esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Trash Bin Model Design and Future for Smart City
## 2 Related Research
### Trash Classification Using Spectroscopy  
Spectroscopy is the study of measuring and analyzing the absorption or radiation of light by dividing it into a spectrum using a spectrometer. Among them, FT-IR spectroscopy provides structural information on the molecular properties of a wide range of compounds  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Model** & **Classify Trash** & **Accuracy** \\ \hline [PERSON] et al. [24] & SVM & Organic, non-organic, non-trash & 82.7\% \\ \hline \multirow{4}{*}{[PERSON] [25]} & CNN & & 89\% \\  & XGBoost & 70\% \\ \cline{1-1}  & SVM & Cardboard, glass, metal, & 65\% \\ \cline{1-1}  & RF & paper, and plastic & 62\% \\ \cline{1-1}  & kNN & & 52\% \\ \hline \hline \multirow{2}{*}{[PERSON] et al. [26]} & ResNet50 & Paper, glass, plastic, metal, & 75\% \\ \cline{1-1}  & RecycleNet & cardboard, trash & 81\% \\ \hline \hline \multirow{2}{*}{[PERSON] et al. [27]} & RecycleTrashNet & Cardboard, glass, metal, & 88\% \\ \cline{1-1}  & paper, plastic, and other trash & & 88\% \\ \hline \hline \multirow{2}{*}{[PERSON] et al. [28]} & DNN-TC & Glass, paper, cardboard, & 94\% \\ \cline{1-1}  & plastic, metal, and trash & & 93\% \\ \hline \hline \multirow{2}{*}{[PERSON] et al. [30]} & AlexNet & Glass, metal, paper, & 91\% \\ \cline{1-1}  & VGG-16 & and plastic & 93\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Studies Related to Trash Image Classification.  
with two generations of infrared spectroscopy, which made a lot of progress in the 1970s, and has the advantage of enabling nondestructive analysis in room temperature and room pressure environments [30].  
FT-IR spectroscopy is used in various fields such as olive oil and wine analysis. In particular, much research has been conducted in the field of waste management and analysis of biological and chemical treatments during the waste disposal process [30; 31; 32; 33].
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 4 Modelling and Planning
### Optimization
#### 4.2.4 Final User Oriented Planning  
* User Costs: include purchasing electricity with TOU, added costs deviating from original patterns to reach the charging station [58; 59; 132], and parking costs [132].
* Penalty Costs: these costs can be useful for quantifying the cost of applying demand response programs, as in [24], and quantifying range anxiety caused by trip unfeasibility, as in [30].
* Overall Downtime: this generally consists of charging, traveling, and waiting time. It is used in multi-objective optimizations [23; 52] or as the only decision variable, as in [48]. Study [56] calculates that solutions with higher quality of service (shorter waiting time) present significantly higher costs of the CI. Waiting time is counted as a cost in [21; 43], calculated through the travel time cost extracted from a survey. In several studies, such as [82], downtime is calculated to assess the quality of the service (QoS) but is not included in the objective function.
* Charging Coverage: together with downtime, this is a critical parameter to ensure an optimal QoS of the CI and is a measure of user satisfaction [84]. In literature, the authors use different factors to quantify charging coverage, e.g., in [62], the factor is the number of charging EVs; in [22], the factor is inversely proportional to the distance between two adjacent CSs on the same road; and [38; 133; 134] aim to maximize the flow captured in their model-driven study. The authors of [81] decide to minimize the overall number of charging locations without compromising the coverage of the trajectories in the mobility scenario. However, they do not take into account any costs in their optimization procedure.
# Production and Refinement of Omega-3 Rich Oils from Processing By-Products of Farmed Fish Species
## 3 Results and Discussion
### Chemical Characterization and Oxidative Stability of Fish Oils
The TOTOX is a parameter used to determine the presence of compounds generated by degradation of PUFAs under pro-oxidant conditions including high temperatures, oxygen, metal compounds and light, and the TOTOX value \(\leq\) 26 under is found to be allowable for fish oil [31]. Therefore, in association to the above mentioned PV and \(p\)-AV values, the TOTOX values of studied by-product oils were above the mentioned limit in crude and refined oils from tuna by-products and just under the limit in seagrass and seabream by-product oil (Table 1). The \(p\)-AV value and TOTOX obtained for crude sardine oil were found to be 16 and 40, respectively [14]. In that study, authors applied the same 4-stage refining process which was more effective for sardine oil and reduced \(p\)-AV value and TOTOX values to 10 and 19, respectively. On the other hand, as reported in Table 1, TBARS assay detected low accumulation of secondary oxidation products. TBARS detects lipid oxidation when thiobarbituric acid and oxidation products from unsaturated FAs react involving several secondary oxidation products, however, the TBARS of by-product oils was lower that the control oil. The Rancimat method, an accelerated method that employs high temperatures and air-flow supply to estimate the oxidative stability and shelf life of oil-containing products in a relatively short time,
## 5 Conclusions  
This paper proposes an NSC formed by the cam-roller mechanism with a quadratic polynomial trajectory. For the QZS vibration isolator equipped with the novel NSC, its static and dynamic models are established to investigate the high-static-low-dynamic stiffness and the dynamic characteristics under QZS condition. Additionally, the vibration isolation performance of the QZS vibration isolator is discussed under various excitations. The main conclusions are as follows:  
(1) The proposed QZS vibration isolator directly has a nonlinear stiffness expressed by a quadratic function, due to the adoption of the QCRM-NSC. When solving the dynamic response, the Taylor series expansion of stiffness is not required, and the error at large response displacement caused by approximate stiffness is avoided.  
(2) The structure parameters have a great influence on the displacement transmissibility of the QZS vibration isolator. Reducing the quadratic coefficient and excitation amplitude weakens the  
Figure 19: RMS of displacement under random excitation.  
Figure 18: Power spectral density under random excitation.  
transmission and nonlinearity of the vibration response. Although a larger damping ratio causes an increase in the transmissibility at high-frequency range, it still performs well around the resonance.  
(3) Compared with the equivalent linear vibration isolator, the QCRM-NSC introduced in the QZS isolator greatly reduces the peak of the resonance response. Under sinusoidal excitation, multi-frequency wave excitation, and random excitation, the QZS vibration isolation system has a lower starting vibration isolation frequency and a smaller displacement response.  
From the above, we can conclude that the proposed QZS vibration isolator maintains the excellent vibration isolation performance of nonlinear isolation. Meanwhile, the structure advantage of the QCRM-NSC makes it possible to reduce the calculation difficulty, and facilitates the application of NSCs in complex vibration isolation structures.  
[PERSON] established the theoretical model and wrote this paper; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed to some key ideas and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Liaoning Revitalization Talents Programs (No. XLYC1905003), the Liaoning PhD Research Start-up Fund (No. 2019 BS181), and the Liaoning Provincial Department of Education Youth Breeding Project (No. LQGD2019007).  
The authors declare no conflict of interest.
## 4 Crystal Plasticity Model
### Kinematic Relations
Hence, the plasticity tensors \(\bar{\mathbf{d}}^{\text{P}}\) and \(\bar{\mathbf{w}}^{\text{P}}\) are expressed via plastic slip rates \(\dot{\gamma}^{(s)}\) resolved onto primary slip planes \(s=1,\dots,\eta_{\text{slip}}\)  
\[\bar{\mathbf{d}}^{\text{P}} =\sum_{s=1}^{n_{\text{slip}}}\dot{\gamma}^{(s)}\mathbf{R}^{\text{ P}}\bar{\mathbf{m}}^{(s)}\mathbf{R}^{\text{P}}, \tag{9}\] \[\bar{\mathbf{w}}^{\text{P}} =\sum_{s=1}^{n_{\text{slip}}}\dot{\gamma}^{(s)}\mathbf{R}^{\text{ P}}\bar{\mathbf{q}}^{(s)}\mathbf{R}^{\text{P}},\] (10) \[\bar{\mathbf{m}}^{(s)} =\text{sym}\Big{(}\bar{\mathbf{b}}^{(s)}\otimes\bar{\mathbf{n}} ^{(s)}\Big{)},\] (11) \[\hat{\mathbf{q}}^{(s)} =\text{skew}\Big{(}\bar{\mathbf{b}}^{(s)}\otimes\bar{\mathbf{n}} ^{(s)}\Big{)}, \tag{12}\]  
where \(\bar{\mathbf{b}}^{(s)}\) denotes the slip direction unit vector and \(\bar{\mathbf{n}}^{(s)}\) is the slip plane unit normal. The curved \"tilde\" overbar indicates that they are in the lattice (reference) frame. The slip rates \(\dot{\gamma}^{(s)}\) depend upon the (unrotated) Cauchy stress and a set of \(n_{\text{hard}}\) internal hardening variables \(\bar{\mathbf{\zeta}}=g^{(1)},\bar{g}^{(2)},\dots,\bar{g}^{(n)}\), written generically as  
\[\dot{\gamma}^{(s)}=\dot{\gamma}^{(s)}(\mathbf{t},\bar{\mathbf{\zeta}}),\quad\bar{ \mathbf{\zeta}}=\bar{\mathbf{\zeta}}(\mathbf{t},\bar{\mathbf{\zeta}}). \tag{13}\]  
Meanwhile, the plastic rotation evolves via the plastic vorticity tensor \(\bar{\mathbf{w}}^{\text{P}}\) according to  
\[\mathbf{F}=\mathbf{F}^{\mathsf{e}}\mathbf{F}^{\mathsf{p}}\approx(\mathbf{I}+ \varepsilon)\mathbf{R}^{\mathsf{e}}\mathbf{R}^{\mathsf{p}}\mathbf{U}^{\mathsf{ p}}. \tag{7}\]0  
At each material point, the coupled set of time-continuous Equations (8) and (13) govern the evolution of the primary unknowns \(\mathbf{t}\) and \(\bar{\mathbf{\zeta}}\). To model them numerically, we adopt the implementation from [46] that employs backwards Euler time discretization for all quantities except the plastic rotation (14) and solves the coupled system with Newton-Raphson supplemented by line search techniques. A salient feature of this approach is that it is modular with respect to the slip rate and hardening models through the linkages (13), for which two specific models are provided in the following sections. The reader is encouraged to consult [46; 48] for linearization and time discretization details.
## 3 Results
### Thermal Emission
Although, from the EDS estimation, cobalt concentration in the MgO phase is higher than in MgAl\({}_{2}\)O\({}_{4}\), with 70 to 80% of the Co present in the sample located in the MgO phase, the contribution to the thermal emission of Co\({}^{2+}\) ions in MgO seems to be negligible. It is worth noting here that the oscillator strength of Co\({}^{2+}\) in MgAl\({}_{2}\)O\({}_{4}\) is greater by three orders of magnitude than in MgO ([17], see below for more details) which could explain the dominant emission of the MgAl\({}_{2}\)O\({}_{4}\):Co phase in the eutectics. Co\({}^{2+}\) ions in MgAl\({}_{2}\)O\({}_{4}\) can occupy sites with tetrahedral symmetry and sites with octahedral symmetry. From previously reported studies [17] the optical properties of MgAl\({}_{2}\)O\({}_{4}\):Co are in agreement with the cobalt located in the tetrahedral sites. Therefore, we performed the analysis of the emission spectra assuming a purely tetrahedral symmetry for the dopant ion environment. The ground state of Co\({}^{2+}\) (d\({}^{7}\)) ions occupying tetrahedral sites is \({}^{4}\)A\({}_{2}\)(\({}^{4}\)F). The measured thermal emission was attributed to the de-excitation of the cobalt ions from the excited state \({}^{4}\)T\({}_{1}\)(\({}^{4}\)F) to the ground state \({}^{4}\)A\({}_{2}\). From Figure 3b, we can observe that some structure is present in the broad emission band. Four lines located at around 1300, 1430, 1500 and 1685 nm were found and related to the spin-orbit splitting of the parent \({}^{4}\)F ionic state (\({}^{4}\)F\({}_{9/2}\) + \({}^{4}\)F\({}_{7/2}\) + \({}^{4}\)F\({}_{5/2}\) + \({}^{4}\)F\({}_{3/2}\)). The positions of these peaks were red-shifted with respect to the absorption bands measured at room temperature [16; 25]. This shift towards low energies of the emission at a high temperature is expected as the thermal expansion of the crystal produces a lengthening of the Co\({}^{2+}\)-O\({}^{2-}\) ligand distance, which  
Figure 4: Thermal emission of all the samples grown at 50 mm/h (solid lines) and 750 mm/h (dotted lines) measured at 1300 \({}^{\circ}\)C. Spectra, labeled with the sample names (see Table 1), are presented normalized for comparison purposes. decreases the cubic crystal field intensity. A similar red shift at high temperatures was also observed in the thermal emission of Ni-doped MgO-MgSZ eutectic composites [15] and in the absorption band of NiO measured up to 1200 \({}^{\circ}\)C [26].
# Model-Based and Model-Free Point Prediction Algorithms for Locally Stationary Random Fields
###### Abstract
This is accomplished by using the Model-Free Prediction Principle of [21; 22]. The key to Model-Free inference is to be able to construct an invertible transformation \(H_{n}:Y_{\underline{t}_{n}}\mapsto\underline{e}_{n}\) where \(\underline{Y}_{\underline{t}_{n}}=(Y_{\underline{t}_{1}},Y_{\underline{t}_{2}},\ldots,Y_{\underline{t}_{n}})\) denotes the random field data under consideration and \(\underline{e}_{n}=(e_{1},\ldots,e_{n})^{\prime}\) is a random vector with i.i.d. components. The rest of the paper is arranged as follows. In Section 2, we set up the framework for defining the causality of random fields in order to enable us to perform point prediction. In Section 3, we visit the problem of Model-Based inference and develop a point prediction methodology for locally stationary random fields. In Section 4, we construct the framework for point prediction of locally stationary random fields using Model-Free inference. In Section 5, we describe how cross-validation can be used to determine the optimal bandwidths for both Model-Based and Model-Free inference. Finally, in Section 6, using finite sample experiments we compare the two novel approaches namely Model-Based of Section 3 and Model-Free of Section 4 using synthetic and real-life data.
# Effect of Lateral End Plates on Flow Crossing a Yawed Circular Cylinder
## 1 Introduction
He found that on the flow crossing a yawed cylinder, besides alternate Karman vortex shedding, symmetrical vortex shedding, and axial vortex should also be taken into consideration. Furthermore, [PERSON] et al. [10] used a wind tunnel to investigate the mechanisms of vortex-induced vibration of inclined cables subjected to oblique cross-flow. It was revealed that the three-dimensional characteristics of vortex shedding are strongly dependent on the end conditions and the yaw angle of the cable. [PERSON] et al. [11] tried to clarify the details of the effect of axial flow, which flows in the near wake along the cylinder axis as the flow is inclined to cross the cylinder. However, they thought that their mechanisms are not sufficiently clear and should be further clarified. Thus, it is necessary to clarify the end effects and moreover provide quantified and sufficient analysis of the influence of the end effects. Hitherto, there are no specific studies on the thorough investigation of the effect of the end plates fitted with a yawed cylinder on the flow behavior in the wake. In this paper, we will strive to resolve this and fill the gap. The boundary condition on the two lateral endplate walls can be generalized as two types. One is nonslip condition, which is commonly encountered in most practical engineering and natural phenomenon, e.g., flow crossing tubes in heat exchangers, wind sweeping strands pulling support of bridges, wind crossing poles supporting electricity-carrying cables, and wind crossing plant stalk and tree trunk. Another is a periodic condition, which is a simplified approximation of a cylinder with a large aspect ratio, e.g., sea stream passing undersea cables. The objectives of this paper are to investigate the effect of the boundary condition of two end-plate walls on the crossflow of a cylinder with a yaw angle. Considering that the crossflow component of velocity over a cylinder should dominate over the in-line component of velocity, the yaw angle in this research is chosen to be not greater than 45\({}^{\circ}\). In the following calculation, three yaw angles are investigated respectively: 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\), corresponding to small, medium, and large yaw angle. The Reynolds number is set to 3900 for all calculations in this research. This Reynolds number is selected based on the following considerations. First, flow in the wake at this Reynolds number is fully turbulent according to previous studies. Second, it has been shown that the sensitivity of hydrodynamic forces to the Reynolds number begins to be weak at this value [12].
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 3 Proposed Neural Network Architectures
### CNNs  
CNNs have the key aspects of sparse interaction and parameter sharing using convolution layers with considerably fewer parameters. CNNs consist of three major components: convolution, pooling, and fully connected layers. In convolution layers, the convolution operation is performed to extract the local features. The convolution operation for two-dimensional case, for example, is as follows:  
\[S_{i,j}=\sum_{a=0}^{m}\sum_{b=0}^{n}I_{i-a,\ j-b}K_{m+a,n+b} \tag{8}\]  
where \(I\) is a matrix containing the input values, \(K\) is a convolution filter matrix sliding across the input, \(a\) and \(b\) denote row and column indices in the input, and \(S_{i,j}\) is output value at a location \((i,\ j)\), respectively. The pooling layer reduces the dimension of output neurons with pooling operation, or down-sampling, which reduces several values into one. The most common pooling operations are max-pooling, average-pooling, and min-pooling, which derive maximum, average, and minimum value in a group, respectively. The fully connected layer is the same with MLP, taking inputs from the previous layer, processing them with activation functions, and transmitting them to the next layer.  
In this study, to apply a CNN to the multivariate climate input data, we represented the input data on one axis for the time sequence and the other axis for each variable. For example, if three days of hourly input data is used, the length of the axis for time is 72, and the other axis is 15 for each variable, for a 72 \(\times\) 15 rectangular input channel.  
Because one axis of the input channel is composed of variables and the order of these variables is independent, we designed one axis of all convolution filters except for the last convolution layer with a length of 1 in order not to cross process each variable. After it passes all the convolution layers except for the last one, the convolution channel will have a time axis length of 1 and a variable axis length equal to the number of variables. Then, we designed the convolution filters of the last convolutional layer to have the same structure as the output channel of the previous layer and for the convolution filters to give a single value as output, respectively. Subsequently, the output consists of as many values as the number of filters, and they are connected to the fully connected layers to forecast temperatures.  
Figure 2 displays the structure of the CNN model. We determined the remaining model architectures through trial and error, and these are shown in Tables 5 and 6. \(T\) and \(m\) in the tables are the length of time of the input data and the number of input variables, respectively.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 5 Results
### Comparison with State-of-the-Art Methods  
Table 5 illustrates the average computational time of the proposed protocol compared to state-of-the-art methods. To facilitate a fair comparison, we scaled the computational values of other protocols to match our testbed specifications [44]. While scaling may not provide 100% accuracy for system runtime due to variations in the instruction set architectures, different numbers of cores and memory subsystems, it offers reasonable estimates that allow for valid relative comparisons.  
The results show that the proposed protocol achieves significantly lower computation time on both the server and client sides, reducing the overall execution time to 7.41 ms, which is 4.55\(\times\), 4.33\(\times\) and 4.20\(\times\) faster than [PERSON] et al. [45], SM2 [46], and PSLA [40]. The substantial reduction in execution time highlights the lightweight nature of our protocol, making it well suited for resource-constrained smart grid environments without compromising security.  
Figure 5: Power usage over time during protocol execution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Server (ms)** & **Client (ms)** & **Total (ms)** & **Speedup** \\ \hline [PERSON] et al. [45] & 15.82 & 17.92 & 33.73 & 4.55\(\times\) \\ SM2 [46] & 15.54 & 16.58 & 32.12 & 4.33\(\times\) \\ PSLA [40] & 16.83 & 14.34 & 31.17 & 4.20\(\times\) \\ Proposed & 3.94 & 3.47 & 7.41 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average computational time of our proposed protocol compared to other state-of-the-art methods.