esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Corrosion Behavior of Cu Wafer under Photocatalysis
From the formula, it can be observed that the impedance of the Cu surface oxidized layer is related to the resistance \(a\) and capacitance \(c\), and the capacitance of the oxidized layer can be defined as [35]:  
\[C=\varepsilon_{0}\varepsilon_{r}\frac{A}{d} \tag{9}\]  
where \(C\) denotes the capacitance, \(\varepsilon_{r}\) is the dielectric constant of the oxidized layer, \(\varepsilon_{0}\) is the dielectric constant of free space, \(A\) represents the surface area of the oxidized layer, and \(d\) is the thickness of the oxidized layer. According to this formula, the magnitude of the impedance \(|Z|\) increases with the thickening of the oxidized layer and decreases with its thinning. Following illumination, the radius of the curves in the Nyquist plots notably increases, signifying that UV-irradiated electrolytes result in higher impedance within the electrochemical system. This implies that as the illumination time progresses, the erosion of the Cu electrode's oxidized layer by the solution diminishes. From the impedance perspective, this indicates a reduction in ion generation from citric acid corrosion after each ten-minute interval of illumination. This observation suggests a decrease in citric acid concentration and confirms the synergistic effect of CeO\({}_{2}\) and H\({}_{2}\)O\({}_{2}\) in degrading citric acid. \begin{table}
\begin{tabular}{c c c} \hline
**Electrolyte Illumination Time** & **Ecorr vs. SCE (V)** & **Icorr (A/cm\({}^{2}\))** \\ \hline
0 min & \(-\)0.323 & 2.00 \(\times\) 10\({}^{-5}\) \\
10 min & \(-\)0.325 & 1.99 \(\times\) 10\({}^{-5}\) \\
20 min & \(-\)0.325 & 1.99 \(\times\) 10\({}^{-5}\) \\
30 min & \(-\)0.323 & 1.97 \(\times\) 10\({}^{-5}\) \\
40 min & \(-\)0.325 & 1.94 \(\times\) 10\({}^{-5}\) \\
50 min & \(-\)0.329 & 1.88 \(\times\) 10\({}^{-5}\) \\
60 min & \(-\)0.325 & 1.77 \(\times\) 10\({}^{-5}\) \\ \hline \end{tabular}
\end{table} 
Table 1: The values of E\({}_{\text{corr}}\) and I\({}_{\text{corr}}\) in the electrolyte at different irradiation times.
# The Free-Energy Landscape of a Mechanically Bistable DNA Origami
## 2 Materials and Methods  
To model the DNA nanostructures we used the oxDNA coarse-grained model [25; 26; 27]. In particular, we used the second version of the model that has been fine-tuned to improve the modelling of origami structure [27]. As a results of its accurate description of the structural [28] and mechanical [29] properties of DNA origami, it has been widely used in the field of DNA nanotechnology, particularly to describe origamis with increasingly functionally complex properties that are otherwise hard to predict [11; 30; 31; 32; 33; 34; 35; 36]. The oxDNA model is particularly well-suited to study jointed origami, as it can also capture the important local features of the junction, e.g., an accurate description of the mechanical properties of single-stranded DNA [26] is important to model the role of the single-stranded linkers on joint flexibility. Furthermore, oxDNA is computationally efficient enough to be combined with techniques such as umbrella sampling [37] and metadynamics [38] to compute the free-energy landscapes associated with mechanical deformations of DNA origamis.  
In the oxDNA model, each nucleotide is represented by a rigid body. The nucleotides interact through a pairwise potential which has terms representing backbone connectivity, excluded-volume interactions, hydrogen bonding between base pairs, stacking interactions, and electrostatic interactions between backbone phosphate groups. Solvent is modelled implicitly as a dielectric continuum, and a Debye-Huckel approach is used to describe the electrostatic interactions. We choose to use an ionic strength corresponding to a salt concentration of [Na\({}^{+}\)] = 1.0 M, which is representative of the high salt conditions typically used for DNA nanotechnology.  
The oxDNA simulation code was used to perform molecular dynamics simulations in the canonical ensemble, in particular making use of its GPU implementation [39]. To sample the free-energy landscapes we used umbrella sampling in a similar manner to our previous work [37]. For each free-energy landscape we ran a series of between 20 and 80 simulations each of which sampled a separate order-parameter window due to the placement of a harmonic biasing potential in the order parameter at the centre of the window. The free-energy landscapes were constructed from the order-parameter probability distributions for each window using the weighted-histogram analysis method (WHAM) [40]. We iterated the sets of umbrella sampling simulations until the WHAM-generated landscapes no longer changed significantly.  
Additional simulation and analysis details can be found in the Supplementary Material.
## 2 Materials and Methods  
### Reagents and Solutions  
The target organic compounds were Chlorpyrifos from PESTANAL(r) (99.8%, HPLC), Tonalide (\(\geq\)98%), and Triclosan, certified reference material, from TraceCERT(r), Sigma Aldrich (Steinheim, Germany). In Table 1, the most significant properties of the compounds are shown.  
Stock standard solutions (452 mg L\({}^{-1}\) for CPS, 510 mg L\({}^{-1}\) for AHTN, and 1017 mg L\({}^{-1}\) for TCS) were prepared in ethyl acetate. From the stock solutions, intermediate solutions at two concentration levels were also prepared in ethyl acetate and replaced every two weeks. These solutions were kept at 4 \({}^{\circ}\)C. Standard solutions for the calibration curve used in the calculation of absolute recoveries were made with ethyl acetate as the solvent.  
Samples for TFME were prepared daily in 0.01 M NaCl in a range of 0.05 mg L\({}^{-1}\) to 0.4 mg L\({}^{-1}\) for CPS and AHTN and 0.2 to 4.0 mg L\({}^{-1}\) for TCS.  
For the preparation of the films, the following reagents were used: cellulose triacetate (CTA) from Acros Organics (Geel, Belgium) as the polymer; dibutyl sebacate (DBS) (\(\geq\)97%, GC) and nitrophenyl octyl ether (NPOE) from Sigma Aldrich (Steinheim, Germany) as the plasticizers. Multi-walled carbon nanotubes (MWCNT), as produced cathode deposit, O.D. \(\times\) L 7-15 nm \(\times\) 0.5-10 \(\upmu\)m, were from Sigma Aldrich (Steinheim, Germany). The XRD spectrum is shown in Figure S1. According to the producer, the C content is \(>\)99% of the TGA results.  
The solvents used were chloroform (CHCl\({}_{3}\)) (\(\geq\)99.8%), Sigma Aldrich (Steinheim, Germany); ethanol (\(\geq\)99.8%, HPLC) PanReac Applichem (Castellar del Valles, Spain); ethyl  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound Name** & **IUPAC Name** & **Molecular Weight** & **Classification** & **CAS** & **Log K\({}_{\text{ow}}\)******* \\  & & **(g mol\({}^{-1}\))** & & **Number** & **Log K\({}_{\text{ow}}\)******* \\ \hline Chlorpyrifos (CPS) & O,O-Diethyl O-(3,5,6- & & & \\ trichloropyridin-2-yl) & 350.6 & Organophosphorus & & \\ insecticide & & & & \\ Triclosan (TCS) & 5-Chloro-2-(2,4- & & \\ dichlorophenoxyoxy)phenol & 289.5 & Antibacterial & & \\ Tonalide (AHTN) & 6-Acetyl-1,1,2,4,4,7- & & & \\ hexamethylletralin & 258.398 & Aroma musk & 21145-77-7 & 5.70 \\ \hline \hline \end{tabular}  
* K\({}_{\text{ow}}\) denotes the octanol-water partition coefficient.  
\end{table}
Table 1: Properties of studied compounds.  
acetate (HPLC; Fisher Scientific, Madrid, Spain) and acetone (HPLC grade) by PanReac Application (Castellar del Valles, Spain).  
All other reagents were of analytical grade. Ultrapure water was obtained from a purified system Milli-Q plus System Millipore Iberica, S.A. (Barcelona, Spain).
## 4 Discussion
The radiomic data derived from MRI could be incorporated into clinical workflows to predict lymph node involvement even before biopsy, offering a non-invasive alternative to traditional methods [20]. The aim is to stratify patients who may avoid extensive pelvic lymph node dissection (ePLND) without compromising staging accuracy and potentially reduce unnecessary ePLND procedures by more accurately identifying high-risk cases. This radiomics-based model may assist clinicians in the preoperative decision-making process by identifying patients at higher risk of lymph node involvement, potentially reducing the number of unnecessary extended pelvic lymph node dissections (ePLNDs) and their related complications. These results underscore the added value of extending radiomics analysis beyond the primary tumor to include the entire gland and PPAT, providing imaging biomarkers for locoregional disease dissemination. The integration of tumor-derived and surrounding tissue radiomics improves the ability to predict both extracapsular extension and lymph node involvement, supporting more tailored clinical decision-making in prostate cancer management. Nevertheless, this study has limitations, including the small sample size and class imbalance within the dataset. To mitigate these issues, we employed a wrapper feature selection method to prevent overfitting and implemented data augmentation techniques to address class imbalance. Despite these limitations, our findings align with emerging evidence on the role of PPAT in prostate cancer aggressiveness and highlight the potential of radiomics in refining risk stratification and guiding personalized management strategies. Despite the promising results, our study has several limitations that should be acknowledged. First, the model was developed and tested using data from a single institution, and no external validation was performed. This limits the generalizability of our findings and highlights the need for multicentric prospective studies. Second, although segmentations were reviewed by two experienced radiologists, the initial segmentation was performed by a single resident, and no formal inter-observer variability analysis was conducted. Third, we did not perform a comparative analysis with other machine learning algorithms beyond the random forest model. Future work should include benchmarking against alternative models to confirm the robustness and comparative advantage of our approach. Future research should focus on validating these findings in larger cohorts and exploring the integration of radiomics with genomic data to further enhance predictive accuracy and inform novel therapeutic approaches. The characterization of PPAT's role in prostate cancer progression holds promise for advancing both diagnostic precision and therapeutic interventions.
## 5 Experimental results and discussion
### Performance evaluation of EE-TGA and \(E^{3}\)-FA on standard unconstrained benchmarks  
For the sake of easier following, this subsection is also divided into two parts. The first part presents the enhanced tree growth algorithm (EE-TGA) performance evaluation, and the second part shows the evaluation of the enhanced firefly algorithm (\(E^{3}\)-FA) for standard unconstrained benchmarks along with respective comparative analysis with original versions.
With this, it is possible to collect a huge amount of data via several sensors attached to the diabetic patient, and then gather it to a smartphone using Bluetooth [7]. Unfortunately, smartphones are not powerful enough to build a glucose model and this computational task should be developed in a more capable computer. Utilizing IoT offers extensive potential for intelligent and robust model systems for enhancing DM1 monitoring. Big data analytics presents the ability to leverage the extensive data hoards achieved by IoT networks to highlight factors that assist in the gaining of key information that can result in a more effective interpretation of glycemia disparities. Whereas Cloud Computing capabilities enable the utilization of time-consuming intelligent data methods in order to present the oscillation of glucose levels. In this research, selected innovative modeling approaches have been utilized on a complete dataset of glycemia-related biomedical features. These have been gathered via a thorough, passive-monitoring operation carried out in real-world conditions with 25 patients diagnosed with DM1, as a means of modeling key aspects of glucose levels by effectively identifying patterns. Achieving this is applicable to enhancing the management of DM1. To this effect, four approaches have been carried out and analyzed within an IoT context--run through CC because of the extensive data involved. An assessment can be made once the chosen techniques have been trialed using the data obtained in order to train the models. After this introduction, some relevant works regarding IoT platforms and IDA are indicated in Section 2, and then, in Section 3, the details of the monitoring campaign are presented. With the data collected, a preliminary study of the recorded features is carried out in Section 4, and then Section 5 states the methods that will be compared. With the results, a discussion is performed in Section 6, and some important conclusions close the paper in Section 7.
# Use of Smart Technology to Improve Management of Utility Tunnels
## 3 Smart System Design
### Physical Layer  
The physical layer includes the tunnel and hosted utilities, as well as their equipment. This layer is subject to strict operation and quality control requirements, including operating conditions and performance, as well as monitoring, inspection, and maintenance.  
A 3D digital model is used for the digital representation of the tunnel and utilities assets. This model includes digital identification and geo-localization of the components of the tunnel and hosted utilities. Each component is equipped with RFID tags (radio-frequency identification), which provide dynamic information about the component including manuals, guides, photos, and videos. BIM (building information modeling) is used for the construction of this model, as well as for the asset management and 3D visualization.
## 4 A Typical Application: Wuhan Huoshenshan Hospital
### Evaluating the Design Scheme of the Air Outlets and Fresh-Air Intakes
#### 4.4.1 Cases with Different Wind Directions  
Provided that the wind speed at the height of 10 m was 1.9 m/s, cases with eight wind directions (i.e., Case-1.9-N, Case-1.9-NE, Case-1.9-E, Case-1.9-SE, Case-1.9-S, Case-1.9-SW, Case-1.9-W, and Case-1.9-NW) were simulated. The simulation results indicated that under four wind directions (i.e., east wind, southeast wind, south wind, and southwest wind), the surrounding high-rise buildings and villas would be affected by the exhausted harmful air from the hospital. Figure 21 shows the typical visualization of the harmful air dispersion under the southeast wind, where the black particles represent the exhausted harmful air dispersing in the air, and the purple curved surfaces represent the isosurfaces with a concentration of \(20\times 10^{-6}\). In the cases with wind speeds of 1.9 m/s, the maximum concentration of harmful air near surrounding buildings under different wind directions are all lower than \(40\times 10^{-6}\), provided that the function of filtration devices is neglected.
## 3 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Element Number** & \(f_{0}\) **(GHz)** & \begin{tabular}{c} **Feeding** \\ **Mechanism** \\ \end{tabular} & \begin{tabular}{c} **Imp. BW** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Peak Gain** \\ **(dBic)** \\ \end{tabular} & \
\begin{tabular}{c} **Peak SLL (dB)** \\ \end{tabular} &
\begin{tabular}{c} **Size (\lambda_{0}^{2}\)** \\ \end{tabular} \\ \hline
[12] & \(16\times 16\) & \(10\) & End feed & \(2.70\) & \(24.5\) & \(-31.3\) dB (E-plane) & \(9\times 8.67\) \\
[13] & \(10\times 10\) & \(10\) & End feed & \(2.40\) & \(22\) & \(-32.3\) dB (E-plane) & \(7\times 5.67\) \\
[17] & \(4\times 16\) & \(27.5\) & Center feed & \(2.18\) & \(21.4\) & \(-17.9\) dB (E-plane) & \(10.3\times 5.7\) \\
[18] & \(4\times 32\) & \(24\) & Center feed & \(1.71\) & \(22.8\) & \(-13.5\) dB (E-plane) & \(15.6\times 3.2\) \\ Proposed Design & \(6\times 16\) & \(10\) & Differential feed & \(7.15\) & \(21.92\) & \(-29.1\) dB (E-plane) & \(14\times 4.83\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the planar low-SLL, SIW-based SAAs. Figure 11: Simulated and measured normalized radiation patterns of the proposed low-SLL, differential-fed, SIW-based SAA in the \(\gamma\)\(\omega\)\(Z\)-plane (\(E\)-plane) and the \(\chi\)\(\omega\)\(Z\)-plane (\(H\)-plane): (**a**) \(9.6\) GHz, (**b**) \(10\) GHz, and (**c**) \(10.25\) GHz.
## 4 Materials and Methods
### Light Transmitter Unit  
A white-LED array was deployed to transmit optical data through a water channel. It consists of 96 LEDs. The maximum power delivered by the LED source is 48 W. The LED array was attached to one side of the water tank to transmit light signals through the water, as shown in Figure 2b. The signal is transmitted using a circuit shown in Figure 3. This circuit is composed of an NPN transistor, a power source of 9 V, and an array of LEDs. This power source is used to enhance the signal to achieve better coverage.
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 3 Sampling Class Imbalance Approaches
### Over-Sampling Methods  
Random Over-Sampling (ROS) compensates the class imbalance; this algorithm randomly duplicates samples from the minority class until a relative class balance is achieved [60].  
Synthetic Minority Over-sampling Technique (SMOTE) produces artificial samples from minority class by interpolating existing instances that are very close each other. The \(k\) intra-class nearest neighbors for each minority sample are found, and synthetic samples are produced in the direction of some or all of those nearest neighbors [61].  
Adaptive Synthetic Over-Sampling (ADASYN) is considered as an extension of SMOTE, which is characterized by the creation of more samples in the vicinity of the boundary in the midst of the two classes than inside the minority class [62].
## 2 Generation of Pore Pressure in Partially Saturated Soils
### Experimental Results  
Basic results of the laboratory investigations are shown in Figures 9-14. A detailed description and deeper analysis of the results from the first experimental programme (Series A) can be found in the study [37]. Detailed laboratory data for all the experiments analysed in this paper are enclosed in the online Supplementary Materials (Repository S1: Experimental Data).  
Changes in pore pressure, normalized by initial mean effective stress in relation to vertical strain \(\varepsilon_{1}\) are shown in Figure 9 for Skarpa sand and in Figure 10 for OZM50 tailings, respectively. It can be seen that generated excess pore pressure increased with saturation and led to the liquefaction of samples with the highest saturation level.  
In turn, changes of corresponding deviatoric stress in relation to vertical strain, \(\varepsilon_{1}\), are presented in Figures 11 and 12 for Skarpa sand and for OZM50 tailings, respectively. It can be seen that lower saturation results in higher maximum and residual stress deviators.  
Figure 11: Stress–strain response of OZM50 tailings at different levels of saturation: stress deviator vs. axial strain.  
Figure 12: Stress–strain response of Skarpa sand at different levels of saturation: stress deviator vs. axial strain.  
The stress paths corresponding to triaxial tests are collected in Figures 13 and 14. As a further confirmation, a clear decrease in the maximum stress deviator can be observed along with an increase in the saturation of the medium for the entire spectrum of saturation levels represented by [PERSON]'s parameter \(B\). When [PERSON]'s parameter \(B\) increases, the response of contractive soil changes from one characteristic of dry soil (dashed line) to a typical response of a fully saturated medium.  
Figure 14: Stress paths corresponding to the behaviour of Skarpa sand at different levels of saturation.  
Figure 13: Stress paths corresponding to the behaviour of OZM50 tailings at different levels of saturation.  
In the presented research results, two stress paths corresponding to tests a6 and c4 differ from the rest. In both cases, failure of the sample took place before the boundary surface characteristic of each soil was reached. In these tests, unlike the others, clear shear surface was observed, typical for the deformation process in a highly compacted non-cohesive soil specimen.
## 3 Technologies of Monitoring in Montado
### Precision Agriculture
#### 3.2.3 Pasture Monitoring
[43] obtained very consistent correlations (r\({}^{2}\) = 0.606 and 0.818) between the values obtained with the capacitance probe and pasture productivity (DM and GM), at all evaluation times (months of December 2015 and June 2016). These facts reveal the practical interest of using the Grassmaster II as a quick method for estimating the productivity of pastures in the south of Portugal. According to [PERSON] et al. [81], in places where pasture humidity is high, productivity is also higher, as well as capacitance values. This same study also found that the less advanced the phenological state of the plants and the greater the percentage of legumes, the greater the capacitance values. [PERSON] et al. [66] carried out a study in pastures under Montado to calibrate and validate the use of a portable NIR sensor in the evaluation of pasture quality. From this study, the authors obtained values of r\({}^{2}\) = 0.73 and 0.69 for calibration and validation,respectively, for the NDF of the pasture samples; for CP, values of r\({}^{2}\) = 0.51 and 0.36 were obtained for calibration and validation, respectively [66]. Several authors state that the main advantages of the portable NIR are its low weight, ease of use, direct measurements in the pasture, non-destructive nature, time-saving in cutting and sample processing, more frequent evaluations, and more timely decision making at the moment of evaluation, thus overcoming the spatial and temporal variability of the pasture [56; 126; 127; 128; 129]. [PERSON] et al. [57] consider that using sensors in pastures is sometimes tricky, with some limitations relating to each specific sensor. Thus, according to [PERSON] et al. [6], sensor fusion is an attractive option for incorporating several variations in scales (vertical and horizontal) and unequal properties. There are three main types of sensor fusion: (1) nearby sensor fusion; (2) fusion of remote and near sensor(s); (3) fusion of remote sensors. The fusion of sensors can lead to more precise and accurate monitoring of the soil and/or pasture since it allows for the acquisition of more than one type of information simultaneously, which can contribute to improving decision making by the farmers and agricultural managers [130]. However, according to [PERSON] et al. [86], sensor fusion can lead to challenges and problems related to the configuration, image capture, validation and data management, and analysis of these data to derive calibrated scientific information.