esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Calibration Performance with Different Input Signals  
The compensation performance of the proposed algorithm with different input signals is presented in this subsection. The TIADC considered in this section is a 4-TIADC with the same nonlinear parameters as in Example 1.  
**Example 3.** In this example, a multi-band signal, which is generated by passing a zero-mean white noise through a dual pass-band filter, is fed into the 4-TIADC. Figure 10 shows the spectra of the multi-band signal with and without calibration. The noise level in the Nyquist band is improved from around \(-50\) dB to less than \(-80\) dB as the figure illustrates. From the figure, we can clearly see that the noise outside the passband of the high pass filter is also greatly suppressed. To quantify the performance improvement after calibration, we adopt the error vector magnitude (EVM) [25] as the figure of merit. The EVMs before and after calibration are \(-45.82\) and \(-80.82\) dB, respectively.  
Figure 8: The convergence behavior of the estimated parameters \(\hat{c}_{k,l}[n]\). (**a**) Second order nonlinear parameters; (**b**) third order nonlinear parameters.  
Figure 9: The spectra of 8-TIADC (**a**) before and (**b**) after calibration.  
The convergence curves for 4-TIADC with multi-band noise input are shown in Figure 11. The estimated nonlinear parameters converge after about \(1.0\times 10^{4}\) samples, and they converge to the true nonlinear parameters as in Example 1.  
**Example 4.** In this example, we evaluate the calibration performance of our method in 4-TIADC with a band-pass multi-tone signal. A multi-tone signal with 11 sinusoids spanning from \(0.305\pi\) to \(0.705\pi\) is adopted. Figure 12 shows the spectra with and without calibration. The SNDR and SFDR before calibration are \(39.6\) and \(43.87\) dB, respectively, while the SNDR and SFDR after calibration are \(74.79\) and \(81.33\) dB, respectively.  
Figure 11: The convergence behavior of the estimated parameters \(\hat{c}_{k,l}[n]\) for dual band noise input.  
(**a**) Second order nonlinear parameters; (**b**) third order nonlinear parameters.  
Figure 10: The spectra of 4-TIADC output with and without calibration for dual band noise input.
## 3 Results and Discussion
### Buildability  
Buildability denotes a printed material's ability to withstand deformation when subjected to a load [47]. Without formwork, freshly mixed materials need to possess adequate buildability to provide sufficient stiffness after extrusion. This stiffness is essential to supporting the material's own weight, the load of any upper layers, and the pressure derived from the extrusion process [48]. In this study, rectangular specimens with dimensions of 220 mm \(\times\) 120 mm \(\times\) 150 mm were printed to evaluate the buildability of each mix design. The test results are shown in Table 7 and Figure 7. From Table 7, it can be observed that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mix No.**} & **Horizontal** & **Vertical** & **Average** & **Flowability** & **Slump** \\  & **(cm)** & **(cm)** & **(cm)** & **(\%)** & **(cm)** \\ \hline S5F0 & 21.8 & 21.8 & 21.8 & 118 & 5.9 \\ SF25 & 21.5 & 21.3 & 21.4 & 114 & 5.4 \\ S0F5 & 20.6 & 20.4 & 20.5 & 105 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Flowability and slump of OPC/GGBFS/FA mortars.  
Figure 6: Extrudability of 3 DPC.  
the specimens printed with all three mix designs were stackable up to 15 layers, with no significant collapse or distortion. The height and tilt angle caused by the material's weight and the extrusion pressure were less than 0.5 cm and 2 degrees, respectively. Therefore, all three 3 DPC designs in this study meet the buildability standards. [PERSON] and [PERSON] [16] indicated that the addition of GGBFS leads to better buildability of 3 DPC. In this study, the inclusion of FA and GGBFS positively impacted buildability by improving the cohesion and stability of the mix, helping the extruded material maintain its form between layers. FA, with its spherical particle shape, helps reduce the material's tendency to sag or collapse after extrusion, while GGBFS improves the overall structural integrity of the mix.
## 7 Application Layer in WMSN
### Traffic Management and Admission Control  
The application layer of WMSN also supports, in addition to multimedia processing source coding techniques and in-network multimedia processing, network traffic management and admission control functionalities which are directly related to application-specific QoS requirements. Therefore, based on the traffic class of the application, WMSN needs to provide a differentiated service.
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 3 Results  
### Datasets  
We used three datasets in our experiments.  
LINEMOD [35] dataset is the most widely used standard dataset in the study of pose estimation. LINEMOD has collected ground truth data, including images, depth images and corresponding pose of each object, for 13 objects.  
Meanwhile, it contains point cloud model for every kind of object. There are many challenging factors in LINEMOD, such as textless objects, cluttered scenes, etc. However, the target objects labeled in each image are not obscured. LINEMOD has an average of 1200 images per type of object, but only 150 images of each type are selected for training.  
Occlusion LINEMOD [36] dataset uses the same objects in the similar scenes as LINEMOD, but the objects in the dataset are very heavily occluded from each other. This brings a particular challenge to pose estimation.  
Truncation LINEMOD [29] dataset is created by randomly cropping each image of LINEMOD. Only 40-60% of the area of the target object is visible after cropping. This results in a very high pixel deficiency of the target object. Although this phenomenon is not easy to occur in practice and this paper is aimed at improving the accuracy of pose estimation when occlusion happens, we still conducted experiments on Truncation LINEMOD as an additional exploration.  
Occlusion LINEMOD and Truncation LINEMOD are not involved in training at all. They are only used for testing.
# Carbon Dots with Up-Conversion Luminescence as pH Nanosensor
## 2 Materials, Their Characterization, and Research Methods
### Electron Microscopy of Obtained Carbon Dots  
Studies of the CDs' structure were carried out by scanning transmission electron microscopy using a high-angle annular dark field detector (HAADF STEM) on a Tecnai Osiris microscope (Thermo Fisher Scientific, USA) at an accelerating voltage of 200 kV. The results are shown in Figure 1a. Elemental analysis was carried out using a special SuperX EDS system, which includes four Bruker silicon detectors, the design of which makes it possible to obtain the distribution of chemical elements in a few minutes. The diagram of elemental analysis is presented in Figure 1b.  
As can be seen from the presented image, the synthesized nanoparticles are about 7 nm in size. The lattice fringes are clearly visible (Figure 1, inset, red lines) with the lattice parameter around 0.189 nm that corresponds to the (102) lattice fringes of graphite.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Integrated Learning Method  
To extract the characteristics of Gaussian and non-Gaussian information in the data for more accurate mode classification in multi-mode process monitoring, a dynamic integrated clustering method based on the kernel ICA-PCA (K-ICA-PCA) model [101; 102] was used for the modeling of the transition mode. Then a transition mode process monitoring method was adopted based on PCA feature extraction and multi-class SVDD pattern classification. The effectiveness of the algorithm was verified by the Tennessee Eastman (TE) process. However, when the algorithm has strict requirements on the selected initial value, an inconsistent initial value may cause a serious loss in its monitoring performance. [PERSON] et al. [103] constructed a serial integrated PCA-kernel PCA-serial PCA (PCA-KPCA-SPCA) model based on the common variable subspace and specific variable subspace decomposed by data to improve the non-linear multi-mode monitoring. The limitation of this method is that it ignores other mixing characteristics of the data. [PERSON] et al. [104] proposed a serial integration dynamic-inner PCA-PCA-KPCA model. The loading matrix and latent variable matrix were obtained by the dynamic-inner PCA iterative data matrix to construct a vector auto-regressive (VAR) model and extract dynamic features. The residual singular values of the model were decomposed to obtain the linear features using PCA. Then, the residual features were decomposed to extract the non-linear features using KPCA. Finally, the extracted features were integrated to construct a unified monitoring statistic.
## 4 Discussion
### Implications from the Nonlinear Relationship Between SIF and the PSII Operating Efficiency  
Numerous studies have found that the leaf \(F^{\prime}_{q}/F^{\prime}_{m}\) is linearly related to the canopy SIF based on ground measurements [17; 18]. This linear relationship is the scientific basis for the application of SIF in detecting the gross primary productivity (GPP) at various ecosystem levels, because SIF and GPP can be separately calculated as: \(\text{SIF}=SIF_{yield}\times APAR\) and \(\text{GPP}=\text{PSII}\) operating efficiency \(\times\) APAR. Indeed, a linear relationship between SIF and GPP was found in various ecosystems at different spatial-temporal scales [10; 11; 13; 15]. Some scientists further utilized SIF to estimate the GPP and obtained more accurate results than that of traditional remote sensing. However, almost all of these studies were conducted over a long time series that covered several plant growth periods, such as green-up, maturity, and senescence. The distributions of leaf pigments during the green-up and mature periods are relatively more homogeneous than at senescence, during which more and more chlorophyll pigment is rapidly broken down and transferred to other organs (e.g., stem and root) [42]. Thus, during the growth stage that covers the green-up and mature periods, the PSII operating efficiency may be relative even at different sites across the leaf and should be more linearly related to the SIF yield. In other words, the goodness of fit of the linear relationship between the SIF yield and the GPP was underestimated when measurements at senescence were considered, since their relationship was not linear in senescent stage as we mentioned above. Therefore, growth stages should be considered when we try to use the SIF to estimate the GPP.  
Furthermore, the linear relationship between the SIF yield and GPP has not always been established, especially when the time scale becomes smaller. [PERSON] et al. (2016) [46] demonstrated that the relationships between the flux tower GPP and the field-measured SIF (760 nm) at hourly time scales are curvilinear. They further demonstrated that the GPP-SIF relationship showed an increasing linearity when the time scale became larger (R\({}^{2}\) = 0.42, 0.74, and 0.84 for hourly, daily, and 16-day measurements) [46]. Similar results were also found based on the ground measurements at different observation regions over different plant types [11; 13; 17; 18]. Our measurements in a diurnal pattern also found a nonlinear relationship between the SIF yield and GPP. This finding indicated that estimating the GPP from the SIF at sub-daily timescales may lead to relatively inaccurate results. Due to the diurnal dynamics of the heterogeneity of the PSII operating efficiency in autumn senescence, such errors of inaccurate estimation of diurnal GPP might become larger. Therefore, more attention should be paid to this situation.
# Assessment on the Properties of Biomass-Aggregate Geopolymer Concrete
## 2 Materials and Methods
### Testing Methods  
#### 2.3.1 Thermal Conductivity  
A heat flux meter (THB 110) manufactured by Lindsays was used to measure the heat flux of a 50 mm thick plate sample, and the heat flux was converted to the thermal conductivity of the concrete by Equation (1).  
\[\lambda=\frac{\Delta\delta\Delta Q}{\Delta t\Delta S} \tag{1}\]  
where \(\Delta Q\) is the heat flow through the flat wall (W); \(\Delta S\) is the area of concrete slab (m\({}^{2}\)); \(\Delta S\) is the thickness of concrete slab (m); \(\Delta t\) is the temperature difference between the two sides  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **GGBS** & **FA** & \begin{tabular}{c} **Natural** \\ **Aggregate** \\ \end{tabular} & \begin{tabular}{c} **Crushed** \\ **Coconut Shell** \\ \end{tabular} & \
\begin{tabular}{c} **Synthesized Biomass** \\ **Recycled Aggregate** \\ \end{tabular} \\ \hline \hline
\begin{tabular}{c} Apparent \\ density \\ \end{tabular} & 2930 & 2910 & 2670 & 1270 & 1585 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Apparent density of different raw materials (kg/m\({}^{3}\)).  
Figure 3: (a) Hardening and molding process of recycled coarse aggregate of synthesized biomass. (b) Synthesized biomass regenerated coarse aggregate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Mix Proportions**} \\  & & \multicolumn{4}{c}{**(kg/m\({}^{3}\))**} \\ \hline \hline
**Coarse Aggregates** & \multicolumn{2}{c}{**Binder**} & \multicolumn{2}{c}{**Sand**} & \multicolumn{2}{c}{**Alkaline Solution**} & **Extra** \\ \hline
**Type** & **Weight** & **GGBS** & **FA** & \multicolumn{2}{c}{**Na\({}_{2}\)SiO\({}_{3}\)**} & **NaOH** & **Water** \\ \hline NA & 1010 & 280 & 70 & 799 & 79 & 193 & 15 \\ CCSA & 508 & 280 & 70 & 799 & 79 & 193 & 15 \\ SBRA & 632 & 280 & 70 & 799 & 79 & 193 & 15 \\ \hline \hline \end{tabular} Notes: The volume fractions of coarse aggregate, cement, fine aggregate, and water in the table are 40%, 11%, 30%, and 19% respectively. Natural aggregate is represented by NA; crushed coconut shell aggregate is represented by CCSA; and synthesized biomass recycled aggregate is represented by SBRA.  
\end{table}
Table 5: Mix proportions of concretes.  
of the concrete slab, (K); and \(\lambda\) is thermal conductivity. The smaller the value, the less heat passing through the material per unit thickness and the better the thermal insulation.
# Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate
## 3 Single Cell Patterning  
The substrate surface after micro fabrication and the relevant treatment can be divided into the hydrophilic part and hydrophobic part, in which the hydrophobic part is used to adhere the proteins. Bovine serum protein is a type of protein with surface adsorption characteristics, and has strong affinity with the hydrophobic surface. It is difficult to be rinsed by general processes when it is adsorbed on the hydrophobic surface. Therefore, using bovine serum protein as the substrate surface adsorption protein, we can obtain good adsorption consistency on the hydrophobic surface.  
In our experiment, we used biotin bovine serum albumin (biotin-BSA) as the underlying adhesive proteins and streptavidin as the middle template layer to provide further binding sites for the following biotinylated biological molecules. Streptavidin is a type of globular protein with four biotin binding sites (two symmetrical pairs), and the affinity between biotin and streptavidin is strong. Via the BSA-biotin-Streptavidin (SA) connections, we converted the patterned surface with hydrophilic and hydrophobic areas into a surface with streptavidin patterns. This is also an effective tool for protein patterning.  
\begin{table}
\begin{tabular}{c c} \hline
**Process** & **Parameters** \\ \hline Cleaning & H\({}_{2}\)SO\({}_{4}\):H\({}_{2}\)O = 7.3 ultrasonic cleaning for10 min; rinsing with deionized water, and drying. \\ Spinning & First: 600 rpm for 10s; acceleration 250 rpm/s; Second: 3600 rpm for 60 s \\ Prebaking & 100 \({}^{\circ}\)C, 45 s \\ Exposure & 10 s \\ Middle baking & 100 \({}^{\circ}\)C, 30 s \\ Development & 0.5\% NaOH solution for 9s \\ Hardbaking & 115 \({}^{\circ}\)C, 60 s \\ HMDS CVD & Cooling in HMDS vapor for 15 min \\ Photoresist & Ultrasonic treatment with acetone for 10 min, rinsing with isopropyl alcohol and deionized water, and drying \\ release & \\ PEG passivation & Immere in PEG–silane solution of concentration 10 mM for 2 h, ultrasonic treatment with methylbenzene for 5 min, rinsing with deionized water, and drying \\ \hline \end{tabular}
\end{table}
Table 1: The optimized processing parameters for the substrate of single-cell patterning. HMDS: hexamethyldisilazane; CVD: chemical vapor deposition. PEG: polyethylene glycol.  
Figure 6: Prepared substrate observed by the scanning electron microscope. (**a**) The large patterns with diameters from 42 \(\upmu\)m to 72 \(\upmu\)m. (**b**) The small patterns with diameters around 20 \(\upmu\)m.  
In the middle of the BSA-biotin-SA-based template layer, we bonded CD45 and CD15 antibodies, separately, which are both expressed on the membrane of the HL 60 cells. Based on the specific immune reaction between antigens and antibodies, the HL 60 cells can be further captured on the surface of the substrate.  
The single-cell patterning principle based on a surface modified substrate is shown in Figure 7.
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 2 Materials and Methods
### Phase 2: Refining the Search  
After manually checking all WoS categories to ensure all retrieved publications were relevant to the topic, the next step involved filtering the results for research quality and focus. Publications in the following categories were excluded: proceeding papers, early access releases, editorial material, and review articles. This decision aimed to concentrate on the most rigorous and influential research, typically found in peer-reviewed academic journals [28]. This filtering process removed 14 proceeding papers and 1 early access paper.  
Figure 2: Flowchart of the systematic review.  
Additionally, only English-language articles were considered for further analysis, excluding two papers in Chinese and one paper in Russian. Through this refinement process, the initial set of 111 publications was reduced to a more focused selection of 93 results.  
Phase 3: Manual Selection  
To further refine the selection, the titles, abstracts, and keywords of each article were manually checked and assessed to determine their alignment with the specific topic of this systematic review. For instance, [PERSON] et al. [29] presented an analytical model to understand the shear behaviour of a fully grouted cable bolt under shear loads. Although the mentioned article is one of the most cited papers in the field, it did not directly address the axial load mechanism of FGRBS. In total, 16 other articles were excluded from the list, which ultimate yielded 77 articles to conduct the systematic review of the topic.  
Phase 4: Bibliometric Analysis  
With the final selection of 77 articles, we employed bibliometric analysis to explore publication trends, prominent journals, and key research areas. VOSviewer version 1.6.20, a free, Java-based software, facilitated this analysis. VOSviewer excels at creating network maps of scientific publications, journals, researchers, and more. These networks can be built based on connections like co-authorship, co-occurrence of keywords, or citations. By analysing the resulting map and its clusters (groups of interconnected items), we gained valuable insights into the research landscape [30]. While VOSviewer was chosen for bibliometric analysis in this research, other tools such as, SciMAT v1.1.06, Bibliometrix v4.2.3, CiNetExplorer v1.0.0, Bibexcel v2016-02-20, and Pajek v5.18, can be valuable for data acquisition and initial analysis.
## 3 Results and Discussion
### Characterization Analysis and Mechanism
#### 3.3.5 Degradation Pathway of BDE 209  
The degradation intermediates of BDE 209 during the MC process at different ball milling times were identified by GC-MS, as detailed in Figures 56-58. It is evident that the peak intensity of BDE 209 weakened, and dibromodiphenyl ether, tribromodiphenyl ether, and 3,5-dibromophenol were observed after 60 min of milling. Furthermore, the  
Figure 4: XRD spectra of sample at different milling times. Conditions: \(n_{\mathrm{LCoO2}}/n_{\mathrm{Fe}}/n_{\mathrm{Br}}=1.5\):1.67:1, \(m_{\mathrm{b}}/m_{\mathrm{p}}=100\):1, rotation speed = 600 rpm.  
peaks of BDE 209 or low brominated aromatics disappeared after 180 min of milling, while aliphatic products such as 2-bromo-3-methyl-hexane-1,5-diol, 3-methyl-1,5-hexanediol, and 3-methyl-1-hexanol were identified (Additional mass spectral data are provided in Figures S9-S15). From all the identified intermediates, we concluded that the decomposition of BDE 209 involved a series of consecutive reactions, namely the C-Br and C-O-C bonds of BDE 209 were first broken, and low brominated aromatic products were produced during the initial period of the MC process. Subsequently, the aromatic structures underwent ring opening to generate aliphatic products; the degradation pathways of BDE 209 are proposed in Figure 7.  
Figure 5: XPS spectra of samples before and after ball milling for 180 min. (**a**) Full XPS spectra analysis, (**b**) Br 3d XPS spectra, (**c**) C 1s XPS spectra, (**d**) Co 2p XPS spectra, (**e**) O 1s XPS spectra, (**f**) Fe 2p XPS spectra. Conditions: \(n_{\text{Li,Co2}}/n_{\text{Fe}}/n_{\text{Br}}\) = 1.5:1.67:1, \(m_{\text{b}}/m_{\text{p}}\) = 100:1, rotation speed = 600 rpm.
## 5 Benders Decomposition Algorithm
### Stopping Criterion  
The Benders decomposition algorithm ends when (a) the solution created by the master problem is feasible; and (b) the slave problem costs computed through the slave problem (upper bound) and the variable \(\alpha\) (lower bound) computed through the master problem are close enough.  
The procedure of the Benders decomposition algorithm for the proposed two stage model of daily VVC is shown in Figure 3.  
According to the flowchart of the Benders decomposition algorithm, the following steps are implemented:  
**Step 1: Initialization**. Initialize the iteration counter, \(v=1\). Solve the initial MIP master problem with objective function Equation (51) and subject to Equations (46)-(50). Its solution provides \(\mathcal{C}Step_{l}^{h(v)}\), \(Tap^{h(v)}\), \(W_{k,D\acute{G}_{l}}^{h(v)}\), \(W_{k,W\tau_{l}}^{h(v)}\), and \(\alpha^{(v)}\). In this step, Equation (52) is not considered for initialization.  
**Step 2: Slave problem solution.** Solve the NLP slave problem for all hours. The solution of this problem is the optimal dispatches of generation units, _i.e._\(Q_{DG,i}^{h}\), \(Q_{WT,i}^{h}\), \(Q_{Disco}^{h}\), \(\Delta P_{Disco}^{L,h}\), \(\Delta P_{D\acute{G},i}^{L,h}\), \(\Delta P_{WT,i}^{L,h}\), \(\Delta P_{Disco}^{Adj,h}\), \(\Delta P_{D\acute{G},i}^{Adj,h}\), and \(\Delta P_{WT,i}^{Adj,h}\), with dual variable values \(\lambda_{l}^{h(v)}\), \(\mu^{h(v)}\), \(\gamma_{k,D\acute{o}_{i}}^{h(v)}\), and \(\gamma_{k,W\tau_{l}}^{h(v)}\).  
**Step 3: Convergence checking.** If the convergence criterion is satisfied, the optimal solution is the last values attained for variables. Otherwise, the algorithm continues with the next step.  
**Step 4: Master problem solution.** Update the iteration counter, \(v=v+1\). Solve the MIP master problem with objective function Equation (51) and subject to Equations (46)-(50) and (52).  
The algorithm continues with Step 2.