esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 5 Discussion
### Operational Water Monitoring
If the long-term and seasonal water regime undergoes significant changes, wetlands' capacity to moderate the effects of extreme droughs and rainfall may be diminished, along with a reduction in habitat and biodiversity and significant ecological losses. We, therefore, suggest that monthly surface water area, annual permanent and seasonal area, as well as annual counts of small water bodies would be useful monitoring metrics in an operational context.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 3 Network Model and Problem Statements  
Network model and problem statements are presented in this section. Some assumptions are summarized first. We assume that every single node has enough power to transmit data to the sink nodes. We also assume that the nodes can vary their transmission power and support different established MAC protocols. We will use a model in which we always have data to transmit and the nearby nodes have co-related data. In this section of the study, we describe the network model and the problem statement for the proposed protocol. We will describe our network model further and mathematically formulate the problem statement.
## 1 Introduction
For steady-state conditions, the downstream pressure can be controlled to achieve a specific pressure gradient along the sample, and many tests under steady-state conditions have been achieved by opening the downstream to the atmosphere, offering atmospheric downstream pressure conditions, and flow rates are measured when the pressure distribution along the sample reaches equilibrium. However, this fails to secure super-critical or liquid CO\({}_{2}\) flow downstream, resulting in phase variation of CO\({}_{2}\) throughout the sample [19]. These issues can be overcome by using transient tests, in which the downstream outlet is closed, giving the opportunity for downstream pressure development [20]. However, the calculation of transient permeability is much more complicated than the steady-state calculation, and researchers have used downstream pressure development curves for this purpose [21]. Depending on the experimental conditions, transient tests and steady-state tests closely correspond to the CO\({}_{2}\) injection process and the CO\({}_{2}\) clean-up process, respectively. In theory, the apparent permeability for CO\({}_{2}\) through sample is independent of different measurement techniques. Existing research shows that permeability results using transient techniques may be up to twice greater than permeability calculated using steady-state approaches in low permeable rocks, due to the many assumptions made in transient calculations [21]. [PERSON] et al. [18] found five times greater permeability using the transient approach compared to the steady-state approach. This exhibits differences among permeability calculations under steady-state and transient conditions [22]. Importantly, permeability calculation under both conditions may have considerable errors if the possible CO\({}_{2}\) phase transition through the rock mass is not considered under steady-state conditions [23]. As a result, precise knowledge of the liquid CO\({}_{2}\) flow behavior in the reservoir rock mass is vital. [PERSON] et al. [24] proposed a new method to calculate the apparent permeability for gaseous and liquid CO\({}_{2}\) under steady-state conditions, and speculated that the permeability for liquid CO\({}_{2}\) at higher than 12 MPa calculated by the traditional method can deviate greatly from the actual value. This study therefore attempts to distinguish the flow behavior of CO\({}_{2}\) at higher pressures considering the CO\({}_{2}\) phase transition between liquid and gaseous phases, and to identify the difference between steady-state and transient conditions, following a comprehensive set of permeability experiments on siltstone based on different confining and injection pressures at room temperature.
To avoid these problems, a solution may consist of incorporating magnetic nanoparticles in the hydrogels: magnetic separation improves the recovery of magnetic microparticles at the end of the sorption (resistance to intraparticle diffusion being limited by the decreasing in diffusion path through small particles) [29, 30, 31]. This is the strategy adopted in the current work. In a first step, magnetic chitosan microparticles were synthesized. To increase their reactivity (density of reactive groups, pH range for application, etc. ), the support was modified by grafting additional functional groups. Several studies have shown interest in increasing the density of amine groups (polyethyleneimine grafting, for example), or inserting carboxylic groups (modulation of metal affinity, and pH effect), among other chemical modifications. Grafting amino acids onto chitosan backbone has been recognized as a promising technique for developing enhanced sorbents [32, 33, 34, 35, 36]. In this study, glycine (one of the simplest and cheapest amino acid) is immobilized on magnetic chitosan microparticles through the insertion of epichlorohydrin (acting both as a crosslinking agent for reinforcing the stability of the polymer and as a reactive spacing arm). The sorbent (G@MChs) is characterized (SEM, FTIR spectroscopy, titration) before investigating its sorption properties for the recovery of Hg(II), Ni(II) and Zn(II) from aqueous synthetic solutions; the effects of pH, sorbent dose are determined before investigating uptake kinetics, sorption isotherms, metal desorption and sorbent recycling, and selectivity issues. These metals are representative of common hazardous metal ions having different levels of toxicity: in drinking water the guidelines are 70 ug Ni L\({}^{-1}\), and 6 ug Hg L\({}^{-1}\) ([37]); WHO did not set guidelines for Zn in drinking water (at the levels found in this type of water bodies). In a second step, the sorbent is tested for the treatment of tailing leachates (produced from Egyptian mining resources). The leachates were pre-treated with pH control and sorption onto DOWEX 50X8 ion-exchange resin (for rare earth elements recovery, REE) before being treated by sorption onto G@MChs.
## 5 Discussion
While phase aberration effects are caused by variations in SOS due to tissue inhomogeneity, reverberation is caused by multiple reflections within an inhomogeneous medium, generating clutter that distorts the appearance of the wavefronts from the region of interest. For fundamental imaging, reverberations have been shown to be a significant cause of image quality degradation and are the principal reason why harmonic US imaging is better than fundamental imaging [39]. Since CoMTUS relies on detecting backscattered echoes from targeted scattered points, the optimization may be corrupted if the reverberation clutter overlays these echoes. The magnitude of this effect will depend mostly on the clutter strength and the signal-to-noise ratio. For all the cases described here, it was possible to detect the backscattered echoesof the targets used for calibration, and CoMTUS optimization succeeded. However, there may be more complex situations in vivo, and further investigations are needed. Future work will focus on the role of redundancy in the large array in averaging multiple realizations of the reverberation signal as a mechanism for clutter reduction. Finally, it should be acknowledged that some choices made in the design of this study may not directly translate to clinical practice, but we believe that they do not compromise the conclusions drawn from these results. The available experimental setup drove the election of the frequency at 3 MHz, which was higher than is traditionally used in abdominal imaging (1-2.5 MHz). However, the use of either lower or higher frequencies would have led to similar CoMTUS results, since the associated image benefits reported here are mainly dictated by the resulting extended effective aperture. In addition, both the simulated and experimental phantoms are a rather simplistic model of real human tissue. Simulations were limited to a two-dimensional model, while real ultrasound propagation occurs in three dimensions. In three dimensions, extra clutter can be generated also by off-axis scattering, arising from scatterers located away from the beam's axis. Second, although the tissue properties were taken from the literature, it is well known that ultrasonic tissue properties vary considerably among individual tissue specimens and different measurement techniques. Finally, the main limitation is the lack of tissue microstructure in the model. Only with a more complete description of tissue microstructure is it possible to capture the phase and amplitude errors observed in vivo in pulse echo measurements. However, the results presented here can capture the main time-shift fluctuations and then help in further understanding of CoMTUS. Previous simulation studies have shown that a simple model like the one used in this study successfully predicts the magnitudes and large-scale trends of time-shift fluctuations, but not the energy-level fluctuations and waveform distortions [29]. Further studies using more sophisticated models are needed to describe the wave distortions, including phase and energy fluctuations.
## 2 Literature Review
### Parametric Approaches  
Parametric approaches, including ARIMA and Kalman filters, are based on pre-determined mathematical structures that assume a fixed relationship between traffic variables [25; 26; 27]. While effective for short-term traffic forecasting in relatively stable conditions, these models are limited in their ability to capture non-linear traffic behaviours [28; 29; 30]. For example, ARIMA models are frequently used to predict traffic speed and volume by identifying patterns in historical time-series data [31]. However, they tend to perform poorly when applied to unpredictable traffic conditions, such as those caused by weather or incidents [13; 32]. Despite their limitations, parametric models have been widely used in traffic prediction. Kalman filters have been particularly useful for real-time traffic monitoring, as they can adjust predictions based on new sensor data [33; 34]. However, like ARIMA, they are less effective in highly dynamic environments, such as arterial roads where interruptions are frequent and difficult to predict [35]. Other novel methods, like the lightweight approach, have been used for real-time vehicle detection, further improving the accuracy of incident detection and response [36]. Combining these algorithms with non-parametric traffic prediction models offers a robust approach to managing traffic on arterial roads, particularly in urban environments where incidents and weather conditions can cause significant disruptions.
## 3 Results and Discussion
### Biological Activity of Coatings  
The antioxidant and antimicrobial activities of coatings were investigated by the DPPH method and disk diffusion assays, as demonstrated by the data provided in Tables 5 and 6, respectively. The biological activities of the coatings correlated with the amount of EO in the coating. Studies of the antioxidant properties of coated paper revealed that high antioxidant activity was characteristic of the clove essential oil emulsion coatings (76-92%) and the eugenol emulsion coatings (87-91%), while the coatings containing cinnamon aldehyde showed quite low antioxidant activity (4-9%). Furthermore, the antioxidant activity of the coatings was determined on Day 28 during the storage at ambient temperature. After this period, the antioxidant activity of the coatings was reduced; however, samples still exhibited relatively high activity, especially at higher quantities of EO in the coatings. Figure 5: FT-IR spectra of samples: (**a**) clove essential oil (CL), paper and coated paper sheets (C-CL); (**b**) eugenol (EU), paper and coated paper sheets (C-EU); (**c**) cinnamaldehyde (CI), paper and coated paper sheets (C-CI). The dashed line boxes and letters show the groups of the absorption peaks: A—1636, 1607, and 1512 cm\({}^{-1}\); B—1670 cm\({}^{-1}\). The antimicrobial activities of coatings against various microorganisms such as Gram-negative, Gram-positive, and yeast are presented in Table 6.  
The disk diffusion method was sensitive in showing the antimicrobial effectiveness of paper samples coated with emulsions containing different quantities of CL, EU, or CI against Gram-positive and Gram-negative bacteria and yeast. Antibacterial activity test results for the Gram-positive bacteria _S. aureus_, _L. monocytogenes_, _B. cereus_, _Gran-negative E. coli_, and _C. albicans_ indicated that the size of inhibition zone diameters (IZDs) correlated with the different percentages of CL, EU, or CI in the coating. The inhibitory effect of the paper samples coated with emulsions containing 2.5% of CL was not detected against Gram-negative and Gram-positive bacteria and yeast _C. albicans_. The results of this study showed that all tested coatings exhibited the highest activity when 10% of essential oil was used in the emulsion preparation. Furthermore, this study showed that the most effective coating was the CI coating. It was determined that _E. coli_ was resistant to C-CI-2.5 and IZD increased to 18.70 \(\pm\) 0.01 mm when C-CI-10 was used. Also, the increase of IZDs was demonstrated after the treatment of _B. cereus_ and _C. albicans_ with the paper coated with emulsions containing different percentages of CI. The size of the IZDs was greater for _C. albicans_, and formed 10.85 \(\pm\) 0.04 mm, 13.60 \(\pm\) 0.02, and 15.49 \(\pm\) 0.02 mm when using 2.5, 5, and 10% of CI in the coating formulation, respectively. The same was observed for _B. cereus_ when the IZDs were 7.59 \(\pm\) 0.02 mm, 17.10 \(\pm\) 0.02, and 18.34 \(\pm\) 0.03 mm using 2,5, 5, and 10% of CI, respectively.
## 3 Design and Implementation of the WSN Based AST System
### WSN Application Layer Design for AST  
The WSN application layer design for AST aims to synchronously control and monitor AST programs. During the strength testing experiments, WSN measurements should be controlled to ensure simultaneous data acquisition of all the strain gauges when a load is applied to the specimen. When the testing engineer wants to query real-time data from some specific nodes to estimate the current status of the particular testing area of a specimen, the WSN application layer interface should allow breaches in normal network operation to transmit control signals back to the sensing nodes. In addition, because some sensing nodes may fail or more sensing nodes may be added to the system over the duration of testing, the WSN application layer interface should be scalable to accommodate changes in number of nodes without affecting the entire system operation. Finally, although the coordinator node and the cluster heads could be supplied by the fixed power, strain sensor nodes are autonomous devices that usually derive their power from a battery mounted on each node. WSN application layer interface should be designed to prolong the lifetime of WSN by effective active and sleep controlling mechanism.  
Figure 10: The flow chart for the WSN application layer design.  
In view of the above considerations, a WSN application layer interface was designed for AST measurement programs. The WSN measurement process for AST includes the following steps: when the user tasks are allocated in the AST application layer interface, the WSN will be waked up for the specific measurement. The user tasks might be triggered periodically or by testing engineers who want to query real-time data from some specific nodes for the particular testing areas of a specimen. Then the base station node send the wake-up instruction to the particular cluster heads which will awaken the own cluster members. After the initialization of the WSN, the relevant network topology will be visualized in the interface. Based on the real-time data transmission scheme, all collected strain data will be gathered to the base station. If no abnormal sensor data or failure nodes exist, the sensor nodes will collect the sensor values with the testing load synchronously. Finally the AST application layer interface will aggregate all testing data and show the integrated result about the specific specimen. The flow chart for the WSN measurement is shown in Figure 10. Figure 11 shows the overview of the WSN application layer interface for AST.
## 4 Correlation Analysis  
Correlation coefficients (\(\rho\)) have been used to test the significance of the relation between different factors (see Table 3). The equation for \(\rho\) is as:  
\[\rho(u,\ v)=\frac{cov(u,\ v)}{\sigma_{u}\sigma_{v}} \tag{7}\]  
where \(cov\) indicates covariance, \(\sigma_{u}\) represents standard deviation of \(u\), and \(\sigma_{v}\) defines standard deviation of \(v\). \(|\rho|>0.8\) signifies a strong relation among \(u\) and \(v\), values from 0.3-0.8 represents a moderate relationship, while \(|\rho|<0.30\) signifies a weak relation [39]. According to [PERSON] et al. [40], a relation is considered as \"strong\" if \(|\rho|>0.8\). \(M\), \(a_{max}\), \(R\), \(D50_{15}\), \(F_{15}\), \(F_{15}\) and \(W\) have moderate to weak relations, as seen in Table 3. As a result, no variables from the lateral displacement estimation model were eliminated. Table 3 reveals that the correlation coefficient has a maximum absolute value of 0.761 and there is no \"strong\" link between different pairs of components.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 2 Materials and Methods
### Research Methodology
#### 2.3.3 PLUS Model  
The PLUS model is a type of CA-based patch generation LUC model. This model allows excavation of the driving factors of land sprawl and changes in the landscape to better model the generation and variation of LU patches [26; 27; 28; 29]. In PLUS, the Land Expansion Analysis Strategy (LEAS) module excavates the driving factors and expansion factors. Then, the development probability of each LU type and the contribution of the driving factors to land expansion is calculated. The CA based on the multiple-type random patch seeds (CARSs) module incorporates random seeds and a patch generation threshold to simulate and predict the future landscape pattern under the control of development probability [30; 31].  
PLUS Model V1.3.5 is used in this study, with the following steps: (1) The selected LU data and driving factors need to be unified to the same projection coordinate system and resolution, and the LU data are converted to unsigned char format. (2) Extract the part of LU expansion during the period. (3) Input the LU expansion data and set various parameters in the LEAS module. The parameters of the simulation phase are as follows: the number of regression trees is 20, the sampling rate is 0.02, and the number of training features is 16. The development probability map of each LU type is acquired. (4) Finally, each parameter is input into the CARS module and debugged to achieve the simulation of future landscape patterns. The parameters are set as follows in the simulation stage: the patch generation threshold is set to 0.8, the expansion coefficient is set to 0.3, and the percentage of seeds is set to 0.0005. The Markov chain is included with the PLUS model, and the calculation of future LU demand can be conducted in the PLUS model.
# Measuring the Performance of Wastewater Treatment in China
###### Abstract  
When a developing country is undergoing a rapid growth period, agricultural wastewater, domestic wastewater, industrial wastewater, and organic matter content in chemical oxygen demand (COD) usually increase in great amounts, causing environmental pollution. Thus, this paper proposes a summary of factors to assess the performance of wastewater discharge costs. Total fixed assets, population growth, and wastewater treatment expenses in various regions of China were used as input factors, while gross regional product, discharged wastewater, and discharged COD were used as output factors. We employed the directional distance function (DDF) method to compare 31 regions of China between 2011 and 2015. The results showed that areas with leading economic development and areas with a small population and vast natural land have good wastewater treatment efficiency. In the past five years, economic development and wastewater treatment expense efficiency in Chongqing have been improving, such that by the end of 2015, this region efficiency was approaching frontier efficiency. We also found that the efficiency of wastewater treatment expense in many areas often falls below 0.6, which is still very low. There is, thus, a large gap between the regions and the leading frontier regions, meaning that the efficiency of wastewater treatment expense needs to be improved.  
Keywords:data envelopment analysis (DEA); DDF (directional distance function) model; chemical oxygen demand (COD); agricultural wastewater +
Footnote †: journal: Applied sciences
## 3 Formulation for a Mathematical Model of the DCBAP  
### Definition of the DCBAP  
**Definition 1**.: _The Dynamic and Continuous Berth Assignment Planning Problem (DCBAP) is a problem with the aim of finding a feasible berth plan that includes six-tuples:_  
\[\text{DCBAP}=(L,\ H,\ V,\ C,\ P,\ Z)\]  
_where_  
* _the length of a quay;_
* _the planning horizon;_
* _a finite set of calling vessels;_ \(V=\{1,2,\ldots,N\}\)_, where_ \(N\) _is the total number of calling ships;_
* _a finite set of berthing constraints;_
* _a set of plans with assignments of berthing positions and start berthing times for ships;_ \(\forall\ p_{i}\in P,\ \exists\ p_{i}=\left\{\cup_{j=1}^{j=N}\left(B_{j},S_{j} \right)\middle|0\leq B_{j}\leq L-l_{j},\ 0\leq S_{j}\right\}\)_, where_ \(\left(B_{j},S_{j}\right)\) _is a pair of assignments, where_ \(B_{j}\) _is the berthing position,_ \(S_{j}\) _is the start berthing time and_ \(l_{j}\) _is length of the calling ship j._
* _an objective function mapping_ \(p_{i}\) _to a time/cot value_ \(Z\)The objective of the DCBAP is to find a \(p_{i}\) or \(p^{*}\left(p_{i},\ p^{*}\in P\right)\) that minimizes the objective function value \(Z\), in which \(p_{i}\) is a feasible solution, while \(p^{*}\) is the optimal solution subjecting to \(\zeta\). For each ship \(j\) a berthing position \(\left(B_{j},S_{j}\right)\) on \(P\) is assigned. Finding the \(\left(B_{j},S_{j}\right)\) for each ship \(j\) (\(j=1,\ldots,n\), where \(n\) is total number of calling ships) is an NP-hard problem.
## 2 Geodetic Monitoring of Co-Seismic Displacement
Another possible way to analyse a time series of coordinates from the processing of long-term GNSS observations is to form baselines between stations and then analyse the rates of length change as demonstrated by [PERSON] et al. [20], using a Kalman filter. In this paper, we will focus on the displacement of permanent stations in Greece during the January 2010 earthquake in Efpalio [13] and the October 2018 earthquake located south of the island of Zakynthos [14]. In contrast to more significant earthquakes, (e.g., Tohoku-Oki with Mw 9.0, where the co-seismic shifts were on a metre level [16] and recorded more shifts in the afterslip phase [19]), the earthquakes in Efpalio and near Zakynthos were relatively small and without or with a very short afterslip phase (shorter than one day). Due to their post-seismic displacements being on a centimetre-level (i.e., close to the accuracy of GNSS method), we also focussed on determining the accuracy of the detected co-seismic displacements based on the results of an anharmonic analysis. The aim of this study was to determine co-seismic displacement and its accuracy, using the mean square error of the difference between two calculations determined by the application of the PPP method. Each of the calculations are derived from the time series, before and after the earthquake. These are determined from an anharmonic spectral analysis of coordinates from the relevant section of the time series.