esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance
## 2 Materials and Methods: Risk Assessment Algorithm
### Risk Assessment  
Once the HI and _Il_ have been calculated, the risk of the unit can be found. Traditionally, the risk index (RI) is obtained by multiplying the HI and the _II_, then, a list or ranking is generated to determine which assessed units should be prioritized. However, under the context of fleet analysis, an asset manager goal's is to arrange the units in groups with similar conditions to implement suitable maintenance strategies. To achieve this goal, risk matrices whose axes are the HI and the _Il_ will be used. The k-means clustering technique is applied to establish the groups or clusters of PT.  
K-means is considered one of the simplest unsupervised learning algorithms that solve the well-known clustering problem [36]. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k-clusters) fixed a priori. This algorithm has as a goal the minimization of an objective function known as the mean square error (MSE) and it is given by (4).  
\[J(V)=\sum_{i=1}^{c}\sum_{j=1}^{c_{j}}\bigl{(}\|x_{i}-v_{j}\|\bigr{)}^{2} \tag{4}\]  
where \(\|x_{i}-v_{j}\|\) is the Euclidean distance between xi and \(v_{j}\), \(c_{j}\) corresponds to the number of data points in the cluster ith and \(c\) is the number of centroids.  
In this paper, three groups or clusters will be designated to assess the risk of units with similar HI and _Il_ as a means to define proper maintenance schemes. Cluster 1 indicates the PT with the lower risk meanwhile Cluster 3 encloses the units that should be prioritized.  
Figure 10(a),b presents a data set example before and after the k-means clustering technique is applied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multicolumn{4}{c}{**Score**} & \multirow{2}{*}{**Weight**} \\ \cline{2-2} \cline{4-5}  & **Low (I)** & & **Medium (2)** & **High (3)** & \\ \hline Manufacturer & Well Known & Known & Unknown & 1 \\ Type/Application & Mobile & Standby & In-service & 3 \\ Load Factor & \textless{}0.6 & 0.6–1.0 & \textgreater{}1.0 & 4 \\ Voltage (\(\mathrm{kV}\)) & 132/33 or 132/13,2 & 220/132 & 500/220 & 4 \\ _N-I_ Criterion & Yes & N/A & No & 5 \\ Location & Rural & Urban & Industrial & 3 \\ Critical Loads & None & A Few & Several & 3 \\ Rated Power (MW) & \textless{}30 & 30-50 & \textgreater{}50 & 3 \\ Mean Load (MW) & \textless{}10 & 10–30 & \textgreater{}30 & 4 \\ Security Level & & & & \\
1. Alarm system & & & & \\
2. Oil Pitch & & & & \\
3. Distance between & & & & \\ transformers more than \(11\
## 3 Results
### Parameters \(T_{opt}\), \(E_{d}\), \(E_{r}\)  
Equation (5) was used to analyze experimental data regarding changes in the activity of horseradish peroxidase under the influence of temperature allowing for the determination of parameters that occur in Equation (5), such as the optimal temperature \(T_{opt}\), deactivation energy \(E_{d}\), and the parameter \(\theta\). Figure 1 shows experimental data on horseradish's peroxidase activity as a function of temperature and activity curves plotted based on Equation (5).  
The values of the parameters \(T_{opt}\), \(E_{d}\), and \(\theta\) for horseradish's peroxidase were determined using the non-linear methods using SigmaPlot 15.0 software, and the values of these parameters are presented in Table 1. Also, they are calculated based on Equation (7), and the \(E_{r}\) values are depicted in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Horseradish** **Peroidase** & \(T_{opt}\) **(K)** & \(\theta\) & \(E_{d}\) **(k/mol)** & \(E_{r}\) **(k/mol)** \\ \hline Native & 303.21 \(\pm\) 1.07 & 1.77 \(\pm\) 0.52 & 63.35 \(\pm\) 5.45 & 40.33 \(\pm\) 10.72 \\ Nanofibrous membrane & 313.64 \(\pm\) 1.15 & 0.06 \(\pm\) 0.03 & 215.00 \(\pm\) 55.04 & 6.39 \(\pm\) 4.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The value of parameters estimated for horseradish's peroxidase immobilized on the modified nanofibrous membrane.  
Figure 1: The activity of horseradish’s peroxidase: (**a**) native and (**b**) immobilized on the modified nanofibrous membrane.  
The value of the regression coefficient \(R^{2}\) was high, above 0.95. Simultaneously, the _SSE_ sum of squared error values below 0.035 is satisfactory. Additionally, the \(F\)-Fisher test values were above 41.00, and the probability for the \(T_{\text{opt}}\) and \(E_{\text{d}}\) parameters was below 0.0175. The statistical data of obtained parameters \(T_{\text{opt}}\), \(E_{\text{d}}\), and \(\theta\) of horseradish peroxidase were satisfactory when the commonly used probability value is much lower and equal to \(p<0.05\)[26; 29]. Additionally, Figure 1 represents standard deviation errors for experimental points, while the 95% confidence limits were marked for the obtained curves. Therefore, the statistical data confirmed that the application of Equation (5) when determining parameters is justified.
## 1 Introduction
At the Earth-Moon system's Lagrange point L1, the MEPI will measure the Earth's comprehensive information of nine spectral bands (400-420 nm, 433-453 nm, 460-480 nm, 480-500 nm, 522-542 nm, 545-565 nm, 590-610 nm, 660-680 nm, 845-885 nm), with three polarization angles of 0\({}^{\circ}\), 60\({}^{\circ}\), 120\({}^{\circ}\), and the spatial resolution of Earth observation up to 10 km. The observation of the whole Earth can obtain information about the whole Earth's disc at the same time, which fundamentally avoids error sources caused by regional splicing, and thus improves the accuracy and reliability of the data. Figure 1: The overall structure of the Multi-Spectral Earth Polarization Observation Imager.
###### Abstract  
In this study, the drying time, effective moisture diffusivity (\(D_{\mathit{eff}}\)), specific energy consumption (SEC), and quality (color, shrinkage, and rehydration) of the ultrasound-pretreated (US) carrot slices were compared when dried by hot air drying (HD), microwave drying (MWD), infrared drying (INFD), and hybrid methods of MW-HD and INF-HD. Five mathematical models were considered to describe the drying kinetics in the carrots. The results show that US+MW-HD and INFD were the fastest and the slowest drying techniques compared to the HD technique with a 73% and 23% drying time reduction, respectively. The \(D_{\mathit{eff}}\) ranged from \(7.12\times 10^{-9}\) to \(2.78\times 10^{-8}\) m\({}^{2}\)/s. The highest and lowest SECs were \(297.29\pm 11.21\) and \(23.75\pm 2.22\) MJ/kg which were observed in the HD and US+MWD, respectively. The color variation indices indicated that the best sample in terms of color stability was the one dried by US+MW-HD with the color variation of \(11.02\pm 0.27\). The lowest and highest shrinkage values were also observed in the samples dried by US+MWD and HD (\(31.8\pm 1.1\%\) and \(62.23\pm 1.77\%\)), respectively. Samples dried by US+MWD and HD possessed the highest and lowest rehydration, respectively. Although the carrot slices dried at a higher pace by US+MW-HD (compared to US+MWD), the shrinkage and SEC of the samples dried by US+MWD were significantly lower than the US+MW-HD (\(p<0.05\)). Therefore, it can be concluded that the application of the US+MWD method can be considered as a proper alternative for drying the carrot slices when compared to the HD, MWD, INFD, and hybrid methods.  
mathematical modeling; color; moisture diffusivity; shrinkage; rehydration ratio 1  
[PERSON] 1,*(c), 2020
## 5 Experimental Evaluation
### Ablation Study  
An ablation study was conducted to observe the individual and combined effects of the UA-RL and PWA modules in our UA-FedRL approach, which can be seen in Table 8. When the UA-RL and PWA modules were implemented independently, the accuracy of the model was increased to 93.34% and 92.54% in the MNIST dataset and 60.95% and 60.34% in the CIFAR-10 dataset, respectively. This highlights the effectiveness of the UA-RL to effectively select epochs in heterogeneous industrial IoT settings and the PWA's ability to boost model accuracy by aggregating model weights in line with their quality.  
However, the UA-FedRL approach achieved good accuracy when both submodules were combined together, achieving an accuracy of 96.87% and 32.35% on the MNIST and CIFAR-10 datasets, respectively. This outcome validates the dynamic combination between the UA-RL and PWA within the UA-FedRL method.  
Table 9 shows the accuracy of UA-FedRL as the number of IoT devices increased on the MNIST and CIFAR-10 datasets. For MNIST, accuracy slightly decreased as the number of devices increased, starting from 96.87% with 100 devices to 96.04% with 250 devices. Similarly, on CIFAR-10, accuracy dropped from 62.73% with 100 devices to 61.98% with 250 devices. It suggests that as more devices participate, the challenge of handling diverse data distributions may slightly affect the overall accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Levels** & **Fed\_Avg** & **Fed\_Prox** & **UA-FedRL** \\ \hline Low & 50.95\% & 60.37\% & 62.75\% \\ Medium & 50.34\% & 49.76\% & 60.87\% \\ High & 35.93\% & 44.78\% & 59.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The acquired accuracy of UA-FedRL, Fed_AVG, and Fed_Prox methods with 90% straggler IoT devices on the CIFAR-10 dataset.  
Figure 13: The accuracy comparison of UA-FedRL, Fed_AVG, and Fed_Prox methods with 90% straggler IoT devices on the CIFAR-10 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multicolumn{2}{c}{**Submodules**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-5}  & **UA-RL** & **PWA** & **MNIST** & **CIFAR-10** \\ \hline \multirow{4}{*}{UA-FedRL} & ✓ & – & 93.34\% & 60.95\% \\ \cline{2-5}  & – & ✓ & 92.54\% & 60.34\% \\ \cline{1-1} \cline{2-5}  & ✓ & ✓ & 96.87\% & 62.73\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The ablation study of UA-FedRL with different combinations of UA-RL and PWA submodules.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Evaluation and Discussion
#### 3.2.2 Model Performance Comparison  
We used the evaluation factors mentioned in the previous section to evaluate the proposed model and several of the classic classification models mentioned in Section 3.1.1, as shown in Table 6. The parameters of these models were the same as in 3.1.1.  
In Table 6, the proposed model is superior to other models in terms of accuracy, precision, specificity and \(F_{1}\) score.
## 2 Assumptions and Methods
### InSAR Coherence Floodwater Mapping in Urban Areas  
Based on the previous considerations, it is important to isolate the pixels that are dominated by the double-bounce mechanism. Furthermore, it is important to optimally exploit the S-1 frequent revisit with constant geometry. The proposed algorithm is therefore composed of two main steps: (1) prior identification of the double-bounce features in the SAR images using a multi-temporal technique; (2) detection of the \(\rho\) drop-off in the previously detected double-bounce map.  
The method is based on a multi-pass approach exploiting a stack of interferometric acquisitions. The coherence map between each consecutive pair of images is extracted using a square moving window. Given t\({}_{0}\), i.e., the date of the image acquired during the flood event, we denote with \(\rho_{\text{co}}\) the coherence of the image pair acquired on t\({}_{0}\) and t\({}_{-1}\), and \(\rho_{\text{pre}}\) the one with images acquired on t\({}_{-1}\) and t\({}_{-2}\). We proceed in the same way to generate coherence maps from previous images of the stack. Figure 1 shows the overall process, summarizing the algorithm steps. Step 1 allows the double-bounce map to be extracted, i.e., the building footprints, and will be described in detail in the next subsection (red blocks in Figure 1). Step 2 combines the double-bounce map and the change of \(\rho_{\text{pre}}\)-\(\rho_{\text{co}}\) (dark blue blocks in Figure 1). The underlying assumption is that urban areas affected by a flood have \(\rho_{\text{co}}<\rho_{\text{pre}}\)To provide a more exhaustive analysis of the event and to frame the new findings in a more general context, flooded bare soils have also been mapped using the algorithm proposed by [PERSON] et al. (2016). The associate processing module is displayed in the \"bare soil flood mapping\" blocks in Figure 1 (light blue). It is based on an adaptive thresholding approach, which at the same time exploits multi-temporal information through change detection and contextual information through region-growing. It makes use of two images: one acquired during the flood event and one from before the event started. The algorithm is operational and available on the on ESA-Grid Processing on Demand (G-POD) platform under the HASARD service ([PERSON] et al., 2016).
# Internet of Drones Intrusion Detection Using Deep Learning
## 4 Results
### Performance Measures  
For the proposed framework's performance, some well-known metrics are used in similar situations. The records in the dataset are classified into normal and attacks. Therefore, the following parameters are considered for the appropriate measure to the performance of the proposed solutions:  
* True Positive (TP): the number of correctly classified records toward the Normal class.
* True Negative (TN): the number of correctly classified records toward the Attack class.
* False Positive (FP): the number of Normal records wrongly classified toward the Attack class.
* False Negative (FN): the number of Attack records wrongly classified toward the Normal class.  
Therefore, based on the previous parameters, the following methods were considered for the solutions performance evaluation in this paper:  
\begin{table}
\begin{tabular}{c c} \hline
**Attack Type** & **TON\_IoT No. of Records** \\ \hline Backdoor & 508,116 \\ DDoS & 6,165,008 \\ DoS & 3,375,328 \\ Injection & 452,659 \\ MITM & 1052 \\ Password & 1,718,568 \\ Ransomware & 72,805 \\ Scanning & 7,140,161 \\ XSS & 2,108,944 \\ Benign & 796,380 \\ \hline \end{tabular}
\end{table}
Table 3: TON_IoT attacks categories.  
Figure 9: CICIDS2017 (a) training data and (b) testing data.  
* Accuracy: as can be seen in Equation (1), the accuracy is computed as the ratio of the correctly identified records to all of the records in the dataset. Certainly, as long as the accuracy is high, the model is considered performing better (_Accuracy_\(\in[0;1]\)). The accuracy test helps calculate the balanced classes of the data we have. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (1)
* Precision: This is an attack measurement method used to calculate the ratio of the number of correctly classified attack records to the number of all identified attack records. Again, the precision is high, the model is considered to be performing better (_Precision_\(\in[0;1]\)). The precision can be computed by Equation (2). \[Precision=\frac{TP}{TP+FP}\] (2)
* True Positive Rate (TPR). Recall, or Sensitivity: This is the probability that an actual positive classification will test positive. Again, the higher the TPR, the higher the performance (\(TPR\in[0;1]\)). The TPR can be computed using Equation (3). \[TPR=\frac{TP}{TP+FN}\] (3)
* F1-Score: This is another valuable performance measure, and it is also called F1-measure. The F-score is the relation between the harmonic mean of the precision and recall measures. If the _F1-score_ is high, the proposed model performance (\(F1-Score\in[0;1]\)) would be considered high. The _F1-score_ is defined as given in Equation (4). \[F1-Score=2\bigg{(}\frac{Precision\ x\ Recall}{Precision+\ Recall}\bigg{)}\] (4)
## 3 Results
### Analysis of the Characteristics of Ridge Formation
#### 3.3.2 Effect of Spacing on Ridge Formation  
Figures 14-16 show that the height of the ridges rise with increasing spacing. In particular, ridge formation began when the cutting spacing was 6 mm with a penetration depth of 3 mm (Figure 14b). Moreover, in cutting with penetration depths of 6 mm and 9 mm, ridge formation began when the spacing was 8 mm and 16 mm, respectively (Figures 15b and 16b). This result supports the fact that the ridges are generated between the cutting grooves when the spacing exceeds 1.5 times the penetration depth, as mentioned in Section 3.2.3. In addition, the ridge reached the height of the rock surface before cutting, when the penetration depth and spacing were cut to 3 mm and 24 mm, respectively  
Figure 15: Cross-sectional profiles according to spacing when cutting a rock specimen of UCS 20 MPa with a penetration depth of 6 mm; (**a**) \(s=4\) mm, (**b**) \(s=8\) mm, (**c**) \(s=12\) mm, (**d**) \(s=16\) mm, (**e**) \(s=20\) mm, (**f**) \(s=24\) mm, (**g**) \(s=28\) mm, and (**h**) \(s=32\) mm.  
(Figure 14h). This result proves that from the case that \(s/p\) is 8, cutting is performed in an unrelieved mode in which no interaction occurs between cuts, as mentioned in Section 3.2.2.  
Figure 11 shows that the cutting volume reaches the maximum when \(s/p\) is 4. However, Figures 14d and 15f show that the ridge formed was relatively high when \(s/p\) was 4. The formation of the ridge is proof that excavation is inefficient. Furthermore, if the cutting volume reaches the maximum, the excavation efficiency obtained is different from the maximum. Therefore, it can be expected that the excavation efficiency depends on the cutting force instead of the cutting volume.  
Figure 16: Cross-sectional profiles according to spacing when cutting a rock sample of UCS 20 MPa with a penetration depth of 9 mm; (**a**) \(s=8\) mm, (**b**) \(s=16\) mm, (**c**) \(s=24\) mm, (**d**) \(s=32\) mm, (**e**) \(s=40\) mm, and (**f**) \(s=48\) mm.
## 2 Materials and Methods
### RNA Isolation and Gene Expression Analysis  
Monolayer cultures (\(n=2\); 23- and 31-year-old) were processed using SV total RNA System (Promega, Madison, WI, USA) following the manufacturer's instruction. DNAse treatment was employed to digest genomic DNA. Chondrogenic 3D pellet cultures were harvested for RNA isolation, by digestion in 1 mL Trizol (ThermoFisher) and subsequent grinding using RNase free stainless-steel balls and 3 min 3000 rpm shakes using a Shake Master NEO (Hirata, Kita, Japan). Subsequent suspensions were processed by Qiagen RNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer's instruction to isolate the respective RNA molecules.  
Quality and quantity of RNA were determined via NanoDropTM Lite spectrophotometer (Thermo Scientific) Subsequently, RNA was reverse transcribed into cDNA using a High-Capacity RNA-to-CDNA kit (Applied Biosystems, Beverly, MA, USA). Relative quantification of mRNA was performed using an SYBR Green Master Mix approach via QuantStudio3 (Applied Biosystems) employing custom primer sequences (Table 1). qPCR analysis of each sample was performed in duplicates. Obtained C\({}_{\text{T}}\) values were compared via 2-\({}^{\text{\tiny{A}}\Delta\text{C}\text{T}}\) analysis, calculating gene expressions level relative to the housekeeping gene GAPDH and the average \(\Delta\text{C}_{\text{T}}\) value of the growth-factor unstimulated MSC.
###### Abstract
As an important application of PPP, the current performance of multi-GNSS PPP time and frequency transfer was evaluated using different GNSS constellation and precise orbit/clock products in [23]. In regard to geodynamics, [PERSON] et al. [24] applied sidereal filtering in the GNSS observation domain to alleviate the centimeter-level PPP noise using multi-GNSS data during the 2017 Mw 6.5 Jiuzhaigou earthquake. The PPP method was also used to study the activity of the Long Point Fault in Houston, Texas with six-years of continuous GPS observations [25]. In this respect, a Stable Caribbean Reference Frame 2018 (CARIB18) using long-term continuous GPS observations was developed and introduced [26]. Research in this special issue is also concerned about glacier and climate change. Continuous GPS observations were employed for 14 years to investigate the ice flow velocity in [27]. By analyzing the vertical position time series from 469 European GPS stations, the post-glacial rebound (PGR) in Europe was comprehensively investigated in [28]. Moreover, [PERSON] et al. [29] estimated the satellite accelerations directly from the onboard GPS data using the point-wise acceleration approach for the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) satellite, and achieved higher gravity field model accuracy than those derived from kinematic orbits. Precise modeling of the refraction caused by the troposphere and ionosphere is also a topic of interest in this special issue. [PERSON] et al. [30] proposed a new empirical model for estimating the tropospheric delay and atmospheric weight mean temperature by using ERA-interim reanalysis data. [PERSON] et al. [31] improved the GNSS zenith wet delay (ZWD) interpolation by utilizing tropospheric gradients. In terms of ionospheric delays, [PERSON] et al. [32] evaluated the performance of four ionospheric models in Multi-GNSS single-frequency positioning over China. Furthermore, with the rapid development of sensors in mobile phones, the most up-to-date phones were reported to be capable of real-time ionosphere monitoring [33]. The Guest Editor of this Special Issue would like to thank all authors who have contributed to this volume for sharing their scientific results and for their excellent collaboration. Special thanks are due to the community of distinguished reviewers for their valuable and insightful inputs. The Remote Sensing editorial team is gratefully acknowledged for its support during all phases of the endeavor to successfully complete this volume. The authors declare no conflict of interest.
## 4 Results
### Water Area Extraction Using SWI  
The best classification results were obtained using the threshold of 0.2 to distinguish water and non-water from the SWI images. Figures 3 and 4 show the extracted water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016, with an interval of 12 days. The average, maximum, and minimum areas of Poyang Lake were 2638.36 km2, 3729.19 km2 (on 17 July 2016), and 1726.73 km2 (on 7 March 2016), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **Date** & **Number of Pixels** & **RMSE** & **MAE** & **RMAPE (\%)** & **R2** \\ \hline Poyang Lake & 9 September 2015 & 225 & 0.21 & 0.17 & 3.99 & 0.92 \\ Taihu Lake & 2 February 2016 & 190 & 0.32 & 0.21 & 5.32 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examination of the Sentinel-1A water index equation using indexes.  
To better understand the dynamic changes of the water surface areas of Poyang Lake, the changed area and the variation rate of the water surface areas in 12-day intervals were analyzed. During the period of analysis, there were 22 changes of more than 100 km\({}^{2}\). The maximum changed area was 875.57 km\({}^{2}\) with a variation rate of 35% from 12 April to 24 April 2016. The average changed area was 197.58 km\({}^{2}\). The variation rate in each 12-day interval showed that there were 11 changes of >10%. The average variation rate was 8.2%. The water surface areas of Poyang Lake had several significant changes within a 12-day window over the course of the study period.  
Figure 3: Spatiotemporal distribution of surface water in Poyang Lake based on the SWI from 24 May 2015 to 14 November 2016. The interval of the time-series was 12 days and there were 38 periods in total; however, five periods (4 August 2015; 8 November 2015; 23 June 2016; 29 July 2016; and 3 September 2016) were missing due to a lack of Sentinel-1A data. The yellow color represents non-water and the blue color represents water.