.I.; writing--review and editing, R.I., M.N. ; visualization, S.G., T.O. ; supervision, D.C., F.G.; project administration, R.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the University Hospital of Pisa, protocol code 32822-19/06/2018. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are contained within the article. The authors declare no conflict of interest.
## 4 Analysis of Data Sources and Evaluation Factor Selection
### Analysis of Evaluation Factor Selection
#### 4.2.1 Elevation  
One of the most significant topographic variables influencing the likelihood of catastrophes is elevation [48]. Elevation influences the stress condition within slopes as well as providing potential energy for geological disasters like slopes. Elevations, slopes and relief are spatially aggregated, and often where elevations are high, slopes and relief are equally high within a certain area, and there is a connection between them. The study area's Digital Elevation Model (DEM) indicates that its elevation ranges from 869 to 4891 m. The research area's elevation is divided into five categories using the natural discontinuity categorization method: 869-1606 m, 1606-2191 m, 2191-2799 m, 2799-3392 m, and 3392-4891 m (Figure 4). Geological hazards exhibit a trend of initially increasing and then decreasing in density as elevation rises. These risks are concentrated in the height range of 1606-2191 m, making up around 54.27% of the total.
# Low Ionosphere under Influence of Strong Solar Radiation: Diagnostics and Modeling
## 2 Methods
### Procedure for Determination of Electron Density: Two-Component Exponential Model  
The procedure was based on the [PERSON]'s two-component exponential model [34], which uses time-dependent parameters \(H^{\prime}\) with values 74-50 km and \(\beta\) with values 0.3-0.6 km\({}^{-1}\), to describe the electron density height profile in the lower ionosphere. We note that there is a lack of agreement for sharpness values obtained by different models and techniques. Although the values of the \(\beta\) parameter are in the range between 0.3-0.6 km\({}^{-1}\), in some papers \(\beta\) values are higher i.e., 1.0 km\({}^{-1}\) at daytime and \(\sim\)3.0 km\({}^{-1}\) at nighttime conditions (see e.g., [35]). Such a deviation illustrates the general deficiency of precision in the research of the ionosphere D-region and the need for new and more precise methods and techniques for its investigation.  
This two-parameter system provides a model of a vertically stratified ionosphere with an electron density profile \(Ne\) that increases exponentially with altitude:  
\[N_{e}(h,H^{\prime},\beta)=1.43\cdot 10^{13}\exp(-\beta\cdot H^{\prime})\exp[( \beta-0.15)\cdot h]\ [\rm m^{-3}]. \tag{1}\]  
Here, \(Ne\) and \(h\) are given in \(\rm m^{-3}\) and km, respectively. The model [34] was successfully used in previous comparisons of VLF measurements and theory (e.g., [9; 33]) and we also use it in our work.  
Important plasma quantities used for describing the characteristics of the D-region are height dependent conductivity parameter; \(\omega_{r}(h)\) defined as  
\[\omega_{r}(h)=\omega_{p}^{2}/\
u(h)=2.5\cdot 10^{5}\exp(\beta\cdot(h-H^{ \prime})), \tag{2}\]where \(\omega_{p}\) [s\({}^{-1}\)] is defined as the plasma frequency and \(v(h)\) is the effective electron-neutral collision frequency. Quantity \(H^{\prime}\) is the reflection height, meaning height at which \(\omega_{\rm r}(h)\) = 2.5 \(\times\) 10\({}^{5}\) rad/s. The positive ion density can be approximated to be equal to the electron density at high altitudes where electron density has values larger than 10\({}^{8}\) m\({}^{-3}\), while at lower altitudes the positive ion density is around 10\({}^{8}\) m\({}^{-3}\) or can be calculated based on charge neutrality.  
As it was noted, the electron densities can be obtained from the observed VLF amplitude and phase perturbations by a trial-and-error method (see Sections 2.1 and 2.2) where density profile is adjusted until the simulated amplitude and phase (using LWPC code) match with observed data. In this way, the obtained Wait's parameters \(H^{\prime}_{\rm per}\) and \(\beta_{\rm per}\) can be used in Equation (1) for further calculations.
# Spectral Separation of Up-Conversion Luminescence Processes
## 1 Introduction
They are actively used for lasers [26; 27; 28; 29; 30] (and micro-lasers, in particular [31]), photoelectric devices (such as temperature sensors [32]), and solar cells [33; 34; 35; 36; 37]. Remarkable success was achieved with UC nanoparticles in optogenetics, bio-imaging, for optical control of the neural activity, i.e., in the widest field of so-called \"theranostics\", combining therapy with sensing [38; 39; 40]. In a majority of these applications, UC processes are realized with optically active media, doped with rare-earth ions (REIs), which are also the staple for modern lasing/amplifying optical systems, optical communication and quantum memories for long-range quantum communication networks, etc. [14; 30; 41; 42; 43; 44; 45]. Of particular note is the use of up-conversion nanoparticles for reaching high nonlinearity (with up to a 26 th power of pump energy density) and super-resolution imaging with up to 12 nm spatial resolution as well [46; 47; 48]. The remarkable feature of our approach is its self-testing. In particular, the validity is manifested by coincidence of obtained components spectra for different regions of the pump power variation. The possibility of such self-testing is very important for the spectral separation method, because it is able to work even for the case when power dependence of the spectral component changes with pump power (which is rather common for UCL processes with REIs). Also, self-testing allows us to observe the emergence of processes, breaking factorization of the spectral, and the pump power dependencies underlying our method (such as, for example, cross-relaxation [15; 16]). We demonstrate our approach in the experiment using fluorophosphate glass doped with rare-earth ions Yb\({}^{3+}\) and Tm\({}^{3+}\) pumped by 975 nm CW diode laser. The outline of this paper is as follows: In Section 2, we discuss our spectral separation method. In Section 3, we describe the experiment and the up-conversion processes occurring in our experiment. In Section 4, the spectral separation procedure is carried on for several spectral regions, in which we observed UCL of ions Tm\({}^{3+}\), sensitized by Yb\({}^{3+}\) ions. There, we also illustrate the self-testing procedure.
## 3 Application examples of CAD for brain diseases
### Multiple sclerosis
\begin{table}
\begin{tabular}{c c c c} \hline Authors & Segmentation & No.cases (No.MS & SI* \\  & & lesions) & \\ \hline [PERSON] _et al._[88] & T1, T2, proton density & 84 (-) & - \\  & maps & & \\ [PERSON] _et al._[89] & Fuzzy C-Means & 10 (-) & 0.62 \\ [PERSON] _et al._[90] & Stochastic model & 50 (-) & 0.51 \\ [PERSON] _et al._[91] & Pipeline analysis & 29 (-) & 0.68 \\ [PERSON] _et al._[92, 93] & AMM**, MRF*** & 20 (-) & 0.75 \\ [PERSON] _et al._[30, 31] & Region growing, LSM\({}^{*}\) & 3 (168) & 0.77 \\ \hline \multicolumn{4}{l}{*SI: similarity index} \\ \multicolumn{4}{l}{**AMM: adaptive mixtures method} \\ \multicolumn{4}{l}{***MRF: [PERSON] random field model} \\ \multicolumn{4}{l}{+LSM: level set method} \\ \end{tabular}
\end{table} 
Table 4: Comparison of methods and performance of CAD systems for detection of MS in MR images. a leave-one-candidate-out test, and the degree of coincidence between MS lesions detected by the proposed method and two neuroradiologists was 0.768 on average. These results indicate the proposed method would be useful for assisting neuroradiologists in assessing MS in clinical practice.
## 4 Proposed Models
### Feature Vector  
Feature vector was created by aggregating Yaw angle signal generated from the gyroscope into 1-s windows, and calculating a set of statistical indices within those windows. The initially considered indices are: mean, standard deviation, sample range, median, interquartile range, kurtosis, root mean square (RMS), skewness, and variance. Given the IMU sampling rate of 50 Hz, all of the statistical indices were calculated using 50 observations. To select the target group of indices, the distribution of their values with the considered classes was checked: \"0\"--no turn, \"1\"--90\({}^{\circ}\) turn, and \"2\"--a slight turn (as shown in Figure 4). The total length of the obtained feature vector is 16,200.  
Figure 4: Boxplots of the initially proposed statistical indexes divided by selected classes.  
The key statistical indices for the classification problem are those for which the most significant differences between the value for class 1 (\"turn\") and the other two classes can be observed. For this purpose, data exploration was performed, and the three most visible relationships identified are presented in Figure 5. Finally, five statistics have been selected to create the final feature vector: standard deviation, dispersion (min-max range), median, interquartile range, and kurtosis.