esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents an algorithm for thermal imaging technology that extracts the features of thermal images--particularly for solar panel applications--to simplify the representation into an easier domain for analysis. Aside from the histogram of an image, the CDF provides significant insight as one of the parameters when dealing with image processing. The experimental results revealed that the enhanced thermal images provided better histogram and CDF features. Implementing histogram equalization on the dataset provided increased contrast and highlighted the elements of the thermal image. AHE improved the image's contrast, which could be observed in the corresponding histogram and CDF of the processed thermal images. In addition, the results of this paper provide an improvement on the training accuracy using both the training and testing datasets of the HE and AHE. The result shows that the AHE dataset provides better classification result against the HE dataset when used in the proposed model. It was also validated that the proposed scheme assesses the health condition of the solar panels/cells by the electroluminescence (EL) technique. The proposed work used the convolutional neural network with SVM (CNN-SVM) to classify the enhanced images and showed an accuracy of classification of 97%. Moreover, it shows that the training accuracy and the training loss presented a better result using the proposed CNN-SVM model. The proposed scheme could promote different thermal image applications--for example, non-physical visual recognition and fault detection analysis.  
In the future, the different resulting features of these thermal images could contribute to neural networks in classification applications, which could promote the development of different thermal image applications--for example, non-physical visual recognition or the prediction of solar power generation efficiency.  
B.K. contributed to the guidance and revision of the paper. [PERSON] devised the experimental strategy, carried out this experiment, wrote the manuscript, and contributed to the revisions. [PERSON]-[PERSON] undertook the leadership on this work. [PERSON] contributed to the validation and data acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1A5A1025137 and No. 2019R1G1A1095215).  
Not applicable.  
Data Availability Statement: Data available on request due to restrictions. The data presented in this study are available on request from the corresponding authors. The data are not publicly available due to project's contract.  
The authors declare no conflict of interest.  
Figure 22: Confusion matrix for the classification using the AHE dataset.
# Si Photonics for Practical LiDAR Solutions
## 4 Si Photonics Optical Phased Array
### OPA System Design
#### 4.1.2 Beam Steering Range  
The requirement on the range of beam steering or FOV is comparably less stringent. Axial rotation-based mechanical beam steering can provide 360\({}^{\circ}\) horizontal coverage, dwarfing any other mapping methods. However, 360\({}^{\circ}\) steering is not always needed in many cases and the OPA solution is mostly in direct competition with MEMS mirror scanning (or other micro motion technologies [74]) or imaging (i.e., flash LiDAR) approaches on cost and manufacturing scalability. A typical commercially available MEMS mirror-based LiDAR (with a single emitter) supports less than 60\({}^{\circ}\) horizonal FOV and 20-30\({}^{\circ}\) vertical FOV. The proposed horizontal FOV requirement for autonomous driving listed in Table 2 is 120\({}^{\circ}\). To accomplish such wide angles, one can in principle combine two or more emitter and receiver modules and this method is also implemented in many MEMS LiDAR product development.  
In a typical OPA design, beam steering is enabled by the optical interference of an antenna array in least one FOV direction (refer to Table 5). The steering range is theoretically limited by at least two factors: aliasing, i.e., high order constructive interference at angles other than the main direction (usually 0\({}^{\circ}\)) and diffraction envelope, i.e., the far field divergence of a single antenna. The aliasing angle \(\theta_{a}\) is the angle between two adjacent constructive interference. For a uniformly distributed antenna array, the Nth order \(\theta_{a}(N)\) is directly related to antenna spacing \(d\) by [75]  
\[\theta_{a}(N)=\sin^{-1}\!\left(\frac{N\lambda}{d}\right)-\sin^{-1}\!\left( \frac{(N-1)\lambda}{d}\right),\ N=1,2,\ldots M,\ M\lambda\leq d. \tag{5}\]  
The 1 st order aliasing practically represents the full steering range (between \(-\theta_{a}\)/2 and \(\theta_{a}\)/2) if aliasing is the sole limiting factor as shown by the example in Figure 3a. A steering angle larger than 60\({}^{\circ}\) requires antenna spacing at most 1.15\(\lambda\) (e.g., 1.8 \(\upmu\)m at 1550 nm) which may lead to unwanted optical coupling between Si waveguides. In addition, such small spacing requires hundreds of antennae for the mm size emission aperture and results in design and control complexity and high power consumption.  
The diffraction envelope originates from the emission profile of a single antenna. It can also be approximated by a Gaussian beam model with Equation (4). The peak beam power follows this envelope when beam is steered across its FOV. When a uniformly spaced antenna array is adopted, the divergence angle of a single antenna is always larger than the aliasing angle because the width of the antenna is less than of the spacing. For antenna arrays with large and unequal spacing to suppress aliasing peaks [52], the diffraction envelope may instead become the limiting factor because the beam power may become too little to maintain a good SNR while steering toward edge angles, as shown by the example in Figure 3b.
## 4 Advanced Agricultural Approaches
### Phenotyping  
The smart methods discussed earlier seem more promising for advancing agriculture, as they have been utilized to grow various crops in a conducive environment. Besides, some advanced technologies are evaluated to control their limitations through advanced sensing and communication technologies, thereby further enhancing crop capabilities. The most efficient method is the phenotyping approach, the advanced genetic engineering technique, and biotechnology, which correlates the genetic sequences of crops for agronomical and physiological aspects, as described in Figure 4. In the last few decades, several modifications have been made in the field of genetic engineering and biotechnology. However, many factors such as grain weight and disease resistance have not been dealt with yet due to a lack of efficient techniques.  
Recent advancements have described that phenotypes play an efficient role in determining many quantitative factors such as plant growth, disease resistance, crop yield, and crop quality in advanced genetic engineering and biotechnology [71]. One of these tools is image-based phenotypes and sensors, which monitor how these resolutions cannot accommodate numerous screen stimulants and help them understand the mode of action, thereby participating in their success [72]. Moreover, other tools such as the IoT and CropQuant-based phenotypes techniques are used to analyze many crop-related factors such as crop traits by genetic engineering and biotechnology for advanced agriculture [73]. Moreover, an automated production site-controlled program was established to continue the generation of data by this program. Machine learning modeling and feature analysis algorithms are given to exploit the association among phenotypes, growth environments, and genotypes.
## 4 Discussion
### Comprehensive Service Life Based on Analytic Hierarchy Process
#### 4.2.4 Artificial Amendment Coefficient  
Even when milled from the same pavement section, the material compositions of RAP are different with various milling types, including single layer milling, double layer milling and full depth milling. Here, a coefficient was used to quantify the effect of the layer combination on comprehensive service life. The worst situation was that samples only contained the upper layers, allocated with value 1.5 while the best was that samples only contained the lower layers, with value 0.7. The artificial amendment coefficients of other situations ranges in the interval [0.7, 1.5]. They are presented in Table 6.
## 3 Results
### Mass Loss Rate Under Different Dissolution Conditions  
Mass loss indicates rock deterioration under dissolution conditions. The dissolution-induced mass loss rate is a key metric for evaluating rock resistance to dissolution and structural stability. In heritage stone conservation, surface mass loss accelerates the progressive degradation of grottoes and carved features. This necessitates a systematic analysis of sandstone mass loss dynamics under dissolution conditions. The mass loss rate is determined as follows:  
\[k_{m}=\frac{m_{0}-m_{i}}{m_{0}}\times 100\%\]  
Here, \(m_{0}\) represents the initial mass and \(m_{i}\) denotes the mass that decreases progressively during the corrosion process.  
Figure 18 reveals that mass loss rates rise progressively over time in both dynamic and static dissolution processes. Dynamic cyclic dissolution shows phased increases in mass loss, with growth rates decreasing and a notable inflection point at 72 h. After 192 h of dynamic cycling, the mass loss rate reaches 0.74%. Static immersion dissolution exhibits a nearly linear mass loss trend, reaching 0.7% after 480 h. After 480 h of immersion, the rate of mass loss increase exhibits a slight deceleration.  
Figure 17: Growth rate of surface moisture content under different dissolution conditions. (**a**) Dynamic cyclic dissolution, (**b**) static immersion dissolution.  
Comparative analysis shows that the average mass loss rate in the dynamic process (0.00385% per hour) is four times that of static dissolution (0.00097% per hour). This necessitates enhanced protective measures for exposed heritage stone surfaces to reduce morphological degradation due to mass loss.
## 2 Materials and Methods Background
### Power Control  
Bypass diodes are advisable to be placed at the level of the photovoltaic panels. Some modules have diodes incorporated in their structure. The diodes switch to the forward direction at a negative voltage, which is caused by the shading of the solar cell equal to the sum of the unshaded cell voltage and the bypass diode voltage.  
The photovoltaic electricity generation is associated with fluctuations caused by climatic conditions, such as cloud shading. Ideally, the operation works at the MPPT point. It can be achieved in several ways:  
* Control on the panel side (by switching their serial or parallel arrangement);
* On the heating resistance side (by changing the internal resistance);
* Using the controller included between the panels and the heating resistance.  
The first two options usually provide only a few operation combinations, thus allowing only a partial approach to MPP. The use of mechanical contactors is not very suitable in terms of switching frequency; the wiring using a series of semiconductor switches is not suitable in terms of their cost and the need for many pieces with serial sequencing [60]. Another control option for consumer connection is using a three-phase inverter that allows asymmetrical control or connecting three single-phase inverters.
## 4 Material and Methods
### Taste and Odor Compounds Analysis
#### 4.2.2 Sample Preparation and Analysis  
The T/O compounds in samples were analyzed via solid-phase microextraction, combined with GC-MS, according to [PERSON] et al. (2000) and [PERSON] et al. (2018) [11; 99]. In detail, one liter or more water samples were filtered immediately through a GF/C filter ([PERSON], Dassel, Germany) at a low vacuum pressure. The dissolved fraction in water (80 mL) was transferred immediately into 125 mL headspace bottles with 25 g NaCl and a magnetic stir bar and measured using GC-MS in 12 h. Simultaneously, the particle-bound fraction retained on the filters was stored at \(-\)20 \({}^{\circ}\)C. For further extraction, the filters underwent three cycles of freeze-thawing and were then rinsed into a 125 mL vial with 80 mL of Milli-Q water. Subsequently, ultrasonication (JY92-2D, Xinzhi Co., Ningbo, China) was performed for 10 min in an ice bath. Enhanced recovery was achieved through the addition of NaCl and a magnetic stir bar. The T/O compound samples were stirred and extracted using a 2 cm-long CAR/DVB/PDMS fiber (Supelco, Inc., Bellefonte, PA, USA) at 65 \({}^{\circ}\)C for 30 min before injection into the GC-MS.  
The T/O compounds, after extraction, underwent analysis utilizing an Agilent 6890 gas chromatograph interfaced with an Agilent 5973 mass spectrometer, equipped with a DB-5 MS column (Agilent Technologies, Inc., Santa Clara, USA). The gas chromatography (GC) temperature program involved an initial holding phase at 60 \({}^{\circ}\)C for 2 min, followed by a gradual increase to 200 \({}^{\circ}\)C for 2 min at a rate of 8 \({}^{\circ}\)C/min, and subsequently elevated to 260 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min. Data analysis was executed in the selected ion monitoring mode. The identification of the targeted T/O compounds was accomplished through the comparison of retention times with established standards, alongside the verification of characteristic ions at m/z 112 and 126 for GSM, 107 and 95 for MIB, 137 and 152 for CYC, and m/z 177 and 91 for ION. The quantification of each analyte was carried out using the corresponding standard curve, with detection limits established at 0.2, 0.2, 0.5, and 0.4 ng\(\cdot\)L\({}^{-1}\) for GSM, MIB, CYC, and ION, respectively.
# Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring
## 2 Materials and Methods
### LEACH-Based K-Means Algorithm  
K-means clustering algorithm is a well-known algorithm in machine learning [8]. Contrary to the LEACH protocol, the K-LEACH uses the K-means clustering algorithm to have uniform node clustering and ensure better choices of CHs [11; 17; 27; 34]. During the first round, the K-LEACH supposes a random initial CH location [15; 37]. Afterward, K-LEACH considers that the lower distance from the cluster center is the criterion for a node to be selected as a CH during the CH selection process (from the second round onwards) [3]. The K-LEACH protocol is divided into several rounds, and each round includes a cluster formation phase and a stable state round [11; 11; 27; 33; 36]. Using K-means as a clustering strategy can minimize overhead during the CHs' re-election [5; 22; 30].  
The K-LEACH algorithm is similar to LEACH but with added machine intelligence to reduce energy consumption and prolong the overall network lifespan [9; 18; 25]. The K-LEACH algorithm chooses CH based on the remaining energy level and distance to cluster members [8; 9; 37]. The K-LEACH algorithm is based on grouping the items according to a  
Figure 1: Flowchart of the LEACH protocol.  
specific criterion, and the algorithm's input is the number of K groups (clusters) [22; 25]. The next step is to measure the Euclidean distance between each node and the centers of the cluster; the smallest distance is chosen to include this node in the nearest cluster center [2; 7; 25; 26; 27; 34; 37]. After all the nodes are grouped, the algorithm determines the new center of gravity for each cluster at each round [30]. The algorithm stops when the groups become stable [5; 7; 18; 23; 24; 29].  
In this algorithm, the CH election is not only based on the remaining energy level as in LEACH, but also depends on the distance to the sensor nodes.  
This in turn has a major effect on increasing network lifetime, as proved by [PERSON] et al. [38] and [PERSON] et al. [39] in their research work. They have presented the total remaining energy of the sensor nodes and the number of dead nodes, which indicates that the LEACH-based K-means can decrease the energy consumption of the sensor nodes throughout the simulation, which will result in a higher network lifetime compared to that of LEACH. Additionally, the number of dead nodes is lower using LEACH-based K-means.
# Machine Failure Prediction Using Survival Analysis
## 4 Materials and Methods
### Types of Machine Failure
#### 4.3.4 Overstrain Failure (OSF)  
Overstrain failure occurs when the forces acting on the tool exceed its design limits, leading to deformation or even breakage of the tool. This can happen when the material being machined is particularly hard or when the machining parameters are not optimized. OSF can lead to significant downtime and repair costs, if not prevented.
# Diffuse-Field Equalisation of Binaural Ambisonic Rendering
## 3 Diffuse-Field Equalisation
### Inverse Filter Calculation  
For the three tested orders of Ambisonics, 240 points around the sphere, distributed using T-design quadrature (21-design), were encoded into binaural Ambisonics. Diffuse-field responses were simulated from the RMS of each binaural Ambisonic render with solid-angle weighting separately for each ear.  
Linear-phase inverse filters were then calculated from the diffuse-field responses using [PERSON] and [PERSON]'s least-mean-square regularisation method [34], which produces perceptually preferred inversions to other currently available methods [35]. 1/4 octave smoothing was implemented using the complex smoothing approach of [36], and the range of inversion was 2 Hz-20 kHz, with in-band and out-band regularisation of 25 dB and 5 dB, respectively. The target response of the inverse filter was the diffuse-field response of the original HRTF dataset.  
The diffuse-field responses, inverse filters and resulting equalised frequency responses of the 1 st, 3 rd and 5 th-order binaural Ambisonic loudspeaker configurations are presented in Figure 4. The plots  
Figure 3: Voronoi sphere plots demonstrating the regularity in spherical distribution of points for four quadrature methods: (**a**) 230 pt. Lebedev, (**b**) 252 pt. Icosahedron, (**c**) 240 pt. Fibonacci and (**d**) 240 pt. 21-Design.  
show how the diffuse-field responses vary significantly for all 3 orders above \(f_{alias}\), with deviations as large as 9 dB for 1 st order.  
DFE was implemented through offline convolution of the calculated inverse filters with the HRIRs used in binaural Ambsonic rendering for each loudspeaker configuration. With truncation of the processed HRIRs, implementation therefore comes at no additional real-time computational cost.  
To assess whether the need for DFE applies to other HRIR datasets, the diffuse-field response calculations were repeated for 5 th-order using the 18 human datasets of individualised HRIRs from the SADIE II (Spatial Audio for Domestic Interactive Entertainment: [[https://www.york.ac.uk/sadieproject/](https://www.york.ac.uk/sadieproject/)]([https://www.york.ac.uk/sadieproject/](https://www.york.ac.uk/sadieproject/)).) database [37]. The calculated diffuse-field responses are illustrated in Figure 5, which shows the necessity for diffuse-field equalisation for all HRIR datasets.
## 5 Discussion and Application
The friction stir welding robot developed for aerospace needs is shown in Figure 14. It mainly applies to the welding of any complex curved surface in three-dimensional space, such as large rocket launchers, space station segments, or aircraft external skin structures in the fields of aerospace and national defense. Figure 14: Friction stir welding robot. Figure 13: Boundary between WNZ and TMAZ: (**a**) Retreating side; (**b**) advancing side. Taking the skin structure of the rocket as an example, the skin is made of 2A14-T6 aluminum alloy. The previous research on welding process parameters can be applied to the welding of the structure. The welding of the rocket skin is mainly divided into two working conditions, namely the welding of the rocket dome and the welding of the cylinder. The structural composition of the rocket top cover and the cylinder is shown in Figure 15. There are five main kinds of welding processes applied to them: melon valve welding, melon top ring seam welding, melon bottom ring seam welding, cylindrical ring seam welding, and cylindrical longitudinal seam welding. The melon flap welding is mainly the welding between the melon flaps of spherical or ellipsoidal components. The welding trajectory is from the bottom to the top of the FSW tool axis. The axial direction of the mixing head is the normal direction of the curved surface of the workpiece to be welded. The welding of the 2A14-T6 aluminum alloy rocket roof was carried out using a large-scale stirring welding robot developed independently. As melon flap welding is relatively difficult in the welding conditions, the welding between the two melon flaps of the roof was carried out first, and the welding process proceeded smoothly. Figure 16 shows the welding results of the front and back sides of the melon flap welding. It can be seen that the surface of the curved weld is smooth and flat, the weld formation is uniform and compact, and there are almost no flash defects, while the back side of the weld is relatively tight, no weld defects are found, and the weld has good forming characteristics. It can be concluded that the above process can be applied well to the welding of large curved 2A14-T6 aluminum alloy, and the welding quality is ideal.
## 2 Current and Voltage Transformations of Delta-Connected Machine  
The PTC algorithm is based on the machine's mathematical model for which the space vector theory is commonly utilized. However, the theory utilizes the machine phase voltages and currents. If the winding is delta-connected, then the currents measured at the inverter output are line currents that differ from the phase currents both in phase and magnitude. Therefore, it is necessary to modify the mathematical transformations that are used in conjunction with a star-connected machine.  
Another important difference that must be considered within the delta winding topology is that for the same inverter switching combinations, different motor phase voltage vectors are created (inverter line-to-line voltage now becomes motor phase voltage). As opposed to a star connection, the voltage vectors are again shifted both in phase and amplitude and have a different effect on the flux and torque control.  
The modifications in the current transformations and the differences between the phase voltage vectors are mathematically analyzed in the following two sections.
###### Abstract
The \"bottom-up\" approach provides an advantage for studying Pine forests productivity on Lesvos, due to the topographic complexity and the forest structural heterogeneity arising from the fire history on the island. In regions of topographic and vegetation heterogeneity the eddy covariance flux tower approach could be biased [38]. Thus, even though the \"bottom-up\" approach is more labour-intensive, it can provide a sound understanding of C fluxes within an ecosystem [38], especially at finer spatial scales. The aim of the study is to explore the key factors that control GPP variation in Mediterranean Pine forests of different post-fire age. Based on forest dynamics theory we expected an increase in GPP with post-fire age, until stands reached a stage of maturity, followed by a gradual decrease in productivity with increasing stand age. We anticipated seasonal GPP variation within stands to be primarily controlled by local weather conditions and phenological variation in foliar properties. Across different stand development stages, competition for light and the ability of canopy to capture radiative energy, expressed through LAI, was expected to be the main driver of GPP. We used three different methods to quantify LAI, based on direct and optical measurements. In order to systematically quantify variation in estimated GPP related to these sources of methodological variation, we used a factorial simulation protocol, which quantified variation in GPP across different methods of LAI and photosynthetic capacity representation.