esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data Sources  
### Study Area  
Zhangjiakou City (\(113^{\circ}50^{\prime}\)-\(116^{\circ}30^{\prime}\)E, \(39^{\circ}50^{\prime}\)-\(42^{\circ}50^{\prime}\)N, Figure 1) is located at the intersection of Shanxi Province, Inner Mongolia Autonomous Region, and Beijing Municipality in China. It administers 16 districts and counties, covering a total area of approximately 36,800 km\({}^{2}\), with an average elevation of 1600 m. The region has a temperate continental monsoon climate, with a frost-free period of 95-145 days per year and an average annual temperature between \(-1\) and \(15~{}^{\circ}\)C. The multi-year average annual precipitation is around 400 mm. Due to limitations in low accumulated temperatures and hydrothermal conditions, the crop growing season is mainly concentrated between May and October [4]. According to the 2022 Zhangjiakou Statistical Yearbook, the major food crops in this region are maize, potato, oats, and sesame, which together account for over 50% of the total arable land area. Local farmers also grow small amounts of cold-tolerant and drought-resistant crops, such as millet and sunflowers. The region's economic crop planting system is diverse, including vegetables, oilseeds, sugar crops, and medicinal herbs. Intensive agricultural activities, inappropriate farming practices, and climatic variability exacerbate environmental issues, highlighting the critical need for accurate remote sensing-based crop classification and continuous monitoring of cropping patterns to support sustainable agricultural management.
## 4 Results and Discussion
### Lossy System  
Figure 5 shows the curves of BER as a function of PTx for the lossy channel are shown. It considers an angle of the UE (hUE) of \(60^{\circ}\), heights of the UE (hUE) = [1, 1.5, 4, 5, 8, 9, 10] m and a frequency range FR1. Upon analyzing the results, it is evident that at a probability of error of \(10^{-3}\), the PTx value for the hUE1 curve is 0.4199 dBm, for the hUE2 curve it is 0.3975 dBm, for the hUE3 curve it is 0.1918 dBm, and for the hUE curve it is 0.1322 dBm.  
Figure 6 shows the BER vs. PTx curves for the constant hUE = 1.5 m, angUE = {\(\pi\)/18, \(\pi\)/12, \(\pi\)/19, \(\pi\)/6} rad for the FR1 frequency range. Upon analyzing the results, it is observed that, at a \(10^{-3}\) error probability, the PTx value for the AngUE\({}_{1}\) curve is 24.28 dBm, for AngUE\({}_{2}\) it is 17.75 dBm, for AngUE\({}_{3}\) it is 13.55 dBm, and for AngUE\({}_{4}\) it is 7.57 dBm. At a \(10^{-2}\), error probability, the PTx value for the AngUE\({}_{1}\) curve is 23.67 dBm, for AngUE\({}_{2}\) it is 17.28 dBm, for AngUE\({}_{3}\) it is 12.97 dBm, and for AngUE\({}_{4}\) it is 6.85 dBm.  
Figure 4: BER vs. SNR for different values of BW, SCS and NBR. (**a**) SCS of 15 kHz, BW = {5, 10, 20, 30} MHz and NBR = {25, 52, 106, 160}. (**b**) NBR = 51, BW = {15, 30, 50} MHz and SCS = {15, 30, 60} kHz.  
Figure 5: BER vs. PTx curves with angUE = 60\({}^{\circ}\), hUE = [1, 1.5, 4, 5] m.  
Figure 7 illustrates the results obtained when considering a hUE = 1.5 m, hUE = 60\({}^{\circ}\), and different frequencies (Fc) = [3.5, 6, 24.5] GHz. It can be observed that, at a \(10^{-3}\) error probability, the PTx value for the Fc1 curve is 0.304 dBm, for the Fc2 curve it is 5.152 dBm, and for the Fc3 curve it is 17.272 dB.
## 3 Results. Maximum Power Point (MPP) Determination
### SELEX Galileo 5-Cell Photovoltaic Assembly
Results point out that the maximum power voltage is predicted quite accurately. Obviously, the best results are obtained with an operating point (\(V_{i}\), \(I_{i}\)) closer to the reference MPP, particularly when this operating point is located to the right of this maximum. Furthermore, the local dispersion showed in the graphs from Figure 6 might be caused by the noise introduced in the calculation of the slope in each operation point (this slope is calculated with four points, the two closest at each side of the operation point). Finally, predictions of maximum power point (\(V_{mp}\), \(I_{mp}\)) compared to the \(P\)-\(V\) experimental curve are included in Figure 7.  
Figure 4: SELEX Galileo 5-cell Photovoltaic Assembly. Figure 5: \(I\)-\(V\) curve of a SELEX Galileo 5-cell Photovoltaic Assembly (see Figure 4). Starting from the measured values of \(R_{\mathrm{sh}0}\), \(I_{\mathrm{sc}}\) and \(V_{\mathrm{cc}}\), each experimental point \(i\): (\(V_{i}\), \(I_{i}\)) measured between \(0.893\cdot V_{\mathrm{cc}}\) and \(0.985\cdot V_{\mathrm{cc}}\) was used as fictional operational point (this bracket was selected as it contains the maximum power point, \(V_{mp}=0.927\cdot V_{\mathrm{cc}}\)). Therefore, the equivalent circuit was calculated for each operational point following the procedure described in Section 2. Figure 6: Values of the maximum power voltage, \(V_{mp}\), **(top)** and the maximum power current, \(I_{mp}\), **(bottom)**, related to the equivalent circuit calculated with the proposed methodology. These variables are plotted (dots) as a function of the voltage level, \(V_{i}\), at the operating point (\(V_{i}\), \(I_{i}\)) selected for each circuit calculation. The experimental reference values of \(V_{mp}\) and \(I_{mp}\) have been included in both graphs (dashed lines). Figure 7: Experimental \(P\)-\(V\) curve of the SELEX Galileo Photovoltaic Assembly (PVA) and location of the estimated maximum power points (\(V_{mp}\), \(I_{mp}\)) calculated with different operating points (\(V_{i}\), \(I_{i}\)), using the proposed methodology. Additionally, in order to check the accuracy of the proposed methodology the normalized root mean square error, \(\xi\) (Equation (5)), has been used to evaluate the error between the \(I\)-\(V\) curve of the calculated equivalent circuits and the experimental data. Results (Figure 8) indicate a quite high accuracy levels, with \(\xi<10^{-2}\) in almost all points, \(i\), of the selected range (\(V_{i}\in[0.893\cdot V_{\alpha\varsigma},0.985\cdot V_{\alpha\varsigma}]\)). These figures agree with those normalized root mean squared error values calculated between the R.T.C. France solar cell and the Photowatt PWP201 solar panel experimental results, and the equivalent circuit models described in Section 1 (Tables 2 and 3) and Section 3.1 (Tables 4 and 5). It should also be pointed out that these results are also similar (better in some cases) than the ones obtained by other authors whose methods implied the use of the MPP to calculate the equivalent circuit parameters.
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model
### Channel Estimation  
#### 2.2.1 White Gaussian Noise Estimation  
The equalizer output includes a Gaussian noise with a total variance G\({}_{\text{N}}\), which is the sum of shot noise and thermal noise besides the ICI contributions using the optical path difference. We disregard the noise contributions from gate leakage current and 1/f noise:  
\[\text{G}_{\text{N}}=\sigma_{\text{shot}}^{2}+\sigma_{\text{thermal}}^{2}+ \gamma^{2}\sigma_{\text{rfSI}}^{2}\text{.} \tag{5}\]  
The received power P\({}_{\text{rfSI}}\) by ICI is [26]:  
\[\text{P}_{\text{rfSI}}\ =\int_{\text{T}}^{\infty}\left(\text{$\sum$}_{i=1}^{ \text{LEDs}}\text{h}_{\text{i}}(\text{t})\otimes\ X(\text{t})\right)\,\text{dt} \text{.} \tag{6}\]  
The shot noise variance is given by [26]:  
\[\sigma_{\text{shot}}^{2}=2\text{q}\gamma\left(\text{P}_{\text{rSignal}}+ \text{P}_{\text{rSI}}\right)\text{B}+2\text{q}\text{I}_{\text{bg}}\text{I}_{ \text{2}}\text{B}\text{,} \tag{7}\]  
where q is the electronic charge, B is the equivalent noise bandwidth, \(\text{I}_{\text{bg}}\) is background current. The noise bandwidth factors I\({}_{\text{2}}\) = 0.562.  
The thermal noise variance is calculated by [27]:  
\[\sigma_{\text{thermal}}^{2}=\frac{8\pi\text{kT}\text{T}_{\text{k}}}{\text{G} }\text{n}\text{Al}_{\text{2}}\text{B}^{2}+\frac{16\pi^{2}\text{kT}\text{T}_{ \text{k}}\Gamma}{\text{g}_{\text{m}}}\eta^{2}\text{A}^{2}\text{I}_{\text{3}} \text{B}^{3}\text{.} \tag{8}\]  
Furthermore, both sides clarified noise from feedback-resistor, and noise from FET channels, respectively. Similarly, k is Boltzmann constant, T\({}_{\text{K}}\) is the absolute temperature, G is the open-loop voltage gain, \(\eta\) is the constant photodetector capacitance (PD) per unit area, \(\Gamma\) is the FET channel noise factor, g\({}_{\text{m}}\) is the FET trans-conductance, I\({}_{\text{3}}\) = 0.0868, T\({}_{\text{k}}\) = 295 K, \(\Gamma\) is the FET channel noise, G = 10 dB, g\({}_{\text{m}}\) = 30 ms, \(\eta\) = 1.5, \(\Gamma\) = 112 pF/cm\({}^{2}\), even B = 100 Mbps. In our work, the surrounding current comes from the direct sum light [27].
# Efficient Adsorbent-Desiccant Based on Aluminium Oxide
## 4 Industrial Adsorbents-Desiccants and Their Characteristics  
The most famous manufacturers of aluminium oxide desiccants are companies such as Axens, Alcoa, Alumac, Albemarle and BASF. The main method of obtaining active aluminium oxide is currently the \"flash process.\" The simplified technology for obtaining active aluminium oxide is as follows. The raw material-bauxite ore--is transformed into the solution by means of sodium carbonate (Baeyer process). Solid impurities are removed, and alumina is isolated by crystallisation of trihydroxide (Baeyer hydrate), which is actually gibbsite. Then, the gibbsite is calcined for a very short time (from 1 s to 10 s) in an air flow (gas flow velocity: from 8 m/s to 25 m/s) at a gas temperature from 400 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C, followed by the separation of the reacted powder from the gas flow and cooling (quenching) to a temperature less than 60 \({}^{\circ}\)C. The result is active aluminium oxide, which has a high specific surface area, high specific pore volume and high adsorption capacity, determining its widespread use in the petrochemical industry.  
In the Russian industry, active aluminium oxide is produced using a similar \"flash process\" by means of the TCA method [54]. The initial material--gibbsite--is activated by the air, which is heated by the heat generator, in the pneumatic transport mode for 5-8 s.  
The latter is heated at the expense of thermal energy, generated by burning diesel fuel. The air temperature in the reaction zone can reach \(\sim\)550 \({}^{\circ}\)C. Depending on the TCA conditions, active aluminium oxide can be obtained with different sets of physicochemical properties.  
Table 2 shows the characteristics of some industrial adsorbents based on aluminium oxide.  
In this way, technological schemes are currently being implemented in the industry that allow for the production of granulated active aluminium oxides of various low-temperature modifications (\(\eta\)-Al\({}_{2}\)O\({}_{3}\), \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) or \(\chi\)-Al\({}_{2}\)O\({}_{3}\)) without precipitation stages. They include materials with the specific surface from 200 m\({}^{2}\)/g to 400 m\({}^{2}\)/g that have both monodisperse and bidisperse porous structures. Spherical granules based on \(\chi\)-Al\({}_{2}\)O\({}_{3}\) are obtained by disk granulation of the gibbsite thermal activation product, and granules of cylindrical or more complex shape based on \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) or \(\eta\)-Al\({}_{2}\)O\({}_{3}\) are obtained by extrusion moulding of hydrated thermal activation products. The catalogue [58] presents a wide range of brands of active aluminium oxide used as adsorbents or catalysts whose granules have a spherical, cylindrical and ring shape. The widespread use of AO as an adsorbent is determined, along with good adsorption properties, by a possibility of geometric and chemical modification, rather high mechanical strength and stability in the cyclic operation.  
Physicochemical Properties of the Surface and Sorption Characteristics of Aluminium Oxide Adsorbents
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 6 E-Health Sustainability Assessment Method
### Procedures of Sustainability Assessment
#### 6.2.1 Assessment Team  
The ideal assessment is conducted by at least three expert assessors: one expert from the IT department, another from the finance department, and one from hospital management. The inclusion of experts from the three domains will ensure a high level of accuracy and validity in the sustainability assessment [46]. The IT expert shall assess the technology dimension, the finance expert shall assess the economic dimension, and the hospital management expert shall assess the organization, resources, and social dimensions. It is worth noting that the assessment of the social dimension must be completed by the users of the e-health solution. Therefore, the selection of the reflective sample is important for results' reliability. Furthermore, as all three expert assessors work in one team, each expert shall help in assessing aspects of other dimensions that are related to their specialty.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 1 Introduction
It has presented a new data protection and route reconstruction mechanism that employs a localized-globalized integrated strategy, effectively addressing data forwarding failures and routing voids caused by node mobility. 4. It has presented the results of extensive simulations to evaluate the performance of the proposed solutions. The results demonstrate that APCDBRP outperforms other corresponding protocols. The rest of the paper is organized as follows. In Section 2, we review related work. In Section 3, we present the proposed APCDBRP. In Section 4, we evaluated the performance of the protocol through theoretical analysis and simulation experiments. In Section 5, we conclude the paper.
# Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models
## 2 Methodology
### Anisotropic Effective Elastic Properties for 2D Fractured Models
#### 2.1.3 Calculation of 2D Equivalent Stiffness Matrix  
After computing the displacement field, the stress and strain components of each element are computed using Equations (3) and (4). The corresponding stress and strain average over the model's volume can be calculated using these parameters  
\[\overline{\overline{\overline{e_{ij}^{m}}}}=\frac{1}{S}\int_{D}e_{ij}^{m}dS, \tag{15}\]  
\[\overline{\overline{\sigma_{ij}^{m}}}=\frac{1}{S}\int_{D}\sigma_{ij}^{m}dS, \tag{16}\]  
where \(m=1\)-\(3\) denotes the \(m\)th test, and \(S\) is the surface of domain D.  
Taking the prerequisite that an equivalent homogeneous anisotropic elastic solid can stand for the average response of the interest model, the average stress and strain components can be related through the Voigt stiffness matrix \(\mathbf{M}\equiv M_{ij}\). In the case of general anisotropy and under plane strain conditions, this relation is given by  
\[\left(\begin{array}{c}\overline{\overline{\sigma_{xx}^{m}}}\\ \overline{\overline{\sigma_{yy}^{m}}}\\ \overline{\overline{\sigma_{yy}^{m}}}\end{array}\right)\equiv\left(\begin{array} []{ccc}M_{11}&M_{12}&M_{16}\\ M_{12}&M_{22}&M_{26}\\ M_{16}&M_{26}&M_{66}\end{array}\right)\left(\begin{array}{c}\overline{ \overline{e_{xx}^{m}}}\\ \overline{\overline{e_{yy}^{m}}}\\ 2\overline{\overline{e_{xy}^{m}}}\end{array}\right) \tag{17}\]  
where \(\overline{\overline{\sigma_{ij}^{m}}}\) and \(\overline{\overline{e_{ij}^{m}}}\) are cells of the averaging stress and strain vector, respectively, and \(m=1\)-\(3\) denotes the \(m\)th simulation test. It is worthy to note that the matrix \(\mathbf{M}\) considers the greatest extent of anisotropy.  
Thus, the six unknown components of the stiffness matrix possibly are calculated utilizing a least-squares procedure. That is, the components of equivalent stiffness matrix \(M_{ij}\) can be obtained by minimizing the cost function \(F_{1}\) (\(\mathbf{M}\)) as follows:  
\[\frac{\partial F_{1}(\mathbf{M})}{\partial M_{ij}}=0. \tag{18}\]  
We can solve the linear system of six equations yielded by Equation (18) to obtain the equivalent stiffness tensor \(M_{ij}\); see Appendix A for more details.
## 2 Materials and Methods  
### CT Dataset from NSCLC Patients  
We utilized two publicly available NSCLC datasets from the TCIA repository [34]. The first was the TCIA-NSCLC Radiomics dataset (LUNG1), which included 422 patients treated at the MAASTRO Clinic in Maastricht, The Netherlands. The patients with in-operable, histologic, or cytologic confirmed NSCLC underwent FDG PET-CT acquisition for radiotherapy treatment planning using Siemens Biograph (SOMATOM Sensation-16 with an ECAT ACCEL PET scanner, Siemens Medical Solutions, Malvern, PA, USA). Patients were then treated with induction concurrent chemoradiotherapy, radiotherapy alone, or chemotherapy followed by radiotherapy. Additionally, the R01 cohort from the TCIA-NSCLC Radiogenomics dataset (LUNG2), which comprises 162 patients from the Stanford University School of Medicine and Palo Alto Veterans Affairs Healthcare System, was considered for external independent validation [32]. This dataset came from a retrospective study, where subjects were scanned using different scanners, protocols, and parameters. Similar to LUNG1, patients affected by NSCLC were treated with concurrent chemoradiotherapy, radiotherapy alone, or chemotherapy followed by radiotherapy. The original works provide specifics about patient recruitment and treatment.  
Pre-treatment CT images and tumor contours manually delineated by experts are available from these datasets. In this study, a subset of patients was considered according to the following exclusion criteria:  
1. More than one tumor exists in the volume.
2. The post-operative survival time was lower than two years, but the patient remained alive.
3. There are misalignments or reading errors between CT images and tumor contours.  
A total of 403 patients from LUNG1 and 130 patients from LUNG2 were thus finally considered. More details about CT images and patients' clinical information are summarized in Table 1, whereas illustrative examples of both datasets are shown in Figure 1. Additionally, patients were dichotomized into non-survivor and survivor based on their overall post-operative survival time, with a cut-off of two years, similar to the median survival time for these patients [9; 35].
## 5 Discussions
### Interpretability Research  
In the field of interpretable research, the Individual Conditional Expectation (ICE) method analyses the effect of a target feature by keeping the other features constant to obtain the relationship between each target feature input and the corrosion rate and presents the results graphically. However, this method has the problem of too many individuals, which leads to the redundancy of the results, and it is not easy to obtain the explanatory information. Meanwhile, Partial Dependence Plots (PDPs) are often used for interpretability analysis. PDPs are generated by averaging the results of ICE, which reduces the complexity of the results. However, this method usually ignores the effect of data heterogeneity on the depicted relationships. Therefore, to cope with the limitations of both methods, this study integrates both ICE and PDP. This integration provides a comprehensive view of the interpretability for the model, demonstrating both the overall impact of the features on the prediction results and preserving the specific effects of each sample.  
The results of the study are shown in Figure 9, and it is found through analysis that the increase of corrosion inhibitor would cause the corrosion rate to decrease significantly, which proves that the corrosion inhibitor can reduce the corrosion rate efficiently. The proportion of carbon dioxide and hydrogen sulfide are positively correlated with the corrosion rate, showing that acidic gases can significantly exacerbate the occurrence of corrosion. Therefore, the above two factors should be considered in the establishment of corrosion control measures. The increase in temperature can accelerate the corrosion rate, indicating that the temperature has a positive effect on corrosion. The pH value and corrosion rate show a negative correlation, indicating that the alkaline environment helps to slow down the corrosion; the effect of this factor on distribution law of the corrosion  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indices** & **R2** & **MAPE (\%)** \\ \hline Statistical prediction model & 0.938 & / \\ Machine learning & 0.925 & 5.73 \\ CEEMDAN-VDM-MPSO-BPNN & 0.978 & 4.13 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of predictive performance of historical documents.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Testing Model** & **Data A** \\ \hline CEEMDAN-VDM-MPSO-BPNN & 0.921 \\ CEEMDAN-MPSO-BPNN & 0.903 \\ EEMD-MPSO-BPNN & 0.885 \\ EMD-MPSO-BPNN & 0.879 \\ MPSO-BPNN & 0.861 \\ PSO-BPNN & 0.854 \\ GA-BPNN & 0.837 \\ BPNN & 0.831 \\ RBFNN & 0.822 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison results of the GRD.  
is similar to the influence of acidic gases. The liquid flow rate and corrosion rate are positively correlated, showing that too high a flow rate can significantly accelerate the onset of corrosion, especially at reducers and elbows. Chloride ions are positively correlated with corrosion rate, indicating that chloride ions are also a key consideration in the corrosion control process.
## 3 Results and Discussion  
### Characterization of the Electrodes  
The morphology of the RVC used in this work can be seen in Figure 2a-c. We can observe the characteristic honeycomb structure of this material with a pore size of approximately 750 \(\upmu\)m for a 60 ppi electrode (Figure 2a). Figure 2b shows this electrode after deposition of the nanoparticles by dipping according to procedure A. The formation of a compact layer of rough texture, with some aggregates of less than 1 \(\upmu\)m can be observed. Figure 2c shows the galvanostatically coated RVC using procedure B. In this case, the formation of spherical nanoparticles, less than 1 \(\upmu\)m, dispersed throughout the entire surface was observed. The formation of more compact coating using procedure A may be due to the heat treatment that was carried out after the deposition. An increase in the treatment temperature results in sintering of the nanoparticles.  
Figure 3a,b show the Mossbauer spectra for the RVC/coated samples of Figure 2b,c, respectively. The spectra show the superposition of the two sextets corresponding to the Fe in the tetrahedral and octahedral position of the spinel structure. The sample obtained by dipping (Figure 3a) has a weight mean isomer shift of 0.311 mm s\({}^{-1}\) derived from the spectrum fit, which corresponds to a 100% maghemite coating. On the contrary, the spectrum of Figure 3b that corresponds to the sample galvanostatically coated, shows a weight mean isomer shift value of 0.465 mm s\({}^{-1}\), which indicates 60% magnetite, the rest corresponding to maghemite. From the measurements made by Mossbauer it is impossible to determine whether there are two types of particles or whether there are magnetite particles with a certain surface oxidation.  
Figure 1: Schematic set up of the electrofenton system.  
Figure 2: SEM images of studied cathodes: (**a**) 60 ppi raw RVC; (**b**) RVC dip coated; (**c**) RVC gal-vanostatically coated.  
Figure 4 shows the diffractogram corresponding to the sample obtained using methodology A. All diffraction peaks match the standard JCPDS number 01-089-5892 which correspond to maghemite, in accordance with the Mossbauer results. There are also two reflections at angles of 25\({}^{\circ}\) and 22\({}^{\circ}\) 20, which corresponds to an amorphous phase of the RVC carbon electrode. The diffractogram confirms that there are no impurities corresponding to other iron oxides or oxyhydroxides. The diffractogram for the RVC coated galvanostatically (figure not showed) correspond to 100% magnetite (JCPDS 01-088-0315) and also all the peaks have been indexed to this phase of iron oxide.