esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area  
We delineated our study area with a bounding box determined by the available reference cocoa data located in Cote d'Ivoire and Ghana (Figure 1a). A total surface area of approximately 17.4 Mha was covered, which corresponded to the most suitable areas for cocoa cultivation [49,50]. The study area fell within latitudes 5\({}^{\circ}\)5\({}^{\prime}\)58.87\({}^{\prime\prime}\)N-7\({}^{\circ}\)19\({}^{\prime}\)16.63\({}^{\prime\prime}\)N and longitudes 0\({}^{\circ}\)18\({}^{\prime}\)31.18\({}^{\prime\prime}\)W-6\({}^{\circ}\)41\({}^{\prime}\)35.13\({}^{\prime\prime}\)W. The Ghanaian regions of Western, Western North, Ashanti, Ahafo, Central, Eastern, and small parts of Bono East and Greater Accra were incorporated into the study area. Accordingly, the Ivorian districts of Comoe, Lagunes, Abidjan, Lacs, Yamoussoukro, Sassandra-Marahoue, Goh-Djiboua and Bas-Sassandra were included (Figure 1b).  
Cote d'Ivoire has four large agro-climatic zones [51], with our study area extent falling within the forest zone and Guinea savannah. There, the climate is wet and tropical, and the annual rainfall ranges from 1000 to 1600 mm [51]. The southern and central part of Cote d'Ivoire experiences two rainy and two dry seasons. The topography is defined by a large plateau featuring undulating plains and mountains in the north-western part.  
Ghana is divided into five agro-ecological zones [52]. Our study area fell mainly under the rainforest zone, deciduous forest zone, transitional zone and coastal savannah. In these zones, the average rainfall ranges from 900 to 1800 mm annually, resulting in a major and a minor growing season [52]. The climate is hot and humid in the southern and western parts, and warm and dry in the coastal zone [52]. Ghana's topography is primarily undulating with mild slopes [52].  
Lowland forests in Cote d'Ivoire and Ghana are part of the Guinean forests of West Africa, which is a globally important biodiversity hotspot [53]. Agriculture managed by smallholder farmers is the dominant land-use in both countries [51,52]. Their cropping systems mainly include perennial cash crops (e.g., cocoa, cashew, rubber and palm oil) and food crops (e.g., rice, maize, cassava and yam) [51,52]. Agricultural activities, especially growing cocoa, have historically been to the detriment of the forests [38,54,55], creating a heterogeneous landscape composed of a mosaic of annual and perennial croplands and forest reserves, which are characteristic in Cote d'Ivoire and Ghana [53,46,52,53].  
Figure 1: (**a,b**) Location of the study area spanning across Côte d’Ivoire and Ghana, and the distributed cocoa reference dataset at plot level. Training, validation and test datasets were split with a minimum in-between distance of 50 km to ensure spatial independence. (**c**) Examples of cocoa plots before (red polygon) and after applying quality refinement (white polygon) overlaid on Google Earth images for finer interpretation (**A–D**). The dates refer to the Google Earth image capture dates.
# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 2 Materials and Methods
### Dynamic Recognition Model for Rock Fragment Size  
The above YOLOv8 instance segmentation model, binocular vision measurement, and [PERSON]'s algorithm can be used to accurately identify the size of rock fragments. The specific implementation process is shown in Figure 7. The binocular camera captures the rock fragment image, and after image preprocessing and Gaussian filtering denoising, the YOLOv8 instance segmentation model is used for instance segmentation. The image captured by the left camera is chosen as the reference. The segmentation obtains all rock masks, and the depth values of all pixels in the image are traversed. The image captured by the left camera is chosen as the reference. The segmentation obtains all rock masks, and the depth values of all pixels in the image are traversed. The depth map and the RGB color image captured by the binocular camera are then aligned at the pixel level. Each pixel in the RGB image can calculate its corresponding 3D coordinates using the depth map information. All rock masks are sequentially extracted, and depth anomalies in the rock masks are removed. The points within the mask are paired to calculate the pixel length and actual length. After calculating the actual length-to-pixel scale, each scale is stored in an array. Then, for each rock, [PERSON]'s algorithm is used to calculate the minimum enclosing circle pixel size as the rock's pixel length. This allows the actual length to be obtained based on the actual length-to-pixel scale.  
Figure 6: YOLOv8 model structure.  
As the mining truck passes the scanning station, the binocular camera dynamically collects a sequence of blast fragmentation images via an automated photography algorithm. Appropriate images are selected to extract point cloud data, which are then used as the source input for the YOLOv8-seg model. Gaussian filtering and histogram equalization are then applied. The preprocessed images are then fed into the YOLOv8-seg model for further analysis. The blasting fragmentation distribution is derived via the data analysis module and ultimately uploaded to the terminal system. A dynamic blast fragmentation recognition system is established by integrating various algorithms with binocular vision, enabling real-time acquisition of rock size distribution during truck haulage. This contributes to enhancing the level of automation in mining operations.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 1 Introduction
Secondly, the algorithm  
Figure 1: Readout circuits in infrared focal plane array (FPA) have different characteristics and such non-uniformity will generate column fixed pattern noise (FPN). (**a**) Block diagram of Uncooled Long-Wave infrared. (**b**) A raw infrared image which contains obvious stripe noise (The image is available under the Creative Commons Attribution (CC-BY) license [9]). directly denoises the vertical component, which can accurately remove the stripe noise and preserve the details of the image. Experiments show that our algorithm is superior to the current advanced stripe correction algorithm on both simulated images and raw infrared images. This paper is organized as follows: in the Section 2, the related work of non-uniform correction algorithm is introduced. In the Section 3, a new infrared image non-uniform stripe correction algorithm is proposed and the implementation details of the algorithm are given in the Section 4. The experimental results of the correction algorithm are given in the Section 5. Finally, the conclusion is drawn in the Section 6.
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
## 3 The Limit Magnitude Detection Capability of the Orbital Optoelectric Tracking System Modeled
### Optoelectric Signal Characterization of Dynamic Targets
The voltage amplitude is the output from the optoelectronic detection CCD element under different illumination levels with the relevant parameters of the orbital optoelectronic tracking system.
## 4 Review of Case Studies: Electric Vehicle Routing Optimization
\begin{table}
\begin{tabular}{c c c c c} \hline
**Study** & **Application Domain** & **Optimization Technique** & **Performance Metrics** & **Results** \\ \hline [PERSON] et al. [30] & Personal electric VRP (pEVRP) & \begin{tabular}{c} mixed-integer nonlinear \\ programming (MNLP), \(c\)-constraint \\ method, GA, PSO \\ \end{tabular} & Travel time, recharging costs & \begin{tabular}{c} Proposed methods effectively optimize \\ travel time and recharging costs, \\ providing valuable insights for decision \\ making in personal electric vehicle \\ routing. \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [22] & IRP & Multi-objective quantum algorithm & Fuel consumption, Pollutant & \begin{tabular}{c} Proposed algorithm outperforms other \\ methods, demonstrating superior \\ performance in optimizing fuel \\ consumption and pollutant emissions. \\ \end{tabular} \\ \hline [PERSON] et al. [32] & EV navigation system & \begin{tabular}{c} Improved Chron-SFT (ICS) \\ algorithm, simplify-charge-control \\ (SCC) algorithm \\ \end{tabular} & Routing and charging policies & \begin{tabular}{c} Both ICS and SCC algorithms are \\ significantly reducing computation \\ completely level of deviating from the \\ optimal cost. \\ \end{tabular} \\ \hline [PERSON] et al. [33] & VRP with time windows (VRPTW) & GA with Pareto ranking & Number of vehicles, total distance traveled & \begin{tabular}{c} Proposed approach avoids search bias \\ and provides a computationally \\ advantageous solution, competitive with existing methods. \\ \end{tabular} \\ \hline [PERSON] et al. [7] & Electric VRP with time windows (EVRPTW) & Hybrid GA & Total electricity consumption & \begin{tabular}{c} Proposed approach outperforms \\ simulated annealing (SA) algorithm and \\ GA in finding better solutions within a \\ computer computation time, optimizing EV \\ routing and energy consumption. \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [34] & Electric VRP with time windows (E-VRPTW) & GA, Tabu search (TS), fuzzy logic & Fitness values, number of vehicles, distance & \begin{tabular}{c} GA-TS method combined with fuzzy \\ logic achieves higher fitness values, \\ shorter changes, and a smaller number of \\ vehicles, optimizing the E-VRPTW \\ problem. \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [5] & Capacitated VRP with time windows (CVRPTW) & GA, Tabu search algorithm (TS), hybrid approach & Total distance traveled, number of vehicles & \
\begin{tabular}{c} Hybrid GA-TS approach yields the best \\ solution, reducing total distance by 15.99% compared to other algorithms. \\ \end{tabular} \\ \hline [PERSON] et al. [26] & Capacitated VRP (CVRP) & GA & Distances, route planning &
\begin{tabular}{c} GA improves transportation efficiency, \\ reduces costs in optimizing shuttle base \\ services. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of GA studies for optimizing electric vehicle routing-based applications.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
###### Abstract  
Although it possesses the capabilities of both anti-contamination and rapid response, the deflector jet servo valve is still confronted with the issue of weak performance robustness, usually manifesting as considerable uncertainty of the product pressure indices. To elaborate on the complex internal mechanism of deflector jet valves and figure out the origins of performance fluctuations, a three-dimensional mathematical model of the deflector jet pilot valve is constructed, in which a series of assumptions are presented to apply specific theorems to different regions of the flow field. Numerical simulations and experiments show that this theoretical system can provide a distinct and logical explanation for both the internal flow distribution and the external performance of the servo valve. On this basis, the causes of performance fluctuations are discussed, concerning the installation error of the deflector and the machining error of the shunt wedge. Calculations show that the latter can bring about remarkable performance variation. Quantificationally, a 10 micron width error of the shunt wedge will induce 7.4% and 3.6% drifts of the receiver pressure and the pressure gain, respectively. However, further analyses confirm that a decrease in the deflector jet distance will lead to dramatic deterioration of the valve's susceptibility to errors. Hence, it is concluded that to enhance the performance robustness of servo valve products, the machining accuracy of the shunt wedge and non-negative errors of the deflector jet distance should both be guaranteed.
## 3 Results and Discussion
### Stress Analysis of BRT Asphalt Pavement Structure
#### 3.2.1 Vertical Stress  
As shown in Figure 7, the pavement structure was in a vertical compression state during the load driving process. The compressive stress increased rapidly as the load approached the analysis point and then decreased rapidly and approached zero after a period of time near the peak value. The slower the initial loading velocity, the longer the duration of the compressive stress. The compressive stress duration was 0.16 s and 0.04 s at 10 km/h and 40 km/h, respectively, the latter being only 25% of the former. The upper, intermediate and lower surface layers were all subjected to high vertical compressive stress, while the vertical compressive stress of the soil foundation was almost zero, which can also be seen in Figure 8. Figure 8 shows the vertical stress in the middle section of the pavement model when the bus load moves over it at the initial speed of 10 km/h. The stress situation shows that the BRT asphalt pavement structure was prone to permanent deformation dominated by unstable rutting at high temperatures.  
The peaks of the vertical compressive stress on the top surface of each layer of the asphalt mixture (i.e., upper, intermediate and lower surface layers) were compared. As shown in Figure 9, the speed slightly influenced the peak of the vertical compressive stress. When the speed was increased from 10 km/h to 20 km/h and then to 30 km/h, the peaks of the compressive stress of each structural layer gradually increased. When the speed was further increased from 30 to 40 km/h, the peaks of the compressive stress decreased.
## 1 Introduction
The noncompliant provision of service is called nonperformance (NP) and the related economic fee is the so-called Nonperformance Penalty (NPP). Regarding FCR, NPP may consist of the deletion of the availability payment (as is, for example, in Denmark [17]) or a financial penalty higher than the availability payment (as, for example, in Italy for Fast Reserve pilot projects [10]). For the analysis of BESS operation, a proper model has to be implemented. In literature, several models [18] with different degrees of accuracy are in place for batteries: accuracy is usually directly proportional to the computational effort. Empirical (analytical) models do not represent electrochemical processes, but empirically fit the KPIs of battery operation throughout past experimental data for estimating the future behavior of BESS [19]. This kind of model has already shown a reduction in computational effort and an acceptable accuracy in predicting battery behavior [20]. Furthermore, to simulate BESS behavior, not only battery cells but also the complementary part of the system should be considered since it has a nonnegligible impact on operational losses, as highlighted by [21; 22]. It includes, usually, a power conversion system (PCS) and other loads providing auxiliary services (e.g., monitoring system, alarms and HVAC system). In [23], authors develop a BESS empirical model that evaluates the efficiency of the BESS from a system perspective. Finally, to properly evaluate the BESS business case, battery aging should be considered. Battery aging is related to operational conditions: as the batteries are charged and discharged there is a capacity loss in the battery. In order to evaluate this phenomenon, different indicators exist in the literature: the most used is the State-of-Health (SoH) [24]. SoH represents a measure of BESS ability to store and deliver electrical energy, compared with a new battery. Depending on its actual application, the end of life (EoL) for a battery is reached with a SoH value between 70% and 80% [25]. In this study, different active SoC restoration strategies are investigated adopting the empirical BESS model, presented in [23], able to provide FCR in Italy. To estimate battery life, a SoH model was developed based on [26]. The SoH model considers both cycle aging and calendar aging in terms of capacity fade. The strategies analyzed are thoroughly developed to be as close as possible to the most innovative regulatory frameworks for BESS integration in ancillary services provision. Resultsare presented in terms of technical and economic performance. Both the perspective of the system operator and of the investor are evaluated and discussed. The rest of the article is organized as follows: Section 2 describes the proposed methodology for SoC restoration strategies, model development and the description of the case study. Section 3 presents the results of the simulations and highlights the main findings. Section 4 presents a broader discussion of the results, summarizes conclusions and takeaways and suggests foreseen future steps.
## 3 Materials and Methods
### Method
#### 3.3.1 The Environmental Impact Calculations
The GHG emission factors of small trucks, three-wheel electric vehicles, and Iveco vehicles at the use phase are 257.7, 81.2, and 300.0 gCO\({}_{2}\)-eq/km, respectively [40]. Figure 2: The GHG emission of different transport modes during the use phase. Therefore, in terms of rebalancing, the GHG emissions of FBSS are:  
\[GHG_{r}=\frac{\sum_{r=1}^{R}n_{rv}\cdot\alpha_{r}EF_{u_{r}}D_{r}}{N} \tag{4}\]  
where:  
\(GHG_{r}\) is the emission of per FFBS during rebalancing;  
\(n_{rv}\) is the total number of dispatched vehicles in Nanjing;  
\(\alpha_{r}\) is the proportion of the rth dispatched vehicles;  
\(EF_{u_{r}}\) is the GHG emission factor of the rth dispatched vehicles in use phase;  
\(D_{r}\) is the average daily rebalancing distance per rth dispatched vehicles; and  
\(N\) is the total number of FFBS in Nanjing. For the maintenance process of FFBS, the following formula is used:  
\[GHG_{m}=\sum_{m=1}^{M}C_{m}\cdot\theta_{m}\cdot P_{m} \tag{5}\]  
where \(GHG_{m}\) is the GHG emission from the FFBS maintenance process and \(P_{m}\) is the maintenance probability of the mth bike part, referred to in [40]. In the disposal phase of FFBS, recycling and waste incineration are mainly considered. It is assumed that 90% of the recovery efficiency is used [19; 48]. Additionally, the remaining components are burned as waste. Referring to the calculation formula of GHG emissions from waste incineration, the GHG emissions \(GHG_{d}\) for non-recyclable partial incineration are calculated as follows:  
\[GHG_{d}=\sum_{d=1}^{D}BC_{d}\cdot EF_{d} \tag{6}\]  
where \(BC_{d}\) is the weight of the dth non-recyclable bike component and \(EF_{d}\) is the GHG emission factor for burning the dth bike component. Therefore, the final GHG emission in the life cycle of FFBS is:  
\[GHG=\sum_{p=1}^{p}GHG_{p} \tag{7}\]  
where \(GHG_{p}\) is the GHG emission of each phase in the life cycle. Because the emergence of FFBS will not have a significant impact on the production and disposal of public transport, and the function orientations of FFBS and cars are different, this paper does not take other phases of other traffic modes into account instead of the use phase.
## 2 Materials and Methods
### Estimation of Total Antioxidant Capacity  
#### 2.4.1 DPPH Scavenging Activity  
DPPH scavenging activity was determined according to the Brand-Williams method [23], using the same extract for the determination of total soluble phenolic content. The reaction mixture contained 20 \(\mu\)L of extract and 980 \(\mu\)L of 0.094 mM DPPH (2,2-diphenyl-1-picrylhydrazyl) previously dissolved in methanol. The reaction was carried out in the dark at 22 \({}^{\circ}\)C for 15 min, with occasional shaking. The standard curve was prepared by dissolving 2.5 mg of Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) in 10 mL of methanol. Absorbance was measured at 515 nm. Total antioxidant activity was expressed as the equivalents of Trolox per g of fresh weight (FW).
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 2 Calculation of 2D Rayleigh Wave Phase and Z/H-Ratio Kernel  
[PERSON] and [PERSON] [9] calculated the 1D depth-sensitivity kernel of the Rayleigh wave dispersion and the Z/H ratio either theoretically or numerically by perturbing the model parameters at various depths. The features of the 1D kernel include that both the dispersion and Z/H ratio are sensitive to changes in the S-wave velocity and the density, but they are less sensitive to the P-wave velocity. For the dispersion, the S-wave velocity sensitivity is positive at different depths, suggesting that the dispersion provides more constraints on the integral velocity feature. In contrast, the S-wave velocity sensitivity of the Z/H ratio changes from positive near a free surface to negative at deeper depths, implying that the Z/H ratio resolves the velocity contrasts better. To study the features of the 2D kernel, we derived equations for the 2D full-wave sensitivity kernels of the phases and the Z/H ratio based on the adjoint-state theory.
# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
###### Abstract  
Multi-source data remote sensing provides innovative technical support for tree species recognition. Tree species recognition is relatively poor despite noteworthy advancements in image fusion methods because the features from multi-source data for each pixel in the same region cannot be deeply exploited. In the present paper, a novel deep learning approach for hyperspectral imagery is proposed to improve accuracy for the classification of tree species. The proposed method, named the double branch multi-source fusion (DBMF) method, could more deeply determine the relationship between multi-source data and provide more effective information. The DBMF method does this by fusing spectral features extracted from a hyperspectral image (HSI) captured by the HJ-1A satellite and spatial features extracted from a multispectral image (MSI) captured by the Sentinel-2 satellite. The network has two branches in the spatial branch to avoid the risk of information loss, of which, sandglass blocks are embedded into a convolutional neural network (CNN) to extract the corresponding spatial neighborhood features from the MSI. Simultaneously, to make the useful spectral feature transfer more effective in the spectral branch, we employed bidirectional long short-term memory (Bi-LSTM) with a triple attention mechanism to extract the spectral features of each pixel in the HSI with low resolution. The feature information is fused to classify the tree species after the addition of a fusion activation function, which could allow the network to obtain more interactive information. Finally, the fusion strategy allows for the prediction of the full classification map of three study areas. Experimental results on a multi-source dataset show that DBMF has a significant advantage over other state-of-the-art frameworks.  
tree species classification; deep learning fusion method; multi-source images classification Article
## 5 Performance Analysis and Discussion
### Performance Metric
#### 5.2.3 Success Rate (\(S\!r\))  
It is defined as the success rate of finding a cost optimized schedule while satisfying the user-defined deadline, as given by Equation (25):  
\[SR=\frac{Number\ of\ simulation\ runs\ that\ successfully\ meet\ deadline}{Total\ number\ of\ simulation\ runs}\times 100 \tag{25}\]
## 4 Future Research Trends for ML-Based IRS-Assisted Wireless Communication
### MmWave Communication  
The mmWave is considered a promising technology for increasing the bandwidth demand in 6G communication [112]. Some studies have been performed using IRS-assisted mmWave communication, but more studies are needed to investigate the impact of IRS. Some research directions can be modulation techniques, antenna array architecture, scheduling algorithms, signal waveform, and the deployed IRS with ML methods.