esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
From the perspective of determining absorbed radiation (\(f_{\text{APAR}}\)), a driving variable for production on this grassland ecosystem, there is less of a saturating problem; near-linear NDVI-\(f_{\text{APAR}}\) relationships are often obtained for grassland ecosystems, demonstrating that NDVI also provides an effective input for productivity models (e.g., the light-use efficiency model [31]). While our results indicate that NDVI saturation is not a serious constraint for biomass estimation in this mixed-grass prairie, this may not always be the case in more productive ecosystems. The economic and ecological importance of the mixed prairie creates a need for accurate and cost-effective biomass monitoring. This study has demonstrated that remote sensing can accurately estimate biomass in these areas and that altered productivity due to interannual weather variability can be detected. These results are important for increasing the confidence of data users in the context of economic applications such as insurance payment programs and carbon markets. Ranchers' perceptions of the accuracy of sampling methods are important for effective adoption and maintenance of remote sensing-based land management programs. Carbon markets would also require accurate monitoring in the mixed prairie, as varying climate and management regimes can undoubtedly influence sequestration. For such purposes, additional sampling (e.g., below-ground sampling) would also be needed. While further work would be required to demonstrate the full utility of NDVI for this purpose, this study strongly supports the use of satellite monitoring for estimating the above-ground component of productivity. Climate change is likely to result in more infrequent, intense precipitation in the areas being studied [1]. This study indicates that satellite methods can accurately monitor above-ground yield changes that are likely to occur with changing precipitation regimes due to interannual variability or as a result of changing climate.
# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
## 5 Simulation Evaluation
### Simulation Results
#### 5.2.2 Dynamic Provisioning  
According to dynamic provisioning, the life times of the requests are chosen at random within the range that is specified in Table 1. In addition, the resources that are occupied by embedded requests in the physical infrastructure are released once the life time of the embedded request has passed, which retains the resources for requests that will be made in the future. The values of all other parameters for PI and request, as well as the assumptions, are made as in the previous case. This strategy is also evaluated by taking into account the number of NR nodes that fall into various categories, such as 10, 20, and 30 within the context of 100, 200, and 300 nodes that make up the physical infrastructure. Dynamic provisioning's utility is demonstrated by comparing the results with those of static provisioning's resource efficiency, acceptance rate, CPU, and bandwidth consumption.  
* Average resource efficiency: It is understandable that resource efficiency is measured in terms of the availability and utilization of the resources available to a given project. Static and dynamic provisioning yield nearly identical resource efficiency when using the proposed approach. Nodes in the PI and the number of requests they receive have an impact on the value, which varies with the number of nodes and requests. Figure 6 illustrates the proposed approach's resource efficiency under both static and dynamic provisioning.
* Average acceptance ratio: When dynamic provisioning is carried out through the proposed strategy, it stands to reason that the acceptance ratio will increase. This is because the occupied resources will be freed up depending on how long the existing requests will remain active, so it is logical that this will cause the increase. Figure 7 provides a visual representation of the acceptance ratio in relation to a selection of possible infrastructure provision scenarios. According to the data, dynamic provisioning results in a greater improvement in the acceptance ratio, with a maximum and minimum percentage improvement of 37% and 19% under the PI equipped with 100 nodes and 300 nodes, respectively. This represents a significant leap from the previous level of improvement. It is abundantly clear from the findings that the greatest percentage of improvement can be accomplished when the network operates with the fewest possible resources.
* Average CPU and bandwidth utilization: Figure 8 illustrates the dynamic provisioning and shows the number of CPUs that are served as well as the bandwidth that is utilized. According to the figure, the provisioning chosen allows for the highest possible utilization of both the CPU and the bandwidth within the context of the system conditions. As with static provisioning, CPU and bandwidth utilization through dynamic provisioning increases as infrastructure and the number of requests increase.  
Figure 5: Resource utilization of static provisioning. (**a**) CPU utilization, (**b**) bandwidth utilization.
# Enhanced Nondestructive Testing Using Pulsed Eddy Current and Electromagnetic Ultrasonic Techniques
## 6 Conclusions  
This study presented the design of a composite nondestructive testing probe, combining pulsed eddy current and electromagnetic ultrasonic technologies, specifically for inspecting marine metal seawater pipelines. Utilizing COMSOL 6.2 finite-element simulation software, a two-dimensional finite-element model of the probe was developed to explore the factors influencing the energy exchange efficiency of the inspection probe.  
Figure 18: Electromagnetic ultrasonic detection signals of defects at different distances from the surface: (**a**) no defects; (**b**) defect is 6 mm from the surface; (**c**) defect is 4 mm from the surface; (**d**) defect is 2 mm from the surface; (**e**) defect is 1 mm from the surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Distance from** & **Actual Wave Velocity** & **Interval of Echo Peak** & **Thickness Calculated** & **Error (\%)** \\
**Defect to Surface (mm)** & **(m/s)** & **Value (10\({}^{-6}\) s)** & **(mm)** & **Error (\%)** \\ \hline
10 (no defect) & 2275 & 8.793 & & \\
6 & 2275 & 5.180 & 5.892 & 1.80 \\
4 & 2275 & 3.550 & 4.038 & 0.95 \\
2 & 2275 & 1.773 & 2.017 & 0.85 \\
1 & 2275 & 0.895 & 1.018 & 1.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Copper plate defect position calculation.  
Based on these insights, a composite inspection probe was designed, and its structural design was optimized to enhance the energy conversion efficiency for nondestructive inspection applications.  
The findings reveal that the pulsed eddy current testing signal can detect defects on the upper surface of a material and up to 4 mm below the surface. Additionally, defects located deeper than 4 mm beneath the surface can be identified using the electromagnetic ultrasonic signal, effectively addressing the limitations of pulsed eddy current detection in accessing deeper defects. The experimental results confirm that the composite probe is effectively capable of detecting corrosion thinning defects within materials.  
X.Z. writing--original draft, N.L. methodology, J.H. supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality.  
We thank the Electric Power System Department of the Naval University of Engineering for providing various resources and experimental conditions. We would like to thank the academic editor and the anonymous reviewers for their helpful suggestions and comments.  
The authors declare no conflicts of interest.
## 3 Data and Methodology
### Data Acquisition and Processing
#### 3.2.3 Inversion of HVSR and Dispersion Curves  
The analysis process involved separate inversion of the HVSR and dispersion curves. Thanks to the inversion of the HVSR curve, it was possible to generate a detailed 1D model of seismic wave velocities (Vp, Vs) and densities. The depth of the investigation for HVSR inversion was limited by the fundamental frequency (\(f_{0}\)), whereas for array applications it was influenced by the array configuration (maximum aperture and inter-station spacing). The ambiguity of the inversion process (multiple models can provide the same response) makes it necessary, especially in the case of HVSR curves, to have some prior knowledge regarding the possible ranges of velocity and thickness of the sedimentary layers that allows constraining of the solution space. These essential data can be obtained from borehole analysis, geological section interpretation, or seismic refraction methods, all of which provide critical constraints on the initial model (Table 1). The Vs and Vp velocity ranges were taken from works previously carried out on the Lower Cheliff and Middle Cheliff Basins also using ambient noise data [6; 32; 45]. The close synergy between all of the collected data (experimental measurements, geologic surveys, boreholes, and seismic surveys) allowed for a further reduction of uncertainties during the data inversion.  
There are several inversion methods available. The authors of [60] summarized the different interpretations suggested in previous studies and proposed a new theory for the HVSR technique and the inversion of its curve to obtain a soil profile based on the diffuse field assumption (DFA). Whichever method is used, the inversion process does not produce a single definitive solution. Instead, it produces several models, each one characterized by a different degree of misfit between the observed HVSR curve and the theoretical Rayleigh wave ellipticity curve. The velocity model with the lowest degree of misfit is considered the most representative. There is a similarity in shape between the HVSR curve and the fundamental mode of the Rayleigh wave ellipticity curve [61].  
In this study, the inversion process was performed using the Sesarray package (release 2.10.1) in Dinver software ([[https://www.geopsy.org](https://www.geopsy.org)]([https://www.geopsy.org](https://www.geopsy.org)), version 2.10.1) [58], which implements the neighborhood algorithm [62]. The input parameters for the inversion, including Vp, Vs, and the density range, are shown in Table 2. The number of layers and their thicknesses were derived from the data obtained from 16 boreholes (Table 1). These borehole data were provided by the \"Laboratoire National de l'Habitat et de la Construction\" (LNHC). A Poisson's ratio between 0.2 and 0.5, representative of typical soil values, was used [63]. The maximum number of iterations was set at 300, with 100 models generated per iteration.  
For each site, the experimental HVSR curve was compared with the theoretical ellipticity curve using a misfit value [64], and the Vs model with the lowest misfit was selected.
## 3 Mathematical Model  
The behavior of the queueing system specified in Section 2 can be described by the \(K\)-dimensional continuous-time Markov chain (CTMC) \(\{(N_{1}(t),\ldots,N_{K}(t)),t\geq 0\}\), where \(N_{k}(t)\) captures the number of \(k\)-type requests in the system at time \(t\). Denote \(N_{k}=\lfloor(C-\sum_{i\
eq k}g_{i})/b_{k}^{\min}\rfloor\) as the maximum number of \(k\)-type sessions in service, then \(\mathbf{n}=(n_{1},\ldots,n_{K})\), \(n_{k}=0,\ldots,N_{k}\), is the number of \(k\)-type sessions that are currently in the system, \(i,k=1,\ldots,K\). Additionally, let us denote \(N_{k}^{S}=\lfloor g_{k}/b_{k}^{\min}\rfloor,k=1,\ldots,K\) as the maximum guaranteed number of \(k\)-type sessions in service. The considered process is defined over the following state space  
\[\mathcal{X}=\{\mathbf{n}:0\leq n_{k}\leq N_{k},k=1,\ldots,K,\sum_{i=1}^{K} \max\{n_{i}b_{i}^{\min},g_{i}\}\leq C\}. \tag{8}\]  
Due to the partially elastic nature of the considered sessions, the amount of resources, \(b_{k}(\mathbf{n})\), \(b_{k}^{\min}\leq b_{k}(\mathbf{n})\leq b_{k}^{\max}\), available to the \(k\)-type sessions is equally distributed between them and depends on the state \(\mathbf{n}\in\mathcal{X}\). That is, we have  
\[b_{k}(\mathbf{n})=\] \[=\min\Biggl{\{}\frac{C-\max\{\sum_{i\
eq k}g_{i},\sum_{i=1}^{k-1} \max\{n_{i}b_{i}(\mathbf{n}),g_{i}\}+\sum_{i=k+1}^{K}\max\{n_{i}b_{i}^{\min},g _{i}\}\}}{n_{k}},b_{k}^{\max}\Biggr{\}}. \tag{9}\]
## 4 Experiments  
### Datasets  
Our model was trained from scratch using the DF2K dataset, comprising two established components: DIV2K [41] and Flickr2K [41]. The DIV2K provides 800 high-quality training images, while Flickr2K contributes 2650 images encompassing broader scene diversity. To generate low-resolution image for training, we implement bicubic downsampling via MATLAB R2022b with scaling factors of \(\times\)4. Post-training evaluation was conducted across five benchmark datasets for single-image super-resolution, including Set5 [42], Set14 [43], BSD100 [44], Urban100 [45], and Manga109 [46]. This totals 328 images used in each evaluation experiment.  
To further highlight the effectiveness of our model, we also validated it using rice images collected from a real processing environment. This domain-specific collection contains 10 high-resolution rice images, each with a resolution of 4096 \(\times\) 2160. The dataset comprises images of polished round-grain rice, polished long-grain rice, and semi-polished long-grain rice, among the semi-polished images including some substandard grains such as broken rice and chalky rice. Although the number of images is limited, each image contains hundreds to thousands of densely packed rice grains with significant intra-image diversity. The grains often appear adhered or overlapping, forming complex texture patterns that pose challenges for local structure reconstruction. Unlike public datasets with diverse scenes and backgrounds, this dataset was acquired under controlled conditions (uniform lighting, background, and camera settings), ensuring high consistency while preserving grain-level variability. To facilitate the execution of the SR task, each image was partitioned into LR patches of size 512 \(\times\) 240, ensuring smooth model operation during testing.  
Through experiments on these datasets, we have validated the superior performance of our model across various scenarios.
## 2 Material and Methods
### Application of 3D Printing TEVG  
3D printing is a field that is currently undergoing rapid development and may serve to advance tissue engineering by incorporating non-uniform characteristics that match patient anatomy and increase the ease fabrication [43; 44]. The 3D printing TEVG approach enables the creation of linear or branched tubular structures using synthetic or natural biomaterials [45; 46]. The main techniques used for 3D printing of biological materials are ink jet, micro extrusion, and laser assisted printing [47; 48]. Within these techniques, hydrogels are the most commonly used material because of their liquid-like state and ease of chemical tunability [49; 50]. However, to date, hydrogels do not have the needed initial mechanical properties required of a TEVG [51]. Therefore hydrogels are often supplemented with other biomaterials or cell seeding.  
3D printed grafts can also forgo the use of hydrogels and use synthetic and/or natural materials. In fact, [PERSON] et al. produced an electrospun graft made out of PCL and chitosan [52]. In a sheep model, a custom mandrel was 3D printed based on preoperative image processing. Afterwards, the scaffold created by electrospinning PCL and chitosan fibers around this custom mandrel showed satisfactory remodeling 6 months post-operation.  
Bioprinting is an extension of the 3D printing field and utilizes cells and/or other media to print tissue- and organ-like structures [53; 54]. As individual cells are difficult to work with, they are often aggregated into \"spheroids\" [55]. 3D bioprinted structures rely on the self assembling nature of spheroids to fuse and create viable neotissue [56] [PERSON] et al. reported the use of bioprinting multicellular spheroids and transplanting the resulting scaffold into rat abdominal aortas (\(n=5\)) [57]. The TEVGs remained patent until the five day end point of the study, but longer term evaluations are needed to further validate this paradigm.  
3D bioprinting as a field has improved immensely in the last decade through the use of spheroids and the improvement of new manufacturing and bioink technologies. While this promising field will likely become more prevalent in tissue engineering applications, focus must be paid to improving ink and scaffold mechanical properties before translation to clinical studies.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison
### Improvement of Computational Efficiency of Parallel Approach
#### 3.2.4 Improvement of Efficiency with Different Number of Stations  
Observation data from 40 to 110 IGS and MGEX stations are selected to analyze the improvement of efficiency of parallel multi-GNSS POD. The results are shown in Figure 6.  
It can be seen from Figure 6 that the parallel POD is obviously more efficient than the non-parallel POD. As the number of IGS/MGEX stations increases, the serial calculation time of non-parallel and parallel POD both gradually increases. The speedup ratio of parallel POD increases with the increase in the number of stations. The efficiency of POD using 80 to 90 stations has been significantly improved. Moreover, the parallel POD of four satellite system can be controlled within one hour, which provides the possibility to increase the update frequency to 1-h for multi-GNSS parallel POD.
## 3 System Model  
In this section, we define the system model by specifying its parts. We start with the deployment model and then proceed with blockage, micro- and macro-mobility, propagation, and antenna models. Finally, we specify the metrics of interest.
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 2 Method
### Data Collection  
To develop a generalized ANN model for BCS evaluation, a real-world dataset [15] of 429 samples, supplied by an industry partner, was initially used. The dataset includes RSC box samples with single-wall construction and flute type A, B, and C [16; 17]. Input parameters include the key influencing parameters such as the edge crush test (ECT) values of corrugated board, and box dimensions (Length, width and depth). An industry analysis indicates that the majority of box dimensions typically fall within the following ranges: length between 8 and 25 inches, width between 5.75 and 19 inches, and depth between 4 and 28 inches. These dimensions account for approximately 90% of the boxes sizes used  
Figure 1: Flow of building generalized ANN model for BCS prediction.  
in the industry. Therefore, to ensure the model was trained on a representative subset, only data points within these ranges were selected from the original dataset. The resulting dataset contains 395 data points, and the box parameter details are summarized in Table 1. Considering the overall goal of this study--to develop a model capable of predicting BCS for the majority of commercial box dimensions--and the limitations in obtaining input data for validation, the most influential parameter (ECT of the corrugated board) along with the box dimensions (length, width and depth) was selected as the inputs to train the ANN model The output of the ANN was the BCS value.  
Figure 2 presents histograms of the input features for the extracted dataset with 395 data points, illustrating their data distributions. The histogram for ECT reveals a small portion of data at higher values that may represent outliers. Box depth also shows the slight presence of outliers at the upper end of its range. In contrast, the distributions of box length and width appear relatively uniform and do not exhibit noticeable outliers.
## 3 Numerical Examples
### Interconversions
#### 3.3.2 Creep Compliance to Relaxation Modulus  
Before converting creep compliance to relaxation modulus, the retardation times of creep compliance need to be determined by the collocation method. Compared with the relaxation times \(\rho_{i}\) of dynamic modulus, the same number of elements 11 is selected to describe retardation times \(\tau_{j}\) of creep compliance.  
Time constants \(t_{k}\) are also required which can be determined by the collocation method or the least squares method [15]. The number of time constants \(t_{k}\) was also selected as 11 to keep consistency with the number of retardation times. However, in the least square method, \(t_{k}=2\times 10^{(k-1)/2-6}s,k=1,2,...,21\). Therefore, in the collocation method, matrix A is \(11\times 11\) and matrix B is \(11\times 1\), whereas in the least square method, matrix A is \(20\times 11\) and matrix B is \(20\times 1\), both matrix calculations were completed in software MATLAB. As can be seen, the matrix utilized in the least square method is more complicated and required more computational time in contrast to the collocation method.  
Regarding the interconversion from creep compliance to relaxation modulus, \(\tau_{j}\) was firstly determined and it was assumed that \(\tau_{j}\) and \(\rho_{i}\) are equivalent as suggested [33]. In this paper, \(\tau_{j}\) was selected between \(2\times 10^{-6}\) to \(2\times 10^{-4}\) to cover the entire range of relaxation modulus. Theoretically, the relaxation times \(\rho_{i}\) are the negative reciprocal of \(s\) when \(\widetilde{E}(s)\) approaches to infinite [15]. The \(\rho_{i}\) can also be determined by root-finding method based on \(\left|\widetilde{E}(s)\right|\) versus \(-1/s\) figure, whereas the calculated \(\rho_{i}\) was almost identical by using the two methods: assuming \(\tau_{j}\) is equivalent to \(\rho_{i}\), and root-finding method [4; 34]. The Prony series coefficients of creep compliance and relaxation modulus are finally calculated in software MATLAB and results are listed in Table 3.
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
## 4 The Protocol Details
### Data Transmission Phase  
The process of data transmission to the BS is divided into two subphases: intra-cluster communication and inter-cluster communication, whose durations are \(T_{3}\) and \(T_{4}\), respectively. The subphases are subsequently described in the following subsections. The details of this phase are given by the pseudo-code in Algorithm 3.