esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Hubs are strategic locations that function as central nodes within clusters of cities, playing a pivotal role in the distribution of goods, services, and connectivity. Identifying these vital hubs--through analyzing influential locations within transportation networks--is essential for effective urban planning, logistics optimization, and enhancing infrastructure resilience. This task becomes even more crucial in developing and less-developed countries, where such hubs can significantly accelerate urban growth and drive economic development. However, existing hub identification approaches face notable limitations. Traditional centrality measures often yield low variance in node scores, making it difficult to distinguish truly influential nodes. Moreover, these methods typically rely solely on either local metrics or global network structures, limiting their effectiveness. To address these challenges, we propose a novel method called Hybrid Community-based Gravity Centrality (HCGC), which integrates local influence measures, community detection, and gravity-based modeling to more effectively identify influential nodes in complex networks. Through extensive experiments, we demonstrate that HCGC consistently outperforms existing methods in terms of spreading ability across varying truncation radii. To further validate our approach, we introduce ThaiNet, a newly constructed real-world transportation network dataset. The results show that HCGC not only preserves the strengths of traditional local approaches but also captures broader structural patterns, making it a powerful and practical tool for real-world network analysis.  
Keywords:transportation networks; connectivity; network analysis; hub identification; influential nodes; regional development; geospatial dataset _Article_
## 2 Methodology
### Dataset Preparation
#### 2.3.2 Principle of Continuous Wavelet Transform (CWT)  
Wavelet transforms have become essential tools in various signal-processing fields, with the continuous wavelet transform (CWT) standing out for its effectiveness in signal analysis. In CWT, the wavelet associated with scale \(a\) and time position \(b\) is expressed as Equation (12):  
\[\varphi_{a,b}(t)=\frac{1}{\sqrt{|a|}}\varphi\left(\frac{t-b}{a}\right) \tag{12}\]  
where \(\varphi(t)\) is the fundamental wavelet that acts as a 'prototype', serving as a bandpass filter. The factor \(|a|^{-1/2}\) ensures energy preservation. The time-scale parameters \((b,a)\) are discretized which can be approached in various ways, leading to different forms of wavelet transforms. Following the conventions of the classical Fourier transform, this research adopts a comparable framework. The concept of CWT was first introduced by [PERSON]. Both the time t and timescale parameters vary continuously as Equation (13):  
\[CWT\{x(t);a,b\}=\int x(t)\varphi_{a,b}^{*}(t)dt \tag{13}\]  
In Equation (13), the asterisk denotes the complex conjugate operation, and the wavelet sequence (WS) coefficients correspond to the sampled continuous wavelet transform (CWT) coefficients. Time is modeled as a continuous variable, whereas the timescale parameters \((b,a)\) are defined within the timescale plane \((b,a)\).  
To enhance data reliability and minimize errors or omissions during transmission, each time-frequency window contains 120 selectively sampled points. Furthermore, the implementation of overlapping data segments ensures seamless and continuous data analysis.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Validation  
In this research, data validation is performed to determine the validity of aerial data based on ground-based data. The data compared between aerial and ground-based nodes are from sensors of the same type. Three experiments were performed to validate the aerial data, which are discussed in the following section.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 2 Principles of Power Flow in Power Systems
### MT-HVDC Systems Power Flow
#### 2.2.3 Interface of the Converter Station AC and DC Sides  
Regardless of the converter station switching losses, the active power balance between converter station AC and DC sides is as follows:  
\[P_{ti}^{p}=V_{DCi}^{p}I_{DCi}^{p} \tag{19}\]  
\[P_{ti}^{n}=V_{DCi}^{n}I_{DCi}^{n} \tag{20}\]  
where \(V_{DCi^{\prime}}^{p}\), \(I_{DCi^{\prime}}^{p}\), \(V_{DCi^{\prime}}^{n}\), and \(I_{DCi}^{n}\) are the DC bus voltage and current of the positive and negative pole of the \(i\)th converter station.  
Also, the active power at the AC-side terminal of the positive and negative pole of the converter stations can be written as follows:  
\[P_{ti}^{p}=V_{ti}^{p}\big{[}V_{ti}^{p}G_{i}^{p}-V_{gi}\big{[}G_{i}^{p}\cos \big{(}\theta_{ti}^{p}-\theta_{gi}\big{)}+B_{i}^{p}\sin\big{(}\theta_{ti}^{p}- \theta_{gi}\big{)}\big{]}\big{]} \tag{21}\]  
\[P_{ti}^{n}=V_{ti}^{n}\big{[}V_{ti}^{n}G_{i}^{n}-V_{gi}\big{[}G_{i}^{n}\cos\big{(} \theta_{ti}^{n}-\theta_{gi}\big{)}+B_{i}^{n}\sin\big{(}\theta_{ti}^{n}-\theta_{gi }\big{)}\big{]}\big{]} \tag{22}\]
## 3 Data and Methodology
### Preparation of Satellite Image and LULC Classification  
Extracting LULC information from the satellite images is one of the most important processes in data preparation. Multiple dates of the Landsat data, acquired from the U.S. Geological Survey (USGS) via the USGS Global Visualization Viewer site, were utilized in this study. The five periods of satellite images were generated using the Landsat 5-TM images in November of 1998, 2003, 2008, and 2011 and by Landsat 8-OLI image for 2014 of path/row 129/50 and 129/51. After the mosaic process, a coupler of image in the same year applied a geometric correction and re-projection into a common UTM zone 47 North. The first 2014 Landsat image was geo-reference using topographic map from the Royal Thai Survey Department (RTSD). Then the other images were corrected through an image-to-image rectification method based on the corrected 2014 image. The total root mean square error (RMSE) of each image was assessed to confirm the geometric error, which was less than one pixel. Furthermore, the Landsat images were classified into five classes by the maximum likelihood classification. This method applies the Bayesian theory [37] to categorize the image data into the interested LULC map. The sets of training data were used to predict the probability of each pixel being classified in each class. Moreover, an accuracy assessment was applied to confirm the quality of the resulted LULC maps by the overall accuracy and Kappa coefficient. The reference data to be used in this process were very high resolution images of IKONOS and QuickBird, Landsat RGB, and ground truth data provided by Land Development Department (LDD) or gathered directly from the field survey. All classified results have an overall accuracy greater than 80% and also a kappa value is higher than 0.8, which shows perfective agreement.
### Motivation and Contributions  
Over the evolution of the human race, humans have evolved to pay different amounts of attention to different regions of a visual scene as it is viewed. This mechanism obviously affects human perceptual quality judgements. Previously published FR-IQA methods [14, 16, 17, 18] utilize visual saliency in the final pooling stage of the algorithm. To be more specific, visual saliency is used as weight in the weighted averaging of local image quality scores, emphasizing image regions that are more salient to the human visual system. The main contribution of this paper was the following. As opposed to other previously published algorithms, visual saliency was utilized by this work in the computation of local image quality and not in the pooling stage, which is usual in the literature. In fact, the human visual system perceives image degradation with higher probability in visually significant regions and tends to neglect degradation in visually less significant regions. In other words, local quality degradation is simultaneously influenced by both local image degradation and visual saliency. The effect of visual saliency on the perception of local image quality is demonstrated below. If humans observe Figure 1a, they to tend to focus their attention on the child's face rather than on the vegetation in the background. To be specific, we added the same amount of noise to two different visually significant areas of the image. In Figure 1a, it was added to the region containing the the vegetation in the left corner, while it was added to the face in Figure 1b. Comparing these two figures, the human visual system was less likely to perceive the degradation if it was not in a visually salient area.  
Based on the above-mentioned observations, a visual-saliency-guided FR-IQA framework was introduced where the visual saliency adaptively adjusted local image quality. Within this framework, ESIM (edge strength similarity-based image quality metric) [19] method was further developed. Furthermore, it was demonstrated that the proposed method was able to improve state-of-the-art performance at low computational costs on four large and widely accepted IQA benchmark databases, i.e., KADID-10k [20], TID2013 [7], TID2008 [21], and CSIQ [8].  
Figure 1: HVS’s perception of local image quality in relation to visual saliency: (**a**) degradation in non-salient region, (**b**) degradation in salient region.
# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Genetic Algorithm (GA)
A fast classification model based on XGBoost was developed to collect stream data features and enable the identification of appropriate services and configurations based on stream data patterns. The success of their approach hinged on the effective utilization of features to characterize and portray the underlying qualities of time-series data. The researchers conducted thorough tests to evaluate the efficacy of features obtained through the genetic algorithm. Experimental evaluations conducted on both synthetic and real datasets demonstrated the superiority of their proposed strategy. It exhibited higher accuracy compared to previous complex approaches and showcased its ability to identify suitable services in diverse situations. Ref. [30] devised an intrusion detection system that effectively detects diverse network intrusions by employing a genetic algorithm with an enhanced starting population and selection operator. The genetic algorithm (GA) is a powerful method that combines exploration and exploitation techniques to expedite the search for attack scenarios within audit files. By utilizing GA, the system efficiently identifies a subset of probable attacks present in the audit file within a reasonable timeframe. Ref. [11] highlights the utilization of Genetic Algorithms (GA) in Network Intrusion Detection Systems (NIDS). The paper provides a concise overview of the Intrusion Detection System, genetic algorithm, and related detection methods. It delves into the properties and evolutionary process of GA in detail. Unlike alternative approaches, this solution tackles the temporal and geographical aspects of network connections by encoding network connection information into IDS rules, making the detection of complex abnormal behavior more streamlined. The study primarily focuses on TCP/IP network protocols.
## 5 Engineering Application
### Reliable Life Prediction and Real-Time Update of IES  
According to Equation (20), the LCL of the reliable life for the IES can be obtained at a given reliability of \(R=0.93\). For comparison, the calculation results using the LM method are also presented. As shown in Table 8, the proposed method predicts a reliable life of 8.46 years, which is 23.5% higher than the 6.85 years estimated using the LM method. This demonstrates the superior accuracy of the proposed approach.  
Assume that 10 IESs of the same type have been in orbit up to time \(t_{0}\) (\(t_{0}\leq~{}8\) years) without any failures (if IESs are as reliable as claimed, it is not expected to fall in a reasonable number of devices within their design life). For these system-level flight data, the system reliability and reliable life derived from component life data can be updated, as outlined in Section 3.2.  
Figure 7 shows the reliable life update results of the IES with increasing in-orbit flight time \(t_{0}\) at a confidence level of 0.9. As shown, the updated reliable life increases  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Acceleration Factor** & **Equivalent Test Time (Years)** & **No.** & **Acceleration Factor** & **Equivalent Test Time (Years)** \\ \hline
1 & 1 & 1.6 & 5 & 3 & 4.8 \\
2 & 1 & 1.6 & 6 & 5 & 8.0 \\
3 & 3 & 4.8 & 7 & 5 & 8.0 \\
4 & 3 & 4.8 & 8 & 5 & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Supplementary test data for rotating devices (no failures).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Required Indicator** & **LM Method** & **Proposed Method** \\ \hline
0.93 & 0.9142 & 0.9354 \\ \hline \hline \end{tabular}
\end{table}
Table 7: LCL of the IES reliability for reassessment.  
monotonically with \(t_{0}\) as long as the flight data remain zero failure. For example, if 10 IESs operate in orbit for 8 years without failure, the system's reliable life is updated to 10.02 years. In comparison with the previously calculated reliable life of 8.46 years, the updating method increases the reliable life by 1.56 years, resulting in an 18.4% improvement. This significantly enhances the accuracy of the system's reliable life prediction.
# Linked Data Triples Enhance Document Relevance Classification
## 7 Discussion  
Latent topics generated by topic modeler provide global semantic meaning [44] for the words in a document. Addition of topic probability distributions enriched the word/sentence representations and improved their classification performance. As a bag-of-words technique, named entities have shown positive effect on GloVe and Transformer models, but they negatively impact the sentence embeddings. Our proposed method considers if linked data triples, containing lexical relationships along with terms from LDA topics and named entities, contain enriching semantic information. When LDA topics and named entities are fused with generalized word/sentence embeddings, as in Section 5, an enriched document representation is created. The addition of these features results in improved performance for a relevance classifier, compared to the baseline models which do not contain them. In contrast with other works, our proposed methodology shows performance improvements without recourse to external knowledge sources, dictionary, ontology, or other knowledge sources.  
Our finding shows that topic-entity triples contain significant semantic value. Triples created from sentences, filtered and formulated using LDA topics and named entities, can be used to create information retrieval systems, such as focused search engines, and we posit that they can be applied in question answering they represent raw facts about a document's meaning.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Document Type** & **Feature Set** & **Accuracy** & **F-Score** \\ \hline Energy-Hub & All & Text & 71.3 & 70.3 \\ Energy-Hub & All & Text + Triples & 77.7 & 81.8 \\ Energy-Hub & Long documents & Text & 71.0 & 68.9 \\ Energy-Hub & Long documents & Text + Triples & 74.4 & 75.4 \\ \hline
20 News groups & All & Text & 83.3 & 82.6 \\
20 News groups & All & Text + Triples & 83.9 & 83.1 \\
20 News groups & Long documents & Text & 71.9 & 67.7 \\
20 News groups & Long documents & Text + Triples & 75.7 & 71.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: BERT model results on long documents from Energy-Hub and 20 News groups datasets. Note: Long documents are filtered from the larger corpus using word length (documents with less than 600 words are excluded).  
In practice, our proposed method has some limitations. As topics evolve, the performance of the model may decrease over time [45], and this called \"topic drift\". One way to address topic drift is to re-train the topic modeler with an updated corpus at regular intervals. Multi-modal architectures provide the ability to merge different models (created for each input) to perform a single task. Creating multiple models increases the number of trainable parameters and often requires greater computing resources and longer training times. The issue of document length, and its impact on classification using BERT by virtue of the limitation of 512 input tokens, is an issue in its own right, and this issue is comprehensively dealt with elsewhere, in Reference [46; 47].
## 4 Hardware Implementation
### Constant of Proportionality  
This chapter briefly explains how the constant of proportionality for the conversion between the unit encoder pulses (EncImp) and meters (m) was determined. The robot drove straight ahead in trajectory mode at 10,000 EncImp. The start and end points were marked, and the route was measured afterwards. The proportionality was determined as follows:  
\[x_{[\text{EncImp}]}=k\cdot x_{[\text{m}]} \tag{56}\]  
where the proportionality constant \(k\) can be determined. The length of the distance traveled, measured with a ruler, was 5.8 m in the test. The constant of proportionality \(k\) was determined accordingly using  
\[k=\frac{x_{[\text{EncImp}]}}{x_{[\text{m}]}}=\frac{10,000\text{EncImp}}{5.8 \text{m}}=1724\frac{\text{EncImp}}{\text{m}}. \tag{57}\]
# Economic Loss of Pine Wood Nematode Disease in Mainland China from 1998 to 2017
## 3 Results
### The Economic Losses of Pine Wood Nematode Disease in Mainland China from 1998 to 2017  
The economic losses caused by the pine wood nematode disease during the 20 years from 1998 to 2017 showed a rising trend. Here, 2008 can be seen as the watershed for the disease losses. The loss before 2008 was relatively small, except for 2001 and 2004. After 2008, the disease loss growth rate was higher than 10%, especially in 2016 and 2017, the disease loss growth rate reached 44.5% and 36.2%, respectively. The downward trend only occurred in 1999-2000, 2008, and 2011 during the 20 years (Figure 3).  
From 1998 to 2017, the total economic loss of pine wood nematode disease was CNY 7.17 billion /year. The direct economic loss accounted for 21.4% of the total economic loss, and indirect economic loss accounted for 78.6% (Table A3). The direct economic losses were mainly the loss of forest material resources and ineffective expenditure for forestry management expenditure, with an average annual economic losses of more than CNY 800 and 600 million, accounting for 53% and 42% of the total direct economic loss, respectively. The average annual economic expenditure of prevention expenditure was less than CNY 100 million, accounting for 5% of direct economic losses (Table A4). Among the tertiary indicators, the main loss was the loss of forest by-products, with the average annual economic loss being more than CNY 770 million, accounting for 50% of direct economic loss. The loss of ineffective forestation was mainly the loss of afforestation expenditure, with the average annual economic loss of around CNY 540 million, accounting for 35% of direct economic loss (Table A5).  
The indirect economic loss is mainly the loss of adjustment service value and the support service value, with the average annual economic losses being about CNY 4 and 1.4 billion, accounting for 70% and 25% of the total indirect economic loss (Table A6). Among the tertiary indicators, the losses of climate regulation services and hydrological regulation services were CNY 940 and 760 million, accounting for 30% and 21% of indirect economic losses, respectively. The average annual economic losses of gas regulation and purification environment were around CNY 320 and 280 million. The loss of support service value was mainly from soil conservation services, with the average annual economic loss of CNY 470 million, accounting for 13% of the indirect economic loss. The average annual economic loss of the biodiversity service value was CNY 350 million, accounting for less than 1% of  
Figure 2: The area of pine wood nematode disease in the province level of mainland China from 1998 to 2017.  
indirect economic losses; the annual loss of aesthetic landscape service value was CNY 150 million, accounting for 5% of indirect economic losses (Table A7).
## 3 Results and Discussion
### Monthly Estimates  
As previously stated, the aggregation of daily data to the monthly scale smooths out the effects of intermittence and rainfall extremes across large areas. As a result, lower levels of variability in the random field (i.e., in the fluctuations around the mean values of the process) are verified in the aggregated process [11] and the performance of statistical methods, with respect to direct imputation of the satellite retrievals, should improve. This conjecture is readily verified in Table 3, which shows the performance indexes of the three methods for filling monthly missing values in the six testing sites. In general, higher levels of linear correlation result at the monthly time scale for all methods. The values of CC were slightly higher for the direct imputation approach (0.90, on average), but, at least with respect to this metric, the MLR models performed just as well (0.89, on average). The GPM product also entailed lower values for MAE (34.6 mm, on average) and RMSE (48.2 mm), with moderate increases in these metrics for the MLR models (37.6 mm for MAE and 53.2 mm for RMSE), but noticeable distinctions for the SLR counterparts (45.3 mm for MAE and 66.7 mm for RMSE). Finally, the underestimation tendencies verified for the regression models, as materialized by the values of PBias, still persist, but to a much lesser extent as compared to the daily time scale (\(-7.9\%\) for MLR models and \(-9.9\%\) for SLR models; one should remember that the \"donor\" sites are different at the daily and the monthly time scales). As for the GPM product, the systematic error amount is again approximately \(4\%\) and is mainly driven by the large positive values of PBias in the Cachoeira de Goias and the Montividiu gauging stations, as discussed in the previous section. Figure 10 depicts the monthly rainfall time series, as obtained from the gauges and from the models. For ease of visualization, the monthly rainfall amounts are shown as continuous lines (although this representation is conceptually inappropriate). Some general remarks can be made from these plots. First, the MLR estimates are often \"oversmoothed\", underestimating the rainfall amounts in the wet season and overestimating otherwise in the testing sites, with the exception of the months of July and August, in which no rainfall was recorded by both the target and the \"donor\" stations. On the other hand, the SLR estimates did not entail general patterns. In effect, they did provide a reasonable fit in the Montividiu gauging station, with some lack of fit between February and April, but failed to do so at the other target sites, mostly underestimating the recorded rainfall amounts during the dry season. This fact might suggest that the best \"donors\" for these stations present lower mean annual rainfall amounts or longer dry spells from February to August, as suggested by the relatively lower values of the correlation coefficient, and reinforces our conjecture that the SLR method is, at least for this case study, less effective for filling missing data over a broad range of time scales (day to month).