esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF\({}_{760}\)) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF\({}_{760}\) tracked GPP well at both diurnal and seasonal scales, and that SIF\({}_{760}\) was more linearly correlated to PAR than GPP was. Therefore, the SIF\({}_{760}\)-GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R\({}^{2}\) value was 0.66 in 2017 and 2018. Based on daily mean observations, the R\({}^{2}\) value was 0.82 and 0.76 in 2017 and 2018, respectively. Secondly, it was found that the SIF\({}_{760}\)-GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF\({}_{760}\) decreased noticeably as the CI increased. Finally, the SIF\({}_{760}\)-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF\({}_{760}\)-based GPP estimates based on daily mean observations: the value of R\({}^{2}\) increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF\({}_{760}\)-based GPP estimation was improved greatly by including the CI, giving a higher R\({}^{2}\) and a lower RMSE. These values improved from R\({}^{2}\) = 0.66 and RMSE = 7.02 mW/m\({}^{2}\)/nm/sr to R\({}^{2}\) = 0.76 and RMSE = 6.36 mW/m\({}^{2}\)/nm/sr for the linear model, and from R\({}^{2}\) = 0.71 and RMSE = 4.76 mw/m\({}^{2}\)/nm/sr to R\({}^{2}\) = 0.78 and RMSE = 3.50 mw/m\({}^{2}\)/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF\({}_{760}\) is a reliable proxy for GPP and that SIF\({}_{760}\)-based GPP estimation can be greatly improved by integrating the CI with SIF\({}_{760}\). These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.  
solar-induced chlorophyll fluorescence (SIF); gross primary production (GPP); clearness index (CI); tower-based observation; automatic observation system; eddy covariance flux; remote sensing 2020 20, 2493; doi:10.3390/s20092493
###### Abstract  
Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In order to estimate land surface temperature (\(LST\)) and the normalized difference vegetation index (\(NDVI\)) from satellite images with cloud coverage, the inpainting approach will be applied to remove clouds and restore the image of the removed region. This paper proposes the use of the neural network LaMa (large mask inpainting) and the scalable model named Big LaMa for the automatic reconstruction process in satellite images. Experiments are conducted on Landsat-8 satellite images of the Amazon rainforest in the state of Aere, Brazil. To evaluate the architecture's accuracy, the _RMSE_ (root mean squared error), _SSIM_ (structural similarity index) and _PSNR_ (peak signal-to-noise ratio) metrics were used. The _LST_ and _NDVI_ of the reconstructed image were calculated and compared qualitatively and quantitatively, using scatter plots and the chosen metrics, respectively. The experimental results show that the Big LaMa architecture performs more effectively and robustly in restoring images in terms of visual quality. And the LaMa network shows minimal superiority for the measured metrics when addressing medium marked areas. When comparing the results achieved in \(NDVI\) and \(LST\) of the reconstructed images with real cloud coverage, great visual results were obtained with Big LaMa.  
remote sensing; deep neural networks; inpainting; cloud removal; image reconstruction; _NDVI_ vegetation index; land surface temperature
## 6 Numerical Results Discussion and Optimization Analysis
### Numerical Results Regarding Comparison Analysis  
In practice, system architects or executives are often faced with the challenge of whether a chief physician should make outgoing calls. To provide useful information for the decision process, this section firstly compares the system without two-way communication and the system with two-way communication in terms of the average time spent in the buffer. In this experiment, displayed in Figure 5, we consider the following parameters:  
Figure 4: The histogram and the Kolmogorov-Smirnov test for the regression residuals.  
\(c=4\), \(\lambda=4,\mu_{1}=3\), \(\mu_{2}=1.5\), \(\mu_{w}=1\), \(\
u=10\), \(\eta=0.3\), \(\delta=0.1\), \(\gamma=1\), \(b_{b}=0.8\), \(b_{r}=0.8\), \(b_{v}=0.8\). It can be seen from this figure that a smaller outgoing generation rate can reduce the average time spent in the buffer. However, the outgoing generation rate is larger, which will increase the average time spent in the buffer. Therefore, in practice, system architects or executives can appropriately consider whether the chief physicians make outgoing calls.  
In addition to whether the chief physician should make a call, the system architect or executive is also concerned about the external environmental factors of the system, such as network coverage or phone reception quality. In response to this problem, we also compare a two-way communication retrial queue without disasters (system I) and a two-way communication retrial queue with disasters (system II) in terms of the idle rate of the chief physicians. As can be seen from Figure 6, the idle rate of the chief physicians in System I is always lower than the idle rate of the chief physicians in System II. Therefore, if the system architects or executives can improve the external environmental factors of the system, this will help reduce the idle rate of the chief physicians.
## 3 Results
### Trends in Vegetation Indices and GPP  
We detected significantly increasing trends, with rates of 0.002 and 0.11 year\({}^{-1}\), for annual mean EVI and LSWI, respectively, over the 2000-2011 period (Figure 2). The inter-annual variations in averaged EVI, LSWI, and GPP within the plant growing season also showed similar significantly increasing trends during the same period (\(R^{2}\)\(\geq\) 0.42, \(p\) < 0.02), at rates of 0.004, 0.005 year\({}^{-1}\), and 16.32 g\(\cdot\) C\(\cdot\) m\({}^{-2}\cdot\) year\({}^{-1}\), respectively (Figure 3a). Correspondingly, we found that the mean annual air temperature at the study site increased by 0.06, 0.07, and 0.12 \({}^{\circ}\)C\(\cdot\) year\({}^{-1}\) during the overall 30-year, first 18-year, and last 12-year periods, respectively (Figure 3b). There were strong and positive linear relationships between annual mean air temperature and vegetation indices (\(R\) = 0.72, \(p\) = 0.005) and GPP (\(R\) = 0.83, \(p\) < 0.0001). Warming explained 52% of the variation in vegetation indices and 69% of the variation in GPP. Regression analysis showed that the warming trend of mean annual air temperature triggered an increasing trend of 0.01 \({}^{\circ}\)C\({}^{-1}\) for EVI, 0.41 \({}^{\circ}\)C\({}^{-1}\) for LSWI, and 128.45 g\(\cdot\) C\(\cdot\) m\({}^{-2}\cdot\) year\({}^{-1}\cdot\) C\({}^{-1}\) for GPP, respectively (Figures 2 and 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Year**} & **Temperature** & \multicolumn{2}{c}{
\begin{tabular}{c} **GPP\({}_{\rm EC}\)\(\geq\) 1** \\ **g** **C/m\({}^{2}\)/d** \\ \end{tabular} } & \multicolumn{2}{c}{**NDVI \(\geq\) 0.3**} & \multicolumn{2}{c}{**EVI \(\geq\) 0.2**} & \multicolumn{2}{c}{**LSWI \(\geq\) -0.1**} \\ \cline{3-10}  & \multicolumn{1}{c}{} & **SOS** & **EOS** & **SOS** & **EOS** & **SOS** & **EOS** & **SOS** & **EOS** \\ \hline
**2008** & 1.58 & 05/01 & 10/10 & 04/14 & 11/08 & 04/30 & 10/15 & 04/30 & 10/07 \\
**2009** & 2.55 & 04/22 & 10/15 & 04/15 & 11/09 & 04/23 & 10/16 & 04/23 & 10/16 \\ \hline \end{tabular}
\end{table}
Table 1: Land surface phenology (the start and end dates of the growing season, SOS, and EOS) during 2008–2009 in the Zoige alpine wetland, as delineated by the estimated daily GPP (GPP\({}_{\rm EC}\)) data from the EC flux tower and MODIS-based eight-day vegetation indices (NDVI, EVI, and LSWI).  
Figure 3: Long-term dynamics of (**a**) growing season GPP, mean EVI, and LSWI during 2000–2011; and (**b**) linear trends of mean annual temperature (Ta) for the overall 30-year (1982–2011), first 18-year (1982–1999), and last 12-year (2000–2011) periods. Dashed lines are the linear trends of growing season GPP, mean EVI, LSWI, and annual mean Ta, respectively.  
Figure 2: Long-term dynamics of (**a**) eight-day EVI, annual mean EVI; and (**b**) eight-day LSWI, annual mean LSWI derived from MODIS imagery during 2000–2011 in the Zoige alpine wetland. Dashed lines are the linear trends of annual mean EVI and LSWI, respectively.
# A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas
## 4 Literature on LMD Vehicle Routing
### Sidekicks
To plan the delivery with sidekicks, delivery nodes (customer locations and intermediate depots) are selected along with the routes of the main vehicles and sidekicks. For the delivery, the main vehicle departs from the main depot toward a series of intermediate depots (ID). Sidekicks then depart from the main vehicle at an intermediate depot to deliver the parcels to customers based on the pre-defined collaboration mode. There are three modes of vehicle-sidekick collaborations. * Mode 1: The main vehicle stays at an intermediate depot until the sidekicks finish their deliveries and return to it [112; 115]. * Mode 2: Sidekicks leave the main vehicle at an intermediate depot and join the main vehicle at another intermediate depot. They return to the main depot together [39; 118]. * Mode 3: Sidekicks leave the main vehicle at an intermediate depot for delivery and return to the main depot by themselves [113]. In the above cases, the main vehicle may deliver parcels by itself or just act as a carrier. The literature on sidekicks in Table 3 shares common characteristics and notable differences in the nature of problems. Drones and main vehicles are commonly capacitated except for [119]. The battery life of the drones is also considered as a capacity limit in two references of [120; 121]. Some papers on sidekick delivery consider time windows of the main vehicles and sidekicks for the collaboration between fleets [122; 123; 124; 118]. Autonomous drones are the most common sidekicks, while [119] consider boats as sidekicks. The main vehicles are mostly human operated, except for [116; 121]. Autonomous vehicles have the advantages of reduced labor cost and delivery time, increased work safety, and higher accessibility. No paper in this survey considers the dynamic and stochastic natures of the problems. The solution methods in sidekick deliveries vary widely, including mixed-integer programming (MIP) and heuristic search algorithms such as clustering-based genetic algorithms and adaptive large neighborhood algorithms. Figure 5: Delivery with sidekick in LMD.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 2 Preliminaries
### Computing Time-Dependent Shortest Paths
#### 2.1.1 Time-Dependent Dijkstra  
The time-dependent variant of [PERSON]'s algorithm (TDD) [23] is a straightforward extension of the classical algorithm that computes the earliest-arrival-times \"on the fly\" when scanning (relaxing) the outgoing edges from a node. TDD grows a shortest-paths tree rooted at an origin \(o\), for a given departure time \(t_{o}\) from it. Analogously to the static case, TDD performs a breadth-first search (BFS) exploration of the graph, settling the nodes in increasing order of their tentative labels (representing the earliest-arrival-times from \(o\), given the departure time \(t_{o}\) from it), until the priority queue of labels becomes empty, or a given destination \(d\) is settled.  
When settling a newly popped node \(u\) from the priority queue, all the outgoing edges are relaxed, implying new evaluations of the corresponding arc-traversal-time functions, given its label (i.e., the earliest-arrival-time at \(u\)) \(t_{u}=Arr[o,u](t_{o})\) at that time. The resulting shortest-paths tree may vary for different departure time choices \(t_{o}\in[0,T)\).
## 1 Introduction
For example, in the research of permanent-magnet linear synchronous motors (PMLSMs), the method of combining an ADRC speed controller and an AFO position estimator can improve the position estimation accuracy and can be applied in the MAR system to enhance the position estimation accuracy of the MRC [19]. The ADRC-enhanced sensorless vector control method of permanent-magnet synchronous motors (PMSMs) has strong anti-interference capabilities and is helpful for improving the anti-interference ability of the MAR system [20]. A new hybrid algorithm for switched reluctance motors (SRMs) can accurately estimate the motor state. When applied to the MAR system, it can improve its survey accuracy [21]. Previous studies have laid the foundation for the maglev ruler: The literature of [1] proposed the structural design and magnetic circuit analysis, while the literature of [2] focused on the suspension control system to stabilize the mover core vertically. However, neither addressed the horizontal motion control of the mover core, which is critical for precise positioning in coordinate measurement. In the existing literature, the horizontal control of the maglev ruler's mover core has not been systematically investigated. The proposed method addresses the inherent nonlinearity and strong coupling characteristics of the horizontal system, overcoming the limitations of conventional linear control strategies such as PID. The contributions of this study include (1) an optimized winding configuration for horizontal control coils and analysis of their operational principles; (2) a novel active disturbance rejection control (ADRC) framework based on SYSTD, achieving decoupling control; and (3) a sensor-less position estimation technology that enables high-precision localization through measurement coils. This paper conducts in-depth research on the autonomous displacement of the MAR system. Besides the abstract and introduction, it consists of seven parts. The second part focuses on establishing the mathematical model of the horizontal system and analyzing the relationship between the Ampere force and the coil current. The third part constructs the dynamic model to describe the movement of the MRC. The fourth part uses ADRC for decoupling. The fifth part designs the improved tracking differentiator SYSTD. The sixth part elaborates on the ESO, NLSEF, and disturbance compensation of ADRC. The seventh part provides the system parameter tuning method and conducts simulations. The eighth part summarizes the research, verifies the system performance, demonstrates its high positioning accuracy, and lays a solid foundation for subsequent research.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### XAI Frameworks
#### 4.1.2 GraphLIMEs  
GraphLIMEs [39] build upon the foundational concept of LIMEs but extend the application to a specialized type of neural network architecture known as graph neural networks (GNNs). Unlike the traditional models, GNNs are designed to handle non-Euclidean data organized in a graph structure [39], making them well-suited for tasks such as node classification, link prediction, and graph classification. Similar to LIMEs, GraphLIMEs aim to derive an interpretable model, specifically the Hilbert-Schmidt Independence Criterion (HSIC) Lasso model, to explain the individual nodes within the input graph. In the training process of GNNs, non-linear aggregation and combination methods utilize neighboring node features to determine each node's representative embedding. This embedding is crucial for tasks like node classification and graph classification, where sorting nodes or graphs into different classes is essential [39; 62].
# Plasmonic Nanomaterials for Micro- and Nanoplastics Detection
## 3 Plasmonic Nanomaterials for MNPs Detection
### Plasmon Enhanced Fluorescence  
Plasmon-enhanced fluorescence (PEF) is a well-established technique that grants huge enhancement factors and the possibility to detect analytes even at the single molecule level. The effect of PEF was observed in the early 1980s in concurrence with the observation of the SERS effect [54]. This effect greatly enhances the emission of organic molecules in the proximity of plasmonic nanostructures, allowing for the detection of weakly emitting species that are not detectable otherwise [55]. PEF has been applied to single-molecule detection [56], the detection of DNA [57], and cell imaging [58].  
Recently, an application has also been envisaged for the visualization of microplastics and fibers [59]. The authors used a gold nanopillar substrate previously engineered as a SERS substrate [60]. The method was tested with low-density polyethylene (LDPE), poly(butylene adipate-co-terephthalate) (PBAT), and epoxy resins: a drop of a microplastic-containing water sample was dried on the substrate and analyzed by means of fluorescence microscopy. PEF allows one to visualize the PBAT and LDPE particles and fibers without the need of a dye. The larger fibers were also detected with optical microscopy, but the use of PEF allowed for the detection of much smaller particles, otherwise invisible to optical microscopy.  
The LOD and LOQ for a microplastic sample in milliQ water were calculated to be 0.35 and 1.2 femtograms, respectively (samples were generated by exposing a plastic film to milliQ water at 23 \({}^{\circ}\)C for 8 weeks, according to a previous published procedure [61]). An enhancement factor of 68 was determined by comparing the signal-to-noise ratio of the fluorescence of microplastics on the nanopillar substrate, related to the S/N ratio of microplastics on bare glass. The method also proved to be reliable on seawater samples, allowing for the detection of microplastics fibers, even when surrounded by salt crystals generated after drying. This paper could be an interesting proof-of-principle for further studies on the use of PEF for micro- and nanoplastics detection.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 6 Further Analysis and Discussion
### Effect of Installation Error \(\Delta H_{s}\) on Pressure Indices  
According to the discussion above, it can be assumed that the deflector outlet velocity is constant. Then, from Equations (23), (24), (27) and (29) we can obtain the balance pressure \(p_{r}\), as a function of the installation error \(\Delta H_{s}\).  
\[p_{r}=\tfrac{1}{5_{r}}\left\{2\rho b_{0}^{\prime}l_{r}^{\prime}{u_{0}^{\prime} }^{2}-\iint_{S_{f}}\left\{\tfrac{1}{2}{\rho^{\prime}}_{0}^{2}+1.415\times 10^{ 8}\exp\left[-25,585(H_{s}+\Delta H_{s})\right]\right.\left.+3.92\times 10^{6} \right.+p_{b}\right\}\exp\left(-\tfrac{\eta^{2}}{b_{r}^{2}}y^{2}\right)dydz\right\} \tag{30}\]  
As depicted in Figure 22, the installation error of the deflector has little effect on pressure. For an error from \(-5\)\(\upmu\)m to \(5\)\(\upmu\)m, the balance pressure \(p_{r}\) rises by 0.08 MPa (1.2% of the standard value), which is one of the reasons that the same valves often have different balance pressures.  
Figure 22: Installation error dependence of balance pressure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Installation Error of Deflector** & & \\ \(\Delta H_{s}\)/\(\upmu\)m & **Deflector Outlet Velocity/m \(\cdot\)s\({}^{-1}\)** & **Relative Error** \\ \hline \(-5\) & 160.78 & 0.16\% \\ \(-3\) & 160.68 & 0.10\% \\ \(-1\) & 160.57 & 0.03\% \\ \(0\) & 160.52 & 0 \\ \(1\) & 160.48 & \(-0.02\%\) \\ \(3\) & 160.39 & \(-0.08\%\) \\ \(5\) & 160.28 & \(-0.15\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Installation error dependence of the deflector outlet velocity.  
According to Equations (23)-(26), (28) and (29), the average pressure gain can be expressed by  
\[K_{p}=\frac{\frac{P_{p}^{2}p_{p}}{k_{f}/95}\left\{\rho{u_{0}^{\prime}}^{2}+2p_{b}+7.84\times 10^{6}+2.83\times 10^{8}\ \exp[-25,585(H_{s}+\Delta H_{s})]\right\}\cdot\left[\int_{0}^{\frac{p(|m-2x _{f})}{2p_{p}}}\exp(-t^{2})dt-\int_{0}^{\frac{p(|m+2x_{f})}{2p_{p}}}\exp(-t^{2} )dt\right] \tag{31}\]  
For different deflector displacements, the installation error dependence of the average pressure gain is depicted in Figure 23.  
Our calculation shows when the installation error ranges from \(-5\)\(\upmu\)m to \(5\)\(\upmu\)m, the pressure gain of the hydraulic amplifier drops by \(2.5\) MPa/mm (\(1.3\%\)), and the influence is approximately linear. Additionally, the average pressure gains for different deflector displacements have a consistent tendency. In any case, such a deviation of the pressure gain will not significantly affect the dynamic performance of the valve.
###### Abstract  
Background: The aim of this research was to evaluate the effects of three different titanium (Ti) implant surfaces on the viability and secretory functions of mesenchymal stem cells isolated from a Bichat fat pad (BFP-MSCs). Methods: Four different Ti disks were used as substrate: (I) DI: smooth Ti, as control; (II) D2: chemically etched, resembling the Kontact S surface; (III) D3: sandblasted, resembling the Kontact surface; (IV) D4: blasted/etched, resembling the Kontact N surface. BFP-MSCs were plated on Ti disks for 72 h. Cell viability, adhesion on disks and release of a panel of cytokines, chemokines and growth factor were evaluated. Results: BFP-MSCs plated in wells with Ti surface showed a viability rate (\(\sim\)90%) and proliferative rate comparable to cells plated without disks and to cells plated on D1 disks. D2 and D4 showed the highest adhesive ability. All the Ti surfaces did not interfere with the release of cytokines, chemokines and growth factors by BFP-MSCs. However, BFP-MSCs cultured on D4 surface released a significantly higher amount of Granulocyte Colony-stimulating Factor (G-CSF) compared either to cells plated without disks and to cells plated on D1 and D2. Conclusions: The implant surfaces examined do not impair the BFP-MSCs cell viability and preserve their secretion of cytokines and chemokines. Further in vitro and in vivo studies are necessary to define the implant surface parameters able to assure the chemokines' optimal release for a real improvement of dental implant osseointegration.  
dental implant surface; chemokines; grow factors; bone damage; osseointegration +
Footnote †: journal: Applied Sciences