esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 5 Performance Evaluation  
In this section, a MATLAB R2018b simulator is applied to evaluate the performance of the DEOR algorithm [40]. First, the simulation settings are introduced before evaluations. Then, DEOR is compared with Opportunistic Routing based on Residual energy (ORR) (ORR) [41], neighbor coverage-based OR (OBRN) [42], and energy-efficient OR (ENSOR) [43] in terms of energy consumption, average hop count, packet delivery rate, and network lifetime. ORR is a centralized opportunistic routing algorithm based on distance and residual energy, OBRN is a distributed opportunistic routing algorithm based on energy and load, and ECSOR is a distributed opportunistic routing algorithm optimized for the forwarder node set.
# Itarare Group Sandstone as a Sustainable Alternative Material for Photon Radiation Shielding
###### Abstract  
The materials typically used for radiation shielding include lead, concrete, and polymers. However, some of these materials can be toxic or very expensive to produce. This raises interest in using more readily available natural materials, such as rocks, as an alternative. In this study, we analyzed the radiation shielding efficiency of sandstones. We evaluated different layers of rock and obtained shielding parameters based on the composition of various oxides. The analysis revealed that these layers showed a predominance of silicon and aluminum oxides. Notably, the lowest photon energies (0.015 MeV and 0.1 MeV) displayed significant differences in photon attenuation, as indicated by linear and mass attenuation coefficients. This suggests that the chemical composition of the samples had a considerable impact on their shielding performance. Samples containing higher amounts of heavier elements proved to be more effective at attenuating radiation, efficiently reducing 50% (half-value layer) and 90% (tenth-value layer) of the photons. Additionally, the presence of these heavier elements decreased the production of secondary photons (buildup factor), further enhancing the samples' efficiency in shielding against radiation. Our results indicate that sandstones hold potential for radiation shielding, particularly when they contain higher quantities of heavier elements.  
radiation shielding; mean free path; linear attenuation coefficient; mass attenuation coefficient; sandstone; energy buildup factor; exposure buildup factor; buildup factor Article
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 2 Materials and Methods
### Flux Partitioning  
Through CO2 flux (NEE) partitioning, we obtained: gross primary production (GPP) and ecosystem respiration (Reco). For the Cerrado and Pantanal sites, NEE is given as a proxy of the turbulent flow. At the other sites, NEE was composed of turbulent flow and storage. We used the flux partitioning method based on nighttime hours [46]. Since GPP = 0 at the nighttime period, NEE is given as:  
\[NEE=R_{eco},\text{ for nighttime hours} \tag{1}\]  
\[NEE=R_{eco}-GPP,\text{ for daytime hours} \tag{2}\]  
where \(R_{eco}\) (umol m\({}^{-2}\)s\({}^{-2}\)) is the sum of autotrophic and heterotrophic respiration. \(R_{eco}\) and \(GPP\) were calculated using the online tool provided by the Max Planck Institute for Biogeochemistry ([[http://www.bgc-jena.mpg.de/~MDlwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDlwork/eddyproc/)]([http://www.bgc-jena.mpg.de/~MDlwork/eddyproc/](http://www.bgc-jena.mpg.de/~MDlwork/eddyproc/)), accessed on 2 February 2022).  
Diurnal corrections of missing NEE data were modeled based on daytime data using the common rectangular hyperbolic light-response curve model [47; 48]:  
\[NEE=\frac{\alpha\cdot\beta\cdot R_{g}}{\alpha\cdot R_{g}+\beta}+\gamma \tag{3}\]  
where \(\alpha\) (umol C J\({}^{-1}\)) is light-use efficiency and represents the initial slope of the light-response curve; \(\beta\) (umol C m\({}^{-2}\)s\({}^{-1}\)) is the maximum CO2 absorption rate of the canopy at light saturation; \(\gamma\) (umol C m\({}^{-2}\)s\({}^{-1}\)) is ecosystem respiration and \(R_{g}\) (W m\({}^{-2}\)) is global radiation. GPP was calculated as:  
\[\text{GPP}=NEE+R_{\text{eco}} \tag{4}\]  
To provide a better comparison with literature data (more in situ studies), we calculated Ecosystem WUE, calculated as:  
\[\text{WUE}=\text{GPP}/\text{ET} \tag{5}\]  
where ET (mm) is evapotranspiration.
###### Abstract  
This research paper presents the application of the microwave infrared thermography (MIRT) technique for the purpose of detecting and characterizing defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. Initially, a numerical model was constructed, which consisted of a broadband pyramidal horn antenna and the specimen. The present study investigated the application of a 360 W power system that operated at a frequency of 2.4 GHz, specifically focusing on two different operational modes: continuous and modulated. The specimen being examined consisted of a solid concrete slab that was coated with an adhesive layer, which was then overlaid with a layer of CFRP. Within the adhesive layer, at the interface between the concrete and CFRP, there was a defect in the form of an air gap. The study examined three distinct scenarios: a sample without any defects, a sample with a defect positioned at the center, and a sample with a defect positioned outside the center. The subsequent stage of the investigation incorporated experimental verification of the numerical modeling results. The experiment involved the utilization of two concrete specimens reinforced using CFRP, one without any defects and the other with a defect. Numerical modeling was used in this study to analyze the phenomenon of microwave heating in complex structures. The objective was to evaluate the selected antenna geometry and determine the optimal experimental configuration. Subsequently, these findings were experimentally validated. The observations conducted during the heating phase were particularly noteworthy, as they differed from previous studies that only performed observation of the sample after the heating phase. The results show that MIRT has the potential to be utilized as a method for identifying defects in concrete structures that are reinforced with CFRP.  
active thermography; microwave heating; fine-element method; defect; CFRP reinforcement 2023
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 3 Results and Analysis
### Impact of Sheet Thickness
#### 3.2.2 Performance Analysis  
Sheet thickness significantly influenced crawling distance for both triangular and trapezoidal patterns, as observed in Figure 5. The 100 \(\upmu\)m sheet achieved the best results: 62 mm for the triangular pattern and 53 mm for the trapezoidal pattern. In comparison, the triangular pattern recorded distances of 54 mm (50 \(\upmu\)m), 48 mm (150 \(\upmu\)m), 44 mm (200 \(\upmu\)m), and 41 mm (250 \(\upmu\)m), while the trapezoidal pattern achieved 58 mm (50 \(\upmu\)m), 42 mm (150 \(\upmu\)m), 38 mm (200 \(\upmu\)m), and 37 mm (250 \(\upmu\)m). These results suggest that 100 \(\upmu\)m offers an ideal compromise: sufficient flexibility for kirigami pop-ups and structural support for effective friction. As discussed in Section 3.1, the triangular pattern at 100 \(\upmu\)m showed better crawling performance than trapezoidal cuts due to increased backward friction reducing overall efficiency. The 50 \(\upmu\)m sheets, while more deformable, exhibited reduced distances, likely due to insufficient rigidity to sustain optimal contact and pop-up stability. Conversely, thicker sheets (150-250 \(\upmu\)m) showed a consistent decline in performance for both patterns, with the 250 \(\upmu\)m sheet achieving the lowest distances (41 mm for triangular and 37 mm for trapezoidal). This decline is attributed to increased stiffness, which limits pop-up formation and reversion post-contraction, retaining buckled shapes and diminishing anisotropic friction, as observed in Figure 5. Although the theoretical pop-up height model \(h=kt\sqrt{\epsilon/\epsilon_{c}}\) suggests that thickness \(t\) appears in both the numerator and denominator (through \(\epsilon_{c}\)), and thus cancels out, experimental results reveal that sheet thickness still significantly affects real-world deformation and recovery behavior. While thinner sheets offer greater compliance and flexibility, excessively thin materials (e.g., 50 \(\upmu\)m) may lack the structural integrity needed for consistent buckling and traction. Conversely, thicker sheets (e.g., 250 \(\upmu\)m) exhibit higher stiffness and risk of plastic deformation, reducing recovery efficiency after repeated cycles. The 100 \(\upmu\)m PET sheet achieves a balance between flexibility and structural resilience, highlighting its suitability for kirigami-based directional frictional properties. These findings underscore material thickness as a critical design parameter, complementing kirigami geometry for locomotion efficiency, with the triangular pattern slightly showing better results than the trapezoidal pattern at optimal thickness due to its higher friction anisotropy.
## 2 Materials and Methods
### Scan Protocol and 4D Cardiac CT Processing  
All cardiac CTAs were performed on a Siemens 64-slice dual-source multidetector CT scanner with ECG gating (SOMATOM Definition Flash, Siemens AG, Munchen, Germany). The sheep were scanned in the prone position with standard acquisition technical parameters: a gantry rotation time of 0.33 s, 100-320 mAs per rotation, a 120 kV tube voltage, matrix 256 with a 16-bit depth, a deviation effective X-ray dose of 15.5 \(\pm\) 11.6 mSv, and a slice thickness of 0.75 mm. CT contrast enhancement was achieved by administering 2-2.5 mL/kg of an iodinated contrast agent at a rate of 5 mL/s. To achieve the ideal synchronization, a bolus tracking method was used for contrast bolus timing in the region of interest on the main pulmonary artery. The 4D cardiac CTA scanning protocol produced 10 continuous frames for the cardiac cycle, from 10% to 100%, with 10% of the RR interval covering the entire cardiac cycle.
# Advancing Digital Twin-Based Collision Avoidance:
## 4 Functional Safety for Wireless Systems
### Background
#### 4.1.1 Functional Safety
parts of the control system, the total \(PFH_{D}\) is determined according to [18] by the sum of each subsystem \(PFH_{D}\):  
\[PFH_{D}=PFH_{D1}+PFH_{D2}+\cdots+PFH_{DN}\]  
Furthermore, there are aspects such as the Mean Time To Dangerous Failure (\(MTTF_{D}\)) which need to be regarded when assigning a performance level. It is given as  
\[\frac{1}{MTTF_{D,\text{total}}}=\sum_{i=1}^{N}\frac{1}{MTTF_{Di}}\]  
where \(MTTF_{Di}\) is the \(MTTF_{D}\) of part \(i\). The calculation of \(MTTF_{Di}\) is defined in [18]. The German Social Accident Insurance (DGUV) and the Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA) propose the matrix method [19] for the development of safety-related software. It involves a comprehensive risk assessment to identify and mitigate potential hazards in the context of safety-related application software for machinery. It includes defining safety functions and determining performance levels (PLs) based on the assessed risks. Using a matrix, the method correlates safety functions with their required performance levels, aiding in allocating appropriate reliability levels. Specific attention is given to software requirements, encompassing aspects such as architecture, development processes, and validation to achieve the designated PL. They emphasize thorough documentation to record risk assessments, safety functions, and measures taken for maintaining safety integrity. The absence of a universally accepted performance level (PL) for communication networks stems from the diverse requirements across industries, the dynamic nature of technology, varied risk tolerances, and the complex interdependencies within network ecosystems. Establishing a coherent PL framework necessitates collaborative efforts, beginning with standardization endeavors involving industry stakeholders and organizations. A comprehensive risk assessment framework should be developed, considering the criticality and impact of communication failures in diverse applications. Industry-specific guidelines should be recognized, acknowledging the unique characteristics of different sectors. To achieve a PL for communication networks, continuous evaluation and adaptation are crucial, reflecting the rapid evolution of technology and the dynamic nature of communication requirements. Any framework should also incorporate cybersecurity considerations, ensuring the security and integrity of network infrastructure. International collaboration is key, engaging with standardization bodies, industry associations, and regulatory authorities to develop universally accepted guidelines. By fostering a collective and adaptive approach, the industry can work toward a standardized performance-level framework that accommodates the diversity and evolving nature of communication networks.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 5 Proposed Algorithm
### Derivation of Fitness Function
#### 5.3.3 Proposed Fitness Function  
In order to determine the optimal positions of the circular areas in our PSO method, it is best to maximize the linear combination of the above two parameters instead of maximizing them individually, since the two parameters do not conflict with each other. Therefore, we use the following fitness function Equation (15) to determine \(G_{best}\).  
\[Fitness=\alpha\times r_{Cov}+\frac{1-\alpha}{IoU},0<\alpha<1 \tag{15}\]  
where, \(\alpha\) is 0.9 in this paper, which is determined by experiments. Because the algorithm removes the circular areas with high _IoU_. Hence, the coverage of the circular areas is mainly considered in the fitness function.  
Figure 8: The circular area (\(IoU<0.7\)) distribution of the proposed algorithm in the simulation experiment.  
Figure 7: The circular area distribution of the proposed algorithm in the simulation experiment.  
For a single circular area, there is no concept of _IoU_. Therefore, in our PSO approach, the fitness function to determine \(P_{besti}\) is the absolute value of the difference between the actual number of nodes covered by the circular area and the number of ideal coverage nodes.  
\[fitness_{i}=\big{|}n_{Cov_{i}}-n_{idali},\big{|},0<i<m \tag{16}\]  
where \(n_{Covi}\) is the number of the nodes covered in a circular area and \(n_{idali}\) is the ideal number of the nodes covered in a circular area. \(m\) is the number of circular areas.  
\(n_{idali}\) in a circular area is related to the density of nodes in the network and the size of the circular area. \(n_{idali}\) is calculated by Equation (17).  
\[n_{idali_{i}}=A_{i}\times\frac{n}{A} \tag{17}\]  
where \(A_{i}\) is the area of the \(i\)th circular area and \(A\) is the area of the whole network.  
\[A_{i}=\pi R_{i}^{2} \tag{18}\]  
where, \(R_{i}\) is the radius of the \(i\)th circular area.  
In each iteration, the velocity and the positions of particles are updated using Equations (4) and (5).
# A Fast and Accurate Method for Computing the Microwave Heating of Moving Objects
## 2 Mathematical Model
### Heat Transfer and Its Boundary Conditions  
For microwave heating, the electromagnetic power \(Q_{e}(t)\) absorbed by the heated sample is taken as the heat source, which can be given by:  
\[Q_{e}(t)=\frac{1}{2}\omega\varepsilon_{0}\varepsilon^{\prime\prime}|E|^{2}, \tag{3}\]  
where \(\varepsilon^{\prime}\) is the imaginary part of the complex relative permittivity of the heated food. \(|E|\) is the magnitude of the electric field. Then, the temperature distribution of the sample can be obtained by solving the heat transfer equation expressed as:  
\[\rho\text{C}_{p}\frac{\partial T}{\partial t}=\
abla\cdot(kVT)+Q_{e}(t), \tag{4}\]  
where \(T\) is the temperature; \(\rho\), \(C_{p}\), and \(k\) are the density, the heat capacity, and the heat transfer coefficient of the heated sample, respectively.  
Besides the heat transfer inside the heated sample, the energy exchange between the sample and its surrounding air is also included in the proposed model by adding the equation of air convection,  
\[-kVT=h_{c}(T-T_{a}), \tag{5}\]  
where \(h_{c}\) and \(T_{a}\) are the convection heat transfer coefficient and the air temperature, respectively.  
Additionally, the energy exchange between the rotating turntable and its surroundings (i.e., the heated food and air) is not taken into account. In other words, the thermal boundaries between the rotating turntable and its surroundings are considered as insulation,  
\[-n\cdot q=0,\]where \(n\) is the normal unit vector of the heated sample surface, \(q\) is the heat flux.
## 2 Design and Analysis
These current distributions provide further insights into the physical mechanism that governs the broadband absorption. The regions exhibiting strong E-field intensities in Figure 5a-c are found to correspond to the areas with significant surface currents. Notably, the current distribution reveals that adjacent split rings form continuous current paths, resulting in a unique current flow pattern. Specifically, the currents in neighboring loops flow in opposite directions, developing localized magnetic dipoles that contribute to the absorption properties of the plasmonic metasurface. These opposite current directions are a direct consequence of the structural design with connecting bars. A key feature of the metasurface design is the presence of connecting bars at the ends of split rings. These connecting bars play a crucial role in enabling current flow between adjacent rings. This connectivity facilitates the coupling and overlapping of resonances between different structural elements, thereby enhancing the capability of the metasurface to absorb EM waves across a wide frequency range. The coupling effect, combined with the structural geometry of the split rings, leads to the formation of multiple absorption peaks that overlap, resulting in the observed broadband absorption.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 4 Analysis and Discussion
### Wave Characteristics  
The power spectral density of GNSS buoy tidal elevations (Figure 12) show that for tidal cycle A, the resultant peak frequency corresponds to a wave period of 5.3 s, whereas tidal cycle B has a peak frequency corresponding to a wave period of 5.1 s.  
Figure 11: Comparison of the GNSS buoy derived sea levels for tidal cycle B and established tide gauges at Liverpool and Heysham. Note: after high water the 900 s boxcar moving average can be seen to match the 15 min Liverpool tidal data.  
Figure 12: Spectral density for tidal cycle A (cyan) and tidal cycle B (red).  
Figure 13a-d shows the power spectral density results from the Cleveleys wave buoy over two-hour periods before and after high water for comparison with the GNSS buoy (see Table 4). The significant wave heights were estimated by calculating four times the square root of the area from under the peak of spectral density [11] between the frequency bands 0.5 and 0.125 Hz.  
The GNSS buoy results compare well with the Cleveleys wave buoy data, giving similar significant wave heights and spectral peak periods. Significant wave height differences vary between 0.0 and 0.3 m; however, in general they are a very close match and indicate that the GNSS buoy is capable of measuring waves as well as a commercial wave buoy. Differences in spectral peak periods within 0.1 s are also very similar. The results here also indicate that wave heights and wave periods increased over the two tidal cycles, and this can also be seen in the sea level data variations (Figures 10 and 11).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**Significant Wave Height**} & \multicolumn{2}{c}{**Spectral Peak Period**} \\  & & **GNSS Buoy** & **Cleveleys Buoy** & **GNSS Buoy** & **Cleveleys Buoy** \\ \hline Tidal cycle A & 2 hr period before HW & 1.1 m & 0.9 m & 4.8 s & 5.0 s \\  & 2 hr period after HW & 1.0 m & 1.0 m & 5.0 s & 4.8 s \\ \hline Tidal cycle B & 2 hr period before HW & 1.5 m & 1.5 m & 4.7 s & 4.8 s \\  & 2 hr period after HW & 1.3 m & 1.6 m & 5.4 s & 5.3 s \\ \hline \end{tabular}
\end{table}
Table 4: Wave parameter comparisons between the GNSS buoy and the Cleveleys wave buoy for two-hour periods either side of high water (HW).
## 5 Workflow Description
### Change Detection  
In this phase, we identify the change of the operational data within the DT. Six steps are performed in this phase listed, as shown in Figure 5. The values of the extracted features of the DT and its adjacent and replica are compared. DT adjacents and replica of the extracted features are equal, and then there is a probability that the data are normal or abnormal. Therefore, the data is sent to the anomaly detection service. The anomaly detection service, which implements machine learning techniques and statistics tests using the stored knowledgebase, finds the correlations of operation data with the historical process data of production plants to predict future maintenance. In the case when no anomaly is detected, no notifications will be sent to decision-makers. Furthermore, the simulated virtualized  
Figure 5: The workflow for auto-detection of erratic operational data using collaborative DTs.  
environment of the production system, i.e., deployed DTs, is considered working in healthy conditions. When all features values of the DT, its adjacents, and replica are unequal. Then, the data acquisition component collects the sensors' data again to overcome the erroneous and noisy data. The rule-based analysis is applied by using the rules as the stored knowledge representation to interpret information from the collected operational data. These knowledgebase rules are used to diagnose and analyse new faults. These rules are sometimes referred to as 'if statements', which depend on the expert system as they tend to follow the line of 'IF X happens, THEN do Y'.