esa-sceva/satcom-chunk-collection huggingface dataset:
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Payload Summary
#### 2.2.4 Deep-Space Platform  
Mission costs are affected more than anything by launch costs. For interplanetary nanospacecraft to become commonplace, launch costs need to be reduced. Developing smaller, denser, and stackable spacecraft could prove advantageous because the fairing volume of the rocket could be used more effectively. For example, SpaceX's Starlink satellites are designed to stack into SpaceX's Falcon 9's fairing most efficiently, showing that one could similarly stack hundreds of nanospacecraft for a launch to an interplanetary environment.  
One such mission concept using fleets of nanospacecraft called the multi-asteroid touring (MAT) [42] exploits this principle of launching tens of nanospacecraft using a single launch vehicle. In such a scenario, the resulting launch cost per satellite would drop significantly, and the scientific return could increase a hundredfold just by launching tens or hundreds of spacecraft.  
However, new systems must be developed and demonstrated before attempting interplanetary exploration using nanospacecraft alone. Miniature hardware implementations of integrated COTS components and systems must be developed and tested in the appropriate environment. Integrated reaction wheels (RWs) and a cold-gas propulsion (CGP) system for attitude and spin control, an integrated ST, autonomous optical navigation near a target of interest and in deep space, and communication solutions that would not rely solely on deep-space networks must be developed and tested. Moreover, various COTS components  
Figure 6: The compact 65 \(\times\) 41 mm\({}^{2}\) satellite module used for testing 15 materials in space, which consists of a printed circuit board (PCB) with coated materials and a cover plate with holes to expose tested materials to atomic oxygen in LEO.  
and systems require characterization with extended testing for a high-radiation environment. An adapted software solution needs to be created for these systems that could deal with adverse effects caused by radiation.  
While most of these tests and experiments are beyond the scope of the ESTCube-2 mission, the satellite will demonstrate the deep-space hardware platform, and software updates will allow the testing of new algorithms providing that the mission lifetime permits this.
# Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction
## 2 Physics Engine Review
### Ode  
Open Dynamics Engine (ODE) is an open-source physics engine that is integrated with several robot simulators, including Gazebo and CoppeliaSim. It has a built-in collision detection system that supports primitive-shape and mesh objects. When calculating the contact force, including friction, ODE uses a polyhedral approximation that relaxes Coulomb's friction cone constraint as a pyramid shape, which transforms the contact problem into a linear complementarity problem (LCP [18]). To obtain the solution of LCP, ODE provides the Dantzig solver [19], which is a type of direct solver that finds an exact solution, and the projected Gauss-Seidel (PGS [20]) solver, which is a type of iterative solver that finds an approximated solution iteratively and quickly. The default setting in CoppeliaSim is PGS. When calculating the contact forces, ODE ignores some contact points to improve the simulation speed, although its details are not fully disclosed. Here, the default maximal number of contact points to generate in CoppeliaSim was 64.The integrator implemented in ODE that determined the numerical accuracy of the simulation was a semi-implicit Euler integrator [21]. Additionally, ODE supports multibody dynamics in maximal coordinate representation, but it does not support generalized coordinate representation, which is more computationally efficient and accurate for heavily constrained systems such as robots with a large number of joints.
## 2 Experimental Implementation  
In our experiment, we implement two different methods to measure the expectation value of the polarization operator \(A=|H\rangle\!\langle H|-|V\rangle\!\langle V|\) (with \(H\) and \(V\) being the horizontal and vertical polarization, respectively): the aforementioned PM, able to estimate \(\langle A\rangle\) with a single reading of the measuring device, and a traditional PJ, in which the expectation value is extracted from the statistics obtained from repeated measurements on an ensemble of identically prepared particles. Both measurements can be described as a von Neumann protocol in which we couple the polarization of an incoming photon, prepared in the linearly polarized state \(|\psi\rangle=\cos(\theta)|H\rangle+\sin(\theta)|V\rangle\), with its transverse momentum \(\mathbf{P}\):  
\[U=\exp\!\left(-\frac{i}{\hbar}g\Pi_{H}\otimes\mathbf{P}\right) \tag{6}\]  
where \(\Pi_{H}=|H\rangle\!\langle H|\) is the projector onto the \(H\) polarization. This interaction causes a shift of the horizontally polarized component of the wavefunction along an axis orthogonal to the photon propagation direction. This is mathematically equivalent to a von Neumann coupling of strength \(g/2\) between the polarization \(A\) and the momentum \(\mathbf{P}\), so from here we will consider a rescaling of our system in order to describe the latter scenario.  
The initial spatial wavefunction of the photon is described by a normal distribution:  
\[\phi_{0}(x)=\langle x|\phi_{0}\rangle=\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\! \left(-\frac{(x-x_{0})^{2}}{4\sigma^{2}}\right), \tag{7}\]  
centered at \(x_{0}\) and with standard deviation \(\sigma\).  
For strong interactions (i.e., \(g\gg 1\)), the two polarization components will be completely separated (Figure 1a). Hence, the expectation value can be evaluated as  
\[\langle A\rangle=\frac{N_{H}-N_{V}}{N_{H}+N_{V}} \tag{8}\]  
where \(N_{H(V)}\) is the number of count events obtained for the polarization \(H(V)\). This is the case of projective measurement [81].  
PMs, instead, in our scheme, consist of a series of weak von Neumann couplings (\(g\ll 1\)) alternating with a protection mechanism, i.e., a projection \(\Pi_{\psi}=|\psi\rangle\!\langle\psi|\) onto the initial state \(|\psi\rangle\). In this case, the photons will fall in a region not corresponding to any eigenvalue of our polarization observable \(A\), but whose position is directly proportional to its expectation value (Figure 1b). Thus, the expectation value of the polarization can be extracted by the formula:  
\[\langle A\rangle=\frac{x-x_{0}}{a} \tag{9}\]  
with \(x_{0}=\frac{x_{H}+x_{V}}{2}\) and \(a=\frac{x_{H}-x_{V}}{2}\), being \(x_{H}\) (\(x_{V}\)) the center of the horizontally (vertically) polarized photon distribution in the PJ framework.
## 4 Tree-Based Protocols  
One of the main features of tree-based protocols is that they are deterministic, since ideally, they are designed to identify the whole set of tags in the interrogation area [36; 37; 38; 39; 40]. These protocols have simple design tags and work very well with a uniform set of tags.  
Tree-based protocols usually work with a muting capability since they need the identified tags to remain quiet after their identification. These protocols usually work using queries, which are broadcast commands transmitted by a reader to require the tags to respond. If a tag's ID does not match the query, the reader command is rejected.  
First, the most popular tree-based protocols are presented here. Then, a selected group of protocols will be presented with the common feature of the use of Manchester coding. The first group includes: Query Tree (QT), Query Window Tree (QwT), and Smart Trend Transversal (STT). Another group consists of tree-based protocols that use Manchester coding: Binary Search (BS), Collision Tree (CT), Optimal Query Tracking Tree (OQTT), and Collision Window Tree (CwT).
## 3 Results and Discussion  
### FTIR Analysis of EB-Irradiated PLC Nanocomposite  
An FTIR analysis was used to determine the structural levels of the PLC nanocomposite after treatment with the various EB irradiation doses, as shown in Figure 1. The Unirr  
Figure 1: Systematic depiction of the PLC nanocomposite synthesis and representative EB irradiation effect.  
PLC nanocomposite exhibited characteristic peaks at 830, 1188, 1341, 1506, and 1621 cm\({}^{-1}\), and all other peaks were due to the presence of dopants in the host polymer. The bands at 1341 cm\({}^{-1}\) were assigned to C-N stretching and at 1621 cm\({}^{-1}\) carbonyl group or C-C ring stretching vibrations. The band at 1188 cm\({}^{-1}\) is associated with the stretching of the aromatic ring, at 830 cm\({}^{-1}\) the C-H-plane bending vibration, and the peak at 1506 corresponds to the quinoid structure present in the main chain of the host polymer [16]. When the PLC nanocomposite was subjected to EB irradiation, the C-H-plane bending at 830 cm\({}^{-1}\) shifted to 834 cm\({}^{-1}\), and the intensity of all other peaks significantly increased with increases in the EB irradiation dose to 40, 80, and 120 kGy. This indicates that EB radiation may affect the molecular structure or oxidation levels through chain scission and cross-linking processes at higher EB energy [21; 28], which means that the interaction improved, confirming the irradiation's effects on the structural properties.
## 5 Earth System Models
### Nitrogen  
An obvious and crucial component for capturing N deposition impacts on plants is the treatment of N, but early versions ESMs only considered the C cycle and neglected the N cycle completely. Carbon-only models miss a significant N deficit and therefore overestimate C sequestration by ecosystems under climate change [102; 103]. Many of the latest versions of ESMs now include N (and a rare few include phosphorus). However, because N is often a limiting nutrient in ecosystems, the focus is on plant response under limited N conditions and on the effects of the progressive N limitation [104] that is expected under elevated CO2. Although some models offer prescribed N, more sophisticated ones employ an N pool (bulk or speciated) that is available to both plants and decomposers. Nitrogen additions come from sources including lightning, deposition, mineralization, and biological fixation. Losses are from plant uptake, immobilization, leaching, and
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 3 Results
### Model Performance Evaluation
#### 3.4.3 Parameter Sensitivity  
To evaluate the impact of different hyper-parameters on the MD-Autoformer's performance, we focused on the number of channels divided in the MSFFN (denoted as \(n\)) and conducted experiments across four datasets. The experimental results are shown in Table 9.  
For small-scale datasets like Apparel Sales and ILI, increasing the number of channel divisions in the MSFFN allows the model to learn more inter-channel interaction features, thereby improving performance. However, for large-scale datasets such as Weather and Exchange, excessive channel division may lead to the loss of interaction information between original hidden dimensions, resulting in decreased model performance.  
In summary, the optimal number of channel divisions in the MSFFN varies by dataset scale. Small datasets benefit from more channel divisions to capture inter-channel interactions, while large datasets require a balance to avoid losing critical interaction information.
# Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network
## 2 Methods
### Numerical Method and Data Preparation
#### 2.3.3 Data Preparation and Processing  
Training of the neural network generally requires a pre-existed dataset with a sufficiently large number of known pairs of input and output. In this paper, as output (pressure and velocity fields) in the dataset is the CFD result of the given input, a number of cases are built up by changing parameters of inputs in three dimensions: the airfoil geometry, AOA, and Reynolds number. Change of the airfoil geometry is conducted by modifying the maximum camber and its position along the chord, during which maximum thickness is kept at a constant value, as usually required by the aerodynamic design of the airfoil. In this manner, 82 airfoil profiles are generated based on the original airfoil NACA0012, which is given in Figure 8, along with 30 examples of modified profiles. Then the SDF representations of these airfoil geometries are obtained by the method given in Section 2.3.2. For each parameterized airfoil, 7 AOAs and 3 Reynolds numbers shall be considered, from the array of \([0^{\circ},1^{\circ},2^{\circ},3^{\circ},4^{\circ},5^{\circ},6^{\circ}]\) and \([3\times 10^{6},5\times 10^{6},7\times 10^{6}]\), respectively. In practice, the value of Reynold number, after being divided by \(1\times 10^{6}\), is merged into SDF to capture the enough mapping from the same airfoil geometry. As a result, the CFD dataset used in this work contains 1722 (\(82\times 8\times 3\)) pairs of input and corresponding CFD output.  
Figure 7: The SDF contour for NACA8412 airfoil on a 208 \(\times\) 340 Cartesian grid at AOA = \(1^{\circ}\). The airfoil boundary is the dashed line in white color.  
As the majority of the flow variations take place within a close region near the airfoil surface, we focus the training and prediction process of CNN-DCNN on a selected box area as the inference region to reduce memory usage, instead of working with the entire CFD domain covered by computational grid. Concentrated on the near wall region, the rectangular box area ranges from \(-0.2c\) to \(1.5c\) in \(x\) direction, and \(-0.52c\) to \(0.52c\) in \(y\) direction, where the coordinates \(x\) and \(y\) are defined in the way as those in Figure 8. The CFD data of pressure and velocity is then extracted from the inference region and interpolated into a matrix with a size of \(208\times 340\). Thus, stacking SDF and the flow field data, the training input of the CNN-DCNN model is designed as  
\[Input=[S,P,U] \tag{24}\]  
where \(S\), \(P\), and \(U\) are all normalized to promote the training efficiency of the network model. Moreover, it should be pointed out that \(S\), \(P\), and \(U\) are all masked with a binary matrix which is filled with 0 and 1 for points inside and outside of the given airfoil, respectively, since there is no need to pay attention to the inside of the airfoil, where there is no flow field.
# Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes
###### Abstract  
Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates. When using an automated volumetric analysis, brain tissue segmentation is necessary, preceded by brain extraction (BE) to remove non-brain tissue. BE remains challenging in neonatal brain MRI, and despite the existence of several methods, manual segmentation is still considered the gold standard. Therefore, the purpose of this study was to assess different BE methods in the MRI of preterm neonates and their effects on the estimation of intracranial volumes (ICVs). This study included twenty-two premature neonates (mean gestational age \(\pm\) standard deviation: 28.4 \(\pm\) 2.1 weeks) with MRI brain scans acquired at term, without detectable lesions or congenital conditions. Manual segmentation was performed for T2-weighted scans to establish reference brain masks. Four automated BE methods were used: Brain Extraction Tool (BET2); Simple Watershed Scalping (SWS); HD Brain Extraction Tool (HD-BET); and SynthStrip. Regarding segmentation metrics, HD-BET outperformed the other methods with median improvements of +0.031 (BET2), +0.002 (SWS), and +0.011 (SynthStrip) points for the dice coefficient; and \(-\)0.786 (BET2), -0.055 (SWS), and \(-\)0.124 (SynthStrip) mm for the mean surface distance. Regarding ICVs, SWS and HD-BET provided acceptable levels of agreement with manual segmentation, with mean differences of \(-\)1.42% and 2.59%, respectively.  
brain extraction; intracranial volume; neonatal MRI; segmentation; skull stripping +
Footnote †: journal: Applied  
Academic Editors [PERSON] and [PERSON]  
1
# 1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary
###### Abstract
However, because the contact force is a 1-dimensional signal, the previously mentioned techniques cannot be applied directly. Hence, the related 1D CNN approaches should be explored. The effectiveness of 1D CNN models has been demonstrated and they have been widely used across a range of applications [34]. However, considering the problem of using 1D CNN to detect and localise the fault simultaneously, it has been found that a conventional machine learning technique is not efficient [35]. Hence, it is suggested to integrate separate 1D CNNs for fault detection and localisation tasks. Considering the above discussion, the objective of this paper is to detect and localise defective droppers in a railway catenary through the pantograph-catenary contact force. The FE catenary model will be developed and used to generate the required contact force data, with and without faults. The impact of the damage to each dropper on the contact behaviour can be analysed and categorised. Noise will be added to the data to imitate real-world disturbance. Once the data are available, two 1D CNNs are integrated (integrated 1D CNN) to detect the degree of damage and location of the defective dropper as independent tasks. The main contributions of this paper are as follows. 1. The impact of dropper defects on railway pantograph-catenary interaction is analysed, and the impact of varying degrees of damage to each dropper is categorised. 2. An integrated 1D CNN is used to improve classification performance compared with a conventional 1D CNN. Approaches including randomly searching the optimal hyper-parameters and K-fold cross-validation are considered to prevent overfitting and ensure model performance regardless of the training data subset selected. 3. The ability of the 1D CNN-based method to evaluate the degree of damage to catenary droppers and locate the position of defective droppers within a catenary system is demonstrated.
## 3 The Proposed Architecture
### Information Manager
After that, the requests become jobs, and request nodes start communicating with the resource nodes to receive the corresponding services. A MCI server may have more than one Information Manager, depending on how large its serving area is and how many requests the system may service simultaneously. Each Information Manager at any moment has its own user nodes, _i.e._, resource nodes and request nodes. But the information of all the resource nodes under an MCI server, e.g., Server A, is collected in Server A's Data Center. A system may consist of several MCI servers. Each has its own Data Center. All the Data Centers are organized as a logical tree in which a lower level Data Center periodically reports its performance table to its immediate parent MCI server, e.g., MCI server P, through Front Unit (see Figure 2). MCI server P collects all its children's performance tables and integrates them with the performance table of its own as P's performance table. When an Information Manager, e.g., Information Manager A (the Information Manager of server A), has insufficient resource nodes to serve its requests, it allocates resource nodes belonging to its sibling Information Managers to serve A's user requests. The data transmitted among Data Center, Information Manager and Resource Broker are all delivered through a backbone network so that the delivery delays are short enough and can be ignored.
## 5 Numerical Results
Figure 3: Convergence condition for proposed GA.  
reconstructing the fronthaul virtual topology and adjusting the operation modes of the active units, energy efficiency of the fronthaul can be improved. Figure 5 presents the transmission efficiency of fronthaul during an operational day with different joint optimization methods. In C-RAN with fixed topology connection relationship, all wavelength resources are occupied in forwarding CPRI signals regardless of load variation, leading to a significant bandwidth waste. Meanwhile, the corresponding transceivers are active all the time, which brings in additional energy consumption. Based on Equation (3), the transmission efficiency \(\eta\) is equal to zero in C-RAN with fixed topology, which is regarded as the benchmark. Moreover, global optimum can always be achieved by INLP method with exponential complexity. Wavelength resources are dynamically allocated based on the aggregated bandwidth requirements from each active ONU. The maximum transmission efficiency increases by about 87% with INLP method in off-peak periods. Whereas in peak time periods, the performance of INLP method is not obvious, since nearly all the wavelength resources are used to satisfy the bandwidth requirements. In addition, the proposed efficient GA achieves closely transmission efficiency enhancement as the INLP method, with linear complexity. Specifically, suboptimal solutions are obtained by adaptive GA, when traffic is decreasing or increasing during an operational day. This is because that there is a tradeoff between evolution times and optimization performance. Figure 6 presents the load balancing performance of TWDM-PON enabled reconfigurable fronthaul, achieved by different approaches. The load balancing \(LB_{fronthaul}\) is defined in Equation (4). As shown in Figure 2, it is observed that traffic changes over time during a day. In C-RAN with fixed wavelength resource allocation scheme, load imbalance is obvious when traffic is decreasing  
Figure 4: Energy consumptions of fronthaul with different methods. Figure 5: Transmission efficiency of fronthaul with different methods. or increasing. Uneven requirements among RRUs results in load imbalance in fronthaul links. Meanwhile, in peak periods, nearly all the wavelength resources are on heavy load, and almost all the wavelengths are on light load in idle periods. Thus, the load imbalance is not clear in peak/idle periods. However, with the proposed INLP method and adaptive GA, the load imbalance of TWDM-PON enabled reconfigurable fronthaul is optimized independent of traffic variation during a day. This is because that the fronthaul network is dynamically reconstructed based on the amount of traffic served by each wavelength. This means that traffic is centralized in less wavelength resources, leading to a load balancing in the wavelength dimensioning.