esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Defined Networks in Wireless Sensor Architectures
## 5 Wireless Cellular Networks
### Software Defined Networks in Wireless Cellular Networks
* Regarding solutions that use programmable RAN and CN, the resource allocation challenge is present both in RAN and in the CN. SoftAir [79] is a wireless network architecture for 5G based on the SDN model. Its control plane is comprised of two components: network management tools and customized applications that provide services to network operators. For its part, the data plane is based on CN switches and base stations that use Software Defined Radio Access Networks. The programmable CN offers efficient network virtualization and traffic classification. Concretely, it uses a three-way virtualization of the network that guarantees effective and non-conflicting allocation of network resources among network operators. Traffic classification is divided into local classification (performed by base stations) and global classification (performed by the controller). Another challenge is the mobility management that SoftAir performs in two steps: location management and QoS handoff-rerouting. Handoff-rerouting is performed exploiting the global network view that the centralized controller gets. The union between optimized placement and the mobility pattern of users establishes an optimal control traffic path from data plane devices through the controller. * A variation of Softair architecture is MyNet [80], a hierarchical deployment of controllers for 5G [81] networks that are responsible for each part of the network in order to reduce the control traffic flow. [PERSON] et al. [82] also propose a hierarchical architecture titled CMaaS that manifests a four-layer controller hierarchy. The bottom layer, called a UE controller, is responsible for managing the available radio access technology selection for a user by local network status. The next layer is called a BS controller that manages the time-sensitive radio resource management and scheduling with a local network view. Continuing with the layered architecture, the RAN controller controls the set of base stations obtaining a regional view. The highest layer, called a Network controller, offers a global network view and manages services such as QoS, mobility management and routing. Control decisions are distributed from lower layers to upper layers. Parallel, upper layers get network information from controllers to gather a global state view and make control decisions. Table 3 gathers the SDN-based solutions for WCN and their features. The four right-side columns describe the properties of each solution. A column named OpenFlow presents if the proposed model is compatible with OpenFlow protocol. Next, the following column is called Scalability, which determines if the proposal is able to be scalable in terms of network size. A Data Plane column shows which data plane component is being improved in the proposal (RAN, CN or both). Finally, the last column stated as Resource allocation indicates if the proposed solution is able to allocate network resources.
## 4 Improved BWOA for Solving
### Design of Fitness Function  
When using BWOA to solve the fuzzy flexible job-shop scheduling problem, the objective function is to minimize the fuzzy maximum completion time. Fuzzy maximum completion time refers to the shortest, most likely, and longest time required to complete all pipelines, taking into account the uncertainty of pipeline processing time and machine allocation. This goal reflects the overall efficiency of the scheduling plan.  
In this context, the design of the fitness function needs to comprehensively consider the processing time of the pipeline, machine allocation, and possible delays. Specifically, the fitness value of each candidate solution (scheduling scheme) is determined by its corresponding fuzzy maximum completion time. First, it is necessary to perform fuzzy calculations on the start and end times of each pipeline. Then, by aggregating the fuzzy completion times of all pipelines, the fuzzy maximum completion time of the entire workshop is calculated.  
The pipeline-processing workshop solved in this chapter aims to minimize the fuzzy maximum completion time as the optimization objective. The optimization objective  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Workpiece**} & \multirow{2}{*}{**Working Procedure**} & \multicolumn{4}{c}{**Machine**} \\ \cline{3-5}  & & \(M_{1}\) & \(M_{2}\) & \(M_{3}\) \\ \hline \multirow{2}{*}{Workpiece1} & \(O_{1,1}\) & (5,6,8) & (4,5,7) & (3,4,6) \\  & \(O_{1,2}\) & (5,7,10) & (6,8,11) & (4,6,10) \\ \hline \multirow{2}{*}{Workpiece2} & \(O_{2,1}\) & (5,9,12) & (6,9,11) & (7,10,11) \\  & \(O_{2,2}\) & (7,11,15) & (9,11,14) & (6,10,13) \\ \hline \multirow{2}{*}{Workpiece3} & \(O_{3,1}\) & (6,9,12) & (7,10,13) & (9,12,16) \\  & \(O_{3,2}\) & (10,14,17) & (8,11,15) & (6,9,13) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Process and fuzzy processing time.  
\(f=\min\Bigl{\{}\widetilde{C}_{\max}\Bigr{\}}\) can be obtained from Equation (14), and the fitness function can be set as follows:  
\[fit=\frac{1}{f}. \tag{14}\]  
In the iteration process of BWOA, the algorithm generates a new scheduling scheme by simulating the reproductive behavior of black widow spiders (including mate selection, mating, and mutation), and it evaluates it using the above fitness function. The higher the fitness of the scheme (i.e., the shorter the fuzzy maximum completion time), the higher the probability of it being selected as the next-generation population. This selection process ensures that the algorithm gradually tends to discover efficient scheduling solutions that minimize the fuzzy maximum completion time. Through this method, BWOA can effectively handle the uncertainty and fuzziness in job scheduling while finding reasonable and effective scheduling solutions. This is of great importance for improving the operational efficiency and flexibility of the workshop, especially when facing complex and dynamic production environments.
# GAN Training Acceleration Using Frechet Descriptor-Based Coreset
## 2 Related Works
### Coreset  
In Coreset, a subset of the data is to be optimally selected such that a model trained on the selected subset will perform as closely as possible to a model trained on the entire dataset. [PERSON] et al. [27] constructed Coreset to accelerate the computation of K-means and K-median clustering algorithms. [PERSON] et al. [28] formulated the active learning problem as a Coreset selection problem where only core samples are to be labeled to improve the performance of Convolutional Neural Network (CNN) models. Coreset has also been adopted in GAN training. For example, [PERSON] et al. [16] developed a Coreset sampling approach to speed up GAN training. A pre-trained Inception classifier was first implemented to extract the Inception embedding from the whole dataset based on which pairwise Euclidean distances were calculated. The Coreset is then derived for training in each iteration through comparing the distance between data samples. While this approach accelerates the GAN training to some extent, it is noted that the Coreset needs to be generated within each iteration of the training, in other words, within the training loop. From iteration to iteration, it is highly likely the same images will be selected for the Coreset which is computationally inefficient, especially considering the long computational time required to generate the Inception embedding. Additionally, the authors of [16] focused on the GAN model, taking a batch of images as Coreset for training, while CycleGANs utilize the Instance Normalization [29] as its normalization method for each layer, and thus, the training of CycleGAN often needs one image instead of a batch of images per iteration. Recognizing the potentials of Coreset and issues from deriving Coreset within the training loop as in [16], we argue that the training of GAN, in this study, CycleGANs, can be greatly accelerated if the Coreset selection is taken out of the training loop. We propose to implement Coreset as a pre-training step instead of a within-training process, and in this case, the Euclidian distance calculated from Inception embedding (derived during the GAN training) for Coreset construction in [16] does not apply.
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
## 3 Stability Analysis
### Impact of Inertial Loop Gain Variation  
The gain of the HPF-based controller is varied from 0 to approximately infinity to examine the controller's stability. Figure 10 depicts the stable operation of the proposed controller over a wide range of gain values. By increasing the inertia gain of the controller, the system produces an overdamped frequency response with a very high settling time for values of gain values more than 10 pu. From this analysis, the optimum value of the inertia gain is estimated as 3.5 pu, where the damping ratio of the dominant eigenvalues is maximized.
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
### Theory  
#### 1.2.1 Separation of the System Response Matrix (SRM) into Axial and Transaxial Components  
The line-of-response (LOR) of a given coincidence event is defined as the line connecting the crystals where the detection took place [27]. These LORs are usually organized in 3D-sinograms with coordinates (\(\rho\),\(\rho\),\(\rho\)1,\(\rho\)2) (see Figure 1). Note that the axial position of the LOR (z) and its axial slope angle (\(\Delta\)) are usually used instead of z1, z2 [9; 28; 29]. For convenience and for easier explanation, in this work, we use the notation z1, z2.  
The SRM contains the probability that a decay at a position (x, y, z) in the image space is detected in a specific LOR with coordinates (\(\rho\),\(\theta\),\(\rho\),\(z\)1,\(z\)2) in the projection space [9; 14; 30; 31]. The SRM in 3D acquisitions is usually too large (of the order of terabytes) to be kept in the memory of current computers. In most iterative methods, these huge matrices are handled with sparse methods [32] or the storage requirement is reduced by exploiting symmetries [9]. However, the computation of the PINV of the SRM does require the use of the whole matrix, because even when the SRM is sparse, the pseudoinverse may not be [33; 34]. To solve this issue, we decomposed the SRM into axial A\({}_{\mathrm{z}}\) and transaxial A\({}_{\mathrm{x},\mathrm{y}}\) components:  
\[A_{x,y,z}\ (\rho_{\mathrm{}}\theta\text{,}\rho_{z1}\text{,}z_{2}\text{,}x\text{,}y \text{,}z)\cong[A_{x,y}\ (\rho_{\mathrm{}}\theta\text{,}x\text{,}y)\text{;}A_{z}\ (z_{1}\text{,}z_{2}\text{,}z)], \tag{1}\]  
The axial matrix can be used for data rebinning and the transaxial matrix for 2D reconstruction of the rebinned 2D slices.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 4 State Estimation
### Link Model Estimation
#### 4.1.2 Accelerometer Model  
Similarly to the gyroscope test, the orientation of Link 1 remained static throughout the execution of all movements for the test using only accelerometers. This is once again due to the lack of an IMU in Link 1. However, because the gravity acceleration vector was approximately parallel to the axis of rotation of Joint 1, the angle of this joint has no relation with any component of the available rotation vectors. Much like the previous test, the system is not fully observable. Nevertheless, the remaining angles can be estimated using the information from the accelerometers.  
Figure 8 shows the evolution of the components of Rotation Vector 2 along with the baseline for the angles of joints 1 and 2. The baseline for Joint 1 (\(\theta_{1b}\)) is only included in the graph to show that it is not related to the rotation vector.  
As expected, the angle of Joint 2 is coincident with \(\theta_{2z}\). Furthermore, there are peaks in acceleration during the transitions, which influence the rotation vector in a visible way.  
Figure 8: Evolution of Rotation Vector 2 when testing the link model using only accelerometer measurements.  
Figure 7: Evolution of Rotation Vector 3 when testing the link model using only gyroscope measurements.  
The accelerometer measurements even justify the encoder errors that were found using the gyroscope test. Once again, there is an overshoot of \(\theta_{2x}\).  
An unexpected detail is the small value for the \(\theta_{2x}\) and \(\theta_{2y}\) variables that starts being noticed at around 5 s. This error is probably associated with imperfections in the initial angle of Joint 3. The initial gravity acceleration vector, and therefore the one expected in the inertial frame, is calculated as the average of the first 100 measurements of the accelerometers of IMU B. If \(\theta_{3}\) is slightly different from zero, then the initial gravity vector for Link 2 will be inconsistent with the one from Link 3. Therefore, the orientation of Link 2 changes slightly to match the initial acceleration vector.  
The graph in Figure 9 displays the coherence between \(\theta_{3z}\) and \(\theta_{3b}\). It also demonstrates that the initial error of \(\theta_{3}\) does not affect the orientation of Link 3, since the slight orientation change of Link 2 produces errors in \(\theta_{2x}\) and \(\theta_{2y}\) that are exact opposites of \(\theta_{3x}\) and \(\theta_{3y}\). The peaks of acceleration during the transitions are also more noticeable in this link as a consequence of it being further from the base.
# Time-Frequency Analysis of Daily Activities for Fall Detection
## 3 Results
### Characteristics of Low-Rigor Activities  
Low-rigor activities, such as standing, walking, sitting down, standing up, lying down, sitting up, and walking up steps, had peak acceleration magnitudes between 1 to 2.1 g, whereas high-rigor activities such as jumping, running, and falls exhibited peak acceleration magnitudes from 2-5.4 g. As expected, standing resulted in 1 g (downward direction) due to gravity. Walking resulted in peak acceleration between 1.5-1.8 g and peak frequency between 2.5-3.5 Hz. Interestingly, walking up/down stairs resulted in higher  
Figure 3: Sample acceleration waveform for various daily activities. Peak accelerations for rigorous activities (i.e., run, jump, falls) overlap. Red lines indicate critical upper fall (UFT) and lower fall thresholds (LFT) typically used for fall detection. Peak accelerations for low-rigor activities such as standing, walking, sitting down/standing up from a chair are well below UFT and LFT thresholds.  
peak acceleration (1.8-2.1 g) and lower peak frequencies (1.5-2.5 Hz), likely due to more careful deliberate motions compared to walking.  
Biomechanically, sitting-from-stand and stand-from-sit are seemingly opposing activities, but are a result of different body motions. When sitting down, the body initially moves downward with the gravitational acceleration vector resulting in the acceleration signal decreasing below 1 g. In the second phase, the acceleration vector acts in the opposite direction to the gravitational acceleration vector and the acceleration signal returns to 1 g when the subject is seated. A stand-from-sit motion produces the reverse observation with an initial increase in acceleration followed by a decrease to 1 g. In the frequency domain, peak PSD was observed between 1-2 Hz.  
Similarly, lying down and sitting-up from a lie produces low peak accelerations (1.4-1.8 g) and a wider range of peak frequencies (0.5-3 Hz) across subjects.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 5 Clinical Applications of Image Fusion in Liver Cancer Diagnosis and Treatment
### PET/CT & PET/MRI
Simultaneously, PET/MRI in the diagnosis of primary liver cancer can clearly distinguish whether the portal and hepatic vein systems display any important pathological features of tumor thrombus involvement, which can provide a foundation for clinical decisions of subsequent treatment. For the fusion of PET-MRI for clinical applications, the wavelet transformation-based method [66; 73; 74; 130], IHS-PCA [31] and deep learning methods [151; 154] are generally used. On the other hand, wavelet transformation-based methods [52; 57; 168; 169] and deep learning [147] are generally applied for the fusion of PET-CT. Here, we show an example in Figure 5, which compares the accuracy of the fused image of PET/MRI and single modal MRI in the correct identification of a patient with liver lesions [183]. The data from this study indicate that the fusion of PET/MRI can increase the identification rate of the liver malignant lesion from 94.4% to 100%.
## 3 Results and Discussion
### When the Aircraft's Load Acts on the Slab Edge  
Figure 8 illustrates how the edge of the neighboring slab experiences both bending tensile stress and compressive stress when a wheel load is applied to it. These stresses are present at both the edge of the slab with a dowel bar and the center position of the slab with a dowel bar. When there is a dowel bar at the wheel load, the vertical stress increases by 14.29% to 0.29 MPa from 0.25 MPa when there is no vertical stress. At the edge of the longitudinal slab, only a minor bending tensile stress exists. When there is no dowel bar, the distribution of the bending tensile stress at the transverse wheel load of the pavement slab is similar. When there is a dowel bar present, the vertical stress increases to around 2.10 MPa from 1.13 MPa when there is no vertical stress, and the bending tensile stress rises by 85.84%.  
When the aircraft wheel load operates on the edge of the adjacent slab, the maximum vertical displacement of the slab is approximately 1.92 mm, with or without a dowel bar, and the maximum vertical displacement of the slab edge is 0.42 mm. Along the transverse direction of the slab, the vertical displacement from the slab's center to its edge reduces by 78.13%. The vertical displacement along the longitudinal axis of the slab diminishes parabolically to zero, and the upward warping deformation occurs 2.65 meters away, on the opposite side of the slab. As can be seen from Figure 9, the positioning of the dowel bar has a minimal impact on the stress and deformation of the slab; however, on the sides of the unloaded slab, the vertical displacement increases by 0.52 mm when a dowel bar is present, and the deflection load transfer coefficient is 27.08%.  
Figure 7: Displacement distribution of the aircraft’s wheel load acting on the pavement slab.
###### Abstract  
The Space-Air-Ground Integrated Network (SAGIN) has emerged as a core architecture for future intelligent communication due to its wide-area coverage and dynamic heterogeneous characteristics. However, its high latency, dynamic topology, and privacy-security challenges severely constrain the application of Federated Learning (FL). This paper proposes a Privacy-Preserving Federated Learning framework for SAGIN (PPFL-SAGIN), which for the first time integrates differential privacy, adaptive transfer learning, and bi-level reinforcement learning to systematically address data heterogeneity, device dynamics, and privacy leakage in SAGINs. Specifically, (1) an adaptive knowledge-sharing mechanism based on transfer learning is designed to balance device heterogeneity and data distribution divergence through dynamic weighting factors; (2) a bi-level reinforcement learning device selection strategy is proposed, combining meta-learning and hierarchical attention mechanisms to optimize global-local decision-making and enhance model convergence efficiency; (3) dynamic privacy budget allocation and robust aggregation algorithms are introduced to reduce communication overhead while ensuring privacy. Finally, experimental evaluations validate the proposed method. Results demonstrate that PPFL-SAGIN significantly outperforms baseline solutions such as FedAvg, FedAsync, and FedAsyncISL in terms of model accuracy, convergence speed, and privacy protection strength, verifying its effectiveness in addressing privacy preservation, device selection, and global aggregation in SAGINs.  
Keywords:space-air-ground integrated network; privacy preservation; federated learning; bi-level reinforcement learning; transfer learning; hierarchical attention mechanism; meta-learning +
Footnote †: journal:
## Soil Moisture & Snow Properties Determination with GNSS  
in Alpine Environments: Challenges, Status, and Perspectives  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1, [PERSON] 3, [PERSON] 4 and [PERSON] 1**  
1 Electronics and Signal Processing Laboratory (ESPLAB), Ecole Polytechnique Federale de Lausanne (EPFL), CH-2000 Neuchatel, Switzerland; E-Mails: [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (P.-A.F.)  
2 Institute for Snow and Avalanche Research (SLF), Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), CH-7260 Davos, Switzerland; E-Mail: [EMAIL_ADDRESS]  
3 Geodetic Engineering Laboratory (TOPO), EPFL, CH-1015 Lausanne, Switzerland; E-Mail: [EMAIL_ADDRESS]  
4 Environmental Fluid Mechanics Laboratory (EFLUM), EPFL, CH-1015 Lausanne, Switzerland; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +41-32-718-3424; Fax: +41-32-718-3402.  
_Received: 8 May 2013; in revised form: 15 July 2013 / Accepted: 16 July 2013 / Published: 19 July 2013_  
**Abstract:** Moisture content in the soil and snow in the alpine environment is an important factor, not only for environmentally oriented research, but also for decision making in agriculture and hazard management. Current observation techniques quantifying soil moisture or characterizing a snow pack often require dedicated instrumentation that measures either at point scale or at very large (satellite pixel) scale. Given the heterogeneity of both snow cover and soil moisture in alpine terrain, observations of the spatial distribution of moisture and snow-cover are lacking at spatial scales relevant for alpine hydrometeorology. This paper provides an overview of the challenges and status of the determination of soil moisture and snow properties in alpine environments. Current measurement techniques and newly proposed ones, based on the reception of reflected Global Navigation Satellite Signals (_i.e._, GNSS Reflectometry or GNSS-R), or the use of laser scanning are reviewed, and the perspectives offered by these new techniques to fill the current gap in the instrumentation level are discussed. Some key enabling technologiesincluding the availability of modernized GNSS signals and GNSS array beamforming techniques are also considered and discussed.  
**Keywords:** snow height; snow water equivalent; soil moisture; remote sensing; reflectometry (GNSS-R); laser scanning