esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.6 The Po River Delta and the Venice Lagoon  
The North Adriatic coastal area in Italy is a large area of geophysical and historical relevance. It includes Venice's city and its lagoon, the Po River delta, and the Emilia Romagna region. The Po River delta is the largest in Italy and is highly threatened by SLR [442; 443; 444]. The Po River Delta has experienced widespread shoreline retreat and salinity intrusion due to anthropogenic-induced subsidence and SLR [445; 446]. Land subsidence of the delta is the most severe in Italy [388; 389]. The irreversible sinking due to the over-extraction of methane water from Quaternary strata in the last century caused average subsidence of approximately 50 cm to 3 m [388]. Consequently, some flat coastal areas have become inundated.  
Sea level in the Venice lagoon is subject to seasonal and long-lasting periodic changes, with fluctuations over a few hundred years and cycles alternating between cold-wet and warm-dry periods. These phenomena are amplified by present-day climate change, and the vulnerability risk increases [447]. The locally known phenomenon of \"Acqua Alta\" in the city of Venice refers to the excessive-high tides responsible for episodic flood events [see, for instance, the recent flooding events that occurred in November 2019 [448; 449]]. This phenomenon has a cascading effect on the media, society, and the scientific community due to the Venice lagoon having a historical and fragile ecosystem. However, recent studies on the land subsidence of the Venice lagoon carried out by using multi-temporal/multi-satellite interferometric SAR analyses at different wavelengths and spatial scales [389; 443] have confirmed that the historical city of Venice is essentially tectonically stable now. In contrast, the northern sector of the lagoon, see Figure 3 in [443], is subject to an average ground displacement of up to 3 cm/year, as observed from 1992 to 2017. Although the amplitude of subsidence reduced in the last few decades, the low-lying coastal areas are still threatened by the combined flooding risk of SLR and elevation loss.
## 3 Quad-Port MIMO Antenna  
### Antenna Geometry and Design Evolution  
This section elaborates a structured and comprehensive analysis of the design methodology and performance assessment of the presented quad-port MIMO variant. The optimized dual-element configuration in the previous section is extended orthogonally in a symmetrical manner to realize a compact 4--element MIMO structure. Figure 15 shows the complete structure. The final dimensions are \(60\times 41\times 1.6\) mm\({}^{3}\). A 50-ohm feedline is used to power each element. A parametric study was performed by varying the parameter  
Figure 14: Three-dimensional (3D) radiation patterns at (**a**) 4.0 GHz; (**b**) 6.125 GHz; (**c**) 9.185 GHz; (**d**) 10.5 GHz.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Diversity Parameters**} & \multicolumn{6}{c}{**Selected Frequencies (GHz)**} \\ \cline{2-7}  & **4** & **5** & **7** & **8** & **9** & **10** \\ \hline ECC & 0.00234 & 0.00096 & 0.0008 & 0.00203 & 0.00318 & 0.00582 \\ \hline TARC [dB] & \(-11.957\) & \(-14.048\) & \(-16.337\) & \(-15.537\) & \(-16.449\) & \(-16.462\) \\ \hline MEG [dB] & 0.1872 & 0.04518 & \(-0.3100\) & 0.0050 & 0.1260 & 0.13069 \\ \hline DG [dB] & 9.98827 & 9.99519 & 9.99558 & 9.98983 & 9.98411 & 9.97085 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Assessment of performance metrics at various frequencies based on S-parameter analysis.  
'L3' between 6.5 mm and 8.5 mm with increments of 0.5 mm. The results are illustrated in Figure 16. Among those tested values, L3 = 7.5 mm yielded the broadest bandwidth of 8.2 (2.6 GHz to 10.8 GHz). The finalized design dimensions are populated in Table 4. Figure 17 depicts the fabricated 4-port MIMO antenna prototypes equipped with 50-ohm SMA connectors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Measurement** & \multirow{2}{*}{**Parameters**} & **Measurement** & \multirow{2}{*}{**Parameters**} & **Measurement** \\  & **(mm)** & & **(mm)** & & **(mm)** \\ \hline L1 & 60 & L10 & 6 & W6 & 1 \\ L2 & 12 & L11 & 38 & W7 & 3 \\ L3 & 7.5 & L12 & 4 & W8 & 2 \\ L4 & 3 & L13 & 0.5 & W9 & 4 \\ L5 & 0.5 & W1 & 41 & W10 & 2 \\ L6 & 1 & W2 & 40 & W11 & 5 \\ L7 & 11 & W3 & 12 & W12 & 1 \\ L8 & 5 & W4 & 0.5 & W13 & 16 \\ L9 & 17 & W5 & 2.5 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Proposed antenna dimension details.  
Figure 15: Quad-port geometry (**a**) top layout (**b**) ground layout.
## 3 Results and Discussions  
### Building the Hybrid ML Models  
Performance of a ML model highly depends on the choice of its parameters. In this study, the parameters for the ANFIS model was selected and used to develop the hybrid models after trial and error test on training dataset (Table 2). In addition, the performance of the model can be affected by the sampling strategy of datasets; thus, the sampling strategy was analyzed by randomly picking data points in the whole dataset to reduce the effect of randomness on the model's performance. Over 100 times of simulation was ran to get the best performance of the models corresponding to the highest values of R and lowest values of MAE and RMSE. Different optimization techniques such as RCSA, CA,  
Figure 4: Methodology flowchart of this study.  
and SFLA were applied for optimizing the parameters of ANFIS and the corresponding parameters of these techniques are summarized in Tables 3-5, respectively. Evaluation of cost functions with respect to R and MAE using ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA for predicting the critical buckling load of I-shaped beams are presented in Figure 5. For each model, the stopping condition was chosen as 1000 iterations. It is shown that ANFIS-RCSA reaches the state of convergence over 100 iterations, whereas it is about 800 iterations for ANFIS-CA and ANFIS-SFLA.  
On the basis of the training phase analysis, it is observed that selection of optimal parameters for training the predictive ML models is highly important before any further investigation. The stopping iteration of a model is also an important factor as it could directly affect the computation time, as well as the efficiency of an algorithm. A more appropriate convergence criterion should be included in the ANFIS-SFLA code in order to better track the information of the cost function (i.e., R, RMSE, and MAE). By default, the 1000 iterations criterion as the stopping condition is selected because it provided a reasonable computation time. Such criterion should be modified in further studies such that the error between the cost function of iteration (i + 1) and iteration (i) exposes no more than a given threshold (i.e., 1% for example). In general, it can be concluded that ANFIS-SFLA is the best ML predictor, following by ANFIS-CA and ANFIS-RCSA in the training phase.
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 4 Mapping an OPM Model to a Graph
### From an OPM Model to a Cypher Query
#### 4.2.2 Applying Cypher Queries on the OPM Model  
Each logical element in the model is an instance of an OPM element, which can be an OPM thing--an object or a process--or an OPM link. In the first phase of the model-to-graph conversion, we apply the OPM Model Flattening Algorithm (Algorithm 1, above). The flattened, single layered OPM model, expressed by a single OPD is equivalent to the entire original model, as it holds all the visual representations of the OPM model things and all the links that connect them, as expressed by the entire set of hierarchal organized original OPD tree.  
Each graph node is associated with a specific OPM thing, and the nodes are linked by edges carrying the original OPM model link semantics. Code 1 is the Cypher code block of the query used for node generation, and Table 2 demonstrates the use of this query.  
**Code 1.** Cypher query for node creation in the Neo4j graph database.  
The OPM links in a graph are the edges that connect the nodes. Each OPM link is checked to identify all its sources and targets. Each \"source\"-\"link\"-\"target\" triple is represented in a customized object structure that contains the following metadata: [**Source** => [Type, Text, Id], **Link** => [Type, Id], **Target** => [Type, Text, Id]). Each triple is unique in that it is based on the combined content of the three elements. Converting the OPM model to such triples can later be used for additional benefits, such as converting it  
\begin{table}
\begin{tabular}{c c} \hline \hline
**OPM Thing** & **Cypher Node Creation Query** \\ \hline Passenger & MERGE (object [name: ‘Passenger’,opm\_id: ‘1’]) \\ \hline Passenger & MERGE (:process [name: ‘Passenger Securing’,opm\_id: ‘2’]) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of converting OPM things to Cypher nodes.  
to the Resource Description Framework (RDF) [11; 37] representation or any other data representation, and for new analysis methods.  
To generate a triple that contains the two nodes and one edge in a graph database, three \"OPM Thing to Cypher\" queries are required: one for the source element, one for the target element, and one to create the link between these two. The Cypher query to generate a single edge includes a search (MATCH) for the two connected nodes and a generating (MERGE) query to create a triple of the two identified nodes and a link between them, as presented in Code 2.  
**Code 2.** Cypher query for relationship creation in a graph database  
```
1:MATCH(n$INDEX$S_OPM_THING_TYPE
2:$SS_OPM_THING_TEXT,opm_id:$S_OPM_THING_ID'))
3:MATCH(n($INDEX+1)$T_OPM_THING_TYPE
4:$T_OPM_THING_TEXT,opm_id:$T_OPM_THING_ID'))
5:MERGE(n$INDEX)-[r:$SOPM_Link_Type' {opm_id: $SOPM_LINK_ID'}]->(n($INDEX+1))
```  
An example of converting an OPM Link to a Cypher relationship is depicted in Table 3.
## 5 Security Analysis, Performance Evaluation, and Discussion
### Key Strength Analysis of Proposed Key/Subkey Generation Technique  
A key schedule algorithm (KSA) should have good confusion and diffusion properties. Any of the compromised subkeys should not reveal any information about other subkeys  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modulation** & BPSK \\ \hline
**Coding** & LDPC \\ \hline
**Code rate** & 1/2 \\ \hline
**Frame size** & 64,800 \\ \hline
**No. of iterations** & 10 \\ \hline
**Channel** & AWGN \\ \hline
**Ciphering** & AES-128 with LDPC coding \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particulars of the parameters used in the proposed scheme.  
Figure 4: AES–LDPC coding.  
or a secret key. In order to understand the key strength, three sets of statistical tests, namely, frequency, bit independence, and bitwise uncorrelation tests [14] were carried out.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 4 Discussion
### Contribution of Diffuse Radiation to GPP Estimations  
The process of photosynthesis can be influenced by multiple environmental factors such as radiative conditions, vapor pressure deficit (VPD), and temperature [24; 70]. Theenhancement effect of LUE due to the diffuse radiation has been widely reported [24; 28; 29]. An approximate 110% enhancement of LUE under cloudy-sky days over clear-sky days was found by [PERSON] [29]. Here, enhancement factors for different vegetation types derived from FLUXNET data by [PERSON] et al. [10] were used to correct LUE under diffuse radiation conditions.  
Meanwhile, environmental factors such as VPD and major volcanic eruptions are also related to the diffuse radiation conditions [24; 70]. Overall, the combined factors can enhance the photosynthesis under diffuse radiation conditions. Therefore, the partition of diffuse and direct radiation in GPP estimations is quite necessary.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 4 Evaluation Metrics
### Discretization Error Quantification  
This subsection focuses on two primary error sources: the Grid Convergence Index (GCI), which quantifies discretization errors, and iterative errors, which arise from incomplete solver convergence. These methods allow for the estimation of numerical uncertainties, enhancing confidence in the CFD solutions.  
The Grid Convergence Index (GCI), rooted in Richardson extrapolation, is a well-established method for estimating discretization error in CFD simulations, while it has been validated against experimental data across diverse aerodynamic applications [55]. The GCI evaluates the convergence of key aerodynamic quantities, such as lift and drag, across systematically refined grids. The procedure is as follows:  
1. **Define a Representative Grid Size**: for three-dimensional grids, Equation (2) is used. \[h=\left[\frac{1}{N}\sum_{i=1}^{N}V_{i}\right]^{\frac{1}{3}}\] (2) Note that \(V_{i}\) is the volume of the \(i_{th}\) cell and \(N\) is the total number of cells.
2. **Grid Refinement**: perform analyses on three grids (coarse, medium, and fine), characterized by grid sizes \(h_{1}<h_{2}<h_{3}\), with a refinement ratio \(r\) (Equation (3)). \[r=\frac{h_{coarse}}{h_{fine}}>1.3\] (3)
3. **Apparent Order of Accuracy p:** Calculate p using Equations (4)-(6). \[p=\frac{1}{\ln(r_{21})}\left|\ln(\frac{\varphi_{3}-\varphi_{2}}{\varphi_{2}- \varphi_{1}})+q(p)\right|\] (4) \[q(p)=ln\left(\frac{r_{21}^{p}-s}{r_{32}^{p}-s}\right)\] (5) \[s=1\times sgn\left(\frac{\varphi_{3}-\varphi_{2}}{\varphi_{2}- \varphi_{1}}\right)\] (6)  
In these equations, \(\varphi\) is the aerodynamic quantity of interest. The equations for \(p\) can be solved using fixed-point iteration.  
4. **Extrapolated Solution:** Use Richardson extrapolation (Equation (7)) to approximate the solution at an infinitely fine grid. \[\varphi_{ext}^{21}=\frac{r_{21}^{p}\varphi_{1}-\varphi_{2}}{r_{21}^{p}-1}\] (7)
5. **Discretization Error (GCI):** The error estimates are reported (Equations (8) and (9)). \[\begin{array}{l}\varepsilon_{31}=\left|\frac{\varphi_{3}-\varphi_{1}}{\varphi _{1}}\right|\\ \varepsilon_{3,ext}=\left|\frac{\varphi_{3}-\varphi_{21}^{2}}{\varphi_{21}^{p} }\right|\\ \\ GCI_{fine,31}=1.25\frac{\varepsilon_{4,31}}{r_{31}^{p}-1}\end{array}\] (8)
## 3 Results
### Electrolyte Performance  
The following section examines the electrochemical behavior of the LSGM electrolyte. Figure 2 illustrates the temperature-dependent electrical conductivity of LSGM samples under atmospheres with various oxygen partial pressures. It can be observed that, under all investigated conditions, an increase in sintering temperature and the addition of the Fe\({}_{2}\)O\({}_{3}\) sintering aid correlates with an improvement in electrical conductivity. However, the activation energy of electrical conductivity remains almost unchanged (Table 1). Figure 1: XRD data for powders LSGM and LSF; for LSF after treatment in hydrogen atmosphere at 800 \({}^{\circ}\)C for 24 h; for mixture LSGM + LSF (mass ratio 1/1) sintered at 1150 \({}^{\circ}\)C for 2 h.  
Figure 2: Temperature dependences of the electrical conductivity for the investigated LSGM samples under atmospheres with various oxygen partial pressures: 0.21 atm **(a)**; \(10^{-22}\) atm **(b)**. To obtain a better understanding of the influence of sintering temperature and introduction of the sintering aid on electrical conductivity of the electrolyte, the contributions of grain bulk and grain boundaries to the electrolyte's conductivity were determined. At temperatures below 400 \({}^{\circ}\)C, two semicircles, corresponding to the bulk and grain boundary resistances, can be clearly observed on the impedance spectra (insert in Figure 3a). Thus, impedance data were analyzed according to the equivalent circuit (RQ)bulk(RQ)gh. Using the obtained values of the resistances, conductivities of the grain bulk and the grain boundaries were calculated (Figure 3a-d). One can see that in both atmospheres, the bulk conductivity remains almost the same, while grain boundary conductivity strongly depends on sintering temperature and Fe2O3 addition. Both the increase in sintering temperature and introduction of the sintering aid led to an increase in the grain boundary conductivity. Activation energy values of bulk and grain boundary conductivity are close for all the samples and conditions, and remain almost the same for all the cases (Table 1). Figure 3e,f also summarize the total, bulk and grain boundary conductivity for all the samples and conditions at a constant temperature. The addition of Fe2O3 along with sintering at 1400 \({}^{\circ}\)C results in a conductivity even higher than that after sintering at 1450 \({}^{\circ}\)C without the sintering aid. Further improvement can be achieved with sintering at 1450 \({}^{\circ}\)C. Sintering at 1350 \({}^{\circ}\)C is not enough even with the Fe2O3 addition. Figure 3: _Cont._  
Microscopic examination of chipped electrolyte samples showed that individual grains are difficult to distinguish in this case (Figure 4). In contrast, the relief of the chip surface is clearly visible, which is especially pronounced for the sample sintered at 1350 \({}^{\circ}\)C without a sintering aid. This indicates weaker intergranular contact and, consequently, lower grain-to-grain conductivity.
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 3 Area of Research
### Connection Techniques in Precast Systems
#### 3.3.1 Dry Connections  
Bolted connections, called dry connections, involve bolts to unite precast elements. Their rapid assembly and disassembly make them especially suitable for temporary structures. Nevertheless, their rigidity may be inferior to that offered by traditional methods. Bolted connections facilitate load transfer through friction and bearing between the bolt  
\begin{table}
\begin{tabular}{c c} \hline
**Stage** & **Load Factor** \\ \hline Yard Handling & 1.2 \\ \hline Transportation & 1.5 \\ \hline Erection & 1.2 \\ \hline \end{tabular}
\end{table}
Table 3: Equivalent load factors to account for dynamic loads [49].  
and the joined elements, effectively distributing shear forces [19] both laterally and vertically [55]. Standard failure modes include bolt yielding, thread stripping, or bending of the bolts [56]. Embedded steel plates, secured by bolts within precast components, simulate monolithic connections and improve performance, particularly in seismic regions [57]. In earthquake-prone areas, bolted connections may require additional reinforcements, such as gusset plates or dampers to improve energy dissipation and prevent instability under dynamic forces. Failures may arise from the yielding of the steel plates or bolts or buckling caused by significant compressive loads. Typical bolted connections, with and without embedded steel plates, are presented in Figures 8 and 9. It can be seen from these figures that bolted connections primarily rely on steel plates and bolts for load transfer. In contrast, embedded plate connections integrate anchoring bars and embedded plates to enhance structural integrity. Given their rapid assembly and disassembly capabilities, dry connections have significant potential for adaptation in modular construction, especially for scenarios requiring efficient on-site assembly.  
suitable for module-to-module connections, particularly in cases where modular structures must resist seismic forces or achieve monolithic performance.
## 3 Results
### Measurement Range of Shock Wave  
The acoustic pressure at the axial and lateral directions is shown in Figure 4. Ultrasonic decay is observed away from the focal point. Figure 4b-d shows the fall in the amplitude of the shock wave away from the focal point. The needle hydrophone was moved forward and backward along the axial direction from the focal point to evaluate the pressure distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Diameter** & \multicolumn{3}{c}{**Positive**} & \multicolumn{3}{c}{**Negative**} \\ \cline{2-7}
**(cm)** & & **R\({}^{2}\)** & **Intercept** & **Slope** & **R\({}^{2}\)** & **Intercept** & **Slope** \\ \hline
2 & 0.98 & 19.45 & 0.019 & 0.64 & \(-\)6.93 & \(-\)0.004 \\
5 & 0.96 & 6.64 & 0.043 & 0.88 & \(-\)10.08 & \(-\)0.0135 \\
8 & 0.99 & 1.039 & 0.066 & 0.92 & \(-\)4.29 & \(-\)0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 2: R\({}^{2}\), intercept, and slope values for 2 cm, 5 cm, and 8 cm transducers for both the positive and negative pressures presented in Figure 3.  
Figure 3: Linear curve fitting showing the relationship between laser energy and the acoustic pressure generated by CNT-PDMS composite transducers. It shows a linear relationship between laser energy and positive and negative acoustic pressures in all transducers, except the transducer with a 2 cm diameter, which shows saturation at \(-\)10 MPa in negative pressure with the increase in laser energy.  
A peak positive pressure of 20 MPa and peak negative pressure of \(-\)10 MPa were observed at 0 mm with reference to the focal point (Figure 4b). When the needle hydrophone was moved along the \(y\)-axis, a peak positive acoustic pressure of \(-\)20 MPa was observed, while a negative peak pressure of \(-\)10 MPa was also observed at 0 mm at the focal point (Figure 4c). Furthermore, when the needle hydrophone was shifted along the \(z\)-axis, a positive peak pressure of \(-\)20 MPa and a negative peak pressure of \(-\)10 MPa were measured at 0 mm with reference to the focal point (Figure 4d). The CNT-PDMS composite transducer used for these measurements was 2 cm in diameter.
## 3 Proposed Design Methodology
### Biasing Networks and Optimization  
The schematic of the ideal three-stage amplifier, including the input, output, and interstage networks, is shown in Figure 10.  
The next step in the design is to implement the biasing networks required to supply the transistors. A DC feed inductor and a DC block capacitor are inserted for each gate and drain terminal. These components are integrated into the IMN, ISMN1, ISMN2, and OMN of the three-stage LNA. The ideal components are then replaced with their counterparts in the WIN PIH-10 PDK. This step is essential, as the physical models account for inductive  
\begin{table}
\begin{tabular}{c c c} \hline \(Z_{in,AD3}\) & \(\Gamma_{in,AD3}\) & \(Z_{L,AD3}\) \\ \hline
12.52–25.25 j & \(-0.37\)–\(0.56\) j & \(18.5+7.27\) j \\ \hline \end{tabular}
\end{table}
Table 4: Source and Load Termination for the Third Stage Amplifier.  
Figure 10: The 3-stage LNA schematic with the ideal components.  
Figure 9: Load-pull simulation for the Amplifier Stage; power delivered to the load (blue), power added efficiency (red), and the optimum output load (black dot).  
effects and losses across different layers [21]. An optimization process is then performed to restore the circuit to meet the original design specifications.  
To enhance the robustness of the device, the gate and drain bias lines are fed through resistors [22]. These resistors help protect the device against input overdrive and, according to simulations, do not negatively affect small-signal or noise performance. Figure 11 shows the complete schematic containing the biasing networks and the 3-stage LNA, which was introduced in Figure 10.  
Finally, a layout is generated for fabrication. Figure 12 shows the final MMIC realization of the three-stage LNA.