esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Studies
### Carbon Dioxide and Dinitrogen
#### 3.3.2 Carbon Dioxide and Dinitrogen Adsorption in K-L  
The zeolite L (structure type LTL) has a hexagonal symmetry featuring an ordered array of parallel (1D) channels running in a direction parallel to the \(c\)-axis of the crystal [101]. These channels have an internal (void) diameter of about 0.75 nm, the actual value depending slightly on the corresponding extra-framework cation.  
Representative variable-temperature IR spectra of CO\({}_{2}\) and N\({}_{2}\) adsorbed on K-L (Si:Al ratio about 6:1) are shown in Figure 11a,b, respectively. Details on the experimental measurements can be found in ref. [102]. The CO\({}_{2}\) adsorption complex shows a single IR absorption band centred at 2346 cm\({}^{-1}\), which should be assigned to the antisymmetric (v\({}_{3}\)) vibration mode of the adsorbed molecules, as already stated above. Regarding N\({}_{2}\), Figure 11b, the corresponding adsorption complex shows also a single band, which peaks at 2330 cm\({}^{-1}\). According to previous reports [103; 104; 105] this band is assigned to the fundamental N-N stretching mode, which becomes IR active when the molecule is perturbed by interaction with the alkali-metal cation.  
Figure 11: (**a**) Variable temperature FT-IR spectra (zeolite blank subtracted) in the antisymmetric (v\({}_{3}\)) stretching region of CO\({}_{2}\) adsorbed on K-L. From top to bottom, temperature goes from 141 to 170 K;  
Figure 10: Van’t Hoff plots corresponding to the set of IR absorption bands at 2346 (blue) and 2355 cm\({}^{-1}\) (red) of CO\({}_{2}\) adsorbed on K-FER (Si:Al ratio 27.5:1), shown in Figure 9 (adapted from [99]).  
and equilibrium pressure from 2.2 to 3.6 mbar. (**b**) Variable temperature FT-IR spectra (zeolite blank subtracted) in the N-N stretching region of N\({}_{2}\) adsorbed on K-L. From top to bottom, temperature goes from 272 to 306 K; and equilibrium pressure from 7.2 to 9.8 mbar (adapted from [102]).  
After computer integration of the full set of spectra obtained for CO\({}_{2}\) and for N\({}_{2}\), the corresponding van't Hoff plots depicted in Figure 12 were obtained. Those linear plots yielded the values of \(\Delta H^{0}=-42.5\) kJ mol\({}^{-1}\) and \(-20.6\) kJ mol\({}^{-1}\) for the adsorption enthalpy of CO\({}_{2}\) and N\({}_{2}\), respectively. This large difference in \(\Delta H^{0}\) should facilitate efficient (thermodynamic) separation of the two gases being considered.
## 4 Experiments  
### TIF Experiment  
To verify the effectiveness of optimizing the TIF by means of the eccentric compression method, we conducted TIF experiments on JGS2 glass using the eccentric pressured small tool polishing device installed on the robotic polishing system.  
Experimental steps: (1) Select a piece of JGS2 glass with good flatness, mark the coordinate directions, and use an interferometer to detect the initial surface shape of the JGS2 glass. (2) Use the industrial robot to drive the new polishing device to conduct fixed-point polishing at the center of the glass, and then measure the surface shape after polishing. (3) Subtract the two surface shapes to obtain the actual TIF. The experimental parameters are shown in Table 3. For the first four parameters, we have selected the values that are consistent with those in the TIF modeling. In addition, we have adopted the rotating speed and the types of polishing powder that are commonly used in our engineering practice.  
In order to facilitate obtaining the shape of the TIF and considering the removal efficiency, the polishing time is set to 60 s. During the experiment, the temperature was maintained at 24 \(\pm\) 0.5 \(  
Figure 11 normalizes the theoretical and experimental TIF. It can be seen from Figure 7 that the theoretical results are in good agreement with the experimental ones, which proves the correctness of the theoretical model. However, there are still slight differences between the theoretical results and the actual results. One possible reason is that during the modeling process, the asphalt in the polishing disk was approximated as an elastic body, so there are still some differences between the experimental TIF and the theoretical TIF. If the viscoelastic properties of asphalt could be introduced into the model, the degree of conformity between the experimental TIF and the theoretical TIF would be further enhanced. In addition, factors not taken into account in the theoretical model, such as the influence of environmental factors and measurement errors during the experiment, have an impact on the accuracy of the theoretical model.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 4 Real-Time ZTD Grid Product Evaluations  
In this section, the accuracy of the generated real-time ZTD grid product (RtZTD) will be first assessed at test stations by comparing with post-processing ZTD solutions. Simulated real-time PPP convergence tests will also be carried out to evaluate the improvements for PPP users by using RtZTD.
## 2 Related Work
### Coordination Algorithms for Evacuation  
Much research has demonstrated that cooperative strategies have a significant positive influence on multi-agent systems, and related studies have emerged for decades as organizational paradigms in agent-based structures [22] and autonomous search by large-scale robotic systems [6,23]. However, most related research on emergency evacuation focuses on developing diverse models [24] to simulate the crowd or coordinated behaviours, such as kin behaviour [25] in emergencies.  
\"Social potential fields\", which were initially used for coordinating robots in a very large-scale robotic system [6], have been employed for emergency navigation in [26]. A self-organizing sensor network is established with \"artificial potential fields\" where artificial forces exist. Exits and dangerous zones generate attractive forces and repulsive forces, respectively, and evacuees move under the actuation of the resultant force. However, the original SPF considers the interactions between robots to avoid congestion, while the artificial potential fields in [26] does not take influences among evacuees into account.  
Emergency evacuation often accompanies the malfunction of communication infrastructures or sensors, which can be caused by an on-going hazard or malicious attacks. The failure of the communication infrastructure can render a centralized system invalid, while the malfunction of sensors will affect the QoI and accuracy of localization. To solve the above problems, some coordinated behaviour-involved algorithms, such as opportunistic communications and delay tolerant networks, have been investigated to increase the resilience of emergency management systems [27, 28, 29]. For instance, [30, 31, 32] have proposed a resilient emergency support system (ESS) with the aid of opportunistic communications [33]. This system consists of sensor nodes (SNs) and communication nodes (CNs). SNs can detect the hazard in its vicinity and inform the evacuees passing by of the location, while CNs are portable devices that are taken by occupants. The civilians initially wander in the environment and can only inform the civilians about the hazard within its communication range. Each civilian calculates the shortest path to the exits with [PERSON]'s algorithm. However, this work concentrates on studying the impact of \"passive\" cooperation among evacuees, who exchange emergent messages with other civilians, and no active mechanism is used to improve the information dissemination efficiency.
# Environmental Remediation of Metribuzin Herbicide by Mesoporous Carbon--Rich from Wheat Straw
## 2 Materials and Methods
### Preparation and Characterization  
The wheat straw biomass was transformed into biochar through slow pyrolysis in an electrical furnace at a heating rate of 10 \({}^{\circ}\)C/min and held 2 h at 800 \({}^{\circ}\)C. Based on preliminary studies, biochar is obtained in an oxygen limited conditions under different temperatures ranging from 200 \({}^{\circ}\)C to 800 \({}^{\circ}\)C [46; 47]. Previous literature indicates that the pyrolysis temperature is one of the key factors determining the physicochemical properties of the biochar [48; 49]. Superior temperatures, similar or upper to 500 \({}^{\circ}\)C, generate biochar with a high specific surface area, pH and C content and less volatile matter [50]. The pyrolyzed specimens were then removed from the furnace and allowed to cool. To remove residual inorganic matters, the obtained biochar was treated with HCl 0.1 n and then washed with ultra-pure water. The wheat straw biochar samples (WSB) were ground and sieved to obtain particles with a size of 500-700 nm. The chemical composition of WSB and WS were determined as follows. By oven drying at 110 \({}^{\circ}\)C, the moisture content was obtained until a constant weight was reached [51]. In the muffle furnace, the ash content was measured by combustion at 750 \({}^{\circ}\)C for 8 h, while the volatile matter content was obtained by pyrolysis at 900 \({}^{\circ}\)C for 5 min.  
The morphological features of wheat straw and biochar were performed via scanning electron microscopy (SEM) (FEI--Field Electron and Ion Company) having an energy dispersive X-ray (EDX) unit. The surface functional groups were identified from FTIR spectra (FTIR spectrometer Interspec 200-X, spectral domain = 400-4000 cm\({}^{-1}\), resolution = 4 cm\({}^{-1}\), KBr pellet technique).
## 4 Challenges and Future Directions
### Storage Capacity  
The adoption of blockchain in the IoUT eliminates the need for a central server to handle the transactions and device data. However, the ledger needs to be stored in the IoUT nodes themselves. The number of such ledgers can increase over time and become quite challenging to handle. IoUT devices usually have very low storage space. The comparatively lower disk space and memory of IoUT devices and network connectivity issues are some of the key factors affecting the adoption of blockchain technology in an IoUT environment. Most of the current blockchain implementations can handle only a few transactions per second. This would be another bottleneck in the case of an IoUT environment. However, the data generated from IoUT sensors are very huge, making it really difficult to handle such big data. The integration of the IoUT with a decentralized and public blockchain is quite hard and should be dealt with considering the increasing data storage requirements. One of the possible solutions includes leveraging cloud resources. The authors in [99] proposed an \"advanced time-variant multi-objective particle swarm optimization algorithm\" for the integration of the IoT and blockchain. However, such techniques can be enhanced and used for the integration of the IoUT and blockchain.
# Multiple Genetic Rare Variants in Autism Spectrum Disorders: A Single-Center Targeted NGS Study
## 1 Introduction  
Autism spectrum disorder (ASD) is defined as a group of clinical heterogeneous disorders characterized by deficits in social behaviors and communication, restricted interests, and repetitive behaviors, often accompanied by other impairments, such as intelligence and language deficits [1]. ASD typically manifests in the first years of age and is persistent throughout life. The overall prevalence is estimated to be 16.8 per 1000 (one in 59) children aged 8 years and varied by sex: males were four times more likely than females to be identified with ASD [2]. The heritability of ASD is supported by studies reporting ASD recurrence in 11-19% of infants with at least 1 older sibling with ASD, while the general prevalence in the US population is between 1% and 2% [3, 4]. Moreover,twin studies revealed high concordance rates for ASD in monozygot twins (88-95%), compared to dizygotic twins (31%) [5], giving further support to the strong influence of genetic factors on ASD. Many studies based on chromosomal microarray, targeted gene sequencing, whole-exome sequencing (WES), and whole-genome sequencing (WGS) identified hundreds of genes associated with autism risk [6; 7; 8; 9; 10], demonstrating that there are several complex genetic factors that contribute to ASD risk. According to the published studies, array-CGH analysis successfully identified causative genomic rearrangements (microdeletions/microduplications named copy number variations, CNVs) in 8-21% of individuals with ASD, the majority of which were affected by severe phenotypes [11; 12; 13; 14; 15]. CNVs can therefore contribute to the genetic susceptibility to ASD in approximately 10% of subjects and provide a diagnostic rate of 5-10%. However, our ability to identify causative genetic risk factors in a single individual is still not satisfactory, and our understanding of the role played by these variants in the causation of diseases, particularly those inherited from seemingly unaffected parents, remains limited. Overall, the effort to identify the genetic background that causes or contributes to ASD symptoms could be hampered by studies on non-homogeneous patients, such as studies involving both complex and essential ASD. The aim of this study is to screen a group of 40 clinically well-determined ASD patients with no dysmorphic features using NGS analysis, based on a custom panel of 120 genes. Our goals were to evaluate the presence of rare inherited or de novo genetic variants in all patients and their parents, when possible, in order to determine their role in ASD onset. We did not take into consideration common variants (shared with more than 1% of the human population), and we focused only on those with possible pathogenetic roles that are not present in the family genetic background.
## 4 Preprocessing of the Measured Data  
The human trapped under the debris generates a time-varying signal caused by physical movements like breathing. The detection of such a signal is a positive indication of the potential presence of a survivor. Although the movement area of the trapped survivors within the debris is narrow, they still create a movement on the body surface of approximately 1-4 mm while they breathe. Additionally, there can be body movements of a few centimeters. The amplitude and phase of the reflected signal change according to the movement of the survivor, which allows for motion detection when a microwave is spread out into the debris. However, as well as recording the vital signal of the human, the background noise is also measured. Thus, preprocessing of the signal to increase the probability of detection (PD) is indispensable. In the following subsection, a brief overview of denoising, STFT, CWT, and EMD is provided. In addition, GANs used for data augmentation in preprocessing techniques are presented. The distribution of the measured signal energy in the time-frequency plane are presented, along with the corresponding analysis parameters.
## 1 Introduction
* A novel multi-objective (MO) function was created that incorporates a strong damping controller architecture for SMIB systems and may be used in large power systems. * The proposed EPSCA is validated with the standard SCA, MFO, and EP techniques. The modelling of an SMIB power system installed with a TCSC, a TCSC SMIB system installed with PIDF controller, and a proposed multi objective function is described in Section 2. Section 3 describes the proposed EPSCA, SCA, MFO, and EP algorithms. The results of simulations of four different loading condition scenarios are represented in Section 4. Section 5 presents the discussion section. Finally, Section 6 condenses the conclusions.
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## Materials and Analytical Methods
### Data Processing  
The obtained XRF-scan intensities of core FXH-B2 were calibrated to element fractions by the aforementioned calibration methods (the calibrated XRF-scan intensity hereafter called \"calibrated-XRF\"). The XRF-scan data was directly compared with conventional-fractions and calibrated-XRF results. Forty samples (hereafter \"reference samples\"), for which conventional-fraction data from core FXH-B2 were available, were used to convert XRF-scan intensities to calibrated-XRF element fractions. The remaining 34 samples (hereafter \"remaining samples\") of conventional-fractions were used to evaluate the differences between the XRF-scan data and conventional-fractions and to assess the performance of the different calibrations. A squared relative error (SRE) and mean squared relative error (MSRE) of elements, as presented below, were used to measure the goodness-of-fit to perform the evaluation:  
\[SRE=\ \frac{1}{n}\sum_{p=1}^{n}\left[(\frac{C_{pq}}{C_{qM}}\cdot W_{qM}-W_{pq})/ W_{pq}\right]^{2} \tag{2}\]  
\[MSRE=\ \frac{1}{D}\sum_{q=1}^{D}SRE \tag{3}\]  
where \(n\) and \(D\) represent the number of the remaining samples and number of the calibrated elements, respectively, \(W_{qM}\) and \(C_{qM}\) represent the median values of the remaining conventional-fractions and remaining XRF-scan/calibrated-XRF of element \(q\), respectively, and \(W_{pq}\) and \(C_{pq}\) are the conventional-fraction and XRF-scan/calibrated-XRF of element \(q\) at depth \(p\), respectively.
## 4 The Time of Occurrence and Frequency Characteristics Identification  
### The Identification of the Start/End Time  
In this section, CWT is used to identify the start/end time of refrigerant flow noise. In this study, the Daubechies 4 wavelet, which has been most widely used, was selected. It is commonly used due to its great performance against irregular signals. Using the Daubechies 4 wavelet, wavelet coefficients can be estimated as follows (Figure 3):  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** \\ \hline \(\mu\) & \(-1.0\times 10^{-3}\) & \(8.0\times 10^{-3}\) & \(3.0\times 10^{-3}\) & \(2.3  
Figure 3: Continuous Wavelet Coefficient: (**a**) Scale: 5, (**b**) Scale: 10, (**c**) Scale: 15, (**d**) Scale: 20, (**e**) Scale: 25.  
The scale represents \(a\) in Equation (1) and can either dilate or compress a signal as the scale changes. High wavelet coefficients refer to a point where signals rapidly change, and this spot is the start or end of the refrigerant flow noise. Figure 4 below shows individual scales' coefficients (5, 10, 15, 20 and 25) for setting the criteria to identify this point:  
Each scale's average is the same with zero, but different variances were found in distribution curves. The CWT coefficients revealed the highest distribution at zero. If original signals rapidly change, the CWT coefficients' absolute values fluctuate greatly. Hence, each scale's 99.5 th percentile of distribution was used to classify a singular point. Table 2 lists the 99.5 th percentiles of CWT coefficients by scale.  
Figure 4: Distribution of continuous wavelet coefficients: (**a**) Scale: 5, (**b**) Scale: 10, (**c**) Scale: 15, (**d**) Scale: 20, (**e**) Scale: 25.  
In Table 2, the most common singular points are 144-170, 198-232 and 313-317 s (see Table 2 for the boldface). A jury test was conducted with 16 experts on air conditioner noise. Each noise interval was heard by experts to determine whether it was slug, churn or annular noise. In this test, three scales were used, and the expert listeners were asked to answer whether they could hear or not, or if they had a neutral opinion (not sure). Then it was identified that they were the starting/ending points of refrigerant flow noises. In addition, different characteristics of refrigerant flow noises were observed in these time intervals through the sound test. According to each time interval of refrigerant flow noise, therefore, 144-170, 198-232 and 313-317 s were categorized as slug flow, churn flow and annular flow, respectively.  
For classification of refrigerant flow noise, an SVM was conducted on the refrigerant flow noise and its frequency characteristics in each time interval.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Probiotic Strain** & **Survival \({}^{\dagger}\) (\%)** & **Moisture \({}^{\dagger\dagger}\) (\%)** \\ \hline _L. rhamn_s & 91.23 \(\pm\) 2.88 \({}^{\text{a}}\) & 2.95 \(\pm\) 0.17 \\ _L. plantarum_ ATCC 8014 & 94.77 \(\pm\) 1.95 \({}^{\text{a}}\) & 3.52 \(\pm\) 0.18 \\ _B. infants_ ATCC 15679 & 97.24 \(\pm\) 2.32 \({}^{\text{a}}\) & 5.77 \(\pm\) 0.20 \\ _B. longum_ ATCC 15707 & 96.07 \(\pm\) 0.07 \({}^{\text{a}}\) & 4.70 \(\pm\) 0.41 \\ \hline \hline \end{tabular}  
* Same letters in the same column indicate not significantly different (\(p<0.05\)) according to [PERSON]’s test. \({}^{\dagger}\) Mean values of two replicates. \({}^{\dagger\dagger}\) Mean values of four replicates.  
\end{table} 
Table 5: Survival of probiotic strains encapsulated by spray drying with chia seed mucilage (0.6% w/v) at 90 \({}^{\circ}\)C and moisture content after spray drying. its viability drastically after 28 days. Meanwhile, after 60 days of storage, viability of _B. longum_, _L. plantarum_, and _B. infants_ reached counts of 1.70 \(\pm\) 0.08, 5.53 \(\pm\) 0.20, and 6.45 \(\pm\) 0.23 log CFU/g, respectively. In this context, _B. infants_ and _L. plantarum_ are more stable than _B. longum_ and _L. rhamn_ at 25 \({}^{\circ}\)C. These results, compared to counts at 4 \({}^{\circ}\)C, represent a loss in viability for _B. longum_, _L. plantarum_, and _B. infants_ of 83, 44, and 36%, respectively. The reduction in the number of viable probiotics stored at 25 \({}^{\circ}\)C can be explained because at this temperature, the metabolic activity is higher and nutrients are consumed quickly [69]. A solution to protect the viability of probiotic bacteria during storage is to perform double encapsulation, where the outer layer has a protective function and the inner layer a nutritional function.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 2 Literature Review
### KNNI Method  
Another imputation method is k-nearest neighbor imputation (KNNI). It should be noted that the k value in the KNNI means that the number of neighbors near each value is k. [PERSON] et al. [38] utilized the Euclidean distance to find the predefined k value of records from the total dataset. They replaced the missing value with the mean value of its neighbors. [PERSON] et al. [39] proposed the weight k-nearest neighbor imputation (WKNNI) to estimate the missing values, and the estimation results showed that the imputation performance of the WKNNI was more robust than that of the original KNNI. In general, if the dataset was insufficient, the KNNI could perform well. However, as the dataset became larger, the KNNI could be exceedingly time consuming because this method would find the k value of similar records from the whole dataset for each missing value.