esa-sceva/satcom-chunk-collection huggingface dataset:
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### K-Out-of-N System  
K-out-of-N is used when there is uncertainty of the lifetime. If at least K numbers of components are in good condition out of N components in a system, this is known as a K-out-of-N system. Smidt-Destombes et al. [123] suggested maintenance operation if the quantity of good components were less than K, wherein all of the components were identical and repairable as well. Another study focused on the methodology of a condition-based preventive maintenance as part of the overall asset management strategy. A maintenance monitoring system subject to false alarms and failure to alarm used K-out-of-N systems with multiple dependent monitors on the basis of the probability matrix being weak MLR (weak multivariate monotone likelihood ratio) [124]. On the basis of their optimal procedure of a monitoring system, a K-out-of-N system can identify an optimal decision quickly. There have been studies conducted for maintenance, spare part inventories, and repair capacity with their interaction for a K-out-of-N system with vacant [125]. Order-restricted hypothesis tests are considered for making the decision about the usual K-out-of-N model or the general sequential K-out-of-N model for given data [126]. Simultaneous maximum likelihood estimation is considered for the model parameters and the distribution parameters with a flexible location-scale family. Many researchers have extended the concept of binary K-out-of-N system to multi-state K-out-of-N systems.
## 3 Framework Description
### Process Controller Adapted to the Simulator
#### 3.2.2 Task Manager  
The task manager is in charge of coordinating all the tasks and subtasks inside the simulation, making automated actors perform different procedures and aiding human actors while performing actions.  
Tasks and subtasks have five statuses: _waiting_, _ready_, _running_, _completed_, and _cannot_complete_ (see Figure 4). When a subtask depends on the finalization of another subtask (parent subtask) and the latter has not finished, it is in a _waiting_ state. When all the parents of a subtask have finished, then the state of the subtask changes to _ready_, which means that it may perform its action. As the goals of the subtask are satisfied, it enters a _running_ state. Finally, when all the goals of the subtask are satisfied, it achieves its _completed_ state. If there is an error related to the action performed, the subtask obtains the status of _cannot_complete_. If the error related to the action is solved, then the subtask shifts to a _completed_ state.  
The task manager listens to the events of the state manager through the goals inside the subtasks.  
Figure 3: State manager operation.  
Once an entity in the simulator (such as an actor, a collision detected by the physics engines, or when a user releases a trigger or performs a given motion... ) changes its status, it triggers an event (such as \(E_{1}\) in Figure 5) which changes the related global condition in the state manager. This triggers the event \(E_{2}\), where the related condition (goal) is checked for satisfaction. If it is satisfied, an event \(E_{3}\) is triggered to make the current subtask verify its new status.  
When a subtask is complete, an event \(E_{4}\) is triggered to notify its parent task that it has finished. If there are no more subtasks in a waiting state inside the current task, the task is considered complete.  
Figure 4: State machine of a subtask status.  
Figure 5: UML sequence diagram of the task manager.  
If a subtask is in a _waiting_ state depending on the finished subtask, it will change its state from _waiting_ to _ready_. The task manager will repeat this management procedure until all tasks of the process are complete.
## 2 Study Area, Data and Methods
### NDVI and Meteorological Dataset  
Long-term satellite data of vegetation greenness using NDVI, a proxy for potential photosynthesis closely related to plant productivity, were used to estimate the sign and magnitude of asymmetric patterns of growth during the growing season. The NDVI dataset used in this study (NDVI3g V1) was produced by the GIMMS group using a series of NOAA-AVHRR satellite imagery ([[https://ecocast.arc.nasa.gov/data/pub/gimms/](https://ecocast.arc.nasa.gov/data/pub/gimms/)]([https://ecocast.arc.nasa.gov/data/pub/gimms/](https://ecocast.arc.nasa.gov/data/pub/gimms/))) [29; 30]. It covers the period 1982-2015 with a temporal resolution of 15 days at 8 km spatial resolution. This NDVI3g product has been used extensively since being published and is the most consistent long-term satellite vegetation dataset currently available [30]. In this version of NDVI, effects of orbital drifts, inter-sensor calibration and aerosol impacts from volcanic eruption have been corrected. The region of Northeastern China was extracted from each image in the raster data stack. Grid cells with NDVI values less than 0.1 were masked out as bare land, water, or urban areas.  
It has been well documented that climate change plays a key role in affecting long-term changes and variability in vegetation [20; 32]. In this study, to quantify climatic effects on the asymmetries in vegetation seasonal cycle, a suite of spatially gridded temperature and precipitation data were employed. These datasets were obtained from [PERSON], et al. [33] ([[http://www.csdata.org/en/p/80/](http://www.csdata.org/en/p/80/)]([http://www.csdata.org/en/p/80/](http://www.csdata.org/en/p/80/))) and cover the study area spanning the time period of 1982 to 2015. Temperature and precipitation datasets with a grid resolution of 1 km were produced from observed surface data obtained from the National Meteorological Information Center (NMIC) of China Meteorological Administration. [PERSON], et al. then calculated eight-day average and total values for temperature and precipitation, respectively, to match the temporal frequency of MODIS data. The ANUSPLIN software was used to interpolate observed meteorological data to a grid with high resolution (1-km), and the results were validated with temperature and precipitation records from AsiaFlux stations ([[http://asiaflux.net](http://asiaflux.net)]([http://asiaflux.net](http://asiaflux.net))). In this study, the spatial resolution of temperature and precipitation data was resampled into an 8 km grid using the nearest neighbor method in ArcGIS 10.3. Using this data, temperature and precipitation asymmetry were calculated in the same manner as the vegetation asymmetric indices.
## 4 Results and Discussion
### Macro-Test on the Backfilling Material  
Spread was measured in order to quantify the workability of the mortars described in Section 3.2 (see Figure 10a and Table 4). As is shown in Figure 10b, there is no workability in the mortar without FA (A-spread). On the contrary, B-spread, which contained CG, 30% FA and _B. pasteurii_ with mineralized solutions, showed good pumping performances, as the initial average spread was 243 mm (Figure 10c).  
Figure 8: (**a**) Image of CG particles (magnified 200 times) and (**b**) SEM of FA particles (magnified 5000 times). OM images of (**c**) FA magnified 1000 times and (**d**) _B. pasteurii_ magnified 1000 times. A typical image is taken from many similar examples.  
Due to the microbial mineralization of CG and FA, the average compressive strength of the mortar B-strength reached 0.471 MPa (refer to Section 3.2 for detailed strength measurement procedures, see Table 4 and Figure 11a), which is higher than the value of 0.0882 MPa measured in the C-strength group of CG and FA materials only (Figure 11b). Therefore, the addition of _B. pasteurii_ to CG and FA aggregates for mineralization can increase the strength of the material by approximately 5.34 times. In other words, in B-strength mortar, microorganisms can not only survive in the presence of CG and FA but also effectively perform the mineralization function.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mortar** & **Compressive Strength (KPa)** & **Spread after 10 s (mm)** \\ \hline A & / & 100 \\ B & 471 & 243 \\ C & 88.22 & / \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average properties of the backfilling material macro-test.  
Figure 10: Mortar spread tests. (**a**) Spread testing bucket; (**b**) A-spread: mortar without FA; (**c**) B-spread: mortar with CG, 30% FA and _B. pasteurii_ with mineralized solutions.  
Figure 9: SEM (magnified 1000 times) of (**a**) CG with its surface plot and (**b**) FA with its surface plot. Schematic diagram of _B. pasteurii_ adhesion is shown on different rough surfaces and anchoring areas. A typical image is taken from many similar examples.  
Combined with the results of rheology tests, we can argue that the backfilling material prepared by microbially mineralized CG and FA not only has the mechanical performance to play the role of a green binder, but its slurry also has excellent pumping properties. Therefore, based on the effect of CG and FA on the mineralizing capacity of _B. pasteurii_ at the micro-level, CG and FA could be accepted well as substrates for biomineralized composites to be used as backfilling materials. Accordingly, the present study also gives new insights into biomineralization in backfill materials and contributes to the sustainable development of the environment by utilizing solid waste in an effective way and reducing the risk of hazardous materials.
## 3 Experiments and Results
### PBL and CEJ Boundary Detection through U-Net  
Figures 4 and 5 show the prediction accuracy of the PBL and CEJ boundaries estimated by U-Net after data augmentation. The PBL and CEJ boundaries were detected using this model architecture, while for both PBL and CEJ, the U-Net model was trained for 30 epochs at a batch size of 16. Table 1 shows the U-Net model accuracy for PBL and CEJ, where the accuracy is slightly higher for CEJ.  
Figure 4: Accuracy of the PBL boundary detection model.
## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.2 Water
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**N** & **Ref.** & **Focus** & **Country** & **Methods** & **Satellites** \\ \hline
1 & [53] & GP & S. Arabia & Fuzzy-AHP & Landsat 8, ALOS PALSAR \\
2 & [61] & GP & Morocco & AHP & Landsat 8 \\
3 & [59] & GP & Iran & ML & ASTER GDEM \\
4 & [62] & GP & Iran & ML & Landsat ETM+ \\
5 & [58] & GP & Iran & ML & Landsat 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research focus (GP: groundwater potential mapping, GQ: groundwater quality, QIW: quantification of irrigation water, ET: evapotranspiration estimates, DP: dam site selection) of the 20 most-cited papers in the water thematic area, the geographical focus (country), the methods used (AHP: analytical hierarchical process, Fuzzy-AHP: fuzzy analytical hierarchical process, ML: machine learning, AI: artificial intelligence), and the satellite data used.
A Review on the Advancements in the Field of Metal Complexes with Schiff Bases as Antiproliferative Agents  
[PERSON]  
1 Department of Pharmacy-Drug Sciences, University of Bari \"[PERSON]\", 70126 Bari, Italy 1  
[PERSON]  
2 Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavaceta di Rende, Italy; [EMAIL_ADDRESS] (M.S.S.); j.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s.s  
ureases inhibitors [25], and occupy a central role in the development of coordination chemistry [26; 27; 28]. Recently, the study of these compounds has attracted interest particularly for the development of new anticancer drugs. Among them, a pyrazolone Schiff base copper complex has been suggested as a potential drug candidate for the treatment of liver cancer, being endowed with high antiproliferative activity against hepatocellular carcinoma (HCC) [29]. Moreover, the use of low frequency electromagnetic fields and Mn(II) complex of a Schiff base derived from the pyridoxal has been described for the treatment of breast cancer [30]. Finally, a recent study reported that the introduction of Schiff bases in the \(N\)-phenylcarbazole/triphenylamine modified half-sandwiched iridium(III) compound led to an enhancement of the antitumor activity of about thirteen times that of cisplatin [31]. Interestingly, Schiff base-Fe\({}^{2+}\) complexes have been incorporated into nanomicelles PAsc/Fe@Cy7 QB, which represent chemodynamic theranostics able to specifically recognize and eradicate cancer cells in solid tumors [32]. Transition metal complexes with Schiff bases are also used as electrode modifiers in sensors to detect analytes of pharmaceutical, environmental and forensic interest, mainly to analyze illicit drugs [33]. Recently, the development of a coumarin Schiff base system has been described as a highly selective fluorescent/colorimetric probe for Cu\({}^{2+}\) and as a tumor biomarker for glutathione detection [34]. Herein, we want to focus the attention on the antitumor activity of complexes with Schiff bases, reviewing firstly the data regarding the antiproliferative activities of Schiff base complexes with transition metals; then, we will dwell on platinum group metals (PGM), such as Pt(II) and Au(III). Finally, we will deepen complexed with lanthanides and discuss their antiproliferative activities [35; 36]. Particularly, we want to highlight the importance of the antiproliferative activity of the metal complex-based Schiff bases, as a significant starting point for the development of new anticancer drugs, giving an update of recent reviews regarding this relevant topic in medicinal chemistry [37; 38; 39; 40; 41; 42].
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 1 Introduction
Finally, simulation analyzes for ship systems operating in the VHF band, performed using the newly created maritime wind turbine propagation channel model, were presented and discussed in the last part of the article. To finish this introductory section and before going to the main body of this paper, it is worthwhile to once again list the key motivation factors for the research presented in the paper. These are as follows:  
* The growing importance of the offshore wind farms as a source of energy;
* A significant number of ongoing investments in Poland (the country of the paper's authors);
* A lack of mathematical tools for modelling radio channel in the VHF (and UHF) bands in the wind farm environment;
* The significance of the VHF band with respect to the maritime applications. This is the band within which some of the most important systems utilized by marine administration (surveillance, safety, GMDSS) and/or border protection units operate. The proper operation of these systems can realistically be affected by wind turbines, mostly due to interference and radio shadowing;
* The wind turbines at sea are generally larger in size than their land counterparts. In the recent years, they have been \"growing\" even more (their size is becoming comparable with the wavelength), which translates to even stronger potential disruptions of the affected radio systems;
* The offshore wind farms' investors in Poland are now obligated by law to present analysis of their farms' impact on the selected maritime radio systems. It can be a problem given the previously mentioned lack of appropriate standards and guidelines. To accomplish all the goals of this research, several obstacles had to be dealt with. The main challenges for the authors were as follows:  
* The necessity of conducting highly complicated measurement campaigns (particularly challenging was maintaining synchronization in time and frequency);
* Potential impact of the land environment during the measurements;
* Lack of offshore wind farms on the Polish waters at the time of this writing (so measurements had to be conducted at land wind farms);
* No reference models, standards, etc., addressing the VHF channel in the wind farm environment. In the authors' opinion the major original contributions and output of this work include to following:  
* The measurement verification of the ITU-R BT.1893-1 recommendation;
* The original channel model for the VHF band in the wind farm environment;* Lessons learned on the effective planning and deployment of maritime radio systems in such environments, while minimizing the potential interference and radio shadowing effects and maximizing the ranges of these systems;
* The simulation tools and measurement equipment allowing multi-faceted analyses of the offshore wind farms impact on radio system, both in Poland and in other countries;
* Identification of the problem (with the remedy proposal), which will increase as the number of existing offshore wind farms grows and the size of the turbines grows.
## 5 Simulation Examples
### Examples with Consideration of Multiple Uncertainties  
In this section, all examples are only supplemented with uncertainties, so this section uses the extensions from Section 4.5. The uncertainties considered are variable time delay, noisy gradient calculation, line losses, and representation of drop-off packets. All three examples in this section are identical to those in the previous one. The parameters, network topology, and generator types used in the previous examples remain unchanged. The only difference lies in the initial algorithm initialization, which will be presented separately for each of the following examples.  
For all the examples below, the gradient noise will be modeled as follows:  
\[\
abla\Phi_{i}(\lambda_{i}(k))+v_{i}(k),\quad v_{i}(k)\sim\mathcal{N}(\mu,\, \sigma^{2})=\mathcal{N}(0,4).\]  
This noise can be designed as a Gaussian noise with a Gaussian distribution (normal distribution). It is, therefore, defined by the mean \(\mu\) and variance \(\sigma^{2}\). Furthermore, the power loss \(P_{\text{loss}}\) was considered as 5%. This value was thus inserted into the required initial load, compensated by this value. The time-varying traffic delay will be modeled as the mean value \(\mu\) of the distribution is considered to be 0 and the variance \(\sigma^{2}\) is considered to be 4:  
\[\tau_{ji}(k)\sim\text{round}\big{(}|\mathcal{N}(\mu,\,\sigma^{2})|\big{)}= \text{round}\big{(}|\mathcal{N}(0,4)|\big{)}.\]  
The maximum value for \(\tau_{\text{max}}\) was considered in all examples as \(\tau_{\text{max}}=10\). The representation of drop-off packets varies across examples and will be explained separately.  
The learning gains \(\gamma\) will be represented as a variable calculated according to Equation (27). Additionally, it is crucial to reset the index learning gain at each change. Otherwise, the algorithm would react slowly and might not achieve the required convergence speed. This is because the learning gain approaches zero as the simulation progresses, as depicted in (27). When uncertainties are introduced, the learning gain cannot remain constant.  
Figure 11: Example 3 results for \(\lambda_{i}\), \(P_{i}\) and BESS capacity \(E_{b_{i}}(t)\).
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 3 Results
### Interannual Change Trends of Ecosystem WUE, GPP, and ET  
The interannual variation of GPP, ET, and ecosystem WUE over a 15-year period is shown in Figure 3a. Ecosystem WUE was highest in 2009 at 3.14 gC mm\({}^{-1}\) m\({}^{-2}\) yr\({}^{-1}\) and lowest in 2005 at 2.62 gC mm\({}^{-1}\) m\({}^{-2}\) yr\({}^{-1}\). The study area experienced drought conditions for three consecutive years from 2009 to 2012. It has been shown that a slight drought at the beginning of the drought can improve the water use efficiency of plants [32], which may be the reason for the large WUE of the ecosystem in 2009. However, persistent drought conditions cause severe water stress to vegetation [33], as demonstrated by the rapid decrease in ecosystem WUE after 2009. Although GPP, ET, and ecosystem WUE all exhibited an upward trend from 2003 to 2017, passing the significance test of \(p<0.05\), the change to WUE was not significant. The variation rate of GPP was much greater than that of ET. Hence, GPP had a greater impact on changes to ecosystem WUE than ET.  
The regions with greater variations to GPP were distributed in central Guangxi, western Guizhou, and the border with Yunnan (Figure 3b). The combination of Figures 2a and 3b illustrates that the rate of change to GPP was greater in regions with a lower annual average GPP, which may be related to the implementation of large-scale ecological projects by the government of China in the ecologically fragile region. The areas with increasing and decreasing GPP trends accounted for 85.60% and 14.40% of the total study area, respectively, and the percentage of the area of the pixels that reached significance (\(p<0.05\)) was 58.10% and 3.41%, respectively. The regions with higher ET variation rates were distributed in southwest Yunnan and northwest Guizhou bordering Yunnan. The areas with increasing and decreasing trends of ET accounted for 85.53% and 14.47% of the total study area, respectively, and the percentage of areas with significant change to the pixels were 46.41% and 2.60%, respectively.  
The spatial distributions of ecosystem WUE and GPP tended to be very similar, indicating that among the effects of GPP and ET on ecosystem WUE, the trends in ecosystem WUE were strongly influenced by variations in GPP, demonstrating the dominant role of GPP. This is the same result derived from the greater interannual GPP variation rate than ET in Figure 3a. The areas with increasing and decreasing trends in ecosystem WUE accounted for 65.89% and 34.11% of the total study area, respectively, while areas with  
Figure 3: (**a**) Interannual variations of GPP, ET, and ecosystem WUE from 2003 to 2017. Spatial trends of (**b**) GPP, (**c**) ET, and (**d**) ecosystem WUE.  
significant variations in pixels accounted for 37.05% and 10.52%, respectively. Ecosystem WUE variations tended to increase with GPP and ET, but were not significant in most of the study area.