esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description  
Ground operations at airports are crucial contributors to overall flight delays, fuel consumption, and emissions. Taxing operations typically account for significant delays due to congestion, inefficient ground handling, and inadequate traffic management systems. These delays are economically costly for airlines and environmentally detrimental due to increased fuel burn during idling and low-speed taxiing [30; 31].  
Therefore, it is essential to optimize the taxi-in and taxi-out phases. Studies on ground handling alternatives show that reducing taxiing time can significantly improve operational efficiency. For instance, reducing taxiing time by 1 to 3 min can lead to meaningful fuel consumption and emissions reductions. Strategies such as single-engine taxiing, onboard systems, or dispatch towing have reduced emissions by up to 30% during taxi-outoperations [32]. This aligns with the research question in this study, which aims to evaluate airport resilience and quantify its weather factors.  
Besides, the need for a more proactive approach to optimizing LTO (landing and take-off) operations cannot be overstated. Ground traffic management systems and runway usage strategies should be designed to minimize idle times and improve sequencing efficiency during peak traffic periods [33]. It is a crucial aspect of managing delays and improving the overall resilience of airport operations.  
[PERSON] et al. [7] surveyed European flight delays from 2008 to 2015 regarding variability in flight phases. Their results indicated that airport delays mainly occur during the take-off and landing phases, among which airport weather is the main influencing factor. When calculating the resilience of flight operations and the degree of weather impact, the following two aspects of change need to be considered, leading to changes in resilience calculation and the importance of various weather factors:  
1. Flight Delays and Cancellations  
Weather changes leading to flight delays and cancellations cause time and economic losses for various stakeholders such as passengers, airlines, and airports. The estimation of losses varies depending on the perspective and bearing capacity of different stakeholders.
2. Weight Calibration of Airport Operation Network  
Besides considering the distance and number of flights operating on each route within a day to determine the weight of each route in the airport network, the impact of the route on airport transportation capacity also determines its importance in the network.  
This research is based on the following assumptions:  
1. All other operational factors remain normal except for weather changes;
2. Pilots fly to minimize delays while ensuring safety;
3. Flight delays in a particular airport include delays caused during taxiing, take-off, and landing.
## 2 Point Cloud Registration Algorithm
### Greedy Projection Triangulation  
Greedy projection triangulation bridges computer vision and computer graphics. It converts the scattered point cloud into an optimized spatial triangle mesh, thereby reflecting the topological connection relationship between data points and their neighboring points, and maintaining the global information of the point cloud data [24]. The established triangulation network reflects the topological  
Figure 1: Point cloud registration chart. FPFH (fast point feature histograms).  
structure of the target object that is represented by the scattered data-set. The triangulation process is shown in Figure 2. The specific steps are given as follows:  
Step 1: A point \(V\) and its normal vector exist on the surface of the three-dimensional object. The tangent plane perpendicular to the normal vector must first be determined.  
Step 2: The point \(V\) and its vicinity are projected to the tangent plane passing through \(V\), denoted as the point set \(\{S\}\), and the point set \(\{S\}\), which forms all N/2 edges between the two points, is linearly arranged in order of distance from small to large.  
Step 3: The local projection method is used to add the shortest edge at each stage and remove the shortest edge from the memory. If the edge does not intersect any of the current triangulation edges, then it is added to the triangulation, otherwise, it is removed. When the memory is empty, the triangulation process ends.  
Step 4: Triangulation is used to obtain the connection relationship of the points and return it to the three-dimensional space, which forms the space triangulation of the point \(V\) and its nearby points.  
Greedy projection triangulation can establish a reasonable data structure for a large number of scattered point clouds in the 3-D space. When positioning a point, the path is unique, and the tetrahedron can be located accurately and quickly, thereby narrowing the search range and eliminating unnecessary matching. This fundamentally improves the overall efficiency of matching feature points.
## 4 Discussion
Generally speaking, for all the methods based on a length parameter, the latter can be related to Irwin's length \(l_{ch}=(K_{lc}/\sigma_{u})^{2}\), with \(l_{ch}\approx 5.18\) nm in the present work. Recalling the synthesis by means of the TCD presented in [30; 36], it could be asserted that all the critical distances are proportional to [53]:  
\[\bar{l}_{c}=\frac{1}{\pi}\left(\frac{K_{lc}}{\sigma_{u}}\right)^{2}=\frac{1} {\pi}l_{ch}. \tag{14}\]  
By substituting numerical values in Equation (14), a critical length \(\bar{l}_{c}\approx 1.65\) nm is obtained. This value obviously agrees with the critical distance evaluated experimentally in [30] i.e., \(\approx 1.3\)-\(1.9\) nm, and it is very close to the critical singular stress field length of 1.5-3 nm at which the continuum-based LEFM breaks down, i.e., 4-5 times the fracture process zone of 0.4-0.6 nm [15; 17; 22]. Interestingly, all the crack advancements presented in Table 3 are well far from the fracture process zone size. Generalizing, Figure 4 shows that \(l_{c}\) can only vary in a range of \(\approx 0.45\)-0.65 \(l_{ch}\), i.e., 2.3-3.4 nm. Again, these values are larger than the fracture process zone size and the critical length. The results confirm that if that low limit is not reached, continuum-based FFM method is still valid, despite the small scale involved. At a smaller scale, e.g., atomic scale, extension and generalization of the approach are possible. The FFM, as introduced earlier, is a coupled criterion, i.e., it requires two conditions to be full-filled, one based on stress and the other on energy considerations. The stress criterion could be developed by considering the virial stress, provided that it is accepted as a representation of the mechanical stress at the atomic scale as recently showed in [20], or by employing a traction vector-based stress recently proposed in [54]. The energy criterion, instead, could be defined based on interatomic potential energy as provided in some recent examples based on the energy release rate [22; 23] and on the averaged strain energy density [17]. The analysis presented here is limited to mode I loading conditions and to ideal brittle material. In case of semi-brittle materials, experimental conditions could affect the elastic-plastic fracture toughness, and scale dependency of fracture properties may occur [32; 55]. This aspect should be carefully considered to define proper mechanical properties and therefore failure conditions of semi-brittle materials.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
###### Abstract  
Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R\({}^{2}\) values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required.  
carbon flux; machine learning; gross primary production; terrestrial ecosystem respiration; net ecosystem exchange +
Footnote †: journal:  
0000-0001-4050913
## 6 Modeling and Simulation Results  
### Net Solar Power Plant (Without Biogas)  
Figures 3 and 4 show the annual net electricity production and the LCOE generated by the solar power plant, respectively, along with the changes in SM at different FLHs for case 6. Figures 3 and 4 show the following: the most significant annual solar electricity production is achieved at SM = 8 and FLHs = 10 h, where \(E_{\rm solar,net}\) = 48.66 \(\rm GW_{\rm hel}\), and the LCOE = 0.2896 EUR/kWh. The lowest cost for produced solar electricity is achieved at SM = 2 and FLHs = 5 h, which amounts to LCOE = 0.1781 EUR/kWh, and the annual electricity production is \(E_{\rm solar,net}\) 25.21 \(\rm GW_{\rm hel}\). To achieve the maximum possible output from the solar plant (48.66 \(\rm GW_{\rm hel}\)), the cost of the produced electricity increases by approximately 61% from the minimum value, almost equivalent to doubling the output compared to the minimum cost scenario.
## 2 Materials and Methods
### Methodology
#### 2.3.4 Indicators' Merging Process  
The health predictions are made for the entire period of time comprised in the test set and information is aggregated to construct a generator health indicator. Anomalies are summarized to a weekly resolution, by comparing the number of anomalies detected in each turbine with respect to the windfarm. The distribution of anomalies within the windfarm is calculated and turbines lying at a distance superior to two standard deviations from the central value are considered anomalous. This is done because particular external conditions lead the entire windfarm to behave anomalously while not undergoing a real fault in the generator system.  
The generator's health indicator is a vector defined in a two-dimensional space. The components of the vector are the processed output of the autoencoder and the counter of key alarms per turbine during the period of the analysis, the module of the vector is calculated as the Euclidean Sum of the two components. A threshold is defined to determine and prioritize the turbines that require maintenance. Alarms' data is used directly in the model, hybridizing and complementing the results of the numerical analysis performed with the autoencoder. The generated status vector considers anomalies in the numerical data and information from the alarm system.  
Figure 1 summarizes all the steps of the proposed methodology showing data reception, its storage and preprocessing and the predicting algorithm.  
Figure 1: Alarm and autoencoder hybrid predictive system pipeline.
## 4 Data and Methodology
### GNSS CORS Data  
To independently assess the results obtained by SAR analysis, we processed the GNSS data obtained at the national GNSS CORS reference station SISA, located on the observed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sentinel** & **Acquisition Date** & **Relative Orbit Track** & **Subswath** & **Initial Burst** & **Last Burst** \\ \hline
1A-SLC & 18 December 2020 & T146 & IW2, IW3 & 6 & 8 \\
1A-SLC & 30 December 2020 & T146 & IW2, IW3 & 6 & 8 \\
1A-SLC & 11 January 2021 & T146 & IW2, IW3 & 6 & 9 \\
1A-SLC & 23 January 2021 & T146 & IW2, IW3 & 6 & 9 \\
1B-SLC & 23 December 2020 & T124 & IW2 & 5 & 7 \\
1B-SLC & 4 January 2021 & T124 & IW2 & 5 & 7 \\
1B-SLC & 16 January 2021 & T124 & IW2 & 5 & 7 \\
1B-SLC & 28 January 2021 & T124 & IW2 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of used Sentinel-1 SAR images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **LOS Displ.** & **UP-DOWN Displ.** & **EAST-WEST Displ.** \\  & **min/max** & **min/max** & **min/max** \\ \hline
1A-IW-SLC & \(-\)30.6/43.0 & \(-\)40.8/57.0 & \(-\)46.0/65.6 \\
1B-IW-SLC & \(-\)17.7/35.4 & \(-\)23.3/46.0 & \(-\)27.2/55.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Displacement values related to each Sentinel orbit separately, in [cm].  
area, in Sisak. SISA station is one of 30 continuously operating reference stations (CORS), belonging to Croatian GNSS network of continuous observing reference stations. CROPOS was established in 2008, with three running services. It provides the accuracy of 2 cm positional and 4 cm in vertical sense for a real-time (RTK) GNSS measurements and sub-centimeter accuracy for post-processing [73]. High level of accuracy was achieved on the grounds that it had continuous millimeter stability. Such stability is needed in order to have centimeter real-time positioning and in order to act as a national reference frame for displacement and other geophysical studies.  
We downloaded and used daily 30 s interval GNSS measurements from SISA reference station for 10 days before and 10 days after the earthquake. The GNSS measurements were downloaded in RINEX (_eng_. _Receiver Independent Exchange_) v. 3.03. format from the GPPS service for the purpose of post-processing. Due to the substantial amount of data, the authors found it appropriate to use the 30-minute interval, and therefore, originally sampled data of 1 s were resampled to 30 s. A period of ten days before and ten days after the earthquake was observed. Median value of ten days prior to the earthquake for each of three coordinates was set as a reference coordinate value for further analysis. Regarding the reference value, the shift in northing, easting, and height direction was computed, given in detail in the next section.
## 3 Hierarchical Controllers for Trajectory Tracking  
Because of its under-actuated nature and the coupling property between translational and rotational dynamics, the hierarchical control structure is used in this section to deal with the quadcopter trajectory tracking problem.  
By assuming that the closed-loop dynamics of rotation are faster than the translational one, it is possible to divide the complete closed-loop system into two independent subsystems [26,27]. However, the displacement equations clearly depend on the rotation components which require the use of a linking block between those subsystems.  
In this section, two controllers' designs based on a hierarchical control strategy are described. The first controller is based on the RM technique, while the second one is optimized using the GA. This control structure consists of two loops in cascade, an inner loop for attitude stabilization and an outer loop for position control. The connection between these loops is provided by a calculation block (see Figure 2).  
Six PD/PID controllers should be computed: one for each flight parameter, \(\phi\), \(\theta\), \(\psi\), \(x\), \(y\), and \(z\) (9). Notice that a PD controller is sufficient in ideal conditions (without an integral action) because the open-loop behavior shows a parabolic divergence (presence of integration) for each flight parameter. However, a PD is not sufficient to stabilize a quadcopter in real design. An integral portion is important to cancel out various disturbances and imperfections (such as nonhomogeneous mass distribution).  
\[\left\{\begin{array}{l}C_{PD_{j}}(s)=k_{p_{j}}+k_{d_{j}}s\\ C_{PID_{j}}(s)=k_{p_{j}}+\frac{k_{j_{j}}}{s}+k_{d_{j}}s\end{array}\right. \tag{9}\]  
\(k_{p_{j}},k_{i_{j}}\), and \(k_{d_{j}}\) are the proportional, integral, and derivative gains, respectively, with \(j=(\phi\), \(\theta\), \(\psi\), \(x\), \(y\), \(z)\).
# Performance of Dynamic Time Division Broadcast Protocol with Rateless Coding
## 3 Performance Analysis
### Outage Probability and Expected Rate  
With the system model description in Section 2, the outage probability of DTDBC-RC, defined as the probability that the system cannot support the give target data rate (i.e., the probability that either source cannot support the the give target data rate), is presented in the following theorem. Figure 2: The working states of the sub-DTDBC-RC protocol. **Theorem 1**. : _For given target data rate \(C\) bits/s/Hz, the outage probability of DTDBC-RC is_  
\[P_{out}=\left(1-e^{-z_{0}/\Omega_{0}}\right)\left(1-e^{-\frac{z_{0}\left(\Omega_ {1}+\Omega_{2}\right)}{1\Omega_{1}\Omega_{2}}}\right)+T_{1}e^{-z_{0}/\Omega_{1 }}+T_{2}e^{-z_{0}/\Omega_{2}}-T_{3}, \tag{1}\]  
_where \(T_{1}\), \(T_{2}\), and \(T_{3}\) are expressed as_  
\[T_{1}=\left\{ \begin{array}{ll}0,&\text{if }0<\alpha\leq\frac{1}{3},\\ e^{-z_{0}/\Omega_{2}}\left(1-e^{-z_{1}/\Omega_{0}}\right)-\Delta_{0}.&\text{if } \frac{1}{3}<\alpha<\frac{1}{2},\end{array} \right. \tag{2}\]  
\[T_{2}=\left\{ \begin{array}{ll}0,&\text{if }0<\alpha\leq\frac{1}{3}\\ e^{-z_{0}/\Omega_{1}}\left(1-e^{-z_{1}/\Omega_{0}}\right)-\Delta_{1}.&\text{if } \frac{1}{3}<\alpha<\frac{1}{2},\end{array} \right. \tag{3}\]  
_and_  
\[T_{3}=\left\{ \begin{array}{ll}0,&\text{if }0<\alpha\leq\frac{1}{3}\\ e^{-z_{0}\left(1/\Omega_{1}+1/\Omega_{2}\right)}\left(1-e^{-z_{1}/\Omega_{0}} \right)-e^{-z_{0}/\Omega_{1}}\Delta_{0}-e^{-z_{0}/\Omega_{2}}\Delta_{1}+ \Delta_{2}.&\text{if }\frac{1}{3}<\alpha<\frac{1}{2}\end{array} \right. \tag{4}\]  
_with \(z_{0}=\left(2^{\left(1-\alpha\right)C/\alpha}-1\right)/\rho\), \(z_{1}=\left(2^{\left(1-\alpha\right)\left(3\alpha-1\right)C/\alpha^{2}}-1 \right)/\rho\), \(\Delta_{0}=\int_{0}^{z_{1}}1/\Omega_{0}e^{-x/\Omega_{0}}e^{-\phi/\Omega_{2}}dx\), \(\Delta_{1}=\int_{0}^{z_{1}}1/\Omega_{0}e^{-x/\Omega_{0}}e^{-\phi/\Omega_{1}}dx\), \(\Delta_{2}=\int_{0}^{z_{1}}1/\Omega_{0}e^{-x/\Omega_{0}}e^{-\phi\left(1/\Omega _{1}+1/\Omega_{2}\right)}dx\), and the integrand \(\phi=\left(2^{\left(1-\alpha\right)C/\left(1-2\alpha\right)}/\left(1+\rho x \right)^{\alpha/\left(1-2\alpha\right)}-1\right)/\rho\)._  
Proof. : Let \(\Theta_{pq}^{i}\) (\(p,q\in\left\{S_{1},R,S_{2}\right\},p\
eq q\)) be the event that the \(p\to q\) link is in outage after the \(i\)-th time slot (\(i=1,2\)) transmission, and let \(\overline{\Theta_{pq}^{i}}\) be its complementary event.
## 4 Results
### Comparative Analysis of PFR-XGB vs. PFR-RF vs. PFR-NN  
In this study, RobustScaler and polynomial feature extraction methods were adopted in all three data processing pipelines. After comparative analysis, it can be seen that the predictive regression framework (PFR-NN) based on a neural network (NN) shows the best effect in terms of oil and gas well productivity fitting. Especially in the productivity fitting of the 4 th and 11 th wells, PFR-NN performed outstandingly, while methods based on XGBoost (PFR-XGB) and Random Forest (PFR-RF) did not perform well. It is particularly worth noting that only PFR-NN performed more accurately when fitting actual gas production. However, all methods failed to achieve the expected results in fitting the low gas production characteristics in the depth interval of 2025 m to 2030 m in the 26 th well. Generally speaking, the neural network is better than the integrated machine learning algorithm in terms of training effect, and it requires fewer parameters, which makes it very suitable for oil and gas reservoir engineering applications and academic research fields.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 5 Discussion
Besides the 3D representation of DT models, IoT Dashboards [87], such as Grafana in particular [28; 73] for real-time data presentation and analysis or even GUI prototypes [81], are proposed, although used on a small scale for facilitating near real-time interaction of DTs with the environment. To reliably transfer and manage data, Blockchain is used, even on a smaller scale, as in [45; 87], while Big Data Analytics, as in [82], AI and ML methods that enable data analysis, pattern recognition, and decision-making based on large datasets, are addressed in [45; 63; 82; 83; 86; 88]. Cloud-based data analytics pose a major issue for the advent of smart agriculture as claimed in [45; 85; 87], not to omit the significance of databases such Mongo DB, Draco, My-SQL as used in [28; 73] or SQLite in [80], and Cloud computing service providers such as Alibaba Cloud, Amazon web services, Microsoft Azure and Google Cloud platform [87]. Various factors, with technical hurdles being the primary challenge, seem to be stepping behind to allow even further the adoption of the DT technologies. It is essential to meticulously design and clearly define intricate agricultural systems to guarantee the accuracy of their corresponding DT models.
###### Abstract  
An optimal maintenance scheduling strategy for bridge networks can generate an efficient allocation of resources with budget limits and mitigate the perturbations caused by maintenance activities to the traffic flows. This research formulates the optimal maintenance scheduling problem as a bi-level programming model. The upper-level model is a multi-objective nonlinear programming model, which minimizes the total traffic delays during the maintenance period and maximizes the number of bridges to be maintained subject to the budget limit and the number of crews. In the lower-level, the users' route choice following the upper-level decision is simulated using a modified user equilibrium model. Then, the proposed bi-level model is transformed into an equivalent single-level model that is solved by the simulated annealing algorithm. Finally, the model and algorithm are tested using a highway bridge network. The results show that the proposed method has an advantage in saving maintenance costs, reducing traffic delays, minimizing makespan compared with two empirical maintenance strategies. The sensitivity analysis reveals that traffic demand, number of crews, availability of budget, and decision maker's preference all have significant effects on the optimal maintenance scheduling scheme for bridges including time sequence and job sequence.  
**Keywords:** bridge network; optimal maintenance scheduling strategy; bi-level programming model; traffic delays; simulated annealing algorithm +
Footnote †: journal: Applied
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 5 Case Studies  
In this section, the proposed methodology has been applied to the two case studies detailed in Table 1. It is important to notice that the number of gateways and cost of systems are obtained when the Q/V band is used in the feeder link.  
In summary, the first part of the methodology is based on the calculation of \(CINR_{b}\) using (16), for different values of the user terminal \(G\)/\(T\) and the number of beams. This means that each \(G\)/\(T\) value corresponds to a different optimal number of beams.  
Tables 11 and 12 of the Appendix A show the variation of the \(a_{ij}\) parameters for different link availabilities. These coefficients have a strong impact on the \(CINR_{b}\) and thus on the optimal values for system design. The \(a_{ij}\) parameters of scenario 1 are smaller than scenario 2, which means that rain has a greater impact on the service area of scenario 1.  
Figure 5: Flowchart for dimensioning using Variable Coding and Modulation (VCM).