esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Evolution of NMR Methodologies
### Multidimensional and Advanced NMR Techniques
#### 2.3.2 Advanced ssNMR for Dynamics and Interactions  
Advanced ssNMR techniques, including proton spin diffusion, heteronuclear correlation experiments (HETCOR), and advanced dipole coupling analysis, have enhanced detailed studies on molecular mobility and interactions.  
These methods are very helpful when it comes to studying the interactions between structure and dynamics in complex polysaccharide systems. [PERSON] et al. used advanced ssNMR technology to investigate the primary cell wall of Arabidopsis. They demonstrated how pectin methyltransferase regulates polysaccharide interactions and dynamics inside the cell wall matrix [52]. Their findings show the role of a dynamic assembly of polysaccharides in determining the mechanical properties of cell walls. This helps us better understand plant biology.  
Another application is a combination of ssNMR time-resolved ssNMR with DNP. [PERSON] et al. studied fungal polysaccharides and plant cell walls using DNP-enhanced ssNMR and found subtle changes in molecular interactions under different environmental conditions [5]. This method provided unprecedented sensitivity, paving the way for the study of low-abundance components. [PERSON] et al. used DNP-enhanced NMR and achieved a 27-fold boost in signal sensitivity, and this made it possible to analyze cell wall polysaccharides in _Aspergillus nidulans_ and _A. fumigatus_[8].  
As shown in Figure 4, they introduced stable double free radicals (e.g., AMUPol) into the sample, and then used microwave radiation to irradiate it at a low temperature (around 100 K). By doing this, the authors significantly enhanced signal detection, allowing them to identify the weak interactions between polysaccharides, such as chitin and \(\alpha\)-1,3-glucan, in the fungal cell walls. The combination of MAS-DNP, long-range 2D NMR, and hydration kinetics provided an unprecedented view of the fungal cell wall structure at the molecular level. It allows us to detect subtle structural differences and interactions that traditional techniques could not detect before.  
In the food science field, [PERSON] et al. used ssNMR to monitor changes in molecular fluidity when a gel is forming. They also studied the interactions between wheat starch and _Mesona chinensis_ polysaccharides [45]. This study offered an understanding of the structural properties of a polysaccharide gel, which is important for food optimization. In general, these examples show the transformative potential of advanced ssNMR techniques in elucidating polysaccharide dynamics and interactions across diverse applications. If combined with other methods like isotope labelling and computational modelling, the field will make more progress in understanding complex polysaccharide systems. These methodological improvements set the basis for dealing with the complexity of polysaccharide rides. In the following parts, we will outline their revolutionary influence in biomedical, food, and environmental applications.
## 4 Experiments
### Static Obstacle Avoidance  
In the experimental paradigm, the robotic arm is tasked to navigate from its initial position to the target position while avoiding two stationary obstacles. The task information of the robotic arm and the definition of obstacles are shown in Table 3. Comparative experiments utilizing the APF method were also conducted. As shown in Figure 9, when using the APF (artificial potential field) method for collision avoidance, the robotic arm directly tries to move towards the target position. This leads to it getting stuck in a local minimum position, unable to reach the target point.  
Figure 8: The comparison between HER and HER-CA in three-dimensional pathfinding tasks.  
Figure 7: Comparison plot of training success rates for different numbers of obstacles, where blue and green correspond to HER-CA and HER, respectively.  
However, our guided APF method enables the arm to circumvent the two obstacles achieving its destined position, as shown in Figure 10. The closest proximity of the entire robotic arm from the obstacles is represented in Figure 11. A collision could potentially occur when this minimum distance reduces to less than 150 mm. However, in this experiment, the shortest distance is consistently greater than this collision threshold. The most minimal distance observed was 161 mm, occurring at 4.6 s.  
Figure 10: Robotic arm motion process using our guidance APF method. The green arrow represents the direction of the temporary target pose given by the action model. From 1 to 4 represents the process of the robotic arm from the beginning to the end of the simulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spherical Obstacle and Robot** & **Information** \\ \hline Start joint value of the robot arm & [\(0^{\circ},0^{\circ},0\) mm, \(0^{\circ},0^{\circ},0^{\circ}\) mm] \\ Goal position and attitude of the end-effector & [8680 mm, 4506 mm, 1700 mm, \(0^{\circ},0^{\circ},0^{\circ}\).] \\ Goal joint value of the robot arm & [38.51\({}^{\circ}\), \(-9.71^{\circ}\), 1515 mm, \(0^{\circ},9.71^{\circ}\), \(-38.51^{\circ}\), 1300 mm] \\ Coordinates of obstacle 1 & [6002 mm, 3106 mm, 1089 mm] \\ Coordinates of obstacle 2 & [6292 mm, 2556 mm, 2389 mm] \\ Obstacle radius & 300 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation information for robotic arms and obstacles.  
Figure 9: The process of real-time collision avoidance movement using only the APF method. From 1 to 4 represents the process of the robotic arm from the beginning to the end of the simulation.
# A Method to Minimize the Effort for Damper-Blade Matching Demonstrated on Two Blade Sizes
###### Abstract  
A method called PCR (Platform Centered Reduction) is designed to more effectively perform complex iterative and nonlinear calculations required for the dynamic response of turbine blades damped by dry friction contacts between rigid dampers and airfoil-to-neck platform. The key feature of PCR is to represent all nonlinear forces on the blade platform by means of only six degrees of freedom at a point located within the platform volume, regardless of the number of damper-platform contact elements. Despite reducing the effort and computational time by more than one order of magnitude, the method proves to be fully accurate by a check against the corresponding nonlinear Finite Elements (FE) calculation. It is also shown that the limit exciting force, indicating the upper capability to dampen vibrations, can be calculated with a simple linear modal analysis. In order to search for the best blade-damper match, the preferred graph represents relevant bending stresses on the airfoil against excitation forces. A detailed application of the method concerns two significantly different blade sizes, by varying parameters such as neck length and damper centrifugal force. Finally, it is emphasized that a final check by a complete FE analysis is still possible as a purely linear solution fed by sets of contact forces previously determined through the PCR at any desired frequency and excitation.
# Linked Open Government Data: Still a Viable Option for Sharing and Integrating Public Data?
## 6 What Factors Are Holding back the Spread of LOGD?
### Data Ownership and Licensing  
Navigating data ownership and licensing agreements in the context of LOGD is complex and fraught with challenges. Different government agencies have varying policies, making _the use and reuse of LOGD under these diverse policies_ a difficult task. [PERSON] et al. [73] highlight a key issue: _the lack of standardized and pluggable license agreements._ This results in a plethora of open licenses that, despite their open nature, often contain conflicting terms. Such discrepancies make it difficult to merge data from different sources, as the licenses may be incompatible. Furthermore, sharing data across various public entities can lead to unclear dataset ownership. This ambiguity around the rightful owner of the data can lead to copyright inconsistencies, impeding the publication of the data. [PERSON] et al. [94] underscore the importance of license compatibility or interoperability, especially for LOGD applications. They observe that many portals apply licenses to some datasets but not all, creating a patchwork of usage terms. Additionally, they note the problematic practice of using legal notices instead of open licenses. These notices, often found in the U.S.'s federal OGD portal, lack standardization and are not machine-readable, posing challenges for LOGD applications. [PERSON] [95] also points out a practical issue: the complexity of understanding what can legally be carried out with mixed datasets. He argues that it is essential for reusers to have a clear understanding of their rights and limitations regarding data usage, ideally \"without asking their lawyers\" or navigating through numerous licenses. This clarity is crucial for the effective and lawful reuse of LOGD.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 4 Experimental Results and Analysis
### Experimental Details  
The method in this paper was implemented using the PyTorch framework. The experimental computing platform comprised an Intel E5-2630 2.20 GHz quad-core CPU, 47 GB RAM, and an NVIDIA Corporation GM200 graphics card with 12 GB VRAM. The network model was trained, validated, and tested using the FVS dataset, with a ratio of 8:1:1 for the training, validation, and testing sets, respectively. Samples from each path type were evenly distributed. During training, the initial learning rate was set at 0.001, the batch size was 128, the mean square error (MSE) was chosen as the loss function, and the Adam optimizer with parameters \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\) was used. Typically, the model converged after around 100 epochs, with the entire training process taking approximately 6 h.
## 2 Related Work
The checkpointing method regularly saves the condition of a process operating on a computational resource so it can restart on another resource in case of resource loss. If any resource faults occur, it invokes the required replicas to meet user application capacity needs. [PERSON] et al. (2011) [23] developed a bi-criteria task scheduling that considers users' satisfaction and fault tolerance. It concentrates on a pro-active fault-tolerant mechanism that considers resource failure history while scheduling jobs. It considers user date and job fulfillment period at all tools, and measures fitness value. Then jobs are scheduled depending on fitness value. Both these heuristic schedulers discussed here have benefits and even certain drawbacks. The duplicitous load balancing does not regard the planned execution period and therefore isweak. The minimum execution time (MET) heuristic algorithm does not consider the completion of jobs, resulting in extreme load imbalance. Minimum completion time (MCT) often implies a low makespan. The Max-Min heuristic procedure is stronger than all these algorithms, but only for the shortest jobs. Of all these evolutionary methods discussed, the Min-Min algorithm is quick, fast, and plays better without being given user satisfaction, considering machine efficiency by reduced makespan. The application demand aware approach works best when taking into account user satisfaction. Many schedulers concentrate on user deadline and task scheduling separately, but no scheduler considers user deadline and resource load. Therefore, there is a wide potential for associated data focusing on both factors. The need to expand the relevant work is the scope of this article, improving both load balancing and fault tolerance with a new selected framework focused on a theory of fractal transformation and entropy. The current methods address both load balancing and fault tolerance in grid environments separately [24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. The method proposed in this paper deals with each of them within one framework by combining some classical methods based on fractal transform and entropy computation for reducing the complexity of data to obtain an optimal method for rendering the grid structure. The study proposes scheduling algorithms that resolve numerous issues, like customer satisfaction, data aggregation, and fault tolerance, by considering criteria such as failure rate, load status, user deadline, and resource usage of scheduling services. In fact, for the first time, we have used the fractal transform and entropy in order to reduce the complexity of the grid.
## 2 Materials and Methods
We will define the growth rate as an _analytic function_ of the growth rate of total body mass of an individual and also of the state of the D-cell population state of health expressed in statistical terms. Thus, these two regions of the epithelium of the ME mucosa are completely defined by the following geometric parameters: _epithelial areas_\(A^{F}\) and \(A^{D}\); _epithelial thickness_ (_height_) \(h^{F}\) and \(h^{D}\). The existence of these two regions is _necessary_ and _sufficiently_ established by the necessity of the two thermodynamic systems \(\varepsilon^{T}\) and \(M^{T}\) to exchange mechanical work to reach thermodynamic equilibrium, opposing the existing pressure difference \(\Delta p_{\sigma}\)[3, 4, 5]. The geometric properties specific to each epithelial area, (8) and (9), reflect the F and D functions they fulfill:  
\[F=F(A^{F},h^{F}) \tag{8}\]  
\[D=D(A^{D},h^{D}) \tag{9}\]  
Establishing a correspondence between the regions and the two _Organs_\(F\) and \(D\) is reduced to establishing a connection between the _physical-physiological processes_ involved and the _geometric characteristics_ of \(F\) and _D epithelium_ (the constant of \(A^{F}\) area, progressive growth of \(A^{D}\) area and the inequality \(h^{F}>h^{D}\)). This correspondence will, in turn, answer all of the following questions:  
* Why is the area \(A^{F}\) constant throughout life? * Why is the area \(A^{D}\) increasing simultaneously to the increase in body mass? * Why are there circumstances when \(A^{D}\) stops growing? * Why does the inequality \(h^{F}>h^{D}\)_even exist_?
## 2 Community Detection across Space and Its Variation across Time
### Network Construction and Its Variation with Different Cell Sizes  
#### 2.1.1 Network Construction  
Due to signal loss or degradation, taxi trajectories are usually recorded with spatial uncertainty. Even if a set of trajectory flows comes from (or drives to) the same regions, the recorded trajectory points are unlikely to share the same coordinates. Therefore, in order to extract the collective regularities from these massive trajectory points, our method proposes that the study region be partitioned into equally sized square cells, each of which represents a place in the urban space. In this way, each trajectory point could be assigned to its nearest cell, and a traffic flow consisting of multiple trajectory points could be represented as a set of partitioned cells. More specifically, by specifying the cell size \(\mathrm{k}\), the whole region would be transformed into a grid of size \(\mathrm{k}\times\mathrm{k}\). There are also other ways to partition the region, e.g., traffic analysis zones (TAZs). However, TAZs are constrained to a fixed scale, and our method can be used for multi-scale spatial analysis with different cell sizes (please see Section 3). In general, for fine scale applications, a small cell can be used, while for coarse scale applications, a large cell would be used. With the tessellation of space, we can then define the nodes and edges of network traffic flow as follows.  
**Definition 1**.: _(Node): Assuming the range of Euclidean coordinates of a cell \(C\) is {[\(x_{min}\), \(x_{max}\)], {\(y_{min}\), \(y_{max}\)}, this cell can be modeled as a node only when there is at least one trajectory point (with coordinate (\(x\), \(y\))) falling into the cell. Specifically, this constraint can be formalized as follows._  
\[\begin{split}& x_{min}<x\leq x_{max}\\ & y_{min}<y\leq y_{max}\end{split} \tag{1}\]  
**Definition 2**.: _(Edge): Assuming there are \(n\) trajectory flows between nodes \(C_{1}\) and \(C_{2}\), the connectivity between \(C_{1}\) and \(C_{2}\) can be modeled as an edge with weight \(n\). As presented in Figure 1, the weighted edge is used here to represent the flow transitions between cells (i.e., sub-regions)._  
The movement of vehicles implies the complex interaction between land uses, and connects distant regions into an integrated system. Since the basic characteristic of this system is connectivity, we propose the construction of a spatially embedded network consisting of nodes (Definition 1) and edges (Definition 2) to represent traffic flow. Based on such a network, we can then employ graph analysis techniques (see Sections 2.2 and 2.3) to discover the hidden regularities in a transport system.
## 3 Concept for a Smart Control Service
### Control Services
#### 3.3.2 SICS Router  
The I/O configuration service of a SICS controller (SICS router) is separated from the SICS runtime for the following reasons:  
* Securing a dynamic reconfiguration; i.e., in the case of an identical control program, the I/O configuration can be changed within a program cycle.
* Identical machines/systems can be operated with the same control program, despite different I/O modules.
* A distributed separate configuration service forms the basis for a future automatic IIoT-based device configuration (IIoT--industrial internet of things).
* SICS routing works according to the following two principles:
* A SICS program (PLCopen XML program) works with absolute I/O addresses.
* The SICS router connects the absolute I/O addresses to the real I/O addresses of the devices (CPS components).  
Figure 8 illustrates the functionality of a SICS router.  
The digital and analogue inputs and outputs of a device, connected via the channel interface, are routed to absolute I/O program addresses and transferred to the SICS runtime via a SICS block channel. The routing rules (interconnection matrix) are defined via an XML file.
## 3 Results and Discussion
### Characterization of PVA/BC/C-Ag Composite Membranes
#### 3.1.2 SEM Analysis  
Pure PVA, PVA/BC, C-Ag, PVA/C-Ag, and PVA/BC/C-Ag were characterized by SEM (Figure 2). The PVA/BC membrane (Figure 2b) had a looser surface topography than the pure PVA membrane (Figure 2a) because the three-dimensional structure of the PVA was split by filling with the BC emulsion. The BC emulsion appeared to be well-combined with the PVA. The silver nanoparticles exhibited a spherical shape and were well-embedded in the surface of the biochar (Figure 2d). The surface of the PVA/C-Ag membrane had spherical protrusions without scalp material because the biochar of the C-Ag material was coated in the PVA membrane, leaving some bare silver nanoparticle protrusions (Figure 2c). Comparing Figure 2c (PVA/C-Ag) and 2e (PVA/BC/C-Ag), it was confirmed that parts of the silver nanoparticles were exposed outside of membrane surface and that the structure of the PVA/C-Ag membrane was more dense.  
The results of the BET measurement (Supplementary Table S1) showed that due to the loading of silver on the surface of biochar and the encapsulation of PVA/BC, although the average pore sizes of C, C-Ag, and PVA/BC/C-Ag are increased, the surface area and the total pore volume appeared to decrease.
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 3 Preliminaries and Problem Definition
### Cross-Domain Spatiotemporal Relationship Network  
In our related research, the scenario where the behavior occurs on different platforms or the behavior objects belong to other platforms is called a cross-domain scenario. A relational network that spans network domains and temporal domains is called a cross-domain spatiotemporal relational network. In this study, the cross-domain environment to which different host nodes and service nodes belong applies to this definition. Referring to the current research results [44], we define the spatiotemporal relationship network as \(G=\left\{V,E,T\right\}\), where \(V=\left\{v_{1},v_{2},\cdots,v_{n}\right\}\) is a set of \(n\) nodes, \(v_{i}\) is associated with a feature vector \(x_{i}\); is the set of edges between nodes, where \(|e_{i}|\subseteq[0,1)\) is the edge relationship weight; \(|e_{i}|=0\) means the edge does not exist; the vector \(|e_{i}|=0\) is the time stamp when the two endpoints \(v_{i}\) and \(v_{j}\) of the edge \(e\) interact.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Geometry**} & \multicolumn{5}{c}{**Property**} \\ \cline{2-5}  & **Curvature** & **Parallel Lines** & **Shape** & **Sum of Triangle Angles** & **Data Types Suitable for Embedding** \\ \hline Euclidean & 0 & 1 & & \(\pi\) & \\ Spherical & \(>0\) & 0 & & \(>\pi\) & \\ Hyperbolic & \(<0\) & \(\infty\) & & \(<\pi\) & \\ Mixed-curvature & / & / & / & / & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of curvature space properties.  
The definition of the cross-space domain (network domain) here are two mapping functions closely related to the network \(G\): node domain type mapping \(f_{v}:V\rightarrow\mathcal{D}\), and inter-node relationship type mapping \(f_{e}:E\rightarrow\mathcal{R}_{\ell}\), \(\mathcal{D}\), and \(\mathcal{R}_{\ell}\) respectively, represent a collection of node domain types and a collection of node relationship types, where the two endpoints of an edge are of the same kind; when \(|\mathcal{D}|=1\), \(|\mathcal{R}_{\ell}|=1\), it indicates that there is only one domain and relationship type from the node source, thus the temporal network is an intra-domain relational network; in addition, when \(|\mathcal{D}|+|\mathcal{R}_{\ell}|\geq 2\), the network is a spatiotemporal relational network, which we also call a cross-domain relational network.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 1 Introduction
### Organisation of the Paper  
The rest of the paper is organised as follows. Section 2 describes the system model and defines the metrics used in the performance evaluation. The average per-user capacity, average SE, and ASE per cell expressions are derived in Section 3. In Section 4, our enhanced and validated beam-pointing algorithm for extended coverage is presented. Section 5 presents numerical results from the derived expressions and shows the performance of the algorithm in comparison to some alternative schemes. The paper is concluded in Section 6.
# Micro Vibration Measurement with Microscopic Speckle Interferometry Based on Orthogonal Phase
## 4 Experiment
### Speckle Interference Experiment
By substituting them into Equation (8), we can get:  
\[I_{1}+I_{2}+2\sqrt{I_{1}I_{2}}\cos[0]=160, \tag{9}\]  
\[I_{1}+I_{2}-2\sqrt{I_{1}I_{2}}\cos[0]=12, \tag{10}\]  
We can get I\({}_{1}\) = 64.91 and I\({}_{2}\) = 21.09. With the values of \(I_{1}\) and \(I_{2}\), the vibration amplitude can be obtained by Equation (7). Taking Figure 9b as an example, the average value of the 20 peaks is 136, so we take \(I_{peak}\) equal to 136:  
\[I_{1}+I_{2}+2\sqrt{I_{1}I_{2}}\cos[\frac{\pi}{2}+\frac{4\pi(-A)}{\lambda}]=136, \tag{11}\]  
By calculation, A is 31.4 nm. The amplitude of Figure 9b is 30 nm, and the relative error is 4.6%. To get the accurate amplitude, we need to know the accurate periodic peak value \(I_{peak}\). As we mentioned in the above experimental part, due to the limitation of the CCD collection frequency, 20 points are collected per vibration period. There is no complete correspondence between the measured vibration period light intensity and the actual vibration period light intensity. Therefore, there is a certain difference between the \(I_{peak}\) we obtained and the actual \(I_{peak}\), which is the main reason for the error. By using a high-frequency camera, the error can be reduced by increasing the number of points collected per vibration period. For vibration trajectory and the light intensity change are corresponding to each other, the vibration frequency can be directly obtained: f = 10 Hz. Finally, we can obtain the vibration frequency and amplitude of the measured object.