esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The chemical reactions that feature pure kinetic-control of the outcome and utilize'spring-loaded' reactants are of considerable interest in multiple applications that include drug discovery, combinatorial chemistry, target-templated in situ chemistry, proteomics, DNA research and bioconjugation techniques [1]. The commonly recognized high yielding, thermodynamically favored and wide-in-scope reactions, such as nucleophilic ring opening reactions of epoxides and aziridines, non-aldol type carbonyl reactions, and additions to carbon-carbon multiple bonds, have been termed by [PERSON] as \"click chemistry\" [2].  
In the above context we have recently developed the tandem Mannich-electrophilic amination reaction of fluorogenic 4,6-dimethylisoxazole[3,4-_b_]pyridin-3(\(\mathit{IH}\))-one or isoxa zolo[3,4-_b_] quinolin-3(\(\mathit{IH}\))-one with formaldehyde and secondary amines that leads to zwitterionic UV-fluorescent Safirinium P and Q dyes, respectively [3, 4]. The latter upon esterification with \(N\)-hydroxy-succinimide (NHS) can serve as fluorescent amine-reactive reagents which are useful as fixed charge derivatization reagents for micellar electrokinetic chromatography (MEKC) and MS proteomic analyses [5], as well as for bioimaging purposes such as stanning of bacterial cells and spores [6, 7, 4]. The tandem reactions of non-fluorescent isoxazolones, formaldehyde and secondary amines, i.e., syntheses of Safirinium dyes, proceed quantitatively, however the reaction rates strongly depend on the substitution pattern, which results in reaction times ranging from several minutes to dozens of hours [4]. The aim of the present study was to describe the tandem Mannich-electrophilic amination reactions using commonly recognized theoretical quantum chemical methods [8, 9, 10] and identify the steric factors that would limit applications of these pro cesses in a fast and sensitive detection of formaldehyde and fluorescent derivatization of secondary aliphatic amines.
## 2 Description of the RC-DTH Air Hammer  
### The Components of the RC-DTH Air Hammer  
As depicted in Figure 2, the valve structure of the DTH hammer is categorized into two distinct types based on the direction of compressed air entering the cylinder as follows: the internal flow structure and the outflow structure. In the outflow piston structure (Figure 2a), compressed air is directed into the cylinder either through the outer tube or via a valve rod separate from the outer tube. Upon reaching a predetermined stroke, the piston's unique external contour design is engineered to either seal or expose the flow channel's inlet and outlet. This mechanism facilitates the transfer of high-pressure gas between the upper and lower chambers of the cylinder, thereby enabling the switching action necessary for the hammer's operation. However, the outflow piston design features slots only on the outer ring of the piston, resulting in a relatively stable overall structure. This design necessitates the structural collaboration with the outer cylinder of the DTH hammer. Consequently, it tends to exhibit areas of structural weakness, and the surface area available for compression is also restricted. In the internal flow piston design (Figure 2b), the compressed air is channeled into the cylinder either through the inner tube or via a valve rod that is distinct from the inner tube. The air distribution channel is integrated within the piston. At a specific stroke point, the configuration of the inner tube and the piston's flow channel undergoes a change, facilitating the transition of high-pressure gas between the cylinder's upper and lower compartments. The internal flow piston structure works in tandem with the valve rod, thereby diminishing the need for structural coordination with the outer cylinder. This design also optimizes the area of the compression surface, maximizing its effective size. The comparative analysis of the two structures reveals that the internal flow piston design boosts superior impact energy and structural integrity. It holds the potential for enhancing efficiency and extending the service life of the RC-DTH hammer during drilling operations in complex geological settings.
## 3 Methodology
### Cell Culture and Cell Aggregates Formation  
HUVECs (frozen stock) were commercially obtained from Clonetics (Cambrex, Inc., Walkersville, MD, USA). Cells were defrosted, thawed, and grown in endothelial growth medium (EGM-2 BulletKit, Lonza, Basel, Switzerland) supplemented with 2% fetal bovine serum, antibiotics (gentamicin sulfate/amphotericin B), 0.4% human fibroblast growth factor-B, 0.04% hydrocortisone, 0.1% human epidermal growth factor, ascorbic acid, heparin, vascular endothelial growth factor, and insulin-like growth factor (R3-IGF-1). Cultures were maintained at 37 \({}^{\circ}\)C in a humidified atmosphere with 5% CO\({}_{2}\). Media were changed at 24 h after thawing and every 48 h onwards. When the cultures reached 80%-90% confluence, cells were subcultured and washed with phosphate-buffered saline (PBS) and detached using 1 mL of trypsin-EDTA. Once cells were detached, the trypsin effect was neutralized with 2 mL of EGM-2 medium and cells were centrifuged at 220\(\times\)\(g\) for 5 min at 25 \({}^{\circ}\)C. Cells were seeded at a minimum density of 10\({}^{3}\) cells/cm\({}^{2}\) in culture flasks or plates. For cell aggregates formation, 1 \(\times\) 10\({}^{6}\) HUVEC cells in passage 10 were harvested by centrifugation and re-suspended with 1 mL EGM-2 medium in 15-mL polypropylene centrifuge tubes. Cells were incubated for 1 h and the tubes' caps were loosened to allow oxygen supply to the cells during culture/incubation. Cell aggregates with diameter in the range of 30-60 \(\upmu\)m formed spontaneously during incubation.  
Figure 4: Fabrication steps for the development of (**A**) microchannel mask; (**B**) mold or microchannel pattern; (**C**) PDMS channel; (**D**) microchip assembly.
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 4 Conclusions  
In this study, a robust synthetic aperture radar (SAR) automatic target recognition approach was presented that applied sparse representation to produce a weighted global feature vector based on spatial pyramid matching. SAR image target recognition was performed on the reconstructed image representation. The experimental results clearly and consistently showed that the proposed framework was significantly more discriminative than the traditional SPM methods. Moreover, the feature coding-pooling framework was shown to perform well in image classification tasks, the unavoidable information loss incurred by the feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout, however, may severely limit the recognition performance. Therefore, more systematic investigations are still required to find a new coding method to improve the classification performance in future research.  
Conceptualization, S.W. and Y.L.; methodology, S.W.; software, Y.L.; validation, L.L.; formal analysis, S.W.; investigation, L.L.; resources, S.W.; data curation, S.W.; writing--original draft preparation, Y.L.; writing--review and editing, S.W.; visualization, L.L.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (Grant No. 61901297) and in part by the National Science Foundation of Tianjin Province of China, (Grant No. 18)CQNJC70600).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Dependence of Solar Energy on Latitude and Ground Albedo  
Up to this point, no mention has been made of the influence of the ground albedo on the solar energy received by the inclined surfaces in the three types of solar system configurations. This factor may be important, as it contributes a ground-reflected radiation component to the inclined surface of the collector. The intensity of this component becomes greater for higher values of the ground albedo and greater tilt angles of the inclined surface, as can be justified by Equation (4). It is easily concluded from this equation that for the system configuration modes-(i) and -(ii), the ground-reflected radiation depends only on the ground-albedo value, as the tilt angle is constant. Nevertheless, this is not the case for the configuration type-(iii) systems, as the tilt angle is always varying throughout  
Figure 8: Intra-annual variation of the solar energy yields, \(\rm H_{g}\), averaged across the 82 sites in Saudi Arabia and the period 2005–2016 under all-sky conditions. The values are shown for the 3 solar system configuration modes and both ISO- and HAY-modelled results. The months on the x-axis are from January (1) to December (12).  
the day. To show this dependence, 3D graphs of the annual mean ratios, \(\rm H_{g,HAY}/H_{g,ISO}\), were prepared as functions of the geographical latitude, \(\varphi\), and the ground-albedo ratio, \(\rm\rho_{r}=\rho_{g}/\rho_{g0}=\rho_{g}/0.2\) of the site for the three configuration modes. We avoided including the tilt angle, \(\beta\), as an independent variable in the graph, especially for configuration mode-(iii), as \(\beta\) does not vary within standard limits at the 82 sites as \(\varphi\) or \(\rm\rho_{r}\) does. Figure 9 depicts the above-mentioned dependence of the solar irradiation ratio on \(\varphi\) and \(\rm\rho_{r}\). The main characteristic in all three figures is the abruptly low ratio values (in the form of \"wells\") in the ranges \(28.5^{\circ}\leq\varphi\leq 31.0^{\circ}\) and \(1.15\leq\rho_{r}\leq 1.55\). The sites that belong to this area of Saudi Arabia lie in two groups: group A, 6, 7, 9, 54, 55, 56, 65, 67, and 68; and group B, 50 and 68. All nine sites of group A belong to SEZ-C, and the two sites of group B lie in SEZ-B. Due to this outcome, sites 50 and 68 should move to SEZ-C (i.e., to the group A of sites); this would result in shifting the SEZ-B/SEZ-C interface downward in order to include the two sites (i.e., a downward shift of the upper black line in Figure 2). Nevertheless, the values of the \(\rm H_{g,HAY}/H_{g,ISO}\) ratios in the wells for the two sites, 50 and 68, were found to be greater (not indicated here) than those at the sites of group A. This fact made us consider that there is no actual need to perform this shift.  
Figure 9: _Cont._  
The best-fitted surfaces to the data in Figure 9 (82 x,y,z triads) have been determined by graphics software (SigmaPlot 14.0) in around 9 s of computational time.
## 2 Materials and Methods
### Gilder Deployment and Data Collection
#### 2.2.2 Echo Sounder Mapping of Shallow Zooplankton Layers  
The Wave Glider and the Sailbuoy were equipped with the Simrad WBT Mini (Kongsberg Maritime), a compact and energy-efficient scientific echo sounder in the EK80 family. The Wide Band Transceiver (WBT) Mini can work with continuous wave (CW) and broadband frequency modulated (FM) pulses. Although initially designed for autonomous operations on stationary platforms, it has found widespread use on small and large autonomous mobile vehicles. The Wave Glider was fitted with two split-beam transducers, Simrad ES70-18 CD and ES333-7 CDK with a center frequency of 70 and 333 kHz,respectively. The Sailbuoy was fitted with one split-beam transducer, the Simrad ES333-7 CDK. The center frequencies were chosen to maximize the signal strength from copepods (_C. fimmarchicus_ in particular) and to continuously monitor the shallow layers of copepods throughout the duration of the mission. In this mission, control of the echo sounder data collection was available on both vehicles through the vehicles' two-way Iridium satellite communication (for additional details of the echo sounder integration, operation, and data collected, see [49]).
# Multirobot Task Planning Method Based on the Energy Penalty Strategy
## 4 GA for Multirobot Task Planning
### Algorithm Implementation  
Considering that there are various obstacles in the actual complex scene, the feasible path distance is no longer a linear distance, and the actual energy consumption of the robot may change, resulting in uneven energy consumption. Therefore, the path planning method should be used to achieve obstacle avoidance, recalculate the robot energy consumption, and optimize its task sequence. The current paper uses A* + GA to achieve the goals of energy optimization and energy balance by re-optimizing the task sequence based on the energy penalty strategy without changing the original number of tasks of the robot. The cost function \(f(n)\) of the A* algorithm is shown in Equation (7), in which n is the number of task nodes, \(g(n)\) is the shortest distance from the start node to the current task node, \(h(n)\) is the heuristic function of the shortest path from the current node to the target node.  
\[f(n)=g(n)+h(n) \tag{7}\]  
The main steps of the algorithm are as follows:  
Step 1: Set the parameters related to the robot and the task, including number of robots and tasks, energy consumption threshold, energy consumption per unit path, and genetic operation parameters such as population size, genetic probability, iterations. Then, determine the optimal mathematical model and coding mode of the robot energy consumption, in which the energy consumption is calculated by the shortest linear distance between adjacent task nodes;  
Step 2: Initialize the individual population to generate the number and sequence of tasks of the robot at a certain scale;  
Step 3: Calculate the fitness function value based on the energy penalty mechanism and analyze and compare the energy consumption data of individual populations;  
Step 4: Through genetic operation, selection, cross and mutation, select the superior individuals in the current population to generate new individuals and the next generation of population;  
Step 5: Repeat Steps (3)-(4) until the iteration termination condition is met and output the optimal solution of the multirobot task assignment and path planning;  
Step 6: Based on the requirements of obstacle avoidance, construct the multirobot operation scene while keeping the number of multirobot task nodes unchanged. Then, re-initialize the individual population of the multirobot task sequence;  
Figure 8: Insertion mutation operation (\(m=14\)).  
Figure 7: Exchange mutation operation (\(m=14\)).  
Step 7: A* algorithm is used to search the optimal planning path under the current task sequence, after which the energy penalty strategy is used to calculate and evaluate the energy consumption target value of the robot;  
Step 8: Select the next generation of individual population via genetic operation. Repeat Step (7) until the iteration termination condition is met and output the optimal task sequence and path of the multirobot.
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
## 4 Single- and Dual-Frequency Noise Reduction Tests
### Single-Frequency Test Result Analysis  
In line with our previous research, we observed commonalities in the overall noise from different types of roads and vehicles. Peak effects surfaced within the frequency ranges of 50-80 Hz, 200-315 Hz, and 800-1250 Hz. Thus, our noise reduction tests targeted the frequency bands of 200 Hz, 500 Hz, 800 Hz, 1000 Hz, and 1200 Hz.  
Given the finite speed of sound propagation, it is necessary for the sound waves emitted by the secondary source to meet the noise at the correct time and place to effectively cancel out the noise. Therefore, before starting the tests, we first adjusted the distances between the various components of the active noise control system--primary source, secondary source, reference sensor, and error sensor--to identify the relative positions that yield the maximum noise reduction [57]. Furthermore, due to the varying wavelengths and phases of sound waves at different frequencies, the optimal distances between the components for effective phase cancellation (i.e., noise reduction) will differ.  
With the primary source set at 70 dBA and the indoor ambient noise at 39.2 dBA, for each test frequency, we iteratively adjusted the distances between the primary source, secondary source, and reference sensor to ascertain the positions that produced the maximum noise reduction. Multiple noise reduction tests were conducted at these positions, and the results showed that each test was very close in outcome. Hence, the median of these test results was selected for comparative analysis, as shown in Figure 9. Figure 10 presents a comparison of the spectral data before and after the activation of the active noise control system at 500 Hz.  
Figure 9: Maximum noise reduction and relative positions under different frequency noise.  
As demonstrated in Figure 10, the active noise control system provided significant noise reduction for single-frequency noise, with a reduction of around 20 dBA. Notably, the 500 Hz noise saw the most significant reduction, at 25.4 dBA, bringing the noise level in the target area close to the ambient noise level. The least reduction was seen at 1200 Hz, which decreased by 8.8 dBA.  
Using testing, we observed that the noise reduction performance for extremely low or high frequencies was relatively inferior. Although theoretically lower-frequency noise should be easier to reduce, the actual noise reduction effect was hindered due to the poor frequency response of the speaker at lower frequencies. On the other hand, the real-time requirements of the active noise control system are higher for higher-frequency noise, which also impacted the noise reduction effect. These findings indicate a close relationship between the noise reduction effect of the active noise control system and the choice of hardware and software configuration.
## 3 Results
### Grazing Acoustic Signal without Flow
#### 3.1.2 Validation of the PVPM Model  
Figure 5 compares CFD and experimental results of acoustic absorption coefficients for H1, H2 and H3 liner absorbers in the case of a grazing acoustic signal, but no mean flow. The numerical results and experimental results are, in general, in good agreement. Peak damping frequencies and the absorption bandwidth for all three perforated liner absorbers are well represented by the proposed PVPM model. Both numerical and experimental results show that peak absorption frequencies increase with increasing liner porosity. The liner H2 with 7.6% porosity displays the greatest peak attenuation effect.  
Differences of up to 15% between the experimental and numerical results do exist, with the numerical results tending to overestimate the attenuation effect near the resonance frequencies for liner H1. The numerical results also tend to generate slightly narrower attenuation curves. This may indicate that the model slightly underestimates flow resistance induced by the grazing acoustic signal in the absence of a mean flow. This discrepancy could be further suppressed by including an additional flow resistance correction term accommodating the impact of a grazing acoustic flow. However, this impact is inappreciable in most conditions where a mean flow is present.  
Despite these subtle differences, the PVPM model provides a robust prediction of the resonance frequencies and also major features such as the sensitivity of the resonant frequency as a function of the liner porosity. These results demonstrate that the PVPM model is able to capture the attenuation effects of perforated liner absorbers where the acoustic signal propagates in parallel with the liner surface.  
Figure 4: Mesh distribution for CFD simulations employing the PVPM model: (a) front view; (b) cross-sectional view.
## 2 Design Parametrization and Methods  
A typical perovskite solar cell arrangement has the following structure (from bottom to top): Au [200 nm]/SPIRO OMeTAD [160 nm]/CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) perovskite [300 nm]/TiO [70 nm]/SiO\({}_{2}\) substrate/air [23; 24], where light is incident from the substrate side (the squared brackets embrace the dimensions of the layers). The layout of this structure is presented in Figure 1a. We modify this structure for sensing application as follows: (i) the SiO\({}_{2}\) substrate moves to the other side, adjacent to the gold layer; (ii) the ITO layer is replaced with a 50 nm-thick silver (Ag) layer (this thickness is optimized for better performance); and (iii) we add an extruded dielectric grating with triangular profile on top of the Ag layer. This nanostructured relief is responsible for the spectral selectivity of the device. The parameters of this nanostructure are its period, \(P\), and the width and height of the triangular shape, GW, and GH, respectively. They will be optimized to maximize the absorption at the active layer maintaining a narrow spectral response for better sensing performance. The triangular nanostructure is made of Si\({}_{3}\)N\({}_{4}\) to have a favorable contrast in the index of refraction of the medium under test (in this case, the sensed material is air). Therefore, the modified structure is (from bottom to top): SiO\({}_{2}\) substrate/Au [200 nm]/SPIRO OMeTAD [160 nm]/CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) perovskite [300 nm]/TiO\({}_{2}\) [30 nm]/Ag [50 nm]/Si\({}_{3}\)N\({}_{4}\) [\(P\), GW, GH]/air (see Figure 1). The insets in Figure 1 represent the mechanism at play in both designs. In the standard arrangement, ITO works as an antireflection coating and as an electrode to extract the photo-generated current from the device. The silver layer in the modified design works as a reflector and rejects most of the incoming radiation except those wavelengths where the nanostructure generates a SPR [25; 26]. These resonances result in strong absorption peaks at the active layer of the cell. Moreover, this absorption is very sensitive to refractive index changes of the medium on top of the grating, i.e., air.  
In this contribution, we have used the FEM package COMSOL Multiphysics to evaluate the field distribution, and the reflection and absorption losses within different layers at the cell. A transverse magnetic polarized light (TM) was set for the incident field with an amplitude of 1 A/m, oriented along the \(Z\) axis.