esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 3 Experimental Setup and Testing Structures  
### Laser-Generated Ultrasonics Damage Detection System  
A laser-generated ultrasonics damage detection system was built in this study to verify the accuracy of the detection technique. A pulsed laser beam with a duration of 20 ns and at a repetition of 20 KHz is generated by a \(Q\)-switched Nd:YAG pulse laser. The wavelength of the pulse is 1064 nm. The laser pulse is slightly focused to a 0.3 mm diameter by a lens, and the maximum pulse energy density is controlled below 1 \(\times\) 10\({}^{7}\) W/cm\({}^{2}\) to avoid ablating the material. Through the galvanometric scanner, the laser pulse can be shot at any desired place on the structure. The generated ultrasonic waves are recorded by a focused transducer mounted at a fixed point on the structure. The transducer used here is a high-frequency transducer product by Olympus (VB213 -RM) with a bandwidth frequency of 0-30.0 MHz, and the crystal diameter of the transducer is 6 mm. This transducer is used for receiving the surface acoustic waves generated by the laser pulse. Glycerin is used as the couplant to fix the transducer to the structure surface. Each dynamic response is post-amplified and recorded by a 14-bit high-speed data acquisition card at a sampling frequency of 100 MHz. There is a sync signal generator contained in the detection system who will send two enable signals to the \(Q\)-switched Nd:YAG pulse laser and the high-speed data acquisition card synchronously. In this way, the synchronization of the laser excitation and the reception of the ultrasonic waves can be guaranteed. The recorded data are transferred to a personal computer (PC) for further analysis. The process of data collection is started by a trigger signal sent out by the PC. A three-dimensional transition stage is used to determine the relative position of the structure. The step interval of the stage is 0.01 mm, which is a sufficient interval to guarantee the repeatability of the test. All the recorded data are averaged 64 times in the time domain to improve the signal to noise ratio.  
An overall schematic of the laser-generated ultrasonics damage detection system used in this study is shown in Figure 5.
# Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
## 3 Method
### Overview of AMKD  
The core idea of AMKD is to enable the small student model \(S\) to learn from the knowledge of the large teacher model \(T\), thereby improving the performance of the student model on specific tasks under the guidance of the teacher model. Both the student and teacher models are composed of an embedding layer, several Transformer layers, and a prediction layer. Each Transformer layer consists of an attention layer and a hidden layer.  
AMKD consists of four distillation components: prediction layer distillation, hidden states distillation, attention matrix distillation, and embedding layer distillation. Assuming that the teacher model contains \(M\) Transformer layers and the student model contains \(N\) Transformer layers, Figure 1 visually presents the overall distillation framework. \(\mathbf{H}_{i}^{S}\) and \(\mathbf{H}_{j}^{T}\) represent the hidden states of the \(i\)-th layer of the student model and the \(j\)-th layer of the teacher model, respectively, \(D^{\prime}\) and \(D\) are the hidden states dimensions of the two, \(l\) represents the input sequence length, and \(\mathsf{T}\) is the matrix transpose operation. \(h\) represents the number of attention heads, and \(\mathbf{A}_{i}^{S}\) and \(\mathbf{A}_{j}^{T}\) represent the attention matrices of the student model and the teacher model, respectively.  
Figure 1: An overview of the AMKD method, showing how to distill the knowledge of an \(M\)-layer Transformer teacher model \(\mathbf{T}\) to an \(N\)-layer Transformer student model \(S\).  
In contrast to previous approaches, we leverage the autocorrelation matrix of hidden states to capture complex feature relationships, rather than focusing solely on the individual features of the teacher model. The student model learns not only the behavior of each feature from the teacher model but also the interactions between features. Furthermore, AMKD effectively resolves the issue of hidden states dimensional mismatches between the teacher and student models, allowing for greater flexibility in the configuration of the student model while reducing computational costs. Next, we will provide a detailed description of the four distillation strategies in AMKD.
# Temperature Distribution Characteristics of Concrete during Fire Occurrence in a Tunnel
## 5 Experimental Results and Analysis  
### Analysis of Vehicle Fire in the Tunnel  
When a fire source corresponding to 27,000 L of diesel was located at the center of the tunnel, the fire intensity was approximately 150 MW, as shown in Figure 6. Figure 7 shows the results of the temperature measurements on the concrete surface and at depths of 20, 40, 60, and 100 mm. The analysis results for the concrete hydrothermal temperatures at each depth showed that for a fire of such a scale (27,000 L), the surface temperature rose to approximately 1000 \({}^{\circ}\)C at approximately 300 s after the occurrence of the fire. This high temperature was maintained for approximately 1800 s.  
The peak temperature of the concrete surface was approximately 1380 \({}^{\circ}\)C. The corresponding temperatures at depths of 20, 40, 60, and 100 mm were 1005 \({}^{\circ}\)C, 750 \({}^{\circ}\)C, 405 \({}^{\circ}\)C, and 110 \({}^{\circ}\)C, respectively. The International Tunneling Association (ITA) [12] specifies that the maximum critical temperatures for concrete and reinforcement bars should be 380 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. On the basis of these results, the maximum critical temperature standard could not be met at concrete depths higher than 60 mm.  
Figure 8 shows the temperature distribution inside the tunnel. Furthermore, the fire exposure experiment was conducted for real concrete members by applying the RABT fire curve where the temperature rose sharply to 1200 \({}^{\circ}\)C within 5 min. The result was similar to the that of the empirical verification of the tunnel structures' characteristics after exposure to high temperatures [12].  
Figure 6: Fire intensity based on vehicle fire analysis.  
Figure 7: Temperature distribution by depth based on vehicle fire analysis.
###### Abstract  
Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. _Arabidopsis thaliana_ plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at \(\sim\)670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 \(\pm\) 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.  
Achidopsis thaliana; water stress; soil water content; reflectance spectra; plant phenotyping; artificial intelligence; machine learning; decision trees; random forests; extreme gradient boosting +
Footnote †: journal: Applied Sciences  
Article  
Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in _Arabidopsis thaliana_  
[PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in _Arabidopsis thaliana_. _Appl. Sci._**2021**, _11_, 6392. [[https://doi.org/10.3390/app11146392](https://doi.org/10.3390/app11146392)]([https://doi.org/10.3390/app11146392](https://doi.org/10.3390/app11146392))
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 2 Background, Related Work, and Contributions
### Beamforming Strategies in 5G New Radio  
According to 3 GPP Rel-15 [1], 5G NR cells may transmit up to eight SSBs. Each SSB is a composed signal that 5G cells broadcast in order to provide essential information to a 5G UE during cell searching that are needed to initiate connection procedures. In particular, an SSB consists of synchronization signals (SS), i.e., Primary (PSS) and Secondary Synchronization Signals (SSS), and PBCH, which also contains a Demodulation Reference Signal (DM-RS); hence, they are also referred to as SS/PBCH blocks. SSBs are periodically transmitted with different time/frequency patterns, depending on the adopted subcarrier spacing and frequency range. Figure 1 shows the pattern for the transmission of eight SSBs, when the subcarrier spacing adopted by the 5G system is 30 kHz. In this case, each SSB spans over four Orthogonal Frequency Division Multiplexing (OFDM) symbols in time, and the duration of an OFDM symbol is 33.33 \(\upmu\)s.  
In the frequency domain, each SSB contains 240 contiguous subcarriers within 20 resource blocks (note that the total number of resource blocks for the maximum transmission bandwidth of 100 MHz is 273) [36].  
Figure 1: Beam-based transmission with N = 8 beams. Each SS/PBCH Block (SSB) is transmitted on a dedicated beam.  
The introduction of multiple SSBs per 5G NR Physical Cell Identifier (PCI) allows for beam-based transmissions, as shown in Figure 1. In this case, SSBs with different indexes (from 0 up to 7) are transmitted over well-defined directional beams, thus allowing a 5G UE-PCI pair to identify a specific beam as their communication link, i.e., the beam corresponding to the SSB detected with highest power. Hence, when the UE is powered on, it receives SS/PBCH blocks from several PCIs, and selects the PCI/SSB pair, and thus a beam, received with the highest power. The UE will then send a preamble to this PCI/SSB in order to initiate a connection procedure that, if successful, will allow the establishment of a communication link for data exchange [37]. The detection and decoding of standardized 5G signals is of great significance in perspective of 5G NR SSB. With TSMA 6, the PBCH/MIB content of each detected SSB is decoded. The 5G NR SSB measurements help to verify 5G NR coverage and the effect of beamforming. Each SSB can be transmitted on different beam (depending on the network configuration), which can be decoded by the scanner. With different SSBs and beams, the scanner results become three dimensional: power, signal-to-noise and interference measurements for each PCI and SSB/beam index deliver a complete set of data to verify the transmission of each SSB/beam. When we have multiple SSBs/beams with their index, we can interpret the propagation characteristics of the detected SSBs/beams.
## 2 Materials and Methods
### Measurements of Photosynthetic Parameters  
The true leaf was used for calculating the net photosynthetic rate, transpiration rate, and stomatal conductance, and for gas exchange determination using a portable photosynthetic apparatus (LI-6800, LI-COR Biosciences Inc., Lincoln, NE, USA). Three replicates of 10 plants each were performed in each treatment.  
Analysis of Malondialdehydedehyde (MDA) Concentration and the Relative Electrical Conductivity of Leaves  
The MDA concentration was measured using the thiobarbituric acid method [5]. Fresh leaves (0.5 g) were homogenized with 5 mM 5% trichloroacetic acid (TCA) and then centrifuged at 5000 rpm for 15 min. After that, the supernatant was thoroughly blended with 0.67% TCA in a boiling water bath. The absorbance was acquired at 450, 532 and 600 nm. The concentration of MDA was calculated using the formula C (\(\mu\)mol/L) = 6.45 (A532 \(-\) A600) \(-\) 0.56A450.  
Fresh seedlings were vacuumized to completely sink the blade in deionized water. Then, a conductivity meter was applied to detect the conductivity of the solution, which was represented by A1. After boiling the leaves in water for 15 min, the conductivity of the solution was detected using a conductivity meter [5], which was represented by A2. The following formula, A1/A2 \(\times\) 100%, was applied to measure the relative conductivity.  
Histochemical Detection and Content Analyses of Hydrogen Peroxide (H\({}_{2}\)O\({}_{2}\)) and Superoxide Radical (O\({}^{2,-}\))  
The 3,3\({}^{\prime}\)-diaminobenzidine (DAB) staining method was applied to measure H\({}_{2}\)O\({}_{2}\) production by staining the leaves with 0.1% DAB solution for 2 h [5; 16]. The nitro blue tetrazolium (NBT) staining method was used to detect the O\({}^{2,-}\) production in leaves by observing the purple-blue color [17]. All the samples were decolorized using 95% ethanol for 24 h at 37 \({}^{\circ}\)C to remove the chlorophyll or superfluous dye. A light microscope was used to observe the decolorized leaves (model: Stemi 2000-C; CarlZeiss, Jena, Germany). The concentrations of H\({}_{2}\)O\({}_{2}\) and O\({}^{2,-}\) were determined using a hydrogen peroxide content detection kit (with visible light spectrophotometry, Solarbio, Beijing Solaibao Technology Co., Ltd., Beijing, China, BC3590-50T/48S) and a superoxide anion content test kit (with visible-light spectrophotometry, Solarbio, BC1290-50T/48S), respectively.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 2 Materials and Methods
### Methods
#### 2.3.3 Local Adaptive Thresholding  
[PERSON] [45] proposed a local thresholding method for digital image segmentation, as follows:  
\[T=m+k\times s \tag{2}\]  
where \(m\) is the local mean, \(s\) is the local standard deviation, and \(k\) is a user-defined parameter, which takes negative values. [PERSON] and [PERSON] [46] modified the method to realize adaptive document image binarization, as follows:  
\[T=m\times\left[1+k\Big{(}\frac{s}{R}-1\Big{)}\right] \tag{3}\]  
where \(R\) is the dynamic range of standard deviation, and parameter \(k\) attains positive values.  
In their experiments, [PERSON] and [PERSON] used \(R=128\) and \(k=0.5\) to obtain good results with eight-bit gray level images. [PERSON] et al. [36] improved their method to segment the thin oil films, as follows:  
\[T=m\times\left[1+k\Big{(}\frac{v}{R^{2}}-1\Big{)}\right] \tag{4}\]  
Figure 10: Determination of effective wave area showing (**a**) gray gradient matrix of Figure 9, (**b**) the mask of gray threshold “20”, and (**c**) the final, pre-analyzed area.  
where \(v\) is the local standard deviation. In their experiments, \(R=128\) and \(k=0.25\) are ideal values. Using this setting, we set the local window to 64\(\times\)16 to analyze the wave area in Figure 11a. The speckles and wake disturbances were then removed in Figure 11b.
## 3 Results
### 3 km SMAP/CYGNSS TBs  
3 km SMAP/CYGNSS TBs include significantly more spatial detail than 9 km SMAP TBs and capture expected soil moisture patterns on the landscape. An example near New Delhi, India (Figure 10) shows a decrease in soil moisture over croplands as time progresses from August to December. The drying of the croplands is evident in both 3 km SMAP/CYGNSS TBs (Figure 10g,h) and 9 km SMAP TBs (Figure 10d,e). However, the spatial pattern of the more productive croplands is obvious in 3 km SMAP/CYGNSS TBs but not discernible in 9 km SMAP TBs.  
The 3 km SMAP/CYGNSS TB maps shown in Figure 10g-h include data that is aggregated over 60 days. As such, adjacent 3 km TBs may be derived from SMAP/CYGNSS data observed on the same day, 5 days apart, or as many as 59 days apart. This introduces noise in the maps. We aggregated SMAP/CYGNSS TBs over 60 days due to sparse daily spatial coverage. Options for increasing daily spatial coverage in SMAP/CYGNSS TBs are discussed in Section 4.5.
## 2 Research Methodology
### Delphi Study with Expert Panels
#### 2.3.2 Delphi First Round  
In the first round, the study started with refining the TQIM-H factors through in-depth interviews with 30 healthcare experts using a Likert scale questionnaire. Then, the questionnaire's results were analyzed by importance and performance analysis (IPA). The process of the first round contains four septs including:  
Step 1. We presented TQIM-H factor to expert panels. The TQIM-H key factors from the literature review and case study analysis were presented to the experts so that they were familiarized with the integrated factors that were developed from the literature review and case studies analysis.  
Step 2. We interviewed the experts about the TQIM-H via questionnaire. An in-depth interview with healthcare experts provided opinions that were used to develop TQIM-H. The opinions on the importance and performance of each TQIM-H key factor were expressed as scores ranging from 1 to 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Expert Categories** & **Required Qualification** & **Number of the Expert Panel** \\ \hline
**Academics** & More than 5 years of experience in the academic area with a Ph.D. & 6 \\ \hline
**CEO/Directors** & Top management in healthcare and more than 5 years of & 6 \\  & experience in the healthcare position. & 6 \\ \hline
**Healthcare quality assurancespecialist** & Healthcare quality assurance specialist with healthcare quality certification and more than 5 years of experience in the healthcare position. & 6 \\ \hline
**Innovation specialist in healthcare** & Healthcare innovation specialist and more than 5 years of experience in the healthcare position. & 6 \\ \hline
**Project development specialist in healthcare** & Project manager/technical specialist and more than 5 years of experience in the healthcare position. & 6 \\ \hline
**Total** & & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Healthcare expert panel criteria.  
Step 3. We analyzed the TQIM-H questionnaire's results via IPA. The results from the TQIM-H questionnaire were analyzed using the IPA methodology.  
Step 4. We summarized the results from IPA. The results of an IPA graph were summarized and shown as refined TQIM-H factors.
## 3 Results
### Analysis the MTF of Dynamic Imaging Interfered by Random Vibration  
In the dynamic frame by frame imaging system, the attitude angle of the camera will change periodically. In this process, the exposure time of the camera is different when the attitude angle of satellite varies at different velocities. Therefore, it is necessary to calculate the exposure time of subframes with different attitude angles. According to Equation (10) and the parameters in Table 1, the exposure time of the camera at different angles is simulated, as shown in Figure 8.  
It can be seen that the camera exposure time becomes larger as the satellite attitude angle increases during the single-frame imaging in Figure 8.  
Figure 8: Camera exposure function.  
Figure 7: MTF curves at different error velocities.  
In order to analyze the influence of the error of velocity \(V_{e}\) and attitude angle \(\theta\) on MTF, spatial frequency is set as the Nyquist frequency \(v=1/2a\), and then, the MTF is transformed into:  
\[\mathrm{MTF}(\theta)=\exp\biggl{\{}\frac{-2\pi^{2}f^{2}\cdot\tan^{2}[V_{e}t( \theta)]}{4a^{2}}\biggr{\}} \tag{22}\]  
The MTF curves with different error velocities and attitude angles are obtained, as shown in Figure 9.  
Comparing Figures 7 and 9, it can be seen that there are similar conclusions in the dynamic imaging model and the satellite nadir imaging model: When the camera's attitude angle is set as a fixed value, the MTF decreases with the increasing error of velocity.  
However, when the error of velocity is set as a fixed value, the MTF value achieves its maximum at the attitude angle \(0^{\circ}\). When the optical axis of the camera deviates from the satellite nadir, MTF decreases significantly, so it appears to be an approximate \"arch\" in the MTF curves.  
It can be explained that the exposure time of the camera at the satellite nadir is the shortest. With the increasing attitude angle, the exposure time of the camera becomes longer. When the camera is disturbed by the error of velocity, caused by random vibration, the error image shift on the camera focal plane is limited by the short exposure time. With the increasing attitude angle, the error image shift in the camera focal plane becomes larger due to the long exposure time. The more error image shifts are introduced into the focal plane of the camera, the more significantly the MTF drops. Therefore, the image quality of the sub-frames, farther from the satellite nadir, is degraded more obviously in a group of single-frame wide-frame images.
## 5 Results
### Analysis of the Laser Scanning Methods Based on Detail and Density
When using the mobile laser scanner Lidaretto, noise-filtering software was applied to eliminate redundant points and improve the quality of the point clouds, although this came at the cost of reduced point density and local accuracy. The average noise value for this Lidaretto dataset reached 0.5 cm. When using the mobile laser scanner Stonex X120 GO, we observed higher noise levels compared to other MLS devices, which could be attributed to several factors, including scanner movement, complex trajectories, and environmental conditions during the measurement process. The average noise level reached 0.8 cm.
## 3 Results
### Quantitative Estimation of Soil pH and EC
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**pH-EC Levels** & **Characteristics** & \begin{tabular}{c} **Geographical** \\ **Background** \\ \end{tabular} & **pH** & **EC (dS/m)** & **TOC (\%)** & **CO3\({}^{2-}\) (mg/L)** \\ \hline \multirow{2}{*}{Strongly alkaline and  strongly saline} & \multirow{2}{*}{\begin{tabular}{c} Sporadic small \\ patches salt \\ crust \\ \end{tabular} } & Margins of & \multirow{2}{*}{10.48} & \multirow{2}{*}{17.80} & \multirow{2}{*}{0.45} & \multirow{2}{*}{8366.70} \\  & & & & & \\ \hline \multirow{2}{*}{Strongly alkaline and  moderately saline} & \multirow{2}{*}{White color} & Margins of & \multirow{2}{*}{10.46} & \multirow{2}{*}{9.42} & \multirow{2}{*}{0.57} & \multirow{2}{*}{2450.54} \\  & & & & & \\ \hline \multirow{2}{*}{Strongly alkaline and  slightly saline} & \multirow{2}{*}{Grey white color} & Margins of & \multirow{2}{*}{10.29} & \multirow{2}{*}{5.64} & \multirow{2}{*}{0.53} & \multirow{2}{*}{977.08} \\  & & & & & \\ \hline \multirow{2}{*}{Strongly alkaline and  non-saline} & \multirow{2}{*}{Grey color} & Margins of & \multirow{2}{*}{10.11} & \multirow{2}{*}{1.39} & \multirow{2}{*}{0.69} & \multirow{2}{*}{296.60} \\  & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Moderately alkaline \\ and non-saline \\ \end{tabular} } & \multirow{2}{*}{Brown color} & \multirow{2}{*}{\
\begin{tabular}{c} flats near plays \\ and pools \\ \end{tabular} } & \multirow{2}{*}{8.65} & \multirow{2}{*}{0.29} & \multirow{2}{*}{1.19} & \multirow{2}{*}{0} \\  & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Slightly alkaline and \\ non-saline \\ \end{tabular} } & \multirow{2}{*}{Dark color} & flats & \multirow{2}{*}{7.94} & \multirow{2}{*}{0.23} & \multirow{2}{*}{2.34} & \multirow{2}{*}{-} \\  & & & & & \\ \hline \multicolumn{2}{c}{Non-affected soils} & \multirow{2}{*}{Dark color} & flats & \multirow{2}{*}{5.91} & \multirow{2}{*}{0.14} & \multirow{2}{*}{3.22} & \multirow{2}{*}{-} \\ \hline \hline \end{tabular} Note: CO3\({}^{2-}\) contents of soil pH below 8.5 are not calculated.  
\end{table} 
Table 3: Descriptive statistics of soil pH-EC levels.