esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nowadays, space-borne imaging spectro-radiometers are exploited for many environmental applications, including water quality monitoring. Turbidity is a standout amongst the essential parameters of water quality that affect productivity. The current study aims to utilize Landsat 8 surface reflectance (L8 SR) to retrieve turbidity in the Ramanga River, a tributary of the Ganges River. Samples of river water were collected from 16 different locations on 13 March and 27 November 2014. L8 SR images from 6 March and 17 November 2014 were downloaded from the United States Geological Survey (USGS) website. The algorithm to retrieve turbidity is based on the correlation between L8 SR reflectance (single and ratio bands) and insitu data. The b2/b4 and b2/b3 bands ratio are proven to be the best predictors of turbidity, with \(R^{2}=0.560\) (\(p<0.05\)) and \(R^{2}=0.726\) (\(p<0.05\)) for March and November, respectively. Selected models are validated by comparing the concentrations of predicted and measured turbidity. The results showed that L8 SR is a promising tool for monitoring surface water from space, even in relatively narrow river channels, such as the Ramanganga River.  
Ramanga River; turbidity; Landsat 8 SR; water quality; Ganges River +
Footnote †: journal: Applied Sciences
# Tomographic Inversion Methods for Retrieving the
## 5 Results
Figure 7: (**a**) True WV field (analysis of 27 July 2020 03:00 a.m.) as a function of angle and altitude; (**b**) retrieved WV field with ERTA and (**c**) the corresponding normalized error field; (**d**) retrieved WV field with LSATR and (**e**) the corresponding normalized error field defined as in Equation (17). Satellite configuration: 1 Tx-10 Rx. ERTA can be ascribed to the fact that the reconstruction depends on the basis functions used to represent the Radon transform and, successively, to reconstruct the WV field. Such functions are quite regular and, in general, exhibit a decay that is slower than the WV field variations. As a consequence, in the attempt to minimize the approximation error, the retrieved WV field comes out to be a monotonically decreasing function, but with a lower gradient with respect to the 'true' WV field. This produces an underestimate of the WV field at the low altitudes. Quantitative results averaged over spatial areas are reported in Table 1, which shows the NRMSE for three different altitude ranges (the first two corresponding to the low and high troposphere and the third one to the whole interval considered) and for the three satellite constellations considered. The results are tabulated for the three considered high resolution atmospheric scenarios and for both ERTA and LSATR. Analyzing Table 1, several observations can be made. The most evident outcome concerns the behavior of the two inversion algorithms with respect to the number of satellites of the constellation. In the case of LSATR, the performance improves (in some cases significantly) when such number increases: this occurs at the low and high tropospheric intervals and, consequently, over the whole altitude range (2-10 km). As to ERTA, instead, the performance is not influenced by the size of the constellation. This occurs because the elements of the basis of the radial expansion of ERTA do not exhibit a sufficiently rapid decay with altitude, so that the characterization of the radial profiles does not benefit from a higher density of the vertical measurements. Another element emerging from Table 1 is that LSATR performs almost always better than ERTA, in particular in the higher tropospheric layer (5-10 km). On the other hand, ERTA appears to be convenient in the lower tropospheric layer (2-5 km) and when the number of satellites is smaller. We can also observe that in the higher altitude range, the percent errors may become quite high, with the performance of ERTA definitely inferior to that of LSATR. Such high NRMSE levels are due to the fact that the average concentration of WV--the denominator in (18)--is much lower in the higher altitude range so that even small absolute errors induce quite high relative errors. However, as mentioned, the number of satellites helps LSATR in improving the performance, but this does not hold for ERTA.
## Discussion  
### Reliability of SIF Retrievals and Vegetation Information  
For the first time, SIF was retrieved in validated physical units along with various plant traits over a complex and heterogeneous peatland and its surroundings. Ground-based validation of airborne products indicated a good accuracy of retrieved vegetation information (Figure 5). Around 10% of the pixels expressed in negative SIF values (these pixels mostly represented the lake area and roads) were excluded from analyses, so they did not impact our results. Similar to [PERSON] et al. [28], we observed higher noise in retrieved SIF maps compared to the VIs maps. Apparent noise effects, however, did not impact our analysis and interpretation. The highest variability (wide variability in signals observed in the map) of \(\text{SIF}_{760}\) and \(\text{SIF}_{687}\) was found for young deciduous forest, grassland, and alder forest areas in the studied vegetation sites, which is in agreement with [PERSON] et al. [16] (for forest), as well as [PERSON] et al. [37] and [PERSON] et. al. [38] (for grasslands). Mowed grassland and cleaned forest areas showed the lowest \(\text{SIF}_{760}\) and \(\text{SIF}_{687}\) signals due to the low amount of photosynthetically-active plant tissue. For heterogeneous landscapes like peatlands, ground-based measurements are essential to assess the quality of RS derived information (as described by [PERSON] et al. [61]). That is why we put substantial efforts into the acquisition of ground-based SIF and reflectance data close to the acquisition time of the airborne sensor.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 2 Electrification of Marine Vessels
### Artificial Intelligence and Applications in Marine Vessels
#### 2.5.2 Optimization-Based Approaches  
These approaches rely on constantly increasing energy usage distribution efficiency and productivity to satisfy specific operational purposes and constraints. These types of approaches deploy various algorithms to find the best solutions under given constraints for optimizing one or multiple objectives, such as minimizing energy consumption, maximizing operational range, or prolonging battery life. Dynamic programming (DP), particle swarm optimization (PSO), genetic algorithms (GAs), and simulated annealing methods can be given as examples of these types of approaches [72; 74; 75; 76; 77].
## Nomenclature  
\begin{tabular}{l l l} \(C\) & Percent change in fitness function & \(C_{d}\) & damping matrix of structure \\ \(H\) & Hessian matrix & \(H_{0}\) & filter gain \\ \(H_{f}(s)\) & \(j\)th bandpass filter transfer function & \(J\) & LQR cost function \\ \(J_{1}\)\(-\)\(f_{6}\) & Control cost functions & \(K\) & LQR feedback gain matrix \\ \(K_{s}\) & Stiffness matrix of structure & \(L\) & saliency (OBS) \\ \(M\) & Mass matrix of structure & \(O\) & fitness function for PSO \\ \(NP_{1}\) & Number of transmitted peak values & \(NP_{2}\) & number of transmitted data points \\ \(P\) & Vector of detected peak values for all filters & \(Q_{0,j}\) & frequency selectivity of each \(j\)th filter \\ \(Q\) & LQR parameter that weighs the cost of the structural response & \(R\) & LQR parameter that weighs the cost \\ \(U\) & Control force & & of the control effort \\ \(\tilde{W}\) & Control weighting vector & \(W\) & control weighting matrix \\ \(d\) & Inter-story drift vector of all floors & \(X\) & vector of partial derivative (OBS) \\ \(f(t)\) & Control force output from actuator & \(g\) & best position of all particles (PSO) \\ \(h_{f}(t)\) & Impulse response of the \(j\)th filter & \(l\) & actuator location matrix \\ \(\iota\) & Ground acceleration influence vector & \(t\) & continuous time \\ \(t_{c}\) & Control time increment & \(x\) & current position of a particle (PSO) \\ \(x_{b}\) & Previous best position of a particle (PSO) & \(x_{g}\) & ground displacement \\ \(y\) & Displacement of structure & \(\overline{y}\) & states of the system (i.e., inter-story displacement \\ \(z_{j}(t)\) & Output of the \(j\)th bandpass filter & \(\gamma_{1},\gamma_{2}\) & acceleration coefficients (PSO) \\ \(\rho_{1},\rho_{2}\) & Random number between 0 and 1 (PSO) & \(\lambda\) & inertia weight (PSO) \\ \(\tau\) & Inertial damping constant (PSO) & \(\delta_{\omega}\) & weight change vector (OBS) \\ \(\xi\) & Filter damping ratio & \(\omega\) & filter center frequency \\ \end{tabular}
## 3 Results and Discussion
### Numerical Analysis
The simulated total electric field distributions in the S2 Ag nanoshell case at its resonance wavelengths are shown in \[\varepsilon_{\mathbf{Draude}}(\mathbf{R},\mathbf{\omega})=1-\frac{\omega_{p}^{2}}{ \Gamma(\mathbf{R})^{2}+\ \mathbf{\omega}^{2}}+j\frac{\Gamma(\mathbf{R})\ \omega_{p}^{2}}{\mathbf{\omega}[\ \Gamma(\mathbf{R})^{2}+\ \mathbf{\omega}^{2}\ ]} \tag{2}\]2 The horizontal panels i and ii in Figure 5 represent the higher order resonance modes at \(\lambda_{1}\) = 470-483 nm and \(\lambda_{2}\) = 553-567 nm, respectively. The vertical panel A gives the corresponding simulated ACS values. The vertical panels B and C represent the corresponding electric field distributions in the YZ and XZ planes at the frequency at which the associated ACS peak \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experiment**} & \multicolumn{6}{c}{**Simulation**} \\ \cline{3-10}
**Sample** & \(\lambda_{1}\) & \(\lambda_{2}\) & \multicolumn{6}{c}{\(\lambda_{1}\) (nm)} & \multicolumn{6}{c}{\(\lambda_{2}\) (nm)} \\ \cline{3-10}  & **(nm)** & **(nm)** & **CST-SDD** & **Mie-SDD** & **CST-JC** & **Mie-JC** & **CST-SDD** & **Mie-SDD** & **CST-JC** & **Mie-JC** \\ \hline S1 & 484 & 568 & 482.7 & 482 & 497 & 496 & 566.7 & 566 & 591 & 588 \\ S2 & 474.5 & 564 & 476.7 & 477 & 469 & 468 & 559 & 557 & 549 & 550 \\ S3 & 471 & 556 & 472 & 470 & 452 & 451 & 553 & 552 & 547 & 545 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measured and simulated wavelengths of the peak absorption cross-section (ACS) values. Figure 4: Comparison of the measured and simulated (CST and Mie theory) results for Sample S2. occurs. Note that the incident EM plane wave propagates along the Z-axis from the bottom to the top of the subplots as shown in panel D. The identification of the higher order resonance modes was accomplished by comparing these numerical results with those obtained from Mie theory [21].
# A Strategic Pathway from Cell to Pack-Level Battery Lifetime Model Development
## 4 Performance-Based Lifetime Modeling
### Lifetime Model Extension to a Pack-Level Use Case  
The capacity degradation and such prediction on the pack-level is the aim of this study considering the crucial features. The unit aging factors are already considered while developing the cell-level lifetime model. Thus, the electrical and thermal elements are introduced when the model is expanded for a series-connected 144 SiP topology.  
The pack topology of the study case is a series-connected configuration of 144 LTO cells. The assembled configuration has a nominal 7.62 kWh pack energy. The cell-level aging model is scaled up according to the cell specifications to build the pack-level model. The scaling is done by simple multiplication for the multicell configuration. Ideal balancing is considered for the cell-to-cell SoC variation having negligible impact on the lifetime. Figure 5 displays the pack-level full first life simulation result with the pack CAD (inset). The prediction basis is converted to travel distance against the pack energy loss. The simulation at 25 \({}^{\circ}\)C estimates the driving distance to be 409,614 km in the first life, however, without considering the electrical and thermal updates of aspects such as increased IR, TMS, etc. The model considers a 16.5 km travel distance per suburban WLTC section that is used for the validation purpose. This proves that the cell can withstand stressed WLTC cycling and still can give a very long mileage. It can also be assumed that with shallow cycling or with a full WLTC (urban and suburban) profile, the LTO pack would be able to survive even longer.  
Figure 5: Pack-level lifetime simulation for the whole first life at ambient 25 \({}^{\circ}\)C.
# Genetic Programming to Remove Impulse Noise in Color Images
## 3 Preliminaries: Color Image Denoising
### Impulse Noise Models  
There are three impulse noise models used for color images in the contemporary literature [42]. These models differ according to the noise correlation of the image channels and how the noise affects the pixels. Let \(x_{i}^{q}\) be a random variable whose output represents a value corrupted by noise in the \(q\)-th channel of pixel \(\vec{x}_{i}\). Then, \(x_{i}^{q}\) behaves according to the following models:  
1. Salt-and-pepper noise. The value of \(x_{i}^{q}\) can only be 0 or 255 with the same probability.
2. Uniform impulse noise. The value of \(x_{i}^{q}\) is chosen independently and uniformly at random from the range \([0,255]\).
3. Correlated impulse noise. The value of \(x_{i}^{q}\) is any number uniformly distributed between 0 and 255 for all \(q\in\{1,2,3\}\).  
Let \(p\) be the probability of the appearance of noise in any pixel \(\vec{x}_{i}\). Then, the amount of noise (or noise density) is distributed randomly over approximately \(p\times 100\%\) pixels of the image.
## 4 Discussion
### Landslide Motion and Rainfall Pattern
We observed in Figure 11 that when changes of the displacement rate were detected previously, a seasonal maximum of rainfall values occurred. Two of the three time periods showed an increase of displacement rate of about 40%, while the total seasonal rainfall (8 months) was quite similar (-700 mm, Table 2). The period September 2017-April 2018 showed an increase of the displacement rate of about 550%. This result was accompanied by a large amount of total rainfall (-1000 mm, Table 2). These findings allowed us to correlate the amount of total rainfall with the positive displacement rate (increase) of an active landslide. Through the cross-correlation method, we computed the time lag between the maximum peaks of InSAR and rainfall (Figure 11 lower panel; Table 2). The mean time lag (of the three periods) was 13.5 days between the maximum value of rainfall and the change in the InSAR displacement rate. The changes in InSAR displacement rates occurred on 28 January 2017, 23 January 2018 and 20 December 2019, respectively. Moreover, due to the large number of ascending orbit data available during the winter-spring period in 2020-2021 (Figure 4), we performed a separate cross correlation using the InSAR position time series (ascending orbit) against the daily rainfall data from the Kato Vlassia station (see Figure S11). Again, we found a 330% increase in the displacement rate of the landslide following a large interval of rainfall with a time lag of 12 days (see Table 2, last line). The change in the displacement rate of the Krini landslide occurred on 18 February 2021. Thus, whatever S1 orbit data we use in the case of Achaia, we always find this pattern, i.e., an increase in the landslide displacement rate following heavy rainfall.
## 2 Overcoming Sagging and Altitude Loss Problems: Effective Solutions  
### Sagging and Altitude Loss Problems of BWB Aircraft  
In BWB aircraft design, a prominent challenge pertains to the management of short-coupled control systems. As shown in Figure 1, the distance from the lever arm of the control surface to the center of gravity (CG) in BWB designs is from approximately two to three times shorter than that in conventional aircraft designs. This shorter lever arm necessitates a greater downward force to achieve the desired pitch-up moment, which is opposite to the direction of lift. Such a configuration often results in the aircraft experiencing descent during the takeoff and landing phases, referred to as \"sagging\" by NASA [51], before achieving the desired pitch angle. This short lever arm length can adversely affect the control of the flight path, especially during critical phases such as landing. Consequently, adjustments in pitch are often accompanied by an unwarranted decrease in altitude, posing a risk to flight safety.  
In addition, aircraft flight requires the appropriate thrust for different flight phases. Traditional aircraft alter their thrust by changing the engine flow rates. In BWB aircraft with distributed BLI propulsors, the suction effect of BLI fans can provide additional lift, so conventional methods of thrust control present a novel, tightly coupled problem, where the additional lift from boundary-layer suction diminishes as the airflow through the BLI fans decreases, which causes an altitude loss problem. Thus, as the thrust is reduced in the landing phase, a decrease in the flow rate results in reduced suction, leading to a decrease in additional lift and a deterioration in the delay of airflow separation, compromising the aircraft's safety and stability.
# Electricity Losses in Focus: Detection and Reduction Strategies--State of the Art
## 3 Methods for Identifying Power Losses
### Non-Technical Energy Losses
#### 3.2.1 Energy Losses Caused by Fraud and Illegal Behavior
The efficacy of the proposed method was assessed through its implementation in a real power grid, and the results obtained demonstrated that the newly introduced model exhibited superior performance in terms of accuracy and precision when compared to previous methods. In the evolving energy market, many residential consumers adopting IoT technologies face problems related to non-technical losses. In [36], a study was presented that highlighted these real challenges in Brazil in terms of energy fraud in low-voltage power grids. To address this critical issue, a novel methodology has been proposed, which involves the strategic placement of thermal sensors to identify affected bus lines within the system and to determine the responsible parties. After the conceptualization of this methodology, empirical trials were executed on a genuine power grid, considering the power flow and a detailed analysis of the load curves representative of the area under study. The findings of this study indicated the efficacy of the proposed method, as it was able to identify a group of fraudulent consumers, and the method could yield better results if applied to the free market. Non-technical energy losses can be categorized based on the underlying cause. In [37], the authors conducted a study on a potential threat to the power system, namely, unauthorized energy consumption by the grid operator. Utilizing intelligent algorithms, they successfully identified illegal connections within power grids. This form of fraud has gained prominence, particularly in underprivileged communities. Conventional methods employed by national energy companies to monitor electricity consumption are often inefficient and labor-intensive. The approach adopted by the authors in their paper involves data classification through a combination of intelligent methods, such as ANNs, decision trees, vector machines, and k-nearest K-nearest classifiers. The data examined in this study are authentic field data from over 300 distribution operators, which renders the data sample complex for the method to be applicable.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 4 Experiments and Results
### Experiment on Context Size  
The context size is the number of words surrounding a target word which is a system parameter. This experiment aims to determine whether the context size affects the result. Experimental context sizes were within a range of 2-14, with intervals of two. We used the best configurations from the previous experiment, i.e., the sense signature (sense definition and sense relations) without the probability of sense distribution using the average of Google word-embedding vectors as the baseline and _n_best_ = 1.  
From Table 4, the results show that the context size slightly affects the results. Providing a few context words or many context words slightly harms the results. The middle context size is recommended to get the best result.
# Fabrication of Silicon Carbide from Recycled Silicon Wafer Cutting Sludge and Its Purification
## 1 Introduction
To concentrate Si and SiC from the discharged Si sludge and to recycle it, proper methods other than landfill can be used, as described in several works [22; 23; 24; 25; 26]. [PERSON] et al. have tried to produce a ferrochrome alloy successfully by reducing chromium ore using Si wafer sludge waste [27; 28]. The second type of Si sludge is obtained through a recently developed method in which a silicon ingot is cut with a sawing wire that the diamond particles are electrodeposited on the surface. Oil or cooling water is supplied continuously to the sawing wire to remove heat generated during cutting and to discharge the kerf generated by cutting. Hence, the sludge generated in this process does not contain SiC but contains only oil, water, Si, and iron particles. So, if oil or water are removed, highly pure Si powder can be easily recovered by diverse methods such as by centrifugation and specific gravity separation [19; 20; 21]. Our aim in the present work was to utilize Si sludge waste by recycling it. Our plan was to recycle Si sludge in the presence of C and to fabricate SiC with moderate quality. In the present study, previously mentioned the second type of silicon sludge was used as the primary material to synthesize SiC powder from the recycling of waste material. In order to determine the optimum condition, an investigation was carried out on the effects of the mixing ratio of Si sludge and carbon and of the synthesis temperature, as well as on the purification conditions to obtain SiC powder. The synthesized SiC as well as raw materials were characterized by X-ray diffraction (XRD) analyses, attenuated total reflectance spectroscopy (ATR), and field-emission scanning electron microscopy (FE-SEM).