esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 What Is the WikiGIS Concept? The authors in [4] defined WikiGIS as \"a collaborative platform 2.0, supported by a wiki that insures traceability of geographical contributions of participants while insuring the quality of produced data in a dynamic visualization and analysis. The WikiGIS has powerful features in editing and design for sketch-mapping and in georpocessing. It thus offers a simple and quick access to the process of GeoDesign\". From this definition, WikiGIS is grounded in a combination of wiki, GIS and design (Figure 1). The strength of the WikiGIS concept is derived from the integration of these three sub-components. The basic concepts of WikiGIS are [4]:  
1. A web 2.0 application based on wiki track editing; dynamic management and consulting contributions. The data may be modified, enhanced, updated or even deleted. All user contributions are archived and can be viewed dynamically with the historical content;
2. A documented traceability of users' contributions to ensure not only the argued documentation of the design process, but also access to this process;
3. The wikiGIS ensures not only the traceability of geometric, graphical and descriptive components of a feature, but also the relationships between different geometric features (topology, intersection, inclusion, _etc._) over time. For example, a user can draw a line directly next to an existing line drawn by another user. These two lines could be aggregated in the future because of their common attribute, much like the extension of a road;
4. An ergonomic and simple mapping interface (Geoweb 2.0) for easy viewing and navigation in space-time versions of contributions;
5. Any contribution is considered as an opinion;
6. These arguments are the main components to qualify external contributions;
7. All previous versions of an opinion are considered as an integral component of the object (not as a different object). In Table 1, we detail the features of each sub-component mentioned above. The WikiGIS is designed primarily to support the creative, iterative and collaborative dimensions of the GeoDesign process. Figure 2 shows the positioning of WikiGIS among GIS products in order to highlight why it is needed in this present research. Figure 1: WikiGIS = Wiki + GIS + Design.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 4 Services in the Architecture
### Service Components on the Control Plane  
An interconnected multi-domain control plane protocol is the main contribution of this article. The most important services in the control plane protocol are the BGP services that exchange SFC route and the encrypted path information (Figure 10). This section explains the route distribution services and the surrounding control services.  
The two main components of the control plane are the network controller and the Compute Node (Hypervisor) BGP services. The architecture is not based on traditional imperative SDN such as OpenFlow or distributed Open Virtualized Switches (OVSs) on the Compute Nodes, but it uses a declarative SDN method by the use of the BGP route distribution. Hence, the network controller is  
Figure 9: Services in the orchestration plane.  
distributing SFC flow routes over BGP in order to inform all Compute Nodes about how to forward SFC packets. Every Compute Node announces its connected VNFs in order to let the network itself calculate the correct SFC paths. Similar to the BGP control plane for SFC [10], every Compute Node acts as a BGP speaker to announce its connected VNFs. Since the network controller knows the location of the VNFs, it can distribute the SFCs for both Virtual and Encrypted Links to every Compute Node. This section explains the functionality of this BGP service, how the edge VPN gateway connects the remote Service Providers and how the Key Management Server (KMS) can automate the setup of tunnels.
## 2 NDT Method Preliminaries
Using analysis of the reflections because of the abnormalities in an internal structure allows measuring the characteristics such as the thickness or depth of any defect. Although advanced ultrasonic tools provide 3D C-scans and cross-sectional B-scans to obtain sufficient information about a structure; quantifying and locating defects might be challenging and require complementary techniques when multiple close reflections [21] or near-surface defects [22] occur. The authors of [22] also showed the challenging points of processing ultrasonic data due to the multiple echoes caused by structural noise. An efficient method for surface and near-surface defects is infrared thermography, which uses thermal wave propagation caused by temperature differences. Figure 1a presents a basic schematic representation of pulsed thermographic inspection. The authors of [26] presented an effective low-velocity impact characterisation for the matrix, delamination and fibre damage of a GFRP structure with thermography. Surface temperature maps have been used to observe local temperature changes and maximum temperature differences to help identify defect types. It should be noted that thin materials restrict infrared thermography due to the challenges of heat penetration into deeper regions of the material [27]. Liquid penetrant testing that uses the capillary action principle might provide a practical solution when surface examination of mass-produced items is performed [28]. A particular liquid, known as a penetrant, fills any discontinuities that may be present when applied to a specimen's surface. Thus, it can be detected by fluorescence or human eyes according to the penetrant's dye type. The main limitation of this technique is being restricted to surface or open-to-surface defects. Figure 1: Basic representations of thermographic, ultrasonic, X-ray and eddy current NDT methods. (**a**) Schematic representation of pulsed thermography [23]. (**b**) Schematic representation of ultrasonic testing [24]. (**c**) Schematic representation of X-ray radiography. (**d**) Eddy current defect representation [25]. As can be seen from Table 1, the magnetic particle and eddy current (Figure 1d) methods are the other surface-effective techniques that use similar principles of forming regular magnetic fields on uniform materials. In the case of a discontinuity, the magnetic field deforms because it tends to follow the path having the least resistance [28,29]. However, magnetic particles and eddy currents are limited by the material types of ferromagnetic and conductive types, respectively. Finally, acoustic emission is a reliable and real-time technique that detects the materials' transient stress waves for early fault detection [30]. While the approach is highly sensitive to small changes in an active defect, it tends not to be suitable for constant (static) defects. Moreover, it seems to be an attractive method for electrical defects rather than mechanical ones, as well as having issues with the defect size and shape aspects [31].
## 4 Background Study
### Literature Review
While the method helps bridge gaps across MRI vendors, it is limited by data imbalances and the absence of ground truth in some datasets. Future work could focus on the standardization and demographic adjustments to improve robustness. [PERSON] et al. (2024) [53] created a multi-modal diffusion model to predict Alzheimer's progression by generating future MRI scans using early imaging and clinical biomarkers like A\(\beta\), t-tau, and p-tau. Their model outperformed GANs and image-only diffusion methods, producing clearer, more detailed scans (PSNR 31.99, SSIM 0.75, FID 11.43). Adding clinical data notably improved results. However, the study used only 50 subjects and did not report diagnostic metrics like accuracy, suggesting the need for larger datasets and broader clinical inputs to boost real-world impact. [PERSON] et al. (2025) [54] addressed the challenge of limited and imbalanced MRI data for Alzheimer's diagnosis by using GANs to generate synthetic brain images. Trained on OASIS data, these images boosted the performance of an EfficientNet model classifying AD, MCI, and NC stages. Accuracy rose from 78.48% to 85.11% when using entirely synthetic data, though GAN training faced stability issues and some overfitting. The study shows promise for GAN-based augmentation, with future work focusing on improving training stability and exploring more robust GAN models. [PERSON] and [PERSON] (2024) [55] developed a dual GAN framework with pyramid attention and a CNN classifier to boost Alzheimer's detection from MRI scans. Trained on ADNI data, the model achieved outstanding results in 98.87% accuracy and a 99.67% F1-score far outperforming standard CNNs. While powerful, its reliance on a single dataset and lack of clinical interpretability limit real-world use. The authors recommend adding data like cognitive scores or genetics and enhancing explainability to improve clinical relevance. [PERSON] et al. (2024) [56] introduced a diffusion model to generate A\(\beta\) and tau PET images from MRI scans, aiming to reduce the cost and radiation of PET imaging in Alzheimer's diagnosis. Their method uses residual learning, prior info like age and gender, and a novel loss function to boost image quality and modality distinction. Tested on 1274 ADNI subjects, it outperformed other models in image quality (SSIM\(\sim\)92%) and achieved strong biomarker classification results (AUC\(\sim\)90%). Though promising, it relies on accurate prior data and high computing power. Future work will aim to improve efficiency and adapt the model to diverse populations.
## 2 Nonlinear Dynamic Modeling of Spur Gear Systems
### Bending Deformation of Driveshafts  
The bending deformation of the driveshaft plays an important role when subjected to a large load. In view of the swing effect caused by the nonuniformly distributed meshing force, two translational DOF are taken into account for each bearing to establish a relatively accurate dynamic model.  
The schematic diagram of the driving gear's shaft that is projected on the plane O\({}_{1}\)YZ is shown in Figure 5. When the gear system is in a static state, \(l_{bi}(i=1,2,3,4)\) denotes the distance from the centroid of the driving or driven gear to the centroid of the corresponding supporting bearing, and \(L_{j}(j=1,2)\) denotes the length of the driving shaft or the driven shaft. Besides, along the Y-axis, the displacement of the gear centroid is \(y_{1}\), and the ideal displacement is \(y_{c1}\) when the drive shaft is regarded as a rigid body.  
When the shaft is regarded as a rigid body, the gear centroid is at point A. However, the bending deformation of the driveshaft will cause the gear centroid to move to point B. Thus, one obtains the following:  
\[y_{c1}=y_{b1}+(y_{b2}-y_{b1})l_{b1}/L \tag{6}\]  
\[\Delta_{1}y=y_{1}-y_{c1}=y_{1}-y_{b2}l_{b1}/L-y_{b1}(L-l_{b1})/L \tag{7}\]  
where \(\Delta_{1}y\) is the elastic deformation of the driving gear shaft along the \(Y\)-axis.  
The elastic deformation of the driveshaft along the \(X\)-axis can be obtained in the same way. Thus, the elastic deformation of the driving and driven shafts at the gear centroid can be obtained as  
\[\left\{\begin{array}{l}\Delta_{1}x=x_{1}-\delta_{1}x_{b2}-\delta_{2}x_{b1}\\ \Delta_{1}y=y_{1}-\delta_{1}y_{b2}-\delta_{2}y_{b1}\\ \Delta_{2}x=x_{2}-\delta_{3}x_{b4}-\delta_{4}x_{b3}\\ \Delta_{2}y=y_{2}-\delta_{3}y_{b4}-\delta_{4}y_{b3}\end{array}\right. \tag{8}\]  
where \(\delta_{i}=l_{bi}/L_{j}\) (when \(i=1,2,j=1\); when \(i=3,4,j=2\)).  
Figure 4: Dynamic model of a gear system, considering the nonuniformly distributed force and elastic deformation.  
Figure 5: Schematic diagram of driveshaft bending deformation.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 6 Simulation Configuration  
We implement the array optimization procedures in MATLAB to enable rapid evaluation of miniaturized acoustic camera designs. The custom cost function represents the mismatch between achieved and ideal beampatterns over angles \(\Theta,\Phi\) and frequencies \(F=[0.5,6.4]\) kHz. At each iteration, filter coefficients are computed analytically then microphone positions are perturbed stochastically to minimize artefacts. The optimization concentrates power within a \(\pm 20\) degree main lobe while suppressing sidelobes. Array performance is assessed by analysing:  
* [noitemsep,topsep=0 pt]
* Directivity--angular discrimination capability;
* White noise gain (WNG)--robustness to fabrication variations;
* Beam patterns and sidelobe levels--imaging artefacts.  
The numerical approach allows for efficient simulation of miniaturized configurations to quantify expected imaging performance and determine plausible hardware parameters. We consider three scenarios:  
1. [noitemsep,topsep=0 pt]
2. A 32\(-\)microphone 0.25 m square array optimized from 2 to 6.4 kHz.
3. A 32\(-\)microphone 0.21 m square array optimized from 2 to 6.4 kHz.
4. A 32\(-\)microphone 0.21 m square array covering [0.5, 6.4] kHz for comparison with _Dual Cam_ specifications (128\(-\)microphone on a 0.5 m square array).  
The different number of microphones and expanded frequency range in Case 3 demonstrates trading off aperture size versus density given constraints. Comparisons with a modelled 128\(-\)element 0.5 m array representing _Dual Cam_ provide context on expected miniaturization imaging trade-offs. The simulation results guide physical prototype development by predicting achievable performance bounds with compact arrays.
## 1 Introduction
[16] studied the flow and heat transfer characteristics in finned-tube heat exchangers based on the porous media method, and analyzed the effect of air flow on the resistance characteristics and heat transfer performance, and the simulation results were verified by the measured data. Therefore, the simplification of the overall heat exchanger using the porous media method will help to achieve an efficient numerical simulation analysis of heat exchangers with complex structures, such as the plate-fin type. Generally, numerical simulation results need to be combined with optimization algorithms to obtain the optimal solution. Many scholars have made many efforts to this end [17; 18; 19]. The non-dominated sequencing genetic algorithm (NSGA-II) is widely used for optimization design due to its advantages such as fast operation and good convergence of the solution set. [PERSON] et al. [20] adopted CFD and NSGA-II to carry out multi-objective optimization designs for the combustion chamber of CO steam boiler, and studied the multi-objective optimization problem with two input parameters and two objectives. [PERSON] et al. [21] used numerical simulation method and NSGA-II for a multi-objective optimization of mini U-channel cold plate with SiO\({}_{2}\) nanofluid to obtain the optimal performance. However, the Pareto frontier obtained by NSGA-II is an optimal solution set, and these studies ultimately use subjective analysis to select the optimal solution from the Pareto solution set. Technique for order preference by similarity to an ideal solution (TOPSIS) can effectively avoid the subjectivity of data, and can well depict the comprehensive impact of multiple impact indicators [22]. [PERSON] et al. [23] comprehensively evaluated the Pareto optimal solution set of NSGA-II using TOPSIS and obtained the optimal performance of concentrated Photovoltaic-Thermoelectric hybrid system. In this study, in order to obtain the optimal performance of the plate-fin heat exchanger, the fin angle of the heat exchanger and the inlet flow rate are optimally designed. Firstly, a fluid-solid coupling model of the heat exchanger is established based on commercial CFD software Fluent, and the serrated staggered fins are simplified using porous media. By changing the fin angle, oil flow rate and water flow rate, 45 numerical simulation test cases are designed. Additionally, based on the numerical simulation results, support vector machine regression (SVR) is used to establish regression models of heat exchanger performance (heat transfer, oil pressure drop, and oil outlet temperature) with respect to fin angle and inlet flow rate, and these regression models are also taken as the optimizedobjective functions. Then, the Pareto optimal solution set of the objective function is obtained using NSGA-II. Finally, the optimal solution that leads to the best performance of the heat exchanger is preferred by the TOPSIS comprehensive evaluation method.
# Influence of the Composition of the Activator on Mechanical Characteristics of a Geopolymer
## 4 Comparisons of Results  
One of the objectives of this study was to find the most efficient composition for the activator, but the results does not give any clear conclusion on this. Nevertheless, it seems that the Base Mix, 1:2 is a good solution, and compositions between 1:1.5 and 1:2.5 are equally acceptable in practical terms. When compared to Type A mixes, the great majority of the specimens of Type B mixes present better results for all the mechanical properties that were studied. For both compression and tensile strength the worst results correspond to 1:4, and the best performance appears to be between the ratios 1:1.75 and 1:2.  
It is known that sodium hydroxide (NaOH) is responsible for breaking down the internal connections of the binder at the beginning of the process. Afterwards, sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)) activates the necessary connections between the binder and the aggregates. Therefore, for a certain amount of metakaolin, an optimal balance between the quantities of NaOH and of Na\({}_{2}\)SiO\({}_{3}\) would be expected for the best performance of the geopolymer. To find out this optimal balance, each of the activators were separated and some graphs for the individual influence of each one of the activators were plotted for the tensile and the compressive strength. As a consequence, 4 graphs were produced (Figures 7-10). Figures 7 and 8 show the evolution of the tensile strength \(\sigma_{\mathrm{t}}\), with the amount of NaOH and Na\({}_{2}\)SiO\({}_{3}\), respectively.  
Figure 8: Influence of sodium silicate (Na2 SiO3) on the tensile strength.  
Figure 7: Influence of sodium hydroxide (NaOH) on the tensile strength.  
Figure 9: Influence of sodium hydroxide (NaOH) on the compressive strength.  
In Figure 8 the influence of this activator seems to have a different trend when compared with Figure 7. This is understandable because the sum of correspondent values of both figures gives the same constant value, the amount of activator (900 g). However, in Fig. 8 the values for Type A mixes are reasonably close to those of Type B mixes. Quantities of sodium silicate between 450 and 600 g seem to lead to values of \(\sigma_{\rm t}\) that are approximately the same.  
Figures 9 and 10 show the variation of the compressive strength, \(\sigma_{\rm c}\), with the amount of NaOH and Na\({}_{2}\)SiO\({}_{3}\), respectively. For this type of strength, and by combining both figures, it can be concluded that 390 g of NaOH and 514 g of Na\({}_{2}\)SiO\({}_{3}\) would be the optimal combination.  
Finally, the results seem to show that combination 1:2 (Base Mix) is the most adequate to obtain a material with good properties for structure applications. However, the activator composition of 300 g of NaOH, 450 g of Na\({}_{2}\)SiO\({}_{3}\) and 150 g of water is more economic than the Base Mix and does not seem to lead to mechanical characteristics of inferior quality.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Command and Data Management System
#### 3.5.5 Reporting and Command Flow  
All SKITH nodes within InnoCube communicate over radio using the Rodos middleware on top of the radio protocol. The middleware topics and publish/subscribe mechanism provide a structured means of onboard communication.  
Figure 14 depicts the flow of information between the apps on InnoCube's SKITH nodes as facilitated by the Rodos middleware: telecommands, depicted in orange, are sent from ground and received by the _Uplink_ app, which publishes them to a topic only the _Commander_ app on the OBC subscribes to.  
After verifying the signature of the telecommand, the _Commander_ publishes any telecommand to be executed immediately to the Telecommand topic. In case the _Commander_ receives a timed telecommand to be executed later, it publishes it to the TimedTelecommand topic, from where the _TimedCommander_ app retrieves and stores it. When the telecommand is due for execution, the _TimedCommander_ publishes it to the Telecommand topic. All apps subscribe to the Telecommand topic and try to execute every command received via this middleware topic that was addressed to them and is formed correctly. Malformed or unknown telecommands are dropped by the app and a non-critical anomaly (black arrow) is published to the anomaly topic. If a severe error occurs, apps can also publish critical anomalies. The _ModeManager_ on the OBC listens for anomalies and switches to safe mode depending on their severity. In save mode the _TimedCommander_ publishes a set of telecommands from its save mode list to the Telecommand topic. There are two kinds of telemetry, both depicted in green: the standard telemetry of a node comprises of a set of telemetry of all of its apps that published periodically by the _Housekeeper_ app to the StandardTelemetry topic. Extended telemetry can be enabled and published by telecommand for each app. Both the _DownlinkManager_ and the _TelemetryHistory_ app subscribe to the telemetry channels. The _DownlinkManager_ schedules the telemetry to be sent down and forwards it via a node local topic to the _Downlink_ app, which uses the transmitter to send it to ground. The _TelemetryHistory_ stores older telemetry, which the _DownlinkManager_ will try to schedule, at a lower priority, when the link is unused.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 3 Extreme Learning Machine-Based Melody Extraction
### Computational Complexity Analysis  
In this subsection, the computational complexity of the proposed method is analyzed. As commonly assumed, one addition, subtraction, multiplication, and division of two floating numbers are treated equally as one basic floating operation (Flops), and they contribute the same to the overall computation load [27].  
The computational load of the proposed method mainly originates from CQT and ELM training and testing. Let \(L_{l}\) and \(L_{t}\) be the down-sampled signal lengths of training and testing recordings, respectively.  
Assume that \(l\) and \(t\) are the numbers of training and testing samples, respectively. Suppose that there are \(m\) pitch classes and \(\overline{N}\) hidden neurons. CQT calculations of the training and testing recordings require Flops on the order of \(\mathcal{O}(Llog_{2}L_{l})\) and \(\mathcal{O}(Llog_{2}L_{t})\)[24], respectively. The computational cost of the ELM solution is \(3\overline{N}^{2}+2\overline{N}^{2}\overline{N}+2(l-1)\overline{N}^{2}+ \overline{N}^{3}+N^{2}l+\overline{N}l^{2}+\overline{N}lm+\overline{N}( \overline{N}-1)l+\overline{N}(l-1)l+\overline{N}(l-1)m\). As \(\overline{N}\gg 1\), \(l\gg 1\), \(l\gg m\), and \(l\gg\overline{N}\), the computation of ELM training is on the order of \(\mathcal{O}(\overline{N}^{3}l+\overline{N}^{2})\). Similarly, ELM testing is on the order of \(\mathcal{O}(\overline{N}^{3}t)\). Therefore, the computational cost of ELM training is on the order of \(\mathcal{O}(L_{l}log_{2}L_{l}+\overline{N}^{3}l+\overline{N}^{2})\), and that of ELM testing is on the order of \(\mathcal{O}(L_{t}log_{2}L_{t}+\overline{N}^{3}t)\).