esa-sceva/satcom-chunk-collection huggingface dataset:
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 3 The FFOM Methodology
### Fractal-Based Structural Design  
Figure 3 compares a traditional hierarchical approach, i.e., Figure 2(a) and FFOM's fractal-based model, i.e., Figure 2(b). In traditional models, each node such as \"Line 1\" or \"Line 2\" manages its own set of processes (e.g., Proc 1, Proc 2, Proc 3, Proc 4), independently. While this standard tree-like structure is straightforward to visualize, it becomes increasingly cumbersome to expand or modify, because each new process or layer often requires substantial restructuring. Over time, inconsistencies can arise between different lines or process branches, making it difficult to enforce uniform data rules or incorporate global updates, such as new quality metrics, across every part of the hierarchy. In contrast, FFOM's fractal concept, detailed in Figure 2(b), provides a single \"Pattern Template\" that can be repeated at different levels of the system. The idea is that every new line, process, or sub-process inherits the same underlying schema, thereby preserving consistency. If a new attribute (e.g., \"EnergyConsumption\") or a fuzzy rule (e.g., \"If temperature is High and pressure is Low, then reduce speed\") needs to be introduced, it can simply be added to the template. All existing and future lines or processes that use this pattern will automatically adopt the update, greatly simplifying maintenance and scalability.  
In practice, this fractal architecture aligns well with fuzzy logic to handle uncertain or variable information. The fractal pattern template includes placeholders, such as \"fuzzy_value\", or \"timestamp\", to record and update these continuous or imprecise values. As each repeated pattern is instantiated for a specific sub-process, the fuzzy parameters can be adjusted to local conditions, without losing the overall structural uniformity. This blend of the fractal concept and fuzzy logic is especially valuable in dynamic manufacturing settings, where equipment states and process parameters are subject to frequent fluctuations. Overall, the FFOM approach offers a more adaptable and consistent framework compared to rigid hierarchical models. By defining a self-similar pattern, it becomes far easier to expand the data structure, integrate new processes, and manage ongoing changes, while accommodating uncertainty through fuzzy logic. This flexibility ensures that large-scale industrial environments can remain coherent and up to date with minimal reworking, supporting real-time decision making and streamlined metadata management.
# Multi-Body Model of Agricultural Tractor for Vibration Transmission Investigation
###### Abstract  
This article analyses vibration transmission on agricultural tractors with the excitation from the road to the driver's seat. A multi-body model of agricultural tractors created in Adams is presented. The main parts for the investigation of vibration transmission are the tractor body, where the only suspension elements are types, the tractor cabin, spring-dampers suspended at the rear and bushings at the front, and the driver seat with its pneumatic spring. A series of measurements were performed, and the model was validated using vertical acceleration values on the tractor body at four different locations. The FTire model (physical FEM-based model) was chosen to describe the behaviour of tyres. The model was created using measured tyre characteristics. Measured characteristics of spring-dampers and front cabin bushings were also implemented. For comfort investigation, ride simulations on ISO 5008 rough roads were performed. The transmission of vibrations in ride simulations was examined. A modal analysis of the linearised model was performed to confirm assumptions of the contribution of suspension elements to overall vibration levels. Finally, three case studies were conducted to better understand the model's vibration transmission properties.  
vibration transmission; comfort; agricultural tractor; multi-body model; suspension; tyres; stiffness; damping +
Footnote †: journal: Applied sciences
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Performance Benchmark
#### 4.8.1 Comparison with Other ASR Systems  
We re-implemented the well-known ASR system Wav2 Letter [33; 65], and compared its performance with our baseline model. We evaluated the speech recognition performances on the dev-clean set of the Librispeech by means of the Character Error Rate (CER) and Word Error Rate (WER), and the alignment performances in Chapter 10, as described in Section 4.4, and as reported in Table 5.  
The results for speech recognition are much better for Wav2 Letter, which was expected, as it was specially designed for this task and has three times more parameters than our model. Surprisingly, however, the Wav2 Letter alignment performances are not as good as those of our model. This indicates that plain- and forced- CTC decoding provides different optimal paths for each task at hand.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Language** & **MAAE [ms]** & **Q50\% [ms]** & **Q95\% [ms]** & **Q99\% [ms]** & **PCO [\%]** \\ \hline English & 116 & 37 & 260 & 1501 & 95.7 \\ French & 2013 & 68 & 7759 & 44,767 & 78.6 \\ German & 1502 & 49 & 5807 & 35,435 & 84.1 \\ Italian & 1596 & 60 & 6628 & 35,557 & 79.9 \\ Spanish & 1571 & 47 & 6281 & 39,648 & 84.1 \\ \hline \end{tabular}
\end{table}
Table 4: Comparing the alignment performances of our CTC-based aligner (trained in English) on singing voice in different languages.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & & **Librispeech** & **Dev-Clean** & \multicolumn{2}{c}{**Chapter 10**} \\ \hline
**Model** & **\# Params** & **CER [\%]** & **WER [\%]** & **MAAE [ms]** & **Q50\% [ms]** \\ \hline Wav2 Letter & 106.5M & **3.6** & **10.6** & 66 & 61 \\ Ours & 37.8M & 8.5 & 26.9 & **51** & **46** \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparison of our model vs. Wav2 Letter.
## 3 Results
### Macro Pore Characteristics of the Aquifer  
In this experiment, 18 groups of core samples were selected to measure the porosity and NWA. The sampling depth of sandstone in the Luo'he formation was 460-570 m; that in the Zhi'luo formation was 750-810 m, and that in the Yan'an formation was 810-890 m. According to the differences in depth, the overburden pressure gradient was approximately 328.08 psi/m.  
Table 2 presents the porosity measurement data of the sandstone core samples from various formations. The average porosities of the Yan'an, Zhi'luo and Luo'he formations were 8.48%, 14.50% and 21.89%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample ID** & **Type** & **PP (\%)** & **APD (\(\upmu\))** &
\begin{tabular}{c} **ASS** \\ **(\(\upmu\)m\({}^{-1}\))** \\ \end{tabular} & **PSC** & **THC** \\ \hline XY-1 & & 20.15 & 51.9 & 1.18 & 0.63 & 28.74 \\ XY-2 & & 19.57 & 47.7 & 1.17 & 0.61 & 26.74 \\ XY-3 & Sandstone & 18.71 & 48.22 & 1.14 & 0.6 & 26.82 \\ XY-4 & in Luo’he & 19.25 & 45.33 & 1.11 & 0.61 & 27.52 \\ XY-5 & Formation & 18.32 & 44.79 & 1.06 & 0.62 & 28.14 \\ XY-6 & & 18.99 & 49.75 & 1.09 & 0.6 & 27.13 \\ \hline XY-7 & & 13.74 & 28.4 & 0.98 & 0.53 & 19.26 \\ XY-8 & & 14.24 & 32.3 & 1.07 & 0.54 & 21.56 \\ XY-9 & Sandstone & 14.57 & 27.2 & 1.03 & 0.57 & 18.69 \\ XY-10 & Formation & 13.32 & 26.75 & 0.97 & 0.56 & 20.88 \\ XY-11 & Formation & 14.55 & 27.64 & 1.01 & 0.54 & 19.76 \\ XY-12 & & 13.67 & 26.88 & 1.04 & 0.57 & 20.65 \\ \hline XY-13 & & 7.07 & 13.7 & 0.62 & 0.5 & 15.47 \\ XY-14 & & 6.31 & 12.4 & 0.61 & 0.48 & 16.44 \\ XY-15 & Sandstone & 5.54 & 11.1 & 0.55 & 0.46 & 14.41 \\ XY-16 & in Yan’an & 6.62 & 12.7 & 0.64 & 0.49 & 14.74 \\ XY-17 & Formation & 4.57 & 10.3 & 0.51 & 0.44 & 13.59 \\ XY-18 & & 5.21 & 10.8 & 0.53 & 0.47 & 14.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic parameters of pore structure test.  
The experimental results of NWA are presented in Table 3. The NWA rates of sandstone in the Luo'he, Zhi'luo and Yan'an formations were 7.42-7.63%, 5.2-5.4% and 4.54-4.68%, respectively, with average values of 7.53%, 5.31% and 4.6%, respectively.
# Lithium Metal: The Key to Green Transportation
## 4 Discussion  
### Manufacturing of Lithium-Ion Battery and Demand  
The rapid rise of LIB and EVs (Figure 10) is inevitable due to the positive mindset of climate change activists towards reducing carbon emissions. The prediction shows that it will reach around 9000 GWh in 2030 [80]. This rapid shift in transportation methods creates competition among governments (with the help of EV manufacturers) to the point where having a large capacity of domestic LIB manufacturing becomes a source of pride for a country for being ahead of the pack in the race of EV technology. The country with the highest manufacturing rate of LIB in terms of GWh corresponds to its access to raw materials. Access to LIB raw materials created a significant gap in the capacity of countries to produce LIB. For instance, as shown by statistics of 2021 [81], China is leading the race with 79% of the global LIB GWh production, while the United States comes second with 6.2%. China is not only dominating LIB production but also leading the battery supply chain as the largest exporter of LIB [82].  
According to [PERSON] and [PERSON] [81], LIB production (GWh) will triple between 2020 and 2025. By accounting for global LIB production, including passenger electric vehicles, electric two/three-wheelers, electric buses, energy storage systems, electric trucks, consumer electronics, and electric off-highway vehicles, it may reach 900 GWh in 2023 [83], and by considering total LIB production regardless of the uses, it may cross 1330 GWh in 2023 [80]. At present, approximately 90% of global battery research is focusing on LIB since NiMH has reached its fundamental technical limits [84]. The decline of NiMH advantages LIB to become the dominant technology as predicted by [85] and proven to be the most promising technology for EVs. The amount of lithium used in LIB varies based on the battery chemistry and type of electric vehicle [86]. However, regardless of the chemistry of the cathode and anode, the lithium amount in a battery can vary between 0.17 kg and 12.68 kg [26]. Thus, both properties are crucial during the estimate calculation of lithium demand.  
Figure 9: Scatter diagram of correlation between electric vehicle stock, electric vehicle sales, and CO2 emission from road transport.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 4 Experiments
### Comparison Algorithms  
We compare the proposed network with two single-view methods and several multi-view subspace clustering approaches by the criteria of ACC and NMI.  
DEC [36] utilizes a fully connected stacked autoencoder network to learn latent representations and use the reconstruction loss to minimize it. Moreover, this network uses the Kullback-Leibler (KL) divergence to make comparisons between the original distribution and the ideal distribution.  
DCCA [49] is an approach designed to learn intricate nonlinear transformations of two views, aiming to produce highly linearly correlated representations. The whole correlation is maximized by simultaneously learning parameters of both transformations.  
BMVC [61] employs binary encoding techniques to jointly optimize binary encoding and clustering from multiple views. Compact collaborative discrete representation learning and binary clustering structure learning are two pivotal components in this framework.  
GMC [62] automatically weights each view and jointly learns the graph structures of each view and the fused graph to obtain the clustering results.  
DGCCA [51] introduces deep neural networks as the transformation functions, allowing for the learning of complex and nonlinear relationships between the views, which is the extension of GCCA.  
DMJC [63] designs a soft assignment mechanism and introduces a novel auxiliary target distribution for multi-view fusion. The fusion process is optimized using KL divergence.  
CMSC-DCCA [64] combines a correlation constraint and a self-expressive layer to leverage the information present in both inter-modal and intra-modal data.  
EDESC [46] utilizes deep neural networks to learn a set of subspace bases and a mapping from samples to the latent space for each subspace. For the new data, the final clustering results can be obtained by computing the latent representations with the learned subspace bases for each subspace.  
DMSC-UDL [55] combines local and global features with self-expression layers and designs a discriminative constraint to increase the discrimination between different views.
# Integrated Earthquake Catalog of the Eastern Sector of the Russian Arctic
###### Abstract  
The objective of this study was to create a representative earthquake catalog for the Eastern Sector of the Arctic zone of the Russian Federation that combines all available data from Russian and international seismological agencies, with magnitude reduction to a uniform scale. The article describes the catalog compilation algorithm, as well as formalized procedures for removing duplicates and choosing the optimal magnitude scale. Due to different network configurations and record processing methods, different agencies may register/miss different events. This results in the absence of some events in different earthquake catalogs. Therefore, merging the data of various seismological agencies will provide the most complete catalog for the studied region. When merging catalogs, the problem of identifying duplicates (records related to the same seismic event) necessarily arises. An additional difficulty arises when distinguishing between aftershocks and duplicates since both are events that are close in space and time. To solve this problem, we used a modified nearest neighbor method developed earlier by the authors. The modified version, which is focused on identifying duplicates and distinguishing between duplicates and aftershocks, uses a probabilistic metric in the network error space to determine the epicenters and times of seismic events. In the present paper, a comparison and regression analysis of the different magnitude types of the integrated catalog is carried out, and based on the obtained ratios, the magnitude estimates are unified.  
merging catalogs; earthquake; clustering algorithm; Arctic region; magnitude unification; duplicate events Article
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 3 Results
### Value of Rice Crops Lost  
Data on the monetary value of rice lost was limited to the Philippines (50 data points). From 1970 to 2018, the average value of rice lost was US$ 42 million per typhoon event, and the value of loss ranged from US$ 377 to US$ 254 million. The lowest value of loss was recorded after Typhoon Nesat in 2017, and the highest was from Typhoon Nalgae in 2011. Notably, Typhoon Nesat was a Category 1 typhoon, while Typhoon Nalgae was a Category 2 event (Supplementary Material S2). On average, the value of rice damaged based on landfall intensity was the highest for Category 4 (US$ 198 million), followed by Category 2 (US$ 76 million), Category 3 (US$ 60 million), and Category 1 typhoons (US$ 39 million). Tropical storms (US$ 22 million) and typhoons that did not make landfall (US$ 9 million) resulted in the least economic loss (Figure 4c; Table 2). Although Category 4 typhoons resulted in the highest value of rice lost, there was only one Category 4 typhoon with rice damage data. Overall, there was a weak relationship between landfall intensity and the value of rice lost (Figure 5d). The relationship between ACE and the value of rice lost was also weak (Figure 6d).
# Advancing in RGB-D Salient Object Detection: A Survey
Various fusion strategies, such as cascade fusion [12], parallel fusion [13], and recurrent fusion [14] have been employed to extract and combine features from these heterogeneous data sources, thus more accurately identifying and highlighting salient objects in a scene. The challenge for all RGB-D SOD tasks lies in effectively utilizing depth information as its quality often varies and is not consistently reliable across different scenes. The primary role of cross-modal fusion is to leverage the strengths of each modality by accurately integrating them without allowing the weaknesses of any one modality to degrade the overall system performance, thereby enhancing object detection performance. As demonstrated by [PERSON] et al. [15], through a depth-guided fusion network and depth attention mechanisms, spatial information can be effectively encoded, thus ensuring that the advantages of each modality are maximized without allowing any modality's limitations to weaken the overall system performance. Attention mechanisms play a critical role in optimizing the fusion of RGB and depth data [16]. By selectively focusing on important features and suppressing less relevant information, attention mechanisms dynamically adjust the contributions of each modality to the task of SOD [17]. This adaptability is particularly crucial when dealing with potentially noisy or incomplete depth data. By focusing computational resources on the richest informational features within each modality, attention models not only optimize the integration of RGB and depth information, but also enhance overall detection accuracy. The purpose of this survey was to systematically explore and analyze the recent advances and disadvantages in cross-modal fusion strategies and attention mechanisms and how to apply them in the field of RGB-D SOD. By examining a wide range of approaches, this survey aims to highlight innovations and assess various fusion and attention strategies. Through this analysis, this survey aims to identify promising directions for future research that can address the remaining challenges in RGB-D SOD, thereby aiding in the development of more complex and capable visual systems. The remainders of the survey are organized as follows. In Section 2, the classification and comparison of various cross-modal fusion strategies are explored. The classification and optimization methods of attention mechanisms, as well as some recent attention models for RGB-D SOD tasks, are discussed in Section 3. In Sections 4 and 5, commonly used datasets and popular evaluation metrics are introduced. Quantitative comparisons of several models are provided. Finally, conclusions are discussed in Section 6.
# Boat-to-Grid Electrical Energy Storage Potentials around the
## 1 Introduction
### The Benefits of V2G Solutions  
Due to the challenges facing the energy and transport sectors at present and in the predictable future, there is no alternative to decarbonization and decreasing pollution, which is not viable without utilizing novel solutions and cutting-edge technologies. This is also the only way to comply with increasingly rigorous international regulation. Today, EVs are widely seen as the solution for attaining sustainable transport, and the efficient management of their charging may also offer additional benefits for the energy sector too. The activity of EV charging, as such, connects both symbolically and practically the above two fast-developing sectors, because of which EVs can double as grid-connected batteries when not moving and connected to the grid. This, in turn, can ensure two things: on the one hand, the necessary range those vehicles can drive as well as charging rates acceptable to the operators of the vehicles, and on the other hand, the sufficient flexibility of the electric grid, provided the infrastructures of charging are suitable and the processes are managed well. Consequently, the dynamic spread of EVs has to be coupled with the concept and practice of optimized charging or \"smart charging\". The deployment of bi-directional chargers can offer further benefits by adopting V2G solutions and foster the integration of the power and transport systems [42].  
As EV batteries can be a source of support for the power grid [43], so can those of various modern watercraft [44]. This is due to the fact that the trend of electrification with a view to mitigating CO\({}_{2}\) emissions and enhancing energy efficiency has also reached military as well as commercial shipping [45; 46; 47]. Vessels with energy storage systems could be of help in terms of peak load shaving, spinning reserve capacity and frequency regulation too [2; 44].
## 5 Numerical Results
The normal inter-arrivals show how the fuzzy models are more robust when the condition changes than the queue models. Figure 7 shows the coefficient of variation of the average working of the stations to evaluate the uniform distribution among the stations. In the case of maximum productivity, the queue models lead to a more uniform working distribution among the stations, but as shown in the previous graphs, the performance of the fuzzy models is always better in this condition. When the inter-arrival time of the raw items follows a distribution, both normal and exponential, the queue models are less robust to the changing of the conditions than the fuzzy models. The fuzzy rules proposed take into account the buffer levels to dynamically change the allocation of the fractional tasks to keep the flow of items through the production line. The simulation tests the fuzzy model compared to the benchmark proposed in the literature considering failures and uncertainty in processing times. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**XQ** & **XFa** & **XFb** & **XFc** & **XFd** \\ \hline Queue & Fuzzy A & Fuzzy B & Fuzzy C & Fuzzy D \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Legend of the simulation results. Figure 4: Conf. Figure 4: Average time in system. Figure 5: Cont. Figure 5: Average time in system—standard deviation. Figure 6: Conf. Figure 6: Work in process. Figure 7: Conf. Figure 7: CV average working. From the analysis of the above figures, the following issues can be drawn:  
* The fuzzy models lead to better performance measures studied when the flow line works at higher productivity or the inter-arrival time of the raw items follows a normal distribution. These results highlight how the fuzzy approach works better when the production is more stable. * The queue models are the best approaches when the inter-arrival items follow an exponential distribution. Then, the queue models can react better to a relevant fluctuation in the inter-arrival time of the raw items. * Generally, the main improvements are obtained in the case of raw items always being available which leads to higher productivity which is often the case of the flow line applications. The hypothesis of a balanced flow line designed to evaluate only the effects of the proposed method leads to a throughput rate very similar among the simulation experiments conducted. The differences are about 1%; for this reason the graphs of the throughput are not reported.