esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Graphene/Graphene Oxide-Based RF Biosensing Platforms
### Graphene-Based Wireless Mobile Health System
#### 4.2.3 Graphene-Based Pathogenic Bacteria Sensor  
As shown in Figure 9, first, a graphene-based sensing element with a wireless readout coil is generated on silk fibroin (Figure 9a). Next, the ultrathin biosensors are intimately bio-transferred from the silk platform onto biomaterials, such as tooth enamel, via dissolution of the supporting silk film (Figure 9b). The extremely large surface area of the graphene and electrodes ensures high adhesive conformability to the rugged surfaces of biomaterials. Because self-assembling designer bifunctional antimicrobial peptide (AMP)-graphene peptides achieve specificity in biological recognition on the graphene (Figure 9c), graphene functionalization can be achieved without degrading its electronic sensing properties.  
Furthermore, Figure 9c shows two other significant functionalities of the hybrid-type sensor unit--battery-free operation and wireless remote sensing capability. On recognizing and binding of specific bacterial targets by the immobilized peptides (Figure 9d)), the electrical conductivity (or resistivity) of the graphene film is modulated and wirelessly monitored using an inductively coupled RF reader device. This shows that the critical functionalities of the graphene/silk hybrid sensing elements are derived from a synergistic integration of the individual material properties and components. In the graphene-based biosensor, graphene film was functionalized with a chemically synthesized bifunctional peptide, consisting of a dodecapeptide graphene-binding peptide (GBP), a triglycine linker, and AMP odorranin-hydroxyprogesterone (OHP), which shows activity toward both Gram-negative bacteria (_Escherichia coli_ and _Helicobacter pylori_) and Gram-positive bacteria (_Staphylococcus aureus_) [78].
## 4 BBN Model Validation  
The developed model is validated against two real world grounding cases: the grounding of the passenger ship \"Zlatni Zal\" and the grounding of the passenger ship \"Cicero\".  
Case \"Zlatni Zal\"--Ship \"Zlatni Zal\" is powered by two diesel engines with a power of 492 kW, 33.5 m long, 5.3 m wide, 1.58 m draft, and 109 GT. The ship has radar. While sailing, he suffered a maritime accident grounding on the island Scedro on 1 April 2017. At the time of grounding, favorable weather conditions and excellent visibility prevailed, and the captain's recklessness was determined as the cause of the accident.  
Case \"Cicero\"--Another example of comparing the model results to a real event is the grounding of a small passenger vessel Cicero. It is a vessel used for a seven-day cruise, 32 m long, which grounded near the Splitska vrata on 18 September 2019 in the afternoon. Weather conditions were favorable at the time of the marine casualty. According to the maritime accident report, the maritime accident did not occur due to a technical failure of the ship, nor the navigation system, nor due to some external or internal influences, but due to human impact, i.e., the assessment and decision of the person operating the ship, ultimately resulting in the grounding. There were 25 passengers on board.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## Materials
### Catalonia SIGPAC (Geographic Information System for Agricultural Parcels) Dataset  
Over the Catalonia region of northeast Spain, the General Direction of Rural Development of the Generalitat of Catalonia provides the Geographic Information System for Agricultural Parcels (SIGPAC) data. The SIGPAC data are based on cadastral plots digitized using aerial images at scale of 1:5000 and 25 cm spatial resolution. The graphical data of the SIGPAC provide the field boundaries. On the other hand, alphanumerical data define each plot by several elements of information including an identification code, surface area, land cover and irrigation indicator. The irrigation indicator shows the presence (100) or absence of irrigation (0). Annual field campaigns are performed each year, in order to update the database mainly for irrigation and land-cover information. In our study, 159,850 plots (123,428 non-irrigated and 36,423 irrigated plots) of different crop types and irrigation coefficients have  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plot** & **Crop Type** & **Surface (ha)** &
\begin{tabular}{c} **Number of** \\ **Irrigations** \\ \end{tabular} & **Period of Irrigation** & **Irrigation Type** \\ \hline P1 & Maize & 1.2 & 30 & 01 June–12 October 2017 & Sprinkler \\ P2 & Soya & 0.8 & 13 & 29 May–13 September 2017 & Sprinkler \\ P3 & Sorghum & 0.44 & 5 & 01 June–08 August 2017 & Sprinkler \\ \hline \hline \end{tabular}
\end{table}
Table 1: Irrigation information over three plots in Montpellier, south-east France.  
Figure 1: Location of the three studied sites. In green, Montpellier of southeast France; in black, Catalonia region in northeast Spain; and in red, Tarbes of southwest France. Irrigated plots are presented in blue while non-irrigated plots are in red.  
been used for the year 2018. Considering only agricultural crops (summer and winter crops) in the study, forests, urban, and orchards plots were eliminated. The surface area of the plots varies between 0.1 ha and 65 ha. In general, winter cereals such as wheat, oat, and barely are rarely irrigated with some exceptions. On the other hand, irrigated plots mainly include alfalfa, maize, grassland, beans, rapeseed, and rice. The study area is mainly irrigated using inundation in the old irrigation district and sprinkler or dripper irrigation in the new irrigation district. Fields that have access to water are always irrigated, and fields that do not have access to water are not. The irrigation period occurs mainly in summer, from May to September, and the frequency depends on the irrigation district (old and new). Since irrigation frequency and dates are not available via the SIGPAC data, the irrigation information over the plots was used to analyze the performance of the proposed approach.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 2 Kalman Filter for Speech Enhancement
### Paradigm Shift of Recursive Equations  
The paradigm shift of the recursive Equations (7)-(11) transforms them in scalar form. It exploits the understanding as well as analysis of the KF operation in the speech enhancement context. The simplification starts with the output of the KF, \(\hat{s}(n|n)=\mathbf{c}^{\top}\hat{\mathbf{x}}(n|n)\), which is re-written as [20, 21]:  
\[\mathbf{c}^{\top}\hat{\mathbf{x}}(n|n) =\begin{bmatrix}1&0&\dots&0\end{bmatrix}\begin{bmatrix}\hat{s}(n| n)\\ \hat{s}(  
The linear algebra operation on \(\mathbf{c}^{\top}\sigma_{w}^{2}\mathbf{d}\mathbf{d}^{\top}\mathbf{c}\), gives:  
\[\mathbf{c}^{\top}\sigma_{w}^{2}\mathbf{d}\mathbf{d}^{\top}\mathbf{c}=\sigma_{w}^{2}, \tag{18}\]  
and \(\mathbf{c}^{\top}\mathbf{\Phi}\mathbf{\Psi}(n-1|n-1)\mathbf{\Phi}^{\top}\mathbf{c}\) represents the transmission of a posteriori error variance by the speech model from the previous time sample, \(n-1\), denoted as [21]:  
\[\mathbf{c}^{\top}\mathbf{\Phi}\mathbf{\Psi}(n-1|n-1)\mathbf{\Phi}^{\top}\mathbf{c}=\alpha^{2}(n). \tag{19}\]  
Substituting Equations (18) and (19) into Equation (17) gives:  
\[\mathbf{c}^{\top}\mathbf{\Psi}(n|n-1)\mathbf{c}=\alpha^{2}(n)+\sigma_{w}^{2}. \tag{20}\]  
From Equations (20) and (16), \(K_{0}(n)\) is given by:  
\[K_{0}(n)=\frac{\alpha^{2}(n)+\sigma_{w}^{2}}{\alpha^{2}(n)+\sigma_{w}^{2}+ \sigma_{v}^{2}}. \tag{21}\]  
Substituting Equations (12), (14), and (15) into Equation (13) gives:  
\[\hat{s}(n|n)=\hat{s}(n|n-1)+K_{0}[y(n)-\hat{s}(n|n-1)]. \tag{22}\]  
Re-arranging Equation (22) yields:  
\[\hat{s}(n|n)=[1-K_{0}(n)]\hat{s}(n|n-1)+K_{0}(n)y(n). \tag{23}\]  
Equation (23) implies that the accurate estimates of \(\hat{s}(n|n)\) (output of the KF) will be achieved if \(K_{0}(n)\) becomes unbiased. However, in practice, the inaccurate estimates of \((\{a_{i}\},\sigma_{w}^{2})\) and \(\sigma_{v}^{2}\) introduce bias in \(K_{0}(n)\), resulting in degraded \(\hat{s}(n|n)\). In [19], [PERSON] et al. introduced a robustness metric, \(J_{2}(n)\) and a sensitivity metric, \(J_{1}(n)\) to quantify the level of robustness and sensitivity of the KF, which can be used to _offset_ the bias in \(K_{0}(n)\). In the speech enhancement context, \(J_{2}(n)\) and \(J_{1}(n)\) metrics can be computed by simplifying the mean squared error, \(\mathbf{c}^{\top}\mathbf{\Psi}(n|n)\mathbf{c}\) of the KF output, \(\hat{s}(n|n)\) as [20, 21]:  
\[\mathbf{c}^{\top}\mathbf{\Psi}(n|n)\mathbf{c}=\mathbf{c}^{\top}[\mathbf{I}-\mathbf{K}  
is the scalar a posteriori mean squared error, \(J_{2}(n)\) and \(J_{1}(n)\) are the robustness and sensitivity metrics of the KF, given as [20, 21]:  
\[J_{2}(n) = \frac{\sigma_{w}^{2}}{\alpha^{2}(n)+\sigma_{w}^{2}}, \tag{27}\] \[J_{1}(n) = \frac{\sigma_{v}^{2}}{\alpha^{2}(n)+\sigma_{w}^{2}+\sigma_{v}^{2}}. \tag{28}\]  
The KF-based SEAs in [20, 21] address tuning of \(K_{0}(n)\) using \(J_{2}(n)\) and \(J_{1}(n)\) metrics for speech enhancement in t WGN condition as described next.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques  
In this section, we will be discussing various privacy-preserving techniques that are used to protect personal information and prevent re-identification or deanonymization. It is important to note that these are just a few examples of the many techniques available to safeguard sensitive information.
## 3 Experimental Results  
### The Characteristics of Patients and Nodules  
Sonographic features of thyroid nodules have led to the emergence of classification systems, and these systems have continuously been updated over the years. Sonographic features of thyroid nodules are indicated as marked for hypoechogenicity, hypoechogenicity, solidity, irregularity/lobulation in their contours, a long axis perpendicular to the skin, microcalcification, and malignancy.  
In the present study, 3261 thyroid nodules are investigated, among which 2695 belong to female patients and 566 belong to male patients. While 2097 (64.3%) were benign, 807 (24.7%) were malignant. Another 357 (11%) were of nondiagnostic cytology.  
Nodules for which an FNA biopsy was performed whereas they should have been followed up according to ACR-TIRADS: 722 in Category 2, 615 in Category 3 (528 neededto be followed up), 1248 in Category 4, (565 needed to be followed up), and 676 in Category 5 (216 needed to be followed up).  
Nodules for which an FNA biopsy was performed whereas they should have been followed up according to K-TIRADS: 406 in Category 2, 852 in Category 3 (407 needed to be followed up), 1363 in Category 4, (260 needed to be followed up), and 640 in Category 5 (232 needed to be followed up).  
Nodules for which an FNA biopsy was performed whereas they should have been followed up according to EU-TIRADS: 61 in Category 2, 1186 in Category 3 (860 needed to be followed up), 669 in Category 4, (185 needed to be followed up), and 1345 in Category 5 (215 needed to be followed up).  
Nodules for which an FNA biopsy was performed whereas they should have been followed up according to ATA and BTA: the numbers of nodules in the very low-risk group were 310 (ATA) and 384 (BTA), while there were 803 (ATA) and 1066 (BTA) nodules in the low-risk group.  
Random Forest, J48 DT, Adaboost, and SVM were used as prediction models to detect whether surgical interventions were correct or incorrect. In the models, a 10-fold cross-validation method was used to calculate performance metrics. As class labels, \"1\" indicates incorrect surgical interventions, and the label \"0\" indicates correct operations on the nodules.  
For Random Forest, the number of leaves was set to 6, and the size of the tree was set to 11 for all experiments. For J48 DT, the confidence factor was set to 0.25, and the minimum number of instances per leaf was set to 2. For the Adaboost algorithm, the weight threshold for weight pruning was 100, the seed was 1, and number of iterations was 10. While training the SVM model, the hinge loss function was used as the loss function, the seed was 1, lambda was \(10^{-4}\), the epochs were 500, and epsilon (e) was set to 0.001. For all experiments, optimization methods and hyperparameters were determined by trial and error.
## 3 Empirical Results
### Robustness Test
#### 3.6.1 Replace the Spatial Weight Matrix  
Compared with the geographical adjacency matrix, the inverse distance spatial weight matrix could measure the relationship between farther spatial units. Thus, this paper adopts the inverse of the center geographical distance between two provinces. \(d_{ij}\) represents the geographical distance between two provinces measured by latitude and longitude. The setting matrix is as follows:  
\[\mathrm{W}_{ij}=\left\{\begin{array}{ll}\frac{1}{\mathrm{d}_{\mathrm{q}}^{*}},\mathrm{i}\
eq\mathrm{j}\\ 0,\mathrm{i}=\mathrm{j}\end{array}\right. \tag{9}\]  
The estimation results show no change in significance and directionality, indicating that the research results are robust and credible from the perspective of replacing the spatial weights.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variables** & **Replace the Spatial Weight Matrix** & **Change Estimation Method** & **Adding Control Variables** \\ \hline L.Ingtfp & 0.1912 * (1.90) & 0.0836 *** & 0.1891 * \\  & (1.90) & (5.26) & (1.89) \\  & \(-\)0.0719 *** & \(-\)0.0253 * & \(-\)0.1171 *** \\  & (\(-\)5.12) & (\(-\)1.67) & (\(-\)4.62) \\  & 0.1580 & 0.4690 * & 0.1587 \\  & (0.97) & (3.45) & (0.98) \\  & 0.2881 *** & 0.1407 *** & 0.2875 *** \\  & (8.59) & (9.34) & (8.61) \\  & \(-\)0.0118 & \(-\)0.0275 *** & \(-\)0.0125 \\  & (\(-\)0.90) & (\(-\)2.77) & (\(-\)0.96) \\  & \(-\)0.0396 & 0.3557 *** & \(-\)0.0436 \\  & (\(-\)0.34) & (4.44) & (\(-\)0.38) \\  & 0.0133 & \(-\)0.1031 & 0.0135 \\  & (0.21) & (\(-\)4.31) & (0.21) \\  & & & 0.0458 ** \\  & & & (2.13) \\  & \(-\)0.0628 * & & \(-\)0.0975 * \\  & (\(-\)1.71) & & (\(-\)1.81) \\  & 1.2089 *** & & 1.1833 *** \\  & (3.83) & & (3.76) \\  & \(-\)0.3069 *** & & \(-\)0.3004 *** \\  & (\(-\)4.12) & & (\(-\)4.08) \\  & 0.0163 & & 0.0190 \\  & (0.694 *** & & 0.6744 *** \\  & (2.86) & & (2.79) \\  & 0.0060 & & 0.0353 \\  & (0.05) & & (0.29) \\  & & & 0.0357 \\  & & & (0.87) \\  & 0.1341 ** & 1.3020 *** & 0.1276 \\  & (2.10) & (\(-\)4.93) & (1.99) \\  & 620 & 620 & 620 \\  & Log-L & \(-\)580.44 & 85.2713 & \(-\)721.2347 \\  & R\({}^{2}\) & 0.5932 & 0.9156 & 0.5685 \\  & Control variables & YES & YES \\  & and spatial terms & & YES \\  & Individual fixed & & YES \\  & effects & YES & YES \\ Time fixed effect & YES & YES & YES \\ \hline \end{tabular} Note: Standard errors in parentheses, *** \(p<\) 0.01, *** \(p<\) 0.05, * \(p<\) 0.1.  
\end{table}
Table 7: The results of robustness test.
## 2 Materials and Methods
### Classification of Urban Spaces  
Suwon City, the capital and largest city of Gyeonggi-do, South Korea with 1.2 million people occupying 12,100 hectares of land, was selected as the study area (Figure 2). Suwon City is basin-shaped and surrounded by mountains with various land uses, such as forests, agricultural land, and low-rise, mid-rise, and high-rise urbanization. Over the last decade, natural land cover such as forests and farmlands have decreased by about 9 km\({}^{2}\) while urbanized areas have increased by about 6.3 km\({}^{2}\). Despite such urbanization, further large-scale urban development projects are still being planned and carried out. In addition, with 40% of Suwon City comprising forests and farmlands, which provide a relatively large number of ES, it is an area where systematic urban ecosystem management is required.  
For spatial assessment of ERS, the study area was classified into a 60 m \(\times\) 60 m grid by applying K-cluster analysis. The study area was classified into the 60 m \(\times\) 60 m grid because satellite images, land cover maps, and soil maps produced by satellite images were mainly used as basic data for assessment of ES. There are 28,148 grids in the study area, and military facilities and areas under development were excluded from the analysis because of difficulties in obtaining data for analysis. In addition, water space was excluded because the selected indices are used mainly for measuring ERS of green areas.
# Impact of COVID-19 Mobility Changes on Air Quality in Warsaw
### Changes in Mobility and Air Quality under COVID-19 Conditions
In Barcelona, an even higher decrease of 45-50% was reported [25]. Lockdown was slightly less important for PM2.5 and PM10 in urban areas and cities, although the data on these types of emissions are not conclusive. A significant decrease in PM2.5 concentrations has been reported in Pakistan [28]. PM10 concentrations decreased significantly (by 28-31%) in Barcelona, however significantly less than NO\({}_{2}\) concentrations [25]. In Puglia, there was no significant difference in PM10 concentrations during the first lockdown phase, while in April 2020, concentrations were higher than in April 2019. This could be due to other factors, unrelated to transport, affecting the levels of these particulates in the air, such as weather and climate conditions and desert dust deposition [24]. The manuscript presents the results of the research project entitled \"Method of assessing the social impact of changes in personal mobility in an epidemic state together with tools to support transport management\", carried out at the Warsaw University of Technology under the \"IDUB against COVID-19\" research project (Excellence Initiative Research University--in Polish, Inicijatywa Doskonalosci Uczelnia Badawcza). Using Warsaw Metropolitan Area (WMA) as a case study, the paper demonstrated the possibility to use data from SIM cards to assess the impact of the pandemic and associated restrictions on personal mobility and thus also the influence of the transportation system on the environment. We mostly focused on the influence of mobility changes on alterations in air quality, with a particular emphasis on nitrogen oxides and particulate matter (PM2.5 and PM10).
## 4 Discussion
This is particularly interesting for applied settings, such as clinical interviews, where it is essential that a continuous measure of load is available without delay, so as to allow the interviewer to pay special attention to what was said or to ask the patient to repeat the information. The problem is, however, that the sensitivity of this measure can be decreased by concomitant emotional content as demonstrated in the negative conditions. Of further interest, the very low correlation between luminance-elicited and load-related pupillary changes indicated that at least the range of luminance values used in the current study does not impose a limit on the sensitivity of the load-dependent pupillary change measured by the algorithm. This was the case despite a significant negative correlation between measured luminance and pupillary responses that indicated that the luminance differences were strong enough to elicit a pupillary response. From an applied perspective, in the future, the increasingly pervasive presence of cameras in laptops and smartphones may be used to measure pupil sizes without the need for additional equipment and, thus, to measure cognitive load without any additional technical requirements. Technologies such as smart glasses may further boost the widespread availability of the necessary camera or eye-tracking equipment. However, we have to admit that, currently, the resolution of cameras built into laptops and smartphones is insufficient, especially in combination with the frequently suboptimal recording conditions with low luminance, shadows, faces turned away from the screens and cameras, or glasses blocking the detection of the comparatively small pupil.
## 4 Analysis of Methods for Forecasting Transportation Flow
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Category** & **Algorithm Name** & **Advantages** & **Disadvantages** & **Application Example** \\ \hline \multirow{8}{*}{Mathematical statistics model} & \multirow{8}{*}{GM} & It is required that the time series data are stable, sensitive to data, and the nonlinear problem is not solved effectively. & Time series problems such as traffic flow prediction. \\  & & Better prediction accuracy for problems with short time period and small amount of data. & Unable to be used as a long-term forecasting tool as the calculation is very cumbersome. & Time series problems such as traffic flow prediction. \\  & & High prediction accuracy and strong nonlinear processing ability. & The model is complex, sensitive to data, and requires a large amount of calculation & Traffic flow prediction, image recognition, etc. \\ \hline \multirow{8}{*}{Intelligent algorithm model} & \multirow{8}{*}{SVM} & Structure of the model is simple, and has good characteristics of time transfer and regional transfer. & The mathematical foundation is weak, the structural rigor of the model is insufficient, and the error of the collective calculation results is large. & Flow forecasting, spatial pattern research, etc. \\  & & Binary classification algorithm, applicable to both linear and nonlinear problems, and has advantages for high-dimensional data. & In addition, and the multiclassification problem is not well solved. & Traffic flow prediction, image recognition, etc. \\  & & The algorithm is simple and has advantages in dealing with missing attribute samples. & Easy to overfit. & Traffic flow prediction, classification, and other issues. \\  & & It has good nonlinear mapping ability, ability, self-organization ability, and self-adaptive ability. & Easy to overfit. & Traffic flow prediction, classification, and other issues. \\  & & & & \\ \hline \multirow{8}{*}{Combined algorithm model} & Combined algorithm model based on ANN & High prediction accuracy and low calculation time. & Traffic flow prediction, image recognition, condition monitoring, etc. \\ \cline{1-1}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table} 
Table 2: Compares common algorithms in the three traffic flow prediction models.