esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Salt Tolerance Mechanism of Halophytes: Role of Secondary Metabolites
#### 1.3.6 Activation of Hormonal Signaling  
Abscisic acid (ABA), jasmonic acid, and ethylene are the main plant hormones produced in response to salt stress. They act as signaling molecules to activate salt tolerance strategies, i.e., halotropism is the tropic movement of Na in the roots, in which halophytes obtain optimum salt in order to maintain growth and development, while non-halophytes show negative halotropism to avoid salt stress. _Limonium bicolor_ is a recretohalophyte with multicellular salt glands. This plant showed positive halotropism, as evidenced from root elongation when treated with 200 mM NaCl stress. IAA was involved in regulating this phenomenon [44]. Phytohormones also maintain the high vitality of anthers and pollens in some halophytes under salt stress. [PERSON] et al. [45] reported a higher number of pollens and pollen activity in _Sueda salsa_ under saline conditions. GA signals partially act with JA signals via regulation of JA biosynthesis that participate in stamen development under saline conditions.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 3 Results and Discussion
### Computational Costs  
This subsection analyzes the computational costs of the WOLA channelization structure. The conventional digital down conversion (DDC) [9] and polyphase filter [13] structures are used for comparison. Suppose the number of sample points is \(N\), the number of channels is \(K\), the decimation factor is \(D\), and the order of the prototype filter is \(L\).  
The three methods all adopt the critical sampling complex channel structure. The number of multipliers consumed by three channelized structures is shown in Table 5. As shown in Table 5, the channelized structure adopted in this paper is as efficient as the polyphase filter structure and superior to the DDC structure. In addition, the IFM algorithm adopted in this paper is efficient because the instantaneous frequency is calculated only by phase difference value.
# Performance Analysis of Pile Group Installation in Saturated Clay
## 3 Two-Pile Group Analyses
### Displacement of Adjacent Pile
#### 3.1.2 Vertical Displacement  
Figure 9(a) demonstrates the effect of \(S\) on the vertical displacement of the adjacent pile. The results indicate that with an increase in \(h\), the vertical displacement first rises rapidly and then increases gradually. It is worth noting that \(S\) has little effect on the  
Figure 8: Lateral displacement of adjacent pile with subsequent pile penetration depth.  
Figure 9: The effect of \(S\) and \(L/\mathrm{D_{e}}\) on the lateral displacement of the adjacent pile.  
initial increase rate of the vertical displacement, whereas the final vertical displacement is largely dependent on \(S\). The peak vertical displacement is positively correlated with \(S\). As \(S\) increases, the peak vertical displacement also increases.  
Figure 10 reports that for small \(L/D_{\mathrm{e}}\), the vertical displacement first rises rapidly and then increases gradually, reaching its peak value once the subsequent pile installation is completed. For large length-to-diameter ratios (\(L/D_{\mathrm{e}}>25\)), the vertical displacement of the adjacent pile can be divided into three stages: a steep increase stage, a gradual increase stage, and a decrease stage. After reaching the peak value, the vertical displacement slowly decreases with further increases in \(h\). Additionally, an increase in \(L/D_{\mathrm{e}}\) results in a larger peak vertical displacement of the adjacent pile.  
Figure 11 illustrates the influence of \(S\) and \(L/D_{\mathrm{e}}\) on the peak displacement of the adjacent pile. The results indicate that the peak vertical displacement is generally higher than the peak lateral displacement. During the installation of the subsequent pile, the adjacent pile is subjected to horizontal lateral pressure and upward friction. In the process of pile installation, horizontal ground movements and vertical displacements can be observed [39]. However, the soil movements are mainly in the horizontal direction, and their inclination increases towards the vertical direction with increasing distance from the adjacent pile's center [40]. Therefore, with the increase in pile center distance, the lateral displacement of the soil around the adjacent pile significantly decreases, while the inclination in the vertical direction increases, leading to an increase in the peak vertical displacement of the pile. In addition, both the peak vertical and lateral displacements of the adjacent pile rise with larger \(L/D_{\mathrm{e}}\).  
Figure 11: Influence of \(S\) and \(L/D_{\mathrm{e}}\) on the peak displacement of the adjacent pile.  
Figure 10: The influence of \(S\) and \(L/D_{\mathrm{e}}\) on the vertical displacement of the adjacent pile.
## 3 Results
### PSGE Modulated the Release of NO and PGE\({}_{2}\)  
In order to examine the cytotoxicity of PSGE, we performed a MTT assay in RAW 264.7 cells. Evaluation on cells treated with different doses (50 to 1000 ug/mL) of PSGE were evaluated. PSGE did not cause cell toxicity up to 400 ug/mL (Figure 2A). Experiments with PSGE concentration ranging from 50 to 200 ug/mL and DXM 20 uM, as positive control, were performed in the study.  
We investigated whether PSGE regulated the formation of inflammatory mediators such as nitric oxide (NO) and prostaglandin E2 (PGE\({}_{2}\)) in LPS-challenged RAW 264.7 cells. PSGE treatment considerably inhibited NO and PGE\({}_{2}\) production, in a dose dependent manner, compared to LPS-treated cells (Figure 2B,C). PSGE (200 ug/mL) suppressed NO production by 0.28-fold and almost completely diminished PGE\({}_{2}\) production when compared with LPS-treated cells. Moreover, it is notable that PSGE caused more extensive inhibition compared to the positive control DXM. We next examined the effect of PSGE on the inducible nitric oxide synthase (iNOS) and the cyclooxygenase (COX)-2 expression levels. The expression of iNOS was significantly increased by LPS but decreased with PSGE treatment dose-dependently (Figure 2D,E). Despite the significant PGE\({}_{2}\) inhibition, COX-2 levels were unaffected suggesting that PSGE might target the downstream of COX-2 in the PGE\({}_{2}\) synthesis cascade (Figure 2D,F). The results of microsomal prostaglandin E synthase (mPGES)-1 expression, a terminal synthase of PGE\({}_{2}\), showed that mPGES-1 also did not influence PGE\({}_{2}\) inhibition by PSGE (Figure 2D,G). We next examined the expression levels of 15-hydroxyprostaglandin dehydrogenase (PGDH), a PGE\({}_{2}\) degrading enzyme. LPS-treated cells showed 0.5-fold decrease in 15-PGDH levels compared to control. PSGEinverted the LPS-mediated 15-PGDH suppression by 2-fold (Figure 2D,H). These results indicate that 15-PGDH induced the PGE\({}_{2}\) inhibition by PSGE.
## 1 Introduction
The appearance of color uniformity, in terms of levelness parameters in dyed fabrics and/or a union shade in dyed fabrics of more than one fiber type, is one of the main concerns in textile manufacturing industries [33]. In order to measure the color levelness of dyed materials, an objective method, named the relative unlevelness index (RUI), which takes into account the coefficients of variation of reflectance measured over the visible spectrum with appropriate consideration of human vision sensitivity, was developed [34]. Decamethylcyclopentasiloxane (D5) is a clear, odorless, colorless and non-oily cyclic siloxane solvent (C\({}_{10}\)H\({}_{30}\)O\({}_{5}\)Si\({}_{5}\)), commonly used in consumer and industrial applications [35; 36]. It has been widely used as a medium in dry cleaning applications, and it has also shown the potential to be used as a medium in home laundering. The impact of D5-washing on fabric performance has been investigated [37]. Recently, attempts have been made for the preparation of D5 reverse micelles and the evaluation of water solubilization performance in the dyeing system [35]. Further experiments were conducted to develop a dye/D5 suspension dyeing system using a selected surfactant [36; 38; 39]. The hydrolysis kinetics of this system was also studied [40]. However, the feasibility of applying the computer color matching system on decamethylcyclopentasiloxane (D5) non-aqueous dyeing of cotton is still unknown. This study has several objectives: (a) establishing the calibration of the dyeing database for both conventional water-based dyeing and D5 solvent-assisted reverse micellar dyeing; (b) dyeing of standard samples with predetermined concentrations of reactive dyes using the conventional water-based dyeing method and the D5 solvent-assisted reverse micellar dyeing method; (c) conducting computer color matching to generate the dyeing recipe by using different color difference formulae to ensure that concentrations of the dyed standard samples match the concentrations of the batch samples; (d) measuring the color levelness of dyed samples; and (e) comparing the differences between conventional water-based dyeing and D5 solvent-assisted dyeing.
## 4 Role of sRNA in Antibiotic Resistance
### Activation of Efflux Pumps
#### 4.3.2 PA0805.1  
_Pseudomonas aeruginosa_ initiates swarming motility in response to semisolid surfaces and nitrogen sources relevant to the human lung. Although the transcriptional regulation of swarming has been well studied, posttranscriptional regulation by sRNAs remains underexplored. [PERSON] et al. found that the sRNA PA0805.1 was upregulated 5-fold under swarming conditions. The overexpression of PA0805.1 in wild-type (WT) PAO1 led to reduced swarming, swimming, and twitching motilities, along with increased adherence, cytotoxicity, and tobramycin resistance, while its deletion (\(\Delta PA0805.1\)) increased tobramycin susceptibility [209].  
Transcriptomic and proteomic analyses of the PA0805.1-overexpressing strain revealed significant alterations in gene and protein expression, with 1,121 differentially expressed genes and 258 proteins identified. Among these were 106 transcriptional regulators, two-component systems, and sigma/anti-sigma factors implicated in antimicrobial resistance and virulence. Notably, the upregulation of multi-drug efflux systems _mexXY_ and _mexGHI-ppmD_ provides a mechanistic basis for increased tobramycin resistance (Figure 3a). Additionally, ClpD, a protease essential for biofilm dispersal and virulence, was upregulated, potentially contributing to adaptive resistance. Further regulatory changes included the downregulation of type IV pilus genes and transcription factors such as PilGH and ExsD, the latter being a negative regulator of the type III secretion system (T3 SS). In contrast, key regulators involved in virulence, quorum sensing, and biofilm formation--AlgR, LasR, RnlR, MvaT, and PsrA were differentially expressed, reinforcing a shift towards an antibiotic-resistant and persistent phenotype [209]. These findings underscore the role of PA0805.1 in modulating antibiotic resistance pathways in _P. aeruginosa_, particularly through efflux system activation and biofilm-associated mechanisms, highlighting its potential as a target for therapeutic intervention.
## 1 Introduction
### Global Optimisation  
Global optimisation algorithms have recently been partitioned, in terms of their theoretical foundations, into four distinct classes [17]:  
* incomplete methods; heuristic searches with no safeguards against trapping in a local minimum;
* asymptotically complete; methods reaching the global optimum with probability one if allowed to run indefinitely without means to ascertain when the global optimum has been found;
* complete; methods reaching the global optimum with probability one in infinite time that know after a finite time that an approximate solution has been found to within prescribed tolerances;
* rigorous; methods typically reaching the global solution with certainty and within given tolerances.  
In heuristic multi-agent systems, the above characterisation is related to the concept of the stability of intermediate solutions, because the probability that any single agent will lose the best solution is often greater than zero. This may result in a lack of stability of the found solutions, or in the worst case, the non-convergence of the algorithm. Thus, for multi-agent systems, it is desirable to characterise the stability of the discovered solutions. For example, it is known that many variants of genetic algorithms do not converge, and so, the optimal solution may disappear from the next population. Consequently, in practice, either an elitist selection mechanism is utilised or the information about the best solution has to be maintained throughout the entire evolution process.  
In this paper, we demonstrate that the solutions discovered by SDS are exceptionally stable, and we illustrate some implications of the resource allocation mechanisms employed by SDS concerning the stability and accuracy of the algorithm, as well as its convergence behaviour.  
In the next section, we will introduce stochastic diffusion search, and we will briefly introduce the models of SDS on which the results of this paper are based. The following section will analyse the resultsobtained contrasting SDS with RANSAC (random sample consensus). The final section will include a discussion and conclusions.
## 4 Experimental Validations
### Assessment of Positioning Performance with the Proposed Quality Control Method
The best dual-system combination is the GR combination, where the positioning performance is close to that of the triple-system combination. The convergence time of the GE and GC combination is about 22 min, while it is about 15 min for the GR combination. For all cases, the north component shows higher positioning accuracy than the east component, which is related to the satellite constellation configuration.
## 5 Experimental Results  
### Experimental Setup  
The experimental platform is shown in Figure 1. The mass of test rotor \(M\) is 1.136 kg. The magnetic lubricating fluid is prepared specially for this study; it is made from ferrous ions and a ferric ion water solution by the co-precipitation method. The surfactant is oleic acid, and the magnetic fluid base carrier solution is SAE 10W/40 engine oil. The properties of the base carrier oil are as shown in Table 1.  
The goal of this study is to determine the incremental stiffness value, \(K_{m}\), of the oil spin instability threshold rising as the magnetic field is applied in the bearing and to check whether the system's rigidity is affected by the magnets. The steps are described below.  
First, measuring the rotor system data using the 108 DAI obtains the initial parameters of the rotor system without any magnetic field to confirm the original fluid stiffness.  
The second step is to add permanent magnets to create an applied magnetic field. Then, the rotor is rotated to check whether the oil spin instability threshold is increased or decreased.
## 2 Data and Methodology
### Synoptic Overview  
The precursor of Medicare Ianos was an upper-level cutoff low that formed over the western Mediterranean around the 8-9 September and slowly moved eastward toward northern Tunisia and the Strait of Sicily. Then, on 14-15 September, as it emerged over the Gulf of Sidra, due to a stratospheric dry air intrusion with high vorticity values (Figure 2c) and anomalously high (\(\sim\)+1.5 \({}^{\circ}\)C) sea-surface temperatures of \(\sim\)27-28 \({}^{\circ}\)C (Figure 2d) [56], a surface low-pressure system quickly developed with a minimum pressure around 1010 hPa. On 16 September, it further deepened and intensified. Wind speeds up to 25 m\(\cdot\)s\({}^{-1}\) were measured by ASCAT sensors (onboard MetOp satellites) around the cyclone's circulation center. From 17 September, the system showed a clear mid-level warm core (Figure 2a) and spiraling cloud bands, especially to the northern side of a well-defined vortex, with a distinct cloud-free eye. Locally large precipitation amounts were recorded in the Calabria region of Southern Italy [57]. It continued to deepen both its surface pressure and its mid-level warm core as it slowly moved east-northeastward toward the Ionian Greek islands (Figure 2b) and Greece mainland, where it made landfall around 3:00 a.m. UTC 18 September. The medicane peak intensity was evidenced by a 995 hPa MSLP minimum and 33 m\(\cdot\)s\({}^{-1}\) ASCAT surface winds, thus becoming classifiable as a minor category 1 hurricane on the Saffir-Simpson scale. Just a few hours earlier, the GPM-CO satellite made an overpass over the Ionian Sea at 10:31 p.m. UTC 17 September, providing valuable DPR measurements for analysis of the mature phase of Ianos's TLC lifecycle. The medicane then crossed Greece on 18-19 September, causing several human casualties and widespread disruption due to wind damage, heavy rainfall, and extensive flooding in the western and central regions (e.g., a total of 228 mm in the Ionian island of Ithaki or the maximum amount of 317 mm in Pertouli, Central Greece [40]). In some areas, the mean annual precipitation was exceeded in a span of 48 h. On the following days, Ianos weakened considerably while it moved south-southeastward and finally dissipated over the northern coast of Egypt on 21 September.
## 4 Conclusions  
This study showed that Raman spectroscopy can be used as an analytical technique to estimate phase fractions for a mixture of duplex oxide-sulphide phases. The study can, therefore, be summarized as follows:  
1. Raman intensity peak ratios between the values of 157 cm\({}^{-1}\) for CaS, 412 cm\({}^{-1}\) for MgO-Al2O3 (MA), 766 cm\({}^{-1}\) for C3A, 517 cm\({}^{-1}\) for C12A7, and 420 cm\({}^{-1}\) for Al2O3 were identified as most suitable for quantifying the binary samples in this study.
2. The linear regression constructed between the relative intensity and phase fraction provided a relatively good coefficient of determination (R2) value, approximately averaged at R2 = 0.96.
3. The calibration model error was assessed in terms of mean absolute error (MAE) with an average value of 7.22 wt. % determined for the full data, and relatively good stability for the most intense Raman peaks for the phases for the set of the studied binary samples.
4. This study presented the prospect of using Raman spectroscopy to characterize inclusions containing CaS.  
Conceptualization, F.G., T.A., M.A., Q.S, and T.F.; Methodology, F.G., T.A., M.A., Q.S., T.V.; software, T.V., and F.G., validation, T.V., and F.G.; formal analysis, F.G; resources, M.H., and T.F.; writing--original draft preparation, F.G.; writing--review and editing, F.G., T.A., M.A., Q.S, T.V., and T.F.; supervision, T.F., M.H., and M.A.; project administration, M.H.; funding acquisition, M.H., and T.F. All authors have read and agreed to the published version of the manuscript.  
Research is funded due I4 Future doctoral program funded by the European Union's H2020 under the Marie Sklodowska-Curie grant agreement No. 713606 and the Academy of Finland Proliferation Project (Academy of Finland, No. 311934).  
The authors of this study wish to acknowledge the support of the I4 Future doctoral program funded by the European Union's H2020 under the Marie Sklodowska-Curie grant agreement No. 713606 and the Academy of Finland Proliferation Project (Academy of Finland, No. 311934).  
The authors wish to declare no conflict of interest.
## 4 Discussion
The present contribution not only revealed the most useful supervised classification algorithm but also identified the most suitable set of parameters between sagittal plane kinematics and sEMG. Our data also make it possible to identify the best set of variables extracted from the sEMG signal. For discriminating between FXS and CS individuals, all the proposed vectors allowed a high prediction accuracy (over 98.3%), but S2, using kinematic parameters, outperformed the other variables. For the classification of FXS-M within FXS-FM individuals the best prediction accuracy was achieved for S1 using all the sEMG parameters and the co-contraction as input features (97.0%). The overall results of the present study suggest that by using sEMG standard parameters coupled with co-contraction indexes (S1), it is possible to classify subjects: FXS and CS as well as FXS-M and FXS-FM. There is a paucity of information regarding specific physical therapy interventions that could impact the characteristic musculoskeletal alterations affecting FXS children. However, rehabilitation therapies for these children are mainly focused on the neurodevelopmental aspects and motor delay, and less attention is paid to actual musculoskeletal alterations [33]. Studies aiming at classifying the different gait motor control impairments associated with FXS could therefore help in overcoming this limitation. The results of our study showed the key role of joint kinematics and muscle activity in characterization and discrimination of the gait pattern of FXS children with respect to controls with a typical neurodevelopment. Furthermore, characteristic gait features were highlighted within FXS children carrying a full mutation versus those with a somatic mosaicism. This information could be used to stratify children according to the gait motor control impairments and associate this classification to specific physical therapy interventions. The study has some limitations that should be acknowledged:* The accuracy of several classifiers was higher for the test dataset than for the training one. There are many reasons for this result: the use of the regularization methods such as dropout, the fact that the validation set may include data that is easier to classify than the training one, the fact that the splitting between training and test sets may not be optimal [34];
* The low differences in the accuracy that might be caused by the small number of subjects forming the testing set (even though the ratio of correctly classified subjects might be the same, the percentage may differ due to the difference in the sets' sizes). Future works should consider increasing the studied population to strengthen the present results;
* The sample size within the study design, as previously mentioned;
* The lack of application of pre-processing procedures for dimensional data reduction (i.e., feature selection methods, PCA, methods for cross-validation and feature normalization) [9]. Future work should explore the possibility of reducing the set of parameters extracted from both kinematics and sEMG with the aim of enabling real-time application.