esa-sceva/satcom-chunk-collection huggingface dataset:
# A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach
## Data
### Reference Data  
While a ground truth is not available for these flood events, different \"flood authorities\" have produced flood extent maps for the regions of interest.  
For the first case, Mozambique, we compare our results with flood maps from two external sources. The Luxembourg Institute of Science and Technology (LIST) provided us with a flood extent map for the Sofala region in Mozambique. They collaborate with the European Space Agency Research and Service Support Team (ESA RSS) for the production of automated Sentinel-1 based flood delineation products. The flood map is obtained by applying an automatic unsupervised change detection algorithm to pairs of Sentinel-1 images (pre-flood and during flood) [16]. The second source of flood maps for Mozambique is Disaster Charter [17], an initiative from ESA, The French National Centre for Space Studies (CNES) and The Canadian Space Agency (CSA), harvesting Earth observation assets from different space agencies. They provide flood maps at three timesteps for the Mozambique flood, based on Sentinel-1 imagery between 13 and 26 March 2019 over the Sofala province and its surroundings.  
The second case, India, was compared with one external flood map, originating from Sentinel-Asia [18], a voluntary basis initiative led by the Asia-Pacific Regional Space Agency Forum (APRSAF) to support disaster management activity in the Asia-Pacific region. They provide a single-date flood map over the region of interest with a spatial resolution of 30 m \(\times\) 30 m. The map is derived from SAR data acquired by Sentinel-1 satellites before (21 May 2019) and during (30 September 2019) the event.
## 1 Introduction
In order to facilitate common understanding of project scope and complexity, the proposed methodology encompasses gathering requirements in the form of UML and SysML models, Story maps and Requirement lists during the requirements workshop, compiling and reconciling requirements list from all provided sources, and estimating project scope by assessing each requirement in story points. There exists a variety of scope estimation (also known as effort estimation) approaches that can be divided into two main groups [14; 15]: algorithmic (or data-based) and non-algorithmic (or expert-based). Algorithmic approaches usually rely on large amounts of historic data and mathematical solutions, and currently also rely on the application of artificial intelligence and machine learning. On the other hand, the non-algorithmic approaches are based on expert experience and deduction. In Agile projects, expert-based methods, such as planning poker and story points, are commonly used [15; 16]. They support basic Agile principles, including the encouragement of teamwork, team consensus, and team-specific estimations. In this paper, we propose an expert-based methodology for software scope estimation, which is constructed on early requirements gathered during the requirements workshop [16; 17]. To properly engage stakeholders of various competencies in the requirements workshop, we encourage using various visual models for recording requirements and support the extraction of requirements into one common set from various sources: Story maps, requirements lists, UML [5] Use case models, UML Activity models, UML Class models, and SysML [6] Requirement models. Due to requirements coming from various potentially disjoint media, we perform requirement reconciliation to derive a requirements list, which is then used for story points-based scope estimation. The tool implementing the principles of the proposed methodology was developed and employed in the presented case study of the animal shelter information system scope estimation. During the scope estimation demonstrated in the case study, the requirements were gathered not only from Story map, but also from various other models. This enabled the acquisition of a more detailed requirement list and a more detailed scope evaluation. The rest of the paper is structured as follows. The second section analyzes related work in the areas of Agile requirements elicitation, modeling usage, and scope estimation. The third section presents the proposed early-phase scope estimation methodology and a detailed description of its main steps: the requirements workshop, compilation of requirements from various sources, and scope estimation. The fourth section describes the tool implementation of the proposed methodology, which provides functionality for importing requirements, facilitating their reconciliation and scope estimation. A case study representing the application of the proposed methodology in the early phase scope estimate of the pet shelter information system is analyzed in the fifth section. The last section concludes the results of the case study and discusses future work.
## 3 System Configuration
### Mid-Infrared Tunable Lasers
#### 3.1.2 Interband Cascade Lasers  
ICL was presented by [PERSON] in 1995 [60]. RT-cw operation of ICL was first demonstrated in 2008 [61]. Only a few years ago, DFB-ICLs became commercially available with an optical power of milliwatts and a spectral tuning range of a few wavenumbers by Nanoplus GmbH [26]. Like QCLs, ICLs employ the concept of bandstructure engineering to achieve an optimized laser design and reuse injected electrons to emit multiple photons. However, ICLs' photons are generated by interband transitions rather than the intersubband transitions used in QCLs, which allows ICLs to achieve lower input powers than is possible with QCLs.  
DFB-ICLs provide single mode, narrow linewidth (\(\sim\)0.7 MHz) [62], low power consumption (hundreds of milliwatts), a compact system solution, and RT-cw emission in the 3-6 \(\upmu\)m range, which fill the MIR gap perfectly. DFB-ICLs have been used for the ppb-level detection of many important gases, especially hydrocarbon species [63; 64; 65; 66; 67; 68], which leads to many important applications in various areas, for example, clinical diagnostics [70; 71], combustion probing [72], environmental monitoring [73; 74], and remote sensing.
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 3 Semiconductor Switch Technologies
### GaN WBG Semiconductors  
GaN transistors are commercially available up to a voltage of 650 V. When they are used as power switching devices, \"cascode\" connection with low-voltage MOSFETs is recommended to ensure normally off operation [142]. GaN devices have a lower threshold voltage of up to 1.5 V, which in combination with the high switching speeds of the complementary transistors can lead to self-turn-on. One of the most efficient measures against this is to set the blocking gate voltage to \(-3\) V [143]. However, this increases the source-drain voltage when reverse conduction is active, resulting in higher reverse conduction power losses during dead time. With this in mind, it is advisable for the designers to (i) consider several options for minimizing the dead time losses [144], (ii) follow best practices in PCB design to minimize parasitic inductance values, and (iii) use gate voltages between 0 V and 6 V. A 600 V GaN device compared to a comparable Si SJ-MOSFET has a similar \(R_{DS,on}\) and 10 times lower gate charge and characteristic capacitances. The release charge values are up to 100 times lower. This enables extremely short switching times with very short time delays during gate signal changes. The voltage turn-on rate of a GaN device can reach up to 80 V/ns, while the turn-off value can exceed 100 V/ns. Compared to SiC MOSFETs with similar \(R_{DS,on}\), GaN devices have four times shorter turn-on times and two times shorter turn-off times [145]. GaN transistors enable operation at switching frequencies of up to 100 MHz. As the switching power losses are considerably lower, operation at higher frequencies appears to be even more sensible [146].  
GaN devices received special recognition when the winners of one of Google's Little Box Challenges used GaN transistors to develop a 2026 VA inverter (400 V DC to 230 V AC) in a 0.23 liter housing with an efficiency of 95.4 % [147]. The main disadvantage of this technology, which stands in the way of a faster dissemination of GaN HEMTs [148], is the fact that the Si components cannot be directly replaced in the applications, as the converters must be adapted accordingly or, even better, developed from scratch, taking into account all the new aspects required by WBG technology [149]. GaN components with the highest-rated voltage are also suitable for bi-directional onboard chargers [124]. However, WBG technologies are not expected to quickly and completely replace the established and affordable Si technology. While the price of GaN devices can gradually compete with that of Si devices [150], GaN voltage ratings are still limited to 650 V. Therefore, the technologies are expected to coexist, especially in the near future (Figure 13) [151; 152].
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
In addition, users can use the DRLR and FRA algorithms to empirically analyze interference decisions and make appropriate anti-interference judgments for dynamic situations under various interference conditions. Because of the various constraints, compared with the routing algorithms of terrestrial networks, the routing algorithms mainly have the following characteristics: (a) The computing speed and storage resources of the CPU of the on-star facilities are severely limited. (b) In satellite networks, ISL exhibits a mesh feature, making none of the inter-star routing paths unique and possibly generating physical loops. (c) They have a highly dynamic network topology with a higher link-switching frequency, a shorter duration of routes, and an extended transmission time between links. Finally, (d) the services on the star have different QoS requirements and priorities. With the continued maturity of routing technology, scholars have gradually shifted the research focus of routing algorithms to satellite networks. In order to better realize the forwarding of onboard data, the satellite network model should include doubling or multi-layer satellite networks in addition to the typical single-layer satellite networks. Therefore, the academic community divides the existing satellite network dynamic routing into single-layer satellite network dynamic routings and multi-layer satellite network dynamic routings. These will be discussed in more detail in the following sections.
## 3 Results and Discussion
### Dimensionless Criterion Correlation Equations Based on Experimental Parameters  
The Nusselt number, Reynolds number and Planter number are the most commonly used dimensionless numbers to characterize convective heat transfer. In this experiment, since the nozzle height and spray coverage were changed during the process, the introduction of the size coefficient \(\mathrm{h}/\mathrm{D}\) was considered, and the two different correlation forms were:  
\[Nu=f(\mathrm{Re},\mathrm{Pr}), \tag{18}\]  
\[Nu=f(\mathrm{Re},\mathrm{Pr},\frac{h}{\mathrm{D}}), \tag{19}\]  
The correlation equations derived from the fitting analysis of the full experimental data were:  
\[Nu=85.49\mathrm{Re}^{-0.673}\mathrm{Pr}^{0.253}, \tag{20}\]  
\[Nu=18.38\mathrm{Re}^{-0.526}\mathrm{Pr}^{0.118}(\frac{h}{\mathrm{D}})^{-1.39}, \tag{21}\]  
Figure 25 shows a comparison between Nu calculated from the experimental parameters and Nu calculated using Equation (20). As can be seen from Figure 23, for the criterion correlation equation considering only Re and Pr, more than 90% of the experimental points fall within \(\pm\)38% error of the correlation calculation results, which is suitable for some performance calculations with low accuracy requirements. The black points are the Nu obtained from each set of experimental data compared with the Nu calculated from the corresponding experimental data. However, its higher error indicates that more dimensional parameters of the spray are needed to increase the accur  
Figure 24: Visualization images of the foam inside the chamber of the ethanol–water mixture at different CTAB concentrations: (**a**) 20 ppm; (**b**) 40 ppm; (**c**) 60 ppm.  
Figure 26 shows a comparison between Nu calculated from the experimental parameters and Nu calculated using Equation (21). Figure 26 shows that after considering the size parameter, more than 90% of the experimental points fall within \(\pm\)25% error of the correlation calculation, indicating that the inclusion of the size parameter in the correlation equation is effective. This demonstrates that the height of the nozzle and the coverage of the spray during the spray cooling process have an effect on the calculation of the cooling performance.  
Figure 26: Comparison of fitted and experimental values when fitting the correlation equation using Re, Pr and h/D.  
Figure 25: Comparison of fitted and experimental values when fitting the correlation equation using Re and Pr.  
Therefore, the criterion correlation formula including Re, Pr and size coefficient can better reflect the performance of spray cooling, and the criterion correlation formula with higher accuracy is Equation (21).
###### Abstract  
Based on monthly precipitation (P), temperature (T) data, and remote sensing images collected from March 2000 to February 2019, this article was constructed to reveal the synergistic effect between P and T for the NDVI in northern China qualitatively and quantitatively by using a one-variable linear regression, the coefficient of variation, multivariate correlation coefficients, and a geodetector. The results show that the NDVI in the study area decreased from 2000 to 2012, increased from 2013 to 2018, decreased in the west, and increased in the east of Northern China. Overall, the NDVI, P, and the average maximum temperature (Tmax) had the strongest multivariate correlations (approximately 43.4% of the total study area passed the 95% confidence level significance test), followed by the average temperature (Tave) and average minimum temperature (Tmin). The explanatory power of the synergistic effect between P and Tmax for the NDVI was the strongest, with the value of explanatory power varying from 0.41 to 0.81, followed by Tave and Tmin. Spatially, the explanatory power of the synergistic effect between P and T for the NDVI was strengthened overall in the study area from northwest to southeast. The annual change rate of the explanatory power showed that the overall explanatory power between P and T for the NDVI in the study area was weakened in the central area and strengthened in the east and the west. Specifically, the synergistic effect between P and T on the NDVI was weakened in both Shaanxi and Ningxia Huizu Zizhiqu, while the opposite occurred in Xinjiang Uygul Zizhiqu, Qinghai, and another five provinces in the eastern part of the study area.  
NDVI; northern China; precipitation; synergistic effect; temperature +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The Synergistic Effect between Precipitation and Temperature for the NDVI in Northern China from 2000 to 2018.  
**J. Sci. 2023, 13, 8425. [[https://doi.org/10.3390/app13148425](https://doi.org/10.3390/app13148425)]([https://doi.org/10.3390/app13148425](https://doi.org/10.3390/app13148425))**  
**Cognition:** 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Detecting Phishing Domains Using Machine Learning
## 4 Methodology
### Implemented Algorithm  
To increase accuracy, this paper utilized the MinMax normalization feature as a preprocessing step in each proposed model. Normalization is a useful strategy for improving the accuracy of machine learning models, and it is required for some models to work properly. The MinMax normalization technique in the suggested model compresses the data to a domain of [0, 1], which improves the model training input quality (see Equations (1) and (2)).  
\[\text{X\_std}=(\text{X}-\text{X.min})/(\text{X.max}-\text{X.min}) \tag{1}\]  
\[\text{X\_scalar}=\text{X\_std}\times(\text{max}-\text{min})+\text{min} \tag{2}\]  
To enhance the model performance and complexities, we used a data normalization strategy, as shown in Table 2. The algorithm selects significant aspects from the initial dataset by determining the prediction outcome, which is performed by filtering it through 30 features. The UCI dataset is split 80/20 into training and testing sets, respectively, by using c5-fold cross-validation, which presented the best performance in the latest research. The prediction model is then taught using machine learning, which employs various learning models. This is particularly useful for making predictions, as utilizing many models ensures that the results are not biased toward a single model. To account for this, we present the results of all the models combined and totaled to establish their maximum accuracies. If most of the models indicate that a domain is phishing, then the model's prediction accuracy confirms that the domain is a phishing attempt.
## 2 Dataset and Methodology
### Dataset and Criteria
#### 2.3.4 Aspect  
Aspect refers to the direction that a slope faces, which significantly affects the amount of sunlight it receives. In the northern hemisphere, south slopes receive the most sunlight throughout the day, making them ideal for solar energy projects. Similarly, east and west slopes receive good solar radiation in the morning and evening, respectively, but south slopes generally offer the highest energy potential. For Gaziantep, aspect is a key factor in site selection due to its varied terrain. The region's landscape includes areas with different directions, and south slopes are generally preferred for solar panel installation because they receive the highest amount of solar radiation annually. ASTER satellite images were used to produce the aspect data for Gaziantep (ASTER 2024). In this context, the south was identified as a highly suitable location for PV farms, while the north was considered to be less suitable. Other directions were evaluated and categorized into sub-criteria based on literature research, reflecting varying levels of solar exposure [17; 49].
## 4 Methodology
### Word Representation  
**Character Level Embedding**  
Most methods use the external context of the words in the large corpus to learn the embedding of words. However, in Chinese, a word is usually composed of multiple characters, each containing rich internal information, for example, the word \"EUR\" (bicycle). The CBOW model can be used to learn the meaning of a word, but it can also be learned by characters: \" \(\mathbb{\hat{E}}\)\" (auto), \"\(\mathbb{\hat{T}}\)\" (walk) and \"\(\mathbb{\hat{E}}\)\" (car). Because of the nature of semantics, the semantic meaning of internal characters may play an important role in semantic meaning. Therefore, an intuitive idea is to consider the internal characters for the embedding of learning words.  
Because the word polysemy is more serious in Chinese, [PERSON] [14] proposed the Position-based Character Embedding model. There are different representations according to the position of each character in the word (begin:(c\({}^{B}\)); middle:(c\({}^{M}\)); end:(c\({}^{E}\))).  
For a word \(x\), (\(x=c_{1}c_{2}c_{3}\), \(\ldots\), \(c_{j}\)) is the character embedding. We use the last hidden vectors from each of the LSTM [15] components and concatenate them together, as shown in Figure 2:  
\[\overrightarrow{h_{k}^{*}}=LSTM(c_{k}^{*},\overrightarrow{h_{k-1}^{*}}); \overleftarrow{h_{k}^{*}}=LSTM(c_{k}^{*},\overleftarrow{h_{k+1}^{*}}) \tag{1}\]  
\[h^{*}=[\overrightarrow{h_{j}^{*}},\overleftarrow{h_{1}^{*}}];m=tanh(W_{m}h^{* }). \tag{2}\]  
**Character-Word Embedding Based on Attention**  
We found that not all words are similar in meaning to their characters, and some foreign words are created from pronunciation, such as \"\(\mathbb{\hat{T}}\)\" (chocolate), which is completely different from \"\(\mathbb{\hat{T}}\)\" (artful), \"\(\mathbb{\hat{T}}\)\" (gram) and \"\(\mathbb{\hat{T}}\)\" (force). They only sound similar. At the same time, some characters may have different meanings but are in the same place.  
Therefore, we need to jointly consider the weight relation between the word level embedding and the character level embedding. Based on [PERSON]'s model, we let the last word embedding \(z\) be:  
\[z=\sigma(W_{z}^{2}t+W_{z}^{1}m) \tag{3}\]  
where \(t\) is the word embedding, and \(W_{z}^{1}\), \(W_{z}^{2}\) are weight matrices for calculating \(z\). They decide how much information to use from the character level embedding or from the word embedding. \(\sigma(0)\) is the  
Figure 1: The architecture for the event trigger and argument recognition in the event extraction.  
logistic function with values in the range [0;1]. \(z\) has the same dimensions as \(t\) or \(m\). This operation can also expand the word embedding dictionary. For words that have regular suffixes (or prefixes), we can share some character level features; words that did not previously have word vectors can also use character level embedding.
# Cationic Lipid Derived from a Basic Amino Acid: Design and Synthesis
## 2 Materials and Methods  
### Design of the Novel CL: Cholesterol-Cadaverine-Lysine (CholCadLys)  
For the design of the CholCadLys lipid, two primary factors were taken into consideration:1. Structural Conformity: The CholCadLys lipid should conform to the structural scheme consisting of a hydrophobic region, a linker, and a hydrophilic region, the main components of an amphipathic lipid.
2. Natural Compatibility: The components used to create the CholCadLys lipid should be molecules that are naturally found as constituents and/or metabolites within eukaryotic cells. Taking these factors into account, the lipid needed to have a molecular structure capable of forming liposomes, associating with DNA, and being non-cytotoxic. Based on these premises, the components selected for lipid synthesis were as follows: * A steroid for the lipid bilayer: Cholesterol chloroform (Sigma-Aldrich, St. Louis, MO, USA) was chosen for its ability to integrate into lipid bilayers. * A small primary amine as a linker: Cadaverine (Sigma-Aldrich) was selected to function as a connector between the hydrophobic body and the hydrophilic head due to its small size and primary amine group. * A basic amino acid for the polar head: L-lysine (Sigma-Aldrich) was selected to confer two positive charges to the hydrophilic region, enhancing its ability to associate with DNA. This design ensures that the amphipathic lipid integrates well within biological membranes, provides effective DNA binding and delivery capabilities, and minimizes potential cytotoxic effects. The components chosen were carefully selected to fulfill the necessary criteria for forming stable and efficient liposomal structures for gene delivery in therapeutic applications.
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 4 Propagation Model  
In this section, we will explore commonly used propagation models in LoRaWAN, including the close-in, floating intercept, and Okumura-Hata models. The study of wave propagation involves analyzing the distribution of energy in a signal through space, and propagation models aid in predicting the received signal power in a wireless network. By considering factors such as the morphology of the environment, terrain morphology, operating frequency, and the height of the transmitting and receiving antennas, we can calculate values and make adjustments for the propagation models [26; 38].