esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 2 Materials and Methods
### Grading Standards and Dataset Construction
#### 2.1.3 Construction of an Apple Grading Dataset  
In the research on apple grading models, we utilized an advanced image acquisition device equipped with an industrial-grade 3206 IR-CUT high-definition distortion-free camera. This camera features an advanced CMOS sensor (1/2.7 inch) and operates at a frame rate of 30 frames per second, ensuring seamless image capture of apples. The device is fitted with an IR-CUT function to correct color deviation and enhance brightness in low-light environments. In well-lit conditions, the IR-cut filter is activated, allowing the CMOS sensor to capture the true colors of apples. When the sensor detects insufficient light, an automatic LED infrared fill light (invisible to the naked eye) is activated and the IR-cut filter is removed, enabling the infrared filter to function. This helps the camera make full use of LED infrared light, significantly improving image capture performance in low-light settings, ensuring the clarity and accuracy of apple images. The industrial camera captures three different angles of each apple, and the images are sent to a local computer for storage. Our chosen image acquisition equipment ensures stable and accurate image capture under varying lighting conditions, meeting the consistency and high reliability required in industrial applications. Our study used 126 apples as the research subjects for developing a classification model. Images of the apples were taken from the top, front, and rear views to extract three features: color, diameter, and shape.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multicolumn{4}{c}{**Apple Grade**} \\ \cline{2-5}  & **Premium Fruit** & **Grade A Fruit** & **Grade B Fruit** & **Substandard Fruit** \\ \hline Stem Feature \(\downarrow\) & Intact & Intact & Intact (minor damage allowed) & Does not meet the \\ Color Feature \(\downarrow\) & \(\geq\)90\% & \(\geq\)75\% & \(\geq\)60\% & Does not meet the \\ Diameter Feature \(\downarrow\) & \(\geq\)80 mm & \(\geq\)70 mm & \(\geq\)65 mm & requirements for the \\ Shape Feature \(\downarrow\) & \(\geq\)0.8 & \(\geq\)0.7 & \(\geq\)0.6 & corresponding grade \\ \hline \hline \end{tabular} \(\downarrow\) represents the order of feature priority.  
\end{table}
Table 1: Apple grading customized standards.
# Time Series Analysis of Acoustic Emissions in the Asinelli Tower during Local Seismic Activity
## 3 Results  
### Correlation between AE and Earthqu  
The plot of the AE signals count rate over the monitoring time (Figure 2) suggests that the masonry wall is undergoing a damage process. The clusters of AE hits, especially around the time 500 h, can be regarded as signature of high crack growth rate. During the monitoring period, frequent seismic events occurred in the region [30], but only those that might have affected the stability of the tower have been considered. Basing on their magnitude and epicentral distance, we have selected the two strongest regional earthquakes (the magnitude 4.1 event that hit the Rimini area on 13 October 2010 at 11:43 p.m. with epicenter about 100 km far from Bologna, as shown in Figure 3, and the magnitude 3.4 event recorded in the Modena Apennines on 21 November 2010 at 4:10 p.m.) and the nearby earthquakes with magnitude \(\geq\)0.5 and epicentral distance \(\leq\)20 km from the monitoring site.  
Figure 2: (a) Time series of the AE signals count rate and nearby earthquakes (extracted from [[http://www.ingvit/it/](http://www.ingvit/it/)]([http://www.ingvit/it/](http://www.ingvit/it/)), see Supplementary Materials) printed from [28]; (b) Expansion of the dashed frame: the red line marks the occurrence time t\({}_{EQ}=486\) h of 13 October; earthquake (EQ).  
Figure 2a shows the AE signals count rate and the earthquake time series, referring to the entire monitoring period, where a correlation in time between AE clusters and seismic events can be observed. A significant example is given by the seismic and the AE sequences that occurred between 1500 and 1800 h. Even more remarkably, the densest AE cluster (formed by 4000 hits and highlighted by the dashed frame in Figure 2) and the magnitude 4.1 earthquake that occurred on 13 October 2010 at 11:43 p.m. appear to be closely correlated.  
Unless uncontrolled factors affecting the measurements, the main seismic shock apparently triggered temporary intensification of the AE activity, revealing local instability until the recovery of equilibrium. The comparatively longer duration of this AE cluster with respect the earthquake duration could be explained by a viscoelastic behavior, assumed for masonry structures [39], which would produce stress and strain over time in response to an impulsive load [40].
## 5 Future Directions
### Integration with Innovative Technologies  
Integrating AI-based IAM systems with innovative technologies like blockchain is increasingly necessary for achieving decentralized and secure access management. Blockchain technology provides a robust, tamper-proof ledger for recording access transactions, ensuring transparency and immutability [79]. When combined with AI, which excels in analyzing patterns and making real-time decisions, this integration enhances the security and efficiency of IAM systems. The decentralized nature of blockchain mitigates the risks associated with centralized data storage, reducing the likelihood of single points of failure and unauthorized access. This synergy between AI and blockchain fortifies access control mechanisms, ensuring secure, transparent, and efficient management of identities across various industrial and organizational landscapes [80].
# Basic Bioelement Contents in Anaerobic Intestinal Sulfate-Reducing Bacteria
## 2 Materials and Methods
### Measurement of Microelements in Bacterial Cells  
The bacterial strains were collected from the end of the exponential growth phase (48 h) and washed three times in a phosphate buffer. The accumulated biomass (1.5 mL) was disintegrated by an ultrasonic device and vortexed prior to the microwave digestion in an MLS 1200 Mega closed vessel digestion unit (Milestone S.r.L., Sorisole, Italy) using a mixture of 2 mL HNO\({}_{3}\) and 0.5 mL of H\({}_{2}\)O\({}_{2}\). A power-controlled digestion program was applied: 2 min-250 W, 2 min-0 W, 5 min-400 W, 2 min-0 W, 2 min-500 W, 2 min-0 W, and 6 min-600 W. The digests were then cooled to laboratory temperature, diluted with ultrapure water in a 10 mL volumetric flask. The blank samples underwent the same procedure as the bacterial samples. To quantify the content of the trace elements, two series of calibration solutions were prepared by diluting single element certified reference materials in ultrapure water. The calibration range was 0.1-1000 \(\upmu\)g L\({}^{-1}\) for Co, Cu, Fe, Mo, Mn, Ni, and Zn and 1-10 000 \(\upmu\)g L\({}^{-1}\) for Ca, K, and Mg, respectively. All used plastic materials andvolumetric glassware were placed into a 10% (\(v/v\)) nitric acid bath for 24 h and were then rinsed three times using ultrapure water and dried.  
The quantitative analysis of chemical elements was carried out by the inductively coupled plasma-mass-spectrometry (ICP-MS) using a 7700\(\times\) instrument (Agilent Technologies, Tokyo, Japan) equipped with an octopole reaction/collision cell, a quadrupole mass analyzer, and an ASX-520 autosampler. The optimized ICP-MS instrumental conditions for He mode were as follows: RF power of 1550 W, plasma gas flow rate of 15.00 L min\({}^{-1}\), auxiliary gas flow rate of 0.9 L min\({}^{-1}\), nebulizer gas flow rate of 1.05 L min\({}^{-1}\), and dwell time 100 ms for all measured isotopes (\({}^{24}\)Mg, \({}^{39}\)K, \({}^{43}\)Ca, \({}^{55}\)Mn, \({}^{56}\)Fe, \({}^{59}\)Co, \({}^{60}\)Ni, \({}^{63}\)Cu, \({}^{66}\)Zn, and \({}^{95}\)Mo) including internal standards (\({}^{45}\)Sc, \({}^{72}\)Ge, \({}^{89}\)Y, and \({}^{115}\)In). Sodium (Na) content was measured by flame spectrometer BWB-EP (BWB Technologies, Newbury, the UK) and the calibration range was 5-1000 mg L\({}^{-1}\). Each bacterial sample was measured in six replicates, and the results are presented as an arithmetic mean \(\pm\) SD, where SD denotes the standard deviation.  
Measurement accuracy was verified by the repeated analysis of a quality control sample at the concentration of 100 ug L\({}^{-1}\) for macro elements; the accuracy for micro elements was tested by the analysis of external certified reference materials (CRM) of water TMDA-64.3 and TM-25.6. Before the application of the method for real bacterial sample analysis, the ICP-MS method was validated by using spike samples at the concentration level of 10 ug g\({}^{-1}\) for Mn, Co, Ni, Cu, and Mo; 100 ug g\({}^{-1}\) for Mg, Fe, and Zn; and 1 mg g\({}^{-1}\) for K and Ca.
# Fate of Soil Carbon Transported by Erosional Processes
## 3 Soil Erosion as a Source or Sink of Carbon
### Slope  
Attributes of land slope that determine specific pedotransformation include gradient (steepness), aspect (north vs. south facing), shape (convex vs. concave), position (summit, side slope, foot slope), and length (short vs. long). The magnitude of sediment generated, and the amount of soil C transported laterally also depend on the soil surface as altered by agronomic management and its interaction with the slope attributes. While assessing the scale-dependency of erosion-induced variation in CO\({}_{2}\) emission from terraced slopes, [PERSON] et al. [15] observed that emissions tend to decline with increase in slope gradient. The declining trend is attributed to increased runoff and more soil erosion on steeper slopes and making eroded soil less habitable for soil biota in terms of their activity and species diversity.  
Slope attributes also affect the deposition of sediments and the associated soil C. [PERSON] et al. [16] evaluated losses of soil C by water erosion in a tropical watershed characterized by a wide land-use diversity. [PERSON] and colleagues observed that of the 126.5 Mg/yr. of the total eroded SOC from the watershed, 111.6 Mg/yr. were deposited in relief depressions and only 14.9 Mg/yr. reached the water body system. [PERSON] et al. [17] observed that bulk soil samples obtained from the summit landscape position emitted the greatest cumulative CO\({}_{2}\)-C (0.49 \(\pm\) 0.04 g C/kg) among all the landscape positions. Furthermore, the CO\({}_{2}\) emission rate from coarse-sized aggregate fraction at depositional slope positions (toe-slope and foot-slope) was significantly greater than those from the eroded slope positions (summit, shoulder slope and back slope).  
Erosion-induced losses of soil C and the attendant CO\({}_{2}\) emissions from a loess and black soil in China were studied by [PERSON] et al. [18] for three slope gradients (5\({}^{\circ}\), 15\({}^{\circ}\)and 25\({}^{\circ}\)) using a rainfall simulator. On average, SOC loss from loess soil was about 1.8 times that from black soil although the SOC concentration in original black soil was 56% higher than that in the loess soil. The cumulative CO\({}_{2}\) emissions from the eroding slopes of the loess ranged from 15.4 to 19.7 g C/m\({}^{2}\) compared with 28.1 to 59.6 g C/m\({}^{2}\) for the black soils. However, when the slope gradient increased from 5\({}^{\circ}\) to 25\({}^{\circ}\), the cumulative soil CO\({}_{2}\) emission decreased by 23.8% on black soil and 12.6% on loess soil. These observations documenting decrease in on-site emissions from steep slopes are similar to those by [PERSON] et al. [15] discussed above.
## 3 Passive Thermal Control Methods
### Heat Pipes  
Heat pipes are two-phase heat transfer devices that are categorized as one of the efficient passive thermal control methods for space applications because they do not need any additional power for their operation [36]. With smaller cross sections and high thermal conductance, these devices are capable of transferring heat over a long distance within the satellite. Heat pipes are broadly divided into three sections, namely: evaporator, adiabatic, and condenser. A typical heat pipe as illustrated in Figure 9[37] has one evaporator section that takes heat from a heat source. The heat causes change of phase of the working fluid (water is used in this study) from liquid to vapor and moves to the adiabatic or transportation section due to increased vapor pressure at the evaporator section and then the vapor reaches the condenser section where condensation rejects the latent heat of the fluid to the sink. The condensed liquid then moves back to the evaporator section due to capillary pumping action [11; 31]. The size of the heat pipe entirely depends on the heat load that it is subjected to. Constant Conductance heat Pipes (CCHP) are the basic and standard isothermal heat pipes whose thermal conductivity also can be varied by changing the physical properties such as pore diameter, porosity, and permeability [38].  
Depending on the working fluid, wick structure, size, and operational temperature, heat pipes undergo various heat transfer limitations [39]. Total and effective heat pipe lengths are given in Equations (21) and (22).  
\[l_{total}=l_{e}+l_{ad}+l_{c} \tag{21}\]  
\[l_{eff}=\frac{1}{2}(l_{e}+l_{c})+l_{ad} \tag{22}\]  
Heat pipe parameters used in this study are given in Table 3 and these values are derived based on available volume within the satellite.  
In this study, the heat pipes are considered to be directly embedded onto both heat exchanger (HX) and radiator aluminum panels to maximize the heat transfer rate. Embedded heat pipes typically increase the effective thermal conductivity by several factors [40].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Values** \\ \hline Vapor core diameter & \(r_{v}\) & 0.003 \\ Wall material & - & Copper \\ Working fluid & - & Water \\ Wall area (m\({}^{2}\)) & \(A_{w}\) & 0.0000026 \(\times\)\(10^{-6}\) \\ Wall shape & - & circular \\ Evaporation length of heat pipe (m) & \(l_{e}\) & 0.08 \\ Adiabatic length of heat pipe (m) & \(l_{ad}\) & 0.08 \\ Condensation length of heat pipe (m) & \(l_{c}\) & 0.12 \\ Effective length of heat pipe (m) & \(l_{eff}\) & 0.18 \\ Thermal conductivity of heat pipe material & \(\lambda_{m}\) & 389 \\ (W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\)) & \(\lambda_{m}\) & 389 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Heat pipe design parameters used.  
Figure 9: Heat Pipe—schematic.
## Materials and Methods
### Passage Detection by LRF
#### 2.3.2 Interpolation of Passage Detection  
In this study, we interpolate the passage. In the conventional method, passage detection is performed at each time point. However, there is a moment when passage detection is not performed. Therefore, in this study we save the center coordinates of the passage once detected. In this study, it is assumed that the system is performed in an unknown environment which does not have an environmental map in advance. Therefore, the coordinates of the passage center are saved as absolute coordinates with the movement start position as the origin. Then, the number of times passage center coordinates included within 0.7 m from the saved coordinates are detected is counted. When this count exceeds 400 times, the passage center coordinates are assumed to be reliable. Then, if no passage center included within 0.7 m from the coordinates has been detected after that, interpolate the passage center coordinates \((\hat{X}_{p}^{w},\hat{Y}_{p}^{w})\) at that coordinate. By interpolating the passages in this manner, more accurate passage detection is performed.  
Figure 6: Passage detection by laser range finder (LRF): (**a**) Result of passage detection; (**b**) Result of interpolation of the passage.  
Figure 6b shows the result of interpolation of the passage center coordinates when turning right. The interpolated center coordinates are marked with a red circle.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 3 Separation Calculation Model Based on Track Prediction
### Track Prediction Model
#### 3.1.1 K-Means Classification Model  
Track data features, such as heading, latitude and longitude, altitude, and speed information, should be extracted firstly, and then the K-means model is applied for training to achieve clustering and classification. By introducing the silhouette coefficient to evaluate the clustering effect under different numbers of categories, the optimal number of categories is ultimately determined.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 3 Element Design
### Amplitude-Control RRA Element  
According to the design concept described in Section 2, after obtaining the equivalent impedance parameters of the THz switch in Section 3.1.3, the task of this section is to make the characteristic impedance of Port 2 equal to the equivalent impedance of the switch in either ON or OFF state. To realize this characteristic in a CMOS process, we selected a split-ring antenna instead of a patch antenna. This is because a patch antenna struggles to meet the metal density requirements of the CMOS process, and the distance between the ground and the patch is less than 10 \(\upmu\)m, which can easily lead to a cavity resonance effect, effectively functioning as a terahertz wave absorber.
# A Hybrid Artificial Intelligence Model for Detecting Keratoconus
## 3 Background
### K-Means  
The _k_-means is one of the well-known heuristic clustering algorithms [40]. The primary aim of _k_-means is identifying similar samples in the dataset based on their particular characteristics (i.e., features). There are many approaches to calculate the similarity among samples [41]. The _k_-means start with a random selection of sample as cluster center (i.e., cluster centroid) and then use similarity measures such as Euclidean distance to calculate the similarity or dissimilarity between the cluster center and the sample. After assigning the closest sample for each cluster, the centroids of the clusters are recalculated based on the average values for all sample features. The _k_-means process is stopped once the predefined number of iterations is meet or until no more changing of the clusters centroids values. The main steps of _k_-means process are implemented in Algorithm 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Flower Pollination Terms** & **Optimization Terms** \\ \hline Pollen & Solution vector \\ Flower constancy & Iteration \\ Lévy flight & Value \\ Pollinators (birds, butterflies, etc.) & Decision variable \\ _Abiotic_ & Local search \\ _Biotic_ & Global search \\ Optimal flower reproduction & Optimal solution \\ \hline \end{tabular}
\end{table}
Table 1: The explanation of the flower pollination algorithm terms in the optimization context.  
Figure 2: Some examples of flower pollinators: (**A**) hummingbird, (**B**) honeybee, and (**C**) butterfly pollination processes.  
```
1:Input: Number of clusters \(k\), row data (samples) \(S\).
2:Output: Matrix contains \(S\) assigned to \(K\).
3: Select random from \(S\) as for each \(K\).
4:while the number of iterations is not meet or no more changing of the clusters centroids values do
5: Calculate the similarity between the clusters centroids and \(S\).
6: Assign \(S\) to the closest cluster centroid.
7: Calculate the clusters centroids.
8:endwhile
```  
**Algorithm 1** Psudo code of \(k\)-means clustering heuristic algorithm.
# Effects of Tunnel and Its Ventilation Modes on the Aerodynamic Drag of a Subway Train
## 3 Measurement Results
### Train Speed Distribution  
As illustrated in Figure 3a, \(V\) varies greatly during the entire trip. \(V=0\) m/s in Figure 3a corresponds to the stops of the train in the stations. The aerodynamic drag, \(D\), on  
Figure 4: Time histories of thermocouple measured temperature in the two ventilation modes.  
a train depends on \(V\), and is proportional to \(V\) with a power of slightly less than two [2]. The energy consumption caused by \(D\) is the product of \(D\) and the distance the train travels. The distance a train travels in a given time is also proportional to \(V\). Therefore, the power consumption caused by aerodynamic drag is approximately proportional to \(V^{3}\). It is then interesting to examine the probability density functions, PDF (\(t\)) and PDF (\(V^{3}\cdot t\)), of \(t\) and \(V^{3}\cdot t\) at various train speeds, where \(t\) is time. PDF (\(t\)) and PDF (\(V^{3}\cdot t\)) are shown in Figure 5 for the two ventilation modes. The maximum speed (\(V_{\text{max}}\)) in the two ventilation modes is the same, i.e., \(V_{\text{max}}\approx 22\) m/s = 79.2 km/h, which accounts for less than 2% of the entire trip. The most likely or representative train speed is about 11.5 m/s (=41.4 km/h), which accounts for 10% and 8% in time of the entire trip for the recirculation and free-cooling modes, respectively (Figure 5a). The contribution to the total power consumption at the representative \(V\) (=11.5 m/s) is only around 8% and 6% in the recirculation and free-cooling modes, respectively (Figure 5b). On the other hand, although the higher \(V\) of 20.5 m/s accounts for only 3.9% of total time, its contribution to the total power consumption reaches about 17% (Figure 5b), irrespective of the ventilation modes. This observation indicates a necessity to examine a representative higher train speed as well as the most likely train speed. As such, two selected train speeds, i.e., the most likely train speed \(V=11.5\) m/s and the representative high train speed \(V=20.5\) m/s, are chosen as examples to analyze the effects of tunnel and ventilation modes on \(\tau_{w}\) and \(D_{p}\).  
Figure 5: Probability density functions PDF of (**a**) \(t\) and (**b**) \(V^{3}\cdot t\) at various train speed \(V\).
## 2 Dataset
### Roti  
To describe the phase fluctuation of GPS signals, [PERSON] et al. [22] defined the rate of TEC index (ROTI) based on the standard deviation of ROT; ROT is the rate of change of TEC. In this study, the slant TEC is converted to the vertical TEC, using only GPS satelliteswith elevations greater than 30\({}^{\circ}\), and the altitude of the ionospheric pierce point is set as 850 km. The mean VTEC data in 20 s are calculated as the background to detrend the VTEC, corresponding to a distance of around 150 km traveled by Swarm satellites. The **ROT** was calculated and converted to the unit of TECU/min according to the following formula, where \(i\) is the corresponding satellite and \(\Delta t\) is 1 s:  
\[\textbf{ROT}(\textbf{\emph{t}})=\frac{\textbf{VTEC}^{t}_{\textbf{\emph{t}}+ \Delta t}-\textbf{VTEC}^{t}_{\textbf{\emph{t}}}}{\Delta t}\times 60 \tag{4}\]  
where **VTEC** is the detrended vertical TEC, and **ROT** is obtained by calculating the standard deviation of **ROT** in the running windows of 20 s, which corresponds to the phase scintillation at a spatial scale of approximately 7.5-150 km:  
\[\textbf{ROT}=\sqrt{<\textbf{ROT}^{2}>-<\textbf{ROT}>^{2}} \tag{5}\]  
where the symbol \(<\)- denotes the mean value in the running window.