esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Digital Pulse Compression  
### Classical Digital Pulse Compression Methods  
As mentioned above, dispersive characteristic mismatches may exist between the expender chirp signal and the SAW compressor in the classical CTS structure. Limited bandwidth and large attenuation of the SAW filter will influence the performance of the CTS system. Nowadays, with the development of modern digital signal processing technology, signal expanding and pulse compressing have been realized by digital processing methods instead of SAW filters in radar systems. The first step in digital pulse compression is to sample the echo signal to get the discrete Linear Frequency Modulation (LFM) signal \(s(n)\). To avoid frequency aliasing, the sampling numbers \(N_{s}\) cannot be smaller than the time-bandwidth products \((T_{s}B)\) of \(s(n)\). After quantizing the echo signal, it is also necessary to quantify the impulse response of the matching filter \(h(n)\). Then, pulse compression \(y(n)\) can be expressed as the convolution of \(s(n)\) and \(h(n)\).  
\[y(n)=s(n)*h(n) \tag{13}\]  
Depending on the implementation approach, there are two common digital methods for pulse compression processing in the time and frequency domains.
## 4 Experimental Results
### F1 Scores
To clarify this, a paired \(t\)-test was performed to examine the statistically significant differences between the experimental models. For this purpose, the \(p\)-value was set to 0.05, and a degree of freedom was set to 4, since an average was taken from five observations. Neither of the two models had t-values within the interval [\(-2.776\), \(+2.776\)] from the t-table, which is needed to accept the null hypothesis [60]. For example, the t-value calculated from the LSTM and BERT models using the original data, where the performance difference was the smallest in Table 11, was about 4.379. When the paired \(t\)-test was performed on the two models with the largest performance difference, including the BERT model with SMOTE and the RNN model with ADASYN, the t-value increased to 9.662. The results showed that the models are statistically different to each other. Figure 8 compares the F1 scores of the models in terms of data imbalance processing algorithms. The left shows the models' F1 scores obtained from the restaurant reviews. In this case, precision is a key factor that determines a model's F1 score. For example, the precision of the LSTM and GRU models was significantly deteriorated after oversampling, as shown on the left side of Figure 2, which leads to the degradation of the F1 scores of the models. The performance of the other two models, including the RNN and BERT models, was not affected by oversampling. The right side of Figure 8 shows the models' F1 scores obtained from the tourist attraction reviews. In this case, recall is a key factor that determines a model's F1 score. For example, the recall of the RNN, LSTM, and GRU models was significantly deteriorated after oversampling, as shown on the right side of Figure 5, which results in the degradation of the F1 scores of the models. The performance of the BERT model was not affected by oversampling. The above results regarding the models' F1 scores can be summarized as follows:  
* None of the oversampling algorithms improved the performance of the models. The models using the original data provided the best F1 scores for both kinds of data sets. * When oversampling was applied for the nearly balanced data, such as the review data sets for the restaurants, the F1 scores of the models were heavily influenced by precision. * When oversampling was applied for the skewed data, such as the review data sets for the tourist attractions, the F1 scores of the models were significantly influenced by recall. * The average F1 score of the models for the nearly balanced data was 0.953, which was higher than the average F1 score of the models for the skewed data, whose score was 0.937. Figure 8: Comparison of the models’ F1 scores in terms of data imbalance processing algorithms obtained from (**a**) 25 review data sets for the restaurants and (**b**) 33 review data sets for the tourist attractions. * The BERT model provided the best performance, with an average F1 score of 0.965. Its performance was not affected by the oversampling algorithms.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### 3.3. Haptic-Based Modality
#### 3.3.2 Marker-Based  
[PERSON] et al. [144] suggested an interactive pen tool that transforms the actions in real space into the virtual three-dimensional CAD system space. To achieve that, they used _fiducial marker recognition_ for markers placed on top of a simple pen to capture its position and orientation. Then, the system could locate any movements and draw virtual lines and shapes into the CAD system space. In [145], [PERSON] et al. presented WireDraw, a 3D wire object drawing using a 3D pen extruder. They also used _fiducial marker recognition_ for identification purposes in the mixed environment and superimposed virtual objects as indicators to help the user design high-quality 3D objects. Yet another pen-based interaction is demonstrated by [PERSON] and [PERSON] [146], this time using a space-occupancy-check algorithm. First, they used a depth map to capture the 3D information of the natural world, and then the algorithm checked if any point in a depth map collides with the geometry of the virtual object. Haptic-based modality also utilizes the _RFID marker recognition_ method.  
Figure 4: Displaying all categories of the haptic-based modality.  
For example, [PERSON] et al. [147] described an interaction that can augment the experience of reading a book with sound effects. To achieve that, they used RFID tags embedded into each page and an electric field sensor located in the bookbinding to sense the proximity of the reader's hands and control audio parameters.
# High-Speed Micro-Grooving of Metal by Angled Irradiation of Single-Mode CW Fiber Laser
###### Abstract  
Laser micro-grooving has excellent potential to improve the surface properties of metals. In this study, high-speed micro-grooving of the surfaces of mild steel SS400 and aluminum alloy A1050 was investigated using a single-mode continuous-wave fiber laser with an average power of 200 W. Experiments were conducted at laser beam scanning speeds of 1 to 6 m/s. The incident angle of the laser beam in the plane perpendicular to the scanning axis was varied from 0 to 60\({}^{\circ}\). A relatively deep groove (approximately 30 \(\upmu\)m in depth) and a high upheaval (10 \(\upmu\)m in height) were formed along the scanning line at an incident angle of 45\({}^{\circ}\) on the SS400, whereas only shallow grooves were formed on A1050. The micro-groove formation mechanism was discussed on the basis of high-speed camera observation, groove shapes, and the thermophysical properties of the metal. Asymmetric molten metal flow and solidification around the keyhole are thought to play important roles in micro-groove formation. Although the laser irradiation conditions must be optimized according to the thermophysical properties of the material, angled laser irradiation has the potential to effectively form micro-grooves.  
micro-grooving; single-mode; CW; fiber laser; angled irradiation; high-speed processing Article High-Speed Micro-Grooving of Metal by Angled Irradiation of Single-Mode CW Fiber Laser
## Acknowledgement to Reviewers of _Applied Sciences_ in 2019
Bielecka, Marzena  
Bielenica, Anna  
Bienvenido-Huertas, David  
Biermann, Martin  
Biffi, Carlo Alberto  
Bigand, Andre  
Bigaud, David  
Bighin, Giacomo  
Bigi, Velia  
Bilan, Yuriy  
Bilen, Sven G.  
Bilger, Andrea  
Billot, Maxime  
Bilotta, Antonio  
Bimonte, Sandro  
Birasova, Vladimirira  
Binello, Arianna  
Bionta, Mina  
Biern-Hansen, Andreas  
Birch, Brian  
Birch, Catherine  
Birdwell, Justin  
Birk, Andreas  
Birowosuto, Muhammad Danang  
Birt, James  
Biscaya, Sara  
Bishay, Peter  
Bisio, Igor  
Bistrian, Diana  
Biswal, Bishnu Prasad  
Biswas, Dipankar  
Bittner, Achim  
Biziuk, Marek K.  
Bizon, Nicu  
Bjorklund, Robert B.  
Blachnik, Marcin  
Blachowicz, Tomasz  
Black, Leon  
Blackman, Sheila  
Blaga, Lucian  
Blair, Christopher  
Blais, Antoine  
Bial, Nawfal  
Banc, Daniele  
Blanche, Pierre-alexandre  
Blanco Fernandez, Matias  
Blanco Rojas, Maria Dolores  
Blanco, Julio  
Blanco, Victor  
Blanco-Murillo, Jose Luis  
Blanco-Salas, Jose  
Blanco-Valera, Maria Teresa  
Blandin, Remi  
Blaser, Nello  
Blaszczak-Bak, Wioleta  
Blavette, Anne  
Blazejewski, Ryszard  
Blazejowski, Marcin  
Blazewska, Katarzyna M.  
Blecharz, Krzysztof * Bleiner _et al._ (2020) Bleiner, Davide Bleiweiss, Max Blekhman, David Bolestis, Costas Blesa, Joaquim Blessent, Daniela Bettterie, Benoit Block, Stephan Bloem, Elke Bloise, Andrea Blok, Boris Blok, Chris Blondel, Philippe Bludov, Yuliy Boanini, Elisa Bobbala, Sharan Bobinger, Marco Bobkowska, Katarzyna Boboc, Razvan Gabriel Bobylev, Nikolai Boccaletti, Chiara Boccia, Edoardo Boccia, Elizabeth Bocia, Elzbieta Bocos, Carlos Boda, Sunil Kumar Bodaghi, Mahdi Bodaghi, Mahdi Bodar, Charles Bodi, Stefan Bodini, Ileana Bodini, Matteo Boehm, Daniela Boe, Carlos Bogacki, Jan Bogdanowicz, Krzysztof Artur Bogdanowicz, Robert Bogdanowicz, Robert Bognar, Gabriella Boguszewski, Dariusz Bohdalova, Maria Bohm, Michal Bojarevic, Valdis Bojic, Mirza Bojic, Chris Bokor, Boglarka Bolboaca, Sorana D. Bolcskei, Hedvig Bolcelsi, Costas Boletis, Costas Boletis, Costas Boloomey, Jean-Charles Bolshov, Mikhail Bolvashenkov, Igor Bolvin, Helene Bolz, Sebastian Bolc, Sebastian Bombek, Gorazd Bombelli, Paolo Bommagani, Shobanbabu Bonaci, Tamara Bonamore, Alessandra Bonardi, Fabien Bonarini, Andrea Bondar, Dali BONDOC, Ionel Bondok, Doaa Bonduka, Stefano Bone, Gary Bonelli, Francesco Bonelli, Valentina Bonga, Beatrice Bonifacio, Maria Addolorata Bonkowski, Piotr Bono, Viviana Bonomolo, Marina Bonyar, Attila Boori, Mukesh Singh Borbely, Yves Borboni, Alberto Bordoni, Massimiliano Bordoy, Joan Bordrouil, Cyril Borek, Wojciech Borelli, Davide Boffac, David Borge Diez, David Borge-Diez, David Borge-Diez, David Borge-Diez, David Borge-Sebastao, Israel Borghetti, Brett Borghi, Alessandra Borghi, Francesca Borghini, Andrea _Appl.
## 2 Antenna Array Design
### Wideband Circularly Polarized Antenna  
In this section, we attached the hybrid feeding network to four elements. This is illustrated in Figure 6a. It is worth noting that the elements were also oriented in a sequential rotation fashion. Figure 6b depicts an image of the manufactured 2 \(\times\) 2 array with the sequential feeding network. The total size of the proposed antenna was 100 mm \(\times\) 100 mm \(\times\) 70 mm. The simulated and measured \(S_{11}\) are shown in Figure 7a. The proposed antenna had a wide bandwidth from 1.46 GHz to 2.63 GHz (57.3%) in simulation and 1.4 GHz to 2.95 GHz (71.2%) in measurement. In the 1.6-2.85 GHz frequency range, the antenna gain ranged from 6 dBi to 9.9 dBi. In addition, the peak gain was 9.9 dBi at 2.45 GHz as shown in Figure 7b. In Figure 8, with sequential rotation, the AR bandwidth of the array was smaller than 3 dB within the wide band of 1.5-2.9 GHz (63.6%).  
Figure 5: The feeding network performances. (**a**) Reflection coefficient; (**b**) amplitude distribution; (**c**) phase distribution.  
As a result, by employing the sequential rotation method, the AR performance was significantly improved. The E-plane and H-plane radiation patterns of the array antenna at 1.8 GHz, 2.1 GHz, 2.45 GHz, and 2.6 GHz are shown in Figure 9. At those bands with half-power beamwidths (HPBW) of roughly 63.6\({}^{\circ}\), 57.9\({}^{\circ}\), 51.9\({}^{\circ}\), and 47.5\({}^{\circ}\), respectively, the antenna was more focused toward the director. The simulation and measurement were quite similar.  
Figure 8: Simulated and measured axial ratio of the wideband and CP antenna.  
Figure 6: (a) Proposed 3D antenna and 2D feeding network; (b) fabricated antenna.  
Figure 7: (a) Simulated and measured reflection coefficient of the wideband and CP antenna; (b) simulated and measured gain of the wideband and CP antenna.  
Table 2 shows a comparison of the proposed antenna and related works. Our antenna had the widest \(-10\) dB reflection bandwidth and 3 dB AR bandwidth. In addition, it had an average size smaller than the antennas in [24; 25; 26; 30; 31], which had lower isolation, complicated design, and a fabricated process, but were bigger than the antenna in [23].
## 2 Materials and Methods  
This study mainly aims to show the techno-economic viability of the minigrid system for Kibran Gabriel island. Using HOMER Pro software, the feasible configurations and system sizing for the optimal system were carried out. The required data for simulation analysis were acquired from multiple sources: the weather data of the island was obtained from NASA surface meteorology and solar energy database integrated in HOMER Pro [29]. In addition, the load data, diesel fuel price, interest rate and inflation rate, grid extension cost, and e-market surveys for equipment costs were required for the analysis.  
* The load profile of the island was estimated and categorized into primary loads (power is required at a specific time) and deferrable loads (power is required but time is flexible).
* Since the island is not fully electrified yet, in the simulation analysis, electrifying the island with diesel generator was considered as a reference for other comparisons. Despite the pollution, the economic feasibility of diesel power generation relative to grid-extension cost was also considered for further comparisons.
* We considered different feasible configurations of PV/wind/battery, PV/wind/diesel/ battery, PV/battery, and PV/diesel/battery systems compared with the stand-alone DG system, and finally the optimal system was determined.
* Once the optimal system was determined, sensitivity analysis was carried out to observe the effects of the variation of important parameters such as global horizontal irradiation, diesel fuel price, and load consumption on the optimal system.
* Finally, the optimal size and techno-economically feasible configuration were determined. The minimum LCOE, and NPC, the total emissions reduced (compared with stand-alone diesel system), RF, capacity shortage fraction, and BGED and other parameters were computed.  
Description of Simulation Software  
HOMER Pro software was developed by US National Renewable Energy Laboratory (NREL). The software is a powerful tool for designing, analyzing and planning of minigrid systems to determine the optimal size of system components through carrying out technoeconomic analysis [29]. Figure 1 depicts the HOMER Pro simulation process architecture diagram.
# Analysis of Controllability in Cyber-Physical Power Systems under a Novel Load-Capacity Model
## 4 Case Study and Discussion
### The Controllability of CPPS in Different Network Types
#### 4.2.1 Case A: IEEE 39-Node System  
Under the three attack strategies, the information network is constructed into a BA network topology of the same scale, which is coupled with the IEEE 39-node system to form the CPPS. The controllability after cascading failure is shown in Figure 7, where (a), (b), and (c) represent the controllability changes of the network under random attack, degree attack, and betweenness attack, respectively. As shown in the figure, after the network is subjected to random attacks, the density of driver nodes gradually increases, while the controllability of the network gradually decreases. After the eighth attack, the network is on the verge of collapse under all four coupling modes. In contrast, after four degree attacks and three betweenness attacks, all four networks suddenly collapse. Prior to this, even though the network was under attack, the network still retained some isolated islands due to the redistribution of loads to neighboring nodes. However, the loads of certain nodes had reached the threshold, and after another attack, the network immediately collapsed completely.  
Figure 6: (**a**) The degree distribution of the IEEE 39-node system, (**b**) the degree distribution of the Chinese 132-node system.  
After we constructed the information network into a WS network of the same scale and coupled it with the IEEE39 node system to form the CPPS, then three attack strategies of random, degree and betweenness are used to attack the IEEE 39-node system. The controllability of the network after the node load redistribution process is shown in Figure 8, and like the CPPS coupled with the information network built by the BA network, the CPPS drive node density under random attacks gradually increases and the network controllability gradually decreases. After the number of attacks reaches the 8 th time, the network almost collapses under the four coupling methods. When the network was subjected to multiple attacks, the CPPS did not collapse most of the network after the third attack as before. Instead, it still survived most of the nodes and suddenly collapsed after the fourth attack. It is worth noting that after the third betweenness attack, the driver node density of the CPPS has been close to 1, which means that most of the CPPS was decomposed into islands at this time, and it can no longer meet the power demand or communicate with other nodes.  
Figure 7: **(a–c)** The \(n_{d}\) variation curves of the CPPS under four different coupling strategies after random attack, degree attack, and betweenness attack, respectively. The CPPS is formed by coupling the IEEE 39-node system and the BA network.
# Iterative Interferometric Denoising Filter for Traveltime Picking
## 4 Field Data Example
### Qademah Fault in King Abdullah Economic City  
We also applied the IDF method to seismic data recorded near King Abdullah Economic City (KAEC) in Saudi Arabia, established a few years ago and very close to the Red Sea. According to the geological survey, the Red Sea coastal plain faults are hidden under the coastal plain sediments. One of these proposed faults is the Qademah fault, a normal rotational fault 25 km long that has been detected and mapped [43,44]. This field dataset includes a 2D seismic profile with 264 sources and geophones with 2 m intervals. The recorded total time and time intervals are 5 s and 1 ms, respectively. Figure 11a,b show the corresponding surface geology map and 40 th CSG (only 301 time steps are displayed); the peak frequency of the recorded data was found to be 20 Hz.  
A tapered window, which was centered around the initial guess of the first-arrival traveltime value, was applied, and the muted CSG is displayed in Figure 12a. The yellow, blue, and black boxes indicate three seismic slices which have obvious random or incoherent data noise, which may lead to first-arrival traveltime picking problems. Using the proposed IDF technology, the artifacts in seismic data become far fewer and easier to pick, as Figure 12b shows. Figure 13a represents the first-arrival traveltime difference between manually picking information on the original and muted datasets. The corresponding histogram of the entire dataset is displayed in Figure 13b. Most traveltime difference values were less than a quarter of the dominant period (10 ms) of the source wavelet; this degree of traveltime difference will not lead to traveltime tomography failure. It illustrates that iterative IDF will keep correct traveltime information while decreasing data noise. In addition, traveltime tomography using first-arrival traveltime information picked from the original and processed IDF datasets is shown in Figure 14a,b. Although there are some differences between these two tomograms, both of them could indicate the normal fault in the middle of the 2D seismic profile.  
Figure 11: (**a**) A surface geology map showing the location of the Qademah fault [43]. (**b**) The 40 th CSG of field data collected near the Qademah fault.  
Figure 10: The traveltime tomography result using the first-arrival traveltime information picked from (**a**) original and (**b**) IDF-processed datasets.  
Figure 12: The 40 th CSG of (**a**) original field data and (**b**) the IDF-processed dataset. The zoom-in of yellow, blue, and purple boxes are some seismic slices which have strong random noises.  
Figure 13: (**a**) The first-arrival traveltime difference between manually picking information on original and muted datasets. (**b**) A corresponding histogram of the entire dataset.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 5 Future Directions
### Wireless Power Transfer for Satellite Power Distribution  
In dusty and harsh environments, where physical connectors may deteriorate, Wireless Power Transfer (WPT) is considered acceptable, especially for small satellites. Although the efficiency of WPT systems is relatively low, with sufficient research and development, it can be a potential method for power transfer in space applications. WPT is especially helpful in improving the reliability and robustness of regulation and transmission of power [10; 147; 148]. This opens up new avenues of research in the design and development of highly efficient DC-DC converters for wireless power transfer in space. A summary of a few identified research gaps in various space applications is provided in Table 10.  
Figure 21: Various deep space missions, along with their power and mass.
## 4 Experimental Validation and Parametric Study
### Parametric Study on Peripheral Resistance  
Parametric studies of peripheral resistance were performed experimentally. The peripheral resistance is controlled through the opening angle of the resistance valve mounted on the reservoir module shown in Figure 4a. The valve angle was adjusted from 90 degrees to 45 degrees over 5 steps, thereby reducing the open area ratio from 0.9 to 0.64. When the valve angle is 90 degrees, the area ratio is 0.9, the maximum, and the resistance is the minimum. As the valve angle decreases, the open area ratio decreases and the resistance increases.  
Figure 9a shows pressure waveform according to area ratio. In the case of ventricular pressure, systolic pressure increases as peripheral resistance increases, diastolic pressure decreases, and as a result, peak-to-peak amplitude increases. In the case of aortic pressure, systolic pressure increases as peripheral resistance increases, diastolic pressure increases more, and as a result, peak-to-peak amplitude decreases. Figure 9b shows the normalized aortic pressure waveform. To compare the pulse pressure, measured aortic pressures were normalized to the baseline aortic pressure in Figure 8b, and shifted to have zero as the minimum value. As the valve angle decreases, the area ratio decreases and the peripheral resistance increases. At the same time, it can be seen that the pulse pressure decreases. This is consistent with the change in pulse pressure according to the peripheral resistance predicted through the simulation model of Figure 7b. Therefore, the mock circulatory platform of this study can reproduce the blood pressure waveform according to the peripheral resistance.