esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Calibration Models Using Full Spectra
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compositions**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Parameters \({}^{\bullet}\)**} & \multicolumn{4}{c}{Calibration} & \multicolumn{4}{c}{Prediction} \\ \cline{4-10}  & & & **R\({}^{2}_{\rm c}\)** & **RMSEC** & **Biasc** & **R\({}^{2}_{\rm p}\)** & **RMSEP** & **RPD** & **Biasp** \\ \hline \multirow{3}{*}{chlorogenic acid} & PLS & 8 & 0.90 \({}^{\bullet\bullet}\) & 0.021 & \(1.04\times 10^{-7}\) & 0.87 \({}^{\bullet\bullet}\) & 0.024 & 2.79 & 0.0024 \\  & ELM & 19 & 0.91 \({}^{\bullet\bullet}\) & 0.020 & \(3.03\times 10^{-10}\) & 0.88 \({}^{\bullet\bullet}\) & 0.023 & 2.91 & \(1.54\times 10^{-4}\) \\  & LS-SVM & 38.7638, 184.1695 & 0.91 \({}^{\bullet\bullet}\) & 0.020 & \(-8.10\times 10^{-10}\) & 0.87 \({}^{\bullet\bullet}\) & 0.024 & 2.79 & \(2.58\times 10^{-4}\) \\ \hline \multirow{3}{*}{luteolin-7-O-glucose} & PLS & 7 & 0.82 \({}^{\bullet\bullet\bullet}\) & 0.020 & \(2.63\times 10^{-7}\) & 0.82 \({}^{\bullet\bullet}\) & 0.019 & 2.42 & \(4.69\times 10^{-4}\) \\  & ELM & 22 & 0.86 \({}^{\bullet\bullet\bullet}\) & 0.018 & \(6.71\times 10^{-12}\) & 0.82 \({}^{\bullet\bullet}\) & 0.019 & 2.42 & \(0.0017\) \\  & LS-SVM & 13.19329, 165.0049 & 0.84 \({}^{\bullet\bullet}\) & 0.019 & \(2.88\times 10^{-16}\) & 0.79 \({}^{\bullet\bullet}\) & 0.021 & 2.19 & \(-0.0039\) \\ \hline \multirow{3}{*}{3,5-O-dicaffeoylquinic acid} & PLS & 8 & 0.85 \({}^{\bullet\bullet}\) & 0.049 & \(-2.41\times 10^{-7}\) & 0.81 \({}^{\bullet\bullet}\) & 0.058 & 2.24 & 0.00057 \\  & ELM & 22 & 0.87 \({}^{\bullet\bullet\bullet}\) & 0.047 & \(-1.89\times 10^{-10}\) & 0.82 \({}^{\bullet\bullet}\) & 0.057 & 2.28 & 0.0084 \\ \cline{1-1}  & LS-SVM & 1,063.54810, 01366, & & & & & & & & \\ \cline{1-1}  & 29,309.77831 & 0.86 \({}^{\bullet\bullet\bullet}\) & 0.048 & \(1.23\times 10^{-11}\) & 0.82 \({}^{\bullet\bullet}\) & 0.056 & 2.32 & 0.0016 \\ \hline \hline \end{tabular}  
* \({}^{\bullet}\): Parameters are the optimal parameters of PLS, ELM, and LS-SVM models. Parameter of PLS is the number of latent variables (LVs): parameter of ELM is the number of neuron nodes in the hidden layer; parameters of LS-SVM are the penalty coefficient C and kernel function parameter \(\gamma\); the ** symbols indicate that the R\({}^{2}\) is significant at the significance level of 0.01.  
\end{table} 
Table 2: Results of calibration models for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid in fresh flowers using full spectra (unit: % DW, DW means dry weight).
## 3 Understanding Data Analysis for IoT
### Challenges in IoT Data Analysis  
The unprecedented volume, velocity, and variety of data generated by IoT devices necessitate the application of diverse data analysis techniques to extract meaningful information. Here are some key techniques relevant to IoT:  
While data analysis offers immense benefits, it is not without its challenges, particularly in the context of IoT [72; 73; 74].  
* Volume and Variety of Data: The sheer volume and diversity of data generated by IoT devices can be overwhelming, posing challenges in data management, storage, and processing.
* Data Quality: The accuracy and reliability of insights depend on the quality of the underlying data. However, IoT data can be prone to errors, missing values, and inconsistencies, affecting the quality of the analysis.
* Data Security and Privacy: With increasing concerns around data breaches and privacy violations, ensuring the security and privacy of IoT data is a significant challenge.
* Lack of Skilled Personnel: Data analysis, especially advanced techniques involving AI and machine learning, requires specialized skills. The scarcity of such skilled personnel can hinder effective data analysis.
* Integration and Interoperability Issues: IoT devices from different manufacturers might use different standards and protocols, posing challenges in data integration and interoperability.  
These challenges need to be effectively addressed to unlock the full potential of data analysis in IoT applications. The upcoming sections delve deeper into these aspects in the context of the maritime industry.
# Portable and Highly Versatile Impedance Meter for Very Low Frequency Measurements
## 3 Results and Discussion
### Preliminary System Testing
It is apparent that if the ENB increases error is introduced because of spectral leakage. The data points used in Figure 9 are encircled in green. It is however apparent that data encircled in blue could have been used without significant error and reduced measurement time. Figure 9: Result of test measurements on the impedance in Figure 6b. DC bias across the DUT was set to 500 mV and the AC amplitude of the test voltage was 10 mV at all frequencies. Quite good agreement is obtained between measurements (symbols) and the expected values (solid lines).
# Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
## 3 Results and Discussion
### Visual Force Sensor
#### 3.1.2 Force Sensing
As seen in the table, the proposed method performs as well as the other existing methods. It is difficult to compare the methods used directly, as the methods of evaluating the performance of the force sensor vary based on the researchers. Another issue in the comparison is the differing stiffnesses of the grippers used in the experiments. A normalized absolute error was found to make the comparison easier by dividing the absolute error by the maximum force value. While still imperfect, it gives a better idea for comparing the metrics. However, the method proposed in this paper does have benefits that are not quantifiable using the metrics above. The most significant difference between the proposed method and the others outlined above is the ability of our method to handle large forces, which was not tested in other research. Other works use fin ray effect grippers with much lower stiffness than the Festo gripper. The Festo gripper deforms significantly less than the custom grippers made for other works, requiring a more sensitive model to predict the force accurately. The Festo finger can also provide significant force for real-world gripping applications. However, an issue affecting the sensor in this work is that it is difficult to measure forces below five Newtons, as the gripper can remain deformed after contact, resulting in an unreliable reading at low forces. This could be fixed by using a gripper with a more elastic material, which can snap back into shape more quickly. Other solutions could be to resolve this in the software.
## 3 Proposed Visualized Malware Multi-Classification Framework
### Performance Evaluation Phase  
In this phase, extensive performance analysis in terms of 15 different evaluation metrics is presented to assess the examined models. Thus, the performance of the eight fine-tuned CNN classifiers was evaluated through detection assessment metrics, including accuracy and loss curves, specificity (TNR) (true negative rate), confusion matrix, NPV (negative predictive value), AROC (Area under the receiver operating characteristic curve), FPR (false positive rate), validation accuracy, FNR (false negative rate), precision (PPV) (positive predictive value), FDR (false discovery rate), recall (TPR) (true positive rate), FOR (false omission rate), sensitivity, F1-Score, and misclassification rate. These classification performance metrics have been comprehensively used in the research community to offer exhaustive evaluations of classification approaches [55; 56].  
The mathematical expressions of these evaluation metrics are formulated as follows:  
\[\text{Accuracy}\ =\frac{TP+TN}{TP+FP+TN+FN} \tag{2}\]  
\[\text{Specificity (TNR)}=\frac{TN}{TN+FP} \tag{3}\]  
\[\text{NPV}=\frac{TN}{TN+FN} \tag{4}\]  
\[\text{FPR}=\frac{FP}{FP+TN} \tag{5}\]  
\[\text{FNR}=\frac{FN}{FN+TP} \tag{6}\]  
\[\text{Precision (PPV)}=\frac{TP}{TP+FP} \tag{7}\]  
\[\text{FDR}=\frac{FP}{FP+TP} \tag{8}\]  
\[\text{Recall (TPR)}=\text{Sensitivity}=\frac{TP}{TP+FN} \tag{9}\]  
\[\text{FOR}=\frac{FN}{FN+TN} \tag{10}\]  
\[\text{F1-Score}=\frac{2 TP}{2 TP+FP+FN} \tag{11}\]  
\[\text{Misclassification rate}=\frac{FP+FN}{TP+FP+TN+FN} \tag{12}\]where _TP_ (true positive) implies that both actual and predicted malware types are positive, _TN_ (true negative) means that both actual and predicted malware types are negative, _FP_ (false positive) implies that the actual malware type is negative, while the predicted malware type is positive, and _FN_ (false negative) means that the actual malware type is positive, while the predicted malware type is negative.  
The _TP_, _TN_, _FP_, and _FN_ values can be estimated as shown in Figure 3, which is the confusion matrix of multi-classification tasks, which is different from the traditional confusion matrix of binary classification tasks. In this paper, we have a confusion matrix of classification with \(N\) malware families (classes). For example, considering a specific malware family \(F_{x}\) where both the actual and predicted values are given, the four different classification results that can be obtained are: true positive (green), true negative (yellow), false positive (blue), and false negative (red).  
More details, debates, and explanations about these evaluation metrics can be explored in [57].
# Veins Depth Estimation Using Diffused Reflectance Parameter
###### Abstract  
In order to perform the standard Intravenous (IV) catheterization, subcutaneous veins must be localized. It is a difficult task, especially in the cases when veins are hard to localize. The factors which affect the veins localization process are the physiological characteristics of patients, mainly darker skin tone, scars, hair, dehydration and low blood pressure. With the help of Near Infrared imaging, subcutaneous veins can be envisioned. This is due to the higher absorption of NIR light energy by Hemoglobin (Hb) found in the veins. Besides a superficial view, the veins depth information is also important in order to avoid their rupture by piercing through the walls during IV catheterization process. Diffused reflectance, measured with a camera sensor, can be used for the depth estimation of blood vessels. In this paper, a method to measure the depth of veins using diffused reflectance parameter, is presented. The well-known Monte Carlo model of light propagation in human tissues is used for the mathematical representation. A four-layered skin model is presented with varying vessel depths to describe the diffused reflectance of light while propagating inside skin tissues. The results are validated with Monte Carlo simulations for light propagation in layered medium. A sensitivity analysis of proposed method is also performed with a 5% alteration in the optical parameters of skin due to the change in operating conditions. The results showed a marginal error of maximum value 6.23% in vessel depth estimation using the standard optical parameters, 1.6% for \(-\)5% and 10.74% for \(+\)5% change in optical parameters.  
Keywords: intravenous catheterization; hemoglobin; subcutaneous veins; diffused reflectance; monte carlo; near infrared +
Footnote †: journal: Applied Soft
# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 2 Contributed Papers
### Rainfall Monitoring and Prediction  
In this category, two papers are published. [PERSON] et al. [8] investigated the potential to use next-generation millimeter-wave backhaul technologies for rainfall monitoring in a dense urban environment. Traditionally, microwave backhaul links are mainly used for communications between cellular base stations. In this study, the authors used the links for measuring the path-averaged rain rate. In particular, they investigated the rain attenuation characteristics in Gothenburgh, Sweden using the new microwave backhaul techniques at different mmWave frequencies and link lengths. They found that estimating the path-average rainfall using mmWave links is very effective. The mmWave link measurement-derived rain rate is very well correlated (R = 0.8-0.9) to the local measurement from rainfall gauges. Their study indicates that there is a great potential to use the mmWave links for monitoring rainfall in urban areas. [PERSON] et al. [9] investigated how the assimilation frequency of radar reflectivity affects the rain storm prediction in the Daginghe basin of northern China, using the Three-Dimensional Variational Data Assimilation (3 DVAR) system of the Weather Research Forecast (WRF) model. Their results show that the WRF-3 DVAR system noticeably improves its performance for predicting the location, tendency, and development of rain storm, using the assimilation of radar reflectivity and Global Telecommunication System (GTS) data collectively. Moreover, this study suggests that it is important to validate and correct the assimilated measurement data before performing data assimilation, which can benefit not only prediction accuracy but also assimilation efficiency.
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 3 Research Methodology
### Training of Generative Models
#### 3.1.3 Pix2 pix Architecture
** The discriminator \(D\) that is used in pix2 pix work is also commonly known as the PatchGAN discriminator (see Figure 4). The architecture of \(D\) is a sequence of convolutional layers. Each layer applies \"Conv 2d\", \"Batch-Norm\", and \"Leaky-ReLU\" operations. In the very first layer, \"Batch-Norm\" is not present. While in traditional GAN, the discriminator classifies the given input as fake or real by a single output neuron unit, the PatchGAN discriminator makes a decision based on the majority voting of several decisions made at an image patch level. A single patch of the input is propagated through the network and then mapped to a single output neuron unit. The same happens to all image patches. The advantage of propagating forward in such a way is that we end up with a network that has fewer parameters. Moreover, the inference can be performed faster as patches can be convolved independently in parallel with less memory consumption. In the formal description of \(D\) (see Equation (5)), \(N\) denotes the total number of patches in the input, and \(D(P_{ij})\) denotes the discriminator's prediction for the patch located at \((i,j)\) within the input tensor, which comprises a fingerprint and minutiae map. \[D(I|I^{*},M)= \begin{cases}1&\text{if }\frac{1}{N}\sum_{i,j}D(P_{ij})\geq 0.5\\ 0&\text{otherwise}\end{cases}  \tag{5}\]  
The patch size varies with reference to the network parameters. However, the patch size is defined based on the network parameters. The individual patch is also referred to as a receptive field. The overall discriminator \(D\) and a single patch convolution can be seen in Figure 4. Please note that in contrast to the original network in [24], which accepts at most 256 \(\times\) 256 pixel images, our network accepts 512 \(\times\) 512 pixel images as input. Consequently, the patch size is 142 \(\times\) 142, which is much larger than the original paper. Figure 3: Pix2 pix generator architecture. For every single training instance, the discriminator loss is the sum of binary cross entropy (\(BCE\)) values of original and synthesized images. Given that \(I\) and \(M\) are sets of original images and their minutiae maps, respectively, the overall loss of \(D\) is given by Equation (6). \[Loss_{D}=BCE(D(I))+BCE(D(G(M))) \tag{6}\]
## 2 Numerical Method and Calculation Procedure
### The Surface Tension Model  
All numerical cases in the present study are in two dimensions; thus, the force analysis of the growing bubble should be carefully carried out to make sure the 2D cases can accurately reveal the real situation. As indicated in previous studies [41; 43], the effect of space dimension on the drag coefficients for 3D spherical and 2D cylindrical bubbles can be neglected when the Reynolds number is small enough. In the current study, the surface tension force shown in Equation (11) is adopted to control the motion of a 2D bubble [45; 53].  
\[P_{g}-P_{l}=\frac{\sigma}{r} \tag{11}\]  
Where \(P_{g}\) is bubble's inner pressure, \(P_{l}\) is the pressure of surrounding fluid, \(\sigma\) is the surface tension coefficient, and \(r\) is the radius of curvature. Surface tension is implemented by computing the curvature of the phase interface, and the pressure gradient, \(\Delta\mathrm{p}=\sigma/r\), is explicitly added to the governing equation shown in Equation (2), where \(\overset{\rightarrow}{F}_{S}=\sigma/r\times\overset{\rightarrow}{n}\) is the surface tension term. Taking the advantage of the re-configuration phase, the local radius of curvature can be easily obtained according to the re-configured vapor-liquid interface.  
It should be noted, however, that the phenomenon of fluid reflux beneath the bubble cannot be entirely described in 2D, as there is a large gap between the bubble departure time in 2D and that in 3D [54]. Thus, we only focus on the bubble growing process on the boiling surface in the present study, and the bubble departure behavior is not the research target.
## 4 Discussion  
### Criteria and Indicators for Forest Degradation Detection from Remote Sensing  
Selection of the indicators from remote sensing data is one of the key points, which have great influence on the accuracy of detecting and assessing the forest degradation. The main principle of selection of the degradation indicators is the reduction of biomass and carbon, the decreasing of canopy fraction cover and the biodiversity. Satellite observations data has the potential to be decidedly instrumental in the assessment of forest degradation at a much lower cost, therefore, becoming an applicable way to detecting forest degradation at a regional and global scale [43]. In previous study, as shown in Table 3, various kinds of indicators from the perspective of biodiversity (such as area of specific forest type, area fragmented, canopy fraction cover), vitality (such as VI) [2,44], carbon storage and productivity (such as above ground biomass and NPP) [12] were all selected as the indicators when detecting the forest degradation, and validated by field data or high resolution satellite data (user accuracy ranged from 82.00% to 88.00%, and producer accuracy ranged from 56.58% to 68.10%). In this paper, indicators from multi-scale remote sensing data on the basis of greenness (NDVI, EVI, RVI, LAI), vitality (FPAR) and productivity (NPP) were all taken into account, therefore, achieving a more persuasive and convincing result (overall accuracy 80.40% for MODIS data, and overall accuracy 84.35% for Landsat data).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean Producer’s** & **Mean User’s** & **Overall** & \\  & **Accuracy(\%)** & **Accuracy(\%)** & **Accuracy(\%)** & **Kappa Coefficient** \\ \hline Maowusu & 80.74 & 80.13 & 80.64 & 0.77 \\ Hulun Buir & 84.89 & 85.45 & 83.32 & 0.80 \\ Khorchin & 82.55 & 78.74 & 80.22 & 0.81 \\ Loess Plateau & 81.54 & 76.55 & 77.41 & 0.76 \\ \hline
**Average** & 82.43 & 80.22 & 80.40 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy of forest degradation from MODIS data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean Producer’s** & **Mean User’s** & **Overall** & \\  & **Accuracy(\%)** & **Accuracy(\%)** & **Accuracy(\%)** & **Kappa Coefficient** \\ \hline Maowusu & 88.54 & 80.63 & 84.78 & 0.81 \\ Hulun Buir & 79.37 & 88.54 & 86.47 & 0.83 \\ Khorchin & 86.23 & 83.55 & 83.50 & 0.85 \\ Loess Plateau & 83.10 & 79.65 & 82.65 & 0.80 \\ \hline
**Average** & 84.32 & 83.09 & 84.35 & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of forest degradation from MODIS-Landsat data.
## 3 Evaluation Method of the Facade Acoustic Insulation for a Corner Room
### Calculation of the Facade Acoustic Insulation of a Corner Room with a Roof  
A particular case is presented by the facade of a room featuring a roof. The facade insulation design of rooms involving a roof is not easy to generalize due to several variables. In this case, the facade insulation depends not only on the geometry of the room and on the height of the floor, but also on the inclination and the shape of the roof. This section reports a simplified case of facade attenuation consisting of one wall and a two-pitched roof (Figure 2a) or two walls and a four-pitched square roof (Figure 2b). The loudspeaker is placed on the ground so that only the walls are directly hit by the sound wave. Another option would be to place the loudspeaker high above the ground, but this solution is not often possible and should only be chosen in the case of noise sources that are mainly acting on the roof (for example, fly by of airplanes).  
In cases of corner rooms having one wall and a two-pitched roof (Figure 2a) or two walls and a four-pitched roof (Figure 2b) exposed to the noise coming from a sound source, the number of variables increases significantly. The following general formulas can be used (subscript 1 and 2 denote walls while subscript 3 is used for the roof):  
\[D_{2\text{m,nT1}} =-10\text{lg}\bigg{(}10^{-\frac{D_{2\text{m,nT1}}}{10}}+10^{- \frac{D_{2\text{m,nT2}}+\Delta D_{21}}{10}}+10^{-\frac{D_{2\text{m,nT3}}+ \Delta D_{31}}{10}}\bigg{)} \tag{10}\] \[D_{2\text{m,nT2}} =-10\text{lg}\bigg{(}10^{-\frac{D_{2\text{m,nT2}}}{10}}+10^{- \frac{D_{2\text{m,nT1}}+\Delta D_{12}}{10}}+10^{-\frac{D_{2\text{m,nT3}}+ \Delta D_{22}}{10}}\bigg{)} \tag{11}\]  
Figure 2: Examples of a corner room located at the top floor of a building. (**a**) One wall and two-pitched roof; (**b**) two walls and four-pitched square roof.  
where  
* \(D_{\rm 2m,nT33}\) is the facade acoustic insulation of the roof (side 3) without attenuation;
* \(\Delta D_{\rm 31}\) is the attenuation due to the diffraction of the edge of the roof (side 3) when the loudspeaker is in position 1;
* \(\Delta D_{\rm 32}\) is the attenuation due to the diffraction of the edge of the roof (side 3) when the loudspeaker is in position 2.  
In the case of a corner room with one wall and a two-pitched roof, only Equation (10) is applicable, and it must be used without considering the second term of the sum inside the argument of the logarithm.  
The different composed acoustic insulations \(D_{\rm 2m,nT}\) can be calculated using (9). Finally, the weighted, standardized level difference, \(D_{\rm 2m,nT,w}\), can be calculated according to the ISO 717-1 standard. It is also possible to apply a simplified method considering only the single number insulation values of the different parts of the facade.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 5 Evaluation
### System Performance Discussion  
In this part, we aggregate our system performance evaluation presented in four previous parts in Table 3 to highlight our system contribution based on comparison with other relevant systems.  
To summarize, in this work, our system shows the advantages of integrating NDN and Edge Computing infrastructure in disaster management, especially in the disaster response phase. Firstly, we demonstrated the benefit of using NDN over IP to achieve fast network convergence time in case of damaged network node replacement. This aspect has not been studied before in any works according to our research. Secondly, we designed a better mobility support mechanism for NDN compared with previously proposed solutions. Thirdly, we show edge computing effectiveness in NDN device information management. Compared with other recent proposed NDN management systems, the usage of edge computing platform not only reduces transmission overhead but also ensures recovery ability in case of intermittent network conditions. Finally, we deploy our proof-of-concept system on real platforms with KubeEdge as the chosen edge platform while most related works are only simulation studies.