esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Insights on Plant Extracts to Prevent and Treat Recurrent Urinary Tract Infections
## 6 Plants Commonly Used in the Treatment of rUTI
### Promising Medicinal Plants to Treat rUTI
#### 6.2.2 A. melanocarpa
The antioxidant activity of the extract was in close relation to the anthocyanin concentration, and it was noticed that the ethanolic extracts proved to be active against 10 out of the 18 tested strains at doses of 2.5-5 mg/mL. Moreover, a significant inhibition of monoculture biofilm formation of 11 out of all tested strains was reported. Antibiotic disks (amikacin, tetracycline, nitrofurantoin, imipenem, and norfloxacin) supplemented with the extract slowly increased the growth inhibition zone in the case of _P. aeruginosa_, _E. coli_, and _M. morganii_[69]. The synergistic antibiotic extract effect that has been shown for pomegranate and black chokeberry could be explained by the presence in the extract of other secondary metabolites with antibacterial activities or the increase in antibiotic activity due to the existence of active biomolecules from the plant extract (polyphenols, tannins, etc.). In this way, the efficacy of an existing drug could be improved with the help of an inexpensive alternative therapy. Accurate and reliable clinical evidence about the efficiency of black chokeberry in UTI treatment is missing; only one clinical trial has been found that reported the reduction in UTI frequency after three months of juice administration in a specific population group (residents from six nursing homes) [70]. Polyphenols, anthocyanin, epicatechin, and quinic acid, with anti-biofilm formation and antioxidant activities, are bioactive substances that raised attention to aronia berries and their role in fighting rUTI. Moreover, _A. melanogaster_ fruits appear to have more excellent health and wellness benefits, higher antioxidant levels, and fewer side effects than cranberry.
## 3 Control Scheme Design  
In this section, a novel fast SMC scheme with a novel fixed-time disturbance observer is presented to improve the trajectory tracking ability of ROVs in time-varying environmental disturbances. First, an innovative reaching law is presented to resolve the contradiction between the system's reaching time and the chatter amplitude in conventional SMC scheme. Then, a fixed-time disturbance observer is proposed to estimate and compensate for the external disturbances in an ROV's working environment. Finally, the system stability analysis and verification is performed. The logic block diagram of the complete FSMC-FDO is illustrated in Figure 2.  
Figure 2: Logic block diagram of the fast-sliding mode control scheme with a fixed-time disturbance observer for ROVs.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 2 Uniaxial Compression Test of Concrete Deteriorated by Accelerated Expansion
### Specimen Fabrication and Testing Methods  
#### 2.1.1 Specimen Fabrication Method  
Table 1 shows the mixing conditions for the specimens used in this experiment. The cement used was ordinary Portland cement (density 3.16 g/cm\({}^{3}\), Na\({}_{2}\)Oeq = 0.51%), and a non-reactive aggregate, crushed limestone sand (surface dry density 2.65 g/cm\({}^{3}\), water absorption 1.50%), was used as fine aggregate. Reactive aggregate (surface dry density 2.54 g/cm\({}^{3}\), water absorption 1.50%) and a non-reactive aggregate, crushed limestone  
(surface dry density 2.70 g/cm\({}^{3}\), water absorption 1.43%), were used as coarse aggregate at a pesimum mixture ratio of 3:7. The reactive aggregate is andesite, showing a mottled texture with opalescent veins, and as a result of the alkali-silica reactivity test (chemical method) of JJS A 1145, \"Alkali-silica reactivity test of aggregates\", the alkali concentration reduction Rc was 168 mmol/L and the dissolved silica content Sc was 710 mmol/L, which were judged \"not harmless\". The maximum size of the coarse aggregate was set at 25 mm to mitigate the non-uniformity in the concrete cross-section due to the mixed coarse aggregate. In accordance with JIS A 5308, \"Ready-mixed concrete\", the target slump and air content were set to 10 \(\pm\) 2.5 cm and 4.5 \(\pm\) 1.5%, respectively. In this experiment, W/C was set as high as 60% to increase the amount of pore water to allow ASR expansion to develop with internally held water only in the fully sealed condition. The mixing ratio was determined by trial mixing in the laboratory to obtain the target slump and air content based on N. In addition, AE water reducer was used to ensure the dispersion of cement in the concrete and to obtain appropriate workability. The specimens were cylindrical in shape, \(\diameter 100\times 200\) mm. To adjust the total amount of alkali, NaCl reagent was added to the mixing water to obtain Na\({}_{2}\)Oeq amounts of 5.5 kg/m\({}^{3}\) (specimen name: R5.5) and 4.0 kg/m\({}^{3}\) (specimen name: R4.0). As a test specimen without expansion, a 1.5 kg/m\({}^{3}\) of NaCl reagent was also prepared (test specimen name: N), which was cement-derived alkali only, and no NaCl reagent was added.  
After demolding, the specimens were sealed and cured using aluminum tape. At 7 days of age, the seals were temporarily unsealed to replenish the internal water content, and the specimens were vacuum-absorbed for 4 h before being sealed and cured again. To replenish the water lost through the hydration reaction and secure sufficient water for the ASR, vacuum absorption was performed again at 50 days of age, and the lumber was resealed and cured with sufficient water inside. ASR expansion was performed at a room temperature of 20 \({}^{\circ}\)C. The amount of ASR expansion was measured over time, and the material was tested after the expansion had been stabilized.
### Data Acquisition and Preprocessing  
Based on the analysis of accident reports or previous research papers, the basic events that may lead to an accident in the DFT model are determined. Experts from various industries are hired to assess the risk of basic events and provide linguistic variables for the accident risk, which are then used in FST to estimate the specific failure probabilities. However, the failure probabilities obtained from this method have some uncertainty due to subjective evaluation. To reduce the uncertainty of subjective evaluation, the Bootstrap algorithm is introduced to perform 1000 Bootstrap sampling of the original data, generating an expanded dataset (where each dataset is centered around the mean and the standard deviation is set to 10% of the mean). This method helps reduce the uncertainty caused by data scarcity, making the results of the proposed method more accurate.  
Additionally, for the time-series variation of failure probabilities, it is necessary to monitor the state changes of each basic event in real-time and obtain the changes in probabilities, which is difficult to obtain in real scenarios. Therefore, for the time-series dynamic characteristics of failure probabilities, a device degradation model based on the principles of Hidden Markov Chains (HMM) is constructed [37], assuming that the state at the next time point is determined solely by the current state, and calculating the failure probability changes of each basic event for the subsequent model dataset.
###### Abstract  
Background: Understanding the diverse acute effects elicited by physiotherapists in soccer players may be pertinent for enhancing performance and aiding in the mitigation of injury risk. Methods: Fifty regional-level soccer players aged 13 to 15 took part in a randomized double-blind trial. They were allocated randomly to either receive diagonal manual mobilization (DM) treatment or undergo a Nordic hamsring curl (NHC) intervention, designated as the control group. Evaluations were carried out before and after the interventions to gauge standing knee extensions (KE) and knee flexers (KF) strength using the ForceFrame Strength Testing System. The recorded variables encompassed average, maximal force, and maximal impulse for both KE and KF. Results: Following the intervention, evaluations showed no significant differences between groups in KF maximal force (\(F_{1,48}=1.238;\,p=0.271;\,\eta_{p}^{2}=0.025\)), KF average force (\(F_{1,48}=0.957;\,p=0.333;\,\eta_{p}^{2}=0.020\)), KF maximal impulse (\(F_{1,48}=0.246;\,p=0.622;\,\eta_{p}^{2}=0.005\)), KE maximal force (\(F_{1,48}=1.514;\,p=0.225;\,\eta_{p}^{2}=0.031\)), KE average force (\(F_{1,48}=0.118;\,p=0.733;\,\eta_{p}^{2}=0.002\)), and KE maximal impulse (\(F_{1,48}=2.540;\,p=0.118;\,\eta_{p}^{2}=0.050\)). Analysis within the DM group showed significant differences in KF maximal force (\(24.2\,\,\mathrm{N};\,p=0.004\)), KF average force (\(23.4\,\,\mathrm{N};\,p=0.001\)), KE maximal force (\(25.8\,\,\mathrm{N};\,p=0.005\)), KE average force (\(20.0\,\,\mathrm{N};\,p=0.044\)), and KE maximal impulse (\(265.9\,\,\mathrm{N};\,p=0.027\)), although no significant differences were found in KF maximal impulse (\(150.1\,\,\mathrm{N};\,p=0.058\)). Conclusions: This study indicates that both DM and NHC elicit similar effects on acute responses in KE and KF strength following intervention, although DM shows more favorable adaptations within the group. Perhaps DM could serve as a suitable warm-up strategy in specific circumstances, immediately influencing readiness for strength activities.  
football; musculoskeletal manipulations; muscle strength dynamometer; biomechanics
## 3 Numerical Model
### Model Parameters  
The nonlinear behavior of soil is realized by a modified Cam clay constitutive model (MCC), and the rock is realized by the Mohr-Coulomb constitutive model. The calculation parameters of the stratum are derived from the geological survey report, as shown in Table 2. Based on the principle of equivalent stiffness, the retaining pile is simplified to a diaphragm wall of corresponding thickness. The calculation parameters of the diaphragm wall, the retaining pile, the concrete cushion, and internal support are shown in Table 3.  
In order to simulate the relative sliding between the diaphragm wall and the soil during the foundation pit construction, the contact interface is set between the wall and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil/Rock** & **Compression Index \(\mathbf{\lambda}\)** & **Swelling Index \(\mathbf{\kappa}\)** & **Critical State** & **Initial Void Ratio \(\mathbf{e_{0}}\)** \\ \hline Miscellaneous fill & 0.095 & 0.006 & 0.543 & 0.98 \\ Pain fill & 0.083 & 0.009 & 0.607 & 0.99 \\ Mucky silty clay & 0.127 & 0.008 & 0.673 & 1.06 \\ Clayey silt & 0.081 & 0.005 & 0.735 & 0.90 \\ Fine silt sand & 0.054 & 0.003 & 1.418 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constitutive parameter of the soil layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure Type** & **Weight (kN/m3)** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** \\ \hline Diaphrag wall & 25 & \(3.15\times 10^{4}\) & 0.2 \\ Concrete support & 25 & \(3.25\times 10^{4}\) & 0.2 \\ Steel pipe support & 78 & \(2.1\times 10^{5}\) & 0.3 \\ Concrete cushion & 25 & \(2.55\times 10^{4}\) & 0.2 \\ Retaining pile (equivalent wall) & 16.36 & \(1.54\times 10^{4}\) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Constitutive parameter of the main structure.  
Figure 2: 3D finite element model.  
soil. The interface is modelled by the Coulomb friction law. The interface friction is equal to the friction coefficient multiplied by the normal effective stress between two contact surfaces. The friction coefficient can be calculated by the interface friction angle and the interface friction angle can be evaluated by the friction angle of soil [23]. It is assumed that limiting shear displacement of 5 mm is used to realize the full adjustment of the interface friction [23]. The friction coefficient of the contact surface is estimated to be 0.3.
# Factorial Design Analysis for Localization Algorithms
## 4 Results
### Impact of Node Density  
Figure 3a shows the variation of the MSE for different node densities \((\rho)\); it is evident that this study factor has greater impact on the CL and REWL range-free algorithms than on the range-based  
Figure 2: MSE vs. Path-loss exponent \((\eta)\).  
algorithms. On the other hand, Figure 3b shows that when the noise level rises (\(\sigma_{dB}=10\:dB\)), the range-based algorithms show greater MSE variation than the range-free algorithms; thus, the noise factor has greater impact on the range-based algorithms. The results shown in Figure 3 were obtained for a path-loss exponent (\(\eta=3\)). The greater the node density, the greater the RN proximity to the NOI, meaning there is less distance separating the NOI from the RNs, which reduces the NOI localization area and leads to less localization error, as shown in Figure 3 for both cases.  
Figure 4 shows the CDF behavior of the localization techniques. As shown in Figure 4a,b, the greater the node density, the greater the CDF value of the localization techniques. Figure 4a shows that the node density (\(\rho\)) has greater impact on the CL and REWL range-free algorithms considering a small noise level (\(\sigma_{dB}=2\) dB), since these algorithms show greater CDF variation for different node densities. In Figure 4b the noise level increases to (\(\sigma_{dB}=10\) dB) for different node densities (\(\rho\)), and the noise level has greater impact on the Hyperbolic algorithm, since this algorithm shows greater CDF variation here than in Figure 4a.
## 2 Materials and Methods
### Quantitative Real-Time PCR (QPCR)  
Total RNA from mouse liver was extracted using TRIzol reagent (Ambion, Carlsbad, CA, USA), according to the manufacturer's standard protocols. Complementary DNA (cDNA) was synthesized by reverse-transcription of two microgram RNA using the High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor (Thermo Fisher Scientific, Waltham, MA, USA). Each sample was assayed in triplicate by a Light Cyler Real-Time PCR System (Roche diagnostics, Mannheim, Germany). The housekeeping gene GAPDH was used for gene expression studies. The following primers sequences of interested genes used in real-time PCR (Q-PCR) were shown: collagen type 1 alpha 2 (col1A2): 5'-TAGGCCATTGTGTGTGATGCAGC-3' (F) and 5'- ACATGTTCAGCTTTGACC-3' (R); \(\alpha\)-SMA, 5'-GTTCAGTGTGGCCTCTGTCA-3' (F), and 5'-ACTGGGACACATGGAAAAG-3' (R); MMP2, 5'-CTCAGATCCGGTGAGAT-3' (F), and 5'-AGGCTGGTCAGGTGG-3' (R); TGF-\(\beta\)1: 5'-CGAAGCGGACTACTATG-3' (F) and 5'-GTTGCTCACACTTGATT-3' (R); Bcl-xl, 5'-GCTGCATTGTTCCGAGAG-3' (F), and 5'-GTTGGCACCTATCTG-3' (R); Bax, 5'-TGCTTCAGGTTTTCCAGG-3' (F), and 5'-GGCGCAATCATCTCTG-3' (R); TNF-\(\alpha\), 5'-TGTAGCCATGTGTAGCAACC-3' (F), and 5'-AGGACCTGGAGTAGATGGA-3' (R); F4/80, 5'-CAAGACTGAACAACG-3' (F), and 5'-ACAGAAGCAGATTATGACC-3' (R); PDGF-BB: 5'-GTCGAGTTGAAAGCTCATCTC-3' (F) and 5'-GAGATGACTTCCAACTCGAC-3' (R); GAPDH: 5'-TCACCACCATGGAAAGG-3' (F) and 5'-GCTAAGCAGTTGGTGA-3' (R).
## 3 Moments and Related Measures
### Residual and Reversed Residual Life Functions  
The \(m\)th moment of the residual life (RL), say \(\Lambda_{m}(x)\), denoted by \(\Lambda_{m}(x)=E\big{[}(T-x)^{m}|T>x\big{]},m=1,2,\ldots,\) uniquely determines the CDF \(G(t)\). The \(m\)th moment of the RL of \(T\) is defined by:  
\[\Lambda_{m}(x)=\frac{1}{\overline{G}(x)}\int\limits_{x}^{1}{(T-x)}^{m}\,dG(t )\,. \tag{8}\]  
Using the binomial expansion, more than one time in (8), then \(\Lambda_{m}(x)\) of the UEHLD can be expressed as follows:  
\[\Lambda_{m}(x)=\frac{\Xi_{\tau,q}}{\overline{G}(x)}\int\limits_{x}^{1}{\delta t ^{\tau+\delta+\delta q-1}\left(1-t^{\delta}\right)^{\varphi-1}dt},\]  
where, \(\Xi_{\tau,q}=\sum\limits_{r=0}^{m}{\sum\limits_{q=0}^{\infty}{(-1)^{q+m-r}(x )^{m-r}2\varphi\bigg{(}\begin{matrix}m\\ r\end{matrix}\bigg{)}\bigg{(}\begin{matrix}\varphi+q\\ q\end{matrix}\bigg{)}}}\).  
Let \(y=1-t^{\delta}\Rightarrow dy=-\delta t^{\delta-1}dt\), then \(\Lambda_{m}(x)\) is as below:  
\[\Lambda_{m}(x)=\frac{\Xi_{\tau,q}}{\overline{G}(x)}\int\limits_{0}^{1-x^{ \delta}}{(1-y)^{\frac{\tau}{\delta+q}}\,y^{\varphi-1}dy}=\frac{\Xi_{\tau,q}}{ \overline{G}(x)}\mathbb{B}\Big{(}\frac{\tau}{\delta}+q,\varphi,1-x^{\delta} \Big{)}. \tag{9}\]  
The MRL of the UEHLD is the expected value of the remaining lifetimes after duration \(t\), which is obtained by putting \(m=1\) in (9). The \(m\)th moment of the reversed RL, denoted by \(\Upsilon_{m}(x)=E\big{[}(x-T)^{m}|T\leq x\big{]}\), \(x>0\), \(m=1,2,\ldots,\) is defined by:  
\[\Upsilon_{m}(x)=\frac{1}{G(x)}\int\limits_{0}^{x}{(x-t)^{m}dG(t)}\,. \tag{10}\]  
Using the binomial expansion, more than one time in (10), then \(\Upsilon_{m}(x)\) of the UEHLD can be formed as follows:  
\[\Upsilon_{m}(x)=\frac{1}{G(x)}\sum\limits_{r=0}^{m}{\sum\limits_{q=0}^{\infty }{(-1)^{m+q}2\varphi\bigg{(}\begin{matrix}m\\ r\end{matrix}\bigg{)}\bigg{(}\begin{matrix}\varphi+q\\ q\end{matrix}\bigg{)}x^{m-r}\mathbb{B}\Big{(}\frac{m}{\delta}+q+1,\varphi,x^{ \delta}\Big{)}}}\,.\]  
The MIT, also known as the mean reverse RL function, is defined as \(\Upsilon_{1}(x)=E\big{[}(x-T)|T\leq x\big{]}\), and reflects the amount of time that has passed after an item has failed \((0,x)\). By setting \(m=1\), we obtain the MIT of the UEHLD.
## 4 Discussion  
Using 8 years of ground-based aboveground net herbaceous production (ANHP) data, we asked whether satellite-based predictions of ANHP were sensitive to ecosite, grazing management strategy, or prediction model type (process-based model vs. empirical model). We found that iAPAR could be used to predict ANHP in the shortgrass steppe and that the relationship between these two variables was sensitive to ecosite but not grazing management strategy. Moreover, we found that a locally calibrated empirical model generally performed better than a continentally calibrated process-based model, though the latter was still moderately correlated with ground observations and performed reasonably well for the dominant ecosite.
## 3 Result And Discussion
### Predictive Models
#### 3.1.2 Fare Prediction  
We evaluated the performance of the fare prediction model across various training and testing set sizes. To achieve this, we divided the dataset into different proportions for training and testing purposes. For instance, we reserved 30% of the entire dataset for testing, utilizing the remaining 70% for training. We repeated this process with nine different testing set proportions: 10%, 20%,..., 90%. Figure 5 displays the testing error in RMSE corresponding to these different testing set sizes.  
The model performs best under 30% of the data being used as the testing set, as shown in the figure. The testing with 10%, 80%, and 90% testing sets yielded the worst results. In general, the overall performance of the fare prediction model is acceptable as the worst situations come from extreme cases.  
We further evaluate our proposed model by comparing it to three baseline models, namely, LR, random forest (RF), and MLP. Table 1 shows the performance comparison of our proposed model and the baseline models.  
It is important to note that the 14-feature model utilizes the original raw data, whereas the 6-feature model represents the data features our model can access after connecting to the front-end.  
As shown in Table 1, our proposed ensemble model demonstrates enhanced performance in comparison to the baseline models when evaluating the 14-feature model. Additionally, a vertical comparison reveals that models focusing on only 6 features demonstrate inferior performance in contrast to those utilizing all 14 features. This observation suggests that the exclusion of certain features leads to an increase in errors and a decrease in the accuracy of the models' predictions. These findings strongly imply that the features within the dataset exhibit interdependencies, which significantly impact the accuracy of the models. In summary, our ensemble model consistently outperforms the other models in various analyses, solidifying its status as the top-performing model.
## 3 Results and Discussion
### Total Petroleum Hydrocarbon Concentration Analysis  
The choice of soil sampling locations is dictated by the fact that one sampling location must be in the vicinity of the well casing (P1), and the rest (P2, P3, P4) are dispersed within the well casing because it is within the well casing that there is the highest risk of pollution. These samples were taken at depths between 0.05 and 0.9 m to capture the TPH values at different depths (up to 0.20 m depth, the substrate is an earth with gravel mixture, and at 0.90 m a layer of sandy clay was identified as the sampling boundary). Sample P1 (Sample point 1 (251 Scarisoara Noua) is taken at the level of the well so that the intervention threshold is reached and exceeded at a depth of 0.3 m. At a depth of 0.05 m it was found that the concentration value of the TPH indicator is above the alert threshold but below the action threshold for sensitive land use. At depths of 0.6 m and 0.9 m the TPH indicator concentration value was below the alert threshold for sensitive land use. For borehole P 3 at a depth of 0.05 m the TPH indicator concentration value was found to be above the alert threshold but below the action threshold for sensitive land use. At depths greater than 0.3 m, the TPH indicator concentration value was below the alert threshold for sensitive land use. For borehole P4 from 0.05 to 0.6 m depth, the TPH indicator concentration value was below the alert threshold for sensitive land use. Only at a depth of 0.9 was it found that the TPH indicator concentration value was above the alert threshold but below the action threshold for sensitive land use. In the case of boreholes P2 and P4, the laboratory results indicate a similar situation so that at all depths analysed, the TPH indicator concentration value was below the alert threshold for sensitive land use (Table 2). Very high hydrocarbon concentration values were recorded for Sample Point 2 (560 Mihai Bravu West), Sample P2 (5684) at a sampling level of 0.05 (m) and for P3 (4315) at a sampling level of 0.9 m.  
For Sample point 3 (561 Mihai Bravu West), the highest concentrations were determined for the central borehole (P1), where the TPH indicator concentration value is above the intervention threshold for less sensitive land use for the sample taken at \(-\)0.05 m depth (5028). At depths greater than 0.30 m, there was a decrease in the TPH concentration value above the alert threshold but below the intervention threshold, which decreased at greater depths (44).