esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
[PERSON] et al. [13] applied DL based supervised image classification model and images gathered using UAV for the forest region classification. The DL technique based stacked Autoencoder (SAE) has shown remarkable potential with respect to the assessment of forest areas and image classification. The experiment result shows that DL technique provides improved performance than other machine learning approaches. The researchers in [14] address the shortcoming of multi-labeling UAV images, usually considered by a higher level of dataset content, by presenting a novel technology based on CNN. They are employed as a means to produce an accurate representation of the query images that are analyzed afterward sub-dividing them into a grid of tiles. The multi-label classification process is implemented by combining a radial basis function neural network and a multi-labeling layer comprised of threshold operation. The researchers in [15] proposed a DL algorithm for classifying UAV images derived from the location and sensor of earth's surface. Initially, the labelled and unlabelled UAV images are fed to a pre-trained CNN to generate deep feature representation. Next, we learned strong domain-invariant features with a further network comprised of two fully connected layers.  
[PERSON] et al. [16] developed a new optimum DL-based scene classification algorithm captured by UAV. The suggested method includes a residual network-based features extraction (RNBFE) that extract feature from the convolutional layer of a DRN system. Furthermore, the various parameters result in configuration errors because of parameter tuning. Hence, self-adoptive global best harmony search (SGHS) approach is applied to tune the parameter of the presented model. The researchers in [17] present a multi-objective optimization algorithm to evolve deep CNN for scene classification that generates the non-dominant solution in an automatic manner at the Pareto front. Then, we used two sets of benchmark data sets for testing the effectiveness of the scene classification algorithm and making an extensive analysis. [PERSON] et al. [18, 19] presented an energy-effective cluster-based UAV system using DL based scene classification model. The suggested method includes a clustering with parameter tuned residual network (C-PTRN) system that operates on two primary processes scene classification and cluster construction.
# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 3 Transfer Reinforcement Learning Approach
### Inter-Task Mapping Transfer Approach
After obtaining the ANN from the source task, the ANN for the target task is generated considering the number of input neurons as the number of components in \(\mathbf{s^{\text{(T)}}}\), the same number of hidden layers and hidden nodes as in the source ANN, and \(3^{N^{\prime}}\) neurons in the output layer. Then, the weights \(\mathbf{\theta^{\text{(S)}}}\) are transferred to \(\mathbf{\theta^{\text{(T)}}}\) according to (4). Next, an iterative process begins where the ANN in the target task is trained using the DQN algorithm to update and tune the weights \(\mathbf{\theta^{\text{(T)}}}\). In each training step \(t\), the DQN algorithm starts by collecting the state/action/reward experiences from the new RAN environment with \(N^{\prime}\) cells, which are stored in the experience dataset of the DQN agent. Then, the algorithm updates the weights according to the mini-batch gradient descent of the loss. This is repeated until a training termination condition is met, obtaining policy \(\pi^{\prime}\) as a result. The termination condition can be expressed in different ways, such as reaching a maximum number of training steps or achieving the convergence of a given metric. For further details on the training operation of the considered RL-based capacity sharing solution, the reader is referred to [11].
# A Review of DSP-Based Enabling Technologies for Cloud Access Networks
## 2 Cloud Access Network Concept  
### Overview  
The basic concept of the CAN is the provision of a SDN-controlled network that is dynamically reconfigurable to offer users' on-demand connectivity according to the users' application requirements, for example, bit rate, latency, security and reliability-level, also new connectivity features, such as ultra-low power idle states can meet the need for greener networks. The proposed CAN offers a number of key characteristics and features:  
* Seamless integration of Metro-networks, fixed access networks, and 4G/5G fronthaul and backhaul networks.
* Provision of a sliceable and dynamically reconfigurable network which can efficiently support virtualised networks at sub-wavelength connectivity levels with elastic bandwidth provision.
* Full support of SDN for efficient control and management of the network configuration, with control extended to the physical layer.
* Support of DSP-based advanced modulation formats to achieve spectrally efficient high-speed interconnections.
* Channel transparency to underlying signal characteristics, such as bandwidth and modulation format.
* Excellent backwards compatibility with existing optical transmission technologies as the baseband region in the optical domain can be allocated to existing TDM-based optical signals.
* Dynamically reconfigurable and highly flexible network elements.
* Exploitation of existing passive optical network (PON) infrastructures by the use of advanced multiple access techniques.
* Enhanced physical layer data security the as full details of the channel multiplexing parameters are required to perform data recovery.
* An inherent \"pay as you grow\" network operation model, as the growing demand for aggregate capacity can be met by adding additional channels to the network as required.
## 3 Methodology
### Spatiotemporal Data Collection and Processing
Figure 4 shows the 3D point cloud data of the bridge after data cleaning using two gradient filtering algorithms. When applying 3D laser scanning technology in bridge spatial morphology detection, the bridge is typically scanned at two different time points to obtain the 3D point cloud models of the bridge at those different times. During data processing, the two point clouds need to be registered to make them as coincident as possible, aligning the two or more point cloud datasets into the same coordinate system. Acknowledging the inevitable errors that arise during the collection of two-year bridge point cloud data, the research team contemplated the use of a local coordinate system. In this scenario, a stable structural feature was chosen as the reference point, such as a pier or a fixed portion of the bridge. Simultaneously, considering the potential deformations of the bridge, the team employed the relevant feature-matching tools provided by CloudCompare to further identify the feature points that remain constant across different time points, serving as critical points in the registration process. The differences between corresponding points in the two registered point clouds represent the changes in the bridge's spatial morphology over that time period. Therefore, the accuracy of the point cloud registration algorithm largely determines the accuracy of the spatial morphology analysis results. Due to ineffective manual registration results (as shown in the Figure 5), in practical bridge point cloud data analysis, typically five methods (as shown in Table 1) are employed for point cloud registration. When evaluating the appropriateness of various point cloud registration methods, it is crucial to consider cases involving \"small transformations\" between two point cloud datasets. These transformations can be characterized by several factors: a small rotation angle, meaning the rotational deviation between the datasets is minimal, as large angles complicate the matching process; a small translation distance, where large distances make it difficult to accurately find corresponding points; minimal shape changes, since significant deformations or structural modifications increase registration complexity; and minimal  
Figure 4: Point cloud filtering of bridge data in 2017.  
scale changes, where substantial differences in size between the point clouds challenge the algorithm's ability to manage these variations. Given the specificity of bridge engineering, where point cloud data typically exhibits small transformations, the Iterative Closest Point (ICP) algorithm is commonly chosen for effective point cloud registration. It iteratively identifies the closest point pairs and optimizes the rotation and translation parameters to progressively reduce differences between two point clouds, ultimately achieving precise alignment.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 4 Experimental Results
### Experimental Environments  
The simulation results were conducted with a Core i5-9400F (up to 4.1 GHz), 32 GB DDR4, and GTX 1080 Ti, and PyTorch was used as the framework. From the KITTI OD dataset, 80% of the samples were used for training and 20% of the samples for testing. The embedding network for re-identification was pretrained on the 37,930 images in the VeRi-776 training dataset. To train the embedding network, we used the Adam optimizer  
Figure 10: Generated ID dataset for KITTI multi-object tracking dataset.  
Figure 9: Appearance distance between objects.  
with default settings (\(\beta_{1}=0.9\) and \(\beta_{2}=0.999\)). The learning rate was set to 0.01, and the scheduler was set to 0.9 after every two epochs. The model was trained for 100 epochs in the pretraining process. Fine-tuning was performed with the reconstructed KITTI MOT dataset. As mentioned, 17 images were used as the training set and 3 as the test set because the dataset does not provide labels for the test set. In Equation (5), \(\lambda\) was set to 0.1, and in Equation (4), \(\alpha\) was set to 1.
# Parameter Analysis of Resilient Precast Concrete Beam-Column Joints
## 3 The Finite Element Model
### Model Establishment
#### 3.1.2 Material Constitutive Relationships  
To simulate the stiffness degradation characteristics of concrete under low-cycle repetitive loading better, the Concrete Damage Plasticity Model was used to define the concrete material. This model employs isotropic elastic damage theory to provide a comprehensive and accurate simulation of the inelastic behavior of concrete. It not only covers the cracking failure phenomena that can occur during the tensile phase of concrete but also considers the crushing failure that can occur under compressive conditions. The stress-strain curve of concrete (Figure 7) refers to the \"Code for Design of Concrete Structures\" (GB 50010-2010) [39].  
In order to accurately simulate the mechanical behavior of reinforced concrete structures under cyclic loading, it is necessary to consider the slip effect when the reinforcement and concrete act together. In this study, the PQ-Fiber subroutine model proposed by [PERSON] et al. was adopted. This model used a bilinear hysteresis model with reloading stiffness degradation (the Clough model) to describe the stress-strain relationship in the reinforcement (Figure 8) and was embedded into ABAQUS as a subroutine to accurately simulate the mechanical response of the reinforcement under cyclic loading.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 3 Results
### Construction of the Spectral Feature Library with Disease Symptom Proportions
#### 3.2.1 Statistical Analysis of \"Three-Edge\" Parameters
Spectral positions of healthy leaves were relatively stable in these waveband regions, which were located at 520, 551, 702 and 672 nm, respectively. However, spectral positions of leaf lesions varied widely due to different severity levels of rice ShB in the ranges of the blue edge, green peak, yellow edge, red valley and red edge. The blue edge position (BEP) changed from 501 to 530 nm centered around 520 nm. BEP had a shift towards shorter wavelengths when the disease was mild in severity. On the contrary, BEP moved to long wavelengths higher than 520 nm. The green peak position (GPP) was relatively stable between 553 and 560 nm centered around 560 nm. The yellow edge position (YEP) moved to longer wavelengths from 550 nm to 583 nm. The red valley position (RTP) was from 641 to 677 nm, mainly concentrated on 641 nm, which had a trend towards shorter wavelengths. The red edge position (REP) was mainly located at 692 and 695 nm. Compared to REP of healthy leaves, it was shown that REP shifted towards blue light. Basic statistics of the ratio and normalized indices formed by the combination of \"three-edge\" areas and amplitudes were computed and analyzed. It was seen from the maximum and minimum values of every variable that the value ranges of some indices from diseased leaf blade fractions were different to those of healthy leaves. They are shown in Figure 3, including ratio indices of SDg/SDb, SDy/SDb, Rg/Ro, Sdnir/SDb, Sdnir/SDr, Sdnir/SDg and normalized indices of (SDg - SDb)/(SDg + SDb), (Sdy - SDb)/(Sdy + SDb), (SDr - Sdy)/(SDr + Sdy), (Rg - Ro)/(Rg + Ro), (Sdnir - SDb)/(Sdnir + SDb), (Sdnir - SDr)/(Sdnir + SDr), and (SDnir - SDg)/ (Sdnir + SDg). These variables indicate the ability of distinguishing the diseased leaves from the healthy ones. They contain not only the above individual parameters with the entirely non-overlapping value range but also the parameters with a range of full overlap and partial overlap. This implies that algebraic combination of spectral amplitudes and areas can amplify the amount of information and make use of parameters with weak disease information.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 3 System Model and Performance Metrics
### Performance Metrics  
Different performance metrics are usually deployed to evaluate every wireless communication system's performance. In this paper, three performance metrics are evaluated and analyzed: throughput (achievable data rate), energy efficiency and fairness index.
## 3 Preliminaries
### JFML-IoT: Design and Features  
JFML-IoT is a lightweight extension of the JFML library, specifically developed to enable the execution of fuzzy inference processes in constrained IoT environments [23] ([[https://github.com/JfMRes/JFML_IoT](https://github.com/JfMRes/JFML_IoT)]([https://github.com/JfMRes/JFML_IoT](https://github.com/JfMRes/JFML_IoT)), accessed on 16 July 2025). It provides a modular and scalable architecture that supports the implementation of FRBSs compliant with the IEEE Std 1855-2016, making it particularly suitable for real-time control tasks in smart building applications.  
The design philosophy of JFML-IoT focuses on bridging the gap between high-level fuzzy modeling and the low-level execution requirements of embedded systems. Its main features include  
* **Optimized execution for edge computing**: The library is adapted for microcontrollers and embedded platforms, offering reduced memory usage and execution time without sacrificing inference accuracy.
* **Modular and extensible architecture**: It provides abstract classes for modeling sensors, actuators, communication modules, and embedded devices, facilitating the rapid development of custom hardware/software configurations.
* **Native support for IEEE 1855-compliant FML**: FRBSs can be loaded, parsed, and executed from XML files using the FML, ensuring both interoperability and human interpretability.
* **Integration with standard communication protocols**: JFML-IoT supports lightweight messaging protocols such as MQTT, allowing sensors and actuators to communicate through publish-subscribe patterns commonly used in building automation. It also supports data integrity through MQTT's built-in QoS levels and timestamped message delivery, ensuring reliable and ordered transmission in constrained environments.
* **Separation of concerns**: The architecture cleanly decouples the fuzzy logic engine from the data acquisition and actuation layers, improving system modularity and maintainability.  
JFML-IoT serves as the computational backbone for the fuzzy inference framework proposed in this work. By leveraging its existing capabilities, we demonstrate how an interpretable, adaptable, and efficient fuzzy controller can be implemented and validated in a smart building scenario, without requiring further extensions to the library.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method  
In response to the suboptimal tracking performance of Siamese tracking algorithms in complex scenarios such as Fast Motion, we propose an object tracking algorithm based on spatial scale attention, SSTrack. SSTrack uses Swin Transformer instead of the convolutional neural network to extract global features of the target object, and builds a spatial scale attention module to enhance the ability to express local details of the target.  
The overall network structure of the SSTrack is illustrated in Figure 2, comprising three main components: the feature extraction sub-network based on Swin Transformer, the feature fusion sub-network based on exemplar attention, and the classification and regression sub-network.  
Firstly, when extracting global features from the input images of the template region and the search region through the Swin Transformer Block in the upper and lower branches, the constructed spatial scale attention module utilizes higher-order statistical quantities and dilated convolutions to enhance the representation capability of object local details. This module enables it to cope with complex environments. Next, the correlation between feature sequences and position information is computed separately in the feature fusion sub-network. Self-attention computation is then performed using exemplar attention, improving tracking speed and meeting the real-time tracking requirements. Finally, the designed classification and regression loss function aids in learning feature representations and position estimates, yielding the ultimate tracking results.
# How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective
## 4 Case Study
### Failure Description Retractable Step  
Product description of the system is based on the technical documentation of the retractable step [50]. The entire system consists of the sliding step and the connection cable to the control unit. The extension unit basically consists of a frame in which the walking zone is stored. The walking zone is equipped with a step sensor and anti-slip coating. The drive takes place by means of a DC motor. The motor is mounted on a compact drive-bearing unit. The driving force is transmitted from the motor shaft via a hollow shaft to a toothed belt wheel. The toothed belt wheel drives the central drive belt. A carrier on the toothed belt established the connection with the sliding step and converts the motor drive power into the extension movement of the extension unit. The sliding step is guided by profile rollers on the extension unit. The upper and lower rails are both made of stainless steel. The profile below the lower rail is made of aluminum. If a vertical load of 150 N or more is applied to the sliding step during extension or retraction, the movement is stopped immediately. A schematic overview of the train door retractable step system is provided in Figure 5.  
According to the research performed by NS, the FLIRT sliding step causes many problems [49]. The most critical problem is that the retractable step gets stuck due to clamping. In that research, clamping was caused by the fact that the left rail was raised about 2.4 mm. To find out why the bottom rail came up, a destructive test was performed. The part of the aluminium profile at the height of the elevation was cut out and the lower rail was removed. Details of the side profile with rollers are presented in Figure 6.  
The rise of the lower rail was caused by the formation of aluminum oxide between the stainless-steel rail and the aluminum profile. More specifically, it was caused by galvanic corrosion of the sliding step. The volume of aluminum oxide pushes the bottom rail up by 2.4 mm.
# Discontinuous Yielding of Fe E420 under High Strain Rate Loading
## 4 Discussion  
The conducted experimental investigation dealt with the DY in Fe E420. The onset of the discontinuous yielding was observed for both the quenched and as-is state of the material tested at higher strain-rates. The quenching treatment produced a reduction in strain to failure and a rise in the maximum stress and hardening. As the strain-rate increased, the onset of a lower and an upper yield point, not present under quasi-static test conditions, became gradually clear. Figure 8 shows the evolution of the lower and  
Figure 6: Fracture surface SEM images of the 1000 s\({}^{-1}\) tested specimen magnified 1500\(\times\). Region close to the section edge (**a**). Central region of the section (**b**).  
Figure 7: Hopkinson bar tests results in terms of stress over strain curves; for 500 s\({}^{-1}\) tested specimens (**a**) and 1000 s\({}^{-1}\) tested specimens (**b**).  
upper yield points at the three considered deformation rates, both for the quenched and as-is material.  
The gap between the lower and upper yield points greatly increases for the higher considered strain-rate of 1000 s\({}^{-1}\). Moreover, this gap is about 250% higher for the quenched material when compared to the as-is state, highlighting an important effect of the heat treatment on the discontinuous yielding phenomenon. The described experimental results lead to the following conclusion:  
* A reserve of elastic strength for the as-is Fe E420 and even more for the quenched Fe E420 when loaded at a strain-rate higher than 500 s\({}^{-1}\) can be considered.  
This represents a beneficial effect in all of those stress-controlled applications where plastic strain has to be inhibited. In addition, since the elastic regime is much more extended, the accumulation of plastic strain can be avoided by preventing low cycle failures under fatigue loading at high strain rates.  
Consequently, the DY phenomenon should not be neglected since it could significantly extend the range of applications of materials in sectors, such as aerospace and defence.  
Conceptualization, L.E. and M.B.; Formal analysis, L.E.; Experimental investigation, G.I. and F.S.; Writing-original draft preparation, M.B.; Writing-review and editing, L.E.; Supervision and project administration, L.E.; All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability StatementData available on request from the authors.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DY & Discontinuous Yielding \\ YPE & Yield Point Elongation \\ DT-SHPB & Direct Tensile Split Hopkinson Pressure Bar \\ WQ & Water Quenching \\ BCC & Body-Centered Cubic \\ FCC & Face-Centered Cubic \\ TRIP & Transformation-Induced Plasticity \\ \end{tabular}  
Figure 8: Upper and lower yield stress over the considered strain-rates, both for the quenched and the as-is Fe E420.