esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Operators
#### 3.2.2 Mutation Operation  
All decision variables are equiprobably modified. The uniform bit flip mutation operator is applied to the selected feature binary vector. Each discretization point in the discretization scheme is also equiprobably altered. Specifically, when a discretization point has been identified for a modification, all of its features are mutated using the polynomial mutation operator. For all of the remaining decision variables, the polynomial mutation is applied whether decision variables are encoded as integers or real numbers.  
```
Input: discretization schemes \(\{\mathcal{L}^{1}_{c},\mathcal{L}^{2}_{c}\}\) of two parent solutions \(\{\mathrm{x}_{1},\mathrm{x}_{2}\}\) Output: discretization schemes \(\{\mathcal{L}^{1}_{c},\mathcal{L}^{2}_{c}\}\) for two offspring solutions \(\{\mathrm{x}_{1}^{\prime},\mathrm{x}_{2}^{\prime}\}\)
1\(N_{off1}\gets random(K^{lower}_{c},\min(|\mathcal{L}^{1}_{c}|+|\mathcal{ L}^{2}_{c}|,K^{upper}_{c}))\)
2\(N_{off2}\gets random(K^{lower}_{c},\min(|\mathcal{L}^{1}_{c}|+|\mathcal{ L}^{2}_{c}|,K^{upper}_{c}))\)
3 fori=1 to\(\max(N_{off1},N_{off2})\)do
4 Sample \(c_{1}\), \(c_{2}\)
5 if\(i>|\mathcal{L}^{1}_{c}|\)then
6 if\(i\leq|\mathcal{L}^{2}_{c}|\)then
7\(c_{1}\gets c_{2}\gets L^{2}_{ci}\)
8 else
9 forj=1 to ndo
10\(c_{1}(j)\leftarrow\) random point in the training space of the gesture \(c\)
11\(c_{2}(j)\leftarrow\) random point in the training space of the gesture \(c\)
12
13 end if
14
15 end for
16 else
17 if\(i>|\mathcal{L}^{2}_{c}|\)then
18\(c_{1}\gets c_{2}\gets L^{1}_{ci}\)
19 else
20 forj=1 to ndo
21\(\{c_{1}(j),c_{2}(j)\}\leftarrow\) SBX\((L^{1}_{ci}(j),L^{2}_{ci}(j))\)
22 end if
23
24 end for
25
26 end for
27\(u\gets random(0,1)\)
28 if\(u\leq 0.5\)then
29 if\(i<N_{off1}\)then\(L^{1}_{ci}\,^{\prime}\gets c_{1}\)
23 if\(i<N_{off2}\)then\(L^{2}_{ci}\,^{\prime}\gets c_{2}\)
24 else
25 if\(i<N_{off1}\)then\(L^{1}_{ci}\,^{\prime}\gets c_{2}\)
26 if\(i<N_{off2}\)then\(L^{2}_{ci}\,^{\prime}\gets c_{1}\)
27 end if
28
29 end for
30
31 end for return\(\{\mathcal{L}^{1}_{c},\mathcal{L}^{2}_{c}\}\)
```  
**Algorithm 1**Rand-length crossover for discretization schemes.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 2 Preliminaries
### Echo State Network  
The echo state network (ESN) proposed by [PERSON] et al. [24] is a new type of recurrent neural network. It consists of an input layer, a reservoir and an output layer. The structure of ESN is shown in Figure 1. \(W_{in}\) is the input weight matrix that connects the inputs and the reservoir, \(\mathbf{W}\) is the internal connection weight matrix of the reservoir, \(\mathbf{W}_{out}\) is the output weight matrix that connects the reservoir and the output, and \(\mathbf{W}_{back}\) is the output-to-reservoir feedback connection weight matrix, which is usually set as a zero vector.  
Assuming that the system obtains a time series \(\mathbf{U}(n)=[u_{1}(n),u_{2}(n),\cdots,u_{K}(n)]^{T}\) at time \(n\), which is the input of the reservoir at this time. The output of the reservoir well be \(\mathbf{y}_{in}(n)=\mathbf{U}(n+\lambda)\), and the prediction of different time steps is realized according to the adjustment of \(\lambda\). The reservoir receives two inputs. The one is \(\mathbf{U}(n)\) from the input layer and the other is \(\mathbf{x}(n-1)\) from the previous state of the reservoir. The matrixes \(\mathbf{W}\) and \(\mathbf{W}_{in}\) are randomly generated and remain unchanged during the  
Figure 1: The basic structure of echo state network.  
training. The weight \(\mathbf{W}\) is a large-scale sparse matrix, in which non-zero elements indicate activated neurons in the reservoir. The update formula of current state of reservoir is as follows:  
\[\mathbf{x}(n+1)=\tanh(\mathbf{W}_{in}\times[1;\mathbf{U}(n+1)]+\mathbf{W}\times\mathbf{x}(n)) \tag{1}\]  
where \(\mathbf{x}(n)\in\mathbf{R}^{L\times 1}\) is the state of the reservoir at time \(n\), and its initial state is a zero vector. The operator \(\tanh(\cdot)\) is the hyperbolic tangent activation function. With the infinite growth of time, the dependence of the current state affected by the initial state of reservoir gradually decreases or even disappears [25]. The information in reservoir is transferred to the output layer with a linear connection, and the output of the network is:  
\[\mathbf{y}(n)=\mathbf{W}_{out}\mathbf{x}(n) \tag{2}\]  
Afterwards, [PERSON] et al. [26] improved the ESN and proposed the leaky integrator ESN (LI-ESN) by introducing leaky integrator neurons into the reservoir. LI-ESN is a variant of ESN, but its reservoir contains the leak integrator neurons. The update formula of the state of reservoir in LI-ESN is:  
\[\mathbf{x}(n+1)=a\times\tanh(\mathbf{W}_{in}\times[1;\mathbf{U}(n+1)]+\mathbf{W}\times\mathbf{x}( n))+(1-a)\times\mathbf{x}(n) \tag{3}\]  
where \(a\in[0,1]\) is the leaking rate. The leaky neuron in reservoir has a leaky integration of its activation, partially remembers its previous activation, and retains the effect of the previous moment on the current state.
## 5 Results Analysis
### Simulation Analysis under Random Excitation  
To further analyze the vibration reduction performance of the new vibration-absorbing wheel structure on the vehicle, a class C road surface model was established [38], as shown in Figure 8.  
The optimized parameters are substituted into Equation (1) and simulated under the road surface excitation. The simulation pairs under random excitation are shown in Figure 9, and the performance indexes are shown in Table 6.  
Figure 8: Road roughness profiles of Class C at a speed of 20 m/s.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Performance Indexes** & **Conventional Suspension** & **PVAWS** & **TDVAWS** \\ \hline RMS of SMA \(\left(\text{m/s}^{2}\right)\) & 19.7211 & 11.1859 & 1.4964 \\ RMS of SD \(\left(\text{mm}\right)\) & 63.3691 & 35.9532 & 5.1521 \\ RMS of DTD \(\left(\text{mm}\right)\) & 72.0473 & 49.3270 & 33.0481 \\ \hline \end{tabular}
\end{table}
Table 5: RMS of vehicle performance indexes under harmonic excitation.  
Figure 9: Simulation comparison under the road excitation. (**a**) sprung mass acceleration comparison chart; (**b**) suspension deflection comparison chart; (**c**) dynamic tire deflection comparison chart.  
It can be seen from Figure 9 that the new wheel absorption structure has a better damping effect on both the sprung mass acceleration and tire dynamic displacement under random excitation. Compared with the conventional suspension, the RMS value of SMA of PVAWS is reduced by 9.59%, the RMS value of SD is reduced by 0.36%, and DTD of tires is reduced by 4.32%; the RMS value of SMA of TDVAWS is reduced by 15.21%, the RMS value of SD is reduced by 0.54%, and the RMS value of DTD is reduced by 12.52%. It can be concluded that both PVAWS and TDVAWS can improve vehicle ride comfort and handling stability, but TDVAWS has a better effect. Ref. [39] is the application of time-delay feedback control to the suspension. Compared with Ref. [39], TDVAWS has a better vibration reduction effect on sprung mass. In conclusion, this structure proposed in this paper can effectively solve the contradiction between vehicle ride comfort and handling stability.
## 1 Introduction
[34] suggested a signal-to-image conversion method that converted raw signals into two-dimensional (2D) images and then entered the obtained 2D images into the CNN model. Diagnostic performance was improved compared to other CNN methods. It is worth noting that there was no spatial relationship between the dimensions of 2D images. In [35; 36; 37; 38], the authors developed a one-dimensional convolutional neural network (1D-CNN) model using the original signal as an input data of a CNN. These methods not only eliminated the signal processing process, but also greatly reduced model complexity. However, due to the use of the original signal as an input data, the 1D-CNN model has a great sensitivity to the noise, so it cannot be used in the case of complex and changeable noise interference in the actual production process. In this paper, a novel intelligent fault diagnosis model, named the multi-dimension input convolutional neural network (MDI-CNN), is developed. The proposed MDI-CNN has multiple input layers so that the raw data can be entered into the model, together with the processed data to extract and identify fault features. Besides, by comparing with the other CNN models, it is proved that the proposed model has higher validity and robustness. The main contributions of this paper can be summarized as follows. (1) Comparing to the traditional CNN, a fusion model is proposed to achieve an effective fusion of different data types; the proposed model has multiple input layers which allows combining the benefits of each signal processing and improving the model generalization ability. (2) The input dataset can contain both raw data and data obtained after the signal processing, which not only can improve the characteristics of the original signal, but also make full use of the advantages of the processed data in terms of clustering and anti-jamming ability; thus, the robustness and the convergence speed of the model are improved. (3) A verification scheme is proposed to verify the validity of the proposed model, which makes the robustness and generalization performance also be validated. This paper is organized as follows: Section 2 briefly introduces the basic theoretical basis of CNNs. Section 3 proposes the MDI-CNN model and introduces its principle. Section 4 introduces the evaluation index and the verification scheme of the proposed model. Section 5 presents the performance of the proposed model is verified by two sets of comparison experiments and analyzes and discusses the experimental results in detail. Section 6 provides our conclusions, and future work guidelines.
## 2 Materials and Methods
### Bacterial Growth Curves  
_M. magnetotacticum_ was grown in media that varied in composition, to evaluate which carbon and nitrogen source resulted in a quicker culturing time, and overall increased cell biomass and nanoparticle yield. Tubes were sealed with parafilm to maintain microarophilic conditions and incubated at 30 \({}^{\circ}\)C for 336 h. This was performed to determine which components were needed to develop an overall optimized medium. Each medium prepared was sterilized using an autoclave for 20 min at 121 \({}^{\circ}\)C, then cooled to room temperature before use.  
The use of lactate or lactic acid as a carbon source has been widely studied in _M. gryphisvaldense_ bioreactor experiments; thus, lactic acid was selected for investigation as a potential carbon source [15]. In addition, glucose and sodium pyruvate were also selected as potential carbon sources. According to a study carried out by [PERSON] et al., 25 mM was found to be the optimum concentration for sodium pyruvate [16]. Thus, 25 mM was employed for all three sole carbon sources investigated for the amount of carbon present in the growth media to be kept constant upon comparison of the results.  
Thereafter, the original nitrogen source present in MSGM, namely sodium nitrate (1.4 mM), was replaced with a different sole nitrogen source. According to comparisons made from different studies, ammonium sulphate, ammonium chloride and sodium nitrate have all been utilized previously [6; 17; 18; 19]. Thus, these components were investigated for use as potential nitrogen sources at a concentration of 40 mM in our study as per a previous study by [PERSON] et al. (2019) who used 40 mM sodium nitrate [16]. The concentration was kept constant to ensure that the amount of nitrogen present in these sources remained the same allowing for a fair comparison to be made. In addition, certain medium modifications were supplemented with one of two additives, namely yeast extract or Isogro(tm) (Sigma-Aldrich, St. Louis, MO, USA).  
Bacterial growth curves were conducted for each MSGM medium modification that are depicted in Table S3 (Supplementary Material). The initial optical density (OD) range for each growth curve sample at time point zero was standardized between a range of 0.1 to 0.2 using an ultraviolet-visible (UV-vis) spectrophotometer (Specord(tm) 210 UV/Visspectrophotometer, Analytik Jena, Jena, Germany). The wavelength of the absorbance readings taken for each of the medium modifications was 600 nm except in the case of MSGM containing sodium pyruvate and lactic acid, where the wavelength used was 450 nm due to the change in colour of the media to bluish-purple.  
The growth curve experimental design was made up of two biological replicates and in each instance three technical replicates per sample were used to ensure reproducibility. The OD readings were taken at time zero (T0) and thereafter every hour for six hours (T0-T6). Subsequent to the 6 h, the OD readings were taken every 24 h for 14 days (336 h) to monitor whether a prolonged incubation time resulted in increased or decreased growth.
###### Abstract  
This paper describes the design, implementation, and testing of a novel multi-function software defined Radio Frequency (RF) system designed for small airborne drone applications. The system was created using an inexpensive Field Programmable Gate Array (FPGA) to combine a coherent linear frequency modulated radar transmitter and receiver, with a Digital Radio Frequency Memory (DRFM) jammer for use with a common RF aperture in simultaneous operation. The system was implemented on a Xilinx Kintex-7 FPGA with a wideband analogue-to-digital/digital-to-analogue (ADC/DAC) converter mezzanine board and tested using hardware-in-the-loop mode to validate its performance. This is the first known account of an integrated multifunction electronic attack and radar system on a single chip, capable of performing a simultaneous, not time shared, operation.  
radar; electronic warfare; FPGA; electronic attack; digital radio frequency memory; electronic support +
Footnote †: journal: Applied Sciences
## 1 Introduction
In the subsequent section, we offer an overview of the future TMSs by highlighting their important conceptual phases and design stages. Further, Section 3 is devoted to addressing the data sensing and gathering (DSG) phase featuring a description of various communications technologies utilized for road traffic monitoring. It also discusses alternative technologies that may improve the quality and the accuracy of the received (collected) data. Then, in Section 4, we outline our constructed environment for modular traffic modeling. Later, in Section 5, our attention is shifted to the performance evaluation of two characteristic experimental scenarios (following the real traffic patterns within the city of Brno, Czech Republic). Here, different embedded devices, such as (i) Intel(r) Galileo; (ii) Edison; and (iii) Raspberry Pi, were employed. In the concluding Section 6, the lessons learned in the course of developing our proposed modular environment for traffic modeling are summarized.
## 4 Time Stepping Schemes
### Explicit Runge-Kutta Scheme  
The explicit three-stage third-order TVD Runge-Kutta scheme [5] is used to advance the solution of Equation (24) from time \(t\) to time \(t+\Delta t\), which reads:  
\[\begin{array}{l}\widetilde{\mathbf{q}}^{(1)}=\widetilde{\mathbf{q}}^{n}+\triangle t \mathbf{M}^{-1}\mathbf{R}(\widetilde{\mathbf{q}}^{n}),\\ \widetilde{\mathbf{q}}^{(2)}=\frac{3}{4}\widetilde{\mathbf{q}}^{n}+\frac{1}{4}\Big{[} \widetilde{\mathbf{q}}^{(1)}+\triangle t\mathbf{M}^{-1}(\widetilde{\mathbf{q}}^{n})\mathbf{R} \Big{(}\widetilde{\mathbf{q}}^{(1)}\Big{)}\Big{]},\\ \widetilde{\mathbf{q}}^{n+1}=\frac{1}{3}\widetilde{\mathbf{q}}^{n}+\frac{2}{3}\Big{[} \widetilde{\mathbf{q}}^{(2)}+\triangle t\mathbf{M}^{-1}(\widetilde{\mathbf{q}}^{n})\mathbf{R} \Big{(}\widetilde{\mathbf{q}}^{(2)}\Big{)}\Big{]}.\end{array} \tag{25}\]  
Figure 3: Schematic illustrations of RBF interpolation: (**a**) straight; (**b**) invalid (displacements of control points); and (**c**) valid (RBF interpolation).  
In addition to the preconditioning mentioned, the local time stepping is also used to improve the convergence speed to steady state solutions. In specific, the local time step \(\Delta t_{i}\) is computed for each element \(\Omega_{i}\) by  
\[\Delta t_{i}=\frac{CFL}{2p+1}\cdot\frac{|\Omega_{i}|}{\int_{\partial\Omega_{i}} \max((|u^{\prime}|+c^{\prime})^{+},(|u^{\prime}|+c^{\prime})^{-}]d\sigma}, \tag{26}\]  
where \(p\) is the approximate order as mentioned, and CFL is the Courant number. In general, CFL could be 1 for 2D and less than 1 for 3D applications of the DG method.
## 6 Results
### Content Server Load  
The average number of contents transferred per second by 13 content servers was recorded in each experiment. The average value of these data indicates that the load on the network content server is used to analyze cache system performance from the content provider's perspective.  
From Figure (a)a,b, the content server load from each cache allocation scheme decreases as the Zipf distribution parameter Alpha increases. From the previous analysis, an increase in Alpha makes hot content more likely to be requested and cached, and a large number of requests are hit at the nodes, which reduces the load on the content server. HCSAS performs best in different Alphas.  
From Figure (c)c,d, the load on the content server decreases as the total cache budget increases. With large cache capacity, the load on the content server decreases more and more slowly. HCSAS has obvious advantages over other schemes in different total cache budgets. To reach the state where the content server transmits an average of 300 content per second, the BC scheme requires a total cache capacity of 0.11, while the HCSAS only requires a total cache capacity of 0.05, which saves significantly the cache space.  
Figure 9: Performance of Content Server Load.  
From the content provider's perspective, the HCSAS scheme is more effective in reducing the load on the content server. The more requests hit the node, the fewer requests reach the server and the lower the server load. Similar to request latency, content server load is inversely proportional to the cache hit ratio. In all schemes, the load on the content server in LFU is lower than that in LRU. In Alpha scenarios, HCSAS can achieve the same performance with less than half of the BC cache budget.
## 6 Conclusions and Applications
### Discussion and Conclusions  
Existing studies also suggest that different pedestrian facility design standards should be prepared according to the purpose and environment of walking. Recently, research [21] into the construction of pedestrian-friendly cities has also been conducted. Some studies also analyzed the effect of walking density on walking [22] and suggested that walking accessibility has an effect on travel decisions [23]. As described above, walking is a transportation method that is always used at the beginning and end of each means despite using various means such as car, train, and air when moving. Some make up the entire trip just by walking. In the case of the elderly, the proportion of walking as a means of transportation is higher than that of other age groups [20]. Therefore, it can be said that the influence of the walking environment on mobility is greater for the elderly.  
However, pedestrian facilities are designed based on the general adult and are constructed in order to eliminate inconvenience for general adults when moving. This makes the elderly feel uncomfortable when using the stairs or using the escalator. In other words, they use facilities that do not fit their body size and walking characteristics, and they complain of discomfort. Such inappropriate facilities can be directly related to safety accidents that can occur while walking for the elderly.  
Therefore, it is judged that research on how to design facilities suitable for the elderly is necessary. In order to design a facility suitable for the elderly, it is fundamentally necessary to know the size of the elderly person's body. This is achieved through the analysis of national statistics. In Korea, there is a Statistical Yearbook [15] that presents the average body size by age. However, it is difficult to publish statistics on how older people walk. This is because even people of the same height and weight demonstrate different gait speeds, step lengths, stride lengths, step widths, and cadences. If the average value of these gait variables is used, the effect caused by the height and the length of the lower extremities cannot be removed, and a kind of collinearity problem may occur. Therefore, in this study, we tried to solve this problem by applying a dimensionless number. The numerical value with the unit removed can be used for a more objective design of facilities because it can remove the factors that affect the height or the length of the lower extremities. By applying the average height to the gait variable values derived in this way, it was possible to derive a more objective average gait variable value for the elderly in that age group. Of course, there may be differences by country, but it is expected that standard gait variable values for each country can be derived if this method is applied. The results obtained in this way are expected to be applicable to the design of new pedestrian facilities.
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.4 Evaluation  
Various metrics, such as accuracy, precision, specificity, recall, F1 score, AUC, PSNR, mIoU, and confusion matrix are used to assess the performance of a proposed model architecture. The AUC measured the overall performance of the binary classifier, indicating its ability to distinguish between positive and negative classes.  
The mathematical formulations for each metric are presented in Equations (1)-(8) [26,27]:  
\[\text{PSNR}=10\text{log}_{10}\bigg{[}\frac{255\times 255}{\text{MSE}}\bigg{]}, \tag{1}\]  
where MSE represents the cumulative squared error between the two images. It is calculated using the following equation:  
\[\text{MSE}=\frac{1}{\text{n}}\sum(\text{actual value}-\text{predicted value})^{2} \tag{2}\]  
\[\text{IoU}=\frac{\text{TP}}{(\text{TP}+\text{FP}+\text{FN})} \tag{3}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{(\text{TP}+\text{FN})+(\text{FP}+ \text{TN})} \tag{4}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{Recall}(\text{Sensitivity})=\frac{\text{TP}}{\text{TP}+\text{FN}}=\text{ TPR} \tag{6}\]  
Figure 4: The proposed classification model for this study.  
Figure 3: The structure of baseline EfficientNet-B0.  
where TPR is true positive rate.  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}=\text{TNR} \tag{7}\]  
where TNR is true negative rate.  
\[\text{F1}-\text{score}=2\times\frac{(\text{Precision}\times\text{Recall})}{( \text{Precision}+\text{Recall})} \tag{8}\]  
where TP is true positive, TN is true negative, FP is false positive, and FN is false negative.