esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Node Satisfaction: A Metric for Optimization
### Optimizing through the Use of Satisfaction Increase  
In the following sections, we will make frequent use of the _satisfaction increase_\(\Delta S^{j}_{i}\) of node \(i\) (where \(S_{i}=\sum\limits_{j\in C_{i}}\Delta S^{j}_{i}\)), due to its choosing of node \(j\) as its \(\left(Q_{i}\left(j\right)+1\right)\)-th highest ranked connection, which can be derived from Equation (1) by considering only the contribution (and possible penalty) of node \(j\) in node \(i\)'s satisfaction, where \(Q_{i}\left(j\right)\leq b_{i}\):  
\[\Delta S^{j}_{i} = \frac{1}{b_{i}}-\frac{R_{i}\left(j\right)-Q_{i}\left(j\right)}{b_ {i}L_{i}}= \tag{4}\] \[= \left(\frac{1-R_{i}\left(j\right)/L_{i}}{b_{i}}\right)+\left( \frac{Q_{i}\left(j\right)/b_{i}}{L_{i}}\right)=S^{s}_{i,j}+S^{d}_{i,j}\]  
Obviously, we can rewrite Equation (1) as:  
\[S_{i}=\sum\limits_{j\in C_{i}}\Delta S^{j}_{i}=\sum\limits_{j\in C_{i}}S^{s}_{ i,j}+\sum\limits_{j\in C_{i}}S^{d}_{i,j}=S^{s}_{i}+S^{d}_{i} \tag{5}\]  
It is easy to see that \(\Delta S^{j}_{i}\) breaks down into two parts \(S^{s}_{i,j}\), \(S^{d}_{i,j}\), which we call the _a priori_ part and the _a posteriori_ part, respectively, since the former is computable for each \(j\) regardless of whether it is currently matched with \(i\) or not, while the latter is computable only for those \(j\) that are currently matched with \(i\) by the solution. Alternatively, we will refer to these parts as _static_ and _dynamic_, respectively. The static part depends only on the rank of node \(j\) in preference list \(L_{i}\), but the dynamic part depends on the rank of node \(j\) among node \(i\)'s chosen connections at any given time. This rank among the nodes connected to \(i\) may depend on various factors in an execution, possibly also choices of nodes that are relatively remote to node \(i\). Knowing that even simple matching is not a locally solvable problem [13], this is no surprise. Note that an algorithm that makes connection decisions based on the static part of \(\Delta S^{j}_{i}\) has all the necessary information available from the beginning of its execution (_i.e._, does not use any runtime information), and therefore, a node's valuation of other nodes (that depends only on that static part) does not change over time, due to its runtime connection decisions. As a result, oscillations, due to cyclic preferences are avoided.  
In the next section, we prove that it is possible to define a variation of the maximizing satisfaction b-matching problem (based on a modified definition of \(\Delta S_{i}^{j}\)) that approximates the original problem and can lead to a simple greedy algorithm.
## 3 Kalman Filter Algorithms for the Case of Correlated Noise Terms
### Contemporaneous Form and Future Form  
In Equations (2) and (3), the \"contemporaneous form\" and the \"future form\" of the dynamical equation of a state space model are defined. The names of these forms refer to the question of whether the state vector, \(\mathbf{x}_{t}\), on the left-hand side of the dynamical equation is contemporaneous to the dynamical noise term, \(\mathbf{\eta}_{t}\), on the right-hand side or whether it has advanced by one time step into the future relative to that noise term; this terminology was introduced by [PERSON] [17].  
The fact that two variants for the dynamical equation can be formulated is a consequence of using discrete-time dynamics; in continuous-time dynamics, there would be no such distinction. The relative choice of the time indices of these two terms may seem like an insignificant technical detail, and in many situations, this is indeed the case. If the two noise terms, \(\mathbf{e}_{t}\) and \(\mathbf{\eta}_{t}\), are uncorrelated, exactly the same Kalman filter algorithm is valid for both the contemporaneous form and the future form.  
However, if the two noise terms are correlated, this no longer holds true. In this case, two different Kalman filter algorithms exist that are not equivalent; these two algorithms will be reviewed below in Sections 3.2 and 3.3. Most papers and textbooks present only one of the two possible algorithms without discussing the issue further; the only exception known to this author is the textbook by [PERSON] [2].
## 3 Results and Discussion
### TPCB's Effect on the Pavement Performance of an Asphalt Mixture  
#### 3.4.1 Mix Ratio of Mixtures  
Commonly used mixtures, AC-13 and AC-20, were used to study TPCB's effect on the performance of asphalt mixtures. AC-13 was selected to represent the mixture used for the surface layer, while AC-20 represented the mixture used for middle and base layers. The GF-70 asphalt with 15% TPCB  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Penetration**} & \multirow{2}{*}{**Ductility (cm)**} & \multirow{2}{*}{**Softening Point (\({}^{\circ}\)C)**} & \multirow{2}{*}{**PI**} & \multirow{2}{*}{\(\mathbf{T_{800}}\) (\({}^{\circ}\)C)} & \multirow{2}{*}{**T1.2**} \\ \cline{3-3} \cline{5-8}  & & **30\({}^{\circ}\)C** & & & & & & \\ \hline
0 & 100.3 & 59.4 & 23.5 & 18.7 & 50.4 & \(-\)0.29 & 51.7 & \(-\)15.8 \\
0.3 & 104.3 & 63.4 & 24.6 & 19.3 & 49.8 & \(-\)0.28 & 51.3 & \(-\)16.4 \\
0.6 & 105.9 & 67.5 & 25.5 & 21.6 & 49.5 & \(-\)0.23 & 51.1 & \(-\)17.1 \\
0.9 & 110.4 & 71.3 & 26.9 & 23.8 & 49.1 & \(-\)0.18 & 50.8 & \(-\)17.9 \\
1.2 & 115.6 & 75.9 & 28.4 & 26.4 & 48.9 & \(-\)0.15 & 50.4 & \(-\)18.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: AHO’s effect on the performance of TPCB/GF-70.  
Figure 7: Stiffness (**a**) and m-value (**b**) of GF-70 and TPCB/GF-70.  
and 1.2% AHO was used as the binder of asphalt mixtures and the optimal oil aggregate ratios of asphalt mixtures were determined according to the Marshall test. The aggregate gradation and optical oil aggregate ratio are given in Tables 7 and 8, respectively. The oil aggregate ratios of asphalt mixtures with TPCB-modified asphalt are 0.1% higher than those of asphalt mixtures without TPCB.
# Laboratory Evaluation of Eco-Friendly Pervious Concrete Pavement Material Containing Silica Fume
## 3 Results and Discussion
### Flexural Strength  
The variation of flexural strength versus silica fume dosage of silica fume-modified pervious concrete is shown in Figure 10. From the data, it can be concluded that silica fumes also have a positive effect on the flexural strength of pervious concrete. The improvement of silica fumes on flexural strength was the same as its effect on compressive strength. The flexural strengths of all groups were much larger than the requirement of 2.5 MPa in the Chinese national standard [34]. The flexural strength increased with the increasing silica fume content. The control group had a flexural strength of 4.84 MPa, while the silica fume-modified groups had the maximum flexural strength of 5.2 MPa with 7.4% improvement. When the silica fume content was 9% and 12%, the flexural strengths of the two groups were almost the same. The improvement mechanism of silica fumes on the flexural strength of pervious concrete was the same as it on the compressive strength. The addition of silica fumes improved the interface characteristics between the bonding layer and aggregate, increased the interface adhesion, and thereby improved the flexural resistance of the pervious concrete.
## 4 Results and Discussion
Figure 15: Application effect of agricultural machinery in the working environment under fuzzy control algorithm. The average distance error during tracking is 0.03 m, the standard deviation of the tracking distance error is 0.02 m, and the maximum distance error does not exceed 0.12 m. The tracking results of the IA *--F-SMC method and MPC method have significant differences compared to the target path, with their maximum tracking accuracy approaching 0.15 m. The tracking accuracy error of EfiMPC changes significantly, with a maximum value exceeding 0.15 m. Subsequently, the tracking error results of the control method in the curve path segment were analysed, and the results are shown in Figure 17.  
The results in Figure 17 indicate that the proposed method has an average tracking error of 0.039 m, a standard deviation of distance error of 0.028 m, and a maximum error value of no more than 0.04 m during path tracking, demonstrating good path tracking performance. Secondly, the IA *--F-SMC algorithm performed well, with an average tracking error of 0.052 m. The standard deviation of distance error for both the EfiMPC and MPC methods exceeded 0.07 m, and the maximum tracking error values exceeded 0.16 m and 0.10 m, respectively, indicating a significant error margin compared to the proposed method.
# Mapping Abandoned Cultivated Land in China: Implications for Grain Yield Improvement
## 5 Conclusions  
In 2017, the area of abandoned cultivated land in China was approximately 9.10 million hectares, with an overall abandoned rate of approximately 5.57%, and more than 95% of counties had abandoned cultivated land, which was wide-ranging and at a large scale. The spatial pattern of cultivated land abandonment showed an \"inverted U\" trend in the east-west direction and gradually presented a trend of spreading from large cities and remote rural areas to the main grain-producing areas with fertile cultivated land, from the areas with depressed economic conditions to the economically developed areas, and from a scattered to a concentrated contiguous distribution. Then, we calculated that the total grain yield loss due to cultivated land abandonment reached 40.89 million tons in China, accounting for 6.48% of the total grain yield, and the grain yield loss caused by cultivated land abandonment reached 254.45 million tons. Against the background of a growing population, unsustainable per capita food, and rapid urbanization, the abandonment of cultivated land has undoubtedly increased the pressure on agricultural production in China. Therefore, it becomes necessary to pay more attention to the phenomenon of cultivated land abandonment. In the future, alternative strategies, including allocation of policy tools such as fallowing, land conversion, land transfer, high farmland construction standards and subsidies, implementation of differentiated land use policies, and optimization of the spatial pattern of farmland use, should be adopted by policy-makers.  
Conceptualization, G.J. and W.M.; Data curation, Y.L. and T.Z.; Formal analysis, Y.L. and T.Z.; Investigation, Y.L.; Methodology, G.J., Y.L. and T.Z.; Supervision, G.J. and D.Z.; Writing--original draft, W.M.; Writing--review and editing, W.M. and G.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42371271, 42001199, 42071249) and the National Social Science Fund of China (20&ZD090).  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflict of interest.
# A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments
## 3 Methodology
### Unsupervised Multipath Estimation
```
0:\(\lambda\), \(\textit{Pts}\), radius
0:\(\textbf{N}_{\textbf{NC}}\)
1: Mark all elements of \(\lambda\) as unvisited;
2: Select an unvisited element \(p\) randomly;
3: Mark \(p\) as visited;
4:\(\textbf{C}_{p}\) is a set whose elements' distance from \(p\) is less than radius;
5:\(\textbf{N}_{\textbf{NC}}\leftarrow\textit{length}\big{(}\textbf{C}_{p}\big{)}\);
6:\(\textbf{IF}\,\textbf{N}_{p}\geq\textit{Pts}\)
7: Create a new cluster, add \(p\) to the cluster;
8:\(\textbf{FOR}\) every element \(p^{\prime}\) in \(\textbf{C}_{p}\);
9:\(\textbf{IF}\,p^{\prime}\) is unvisited
10: Mark \(p^{\prime}\) as visited;
11:\(\textbf{C}_{p^{\prime}}\) is a set whose elements' distance from \(p^{\prime}\) is less than radius;
12:\(\textbf{IF}\,\textit{length}\big{(}\textbf{C}_{p^{\prime}}\big{)}\geq\textit{ Pts}\)
13: Add all elements of \(\textbf{C}_{p^{\prime}}\) to the new cluster;
14:\(\textbf{END}\,\textbf{IF}\)
15:\(\textbf{IF}\,p^{\prime}\) doesn't belong to any other cluster
16: Add \(p^{\prime}\) to the new cluster;
17:\(\textbf{END}\,\textbf{IF}\)
18:\(\textbf{END}\,\textbf{IF}\)
19:\(\textbf{END}\,\textbf{FOR}\)
20:\(\textbf{END}\,\textbf{IF}\)
21:\(\textbf{N}_{\textbf{mode}}\) is the element number of the cluster with the largest number of elements;
22:\(\textbf{N}_{\textbf{NC}}\leftarrow\textit{length}(\lambda)-\textbf{N}_{ \textbf{mode}}\)
```  
**Algorithm 1** Unsupervised Multipath Estimation.
## 3 Results and Discussion
### Corrosion Potential (\(E_{corr}\))
In the case of specimens with galvanized steel in the curing stage, a behavior similar to that reported in the specimens exposed to the control medium is presented. \(E_{\text{corr}}\) values in the range of \(-1150\) mV to \(-750\) mV are observed on day 7, increasing towards more positive values in the range of \(-580\) mV to \(-730\) mV on day 28, at the end of the curing stage. A passivation stage is also observed when exposed to the aggressive environment (3.5 wt.% MgSO\({}_{4}\) solution), according to the literature [94; 95], said passivation period lasts until day 126, reaching values up to \(-355\) mV for the CC-MS-GS specimen and \(-400\) mV for the BSC1-MS-GS and BSC2-MS-GS specimens, to later present a trend towards more negative \(E_{\text{corr}}\) values, which remain stable from day 154 to 252 in a range between \(-400\) and \(-440\) mV, indicating 90% risk corrosion according to the ASTM C-876-15 standard [92]. Later, a pronounced tendency to more negative values is observed, associated with the degpassivation or activation of the steel-concrete system, reaching values of \(-620\) mV for the CC-MS-GS specimen, followed by the BSC1-MS-GS with an \(E_{\text{corr}}\) of \(-590\) mV and the BSC2-MS-GS specimen with the best performance, with a corrosion potential of -510 mV at the end of the monitoring. The behavior indicates a better performance, evaluated according to what is indicated in the ASTM C-876-15 standard, of the binary sustainable concretes made with 10% SF, BSC2-MS-GS, followed by the one made with 10% SCBA, BSC1-MS-GS and finally the specimen made with 100% CPC 30R, CC-MS-GS [92]. Figure 5: \(E_{\text{corr}}\) monitoring of GS and CS reinforced concrete exposed to 3.5 wt.% MgSO\({}_{4}\) solution.
## 4 Results Characterization
### XPS Characterization
The C-F bond may be a C\({}_{5}\)F\({}_{10}\)O molecule adsorbed on the surface of a copper sample or a small molecule product containing C and F that was produced after the decomposition of C\({}_{5}\)F\({}_{10}\)O.  
Figure 7: Copper surface element XPS energy spectrum. Figure 7c presents the photoelectron spectroscopy of the 1s orbital of the C element on the surface of the copper sample. The energy peaks detected at photoelectron binding energies of 284.70, 286.30, and 286.60 eV were C 1s, COO, and C=O, respectively. The peaks of COO and C=O did not differ between the control group and each experimental group, and the relative content of the C 1s peak was slightly reduced. Figure 7d presents the photoelectron spectroscopy of the 1s orbital of the O element on the surface of the copper sample. In the C\({}_{5}\)F\({}_{10}\)O/air gas mixture experiment groups, the three peaks with electron binding energies of 529.8, 530.3, and 531.8 eV were Cu\({}^{2+}\)(CuO or CuF\({}_{2}\)), Cu\({}^{+}\)(Cu\({}_{2}\)O), and O\({}_{2}\)/Cu (oxygen on copper, O\({}^{2-}\), O\({}^{2-}\) or O\({}^{-}\)), respectively. In other gas mixture experiment groups or the control group, Cu\({}^{+}\)(Cu\({}_{2}\)O) was detected at photoelectron binding energies of 530.3 eV and 530.7 eV. O\({}_{2}\)/Cu (oxygen on copper, O\({}^{2-}\), O\({}^{2-}\) or O\({}^{-}\)) was detected at photoelectron binding energies of 531.8 eV and 532.9 eV. Cu\({}^{2+}\) (or Cu\({}^{+}\)) with different binding energies are copper oxide or cuprous oxide crystals of different crystal types, and the oxygen on copper was detected in the control group because oxygen in the air was adsorbed on the surface. Figure 7e presents the photoelectron spectroscopy of the N-element 1s orbital of the copper sample surface. Cu-C-N compounds and N-O bonds were detected at photoelectron binding energies of 398.60 eV and 400.10 eV. As the experiment temperature increased, the relative content of the N-O bond gradually decreased and an N-O bond was not detected in the C\({}_{5}\)F\({}_{10}\)O/air gas mixture experiment group at high temperatures. Meanwhile, a Cu-C-N compound was detected in the C\({}_{5}\)F\({}_{10}\)O/N\({}_{2}\) gas mixture experiment group. The results of the XPS test reveal that C\({}_{5}\)F\({}_{10}\)O reacts with Cu at the gas-solid interface and that C\({}_{5}\)F\({}_{10}\)O reacts differently with N\({}_{2}\) and air buffer gas. This finding is consistent with the results of previous research.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
#### 3.3.2 Calibration Quality Assessment  
The cornerstone of any uncertainty quantification system lies in the calibration quality of its confidence intervals. Proper calibration ensures that stated confidence levels correspond accurately to empirical coverage rates, making the uncertainty estimates truly useful for practical engineering applications rather than merely statistical artifacts.  
The uncertainty quantification system demonstrated exceptional calibration performance across multiple confidence levels, establishing its reliability for real-world geotechnical applications. For 68% confidence intervals, the system achieved 67.6% empirical coverage, representing nearly perfect calibration with a calibration score of 99.6%. This close agreement between theoretical and observed coverage indicates that when the system reports 68% confidence, engineers can trust that approximately two-thirds of true values will indeed fall within the predicted intervals.  
For 95% confidence intervals, the system achieved 93.5% empirical coverage with a calibration score of 98.5%, again demonstrating the slight conservative bias that enhances practical utility. This consistency across different confidence levels indicates that the calibration quality is stable and reliable rather than coincidentally good at a single confidence threshold.  
The calibration consistency across cross-validation folds further validates the robustness of the uncertainty estimates. Rather than achieving good calibration through fortunate data splits, the system demonstrated stable performance across different data partitions, indicating that the calibration quality will likely generalize to new data from similar experimental conditions (Figure 10). This stability is crucial for deployment in practical applications where data characteristics may vary slightly from the training conditions.
## Materials and Methods
### Particle Characterization: Size, Polydispersity and Surface Charge  
The prepared particles were characterized through scanning electron microscopy (SEM) and dynamic light scattering (DLS), also referred to as photon correlation spectroscopy. The numerical size distribution is shown in terms of polydispersity index (PI) obtained via DLS. The presence of the surfmers on the surface of the particles was investigated through electrophoretic light scattering (ELS, for zeta potential) and polyelectrolyte titration (for charge quantity), in which particles functionalized with AUPDS were used both unmodified and after a hydrolysis process. Hydrolysis of the active ester groups was accomplished by dispersing the particles in NaOH solution (0.1 M) overnight. Particles were recovered through centrifugation and were redispersed in sodium and potassium phosphate buffer (c = 4 mM, pH 7.5).  
DLS and ELS were performed in a Zetasizer Nano ZS device (Malvern Panalytical, Worcestershire, UK) using the corresponding software Zetasizer Nano ZS Version 7.12. Size measurements were executed in the non-invasive backscattering mode (NIBS) at a scattering angle of 173\({}^{\circ}\), using PS disposable cuvettes. Zeta potential (ZP) was determined in ELS mode, using folded capillary zeta cells (DTS1070, Malvern Panalytical, Worcestershire, UK). All measurements were carried out in triplicate, at 25 \({}^{\circ}\)C, with the number of runs, attenuator value, and laser position set to automatic. Samples were prepared by further diluting the particle stock suspensions in water (1:20 \(v/v\)), without prior use of ultrasonication or filtration processes. The measurements were executed and evaluated according to the ISO 22412 [48].  
SEM measurements were executed using a LEO 1530 VP microscope (Zeiss, Oberkochen, Germany). Samples were prepared in the same manner as for DLS measurements and 5 \(\mu\)L droplets were placed on clean silicon wafers and left to dry overnight at room temperature.  
Polyelectrolyte titrations were carried out via potentiometry using a particle charge detector (model PCD-03, BTG Mutek, Herrsching, Germany) coupled to a Titrino 702 SM (Metrohm AG, Herisau, Switzerland) using the corresponding BTG Mutek PCD software. All measurements were performed in triplicate, under agitation at RT, where 0.01 mL of titrant solution (c = 1 mM) was added to the colloidal suspension every 20 s, until the charge was neutralized. For sample preparation, at least 10 mg of dispersed particles were added to sodium and potassium phosphate buffer (c = 5.2 mM, pH 7.0) until total volume of 15 mL. PES (anionic) was used to titrate positively-charged particles and PDADMAC (cationic) for the negatively-charged particles.
## 3 Methods
### Experiment Setting
#### 3.2.1 Conventional Excavation  
The experiment took place on a construction site in Hwaseong, Gyeonggi Province, South Korea, in November 2021. The weather was clear, with temperatures ranging from 2 to 6 degrees Celsius. A 30-ton excavator (Hyundai CE HX 300A, Seongnam, Republic of Korea) was used for the experiment. Two cameras were installed, one inside the excavator cabin and one outside, to capture video footage. The experiment involved excavating a total of eight footings, four using the conventional method and four using machine guidance. The conventional excavation task was conducted using a crawler-type excavator with the assistance of a signal person equipped with a hand-held GNSS receiver. The study area corresponded with the site's natural ground conditions, and the soil properties were typical. The excavation area comprised four 4 \(\times\) 4 m square footings, with a depth of 1.2 m and 1:0.2 side slopes, as shown in Figure 2.  
Figure 2: Footing excavation for (**a**) conventional and (**b**) MG methods.  
Four spray markers were placed at the corners of the foundation to serve as visual reference points. At the beginning of the task, the signal person verbally instructed the operator. The operator then proceeded with the excavation. When ready for depth verification, the operator signaled using the horn, prompting the signal person to measure the depth at the designated control points using GNSS. The remaining areas for excavation and the excessive excavation depths were marked on the ground with red spray paint, providing feedback to the operator. The operator continued the excavation, making adjustments based on the markings, until the signal person confirmed that all the target depths were met, by marking 0 (or the required depth) at the control points. Once the excavation was deemed complete, the signal person provided a hand signal to indicate task completion, as shown in Figure 3.