esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK.  
SCADA; wind energy; operating data; high frequency; information loss; data storage; downsampling; temporal aggregation Article
###### Abstract  
In this paper, a novel design method of circularly polarized folded transmitarray antenna (CPFTA) is presented by applying sequential rotation technology. Compared with the general design method, the novel design method can reduce the design difficulty and improve the axis ratio (AR) bandwidth significantly without adding any additional structure. To verify the proposed method, both a general CPFTA (GCPFTA) and a novel CPFTA (NCPFTA) are designed, fabricated, measured and compared. Good agreements between simulated and measured results are obtained. Thanks to the creative design, the integrated radiation and scattering control of the CPFTA is realized for the first time. The proposed NCPFTA has the advantages of broadband, high gain, planar structure, low profile, convenience in installation and low radar cross section (RCS), which has potential application in mobile satellite communication.  
high-gain antenna; low RCS; axis ratio bandwidth; beam pattern +
Footnote †: journal: Sensors
## 1 Introduction  
For successful endodontic treatment, an understanding of the complex root canal anatomy as well as surrounding root dentine architecture is essential. Although the mandibular incisors usually have a single canal (72% ([PERSON] et al., 2014) [1], 91.1% and 82.5% ([PERSON] et al., 2014) [2]), they pose the greatest difficulty in access preparation owing to the narrowness of the crown and pulp space in the meso-distal direction [3]. Furthermore, mandibular incisors often have mesial and distal curves in their apical third [3], which makes the root canal preparation in this area even more difficult.  
In terms of root canal preparation, the root canal width, dentin thickness around the root canal, and 3-dimensional (3D) direction and degree of the root canal curvature are relevant. However, previous studies on the root canal anatomy of the mandibular incisors were mainly focused on the root canal type, the number of root canals, and the shape of root canals [1,2]. These factors provide valuable information on the root canal anatomy itself. However, more clinically relevant information for root canal preparation is the narrowest and the widest root canal width, the dentin thickness, and the direction and degree of the root canal curvature. There have been no previous studies investigating the 3D direction or degree of root canal curvature of human mandibular incisors.  
Micro-computed tomography (MCT) with mathematical modeling is an accurate and non-destructive tool for studying root canal anatomy [4]; it provides detailed information on root canal dimensions, dentin thickness, and direction and degree of the root canal curvature. Additionally, image analysis software is essential to analyze the information obtained via MCT. Our previous studies used custom-made Kappa 2 software, which produced detailed 3D measurements of various aspects of canal anatomic parameters [5, 6, 7]. This method used slices of images perpendicular to the central axis of the root canal (Figure 1A), not the long axis of the tooth, which can reduce distortions of measurements in areas where a curvature is a present (Figure 1B). Our previous studies measuring the 3D root canal anatomy of the lateral incis [8], maxillary first molar [5,9], and mandibular first molar [6] confirmed the validity of this custom-made image analyzing software (Kappa 2) in coordination with MCT.  
The aim of this study was to determine the minimum and maximum root canal widths, dentin thickness, and 3D direction and degree of the root canal curvature in extracted human mandibular incisors.  
Figure 1: Slice of reconstructed image. (**A**) When sliced perpendicular to the long axis of the root canal, no distortions are produced. (**B**) When sliced perpendicular to the long axis of the tooth, distortions are produced in the images of the root canal and surrounding dentin.
## 3 Experiments, Results and Discussion
### Indoor Positioning  
All signals are attenuated and multipath becomes a permanent feature inside objects. This causes large errors in positions which were estimated using code-based positioning. As seen in Figure 5, the discrepancies in reference position exceed 100 m in a single component in environment (c).The best solution is also illustrated by way of comparison. The best solution is the custom procedure using C1 and D1 with the application of the Klobuchar ionosphere model and the troposphere model, consisting of the Hopfield zenith delay estimation and the Niell mapping function. Basic statistics of both solutions are listed in Table 3.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 4 Channel Estimation Method Based on Deep Learning
### The Super-Resolution Denoising Residual Network Model Based on OMP Algorithm
The basic structure of the residual block consists of two parts: direct mapping and residual mapping, and the output of each block can be expressed as:  
\[y^{(j)}=\mathcal{F}\Big{[}x^{(j)}\Big{(}w^{(j)}\Big{)}\Big{]}+x^{(j)} \tag{33}\]  
where \(j\) represents the number of residual blocks, \(x^{(j)}\) and \(y^{(j)}\) are the input and output of the residual blocks, and \(w^{(j)}\) is the set of parameters for these operations. Residual mapping \(\mathcal{F}\Big{[}x^{(j)}\Big{(}w^{(j)}\Big{)}\Big{]}\) usually consists of two or three convolution layers, each of which is followed by batch normalization (BN) and ReLU activation functions. The final output result can be expressed as:  
\[y_{SDRNet}=y^{(j)} \tag{34}\]  
where \(j\) represents the value of the last residual block, which needs to be converted into complex-valued channel information \(H_{SDRNet}\). We can express the estimated channel matrix \(H_{SDRNet}\) as:  
\[H_{SDRNet}=\frac{y_{SDRNet}}{X_{esti}} \tag{35}\]  
Through adequate training, SDRNet successfully introduced the concept of residual learning into the task of channel estimation. The structure of the residual block allows the network to learn the residual representation between the input and output, thereby simplifying the learning difficulty and avoiding the problem of vanishing gradients. The integration method adopted in this paper can significantly improve the denoising performance of neural networks. The loss calculation and backpropagation sections are similar to the SDNet framework proposed in the previous section and will not be elaborated on in detail here.
# Optimal Robust LQI Controller Design for Z-Source Inverters
## 2 Circuit Analysis
### Extended Model  
There exist several closed-loop techniques, either single or dual loops, to control the output voltage of a ZSI around a value, so as to achieve a near stable voltage. While the single-loop control causes fluctuation for the peak voltage across the dc-link in response to the source voltage fluctuations, as depicted in Figure 3a, an inner current loop is embedded within dual loop control systems to not only stabilize the peak voltage across the dc-link but realize a fast response as well. As depicted in Figure 3b, a similar approach can be adopted to the SF by introducing an integrator as the outer loop. The additional integral state is a requisite for the zero steady-state error and the regulated output of the system.  
Figure 3: Dual loop control of a ZSI (**a**) with PI controller and (**b**) with state feedback (SF) controller.  
Herein, because of the simplicity in measurement, voltage across the capacitors is chosen as the control variable to control the dc-link voltage. To make sure that the capacitor voltage is controlled to the desired values, as shown in Figure 3b, another state, \(x_{ext}\), is defined as the integral of the difference between the reference voltage and the capacitor voltage, as given by (12).  
\[x_{ext}=\int_{0}^{t}(v_{C}^{ref}-v_{C})d\tau. \tag{12}\]  
Furthermore, as shown in Figure 4, the disturbance vector in load \(w(t)\) is modeled as an output current source \(I_{dis}\). Therefore, the overall system equations can be modified as (13).  
\[\begin{cases}\dot{\mathbf{x}}(t)=A_{tot}\mathbf{x}(t)+B_{u}u(t)+B_{w}w(t)+B_{ ref}v_{C}^{ref}\\ \mathbf{y}(t)=\mathbf{Cx}(t)+D_{u}u(t)+D_{w}w(t)\end{cases} \tag{13}\]  
where \(C=[0,1,0,0]\), \(D_{u}=0\), \(D_{w}=0\), \(w(t)=I_{dis}(t)\) and \(u(t)=d\) while \(A_{tot}\), \(B_{u}\), \(B_{w}\) and \(B_{ref}\) are expressed by (14). The capacitor voltage is considered as the controlled output \(y(t)\) whose response has to fulfill the control requirements.  
\[A_{tot}=\left[\begin{array}{cccc}-\frac{f}{L}&\frac{2D-1}{L}&0&0\\ -\frac{2D-1}{C}&0&-\frac{1-D}{C}&0\\ 0&\frac{2(1-D)}{L_{o}}&-\frac{R_{u}}{L_{o}}&0\\ 0&-1&0&0\end{array}\right],B_{u}=\left[\begin{array}{c}\frac{2V_{C}-V_{in}}{ L_{o}}\\ \frac{1-D}{C}\\ \frac{2V_{C}-V_{in}}{L_{o}}\\ 0\end{array}\right],B_{w}=\left[\begin{array}{c}0\\ -\frac{1-D}{C}\\ 0\\ 0\end{array}\right]\text{and }B_{ref}=\left[\begin{array}{c}0\\ 0\\ 0\\ 1\end{array}\right] \tag{14}\]  
In order to investigate uncertainties that frequently occur in practice, load \(R_{o}\) and shoot-through duty-cycle at the operating point \(D\) are considered to be uncertain or time-varying parameters with known limitations as \([R_{min},R_{max}]\) and \([D_{min},D_{max}]\). This is beneficial to photovoltaic integration and EV applications of ZSIs as the maximum power point tracking of photovoltaics, charging the batteries in EVs, and bidirectional EV integration, where robust controllers are needed in order to tackle uncertainties such as parameter perturbation, unmodeled dynamics, and load variations.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 5 Code Construction and Simulation Results
### Simulation Results  
We have simulated the MO helper data scheme using Monte Carlo simulations and the statistical model for SRAM PUFs that was presented in Section 2, with \(\lambda_{1}=0.51\) and \(\lambda_{2}=0\) and average error probability \(\overline{\psi}\approx 0.15\). We plot the resulting error probability of the key reconstruction (FER) for various key rates \(R\) and number of enrollment observations \(t\) in Figure 6.  
Observe that the error probability can be reduced by decreasing the secret-key rate \(R\) and by increasing the number of enrollment observations \(t\). We choose \(\text{FER}\leq 10^{-6}\) as the target error probability and find that \(R=1/6\) is the best (highest) achievable secret-key rate that achieves the target FER. Furthermore, at least 5 enrollment observations are required to achieve the target FER with this key rate, and the corresponding helper data rate is \(R^{\text{hd}}\approx 15.5\). Another rate-enrollment pair that achieves the target FER is \(R=1/7\) for \(t=3\) enrollment observations, with corresponding helper data rate \(R^{\text{hd}}\approx 14\). Please note that the classic single enrollment scheme (corresponding to MO scheme for \(t=1\)) achieves the target FER for \(R=1/11\) or worse key rates. Furthermore, the helper data rate \(R^{\text{hd}}=11\) bits per key bit in this case. Therefore, the MO helper data scheme achieves a secret-key rate that is \(11/6\approx 1.8\) times higher than the single enrollment scheme. However, the improved secret-key rate comes at a cost since the helper data rate is \(\frac{15.5}{11}\approx 1.4\) times as high in comparison to the classic scheme.  
We conclude that a trade-off must be considered between the required enrollment time, helper data storage, and the number of SRAM cells, when selecting the parameters (enrollment observations and secret-key rate) for a given setting.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in D2D
#### 4.1.4 Interference Mitigation for Vehicular-to-Everything and NTN-Enabled D2D  
In addition to traditional D2D applications, 3 GPP has expanded support for side-link (SL) communication in advanced scenarios such as vehicular-to-everything (V2X) and NTNs, starting from Release 14 and further enhanced in Release 16 and beyond [173; 174; 2; 175]. For V2X, SL-based D2D is crucial for enabling low-latency and highly reliable communication between vehicles, particularly in high-mobility environments. However, these setups suffer from rapid link fluctuations, frequent topology changes, and cross-link interference, especially at road intersections or in multi-lane highway scenarios [175]. To address these challenges, 3 GPP specifies advanced SL control resource configurations and dynamic resource reservation schemes that allow for proactive IMI [176; 177].  
In NTN environments such as UAV-assisted or satellite-based networks D2D communication faces unique impairments, including Doppler shifts, long propagation delays, and spatial beam overlap. These factors complicate POC and scheduling due to desynchronized link behavior across altitude levels [177]. 3 GPP has initiated study items under Release 17 and 18 to explore Doppler compensation mechanisms, predictive beam steering, and adaptive link control to maintain SL reliability while minimizing interference in these dynamic topologies [178; 179].
## 2 Materials and Methods  
### Study Area  
The Beibu Gulf (107\({}^{\circ}\)57\({}^{\prime}\) E-109\({}^{\circ}\)48\({}^{\prime}\) E, 21\({}^{\circ}\)00\({}^{\prime}\) N-22\({}^{\circ}\)15\({}^{\prime}\) N, Figure 1) is a coastal region in Guangxi Province, included in the administrative region of Qinzhou, Beihai, and Fangchenggang. The study area can be roughly divided into the Qinzhou Bay, Fangcheng Bay, Dafeng Estuary Bay, Nanliu Estuary Bay, Tieshan Port Bay, and Pearl Bay.  
Located south of the Tropic of Cancer, the Beibu Gulf of Guangxi is dominated by a subtropical climate with an oceanic monsoon, exhibiting transitional characteristics from subtropical to tropical [38]. The average sea surface temperature in the Beibu Gulf of Guangxi is approximately 22.6 \({}^{\circ}\)C, with high temperatures experienced from June to August and low temperatures occurring from December to March. Rainfall is cyclical, with the wet season ranging from May to October and the dry season ranging from November to the following April. The annual average rainfall is approximately 1500 mm, and the rivers in Guangxi are mainly replenished by rainwater, particularly during the wet season and a small amount in the dry season.
# Interactions between Phase-Separated Liquids and Membrane Surfaces
## 1 Introduction  
Liquid-liquid phase separation and the formation of biomolecular condensates have recently been identified as important contributors to cellular organization. Phase-separated condensates are implicated in more and more biological structures: signaling complexes [1; 2; 3], the nucleolus [4; 5], P granules [6; 7] and nuage [8], the nuclear pore complex [9], stress granules [10], and many others [11]. As such, the body of scientific work characterizing this collection of biomolecular condensates is ever-expanding. These entities have been of scientific interest not only because of their seemingly widespread occurrence, but also because of their versatility in function: biomolecular condensates are responsive to stimuli, concentrate biomolecules with specificity, buffer protein concentrations, and nucleate larger cellular structures, among other functions [12]. Beyond this diversity of function, liquid-liquid phase separation attracts attention because it is a mechanism for the formation of so-called \"membranes organelles\" [9]. In this way it provides a principle of cellular organization distinct from traditional, membrane-bound organelles [11; 13].  
Membranes are ubiquitous in cells, and many biomolecular condensates interact with membrane surfaces. These hybrid phase-separated condensate and membrane systems appear in different forms, with different consequences for the function of the biomolecular and membrane components. As we will show in this review, they range from clusters of integral or peripheral membrane proteins in the plane of the membrane to spherical droplets wetting membrane surfaces to droplets containing small lipid vesicles.  
In this review, we consider phase-separated liquids that interact with membrane surfaces and we discuss the consequences of those interactions. The functions performed by biomolecular condensates at membrane surfaces are broad. We will first examine how membranes may be reshaped and laterally reorganized through contact with distinct liquidphases with different physical properties. Then we will turn our attention to how liquid-liquid phase separation can have a role in constructing membrane-associated protein structures and controlling their function. Finally, we will discuss the organization of collections of lipid vesicles by biomolecular condensates and how active processes and other cellular regulatory mechanisms modulate the behavior of membrane-associated condensates. Throughout, our focus will be on experiments using biomimetic membranes. However, at times discoveries made in silico or in cells or cell-derived membranes will be presented to better contextualize the state of research in this area.
## 4 Case Study
### Simulation Results
#### 4.2.4 Step-By-Step Case with the Virtual Leader (Leader-Follower Consensus Algorithm)  
This case is used to test the leader-follower consensus algorithm proposed in this paper. The communicate topology of all BESSs in Figure 3e is adopted. The consensus controller begins to work at five seconds. What is more, the same load change of each load node as the case 3 occurs at 50 s and 90 s. It is shown in Figure 8 that energy level, voltage, frequency, active power and reactive  
Figure 6: Simulation result without leader considering geographic factor (case 2).  
Figure 7: Simulation results of step-by-step consensus algorithm without the virtual leader (case 3).  
power can reach consensus in two steps just as case 3. Furthermore, frequency and voltage restore to the nominal value 1 \(p.u\) whatever load changes. It can be observed that the leader-follower consensus controller can play a part of the secondary frequency/voltage controller.
# Solar Sail-Based Mars-Synchronous Displaced Orbits for Remote Sensing Applications
## 3 Numerical Results
### Evaluation of the Mars-Spacecraft Distance
Accordingly, the angular offset between the spacecraft and Mars, defined as  
\[\phi\triangleq\theta-\
u=\omega\left(t-\overline{t}\right)-\left(\
u-\overline {\
u}\right) \tag{20}\]  
is an explicit function of \(\{\
u,\,\overline{\
u}\}\) according to Equations (15)-(18), so it changes throughout the period of revolution of Mars. However, as already shown in [27], its mean integral value with respect to \(\
u\), that is, the function  
\[\Phi\triangleq\frac{1}{2\pi}\,\int_{0}^{2\pi}\phi\,\mathrm{d}\
u\equiv\theta _{0} \tag{21}\]turns out to coincide with the angular displacement between the solar sail-based spacecraft and Mars when the latter passes the perihelion. The distance \(d\) between the spacecraft and the center of mass of Mars, given by  
\[d=\sqrt{\left(\rho_{\mathcal{G}}\,\cos\
u-\rho\,\cos\theta\right)^{2}+\left( \rho_{\mathcal{G}}\,\sin\
u-\rho\,\sin\theta\right)^{2}+H^{2}} \tag{22}\]  
is an explicit function of \(\{\rho,\,H,\,\
u,\,\overline{\
u}\}\) and is represented (normalized with respect to \(a\)) in Figure 7 for \(H=0.005\,a\). In particular, \(\rho/a\) ranges between \((1-e)\simeq 0.9066\) (which corresponds to the perihelion of Mars orbit) and the maximum value that ensures a feasible (i.e., positive) value of \(\beta\). This last condition holds if \(r\,\rho^{2}<a^{3}\); see also Equation (9). Figure 8, instead, shows the variation of \(d/a\) as a function of \(\{\
u,\,\overline{\
u}\}\) in the scenario \(A\). The corresponding figure for scenario \(B\) is substantially coincident with Figure 8 and has been omitted. Note that Figure 8 also displays the radius of Mars SOI, which is given by Equations (10) and (11) as a function of \(\
u\). Finally, Figure 9 shows the variation of \(\min_{\
u}(d/a)\) with \(\overline{\
u}\) for scenarios \(A\) and \(B\). \[t=\sqrt{\frac{a^{3}}{\mu_{\odot}}}\left(E-e\,\sin E\right) \tag{15}\]0 Variation of \(\theta_{0}\) with \(\overline{\
u}\) as given by Equation (19). \[t=\sqrt{\frac{a^{3}}{\mu_{\odot}}}\left(E-e\,\sin E\right) \tag{15}\]1 _Cont._  
\[t=\sqrt{\frac{a^{3}}{\mu_{\odot}}}\left(E-e\,\sin E\right) \tag{15}\]2 Variation of the ratio \(d/a\) with \(\{\
u,\,\overline{\
u}\}\) for scenario \(A\).