esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Several quantitative models have been developed to select IS, including principal component analysis [33], context-sensitive joint species distribution model (JSDM) [34], probability of species occurrence combined with environmental factors [35], maximum conditional co-occurrence [36], and threshold indicator taxa analysis (TITAN) [37]. However, few studies have quantitatively selected ISs from completely assembled species data, especially for benthic organisms. Many of the well-known wetland values are related to their biodiversity [9]. The number of species is determined by the birth, death, immigration, and emigration rates of species in an area [7]. Biodiversity is one of the primary criteria used in the protection of wetlands [9]. Wetlands as ecotones are unique environments exhibiting considerable potential for biodiversity [38]. Measuring species-level biodiversity involves determining a diversity index based on individual species abundances. The elements that affect such a biodiversity index are the number of unique species (species richness) and the evenness in the distribution of species abundances. Numerous indices are applied in ecological research to measure species-level biodiversity [39]. Species abundance could be measured either in terms of biomass or the number of individual species [40]. Biodiversity is a quantitative measure that reflects how many different species exist in a community and simultaneously considers how evenly the basic entities are distributed among those living organisms. Therefore, we explored the value of individual species, which is based on the contribution to biodiversity, to identify ISs. Contribution to biodiversity from each species was employed because we sought to calculate the probabilities of biodiversity from each individual species when survey data were available. Therefore, the desired individual probabilities and expectations were treated as conditions of contribution. Thus, we developed a novel, quantitative method that was useful for determining the specific contribution of species to biodiversity. In this case study, the mangrove removal project in the Siangshan Wetland Conservation Area, Hsinchu, Taiwan, was used and analyzed.
###### Abstract  
The direct synthesis of dimethyl ether (DME) is an ideal process to achieve the environmental objective of CO\({}_{2}\) conversion together with the economic objective of DME production. The effect of the reaction conditions (temperature, pressure, space time) and feed composition (ternary mixtures of H\({}_{2}\) + CO + CO\({}_{2}\) with different CO\({}_{2}\)/CO and H\({}_{2}\)/CO\({}_{\mathrm{x}}\) molar ratios) on the reaction indices (CO\({}_{\mathrm{x}}\) conversion, product yield and selectivity, CO\({}_{2}\) conversion) has been studied by means of experiments carried out in a fixed-bed reactor, with a CuO-ZnO-MnO/SAPO-18 catalyst, in order to establish suitable ranges of operating conditions for enhancing the individual objectives of CO\({}_{2}\) conversion and DME yield. The optimums of these two objectives are achieved in opposite conditions, and for striking a good balance between both objectives, the following conditions are suitable: 275-300 \({}^{\circ}\)C; 20-30 bar; 2.5-5 g\({}_{\mathrm{cat}}\) h (mol\({}_{\mathrm{C}}\))\({}^{-1}\) and a H\({}_{2}\)/CO\({}_{\mathrm{x}}\) molar ratio in the feed of 3. CO\({}_{2}\)/CO molar ratio in the feed is of great importance. Ratios below 1/3 are suitable for enhancing DME production, whereas CO\({}_{2}\)/CO ratios above 1 improve the conversion of CO\({}_{2}\). This conversion of CO\({}_{2}\) in the overall process of DME synthesis is favored by the reverse water gas shift equation, since CO is more active than CO\({}_{2}\) in the methanol synthesis reaction.  
CO\({}_{2}\) utilization; STD process; syngas; dimethyl ether; bifunctional catalyst; SAPO-18
## 4 Results and Discussion  
The Lung-PET-CT-Dx was the dataset used to train and validate all models, Table 1 presents the division of the dataset by the train, validation and the internal test subsets. The division of patients by the different subsets was random and remained consistent through all the experiments. For external evaluation of the model, the NSCLC-Radiomics and NSCLC-Radiogenomics datasets were used for testing.  
First, for the more simple architecture of each pipeline (RF and ResNet), it was compared the impact of using just one tumour slice, the larger slice, with the use of the tumour volume. Then this input type was used to construct the more complex models (XGBoost and Hybrid ViT). The obtained results are presented in Tables 7 and 8.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyper-Parameter** & **Values** \\ \hline ViT Layers & [6, 8, 10] \\ Learning Rate & [0.00001, 0.00005, 0.0001] \\ Weight Decay & [0, 0.001, 0.01] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyper-parameter values for the Hybrid ViT.  
\begin{table}
\begin{tabular}{l l l c c c c} \hline \hline Classifier & Input & Test Set & AUC &
\begin{tabular}{c} Balanced \\ Accuracy \\ \end{tabular} & Precision & Recall & Specificity \\ \hline \multirow{4}{*}{Random Forest} & \multirow{2}{*}{2D} & Lung-PET-CT-Dx & 0.806 & 0.712 & 0.867 & 0.886 & 0.538 \\  & & NSCLC-Radiomics & 0.543 & 0.497 & 0.164 & **0.936** & 0.059 \\  & & NSCLC-Radiogenomics & 0.659 & 0.531 & 0.814 & 0.941 & **0.120** \\ \cline{2-6}  & \multirow{2}{*}{3D} & Lung-PET-CT-Dx & **0.836** & **0.762** & **0.889** & **0.909** & **0.615** \\  & & NSCLC-Radiomics & **0.560** & **0.509** & **0.168** & 0.830 & **0.189** \\  & & NSCLC-Radiogenomics & **0.676** & **0.555** & **0.821** & **0.990** & **0.120** \\ \hline \multirow{4}{*}{ResNet-34} & \multirow{2}{*}{2D} & Lung-PET-CT-Dx & 0.783 & 0.660 & 0.861 & **0.705** & 0.615 \\  & & NSCLC-Radiomics & **0.577** & **0.598** & **0.222** & **0.638** & 0.559 \\ \cline{1-1}  & & NSCLC-Radiogenomics & 0.495 & 0.527 & 0.814 & **0.814** & 0.240 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{3D} & Lung-PET-CT-Dx & **0.841** & **0.700** & **0.955** & 0.477 & **0.923** \\ \cline{1-1}  & & NSCLC-Radiomics & 0.576 & 0.550 & 0.219 & 0.340 & **0.761** \\ \cline{1-1}  & & NSCLC-Radiogenomics & **0.589** & **0.555** & **0.839** & 0.510 & **0.600** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Test results (AUC, balanced accuracy, precision, recall and specificity) of Random Forest and ResNet-34 on three different test sets (Lung-PET-CT-Dx, NSCLC-Radiomics, and NSCLC-Radiogenomics) for 2D and 3D input types. The highest values within each metric are highlighted in bold for each model type and test set.
## 3 State of the Art
### Enhancing EEG in Collaborative Robot Control with Reinforcement Learning
In a study carried out in June 2022 [49], it is confirmed that it is possible to modify the way a cobot behaves based on the feelings of a human being. The intention is to be able to modify the behavior of the robot to achieve a level of empathy, in this case, the robot acts in a very similar way to the emotions felt by the human. The experiment is carried out in a simple and controlled environment, but it is very useful to demonstrate that it is indeed possible to modify the behavior of a robot based on the brain measurements of a human. After looking at the most recent studies, brainwave measurement is a reliable method for modifying a robot's behavior. However, reinforcement learning is not an area where this approach is routinely applied, even though it allows for emergent behaviors and greater adaptability. Although it is true that most of the studies that apply RL focus only on the detection of errors in executions to reward or penalize an agent, it could be very useful to use similar techniques to modify the behavior of a collaborative robot depending on the user's emotions. During this review article, the filtering of brain signals has been covered on several occasions. However, it is important to note that other relevant studies propose different techniques to filter the signals and achieve the desired emotion or potential error detection. The use of Convolutional Neural Networks (CNNs) is proposed as a valid method to filter and classify the EEG signals. However, as the classification proposed was done in binary terms considering only if the experiment was done correctly or incorrectly; it's difficult to relate if this could be useful for more complex experiments where different emotions need to be taken into account [50]. The filtering of EEG signals and their subsequent classification is one of the biggest challenges in this technology [41]. In addition to that, everything related to brain signals is a relatively recent topic that has emerged during the last few years. That is why several techniques have been used in the quantification of signals that can enable Human-Robot Interaction. In this section it became clear that the brain signals measured by EEG sensors are perfectly valid to carry out different investigations around collaborative robotics and reinforcement learning. In the following section, other relevant techniques related to the capture of different valuable signals for Human-Robot Interaction will be discussed. Figure 5: Real experimentation using EEG for an assembly task [33].
# Threat Analysis for Smart Homes +
## 4 Threat Analysis  
### Method  
Threat analysis is a statement of threats that are related to vulnerabilities of assets and threat agents [32]. As such, threat analysis is part of the risk assessment process [33]. In dynamic environments, it is important to use a threat analysis method which allows the consideration of potential changes in the targeted environment. Two distinct threat analysis approaches can be identified in the literature, namely the attacker's perspective and the defender's perspective. The former is more complex, whilst the latter examines the targeted systems thoroughly, taking into consideration also defensive techniques. The methodology to be used is important for the identification of all vulnerabilities, threats and attacks within the smart home architecture. Focusing on methods which are able to identify threats automatically by means of the use of a supporting tool, we opted to employ the STRIDE (_Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service and Elevation of Privilege_) method which is supported by Microsoft's threat modeling tool. The method was developed by [PERSON] and [PERSON] and has been used by both academia and industry; it allows the extraction of rigorous results for the risks that the target systems face [34] and can be applied as early as the design phase. The STRIDE threats were described by [PERSON] [35]. Further details on the use of the method can be found in [36].  
Microsoft's threat modeling tool allows the identification of potential threats which target data flows and back-end services of the reference model of [PERSON] et al. [31]. This tool allows the identification of security problems in processes, data stores and data flows, as the analysis is conducted using DFDs. Hence, DFDs for the smart home ecosystem, each corresponding to a different topology, need to be created. The identification of threats is achieved by using the STRIDE threat taxonomy. The analysis proceeds as follows:  
_Step 1--Describe Scenario:_ The scenario description must include all relevant elements within the scope of the examined environment. In this paper, the focus is on the smart home ecosystem and in particular on the data flows and the back-end services.  
_Step 2--Identify Assets:_ The assets of the target system must be identified. Such assets include information assets and physical assets.  
_Step 3--Create DFDs:_ By leveraging the simplicity of such diagrams, an analyst is able to represent devices, services, and data flows between the assets identified above.  
_Step 4--Identify constraints for each vulnerability:_ Each of the identified assets has various security vulnerabilities which have already been analyzed and can be found in existing vulnerability databases (e.g., [37; 38]).  
_Step 5--Determine Threats:_ The analyst develops different attack scenarios, considering the identified assets and their interconnections. The tool automatically identifies threats, also taking into account the predefined constraints.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Improvement and Refinement of Flood Maps
#### 3.4.2 Ancillary Ground Truth Data  
We used ancillary data such as field observations, civil protection reports, geolocated photos or news found on reliable websites. This type of data is currently an essential source of information for flood map assessment [85; 86]. For the Po basin, we also accessed volunteer geographic information that provided us photos that were georeferenced by using Google Streetview. These data provided spot information that allowed confirming of the reliability of flood maps. As mentioned previously, other ancillary data (such as permanent water body masks) were used to improve flood mapping.
# Half-Spectrum Suppression in Dynamic Resonant Tunneling
## 6 Half-Spectrum Suppression Leads to 100% Reflection and Fano Resonance
The parameters are \(LU^{1/2}=7\), \(\beta U^{-1/2}=0.4\) and \(\omega=0.05\). reason is that the lower part of the spectrum is suppressed and the other part of the spectrum consists of positive energies with finite probability to tunnel out of the barrier (see Figure 11). On the other hand, around the black arrows, the difference between the suppressions in the two scenarios is fundamental. For the case \(\Omega<\omega\) (black curve), the current reaches zero since the high spectral components are suppressed and all the remaining spectral components are negative \((\Omega-n\omega)<0,\ n=1,2\ldots\) (see Figure 10). However, for the case of \(\Omega>\omega\) (dotted red curve), although the high spectral components are suppressed, there is still a component (or components) with positive energy, \(\Omega-n\omega>0\), and therefore with a finite probability to escape and tunnel out of the barrier. In Figure 10, the spectra are presented for the case marked by the left black arrow in Figure 9. Although the difference between the incoming energies is very minor, and although the spectra are almost identical, for the case \(\Omega>\omega\) (dotted red curve) there is a single component (\(n=1\)) with positive energy \(\Omega-\omega>0\) (see Figure 10b). This minor difference is sufficient to prevent a total reflection since the positive spectral component has a finite probability of escaping from the varying well out through the barrier. Although the current does not reach zero for the case \(\Omega>\omega\) (dotted red curve), the black arrows' region is lower than the one marked with blue arrows, the reason is that there are fewer remaining spectral components (or even a single component) with positive energies, and those remaining components obviously have low energies, \(\Omega-n\omega<\Omega\), which have a lower probability to tunnel through the barrier. It should be noted that the spectral width of the Fano resonances, i.e., the zero transmission region, is proportional to \(\beta^{2}\) but also depends on the order of the resonance (see Ref. [8]). It should be noted that in cases where the minimum instantaneous resonant energy is negative and the incoming particle's energy matches one of the QBSS energies, i.e., \(\Omega=\langle\Omega_{R}\rangle+n\omega\), then only the '\(-\)' sign (in Equation (48)) is valid, and in this case the upper half of the spectrum vanishes. Therefore, if, in addition, the incoming particle's energy is lower than the oscillation frequency \(\Omega<\omega\), a total reflection occurs.
# Powder Bed Selective Laser Processing of Alumina: Scanning Strategies Investigation
## 4 Results and Discussion  
### Process Parameters Selection  
It is essential to select the laser power and scanning speed in the range that can melt the layer thickness and adhere it with the previously scanned layer to study the effect of scanning strategies properly. Additionally, at the same time, the generated temperature should be in the acceptable range above the melting point and not evaporate the entire scanning path. In this study, the developed numerical model was used to test different laser powers and scanning speeds until they reached the values that came up with the conditions mentioned earlier. Using laser power 95 W with scanning speed of 200 mm/s was found to melt the whole layer thickness (100 \(\upmu\)m) and gave suitable temperature distribution that did not affect the top surface due to high excess heat input (Figure 10a,b). Figure 10c shows the SEM image of the alumina sample top surface printed using 95W and 200 mm/s where no defect can be seen due to high input heat. It was concluded that using a laser power of 95 W and a scanning speed of 200 mm/s is suitable for analyzing the effect of the scanning strategies.  
Figure 9: Comparison of the experimental data with the numerical model: (**a**) two adjacent melting paths width measured using SEM, (**b**) two adjacent melting path widths measured numerically, (**c**) temperature contour obtained using the numerical model [12].  
Hatching distance is a critical process parameter as it controls the contact between the adjacent scanning paths and affects the mechanical and physical properties. Therefore, it is crucial to choose the most appropriate hatching distance for alumina's PBSLP. It was concluded that hatching distance should be selected below 70 \(\upmu\)m to ensure that adjacent paths are connected because the width of the melting path is about 70 \(\upmu\)m measured numerically as can be seen in Figure 11. Thus, three different hatching distance cases were studied, such as 50, 40, and 30 \(\upmu\)m, as shown in Figure 12. By using a hatching distance of 30 \(\upmu\)m, it was found that the alumina specimen was destroyed entirely due to the high heat accumulation that resulted from a small hatching distance because the laser source, in this case, scans almost the same position two times. Alumina samples were produced when the 40 \(\upmu\)m hatch distance was selected. However, high distortion occurred, especially at the sample base, due to the significantly increased thermal stress due to high temperatures. A full-size alumina sample was able to produce without any problems using a 50 \(\upmu\)m hatching distance. Therefore, it is recommended to use a hatching distance of 50 \(\upmu\)m. Otherwise, when a more considerable hatching distance is used, discrete scanning paths may occur.  
Figure 11: Melting contour achieved using laser power of 95 W and scanning speed of 200 mm/s.  
Figure 10: (**a**) The melting contour for the vertical section of the scanning path, (**b**) the temperature distribution for the layer top surface, (**c**) the SEM image of top layer surface.
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 3 Materials and Methods  
### Traditional Three-Level Cloud-Fog-Edge Architecture  
The traditional three-level architecture, comprising cloud, fog, and edge, represents a tiered approach to data processing and management in geographically distributed networks. Each level is uniquely positioned in the data processing chain, offering specific functionalities that cater to distinct needs (Table 1). This table offers a summarized overview of each layer's characteristics, which stem from a convergence of widely accepted and recognized information within the field. The typical three-level architecture is shown in Figure 1.  
Figure 1: Traditional cloud–fog–edge architecture. At the top is the cloud tier, comprised of massive, centralized data centers providing pooled compute, storage, and application services on demand. Next is the fog layer, which distributes some networking, computing, and storage capacity into local access networks to reduce latency and network load. Fog nodes include routers, gateways, micro data centers, and more. The edge tier aggregates and processes data flows from the IoT layer below it. It consists of devices like desktop computers, cameras, base stations, and local servers. The edge provides the first level of computing/storage resources outside end devices. The IoT sublayer contains the plethora of endpoint sensors, wearables, appliances, vehicles, control systems, and other devices embedded in the physical environment. The IoT layer generates raw data from the field. By separating the edge and IoT levels, it is possible to obtain a clearer understanding of the distribution of computational tasks and data flow within a multi-tier system. The IoT level's primary job is data capture with minimal processing, while the edge level acts as a local decision maker, processing and analyzing the data in near real time. In operation, IoT devices transmit data to the edge for localized processing. The edge passes summarized or filtered information into the fog layer for intermediate processing and buffering closer to users. The cloud finalizes processing, analyzes aggregated data, and provides global services. This four-tier model provides a more complete picture of multi-level architectures spanning the centralized cloud, distributed fog/edge computing, and dispersed IoT endpoints. The IoT layer highlights the role of smart edge devices and systems producing and consuming data.
# Salt-Fog Corrosion Behavior of GCr15 Steels Treated by Ultrasonic Strengthening Grinding Process
###### Abstract  
In this paper, the corrosion resistance of four GCr15 steel samples has been investigated. Three samples were initially surface-treated by ultrasonic shot peening (USP), a dry ultrasonic strengthening grinding process (DUSGP), and a wet ultrasonic strengthening grinding process (WUSGP), respectively, while another one was only polished. Then, all the processed samples were subjected to salt spray corrosion. Sample properties, such as capacitance arc, self-corrosion potential (E\({}_{\text{corr}}\)), self-corrosion current density (I\({}_{\text{corr}}\)), corrosion morphology, and corrosion products were analyzed. Results show that the sample treated by WUSGP has the best corrosion resistance, which can be attributed to the high dislocation density, small grain size, needle-like and lath-like shape of martensite content formed in the treatment, which hinders Cl\({}^{-}\) from eroding the matrix. The obtained corrosion pits and cracks in Sample WUSGP show a width of approximately 1.4 \(\upmu\)m and 2.1 \(\upmu\)m, respectively, degrading 78% and 75% compared to polishing. The possible corrosion mechanism of the samples has been discussed. The findings denote that the treatment fused ceramic balls, strengthened liquid, and corundum in USP could be an efficient method to improve the corrosion resistance of some mechanical parts.  
GCr15 steel; ultrasonic strengthening grinding process; salt spray corrosion; corrosion resistance +
Footnote †: journal: Applied Sciences  
_Applied_  
_sciences_
## 5 Experimental Results
### Visual Servo Control with Large Payload
In general, it can be seen that the swing period, which is reduced, enables the flexible base manipulator system to stabilize faster when the visual servo enters the exit domain and switches to standby mode, improving the positioning accuracy of servo control. Figure 13: Comparison of the 1500 kg load visual servo with or without base FTS feedback. (**a**) The relative position of the visual servo without FTS feedback, (**b**) the relative position of the visual servo with FTS feedback, (**c**) the reaction torque form base FTS without FTS feedback, (**d**) the reaction torque form base FTS with FTS feedback.