esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In any case it is important to recognize the heterogeneity and to characterize the different sites. This is commonly done by determining the binding isotherms in given concentration ranges depending on the detection or transduction technique considered. Then a model is fit to the binding isotherm to enable the estimation of the binding parameters. The most commonly used ones are the Langmuir and Langmuir-Freundlich isotherms [24; 25; 26; 27; 28; 29; 30; 31]. The binding isotherms are most often obtained by equilibrating the solid with a solution containing the substrate and determining the concentration of the target substance in solution after equilibration with various methods [32]. This approach is quite demanding in terms of amount of solid required and complexity of the experimental procedure. In the present investigation the binding isotherms of 2-FAL on a MIP prepared by non-covalent imprinting have been determined not only by batch equilibration but also by the SPR sensor based on POF. The sensing method could improve the detection limits with respect to the batch equilibration, due to the fact that the template is preconcentrated at the electrode surface when it combines with the imprinted sites. For the sake of comparison, the binding isotherms of 2-FAL on the corresponding MIP have been obtained also with a sensor based on a completely different transduction principle, i.e., electrochemistry. Electrochemical sensors are chemically modified electrodes at the surface of which the species capable of specifically interacting with the desired analyte are fixed [33; 34; 35; 36]. Various MIP based electrochemical sensors have been developed in the last years [35]. Our research in this field was focused on devices created by depositing a thin MIP layer over a screen printed electrochemical (SPC), so obtaining sensors for the determination of small molecules by different electrochemical techniques [36; 37; 38]. The MIP layer used in all these devices is easily and rapidly prepared, so it is particularly suitable to be used for rapid characterization purposes. The sensor developed in this investigation gives a voltammetric signal, generated by the substrate itself, i.e., 2-FAL, which is electroactive [39]. Sensing by different transduction methods would make it possible to examine the binding of 2-FAL to the same MIP in different concentration ranges, and to compare the results obtained. 2-FAL has been selected as a target molecule, as a proof of principle, for the development of an SPR-POF sensor for a small molecule, a kind of device which is sometimes believed to not be sensitive enough for practical application [17]. On the other hand, the development of a sensor for furanic compounds has an interest for controls in several fields, as in food technology and in biomass for energy production.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 2 Analysis, Design and Experimental Verification
### Design of the FSR
#### 2.2.2 Flexible Ultra-Wide Passband FSS  
The band-pass FSS is designed based on the relative position and bandwidth of the transmit-band of RS. According to the analysis in Section 2.1, the design of bandpass FSS should satisfy at least two requirements: firstly, it is crucial to ensure that the resonance frequency \(f_{12}\) of FSS should align with \(f_{t}\) in order to achieve optimal transmission; secondly, broadband total reflection should be achieved in the out-of-band frequency band to serve as a ground for low-band absorption design. This design approach is widely documented in the literature, such as in [17; 18; 19; 22; 23; 24; 25].  
However, when evaluating FSR structures, \(S_{21}\leq-1\) dB is typically considered the standard for high transmittance. Therefore, focusing solely on a single frequency point is insufficient. By analyzing the change trend of the \(S_{21}\) curve in Figure 3, it is evident that the high-frequency side of the curve changes more gradually near the center frequency of transmission compared to the low-frequency measurement curve. Therefore, to ensure efficient utilization of the RS transmission band while maintaining a greater FSS passband than RS, it is necessary to ensure \(f_{t_{2}}>f_{t}\) when the FSS passband perfectly covers the RS transmission band.  
Sandwich-type FSS with bilateral dielectric loading is an effective solution to achieving a wide passband. However, this method will lead to a significant increase in the thickness of FSS (\(d_{f}\)). In commonly used PCB flexible materials such as FR4, PTFE, Rogers RT5880, etc., when the thickness exceeds 0.4 mm, it will become a rigid medium. Therefore, FSS structure is faced with the contradiction between flexibility and broadband, which has also become the main factor restricting the conformity of FSR structures with a three-dimensional surface. Therefore, ensuring that the passband of FSS covers the transmission band of RS, while maximizing \(f_{t_{2}}\) over \(f_{t}\), is an effective approach to minimizing the thickness of FSS.  
According to the aforementioned concepts, the FSS structure designed is illustrated in Figure 8. The dielectric substrate also utilizes Rogers RT5880 with \(d_{f}\) = 0.127 mm, and the value of other structure parameters are shown as follows: \(l\) = 4 mm, \(l_{f}\) = 3.1 mm, and \(W_{f}\) = 1 mm. The two dielectric substrates are bonded with WL-PP280-045-50N semi-cured adhesive.  
The simulation results are depicted in Figure 9. The FSS shows good angular and polarization stability, and its passband (8.56-18.00 GHz) effectively covers that of RS (8.72-16.80 GHz) and ensures a wide wave transmittance bandwidth. Furthermore, it is evident that the FSS possesses a remarkably low thickness and exhibits flexibility, which is important to the conformal design.  
Figure 8: The structure of the FSS unit cell.
## 5 Conclusions  
To assure the successful implementation of the AWM technology, both blade loads and energy capture performance of wind turbine rotor are investigated in this paper under various combined conditions of wind speed, rotor speed, yaw angle, pitch angle, wind shear, the deflections of the blade, and its azimuth angle. From the research depicted above, the following conclusions can be drawn:  
* An appropriate definition of the flow field is critical to the computational efficiency and accuracy of CFD calculations. Through comparing the volumes of the flow field as well as the number of meshes obtained at different outlet-diameter-to-inlet-diameter ratios, it is found that a sharp cone-shaped flow field should be avoided in CFD calculations, especially when the inlet diameter of the flow field is not large enough.
* During yaw operation, the bending moments in both flapwise and edgewise directions of the blade are not only dependent on wind speed, rotor speed, yaw angle, and pitch angle but are also dependent on the matching relationship of them. For example, the bending moments usually decrease with the increase of the yaw angle. However, the bending moments may show a different tendency when wind speed is low. Particularly, when rotor speed and wind speed are mismatched, negative bending moments can be observed.
* With the aid of the orthogonal experiment design, the mathematical models of the blade loads under combined environmental and turbine control conditions are established, which are of great significance to guide the control of the hydraulic yaw brake during yaw operation.
* Wind shear will not only reduce the energy capture performance of the wind turbine rotor but will also cause fluctuation to the power produced by the wind turbine. That will challenge the power quality of the wind farm if no measures are taken.
* A blade's flapwise and edgewise deflections have different influences on the energy capture performance of the turbine rotor. Usually, the influence of the former is larger than that of the latter. However, it is interestingly found that the corresponding influence is reduced when the blade deflects simultaneously in both flapwise and edgewise directions, especially when wind speed is higher.  
Following the above research, the impact of wind turbulence on the aerodynamics and power harvesting performance of the wind turbine rotor during yaw operation will be investigated. The relevant achievements will be reported in separate papers.  
J.D.: Methodology, Supervision, Writing-Original draft preparation, Funding acquisition X.Y.: Software, Writing-Original draft preparation W.Y.: Conceptualization, Supervision, Writing-Reviewing, and Editing G.G.: Software M.L.: Software. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51675175 and the UK EPSRC, grant number EP/R021503/1.  
The authors declare no conflict of interest.
## 1 Introduction
### Literature Review of the Existing Studies  
Based on a large number of different models for estimating the temperature distribution in the PV module, a comprehensive review of existing studies was performed in this part of the study. Many static and dynamic models have been developed to estimate the temperature distribution in the PV module accurately. Since temperature distribution in the PV module is described differently for many types and designs, [PERSON] [18] provides an overview of design requirements, design analysis and test methods for flat-plate PV modules. At that time, the NOCT model was usually used for temperature assessment of the PV module. Similarly, [PERSON] [19] presents the flat-plate PV module outdoor operating temperature assessment using a simple modified form of the Hottel-Whillier-Bliss (HWB) equation. Furthermore, Sandia National Laboratories [20] proposed a new empirically-based thermal model for flat-plate PV modules mounted in an open rack. The thermal model consists of temperature calculation of the backsheet layer and PV cell. This model has proven to be adaptable and adequate for flat-plate PV modules with an accuracy of \(\pm\)5 \({}^{\circ}\)C. The results show that the temperature of the PV cell and the temperature of the backsheet layer can differ significantly, especially for different types of PV modules. In their study, [PERSON] et al. [21], review two different approaches for determining a physical model of the PV module. The first model consists of a detailed temperature description, while the second electric model, is described by an explicit equation. The second model consists of a detailed electric model for I-V curve prediction, while the NOCT model describes the operating temperature of the PV module. [PERSON] et al. [10] propose a comprehensive multi-state dynamic thermal model for temperature estimation of the PV cell. Temperatures are modeled as internal states and are corrected according to the observations, which leads to high accuracy of the proposed model and reliable prediction of PV cells temperature and output power. [PERSON] et al. [22] present a simple dynamic thermal model using non-linear optimization with four parameters. The proposed model reduces RMSE between measured and modeled PV module temperature to 1.58 \({}^{\circ}\)C on average. However, it does not consider optical losses and losses due to power generation but uses both global and diffuse solar radiation as an input parameter. In their study, [PERSON] et al. [23], focus on the 2D temperature-irradiance coupling model of the flat-plate photovoltaic/thermal (PV/T) collector, which is very similar to the described model in [21], without the energy balance equation of water flow in copper tubes. This model includes the global and diffuse solar radiation as its input and considers the change of the absorptivity, transmissivity and reflectivity at a specific incident angle, which can be calculated by [PERSON]'s law.
# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 3 Proposed Method
### Social Network Noise Simulator
This simulation retains low-frequency DCT coefficients (\(5\times 5\) region for the Y channel and \(3\times 3\) region for U,V channels) while zeroing out high-frequency components, which approximates the quantization effects of JPEG compression while maintaining differentiability. The simulation process of JPEG-Mask is illustrated in Figure 5. In the testing phase, where gradient computation is not necessary, we apply real JPEG compression to evaluate the model's real-world performance, where quality factor \(q\) determines the compression strength. We also apply Gaussian noise, with kernel width \(\sigma\) controlling the noise intensity. The resize operation, which involves downsampling with ratio \(r\) followed by upsampling to the original dimensions \(HW\), can also be considered as frequency domain distortion as it essentially acts as a low-pass filter, causing the loss of  
Figure 5: Visualization of noise attacks simulated by SNNS. The left panel shows the cover image \(I_{cov}\) and the watermarked image \(I_{wm}\). The right panel shows the effects of different noise attacks from SNNSpoolsingle, where the upper row displays the noise-attacked watermarked images \(NIwm\) and the lower row shows the corresponding residual maps \(|NI_{wm}-I_{wm}|\) for cases (**a**–**h**). While the framework implements dropout and dropout, which require cover image information, we demonstrate their effects using simpler crop and drop operations for clearer interpretation of the noise patterns. high-frequency details. This operation is necessary to ensure compatibility with our secret recovery module (SRM), which requires consistent input dimensions. In the geometric domain, we employ two distinct types of transformations. Rotation transforms with rotation angle \(\theta\) enable us to simulate different image orientations encountered in real-world scenarios. Meanwhile, affine transforms with a fixed translation ratio and scaling factor are applied with varying shearing degree \(s\), allowing us to simulate various viewing perspectives. In this experimental setup, we examine the effects of orientation and shearing variations through adjusting \(\theta\) and \(s\), respectively. To summarize, the aforementioned noise types can be organized into three main domains:  
1. Spatial Domain Distortions: cropout and dropout manipulate pixel values through selective replacement. 2. Frequency Domain Distortions: our differentiable JPEG compression, Gaussian noise, and resize operations affect the signal's frequency components through compression, noise addition, and resampling, respectively. 3. Geometric Domain Distortions: affine and rotation transforms modify the geometric structure. Each noise type is controlled by specific hyperparameters that determine the intensity of the distortion, allowing us to simulate various degrees of image manipulation encountered in real-world social media sharing scenarios. The specific parameters and intensity levels for each type of noise are detailed in Section 4.1.
## 4 Experiment Results
### Timing Comparison  
All mentioned algorithms are compared the average recognition time for test images. In addition, the recognition time is not be evaluated on the CMU-KO8 dataset, because very little emphasis is placed in scale and rotation changes and only 9 templates are used to match. Thus, the detection time is computed as an average over all the test images in D-Textureless dataset. The size of per test image is 640 \(\times\) 480 in the D-Textureless dataset. The comparison results are given in Figure 13. For a fair comparison, all the algorithms are implemented on a laptop with an Intel Core i5 processor and 4 GB memory. As OCM-DRW is predominantly a binary-based detector, it has fast matching speeds. Regarding the processing time, OCM-DRW is faster than BOLD, BORDER and SIFT and slower than LINE2D because we do not implement their cache-friendly response map.  
In the Existing system, in order to further accelerate detection speed, the parallel processing cores of the GPU hardware will be considered in the future. The major computation in OCM-DRW is the similarity measure for each location of test image. By using Compute Unified Device Architecture (CUDA), the main process of OCM-DRW can be parallelized, and the execution of program is accelerated.  
Figure 12: Some detection results are achieved by OCM-DRW, BORDER and BOLD respectively from CMU-KO8 dataset. (**a**) OCM-DRW successfully detects; (**b**) BORDER fails to detect; (**c**) BOLD also fails to detect.
# Modeling Non-Equilibrium Dynamics and Saturable Absorption Induced by Free Electron Laser Radiation
## 1 Introduction
Those results indicate that the description of photon-matter interaction at high intensities requires specific models to be devised, accounting for the various effects contributing to a modification of the X-ray and EUV absorption cross-section (relaxation of final state, ultrafast electron heating, and so on). There have been several theoretical and computational works on Al EUV and X-ray FEL intense pulse absorption. Examples range from \"dynamical\" (small cluster) [11; 12] to \"steady\" model [13; 14] approaches, including application of the SCFLY code [15]. In previous works, we have developed a simple three-channel model (involving ground, excited and relaxed states) for calculating the transmission of ultrathin metal films for increasing photon fluences using FEL radiation up to the saturation limit [16]. The model was conceived to provide a reliable and physically-intuitive calculation scheme for saturable absorption, also allowing direct inspection of the interaction dynamics including size effects of the pulse width and finite thickness of the absorbing medium at the nanometric scale. The three-channel model was found to provide simulations in excellent agreement with EUV transmission data in an extended range of pulse fluence (0-200 J/cm\({}^{2}\)) [8; 16]. Simulations allow us direct estimates of time and space profiles for the photon absorption, and a benchmark for calculating observables of interest (transmission) and the deposited energy in nanosized condensed matter for evaluation, for example, of local temperatures. The effects of a local increase of the electron temperature can also be introduced in the calculations as reported in Reference [8]. In this work, we extend previous results for the calculation of EUV transmission of ultrashort pulses through Al films of nanometric thickness. We show the details of the temporal dynamics of the photon-matter interaction at fs resolution, also including possible multi-photon absorption and electron-heating effects.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 6 Security Analysis
### Session Key Establishment  
In the D2D environment, it is of great significance to generate the shared session key between the SN and all the UEs with the intention to guarantee the data confidentiality and transmission security.  
**Theorem 3**: _Our authentication scheme is able to provide the shared session key \(\gamma\in\mathbb{Z}_{\mathcal{P}}\) between SN and all the validated UEs._  
**Proof of Theorem 3**: According to our design, the group key \(\gamma\) generated by SN is finally delivered to valid UEs in a secure way. The \(\gamma\) is adopted as the group key between SN and all the legitimate devices. Specifically, fast key updating operations can be guaranteed in the corresponding updating process. Within the updating phase, updating the related key for decryption is not necessary for the currently legitimate devices. In other words, the newly distributed key \(\gamma^{new}\) can easily be derived with the formula \(f(x)\), where \(f(\varphi_{i})=\gamma^{new}\) holds for all validated UEs. However, the updated group key does not involve the information of revoked devices. In this way, the revoked device cannot derive the new group key using the expired \(f(\varphi_{i})\). The group key can be successfully distributed and updated in our scheme.
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 3 Method
### Architecture of the Decoder  
The encoders of previous segmentation models [5; 6; 20] are often pre-trained on the ImageNet dataset. To capture high-level semantics, it is common to employ a decoder on top of the encoder. This paper aggregates features from the last three stages and utilizes the lightweight Hamburger model [9] for further modeling of the global context. The Hamburger model utilizes optimization methods to solve the matrix factorization problem, decomposes the input representation into submatrices and reconstructs the low-rank embedding. When carefully handling the gradients backpropagated by matrix factorization, the Hamburger structure with different matrix factorizations performs better than self-attention in the process of modeling the global context module. Combining the powerful convolutional attention encoder from the article, the use of a lightweight decoder contributes to improved computational efficiency, as depicted in Figure 7. Here, \(Stage_{i}\) represents the outputs from the four stages of the main path (\(i=1,2,3,4\)). The encoder part concatenates the results from the second, third and fourth stages of the main path. It further incorporates a lightweight Hamburger module for additional global context modeling.  
The concatenated features then pass through a simple convolution block (convolution, normalization, activation) before being fed into a straightforward segmentation head to obtain the final decoder output. The formulation is represented as (19) and (20):  
\[f=Ham(Cat(S_{2},S_{3},S_{4})) \tag{19}\]  
\[Out=MLP(Conv(f)) \tag{20}\]  
where \(S_{2},S_{3},S_{4}\) represent the outputs of the main path in the second, third and fourth stages, respectively. Here, we do not aggregate the features of stage 1 as well, because the features of stage 1 contain too much low-level information, and the information fusion of the next three stages is necessary. The operation \(Cat\) denotes concatenation; \(Ham\) represents the lightweight Hamburger module; \(Conv\) is a convolution block comprising convolution, normalization and  
Figure 6: Fusion diagram of the main path and branch path.  
activation; and _MLP_ signifies the fully connected layer of the segmentation head. With these components, we obtain the final output of MCAG.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 3 Methods  
### WIST Algorithm  
The WIST algorithm includes three steps. First, cloud-free satellite observations are smoothed and gap-filled using a local moving Savitzky-Golay (SG) filter. This step generates a daily NDVI time-series. Second, a local Moving Average Convergence Divergence (MACD) approach [35] is used to detect the downward trend of the NDVI time-series and find the senescence and dormancy onsets (senescent period). In general vegetation types, this approach defines the senescence onset as the date at the beginning of the downtrend and the dormancy onset as the date at the end of the downtrend (or end of the season). In cover crops, this inflection is associated with termination or harvest rather than dormancy. The last step is to identify the termination date as the midpoint between the two adjacent imagery dates that show the most-rapid rate of decrease during the senescent period, using the originally unsmoothed observations.