esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Algorithm Efficiency Improvement
#### 2.3.2 OpenMP and GPU Collaborative Acceleration Strategy  
To address the issue of CPU and GPU collaborative parallel computation, this paper proposes using a fork/join model to implement the collaborative parallel computation of STSU-SAR-SIFT. First, during the construction of the feature pyramid, the CPU uses OpenMP to allocate tasks for each pyramid layer to sub-threads, while the GPU performs parallel computation of feature scales using CUDA, as shown in Figure 5. Once the feature pyramid is built, OpenMP assigns the tasks of feature point search and the calculation of the principal orientation of each layer's features to sub-threads. Subsequently, the parallel task of constructing feature descriptors is allocated to sub-threads.  
The main concurrency model of OpenMP is the fork/join model, which includes a master thread and worker threads. In the master thread, parallel regions can be specified,  
Figure 4: Schematic diagram of overlapping regions among different images.  
Figure 5: A parallel computing model for accelerated algorithms.  
and the number of worker threads can be allocated. Each thread is mapped to a scalar processing core via multiple processors, and multiple scalar threads can execute tasks in parallel. The pyramid constructed by SAR-SIFT differs from the SIFT pyramid. In the SAR-SIFT algorithm, the original image is used as the base image, and an eight-layer pyramid is constructed by gradually increasing the Gaussian blur at a certain ratio. During thread compilation, tasks are divided into eight independent subtasks within parallel regions. Each subtask involves the following operations: as shown in the OpenMP section of Figure 5, performing Gaussian blurring; computing gradient magnitude and direction; computing pixel correlation matrices; and calculating the SAR-Harris response function for the current layer. Since the GPU is better suited for image processing, using the GPU to compute the response functions for each pyramid layer accelerates the computation process. As shown in the GPU section of Figure 5, the feature scales are computed using CUDA. All eight layers of the pyramid are first loaded into GPU memory, and the results of the response functions are finally returned to memory, thus, avoiding the complexity of frequent data transfers between memory and GPU memory during image processing.
## 2 Related Works
### Inductive Methods  
Inductive methods use location techniques based on RSS profiles. This technique consists of building a map according to the signal strength behavior with respect to the coverage area. A sensor location can be estimated with the gathered information from several base stations. These access points or base stations work with the signal strength vector. The vector is obtained from the RSS model and probabilistic techniques or various methods based on neighbors. With this information the system can estimate the possible area where the sensor could be located.  
This method uses two parameters: a) the likelihood that an object is in this area and b) the precision of the signal strength. This second parameter depends on, for example, the size and the type of the location area. With these types of systems, the final user does not require any additional hardware for the localization process. These algorithms give very low localization errors using the IEEE 802.11 technology [21]. According to reference [21], we can consider three different techniques used in algorithms based on patterns or areas: a) single point adaptation, b) likelihood based on areas and c) Bayesian networks.  
There are statistical models based on the signal strength, where the distance between different sensors is obtained by the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements (see reference [14] for more details). This is a useful tool to help system designers and researchers select measurement technologies and evaluate localization algorithms.  
Reference [22] shows a localization system based on the RSS, which considerably reduces the number of broadcasting stations. This system is called Location Estimation Assisted by Stationary Emitters (LEASE) for indoor RF wireless networks and uses a few stationary emitters and sniffers in a novel way to solve the location estimation problem. The estimation engine uses non-parametric modeling techniques that automatically capture the anisotropy of the RSS encountered in indoor environments.  
[PERSON] _et al_. present in [23] another work where we can see the use of Bayesian networks. In this paper, the authors use a robot to retain samples. These samples are used to know the location by means of a probability density function. Each time the robot moves or senses the signal strength of an AP, a Bayes filter is used to recursively update the belief function (Monte Carlo localization (MCL) algorithm). Their results show that accurate localization (\(\sim\) 2 m) is achieved in most test cases and the average localization error decreases with time.  
Another important inductive location system is LANDMARC (see reference [5]). It is a localization prototype that uses RFID technology to locate objects inside the buildings. Although RFID is not designed for indoor location, the authors demonstrate that active RFID is a viable and cost-effective candidate for indoor location. References [4,13,20,21] can be consulted to examine some inductive methods in depth.
# A Phase Error Correction System for Bioimpedance Measurement Circuits
## 2 Methods
### Phase Error Analysis
Subsequently, phase differences between Vsense and the reference sinusoidal voltage (V\({}_{\text{in}}\)) are compared using a Type II phase detector. Phase differences provide a pulse that tunes the phase synchroniser via a microcontroller until an equivalent phase delay is added to the I and Q demodulation signals. This step causes the phase detector pulse to become low and remain low until another phase difference occurs. Vmeas is now multiplied with demodulation signals after phase correction, and low-pass filtered to eliminate AC components. The resultant DC voltages are further analysed after the low-pass filter to obtain real and imaginary components of the impedance and express them in polar form according to Equations (10) and (11).
## 3 Results and Discussions  
### Structure Characterization  
The morphology of the thin film was observed using field-emission scanning electron microscopy (FE-SEM; JEOL JSM-7600F). The cross-sectional FE-SEM image of the CaF\({}_{2}\) film on glass are clearly shown in Figure 1a. The image showed the information about the ARC thickness, its uniformity and the interaction between the coating and substrate. X-ray diffraction (XRD) patterns were recorded in a 20 range of 20-70 degrees using an X-ray diffractometer (Bruker, D8 Advance, Turbo X-ray source 18 KW). The typical XRD pattern for the CaF\({}_{2}\) thin film on glass are presented in Figure 1b. It shows that the CaF\({}_{2}\) film is polycrystalline and has a strongest peak of 111, which is in good agreement with the [PERSON] and Coogan result [26]. The subsequent diffraction lines at 49.6 and 55.6 were identified as 220 and 311 reflexes of the CaF\({}_{2}\) phase, respectively [27].
## 3 Research Environment and Plan of Experiment
### Flight Plan and Its Realization for Estimating Vertical Gusts
A highly hazardous downdraft developed, with the vertical gust speed increasing to 9.6 m/s (1890 ft/min), then decreasing  
Figure 23: (**A**) Altitude, (**B**) IAS, and (**C**) VVI recorded during simulated flight—descent phase. Figure 24: Registered ideal gusts measurements from the simulation weather section—descent phase; (**A**)—wind speed as a function of time, (**B**)—wind speed as a function of altitude. to zero by the 70 th second of the simulation. The speed of vertical gust increased until it reached 9.6 m/s--(1890 ft/min) and then decreased to zero in the 70 th second of the simulation. The microburst phenomenon poses a significant threat to flight safety due to the combination of a downdraft and the shift from a headwind to a tailwind. The occurrence of such sudden and varied atmospheric gusts caused significant changes in flight parameters (Figure 25). Returning to the pre-microburst condition required a significant amount of time. The automatic control systems restored the flight parameters to the values set before the microburst occurred, 30 s after the gusts had ceased. The third microsquall simulation with flight parameter recording, was conducted during a climb in LVL CHG autopilot mode. After initiating a climb from an altitude of 8000 ft (2439 m) and stabilizing flight parameters, a microburst was generated from the instructor station at an altitude of 10,000 ft (3048 m), 68 s after the simulation began (Figure 27). In this case, the downdraft reached a maximum of 6.1 m/s (1200 ft/min), while the frontal and then rear gusts reached 25 m/s. The observed lateral gust was minor, with a maximum value of 2 m/s, and shifted direction during its occurrence (Figure 28). The microsquall occurred between 68 and 96 s of the simulation; during this time, the aircraft was climbing from 3048 m to 3400 m. Once the wind subsided, the aircraft completed its climb to an altitude of 3658 m (12,000 ft) and stabilized in level flight. Figure 26: Registered ideal gusts measurements from the simulation weather section—level flight; wind speed as a function of time. Figure 25: (**A**) Altitude, (**B**) IAS, and (**C**) VVI recorded during simulated level flight. Given the steady increase in altitude and climb rate, it may seem that a microsquall does not pose a significant threat during this phase of flight. However, it is important to highlight the substantial decrease in IAS speed--drop of 42 m/s from 128.6 m/s to 86.6 m/s. By comparison, in level flight, the IAS decreased by nearly 20 m/s to 108.7 m/s, despite the slightly weaker frontal gust (Ug = 22 m/s versus 25 m/s during climb). This sharp drop in IAS during climb, especially when occurring shortly after takeoff at lower airspeeds, can be critical. A loss of this magnitude may lead to an aerodynamic stall, posing a serious risk to flight safety.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 2 Background and Motivation
### What's the Roles of Bioinspired Algorithms in FANETs?
The rest of the group follows their lead to keep up with the alpha, beta, and delta wolves. Each wolf will be considered as a solution to the optimization issues. As an alpha solution, it is considered the best one. CAVDO [22] utilized the dragon fly algorithm's feature extraction to resolve the problem of clustering and selecting the best CH. The fundamental drawback of artificial intelligence approaches is that they require a large amount of computer power, even though they generate better outcomes. It takes a long time for them to get to an ideal solution. A vast number of random selections and random population sizes slow down the convergence of these methods. Due to the high mobility of UAVs, artificial intelligence approaches take too long to generate a correct result. Hence, they cannot be employed alone for a changing network architecture due to their low processing capacity. In Tables 1 and 2 various techniques have been compared using different metrics. Energy-constrained networks are not that suited for computationally intensive methods. So, our aim is to enhance the bio-inspired algorithm to perform efficiently and with less energy consumption in the FANET environment. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Protocol** & **Year** & **Network Type** & **Cluster Method** & **Complexity** & **No of CH\({}^{\star}\)** & **No of Nodes in Cluster** & **Mobility** & **Energy Efficiency** \\ \hline LEACH & 2000 & Homogenous & Distributed & Low & Uncertain & Unforeseeable & Inactive & Yes \\ LEACH+C & 2002 & heterogenous & Centralized & Low & Certain & Unforeseeable & Inactive & Yes \\ \hline CRLADSR & 2012 & Heterogenous & Distributed & High & Uncertain & Unforeseeable & Inactive & Yes \\ \hline CACONET & 2016 & Homogenous/heterogenous & Centralized & High & Uncertain & Unforeseeable & Inactive & Yes \\ \hline PSONET & 2011 & Homogenous/heterogenous & Centralized & Very high & Uncertain & Unforeseeable & Inactive & Yes \\ \hline GWCONET & 2014 & Homogenous/heterogenous & Centralized & Very high & Uncertain & Unforeseeable & Inactive & Yes \\ \hline CAVDO & 2018 & Heterogenous & Distributed & Medium & Uncertain & Unforeseeable & Inactive & Yes \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of various algorithms. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline
## 6 Summary and Future Prospects  
This paper introduces VRIS, an image steganography model capable of simultaneously deceiving both the human visual system and machines. By replacing the generator role in Generative Adversarial Networks (GANs) with an autoencoder, VRIS achieves the steganography of secret images. Random Gaussian noise is added to the encrypted image to ensure that unauthorized users cannot access the specific information of the secret image, thereby preserving its security and concealment. Experimental validation of the misclassification rates of the encrypted images generated by VRIS on InceptionV3 and ResNet50 demonstrates its strong security and robustness, particularly against various attacks, including those based on deep learning models.  
However, as the noise factor increases, slight color differences emerge between the reconstructed secret image and the original, potentially compromising the integrity and accuracy of the secret information, particularly in applications where image quality is stringently required. Therefore, future research should focus on reducing the noise introduced during embedding through noise suppression techniques or enhancing the quality and accuracy of the secretly reconstructed image by optimizing the embedding algorithm. Furthermore, considering the various interference factors that may exist in practical application scenarios, deep learning methods can be explored to improve the robustness of the embedding algorithm, enabling effective embedding and extraction of secret images in diverse environments.  
Given that the current VRIS model primarily focuses on image steganography, future endeavors can explore extending the model to multimodal data such as video and audio to cater to diverse application needs. This necessitates addressing the unique chal  
Figure 9: Histogram comparison between secret image and reconstructed secret image.  
lenges of multimodal data, including synchronization and continuity while maintaining high concealment.  
Conceptualization: F.Z.; software: F.Z.; formal analysis: F.Z.; investigation: F.Z.; data curation: F.Z.; writing--original draft preparation: F.Z.; writing--review and editing: [PERSON] and H.S.; visualization: F.Z.; supervision: [PERSON] and H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Jilin Province Science and Technology Development Plan Project--Youth Growth Science and Technology Plan Project (20220508038 RC), New Generation Information Technology Innovation Project of China University Industry, University and Research Innovation Fund (2022 IT096), Jilin Province Innovation and Entrepreneurship Talent Project (2023 QN31), Natural Science Foundation of Jilin Province (No. YDZJ202301 ZYTS157, 20240601034 RC, 20240304097 SF), Innovation Project of Jilin Provincial Development and Reform Commission (2021C038-7).  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
## 3 Methodology
### POD-MCNN Model
#### 3.3.1 POD Extraction of Physical Field Features  
The parameter space \(C^{(i)}\) is obtained by the Latin hypercube sampling of each process parameter. The temperature and pressure field data are \(X=[x_{1},x_{2},\cdots,x_{r}]\in\mathbb{R}^{n\times r}\), where n is the number of nodes in the computational domain and r is the number of samples in the parameter space. The flow field data are high-dimensional, so it is unrealistic to directly map the input parameters \(C^{(i)}\) to the flow field data \(X^{(i)}\) through the neural network. Therefore, it is necessary to use POD to reduce the dimension of the flow field data.  
The mean value of the temperature and pressure field is calculated as:  
\[\overline{x}=\frac{\sum_{i=1}^{r}X^{(i)}}{r} \tag{1}\]  
The standardized sample data are:  
\[\overline{X^{(i)}}=X^{(i)}-\overline{x} \tag{2}\]  
First, calculate its covariance matrix and n eigenvalues for the standardized sample data, \(\overline{X}\), and arrange them from large to small. Then, take the eigenvectors \(q^{(1)},q^{(2)},\cdots,q^{(m)}\) corresponding to the first m eigenvalues to form the mapping matrix \(U\):  
\[U=[q^{(1)},q^{(2)},\cdots,q^{(m)}]^{T} \tag{3}\]  
Figure 4: General framework of POD-MCNN model.  
where each eigenvector represents a fundamental mode of the flow field. The reduced order matrix \(Y\in R^{m\times r}\) can be obtained with:  
\[Y=U\overline{X} \tag{4}\]  
When \(m\) is taken to a suitable value, it is guaranteed that:  
\[X^{(i)}\approx U^{\prime}Y^{(i)}+\overline{x} \tag{5}\]  
where \(U^{\prime}\) denotes the pseudo-inverse matrix of \(U\). The m-dimensional data \(Y^{(i)}\) can be used to characterize the high-dimensional flow field data \(X^{(i)}\) with a low error rate. By POD modeling, the basic modes and the corresponding coefficient matrix can be obtained, where the basic modes are used for the flow field reconstruction and the coefficient matrix is used for MCNN modeling and flow field prediction, together with the parameter space matrix.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 7 Discussion
### On Repeatability in Field Measurements  
One particularity of field measurements in wind energy is that atmospheric conditions cannot be controlled. Therefore, repeatability does not formally exist. Repeatable conditions could be defined by grouping data according to wind speed, turbulence intensity, temperature, aerosols concentration, thermal stability, etc. Obtaining sufficiently large calibration datasets under repeatable conditions would require years of measurement data and is consequently not reasonably feasible. Thus, LOS velocity measurement uncertainties cannot be assessed using only statistical methods (of 'type A', [11]). Studies of long-term measurement datasets would provide valuable information on the statistical uncertainties of lidar measurements, both at the LOS velocity and reconstructed parameter level. Such studies may even allow to obtain a 'golden' calibrated lidar that could then be used as a reference for other lidars calibration. However, to the author's knowledge, no such studies exist or are ongoing at the time of writing.
## 4 Control Packet Collision
### Collision Avoidance with Proactive Interval Setting
In fact, CSMA/CA cannot completely avoid packet collisions even when two nodes are hidden with respect to each other. Also, SREQ collisions tend to occur more often as more backward nodes contain data packets. Therefore,  
when the packet generation rate is high, SREQ collisions occur more frequently. In an actual multi-hop network, a node sends data packets not only to forward nodes, but also to sideward nodes and backward nodes since \(P_{SREQ}\) in an actual network is difficult to estimate. Moreover, the actual average number of data packets received in one second increases due to retransmissions.
## 3 Experimental Scheme and Analysis of Results
### Experimental Results and Cause Analysis
Nonetheless, compared to the Gaussian noise-inflicted image, the object's image is clearly discernible. Figure 10 presents the PSNR values of the reconstructed object images utilizing the four different algorithms after Gaussian noise addition. The PSNR values indicate an increase with higher sampling rates, with the untrained DL algorithm consistently exhibiting superior performance at equivalent sampling rates. The introduction of Gaussian noise further substantiates the efficacy of our proposed approach. By amalgamating the SPI-based scheme with a ToF camera, we mitigate noise's substantial impact on image quality, thereby yielding higher-quality images. Figure 8: Plots comparing the PSNR and SRs for the reconstruction of intensity images through scattering media using different algorithms.
###### Abstract  
Nanoscale defects on the surface of ultra-precision optical elements seriously affect the beam quality in optical systems. In response to the challenge of detecting nanoscale defects on optical component surfaces, we propose a method for the detection and classification of various types of defects on optical component surfaces via reflection Mueller matrix spectroscopy (RMMS). Firstly, an electromagnetic scattering theoretical model for various types of defects on the surface of optical elements and the incident and scattered fields were established by combining the bidirectional reflection distribution function (BRDF) and the Rayleigh-Rice vector scattering theory. Then, the optimal conditions for RMMS measurements were determined by numerically simulating the BRDF. On this basis, the surface roughness and pockmarks of the optical test plate were simulated and analyzed via RMMS, and the results were verified experimentally; then, dirty particles and pockmarks above the surface of the optical element and subsurface bubble defects (SSBD) were simulated and analyzed via RMMS. The results showed that some elements of the Mueller matrix could significantly distinguish defects on the surface of the optical element with dimensions smaller than the visible wavelength, and the dimensions of various types of defects of the element could be inverted using the values of the Mueller matrix elements. This method provides a theoretical basis and reference for the detection and classification of various types of defects in precision optical components.  
Rayleigh-Rice vector scattering theory; Mueller matrix; optical component quality evaluation; defect detection; polarized bidirectional reflection distribution function (pBRDF)