esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parametric Simulations
Therefore, \(w\) can be chosen to satisfy the low-loss passband requirement and the Q-factor. In this design, \(w\) is chosen as 4 mil by considering the used chemical etching fabrication condition. Figure 6 shows the frequency response variations related to other parameters (\(d\), \(p\) and \(g\)). With the selected \(w\), the resonant frequency of the loop-type FSS filters is determined by the loop length \"\(d\).\" As shown in Figure 6a, a change of the loop length \(d\) significantly shifts the resonance frequencies of both square- and circular-loop cases. A small decrease of 0.5 mil in \(d\) shifts the resonant frequencies upwards by 2.96 GHz and 2.16 GHz for square- and circular-loop FSSs, respectively. Further theoretical details regarding the effect of \(d\) on the resonant frequency were elaborated in Reference [30]. Figure 4: Frequency responses of square-loop and circular-loop FSSs: (**a**) normal incidence, (**b**) TE excitation, (**c**) TM excitation. The main effect of choosing \(p\) (period of the unit cell) and \(g\) (gap between the loops) values is determination of the frequency where grating lobes occur as shown in Figure 6b,c. As values of \(p\) or \(g\) become smaller, the frequency of the grating lobes occurs farther from the resonant frequency. Also, similarly as \(w\), the gap \(g\) between loops also affects the Q-factor. The higher Q-factor can be achieved by increasing the \(g\), but, as a tradeoff, it results in producing closer grating lobes from resonant frequency, thereby lowering oblique incident angle performance. Increase of 10 mil in \(p\) or \(g\) results in the higher Q-factor but decreases the notch-rejection by 3.3 dB. It also causes the grating lobes to occur very closer to the resonant frequency. When the oblique incidence angle performance of the FSS is important, the gap between adjacent elements \(g\) should be less than half of the free space wavelength to avoid the scattering of the signal. However, for the normal incidence, the gap \(g\) can be equal to or less than one wavelength [30; 31]. Though there exist the distinct main influences of the aforementioned parameters on the frequency response of the FSS, however, they all contribute more or less in the shift of the FSS resonant frequency. Furthermore, the variations in the dielectric properties (i.e., loss tangent and dielectric constant) of the substrate can slightly influence in the frequency shift. It is known that the frequency increase results in decreasing the dielectric constant of the substrate, which can shift the resonant frequency upwards. The change in the loss tangent also effects on the level of the notch-rejection. Figure 5: Effects of the loop width \(w\) on the loop-type FSSs: (**a**) Fixing \(d\), \(p\) and \(g\) parameters, (**b**) Adjusting \(d\) with fixed \(p\) and \(g\). Figure 6: _Cont._
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 3 Preliminary Research  
### EdgeX Foundry  
EdgeX Foundry is an open-source-based IoT edge computing platform located between cloud and IoT devices (device/sensor) to interact with IoT devices [3] and play the role of edge nodes [21] in an edge computing environment. In other words, EdgeX Foundry can collect, store, analyze, and convert data from IoT devices and transfer the data to a designated endpoint. Moreover, the user can directly control the operation of IoT devices and monitor the device data through EdgeX Foundry.  
EdgeX Foundry contains four main layers: _Device Services_, _Core Services_, _Supporting Services_, and _Export Services_. The _Device Services_ layer interacts with IoT devices and abstracts their connectivity protocols. Microservices in EdgeX Foundry can request data collected from IoT devices or transmit the data to other microservices, such as the _Core Services_ layer. The _Core Services_ layer is an intermediary between the upper layers and _Device Services_ layer. The _Core Services_ layer includes initial information and sensor data about IoT devices connected to edge nodes, which are stored in a local database until they are sent to the upper layers and cloud systems. The _Core Services_ layer manages information on microservices, such as hostname and port number, and provides a connection between microservices. When one microservice wants to connect to another microservice, it calls the _Core Services_ layer to obtain the microservice information necessary to connect the microservice. The _Supporting Services_ layer performs common software functionalities, such as scheduling, notifications, and alerts; it allows each microservice to execute a designated task at a specified time or under given rules. In addition, the _Supporting Services_ layer collects logs inside EdgeX Foundry, records them to log files or databases, and notifies or alerts external systems or users connected to EdgeX Foundry. The _Application services_ layer contains a set of functions that process messages in a specific order based on the function pipeline concept. Furthermore, the _Applications Services_ layer prepares (e.g., transforms, filters, etc.), formats (e.g., reformats, compresses, encrypts), and exports data to an external service designated as an endpoint.
## 3 Cutting-Edge Online Fault Diagnosis Techniques for Al-Caps
### Model-Based Online Fault Diagnosis Techniques (MB-ON-FDTs)  
Model-based online fault detection techniques (MB-ON-FDTs) rely on the physical system model to which they are applied, which makes them well-suited for PCs, as these hybrid dynamical systems can be effectively described using state-space models. Therefore, in [38], the authors apply the recursive least mean squares (RLMS) algorithm to identify the coefficients of the state space model of a buck converter, which naturally includes the capacitor parameters. Later, in [39], a simplified regression model for buck and boost converters was proposed that reduced both the computational cost and time required for calculations. Although accurate, the two previous methodologies require a high sampling frequency. To address this issue, in [40], a continuous-time model of the buck converter was used in conjunction with the LMS algorithm to estimate the ESR value. This approach focuses solely on the non-conduction stage of the buck converter. Subsequently, the previous solution was adapted for buck-boost and boost converters [41]. However, as the two previous solutions use samples associated with a specific converter state, the size of the duty cycle may restrict their implementation. Thus, in [42], a unified method is proposed for three non-isolated converters: buck, buck-boost, and boost converters. This new method uses samples from the converter state that has the largest number of samples available. Subsequently, the concept of a digital twin is introduced and applied to assess the health status of the capacitors used in the output filter of a buck converter [43]. Using the circuit model, the authors employ the particle swarm optimization (PSO) algorithm to estimate the main circuit parameters, namely the C and ESR, by minimizing the discrepancy between the digital twin and the physical system.
## 2 Related Work
Experimental results demonstrated a significant 96% improvement in system-level energy efficiency, with a minimal impact on signal quality (2%). [PERSON] et al. in [29] addressed the importance of computer-aided patient health monitoring, thus emphasizing the role of body wearable sensor nodes and sensor networks in collecting health-related data. They highlighted the critical nature of timely data transmission in these networks and the need for efficient monitoring systems to identify diverse symptoms without invasive instruments. The proposed scheduling strategy, termed Data Aggregation and Precedence by Delay Sensitivity (DAP-DS), aims to schedule collected messages from various sensors based on the critical objective of delay sensitivity. Additionally, the study suggested methods to minimize controller waiting time. Through simulation studies, the robustness and scalability of the proposed scheduling scheme were demonstrated. [PERSON] et al. in [30] addressed the challenge of energy consumption in WBSNs by proposing an adaptive routing protocol based on ant optimization techniques. This protocol aims to efficiently distribute energy utilization among nodes, thus extending the nodes' lifecycle and preventing potential tissue damage in patients' bodies. The authors compared their proposed protocol with the conventional LEACH routing protocol to demonstrate its effectiveness in prolonging node lifespan. Experimental validation using a network setup confirmed the efficiency of the proposed algorithm, thereby showing significant reductions in energy consumption compared to conventional and developed routing protocols. [PERSON] et al. in [31] proposed an adaptive rate energy-saving data collecting technique, AREDaCoT, for efficient health monitoring in WBSNs. With the aim of reducing costs in healthcare systems, WBSNs offer continuous remote monitoring of patient health. AREDaCoT optimized data transmission by removing redundancy and adapting sampling rates based on patient risk levels. Through simulations on real health datasets, AREDaCoT demonstrated significant energy savings while preserving data accuracy and integrity compared to existing approaches.
## 5 Experiment Analysis  
To practically verify the performance of the proposed CCES scheme, the prototype system was built under the end hopping system in [18], including an extra adversary \(P_{atk}\), who can launch a network attack to thwart the covert communication. All of the communication participants are deployed on Linux PCs, and the specific PC configurations are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Role** & **CPU** & **OS** & **Memory** & **Storage** \\ \hline \(P_{s}\) & Core i5-5200U & Ubuntu16.04 & 8G & 512G \\ \(P_{r}\) & Core i7-7700 & Ubuntu16.04 & 8G & 1T \\ \(P_{atk}\) & Core i7-6700 & Ubuntu16.04 & 8G & 512G \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configurations of Linux PCs.  
Based on the evaluation metrics proposed in Section 4, three kinds of performance tests were conducted, and below is the specific experimental analysis.
## 3 Methodology
### Space Syntax Monitoring Scene Analysis
#### 3.2.2 Visibility Analysis  
In space syntax, visibility analysis provides the visible range from a given location by constructing a visibility polygon for that point. The characteristics of the visible area can be described by the following attributes:  
* Area: The area of the visibility polygon, where a larger area indicates a broader field of view, implying a relatively more significant geographical position.
* Circumference: The perimeter length of the visibility polygon.
* Radius: The distance from the viewpoint to the boundary of the visibility polygon. The shortest radius is the minimum distance from the viewpoint to the boundary, the longest radius is the maximum distance, and the average radius is the mean distance in all directions from the viewpoint to the boundary. This can similarly produce the shortest, longest, and average diameters. The longest diameter represents the maximum visible distance from a given point and is a critical variable in assessing spatial perceptibility.
* Occlusivity: The proportion of the total perimeter represented by virtual boundaries formed by lines of sight, indicating openness of view.
* Compactness: The ratio of the average to the longest diameter of the visibility polygon.
* Circularity: Defined as the ratio of the circular area calculated from the average radius to the actual visibility area. Both compactness and circularity not only describe the roundness of the visibility shape but also reflect the centrality of the viewpoint within its visible range. With other factors being equal, higher compactness and circularity suggest that the viewpoint is closer to the center of the space.  
In line with the feature requirements of video surveillance systems, three key metrics--visibility area, longest diameter, and occlusivity--are used as evaluation standards. To enhance computational efficiency, this study calculates visibility attributes after rasterizing, which reduces the calculation of the visible polygon. The three generated attributes are also normalized and average-weighted together to create the visible map, as shown in Figure 3. All attributes are normalized into 0 to 1 and visualized in a heat map in which the red represent 1 and blue represent 0. In Figure 3, the inaccessible parts are colored in black, and the warmer the color, the higher its visibility value. We can see that the center part of the blueprint has a better field of view than the corner parts, which is reasonable.
## 4 Discussion  
### Evaluation of Path Planning  
The path is 3D and its constituent elements can be divided into 2D planar elements and a one-dimensional (1D) elevation element. A 2D plane element can be evaluated through a set overlap, and the 1D height element can be evaluated through a set GSD.  
The analyzed GSD is close to the target value of 2 mm. The horizontal and vertical dimensions of the GSD may differ depending on the size of the electronics and the number of pixels in each direction. The GSD in the horizontal direction had an error of only 0.1 mm, as shown in Table 6, and its error rate was 7.6%. Conversely, the vertical GSD was 1.4 times greater than the horizontal GSD, and its error rate was 27.8%. The simulator sets the height based on the horizontal GSD; therefore, the error arose from the difference in the physical pixel size between the two directions.  
There was a slight difference between the forward and side overlaps. The error was small in the longitudinal direction and large in the transverse direction (Table 7).
###### Abstract  
This study conducted experimental tests on the undisturbed Nanjing Yangtze River floodplain soft soil using the bender element instrument to determine the maximum dynamic shear modulus of the Yangtze River floodplain overconsolidated soft soil. The \(G_{\text{max}}\) of floodplain soft soil with different overconsolidated ratio OCR, initial effective confining pressure \(\sigma_{\text{3c}}\)\({}^{\prime}\), and void ratio \(e\) are discussed. The results indicated that \(G_{\text{max}}\) reduced as \(e\) rose for given \(\sigma_{\text{3c}}\)\({}^{\prime}\) and OCR. In addition, an increase in OCR contributed to a gradual decrease in the decay rate of \(G_{\text{max}}\), while the \(G_{\text{max}}\) decay rate is insensitive to the change of \(\sigma_{\text{3c}}\)\({}^{\prime}\). The void ratio-normalized maximum shear modulus \(G_{\text{max}}/F(e)\) improved with the increase in the stress-normalized initial effective confining pressure \(\sigma_{\text{3c}}\)\({}^{\prime}\)/\(P_{\text{a}}\), whereas the growth rate gradually drops, and a power relationship is then obtained between \(G_{\text{max}}/F(e)\) and \(\sigma_{\text{3c}}\)\({}^{\prime}\)/\(P_{\text{a}}\). Based on the regression analysis, a \(G_{\text{max}}\) prediction method is established for reasonably characterizing Yangtze River floodplain soft soils with various over-consolidation states, initial stress conditions, and compactness levels, with a prediction error of less than 10%.  
floodplain soft soil; overconsolidated ratio; Initial effective confining pressure; void ratio; bender element testing
# A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System
## 4 Detection Methods
### Complexity Analysis  
To demonstrate the advantages of the proposed algorithm, we will analyze the computational complexity of ML-MRC detection algorithm and the proposed IVC-OMP detection algorithm separately from the perspective of time complexity.  
As for the ML-MRC detection, which is a two-step detection algorithm, the computational complexity consists of both ML and MRC components. In ML detection, in order to obtain the index vector \(\hat{\mathbf{v}}_{l}\), we successively used descending and ascending sorting methods, and the computational complexity required for this process can be approximately estimated as \(O(NN_{r})\). In the ML stage of estimating the signal vector \(\hat{\mathbf{s}}_{l}\), considering the modulation order and generalized operation of QAM, its computational complexity can be approximated as \(O\left(2^{\lfloor\log_{2}(C(N_{r},N))\rfloor}N_{r}M^{N}N\right)\). In MRC detection, the computational complexity mainly focuses on the calculation of weight factors, which can be approximated as \(O(N_{r}M)\). Hence, the total computational complexity of the ML-MRC method can be expressed as \(O\left(2^{\lfloor\log_{2}(C(N_{r},N))\rfloor}N_{r}M^{N}N\right)\).  
As for the proposed IVC-based OMP detection, a total of \(N\) iterations are required in Algorithm 1, and each iteration's operations are mainly concentrated in two parts, including the inner product operation of (12) and solving least squares problem of (16). The computational complexity of the inner product operation part is related to the number of LEDs and PDs and can be approximately evaluated as \(O(N_{r})\), while the computational complexity of solving least squares problem is \(O\left(N_{r}^{2}\right)\). However, the illegal vector correction operation occurs outside the iteration and is only required once, leading to the negligible computation complexity. Therefore, the total computational complexity of the proposed detection method can be expressed as \(O\left(N_{r}^{2}N\right)\).  
Based on the above analysis of the complexity of the two algorithms, we find that the complexity of the ML-MRC algorithm increases exponentially with the increase in the number of activated LEDs. However, the proposed IVC-based OMP algorithm takes advantage of the sparse characteristics of the received signal and will first detect the indexes of the activated LEDs and then estimate the received symbols according to these indexes by solving the least squares problem, which makes the impact on its complexity when the number of active LEDs increases much smaller than that of ML-MRC. Table 4 provides a brief comparison of the average complexity in the cases of \(N_{t}=4\), \(N_{r}=4\), and \(M=16\).
# Review of Adaptive Shock Control Systems
## 5 Outlook  
Future investigations of adaptive SCBs should look into drag reduction and overall system weight increase at the same time. Only with such an analysis can a conclusion be drawn as to how large the overall reductions in fuel consumption and emissions really are. However, since environmental aspects and goals are one of the main drivers for adaptive SCBs, the ecological design of such morphing systems and, e.g., the recycling should also be considered, which were not analyzed in this review. Furthermore, optimizers which use coupled FSI simulations for morphing systems offer a great potential for a fast and detailed design and should be considered more often for adaptive SCBs. To attempt an outlook, in the next five to ten years morphing components like adaptive spoiler SCBs could increase in their technological readiness. They can play an important role in reaching global emission targets and making aviation more efficient and environmentally friendly.  
In the project Move-IntegR (FKZ: 20W1729C), position and height variable SCBs are to be designed for a wing with hybrid laminar flow control (HLFC). Various concepts to form SCBs on the spoiler will be considered and applied to performance and buffet-onset optimized designs, with and without VC.  
[PERSON] has executed the literature research and was responsible for writing this review. [PERSON] and [PERSON] supervised, reviewed and edited the work. [PERSON] reviewed the parts of the Tube Spring and fish-mouth actuators. [PERSON] worked in the former LuFo project LDAinOp and participated in developing the one- and two-actuator as well as pressurized chamber concepts and reviewed the respective sections of this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded in scope of the 5 th Federal Aeronautical Research Program (LuFo) V-3 by the German Federal Ministry of Economic Affairs and Energy (BMWi) and is part of the project Move-IntegR (FKZ: 20W1729C).  
Not applicable.  
Data Availability StatementData are stored on DLR institute servers and all papers and sources used for this review are inserted above.  
Special thanks go to colleagues of the project partners in Move-IntegR from Airbus, the university of Stuttgart and DLR for the ongoing collaboration.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
2D Two-dimensional  
3D Three-dimensional  
BMWi Federal Ministry of Economic Affairs and Energy (of Germany)  
CFRP Carbon fiber reinforced polymer  
ERA Environmentally Responsible Aviation  
FE Finite-element  
FKZ Grant number (DE: Forderkennzeichen)  
FSI Fluid-structure interaction  
GFRP Glass fiber reinforced polymer  
HLFC Hybrid laminar flow control  
LE Leading edge  
LDAinOp Low Drag Aircraft in Operation  
LuFo Federal Aeronautical Research Program  
NASA National Aeronautics and Space Administration  
NiTi Nickel-titanium  
\(p\) Pressure  
SCB Shock control bump  
SMA Shape memory alloy  
TE Trailing edge  
VC Variable camber