esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Friction Models for Starting Up Water Installations Containing Trapped Air
## 1 Introduction
A differential pressure of 2 psi is recommended for computing the injected air volume. A compressible flow-through equation is suggested to compute the air valve capacity. However, the manual does not contain an extensive explanation for simulating the hydraulic and thermodynamic behavior of the water and air phases, respectively. Thus, water utilities cannot use the manual to determine the air pocket pressure evolution along water installations, where it of utmost importance to confirm whether the resistance pipe class is higher than the air pocket pressure in order to avoid a pipeline rupture. During transient events, the friction factor should be addressed considering unsteady friction models; therefore, numerical, and experimental approaches have been employed in this field [10; 11; 12; 13]. Particularly, [PERSON] et al. (2020) [14] leveraged the method of characteristics as a solver for transient equations, incorporating an unsteady friction factor in the context of starting up water pipe systems. Table 1 presents a summary of different mathematical models used in recent years, showing the implementation of the rigid water column model (RWCM), the elastic water column model (EWCM), and the CFD model. Both the RWCM and EWCM provide similar results, since the air elasticity is much higher compared to that of the water and the pipe. This paper endeavors to develop a mathematical model tailored to the analysis of filling operations, emphasizing an unsteady friction model and the application of the rigid water column model, which has not been addressed in the current literature. The proposed model can be employed to calculate the maximum air pocket pressure during the start up of water pipelines. This calculation helps determine whether the pipe resistance can withstand this extreme value. The proposed model introduces the Brunone friction coefficient for simulating this operation for comparison with previous models. Notably, this model is particularly well-suited for the small pipe diameters within hydraulic installations. In addition, the current literature does not contain information regarding the use of the rigid water column model in combination with the unsteady friction model to simulate starting up water installations. To validate the mathematical model, experimental tests were conducted employing a pipe with a length of 7.36 m. Subsequently, the mathematical model, enriched by the incorporation of an unsteady friction factor, is applied to a practical scenario to observe variations in air pocket pressure, water velocity, and the length of the water column.
# Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?
## 5 Study 2  
In this section, we present our second study performed to compare the effects of conversational and gestural fillers on users' perception of the digital human and the latency in conversation. We again used two conversation types: informative and casual, and chose the appropriate gestural filler per each conversation type based on the results of Study 1. The filler types compared in this study were non-filler, conversational filler only, gestural filler only, and gestural filler accompanied by conversational filler (NF, CF, GF, and GCF, respectively).
## 4 Experiment Results and Analysis
### Comparison Experiments with Other Modules  
To prove that the attention mechanism we have chosen has the best performance among the modules those are widely used in other YOLO models, we designed the comparison experiments with DLKA and the other four attention mechanisms, which are parallelized patch-aware attention (PPA), efficient multi-scale attention (EMA), deformable attention (DAttention), and mixed local channel attention (MLCA). We selected precision (P), recall (R), mAP@50, and F1 score to be the measure of performances. As the results demonstrate, shown in Table 7, the DLKA achieves the highest precision, mAP@50, and F1. Although, its recall is not the best, we can raise it in other ways, as the attention mechanisms are usually used for increasing the precision with their amplification effect on detail features.
## 3 Results and Discussion
### Variable Humidity
#### 3.3.2 Carbonation Depth  
The relationship between the carbonation depth of GC0 and GC0.05 with different humidity is shown in Figure 10.  
It can be found that the carbonation depth of the two kinds of concrete is gradually increasing, but the carbonation depth of GC0.05 is generally 15% to 30% lower than that of GC0. These data indicate that the incorporation of GNP can slow down the carbonation process in these changing relative humidity scenarios. When the humidity is 78-85% (time is 28 to 35 days), the carbonation depth of graphene concrete and ordinary concrete increases slightly, which indicates that with the extension of time, the carbonation reaction of the two kinds of concrete almost stops when the relative humidity of the external climate is high. This may be because the pore water saturation of graphene concrete and ordinary concrete is relatively high within a certain period of time. At the same time, the carbonation reaction will produce new water and more water in the concrete, which will hinder the penetration speed of CO2 gas into the concrete and then prevent the chemical reaction in the process of carbonation.
## 3 Software Architecture and Measurement Algorithm  
### Software Architecture  
The software consists of two different parts: a shell script controlling the measurements, and a C++ program using GNURadio libraries. The first script starts the measurements with the correct parameters and is responsible for the timing of measurements and file storage. The second programme performs the measurements.
# Production of Alternative Fat from Adipose-Derived Stem Cell from Bovine in 3D Culture
## 2 Materials and Methods  
### bADSC Preparation and Culture  
Intermuscular adipose tissues were immediately harvested from 29- to 33-month-old castration male or female Hanwoo cattles slaughtered at a local slaughterhouse (Kwell LPC, Hongcheon, Republic of Korea). Subsequently, the retrieved adipose tissues were used as donors of bADSCs. All experimental procedures were conducted to comply with the Animal Care and Use Guidelines of Kangwon National University and approved by the Animal Care and Use Committee (IACUC) of Kangwon National University (IACUC approval no. KW-240809-5).  
The excised adipose tissue was then immersed in a sterile solution of Dulbecco's phosphate-buffered saline (DPBS, LB001-02, Welgene, Gyeongsan, Republic of Korea) supplemented with 1% Antibiotic-Antimycotic solution (A/A; Corning, 30-004-Cl, Corning, NY, USA). The adipose tissue was minced on a culture dish using sterile forceps and scissors, and subsequently incubated with 0.1% collagenase type 1 (Gibco, 17101015, Waltham, MA, USA) diluted in DPBS at 37 \({}^{\circ}\)C for 1 h. To inactivate collagenase activity, an equal volume of complete culture medium consisting of DMEM (Gibco, 11995073) supplemented with 1% A/A and 10% fetal bovine serum (FBS; Gibco, 16000044) was added. The sample was centrifuged at 350\(\times\) g for 5 min, and the supernatant was discarded. The pellet was resuspended in DPBS and then resuspended cells were passed through a 100 \(\upmu\)m mesh filter to ensure homogeneity and remove debris. After centrifugation, the supernatant was removed and the SVF pellet was resuspended in DMEM supplemented with 10% FBS and 1% A/A. SVF was cultured at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\). After 24 h of incubation, the culture medium was changed to remove unattached cells. At approximately 90% confluence, the cells were harvested using 0.25% trypsin-EDTA (Gibco, 25200072) and then were subcultured at a density of 2 \(\times\) 10\({}^{3}\) cells/cm\({}^{2}\).
###### Abstract  
The new third runway of the Hong Kong International Airport was commissioned in November 2022. With the increased distance away from the mountains of Lantau Island to its south, it is not certain if terrain-induced windshear could still happen over the corridors of this runway, particularly the usual landing runway corridor (07 LA) for aircraft from the west. With the rebound of air traffic in 2024 in Hong Kong, it is now possible to find out the impact of the Lantau terrain on the winds over 07 LA, at least based on case studies. It is found that, though 07 LA is located at least 4 km from the foothills of Lantau Island, which has peaks rising to about 1 km above mean sea level, the terrain-induced airflow disturbances could, at times, extend to 7-8 km away from the mountains to bring about low-level windshear and turbulence to this runway corridor. This conclusion is drawn based on actual pilot windshear reports and observations from Doppler light detection and ranging (LIDAR) systems and confirmed by simulations using a mesoscale to microscale numerical weather-prediction model. It is consistent with the definition of open terrain for a height-over-distance ratio of 1:10. Based on the experience in Hong Kong, it is found that, for runway corridors located at distances within 10 times the height of the nearby terrain, it is still necessary to set up windshear detection equipment, such as Doppler LIDAR, to safeguard aircraft operation.  
Keywords:windshear; LIDAR; numerical weather prediction +
Footnote †: journal: Applied Sciences
## 3 Flow Velocity Measurement Method
### The Principle of Main Steps and Analysis
#### 3.2.3 Iterative Multi-Grid Interrogation  
The analysis process of ultrasonic images includes multi-grid analysis and iterative PIV interrogation analysis. The interrogation window size is gradually decreased when the cross-correlation is applied during the multi-grid analysis process. It can eliminate some rule constraints, usually terminated when the desired window size (8 \(\times\) 8) is applied. Iterative PIV interrogation analysis can further improve the accuracy of PIV image deformation and enhance spatial resolution. The whole process will be terminated when the required incremental change is applied.  
The iterative PIV interrogation analysis process can be described as a predictor-corrector process and shown as the following formula:  
\[\Delta s_{k+1}(x)\ =\ \Delta s_{k}(x)+\Delta s_{corr}(x) \tag{4}\]  
where \(\Delta s_{k+1}(x)\) is the evaluated result of the \(k\)-th iteration and \(\Delta s_{corr}(x)\) is the correction term, which can be described as the residual displacement. Using a central difference function to calculate the deformed ultrasonic image and interrogating several times obtains a more accurate displacement field. In practice, two or three iterations have been enough to obtain a converged result, because most in-plane imaging particle motions can be compensated effectively through image deformation. The spatial resolution of the image after iterative analysis can be higher than twice the discrete or single-step window-shift algorithm.  
Therefore, the iterative multi-grid interrogation process can be improved by using a hierarchical approach. The sampling grid of the algorithm can be refined continually. At the same time, the size of the interrogation window can be decreased simultaneously, which can improve the capability of interrogation window sizes by using smaller particle image displacement. This process can increase the dynamic spatial range. It is especially useful for the pretreatment of ultrasonic images with both higher particle image density and a higher dynamic range in the displacements and detected offsets. The basic principle of the iterative multi-grid interrogation process can be shown in Figure 5. The line arrow presents the direction of the flow in the box. The box is divided from one to four and then to sixteen, which means that the direction of the flow becomes more precise and clearer.  
Additionally, particle image density can affect the choice of the final interrogation window size. When the particle image density or the number of particles decreases to a certain extent in the targeted interrogation window, the detection rate will decrease rapidly. The detecting speed, that is, the calculated feature vector, may be increased significantly by downsampling the image during the initial interrogation process at the same time. It can be realized by merging neighboring pixels by using the sum of N \(\times\) N block pixels. This process can help the detection process become much faster when using interrogation samples [36].
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 2 Medical Data Management Platform
### Blockchain and Medical Data Provenance  
Ref. [43] uses blockchain and trusted execution environment (TEE) technology to solve the fragmentation of mHealth data information traceability and security issues. Blockchain is used to record and enforce data access policies, ensuring that only authorized users can access data. TEE is used to protect the confidentiality and integrity of data during processing. Ref. [44] combined blockchain and IoT to develop an intelligent health supply chain management system that tracks medical products, avoids counterfeit products and medical device damage, reduces management costs, and allows users to understand the usage status of products from manufacturing to use. Ref. [45] proposed an effective and reliable vaccine-tracking and -monitoring scheme based on blockchain and smart contracts, allowing for the complete history of each vaccine from receipt to use or expiration date to be traced to ensure the safety of vaccine use. Similar methods were also used in [46; 47] for drug tracking. Ref. [48] designed an intelligent tracking and tracing platform for decentralized drug supply chain tracing using a five-layer blockchain and IoT technology. The platform establishes on-chain and off-chain standards to determine whether drug information is stored on-chain or off-chain, and uses smart contracts to address drug quality problems.  
In summary, the aforementioned works have confirmed the function of blockchain, smart contracts, and IoT technologies in ensuring the trusted storage, secure sharing, and traceability of medical data. Nevertheless, to the best of our knowledge, the majority of current research endeavors primarily focus on managing original medical data, without exploring the utilization of blockchain for credibly managing the entire life-cycle of medical data, encompassing raw data, operational behaviors data, intermediate data, and final data. Additionally, no relevant tracking and tracing methods have been proposed on the blockchain, especially under the constraints of block intervals, time intervals, and other permissions.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 2 Background and Motivations
### Wear-Out Mechanisms in DRAM
#### 2.5.4 Radiation Failure  
Ionizing radiation from natural or artificial sources may create hundreds of electron-hole pairs in the gate oxide, which can degrade the transistor and even disrupt performance [23]. The electrons get swept away out of the gate oxide due to high mobility, leaving trapped positive charge at the Si-SiO\({}_{2}\) interface. Consequently, it results in a shift of threshold voltage and increases the channel leakage current [20; 24]. Over an extended period of time, trapped charges at the interface and oxide may cause the TDDB effect and a complete breakdown of the dielectric oxide.  
An integrated circuit's performance slowly but gradually degrades over time due to such wear-out phenomena. The degradation rate might depend on the switching activity, the gate voltage, the content of the DRAM cell, and several other factors [25; 26].
# Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
## 2 Materials and Methods
### Development Workflow
#### 2.2.1 Case Study and Data Collection  
The case study under consideration is based on the realization of welds on bases for an offshore plant on an FPSO vessel. No special materials were used for this work; almost all of the basement is made of S355J2 structural steel.  
Multi-pass welds were performed using a Miller XMS 425 MPa in Figure 7 and a gas composed of 82% of argon and 18% carbon dioxide. The wire, with a 1.2 mm diameter, is AWS certified as A5.18:ER70S-6.  
Miller Insight Core software was used to monitor welding operations and subsequent collection of welding parameters; this software allows monitoring in real-time and storing all the process parameters on a cloud. Together with the WPS, this data was exported in excel format and then pre-processed in a python environment.  
Ultrasonic, magentoscopic, and penetrant liquids tests were performed on the welds. Figure 8 shows an image of one of the tests performed using penetrant liquids, which  
Figure 7: Miller XMS 425 MPa welding machine.  
revealed the presence of a crater. Moreover, through an ultrasound inspection, it was possible to highlight a lack of fusion at a certain depth from the surface, corresponding to a certain welding pass to which certain welding parameters were associated. In this way, it was possible to assign labels to each welding pass whose process values were known. All of this information has been collected in a single file, which followed a pre-processing step in Python.  
Pre-processing of the data involved scaling of the values to reference values for all process variables, to guarantee the same scale to all variables in input in neural network:  
\[e_{i}=\frac{r-v_{i}}{r} \tag{6}\]  
where \(r\) is the reference and \(v_{i}\) the actual value of variable. So for each weld the scale errors were collected in a \(4\times 1\) vector of features, and Equations (2)-(5) become:  
\[e_{1}=\Delta v_{mean}=\frac{v_{mean}^{WPS}-v_{t}^{eff}}{v_{mean}^{WPS}} \tag{7}\]  
\[e_{2}=\Delta I_{max}=\frac{I_{max}^{WPS}-I_{eff}}{I_{max}^{WPS}} \tag{8}\]  
\[e_{3}=\Delta I_{min}=\frac{I_{min}^{WPS}-I_{eff}}{I_{min}^{WPS}} \tag{9}\]  
\[e_{4}=\Delta V_{mean}=\frac{V_{mean}^{WPS}-V_{eff}}{V_{mean}^{WPS}} \tag{10}\]  
where \(V^{WPS}\) is the voltage reported in the \(WPS\) and \(V_{eff}\) is the measured voltage. The same is for the others parameters like welding speed \(v_{t}\) and current \(I\). Following the non-destructive tests, it was possible to associate the corresponding label with each feature vector corresponding to the single welding pass, as mentioned before.
## 5 Analysis on Time and Area Overheads
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**MRM Types** & **Scheme** & \(P\) & **Operand Size (bit)** & **f (MIBZ)** & **Total Area (Q gate)** & **Extra Overhead** & **Extra Overhead Ratio** & **Error-Reporting Delay (seq)** \\ \hline \multirow{6}{*}{Type-32} & \multirow{6}{*}{Parity [30]} & 128 & & 59.29 & \multirow{6}{*}{3} & 35.13 & 46.65 & 142.44 & 5.80 \\  & & 256 & & 114.26 & & 64.27 & 45.82 & 122.90 & 8.70 \\  & & 52 & 690 & 27.73 & & 123.88 & 45.41 & 108.81 & 14.50 \\  & & 1024 & & 57.68 & & 246.09 & 45.20 & 89.91 & 26.10 \\ \cline{2-10}  & & 128 & & 34.11 & & 9.45 & 0.76 & 38.31 & 4.00 \\  & & 256 & & 1000 & 71.15 & 2 & 18.67 & 0.38 & 38.21 & 6.00 \\  & & 512 & & 151.91 & & 38.05 & 0.19 & 33.03 & 10.00 \\  & & 1024 & & 34.06 & & 74.07 & 0.09 & 26.96 & 18.00 \\ \hline \multirow{6}{*}{Type-32} & \multirow{6}{*}{3} & 128 & 800 & 41.95 & \multirow{6}{*}{2} & 17.51 & 25.95 & 71.62 & 2.50 \\  & & 256 & 740 & 82.24 & & 30.82 & 35.51 & 59.94 & 2.70 \\  & & 512 & 714 & 175.16 & & 61.30 & 40.26 & 53.85 & 2.80 \\  & & 1024 & 714 & 395.10 & & 122.51 & 40.13 & 44.94 & 2.80 \\ \cline{2-10}  & & 128 & 667 & 45.40 & & 21.01 & 51.14 & 86.13 & 3.00 \\  & & 256 & 667 & 94.43 & & 42.02 & 50.57 & 81.75 & 3.00 \\  & & 512 & 645 & 107.89 & & 83.05 & 58.29 & 72.94 & 3.10 \\  & & 1024 & 645 & 400.67 & & 167.95 & 55.14 & 61.59 & 3.10 \\ \cline{2-10}  & & 128 & 625 & 51.40 & & 27.01 & 61.22 & 110.71 & 3.20 \\  & & 256 & 625 & 100.38 & & 48.97 & 60.00 & 95.28 & 3.20 \\  & & 512 & 606 & 211.34 & & 97.50 & 65.31 & 85.64 & 3.30 \\  & & 1024 & 606 & 447.46 & & 194.89 & 65.15 & 71.50 & 3.30 \\ \hline \multirow{6}{*}{Parity [30]} & \multirow{6}{*}{64 bit} & 128 & & 55.64 & \multirow{6}{*}{3} & 31.81 & 47.13 & 133.46 & 3.20 \\  & & 256 & & 111.73 & & 63.07 & 46.28 & 129.62 & 6.40 \\  & & 512 & & 284.41 & & 215.61 & 48.67 & 122.83 & 6.90 \\  & & 1024 & & 478.77 & & 250.68 & 45.66 & 109.90 & 16.00 \\ \hline \multirow{6}{*}{Type-64} & \multirow{6}{*}{3} & 128 & & 34.03 & & 10.19 & 0.77 & 47.88 & 2.20 \\  & & 256 & & 61.25 & & 12.59 & 0.38 & 25.89 & 4.40 \\  & & 512 & & 120.81 & & 18.01 & 0.19 & 17.52 & 6.60 \\  & & 1024 & & 256.36 & & 28.87 & 0.09 & 12.66 & 11.00 \\ \cline{2-10}  & & 128 & 800 & 39.48 & & 15.64 & 14.51 & 65.66 & 2.50 \\  & & 256 & & 800 & 79.45 & & 30.99 & 14.09 & 63.20 & 2.50 \\  & & 512 & 740 & 163.94 & & 61.14 & 22.96 & 59.47 & 2.70 \\  & & 1024 & 740 & 360.40 & & 122.31 & 28.84 & 53.63 & 2.70 \\ \cline{2-10}  & & 128 & 645 & 43.36 & & 19.51 & 41.99 & 81.79 & 3.10 \\  & & 256 & 645 & 87.30 & & 39.01 & 41.44 & 80
## 2 RPC-FST Model Based on ALE Method
### Fluid-Solid Interaction of RPC-FST  
The approach for solving FSI problems is the ALE formulation for the fluid domain and the Lagrangian formulation for the structure domain. This approach is called the ALE method, and it has been intensively used for problems involving small and large structure displacements with no topological changes in the structure. Therefore, the ALE method from LS-DYNA provides a possibility to model multi-phase highly dynamic problems. The nonlinear dynamic analysis program LS-DYNA is used to analyze the dynamic response of RPC-FST columns under explosion load. A numerical model composed of four parts is established. The TNT and air domain are treated as ALE parts, while the RPC-FST columns are treated as Lagrangian parts.
## 3 System Model
### Optimal Relay Selection
#### 3.2.2 Delay Model
The level of packet loss is \(F_{t}\), which can be expressed as  
\[F_{t}=\max[0,(Q_{t-1}+A_{t})-M]. \tag{19}\]  
When the train or wayside relay steadily sends packets to the next hop, \(A_{t}\) and \(F_{t}\) remain constant during transmission. Therefore, \(\lim\limits_{t\rightarrow\infty}A_{t}=A\) and \(\lim\limits_{t\rightarrow\infty}F_{t}=F\). In time slot \(t\), the packet loss rate of this node can be calculated by  
\[p_{fl}=\lim\limits_{t\rightarrow\infty}\frac{\sum\limits_{t=1}^{T}F_{t}}{\sum \limits_{t=1}^{T}A_{t}}=\frac{E\{F\}}{E\{A\}}=\frac{E\{F\}}{\lambda_{b}\Delta t}, \tag{20}\]  
where \(E\{x\}\) is the mathematical expectation. If there is no packet overflow, then the packet loss rate is zero. Otherwise, the packet loss rate is the number of packet losses divided by the number of node arrivals. The packet loss rate and the packet error rate are calculated in Equations (18) and (20), respectively, and then introduced into Equations (14) and (15) to calculate the queuing delay. Therefore, the total delay of the \(k\) hop from node \(a\) to node \(b\) can be expressed as  
\[D_{a,b}^{k}=T_{p}+T_{c}. \tag{21}\]