esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
An HSI technique for the quantitative evaluation of FWCs in OF within the Vis/NIR region was investigated; the HSI system, image processing method, chemometrics models, and model preprocessing methods were developed and analyzed. The reflectance spectra in the Vis/NIR region, which were extracted from the hyperspectral images of mixture samples, were processed into the EW region (500 to 750 nm) for LS-SVM and PLS models. In the EW region, the spectral band of the lipid components between 600 and 700 nm mainly affects the hyperspectral images for determination of the FWCs in OF. After preprocessing was applied to both models, the LS-SVM model with range normalization was selected because of the stable performance of the coefficients of determination and the low SEP and SEC values. The \(R_{p}^{2}\) obtained for evaluation of different FWC levels (0, 5, 10, 15, 20, 30, 40, 50, and 100%) was approximately 0.83 because of the different particle sizes in the mixture samples. However, the dynamic changes in the FWCs with increased concentration were clearly distinguished using distribution maps visualized using the LS-SVM model. Hence, the developed HSI technique may constitute an alternative method for a rapid and accurate inspection system for real-time quality screening to prevent excessive addition of FW fertilizer to OF. This approach introduces a promising research avenue for development of various nondestructive quality and safety evaluation systems for particle-based fertilizers.  
Conceptualization, H.L. and B.-K.C.; methodology, H.L. and B.-K.C.; software, H.L. and I.B.; validation, H.L. and G.K.; formal analysis, H.L. and G.K.; investigation, H.L., I.B. and G.K.; resources, H.L. and B.-K.C.; data curation, H.L. and G.K.; writing--original draft preparation, H.L. and G.K.; writing--review and editing, H.L., G.K., B.-K.C. and M.S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Agri-Bio industry Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (319114-3).  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository.  
The authors declare no conflict of interest.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 2 Materials and Methods
### Model Training
#### 2.5.3 Repeat Verification  
The 100 iterations of validation yielded high reliability. In the process, different datasets were randomly used for training, and an average value was used to evaluate the performance of the model. The mean values of \(R^{2}\), \(MAE\), and \(MSE\) for the protein model were 0.9825, 0.0084, and 0.0012, respectively. The variations in these evaluation indices indicated relatively accurate predictions. The mean \(R^{2}\), \(MAE\), and \(MSE\) values for the fat model were 0.9981, 0.0070, and 0.0013, respectively. Compared to the protein model, there were individual indicators with relatively large values but within the margin of error. Thus, the prediction model was repeatedly validated to demonstrate that the XGBoost model has good learning performance and can be used for the measurement of protein and fat content in milk.
# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
## 4 Multi-Threshold Segmentation Algorithm Based on Improved PSO  
The classical PSO algorithm is an effective method to deal with the optimization problem of threshold segmentation. However, due to the mechanism of particle swarm optimization algorithm, when used in image processing, a large number of calculations will make the image processing time longer, and premature convergence will fall into local optimization, thus reducing the overall work efficiency and affecting the accuracy of threshold segmentation. This paper proposes an improved particle swarm optimization algorithm to optimize OTSU multi-threshold image segmentation. The speed and accuracy of multi-threshold image segmentation are improved by optimizing the basic parameters of the particle swarm optimization algorithm and proposing an elite particle search strategy based on the classical particle swarm optimization algorithm.
## 3 Model and CFD Methods
### Spatial Motion Simulation
#### 3.3.2 Body Force Propeller Model  
The weighted integration of the flow velocity onto the propeller's inflow plane provides the propeller's inflow velocity, which is substituted into the propeller's known hydrodynamic curve to estimate the thrust and torque. Subsequently, by adding source terms, an equivalent force and moment are exerted on the fluid within the specified volume force field. This method is referred to as the body force model [33]. Compared to conducting actual propeller calculations, this method has a higher computational efficiency. Therefore, the propeller was replaced by a virtual propeller based on the volume force model. This virtual propeller adopts the DTMB 4382 propeller parameters, which are reported in Table 3. Additionally, Figure 6 depicts the virtual propeller's location within the submarine, the inflow plane diameter of the blade disk (\(D_{i}=1.1D_{P}\)), and the offset of the inflow plane (\(d_{s}=0.1D_{P}\)).  
Figure 4: The computational regions. (**a**) Hull region; (**b**) rudder region.  
Figure 5: The region grids. (**a**) Hull region grid; (**b**) rudder region grid.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Blades & - & 5 \\ Diameter & \(D_{P}\) & 0.15 \\ Hub-to-diameter ratio & \(d/D_{P}\) & 0.2 \\ Disk aspect ratio & \(A_{o}/A_{0}\) & 0.725 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main parameters for DTMB4382.  
Figure 7 illustrates the open-water performance curve. In order to obtain the same performance as the propeller, its characteristics should be incorporated into the volume force model, where the volume force depends on the thrust coefficient \(k_{t}\) and torque coefficient \(k_{q}\). These coefficients are obtained from open-water experiments and are related to the advance ratio \(J\):  
\[J=V/(nD_{p}) \tag{25}\]  
where \(n\) represents the rotational speed of the propeller, \(D_{p}\) is the propeller's diameter, and \(V\) is the fluid velocity at the propeller location.
## 3 Restoration of Proto-Type Basins and Lithofacies Paleogeography
### Passive Continental Margin Stage  
From the Early Cretaceous Neocomian, seafloor spreading continued in Somalia and the Mozambique Channel Basins, and the Paleo-Tethys transgression reached the southern end of the African Plate (Figure 3c). East Gondwana drifted southward relative to Africa along large submarine-transforming faults such as the Davie Ridge. In the northern part of the eastern margin of the African Continent (Kenya, Somalia), it might be dominated by tensile movement, while in the Tanzania-Rovuma Basins, there was a transition margin similar to Cote d'Ivoire-Ghana in West Africa, dominated by strike-slip movement. In the northern part of the Angoche Basin, the main movement was pulling apart, and there was also strike-slip movement, which made the East African margin show obvious segmentation activity, and the movement modes of each segment are different [4; 7]. Moreover, extensional faults were developed between Madagascar on the eastern Gondwana Continent and the northern part of the Indian landmass [4].  
During the Early Cretaceous, with the rise of global sea level, extensive transgressive sequences were developed at the margin of East Africa, and relatively single argillaceous rock stratigraphic sequences were deposited in the whole region. These argillaceous sequences were gradually developed towards the continental slope (Figure 3c). Volcanic activities were relatively developed in Madagascar, the southern part of the India massif, and the adjacent Mozambique Channel and Mascarene Basin, where thick pyroclastic rock and volcanic-sedimentary stratigraphic sequences were deposited. These volcanic activities might be related to the separation of the India and Seychelles Plates from Madagascar (84 Ma) [6; 7]. During the Late Cretaceous, influenced by volcanic orogen, both the East African Continent and Madagascar had a more abundant source supply and a significant increase in coarse debris deposition (Figure 3d).  
The Paleocene and Eocene were the stable periods of the East African continental margin. Shallow-water shelf carbonate deposits were widely distributed on the East African continental margin, and reef facies belt deposits can be seen along the outer margin of the shelf. In the central-northern part of the Lamu Basin and the Juba Basin, larger delta deposits were developed.  
From the Oligocene to the Miocene, with the decline of global sea level, the African Craton rose. The passive continental margin of East Africa underwent progradation sedimentation, and a series of highly constructive delta basins (such as Rovuma and Juba) were developed from south to north [22] (Figure 3e).
# Performance Modeling and Analysis of LTE/Wi-Fi Coexistence
## 3 Modeling  
### LTE/WI-Fi Model Integration  
Wireless broadband access technology can provide end-users with diverse services through multiple technologies, while maintaining a better performance and quality of service of the network. On the other hand, the core objective is to offer an interoperability solution that includes a deployment of different technologies to accomplish the enhanced performance of the network, so that the end-user can enjoy the service at any time and anywhere [24; 25]. By suggesting the LTE/Wi-Fi interoperability model and incorporating different technologies, shared characteristics are achieved for the benefit of end-users in diverse non-line-of-sight environments. The proposed scenario is to obtain a hotspot unlicensed Wi-Fi zone surrounded by a set of licensed LTE zones, in which users can have endless connectivity in different conditions, as shown in Figure 1. LTE is the primary macro-cell zone (i.e., zone 2) with a licensed spectrum to manage all the data connections, while Wi-Fi is the secondary micro-cell zone with an unlicensed spectrum to handle the best effort user data. Considering the above assumptions, the following scenario works: allocating best effort traffic via the Wi-Fi interface and QoS-sensitive traffic via the LTE interface boosts user involvement in both traffic classes by monitoring the large bandwidth available on the Wi-Fi link and releasing resources on the LTE link.
## 2 Materials and Methods
### Calculation of Thermal Properties
#### 2.1.2 Thermal Conductivity  
Another important thermal property is thermal conductivity. Heat transfer coefficient is found according to [PERSON] et al.'s study ([PERSON] et al., 2017). First of all, only the ingredient's heat transfer coefficient effects are taken into account in Equation (5).  
\[\lambda_{c}^{para}=\sum_{j}\varepsilon_{j}\lambda_{j} \tag{5}\]  
\(\lambda\) represents the conductivity, \(c\) represents the continuum phase for the ingredients, i.e., the liquid phase of the ice cream mixture. \(j\) represents the number of ingredients, and \(\varepsilon\) is described in Equation (6).  
\[\begin{split}\varepsilon_{j}=X_{j}\frac{\rho_{c}}{\rho_{j}}\\ \rho_{c}=\frac{1}{\sum_{j}\frac{X_{j}}{\rho_{j}}}\end{split} \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sucrose Amount Dissolved** & **Freezing Point** \\
**in 100 g Water (g)** & **Depression (\({}^{\circ}\)C)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Freezing point depression according to sucrose equivalence.  
\(\rho\) represents the density of the mixture. To be able to find the heat transfer coefficient for ice cream, ice also needs to be considered in the heat transfer coefficient formulations. To do that, Equation (7) from [7] is used.  
\[\begin{split}\lambda_{mix}=\lambda_{c}^{para}\frac{1-\varepsilon_{ ice}+\varepsilon_{ice}F\frac{\lambda_{ice}^{para}}{\lambda_{c}^{para}}}{1- \varepsilon_{ice}+\varepsilon_{ice}F}\\ F=\frac{1}{3}\sum_{i=1}^{3}[1+(\frac{\lambda_{ice}}{\lambda_{ c}^{para}}-1)g_{i}]^{-1}\end{split} \tag{7}\]  
The \(g\) values are \(g_{1}\)=\(g_{2}\)=1/11, \(g_{3}\)=9/11, respectively. Finally, air must be included in the heat transfer coefficient formulation to finalize the heat transfer coefficient correlation.  
\[\lambda_{IC}(T)=\lambda_{mix}(T)\frac{2\lambda_{mix}(T)+\lambda_{air}-2 \varepsilon_{air}(\lambda_{mix}(T)-\lambda_{air})}{2\lambda_{mix}(T)+ \lambda_{air}+\varepsilon_{air}(\lambda_{mix}(T)-\lambda_{air})} \tag{8}\]  
\(\lambda\) represents the heat transfer coefficient and \(\lambda_{IC}\) represents the ice cream thermal conductivity coefficient.
## 3 System Design
### Tracking  
The role of a tracking algorithm is to follow the evolution of the signal synchronization parameters: code phase \(\tau(t)\), Doppler frequency \(f_{d}(t)\) and carrier phase \(\phi(t)\). The proposed receiver implements tracking loops to continuously monitor and adjust to the code and carrier parameters of the incoming signal. Specifically, a Delay Lock Loop (DLL) is employed to track the signal's code delay, a Phase Lock Loop (PLL) is dedicated to monitoring and adjusting to the signal's phase, and a Frequency Locked Loop (FLL) can be enabled to monitor the signal's frequency.  
The tracking multicorrelator hardware accelerators in the FPGA perform the Doppler wipe-off and the multicorrelation of the incoming signal with the local replica of the PRN codes. The embedded processor executes the PLL, the FLL, and the DLL. The PLL and the FLL employ a four-quadrant arctangent discriminator. The DLL employs a noncoherent Very Early Minus Late Power (VEMLP) normalized discriminator when tracking Galileo E1b/c signals, and a noncoherent Early Minus Late envelope-normalized discriminator when tracking Galileo E5a, GPS L1 C/A, and GPS L5 signals.  
The tracking multicorrelator hardware accelerators are configured as shown in Table 8. When tracking GPS L1 C/A signals, the receiver follows the data component of the received signals. For other signals, the receiver tracks the pilot component, utilizing an additional correlator dedicated to demodulating the data component.  
For details on the implementation of the tracking multicorrelator hardware accelerators, refer to [21].
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for Clustering  
WSN and IoT networks tend to have a large and complex topology. The network may consist of a large number of nodes. The nodes are expected to collaborate with each other in delivering information from the environment or performing a specific task.  
Network clustering is one of the efforts to save energy and prolong the lifetime of the network. The clustering will transform a large network into many small groups and makes possible for data aggregation, so that data does not need to be sent directly to a distant node. It is collected in advance at one point in a group, which is called Cluster Head (CH). From the CH, data can be taken to another node in another group and forwarded again in the same way toward the destination node. This mechanism will save energy because the required power for each node to send data becomes smaller. The clustering mechanism is also dynamic and can adapt to the change in the topology and the number of nodes in the network. It supports the self-organization property for an IoT network.  
There are many studies that have been conducted to offer the most optimum protocol design for network clustering. Each protocol has a different algorithm. Some algorithms are conventional methods based on some theories or ideas that are validated mathematically, while some others are inspired by the behavior of living creatures in nature, which are referred to as an bio-inspired algorithm.  
One of the most popular protocols for network clustering is the Low Energy Adaptive Clustering Hierarchy (LEACH), which was proposed by [PERSON] [16]. LEACH is a clustering protocol that randomly rotates the CH function among nodes in a cluster. It is expected to distribute the energy consumption evenly to all nodes in the network. However, this LEACH approach will have weaknesses. The selection of nodes at random by the CH means it does not pay attention to the conditions that exist at the nodes, which may be different from each other. LEACH also transmits the data directly from CH to the base station in a single hop, causing high energy consumption in CH.
# Numerical Modeling Technique of Damage Behavior of
## 2 Introduction of XFEM
### Basic Principle  
The displacement field for the XFEM approximation can be expressed as:  
\[\mathbf{u}(x)=\sum_{l}N_{l}(x)\mathbf{u}_{l}+\sum_{l}N_{l}(x)\big{(}H(\phi(x) )-H\big{(}\phi(x_{l})\big{)}\big{)}\mathbf{b}_{l}+\sum_{K}N_{K}(x)\left(\sum_{l =1}^{4}(\Phi_{l}(r,\theta)-\Phi_{l}(x_{K}))\epsilon_{K}^{l}\right) \tag{1}\]  
where the three terms on the right side represent standard finite element approximation, crack discontinuity enrichment and crack tip enrichment, respectively. A geometric enrichment strategy [46] is adopted in this paper for crack tip enrichments, which ensures that the enrichment domain is independent of the mesh size. Since degrees of freedom are enriched on nodes of existing elements, the modification of topology can be avoided.  
The modified Heaviside step function \(H(\phi(x))\) is given by:  
\[H(\phi(x))=\begin{cases}+1\text{ if }\phi(x)>0\\ -1\text{ if }\phi(x)<0\end{cases} \tag{2}\]  
This function implies that the discontinuity occurs at the location of the crack surface.  
For a cohesive crack, the crack tip enrichment functions \(\Phi_{l}(r,\theta)\) can take the following formats [47]:  
\[\Phi_{l}(r,\theta)=\left\{r\sin\frac{\theta}{2},r\cos\frac{\theta}{2},r\sin \frac{\theta}{2}\sin\theta,r\cos\frac{\theta}{2}\sin\theta\right\} \tag{3}\]  
where \((\mathbf{r},\theta)\) is a local polar coordinate system at the crack tip. The functions are used to describe the displacement field around a crack tip. The first function \(r\sin\theta/2\) in Equation (3) is discontinuous across the crack surface, whereas the last three functions are continuous.  
In fact, these functions are allowed to be omitted, and the modified Heaviside step function is suitable for the entire crack, including the crack tip, since near-tip fields are no longer singular for the cohesive crack [48; 49; 50]. Nevertheless, the use of crack tip enrichment functions allows the crack tips to be inside a finite element, rather than being located on the element boundaries. In this work, step and crack tip enrichment functions are both retained for modeling concrete columns and beams, whereas only a step enrichment function is adopted for masonry blocks, considering the fact that masonry blocks in the infill panel are isolated with each other.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 3 System Model and Problem Formulation  
In this section, we consider a typical software-defined STIN model as the network architecture and then establish the energy consumption problem.
## 1 Introduction
Few studies have been conducted in large metal mines, especially those with a kilometer depth characterized by broken rock mass and high in-situ stresses. Therefore, the Jinchuan nickel mine in China was selected as the research area in this study and a detailed field investigation into the deformation and support of deep roadways under broken rock mass was performed. We then conducted a broken rock zone test in this area to assess the primary control method's supporting effect. Moreover, to compensate for its disadvantages, a new hybrid control system with multistage anchorage and a concrete-filled steel tube (CFST) was designed. Numerical calculations using the Fast Lagrangian Analysis of Continua in 3 Dimensions (FLAC\({}^{\text{3D}}\)) were performed for a simulation and comparison of the evolutionary processes of the deformation, as well as the stress of roadways and support structures. Additionally, on-site experiments, field monitoring and economic analyses were performed to assess the new support scheme's control effect.
## EUV Beamline Experimental Characterization
### EUV Spectral Bandwidth  
Photo-electron spectroscopy requires a narrow spectral bandwidth of the EUV radiation. The spectral profile at the output of the monochromator depends on the size of the HHG source, on the monochromator instrumental response (i.e., the bandwidth transmitted through the slit) and on the intrinsic bandwidth of the harmonics. If the intrinsic bandwidth of the harmonics is lower than the bandwidth transmitted through the monochromator, the energy resolution is limited by the source itself. In this case, the monochromator is acting as a broadband filter that is used to select a single harmonic and filter out all the adjacent harmonics. On the other side, if the transmitted bandwidth is lower than the bandwidth of the harmonics, the monochromator, as well as filtering out all the adjacent harmonics, is also selecting a spectral portion of the single harmonic.  
The actual bandwidth at the output of the monochromator has been estimated by measuring the HHG spectral profile for different widths of the slit. The widths of the spectral profiles measured for H15 and H21 using G3 are shown in Figure 9. The measured values for H15 and H21 are fitted assuming, respectively, 100-\(\upmu\)m and 80-\(\upmu\)m source size and 200-meV and 180-meV source bandwidth, the dashed lines in Figure 9. The size of the source that is found by the fitting procedure (80-100 \(\upmu\)m) is well in agreement with the measurements of the EUV spot size done on the ARPES chamber using the CE:YAG crystal. Similarly, the source bandwidth (180-200 meV) is in agreement with the resolution measured from the static ARPES spectra, which is ~190 meV after space-charge minimization. Therefore, the actual spectral resolution of the beamline is limited by the intrinsic bandwidth of the source. Indeed, the monochromator acts as a tunable filter with high efficiency in the whole spectral bandwidth of operation.  
Figure 8: HHG spectra generated by the SHG laser pulse (400 nm). (**a**) Ar spectrum measured with grating G2 (300 gr/mm); and (**b**) Ne spectrum measured with grating G3 (600 gr/mm). The absolute photon flux is measured after the monochromator slit.