esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Critical Analytical Parameters
### Salt Content of the Sample  
OSPWs may contain high levels of natural salts from some oil sands formations or from high levels of water reuse in the bitumen extraction process. Salting-out effects in MS analysis have been noted for crude oil acids in sea water, and the same may be true in OSPW with high salt content [21]. Salinity values over 2800 mg/L have been reported in OSPW samples [22], and these concentrations may affect the partitioning behaviour and separation of NAFCs by the formation of ion-pair complexes between the salt cations and NA anions.  
A comparison of NAFC extracts in a DCM/ACN solvent mixture showed significant spectral differences when a saturated salt solution was added [21]. Intensities were en  
Figure 4: Effect of solvent on FT-ICR MS abundances of z-series NAs for DCM/acetonitrile, Milli-Q water/acetonitrile, and 1-octanol/acetonitrile for O\({}_{2}\)-NAs (**a**,**c**,**e**) and O\({}_{4}\)-NAs (**b**,**d**,**f**) [19].  
hanced for shorter carbon chain acids with lower z-values for C\({}_{\rm n}\)H\({}_{\rm 2n-z}\)O\({}_{2}\), C\({}_{\rm n}\)H\({}_{\rm 2n-z}\)O\({}_{3}\), and C\({}_{\rm n}\)H\({}_{\rm 2n-z}\)O\({}_{4}\) species but suppressed for longer chain, higher z-value acids. The authors attributed this decrease in response to salting-out effects. High sodium content can lead to the formation of anionic sodium dimers [23], with masses corresponding to [2M-2H + Na]\({}^{-}\), where M is the mass of the neutral compound. The mass range would be approximately double the expected value for the [M-H]\({}^{-}\) anion monomer. The acids forming the dimer may not be the same species, which further complicates spectral evaluation. A possible remedy to these sodium-bridged dimers would be to reduce the sodium in the source of the MS when the acids are present by injecting the sample onto an LC column rather than infusing the sample directly into the source. Sodium ions would be unretained on the column and directed to waste. Alternatively, off-line extraction of NAFCs from water can be carried out using solid phase extraction (SPE) by adjusting the pH to <3 for SPE loading, removing salts with the subsequent wash step, followed by elution of the NAs with organic solvent [24]. Sodium adducts in positive mode ESI can be reduced by adding ammonium to the mobile phase, or by decreasing source temperatures. The same may be true in negative mode. After dimer formation in the source, increasing the TOF fragmentor voltage may also be used to fragment dimers into their monomers. Care must be exercised when using this technique as it can lead to unwanted fragmentation of ions, reducing the abundance of precursor ions.
# Stressing State Analysis of CFST Arch Supports in Deep Roadway Based on NSF Method
## 2 Method and Theories of Structural Stressing State  
### Method of Numerical Shape Function  
In the structure analysis, the experimental data could reflect the working behavior characteristics of the global/local structure to some extent indeed, while due to the limitation of measuring instruments, etc., it is not enough to fully demonstrate the structural response mechanism and characteristics. Hence, a numerical shape function (NSF) interpolation method, which has exact physical significance, is proposed to interpolate/expand the limited experimental data in order to obtain more information of the structure. The NSF method applies generalized numerical simulation of a specific ideal physical model to construct discrete weighting function based on the concept of shape function in the finite element method (FEM), so it could not only overcome the shortcoming of conventional shape functions but also meet the accuracy requirements for in-depth experimental analysis [27; 28].  
In order to vividly introduce this method, the deflection field of a circle section with four measuring points is used as an example. As shown in Figure 1a, the section is constructed and properly meshed with element Shell 181 by ANSYS, and 4 measuring points are adopted as the basic nodes of the numerical shape functions. Then, a unit displacement at measuring point \(i\) along the z-axis are applied to the section, while the other ones are fixed and restricted without rigid z-directional displacements. Hence, the numerical shape function \(\mathbf{N}_{i}\) of the \(i\)th measuring point can be obtained, such as \(\mathbf{N}_{1}\) and \(\mathbf{N}_{2}\) shown in Figure 1b,c. Without considering large deformation or elastoplasticity, the displacement field constructed by [PERSON]'s theorem is independent of loading paths, and the simulative results with explicit physical meanings can be superimposed linearly. Therefore, according to the deflection of the measured points, the deflection field is obtained by Equation (2),  
\[\mathbf{D}=\sum_{i=1}^{m}u_{i}\mathbf{N}_{i},\ \mathbf{N}_{i}=\left[N_{i}(x_{1}), \ N_{i}(x_{2})\cdots N_{i}(x_{j})\cdots N_{i}(x_{n})\right] \tag{1}\]  
where \(\mathbf{D}\) is the deflection field of the section, \(\mathbf{N}_{i}\) is the numerical shape functions of \(i\)th measuring point, \(N_{i}(x_{j})\) is the function value at element node \(x_{j}\), \(n\) is the total number of element nodes, and \(m\) is the total number of measured points, here \(m=4\).  
Figure 1: Finite element model and contour maps of numerical shape functions: (**a**) The square plate model; (**b**) shape function \(N_{I}\); (**c**) shape function \(N_{2}\).  
Correspondingly, the limited measuring strain on the cross section could be expanded by the shape function interpolation to obtain the strain filed, and the stress filed could be gained through the constitutive relation of the material. Hence, the contour maps of various fields for structure responses could be depicted through the expanded experimental data above, as well as the internal forces distribution of the arches.
## 5 Conclusions  
This study evaluated how much GPP variation can be explained by spatio-temporally continuous Sentinel-2 satellite-based red-edge data in grassland and evergreen forests. We found that GPP based on CIr \(\times\) PAR\({}_{\text{in}}\) had the highest correlation with GPP\({}_{\text{EC}}\) across grassland sites, and it also showed low bias with GPP\({}_{\text{EC}}\) at each forest site. The networking system of Sentinel-2A and B enhanced the ability to track the seasonal canopy chlorophyll changes, which improved the temporal representativeness of GPP. The high-spatial-resolution remote-sensing data specifically characterized the spatial GPP variance over a heterogeneous landscape. The GPP\({}_{\text{CIr}}\) and GPP\({}_{\text{EVI}}\) based on the coefficients for each site exhibited better performance with GPP\({}_{\text{EC}}\) than the MOD17A2H product. The coefficients derived for the empirical relationship of CIr \(\times\) PAR\({}_{\text{in}}\) with GPP\({}_{\text{EC}}\) were largely transferable between the grassland sites. This result indicated that the vegetation red-edge information-based GPP\({}_{\text{CIr}}\) was suitable for mapping spatio-temporally continuous GPP in grasslands at regional and continental scales.  
However, as the Sentinel-2 data's revisit cycle is not as high as other medium-spatial-resolution satellite data and the VI-based GPP estimation method is more related to seasonal chlorophyll change, the vegetation red-edge-based empirical relationship is more suitable for capturing the seasonal change of GPP. Further research on satellite-based GPP mapping with high-spatial-resolution data should focus on (1) improving the number of effective data during the high-cloud-cover seasons by using multisource data, (2) calibrating the coefficients with more in situ measurement data under cloudy and clear sky conditions, and (3) investigating whether there is a series of transferable coefficients to estimate GPP under multiple EBF sites.  
Conceptualization, S.L. and J.L.; methodology, S.L.; writing--original draft preparation, S.L. and J.L.; writing--review and editing, Q.L., L.L., J.Z., and W.Y.; supervision, J.L. and Q.L.; funding acquisition, J.L. and Q.L.  
This research was funded by Key Development Project of the Chinese Academy of Sciences, grant number KFZD-SW-316-1; the National Natural Science Foundation of China, grant number No. 41671374; the GF6 Project, grant number 30-Y20A03-9003-17/18 and \"The APC was funded by Key Development Project of the Chinese Academy of Sciences.  
We thank the OZflux provides carbon flux data. We thank [PERSON] for offering valuable comments on this paper. We also thank [PERSON] for proofreading this paper.  
The authors declare no conflict of interest.
## 5 Discussion  
The DSM uses the mean position of each satellite pair's IPP and the intermediate position of the network to interpolate the ionospheric delay of users, which takes the variation in the ionospheric delay of each satellite pair in different directions into account fully and is easy to implement. The interpolation accuracy of DSM is better than that of DIM and USM, which are 1.4 cm, 3.2 cm, 2.2 cm, and 1.4 cm, respectively, in four scenarios. Compared with DIM whose interpolation accuracy is second only to the DSM, the interpolation accuracy represented improvements of 22.2% and 25.6% in the medium-scale and large-scale networks, respectively. What is more, for the scenario with three reference stations, the interpolation accuracies of DIM and DSM were no different from those for four reference stations, indicating that the server can still try to provide ionospheric correction service under the condition of fewer reference stations. In contrast, USM could not provide service because it lacked a sufficient number of reference stations. For users, the most concerning problem is the positioning performance after using interpolation correction. When the mean squared errors of the ionospheric delays by experience were determined and matched the interpolation accuracy basically on the whole, the positioning performance of DSM was better than that of DIM, especially in the large-scale networks. Hence, the DSM provides a reference for using the atmospheric delay coefficient based on the IPP to characterize the SD ionosphere delay, which can advance the application of PPP-RTK.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Coordinate**} & \multirow{2}{*}{**Fixing Percentage**} & \multirow{2}{*}{**TTF**} & \multicolumn{3}{c}{**Percentage (\%)**} \\  & & **(\%)** & & **(s)** & **1 min** & **2 min** & **3 min** \\ \hline DIM & \multirow{2}{*}{horizontal} & 71.9 & 46.3 & 67.5 & 89.2 & 98.9 \\ DSM & & 84.8 & 30.4 & 78.2 & 91.3 & 97.8 \\ \hline DIM & \multirow{2}{*}{three-dimensional} & 67.8 & 48.9 & 65.4 & 88.5 & 98.9 \\ DSM & & 82.2 & 33.1 & 77.0 & 89.2 & 97.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of PPP-RTK simulating dynamic convergence results for large-scale networks.  
Figure 9: Positioning bias at TTFF (**left**) and RMS values after convergence (**right**) for large-scale networks.
## 4 Discussion
### Challenges and Limitations
#### 4.4.4 Organizational and Human Factors in System Adoption  
Beyond technical considerations, the successful implementation of blockchain-based circular traceability systems confronts significant organizational and human challenges that can determine adoption success regardless of technological merit. Change management emerges as a critical factor, as supply chain participants must transition from established paper-based or siloed digital systems to collaborative blockchain networks requiring new workflows, training protocols, and inter-organizational coordination mechanisms. Mid-tier suppliers, particularly in developing economies, may lack the organizational capacity to integrate blockchain systems without substantial workforce development initiatives and process reengineering support. The human element becomes particularly pronounced in data entry accuracy and consistency, as the \"garbage in, garbage out\" principle means that blockchain immutability amplifies rather than corrects human errors in initial data recording.  
Cultural resistance to transparency represents another substantial barrier, especially in supply chains where opacity has historically protected competitive advantages or masked compliance deficiencies. Recyclers and waste management entities may resist detailed transaction logging if it exposes operational inefficiencies or regulatory gaps, while suppliers may fear that enhanced visibility could lead to increased scrutiny from buyers or regulatory authorities. Trust-building initiatives, stakeholder workshops, and phased implementation strategies become essential to address these human factors, requiring significant investment in change management processes that extend far beyond technical deployment costs.
## 4 Performance Analysis  
Capitalizing on the previous derived statistical characteristics of the single-link and MRC systems, some performance metrics of interest, such as the outage probability, the ABEP/ASEP, the ergodic capacity, and the effective capacity are evaluated and discussed in this section, respectively.
## 4 Analysis and Results
### Indirect Effects with Bootstrapping  
The indirect effect of the bootstrapping test has better statistical power than the causal method and coefficient product method [55; 56]. Additionally, the bootstrapping method does not need to confirm the normal distribution of the sample, as the coefficient product method (such as the B-K method) does. Bootstrapping uses the sampling with replacement method, which draws a sample from the original samples until reach the same number as one process. [PERSON] [57] suggested repeating this process at least 1000 times and recommended implementing this process 5000 times. Therefore, when the process repeats 1000 times, there would be 1000 indirect effects (a*b). This 1000 indirect figures would form its own data distribution. Then, the standard error and confidence interval can be produced. In short, bootstrapping can provide confidence interval with a statistical power of indirect effect, especially, bias-corrected bootstrapping [56; 58].  
The examination of the indirect effects, including intrinsic motivation \(\rightarrow\) learning attitude \(\rightarrow\) well-being, is shown in Table 7. The confidence interval does not include 0, which means the indirect effect exists. Then, the second indirect effect is from extrinsic motivation through learning attitude to well-being. The confidence interval also does not include 0. The indirect effect exists.
### Electrical Properties of Rocks and Ores  
The resistivity and chargeability parameters of typical rocks and ores in the mining area are detailed in Table 1, which reveals significant correlations between different lithologies and SSIP anomalies. High-resistivity rocks, such as granite, marble, and quartz sandstone (resistivity \(>\) 350 \(\Omega\cdot\mathrm{m}\)), generally exhibit low chargeability (<1%), primarily due to the insulating properties of their silicate mineral composition, which restricts charge migration. In contrast, mineralized bodies display distinct low-resistivity and high-chargeability characteristics: vein-type orebodies are most prominent with extremely low resistivity (55 \(\Omega\cdot\mathrm{m}\)) and exceptionally high chargeability (41%), resulting from continuous electronic conduction networks formed by sulfide minerals coupled with strong polarization at metal-electrolyte interfaces. Although disseminated tungsten-tin ores show relatively higher resistivity (206 \(\Omega\cdot\mathrm{m}\)), their 2.1% chargeability remains significantly elevated compared to wall rocks, reflecting the electrode polarization effects of isolated metal particles. Particularly noteworthy are pyrite and carbonaceous shale, which, despite their moderate resistivity (200-290 \(\Omega\cdot\mathrm{m}\)), generate high chargeability anomalies of 11% and 3.7% respectively--the former originating from electrochemical noise caused by surface redox reactions, and the latter associated with semiconducting properties induced by graphitized organic matter. These interference sources substantially increase the complexity of SSIP data interpretation. The variations in electrical parameters of skarn (360 \(\Omega\cdot\mathrm{m}/0.35\%\)) and scheelite-bearing marble (90 \(\Omega\cdot\mathrm{m}/0.25\%\)) directly reflect differing degrees of metallic mineral introduction during hydrothermal alteration. These electrical property differences provide crucial criteria for distinguishing mineralization-related anomalies from interference anomalies in SSIP exploration. Of particular significance, granite samples collected from the pluton top and near contact zones with sedimentary rocks exhibit markedly reduced resistivity values compared to regional background levels.
# Intelligent Reflective Surfaces for Wireless Networks:
## Abbreviations  
The following abbreviations are used in this manuscript:  
**SDP**: semidefinite programming
**NN**: neural network
**PBIT**: passive beamforming and information transfer
**SER**: symbol-error-rate
**CNN**: convolutional neural network
**SAA**: sample average approximation
**RL**: reinforcement learning
**LOS**: line-of-sight
**DC**: direct current
**ISL**: intelligent spectrum learning
**NMSE**: normalized mean-square error
**OMA**: orthogonal multiple access
**OMP**: orthogonal matching pursuit
**DDPG**: deep deterministic policy gradient
**OFDM**: orthogonal frequency division multiplexing
**DRL**: deep reinforcement learning
**CS**: compressive sensing
**TS-OMP**: twin-stage orthogonal matching pursuit
**BCD**: block coordinate descending
**MEC**: mobile edge computing
**QoS**: quality of service
**FWOL-FPHC**: fuzzy win or learn fast-policy hill-climbing
**NLOS**: non line-of-sight
**FPGA**: field-programmable gate array
**RCG**: Riemannian conjugate gradient
**TDMA**: time-division multiple access
**PIN**: positive-intrinsic negative
**DF**: decode-and-forward
**MISO**: multiple-input single-output
**SIMO**: single-input multiple-output
**UAV**: unmanned aerial vehicle
**SINR**: signal-to-interference-plus-noise ratio
**PG**: projected gradient
**SNR**: signal-to-noise ratio
**SDR**: semidefinite relaxation
**mmWave**: millimeter-wave
## 4 Discussion
PRF and CGF were applied bilaterally during the root coverage procedure, and the outcome of the treatment after three months was compared. Histological analysis of the two clots was also performed. A root coverage of 100% was achieved with both PRF and CGF membranes. Despite this, the side treated with CGF showed less postoperative discomfort and accelerated wound healing (by the 10 th postoperative day), with respect to the side treated with PRF [42]. Recently, a three-arm parallel clinical study compared advanced PRF (A-PRF) and CGF as an adjunct to guided tissue regeneration (GTR) procedures in periodontal intrabony defects (IBD) [43]. A group treated with GTR alone was used as control. Standard periodontal parameters (probing pocket depth, clinical attachment level (CAL), intrabony component (IC) depth, radiographic bone level (RBL) and bone defect filling) were measured preoperatively and after 6 months. A-PRF and CGF showed similar effectiveness in improving GTR clinical and radiographic outcomes as compared to control in IBD treatment [43]. In a recent three-arm randomized trial on lower third molar surgery, [PERSON] et al. compared CGF, advanced PRF (A-PRF), and natural healing [44]. They evaluated VAS score, analgesics, edema, trismus in the first post-operative week, and found no significant benefits of CGF or A-PRF over control in any of the variables assessed. This study is hardly comparable to ours, for different reasons. First, they did not evaluate healing index or socket closure, which are our main objective outcomes; second, it was a parallel study, as opposed to ours, and comparing subjective outcomes such as VAS scores between different subjects, should be performed cautiously. Furthermore, A-PRF is obtained with a much different centrifugation system as compared to the one used in the present study. As far as we know, the present split-mouth study is the first reporting clinical results of a comparison between CGF and L-PRF obtained with the Intra-Lock system. The latter was reported to be the best among different centrifugation systems for L-PRF, and its efficacy in improving tissue healing is widely documented in several oral surgery procedures [40].
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 4 Methodology
### Byzantine Mechanism and Privacy Preservation  
In our implementation, we add a simulator on the worker side to simulate Byzantine failures and send malicious values to the servers. It turns out that such a simulator can also be used to generate random noises for privacy preservation. On the server side, the random noises will then be treated as values from Byzantine workers. Formally, we introduce the Byzantine mechanism as follows.  
**Definition 7**.: _In the Byzantine mechanism denoted as \(\mathcal{M}_{\text{byz}}\), the \(t\)th worker sends the vector_  
\[\tilde{v}_{i}=\mathcal{M}_{\text{byz}}(v_{i})=\begin{cases}v_{i}&\text{with probability $1-p_{\text{byz}}$;}\\ \text{noise}\sim\mathcal{D}_{\text{noise}}&\text{otherwise},\end{cases}\]  
_where \(v_{i}\) is the correct vector. In other words, with probability \(p_{\text{byz}}\), the worker sends the value noise, which is randomly drawn from the distribution \(\mathcal{D}_{\text{noise}}\) instead of the correct value \(v_{i}\), to the server._
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 4 Simulation Results  
To verify the effectiveness of the proposed Bezier curve-based shaping approach, the trajectory planning for the MR-SPS in different time of the year is conducted. The order of the Bezier curve function is selected as \(n_{\gamma}=n_{\theta}=20\) and the number of LG discrete points is selected as \(m=50\). The nonlinear programming problem is solved using sequential quadratic programming. The parameters of the MR-SPS are listed in Table 1.
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology
### First-Stage Prediction  
This preliminary step ensures that the data distribution will converge. This step made use of three ECG signal prediction functions that were proposed in previous studies namely linear, slope, and adaptive trending functions by [PERSON] et al. [6], [PERSON] et al. [18] and [PERSON] et al. [23], respectively. These functions were simulated and tested to predict a given ECG signal. Table 1 lists the prediction functions and their respective compression rate on the ECG signals provided by the MIT-BIH arrhythmia database [24]. The adaptive trending function had the highest compression rate achieved.