esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 3 Results  
### Mold vs. Dust Mycobiota by DNA Metabarcoding  
The fungal community composition was clearly different between the two sample types, the mold and dust samples, as shown by the NMDS ordination of the full dataset (Figure 2a). According to the PERMANOVA, there was a significant (\(p<0.001\)) difference between the community composition of the two sample types, accounting for 12.3% of the overall variance. The similarity among the dust samples was clearly higher than among the mold samples. As expected, the dust samples showed a significantly higher alpha diversity compared to the mold samples (Figure 2b). The number of observed OTUs per sample (richness) ranged from 44 to 424 (mean = 211.9) among the dust samples and from 2 to 65 (mean = 16.6) among the mold samples.  
Among the 1829 OTUs detected in the overall dataset, 1562 (85.4%) were only present among the dust samples, while 38 (2%) were uniquely present in the mold samples. Altogether, 229 OTUs (12.5%) were detected in both substrates. Considering the most abundant genera, those represented by \(>\)2% of the total number of sequences, we observed some marked trends in the two types of samples (Figure 2c): _Acemonium, Aspergillus, Chuedomium, Derbaromyces, Stachybtors,_ and _Trichoderma_ were more abundant in the mold samples, while _Cladosporium, Fomitopsis, Penicillin, Rlodotton, Sacchranomyces, Serpula_ (initially misidentified as _Austropaxillus_), and _Thekopson_ were dominant in the dust samples. The details about the 200 most abundant OTUs in the complete dataset are reported in Supplementary Table S1.  
When we analyzed the mold samples separately, we observed that the type of building material accounted for 22.05% (\(p<0.002\)) of the compositional variance (Figure 3a). Despite the low number of samples analyzed for some materials, we detected some specific associations between certain fungal genera and types of materials (Figure 3b): _Acemonium_ on gypsum board; _Alternaria_ on wood; _Chaetomium_ on gypsum board, chipboard, and MDF;  
Figure 2: Comparison of dust and mold mycobiota, as revealed by DNA metabarcoding: **(a)** NMDS plot showing the differences in the fungal community composition of 42 dust samples and 48 mold samples; **(b)** variation of the alpha diversity indices; **(c)** the most abundant fungal genera (RA \(\geq\) 2%) identified in each sample type. Note that the genus _Serpula_ was initially misidentified as _Austropaxillus_ in the first automatic taxonomic assignment (*).  
Derbaryomyces, Monicillium, and Mucor on wall paper; _Naganishia_ on chipboard; _Penicillium_ on MDF, building paper, and wall paper; _Rhodotorula_ on chipboard; _Stachybotrys_ on gypsum board, wood, and building paper; _Trichoderma_ on wood; and _Wallemia_ on wall paper.
## 3 Results and Discussion
### Mechanical Properties
Comparatively, the polymer with the best breaking energy properties is the EVA copolymer and with percentages of GTR of up to 20%, a good behavior of the energy at break (20.24 J) is observed for particles of size \(<\)200 \(\upmu\)m, unlike for percentages of GTR greater than 20% (40%, 50%, 70%) the behavior of the energy at breakdown (10.16 J, 4.51 J, 3.86 J, respectively) at values close to 40% value of the polymer without additive. For the PP, ABS, and to a lesser extent the HDPE, the addition of GTR affects very negatively the properties of resistance to breakage (J), and suffers a vertiginous fall of the resistance to breakage for percentages in GTR from 5% of GTR, not reaching values of 5% with respect to values of the matrix without GTR. For HDPE, the addition of GTR does not so severely affect the breaking Strength. Very prominent cases are the polymers PVC, PA, and PS, which have very low energy breaking properties, comparatively have very little properties of breaking energy for these polymers, and with the addition of GTR, they worsen said characteristics. As it can be seen in Table 3, mainly, the highest values of property are corresponding to the pure polymer (0% GTR), with some important exceptions as are highlighted in the table. For EVA, HDPE, PA, and PS, some mechanical properties are improved by the addition of microparticles (\(<\)200 \(\upmu\)m) of GTR in the matrix of the polymer. The mechanical parameters are significantly influenced by the GTR. Basically, it is observed that at the mechanical levels, the properties of the pure matrix of the polymer are maintained, approximately, between 10 and 20% contribution of GTR particles (for particle size \(\leq\)200 microns). The interaction of the matrix with the GTR particles is rather low, and therefore, it accepts this low content of GTR in its matrix. For values higher than 20% of GTR in the polymeric matrix, mechanical properties decrease dramatically. Adding the GTR in the matrix (5-10%) for EVA, PS, and HDPE composites, the Young's modulus of the compound increases, although other mechanical properties decrease as it happens, and for the rest of the cases, the Young's modulus gets worse. This behavior may be due to the fact that reinforcement matrix is correct for these formulations, and therefore, some mechanical properties such as stiffness improve. However, for GTR concentrations above 20%, all mechanical properties decrease, except for PA composites.
## 3 Experimental Results and Discussion
### Load and Deflection Relationship  
The load and deflection curves for each comparison group are shown in Figures 8-10, and the results are listed in Table 6. The stiffness of TRM and control specimens were similar, except for T2, T2A, and T2 AP. The decrease of the stiffness of the T2 was observed due to damage of the concrete beam during the grinding process. Increasing stiffness of T2A and T2 AP was affected by applying a mechanical end anchorage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Experiment Results**} \\ \cline{2-7}
**Name** & \(P_{y}\) & \(\delta_{y}\) & \(P_{u}\) & \(\delta_{u}\) & \(\mu\) & **Failure Mode** \\  & **(kN)** & **(mm)** & **(kN)** & **(mm)** & **(\(\delta_{u}\)/\(\delta_{y}\))** & \\ \hline RC & 32.06 & 6.75 & 35.39 & 18.92 & 2.8 & Concrete crushing \\ T1 & 33.66 & 7.48 & - & - & - & Textile rupture \\ T1N & 38.96 & 7.85 & - & - & - & Textile rupture \\ T1O & 38.22 & 8.18 & 39.21 & 10.79 & 1.32 & Debonding and textile rupture \\ T1M & 35.38 & 8.42 & 37.11 & 17.48 & 2.08 & Textile slippage \\ T2 & 34.89 & 10.02 & 36.49 & 15.86 & 1.58 & Debonding and textile rupture \\ T2E & 40.69 & 8.85 & 42.29 & 12.45 & 1.41 & Textile rupture \\ T2A & 40.19 & 7.31 & - & - & - & Textile rupture \\ T2 AP & 38.34 & 7.41 & 40.19 & 9.03 & 1.22 & Textile rupture \\ T3 & 38.71 & 8.53 & 41.92 & 12.79 & 1.50 & Textile rupture \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test results.  
Figure 8: Load and deflection curve: Textile reinforced ratio.  
Figure 7: Failure modes of TRM: (**a**) Textile rupture of T2 AP; (**b**) debonding and textile rupture of T2; (**c**) textile slippage of T1M.
## 1 Introduction  
Among all biopolymers, chitosan (CS) is among the most interesting ones, as being a polycationic biopolymer obtained from chitin grants it elevated biodegradability, biocompatibility and an almost complete lack of toxicity. This led the FDA to approve it for various biomedical topical applications, particularly in those related to wound healing [1]. Furthermore, the plethora of biological properties presently ascribed to chitosan, namely antimicrobial, make this molecule a highly sought one for the advancement of biomaterial solutions for tissue engineering, wound healing and drug delivery [2; 3; 4; 5; 6]. When considering this particular set of characteristics, chitosan was selected as a material of interest for the development of nanoparticles (NPs) [7; 8].  
Chitosan NP's production was first described by [PERSON], [PERSON] [9]. Since then, various NP production methods have appeared, with electrostatic complexation being the most used one, as it permits the production of small-sized NPs with narrow population distributions in a highly reproducible manner in an aqueous solution and without requiring the use of organic solvents or cross-linking agents with known toxicity [10; 11]. The formation process _per se_ is based on the protonated amino groups present in chitosan (only in acid medium) interacting through electronic complexation, with anionic compounds such as citrate or (poly)phosphate resulting in the production of the NPs. This method is dubbed ionic gelation and is the preferred one to produce chitosan NPs, especially through the use of the ionic agent sodium tripolyphosphate (TPP) [10; 12]. Widely used as a nanometric delivery system for bioactive molecules, chitosan-TPP NPs' inherent biological properties(non-toxic [13; 14; 15], antimicrobial activity [16; 17; 18], mucoadhesivity [19; 20] and haemocompatibility [21]) make them an interesting option for various industries, including the cosmetic and textile industries. Additionally, the ionic gelation method is highly controllable and customizable. Authors showed that alterations to chitosan and TPP concentration, environmental pH value and even the biopolymer-to-TPP ratio may alter the size and charge of the produced NPs [22; 23]. With this in mind, this work sought to understand how a fixed-point optimization of three physical parameters (TPP addition time, overall reaction time and rotation speed) associated with chitosan NP production at two different pH values would modulate particle charge and size. Additionally, freeze-drying with and without cryoprotectants and long-term storage stability for the optimized NPs produced were also assessed. Finally, considering potential skin related applications, the biocompatibility of the NPs with human keratinocytes was also assessed.
## 2 Methods
### Adversarial Loss and Cycle Consistency Loss  
The adversarial loss is used to judge the distribution similarity between the transformed images and the reference images. Applying least squares generative adversarial networks (LSGAN), the forward adversarial losses is defined as  
\[\begin{split}&\mathcal{L}_{LSGAN}\left(G_{forward},D_{forward},X_{n},X_{N} \right)\\ &=\mathbb{E}_{\{x_{N}\}}\left[\left(D_{forward}\left(x_{N} \right)-1\right)^{2}\right]+\mathbb{E}_{\{x_{n}\}}\left[D_{forward}\left(G_{ forward}\left(x_{n}\right)\right)^{2}\right].\end{split} \tag{1}\]  
In GANs, the objective of generator optimization is to \"fool\" the corresponding discriminator by reducing the discriminator result gap between generated images and reference images while that of discriminator optimization is to better distinguish the two kinds of images. Therefore, the objective of the forward adversarial loss is \(\underset{G_{forward}}{\min}\underset{D_{forward}}{\max}\mathcal{L}_{LSGAN} \left(G_{forward},D_{forward},X_{n},X_{N}\right)\), and that of the backward one is \(\underset{G_{backward}}{\min}\underset{D_{backward}}{\max}\mathcal{L}_{LSGAN} \left(G_{backward}\left(n,D_{backward}\left(n,X_{N},X_{n}\right)\right)\).  
The forward generator and the backward one is combined to form an end-to-end structure similar to the self-encoder. Therefore, the \(G_{forward}\left(x_{n,m}\right)\) is treated as the \"code\". To ensure the preservation of image-specific feature in the \"code\", cycle consistency loss is applied to compare \(x_{n,m}\) with \(G_{backward\ n}\left(G_{forward}\left(x_{n,m}\right)\right)\) and compare \(x_{N,m}\) with \(G_{forward}\left(G_{backward\ n}\left(x_{N,m}\right)\right)\). Utilizing the \(L_{1}\) norm, the \(n\)th cycle consistency loss function is  
\[\begin{split}\mathcal{L}_{cycle}\left(G_{forward},G_{backward\ n} \right)&=\ \mathbb{E}_{\{x_{n}\}}\left[\left\|G_{backward\ n}\left(G_{forward}\left(x_{n} \right)\right)-x_{n}\right\|_{1}\right]\\ &+\mathbb{E}_{\{x_{N}\}}\left[\left\|G_{forward}\left(G_{backward\ n}\left(x_{N}\right)\right)-x_{N}\right\|_{1} \right].\end{split} \tag{2}\]
## Appendix B
### Flight Path for a MISTiC Observation Demonstration Flight on 4 December 2017  
The path of this flight is shown on the left in Figure 10, and closely followed the plan developed with the pilot and flight planning support at NASA Armstrong. Following launch from Palmdale (approximately 10:30 AM PST), and ascent-stage maneuvers, the ER2 climbed above 60 kft above MSL, and MISTiC hyper-cube collection was initiated while flying to the southwest to the east of Oxnard (there is evidence of one of the many fires in the region in one portion of the first hypercube of this flight). After leveling off at the observation altitude of 65 kft, the ER2 executed 2 \(\frac{1}{2}\) passes around the orbit following a \"racetrack\" pattern, for the first Orbit, just south of the Channel Islands. The ER2 then proceeded to a second Orbit (2 \(\frac{1}{2}\)), which included both land and open-ocean features near Vandenberg AFB. The third Orbit location (2 \(\frac{1}{2}\)) is over Edwards AFB, and the fourth Orbit location is to the east, over the Twenty-Nine Palms region. The fourth Orbit was followed by return-to-base. These Orbit locations were chosen in part to support independent observations of the weather features for comparison to the MISTiC observations. The first two Orbits are close to the Vandenberg AFB National Weather Service RAWINSONDE launch site, which provided measurements of wind velocity and the atmospheric thermodynamic properties. Two additional radiosondes were launched from Vandenberg on 4 December, supplementing the standard \(\sim\)0Z and 12Z launches. The first Orbit is over the open ocean--just following the IASI-B overpass--providing the simplest situation for comparing MISTiC and IASI-B spectra, and refining MISTiC spectral calibration. The first Orbit also over-flow departure pathway of commercial airliners flying out of LAX, potentially allowing comparisons with ACARS data. The third Orbit was executed over Edwards AFB, when a special RAWINSONDE was launched for MISTiC by the [PERSON] weather support staff. Finally, the Twenty-Nine Palms Orbit is over the departure and arrival pathway to LAX from the east, providing another opportunity for comparison with ACARS data. Aqua, with AIRS over-flew the area during the third Orbit, as did Soumi NPP and JPSS-1, each hosting a CrIS instrument.
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 5 Experiment Results
### Performance Evaluation
#### 5.2.2 Multi-User Legitimacy Authentication  
In the multi-user legitimacy authentication, we conducted separate tests in scenarios involving double-user, triple-user, and four-user scenarios. The specific procedures were as follows: we initially partitioned 70% of the legitimate single-user dataset for training. The data on legitimate and illegitimate combinations of different user counts were used for testing to evaluate the system's performance in the following aspects in multi-user legitimacy authentication. The evaluation results are elucidated using a confusion matrix in Figure 10b-d.  
The overall performance, as depicted in Figure 11, showed that the Multi-WilR achieved accuracy rates of 92.64%, 90%, 87.91%, and 85.11% in the single-user, double-user, triple-user, and four-user scenarios, respectively. This result indicated that our accuracy remained, even in private settings. This achievement can be attributed to the utilization of a multi-branch approach within our system for extracting the features of a single-user, thereby mitigating potential interference from other users. Notably, within the context of triple-user and four-user scenarios, the precision significantly surpassed the other scenarios, owing to the lower presence of illicit users juxtaposed with a higher prevalence of legitimate users, consequently resulting in the highest precision upon reevaluation. Importantly, within this model, the overall false positive rate stood at a mere 3.2%, thus decisively validating the system's efficacy in preempting unauthorized intrusions.  
Figure 10: Confusion matrix for legitimacy authentication across different numbers of users. (**a**) Single-user legitimacy authentication; (**b**) double-user legitimacy authentication; (**c**) triple-user legitimacy authentication; (**d**) four-user legitimacy authentication.
## 4 Experiments
### Characteristic of the Convolutional Neural Network Model
As a sufficient and conclusive data set could not be obtained in the initial weeks, especially in the first week, where the previous instance was unavailable, the experiment provided the loss and accuracy correction from week 6. Figure 3 exhibits the progress for accurate earlier prediction using the testing subset. On average, for three LSTM-based models, the accuracy scale ranged from 0.865 in the sixth week to 0.879 achieved in the eighth week up to 0.942 obtained in the last, 16 th, week. A gradual improvement in the accuracy was observed since the 10 th week, indicating the efficiency of the deployed model in learning the distinct multivariate patterns of students' performance with accuracy over 0.871, especially using both CNN-LSTM and Conv-LSTM. The figure depicts that the loss values continuously degrade, signifying a progressively smaller difference between the target class label and the predicted class label. In addition, overfitting became another challenge in the AI model. Therefore, how well the proposed models perform on a new data set cannot be known unless tested on actual data. To address this issue, a 10-fold cross-validation was performed on separate training and testing subsets, as illustrated in Figure 4. In general, the 10-fold cross-validation accuracy of CNN-LSTM and Conv-LSTM from week 6 to the final week was higher than those of the LSTM model. The longer the data set, the better the performance of the proposed models with an accuracy of more than 0.950 when using a 16-week-wise data set.
# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 4 Deep Visual Saliency Models
### Static Models
[PERSON] [76] used adversarial networks for training, and it consisted of two parts, a generator and a discriminator. The network learned the parameters through the backpropagation of the downsampled binary cross entropy loss calculation. The success of the model indicated that the choice of an appropriate loss function can be treated as a method for improving the prediction effect. In recent years, some excellent models have been proposed for saliency prediction. [PERSON] et al. [77] proposed a saliency model called EML-Net based on the similarities between images and the integration of Extreme Learning Machines (ELMs). [PERSON] et al. [78] proposed the Deep Visual Attention (DVA) model in which the architecture was trained in multiple scales to predict pixel saliency based on a skip-layer network. The model proposed by [PERSON] [79] used shared attention to enhance saliency prediction. [PERSON] et al. [80] proposed a model called MxSalNet, which was formulated as a mixture of experts. [PERSON] et al. [81] proposed a deep feature-based saliency (DeepFeat) model to utilize features by combining bottom-up and top-down saliency maps. [PERSON] et al. [82] proposed the MSI-Net based on an encoder-decoder structure and it includes a module with multiple convolutional layers at different dilation rates to capture multi-scale features.
## 3 Results
### Global Efficiency Indices  
#### 3.1.1 Single-Value Global Environmental Efficiency Index  
Table 2 shows the value obtained, using the ReCiPe methodology, for the single-value environmental impact index, for each aspect analysed for 2019 and 2020.  
The measurement of this index has resulted in a single-value representing the environmental impacts for 2019 and 2020  
\[\text{GEEI}\ 2019=6.06\ \times\ 10^{4}\]  
\[\text{GEEI}\ 2020=7.10\ \times\ 10^{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **GEEI** & **GEEI** & **\% Variation** \\  & **2019** & **2020** & **\% Variation** \\ \hline Natural resources & \(1.39\ \times\ 10^{2}\) & \(1.52\ \times\ 10^{2}\) & 9.35\% \\ Medicinal gases & \(2.91\ \times\ 10^{2}\) & \(3.74\ \times\ 10^{2}\) & 28.52\% \\ Wastes & \(2.99\ \times\ 10^{3}\) & \(4.43\ \times\ 10^{3}\) & 48.16\% \\ Discharges & \(1.69\ \times\ 10^{2}\) & \(1.90\ \times\ 10^{2}\) & 12.43\% \\ Means of transport used & \(2.21\ \times\ 10^{4}\) & \(2.68\ \times\ 10^{4}\) & 21.27\% \\ Transport of patients & \(3.48\ \times\ 10^{4}\) & \(3.91\ \times\ 10^{4}\) & 12.36\% \\ Consumption of materials & \(1.57\ \times\ 10^{1}\) & \(2.52\ \times\ 10^{1}\) & 60.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global environmental efficiency index (2019–2020).  
Obtaining these single-values facilitates the analysis and makes it possible to compare the environmental impact produced by hospital activity in 2019 and the impact of the COVID-19 pandemic on the same index, during 2020.  
The resulting index obtained for 2020 shows the increase in the impact this hospital had on the environment, compared to 2019.
## 5 Evaluation in Window Analysis and Clustering Algorithms
### Evaluation in the Window Analysis Method
#### 5.1.2. Discussion in Window Analysis-FDH, BCC and CCR Models

Table 11: _Cont._  
Figure 8: Average of total efficiency for the CCR model. Based on the CCR model for Table 13:  
* The 22 nd company has the highest efficiency score with an efficiency score of 1 for all years. * 19 th (0.950, 0.995, 1, 1, 1, 1), and 23 rd (0.922, 0.955, 1, 1, 1, 1) companies have an ascending trend of efficiency from the beginning (1 st year or 2014) to the end (6 th year or 2019). * There is no descending trend of efficiency from the beginning (1 st year or 2014) to the end (6 th year or 2019). * Other cement companies have both descending and ascending from the beginning (1 st year or 2014) to the end (6 th year or 2019). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** \\ \hline Company 1 & 1.00 & 1.00 & 0.94 & 0.86 & 1.00 & 1.00 \\ Company 2 & 1.00 & 0.96 & 1.00 & 0.97 & 1.00 & 0.98 \\ Company 3 & 0.89 & 0.96 & 1.00 & 0.97 & 1.00 & 0.98 \\ Company 4 & 1.00 & 0.94 & 0.89 & 0.89 & 1.00 & 1.00 \\ Company 5 & 1.00 & 0.92 & 0.95 & 0.97 & 1.00 & 0.86 \\ Company 6 & 1.00 & 0.91 & 0.89 & 0.85 & 1.00 & 0.93 \\ Company 7 & 0.92 & 1.00 & 0.93 & 1.00 & 1.00 & 1.00 \\ Company 8 & 1.00 & 1.00 & 0.94 & 0.85 & 1.00 & 1.00 \\ Company 9 & 1.00 & 1.00 & 1.00 & 0.87 & 1.00 & 1.00 \\ Company 10 & 0.92 & 1.00 & 0.96 & 0.97 & 1.00 & 1.00 \\ Company 11 & 1.00 & 0.95 & 1.00 & 0.85 & 1.00 & 1.00 \\ Company 12 & 1.00 & 1.00 & 0.99 & 0.98 & 1.00 & 1.00 \\ Company 13 & 1.00 & 1.00 & 1.00 & 0.95 & 1.00 & 1.00 \\ Company 14 & 1.00 & 0.93 & 0.98 & 0.95 & 1.00 & 1.00 \\ Company 15 & 1.00 & 1.00 & 0.91 & 0.96 & 1.00 & 1.00 \\ Company 16 & 1.00 & 0.99 & 1.00 & 1.00 & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 13: Average efficiency in CCR model for each year. \begin{table}
\begin{tabular}{c c c c c} \hline  & **2014–2016** & **2015–2017** & **2016–2018** & **2017–2019** \\ \hline Company 1 & 0.99 & 0.98 & 0.90 & 0.93 \\ Company 2 & 0.98 & 1.00 & 0.97 & 0.99 \\ Company 3 & 0.94 & 1.00 & 0.94 & 0.99 \\ Company 4 & 0.92 & 0.94 & 0.87 & 0.98 \\ Company 5 & 0.89 & 1.00 & 0.97 & 0.95 \\ Company 6 & 0.89 & 0.98 & 0.87 & 0.88 \\ Company 7 & 0.97 & 0.96 & 0.96 & 1.00 \\ Company 8 & 1.00 & 0.99 & 0.87 & 0.92 \\ Company 9 & 1.00 & 1.00 & 0.93 & 0.94 \\ Company 10 & 0.96 & 0.96 & 0.99 & 1.00 \\ Company 11 & 0.97 Therefore, based on the abovementioned results:  
* 22 nd company has the highest efficiency score in all FDH, BCC and CCR models
* 6 th company has the lowest efficiency score in all FDH, BCC and CCR models
* FDH, CCR and BCC models have the same ranking for all DMUs (The first, second and third ranks for 22 nd, 16 th, and 19 th companies respectively and the lowest level for 6 th company)
* FDH model has the first rank and the highest total average efficiency score for all 24 DMUs
* BCC model has the second overall average efficiency score for all 24 DMUs
* CCR model has the third overall average efficiency score for all 24 DMUs