esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analysis
### Analysis of Simulation Results  
The study of the findings in this section is divided into two parts. The first part is to validate the presence as outlined in this research report. This research evaluates the system in relation to the current literature, specifically focusing on two key aspects: the access rate of VUEs and the throughput. The subsequent section examines the impact of parameter modifications on the system model's throughput for VUEs, as indicated by the graphical representation of the results.
# Numerical Technique for Study of Noise Grating Dynamics in Holographic Photopolymers
## 5 Conclusions  
The results show that the development of both intended holographic signal and unwanted noise gratings in holographic photopolymer can be studied by numerically solving the coupled physics of optical diffraction, reaction, diffusion and index formation. The technique is demonstrated to accurately reproduce a closed-form solution to the two-beam recording condition including the shape and amplitude of the index grating as well as the Bragg selectivity curve. Numerical studies of signal and haze development reveal the dynamics of haze formation as a function of experimental parameters. Integrated haze is found to depend linearly on initial scatter strength and super-linearly on sample thickness. Haze growth is shown to be largely suppressed for diffusion-limited recording. This condition can be achieved via a number of experimental routes including high intensity, short exposures. The required intensity to record in the diffusion-limited regime can be found from the unitless parameter \(R_{m}\), originally defined in [12], which is inversely proportional to intensity. In summary, the scaling relationships revealed by these numerical studies should provide routes for materials and process design to maximize SNR in demanding applications.  
Figure 11: Haze growth above the initial scatter as a function of normalized exposure time \(\tau\) and sample thickness, z. On the left, scattering centers are distributed randomly throughout the volume of the polymer, while on the right the same number of scattering sites are exclusively on the incident substrate. Data points produced by the numerical method are connected by cubic polynomial fits as visual guides. The transmission hologram has period \(\Lambda=1\) μm and initial scatter is set to 0.5%. As in previous figures, the material is 10 wt% bisphenol A diacrylate in polyurethane.  
**Funding:** The author would like to thank Facebook Reality Labs for support of this work.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Results
### RRT Medium-Term Effects
#### 3.3.2 Pre-Follow-Up Effects on Secondary Outcomes  
Reading speed of all verbal materials was still improved 3 months after the end of the interventions, as showed by significant Phase main effects (short non-words: F(1,31) = 32.9; \(p\) < 0.001; \(\eta^{2}\) = 0.13; long non-words: F(1,31) = 23.60; \(p\) < 0.001; \(\eta^{2}\) = 0.08; high-frequency short words: F(1,31) = 28.78; \(p\) < 0.001; \(\eta^{2}\) = 0.09; high-frequency long words: F(1,31) = 12.73; \(p\) = 0.001; \(\eta^{2}\) = 0.04; low-frequency short words: F(1,31) = 16.94; \(p\) < 0.001; \(\eta^{2}\) = 0.06; low-frequency long words: F(1,31) = 11.08; \(p\) = 0.002; \(\eta^{2}\) = 0.03). No significant Phase \(\times\) Condition effect was found on any reading speed test (ps ranging from 0.22 to 0.72).  
Figure 1: Reading speed z-scores under the three conditions (i.e., Visual cue, Rhythm, No intervention) at each timepoint (i.e., Pre, Post, Follow-up).  
Non-significant pre-follow-up accuracy changes were found in any reading test (ps ranging from 0.06 to 0.96).  
Pre-follow-up comparisons were significant for RAN (Phase: F(1,31) = 4.16; \(p\) = 0.050; \(\eta^{2}\) = 0.03), with no difference between RRT conditions (Phase \(\times\) Condition: F(1,31) = 0.27; \(p\) = 0.607).  
Similarly, phonemic elision was improved 3 months after intervention (Phase: F(1,31) = 6.65; \(p\) = 0.015; \(\eta^{2}\) = 0.04) and no difference between conditions emerged (Phase \(\times\) Condition: F(1,31) = 0.02; \(p\) = 0.884).  
Finally, Rhythm reproduction was significantly higher at follow-up (Phase: F(1,28) = 14.64; \(p\) < 0.001; \(\eta^{2}\) = 0.04) in both RRT conditions (Phase \(\times\) Condition: F(1,28) = 1.81; \(p\) = 0.190).  
Descriptive statistics of pre, post, and follow-up measures are reported in Table S1.
## 2 Materials and Methods
To allow comparison with the existing studies, we also created an _\"alternative\"_ model, which did not fit the data so well as the \"calibrated\" model but retained theoretically relevant variables such as season, NDVI, and the length of ecotone boundaries. Finally, we compared these models, using AICc and pseudo-R\({}^{2}\), against a _\"null\"_ model that contains a constant term only. The _null_ model provides a baseline scenario where the relationships between explanatory variables and response variables are random. Furthermore, we applied generalized additive models (GAM) to examine which factors have nonlinear interactions with the number of question ticks [60; 61]. While the negative binomial models are effective at testing the impacts of environmental predictors on the numbers of question blacklegged ticks, they assume linearity. However, the general pattern visualizations showed signs of nonlinearity. Therefore, we applied GAM, with the same negative binomial distribution and log link function, to explore this nonlinearity. GAM is a type of non-linear regression technique, which is essentially a penalized GLM with a smoothing basis and especially useful for \"wiggly\" data that are common in environmental and ecological applications. GAM commonly applies high-order polynomial smoothing functions to predictors to smooth and fit complex patterns. Together, these methods offer a comprehensive depiction of the complex relationships between the quantity of question blacklegged ticks and the micro-scale environmental factors at those four parks. They are particularly valuable at exploring multicollinearity, nonlinearity, and thresholds from different perspectives.
## 3 Numerical Scheme
### Energy Analysis
#### 3.5.4 Non-Negativity of the Numerical Energy and Stability Condition  
The stability of this scheme now reduces to a condition on the non-negativity of \(\mathrm{H}^{n}=\mathrm{H}_{w}^{n}+\mathrm{H}_{u}^{n}\) at all times. For the vertical polarisation, as \(\mathbf{\Phi}^{n}\geqslant 0\) by construction, it is straightforward to see from Equation (59c) that \(\mathrm{H}_{\Phi}^{n}\geqslant 0\); the modelling of collision interactions does not have an effect on numerical stability. In the horizontal direction, \(\mathrm{H}_{u,FB}^{n}\geqslant 0\) follows directly from Equation (63c); the energy of the finger and bow is unconditionally strictly positive, thanks to the use of the averaging operator in Equation (57).  
\(\mathrm{H}^{n}\geqslant 0\) is then equivalent to the isolated string energy being non-negative, that is \(\mathrm{H}_{w,s}^{n}+\mathrm{H}_{u,s}^{n}\geqslant 0\). This is only ensured at all times if both \(\mathrm{H}_{w,s}^{n}\geqslant 0\) and \(\mathrm{H}_{u,s}^{n}\geqslant 0\), which are verified for this particular scheme under the same condition, linking the time step \(k\) and grid spacing \(h\)[18]:  
\[h\geqslant\sqrt{\frac{1}{2}\left(\frac{Tk^{2}}{\rho}+2\lambda_{2}k+\sqrt{ \left(\frac{Tk^{2}}{\rho}+2\lambda_{2}k\right)^{2}+16k^{2}\frac{El_{0}}{\rho}} \right)} \tag{67}\]
## 3 Methodology
### DESTformer Architecture
#### 3.2.1 Frequency Decomposition  
Compared to directly extracting seasonal and trend features on the original sequence (e.g., COST [29]), the approach of first decomposing and then extracting targeted features can effectively reduce interference from other features. It has been widely utilized in various data-denoising and pattern-filtering tasks [30; 31]. In long-term forecasting problems, time series decomposition can learn complex temporal representations. Unlike traditional methods that obtain trend components through fixed window moving averages, a new time series decomposition method is used that maps the sequence to the frequency domain and takes high frequencies as seasonal components and low frequencies as trend components. Compared to traditional decomposition methods, the effect of the frequency domain transform is more obvious [30; 31]. It effectively avoids the overall impact of outliers' on-trend components in traditional methods:  
\[\hat{X}_{S},X_{T}=\mathcal{F}^{-1}(\mathcal{F}(X)[:\xi],\mathcal{F}(X)[\xi:]), \tag{1}\]  
Figure 1: DESTformer architecture. The encoder combines traditional STL decomposition ideas to achieve separation and modeling of seasonal and trend components through representation learning of the original sequence. The decoder adopts an innovative frequency domain decomposition and representation learning method to further optimize and enhance the seasonal–trend representation.  
where \(\mathcal{F}\) denotes the FFT and \(\mathcal{F}^{-1}\) is its inverse, \(\mathcal{F}(X)\in\mathbb{R}^{I\times K}\), where \(I=\lfloor T_{x}/2\rfloor+1\) and \(\xi=3\). At this point, the seasonal component \(\hat{X}_{S}\) contains a large amount of noise. In long-term forecasting tasks, the presence of noise often reduces the generalization ability of the model. Therefore, further denoising of the seasonal component is performed by selecting the Top-K frequencies corresponding to the amplitude to obtain the final seasonal component \(X_{S}\).
## 4 Conclusions  
In this work, a novel approach for the inverse design of metasurface unit cells aimed at achieving RCS reduction was proposed. Instead of relying on a matrix or pixel-based representation, we modelled the unit cells using a limited set of parameters combined with rotational symmetry. This strategy allowed us to achieve a broader range of structural designs without the requirement for extensive data. Additionally, by optimizing the phase difference between the parameterized unit cells using an SA algorithm, we successfully achieved broadband RCS reduction. The optimized unit cell pair demonstrated a 10 dB reduction in RCS over the 7.6-15.5 GHz frequency range with excellent angular stability. The adaptability of this method enables optimization across arbitrary frequency bands by adjusting the substrate thickness and unit cell period. While the method's computational cost and fabrication constraints present challenges, the approach can be extended to design other types of metasurfaces, such as PGMs and PVMs, by incorporating different objective functions and rotational symmetry strategies.  
Conceptualization, H.X.; methodology, H.X.; software, H.X. writing--original draft preparation, H.X.; validation, X.L.; formal analysis, B.J.; investigation, P.S.; data curation, Z.C.; supervision, N.X.; writing--review and editing, S.P.; All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Science and Technology Major Project, grant number 2021 ZD0114600; the Fundamental Research Funds for the Central Universities, grant number 2024-LXX-B1-01; the National Natural Science Foundation of China, grant number 62104174; and partly by the National Innovation & Entrepreneurship Training Program for College Students, grant number 20240100435.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: 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.
## 2 Related Work
### Agent Mobility  
A major concern with trust and reputation systems is their lack of interoperability across applications, known as the issue of the mobility of agents. When entities move between different applications, their trust and reputation data do not follow them; instead, this information remains isolated, requiring entities to rebuild their reputation in each new application. Enabling the transfer of trust and reputation data across applications remains an open challenge.  
To address this issue, [PERSON] [5] proposed a general framework to enable the exchange of trust and reputation information. This framework establishes a set of messages and a protocol that allows trust and reputation systems to request ratings, provide responses, and communicate errors. Additionally, to streamline message creation, they developed a grammar for generating queries from strings and introduced a procedure for parsing these query messages.  
In [2], [PERSON] et al. proposed a hybrid trust management framework which enables trust evaluation between entities using both centralized and distributed approaches, specifically addressing agent mobility. The network is divided into multiple sub-networks to improve management efficiency and scalability. Each sub-network contains a resource-rich fog node, known as the kingpin node, responsible for sharing service provider reputation scores upon request. For global reputation assessment, kingpin nodes aggregate reputation scores and forward them to the cloud. When a service provider moves to a different sub-network, it can present the ID of the kingpin from its previous sub-network. The new kingpin can then retrieve the service provider's reputation from the cloud using this ID.  
In the CA approach, however, a service provider retains all trust-related information concerning itself in the form of stored connections. This allows the service provider to directly access its own trust data and manage mobility by calculating its trustworthiness as a global reputation.
# Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility
## 3 GNSS Receiver Antenna Calibration Facility  
A calibration system can be divided into two main blocks, namely, hardware and software. The main hardware components include an industrial robot, external oscillator, antennas and receivers, robot mounting set, and a personal computer with appropriate wiring. The software consists of the robot motion and control software, data collection software, and PCC modeling software.  
The system design of the proposed method for the absolute field GNSS antenna calibration is explained in Figure 3. On the short baseline (-5 m), the reference antenna and test antenna (mounted on the robot) are connected to the receivers and linked to the external frequency standard. Connecting to an external frequency standard is crucial for the reduction of individual receiver clock errors. A PC is used to acquire raw GNSS data and keep control of the robot (initialization and control robot positioner). A robot control device (RCD) is used to control the robot movements, where the two main parts are the PLC and control unit (CU).  
Figure 2: Robot vs. GNSS time synchronization.  
The antenna that is to be calibrated is mounted on the robot arm and then tilted and rotated around a fixed point The optimal duration of a single robot arm movement appropriate to obtain two successive positions is 1-5 s. A full set of such movements will result in precise GNSS measurements covering the entire antenna hemisphere.
## 4 Materials and Methods
### LC-ESI-LTQ-MS\({}^{n}\) Qualitative Analyses  
LC-MS\({}^{n}\) analyses were performed using a Surveyor HPLC system coupled to a linear ion-trap mass spectrometer (Linear Trap Quadropole [LTQ], Thermo Fisher Scientific, Bremen, Germany). HPLC separation was performed at 45 \({}^{\circ}\)C on a Discovery C18 column, 250 \(\times\) 4.6-mm i. d., 5-um particle size, equipped with a Discovery C18 20 \(\times\) 4 mm i. d. security guard cartridge (Supelco Inc., Bellefonte, PA, USA). H\({}_{2}\)O containing 0.1% formic acid (solvent A) and ACN containing 0.1% formic acid (solvent B) were used for chromatographic separation [6]. The following elution program was adopted: 0-17 minfrom 35%:65% (A:B, \(v/v\)) to 5%:95%, 17-22 min from 5%:95% to 5%:95%, and 22-24 min from 5%:95% to 35%:65%. The flow rates were 1 mL/min in the column and 200 \(\mu\)L/min in the electrospray ionization (ESI) source (split ratio 4:1). Positive electrospray ionization, ESI(+), was chosen for the detection of CBD, THC, and CBN; instead, ESI(\(-\)) was employed for acidic form, CBDA, and THCA. The LTQ mass spectrometer was calibrated according to the manufacturer's instructions using a solution of sodium dodecyl sulfate (\(m/z\) 265) and sodium tautocholate (\(m/z\) 514). The source voltage was set at 4.60 kV, the heated capillary temperature was set at 350 \({}^{\circ}\)C and the applied voltage was set at \(-\)28 V. The sheath gas (N\({}_{2}\)) flow rate was 80 arbitrary units (a.u.) and the auxiliary gas was set to zero. Full-scan MS experiments were performed in the linear ion trap in the \(m/z\) range 100-1000/MS\({}^{n}\) experiments were performed by selecting the precursor ion of interest and subjecting it to collision-induced dissociation (CID) in the linear ion trap. Helium was used as a collisional gas and the collision energy was selected according to the stability (typically 20-50 eV). Identification was based on retention time comparison and fragments match (\(m/z\) and intensity). HPLC-MS data were acquired in full scan mode and then elaborated to obtain the chromatographic profile of the ions of interest, with specific \(m/z\) values. The data acquisition was carried out with the Xcalibur package (version 2.0.7 Thermo Electron). Raw chromatographic data were imported, processed, and plotted by SigmaPlot. 12.5 (Systat Software, Inc., London, UK).
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 7 Conclusions  
In this paper, a structure damage detection method based on one-dimensional convolutional neural network is proposed. The interlayer displacement of each node is collected under different damage states. The data are directly inputted into the proposed convolutional neural network, the data features are learned autonomously, and the damage detection result of the frame structure is outputted. Sections 3 and 4 both verify the effect of the structural damage location detection. Section 5 verifies the effect of the damage degree detection. The results showed that the proposed method has high detection accuracy in all three types of damage detection. Furthermore, the method does not require feature extraction. The proposed method allows high-precision identification for various types of damage to building structures. However, it is difficult to build accurate finite element models in practical inspection. Therefore, further validation of the effect of the rough finite element model on damage identification is required. In addition, displacement is used as the input to the network in this paper. For large infrastructures where displacement is not easily detected, acceleration-based damage identification methods can be further considered. In the last section, the convolutional kernel and the outputs of some hidden layers are visualized to understand more intuitively how the convolutional neural networks work. Although the findings gained by studying convolutional neural networks are still unclear, visualization is the first step in understanding convolutional neural networks. In the future, the further research of the hidden layers of convolutional neural networks is required.  
The work presented here was carried out in collaboration between all authors. [PERSON] contributed the conception of the proposed structure damage detection method. [PERSON] designed the three-layer framework model. [PERSON] and [PERSON] analyzed the test data and prepared the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Plan Project of State Administration of Market Supervision (No. 2021 MK044), the Basic Science (Natural Science) Project of Colleges and Universities in Jiangsu Province (No. 21 KJB470031), and Science and Technology Plan Project of Jiangsu Market Supervision Administration (No. KJ196043).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Count**} & \multirow{2}{*}{**Actual Label**} & \multicolumn{4}{c}{**Predicted Probability**} \\ \cline{3-6}  & & **c0** & **c1** & **c6** & **c11** \\ \hline
1 & c0 & 0.999811351 & 0.000161445 & 0.000027148 & 0.00000002 \\
2 & c0 & 0.998547852 & 0.001
# DRUNet: A Method for Infrared Point Target Detection
## 2 Related Work
### Object Detection by Keypoint Estimation  
To detect small IR targets, the prediction head uses a keypoint detection mechanism. There are two main ideas for the anchor-free model based on keypoints: one is grouping keypoints at a specific location, and the other is combining the central keypoints for regression prediction. The anchor-free model based on keypoint grouping detects specific keypoints from the feature map and generates high-quality detection frames based on matching keypoints, reducing the problems of feature mismatch and computational redundancy of predefined anchor frames. Such models include CornerNet [22] based on corner points and its optimized version CornerNet-Lite [23], ExtremeNet [24] based on extreme points, and so on. The anchor-free model that combines the central keypoints outputs the probability of it being the target centroid, the regression target scale, and the offset at each position of the feature map for the prediction of the edges. CenterNet [25] and series work are two examples of such models. To solve the problem of CornerNet, ExtremeNet relies on the postprocessing step of grouping keypoints, which reduces the detection speed. CenterNet provides a more concise idea of extracting the centroids of each target without grouping multiple keypoints. Similar to the anchor-based algorithm, CenterNet analogizes the centroid to a single anchor of an unknown shape. Because this anchor is only position related, there is no overlapping of anchors and no need to set artificial thresholds to distinguish between foreground and background. In terms of the design of the detection process, CenterNet does not postprocess the grouping of keypoints, thus greatly reducing the number of network parameters and computational effort.