esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 2 Material and Methods  
The visual-based positioning system proposed in this work allows to locate and maneuver a commercial drone over a transmission grid. As shown in Figure 2, in the proposed approach the UAV acquires visual images of the conductors using a monocular camera and sent each image to a ground station. The system position is estimated and the navigation commands are sent back to the UAV in order to maintain the position respective to the transmission lines.  
1. A commercial UAV is positioned over a transmission grid, controlled by an operator. The distance from the UAV to conductors varies depending on the safety, lighting conditions and camera characteristics, requiring at least 1 m between the conductors and the vehicle.
2. The exteroceptive information is acquired with a monocular camera, which is mounted at bottom of the UAV.
3. To avoid the direct incidence of sunlight, a two-axis hand-held gimbal changes the camera vision point to avoid sensor saturation.
4. The positioning process computes the placement of the camera based on conductors geometric design. In addition, the system is capable of storing a compressed image, with geo-referenced information as a back up and as support to a future inspection work.
5. The current version for the positioning system only performs the flight over three-phase transmission grids with conductors approximating straight lines without interruption. Its operating system is flexible in order to add other features. Transmission lines with other distribution can be detected using Hough transform variations since the system is based on the geometric pattern.
6. A navigation strategy consists of two stages: (i) power-line detection; and (ii) electrical tower detection. Our visual positioning system is focused on the first goal. Therefore, the system is disabled when the UAV is near an electrical tower, changing to manual mode. The maximum speed is 75 \(\frac{\text{km}}{\text{h}}\). However, we limit this velocity to 25 kph to increase the probability of power-lines detection and to reduce blurring effects.
7. The drone has a flight autonomy of 30 min (empirically determined). If the drone detects a low energy level of its batteries, the vehicle selects between two flight modes: land (attempts to bring the UAV straight down) or return to the launch (the UAV navigates from its current position to hover above the home position), depending on the distance to starting point.
8. The drone performs inspection tasks along a length of about 10 km in 1 h intervals (Battery charging time).
9. The proposed drone operates in a dry ambient, ideally at a standard environment of 20 \({}^{\circ}\)C and 50% humidity. However, the aerial platform can also be placed in a rugged ambient, whose temperature is not less than 5 \({}^{\circ}\)C or more than 40 \({}^{\circ}\)C and whose humidity is not more than 80%. The apparatus cannot be directly exposed to rain and it is capable of facing wind gusts of up to 10 \(\frac{\text{km}}{\text{h}}\).
## 4 Data Collection and Correlation Analysis
### Pearson Correlation Analysis  
Before applying the DEA model, we need to verify the suitability of the selected input and output variables. One prerequisite for a DEA model is that the selected input and output should have an isotonic relationship, which means that an increase in input will lead to an increase in output, or at least the same output level.  
Pearson correlation analysis is a statistical approach applicable to verify the isotonic relationship between input and output variables. Equation (4) shows the formula for calculating Pearson correlation coefficient (\(C\)), which can be used to measure the magnitude and direction (\(+\) or \(-\)) of two variables, \(a\) and \(b\).  
\[C=\frac{\sum_{i=1}^{n}(a_{i}-\overline{a})\big{(}b_{i}-\overline{b}\big{)}}{ \sqrt{\sum_{i=1}^{n}((a_{i}-\overline{a})^{2}}\sum_{i=1}^{n}\big{(}\big{(}b_{i} -\overline{b}\big{)}^{2}} \tag{4}\]  
The correlation coefficient \(C\) is within the interval \([-1,1]\). The closer to \(-1\) or \(1\), the higher the correlation between the two variables \(a\) and \(b\). On the contrary, the closer the value is to \(0\), the weaker the correlation between the two variables. If \(C>0\), it indicates that the two variables have a positive correlation; if \(C<0\), it indicates a negative correlation.  
Table 5 shows the Pearson correlation coefficients between input and output variables in the year 2017. As all correlation coefficients between any two variables (X1, X2, X3, and X4) are positive, ranging between 0.5153736 and 0.8775034, it indicates that these input variables are positively related. In addition, it is found that there is a negative correlation between the input variable X3 (renewable energy) and undesirable output variable Y2 (CO\({}_{2}\) emissions per capita), which indicates that more use on renewable energy can lower the CO\({}_{2}\) emissions. As the three input variables, X1, X2, and X4, each have a positive correlation, with C ranging between 0.0364256 and 0.28292, this indicates that an increase in these inputs will increase CO\({}_{2}\) emissions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & & **X1**
## 5 Results and Discussion
### DWFs' Optimization of the Sample  
As described in Section 3.3, two bands containing more transient impact information, \(iwp_{1}^{2}\) and \(iwp_{2}^{2}\), were extracted using the DWFs algorithm. Two data sets were created based on the spectra of the two sub-spectrum, respectively. In order to verify the beneficial effects of the DWFs, this paper also created a 2-D data set based on the complete fast Fourier transform spectrum of the raw 1-D sample. Its frequency band was 0-6400 Hz, which was twice that of \(iwp_{1}^{2}\). Repeated modeling experiments were performed 100 times with 100 epochs, and the results are shown in Figure 19. It can be seen that the effect of the frequency band \(iwp_{2}^{2}\) was obviously less than that of \(iwp_{1}^{2}\) and the full spectrum. Although the bandwidth of this band was narrower, more noise could be filtered out, and the impact characteristics were more prominent in the time domain. However, the impact forms of the cutting edge and the work piece were similar under different degrees of wear. After normalizing the spectrum, only the information retained in this narrow frequency band might be insufficient. \(iwp_{1}^{2}\) had improved performance compared to the full spectrum. This might be due to the elimination of interference from other frequency bands, especially the noise in the high frequency band and the steady state signal in the low frequency band. More notably, \(iwp_{1}^{2}\) was reduced by half compared to the full spectrum calculation, which significantly improved the recognition speed of the model.  
Figure 18: Performance comparison between 1-D CNN and 2-D CNN.
## 5 Results and Discussion
Hence, protective coatings and encapsulation methods are proposed to improve resistance to moisture-related deterioration. Figure 21: The power output of hybrid RF-PSC configurations varies across different levels of RF input power, particularly at the peak power point of the PSC array under irradiation of 1000 W/m\({}^{2}\). In terms of RF propagation, atmospheric conditions such as rain and fog introduce signal attenuation, necessitating frequency diversity. Adaptive filtering techniques will be used to optimise energy reception under varying environmental conditions. Moreover, multiple RF sources in smart city environments introduce potential interference, which will be addressed through strategies such as frequency diversity, spread-spectrum techniques, and adaptive filtering. Long-term system stability will be examined, including material degradation rates and performance retention over extended deployment periods.
# Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection
## 4 Text Similarity Detection
### SimHash Algorithm  
The SimHash [23] algorithm is an efficient method of processing high-dimensional data by converting high-dimensional feature data into low-dimensional hash codes that maintain the approximate similarity of the original spatial data. The key idea of the SimHash algorithm is to reduce the dimensions of large-scale and high-dimensional feature vectors to a fixed-length bit string (usually 64 bits), while keeping the Hamming distance between the bit strings corresponding to similar feature vectors small. In this way, the Hamming distance between two bit strings can be calculated to approximately represent the similarity of the original feature vectors, as shown in Figure 3.  
When performing the SimHash operation, features are first extracted from the text. Features are usually in the form of words or phrases, and the extraction process includes preprocessing, word segmentation, stop word removal, stemming, and other steps. A hash function maps each feature to a fixed-length binary string. This mapping should be homogeneous, i.e., any input has an equal probability of being mapped to any position in the hash space.  
The next step is feature weighting. In this process, the algorithm considers the weight of each feature, which can be derived in several ways; for example, the widely used TF-IDF (term frequency-inverse document frequency) algorithm [24] is often employed to calculate weights. TF-IDF is a common algorithm in text mining that quantifies the importance of a word in a document relative to its frequency in a larger corpus. The 'TF' part measures the frequency of a word in a document, and 'IDF' reduces the weight of words that appear frequently across many documents, thereby ensuring that common words like 'and', 'the', etc., do not unduly influence the analysis. The weight reflects the importance of the feature in the text. The higher the weight, the greater the influence of the feature when generating the SimHash value.  
The algorithm then weighs and aggregates the hash values based on the feature weights. Specifically, the algorithm assigns a weight to each bit of the hash value of each feature, then adds up the weighted hash values of all the features and finally decides whether each bit of the final SimHash value is a 0 or a 1 by comparing the size of the weighted sum of each bit with zero.  
Figure 3: SimHash algorithm diagram.  
Finally, the SimHash algorithm estimates the similarity of the original data by calculating the Hamming distance (i.e., the number of corresponding bits of the binary string that are different) between the two hash values. Since SimHash retains the structure and content information of the original data, and the Hamming distance can better reflect the similarity between the original data.
## 4 Discussion and Conclusions  
In this paper, the effects of additional structural quality, Y-direction and Z-direction stratification, particle material, and filling factor of the particle damper on the damping effect are investigated by experiments. The main conclusions are as follows:  
1. The differences between the resonance frequencies of the primary structure with and without particle dampers are small. For the scheme in which the additional mass (The sum of the mass of the adapter and the particle damper) does not exceed 1000 g and the maximum response of the primary structure can be attenuated by more than 50%, and the first-order frequency of the primary structure decreases most when the A2_WC_0.9 type particle damper is installed, which is 5.57%.
2. Layering the cavity in the direction of the main vibration can improve the efficiency of particle damping. This is because the multi-unit damper can increase the number of collisions between the particles and the wall. But in the direction of a small vibration amplitude, layering the damper does not significantly improve the particle damping efficiency.
3. With the same filling factor, the tungsten carbide powder has the best damping effect. When the filling coefficient and the cavity is kept constant, the damping effect of the particle damper is positively correlated with the particle density.
4. There is an optimal filling coefficient, but the values corresponding to different cavities are slightly different. For the A1 cavity, the optimum filling coefficient for filled tungsten carbide powder, 2 mm lead, and stainless steel particles is 1.05, while for the A2 cavity, the best filling coefficient is 0.9.  
Figure 18: Influence of filling coefficient on the amplitude of amplification coefficient: (**a**) Sinusoidal excitation; (**b**) Random excitation.  
The structural shape of the cavity affects the collision between the particles, which in turn affects the energy consumed in a single cycle. The interaction between particles during motion can be found in reference [23].  
It should be noted that the optimal damper design was determined in this paper from the trial-and-error method. But the Kalman filter and the Taguchi method are more efficient optimization methods for experimental design [25; 26; 27]. In addition, the damper proposed in this paper requires a large installation space and is not suitable for tight structures. For tight structures, multiple small damper combinations are required to achieve vibration damping effect [22]. And the use of a viscoelastic material as a cavity can further increase the damping effect of the damper [28].  
[PERSON] and [PERSON] designed the experiments. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] wrote the paper. [PERSON] revised the paper.  
This research was funded by the National Natural Science Foundation of China, grant number 51475022.  
The authors declare no conflicts of interest.
## 3 Materials and Methods
### Empirical Data and Preprocessing
#### 3.2.2 Data Preprocessing  
Wireless signal data is the basis of the vehicle wireless positioning experiment. To improve the accuracy of the experimental results, the data need to be preprocessed first by extracting and integrating the signal data in the data packet. Using the longitude and latitude of the vehicle at each time in the GPS data, its motion trajectory can be obtained, as shown in Figure 3. The vehicle running track basically follows the road direction. Therefore, the vehicle position positioned by GPS can be regarded as the real driving track of the vehicle because of the high GPS positioning accuracy in the experiment.  
By integrating the three kinds of data with timestamp data, the GPS data, base station data, and vehicle data at each time can be obtained, where the complete data information can be sorted according to the timestamp. A total of 1190 groups of data were extracted from the period of 7:40:54-7:46:13. The base station IP number is 1-11. An experimental motion coordinate system is established for the GPS coordinates of the vehicle and the base station, which is used in the verification of the fusion positioning algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Data Name** & **Data Sample** \\ \hline \multicolumn{3}{c}{S is the packet sent by the} \\
1 & S/R: packets status & base station \\ \multicolumn{3}{c}{R is the data packet received by} \\
2 & Timestamp when the packet was logged & T06:48:18.5156250-08:00 \\
3 & Interface IP addresses of the packet source & 10.198.17.02 \\
4 & Experiment ID of the packet sourcing application & 1 \\
5 & Source-specific sequence number of the packet & 309 \\
6 & Transmission rate (in Kbps) & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vehicle data fields.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Data Name** & **Data Sample** \\ \hline
1 & Timestamp & T06:47:45.4062500-08:00 \\
2 & Upper 32 bits of the hardware timestamp & 471 \\
3 & Lower32 bits of the hardware timestamp & 2566571334 \\
4 & Frequency of the logged packet & 2412 \\
5 & Status of this packet. & 0 means a packet has been received \\
6 & 102 means packet sent & \\
7 & Other values are ignored & \\
8 & RSSI & 9 \\
9 & Transmission rate of the packet & 1000 \\
10 & Packet size & 89 \\
11 & Retry & MGMT\_BEACON F \\
12 & Source MAC address & 00:02:6F:3E:1D:77 \\
13 & Destination MAC address & FF:FF:FF:FF:FF \\
14 & BSSID & 02:03:04:05:06:07 \\
15 & DS field values; FromDS:ToDS & DS:00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Wi-Fi data fields.  
The sorted data fields are presented in Table 5.
## 1 Introduction
[PERSON] conducted experiments on four different materials at mineral filler volume fractions of 5%, 10%, and 25%. The experimental results were compared with predictions from the Hashin model and the generalized self-consistent model. The study found that the predictions aligned more closely with the experimental results at lower mineral filler volume fractions, suggesting that interparticle and physicochemical interactions enhance the performance of asphalt-aggregate mixtures [34]. [PERSON] evaluated and compared 12 existing micromechanics models. Based on the limitations of these models, a new micromechanics model was proposed, incorporating physicochemical interactions. The results showed that this new model provides accurate predictions for asphalt mastic with mineral filler volume fractions of up to 40% [35]. In recent years, micromechanics models have been increasingly applied to calculate the thickness of adsorbed asphalt films. [PERSON] et al. proposed a micromechanics-based method for determining the thickness of adsorbed asphalt films while investigating the generalized self-consistent (GSC) model's ability to predict the mechanical properties of asphalt mastic. Using this method, the calculated thickness ranged from 0.02 to 0.1 \(\upmu\)m [33]. [PERSON] et al. employed the Hashin model to calculate the thickness of the adsorbed asphalt films and evaluated the physicochemical interactions between asphalt and mineral filler based on the derived film thickness [36; 37]. Utilizing micromechanics methods to calculate the thickness of adsorbed asphalt films effectively mitigates the influence of experimental and measurement errors, thereby improving the accuracy of the results. However, existing studies have predominantly relied on a single micromechanics model for these calculations. Further research is needed to assess the applicability of various micromechanics models for calculating the thickness of adsorbed asphalt films and to analyze the factors that influence this thickness in greater detail. The research objectives of this paper are as follows:  
1. To calculate the thickness of the adsorbed asphalt film using the Hashin, Mori-Tanaka (MT), and generalized self-consistent (GSC) models, and to perform a comparative analysis of the values obtained from these models to identify the most appropriate one. 2. To determine the critical volume fraction of mineral fillers using nonlinear fitting, and to refine the interaction evaluation indices based on micromechanical models, removing their dependence on the volume fraction of mineral fillers. 3. To analyze the influence of the chemical composition, testing frequency, and temperature of mineral fillers on the interaction between asphalt and mineral fillers through a micromechanical approach.
## 5 Evaluation without JMH  
### Java Persistence API  
We will summarize the results of the previous research to make it clear where the new steps started from. Figures 4-7 provide the results of the execution times without JMH, using Java Persistence API as a framework, and different RDBMSs, as conducted and published in [6; 7; 8]. Then, the research will focus on the improvements of using JMH with one warm-up iteration and one measurement iteration (Section 6) and the bottlenecks investigation (Section 7).  
The combination MySQL-JPA needs less than 20 min for all four CRUD operations working with 500 records. The READ operation is the fastest, CREATE is a little slower than UPDATE, while DELETE doubles the execution time with the doubling of the records, exhibiting a linear behavior.  
For Oracle, the DELETE operation requires almost 2 h on 500,000 entries. The CREATE operation is about 10 times quicker, but still much slower than for MySQL. However, the UPDATE operation behaves better than for MySQL.  
Microsoft SQL Server has clearly the best execution times for the UPDATE operation. This advantage is covered by a greater disadvantage: the DELETE operation runs for 4.5 h for 500,000 entries which is a very bad performance when looking at the MySQL test. READ has the best time for this RDBMS for over 100,000 entries, while CREATE fits in the average time so far in the tests.  
PostgreSQL is a very good choice when working with a reduced number of records (up to 5000). The performances, however, decrease for a larger number of entries, with remarkably bad behavior for the DELETE operation at 500,000 entries.  
Figure 4: Create execution times using JPA without warm-up.  
Figure 5: Read execution times using JPA without warm-up.
## 2 G-LiHT Design and Instrumentation
### Profiling LiDAR  
G-LiHT's profiling LiDAR is an LD321-A40 (Riegl USA, Orlando, FL, USA), multi-purpose laser distance meter that is similar to the LD90-3800-VHS used in the Portable Airborne Laser System (PALS) [16]. The LD321-A40 provides a continuous profile of canopy height measurements, which, from a regional sampling perspective, provides data similar to other space-based profilers that have been flown (Ice, Cloud, and Land Elevation Satellite Geoscience Laser Altimeter System, ICESat GLAS), proposed for flight (Deformation, Ecosystem Structure and Dynamics of Ice, DESDynI) or which may be flown in the near future (ICESat-2 ATLAS). In addition, profiling data is important for studying horizontal landscape patch structure [13] and ensuring continuity between ALS and PALS datasets that have been collected worldwide. Real-time digital echo signal processing with the LD321-A40 enables precise distance measurement for complex multi-target situations, resolving up to five target distances per pulse. Distance measurements parallel to the flight line are used to continuously measure vegetation height and structure along a sampling transect. A Class 1M laser  
Figure 4: Canopy height model from (**a**) small footprint (10 cm) G-LiHT LiDAR during June 2012; and (**b**) large footprint (25 m) Land, Vegetation and Ice Sensor (LVIS) LiDAR during August 2009 [17], for a commercial forest near Howland, ME, USA (45.2220\({}^{\circ}\)N 68.7423\({}^{\circ}\)W). Discrete returns from small footprint LiDAR are able to detect small gaps and characterize fine-scale disturbance (_i.e._, strip harvesting), which are challenging to deconvolve from large footprint LiDAR waveforms.  
diode emits a 905 nm beam with a divergence of 1.5 mrad to produce a 50 cm diameter footprint at the nominal operating altitude of 335 m.