esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 4 Implementation and Simulation Results of the N78 Band Sub-6 GHz DPA  
The final circuit design of the DPA is shown in Figure 14 and is composed of several parts: an unequal Wilkinson power divider to split the input signal, a phase compensation network to correct the phase difference between the carrier and peaking branches, and input-matching networks, consisting of two identical CG2H40010F devices. Additionally, the design features output-matching networks, an impedance inverter network, and a harmonic suppression network with a 35.35-ohm impedance transformer. To enhance performance, bypass capacitors with a high Q factor of 500 were added to the DC biasing line for amplifiers C\({}_{1}\), C\({}_{2}\), C\({}_{3}\), and C\({}_{4}\), with capacitance values of 1 pF. The DC coupler  
Figure 12: Harmonic suppression network design. (**a**) Ideal transmission line schematic. (**b**) Layout design with dimensions in mm.  
Figure 13: Results of the electromagnetic simulation of S-parameters for the proposed harmonic suppression network.  
comprises surface mount device (SMD) capacitors C\({}_{5}\), C\({}_{6}\), C\({}_{7}\), and C\({}_{8}\) with capacitance values of 15 pF. The DPA was fabricated on a Rogers RT/duroid 5880 substrate, which has a relative dielectric constant (\(\epsilon\)r) of 2.2, a substrate thickness (H) of 31 mil, a loss tangent (Tan \(\delta\)) of 0.0009, and copper conductors with conductivity (\(\sigma\)) of 5.8 \(\times\) 10\({}^{3}\) and a thickness (T) of 0.035 mm. The main and auxiliary amplifiers are powered by the same drain bias voltage (V\({}_{\rm DS}\)) of 28 V, with the main gate bias voltage being set at \(-\)2.7 V and the auxiliary gate bias voltage set at \(-\)4 V. The scatter parameters of the designed DPA are displayed in Figure 15. The simulation results indicate that despite a slight shift towards lower frequencies, the DPA maintained a small-signal gain of approximately 14 dB within a 600 MHz bandwidth ranging from 3.20 GHz to 3.80 GHz, providing full coverage of the N78 frequency band. Additionally, input matching was achieved with better than 13 dB across the operating range from 3.20 GHz to 3.90 GHz. The output matching was approximately 13 dB in the higher portion of the operating band, ranging from 3.2 to 3.9 GHz, but slightly decreased towards the lower end of the band.  
Figure 14: Completely layout design of the proposed Doherty power amplifier.  
Figure 15: The scattering parameters of the proposed DPA.
## 3 Methodology  
### Experimental Setup  
To evaluate the algorithm, an OCC system was implemented in different scenarios, and communications were recorded in these settings. Subsequently, the algorithm was adapted to extract images from the video sequences as if it were real time.  
The OCC system was implemented using embedded systems. On one hand, four Arduino Nano 33 devices were used, to which various types of LEDs were connected as transmitters. These devices transmitted all possible frames consecutively and indefinitely. On the other hand, a Raspberry Pi v3 was used with a high-performance camera as the receiver. The recorded videos had a minimum duration that allowed the recording of all 256 possible transmitted frames (1 byte of payload). Due to the memory and processing capabilities of the Raspberry Pi, the algorithm was executed on the videos using a laptop and an external hard drive. The characteristics of the main elements used can be observed in Table 2.  
The system was implemented in three scenarios: 1 indoor, which can be seen in Figure 4, and 2 outdoors, which can be seen in Figures 5 and 6. Different wavelength LED devices (red, green, blue and white) were used in these scenarios to assess how the algorithm's performance varied with respect to the emission spectrum. Regarding the distance, minimum distances were maintained to achieve sub-pixel projections as defined in Equations (1) and (2). Specifically, a link range of 32 m was used in the indoor scenario, whilst 8 m and around 70 m were used for the outdoor tests during the daytime and nighttime, respectively. To evaluate these scenarios under different lighting conditions, several videos were recorded in some of them by modifying the exposure time and sensitivity parameters. Specifically, 3 videos were recorded in the indoor scenario, 2 in the daytime outdoor scenario, and 1 in the nighttime outdoor scenario.  
Figure 4: Indoor experimental setup. The resulting link range in the depicted corridor was 32 m.  
Figure 5: Outdoor daytime experimental setup. The resulting link range was 7 m.
# Design Optimization of Underground Mining Vehicles Based on Regenerative Braking Energy Recovery
## 2 Design of Regenerative Braking System for Underground Mining Vehicles  
This study specifically examines the energy consumption analysis and regenerative braking design of the CTY5/6 GB explosion-proof electric motor car manufactured by the Xiangtan Yutong Company, as depicted in Figure 1. This motor car features an optimal overall structure, dependable explosion-proof performance, high efficiency and energy-saving capabilities, and a significant starting torque, guaranteeing stable operation. It is equipped with various protective devices, requires minimal maintenance, and provides the option of mechanical braking, regenerative braking, and hydraulic braking, thereby enhancing the vehicle's safety and reliability.  
Regenerative braking operates by harnessing the reversible principle of the electric motor/generator. Its fundamental principle is as follows: due to inertia, the rotational speed of the wheels transmitted to the motor rotor through the reducer exceeds the motor's synchronous speed. Consequently, the rotor cuts the magnetic flux lines in the opposite direction, generating a reverse electromotive force greater than that of the stator coil. This causes the driving motor to enter the energy recovery mode, and the traction motor exerts a reverse force on the driving axle, creating a regenerative braking force on the wheels [13; 14]. However, it is important to note that while regenerative braking can recover braking energy and provide partial braking force to the wheels, it cannot completely halt wheel rotation. The effectiveness of regenerative braking is influenced by various factors, including the motor, battery, and speed. In instances of emergency braking or high-intensity braking, regenerative braking alone may not fulfill the braking requirements. Therefore, to ensure the braking safety of the mine car, the use of motor regenerative braking must be supplemented with the locomotive's own braking system. This approach ensures both the braking safety of the mine car and the recovery of a substantial amount of energy [15].
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 2 Related Work
### Heuristic Methods  
The major drawback of purely mathematical models is the complexity of the model itself; therefore, roboticists turned to heuristics to generate a gait pattern while still using the precomputed trajectories from an external optimization process under energetic criteria or faulty conditions.  
In order to use a simpler model during the gait generation, heuristic approaches enclose a biological notion of locomotion learned from analyzing the movement of animals, expressed as simple rules for the movement of legged robots. Heuristics such as genetic algorithms (GA) can help to generate the walk pattern for a virtual legged robot [22; 23], where the main criterion for fitness calculation is the stability of the robot while walking in a straight line over a flat surface, within preset borders and stability [2]. The energy efficiency, traveled distance, and deviation of trajectory from the straight line are used as feedback information for the GA [24]. The final result is a set of static gait patterns for the robot, learned without an explicit mathematical model, from which the robot can choose during locomotion.  
Also, Finite State Automata (FSA) can be used to generate walking patterns [25] as well as flow charts [6], as these models can encode a sequence of movements for every limb used during locomotion maneuvers. By definition, these approaches are also static, andadaptability to faulty conditions comes at the cost of increasing the complexity of the FSA or the flow chart.  
These approaches can also emulate reflexive motions using a reactive control scheme, allowing a legged robot to react to the irregularities of the terrain it is walking on. These reflexes are triggered when a limb or foot collides unexpectedly with the terrain or an obstacle. The collision detection can be performed with a set of touch sensors [14] or by measuring the electric current, forces, and torques consumed by the actuators on every joint [10].  
Heuristics allow for _blind-walking_, i.e., the robot is able to walk through irregular terrain based only on proprioceptive information [26]. Neural networks can be implemented in hardware with miniaturization and energy efficiency as the main objective [27]. The Virtual Model Control [27] is one of the most relevant heuristics in order to obtain a model from experimental data, simulating the same dynamic behavior of complex mechanical systems using much simpler components such as springs, dampers, masses, etc. The resulting model is less complex but accurate enough to compute the forces acting on the robot body [28; 29].
# Future Perspectives for Gamma-ray Burst Detection from Space
## 10 Strobe-X
### Instrument Design  
STROBE-X carries three instruments. The Low-Energy Modular Array (LEMA) covers the soft or low-energy band (0.2-12 keV) with an array of lightweight optics (3 m focal length) that concentrate incident photons onto small solid-state detectors with a CCD-level (85-175 eV) energy resolution, a 100 ns time resolution, and low background rates. This technology has been fully developed for NICER and will be scaled up to take advantage of the longer focal length of LEMA, which provides a factor of 8.5 improvement in effective area over NICER with over 1.6 m\({}^{2}\). The High-Energy Modular Array (HEMA) covers the harder or higher energy band (2-30 keV or beyond), with modules of Si drift detectors and micro-pore collimators originally developed for the European LOFT and eXTP mission concepts. HEMA provides a factor of 5.5 improvement in effective area (3.4 m\({}^{2}\)) and \(\sim\)3 in spectral resolution (200-300 eV) over the RXTE/PCA. The Wide-Field Monitor (WFM) comprises a set of coded-aperture cameras operating in the 2-50 keV band, which have a combined instantaneous field of view of 1/3 of the sky, with arcmin localization capability. It will act as a trigger for pointed observations of X-ray transients and will also provide high duty-cycle, high time-resolution, and high spectral-resolution monitoring of the dynamic X-ray sky. The WFM will have 15-times the sensitivity of the RXTE All-Sky Monitor, enabling multi-wavelength and multi-messenger investigations with a large instantaneous field of view, down to a new, order-of-magnitude lower flux regime. On-board processing will detect bursts in real time and provide notifications to the ground, as well as triggering autonomous slews to get the pointed instrument on source in a few minutes.  
Figure 16: The StarBurst effective area averaged over azimuth, as a function of the angle from the instrument boresight (**left**). The StarBurst effective area as a function of energy (**right**).
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 5 Experiments on Variations in the Model Configuration and Batch Size
The total number of parameters reflects the overall number of parameters in each model and shows that the proposed HCTC model indicates more complexity as compared to those of the other models. Trainable parameters are the parameters adjusted during training, whereas non-trainable parameters remain constant during training. The HCTC model shows the highest number (361,611) of trainable parameters, indicating the model's learning capacity and superiority over the other models in terms of the accuracy. Table 9 also shows the performance measures in terms of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model (Number of Heads, Depth, Number of FF Dimensions)** & **Number of Parameters** & **Accuracy (0 dB)** & **Average Accuracy (0–18 dB)** \\ \hline HCTC (2, 8, 256) & 209,579 & 85.45 & 88.79 \\ HCTC (2, 16, 256) & 139,707 & 85.45 & 88.79 \\ HCTC (2, 64, 256) & 361,611 & 87.03 & 90.45 \\ HCTC (4, 8, 256) & 106,867 & 83.09 & 84.4 \\ HCTC (4, 16, 256) & 141,851 & 86.96 & 89.3 \\ HCTC (4, 64, 256) & 394,763 & 87.75 & 89.35 \\ HCTC (8, 8, 256) & 107,987 & 82.60 & 84.24 \\ HCTC (8, 16, 256) & 146,139 & 85.27 & 88.87 \\ HCTC (8, 64, 256) & 461,067 & 87.03 & 88.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracies and numbers of parameters for specific HCTC models represented by the numbers of heads, depths, and numbers of FF dimensions. Figure 12: Results of the ablation study for model accuracy with the RML2016a dataset. the average accuracies of the models at signal-to-noise ratios of 0-18 dB. The proposed HCTC model exhibits the highest average accuracy (90.46%), showcasing its superior performance compared to those of the purely convolutional (83.73%) and pure transformer models (63.90%).
## 3 Results  
### Frequency Response  
We used an 11-second logarithmic sweep from 18 Hz to 22 kHz as excitation signal for each of the six modulations (using a modulation index of m = 1). Magnitude curves of the transfer functions are shown in Figure 5. The curves were smoothed by applying 1/12 th bandwidth spectral averaging. The parametric speaker barely reproduced low frequencies and the response increased by about 12 dB/octave between 300 and 1500 Hz. After 1500 Hz, the transfer functions varied slightly for each modulation. LSBAM showed the largest difference with a marked decrease at 6.5 kHz. This is due to the asymmetric response of the loudspeaker array that also had a marked decrease at approximately 33.5 kHz (which is 6.5 kHz below the carrier frequency of 40 kHz; Figure 2b). This minimum was not present in the USBAM since this modulation only uses the upper sideband. Beyond 10 kHz, all responses dropped abruptly due to the limited frequency response of the recording microphone. Similar curves, with a lower relative level, were obtained for each modulation if the modulation index was reduced.
# Effect of Ion Release on Color Stability of Zirconia: A Comparative Study
###### Abstract  
Zirconia ceramics are widely used in dentistry, but maintaining long-term color stability remains challenging. This study investigated the combined effects of specimen thickness, immersion duration, and aging of coloring solutions on the color stability of two multilayer commercial zirconia materials: TT ONE Multilayer (TT) and DD cubeX\({}^{2}\) ML (DD). Discs (1.0-2.5 mm thick) were immersed in A2-shade coloring liquids for 30 s, 1 min, 3 min, and 5 min and evaluated after three months of solution aging. Color parameters (L\({}^{\star}\), a\({}^{\star}\), b\({}^{\star}\), C\({}^{\star}\), \(\Delta\)E) were assessed, along with pH variation and Fe/Er ion concentrations using ICP-MS. Thinner specimens showed higher \(\Delta\)E values and greater chromatic shifts than thicker ones. Aging of the coloring solutions increased L\({}^{\star}\) values and discoloration, particularly in TT. ICP-MS revealed rising Fe and declining Er levels, correlating with observed optical changes. DD showed greater chemical and optical stability under identical conditions. These findings highlight the need to control zirconia thickness and coloring solution aging to preserve long-term esthetics.  
zirconia; color stability; ion release; optical properties 2025156364  
1
## 3 Results
### Viability Evaluation by the MTT Reduction Method  
Depending on the experimental model, the MTT salt itself may induce significant cytotoxicity. For instance, after incubating U87 glioblastoma cells for 3 h (at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\)) with 0.75 mg/mL MTT (final concentration), as indicated in published protocols [44,45], we found 40% cell death as indicated by decreased OD\({}_{570}\) (data not shown). At this level of baseline viability, it would be impossible to accurately measure cytotoxicity by methylene blue. Therefore, to determine the optimal concentration of MTT, U87 cells, plated at 1 \(\times\) 10\({}^{4}\) and maintained for 48 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\), had their media replaced with media containing 0.125 to 0.500 mg/mL MTT for 1 h to 4 h and were then subjected to the MTT assay.  
Figure 3 shows that 3 h is indeed the optimal incubation time for the assay, as OD\({}_{570}\) reaches a plateau after this time. Likewise, incubation with 0.375 mg/mL MTT resulted in the highest OD\({}_{570}\) at all time points. With 0.500 mg/mL MTT incubation, OD\({}_{570}\) was lower than with 0.375 mg/mL MTT. A cytotoxic effect of MTT might explain this result, as described elsewhere [27]. Thus, the concentration of MTT chosen for the viability assays was 0.325 mg/mL for all three cell lines, although the results were obtained with the U87 cell line, no adverse effects were observed with this concentration for the other cell lines.  
\begin{table}
\begin{tabular}{c c c} \hline
**Cell Line** & *** Number of Cells** & **Z’Factor** \\ \hline  & 2000 & \(-\)0.4 \\ \cline{2-3}  & 5000 & 0.7 \\ \cline{2-3}  & 10,000 & 0.8 \\ \cline{2-3}  & 20,000 & 0.8 \\ \cline{2-3}  & 30,000 & 0.9 \\ \cline{2-3}  & 40,000 & 0.7 \\ \hline  & 2000 & \(-\)1.2 \\ \cline{2-3}  & 5000 & 0.5 \\ \cline{2-3} U87 & 10,000 & 0.7 \\ \cline{2-3}  & 20,000 & 0.8 \\ \cline{2-3}  & 40,000 & 0.8 \\ \hline  & 4000 & \(-\)0.7 \\ \cline{2-3}  & 6000 & 0.6 \\ \cline{2-3} GL261 & 8000 & 0.5 \\ \cline{2-3}  & 10,000 & 0.8 \\ \cline{2-3}  & 12,000 & 0.9 \\ \cline{2-3}  & 20,000 & 0.8 \\ \hline \end{tabular}  
* Number of cells per well in a 96-well plate.  
\end{table}
Table 3: Z’factor values for SHSY5Y, U87, and GL261 cell lines.
# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 4 Results
### Intercomparison of SLC, AOT and WVP Maps  
Intercomparisons between SLC, AOT and WVP maps from ESA-L2A and S2C-L2A products for Barrax, La Crau and Harth sites are presented in Figure 4. Data from the five acquisition dates (Table 1) were merged for each site and each parameter.  
SCL (Figure 4a) showed an overall agreement exceeding 99% for all sites. The land area classified as clear sky bare soil (clear sky vegetated) was 94% (5%) for Barrax, 25% (66%) for La Crau and 9% (84%) for Harth.  
AOT estimates from both products ranged from 0.1 to 0.3 at Barrax and La Crau and from 0.1 to 0.2 at Harth (Figure 4b). AOT estimates were relatively uniform over each image (not shown) resulting in clusters in Figure 4b corresponding to individual product dates. Over all sites, ESA-AOT were positively biased in comparison to S2C-AOT by \(\sim\)0.02 for La Crau and Harth sites (15% relative to the average AOT for the site across all dates) and by \(\sim\)0.01 for Barrax (5% relative to the average AOT  
Figure 4: Intercomparison of SCL, AOT and WVP maps between S2C-L2A and ESA-L2A products for five dates over Barrax, La Crau and Harth.  
for the site across all dates). The RMSE was nearly constant (\(\sim\)0.04) across all sites (\(>\)15% relative to the average AOT for the site across all dates). This suggests that there are both small systematic and random differences between ESA-AOT and S2C-AOT. Bias and RMSE did not change when considering only vegetated areas. Correlation coefficients varied across sites, although this is likely due to the variation in the range of AOT between sites (\(R^{2}\) from 0.67 to 0.69 for Barrax and La Crau, and \(R^{2}\) = 0.14 for Harth) rather than an indication of actual product differences. This was supported by the absence of spatial structure in maps of residuals (not shown).  
WVP estimates from both products ranged from 0.25 cm to 3 cm at Barrax, from 0.10 cm to 3.5 cm at La Crau and from 0.10 cm to 4.5 cm at Harth (Figure 4c). Over all sites, ESA-WVP slightly overestimated S2C-WVP by \(\sim\)0.08 cm (\(\sim\)6% relative to the mean WVP for the site). The RMSE between ESA-WVP and S2C-WVP was also small and did not vary substantially between sites (\(\sim\)0.09 cm or 7% relative to the mean WVP value). As with AOT, differences in WVP between products were similar for vegetated areas. These observations were confirmed by the absence of spatial structure in maps of residuals (not shown). This is further supported by the almost perfect correlation (\(R^{2}=0.99\)) when intercomparing WVP at each site.
## 4 Results and Discussion
### Transfer Learning Process from a Pre-Trained YOLOv5s to a Fine-Tuned YOLOv5s
The edge contours belonging to the surface defects of an arc magnet extracted by the transferred model were generally sharper, implying more accurate feature extraction results. Moreover, the image information extracted by the transferred model was more concentrated and had less redundant data, facilitating the filtering and reduction of the output. These results demonstrate that the design of both freezing and fine-tuning the YOLOv5s model that was adopted in our proposed framework was effective. Considering the significant difference between the dataset for pre-training and our dataset for transferring, these freezing and fine-tuning results also indicate a noteworthy phenomenon in transferring a model in the case of large differences in training data: layers frozen in the pre-trained model decrease, while those to be fine-tuned increase. The performance of the transferred and un-transferred YOLOv5s model in identifying arc magnet surface defects was also evaluated on our dataset's testing set. The un-transferred model refers to the pre-trained model that had only been retrained by the training set in our dataset, instead of the transferred one that had been frozen and then fine-tuned under our dataset. As shown in Figure 5d, the transferred model performed better in each accuracy indicator that was related to identifying different surface defects in the testing set of our dataset. Compared to the pre-trained model dataset, our dataset's training volume was only 12.20% (14,400:118,000). This suggests that our transfer learning strategy allowed the pre-trained model to be adapted and become competent for the arc magnet surface defect identification task with relatively low dependence on the training volume. This also implies that the performance of the pre-trained model that was suitable for surface defect identification was effectively inherited and improved.