esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Results
#### 4.2.2 Value-Added Creation Effects  
The results of analyzing value-added creation effects are showed in Table 3. In South Korea, the value-added creation effects of other industries due to investment in the NG supply sector was high in the order of \"23. Finance and insurance\", \"2. Mined and quarried goods\", and \"26. Business support services\". In Japan, the value-added creation effects of other industries due to investment in NG supply sector was high in the order of \"26. Business support services\", \"20. Transportation\", and \"19. Wholesale, retail trade, and commodity brokerage services\". From the perspective of the overall national economy, an investment of USD 1 in the NG supply sector in South Korea induced a total of USD 0.20246 of value added: USD 0.03668 in other industries and USD 0.16578 in its own industry. On the other hand, in Japan, an investment of USD 1 in the NG supply sector induced a total of USD 0.51990 of value added: USD 0.20548 in other industries and USD 0.31443 in its own industry. In other words, it was found that the value-added creation effects of the NG supply sector in Japan were greater than those in South Korea.
# Agent-Based Model for Automaticity Management of Traffic Flows across the Network
## 4 Architecture of the Agent-Based Model
### Multi-Agent Architecture for Management of Network Flows  
The architecture of the proposed multi-agent model is shown in Figure 2. The Multi-Agent Architecture for Management of Network Flows, TELEKA, is organized by hierarchy using three levels: Network, Control and Negotiation.  
* Network: In this level, all nodes are set up with the type of flows that will transmit and their destination. In addition, the agents of each node are initializing with its hierarchy obtained by Equation (1), and these preferences are assigned by the network administrator depending on their policies, and their utilities are set to zero. The agents collect information related to congestion status by sensing the current state of the nodes. When a congestion is recognized by exceeding a threshold, an _ECG_ flag changed their value to one, and this will trigger the activation of the negotiation level.
* Control: Here, the agents detect and classify the flows that arrive at each node. After this, the agents provide the data to the Negotiation level, which holds the decision-making module.
* Negotiation: The algorithm SEHA (Social Election with Hidden Authorities) is triggered in this level when a congestion situation emerges. As a result, a set of actions to be performed by the lower-level agents is selected, achieving with this the optimization of the network status.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model
### The Model of Radar Power and Communication Power  
The radar detection power of the \(i\)-th UAV in the \(t\)-th time slot could be defined as \(p_{i}^{t}\), and the communication transmission power defined as \(q_{i}^{t}\). In each time slot, the sum of radar, the detection power, and communication transmission power of each UAV was a fixed value, i.e., \(p_{i}^{t}+q_{i}^{t}=E_{\max}\). \(E_{\max}\) was defined as the electromagnetic domain of power.
## 3 Results
### The WUE of Different LUCC
#### 3.2.2 Variations in WUE in Different Ecosystems  
The WUE of different ecosystem types revealed a gradual upward trend from 1982 to 2018 in the YRB (Figure 6). The WUE of forest ecosystems showed a slowly increasing trend from 1982 to 2000, while it exhibited a significant upward trend starting from 2000 to 2018 at the rate of 0.79 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O-a\({}^{-1}\) (R\({}^{2}\) = 0.548). The WUE of farmland ecosystems in 1982-2000 and 2000-2018 showed an increasing trend at rates of 0.63 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O-a\({}^{-1}\) (R\({}^{2}\) = 0.237) and 0.55 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O-a\({}^{-1}\) (R\({}^{2}\) = 0.242), respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**Z\({}_{\epsilon}\)**} & \multirow{2}{*}{**WUE Trend**} & \multicolumn{3}{c}{**Different Periods of Time**} \\ \cline{3-5}  & & & **1982–2018** & **1982–2000** & **2000–2018** \\ \hline \(\geq\)0.0005 & \(\geq\)1.96 & Significantly increase & 47.07 & 8.23 & 45.35 \\ \(\geq\) 0.0005 & \(-\)19.96–1.96 & Slightly increase & 21.03 & 55.35 & 16.92 \\ -0.0005–0.0005 & \(-\)1.96–1.96 & Stable & 4.32 & 4.78 & 4.11 \\ \(<\)\(-\)0.0005 & \(-\)1.96–1.96 & Slightly decrease & 12.94 & 29.82 & 15.05 \\ \(<\)\(-\)0.0005 & \(\leq\)-1.96 & Significantly decrease & 14.64 & 1.82 & 18.57 \\ \hline \hline \end{tabular} Note: Slope (-0.0005, 0.0005) and Z\({}_{\epsilon}\) (-1.96, 1.96) were defined as stable.  
\end{table}
Table 2: The percentage of area with significant variation trend of WUE.  
Figure 5: Variation in WUE in different land cover types.  
The interannual rates of WUE in grassland ecosystems were 0.41 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\) (R\({}^{2}\) = 0.49) and 0.44 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\) (R\({}^{2}\) = 0.45) for 1982-2000 and 2000-2018, respectively. There are interannual differences in WUE between different ecosystem types, with forest ecosystems showing higher WUE than farmland, grassland, and other ecosystems. Moreover, the rate of increase in WUE of forest ecosystems from 2000 to 2018 was higher than other types.
## 3 Seismic Action
### Earthquake Scenario
#### 3.2.1 Ground-Motion Prediction Equations  
It is frequent to use GMPEs to assess the effects of earthquakes, which are also usually designated as attenuation laws. Normally, GMPEs are functions of the type of seismic fault (a strike-slip fault, a normal fault or a reverse fault), earthquake magnitude (\(M\)), a distance to the earthquake and the local geological characteristics of the studied site.  
The result of the GMPEs may be an earthquake intensity, the PGA or some spectral coordinates of a response spectrum. In the proposed approach, only the latter GMPEs were implemented in the developed software. Moreover, three types of sources were considered: A point source, a line source and a fault plane (rectangular). Therefore, four possible distances were also considered (Figure 4) for an earthquake with a given focus depth (\(z_{\prime}\)): The epicentre distance (\(D\)); the focus distance (\(R\)); the closest distance to the fault rupture (\(R_{\prime}\)); and the distance to the surface projection of the closest distance to the fault rupture (\(D_{\prime}\)).  
The results may be very different, depending on the option selected by the user, especially for near source earthquakes with high focus depth, or for very high magnitude earthquakes that present huge rupture lengths. When considering line sources or fault plane sources, the definition of the percentage of the fault length where the focus is located (related to the fault origin) and the azimuth (\(\phi_{s}\)) of the fault trace (the angle to the north direction) is also necessary. The dip angle (\(\delta\)) is also necessary for fault plane sources, as presented in Figure 4. This means that results will be dependent on the quality of the earthquake data. If just the earthquake epicentre location is known, obviously a point source and the epicentre distance must be considered. If the focal mechanism is also known, or just guessed, it is possible to compute rupture dimensions based on empirical expressions.  
Figure 3: Input/output Eurocode 8 (EC8)-type response spectrum user interface.  
It is also well known that the ground motion duration is an important issue in damage assessment and that is also possible to adopt empirical expressions to compute the vibration durations.  
All the described approaches were implemented in the developed seismic assessment tool.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 4 Discussion  
The examples of destriping shown here were produced using our off-line post processing software where a DNB SDR dataset is input, and a corrected SDR is output. Ideally, though, this process would be incorporated into the operational calibration process. In order to do this, it is important that destriping be integrated into the entire calibration process. In this way, the process is done in the proper order. For example, the NCC is produced from the DNB SDR, but once this is done, the NCC imagery cannot be destriped as a post-process because it is projected into the Ground-Track Mercator Projection (this is why the NCC examples shown here must use a simplified off-line version of the NCC algorithm).  
Another problem with removing striping as a post-process is that striping from other causes, especially nonlinearity, become mixed with the stray light correction and this striping becomes impressed into the stray light correction tables.  
Therefore, we recommend that the HMM destriping algorithm described here be incorporated into the operational software. In the discussion that follows, we consider what would be required to do this.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
###### Abstract  
Underwater acoustic communication is fraught with challenges, including signal distortion, noise, and interferences unique to aquatic environments. This study aimed to advance the field by developing a novel underwater modem system that utilizes machine learning for signal classification, enhancing the reliability and clarity of underwater transmissions. This research introduced a system architecture incorporating a Lattice Semiconductors FPGA for signal modulation and a half-pipe waveguide to emulate the underwater environment. For signal classification, support vector machines (SVMs) were leveraged with the continuous wavelet transform (CWT) employed for feature extraction from acoustic signals. Comparative analysis with traditional signal processing techniques highlighted the efficacy of the CWT in this context. The experiments and tests carried out with the system demonstrated superior performance in classifying modulated signals under simulated underwater conditions, with the SVM providing a robust classification despite the presence of noise. The use of the CWT for feature extraction significantly enhanced the model's accuracy, eliminating the need for further dimensionality reduction. Therefore, the integration of machine learning with advanced signal processing techniques presents a promising research line for overcoming the complexities of underwater acoustic communication. The findings underscore the potential of data mining methodologies to improve signal clarity and transmission reliability in aquatic environments.  
underwater acoustic communication; signal processing; support vector machines; continuous wavelet transform; FPGA; feature extraction; modulation techniques; noise reduction
## 3 Numerical Examples
### Advection of Density Perturbation  
The numerical precision of URLBFS in WENO5 and WENO7 schemes is verified using density perturbation advection and compared with the traditional numerical scheme. The initial conditions are as follows [47]:  
\[\left\{ \begin{array}{l}\rho(x,y)=1+0.2\sin(\pi(x+y))\\ u(x,y)=1\\ v(x,y)=1\\ p(x,y)=1\end{array} \right., \tag{45}\]  
The exact solution is as follows:  
\[\left\{ \begin{array}{l}\rho(x,y,t)=1+0.2\sin(\pi(x+y-t))\\ u(x,y,t)=1\\ v(x,y,t)=1\\ p(x,y,t)=1\end{array} \right.. \tag{46}\]  
The computational domain is [0, 2] \(\times\) [0, 2], and periodic boundary conditions are imposed on all sides. To avoid the error caused by the time step, a fixed-time step \(DT=1\times 10^{-4}\) is used. The ultimate evolution time \(t=2\) and the sizes of the uniform grid \(h=1/5,1/10,1/20,1/40\), respectively. The density accuracy errors and the order of the schemes under WENO5 and WENO7 are shown in the following table. Table 1 shows the density errors and corresponding accuracy order obtained by the WENO5-LF, WENO5-ROE, WENO5-LBFS, and WENO5-URLBFS schemes. Figure 6(a) shows the relationship between the \(L_{2}\) error and the grid size, and the accuracy order of different schemes is obtained by fitting the least squares method. It can be seen that the URLBFS scheme has a higher accuracy order. Figure 6(b) shows the relationship between the grid size and CPU calculation time of these schemes. Since the LF scheme reconstructs the numerical flux directly on the interface, while the other three schemes need to reconstruct  
at the interface to obtain the flow variables on both sides of the interface and then substitute them into the solver to obtain the numerical flux at the interface, the calculation time of the LF scheme is faster than the other three schemes, but at the same grid size, the URLBFS scheme has the highest accuracy. Table 2 shows the density error and corresponding accuracy order obtained by the WENO7-LF, WENO7-ROE, WENO7-LBFS, and WENO7-URLBFS schemes. Figure 7(a) shows the relationship between the \(L_{2}\) error and the grid size, and the accuracy order of different schemes is obtained by least squares fitting. It can be seen that the URLBFS scheme has a higher accuracy order. Figure 7(b) shows the relationship between the grid size and CPU calculation time of these schemes. It can be seen that the calculation time of all schemes will increase due to the use of higher-order reconstruction methods. The LF scheme directly reconstructs the numerical flux at the interface, so the procedure is relatively simple and the calculation time is faster than the other three schemes. However, at the same grid size, the URLBFS scheme has a smaller error and higher accuracy.
## 3 Proposed Framework
### Data Pre-Processing
#### 3.2.1 Tokenization and Lemmatization  
In this process, we split our tweet text into separate words or chunks; for example the emergency alert in the state of Alabama because of COVID was converted into 'emergency', 'alert', 'in', 'the','state', 'of', 'Alabama', 'because', 'of', 'covid'. We used NLTK method word_tokenize() to split a sentence into words; we utilized the Spacy function with the core English language model for lemmatization ([[https://spacy.io/usage/models](https://spacy.io/usage/models)]([https://spacy.io/usage/models](https://spacy.io/usage/models)) (accessed on 1 June 2020)). The beauty of this Spacy function is that it gives you part of the speech detail of every sentence, and you can chose from that which part of speech you need for further processing in the specific context. Spacy is capable of also giving sentence dependencies in case you need them while performing graph embedding. After tokenization, we need to see which part of the sentence we need and also need to extract the words into their original forms. Both the lemmatization process and the stemming process are used for this purpose. Many typical text classification techniques use stemming with the help of a port stemmer, and snowball stemmer, with which the words 'compute', 'computer', 'computing', 'computed' would be reduced into the word 'comput'; a little draw back with stemming is that it reduces the word into its root form without looking into if the word is found in dictionary of that specific language or not, as you can see 'comput' is not a dictionary word. This is where the lemmatization is used; with Spacy we performed the lemmatization. Lemmatization also reduced the word into its root form but whilst keep in mind the dictionary database. With lemmatization, the above examples of words ('compute', 'computer', 'computed', 'computing') would be reduced to root form as ('compute', 'computer', 'computed', 'computing'), respectively by keep in in mind the dictionary.
# Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models +
## 6 Discussion
The first can be used with neural networks, but not with decision trees, while the others are agnostic. Fidex is local but not agnostic, although it is currently used with MLPs, DTs, SVMs and CNNs. For SHAP, the global scores of the variables for each class can be obtained by averaging the Shapley scores of all samples. Therefore, SHAP can be used as a global method. FidexGlo is global and its computational complexity scales quadratically with the number of training samples, whereas SHAP scales exponentially with the number of input variables. Furthermore, LIME learns a new linear model in the neighborhood of a target sample, but there is no guarantee that the new local model matches the black-box model. This is in contrast to Fidex, which does not perform any new learning, but searches for the best hyperplanes, according to the fidelity on the training samples. Finally, while Grad-CAM, LIME, and SHAP can highlight the most important regions in an image, they may not provide detailed insights into why a specific prediction has been accomplished. This can be a drawback in cases where fine-grained understanding is required. The main limitation of FidexGlo is its quadratic algorithmic complexity with respect to the number of samples. With time series containing a reasonable number of samples (not millions), we can imagine a CNN learning this type of data. Propositional rules can then be extracted and visualized, as accomplished in this work. The same applies in a multimodal setting, e.g., with time series and tabular data as input variables, except that the extracted rules will have both temporal and tabular data in the antecedents. Furthermore, FidexGlo could be applied to recurrent neural networks such as Jordan/Elman networks by inserting a DIMLP layer after the input layer. In this way, the generated rules will have in the rule conditions the input variables of the input data, with the addition of hidden states corresponding to the neuron states of the feedback loops. This is certainly an open question for research and one that is worthy of further investigation in the future. This work does not address the behavior of the rules or explanations extracted by the methods under adversarial conditions or with noisy data inputs. Our main goal was to determine the characteristics of the rules extracted from several benchmark datasets and to find out whether the rules are understandable with the centroids represented as images. In the future, it will be very important to investigate the behavior of the rules under adversarial conditions to determine whether they are robust to attacks.
## 5 Discussion
### Study Limitations and Recommendations for Future Work  
The results of this LCSA are specific to the described scope and are not necessarily representative of other applications of MICP, as the engineering performance and predicted impacts of MICP treatment, including process emissions, may vary for different soil types or treatment volumes. Some factors, such as life cycle costs, are expected to vary widely between projects and geographic regions. Other modeling choices, however, may have little influence on the LCSA results. For example, the sensitivity of the results to the reference LCI dataset selected for modeling electricity production is expected to be small, as energy use impacts were minimal relative to materials production impacts for MICP. In fact, when the U.S. average electricity grid mix is substituted with the electricity grid mix for California (i.e., the CAMX subregion), which relies less on burning coal and more on natural gas and renewables (e.g., hydro, wind, and solar power), total global warming potential is reduced by less than 5% for all columns.  
Results obtained from this study may help prioritize areas for future research and development efforts in order to improve the sustainability of MICP for geotechnical and geoenvironment applications [55]. Based on the LCSA results obtained, it is recommended that the following research areas be prioritized in future studies: (1) the optimization/minimization of materials identified as environmental/economic hotspots (i.e., urea, calcium chloride, ammonium chloride, and sodium acetate); (2) the optimization of MICP engineering performance per unit mass of CaCO\({}_{3}\) (i.e., reducing the mass of CaCO\({}_{3}\) needed to achieve a specific engineering performance metric); (3) the reduction in produced nitrogen byproducts near the end of MICP treatments through treatment strategy modifications; (4) a further investigation of the fate and transport of produced nitrogen species resulting from MICP; (5) the evaluation of the relevant impacts and costs that would occur during MICP field-scale implementation, including materials transportation, equipment mobilization and use, construction consumables, effluent management, and labor costs; and (6) a comparative LCSA of MICP against other conventional soil improvement techniques to identify applications where MICP may offer large sustainability gains (e.g., liquefaction mitigation under existing structures where many ground improvement methods may not be effective or would be cost-prohibitive).
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 1 Introduction
On the one side, there is a need to reduce the computational and storage load on the physical device to perform timely actions, but simply offloading to the edge may not improve as the edge also has limited resources. On the other side, how to use all the technologies and devices available to improve situational awareness for first responders is nontrivial due to heterogeneity in communication protocols, semantics and data formats (e.g., generating a collaborative map of the area using the information from drones and robots considering that data fusion from heterogeneous data sources is a challenging task). In this paper, we will investigate how the cloud-to-edge-to-IoT continuum can support and enable post-disaster SAR operations, and we will propose solutions for the challenges and technological/research questions introduced above. Indeed, distributed applications paired with advanced resource orchestration solutions over the continuum reaching out to the edge of the network can enhance the response time and effective intervention for SAR operation [4; 5; 6]. The challenges will be discussed, and the possible solutions will be described as part of the Horizon Europe NEPHELE project (NEPHELE project website: [[https://nephele-project.eu/](https://nephele-project.eu/)]([https://nephele-project.eu/](https://nephele-project.eu/)) (accessed on 19 January 2023)). This project proposes a lightweight software stack and synergetic meta-orchestration framework for the next-generation compute continuum ranging from the IoT to the remote cloud, which perfectly matches the needs of SAR operations. The paper is organized as follows. Section 2 reports the related work on the main related technologies in cloud computing, sensor networks, the Internet of Things (IoT), robotic applications and cloud robotics. Section 3 describes the reference use case for SAR operations, with its requirements. Section 4 describes the proposed solutions based on the approach followed in the NEPHELE project. Section 5 concludes the paper.