esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Real-Time Virtual 3D Object Composition Method for 360\({}^{\circ}\) Video
## 2 Background Theory and Related Studies
### Stereo Vision  
Representative algorithms for estimating the location of a device in real space and generating a map of the surrounding environment are simultaneous localization and mapping (SLAM) [4; 5; 6] and visual inertial odometry (VIO) [17; 18]. SLAM and VIO can be applied to different types of sensors such as stereo vision, time-of-flight (ToF), and lidar, depending on the environment. Among them, stereo vision uses two cameras to extract the depth map and calculate the three-dimensional position of the feature point to calculate the relative motion. It has the advantage of being relatively inexpensive when compared with lidar and it can measure a wider distance than ToF [19].  
In this paper, we used a ZED, which was developed by Stereo Lab [20]. A ZED is a stereo-vision device which uses the SLAM algorithm to provide various software tools, a software development kit (SDK) to generate 3D environment mapping and point clouds from real scenes for estimating position tracking in real-time. Various studies have been conducted on the accuracy of ZED. [PERSON] et al. investigated various Robot Operating System (ROS)-based visual SLAM methods and analyzed their feasibility for a mobile robot application in a homogeneous indoor environment. It was verified that the odometry errors of the ZED are as low as those of lidar [21]. In addition, [PERSON] et al. compared the ZED with OptiTrack to analyze its accuracy [22].  
Figure 2: Errors of stitching in a 360\({}^{\circ}\) video.  
In this study, we used the real-time positional tracking value of the ZED as the external parameter value of a mounted 360\({}^{\circ}\) camera. In addition, we converted the extracted data into a script suitable for a 3D program (e.g., 3D Max, Maya, Unity) to create a virtual camera.
## 3 Experimental Results and Discussion
### Load and Deflection Relationship
#### 3.2.2 Textile Lamination and Geometry  
Figure 9 shows flexural behaviors of T1, T1N, T1O, and T1M according to textile lamination and geometry. The yield load of T1N was 38.96 kN, which was 16% higher than that of the T1 specimen. The yield load of T1O was 38.22 kN, which was 14% higher than that of T1. The ultimate load of T1O was 39.21 kN, which was a little higher than its yield load. T1O and T1N improved the load-bearing capacity with nearly the same increment. The textile of T1 was vulnerable to damage, as described in the previous section, Section 3.2.1. However, in case of T1O, because the textile applied at the same reinforcing axis by lamination, all filaments in the textile resist the loads acting on the TRM layer simultaneously, even if the inner filaments have a higher ratio in the total cross-section area of the textile. Therefore, T1O increased load-bearing capacity higher than that of T1. In the case of T1N with the process of pressing the mortar with a trovel after the application of the textile without fixing both ends of the textile, the spacing of the textile layer of T1N was decreased compared to that of T1, so that same effect of textile lamination of T1O was observed. However, unlike other specimens, the uncertainty in the fabrication process of non-stretching caused unstable behavior in which the load suddenly decreased due to the textile rupture at the yield load.  
The yielding and ultimate load of T1M was 35.38 kN and 37.11 kN, respectively. The textile mesh size of T1M was changed from 8 mm to 24 mm. The yield load of T1M was 5% higher than that of T1, but the yield and ultimate load were decreased compared with T1N and T1O. Because the werf (transverse direction) roving of the textile applied to beams or one-way slabs is perpendicular to the longitudinal axis, there is no load-bearing capacity but it performs a mechanical interlock such as an anchor of warp (longitudinal direction) roving between interaction points [25]. Therefore, T1M had wider spacing of anchors that fix warp roving than other TRM specimens, so that the possibility of textile slippages increased, and load-bearing capacity decreased [16].
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Analysis Measurements
* _Knowledge expandability_--Expandability can be evaluated by examining whether the representation supports mechanisms for inference, induction, or the incorporation of new facts, rules, or domain-specific knowledge over time. An indication of knowledge expandability would be given by the efficiency of deriving new insights relative to the addition of new knowledge. * _Knowledge scalability_--Scalability can be assessed by evaluating the performance of the knowledge representation as the size and complexity of the knowledge base grow. It involves examining factors such as response times, resource utilization, and efficiency of storage. An indication of knowledge scalability would be given by the system's performance under maximum load over its baseline performance. These criteria collectively help evaluate the suitability and quality of a knowledge representation formalism for various applications and domains. The choice of a representation should align with specific objectives and requirements related to the criteria. Based on the above description of measurements, Table 1 lists a set of defining questions to be answered for the qualitative assessment. These questions constitute a methodology that is used to assess conformance to the criteria in the range of high, moderate, and low conformance. It should be emphasized that the comparison of the related work in this survey relies on assessments based on these qualitative measurements. For further clarity of the criteria, some indicators have been also presented. However, a rigorous formalization of the criteria with well-defined quantitative metrics is an interesting direction for future research towards a unified knowledge framework.
## 5 Methods
### Parameter Estimation  
Among various methods for estimating the parameters of the probability distribution, the maximum likelihood method (MLM) is the most preferred one due to its good asymptotic properties, including efficiency and consistency. [PERSON] in [60] compared six methods for estimating the parameters of the Weibull distribution and showed that the MLM provided more accurate estimates than other methods in both the simulated and observed datasets. [PERSON] et al. in [61] proposed the method of moments (MOM) and the MLM as the most efficient methods for estimating the parameters of W2 based on a comparative study of seven estimation methods. [PERSON] et al. in [62] compared the MLM with the modified MLM and the Energy pattern factor method (EPF) in estimating the parameters of W2 using ten-minute averaged wind speed data from Jhampir, Pakistan. Based on the results of the root mean square error, the coefficient of determination and the \(\chi^{2}\) test, they demonstrated better performance of the MLM as compared to the others. Many authors have used the MLM as the method for parameter estimation when modelling the wind speed using various probability distributions, for example, in [63; 64]. Therefore, the MLM was utilised for estimating the parameters in this study.  
The log-likelihood function \(\ln L=\ln L(x_{1},x_{2},\ \ldots,x_{n};\theta)\) of the probability distribution with PDF \(f(x,\theta)\) is defined as,  
\[\ln L(x_{1},x_{2},\ \ldots,x_{n};\theta)=\sum_{i=1}^{n}\ln f(x_{i};\theta) \tag{9}\]  
where \(\theta\in\Theta\) is the unknown parameter (in general, it is a vector parameter), and \(x_{1},x_{2},\ldots,x_{n}\) is a realization of the random sample \(X_{1},\ X_{2},\ \ldots,\ X_{n}\) of size \(n\) from the distribution with PDF \(f(x,\theta)\). After taking the partial derivatives of the log-likelihood function \(\ln L\) with respect to each parameter and equating each derivative to zero, the likelihood equations are obtained. The solution to these equations is called the maximum likelihood estimate of the parameters. The log-likelihood functions of the W2, W3, and EW distributions, respectively, and their likelihood equations are given in Table 4. As we may see, in the majority of cases the likelihood equations need to be solved iteratively.
# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 3 Research Methodology
### The Process of Training, Testing, and Validating Models
#### 3.1.1 Automatic Training Process
When using a single class, class loss is not significant and does not affect the assessment of the model's effectiveness. In such a case, class loss does not provide meaningful information about the model's performance, as there is no possibility of confusion between classes. The above results were calculated on a separate test set, ensuring an objective evaluation of the model's generalization capabilities. In the research process, charts showing changes in precision and recall as a function of the number of epochs were used. The resulting metrics enabled verification of the model's quality, as they allowed monitoring of how effectively the model minimized the number of false alarms during the training process. The precision and recall curves are shown in Figure 9.  
For the given values of precision and recall, the calculated F1 score was 0.32. Additionally, Roboflow enabled the visualization of results on sample validation images and the  
Figure 8: Object classification error graph. Figure 9: Change in precision (**A**) and recall (**B**) as a function of the number of epochs during automatic model training. Blue lines represent the progression of the metric values over successive epochs, while orange dots indicate the averaged values for each epoch. determination of the model's performance. Example images from the validation process are shown in Figure 10.  
This approach ensures the repeatability of the experiment and enables an objective comparison of the obtained results with other solutions available in the literature.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 3 Results
### Ferric Reducing Antioxidant Power (FRAP)
#### 3.3.2 Cytotoxicity Assay of Biosynthesisized ZnO-NPs  
The cytotoxic effects of the biosynthesized ZnO-NPs of _U. lactuca_ at concentrations ranging from 1.56-100 ug/mL were detected using the WI38, HepG2, HCT116, and MCF7 cell lines based on the results of the MTT assay. The obtained results showed very weak effects of the biosynthesized ZnO-NPs on WI38 normal cells (84.98 \(\pm\) 4.6). This result confirmed its safety and non-toxicity. The recorded IC\({}_{50}\) value of the ZnO-NPs of _U. lactuca_ showed moderate cytotoxic effects on HepG-2 cell line (46.66 \(\pm\) 2.8 ug/mL) and MCF-7 cell line (30.60 \(\pm\) 2.1 ug/mL), whereas the ZnO-NPs recorded weak cytotoxic effects on the HCT-116 cell line (58.69 \(\pm\) 3.3 ug/mL) compared to the doxorubicin (a chemotherapeutic drug) (Table 5).
###### Abstract  
The desertified ecological restoration vegetation of Wuzhumuqin grassland plays an important role in the ecological restoration and protection of the region. However, there are few studies on the monitoring of the changes in ecological restoration vegetation in grassland sandy land in the past. In order to improve the low efficiency of ecological restoration vegetation monitoring, this study used Gaofen-6 (GF-6) remote sensing data to calculate the kernel Normalized Difference Vegetation Index (_kNDVI_) and vegetation coverage of ecological restoration vegetation and analyze their spatial and temporal trends. At the same time, a transform three-branch network structure based on deep learning is proposed to extract visual features. The kernel Normalized Difference Vegetation Index-position-temporal awareness transformer (_kNDVI_-PT-Former) model monitoring method based on two-phase remote sensing image features combined with _kNDVI_ for spatio-temporal feature extraction can accurately obtain the vegetation changes in desertification ecological restoration in Wuzhumuqin grassland. The results show that the _kNDVI_ of the study area shows an increasing trend from 2019 to 2024. The _kNDVI_ value is 0.4086 in 2019 and 0.4927 in 2024. From the perspective of the change trend of vegetation coverage, the overall vegetation coverage of the Wuzhumuqin desertification restoration study area showed a gradual increase trend from 2019 to 2024, and the vegetation coverage increased by 19% in 2024 compared with 2019. The transformation of vegetation coverage from low level to high level in the study area is more prominent. Based on the self-built monitoring dataset of more than 5.2 million pairs of grassland vegetation changes, through model comparison and analysis, the _kNDVI_-PT-Former model obtains that the Class Pixel Accuracy (CPA) is 0.7295, the Intersection over Union (IoU) is 0.7228, and the overall monitoring accuracy of the model is improved by 11%. Furthermore, the stability of the model's performance was confirmed through evaluation with five-fold cross-validation.  
kNDVI; vegetation coverage; change monitoring; ecological restoration; remote sensing +
Footnote †: journal: Applied sciences  
Article
## 4 Discussion
### Limitations of Data Sets  
This study employed three machine learning datasets, two ecosystem model datasets, and one remote sensing dataset to compare site-level and global-level carbon fluxes. However, there are some limitations in this study that affect the assessment of carbon flux product reliability. Firstly, the comparison of carbon flux datasets is inadequate due to the inclusion of only a limited number of products. Numerous datasets on carbon fluxes are available, and their omission restricts the comprehensiveness of the comparison. Secondly, the selected carbon flux products generally have low spatial resolution, which prevents a detailed analysis of the predicted spatial distribution of each dataset in a local area. Consequently, the evaluation of spatial distribution can only be conducted on a larger scale. To improve the reliability assessment of the GCFD product at the regional level, it is necessary to incorporate higher-resolution carbon flux products. Thirdly, the sites used for validation are limited in number and exhibit uneven spatial coverage, and there is a scale mismatch between the site-level observations and the grid-level estimations of carbon fluxes. These factors introduce uncertainties in the validation results of various carbon flux products. Fourthly, despite their ability to represent the actual distribution of carbon fluxes, remote sensing products have their own limitations. Satellite observation can be affected by atmospheric conditions, while factors such as instruments and data processing algorithms can affect remote sensing data, thereby compromising their accuracy and reliability. Therefore, future studies on carbon fluxes require high-quality data to overcome these limitations.
# An Abstraction Technique for Verifying Shared-Memory Concurrency +
## 3 Formalisation
### Programs
#### 3.3.4 Fractional Permissions  
In the assertion language, all heap/process ownership predicates have an associated rational number \(\pi\in\mathbb{Q}\). There are used to express the \"amount\" of ownership that is available to the corresponding heap location or program model.  
We define a rational number \(\pi\) to be a _(Boyland) fractional permission_ in case \(\pi\in(0,1]_{\mathbb{Q}}\)[36]. The original work of [PERSON] uses fractional permissions to distinguish between write access (\(\pi=1\)) and read access (\(0<\pi<1\)) to some shared resource. However, in our work this is slightly different, since the fractional access permissions \(\pi\) annotated to \(\stackrel{{\pi}}{{\rightarrow}}\)\(\mathsf{proc}\) predicates never provide write access.  
To conveniently handle fractional permissions, we define basic notions of _validity_ (\(\mathsf{valid}_{\mathbb{Q}}\)) and _disjointness_ (\(\bot_{\mathbb{Q}}\)) of rational numbers, as follows.  
**Definition 17** (Permission validity, Permission disjointness).: \[\mathsf{valid}_{\mathbb{Q}}(\pi)\triangleq 0<\pi\leq 1 \pi_{1}\perp_{\mathbb{Q}}\pi_{2}\triangleq 0<\pi_{1}\wedge 0<\pi_{2} \wedge\pi_{1}+\pi_{2}\leq 1\]  
The predicate \(\mathsf{valid}_{\mathbb{Q}}:\mathbb{Q}\to\mathit{Prop}\) determines whether the given rational number is within the range \((0,1]_{\mathbb{Q}}\), that is, is a valid Boyland fractional permission. (Here \(\mathit{Prop}\) is the sort of propositions.) The binary relation \(\perp_{\mathbb{Q}}:\mathbb{Q}\to\mathbb{Q}\to\mathit{Prop}\) determines _disjointness_ of two rationals. Disjoint rational numbers do not overlap, in the sense that both operands are fractional permissions, as well as their addition.  
**Lemma 4**.: \(\mathsf{valid}_{\mathbb{Q}}\) _and \(\perp_{\mathbb{Q}}\) satisfy the following properties._  
1. _If_ \(\pi_{1}\perp_{\mathbb{Q}}\pi_{2}\)_, then_ \(\pi_{2}\perp_{\mathbb{Q}}\pi_{1}\)_,_ \(\mathsf{valid}_{\mathbb{Q}}(\pi_{1})\)_, and_ \(\mathsf{valid}_{\mathbb{Q}}(\pi_{1}+\pi_{2})\)_._
2. _If_ \(\pi_{1}\perp_{\mathbb{Q}}\pi_{2}\) _and_ \((\pi_{1}+\pi_{2})\perp_{\mathbb{Q}}\pi_{3}\)_, then_ \(\pi_{2}\perp_{\mathbb{Q}}\pi_{3}\) _and_ \(\pi_{1}\perp_{\mathbb{Q}}(\pi_{2}+\pi_{3})\)_._
# Time Series Irradiance Synthesizer for Optical GEO Satellite Downlinks in 5G Networks
###### Abstract  
Next generation 5G networks generate a need for broadband, low latency and power efficient backhauling and data-relay services. In this paper, optical satellite communications links, as an integrated component of 5G networks, are studied. More specifically, the Geostationary (GEO) satellite-to-ground optical communication link is investigated. Long-term irradiance statistics based on experimental measurements from the ARTEMIS program are presented and a new time series generator related to the received irradiance/power fluctuations due to atmospheric turbulence is reported. The proposed synthesizer takes into consideration the turbulence-induced scintillation effects that deteriorate the laser beam propagation, on the assumption of the Kolmogorov spectrum. The modeling is based on Rytov theory regarding weak turbulence conditions with the incorporation of first order stochastic differential equations. Summing up, the time series synthesizer is validated in terms of first and second order statistics with experimental results from the European Space Agency's ARTEMIS experimental optical downlink and simulated received power statistics for various weather conditions are presented using the proposed validated methodology. Some important conclusions are drawn.  
5G satellite; optical satellite communications; atmospheric turbulence; free space optics; downlink; scintillation; long-term statistics; ARTEMIS +
Footnote †: journal:  
_Future Internet_
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 5 Experimental Results
### Chip Performance Comparison
In April 2014, Velodyne and Kitware collaborated to release VeloView 2.0 in SPAR International 3D Measurement and Imaging Conference. VeloView displays the distance measurements from the LidAR as point-cloud data such as intensity-of-return, time, distance, azimuth, dual return type, and laser ID. Correctness verification is performed using a correlation criterion in which the \"similarity\" between the LiDAR decoded packet data s and the ground truth w, Sim(s, w), is calculated as follows:  
\[Sim(s,w)=\frac{\sum\limits_{i=1}^{m}{(s_{i}\cdot w_{i})}}{\sum\limits_{i=1}^{m}{ (w_{i}\cdot w_{i})}} \tag{17}\]  
We calculate the similarity value. The results are included in Table 11. The experimental results show the Sim values all to be 1. The experimental results prove that the LiDAR decoded packet results are exactly the same as the ground truth. \begin{table}
\begin{tabular}{c c c c} \hline
**Process** & **Frequency** & **Chip Area** & **Power** \\ \hline
90 nm & 200 MHz & 219,990.00 μm\({}^{2}\) & 10.625 mW \\
65 nm & 276 MHz & 109,995.25 μm\({}^{2}\) & 5.312 mW \\
45 nm & 400 MHz & 54,997.60 μm\({}^{2}\) & 2.656 mW \\ \hline \end{tabular}
\end{table} 
Table 10: Chip scaling. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Process** & **Type** & **Architecture** & **Frequency (MHz)** & **Max** & **Throughput** \\
**MMCM** [30] & 45 nm & Chip & LUT & 200 & - & - \\ FBW [30] & 45 nm & Chip & LUT & 200 & - & - \\ Qi [25] & 65 nm & Chip & CORDIC & 35 & 16 & \(1.2572\times 10^{8}\) \\ Wu [26] & 0.18 μm & FPGA & CORDIC & 40 & 16 & \(1.4368\times 10^{8}\) \\ Ray [27] & 0.18 μm & Chip & CORDIC & 125 & 16 & \(4.4901\times 10^{8}\) \\ Proposed & 0.18 μm & Chip & CORDIC & 100 & 7 & \(8.2105\times 10^{8}\) \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison of hardware performance between CORDIC circuit and related literature.
## 5 Case Studies
### Numerical Experiment A: A Highly Nonlinear System  
Assuming a nonlinear industrial system consisting of two failure modes, the two corresponding limit state functions can be expressed as \(g(\mathbf{X})=0.018-74.769\frac{x_{1}}{x_{2}^{3}}\), where \(\mathbf{X}=(x_{1},x_{2})\) is the basic random variable, satisfying \(x_{1}\sim N(2000,200)\) and \(x_{2}\sim N(250,37.5)\). Furthermore, to verify the applicability and superiority of the proposed approach in the calculation of failure probability, three comparative methods are employed, encompassing the Monte Carlo method, response surface method and Kriging method. Notably, the pre-set convergence criterion is that the relative error of the failure probability between two calculations is less than 0.001. The calculation results of four diverse approaches are shown in Table 1. It can be found that the proposed method implies an apparent superiority with least iteration cycles and highest computation accuracy.
## 6 Propagation Time for a Signal, Emitted and Intercepted by Satellites on a Space-Oriented Orbit
### Analytical Calculation of the First \(O(\frac{1}{e})\) Correction by Means of Elliptic Integrals
After presenting the calculation for the second \(O(c^{-3})\) propagation time correction in the next sections, it will become clear that due to the important transformation (92), it will be possible to prove the real-valuedness of terms with second-order elliptic integrals in the final expression for the propagation time for the case of space-oriented orbits.