esa-sceva/satcom-chunk-collection huggingface dataset:
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
## 4 Discussion
### Green Trombe Wall's Energy Contribution  
The modified TW's absorption efficiency decreased by 30.2% compared to its baseline state. This reduction stems primarily from lower air temperatures within the cavity, which reduce conductive heat transfer to the building. The additional heat mass was placed closer to the glazing, both shading its interior and hindering the penetration of heat deep into the space of the TW by conduction (Figure 13). The increased ventilation also contributed to energy loss, intercepting the heat from the bottles and water and allowing it to leave the system freely. The surface color and reflectivity changes were not as influential as expected due to the effectivity of the glazing in place.  
Figure 12: The original studied (**A**) and the proposed improved functional diagram for a green TW (**B**).  
Despite these changes, the TW remains functional year-round, providing 10,893.73 MJ (3025.8 kWh) of energy annually, compared to 13,767.79 MJ (3824.16 kWh) in its baseline state (Table 6). At the current electricity price at our location, which is used to power the AC unit heating the space, we would have saved the building owners JPY 12,103.2 (USD 78.39) with the green Trombe wall and JPY 15,296.6 (USD 99.08) with a traditional one.  
Equivalent solar energy gains are mainly offset by the conductive losses in the baseline configuration, while the green Trombe wall is dependent on a diversity of factors, with the appliances and evaporative processes being the most relevant ones. The conductive losses were reduced due to the lowering of the temperatures, despite still being the most significant (Figure 14). The ventilation-induced gains and losses were minimal, not appearing at the scale of the figure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Study Period** & **Total Energy Contribution, MJ** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Autumnumn{1}{c}{(102 days)} \\ \end{tabular} } & Baseline TW & 8465.683 \\ \cline{2-3}  & Green TW & 6919.39 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Winter \\ (66 days) \\ \end{tabular} } & Baseline TW & 5302.11 \\ \cline{2-3}  & Green TW & 3974.34 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Total \\ (168 days) \\ \end{tabular} } & Baseline TW & 13,767.79 \\ \cline{2-3}  & Green TW & 10,893.73 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Estimated yearly energy contribution of the original and the green TW.  
Figure 13: Section of the Trombe wall depicting its temperatures during an average October day before (in orange), during (in green), and after the experiment (in dark red).
# Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films
## 3 Results  
In our previous work [20], we analyzed the vibrational signatures of DNA thin films under different hydration conditions obtained by controlling the time spent in a vacuum chamber, from 3 to 40 min. The main absorption bands of DNA thin films of different hydrated states were presented in the range from 1350 to 750 cm\({}^{-1}\) and assigned according to the literature. On the basis of the visual categorization of the spectra, a detailed band shape analysis in the phosphate (1150-1000 cm\({}^{-1}\)) and sugar-phosphate (900-750 cm\({}^{-1}\)) region and an estimate of %B-form calculated utilizing integrated intensities of the 860, 836, and 805 cm\({}^{-1}\) bands, a rough classification of the spectra into two groups was obtained: 3 and 5 min films are in the first, and 10-40 min films are in the second group. In the first group, 3- and 5-min films showed distinct spectral signatures in the sugar-phosphate region, 900-750 cm\({}^{-1}\), corresponding roughly to 40% and 60% B-form, respectively. On the other hand, the spectral signatures of 10-40 min films all showed higher level of B-form, about 75%, indicating the end of conformational transitions after 10 min. Details related to the formation kinetics of the B-form, as well as relevant calculations are presented in our previous work [20]. Next, it was shown that the changes in phosphate and sugar-phosphate vibrations, namely the 1232, 1089, 1055, 1030, and 765 cm\({}^{-1}\) bands, originate from the changes in the hydration of thin films and are mostly unrelated to the conformational changes that seem to saturate after 10 min (changes on the \(\approx\)890, 860, 837, and 805 cm\({}^{-1}\) bands). In this work, we expand the spectral range, and for the first time present vibrational signatures in the base region, 1800-1350 cm\({}^{-1}\). We first try to detect and untangle the signatures related to conformational transitions from the hydration-related signatures in the base region and then develop automated procedures for DNA conformation quantification.
# Mutated Channelrhodopsins with Increased Sodium and Calcium Permeability
## 3 Results
### Mutation of the Aspartate in TM4 Influences the K\({}^{+}\) Permeability  
As tools for light-induced depolarization, ideal cation-permeable channelrhodopsins should be more Na\({}^{+}\) conductive and less K\({}^{+}\) conductive, because K\({}^{+}\) efflux across the plasma membrane would lead to a more hyperpolarized membrane potential. To test the potential influences on K\({}^{+}\) permeability of different mutations, we measured photocurrents and calculated the reversal potential shift of these mutants when systematically changing bath solutions from 120 mM K\({}^{+}\) to 1 mM K\({}^{+}\) in comparison to changing pH from 7.6 to 9.6.  
ChR2 had a slightly weaker K\({}^{+}\) conductance in comparison to Na\({}^{+}\) with a P\({}_{\text{Na+}}\)/P\({}_{\text{K+}}\) = 1.2. XXL increased the Na\({}^{+}\) and K\({}^{+}\) permeability slightly and equally. XXM increased the Na\({}^{+}\) permeability more than that for K\({}^{+}\), and the P\({}_{\text{Na+}}\)/P\({}_{\text{K+}}\) of XXM reached 2.2 (Figure 4 and Table 2). _P_S_ChR showed a higher Na\({}^{+}\) permeability, together with an enhanced K\({}^{+}\) permeability in comparison to that for H\({}^{+}\), with a similar P\({}_{\text{Na+}}\)/P\({}_{\text{K+}}\) as ChR2 (Figure 4 and Table 2). Interestingly, the D139H mutation increased the Na\({}^{+}\) permeability five-fold, while changing the K\({}^{+}\) permeability only 1.6-fold, thus the P\({}_{\text{Na+}}\)/P\({}_{\text{K+}}\) of _P_S_ChR D139H increased to 3.5 (Figure 4 and Table 2). The increased P\({}_{\text{Na+}}\)/P\({}_{\text{K+}}\) makes _P_S_ChR D139H even more suitable as a depolarization tool.  
Chronos, Chronos D173H, and Chronos D173C had much lower K\({}^{+}\) permeability and the highest P\({}_{\text{Na+}}\)/P\({}_{\text{K+}}\) value among the tested constructs (Figure 4 and Table 2). However, the H\({}^{+}\) permeability was the highest for all Chronos variants (Table 2).
## 4 Results and Discussion
In addition, an increase in temperature can lead to an increase in sensitivity and a decrease in response time. The essence of this phenomenon is that it affects the dielectric and diffusion coefficients of the material. If the material parameters are changed through material modification, similar results can also be obtained. The phenomena above can be explained by the humidity-sensing mechanism of MXene. MXene absorbs water molecules in the air, causing changes in its dielectric coefficient. In different humidity environments, different numbers of water molecules are attached to the membrane, so the humidity sensor outputs different capacitance values. The adsorption of water molecules on MXene is shown in Figure 9.  
In a low-humidity environment, due to the abundant hydrophilic functional groups on the surface of the humidity-sensitive material, water molecules can easily adsorb to the humidity-sensitive material through physical adsorption, thereby changing its dielectric coefficient. However, in a low-humidity environment, more free water molecules cannot be provided, and the water molecules adsorbed on the surface of the humidity-sensitive material are only at sites such as hydrophilic functional groups and do not form a continuous water film. At this time, it is the intrinsic properties of the material that affect the conduction. With the increase in ambient humidity, a large number of water molecules occupy the hydrophilic functional group on the surface of humidity-sensitive materials. At the same time, the adsorption between water molecules has begun due to the action of hydrogen bonds. Because of the presence of certain water molecules on the surface of humidity  
Figure 9: Schematic diagram of adsorption of water molecules on the MXene. sensitive materials, water molecules are more easily absorbed by the environment. Finally, the adsorption of humidity-sensitive materials by water molecules in the air reaches a saturation state in this high-humidity environment. The adsorption forms a water film covering the surface of a humidity-sensitive material. At this time, under the action of an electric field, the water-membrane layer will electrolyze to form conductive hydronium ions. At this time, the humidity sensor has two conduction mechanisms, one is intrinsic to the material and another is the electrolytic conduction by liquid aqueous electrolysis [28, 34]. This is why the capacitive humidity sensor based on MXene increases nonlinearly with humidity at a certain temperature. The higher the temperature or the thinner the material, the more efficiently the water molecules binds to the film, indicating that the response time decreases when the thickness decreases or the temperature increases. However, decreasing the thickness leads to a decrease in the absorption of water molecules, and increasing the temperature leads to an increase in the absorption of water molecules. This is the reason why the sensitivity decreases with decreasing thickness and increases with increasing temperature.
# An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
## 4 Deep Learning Methods
### Deep Learning for Cloud Masking
The pre-existence of data and their ground truths, is a necessary requirement to develop Deep Learning cloud segmentation models for new satellites. This limits the application of such methods to the time of the data availability and implies the use of more traditional methods that have a higher portability, although they are less accurate, until the satellite becomes operational. Alternatively, one could consider models trained on sensor data with similar characteristics to those to be launched, using a transfer learning approach, or exploit the advantages offered by SEnSeI by taking the data regardless of the type of source sensors.
## 6 Biological Properties
### Anti-Inflammatory Effects  
Inflammation can lead to detrimental effects of different pathologies such as neurodegenerative and cardiovascular diseases, cancer, diabetes, and others [21]. It is also essential to find compounds that can tackle this problem by inhibiting the pro-inflammatory molecules and associated pathways [53].  
[PERSON] et al. [52] reported that _A. altissima_ seed oil could induce anti-inflammatory effects on edema (in vivo experiment), by reducing it more than 60% after 3 h, via the administration of doses of 0.2 and 1 g/kg. The same study also demonstrates the analgesic capacity of the oil due to its complex composition. The acute toxicity study and the analgesic effect of the seed oil was compared with a widely marketed drug, acetylsalicylic acid (ASL), in which the average lethal dose of the oil was estimated to be more than 2 g/kg and the analgesic effect was almost as potent as ASL at a dose of 1 g/kg. Other research [54] has suggested that the ethanolic extract of _A. altissima_ (leaves and branch) can inhibit inflammatory mediators, both in vitro and in vivo, mainly by the inhibition of cyclooxygenase-2 (COX-2). By extractinganthin-6-one, an alkaloid, from the stem barks of _A. altissima_, [PERSON] et al. [55] has proven this compound's potential effect in exerting anti-inflammatory effects of macrophages dysregulating pro-inflammatory cytokines and pathways. This same family of compounds was the target of the research conducted by [PERSON] et al. [56], amongst other compounds. The authors reached the same conclusion regarding the anti-inflammatory properties. [PERSON] et al. [57] have shown the potent inhibitory effects of _A. altissima_ decoction on the decrease of cytokine levels, such as tumour necrosis factor (TNF) and interleukin (IL)-6 and IL-8 as well as on the reduction of histamine levels, by using both in vitro and in vivo models.  
Moreover, the nuclear factor kappaB (NF-kB) pathway can also be inhibited by _A. altissima_ compounds. [PERSON] et al. [58] used the ethanolic extract of _A. altissima_ leaves to assess the anti-inflammatory properties in astrocytes, where inflammation was induced by lipopolysaccharide. Results showed that the anti-inflammatory mechanism functions through the inhibition of COX-2 and other cytokines and the inhibition of the NF-kB pathway. These findings may lead to the use of _A. altissima_ as a new therapeutic approach to neuroinflammatory diseases.  
Overall, studies about the anti-inflammatory effects of the _A. altissima_ compounds, whether they come from the leaves, bark, or seed oil, all conclusions favour the beneficial effects against pro-inflammatory molecules.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 5 Experiment and Result Analysis
### Evaluation System  
Since real and clean signals are unobtainable, denoising performance could not be verified directly through evaluation indicators. Thus, the \"Blocks\" and \"Bumps\" signals in Matlab were used to simulate non-stationary random signals, with varying noise intensities added to emulate AWGN (where noise variance represented intensity), as shown in Figure 9.  
The signal-noise ratio (SNR) was used as an evaluation metric to measure denoising effectiveness [45]:  
\[\text{SNR}=10\log_{10}\!\left(\frac{\|x(n)\|^{2}}{\|y(n)-x(n)\|^{2}}\right)( \text{dB}), \tag{16}\]  
where \(n\) represents the number of signal sampling points, \(y(n)\) represents the noise signal, and \(x(n)\) represents the clean signal. The higher the SNR, the closer the noise-reduced signal to the clean signal, indicating better denoising effectiveness. Classification performance was evaluated with Accuracy, F1 score, Kappa, Precision, and Recall.
# Aveiro Canyon Head (Portugal) Submarine Slope Instability Assessment
## 3 Methods
### Information Value (IV)  
The information value (IV) [23] is a statistical method that correlates the unstable areas with the predisposing factors. Each predisposing factor is divided into classes or variables (e.g., factor--mean slope angle, variable--0-5\({}^{\circ}\)) [19] in order to compute a score or susceptibility score for each one. The analysis of each factor is done in geometrical basis (pixels) or in predefined polygons, i.e., terrain units.  
The score of the IV (I\({}_{i}\)) of each variable (X\({}_{i}\)) is given by [23]:  
\[\mathrm{I}_{i}=\log\frac{\mathrm{S}_{i}/\mathrm{N}_{i}}{\mathrm{S}/\mathrm{N}} \tag{1}\]  
where S\({}_{i}\) is the number of terrain units that are unstable with the variable X\({}_{i}\); N\({}_{i}\) is the number of terrain units with the variable X\({}_{i}\); S is the total number of terrain units that are unstable; N is the total number of terrain units in the study area.  
Negative values of I\({}_{i}\) mean that the presence of the variable is not favourable to the occurrence of instabilities, whereas positive values of I\({}_{i}\) indicate that there is a relationship between the variable and the occurrence of instabilities [23].  
The total IV (I\({}_{j}\)) for a given terrain unit (j) is obtained by the following equation [23]:  
\[\mathrm{I}_{j}=\sum_{i=1}^{m}\mathrm{X}_{ij}\mathrm{I}_{i} \tag{2}\]  
where m is the number of variables; and X\({}_{ij}\) is 0 if the variable is not present in the terrain unit j, or 1 if the variable is present. Therefore, the susceptibility of a terrain unit to the occurrence of a movement is given by the final information value (IV) I\({}_{j}\)[32].  
The use of this method allows a simple susceptibility assessment of the occurrence of movements based only on the spatial distribution of the classes of predisposing factors (variables) and the presence or absence of instabilities in each terrain unit [19]. As a final result, susceptibility scores are obtained for all the terrain units of the study area.  
This very simplified method allows a direct evaluation of results using the variable scores. Its main limitation results from its bivariate character, i.e., it does not take into account correlations that may exist between variables [19; 32].
## 1 Introduction
It was shown that the noise of the accelerations derived by the carrier phase differentiation method was lower than that derived by the orbit differentiation method in all three components, especially in the cross-track component, which showed an improvement of about 20%. Thus, the recovered gravity field based on the carrier phase differentiation accelerations had a slightly higher accuracy when compared with the solution based on the orbit differentiation method. It should be noted that [PERSON] et al. [25] estimated the average satellite accelerations in two steps: first, the position differences epoch by epoch were derived directly from the GPS carrier phase measurements; the average satellite accelerations were then obtained with the position differences. In this study, we proposed to recover the gravity field model based on the point-wise accelerations of the GOCE satellite estimated by the carrier phase differentiation method from the onboard carrier phase observations. This idea has not yet been addressed and differs from the average acceleration approach used for gravity field modeling and the approach of estimating the satellite accelerations in [PERSON] et al. [25]. The paper is organized as follows. Section 2 describes the functional models of satellite acceleration and gravity field determination. Section 3 shows the experimental results. Section 4 discusses the findings of this research, and Section 5 presents our conclusions.
# A Machine Learning View on Momentum and Reversal Trading
## 5 Discussion
### Analysis of Machine Learning Models  
In Tables 6-9, we can see that most of the returns were positive, which indicates the machine learning approaches are helpful for forecasting the momentum and reversal effects.  
We calculated the average returns and Sharpe ratios of different observation and holding periods for each model, and these are listed in Table 10. The results clearly imply that (1) the performance of the reversal strategy was better than that of momentum one (51.95% vs. 21.73% and 0.48 vs. 0.26). The average values of the momentum strategy were even worse than the benchmark. The CSI 300 market appears to be a reversal market. This finding is quite similar to other research, such as that presented in [32]. (2) The average results of the machine learning models exceeded both the momentum and reversal strategies, except for the LSTM. (3) Even so, the LSTM still bet the benchmark and was just a little below the standalone reversal strategy.  
As for the best candidate in each model, it is obvious that the best results obtained with each machine learning model were much better than the benchmark as well as the standalone momentum and reversal strategies. For example, the highest return with DT occurred for the case of (_J_ = 15, \(K\) = 10). Its return reached 207.17%. Among all models, the SVM was the best with an averaged return of 66.48% and the highest return of 239.43% and a Sharpe ratio of 1.68 for the case of (_J_ = 15, \(K\) = 10).  
In fact, the measurements of the average and best performances are meaningful for real-world trading. The investor can bet on the best strategy to acquire the highest potential return, and he or she can allocate the capital to the strategies with different observation and holding periods to decrease the risk.
## 1 Introduction
surface deformation exhibited an increasing pattern as the excavation depth continued to increase. The on-site power personnel's operational experience indicates that the uneven settlement amount of the self-supporting straight tower must be limited within the range of 12.7-25.4 mm to ensure its structural stability. This is in accordance with the pertinent regulations and rules in the power industry, which state that the vertical straight pole's inclination displacement safety threshold is 105 mm. The transmission line was in a safe and stable condition, according to the findings of the numerical simulation, and all of the data in the monitoring line fell within the settlement threshold range. To confirm the correctness of the numerical simulation's findings on the unloading impact of rock and soil during foundation pit excavation, the vertical displacement data of the vertical transmission towers were collected at the excavation site. The distances between the vertical transmission towers DX1, DX2, and DX3 and the crest of the working slope were 25 m, 50 m, and 100 m respectively. Following a methodical organization of the acquired vertical displacement data, the corresponding displacement curves were displayed and contrasted with the outcomes of the numerical simulation. During the data analysis process, statistical methods were used to calculate the Pearson correlation coefficients between the three groups of displacement curves and the corresponding numerical simulation curves. According to the computation findings, the average Pearson correlation coefficient was 0.9754, while the minimum Pearson correlation value was 0.9514. This outcome confirms the precision and potency of the numerical simulation's findings. Figure 6 displays the displacement curves for comparison analysis, and Table 1 displays the Pearson correlation coefficients for each set of curves. Figure 3: Foundation pit excavation model diagram: (**a**) excavation depth of 10 m; (**b**) excavation depth of 20 m; (**c**) excavation depth of 30 m; (**d**) excavation depth of 40 m; (**e**) excavation depth of 50 m.  
Figure 4: Numerical simulation results at each excavation depth.
# Energy Management Integrated Circuit for Multi-Source Energy Harvesters in WBAN Applications
## 4 Circuit Block Designs  
The energy management circuit has an analog part and a digital part. The role of the analog part is to generate clock signals, monitor the state of the harvested energy, and to choose a path from the input port to the output port. The digital part makes sure the path combinations are energy efficient.