esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Comparison with Existing Algorithms  
We compared the newly proposed ADN and ADN_LS with two existing algorithms: ALNS [25] and DRL [39]. The average results for each task size for total profit and runtime are listed in Table 7, with the best results for each task size shown in bold font. Evidently, the ADN_LS obtained the best results in all scenarios. Figure 9 shows the trends of the results, which varied with the number of tasks. It can be observed from Figure 9 that when the number of tasks in the scenarios reached 500, ADN and ALNS performed significantly better than DRL. A significance test was performed for the pairs of each algorithm and the other three algorithms. The detailed significance test results are shown in Table 8. Table 8 indicates that ADN_LS significantly outperforms the other three algorithms, and ADN outperforms ALNS and DRL. As shown in Figure 9, the runtime of ALNS increases sharply as the number of tasks increases. In contrast, a slight increase in the runtime of ADN and ADN_LS indicates that the proposed method has good scalability and the potential to efficiently address larger-scale problems.  
ALNS consumed significantly more running time than the methods based on DRL (Table 7). This is because the efficiency of the trained decision network was equivalent to that of a heuristic rule. ALNS also exhibited considerable time efficiency in the 100-task scenarios because all tasks were included in the initial solution, and thus, the algorithm terminated before the neighborhood search. ADN_LS obtained better solutions by consuming more time than ADN. This is because the local search process is an extra procedure that optimizes the observation time of scheduled tasks so that unscheduled tasks can be inserted into the observation plan generated by the ADN. Although the optimization of observation time consumes more time than the ADN, it also exhibits considerable time efficiency.  
Figure 10a,b present box diagrams illustrating the profit acquisition rates of the algorithms in various scenarios. The results were divided into two parts to prevent the maximum and minimum values on the \(y\)-axis from significantly differing. Due to the oversubscription scenario, the profit acquisition rate decreases as the number of tasks increases. It can be observed from the figure that the newly proposed ADN and ADN_LS achieved higher median values than the other two algorithms. Table 9 shows the maximum value, minimum value, and the range of profit acquisition rates of the
# A Space-Interconnection Algorithm for Satellite Constellation Based on Spatial Grid Model
## 2 Methods
### Grid Network Topology Model and Routing Algorithm
#### 2.3.1 Grid Network Topology Model  
First, a basic model of space virtualization routing is established for low-orbit satellite constellation. The main idea is to divide the surface of the earth into different areas, namely the grid division of the aerospace grid model. Each area is assigned a virtual node (VN), which is marked as the grid coordinate code. Thereafter, OneWeb is taken as an example to allocate virtual nodes (Figure 6a).  
In the grid network topology model, when the satellite is in a grid cell, the network node related to the cell is held by the satellite. When the satellite leaves the cell, it also loses its holding relationship with the cell. When the satellite enters the area from a neighboring area, it enters from a neighboring cell, and then leaves the cell to go to another neighboring cell. When entering a certain grid routing, the virtual node will identify the six directions in relation to the grid cell (up, down, left, right, front, and back), and the global coordinate system uses the six directions, comprising top, bottom, west, east, north, and south, which correspond to the six neighbors of the cell (Figure 6b). After selecting the appropriate neighboring cell, directional enhancement of the grid routing will be performed. As soon as the satellite enters the cell, it matches the grid code to obtain the corresponding routing table and grid-related information. Combined with the aerospace grid indexing BigTable in Section 2.1.2, it can be seen that if the satellite enters a grid cell, the information associated with the BigTable can be found.  
Furthermore, the satellite grid virtual nodes can be divided into sub-grids (smaller and larger scale grids) according to the requirements of applications, as shown in Figure 7. The grids in figure represent both the location range and the virtual nodes. The main idea of the model is to convert the satellite's highly variable topology into a connection graph between grids. The smaller grid at the center of each grid virtual node will indicate which satellite the virtual node is currently occupying. The stations or users on the ground can also be mapped as nodes in fixed grids. Because GeoSOT-3D is extended by GeoSOT in the height dimension, the ground (2D) and satellite grid nodes (3D) can be integrated and easily converted.  
Figure 6: Grid network topology model: (**a**) LEO (low earth orbit) satellite constellation; (**b**) Six cell neighborhood.  
Figure 7: Grid network topology model: (**a**) virtual nodes (using the grid as a virtual node to connect the network in this area); (**b**) multi-scale virtual nodes and neighboring grids.
# Stereo Digital Image Correlation in MATLAB
## 1 Introduction
More specifically, although some publications release code that is consistent with the theory presented [50; 51], these codes focus on robustness and ease of use which, despite making them more suitable for real world applications, results in a complex code which is ineffective as a learning resource. This lack of resources acts as a barrier to newcomers intending to further the capabilities of DIC, thereby limiting the development rate of the field. It is for this reason that the authors published a paper bridging the gap between the theory of a 2D DIC framework (ADIC2D) and its practical implementation as a modular 117 line MATLAB code [53]. However, 2D DIC, being limited to determining in-plane displacements, is susceptible to errors in the presence of out-of-plane motion, which is generally unavoidable especially outside the confines of a laboratory [54]. As such, Stereo-DIC has become favored since it accounts for out-of-plane motion, allowing for more reliable use across a broader range of applications and fields. This is supported by the significantly larger number of publications of Stereo-DIC relative to 2D DIC. This paper builds upon the work of the preceding paper by extending ADIC2D to Stereo-DIC with the aim of bridging the gap between the theory and implementation of Stereo-DIC. This is done by first presenting the theory of a subset-based, Stereo-DIC framework that is predominantly consistent with current state-of-the-art techniques. How this theory is implemented as a modular 202 line MATLAB code is then discussed. These theory and implementation sections focus on aspects of Stereo-DIC which differ from that of ADIC2D. Thereafter the framework is validated using image sets provided by the Stereo-DIC Challenge [55]. More specifically, ADIC3D's results are compared to those of LaVision's StrainMaster software and DICe [56] in order to make observations about its capabilities. Finally, the proposed framework's modularity, limitations, and overall performance are discussed. The framework, referred to as ADIC3D, was developed in MATLAB since its simple syntax lets the reader focus on the core mathematics of the code. Furthermore, MATLAB's efficient built-in functions are leveraged to simplify the code and reduce its computation time. The code is designed to be modular, so that the link between the theory and code is clear and enables readers to systematically develop an understanding of the code. Additionally, this modularity allows the code to be easily altered, urging readers to further the capabilities of DIC.
## 6 Electromagnetic Shielding by Nanofiber Mats in Other Frequency Bands  
Most of the investigations of EMI shielding in the K-band were combined with measurements in the Ku- and often also the X-band [62; 78; 91; 94; 98; 99], while only few studies can be found concentrating on the K-band only [100; 101; 102]. Thus, no new approaches can be found in the literature which have not yet been reported for X- and Ku-band.  
Few studies were published regarding EMI shielding by electrospun nanofiber mats in the Ka-band (27-40 GHz) [103] and in the C-band (4-8 GHz), typically applying carbon/metal nanofibers [104; 105]. Generally, it is necessary to reach a good impedance matching in the desired frequency range to avoid direct reflection of the incident EM waves causing secondary pollution, which can often be tailored by modifying the conductivity and also the thickness of a composite [106].  
The pore sizes of electrospun nanofiber mats, however, can only be tailored to a certain extent, in this way limiting the wavelengths of the electromagnetic radiation that can undergo multiple reflections inside the nanofiber mats. This is why lower frequencies, i.e., higher wavelengths, are less often shielded with electrospun nanofiber mats and then typically due to reflection losses [107; 108; 109]. In addition, lower frequencies are of less technological interest since the microwave range is largely used in communication technologies, with 5G mobile networks extending the earlier mobile communication bands to higher frequencies above 6 GHz for high-speed data transfer, and lower frequencies corresponding to lower energies resulting in reduced danger of health effects on humans [110].
## 3 System Model
### Block Access Probability Model  
In SloV, when vehicles establish social relationships or engage in information exchange, they typically need to verify each other's identity and reputation information. In this system, a vehicle's reputation update information is packaged into blocks by RSUs and becomes part of the blockchain. Due to the random block packaging mechanism in blockchain, a single block may contain reputation update records for multiple vehicles. Similarly, a vehicle's identity and reputation updates may be stored across different blocks. For a given vehicle, the proportion of its relevant information in each block varies, leading to different access preferences for different blocks during identity and reputation verification. To better account for vehicle access preferences in block deployment, we define the access probability of vehicle \(v_{m}\) to block \(b_{k}\) as \(P_{mk}\), which is expressed as follows:  
\[P_{mk}=cor_{mk}\cdot P_{f}(b_{k},A_{k}) \tag{2}\]  
where \(cor_{mk}\) represents the correlation between vehicle \(v_{m}\) and block \(b_{k}\), which is determined by the random block packaging mechanism of the blockchain. When a block contains multiple transactions related to the same vehicle, its correlation with that vehicle is stronger. Consequently, when the vehicle's reputation information needs to be verified, this block is more likely to be requested. Consider a block \(b_{k}\) that contains \(n_{l}\) transactions, represented as \(Tx^{k}=\{Tx_{1}^{k},Tx_{2}^{k},\cdots,Tx_{n_{l}}^{k}\}\), where each transaction may involve multiple vehicles. Let \(y_{m}^{k}\) denote the relevance of a transaction within block \(b_{k}\) to any vehicle in the cluster. If transaction \(Tx_{l}^{k}\) is associated with vehicle \(v_{m}\) (i.e., it contains the reputation information of vehicle \(v_{m}\)), then \(y_{lm}^{k}=1\). Otherwise, if transaction \(Tx_{l}^{k}\) does not involve node \(v_{m}\), then \(y_{lm}^{k}=0\). Thus, the correlation between vehicle \(v_{m}\) with block \(b_{k}\) is defined as follows:  
\[cor_{mk}=\frac{|Tx_{l}^{k}|y_{lm}^{k}=1|}{n_{l}} \tag{3}\]  
Additionally, \(P_{f}(b_{k},A_{k})\) represents the baseline query probability based on historical query frequency. Since different blocks exhibit varying access frequencies, with newer blocks generally containing more recent vehicle reputation updates, we incorporate both correlation and access frequency into our analysis. By examining historical social behavior patterns, we estimate block access behavior within the current cycle. Following the definition provided in reference [21], we assume that the query frequency follows an exponential decay pattern. Thus, the baseline query probability for block \(b_{k}\), given its block age \(A_{k}\), is expressed as follows:  
\[P_{f}(b_{k},A_{k})=[\int_{0}^{A_{k}}F_{0}(b_{k})e^{-\alpha t}d(t)]^{-1} \tag{4}\]  
where \(F_{0}(b_{k})\) denotes the initial query frequency of \(b_{k}\) at the time of block generation, and the block age \(A_{k}\) starts from an initial value of 0 at the time of block creation and gradually increases as new blocks are generated.
# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
###### Abstract  
Active and passive vibration control systems are of paramount importance in many engineering applications. If an external load excites a structure's resonance and the damping is too low, detrimental events, such as crack initiation, growth and, in the worst case, fatigue failure, can be entailed. Damping systems can be commonly found in applications such as industrial machines, vehicles, buildings, turbomachinery blades, and so forth. Active control systems usually achieve higher damping effectiveness than passive ones, but they need a sensor to detect the working conditions that require damping system activation. Recently, the development of such systems in rotating structures has received considerable interest among designers. As a result, the development of vibration monitoring equipment in rotating structures is also a topic of particular interest. In this respect, a reliable, inexpensive and wireless monitoring system is of utmost importance. Typically, optical systems are used to measure vibrations, but they are expensive and require rather complex processing algorithms. In this paper, a wireless system based on a commercial MEMS accelerometer is developed for rotating blade vibration monitoring. The proposed system measurement accuracy was assessed by means of comparison with a reference wired measurement setup based on a mini integrated circuit piezoelectric (ICP) accelerometer adapted for data acquisition in a rotating frame. Both the accelerometers were mounted on the tip of the blade and, in order to test the structure under different conditions, the first four blade resonances were excited by means of piezoelectric actuators, embedded in a novel experimental setup. The frequency and amplitude of acceleration, simultaneously measured by the reference and MEMS sensors, were compared with each other in order to investigate the viability and accuracy of the proposed wireless monitoring system. The rotor angular speed was varied from 0 to 300 rpm, and the data acquisitions were repeated six times for each considered condition. The outcomes reveal that the wireless measurement system may be successfully used for vibration monitoring in rotating blades.  
MES; accelerometer; rotating frame; vibration monitoring +
Footnote †: journal: Applied Sciences
# Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
## 3 Deep Kernel Method for Dynamic MRI Recovery  
### Feature Extraction Using CNN  
In kernel representation (1), one of the most important pieces is the feature vector \(\varphi\). The feature vector \(\varphi\) is obtained using the feature extraction operator \(\Phi\), which applies to the image prior to \(Y\). Specifically, in the free-breathing and ungated cardiac MRI setting, we assume the prior \(Y\) has \(M\) prior images \(\mathcal{A}_{i}^{H}\mathbf{b}_{i},\ i=1,\cdots,M\). Then the feature operator \(\Phi\) is applied on the \(M\) prior images to get the feature vector \(\varphi\) with pre-determined length \(\ell\) for each pixel location. In this work, we choose \(\ell=64\). We optimize \(\ell\) on a single dataset such that the reconstructions are visually optimal. It is then kept fixed for the remaining datasets. Mathematically speaking, we have  
\[\varphi=\Phi(Y).\]  
In classical machine learning, popular feature extraction techniques include principal components analysis (PCA) [28], independent component analysis (ICA) [29], and \(t\)-distributed stochastic neighbor embedding (\(t\)-SNE) [30]. Readers may refer to the references listed for the details of these techniques.  
Deep CNN now provides a better way for automatic feature extraction. In this work, we implement the feature extraction operator \(\Phi\) as a deep CNN with network parameters \(\theta\). We denote the feature extraction network as \(\Phi_{\theta}\). The structure of the feature extraction network is illustrated in the upper part of Figure 1.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 4 Discussion
### Path Selection Performance  
Figure 6 shows the categorical distribution achieved by the RL agent at convergence in each context. These are the probabilities \(P(1)\), \(P(2)\) and \(P(1,2)\) for transmitting with satellite 1 (\(sat_{1}\)), satellite 2 (\(sat_{2}\)) and both satellites (\(sat_{1,2}\)), respectively. According to [28], the higher the elevation angle, the higher the LOS probability, since there are fewer obstacles such as buildings at high elevation angles than at low elevation angles. It can be seen that our agent can recognize this pattern in each context and transmit more on a link with a higher elevation angle (i.e., with a higher LOS probability). In the second context (60\({}^{\circ}\) and 45\({}^{\circ}\)), the agent transmitted more on satellite link 1, which was at 60\({}^{\circ}\), than on satellite link 2, which was at a lower elevation of 45\({}^{\circ}\). In the scenario where both satellites had the same elevation angle, as in contexts 4 (60\({}^{\circ}\) and 60\({}^{\circ}\)) and 7 (70\({}^{\circ}\) and 70\({}^{\circ}\)), the agent distributed traffic equally between the two links. Another observation is that the agent prefers double transmissions when both satellites are at lower altitudes (i.e., it uses replicas to compensate for the loss due to low LOS probabilities at low elevations). This was evident in context 2 (60\({}^{\circ}\) and 45\({}^{\circ}\)) and context 3 (45\({}^{\circ}\) and 60\({}^{\circ}\)). On the other hand, when all links were favorable, as in context 6 (70\({}^{\circ}\) and 70\({}^{\circ}\)), the model transmitted with single links (without replicas) to save bandwidth. The same happened when the two satellites differed significantly in their elevation angles, as in context 1 and context 5. Figure 6 also shows the comparison between the categorical distributions achieved by the AC and those achieved by the optimal policy, where the system knew the channel model in advance and therefore had a relatively optimal prediction. It can be seen that our AC agent achieved a quasi-optimal scheduling policy in all contexts, even under non-stationary conditions without a priori knowledge of the channel model.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### 5.2.2 Access Level Contribution (Solutions)  
By way of access network approaches, one of the significantly suggested notions, and also parallel to LTE twofold connectivity, is the idea of a phantom cell [85]. It permits the UE to camp its CP (control plan) on a macro cell (MC), whereas its DP is controlled at the small cells (SCs), which reside inside the coverage of the above-stated MC. Specifically, the MC handles the radio resource management for the phantom cells, and henceforth, the CN signaling is shunned for the duration of handovers between the phantom cells [55]. Next, due to the softwarization of the whole network, the information interchange among various OSI layers, i.e., implementation of the cross-layer scheme, is eased [102; 103]. However, to grasp the cross-layer approaches, substantial modifications will be required to the protocol stack of the software architecture [84; 103]. Another result of the softwarization procedure is cloud-RAN (C-RAN), which offers on-demand provision of network resources at the access level, subject to the user and network situation [104; 105; 106].  
Furthermore, the proposed multi-RAT solutions are broader categorizations for the various RAT selection procedures (RSSI-centric, genetic algorithm-centric, optimization-based, fuzzy logic-centric, etc.) [27; 88; 90], since, from our previous discussions, it is noticeable that RSSI-based approaches are simple but not optimal for the selection of RAT due to non-consideration of other factors such as network or user policies, network burden, and backhaul conditions. Hence, another context-aware method is proposed [92; 107], which will provide an optimal solution for even real-time situations.  
Another critical aspect required to be mentioned here is that the above-stated handoff decision may be performed either by the combined effort of UE and the network (hybrid approach), by the network, or only by the UE. Along with the consideration of each aspect and in light of the discussion in Table 2 of Section 3, we describe the advantages and drawbacks of each approach (smart AP/RAT, cross-layer, radio access network as service, and phantom cell approach). These are as follows:  
**Advantages of Smart AP/RAT choice**  
* Offers the capability to choose multiple RATs (AP) [87]
* Use of the parameters such as the context of the user, the burden on the access point, the profile of the user, etc. [88; 89; 90]
* Provides the option of selecting AP on the basis of per flow, per user, or per slice.
* It is an enhanced (optimized) method of selecting the access point (AP) [5].  
**Drawbacks of smart AP/RAT choice**  
* The QoS requirements in 5G are disturbed, owing to the complexity of the RAT selection algorithm [87].
* The appropriate selection of the RAT requires rapid and accurate information about network conditions.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 2 Model and Data
### LDAS-Monde  
Within the SURFX modeling platform, the joint assimilation of SSM and LAI satellite-based products is possible using the LDAS-Monde tool. LDAS-Monde assimilates satellite products using a simplified extended Kalman filter (SEKF) technique. The variables analyzed were LAI and soil moisture at several depths (up to 1 m). Each analyzed variable had 12 different values corresponding to 12 land surface patch classes. The patch fraction for each model grid cell was calculated from the ECOCLIMAP-II land cover database [40], including bare soil, rocks, permanent snow, and ice surfaces, and nine plant functional types (needleleaf trees, evergreen broadleaf trees, deciduous broadleaf trees, C3 crops, C4 crops, C4 crops, C4 irrigated crops, C3 grasslands, C4 grasslands, and wetlands). The assimilation process was performed every 24 h, and the analyzed variables were used as initial conditions for the next 24 h window. Instead of calculating each variable at the beginning of each cycle, they were adjusted based on available observations and their relative errors. LDAS-Monde has been already used in several studies to assimilate and validate different satellite products over different regions and at different scales (e.g., [34; 41; 42; 43; 44]). In this study, LDAS-Monde was adapted to the kilometer scale.