esa-sceva/satcom-chunk-collection huggingface dataset:
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 4 Conclusions  
Since the traditional methods for identifying the Chinese walnut varieties are highly time-consuming, destructive, or subjective, a method based on the hyperspectral imaging (HSI) technique coupled with chemometrics was successfully applied for classifying Ningguo and Lin'an Chinese walnuts. The first two principal component (PC) score plots and transformed PC score visualization images were drawn to clearly show the differences between Ningguo and Lin'an varieties. The partial least squares-discriminant analysis (PLS-DA) based models showed strong ability for varieties classification (accuracies of 97.33%, 95.33% and 92.00% in calibration set, cross-validation set, and prediction set). In order to reduce data dimensionality and further reduce the modeling time, the most effective 15 wavelengths selected by competitive adaptive reweighted sampling (CARS) were used to build a new PLS-DA model yielding 96.33%, 95.67%, and 91.00% accuracies in the three sets. These 15 wavelengths can be likely the most potential ones in further developing a multispectral instrument to discriminate Chinese walnut varieties rapidly and non-destructively. Final visualization map was successfully generated to observe the specific variety of each sample in an intuitive way. Visual differences between walnut samples of different varieties as well as different areas of the same sample were clearly displayed. In summary, all the results showed that HSI was feasible and beneficial in further building online large-scale Chinese walnut varieties identification system. More  
Figure 5: Classification visualization of Ningguo and Lin’an walnut varieties, (**a**) false-color images of Ningguo walnuts, (**b**) false-color images of Lin’an walnuts, (**c**) classification maps of Ningguo walnuts, and (**d**) classification maps of Lin’an walnuts.  
Chinese walnuts originated from different planting areas will be further considered to improve the models' robustness.  
Conceptualization, H.J.; methodology, H.J.; software, H.J. and L.Y.; validation, H.J., L.Y. and X.L.; formal analysis, M.S.; investigation, M.S.; resources, H.J.; data curation, L.Y.; writing--original draft preparation, H.J.; writing--review and editing, M.S.; visualization, L.Y.; supervision, X.L.; project administration, H.J.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Jiangsu Agriculture Science and Technology Innovation Fund, grant number CX(20)3040, and the Key Laboratory of on Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, China.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors acknowledge the device support provided by NBL Imaging Systems Ltd.  
The authors declare no conflict of interest.
## 3 Comparative Analysis of the Algorithm Implementation Performance  
### Data Source  
We used more than 4000 forest fire risk images as the original data that ranged in size from 200 \(\times\) 200 to 4000 \(\times\) 4000 pixels and were taken in the Guangdong Longshan and Jiangmen Sihui forest farms. The input images were normalized to produce a standard data set. UAV images usually contain cluttered information, and so some important features can become submerged when their size is directly adjusted.  
We could quickly locate the core fire area in a forest fire risk image despite a complex background by dividing the detection area into a recommended area and a selection area by using significance detection and a logistic regression classifier. The specific steps were as follows.  
1. The region of interest (ROI) proposal. Bayesian-based saliency detection was applied to an original image I; the saliency was mapped to the planarization in the image mask IM; and the original image was masked to areas R1-Rn.
2. The color components, image energy, and entropy (vectors X1-Xn) of each ROI were determined to decide whether the characteristics vector in the ROI corresponded to flame or smoke, after which MBR and a fixed size boundary were used to divide the fire area.  
We used the color matrix value as the color feature descriptor and the energy and entropy of the angular second-order matrix of an image as the texture descriptor.
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
## 4 Dropped Object Frequency and Consequence Analyses
The probability of falling was derived from the \"Mechanical lifting failures\" of Oil and Gas Producers (OGP) [27] by citing the dropped object probabilities for fixed installation data:  
\[E=mgh \tag{2}\]  
where \(m\) = drop mass, \(g\) = gravitational acceleration, \(h\) = drop height. Figure 5: Grand QRA assessment scheme. Figure 6: Detailed procedure of dropped object assessment. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multirow{2}{*}{**Drop Zone**} & \multirow{2}{*}{**Service Item**} & \multirow{2}{*}{**Item to be**} & \multicolumn{2}{c}{**Predicted**} & \multirow{2}{*}{**Weight**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Light**} & \multirow{2}{*}{**Lifts Annually**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Object**} & \multirow{2}{*}{**Impact Frequency**} \\  & & & **Handed/** & **Impact Energy** & **(MT)** & **Height** & **Lifts Annually** & & **Object** \\ \hline \multirow{11}{*}{**Fractionation**} & & & Pump 1 & 2.94 & 0.10 & 3.0 & 2 & \(1.00\times 10^{-4}\) & 2.00 \(\times 10^{-4}\) \\ \cline{3-8}  & & & Pump 2 & 1.47 & 0.05 & 3.0 & 2 & \(1.00\times 10^{-4}\) & 2.00 \(\times 10^{-4}\) \\ \cline{3-8}  & & & Heather bundle 1 & 1.47 & 0.05 & 3.0 & 2 & \(1.00\times 10^{-4}\) & 2.00 \(\times 10^{-4}\) \\ \cline{3-8}  & & & Filter 1 & 1.47 & 0.05 & 3.0 & 10 & \(1.00\times 10^{-4}\) & 1.00 \(\times 10^{-3}\) \\ \cline{3-8}  & & & Cooler bundle 1 & 11.77 & 0.40 & 3.0 & 2 & \(1.00\times 10^{-4}\) & 2.00 \(\times 10^{-4}\) \\ \cline{3-8}  & & & Filter 2 & 5.89 & 0.20 & 3.0 & 10 & \(1.00\times 10^{-4}\) & 1.00 \(\times 10^{-3}\) \\ \cline{3-8}  & & & Demister 1 & 2.94 & 0.10 & 3.0 & 10 & \(1.00\times 10^{-4}\) & 1.00 \(\times 10^{-3}\) \\ \cline{3-8}  & & & Demister fan 1 & 0.15 & 0.01 & 1.5 & 0.4 & \(1.00\times 10^{-4}\) & 4.00 \(\times 10^{-5}\) \\ \cline{3-8}  & & & Motors & 0.15 & 0.01 & 1.5 & 0.4 & \(1.00\times 10^{-4}\) & 4.00 \(\times 10^{-5}\) \\ \cline{3-8}  & & & TCV & 0.15 & 0.01 & 1.5 & 0.4 & \(1.00\times 10^{-4}\) & 4.00 \(\times 10^{-5}\) \\ \cline{3-8}  & & & Demister fan 2 & 5.89 & 0.20 & 3.0 & 0.4 & \(1.00\times 10^{-4}\) & 4.00 \(\times 10^{-5}\) \\ \cline{3-8}  & & & Heather bundle 1 & 25.62 & 0.653 & 4.0 & 1 & \(1.00\times 10^{-4}\) & 1.00 \(\times 10^{-4}\) \\ \cline{3-8}  & & & Tube Burdle 2 & 117.72 & 3.00 & 4.0 & 1 & \(1.40\times 10^{-5}\) & 1.40 \(\times 10^{-5}\) \\ \cline{3-8}  & & & Dentenhainer & 52.97 & 1.35 & 4.0 & 0.4 & \(1.40\times 10^{-5}\) & 5.60 \(\times 10^{-6}\) \\ \cline{3-8}  & & & Dentenhainer & & & & & & \\ \cline{3-8}  & & & PartF3 & 0.78 & 0.02 & 4.0 & 0.4 & \(1.00\times 10^{-4}\) & 4.00 \(\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dropped object impact energy in the drop zones UD01-FD04.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 3 Methodology
### Multicollinearity Analysis  
When using landslide assessment factors as model inputs, the presence of high correlation or linear dependency among the input features can lead to inaccurate parameter estimation of the model, reduce its stability, and pose the risk of overfitting [7]. In this study, the _VIF_ was employed to assess the presence of collinearity among the input features, as specified in Equation (1):  
\[VIF_{i}=\frac{1}{1-R_{i}^{2}} \tag{1}\]  
where \(R^{2}\) represents the correlation between a given variable among the independent variables and the remaining independent variables through multiple linear regression. The resulting model accuracy is used as a measure of the correlation between this variable and the remaining independent variables. When the VIF is close to 1, it indicates that there is no multicollinearity among the independent variables, and each variable independently explains the dependent variable, resulting in good model stability. When the VIF is greater than 1 but less than 10, it suggests a certain degree of multicollinearity, which may affect  
Figure 6: Land cover dynamic landslide assessment factors: (**a**) 2000–2004; (**b**) 2005–2009; (**c**) 2010–2014; (**d**) 2015–2019.  
the interpretability of the model but not significantly impact its performance. However, when the VIF is greater than 10, it indicates severe multicollinearity, leading to unstable parameter estimation and potentially severe impacts on both the interpretability and predictive performance of the model.
## 3 Materials and Methods
### Drone Technology and Flight Dynamics  
Drone technology has rapidly advanced, significantly improving flight dynamics and performance. Modern drones leverage sophisticated flight control systems, aerodynamic designs, and advanced materials to enhance stability, maneuverability, and efficiency. Flight dynamics, which encompass the study of forces and moments affecting a drone in flight, are critical for optimizing its performance. Factors influencing flight dynamics include payload weight, battery life, aerodynamic drag, and thrust generation. The table below summarizes various drone types and their corresponding flight characteristics, while the figure illustrates the fundamental forces of flight dynamics.  
Figure 2 shows fundamental forces in drone flight dynamics, including thrust, drag, lift, and weight. Table 2 compares drone types, highlighting their payload capacities, flight times, maximum speeds, and control systems. Multirotor drones, known for their vertical takeoff and landing capabilities, are limited by their shorter flight times and lower speeds but offer excellent maneuverability. Fixed-wing drones, on the other hand, are designed for longer flight times and higher speeds due to their aerodynamic efficiency, making them suitable for extended missions. Hybrid VTOL drones combine the benefits of both designs, providing vertical takeoff and landing with the extended flight times and speeds of fixed-wing drones. Figure 2 illustrates the fundamental forces acting on a drone during flight: thrust, which propels the drone forward; drag, the resistance encountered; lift, which counteracts weight and keeps the drone airborne; and weight, the force of gravity pulling the drone down. Understanding these forces is crucial for optimizing drone performance and enhancing flight stability.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Drone Type** & **Payload Capacity** & **Flight Time (mins)** & **Maximum Speed (km/h)** & **Control System** & **Aerodynamic Design** \\ \hline Multirotor & 0.5–2 kg & 20–30 & 30–50 & GPS, IMU, ESC & Fixed rotor blades \\ \hline Fixed-Wing & 2–5 kg & 60–120 & 50–80 & GPS, Inertial Navigation & Streamlined wing structure \\ \hline Hybrid VTOL & 1–4 kg & 30–60 & 40–70 & GPS, IMU, Advanced Control & Combination of rotor and wing \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison for different drone types.  
Figure 2: Fundamental forces in drone flight dynamics, including thrust, drag, lift, and weight.
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### Abnormal KPI Screening Based on 1D-CNN-TCN Model  
#### 3.3.1 Data Preprocessing  
It is necessary to preprocess data before using a model for prediction. The original data obtained via system monitoring will appear lacking, and will contain too much noise. Interference factors in these original data will affect the training of the following model, and thus ultimately affect the accuracy of the output results. Therefore, before model training, this paper first conducts a simple preprocessing of the original data. In this paper, the linear interpolation method is used to fill in the missing values, which reduces errors and their impact on the ensuing model training. After filling in missing values, the Z-score method is used to standardize the processed data. All data are scaled to remove the unit limitation, and to convert them into dimensionless pure values, which can improve the reliability of the results.
###### Abstract

Table 1: Summary of fieldwork data acquisitions. Figure 1: Location of the three 2020 wheat fields near Mount Brydges in southwestern Ontario, Canada, over a Google Earth image. placed in a four-by-four grid, spaced 60 m from one another. A minimum distance of 50 m from roads was used to reduce possible effects of transportation pollution. From Google Earth, the points were exported to a KML file and downloaded onto mobile devices. On the first fieldwork date, the KML file was used to navigate to the pre-determined sample points and red flags were placed for accurate positioning in following fieldwork sessions. To measure the biomass at each sampling point, a square guide made of plastic tubing of 50 cm \(\times\) 50 cm was placed around a patch of wheat. The plants within the guide were destructively collected by cutting at the stem base above ground, then placed in paper bags. The average distance between rows was 17 cm, and mostly three rows of wheat would be collected from the 0.25 m\({}^{2}\) biomass block. On the same day following fieldwork collection, the fresh biomass was weighed in grams then fully dried in an oven at 60 \({}^{\circ}\)C for 48 h. Dry biomass was weighed before being sent to A&L Canada Laboratories for plant analysis. The biomass was ground into a fine powder able to pass through a 1 mm sieve before being used in the Laboratory Equipment Company (LECO) FP628 nitrogen combustion method to obtain the leaf N content percentages [35]. Six measurements of plant height in centimeters were taken around each sampling point within a 1 m\({}^{2}\) block on every fieldwork date, and an average height was calculated. Six measurements of soil moisture were collected within a 1 m\({}^{2}\) block around the sampling point using an ML3 ThetaProbe (Delta-T Devices Ltd., Burwell, Cambridge, UK) and averaged [36]. Plant phenology was recorded at each sample point using the BBCH scale to determine growth stage during data collections. The leaf area index (LAI) was measured non-destructively using a LAI-2200C Plant Canopy Analyzer (Li-Cor, Inc., Lincoln, NE, USA) [37]. Following manufacturer recommen  
Figure 2: Digital elevation model showing the variation of the wheat field’s topography at each sample point for (**a**) W1 field, (**b**) W2 field, (**c**) W3 field. dations, five measurements were taken along the row at each sample point during clear skies or uniform overcast conditions. Files from the device were transferred to and processed with the Li-Cor software File Viewer FV2200. Processing options include scattering correction based on the field conditions, and output text files with final LAI measurements.
## 3 Theoretical Analysis of the Anchoring Action of Oil Wells on the Movement of Overburden Rock
### Mechanics of Oil Wells in the Formation
#### 3.2.3 Effect of Oil Well Casing on the Compressive Strength of the Rock Formation  
Surrounding It  
Analyzing along the axial direction of the casing, the casing borehole undergoes a transition from a bi-directional force system to a tri-directional force system. This transition occurs due to the potential deformation of the casing, which induces axial stress on the surrounding rock, thereby enhancing its compressive strength in the horizontal direction.  
According to the Mohr-Coulomb strength criterion, the compressive strength of the casing-anchored solid in the horizontal direction is given as follows:  
\[\sigma_{s}=\frac{2c\ cos\varphi}{1-sin\varphi} \tag{6}\]  
The uniaxial compressive strength of the anchored surrounding rock mass of the oil well is expressed as follows:  
\[\sigma_{c}=\frac{2c_{r}\ cos\varphi}{1-sin\varphi} \tag{7}\]  
The increment of uniaxial strength of the surrounding rock after casing insertion is expressed as follows:  
\[\Delta\sigma=\sigma_{x}-\sigma_{c}=2 nS(c_{b}-c_{r})\frac{cos\varphi}{1-sin \varphi}+2\sigma_{b}\ tan\varphi \tag{8}\]  
In the formula, \(c\)--cohesion of surrounding rock after casing insertion, and \(c_{r}\)--cohesion of surrounding rock before casing insertion.  
Based on the calculations from Equations (6) and (7), the uniaxial compressive strength of the casing-anchored solid in oil wells in the horizontal direction reaches 37.2 MPa after the casing is embedded into the rock formation. Simultaneously, the increment in uniaxial compressive strength due to casing anchorage is determined to be 13.06 MPa. These results indicate that the insertion of the oil well casing significantly enhances the mechanical properties of the surrounding formation, effectively upgrading its original strength parameters.
## 2 Background of Scale Model Similitude Theories
Theoretically, for a given scale-modelling issue, there is no unique set of \(\pi\), but the variables should be correctly identified, and \(\pi(s)\) should be constructed correctly. The scaling relationships may then be established by connecting the model \(\pi_{i,m}\) to the corresponding prototype \(\pi_{i,p}\), where \(i\) ranges between 1 and \(\eta\). As stated earlier, the similitude theory endeavours to realistically characterise the problem by devising \(\pi\) term forms that are grounded in the prevailing forces in the system [10]. The stress components of the time history \(\sigma_{ij}(r,t)\) for a scale model are evaluated by analysing the formation of \(\pi\) terms resulting from an imposed acceleration time history \(a(t)\), where \(i,\;j=1,\;2,\;3\), etc. In order to satisfy the criteria for (true) scale modelling, it is imperative to adhere to two fundamental requirements, namely the Froude and Cauchy conditions. The indicated stress is a function of a number of variables involved in the system (see Equation (11)). Subsequently, the \(\pi\) terms can be developed as given in Equation (12). \[\frac{L_{m}}{L_{p}}=\lambda \tag{1}\]2\[\frac{L_{m}}{L_{p}}=\lambda \tag{1}\]3  
In these equations, the variables \(r,t,\rho,E,a,g,l,\sigma_{o}\), and \(r_{o}\) represent the position, time, material density, elastic modulus, acceleration, acceleration of gravity, object length, initial stress, and initial position, respectively. In 1g scale modelling, the dimensionless product \(a/g\) and Froude's number \(v^{2}/lg\) must be equal to unity, which means that the model-prototype ratio of a specific stiffness \((E/\rho)\) is equivalent to the geometric scaling factor \(\lambda\). This is referred to as the Cauchy condition and may be expressed in terms of the shear wave velocity (\(V_{S}\)):  
\[\frac{L_{m}}{L_{p}}=\lambda \tag{1}\]4  
[PERSON] and [PERSON] (2006) [10], also showed that in a dynamic model system, the Cauchy condition is an essential requirement for synchronous replication of restoring, inertial, and gravitational forces. However, the challenge in designing a true scale model is in selecting the model materials to satisfy the Cauchy condition with an amalgamation of a small elastic modulus and a large density. Alternatively, [PERSON] and [PERSON] (2006) proposed two alternatives to perform scale-modelling tests; these included simulating the artificial mass and ignoring gravitational effects.
## 2 Materials and Methods
### Deep Learning  
With recent advances in computation and the availability of large datasets, deep learning algorithms have been shown to be comparable with, and even exceed, human performance in image recognition tasks, including applications to diagnose human disease (e.g., [13] for ImageNet challenge, [14] for breast cancer histology images, and [15] for skin cancer images) as well as object detection [16; 17], and semantic segmentation [9; 18]. Convolutional neural networks (CNNs) learn key features directly from the training images by the optimization of the classification loss function and by interleaving convolutional and pooling layers, (i.e., spatially shrinking the feature maps layer by layer) [8; 19]; in other words, CNNs exploit feature representations learned from raw image pixels and have minimal need for a priori knowledge and pre-defined statistical features [14; 20]. Several studies have successfully implemented deep learning approaches and CNNs for various vegetation segmentation and classification tasks on satellite imagery and have achieved high accuracy [21; 22; 23].  
A fully convolutional network (FCN) is a deep learning network for image segmentation [24]. Leveraging the advantages of convolutional computation in feature organization and extraction, an FCN establishes a multilayer convolution structure and de-convolution layers to realize pixel-by-pixel segmentation [9; 25; 26]. Segmentation models, such as FCNs, are effective because the multilayer structure of these models adeptly handles the detail features of images.  
U-net--a specific type of FCN--has received a lot of interest for the segmentation of biomedical images using a reduced dataset [11] but has also proven to be very efficient for the pixel-wise classification of satellite images [22; 27]. The U-Net architecture (Figure 4) consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. Here, we adapted U-Net for semantic segmentation on aquatic vegetation using multispectral satellite imagery. For the design of our model, we followed the classical U-Net scheme that produced a state-of-the-art CNN encoder-decoder [11]. The workflow of our deep learning approach is presented in the next section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme 397** & **Wavelength (nm)** \\ \hline Coastal & 397–454 \\ Blue & 445–517 \\ Green & 507–586 \\ Yellow & 580–629 \\ Red & 626–696 \\ Red Edge & 698–749 \\ Near-IR 1 & 765–899 \\ Near-IR 2 & 857–1039 \\ \hline \hline \end{tabular}
\end{table}
Table 1: WorldView-2’s eight multispectral bands.
## 4 Hardware Customization
### LoRa-Based Gateway  
The purpose of the LoRa-based gateway is to receive LoRa-modulated RF messages from the RF and LoRa Coordinator and sends the same information to the main server using the Internet. The block diagram is shown in Figure 13(a); it uses the ESP8266 Wi-Fi module and LCD.  
The customized hardware of the LoRa gateway is shown in Figure 13(b), the ESP8266 module helps to connect to the Internet through Wi-Fi. The technical specifications of all of the modules are listed in Table 8.