esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Studies
### Old Crafts Virtual Museum
#### 3.2.2 Application Structure  
According to the recommendation from the Museum of Sarajevo curators, we selected three crafts where nobody will inherit the skill after the last craftsm. These are cetkar (the brush maker), kazaz (the tailor decorator) and bozadzija (the maker of a drink called boza). The Virtual Reality application has three parts:  
* Stories about crafts told by the actor dressed as each of three craftsm  
Figure 1: Nine dissidents project workflow.  
Figure 2: Nine dissidents—gameplay video screen shot.  
* Stories about crafts told by original craftsmen in their workshops, while showing their work processes;
* Virtual Reality videos of Bascarsija with superimposed names of crafts recorded in three censuses through history and explanations of their meanings.  
The application consists of four virtual environments (Figure 3). The first virtual environment is a drawing of Bascarsija created as a 360 image. The actor recorded against the green screen is wearing the traditional costumes of the craftsmen and telling a story about each craft. Clicking on the entrance behind the actor, the user enters into the VR video of the craftsman's workshop, where a story told by the last craftsman of that craft is superimposed. The real craftsman presents the process of his craft making.  
On clicking on the arrow positioned on the street pavement, the user moves to the second virtual environment, a VR video of Bascarsija square, where the story about crafts recorded in the oldest census from the 15 th century is told. Around the user are names of crafts which existed at that time and explanations of their meaning. From that virtual environment the user can move to the third one where the story about crafts from the 16 th century is told and they can see the names of those crafts around them. The last virtual environment offers the story about crafts from the 17 th and 18 th centuries, together with their names and explanations.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.15 Sutra  
SUTRA uses both finite element and finite difference methods to simulate density-dependent groundwater flow in variable saturated media and reactive solute transport, including sorption and decay reactions. SUTRA also allows the simulation of heat transport in liquid and solid media (aquifer and its matrix). SUTRA can be used for areal and cross-sectional modelling, and reactive simulations can be used for pristine areas and brownfields. The latter makes Sutra useful for solving contaminant transport problems and remediation scenarios. Examples of the use of Sutra in reactive transport studies can be found in the works of Koch and Zhang [726], Benes and Elias [727], Rashid and Kaluarachchi [728], El-Kadi [729], and Plampin and Provost [730].
## 3 Results
### Identification and Quantification of Vitamin C  
Vitamin C belongs to the class of non-enzymatic antioxidants and exhibits an anti-allergic effect through the mechanism of prostaglandin inhibition [39]. Vitamin C is the main water-soluble antioxidant [39; 40] present in fruits and vegetables [41; 42]. In addition to its antioxidant and anti-allergic properties, it also has anti-inflammatory characteristics [3]. According to the literature, it is considered beneficial both in the prevention and treatment of allergic conditions [3; 43]. The anti-allergic capacity of vitamin C is based on its antihistamine action, which results from its non-enzymatic reaction with histamine [44], interfering with its chemical structure and breaking down the imidazole ring [41].  
In plants, the role of vitamin C is to protect against oxidative stress and regulate electron transport in the process of photosynthesis [35], contributing to maintaining cell division and growth [45]. In the human body, vitamin C provides protection against radicals and pollutants by donating electrons, helping to lower histamine levels in the blood [46]. Vitamin C is involved in various reactions in the body, such as collagen and serotonin synthesis, acting as a cofactor for many essential enzymes [47]. Incorporating fruits and vegetables rich in vitamin C into the diet is all the more important since the human body is unable to synthesize ascorbic acid [39; 46], due to the lack of the enzyme gulonolactone oxidase [43; 45]. Horseradish contains vitamin C in a high proportion [16; 24; 28; 46], about three times more than citrus fruits [48]. The high vitamin C content makes it useful in treating scurvy [33; 48; 49].  
Figure 7 shows the chromatograms for vitamin C at a wavelength of 245 nm for the optimized horseradish root extracts obtained.  
The vitamin C content was influenced by the type of solvent used in the extraction process. Thus, the hydroalcoholic extracts recorded a concentration of 105.32 mg/100 g of dry product, higher than that obtained from the aqueous extracts (90.35 mg/100 g of dry product), suggesting a higher efficiency of methanol in releasing and stabilizing ascorbic acid.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 4 MADM Handover Algorithm
### Handover Decision-Making
#### 4.2.1 Evaluation of the Current Network  
Before the handover execution, our algorithm makes an assessment as to whether the current network meets the QoS of the running application. This evaluation is based on the QoS parameter threshold of the different applications as shown in Table 2, and these values are defined in accordance with the real application requirements [27; 30]. If any QoS parameter value exceeds its required threshold, the handover process will trigger; otherwise, the current connection is sustained.
## 3 Results  
Eighty-five children with posterior crossbite were screened. Eight of these patients were excluded based on the inclusion/exclusion criteria and three patients were subsequently lost to follow-up (Figure 1). The age range of the patients was 5-9 years. The final group study therefore consisted of 74 patients (44 girls and 30 boys, mean age \(\pm\) SD = 7.7 \(\pm\) 1.1 years) divided into two groups: the first group (called Group A) consisted of 38 subjects (21 girls and 17 boys, mean age \(\pm\) SD = 7.7 \(\pm\) 1.1 years) treated with RME, and the second group (called Group B) consisted of 36 subjects (23 girls and 13 boys, mean age \(\pm\) SD = 7.6 \(\pm\) 1.0 years) treated with SRPE. The CONSORT flow diagram (Figure 1) demonstrates patient flow through the clinical trial. At baseline (T0), the two groups do not show significant differences for any of the skeletal variables (Table 1). The mean (\(\pm\)SD) treatment duration was seven months (\(\pm\)one month). Dental baseline (T0) measurements for both the groups are not significantly different (Table 2). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Group A (n = 38)**} & \multicolumn{2}{c}{**Group B (n = 36)**} & \\ \hline
**Cephalometric Measurements** & **Mean** & **SD** & **Mean** & **SD** & **95\% CI for Mean** & \(p\) **Value (\(t\) Test)** \\ \hline ANB (degrees) & 4.6 & 2.4 & 4 & 2.1 & 0.57 & 0.1 \\ \hline SNB (degrees) & 76.3 & 3.1 & 75.7 & 3.1 & 0.53 & 0.3 \\ \hline SNA (degrees) & 80.8 & 3.4 & 79.7 & 3 & 1.1 & 0.2 \\ \hline SN-GoGn (degrees) & 32.8 & 4.3 & 33.8 & 5.2 & \(-\)1 & 0.5 \\ \hline SN\_ANS\_PNS (degrees) & 9.3 & 3.2 & 9.3 & 3.6 & \(-\)0.1 & 0.9 \\ \hline ANS\_PNS\_GoGn (degrees) & 26.2 & 4.2 & 27.1 & 4.8 & \(-\)0.8 & 0.8 \\ \hline P-A face height (percentage) & 62.9 & 3.8 & 62.2 & 4.1 & 0.7 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline cephalometric comparison of experimental groups. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Group A (n = 38)**} & \multicolumn{2}{c}{**Group B (n = 36)**} & \\ \hline
**Baseline Data** & **Mean** & **SD** & **Mean** & **SD** & **95\% CI for Mean** & \(p\) **Value (\(t\) Test)** \\ \hline Maxilla & & & & & & \\ \hline Intermolar distance & & & & & & \\ (mesibuccal cusp tips) & 45.1 & 3.9 & 44.7 & 5.6 & 0.4 & 0.87 \\ \hline Intercanine distance & & & & & & \\ (mesibuccal cusp tips) & 29.6 & 3.7 & 28.4 & 5 & 1.2 & 0.51 \\ \hline Mandible & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Baseline measurements (mm) for comparison of experimental groups. The cephalometric measurements significantly changed in Group B from T0 to T1 for the SN-GoGn angle (increasing value) (\(t\) test = 0.009; \(p\) < 0.001) and for the P-A face height percentage (decreasing value) (\(t\) test = 0.008; \(p\) < 0.001), as shown in Table 3. The dental measurements significantly changed in Groups A and B from T0 to T1 (Table 4) for the following measurements: the maxillary interpolar and intercanine distance (Group A, \(t\) test = 0.000; Group B, \(t\) test = 0.000, both for \(p\) < 0.01); the mandibular intercanine distance (Table 5).
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 4 Conclusions  
By establishing an optimal nominal trajectory that takes into account the minimum attack time and the rate of change in the control quantity, combined with the approach of high-order sliding mode control and self-adaptation, this paper proposes a trajectory tracking control method based on the adaptive high-order sliding mode, thus achieving good tracking control for nominal trajectories. The main conclusions of this paper are as follows:  
1. This paper establishes the nominal trajectory that takes into account the flight time and the rate of change in the control quantity by using the Gaussian pseudospectral method and nonlinear programming;
2. Based on the sliding mode control disturbance rejection, the high-order sliding mode control introduced in this paper reduces the chattering problem of the control variables to a greater extent than in the ADRC method;
3. By introducing the adaptive control method, this paper ensures that the controller design maintains a good control effect in the presence of initial state interference.
4. The control variables and state variables for the trajectory tracking control method, based on the adaptive high-order sliding mode, can effectively converge to the nominal trajectory, and the terminal error is small.  
Conceptualization, [PERSON] and Y.W.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON], [PERSON] and C.Z.; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Aeronautical Science Foundation Project] grant number [20180153002, 20200001053001].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Terminal State** & **AHSTC** & **LQR** & **ADRC** \\ \hline \(y\) coordinate/\(m\) & 4.3235 & \(-9.1456\) & 3.6223 \\ Trajectory & 0.0403 & 0.01858 & 1.4914 \\ inclination \(\theta\)/\({}^{\circ}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tracking error based on Simulation Analysis 3.  
**Data Availability Statement**: All data in our paper are included within the article.  
**Conflicts of Interest**: All the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Recent Advances in Optical Detection of Aminoglycosodies
## 2 Fluorescent Assays
### Turn-on Fluorescent Analysis Strategies
The binding of kanamycin to the aptamer changed the aptamer structure into a folding state. Thus, the addition of kanamycin caused the aptamer to lose its binding ability to the SSB and led to the subsequent FL recovery. The GQDs-based FL sensing platform showed a linear range of 0.017-154.4 nM for the detection of kanamycin and a LOD of 0.01 nM [37]. [PERSON]'s group prepared FL GQDs by using citrate and glutathione as the starting materials. Fe\({}^{3+}\) showed a significant FL quenching effect on the GQDs due to the formation of a charge transfer complex with the GQDs. The coordination of Fe\({}^{3+}\) with GQDs could be disrupted by neomycin, causing the turn-on of the FL intensity around 430 nm. Thus, the GQD-Fe\({}^{3+}\) probe was employed as an FL probe for the sensing of neomycin sulfate. The linearized analytical response covered three orders of magnitude from \(10^{-7}\) to \(10^{-5}\) M and the LOD was 74 nM [38]. Furthermore, nanomaterials can act as energy acceptors to construct FRET assays. These nanomaterials include Au NPs, Ag NPs, graphene oxide (GO), carbon nanotubes (CNTs), MoS\({}_{2}\) nanosheet, etc. [40; 41; 42]. Au NP was employed in the establishment of a FRET-based assay for the detection of kanamycin by using Ag NCs and aptamers as the fluorophore and recognition elements, respectively. The well-dispersed Au NPs could quench the FL of Ag NCs efficiently, while the aggregated Au NPs had a weak quenching ability. The adsorption of aptamers on the surface of Au NPs protected the Au NPs from aggregation in a high-concentration of salt solution, leading to an efficient FRET between Au NPs and Ag NCs. The formation of the aptamer-kanamycin complex resulted in the aggregation of Au NPs in the salt solution and subsequent FL recovery. The fluorescence increased linearly with the increase in kanamycin in the 5-50 nM concentration range. The LOD was 1.0 nM [17].
## 4 Simulation and Verification
### Simulation Model
The capacity of DGs and the location positions of DGs would simultaneously affect the short-circuit currents. In the meantime, the short-circuit currents also vary with time because of the fluctuation in electricity loads and the output power of DGs. In the third case, the short-circuit currents of some branches are also obtained by using our proposed method. On sunny days, the calculated currents are shown in Figure 11a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Branch**} & \multicolumn{2}{c}{**Short-Circuit Current on**} & \multicolumn{2}{c}{**Short-Circuit Current on**} & \multicolumn{2}{c}{**Short-Circuit Current on**} \\  & \multicolumn{2}{c}{**Sunny Day/kA**} & \multicolumn{2}{c}{**Cloudy Day/kA**} & \multicolumn{2}{c}{**Rainy Day/kA**} \\ \cline{2-7}  & **4:00** & **13:00** & **4:00** & **13:00** & **4:00** & **13:00** \\ \hline
1-2 & 1.20 & 1.2167 & 1.20 & 1.2182 & 1.20 & 1.22 \\ \hline
3-2  
under the three-phase short-circuit fault with a resistance of 0.1 \(\Omega\), while the calculated currents on cloudy days and rainy days are shown in Figures 11b and 11c, respectively. Figure 11: Short-circuit currents considering the fluctuation in loads and DGs in case 3: (**a**) short-circuit currents on sunny days; (**b**) short-circuit currents on cloudy days; (**c**) short-circuit currents on rainy days. As shown in Figure 11a, on the sunny days in case 3, the minimum short-circuit current equals 0.9384 kA at 4:00, while the maximum short-circuit current equals 1.0055 kA at 20:00 for branch 4-5. But on cloudy days and rainy days, the short-circuit currents vary over time. Take short-circuit currents at 13:00 in branch 4-5 as an example: the short-circuit current equals 0.9552 kA on sunny days, while the current equals 0.9595 kA on cloudy days and 0.9646 kA on rainy days. In other words, the short-circuit currents vary with the weather.
# Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem
## 1 Introduction
Past studies related to PhenoCam-derived GCC and EC-measured fluxes mainly focus on temperate and boreal forests [25], and only a few recent studies have focused on dryland ecosystems (e.g., [29]). In the iPNW, there remains a critical need to better understand the relationship between vegetation phenology and the carbon balance of dryland ecosystems. The objective of this work was to evaluate the response of carbon fluxes to snowpack variability in a semiarid sagebrush ecosystem using a combination of eddy covariance, soil chamber, and phenology camera measurements during the years 2019 and 2020. In 2019, the semi-arid site experienced a cold and wet winter with a heavy snowpack that melted until the middle of March, while in 2020, the site experienced a warm and dry winter. As a result of the impact of snowpack in 2019, the seasonality of vegetation phenology was delayed, and we hypothesized that the ecosystem carbon balance (i.e., NEE, GPP, and Reco) would also be altered. Specifically, our objectives were (1) to characterize how the snowpack influences vegetation phenology change and carbon uptake, as well as the diel and seasonal patterns of carbon fluxes in the semiarid ecosystem; (2) to compare the calculated Reco and measured soil respiration, and evaluate their performances in representing the respiration of dryland ecosystems.
## 2 Evolution of the Fronthaul Network Technologies in C-RAN Architecture
The investigated techniques are preliminary and still in a study phase. Traditional C-RAN solutions as above have been deemed not adequate because of: (i) the high bandwidth resource consumption in all of the fronthaul segment from the BBUs to the RRUs due to the high bit rate of the CPRI flows; and (ii) the high extension of the fronthaul network that compromises the Hard Automatic Repeat reQuest (HARQ) and Automatic Repeat reQuest (ARQ) real-time functionalities of the mobile network when these functionalities are located in the BBUs. The scientific community is converging towards a solution ad hoc for 5G in which the 5G RAN functions are re-split [1], as illustrated in Figure 2. The original BBU and RRU are reconstructed as three functional entities: Centralized Unit (CU), Distributed Unit (DU) and RRU. The CU mainly provides the non-real-time wireless high-level protocol processing function such as radio resource management; it can use general hardware platform and be deployed together with mobile edge computing. A DU mainly processes physical layer functions and the real-time HARQ and ARQ ones through a dedicated equipment platform or a general+dedicated hybrid platform. The network segment between the CU and DU is referred to as Midhaul and its bandwidth dimensioning is comparable to the one of the backhaul of traditional mobile networks. The RRU could host the radio functions as well as some physical layer functions to reduce the bandwidth needed between the DU and RRU. The network segment between DU and RRU is the new fronthaul. Its extension is limited with respect to the previous solution and that allows us to overcome the two limitations previously mentioned. The industry has not yet reached a consensus on the standardization of the underlying function division solution between DU and RRU. The objective of this paper is to propose and investigate the performance of a fronthaul network based on OTN/WDM transport technology. The proposed solution allows for a dynamic bandwidth allocation. We introduce an analytical model to evaluate the advantages of the proposed solution with respect to a static bandwidth allocation strategy.
## 2 What VIs Measure
The EVI is more functional on NIR reflectance than on Red absorption, and therefore it does not \"saturate\" as rapidly as NDVI in dense vegetation, and it has been shown to be highly correlated with photosynthesis and plant transpiration in a number of studies [3]. The EVI is one of the two VIs available from the MODIS sensors and it is increasingly used in phenological, productivity and evapotranspiration (ET) studies, as documented in **Section 4**. Where soil effects on NDVI are a problem, alternative VIs such as the Soil Adjusted Vegetation Index or the Scaled Difference Vegetation Index can be used [e.g., 15].
## 2 Materials and Methods
### Adsorption Kinetics and Individual Material Adsorption Capacity  
The contact time and adsorption kinetics of graphene oxide (GO) and zeolite were studied to determine their individual MB adsorption capacities. All adsorption investigations were carried out in triplicate. In 250 mL Erlenmeyer flasks, 100 mL of a 100 mg L\({}^{-1}\) MB solution was mixed with 100 mg of the adsorbent material. The mixture was stirred at 250 rpm. Aliquots were taken every 3 min for the first 60 min, then every 10 min until 180 min. MB concentration in each sample was analyzed by UV-vis spectroscopy. This procedure was repeated for the ZGO2.5, ZGO5, and ZGO10 composites. The effect of pH on MB adsorption was investigated using a batch test. The procedure was similar to the one described above, with the MB solution pH adjusted from 2 to 10 using 0.1 N NaOH and HCl solutions. Adsorption isotherm experiments were conducted at different temperatures (298 K, 303 K, 313 K, and 333 K) using 10 mL of solutions at concentrations of 10, 25, 50, 75, 100, 250, 500, and 1000 mg L\({}^{-1}\). Adsorption was studied using a batch test at a fixed pH of 6.5, using 10 mg of the nanocomposites. Methylene blue adsorption capacity (\(qt\)), defined as the amount of adsorbed MB per unit mass of adsorbent, was determined using the following equation [5; 38]  
\[\mathrm{qt}=\frac{(\mathrm{C}_{0}-\mathrm{C}_{\mathrm{e}})\mathrm{V}}{\mathrm{ W}} \tag{1}\]  
where C\({}_{0}\) (mg g\({}^{-1}\)) and Ce (mg L\({}^{-1}\)) are the initial and final methylene blue concentrations, respectively. \(V\) (L) is the volume of the methylene blue solution, and W (g) is the mass of the nanocomposite. In the adsorption equilibrium conditions, Ct = Ce and qt = qe. Ce is the equilibrium concentration, and qe denotes the equilibrium adsorption capacity. The removal efficiency (RE%) of the nanocomposite can be calculated as follows:  
\[\mathrm{RE\%}=\frac{(\mathrm{C}_{0}-\mathrm{C}_{\mathrm{e}})}{\mathrm{C}_{0}} \times 100 \tag{2}\]
## 4 Experiments and Analysis of Results
### Data Embedding Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threshold Value** & \(B=32\) & \(B=64\) & \(B=128\) & \(B=256\) & \(B=512\) \\ \hline (2, 2) & 5.1978 & 5.1930 & 5.1897 & 5.1867 & 5.1839 \\ (3, 3) & 6.1318 & 6.1287 & 6.1265 & 6.1245 & 6.1226 \\ (3, 4) & 5.1978 & 5.1930 & 5.1897 & 5.1867 & 5.1839 \\ (4, 4) & 6.5989 & 6.5965 & 6.5948 & 6.5933 & 6.5919 \\ (4, 5) & 5.7582 & 5.7544 & 5.7518 & 5.7494 & 5.7471 \\ (5, 5) & 6.8791 & 6.8772 & 6.8759 & 6.8746 & 6.8735 \\ (5, 6) & 6.1318 & 6.1287 & 6.1265 & 6.124 & 6.1226 \\ (5, 7) & 5.5981 & 5.5940 & 5.5912 & 5.5886 & 5.5862 \\ (6, 6) & 7.0659 & 7.0643 & 7.0632 & 7.0622 & 7.0612 \\ (6, 7) & 6.3987 & 6.3960 & 6.3941 & 6.3924 & 6.3907 \\ (6, 8) & 5.8983 & 5.8947 & 5.8923 & 5.8900 & 5.8879 \\ (12, 12) & 7.5330 & 7.5327 & 7.5320 & 7.5311 & 7.5306 \\ (12, 14) & 6.7991 & 6.7975 & 6.7968 & 6.7949 & 6.7931 \\ (12, 16) & 6.2487 & 6.2370 & 6.2463 & 6.2426 & 6.2399 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Embedding rate under different parameters of the algorithm. values, surpasses [PERSON] et al. 's by approximately 2.12 bpp. Notably, our algorithm based on (6, 6)-threshold achieves an average embedding rate exceeding 7 bpp across these six images, significantly outperforming existing algorithms. This marked advancement positions our algorithm as a more practical solution, capable of embedding significantly larger volumes of data within images.