esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Air Quality Assessment  
The Air Quality Index (AQI), a numerical metric, is utilized to quantitatively define the level of air quality. It has the capacity to mirror both the long-term temporal trends and spatial distribution characteristics of air quality within a particular region. This numerical representation, marked by its simplicity and lucidity, ranges from 0 to 500.  
In line with the Ambient Air Quality Standards (GB 3095-2012), six conventional atmospheric pollutants are adopted for air quality evaluation: sulfur dioxide (SO\({}_{2}\)), nitrogen dioxide (NO\({}_{2}\)), particulate matter with an aerodynamic diameter smaller than 10 \(\upmu\)m (PM\({}_{10}\)), particulate matter with an aerodynamic diameter smaller than 2.5 \(\upmu\)m (PM\({}_{2.5}\)), ozone (O\({}_{3}\)), and carbon monoxide (CO).  
Figure 4: 3D contour map of the study area.  
As shown in Figure 5, which graphically depicts the AQI levels along with their corresponding color-coded designations, the AQI values are categorized into six distinct grades, each corresponding to a specific air quality level. A wider index range and a higher grade imply more severe pollution and a heightened potential risk to human health [30].  
This research paper focuses on the dispersion patterns of typical winter pollutants in Urumqi under the prevailing topographical conditions. In this area, NO2 stands out as the dominant pollutant during the winter months.  
The formula for calculating AQI values is shown below:  
\[I=\frac{I_{\text{high}}-I_{\text{low}}}{C_{\text{high}}-C_{\text{low}}}(C-C_{ \text{low}})+I_{\text{low}} \tag{1}\]  
where: \(I\) represents the air quality index, i.e., the AQI output value, \(C\) represents the pollutant concentration, which is the input value, and the constants \(C_{\text{low}}\) is the concentration limit less than or equal to \(C\), \(C_{\text{high}}\) is the concentration limit greater than or equal to \(C\), \(I_{\text{high}}\) corresponds to the exponential limit value of \(C_{\text{low}}\), and \(I_{\text{high}}\) corresponds to the exponential limit value of \(C_{\text{high}}\).  
AQI data can reflect the long-term change trends and spatial distribution characteristics of air quality in a region.  
Due to the needs of local factories and residents for heating, the emission of NO2 is relatively high in winter. The local climate is cold and dry, which makes people's respiratory tracts relatively vulnerable. The irritation of NO2 will further aggravate the discomfort of the respiratory tract. Prolonged exposure to an environment containing NO2 may also lead to chronic respiratory diseases such as chronic bronchitis and emphysema.
## 3 Results and Discussion  
### Morphology of the Electrospun Nonwoven Fabrics  
In this research, various solutions containing different concentrations of HPO were prepared and used in the development of fibers to investigate the effect of electrospinninging solution composition on the structure of the nonwoven fabrics and the electrospinning process. SEM was used to evaluate the morphologies and fiber diameter distributions of the PLA and HPO/PLA electrospun nonwoven fabrics, as shown in Figure 1. PLA electrospun fibers were estimated to have a diameter of 1.71 \(\pm\) 1.02 \(\upmu\)m. This result is in agreement with the data previously reported by [PERSON] et al., who founded a similar average diameter for electrospun PLA fibers (-1.4 \(\upmu\)m) [73]. Smooth cylindrical and continuous fibers were obtained from the PLA electrospinninging solution. The average diameters of 3 HPO/PLA and 5 HPO/PLA electrospun fibers were 1.68 \(\pm\) 0.58 \(\upmu\)m, and 1.51 \(\pm\) 0.64 \(\upmu\)m, respectively. The diameter of the fibers decreased slightly after adding HPO. In addition, the fiber diameter distribution of HPO/PLA fibers was narrower than that of PLA fibers, as measured by the lower standard deviation in the graphs presented in Figure 1. This is possible since HPO is a natural surfactant, and its addition induces a reduction in surface tension of the resulting solution [74]. Lower surface tension reduces the resistance to the applied electric force, which causes the jet to stretch more, resulting in a thinner fiber diameter [75]. The fiber diameter of the HPO/PLA electrospun mats containing various amounts of HPO revealed that the fibers were randomly aligned and had thin diameters. [PERSON] et al. observed that increasing the amount of _Hypericum capitatum_ extract within the PLGA/GE nanofibrous mats caused the fiber diameter to drop as the viscosity of the polymer solution decreased [38]. In addition, HPO loaded-PLA electrospun nonwoven fabrics appear to have favorable fiber diameter (1.5-1.7 \(\upmu\)m) for cell adhesion and attachment, which is an advantage in biomedical applications, such as wound dressings and scaffolding. PLGA fibers with diameters ranging from 0.4 to 1.4 \(\upmu\)m are suitable for cell attachment, according to researchers who have investigated the importance of the fiber diameter on the attachment of fibroblast cells [76]. To sum up, SEM images show that both PLA and HPO/PLA electrospun mats displayed a smooth and uniform morphology.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 2 Study Site and Database
### Database
#### 2.2.2 Ground Measurements  
During two cycles of cereal fields (from December 2015 to May 2016, and from December 2016 to March 2017), ground measurements were carried out at the same time as several S1 sensor acquisitions (Figure 2). In the Kairouan plain, early December generally corresponds to the sowing season, whereas the end of March corresponds to the period of maximum cereal growth. Cereal senescence occurs prior to harvesting, between mid-April and the end of May. For each cycle, more than 20 reference fields ranging in size between 2 and 5 ha were considered, under conditions of either bare soil with varying degrees of roughness, or bearing cereals at different stages of growth (irrigated and non-irrigated) (Table 1). The first campaign in each agricultural cycle (during the month of December) was carried out on bare soil, prior to cereal growth. The fields are sufficiently large to ensure that the mean values of the radar measurements, which are affected by the presence of speckle noise, are sufficiently accurate. They were observed during both cereal cycles; moisture and roughness measurements were carried out for the soil characterization. Leaf area index (LAI) and canopy height (H) measurements were recorded for the fields with a vegetation cover. Table 2 lists the dates of the field campaigns, and the corresponding types of measurement. 1. Soil Moisture  
On each of the dates listed in Table 2, approximately 20 handheld Theta Probe measurements were made in each reference field (irrigated or non-irrigated) at a depth of 5 cm. The samples were taken from various locations in each reference field, within a two-hour time frame of the concurrent satellite acquisitions, and the Theta Probe measurements were calibrated with gravimetric measurements recorded during previous campaigns [30]. This semi-arid region is characterized by strong temporal variations in rainfall, which were revealed by the very high range of soil moisture content values (between 3.9 vol. % and 45 vol. %) measured during the experimental campaigns (Table 2). In addition to the moisture measurements carried out in reference fields, a network of seven continuous Theta Probe stations (Figure 1), installed at various bare soil locations over the studied site, was used to provide moisture measurements every 3 h. At each station, the measurements were made at depths of 5 and 40 cm. Figure 3 plots the daily values of precipitation and the mean daily volumetric surface soil moisture, estimated by calculating the mean value of the continuous recordings given by Theta Probe measurements at a depth of 5 cm during the two studied agricultural seasons. A strong correlation can be noted between the soil moisture estimations and the precipitation events. 2. Soil roughness parameters  
Radar signals are highly sensitive to the geometry of scattering surfaces. In the present context, roughness is a measure of the variations in micro-topographic height of the soil surface. Measurements
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Chemiresistors  
Chemiresistors are one of the most promising types of gas-sensors. They are characterized by simple configuration and working principle [13], increased reliability, decreased size and weight, while they are suitable for automatic packaging in wafer level thus permitting the mass-production of portable, on-chip sensor arrays [51]. Two pairs of electrodes are connected with an overlying sensing layer that is either semiconducting or metallic. Interdigitated electrodes (IDEs) are commonly used for enhanced sensing response [13]. Sensor-resistance changes upon gas exposure, while constant current/potential is applied between the two electrodes [13; 51]. Quantitative analysis of the analytes is possible by measuring the change of resistance/current [13]. A schematic representation of typical chemiresistors is presented in Figure 4.
## 3 Methodology
### Decomposition-Ensemble Learning Model for Landslide Displacement Prediction
#### 3.4.1 Data Decomposition  
The data decomposition technique is useful for the accurate prediction of landslide displacement, as it can reduce the complexity and improve the interpretability of nonlinear time series. In the present study, EEMD and kurtosis criteria were applied for landslide displacement decomposition and construction of trend and periodic components for further landslide displacement prediction.  
Kurtosis is a dimensionless parameter [44; 45] describing the waveform peak that is formulated as follows:  
\[K=\frac{1}{M}\sum_{t=1}^{M}\left[\frac{x(t)-\mu}{\sigma}\right]^{4} \tag{17}\]  
where \(M\) is the signal length, \(\mu\) presents the average of the signal, and \(\sigma\) presents the standard deviation. A decomposed component with a higher kurtosis retains more deformation characteristics.  
EEMD was used to decompose the landslide displacement data shown in Figure 3 into six IMFs and one residual (Figure 7). According to previous works [32], the noise added to the original signal and the maximum number of iterations were set to 0.2 and 100, respectively. The decomposed IMFs oscillate in descending order. The corresponding kurtoses for the decomposition components are listed in Table 1. The obtained kurtoses indicate that the decomposed residual term retains the overall deformation trend of the original time series with the largest kurtosis. Therefore, the residual component was treated as the main trend series for further landslide displacement prediction. The periodic series was obtained by subtracting the trend series from the original series [15]. As shown in Figure 7, the obtained trend components (\(y_{T}\)) and periodic components (\(y_{P}\)) show two characteristics: The trend components show an approximate monotonic increase in displacement with time, and the periodic components exhibit characteristics of a chaotic time series.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{K_{IMF1}}\) & \(\mathbf{K_{IMF2}}\) & \(\mathbf{K_{IMF3}}\) & \(\mathbf{K_{IMF4}}\) & \(\mathbf{K_{IMF5}}\) & \(\mathbf{K_{IMF6}}\) & \(\mathbf{K_{Residue}}\) \\ \hline \(-18.318\) & \(-26.058\) & \(-46.556\) & \(-36.069\) & \(-17.362\) & \(0.137\) & \(28.819\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The kurtoses for the decomposition components.  
Figure 6: Overall process of the decomposition-ensemble learning model based on EEMD and RNN.
## 7 Corrosion Control and Monitoring
[135], under the National Robotics Program funding, developed a novel autonomous metal thickness inspection robot for ship hull inspection during the dry-docking condition. The proposed climbing robot, named Sparrow, is capable of conducting multiple inspection tasks on a ship hull and navigating autonomously on the vertical metal surface. According to its description, the robot platform consists of the Sparrow bot with a thickness gauge, an app with control and a video feed, storage, and the communication unit. The HMI App screen layout is split into the upper part showing the video feed from the camera and the lower part for the locomotion control buttons and autonomous buttons. An overview of the Sparrow robotic system is illustrated in Figure 29. The hardware architecture of the Sparrow robot (see Figure 30)  
Figure 28: The construction module of a testing facility [134]. comprises six functional units, including a Jetson Nano kit as the central computing unit; a sensor unit comprising a VectorNav IMU, a wireless 1080 p camera, and Marvelmind beacons; a thickness measuring unit; a locomotive Unit (Arduino Mega 2560 and motors); and a communication unit comprising a Bluetooth adapter. For thickness measurement, a 38 DL PLUS Ultrasonic Thickness Gauge from Olympus is attached to the center part of the robot. The gauge is controlled using a linear solenoid, which makes the gauge touch the ground and obtain the thickness measurement. A series of experimental tests, including a slippage test and a thickness measurement test under various scenarios, were conducted to validate and verify the robot's parameters. It can be concluded from the test results that the proposed robot could successfully measure the metal plate's thickness in all considered scenarios. Future developments should aim at improving navigation on the curved structure and applying machine learning to classify the metal surface corrosion level.
## 2 Materials and Methods  
### Participants  
We included 18 participants with PFP (13 females and 5 males, mean age: 24.6 \(\pm\) 12.5 years, mean body height: 171.1 \(\pm\) 10.1 cm, mean body mass: 67.3 \(\pm\) 17.2 kg) and 37 participantswithout PFP (26 females and 11 males; mean age: 21.6 \(\pm\) 8.8 years, mean body height: 170.6 \(\pm\) 8.9 cm, mean body mass: 64.1 \(\pm\) 12.4 kg), who served as the control group (CG) (Table 1). Sample size calculations were performed using the G Power software package (version 3.1.9.2) and based on trunk lateral flexion strength as the primary outcome. We calculated the required minimal number of participants in the study (\(n\) = 46) to reach statistical power, \(\beta\) = 0.80 and \(\alpha\) = 0.05 [14]. Participants in the CG were matched based on their gender, age, activity level, body mass and body height. Participants with PFP were recruited by a licensed sports medicine physician according to two specific inclusion criteria. These criteria included (i) pain \(\geq\) 3 on the Visual Analogue Scale (VAS) during at least two of the following activities: stair climbing/descending, squats, running, jumping, prolonged sitting and isometric quadriceps contraction; (ii) positive patellar tilt or compression test and (iii) duration of PFP \(\geq\) 3 months prior to the inclusion in the study [25]. Participants with unilateral and bilateral symptoms were included in the study, with the worst side determined as \"affected\". All subsequent analyses were performed separately for participants with unilateral or bilateral PFP. In the recruited participants with PFP who met the inclusion criteria and volunteered to take part in the study, the following clinical tests parameters have been observed and reported based on the unilateral or bilateral presentation of PFP (Table 2): patellar tilt or compression, excessive femoral internal rotation [26]; lateral patellar tilt; patellar hypo- or hyper-mobility; contralateral pelvic drop (>3\({}^{\circ}\)) and knee valgus during the single leg-squat [27]; havicular drop test or poor foot flexibility [28; 29] and high-riding patella (Insall-Salvat index > 1.2) [30]. Participants were excluded from the study if they showed symptoms or signs of any of the following conditions: patellar tendinopathy; pre/infrapatellar bursitis; a history of previous surgery on the hip or knee; meniscal or intra-articular injury; involvement of the crucial or collateral ligaments; sign of patellar apprehension and Osgood-Schlatter or Sinding-Larsen-Johansson syndromes [27; 31]. Participants in the CG were recruited through regional sports clubs and were excluded from the study if they presented any current of previous injury of the lower extremity for at least one year prior to participating in the study. All participants gave written informed consent prior to participation. For underaged participants, informed consent was provided by the parents. The study protocol was conducted in accordance with the Declaration of Helsinki and approved by the Slovenian National Medical Ethics Committee (0120-99/2018/5).
# Current Status of HbA1c Biosensors
## 2 HbA1c Biosensors
### Affinity Biosensors Based on Boronic Acid Derivatives
#### 2.1.2 Biosensors Based on Thiophene-3-Boronic Acid (T3 BA)  
Electrochemical impedance spectroscopy (EIS) has been widely used to characterize sequential chemical modifications on an electrode surface by measuring changes of conductivity or dielectric constant, and can be adopted for the design of label-free biosensors. The instrument puts a small oscillating voltage across the membrane over a range of frequencies. The resulting current and its phase shift are the data that provide information about the interface. For example, T3 BA-SAM is bound to the gold electrode surface via the Au-S covalent bonds (Figure 1B). The formation of T3 BA-SAM followed by HbA1c on the Au electrodes are confirmed and characterized by QCM, atomic force microscopy, and EIS methods [27]. The concentration of HbA1c on the T3 BA-modified biosensors is measured by monitoring the changes of charge-transfer resistance, which are owed to the blocking of the electrode surface by the immobilized HbA1c, with hexacyanoferrate (HCF) as the redox indicator [27; 28]. Some sensors are developed to utilize the capacitive behaviors of the electrode interface to measure the surface-bound HbA1c without a need of redox indicator [29]. However, the dynamic detection range of this method is yet to match the physiological range of HbA1c from 3 to 13 mg/mL.
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 4 Results and Discussion  
### Suction Change Process  
Figure 2 shows the variation of the suction value versus time of the entire buffer layer. It can be seen that in the horizontal direction, the suction value gradually decreased from the outside to the inside, and the variation pattern in the vertical direction was consistent. There was no significant difference in the suction value of the buffer layer due to the addition of water and the gravity effect of water. In the vertical direction, there was a slight difference in the suction value between the middle and the two ends, which is because the upper and lower hydraulic boundary conditions in the model area were impermeable, and the water in the upper and lower parts could only penetrate towards the middle.  
As shown in Figure 3a, as the experiment progressed, the suction values at each point continued to decrease. The suction value close to the water injection boundary rapidly decreased to close to 0 at the beginning of the experiment. The closer it was to the water injection boundary, the steeper the curve, and the faster the suction value decreased. The closer it was to the heater, the slightly higher the curve in the early stage of the experiment, which then gradually flattened out. After about 100 days, the suction value began to decrease, and the rate of decrease was relatively small when it was close to the surrounding rock.  
Figure 3: (**a**) Variation curve of suction with time; (**b**) variation curve of suction with radial distance.  
Figure 2: Variation of suction value with time.  
As shown in Figure 3b, it can be seen that the suction value near the heater changed very little in the 100 days before the test, with suction values ranging from 60 MPa to 80 MPa. The suction value near the water injection boundary rapidly decreased and tended to zero. As the experiment progressed, the suction value gradually decreased from the heater boundary to the hydraulic boundary. At 100 days, the suction value decreased from 64.31 MPa at the edge of the heater to 0 at the hydraulic boundary edge. At 0.09 m from the edge of the heater, the suction value decreased from 72.77 MPa at 10 days to 11.36 MPa at 1000 days, reflecting the infiltration process of groundwater into the buffer layer. After 100 days, there was a significant change in the suction value at the boundary of the heater, indicating that groundwater infiltration had an impact on the buffer layer near the heater side, and the gradient of the curve significantly decreased, indicating that the suction value of the buffer layer tended to be consistent.
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 5 Conclusions  
Often, Linked Data applications pre-fetch data and place it in a cache for future access. Due to the continuous evolution of the Linked Data Cloud, the local cache becomes outdated. In this paper, we proposed a maintenance policy that performs the local cache update jobs before query evaluation. The proposed maintenance policy utilizes a change metric together with a query similarity measure to identify and update changed items. Most of the queries issued by the Linked Data client are  
Figure 4: Quality of updates performed by the proposed approach as compared to the existing approaches.  
Figure 5: Hit Rate achieved by CAMP as compared to existing approaches.  
similar in structure. Therefore, instead of running the queries repeatedly, we pre-fetched the results of these queries to improve cache hit rates. For the maintenance jobs, we combined the smaller task into one job to reduce resource utilization. We compared the effectiveness of our proposed approach to state-of-the-art approaches, namely Eager, _TTL_, _PageRank_, _Size_, _ChangeRatio_ and _ChangeRate_. The proposed approach outperformed the existing policies in terms of lower maintenance cost, higher maintenance quality and better cache hit rates. In the future, we will investigate the benefit of our proposed approach to accelerate the query time in real-world data analytics application.  
[PERSON] was the principal researcher of this study. He proposed and formulated the idea, designed and performed the experiments and wrote the paper; A.S.A. edited the manuscript for language and clarity; [PERSON] provided advisory comments, remarks, and financial aid for the paper.  
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support programme (IITP-2017-0-01629) supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP). This work was supported by an IITP grant funded by the Korean government (MSIT) (no.2017-0-00655) and NRF-2016K1A3A7A03951968.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAMP & Change-Aware Maintenance Policy \\ KBs & Knowledge Bases \\ LOD & Linked Open Data \\ DCS & Dynamic Characteristics Set \\ SPARQL & SPARQL Protocol and RDF Query Language \\ TTL & Time To Live \\ PR & Page Rank \\ DYLDO & Dynamic Linked Data Observatory \\ \end{tabular}