esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Sustainable Water Use in Two University Student Residences: A Case Study
## 3 Results and Discussion
### Characterization of Water Devices and Their Use Patterns
#### 3.1.4 Taps  
* WC basin single-lever taps: These taps had an average flow of 13.29 L/min, and the estimated number of total uses was around three times/day/user; each use is on for approximately 0.18 min (Residence I). In Residence II, these taps presented an average flow of 14 L/min, and the estimated number of total uses was around 3.4 times/day/user; each use was on for approximately 0.23 min. The water category efficiency of these devices is E.
* Timed flow tap: The average flow is 3.63 L/min in both residences. Each use is on for approximately 7.45 s, and it is used 0.33 and 0.38 times/day/user in the male and female residences, respectively. Their water category efficiency is A.
* Kitchen tap: The average flow is 10.6 L/min, and it is used for 6.67 min/day/user (Residence I). In Residence II, the average flow is 9.9 L/min, and it is used for 2.20 min/day/user. Their water category efficiency is C.
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 2 Theoretical Model Derivation
### Long-Channel Threshold Voltage Model
#### 2.1.2 The Effect of Substrate Bias on Threshold Voltage  
If a reverse bias \(V_{bs}\) is applied between the substrate and the source, the surface potential \(\phi_{S}\) with the source as the reference point can be determined as follows:  
\[\phi_{s}=\varphi_{s}=V_{bs}, \tag{19}\]  
When the substrate bias is zero, \(\phi_{s}=\varphi_{s}\) and \(V_{gs}=V_{gb}\). If the substrate bias is not zero, the threshold voltage is defined as the gate-source voltage (\(V_{gs}\)) at which the surface potential \(\phi_{s}\) equals the threshold surface potential, as obtained from Equation (18):  
\[V_{TH}=V_{FB}+\phi_{s}+\gamma_{2}\bigg{(}\sqrt{\phi_{s}-V_{bs}+A_{1}}+A_{2} \bigg{)}, \tag{20}\]  
Substituting Equation (18) into Equation (20) yields the following result:  
\[V_{TH}=V_{TH0}+\Delta V_{TH}, \tag{21}\]  
where \(\Delta V_{TH}=\gamma_{2}\big{(}\sqrt{\phi_{s}-V_{bs}+A_{1}}-A_{2}\sqrt{\phi_{ s}+A_{1}}\big{)}\), taking into account the non-uniformity of substrate doping and the influence of substrate voltage [14], and introducing process fitting parameters \(K_{1}\) and \(K_{2}\), the threshold voltage equation for a long-channel strained Si NMOSFET, in this case, is as follows:  
\[V_{THL}=V_{TH0}+K_{1}\Big{(}\sqrt{\phi_{s}-V_{bs}}-\sqrt{\phi_{s}}\Big{)}+K_{2 }V_{bs}. \tag{22}\]The Influences of Short-Channel Effect and Drain-Induced Barrier Lowering Effect on Threshold Voltage  
As the device dimensions (\(W\) and \(L\)) continue to decrease, the electric field along the channel direction becomes stronger. The assumption of a gradually varying channel no longer holds, and when L becomes comparable to the depletion region width at the source-drain junctions, the MOSFET is considered to be a short-channel device. In this case, the one-dimensional [PERSON]'s equation is no longer applicable. For the depleted region in the relaxed \(SiGe\) layer, a quasi-two-dimensional analysis method is adopted to establish the threshold voltage model for short-channel strained Si NMOSFETs.
## 3 Modelling, Parameter Adjustment, and Results
### Modelling the Material Characteristics of the Envelope  
The characteristics of the experimental room envelope are important for a correct consideration of the temperature evolution over time. Hence, their correct assessment is essential for good and reliable model results. The main characteristics of the materials, which are relevant to the present study, are the: walls material density, the walls specific heat, and thermal conductivity. The specific density and heat used the values from the data sheet of the facade panels: \(\rho=120\) kg/m\({}^{3}\) and c\({}_{\rm p}=0.840\) kJ/kg/K.  
The distribution of temperatures inside the booth walls and heat loss through the walls depends on their thermal conductivity [31], and its influence is further transferred through convection to the indoor air temperature. It was necessary to determine an equivalent thermal conductivity \(\lambda_{\rm eqv}\) W/m/K of the entire booth envelope to match the temperature profile of the numerical model to the experimentally obtained profile. Generally, suppliers provide determined thermal conductivity, under standard conditions, for panels (field thermal conductivity). However, it is necessary to correct this value, taken from technical data sheets, due to the existence of doors and windows on the real booth that are not present in the model. The equivalent thermal conductivity must also consider the existence of thermal bridges, such as the joints of the panels, intersection with the door, the windows, corners of the container, in between metal structure of the panels, and so on.  
Five different values of the equivalent thermal conductivity were analyzed in the FDS model: 0.024 W/m/K, 0.044 W/m/K, 0.061 W/m/K, 0.078 W/m/K, and 0.098 W/m/K. The temperature evolution profiles in point S24 corresponding to the five different equivalent thermal conductivities. Figure 4 presents similar values for the beginning of the monitoring period (0-200 s) and differ considerably beyond this time interval.  
It can be observed that for \(\lambda_{\rm eqv}=0.061\) W/m/K, the overlap of the numerical prediction of the temperature evolution (violet curve in Figure 4) over the experimental profile (black curve in Figure 4) is the closest. The model accuracy is indicated by a parameter re-grouping the temperature prediction error (Equation (1)) at all-time steps during the entire  
Figure 4: Temperature evolution graph at different values of the thermal conductivity.  
monitoring interval. We used the error standard deviation (Equation (2)) to characterize the numerical model precision for the entire time interval (i = 0 \(\div\) 760 s).  
The variation of the errors, corresponding to the five equivalent thermal conductivities (Figure 5a), are characterized by different standard deviations (Figure 5b). The minimum value of the error standard deviation is \(\sigma_{\rm Err}\) = 1.5 (\({}^{\circ}\)C), corresponding to \(\lambda_{\rm eqv}\) = 0.061 W/m/K.
# Finite Mixture Model-Based Analysis of Yarn Quality Parameters
###### Abstract  
This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and reps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation-maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and reps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry.  
finite mixture model; expectation-maximization algorithm; Poisson mixture; gamma mixture; yarn quality Article
## 2 Materials and Methods
### Calculations of BS, BMI, LMI, and FMI  
Body surface area was calculated using [PERSON]'s formula [29] as follows:  
(i) Body surface area (BS, cm\({}^{2}\)) = k Mb2/3, where Mb = body mass (g), and k = constant.  
The literature reports various k values for rats and mice, for example, C57 BL6 mice, k = 9.82 [30]; C57 BL6 mice, k = 9.617 (8 AM) and k = 9.802 (4 PM); Wistar rats, k = 9.83 [31]. Here, we used k = 9.82 [30].  
From the body composition parameters measured with DXA, several derivative values were calculated, as described in [PERSON] et al. [32], to investigate correlations with blood pressure during development in the mice, as follows:  
(i) Body mass index (BMI, g/cm\({}^{2}\)): Body weight (g)/Body surface area (cm\({}^{2}\)).  
(ii) Lean mass index (LMI, g/cm\({}^{2}\)): Lean (g)/Body surface area (cm\({}^{2}\)).  
(iii) Fat mass index (FMI, g/cm\({}^{2}\)): Fat (g)/Body surface area (cm\({}^{2}\)).
## 3 Conventional Active Disturbance Rejection Control Problem
### Tracking Differentiator (TD)  
In the tracking differentiator, the output profile of the nonlinear system, in Brunovsky canonical form [26], must track the transient profile of the reference signal to resolve the problem of set-point jump in the traditional proportional-integral-derivative (PID) controller as stated in the seminal work [2]. In this manner, when a rapid change occurs in the set point for any reason, the output signal of the plant will follow the output of the TD and will change gradually to reach the desired set-point, and the TD can be represented as [2]:  
\[\left\{\begin{array}{c}\dot{r}_{1}=r_{2}\\ \dot{r}_{2}=-R\;sign\Big{(}r_{1}-r(t)+\frac{r_{2}\;|r_{2}|}{2r}\Big{)}\end{array}\right. \tag{6}\]  
where \(r_{1}\) is the tracking signal of the input \(r\), and \(r_{2}\) is the tracking signal of the derivative of the input \(r\). To speed up or slow down the system during transient effects, the coefficient \(R\) is adapted, making it application dependent [27]. Other versions of enhanced TD are proposed in [28; 29; 30; 31].
## 5 Conclusions  
1. The experimental values of ultimate tensile bearing capacity obtained from the bolted joint specimens are close to those obtained from the traditional welded joint specimens. Moreover, both values exceed the ultimate tensile bearing capacity of the pile shaft calculated using the empirical formula specified in the regulations. The ratio of these values to the calculated ultimate tensile bearing capacity of the pile shaft is greater than 1.29, meeting the requirements for the load-bearing capacity of the joint in pile connections. Thus, this type of high-strength bolted connection proves to be a reliable connection method.
2. The experimental value of ultimate tensile bearing capacity obtained from the improved bolted joint specimens exceeds that of the normal bolted joint specimens. By positioning the bolt holes of the end plate closer to the pile shaft in the improved bolted joint, the load-bearing capacity of the bolted joint can be enhanced.
3. Both bolted joints and welded joints experienced prestressing steel bar rupture at the head of the prestressed reinforcement during the final failure of the experiment, with no failure occurring in the joints themselves. This indicates that both connection methods have a certain degree of load-bearing capacity surplus.
4. The strains at the prestressing steel bar holes in the end plate of the joint specimens are relatively small and develop slowly, remaining essentially within the range of  
Figure 12: Comparison of test and simulated load–displacement curves.  
0.002. Conversely, the strains at the bolt holes in the end plate are larger and develop rapidly, entering the yield phase earlier in the loading process.
5. The load-displacement curves obtained from the numerical models established using ABAQUS closely match the curves obtained from experiments, indicating that they can reasonably predict the entire process of joint tensile loading until failure.  
It is worth noting that this study focused solely on the joints and did not consider the connection performance in the presence of the pile. Additionally, parameter analysis of the joints to optimize their connection performance was not conducted. These aspects could be explored in future research endeavors.  
Conceptualization, Q.D.; Methodology, Q.D., Z.W., R.Z. and X.X.; Validation, Z.W.; Writing--original draft, Q.D.; Writing--review and editing, Z.W., R.Z. and X.X.; Visualization, Q.D.; Supervision, Z.W. and X.X.; Project administration, Z.W. and R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ningbo Major Special Projects (No.2022Z030) and Ningbo Youth Science and Technology Innovation Leading Talent Project (No.2023 QL018).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company ZCONE High-Tech Pile Industry Holdings Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 2 Experiments and Data
### Data Processing Algorithm
#### 2.3.4 Method to Identify Phase Shift Induced by Raindrops  
For each satellite, data from rainy days also suffers from multipath effect. We compare each \(\Delta\phi^{\prime}(\beta)\) with the standard deviation of \(\Delta m\) (\(\sigma_{\Delta m}\)) to analyze the effect of hydrometeors. Here, we use \"\(2\sigma\)\" principle to obtain phase shift: 95% of the data should be within \(\pm 2\sigma\) in statistics, the other 5% ought to exceed \(\pm 2\sigma\)[24]. Since there is no absolute reference for phase shift, \(\pm 2\sigma\) is a threshold. The minimal value where the difference is between \(\Delta\phi^{\prime\prime}(\beta)\) and \(-2\sigma\) can be calculated. Then, compute each \(\Delta\phi^{\prime\prime}(\beta)\) by the equation:  
\[\Delta\phi_{C}(\beta)=\Delta\phi^{\prime\prime}(\beta)-\left(\Delta\phi^{ \prime\prime}(\beta)+2\sigma(\beta)\right)_{\text{min}} \tag{7}\]Here, considering \(+2\sigma\) as the no-rain noise level, the \(\Delta\phi_{\mathrm{C}}(\beta)\) can be understood as corrected phase shift. Its value that surpass \(+2\sigma\) is regarded as phase shift induced by hydrometeors and can be obtained from:  
\[\Delta\phi_{+}(\beta)=\left\{\begin{array}{ll}\Delta\phi_{\mathrm{C}}(\beta)- 2\sigma(\beta)&\Delta\phi_{\mathrm{C}}(\beta)>2\sigma(\beta)\\ 0&\Delta\phi_{\mathrm{C}}(\beta)\leq 2\sigma(\beta)\end{array}\right. \tag{8}\]  
where \(\Delta\phi_{+}(\beta)\) is a relative phase shift beyond the no-rain noise level. Its positive value comes from the contribution of hydrometeors, which includes not only raindrops but also ice crystals and melting particles. From our previous research, due to the fact that the number concentration of ice particles is often sparse at high altitude, the effect of ice crystals along the path is quite weak, which can be neglected [25]. Compared with the effect of larger and more concentrated raindrops, the melting particles have some effect but not that obvious. Besides, the effect of ionosphere has been also assessed. Its phase delays, Faraday rotation and Cotton-Mouton effects all have no influence on our measurements [25].
## 4 Operational Modal Analysis (OMA)  
To obtain the modal parameters of the structures, operational modal analysis (OMA) is used. The extraction of the modal parameters from the recorded data was performed using ARTeMIS 2.13 software [38]. Figure 12 shows the model used to identify the modal parameters of the bell tower. Prior to processing, the recorded data was filtered with a low-pass filter and decimated to the 0-25 Hz frequency range of interest. These 16 min of data were segmented and processed with a frequency resolution of 1024.  
In this work, modal parameter identification was performed by two different techniques: enhanced frequency domain decomposition (EFDD) and stochastic subspace identification-unweighted principal components (SSI-UPC). The EFDD technique is an enhanced system identification technique that results only from frequency domain decomposition and provides a more accurate estimate of natural frequencies and mode shapes. This technique is suitable for closely spaced modes and repeated modes but has drawbacks in accurately identifying damping [39]. The SSI technique is a stochastic method for estimating system matrices from measured oscillations based on an algorithm developed by [PERSON] et al. [40]. Unweighted Principal Component (UPC), the simplest SSI algorithm, is used in this work because it has the highest estimation accuracy compared to other algorithms [41, 42].  
Figure 11: The acceleration time histories recorded by the sensors at measurement point 7 (TEST SETUP 1).  
Figure 10: The acceleration time histories recorded by the sensors at measurement points 14, 15, and 16: (a) TEST SETUP 1; (b) TEST SETUP 2.  
Figure 13 shows the singular values of the spectral density matrices obtained using the EFDD technique, while Figure 14 shows the stabilization diagram of the state-space models obtained using the SSI-UPC technique. Despite the relatively low vibrations during the vibration test, the modal parameters of the bell tower structure are identified. From the EFDD results (Figure 13), it is clear that the natural frequencies of the bell tower are in the range of 1 to 20 Hz. Table 1 shows the frequencies, damping and complexity (or the so-called MCF factor) of the selected modes. The modes were selected based on the criterion that the modes with the lowest complexity should be chosen [43].  
Figure 12: Model for dynamic identification. Accelerometer positions and selected channels for identification in TEST SETUP 1 and TEST SETUP 2.  
Figure 13: Singular values of spectral densities by EFDD technique.
## 1 Introduction
### Previous Research on High-Velocity Jet Behavior  
Characterizing the flow of a jet at the impinging section and the interaction with the bottom of the plunge pool is crucial, and previous studies have focused on the mean and fluctuating pressures, often treating them as non-dimensional parameters relative to the impinging jet velocity [3; 4; 5; 6; 7; 8]. However, this approach overlooks scale effects related to jet velocity, plunge pool size, and aeration, which are critical for an accurate simulation and proper design [9]. [PERSON] [10] conducted experiments using a jet with a diameter of 0.072 m, studying the pressure field at the plunge pool bottom and within a rock joint for jets with velocities up to 37.2 m/s. This work provides valuable insights into the pressure distribution relevant to understanding the behavior of high-velocity jets in plunge pools. [PERSON] [11] explored the influence of plunge pool geometry on rock scour, while [PERSON] [12] assessed the stability of rock blocks subjected to high-velocity free-water jets, with velocities ranging from 4.9 to 22.1 m/s and air concentrations from 0% to 23%. [PERSON] [13] continued this line of research, examining the response of rock blocks in terms of pressures and accelerations. [PERSON] et al. [14] experimentally characterized dynamic pressures near the stagnation zone for circular jets. These studies highlight the importance of understanding the interaction between jet velocity, air-water mixtures, and solid boundaries in high-velocity jet applications.
## 5 Experiments
### Tuning the Amplification Factor  
In the force amplification strategy we amplify the forces applied by the user on the joystic, detected by the FT sensor and input to our control scheme (see Figure 1). For the robot to provide assistive forces we had to determine the amplification factor \(\eta\) for each degree of freedom as shown in Equation (18):  
\[F_{cmd}=\begin{bmatrix}\begin{smallmatrix}eF_{x}\\ eF_{y}\\ eF_{z}\\ eF_{Az}\\ eF_{By}\\ eF_{Cx}\end{smallmatrix}\end{bmatrix}=\begin{bmatrix}\begin{array}{c}\eta_{x} \\ \eta_{y}\\ \rho_{z}\\ \rho_{x}\\ \rho_{z}\\ \rho_{y}\\ \rho_{z}\\ \rho_{x}\end{array}\end{bmatrix}\begin{bmatrix}\begin{smallmatrix}\rho_{x} \\ \rho_{y}\\ \rho_{x}\\ \rho_{y}\\ \rho_{z}\\ \rho_{z}\\ \rho_{z}\\ \rho_{z}\\ \rho_{y}\\ \rho_{z}\end{smallmatrix}\end{bmatrix} \tag{18}\]  
While a high amplification factor would ease the load on the user, it could also give the user the perception that he is no longer in control of the operation and reduce his comfort with the system (as was realized during the experiments). This is the reason we cannot have the robot simply apply the highest forces possible.  
The tuning of the comfortable amplification factors was a continuous process where several iterations of meat cutting were performed by a professional butcher. It was decided collectively that there would be only two amplification factors: one common \(\eta_{f}\) for forces and one \(\eta_{\tau}\) for torques (i.e., \(\eta_{f}=\eta_{x}=\eta_{y}=\eta_{z}\) and \(\eta_{\tau}=\eta_{Az}=\eta_{By}=\eta_{Cx}\)). This iterative tuning process finished when the butcher found that the assistance behavior was comfortable, as explained in Figure 16.  
The meat cutting operation involves both the application of forces and torques, as such the force amplification factor \(\eta_{f}\) cannot be determined independently of the torque amplification factor \(\eta_{\tau}\). In earlier experiments the user felt that \(\eta_{\tau}=3\) was ideal for him and hence to determine \(\eta_{f}\) a series of consecutive experiments were done as shown in Table 6 enabling the user to make a subjective comparison.  
At the end of the experiment it was concluded that the user preferred \(\eta_{f}\in[10,20]\) and \(\eta_{\tau}=3\). With \(\eta_{f}=20\) and \(\eta_{\tau}=4\), the user found the system to be too reactive and he felt he was no longer in control. These 6 experiments and the temporal evolution of the cutting forces applied by the user after applying these optimal force amplification factors are shown in the next section.