esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Color Assessment  
The colorimetric profile of fresh and dried sea fennel was defined using a Chroma Meter CR-200 (Minolta, Japan) to determine lightness (L), redness-greenness (a*: + red;\(-\) green), and yellowness-blueness (b*: + yellow; \(-\) blue) coordinates according to the CIELab color space system with a D65 light source. Moreover, the hue angle (h\({}^{\circ}\)) was calculated using the formula h\({}^{\circ}\) = 180 + arctg (b*/a*) [78], and the chroma (C) was calculated using the formula C = [(a*2 + b*2)]\({}^{1/2}\). For color assessment, whole fresh leaves were bound together to create a homogeneous surface, while the dried samples were ground to powder. The instrument was calibrated using standard white coordinates, and the colorimetric measurements were performed in triplicate. The results were expressed as the mean of three replicates per batch \(\pm\) standard deviation.  
Determination of Volatile Compounds via Headspace/Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS/SPME-GC/MS)  
The volatile compounds were analyzed according to [PERSON] et al. [79] with some modifications. In detail, aliquots (2 g) of chopped fresh and dried sea fennel leaves were placed in a 20 mL headspace vial (Gerstel GmbH & Co., Mulheim, Germany), and 5 uL of 4-methyl-2 pentanol (internal standard, 100 mg L\({}^{-1}\) standard solution) was added. The samples were subjected to equilibration at 40 \({}^{\circ}\)C for 2 min at 250 rpm using a Gerstel MPS2 automatic sampling system (Gerstel GmbH & Co., Mulheim, Germany). The analysis was performed using a GC/MS system (Agilent 7890/5975 Inert, Agilent, Santa Clara, CA, USA) with helium as the carrier gas (1 mL min\({}^{-1}\)). A coated divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (Sigma Aldrich S.r.l., Milan, Italy) was exposed to each sample headspace for 15 min, while maintaining the sample temperature at 40 \({}^{\circ}\)C. The fiber was desorbed for 5 min at 240 \({}^{\circ}\)C in the injection unit in the split ratio 50:1). The separation was accomplished in a capillary column (HP-Innowax, Agilent Technologies, USA) (30 m \(\times\) 0.25 mm id. \(\times\) 0.50 \(\mu\)m film thickness). The GC oven's temperature program started at 35 \({}^{\circ}\)C for 5 min, was then ramped to 150 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\) and to 240 \({}^{\circ}\)C at 15 \({}^{\circ}\)C min\({}^{-1}\), and maintained at the final temperature for 1 min. The mass spectrometer operated with an ion source of 230 \({}^{\circ}\)C, a quadrupole temperature of 150 \({}^{\circ}\)C, and 70 eV electron energy, and acquired in the TIC mode from m z\({}^{-1}\) 30 to 350\(\mu\)m. Identification of volatile compounds was carried out by comparing the mass spectra with the Wiley library (Wiley7, NIST 05). The results were expressed as RAP = relative peak area (peak area of compound/peak area of internal standard) \(\times\) 100 (RAP \(\pm\) SD). Blank experiments were conducted in two different modalities (blank of the fiber and blank of the empty vial) and analyzed after every 4 analyses. All analyses were performed in duplicate.
## 4 Discussion
Consequently, diode lasers, similar to CO\({}_{2}\) lasers, have proved to be useful in obtaining a better disinfection of the implant surfaces colonized by bacteria and in stimulating the epithelial healing processes [18]. The clinical trials included in our paper highlighted that there is no single method for decontamination of implant surfaces, which is the best choice in relation to long-term clinical results. Dental lasers have been shown to be effective in treating mild-to-moderate peri-implant lesions in studies with a follow-up mean of 6 months [12]; severe peri-implantitis treatment and longer follow-ups have shown unstable results [13; 14]. According to some authors, the only non-surgical treatment does not guarantee predictable results in the treatment of mild/moderate peri-implantitis and must be associated with surgical therapy to treat the peri-implant pockets and the bone defects. Non-surgical treatment of mucositis and initial peri-implantitis generally provides clinical improvements in reducing the bleeding on probing and, in some cases, the probing depth. Early diagnosis and consequent timely intervention remain fundamental elements for the management and success peri-implantitis treatment [33]. Caution is required before interpreting the relevant findings of this study and some of limitations should be borne in mind. First of all, our review comprised studies conducted by several authors with different surgical skills and equipment (instrumentations and bio-material) and who have chosen different approaches to treat mucositis and peri-implantitis. Furthermore, the study design, the methodology applied in data collecting, the medical and demographic characteristics of the patients treated, the severity of the periodontal disease, the implant surfaces and the duration of the post-operative follow-up periods varied between the analyzed studies. Consequently, only on the basis of the data reported in the literature, no statistically significant differences were found between the adjunctive laser irradiation and mechanical therapy alone in terms of PPD reduction and CAL gain in peri-implantitis treatment. However, the use of a dental laser to obtain a complete implant surfaces decontamination is convenient to conduc faster intervention and more accepted therapy by the patients. The results collected in our study were strongly influenced by the heterogeneity of the included studies, which present a combination of different therapeutic approach. For this reason, it is not methodologically possible to establish a single therapeutic strategy to obtain a better implant decontamination in the treatment of peri-implantitis; future research must be encouraged to establish an effective clinical protocol against peri-implant lesions.
## 3 System Model and Preliminaries
### Underwater Layering and Sound Speed Profiling  
The underwater medium is viewed as a layered model based on the observed SSP of the water column. Since the horizontal changes in SS are limited whereas changes in the vertical direction are significant, we use a layered approach to discretize and capture SS changes along the depth [21]. In principal, the SSP of the water column is divided into isogradient layers where each layer has a constant gradient. Since the operational environment is 3D, we represent each layer as a disk with a thickness that is equivalent to the thickness of layer along the \(z\)-axis. Therefore, the layered model could be visualized as stacked disks, as shown in Figure 3, where the SS gradient in each layer is assumed to be constant. We further assume that nodes obtain the SSP of the water column either by measuring the water column SSP or referencing data collected by field experiments in the same deployment area [22]. Finally, we have proposed a method to determine the SSP from established communication links where we exploit the fact that the paths of acoustic signals are highly correlated to the SSP of the water column [23]. In the event that the SSP changes, we assume that either such a technique can be leveraged to deduce the changes in the SSP, or new measurements are taken by the node to reflect the latest SSP.
## 4 Simulation Study
### Comparison of Traditional PI and Fuzzy PI  
The speed responses of the two speed control methods are shown in Figures 9 and 10. When the electric motor starts, the overshoot of the traditional PI control and fuzzy PI control are 12 r/min and 6 r/min, respectively. The time to steady state is 40 ms and 31 ms, respectively. When the speed changed suddenly, the overshoot of the traditional PI control and fuzzy PI control are 8 r/min and 5 r/min, respectively. The time to steady state is 24 ms and 16 ms, respectively. A 15 N-m load is added at 0.2 s, the electric motor speed drop of the traditional PI control and fuzzy PI control are 10 r/min and 6 r/min, respectively. Moreover, the adjustment time is 16 ms and 7 ms, respectively. Therefore, when the fuzzy PI control is used, the motor speed output overshoot is smaller, the response is faster, the load disturbance has less affection, and the stability is higher.  
Figure 8: Vector control system-positive flow system co-simulation model.  
Figure 9: The comparison curve of speed changes.  
The change curve of the electric motor output torque under load mutation condition is shown in Figure 11. A 15 N\(\cdot\)m load is loaded at 0.2 s, the adjustment times of the two control systems are 15 ms and 7 ms, respectively. The torque overshoot of traditional PI control is smaller, but the adjustment time is slower than that of fuzzy PI control. At the same time, the adjustment process of traditional PI control oscillates obviously. Even when the torque reaches the steady state, there are fluctuations. It can be seen that when the fuzzy PI control is used, the electric motor output torque overshoot is slightly increased, but the responsiveness and stability are significantly improved.
# Evaluation of the Performance of Previous Concrete Inspired by CO\({}_{2}\)-Curing Technology
## 2 Materials and Methods
### Runoff Passage Testing  
Aqueous solutions dissolved with 70% concentrated HNO3 and lead nitrate and zinc sulfate salts were prepared using analytical grade reagents obtained from Merck. These solutions, each of 2.5 L capacity, were passed through the concrete samples mounted on the permeameter setup for ten repeat cycles. A 10 mL quantity of water sample was collected at the end of every cycle and used for further investigation. The water samples were filtered using a 0.45 \(\upmu\)m disk filter, diluted 1000 times, acidified with 2% HNO3, and analyzed using ICP-MS of the Perkin Elmer NexION-300X model to detect the concentration of calcium, lead, and zinc ions dissolved in them. These metal ions of up to 10\({}^{-5}\) mg/L can be detected with the ICP-MS used in this study because argon was used as the plasma source and operates at a flow rate of 18 L/min. The pH and turbidity of these water  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{Sieve Size in mm} & 0.3 & 1.18 & 2.36 & 4.75 & 9.5 & 12.5 & 19 & 25 \\ \hline \multicolumn{1}{c}{Passing in \%} & 5 & 5 & 25 & 45 & 100 & 100 & 100 & 100 \\ \hline \end{tabular}
\end{table}
Table 1: Gradation of crushed aggregates.  
Figure 3: Details of the percolation rate and runoff passage experiments.  
Figure 2: Various stages involved in the preparation of pervious concrete samples.  
samples were also measured using battery-operated laboratory devices of the Eutech model. The pH meter was calibrated at three points (pH 4.01, 7, and 9.21), while the turbidity meter was calibrated at four points (turbidity 0.02, 20, 100, and 800 NTU), according to the NIST and ISO guidelines. Sodium-based bentonite clay powder was obtained from a local chemical supplier and thoroughly mixed in distilled water to obtain highly turbid water, which was later passed through concrete samples for ten repeat cycles to obtain information on the reduction in concrete permeability. The average size of this clay reported by the manufacturer was 10 \(\upmu\)m. Figure 3 also shows the details of this runoff passage investigation.
## 3 System Model and Problem Formulation  
The specific objective of this study was to propose an intelligent task offloading scheme in a multi-user and multi-server MEC environment that can offload each task to the corresponding MEC server through centralized control to reduce the service delay and terminal energy consumption.  
SACDTO is a DRL-based offloading framework integrated into the MEC platform defined by ETSI [28]. As shown in Figure 1, the overall design of the SACDTO architecture is depicted. This system consists of three levels: the UE level, MEC level, and cloud level.  
The system model is a multi-user and multi-server application scenario. There are N tasks and M MEC servers, and the task data to be offloaded is transmitted between the MEC server and the terminal device over the wireless communication link. This paper assumes that each terminal device can execute or offload the task locally, the task can only be offloaded to one MEC server for calculation, and each terminal device is within the range of wireless connection. Because the computing capacity of each MEC server is limited, it cannot accept the offloading requests of each terminal simultaneously. For the convenience of understanding, the mathematical notations are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definition** \\ \hline \(\pi(\cdot\cdot\cdot 1)\) & Offloading policy \\ \(\alpha_{i}\) & The number of clock cycles required to process each bit of data \\ \(D\) & Replay buffer \\ \(P_{sendt},P_{rec}\) & The transmitted and received power \\ \(\omega\) & The parameter that determines the send or receive rate \\ \(O_{1:i}\) & Set of offloading decisions from task v\({}_{1}\) to v\({}_{i}\) \\ \(\xi\) & Directed acyclic graph \\ \(\beta_{1},\,\beta_{2}\) & The weight coefficient of energy consumption and delay ratio \\ \(\theta 1,\,\theta 2,\,\phi\) & Parameters of SACDTO \\ \(\alpha\) & The temperature parameter \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mathematical notations.  
Figure 1: DRL-based MEC Offloading Scheme.
# The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
## 2 Materials and Methods
### Image Dataset  
In this study, we utilized a public sunflower image dataset [13], which contained 467 original images and 1868 augmented images. These images included four categories, such as gray mold, downy mildew, leaf scar, and fresh leaves. In this dataset, augmentation was achieved through rotation, scaling, and shearing. The augmented 1868 images were used for model training and testing. The distributions of all images are shown in Table 3.  
Downy mildew disease can cause chlorotic spots on the leaves of sunflower, with a white velvety mold layer on the underside of the leaves. Then the leaves turn brown and scorched. The infected plants become stunted, and the flower heads become deformed.  
Gray mold disease mainly affects the flower heads of sunflowers. In the early stages of infection, the flower heads exhibit water-soaked decay. When the humidity is high, gray mold layers grow, leading to the rotting of the flower heads and failure to bear seed.  
Figure 1: The process used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Classes** & **Number of Original Images** & **Number of Augmented Images** \\ \hline Downy mildew & 72 & 398 \\ Gray mold & 120 & 470 \\ Leaf scar & 141 & 509 \\ Fresh leaves & 134 & 491 \\ \hline Total & 467 & 1868 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The distribution of dataset images.  
Leaf scar disease is characterized by the appearance of circular or nearly circular scar-like lesions on the leaves. The edges of these lesions are typically dark brown, while the centers are grayish-white, and there may also be the presence of a gray mold layer. Although this disease does not directly cause the death of sunflowers, it reduces the photosynthesis capability of the leaves, thereby affecting the yield. Four types of image samples are displayed in Figure 2.
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
## 5 Oil-Water Separation via FS Laser Structuring
### FS Laser Fabrication of Arrays of Micro-Through Holes
(**b**) Magnified image of the treated foil showing that the rim of the pores is decorated with microstructures; (**c**) underwater contact angle of a light oil (C\({}_{8}\)H\({}_{18}\)) droplet on the laser-treated aluminum foil; and (**d**) underwater contact angle of a heavy oil (C\({}_{2}\)H\({}_{4}\)Cl\({}_{2}\)) droplet on the laser-treated aluminum foil [70]. OCA: oil contact angle. Reproduced with permission from [70] Royal Society of Chemistry 2016. Figure 12: (**a**) Oil–water mixture of heavy and light oil. (**b**) Schematic of the light oil–water separation process; (**c**) oil–water separation was completed within 13 s in the case of the octane light oil. (**d**) Tube with side opening and schematic of the heavy oil–water separation process; (**e**) the heavy oil–water separation process was completed within 31 s [70]. Reproduced with permission from [70] Royal Society of Chemistry 2016. In another approach, a FS laser-processed titanium membrane filter showed high oil-water separation efficiency [69]. In that case, the microholes formed with a spacing of 100-300 \(\upmu\)m, using a laser fluence of 3.1-15.5 J/cm\({}^{2}\). In particular, the titanium filter prepared with a fluence of 12.4 J/cm\({}^{2}\) and 100 \(\upmu\)m interspacing showed a remarkable separation efficiency of 99%, with high recyclability. Figure 14 shows a typical SEM image of the titanium membrane filter and the respective energy dispersive x-ray (EDX) analysis of the microholes. Laser ablation leads to the creation of microprotrusions and extensive oxidation of titanium due to oxygen presence in the ambient air. Figure 13: (**a**) Schematic of the modification of a FS laser-drilled aluminum foil with 1H,1H,2H,2H-perfluorodecyltriethoxysilane. Following this process, the water contact angle (WCA) turned from superhydrophobic (WCA \(\sim\)5.1\({}^{\circ}\)) to superhydrophobic (WCA \(\sim\)154.4\({}^{\circ}\)); (**b**) schematic illustration of the continuous separation of an oil–water mixture by superhydrophobic and superhydrophobic foil; (**c**) the continuous separation of an oil–water mixture, which was completed in 28 s [70]. WCA: water contact angle. Reproduced with permission from [70] Royal Society of Chemistry 2016.
## 2 Materials and Methods
### Preparation of CTS-MBs  
The technical process for preparing the CTS-MBs with surface-grafted aldehyde groups (i.e., the activated CTS-MBs, aCTS-MBs in brief) and the complex with immobilized _P. putida_ cells (i.e., the aCTS-MBs complexed with _P. putida_ cells, bacCTS-MBs in brief) is illustrated in Figure 1. First, 2 g of CTS powder was dissolved in 100 mL of a 1% (\(v/v\)) acetic acid solution, followed by vigorous stirring with a magnetic stirrer for at least 12 h. To prepare CTS-MB with different particle sizes, we designed and self-assembled a set of facile preparation devices consisting of a constant flow pump, a DC high-voltage power supply (HV-PS), a magnetic stirrer and a medical needle (30 G) that was placed at a height of 3.5 cm over a NaOH-ethanol solution (Figure S1). The negative electrode of the HV-PS was connected to a copper ring that was immersed in a NaOH-ethanol solution and the positive electrode of the HV-PS was connected to a needle. CTS-MBs formed in the NaOH-ethanol solution when the CTS solution was titrated from the needle at a flow rate of 0.05 mL min\({}^{-1}\) and was stirred at 200 rpm. By adjusting the voltage of the HV-PS from 5.3 kV to 0 kV, five CTS-MB preparations with particle diameters ranging from 450 \(\upmu\)m to 2100 \(\upmu\)m were made. For each, the collected CTS-MBs were washed with double distilled water (ddH\({}_{2}\)O) to remove NaOH.  
Figure 1: Schematic illustration showing the preparation of the bacCTS-MB complex. 1, Preparation of the CTS-MBs; 2, Surface grafting of aldehyde groups on the CTS-MBs (aCTS-MBs); 3, Immobilization of _P. putida_ MB285 cells onto the aCTS-MBs via covalent cross-linkages to prepare the bacCTS-MB materials.
## 2 Materials and Methods
### Scaling 3D Data  
When a 3D point gets reconstructed, its coordinates (\(x\), \(y\), \(z\)) are computed based on the local coordinate (\(u\), \(v\)) of the overlapping images utilized to triangulate this point. Therefore, the size of the 3D data does not represent the actual object size. In order to extract any geometrical measurements, the 3D data must be scaled using ground control points (GCPs). In this study, four coded targets, 12-bit type with a center point radius of 10 mm, were used to scale the specimens' models, Figure 4b. The actual distances between every two targets were measured and entered as scale bars to calibrate the 3D data. In this study, two scale bars were created for all data sets. The first bar, between targets 1 and 2, was used to scale the models. The second bar, between targets 3 and 4, was used to check the scaling distance and add further statistical confidence. The same four targets with the same scaling bars were used for all data sets.  
It is important to use coded targets that can be automatically detected by the photogrammetric pipeline. For instance, Agisoft Metashape identifies its own targets' configurations, precisely marks their exact center, and labels them with their associated numbers printed next to them, Figure 4a. Some studies, for instance [27], used control points that were manually marked. This can contribute to the overall geometrical estimation errors due to the imprecise manual selection of the scale bars' starting and ending points.
## 3 Settlement Calculation with Error Corrections and Resampling
### MDD/GNSS Data Error Correction
#### 3.4.2 MDD/GNSS Data Analysis by a Resampling Method  
Resampling methods are statistical techniques used to estimate the properties of a population or to validate the performance of a statistical model. These methods involve repeatedly drawing samples from the available data, often with the goal of obtaining more accurate estimates or assessing the variability of statistical measures [28]. This study adopted the bootstrapping resampling method. Bootstrapping is a resampling method in statistics that involves creating multiple new datasets by sampling observations from the original dataset with replacement. It is commonly used for estimating the sampling distribution of a statistic, constructing confidence intervals, and assessing the variability of estimates [29]. The application of bootstrapping to GNSS and MDD smoothed data obtained from slab track settlement measurements based on time (days) is evident from Figures 9 and 10. To accomplish this, bootstrap samples were generated by randomly selecting days from the available data with replacement. It is important to note that each bootstrap sample for both MDD and GNSS should contain the same number of days as the original dataset. The process of resampling with replacement allows for the possibility of selecting certain days multiple times in a bootstrap sample while excluding others entirely. A comprehensive explanation of the resampling method utilized in this context can be found in the work of [PERSON] and [PERSON] [30].  
Figure 9 displays the original data of the total track settlement acquired through the GNSS, accompanied by the smoothed curve determined using the LOESS trend model. Figure 10, on the other hand, depicts the original data of total track settlement obtained from the GNSS estimated through the resampling method. The trend model's standard deviation error was calculated to be \(\sigma=3.275\) mm.
## 2 Theoretical Background
### Failure-Rate Model  
The reliability of a component is the probability of its successful operation until a specific time [17]. It is necessary to obtain historical failure data about failure modes to determine the component reliability [17]. Therefore, it is necessary to study how failures occur over time for life cycle analysis [17].  
In addition, every piece of equipment will fail during its operational life [18]. Accordingly, the failure rate of components is given by the following formula [18]:  
\[\text{failure rate, }\lambda=\frac{\text{\emph{number of failures}}}{\text{ \emph{total operating time of units}}} \tag{1}\]  
The relationship between failure, fault, error, and time to failure is illustrated in Figure 1 for continuous and discrete scenarios [19].