esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulated Result of Proposed RDRA  
Dual-band radiation of the proposed RDRA has been attained by engendering quasi-55,111 mode through the single feed, and quasi- TE111 and TE113 mode because of the cross-shaped feed. With the help of a parasitic patch on top of RDRA, Not only is the Impedance matching of two bands improved but so are the CP performances of two bands (particularly the higher band).  
At 3.47 GHz and 4.41 GHz, two resonant modes were concurrently excited, as seen in Figure 7. The bands of quasi-TE111 and quasi-TE113 modes can be blended together and a large CP bandwidth is obtained by properly setting the values of l1 and w1 of the floating parasitic patch on top of RDRA. The frequency ratio of \(f\)TE113/\(f\)TE111 should be close to 1.25 in order to even achieve a wide CP bandwidth in the upper band. Because a small \(f\)TE113/\(f\)TE111 results in a narrow CP bandwidth in the upper band, a large \(f\)TE115/\(f\)TE113 results in the upper band splitting into two bands.  
The optimized design yielded wide 3 dB bandwidths of 9.0% (1.81-1.98 GHz) and 20.2% (2.40-2.94 GHz) for the lower and upper bands, respectively, as illustrated in Figure 7. Because the proposed antenna contains a conformal metal parasitic patch that plays at the upper band, the CP bandwidth of the lower band is nearly three times the typical 3-dB axial-ratio bandwidth for a regular single-point-fed RDRA working in its fundamental mode. However, because the resonances of DRA and cross-shape feeds are so close, it appears that the lower CP band has only one AR minimum. On the other hand, S11 features are influenced by the length (L) and width (w) of the metallic ground PEC plane as shown in Figure 2a, as well as the gap between the partial ground plane and the DR (G), because this parameter serves as a matching network for the proposed RDRA. The capacitance (C) created by the gap between the conformal metal parasitic strip and the DRA top corner logically balances the proposed RDRA's inductance (L). As a result, the Q-factor was  
Figure 6: Simulated S-parameter and Axial ratio of the cross-shaped RDRA.  
reduced [8]. This factor plays a significant role in widening the S11 bandwidth. The upper-band impedance was improved in particular, with little effect on lower-band performance.
## 3 Results
### Physical Models  
Another method of deriving LAI from spectral data is through the inversion of radiative transfer (RT) or physical process models. Physical models are based on the propagation of light in plant canopies and are used to retrieve biophysical parameters of vegetation such as LAI. Most physical algorithms are rooted in the principles of reflectance model inversion and are operated through minimizing a merit function. Such a function yields a value for LAI by minimizing the summed differences between simulated and measured reflectances for all wavelengths [35].  
Radiative transfer models (RTM) were shown in a number of studies as one of the successful methods of estimating LAI from remote sensing data [34; 35; 36; 44]. The widely used and well validated RTMs included PROSPECT, which is a 1-Dimentional leaf reflectance model [45] and SAIL, which is a canopy reflectance model [46]. The performance of physical models in terms of R\({}^{2}\) (0.71-0.99) was relatively consistent compared to empirical models (0.14-0.99). The model with the highest number of applications in forest, crop and grassland studies was PROSPECT and SAIL radiative transfer models (PROSAIL), which could be attributed to its ease of use and robustness. From the studies that were reviewed in this paper, there were no crop and grassland LAI studies that applied the PROSPECT + discrete anisotropic radiative transfer model (DART), while four-scale bidirectional reflectance distribution (BRD) and DART models had the highest number of applications in forest environments.  
Ref. [47] used the combined version of PROSPECT and SAIL known as PROSAIL to retrieve the LAI of saltmarsh from Sentinel-2 and RapidEye satellite data. This study achieved an R\({}^{2}\) of 0.59 and RMSE of 0.16 for Sentinel-2 and an R\({}^{2}\) of 0.65 and RMSE of 0.11 for RapidEye. The adaptability of this model was highly dependent on the type of remote sensing data, the ecosystem and the texture of the vegetation canopy. This model was not found to work well with coarse spatial resolution data; furthermore, it was more adaptable to homogeneous vegetation canopies.  
Ref. [48] used the radiative transfer model to estimate the LAI of grasslands. The RTM-based method showed a higher accuracy of R\({}^{2}\) = 0.64 and RMSE = 42.67 gm\({}^{-2}\) when compared to other models. However, the disadvantages of the RTMs are that they are not as fast as other methods and they have many parameters which are difficult to acquire [48].
## 4 DL Models  
In an AI-powered seizure prediction system using EEG input (Figure 2), traditional machine learning (ML) models, such as support vector machines (SVMs), decision trees (DTs), and random forests (RFs), require a preprocessing stage where raw EEG signals are transformed into engineered features (e.g., statistical, spectral, or nonlinear). These models then classify EEG segments based on these manually extracted features. While relatively efficient, they are limited by their dependence on an expert-driven feature design.  
In contrast, deep learning (DL) approaches [18; 19; 20], especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs), including long short-term memory (LSTM) variants, have revolutionized seizure prediction by directly learning from raw EEG data. CNNs are widely adopted due to their capacity to capture spatial and spectral patterns [21], particularly when EEG signals are represented as time-frequency spectrograms. CNN architectures typically include convolutional, pooling, and fully connected layers, allowing for automatic feature extraction and efficient classification between seizure and non-seizure states.  
LSTMs, on the other hand, are tailored to sequential data and are effective at modeling temporal dependencies across EEG time windows, making them ideal for predicting transitions into ictal states. Hybrid models combining CNNs and LSTMs have also been proposed to leverage both spatial and temporal dynamics in EEG data.
## 3 Mechanically Tunable PhC Cavity Sensors
### Sensing Applications of Mechanically Tunable PhC Cavities  
On-chip tuning PhC resonator devices have merits of simple system configurations and fabrication processes, dynamic control capability and low energy consumption. When certain mechanical action is applied to PhC cavity, resulting in variation of effective RI perturbation, deformation of resonator as well as mode-splitting in coupled cavities, could strongly affect the transmission spectrum of PhC cavity. Therefore, the amount of such spectral shift can be exploited to detect the applied mechanical actions. Now, we turn to review the sensing applications based on mechanically tunable PhC cavities.
## 4 Discussion of Simulation Results
### Glide Simulation Research
#### 4.2.2 Gliding Research When Blade Folded
According to the gliding simulation results, the drag caused by the propeller accounted for a large proportion of the overall drag of the hybrid-driven underwater glider, about 44-48%. However, the negative lift generated by the propeller has a relatively limited effect on the overall lift of the hybrid-driven underwater glider, and its value is about 2.6-5.0% of the overall lift. The propeller not only increases the overall drag of the hybrid-driven underwater glider, but also weakens its overall lift, which is not conducive to the gliding movement of the hybrid-driven underwater glider. However, by folding the propeller blades, the adverse effects can be greatly reduced, and the glide resistance caused by the propeller can be reduced by about 45-55%, while the negative lift caused by the propeller can be reduced by about 68-85%. Although this article provides a detailed and comprehensive hydrodynamic study on the series of Kappel tip pitch propellers and their hybrid-driven underwater gliders, due to time constraints, there are still some aspects that have not been fully addressed and require further in-depth research, such as evaluating the simulation effects of different aerodynamic numerical models and combining them with noise models in STARCCM+ 2021.3 commercial software to complete a noise analysis, and comparing and applying different types of cavitation models and couple noise monitoring models in cavitation simulation to evaluate the cavitation noise during the operation of Kappel propellers. By analyzing the streamline distribution and y + distribution on the surface of the propeller, the transition phenomenon during the operation of the Kappel propeller is presented, and the key role of the gamma transition model in propeller simulation is demonstrated. When using different simulation strategies, there are often significant differences in the calculation results of high-speed operating conditions. Exploring the transition phenomenon can help reveal the underlying reasons and provide a direction for optimizing numerical simulation strategies. Conceptualization, C.-W.C.; methodology, J.-L.L. ; software, X.-PC. ; validation, D.-J.W. ; formal analysis, J.-L.L. and X.-P.C. ; investigation, J.-L.L. and X.-PC. ; resources, J.-L.L. and X.-P.C. ; data curation, J.-L.L. and X.-P.C. ; writing--original draft preparation, X.-P.C., J.-L.L. and C.-W.C.; writing--review and editing, C.-W.C., [PERSON]-L.L. and X.-P.C. ; visualization, D.-J.W. ; supervision, C.-W.C.; project administration, C.-W.C.; funding acquisition, C.-W.C. All authors have read and agreed to the published version of the manuscript. This research was funded by [National Natural Science Foundation of China] grant number [U23A20616]. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflict of interest.
# Experimental Study on the Behavior of Polyurethane Springs for Compression Members
## 4 Experimental Results and Discussion
### Energy Dissipation  
Energy dissipation is an essential factor for evaluating the cyclic behavior of seismic devices, such as dampers and seismic isolators [38]. The energy dissipation of the polyurethane spring as a compression member can contribute to the energy dissipation capacity of the seismic device [39, 40]. The energy dissipation of a pseudo static test can be defined as the area of the closed curve. Polyurethane rubber springs exhibit significant stress reduction after the first cycle. Here, the energy dissipation also decreased greatly.  
Figure 14: Behavior of three types of strain corresponding to each maximum strain in Type 2. (**a**) Unloading strain; (**b**) residual strain; (**c**) recover strain.  
By calculating the reduction ratio of the energy dissipation defined by Equation (4), the amount reduced after the first cycle was confirmed. Here, E\({}_{1}\) means the energy dissipation of the first cycle, and E\({}_{2}\) means energy dissipation of the second cycle. Therefore, the reduction ratio means the ratio of the amount of reduction compared to the energy dissipation of the first cycle.  
\[\text{Reduction Ratio}=\frac{\text{E}_{1}-\text{E}_{2}}{\text{E}_{1}}\;100\;( \%) \tag{4}\]  
Figure 15 shows the reduction ratio of all the cases, but two cases were excluded as they were measured through retest. Since polyurethane springs experience a significant reduction in energy dissipation from the second loading once they experience a load that exceeds the elastic range, the reused specimens were excluded from the results. As can be observed in Figure 15, the reduction ratio of the energy dissipation demonstrated a slight deviation for all cases, but was concentrated near an average of 61.81%. The reduction rate of the energy dissipation by compressive stiffness and size did not demonstrate a significant trend, which was judged according to the material properties of the polyurethane spring used in this study. However, the average values of the reduction ratios of Type 1 and Type 2 for each maximum strain were 59.38%, 60.79%, 62.88%, and 63.97%, respectively, demonstrating a greater decrease in energy dissipation as the maximum strain increased. The reduction ratio after the second cycle was an average of 10.81%, which decrease to about 1/6 of the reduction rate after the first cycle, and it can be seen that the energy dissipation was slightly decreased but relatively stable.
## 3 Consolidation Properties
### Principal Component Analysis of Compression Index in Stabilized Soil  
Principal component analysis of compression index in stabilized soil demonstrates a highly deterministic relationship where PC1 accounts for 99.75% of total variance, with the first two components explaining approximately 94% of variability, establishing stabilizer content as the predominant factor governing soil compressibility reduction. The scree plot exhibits a precipitous decline after PC1 with subsequent components contributing less than 0.5%, confirming the compression index as essentially a single-dimensional phenomenon controlled by lime and cement additions (Figure 9(a)). The PCA biplot reveals moderate data dispersion indicating nonlinear relationships between stabilizer content and compression index reduction, where compressibility reduction rates vary across stabilization levels due to complex physicochemical interactions within the soil matrix (Figure 9(b)). The lime content gradient demonstrates a clear negative correlation with compression index values through flocculation mechanisms that aggregate clay particles into larger, more stable clusters, while simultaneous pozzolanic reactions between lime and clay minerals form cementitious compounds that bind soil particles permanently. Cement stabilization enhances these effects through hydration processes that produce calcium silicate hydrate and calcium aluminate hydrate gels, creating a rigid three-dimensional network that encapsulates soil particles and dramatically increases inter-particle bonding strength. These combined mechanisms fundamentally alter the soil microstructure by reducing void ratios, increasing particle interlocking, and establishing strong chemical bonds that resist deformation under applied loads, resulting in substantial compression index reduction while maintaining high predictive reliability across varying stabilization scenarios.  
Figure 10: Principal component analysis of compression index in lime–cement stabilized soil.
## 3 Materials and Methods
### Model Formulation
#### 3.2.2 Objective Function  
The objective function, expressed in Equation (1), minimizes the total cost associated with quality control activities. This cost consists of four components: the investment cost (\(c^{inv}\)) for the adoption of inspection methodologies, the variable inspection cost (\(c^{var}\)) related to the number of inspections performed and the type of technology adopted, the penalty cost (\(c^{pen}\)) for non-compliant products delivered to customers, and the rework cost (\(c^{rep}\)) associated with repairing the detected defects.  
\[\Phi=min\Big{(}c^{inv}+c^{var}+c^{pen}+c^{rep}\Big{)} \tag{1}\]  
The investment cost, defined in Equation (2), is calculated as the sum of the costs associated with the adoption of inspection methodologies at the assembly stations, where the installation status is defined by the binary variable \(Y_{s,h}\).  
\[c^{inv}=\sum_{s\in S}\sum_{h\in H}C_{h}^{inv}.Y_{s,h} \tag{2}\]  
The variable inspection cost is directly proportional to the number of inspections \(\Big{(}N_{s,p,h,k}^{contr}\Big{)}\), while the penalty cost is directly proportional to the number of non-compliant products (\(NC_{p,k,t}^{mod}\)), as shown in Equations (3) and (4), respectively. The former reflects the operational expenses associated with performing inspections, whereas the latter accounts for the economic losses due to non-compliant products.  
\[c^{par}=\sum_{s\in S}\sum_{p\in P}\sum_{h\in H}\sum_{k\in K}\sum_{t\in T}C_{h }^{par}\cdot N_{s,p,h,k,t}^{contr} \tag{3}\]  
\[c^{pen}=\sum_{p\in P}\sum_{k\in K}\sum_{t\in T}C_{p,k}^{pen}\cdot NC_{p,k,t}^{ pred} \tag{4}\]  
The rework cost, representing the expenses incurred to repair defects detected during quality inspections, is calculated as in Equation (5). The rework cost is directly proportional to the number of defects correctly detected during inspection activities, which depend on the defect probability, the detection probability, and the number of inspections performed (\(N_{s,p,h,k,t}^{contr}\)).  
\[c^{rep}=\sum_{s\in S}\sum_{p\in P}\sum_{h\in H}\sum_{k\in K}\sum_{t\in T}C_{s,p, k}^{rep}\cdot def_{k,p}^{prob}\cdot det_{s,p,h,k}^{prob}\cdot N_{s,p,h,k,t}^{contr} \tag{5}\]
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 6 Discussion
### Mutual Coupling in Original Antenna Array  
The mutual coupling \(M_{\text{chip}}\) in Figure 11, i.e., \(|S_{21}|\) in Figure 2, is one of the dominant factors determining \(W\). If \(M_{\text{chip}}\) approaches 0 dB, it means that a chip antenna is transferring the signal to the other chip antenna rather than radiating it to the air; thus, it is virtually a single-antenna transceiver. This is interpreted as \(\text{rank}(W)=1\). In this case, Augmented MIMO cannot contribute to increasing the channel capacity.  
In our experiment, the mutual coupling \(M_{\text{chip}}\) was about \(-14\) dB, and the proximity coupling (\(-L_{\text{prox}}\)) was about \(-6.8\) dB. The condition \(-L_{\text{prox}}>M_{\text{chip}}\) means that the signal fed to one of the watch antennas is transferred more to the proximity-coupled cuff antennas than to the other watch antenna. This may have had a positive impact on the throughput improvement with Augmented MIMO.  
Figure 11: Antenna gains and losses in peripheral components in (**a**) chip-to-AP and (**b**) Augmented MIMO links.  
Acceptable conditions for \(-L_{\text{prox}}\) and \(M_{\text{chip}}\) should be further investigated in future work. In addition, as we have seen in Figure 6, the proximity coupling can also be further increased by design optimization.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 2 State of the Art
### Ultra-Wideband (UIWB)  
IEEE 802.15.4a appeared in 2007 as an amendment to the IEEE 802.15.4-2006 standard for low-rate wireless personal area networks (LR-WPANs) [43]. Two additional physical (PHY) layers were defined to support higher throughput, longer ranges, lower power consumption, and precision ranging capabilities: chirp spread spectrum (CSS) and ultra-wideband (UWB) [44].  
UWB uses a high-rate pulse-repetition scheme consisting of very short pulses (typically less than 2 ns wide, with an instantaneous bandwidth greater than 500 MHz), which results in an extremely high time resolution of signals even in environments with multipath propagation. This feature allows one to determine the ToF (and, consequently, the distance) between two devices at the centimeter level as long as they are in LOS conditions [19; 45]. Conversely, signal-based ranging-estimation techniques such as RSSI deliver meter-level accuracy. Despite being widely employed in technologies such as WiFi and Bluetooth, RSSI records are more prone to instability caused by interference and fading [46].  
For its part, UWB is rarely affected by interference from other communication devices or external noise except for the unlicensed use of the 6 GHz band (e.g., in WiFi 6), which shares some of its channels [47]. Lastly, the particular wavelength of UWB signals holds good penetration capabilities for most common building materials such as concrete, glass, and wood, thus enabling positioning even in NLOS conditions. Altogether, this makes UWB an eligible technology for IPSs operating in heterogeneous sectors, even those involving fast-moving objects, with fine localization requirements both in accuracy and time.
## 4 System Design
### Interaction Model  
One of the vital factors in the design of any architecture is the analysis of the interaction among its different modules. This interaction can be best described using a Unified Modeling Language (UML) sequence diagram. In this subsection, we describe the interaction of a sequence among various processes of the proposed system. The proposed system communicates across two main subsystems, i.e., the embedded plane and mission-planning plane. On the mission-planning plane, tasks are generated, mapped, and the configuration is stored. The tasks are then allocated based on the persisted configuration. The sequence diagram is shown in Figure 6. As can be seen, in this work, we have two main subsystems. The embedded plane and mission-planning plane. In the IoT Gateway, which hosted on the embedded plane, resides a Flask-based IoT server which at first initializes and registers the physical devices connected with it. The devices can be sensors and actuators. The registry information is passed onto the application. The bootstrapper component of the application is the central place which runs for the whole lifespan of the application. The bootstrapper prepares the tasks' parameters and passes them to the task generator. The task generator creates the tasks in loop based on the parameters passed to it by the the bootstrapper. Once the tasks are created and persisted, the resultant task data are loaded to the mapper. The physical device data is also loaded to the bootstrapper which in turn passed to the VO composer. The VO composer creates virtual objects based on the physical device information and passes it to the mapper. The mapper passes all the data to the correlator module which analyzes the tasks with the provided VO and returns the correlation index. On the basis of the correlation index, the tasks are mapped onto the best virtual objects. The information is communicated to the task scheduler which schedules the tasks and returns the status information to the bootstrapper, which in turn passes it to the IoT gateway. The IoT gateway uses the information to deploy the correct task on the physical devices.  
Figure 5: Semantic modeling of proposed system.