esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modelling of the Problem  
A beam without mass systems with commonly used boundary conditions at each end and including spatial derivatives of up to the third order is shown in Figure 1. The boundary conditions with the name at the top and results at the bottom of each case are based on the Euler-Bernoulli beam theory, where \(w_{1}\) and \(w_{2}\) are the dimensionless left and right deflections [3]. Similarly, the prime denotes differentiation with respect to the spatial variable \(x\)[39].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters (Symbol)** & **Values (Units)** \\ \hline Length (\(l\)) & 1.0 m \\ Breadth (\(b\)) & 0.10 m \\ Height (\(h\)) & 0.03 m \\ Area of Cross Section (\(A\)) & 0.266 m\({}^{2}\) \\ Moment of Inertia (\(l\)) & 2.25 \(\times\) 10\({}^{-7}\) m\({}^{4}\) \\ Number of Modes (\(n\)) & 1–20,000 \\ Young’s Modulus (\(E\)) & 210 \(\times\) 10\({}^{9}\) N/m\({}^{2}\) \\ Poisson’s Ratio (\(v\)) & 0.3 \\ Density (\(\rho\)) & 7850 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters and properties of the prismatic cantilever beam.
# The Electromagnetic Field Analytical Solution Analysis to Downhole Current Injection [PERSON]
## 2 Signal Model and Electromagnetic Field Distribution Analysis  
### Signal Model  
A standard current-injection ranging system, as depicted in Figure 1, consists of a surface electrode, a downhole electrode, and a sensing tool with magnetometers. In operation, the downhole electrode introduces a low-frequency AC current into the surrounding formation, where it disperses and weakens over distance. Since the conductivity of the target well (casing) is considerably greater than that of the stratum, the current is disturbed by short-circuit effects. A low-frequency alternating magnetic field will be generated when the current converges on the target well. Sensors positioned in the relief well detect this magnetic field, allowing for the estimation of the relative distance between two wells [31].  
To achieve accurate well ranging results, the electromagnetic field distribution around the target well needs to be calculated. Nevertheless, since the analytical solution for the  
Figure 1: Schematic diagram of current injection ranging in relief well.  
multi-layer medium cannot be obtained, the downhole current injection model utilized in Figure 1 must be further simplified. Considering that the conductivity of the target well is substantially higher than that of the stratum and relief well, our work simplifies the multi-layer model in Figure 1 to a two-layer model shown in Figure 2, where the injection current is modeled as a point current.  
Based on the two-layer model, the distribution of the primary and secondary electromagnetic fields can be expressed in an analytical form. Firstly, the primary electromagnetic field generated in the active region is calculated.
## 4 Analysis of Influencing Factors
### Influence of the Horizontal Bearing Stiffness
#### 4.5.2 Tunnel Vertical Displacement  
Figure (b)b shows the tunnel's maximum vertical displacement upon completion of the foundation pit excavation under varying horizontal bearing stiffnesses. The diagram shows that from three steel bracings to five concrete bracings, the uplift of the north side of the tunnel is reduced by 18.89%, and the settlement of the south side of the tunnel is reduced by 38.87%. Thus, increasing the stiffness of the horizontal bearings is more effective than increasing the horizontal displacement for controlling the tunnel's vertical displacement.
## 5 Experimental Results
The upper panel refers to the single ranking strategy for feature selection, whereas the lower panel shows the results considering the total ranking option. Adaboost with the GTV and PTV (second column) and the DT, RF, and XGBoost with the CTV (third column). Note that finding zeros in the occurrences of Table 4 does not exclude the presence of other LBP features in the best subsets besides those chosen in the experiment with largest performance. To deepen the discussion on the effectiveness of 3D LBPs, we excluded them from each signature selected by our approach and train again each learner. The results are reported in Table 5, where \"-\" indicates that the classifier was not trained, since the best set included only 3D LBP descriptors. When comparing these results with Table 3 we notice that there is not an evident pattern in the performance differences between the two experiments. Despite that, the use of 3D LBPs improves the largest accuracy obtained: indeed, without them, the overall best accuracy score decreases from 83.51% to 76.29% and from 77.32% to 75.26% for the single ranking and the total ranking, respectively. This trend is also manifested by the other performance measures reported for the best cases, indicating that LBPs actually give an important contribution to the classification task at hand. Furthermore, we statistically pairwise compared the results reported in Table 3 with those in Table 5 using the [PERSON] signed rank test. In the case of single ranking, for AdaBoost+CTV, DT+CTV, RF+CTV, RF+PTV, and XGBoost+GTV the use of 3D LBPs provides performance larger and statistically different from those attained excluding these descriptors (\(p<0.1\)). In the case of total ranking, at the same level of confidence, the results of the test show that the use of 3D LBP provides larger and statistically different performance for AdaBoost+PTV and XGBoost+CTV. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Features Selected for AdaBoost CTV** & **\#Occurences in AdaBoost GTV and PTV** & **\#Occurences in DT, RF and XGBoost with CTV** \\ \hline U 3D LBP kurtosis & 0 & 3 \\ \hline
3D LBP energy around maxAss & 0 & 1 \\ \hline RI 3D LBP energy & 0 & 1 \\ \hline U 3D LBP energy around maxRel & 0 & 1 \\ \hline U 3D LBP entropy & 1 & 3 \\ \hline U 3D LBP skewness & 0 & 3 \\ \hline inverse GLCM (\(-1\),\(-1\),\(0\)) & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best radiomic signatures. The fist column lists the feature selected in the best performance. The other two columns indicate the number of times that each descriptor was selected in the best feature set. Abbreviations: U—uniform, RI—rotation invariant.
## 5 Results and Verification
### Model Structure  
TAN learning was conducted using MATLAB code for the 403 cases of data out of the collected 443 cases of RORC event data. The vertical alignment, vertical slope, and the compound curve showed a relatively high correlation with the RORC severity, and the horizontal alignment, the radius of the curve, and compliance with the minimum radius of the curve design criteria showed some correlation with severity. The causal relationships between the variables and the independent variables were connected by links. The vertical slope and radius of the curve have a causal relationship with the compound curve variable, and the radius of the curve is the variable that has a causal relationship with the variables of the reverse curve, continuous curve, and compliance with the minimum radius of the curve design criteria.  
In summary, as shown in Figure 4, the structure of the BN model for predicting the RORC severity was developed through the TAN learning process.  
Figure 4: BN structure for RORC severity prediction.
###### Abstract  
The present study investigates the characteristics of ionospheric irregularities at middle latitudes by examining the association between spread F (SF) events detected by Digisondes and medium-scale travelling ionospheric disturbances (MSTIDs) detected by GNSS with a special emphasis on the coupling with topside irregularities observed by Swarm satellites based on in situ electron density (Ne) measurements. We analyzed SF events over the European midlatitude region from 2015 to 2017, over six Digisonde stations coinciding with Swarm satellite overpasses. Swarm latitudinal Ne profiles were used to identify topside irregularities, while GNSS d-TEC and ROTI maps were used to track MSTIDs and irregularities, respectively. Based on ten selected cases demonstrating concurrent SF and topside irregularities, our findings suggest a strong association between SF in the bottomside ionosphere and fluctuations in topside Ne.  
middatitude ionosphere; spread F; swarm; topside irregularities; MSTIDs; ROTI Article
## 2 Materials and Methods  
### Study Area  
The islands of Malta and Sicily are characterized by distinctive environmental and climatic similarities, as they are both located in the center of the Mediterranean Sea (Figure 1). They have, in fact, many geological similarities since they form a spur on the northern edge of the African continental plate that includes Malta, SE Sicily, the Pelagian Islands, eastern Tunisia, and the northwestern Libyan shelf [45].  
During the last one million years, following eusstatic variations, both islands have alternately been physically linked to each other and to the Italian peninsula [45; 46; 47]. In particular, during the Last Glacial Maximum they were physically connected by an isthmus and formed a territorial unicum, located in the center of the Mediterranean, acquiring the shape of the three-legged island [48].  
Despite this, while Sicily shows a great geodiversity, due to the geologically complex history dating from the Paleozoic to Pleistocene periods, involving metamorphic, magmatic, and sedimentary outcrops, the geology of the Maltese Islands are predominantly composed of marine sedimentary rocks, mainly limestone with subsidiary marls and clays [49; 50], deposited in shallow marine conditions between the late Oligocene and Miocene age [49], with sporadic occurrences of quaternary deposits in some areas (Figure 2).  
Sicily is the second largest island in the Mediterranean Sea, with a 1623 km coastal length, divided into 26% of rocky coasts and 74% of sandy and/or pebbly beaches [51; 52]; among them 110 PBs consist of both natural and artificial features, having different dimensions and various planform geometries, differently exposed to incident wave energy and with limited sediment sourcing by drainage basins.  
The Maltese Archipelago consists of the three main islands of Malta, Gozo, and Comino and a number of other minor islands and rocks [53; 54]. The entire coastline measures 272 km [55], with the predominant shore type being rocky (90.5%); sand and shingle shores only comprise 2.4% of the remaining coastline [56]. The coastal morphology of the Maltese Islands has been largely determined by tectonic activity, primarily in the Holocene period, which has been uninterrupted to present day [57].  
The PBs of the Maltese islands (22 PBs) are exclusively small (the largest, Ghadira, is only 1 km long), flanked between rocky headlands or anthropogenic infrastructures, and there is limited sediment exchange between beaches located at various spatial scales [58; 59].  
Figure 1: Geographical framework of the study area (map adapted from ESRI, GEBCO, NOAA, National Geographic, Garmin, HERE, Geonames.org, and other contributors).
## 3 Results and Discussion
### \(N_{2}\) Broadening Coefficient  
In a similar way to the previous experiment, an HCl/N\({}_{2}\) gas mixture with 500 \(\pm\) 30 \(\upmu\)mol/mol HCl in N\({}_{2}\) was employed to determine the N\({}_{2}\) broadening coefficient. The HCl-N\({}_{2}\) broadening measurements were performed in a flow mode, the cell was filled with the HCl/N\({}_{2}\) gas mixture at a steady flow rate of 200 sccm. As example, Figure 3a shows a measured absorption spectrum at the pressure of 173 mbar. The measured spectrum was averaged for 10 scans and fitted by the Voigt profile. Typical average residuals were around (1\(\sigma\)) 1 \(\times\) 10\({}^{-3}\) optical density, which corresponds to a SNR of 744. Comparing to other foreign broadening measurements, here the HCl-N\({}_{2}\) broadening coefficient was interfered with the self-broadening contribution in Equation (4) (first term) which was subtracted due to the relative high concentration of HCl. Using the self-broadening coefficient of 0.223 cm\({}^{-1}\)/atm from HITRAN [20]. The respective Lorentzian widths show very good linearity with pressures with an intercept of (2.73 \(\pm\) 3.99) \(\times\) 10\({}^{-4}\) cm\({}^{-1}\). The measured HCl-N\({}_{2}\) broadening coefficient is \(\gamma_{\rm{N2}}\) = 60.92 (63) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm at 294 K.  
Figure 3: (**a**) Measured N\({}_{2}\)-broadened HCl absorption spectra at 173.3 mbar, and the fitting residual; (**b**) Measured N\({}_{2}\) induced pressure broadening with a linear fitting model. The residual between fit and data is shown below.  
Figure 2: (**a**) Measured CO\({}_{2}\)-broadened HCl absorption spectra at different pressures. The fitting residual is depicted for the spectrum at 943.5 mbar; (**b**) Measured CO\({}_{2}\) induced pressure broadening with a linear fitting model. The residual between fit and data is shown below.
# Augmented Reality for PCB Component Identification and Localization
## 4 Materials and Methods
### Experiment 1-Methods
#### 4.2.2 Procedure  
Participants (\(n=10\)) were electronics engineers from the same local electronics company actively engaged in PCB manufacturing tasks. All had prior experience with traditional schematic-based identification, but no hands-on experience with head-mounted AR systems such as the Microsoft(tm) HoloLens(tm)tm 2. To reduce learning bias, each session began with a briefing and a 15 min familiarization period using the AR application. Participants were divided into two groups based on industry-standard classifications of expertise: experts (\(\geq\)8 years of experience, \(n=6\)) and non-experts (<8 years, \(n=4\)). The threshold of \(\geq\)8 years was selected based on internal classification practices, where professionals with this level of experience are typically considered senior. In this study, participants' experience ranged from 4 to 15 years, making this threshold a practical and meaningful division for comparing expert and non-expert groups. They were tasked with identifying 10 components on a PCB--including resistors, capacitors, diodes, and integrated circuits--that varied in size from 0.5 mm to 15 mm. The components were intentionally distributed across dense clusters and isolated areas to evaluate the AR system's tracking performance under spatial variability. All tasks were performed from an overhead view to maintain consistency in angle and minimize depth distortion.  
To establish a baseline for comparison, participants also identified the same components using traditional methods by referencing a digital schematic of the PCB in PDF format (viewed on a screen using Adobe Acrobat Reader DC, version 2023.006.20380). This allowed for a direct measurement of the time taken and the accuracy of component identification between the AR method and conventional methods. Upon completion of  
Figure 3: Texture-mapped digital twin of the PCB Unity(tm) game engine.  
the tasks, participants filled out a survey to assess various aspects of the AR system's usability, including ease of use, perceived accuracy, speed, and overall satisfaction. The survey provided qualitative data that complemented the quantitative measurements from the component identification tasks.  
The results from Experiment 1, which highlighted the efficiency of the HoloLens(tm) 2 in comparison to traditional methods, revealed the need for further investigation into the accuracy of the AR system. This motivated Experiment 2, which focuses on how different viewing angles impact the AR system's accuracy and efficiency in component identification.
## 6 Results and Discussion
### Discussion  
This paper evaluates two distinct cascading controllers within a hybrid microgrid setting, namely the PI-PI controller and the FOPI-FOPI controller. The key unique features and advantages of the proposed approaches are:  
1. Integration of Advanced Controller Types: \(\bigcirc\) PI-PI Controller: A well-established controller that effectively regulates voltage and current within the hybrid microgrid. \(\bigcirc\) FOPI-FOPI Controller: Introduces fractional-order control, providing enhanced responsiveness and performance in dynamic conditions, thereby improving system stability.
2. Innovative Hybrid Optimization Algorithm (WSO-WOA): \(\bigcirc\) The novel WSO-WOA algorithm was created specifically to optimize controller parameters efficiently across various operational scenarios, including significant power quality disturbances. \(\bigcirc\) Extensive comparisons against other optimization techniques--WSO, WOA, GWO, and SCA--demonstrated the superior efficacy of WSO-WOA in achieving optimal solutions.
3. Improved Accuracy in Controller Tuning: The WSO-WOA algorithm achieved a tuning accuracy improvement of approximately 7.29% to 14.1% for the PI-PI controller and 8.5% to 21.2% for the FOPI-FOPI controller compared to other algorithms. This substantial enhancement underscores the algorithm's capability to fine-tune controllers for optimal performance.  
Figure 14: Regulation of the load voltage at B4 under three-phase balanced swell.  
Figure 16: Regulation of the load voltage at B4 under three-phase-to-ground fault.  
Figure 15: The outcomes of the swell scenario of phase (**a**) using the DSTATCON using the FOPI-FOPI: (**a**) the load voltage without enhancement, (**b**) the load voltage with enhancement, and (**c**) the injected voltage and current.
## 3 Results  
As previously stated, the computations were performed, for each of the two boilers, for a set of convective heat loads, and kept constant for the various operating situations. The corrugated boiler was set for 27.5 kW and the finned pipe boiler was set for 24.0 kW. The stoichiometric values correspond to a theoretical complete burning, without any excess air. For each boiler, the operating conditions were computed for three different H\({}_{2}\) concentrations in the gaseous combustible (pure methane--CH\({}_{4}\)): 0% H\({}_{2}\) (reference case), 10% H\({}_{2}\) (intermediate case), and 20% H\({}_{2}\) (limit case). The H\({}_{2}\) concentrations were chosen in this manner because a larger content of H\({}_{2}\) may determine faulty operation of the burner. This is due to the variation in the burning speed of the combustion mixture and the variation in the minimal ignition diameter that can generate backfires in the burner's mixing fan. The main burning characteristics of the gaseous combustible in the three previously stated cases are presented in Table 3.  
One very important conclusion to be drawn after evaluating Table 3 is that the burning parameters, as air and flue gases stoichiometric volume, decrease along with the H\({}_{2}\) increasing concentration. The same trend is also valid for the net calorific value (or lower calorific power) the decrease reaching 15% for the 20% H\({}_{2}\) concentration. What is also of great interest is the water vapor volume variation, which is also decreasing, but only by approx. 10%. Considering the decrease in the net calorific value at a higher rate than the water vapor concentration, it is to be expected that, due to fuel flow increasing (under the reserve of constant convective boiler efficiency at H\({}_{2}\) concentration \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Calculated Results** & **Measured** & **Relative Error** \\  & **(Modelling Program)** & **Values (Experimental Stand)** & **\%** \\ \hline Inlet water temperature & [\({}^{\circ}\)C] & 31.7 (input) & 31.7 & \\ Outlet water temperature & [\({}^{\circ}\)C] & 51.5 (input) & 51.5 & \\ \hline Combustible flow & [m\({}^{3}\)/h] & 2.792 (input) & 2.792 & \\ \hline Excess air at the burner & [-] & 1.17 (input) & 1.17 & \\ \hline Flue gas exit temperature & [\({}^{\circ}\)C] & 35.5 & 37.1 & \(-\)0.1 * \\ \hline Total useful thermal load & [kW] & 29.48 & 29.16 & 1.1 \\ \hline Sensible useful thermal load & [kW] & 27.52 & 27.19 & 1.4 \\ \hline Condensing useful thermal load & [kW] & 1.96 & 1.97 & \(-\)0.5 \\ \hline Thermal efficiency (by respect to the Net calorific value) & [\%] & 105.1 & 104.9 & 0.2 \\ \hline \hline \end{tabular}  
* Error related to the entire temperature span for the flue gases: theoretical burning temperature to chimney temperature.  
\end{table} 
Table 2: Experimental vs. computational results for corrugated pipes boiler.
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 3 Typical Engineering Case Study
### Downburst Wind Load Simulation  
In this study, the downburst wind field with different combinations of direction angles and radial distances is addressed. Among them, four direction angles, including \(0^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\), are considered while the radial distances \(R\) range from \(0.1D\) to \(3D\) with an interval of \(0.1D\), where \(D\) = 600 m. Apart from the direction angles and radial distances, the other parameters related to the mean wind speed and fluctuation are summarized in Table 5. Note that the horizontal maximum wind speed associated with the Vicroy wind speed profile model is a result of a 3 s time interval. In order to be consistent with Chinese specifications of transmission line design in the practical application (DL/T 5154-2012), the 3 s mean wind speed will be converted to a 10 min mean wind speed according to the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Uncertainty Source** & **Random Variable** & **Average Value** & **COV** & **Distribution Type** \\ \hline Elastic modulus & \(E_{S}\) & 206 Gpa & 0.03 & Lognormal \\ Poisson ratio & \(v\) & 0.3 & 0.03 & Lognormal \\ Density & \(\rho\) & 7800 kg/m\({}^{3}\) & — & Deterministic \\ Yield strength for Q235 & \(f_{y}\)\({}_{-0235}\) & 263.7 Mpa & 0.07 & Lognormal \\ Yield strength for Q345 & \(f_{y}\)\({}_{-0345}\) & 387.1 Mpa & 0.07 & Lognormal \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical information of material variables.  
Figure 10: Generated samples of yield strength.  
Figure 9: Generated samples of elastic modulus.  
converted formula [44]. The results are shown in Table 6. For the fluctuation, the tower is divided into six sections along the height when simulating the downburst wind field, as shown in Table 6. Hence, a total of six points are required to be simulated. The ensemble autocorrelation function of one point and the cross-correlation function between two points estimated from 2000 samples are shown in Figure 11. It can be seen that they agree with the targets well, which verifies the simulation accuracy of the downburst fluctuating winds. Once the time history of the downburst wind field has been generated, the time histories of wind force can be calculated based on Equations (11)-(14) and Table 1. The wind-induced response of the transmission tower can then be conducted.