esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Background
### Classification  
Terpenes are the largest classification of biologically produced organic molecules accounting for approximately one third of all characterized natural products. These compounds are structurally classified as combinations of monomeric isoprene units because are likely to follow the isoprene rule. In 1953, this rule was proposed to rationalize the biogenesis of terpenoid compounds. The isoprene rule not only clarified the biochemical origin of this class of molecules but also serves as the basis for their classification [9].  
The subgroups of terpen are defined by the number of isoprene units present in the molecule. Because all terpenes are composed of a number of isoprene units, they will share the hydrocarbon formula (C\({}_{5}\)H\({}_{8}\))\({}_{\rm n}\). Activated isoprenoid precursors, isopentyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), polymerize in a variety of combinations through enzymatically catalyzed coordination-insertion, coordinative chain growth and radical ionic mechanisms. These polymerizations afford higher order terpenoid compounds (Figure 1). The various combinations of IPP and DMAPP as well as the potential for rearrangement mechanisms at each step yields a high degree of structural diversity in this class of molecules.  
Monoterpenes are the smallest molecules of the isoprenoids with a conserved hydrocarbon formula of C\({}_{10}\)H\({}_{16}\). There are more than 400 known chemical structures that have been classified as monoterpenes [10]. There have been many monoterpenes isolated and elucidated from natural sources that contain unique and intricate structures each having their own properties in biological systems. These molecules can further be classified as a-, mono-, or bicyclic systems.
# Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization
## 2 Related Works
### Hyperparameters  
As mentioned in the previous section, activation function and optimisation algorithm are the core components of the CNN. In addition, these hyperparameters will increase the convergence speed of the model, and greatly change the score of the models by avoiding overfitting [15] and vanishing gradient problems [16].  
The activation function is used to introduce non-linearity to the network. Various activation functions are suggested. Rectifier Linear Unit (ReLU) [17], which is derived from the action potential of neurons, is one of the most popular activation functions. As ReLU outputs all negative numbers to zero, negative nodes will die out. In order to solve this problem (dying ReLU), Leaky ReLU [18] is introduced. In this function, negative values are multiplied with a small parameter, \(\alpha\), and not zeroed out. Finally, Clipped Relu [19] is introduced in order to prevent the exploding gradient problem of ReLU. It sets a ceiling value so that large values are 'clipped' to the ceiling number.  
On the other hand, an optimisation algorithm is used to find a model's parameters that minimise the loss function of the model. One popular method is Stochastic Gradient Descent (SGD) [20], which is a modification of GD [9]. The difference between GD and SGD is that GD uses the entire dataset to update gradient, whereas SGD updates the gradient with a random sample within the dataset. As parameter values calculated with part of the dataset are approximate values, this method is a stochastic method. In addition, the procedure of SGD might introduce oscillation into the path of the algorithm. To prevent the oscillation problem, a momentum value is introduced. This algorithm is called Stochastic Gradient Descent with Momentum (SGDM) [21].  
SGDM uses a fixed learning rate to all parameters. On the other hand, Root Mean Square Propagation (RMSProp) [22] automatically applies a different learning rate to each of the parameters. It uses exponential moving average to find the gradient of the parameters. RMSProp effectively decreases the learning rate for parameters with a large gradient, and vice versa for small gradient parameters. Finally, Adaptive Moment Estimation (Adam) [23] uses moving averages of both gradient and squared gradient, which are estimations of mean and variance values, respectively. If the gradient change is minimal, a parameter update can get some momentum from the moving average. On the other hand, if the gradient is small, the moving average of the gradient also decreases, leading to a small change in parameter values.
## 6 Practical Analysis  
### Analysis of Settlement Test Results  
In order to study the ground deformation under load at different locations of the existing buried high pressure gas pipe section of the soft soil roadbed, the monitors chose to set up monitoring points at three locations on the left, middle and right of the road. The measurement period was from 20 March 2019 to 20 August 2019, and the measurement  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Phase** & **Project** & **Unit** & **CO\({}_{2}\)** & **SO\({}_{2}\)** & **NO\({}_{\mathbf{x}}\)** & **CO** & **Quantity** & **Carbon** \\ \hline Materialization & PS32.5 & kg/t & 677.68 & 0.202 & 1.087 & 0.292 & 50 kg & 0.034 \\ \hline Transportation & road transportation & kg/t\(\cdot\)km & 18.2 & 14,200 & 12.9 & 261 & 67 km & 48.549 \\ \hline Construction & \begin{tabular}{c} deep mixers \\ (thermal) \\ \end{tabular} & kg/wh & 1.14 & 0.0001 & 0.0052 & 0.0028 & 227 kwh & 260.617 \\ \hline \multirow{3}{*}{\begin{tabular}{c} \end{tabular} } & \
\begin{tabular}{c} lifting equipments \\ (diesel) \\ \end{tabular} & kg/kg & 0.0734 & 0.0026 & 0.0003 & 0.0007 & 32.25 kg & 0.002 \\ \cline{2-6}  &
\begin{tabular}{c} Curing agent \\ preparation \\ systems (thermal) \\ \end{tabular} & kg/wh & 1.14 & 0.0001 & 0.0052 & 0.0028 & 40.17 kwh & 46.119 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Inventory of carbon emission per unit length of cement mixing pile.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Project** & **Quantity** & \begin{tabular}{c} **Unit Carbon** \\ **Emissions/** \\ **(kg\(\cdot\)m\({}^{-1}\)(m\({}^{-3}\)))** \\ \end{tabular} & \
\begin{tabular}{c} **Total Carbon** \\ **Emissions/t** \\ **Emissions/t** \\ \end{tabular} &
\begin{tabular}{c} **Total Carbon** \\ **Emissions of the** \\ **Programme/t** \\ \end{tabular} \\ \hline Plain concrete piles & 29,661.50 & 1,217,218.13 & 36,104,515.450 & \multirow{2}{*}{Scheme I: 41,974,810} \\ CFG piles & 4900.80 & 324,558.3267 & 1,590,595.447 & \\ Cement mixing piles & 12,044.60 & 355,320.9858 & 4,279,699.146 & \\ FLC & 27,319.90 & 547,335.85 & 14,953,160.752 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Scheme I and II carbon emission inventories.  
results are shown in Figure 7. As can be seen from it, the cumulative settlement of the foam lightweight concrete roadbed in the test section stabilised at less than 0.9 cm after work, satisfying the theoretical value.
# The Role of Data-Driven Methodologies in Weather Index Insurance
## 3 Theoretical Framework
### Index Insurance  
Traditional insurance tools assess the risk to the insured. One of the main risk factors in agriculture is the weather, which is a correlated risk. This means that when an event  
Figure 2: Confusion matrix [29].  
occurs, many policyholders are affected simultaneously [30]. This situation can involve high costs and is a critical factor for the financial viability of these insurances as a single event could result in losses that exceed the available capital if careful planning is not carried out [4].  
Unlike traditional loss-based insurance, index insurance pays out to the holder according to a predefined index which is highly correlated with a loss variable [4]. These indices are based on specific measures, such as precipitation or temperature, which can show a clear correlation with a response variable such as crop production, yield production, or a proxy of both. One of the advantages of index insurance is that it exhibits lower costs compared to traditional loss insurance. For example, lower transaction costs which makes it potentially more affordable in low-income economies [4].
## 4 Performance Evaluation
### SE and IF for the Proposed DPC with Different \(\lambda\) in the Absence of Cellular Users  
Figure 6 depicts the IF results of the proposed DPC scheme according to \(\lambda\) in the loss function in Equation (14) for Rayleigh fading channels when \(IF_{thr}=1\). In the figure, it is shown that the IF result becomes closer to 1 as \(\lambda\) decreases, and the maximum value of \(\lambda\) that meets the maximum IF (i.e., \(IF=1\)) is 0.0001. It is very important to find the maximum value of \(\lambda\) because when \(\lambda\) is close to 0, the SE maximization is rarely taken into account, as specified in Section 3. Additionally, the figure illustrates that the IF results become away from one as \(\lambda\) increases.  
Figures 7 and 8 show the SE and IF results of the proposed DPC scheme with \(\lambda=1\) and 0.0001 in Rayleigh fading channels, respectively. In Figure 7, the proposed DPC with \(\lambda=0.0001\) and the maximum IF has lower SE results than the proposed DPC with \(\lambda=1\) for all of the area sizes. However,  
Figure 4: Average spectral efficiency of the conventional schemes and the proposed scheme for various area sizes in Rayleigh fading channels.  
Figure 5: Index of fairness of the conventional schemes and the proposed scheme for various area sizes in Rayleigh fading channels.  
in Figure 8, the proposed DPC with \(\lambda=0.0001\) and the maximum IF has higher IF results than the proposed DPC with \(\lambda=1\) for all the area sizes, and it also achieves perfect fairness performance. From Figures 7 and 8, it is recognized that the fairness performance of the proposed DPC with \(\lambda=0.0001\) improves at the cost of SE.  
Figure 6: Index of fairness of the proposed deep power control (DPC) scheme according to \(\lambda\) in the loss function under Rayleigh fading channels.  
Figure 7: Average spectral efficiency of the proposed DPC scheme with \(\lambda=1\) and 0.0001 in Rayleigh fading channels.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 4 Optimal Formulation for SF and Channel Assignment
This is because, for our objective, the SF selection is independent of what channels the devices are using, as attested to in Equation (7). Finally, the joint assignment algorithm can either be semi-distributed, by using the best response algorithms to solve for the NEs, or completely distributed, by using the learning approach.
# Reasoning about Confidence in Goal Satisfaction
## 4 Data Quality Tagging and Propagation Mechanism
### Data Quality Tagging  
In goal modeling, there are several systematic approaches for determining quantitative contribution and importance levels in goal models, such as the use of the Analytical Hierarchy Process (AHP) [24] and Delphi-based consensus [25], applied to GRL [26] and other goal modeling languages [27; 28; 29; 30; 31]. In our context, we want to provide quantitative values to data quality qualitative types. The data quality types are identified based on the procedure followed to collect or obtain the data. For each type, we have a qualitative value and a corresponding quantitative value. A qualitative value reflects the validity of the source from which data was collected. We identified sources and the associated qualitative attribute through several iterations of validation with industry practitioners, mainly from a healthcare domain. In addition, we propose a tagging mechanism that estimates the impact of the quality of collected data on goal satisfaction levels by giving it a quantitative _confidence value_, a concept that does not exist in standard GRL. These quantitative values were also identified through a consensus-building process among industry practitioners. Table 1 defines the proposed types of quality and corresponding confidence levels. Constructing the table is essential to propagate the confidence value regardless of the data quality types, which could change from one context to another.  
_Valid (100)_ and _Borrowed-Valid (100)_ are the best data quality types because the data have been measured and validated, resulting in the highest confidence. An example of data tagged with _Borrowed (75)_ in a healthcare system context is data collected for patient information documentation in an Emergency Room but that will be reused in documenting patient information in a Surgery Room. _Estimated-Context (50)_ is meant to be used when data is not available but can be estimated based on similar tasks. For example, one could estimate data for 'write patient report after a surgery' based on data of a similar activity such as 'write patient report for consultation'. In case data cannot be estimated from similar activities, it could be estimated from the literature or studies of similar systems, and be tagged with _Estimated-Literature (25)_. _Unknown_ is used to highlight missing data and its nullifying impact on the interpretation of goal/element satisfaction.  
In each evaluation strategy corresponding to a certain design alternative, the confidence of goal-model element satisfaction values will be calculated and propagated. The following section presents how the confidence of a satisfaction value is propagated along different GRL links between model elements.
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 2 Automatic Recognition Method-Based Spectrograms for Hard Rock Crack Types
### Sound Signals of Tensile and Shear Cracks Collected during Rock Tests
#### 2.2.2 Sound Signals of Tensile and Shear Cracks  
The waveforms of the obtained sound signals are presented in Figure 6. It can be seen that the sound signal of the tensile crack from the Brazilian splitting test is different from that of the shear crack from the direct shear test.  
Figure 6a shows the sound signal obtained from the Brazilian splitting test. In the early stage of the test, the rock specimens are in a calm state, and there are no cracking sounds. In the late stage of the test, a small number of cracking sounds appeared gradually. The amplitude of these signals is low, indicating the small-scale failure in the specimen. At  
Figure 4: Experimental setup: True-triaxial rockburst machine testing system and sound recorder.  
Figure 3: Rock test specimens of (**a**) disc granite specimen for the Brazilian splitting test to collect the sound of tensile cracks and (**b**) cube granite specimen for the direct shear test to collect the sound of shear cracks.  
the end of the test, the sound signal with the maximum amplitude occurs, and the final macro failure occurs quickly. The dull \"bang\" sound can be heard by the human ear.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Type** & **Specimen Number** & **Pre-Loading** & **Loading Rate** \\ \hline \multirow{3}{*}{Brazil splitting test} & GBD-S-1 & \multirow{3}{*}{\(F_{1}\): 5 kN} & \multirow{3}{*}{\(F_{1}\): 50 N/s} \\  & GBD-S-2 & & \\ \cline{1-1} \cline{2-4} Direct shear test & GDS-1 & \multirow{3}{*}{\(F_{1}\): 10 kN} & \multirow{3}{*}{\(F_{2}\): 500 N/s} \\  & GDS-2 & & \\ \cline{1-1} \cline{2-4}  & GDS-3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loading scheme.  
Figure 5: Loading device and stress state of the tested specimen in (**a**) Brazilian splitting test and (**b**) direct shear test.  
Figure 6: Waveform of the tested specimen from (**a**) the Brazilian splitting test and (**b**) the direct shear test.  
Figure 6b presents the sound signal from the direct shear test. In the early stage of the test, there are still no cracking sounds. After loading for a while, although the cracks of rock specimens are invisible due to the obstruction of the loading device, the successive ringing of a \"ka\" sound can be detected by the human ear, indicating the successive formation of small cracks. The waveform of sound signals at this stage is dense. In the late stage of the test, the rock specimens produce a continuous sound of frictional fracture between the rock grains. The cracking sounds gradually increase, and it changes from crisp to low. At the end of the test, the final failure of the rock specimen occurs with the dull \"bang\" sound, the corresponding waveform of sound signals is denser, and the amplitude of the sound signal is higher.  
It should be pointed out that to eliminate the influence of noise, the obtained sound signals should be first filtered before being processed.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 2 Materials and Methods
### IF1213 Observation Model  
The IF combination can eliminate the ionospheric first-order term from the original observation equation. Using the IF combination and ignoring the ionospheric second-order term and above, the number of parameters that need to be estimated in the equation can be effectively reduced. A single-system dual-frequency IF combined observation equation is formed by Equation (1), which can be expressed as follows:  
\[\left\{\begin{array}{lcl}p_{r,IFij}^{s,Q}&=&\alpha_{ij}\cdot p_{r,i}^{s,Q}+ \beta_{ij}\cdot p_{r,j}^{s,Q}\\ &&\\ &=&\mu_{r}^{s,Q}\cdot x+dt_{r}-dt^{s,Q}+m^{s,Q}\cdot Z_{r}+d_{r,IFij}-d_{IFij}^ {s,Q}+\varepsilon_{r,IFij}^{s,Q}\\ &&\\ I_{r,IFij}^{s,Q}&=&\alpha_{ij}\cdot l_{r,i}^{s,Q}+\beta_{ij}\cdot l_{r,j}^{s,Q }\\ &&=&\mu_{r}^{s,Q}\cdot x+dt_{r}-dt^{s,Q}+m^{s,Q}\cdot Z_{r}+\overline{N}_{r,IF ij}^{s,Q}+\delta\varphi_{r,IFij}-\delta\varphi_{IFij}^{s,Q}+\bar{\varepsilon}_{r,IF ij}^{s,Q}\end{array}\right. \tag{4}\]  
where  
\[\left\{\begin{array}{lcl}d_{r,IFij}&=&\alpha_{ij}\cdot d_{r,i}+\beta_{ij} \cdot d_{r,j}\\ d_{IFij}^{s,Q}&=&\alpha_{ij}\cdot d_{i}^{s,Q}+\beta_{ij}\cdot d_{j}^{s,Q}\\ \delta\varphi_{r,IFij}&=&\alpha_{ij}\cdot\delta\varphi_{r,i}+\beta_{ij}\cdot \delta\varphi_{r,j}\\ \delta\varphi_{IFij}^{s,Q}&=&\alpha_{ij}\cdot\delta\varphi_{i}^{s,Q}+\beta_{ij} \cdot\delta\varphi_{j}^{s,Q}\\ \overline{N}_{r,IFij}^{s,Q}&=&\alpha_{ij}\cdot\overline{N}_{r,i}^{s,Q}+\beta_{ ij}\cdot\overline{N}_{r,j}^{s,Q}\end{array}\right. \tag{5}\]  
and \(d_{r,IFij}\) and \(d_{IFij}^{s,Q}\) are IF combinations of the code hardware bias of the receiver and satellite, respectively. Furthermore, \(\delta\varphi_{r,IFij}\) and \(\delta\varphi_{IFij}^{s,Q}\) are the IF combinations of the phase hardware bias time-varying part of the receiver and satellite, respectively. \(\overline{N}_{r,IFij}^{s,Q}\) is the ambiguity IF combination.  
The IF1213 observation equation is based on Equation (4), which consists of the IF12 and IF13 observation equations and can be expressed as follows:\[\left\{\begin{array}{lcl}p_{r,IF12}^{s,Q}&=&\mu_{r}^{s,Q}\cdot x+dt_{r}-dt^{s,Q}+m ^{s,Q}\cdot Z_{r}+d_{r,IF12}-d_{IF12}^{s,Q}+\varepsilon_{r,IF12}^{s,Q}\\ \par l_{r,IF12}^{s,Q}&=&\mu_{r}^{s,Q}\cdot x+dt_{r}-dt^{s,Q}+m^{s,Q}\cdot Z_{r}+ \overline{N}_{r,IF12}^{s,Q}+\delta\varphi_{r,IF12}-\delta\varphi_{IF12}^{s,Q}+ \tilde{\varepsilon}_{r,IF12}^{s,Q}\\ \par p_{r,IF13}^{s,Q}&=&\mu_{r}^{s,Q}\cdot x+dt_{r}-dt^{s,Q}+m^{s,Q}\cdot Z_{r}+ d_{r,IF13}-d_{IF13}^{s,Q}+\varepsilon_{r,IF13}^{s,Q}\\ \par l_{r,IF13}^{s,Q}&=&\mu_{r}^{s,Q}\cdot x+dt_{r}-dt^{s,Q}+m^{s,Q}\cdot Z_{r}+ \overline{N}_{r,IF13}^{s,Q}+\delta\varphi_{r,IF13}-\delta\varphi_{IF13}^{s,Q}+ \tilde{\varepsilon}_{r,IF13}^{s,Q}\end{array}\right. \tag{6}\]
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm
### Cluster Head Selection
#### 3.3.5 Join  
We measure CH based on the available power, signal strength, and distance mentioned above. Considering the available power, signal strength, and distance, the joint metric is defined by Equation (6):  
\[Cost(x_{i})=AP(x_{i})+RS(x_{i})-d(x_{i}) \tag{6}\]  
We calculate \(Cost(x_{i})\) for all potential cluster heads, and we then select the cluster head with the minimum \(Cost(x_{i})\).  
Figure 3: Flowchart for cluster head selection.  
First, a node with more energy power and stronger signal has more probability to be the cluster head in a cluster. Thus, the node with the minimum cost becomes the cluter head candidate. Second, a non-cluster head node with higher energy power than those of neighbor nodes may become a cluster head candidate. The selected cluster head candidate has to notify its neighbor nodes of cluster head candidate selection (NOTICE_CH_CANDIDATE). Third, cluster members that are not the cluster head broadcast join request message (REQ_JOIN) to the nearest cluster head. If a node is not the cluster head candidate (NOT_CH_CANDIDATE), then the node forwards to neighbor nodes that the node is a cluster member. The whole process is shown in Figure 3, and the corresponding cluster head selection algorithm (Algorithm 1) is given as follows.  
```
Input: Nodes' information in a Node Cluster Output: CH Node begin broadcast E\({}_{i}\) in Cluster Radius receive E\({}_{j}\) in Cluster Radius \({\it FRD(x_{i})=\frac{E_{i}(t)}{\sum\limits_{j=1}^{n}E_{j}(t)}\ \times\mu(x_{i})}\) If (FRD( \(x_{i}\))==max(FRD( \(x_{j}\)) |j=1,2,...,n)) then begin broadcast NOTICE_CH_CANDIDATE(i) in Cluster Radius receive REQ_JOIN(i,j) in Cluster Radius Cluster(i)=Cluster(i) \(\cup\) {j} calculate the available power calculate the received signal strength calculate the distance for the cluster heads search min \({\it Cost(x_{i})=AP(x_{i})+RS(x_{i})-d(x_{i})}\) if (i!=j) then begin send NOT_CH_CANDIDATE end else CH_CANDIDATE=FALSE; receive NOTICE_CH_CANDIDATE(j) in Cluster Radius CH(i)=CH(i) \(\cup\) {j} if (CH(i)!=O) then begin broadcast REQ_JOIN(i,j) else CH_CANDIDATE=TRUE; end end end end end
```  
**Algorithm 1**Cluster head Selection.
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 2 Materials and Methods
### Interactive Satelite Map Creation  
The main program software utilizes several Python libraries: folium (for creating interactive maps [16]), shapely (commonly used for geospatial analysis [17]), pyproj (for geospatial data transformation [18]) and geopy (enabling easy access to geocoding services for address-to-coordinate conversions and vice versa [19]). These libraries facilitate the analysis of SR coverage areas on geographic maps where TRs are located.  
Python's folium library was utilized to generate interactive satellite maps, providing a visual representation of the geographical features within the selected study area. Folium utilizes OpenStreetMap data [20] to generate interactive maps directly in Python. To enhance both visual appeal and accuracy, we integrated Esri Satellite map tiles [21] into the maps.  
The program is initiated by receiving inputs from a data file, which includes SR parameters such as locations, ranges, angular ranges of detection, as well as positions of known TRs. Subsequently, the folium map is centered at a specified location, and pop-up controls are added for visualization purposes to display mouse position (latitude-longitude). Additionally, known TR locations are marked on the map.