esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In contrast to the two interferometer arrangements described by [PERSON] et al. [14,15] Mirau-type CSI instruments are usually preferred in industrial and other practical applications due to their advantages, namely, a sufficient numerical aperture (NA), a compact design, as well as low dispersion distortions. Furthermore, Mirau systems encompass a simple handling due to less demanding adjustment compared to, e.g., a Linnik setup. Moreover, significantly lower system costs result from the configuration with only one microscope objective lens [23]. Therefore, extending the range of application and especially making the method accessible for industrial use requires the transfer of the previously described vibration compensation technique to a Mirau CSI. However, due to the need of a sinusoidally oscillating reference mirror, this method is not directly applicable to conventional Mirau-type CSI system. To overcome this, and to make the method described above suitable for use with a Mirau interferometer, a self-designed and build Mirau setup with oscillating reference mirror is presented in this article. Therefore, a microscope objective lens providing a long working distance and a glass thickness compensation is used and extended to a Mirau interferometer by applying a Mirau attachment (MA). The MA consists inter alia of the oscillating reference mirror that enables the IDS to share the optical path with the CSI. The sensor setup is introduced in Section 2 with focus on the Mirau extension. Afterwards, the IDS signal processing and correction of the CSI signals is described in Section 3. In Section 4, we present measurement results to validate the transfer characteristics and vibration compensation capabilities of our Mirau CSI on several specimens. A conclusion is drawn in Section 5 and an outlook to further investigations will be given.
## 5 Methodology for Flood Analysis
### Estimation of River Overflow Pattern from Water Level Data
#### 5.1.1 Rate of River Overflow Flow at Points A and B  
At the two points where river overflow occurred due to the existence of old road bridges indicated with A and B, the river water level record during the flood by 3 L water gauges was obtained (see \(H_{A}\) and \(H_{B}\) in Figure 9). Therefore, the overflow rate was calculated using the formula describing the relation between flow rate and overflow depth proposed by [19], and the calculation results were used in the simulation of the entire flow field.  
\[q=\left\{\begin{array}{cc}0.35\ h_{1}\sqrt{2 gh_{1}}\ \text{when}\ \frac{h_{2}}{h_{1}}\leq \frac{2}{3}\\ 0.91\ h_{2}\sqrt{2g(h_{1}-h_{2})}\ \text{otherwise},\end{array}\right., \tag{1}\]  
where \(h_{1}\) and \(h_{2}\) are the water level on the two sides measured from the levee top (\(h_{1}>h_{2}\)) and \(q\) is the flow rate per unit width on the embankment.
## 2 Detection Algorithms  
In FMCW radar systems, the target information is extracted from the beat signal via spectral analysis. Accurate peak detection within the frequency domain is critical as it directly impacts the radar's ability to estimate range and velocity. In this section, we present the mathematical models of five peak detection techniques evaluated in this work: FFT-based detection, CA-CFAR, a simplified Matrix Pencil Method (MPM), SVD-based subspace detection, and the proposed Learned Thresholded Subspace Projection (LTSP) method.
## 2 Materials and Methods
### Missions: Common Procedures
Figure 1: Schematic representation of the hardware and experimental designs used in the mission payloads. (**A**) FPA bioreactors were used for the Micro-6/8 and Micro-14/FPA payloads and were loaded with two or three chambers, loaded in sequence, and separated by rubber septa. Medium was  
loaded in chamber A (2.7 mL in three chamber design, 5 mL in two chamber design) and cell inoculum was loaded in chamber B (0.5 mL). In the three chamber design, chamber C was loaded with 2.2 mL of fixative or fresh medium. Chambers were sequentially mixed on a predetermined schedule by depressing a plunger, and consequently the column of reagents and septa, to allow mixing of reagents through the bypass in the glass barrel. All eight FPAs within a GAP were processed as a unit. At the conclusion of the experimental operations, the FPAs were stored within the GAP at the prescribed temperature. (**B**) FEP bags were used for the Micro-14/FEP payload, allowing for dilution (D\({}_{1}\) and D\({}_{2}\) bags) and serial subculture procedures. To activate growth, yeast (1 mL, 4 \(\times\) 10\({}^{4}\) cells/mL) was injected into the first Large FEP bag (Bag A containing 11 mL YPD) and cultured for 24 h +/ - 1 h at 30 \({}^{\circ}\)C. At the end of this growth stage, an aliquot was removed and diluted 20-fold (Bag A\({}_{\rm{DI}}\)). From this dilution, the next Large FEP bag was inoculated (0.25 mL into Bag B containing 11.8 mL YPD) and cultured for 24 h +/ - 1 h at 30 \({}^{\circ}\)C. This process was repeated until the cells had been cultured for five days (through Bag E). On each of the first four days, samples were also prepared to analyze antifungal sensitivity. For these samples, the dilution series was extended from the D\({}_{1}\) dilution bag to the D\({}_{2}\) dilution bag (1:10) and aliquots (0.2 mL) from D\({}_{2}\) were used to inoculate Small FEP bags (A1-A12, B1-B12, etc.) containing antifungal agents or control medium (2.8 mL). Each day following the 24 h growth period and any necessary sampling, FEP bags (Large FEP bags A-E, dilution D\({}_{1}\) bags A-D, and Small FEP antifungal bags) were frozen at \(<\)\(-\)80 \({}^{\circ}\)C for the remainder of the mission. (**C**) An overview is provided to summarize the experimental details for each payload. Cell inoculation, time of cultivation, relative time within the mission, and storage conditions are provided. In addition, the outcome measurements employed for each payload are indicated. Outcome measures that were performed/completed during flight with cells fixed in microgravity are indicated in black text. Outcome measures that were performed using cells returned viable (chilled or frozen) and processed post flight in the home laboratory are indicated in green text.
## 4 Discussion  
### Evolution of U-Mo Matrix  
The as-received U-7 wt.% Mo sample showed the presence of \(\gamma\)'-U\({}_{2}\)Mo, \(\alpha\)-U phases, and Mo-alloyed \(\gamma\)-U-Mo from the parent \(\gamma\)-U-Mo matrix, along with presence of U-C phase along the grain boundaries. It has been shown previously that discontinuous precipitation (DP) plays a major role during eutectoid transformation in U-10 wt.% Mo at 500 \({}^{\circ}\)C and higher [41]. DP results in the formation of a lamellar microstructure consisting of \(\alpha\)-U and Mo-enriched bcc \(\gamma\)-U-Mo phase along the prior \(\gamma\)-U-Mo grain boundaries. Lower Mo contents, such as 7 wt.% in the present case, can also lead to a thermally induced phase decomposition from \(\gamma\)-U to \(\alpha\)-U, U\({}_{2}\)Mo, and Mo-alloyed \(\gamma\)-U during fuel plate fabrication, which involves several heat treatments. Such behavior has already been reported on plates produced in the SELENIUM project [14; 23; 34]. However, after irradiation, the U-Mo sample at intermediate (M11) and high burnup (M14) did not show remnants of \(\alpha\)-U, U\({}_{2}\)Mo, and UC phase. This has also been widely reported for a wide range of irradiated U-Mo fuels [17; 23; 33].  
Irradiation promotes \(\gamma\)-U-Mo because the fission spikes melt the material locally, after which it is quenched. This leads to stabilization of the high \(\gamma\) phase independent of Mo content. Recrystallization is likely caused by the lattice pressure due to fission products and defects. The lower Mo content near grain/cell boundaries reduces the stability of that crystal structure, but even in heat-treated U-Mo, where no Mo gradient is present, recrystallization starts at the grain boundary.
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 1 Introduction  
Fungi are known for their ability to live in different ecological niches [1]. They can adapt to a wide range of extracellular stimuli such as low and high temperatures, UV radiation, low water availability, high salt concentrations, osmotic pressure, etc. In many cases, the fungi have to deal with a combination of these stimuli. Antarctica is one such habitat, characterized by extreme environmental conditions. Nevertheless, representatives of different phyla of the Kingdom Fungi have been isolated from this region [2, 3]. Their survival is a complex process, and mechanisms have been identified at the morphological, physiological, biochemical, and genetic levels [4, 5, 6, 1].  
All the noted environmental conditions have a common mechanism of action. This is the phenomenon of oxidative stress (OS), which results from the exposure of fungal cells to biotic and non-biotic agents found in nature [7]. The term \"oxidative stress\" is widely used in many areas of science. It is one of the keywords of the 21 st century, along with global warming and drug resistance. Oxidative stress is a complex process having negativeconsequences on aerobic cells, characterized as a state in which cells generate reactive oxygen species (ROS) at a higher rate than internal antioxidant defenses [8]. ROS are characterized by the presence of free radicals such as the superoxide anion (\({}^{\bullet}\)O\({}_{2}\)\({}^{-}\)), and the hydroxyl radical (\(\bullet\)OH), along with nonradical molecules like hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and singlet oxygen (\({}^{1}\)O\({}_{2}\)), among additional species. In optimal growth environments, the synthesis of these substances occurs primarily at low levels within the organelles of fungal cells, such as mitochondria and peroxisomes [9]. However, under conditions of stress, there is a notable increase in the ROS generation rate.  
A variety of environmental stresses, including temperature, drought, salinity, chilling conditions, metal toxicity, and UV-B radiation, as well as the impact of pathogens, have been demonstrated to contribute to an increased production of ROS in fungi [10]. Temperature is a critical factor influencing their growth and development. Exposure to extremely low temperatures induces oxidative stress, prompting a distinct response characterized by various stress tolerance strategies at the morphological, physiological, and molecular levels [3; 6; 11]. Despite the extensive research conducted on the cold stress responses of bacteria and plants, the adaptation strategies employed by fungi to cold conditions have not received adequate attention. Moreover, there is a notable lack of knowledge regarding the cellular response of Antarctic fungal isolates to low-temperature stress. Therefore, the present review was focused on the impact of low temperatures on the release of cold stress in the fungi isolated from Antarctica as well as on the cell response as a component of their adaptive strategies to survive under so harsh conditions.
## 2 Finite Element Model and Research Method
### Central Composite Design (CCD)  
The fitting accuracy of the RSM depends largely on the distribution of sample points in the design space. The CCD method follows specific rules when selecting the sample points [44; 45; 46]. According to the traditional method, when three variables and five levels are selected for experiments, at least 125(5\({}^{3}\)) experiments are needed. The CCD can minimize the number of experiments on the premise of good accuracy.  
Figure 3 shows the distribution of sample points, taking three variables as an example [29]. Where \(\alpha=2^{n/k}\); (0,0,0) is the center point; (\(\pm\)1,\(\pm\)1,\(\pm\)1) is the cubic point; and (\(\pm\)\(\alpha\),0,0), (0,\(\pm\)\(\alpha\),0), and (0,0,\(\pm\)\(\alpha\)) are the axial points.
# Gut Microbiome in Patients with Obstructive Sleep Apnoea
## 2 Materials and Methods
### Stool Sample Collection and Analysis  
The stool sample collection and analysis followed the protocol published previously [17]. Briefly, following DNA extraction, we performed library extraction for the hypervariable region V3-V4 of microbial 16S ribosomal RNA. Libraries labelled with individual index pairs and validated with Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) were sequenced after pooling on an Illumina MiSeq platform by running a 600-cycle MiSeq Reagent Kit v3 (Thermo Fischer Scientific, Waltham, MA, USA).  
The quality of raw reads was assessed with FastQC and MultiQC [18]; the low-quality sequences were filtered and trimmed with Trimmonmatic [19], and we kept only sequences with a minimal length of 50. The low-quality and the first 12 base calls were discarded (Phred score \(<20\), sliding window size 5). The read classification was performed with a Kraken2 [20], with k-mer size 31 against the SSU Ref NR 99 database (release 132) of SILVA [21]. The microbiome composition and taxa abundances were estimated with Bracken [22].  
The microbiome data were further processed as follows. The taxa with low abundance (present maximum of 10% in the OSA or control groups) and with low support (if at maximum 100 reads were classified as that taxa) were discarded. After the filtering process, a Bayesian-multiplicative replacement of zeros was carried out using the zCompositions R package [23], which was followed by a centred log-ratio (CLR) transformation of count and ratio values as implemented in scikit-bio 0.5.6 [24]. The Simpson's, Shannon, and richness alpha diversity metrics were calculated on the filtered and pre-processed taxonomic composition table. The composition similarities were investigated with PERMANOVA [25]. To obtain potential biomarker candidates for OSA, we performed LASSO analysis as implemented in scikit-learn 1.0.2. The optimal hyperparameters were selected via 5-fold cross-validation procedure using ROC-AUC as target metric.  
The distribution of the clinical data and relative abundances were analysed with the Shapiro-Wilk test. The OSA and control groups were compared with Mann-Whitney and t-tests. The relative abundances of bacteria were correlated to clinical variables with Mann-Whitney and Spearman tests. The comparison in the microbiome data between the OSA and control groups was adjusted for age, gender, BMI, HDL-C, and triglyceride using multivariate logistic regression analysis. The JASP 0.14 (JASP Team, University of Amsterdam, Amsterdam, The Netherlands) software was used for statistical analysis.  
Data are presented as mean \(\pm\) standard deviation or median/interquartile range/. A \(p\)-value \(<0.05\) was considered significant.  
As the results were obtained from a limited population, no formal power calculations were performed. Post hoc sensitivity analyses demonstrated that we could detect larger differences between the two groups than an effect size of 0.95 with a power of 0.80 and an \(\alpha\) error probability of 0.05 [26].
## 4 Results and Analysis
### PSC Level Analysis and Evaluation
The fact that employees under the age of 50 and male employees have a higher PSC level in megaprojects may be because young employees lack social experience and great life pressure and hope to be cared for and protected by senior managers for their psychological health in their work. Therefore, their perception varies greatly in different organizational environments. Older employees may tend to be lazy and less enthusiastic about their work because of their experience and long hours and less likely to care whether senior managers are concerned about their psychological health. Many female employees may be focusing more on their families than work and pay less attention to workplace communication and management's policy commitment: therefore, in different organizational environments, they can have a low common perception of senior managers' emphasis on their psychological health. Table 8 shows the results of the one-way ANOVA tests on the within-group means for the three characteristic groups of position, age, and marital status, and independent sample \(t\)-test for the gender, indicating the within-group respondents to be significantly different only for position and gender for both megaprojects and non-megaprojects. [PERSON]'s test indicates that the within-group variances are all sufficiently homogeneous for the ANOVA to be applied. Table 9 shows the results of [PERSON]'s least significant difference (LSD) test on the impact of position on PSC, indicating that the PSC of construction workers is significantly different from that of junior managers for megaprojects and of construction workers and both junior and intermediate managers for non-megaprojects. Of interest is that construction workers have a higher PSC level than the junior managers in megaprojects, but lower than both types of managers in non-megaprojects. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Respondents Characteristic**} & \multicolumn{2}{c}{**Megaprojects**} & \multicolumn{2}{c}{**Non-Megaprojects**} & \multirow{2}{*}{\(p\)} \\ \cline{2-2} \cline{4-5}  & **Mean** & **SD** & **Mean** & **SD** \\ \hline Position & & & & \\ Construction Workers & 51.59 & 9.17 & 40.26 & 9.45 & \textless{}0.001 \\ Junior Managers & 47.43 & 7.83 & 45.12 & 9.34 & 0.089 \\ Intermediate Managers & 48.95 & 5.58 & 45.69 & 8.19 & 0.079 \\ \hline Age & & & & \\
18–25 & 49.40 & 7.86 & 42.44 & 10.30 & \textless{}0.001 \\
26–35 & 48.00 & 7.20 & 44.32 & 9.65 & \textless{}0.001 \\
35–50 & 51.03 & 7.42 & 43.98 & 8.43 & \textless{}0.001 \\ Above 50 & 47.86 & 16.45 & 41.5 & 9.21 & 0.153 \\ \hline Gender & & & & \\ Male & 49.92 & 7.46 & 43.68 & 9.42 & \textless{}0.001 \\ Female & 42.27 & 12.00 & 41.62 & 10.05 & 0.860 \\ \hline Marital status & & & & \\ Single & 47.91 & 8.68 & 42.34 & 10.68 & \textless{}0.001 \\ Married & 50.46 & 7.74 & 44.16 & 8.77 & \textless{}0.001 \\ Other & 46.50 & 7.33 & 44.73 & 7.51 & 0.664 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Descriptive statistical results of PSC scale.
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 3 CISNet Method  
The overall architecture of CISNet, as depicted in Figure 2, comprises three main components: transformer-based backbone, cross-level iterative structure (CIS) (incorporating multi-scale strip convolution subtraction (MSCSub)), and enhanced guidance attention (EGA). We adopt the PVTv2 [13] as the backbone to generate multi-scale features \(\{E_{i}\}_{i=1}^{4}\) from the input image \(I\in\mathbb{R}^{H\times W\times 3}\). The resolution of \(\{E_{i}\}_{i=1}^{4}\) is \(\{\frac{H}{2^{i+1}}\times\frac{W}{2^{i+1}}\}_{i=1}^{4}\), and the number of channels correspond to 64, 128, 320, and 512. Firstly, we separately adopt a \(1\times 1\) convolution to compress the number of channels in each feature map obtained from the backbone to 64, which can reduce the parameters. Then, to make more effective use of distinctive characteristics between different levels, we feed features from the highest level and all lower levels into the MSCSub separately. This allows the extraction of different information between cross-levels and generates complementary features \(F_{h-i}\), where \(h\) and \(i\) represent the level of the highest-level feature and lower-level feature, respectively. This process is iterated to capture higher-order complementarity information across multiple levels. The results of MSCSub at the same level are then fused using the addition operation, yielding complementarity-enhanced features \(\{F_{level_{i}}\}_{i=1}^{3}\). Subsequently, these enhanced features are fed into the EGA module to deeply mine local context information, outputting the refined features \(\{X_{i}\}_{i=1}^{3}\). We use EGA to refine \(F_{level_{i}}\) with \(X_{i+1}\) in a top-down manner to improve the accuracy of camouflaged object segmentation progressively. Finally, we use \(X_{1}\) generated by the last EGA to predict camouflaged map \(P\), which is supervised by ground truth.
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 4 Discussion
### Summary  
The simulation results presented in previous sections could be concluded as follows in Table 1.  
Based on the comparison above, it could be found that the proposed method has the best convergence rate and robustness to model uncertainty and disturbance, and the system has a fault tolerant capability when the actuator failure occurs. In addition, the steady accuracy is somehow lower than the finite time controller; however, the chattering issue is solved by the integral term. Hence, it could be concluded that the proposed method has the best convergence rate and robustness to model uncertainty and disturbance, and the system has a fault tolerant capability when the actuator failure occurs. In addition, the steady accuracy is somehow lower than the finite time controller; however, the chattering issue is solved by the integral term. Hence, it could be concluded that the proposed method has the best convergence rate and robustness to model uncertainty and disturbance, and the system has a fault tolerant capability when the actuator failure occurs.  
Figure 11: Control torque of the proposed controller with actuator failure and without actuator failure.  
Figure 10: Curve of attitude quaternion under the controller proposed in this paper with actuator failure.  
method combined the advantages of the standard PD controller and finite time controller; meanwhile, some of the drawbacks of these methods are avoided.