esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Study Area  
The Thies region spans from latitude 10\({}^{\circ}\)44\({}^{\prime}\)46\({}^{\prime\prime}\) to 10\({}^{\circ}\)52\({}^{\prime}\)46\({}^{\prime\prime}\) N and from longitude 78\({}^{\circ}\)39\({}^{\prime}\)11\({}^{\prime\prime}\) to 78\({}^{\circ}\)44\({}^{\prime}\)13\({}^{\prime\prime}\) W. It covers approximately 6669.6 km\({}^{2}\), which accounts for 3.35% of Senegal's total land area. Considering the importance of peanut and vegetable production, the Thies region plays a critical role in Senegal's economy. In 2020, peanuts and millet collectively constituted 78.1% of the total crop production, while vegetable cultivation, covering one-third of cultivated areas, contributed approximately 30.25% to the national output. The \"Niayes Zone\", situated on the Thies region's coastal line, contributes significantly to employment through market gardening, which fulfills more than 50% of Senegal's need for fruits and vegetables (CIDRAD, 2022). In addition, from 1986 to 2021, the shrub savannah area decreased from 10,883.45 hectares to 7414.46 hectares, marking a decline of 29.29%. Exploring the data from the National Agency of Statistics and Demography (ANSD) in 2020, we found that about 51.8% of farmers' plots faced socio-economic or  
Figure 1: The interconnection between natural and socio-economic factors.  
natural factors. In 2020, this accounted for about 2,162,831 inhabitants (ANSD). From 2009 to 2018, urban growth in the Thies region exceeded 7% [8]. The urbanization trend will be poised to amplify through the initiative of multiple projects, including establishing the \"Ndayane\" port. Accordingly, this context highlights the dual challenge of substantial socio-economic development and the imperative for effective land management.  
Regarding land use classification, Figure 2 indicates that agricultural land constituted 52.40% of the total in 2005, decreasing to 48.40% by 2020, while grassland area accounted for 14.61% in 2005 and increased to 18.4% in 2020. Conversely, artificial surfaces represented 3.5% in 2020 and 1.85% in 2005. Consequently, agricultural and grassland dominated the land use morphology, comprising 66.78% of the total land area in 2020. In summary, two significant conclusions can be drawn: decreases in agricultural land (\(-0.005\%\)), forested areas (\(-0.015\%\)), and wetlands (\(-0.043\%\)) from 2005 to 2020, coupled with simultaneous increases in grassland (0.017%), bare land (0.017%), and artificial surfaces (0.059%) from 2005 to 2020. The topography of the Thies region is predominantly flat, except for the \"Plateau of Thies\", reaching an altitude of 141 m. The highest recorded temperature in the area is 33.2 \({}^{\circ}\)C. According to data compiled by the National Agency of Civil Aviation and Meteorology (ANACIM), the interannual rainfall patterns showed an average of approximately 461.65 mm from 2000 to 2020. The prevalent soil characteristics include mild leaching and tropical sandy soils with a ferruginous component [10].
## 4 Discussion
### HSPs' Induction  
HSP60 and HSP70 expression levels, both in the gills and in the liver, were maintained at high levels throughout the year. During colder and warmer months, when salinity and temperature showed minimum and maximum values, respectively, both HSP60 and HSP70 in the gills exhibited their highest levels. This was also depicted in the two dimensional plot of the results of PC analysis (Figure 7). Generally, HSP members in both gill and liver samples were positioned on the positive part of PC1, where the samples of the cold months are placed, having a negative correlation to salinity and conductivity. It has been demonstrated that several molecular chaperones are upregulated in fish gills when exposed to stress provoked by salinity variations [18; 55; 56]. Specifically, in black sea bream _Mylo macrocephalus_ ([PERSON], 1855), HSPs (HSP70, HSP90 and HSP60) were induced after acclimation to both plasma-hypersomotic and plasma-hyposmic salinity [57]. According to [PERSON] et al. [58], low conductivity appears to elicit responses in fish similar to heat stress response [59; 60; 61]. In fact, the above claim has been confirmed by [PERSON] et al. [62], who showed that low water conductivity values were accompanied by increased HSP90 expression in the fish species _Astyanax mexicanus_ ([PERSON], 1853). These indications are very similar to the ones obtained in the present work, since increased levels of HSP members in the gills were mostly negatively correlated to conductivity, as shown by the PCA. We cannot support with certainty whether salinity is the single stress factor, as PCA has shown that, in gill samples, HSP levels are positively related to temperature, since they are positioned on the positive part of the PC2 axis. However, the opposite stands for HSP measured in liver samples that were placed on the negative part of PC2. (Figure 7). Because cold and heat stress destabilizes the hydrophobic interactions of polypeptide chains, leading to non-functional proteins, HSPs provide protection against protein structural changes [63; 64; 65; 66]. Similar results of Heat Shock Response (HSR) as a means of fish acclimatization to cold [67; 68; 69; 70] as well as to increasing ambient temperatures were reported by [PERSON] et al. [71], who investigated seasonality effects in the gilthead sea bream _Sparus aurata_ ([PERSON], 1758). Regarding HSP90, its expression levels exhibited seasonality in both gills and liver. Specifically, in the liver, its levels were consistently high during the colder and low in the warmer months. It has been argued that HSP induction in fish has a protective role against a number of environmental stressors [72; 73]. However, according to [PERSON] et al. [8], HSPs increased levels in fish may be more of a stress indication rather than a measure of its degree. Differences observed between gills and liver HSR can be attributed to the well-known tissue specificity observed in fish species (e.g., [13; 71]).
# Uncertainty Propagation through a Point Model for Steady-State Two-Phase Pipe Flow
## 2 Materials and Methods
### Uncertainty Quantification
#### 2.2.4 Statistics  
The uncertainty analysis can be summarized by some key figures. We have a good overview of the propagated uncertainty if we know the mean \(\text{E}\left[Y\right]\), variance \(\text{Var}\left[Y\right]\) and the quantiles \(y_{0.025}\), \(y_{0.05}\), \(y_{0.95}\) and \(y_{0.975}\). Furthermore, we can list the contribution to \(\text{Var}\left[Y\right]\) from each input. If the input \(Z_{i}\) contributes much to \(\text{Var}\left[Y\right]\), we have much to gain from reducing the measurement error in \(Z_{i}\). The reduction in \(\text{Var}\left[Y\right]\), if we could eliminate the measurement error in \(Z_{i}\), is equal to \(\text{Var}_{Z_{i}}\left[\text{E}_{Z_{\sim i}}\left[Y|Z_{i}\right]\right]\), where \(Z_{\sim i}\) are all inputs except \(Z_{i}\). The relative reduction in output uncertainty is  
\[S_{i}=\frac{\text{Var}_{Z_{i}}\left[\text{E}_{Z_{\sim i}}\left[Y|Z_{i}\right] \right]}{\text{Var}\left[Y\right]}, \tag{18}\]  
which is known as the first-order sensitivity index proposed in Sobol [29]. The same article defines the total sensitivity index \(S_{Ti}\) which also includes the interaction effect with other variables. The total index is the remaining output variance when we fix all inputs but \(Z_{i}\). That is  
\[S_{Ti}=1-\frac{\text{Var}_{Z_{\sim i}}\left[\text{E}_{Z_{i}}\left[Y|Z_{\sim i }\right]\right]}{\text{Var}\left[Y\right]}. \tag{19}\]  
Sections 2.3 and 2.4 each provide a simulation method. When we talk about sensitivity indices for multiple cases, \(AS_{i}\) is the average index weighted by \(\text{Var}\left[Y\right]\) for each case. That is  
\[AS_{i}=\frac{\sum_{c}S_{i}^{c}\text{Var}\left[Y_{c}\right]}{\sum_{c}\text{Var }\left[Y_{c}\right]}, \tag{20}\]where \(S_{i}^{c}\) is the first-order sensitivity index of input \(Z_{i}\) for case \(c\), and \(\text{Var}\left[Y_{c}\right]\) is the output variance for case \(c\).
## 4 Verification Numerical Examples and Discussion
### Second Example
Therefore, an increased number of finite elements (\(N_{e}\)) was required to obtain visually perfect agreement of the results (although some small discrepancies in the vicinities of the cracks remain still noticeable). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Solution** & \(\mathbf{\
u_{\mathrm{A}}}\) **(mm)** & \(\mathbf{\upsilon_{\mathrm{C1}}}\) **(mm)** & \(\mathbf{\upsilon_{\mathrm{B}}}\) **(mm)** & \(\mathbf{\upsilon_{\mathrm{C2}}}\) **(mm)** & \(\mathbf{\upsilon_{\mathrm{C}}}\) **(mm)** \\ \hline GDEs & \(-7.10145\) & \(-0.82797\) & \(-0.43899\) & \(-1.15046\) & \(-8.99332\) \\
2 cb2 psFE & \(-7.08739\) & \(-0.88593\)* & \(-0.42891\) & \(-1.22572\)* & \(-8.97526\) \\
4 cb2 psFE & \(-7.09083\) & \(-0.84549\)* & \(-0.44200\) & \(-1.17304\)* & \(-8.98006\) \\
6 cb2 psFE & \(-7.09121\) & \(-0.83208\) & \(-0.44032\) & \(-1.15551\) & \(-8.98052\) \\
8 cb2 psFE & \(-7.09091\) & \(-0.84219\)* & \(-0.43912\) & \(-1.16822\)* & \(-8.98014\) \\
10 cb2 psFE & \(-7.09237\) & \(-0.83551\)* & \(-0.43905\) & \(-1.15987\)* & \(-8.98197\) \\
12 cb2 psFE & \(-7.09418\) & \(-0.82692\) & \(-0.43902\) & \(-1.14915\) & \(-8.98423\) \\ \hline \hline \end{tabular}  
* values obtained from interpolation functions of cb2 psFE model.  
\end{table} 
Table 2: Results of transverse displacements for several solutions and locations. Figure 6: Comparison of transverse displacements for two (**a**) and four (**b**) FE meshes, respectively. At last, functions of shear force distributions were calculated. Since these functions are smoother than the bending moment functions, fewer finite elements were required to obtain a very fine agreement of the results. The horizontal axis in Figure 8 represents the distance from the left end of the beam, and the vertical axis represents the results of the shear force. For a small number (i.e., four) of finite elements, as shown in Figure 8a, the H2 interpolation functions (dark blue) produced evidently better matching against GDEs solutions (red) as the H1 (light blue) interpolation functions. However, since the model number of FEs is actually governed by the bending moment functions, the differences between both interpolations became almost irrelevant with the increasing number of FEs as the H1 interpolation functions already produced good matching, as shown in Figure 8b. Figure 8: Comparison of shear function for four (**a**) and six (**b**) FE meshes, respectively. Figure 7: Comparison of bending moments for various FE meshes: (**a**) \(N_{e}=10\); (**b**) \(N_{e}=20\); (**c**) \(N_{e}=30\); (**d**) \(N_{e}=50\).
# A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions
## 5 Results
### Propagation Conditions
For each measurement points, the assessment was made and the NDEI error was determined for each individual distribution, as shown in Figure 7.2 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Point** & **Weibull** & **Rayleigh** & **Nakagami** \\ \hline
1 & 0.517 & 0.93266 & 0.66894 \\
2 & 0.77901 & 0.96381 & 1.0891 \\
3 & 0.87935 & 1.081 & 1.2216 \\
4 & 0.89289 & 1.0866 & 1.1133 \\
5 & 0.42217 & 0.80168 & 0.53029 \\
6 & 0.44172 & 0.83668 & 0.58009 \\
7 & 0.46689 & 0.76069 & 0.54274 \\
8 & 0.4166 & 0.77924 & 0.53395 \\
9 & 0.38014 & 0.5733 & 0.44018 \\
10 & 0.48478 & 0.63987 & 0.53011 \\
11 & 0.45607 & 0.75739 & 0.56195 \\
12 & 0.58868 & 0.82631 & 0.66981 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NDEI error for Weibull, Rayleigh, Nakagami distributions at individual points. Figure 10: Histogram of normalized amplitudes of replicas. Probability density functions of replica amplitudes for Weibull, Rayleigh and Nakagami distributions at measurement point No. 5. The results summarized in the Table 1 show that at each measurement point, the lowest value of the NDEI error was obtained for the Weibull distribution, making it the distribution that best reflects the propagation conditions in the laboratory tests. This conclusion is similar to that presented in [13].
## 3 Materials and Methods
### Methods
#### 3.2.5 Proximity Calculation  
The show method of the offerController controller receives the geographic coordinates sent by the application and calculates the distance between the shopping center and the mobile device (MD).  
Algorithm 1 describes the steps to calculate the distance between the mobile device and the shopping center, taking into account the geographical coordinates. The API receives the coordinates and returns a list of offers from any shopping center located within 600 m or less.  
```
Data: String with coordinates ($coordinates) Result: List of items from stores within 600 ms
1. Split $coordinates into latitude and longitude by separating at the letter \"u\". Obtain latitude1 and longitude1;
2. Retrieve the coordinates of all shopping centers from the database;
3. foreachshopping center in storesdo
4 id\(\leftarrow\) ID of the current store;
5 name\(\leftarrow\) name of the current store;
6 latitude2\(\leftarrow\) latitude of the current store;
7 altitude2\(\leftarrow\) altitude of the current store;
8. Calculate the distance between (latitude1, longitude1) and (latitude2, altitude2) using the Haversine formula;
9. ifdistance\(\leq\)600 then
10 Retrieve items (name, price, discount, sale, image) from the store;
11 return items from the store;
12
13 end if
14
15 end for
16. If no stores are within 600 ms, finish without returning specific results;
```  
**Algorithm 1**Show Nearby Stores.  
If the calculated distance is less than or equal to 600 m, the API will send the available offers from the shopping center to the mobile device (MD).
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 5 Conclusions  
In this paper, we discussed the prospects of out-of-band NLIN mitigation using equalization. At the heart of this concept is the time-varying ISI model, and the many types of correlations that it entails. There are several points to consider when one attempts to choose an equalization algorithm; we have shown that the more complex algorithms (e.g., Kalman filtering) provides superior NLIN cancellation capability. However, this comes with a significant increase in computational cost. While designing an adaptive equalization algorithm, one must be aware of the statistical properties of NLIN in the system. For example, very short systems tend to be dominated by the 0-th order ISI term, corresponding to PPRN. They are also characterized by relatively short correlation times. In longer systems, higher order ISI terms become more significant, and the correlation lengths increase. Thus, a higher number of equalizer taps will be needed as the link length increases. In cases of polarization-multiplexed transmission, it is highly advantageous to treat the two polarization channels jointly. This allows both to compensate for cross-polarization effects, and to utilize the correlation between the NLIN components affecting the two polarizations.  
Another important aspect that was discussed is the type of symbol estimation method that is used. Incorporating the decoder with the equalizer in a turbo-equalization (or similar) setup have been shown to significantly improve performance. Naturally, the choice of code-type and decoding method can significantly affect the equalizer's performance.  
The equalization algorithms reviewed in this paper represent the main types discussed in the literature, although many variants of these algorithms exist. We have showed that significant gain can be achieved by using these equalization methods, and that adaptive equalization is a viable method for NLIN mitigation. Still, there is much work to be done on this subject, such as designing new and efficient algorithms tailored for NLIN mitigation, as well as real-time implementation of the current algorithms.  
[PERSON] is responsible for the gathering of materials, study of the various equalization schemes, the evaluation of their performance, and preparation of the original draft. [PERSON] contributed to the professional guidance and editing of the final version. [PERSON] provided professional guidance and academic supervision, and participated in the editing of the final version of the paper.  
This work was supported by the Israel Science Foundation under Grant 1401/16.  
The authors declare no conflict of interest.
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Considered Intensity Standardisation Techniques
#### 2.3.6 Piecewise Linear Histogram Matching  
The piecewise linear histogram matching method proposed by [PERSON] and [PERSON] [26] learns an intensity mapping function from a reference scan and uses it to standardise input scans. In the training step, the method identifies a set of \(p\) landmarks (e.g., percentiles or modes) from the histogram of the reference scan and creates a piecewise linear function by interpolating linear segments between consecutive landmarks. The method avoids the minimum and maximum values to add robustness against outliers. In the transformation phase, the same landmarks are identified and intensities updated according to the estimated mapping function. In this work, we assumed the reference scan was the baseline scan of each subject.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 3 Computational Results and Discussion
### Sensitivity Analysis: Effect on Simulation Accuracy, Location, Electricity Price, and Costs
#### 3.4.3 Effect of PHS Cost  
Two new cases of PHS CAPEX were considered to observe the effect of the PHS CAPEX on the optimisation:  
1. PHS CAPEX 30% lower than that in Section 3.2: 700 EUR/kW for all PHS components except the reservoirs + 17.5 EUR/m\({}^{3}\) for the reservoirs. These values can be considered to be optimistic.
2. PHS CAPEX data from the publication of [PERSON] et al. [28] were used: a CAPEX of 547 EUR/kW for machines and civil works and 2.7 EUR/m\({}^{3}\) for reservoirs. These data are much lower than the PHS cost used in Section 3.2, and it seem to be real (too optimistic) when compared with the rest of the publications discussed in Section 3.2.4.  
For LSS systems, Figure 32 shows the configuration and NPC of the optimal system found for each type of project with different PHS CAPEX considered. For the cost of 700 EUR/kW + 17.5 EUR/m\({}^{3}\), the optimal system with PHS has a lower NPC than the case without PHS, making it worth, in this case, the use of PHS. Using the optimistic CAPEX of 547 EUR/kW + 2.7 EUR/m\({}^{3}\), the PHS of the optimal system has a higher power and duration (1.5 MW and 18 h duration for A and 16 h for B).  
Figure 31: Optimal systems found for the different RTP electrical price: PGS system.  
Figure 30: Optimal systems found for the different RTP electrical price: LSS system.  
Considering FGS systems, Figure 33 shows the configuration and NPV of the optimal system for each type of project with different PHS CAPEX. The cost 700 EUR/kW + 17.5 EUR/m\({}^{3}\) of the optimal system with PHS has a similar NPC to the case without PHS. In the case of the lowest PHS CAPEX, the optimal system includes a pump turbine of 1.5 MW and a reservoir of 18 h in the Type A project and 16 h in the Type B project.  
In the case of low PHS CAPEX, both in LSS and PGS, the difference in arbitrage between type A and type B has no significant effect because the pump turbine is of a higher size: the NPC of both systems is similar, but one has a 16 h duration and the other has a 18 h duration.
## 1 Introduction
Thus, considering these arguments, effectively using the hammer in DS measurements, and being able to obtain comparable results with those of the shaker in terms of accuracy, is very attractive. Hopefully, a validated and accurate hammer methodology to measure pavement DS will finally allow us to produce the amount of DS measurements needed to understand the relationship between DS and TRN. The present work aims to fine-tune the non-resonant method with the impact hammer to achieve similar reproducibility and accuracy to that obtained with the shaker [36] and to provide a more easy-to-use and easy-to-setup method for determining the DS of pavements. To this end, the authors designed and 3D printed a low-cost device named Test Automation Device (TAD) to perform a semi-automatic measurement and to allow for better control of the impact. Using the TAD, the limits of the handheld usage of the hammer were identified and a best practice procedure for the non-resonant road pavement DS measurement was defined. The fine-tuned measurement chain was then used to correctly reproduce the predicted frequency response proposed in [36], with \(R^{2}\) values very close to the unity for boot in situ pavements and pavement samples. The in situ pavements tested and the sample used for the optimization process were developed under the LIFE E-VIA (Electric Vehicle noise control by Assessment and optimization of tire/road interaction) project. The project aims to address road traffic noise pollution by focusing on a future perspective where electric and hybrid vehicles are a substantial part of the flow. The other sample was provided by the LIFE NEREiDE project (Noise Efficiently Reduced by Recycled Pavement), which aims to define the guidelines and best practices for the design, building, and monitoring of pavements with high environmental sustainability. In the Sections 2 and 3 a description of the measurement principle and a brief literature review are provided. In Section 4, the methodology and the designed mechanical device are described in depth. Sections 5 and 6 describe the experimental setup and calibration. The results are then presented in Section 7, including the in situ and in laboratory measurements. A discussion of the results is then provided in Section 8.