esa-sceva/satcom-chunk-collection huggingface dataset:
# ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries
## 4 Research Methodology
### Empirical Model Specification and Estimation Techniques
Taking the first difference of Equation (5), it can be parameterised as follows:  
\[\Delta\text{Y}_{it}{=}\ (\ \alpha\ -\ 1)\ \Delta\text{Y}_{it-1}{+}\beta\Delta \text{X}_{it-1}{+}\Delta\epsilon_{it} \tag{6}\]  
The GMM, Equation (6), is therefore specified as follows:  
\[\Delta SMTV_{it}=\ \alpha\Delta\text{SMTV}_{it-1}+\alpha_{1} \Delta NBU_{it}+\ \alpha_{2}\Delta NMUI_{it}+\ \alpha_{3}\Delta IU_{it}+\alpha_{4}\Delta NFTU_{it}+\alpha_{5}\Delta FFI_{it}+ \alpha_{6}\Delta GDP_{it} \tag{7}\] \[+\mu_{i}+\epsilon_{it}\] \[\Delta SMC_{it}=\ \theta_{0}\Delta SMC_{it-1}+\theta_{1}\Delta NBU_{it}+ \ \theta_{2}\Delta NMUI_{it}+\ \theta_{3}\Delta IU_{it}+\theta_{4}\Delta NFTU_{it}+\theta_{5}\Delta FFI_{it} +\theta_{6}\Delta GDP_{it}\] (8) \[+\mu_{i}+\epsilon_{it}\] \[\Delta SMRR_{it}=\ \gamma_{0}\Delta SMRR_{it-1}+\ \gamma_{1}\Delta NBU_{it}+\ \gamma_{2} \Delta NMU_{it}+\ \gamma_{3}\Delta IU_{it}+\gamma_{4}\Delta NFTU_{it}++ +\gamma_{5}\Delta FFI_{it}+\gamma_{6}\Delta GDP_{it}\] (9) \[+\mu_{i}+\epsilon_{it}\] \[\Delta NLC_{it}=\ \beta_{0}\Delta NLC_{it-1}+\ \beta_{1}\Delta NBU_{it}+\ \beta_{2} \Delta NMU_{it}+\ \beta_{3}\Delta IU_{it}+U+\beta_{5}\Delta FFI_{it}+\beta_{6}\Delta GDP_{it} +\mu_{i}+\epsilon_{it} \tag{10}\]  
\(\alpha_{0}\), \(\theta_{0}\), \(\gamma_{0}\) and \(\beta_{0}\) = Each model's intercept, respectively;  
\(\alpha_{i}\), \(\theta_{i}\), \(\gamma_{i}\) and \(\beta_{i}\), where \(i\) = 1, 2, 3 and 4 and represent the coefficient of the model explanatory variables, while the time invariant country specific effects are captured by \(\mu_{i}\), whilst \(\epsilon_{it}\) the error term and \(\Delta\) is the difference operator.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 3 Lightning Analysis of PR Data
### Remarks on the Studies of Lightning Accompanied by Rainfall  
From the results of PR observations, it could be concluded that the lightning probability and high flash rates were more likely to occur over land for convective rainfall than over oceans for stratiform rainfall, and with higher radar reflectivity and weaker vertical reflectivity gradients. Nevertheless, due to the low sensitivity to weak precipitation and phase states of drops, the formation process of lightning was difficult to be analyzed by PR data alone. Spaceborne microwave radiometer, cloud-aerosol lidar and cloud radar are possible collaborative instruments as assistance to provide important information such as water vapor, aerosols and small drops.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Period** & **Location** & **Products (Version) of PR** \\ \hline [PERSON] et al. [75]; [PERSON] and & Dec. 1997 to Dec. 1998 & tropics & 2A23 and 2A25 (version4) \\ [PERSON] [76] & & & \\ [PERSON] et al. [82] & Jan. and Feb. of 1998–2003 & western North Pacific & 2A25 (version5) \\ [PERSON] et al. [78] & 1998–2010 & tropics & the University of Utah \\ [PERSON] and [PERSON] [73] & Dec. 1997 to Feb. 2000 & tropics & 2A25 (version5) \\ [PERSON] and [PERSON] [77] & 1998–2009 & tropics & 2A23 and 2A25 (version7) \\ [PERSON] and [PERSON] [85] & Jul. to Dec. 2004 & African continent & PR precipitation products \\ [PERSON] et al. [74] & Aug. to Oct. 1998 & Africa, South America, east & 2A25 (version4) \\ [PERSON] et al. [83] & 2002–2005 & tropics & 2A23 and 2A25 (version6) \\ [PERSON], [PERSON] et al. [84] & 2007–2008 & Nanjing area & 2A25 (version7) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Key information of the studies on lightning from PR data.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 5 Discussions
### The Contribution of Each Component in the HC-MMK Approach
#### 5.2.3 Hierarchical Classification  
Hierarchical classification is an effective land cover mapping method for those areas with complex and heterogeneous landscapes by increasing classification time and decreasing the number of land cover classes in a single classification process [52; 53; 54]. The conceptual hierarchical structure is the core of this approach. A unique conceptual hierarchical structure was built in most research [53; 54], which cannot meet the demands for land cover mapping over large regions with complex terrain, such as Southwestern China, because there are 38 land cover classes, 190 mapping units and various land cover classes. To solve this problem, a series of conceptual hierarchical structures aimed for each unique mapping unit were built in this study, which obviously improved the accuracy of the CLC-SW2010 product. This strategy also provided a useful reference for future research on land cover mapping over large areas.
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
## 3 Time-Frequency Analysis Method Based on Wavelet Threshold Denoising and HHT  
In real engineering applications, the dynamic observation data collected from the GNSS receiver may contain many components aside from the vibration signals from the bridge structure itself, and the data may also contain interference from environmental noise. Therefore, the signal-to-noise ratio (SNR) is relatively low, which greatly affects the recognition accuracy of the main vibration frequency. Only by filtering out noise effectively can useful information and reasonable and reliable conclusions be obtained. For the EMD method, the modal aliasing phenomenon may occur due to intermittent events and the interaction between signals, resulting in incomplete decomposition. The number of decomposition layers from the EMD decomposition process and the margin effect on signal analysis must be reduced via denoising pre-processing to improve the accuracy and timeliness of signal feature extraction.
###### Abstract  
Context: The use of controlled vocabularies (CVs) aims to increase the quality of the specifications of the software requirements, by producing well-written documentation to reduce both ambiguities and complexity. Many studies suggest that defects introduced at the requirements engineering (RE) phase have a negative impact, significantly higher than defects in the later stages of the software development lifecycle. However, the knowledge we have about the impact of using CVs, in specific RE activities, is very scarce. Objective: To identify and classify the type of CVs, and the impact they have on the requirements engineering phase of software development. Method: A systematic mapping study, collecting empirical evidence that is published up to July 2019. Results: This work identified 2348 papers published pertinent to CVs and RE, but only 90 primary published papers were chosen as relevant. The process of data extraction revealed that 79 studies reported the use of ontologies, whereas the remaining 11 were focused on taxonomies. The activities of RE with greater empirical support were those of specification (29 studies) and elicitation (28 studies). Seventeen different impacts of the CVs on the RE activities were classified and ranked, being the two most cited: guidance and understanding (38%), and automation and tool support (22%). Conclusions: The evolution of the last 10 years in the number of published papers shows that interest in the use of CVs remains high. The research community has a broad representation, distributed across the five continents. Most of the research focuses on the application of ontologies and taxonomies, whereas the use of thesauri and folksonomies is less reported. The evidence demonstrates the usefulness of the CVs in all RE activities, especially during elicitation and specification, helping developers understand, facilitating the automation process and identifying defects, conflicts and ambiguities in the requirements. Collaboration in research between academic and industrial contexts is low and should be promoted.  
**Keywords:** controlled vocabularies; requirements engineering; software development; evidence-based software engineering; systematic mapping study +
Footnote †: journal: Applied Soft. Sci. 2020, 10, 7749; doi:10.3390/app10217749
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 2 Sensor Technologies
### Flow Rate Measurement
#### 2.2.1 Ultrasonic Meter  
Ultrasonic meters are devices that are composed of a receiver and transmitter and that use the phenomenon of sonic waves. The device is attached to the exterior of a pipeline and used to determine the flow rate. The transmitted waves are detected by the receiver. The modification of the wave frequency and amplitude is indicative of the properties of the fluid/gas. There exist several methods that can be used to calculate the final flow rate, for example, based on the time of transit of the sonic waves [36], the Doppler Effect [37], cross-correlation [37] or changes in wave amplitude [38]. Ultrasonic sensors are used in many areas of industry. In underground structures, at least three sensors are used at different points [28], for example, at different locations along a channel [27]. The advantage of an ultrasonic meter is that it does not come into contact with the fluid/gas medium and is positioned on the outside of the pipeline [27].
# Spatial Image-Based Walkability Evaluation Using Regression Model
## 2 Proposed Method
### Data Acquisition
#### 2.1.3 Semantic Segmentation  
Semantic segmentation is used to classify pixel areas of images, identifying features that constitute the walking environment. The more detailed the pixel areas, the easier it is to distinguish the components of the walking environment in road images. Therefore, this study employs the Unified Perceptual Parsing network (UPerNet) model, trained on the ADE20K dataset, which provides 150 classes. The model utilizes the Swin Transformer [31] as its backbone network [32]. The UPerNet (Swin-B) model, which has demonstrated state-of-the-art semantic segmentation performance for the ADE20K dataset, divides the pixel  
\begin{table}
\begin{tabular}{c c c} \hline \hline Image Name & & \\ Number of comparisons & 16 & 16 \\ Number of wins & 3 & 15 \\ Trueskill\_mean & 17.226043 & 34.964508 \\ Trueskill\_std & 2.8507967 & 3.0705123 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image scores dataset of pairwise comparison.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{Image name} & & \\ Mean\_before & 36.153447 & 23.979166 \\ Std\_before & 3.4737805 & 2.7657728 \\ Result & & left \\ Mean\_after & 36.362659 & 23.979166 \\ Std\_after & 3.4737805 & 2.7657728 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image information dataset for two images.  
areas of the image into their corresponding features from the 150 features. Figure 3 shows a visualization of the semantic segmentation results, a road image of Jeonju-si. Table 3 presents the semantic segmentation results of Figure 3, showing the ratio of the number of pixels for the extracted features. The feature numbers are displayed according to the order presented in the ADE20K dataset. From the image shown in Figure 3, the following \(11\) features were extracted: road, sky, tree, plant, sidewalk, car, building, grass, mountain, house, and streetlights.  
However, expressing the crowdsourcing results in numerical form, along with semantic segmentation results and image scores calculated by the TrueSkill algorithm, presents difficulties in generalizing the criteria of the walkability evaluators or visually understanding the walking environment preferred by pedestrians. To address these issues, a web application is built to visually represent the data.
## 2 Materials and Methods
### Related Works
In contrast to traditional GNNs, heterogeneous graph neural networks take into account the relationships between various types of nodes and edges alongside node features and adjacency information. This comprehensive approach enables the optimal representation of the intricate and multifaceted information present within the network. Heterogeneous graph neural networks are applied in various agricultural areas, including crop yield prediction [32], pest and disease identification [33], and price forecasting [4], etc. Beyond agriculture, heterogeneous graphshave found extensive applications in other domains, highlighting their versatility and effectiveness in capturing intricate relationships. Notable applications include recommendation systems [34], knowledge graphs [35; 36], bioinformatics analysis [37], and social network analysis [38; 39]. Link prediction is a pivotal task in network analysis, involving the determination of whether a connection exists between two target nodes based on the structural and feature information of those nodes [40]. This process is crucial in forecasting potential relationships within a network, such as predicting links within the Xinjiang juijube sales market. Such predictions are invaluable in expanding sales channels and refining sales strategies. In the context of agricultural trade, links represent existing trade relationships, with new links emerging and others being terminated annually. It is worth noting that different links hold varying trade values. Consequently, the pursuit of identifying optimal sales channels that correspond to diverse trade demands emerges as an ongoing area of research. Notable heuristic methods such as Katz [41], PageRank [42], and SimRank [43] have shown effectiveness in link prediction. However, these methods face limitations, especially in their reliance on manually set link similarity scores, which makes them less adaptable to dynamic and complex networks. Overcoming the manual setting constraints of heuristic methods, graph embedding-based methods [44] such as DeepWalk [25] and node2 vec [26] leverage random walks to generate node embeddings using Skip-Gram [45]. While successful, these methods may face challenges in link prediction due to their focus primarily on local structures, potentially neglecting broader, global patterns. GNNs [46], viewed as extensions of DeepWalk and node2 vec, bring a transformative approach to link prediction. They deploy multi-layer convolutional neural networks to learn node embeddings, capturing intricate structural information within the graph. Unlike graph embedding-based methods, GNNs excel in representing both local and global structures, addressing over-fitting concerns. Moreover, GNNs perform link prediction without explicitly computing similarity matrices between node pairs, making them well-suited for large-scale graph applications.
## 7 Overview of Deep Learning in 5G Security
### Deep Learning Applications in 5G Security
#### 7.3.2 Mitigating Malware Risks Using Deep Learning  
As the complexity and sophistication of cyber threats grow, particularly in the context of 5G technology, the risks posed by malware--malicious software designed to cause harm to a system or network--become increasingly significant. Malware can take many forms, including viruses, worms, trojans, ransomware, and spyware, and the detection and prevention of such threats is a key aspect of maintaining 5G network security [128]. Deep learning techniques have shown remarkable potential in mitigating malware risks in 5G networks. These techniques can go beyond traditional, signature-based malware detection methods, which are often ineffective against zero-day and polymorphic malware, by learning to recognize malicious patterns and behaviors in software applications or network traffic [129] For instance, deep learning models like RNNs and LSTM can analyze sequences of system calls made by applications, enabling the detection of previously unseen or unknown malware based on their behavioral patterns. Furthermore, CNNs, known for their prowess in image recognition tasks, have been applied successfully to malware detection, treating binary files as images and identifying harmful patterns therein. Additionally, autoencoders, a specific type of artificial neural network, have been utilized in unsupervised learning scenarios for malware detection. They can learn the normal behavior of a system and identify any deviations which could signify a malware infection [95].
## 4 Proposed Protocol
### V2I Communication  
V2I communication is only possible when the vehicle is registered on the CA Server and has an active session. On the vehicle side, The OBU computes \(ID_{i}\), \(REF_{i}\), \(CL_{i}\), \(LUI_{i}\), and \(t_{i}\). \(CL_{i}\) is composed of the latitude and longitude of the vehicle. The CA Server gets \(\{ID_{i},REF_{i},CL_{i},LUI_{i},t_{i}\}\) as parameters. \(ID_{i}\), \(REF_{i}\), \(t_{i}\) and \(t_{s}\) are concatenated to form \(IT_{s}\). If the CA Server receives a value regarding the current location of the OBU, it extracts latitude and longitudes from \(CL_{i}\). Otherwise, it returns null values in response. The parameters \(\{lat,long\}\) are then passed to the vehicle for parsing purposes (see Table 7).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Vehicle OBU** & **CA Server** \\ \hline \multicolumn{2}{l}{Login and authentication:} \\ \multicolumn{2}{l}{Inputs \(E_{i},PW_{i}\) OR \(VCA_{s}\)} \\ \multicolumn{2}{l}{Computes} \\ \(t_{i}\) & \\ \(ID_{i}\) & \\ \(REF_{i}\) & \\ \(AT_{i}\) & \\ \(ID_{i},REF_{i},((E_{i},PW_{i})OR(VCA_{s})),AT_{i})\) & \\ \(ID_{i},REF_{i},((E_{i},PW_{i})OR(VCA_{s})),AT_{i})\) & \\ \(AT_{i}\) & \\ \(if(AT_{i}==0)\) & \\ \(C\) & \\ \(IF(Matched)\) & \\ \(Message=\) “_Success_” & \\ \(SK_{s}=(ID_{i}||t_{s})\) & \\ Updates logs table & \\ \(Message=\) “_Retry_” & \\ \(SK_{s}=null\) & \\ \(else\) & \\ \(AS_{i}=\) “_OK_” & \\ \(else\) & \\ \(AS_{i}=\) “_Failed_” & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Login and authentication phase.
# Optimal Energy Management within a Microgrid: A Comparative Study
## 3 Generic Model of OPF for Microgrid  
For electric demand curves, solar radiation, and predicted wind speed for an interval of time \(T\), the general model of OPF is given by Equations (22)-(25). It should be noted that, in this model, it is considered that the period of time \(T\) is composed of a set of time stages \(t_{z}\) (\(\forall=1\),..., end), such that \(T=\{t_{1},...,t_{\mathrm{end}}\}\).  
\[\mathrm{Minimize}\ F_{T}=\sum\limits_{t_{z}=1}^{t_{\mathrm{end}}}f^{t_{z}} \big{(}y^{t_{z}}\big{)} \tag{22}\]  
\[\mathrm{Subject}\ \mathrm{to}\ h^{t_{z}}\big{(}y^{t_{z}}\big{)}=0;\ \forall t_{z}\in T \tag{23}\]  
\[g^{t_{z}}\big{(}y^{t_{z}}\big{)}\leq 0;\ \forall t_{z}\in T \tag{24}\]  
\[\underline{y}\leq y^{t_{z}}\leq\overline{y};\forall t_{z}\in T \tag{25}\]  
Figure 5: Electric vehicle scheme.  
Taking into account that \(t_{z}\) represents the \(z\)th time stage, the description of the terms of the model, Equations (22)-(25), is as follows: \(F_{T}\) is the objective function to optimize along the interval \(T\); \(h(y)\) is a set of equality constraints, which represents the balance equations of active and reactive power on all the nodes in the microgrid, as well as other operating conditions that must be fulfilled unconditionally; \(g(y)\) is a set of constraints of inequalities that represents the physical and operating limits of the elements that make up the microgrid; and \(y\) is the set of decision variables (to be optimized) composed of subsets \(y_{RD}\), \(y_{MF}\), and \(y_{B}\) such \(y=[y_{RD},y_{MF},y_{B}]\). As has been pointed out in Section 2, \(y_{B}\), \(y_{RD}\), and \(y_{MF}\) represent the battery variables, distribution grid, and photovoltaic modules, respectively. The upper and lower limits, \(\overline{y}\) and \(\underline{y}\), of these variables are formulated through the inequality constraints; see Equation (25).
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 2 Materials and Methods
### Description of ET Models
#### 2.1.1 Mapping Evapotranspiration at High Resolution with Internalized Calibration, METRIC  
Sensible heat flux in METRIC [32] is derived in a contextual manner by finding hot and cold pixels (Equation (2)).  
\[H =\rho C_{p}\frac{\delta T}{R_{AH}} \tag{2a}\] \[\delta T =c+m\ T_{rad} \tag{2b}\]  
where \(\delta T\) is the estimated gradient between aerodynamic and air temperature, estimated as a linear equation function of \(T_{rad}\) with \(c\) and \(m\) parameters are linearly solved from expressing Equation (2b) from two cold and hot endpoints:  
\[m =\frac{\delta T_{hot}-\delta T_{cold}}{T_{hot}-T_{cold}} \tag{3a}\] \[c =\delta T_{hot}-m\ T_{hot} \tag{3b}\]  
METRIC scales \(\lambda E\) between these two hot (\(T_{hot}\)) and cold (\(T_{cold}\)) endmembers based on a linear relationship between actual ET and reference ET using the standardised ASCE Penman-Monteith equation for an ideal alfalfa field [55]. Therefore, METRIC, as opposed to SEBAL [31], does not assume zero sensible heat flux at the cold pixel, which can have a positive impact on model accuracy at well watered areas under large vapour pressure deficit conditions. According to [PERSON] et al. [32], cold pixels yield a 5% larger ET than the reference ET (\(\lambda E_{cold}=1.05\lambda E_{ref}\)), but earlier in the season and off-season, cold pixel ET is instead a function of fractional cover or NDVI: \(\lambda E_{cold}/\lambda E_{ref}=f\left(NDVI\right)\). On the other hand, METRIC overcomes the issue of estimating \(kB^{-1}\) by computing \(R_{AH}\) using the profile at two different heights above \(z_{0H}\). Finally the authors stated the need for either computing an \"excess resistance\" in aerodynamically rough and dry surfaces when using the \(\delta T\) calibration performed over agricultural areas, or calibrating different \(\delta T\) slopes at different land covers/environmental conditions [32].  
For Equation (2) to hold true, \(\delta T\) and \(H\) require constant wind speed at the application domain, so the model uses wind speed at blending height to overcome this issue. It also requires constant irradiance and air temperature, i.e., \(\delta T\) changes are only either due to root-zone soil moisture or aerodynamic roughness. Furthermore, the model requires heterogeneity in hydrologic and vegetation conditions and therefore we applied METRIC over two different vegetation domains, short vegetation (crops, grass and shrubs) and tall vegetation (broadleaved and conifer forests as well as wooded savannas). Finally, METRIC is sensitive to the definition of hot and cold pixels. Several different methodologies to find those endmember values were proposed, which can be especially challenging in heterogeneous areas where pixels become mixed at coarse spatial resolution. In our case we adopted the Exhaustive Search Algorithm solution proposed by [PERSON] et al. [56].