esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study of the CMT+P Process on 316L Stainless Steel Additive Manufacturing
## 4 Results and Analysis
### Microstructure
The micromorphology of four as-deposited samples obtained by SEM are displayed in Figure 7. The morphology was clean in which almost no obvious oxidation was observed, as shown in sample #1. However, with the linear energy increased to 2.37 kJ/cm, some oxide occurred and were distributed on the austenite matrix because of the higher temperature manufacturing environment caused by the higher linear energy, but only a small amount of oxides was observed in sample #2. With the linear energy further increased to 2.62 kJ/cm, larger size and larger quantities of oxides appeared, and a  
Figure 5: Microstructure of the middle region of as-deposited samples: (#1-c) sample #1, (#2-c) sample #2, (#3-c) sample #3 and (#4-c) sample #4.  
phenomenon of a certain degree of material stripping could be observed on the grain boundaries, as shown in sample #3, which would have adverse effects on the material mechanical properties, because their presence destroyed the continuity of the metal matrix, increasing the risk of brittle fracture of materials under tension. When the linear energy further came to 2.85 kJ/cm, the stripping of the material even evolved into a large amount of deeper and larger cracks, which would significantly decrease the tensile strength, this phenomenon was consistent with the subsequent tensile test results. Thus, the microstructure of samples #1 and #2 are better than that of samples #3 and #4, but, although sample #1 have a good micromorphology, bad macroscopic appearance requires a large amount of machining costs to remove it. Thus, all things considered, the linear energy of 2.62 kJ/cm was the most appropriate parameter for 316L stainless steel CMT+P-AM process. Figure 6: (a) microstructure of sample #4 and (b) bar chart of element percentage of two particles. Figure 7: Micro morphology defect recognition of the samples observed by SEM. In the middle of sample #1, the SEM image was taken for observation and the energy spectrometer was used to analyze the specific region as shown in Figure 8. The EDS scanning result in the figure showed that when the scanning beam spot passes through ferrite, the counting rate of Cr and Mo increased significantly, while that of Ni decreased obviously. The similar element distribution on the phase-interface between ferrite and austenite was also observed in other three samples. These show that the content of Cr and Mo in ferrite is more than that in austenite, and the content of Ni is less than that in austenite. Cr and Mo probably promote the formation of ferrite, while Ni is the opposite.
###### Abstract  
Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise. However, their efficacy depends on the complexity of the algorithm, which can be characterized by the number of parameters. In this research, we presented a deep learning algorithm for AMC, inspired by residual learning, which has remarkable accuracy and great representational ability. We also employed a squeeze-and-excitation network that is capable of exploiting modeling interconnections between channels and adaptively re-calibrates the channel-wise feature response to improve performance. The proposed network was designed to meet the accuracy requirements with a reduced number of parameters for efficiency. The proposed model was evaluated on two benchmark datasets and compared with existing methods. The results show that the proposed model outperforms existing methods in terms of accuracy and has up to 72.5% fewer parameters than convolutional neural network designs.  
automatic modulation classification; deep neural network; residual learning; squeeze and excitation
# A Project Scheduling Game Equilibrium Problem Based on Dynamic Resource Supply
## 5 Practical Application
### Project Description  
In this study, a large hydropower project located in southeast China was considered as an application example. The project had a variety of hydraulic structures such as river dams, flood discharge structures, and hydraulic power generation systems. The river dam was a concrete double-curvature arch dam with a height of 610 m.  
The concrete double-curvature arch dam construction project, which consists of 17 engineering activities, is the most important part. A flowchart is shown in Figure 5. Each activity has several optional modes, and each mode has a certain duration and resource demand. At the construction site, there are two large-scale resource demand points to allocate resources for each activity within the project, and the three resources required by the demand points are supplied by an external resource supplier with four resource supply points.
# Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing
## 4 Experiment and Analysis
### Results Comparison
#### 4.2.1 Task Response Time
When there are few tasks, the task success rate changes greatly. This is because there are few tasks and the resource demand is met easily. However, due to visibility and satellite movement, some tasks will fail, which makes the proportion of task failure change greatly when there are few tasks. Figure 8: Average waiting time of tasks. Figure 7: Average response time of tasks.
## 2 Data Collections and Method  
### Description of the Study Sites and Data Collection Method  
The study sites were highway and urban land development and redevelopment construction project sites in South Korea, as shown in Figure 2b. The datasets used for this study were collected from blasting operations during the pre-construction phase of these highways and urban land and redevelopment projects. The geology of South Korea is characterized by three main massifs: the Precambrian Nangrim, Gyeonggi, and Yeongnam massifs. The Gyeonggi and Yeongnam massifs are composed of basement Precambrian gneiss complexes consisting of 2.7 to 1.1 Ga high-grade gneiss and schists. The two massifs are separated by the Okcheon fold belt, which comprises metasedimentary rocks and  
Figure 1: Blastwave propagation and its unwanted environmental events (adapted from [13]). bimodal meta-volcanic rocks, such as metapelites, quartzites, conglomeratic phyllites, marble, and calc-silicates, as shown in Figure 2a [37; 38]. However, the rock exposures and bedrock found at the study sites included granite, speiss, shale, tuff, sandstone, andesite, schist, and rhyolite. Following [39; 40; 41; 42]'s classification methods, the rock weathering degrees observed in all the sites under study were classified as grades II to IV. Three seismographs, Blastmate II and III, and Minimate seismographs (Instatel Corporation Canada), each outfitted with two accessories (triaxial geophones and microphones), were used to measure the AOp (dB) in these construction sites. These devices recorded AOp values that ranged from 100 dB (2 Pa) to 148 dB (256 Pa). The operating frequency response of the microphones was 2-300 Hz, which was sufficient to accurately measure the AOp in the frequency range critical for important utilities, structures, and human hearing. Before the blast, two seismograph accessories were connected to the seismographs and were placed at predetermined points in the blast area. The monitoring distances of the AOp ranged from 14 m to 1344 m and were not the same for each site. In total, 115 blasting events were recorded at these sites. The dataset comprised the hole diameter (D), blashde depth (L), powder factor (PF), charge per delay (Q), spacing (S), stemming length (T), burden (B), rock mass rating (RMR), monitoring distance (DIS), and AOp (dB) for each blast. The explosives used in all blasting were 32 mm- and 50 mm-diameter emulsion explosives with a millisecond delay electronic detonator. Stemming materials included sand, gravel, and rock dust. The diameters of the blastholes at all sites were 51 and 76 mm, respectively. The distance between the AOp monitoring point and blast initiation was measured using a handheld global positioning system (GPS). The RMR values were obtained from experiments conducted on core logs from each site. A breakdown of the dataset statistics used in this study is presented in Table 1. Figure 3 shows the correlation plots between the model input variables and between the inputs and outputs.
## 4 Conclusions  
In this paper, we propose an elastic full-waveform inversion method based on CNEWI. The CNEWI method uses the normalized cross-correlation of near-offset direct waves to formulate an objective function, thereby mitigating the influence of inaccurate amplitudes or bad traces in seismic data during source wavelet inversion. Elastic full-waveform inversion based on CNEWI shows robustness in the presence of bad traces. Moreover, our proposed method addresses the noise and surface wave residuals commonly found in land seismic data, thereby improving the accuracy of both the source wavelet and velocity inversion processes. Incorporating multi-component seismic data enhances the precision of both source wavelet and velocity inversion. The numerical examples provided validate the robustness of our method in source wavelet inversion and the inversion of P-wave and S-wave velocities. The proposed method can provide relatively accurate source wavelet estimation in some complex cases, and also has good application potential for other cases that require high-accuracy source wavelet estimation, such as time-lapse seismic data processing.  
Conceptualization, Q.Q. and W.H.; methodology, Q.Q.; software, D.Z.; validation, Q.Q., W.H. and D.Z.; formal analysis, Q.Q.; investigation, W.H.; resources, W.H.; data curation, D.Z.; writing--original draft preparation, Q.Q.; writing--review and editing, W.H.; visualization, D.Z.; supervision, L.H.; project administration, L.H.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42130805 and 42074154, and the Lift Project for Young Science and Technology Talents of Jilin Province, grant number QT202116.  
Not applicable.  
Data Availability StatementData used are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
_Algorithms_**2015**, \(8\), 965-981; doi:10.3390/a8040965  
_Algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution**  
**[PERSON], [PERSON] * and [PERSON]**  
Department of Information and Communication Engineering, Harbin Engineering University,  
Harbin 150001, China; E-Mails: [EMAIL_ADDRESS] (C.Z.);  
[EMAIL_ADDRESS] (Y.W.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-150-4562-2396; Fax: +86-451-8258-9811.  
Academic Editor: [PERSON]  
_Received: 19 August 2015 / Accepted: 27 October 2015 / Published: 3 November 2015_  
**Abstract:** In this paper, we address the problem of detecting and tracking targets with a low signal-to-noise ratio (SNR) by exploiting hybrid differential evolution (HDE) in the particle filter track-before-detect (PF-TBD) context. Firstly, we introduce the Bayesian PF-TBD method and its weaknesses. Secondly, the HDE algorithm is regarded as a novel particle updating strategy, which is proposed to optimize the performance of the PF-TBD algorithm. Thirdly, we combine the systematic resampling approach to enhance the performance of the proposed algorithm. Then, an improved PF-TBD algorithm based on the HDE method is proposed. Experiment results indicate that the proposed method has better performance in detecting and tracking than previous algorithms when the targets have a low SNR.  
**Keywords:** track-before-detect; particle filter; hybrid differential evolution
## 3 Results  
### Pump Wavelength  
First, we test the effect of the pump wavelength (\(\lambda_{p}\)) on the NLI patterns. When we change the pump wavelength, \(\Delta k_{\text{DSF}}\) and \(\Delta k_{\text{SMF}}\) are varied, simultaneously. The NLI system under this test consists of two 100-m DSF sections and a 50-m SMF section located between two DSF sections. Figure 2a-c show the measured JSIs for various pump wavelengths, \(\lambda_{p}\) = [(a) 1550.92 nm (ITU ch.33), (b) 1555.75 nm (ITU ch.27), (c) 1560.61 nm (ITU ch.21)]. The patterns show the constructive and destructive interference patterns as an island arc along the diagonal dashed white line as seen in Figure 2a, and the first bright island (\(m\) = 1) is about 400 GHz away from the pump frequency. The JSI spectrum in Figure 2a-c looks very similar to each other for various \(\lambda_{p}\)'s, but as seen in Figure 2d, the normalized diagonal JSI spectrum (like the dotted white line in Figure 2a) against the frequency detuning from the pump frequency shows the slow JSI shift towards \(\lambda_{p}\) as \(\lambda_{p}\) increases.  
Figure 2: The measured joint spectral intensity (JSI) for various pump wavelengths, (**a**) 1550.92 nm, (**b**) 1555.75 nm, and (**c**) 1560.61 nm. (**d**) The plot of the diagonal line (white dotted line in (**a**)). (**e**) Peak frequency against the pump wavelength for \(m\) = 1, 2, and 3. Lines are theoretical predictions extracted from the calculated JSI by using Equation (1).  
Figure 2e shows the difference between the pump center frequency and the peak frequency of the first, second, and third islands (\(m=1\), \(2\), and \(3\), respectively) against \(\lambda_{p}\). The lines are the theoretical predictions extracted from the calculated JSI using Equation (1). In the JSI calculation, the zero group-velocity-dispersion (GVD) wavelength of DSF (\(\lambda_{0,\text{DSF}}\)) is \(1555.5\) nm, and its dispersion slope is \(71.5\) s/m\({}^{3}\). The zero GVD wavelength of SMF (\(\lambda_{0,\text{SMF}}\)) is known to be about \(1300\) nm, and the dispersion of SMF at \(1550\) nm used in the calculation is \(18\times 10^{-6}\) s/m\({}^{2}\) with a dispersion slope of \(53.3\) s/m\({}^{3}\). As seen in Figure 2e, the measured peak shift rates with increasing \(\lambda_{p}\) are approximately \(-2.5\) GHz/nm, \(-3.6\) GHz/nm, and \(-4.3\) GHz/nm for the interference mode number \(m=1\), \(2\), and \(3\), respectively. As the mode number, \(m\), increases, the peak frequency moves faster towards the pump frequency with increasing \(\lambda_{p}\), and the measured results match well with the theoretical predictions. This slow frequency shift yields the fine tunability of the center frequency of islands.
## 5 Discussion
### Future Directions
#### 5.2.3 Application to LVDC Systems  
LVDC distribution systems are typically designed under the assumption that both the power lines and the connected loads operate on DC. However, in practical deployments, the need to supply conventional AC loads is expected to arise. In this context, the proposed STPC can serve as a viable interface solution. It is therefore suggested that the control and conversion principles presented in this paper may be applicable to future LVDC systems requiring AC load compatibility [31; 32].
# Effects of Arm Swing in Lower Limb Muscle Activation and Coordination During Treadmill Walking
## 4 Discussion
While this sample size is consistent with previous studies analyzing muscle synergies in healthy subjects during various locomotor tasks [24,28,33,35,37,38,50], future work should enlarge the sample size to enhance the robustness of the findings and increase the statistical power of the results. The generalizability of our findings is limited by the homogeneity of the sample, which consisted of young, healthy subjects within a narrow age range (24.3 \(\pm\) 2.26 years). While this minimized inter-subject variability and is in line with similar studies using mixed-sex samples to investigate muscle synergies during treadmill walking [3,28,37,56,65], it does not capture the variability present in broader populations. Although our sample included both sexes, as other synergy analyses across different tasks [24,28,36,37], it would be interesting, with a larger cohort, to investigate potential sex-related differences in muscle synergies, as has been studied in terms of kinematics during running [13]. Additionally, while this study focused on muscle activation and coordination, incorporating kinematic measurements could provide a more comprehensive understanding of how the speed and frequency of arm swing affect muscle activation and coordination, offering a more complete perspective on the biomechanics of locomotion. Furthermore, exploring these effects under different conditions such as overground walking or varying speeds could provide insights into the role of arm swing in more realistic locomotor scenarios. This approach would enhance our understanding of neuromuscular adaptations to varying mechanical demands and offer valuable guidance for rehabilitation applications. Further studies could also investigate the metabolic costs, providing deeper insights into the physiological mechanisms underlying these changes. Although we controlled gait cadence in both tasks, we cannot determine participants' natural cadence without arm swing, which should be addressed in future studies. Furthermore, investigating how arm swing influences LL muscle synergies under varying conditions, such as on different terrains or at different speeds, would enhance our understanding of its biomechanical implications. Despite these limitations, this study was designed as an initial step toward understanding the effects of arm swing on muscle coordination under controlled conditions. Future research should extend these findings to clinical populations, such as stroke survivors, alongside age-matched healthy controls. This approach would help evaluate the relevance of these findings in clinical contexts and assess their potential applications in rehabilitation.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 3 System Models and Problem Analysis
### Network Model  
We consider an IWSN with a reference node or root node. Each node has a unique identity. The ordinary node communicates with the reference node in a multi-hop manner, namely, the IWSN with the reference node is a layered network, as shown in Figure 1. Obviously, for an ordinary node, it has at least one parent node and one child node. However, the edge nodes only have parent nodes and the reference node only has child nodes. Although the layered network topology is simple and has a high transmission efficiency, it remains a practical problem that attacking the node may result in the failure of network branches starting from this node.
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 2 Materials and Methods
### Methods  
The proposed fusion framework can be decomposed into four steps for the preprocessed images: (1) selection of training pixels, (2) classification, (3) feature extraction, and (4) learning-based image fusion; they are shown in Figure 5. In the first step, the pixels to be used for the training are selected, and in the second step, classification is performed on the SAR and panchromatic images. In the third step, feature descriptors are extracted to be used for training as the SAR image, and in the fourth step, fusion is performed by establishing a relationship based on learning. These steps are described below.  
Figure 4: Experimental images from Site 3: (**a**) synthetic aperture radar image acquired on 10 September 2015; and (**b**) panchromatic image acquired on 7 August 2015.  
Figure 3: Experimental images from Site 2: (**a**) synthetic aperture radar image acquired on 10 September 2015; and (**b**) panchromatic image acquired on 7 August 2015.
## 3 LPS transport to the Outer Membrane
### Molecular Mechanism of Lpt Transport: Towards the Transenvelope Bridge Model
In the work by [PERSON] and coworkers, the residues identified for LptA-LptA dimer interaction are the same involved in LptA interaction with LptC or LptD and the authors claim that this could be an artifact due to LptA overexpression [64]. Based on structural data, it has been suggested that four OstA superfamily domains are necessary to span the width of the periplasm [66]: if LptC and LptD periplasmic domains are taken into account, LptA would function as a dimer within the transenvelope bridge. Alternatively, the periplasm could be constricted in the vicinity of Lpt bridges, and thus only one LptA molecule would be sufficient to build the bridge. Finally, [PERSON] and co-worker developed a system to dissect LPS transport into different steps, shedding light into the role of ATP hydrolysis during the transport process [68]. In this work, the authors identified different sites inside the \(\beta\)-jellyroll structure of LptA and LptC that are involved in LPS interaction _in vivo_, confirming that the periplasmic transit of LPS occurs inside the hydrophobic groove formed by the oligomeric protein bridge. More interestingly, the authors suggested that LPS transits along Lpt components in an ordered process that requires LptBFG to transfer LPS to LptC and then LptBFGC to deliver LPS to LptA. Remarkably, energy (ATP hydrolysis) is not required for assembly of the bridge, instead two energy dependent steps are necessary to translocate LPS from IM to OM: the first requiring energy step is the extraction of LPS from the IM and the second is the transfer of LPS from LptC to LptA. In conclusion, the current model of LPS transport suggests that ATP hydrolysis is used to push a continuous stream of LPS through the transenvelope Lpt bridge in discrete steps against a concentration gradient [68].
## 3 Background
### General CubeSat Specifications
#### 3.1.2 Propulsion System Specifications  
Many propulsion systems, such as cold gas and pulsed plasma thrusters, will require a fuel storage vessel to securely transport propellant. Any vessel on board a CubeSat is restricted to a maximum pressure of 0.12159 MPa (1.2 atm) [1] and must incorporate a minimum safety factor of 4 for the vessel. This restriction has a detrimental effect on thrusters which require pressure differential potential energy to generate thrust and specific impulse to maneuver and maintain altitude.  
The incorporation of pyrotechnics into any aspect of CubeSat design is also strictly prohibited [1]. Typically, pyrotechnics are used as an igniter for chemical propulsion systems and a propellant isolator in pyrotechnic valves for propulsion systems. Any propulsion systems will be designed integrated and tested in accordance with AFSPCMAN 91-710 volume 3 and will have at least 3 Inhibits to activation [1]. Total stored chemical energy will not exceed 100 W-hr and any hazardous materials incorporated into the design, for example propellant, will also conform to AFSPCMAN 91-710, Volume 3.