esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion of Results
### Test No. 1: Payload  
Test no. 1 was performed to determine the payload, i.e., the maximum weight that the robotic manipulator can support both when stationary and when moving. When the motors of the manipulator are not driven, the load is supported by the torque created in  
Figure 15: Final assembled robotic arm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Filament**} & \multicolumn{2}{c}{**Temperature [\({}^{\circ}\)C]**} & \multicolumn{2}{c}{**Print Speed**} & \multicolumn{2}{c}{**Layer Height**} & \multicolumn{2}{c}{**Infill**} \\  & **Print** & **Bed** & **[mm/s]** & **[mm]** & **Density [\%]** \\ \hline PLA & 215 & 70 & 80 & 0.2 & 20–50 \\ PETG & 240 & 80 & 80 & 0.2 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main print parameters for different filaments.  
the gearbox. When in motion, it is ensured by the torque produced by the motor(s). To perform this test, we resorted to the physical consideration that the force is maximum for a given weight and the horizontal distance (0\({}^{\circ}\)) of the mass to the center of movement is greater. The robotic arm was extended horizontally, which corresponds to zero degrees (0\({}^{\circ}\)) for axes two and three, and weight was gradually added in the center of the final effector until the arm began to sag. Subsequently, the masses were weighed, obtaining a maximum weight of 314 g. The results of test no. 1 (payload) determine the maximum weight that the robotic arm supports while stationary or moving and with or without the arm fully extended. The developed robot supports 0.314 kg. This result is quite acceptable, since the developed arm is entirely made of PLA, including the pinion and gear set, motor without mechanical lock, and low electromechanical torque.
## 3 Multi-Dimensional Fusion Feature-Enhanced Prediction Method
### Data Augmentation of Multi-Dimensional Feature Engineering
#### 3.1.1 Technology Index  
Technical indicators are statistics calculated based on historical price data. They play an important role in reflecting market trends, trading signals, and price volatility characteristics, and can provide basic market dynamic information for price forecasting.  
(1) Relative strength index (\(RSI\))  
The \(RSI\) measures the relative strength of price rises and falls over a period of time. Its calculation formula is as follows:  
\[RSI=100-\frac{100}{1+\frac{AverageCain}{AverageLoss}}. \tag{1}\]  
Among them, \(AverageGain\) is the average value of the increase in the selected time period, and \(AverageLoss\) is the average value of the decline in the same period. In the actual calculation, the daily price change value (rise is positive, fall is negative) is usually calculated first, and then the cumulative rise and fall are calculated, respectively, and the average is calculated according to their respective cycles (14 days in this study). Finally, the \(RSI\) value is calculated by substituting the above formula. Its value range is usually  
Figure 2: Multi-dimensional feature engineering diagram (shows the multi-dimensional feature engineering framework, covering the design and integration of technical indicators, volatility indicators, time characteristics, cross-variety linkage characteristics, and lag characteristics).  
between 0 and 100. When the \(RSI\) value is higher than 70, it indicates that the market is overbought and the price may be called back. When the \(RSI\) value is below 30, the market is oversold and the price may rebound. \(RSI\) provides investors with a reference to short-term market sentiment by measuring the relative strength of market buying and selling power.  
(2) Bollinger bands (BBANDS)  
The Bollinger belt is composed of an upper rail, middle rail, and lower rail. The calculation process is as follows:  
Middle rail: Typically, this is represented by the moving average; for instance, a 20 day moving average. The computation method aligns with the standard moving average formula mentioned earlier.  
\(Upper\) rail and \(Lower\) rail:  
\[UpperBand =MA_{n}+k\times\sigma \tag{2}\] \[LowerBand =MA_{n}-k\times\sigma \tag{3}\]  
where \(MA_{n}\) is the daily moving average of \(n\) (e.g., \(n=20\)), \(\sigma\) is the standard deviation of the price in the corresponding period, and \(k\) is a constant (generally 2).  
The fluctuation in the belt's width can also signal shifts in market volatility. A decrease in the bandwidth might suggest an impending significant price movement. If the price nears the upper boundary, it could indicate that the market is becoming overbought; conversely, if the price approaches the lower boundary, it may suggest that the market is oversold.
## 3 Results and Discussion
### Bioactive Compounds of Camelina Oils
#### 3.2.1 Fatty Acid Composition and Nutrient Values
The quality of dietary fat depends on its level of SFA, MUFA, PUFA, and especially on the ratio of \(n\)-6 to \(n\)-3 fatty acids [15; 16; 32; 45]. [PERSON] et al. [46] summarized the effects of the quantity and quality of dietary fat on risk coronary heart disease (CHD), factors for type-2 diabetes, body weight, and cancer in healthy subjects and in subjects at risk of these diseases. In 1991, [PERSON] and [PERSON] [32] recognized seven dietary factors that can be associated with CHD. Saturated fatty acids, which raise cholesterol levels, and thrombogenic SFA are promoters of CHD, whereas the \(n\)-3 and \(n\)-6 isomers of PUFA, MUFA, and antioxidants have been described as protecting against CHD. For assessment of the nutritional value of oils, three nutritional quality indices were also determined: the atherogenicity index (AI), the thrombogenicity index (TI), and the hypocholesterolemomic: hipercholesterol FA ratio (HH) were calculated on the basis of the fatty acid composition of the amelina oils. These indices are more useful than fatty acid composition in evaluating oils nutritionally. The AI values of all camelina oils amounted to between 0.05 and 0.07, and did not differ statistically at \(\alpha=0.05\). [PERSON] and [PERSON] [32] showed that the AI of oils from coconut, palm and olive are 13.63, 0.88 and 0.14, respectively. [PERSON] et al. [37] determined AI value oils for avocado fruit (0.40) and borage (0.18), while similar values were shown for apricot kernel (0.05) and dill seed (0.08) oils. The studied amelina oils were also characterized by low TI (0.1), not statistically different. A similar TI value was provided by [PERSON] et al. [37] for apricot (0.11), and for oils obtained from blackcurrant and hemp seed--0.23. Significantly higher TI values for coconut (6.81) and palm (2.07) oils were demonstrated by [PERSON] and [PERSON] [32]. The HH ratio of tested amelina oils ranged from 11.7 to 14.7 and was similar to oils obtained from dill (12.56), hemp (14.88), and blackcurrant seed (13.82) [37]. Therefore, enriching the diet with amelina oil, which is characterized by low AI and TI and a relatively high HH ratio, can make the diet less atherogenic without making it less thrombogenic, thus contributing to a decrease in the incidence of coronary heart diseases (CHD) [32; 37; 46].
## 4 Simulation and Experimental Results  
In this section, we verify the feasibility and effectiveness of the designed circular formation controller with obstacles avoidance through simulations and experiments. We first use MATLAB R2018b platform to carry out numerical simulations. Then, we utilize three nonholonomic mobile robots based on Ubuntu 18.04 with ROS (Robot Operating System) to further verify the performance of the proposed controller.
## 3 Results
### Potential Landslide Scar Mapping from SBAS Method  
Since the accuracy of the SBAS method is verified in this area with GNSS and slope inclinometer, landslide mapping based on this method is possible in the study area. In order to set up a standard operating procedure to map the landslide area, the following steps are executed after several tests.  
1. Calculate vertical displacement from LOS displacement after SBAS analysis.
2. Interpolate vertical displacement to raster format.
3. Overlap raster vertical displacement with shaded hill derived from the digital elevation model.
4. Compare displacement with LiDAR identified scars.  
The potential landslide scars are mapped after the procedures, as shown in Figure 12. The landslide scars from LiDAR are also shown in the figure to compare the differences. The figure shows that most of the LiDAR base landslide area is moving, which means mapping landslides from LiDAR data is correct. However, the sliding displacement also means the priority to do further monitoring or engineering treatment. The SBAS method shows a possible procedure to select sites for hazard mitigation. The method based on this research mapped more potential landslides than LiDAR mapped landslides. These potential landslides are moving, but the sliding threshold is difficult to define due to continuous regular sliding. The primary observation from this LiDAR DEM-based SBAS deformation shows that most of the sinking displacement occurs at the mapped scar's crown. Also, some uplift deformation can be observed at a lower elevation of the slope.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 3 Experiment and Result Analysis
### Experimental Results and Analysis
#### 3.3.2 Comparison of Mimo-Net and Y-Net at 256 \(\times\) 256 Scale  
The reconstructed results of the Mimo-Net and Y-Net models were compared using images at 256 \(\times\) 256-scale, the enlarged comparison figures are shown in Figure 6.  
Figure 6 shows that the intensity and phase images reconstructed by the proposed Mimo-Net network have better results than the reconstructed images from the Y-Net network. Table 3 shows the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) between the reconstructed images from the Mimo-Net and Y-Net networks and the original images.  
Table 3 shows that the Mimo-Net network model has improved the PSNR of the reconstructed intensity and phase images by 2.4201 dB and 0.3031 dB, respectively, compared to the Y-Net network. The SSIM values have also increased by 0.0212 and 0.0044, respectively. Therefore, the Mimo-Net network exhibits better reconstruction performance.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 1 Introduction
Some harmonies may lead a node's final position to be far from the target; however, such harmonies are not that useful. The closer the distance between node and target, the better the performance may be. Therefore, we adjust the probability of a new harmony being generated as increasing as the distance to the target decreases, in order to improve tracking accuracy and searching speed. The main contributions of this paper are threefold. Firstly, we propose a node depth adjustment scheme to dynamically improve tracking accuracy in UWSNs. This is a novel idea that has not been done before. Secondly, we employ the relationship between FIM and nodes' depth as an objective function, and determine moving distance in the framework of optimization problems. Thirdly, an improved HS is proposed to solve this optimization problem, improve tracking accuracy and searching speed. The rest of the paper is organized as follows. In Section 2, an overview of the related work about this paper is provided for readers. In Section 3, the target tracking problem in UWSNs is formulated based on node depth adjustment. In Section 4, the relationship between FIM and nodes' depth is employed to determine nodes' depth, and an optimization optimization problem is formulated. An improved Harmony Search algorithm is proposed to solve the optimization problem in Section 5. Simulation results are presented in Section 6 and conclusions of this paper are drawn in Section 7.
# Degradation Characteristics and Reliability Assessment of 1310 nm VCSEL for Microwave Photonic Link
## 5 Conclusions  
In this work, the degradation characteristics and lifetime evaluation of the 1310 nm VCSEL are demonstrated. When the electrical stress is 6 mA, 8 mA and 10 mA, the maximum output power reduces by 5%, 6% and 15% of the initial value, respectively. We find that leakage current increases and reverse bias breakdown voltage decreases after the accelerated current aging test, and the increase in leakage current is explained by the tunneling mechanism. The current noise power spectral density, the frequency index factor and noise amplitude gradually increase with bias current increase from 0.001 mA to 0.5 mA. Meanwhile, the current noise power spectral density increases after the device aging, and the noise increases with the augment of the electrical stress. The result indicates that with the increase of the electrical stress, the active region dislocation and oxide layer defects of the device increase. In addition, lifetime fitting curves of the devices at accelerating current of 6 mA, 8 mA and 10 mA are obtained, and the median lifetime of 67 years at operating current is extrapolated. This technique can also be used in degradation characterization and lifetime evaluation of 1550 nm VCSEL, which is of great significance for the reliability assessment of optical communication devices.  
Conceptualization and experiment design, W.L.; Measurement and evaluation, W.L., R.G. and J.Z.; Investigation, W.L. and S.L.; Methodology, H.N. and S.Y.; Supervision, C.L.; Writing, review and editing, W.L. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory, grant number 6142806200304.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experiment
### Comparison Experiment Result Analysis  
We compare the TGAN and GAN in both generated page layout and subjective-objective scoring to highlight the performance of the TGAN. Firstly, we randomly selected 25 layouts from the results of the TGAN. The layout diagrams are shown in Figure 9, and the correspondence between object types and colors is illustrated in Figure 9.  
Similarly, we randomly selected 25 layouts from the results generated by the GAN. The layout diagrams are presented in Figure 10, and the correspondence between object types and colors is depicted in Figure 10.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Alignment** & **Overlap** & **Balance** & **White Space** & **Boundary** & **Scale** \\ \hline Standard GAN & 0.62 & 0.31 & 0.58 & 0.61 & 0.72 & 0.65 \\ TGAN w/o Self-Attention & 0.68 & 0.25 & 0.63 & 0.66 & 0.78 & 0.70 \\ TGAN w/o Conditioning & 0.74 & 0.19 & 0.71 & 0.70 & 0.82 & 0.75 \\ Fixed-Weight & 0.82 & 0.12 & 0.78 & 0.75 & 0.87 & 0.81 \\ Adaptive-Weight (Full) & **0.89** & **0.07** & **0.85** & **0.82** & **0.93** & **0.88** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Component-wise ablation study on layout quality metrics (bold represents the best result).  
Figure 9: TGAN layout results.  
Compared to the results generated by the GAN, the TGAN exhibits a more aesthetically pleasing layout overall. In summary, the following points highlight the differences:  
1. Results generated by the TGAN exhibit better alignment and fewer instances of overlap, facilitating more efficient information conveyance.
2. TGAN layouts adhere to topological constraints among objects, arranging elements with constraints in a logical manner on the page, making the relationships between elements clear and maximizing their expressive potential.  
In order to better assess the subjective and objective scores of the TGAN, we selected 200, 400, and 600 generated samples from the TGAN, GAN, LayoutTransformer [6], VTN [7], L-CGAN [5], and PLAY [8], and we evaluated the results using subjective and objective assessment methods. The evaluation results are shown in Table 2.  
The results in Table 2 indicate that in subjective evaluation, under the three different sampling approaches, the mean layout scores of the TGAN are consistently higher than those of the other methods, while the variances are consistently lower. Notably, our TGAN demonstrates progressive quality improvement with more training samples, whereas baseline methods plateau after 400 samples. This suggests superior learning capability for complex layout patterns. In terms of objective evaluation, both the mean and variance of the energy item weighted sum for the TGAN are lower than those of the other methods.  
Figure 10: TGAN layout results.  
Overall, this suggests that the TGAN exhibits higher layout quality and stability compared to other methods.
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
###### Abstract  
Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (\(P\)-\(V\)) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.  
renewable energy; energy storage system; hybrid multi-objective particle swarm optimization; back-propagation neural network; power-voltage sensitivity analysis; three-point estimation method +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Testing Methods
#### 2.3.2 Thermogravimetric Analysis  
Thermogravimetry (TGA) was performed using a NETZSCH 209 F1 Libra gravimetric analyzer (Selb, Germany). Samples of 4 \(\pm\) 0.2 mg were placed in Al\({}_{2}\)O\({}_{3}\) crucibles. Measurements were conducted under nitrogen (flow of 20 mL/min) in the 30-800 \({}^{\circ}\)C range and a 10 \({}^{\circ}\)C/min heating rate. Sensitivity: 0.1 \(\upmu\)g.
# Research Progress in the Construction and Application of In Vitro Vascular Models
## 3 Application
The importance of 3D bioprinting in specific vascular applications lies in its ability to precisely control and customize vascular structures to meet medical and research needs. This technology can print complex vascular structures with micrometer-level accuracy, including various components such as endothelial cells, matrix scaffolds, and vessel walls. This precision is crucial for mimicking the morphology and function of natural blood vessels. Bioprinting materials must possess compatibility and biocompatibility with the body's environment to support cell adhesion, proliferation, and differentiation [89]. Choosing appropriate bioinks and scaffold materials is a key technological challenge. Designing various types of vessels, including those with different diameters, branches, bends, and diverse biological and physical characteristics according to specific needs, demonstrates the flexibility of this technology, making it suitable for various clinical applications and research scenarios. By integrating multiple cell types (such as endothelial cells, smooth muscle cells, and fibroblasts) during printing, it is possible to better simulate the complex structure and function of natural blood vessels, thereby enhancing the success rate and functional performance of vascular construction [91]. Controlling the printing microenvironment and the release of growth factors can promote vascular generation and regeneration, which is crucial for repairing damaged vessels or improving blood supply. Laser degradation and laser cavitation technologies have significant application value in constructing vascular models. These technologies utilize precise energy control and local action characteristics of lasers to simulate and reconstruct complex vascular structures. By using high-energy laser pulses, the focus and energy of the laser can be precisely controlled to construct fine structures and channels on different material surfaces or within tissues [49]. Therefore, in constructing vascular models, this technology can be used to create highly refined microstructures, such as the intricate internal structures of vessel walls or various complex forms mimicking vessel walls. Lasers can achieve local evaporation or chemical changes on different material surfaces or tissues to form specific shapes or structures. When constructing vascular models, different types of biocompatible materials or simulated tissues can be used, and their shapes and microstructures can be precisely controlled using laser technology to simulate the characteristics of real blood vessels. This technology allows high-precision processing at the microscopic scale, enabling constructed vascular models to possess cellular-level structural features [50]. Such precision and complexity enable researchers to more accurately study the physiological and pathological processes of blood vessels, such as hemodynamics and mechanisms of thrombosis, thereby advancing vascular model development.
## 2 Modeling and Blade Modal Identification of AMB-Rotor System
### Blade Modal Identification by Sinusoidal Sweep Excitation
The rated speed of the MSTMP is 24,000 rpm, which exposes the multi-stage blades to the comprehensive effect of thermal stresses, centrifugal forces, and alternating loads during acceleration and deceleration. These considerable forces generated by high-speed rotating machinery induce two effects: stress stiffening and spin softening. Consequently, the multi-stage-blade modal frequencies \(w_{fi}\) are not constants but vary with the rotor speed. As shown in Figure 4b, multi-stage-blade modes bifurcate into forward (FW) modes and backward (BW) modes. The labels in Figure 4b show frequencies during static suspension (0 rpm: 419 Hz, 525 Hz, 583 Hz, 674 Hz, 710 Hz, 1598 Hz) and after reaching the rated speed (24,000 rpm: 350 Hz, 463 Hz, 530 Hz, 628 Hz, 745 Hz, 980 Hz). In short, the multi-stage-blade mode frequencies \(w_{fi}\) are constantly changing in the full speed range. Modeling and blade modal identification of the multi-stage-blade AMB-rotor are crucial to subsequently determine the precise notch frequency, select the appropriate notch width, and adjust the optimal notch depth of the parallel co-frequency ANF and cascaded multi-frequency improved DTNFs.