esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Uncertainties and Future Works  
The input climate data for AEM are of critical importance for the simulation results. [PERSON] et al. [37] found that the NPP of the ecosystem in Xinjiang had different spatial and temporal patterns under three reanalysis data sets. Under the same RCP scenario, different global circulation models perform differently. To minimize these uncertainties, more GCM results with better performance in the studyarea are needed to force the regional climate model or outcomes of different regional climate models could be used. However, we only used one data set to force the AEM under each RCP scenario. In addition to the changes in climate and CO\({}_{2}\) concentration, the human activities, such as grazing and farmland management activities, have an important impact on the arid ecosystem [5; 11; 12; 18; 25; 62]. However, these human activities are not considered in this study.  
However, we take several measures to reduce uncertainties. First of all, our team improved the structure of the AEM model, such as the vertical root distribution and water movement from groundwater to the canopy. This was necessary to allow the AEM to better reproduce the biogeochemical and biophysical processes of the arid ecosystem than other ecosystem models [3; 14; 17; 35]. Then, the parameterization of the AEM has been improved and the corresponding simulation results have been well validated in the arid areas of central Asia and China, which will enhance the robustness of AEM and reduce the uncertainties of the simulations [2; 4; 14; 17; 18]. Moreover, the high resolution climate forcing data were produced by dynamic downscaling, which could reduce some uncertainties from the original outputs of GCM [52].  
Moreover, the Tianshan North Slope is the core area for the Belt and Road Initiative, and is expected to experience significant urbanization and oasisization. So, in the future, we should run the model by more climate forcing data sets from different GCMs or regional climate models, and take the human activities into consideration to quantify the effects of climate change, CO\({}_{2}\) fertilization, and human activities.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Comparison Between Integrated Bearings and Deep-Groove Ball Bearings  
Under the same operating conditions (such as rotational speed, load, etc.), the vibration characteristics of integrated bearings and deep-groove ball bearings with similar structural dimensions were compared. As shown in Figure 16, the trends of vibration velocity and vibration acceleration level with respect to speed are almost identical for both integrated bearings and deep-groove ball bearings. However, the vibration acceleration level of the integrated bearing is slightly lower than that of the deep-groove ball bearing. This indicates that under the same operating conditions, the vibration characteristics of the integrated bearing and the deep-groove ball bearing are generally similar, with the integrated bearing having a more compact structure, making it suitable for more confined working environments.  
The structural parameters of the deep-groove ball bearing are presented in Table 4.  
Figure 16: Comparison between integrated bearings and deep groove ball bearings. (**a**) Trends in vibration velocity variation. (**b**) Trends in vibration acceleration variation.  
Figure 15: Trend of vibration with variation in the bearing speed. (**a**) Trends in vibration velocity variation. (**b**) Trends in vibration acceleration variation.
## 2 Materials and Methods
### Investigated Mixture Composition  
Concrete mixtures were designed with a water/cementitious materials ratio of 0.4 and contained 335 kg/m\({}^{3}\) of cementitious materials, 147 kg/m\({}^{3}\) of water, 1038 kg/m\({}^{3}\) of gravel, and between 701 kg/m\({}^{3}\) to 731 kg/m\({}^{3}\) of natural sand. The basic element was the Portland Cement Type II-V (TII-V), which is widely used in California due to the sulphate attack problem. It is to be noted that Type I cement is prohibited in California. Various SCMs were utilized, namely, Class C fly ash (C), Class F fly Ash (F), Silica fume (SF), ground granulated blast furnace slag of grade 120 (G120S), Metakaolin (M), and Volcanic Pumice Pozzolan (P). Volcanic pumice was the major ingredient, as it is incorporated in all binary and ternary concrete mixtures. The specific gravity of Cement TII/V is 3.15, that of Class C Fly Ash is 2.32, that of Class F fly Ash is 2.32, that of GGBFS 120 is 2.92, that of Silica Fume is 2.22, that of Metakaolin is 2.6, that of Pumice is 2.35, that of Natural Sand is 2.66, and that of Gravel is 2.71.  
Furthermore, a water-reducing admixture (Glenium 7500) and an air-entraining admixture (MBVR AE90) were used to achieve better processability and other workability specifications. Mixtures were marked according to the cementitious materials and their percentage replacement by mass, e.g., 70 VIII-V/15F/15P stands for 70% Type II-V Cement, 15% Class F fly ash, and 15% Pumice (see Figure 1). It can be noticed that the first concrete mixture was taken as a reference for all calculations, and was designated as OPC.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Chemical [\%]** & **Cement Type II/V** & **Class C Fly Ash** & **Class F Fly Ash** & **Slag G120** & **Silica Fume** & **Metakaolin** & **Pumice** \\ \hline SiO2 & 21.20 & 34.02 & 45.05 & 36.81 & 97.90 & 51.95 & 87.40 \\ Al2O3 & 3.90 & 18.20 & 23.71 & 9.66 & 0.18 & 44.27 & 10.52 \\ Fe2O3 & 3.50 & 6.59 & 16.43 & 0.61 & 0.07 & 0.41 & 0.174 \\ MgO & 2.10 & 5.06 & 0.88 & 10.03 & 0.21 & 0.05 & 0.126 \\ CaO & 2.70 & 2.70 & 0.68 & - & 0.17 & 0.02 & - \\ Na2O + K2O & 63.10 & 27.18 & 3.78 & 36.77 & 0.42 & 0.06 & - \\ MnO & 0.54 & 1.91 & 2.26 & 0.66 & 0.71 & 0.30 & - \\ ZnO & - & - & - & - & 0.03 & - & - \\ SiO2 & - & - & - & - & 0.08 & - & - \\ SrO & - & - & - & 1.10 & - & - & - \\ CI & - & - & - & 0.05 & - & - & - \\ Fe2O4 & - & - & - & - & 0.09 & - & 0.194 \\ Na & - & - & - & - & - & - & 0.128 \\ K & - & - & - & - & - & - & 0.099 \\ Ca & - & - & - & - & - & - & 0.090 \\ TiO2 & - & - & - & - & - & - & 0.0074 \\ S04 & - & - & - & - & - & - & 0.0041 \\ H2O & & & & & & & 1.110 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cementitious materials [26].
## 2 Materials and Methods
### The Construction of Fault Diagnosis and Recovery Algorithm  
We combined the characteristics and specific implementation process of the four functional models of temperature data acquisition, fault diagnosis, fault location, and data recovery. In this study, each function was connected in series according to the data flow to form an overall algorithm for fault diagnosis and recovery, the structure of which is shown in Figure 4.  
The algorithm first sorts the temperature data obtained by the three-way sensors, selects the maximum value in the three-way, and defines it as the temperature X. The remaining temperature values are defined as temperature Y and temperature Z. The temperature data at this time cannot be judged whether it is correct or not. It can only be divided into two situations: the steady state signal and sudden change signal. The PCA model diagnoses the three-way data, and distinguishes three situations: the steady state signal, the normal sudden change signal, and the fault signal. The steady-state signal and the normal mutation signal are the normal conditions, and the fault signal is the abnormal condition. For normal conditions, the temperature signal is output directly. For abnormal situations, the three-way data are calculated by the fault location algorithm and the abnormal situations are classified according to seven abnormal scenarios. Then, the LSTM predictor is started, bringing the predicted value P into this abnormal situation, and finally determining the faulty sensor and diagnosing the fault type. If it does not exceed the threshold, it means that the data are still normal data at this time, and the temperature signal is output directly. If the threshold is exceeded, one or more sensors are faulty. This part of the data is isolated and the temperature signal predicted by the predictor is output.  
Figure 4: The sensor fault diagnosis and restoration algorithms.
## 1 Introduction
The space-based InSAR has become a routine remote sensing technology to image the Earth because it can provide full-weather and full-time measurements with large scale coverage, high resolution, and high accuracy, resulting in its use in glacier environment [29; 30; 31; 32; 33; 34; 35]. LiDAR is another remote sensing method that uses actively dissipate LASER (Light Amplification by Stimulated Emission of Radiation) pulses to locate the positions of targets [36], which can provide a high-accuracy digital elevation model (DEM) and 3D displacement with point cloud data. More and more research is using LiDAR on the cryosphere [37; 38]. InSAR can fail in some lake ice applications due to a loss of coherence, and high costs still hinder LiDAR applications. The POT method surveys displacement by calculating the pixel offset in a pair of correlated images, which can overcome the deficiencies of InSAR. In addition, both SAR and optical images (e.g., Sentinel 1/2, ALOS1/2, Landsat 8 OLI, and SPOT images) can be used with the POT method, thus increasing its applicability, and reducing its cost. Therefore, the POT method is more suitable for ice-movement monitoring than LiDAR and InSAR [39; 40]. In this work, we utilized COSI-Corr software to measure the movement of Lake Khovsgol based on 112 time series of Sentinel-2 images. Lake Khovsgol is an important seasonal ice lake located in Northwestern Mongolia. The second largest lake in Asia, Khovsgol Lake contains about 70% of Mongolia's freshwater and is used as a transport. Previous researchers have studied Lake Khovsgol's hydrology changes and surrounding complex soil ecology [41; 42; 43]. [PERSON] et al. [44] used remote sensing images to study ice rings appearing on the surface of Lake Khovsgol. However, little research has been conducted on the movement of lake ice and its causes. Integrating a series of Sentinel-2 images and the COSI-Corr technique, the objectives of this work include three parts as follows:  
1. To determine the suitability of the POT method for studying lake ice in general. 2. To explore the duration of the ice period and the variation of lake ice by POT. 3. To analyze the correlation between lake ice and climate based on the meteorological data we collected.
## 1 Introduction
* Extensive numerical experiments are performed, which show the advantages of the PBA and the performance of the packet-switched Xhaul network in different network slicing scenarios. The remainder is organized as follows. Section 2 discusses related works. Section 3 presents the main assumptions of the considered network scenario. Section 4 formulates the network slicing-planning problem in the packet-switched Xhaul network as a MILP problem. Section 5 describes the PBA optimization. Section 6 reports and discusses the numerical results. Eventually, Section 8 presents the final conclusions.
## 3 Results and Discussion
### Ozone Deposition Velocities
#### 3.1.2 On Synthetic Fibers  
The \(v_{\mathrm{d,s}}\) values on the four types of synthetic fibers were determined after two series (i.e., \(y_{\mathrm{ozone,in}}\) = 100 ppb or \(y_{\mathrm{ozone,in}}\) = 300 ppb) of experiments and are summarized in Figure 4.  
As shown in Figure 4, the ozone deposition velocities on various synthetic fibers showed large discrepancies. The fastest velocities, which surpassed 0.055 cm\(\cdot\)s\({}^{-1}\) by two times, were found for the interactions with flannel, for which it took almost 3 h for ozone deposition to reach a steady state after introducing ozone at a concentration of either 100 ppb or 300 ppb. The most rapid deposition was found on the carpet, which was covered by a surface of napped velvet. The steady-state ozone concentrations were around 46 ppb (after 1.5 h, and the concentration was equivalent to 98.6 \(\mathrm{\mu}\)g\(\cdot\)m\({}^{-3}\)) and 121 ppb (less than 3 h, equivalent to 259.3 \(\mathrm{\mu}\)g\(\cdot\)m\({}^{-3}\)). The slowest velocities (smaller than 0.022 cm\(\cdot\)s\({}^{-1}\) at both ozone concentrations during the experiments) were found for the microfiber lining coated with a  
Figure 3: Ozone deposition velocities at a steady state on wood-based panels.  
smooth layer of polyurethane. The texture of the lining was similar to that of leather, albeit the ozone deposition progress was observed at a lower ozone concentration of 100 ppb. Notably, the deposition velocities were very close at a higher ozone concentration of 300 ppb for the leather and the lining, at about 0.016 cm\(\cdot\)s\({}^{-1}\). When purging a higher concentration of ozone into the chamber, the ozone deposition velocities decreased on all the fiber specimens, except for the carpet, on which the mean deposition velocities increased from 0.024 to 0.034 cm\(\cdot\)s\({}^{-1}\).
# Weakening Detection of Composite Structure Adhesive Layer Based on Nonlinear Guided Waves
###### Abstract  
In this study, a detection method utilizing nonlinear ultrasonic guided waves is presented to tackle the difficulties in detecting localized damage and weakening in bonded composite structures. For a three-layer structure made of polystyrene, acrylic resin, and aluminum plate, dispersion equations for ultrasonic guided waves were developed using the spring model and wave equation. The A1-S1 mode was selected by examining the material parameters' influence on the adhesive layer's dispersion curves. The finite element method was employed to simulate the propagation characteristics of ultrasonic guided waves within the composite structure. The error between the theoretically calculated and simulated group velocities was less than 5.15%. As the propagation distance increased, both the nonlinearity coefficient and the amplitude of the second-order harmonic showed an upward trend. This indicates a significant accumulation effect at the second harmonic of nonlinear guided waves. Compared to without adhesive layer weakening, localized and overall weakening resulted in higher amplitudes of the second-order harmonic. Experimental testing of ultrasonic guided waves was conducted to investigate the nonlinear properties of the composite structure. The error between the experimentally measured and theoretically calculated group velocities was less than 6.96%. The experimental results corroborated the propagation accumulation effect of the second-order harmonic amplitude.  
composite structures; dispersion curve; nonlinear guided wave; adhesive layer; weakening +
Footnote †: journal: Applied sciences
# Nutritional Support for Liver Diseases
## 2 Nutritional Support for Liver Diseases
### Cholangiocarcinoma
#### 2.2.1 Overall Recommendation  
To date, one specific diet for NAFLD patients has yet to be identified. The 2019 European Society for Clinical Nutrition and Metabolism (ESPEN) [39] guidelines state that in overweight and obese NAFLD/NASH patients, 7-10% weight loss shall be aimed for to improve steatosis and liver biochemistry. Moreover, the grade B recommendation points out the Mediterranean diet as the recommended diet for NAFLD patients, which reduces steatosis and increases insulin sensitivity. There is a consensus that patients with NAFLD should increase their physical activity, mainly strength training (grade A recommendation; 100% consensus); this recommendation especially applies to people for whom weight reduction cannot be applied (lean-NAFLD) [39]. The nutrition guidelines for NAFLD patients are not consistent and specific. Although scientific associations, such as ESPEN, the Asian Pacific Association for the Study of the Liver (APASL), and the American Association for the Study of Liver Diseases (AASLD) [28], emphasize the importance of losing weight using an energy-restricted physical activity in reducing insulin resistance and general health, there are still no specified recommendations describing the type of diet and exercise [5; 40; 41]. Most scientific societies indicate the Mediterranean diet as the most beneficial model of nutrition for NAFLD patients, which has been confirmed by numerous scientific studies [5; 42]. However, at the same time, there is currently a lack of evidence validating what macronutrient distribution of the diet is the most appropriate.  
Additionally, salt intake should be limited already in the earliest stages of the disease for the purposes of the prevention of hypertension and cardiovascular diseases. In addition, a systematic review and meta-analysis of observational studies showed that populations with a high sodium intake have a 60% greater risk of developing NAFLD compared to those with low consumption, but the explanation of the mechanism of this phenomenon requires further research [43].  
Interestingly, due to the presence of antioxidant compounds in coffee, such as caffeine, chlorogenic acid (CGA), cafestol, kahweol, and trigenolin, numerous studies on cell cultures, animal models, and humans have shown that coffee protects against metabolic syndrome and NAFLD/NASH. National Health and Nutrition Examination Surveys (NHANES 2001-2008) found that caffeine intake is independently associated with a lower risk of NAFLD [44].
###### Abstract
However, most of these solutions suffer drawbacks such as increased profile height [9; 10; 11; 12], an asymmetric scanning pattern [16], design complexity [13; 18], and additional cost [17], making them unsuitable for some applications. Therefore, we should take into account ways of simplifying design, reducing costs, and suppressing coupling in practical application design. On the other hand, the cross-polarization (cross-pol) level of the array is typically better than \(-50\) dB in E-plane. In contrast, the cross-pol levels are relatively large in H-plane, especially in the wide angular range. Fortunately, the sequential rotation technique (SQT) has been demonstrated to effectively improve the cross-pol levels in H-plane [21; 22; 23; 24]. On the basis of the above considerations, we present a PAA with high scanning performance by using a modified SIW cavity and SQT. Owing to the \"modified SIW cavity\", the beamwidth of the center unit in PAA is expanded. Furthermore, SQT can obtain a symmetric scanning pattern in E-plane and significantly reduce the cross-pol levels in H-plane. As a result, the proposed PAA simulated results exhibit low gain fluctuation of \(2.2\) dB and low cross-pol levels of \(-68.1\) dB at a two-dimensional (2D) scanning of \(\pm 45^{\circ}\). Without any parasitic structures, the design process is very simple and flexible. Therefore, the proposed design is an attractive candidate for satellite/\(5\)G communication systems, radar systems, and various wireless systems, such as wireless power transmitting systems. This paper is organized as follows. In Section 2, the representative antenna element is described. Section 3 describes the infinite array performance. The operation performances of the proposed method are discussed in Section 4. The measured results and discussion of the array are presented in Section 5. Finally, a brief conclusion of this work is provided in Section 6.
## 2 Materials and Methods
### Extraction of Navigation Networks  
Extraction and generation of navigation networks requires a method for locating and retrieving networks, followed by a method for storing the network. To generate the navigation network from IFC files, the algorithm defined by [PERSON] and [PERSON] [22] can be used. The algorithm uses IFC XML files and searches for specific entities in the building structure to identify spaces and their relationships. The algorithm relies on room definitions extracted from floor plans. Rooms are organised by spatial adjacencies, and paths between rooms are defined by openings that allow passageways. Using attribute information about entities, the algorithm extracts additional accessibility information useful for navigation. IFC entities are used as follows:  
* _IfcSpace_ defines the spaces and is used to add nodes to the navigation network,
* _IfcWall_ defines the boundary(s) between rooms and is used to compute the room neighbourhood,
* _IfcDoor, IfcWindow, IfcStair_ define the passage(s) from one room to another and are converted into edges of the navigation network.  
In the model, the IFC entities listed above are connected by additional IFC objects. Nodes and paths are extracted from the model using the algorithm of [PERSON] and [PERSON] in the following way. Two spaces are adjacent if two _IfcSpace_ entities refer to the same _IfcWall_ or to the same _IfcWindowStandardCase_ using the _IfcRelSpaceBoundary_ relationship. Two spaces are accessible if the wall between them has an opening or door. Therefore, the _IfcWall_ or _IfcWindowStandardCase_ entity should refer to _IfcOpeningElement_ using the _IfcRelVoidsElement_, or in addition, the _IfcDoor_ entity should refer to _IfcOpeningElement_ using the _IfcRelFillsElement_ relation (which means that the opening is filled with a door). This is an example of extraction where _IfcDoor_ is used as a node of the transition from room to room (an edge in the resulting navigation graph). Similarly, other \"room transition\" elements could be used. All rooms belonging to a floor are grouped in the IFC model by _IfcRelAggregates_, which allows for the construction of a horizontal navigation graph (see Figure 2). Each spatial entity in the IFC model inherits spatial coordinates from parent  
Figure 1: Transformation process from IFC to Transmodel’s Fixed-Object Model (NeTEx).  
classes and the system uses _IfcLocalPlacement_ to extract the coordinates of nodes and links in the navigation network.  
Similarly, _IfcWindow_ could be used as the entrance to a space or vertical connections could be extracted using _IfcSlab_ as space delimiter and _IfcSfair_ as the entrance to a space. For a more detailed description of the algorithm, see the original paper [22].
## 3 Results and Discussion
### Acetaldehyde Degradation Characteristics according to Light Source Conditions  
Different light sources were also tested. When the catalysts were irradiated using the UV-A (365 nm) light source, all three showed conversion rates of less than 5% (Figure 4). Irradiation with the 365 nm light resulted in almost no oxidation of the contaminants, which is consistent with a previous paper [47]. Conversely, with the UV-C light source, the conversion rate achieved with the catalyst heat-treated at 450 \({}^{\circ}\)C reached 42% before leveling out; additionally, the conversion rate of the catalyst heat-treated at 650 \({}^{\circ}\)C reached up to 57% 45 min after irradiation and that of the catalyst heat-treated at 850 \({}^{\circ}\)C was 23% at its highest; however, this gradually decreased and stabilized in the 10% range. Therefore, the UV-C light source induced higher conversion rates than those of the UV-A source, presumably because UV-C light has a higher light flux than that of UV-A light. Another study demonstrated greater photon flux of short-wavelength light than long-wavelength light, along with the high acetaldehyde removal efficiency of short-wavelength light [48].  
Furthermore, experiments with and without a catalyst in the reactor were conducted to determine the possibility of direct acetaldehyde decomposition in the presence of a light source only. The conversion rate was 4.7% when only UV-C irradiation was used in the reactor for 45 min, which decreased to a constant value of 3% after 120 min (Figure 8). However, when the catalyst was present in the reactor (flow rate: 40 sccm, UV-C light), the conversion rate of acetaldehyde reached as high as 57%, suggesting that irradiation with a light source did not affect the direct oxidation of acetaldehyde.