esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 4 EVSP Process
### Vehicular Network Communication Protocol  
The STSC system communication protocol defines the message, format, frequency between the OBU, and the RSU, as shown in Figure 7. OBU periodically broadcasts the vehicle-to-RSU (V2R) message every second, the message fields are OBU_ID, TIME_STAMP, POSITION, SPEED, DIR, ACC, VEHICLE_TYPE, DUTY_FLAG, where time, position, and speed information collects from GPS module, id, type, and duty are the vehicle internal status. The DIR field determines the direction range (from 0-7), converted from the GPS data, is applied for matching the vehicle direction and RSU location, which is applied for the host RSU determination algorithm. DUTY_FLAG indicates whether the emergency vehicles is on duty or not.  
RSU periodically broadcasts an RSU-to-vehicle (R2V) message every second, where message fields including RSU_ID, TIME_STAMP, RSU_POSITION, SPEED_LIMIT, and EVENT, represents unique RSU ID, timestamp, the location of the RSU, speed limit, and warning events (ex. accident, EV, road work), respectively.
## 4 Results
### Clustering Parameter Selection and Result of Cluster
Table 10 presents the results of clustering segmentation for product behavior, categorizing products into three groups: best-selling, profitable, and VIP. Cluster 1 represents the best-selling products, with a higher transaction frequency mean (155.932 times) than cluster 2 (15.163 times). Additionally, cluster 1 showed a shorter average duration (10.050 days) than cluster 2 (91.096 days). Regarding monetary value, cluster 2 (USD 58.806) was more profitable than cluster 1 (USD 454.953), considering the respective transaction frequencies. Cluster 3 is an outlier in the data, displaying extremely high values compared to the other clusters. Cluster 3 exhibited an average transaction frequency of 900.722 times and an average duration of 249.059 days. The monetary value for cluster 3 was significantly higher, reaching USD 14,780.086. Comparing the frequency and monetary values, Cluster 3 generated substantially greater income than the other two groups. Best-selling items are typically inexpensive and sold in great quantities in the real world. Office supplies are one of the best-selling items since they are frequently purchased. Products with a high selling value but a low sales volume are known as VIP products. This category includes household furniture and appliances in general. Among them are profitable products with a medium frequency of sales and a reasonably medium monetary value. This category of merchandise includes apparel, accessories, and kitchenware.
## 2 Materials and Methods
### EEG Acquisition and Preprocessing  
Brain activity in response to continuous speech sentences was recorded using a 64-channel EEG system at a sampling rate of 2048 Hz (Biosemi Co., Amsterdam, The Netherlands). The EEG data were preprocessed using average-referencing and 1-57 Hz band-pass filtering and then resampled at a rate of 256 Hz. Each epoch had a \(-\)0.5 to 2.5 s interval relative to the onset of the spoken sentence. Epochs and channels with large-scale artifacts were rejected, and specific channel data were spherically interpolated using nearby channels. Independent component analysis was performed to remove eye blink-related artifacts using EEGLAB [24]. Finally, the data were filtered using a zero-phase anti-aliasing 10-Hz low-pass filter after baseline correction.
## 3 Equipment and Testing
### Pedestrian Data in Natural Canyon  
Unlike urban canyons where multipath is more specular, the multipath in natural canyons is generally more diffuse. The variation in the type of multipath while still limiting geometry and availability provides a unique test of the algorithms presented herein. Using a similar equipment setup as the urban scenario, data was thus collected in a natural canyon (King's Creek Canyon) in Kananaskis Country, AB, Canada. GNSS data collected using two receivers, namely OEM6 and u-blox6, were separately integrated with the IMU data collected using the ADIS16488 sensor unit. The test environment is shown in Figure 6.  
The satellite mask angles in this environment varied between 50 and 80 degrees. The test duration was two hours. The reference trajectory obtained for the natural canyon data set is shown in Figure 7. The trajectory starts at Highway 40 and follows the Creek until its end, 1,500 m later. The same snow path was easy to follow exactly on the way back. The fact that the same path was followed was followed forth and back was also used to assess trajectory repeatability. However, despite following  
Figure 5: Reference trajectory (urban canyon).  
Figure 6: Data collection environment (natural canyon).  
the same path in both directions, the error characteristics and signal availability does not remain the same due to continuous changes in satellite geometry.
# An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
## 3 System Model
### Mean Packet Transmission Delay for Delay-Sensitive UE QoS Requirement
Figure 3: Packet transmission delay for delay-sensitive UE as a function of unlicensed spectrum allocation ratio \(\gamma\). Figure 5 shows total packet transmission delay for all UE in the network as a function of unlicensed spectrum allocation ratio \(\gamma\) for various power allocation ratios \(\xi\). It shows that the total packet transmission delay has the same variation tendency as the packet transmission delay for rate-sensitive UE, as shown in Figure 4, which means that the total packet transmission delay is mainly caused by the rate-sensitive UE in the system. Then, this reveals that it is essential to choose the most accurate performance criteria for the UE in the system. Then, all in range CCs of the UE \(u\) are arranged based on their signal qualities. The CC offering the highest signal quality in 5G receives an assignment of identify function with 1 for the PCC by the UE \(u\), and the remaining CCs in both 5G and 5G-U can also receive identify function with 1 for SCCs based on the association mechanism. The delay-sensitive UE and rate-sensitive UE have a minimum QoS requirement on delay and rate, which are \(D_{max}\) and \(c_{min}\), respectively. Then, an association bias \(\psi\) can be achieved based on the proportional fairness of each UE in the network. From the perspective of APs, each AP allocates resources to UE in its cell coverage with the highest association bias \(\psi\), which is shown as  
\[\psi=\frac{\text{\emph{Minimum QoS Requirement}}}{\text{\emph{Real Time QoS}}}. \tag{30}\]  
The procedure of dynamic unlicensed spectrum allocation is presented in Algorithm 1. Figure 4: Packet transmission delay for rate-sensitive UE as a function of unlicensed spectrum allocation ratio \(\gamma\). Figure 5: Total packet transmission delay as a function of unlicensed spectrum allocation ratio \(\gamma\). ```
Algorithminput: All in range CCs in 5G and 5G-U. Initialize Primary and secondary CCs allocation. begin Identify function to the AP serving highest signal quality \(\leftarrow\) 1; ifThe AP also assign an identify function with 1 to UE \(u\)then  aggregation set \(\leftarrow\) CC;  Break; else  Remove AP from the list and return to Step 3;  end if  end if Algorithmoutput: PCC and SCCs. ```  
**Algorithm 1**Proposed algorithm for primary and secondary CCs association  
With this dynamic primary and secondary CCs association mechanism, UE in the network can be optimally allocated primary and secondary CCs by considering proportional fairness based on carrier quality and service type. A simple flowchart representing the process of this work is shown in Figure 6.
## 4 Design Case
### Evaluation of Driving Performance
Similar trends can be observed for the two right wheels. Equations (13) and (14) show that either \(\omega_{m}\) or \(\omega_{dm}\) depends on both \(\omega_{wl}\) and \(\omega_{wr}\) which can be calculated by  
\[\omega_{wl}=\frac{v}{R}\frac{R_{fl}}{R_{w}} \tag{47}\]  
\[\omega_{wr}=\frac{v}{R}\frac{R_{fr}}{R_{w}} \tag{48}\]  
Thereby, \(\omega_{m}^{\circ}\) and \(\omega_{dm}^{\circ}\) can be attained using \(R=R_{fl}=R_{fr}\), while \(\omega_{m}^{\star}\) and \(\omega_{dm}^{\star}\) are obtained as \(R_{fl}=R_{fl}^{\ast}\) and \(R_{fr}=R_{fr}^{\ast}\). The algorithm for finding the operating conditions of M and DM corresponding to the optimal turns are described below:  
1. Letting the required driving torque of wheel be equal to \(F_{trac}R_{w}\), the values of \(T_{m}^{\circ}\) at straight driving are computed based on Equation (12) with a transmission efficiency, \(\eta_{t}\), of 0.97 considered; i.e., \(T_{m}^{\circ}=F_{trac}R_{w}/(r_{m}\eta_{t})\). 2. \(T_{dm}\) is determined with the value of \(\xi_{lr}\) in Equation (20) set to be \(\xi_{lr}^{\ast}\), 0, or \(-\xi_{lr}^{\ast}\) corresponding to left turn, straight, or right turn, respectively. 3. The required driving torque of M, \(T_{m}^{\prime}\) is obtained from Equation (18). 4. \(\omega_{m}\) and \(\omega_{dm}\) are calculated from Equations (13) and (14), respectively, using the optimal values of \(R_{fl}^{\star}\) and \(R_{fr}^{\star}\), as shown in Figure 20.
5. Then, the operating points of M and DM in terms of \(\omega\) and \(T\) are acquired. As a result, the operating points of M for left turn, straight driving, and right turn at cruise driving are shown in Figure 21 with \(v\) in the range of 20 to 120 km/h, while those of DM are illustrated in Figure 22. It should be noted that \(T_{dm}=0\) when driving straight. Furthermore, the specifications of DM are scaled down to rated maximum torque of 244 N\(\cdot\)m, rated power of 6 kW, and maximum rotational speed of 1500 rpm so that the operating points for \(v\) in the range of 60 to 100 km/h are well situated in a high-efficiency spot as indicated in the area circled by the red dotted line in Figure 22.  
This section has presented the assessment of the driving performance for the designed vehicle. The power capacity of DM has been determined based on the optimal cornering performance, being merely 7.5% that of M. Moreover, the performance map of DM has been so scaled that the operating points in the mid-speed range where the torque vectoring is frequently activated are favorably located in an energy efficiency zone.
## 2 Materials and Methods
### Laser Setup  
A fiber laser with a maximum continuous laser power output of 250 W and a wavelength of 1080 \(\pm\) 5 nm was used. The laser was mounted with a collimation unit, a beam expander and a galvanometric scanner in a constructed mount seen in Figure 3a. A f-theta lens ensured an orthogonal laser beam over the whole scanning area.  
For the laser joining experiments, a specimen holder for three joints with an overlap width of 12.5 mm and an applied pressure of 0.4 MPa through compression springs was utilized, as illustrated in Figure 3b. The distance between the sample surface and the f-theta lens was maintained constant to achieve a laser spot diameter of 5 mm. A simple line was chosen as the scanning geometry, with an additional 5 mm before and after the first and last sample to ensure homogeneous heating. The indirect heating method was applied for all samples. The laser joining processes were conducted under atmospheric conditions at approximately 20 \({}^{\circ}\)C and 50% humidity. The variation in laser power and scanning speed is detailed in Table 2. To facilitate comparison with other LAMP publications, the laser power was divided by the scanning speed to obtain the energy input per unit length, also known as line energy. These parameters were varied in identifying conditions yielding the maximum lap shear strength with an adhesive failure mode. The investigated parameter range was determined based on experience, considering the melting ranges of different plastics and the significance of metal thickness when utilizing the indirect joining method. Thicker metals typically require higher laser power or reduced scanning speed to achieve sound joints.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Plastic**} & **Laser Power** & **Scanning Speed** & \multicolumn{2}{c}{**Line Energy**} \\  & **[W]** & **[mm/s]** & & **[J/mm]** \\ \hline \multirow{3}{*}{Polyamide 6.6} & \multirow{3}{*}{225, 237.5, 250} & \multirow{3}{*}{1, 1.5, 2} & 225 & 238 & 250 \\ \cline{3-5}  & & & 150 & 158 & 167 \\ \cline{3-5}  & & & 113 & 119 & 125 \\ \hline \multirow{3}{*}{Polypropylene} & \multirow{3}{*}{150, 162.5, 175} & \multirow{3}{*}{1.5, 2, 2.5} & 100 & 108 & 117 \\ \cline{3-5}  & & & 75 & 81 & 88 \\ \cline{3-5}  & & & 60 & 65 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Laser parameters used in this research.  
Figure 3: CAD-generated image of the (**a**) laser mount with sample holder and (**b**) sample holder without the cover plate.
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis  
### Experimental Set-Ups  
#### 4.1.1 Implementation Details  
In this study, the tensorflow2.0 suite within Python was used as the platform for the experiment. The hardware and system configuration included a Windows 10 version 1703 system. An NVIDIA GTX 1080 TI graphics card was used; the computing core was an i7-6700 3.40 GHz 8-core central processing unit (CPU) with 32 GB memory. Different pre-trained model parameter settings were used, and the results of using these were compared. Attempts were made to adjust the settings for training methods with different stages. The size of the batches in the experiment was set to a uniform size of 64, which was more in line with the memory capacity of the graphics card; the image length and width were set to 224 pixels in all cases. This study designed the training set size based on earlier studies. For the UC Merced land-use dataset, two training configurations--80% training and 50% training--were used. For the RSSCN7 dataset, 50% training and 20% training were used, and, for the WHU-RS19 dataset, the two modes used were 60% training and 40% training Two modes and 10 repeated random training cycles were used to verify and evaluate the experimental results.
## 3 Proposed Framework
### Feature Selection  
Feature selection has become a key component for attaining the most desirable subset of features. An algorithm for feature selection proposes a solution based on the distinct feature subsets and ranks those subsets based on three criteria: embedded, wrappers, and filters. This work examines two methods, wrapper and embedded.
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 2 Methodology
### Generation Units
#### 2.2.3 Pv  
In HOMES, the electrical power generation from each PV system \(u\) is treated as a parameter \(p_{u,t}^{PV}\) provided as input to the optimization model, similar to the treatment of non-dispatchable loads. As a result, to enable the use of HOMES for day-ahead scheduling, a dedicated forecasting model for PV generation or appropriate assumptions must be adopted.
## 3 NARX Model on the Rotor-Bearing System
### NARX Model Subjected to Multi-Harmonic Excitation  
NARX approach is used in the system identification of the rotor-bearing system which is shown in Figure 1, where the system is subjected to unbalanced force \(meu^{2}\sin(\omega t)\) in the horizontal direction. Considering the advantages of the speed-up process which have been discussed in the introduction, the multi-harmonic signal \(\sin(\omega t),\ \omega\in[1,450]\) rad/s is defined as the system input excitation, and the output of the system is defined as the horizontal vibration response of the unbalanced disc.  
The NARX model for the rotor-bearing system in Figure 1 is identified as:  
\[\begin{array}{l}y(k)=\sum\limits_{i=1}^{15}\hat{\rho}_{i}(k)\tilde{\theta}_{ i}\\ =1.5806y(k-1)-0.4635y(k-2)+2.9713\times 10^{-4}u(k-2)+\cdots\\ -8.0035\times 10^{-4}u(k-9)u(k-9)\end{array} \tag{25}\]  
The 15 selected model terms, ranked in order of significance, are shown in Table 1.  
In the following study, the NARX model (25) is validated under the excitations of the low speed (\(\omega\) = 100 rad/s), the critical speed (\(\omega\) = 320 rad/s), and the over critical speed (\(\omega\) = 500 rad/s), respectively. The predicted output responses by the NARX model are compared with the simulation results in both the time and the frequency domain. Moreover, the OSA and the MPO methods are also discussed. The results are shown in Figures 6-8. Also, the model is also evaluated by RMSE, which is shown in Table 2.  
model identified over a wide frequency range cannot be used to reproduce the structure of the nonlinear, rotor-bearing system.  
To address this issue, in the following study, NARX models are separately identified under, a more narrow frequency range, such as the low frequency range, the natural frequency range, or the high frequency range, respectively.  
Figure 6: Output comparison in time and frequency domain when \(\omega=100\) rad\(/\)s.  
Figure 7: Output comparison in time and frequency domain when \(\omega=320\) rad\(/\)s.
## 2 Materials and Methods
### Multi-Scale NSST-CNN Feature Extraction
#### 2.1.2 Construction of an NSST-CNN Feature Extraction Network  
The CNN is a classical deep neural network model for image processing. Compared to other deep neural networks, they have fewer parameters and faster training speed, so they have a huge advantage in the image field [18]. In this paper, a VGG16 network was selected. The VGG16 network consists of 13 convolutional layers, five pooling layers, and three fully connected layers, of which 16 are network layers containing parameters. After NSST decomposition, the original foam image outputs a multi-scale image with a size of 224 \(\times\) 224 \(\times\) 3 which is fed to the CNN network. After a series of convolution and pooling operations, the output size becomes 2 \(\times\) 2 \(\times\) 256, and finally a 256-dimensional feature vector is output after a fully connected layer. Compared with the simple and easy to train AlexNet network, the VGG16 not only inherits these features perfectly, but also increases the depth of the network. The VGG16 can produce image features more comprehensively, so it effectively improves the network's performance [19; 20; 21]. In this paper, the four frequency sub-bands of the NSST triple decomposition are extracted separately, and then the extracted 1024 \(\times\) 4 dimensional features are fused, and the fused feature vector is 1 \(\times\) 4096 dimensional.  
Foam flotation condition recognition works by building a convolutional neural network using the NSST processed image for training and testing. After the NSST's decom  
Figure 3: Working condition 1: Three-dimensional display of bubble image.  
Figure 2: 3-stage NSST multiscale decomposition of bubble image.  
position into multiple scale images, low and high-frequency images can reflect more of the foam image's multi-scale details and contours, and the characteristic vectors output from each scale are fused in series, which can thereby obtain a high classification accuracy. Figure 4 shows the NSST-CNN network feature extraction model.
# Covert Channel Based on Quasi-Orthogonal Coding
## 6 Practical Implementation  
The concept of a covert channel based on quasi-orthogonal coding was implemented by using the USRP-2920 [34] hardware platform manufactured by National Instruments. USRP is the essential hardware part for generating a radio signal, while the software part is provided by the LabView software (with Matlab scripts) installed on a personal computer (PC). An Ethernet network adapter with a bit rate of 1 Gb/s is used to provide communication between USRP and the PC. Two USRP-2920 were used to implement a test stand (Figure 14) for detectors (in the transmitter-receiver system) connected with the computer by an unmanaged switch. The system was placed in an office room, and the distance between the transmitter and receiver was 5 m. The line-of-sight (LOS) propagation conditions were ensured disturbance only by office equipment such as PCs and monitors.  
The data were preceded by a short and long training sequence (Figure 15) as defined in [35]. On this basis, transmission channel parameters were estimated and synchronization and frequency and phase correction were made. For performance analysis, there was no channel encoding during the signal transmission. In order to compare the results obtained, tests were performed for the case of transmission with and without sorting. Sorting was done as in simulation #2 in Section 4. The results obtained are shown in Figure 16. Estimated SNR value refers to cover signal. Cover detection are intended to show that a certain minimum SNR for the cover channel is required to receive the covert channel. The cover signal has to be detected correctly first and then the covert signal can be received. During the test, a low power covert signal was selected deliberately. First, the authors intended to make the signal as difficult to detect as possible, and second, to obtain conditions under which it is more sensitive to channel estimation errors. The test verified the previously assumed and simulation-validated thesis that sorting aimed at mutual quasi-orthogonality of signals can improve the bit error rate. The difference for a signal with sorting versus without sorting for parameters defined in Table 2 is about 3 dB. The proposed algorithm effectively reduces channel estimation error and improves SIC operation. Significant gain was achieved, although the channel parameters were estimated every 64 blocks, which should give small channel estimation error. Improved CSI (lower error) could be achieved by, for example, additional pilot signals and training sequences, however, this would come at a cost of system resources and maximal bit rate.  
Figure 14: Test system used for examinations.  
Figure 15: Structure of the transmitted signal.