esa-sceva/satcom-chunk-collection huggingface dataset:
## Broader Implications  
The adoption of forecasting models has broader implications for the aviation industry with the research base, as shown in Table 5.  
The table shows a clear drop in SmartLynx Airlines' AOG downtime, falling from 630 h in 2020 to 320 h in 2024, a 49% reduction. The most significant improvement happened between 2022 and 2023, with downtime decreasing from 560 to 430 h, marking a key period of operational progress.  
This decline reflects the airline's improved maintenance efficiency and use of predictive tools to catch issues early. The airline has reduced grounding time, lowered costs, and boosted fleet availability by addressing potential problems like landing gear or air conditioning failures before they escalate.  
The table \"Statistical Overview of AOG Incidents in SmartLynx: 2020-2024\" shows how the research base airline has significantly reduced Aircraft On-Ground (AOG) incidents over five years with the help of those above-mentioned tools, with the total incidents dropping by 57%, from 147 in 2020 to 63 in 2024.  
Downtime hours also fell by 49%, from 630 h in 2020 to 320 h in 2024, improving fleet availability and operational efficiency. Financially, AOG costs were reduced from EUR 1.47 million to EUR 630,000, saving SmartLynx approximately EUR 840,000 annually. Specific systems, such as landing gear, saw a 30% reduction in AOG incidents between 2022 and 2024, while communication and air conditioning systems experienced a 25% decrease. These results highlight the effectiveness of proactive maintenance strategies and suggest further opportunities for improvement through better supplier coordination and inventory management systems.
## 4 Methodology for the Modernization of ATLAS-LG System Software Operational Algorithms
### System Software Operation Algorithm after Upgrading  
When the ATLAS-LG system operates under the regulation referred to in Section 4.1, the following software operation algorithm is recommended:  
1. The ATLAS-LG system continues to operate in its standard mode. On the basis of the recommendations specified in Section 4.1, the main results of the system are used for processing and evaluation.
2. The rolling stock type is determined and attributed to one of the following categories: - Freight wagon with variable load, including tanks, platforms, bunker type wagons, etc.; - locomotive; - passenger wagon; - special rolling stocks.  
3. The following parameters, which are standardized for the ATLAS system, are used to continue calculations: * \(P_{max}\)--maximum value of the vertical force (kN); * \(P_{average}\)--average value of vertical force (kN); * \(v\)--speed at which the carriage passes a distance section (km/h).
4. Depending on the categories of rolling stock according to the Equations (17)-(19) and with the instructions are given in the second and third conclusions in Section 4.1, the \"derivative\" value of the vertical force \(P_{derivative}\) is determined.
5. The resulting \(P_{derivative}\) value is aligned with the limit values given in Section 4.1 (two limits are set) for the relevant categories of rolling stock.
6. When the relevant limit value is exceeded, the required level of risk is formed and the expected actions corresponding to this level of risk are formed.
## 3 Results and Discussion
### Offsetting Environmental Impacts: CO\({}_{2}\) Emissions
Based on the areas of vine cultivation available in each territory, as well as the net value of CO\({}_{2}\) emissions associated with vine cultivation and finally, taking into consideration the result generated in the research in which a value of 4.30 kg CO\({}_{2}\) is emitted for each ton of NCA produced in gravel pits, we want to estimate the production of this type of aggregates that would be associated with being free of CO\({}_{2}\) emissions in Spain. The results are shown in Figure 6, where Figure 6a shows the vineyard area (km\({}^{2}\)) for each Autonomous Community according to data from the Ministry of Agriculture, Fisheries and Food [14] as of July 2023. Figure 6b shows the net CO\({}_{2}\) sequestration (kt CO\({}_{2}\) eq) according to the current vineyard areas. Figure 6c shows the scenario of production of CO\({}_{2}\)-free NCA (Mt) obtained in gravel pits, taking into account the hypothesis that the entire current vineyard area would be used to mitigate the CO\({}_{2}\) emissions associated with the production of natural coarse aggregates in gravel pits. As can be seen in all the Autonomous Communities, there is the potential to produce emission-free coarse natural aggregates; however, in some territories, the amount of aggregate produced is very small (-0.1 Mt) and therefore, in other areas, production is exponentially high (-389.6 Mt). This is justified by the fact that a lower level of vine cultivation area in a territory results in a lower net CO\({}_{2}\) uptake that can be used to mitigate the environmental impacts of producing aggregates as clearly seen in Figure 6. In this scenario, the total amount of all natural coarse aggregates from gravel pits produced in Spain free of CO\({}_{2}\) emissions would be 816.36 Mt. This initiative aims to raise awareness not only in Spain but also in other international countries, such as China, where the estimated production of NCA is 15 billion tons [2] and this would be a good measure to at least mitigate the environmental effects.
## 4 Results
The obtained scores were then summed over the time-lag values \(l\) to obtain the total score \(\text{AutoML}_{\text{score}}^{\text{T}}(m,f)\) for each combination of AutoML model \(m\) and feature \(f\). The lower the total score, the better the considered combination of model and feature. In Table 5, the \(\text{AutoML}_{\text{score}}^{\text{T}}\) is reported for each combination of AutoML model and feature. Finally, the top performing AutoML model for each input feature was additionally run on the test data for each of the selected time-lag, \(1\leq l\leq 3\). The latter data were unseen by the trained AutoML models for all the selected input features. According to Table 5, the top performing AutoML models for each input feature set according to the \(\text{AutoML}_{\text{score}}^{\text{T}}\) metric were, respectively, H2O AutoML for Thiessen, auto-sklearn for PERSIANN, H2O AutoML for PERSIANN-CCS, AutoKeras for PERSIANN-CDR, and auto-sklearn for PDIR-Now. The average streamflow observed data and predictions obtained from the AutoML models were reported in Figure 8.  
Regarding Figure 8, the performance of the AutoML models was evaluated across different seasons to understand their robustness and reliability under varying climatic conditions. The Amazon basin experiences distinct wet and dry seasons, which significantly influence streamflow patterns. During the wet season (October to April), the models demonstrated higher prediction accuracy due to the increased availability of rainfall data, which is a primary driver of streamflow. Conversely, during the dry season (May to September), the prediction accuracy slightly decreased, reflecting the reduced rainfall input and the increased reliance on historical streamflow data. This seasonal variation highlights the models' dependency on accurate and timely rainfall data for optimal performance. Figure 8: Average streamflow observed data and predictions obtained from the top performing AutoML model for each input feature. In the figure, AutoML models were evaluated on the data contained in the respective test set kept for each feature set (refer to Table 3) and for each selected time-lag (\(l\leq 1\leq 3\)). The latter data were unseen by the trained AutoML models for all the selected input features. The reported top performing AutoML models and respective input features according to the AutoML\({}_{\text{True}}\) metric were H2O AutoML for Thiessen, auto-sklearn for PERISANN, H2O AutoML for PERISANN-CCS, AutoKeras for PERISANN-CDR, and auto-sklearn for PPLoSNuC. Observed average streamflow data were reported in red color, while time-series data predicted from AutoML models were reported with blue dashed lines. Horizontal thick black lines define the boundaries outside which predictions were not computed (since data were contained in the training set or were not available for the considered feature set).
## 4 Results and Analysis
### Analysis of Meteorological Factors on Historical Forest Fire Occurrences
#### 4.1.5 Impact of Wind Speed on Forest Fires  
Between 1991 and 2021, the occurrence and affected areas of forest fires in Yunnan Province were correlated with average wind speed. Figure 5g shows that the number of fires peaked in 1997, 2004, and 2010, coinciding with higher average wind speeds in these years, suggesting that increased wind speeds may have facilitated the occurrence and spread of fires. The trend of the affected area increasing with wind speed, as shown in Figure 5h, further confirms the influence of wind speed on the propagation of fires. In particular, in years with significant fire occurrences and affected areas, the characteristic of higher wind speeds is evident. This finding emphasizes the role of wind speed in the dynamics of forest fires, which is crucial for risk assessment and prevention strategies for forest fires [52]. Future research should delve deeper into the intrinsic mechanisms of the relationship between wind speed and forest fires and consider climate factors like wind speed in predictive models.
## 4 Results and Discussion  
### Using AE Time Intervals to Study the Variability of the Entropic Index \(q\)  
The following analysis aims to determine the variability of the entropic index \(q\) in the various loading stages until fracture by utilizing the time intervals of the AE data that were recorded during the two aforementioned loading schemes. The time interval \(\left(\delta\tau\right)\) between two consecutive AE hits is defined as:  
\[\delta\tau_{i}=\left(t_{i+1}-t_{i}\right) \tag{4}\]  
where \(t_{i}\) is the recording time of the \(i^{th}\) hit; and \(t_{i+1}\) that of the consequent one. In previous works [25; 26; 27; 28; 29; 30; 31; 32; 33], the time intervals of two consecutive AEs have been exploited in the context  
Figure 4: The temporal evolution of the AE time intervals (\(\delta\tau\)) in juxtaposition with the stress applied, (**a**) using a conventional time-scale, (**b**) using the \(\left(t_{f}-t\right)\) time-scale parameter for the case of the UC loading protocol.  
of NESM. It has been confirmed that the CDFs of the time intervals obey \(q\)-exponential functions of the form:  
\[P(>\delta\tau)=\exp_{q}\bigl{(}-\beta_{q}\cdot\delta\tau\bigr{)}=\bigl{[}1+(q-1) \beta_{q}\cdot\delta\tau\bigr{]}^{\frac{1}{1-q}} \tag{5}\]  
The procedure that was applied to study the temporal variability of the entropic index \(q\) and the parameter \(\beta_{q}\), contained in the CDF of Equation (5), is described herein. According to the total number \(n\) of AE hits recorded from the beginning of the loading protocol until the specimen's fracture, the AE hits are divided into \(k\) consecutive groups, with a degree of overlap, as will be described in detail in the sections below. Then, the time intervals \((\delta\tau)\) of the AE hits of each group were calculated; the corresponding CDF \([P(>\delta\tau)]\) was plotted for each AE hit group; and, through fitting curves, the values of the entropic index \(q\) and the parameter \(\beta_{q}\) were estimated.
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 2 Materials and Methods  
### Hongtu-1 (HT1, Also Known as PIESAT-01)  
HT1 is the world's first multi-static SAR satellite system operating in a cartwheel formation [32]. The cartwheel formation is a unique satellite constellation in which slight differences in the eccentricity of each satellite's orbit ensure that the main satellite remains in the center of the formation at all times and three auxiliary satellites are evenly spaced around it at 120-degree intervals [32]. Precise orbit control maintains a planar configuration of the four HT1 satellites at all times. The main satellite, HT1-A, is located in the center of the formation, and the three auxiliary satellites, HT1-B, HT1-C and HT1-D, orbit the main satellite on the same elliptical path [32]. The active main satellite, HT1-A, transmits and receives radar signals, i.e., it corresponds to a normal monostatic radar. The three additional satellites, HT1-B, HT1-C and HT1-D, each contain a passive radar receiver which receives the radar signals transmitted by HT1-A and reflected on the Earth's surface. An HT1 acquisition therefore comprises one monostatic SAR image and three bistatic SAR images. The distance between the passive receivers and HT1-A is small (<1 km) compared to the distance of the satellites to the imaged ground (~700 km), so the bistatic angles are very small. The sun-synchronous orbit of HT1 has a repeat cycle of 15 days [32].
## 3 Results and Discussion
### Pretreatment-Thickness  
Many researchers have reported \(D_{\mathit{eff}}\) for other products with ultrasonic pretreatment. For instance, the obtained moisture diffusivity coefficient values for potatoes in the hybrid microwave-hot air dryer with ultrasonic pretreatment ranged from 1.07 \(\times\) 10\({}^{-7}\) to 1.283 \(\times\) 10\({}^{-7}\) m\({}^{2}\)/s [11]. [PERSON] et al. obtained the \(D_{\mathit{eff}}\) for kiwifruit in a hot air dryer with ultrasonic pretreatment ranging from 3.67 \(\times\) 10\({}^{-11}\) to 9.45 \(\times\) 10\({}^{-11}\) m\({}^{2}\)/s [5]. [PERSON] et al. calculated the diffusivity coefficient for the drying of walnut in the microwave and hot air dryers at different ultrasonic pretreatment times between 3.12 \(\times\) 10\({}^{-9}\) and 8.99 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s and between 2.77 \(\times\) 10\({}^{-9}\) and 5.56 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s [43].
## 3 Results
### Comparisons of Products
GCFD predictions in tropical Africa were slightly lower than the two machine learning datasets, but the underestimation was relatively minor. These outcomes might be attributed to the similarity in the covariates used in the GCFD modeling process compared to those employed by [PERSON] et al., resulting in closer predictions. Figure 2: Distribution of the differences between the three carbon fluxes predicted by the GCFD and the predictions of FLUXCOM (**a–c**) and [PERSON] et al. (**d–f**); differences between GPP predicted by GCFD and TRENDY (**g**), [PERSON] et al. (**h**), and GLASS (**i**), with the data in the plots averaged over the year 2010. Values in brackets are average absolute differences between products. The GPP values predicted by the GCFD exhibited varying degrees of over- or underestimation in certain regions when compared to ecosystem model data. In tropical Africa, the GPP values predicted by GCFD were slightly lower than those obtained by both models. Similarly, in southern Asia and certain coastal areas of South America, the GCFD predictions were also lower compared to the model predictions. Conversely, in the remaining regions across the globe, the GCFD predictions for GPP tended to be higher than the model predictions to varying extents. The most substantial differences were primarily observed in areas adjacent to the southern Sahara, the northern part of the Indochinese Peninsula, the central United States, and some regions of south-central South America. Overall, the disparity between GCFD and ecosystem models was more pronounced than that between machine learning products. The GCFD product exhibited significantly higher values in the Amazon region compared to GLASS. The range of high values obtained by GCFD was greater than that of GLASS in the high-value region of southeastern China. In the eastern USA and western Europe, the GCFD exhibited similar characteristics. However, in tropical Africa and Southeast Asia, GLASS showed more prominently high values. Overall, the GCFD product effectively captured the accurate distribution of GPP in most regions. Based on the global absolute difference, it is evident that the smallest disparity was found between the GCFD and [PERSON] et al. In comparison to machine learning products, the absolute values between the GCFD and the remaining datasets were relatively higher. Consequently, it can be inferred that the GCFD exhibits the highest similarity to [PERSON] et al. among the three categories of carbon flux products.
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 4 Discussion  
SIBERIA represents a significant advancement in the implementation of authentication mechanisms for industrial environments by integrating voice and behavioral biometrics, combined with SSI principles. This innovative approach overcomes the limitations of traditional models based solely on passwords or physical tokens, offering an architecture that ensures not only robust identity verification but also CA throughout the user's active session.  
One of the system's most relevant contributions is the incorporation of voice-based biometric credentials as another authentication factor, eliminating the need for additional hardware. This approach aligns with data minimization and user-control principles fundamental to SSI frameworks, while also ensuring the confidentiality of biometric information through the use of HE techniques. This mechanism guarantees that voiceprints are processed in their encrypted form and are never exposed in plaintext.  
The decentralized management of VCs, stored locally in user-held wallets, provides granular control over the entire identity lifecycle, including issuance, revocation, and recovery. Furthermore, the technical structure of the behavioral biometric model, based on a dual architecture that separately processes mouse and keyboard signals, enables the precise and complementary characterization of temporal interaction patterns. This enhances the system's robustness against individual variability and improves the reliability of CA.  
Operationally, SIBERIA balances security and usability, integrating mechanisms that ensure secure credential reissuance in case of wallet loss while maintaining a smooth user experience. The adoption of advanced cryptographic methods, combined with decentralized identity management, reinforces system integrity and aligns with privacy-by-design principles. As a result, SIBERIA is positioned as a reliable and adaptable solution for modern industrial access management challenges.
# Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
## 1 Introduction  
Graphs are a canonical way of representing objects and relationships among them. They have proved remarkably well suited in many fields such as chemistry, biology, social sciences or computer science in general. The connectivity information (edges) is often completed by discrete labels or continuous attributes on nodes, resulting in so-called attributed graphs. Many real-life problems involving high dimensional objects and their links can be modeled using attributed graphs.  
Machine learning offers several ways to solve problems such as classification, clustering or inference, provided that a sufficient amount of training examples is available. Yet, the most classical frameworks are devoted to data living in regular spaces (e.g., vector spaces), and they are not suitable to deal with attributed graphs. One way to overcome this issue is to represent or encode the attributed graphs in such a way that usual machine learning approaches are efficient. A recent take on that is known as graph representation learning [1]: the graphs are embedded in a fixed dimensional latent space such that similar graphs share similar embeddings.  
Three properties are desirable in order for a method of attributed graph representation learning to be widely applicable and expressive enough. We expect a method to be: I. unsupervised because labels are expensive, and not always available; II. inductive so that computing the embedding of anunseen graph (not belonging to the training set) can be done on the fly (in contrast to transductive methods); III. hierarchical so as to take into account properties on both local and global scales; indeed, structured information in graphs can reside at various scales, from small neighborhoods to the entire graph.  
In order to obtain these three desirable properties for attributed graphs representation learning, the present work introduces a new Hierarchical Graph2 Vec (HG2V) model. Like Graph2 Vec [2] with which it shares some similarities, it is based on the maximization of some mutual information. Thanks to a proper use of coarsening, as proposed by [PERSON] [3], it is hierarchical and incorporates information at all scales, from micro-structures like node neighborhoods up to macro-structures (communities, bridges), by considering a pyramid of attributed graphs of decreasing size.  
The article is organised as follows. Section 2 presents some related work in the literature. In Section 3, we introduce the notation and the required background. Section 4 is dedicated to the detailed presentation of our main contribution: the Hierarchical Graph2 Vec method. In Section 5, an experimental study is reported that demonstrates the effectiveness of the framework for various tasks.
## 2 Methods and Modeling
### Conservation of Charge
#### 2.3.1 Surface Exchange  
It is assumed the oxygen (O\({}_{2}\)) that fills the pores of solid (i.e., mixed ionic-electronic conductive--MIEC) material is adsorbed into the cathode surface. Oxygen ions (O\({}^{2-}\)) are formed with a double negative charge by accepting electrons (e\({}^{-}\)) from the cathode. The flow of O\({}^{2-}\) (\(J_{\text{O}^{2-}}\)) can be modeled as follows [16]:  
\[\overrightarrow{n}\cdot\overrightarrow{J}_{\text{O}^{2-}}=-k_{\text{surf}} \cdot\left(C_{\text{O}^{2-},\text{ eq}}-C_{\text{O}^{2-}}\right) \tag{13}\]  
The surface exchange coefficient is denoted with \(k_{\text{surf}}\). It depends on the temperature and the partial pressure of O\({}_{2}\). The equilibrium oxygen ion concentration is denoted with \(C_{\text{O}^{2-},\text{ eq}}\) and depends on the partial pressure of O\({}_{2}\) within pores. The concentration of oxygen ions (\(C_{\text{O}^{2-}}\)) decreases during the increasing electric current load (\(C_{\text{O}^{2-}}<C_{\text{O}^{2-},\text{ eq}}\)).
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 5 Testbed Implementation
### Localization  
First, the target distances from the Anchor nodes are estimated. To do this, three reference nodes were set with the following coordinates, (0;0), (5;0), and (0;5), where each single unit in the reference system corresponds to 1 m. The target device was then positioned within the area delimited by the Anchor nodes. (In the case of triangulation, the device at position (0;5) is not present for obvious reasons).  
On each reference device, the first code was executed to continuously send packets towards the target node. The target device executes the code that first collects the RSSI values for each device, with correct parameters \(A\) and \(n\). It then applies Equation (4) to estimate the distance between the target node and each reference node.  
Next, the program performs the positioning phase; that is, it applies the localization algorithms and estimates the position of the target node. The Min-Max, triangulation, and trilateration source codes are all available on github.