esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Guidance  
#### 2.3.1 Aircraft Control  
Assuming a constant throttle setting, the aircraft's rate of climb and airspeed may be simultaneously controlled by properly adjusting its flight path angle. From the definitions of Specific Energy (\(E_{s}\)) and Specific Excess Power (\(P_{s}\)) [3]:  
\[E_{s}=h+\frac{V2}{2g} \tag{9}\]  
\[P_{s}=\frac{T-D}{mg}V \tag{10}\]  
\[P_{s}=\frac{dE_{s}}{dt}=\dot{h}+\frac{V}{g}\dot{V} \tag{11}\]  
Using the chain rule:  
\[\frac{dh}{dM}=\frac{dh}{dV}\frac{dV}{dM}=\frac{dh}{dV}a=\frac{dh}{dt}\frac{dt}{ dV}a=\frac{\dot{h}}{\dot{V}}a\Rightarrow\dot{V}=\frac{\dot{h}a}{\left[\frac{dh}{ dM}\right]} \tag{12}\]  
Combining Equations (11) and (12),  
\[P_{s}=\dot{h}+\frac{V}{g}\frac{\dot{h}a}{\left[\frac{dh}{dM}\right]}\Rightarrow \dot{h}=\frac{P_{s}}{1+\frac{Va}{g[\frac{dh}{dM}]}} \tag{13}\]  
Knowing that \(\dot{h}=V\sin\gamma\), Equation (13) becomes:  
\[\gamma=\sin^{-1}\frac{P_{s}}{\left(1+\frac{Va}{g[\frac{dh}{dM}]}\right)V} \tag{14}\]  
It is thus possible to fly in a particular direction in the H-M plane only by controlling the aircraft's flight path angle. Limitations, however, do exist:  
For \(\gamma>0\):  
\[\frac{dh}{dM}<\frac{V^{2}a}{g(P_{s}-V)} \tag{15}\]  
The limiting value corresponds to the aircraft climbing vertically.  
Equivalently, for \(\gamma<0\):  
\[\frac{dh}{dM}>\frac{-Va}{g(\frac{P_{s}}{V}+1)}, \tag{16}\]  
the limiting value corresponding to a vertical dive.  
Both limitations are presented graphically in Figure 3, the shaded area denoting the range of physically possible transitions in the h-M plane.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 2 Related Works
### Stretch Optimal Model  
In stretch optimal scheduling, the algorithm takes the variable sizes of data items into consideration (i.e., the service time to different data items). A metric called _stretch_ is proposed in ([PERSON] and [PERSON], 1998). It is the ratio of the response time to the service time of the requested data item. The response time refers to the duration from the instance when the request is submitted to the time when the request is satisfied, whereas the service time is the duration for transmitting the data item. [PERSON] and [PERSON] (2017) proposed a stretch optimal scheduling algorithm called LTSF (Longest Total Stretch First). In LTSF, the data item with the largest current stretch is broadcast first. [PERSON] and [PERSON] (2018) reduced the computation overhead to make the stretch-based scheduling more scalable in on-demand broadcast environments. STOBS (Summary Tables On-demand Broadcast Scheduler) is proposed in ([PERSON] and [PERSON], 2018), which broadcasts the data item with the maximum \(\frac{R\times W}{5}\) value. Here, \(R\), \(W\) and \(S\), respectively, denote the number of pending requests for the data item, the waiting time of the oldest request and the data item size. [PERSON] et al. (2018) proposed PRDS (Preemptive Request Deadline Size), for minimizing the response time and stretch in scheduling size variant data item.
# Online Streaming Feature Selection via Conditional Independence
## 5 Experiments and Analysis
### Comparison of ConInd with Two Online Algorithms
#### 5.3.2 The Number of Selected Features and Running Time
On the one hand, _ConInd_ significantly outperforms _OSFS_ and _Alpha-investing_ in mining the elements in the _Markov blanket_. It can find many more elements than _OSFS_ in the _Markov blanket_. On the other hand, the number of #_SIC_ is much smaller than the number of #_NIC_, as presented in Table 4. This also means that the size of the feature subset for the #_SIC_ condition is smaller than the subset of #_NIC_. Therefore, there is a low possibility that the feature can be discarded. * _A summary of running time of algorithms_ A summary of the performance of three algorithms in terms of running time is reported in Table 6. Obviously, _Alpha-investing_ is much faster than _OSFS_ and _ConInd_ for all datasets. This is because _Alpha-investing_ considers only new features that are added; the discarded features are never considered again. This also leads to generally low prediction accuracy (Figure 7), such as those for colon, lucap0, reged1, leukemia, and Smk_can_187 classifiers. The performances of _ConInd_ and _OSFS_ in terms of running time are significantly different. The results in Table 6 indicate that _OSFS_ is much faster than _ConInd_ on datasets wdbc, colon, lucas0, ionosphere, lung, prosate_GE, arcene, leukemia, and Smk_can_187. Conversely, _ConInd_ is much faster than _OSFS_ on datasets sylva, cina0, lucap0, and reged1. These datasets are highlighted in bold. The differences arise from the feature relevance and the redundancy of the datasets. We find that the datasets in bold have more selected features (as indicated in Column 6 of Table 6); these datasets have low redundancy and high strong and weak relevance because the runtime for the two algorithms is significantly influenced by the size of candidate selected features. Table 7 presents that, when the datasets are lowly redundant and highly relevant, the average running time for _ConInd_ is reduced by 53.56% compared to _OSFS_. This indicates that _ConInd_ has perfect efficiency in terms of running time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Running Time**} & \multirow{2}{*}{\(\mathbf{Av_{T}}\) (\%)} & \multirow{2}{*}{\(\mathbf{AverageAv_{T}}\)} \\ \cline{2-2} \cline{4-4}  & \(T_{A}\) & & & \\ \hline sylva & 247.9366 & 189.8111 & \(-\)0.2344 & \\ cina0 & 721.3638 & 407.7689 & \(-\)0.4347 & \\ lucap0 & 1.43 \(\times\) 10\({}^{7}\) & 225.7368 & \(-\)0.9998 & \\ reged1 & 121.2839 & 63.9082 & \(-\)0.4731 & \\ \hline \hline \end{tabular} \(T_{A}\), running time of Alpha-investing \(T_{C}\), running time of _ConInd_; \(\mathbf{Av_{T}}=(T_{A}-T_{C})/T_{A}\), variation in the ratio of the running time.  
\end{table} 
Table 7: Variation in the ratio of running time for _Alpha-investing_ and _ConInd_.
###### Abstract  
Water resources are depleting, and the availability and supply of clean, potable water are a global concern. Advanced oxidation processes (AOPs) possess immense prospects in water and wastewater treatment settings. This study investigated and optimized the photocatalytic treatment of wastewater using titanium dioxide (TiO\({}_{2}\)) as the photocatalyst. The one-factor-at-a-time (OFAT) technique was employed to evaluate the effects of reaction time (20-100 min), mixing speed (20-100 rpm), and catalyst load (0.3-1.5 g/L) on pH, colour, turbidity, and chemical oxygen demand (COD) removal from actual municipal wastewater. Reaction time and catalyst load were then identified as the two key factors selected to be modeled and were optimized for turbidity and COD removal using the Central Composite Design (CCD) of response surface methodology (RSM). These statistical models were developed and used to optimize the operating conditions. The results obtained showed a desirability efficiency of 74.7% at a 95% confidence level. The RSM model predicted results at the optimum conditions and showed reasonable agreement with the experimental results obtained. The optimal responses achieved were 32.64% COD removal and 95.17% turbidity removal. A comparative study between UV light and visible light was also conducted at optimum conditions, whereby the UV light was demonstrated to be highly effective for turbidity and COD removal. The optimal responses achieved were 25.58% COD removal and 66.88% turbidity removal for visible light.  
Keywords:photocatalysis; titanium dioxide; chemical oxygen demand; municipal wastewater; response surface technology (RSM) +
Footnote †: journal: Applied
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 2 Transparent Antennas: A Review
### Transparents Conductive Oxides
#### 2.2.1 Mono-Layer  
In Reference [16], solar transparent patch antennas operating at 28 GHz, which combine function of antenna and solar cell, is presented using TCO films. Transparent antennas help to reduce the shadowing region of the antenna to ensure maximum efficiency of the solar cell. As an antenna part, the rectangular microstrip patch antenna designed with a solar cell is illustrated in Figure 4a. Four types of TCO films (ITO, fluorine-doped tin oxide (FTO), silver-coated polyester films (AgHT-4, AgHT-8)) were used with a glass substrate, which has a dielectric constant of 4.82 and a thickness of 1.1 mm. The sheet resistances of the four TCO films (ITO, FTO, AgHT-4, AgHT-8) are 10, 24, 4.5, and 8 \(\Omega\)/sq, respectively. The TCO-based transparent antennas show a wide bandwidth property since they used a thicker glass substrate compared to the RT5880 substrate of copper film, which has a dielectric constant of 2.2 and a thickness of 0.254 mm. Also, FTO provides the highest bandwidth while ITO gives the best performance of return loss.  
In Reference [17], ITO- and FTO-based transparent U-shape patch antennas that operate at 750 GHz are analyzed. The ITO (t = 400 nm, \(\sigma\) = 2.88 \(\times\) 10\({}^{5}\) S/m), FTO (t = 400 nm, \(\sigma\) = 1.67 \(\times\) 10\({}^{5}\) S/m), and reference thin copper (t = 400 nm, \(\sigma\) = 5 \(\times\) 10\({}^{6}\) S/m) are fabricated on a transparent polyimide substrate, and the optical transmittance of ITO and FTO is greater than 80%. The thickness, relative permittivity, and loss tangent of the transparent substrate are 20 \(\mu\)m, 3.5, and 0.008, respectively. The bandwidths of the ITO and FTO antennas are 9.54% and 11.49%, which are both is higher than the 3.86% of the reference copper antenna, shown in Figure 4b. The peak radiation efficiencies of the ITO, FTO, and reference antennas are 49.45%, 43.22%, and 79.47%.  
Figure 4: (**a**) Structure of designed antenna [16] and (**b**) frequency versus return loss (dB) for the antennas [17]. (With permission from Elsevier and IOP).
## 3 Data and Methods
### Development of Training Materials
Furthermore, to explain local processing of Sentinel-1, an exercise using SNAP software was explained to the participants, with special emphasis on the automation of processing Sentinel-1 GRD data using a processing chain adapted from Filipponi [47]. To complete this session, an interferogram exercise was adapted from the Alaska Satellite Facility to process Sentinel-1 SLC data and validate the reported subsidence in southern Ecuador [48] (Figure 5). Despite the Bolivian panel discussions not being planned to include training sessions, stakeholders appreciated demonstrations of the abovementioned Copernicus services, accessing data, and applications. To fulfill these requests, considering the time constraints, demonstrations of accessing data were prioritized, especially for the CLMS service, as most stakeholders were interested in their products. Other specialized sessions, such as the session on the Security pillar, included showcasing the Sentinel 1-2 Metrics Extraction script to identify suspicious infrastructure.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 3 Results
### Spatial Consistency Comparison  
#### 3.3.1 Spatial Consistency of Eight Datasets over Different Years  
Figure 6 is the spatial consistency map among eight datasets. It can be seen that the consistency of forest distribution in Myanmar is generally good. Sub-areas A and B are regions with a better distribution of forest consistency. The best consistency is found in northern Kachin and northwestern Sagaing (Sub-area A): Northern Myanmar is a highly mountainous region. These areas have high topography, concentrated forest distribution, better continuity, and a relatively homogeneous geographic landscape, so each dataset can identify forests more accurately. Forest consistency is also better in southeastern Shan (Sub-area B), but there are still gaps compared to area A: This is related to the year of the datasets, where deforestation or reforestation affects the temporal distribution of forests. It is also related to the undulating topography of Shan. As for sub-areas C and D, the distribution of forest consistency is less desirable. The spatial distribution of forests in Rakhine and is less consistent (Sub-area C): They are located in southwestern Myanmar with a complex landscape. They are also hotspots of deforestation [39], so the consistency is lower. Mandalay, Magway, and Southern Sagaing are located in the central part of Myanmar (Sub-area D): These regions are flat and dominated by agriculture, the cover type is mostly cropland. Most datasets can classify the land as non-forest. However, occasionally a few datasets are misclassified, resulting in the \"salt and pepper\" phenomenon.  
Figure 5: Percentage of forest cover by state in different datasets.
# Experimental Identification of Characteristic Curves of
## 4 Results
### Results on the Prediction of the Evacuation Time  
The TET is a key metric for vacuum ejectors. Accurate PTET, as discussed in Section 3.1, relies on precise data capture to fit the polynomial coefficients of the characteristic and polytropic curves (Section 3.1). Table 4 displays the predicted and experimental evacuation times to reach the ejector minimum pressure from atmospheric pressure.  
Figure 9 compares the deviation of predicted and experimental evacuation time for setups 1, 2, 3 and 4. The errors between PTET and experimental values are as follows: setup 1 has the highest error with a deviation of 14.5s (7.6%), setup 2 shows an error of 10.2 s (5.3%), setup 3 has an error of \(-5.5\) s (2.9%), and setup 4 has the smallest error of 2.6 s (1.4%), closely matching the experimental TET.  
Setup 5 characteristic curve coefficients are given in Section 3.3. The polytropic curve obtained with setup 4 was used due to characteristic curve similarity. Figure 10 shows the predicted evacuation time for setup 4 and 5. The right graph zooms in at the end of the evacuation time. As anticipated in [5], the minimum pressure of setup 5 is lower than the tested with setups 1, 2, 3 and 4, due to its optimized internal geometry.  
Figure 11 illustrates the deviation between the experimental evacuation time and the predicted time from setup 5. At higher pressures, the predicted evacuation time exceeds the experimental values, with a maximum difference of 2 s at 0.5 bar(a). However, at lower pressures, setup 5 demonstrates improved performance by reducing the PTET by 8.1 s (4%) compared to the experimental TET. These data are further corroborated in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Pressure** **[bar(a)]** & **Experimental** **[s]** & **Setup 1 [s]** & **Setup 2 [s]** & **Setup 3 [s]** & **Setup 4 [s]** & **Setup 5 [s]** \\ \hline
1.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\
0.9 & 4.9 & 5.4 & 5.1 & 5.3 & 5 & 5.4 \\
0.8 & 11.2 & 11.9 & 11.4 & 11.7 & 11.2 & 12.1 \\
0.7 & 19.7 & 19.8 & 19.2 & 19.6 & 19.8 & 20.5 \\
0.6 & 30.2 & 29.9 & 29.4 & 29.8 & 30.3 & 31.3 \\
0.5 & 43.7 & 43.3 & 43.3 & 43.6 & 43.8 & 45.5 \\
0.4 & 64.0 & 62.8 & 63.9 & 63.5 & 64.3 & 65.4 \\
0.3 & 97.9 & 96.1 & 99.3 & 97.1 & 98.7 & 97.7 \\
0.2 & 190.0 & 204.5 & 200.2 & 184.5 & 192.6 & 181.9 \\ \hline \end{tabular}
\end{table}
Table 4: Temporal comparison at specific vacuum pressure levels.  
Figure 8: **(Left)**: Characteristic curves, \(\hat{m}_{s}(p)\), captured by setup 4 and setup 5 [5]. **(Right)**: Polytropic curve, \(n(p)\), captured by setup 4.
# Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis
## 3 Results and Discussion
### Thermogravimetric Analysis of Biochar  
The effects of the feedstock and the heating rate on the TGA are expressed in Figures 2 and 3.  
The maximum mass reduction of about 98 wt. % was determined for the biochar derived from softwood carbonization. The TGA revealed two-staged conversion: devolatilization (stage 2) and char combustion (stage 3). Stage 1, corresponding to the water mass loss up to 180 \({}^{\circ}\)C, was not considered because the samples showed low moisture content. Due to the reduced biochar's content of volatiles (Table 1), stage 2 was weakly expressed in comparison to the TGA of biomass [3] and occurred in the temperature range from 180 to 560 \({}^{\circ}\)C. Stage 3 took place between 400 and 720 \({}^{\circ}\)C for all studied biochar types and heating rates.  
Figure 3: Effect of heating rate on cherry stone biochar oxidation, TGA and DTG in air.  
Figure 2: Effect of feedstock on the oxidation of six types of biochar at given heating rate (20 and 40 \({}^{\circ}\)C/min), TGA and DTG in air.  
Comparison of the DTG data for colza, sunflower husks, and softwood revealed that their maximum mass reduction was between 370 and 400 \({}^{\circ}\)C, which corresponds with the high percentage of cellulose in their feedstock composition. The process of thermal decomposition of these types of biochar required less energy to initiate the combustion process than the one obtained from cherry and peach stones.  
The DTG curves of peach and cherry stones and grape pomace showed their maximum at 400-500 \({}^{\circ}\)C and released a higher amount of heat during the combustion. The effects were associated with the highest lignin content, reported in [2,3], and the highest HHV of the biochar, derived from kernel/seed-containing biomass.  
The weight loss rate decreased when increasing the heating rate. Both TGA and DTG curves were slightly shifted to the right with an increase in heating rate (Figure 3). The same effects were described by [PERSON] et al. [37]. The authors explain the appearance of this phenomenon through the stronger thermal shock occurring in a short time and a greater temperature gradient between inside and outside that the sample develops. When the heating rate increases, the temperature of the biomass particle drops more abruptly, showing greater energy absorption [58].
# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 5 Recommendation Evaluation Process
### Penalty Process  
The penalty process aims to adjust the trust value of a node based on its history of violations. The trust value for node \(k\) obtained by \(i\) at time \(t\), denoted as \(T_{ik}(t)\), is subject to a penalty factor that diminishes the trust based on the number of violations, \(\lambda\), recorded for the node. This adjustment is represented by the following equation:  
\[T_{ki}=T_{ki}(t)\bigg{(}1-\frac{1}{\exp^{\lambda}}\bigg{)} \tag{11}\]  
In this equation, \(\lambda\) represents the number of violations where the node has been alarmed during the recommendation evaluation process. The penalty factor, \(\Big{(}1-\frac{1}{\exp^{\lambda}}\Big{)}\), decreases the trust value exponentially as the number of violations increases. This model ensures that each additional violation results in a progressively smaller decrement in the trust value, reflecting an exponential penalty mechanism as shown in Figure 7.  
Violations are determined based on two criteria: the count of appearances in the initial filter and the belief score of the nodes. Penalties are applied if a node is detected in the filtering process and has a belief score lower than the average belief score of all recommenders. This method ensures that nodes frequently flagged by the filter and with relatively low belief scores are appropriately penalized.
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
## Materials and Methods
### Ex Vivo Transport Experiments across Rabbit Nasal Mucosa  
The transport of simvastatin across rabbit nasal tissue was evaluated by using Franz-type vertical diffusion cells with a receptor volume of 4.5 mL and a diffusional area of 0.58 cm\({}^{2}\). On the day of the experiment, nasal mucosae were freshly excised from rabbits obtained from a local slaughterhouse (Pola, Finale Emilia, Italy), and cleaned to remove the adhering submucosal tissue [53]. The rabbit nasal mucosa were placed between the donor and the receptor compartments of the diffusion cells. Then, in order to check the mucosa integrity, the donor compartment was filled with medium to confirm that no liquid leaked into the cell receptor compartment. If the nasal mucosa passed this test, the donor compartment received 200 uL of one of the three tested preparations, i.e., LNC\({}_{\text{SVT-LMWchit}}\), LNC\({}_{\text{SVT-HMWchit}}\), or SVT\({}_{\text{suspension}}\), equivalent to 200 uG of SVT. The Franz cells were maintained at 37 \({}^{\circ}\)C under mild magnetic stirring. At predetermined time intervals, 500 uL of the receptor medium, SNES containing 0.5% w/v of polysorbate 80, was withdrawn, and the receptor compartment was refilled with an equivalent volume of fresh medium. All samples were analyzed by HPLC-UV. In order to evaluate the retention of SVT in the nasal tissue, mucosa samples were placed in a volumetric flask (10 mL) containing a solvent of extraction (acetonitrile), and subjected to vortexing (2 min) and sonication (15 min). The solvent of extraction was then filtered (0.45 um, Millipore(r)) and simvastatin was quantified using HPLC-UV. The experiments were conducted in triplicate for each formulation.
# Wildfires in the Siberian Arctic
## 2 Materials and Methods
### Statistical Analysis and Regression Modelling of Wildfire Parameters  
Significant differences among sectors within the Siberian Arctic for decadal fire frequency and the extent of burnt areas, fire season duration, and the timing of the onset and conclusion of the fire season were identified using the Kruskal-Wallis ranked Analysis of Variance test (KW-ANOVA, \(p<0.10\) and 0.05). Differences in the underlying distribution for the wildfire parameters by sector were detected using the Mann-Whitney U test (\(p<0.10\) and 0.05). These tests were made with Statsoft Statistica ([[https://www.moonsoft.net/products/000649.aspx](https://www.moonsoft.net/products/000649.aspx)]([https://www.moonsoft.net/products/000649.aspx](https://www.moonsoft.net/products/000649.aspx)) (accessed on 5 May 2022)).  
The correlations of annual fire frequency, burnt area extent, fire season duration, and the position of the northern boundary of wildfire occurrence to the climate variables to the climate variables (\(T_{j\mu}\), \(T_{m\mu js}\), \(P_{j\mu}\), \(P_{mijs}\), \(SPEI_{j\mu}\), \(SPE_{mijs}\), \(W_{j\mu}\), \(W_{mijs}\)), were tested usingthe Pearson correlation test for variables with a Gaussian distribution and the Spearman correlation tests for variables with a non-Gaussian distribution.  
We used a multiple linear and hierarchical regression analysis realized in the R-project for statistical computing (R version 4.1.3, name \"One Push-Up\"; [[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/)) (accessed on 5 May 2022)) and RStudio (version 2022.02.3+492, name \"Prairie Trillium\" [[https://www.rstudio.com](https://www.rstudio.com)]([https://www.rstudio.com](https://www.rstudio.com)) (accessed on 5 May 2022)). R libraries gylvma v.1.0.0.3, AICmodavg v.2.3-1, trafo v.1.0.1, readxl v1.4.0, writexl v.1.4.0 were used. A multiple model was used to estimate the dependence of hotspots and burned area on the climate variables. We used hierarchical linear regression to test the improvement of model relationships between wildfire parameters and individually added environmental variables for the 2001-2021 years [39]. Candidate selection was based on correlation analysis (Pearson r and non-parametric Spearman rho), guided by the corrected Akaike information coefficient (AICc) [40]. That analysis allowed us to determine dispersions intercepted by each independent variable.