esa-sceva/satcom-chunk-collection huggingface dataset:
# SLR, GRACE and Swarm Gravity Field Determination and Combination
## 2 Materials and Methods
### Swarm  
A number of Earth observation LEOs at orbit altitudes of 400-600 km that are equipped with GPS receivers may also be exploited to derive information on gravity field variations [17; 56]. Eminent candidates to bridge the gap, be it at lower spatial resolution, between GRACE and the GRACE-FO mission are the three satellites of ESA's Earth's Magnetic Field and Environment Explorer (Swarm) mission [18] that were launched in November 2013. All three satellites of the constellation circle the Earth in near circular polar orbits, Swarm A and C at 460 km, Swarm B at 530 km altitude (in 04/2014). They are equipped with GPS receivers and accelerometers, but the data of the latter turned out to be disturbed by slow temperature-induced bias variations and sudden bias changes [73] and are not routinely used for orbit and gravity field determination.  
At AIUB, kinematic orbits of all three Swarm satellites are determined routinely [21]. While for the first mission phase until June 2014 gravity field results are deteriorated due to high solar and consequently ionosphere activity and not optimal GPS receiver settings [74; 75], for the time period critical to bridge the gap between GRACE and GRACE-FO, the data quality corresponds to the nominal. By evaluation of the kinematic Swarm orbits, monthly gravity fields can be determined that are sensitive to temporal gravity variations up to about degree 13 [47].  
The background force model for the Swarm processing is defined correspondingly to SLR and GRACE (see Table 2). Again the static part of AIUB-GRACE03S is used as a priori model and AOD is applied for de-aliasing of short-term variations in the atmosphere and ocean masses. As in the case of GRACE, no models for surface forces are applied. To compensate for the not used accelerometer observations, the constraints on the pseudo-stochastic accelerations are set less strict [21]. Details on the Swarm orbit parameterization can be extracted from Table 4.
# A Solution Procedure to Improve 3D Solid Finite Element Analysis with an Enrichment Scheme
## 3 Improving 3D Solid FE Solutions
### Cover Function Selection Scheme  
The cover function selection scheme aims to identify the optimal nodes and the corresponding degrees of cover functions with minimal artificial parameters. In this study,  
Figure 4: Ad hoc problem: (**a**) problem description (\(E=2.0\times 10^{11}\) N\(/\)m\({}^{2}\) and \(\
u=0.3\)) and (**b**) meshes when \(NE=6\) for HEX8 and TET4 elements.  
Figure 5: The normalized index and the normalized error indicator graph of the ad hoc problem: (**a**) HEX8 element mesh and (**b**) TET4 element mesh.  
TET4: standard 4-node tetrahedral element  
Note that the previous approach to automatically enhance the accuracy of finite element solutions only considers the TET4 element because of LD problems [19] and different artificial constants are adopted for each example. However, this study considers various types of elements widely used and no arbitrary constant is included.  
It is important to note that essential boundary conditions were imposed when applying the proposed scheme to eliminate the rank deficiency problem. Only up to quadratic cover functions were considered in this paper, but higher-order cover functions can be easily incorporated by refining the cover function selection scheme in (26).  
The solutions obtained using the adaptively enriched elements were compared with those obtained using standard elements on the same finite element meshes. To confirm the effectiveness of the proposed scheme, we evaluate displacements and von Mises stresses at specific locations. Also, the relative error in strain energy (\(E\)) and maximum von Mises stress (\(\tau\)) are measured by  
\[e_{E}=\frac{\left|E_{ref}-E_{h}\right|}{E_{ref}}, \tag{28}\]  
and  
\[e_{\tau}=\frac{\left|\tau_{ref}-\tau_{h}\right|}{\tau_{ref}}, \tag{29}\]  
where the subscripts \"_ref_\" and \"_h_\" denote that the values are calculated from the reference and finite element solutions, respectively. The reference solutions are either analytical solutions or sufficiently converged numerical solutions obtained using fine high-order elements.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 3 Near Infrared Spectroscopy
### NIRS Parameters  
As previously stated, NIRS techniques allow for the monitoring of the oxidative metabolism and O\({}_{2}\) consumption rate of skeletal muscle. In particular, the parameters that can be directly extrapolated are O\({}_{2}\)Hb and HHb hemoglobin. The sum of these two represent the total content of hemoglobin (tHb), which is relative to the blood volume under the probe. The ratio between O\({}_{2}\)Hb and tHb represents the oxygen tissue saturation (SO\({}_{2}\) or SO\({}_{2}\)%). Moreover, there is a lack of standardization in NIRS parameter nomenclature in the literature and different acronyms can be found to indicate the same quantities. Hence, besides the one already reported, it is also possible to find the terms StO\({}_{2}\) or SO\({}_{2m}\) for muscle oxygen saturation or TOI and TSI, especially in studies that have employed CW-NIRS devices. Furthermore, to highlight the fact that, with NIRS, it is not possible to discriminate the contributions of hemoglobin and myoglobin species, the terms oxy[Hb + Mb], deoxy[Hb + Mb], and total[Hb + Mb] are frequently reported in muscle studies. In this review, the shorter acronyms will be used.  
When TD or FD instruments are used, besides these physiological parameters, it is also possible to investigate the absolute values of \(\mu_{\alpha}\), \(\mu_{\delta}\), and DPF.  
Overall, the NIRS derived parameter that seems to be more representative of muscle oxygenation metabolism (and the more studied, to present) is HHb [5; 66], which reflects the balance between O\({}_{2}\) delivered and O\({}_{2}\) consumption and, thus, can be indicator of the physiological status and performance of a muscle.
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 5 Results and Discussion
### Flow Field Structure Analysis  
The variation of the spacing of the spur dikes caused the change of the flow structure near them. Studying the changes in the flow field around a spur dike enables better analysis of the local scouring morphology. Numerical calculations were used to simulate the variation of the velocity field and streamlines, turbulent flow field, and shear stress on the fixed and moveable beds.
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 5 Experiments
### Synthetic Networks  
In this section, we construct several synthetic networks to assess the performance of various graph clustering algorithms. We use the LFR benchmark network [9], which facilitates easy modifications of degree distributions and cluster sizes. Due to the high time complexity of GN and SCAN, our comparisons are limited to clustering algorithms such as DistanceSCAN, DPSCAN, GS*-Query, and FluidC.  
**Cluster Density:** To assess the algorithms' responsiveness to network variations, the average degree (\(k\)) was adjusted, maintaining a fixed mixing parameter (\(\lambda=0.1\)) while varying \(k\) from 5 to 25. Each network consisted of 5000 nodes. As \(k\) increased, the performance of five methods, evaluated by NMI, is displayed in Figure 5(a). In Figure 5(a), DistanceSCAN shows moderate performance at lower \(k\) values, but its performance improves as \(k\) increases. In contrast, FluidC's performance diminishes with increasing \(k\). ADPSCAN maintains stable performance across the range, particularly excelling at \(k=15\), where it nearly achieves perfect clustering.  
**Inter-Cluster Edge:** To evaluate the algorithms' sensitivity to changes in inter-cluster edges, the \(\lambda\) was varied from 0.1 to 0.5, with \(k=15\), generating networks with different inter-cluster connectivity. Each network comprised 5000 nodes. As \(\lambda\) increased, the performance of all five methods, assessed by NMI, is depicted in Figure 5(b), Figure 5(b) shows a sharp decline in performance for all algorithms at \(\lambda=0.4\), with GS*-Query and FluidC experiencing the most rapid declines. Both ADPSCAN and GS*-Query approached perfect clustering at \(\lambda=0.1\).  
**Network Scale:** To assess the adaptability of algorithms to networks of varying sizes, we fixed the mixing parameter \(\lambda=0.1\) and the average degree \(k=15\), and varied the number of nodes in the network from 500 to 2500. This setup allows us to observe the performance and stability of the algorithms as they handle networks of different sizes. As shown in Figure 5(c), ADPSCAN consistently performs well, peaking at 500 nodes; DistanceSCAN steadily improves; DPSCAN peaks at 500 nodes but declines afterward; FluidC exhibits significant fluctuations on smaller datasets but gradually stabilizes as the node  
Figure 6: Performance of different algorithms on the LFR benchmark networks.  
size increases.; GS*-query starts lower but shows significant improvement, highlighting each algorithm's scalability and efficiency.
## 3 Results
### Data Acquisition
#### 3.1.1 Three-Dimensional Digitization of the Church  
The digitization of the exterior of the church was based on photogrammetric measurements and was carried out using a UAS, model DJI M30T, produced by SZ DJI Technology Co., Ltd (Shenzhen, Guangdong, China). It features three cameras that shoot at the same time: a wide camera (12 megapixels), a zoom camera (48 megapixels), and a thermal camera (640 \(\times\) 512 pixels). There are a couple of high trees on the church's west side, where the entrance portal is located, as well as on the north narthex wall (see the red-marked area in Figure 3), making it impossible to access with the UAS. For this reason, these sectors were digitized using a Sony A7R3 camera (42 megapixels) from the ground level, with a monopod for higher perspectives. For alignment purposes and scale, both image setsincluded 12 ground control points (coded targets) around the church, with the precise distance measured between them. In total, 1556 images from the wide camera were used for aerial photogrammetry processing, along with 1114 images for the ground level. To have a uniform visual aspect in both image sets, a colour calibration chart and a white balance chart were also used.  
Both image sets were processed in Agisoft Metashape PRO v2.0 at the ultra-high-quality setting. For the aerial image set, with a 12-megapixel wide camera (see Figure 4), the final mesh amounted to 134 million polygons, while the ground image set had a sum of 113 million polygons. As part of the optimization process, two models were exported from each image set processed in Wavefront OBJ file format: a high-poly mesh (10 million polygons) and a low-poly mesh (500,000 polygons). The low-poly meshes will be the base models for the following data preparation stage, so they also had high-resolution textures (8192 \(\times\) 8192 pixels) exported as well.  
Figure 4: Aerial photogrammetry result and camera positions.  
Figure 3: Golesti Church. **Left**: General aerial view. **Right**: Top view with aerial limits on the west and north sides (Photos: L.M.A.).
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 5 Discussion
### Factors Affecting the Performance of the System  
One of the factors affecting the retrieval performance is the size of the visual vocabulary. We have conducted experiments with visual vocabulary of different sizes to determine the optimal performance of the proposed representation as discussed in the preceding sections. Another factor affecting the performance of the system is the ratio of the training images used to train the classifier. Figure 19 provides a comparison of different training test ratios i.e., 70:30, 60:40, 50:50 for the Corel-1K image dataset. It can be seen that the performance of the system increases at higher training test ratios. However, to be consistent with related approaches [8], 50:50 is used to report the precision and recall retrieval results for the experimental comparisons presented in Section 4.
## 3 Interval-Based IGM
### Local Part: Interval-Based GM  
Interval-based GM is designed using the IFCM clustering method, as described in Section 2.2. The interval-based GM modeling of the local part is modeled using input-error data, which combine the input data and the error obtained from the global part, rather than the general input data. When input-error data enter the input of the interval-based GM, the intervals are generated in the output and a cluster corresponding to each interval is generated in the input space. Rules can be created through the intervals and clusters created here. The results obtained for the IFCM clustering method in Layer 3 are combined to generate the interval-based GM output and with the LR model output for calculating the final output of the interval-based IGM. Then, the LR's error is compensated. Therefore, the model's performance is enhanced. The procedure of interval-based IGM is described as follows.  
1. Firstly, the LR model is designed from numerical data. The error is obtained by using the LR model, and input-error data are generated.
2. The intervals are created in the error space, by using Cases 1, 2, and 3.
3. IFCM clustering is performed on the input-error data points. The numbers of intervals and clusters are selected by the user.
4. The clusters in each interval are estimated, and the output value is calculated by the interval determined through the interval. GM's output obtains the number of fuzzy in the form of intervals.
5. The interval-based IGM's output is calculated by combining the outputs of the LR model and interval-based GM.
###### Abstract  
For space-based gravitational wave detection, a laser interferometric measurement system composed of a three-spacecraft formation offers the most rewarding bandwidth of astrophysical sources. There are no oscillators available that are stable enough so that each spacecraft could use its own reference frequency. The conversion between reference frequencies and their distribution between all spacecrafts for the synchronization of the different metrology systems is the job of the inter-spacecraft frequency setting strategy, which is important for continuously acquiring scientific data and suppressing measurement noise. We propose a hierarchical optimization algorithm to solve the frequency setting strategy. The optimization objectives are minimum total readout displacement noise and maximum beat-note frequency feasible range. Multiple feasible parameter combinations were obtained for the Taiji program. These optimized parameters include lower and upper bounds of the beat note, sampling frequency, pilot tone signal frequency, ultrastable clock frequencies, and modulation depth. Among the 20 Pareto optimal solutions, the minimum total readout displacement noise was 4.12 pm/\(\sqrt{\mathrm{Hz}}\), and the maximum feasible beat-note frequency range was 23 MHz. By adjusting the upper bound of beat-note frequency and laser power transmitted by the telescope, we explored the effects of these parameters on the minimum total readout displacement noise and optimal local laser power in greater depth. Our results may serve as a reference for the optimal design of laser interferometry system instrument parameters and may ultimately improve the detection performance and continuous detection time of the Taiji program.  
frequency distribution scheme; Taiji program; hierarchical optimization algorithm; laser interferometry system 2023239431. [[https://doi.org/10.3390/s2329431](https://doi.org/10.3390/s2329431)]([https://doi.org/10.3390/s2329431](https://doi.org/10.3390/s2329431))
## 4 Direct Cooperation via In-Band Energy Level Patterns
### Enhanced CSAT
The creation of the new slots will be decided based on the same predefined mechanisms for both LTE and Wi-Fi networks. Moreover, the length of the LTE and Wi-Fi slots will be decided based on the common scheduling mechanism that is used by LTE and Wi-Fi. Furthermore, during a time slot, the eNBs and the APs will measure the channel utilization in order to further adapt the slots for the next frame. As we mentioned above, the TTX phase is divided into Wi-Fi and LTE slots. During the Wi-Fi slots, the different nodes will be able to compete for the medium using the traditional CSMA/CA method, as has been described in Section 3.1.2. On the other hand, during an LTE slot, one or more eNBs can schedule transmissions to their attached UEs. Up to today, much work has been done towards interference mitigation between different eNBs, known as eICIC, which was initially introduced in 3 GPP LTE Release 10 [19]. Additionally, eNBs could sense the medium before a transmission to further reduce interference. Figure 9 shows the flowchart of the algorithm described above. From the description of the method above it is clear that such a cooperation method, for all the advantages that it offers such as intra-technology synchronization, interference management and indirect technology recognition, also has some aspects that require further investigation. For instance, mechanisms should be included that are capable of merging separate co-located networks that have been set up using this cooperation technique or mechanisms that can cope with potentially lost special patterns. This is outside the scope of this paper. The described technique requires small changes for both LTE eNB and Wi-Fi AP. Firstly, both wireless technologies must be able to transmit, detect and recognize the different energy level patterns that have been described above for synchronization and scheduling purposes. Moreover, the MAC protocols of LTE and Wi-Fi need to be modified in order to create and access the LTE and Wi-Fi slots based on the received TIDENT. Figure 8: Enhanced CSAT time frame.
# Smartphone-Based NFC Potentiostat for Wireless Electrochemical Sensing
## 2 Materials and Methods
### Ascorbic Acid Measurement  
To demonstrate the application of the NFC potentiostat, the detection limit of standard ascorbic acid was determined by cyclic voltammetry. In this experiment, different concentrations of ascorbic acid, ranging from 0.1-1 mg/mL, were drop-cast onto a commercial screen-printed carbon electrode. Cyclic voltammetry was performed over the potential range from 200 to 800 mV, and the current at 550 mV was used to determine the ascorbic acid concentration. Commercial drinking water was spiked with ascorbic acid into different concentrations. The concentration of ascorbic acid in spiked commercial drinking water was analyzed from the calibration curve.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 1 Introduction
### Organization  
The remainder of the paper is organized as follows. The considered system model is introduced in Section 2. Section 3 details the fronthaul signal processing techniques employed to cope with the impairments of the A-RoC fronthaul links. The eMBB rand URLLC information rates are discussed in Sections 4 and 5 for OMA and NOMA, respectively. Numerical results are presented in Section 6. And Section 7 concludes the paper.
## 5 Experiments
### Model Performance  
Table 2 details the node classification accuracy of the model on seven public datasets. Three teacher model architectures were used for the teacher model: GCN, GAT, and GraphSAGE. NOSMOG served as the baseline model for comparison. All experiments were conducted in both transductive and inductive settings, and the results in the production (prod) setting represent a combination of tran and ind results in a certain proportion. It can be observed that the performance impact of the three different teacher model architectures on the student model was not significant. This suggests that a well-performing teacher model does not necessarily transfer effective knowledge, and conversely, an average-performing teacher model may still distill into a better-performing student model. Therefore, the results of the student model were obtained using GraphSAGE as the teacher model architecture. The proposed KDGCL method demonstrated the ability to enhance the performance of the student model to varying degrees in both transductive and inductive settings, narrowing the gap in classification accuracy between the student model and the teacher GNN.  
Additionally, KDGCL maintained the same parameter count as the baseline model, NOSMOG, thereby demonstrating superior competitiveness in practical environments.  
In Table 2, \(\uparrow\) indicates increase and \(\downarrow\) indicates decrease. Under the same experimental settings, the proposed model was compared with a GNN, an MLP, and the best-performing NOSMOG. In the transductive setting, our method outperformed the baseline method on all datasets, achieving an increase of 1.63% in node classification accuracy. Particularly notable performance improvements were observed on the Citeseer and Cora datasets, reaching 3.74% and 2.93%, respectively. In the inductive setting, KDGCL outperformed the baseline model on 6 out of 7 datasets, with significant improvements observed on the Citeseer, A-computer, and Arxiv datasets (by 1.53%, 0.95%, and 0.95%, respectively). On average, the node classification accuracy was improved by 0.8% compared to the baseline model. Furthermore, comparing the standard deviations of the results between KDGCL and the baseline model, the standard deviation of the results obtained with our method was smaller than that for the baseline model, indicating that the proposed method is more stable.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
## 3 Proposed Algorithm
### Optimizing \(P\),\(\Theta\) Given \(w_{k},\rho_{k},t_{k}\)  
After solving the problem (P3), the next step is to find suitable \(\mathrm{P}\) and \(\Theta\) so that they satisfy the constraints of (P2), and we define \(\theta=\left(p_{1}e^{i\theta_{1}},\cdots,p_{N}e^{i\theta_{N}}\right)^{H}\in \mathbb{C}^{N\times 1}\). To make the problem easier to deal with, we use the variables \(a_{k,i}=\mathrm{h}_{b,k}^{H}\mathrm{w}_{i}\) and \(\theta^{H}b_{k,i}=\mathrm{h}_{r,k}^{H}\mathrm{Y}\mathrm{G}\mathrm{w}_{i}\), where \(Z_{k}=\mathrm{diag}\Big{(}\mathrm{h}_{r,k}^{H}\Big{)}\mathrm{diag}(\mathrm{h }_{r,k}),\forall k\in\mathcal{K}\), \(Q_{k}=\mathrm{diag}(\mathrm{G}\mathrm{w}_{k})(\mathrm{diag}(\mathrm{G} \mathrm{w}_{k}))^{H}\), and \(b_{k,i}=\mathrm{diag}\Big{(}\mathrm{h}_{r,k}^{H}\Big{)}\mathrm{G}\mathrm{w}_{i}\). This way, the optimization problem (P4) can be obtained via the following steps. For (P2), due to \(\mathrm{h}_{k}^{H}=\mathrm{h}_{r,k}^{H}\mathrm{P}\Theta\mathrm{G}+\mathrm{h}_{ b,k}^{H}\), (16) can be rewritten by the following process:  
\[\mathrm{h}_{k}^{H}=\mathrm{h}_{r,k}^{H}\mathrm{Y}\mathrm{G}\mathrm{w}_{k}+ \mathrm{h}_{b,k}^{H}\]  
\[\left|\mathrm{h}_{k}^{H}\mathrm{w}_{k}\right|^{2}=\left|\mathrm{h}_{r,k}^{H} \mathrm{Y}\mathrm{G}\mathrm{w}_{k}+\mathrm{h}_{b,k}^{H}\mathrm{w}_{k}\right|^{ 2}=\left|\theta^{H}b_{k,k}+a_{k,k}\right|^{2}\]  
\[\sigma_{v}^{2}\|\mathrm{h}_{r,k}^{H}\mathrm{P}\Theta\|^{2}=\sigma_{v}^{2}\Big{(} p_{1}e^{i\theta_{1}},\cdots,p_{N}e^{i\theta_{N}}\Big{)}\mathrm{diag}\Big{(} \mathrm{h}_{r,k}^{H}\Big{)}\mathrm{diag}(\mathrm{h}_{r,k})\Big{(}p_{1}e^{i \theta_{1}},\cdots,p_{N}e^{i\theta_{N}}\Big{)}^{H}.\]  
\[\sigma_{v}^{2}\|\mathrm{h}_{r,k}^{H}\mathrm{P}\Theta\|^{2}=\sigma_{v}^{2} \theta^{H}Z_{k}\theta\]\[\frac{\big{|}\theta^{H}b_{k,k}+a_{k,k}\big{|}^{2}}{\sum\limits_{ \begin{subarray}{c}j=1\\ j\
eq k\end{subarray} }^{K}\big{|}\theta^{H}b_{k,j}+a_{k,j}\big{|}^{2}+\sigma_{v} ^{2}\theta^{H}Z_{k}\theta+\widetilde{\sigma}_{k}}\geqslant\gamma_{k}.