esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Emerging Microwave NDT Techniques
### Conclusions  
Emerging technologies related to MNDT techniques have shown potential in various applications, with some limitations. An overview of the working principle, probe types, advantages, limitations, and applications of the discussed emerging techniques are summarized in Table 4.  
\begin{table}
\begin{tabular}{p{128.0 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline  & **Microwave Microscopy** & **Active Microwave Thermography** & **Chipless RFID** \\ \hline \multirow{3}{*}{**Working Principle**} & Employs microwave interaction with the sample at a microscopic level to generate high-resolution images or data & Heats the specimen with microwave signals and uses thermal cameras to identify temperature variations due to defects & Uses encoded electromagnetic fields reflected by chipless objects to transmit data \\ \cline{3-4}  & \(\bullet\) & Evanescent microwave probe [111] & \(\bullet\) & Horn antennas [114; 122; 115; 121] & \(\bullet\) & Printed antennas [125; 126; 127; 128; 129; 130; 131] \\  & \(\bullet\) & Coaxial evanescent microwave probe[113] & & & \\ \hline \multirow{3}{*}{**Advantages**} & \(\bullet\) & High-resolution imaging & \(\bullet\) & High sensitivity to surface defects & \(\bullet\) & No need for physical tags \\  & \(\bullet\) & Suitable for a wide range of materials & \(\bullet\) & Fast inspection over large areas & \(\bullet\) & Durable and cost-effective environments \\  & \(\bullet\) & \(\bullet\) & Non-contact method & \(\bullet\) & Can operation in harsh environments \\ \hline \multirow{3}{*}{**Limitations**} & \(\bullet\) & Limited sample size due to high resolution & \(\bullet\) & Limited to surface or near-surface defects & \(\bullet\) & Limited data storage capacity \\  & \(\bullet\) & Requires sophisticated equipment and expertise & \(\bullet\) & May require complex data analysis & \(\bullet\) & Shorter read range compared to chipped RFID conditions \\  & \(\bullet\) & Material characterization & \(\bullet\) & Detection of surface cracks in metals [115; 122] & \(\bullet\) & Sensitivity to environmental conditions \\  & \(\bullet\) & Semiconductor inspection & \(\bullet\) & Evaluation of composites & \(\bullet\) & Structural health monitoring \\  & \(\bullet\) & Biological sample analysis [111] & \(\bullet\) & Inspection of aerospace structures [122; 123; 124] & \(\bullet\) & \(\bullet\) \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of emerging MNDT techniques.
# The Problem of Stability in Mechanical Systems Using the Example of Mine Hoist Installations
## 3 Parametric Vibrations of the Mining Skip  
The results of the performed dynamic analysis, as presented in [1; 24], provide a practical basis for solving questions regarding the influence of variable guide shaft elasticity on the horizontal vibration of a mining skip. This work is not just theoretical but also haspractical applications that can be confirmed by simplifying the assumptions contained in [1; 24]. The original assumption for the model was linear dependence for the damping coefficient (\(2h\)) and stiffness coefficient (\(k\)) guiding system (rolling guides and others) and was summarized in [1]. The guiding system contains rolling guides, which roll on the series attached while riding the mining skip. The guides are perpendicularly connected to the steel shaft beams, that is, horizontal beams attached to the shaft's walls (Figure 2). If the horizontal displacements are minimal, the damping coefficient and stiffness coefficient could be accepted as linear. When the location of the guide's attachment to the steel shaft is rigid enough, the guide could be regarded as a beam on an elastic surface, and its length \(l_{0}\) is equal to the distance between the supports. The elasticity of the shaft steel is the average value for the shaft guide, which is caused by the places where the guide shafts are mounted. The guide shaft is mounted on two places to the shaft steelwork. The assumption, which is very similar to the actual situation, is that on the mounting places, the elasticity factor is different from what it is between the places of mounting, which is shown in some articles [2; 3; 4]. One possible assumption to maintain is the basis [27; 28; 29; 30; 31] that the elasticity of the steel shaft (supports and guides) could be defined as:  
\[k_{z}=\frac{k_{\mathrm{d}}+k_{\mathrm{P}}}{2}-\frac{k_{\mathrm{d}}-k_{ \mathrm{P}}}{2}\mathrm{cos}\,\omega t, \tag{6}\]  
where:  
\(k_{z}\)--equivalent elasticity coefficient of the shaft steelwork,  
\(k_{\mathrm{d}}\)--equivalent elasticity coefficient of the shaft steelwork at the point the guide rail is mounted on the button,  
\(k_{\mathrm{P}}\)--equivalent elasticity coefficient of the shaft steelwork at mid-span between two buttons. Figure 2: Diagram of the hoist installation with actual representations from a real object, adapted from [24; 26]. Introducing the substitution:  
\[ \begin{array}{l}\frac{k_{4}+k_{\rm p}}{2}=C,\\ \frac{k_{4}-k_{\rm p}}{2}=D.\end{array}  \tag{7}\]  
\(k=E\)--elasticity factor of the guide rail.
# E-Mail Network Patterns and Body Language Predict Risk-Taking Attitude
## 2 Data and Methods
### Study A: E-Mail Analysis
#### 2.2.3 Content Variables  
Similar to the structure variables, we employ Condor to calculate content variables based on the participants' e-mail messages, which contain virtual \"tribe\" signals and emotion signals.  
* Virtual tribe signals  
Tribes are groups of people homogenous within themselves and heterogeneous to other groups. Given that a tribe is generally helpful for reflecting one's identity, place in society, and ethnic partiality [45; 46; 47], and has demonstrated usefulness in different scenarios such as marketing [47] and human resource management [22], it is important to investigate correlations between the users' risk-preference and their underlying virtual tribe. To identify the tribes of users in the e-mail network, we employ TribeFinder [47], which is a novel AI-based tool trained on Twitter. Specifically, TribeFinder finds the different types of tribes and their leaders on Wikipedia, then looks at the language of the leaders on Twitter. People are then assigned to tribes if their word usage is similar to the aggregate of all \"leaders\" of a tribe. We identify tribes for e-mail users according to words usage in their e-mails, using five pre-existing macro-categories [22]: Alternative Realities, Personality, Recreation, Ideology, and Lifestyle [47], each of which consists of four dimensions (see detailed explanations for each dimension within each macro-category in Table 1). For each user, TribeFinder computes his/her probability of being in a certain dimension.  
* Emotion signals  
To calculate the e-mail sentiment, we employ Condor's built-in sentiment analysis function, which is a Bayesian-based classifier trained on billions of tweets achieving over 80% accuracy on English e-mail corpora [47]. The sentiment score of each e-mail ranges from 0 (negative) to 1 (positive). A user's average sentiment is denoted as the mean value of all sentiment scores from her/his e-mails. In addition, we use TribeFinder to further quantify each user's explicit mood state. Similarly, we set up an additional mood \"tribe\", which depicts the individual's mood by four dimensions (happy, sad, angry, and fear) through assigning a likelihood of belonging for each dimension.
## 1 Introduction
By integrating IoT devices into agricultural practices, this system enables the collection of dynamic data, empowering farmers and agronomists with an unobstructed view of their crops' health and growth. 2. Precise predictive modeling: Utilize advanced AI models to analyze the data derived from IoT devices with precision. These models are specifically designed to provide timely and accurate information about crop health and growth trajectories. By leveraging the power of predictive modeling, stakeholders can implement targeted interventions, optimizing resource allocation and enhancing crop quality. In summary, this research demonstrates the transformative potential of combining IoT and AI technologies in agriculture. By closing the gap in crop health prediction, it paves the way for precision agriculture. Continuous crop monitoring and precise predictive modeling enable informed decisions that enhance crop growth, sustainability, and productivity.
# Characteristics of Vibration Velocity Signal Using Liquid Carbon Dioxide Rock-Breaking Technology
## 2 Field Test
### Monitoring Program  
Both pressure and blasting vibration were monitored. For the L-CDRB tests, the fracturing tube bore pressure and the blast vibration velocity were monitored. For the explosive tests, the blast vibration velocity was monitored.  
The pressure testing system is mainly composed of a pressure sensor, a multi-functional collector, and control software. The connection method is shown in Figure 4a. In order to obtain a complete internal pressure curve of the cracking tube, we machined a threaded hole of suitable sensor size on the top of the fracturing tube and fixed the sensor to the end of the fracturing tube; as shown in Figure 4b, the pressure sensor was a KD2000 series large-range piezoelectric sensor. The pressure-sensing part of the sensor is located inside the tube. Then, the data are transmitted to the acquisition instrument through a data line, and finally, the pressure signal is recorded and stored by the acquisition software.  
In this test, a TC4850 self-registering instrument monitoring system was used to record the vibration velocity in three directions: vertical, horizontal-radial, and horizontal-tangential. When the monitoring points were placed, the sensors were fixed to the intact bedrock surface by using quick-setting plaster, with the X direction being that of the blast zone (see Figure 5). Three to seven pieces of monitoring equipment were placed for each test, depending on the site's actual geological conditions, and the equipment was distributed within 9.7-260.5 m from the center of the blast zone to ensure that adequate vibration data were obtained.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test No.** & **No. of Holes** & **Hole Depth** & **Total Charge** & **Segmented** & **Consumption** \\  & & **(m)** & **(kg)** & **(kg)** & **(kg/m\({}^{3}\))** \\ \hline
04 & 58 & 16.8 & 15,070 & 280 & 0.71 \\
05 & 49 & 17.0 & 12,880 & 280 & 0.61 \\
06 & 101 & 14.4 & 11,945 & 300 & 0.68 \\ \hline \end{tabular}
\end{table}
Table 4: Explosive blasting test parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Test No.** & **Uniaxial Strength (MPa)** & **Elastic Modulus (GPa)** & **Poisson’s Ratio** \\ \hline U-1 & 116.70 & 44.83 & 0.51 \\ U-2 & 106.80 & 53.22 & 0.34 \\ U-3 & 114.41 & 55.59 & 0.11 \\ U-4 & 119.79 & 54.93 & 0.27 \\ Average & 114.43 & 52.14 & 0.31 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics of uniaxial compression test results.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test No.** & **No. of Holes** & **No. of Tubes** & **Fill Pressure** (MPa)** & **Fill Quantity** (kg)** & **Hole Depth** (m)** \\ \hline
01 & 8 & 40 & 8 & 208 & 16.5 \\
02 & 13 & 97 & 8 & 504.4 & 16.5 \\
03 & 21 & 128 & 8 & 665.6 & 16.5 \\ \hline \end{tabular}
\end{table}
Table 3: L-CDRB test parameters.
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 3 Results  
In the area-level analysis, the monthly average RDVI values for both healthy and affected areas (Figure 4) showed a decline throughout the spring-summer period of each year. Nevertheless, the RDVI trend in the control area differed significantly from that of the areas affected by _T. parviformis._ Specifically, the control area did not exhibit the pronounced decline observed in the infested zones during the summer months, maintaining relatively stable photosynthetic activity levels.  
The analysis of RDVI values in the infested areas revealed that the lowest average values recorded showed an increasing trend over time. Specifically, the minimum peak reached an average value of 26.6 in 2022, much better compared to the value of 21.9 in 2020.  
In the single-plant level analysis, the normal distribution (Equation (2)) of RDVI\({}_{\text{Sua}}\) presented a clear separation between healthy and infested areas (Figure 5).  
Based on this evidence, some RDVI\({}_{\text{Sua}}\) thresholds were tested through the construction of a confusion matrix (Table 4). The matrix assessed the classification accuracy of each threshold-based classification compared to the field-survey data. A threshold value of 33.1 optimized the classification accuracy, calculated using the overall accuracy (0.996), producer accuracy (0.999), and user accuracy (0.996). An RDVI threshold of 33.1 ensured the highest accuracy in distinguishing between affected and healthy plants.  
Figure 4: Monthly average RDVI of infested area (shades of grey) and healthy area (blue).  
A visual evaluation of PlanetScope imagery also demonstrates temporal changes. In 2022, the areas dominated by _P. pinea_ displayed a lower response in NIR bands (as shown by the darker red areas in Figure 6) when compared to 2019, suggesting healthier conditions in 2019 [48]. Additionally, the crown density appeared to have decreased over time (Figure 6).
## 3 Implementation of X-IoCA for SAR Missions: SAR-IoCA
### Feedback Information System for SAR Missions
#### 3.2.2 SAR-FIS
As illustrated in Figure 5, the planned paths are also represented on the environment map, and the agents are shown in their current location, allowing visual tracking. Figure 5 also illustrates the status of a sensory target, showing data such as:  
* The SG geolocation in geographic or Universal Transverse Mercator (UTM) coordinates. * The list of sensor-nodes of the SG, showing, for each LoRa sensor-node, the current values of: spread factor (SF), received signal strength indicator (RSSI) and signal to noise ratio (SNR), radio frequency channel used, and battery percentage. * The list of acquired physical magnitudes, showing the current values received from the sensor network. * The date and time of the last data received from the SG. * A time evolution chart. Figure 5: SAR-FIS displaying sensory information from a sensory target with path-planning for UGVs Cuadriga and Rambler. The user can configure what information is shown on the graphical interface. Thus, e.g., Figure 5 shows the time evolution chart related to RSSI and radio frequency channel used by sensor-nodes belonging to Sensor Group 3. Similarly, Figure 6a illustrates the status of an agent (Cuadriga) in which, in addition to the aforementioned data, the image captured by the onboard IP camera is displayed. To facilitate the data analysis related to an experiment, SAR-FIS saves in a data structure the information received from the LoRa H-WSN and from the GPS devices on board the agents. This data structure is saved in a file, constituting a real dataset. The file with the generated dataset can be restored in SAR-FIS for further data analysis.
# High-Density 3D Printable Chipless RFID Tag with Structure of Passive Slot Rings
## The Principle of the Design  
### Working Principle of Chipless Tag RFID System  
The working principle of the RFID system with chipless tag as shown in Figure 1. The UWB signal is transmitted to the tag through the radiation of the reader transmitting antenna. The feedback UWB spectrum from the reader is tuned accordingly because of the resonant structure existing on the tag. In this way, the data carried on the tag is encoded into the UWB signal and backscattered to the reader receiving antenna.  
The structure in this paper is the rectangular slot ring printed on insulated substrate, which is depicted in Figure 2a. It can be seen that the resonator structure is the concave-down slot ones, which is obviously different from the strip structure [32]. In our design, the coverage of the deposited structure on the substrate is above 95%. On the contrary, the metal coverage of the strip structure [32] is below 5%. The designed tag is implemented by 3-D printing technology. The nanoparticle silver paste is used in the 3D printer (Voxel8) as conductive material. Nanoparticle silver paste is provided by Silver Paste-SP3012 of Guangdong Nanhai ETEB Technology Co., LTD. The printing system is Voxel8, manufactured by Somerville, USA. After the subsequent heat treatment process, the curing temperature is set to 200 \({}^{\circ}\)C and the bulk resistivity of the nanoparticle silver paste reaches 3 \(\upmu\Omega\)-cm. The adopted substrate here is Teflon woven glass fabric copper-clad laminates (F4 BM), with thickness 1 mm, permittivity 2.2 and loss tangent 0.0007.  
Figure 1: Working principle of RFID system, in which UWB signal is transmitted by the reader. The tag receives the signal and submits its reaction in resonant way. The reader receives the encoded signal.
## 4 Results and Discussions
### The Result of Fuel Consumption Prediction  
After three Bayesian searches and cross-validation to optimize hyperparameters, this study evaluated model performance on different data sets. The results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Data Sets** & **MAE** & **MSE** & **RMSE** & **MAPE** \\ \hline \multirow{6}{*}{XGBoost} & Set\({}_{1}\) & 0.4173 & 0.2801 & 0.5292 & 0.3148 \\  & Set\({}_{2}\) & 0.4038 & 0.2924 & 0.5408 & 0.300  
As presented in Table 3, the SA-LSTM model shows the best prediction performance on the feature subset selected through the embedding method, whereas SVR has the highest prediction error. Compared to the pre-embedding state, the four evaluation indicators for the SA-LSTM model on the five data sets decreased noticeably, with MAPE decreasing by up to 12%. The specific results are depicted in Figure 8.  
It can be found that adding the self-attention mechanism significantly impacts the SA-LSTM across each data set, reducing MAE by up to 17.5%, MSE by up to 22%, RMSE by up to 13%, and MAPE by up to 12%.
###### Abstract  
The use of a new fluorescent composite can reduce some of the problems related to procedures of de-bonding orthodontic bracket (enamel damage, dentine lesions, and composite residuals). The aim of the presented study was to compare the effect of fluorescent and conventional non-fluorescent composite on dental surface and composite remnants by in vitro de-bonding tests. De-bonding oforescent composite (DFC) and the de-bonding of standard composite (DSC) were performed by operators on an in vitro sample of 48 teeth under UV light (360-370 nm min 20 mW/cm\({}^{2}\)). Modified ARI (Adhesive Remnant Index), scored under 5.0\(\times\)/235 magnification, was used for evaluation of dental surface after the procedure, and the duration required for de-bonding was measured. Significant differences in ARI between the two groups were observed (Pearson two-tailed \(p\) = 0.006 1.4 \(\pm\) 0.1 95% C.L.), and the average duration of de-bonding was 38 s (DFC) and 77 s (DSC) per tooth, respectively (Mann-Whitney test \(p\) = 0.015; 57.7 \(\pm\) 19.9 95% C.L.). The use of fluorescent composite could significantly improve the quality of de-bonding by reducing the quantity of composite residuals and visible enamel damage, while reducing time needed for successful procedure performance.  
composite; fluorescence; de-bonding; residuals; enamel damage; orthodontics 202120212 July 20212  
most of the composite residuals on the enamel surface. However, in most cases, adhesive residuals are present on the enamel surface even after cleaning and polishing with rotary instruments [5].  
A simplified de-bonding technique using a novel fluorescent composite and UV lamp was proposed. The method may significantly decrease the presence of the composite residuals, along with the risks related to the standard practice. BrackFix(r) composite (Voco GmbH, Cuxhaven, Germany) has fluorescent properties due to fluorescent dye mixed with the composite base. This characteristic allows the compound materials used to react under UV light and to become easily detectable. The aim of the study was to evaluate the efficacy of a novel type and simplified de-bonding procedure of fluorescent adhesive composite for orthodontic brackets and to critically analyze its advantages in comparison to the standard composite during the procedures of composite clean-up. We hypothesized that this new product could reduce the operator-depending factor and, therefore, can increase de-bonding safety and quality.
## 4 Advanced Development of ANNs
### Wavelet-Based Neural Networks  
As discussed above, the standard BP algorithm may produce problems in training, for example, converge to local optima. Besides, the determination of the network structure in BP training, such as the number of hidden nodes, is still subjective. With the intelligent integration of wavelet transform, ANNs can be able to approximate data with non-linear and non-stationary properties and to conduct self-learning adaptive structure identification and parameter estimation in modeling process.  
Wavelets are \"small\" waves that should integrate to zero around the x-axis. They localize functions well and a \"mother\" wavelet can be translated and dilated into a series of wavelet basis functions. The basic idea of wavelets can be traced back to very early in this century. However, the development of the construction of compactly supported orthonormal wavelets [82] and the wavelet-based multiresolution analysis [83] has resulted in extensive research and applications of wavelets in recent years. In comparison to the more widely known Fourier transform, wavelet basis functions are local both in frequency and time while Fourier basis functions are local only in frequency. Wavelet analysis has been widely used in a variety of fields, especially in signal and image processing.  
Wavelet analysis is becoming a common tool for analyzing localized variations of power within a time series [84]. By decomposing a time series into time-frequency space, one is able to determine both the dominant modes of variability and how those modes vary in time. Wavelet-based ANNs (WANNs) have been developed and used for function approximation [28-30]. WANNs have been further developed for nonlinear time series forecasting [31]. WANNs have a strong ability of function approximation for optimal time series pattern recognition and their modeling algorithms are different from conventional BP neural network training algorithms. Therefore, WANNs have the potential to overcome the problems that conventional ANNs had.