esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Then, based on the completed data, the future traffic flow is predicted. The main contributions of this paper include the following. 1. Combined with linear regression, univariate spline and CP decomposition, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD). The linear regression and the univariate spline are used to obtain \"day-hour trend\" features and \"hour-minute trend\" features, respectively. Then, we get the residual value tensor by eliminating the \"day-hour trend\" features and the \"hour-minute trend\" features. We apply CP decomposition for the residual value tensor, and add \"day-hour trend\" features and the \"hour-minute trend\" features after reconstruction, which can recover missing data better;
2. We propose the Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The united tensor is composed by the historical data that recover the missing data and the prospective data that need to be predicted. The prospective data is regarded as missing data, which can be determined by using data completion strategy;
3. We verify the proposed Algorithms by experiments. The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. And the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method. The rest of the paper is organized as follows: Section 2 introduces the related works. Section 3 introduces the theoretical background. Section 4 proposes the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD). Section 5 proposes a Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). Section 6 explains the experiment's results, and Section 7 provides a conclusion.
# Experimental Study on Slender CFRP-Confined Circular RC Columns under Axial Compression
## 4 Comparisons for FRP-Confined RC Columns
### Slenderness Limit  
In general, the slenderness limit for short RC columns is suggested to ignore the effect of second-order moment. ACI 318-19 [20] and CSA A23.3-14 [21] define the slenderness limit for short columns based on 5% axial load reduction, which was mainly developed by [PERSON] [22]. If the slenderness ratio of columns exceeds this limit, the column should consider the effect of second-order moment.  
Figure 8 shows the vertical lines to indicate the slenderness limit based on 5% axial load drop. Therefore, according to the experimental data, the slenderness limit for CFRP-confined RC columns and control columns are 26.5 and 38.6, respectively. Figure 8 also shows the slenderness ratio of 42 for RC columns that corresponds to the 5% stability coefficient reduction in GB 50010, and this slenderness limit is close to the test results of control columns.  
Figure 8: Effect of slenderness on ultimate axial load.  
Figure 7: Stability coefficient versus slenderness curves.  
In recent years, several design guidelines of concrete structures (e.g., ISIS Canada [23]) and researchers proposed expressions along with a safe value for the slenderness limit of FRP-confined columns. However, the existing slenderness limits are also moderately different from one another because the experimental parameters are different. [PERSON] et al. [2] recommended the slenderness limits of 22 for CFRP-confined RC columns and 11.4 for GFRP-confined RC columns. [PERSON] et al. [24] suggested that the slenderness limit of 12 for steel-reinforced CFFT columns should be taken as the security value of design.  
The slenderness limit of 26.5 obtained in this experiment is different from other studies. This result can be attributed to the effect of concrete compressive strength and FRP type [24]. In addition, many factors--such as initial material imperfections, accidental load eccentricities, size effect, and ratio of longitudinal reinforcement--affect the stable bearing capacity of slender FRP-confined RC columns. Further study is required to provide accurate slenderness limits for FRP-confined RC column design.
## 4 Applications for IoT Technology in Smart Buildings
### IoT-Controlled HVAC System  
HVAC is responsible for 40% of the total energy consumed in a building. An intelligent HVAC system offers sufficient heating, high ventilation, and a more economical air conditioning system for a facility. Smart buildings should be operated according to a layered and flexible method for offering a standard degree of interoperability across the multiple modules. HVAC energy can be measured mathematically [92]. Implementing IoT in an HVAC system is mandatory to achieve an eco-friendly working environment and conserve energy. Intelligent HVAC systems use smart thermostats, smart meters, and smartphone applications. Smart Building Energy Management System (SBEMS) describes energy utilization and predicts potential energy consumption [93]. By activating the sensors in the HVAC monitoring system, the Raspberry Pi 3 can be linked to humidity, temperature, and the current sensor. The gathered data from sensors is sent to a smartphone using the MQTT protocol on a Raspberry Pi 3, acting as a sink node. As a binary protocol, MQTT consumes very little electricity and is very small in weight [94]. Using a Wifi module as a network connection, data from sensors is sent to the cloud service, where it is stored indefinitely.
# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 3 Measurement Results and Analysis
### The Auto-Correlation Function  
One method of characterizing the periodicity in a fading signal envelope is to calculate the auto-correlation function (ACF) of the signal. The ACF provides a useful measure of the degree of time dependency among the observations of stationary signals. For real discrete sampled data \(x(n)\), the empirical ACF is given as [12; 13]:  
\[r_{xx}(\tau)=\sum_{n=1}^{N-\tau}(x(n)-\mu)(x(n-\tau)-\mu), \tag{1}\]  
where \(\tau\) is the time delay, \(N\) is the length and \(\mu\) is the mean of the sampled data. The correlation was performed over the entire data sample, and was normalized using \(r_{xx}(0)\) to give \(\rho_{xx}(\tau)\). Figure 5 shows plots of the normalized ACF \(\rho_{xx}(\tau)\), for lags of up to 3 s (\(\tau=750\) samples). It can be observed that the ACFs resemble exponentially decreasing sinusoids with an approximately constant period. For running activity, the period is consistent with the oscillatory movement of the upper arms where  
Figure 4: Example of measured time series collected in Scenario 2, (subject beside the other). (**a**) running; (**b**) cycling.  
Figure 3: Example of measured time series collected in Scenario 1, (subject behind the other). (**a**) running; (**b**) cycling.  
the transceivers are attached, and was found to be approximately 0.7 s. For the case of cycling activity, where there is no backward and forward movement of the upper arm, there still exists a minor periodic movement from leaning and tilting of the body as the subjects pedal the bicycles. This movement is comparatively smaller, and hence can only dominate the ACF in the presence of a permanent LOS condition. In the absence of permanent LOS, such as in Scenario 2 (subject beside the other), other factors such as change in the surrounding environment and subjects' relative positions start to dominate.  
Another important piece of information that can be deduced from the figures depicting ACFs is the channel coherence time \(T_{C}\). It is used to describe the time-varying nature of the channel caused by relative motion between the transceivers, and characterizes the frequency selectivity of the channel in time domain. It gives us the lower limit on the transmission rate for the channel not to cause distortion due to motion. It can be defined as the time lag for which the channel correlation coefficient remains above 0.7 [13]. The channel coherence time \(T_{C}\) for each scenario and activity are given in Table 1. When the subjects' motion dominates (e.g., running all scenarios), we observe smaller coherence times. The largest coherent time was 92 ms observed in Scenario 2 (subject beside the other) of cycling.
# Performance of the Compliant Foil Gas Seal with Surface Micro-Textured Top Foil+
## 2 Materials and Methods  
### Geometric Model  
The structure of the compliant foil gas seal is shown in Figure 1. It mainly consists of a sealing cavity, flexible sealing surface, and rotating shaft. The flexible sealing surface includes a bump foil and a top foil with flexible support. The top foil is bent on the high-pressure side to form an extension, which serves to fix the foil and block the leakage channel.  
In Figure 1, \(\theta_{1}\) is the angle between the free end of the bump foil and the fixed end, \(p\) is the gas film pressure, \(h\) is the thickness of the gas film, \(t_{\text{b}}\) and \(t_{\text{p}}\) are the thicknesses of the  
Figure 1: (**a**) Schematic of the compliant foil gas seal. (**b**) Mechanisms for the lubricating gas film formation.  
bump foil and the flat foil, respectively, \(h_{\text{c}}\) is the texture depth, \(e\) is the eccentricity distance, R is the outer diameter of the rotation axis, and \(O_{1}\) and \(O_{2}\) are the centers of the sealing cavity and the rotation axis, respectively.  
The rotor is installed eccentrically in the sealing cavity. When the compliant foil gas seal is in the normal working condition, the bump foil and top foil are slightly deformed under the viscous shear of the sealing medium, as shown in Figure 2a. A micro-scale wedge-shaped gap of 10-30 \(\upmu\)m is formed between the friction pairs, which separates the friction and sealing pairs and produces a good hydrodynamic pressure effect, thereby forming a rigid pressurized air film to achieve lubrication and sealing. The structural parameters of the bump foil are shown in Figure 2b.  
In Figure 2b, \(u\), \(r_{\text{b}}\), \(s\), \(l\), and \(\alpha\) are the deformation, radius, pitch, half chord length, and wrapping angle of the foil, respectively, and \(L\) is the seal width.
## 5 Simulation and Performance Analysis
### Experiment Setting  
#### 5.1.1 Simulation Scenario Setting  
The LEO satellite network considered in this paper consists of 6048 LEO satellites, ground stations, and user terminals. Specifically, it is a mega constellation of 6048 satellites constructed with reference to the parameters of StarLink, with 84 constellation orbits, 72 satellites uniformly distributed in each orbit, an orbital inclination of 53\({}^{\circ}\), and an orbital altitude of 550 km [51]. The satellite nodes are randomly assigned the TM, gNB, and NGC functions. For the ground station, StarLink has already completed the deployment of the data [52]. We constructed user terminals of different sizes based on the World Grid Population v4 dataset [53]. The distribution of remote servers uses the same data as the Starlink ground station, and the source node is randomly chosen from one of the user terminals, while the destination node is randomly chosen from the remote servers. The data flow size is in the range of [5, 100] Mb and satisfies the lognormal distribution. In order to verify the performance of the proposed algorithm, we conducted 1000 simulation experiments. The algorithms were implemented in Python (Version 3.13.2). The critical simulation parameters are shown in Table 3.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 1 Introduction
### Smart Rings  
SRs are compact devices worn on the finger that can monitor a variety of health metrics in real time [15], such as oxygen saturation (SpO2), heart rate (HR), sleep quality (SQ), physical activity level, and stress level [15]. SRs can be connected to smartphones or other devices [16], allowing users to view and monitor their data in real time [17] (Figure 1B). Two key benefits of SRs are their unobtrusive nature and user-friendliness, making them more comfortable to wear for extended periods of time [15], in contrast to traditional medical equipment that requires attaching sensors and wires to the body, or stationary applications that need a constant power supply [18]. The low-power design of SRs enables a prolonged usage [18] and continuous monitoring of physiological parameters in a range of clinical settings [19]. This feature allows the identification of clinical patterns that would otherwise be hard to detect [5; 20]. SRs may help patients in managing their chronic disease by providing real-time feedback on their activity levels and vital signs. For example, patients with heart disease could use SRs to monitor their HR and receive alerts if it becomes abnormal [21]. Another explored application of SRs is in the mental health field, where metrics such as sleep quality, activity levels, and HR variability may provide  
Figure 1: (**A**) SR’s concept of function. The SR registers and collects the health data (SpO2, heart rate and others) communicating with smartphones or other devices. These data can be used to monitor patient vital signs and alert healthcare professionals to potential problems or changes in a patient’s condition. (**B**) IoMT ecosystem architecture and layers. The collection layer consists of medical sensors. Devices collect the health data and transmit them to a gateway node. The communication layer stores those data and analyses them using conventional threshold values to report any abnormality. Last is the application layer: using a web-based interface, medical professionals can check and verify the diagnostics and take corresponding measures.  
insight into a patient's emotional and psychological state allowing for early detection of mental health issues, such as depression and anxiety, and help patients better manage their condition [22].  
Furthermore, continuous monitoring may act as a positive feedback mechanism, nudging patients towards healthy habits [23], such as engaging in more physical activity and improving sleep hygiene. One particular SR, the Oura Ring(r), has become compatible with Natural Cycles, a Food and Drug Administration (FDA)-cleared birth control app [24]. This app analyses body temperature and other fertility indicators to determine fertility status, facilitating pregnancy planning or prevention. According to the current literature, multiple SR applications have been theorized, simulated, and commercialized, ranging from monitoring hand hygiene compliance [25,26] to providing feedback systems for evaluating chest compression efficacy [27].
# Lessons Learned on Geosynthetics Applications in Road Structures in Silesia Mining Region in Poland
## 2 Materials and Methods
### Evaluation of Mining Waste Mechanical Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Embankment Material** & **Aggregate in Mattresses** \\ \hline _physical_ & & \\ \(\mathrm{dmax}\) (mm) & \(<\)150 & 63 \\ \(\mathrm{U=d_{\phi 0}/d_{10}}\) (-) & 8.6–41.0 & 11.4–15.3 \\ Type & Unburned coal mining shales & Blast furnace slag \\ Carbon cont. \(\mathrm{f_{C}}\) (\%) & 8.8 & n/a \\ \(\mathrm{w_{opt}}\) (\%) ac. Proctor & 9.65 & 25.7 \\ \(\mathrm{\rho}\mathrm{d}\mathrm{s}\) (g/cm\({}^{3}\)) ac. Proctor & 1.85 & 1.46 \\ Passive capillary (m) & 1.75 & n/a \\ Frost resistance (\%) & 71 & 99.7 \\ \hline _mechanical_ & & \\ Internal friction angle \(\mathrm{\varphi}\) [\({}^{\circ}\)] & 22.7 & 40 \\ Cohesion c [kPa] & 50 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical and mechanical parameters (based on back analysis) of the filling material. Figure 5: Finite element method (FEM) model used for mechanical parameters determination: (**a**) Model geometry; and (**b**) calculation characteristic curve of mechanical parameters referring to monitoring results. Therefore, for the purposes of constructing the motorway in mining areas, a completely innovative approach was applied. The typical parameters for geogrids used for reinforcement are tensile strength \(R_{max}\) [kN/m] and elongations at 2% and 5% (\(R_{2\%}\)_i_\(R_{5\%}\)), respectively. Commonly the elongation at the breaking point \(\varepsilon_{max}\) is also determined. In Figure 7, two extremely different behavioral models of geogrids are presented. Model (1) is characterized by high initial stiffness \(E_{i}\) (1) and moderate strength \(R_{max}\) (1). In contrast, the high strength \(R_{max}\) (2) of model (2) is accompanied by low initial stiffness \(E_{i}\) (2). Figure 7, shows a typical presentation of material characteristics, that usually are insufficient for a fully covered description of the material's mechanical performance. From the point of view of a proper protection of the motorway, mobilization at a very small deformation is very much of interest. Thus, the initial rigidity is \(E_{i}\) tangential to the \"force-elongation\" curve, at the beginning of the stress-strain relationship, or the \(E_{S}\) secant stiffness is a limited, deformable interval, up to 0.5%. The geogrid's built-in aggregate layer behaves differently to a geogrid tested in an in-situ condition.Thanks to mechanical compaction, the grains of sizes no bigger than the apertures get clogged in the apertures, practically eliminating the ability to deform. The stiffness of a geogrid built into the aggregate is; therefore, greater than the standard sample in the laboratory.
###### Abstract  
The purpose of this study is to investigate the effect of principal stress direction on the efficiency of hydraulic fracturing treatment. There are two different drilling scenarios: 1. Four horizontal wells drilled in four orthogonal directions regardless of in-situ stress condition (\"Actual\"). 2. Three horizontal wells drilled equivalent to \"Actual\" case by considering the direction of principal stress (\"Proposed\"). The hydraulic fracturing modeling was carried out based on well logging data and completion reports of Brushy Canyon formation, Permian Basin. In the results of \"Actual\" case, transverse fractures were generated in two horizontal wells drilled parallel to \(\sigma_{\text{hmin}}\)-dir (direction of \(\sigma_{\text{hmin}}\)), similar to \"Proposed\" case. Meanwhile, for two other wells drilled perpendicular to \(\sigma_{\text{hmin}}\)-dir, longitudinal fractures were generated. These obliquely deviated fractures significantly decreased the fracture spacing between the stages up to 26%. This induced great stress shadow, however, the fractures propagated straight due to the large stress anisotropy of 2000 psi (\(\sigma_{\text{Hmax}}/\sigma_{\text{hmin}}=1.4\)). Therefore, it was found that due to the different direction of fracture propagation in \"Actual\" case, \"Proposed\" case was 14.6% of stimulated reservoir volume (SRV) higher. In conclusion, for successful hydraulic fracturing treatment, the direction of horizontal well must be determined in consideration of the principal stress direction as well as stress anisotropy.  
in-situ principal stress; horizontal well direction; hydraulic fracturing efficiency; transverse fracture; longitudinal fracture; stress shadow; SRV
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
## 5 Conclusions  
This research mainly uses a CFD solver that has been developed based on RANS to perform high-precision and high-efficiency steady-state simulation for the internal flow of a two-dimensional supersonic inlet/isolator, with high order spatial schemes. These findings establish a strong agreement between computational and experimental results, and also show a comparison of the 2D/3D simulation results. Moreover, the commercial software ANSYS Fluent 18.0 was utilized to carry out DES and LES simulations, obtaining the flow field details. In summary, the main conclusions of this research are as follows:  
(1) The high-order reconstruction schemes can accurately capture the details of the flow field, which is particularly important for computing the SWTBLI. In this work, by combining the currently widely used turbulence model \(k-\omega\) SST with the 3 rd-order MUSCL or the 5 th-order WENO/MP reconstruction scheme, the accurate capture of the shocks' positions inside the inlet/isolator was realized, and the simulation results of the pressure distribution on the cowl/ramp were obtained and were found to be highly consistent with the experiment;  
(2) The side wall of the two-dimensional supersonic inlet has a vital impact on the internal airflow conditions, and the 3D effects are obvious. By comparing the pressure distribution on the cowl/ramp obtained by 2D/3D simulations, it can be found that 3D computation can make up for some defects in the 2D results and capture the shocks' positions and intensities more accurately. Through a detailed analysis of the results of the  
Figure 16: LES simulation: Mach field.  
Figure 17: LES simulation: average fields of density gradient and pressure.  
3D flow field, it is observed that the side wall of the inlet will induce a strong lateral flow, which interferes with the separation bubbles generated near the throat of the ramp;  
(3) The DES and LES computational methods can be used to obtain the flow details near the flow separation area and the boundary layers, but these methods need to be combined with high-order spatial discretization schemes. Otherwise, they cannot accurately capture the vortex structures in the complex flow, leading to certain deviations between the computational and experimental results.  
Investigation, Z.L.; methodology, Z.L. and Z.X.; software, Z.L.; formal analysis, Z.L. and Z.Z.; data curation, Z.L.; visualization, Z.L., C.W. and K.Z.; writing--original draft, Z.L., Z.X. and C.W.; writing--review & editing, Z.L., C.W., K.Z., Z.Z. and Z.X.; resources, Z.X.; conceptualization, Z.X.; supervision, Z.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Thanks are due to [PERSON], [PERSON] and [PERSON] of Tsinghua University for valuable discussion.  
The authors declare no conflict of interest.
## 4 Model Validation
### Model Result and Analysis
#### 4.3.4 Comparison and Analysis  
In order to verify the superiority of the model in this paper, a linear regression analysis was performed on the data from 64 projects. We use linear regression and the RBF neural network constructed in this paper to forecast SPI(t\({}_{2}\)) and SPI(t\({}_{3}\)) for 64 samples, respectively, and the forecasting results are shown in Figures 7 and 8. The data obtained from the linear regression analysis are compared with the data obtained from the model constructed in this paper, as shown in Table 7. It can be seen that although the R\({}^{2}\) of SPI(t\({}_{2}\)) obtained by linear regression is 0.989, which is extremely close to 1, the R\({}^{2}\) of SPI(t\({}_{3}\)) is lower, which is only 0.637. This indicates that although the linear regression model has a better forecast effect on SPI(t\({}_{2}\)), the forecast effect of SPI(t\({}_{3}\)) is poorer.  
On the contrary, the RBF neural network model constructed in this paper extends the sample size from 64 to 200 by expanding the data samples. Then, 95% of the samples are selected as the training set and 10 groups of independent random experiments are conducted. At this time, both the SPI(t\({}_{2}\)) and SPI(t\({}_{3}\)), whose R\({}^{2}\) values are closer to 1, can better forecast the project performance indicators and thus provide help for assessing project resilience.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Hypothesis-Driven Studies in Identification of mTOR Signaling Components
#### 4.4.3 Hypothesis-Driven Approach to Identify Rheb in Activating mTORC1  
[PERSON] et al. hypothesize that Rheb might be a TSC2 GAP substrate to regulate mTORC1 based on the known knowledge that (1) _Rheb_ deletion in fission yeast led to G0/G1 arrest that mimics nutrient starvation [41], (2) _Rheb_ deficiency in fission yeast increased arginine and lysine uptake [85], and (3) as a Ras-related GTPase, together with Raf1, Rheb was able to transform NIH3T3 cells [86]. As a test result, overexpression of Rheb in HEK293E cells activated S6K1 but not Akt and RSK1, and TSC2 inhibited Rheb activity in HEK293E cells [87].