esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing
## 3 Data and Study Area  
Chongqing is in the transition zone between the Qinghai-Tibet Plateau and the middle and lower reaches of the Yangtze River Plain. This city has mountainous terrain with large topographic undulations and an average elevation of 400 m. The peculiarities of the topography of Chongqing often require winding routes to be followed between two locations. This results in a mismatch between straight-line distance and time cost and difficulty in reflecting actual food accessibility. Chongqing contains 26 municipal districts, 8 counties, and 4 autonomous counties. With consideration of the distribution of urban areas, the present study analyzed nine main urban areas in Chongqing, namely, the districts of Yuzhong, Jiangbei, Nanan, Jiulongpo, Dadukou, Shapingba, Yubei, Banan, and Beibei, shown as Figure 1.  
Figure 1: Map of Chongqing, China.
## 2 Critical Infrastructures (CIs)
### Smart Interdependent Critical Infrastructures (ICIs)  
Interdependence is an interdependence between two infrastructures in which the operational effectiveness of one is intrinsically linked to the performance of the other. Consider the electric power network and the natural gas network, for instance. The preponderance of electric power generators relies on natural gas as a fuel source, while certain components of the natural gas infrastructure, such as compressors, require electricity to function. In such a scenario, any disruption to the electrical power grid may cause a disruption in the natural gas network. In contrast, a decrease in natural gas pressure could hinder power generation. Smart Interdependent Critical Infrastructures (Smart ICIs) adhere to the same interdependence principle as traditional ICIs. However, the incorporation of advanced Information and Communication Technology (ICT) could substantially amplify their interdependence [17; 18; 19].  
Infrastructure interdependencies are more than just theoretical concerns. Numerous policy reports have recognized the importance of understanding the relationships between infrastructures [20]. These reports highlight the growing attention at the governmental policy level toward the significance of incorporating interdependencies in national strategies to protect and defend critical infrastructures. While infrastructure service providers possess extensive experience in responding to and mitigating blackouts or minor disruptions, there is a crucial need for the nation to prepare for and recover from critical interruptions which may arise due to terrorist attacks or natural disasters.  
Furthermore, as the world becomes more interconnected and digitized, the complexities of smart interdependent critical infrastructures continue to evolve. This progression necessitates continuous advancements in the development of resilience metrics and models to understand and manage the potential impacts of disruptions on these interconnected systems. By enhancing the resilience of Smart ICIs, stakeholders can better safeguard the essential services that these infrastructures provide, ensuring the continuity of these services and the well-being of society as a whole.
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 2 Description of the Calibration Methods
### Normalized Median-Scaled (NMS) Calibration  
The NMS calibration involves three stages: (i) the XRF-scan intensities are scaled to the range of element compositions using the median value of conventional-fractions, which attempts to reduce the specimen effects (see detail in [9]); (ii) the scaled data are normalized by constraining the sum of element components of each sample to 100%; and (iii) the normalized XRF-scan data rather than the raw XRF-scan intensities are converted to element fractions using linear regression equations.
# Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem
## 5 Heuristics for the MD-GmTSP  
In this section, we present a heuristic based on variable neighborhood search (VNS) to solve the MD-GmTSP. VNS is a metaheuristic proposed by [PERSON] and [PERSON] [39; 73] that employs the idea of systematic exploration of neighborhoods of an initial solution and improves it using local search methods. The neighborhood exploration involves descent to a local minimum in the neighborhood of the incumbent solution or an escape from the valleys containing them to obtain better solutions (global minimum).  
The heuristic approach can be divided into two broad phases: (1) construction phase--initial solution and (2) improvement phase--neighborhood search. In the construction phase, a tour construction heuristic is used to generate an initial feasible solution for the given problem. Based on this initial solution, the neighborhood search phase aims to obtain better solutions by making incremental improvements. The tour improvement phase starts by defining a set of neighborhood structures in the solution space. These neighborhood search structures, denoted as \(N_{1},N_{2},\ldots,N_{kappa}\), can be chosen either arbitrarily or based on a sequence of neighborhood changes with increasing cardinality. Once the neighborhood search structures are fixed, different neighborhoods are explored in deterministic or stochastic ways, depending on the type of VNS metaheuristic used. If a better solution is found in the \(\kappa\)-th neighborhood, then the search is re-centered around the new incumbent.  
Otherwise, the search moves to the next neighborhood, \(\kappa+1\). Depending on the chosen neighborhood structures, different neighborhoods of the solution space are reached. VNS does not follow a predefined trajectory but explores increasingly distant neighborhoods of the current solution. A jump to a new solution is made only if an improvement is achieved, often retaining favorable characteristics of the incumbent solution to obtain promising neighboring solutions.
# A Global Overview of SVA--Spatial-Visual Ability
## 8 Implications of the Study  
The exclusion of sources such as newspaper articles, magazines, books, website content, blog posts, etc. was primarily justified by the study's scope, which sought to compile the most recent and relevant research findings from peer-reviewed journals and scholarly publications. Although these sources likely offered valuable insights, their omission was imperative in order to maintain the review's relevance and precision with regard to the research inquiry. By covering literature spanning several decades, this study is able to broadly touch upon the historical development of the concepts of cognitive abilities, the investigation of underlying subconstructs, methodologies, and assessment techniques pertinent to the domain of spatial-visual abilities. This overview is multi-disciplinary, including literature from various disciplines in order to ensure a thorough comprehension of spatial skills.  
Among the many implications of this study, first and foremost is the contribution to knowledge of spatial skills across diverse domains. It also identifies cognitive skills needed for academic and vocational success. This inquiry sheds light on the gender disparities apparent in most of research articles examined, which call for more training of spatial abilities in various sectors. To address the gender gap, the design of the instruction material should focus on tasks that re particularly beneficial to female students. Many of the spatial skills assessments discussed in this paper can be utilized as a framework to develop novel and domain-specific evaluation instruments. Several CAD, virtual reality (VR), and augmented reality (AR)-based strategies are discussed that can improve spatial abilities in problem-solving scenarios, especially for STEM-related outcomes. Additionally, this inquiry also provides a framework for understanding spatial abilities in architecture and interior design.
# Climate Warming Dominates Vegetation Productivity in the Hanjiang River Basin, China
## 3 Results
### Contribution of Climate Factors to Vegetation Productivity  
We quantified the relative contribution of each climate factor to different vegetation types in their linear models using SHAP (Figure 10). The analysis results indicate that Tem and PET have significant impacts on GPP variations in the HJRB. Consistent with the partial correlation analysis, the contribution of Pre to GPP variations is significantly higher in the upstream compared to the downstream. Tem contributes over 40% to DBF, MF, and WSA in the upstream (44.65%, 45.05%, and 42.53% respectively) and over 40% to DBF and MF in the downstream (47.23% and 40.55% respectively). PET contributes over 40% to SAV in the upstream (42.27%) and to SAV, GRA, and CRO in the downstream (43.67%, 40.74%, and 56.30%, respectively), while it is close to 40% in WSA and CVM (39.99% and 38.57%, respectively). Notably, Pre only contributes over 10% in GRA in the upstream (11.86%). The contribution of SPEIs is limited in all vegetation types. Comparatively, SPEI1 and SPEI3 have higher contribution rates than SPEI12, indicating that vegetation productivity in the HJRB is more sensitive to short-term drought conditions. The contribution rates of SSD and WS show minor differences between upstream and downstream. WS contributes significantly (>5%) in forest (DBF and MF) and just over 5% in GRA among other vegetation types. SSD contributes between 5 and 10% in all vegetation types with minimal differences.  
Figure 9: Response characteristics between vegetation productivity and climate factors in the HJRB from 2000 to 2020: (**a**) Tem, (**b**) Pre, (**c**) PET, (**d**) SSD, (**e**) WS, (**f**) SPEI, (**g**) SPEI3, (**h**) SPEI12.
## 4 Evaluation
### Performance of Trajectory Prediction
It was confirmed that the change in the location did not significantly impact the performance of the proposed method. The standard deviation of the proposed method was 0.008 m, whereas that of the method proposed by [21] was 0.032 m. Furthermore, the proposed method achieved the best accuracy at all locations compared to the previous methods. It means that the real-time estimation of operation characteristics can account for the individual difference in driver behavior and the change in driving conditions appropriately. Based on this comparison, it was demonstrated that the real-time optimization of the operation characteristic variables is significantly effective in improving the robustness of the trajectory prediction.
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results
### Evaluation Methods  
With the continuous development of remote sensing image fusion technology, a growing number of fusion algorithms have emerged. Due to the varying principles underlying these methods, the quality of the generated fused images differs significantly. Therefore, adopting scientifically sound and effective quality evaluation methods to assess the performance of these fusion techniques is of utmost importance [29]. This paper employs both subjective and objective evaluation approaches to comprehensively assess the performance of remote sensing image fusion methods.
# Bounded-Error Parameter Estimation Using Integro-Differential Equations for Hindmarsh-Rose Model
## 2 Parameter Estimation Method
### Interval Set Inversion  
This section recalls the algorithm SIVIA (Set Inverter Via Interval Analysis), well known in the interval-analysis community. This algorithm, proposed in [19], leads to characterize the solution set of a system of nonlinear real constraints, by enclosing it between internal and external unions of interval boxes (pavings).  
Consider the problem of determining a solution set \(\mathbb{S}\) for the unknown quantities \(u\), belonging to an a priori search set \(\mathbb{U}\), defined by:  
\[\mathbb{S}=\{u\in\mathbb{U}|f(u)\in[y]\}=f^{-1}([y])\cap\mathbb{U}, \tag{5}\]  
where \([y]\) is a priori known, and \(f\) is a nonlinear function, not necessarily invertible in the classical sense. (5) involves computing the reciprocal image of \(f\) and is known as a set inversion problem, which can be solved using the algorithm. SIVIA is a recursive algorithm, which explores all the search space without losing any solution. This algorithm makes it possible to derive a guaranteed enclosure of the solution set \(\mathbb{S}\), as follows:  
\[\underline{\mathbb{S}}\subseteq\mathbb{S}\subseteq\overline{\mathbb{S}}. \tag{6}\]  
The inner enclosure \(\underline{\mathbb{S}}\) is composed of the boxes that have been proven feasible. To prove that a box \([u]\) is feasible, it is sufficient to prove that \(f([u])\subseteq[y]\). Reversely, if it can be proven that \(f([u])\cap[y]=\emptyset\), then the box \([u]\) is unfeasible. Otherwise, no conclusion can be reached, and the box \([u]\) is said to be undetermined. The latter is, then, bisected and tested, again, until its size reaches a user-specified precision threshold \(\varepsilon>0\). Such a termination criterion ensures that SIVIA terminates after a finite number of iterations.  
Thus, the algorithm SIVIA allows to obtain these two subpavings, with a required precision \(\varepsilon\), based on an inclusion test. The relation between the two subpavings can be characterized as:  
\[\Delta\mathbb{S}=\overline{\mathbb{S}}\backslash\underline{\mathbb{S}}, \tag{7}\]  
where \(\Delta\mathbb{S}\) is called the inclusion test uncertainty, in which no decision can be made during the test. The properties of solutions are:  
* if \(\overline{\mathbb{S}}=\emptyset\) the problem (5) has no solution;
* if \(\underline{\mathbb{S}}\
eq\emptyset\), there exists at least one verified solution for (5).  
A further alternative to the parameter identification proposed above consists of using interval methods, with a subsequent subdivision of parameter domains, in order to reliably identify the implausible parameter subintervals. This method, however, is much more computationally demanding and may, significantly, be affected by the wrapping effect of interval analysis, if specific properties, such as cooperativity, are not satisfied by [20] or [21].
## 4 Experiments
### Implementation Details  
All modules were implemented using the Python 3.8.5 programming language. Deep learning networks were also implemented using the PyTorch framework (version 1.8.0). YOLOv3 network was trained using an image size of \(416\times 416\), a fixed learning rate of \(10^{4}\) over 50 iterations, a mini-batch of 6 images, and the ADAM optimizer. In addition, the YOLOv3 weights were initialized using the COCO pre-trained model. To perform evaluations on the SORT method [10], the number of frames to hold a track without associations before deleting that track was set with \(T_{\mathit{Lost}}=1\), the minimum number of object detections to start a new track was set with \(hit_{\mathit{min}}=3\), and the minimum threshold value for bounding box association was set with \(th_{\mathit{cost}}=0.3\). For the Deep-SORT, the following constant values were used: \(\lambda=0\) (hyperparameter to control the influence of each metric on the association cost), \(T_{\mathit{Lost}}=30\), and an association gating threshold, \(dist_{\mathit{max}}^{1}=0.2\). Moreover, all experiments were performed using an Nvidia RTX 2060 super GPU, 32 GB RAM, and an AMD Ryzen 5 3600 CPU.
# Experimental Study of Complex Coupled Deep Tillage Mechanism and Drag Reduction
## 4 Discussion  
In this study, a complex coupled deep tillage operation was successfully developed by combining bionic design with pneumatic pressure-assisted deep tillage, and a deep tillage sample subsoiler was fabricated using 3D printing technology for field tests, and the field test results verified its significant drag reduction effect. Although 3D printing technology used to fabricate deep tillage sample subsoilers meets the requirements of field tests, in practical operational applications, the group suggests the use of metal materials to ensure durability and reliability; secondly, the field tests in this study were only conducted in loamy soils, a choice that helps to control the variables and ensure the accuracy of the results, but at the same time restricts the generalizability of the results, and future research will be extended to other soil types to validate and optimize the adaptability and performance of deep tillage subsoilers. Although this study focused on the technical performance of the deep tillage subsoiler, its environmental and economic significance in sustainable agriculture cannot be ignored. From the environmental point of view, the reduction in drag force and energy consumption can significantly reduce fuel consumption and greenhouse gas emissions, which is important for combating climate change and reducing the carbon footprint of agriculture. From an economic perspective, reduced traction resistance can reduce operating costs and improve operational efficiency, thereby increasing farmers' economic returns. However, these potential environmental and economic impacts were not quantitatively analyzed in this study. Future studies should systematically assess the environmental and economic benefits of this technology in different application scenarios to fully demonstrate its potential in sustainable agriculture.
## 2 Materials and Methods
### SCC Design  
Conventional SCC design following the previous experience of the authors in SCC with different wastes was conducted [9; 17; 22]. SCC with 0% vol. RCWTB (_RW_ mix) was first designed to reach an SF2 slump-flow class as per EN 206 [57]. To perform this, 320 kg/m\({}^{3}\) of cement, a water/cement ratio of 0.48, and a superplasticizer amount of 2.4% wt. of cement mass were considered. Furthermore, the joint gradation of the aggregates was determined by [PERSON]'s curve, although the amount of fine aggregate was defined through the Fuller's aggregate gradation, as can be observed in Figure 5. In this way, both adequate particle packing and fine aggregate content were ensured [7].  
1.5% vol. and 3.0% vol. RCWTB (_W_1.5 and \(W\)3.0 mixes) were subsequently added to SCC as overall additions. The water/cement ratio was increased by around 0.08 per 1.5% RCWTB to partially compensate its negative effect on flowability according to our experience [40]. The superplasticizer content was not modified in order not to introduce more variables that could affect the fresh behavior of the SCC, since this research was the first approach for the development of SCC containing RCWTB.  
Figure 4: RCWTB components: (**a**) GFRP fibers; (**b**) GFRP micro-fibers and small non-separable particles; (**c**) polymers; (**d**) balsa wood.  
Figure 5: Overall gradation of the aggregates.  
The composition of the three SCC mixes developed in this study is detailed in Table 2. The use of RCWTB as an overall addition while maintaining the amount of the rest of the components constant caused the volume of SCC produced to increase, as can be perceived in the last row of Table 2. Thus, the composition of SCC is shown both in comparative terms and in kg/m\({}^{3}\) in it. The former allows us to comprehend the proportioning of the mixes comparatively, while the latter allows us to understand the decrease in cement content caused by the addition of RCWTB. Therefore, the use of RCWTB also increased the sustainability of SCC, similar to its use in conventional-workability concrete [41].
## 3 Numerical Simulation and Results
### Numerical Simulation Results
#### 3.2.3 Cutting Force  
The tool-tube model's cutting force versus displacement curve is shown in Figure 8. Compared with the experimental curve (Figure 3), the force of both changes in a similar trend. In the initial stage, from 0 to 30 mm, the cutting force also fluctuates significantly after an initial peak. Its fluctuation frequency is lower than the test (corresponding to the AB segment in Figure 3). From 30 to 83 mm, the cutting state stands in the steady-state stage. The force gradually increases under the effect of chip accumulation. It then stabilizes at about 55 kN, slightly smaller than the steady-state values of the test (340/6 = 56.7 kN) in Figure 3. The simulation cannot directly simulate the real tool failure. Hence, the cutting state remains in the steady-state phase subsequently, and the force stays stable. In  
Figure 8: Curves of force and temperature versus displacement.  
Figure 7: Tool temperature distribution during the cutting process.  
contrast, the test showed a continuous increase in force and aggravated oscillation after the steady-state phase. Therefore, the comparison between numerical and experimental results confirms that the worn and broken tool results in decreased cutting performance and increased cutting force and oscillation.
## 3 Neutral Formats for Data Exchange
### STEP Standard  
STEP (Standard for the Exchange of Product Model Data) is a standard that defines the rules for writing a product model based on geometric, topological, technological, and material data. The product model and its mapping data record are created based on the ISO 10303 Standard, including the STEP standard specification [23; 24; 25; 27; 30; 32; 41; 42; 43; 44; 45; 46; 47; 44].  
This translator was developed through a program funded by large industrial corporations wishing to bring about standardization in production, mainly to reduce intermediate production costs. When developing the STEP standard, the best features of standards developed in different countries were selected. The IGES standard played the greatest role here [60; 82].  
The STEP standard defines the rules for creating a product model in a way that allows for the processing and storage of information about the product and its manufacturing processes. As the STEP standard evolves, it becomes possible to flexibly configure CAx software and hardware components while avoiding incompatibility between data and network communication protocols.  
All data in this standard are transported (similar to IGES) in the form of ASCII files. This facilitates their transfer between computers and CAx systems with different hardware and software platforms. The application of the STEP standard is based on application protocols, two of which are very important for the typically cooperative automotive industry. These are AP 214, for applications in the mechanical area, and AP 212 for applications in the electrical engineering area, as described in Table 1.  
In 2014, the STEP AP 242 application protocol was introduced, which combines the common features of the above protocols, as shown in Table 1. The main advantages of Step AP 242 are the functionality and capability to support all AP 203 and AP214 protocols, as well as 3D semantic PMI, high-quality 3D shapes, 3D kinematic assemblies, and 3D wire harnesses and pipelines. Due to its complex functionality, the AP242 application protocol is the industry's preferred STEP development protocol.  
The main tool (programming language) for modeling the product structure in the STEP application is EXPRESS-M (Modeler) and EXPRESS, which is a text language for describing the data model.  
The EXPRESS language is the basic element of the comprehensive, standardized description of a product model in the scope of the ISO 10303 Standard. Apart from the EXPRESS language, the EXPRESS-G graphic module has also been developed. An important benefit resulting from the consistent use of formal language is the unambiguous definition of the data transferred in the application. This makes it possible to test the specification of the data model by means of correct syntax and to generate the so-called adaptation programs, e.g., CAD pre- and postprocessors [14; 19; 36; 70].