esa-sceva/satcom-chunk-collection huggingface dataset:
# A _uv_-Cellular Network: Design, Implementation and Experiments
Moreover, achieve the target functions, we also developed several application protocols including processing sensor data and remote commands. In order to verify the performance of the _uw_-Cellular network with the introduction of the CLA method, we implement the CLA method at the routing layer. We compare it with the VBF and MFLOOD protocols on some network metrics in the simulation environments. And the simulation test results prove the CLA method can achieve better performance in a network with frequent node movement. As a field case study, we deployed a twenty-node _uw_-Cellular network in the real underwater environment. The network has a single fixed Gateway node in order to connect terrestrial and underwater networks. And there are three fixed nodes act as the BS nodes. Ten fixed and six mobile terminal network nodes are deployed within the access point coverage area. During the sea trial, this large-scale network accumulated more than 240 h of trouble-free operation. The experimental results show that the _uw_-Cellular network with both fixed and mobile network nodes can be used in the practical application scenario with good robustness and performance. The main contributions of this paper are summarized as follows. 1. We design the _uw_-Cellular network, with the goal of realizing a large-scale monitoring hydroacoustic network, which can be long-term deployed in the real underwater environments. The network is composed of three parts, the Gateway node, the BS nodes and the terminal nodes. The Gateway node has the responsibility to connect the networks above and below the surface. The BS nodes provide access and forwarding services for the terminal nodes. According to different practical applications, various types of terminal nodes can be added to the network for a variety of different tasks. 2. For a hydroacoustic network that requires long-term deployment, the communication link is highly time-varying because of the changes in the underwater sound field and network topology. We design protocols in each network layer in order to ensure reasonable channel sharing, data forwarding path selection and data reliability. The protocols used to process sensor data at the application layer are also implemented in order to face the need of practical scenarios. 3. We implement and deploy a twenty-node _uw_-Cellular network in the real underwater environment. The results of experiments in real environment show that the large-scale monitoring network can be used in the practical application scenario with good robustness and performance. The rest of this paper is organized as follow. The related previous works is presented in Section 2. The design of _uw_-Cellular network is presented in Section 3. Section 4 introduces the experiments in simulation environment and verifies the performance of the protocol. At the same time, we give a detailed description about the filed environment in Section 5. Finally, we summarize the contributions of our work and discuss the directions of future work in Section 6.
## 3 Results
### NEE Response to Environmental Factors  
The results of the Anderson-Darling test verified the non-normality of the data, except for Ts. Then, the correlations between the environmental factors, Reco, and NEE were analyzed. Coefficients close to 1 and \(-1\) indicate strong positive and negative correlations, respectively (Figure 8). NEE was positively and significantly correlated with RH\({}_{\rm C}\) (R = 0.66, \(p\) < 0.05) and T\({}_{\rm s}\) (R = 0.26, \(p\) < 0.05). Furthermore, it showed a highly significant negative correlation with Rn (R = \(-\)0.75, \(p\) < 0.05) and T\({}_{\rm c}\) (R = \(-\)0.69, \(p\) < 0.05). On the other hand, Reco was highly positively associated with T\({}_{\rm c}\) (R = 0.7, \(p\) < 0.05) and Rn (R = 0.73, \(p\) < 0.05). In addition, it showed a significant negative correlation with RH\({}_{\rm C}\) (R = \(-\)0.55, \(p\) < 0.05) and T\({}_{\rm s}\) (R = \(-\)0.4, \(p\) < 0.05).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hour**} & \multicolumn{2}{c}{**PPFD**} & \multicolumn{5}{c}{**DPS**} \\ \cline{2-7}  & **[PERSON] et al. [(2018)]** & **82** & **89** & **102** & **111** & **126** \\ \hline
0 & 10 & \(-\)0.061 \(\pm\) 0.09 & 0.143 \(\pm\) 0.25 & \(-\)0.034 \(\pm\) 0.17 & 0.081 \(\pm\) 0.19 & \(-\)0.084 \(\pm\) 0.15 \\
2 & 10 & \(-\)0.069 \(\pm\) 0.05 & 0.309 \(\pm\) 0.29 & \(-\)0.041 \(\pm\) 0.05 & 0.194 \(\pm\) 0.25 & \(-\)0.065 \(\pm\) 0.19 \\
4 & 10 & \(-\)0.041 \(\pm\) 0.12 & 0.361 \(\pm\) 0.39 & \(-\)0.036 \(\pm\) 0.04 & 0.313 \(\pm\) 0.47 & 0.059 \(\pm\) 0.4 \\
6 & 10 & \(-\)0.064 \(\pm\) 0.03 & 0.171 \(\pm\) 0.21 & 0.318 \(\pm\) 0.3 & 0.290 \(\pm\) 0.47 & 0.097 \(\pm\) 0.5 \\
8 & 200 & \(-\)4.148 \(\pm\) 0.17 & \(-\)3.860 \(\pm\) 0.54 & \(-\)4.997 \(\pm\) 0.59 & \(-\)5.550 \(\pm\) 0.36 & \(-\)5.021 \(\pm\) 0.28 \\
10 & 600 & \(-\)9.511 \(\pm\) 0.08 & \(-\)9.245 \(\pm\) 0.54 & \(-\)13.626 \(\pm\) 0.54 & \(-\)12.901 \(\pm\) 1.52 & \(-\)12.489 \(\pm\) 1.12 \\
12 & 400 & \(-\)6.555 \(\pm\) 0.53 & \(-\)6.777 \(\pm\) 0.91 & \(-\)8.829 \(\pm\) 1.29 & \(-\)9.720 \(\pm\) 0.32 & \(-\)8.468 \(\pm\) 0.35 \\
14 & 300 & \(-\)4.710 \(\pm\) 0.9 & \(-\)5.141 \(\pm\) 0.63 & \(-\)7.336 \(\pm\) 0.48 & \(-\)7.202 \(\pm\) 0.36 & \(-\)6.330 \(\pm\) 0.28 \\
16 & 100 & \(-\)0.928 \(\pm\) 0.24 & \(-\)1.233 \(\pm\) 0.77 & \(-\)2.123 \(\pm\) 0.73 & \(-\)1.881 \(\pm\) 0.69 & \(-\)1.899 \(\pm\) 0.8 \\
18 & 10 & 0.686 \(\pm\) 0.89 & - & 0.171 \(\pm\) 0.21 & 1.003 \(\pm\) 0.6 & 0.218 \(\pm\) 0.42 \\
20 & 10 & 0.015 \(\pm\) 0.18 & 0.280 \(\pm\) 0.28 & 0.110 \(\pm\) 0.09 & 0.069 \(\pm\) 0.21 & 0.074 \(\pm\) 0.28 \\
22 & 10 & \(-\)0.014 \(\pm\) 0.04 & 0.247 \(\pm\) 0.21 & 0.282 \(\pm\) 0.24 & 0.321 \(\pm\) 0.52 & 0.069 \(\pm\) 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Diurnal NEE (umolCO2 m\({}^{2}\) s\({}^{-1}\)) for different growth stages in rice fields.  
Figure 7: Diurnal NEE for different growth stages of rice fields.
## 5 Case Study for Mitigating Security Attacks in Smart Healthcare System
### Malware Analysis Layer  
The malware samples collected at the data layer are forwarded in this layer, where we utilized dynamic malware analysis techniques to find intuitive features associated with the malware. For that, Pestudio, process explorer, Sysintemal tools, and Hexeditors are used to analyse the runtime behaviour of the malware. Dynamic analysis helps in bifurcating the malware and non-malware files by their specific signatures shown in the dynamic analysis tools. Few binaries are analysed using an automated malware analysis tool, like a Hybrid analysis web-based tool that gives information, such as hash signatures, threat score, risk assessment, and scope of the MiTM attack. This layer discards all the malware samples from the proposed architecture; only the non-malware sample is forwarded to the intelligence layer.
## 5 Conclusions  
This article presents the results of an experimental study, which assessed the influence of foam on a mixed soil consisting of silty clay, fine-medium sand, and sandy gravel utilizing several geotechnical methods to provide a reference for soil conditioning on mixed soils often encountered in EPBS tunneling. Based on the preliminary tests conducted in this study, the main findings specific to the soils and conditioners tested in this study can be summarized as follows:  
Foam microstructure changes with the decay of the liquid film. The microstructure test carried out in this study showed that the bubbles decayed as the time went by and the decay mechanism was the drainage of water in the liquid film caused by the gravity and pressure difference, and the foam fusion among various size bubbled due to the pressure difference.  
Water plays an important role in modifying the characteristics of the mixed soil. When the water was at the low level (\(w=25\%\)), the foam failed to modify the properties of the mixed soil because of its failure behavior of foam particle induced by the tested soil with insufficient water content. However, when \(w=40\%\), the flowability increased and viscosity decreased dramatically.  
Foam plays an important role in increasing flowability and decreasing viscosity when the water is sufficient in the mixed soil. The mixed soil in this study could be modified into a proper state to meet the slump requirement of 100-200 mm by mixing the foam with the water content of 40%. Foam could maximum its effect when the mixed soil with sufficient water content (i.e., 40%).  
When the water content was 40%, the mixing time, net power value and fluctuation value, slump value, external friction angle and friction coefficient were better than soil samples with water content of 25%. The net power caused by mixed soil with \(w=40\%\) and \(FIR=30\%\) could decrease by approximately 50%. The foam could improve the properties of mixed soil further when the water is sufficient.  
Figure 15: Variation of external friction angle and friction coefficient.  
**Author Contributions:** Conceptualization, Y.W.; methodology, Y.W.; software, Y.W., J.L.; validation, J.L.; formal analysis, Y.W. and D.W.; investigation, Y.W. and D.W.; resources, Y.W.; writing--original draft preparation, Y.W.; supervision, [PERSON] and [PERSON]; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC), Grant number41472278, and the National Key Research and Development Program of China, Grant number2017 YFC0805008.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Occlusion Vehicle Target Recognition Method Based on Component Model
## 3 Experiment and Result Analysis  
### Experimental Data Set  
In this paper, the PASCAL VOC2012 data set, Vehicle Occlusion data set, UA-DETRAC data set, and self-collected real vehicle data set are used to verify the vehicle detection effect of the proposed algorithm. PASCAL VOC2012 is a standard data set that contains a variety of vehicles. The Vehicle Occlusion data set is a synthetic occlusion data set, in which the target object randomly overlaps with two, three, or four unrelated occlusions. UA-DETRAC is a challenging benchmark for real-world multi-target detection and multi-target tracking, it consists of 10 h of video captured by a Cannon EOS 550D camera at 24 different locations in Beijing and Tianjin, China. This paper only trains the vehicle targets in the data set.  
During the experiment, 6713 car images of the three data sets were used as training sets, and the number of training sets was expanded by image enhancement processing methods such as scaling, rotation, and mirroring to further improve the performance of the module. The test uses 500 images collected by ourselves, and the vehicle target in the image has random occlusion. And 748 vehicle images in three public data sets form a data set for module verification.  
Based on the above training data set and test data set, the accuracy rate, mIoU, standard average precision (AP, average precision), and mean average precision (mAP, mean average precision) were used as detection and evaluation indicators to evaluate the performance of the VSRS-VCFEC recognition net designed in this paper.  
The vehicle components are divided into six categories, as shown in Table 1.
## 4 Conclusions  
In order to remove the topography of the tungsten surface during the CMP process, the effect of picolinic acid on tungsten dissolution was studied. First, the adsorption behavior of picolinic acid on tungsten oxide was analyzed using the Langmuir and Freundlich model equations. The adsorption of picolinic acid on tungsten oxide fit well with the Langmuir model and saturated at 1.5 wt%. The removal and dissolution rates were tested at selected picolinic acid concentrations and were shown to decrease with picolinic acid concentration until saturation at 1.5 wt%, which is in good agreement with the adsorption behavior. To analyze the surface topography of each sample, AFM and SEM were used. Tungsten grains were well removed with 1.5 wt% picolinic acid because the valleys of tungsten did not dissolve due to the adsorption of picolinic acid, while the tops were polished until the entire surface flattens, resulting in minimizing the tungsten topography below 10 nm. The experimental results in this study are expected to play a role in ameliorating defects and enhancing the buffing efficiency during the tungsten CMP process for next generation semiconductor process.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12031227/s1](https://www.mdpi.com/article/10.3390/app12031227/s1)]([https://www.mdpi.com/article/10.3390/app12031227/s1](https://www.mdpi.com/article/10.3390/app12031227/s1)), Table S1: Detailed CMP process conditions; Figure S1: Hydrodynamic diameters of SiO\({}_{2}\) abrasive as a function of the concentration of picolinic acid; Table S2: Hydrodynamic diameters and Zeta potential of SiO\({}_{2}\) abrasive as a function of the concentration of picolinic acid.  
Conceptualization, K.L.; methodology, K.L.; investigation, K.L. and J.S.; data curation, K.L.; writing--original draft preparation, K.L.; writing--review and editing, K.L. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 4: (**a**) Dissolution rates and R\({}_{\text{a}}\) values from the tungsten dipping test and (**b**) removal rates and R\({}_{\text{a}}\) values from the tungsten CMP test for various picolinic acid concentrations.
# Fabrication of Low-Twist and High-Strength Metallic Fibre Hybrid Spun Yarns
## 3 Results and Discussion
### Effect of Yarn Count (Ne) on Tensile Properties  
The tensile properties of MFHSYs produced at different yarn counts are shown in Figure 3, where PS3, PS4 and PS5 represent yarn counts Ne 10 s, Ne 20 s and Ne 30 s, respectively. Whereby the Ne 10 s represents a coarser yarn count while Ne 30s indicates a relatively finer yarn count. All three yarn counts were produced at a fixed level of TC, i.e., 3.3 to keep an identical angle of twist in the yarn [44]. The decrease in tenacity and elongation with a stepwise increase in yarn count can be assigned to the decrease in the total number of fibres constituting the yarn [45]. Since SSSF is about six times heavier (8.0 g\(\cdot\)cm\({}^{-3}\)) than that of polyester fibre (1.34 g\(\cdot\)cm\({}^{-3}\)), which means that one low tenacity SSSF fibre (19 cN tex\({}^{-1}\)) with minor elongation (1.0%) can replace about three high tenacity polyester fibres (46 cN tex\({}^{-1}\)) with superior elongation (32%) in the yarn. In addition, the total number of fibres in the yarn cross-section also tend to reduce with increasing yarn fineness (greater value of Ne yarn count) [46,47]. As a result, in a finer yarn with an overall smaller number of fibres in the yarn cross-section, the replacement of polyester fibres by SSSF will reduce the yarn strength and elongation. While in coarser yarn count, due to higher frictional coefficient o SSSF and a greater number of total fibres in the yarn cross-section, a stronger inter-fibre cohesion evolves in the yarn [48]. Thereby, for coarse yarns the resistance to slippage among the fibres in the yarn is increased under a tensile load producing a stronger yarn with higher elongation [49]. However, at a higher yarn count, the contribution of TC towards tensile properties of yarn has appeared to decrease with a stepwise increase in yarn count. Hence, at a higher yarn count, a further increase in TC beyond 3.3 can try to achieve even better tensile characteristics of the yarn.  
Figure 3: Effect of yarn count on tensile properties of conventional ring-spun MFHSYs.  
The regression equations for yarn count and tensile properties are given in Table 5, where the negative signs of slope coefficients confirm an inverse relationship of yarn count with tenacity and elongation. The R\({}^{2}\) values for tenacity and elongation are 0.8439 and 0.931, respectively, reflecting a strong interaction of the parameters. However, a significant decrease in tenacity is observed from 26.09 cN tex\({}^{-1}\) to 23.90 cN tex\({}^{-1}\) for PS3 to PS4, which further declines by a small amount to 23.58 cN tex\({}^{-1}\) for PS5. A relatively smaller value of R\({}^{2}\) associated with tenacity also indicates that the gradual change in tenacity is not perfectly linear.
## 2 Materials and Methods
### X-ray Absorption Spectroscopy (XANES, EXAFS)  
X-ray absorption near edge structure (XANES) spectra at the Pt L\({}_{3}\) absorption edge were recorded ex situ at the P65 beamline of the PETRA III synchrotron radiation source (DESY, Hamburg, Germany) in transmission mode. The energy of the X-rays was selected using a Si(111) double crystal monochromator, higher harmonics rejection was performed using a pair of Rh-coated Si mirrors bent at 2.5 mrad, and the beam size was selected by slits as 0.4 (vert.) \(\times\) 1.5 (hor.) mm\({}^{2}\). Catalyst samples were measured as 100-200 \(\upmu\)m sieved powders packed in quartz capillaries (o.d. = 1.5 mm, 0.02 mm wall thickness). The spectra were corrected for the energy shift using a spectrum of Pt foil measured simultaneously and then normalized using the Athena program from the IFFEFIT software package [34]. For the analysis of extended X-ray absorption fine structure (EXAFS) the spectra were background-subtracted, k\({}^{2}\)-weighted and Fourier-transformed in the k-range 2.5-11.0 A\({}^{-1}\) and multiplied by a Hanning window with sill size of 1 A\({}^{-1}\). The amplitude reduction factor S\({}_{0}\)\({}^{2}\) = 0.74 was obtained by fitting the Pt foil and the PtO\({}_{2}\) (Alfa Aesar, 99.95%) reference spectra to structural models as reported in the Inorganic Crystal Structure Database (ICSD, CCs = 64,923 and 4415). The fits were performed on k\({}^{1}\), k\({}^{2}\), and k\({}^{3}\)-weighted data using Artemis [34] by a least square method in R-space between 1.0 and 3.0 A. Coordination numbers, interatomic distances, energy shift (\(\delta\)E\({}_{0}\)) and mean square deviation of interatomic distances (\(\sigma^{2}\)) were refined during the fitting. The absolute misfit between theory and experiment was expressed by \(\rho\).
## 4 Discussion
Moreover, our spectral measurements were carried out at the conditions when we could not distinguish the emission from the target surface and emission from nearby (-0.2 mm above the ablated surface) plasma. At the moderate ablation conditions (i.e., at the fluence of 2 to 7 J cm\({}^{-2}\) on the target surface) the nanostructures are not affected by the temperature of surrounding material (-900 K) but rather safely elevate from the surface. The survival of carbon nanostructures at these conditions of ablation and their presence at the moment of propagating the driving femtosecond pulses through such plasma formations was confirmed by the SEM analysis of the debris deposited on the nearby surfaces. To summarize, we considered two models: the model of plasma spreading and the model of harmonics generation in the plasmas comprising of the same element (carbon, in our case) either in a single atomic mode or complex morphology state. The first model allowed us to confirm the similarity in the velocities of single-atomic and multi-atomic debris, which corroborates with our observations of the maximal yield of harmonics at approximately same delay between the heating and driving pulses for each of these species. The second model offers the consideration of C and C\({}_{n}\) (\(n\) refers to the number of atoms in cluster) particles as the harmonic emitters from the point of view of their ionization potentials and harmonic yield. An approximate similarity in the ionization potentials led to similarity, to some extent, of the cutoff harmonics, while a larger number of atoms in nanostructures allowed for increasing the cross section of recombination of the accelerated electron with the parent particle. The latter peculiarity distinguished the harmonic yields from the single-atomic plasma produced from ablated graphite and the multi-atomic plasma produced from other studied carbon-contained species (fullerenes, multiwalled nanotubes, nanofibers, diamond nanoparticles, and graphene). Thus, by combining the analysis of delay dependences of the integrated harmonic signals and HHG spectra in the 30-100 nm wavelength range, the roles played by the carbon monomers, small-sizedcarbon molecules, and large nanoparticles in the former process were revealed. A search of new approaches in this direction allows for demonstrating the advantages of high-order nonlinear spectroscopy of ablated nanostructured solids.
# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 2 Related Works
### Real-Time Semantic Segmentation  
Because many previous segmentation models cannot achieve real-time performance and cannot meet real-time application scenarios, efficient lightweight networks have been constantly proposed in recent years. ENet [18] is a lightweight real-time segmentation model based on SegNet [4], which is the first network to realize real-time semantic segmentation. Aiming at the problem of the low timeliness of previous deep neural networks, a new effective deep neural network is proposed. The bottleneck module used in this method has a jump connection structure, which can enhance the expression ability of features, so it can greatly improve the inference speed without much loss of accuracy. However, it gives up the final stage of the model, which is not large enough for the perception field of large objects, which will lead to poor recognition ability. DFANet [19] adopts a lightweight backbone to reduce the computational cost and design a cross-level feature aggregation module to transfer semantic and spatial information between different network layers, so as to solve the problem of spatial information loss caused by the increase of the number of network layers. DFNet [20] uses a \"partial order pruning\" algorithm to obtain a lightweight backbone and efficient decoder. Researchers also design a multi-path scheme to achieve the balance between segmentation speed and accuracy. ICNet [5] combinesmedium-resolution and high-resolution features, considers segmentation accuracy and uses cascading strategies to accelerate real-time image semantic segmentation. BiSeNet v1 [6] and BiSeNet v2 [7] propose two flow paths for low-level details and high-level context information, respectively.  
In this paper, we propose an efficient lightweight backbone to provide different receptive field information and high-level semantic information. In addition, we set up a low-cost detail guidance path and a semantic information supplement path to guide the backbone network to learn low-level details and supplement semantic information, so that the network can achieve a balance between speed and accuracy.