esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Features Extraction Using Transfer Learning Models
#### 3.7.1 Vgg-19  
The architecture of the fine-tuned VGG19 model and the basic architecture of VGG19 is shown in Figure 7. The number of total learnable parameters is 59,604,784. The VGG19 consists of five convolutional blocks. In the first block, two convolutional layers with output shapes (100 \(\times\) 100 \(\times\) 64) with 1792 and 36928 trainable parameters, respectively, are connected to the MaxPooling layer with an output shape (50 \(\times\) 50 \(\times\) 64). In the second block, two convolutional layers are connected with the max-pooling layer, the output shape of the convolutional and max-pooling layers are (50 \(\times\) 50 \(\times\) 128) and (25 \(\times\) 25 \(\times\) 128) with 73,856 and 147,584 learnable parameters, respectively. The third block contains four convolutional layers with an output shape (25 \(\times\) 25 \(\times\) 256) and 295,168, 590,080, 590,080 and 590,080 learnable parameters, respectively, connected to the max-pooling layer of output shape (12 \(\times\) 12 \(\times\) 256). The fourth block consists of four convolutional layers with 1,180,160,  
Figure 6: **(a)**: Decomposition of signal using bior 6.8 wavelet **(b)**: Decomposition of signal using Sym5 wavelet **(c)**: Decomposition of signal using Haar wavelet **(d)**: Decomposition of signal using Dney wavelet **(e)**: Decomposition of signal using db2 wavelet and **(f)**: Decomposition of signal using CoiI5 wavelet.  
2,359,808, 2,359,808 and 2,359,808 learnable parameters, respectively, and the shape of the convolutional layers is (12 \(\times\) 12 \(\times\) 512) and afterwards, the connected max-pooling layer is (6 \(\times\) 6 \(\times\) 512). The fifth block contains four convolutional and max-pooling layers, the learnable parameters and output shape of all convolutional layers is 2,359,808 and (6 \(\times\) 6 \(\times\) 512) and the shape of the max-pooling layer is (3 \(\times\) 3 \(\times\) 512). Further layers are added to train the Dataset according to their output shape. The batch normalization layer normalizes the features of the above layer, and there are 3584 learnable parameters for the batch normalization layer. The global average pooling layer converts a three-dimensional into a one-dimensional array with an output feature length of 512. After the global average pooling layer, the four dense layers and one dropout layer are connected, the first two layers with 512 and 256 output units, respectively, and the last two layers with 256 and 128 output units and the dropout layer is applied between them. The accuracy score for that fine-tuned model is shown in Table 9.
# Weight Effects on Vertical Transverse Vibration of a Beam with a Nonlinear Energy Sink
## 1 Introduction
For example, [PERSON] investigated the effects of the self-weight of a Timoshenko beam on dynamic deflection [52]. [PERSON] et al. analyzed the effects of the self-weight of a beam on the vibration under thermal stress [53]. [PERSON] et al. analyzed the effects of the self-weight of a beam on the motion period [54]. These works are totally different from the present investigation into the dynamic effects of weights due to nonlinearities. As reviewed above, all the works on the use of nonlinear energy sinks to reduce the transverse vibrations of beams [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34] do not consider the dynamic effects of the weights of the nonlinear energy sinks, while all the works accounting for weights [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54] do not concern beams with nonlinear energy sinks. Therefore, the effects of the weights of nonlinear energy sink on the vertical transverse vibrations of beams have not been investigated. To address the lack of research in this aspect, the present work investigates the effects of nonlinear energy sink weight on the vertical transverse vibration of a beam with a nonlinear energy sink. The beam's responses, considering nonlinear energy sink weights, are in good agreement with the responses simulated by ANSYS software (version number:2.2.1). The main contribution of this work is to highlight the effect of nonlinear energy sink weight on the vertical transverse vibration of a beam with a nonlinear energy sink. This manuscript is organized as follows. Section 2 derives the differential equations of the beam coupled with a nonlinear energy sink, both considering and neglecting weight. In Section 3, the natural frequencies of the system of the beam coupled with a nonlinear energy sink are obtained. In Section 4, the differential equations are discretized by the Galerkin method to analyze the convergence. The results calculated by the Runge-Kutta method are required to ensure the correctness of harmonic balance method. Section 5 numerically discusses the influence of weight on the beam attached to the nonlinear energy sink. Section 6 utilizes ANSYS software (version number:2.2.1) to validate the numerical solution calculated by the Runge-Kutta method. The approximate analytical solutions calculated by the harmonic balance method are also validated by ANSYS software (version number:2.2.1). Section 7 concludes all the results.
## 3 Results
Within the travelling distance of \(\sim\)20 mm, the S\({}_{0}\) mode and the A\({}_{0}\) mode do not separate in the time domain, and the wave packet (the signal of black line in Figure 5c) exhibits strong dispersion, where A\({}_{0}\) mode has not disappeared completely. As the propagating distance increases (the red, blue and green lines in Figure 5c), the incident A\({}_{0}\) mode with strong dispersion disappears, leaving the non-dispersive S\({}_{0}\) mode and the reflected A\({}_{0}\) mode resulting from mode conversion (marked as 'S\({}_{0}\)-rA\({}_{0}\)'). When the S\({}_{0}\) mode propagates a distance of 40 mm, i.e., when it arrives at the edge of the defect (shown by the purple line in Figure 5c), the amplitude of the wave is increased because of the overlapping of the incident S\({}_{0}\) mode and the converted A\({}_{0}\) mode. This significant increase in wave amplitude from the near-field interactions of the S\({}_{0}\) Lamb waves with the defects can be used to estimate the depth of hidden defects in dry condition [13]. However, due to the small out-of-plane displacement of the S\({}_{0}\) mode and the dispersion of the A\({}_{0}\) mode by the pulsed laser irradiating the surface, the width of the defect is hard to detect using the top generation configuration. On the other hand, by using the lateral generation configuration, the S\({}_{0}\) mode is enhanced, and its non-dispersive characteristic leads to a narrow wave packet in the time domain, meanwhile the A\({}_{0}\) mode is efficiently restrained. Therefore, the width of the notch can be estimated utilizing the lateral generation configuration, i.e., the probe system shown in Figure 4, but with a higher spatial resolution. By reducing the scanning step to 50 \(\upmu\)m per step and focusing the probe laser onto a circular spot with a diameter of \(\sim\)10 \(\upmu\)m on the plate surface, the B-scan image at the defect area is obtained, and the result is shown in Figure 6a, with the corresponding peak-to-peak amplitude of the out-of-plane displacement being shown in Figure 6b. It can be seen that the amplitude decreases at both edges of the notch, the notch width of 0.4 \(\pm\) 0.1 mm can be estimated by the scanning distance between the two troughs in Figure 6b.
# Investigation of Regenerative Braking Performance of Brushless Direct Current Machine Drive System
## 3 Proposed Braking Strategy Based on Critical Duty Ratio Determination
The line-to-output (\(H_{g}(s)\)) and control-to-output (\(H_{c}(s)\)) transfer functions have been obtained to examine the control performance of the specific duty ratio as shown in Equations (7) and (8). \[ \begin{split} H_{g}(s)&=\frac{D_{r}(1+sCR_{c})}{s^{ 2}C(1+\frac{R_{c}}{R_{i}})2L_{s}}+\frac{D_{r}(1+sCR_{c})}{s(C(1+\frac{R_{c}}{R_ {i}}D_{r}(R_{T}-R_{D})))}\\ &+\frac{D_{r}(1+sCR_{c})}{\frac{2L_{s}}{R_{i}})+CR_{c}}+\frac{D_{r }(1+sCR_{c})R_{i}}{D_{r}(R_{T}-R_{D})+2R_{s}+R_{D}+R_{i}}\end{split}  \tag{7}\]  
\[ \begin{split} H_{c}(s)&=\frac{(E_{L}-i_{d}(R_{sw}-R_ {d})-V_{th})(1+sCR_{c})}{s^{2}C(1+\frac{R_{c}}{R_{i}})L_{s}}\\ &+\frac{(E_{L}-i_{d}(R_{T}-R_{D})-V_{th}))(1+sCR_{c})}{s(C(1+ \frac{R_{c}}{R_{i}}D_{r}(R_{sw}-R_{d})+\frac{2L_{s}}{R_{i}}+CR_{c}))}\\ &+\frac{(E_{L}-i_{d}(R_{sw}-R_{d})-V_{th}))(1+sCR_{c})R_{i}}{D_{r }(R_{sw}-R_{d})+2R_{s}+R_{d}+R_{i}}\end{split}  \tag{8}\]  
where \(L_{s}\), \(R_{c}\), \(R_{d}\), and \(C\) are the terminal inductance, the equivalent series resistance of the DC filter capacitor, diode on-resistance, and capacitance, respectively. Moreover, the closed loop controller including the current and speed loops, the parameters are analyzed for the high stability, the maximum efficiency and the useful regenerative braking operation time. The \(D_{r}\) value is determined to be 0.78 and is used as a reference for the experimental studies. This value is assigned as a specific regenerative braking duty ratio at 25 \({}^{\circ}\)C.
## 3 EV Emulator Design  
### Overview  
A block diagram of the complete EV emulator system proposed with the HUT EV charger connection is shown in Figure 1.  
The power electronics system is built up of one AC/DC grid side converter and one DC/DC vehicle converter, whose specifications are listed in Table 2. The two components communicate through an embedded low cost gateway, which is called Energy Box (EBox) [22], via Modbus RS485. The EBox also communicates via Modbus RS485 with the grid analyzer to measure in real time the active and reactive power of the HUT and with the Human-Machine Interface (HMI) via TCP/IP. Furthermore, a CHAdeMO protocol communication via CAN has been implemented in the DC/DC, which allows the emulator to interact with HUT, setting the power limits and the desired current during the test. In order to have galvanic isolation in the whole system, a three-phase transformer \(\lambda\)/\(\Delta\) is connected between the AFE and the electric grid at 400 Vrms and 50 Hz.  
Figure 1: General EVE testbench block diagram, pointing out the main power subsystems of the EVE (blue) and the HUT (red), as well as the communication interfaces defined between them (dark and light green).
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 3 Methodology
### Method of Estimating Critical Latency
#### 3.1.3 Evaluation of Estimated Performance  
The estimated performance of critical latency was evaluated based on scenarios, which were designed for OS-TS collision situations where COLREGs Rule 14 (head-on situation) and COLREGs Rule 15 (crossing situation) could be applied.  
Figure 3 illustrates 4 scenarios designed using 1 OS and 4 TSs-SC1, SC2, SC3, and SC4-as follows: SC1 is a head-on situation with the encountering angle of \(1^{\circ}\), SC2 is a crossing situation with the encountering angle of \(45^{\circ}\), SC3 is a crossing situation with the encountering angle of \(90^{\circ}\), and SC4 is a crossing situation with the encounter angle of \(135^{\circ}\). Here the encountering angle is measured clockwise relative to the fixed heading (\(90^{\circ}\)) of OS. In addition, OS-TS relative distances are given at random to allow a collision to occur. CP  
represents the point of a possible collision, and O and T1-T4 represent the initial positions of the OS and targets, respectively.
## 3 System Model  
In this section, the model of multi-beam satellite communication systems is introduced. On this basis, the connection mode of different satellites and different beams and the multi-dimensional mapping model between packets and radio resources are analyzed and modeled.
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
## 4 Implementation of the V2x Data Dissemination over diSRsVN and Its Results
### Simulation Scenario  
The aim of the simulation scenario is to disseminate vehicle safety messages (query vehicle information from the nearest vehicle) inside the diSRsVN architecture. Two main scenarios are simulated to evaluate the proposed diSRsVN architecture as follows.  
We use three performance metrics: average end-to-end (E2E) delay, packet delivery ratio (PDR), and average throughput. They are defined as follows:  
* E2E delay measures the average time it takes for transmitted packets to reach their respective destination nodes.
* PDR calculates the ratio of successfully delivered packets to total packets transmitted.
* The average throughput allows you to calculate the average rate at which packets are successfully received by the destination node.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline small-cell-RSU & \\ \hline application Type & TraCIDemoRSU11p \\ beacon Interval of application & 1s \\ accident Count on mobility model & 1 \\ \hline Topology discovery parameters & \\ \hline Transmission range of clusters & 500 m \\ Processing time for queries & 10 ms \\ Broadcast of channel information & 10 ms \\ \hline IEEE 802.11 parameters & \\ \hline Transmission range & 500 m \\ Carrier frequency & 5.890 \(\times 10^{3}\) Mhz \\ Data rate & 6 Mbps \\ The number of available channels & 1 CCH and 4 SCH \\ \hline Propagation model & \\ \hline pathloss & two-ray interference \\ fading & Nakagami m = 1.3 \\ General parameters & \\ \hline Number of small-cell-RSU & 10 \\ Number of vehicles & 10–100 \\ Number of small-cell controller & 2 \\ Number of openFlow Controller & 1 \\ Number of openFlow switch & 2 \\ Simulation time limit & 400 s \\ Repetition run simulation & 5 \\ Mobility Model & Freeway \\ simulation area & 5000 m \(\times\) 5000 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main simulation parameters and settings.
## 2 Principles and Methods
### Signal Preprocessing and Feature Extraction
#### 2.1.3 WPES Feature Extraction  
The structural damage information can typically be characterized by the distribution of Wavelet Packet Energy Spectrum (WPES) [19; 20].  
The original signal undergoes a wavelet packet decomposition into \(n\) levels, yielding \(2^{n}\) signal frequency components \(\{Y_{1},Y_{2},\ldots,Y_{2}{}^{n}\}\). Each component \(Y_{i}\) (\(i\) = 1, 2, \(\ldots\), \(2^{n}\)) can be represented as:  
\[Y_{i}=[y_{i,1},y_{i,2},\ldots,y_{i,L}] \tag{5}\]  
In this equation, \(y\) represents the amplitude of the reconstructed signal at the discrete points, \(L\) corresponds to the number of samples in a frame, i.e., the frame length, and \(i\) is the frequency band index. Therefore, the energy \(E_{i}\) of the \(i\)th component \(Y_{i}\) is given by:  
\[E_{i}=\parallel Y_{i}\parallel_{2}^{2}=y_{i,1}^{2}+y_{i,2}^{2}+\cdots+y_{i,L} ^{2} \tag{6}\]  
Subsequently, according to Equation (6), a feature vector can be constructed with each frequency band energy as its element:  
\[E=[E_{1},E_{2},\ldots,E_{2^{n}}] \tag{7}\]  
To better illustrate the energy difference between intact and damaged signal samples, this paper employs a dimensionless energy ratio \(R\) as the feature vector:  
\[R=[R_{1},R_{2},\ldots,R_{2^{n}}] \tag{8}\]  
where the expression for the energy ratio \(R_{i}\) for each frequency band is:  
\[R_{i}=\frac{E_{i}}{\sum_{i=1}^{2^{n}}E_{i}} \tag{9}\]
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 4 Experimental and Analysis  
### Multisim Simulation Experiment  
#### 4.1.1 Power-Amplifier Circuit--Multisim Simulation Experiment  
In the Multisim simulation software version 14.1, the simulation verification of the excitation-signal power-amplification circuit is conducted. The excitation input signal is set to a 7.2 Vp-p, 10 kHz sine wave with a phase offset of 0\({}^{\circ}\). AD8397 ARDZ is a dual-channel operational amplifier. In the Multisim simulation, a +12 V power supply can be applied to the power pin of one of the operational amplifiers to ensure normal operation. The \(V_{REF}\) voltage is set to 6 V. The other component parameters and circuit connections are as shown in Figure 6.  
In Figure 6, XSC1 is a dual-channel oscilloscope used to observe the waveform of the input excitation signal before and after passing through the power-amplification circuit. The simulation results are shown in Figure 7.  
The waveforms, from top to bottom, correspond to CH1 and CH2 represents the original excitation signal and the signal after power amplification, respectively. The peak-to-peak values of the signals are 7.16 V and 7.75 V, with frequencies both close to 10 kHz. From the simulation results, it can be observed that the excitation signal undergoes a slight increase in amplitude after power amplification, while maintaining good sinusoidal characteristics. The phase of the amplified signal is opposite to that of the input waveform. Overall, the results align with the theoretical expectations and meet the design criteria of the decoding circuit for the excitation signal.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### UAVs for Inspection of Overhead Power Lines  
Detection and prevention of faults from power lines is crucial for the availability and reliability of electricity supply. The drawbacks of traditional techniques include high cost, cumbersome deployment and hazardous risks. Therefore, UAV-aided power line distribution and transmission lines inspection as shown in Figure 12 have gained significant interest by researchers. Inspection of power lines also refers to the safety of a power transmission grid. UAV equipped with digital camera to take images of power lines corridors is a convenient approach to support these inspection tasks [110]. UAVs can be used to trace power pylons for damaged bolts, corrosion or rust and lightning strikes. Short-circuiting of these power lines usually occurs due to harsh weather conditions, bush fires and tree falls. In a recent study [111], authors discussed the installation of UAVs in the overhead power lines to identify faults. Both UAVs and climbing robots can be used to locate faults. UAVs can perform these inspection operations at lower cost than helicopters and low risk associated to conventional foot patrol. Among different UAVs, fixed-wing UAVs that can fly faster and higher are mostly preferred for rough inspection and vegetation monitoring. In contrast, multi-rotor UAVs are utilized to get images by hovering in the air at a closer position to the objects. Multi-rotor UAVs are suitable due to high 3D maneuverability. Despite these potentials, use of multi-rotor UAV in a confined or complex environment is challenging for the autonomous mission plan and pilot. In the future, advanced data gathering, sharing and processing algorithms should be investigated for cooperative UAVs network to support reliable, efficient and faster inspections.
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
###### Abstract  
The IEEE 802.15.4 standard is widely recognized as one of the most successful enabling technologies for short range low rate wireless communications and it is used in IoT applications. It covers all the details related to the MAC and PHY layers of the IoT protocol stack. Due to the nature of IoT, the wireless sensor networks are autonomously self-organized networks without infrastructure support. One of the issues in IoT is the network scalability. To address this issue, it is necessary to support the multi-hop topology. The IEEE 802.15.4 network can support a star, peer-to-peer, or cluster-tree topology. One of the IEEE 802.15.4 topologies suited for the high predictability of performance guarantees and energy efficient behavior is a cluster-tree topology where sensor nodes can switch off their transceivers and go into a sleep state to save energy. However, the IEEE 802.15.4 cluster-tree topology may not be able to provide sufficient bandwidth for the increased traffic load and the additional information may not be delivered successfully. The common drawback of the existing approaches is that they do not address the poor bandwidth utilization problem in IEEE 802.15.4 cluster-tree networks, so it is difficult to increase the network performance. Therefore, to solve this problem in this paper we study a relay transmission protocol based on the standard protocol in the IEEE 802.15.4 MAC. In the proposed scheme, the coordinators can relay data frames to their parent devices or their children devices without contention and can provide bandwidth for the increased traffic load or the number of devices. We also evaluate the performance of the proposed scheme through simulation. The simulation results demonstrate that the proposed scheme can improve the reliability, the end-to-end delay, and the energy consumption.  
Internet of Things; IEEE 802.15.4; relay communication; wireless sensor network +
Footnote †: journal: Sensors
## 3 Results
### Inconsistency between Vegetation Greenness and Productivity  
Figure 4 showed that the areas exhibiting significant growth in both greenness and productivity constituted the largest share, exceeding 35% in each agglomeration. Especially in BTH, this proportion was the highest, making up 69.09% of the entire region. In contrast, the proportion of regions where both greenness and productivity changed insignificantly was 34.08% and 23.66% in the PRD and YRD, respectively, while in BTH, this proportion accounted for only 9.75%. Additionally, there were regions where productivity increased significantly but greenness changed insignificantly. This pattern was most evident in BTH and YRD, with proportions of 17.12% and 23.31%, respectively, while in the PRD, it was only 7.37%. Conversely, there were also regions where greenness decreased significantly despite insignificant changes in productivity. This trend was most prominent in the YRD, where it accounted for 8.57% of the total area. Both greenness and productivity significantly decreased in 6.99% of the regions in the YRD and 2.67% in the PRD, while the proportion was 0.78% in BTH. Overall, regions with consistent changes in greenness and productivity accounted for 69.87% of BTH but only 45.65% and 42.93% of the PRD and YRD.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Particular Types of Accelerometers
#### 1.4.3 Strain Gauge Accelerometer  
A strain gauge is a sensor that exhibits a change in electrical resistance in response to applied forces. It effectively converts mechanical quantities, such as force, pressure, tension, and weight, into measurable variations in electrical resistance. This characteristic enables the precise measurement of these mechanical parameters [16; 17].