esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Purpose and Applications of Image-Based Structural Health Monitoring
#### 4.1.3 To Automate and Increase Efficiency  
According to [59], the vision-based technique is quickly gaining traction as the most effective structural assessment and monitoring tool. Noncontact bend measuring, steel corrosion identification, and spalling detection, are examples of recent developments in vision-based inspection and surveillance. The vision-based strategy, on the other hand, has significant disadvantages. In the actual world, it is difficult to create an algorithm that can protect against all unexpected events. DL has emerged as one of the most promising solutions to this challenge in recent years. Deep learning refers to machine learning approaches that use hidden layers in ANN to increase performance. It has demonstrated exceptional performance, particularly in object identification, natural language processing, advertising, and biology [59]. Deep learning has been used for the fields mentioned above and other engineering problems. For instance, ref. [60] developed a traffic surveillance system that analyses aerial data to follow cars and their movements using deep learning and a SURF-based technique. Although CV-based algorithms have been designed to beat qualified human resources in graphic assessment and to detect structural impairment in photos remotely, most systems detect only specific types of damage, such as concrete or steel fissures. As [6] suggests, the usage of R-CNN, a sort of machine learning modality utilized for technological vision tasking, is primarily object detection. In their methodology, the faster R-CNN approach is employed for the quasi-real-time image and video processing to detect and locate forms of damage. The reasons and functions of image-based SHM also cover the improvement of infrastructure maintenance practices' efficiency and safety, by assessing the cracks of reinforced concrete structures using stereo cameras [61].
## 2 Pass-By Noise Annoyance Evaluation Experiment
### Results
All these weightings are also in line with the feedback of the panel members. Figure 3:0  
where AI is the \"annoyance index\", N5 is the percentile loudness in some (according to [39]), T5 is the percentile tonality in tu (according to [42]), R is the roughness in asper (according to [43]), and FS is the fluctuation strength in vacil (according to [44]). A regression analysis between the developed annoyance index and perceived annoyance ratings resulted in a coefficient of determination of R\({}^{2}\) = 0.91. Figure 6 shows the results of regression analysis between the annoyance index and annoyance ratings. The relationship between the annoyance index (AI) and annoyance ratings (AR) was determined by linear fitting and can be described as:  
Figure 3:1  
[PERSON] et.al. proposed the Total Energy of Tonal Components (TETC) Index, which is presented in Equation (3), to characterize the high-frequency content due to the squeal noise of single tramway pass-by sounds [45]. [PERSON] et al. showed that the TETC Index can characterize the breaking noise of urban road single-vehicle pass-by noises and annoyance-relevant sensation of dull/shrill due to single pass-by noises very well [27]. The driving condition and types of vehicles of this study differ from these investigations [27; 45]. However, it is interesting to analyze the suitability of the TETC Index for the characterization of the perceived annoyance caused by the pass-by noise from internal combustion engine, electric, and hybrid passenger cars in this study. Figure 3:2  
where TETC is the Total Energy of Tonal Components within critical bands from 16 to 24 barks and Lz is the maximal (across time) level of the tonal components as a function of the critical band rate z. As the sharpness results are not suitable to characterize the pass-by noise evaluation results in this study, the analysis of the TETC index can be useful. The TETC Index values of the vehicle sounds and the annoyance ratings are shown in Figure 7a and the sharpness values of the vehicle sounds and the annoyance ratings are shown in Figure 7b (sharpness model: [PERSON] [39]). The results show that the TETC Index has a clearly better correlation with the annoyance evaluation results than sharpness. According to these results, the consideration of the TETC index at the annoyance index (Equation (1)) makes sense. However, the participation of the TETC index to the annoyance index, which was proposed in this study, did not cause better estimation of the annoyance index. The relationship between the index (AI = N5 + 60 \(\times\) T5 + 2 \(\times\) R + 50 \(\times\) FS + TETC) and the annoyance ratings are shown in Figure 3:3 The regression resulted in a coefficient of determination of R\({}^{2}\) = 0.84. Taking this result into account, it was decided to propose the annoyance index from the Equation (1) to characterize the perceptual annoyance ratings of the car pass-by noises. The possible reason why TETC cannot strongly support the annoyance index, which is proposed in this study, will be discussed in the next section.
# Visual Ranging Based on Object Detection Bounding Box Optimization
## 3 Methods
### YOLOv5 Object Detection
#### 3.1.2 Data Set Preparation  
The experiment employed a remote-control car as the detection object and used the MER-500-7 UC camera with a resolution of 2592 pixels \(\times\) 1944 pixels to collect 150 pictures as the initial samples for the remote-control car dataset. In general, larger datasets can lead to improved performance of deep learning models [30; 31]. Data augmentation is a way to improve network performance by expanding the number of samples [32]. Common methods of data augmentation include flipping, rotation, translation, noise addition, color space transformation, brightness change, and so on [33].  
The data augmentation technique is used to increase the number of samples in order to improve network performance, allowing the network to better learn features and create an object detection model. In this study, the sample count was increased by flipping, adding noise, and changing brightness (as shown in Figure 2). Ultimately, we obtained 750 samples, which were annotated using the Labellmg tool. The samples were then divided into training and testing sets, with 525 images in the training set and 225 images in the testing set. The specific experimental environment and configurations are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Parameter Settings** \\ \hline Camera model & MER-500-7 UC \\ Image resolution (pixel) & 2592 \(\times\) 1944 \\ Number of dataset images & 750 \\ Number of training set images & 525 \\ Number of testing set images & 225 \\ Image annotation tool & Labellmg \\ Network model & YOLOv5s \\ CPU & AMD EPYC 7601 \\ GPU & 3070-8G \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment and configurations.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 4 Measurement Scenarios and Data Preparation
### Data Collection Process
#### 4.2.1 Labeling the Measured Data and Dealing with the Class Imbalance Case  
The class labels (LOS, NLOS, and MP) were manually annotated in the data preprocessing phase after the measurement campaign. During the data measurement process, a block of observations for each trial regarding the three categories (LOS, NLOS, MP) was carried out and saved individually into the PC. The block referred to a collection of data belonging to the same class that was saved separately in the computer as a single file. For instance, we collected the random movement data in Corridor 2 (Figure 3) for 5 min using a data update rate of the system as 20 Hz. At the same time, we made sure that there were no obstacles between the two transceivers, and the antenna was held still at 90\({}^{\circ}\) during the process. Then, we annotated the block of this whole measured data as an MP class to be used in our evaluation. In this way, the whole block of that data in each evaluated scenario could easily be labeled as an LOS, NLOS, or MP.  
The initial UWB dataset achieved from the measurement was imbalanced for the three demanded classes (LOS, NLOS, and MP), which is a typical phenomenon in data collection. Class imbalance refers to a scenario where the number of observations in each class is not the same in the measurement.  
Figure 3: Illustration of the scenarios where training and test data were collected for evaluation: (**a**) A sketch of the building where the experimental data for training and test were collected. (**b**) Training data were collected for LOS, NLOS (including human blocking) and MP conditions in a laboratory, a large hall, and a corridor (blue color in (**a**)). (**c**) Similar to (**b**), test data were collected in a different room (including different types of furniture and NLOS human blocking) and a different corridor (red in (**a**)).  
In other words, the number of samples in one class or more classes is significantly lower or higher than those belonging to the other classes. There are several techniques to deal with the imbalanced data in classification problems including resampling techniques and algorithms [42].  
We chose a random undersampling technique [42] in our evaluation to balance the three mentioned classes equally. This ensured that no artificial data points were created outside of the measured experimental data. The undersampling was performed by setting the class belonging to the smallest number of observations as a base class. Then, the classes belonging to higher samples of observation were reduced to balance with the total number of the base class by randomly selecting their elements.
# Research and Progress on Truing and Sharpening Process of Diamond Abrasive Grinding Tools
## 3 Dressing Method of Diamond Abrasive Grinding Tools  
### Truing Technology of Diamond Abrasive Grinding Tools  
Truing technology of diamond abrasive grinding tools refers to the process of micro-dressing diamond abrasive grains to make their tips finely broken and obtain appropriate shape accuracy to obtain the grinding edge. At present, the truing technology of diamond abrasive grinding tools mainly includes the diamond pen truing method, rolling method, grinding method, EDM truing method, and the laser truing method.
# Development of a Motorized Hospital Bed with Swerve Drive Modules for Holonomic Mobility
## 3 Results and Discussion  
### Fabrication of the Prototype and PID Tuning of the Propulsion Motor  
The results of the fabricated prototype of the swerve drive module are shown in Figure 3a. In addition, Figure 3b shows a photo of the whole system when four modules were installed on the frame of a commercial hospital bed. The forward direction is shown by the arrow, and each motor is numbered according to the photo for the performance evaluation.  
Figure 3: (**a**) Front view and (**b**) side view of fabricated prototype of the swerve drive module and (**c**) Prototype of the motorized hospital bed with numbered motors.  
The DC motor for propulsion has an encoder that generates seven pulses per revolution. The motor and encoder were connected via a gearbox with a gearbox with a gearbox (motor):19.2 (encoder). To obtain the revolution of the motor, the quadrature encoder method was utilized. A single revolution corresponded to 537.6 pulses, and it was converted to 360 degrees/537.6 pulses. PID tuning was conducted on the trajectory outputs of the quintic polynomial. The sampling rate for trajectory planning and PID control was 5 ms. After a series of trials and errors, the coefficient values of \(K_{\text{P}}=0.95\) and \(K_{\text{i}}=1.23\) were set, which resulted in satisfactory results with a substantially small error (difference between \(\theta_{\text{input}}\) and \(\theta_{\text{actual}}\)), as shown in Figure 4.
## 3 Software Design of Frequency Stabilization System
### Obtaining Reference Voltage and Error Fragment (S2)
#### 3.1.5 Reference Voltage Extraction of PZT  
The position of the zero point can obtain the reference voltage. Because the scanning signal has a linear correspondence with the position of the error signal, the position of the locking point allows indexing the value of the scanning voltage at that point, i.e., the desired reference voltage, and the reference voltage extraction program is shown in Figure A5 of the Appendix A. These voltages will depend on the temperature and current of the ECDL. For example, for the data shown in Figure 4 in this study, the scanning voltage offsets of the 6 locking points are 4.18646 V, 4.31604 V, 4.53534 V, 6.18896 V, 6.23666 V, and 6.33136 V, which are fitted with the frequencies and wavelengths in Table 1. We can see that the scanning voltages and the changes in the laser frequencies and wavelengths have a good degree of linearity, as shown in Figure 8. It can be concluded that the slope of the error signal is proportional to the frequency offset of the light, so the center frequency of the SAS corresponds to the over-zero position of the error signal. The scanning voltage value indexed by the over-zero position of the error signal is the initial feedback value of the PZT voltage.
## 3 Traffic Flow Modeling
In contrast, for a large traversed time, the change to attain the desirable velocity is small, and, consequently, the driver sensitivity is low. The change to acquire the desirable speed depends on the speed behavior for the required traversed time over a road section. Therefore, the driver sensitivity is characterized with the second order time derivative of speed behavior to determine the change expected from (2). Then  
\[\frac{\partial^{2}v(t)}{\partial^{2}t}=\frac{10.77}{t^{2}}. \tag{6}\]  
For a normalized driver sensitivity, (6) becomes  
\[\frac{1}{10.77}\frac{\partial^{2}v(t)}{\partial^{2}t}=\frac{1}{t^{2}}. \tag{7}\]  
Then, the normalized driver sensitivity \(\eta\) from (7) takes form as  
\[\eta=\frac{1}{t^{2}}. \tag{8}\]  
As \(\alpha_{t}=\frac{v_{t}}{\tau}\), where \(\tau\) is the relaxation time required to align to forward conditions during transition; \(v_{t}\) is the traffic transition speed during alignment. Then, (5) from (8) is  
\[v_{t}=\frac{(d_{t}-d_{s})\tau}{t^{2}}. \tag{9}\]  
This speed changes with the variation in the transition distance, traversed time, and driver reaction time. In contrast, the alignment to forward conditions considers a uniform speed, which is inappropriate and ignores the traffic evolution, whereas \(v_{t}\) is impacted by the traffic conditions. Substituting \(v_{t}\) from (9), the proposed system from (3) becomes  
\[ \begin{split}\rho_{t}+(\rho v)_{x}=0\\ (\rho v)_{t}+\left(\rho v^{2}+\left(\frac{(d_{t}-d_{s})\tau}{t^{2 }}\right)^{2}\rho\right)_{x}=\frac{v(\rho)-v}{\tau}.\end{split}  \tag{10}\]  
According to the proposed system (10), the spatial evolution is based on the driver sensitivity, transition distance, and the safe distance headway. In other words, the changes perceived on a road by a driver during traffic transitions depends on the sensitivity and maintenance of the safe distance headway. Unlike the traffic systems in the literature, the driver sensitivity is not a constant in the proposed system. When the transition distance is comparable to the safe distance headway, there is no spatial evolution, and changes in traffic does not occur. For a larger difference between the transition distance and safe distance headway, large traffic changes are expected.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
### Contributions  
We present a three-hop mixed RF/FSO/UWOC system where the RF link, FSO link, and UWOC link are modeled by Nakagami-\(m\) fading distribution, Gamma-Gamma distribution under the influence of pointing errors, and Exponential Generalized Gamma (EGG) distribution, respectively. The first relay node receives information from the source node located at a distance via RF link. The first intermediate DF relay node then transmits the signal towards the second intermediate DF relay node via FSO link. Furthermore, the second relay node decodes and forwards the received signal towards the destination receiver on the UWOC link. To investigate the performance of the proposed three-hop mixed system model, the SNR statistics of the considered multi-hop system are derived in terms of the cumulative distribution function (CDF) of the end-to-end instantaneous signal-to-noise ratio (SNR) of the system. Later, these statistics are utilized to derive the closed-form analytical expression for the system outage probability. The proposed mixed system's behavior under high-SNR conditions is monitored. Additionally, numerical simulation is used to confirm the mathematical analysis of the suggested model while taking into account the influence of different link factors, such as the effect of path loss, atmospheric turbulence, pointing errors, angle-of-arrival (AOA) fluctuations, multipath fading parameter, water salinity, scintillation caused by air bubbles, and temperature gradient. The proposed three-hop communication system model and channel models of RF, FSO, and UWOC links are presented in Section 2. The outage probability analysis of the proposed system and asymptotic outage analysis are carried out in Sections 3 and 4, respectively. The simulation results and numerical results are demonstrated in Section 5, and lastly, the paper is concluded in Section 6. For ease of reference a list of abbreviations and symbols utilized in the paper are given in Table 1 and Table 2, respectively.
## 3 Uncertainty Modeling Techniques
### Point Estimation Methods (PEMs)  
PEMs have been increasingly applied in power systems to address uncertainty, particularly with the rise in renewable energy sources such as wind and distributed generation (DG). The improved multi-PEM proposed in [32] is specifically tailored for probabilistic transient stability assessments in systems with wind power. By leveraging an extreme learning machine to model wind power uncertainty and utilizing a Gauss-Hermite integral-based approach, this method improves the accuracy of stability predictions without sacrificing computational efficiency. Similarly, in the context of power system state estimation, the proposed method in [33] combines kernel-based objective functions with a primal-dual Interior Point approach to optimize the similarity between actual and estimated states. This method offers significant advantages, such as addressing leverage points and identifying bad data, which are critical for accurate state estimation. Lastly, the application of an improved three-PEM and maximum entropy theory in [34] addresses the probabilistic harmonic power flow in distribution networks with DG.  
The output variable in relation to vector \(X\) can be calculated as follows:  
\[Y=f(X) \tag{38a}\] \[X=[\mu_{k,1},\ldots,\mu_{k,i},\ldots,\mu_{k,n}],\quad\forall i\in\{1,2\} \tag{38b}\]  
Further, Equations (39a) and (39b) should be calculated:  
\[\mathbb{E}(Y)^{k+1} \cong \mathbb{E}(Y)^{k}+\sum_{i=1}^{2}P_{k,i}\cdot h(X) \tag{39a}\] \[\mathbb{E}\Big{(}Y^{2}\Big{)}^{k+1} \cong \mathbb{E}\Big{(}Y^{2}\Big{)}^{k}+\sum_{i=1}^{2}P_{k,i}\cdot h^{2 }(X) \tag{39b}\]  
Finally, for statistical analysis provided by the two-point estimate method, the expected value and standard deviation of \(Y\) can be shown as follows:  
\[\psi_{Y}=\mathbb{E}(Y) \tag{40a}\] \[\sigma_{Y}=\sqrt{\mathbb{E}\Big{(}Y^{2}\Big{)}-\left(\psi_{Y} \right)^{2}} \tag{40b}\]
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 6 Conclusions  
This study presents the RST-FLARE performances in characterizing the Niger Delta region gas flaring activity, using multi-year time series of nighttime infrared (both MIR and TIR) MODIS data.  
The main achievements are summarized as follows:  
1. sites affected by flaring activity are automatically identified, at different temporal scales (both annual and multitemporal), using a standardized variable index measuring the local variation of infrared radiation from flare;
2. the volumes of gas annually emitted, at national scale, are computed by developing a regression model using the Brightness Temperature Excess (BTE), measuring the difference between the 4-\(\upmu\)m source emission and its surrounding expected background, as well as official data on gas volumes emitted by the plants to quantify the emissive power of the hot sources;
3. the long term variation in flaring activity, in terms of flaring sites and flared volumes, is evaluated considering annual estimates within the 17-year investigated period.  
In summary, based on these achievements, RST-FLARE seems to be a reliable semi-automatic tool to be used to identify gas flaring sites, to estimate gas flared volumes and to analyze possible spatiotemporal dynamics at flaring sites. With this capability, and thanks to its exportability on different satellite data and geographic area, the proposed approach can be implemented to whatever country to investigate gas flaring activity provided that a long-term time series of useful (in terms of spectral, spatial and temporal resolution) satellite data and independent official data on gas flared volumes are available. Therefore, results specific of each considered region of interest will be produced.  
Works in progress are currently focused on the calibration of a new version of RST-FLARE aimed at exploiting the great potential of SWIR VIIRS (since 2012) band, which, thanks to its spectral and spatial resolutions, should improve RST performances in detecting flares and measuring their radiant power. In the next future, also the Sea and Land Surface Temperature Radiometer (SLSTR) SWIR bands capability will be explored as soon as a consistent dataset will be available for RST implementation.  
[PERSON] and [PERSON] conceived and designed the analyses; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper; [PERSON] revised the paper.  
This research received no external funding.  
The authors declare no conflict of interest.
## 7 Users' Reception  
Besides the performed field tests, the framework for collecting VGI regarding oil spills was evaluated by analyzing the users' acceptance and the actual use of the ARGO Sentinel app. A high number of users sent information using ARGO Sentinel: specifically, from 7 September 2012-31 August 2013, we received 186 reports. Even if our test sites were very narrow, we received reports from many areas in the world. For the most part, these were only tests; however, by integrating received reports with other information on the MIS, we deducted that 19 could not be false positives, and thus, we forwarded them to the Italian Coast Guard. The media and social impact has been unexpectedly high; indeed, after ten days from the publication of the app, 7-17 September 2012 from, there were more than 25 million results by searching ARGO Sentinel on Google with comments positive or neutral, and more than 500,000 Twitter accounts were informed about the initiative. In Figure 6, the dissemination of media after the first ten days is shown. Besides web sites, blogs, social channels and news aggregators, also local and international newspapers, radio and televisions provided information about ARGO Sentinel, reaching a very high number of people all over the world.
## 3 Shadow Encoding Scheme for Power Spectrum Density  
### Power Spectral Density (PSD)  
PSD is an important feature for showing the strength of frequency variations. PSD makes it easier to know which change has more and which has less power. The power of frequency is computed as per the width of obtained energy from the specific waveform unit. The waveform of a line coder shows the PSD behavior of periodic digital data. Equation (1) shows how to calculate PSD.  
\[P_{s}(f)=\frac{\left|F(f)\right|^{2}}{T_{s}}\sum_{-\infty}^{\infty}R(k)_{ei}^{ \int 2\pi KT_{s}} \tag{1}\]  
\(\mathbf{F(f)}=\) Fourier transform of the pulse f(t) and R(k)  
\(\mathbf{T_{s}}=\) digital binary representation time symbol \(\mathbf{T_{s}}\)= \(\mathbf{T_{b}}\)