esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of a Wind Tunnel Propeller Dynamometer for Group 2 Unmanned Aircraft
###### Abstract
However, BEMT models are subject to limitations, particularly at low Reynolds numbers and low advance ratios [1]. Researchers have demonstrated the ability to accurately predict propeller performance using computational fluid dynamics (CFD), even in difficult-to-resolve flow conditions [2; 3]. Although such examples commendably replicate performance characteristics based on available data, CFD practitioners still require validation of their modeling results. Besides its use as a validation tool, experimental propeller characterization remains a viable option, especially for the UAS community, due to the smaller wind tunnel facility requirements compared to full-scale aircraft propeller testing. There is an ever-growing body of knowledge from wind tunnel experiments using different configurations to assess propeller performance; however, there does not appear to be a unifying method to ensure that the different configurations are valid. [PERSON] et al., 2022, studied the aerodynamic performance of propellers with various pitch in a wind tunnel for electric propulsion applications [4]. [PERSON] et al., 2020, conducted experimental tests of variable pitch propellers for UAVs [5], the study involved a propeller of 16 inches in diameter and various pitch. [PERSON] et al. designed and built a measuring station that operated similarly to a propeller dynamometer. [PERSON] et al., 2020, developed a test bench for measuring propeller aerodynamic performance and electrical parameters; this involved using measurements of thrust, torque, and electric power to validate models used for preliminary designs of UAVs [6]. [PERSON] and [PERSON], 2019, developed a small propeller test bench system; this study involved the use of a rig with loads cells to measure thrust and torque for small propellers (10 inches in diameter) [3]. Experimental measurements were compared to results obtained from CFD and BEMT [3]. These studies [3; 4; 5; 6; 7; 8] have formed a basis of knowledge useful to Group 2 UAS; however, they do not specifically address a method for validating a wind tunnel dynamometer, which is essential for credible experimental results. There are many potential sources of experimental artifacts that can affect data and yet not be manifest from an uncertainty analysis. For example, the presence of fluid-structure interaction between the propeller, motor, instrumentation and support structure can influence results in a way that does not effect bias (systematic) or precision (random) error. This current paper proposes a novel method to be adopted as common practice for validating such wind tunnel dynamometers.
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 2 Research Approach and Numerical Simulation
### Load Application  
For simulating the traffic load, a single-axle dual-wheel assembly with a gross weight of 80 kN is utilized (according to IRC 37:2018 [17]). While the actual tyre imprints are semi-elliptical, circular tyre imprints are adopted in this analysis for simplicity (refer to Figure 1). A radius of 107 mm is chosen for the tyre imprint, and the horizontal gap between the two wheels is set at 343 mm [18]. Typically, a single-axle dual-wheel configuration consists of two pairs of wheels, totalling four wheels, resulting in a 20 kN load applied to each wheel. Due to the symmetry of the pavement section and the dual-wheel assembly, only half of the pavement is analysed in this study. The model utilizes repeated loading to simulate traffic conditions, with a total of 20,000 load repetitions at a frequency of 10 Hz, meaning the load repeats 10 times per second. The pressure applied on each tyre is 0.56 MPa.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 3 Experiment Results and Analysis
### Effectiveness Evaluation of Proposed Method  
The forecasting accuracy of the whole proposed method is compared with the proposed method removing CEEMDAN (Prophet-ARBiLSTM), the proposed method removing Prophet (CEEMDAN-ARBiLSTM), and the proposed method removing ARBiLSTM (Prophet-CEEMDAN-Dense), validating the effectiveness of the Prophet-CEEMDAN-ARBiLSTM model. Figure 8 illustrates the training losses of these methods. Compared to Prophet-ARBiLSTM, CEEMDAN-ARBiLSTM, and Prophet-CEEMDAN-Dense, the Prophet-CEEMDAN-ARBiLSTM model demonstrates a significant reduction in training loss. It indicates that the forecasted model incorporating Prophet, CEEMDAN, and ARBiLSTM indeed enhances forecasting performance significantly.  
This study further compares the next 24 h forecasting value of different modules in Prophet-CEEMDAN-ARBiLSTM on the test set, as presented in Table 1. Comparing the data from rows 1 and 4, it is evident that the ARBiLSTM block significantly contributes to the forecasting performance. The MAPE, MAE, and RMSE exhibit reductions of 3.83%, 578.11, and 760.82, respectively. The enhanced performance can be attributed to the residual connections and BiLSTM. Through these connections, temporal information is directly propagated to the subsequent BiLSTM layer, enabling the extraction of temporal features from adjacent and preceding layers, thereby promoting performance. Moreover, comparing the data from rows 3 and 4, it is apparent that the Prophet algorithm demonstrates notable performance in reducing forecasting errors, with MAPE, MAE, and RMSE decreasing by 0.71%, 112.35, and 143.57, respectively. It indicates that the Prophet algorithm can learn the periodic and trend information, thereby enhancing the model generalization ability. Finally, the comparison of rows 2 and 4 demonstrates that the utilization of the CEEMDAN algorithm can result in the reduction in the forecasting errors, with MAPE, MAE, and RMSE decreasing by 1.34%, 216.28, and 324.93, respectively.
## 2 Planar Deformation Measurement Methods in Masonry Specimens
### Proposed Optical Approach
#### 2.2.2 Measurement System  
The implemented optical measuring system for the masonry specimen testing setup is presented in Figure 6. In this figure, the main elements are numbered and described as follows:  
1. Digital camera (brand Allied Vision Technologies, model Prosilica GX1050, Stadtroda, Germany), which includes the image sensor (CCD--charge-coupled device type, \(1024\times 1024\) pixels, \(3.6\) mm x \(4.8\) mm dimension, \(5.5\)\(\upmu\)m squared pixel, acquisition frequency of 100 images per second, 14 bits radiometric resolution for gray-level images, exposure time between 10 \(\upmu\)s and 26.8 s, GigE data communication and \"mono8\" image format), and an optical lens (with variable focal distance between 8 mm and 48 mm and C-type connection);
2. Telemetry device (brand Videotec, model DTMRX224, Schio, Italy) for remote adjustment of the camera's focal distance, aperture, and lens focus, supported by a LabVIEW(tm) (National Instruments, Austin, Texas, USA) computational routine;
3. Signal generator (LNECs Scientific Instrumentation Centre, Lisbon, Portugal) for the synchronization of additional cameras and remaining instrumentation of the tested specimens (50% duty-cycle, frequency between 0.1 Hz and 114 Hz and variable frequency increments of 0.1 Hz and 1 Hz);
4. AVT Vimba computational application (version 1.3.0) for use with Allied Vision Technologies cameras, for image acquisition configuration, visualization, and image record purposes;
5. Measurement referential (LNEC's Scientific Instrumentation Centre, Lisbon, Portugal), with eight reference targets (circular geometrical shape and 4 mm diameter, with known world coordinates), with a white color in order to have a high contrast relative to the remaining black surface of the referential;  
Figure 5: Functional diagram of the quantities and mathematical models related to the proposed optical approach.  
Monitoring targets (LNEC's Scientific Instrumentation Centre, Lisbon, Portugal), with unknown world coordinates, applied to the tested specimen, with the dimension and shape similar to the reference targets in the measurement referential.
## 2 Materials and Experiments
### Microstructure Examination  
As-welded and post-creep test microstructures were characterized. Transmission electron microscopy (TEM) analyses were performed at the different locations of the crept specimen, namely, the fusion zone (FZ) weld metal, heat-affected zone (HAZ) adjacent to the fusion boundary, and the base metal (BM) close to the fracture surface in the gauge section, to analyze the plastic deformation behavior and phase evolutions in each region. The microstructure of the threaded end section was also analyzed. This section experienced negligible deformation but had the same thermal exposure history as the gauge section, and their comparison would help distinguish the creep deformation mechanism from the high-temperature ageing effect.  
The cut slices from the specimens were first mechanically ground to a thickness of 0.2 mm, then electropolishing was conducted at 17 V DC and \(-\)30 \({}^{\circ}\)C with an electrolyte composed of 95% methanol and 5% perchloric acid. The electron-transparent foils were examined using FEI OSIRIS scanning TEM (STEM), FEI, Hillsboro, OR, US, operated at 200 kV. A nano-beam electron diffraction (NBED) technique was used to analyze the crystal structure of precipitates. Local chemical compositions were investigated by ChemiSTEM energy-dispersive X-ray spectroscopy (EDS) using the Cliff-Lorimer quantification approach implemented within the Brucker ESPRIT software, Version 1.9. The fracture surface was examined with Hitachi scanning electron microscopy (SEM), Hitachi, Marunouchi, Chiyoda-ku, Tokyo, Japan.
## 3 Research Status of the Multipath Error Data Processing Algorithm
### Multipath Error Mitigation Method Based on the Function Model
#### 3.2.1 Multipath Error Mitigation Method Based on the Coordinate Domain
[52] combined the advantages of the empirical wavelet transform (EWT) algorithm in decomposing complex signals with the diurnal repetition characteristics of GNSS multipath errors, and proposed a new method for extracting and correcting multipath errors based on EWT. [PERSON] et al. [53] used the height of the high-speed rail track as a constraint to solve GPS data, and then analyzed the coordinate sequence with wavelet, which weakened the multipath error of rail train positioning in a dynamic environment. [PERSON] et al. [54] compared and analyzed the ability of two least-squares harmonic estimation methods, unit harmonic estimation and multi-harmonic estimation, to extract multipath errors in the coordinate domain. [PERSON] et al. [55] proposed an adaptive fast independent component analysis (AF-ICA) method,which can effectively separate and weaken multipath signals when applied to GNSS deformation monitoring. The algorithm flow of the sidereal filtering method based on the coordinate domain to weaken the multipath error is shown in Figure 2. Firstly, the GNSS baseline monitoring data of the first period is solved to obtain the coordinate solution of the corresponding station. The first-period data are pre-processed, and BDS MEO satellites are not considered in the case of GPS/BDS fusion positioning. Then, filter processing, such as wavelet analysis, is performed to extract the low-frequency components of the data, so as to obtain the periodic error sequence related to the multipath effect. Then, the calculated average repeat period is used to obtain the multipath error correction model based on SF. This model is used to correct the coordinate data of the second period epoch by epoch, and finally, the coordinates after multipath error correction are obtained. Figure 2: Flowchart of the SF algorithm based on the coordinate domain. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Orbit Type** & **Repeat Period** \\ \hline GPS & MEO & 1 day \\  & GEO & 1 day \\ BDS & IGGSO & 1 day \\  & MEO & 7 days \\ GLONASS & MEO & 8 days \\ Galileo & MEO & 10 days \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GPS/BDS/GLONASS/Galileo Repeat period.
## 2 Methodology  
### Measurement System  
The channel sounder implemented at 700 MHz is shown in Figure 1. This channel sounder consists of an Hewlett HP8648C signal generator in the Tx vehicle transmitting an unmodulated carrier at 700 MHz and a Hewlett Packard HP8590L spectrum analyzer (SA) in the Rx vehicle. A SPAN of zero was selected in the SA to measure received power on 401-point traces. The antennas used are monopoles with a horizontal plane gain of approximately \(-5.43\) dB. For transmission, a power amplifier was used that allowed transmission with an equivalent isotropic radiated power (EIRP) of 26.3 dBm. For reception, an amplifier with a gain of 32.75 dB was used.  
The channel sounder implemented at 5.9 GHz is shown in Figure 2. This channel sounder consists of a Hewlett Packard HP83623A signal generator in the Tx vehicle, which transmits an unmodulated carrier at 5.9 GHz, and the Rohde & Schwartz ZVA24 vector network analyzer (VNA) in the Rx vehicle. The VNA was used in power meter mode, directly measuring the \(\mathrm{b}_{2}\) parameter in traces of 5000 points. The antennas used in Tx/Rx are monopoles at \(\lambda/4\) with a gain in the horizontal plane of about \(-2.56\) dB and a scattering parameter \(S_{11}\) lower than \(-22\) dB. A power amplifier was used for transmission, allowing transmission with an EIRP of 23.8 dBm. For reception, two amplifiers were used in series with a total gain of 68.12 dB.  
The vehicles were equipped with global position systems (GPS) receivers controlled by laptops to obtain information on the time of measurement, relative speed, and Tx-Rx separation distance. All laptops were synchronized in time to correlate the measurements taken by the VNA with the information provided by the GPS receivers.  
The vehicles used for the measurements were a Renault (Tx) (Figure 3a) and a Peugeot (Rx) (Figure 3b). The antennas were mounted on the roof of the vehicles at a height of approximately 1.41 m and 1.45 m above the ground for Tx and Rx, respectively. Part of the on-board equipment is also shown in Figure 3. In each of the vehicles, 75 Ah batteries and 12 V DC to 220 V AC inverters were used as the power supply system, allowing an autonomous time of approximately 90 min.  
Figure 1: Channel sounder at 700 MHz.  
Figure 2: Channel sounder at 5.9 GHz.
## 4 Qualitative Comparison with Existing Surveys on Detection Approaches of DDoS Attacks on CC  
Several existing review papers discuss DDoS attack detection techniques. This section provides a qualitative comparison between our survey with the existing ones to highlight its originality. Table 3 lists the metrics used for qualitative comparison, namely: (i) taxonomy of attacks on CC; (ii) taxonomy of DDoS attacks on CC; (iii) taxonomy of DDoS attacks detection approaches, and (iv) parameters for determining DDoS attacks.  
We proposed these metrics after thoroughly reviewing many existing DDoS detection techniques. Such a comparison is necessary to comprehend the fundamental aspects surrounding DDoS attacks on CC in order to propose a more efficient detection method. This survey is benchmarked with three existing reviews [13; 39; 40].  
This survey paper accomplishes the basic objective of gathering work from various research papers. It introduces the existing research on CC security broadly and quickly. Since it is challenging and time-consuming to comprehend the entire research area, this survey attempts to assist the effort by introducing a thorough rundown of the state-of-the-art literature in one place.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Other Survey Works**} \\
**Scope of Work** & **This Work** & \multicolumn{4}{c}{**(A Brief Discussion Only)**} \\  & & [13] & [39] & [40] \\ \hline \multicolumn{5}{c}{Taxonomy of Attacks on CC} \\ \hline Suffocative & ✓ & ✗ & ✗ & ✗ \\ Protocol & ✓ & ✗ & ✗ & ✗ \\ Application Layer & ✓ & ✗ & ✗ & ✗ \\ Economically & ✓ & ✗ & ✗ & ✗ \\ Permanent Economic & ✓ & ✗ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Taxonomy of DDoS Attacks on CC} \\ \hline SYN Floods & ✓ & ✓ & ✓ & ✗ \\ UDP Floods & ✓ & ✓ & ✗ & ✗ \\ Ping of Death & ✓ & ✓ & ✓ & ✗ \\ ICMP Flood & ✓ & ✓ & ✗ & ✗ \\ HTTP Flood & ✓ & ✓ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Taxonomy of DDoS Attacks Detection Approaches} \\ \hline Signature Based (Used for Recognizing the Network Attacks) & ✓ & ✗ & ✗ \\ Anomaly-based (Used for Recognizing the Network Attacks) & ✓ & ✗ & ✗ \\ Hybrid-based (Used for Recognizing the Network Attacks) & ✓ & ✗ & ✗ \\ Entropy-based (Deployed in Application Layer) & ✓ & ✗ & ✗ & ✗ \\ Filtering Tree (Deployed in Application Layer) & ✓ & ✗ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Parameters for Determining DDoS Attacks Solution} \\ \hline The real-time Response Detection mechanism & ✗ & ✓ & ✗ & ✓ \\ Over-Under Mitigate & ✗ & ✓ & ✗ & ✓ \\ Reporting & ✗ & ✓ & ✗ & ✓ \\ Performance Degradation & ✗ & ✓ & ✗ & ✓ \\ Ability to Auto-scale & ✗ & ✓ & ✗ & ✓ \\ Throughput & ✗ & ✓ & ✗ & ✓ \\ Request Response Time & ✗ & ✓ & ✗ & ✓ \\ Zero-Day Attack Detection Ability & ✗ & ✓ & ✗ & ✓ \\ Efficient & ✓ & ✗ & ✗ & ✗ \\ Accurate & ✓ & ✓ & ✗ & ✗ \\ Lightweight & ✓ & ✗ & ✗ & ✗ \\ Easy & ✓ & ✗ & ✗ & ✗ \\ Functional & ✓ & ✗ & ✗ & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Qualitative comparison between this work and existing surveys.
## 4 Discussion
After aligning the three-dimensional occlusal surface and the T-Scan registration, the resulting contacts are projected onto the patient's occlusal surfaces, producing occlusal forces over time. The results received with this procedure demonstrate the relevance of integrating different tools and software and the complete integration of this procedure into a dental digital workflow [25]. Classical occlusal analysis using articulation paper only provides some necessary information to achieve balanced occlusal contacts. Intraoral scanners, on the other hand, can provide similar information for clinical examinations. Due to the possibility of multiple magnifications and views from different angles (including from inside the mouth and the throat), the analysis is much more accurate and thorough. Digital T-Scan analysis can also provide additional valuable information, including force intensity, the moment of first contact, and localization of severe traumatic contact, which can be used for analysis and treatment [28; 29]. According to [PERSON] et al., intraoral scanners appear reliable in intermaximillary occlusal contact registration compared to intra- and extraoral digital scanners and T-Scan III system [27]. Other studies comparing intraoral scanning and T-Scan in natural dentition confirm our results. Their conclusion is that T-Scan is a reliable method for measuring occlusal contact area but 3D surface scans are not. Occlusal registration shows high validity [29]. Intraoral scanning can be a great method of checking occlusion in general. The T-Scan methodology is only aimed at determining occlusion. In addition to diagnostics, it can also be used for treatment by establishing preliminary contacts and overloading certain areas, which is especially important in the integration of implants, treatment of bruxism, and TMJ diseases [53; 54; 55].
## 4 LeSSA--Implementation and Technical Details
### Linguistic Rules
#### 4.2.4 Initial Training Set Selection for Self-Training, Active Learning and Co-Training  
In semi-supervised learning, the selection of an initial training set for first classifier learning is very important. This initial training set may affect the performance of semi-supervised learning. In previous studies on semi-supervised learning, the initial training set is usually induced at random. However, due to the complexity of unstructured unlabeled data distribution, it is hard to say that the randomly selected subset for initial training set will be the most representative samples to the unstructured unlabeled data. There have been many works on how to select the initial training set such as employing clustering and sentiment lexicons [14; 49; 50]. Motivated by the method proposed in [40], sampling by clustering (SBC) scheme is adopted for initial training set generation. In this scheme, the unlabeled textual data is divided into known number of k clusters utilizing k-means clustering algorithm. The k-means algorithm groups the unlabeled examples into k-clusters of similar ones. In order to obtain the most representative initial training set, first we cluster the documents into k- clusters, and then, a single document from each cluster closest to the respective centroids based on cosine similarity is selected, which is considered the most representative sample from each cluster. The selected documents are annotated, and an initial classifier is trained on them to predict the labels for unlabeled data. From the unlabeled data the most reliable examples with their predicted labels are selected and added into the initial training set based on some strategy (high confidence, high uncertainty).
## 3 Introducing the Proposed System  
As shown in Figure 1, the multi-agent combination, in which each agent performs its tasks to achieve the overall goal of the system, is called MCS. Generally, MCS is divided into three layers: upstream network, MG, and field, as shown in Figure 2. These three layers consist of eight agents. The agents are the upstream network, micro-grid, thermal, hydrogen, RB unit, renewable, storage, and load collector. The upstream grid agent is located in the first layer, which includes the natural gas grid and the electricity grid. This agent is used as an additional resource in case of a lack of energy production.  
Figure 1: Structure of the proposed MEMG.  
The micro-grid agent is located in the second layer, which is responsible for coordinating the production and consumption of electrical and thermal energy. This task is performed under the optimal performance of agents while observing the constraints.  
The other six agents associated with the production or consumption of electrical and thermal energy and hydrogen are located in the field layer. The overall structure of the proposed method of this study is shown in Figure 3. As shown in Figure 3, in the first step, we select the data related to wind speed and energy demand, and apply them to the LSTM block as input data. In the second step, using the recursive neural network (LSTM) method, we predict the diagrams related to electrical energy data of the wind turbine output, energy demand, and energy price. In the third step, we use a mixed-integer linear programming method and optimize the total cost function, while meeting the existing constraints. This step is carried out according to the modeling of MEMG agents, which is discussed in the next section. Moreover, uncertainty data are modeled with the LSTM block. In the fourth step, the optimal performance of each agent is determined, while minimizing the objective function.  
Figure 3: The overall structure of the proposed method.  
Figure 2: Architecture of the MCS and date exchange.