esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Metagenomics  
DNA was isolated from murine lung using glass beads (Sigma Aldrich, Saint Louis, MO, USA) followed by QIAmp DNA Mini Kit (QIAGEN, Valencia, CA, USA). The bacterial microbiota was evaluated by 16S rRNA. The V3-V4 region of the bacterial 16S genes were sequenced using MiSeq platform (Illumina, San Diego, CA, USA) with primers pair 341F/785R. Sequencing libraries were prepared using NEXTERA XT DNA sample preparation kit (Illumina). Demultiplexing of all libraries for each sequencing lane was accomplished by the Illumina bcl2 fastq 2.17.1.14 software. Only reads with at least 100 nucleotides (nt) were retained and then primer sequences were detected, clipped, and oriented into forward-reverse primer orientation. The forward and reverse paired-end reads were imported and analyzed by using Qime2 platform [38] in a genomic cloud-computing environment based on [39; 40] and oriented for biological nano-communication systems in blood vessels for early medical tumor diagnosis [41]. At first, paired-end sequences were denoised, deregplicated, and filtered by any phiX reads and chimera (consensus) by using q2-dada2 quality control method [42] for detecting and correcting (where possible) Illumina amplicon sequence data. In particular, the q2-dada2 method makes use of sequence error profiles to obtain putative error-free sequences, referred to as either sequence variants (SVs) or 100% operational taxonomic units (OTUs). SVs were assigned taxonomy using a Naive Bayes classifier model trained on the Silva138 99% database trimmed to the V3-V4 region of the 16S. The classifier was then applied to the obtained SVs for mapping them to taxonomy. A phylogenetic tree was constructed via sequence alignment with MAFFT [43], filtering the alignment and applying FastTree [44] to generate the tree. The between-samples beta diversity was assessed based on the 16S rRNA, and estimates were calculated on the SVs within QIIME2 using Jaccard and Bray-Curtis distances between samples [45]. An abundance feature table was rarefied according to the lower sample depth (1600 reads) and was aggregated at the phylum level. Significant differences between strains of taxa proportions were evaluated by applying the unpaired non-parametric Wilcoxon test (R v.4.0.2) to differences between the reference group (wild-type) and the strain of interest at a given dpi (0, 3 or 7). The \(p\)-value was adjusted according to the Hochberg correction. Differences in relative abundances between each strain at a given time-point against all other samples were evaluated by q2-ALDEx2 [46; 47]. q2-ALDeX2 was applied to the centered-log ratio transformed data, and significant taxa represented on a MW-plot that shows the fold expression changes (difference) versus the maximum within-condition expression differences (dispersion). A significance level of 0.05 was used, together with the Benjamini-Hochberg corrected \(p\)-value.
# Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review
## 1 Introduction  
With the improvement of industry, living standards and the emphasis on the environment, the detection of toxic and hazardous gases is facing higher challenges. In addition, sensors sensing the surrounding gas environment play an important role in this field. Gas sensors are devices that convert the gas volume fraction into corresponding electrical signal [1]. They are of crucial importance in environmental monitoring, industrial chemical processing, public safety, agriculture and medicine [2]. With the development of science and technology, the development of gas sensors towards high sensitivity, high selectivity, fast response, low cost, low power consumption, stability and portability has led to the search for new and superior gas-sensing materials [3].  
Metal oxide semiconductor (MOS) gas sensors are the most wildly used gas sensors in the world for production and use due to its high sensitivity and fast response time [4]. The sensing mechanism of the MOS gas sensors is attributed to changes of electric charge carriers caused by oxidation or reduction reactions occurring at the surface of the metal oxide [5]. However, they still have the disadvantages of short life, poor selectivity and high operating temperature. Sensitivity is not sufficient for the application of precision measurement. The key indicator of material gas sensitivity is the specific surface area, which is the total surface area of a material per unit of mass [6]. The large specific surface area of nanomaterials facilitates the adsorption of gas molecules, thereby enhancing the sensitivity of gas detection. Theoretical and experimental results showed that [7] graphene and its derivatives, such as graphene oxide (GO) and reduced graphene oxide (rGO), exhibit large specific surface area,excellent conductivity, and easy adsorption of gas molecules, and the surface can easily be modified by functional groups, so it has good gas sensing properties.  
Graphene, a monolayer of graphite sheet consisting of sp\({}^{2}\) hybridized carbon atoms covalently bonded to three other atoms which was first isolated by [PERSON] and [PERSON] using micro-mechanical peeling of graphite in 2004, so they won the 2010 Nobel Prize in Physics [8]. Graphene is the thinnest and highest strength material in nature at present and has the advantages of strong electric conductivity and heat conductivity, and is almost transparent and dense, thus attracts people's attention [9].  
Nowadays, there are many studies about graphene gas sensors focused on the performance improvement in the field of composite materials, computational chemistry and Micro-Electro-Mechanical System (MEMS). Considering the practical application of graphene gas sensor, we need to find the most potential direction. In this article, the development process and the state of art of graphene gas sensors are introduced. The direction of graphene gas sensors for the future is also provided. The review provides important reference for follow-up research work.
# Manipulating Stress Responses during Spaceflight Training with Virtual Stressors
## 5 Results
### Heart Rate and Heart Rate Variability  
The main effect of stressor level on the change in heart rate (\(N=35\)) was significant, \(F(2,63.7)=4.34\), \(p=0.017\), with a large effect size, \(d=0.90\) (Figure 6a). Pairwise comparison indicated the change in heart rate was significantly higher (\(p=0.017\), \(d=0.58\)) for participants in high stressor (\(M=1.41\), \(SD=3.05\)) compared to low stressor (\(M=0.37\), \(SD=1.42\)), but not significantly different (\(p=0.16\)) for high stressor compared to medium stressor(\(M\) = 0.58, \(SD\) = 1.59) and not significantly higher (\(p\) = 0.99) the for medium stressor compared to low stressor.  
The main effect of stressor level on the change in RMSSD (\(N\) = 35) was significant, \(F\)(2,170) = 3.29, \(p\) = 0.04, with a medium effect size, \(d\) = 0.48 (Figure 6b). Pairwise comparison indicated the change in RMSSD was significantly lower (\(p\) = 0.048, \(d\) = 0.37) for participants in high stressor (\(M\) = \(-\)0.25, \(SD\) = 2.44) compared to low stressor (\(M\) = 0.63 \(SD\) = 3.07), but not significantly different (\(p\) = 0.18) for high stressor compared to medium stressor (\(M\) = 0.48, \(SD\) = 2.78) and not significantly higher (\(p\) = 0.99) the for medium stressor compared to low stressor.  
Figure 6: Heart rate metrics: (**a**) Heart Rate; (**b**) RMSSD; (**c**) pNN50. Error bars representing 95% confidence intervals.  
The main effect of stressor level on the change in pNN50 (\(N=35\)) was not significant, \(F\)(2,185) = 0.079, \(p=0.92\) (Figure 6c) for the low stressor (\(M=-0.02\), \(SD=1.42\)), medium stressor (\(M=0.13\), \(SD=1.81\)), or high stressor (\(M=-0.076\), \(SD=1.51\)).
# Consistency Models of NoSQL Databases
## 3 Consistency Models
### Weak Consistency  
As the name implies, this model weakens the consistency. It states that a read operation does not guarantee the return of the latest value written. It also does not guarantee a specific order of events [15].  
The time period between the write operation and the moment that every read operation returns the updated value is called _the inconsistency window_[16]. This model leads to a highly scalable system because there is no need to involve more than one replica or node into a client request.
## 3 Results and Discussion  
### XRD Analysis  
X-ray diffraction was performed at ambient temperature to examine the crystal structure and size of the prepared MnO\({}_{2}\) phases. Based on the synthesis conditions and amount of precursor used, the XRD spectrum of all the tunneled structures synthesized via the facile hydrothermal route is shown in Figure 3a. The sharp peaks (crystalline nature) at 20 equaled 12.7, 18.1, 28.8, 37.4, 41.9, 49.8, 56.3, 60.2, 65.1, 69.7, 72.7, and 78.5 (JCPDS 440141) for the \(\alpha\)-MnO\({}_{2}\) (tetragonal structure with a = 9.375 A\({}^{\alpha}\) and c = 2.908 A\({}^{\alpha}\)) phase [13]. The interplanar spacing d calculated for plane (110) was 0.69 nm. The peaks obtained at 20 equaled 28.6, 37.3, 40.9, 42.8, 46.1, 56.7, 59.3, 64.9, 67.3, and 72.4 for \(\beta\)-MnO\({}_{2}\) (rutile structure) [14]. The five distinct diffraction peaks at 20 equaling 22.2, 37.6, 42.5, 56.2, and 65.3 corresponded to \(\gamma\)-MnO\({}_{2}\) (ramsdellite structure) [15]. The peaks corresponding to \(\gamma\)-MnO\({}_{2}\) were not very sharp and exhibited a semi-crystalline behavior. The crystallite size of \(\alpha\)-, \(\beta\)-, and \(\gamma\)-MnO\({}_{2}\) was 19.7, 22.2, and 8 nm, respectively, calculated using the Debye-Scherrer formula. Thus, the XRD pattern demonstrates the successful synthesis of polymorphous MnO\({}_{2}\) phases.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 2 Related Works
### Game Theory-Based Techniques  
Ref. [28] introduced a mathematical framework to detect and mitigate the effects of GPS spoofing attacks on UAVs; the approach models the attacker and a set of UAV strategies as a dynamic Stackelberg game. However, this technique required communication among UAVs to determine the real position of the target UAV through cooperative localization.  
A game-theoretic security mechanism was proposed in [29] to detect and mitigate GPS spoofing attacks based on a continuous kernel signaling game. In their model, a GPS receiver and a spoofer were depicted as two players. The receiver player faces a critical decision, either accepting a received GPS signal without verification, potentially exposing itself to deception, or estimating its position based on the signal. On the other hand, the spoofer player strategically injects counterfeit signals in an attempt to manipulate the receiver's navigation system and mislead its position.
## 2 Materials and Methods  
In this study, the two handheld scanners based on a laser (ViuScan from Creaform Inc., Levis, QC, Canada) (Figure 2a) and white light (Goscan from Creaform Inc., Levis, QC, Canada) (Figure 2b) are evaluated.  
Figure 2: Handheld scanners (**a**) laser scanner; (**b**) white light scanner.  
The two handheld scanners evaluated in this work were manufactured by Creaform (Creaform Inc., Quebec, Canada) [8]. These scanners are portable and self-positioning, meaning that they use positioning targets for reference. With the laser-based handheld scanner, two crossing laser lines are projected onto the object surface, while with the white light scanner, a light pattern is projected onto the object surface. The technical specifications as defined by the manufacturer can be seen in Table 1.  
The test part considered in this investigation was the automotive car door shown in Figure 3. This component was selected because it provided a free-form surface and was large enough to evaluate the performance effectively. Besides accuracy and scanning time, the factors such as portability and ease of use are also important factors that govern the selection of scanners for the desired application. The portability in the current scenario has been defined as the ease or proficiency with which the scanner can be transferred or moved from one place to another. The portability can be determined by the weight of the scanner, setup time, etc. Similarly, the ease of use defines the user-friendliness, ease of learning, and applicability of the scanner. The ease of use may depend on the number of the scanning parameters, set up procedure (easy or difficult), and number of operators required, amongst other factors.  
Besides analysis of variance (ANOVA) and main effect plots, the optimization plots were utilized to optimize the different factors involved in achieving the desired response values. Since, different responses were involved in this study, composite desirability instead of individual desirability was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technical Specifications** & **Laser Based** & **White Light** \\ \hline Weight (kg) & 1.3 & 1.1 \\ Dimensions (mm) & \(172\times 260\times 216\) & \(127\times 156\times 262\) \\ Laser Class & 2 & White light (led) \\ Resolution (Normal) (mm) & 0.100 & 0.500 mm \\ Accuracy (Normal) (mm) & Up to 0.05 & Up to 0.100 \\ Volumetric Accuracy (mm/m) & 0.020 + 0.025 & 0.300 \\ Standoff Distance (mm) & 300 & 400 \\ Depth-of-Field (mm) & \(\pm 150\) & 250 \\ Field of View (mm) & \(220\times 220\) & \(380\times 380\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the two data acquisition systems.  
Figure 3: Test part selected for the comparative evaluation.  
used to optimize the set of responses. This approach was based on the desirability function for the optimization of multiple response processes [21]. Specifically, the overall desirability can be defined as the geometric mean of the desirability for each response. A desirability value of one represents the ideal case. An optimal solution occurs when composite desirability attains its maximum.
# Industrial Application of AI-Based Assistive Magnetic Particle Inspection
## 4 Software
### Defect Detection  
#### 4.2.1 Faster-RCNN Architecture  
The usual available AI-based architectures for object detection are:  
* Yolo [21];
* Faster R-CNN [22];
* Transformer-based (Deformable-DETR [23]).  
Real-time detection is not a requirement for our project (the maximum allowed detection time is set to 60 s), but having a high PoD (which is similar to high recall) is mandatory. This eliminates Yolo-based architecture which is faster than the accurate sort due to its single stage architecture, contrary to Faster-RCNN [19; 24] built around a two-stage architecture. Transformers (*-DETR) using ViT cannot be used because of the non-uniformity of the lighting. Indeed, the attention mechanism will detect non-uniformity as possible defect zones, leading to a high number of false detections.  
Faster-RCNN is based on feature extraction (backbone), then a Region Proposal Network (RPN) followed at the end by the classification. Backbone based on convolution layers provides features to the RPN. RPN selects the best possible regions of interest (RoIs), to feed the classifier (Figure 14). The RPN part is relevant to our defect detection. Indeed, this defect represents \(1\) to 5 % of the whole image. Therefore, selecting RoI is more efficient. In this study, we used the official Pytorch implementation of Faster R-CNN.  
During training, trainset images were compressed to be able to train with the CCUB (Centre de Calcul de l'Universite de Bourgogne) cluster. The validation on the testset was carried out with the original image size. Data augmentation [9; 11; 13; 20] was performed on the trainset only, with rotation as well as horizontal and vertical flips. This is motivated by the fact that during machine utilization defects can appear rotated and horizontally or vertically flipped. Data augmentation is implemented using a Python library called 'Albumentations'.  
Transfer learning [11; 25] using argument 'weights' set to 'IMAGENET1K_V2' was used with Pytorch implementation of Faster R-CNN to obtain the best possible recall with a limited number of training epochs. Indeed, only the fully connected layers used for classification are trained; the backbone remains frozen. This is useful for re-using the feature extraction capability of the backbone trained with a huge dataset, while specializing our network for our specific detections.  
The PARADES machine is a commercial product; thus, the source code and implementation details cannot be released.  
Figure 13: Example of stress-induced defect on a rod, acquired with the PARADES machine.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 3 Manifolds-Based Low-Rank Dictionary Pair Learning
### MbLRDPL-GM  
Different from MbLRDPL-SPD, MbLRDPL-GM uses linear subspace \(Y_{i}^{k}\) to model image set \(X_{i}^{k}\), and the subspace matrix \(Y_{i}^{k}\) can be solved by solving the following problem:  
\[X_{i}^{k}{X_{i}^{T}}=Y_{i}^{k}\Delta_{i}^{k}{Y_{i}^{k}}^{T}, \tag{6}\]  
where \(\Delta_{i}^{k}\) is the eigenvalue matrix, and \(Y_{i}^{k}\) is the eigenvectors matrix. Since the linear subspace \(Y_{i}^{k}\) is located on a Grassmann manifold, we first embed it to its tangent space with \(Y_{i}^{k}(Y_{i}^{k})^{T}\).  
Then, the objective function of MbLRDPL-GM can be constructed as  
\[\begin{array}{ll}\min&\sum\limits_{k=1}^{K}\sum\limits_{i=1}^{n_{k}}||Y_{i }^{k}{Y_{i}^{k}}^{T}-D_{k}P_{k}Y_{i}^{k}(Y_{i}^{k})^{T}||_{F}^{2}\\ &\quad+\lambda_{1}\sum\limits_{k=1}^{K}\sum\limits_{j=1}^{n_{k}}||P_{k}\bar{Y }_{i}^{k}(\bar{Y}_{i}^{k})^{T}||_{F}^{2}+\lambda_{2}\sum\limits_{k=1}^{K}||D_ {k}||_{*}\\ \text{s.t.}&||d_{j}^{k}||_{2}^{2}\leq 1,\ k=1,\cdots,K;\ j=1,\cdots,m,\end{array} \tag{7}\]  
where \(\bar{Y}_{i}^{k}\) is the complementary matrix.  
This problem is solved in Section 3.4 to obtain the optimal dictionaries \(D_{k}\), \(P_{k}\), and \(k=1,\cdots,K\). When a new probe image set \(Z\) is considered, we only need to solve the following problem to obtain its label:  
\[label(Z)=\underset{k}{\arg\min}\ ||Y_{z}Y_{z}^{T}-D_{k}P_{k}Y_{z}Y_{z}^{T}||_{F}^{2} \tag{8}\]  
where \(Y_{z}\) is the subspace model of \(Z\).
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 2 Non-Mechanical Beam Steering with PGs
### Optical Properties of PG-Based Non-Mechanical Steering Systems  
#### 2.2.1 Spectral Properties  
The useful spectral range of this technology is broad over optical wavelengths and limited primarily by material absorption constraints. In the UV, the primary challenge is the degradation of many LC-based polarization handedness switch architectures. Specifically, many nematic liquid crystals and polyimide alignment layers are known to degrade under UV illumination [34; 35]. In the infrared, increasing absorption of many transparent conductors, such as indium tin oxide (ITO) and indium molybdenum oxide (IMO) gradually decreases per-stage efficiency as the design wavelength increases beyond 1 um [36]. This is exacerbated by strong molecular vibration absorption bands in LC throughout the MWIR and long wave infrared (LWIR) for many liquid crystals [37], though these bands can be shifted through chemical modifications to the LC mixtures [38; 39].  
The spectral bandwidth of a specific design depends on the retardance error of both the PG and handedness switch over the desired spectrum. For example, Figure 2b displays the transmission spectrum of a PG fabricated for 1550 nm, where the null indicates the wavelength of peak diffraction efficiency. For this particular PG, 0.06% of incident light is transmitted into the zero order at 1550 nm and this leakage remains <3% over \(\pm\) 105 nm. The PG design may be further achromatized with a dual-layer approach in which chiral dopants are used to generate opposing twist in each layer that serves to self-compensate chromatic dispersion in the geometric phase delay [40]. It is worth pointing out, however, that although the efficiency is achromatized, the steering angle still displays chromatic angular dispersion according to Equation (1).
## 2 Results and Discussion
### ppGpp Stimulates the Transcriptional Expression of the hly_CABD_\({}_{II}\)_Open_
A further decrease was observed in a ppGpp-deficient genetic background, consistent with the results at the protein level (Figure S1, Supplementary Materials). These results suggest that the ppGpp-mediated regulation of the _hlyCABDII_ operon is also independent of RfafH. Zur, a regulator that senses the intracellular levels of zinc in the cell [28], represses the _hlyCABDII_ operon. The derepressed expression of the \(\alpha\)-hemolysin by the lack of Zur repressor, observed previously in the J96 strain, was also detected in the K12 genetic background (Figure 4C). ppGpp deficiency caused a drastic drop in the \(\alpha\)-hemolysin expression even in the absence of the Zur repressor. These results suggest that Zur is not involved in the ppGpp-mediated regulation of the _hlyCABDII_ operon. The H-NS protein has been found to silence the expression of the \(\alpha\)-hemolysin encoded in the _hlyCABD_ operon from the conjugative plasmid pHy152 [29]. To study the H-NS effect in the _hlyCABDII_ operon, the _hns_ allele was introduced in Wt and ppGpp-deficient derivatives (Figure 4D). Under the experimental conditions used, only a slight increase in the \(\alpha\)-hemolysin expression level was detected in the _hns_ mutant as compared with the isogenic Wt counterpart. Similar results were obtained when characterizing the effect of the _hns_ mutation in the UFEC strain 536 [30]. Nonetheless, the decrease in the \(\alpha\)-hemolysin expression caused by the ppGpp deficiency was independent of the presence or absence of H-NS, indicating that H-NS is not involved in the ppGpp-dependent regulation of the _hlyCABDII_ operon. Overall, our data indicate that the ppGpp-mediated stimulation of the _hlyCABDII_ operon is independent of RpoS and the previously described \(\alpha\)-hemolysin regulators RfafH, Zur, and H-NS. Further studies will be required to fully dissect the exact mechanism by which ppGpp mediates the stimulation of _hlyCABDII_ operon expression.