esa-sceva/satcom-chunk-collection huggingface dataset:
# Hands-On Fundamentals of 1D Convolutional Neural Networks--A Tutorial for Beginner Users
## 3 1D CNN Architecture  
CNNs typically perform a series of operations to extract features from an input signal. For simplicity, this analysis focuses on a model with two convolutional layers \((CL_{1},CL_{2})\) followed by a fully connected layer \((FCL)\), as indicated in the \"Layer\" column of Table 1.  
In turn, each convolution layer comprises convolution (Conv), activation (Act), and pooling (Pool) operations, as detailed in the \"Operation\" column of Table 1. The general features of the CNN architecture in terms of sizes are summarised in the \"Filter\" and \"Output size\" columns of Table 1. In \(CL_{1}\), the convolution is performed using a single-channel input \((I)\) with dimension \(n=18\) and \(p=4\) filters with dimension \(s^{(1)}=3\). A bias array is then added to the output, and the sigmoid activation function is applied. A new array \((C_{\sigma}^{(1)})\) of \(p\) columns with \(n-s^{(1)}+1=16\) dimensions is obtained. It is then down-sampled (using max or average-pooling) with a factor \(SS^{(1)}=2\), resulting in a new array \((S^{(1)})\) of \(p\) columns with size 8.  
In \(CL_{2}\), the convolution operation is performed using \(S^{(1)}\) as the input signal and \(p\times q\) filters with \(q=6\) and \(s^{(2)}=3\) as the dimension of each filter.  
Similar to \(CL_{1}\), a bias array is added here, and an activation function \((\sigma)\) is applied. This results in an array \((C_{\sigma}^{(2)})\) of \(q\) columns of length 6. It is down-sampled (using max or average-pooling) by a factor of \(SS^{(2)}=2\). The resulting \(S^{(2)}\) is then represented by an array of \(q\) columns of size 3.  
Finally, the output of \(CL_{2}\) is flattened, obtaining a new vector \((f)\) with dimension 18. This latter vector represents the input for the FCL. The output of the 1D CNN is represented by an array with two dimensions. This implies that a weight matrix \(2\times 18\) is required in the FCL.
###### Abstract
In other words, the characteristic angle \(\beta_{n}\) can be calculated as in Equation (5). The characteristic angle has the advantage that its value changes more quickly making it ideal for analysis. \[\beta_{n}=180-tan^{-1}(\lambda_{n}) \tag{5}\]  
The CMA analysis is carried out using CST microwave studio using two different solvers, which are a surface integral solver and a multi-layer solver. The model significant \((MS_{n})\), characteristic angle \((\beta_{n})\), and the eigenvalue \(\lambda_{n}\), all indicate the behavior of the concerned structure. At resonance mode, the value of \(\lambda_{n}\) is equal to \(0\), while \((MS_{n})\) is equal to \(1\) and the value of the characteristic angle \((\beta_{n})\) is equal to \(180^{\circ}\). The inductive mode that stores the magnetic energy occurs when \(\lambda_{n}>0\) or \(\beta_{n}<180^{\circ}\). In contrast, the capacitive mode stores the electric energy when the value of \(\lambda_{n}<0\) or \(\beta_{n}>180^{\circ}\). Herein, in this paper, a graphene plasmonic microstrip nano-antenna is proposed to be used in the 6G wireless communication system applications to operate at a 3.5 THz frequency band. A three configurations graphene plasmonic two-ports MTM microstrip patch antenna is reported based on the theory of characteristic mode analysis (CMA), which gives us the opportunity to reach the optimum design of the antenna without wasting a lot of time in the try and error iteration process. The CMA is also used to detect the optimal configuration that gives high isolation and good performance to withstand the current distribution and the model significance. In order to reduce the mutual coupling between the two-port graphene plasmonic microstrip nano-antenna, a metamaterial unit cell of Dumbbell-structure is introduced. The diversity performance is verified by the usage of the envelope correlation coefficient (ECC), channel capacity loss (CCL), and diversity gain (DG). The strategy of the proposed design is demonstrated in the following order: firstly, the design procedure of a single element graphene plasmonic nano-antenna at the resonance frequency of 3.5 THz by the usage of CMA to obtain the optimum dimensionsthen a verification of the desired is carried out by another software such as HFSS and ADS to invoke the equivalent circuit model to verify the intended performance. Secondly, the graphene plasmonic two-port MIMO array antenna is used to increase the gain with three configurations. Thirdly, the characteristics of the Dumbbell-structure MTM unit cell is investigated and etched in the antenna to reduce the mutual coupling between the two elements. Finally, the diversity performance of the graphene-MIMO configuration is also carried out with and without the MTM, and this design is validated using the HFSS and ADS software.
## 3 Results and Discussion  
### Performance on Different Data Sets  
To demonstrate the superiority of the proposed CNN in this paper over other network models, the performance of the CNN trained on the NYU Depth V2 dataset and asphalt macro-texture RGB-D datasets were compared with some publicly available research results, as shown in Table 3.  
According to Table 3, the RMSE, REL, and accuracies for \(\delta\) = 1, \(\delta\) = 2, and \(\delta\) = 3 on the NYU Depth V2 dataset for the proposed CNN are 0.630, 0.135, 0.801, 0.951, and 0.986, respectively. For the asphalt macro-texture dataset, the corresponding values are 0.491, 0.102, 0.931, 0.979, and 0.991. Compared to other models, the proposed CNN achieves lower errors and higher accuracy, indicating its superior performance for accurate asphalt macro-texture reconstruction. Additionally, these results demonstrate that the established RGB-D asphalt pavement texture dataset is effective and suitable for training the network model.
## Nomenclature  
* temperature, K
* heat flux, W/m\({}^{2}\)
* heat transfer coefficient, W/(m\({}^{2}\) K)
* diffusion coefficient, m\({}^{2}\)/s
* molar mass, kg/mol
* gas constant, J/(mol K)
* thermal conductivity, W/(m K)
* Schmid number
* Prandtl number
* gas volume fraction, %
* characteristic length, m
* mass fraction, %
* gas
* liquid
* steam
* wall
* noncondensable gas
# Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Passage** & **Roll (\({}^{\circ}\))** & **Yaw (\({}^{\circ}\))** & **Number of Points** & **X (m)** & **Y (m)** & **Z (m)** & \(\sigma_{\rm Z}\) (cm)** \\ \hline P1 & \(-\)4.95 & 4.2 & 1583 & 0.28 & 0.08 & 77.85 & 5.7 \\ P2 & \(-\)5.44 & \(-\)39.47 & 1290 & 0.12 & 0.33 & 77.75 & 6.7 \\ P3 & 0.58 & 98.49 & 1405 & 0.10 & 0.03 & 77.79 & 6.1 \\ P4 & 10.57 & \(-\)121.28 & 1382 & 0.00 & 0.24 & 78.02 & 9.8 \\ P5 & \(-\)23.62 & 58.35 & 866 & 0.28 & 0.08 & 77.85 & 7.3 \\ P6 & \(-\)0.66 & 4.92 & 1068 & 0.41 & 0.21 & 77.43 & 9.2 \\ P7 & 5.68 & \(-\)179.02 & 867 & 0.16 & 0.00 & 77.69 & 5.7 \\ P8 & 0.79 & \(-\)86.38 & 1537 & 0.18 & 0.25 & 77.75 & 5.1 \\ P9 & \(-\)0.39 & 88.76 & 791 & 0.42 & 0.00 & 77.73 & 7.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the uncertainty assessment of LiDAR height measurements in in-flight conditions. X and Y are the offset (in m) between the lower left angle of the target and the origin indicated in Figure 6.  
Figure 11: Returns from the top of the box in (**a**) P4 and (**b**) P8, in green, and ground returns, in black. The red line is the expected box top position. The cluster of returns on the right-hand side of each figure is another ground target (a sphere) that is not included in the analysis. Summarizing, uncertainty in Z in angular static conditions is equal to 3.8 cm, which increases to 3.9 cm in angular dynamic conditions. Uncertainty in Z in in-flight conditions ranges between 5.1 cm and 9.8 cm, while in multiple passages, obtained as a combination of nine single passages, it ranges between 5 cm and 16 cm.
# An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
## 2 Preliminaries
### Basic Idea of MOEA/D  
MOEA/D is a representative algorithm based on decomposition, which was proposed by [PERSON] and [PERSON]. In addition, [PERSON] and [PERSON] had proven that it is superior to the other state-of-the-art MOEAs in 2007 [18]. Moreover, a variant of MOEA/D, which is named MOEA/D-DRA (MOEA/D with dynamical resource allocation), had achieved first place at CEC2009 (2009 IEEE Congress on Evolutionary Computation) [29]. The basic idea of MOEA/D is using some kind of aggregation function to decompose an MOP into some single objective optimization sub-problems and to use some other evolutionary algorithm to optimize them simultaneously.  
In the general MOEA/D framework, let \(N\) be the population size, \(m\) be the number of the objectives, for all \(i=1,\ldots,N\), and \(\lambda_{1},\lambda_{2},\ldots,\lambda_{N}\) be a set of aggregation coefficient weight vectors, where \(\lambda_{i}=(\lambda_{1}^{1},\lambda_{i}^{2},\ldots,\lambda_{i}^{m})^{T},j=1, \ldots,m\), and subject to \(\lambda_{i}^{j}\geq 0\), \(\sum_{j=1}^{m}\lambda_{i}^{j}=1\). With these weight vectors, MOEA/D employs a decomposition method (discussed in Section 2.2) to decompose an MOP (1) into single objective optimization subproblems and minimizes all of these \(N\) subproblems in a single run. Each subproblem \(i\) corresponding to vector \(\lambda_{i}\) is optimized by using the information just from its neighborhood. The neighborhood of weight vector \(\lambda_{i}\) is defined as a group of its \(T\) closest weight vectors in the entire set \(\{\lambda_{1},\lambda_{2},\ldots,\lambda_{N}\}\). Therefore, the neighborhood of the \(i\)-th subproblem is composed of all of the subproblems whose weight vectors are from the neighborhood of \(\lambda_{i}\). The constantly-updated population consists of the best solution found up to now for each subproblem. Only the present solutions to these neighboring subproblems are utilized for optimizing a subproblem in the algorithm. For more details about MOEA/D, readers may refer to [18].
## 3 Methodology
### Different Components of Solar Irradiance on Rugged Terrain  
6S radiative transfer model can be run to compute relevant atmospheric parameters and solar irradiance reaching the horizontal surface. From the direct and diffuse components of irradiance on horizontal surface \(E_{h}^{dir}\) and \(E_{h}^{dif}\), corresponding components of irradiance on rugged terrain, namely \(E^{dir}\) and \(E^{dif}\), can be calculated based on cosine correction formula and [PERSON]'s insolation model [41].  
\[E^{dir}=E_{h}^{dir}\varphi\frac{\max(\cos(i_{t}),0)}{\cos(\theta_{s})} \tag{4}\]  
\[E^{dif}=E_{h}^{dif}\left\{k\frac{\cos(i_{t})}{\cos(\theta_{s})}+(1-k)V_{d} \right\}+E_{h}V_{t}\rho_{adj} \tag{5}\]  
where \(\varphi\) is a binary coefficient, set to 0 when no direct irradiance reaches tilted surface, and set to 1 otherwise; \(\rho_{adj}\) is average reflectance of the neighboring area. Details about the derivation of sky clarity index \(k\), terrain view factor \(V_{t}\), and sky view factor \(V_{d}\) can be found in [42]. Now define ratio factors \(R^{dir}\), \(R^{dif}\) and \(R\) by referring to [20], and Equation (3) can be converted to the following form:  
\[\frac{\rho_{m}}{1-S\rho_{m}}=R^{dir}\left[f_{v}\rho_{s}(i_{t},e_{t},\delta \varphi_{t})+(1-f_{v})\overline{\rho^{\prime}}(i_{t})\right]+R^{dif}\left[f_{ v}\overline{\rho}(e_{t})+(1-f_{v})\overline{\overline{\rho}}\right]+R\frac{S \overline{\overline{\rho}}^{2}}{1-S\overline{\overline{\rho}}} \tag{6}\]  
where parameters, like \(f_{v}\), \(S\) and surface reflectance under Lambertian assumption \(\rho_{m}\), can be obtained from the results of running 6S model. Atmospheric state parameters, like aerosol optical depth (AOD) and water vapor content, are needed to drive the 6S model. The highly variable AODs are inversed by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) code, and the technique details can be found in references [43,44]. Atmospheric water vapor data are derived from NOAA NCEP (National Centers for Environmental Prediction) reanalysis product ([[http://www.ncep.noaa.gov/](http://www.ncep.noaa.gov/)]([http://www.ncep.noaa.gov/](http://www.ncep.noaa.gov/))), and ozone content data are obtained from TOMS (Total Ozone Mapping Spectrometer, [[http://disc.gsfc.nasa.gov](http://disc.gsfc.nasa.gov)]([http://disc.gsfc.nasa.gov](http://disc.gsfc.nasa.gov))).  
Now, only four reflectance quantities in Equation (6) are unknown, namely \(\rho_{s}(i_{t},e_{t},\delta\varphi_{t})\), \(\overline{\rho^{\prime}}(i_{t})\), \(\overline{\rho}(e_{t})\) and \(\overline{\overline{\rho}}\). Bi-directional reflectance factor \(\rho_{s}(i_{t},e_{t},\delta\varphi_{t})\) is the final solution pursued.
# Cationic Moieties in Polystyrene Gels Swollen with D-Limonene Improved Transdermal Delivery System
## 4 Conclusions  
In conclusion, the limonene oil gels were successfully fabricated from cross-linked PS and its derivatives, which included 4-vinylpyridine and (vinylbenzyl) trimethylammonium chloride. They were prepared for TDS by using the advantages of d-limonene as an effective chemical permeation enhancer and a low-toxic organic solvent. The stable chemical network of the PS gels swollen in limonene illustrated the highest elastic modulus at 98 kPa. Moreover, the efficiency of the permeability of ibuprofen was successfully enhanced by d-limonene and the controllable of the network density indicated by relative permeability coefficient from 53.1 \(\pm\) 0.03 to 17.9 \(\pm\) 0.20 \(\times\) 10 cm/h. Cationic moieties were introduced to control the drug releasing behavior of the gel with the slowest steady flux at 14.1 \(\pm\) 0.25 cm/h of 5 mol %-4 VP gel because of drug-polymer chain interaction. However, upon increasing the cationic moiety repulsive effects appeared. Overall, we achieved a steady controlled release of ibuprofen from modified PS gels swollen in limonene.  
The following are available online at [[http://www.mdpi.com/2073-4360/10/11/1200/s1](http://www.mdpi.com/2073-4360/10/11/1200/s1)]([http://www.mdpi.com/2073-4360/10/11/1200/s1](http://www.mdpi.com/2073-4360/10/11/1200/s1)), Figure S1: HPLC chromatogram of ibuprofen in PBS solution with 6.5 \(\mathrm{\SIUnitSymbolMicro m}\) (**A**), 13 \(\mathrm{\SIUnitSymbolMicro m}\)/mL (**B**), 26 \(\mathrm{\SIUnitSymbolMicro m}\)/mL (**C**), 46 \(\mathrm{\SIUnitSymbolMicro m}\)/mL (**D**) and standard calibration curve of peak area and concentration (**E**), Figure S2: Schematic illustration of the interaction with drugs and scheme of polymer network with and without cationic moieties at the interface of aqueous media, Figure S3: (**A**) FT-IR spectrum of 4-vinylpyridine (**a**), _d_-limonene (**b**), Ibuprofen (**c**), and 4-vinylpyridine: ibuprofen, 1:1 in limonene (**d**) and (**B**) the expected intermolecular interaction between 4-vinylpyridine and ibuprofen, Figure S4: (**A**) FT-IR spectrum of (vinylbenzyl) trimethylammonium chloride; VBAC (**a**), _d_-limonene (**b**), Ibuprofen (**c**), and VBAC: ibuprofen, 1:1 in limonene (**d**) and (**B**) the expected intermolecular interaction between VBAC and ibuprofen. Figure S5: The correlation of cross-linker ratio between 5 and 10 mol % against to elastic moduli (G') and steady flux permeation of PS gel (Gel A-D) with 4 and 8 M limonene concentration and Table S1: Storage modulus and permeation parameter of PS gel.  
Conceptualization, H.A.; Data curation, P.C.  
This research was supported by 'NAIST Grant-In-Aid' funding from Nara Institute of Science and Technology (NAIST) and Japanese Government (MEXT) Scholarship Program. This was partly supported by Bilateral program: Joint research Thailand-Japan (JSPS-NRCT). The rat skin in permeation studies was received from [PERSON], System Neurobiology and Medicine Laboratory, NAIST, Japan.  
The authors declare that they have no conflict of interest.
## Materials and Methods  
### DED Process  
DED alnico alloy feedstock powders were produced via HPGA by Ames Laboratory's (Ames) Powder Synthesis and Development Facility (PSDF). These powders were screen classified using ASTM screen sizes and an Elcan Hi-Sifter to 45-106 \(\upmu\)m for the DED experiments. Preliminary DED builds using an Optomec MR-7 system were described in a previous paper [16] and additional processing space was systematically explored in this work as described in Tables 1 and 2 to promote heat extraction along the vertical axis of the builds (i.e., draw versus raster). For the samples utilizing the melt pool sensor (MPS) the laser power was varied by the MPS to maintain a constant size of a melt pool with 100 W for the set power level, 200 W for the upper limit, 70 W for the lower limit, and actual variation during the build between 130 and 160 W. Goal cylindrical geometries were \(\geq\)3 mm diameter by 25 mm tall.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample ID** & **Powder Feed (rpm)** & **Print Method** & **Speed (Steps/s)** & **Power (W)** \\ \hline A & 12 & Draw & 1200 & 120 \\ B & 16 & Draw & 2000 & 160 \\ C & 16 & Draw & 3000 & 160 \\ D & 16 & Draw & 3000 & 120 \\ E & 8 & Draw & 800 & 90 \\ F & 16 & Draw & 4000 & 160 \\ G & 16 & Draw & 3000 & 100 * \\ H & 12 & Draw & 3000 & 100 * \\ I & 9 & Draw & 3000 & 100 * \\ J & 9 & Draw & 2000 & 100 * \\ K & 9 & Draw & 1500 & 100 * \\ \hline \hline \end{tabular}  
* refers to the melt pool sensor (MPS) control.  
\end{table}
Table 1: DED processing parameters for full-Co alnico build samples.
# Movement-in-a-Video Detection Scheme for Sign Language Gesture Recognition Using Neural Network
## 4 Conclusions and Future Works  
In this paper, a sign language recognition method with the movement-in-a-video detection scheme is proposed. In the proposed method, the movement-in-a-video detection scheme was applied to extract unique spatial and temporal features from each gesture. The extracted features were subsequently used with a pre-trained CNN to classify sign language gestures. In the movement-in-a-video detection scheme, a series of frames were extracted from each video. Thereafter, each extracted frame was subtracted from its preceding frame. Finally, the differences between consecutive frames were added together. The proposed method was used to identify sign language with short, medium, and long gestures in the Argentinian and Chinese sign language datasets. The proposed method identifies sign language with higher accuracies of 90.32% and 40% for short and medium gestures, respectively, compared with those (69% and 43.7%, respectively) achieved using methods in other recent works. The results clearly show that the proposed method can be used to recognize sign language gestures. Although the proposed method is tuned for short gestures only, the results show that it can be extended to medium gestures. This will enable real-time processing and recognition of videos and avoid overestimation by using methods with complex and expensive computation for short and medium gestures.  
In future works, we intend to extend the study by improving the extraction of information from videos and storage to an image for long gestures. Furthermore, apart from Inception V3, other types of CNNs that are capable of solving image classification tasks will be used.  
Conceptualization, A.C.C., H.-J.H., S.-H.K. and W.L.; methodology, A.C.C. and W.L.; software, A.C.C. and W.L.; validation, H.-J.H., S.-H.K. and W.L.; formal analysis, A.C.C.; investigation, A.C.C. and W.L.; resources, H.-J.H., S.-H.K. and W.L.; data curation, A.C.C.; writing--original draft preparation, A.C.C. and W.L.; writing--review and editing, H.-J.H., S.-H.K. and W.L.; visualization, A.C.C. and W.L.; supervision, H.-J.H., S.-H.K. and W.L.; project administration, H.-J.H., S.-H.K. and W.L.; funding acquisition, H.-J.H., S.-H.K. and W.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT (2020R1A4A1017775).  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Kumoh National Institute of Technology (202007-HR-003-04).  
Not applicable.  
The authors declare no conflict of interest.
# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 3 BPNN Model for Noise Limits Prediction during Vehicle Acceleration
### Architecture of the Proposed BPNN Model  
#### 3.1.1 Data Normalisation and Classification  
The accuracy of a BPNN model relies on the selection of input data and the preparation of training samples. For data selection, the representativeness of the samples and the elimination of contradictory samples should be considered, and then the data need to be normalised. In MATLAB, a built-in normalisation function is available, which can easily rigidise and convert the data into a data mapping between [-1, 1] or [0, 1]. Data are classified into three sets: (a) Training data, (b) validation data, and (c) test data. The training data are used for neural network learning and training. The validation data are used to confirm that the sample error of the validation data does not fall or rise after six consecutive iterations. The network training is terminated. It can effectively avoid overfitting, that is, in learning and training, the neural network achieves a very high approximation accuracy for the learning samples. However, for non-learning samples, there is a possibility of singular phenomena [12]. For example, generated approximation errors on the outputs by using the validation data decreases first with the number of training times, but then rises. Therefore, these are also used to measure network generalisation, and to halt training when the generalisation has no further improvements. The test data, as the third set, have no effects on training and so provide an independent measure of network performance during and after training.
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Reactive  
This section presents two reactive routing schemes for FANETs.
# Effects of Corner Modification on the Wind-Induced Responses of High-Rise Buildings
## 3 Aerodynamic Forces on the Building
### Standard Deviation of Fluctuating Base-Overturning Moment  
The standard deviations of the base overturning moments \(M_{x}\) and \(M_{y}\) in the \(x\)-direction and \(y\)-direction of the five models are shown in Figure 4. It is found that the maximum standard deviation of \(M_{x}\) occurs at azimuth 270\({}^{\circ}\). The pronounced standard deviation at the azimuth is mainly led by the cross-wind fluctuations originating from wake excitation. Among the five models, the RO10% model shows the smallest value at the azimuth. The standard deviations of \(M_{x}\) at azimuth 0\({}^{\circ}\) and 180\({}^{\circ}\) are much lower than that at azimuth 270\({}^{\circ}\), which corresponds to the along wind fluctuating aerodynamic forces typically led by the approaching turbulence.  
Furthermore, even in the along wind direction, the RO10% model shows the most beneficial effect in reducing the standard deviation of moments, indicating that the curved surfaces produce a less intensified vortex than the sharp edges. The modification of altering the edges can disrupt the vortex shedding process and mitigate the strength of cross-wind excitation forces [7]. The most unfavorable angle of the standard deviation of \(M_{y}\) lies at azimuth 0\({}^{\circ}\), and the Ref model presents the maximum value among the five models. Similarly, the RE10% model shows the most favorable effectiveness in attenuating the fluctuating base overturning moment.  
Figure 4: Standard deviation of base overturning moment: (**a**) the \(x\)-direction. (**b**) the \(y\)-direction.  
Figure 3: Mean values of base overturning moment: (**a**) \(M_{x}\). (**b**) \(M_{y}\).