esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The development of sensors working in a large range of temperature is of crucial importance in areas such as monitoring of industrial processes or personal tracking using smart objects. Devices integrating GaN/Ga2O3 core/shell nanowires (NWs) are a promising solution for monitoring carbon monoxide (CO). Because the performances of sensors primarily depend on the material properties composing the active layer of the device, it is essential to control them and achieve material synthesis in the first time. In this work, we investigate the synthesis of GaN/Ga2O3 core-shell NWs with a special focus on the formation of the shell. The GaN NWs grown by plasma-assisted molecular beam epitaxy, are post-treated following thermal oxidation to form a Ga2O3-shell surrounding the GaN-core. We establish that the shell thickness can be modulated from 1 to 14 nm by changing the oxidation conditions and follows classical oxidation process: A first rapid oxide-shell growth, followed by a reduced but continuous oxide growth. We also discuss the impact of the atmosphere on the oxidation growth rate. By combining XRD-STEM and EDX analyses, we demonstrate that the oxide-shell is crystalline, presents the \(\beta\)-Ga2O3 phase, and is synthesized in an epitaxial relationship with the GaN-core.  
core/shell nanowires; GaN; Ga2O3; metal-oxide semiconductor; gas sensor devices +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: Centre de Nanosciences et de Nanotechnologies—CNRS-UMR9001, Université Paris-Sud, Université Paris-Saclay, 10 Boulevard Thomas Gobert, F-91120 Palaiseau, France  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +33-(0)1-70-27-05-49  
* [FOOTNOTE:2]Footnote 2: email: *
## 2 Materials and Methods
### Evaluation of Biological Activities of Prepared Extracts
#### 2.4.4 Permeability Simulation by the PAMPA Method  
The PAMPA (Parallel Artificial Membrane Permeability Assay) test was used to predict the intestinal absorption of polyphenols from grape pomace extracts into the intestinal membrane [28]. The PAMPA test consists of donor and acceptor wells filled with a buffer and separated by an artificial membrane with a filter through which components are transported by passive diffusion. Artificial membranes serve as an ideal system for mimicking gastrointestinal or other types of biological membranes in assessing the absorption potential of individual compounds [29]. A Multi-Screen Permeability Plate Assembly with a 96-well filtration plate (Millipore, MA, USA) was used as the carrier of the artificial membrane and as the acceptor plate. The filter in each well of the filtration (donor) plate was coated with 5 \(\mu\)L of a solution of L-\(\alpha\)-phosphatidylcholine in 1% (\(w/v\)) dodecane. The donor plate was then placed onto the acceptor plate, which was pre-filled with 300 \(\mu\)L of a phosphate-buffer solution at pH 7.4. Next, 125 \(\mu\)L of extract was added to each well of the donor plate. The plate system was sealed with a plastic cover to prevent evaporation and incubated for 24 h at room temperature. After incubation, the volumes in the donor and acceptor wells were measured, and the concentrations of TPs on the acceptor plate were determined as previously described.  
The apparent permeability coefficients (\(P_{\text{app}}\)) were calculated using the following equations:  
\[\%T=100\times\frac{C_{R}\times V_{R}}{C_{D}\times V_{R}} \tag{1}\]  
\[P_{app}=\frac{V_{D}\times V_{R}}{(V_{D}+V_{R})\times S\times t}\text{ln}\left[ \frac{100\times V_{D}}{100\times V_{D}-\%T\times(V_{D}+V_{R})}\right] \tag{2}\]  
\(V_{D}\) and \(V_{R}\) are the volumes of the donor and receiving solutions, respectively (mL); \(C_{D}\) and \(C_{R}\) are the concentrations of TP in the donor and receiving, respectively; \(S\) is the surface area of the artificial membrane (0.28 cm\({}^{2}\), according to the manufacturer); \(t\) is the incubation time (s); and \(\%T\) is the extent of transport across the membrane.
# Transcriptome Landscape Variation in the Genus _Thymus_
## 3 Results and Discussion
### Thymol Biosynthetic Pathway
The amino acid comparison showed the highest similarity (~93%) between TPS2 and TPS3, followed by TPS4 and TPSS (~89%). In contrast with this, the sequence of TPS4 displayed low levels of similarity (max. 63%) to TPS2. TPSs are divided into three subclasses based on their functional roles and product formation, namely, monoterpene synthases (Mono-TPSs), diterpene synthases (Di-TPSs), and sesquiterpene synthases (Sesqui-TPSs). Based on sequence homology, TPSs are classified into seven families, from TPSa to TPSg [66; 82]. However, most monoterpene synthase genes, especially in _T. vulgaris_, belong to the Mono-TPSb subfamily [85; 89]. Here, we used the TERZYME program [90] to classify TPSs. As anticipated, all putative TPSs identified in the _Thymus_ transcriptomes were assigned to the Mono-TPSb subfamily, supporting the existing view that _Thymus_ TPSs belong to a distinct clade. All the discovered TPSs were investigated for the presence of terpene synthasesconserved motifs. The deduced amino acids of all TPSs contained the DDX motif, which is a highly conserved motif in TPSs and plays a role in the complexation of the diphosphate group after the ionization of substrates [82; 91]. To obtain further information, phylogenetic analysis was performed to distinguish the evolutionary relationships of terpene synthases. Phylogenetic analysis placed TPS2 and TPS3 in a clade (Figure 5), while TPS5 was determined in a different clade. In the tree, it was observed that all TPSs identified in this study clustered into the TPS-b subfamily, which consisted mainly of Mono-TPSs. Phylogenetic analysis shows that TPSb is closely related to TPSg, which is congruent with previous phylogenetic studies of the TPS gene family [81; 91]  
TPSs along with CYPs organize the core components of terpene biosynthetic pathways [92]. It has been reported that the terminal gene involved in thymol biosynthesis is a regio-, stereo-, -specific Cytochrome P 450, specifically a member of the CYP71D family [93]. One previous study suggests that TPS genes are predominantly found in combination with CYP71 clan genes in both eudicots and monocots, and that among the TPS/CYP pairings there is a significant correlation between the TPSb and CYP71D subfamilies [92]. In the _Thymus_ transcriptome data, at least 45 putative CYP450s transcripts were identified belonging to five families including CYP71D, CYP72A, CYP736A, CYP749A, and CYP82d, with the majority of CYP716B2 family members. Thus, from this data we can conclude that the CYP genes may be involved in thymol and carvacrol biosynthesis.
## 2 Literature Review
### Review of Existing Studies on HCAHPS Dataset  
There have been several studies on the HCAHPS dataset. [PERSON] [4] defined a number of objectives to extract useful knowledge from the HCAHPS survey data and studied the effect of such knowledge on hospital care improvement.  
[PERSON] et al. [5] investigated the relationship between postoperative morbidity and mortality and patients' perspectives of care in surgical patients. In their article, the overall satisfaction score is used along with Michigan Surgical Quality Collaborative clinical registry as a measure of patients' perspective of care.  
Quite a few studies have explored specific relationships between a single satisfaction question and one or more of patients' demographic information. [PERSON] et al. [6] conducted an analysis of racial/ethnicity in patients' perceptions of inpatient care. Using regression, they concluded that non-Hispanic Whites on average tend to go to hospitals that deliver better patient experiences to all patients as compared to the hospitals that are typically used by African American, Hispanic, Asian/Pacific Islander, or multiracial patients [6].  
[PERSON] et al. analyzed the association of gender with different aspects of satisfaction, [7] and, in a separate study, analyzed hospital ranking variation with patient health status and race/language and slightly with patient's education and age [8].  
[PERSON] [9] explored the relation between the willingness to recommend the hospital and other satisfaction identifiers. This paper discovers that hospitals that focus resources on improving  
\begin{table}
\begin{tabular}{c c c} \hline _Section_ & _Number of Question in Each Section_ & _Number of Choices for Each Question_ \\ \hline _Care from Nurses_ & 5 & 4 \\ \hline _Care from Doctors_ & 3 & 4 \\ \hline _Hospital Environment_ & 2 & 4 \\ \hline _Experience in Hospital_ & 5 & 4 \\ \hline _When You Left_ & 1 & 2 \\ \hline _Overall Rating_ & 1 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: Satisfaction Questions Categories.  
\begin{table}
\begin{tabular}{c c} \hline _Questions_ & _Number of Choices_ \\ \hline _Age_ & - \\ _Discharge Date_ & - \\ _State_ & 44 \\ _Racial Category_ & 6 \\ _Overall Health_ & 5 \\ _Education_ & 6 \\ _Etlmicity_ & 5 \\ _Patient-Filled Race_ & 5 \\ _Patient’s Language_ & 3 \\ _Gender_ & 2 \\ _Principal Reason_ & 3 \\ _Admission Source_ & 10 \\ _Survey Language_ & 2 \\ \hline \end{tabular}
\end{table}
Table 2: Demographic Data Types.  
interpersonal aspects of care such as nurses and doctors' courtesy, respect, listening, room cleanliness, etc. will be most likely to see improvements in satisfaction scores. The paper does not consider patients' demographic data.  
The existing literature on analysis of the HCAHPS dataset is mostly hypothesis-driven and only considers specific aspects of patient satisfaction or demographics. In contrast, the methodology presented in this paper does not assume any specific hypothesis. Instead, we run a data-driven exploratory analysis which inspects all aspects of patient satisfaction as well as patient demographics and discovers interesting associations in the HCAHPS dataset.
## 4 Experiments
### Experimental Results and Analysis
Unlike the conventional GWO algorithm, IGWO ensures a more uniform distribution during population initialization, facilitating better exploration of high-dimensional parameter spaces. This prevents premature convergence and contributes to improved generalization capability and model stability. To verify the robustness of the reported performance improvements, we conducted statistical significance testing across multiple random splits of the dataset. Specifically, we \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Models** & **MSE** & **MAE** & **MAPE** & **R\({}^{2}\)** & **Training Time (min)** \\ \hline  & RNN & 6.82 & 1.965 & 6.51\% & 0.988 & 10.41 \\  & GRU & 5.60 & 1.785 & 5.52\% & 0.992 & 13.45 \\  & LSTM & 4.52 & 1.587 & 4.47\% & 0.993 & 15.71 \\  & BILSTM & 4.03 & 1.446 & 4.36\% & 0.994 & 16.15 \\  & PSO-LSTM & 3.61 & 1.346 & 3.21\% & 0.994 & 58.31 \\  & CS-LSTM & 2.92 & 1.163 & 3.18\% & 0.996 & 60.14 \\  & Transformer & 2.67 & 1.123 & 3.82\% & 0.996 & 25.36 \\  & N-BETAS & 2.64 & 1.102 & 4.02\% & 0.996 & 33.76 \\  & LSTM-Transformer & 2.75 & 1.233 & 3.79\% & 0.993 & 27.90 \\  & IGWO-MALSTM & 2.63 & 1.091 & 3.70\% & 0.997 & 65.15 \\ \hline  & RNN & 11.79 & 10.817 & 11.19\% & 0.988 & 21.14 \\  & GRU & 10.67 & 10.387 & 11.13\% & 0.991 & 25.99 \\  & LSTM & 10.36 & 10.538 & 10.21\% & 0.989 & 24.45 \\  & BILSTM & 10.21 & 10.228 & 9.36\% & 0.987 & 20.60 \\ CSI 300 & PSO-LSTM & 9.26 & 9.964 & 9.36\% & 0.991 & 119.16 \\  & CS-LSTM & 9.76 & 9.222 & 9.33\% & 0.993 & 111.36 \\  & Transformer & 8.51 & 9.415 & 8.65\% & 0.993 & 28.47 \\  & N-BETAS & 9.96 & 6.159 & 9.32\% & 0.991 & 36.15 \\  & LSTM-Transformer & 6.48 & 4.595 & 8.45\% & 0.993 & 36.52 \\  & IGWO-MALSTM & 4.17 & 3.466 & 6.92\% & 0.996 & 116.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results of models. performed paired \(t\)-tests and Wilcoxon signed-rank tests comparing the IGWO-MALSTM model to each baseline model on key evaluation metrics (MSE, MAE, MAPE, R\({}^{2}\)). For each model, we repeated training and testing over 10 randomized experiments, using different seeds and dataset splits while maintaining the same training ratio. Results showed that the performance differences between IGWO-MALSTM and all other baseline models were statistically significant at the 95% confidence level (\(p\) < 0.05) for all metrics. Table 7 summarizes the \(p\)-values for these comparisons.
## 2 Materials and Methods  
### [PERSON]'s FOS/TAC Titration  
#### 2.1.1 Presentation of the Method  
[PERSON] (1977) developed the titration technique for determining alkalinity and VFA content of sewage sludge. This method was introduced and adapted to samples from biogas plants by [PERSON] in 1968 [7,8].  
The buffering capacity of the system (TAC) was determined by titrating the 20 mL samples from their initial pH to pH = 5 with sulfuric acid. The TAC could, thus, be calculated according to Equation (1).  
\[\text{TAC}=\text{A}\times 250 \tag{1}\]  
where TAC (mg CaCO\({}_{3}\cdot\text{L}^{-1}\)) is the total alkalinity and A (mL) is the equivalent volume of 0.1 N H\({}_{2}\)SO\({}_{4}\) to reach pH = 5.  
The determination of FOS (VFA concentration) was based on titration of the solution from pH = 5 to pH = 4.4. Equation (2) was then obtained by using the linear relationship of acid consumption versus FOS as described by [PERSON] (1968) [8].  
\[\text{FOS}=\left(\text{B}\times 1.66-0.15\right)\times 500 \tag{2}\]  
where FOS (mg HAc:L\({}^{-1}\)) is the VFAs content with regard to acetic acid; B (mL) is the equivalent volume difference of 0.1 N H\({}_{2}\)SO\({}_{4}\) between pH = 5 and pH = 4.4.  
It should be noted that Equations (1) and (2) are based on the titration of a 20 mL sample [7]. The coefficient values in these formulas may vary if a different titration volume is applied.
# THz Rectennas and Their Design Rules
## 2 Historical Notes
### Rectification of Electromagnetic Waves and Rectennas  
The concept of rectifying an electromagnetic wave into DC power may be attributed to [PERSON] who proposed a rectenna system in the first paper submitted to The Journal of Microwave Power in 1966 [15]. In his paper, [PERSON] deploys \"An array of Half-Wave Dipoles Terminated in a Bridge-Rectifier Array of Point-Contact Silicon Diodes.\" Although the diodes that [PERSON] uses are  
Figure 1: Energy-band diagram of a silicon p-n junction solar cell (Reproduced with permission from [8], [PERSON], 2017).  
assembled in a bridge form, the diagram of Figure 2 may be considered as a generic representation of rectennas based on its simple structure and the expandability of the model to higher frequencies.  
Figure 2 shows the generic diagram of a rectenna system where the signal received by the antenna is directed to the diode, which rectifies the AC waveform into a DC signal that can deliver power to the load \(\mathrm{R_{L}}\). The low pass filter (LPF) unit will clearly restrict all components higher than the DC from passing into the load, thus keeping the AC part separated from the DC one. The letters \(\eta_{a}\), \(\eta_{c}\),... refer to the efficiencies of the respective parts and will be discussed later in Section 2.3.  
In a later trial, [PERSON] managed to increase the efficiency of his rectenna system to 92% [4]. This very good figure may be attributed to (a) the use of a Schottky barrier diode that features a fast switching time, as well as relatively low voltage drop and (b) the use of a filter between the dipoles and the diode network, which suggests that matching has been achieved between the two.  
This work has drawn attention to the possibility of reaching significantly high efficiencies at high frequencies in the microwave region. For example, the work of [PERSON] et al. [17] proved experimentally that an efficiency up to 72.5% is reachable at 35 GHz. Furthermore, more than 35% efficiency has been reported in the experiment of [PERSON] and [PERSON] [18] in 2010 at the 94-GHz band for a dual-band design thanks to using filtering and matching techniques for a pair of Schottky diodes at the circuit termination points. The interested reader may refer to [19] (Figure 7) for a detailed representation of the recent work in this field.  
The difference between rectennas and p-n junction solar cells lies in the fact that rectennas receive the radiation in its wave form as opposed to solar cells, which employ the quantum nature of light. However, quantum mechanics are frequently deployed in explaining the theory of operation of the rectifying diode as will become clear later on; thus paving the way for what is referred to as semiclassical models [20]. In these models, the radiation is perceived as an oscillating wave that interacts with the charge carriers whose behavior may be explained by quantum mechanics.
# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 3 Results and Discussion
Due to this rapid recovery of the NDVI value, grass-containing land cleaning events may go unnoticed because (a) no valid images are available, without cloud cover, on dates close to the vegetation removal events and (b) the NDVI variation in the period near the cut does not interfere significantly in the set analysed in the Welch \(t\)-test. These small changes, undetected by the satellite, are easier to identify on a field visit. Therefore, a GNR supervisor can identify fuel management 'compliances' that go unnoticed in the model. Another factor that can interfere with the good functioning of the model is the temporality of the land cover map used. COSsim 2018 was prepared for the interval between September 2017 and October 2018. The proposed model used the available 2020 images, which indicates that some areas mapped in COSsim may already have changed. With this, we can be applying the model to an area that in the official mapping was considered fuel and today it is no longer. The opposite is also true. Our results show that this model can be used to identify areas that need management, either by private individuals or by public entities, as well as to assist in the planning of supervision by the competent agencies. The elaborated model has the potential to be used on a national scale to verify changes in the fuel management bands.
## 3 Results
### SMAP/CYGNSS TB Spatial Heterogeneity  
As expected, 3 km SMAP/CYGNSS TBs exhibit greater spatial variability than 9 km SMAP enhanced TBs. Comparing the 3 km SMAP/CYGNSS TBs associated with each 9 km SMAP TB allowed us to assess the spatial heterogeneity of 3 km SMAP/CYGNSS TBs. We calculated the RMSD between 9 km SMAP TBs and the corresponding 3 km SMAP/CYGNSS TBs for each SMAP observation with concurrent CYGNSS data during 2020. We then calculated the median RMSD TB for each 9 km grid cell. The median RMSD between 9 km SMAP TBs and 3 km SMAP/CYGNSS TBs range from 0.06 to 12.63 K (5 th to 95 th percentile) with a median of 3.03 K (Figure 6c). Many of the near-zero RMSD TBs are due to likely-erroneous, near-zero \(\beta\) values (discussed below, in Section 3.3). We can more accurately describe the spatial heterogeneity of 3 km SMAP/CYGNSS TBs by eliminating the near-zero RMSD TBs caused by near-zero \(\beta\) values. By only including \(\beta\) values with correlations less than \(-0.4\), we exclude near-zero \(\beta\) values (Figure 6b). We discuss the reasons for choosing a correlation cutoff of \(-0.4\) in Section 4.3. When only considering grid cells with \(\beta\) values with R \(<-0.4\), the median annual RMSD between 9 km SMAP TBs and 3 km SMAP/CYGNSS TBs is 5.16 K (Figure 6c).
## 3 Results and Discussion
### Modeling Based on Spectral Data  
Full spectral data and the corresponding DL values were used to develop PLS-DA classification models. The ideal number of latent variables (LVs) of the PLS-DA classification model was determined at the lowest value of error rate of cross validation. As an example shown in Figure 3, seven LVs were retained to build the full-wavelength model. As shown in Table 2, the correct classification rate (CCR) of calibration, cross-validation, and prediction sets were 85%, 78%, and 78%, respectively. According to conclusions reported in previous researches [5; 20], selecting optimal or informative wavelengths from full wavelengths is an effective way to reduce computational complexity of image processing and to simplify the classification model, and may obtain an equal or better result compared to the full wavelengths in modeling. In this study, eight wavelengths (441, 469, 506, 566, 596, 617, 939, and 999 nm) were selected using SPA. However, only the first seven wavelengths were adopted to develop the simplified classification model. Spectral data of 999 nm was removed from the optimal multi-spectrum subset due to the low signal-to-noise ratio of the data at the edge of the spectral range. Compared to models based on full wavelengths, models based on seven selected wavelengths showed slightly poorer performances for the classification. The CCR of calibration, cross-validation, and prediction sets were reduced to 76%, 72%, and 73%, respectively. However, the number of wavelengths involved in modeling reduced from 472 to 7, accounting for only 1.2% of all wavelengths, indicating that the first derivative processed spectra can be used to select optimal wavelengths to build simplified classification models in this research.
# An Integrated Cellular Automata Model Improves the Accuracy of Secondary Fragmentation Prediction
## 4 Mine-Scale Application
### Mine Scale Results  
Figure 11 presents the results obtained from the large-scale flow simulation, including the new fragmentation model. This figure reports the d\({}_{10}\) and d\({}_{20}\) obtained during mineral extraction. Different \"fine sizes\" are also reported according to the criteria of three different block caving operations.  
In the case of the Esmeralda criterion, 100% of the material corresponding to d\({}_{10}\) was classified as fine, and 40.65% of d\({}_{20}\) also fell into this category. This high amount of fine material could generate the previously mentioned operational problems, which highlights the need for an adequate predictive model for this type of fragment.  
At the Diablo Regimiento criterion, 100% of the material of d\({}_{10}\) and 80.43% of d\({}_{20}\) would be classified as fine, indicating an even greater tonnage of fine material, increasing the likelihood of operational problems such as fine bursts or the formation of cohesive arcs at extraction points. Finally, in Palabora Lift 1, 80.43% of the d\({}_{10}\) material would be classified as fine, but the size corresponding to d\({}_{20}\) would not be included in this classification, as it exceeds 1 cm.  
Because the accumulation of fine material can lead to operational problems, such as fine nirushes, mud rushes (in presence of humidity) and cohesive hangups, it is crucial to maintain rigorous control over the size of the fragments during the extraction process. These issues pose a risk not only to safety but also to the stability of the operation, highlighting the importance of a model that assesses the behavior of fine material as a function of extraction to prevent operational complications.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Unit** \\ \hline Density & 2600 & kg/m\({}^{3}\) \\ d\({}_{10}\) & 0.049 & m \\ d\({}_{20}\) & 0.107 & m \\ d\({}_{50}\) & 0.340 & m \\ d\({}_{80}\) & 0.809 & m \\ UCS & 70.0 & MPa \\ \hline \end{tabular}
\end{table}
Table 6: Input material properties used for mine-scale gravity flow simulation.  
Figure 11: Evaluation of fine material according to mine criteria.  
Future research should include an analysis of the effect of scaling the strength of the material tested to a laboratory scale, as well as the effect of fragmentation when extracting from multiple drawpoints or drawbacks. Since friction is a key parameter, analyzing materials with different amounts of friction [37] or the heterogeneities of the rock mass [38] can also enrich the proposed model. Finally, if a robust database of extraction and rock characterization is available, the model could be validated on a larger scale. However, the results presented in Section 4 are within the range observed in caving mines [39; 40; 41; 42; 43].