esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Sector Development: System Dynamics Analysis
## 2 Research Method
### Model Validating/Testing  
The validating of system dynamics models commonly involves structural and behavioural tests. Structural tests assess whether the structure of the model represents the real system. Behavioural tests assess whether the model provides a reasonable output behaviour [30].  
In relation to structural tests, the following tests have been applied: dependency and unit consistency test, feedback loop test, laws of conservation and accumulation test, and negative stock test. Dependency and check unit consistency was performed using the \"dependency tracking\" feature in the software used (Sysdea) [31] to check the relationship between parameters and thus track their units. The feedback loop test was used to check the behaviour of feedback loops, as reinforcing loops should follow reinforcing behaviour and balancing loops should follow balancing behaviour. The stock and flow test implies that the value of the stock must equal the sum of inflows minus the sum of outflows. The negative stock test implies that the stock can go to zero, but cannot go below zero.  
In regard to the behavioural tests, the following points are important: the model should include a number of historical time-periods. The current study used a historical time series consisting of 28 years from 1990-2017 for most resources. The simulated values calculated by the model (blue line) should match these real-world values (red line). This matching can be given a value from 0 (perfect predictions) to 1 (worst predictions) called the discrepancy coefficient. Values between 0.4-0.7 indicate good to average models [32]. This test has been used for energy production capacity for every resource, total energy consumption, total energy production and CO\({}_{2}\) emissions to compare modelled with historical trends between 1990 and 2017 for most resources and between 2007 and 2017 for CO\({}_{2}\) emissions due to data availability.  
Extreme conditions tests were used to assess the robustness of the SFM under different extreme conditions. For example, (1) the gross demand growth rate was set to (0%, 0%) (no growth) for coal and wind respectively, (2) base case scenario with gross demand growth rate was set to (0, 3.25%) and(3) gross demand growth rate was set to (10.3%, 52.4%) (maximum), to determine their influence on energy production capacity, capacity under construction, wholesale price, total supply cost, capital employed, capex (capital expenditure) and reserves depletion. In addition, we conducted three extreme condition tests for total CO\({}_{2}\) emissions for black coal, brown coal, gas and oil. The test scenarios were as follows: (1) gross demand growth rate set to 0% (no growth) for black coal, brown coal, gas and oil, (2) gross demand growth rate set to (0%, -8%, 22.7%, 2%) (current trend) for black coal, brown coal, gas and oil, respectively, (3) gross demand growth rate set to (10.3%, 13.8%, 27.3%, 7.5%) (maximum) for black coal, brown coal, gas and oil, respectively.
## 3 Results and Discussion
### Limitations and Future Goals  
The accuracy of the regression model between ICMV and ISMV is a crucial issue in the application of IC technology. Currently, in terms of input of regression model, previous studies had considered soil parameters and drum vibration signal separately, rather than comprehensively [40; 41]. Regarding output, only compaction degree had been targeted for regression or classification, while the study of subgrade modulus was lacking [40; 41; 42]. For the perspectives of regression algorithm and regression features, this paper explores methods to improve the reliability of using ICMV to estimate compaction degree and subgrade modulus. Some conclusions that are beneficial to practical engineering are obtained. However, there are still some issues that need to be addressed:  
* This paper used moisture content as an input in the regression, but the continuous detection method of subgrade moisture content in practical engineering still needs further research. Therefore, when using the regression method proposed in this paper, moisture content can be removed from the regression input as appropriate, however, it will reduce the accuracy of ISMV estimation.
* In the analysis of the drum vibration signal, this paper adopted the conventional indicators in the field of IC technology, namely CMV, CCV and \(E_{\text{vib}}\). Other parameters of vibration signal are not considered, such as shape factor, kurtosis, skewness etc.  
Figure 11: Comparison of actual and predicted ISMV of different algorithms after monadic linear regression, (**a**,**b**) actual \(E_{\text{LWD}}\) vs. predicted \(E_{\text{LWD}}\), (**c**,**d**) actual compaction degree vs. predicted compaction degree.  
It may lead to the inadequacy of the analysis of vibration signals. Thus, further discussion and research are necessary to address this issue in future studies.
## 1 Introduction
The final model is a weighted combination of these weak classifiers, with their contributions determined based on their performance on adjusted sample weights. However, indoor localization is challenging due to noise and outliers in real-world data, making AdaBoost sensitive to these factors, which can significantly impact positioning accuracy [36]. To tackle these challenges, we proposed an AP selection method that ensures the selected AP always appears in the input dataset (referred to as a _probability-one_ (PONE) selection in this study). This strong requirement in AP filtering can reduce the available features in the dataset but can be effective in filtering out noise and retaining reliable APs, especially when the number of detected APs is huge, like in campus buildings. The PONE selection is then combined with an AdaBoost-based localization algorithm to enhance indoor positioning accuracy. This two-step approach helps to mitigate the effect of noisy data and leverages the boosting capability of AdaBoost to detect the specific location of arbitrary objects more effectively. The contributions of this paper can be summarized as follows:  
* The development of a novel indoor positioning scheme combining PONE AP selection with an AdaBoost-based localization method;
* Data collection with various types of WiFi devices and evaluation of the proposed approach for real-world multi-story campus buildings;
* Performance comparison with other well-known localization algorithms to validate its effectiveness in terms of accuracy and robustness. The numerical results show that the average positioning accuracy of the proposed method is 95.55%, representing an improvement of 5.55% to 16.21% over the machine learning methods it was compared to. Additionally, the positioning accuracy remains consistent across different tested devices. The two-dimensional positioning error ranges from 0.25 to 2.3 m.  
The remainder of this paper is structured as follows: Section 2 presents the system architecture and the fundamental concepts of the proposed fingerprint localization scheme. Section 3 provides the experimental evaluation and compares the proposed method with other well-known approaches. Finally, Section 4 concludes the paper, addressing the limitations of this study.
## 5 Conclusions  
From the above analysis, we can draw the following conclusions.  
1. This research improved the traditional VFS method from two aspects: (i) simplifying the RMD calculation process and (ii) optimizing SRMD, which was applied to the prediction of rockburst strength. Good results have been achieved.
2. Compared to the traditional VFS method, the improved VFS has a clearer, more efficient calculation process and a more credible and stable prediction. The improved VFS method simplified the RMDcalculation process by using the characteristic relationship of RMD at different levels and verified the correctness of RMD calculation results through these characteristics at all times. Besides, the improved VFS also uses the BP neural network to optimize the SRMD, which improves the prediction accuracy of SRMD. By this way, the influence of weight change on SRMD is also effectively avoided. Therefore, the improved method has higher fault tolerance rate and anti-jamming ability.
3. The original index data, the rockburst occurrence, and the intensity in underground projects all have certain dynamic variability and fuzziness, so it is difficult to express the rockburst criterion with an accurate relational expression. A more reasonable results can be obtained by using RMD and SRMD to predict. However, the application of improved VFS to rockburst prediction is still in the phase of theory and there are still some problems to be further explored, for example, how to reasonably construct RMD function, how to select and calculate the layers of BP neural network, the number of nodes and the connection weight matrix, etc. to make the rockburst prediction results more in line with the actual situation, especially for the accurate prediction of mix-level and intermediate-level rockburst.  
Formal analysis, Y.X.; Funding acquisition, L.N. and Y.X.; Investigation, T.Z. and Y.W.; Methodology, H.W.; Validation, Y.L. and C.D.; Visualization, Y.H.; Writing--original draft, H.W.  
This work was supported by the National Natural Science Foundation of China (Grant No. 41702300 and Grant No. 41572254).  
The authors declare no conflict interest.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 5 Case Studies and Estimation Costs  
At a large scale, the choice of technique for the removal of heavy metals and metalloids must consider several factors: removal efficiency, cost, treatment duration, pollutant concentration, and soil properties. In the literature, there are few case studies and field applications of various techniques for heavy metal remediation. Some examples are summarized in Table 5.  
Several companies are specialized in the stabilization/solidification technique at an industrial scale, but they do not usually publish financial information.  
It is difficult to estimate actual costs due to a lack of recent and large-scale data. For example, by referencing 2010, the cost for an electrokinetic method that implements a 1 V\(\cdot\)cm\({}^{-1}\) voltage slope to eliminate copper is about 13-40 USD\(\cdot\)m\({}^{-3}\) of soil [89].  
[PERSON] and [PERSON] [146] had estimated the costs for soil washing and phytoextraction (year 2000):  
* for soil washing, the cost is in the range of 200 USD\(\cdot\)m\({}^{-3}\) (small sites) to 70 USD\(\cdot\)m\({}^{-3}\) (large sites);
* for phytoextraction, the cost can go from 35 USD\(\cdot\)m\({}^{-3}\) (small sites) to 10 USD\(\cdot\)m\({}^{-3}\) (large sites).
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 4 Handover Signaling Cost Analysis
### Processing Cost  
We define the handover processing cost \(C_{P}\) based on the required number of signaling messages during a handover. Therefore, handover processing cost reduction can be given by  
\[C_{P-Reduction}=[\frac{1}{NM_{np}}(NM_{np}-NM_{p})]\times 100\% \tag{7}\]  
where \(NM_{np}\) and \(NM_{p}\) denote the number of messages needed for non-predictive and predictive scenarios, respectively.
###### Abstract  
This paper proposes a novel high-gain, wideband, circularly polarized (CP) metasurface (MTS) antenna. The antenna is composed of a centrally symmetric MTS and a slot-coupled feeding network. Through characteristic mode analysis (CMA), parasitic patches and mode-suppressing patches are added around the MTS to enhance the desired modes and suppress the unwanted modes. Subsequently, a feeding network that merges a ring slot with an L-shaped microstrip line is utilized to excite two orthogonal modes with a 90\({}^{\circ}\) phase difference, thereby achieving CP and high-gain radiation. Finally, a prototype with dimensions of 0.9\(\uplambda\)\(\times\) 0.9\(\uplambda\)\(\times\) 0.05\(\uplambda\)\(\uplambda\) is fabricated and tested. The measured results demonstrate an impedance bandwidth (IBW) of 39.5% (4.92-7.37 GHz), a 3 dB axial ratio bandwidth (ARBW) of 33.1% (5.25-7.33 GHz), and a peak gain of 9.4 dBic at 6.9 GHz.  
circularly polarized (CP); slot antenna; wideband; metasurface (MTS); characteristic mode analysis (CMA) Article  
1
## 5 Discussion
### Opportunity to Reduce Business Costs  
Market research can be an exorbitant and time-consuming process, but these organizations have switched to social media as an economical and extensive tool for obtaining insights into their market, customers, branding and other crucial market research factors. These organizations utilize social media platforms such as Facebook, Twitter and Instagram to track trends and carry out market research. By just looking through the most recent posts and popular terms by their customers, they can easily get insight into arising trends and discern what the customers are discussing about their business in real-time. Social media platforms also enable the organizations to participate in an open discussion with their target customers. This technique for research provides the organizations with significant data and a more profound understanding into their customers' thoughts and preferences. In other words, it enables the organizations to recognize the actual desires of their target customers. In comparison with traditional market research, the cost of conducting market research on social media platforms is considerably less and it is also less time consuming.  
Carrying out market research on social media platforms can enhance the effectiveness of the organizations' marketing strategies as they are more knowledgeable about their target market. The information that has been accumulated from the market research are utilized as a guide for their marketing strategies including product launching and follow-up marketing. In accordance with an interview conducted, it was mentioned that after conducting market research on social media platforms, they found that their customers prefer advertisements through social media as opposed to traditional advertisements. The utilization of social media for market research is more effective rather than investing time and money into building a marketing plan where the outcome is uncertain. This prevents wastage of money and efforts which will overall reduce business costs.
## 1 Introduction
These enzymes (NR, NiR, GS, GOGAT) are critical and involved in N metabolism, and their activities determine the N accumulation in plants. [PERSON] et al. reported that nano-MoO\({}_{3}\) increased the activities of essential enzymes associated with nitrogen metabolism and nitrate utilization in rice [27]. NPs regulating soybean growth and nitrogen assimilation have been reported previously. [PERSON] et al. found that NiO NPs at 50 mg/kg increased the activities of nitrate reductase, urease, glutamine synthetase, and glutamate synthesis in plants [29]. The use of ZnO NPs at 25 mg/kg increased the biomass and nitrogen assimilation of _Phaseolus vulgaris_[30]. [PERSON] et al. reported that Mn NPs acted as cofactors in a series of enzymatic reactions in the process of assimilating nitrate into organic nitrogen compounds and subsequently improved the nitrogen content in plants [31]. MoS\({}_{2}\) NPs released molybdenum and sulfur, both of which participate in enzymes involved in nitrogen metabolism, and then altered soybean growth [32]. Only a few studies have focused on the relationship between CuO NPs and soybeans. For example, [PERSON] et al. reported that the toxicity of CuO NPs to soybean seedlings was dependent on size and use doses [33]. Hence, although a large body of research has been conducted on NPs, there is still a lack of understanding of the differential regulation of soybean growth and nitrogen assimilation by different sizes of CuO NPs. The objective of the present work was to investigate the physiological responses of soybeans upon exposure to different sizes of CuO NPs. Particularly, the pattern of N\({}_{2}\) assimilation in soybeans as affected by different sizes of CuO NPs was elucidated. At harvest, physiological parameters, including biomass, nitrogen assimilation-related enzymic activities (e.g., NR, NiR, GS, GOGAT), and nitrogenous compounds (e.g., NO\({}_{3}\)\({}^{-}\)-N, NH\({}_{4}\)+-N, GSH, amino acids, proteins), were measured across all the treatments. Additionally, the contents of both micro- and macro-nutrients in soybean shoots and roots as a function of particle size were determined. Overall, the present study contributes to a better understanding of the potential implications of different sizes of CuO NPs in altering nitrogen assimilation in legume crops.
## 1 Introduction  
### Motivation  
The sustainability of cities depends, to a large extent, on urban transport, which has many negative impacts such as traffic congestion, air pollution, health issues, and decreased traffic safety. In this context, public transport is a viable alternative to private car transport even for medium and long distances. Urban areas with a good public transport supply also favor pedestrian and bicycle travel [1] when safe and comfortable infrastructure is provided [2]. In many countries, the authorities have made efforts to solve urban transport problems by building more infrastructure; introducing more traffic signalization; introducing traffic management, especially for car flows; enhancing the quantity and quality of information (ITS); introducing road pricing; introducing subsidies to make public transport more attractive; and better integrating urban transport with land-use planning [3].  
Measuring the performance of public transport is essential for transport authorities, enabling them to verify whether services are provided efficiently and effectively, to identify areas where performance may need to be improved, to ensure user satisfaction, and to support decision makers in making informed decisions [4].  
For tracking all these factors in a fast and coherent manner, the rapid digitization of the public transport network is necessary. Digital maps allow transport authorities and/or operators to analyze, forecast, and simulate the evolution of urban public transport in correlation with spatial development, in the context of developing new forms of mobility, such as micro-mobility and the inevitable introduction of autonomous and connected urban public transport.  
The digitization of the transport network by integrating the road network, stations and terminals, lines, transport schedules, the number of passengers, and accessibility is essential for carrying out analyses leading to the better adaptation to demands, increased performance and, thus, increased attractiveness of the urban public transport system. In recent years, special attention has been paid to the use of geographical information systems (GIS) for modeling mobility and transport studies [5; 6; 7].
# Research on TBM Cutterhead Crack Damage and Fatigue Reliability
## 2 Analysis of TBM Cutterhead Crack Damage Characteristics  
### Dangerous Area Analysis of Cutterhead  
The focus of this study was the Xinjiang cutterhead with a diameter of 8 m (Figure 2a), which was imported into ANSYS (ANSYS 19.1, ANSYS Inc., Canonsburg, PA, USA) for mesh generation and finite element analysis. A node, which was rigidly coupled with the cutterhead, was established at the center of the cutter hole of each hop. The loads were applied on the node (Figure 2b) and the back of the cutterhead was fixed as the constraint condition (Figure 2c). The finite element model of the TBM cutterhead is shown in Figure 2d, where there were 248,713 elements and 348,670 nodes.  
Figure 1: The overall flowchart. TBM: tunnel-boring machine.  
The material of the cutterhead was Q345B steel, whose behavior was assumed to be linear-elastic; its properties are shown in Table 1.  
Based on the research data of hob loads under different rocks [28], starting with marble geology, the finite element analysis of the cutterhead was carried out. Through static strength analysis, large stress and deformation areas were found. The stress and deformation nephogram are shown in Figure 2e,f, respectively. It can be seen that the area  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density** & **Young’s Modulus** & **Poisson’s Ratio** & **Yield Strength** \\ \hline
7850 kg\(\cdot\)m\({}^{-3}\) & 210 GPa & 0.3 & 345 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of Q345B steel.  
Figure 2: Finite element analysis of a TBM cutterhead: (**a**) three-dimensional models, (**b**) loading mode, (**c**) constraint conditions, (**d**) finite element model, (**e**) stress distribution, and (**f**) deformation distribution.  
with higher stress and deformation was distributed in the center of the cutterhead because of the supporting effect of the stiffened plate in the peripheral area. Therefore, cracks were more likely to occur in the central area, which is consistent with the actual engineering data [12]. Hence, the stress intensity factor calculation and crack fatigue reliability study focused on this area.
# Innovative Tool for Automatic Detection of Arterial Stenosis on Cone Beam Computed Tomography
## 2 Materials and Methods
### Segmentation  
The first step is the segmentation. Accurate vascular segmentation is crucial as further analysis of vessels' properties depends on the accuracy of this procedure. In this study, we exploited a segmentation algorithm presented in a previous paper by [PERSON] et al. [18] and briefly summarized below. This algorithm consists of two consecutive steps: bone segmentation and vessel segmentation. The images are acquired with two CBCT scans, carried out before and after the administration of the contrast medium, respectively. The first acquisition is used to segment the bones. This step is necessary to obtain a binary mask to apply on the contrast image. This binary mask improves the segmentation of vessels in the contrast image, avoiding errors related to a similar gray level distribution of bones and vessels with contrast. Thus, segmented vessels are obtained first by subtracting the segmented bones from the image with contrast and, then, applying a second phase of segmentation on the resulting image. Both segmentations are based on a threshold approach (with an empirically selected threshold), which provides provisional segmentation, further refined by a region-growing method, as explained in the following. The region-growing method begins with a seed point, which is determined with the help of a function that is able to identify all spherical shapes in the frame by exploiting the circular Hough transform  
its minimum level. In this case, it takes the binarized volume as the input and outputs a 3D binary image with the same measurements as the reference binarized volume. Vessel skeletonization is performed through an appropriate function present in MATLAB. The skeletonized vessel tree provides a simplified representation of the complex vascular ramifications and pixels that form it. By reducing the volume of blood vessels to their skeleton, the overlap between vascular segments is eliminated accordingly, increasing the understanding of the flow path followed by the blood. The structure of the skeletonized image is then analyzed with the algorithm developed by [PERSON] (2010), which, starting from a 3D binary skeleton, produces a topological description of the tree providing the adjacency matrix of the graph and two structures containing information about the nodes (coordinates, list of connections for each node, etc.) and links, respectively. By the term \"nodes\", we refer to the points of bifurcation where a vessel splits into two smaller vessels, while by \"link\", the set of points that are on the branch.