esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 4 Simulation and Result Analysis
### Evaluation Considering Node Mobility  
In this experiment, the performance of the protocols is assessed by changing the mobility of the nodes between 4-20 m/s while other parameters stay constant. The percentage of malicious nodes is fixed to 20%.  
Packet delivery ratio (PDR): As shown in Figure 5, as the mobility increases from 4 to 20 m/s, PDR of AODV decreases from around 50 to 37% while that of ETRS-PD decreases from around 71 to 52%. As the speed gets higher, there is an expanded number of link failures, causing packet loss. TSQRS shows improvement in PDR from 85 to 75% compared to AODV and ETRS-PD. As mobility level increases, there are an increasing number of link breakages, resulting in more path failure and unusual packet loss.Ways of improving the result include (1) using the secure route to the destination and (2) considering link quality for the selection of forwarding node.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & NS 2.34 \\ Routing Protocol & AODV, Adversary Model, TSQRS \\ Scenario Size & 1000 \(\times\) 1000 m\({}^{2}\) \\ Number of Nodes & 50 \\ Misbehaving Nodes & 0–40\% \\ Simulation Time & 240 s \\ Traffic Type & CBR/UDP \\ Number of connections & 15 \\ Pause Time & 5 s \\ Mobility & 4–20 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters. Constant Bit Rate (CBR); User Datagram Protocol (UDP).  
Routing overhead (RO): As shown in Figure 6, RO of AODV is between 4.5 and 8.5 while that of ETRS-PD is between 2.7 and 7.07 which is somewhat improved. Meanwhile, the RO of TSQRS shows great improvement with values between 1.64 and 4.8. High mobility leads to more frequent path failure and route discovery, resulting in higher routing overhead. In TSQRS, the selection of intermediate nodes is based on QoS parameters which would lower the routing overhead.  
Figure 5: Packet delivery ratio (PDR) against mobility.  
Figure 6: Routing overhead (RO) against mobility.  
Energy consumption (EC): We also analyzed the performance of ETRS-PD and TSQRS in terms of energy consumption. As shown in Figure 7, EC of ETRS-PD is between 313.96 and 314.53 J under the adversary model while TSQRS provides improved results, i.e., between 310.2 and 310.9 J. EC depends upon the number of packets sent and received. More link failures reported by a protocol would result in a large amount of energy consumption in the network. TSQRS consumes less energy because it considers link quality and residual energy in the nodes for routing.
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## Abbreviations  
The following abbreviations are used in this manuscript:  
HY-2 Haiyang-2 Haiyang-2B HY-2C Haiyang-2C HY-2D Haiyang-2D NSOAS National Satellite Ocean Application Service NOAA National Oceanic and Atmospheric Administration NASA National Aeronautics and Space Administration CNES Center National d'Etudes Spatiales EUMETSAT European Organization for Exploitation of Meteorological Satellites ESA European Space Agency HYCOM Hybrid Coordinate Ocean Model DUACS Data Unification and Altimeter Combination System CLS Collect Localization Satellites CMEMS Copernicus Marine and Environment Monitoring Service SLA Sea Level Anomaly OI Optimal Interpolation SSH Sea Surface Height CLS Collect Localization Satellites
# Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition
## 1 Introduction
Concretely, the GCN module is called graph embedding, having an efficient separate convolution operation, and we introduce two kinds of transformer blocks: spatial-temporal transformer blocks and joint transformer blocks. The spatial-temporal transformer block calculates the spatial self-attention and temporal self-attention separately, while the joint transformer block discovers the global self-attention of all nodes in the graphs. The main contributions of this work are summarized as follows:  
* A pyramid spatial-temporal graph transformer backbone (PGT) is proposed in this work which shows a powerful ability to learn robust features for skeleton graphs with dynamic attention. * Two kinds of transformer blocks are introduced and applied to discover the long-range spatial-temporal correlations in human actions, and the separate convolution operation in graph embedding makes the model go deeper with high-level semantics. * Extensive experiments are performed in this work, and the ablation study demonstrates the effectiveness of our backbone. Finally, the experiments show better comparable performance with the state-of-the-art methods and show the potential of this backbone. The rest of this paper is organized as follows. Section 2 describes the related work in the field of skeleton-based action recognition and some work on the vision transformer method. Section 3 presents the architecture design of our PGT. Section 4 provides the experiment results, ablation study and comparisons with the state-of-the-art methods. The conclusions are drawn in Section 5.
# Seventy-Five Years since the Point-Contact Transistor: Germanium Revisited
## 2 Transistors and Artificial Intelligence  
Artificial intelligence applications are mushrooming in the industrial, retail, education, health care, military, research, resource management, and consumer services sectors. Much of this growth is enabled by machine learning capabilities, that is, the processing of massive amounts of data in order to create models that can be used quickly (ideally in real time) for the accurate classification of newly encountered problems that can subsequently be solved by applying learned problem-solving approaches. This creates new challenges for semiconductor design and implementation and new opportunities for growth for the semiconductor industry [8]. The global AI market is expected to grow to USD 1811.8 billion by 2030, and demand for semiconductor chips is expected to reflect this rise. According to McKinsey, AI accelerator chips will be in demand five times more than semiconductors used in non-AI applications [9].  
All AI applications rely on hardware. First, the amount of data that needs to be stored and processed requires efficient memory systems through which data can be moved fast. Second, the integration of nonvolatile memory and processing logic renders \"system on a chip\" processors possible in order to meet the requirements of AI algorithms. It is quite expensive to build special purpose AI chips for each potential application, and a generic AI platform that could be adapted to the specific features of an application area would be more cost efficient and provide a common ground for a whole ecosystem of applications.  
The semiconductor industry itself can benefit from AI for the reduction in material loss and production time and increased quality control of the production process and the final product. Very recently a new type of adaptive transistor, which can be dynamically switched during run-time to perform different logical tasks, was announced by scientists at the technical University of Vienna who have been working on adaptive electronics. The new flexible transistor is based on pure germanium and does not require doping and therefore no new manufacturing processes [10]. The flexible transistor creates new possibilities for chip design and for both schools of artificial intelligence, namely symbolic-based approaches, which can potentially use multivalued logics and life-based approaches that employ neural networks. The functional diversification of the new transistor is achieved by exploiting the electronic structure of germanium and modulating circuit behaviour via a control electrode, for instance to change the behaviour of an NAND gate into an NOR, as the need arises. This adaptability results in smaller numbers of transistors for the performance of computations, hence leading to energy and time savings.
# VOC Removal from Manure Gaseous Emissions with UV Photolysis and UV-TiO\({}_{2}\) Photocatalysis
## 2 Results and Discussion
### UV Treatment of Odorous VOCs in Synthetic Mixture
#### 2.1.1 Effects of UV Light dose, Wavelength, and Catalyst on VOC Conversion
The effects of light intensity on \(R\) is presented in Figure 3. The effects of catalyst were tested in this case for the more effective UV\({}_{185+254}\) treatment only. The \(R\) conversion was proportional to the light intensity with R\({}^{2}\) > 0.90 for all VOCs except for VFAs. Lower light intensity (i.e., \(I\) < 0.084 mW cm\({}^{-2}\); Figures 1 and 2) resulted in lower \(R\). The absence of a catalyst (Figure 3B) resulted in poor or no conversion of VFAs. Specifically, concentrations of VFAs with low MW, i.e., acetic and sometimes also propanoic acid, increased. Notably, acetic acid concentrations \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound Name**} & \multicolumn{4}{c}{_p-Values of Paired \(t\)-Test_} \\ \cline{2-5}  & \multicolumn{2}{c}{**Effect of Deep UV (UV\({}_{185+254}\))**} & \multicolumn{2}{c}{**Effect of Catalyst**} \\ \cline{2-5}  & **UV\({}_{185+254}\) \textgreater{} UV\({}_{254}\)** & **UV\({}_{185+254}\) \textgreater{} UV\({}_{254}\)** & **TiO\({}_{2}\) \textgreater{} No TiO\({}_{2}\)** & **TiO\({}_{2}\) \textgreater{} No TiO\({}_{2}\)** \\  & **(with TiO\({}_{2}\))** & **(no catalyst\$)** & **(UV\({}_{185+254}\))** & **(UV\({}_{254}\))** \\ \hline Methyl mercaptan & 0.023 & 0.001 & 0.0033 & 0.000041 \\ Ethyl mercaptan & 0.019 & 0.00163 & 0.0077 & 0.0004 \\ Dimethyl sulfide & **0.052** & 0.0027 & 0.0079 & 0.0011 \\ Butyl mercaptan & 0.021 & 0.0072 & 0.028 & 0.0093 \\ Acetic acid & **0.21** & 0.0031 & 0.0037 & 0.0045 \\ Propanoic acid & **0.10** & **0.17** & 0.0021 & 0.0081 \\ Butyric acid & **0.24** & **0.17** & 0.0046 & 0.0041 \\ Isovaleric acid & **0.12** & **0.23** & 0.0067 & 0.0013 \\ \(p\)-Cresol & 0.008 & 0.007 & **0.085** & 0.0022 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical analyses. Effects of UV wavelength and catalyst on treatment effectiveness (expressed as \(R\)). The table represents \(p\)-values of paired \(t\)-tests. Bold fonts indicate no statistical significance (\(p\leq 0.05\)). increased linearly with the increase of light energy dose. This is presumably caused by the oxidation of aldehydes, alcohols, or the sequential decarboxylation of longer chain acids [44].
## 3 Results and Discussion
Moreover, after the bleaching treatment, the surface of the coir fiber contained a large number of regularly placed pores or holes. In a study by [PERSON] et al. [36], the fatty deposits or impurities that were buried inside the surfaces of the untreated fiber were eliminated, resulting in empty spaces on the coir fiber surface, producing a rough surface with globular markings. Energy-dispersive X-ray (EDX) results are summarized in Table 3, indicating that the elemental composition of a nonwoven coir geotextile is carbon (C), oxygen (O), silicon (Si), and calcium (Ca). [PERSON] et al. [37] stated that the oxygen-to-carbon ratio (O/C) of cellulose, hemicellulose, and pectin equals 0.83, whereas lignin has an oxygen-to-carbon ratio of 0.35. Moreover, [PERSON] et al. [38] studied the effect of chemical treatments on the properties of green coconut fiber; the O/C of the treated fibers in the study resulted in a value less than the O/C of cellulose, hemicellulose, and pectin reported in the research by [PERSON] et al. [37]. They concluded that the surface of the green coconut fiber contains a significant proportion of waxes. Likewise, an increase in the value of O/C of the treated fibers indicates that the chemical treatments reduced the proportion of waxes on the surface. The value of O/C, as shown in Table 3 for untreated, mercerized, and bleached samples, is equal to 0.63, 1.61, and 1.75, respectively. The O/C value of the un  
Figure 11: Micrographs of bleached nonwoven coir geotextiles. (**a**) at 500\(\times\) magnification. (**b**) at 1500\(\times\) magnification. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{3}{c}{**Weight Percentage of Composition (\%)**} \\ \cline{2-4}  & **Untreated** & **Mercerized** & **Bleached** \\ \hline C & 54.49 & 22.32 & 30.47 \\ \hline O & 34.51 & 36.03 & 53.19 \\ \hline Si & 1.33 & 0.73 & 0.43 \\ \hline Ca & 0.5 & 5.72 & 4.32 \\ \hline O/C ratio & 0.63 & 1.61 & 1.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Elemental weight percentage of the untreated and treated nonwoven coir geotextiles. than 0.83, and the O/C value for hemicellulose, cellulose, and pectin, indicates that there are significant lignin and wax contents on the surface of the coir fiber. Furthermore, the O/C values for the treated samples are higher than the 0.83 O/C value reported in the study by [PERSON] et al. [37]. This signifies that the chemical treatments (mercerization and bleaching) are effective in reducing waxes and impurities on the fiber surface.
# Fast 10-Point DFT Algorithm for Power System Harmonic Analysis
## 1 Introduction
There are also many methods that are complementary to the FFT that allow the improvement of the efficiency of this algorithm, such as spectrum interpolation methods [26; 27; 28; 29; 30] and the zero padding method combined with zero-sampling elimination (pruning FFT) [31; 32]. Both methods increase computational requirements and only reduces the error caused by the discrete nature of the DFT spectrum, without reducing the error caused by the effect of spectrum leakage from other components. Time windows with a shape other than rectangular are also used; however, they reduce the frequency resolution of the analysis and amplify the noise in the DFT spectrum [33; 34; 35; 36]. For the satisfactory operation of a power system, the fundamental frequency should remain constant. In such a case, it can be assumed that there is a coherent signal; then, the FFT remains the best solution because of its efficient and simple structure. However, as mentioned above when a standard radix 2 or 4 FFT is used for the harmonic analysis of a power system, the problem of spectrum leakage remains. In [37], a solution to this problem by using \(N=L\times 10^{M}\)-point DFTs was proposed to reduce spectral leakage and provide high-accuracy harmonic parameters. An original approach to computing a 10-point DFT was described in [38; 39]. Developing this method, we propose a new 10-point DFT algorithm with less multiplicative complexity.
## 3 Qualitative Tool Condition Monitoring System
### CNN Image Classifier Trained for TCP  
The base model developed and discussed in Section 3.1 forms the central intelligence for the TCM. The base model helps narrow down the ROI and extract useful features and descriptions of the tool, as shown in Figure 2. This intelligence is developed in the base model and rolled out as a trained network. The offline stage of the system is in the machine shops, where the model has to be repurposed to identify and implement different TCPs. Considering that there are thousands of different TCP unique to each machine shop, retraining a complete network presents a significant data and training time challenge. TL is one of the lifelines to overcome this data and training time challenge.  
Given the importance of TL, we now adapt the definitions of TL in [42] for our application. In the proposed system, the knowledge developed during the training of the wear classification model to identify what type of wear pattern or damage the cutting tool has is optimized using TL to differentiate between a good tool that produces conforming parts and a bad tool that produces non-conforming parts. Every classification model has a domain D, which forms the pool for data extraction and a task which, in the case of this study, is classification. Pan and Yang  
since the neural networks are black-box models. There is no way to know if the same or a different function was used for source and target tasks.  
\[\mathrm{Y_{S}}=\left\{\begin{array}{ll}0&\text{if the insert is damaged}\\ 1&\text{if the insert has deformation}\\ 2&\text{if the insert has normal wear}\end{array}\right. \tag{4}\]  
\[\mathrm{Y_{T}}=\left\{\begin{array}{ll}0&\text{if the component is conforming }(\text{GO})\\ 1&\text{if the component is non - conforming }(\text{NO GO})\end{array}\right. \tag{5}\]  
The target task is tied up to the traditional concepts of GO/NO GO gauges discussed at the start of Section 3. GO/NO GO gauges are one of the most popular gauges to evaluate the material conditions in holes and shafts. The tool condition monitoring system developed extends and generalizes this definition of GO/NO GO gauges to other quality requirements. In the target task, the model is retrained to identify a GO part producing tool and NO GO part producing tool. This concept makes the proposed methodology qualitative and gives the model the flexibility to adapt its knowledge across different TCPs. The offline state requires an expert to generate the GO/NO GO labels for the training and adaptation of the task to the TCP.
###### Abstract  
Mass-produced, off-the-shelf automotive air compressors cannot be directly used for boosting a fuel cell vehicle (FCV) application in the same way that they are used in internal combustion engines, since the requirements are different. These include a high pressure ratio, a low mass flow rate, a high efficiency requirement, and a compact size. From the established fuel cell types, the most promising for application in passenger cars or light commercial vehicle applications is the proton exchange membrane fuel cell (PEMFC), operating at around 80 \({}^{\circ}\)C. In this case, an electric-assisted turbocharger (E-turbocharger) and electric supercharger (single or two-stage) are more suitable than screw and scroll compressors. In order to determine which type of these boosting options is the most suitable for FCV application and assess their individual merits, a co-simulation of FCV powertrains between GT-SUITE and MATLAB/SIMULINK is realised to compare vehicle performance on the Worldwide Harmonised Light Vehicle Test Procedure (WLTP) driving cycle. The results showed that the vehicle equipped with an E-turbocharger had higher performance than the vehicle equipped with a two-stage compressor in the aspects of electric system efficiency (+1.6%) and driving range (+3.7%); however, for the same maximal output power, the vehicle's stack was 12.5% heavier and larger. Then, due to the existence of the turbine, the E-turbocharger led to higher performance than the single-stage compressor for the same stack size. The solid oxide fuel cell is also promising for transportation application, especially for a use as range extender. The results show that a 24-kWh electric vehicle can increase its driving range by 252% due to a 5 kW solid oxide fuel cell (SOFC) stack and a gas turbine recovery system. The WLTP driving range depends on the charge cycle, but with a pure hydrogen tank of 6.2 kg, the vehicle can reach more than 600 km.  
turbocharger; supercharger; E-turbocharger; Proton-exchange membrane fuel cell; solid oxide fuel cell; range extender
## 2 Mathematical Model
### Mathematical Model and Numerical Methods
The thermal effects of dissociation, melting, and vaporization at the particle--gas medium interface and the heat supply by conduction from the heated substrate were taken into account in the boundary condition:  
\[y=0,\;0\;<\;x\;<\;H,-\lambda_{1}\frac{\partial T_{1}}{\partial r}=q_{\rm cond}. \tag{19}\]  
The density of the heat flux supplied to the fuel sample by conduction is given by  
\[q_{\rm cond}=\lambda_{\rm sub}(T_{\rm sub}-T_{1}). \tag{20}\]  
The rate of the gas hydrate particle dissociation can be calculated using the following equation [39,40]:  
\[\frac{\partial X_{H}}{\partial t}=-\frac{6k_{R}}{B\rho_{H}d_{0}}\frac{\text{ peq}-P^{0}}{1+\gamma}X_{H}^{2/3} \tag{21}\]  
where \(X_{H}\) is the percentage of the remaining hydrate (hydrate conversion degree); \(k_{R}\) is the kinetic coefficient of the dissociation rate; \(B\) is the initial content of gas in the hydrate;\(\rho_{H}\) is the density of hydrate particles; \(d_{0}\) is the average size of hydrate particles; \(P^{\text{eq}}\) is the equilibrium pressure of gas over the hydrate; \(P^{0}\) is the partial gas pressure; \(\gamma\) is the coefficient representing the effect of pore resistance. Since ignition occurred within a short time, the fuel sample temperature did not change significantly. This is why modeling in a first approximation is possible with an assumption about the constant dissociation rate. Below are the modeling results at several gas dissociation rates ranging from the minimum to maximum at a corresponding pressure of the external gas medium. To increase the solution accuracy, a time increment of \(10^{-5}\) s was used. The adopted sizes of the solution domain (\(x=H\), \(y=L\)) were ten-fold larger than the particle sizes (\(R_{\text{g}}=x_{1}\), \(D=y_{2}-y_{1}\). The problem was solved by the finite element method. The scheme of the problem solution area was with a physics-controlled mesh (8700 elements). The calculations were performed using a 4-core computer (processor 3.4 GHz Intel Core i5-3570 CPU, 32 GB, 64 K L1 cache, 1 MB L2 cache). The calculation time ranged from 2 to 20 min. The source data for modeling are presented in Table 1.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 1 Introduction
### Contributions  
In view of the existing work discussed in the previous section, this paper reports a path planning and UAV relay system for rural environments. The main contributions of this work are as follows:  
1. A two-stage framework is proposed to allow coverage to areas with poor signal propagation by relaying signals from the available cell-towers.
2. A viewshed analysis was conducted to determine the mutual visibility region between points of interest and cell-towers.
3. A blockage map was created to prevent the UAV from passing through no coverage areas.
4. The approach enables the UAV to determine the optimal time to head back towards the charging station.