esa-sceva/satcom-chunk-collection huggingface dataset:
# Central and Periodic Multi-Scale Discrete Radon Transforms
## 5 Results and Discussion
### Convergence of Inversion Algorithm  
With the aforementioned adjoint operators, the same technique described by [PERSON] (1966) for conventional DRT has been applied to central and periodic DRT.  
Convergence of inversion of periodic DRT is as fast as that of conventional DRT.  
In the case of central DRT, even if the eigenvalues of the multiplication of the adjoint and forward operators, expressed as matrices, are lower than one, the convergence of its backward transform is fast, only in the previously mentioned rhomboid central zone. Outside of that zone, it converges so slowly that it ruins the speedup gained on the forward step.  
The inversion method, proposed by [PERSON], reduces the error on image domain, based on observations of the error on Radon domain, where the trace of cornered line integrals are basically absent.  
Figure 18 shows the behavior of combined forward and back-projection transforms for each DRT type, operating on an input with a horizontal line inside the rhombus, inscribed in a square zone, described for central DRT and additional smaller lines, slanted and positioned closer to each corner.  
The cornered lines are part of those not considered by central DRT, and they leave no trace, neither in the forward transform nor in the back-projection. On the other hand, the back-projection, in the case of periodic DRT, is good enough for the method to converge.  
Figure 17: Top row, from left to right, the respective adjoint operators applied to the conventional, central, and periodic forward DRTs of an input, constituted by \(16\times 16\) equispaced black pixels. Bottom row, details of top left corners of back-projections in the same order.  
Figure 19 shows the quality of inversion achieved for each type of DRT after three and five iterations. Surprisingly, periodic DRT performs even better than conventional. Central DRT inversion is only valid in the central rhomboid zone.  
Figure 18: Forward and back-projected transforms of an input, consisting of one horizontal line in the center and four lines closer to the corners. From top to bottom: conventional, central, and periodic DRT. (**a**) Forward transforms; (**b**) Input and back-projections.
# Supplement Use Among Athletes: Insights from Gyms in Sarajevo
## 2 Materials and Methods
### Participant Profiles and Measurement Tools  
Data collection was conducted using an online questionnaire adapted from [PERSON] et al. (2022) [22] and distributed via Google Forms. This digital platform was chosen for its accessibility, practicality, and environmental benefits. Access to the questionnaire was facilitated through QR codes displayed in 17 fitness centres across the city of Sarajevo.  
The questionnaire consisted of 21 items, systematically organized into three thematic sections:  
Section I: This section focused on demographic and lifestyle characteristics, including age, gender, educational attainment, history of illness, smoking, and alcohol consumption.  
Section II: This part assessed physical activity-related variables, encompassing the type, frequency, and duration of training, as well as participants' history of consultations with nutrition professionals.  
Section III: This section examined dietary supplement usage behaviours, specifically the types of supplements consumed, motivations for use, timing of intake, and sources of information.  
The questionnaire included a mix of categorical (multiple-choice) items and Likert-scale items, the latter capturing levels of agreement or frequency (e.g., ranging from \"strongly disagree\" to \"strongly agree\"). Categorical variables were used to classify fixed responses, such as types of supplements, while Likert-type items evaluated attitudes and motivations related to supplementation.  
To ensure the clarity, appropriateness, and reliability of the instrument, a pilot study was conducted involving 19 undergraduate students from the Faculty of Sport and Physical Education. The pilot study employed a test-retest protocol with a 10-day interval between administrations. To minimize potential learning effects during the re-test phase, selected items were presented with inverted scaling.  
Content validity was assessed through expert reviews conducted by two licensed nutritionists. Measures of internal consistency demonstrated acceptable reliability, with Cronbach's alpha values ranging from 0.747 to 0.923. Pearson's correlation coefficients ranged from 0.67 to 0.93, yielding an overall correlation coefficient of 0.84, indicating a high level of test-retest reliability. Based on feedback from the pilot study, minor revisions were made to improve question clarity and structure. Although the questionnaire was adapted from established instruments, this was its first application in the Bosnian and Herzegovinian gym-goer population.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 4 Error Factors of TCR RCS
### Interplate Orthogonality  
The interplate orthogonality means ensuring the angle between the two plates is 90\({}^{\circ}\) at their intersections, but in practical manufacturing, the plates need to be assembled together such as using welding or other techniques, so it is difficult to ensure the orthogonality between the two plates. When  
Figure 8: The bistatic RCS pattern of the TCR based on FEKO electromagnetic simulations as the TCR boresight changes with the bistatic angle is considered. (**a**) The bistatic RCS elevation pattern of the TCR; (**b**) the bistatic RCS azimuth pattern of the TCR.  
analyzing the bistatic RCS observation geometry of the TCR (see in Figure 6), points A and B move \(n^{\circ}\) outward or inward, and each point moves \(n^{\circ}/2\) (\(n=0,0.1,0.2,0.3,0.4,0.5\)), respectively. In this condition, points A and B are still in the XOY plane, and \(OA=OB=b=0.4\) m; we can calculate the azimuth bistatic RCS pattern.  
Define \(\Delta\sigma\) as the peak RCS reduction of the trihedral:  
\[\Delta\sigma=\sigma_{\text{max}}-\sigma \tag{9}\]  
where \(\sigma_{\text{max}}\) is the peak RCS of the ideal TCR, and \(\sigma\) is the peak RCS of the TCR with errors. The effect of the interplate angle deviations on RCS can be obtained, and we can also calculate the relationship between the interplate angle errors and the peak RCS errors, as shown in Figure 9.  
When only the angle between the two vertical plates is varied, we found that the RCS changes were negligible, as the interplate angle was smaller than 0.5\({}^{\circ}\). For monostatic calibration, the interplate angle error will result in a reduction in RCS. However, the effect of interplate angle error on the RCS at different bistatic angles is irregular, which may be due to the complexity of the bistatic system, and the interplate angle error will change the original structure of the TCR. Even in the case of small bistatic angle (less than 6\({}^{\circ}\)), the RCS errors are negative numbers, as the interplate angle is less than 90\({}^{\circ}\) (i.e., the interplate angle errors are negative numbers). The errors were more severe when the interplate angle was more than 90\({}^{\circ}\), rather than less than 90\({}^{\circ}\). The current level of machining can ensure the interplate angle error is less than 0.5\({}^{\circ}\), and the maximum RCS error will not exceed 0.2 dB for different bistatic angles. Generally, the RCS loss of the TCR is about 0.23 dB if the interplate angle deviation is 0.5\({}^{\circ}\) for monostatic calibration [27]. So, RCS errors due to interplate angle errors are more severe for monostatic calibration at this machining level. Moreover, the interplate angle deviation will only cause a loss of RCS, and will not affect the beamwidth and stability requirements when the TCR is used as a bistatic calibration target.
# Optimal Order of Time-Domain Adaptive Filter for Anti-Jamming Navigation Receiver
## 2 Mathematics Model  
### Navigation Receiver Model  
The structure of a satellite navigation receiver includes two parts: radio frequency front-end (RFFE) processing and baseband digital signal processing (DSP) [26]. Navigation receivers generally add an anti-jamming module to suppress interference such as narrow-band interference [27]. As shown in Figure 1, the receiver RFFE performs analog signal processing on the antenna received signal, including low-noise amplifier (LNA), down-conversion (DC), amplifier, and analog to digital converter (ADC). The baseband DSP includes digital down-conversion (DDC), anti-jamming, acquisition and tracking, navigation, and positioning [28].  
Suppose that the intermediate frequency (IF) signal, noise, and interference received from an antenna and processed after DDC are \(s[n]\), \(n[n]\) and \(j[n]\), respectively, and they are all complex signals [29]. Then the signal before anti-jamming can be expressed as [27].  
\[x[n]=s[n]+j[n]+n[n] \tag{1}\]  
The carrier-to-noise ratio (CNR) is an essential parameter for evaluating navigation signal quality. Different from communication systems, navigation systems take ranging and positioning as the ultimate goal [30; 31]. Therefore, the CNR can evaluate the receiver performance while there is no need to consider the decoding processing as performed in communication systems [32]. The ideal CNR after the anti-jamming filter is the CNR without interference, which can be expressed as [6].  
\[\left[C/N\right]_{0}=\frac{C}{N}\cdot B_{N}=\frac{\int_{-\infty}^{\infty}S_{s} (f)df}{\int_{-\infty}^{\infty}S_{n}(f)df}\cdot B_{n}, \tag{2}\]  
where \(S_{s}(f)\) and \(S_{n}(f)\) are the power spectral densities of the IF signal and noise, respectively.  
The weight vector of an anti-jamming filter can be defined by [28].  
\[\mathbf{W}_{M}=[w_{0},w_{1},\ldots,w_{M}], \tag{3}\]  
where \(M\) is the order of the time domain filter, then the filter length can be expressed as [33].  
\[N=M+1 \tag{4}\]  
The output signal after the anti-jamming filter can be expressed in the time domain as [33].  
\[y[n]=x[n]*\mathbf{W}_{M}=\sum_{k=0}^{M}x[n-k]\mathbf{W}_{M}[k] \tag{5}\]  
The frequency response of the anti-jamming filter can be written as [33].  
\[H(f)=\mathrm{DTFT}[\mathbf{W}_{M}] \tag{6}\]  
The estimated CNR after anti-jamming is a vital indicator to estimate the interference suppression performance, which can be expressed as [6].  
\[\left[C/N\right]_{\mathrm{ajm}}=\frac{\int_{-\infty}^{\infty}|H(f)|^{2}S_{s} (f)df}{\int_{-\infty}^{\infty}S_{y}(f)df-\int_{-\infty}^{\infty}|H(f)|^{2}S_ {s}(f)df} \tag{7}\]  
Figure 1: Navigation receiver structure.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Requirements on Tools in MLOps
#### 5.1.5 Operations and Monitoring Phase (OMP)  
Different monitoring metrics are feasible depending on the MLOps project's structure and approach to solve a specific problem. As the model performance depends upon input data, as well as the overall system infrastructure, different observation aspects and strategies for the overall status of the ML environment are summarized in the following.  
OMP-1: System Monitoring (SM)  
The constant monitoring of the deployment infrastructure (for example, the systems latency, throughput, or server load) is the foundation of counteracting deployment-related errors. Different metrics for monitoring the overall system utilized for ML tasks are depicted in Table 2.  
OMP-2: Input Data Monitoring (IDM)  
When not permanently monitoring the incoming data of an application, error factors like data or concept drift are easily overlooked, and impacts are to be expected in the subsequent model utilization. Different aspects of monitoring the respective data sets are overviewed in Table 3.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Metric** & **Remarks** \\ \hline Serving latency & The metric measures the response time. Low latency is one of the critical aspects of a good user experience. In order to create an expressive measurement, the responses of every tier must be taken into consideration. Next to the average time to answer a request, other aspects like perceived page load time may influence the metering [14]. \\ \hline Throughput & Average predictions returned in one second. High throughput is representative of a healthy system. Additionally, measurements regarding the throughput could be impacted by additional metrics, e.g., total hits per week, average hit size, or the occurring error rate [14]. \\ \hline Disk utilization & Monitoring free disk space helps prevent data loss and avoid server downtime. The statistics of the respective server’s filesystem may also help in determining and understanding the applications health status [13] \\ \hline System’s uptime & System uptime is an important measure of the reliability of the system. It accounts for the availability of the service. Therefore, this performance metric type is especially useful when serving web-based applications [14]. \\ \hline CPU/GPU usage & Monitoring GPU/CPU usage helps identify whether some inputs result in longer computation time. This information is beneficial for tuning the model for a better user experience. While fine-tuning the capacity and configuration of infrastructure, these resources may be resized in order to fit the required serving capability [14]. \\ \hline Number of API calls & While serving the model to many users, monitoring the number of API calls is a good metric for approximating the maximum load and systems availability. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics for system monitoring.
# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## 1 Introduction
The present research is a continuation and extension of previous studies by the same authors. The current research aims at comparing the two products, namely, GPM IMERG and TRMM 3B43, in order to determine and highlight possible differences, advantages and disadvantages of each one of them, based on the performance of several statistical skill scores, along with cross-evaluation against the dense rain gauge dataset over Cyprus during the period from April 2014 to June 2018.
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 4 Conclusions  
In this study, we proposed a novel segmentation framework for accurate left atrium segmentation in medical imaging, addressing the challenges of complex anatomy, subtle boundaries, and inter-patient variability. By integrating the CDE for multi-scale feature extraction, MPCM for long-range contextual dependency, AFDB for global-local fusion, AFAB for boundary refinement, and SFFH for spectral-spatial synthesis, our model achieved a Dice score of 93.35, Precision of 94.12, and Recall of 92.78. The Laplacian-approximation-based uncertainty estimation further enhanced interpretability, providing insights into low-confidence regions.  
Figure 11: Uncertainty estimation of the predicted results using Laplacian approximation.  
Figure 12: Calibration error of the different data shift intensity for baseline and Bayesian models. Diamond represents the outlier.  
While our framework demonstrated state-of-the-art performance, some limitations remain. The computational complexity, particularly with transformer-based components, may limit its applicability to resource-constrained environments. Additionally, the model's performance may vary when applied to datasets with significant imaging artifacts or noise.  
Future work will focus on optimizing the framework to reduce computational costs, making it more suitable for real-time clinical use. We also aim to validate the model on larger, multi-institutional datasets to improve generalization. Furthermore, integrating semi-supervised or self-supervised learning techniques could reduce the reliance on large annotated datasets, expanding the applicability of our approach in clinical settings.  
Methodology, A.S.M.S.S. and M.Z.I.; software and coding, A.S.M.S.S.; experimentation and formal analysis, M.Z.I., A.S.M.S.S. and J.T.; writing--original draft preparation, M.Z.I. and A.S.M.S.S.; writing--review and editing, M.Z.I., A.S.M.S.S. and J.T.; supervision, H.S.K.; funding acquisition, M.Z.I. and H.S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) Grant by the Korean Government through MSIT under Grant 2022R1F1A1063662 and the Strengthening R & D Capability Program of Sejong University.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank the MONAI team for publicly providing an excellent medical image analysis framework.  
The authors declare no conflicts of interest.
## 3 Research Results
### Analysis of Power Supply and Demand at Each Facility
#### 3.1.1 Development of Load Pattern for Operation Plan Line  
As shown in Figure 10, when the power load of the operation plan line was analyzed, it was expressed by the step values during non-operation and operation. It was evident that the power load during operation differed depending on the processing method of the facility. Therefore, Equation (1) calculates the daily load pattern. In addition, the operating power, non-operating power, and the prediction errors of each facility are shown in Table 3.  
\[P_{c}=P_{1}\times A_{1(t)}+P_{2}\times A_{2(t)}+\cdots+P_{n}\times A_{n(t)} \tag{1}\]  
where, \(P_{c}\) is the consumed power (kW), \(P_{1-n}\) is the power value for conditions 1 through \(n\) (kW), \(A_{1(t)}\) is the binary value for conditions 1 through \(n\) at \(t\) 0 or 1, and \(t\) is time.
# Torsional Fracture Resistance of Niti Rotary Glide Path Files under Flexural Stress  
[PERSON]  
1 Department of Conservative Dentistry, Kyung Hee University Medical Center Dental Hospital,  
23 Kyungheedaero, Dongdaemun-gu, Seoul 02447, Korea; [EMAIL_ADDRESS] (S.O.); [EMAIL_ADDRESS] (H.-J.K.); [EMAIL_ADDRESS] (K.-K.C.)  
invasive root canal preparation. TruNatomy Glider has a variable taper that averages 2%, with tip size of #17 [6].  
The NiTi alloy is composed of nickel and titanium with an equi-atomic ratio [7]. The NiTi alloy can exist in different crystal structures depending on temperature, i.e., austenite and martensite [7; 8]. The martensitic phase of the NiTi alloy has lower Young's modulus and yield strength compared to those of austenitic phase of the NiTi alloy [7]. Namely, martensite is easily deformed and is soft and ductile [7; 8]. Conventional NiTi files mainly consist of austenite and exhibit superelasticity, which allows deformation of 8% strain to be recoverable [7; 8].  
NiTi files made from a thermo-mechanically treated alloy, Controlled Memory (CM) wire, do not possess superelastic properties. CM wire files contain some martensite at room temperature [8], which have demonstrated remarkable flexibility [9; 10]. These include V Taper 2H (SS White, Lakewood, CA, USA) and HyFlex EDM (Coltene-Whaledent, Alistatten, Switzerland) [10]. HyFlex EDM files produced by electrical discharge machining of CM wire exhibited superior resistance to cyclic fatigue fracture [11]. HyFlex EDM glide path files are available in sizes #10/05 and #15/03, and the latter released more recently has had limited assessment of its mechanical properties.  
Heat treatments with electropolishing during manufacturing produce blue surfaces with enhanced properties, as in RaCe Evo (FKG Dentaire, La Chaux-de-Fonds, Switzerland) [12]. However, the torsional fracture resistance of RaCe Evo glide path files has not been compared to original RaCe (FKG Dentaire).  
When the instrument's tip becomes tightly bound within a canal and the handpiece continues to rotate beyond the maximum strain it can withstand, torsional fracture occurs [13]. Testing torsional resistance in vitro according to ISO 3630-1 involves immobilizing 3 mm of the instrument's tip, while rotating the shaft continuously until fracture [14]. Although these tests are performed on straight instruments, actual canals are curved, and instruments resist both torsional and bending stresses simultaneously.  
This study aimed to compare NiTi rotary glide path file's design, phase transformation behavior, and torsional fracture resistance under flexural stress for six brands.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 3 Joint Optimization
### Joint Optimization Problem  
As shown in Algorithm 1, the key idea of the proposed algorithm is to alternately optimize the height and elevation subproblem and the power and path loss subproblem. In each iteration, the main complexity of the proposed algorithm lies in steps 4 and 6, which is needed to solve a series of convex problems. In iteration \(l\), let \(S^{l}=\sum_{l=1}^{T}P_{e}^{t}\), when \(S^{l}-S^{l-1}\leq\epsilon\), the joint optimization algorithm converges, where \(\epsilon\) is a predefined error tolerance threshold.  
```
1:Initialize \(l=0\), \(S^{0}=0\), \(P_{\text{G}}^{t}=P_{\text{U}}^{t}=P_{\max}\), \(\forall t=1,\dots,T\)
2:Repeat
3:\(l=l+1\);
4: For \(t=1:T\)
5: solve the height and elevation subproblem (14).
6: For \(t=1:T\)
7: solve the power and path loss subproblem (15).
8:Until \(S^{l}-S^{l-1}\leq\epsilon\)
```  
**Algorithm 1**The joint optimization algorithm
## 3 Results
### Test Results and Analyses of Small-Sized Granular Materials
#### 3.1.2 Effect of Particle Diameter on the Shear Strength of Small-Sized Granular Materials  
According to the \(q_{\text{f}}\) obtained under different confining pressures, as shown in Figure 6, the cohesion(\(c\)) and internal friction angle (\(\varphi\)) of the glass bead specimen of each size was calculated (Table 2). The relationship between the internal friction angle (\(\varphi\)) and particle diameter was then plotted, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Particle Diameter (mm)**} & \multicolumn{2}{c}{**Shear Strength Parameters**} \\ \cline{2-3}  & \(c\) **(kPa)** & \(\varphi\) (\({}^{\circ}\)) \\ \hline
2 & 13.06 & 14.51 \\
3 & 9.89 & 18.57 \\
4 & 5.67 & 19.99 \\
5 & 10.81 & 20.01 \\
6 & 1.32 & 22.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The shear strength parameters of granular materials with different sizes.  
Figure 6: Variation curves of peak shear stress \(q_{\text{f}}\) with particle diameter (\(d\)) for glass beads of different size under four levels of confining pressure.  
Figure 7 reveals that the internal friction angle (\(\varphi\)) exhibits a non-monotonic variation with particle diameter (d) within the range of 2 mm to 6 mm. Specifically, when the particle diameter increases from 2 mm to 3 mm, the \(\varphi\) increases by 4.06\({}^{\circ}\) (approximately 25%). From 3 mm to 4 mm, \(\varphi\) increases by 1.42\({}^{\circ}\) (approximately 8%). Between 4 mm to 5 mm, the increase in \(\varphi\) is minimal at 0.02\({}^{\circ}\) (approximately 0.1%), indicating a plateau in the relationship. Finally, as \(d\) increases from 5 mm to 6 mm, \(\varphi\) rises by 2.59\({}^{\circ}\) (approximately 12%). These results suggest that while the internal friction angle generally increases with particle diameter, the relationship is not strictly monotonic. Instead, there is a notable interval of minimal change between 4 mm and 5 mm, indicating a complex interplay between particle size and shear strength parameters. This observation highlights the importance of considering the specific particle size range when evaluating the shear strength characteristics of granular materials.