esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Graph Neural Network Model Based on Edge Graph Structure  
A series of studies [36; 37; 38] have confirmed that the performance of a classifier is mainly determined by the majority class [39]. However, there is another aspect of imbalance in graph-structured data, which is the imbalance of topological structure on the graph, which is also called edge graph structure. Considering the generality of this problem, the literature [40] proposed a ReNode framework for solving the problem of edge graph structure. It first reweights nodes based on how close each labeled node is to its class boundary [41; 42; 43; 44]. The weights of training nodes that are close to the category boundary and are likely to cause decision boundary shifts are reduced, while the weights of training nodes close to the center of the category are increased. This makes the node's influence boundary and the real category boundary more coincident, reducing the decision boundary offset problem caused by the imbalanced topology. However, this type of method needs to measure the distance from the node to the class boundary. This index has no specific value and is not easy to control. Moreover, the model has poor portability and is difficult to apply to graph-related tasks other than node classification. The literature [45] noted that although graph neural networks can learn node representations, they treat all nodes uniformly, without paying attention to a large number of tail nodes, which have less structural information, resulting in poor performance. It is based on the assumption that all nodes have similar labels to their neighbors, and use the transformation operation to transfer the head node to the tail node to simulate the missing neighborhood information. By modeling the relationship between a node and its neighbors, the transformation vector is finally used to calculate the missing neighborhood expressions of all tail nodes, so as to improve the robustness of tail node embedding. Although this method completes the neighbors of the long-tail nodes in the graph, it ignores that the real graph data do not all follow the long-tail distribution, and there are some nodes that fail to reach the transfer index and are ignored, which is obviously not conducive to feature representations for all nodes. This paper proposes an adaptive method to discover all edge nodes in the graph, which may better play the performance of the GNN classifier.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 4 Data Processing
### Data Dissemination
#### 4.4.1 Short- to Medium Term Hazard Level  
In order to assess the short- to medium-term hazard level, mainly the triggering factors rainfall and groundwater height are considered. On the one hand, time series analyses will be performed to identify causal and temporal relationships between short-, medium- and long-term rainfall and groundwater levels [68]. On the other hand, the hazard level is determined using groundwater level thresholds at, e.g., 50, 75 and 90 percent of the critical water table derived from the geological/geomechanical models created during the hazard analysis. The threshold values thereby are determined individually for different geological homogeneous zones throughout the project area and are applied to the according sensors in this area.
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 6 Conclusions  
In this paper, a sector-designation method based on the mean shift algorithm is proposed to obtain different designation schemes for busy and idle time segments based on the clustering of regional flight conditions, and the main innovations are as follows:  
(1) The conflict network constructed based on the speed-obstacle relationship between aircraft extends the conflict detection to three dimensions, which can objectively and accurately reflect the flight situation between aircraft;  
(2) The radar data of the whole time of recorded flights are selected and an EM clustering algorithm is utilize to cluster and analyze all the aircraft nodes on different dates, which provides extensive data support for sector designation;  
(3) The mean shift algorithm searches and clusters according to the distribution of the center position of the node area and the sum of the strength of the aircraft nodes. Combined with the principle of a Voronoi diagram, the busy period and the idle period sector-division schemes are formed, respectively. Compared with other sector division methods, the method proposed in this chapter is based on the original data of the air traffic control radar to explore more comprehensive and objective information in the flight conflict situation.  
According to whether the airport is in a busy or idle state, different sector-classification methods are adjusted and planned, so as to facilitate the controller allocating energy more scientifically and reasonably, improving the safety of regional operation, and providing a new reference for current sector-delineation work.  
In the process of airspace sector division in idle/busy periods, although the flight conflict situation and air traffic characteristics were comprehensively collected based on historical radar flight data, the selected historical data have certain limitations. In order to improve the universality of the model, the common topological characteristics of radar data should be deeply explored in the next research, and the instance data of multiple control sectors should be used for verification.  
Conceptualization, Y.P. and X.W.; methodology, Y.P.; figure, Y.P. and J.K.; data analysis, Y.P. and Y.M.; writing original draft preparation, Y.P.; writing--review and editing, J.K. and M.W.; supervision, X.W. and M.W.; project administration, M.W. and X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Science Foundation of China under Grant 71801221.  
The data that support the findings of this study are available from the corresponding author, [X.W.], upon reasonable request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Conclusions  
In this paper, a BSN signal transmission system based on the human body medium using a Mach-Zehnder EO sensor has been proposed. We demonstrated that, compared with the signal transmission system based on an electrical sensor, the proposed system based on a Mach-Zehnder OE sensor has a steady frequency response in the frequency range of 2 MHz-30 MHz. Furthermore, the corresponding _in-vivo_ signal transmission experiments with different carrier wave frequencies, baseband frequencies and multi-paths were implemented, and some conclusions can be drawn as follows: (1) generally, the carrier frequency used in BSN based on the human body medium should be higher than 1 MHz, while 8 MHz is a suitable frequency; (2) using the proposed method, signal transmission with the BF range of 100 kHz-8 MHz can be achieved under the condition of CF = 8 MHz; (3) the signal transmission path has less influence to the quality of the discussed signal transmission based on the human body medim (electrostatic coupling type). Our results indicate that the proposed system will help to achieve reliable and high speed signal transmission based on the human body medium in BSNs used for healthcare and other related fields.
## 2 Research Area and Data Sources
### Data Sources  
The data used include land use/cover, meteorological, vegetation type data, and NDVI data (Table 1). From the Geospatial Data Cloud Platform ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn)), accessed on 14 October 2023), we obtained three remote sensing images from Landsat TM in 2000 and 2010, and Landsat OLI in 2020, with a spatial resolution of 30 m.  
Meteorological data such as temperature and precipitation are sourced from the National Earth System Science Data Center ([[https://www.geodata.cn](https://www.geodata.cn)]([https://www.geodata.cn](https://www.geodata.cn)), accessed on 12 June 2024). The spatial resolution is 0.008333\({}^{\circ}\) (approximately 1 km). The temperature resolution is 0.5\({}^{\circ}\), and the precipitation resolution is 0.1 mm. The raw data were subjected to a  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Source of Data** & **Accuracy** \\ \hline \multirow{3}{*}{LULC} & Geospatial Data Cloud & \\  & ([[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)), & 30 m \\  & accessed on 14 October 2023) & \\ \hline \multirow{3}{*}{Temperature and Precipitation} & National Earth System Science Data Center & \\  & ([[https://www.geodata.cn](https://www.geodata.cn)]([https://www.geodata.cn](https://www.geodata.cn)), & 30 m \\  & accessed on 14 October 2023) & \\ \hline \multirow{3}{*}{NPP} & USGS ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)), & \\  & accessed on 14 October 2023) & 30 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Source of data.  
series of preprocessing steps, including data screening, correction, validation, and quality control. To ensure uniformity in spatiotemporal resolution, ArcGIS 10.8 was utilized for format and projection transformations, masking operations, and nearest-neighbor resampling.
## 3 Experimental Verification
### Measurement of the TDC Signal  
In order to determine the crank angle, a remote optical sensor was installed at the compressor frame, which generated a pulse signal as the piston arrived at the TDC (top dead center) during each operating cycle. Thus, the crank angle was set to \(0^{\circ}\) at this time and the operating time was converted to the crank angle for Sections 3.2 and 3.3.
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity through an Approximation of the Failure Domain Indicator Function
#### 4.3.2 Statistical Properties of the Weak Approach with Crude MC
Consequently, the sensitivities of \(P_{f}(\mathbf{s})\) computed with the Weak approach and the failure probability estimate do not depend on the same quantities. In other words, improvements on the failure probability estimation will not necessarily lead to improvements on the failure probability sensitivity estimation and vice versa, in the Weak approach framework.
## 3 Algorithms Used in Correlation Mining Analyses
### Machine Learning-Based Algorithms
#### 3.3.2 Decision Tree Method  
The decision tree is a popular machine learning algorithm used for solving classification and regression problems due to its easy understanding and interpretation. This algorithm constructs a tree structure from the training data, representing a series of decisions to predict the target variable. It recursively divides the data into subsets based on features that provide the most information for making a final decision. The structure of a decision includes root nodes, branches, and leaf nodes. Each internal node represents a test on a feature, each branch represents the outcome of that test, and each leaf node holds a class label. The highest node in the tree is the root node. This technique is user-friendly, requiring no knowledge, and its learning and classification steps are straightforward and quick to execute [58; 59; 60].  
So, a decision tree is a hierarchical model that makes decisions based on a series of questions or conditions. Thus, similar to linear regression, the same features (longitudinal levels) as input data and same target (vertical rail deflection and ballast stiffness) as outcome data were used in the current analyses. Decision trees split the data based on feature values to create a tree-like structure, as depicted in Figure 5(b). Each internal node represents a test on a feature, and each branch represents the outcome of that test. The leaf nodes (terminal nodes) contain the predicted deflection values. Input data should be organized in a tabular format, with rows representing measurements and columns corresponding to features.
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 3 Methodology and Machine Learning Methods (MLMs)
### MLMs
#### 3.1.1 Random Forest (RF)  
Random forest algorithms can be used in both classification and regression problems, like decision trees. The logic of the approach is to create more than one decision tree and produce average results with the help of these trees. The reason why this algorithm is called random is that it offers extra randomness during the creation of the tree structure. When splitting a node, instead of looking for the best attribute directly, it looks for the best attribute in a subset of random attributes. This situation creates more diverse trees [23; 24; 25].  
The brief main stages of RF are as below:  
if stopping_condition_met(data) then:  
return CreateLeafNode(data)  
selected_features = RandomSubset(features)  
best_feature = FindBestSplit(data, selected_features)  
subsets = SplitData(data, best_feature)  
node = CreateNode(best_feature)  
for each subset in subsets do:  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \(\mathbf{H_{2}}\) & \(\mathbf{CH_{4}}\) & \(\mathbf{C_{2}H_{6}}\) & \(\mathbf{C_{2}H_{4}}\) & \(\mathbf{C_{2}H_{2}}\) & **CO** & **Fault** \\ \hline
127 & 24 & 0.01 & 32 & 81 & 0.01 & PD \\ \hline
54 & 0.01 & 0.01 & 4 & 0.01 & 106 & OH \\ \hline
246 & 43 & 0.01 & 21 & 53 & 218 & Normal \\ \hline
9474 & 40.02 & 353 & 6552 & 12,997 & 553 & PD \\ \hline
47 & 12 & 0.01 & 8 & 0.01 & 115 & OH \\ \hline \hline \end{tabular}
\end{table}
Table 2: A sample from the RRM dataset.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \(\mathbf{R1}\) & \(\mathbf{R2}\) & \(\mathbf{R5}\) & \(\mathbf{Fault\_Rogers}\) \\ \hline
0.000185185 & 0.0025 & 400 & Normal \\ \hline
1.375 & 0.005 & 0.030769231 & OH \\ \hline
1.181818182 & 0.181818182 & 4.395604396 & OH \\ \hline
0.004224193 & 1.983669109 & 18.56090652 & Normal \\ \hline
3.32 \(\times\) 10\({}^{40}\) & 2.002 & 2.5 & ARCING \\ \hline
0.119748226 & 5.340683572 & 9.350515464 & ARCING \\ \hline \hline \end{tabular}
\end{table}
Table 3: A sample from the DTM dataset.  
child_tree = BuildRandomTree(subset, features) node_add_child(child_tree) return node votes = {}// Initialize a dictionary to count votes for each class for each tree in forest do: prediction = tree.Predict(test_instance)//Get prediction from each tree if prediction not in votes then: votes[prediction] = 0 votes[prediction] += 1//Count the vote for this prediction final_prediction = ArgMax(votes)//Get the class with the highest vote count
## 4 Outcome  
### Air/Energy Consumption Analysis  
The initial data collection for the Wiedemann Turret Punch Press (TPP) took place in April 2024 (Figures 10 and 11). During this period, the machine consumed 6783 kWh of electricity and 4270 cubic meters of air, averaging 270 kWh of energy and 140 m\({}^{3}\) of air per weekday.  
Figure 10: Power consumption of Wiedemann TPP in April 2024.  
Figure 9: Interactive dashboard for real-time and historical energy and air consumption monitoring.  
As discussed in Section 2.1, the machine was frequently idle in automatic mode due to production scheduling uncertainties. Analysis of the hourly electricity consumption distribution (Figure 12) indicated that values below 10 kWh per hour corresponded to non-operational periods.  
To address this inefficiency, operators were instructed to power down the machine when not in use. A comparison of energy consumption between April and June (Figure 13) showed a significant 51.5% reduction, from 6783 kWh to 3293 kWh.  
Figure 11: Compressed air consumption of Wiedemann TPP in April 2024.  
Figure 12: Histogram of hourly power consumption of Wiedemann TPP.  
Figure 13: Energy consumption comparison of the Wiedemann TPP: April vs. June 2024.  
Further analysis over the following months (Figure 14, Table 1) confirmed a steady improvement in energy efficiency. The high consumption recorded in April 2024 was never repeated, establishing a benchmark for energy-saving measures. Monthly savings ranged from a minimum reduction of 1741 kWh (25.7%) in May to a maximum of 4564 kWh (56%) in October 2024, demonstrating the success of data-driven optimization strategies.
## 3 Results
### Qualitative Synthesis
#### 3.1.2 Diabetes Disease  
The combination of IR imaging with AI algorithms has proven its usefulness in diabetic foot detection. [PERSON] et al. retrieved features representative of 3D morphological patterns and position of the foot, which achieved ACC values of 94.33% with an ANN classifier [52]. High classification rates (91%) were attained more recently with a learner, also based on neural networks, fed with inputs collected after the analysis of surface temperature distribution of the foot [6]. [PERSON] et al. decomposed images of left, right and bilateral foot to calculate entropy and texture features. The best performance was achieved by a k-NN learner (ACC = 93.16%, SN = 90.31%; SP = 98.04%) [53]. Good classification metrics for k-nearest neighbor were also presented by [PERSON] et al. The authors first used steady-state thermal images and built a computational tool for automatic image processing and classification with a success rate of 92.5% [54]. Later on, the use of dynamically acquired thermograms was tested by the same research group, achieving ACC = 81.25%, SP = 80% and SN = 100% with k-NN [55]. Great results were also recently achieved by [PERSON] et al. [56] for the detection of changes in the plantar temperature of the foot. The authors were the first to apply a deep learning structure for diabetic foot assessment, retrieving ACC and SN values of 85.3% and 91.67%, respectively. [PERSON] et al. presented the only study pertaining to diabetes disease that did not report on the evaluation of diabetic foot [57]. Instead, thermal data and artificial intelligence were used as a pre-screening tool for type II diabetes, through the analysis of tongue thermograms. The ANN classifier was the top learner with an ACC of 94.28%.
## 4 Forecasting Results and Discussion
### Single Models  
Four single models were developed to be tested using the electricity demand dataset. The outputs from these models are computed, and their performances are compared using the three metrics \(R^{2}\), MAE, and RMSE, as highlighted earlier in Section 3.2. Table 4 shows a comparison which is further visualised in Figure 6. The performance evaluation is performed on the models after training them on the electricity demand dataset with and without the use of the temperature as a training feature.  
As seen in Table 4 and Figure 6, the LSTM outperforms all other models with an \(R^{2}\) of 87.20%, an MAE of 1.55, and an RMSE of 2.08. Figure 7 shows the predicted/forecasted output against the testing data, which span the period between 1 December 2018 and 28 February 2019, as shown earlier in Figure 3. The figure visualises a close similarity between both datasets with minor differences. It is also worth mentioning that the addition of the temperature as a feature did not improve the results. There is a negligible improvement with the LASSO and SVR models, and a decrease in performance for the FBP. This indicates that the energy consumption is not impacted by the changes in temperature, and this can also be visually seen in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & \multicolumn{2}{c}{\(R^{2}\)} & **MAE** & **RMSE** \\ \hline \multirow{4}{*}{Without Temperature as a Feature} & \multirow{2}{*}{Single} & LASSO & \(-0.76\%\) & 5.05 & 5.83 \\ \cline{3-6}  & & SVR & 44.35\% & 3.17 & 4.34 \\ \cline{3-6}  & & FBP & 68.68\% & 2.51 & 3.25 \\ \cline{3-6}  & & LSTM & 87.20\% & 1.55 & 2.08 \\ \hline \multirow{4}{*}{With Temperature as a Feature} & \multirow{2}{*}{Single} & LASSO & 10.15\% & 4.61 & 5.51 \\ \cline{3-6}  & & SVR & 44.99\% & 3.12 & 4.31 \\ \cline{3-6}  & & FBP & 27.61\% & 3.82 & 4.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Forecasting performance results for all single models: \(R^{2}\), MAE, and RMSE values.  
Figure 6: MAE, \(R^{2}\), and RMSE values for the single models with and without temperature as a feature. (**a**) Mean Absolute Error (MAE). (**b**) Coefficient of Determination (\(R^{2}\)). (**c**) Root Mean Square Error (RMSE).
## 5 Discussion
### The Purpose of the System  
This system is the basis for utilization in urban planning, civil engineering, and emergency management. It involves a more accurate perception of the real world, supported by virtual worlds and visualizations. The identification and 3D modeling of building solids, including roof shapes, is important for urban planners, architects, and heritage professionals. The additional attributes that can be added to the system will have a significant impact on crisis management, including environmental management.  
AR techniques used in the system have been shown to have a significant impact on the field of urban design and architects, primarily by enhancing community participation in the decision-making process, promoting a more comprehensive understanding of the subject matter, and enabling interactive feedback [17; 18; 19; 20; 21]. These techniques assist designers in evaluating and modifying ideas based on community input, thereby leading to more accurate design decisions. Furthermore, AR has been demonstrated to contribute to the enhancement of a city's image by showcasing both future and historical content, thus making it a valuable tool in urban development projects that emphasize the city's identity. In addition to this, AR plays a pivotal role in utilizing user experience to improve urban sensing and data collection. The effectiveness of AR in urban projects is contingent on the availability of technologies, expertise, and the scale of the project.
## 1 Introduction
Through Matlab/Simulink simulations, it demonstrates superior performance in terms of speed control, reduced overshot, and quicker settling times compared to traditional PID controllers. Other researchers [31; 32; 33; 34; 35; 36] present the development of a control system for a mobile workbench using Mecanum wheels, controlled by a PLC for versatile movement capabilities. The paper highlights mechanical selection, system design, and the exploration of advanced control methods for future enhancements. In [37; 38; 39; 40], [PERSON] et al., provide a comprehensive look at designing a Mecanum-wheeled system for a mobile workbench, emphasizing control system design, mechanical component selection, and potential for future advancements in control strategies. Most of the research involving fuzzy decision algorithms relies on decision-making regarding precision during driving or precision concerning trajectory tracking. Additionally, refs. [41; 42] present various complex algorithms that are utilized for trajectory planning and decision-making regarding trajectory optimization. In [43; 44] [PERSON] focuses on advanced control strategies for precision and adaptability in robotic systems, although specific details are truncated and thus not included in this summary. Also, the trajectory and heading tracking for a Mecanum-wheeled robot using fuzzy logic control illustrates the use of fuzzy logic in improving the trajectory following and heading control of a robot with Mecanum wheels, showcasing enhanced path tracking and orientation management through intelligent control algorithms. This work introduces a novel idea: that of using fuzzy logic concerning the driving mode onto an imposed trajectory or the type of movement for following the imposed path. Based on the dynamic model of the robot, the energy consumption of the robot can be monitored.