esa-sceva/satcom-chunk-collection huggingface dataset:
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Concepts
#### 7.2.2 Swath Mapping  
The transition from single-beam to swath-mapping LiDAR, facilitated by the use of pixelated detectors, represents an advancement in remote sensing technology. Using coverage with tens to hundreds of simultaneous measurements, the LiDAR swath mapping vastly improves spatial resolution, improving the ability to capture fine surface features and morphological variations [2]. Traditional single-beam systems rely on discrete pulses to map topography at predefined intervals. However, swath-mapping LiDAR operates by distributing an array of laser beams across a wide field of view. This transition can bring about improvements in planetary science and Earth observation. It allows the generation of high-resolution topographic data sets and can be helpful for applications such as geodetic control, surface roughness analysis, and tidal deformation studies. However, the implementation of swath-mapping LiDAR requires careful consideration of power constraints, because increasing the number of simultaneous measurements often necessitates higher laser power to maintain sufficient signal-to-noise ratios [2].
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
###### Abstract  
Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. Existing methods are often plagued by modality discrepancies and fragmented temporal reasoning. To address these issues, we introduce MT-CMVAD, a hierarchically structured Transformer architecture that makes two key technical contributions: (1) A Context-Aware Dynamic Fusion Module that leverages cross-modal attention with learnable gating coefficients to effectively bridge the gap between RGB and optical flow modalities through adaptive feature recalibration, significantly enhancing fusion performance; (2) A Multi-Scale Spatiotemporal Transformer that establishes global-temporal dependencies via dilated attention mechanisms while preserving local spatial semantics through pyramidal feature aggregation. To address the sparse anomaly supervision dilemma, we propose a hybrid learning objective that integrates dual-stream reconstruction loss with prototype-based contrastive discrimination, enabling the joint optimization of pattern restoration and discriminative representation learning. Our extensive experiments on the UCF-Crime, UBI-Fights, and UBnormal datasets demonstrate state-of-the-art performance, achieving AUC scores of 98.9%, 94.7%, and 82.9%, respectively. The explicit spatiotemporal encoding scheme further improves temporal alignment accuracy by 2.4%, contributing to enhanced anomaly localization and overall detection accuracy. Additionally, the proposed framework achieves a 14.3% reduction in FLOPs and demonstrates 18.7% faster convergence during training, highlighting its practical value for real-world deployment. Our optimized window-shift attention mechanism also reduces computational complexity, making MT-CMVAD a robust and efficient solution for safety-critical video understanding tasks.  
m +
Footnote †: journal: Applied sciences  
ulti-modal transformer; LoRA; video anomaly detection; self-attention mechanism; cross-modal learning
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 1 Introduction
To address the aforementioned challenges, we present a novel federated few-shot learning-based machinery fault-diagnosis scheme, named FedCDAE-MN, which utilizes a combination of convolutional denoising auto-encoder and feature-space metric learning across multiple domains. Figure 1 illustrates the basic framework of our proposal. In particular, we consider two primary scenarios: (1) a single source domain to target domain scenario, where the working conditions remain the same, but the fault types vary, and (2) multi-source domain scenarios, where the source domains exhibit varying working conditions while the fault type remains consistent. First, to enhance the model's generalization performance under diverse conditions, we propose a few-shot learning method that relies on a convolutional denoising auto-encoder and feature-space metric learning. By effectively extracting fault features using the convolutional denoising auto-encoder, we improve the model's domain generalization, robustness, and noise reduction capabilities. Furthermore, the metric embedding module encodes the extracted features, and the meta-learning training method exploits sample similarity information. Importantly, the entire metric learning process operates within the feature space, allowing for effective utilization of label information from the source domain. In addition, to address privacy concerns, the proposed model is developed within the federated learning framework, where individual factories are treated as distinct participants, representing multiple source domains in the few-shot fault diagnoses. The main contributions are summarized as follows:  
1. We proposed a federated few-shot learning-based fault-diagnosis framework, which leverages fault data from multiple source domains to construct a unified few-shot fault-diagnosis model. By doing so, we address the issue of limited fault samples in the target domain, therefore enhancing diagnosis accuracy. Moreover, our framework ensures privacy preservation by avoiding the need to share sensitive data. 2. We proposed a novel few-shot fault-diagnosis method that utilizes a convolutional denoising auto-encoder and feature-space metric learning. This approach enhances the model's generalization across various domains and exhibits strong adaptability to diverse working conditions, new fault types, and noises. 3. We conduct extensive experiments on real-world datasets to evaluate the effectiveness of our approach. Experimental results demonstrate that FedCDAE-MN outperforms existing methods and significantly enhances the accuracy of fault diagnosis. The rest of the paper is organized as follows. We review work related in Section 2. In Section 3, we state the preliminaries and problem definitions. In Section 4, we introduce the details of our approach. We present the experimental results and analysis in Section 5. Finally, we conclude our work in Section 6.
# Application of Workflow Technology for Big Data Analysis Service
## 2 Big Data Analysis Oriented Cloud Workflow System
### Workflow Execution Engines  
The workflow-parsing engine parses workflows into JPDL and DAG, which can be stored into workflow description and workflow instance databases. The workflow execution engine reads workflow information from the workflow instance database. During the workflow execution process,  
Figure 4: Workflow parsing engine. DAG: directed acyclic graph; UI: user interface; XML: Extensible Markup Language.  
Figure 5: Workflow operation tree.  
different tasks are executed in different ways. This section introduces the interactions between the workflow execution engine and the distributed computing platform, as well as the process of the cloud tasks implemented in the cloud platform as shown in Figure 6.  
1. The web server parses workflow configurations preliminarily. Workflow aspect: the workflows are marked with normal and cloud businesses, and the marked workflow information is transferred to the workflow engine (1); Data aspect: the submitted data can be transferred to the specified location of the cloud platform (2) according to the configurations.
2. The workflow engine parses the workflows received from the web server and stores the parsed results (which involves JPDL and DAG) into the database. The workflow execution engine initiates an executive request as a RESTful web service to the distributed computing platform (3).
3. The cloud platform starts a daemon, which parses the control and data streams of workflows when receiving the initiated executive request (4). In our system, the cloud businesses are divided into batch and online cloud businesses. The batch cloud businesses apply to offline batch data analysis tasks, whereas the online cloud businesses apply to online real-time data streaming analysis tasks. In the cloud platform, batch cloud businesses are implemented using one or more jobs (as shown in Figure 7), and online cloud businesses are executed by Topology structure (the detail is descripted in Section 2.2.2). The daemon invokes job or topology according to the configured control and data streaming to implement tasks in order until web they are finished (5). The daemon sends the results to the web server (6) that applied to execute normal businesses, which mainly include data visualization and report exhibition in big data analysis applications.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 2 Materials and Method
### Algorithmic Principle
#### 2.2.3 Classic Free Network Adjustment and Constraint Adjustment  
Classical free network adjustment means that the control network contains only the essential starting data. The essential starting data for a triangulation and trilateral control network is a known point and a known azimuth. It is called constraint adjustment if more starting data are employed, such as two known points. In both cases, the coefficient matrix of the normal equations is full-rank, so indirect adjustment can be used to solve the problem.
## 3 Results and Discussion
### SWCC and Pore Size Distribution (PSD) Curves  
Mercury and air are nonwetting fluids. For pores with the same diameter, the process of mercury intrusion into porous media is similar to that of air intrusion into soil under the dry path of the SWCC [43; 44; 45; 46; 47]. The mercury infiltration curve is converted to a PSD curve with the help of the Young-Laplace equation for the capillaries [48], and the PSD density obtained from mercury intrusion porosimetry (MIP) can be converted into a relationship between the pore size and matric suction and is thus related to the SWCC. As Figure 12 shows, the SWCCs for soils exhibit double S-shapes. [PERSON] et al. reported that SWCCs tend to flatten out in the intermediate suction range, regardless of the pore size distribution density, and that the two pore systems correspond to the two stages of the double-down SWCC [49]. The water loss process of pores between saturated aggregates corresponds to the first stage of the SWCC, while the water loss process of pores within saturated aggregates corresponds to the second stage of the SWCC. Consequently, compacted soils are expected to undergo two distinct stages of water loss during the drying process. Water is lost more easily in the first stage than in the later stage because of the existence of a large-volume portion of macropores or interaggregate pores, similar to the observations of [PERSON] et al., [PERSON] et al. and [PERSON] et al. [50; 51; 52].  
In this paper, the double-down SWCC was divided into three zones based on the typical \"S\"-type SWCC (Figure 12). The first is the saturation zone (\(\psi<\psi_{\text{a}}\)). In the drying path, the first zone is the saturation zone controlled by the total porosity of the soil sample. In this zone, the soil sample is completely saturated until the matric suction reaches the air-entry value. At this stage, the saturated water content of the soil sample is controlled by the total porosity of the soil sample; when the matric suction rises exponentially, the volumetric water content changes slightly. The second zone is the transition zone (\(\psi_{\text{a}}\leq\psi<\psi_{\text{i}}\)). In the drying path, when the matric suction is on the right of the air-entry value, the water in pores between saturated aggregates or the interaggregate macropores is preferentially drained from the soil sample as the matric suction increases, and due to the large size and volume of the interaggregate pores, when the suction rises exponentially, the water in the  
Figure 11: Average infiltration rate at the upper surface of the soil column.  
macropores decreases relatively fast, and the slope of the SWCC is relatively large. The third zone is the residual zone (\(\uppsi\geq\uppsi_{\rm i}\)). When the suction exceeds the inflection point of the SWCC, the small pores in the aggregates in the soil sample are in a saturated state. As the suction continues to increase, the water in the aggregates slowly drains out, and under the same matric suction increment, the water loss rate is far lower than that in the intergranular macropores.
# Kernel Geometric Mean Metric Learning
## 3 Main Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Datasets** & **GMML** & **DMLMJ** & **LMNN** & **GB-LMNN** & **Euclidean** & **Low-Rank** & **KGMML** \\ \hline
1 & Pima & 27.66 & 30.18 & 33.82 & 37.14 & 27.27 & 29.58 & **25.17** \\
2 & Vehicle & 22.09 & 25.75 & 46.53 & 41.24 & 33.53 & 41.32 & **21.21** \\
3 & German & 27.41 & 24.79 & 30.50 & 29.32 & 31.53 & 26.96 & **24.40** \\
4 & Segment & 4.13 & 3.64 & 5.19 & 4.55 & 6.93 & 5.59 & **3.22** \\
5 & Heart-Disease & 20.82 & 19.73 & 31.52 & **18.51** & 33.33 & 22.52 & 18.97 \\
6 & Lymphography & 56.88 & 73.62 & 70.76 & 60.21 & 75.13 & 57.57 & **53.75** \\
7 & Liver-Disorders & 35.00 & **30.05** & 30.46 & 34.88 & 31.88 & 41.86 & 30.17 \\
8 & Hayes-Roth & 37.69 & 16.84 & 31.33 & 31.35 & **16.67** & 39.55 & 19.52 \\
9 & Ionosphere & 15.34 & 11.27 & 5.71 & 4.29 & **1.43** & 17.26 & 11.54 \\
10 & Glasses & 36.96 & 33.08 & 30.20 & **23.30** & 30.23 & 33.64 & 32.47 \\
11 & Balance-Scale & 12.84 & **8.62** & 12.80 & 15.20 & 14.40 & 12.31 & 9.19 \\
12 & Usps & 3.72 & 2.88 & 33.11 & 10.60 & 10.55 & 4.08 & **2.82** \\
13 & Mnist & 9.65 & 16.44 & 86.80 & 82.62 & 17.12 & 75.34 & **8.32** \\
14 & DNA & 23.65 & **21.75** & 22.32 & 22.84 & 27.63 & 26.24 & 23.05 \\
15 & Spambase & 19.33 & 18.27 & 38.60 & 15.80 & 16.09 & 11.43 & **11.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification errors on UCI dataset. The best result is highlighted in boldface.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 5 Conclusions  
This study has evaluated the potential of ELM neural networks as a new burned area classifier of multispectral Sentinel-2 satellite images. ELM showed promising results in the classification performance on the test dataset and shorter training times than the other algorithms. This characteristic is a great advantage for ELM in order to address burned area mapping with medium-high spatial resolution images at national and global scales. However, like the other machine-learning algorithms, its practical implementation requires preprocessing stages that include an atmospheric and topographic correction and samples collection to generate training datasets with enough spectral and spatial representativity of the burned areas.  
In the current context of climate change, where an increase in the frequency and magnitude of wildfires is forecasted, there is a growing need for precise information about burned areas. This information is essential to fire risk systems and burned-area records used to design prevention and fire-combat strategies and provides valuable knowledge on the effect of fires on the landscape and the atmosphere.  
Conceptualization, [PERSON] and M.M.; methodology, J.G. and M.M.; software, G.V.-N.; validation, J.G., M.M. and G.V.-N.; formal analysis, J.G., M.M. and M.C.-B.; investigation, J.G., M.M. and G.V.-N.; resources, J.G. and M.M.; data curation, J.G. and G.V.-N.; writing--original draft preparation, J.G., M.M., M.C.-B. and G.V.-N.; supervision, [PERSON] and M.M.; project administration, [PERSON]; funding acquisition, [PERSON] and M.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Fondo de Investigacion del Bosque Nativo (FIBN) from Corporacion Nacional Forestal de Chile (CONAF), grant number FIBN010/2017.  
The authors of this study offer their special thanks to the FONDECYT Regular Project 2020 No. 1200810 \"Very Large Fingerprint Classification based on a Fast and Distributed Extreme Learning Machine\", National Agency of Investigation and Development, Science, Technology, Knowledge and Innovation Ministry, Government of Chile, from where the methodology of work of ELM networks was obtained.  
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.  
Figure 6: Burned area classifications for the validation area (**A**), CONAF’s Method (**B**), FIRECCI Algorithm (**C**) and ELM (**D**). White shows spatial agreement, and magenta indicates omission and green shows commission.
# Performance Analysis of Tunnel Boring Machines for
## 4 Case Study: The Moncenisio Base Tunnel
### Application of Correlation Models between the Mechanical Parameters of TBMs to the Case Study
#### 4.1.1 \"GEA\" Open TBM  
Table 3 shows the equations that correlate the diameter D with the three mechanical parameters (power (P), torque (C), thrust (S)), in the case of open TBMs. Through these equations, by entering a diameter of 6.3 m, the minimum values of P, C, and S are obtained. The results are then compared with the real TBM values (Table 4).  
From the analysis of the results of Table 3, it is clear that the power supplied to the excavation head and the thrust are perfectly estimated by the experimental prediction models obtained in the present study. Particularly noteworthy is the forecasted value of the thrust that underestimates the real value of only 3.6%.  
Pertaining to the cutterhead power, the discrepancy is slightly higher but, however, it is aligned with the prediction value proposed by [43]. Finally, an overestimation of the torque of about 45% with respect to the real value is observed. However, even references from [42; 43] provided overestimated torque values with a discrepancy greater than 20%. This greater gap between the real value of the torque and the outcomes of the prediction models probably denotes the influence of the variability of the mechanical characteristics of the rock mass crossed on the performance of the machine.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Westimated Values** & **Experimental Results** & [42] & [43] & **Real Value** \\ \hline Cutterhead Power P (kW) & 1905 & - & 2378 & 2203 \\ \hline Torque C (kNm) & 3769 & 2674 & 2810 & 2083 \\ \hline Thrust S (kN) & 12,355 & 11,573 & - & 12,800 \\ \hline \hline \end{tabular}
\end{table}
Table 4: P, C, S values estimated with the experimental models and real values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Model** & [42] & [43] \\ \hline P = 434.55D\(-\)832.66 & - & P = 350.6D + 169.62 \\ \hline C = 1354.4D\(-\)4764.2 & C = 1089.3D\(-\)4188.8 & C = 1834.63D \(-\) 8748.52 \\ \hline S = 12,185 ln(D)\(-\)10,072 & S = 1777.1D + 377.72 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Equations that link the diameter D to the power supplied to the excavation head (P), to the torque (C) and to the thrust (S) for the open TBMs.
## 3 Damage Detection Using Machine Learning Techniques  
This section highlights an approach to monitor and detect damage using ML techniques. In the context in which we work, SHM means training an AI to associate a state or class of damage with a vector of measurements of the structure of interest. The vectors of measurements should consist of quantities, i.e., features, which are sensitive to the type of damage being studied. The features could be, for example, the first identified fundamental frequencies of vibration, statistical parameters of the recorded signal, wavelet coefficients, or artificial scalar quantities computed from seismic signals recorded at the structure, among others. Once the appropriate features are determined, which is not straightforward, a \"map\" between the features and the damage class of the structure can be created to extract mathematical models that can automate the SHM process and eliminate the intervention of human technicians as much as possible. The goal of SHM is to gather enough information to take appropriate corrective and preventive actions to restore and preserve the artifact, or at least to ensure its safety.  
Figure 1: General framework investigated.  
The approach discussed here for solving the classification level is based on the idea of pattern recognition (PR). In a general sense, a PR algorithm is simply an algorithm that assigns a label, a class label, to a sample of measurement data. The class labels encode the location and rating of the damage. The training method, in which the diagnosis is trained by linking labels and a full set of measurement data (including those of the different damage classes), is called supervised learning. A supervised learning approach, unlike unsupervised learning, imposes very stringent requirements on the data needed for learning. First, as described earlier, measurement data are needed for every possible damage configuration, and such data are difficult to find in the civilian domain. One option would be physical FE modeling. However, FE modeling could be hampered by several problems, such as: the a priori choice of the damage type, and the model could be complex in geometry and material definition and, therefore, the analysis could take a lot of time. Moreover, it could be difficult to model the damage accurately, especially in the case of nonlinearities or in the case of fatigue cracks with cyclic opening-closing phenomena. Finally, there are uncertainties about present and future loading and environmental conditions. An alternative solution could be to make small-scale or full-scale copies and then damage them. the number of copies should correspond to the combination of the different types of damage that can be assumed based on their varying severity. However, this solution is simply not feasible for construction works. The proposed framework is shown schematically in Figure 2. Briefly, the framework uses a parametric FE model (here using the FE software OpenSees [26]) for damage definition and stochastic variables for environmental conditions. The FE model is used to train a ML model, using the SVM method. Details are provided in the following sections.
## 3 Materials and Methods
### The Proposed ECT Image Quality Assessment Methods
#### 3.2.2 Group MSE and Group PSNR  
Group Mean Square Error (GMSE) and Group Peak Signal-to-Noise Ratio (GPSNR) address the object-shifting problem via leveraging node grouping, thus focusing on group-level comparisons instead of individual node values. We first determine an average value for each node group (depth 1 or 2, as defined above). Then, we calculate the MSE or the PSNR for these average group values, treating them as pixel values in a traditional image.  
Both the GPSNR and the GMSE rely on node grouping. When determining the Group PSNR for each node, an average value is calculated from its assigned group (similar to calculating the mean for a pixel neighborhood). The PSNR is then computed for these average group values, using a maximum node value of 3. In the case of the Group MSE, the squared differences between the corresponding average group values (from reference and other images) are calculated and averaged.