esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing  
#### 2.4.1 Radiometric Correction: Digital Number (DN) to Physical Values  
The DN values of pan-sharpened high-resolution hyperspectral image were transformed to absolute radiance (W/m\({}^{2}\)/nm/sr) values using per-band per-pixel sensitivity factors that were determined in a photonics laboratory of the Technical University of Denmark before the campaign. The spectral irradiance (W/m\({}^{2}\)/nm) measured by the on-flight spectrometer was converted to per-band per-pixel irradiance value for the Cubert camera using the spectral response function of each pixel that was determined in the same laboratory. See [33] for details about the Cubert camera calibration. The reflectance of each pixel was calculated as:  
\[\rho=\pi\frac{radiance}{irradiance}, \tag{1}\]The brightness temperature was calculated using calibration parameters and the inverse of Planck's Law following [PERSON] [34]:  
\[T_{b}=\log^{-1}\left(\frac{k_{1}}{DN+k_{2}+k_{3}T_{c}}+1\right), \tag{2}\]  
where \(k_{1}\), \(k_{2}\), and \(k_{3}\) are calibration parameters, \(DN\) is the digital number of FLIR camera images, and \(T_{c}\) is the camera detector core temperature.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 5 Experimental Results
### Cross-Subject Model Experiment
#### 5.3.1 Colour Naming Session  
Table 2 lists the machine learning results for cross-subject models. In this experiment, we created a model for speech detection using signals from 3 subjects and tested on the rest quarter. The data used for these models were selected only from the first session, where the subjects were presented with colours in the form of an image and they named them.
## 4 Experimental Study in Long Delay Regime
### Frequency Noise Reduction  
The PSD of FM noise of locked DFB lasers under different coupling strength and length are shown in Figure 14, obtained by delayed self-heterodyne measurement with 2 km fiber. The peaks at multiples of 100 kHz (reciprocal of delay time) are artefacts caused by converting the measured RF phase noise to laser frequency noise [17; 40]. A strategy in [41] was employed to partially overcome these artefacts. The measured PSD of FM noise spectra suffer from \(1/f\,^{n}\) noise in low frequency range and white noise dominates above 100 kHz. Increasing the coupling strength leads to reduction of both types of frequency noises, and two lasers exhibit similar performance. Increasing the coupling length, i.e., the coupling delay, is also helpful for frequency noise reduction. The lowest white noise achieved in the system is about \(S_{0}=20\) Hz\({}^{2}/\)Hz, corresponding to an intrinsic linewidth of \(\Delta v_{int}=\pi S_{0}=62.8\) Hz.
# Quantum Game Application to Recovery Problem in Mobile Database
## 2 State-of-the-Art
#### The Need to Extend the Related Work  
According to the review, the following are the current areas of research: (1) The majority of recovery studies employed a variety of techniques, including log management, checkpointing, movement-based checkpointing, and an agent-based logging scheme; (2) Because these techniques are so dissimilar, one cannot be used in place of another; this means that each algorithm has a distinct parameter set and different assumptions; (3) Despite the fact that some plans tried to merge several methods into a single contribution (hybrid method), they were damaged by the difficulties of selecting the optimal fusion from this pool of options. As a consequence, recovery costs may be high and the recovery mechanism may be excessively complicated; (4) The majority of schemes did not include environmental variables as influencing elements in the recovery process; and (5) As the demand for network applications grows, researchers are continuously developing new ways to solve the issue of high mobility or network connection loss owing to a variety of new or changing conditions. Thus, more fault-tolerant methods are needed to guarantee the continued functioning of mobile devices. As a consequence of the above, the use of recovery algorithms is constrained in a realistic manner. It is essential to design a plan that maximizes success via the selection of the most suitable recovery methods for the present situation. We selected quantum game theory over conflict analysis or interactive decision theory, as it enables us to compare the recovery possibilities available.
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 5 Security Analysis and Performance Evaluation
### The Performance Evaluation
The message lengths of each scheme are summarized in Column 4 of Table 4. For example, the total bit length of our D2S anonymous authentication is \(9L_{h}+3L_{ID}\) under the parameter setting we specify, where MAC is implemented using the HMAC algorithm, and the hash function is assumed to be SHA-256. The bit length of the initial request from the device to the server in our D2S anonymous authentication is \(3L_{h}+1L_{ID}\). So, if we assume the bit length of an identity is 80, then the bit length of the initial request and the total bit length of our D2S anonymous authentication are respectively 848 and 2544. In Table 4 we further differentiated D2D schemes into either D2S2D cases or direct D2D cases. [PERSON]'s schemes [11; 25] adopt the concept of time zone in which the entities share the same authorized tokens to derive the successive session keys; therefore, the cost per session is amortized and is greatly reduced; for example, [PERSON]'s scheme [11] apply pairing operation, but the cost is amortized among \(m\) times of accesses within a time zone;
# Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
## 3 Experimental Results
### Discussion  
We compared the performance of the proposed 2D CNN with Taguchi parametric optimization with the original 2D CNN model on the LIDC-IDRI and SPIE-AAPM datasets. We optimized the convolutional neural network model with new data on the LIDC-IDRI and SPIE-AAPM datasets using the best parameter configuration to improve accuracy. The comparison with the original accuracy, and parameters settings are shown in Tables 11 and 12, respectively. On the LIDC-IDRI dataset, the original 2D CNN model attained an accuracy of 91.97%. After three more experiments with the best combination of parameters, the average accuracy rate was 98.83% (98.04%, 98.91%, and 99.53%). On the SPIE-AAPM dataset for the original 2D CNN model the accuracy was 94.68%. After three more experiments with the best combination of parameters, the average accuracy rate was 99.97% (100%, 99.90%, and 100%). Therefore, compared with the original model, the accuracy significant improved 6.86% and 5.29% for the two datasets, respectively. The results showed that the design model for 2D CNN with parametric optimization is necessary.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Sensitivity** & **Specificity** & **False Positive Rate (FPR)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline [PERSON] et al. [22] & 84.87 & 81.22 & 82.97 & 17.03 \\ [PERSON] and [PERSON] [23] & 97.20 & 100 & 94.4 & 5.60 \\
2D CNN with Taguchi parametric optimization & 99.97 & 99.94 & 99.94 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Recognition results of different techniques on the SPIE-AAPM dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Original 2D CNN** & **2D CNN with Taguchi Parametric Optimization** \\ \hline conv1\_Kernel size & 5 & 3 \\ conv1\_Filter & 6 & 4 \\ conv1\_Stride & 1 & 1 \\ conv1\_padding & 0 & 1 \\ conv2\_Kernel size & 5 & 5 \\ conv2\_Filter & 16 & 32 \\ conv2\_Stride & 1 & 1 \\ conv2\_padding & 0 & 1 \\ Accuracy & 91.97\% & 98.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: 2D CNNs architecture parameters setting comparison on the LIDC-IDRI dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Original 2D CNN** & **2D CNN with Taguchi Parametric Optimization** \\ \hline conv1\_Kernel size & 5 & 3 \\ conv1\_Filter & 6 & 4 \\ conv1\_Stride & 1 & 1 \\ conv1\_padding & 0 & 1 \\ conv2\_Kernel size & 5 & 5 \\ conv2\_Filter & 16 & 32 \\ conv2\_Stride & 1 & 1 \\ conv2\_padding & 0 & 1 \\ Accuracy & 94.68\% & 99.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: 2D CNNs architecture parameters setting comparison on the SPIE-AAPM dataset.
## 2 Methods: FMTL Framework  
The overall system structure is shown in Figure 1. The FMTL framework is deployed across multiple clients, each corresponding to a dataset from a different institution for simulations. At the start of each communication round, the server broadcasts the current global model parameters to all clients. Each client then performs local training on its private data, which consists of 30 s PSG signal epochs, including EEG, EEG, ECG, respiratory, EOG, EMG, SpO2, and snoring. After completing its local updates, each client sends only the updated model parameters back to the central server. The server aggregates these parameters using the federated averaging (FedAvg) algorithm [31], producing a new global model that is then re-distributed to every client. This loop of \"broadcast \(\rightarrow\) local training \(\rightarrow\) parameter aggregation \(\rightarrow\) broadcast\" continues for multiple rounds until the global model converges.
## 2 Materials and Methods
### Boundary Conditions and Loading  
In this study, the geometry of the maxillary bone where the boundary condition was applied and the geometry of the crown where the mastication load was applied were identical in all four experimental groups. Therefore, for all four experimental groups, the bottom surface of the maxillary bone was taken as the fixed support, and, to simulate natural chewing loads on the palatal surface of the maxillary central incisors, according to the previous finite element analysis studies on similar topics [4; 25], a static load of 100 N was distributed to the palatal surface of the esthetic ceramic crown at a 45\({}^{\circ}\) angle to the tooth extraction socket long axis (Figure 4). The interface between RAIs and crowns, together with the interface between cancellous and cortical bones are considered bonded in which neither separation nor sliding is allowed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** \\ \hline Cortical bone & 13.7 & 0.3 \\ Cancellous bone & 1.37 & 0.3 \\ Titanium grade 5 (T6 Al4V) & 113.8 & 0.342 \\ Esthetic ceramic & 69 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of all materials used.  
Figure 3: The sectional view of assembled root analogue implant (RAI) components of each experimental group. NM: group implanted with the non-modified surface designed RAI; WG: group implanted with the RAI with a wedge shape added at mesial and distal root surface; LT: group implanted with the lattice shape added at root surface RAI; AA: group implanted with the apex-anchor added RAI.
## 3 Results and Discussion
### X-ray Studies
XPS spectra of SN1 sample before and after annealing at the characteristic energy of Si 2p core levels are presented in Figure 4. The formation of Si nanoclusters (T\({}_{\text{ann}}\) = 900 \({}^{\circ}\)C) and Si NCs (T\({}_{\text{ann}}\) = 1150 \({}^{\circ}\)C) can be attributed to an appearance of a shoulder at 99.7 eV, which corresponds to Si-Si bonds (Si\({}^{0}\) peak). In turn, evidence of a significant phase separation within SiN\({}_{\text{x}}\) layers at T\({}_{\text{ann}}\) = 1150 \({}^{\circ}\)C follows out of an appearance of Si\({}^{4+}\) peak with a characteristic energy 101.7 eV, which corresponds to Si-N bonds in Si\({}_{\text{3}}\)N\({}_{\text{4}}\) matrix [25]. Figure 4: XPS spectra of SN1 sample (as prepared and annealed at different temperatures) measured in the energy region of Si 2p levels. Figure 3: XRD scans around the position of Si (111) diffraction peak (10.3\({}^{\circ}\) for photon energy used) for SN2 sample annealed at different temperatures. Inset: XRD scans of SN1 and SN2 samples annealed at 1150 \({}^{\circ}\)C.
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 3 Experiments
### Experiments Conducted on UAV-Mounted miniSAR-Ku Strip Images
#### 3.3.2 Experimental Results
For spaceborne SAR, the related documents [21; 28; 29] and the results of this paper mutually verified that the positioning accuracy of the ICRD model, the classical R-D model, and the RFM was highly consistent under the same conditions.
# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
### The Methodology's Novelty and Contributions
Overall, this strategy employs two supervised algorithms: (i) a deep hybrid neural network model using a one-dimensional convolutional layer LSTM (Conv1D-LSTM) to predict traffic behavior according to the traffic pattern; (ii) a one-dimensional convolutional network (CNN1D) to classify the incoming types of attack. Five classes (or categories) of traffic behaviors were chosen from the AWID3 for our research, including Website Spoofing, Evil twin, Botnet, Malware, and Normal. The paper is structured as follows: Section 1 introduces the topic and reviews the existing related work; Section 2 presents definitions of the problem and the preparation of data; Section 3 describes our proposed HRC strategy; Section 4 shows the experimental results of the individual model used in the strategy; Section 5 evaluates the goodness of the HRC strategy when integrated into an IDS framework; Section 6 concludes the paper with an evaluation and future works.
# Structured Alumina Substrates for Environmental Catalysis Produced by Stereolithography
## 2 Materials and Methods
### Washcoating with Catalytically Active Material  
One 3D-printed alumina substrate (size, 12 mm \(\times\) 17 mm \(\times\) 54 mm; volume, 8 cm\({}^{3}\), specific surface, 13.3 cm\({}^{2}\cdot\)cm\({}^{-3}\)) and two conventional cordierite honeycomb substrates (M1 and M2; 400 cpsi; 12 mm \(\times\) 17 mm \(\times\) 50 mm; volume 10.3 cm\({}^{3}\); specific surface 25.1 cm\({}^{2}\cdot\)cm\({}^{-3}\)) were coated using a standard V\({}_{2}\)O\({}_{5}\)-WO\({}_{3}\)-TiO\({}_{2}\) catalyst for selective catalytic reduction of NO by NH\({}_{3}\)[26], as summarized in Table 1. The HC specimens were cut out from a monolith with the following dimensions (Corning GmbH, Germany): diameter 267 mm, length 178 mm, cell density 400 cpsi, wall thickness 7 mils (0.17 mm). The catalyst was obtained in powder form by impregnation of commercial 10 wt% WO\({}_{3}\)-TiO\({}_{2}\) (CristalActive DT-52, kindly obtained from Cristal Global) with an aqueous solution of ammonium metavanadate (NH\({}_{4}\)VO\({}_{3}\), assay \(\geq\)99.0%, Sigma-Aldrich, Burlington, MA, USA) followed by drying at 120 \({}^{\circ}\)C for 2 h and calcination in muffle furnace at 550 \({}^{\circ}\)C for 3 h. The powder was then suspended in water (3 eq. of catalytic material), sonicated for 10 min, and homogenized with a disperser for 5 min at 20,000 rpm (d-8, Micera, DE). The monoliths were repeatedly immersed in the slurry and dried with an air blower to reach a loading of the active material of ca. 70-71 g\(\cdot\)L\({}^{-1}\) (3D-printed and M1). One cordierite monolith (M2) was washcoated to obtain 124 g\(\cdot\)L\({}^{-1}\), a typical value for a practical application. Finally, the monoliths were dried and calcined at 450 \({}^{\circ}\)C for 10 h in a muffle furnace. The washcoat loading was determined from the mass change of the substrate. The washcoat thickness was estimated from geometric considerations and taking into account the density of the catalyst powder (ca. 1-1.1 g\(\cdot\)cm\({}^{-3}\)).
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 1 Introduction
Another new metric used to process dual-polarization coherent and incoherent SAR data for coastline extraction purposes allowed discriminating land from sea using an unsupervised classification scheme [12]. However, there are challenges in interpretation and analysis of SAR images due to the inherent speckle noise and possible overlap of backscatter from water and adjacent ice or land. Previous coastline data sets of the Antarctica are available. A high-resolution Circum-Antarctic coastline has been derived from Radarsat-1 images collected during 1997 using a sequence of automated image processing algorithms [3]. A digital image mosaic of surface morphology of the Antarctic continent, assembled from 260 MODIS images, was generated. This data set was then used to determine the coastline vector files [13]. There are also some existing Antarctic coastline products, such as the Mosaic of Antarctica (MOA) coastlines ([[https://nsidc.org/data/moa](https://nsidc.org/data/moa)]([https://nsidc.org/data/moa](https://nsidc.org/data/moa))) and Antarctica Digital Database (ADD) coastline (British Antarctic Survey, BAS; [[https://www.scar.org/data-products/Antarctica-digital-database/](https://www.scar.org/data-products/Antarctica-digital-database/)]([https://www.scar.org/data-products/Antarctica-digital-database/](https://www.scar.org/data-products/Antarctica-digital-database/))). Based on these data sets, we conducted a comprehensive analysis of the dynamic change of ice sheets at a finer spatial scale than previously. However, there are also some issues with these data sets. For one, the resolution of the MOA coastline product is 125 m and the resolution of our productsis 30-75 m, which could provide a better analysis on the change of coastline as well as ice shelves. Traditionally, the coastlines are delineated by experts using optical images at a range of resolutions. Our automated method improves coastline extraction efficiency by removing biases associated with the subjective assessment. In this study, we present detailed information of the Antarctic coastlines using high-resolution remote sensing data for three years: 2005, 2010, and 2017. The data were obtained from Landsat, Advanced Synthetic Aperture Radar (ASAR) on ENVISAT and SAR onboard Sentinel-1A/B (S1A/B). We modified the Canny edge algorithm [14] to improve the automated identification of the coastline.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 4 Evaluation
### The Experiment of Feature Selection
When the proportion of the training set was between 20% and 60%, it was obvious that the classification accuracy was enhanced with the increased percentage of the training sets. When the percentage was set to 70% (larger than 60%), the final classification accuracy tended to be stable, which means that 60% of the training data is enough to build a robust model. Moreover, in order to further illustrate that the selected features can accurately distinguish between normal scripts and malicious scripts, another experiment was conducted by using the dataset of 2112 samples (1056 normal samples and 1056 malicious web shell samples) and 16 features selected by the Fisher score. For each dimension data of the feature vector, the center point of various scripts (normal ones and web shells) was calculated first, which were denoted as \(c_{n}\) and \(c_{m}\), respectively. Then, the average radius of the Euclidean distance from the corresponding dimension data to the center points \(c_{n}\) and \(c_{m}\) in the data samples of the normal and malicious scripts were recorded as \(r_{n}\) and \(r_{m}\), respectively. Finally, the Euclidean distance between the center points of the normal and malicious scripts was calculated and recorded as \(\ dc_{m,n}\). For the 16 dimensions (features), the final results are shown in Table 2. It can be seen from Table 2 that the values of \(r_{n}\) and \(r_{m}\) are much smaller than \(dc_{m,n}\), which shows that the features extracted in this paper can distinguish between normal scripts and web shells. Figure 3: Classification accuracy results with different numbers of selected features. Training result by SVM model. Each result is based on 100 independently repeated trials.
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 5 Experimental Results
### Comparison Experiment of Optimal-Path Model Optimization
#### 5.2.2 Parameter Analysis  
Since the modified differential evolution algorithm is very sensitive to parameters, different parameters will lead to different programming results for the same calculation. Taking the space-robot cluster at 6000 energy as an example, the optimization parameters are analyzed. The population number \(NP\) is selected according to experience, the commonly used range of 5\(D\)-10\(D\). In this paper, an adaptive mutation operator F is adopted, whose value can be adjusted adaptively. The crossover operator selects an appropriate value according to experience. In the modified differential evolution algorithm, the proportion of subpopulations \(\lambda\) (as \(\lambda_{1}=\lambda_{2}=\lambda_{3}=\lambda\)) and \(ng\) have a great influence on the final optimization results. In order to obtain the optimal solution, this paper conducts a separate analysis on the influence of these two parameters, as shown in Table 6.  
Figure 5: Comparison of maintenance routes when the space-robot cluster energy is 7000. (a) Optimal-path model; (b) shortest-path model.  
We obtained by comparing multiple groups of experiments that when \(\lambda=0.25\), \(ng=20\), the effect of this experiment is the best. Different parameters may need to be set for different space maintenance problems. This paper provides a feasible parameter selection scheme.
###### Abstract
In the DaCoTA study [15], a two-dimensional index was created, where countries were ranked simultaneously on the basis of the final outcome index and the safety performance index. The aforementioned studies clearly demonstrated the feasibility and usefulness of developing an overall index for road safety performance evaluation among a group of countries. However, all these studies concentrated on the index development for only one year. In other words, there is still a lack of cross-year comparison based on the constructed safety performance index [18]. In fact, by collecting the SPI data and computing the corresponding index score at regular intervals, road safety policymakers can systematically evaluate the results of various policy interventions, monitor the progress of road safety performance in each country, and make meaningful comparisons among different countries [19]. Hence, the current research gap motivated us to focus on the comparison of road safety performance for a number of countries between different years based on the developed index. To this end, comparable countries are first grouped, as it is more reasonable for countries to learn from similar ones than from those differing in physical and social-economic characteristics. More specifically, 21 European countries are considered in this study, and they are divided into two groups by using the hierarchical clustering analysis. After that, seven quantitative SPIs that currently have data available for all these countries are selected, and corresponding data for both 2008 and 2015 are collected. The principal component analysis (PCA) approach is then applied to combine these SPIs into an overall index for these two years, respectively. Cross-year comparisons on country grouping, index ranking, and weight allocation are conducted afterwards to assess the road safety development of these countries during this period, identify areas of their underperformance, and formulate road safety priorities for policy action. The remaining part of this paper is organized as follows. In Section 2, the indicators used and the data collected for this study are briefly introduced. Section 3 focuses on the methodology for country grouping and safety performance index construction. The results on cross-year comparison are presented and discussed in Section 4. Finally, the paper ends with main concluding remarks and directions for future research in Section 5.