esa-sceva/satcom-chunk-collection huggingface dataset:
# The Free-Energy Landscape of a Mechanically Bistable DNA Origami
## 3 Results
### Free-Energy Landscape  
The free-energy landscape was calculated using umbrella sampling and WHAM. We chose the order parameter in the umbrella potential as the distance \(R\) between the centres of mass of two groups of nucleotides in the crank block and the frame block, respectively. The groups of nucleotides are highlighted in Figure 2a. We chose to use \(R\) as the order parameter rather than \(\theta\) because calculating the forces on the nucleotides due to the umbrella potential is then much simpler and had already been implemented in the oxDNA simulation code [37]. The values of \(R\) and \(\theta\) were tracked during the umbrella sampling simulations so that we can then transform the free-energy landscape from a function of \(R\) to a function of \(\theta\). The resulting landscape is shown in Figure 2b.  
Contrary to previous expectations, the landscape obtained using the oxDNA model only shows a single minimum at \(\theta\approx 80^{\circ}\), which corresponds to state S2. There is no sign of any second minimum corresponding to state S1. Instead, the free energy continuously increases as \(\theta\) decreases. At \(\theta\approx 20^{\circ}\), where state S1 is expected to occur, the free energy is more than 20 \(k_{B}T\). This is significantly different from the expectation based on the experimental results, where about 30% of the origamis were observed to be in the closed state.  
Figure 1: Representative oxDNA configurations of the origami in (**a**) the closed state S1 (\(\theta\approx 20^{\circ}\)) (**b**) the intermediate state U (\(\theta\approx 45^{\circ}\)) and (**c**) the open state S2 (\(\theta\approx 80^{\circ}\)). The four joints are numbered in (**c**).  
Given the seeming disagreement between the obtained free-energy landscape and the experimental results, we further explored the dependence of the landscape on the solution conditions and temperature. Given the relative simplicity of the description of ionic interactions in the oxDNA model, we first checked that the destabilization of the closed state is not due to an overly strong electrostatic contribution to interhelical repulsions [41]. We found that even when the electrostatic interactions were tuned off, the landscape still had a single free-energy minimum corresponding to the open state (Figure S1). Secondly, we explored the temperature dependence of the free-energy landscape but found that there was little discernible change over the 40 K window that we considered (Figure S2). These results confirm that the the single-minimum character of the free-energy landscape is a robust feature of the oxDNA simulations.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 4 Multi-Carrier Transmission Schemes with Dimming Control
### Aco-Ofdm  
In ACO-OFDM system, the signal generation is similar to that of DCO-OFDM systems but utilizes a different frame structure. Only the odd subcarriers of the first half of the OFDM frame with index \(k=1,3,5,\ldots,K/2-1\) are used to transport the information when performing a \(K\)-point fast Fourier transform (FFT), whilst the even subcarriers are left unused, leading to a total number of \(K^{\prime}=K/4\) information-carrying subcarriers. To get the real-valued symbols following the IFFT operation, Hermitian symmetry is applied to the remaining OFDM frame and a similar time domain signal as (25) is obtained. To ensure that \(x[n]\) is with unit variance, the variance of the signal should be \(\sigma^{2}_{\rm X}=2\) when taking into account the uniform power distribution over the subcarriers [34]. To guarantee that the time domain signal is transmitted with non-negative values, it has to be properly clipped at a top clipping level, \(\kappa\). Hence, the clipped signal, \(\hat{x}[n]\), can be expressed as  
\[\hat{x}[n]=\begin{cases}\kappa&\text{if}\ \ x[n]\geq\kappa,\\ x[n]&\text{if}\ \ 0<x[n]<\kappa,\\ 0&\text{if}\ \ x[n]\leq 0.\end{cases} \tag{46}\]  
Because of the time domain signal structure in ACO-OFDM and DCO-OFDM, the optical power of the \(n\)th time-domain OFDM sample emitted from the source is given by \(x_{t}[n]=\alpha\hat{x}[n]\). This will lead to \(\alpha=P_{\text{max}}/\kappa\). Therefore, the average transmit optical power is given by  
\[\overline{P}_{\text{TX}}=\alpha\Big{[}1/\sqrt{2\pi}-\phi(\kappa)+\kappa Q( \kappa)\Big{]}, \tag{47}\]  
ACO-OFDM dimming is achieved in a similar manner as that of its DCO-OFDM counterpart, but with a decrease of the dynamic range of the utilized power source, i.e., \([0,\gamma P_{\text{max}}]\).
## 2 Materials and Methods
### Three-Stage Resilience Level Assessment  
The evaluation results of three stages (pressure, state and response) were respectively calculated in this study on the PSR framework, shown in formula (5). The higher the evaluation result of the stress index (\(U_{pressure}\)), being faced with minor infrastructure risk and crisis; the higher the evaluation result of the state index (\(U_{state}\)), the healthier the state;the higher the evaluation result of response index (\(U_{response}\)), the timelier the response; and the healthier the infrastructure system.  
\[U_{pressure/state/response}=\sum_{j=1}^{m}\left(\omega_{j}\times r_{ij}\right), \tag{6}\]  
where, \(m\) indicates the number of indicators in each stage (i.e., pressure, state, response) and \(r_{ij}\) is the normalized value in matrix \(X\). The weighted model was the most common method for evaluating the resilience levels on the PSR due to its simple operation. Specific calculations are as follows:  
\[R=W_{p}U_{pressure}+W_{S}U_{state}+W_{R}U_{response}, \tag{7}\]  
\[W_{i}=\sum_{j=1}^{k_{j}}\omega_{j}, \tag{8}\]  
where \(k_{j}\) is the number of assessment indicators in criterion \(j\). \(W_{i}\) represents the weight of the stage \(i\) (\(W_{p}\) for pressure, \(W_{S}\) for state, and \(W_{R}\) for response), and \(R\) is the urban infrastructure resilience level based on PSR.  
Though the weighted model has been extensively used in resilience assessment, it is noteworthy that it tends to sum the evaluation results of each stage. No matter which criterion layer the evaluation index was placed on, it barely affected the final comprehensive resilience levels. Therefore, it fails to display the coordination degree of the three stages since it cannot effectively reflect the causal logic of the PSR model, and the evaluation result may mislead the judgment.  
Post-disaster resilience was one of the core factors that give prominence to the concept of disaster resilience of UI and the primary criterion for measuring resilience [26]. Previously, disaster preparedness planning focused on the prevention of destructive events. This strategy may not be sufficient to resist destructive events, especially anti-normal destructive events [52]. In practice, financial constraints make it impossible to strengthen the resilience level of the infrastructure system at all stages to resist all types of destructive events. So, When UI is under pressure, in the current state, the stronger the recovery, the higher the resilience, as shown in Equation (9). Therefore, it is evident that the larger the \(R^{*}\), the higher the resilience of the urban Infrastructure. Meanwhile, based on [PERSON] et al. [38] and [PERSON] et al. [53], the \(U_{pressure/state/response}\) and \(R^{*}\) were divided into five stages by the Non-equidistant division method in this study, as show in Table 1.  
\[R^{*}=\frac{U_{response}}{U_{pressure}+U_{state}}, \tag{9}\]
## 3 Results and Discussion
### Influence of Aspect Ratio of Width to Height  
Setting the values of the aspect ratios \(\beta\) as 1, 1.2, 1.4, 1.5, 1.6, 1.8, and 2 respectively, based on the generally available section size of the utility tunnel, the relationship curve  
Figure 5: Influence of joint stiffness ratio: (**a**) stiffness reduction coefficient and (**b**) bending moment adjustment coefficients.  
between \(\beta\) and \(\eta\) was obtained, as shown in Figure 5(a), and the relationship curves between \(\beta\) and \(\xi_{t}/\xi_{b}\) are shown in Figure 5(b). In the FE simulations performed for this investigation, an increase in \(\beta\) corresponded to an increase in the width while keeping the height of the sidewalls constant.  
It can be seen from Figure 5(a) that, when \(\beta\) increased from 1 to 2 gradually, the stiffness reduction coefficient decreased from 0.64 to 0.53, indicating that the stiffness reduction coefficient of the sidewalls decreased with the increase in the aspect ratio, in general. The main reason for this was that a larger \(\beta\) led to a smaller overall structural stiffness, leading to a smaller \(\eta\), according to Equation (1). However, it was found that the aspect ratio had less effect on the bending moment adjustment coefficients (\(\xi_{t}/\xi_{b}\)), as shown in Figure 5(b), with \(\xi_{t}\) ranging between 0.13 and 0.14 and \(\xi_{b}\) ranging between 0.09 and 0.12. The reason for this less obvious effect on \(\xi_{t}\) and \(\xi_{b}\) may have been because an increase in \(\beta\) led to a smaller overall structural stiffness and an increase in loading (due to increasing width), indicating corresponding changes in the bending moments for both the beam-spring model and the stiffness reduction model. As a result, all the bending moments at the upper and lower ends (\(M_{t}\), \(M_{t1}\), \(M_{b}\), and \(M_{b1}\)) changed in a similar way, leading to steady values for \(\xi_{t}\) and \(\xi_{b}\), according to Equations (2) and (3).
# Choroidal Vascularity Index in Adult-Onset Foveomacular Vitelliform Dystrophy: A Pilot Study
## 3 Results
#### Analysis of Choroidal Vasculature  
CVI was significantly different among groups (\(p\) = 0.004). On further analysis, the luminal area demonstrated the greatest difference between groups (\(p\) = 0.04). Box plots summarize the data distribution of the three different subgroups graphically (Figure 4). Choroidal vascular parameters are detailed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **AOFVD** & **AMD** & **Controls** & \(p\)**Value** \\ \hline
**No. of eyes** & 18 & 18 & 16 & \\
**No. of patients** & 18 & 18 & 16 & \\
**Age (years)** & 74.6 \(\pm\) 9.2 & 76.1 \(\pm\) 13 & 76.4 \(\pm\) 6.9 & 0.80 \\
**Gender (female)** & 10 (55.5\%) & 9 (50\%) & 9 (56.2\%) & 0.06 \\ \hline \hline \end{tabular} No.: number, %: percentage. AOFVD: adult-onset foveomacular vitelliform dystrophy; AMD: age-related macular degeneration.  
\end{table}
Table 1: Baseline characteristics of the overall population.  
Figure 4: Box plots for choroidal vascularity index (CVI) analysis (top, blue) and luminal component (bottom, orange) into the three different subgroups, including controls, age-related macular degeneration (AMD), and adult-onset foveomacular vitelliform dystrophy (AOFVD).  
Post hoc analysis for the relevant choroidal parameters, including CVI and the luminal area, is reported in Table 3. On post hoc analysis, eyes with AOFVD presented a higher CVI than eyes with CD (+0.03 CI 95%: \(-0.57\), \(-0.01\), \(p\) = 0.001) and controls (+0.02, CI 95%: 0.0007, 0.04, \(p\) = 0.03). The luminal component was larger in AOFVD eyes when compared to controls (+0.26, CI95%: 0.05, 0.48, \(p\) = 0.02), and in CD eyes, this was close to statistical significance (+0.20, CI95%: \(-0.002\), 0.41, \(p\) = 0.05). CT at various locations did not significantly differ among subgroups, as reported in Table 2.  
In the AOFVD group, increasing age was associated with a thinner subfoveal CT (r = \(-0.50\), \(p\) = 0.02), whereas CVI was not influenced by age (r = \(-0.33\), \(p\) = 0.17). In the AMD group, neither subfoveal CT (r = \(-0.11\), \(p\) = 0.66) nor CVI (r = \(-0.02\), \(p\) = 0.33) were influenced by age.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 6 5G Public Safety Use-Case  
As discussed, because the NGPaaS workflow is based on the concepts of build-ship-run and build-to-order, it allows for the deployment of a wide-range of platforms, services and technologies. So far, this paper focused on the Telco PaaS use case and the VNFaaS PoC. However, to demonstrate the flexibility of the NGPaaS workflow another use case has also been considered, namely the 5G Public Safety use case. This use case is briefly demonstrated herein, through the Mission Critical Push To Talk (MCPTT) PoC.  
The idea of the 5G Public Safety use case is that an end-to-end cloud native mobile network supporting the MCPTT service is provisioned on-demand, through NGPaaS. Then it can be used by firemen during an intervention at an emergency incident (e.g., a building fire). Depending on the location of the Radio Access Network (RAN), this use case could involve different scenarios. In the prototype presented herein, the fire truck is assumed to have its own, mini, data centre with the capability to host VNFs. In this way the U-Plane as well as the Cloud RAN and MCPTT related VNFs can be hosted in the fire truck, while the MCPTT control plane VNFs can be hosted in a centralised data centre. In this PoC instead of CORD, two separate Kubernetes platforms are deployed: A Core Kubernetes PaaS and an Edge Kubernetes PaaS, tailored respectively for the Core and RAN service requirements. Both Kubernetes platforms are fully deployable and configurable through the NGPaaS workflow, the same workflow used for the Telco PaaS use case. Finally, the VNFs comprising the MCPTT PoC are deployed on their corresponding platforms, similarly to that of the VNFaaS PoC. Figure 16 illustrates the architecture and functional elements of this PoC. More details are available in the technical documentation of the NGPaaS [37].
# A Compact Dual-Band Microstrip Patch Antenna for C- and X- and Ku-Band Applications +
## 2 Design of Antenna
### Step 1 Antenna  
The antenna is simulated using HFSS (high-frequency Simulation software) with an overall dimension of 16 \(\times\) 16 mm\({}^{2}\). The proposed antenna consists of a pentagonal-shaped patch with a full ground configuration. The design of the first-step antenna is shown in Figure 1a.
# Lightweight Design and Evaluation of Square Reflector
## 1 Introduction  
The rapid development of space remote sensing technology has played a significant role in alarm reconnaissance, resource detection, and meteorological monitoring. With the steady improvement in scientific and technological levels, the demand for space remote sensing payload resolution is also gradually increasing. Large-aperture optical components with wide-field imaging capabilities have become the development direction of space remote sensing payload [1; 2; 3]. The off-axis optical system has the advantages of having fewer components, no block, a long focal length, a large field of view, a wide band, and a high modulation transfer function (MTF), which has become a trend in the development of long focal length and large field of view space remote sensing payloads in the world. However, its optical components are asymmetrical and need a lightweight design, bringing considerable difficulty to the design and processing [4]. Therefore, the development of space remote sensing loads using such optical systems has become an internationally recognized challenge, especially for the development of asymmetric square optical elements with a diameter of more than 400 mm, which has more significant technical difficulties and high risks [5].  
The square mirror studied in this paper is the secondary mirror in the off-axis optical system and the farthest optical element in the system, which needs to withstand the harshest mechanical and thermal environment, and its surface shape accuracy directly affects the imaging quality of the entire remote sensing payload. As the farthest end of the system, the optical element needs to have a relatively light weight and high thermal dimensional stability when exposed to harsh thermal environments, and high stiffness is also required under harsh mechanical environments [6]. However, light weight, high stiffness, andthermal dimensional stability are usually contradictory, and a successful design seeks a balance so that its performance indicators meet the requirements.  
This paper dives deep into the selection of raw material for mirrors, the design of the diameter-to-thickness ratio of mirror blanks, the optimization of the number of supporting points of mirror blanks, and the lightweight design of mirror blanks, providing detailed rationales with the primary goal of reducing weight, increasing stiffness, and ensuring surface shape accuracy. The engineering analysis of the designed mirror components is conducted by applying finite element simulation analysis technology. The design is further optimized according to the data results, and the obtained results meet the requirements of the design index.
## 4 Discussion
### Concluding Remarks  
An operational algorithm based on the Levenberg-Marquardt multivariate optimization technique is developed for processing remote sensing data in the visible. The algorithm allows to retrieve simultaneously the concentrations of three main colour producing agents such as phytoplankton chlorophyll, suspended minerals and dissolved organic matter. These are natural water constituents frequently considered as water quality parameters. The algorithm is outfitted with the quality control module increasing the validity of retrieval results.  
Figure 8: Diurnal cycle of the phytoplankton fluorescence (0 m, relative units) recorded in the Riga Bay, May 1975 [41]; \(t\)- local time (hrs).  
The developed algorithm is neither area- nor satellite sensor- specific: indeed, the presented results explicitly indicate that SeaWiFS, MODIS and MERIS data on a variety of inland and marine coastal waters on the European and North American continent can be successfully processed by our algorithm provided with an adequate hydro-optical model. It can easily be used for processing images taken not only by any present but also future ocean colour sensors. The algorithms is intended to be utilized for processing satellite images taken over case 2 waters, for case 1 waters standard NASA and ESA algorithms are more efficient [6].  
The essential element of the algorithm is the hydro-optical model, which is water-body specific. However, several examples are given how this difficulty/impediment can be overcome in specific cases. Extensive validation experiments conducted in a variety of water bodies indicate that the algorithm is robust and efficient in the case of both marine coastal and inland/lacustrine waters.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 8 Limitations and Ethical Considerations
### Accountability for Errors  
One of the most critical ethical considerations in security systems like IoTBystander is the issue of accountability for errors. In scenarios where the system fails to detect a security threat or mistakenly classifies a harmless event as an anomaly, the consequences could be significant, potentially leading to property damage, false alarms, or missed security breaches. This is particularly concerning for high-stakes events, such as gas leaks or intrusions, where a false positive or negative could result in harm or significant loss.  
To address accountability, a redundancy mechanism is incorporated (see Section 6.2), where data from both audio and network traffic channels are cross-verified, ensuring that errors in one channel are less likely to lead to incorrect conclusions. In addition, multi-faceted approaches are considered to be effective in further mitigating this issue. First, we will ensure that the system undergoes extensive validation and testing using diverse real-world data to assess its accuracy and reliability across a wide range of events. Moreover, designing mechanisms for users to report false positives or false negatives, allowing continuous learning and improvements based on real-world feedback, is an actionable strategy. Finally, the deployment of IoTBystander in high-risk scenarios (e.g., gas leak detection) will be coupled with manual oversight or additional safeguards to ensure that critical decisions are not solely based on the automated system.
## 8 Performance Evaluation
### Tracking  
The estimated track accuracy is measured based on the RMSE in Figure 10. The RMSE value of the EKF for the target position was 0.21, whereas that of the LKF was 0.36. Based on the RMSE analysis, the EKF was more accurate than the LKF. The EKF and LKF are both methods used for dealing with 3D-radar-tracking systems. These filters aim to approximate  
Figure 8: The proposed CNN model for drone target recognition.  
the non-linear functional model of a tracking system by using analytical techniques. In both the EKF and LKF, the non-linear equations of the model are approximated using a first-order expansion. This allows the use of the KF to estimate the state of the system after linearization. The main difference between the EKF and LKF lies in how they linearize the state space model. The EKF linearizes the model with respect to the estimated track, which is continuously updated using the collected information. On the other hand, the LKF linearizes the model with respect to a nominal track that has been pre-compiled without considering the collected information. It is important to note that the accuracy of the tracking performed by the LKF is heavily dependent on the accuracy of the nominal track that is predetermined. If the nominal track is inaccurate, it can lead to filter instability and poor tracking performance.  
Both the EKF and LKF are powerful tools for approximating non-linear models in radar-tracking systems, but they have different strategies for linearizing the models and estimating the state. It is important to carefully consider the trade-offs and limitations of each filter when deciding which to use in a specific application.  
The RMSE for the target position is less than that for the bounding box size. The bounding box size is determined after cloud point clustering, which is used to extract the predicted track. In rare circumstances, the algorithm fails to extract the precise bounding box of the tracked object when noise points are not filtered from the FoV. In addition, EKF has a shorter computational time since transition matrices are not required for the calculation due to the linearization effect.  
Figure 10: Tracking accuracy results.  
Figure 9: Clustering accuracy results.
## 3 Materials and Methods
### The Normalized Difference Vegetation Index  
The normalized difference vegetation index (NDVI) is a vegetation index which is defined as the normalized difference of the spectral reflectance measurements acquired in the \"Near Infra-Red\" (\"NIR\") and \"Red\" wavelength zones. It makes it possible to discriminate the pixels, which represent the vegetation. The NDVI calculation is given as follow:  
\[NDVI=\frac{(PIR-Rouge)}{(PIR+Rouge)} \tag{1}\]  
In the present case, the \(NDVI\) for Landsat 8 were computed by the following equation:  
\[NDVI=\frac{(Band\;5-Band\;4)}{(Band\;5+Band\;4)} \tag{2}\]  
Which explain that band 4 correspond to RED and NIR correspond to band 5.  
The theoretical NDVI value varies between \(-1\) and \(1\). In practice, a surface of open water (ocean, lake, etc.) will take NDVI values close to 0, bare ground will take values of 0.1 to 0.2, whereas a dense vegetation will have values ranging from 0.5 to 0.8.