esa-sceva/satcom-chunk-collection huggingface dataset:
_Appl. Sci._**2015**, \(5\), 1699-1714; doi:10.3390/app5041699  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Feasibility Study of Sensor Aided Impact Acoustic Sorting of Plastic Materials from End-of-Life Vehicles (ELVs)  
**[PERSON] \({}^{\dagger,\star}\), [PERSON] \({}^{\dagger}\) and [PERSON] \({}^{\dagger}\)**  
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Daxue Rd. 1, Xuzhou 221116, China; E-Mails: [EMAIL_ADDRESS] (Z.B.);  
[PERSON]_ADDRESS] (S.L.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-516-8388-3100; Fax: +86-516-8359-1330.  
Academic Editor: [PERSON]  
_Received: 16 September 2015 / Accepted: 2 December 2015 / Published: 9 December 2015_  
**Abstract:** The purpose of this feasibility research was to study a novel sensor based separation method for recycling of plastic materials from end-of-life vehicles (ELVs) by using eigen-frequency response of impact acoustic emission. In this research three kinds of commonly used plastics, polypropylene (PP), acrylonitrile-butadiene-styrene (ABS), and styrene-maleic-anhydride (SMA) sampled from end-of-life vehicles, were researched. Almost all the crushed plastic scraps had a flake structure, theoretically their impact response behaviors were determined by their diameters and thicknesses. The equivalent diameters of the scraps were characterized by fine sieving and their thicknesses were measured online by a 3D laser triangulation sensor above the conveying path. Following this the scraps were free dropped one-by-one to impact with an impact passive body on which impact acoustic emission (AE) signals were generated and acquired by an acoustic pickup sensor. Thirdly, the AE signals which carried eigen-frequency response features were processed and characterized. Results demonstrated that the scraps with diameters \(<8\) mm were too weak for the actual devices to process; the scraps with diameter from 8-13 mm still generated quite a lot of AE signals of inadequate intensity. Finally the general characterization and recognition yields were 64.6%, 61.7%, and 63.9% of PP, ABS, and SMA in mass, respectively of tested materials.  
**Keywords:** end-of-life vehicles (ELVs) recycling; automobile shredder residues (ASRs); sensor aided sorting; impact acoustic emission (AE)
###### Abstract  
Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.  
Keywords:wireless sensor networks (WSNs); hierarchical network structure; feasible routing sets; maximum minimum criterion; combinatorial optimization; balancing energy consumption +
Footnote †: journal: Sensors
## 1 Introduction  
The physics and applications of ultra-nanocrystalline diamond (UNCD) films have been and are currently being investigated due to their unique combination of properties such as: high wear resistance [1], highest hardness relative to any other films [1], lowest friction coefficient [1] compared with metal and ceramic coatings chemical inertness to corrosion [1], negative electron affinity low work function for electron emission [1], high electrical conductivity via boron (B) atoms doping of UNCD producing B-UNCD films [2, 3]. N atom insertion into grain boundaries of UNCD films, producing the N-grain boundary incorporated ultrananocrystalline diamond (N-UNCD) films, as previously demonstrated by different groups worldwide experimentally [4, 5, 6, 7, 8], and theoretically [9]. The combination of these properties makes doped UNCD films suitable for many applica-tions like corrosion resistant electrically conductive B-UNCD coatings on metal electrodes, for electrolysis-based water purification systems [3] and Li-ion batteries electrodes, thermionic and field emission devices, and high-power electronic devices [2, 10]. The R&D described in this paper focused on exploring the effect of total pressure and microwave power on the electrical conductivity of N-UNCD films grown by microwave plasma chemical vapor deposition (MPCVD) in order to maximize the electrical conductivity of these films.  
[PERSON] et al. [11] recently demonstrated to dope the diamond with nitrogen using the CVD technique, however, our research is novel because we use the MPCVD technique and the diamond size scale that we produce is much smaller.
# High-Speed Tomography--A New Approach to Plasma Bulk Velocity Measurement
## 1 Introduction
In this study, the plasma torch was operated at atmospheric pressure with a current of 200 A and argon as the plasma carrier gas. The filtered back-projection was then used for the reconstruction [29]. As discussed above, there are many diagnostics for determining the plasma velocity of different measurement objects. Here, the tomographic diagnostic tool coupled with high-speed imaging is used for velocity measurements. One advantage of this approach is that it is a non-invasive measurement technique. In addition, compared to laser or spectroscopic diagnostics, the used method promises low complexity. The combination with a high-speed imaging system enables the detection of fluctuations of the plasma plume in the range of microseconds and the tracking of these fluctuations in the reconstructed images to determine the velocity of the plasma. The aim of this work is therefore to determine the velocity of the plasma bulk with such a high-speed tomographic system. To validate the proposed setup, a DC plasma spray torch operated at a pressure of 20 mbar was used as the measurement object. However, the proposed setup can be used for other applications such as electric propulsion. In order to achieve the highest possible spatial resolution and, on the other hand, due to geometrical limitations, the torch under study was observed from six directions of view. As a result, a reconstructed three-dimensional model of the plasma plume was delivered.
## 6 Conclusions  
In this paper, we proposed a protection scheme in a QKDN with partially trusted relay. In the partially trusted relay-based QKDN, the topology abstraction method can handle the trusted and untrusted relay in different ways, which was conducive to the subsequent key resource allocation. In the newly formed abstract topology, the working and protection paths were found. Lastly, key resources were allocated to the services required for keys. In addition, the key protection threshold was proposed to ease up the conflict of working and protection paths on key resources. The protection scheme can improve the stability and reliability of transmission services in the QKDN with partially trusted relay. The simulation results show that there is a trade-off between the blocking rate of secret key requests and the success rate of protection path construction, which means a balance is needed to maintain the resource allocation between working paths and protection paths.  
Conceptualization, Q.Z. and X.Y.; methodology, X.Y. and Y.Z.; software, Q.Z. and Y.L.; validation, Q.Z. and Y.L.; writing--original draft preparation, Q.Z.; writing--review and editing, X.Y. and Y.Z.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the NSFC project (62150032, 61971068, 62021005), Fund of National Key Research and Development Program of China (2020 YFE0200600), Fund of State Key Laboratory of Information Photonics and Optical Communications, BUPT (IPOC202 ZT04) and the Fundamental Research Funds for the Central Universities (2019 XD-A05).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Figure 10: Simulation results: (**a**) protection path construction success rate varies with key resource in the case of different untrusted node numbers in NSFNET; (**b**) protection path construction success rate varies with key resource in the case of different untrusted node numbers in USNET.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Biofilm  
The _mqsRA_ was first identified for its role in regulating biofilm [73]. The expression of _mqsRA_ is induced by the quorum-sensing autoinducer (AI-2) [73]. MqsRA regulates the expression of many genes, including genes involved in curli and cellulose production, motility and stress resistance through the transcriptional regulation of global regulators such as _rpoS_ and _cspD_[39; 41]. The cold shock protein CspD is a master regulator of curli and cellulose production, which are the main constituents of biofilm matrices [146; 147; 148]. MqsA binds palindromic sequences in the promoter region of _cspD_ and represses its expression under favorable growth conditions [41]. Under stress, such as nutrient limitation, MqsA is degraded by proteases. As a result, the _cspD_ expression will be de-repressed, leading to the production of curli and cellulose [39; 41]. Simultaneously, the degradation of MqsA also leads to the induction of sigma S expression, which in turn represses the expression of the motility gene via FluDC, which also favors cells to form biofilms [73].  
Other TA systems, whose toxins do not possess nuclease activities, are also implicated in biofilm formation. In _B. subtilis_, TpxA and YqcG toxins are expressed while under nitrogen limiting conditions and are important for the proper development of biofilms [149]. YafQ affects the number of cells in biofilm that survive antibiotic treatment [150]. These results demonstrate plausible links between biofilms, stress response and TA systems.
## 2 Experimental Procedure
### Photocatalytic Evaluation  
The photocatalytic performances of MnFe\({}_{2}\)O\({}_{4}\), MnCe\({}_{0.1}\)Fe\({}_{1.9}\)O\({}_{4}\), MnCe\({}_{0.3}\)Fe\({}_{1.7}\)O\({}_{4}\) and MnCe\({}_{0.5}\)Fe\({}_{1.5}\)O\({}_{4}\) NCs were examined under a visible light (Xenon lamp 300 W; \(\lambda\) > 400 nm) treatment. The photocatalytic activity was analyzed at RT. Exactly 100 mg of the nanocatalyst was distributed in 100 mL of methylene blue (MB) (10 mg/L), which was reserved in a quartz glass photocatalytic reactor. Before irradiation, the aliquot was stirred 30 min in dark conditions and it was ensured that the catalyst equilibrium of adsorption-desorption was reached between the dye and photocatalyst. The degradation efficiency was examined using UV absorption readings, which lead to govern the dye absorptions at a certain time interval. The photo-catalytic performance of the pure MnFe\({}_{2}\)O\({}_{4}\) and cerium doped MnFe\({}_{2}\)O\({}_{4}\) NCs at various percentages was also investigated.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 4 Discussion
### Impact of Prescribed Fire on Ecosystem-Scale Phenology in Early Spring  
Prescribed fire is a common forest management method in the southern United States [56] and has been shown to delay the timing of understory green-up (Appendix B, Table A1). Since prescribed fire is implemented during the dormancy period (winter to early spring) [44; 45], burning of the understory caused short-term shifts in aboveground biomass and photosynthetic capacity [57]. Fire may increase soil nutrients, which can promote rapid recovery of herbaceous plants and thus ecosystem productivity [56; 58]. This indirectly improves light use efficiency, which may increase the sensitivity of spring phenology to air temperature and PAR (Table 3). This may have contributed to the slight difference in SOS over the course of the study, since low-intensity prescribed fires did not cause functional disruption of forest structure and physiological function [44; 45; 58].  
Site-level soil water holding capacity was an important influencing factor for the sites' phenological response to prescribed fire. Increased post-fire soil hydrophobicity has been shown to lead to short-term water competition, which limits the development of understory leaves, thus reducing GPP which drives our phenological model [59]. Greater soil water holding capacity is related to greater understory plant diversity and biomass [40], which resulted in a relatively longer recovery time for productivity following fire. To fully understand the meteorological and biological mechanisms of how prescribed fire influences spring phenology, it may be necessary to study fire intensity by site, as greater fire intensity may be associated with more vegetation loss on ignition, leading to a longer period of recovery [59].
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method
### Model Architecture
#### 2.4.3. Gated Recurrent Unit (GRU)  
GRU [35; 36] is a gating mechanism for recurrent neural networks, introduced by [PERSON] et al. in 2014 [37], in order to improve the slower execution speed of LSTM, to speed up execution and reduce memory consumption. At the same time, it has a pretty good performance in a huge dataset. As shown in Figure 12, GRU replaces the Forget Gate and Input Gate in the LSTM with an Update Gate and changes the Cell State and Hidden State (\(h_{t}\)) to Merging, so it has fewer parameters than LSTM.  
The principle behind GRU is very similar to LSTM, using the Gate mechanism to control input, memory and other information. The GRU has two gates, one is the Reset Gate and the other is the Update Gate. Resetting the gate will determine that the new input information is combined with the memory at time \(t-1\). Here, \(z\) is the vector of the update gate defined as [36]  
\[z_{t}=\sigma_{g}(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}), \tag{17}\]  
where \(W\) and \(U\) are the weight parameters, \(b\) is the partial weight, and \(\sigma_{g}\) is the Sigmoid function.  
The Update Gate defines the amount of time (\(t-1\)) memory saved to the current time \(t\), \(r\) is the Reset Gate vector, \(h\) is the output vector, and the retained characteristic value is passed to the next time. They are defined as [36]  
\[r_{t}=\sigma_{g}(W_{r}x_{t}+U_{r}h_{t-1}+b_{r}) \tag{18}\]  
and  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\sigma_{h}(W_{h}x_{t}+U_{h}(r_{t}h_{t-1 })+b_{h}), \tag{19}\]  
respectively.  
As shown in Figure 13, in order to set 30 inputs, respectively, the (\(x,y\)) coordinates of  
Figure 12: Gated recurrent unit (GRU): (**a**) Reset Gate; (**b**) Update Gate [36].  
15 joint points are input as feature values. One hidden layer was set up for RNN, LSTM, and GRU. Since the recurrent neural network is more complex than other neural networks such as ANN, there is no need for too many hidden layers, otherwise the model will be too complicated. The Softmax layer is added, which normalizes the output of multiple neurons to (0, 1) interval. For example, in the end, two categories will have two scores with 0.7 and 0.3, and the Softmax layer will output the highest score value and complete the classification.
###### Abstract  
Preservation of perishable food produce is a major concern in the cold chain supply system. Development of an energy-efficient on-farm cold storage facility, hence, becomes essential. Integration of thermal storage into a vapor compression refrigeration (VCR)-driven cold room is a promising technology that can reduce power consumption and act as a thermal backup. However, designing a latent heat energy storage heat exchanger encounters challenges, such as low thermal conductivity of phase change materials (PCMs) and poor heat exchanger efficiencies, leading to ineffective charging-discharging cycles. The current study investigates the effect of the integration of a Phase Change Material (PCM) in terms of the selection of the PCM, the optimal positioning of the PCM heat exchanger, and the selection of heat exchanger encapsulation material. Numerical analysis was undertaken using 3D Experience software (version: 2024x.D31.R426 rel.202403212040) by creating a 3D model of a 3.4 m\({}^{3}\) micro-cold storage unit to understand the inner temperature distribution profile. Further, the experimental setup was developed, and tests were conducted, during which the energy consumption of 1.1 kWh was recorded for the total compressor run time of 1 h. Results indicated that an improved cooling effect was achieved by positioning the PCM trays on the wall opposite the evaporator. It is seen that a temperature difference in the range of 5 to 7 \({}^{\circ}\)C exists between the phase change temperature of PCM and the optimal storage temperature depending on the encapsulation material. Hence, PCM selection for thermal storage applications would have an important bearing on the material and configuration of the PCM encapsulation.  
micro-cold storage; phase change material (PCM); thermal storage; PCM encapsulation Article  
Numerical and Experimental Investigation on Performance of Thermal Energy Storage Integrated Micro-Cold Storage Unit
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Discussion
#### 6.5.1 Security Features  
Regarding security capabilities, SL_GAS is superior to SGMS, SEIP, and CGM6 because it offers a complete range of security features. Several critical attacks against which it provides strong resistance, including key leakage, insider attacks, and traceability attacks. A significant difference between SL_GAS and other schemes is that SL_GAS provides PFS. To enhance protection against IP spoofing and Sybil attacks, the SL_GAS dynamically updates secret keys, identities, random values, and other parameters after each successful authentication. The scheme is also resistant to privileged insider attacks, one of the most common vulnerabilities in SEIP and CGM6. The compliance of SL_GAS with the CK and eCK adversary models for session key (SK) security reinforces its superiority over other schemes that fail to meet these advanced adversary model criteria. Thus, SL_GAS has a well-rounded design that anticipates a broader range of security threats. This contributes to its reliability in real-world applications, particularly those operating in sensitive or resource-constrained environments, such as 6 LoWPAN.
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 3 Proposed Algorithm
### Fitness Function  
In this section, we employ a fitness function that optimizes the route based on the maximum residual energy in its nodes. This function is defined as [31]:  
\[F_{e}=E_{x}/E_{a} \tag{1}\]  
where \(F_{e}\) is the fitness function based on the energy. \(E_{x}\) is the residual energy at each node \(x\) of one route and \(E_{a}\) is the residual energy of all the nodes.
## 4 Experimental Results and Analysis
### Comparative Experiments and Analysis
#### 4.5.1 General Assessment
Table 5 shows the comparative experimental results, with average results presented for each group of images. Our method outperformed Tsallis-BE significantly on the metrics of JC and DSC for all groups of images except the first group. On the BrainWeb dataset, our method achieved average accuracy, JC, and DSC of 97.09%, 95.08%, and 97.44%, respectively. Compared to the Tsallis-BE method, these metrics were improved by 0.5%, 0.8%, and 0.4%, respectively. The possible reason why Tsallis-BE performed slightly better in the first group of images is that the pixels of each part of the brain in the low-index slices were relatively few, and the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Otsu** & **Tsallis-BE** & **Cauchy-TB** \\ \hline BrainWeb & 6.7194 \(\times\) 10\({}^{-5}\) & 4.7576 & 5.1973 \\ MRBrainS13 & 5.129 \(\times\) 10\({}^{-5}\) & 6.4871 & 6.5095 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average time performance comparison. Figure 8: The sensitivity, specificity and precision curves of image thresholding results obtained by using the Cauchy-TB, Tsallis-BE, and Otsu method on nine groups from the BrainWeb dataset.
## 4 Experiments
### Experiment Settings  
We implemented our GSTGCN model based on the Pytorch framework and conducted experiments on a computer with one Intel(R) Xeon(R) W-2123 CPU @ 3.60 GHz and one NVIDIA Quadro P2000 GPU card. The dynamic temporal module in the model contained four residual blocks. The residual blocks consisted of two stacked dilated casual convolutions. The kernel size was set as 3. The dilation factors of three residual blocks were 1, 2, and 4. The number of kernels in localized graph convolution and the hidden channels was set to 8. The hyperparameter \(\alpha\) was set as 2. For external component, we set the output channels of the first fully-connected layer to 22, and the output channels were reduced to 1 by the next fully-connected layer. During the phase of constructing adjacency matrix, \(\kappa\), \(w_{1}\) and \(w_{2}\) were set as 0.5. The batch size was 256, and we trained the model for 30 epochs. We used Adam optimizer to train our model with the initialized learning rate of 0.001. During the testing phase, we predicted the traffic speed in the next hour (12 steps) based on 12 historical speeds.