esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 4 Evaluation Results of Proposed Method
### Robustness
#### 4.2.1 Evaluation on Real Recordings
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test** & \multirow{2}{*}{**Dataset**} & **TP** & **FP** & **FN** & **PPV(\%)** & **SEN** & **F1-Score** \\
**Record** & & **AD\_0** & **627** & **10** & **17** & **98.43** & **97.36** & **97.89** \\  & & AD\_5 & 643 & 1 & 1 & 99.84 & 99.84 & 99.84 \\ r01 & AD\_10 & 644 & 0 & 0 & 100.00 & 100.00 & 100.00 \\  & AD\_Origin & 644 & 0 & 0 & 100.00 & 100.00 & 100.00 \\ \hline  & & AD\_0 & **469** & **132** & **163** & **78.04** & **74.21** & **76.07** \\ r04 & AD\_5 & 600 & 4 & 32 & 99.34 & 94.94 & 97.09 \\  & AD\_10 & 629 & 0 & 3 & 100.00 & 99.53 & 99.76 \\  & AD\_Origin & 632 & 0 & 0 & 100.00 & 100.00 & 100.00 \\ \hline  & & AD\_0 & **624** & **1** & **3** & **99.84** & **99.52** & **99.68** \\ r07 & AD\_5 & 627 & 0 & 0 & 100.00 & 100.00 & 100.00 \\  & AD\_10 & 627 & 0 & 0 & 100.00 & 100.00 & 100.00 \\  & AD\_Origin & 627 & 0 & 0 & 100.00 & 100.00 & 100.00 \\ \hline  & & AD\_0 & **643** & **7** & **8** & **98.92** & **98.77** & **98.85** \\ r08 & AD\_5 & 650 & 1 & 1 & 99.85 & 99.85 & 99.85 \\  & AD\_10 & 649 & 1 & 2 & 99.85 & 99.69 & 99.77 \\  & AD\_Origin & 651 & 0 & 0 & 100.00 & 100.00 & 100.00 \\ \hline  & & AD\_0 & **393** & **190** & **244** & **67.41** & **61.70** & **64.43** \\ r10 & AD\_5 & 606 & 33 & 31 & 94.84 & 95.13 & 94.98 \\  & AD\_10 & 630 & 4 & 7 & 99.37 & 98.90 & 99.13 \\  & AD\_Origin & 632 & 0 & 5 & 100.00 & 99.22 & 99.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of the proposed U-Net across different SNRs. The results for the 0 dB case are highlighted in bold. Figure 5: An example illustrating a 10-s segment for each record in the original A&D FEC database. The red boxes highlight the amplitude fluctuations observed in records r04 and r10, which exhibit distinct differences compared to the other records. Figure 6: Detection of the fetal R-peak on (**a**) recording r01 and (**b**) recording r04 with SNR of 0 dB (left case) and 10 dB (right case). Here, (**1**) shows the FECG segments used for testing; (**2**) shows the predictions of the model, indicating segments of potential fetal R-peak locations; (**3**) shows the remaining potential R-peak locations after filtering by the fixed threshold \(Thr_{1}\) in post-processing; and (**4**) shows the final detected fetal R-peaks, label as green stars. Blue regions highlight missed beats. Red regions highlight TP predictions after post-processing.
## 2 Underwater Rock-Plug Blasting Experiments
### Experimental Results and Analysis
#### 2.3.3 Comparison of Penetration Effects  
Figure 9 displays the effects of rock-plug blasting with different opening angles and heads of water. Two experiments were conducted under each condition. The research provided highly representative results, and the images were taken from the downstream water tank to the upstream one. As shown by Figure 9a-c, a conical funnel was formed on the downstream end face after the outflow of the block group from the blasting action when the charge was 0.7 g and the opening angle was 30\({}^{\circ}\). Whether the rock plug was penetrated or not was dependent on the motion of the block group at the upstream end face under the effects of water scouring. When \(\beta\) = 30\({}^{\circ}\) and h = 105 mm, it was observed in the experiment that most blocks from the upstream end face were accumulated in the rock plug and did not collapse under the effects of water scouring. With the opening angle \(\beta\) at 30\({}^{\circ}\) and h = 205 mm, the number of accumulated blocks at the upstream end face was found to be much lower than that under a head of water of 105 mm. When \(\beta\) = 30\({}^{\circ}\) and h = 305 mm, the rock plug could be penetrated and the minimum diameter of the channel was about 60 mm, with two rings of block particles remaining on the upstream end face and only one ring on the downstream end face. The rock-plug blasting effect when the head of water was kept unchanged and the opening angle was changed to 40\({}^{\circ}\) is shown in Figure 9d. After increasing the opening angle to 40\({}^{\circ}\), the penetration effect for the rock plug was similar to that with \(\beta\) = 30\({}^{\circ}\) and h = 305 mm. Under these conditions, the minimum diameter of the through-going channel reached 80 mm, and only a ring of block particles was retained around the whole channel in the rock plug. It is worth noting that the rock-plug sample was connected to the upstream and downstream water tanks via screw-type connectors nested in reserved segments with lengths of 25 mm on two sides of the polyvinyl chloride cylinder. Therefore, block particles in the outermost ring of the rock plug were affected by a clamping force resulting from the squeezing of the screw-type connectors that connected the upstream and downstream water tanks with the rock plug.  
In summary, the rock-plug blasting effect gradually improved with increasing heads of water in the upstream water tank when the charge remained unchanged.
# Natural Virtual Reality User Interface to Define Assembly Sequences for Digital Human Models
## 3 Results
### Objective Measures  
Table 1 shows the results of the task completion time (TCT) for the WIMP and VATS. The WIMP interface has the highest value of 435 s in the first trial and the lowest in the eighth trial (128 s). Individual \(t\)-tests were calculated between each trial. No ANOVA was calculated for all data, since only the main effects, i.e., general differences between VATS and WIMP and the eight trials, can be determined with the ANOVA. For us, the differences between the trials within one system (WIMP or VATS) are relevant. Since each data set was used twice, the \(p\)-value was corrected to 0.025. With the WIMP interface, there are significant differences between the trials one until four and significant differences between the fifth until the seventh trial. The VATS interface has a TCT of 59 s in the first and 23 s in the eighth trial. There only exist significant results between the first until the third trial. After that, no significant differences occur (Table 1). Besides, Figure 9 shows the errors for the WIMP interface and the VATS. An error in the VATS was counted when a user did not select the right grip type to perform the task. An error with the WIMP interface was counted when users were not able to fulfill the task.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **R1** & **R2** & **R3** & **R4** & **R5** & **R6** & **R7** & **R8** \\ \hline mean TCT VATS & 59 s & 35 s & 25 s & 27 s & 24 s & 25 s & 24 s & 23 s \\ \(p\) (\(t\)-test R/R + 1) VATS & 0.001 & 0.018 & 0.601 & 0.582 & 0.769 & 0.867 & 0.735 & 0.186 \\ mean TCT WIMP & 435 s & 281 s & 210 s & 179 s & 166 s & 142 s & 132 s & 128 s \\ \(p\) (\(t\)-test R/R + 1) WIMP & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & 0.193 & \textless{}0.01 & 0,02 & 0.314 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results for eight trials for the VATS as well as the WIMP. In the VATS, nine trials were conducted. Thus, we also tested the trial eight vs. trial nine. The \(p\) values with a grey background indicate the significant results of the \(t\)-test between trial R(X) and trial R(X + 1).  
Additionally, Figure 9 shows the errors for the WIMP interface and the VATS. On average, seven out of 29 users made errors with the VATS in one trial. However, the errors decrease throughout use. This means that even during repeated use, users selected incorrect grip types. With the WIMP application, an error was counted when the task was not completed correctly. The average amount of errors was one.
## 2 The Evolution and Trends of 6G Network Architecture
### The Design Fundamentals of 6G Networks
#### 2.1.1 Superconvergence  
Wired and radio systems that are not 3 rd Generation Partnership Project (3 GPP)-native will be essential parts of the 6G ecosystem. It would be impossible to implement many of the more radical changes that are covered below without a more seamless and scalable convergence between various technology families. For these convergent network segments, the emphasis will be on mutual or 3 GPP-driven security and authentication. In order to protect the unified network, wireline and wireless technologies such as Wi-Fi, WiGig, Bluetooth, and others will adaptively integrate with 6G by utilizing the strong security and authentication techniques of 3 GPP. This integration will make it easier to onboardand offload traffic between networks with different loads, which will greatly help with balancing traffic. Additionally, it will improve resilience because traffic delivery can be varied among many technology families.
## 2 Materials and Methods
### Thermoelectric Material Simulation  
To simulate the P-type BiSbTe\({}_{3}\), the structural coordinates of Sb\({}_{2}\)Te\({}_{3}\) (R\(-\)3 m, 166) were built, which are reported in inorganic crystal structure databases [29]. As shown in Figure 1a, three layers of Sb were substituted as Bi in the 2 \(\times\) 2 \(\times\) 1 supercell of Sb\({}_{2}\)Te\({}_{3}\) (totally 60 atoms) [30]. The Bi 5d, 6s and 6p, Sb 4d, 5s and 5p, and Te 5s and 5p electrons were treated as valence electrons. The Vienna ab initio simulation package (VASP) [31] was used for the Density Functional Theory (DFT) calculations using the projector augmented wave (PAW) method [32] and the Perdew-Burke-Ernzerhof (PBE) functional [33]. The cutoff energy of the basis set was set to 550 eV. The energy and the force of the ions were converged within 10\({}^{-5}\) eV and 10\({}^{-3}\) eV/A, respectively. The k-point meshes were set to 5 \(\times\) 5 \(\times\) 5 by the Monkhorst-Pack method [34]. The electronic thermal conductivity, electrical conductivity, and Seebeck coefficient were estimated by solving the Boltzmann Transport Equation (BTE) with Relaxation Time Approximation (RTA) [35; 36] using BoltzTraP code [37]. A constant relaxation time was used for the calculations to match the result with the experimental electrical resistivity of the Bi\({}_{0.5}\)Sb\({}_{1.5}\)Te\({}_{3}\) at carrier concentration of 2 \(\times\) 10\({}^{21}\) cm\({}^{-3}\). The lattice thermal conductivity was also taken from the experimental value of Bi\({}_{0.4}\)Sb\({}_{1.6}\)Te\({}_{3}\)[38].  
Figure 2 shows the data obtained from the simulation and the ZT graph at room temperature (300 K) at each carrier concentration. The data were extended through curve fitting for further analysis. As theoretically suggested, the thermal conductivity and the electrical conductivity increase with the carrier concentration while the Seebeck coefficient decreases and the ZT has a maximum at around 10\({}^{19}\) cm\({}^{-3}\).  
Figure 1: **(a)** Crystal structure of P-type BiSbTe\({}_{3}\) **(b)** Thermal resistance model of the human body application. Peltier effect and load matching were considered. \(R_{t,air}\), \(R_{t,tkg}\), and \(R_{t,body}\) are thermal resistances of air, thermoelectric generator (TEG), and body, respectively. \(T_{air}\) is ambient temperature and \(T_{body}\) is core temperature of the body. \(Q_{j}\) is Joule heat generated by current through the device, and \(Q_{peltier}\) is heat flux generated by the Peltier effect.
## 6 Results and Discussion
### Method-Specific Insights  
#### 6.2.1 High-Performance Methods  
* The SWT's Advantage: The scattering network's invariance to small deformations explains its experimental dominance:
* The WVD's Trade-off: While achieving 94.74% experimental accuracy, its performance is bounded by the Heisenberg uncertainty principle: \[\sigma_{t}\cdot\sigma_{f}\geq\frac{1}{4\pi}\] (12)
# An Ordered Universal Accumulator Based on a Hash Chain
## 2 Background
### Definitions
\(\left(\mathfrak{Acc}_{i},m_{i},w_{i}\right)=Update(x_{i},\mathfrak{Acc}_{i-1}, m_{i-1})\) _for all_ \(1\leq i\leq n\)_._  
_Then, the accumulator value \(\mathfrak{Acc}\) represents the set \(X\subseteq M\), denoted by \(\mathfrak{Acc}\Rightarrow X\). If no such sequence exists, then \(\mathfrak{Acc}\) does not represent the set \(X\), denoted by \(\mathfrak{Acc}\ltimes\times\)._  
**Definition 3** (Security of Strong Universal Accumulators with Memory). : _A strong universal accumulator with memory is secure if, for every probabilistic polynomial-time adversary \(A\), the following conditions hold:_  
1. _Consistency_ \[pr\left[ \begin{array}{c}(x,w_{1},w_{2},X,\mathfrak{Acc})\leftarrow A(k)\\ \mathfrak{Acc}\Rightarrow X,x\in X,Belongs(x,w2,\mathfrak{Acc})=0\\ \mathfrak{Acc}\Rightarrow X,x\
ot\in X,Belongs(x,w1,\mathfrak{Acc})=1\end{array}  \right]=negl(k)\]
2. _Secure addition_ \[pr\left[ \begin{array}{c}\left(X,\mathfrak{Acc}_{bf},x,\mathfrak{Acc}_{af}, w\right)\leftarrow A(k)\\ CheckUpdate\left(x,\mathfrak{Acc}_{bf},\mathfrak{Acc}_{af},w\right)=1\\ \mathfrak{Acc}_{bf}\Rightarrow X,\mathfrak{Acc}_{af}\ltimes X\cup\{x\}\end{array}  \right]=negl(k)\]
4. _Secure deletion_ \[pr\left[ \begin{array}{c}\left(X,\mathfrak{Acc}_{bf},x,\mathfrak{Acc}_{af}, w\right)\leftarrow A(k)\\ CheckUpdate\left(x,\mathfrak{Acc}_{bf},\mathfrak{Acc}_{af},w\right)=1\\ \mathfrak{Acc}_{bf}\Rightarrow X,\mathfrak{Acc}_{af}\ltimes X\backslash\{x\} \end{array} \right]=negl(k)\]  
Since the proposed scheme is based on a hash chain, it is not quasi-commutative. And, its accumulated set is an ordered set; that is, the accumulator value \(\mathfrak{Acc}\) depends on the specific sequence of the update operation. In other words, for any accumulator value, there is only one specific sequence to compose, which will be demonstrated later in this paper. **Security Game \(\mathbf{G_{uni}}\):** The challenger initializes the accumulator \(\mathfrak{Acc}\) for an empty set \(X^{\prime}\). The adversary \(A\) adaptively queries the oracle O to insert elements into \(X^{\prime}\), updating \(\mathfrak{Acc}\) and requesting proofs for elements. \(A\) can arbitrarily construct the set \(X^{*}\subseteq M\) and \(X^{*}\
eq X^{\prime}\). If \(\mathfrak{Acc}\Rightarrow X^{*}\), then \(A\) wins the game.
# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### Transformative Role of ML in 3D Printing  
The transformative role of machine learning in 3D printing has revolutionized design, manufacturing, and quality assurance processes. ML algorithms enable the optimization of 3D designs by analyzing complex geometries and suggesting improvements in performance, strength, or material yield. In materials science, machine learning predicts the behavior of new materials under different conditions, accelerating the development of advanced fibers and composites. During the printing process, ML-based monitoring systems detect and correct errors in real time, reducing waste and ensuring higher-quality prints. Predictive modeling helps tune printing parameters such as speed, temperature, and layer thickness, increasing precision and reliability. ML also enables adaptive control, allowing printers to respond dynamically to changes in the environment or material properties. Generative design algorithms powered by ML create innovative structures that maximize productivity while minimizing material consumption, often leading to designs unattainable by traditional methods [9]. Quality assurance benefits from ML's ability to analyze post-print inspection data, identify defects, and suggest corrective actions [10; 11; 12]. Additionally, ML integration facilitates predictive maintenance, reducing downtime by predicting printer part failures. As 3D printing scales to mass production, ML ensures consistent quality, productivity, and innovation, making it essential to the industry's growth (Figure 2) [13].  
Emerging applications of machine learning in 3D printing that have not yet been defined may include, for example, real-time adaptive printing, i.e., ML can enable 3D printers to adapt to anomalies or unexpected changes during the printing process, such as changes in temperature, material consistency or layer alignment, providing higher-quality prints. ML could facilitate in-process quality control by identifying imperfections in prints as they are being formed and suggesting or applying real-time adjustments to reduce or compensate for defects. In addition, ML algorithms could predict when 3D printer components might fail or require maintenance, reducing downtime and increasing productivity. Through ML integration, 3D printers could optimize the reuse of failed prints and waste materials, identifying the best way to recycle them into new printing materials with minimal degradation. More broadly, ML could analyze consumer and industry trends to suggest innovative 3D printing applications, material combinations, and design ideas, driving faster adoption in niche markets, including within sustainable 3D printing or material or energy availability constraints.
# Design and Modelling of an Amphibious Spherical Robot Attached with Assistant Fins
## 1 Introduction
Four Omni-Paddles are disposed in a radical pattern around the outside of the robot, with which the robot is capable of moving towards any direction. The Omni-Paddle works as not only a driving mechanism but also a source of buoyancy in one prototype of the robot. Each of the amphibious robots mentioned above has its advantages and disadvantages. A snake-like robot adapts well to complex terrains; however, it usually needs many units of bodies in order to achieve a high terrain adaptability, as a result requiring a large amount of motors. This leads to low robustness and high energy consumption, which is the bottleneck in field applications. A robot equipped with water-jet propulsion system can control its position and attitude precisely underwater. However, another actuation system is needed for its locomotion on land, resulting in a complex actuation system and extra weight. A robot with paddles mimics the locomotion modes used by amphibians such as ducks and turtles, which is advantageous in that it uses the same set of actuation system for both on land and in water locomotion; however, it calls for complex algorithms for gait planning in order to reach dynamic stability. In addition, the existence of feet increases risk of being entwined by water plants or shrubs. A spherical robot bears very few motors that are sufficient for both on land and in water locomotion. Because of its inherent sealing characteristic, good stability and low energy cost, spherical robots have a great potential to be used in amphibious locomotion in field applications. However, the relatively slow locomotion speed on the water due to frictionless spherical shell and the poor maneuverability are main hindrances in using a spherical robot as an amphibian. To overcome these limitations, a novel amphibious spherical robot design is proposed in this paper, by attaching assistant fins to the outside surface of the spherical shell to increase its locomotion speed, and by using a combination of pendulum and rotator as the Inside Drive Unit (IDU) to propel the robot agilely. It is shown that this design strategy significantly improves the mobility capacity of the robot, and enhances its resistance to environment undulation, making it practicable to use a spherical robot both on land and on water. The remainder of the paper is organized as follows. In Section 2, the mechanical design, the electrical actuation and the control system of the amphibious spherical robot are introduced. In Section 3, the equations of the robot's motion are deduced based on kinematic analyses and force analyses. In Section 4, simulations of the dynamic model are presented, and the experiments to test the performance of the robot are described in detail. Finally, the conclusion is given in Section 5.
## 3 Results and Discussion
### Validation of the Key Assumption of the Study
#### 3.4.3 Performance of Models with Lags of the Same Variable  
To investigate auto-correlation, we ran both VIF and performance test on the 40 possible models with lags of the predictor variables. Only three models of the possible 40 returned VIF > 5 implying multi-collinearity. Despite the results showing that the lags for the same variable can be used in the same model for the balance of 37 models, an actual investigation of their performance, as provided in Figure 16, provides contrary evidence.  
Only 17 models post a gain in performance of 1% or more. All the models that post an \(R^{2}\) of at least 0.5 either have a loss of 6% in performance to a gain of 1% implying poor return in having multiple lags of the same variable in a model.
## 3 Results and Discussion
### Superiority Verification of the Optimized DDARF Program  
#### 3.2.1 Model Building and Loading Mode  
In order to research the advantages of the optimized DDARF in grid generation, calculation accuracy and calculation efficiency, uniaxial compression experiments were simulated by the optimized DDARF and original DDARF. The fracture opening of rock has self-similarity, and its analysis can be conducted by using fractal theory, which can simulate the expansion law of joints in rock mass. Some scholars have also obtained important conclusions through fractal modeling [29]. Based on fractal theory, the following models can be established.  
Three cases of rock models are established: Case 1, Case 2 and Case 3. The geometric parameters of the rock models in the three cases are shown in Figure 8. The dimensions of the rock models in the three cases are 140 mm \(\times\) 70 mm. A steel plate with a thickness of 20 mm is added to the bottom for fixing, and there is a preset joint with a length of 30 mm in the middle of the rock model. The mechanical parameters of the rock models are shown in Table 2, the elastic modulus of the lower steel plate is 100 GPa, and the Poisson's ratio of the steel plate is 0.25. A total of eight measuring points are set in the upper, middle and lower parts of the model, which can record the stress  
The boundary conditions of the uniaxial compression experiment are as follows: a steel plate is placed at the bottom of the rock block, the steel plate is fixed, a uniform load is applied on the upper part of the rock block, and the value of the uniform load increases slowly from 0 until the calculation finished and the remaining parts of the rock block are free.  
In Case 1, the original DDARF program is used to generate the grid, as shown in Figure 9a. The grids are uniform with a total of 817. After calculation, the area of each grid is about 12 mm\({}^{2}\).  
Case 2 uses the optimized DDARF program for grid generation, as shown in Figure 9b, in order to observe the expansion of the preset joint. Dense grids are generated in the area around the preset joint, while in the area away from the preset joint sparse grids are generated, and the total number of grids is also 817. The area of each dense grid is around in 1-5 mm\({}^{2}\) while that of each sparse grid area is about 40 mm\({}^{2}\)-50 mm\({}^{2}\).  
In Case 3, the original DDARF is used for grid generation. In order to compare the calculation accuracy, the area of each grid in this case is also about 1 mm\({}^{2}\)-5 mm\({}^{2}\); however, since the original DDARF can only generate uniform grids, an average area value of 3 mm\({}^{2}\) is taken as the area of each grid. Thus, there should be 3267 grids generated and a uniform grid model with 3267 grids, as shown in Figure 9c.  
A uniformly distributed load is applied above the model, as shown in Figure 8,to conduct a numerical simulation experiment of uniaxial compression. Screen recording software is used to record the whole process.