esa-sceva/satcom-chunk-collection huggingface dataset:
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 2 Related Work
### Network Pruning  
Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings, such as UAVs and other edge computing equipment [29]. A typical pruning process is a three-stage pipeline, i.e., training (a large model), pruning, and fine-tuning [30; 31].  
In early research, [PERSON] et al. [32] showed that network pruning and quantization could effectively reduce network complexity. In [33], a simple magnitude-based three-step method was presented, including pruning, quantization, and Huffman coding, to remove the unimportant connections. [PERSON] et al. [34] introduced dynamic network surgery to iteratively reduce the network's complexity by pruning and splicing. In structured pruning, it prunes a complete structure, such as channels, filters, and layers. [PERSON] et al. [35] proposed the pruning of convolutional layer filters, which rank the filters according to their L1-norm and prune the low-ranking filters from each layer. [PERSON] et al. [30] proposed a simple and effective method to prune channels, called network slimming. It directly adopted the BN layer scale factor as a pruning indicator and introduced L1 regularization to obtain a channel-wise sparsity model.  
The network pruning is different from the compact network design and scale network in reducing the model. Compact network design is a method of choosing a small and compact network at the initial stage of model construction [30]. The most representative method is the MobileNet series [36]. Tiny-YOLOv3 is a designed compact network version of the original YOLOv3. Compared with other networks, these networks are smaller, faster, and less computational. However, considering the characteristics of infrared aerial photography data and the deployment of algorithms on mobile platforms, the algorithm's robustness and its effective applications need further research. [29] implies that the pruned architecture itself, rather than a set of inherited \"important\" weights, is more crucial to the efficiency of the final model. \"Lottery ticket hypothesis\" [37; 38] shows that finding effective subnetworks is significant for model compression. Facing a specific scene with limited data, it is quite difficult to design an effective network directly.  
In scale networks [22; 39; 40; 41], the higher accuracy is obtained by repeating feature networks [41]. When reducing these networks, this usually will reduce the effectiveness of the model. The pruned network after fine-tuning does not greatly reduce the accuracy and usually can achieve comparable detection accuracy to the original model.  
In this paper, inspired by network slimming [30], which is proven to be an effective method to channel-wise prune [7; 42], we use Smooth-L1 instead of L1 regularization to avoid using sub-gradient [30] and adopt the scale factor to evaluate the convolutional channels to prune.
## 2 Experimental Setup, Methods, and Procedures
### Assembly for Measuring Microhardness and Hardness  
Microhardness and hardness measurements were carried out using the Vickers method. A diamond quadrilateral pyramid with an apex angle of 136\({}^{\circ}\) was pressed into metallographically prepared samples in their initial state, perpendicular to the rolling direction. The Zwick/Roell ZH\(\mu\) microhardness tester (ZwickRoell Ltd., Ulm, Germany, Figure 2a) was used for the microhardness measurements, with a load of 0.5 kp (HV 0.5) and a 10 s load time at a room temperature of 20 \({}^{\circ}\)C. The measurements were taken at the center of the sample due to the expected symmetry of microhardness results from the beginning to the end of the sample. The investigation of changes in sample hardness based on anneal  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **C** & **Mn** & **Cr** & **Si** & **Ni** & **Mo** & **S** & **P** & **Fe** \\ \hline
**AIS 304** & 0.0308 & 1.44 & 18.87 & 0.315 & 7.8 & 0.24 & 0.0493 & 0.0236 & rest of \\
**STN 17 240** & \(\leq\)0.07 & \(\leq\)2 & 17.0 \(\div\) 20.0 & \(\leq\)1 & 9.0 \(\div\) 11.5 & N/A & 0.03 & 0.045 & rest of \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the experimental material AISI 304 compared with the standard (wt %).  
Figure 1: Prepared samples after heat treatment at different temperatures (IS = initial state).  
ing temperature was conducted using a hand-held Vickers hardness tester with a 10 kg load (HV 10) at a consistent load time and temperature for all measurements, as seen in Figure 2b.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 5 Algorithm Simulation and Result Analysis  
To more accurately evaluate the strengths and weaknesses of the HDQN algorithm proposed in this paper, Matlab R2022b was used as the simulation tool. The simulation experiment platform was configured with an Intel Core i7 3.6 GHz processor and 16 GB of RAM. Assume sensor nodes are randomly distributed within a square area measuring 200 m by 200 m (denoted as: 200 m \(\times\) 200 m), with the BS initially positioned at coordinates (100, 100). The HDQN algorithm is compared with five other algorithms--Energy-Efficient Hierarchical Cluster Routing (EEHCHR) [9], Two-Level Heterogeneous Gateway-Based Energy-Aware Multi-hop Routing (2L-HMGEAR) [10], a Network-Configurable Optimized Genetic Algorithm (NCOGA) [11], Distributed Energy-Efficient Clustering (DEEC) [12], and Stable Election Protocol (SEP) [13]--based on metrics such as average remaining energy, node survival, and data transmission volume. The comparative algorithms are as follows:  
1. EEHCHR: A hierarchical cluster routing algorithm that focuses on energy efficiency and aims to reduce energy consumption in the network.
2. 2L-HMGEAR: Suitable for dual-layer HWSNs, optimizing multi-hop routing through energy-aware methods.
3. NCOGA: A network configuration optimization method based on genetic algorithms, designed to improve the performance of wireless sensor networks.
4. DEEC: This algorithm optimizes the LEACH algorithm by considering the remaining energy of nodes during cluster head election, thereby extending the network lifecycle.
5. SEP: This protocol sets different thresholds for HWSNs, enabling advanced nodes to take on more cluster head roles and thus improving the network stability.  
The simulation experiment parameters for HWSNs are shown in Table 1.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Simulation Parameter**} & \multicolumn{1}{c}{**Values**} \\ \hline Sensor node monitoring area (m\({}^{2}\)) & 200 m \(\times\) 200 m \\ BS locations (m) & (100, 100), (150, 150), (200, 200) \\ Number of wireless sensor nodes & 100, 200, 300 \\ Maximum round & 3000 \\ Energy-heterogeneous node type & super nodes, advanced nodes, ordinary nodes \\ Proportion of energy-heterogeneous nodes & 1:2:7 \\ (super nodes/advanced nodes/ordinary nodes) & 1:2:7 \\ Normal energy-heterogeneous node initial & 300 \\ energy (m) & Advanced energy heterogeneous node initial & 600 \\ Energy (m) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of parameters of HWSNs simulation experiment.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models
### GNSS Code and Phase Observations
#### 3.1.3 Wide-Lane (WL) Phase Observation  
Another useful combination is the wide-lane phase \(\Phi_{WL}\), derived by setting \(\alpha_{WL}=f_{1}/(f_{1}-f_{2})\) and \(\beta_{WL}=-f_{2}/(f_{1}-f_{2})\) in (30). \(\Phi_{WL}\) introduces a new WL ambiguity \(N_{WL}=N_{1}-N_{2}\) corresponding to a wavelength \(\lambda_{WL}=c/(f_{1}-f_{2})\). The WL wavelength exceeds L1 and L2 wavelengths, e.g., \(\lambda_{WL}=86.2\) cm for GPS. The WL phase proves valuable in cycle slip detection and integer ambiguity resolution.
## 3 A Damage Quantification Method Based on Multi-Frequency Excitations  
With the understandings of the torsional wave-damage interaction, the inverse problem is investigated as damage quantification in terms of the width (\(w\)) and polar moment of inertia of the cross-section (\(I_{p}\)). Material parameters (\(\rho\) and \(G\)) are assumed to be unchanged by the defect so that the wavenumbers (\(k\)) of the torsional waves in the intact and damaged segments are identical. According to Equations (15) and (16), the reflections coefficients from both the defect and end are significantly affected by the wavelength (\(\delta\)) which is explicitly related to the frequency (\(f\)). Accordingly, a damage quantification method is proposed by using multiple excitations at different frequencies. By cross-checking the variation patterns of the wave reflection coefficients with respect to the frequencies from the tests and theoretical analyses, the defect sizes can be quantified with the least square method. Details of the damage quantification method are explained as follows.  
In practical pulse-echo tests, the amplitude of the incident wave is usually difficult to measure due to electromagnetic interference. Consequently, the reflection coefficients from the defect cannot be directly obtained according to Equation (15). Alternatively, as both reflected waves from the defect and end can be measured in the tests, a Synthesized Reflection Coefficient (\(SRC\)) is defined as,  
\[SRC(f)=\frac{\hat{\varphi}_{d}(f)}{\hat{\varphi}_{e}(f)} \tag{18}\]  
With this definition, the influence of the incident wave is effectively eliminated.  
For an assumed damage size \((w,I_{p})\), the theoretical analyses are applied to calculate the \(SRC\) at different frequencies according to Equations (15) and (16). Based on the least square principle, the deviation (\(DV\)) of the variation patterns of the \(SRC\) with respect to frequency between the test results and theoretical predictions is defined as,  
\[DV(w,I_{p})=\int\bigg{(}SRC_{test}(f)-SRC_{theory}(f)\bigg{|}_{w,I_{p}}\bigg{)} ^{2}\text{d}f \tag{19}\]  
A larger value of \(DV\) indicates the larger difference between the test and theoretical results, which means the less possibility of damage sizes of the assumed \((w,I_{p})\). A damage index (\(DI\)) is further defined as the inverse of \(DV\) as,  
\[DI(w,I_{p})=\frac{1}{DV(w,I_{p})} \tag{20}\]which directly characterizes the possibility of damage sizes of \((w,I_{P})\). Finally, through varying the assumed damage sizes in the theoretical calculations, a damage image in terms of the \(DI\) is constructed. The damage size can be quantified as \(\left(\widetilde{w},\widetilde{I_{P}}\right)\) which produces the maximum \(DI\) in the damage image as,  
\[\left(\widetilde{w},\widetilde{I_{P}}\right)=\underset{(w,I_{P})}{\text{argmax }}(DI(w,I_{P})) \tag{21}\]
###### Abstract  
The purpose of this study is to establish the minimal injection doses of magnetic resonance imaging (MRI) contrast agents that can achieve optimized images while improving the safety of injectable MRI drugs. Gadolinium-diethylenetriamine penta-acetic acid (Gd-DTPA) and ferroucarbotran, commonly used in clinical practice, were selected and evaluated with in vitro and in vivo experiments. MRI was acquired using T1-weighted (T1W) and T2-weighted (T2W) sequences, and the results were quantitatively analyzed. For in vitro experiments, results showed that T1W and T2W images were optimal when Gd-DTPA-bisamide (2-oxoethyl) (Gd-DTPA-BMEA) and ferroucarbotran were diluted to a volume percentage of 0.6% and 0.05%; all comparisons were significant differences in grayscale statistics using one-way analysis of variance (ANOVA). For in vivo experiments, the contrast agent with optimal concentration percentages determined from in vitro experiments were injected into mice with an injection volume of 100, and the images of brain, heart, liver, and mesentery before and after injection were compared. The statistical results showed that the \(p\) values of both T1W and T2W were less than 0.001, which were statistically significant. Under safety considerations for MRI contrast agent injection, optimized MRI images could still be obtained after reducing the injection concentration, which can provide a reference for the safety concentrations of MRI contrast agent injection in the future.  
contrast agent; ferroucarbotran; Gd-DTPA-BMEA; MRI +
Footnote †: journal: Applied Sciences  
1
Footnote 1: Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology,  
Hsinchu 30015, Taiwan; [EMAIL_ADDRESS] (W.-T.H.); [EMAIL_ADDRESS] (Y.-H.C.); [EMAIL_ADDRESS] (J.-W.T.); [EMAIL_ADDRESS] (A.-Y.W.)
# Study of Standing-Wave Thermoacoustic Electricity Generators for Low-Power Applications
## 4 Results and Discussion
### Two-Stage Engine with Loudspeaker Coupled in Side Branch Arrangement
Using the above values of \(R_{b}\) and \(X_{b}\) the engine is then optimized by varying the stack location, the stack length, and the branch location while adjusting the HXs length to match exactly the local peak-to-peak acoustic particle amplitude. The geometrical dimensions of the engine deriving from the optimization procedure are given in Table 2 (excluding the branch whose dimensions are reported in Figure 8). The performance of the engine can be inferred from Figure 8 where the output power values \(W_{a}\), \(W_{e}\) and the conversion efficiencies \(\eta_{ha}\), \(\eta_{he}\) are reported as a function of the heating temperature \(T_{H}\) which varies as a consequence of heat input change (from 100 to 2200 W by steps of 100 W). In these simulations, the HX lengths were held fixed to the values relative to \(W_{e}=100\) W. From the graph it can be observed how all the investigated parameters grow with \(T_{H}\) (reflecting the growth of the stacks conversion efficiency with \(T_{H}\)) and that the onset temperature of the engine falls around 360 \({}^{\circ}\)C. When the temperature of the HHX reaches 719 K the LA extracts 50 W of electrical power with the design \(\eta_{ae}\) efficiency (70%) and with an overall \(\eta_{he}\) efficiency equal to 4.3%. At this operative point the amplitude of the driving pressure in front of the diaphragm and its peak-to-peak stroke amount to 2 \(\times\) 10\({}^{3}\) Pa and 8.7 mm, respectively. When \(T_{H}=773\) K, the engine produces the target (maximum) power (\(W_{e}=100\) W) with an the overall \(\eta_{he}\) efficiency equal to 4.6%. In this case the amplitude of the driving pressure and its peak-to-peak stroke amount to 3 \(\times\) 10\({}^{3}\) Pa and 12 mm, respectively. Figure 7: The pressure and volumetric velocity distributions along the branch to the alternator for \(f=194.5\) Hz and \(R_{L}=19\) \(\Omega\). Note that the driving pressure is of the same order of magnitude of the driving pressure relative to the two-stage engine with push-pull configuration. In this case, however, the engine is able to achieve the target output power since the use of the branch allows us to decouple the acoustic filed driving the alternator from the one sustained by the trunk where the engine components (stack and HXs) are placed. In this way, the stacks are interested by high pressure levels (which enable high acoustic powers) while the trunk and the back volume regulations concur in selecting a \(p\)-\(U\) time-phase in front of the LA different from the standing wave phasing (\(\sim\)90\({}^{\circ}\)) typical of the trunk, which can be as low as 10\({}^{\circ}\).
## 2 Via-Point Trajectory Generalization and Initial Distributions Encoding
### Learning New Skills in Cartesian Space with Via-Point Trajectory Generalization
The elementary trajectory \(h(\mathbf{o})\) is derived using the spherical linear interpolation (SLERP) as follows:  
\[h(\mathbf{o})=\frac{\sin((1-\alpha(\mathbf{o}))\beta)}{\sin(\beta)}h(\mathbf{o}_{1})+\frac {\sin(\alpha(\mathbf{o})\beta)}{\sin(\beta)}h(\mathbf{o}_{\text{end}}) \tag{8}\]  
where \(\cos(\beta)=h(\mathbf{o}_{1}).h(\mathbf{o}_{\text{end}})\), \(\alpha(\mathbf{o})\) is the ratio of SLERP, ranging from 0 to 1. \(h(\mathbf{o}_{1})\) and \(h(\mathbf{o}_{\text{end}})\) are the start and end points of the elementary trajectory:  
\[y(\mathbf{p})=h(\mathbf{p})+f(\mathbf{p}) \tag{1}\]0  
In order to learn \(f(\mathbf{o})\) from demonstration, we set \(\alpha(\mathbf{o})\) to distribute uniformly over \(T\) timesteps and set \(f(\mathbf{o}_{1})\), \(f(\mathbf{o}_{\text{end}})\) to \([1,0,0,0]\). Once new start quaternion sets \(\mathbf{o}_{1}^{\text{new}}\) and end quaternion sets \(\mathbf{o}_{\text{end}}^{\text{new}}\) are required during generalization, we compute a new elementary trajectory \(h(\mathbf{o})^{\text{new}}\) using Equations (8) and (9), with learned shape modulation \(f(\mathbf{o})\) superimposed to generate a new orientation trajectory \(y(\mathbf{o})^{\text{new}}\) using Equation (7). Note that there is no need to generalize via-points in orientation, as we can achieve via-points generalization in 3D positions while keeping the original orientations.
## 3 Results and Discussion  
### The Flowability of Fresh Mixture  
Figure 6 presents the effects of steel fiber content, water-binder ratio and superplasticizer dosage on flowability of fresh UHPC mixtures. For all four different water-to-binder ratios, the flowability decreased with the increase of steel fiber content when the superplasticizer dosage was kept the same. The addition of 1% steel fiber led to a notable reduction of flowability when compared with the blank mixture without fiber, and a relatively slight decrease of flowability occurred when the addition of fiber increased from 1% to 3% for most of mixtures. For instance, the flowability of mixtures with 1%, 2%, and 3% steel fibers were reduced by 22.3%, 25.8%, and 28.8% compared to that of the blank mixture with a water-to-binder ratio of 0.20, containing 1.4% superplasticizer. This flowability reduction effect might be attributed to the increased specific surface area of steel fiber [16], leading to an increased demand of cement paste for covering the surface of fibers and aggregates. On the other hand, the steel fibers were randomly distributed in the mortar matrix and acted as a skeleton, and eventually prevented the flow of fresh UHPC mixture [14].  
When the same water-to-binder ratio and steel fiber content was used, the more dosage of superplasticizer increased the fluidity as expected. To increase the flowability, a higher dosage of superplasticizer was needed for UHPC mixtures with a lower water-to-binder ratio, due to a smaller amount of existing free water. For water-to-binder ratios of 0.24 and 0.22, the flowability of the mixtures reached as high as 200 mm by using more superplasticizer, which is favorable for casting. However,  
Figure 6: Effects of steel fiber content, water-binder ratio and superplasticizer dosage on flowability of fresh UHPC mixtures. (**a**) \(w/b=0.18\); (**b**) \(w/b=0.20\); (**c**) \(w/b=0.22\); (**d**) \(w/b=0.24\).  
for water-to-binder ratios of 0.18 and 0.20, the increased dosage of superplasticizer led to a limited increase of flowability. Therefore, the fluidity was more sensitive to superplasticizer dosage in higher \(w/b\) than in lower \(w/b\) mixtures [22]. Such results reveal that a very low water-to-binder ratio may result in increased difficulty in compacting UHPC mixtures.
## 3 Results
### Recovery of Gamma-Decalactone by Liquid-Liquid Extraction  
Solvent extraction is a very simple and rapid method of isolating fragrance compounds. Research on the separation of GDL began with analyzing the influence of the type of solvent used at the level of its recovery. For this purpose, model solutions were used with a composition that reflected the microbiological media for lactone synthesis. The model solutions contained GDL at the concentration of 3 g/L and castor oil. The concentration of the ingredients was selected based on the previous research on GDL synthesis, in which, an average of about 3 g/L of lactone was obtained from 10 g/L of castor oil by biotransformation reactions with the participation of _Yarrowia lipolytica_ yeast [35].  
In this study, five different organic solvents with different polarities were compared in order to obtain the best recovery of GDL. Table 2 presents a comparison of the results of the recovery test with two different solvent-to-sample ratios.  
The data indicate that diethyl ether is one of the best solvents for GDL extraction. The recovery with its participation reached almost 100% (99.9 \(\pm\) 1.8%). Slightly lower or less accurate (burdened with a bigger error) extraction efficiency was obtained with the use of chloroform or dichloromethane (with a recovery of 98.8 \(\pm\) 1.3% and 101.1 \(\pm\) 2.5%, respectively). The use of a larger volume of organic solvent for lactone extraction clearly improved the level of its recovery, regardless of the solvent used. With a 1:1 (_v:v_) solvent-to-sample ratio, 0.1% (for diethyl ether) to 13% (for heptane) of the GDL was not extracted. For a 1:2 (_v:v_) solvent-to-sample extraction ratio, 10.6% (for dichloromethane) to 27.9% (for heptane) of the GDL remained in the solution. The RSD values were less than 5% in all the experiments, with higher deviations for the 1:2 (_v:v_) extraction, regardless of the type of solvent. The extraction method that used equal amounts of solvent and sample for diethyl ether, chloroform, and dichloromethane showed good recoveries and high repeatability.  
However, the problem with using this technique is the purity of the extracted compound. In the extracts, the GDL purity was only about 51.5 \(\pm\) 1.2%-53.0 \(\pm\) 1.8% (the results obtained using GC-MS analyses). The samples were contaminated mainly with ricinoleic acid derived from the hydrolysis of castor oil as well as with triglycerides of the unreacted substrate.
# Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs
###### Abstract  
This study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (EOS) (AMSR-E) sea surface temperature (SST) footprint associated with the remote sensing systems (RSS) level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed to each \(4\times 4\) km square contributing to the SST value of a given (AMSR-E) pixel. High-resolution L2 SST fields obtained from the moderate-resolution imaging spectroradiometer (MODIS), carried on the same spacecraft as AMSR-E, are used as the sub-resolution \"ground truth\" from which the AMSR-E footprint is determined. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a constrained least square approximation based on the bootstrap sampling procedure. The method yielded an elliptic-like Gaussian kernel with an aspect ratio \(\approx\)1.58, very close to the AMSR-E 6.93 GHz channel aspect ratio, \(\approx\)1.74. (The 6.93 GHz channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be aligned with the instantaneous field-of-view of the sensor as expected from the geometric characteristics of AMSR-E. Footprints were also analyzed year-by-year and as a function of latitude and found to be stable--no dependence on latitude or on time. Precise knowledge of the footprint is central for any satellite-derived product characterization and, in particular, for efforts to deconvolve the heavily oversampled AMSR-E SST fields and for studies devoted to product validation and comparison. A preliminary analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained ignoring the shape and size of the footprint as has been the practice in such comparisons to date.  
SST; AMSR-E; MODIS; footprint; constrained least square; bootstrap 2019 11 715 10.3390/rs11060715