esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Accuracy of PLS and RF Regression Model
Figure 6: Relationships between actual plant density derived from the YOLOv3 model and estimated plant density (plants m\({}^{-2}\)) derived from (**a**) SR-only PLS; (**b**) NDVI-only PLS; (**c**) SR + NDVI-PLS; (**d**) SR + Climate-PLS; (**e**) NDVI + Climate-PLS; (**f**) SR + NDVI + Climate-PLS of test data set. The red line represents a regression line. The black line represents a 1:1 relationship. Different color points represent the field names. and RMSE 1.78 plants m\({}^{-2}\)). The model with the lowest accuracy was the RF model using NDVI only predictors (NDVI-only RF). The accuracy of RF models was surpassed the PLS models in estimating plant density in test fields. In addition to the RF analysis, we examined the variable importance of the RF model using all available predictors, and the results are shown in Figure 8. The results revealed that NIR at 40 DAS was the most influential variable for the model with more than 12,000 values of _IncNodePurity_, followed by precipitation before the sowing date, temperature at 10 DAS, and NIR at 30 DAS. The NDVI variables in the later DAS (40, 20, and 30 DAS) dominated the top ten variables. The temperature variables had little impact on the model accuracy, excluding that on DAS 10. The result indicated that most of the predictors were less important for the model to estimate the response variable, since the _IncNodePurity_ was less than 20% of the highest variable, NIR at 40 DAS. Based on the variable importance of the RF model using all available predictors, we developed another model using the top ten variables with the highest _IncNodePurity_ score (10 VarImp-RF). The newly developed RF regression model, 10 VarImp-RF, scored the best performance (\(r^{2}\) 0.68 and RMSE 1.72 plants m\({}^{-2}\)), as presented in Figure 9, which outperformed the best RF regression model without variable selection, according to the variable importance (NDVI + Climate-RF) and the best PLS regression model (SR + NDVI-PLS) in the test dataset, as presented in Table 3, Figures 6c and 7e. The plant density estimated by the 10 VarImp-RF model ranged from 0.40 to 11.70 plants m\({}^{-2}\). Figure 7: Relationships between actual plant density derived from the YOLOv3 model and estimated plant density (plants m\({}^{-2}\)) derived from (**a**) SR-only RF; (**b**) NDVI-only RF; (**c**) SR + NDVI-RF; (**d**) SR + Climate-RF; (**e**) NDVI + Climate-RF; (**f**) SR + NDVI + Climate-RF of the test data set. The red line represents a regression line. The black line represents a 1:1 relationship. Different color points represent the field names.
## 3 Background and Definitions  
### System Model  
Distributed systems have specific characteristics such as: communication between processes is done by exchanging messages, time is not global for all processes, each process can have its own time, and processes do not share a common memory. Processes can be one or more nodes, and processes consist of a single thread or any number of threads (a process can be a computer, a service, a cell phone, a video game console, etc.). In a distributed computation \(P=\{P_{1},P_{2},\ldots,P_{n}\}\) represent a finite set of processes; where communication channels can be unpredictable, presenting finite delays, however, these channels are considered asynchronous and reliable. In a distributed system, there exist two types of events _internal_ and _external_.  
An _internal_ event reflects a change in the state of a process, for example, when checkpointing. A checkpoint contains the state of a process such as heap, registers, stack, and the kernel state. We denote \(C_{i}^{x}\) as checkpoints that contain the state of a process \(i\) and \(E_{i}\) denotes all checkpoints previously taken. We consider _external_ events, those that affect the state of the system globally, for instance, _send_ and _delivery_ of messages, where \(m\) is a message; \(send(m)\) denotes the sending of \(m\) by any process \(P_{i}\in P\); and \(delivery(P_{j},m)\) denotes the delivery of message \(m\) to another process \(P_{j}\in P\), where \(P_{i}\
eq P_{j}\). The set of messages \(M\) containing external events is given by the following expression:  
\[E_{m}=\{send(m):m\in M\}\cup\{delivery(P_{i},m):m\in M\wedge P_{i}\in P\}\]  
where \(E_{m}\) is the set of events associated with \(M\). All set of events in the system, \(E\), are:  
\[E=E_{i}\cup E_{m}\]  
Therefore, modeling of a distributed computation is done through a partially-ordered set \(\widehat{E}=(E,\rightarrow)\) where \(\rightarrow\) and denotes [PERSON]'s _happened-before relationship_[26].
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 3 Development of the MINLP Model Formulation for Truss Superstructures
### The MINLP Model Formulation for Truss Superstructures
#### 3.2.3 Objective Function  
In the present truss synthesis, the objective function Equation (1) represents the mass of the truss superstructure, where \(\rho\) denotes the unit mass of the material and \(A_{i,j}^{\text{sf}}\) and \(s_{i,j}^{\text{sf}}\) are the cross-section area and the length of element \(i\)\(\rightarrow\)\(j\), respectively.  
In its simplest form, the objective function can be linear, when the cross-section areas of bars are considered as independent sizing variables and the joint coordinates of the truss are fixed (i.e., the lengths of the elements are fixed parameters). In the engineering structures, however, the bars are built from standard cross-sections. Each cross-section area generally depends on several dimensions. This is important, especially when buckling constraints are included, as buckling resistance of a bar is dependent not only on its cross-section area but also on the shape and individual dimensions of its cross-section. By considering circular hollow sections (tubes), for example, each cross-section is defined by two independent sizing variables, i.e., the tube diameter \(d_{i,j}\) and the wall thickness \(t_{i,j}\). The cross-section area is therefore evaluated from the cross-section dimensions and in the constraints defined as a substituted function \(A_{i,j}^{\text{sf}}\); see Equation (9). In addition, when the shape of the structure is also optimized, the joint coordinates become variables, and consequently, the lengths of the bars also become substituted functions \(s_{i,j}^{\text{sf}}\); see Equation (10). In effect, the mass of the structure becomes a non-linear function (Equation (1a)):  
\[MASS=\rho\cdot\pi\cdot\sum_{i=1}^{n}\sum_{\begin{array}{c}j=1\\ el_{i,j}=1\end{array}}^{n}\left[t_{i,j}\cdot\left(d_{i,j}-t_{i,j}\right)\cdot \sqrt{\left(c_{x_{j}}-c_{x_{i}}\right)^{2}+\left(c_{y_{j}}-c_{y_{i}}\right)^{2 }}\right]\] (1a) where \[d_{i,j}\] and \[t_{i,j}\] denote the tube diameter and the wall thickness of element \[i\)\(\rightarrow\)\(j\), respectively, and \[c_{x_{j}}\], \[c_{y_{j}}\], \[c_{y_{j}}\]  and \[c_{y_{j}}\] are the coordinates of joints \[i\] and \[j\].  
Figure 1: Definition of a truss element.
## 3 Experimental Results and Discussion
### Sensor Data Analysis
#### 3.5.3 Soil Parameters
This suggests that the soil moisture data should be considered in the soil NPK data analysis or interpretation. Figure 21: Recorded soil moisture and EC data graphs of Plant-1. Figure 22: Recorded soil moisture and EC data graphs of Plant-2. Figure 23: Recorded soil EC data graphs. Figure 24: Recorded soil pH data graphs. Figure 25: Recorded soil moisture and N, P, K data graphs of Plant-1. Figure 27 shows the recorded soil NPK data graphs, where the red, green, and purple lines represent the Plant-1 soil N, P, and K, respectively. The blue, aqua, and orange lines represent the Plant-2 soil N, P, and K, respectively. The figure shows that the soil NPK is higher for Plant-2 than Plant-1. This conforms with the fact that Plant-2 soil is in fertilizer soil, which has higher NPK concentrations than Plant-1 soil. From the above discussion, we may summarize the findings in the JXCT soil sensor reading as follows:  
1. Soil temperature readings can be independently interpreted. 2. Soil moisture readings should be interpreted by considering the soil temperature changes and watering treatment. 3. Soil EC, pH, N, P, and K readings should be interpreted by considering the soil moisture reading.
# Experimental Study on the Dynamic Characteristics of Fractured Coal Under Cumulative Impact
## 3 Results and Analysis
### Fractal Characteristics of Fragmentation
This phenomenon arises from distinct failure mechanisms: elongated fragments result from axial-stress-induced tensile failure along dominant cracks, whereas polygonal/powdered debris forms through tension-shear \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Number**} & \multicolumn{6}{c}{**Mass Percentage of Fractured Coal Sample of Each Grain Group/\%**} \\ \cline{2-9}  & **\textgreater{}30 mm** & **20–30 mm** & **10–20 mm** & **5–10 mm** & **1–5 mm** & **0.5–1 mm** & **\textless{}0.5 mm** \\ \hline No fracture-impact effect times-1 & 20.20 & 23.68 & 38.91 & 10.52 & 5.79 & 0.64 & 0.27 \\ No fracture-impact effect times-3 & 0.0 & 8.66 & 41.74 & 31.88 & 14.37 & 2.14 & 1.21 \\ No fracture-impact effect times-5 & 0.0 & 6.33 & 33.59 & 28.69 & 24.16 & 4.14 & 3.08 \\ fracture angle 30\({}^{\circ}\)-impact effect times-1 & 0.0 & 23.55 & 42.97 & 12.86 & 16.18 & 2.95 & 1.47 \\ fracture angle 30\({}^{\circ}\)-impact effect times-3 & 0.0 & 13.18 & 35.54 & 17.03 & 24.34 & 5.92 & 3.99 \\ fracture angle 30\({}^{\circ}\)-impact effect times-5 & 0.0 & 12.99 & 8.81 & 18.50 & 40.82 & 10.96 & 7.91 \\ fracture angle 60\({}^{\circ}\)-impact effect times-1 & 0.0 & 18.97 & 37.64 & 25.61 & 14.79 & 1.94 & 1.04 \\ fracture angle 60\({}^{\circ}\)-impact effect times-3 & 0.0 & 7.47 & 42.04 & 24.11 & 19.62 & 3.83 & 2.93 \\ fracture angle 60\({}^{\circ}\)-impact effect times-5 & 0.0 & 4.07 & 25.82 & 31.53 & 29.54 & 5.74 & 3.30 \\ fracture angle 90\({}^{\circ}\)-impact effect times-1 & 0.0 & 36.06 & 42.37 & 13.58 & 6.52 & 0.80 & 0.68 \\ fracture angle 90\({}^{\circ}\)-impact effect times-3 & 0.0 & 22.9 & 29.3 & 23.3 & 18.79 & 3.56 & 2.08 \\ fracture angle 90\({}^{\circ}\)-impact effect times-5 & 0.0 & 23.57 & 24.30 & 22.88 & 21.62 & 4.79 & 2.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Screening results of fractured coal samples. interactions within complex crack networks generated by repeated impacts, where multi-directional stress fields and inter-fragment collisions induce mixed-mode failure. Figure 6: Logarithmic curve of fragment mass distribution of fractured coal samples. (**a**) No fracture and fracture angle 30\({}^{\circ}\); (**b**) Fracture angle 60\({}^{\circ}\) and fracture angle 90\({}^{\circ}\). Figure 7: Relationship diagram between fractal dimension and prefabricated fracture dip angle.
## 4 Influence of MICP on Properties of Unsaturated Soil  
### Microstructure of MICP Unsaturated Soil  
The micro bonding form determines the mechanical properties such as macro strength, to a certain extent. At present, the main ways to study the microstructure of soil are X-ray diffraction analysis and scanning electron microscope (SEM) observation [99]. [PERSON] et al. [100] carried out SEM analysis to observe the changes in the product morphology and microstructure of MICP soil. Figure 5 presents the microscopic morphology of untreated sand soil using SEM. It shows that there are a few clay minerals attached to the surface of soil particles in the untreated soil, and no evident CaCO\({}_{3}\) crystals are found. The SEM results of MICP-treated sand soil are shown in Figure 6. It is evident that a large number of CaCO\({}_{3}\) crystals are generated in the shape of parallelepiped calcite, and the size of crystals is 1-10 \(\upmu\)m. [PERSON] et al. [101] explored the relationship between the concentration of the bacteria solution and the concentration of the bio-cement solution in the MICP process from a microscopic perspective. Some of the CaCO\({}_{3}\) crystals were dispersed in the soil pores and played the role of filling pores among soil particles. It was proved that the CaCO\({}_{3}\) crystal played a role in bio-cementing soil particles.  
[PERSON] et al. [102] observed the microscopic morphology of the MICP of the experimental device under different solid particle compositions. It was found that the CaCO\({}_{3}\) crystals mainly play the role of encapsulation and can evenly fill the internal pores of soil samples. The permeability of ordinary sand can be reduced by three orders of magnitude due to the CaCO\({}_{3}\) in the pores after treatment. [PERSON] et al. [103] investigated the morphology of the sand column after curing with BP and found that CaCO\({}_{3}\) played a bio-cementing role in greatly improving the connection strength between sand grains. SEM results show that a layer of white and dense hardened shell formed on the surface of  
Figure 5: Micromorphology of untreated sand soil: (**a**) pore; (**b**) flaky clay mineral.  
the modified soil sample, and the generated CaCO\({}_{3}\) presented a lamellar cleavage [104]. In addition, given that the solubility of CaCO\({}_{3}\) is small, the MICP process is very helpful to improve the water stability of soil.
# Fracture Threshold Analysis and Parameter Matching of Cut-Out Induced Bolts for Subway Couplers
###### Abstract  
The overload protection device is crucial in ensuring the orderly absorption of kinetic energy by the coupler buffer device. This paper studies an overload protection bolt with a cut-out zone. In the bolt impact experiment, a premature fracture of 10.9-grade M24 bolts was observed. Based on the analysis of the results, it was concluded that this phenomenon was caused by the mismatch between the mechanical properties of the bolts and the dynamic performance of the coupler. Building on this test, a numerical simulation model was established and subsequently validated. The width and depth of the inducing structure were selected as the research objects. Using the Latin Hypercube method, 78 sets of cut-out zone structure parameters were generated, and numerical simulations were performed on the cut-out induced bolts. The simulation results indicate that the peak force generated by the coupler collision leads to necking in the cut-out induced bolts, which consequently weakens their mechanical properties to some extent. Therefore, it is necessary to consider a strength margin when designing cut-out induced bolts. Based on the simulation results, a surrogate model was constructed, and the optimal bolt cut-out zone was obtained through optimization: a width of 17.74 mm and a depth of 1.37 mm. The surrogate model predicted a fracture force of 1894.13 kN for the bolts. An impact test was conducted to verify the performance of the optimized cut-out induced bolts. The experimental results showed that the cut-out induced bolts broke after the crush tube completed its kinetic energy absorption, with a fracture force of 1828.44 kN, which was a 3.59% difference from the predicted value of the surrogate model. After optimization, the fracture force of the cut-out induced bolts increased from 1147.5 kN to 1828.44 kN (a 59.34% improvement), while the fracture time extended from 20.9 ms to 69 ms, fully meeting the design requirements of the overload protection device.  
overload protection bolt; cut-out zone; impact test; optimization strategy +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Paper Contribution  
This paper introduces a groundbreaking compact hybrid systolic array architecture specifically developed for the Dickson basis multiplier, showcasing remarkable improvements in both space and power efficiency when compared to traditional designs. By seamlessly integrating serial and parallel inputs, the proposed systolic array enhances data processing flexibility while maintaining a streamlined structure that effectively consolidates input types into parallel outputs.  
A key feature of this architecture is its linear complexity, which contrasts sharply with the quadratic complexity typically found in many existing designs. Linear complexity means that the resources needed for computation increase directly with the size of the input; for instance, if the input size doubles, the resources required also approximately double. In contrast, quadratic complexity indicates that the resources required grow at a rate proportional to the square of the input size, leading to a fourfold increase in resource demands when the input size doubles. By maintaining linear complexity, the architecture minimizes the number of processing elements (PEs) and connections needed, which directly contributes to lower space and power usage. The streamlined design reduces the overall number of active components during operation, leading to further energy savings. Consequently, the proposed architecture not only occupies less physical space but also operates more efficiently, making it particularly suitable for applications where power efficiency is critical.  
Crucially, even with this reduction in area and power consumption, the multiplier's performance remains intact, achieving timing delays that are comparable to those of conventional two-dimensional multipliers. Furthermore, the modular design and close connectivity among PEs greatly enhance its suitability for VLSI implementation. These tight interconnections not only improve overall performance but also minimize wire delays, which can considerably impact circuit efficiency. Consequently, this multiplier structure is particularly well-suited for compact IoT edge nodes designed for disabled people, providing substantial advantages in terms of space efficiency and power savings. The characteristics of this approach render it an attractive option for applications tailored specifically to individuals with disabilities, particularly in environments where high efficiency is essential. This innovation holds the potential to significantly benefit those who rely on assistive technologies. The experimental analysis strongly supports the advantages of this design, laying the groundwork for the effective implementation of cryptographic algorithms on IoT edge nodes designed for people with special needs. Moreover, the success in deploying these cryptographic algorithms within resource-constrained environments will not only enhance security but also ensure safe access to vital assistive devices. This improvement is instrumental in promoting a better quality of life for individuals with disabilities, empowering them to navigate their daily lives with greater confidence and independence.
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 2 Materials and Methods  
### The Dataset Description  
A total of 7690 forest fire images of diverse types were collected from Guangdong Province and the internet to build a dataset. The dataset contains top-view image information for trees, lakes, roads, and other forest environments captured using a UAV from the air, collected under sufficient light and low-illumination conditions.  
The dataset was divided into training and testing sets in an 8:2 ratio (i.e., 6152 image samples for training and 1538 images for testing, collected with the same background). The rotation angle method increased the number of training set samples. Six angles were used to rotate the training set images: 30\({}^{\circ}\), 75\({}^{\circ}\), 180\({}^{\circ}\), 210\({}^{\circ}\), 255\({}^{\circ}\), and 300\({}^{\circ}\). This approach increased the training sample diversity, prevented overfitting during training, and improved the model detection accuracy. Forest fire images were recognized as different images in the training network using various rotation angles. The original image dataset can be increased six-fold through rotation angles. The number of samples before the dataset expansion was 7690, and the number after the expansion was 53,830. The expanded sample data were divided into training and validation sets. Table 1 presents the data distribution of the training, verification, and testing sets.  
This study constructed a dataset with 3840 images from the internet and forest fire images collected in Longshan Forest Farm (23\({}^{\circ}\)12\({}^{\prime}\)N, 113\({}^{\circ}\)22\({}^{\prime}\)E), which is located in Lechang City, Shaoguan, Guangdong Province, China. The forest farm mainly grew Chinese fir, as well as Lechang Michelia, camphor, and various local broad-leaved trees. The generalizability of model training and the effectiveness and efficiency of extracting relevant features from images were ensured. The dataset was divided into training and testing sets. The training set consisted of 3072 images, including 320 images with fire and 2752 without fire. The testing set had 768 images: 384 with fire and 384 without fire. The training set distribution was unbalanced, but the testing set distribution was balanced; therefore, whether the model could be used in real-world scenarios could be assessed. The dataset contained images of various scenes, such as forest fires with various sizes and varying lighting intensity at night and in the morning. Non-fire images were captured in bright light, during early morning, and at sunset. Figure 1 depicts representative images of forest fire datasets in diverse environments.
## 4 Discussion
The positive correlation between efficiency gain and flow rate--with minimal gains at low flow rates (<100 m\({}^{3}\)/d) and significant enhancement at high flow rates--indicates that biomimetic surfaces offer particular advantages during production peaks. This characteristic aligns closely with operational requirements in offshore oilfields. The implications of this work extend beyond ESP optimization. By expanding high-efficiency operating ranges, the technology reduces pump replacement frequency and energy consumption, directly addressing sustainability goals in offshore oil production. The 3D-printed prototype's successful fabrication further highlights additive manufacturing's potential for rapid iteration of complex bio-inspired geometries, a critical enabler for industrial adoption. However, long-term durability under abrasive multiphase flows remains unverified. Future research should prioritize the following three areas:  
* **Wear Resistance Evaluation:** Investigating material coatings or hybrid surface textures to enhance durability in abrasive environments. * **Multi-Objective Optimization:** Integrating machine learning with CFD to explore synergistic effects of dimple parameters (depth, spacing, distribution) and alternative bio-inspired geometries (e.g., riblets, hierarchical structures). * **Field-Scale Validation:** Testing optimized impellers in real-world offshore conditions to assess performance under variable oil-water ratios, gas ingress, and thermal stresses. By addressing these challenges, bio-inspired surface engineering could revolutionize not only ESP systems but also broader applications in turbomachinery and renewable energy systems, fostering a new paradigm of energy-efficient fluid handling technologies.
## 2 Problem Statement and Preliminaries
### Neighbourhood of a Station  
As stated previously, the planned rebalancing operations must be local and redistribute bicycles among nearby stations which distance is not greater than a maximum distance threshold \(d\). Thus, we define the neighbourhood of a station \(N(s)\) as the set of stations within a range of \(d\) meters from its position. The station \(s\) itself belongs to its neighbourhood. More formally, given a set of stations \(S\), the neighbourhood of a station \(s_{i}\in S\) is defined as:  
\[N(s_{i})=\{s_{j}\in S|dist(s_{i},s_{j})\leq d\}\]  
The distance is computed using the haversine distance and thus does not take into account streets and pedestrian areas.