esa-sceva/satcom-chunk-collection huggingface dataset:
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 6 Conclusions  
This work describes an imaging method based on the analysis of micro-motions present on volcanoes and generated by underground Earth tremors. We showed a series of tomographic maps representing the internal echography of volcanoes. Processing the coherent vibrational information present on the single SAR image, in the single-look-complex configuration, we exploited the sound information penetrating tomographic imaging over a depth of about 3 km from the Earth's surface. The experimental results were calculated processing an SLC image from the COSMO-SkyMed Second Generation satellite constellation of the Italian volcano Vesuvius. Tomographic maps revealed the presence of the solidified magma, and the main and the secondary feeder conduit. In addition, both the main and secondary fractures are visible. Concluding, we also declare that although the results of the considered case study are promising, they should be confirmed by using other case studies in the future.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data used for this work can be requested at [[https://www.e-geos.it/orathttps://www.asi.it/](https://www.e-geos.it/orathttps://www.asi.it/)]([https://www.e-geos.it/orathttps://www.asi.it/](https://www.e-geos.it/orathttps://www.asi.it/)) (accessed on 1 April 2022).  
**Acknowledgments:** We would like to thank [PERSON] for making the SARPROZ software available, through which many calculations were carried out more easily and quickly. We also thank the Italian Space Agency for providing the SAR data. The in situ seismic numerical data were kindly provided by the Italian Institute of Geophysics and Volcanology (INGV), which can be downloaded from the website: [[https://eida.ingv.it/it/](https://eida.ingv.it/it/)]([https://eida.ingv.it/it/](https://eida.ingv.it/it/)) (accessed on 23 April 2022). Finally, We thank [PERSON], Professor of Industrial Chemistry at the Faculty of Chemistry, University of Pisa (Italy), for guiding me in formulating the idea of extrapolating phononic information through photonic processing of SAR data. ([[https://corradomalangexperience.com/](https://corradomalangexperience.com/)]([https://corradomalangexperience.com/](https://corradomalangexperience.com/)), accessed on 1 January 2022).  
**Conflicts of Interest:** The author declare no conflict of interest.
## 4 FSO Channel Model  
There are a variety of factors that degrade optical signals transmitted over FSO links; primarily, the attenuation of the laser power in the atmosphere and the geometrical loss caused by the spreading of the transmitted beam between the transmitter and the receiver.  
The link equation can be expressed as [24]:  
\[P_{Received}=P_{Transmitted}\frac{d_{R}^{2}}{(d_{T}+\theta R)^{2}}10^{-\alpha \overleftarrow{\beta}}, \tag{1}\]  
where \(d_{R}\) is the receiver aperture diameter (m), \(d_{T}\) is the transmitter aperture diameter (m), \(\theta\) is the beam divergence (milliradians), \(R\) is the range (km) and \(\alpha\) is the atmospheric attenuation (dB km\({}^{-1}\)).  
Additional losses include the transmitter and receiver losses caused by the fiber-telescope interface, mis-pointing and coupling deficiencies. Furthermore, there is a significant loss due to scintillation. Intensity scintillation results mainly from the random variations in temperature and pressure of the air, causing index-of-refraction fluctuations, or in other words, optical turbulence. There are various channel models that can be used to describe the intensity scintillation of an optical signal. The gamma-gamma channel model can mimic a broad range of turbulence conditions.  
In this case, the probability density function of the normalized light intensity \(I\) is given by:  
\[P(I)=\frac{2(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{\Gamma(\alpha)\Gamma(\beta )}I^{\frac{\alpha+\beta}{2}-1}K_{\alpha-\beta}\left(2\sqrt{\alpha\beta I}\right) \tag{2}\]  
\[\alpha=exp\left[\frac{0.49\sigma_{R}^{2}}{\left(1+1.11\sigma_{R}^{\frac{12}{ \beta}}\right)^{\frac{5}{6}}}\right]-1 \tag{3}\]  
\[\beta=exp\left[\frac{0.51\sigma_{R}^{2}}{\left(1+0.69\sigma_{R}^{\frac{12}{ \beta}}\right)^{\frac{5}{6}}}\right]-1 \tag{4}\]  
\(\alpha\) and \(\beta\) are the numbers of small and large turbulence cells, \(\Gamma(\cdot)\) is the gamma function and \(K_{\alpha-\beta}(\cdot)\) is a modified Bessel function of the second kind [13].  
The intensity variance \(\sigma_{R}^{2}\), which directly depends on the magnitude of atmospheric turbulence, is given by:  
\[\sigma_{R}^{2}=1.23C_{n}^{2}k^{2}\xi^{\frac{7}{6}}z^{\frac{11}{6}} \tag{5}\]  
where \(C_{n}^{2}\) is the refractive index structure parameter with value ranges from \(10^{-13}\)\(m^{-2}\) for strong turbulence to \(10^{-17}\)\(m^{-\frac{2}{3}}\) for weak turbulence, \(k=\frac{2\pi}{\lambda}\) is the optical wavenumber, and \(z\) is the range of the FSO link. Channel time fluctuations are calculated based on the theoretical quasi-static model, also known as the frozen channel model. This model is based on the idea that channel fading stays the same over the course of a frame of symbols (coherence time), changing to a new value between frames.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### Application  
We designed a modern logistics management system in [38], and located the express parcels through the fingerprint library method, as described in Section 4.1. The defect makes is obvious that the deployment is complicated. Accordingly, we can improve the positioning scheme of express parcels through ANTspin. Usually, the express parcels are divided into large and small pieces and they are placed in different places. Take a courier station as an example, as shown in Figure 21a, the shelves of large express parcels are placed around the room, which can be seen as four planes to be positioned. According to ANTspin, all of the express parcels can be located by placing a rotating antenna in the middle of the room and rotating by 360\({}^{\circ}\) to scan all of the shelves in the room. As shown in Figure 20b, the express parcels are placed in dense shelves to form a 3D space to be positioned. According to ANTspin, all of the express parcels can be located by placing rotating antennas on both sides of the shelves and rotating by 180\({}^{\circ}\) to scan the shelves. In addition, different express post stations have different layouts, which requires different deployment schemes of ANTspin in the field. After locating the express parcels, the system combines the communication technology to send the positioning information to the user via SMS, and the user can quickly find the parcel after receiving the short message.
# TEC and ROTI Measurements from a New GPS Receiver at BOWEN University, Nigeria
## 4 Results and Discussion
Another conspicuous feature that is evident in Figure 5 is the value of TEC at the BJCO, which is close to that of our observations. For instance, we observed 40 TECU, 45 TECU, and \(\sim\)45 TECU compared to 45 TECU, 45 TECU, and \(\sim\)50 TECU in September, October, and December at BJCO and Bowen, respectively. These disparities in the TEC values between both GPS stations can be traced to the annual solar flux index playing a primary role and having a value of \(\sim\)82 sfu and \(\sim\)80 sfu in 2021 and 2010, respectively. The longitudinal effect may not be significant in the varying TEC value between BJCO and Bowen. This is due to their longitudes, which are closer as BJCO is located at 4.14\({}^{\circ}\) E and Bowen is at 2.45\({}^{\circ}\) E. For clarity, the difference in local time due to the longitude where each of the GPSs are located is less than one hour. For example, the calculated local time (LT) at BJCO when the universal time (UT) is zero is 0.276. In the case of the Bowen location, it is 0.163. Therefore, the difference between both LT is 0.113. This value is smaller and may not have a significant impact on the varying TEC between both GPS locations. However, it may play a secondary role. It is important to note that both 2010 and 2021 are characterized by periods of low solar activity and belong to solar cycle 24. It is also important to note that the solar cycle effect is negligible for both years. Therefore, this difference in TEC magnitude may be due to the combined effect of the longitudinal difference and the solar flux index modification (Figure 6). In addition, Figure 7 displayed good agreement with the total suppression of ionospheric irregularities observed at BJCO when compared to Bowen. Figure 6: Monthly TEC variation over the BJCO receiver during a low-solar-activity period (2010). The comparisons of ROTI results from both stations confirm similarities in their variability. These indicate that a CASES GPS receiver located at Bowen has similar capacities and reliability as that at BJCO. Therefore, it can now be known how ionospheric irregularities over the Nigerian ionosphere affect our technologies related to navigation and positioning.
## 2 FCM Clustering Algorithm  
### Measurement of Similarity  
The FCM clustering algorithm was first proposed by [PERSON] (1974) in 1974 and then extended by [PERSON] (1974). In the FCM algorithm, the membership degree of each sample point to all class centers is obtained by optimizing the objective function, thereby determining the class membership of the sample points and achieving the automatic classification of sample data. Each sample is assigned a membership function belonging to each cluster, and the samples are classified according to their membership values. Among numerous fuzzy clustering algorithms, the FCM clustering algorithm is the most widely and successfully applied algorithm, which has been used in fields such as data mining and image processing.  
A sample is a point in a feature space, and the distance between points reflects whether the corresponding samples belong to different types [PERSON] et al. (2018). According to the distribution of main discontinuity sets in rock masses, the dip direction and dip angle parameters describing the occurrence of the structural planes are usually used as statistical indicators for cluster analysis. To facilitate analyses and comparisons, it is necessary to standardize the statistical indicators of each representative point. A good standardization method, while dimensionless, should also maintain the resolution of the original statistical indicators, that is, the magnitude of variability. For the clustering results and the proximity between the clustering center and the samples, the sine value of the included angle between normal vectors, similarity coefficient, or Euclidean distance are often used to represent them. Due to the characteristics of rotation invariance, sample features being unit vectors, and there being no need for the linear transformation of coordinate units, the Euclidean distance is adopted to measure the similarity between discontinuity sets in this study.
## 4 Conclusions  
The findings of the present work showed that PLSR, in combination with vis-NIR reflectance spectroscopy, could be regarded as a promising and helpful method for a quick and inexpensive prediction of PCBs18, as well as of Hepta-CB and Penta-CB congeners, and EOX.  
Although the number of samples is limited, it must be considered adequate with respect to the reduced extension of the investigated area. Moreover, in accordance with the international literature, until now there have been no research studies concerning the application of vis-NIR spectroscopy to the prediction of PCBs; therefore, the results achieved in this work should be considered worthy of attention.  
Certainly, this preliminary study used soil samples with high concentrations of PCB. However, further soil sampling, concerning a very large area (called \"_Area Vasta_\") of particular environmental interest, where, presumably, the contents of PCBs are more variable, has already been started. The completion of the study will give a better answer to the applicability of the vis-NIR reflectance spectroscopy to the more generalised monitoring of these contaminants. The fact remains that the method can be considered useful in conditions of high concentrations of the same contaminants, not to be excluded in other sites from the \"_Area Vasta_\".  
In light of these considerations, further investigations are needed to evaluate the effective potential of vis-NIR spectroscopy in the quantitative evaluation of PCBs and EOX, enlarging the area of interest. In fact, these investigations have already been launched and will concern the entire \"_Area Vasta_\" of Taranto, where the problem of contamination from PCBs (and other contaminants) is particularly felt.  
Conceptualization, N.L., V.A. and A.P.L.; methodology, N.L., V.A., C.G. and A.P.L.; validation, N.L. and A.P.L.; formal analysis, N.L., V.A. and C.G.; investigation, C.G.; resources, V.F.U. and C.M.; data curation, N.L. and A.P.L.; writing--original draft preparation, N.L., V.A., C.G. and A.P.L.; writing--review and editing, N.L., V.A., C.G. and A.P.L.; visualization, N.L., V.A., C.G., C.M. and A.P.L.; supervision, V.F.U. and A.P.L.; project administration, V.A., C.M. and V.F.U.; funding acquisition, V.A., C.M. and V.F.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request due to restrictions.  
We would like to express our gratitude to [PERSON] (IRSA-CNR) for his insightful comments and discussion. We also want to thank [PERSON], [PERSON] (IRSA-CNR), and [PERSON] (IBBR-CNR) for their invaluable assistance with the PCB and EOX data assessments.  
The authors declare no conflict of interest.
## Materials and Methods  
### Sample Preparation  
Pineapple by-products (_Ananas comosus_ L.) were provided by Campotec S. A., located in Torres Vedras, Portugal. The pineapple by-products (-16 kg of pineapple shell and -16 kg of pineapple core) were stored under refrigeration (4 \(\pm\) 1 \({}^{\circ}\)C) approximately 18 h before packaging and abiotic stresses application. Half of the amount of by-productswere cut into specific dimensions: \(\sim\)8 kg pineapple core cylinders (\(\sim\)52.5 \(\times\) 30 mm) and \(\sim\)8 kg pineapple shells (\(\sim\)110 \(\times\) 40 mm). The remaining 8 kg of pineapple shell and the remaining 8 kg of pineapple core were wounded separately (700 g/batch) in a Thermomix (TM31, Vorwerk Thermomix) at a speed of 5650 rpm for 25 s. Subsequently, all the by-products were packaged in separate portions (\(-\)40 g) in PA/PE-90 (Alempack--Embalagens Flexiveis, Elvas, Portugal) and vacuum sealed (85% of vacuum). The thermal treatments were carried out through the bags with pineapple by-products immersed in a bath of hot water at 30 \(\pm\) 1 \({}^{\circ}\)C or 40 \(\pm\) 1 \({}^{\circ}\)C for 10 min. Heat treatment conditions (temperature and time) were selected based on a previous study [21]. After heat treatment, the samples were immediately placed in a water and ice bath to stop the reactions caused by the heat in the samples. Samples that were not immersed in the hot water bath were used as controls. After the application of heat stress, the pineapple bags were stored for 24 h or 48 h at 4 \(\pm\) 1 \({}^{\circ}\)C so that the enzymes had time to act and, later, they were frozen at \(-\)80 \({}^{\circ}\)C until the moment of the analyses. The storage time control samples were frozen at \(-\)80 \({}^{\circ}\)C immediately after the heat treatment. All samples were prepared in triplicate for each treatment.
## 4 Results and Discussion
### Performance Analysis of the Proposed HT-RLSTM
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Techniques**} & \multicolumn{5}{c}{**Performance Metrics (\%)**} \\ \cline{2-6}  & **Proposed HT-RLSTM** & **KNN** & **ANFIS** & **SVM** & **ANN** \\ \hline
**Specificity** & 94.72 & 83.16 & 84.45 & 82.68 & 91.92 \\ \hline
**Sensitivity** & 95.45 & 86.46 & 87.44 & 85.65 & 92.66 \\ \hline
**Precision** & 96.42 & 91.23 & 88.56 & 86.35 & 94.48 \\ \hline
**F-Measures** & 97.23 & 93.35 & 91.13 & 87.12 & 95.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance analysis of the proposed HT-RLSTM based on specificity, sensitivity, precision, and recall. Figure 4: Comparative analysis of the proposed HT-RLSTM based on specificity, sensitivity, precision, and recall. Figure 5 compares the evaluation metrics, such as FPR, FNR, and MCC of the proposed work with existing works. The significance of the model is determined by the low value of FPR and FNR rates and the high value of MCC. In accordance with the above-mentioned statement, the FPR and FNR rates of the proposed work are notably lower and the MCC rate achieved by the proposed work is higher than the existing approaches. Hence, the proposed method outperforms the other state-of-art methods and delivers better outcomes under complex situations.
# Therapeutic Potential of Polymer-Coated Mesoporous Silica Nanoparticles
## 4 Polymer Coated MSNs for Inflammation  
Ibuprofen was the first drug molecule to be loaded and released from mesoporous silica in 2001 and has been vastly employed as a model drug in the context of mesoporous silica ever since. Also, stimuli-responsive systems have been designed based on ibuprofen release. For instance, MSNs grafted with thermo-responsive polymer poly(N-isopropylacrylamide-co-3-(methacryloxymethyl) trimethoxysilane) (PNIPAM-co-MPS, LCST--40 \({}^{\circ}\)C) were developed by [PERSON] et al. (Figure 11). The MSNs were magnetised by encapsulating iron oxide nanoparticles using the solvothermal method. The author's observed low loading efficiency of the drug in polymer coated MSNs (6.2 wt%) compared to uncoated MSNs (24 wt%), and this was attributed to the poor diffusion of drug through the polymer chains during the loading process. A temperature dependent release behaviour was observed from hybrid MSNs, where only 20% release was observed at 20 \({}^{\circ}\)C in 16 h but complete drug release at 40\({}^{\circ}\) C was observed within 24 h due to the coil-to-globule transition of the LCST polymer [51].  
PEI-b-PEG coated MSNs loaded with dexamethasone was reported by [PERSON] et al. for the treatment of airway inflammation. PEG-PEI coating improved the dispersibility of drug-loaded particles and suppressed unfavorable interactions within the airways. The coating was performed by electrostatically adsorbing PEI-b-PEG onto silica particles loaded with the drug. The aerosol formulation prepared using MSNs were administered in vivo via a nebulizer in two different mice groups with pulmonary inflammation induced by either melphalan or lipopolysaccharideacharide. The obtained results suggested that drug loaded MSNs were as effective as free drug for treating the inflammation induced by melphalan. However, surprisingly, it was found that the blank particles were also capable of reducing inflammation in a lipopolysaccharideacharide induced animal model [52].  
Eudragit coated Santa barbara amorphous-15 (SBA-15) and MCM-41 silica nanoparticles were developed with the aim to enhance the efficiency of sulfasalazine through the oral route. Amine  
Figure 11: Preparation of polymer coated magnetic MSNs for delivery of Ibuprofen. Reproduced with permission from reference [51].  
terminated MSNs were prepared using 3-aminopropyltriethoxysilane (APTES) and loaded with drug followed by coating with pH sensitive Eudragit S and Eudragit RL to control the drug release. The polymer coating restrained the drug release at pH 1.2 but accelerated the release at target pH, i.e., 6.2, as expected [53].
_Article_  
Uncertainty Propagation of Spectral Matching Ratios  
Measured Using a Calibrated Spectroradiometer  
[PERSON] \({}^{\ast}\)\({}^{\copyright}\), [PERSON]\({}^{\copyright}\) and [PERSON]  
Diectorate C--Energy, Transport and Climate, European Commission Joint Research Centre,  
1-21027 Ispra, Italy; [EMAIL_ADDRESS] (R.G.); [EMAIL_ADDRESS] (R.P.K.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +39-0332-786449  
Received: 29 November 2017; Accepted: 19 January 2018; Published: 26 January 2018  
**Featured Application: The results of this paper are needed in the calculation of measurement uncertainty associated with the performance of Concentrator Photovoltaic (CPV) devices, in particular for measurements taken outdoors. As of today's state-of-the-art capability in measuring CPV devices, outdoor measurements are the most reliable.**  
**Abstract:** The international standard IEC62670-3 (International Electrotechnical Committee) \"Photovoltaic Concentrators (CPV) Performance Testing--Part 3--Performance Measurements and Power Rating\" sets the guidelines for power measurements of a CPV device, both in indoor and outdoor conditions. When measuring in outdoor conditions, the acquired data have to be filtered a posteriori, in order to select only those points measured with ambient conditions close to the Concentrator Standard Operating Conditions (CSOC). The most stringent requirement to be met is related to the three Spectral Matching Ratios (SMR), which have all to be within the limit of 1.00 \(\pm\) 0.03. SMR are usually determined by the ratio of the currents of component cells to monitor the outdoor spectral ratio conditions during the CPV device power measurements. Experience demonstrates that obtaining real world data meeting these strict conditions is very difficult in practice. However, increasing the acceptable range would make the entire filtering process less appropriate from a physical point of view. Given the importance of correctly measuring the SMR, an estimation of their associated measurement uncertainties is needed to allow a proper assessment of the validity of the 3% limit. In this study a Monte Carlo simulation has been used, to allow the estimation of the propagation of uncertainties in expressions having the and integral form. The method consists of applying both random and wavelength correlated errors to the measured spectra and to the measured spectral responses of the three CPV cell junctions, according to the measurement uncertainties of the European Solar Test Installation (ESTI). The experimental data used in this study have been acquired during clear sky conditions in May 2016, at ESTI's facilities in Ispra, northern Italy (45\({}^{\circ}\)49\({}^{\prime}\) N 8\({}^{\circ}\)37\({}^{\prime}\) E).  
**Keywords:** concentrator photovoltaics; measurement uncertainty; International standards; Monte Carlo simulation
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 4 Evaluation Framework
### Metrics
#### 4.2.1 Metrics Related to Effectiveness  
For \(k+1\) classes (+1 class corresponds to background) and \(p_{ij}\) the number of pixels of class i predicted/presumed as belonging to class \(j\) we define the following metrics [64]:  
* _Pixel Accuracy:_ is defined as the ratio of correctly classified pixels divided by their total number. \[PA=\frac{\sum_{i=0}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}}\] (1)
* _Mean Pixel Accuracy:_ is an extension of Pixel Accuracy, which calculates the ratio of correct pixels in a per-class basis and then averaged over the total number of classes. \[MPA=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}}\] (2)
* _Intersection over Union (IoU):_ is a very popular metric used in the field of semantic image segmentation. IoU is defined the intersection of the predicted segmentation map and the ground truth, divided by the area of union between the predicted segmentation map and the ground truth. \[IoU=\frac{\sum_{i=0}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}+\sum_{i=0}^ {k}\sum_{j=0}^{k}p_{ji}-\sum_{i=0}^{k}p_{ii}}\] (3)
* _mean Intersection over Union (mIoU):_ is the most widely used metric for semantic segmentation. It is defined as the average IoU over all classes. \[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0}^ {k}p_{ji}-p_{ii}}\] (4)