esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress and Engineering Applications of Viscous Fluid Mechanics
## 2 Advances in the Study of the State of Motion of Viscous Fluids
### Experimentation and Research
#### 2.1.3 Experiments and Studies on the Flat-Plate Gap Method
[72] envisioned a fluid viscoelastic model consisting of a viscoelastic plate and a half-plane filled with a viscous fluid, which is shown schematically in Figure 8. Using the exact equations of viscoelastic dynamics for describing the motion of the plate and the linearized N-S equations for describing the fluid flow, the problem of forced vibration of the system under plane strain was investigated. Figure 8: Schematic of a hydroviscoelastic system: 1—viscoelastic plate; 2—viscous fluid. Based on the Cartesian coordinate system \(Ox_{1}x_{2}x_{3}\) on the surface of the upper plate, a hydroviscoelastic system consisting of a plate of viscoelastic material and a half-plane filled with compressible viscous fluid is established. In this system of coordinates, the plate occupies the area \(\{|x_{1}|<\infty,-\infty<x_{2}<0,|x_{3}|<\infty\}\) and the fluid occupies the area \(\{|x_{1}|<\infty,-\infty<x_{2}<-h,|x_{3}|<\infty\}\), where h is the thickness of the plate. It is assumed that the system is subjected to a normal harmonic force of amplitude p, uniformly distributed on axis \(Ox_{3}\). The plane strain state is generated in the plate, and the liquid plane flow occurs in the plane \(Ox_{1}x_{2}\)[72]. Figure 7: Systematic diagram of squeeze-film bearing with porous circular plate. The digital model assumes that the velocity of the upper plate in the negative direction of the \(Z\)-axis of the Cartesian coordinate system drives the collision and extrusion between the parallel circular perforated plate and the fixed lower plate and predicts the bearing characteristics of the extrusion die of the parallel circular perforated plate. The pressure results under different viscosity–pressure parameters are numerically calculated and compared with the isoviscous coupling stress and Newtonian lubricant. The results show that the pressure of the piezo-viscous non-Newtonian fluid is significantly higher than that of the isoviscous Newtonian fluid and the isoviscous non-Newtonian fluid [70]. As can be seen from the above researchers' studies on the motion of viscous fluids between plate gaps, this type of experiment is complicated by the experimental setup, so much so that most of the researchers use digital models for predictive studies. In terms of scientific research, this approach helps to gain a deeper understanding of the flow properties of viscous fluids at the microscopic scale, explore the complex rheological behavior of fluids, and save experimental costs. In engineering applications, this digital model prediction method can provide important data support for designing viscous fluid lubrication systems, optimizing fluid transport pipelines, and so on.
###### Abstract  
Small-size enterprises drying cassava in Africa mostly use fixed-bed dryers or pneumatic dryers. The objective of this study was to determine which of those two dryers is the best choice for this operation. Energy performance, product quality and costs were measured and analysed using a comparative experiment design. Each dryer was considered as a treatment and experiments were performed in quintuplicate at a cassava processing small-size enterprise in Ghana. The energy performance of the pneumatic dryer was superior because of the better contact between the cassava grits and the drying air, resulting in greater heat and mass transfer. The cassava flour obtained from the fixed-bed dryer had a higher Whiteness Index, but the same level of lightness, and the staff responsible for managing product quality was not able to visually distinguish them. As a result of the pneumatic dryer's better energy performance, its operating cost was lower. The capital cost of this dryer was higher, but to recover the additional investment only 194 days of operation were needed. Therefore, it was concluded that pneumatic dryers are a better choice of equipment for cassava drying by small-size enterprises in Africa.  
convection dryer; pneumatic dryer; flash dryer; fixed-bed dryer; flatbed dryer; energy efficiency; product quality; costs +
Footnote †: journal: Applied Soft. 2020, 10, 7863; doi:10.3390/app10217863
# Cavitator Design for Straight-Running Supercavitating Torpedoes
## 3 Verification of Mathematical Models
### Experimental Verification
The corresponding trim condition was determined by solving the moment equilibrium equation (Equation (3)). The calculated cavity shape is shown in Figure 9b. The numerical trim condition for this case was determined to be \(\delta_{c}=-9^{\circ}\) and \(\alpha_{B}=1.924\), which agreed well with the experimental one. This means that the mathematical models used in the present design method provide a reliable prediction of the hydrodynamic forces acting on the disk-type cavitator and the wetted part of the vehicle planing in the cavity. Additionally, the stationary planing motion scheme was proven to be reasonable through the present experimental results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Section No.** & **Distances (mm)** & \
\begin{tabular}{c} **Diameter (mm),** \\ **Experiment** \\ \end{tabular} &
\begin{tabular}{c} **Diameter (mm),** \\ **Calculation** \\ \end{tabular} & **Deviation (\%)** \\ \hline
1 & 50 & 66 & 66.96 & 1.45 \\
2 & 100 & 81 & 81.80 & 0.99 \\
3 & 150 & 92 & 92.01 & 0.01 \\
4 & 200 & 101 & 99.90 & 1.09 \\
5 & 250 & 108 & 105.87 & 1.97 \\
6 & 300 & 112 & 110.30 & 1.52 \\
7 & 350 & 115 & 113.40 & 1.39 \\
8 & 400 & 117 & 115.25 & 1.50 \\
9 & 450 & 117 & 115.89 & 0.95 \\
10 & 500 & 116 & 115.31 & 0.59 \\
11 & 550 & 113 & 113.57 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the cavity dimensions obtained from the experiment and calculation. Figure 8: Experimental and calculated cavity shapes at a fixed zero trim angle: (**a**) experiment; (**b**) calculation. Similarly, an experiment was performed for the model coupled with the cone-type cavitator and four control fins. The purpose of this experiment was to validate the mathematical models for the hydrodynamic forces acting on the cone-type cavitator and control fins. The deflection angles of the vertical and horizontal fins were set to \(0^{\circ}\) and \(2^{\circ}\), respectively. The experimental results indicate that the model was stabilized in a stationary bottom planing mode at \(\delta_{c}=2.1^{\circ}\) and \(\alpha_{B}=2.92^{\circ}\), as shown in Figure 10a. The corresponding trim condition was found to be \(\delta_{c}=2.1^{\circ}\) and \(\alpha_{B}=3.075\), as shown in Figure 10b. The numerical trim condition with the calculated cavity shape also agreed well with the experimental one, meaning that the hydrodynamic forces acting on the cone-type cavitator and control fins can be adequately predicted by the given mathematical models.
## 2 Results and Discussion  
The experimental results are given in Table 1. In all 54 volatile compounds were identified in the three wines studied, including 23 alcohols, 13 esters, eight acids, three terpen and seven aldehyde and ketone compounds. Many of these volatile compounds are commonly found in wines and are derived from grapes and yeast strain fermentation and the vinicification process [21]. \begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{
\begin{tabular}{c} **Threshold** \\ **(\#g/L)** \\ \end{tabular} } & \multirow{2}{*}{**Odor descriptor**} & \multicolumn{3}{c}{**Concentration (\#g/L)**} \\ \cline{4-6}  & & & **Cabernet** & **Cabernet** & **Cabernet** \\ \hline
**Alcohols** & & & & **Sauvignon** & **Gernischet** & **Chardonnay** \\
1-Propanol & 306,000\({}^{\text{d}}\) & Fresh, alcohol & ND & 2715.9 & 6491.9 \\ Isobutyl alcohol & 40,000\({}^{\text{a}}\) & Fusel, alcohol & 22004.1 & 19778.1 & 9379.0 \\
1-Butanol & 150,000\({}^{\text{c}}\) & Medicinal, alcohol & 1587.6 & 1083.4 & 945.2 \\ Isoamyl alcohol & 30,000\({}^{\text{a}}\) & Cheese & 185587.5 & 144784.9 & 103021.5 \\
1-Pentanol & 80,000\({}^{\text{j}}\) & Fruity, balsamic & 176.1 & 146.8 & ND \\
4-Methyl-1-pentanol & 50,000\({}^{\text{b}}\) & — & 106.8 & 81.0 & 28.5 \\
2-Heptanol & 70\({}^{\text{e}}\) & Fruity, mouldy, & 14.7 & 8.1 & ND \\  & & musty & & & \\
1-Hexanol & 8000\({}^{\text{a}}\) & Green, grass & 4017.7 & 2542.8 & 1163.7 \\
3-Hexen-1-ol,(\(E\))- & 400\({}^{\text{a}}\) & Green, floral & 133.8 & 92.6 & 55.0 \\
3-Hexen-1-ol,(\(Z\))- & 400\({}^{\text{a}}\) & Green & 135.8 & 129.2 & 54.4 \\
2-Hexen-1-ol,(\(E\))- & 400\({}^{\text{j}}\) & Green grass, herb & 47.3 & ND & ND \\
2-Hexen-1-ol,(\(Z\))- & 400\({}^{\text{j}}\) & Green grass, herb & ND & ND & 16.6 \\
2-Octanol & 120\({}^{\text{a}}\) & — & 24276.6 & ND & ND \\
1-Heptanol & 1000\({}^{\text{j}}\) & Grape, sweet & 212.2 & 129.0 & 22.3 \\
2-Ethyl-1-hexanol & 8000\({}^{\text{j}}\) & Mushroom, sweet & trace & ND & ND \\  & & fruity & & & \\
2-Nonanol & 58\({}^{\text{e}}\) & — & 6.3 & ND & 3.7 \\
2,3-Butanediol,[\(R\)-(\(R\)*, 120,000\({}^{\text{j}}\) & Butter, creamy & 405.4 & 475.1 & 527.9 \\ \(R\)*)]- & & & & & \\
1-Octanol & 120\({}^{\text{a}}\) & Intense citrus, roses & 44.2 & 25.2 & 17.0 \\ \hline \end{tabular}
\end{table} 
Table 1: The threshold values and concentration of volatile components found in the Cabernet Sauvignon, Cabernet Gernischet and Chardonnay young wines.
## 4 Assumptions and Definitions
#### Assumptions  
This article assumes that both the ship and a drone's 3D Cartesian coordinates are measured in real time. Global Positioning Systems (GPSs) and Inertial Measurement Units (IMUs) can be used for this localization.  
Furthermore, the ship can measure the target's 3D Cartesian coordinates at every sample-index. Position measurements can be provided by various sensors, such as radar sensors or laser sensors. Therefore, the ship at sample-index \(k\) can derive \(L_{k}\).  
Furthermore, based on the target's recent trajectory, the ship at sample-index \(k\) can predict \(\mathbf{r}_{k+1}^{t}\), the target's 3D Cartesian coordinates, after one sample-index in the future. Section 5.1 shows how to predict the target's 3D Cartesian coordinates after one sample-index in the future.  
The ship can also predict \(\mathbf{r}_{k+1}^{s}\), the ship's 3D Cartesian coordinates, after one sample-index in the future. This is feasible because the ship has GPS and IMU. Therefore, the ship can predict \(\bar{L}_{k+1}\), which crosses both \(\mathbf{r}_{k+1}^{t}\) and \(\mathbf{r}_{k+1}^{s}\).  
It is desirable that as the target is caught, it is sufficiently far from the ship. Otherwise, the ship may be partially caught by the debris of the target. Let \(\beta>0\) define the _safety distance_. The safety distance is set by the operator of the drones. As the target is caught, it is desirable that its distance from the ship is bigger than the safety distance \(\beta\). This way, we can assure the safety of the ship.
# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 3 Experimental Results  
In this section, the performance of the proposed SP-MCFI algorithm is evaluated by testing on different video sequences, and then the results are compared with recent state-of-the-arts ME algorithms Dual-ME from [24] and DS-ME from [25]. All test sequences used for the experiments are in the standard CIF (352 \(\times\) 288) formats and 30 frame/s. In the proposed algorithm, we need to set the two parameters: block size \(s\) and radius \(r\) of the search window. To make a good balance between the interpolation quality and computational complexity, \(s\) and \(r\) are both set to be 16. The comparing algorithms keep their original parameter settings. The quality of the interpolated frames will be evaluated from subjective and objective perspectives, and the objective evaluation is to be done by using the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) [26]. All experiments are conducted on a Windows machine with an Intel Core i7 3.40 GHz CPU and a memory of 8 GB. All algorithms are implemented in MATLAB.
## 3 Results and Discussion  
We designed and simulated an IPD resonator using ADS, and compared the test data with the simulation data. Furthermore, we measured and recorded the transmission and reflection parameters of the product using an Agilent 8510C vector network analyzer (VNA), as shown in Figure 8a. The aluminum cube (\(2^{3}\) cm\({}^{3}\)) acts as a GND to reduce noise, and the PCB was mounted on it. The two ports of the PCB are connected to the VNA using subminiature version A (SMA) connectors. The chip was wire-bonded on the PCB, as shown in Figure 8b. As shown in Figure 8c, the size of the product is 1538 \(\upmu\)m \(\times\) 800 \(\upmu\)m, which is marked in a scanning electron microscope (SEM) image. As seen in Figure 8d, the enlarged view of air-bridge structures and a cross-section clearly shows its three-layer structure.  
Figure 9 compares the simulation and measurement parameters of the IPD, showing a good consistency. In the simulated result, the center frequency is located at 1.55 GHz with the insertion loss and return loss being 25.17 dB and 0.08 dB, respectively. The center frequency was measured at 1.53 GHz with a 3-dB passband of 0.99-2.07 GHz and a fractional bandwidth of 70.59%. Only 0.02 GHz frequency shift and 1.32 dB return loss variation, respectively, are acceptable manufacturing errors. The transmission zero with a frequency and magnitude of 4.16 GHz and \(-\)35.86 dB, respectively, is located on the right side of the passband. The insertion loss is 0.53 dB, return loss is 26.49 dB, and the Q-factor is 49.29. Table 4 compares the proposed IPD BPF with four researched BPFs to demonstrate that the proposed device exhibits relatively small chip size, wide fractional bandwidth, and good insertion and return losses.  
Figure 8: (**a**) Measurement setup (VNA); (**b**) BPF fixed on an aluminum cube and the top view of the fabricated product; (**c**) top view of the SEM image; (**d**) enlarged view of air-bridge structure and cross-section of the three metal layers.  
Figure 9: Simulation and measurement results of the S11 and S21 parameters.  
Table 5 compares the present study with other works using various manufacturing technologies, thereby demonstrating the merits of a wide passband and smaller dimensions of the proposed GaAs-based IPD BPF.
# A Two-Time-Scale Turbulence Model and Its Application in Free Shear Flows
## 4 Model Validation
### Plane Jet Results

Table 2: _Cont._  
Figure 2: Prediction of (**a**) mean axial velocity, (**b**) shear stress, and (**c**) TKE profiles for plane jets [35; 36; 38; 40]. spreading-rate far more accurately with a value of 0.109, and the maximum shear stress levels are also consistent with the revised model. This is not unexpected considering that the coefficients are modified in order to achieve optimal agreement among the turbulence quantities, spreading rate, and mean velocity. The time scale \(\tau\) can be normalized in two ways, either by \(\tau^{*}=\tau U_{CL}/y_{h}\) or \(z=\varepsilon\tau/k\). The latter can also be expressed as the ratio of two different time scales and, it should be comparable to the reciprocal of \(C_{\varepsilon 2}\) of the standard \(k\)-\(\varepsilon\) model (1/1.92). Figure 3 shows time scales \(\tau^{*}\) and \(z\), respectively. \(\tau^{*}\) is compared with values of \(k/\varepsilon\) (times \(1/C_{\varepsilon 2}\) of the \(k\)-\(\varepsilon\) model) predicted from both the \(k\)-\(\varepsilon\) and \(k\)-\(\varepsilon\)-\(\tau\) models. Time scales of \(k/\varepsilon\) from both models go in parallel, the one predicted from the \(k\)-\(\varepsilon\)-\(\tau\) model being 4% higher, across the jet. Time scale \(\tau^{*}\) starts with the same value of \(k/\varepsilon\) of the \(k\)-\(\varepsilon\) model in the centre of the jet and slightly increases until about 1.5 \(y_{h}\), yielding, at the maximum, a 7% higher value between 0.9 \(y_{h}\) to 1.4 \(y_{h}\). Although the \(k\)-\(\varepsilon\) model suggests that its \(C_{\varepsilon 2}\) coefficient depends only on decay exponent, variation in \"z\" across the jet implies that the coefficient \(C_{\varepsilon 2}\) of the \(k\)-\(\varepsilon\) model would not be a true constant. The TKE budget, as found by the modified \(k\)-\(\varepsilon\)-\(\tau\) model is compared in Figure 4 with the Bradbury's energy balance, which is found to be more consistent than [PERSON]'s, according to [PERSON] [44]. Agreement is again very good and consistent with the above findings. Figure 4: Plane jet energy budget. Figure 3: Estimation of z-profiles and the turbulence time scale in plane jet.
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 2 Theoretical Conceptualization  
The SHM strategy implemented in this work encompasses two main techniques:  
* **Unsupervised Machine Learning using AutoEncoders**: This step involves the application of VAE, SAE, and CAE models to the dynamic data. The models extract features that effectively characterize the monitored vibration signals and serve as a dimensionality reduction tool. It is important to mention that the application of AEs in SHM tasks has been showing promising results, as reported in [27].
* **[PERSON]'s \(T^{2}\) Control Chart**: This chart displays the values of the \(T^{2}\) statistical metric, which is computed from the reduced parameters obtained through the VAE, SAE, and CAE models. The present study uses the \(T^{2}\) Control Chart to visually and objectively assess the AutoEncoder models' capability to detect structural changes in railway wheels.  
AutoEncoders are neural network models designed to learn compact latent representations of input data to reconstruct the original data accurately. Their architecture consists of two main parts: an encoder and a decoder. This design enables AEs to capture the data's intrinsic features and statistical patterns. The encoder maps the input to a lower-dimensional latent space \(\mathbf{h}=f(\mathbf{x})\), trying to preserve the most relevant data features. On the other hand, the decoder reconstructs the original data from this latent representation, denoted as \(\mathbf{r}=g(\mathbf{h})\). Essentially, it learns to reverse the transformation performed by the encoder [35], as shown in Equation (1):  
\[L(\mathbf{x},g(f(\mathbf{x}))), \tag{1}\]  
where \(L\) is a loss function that penalizes the output \(g(f(\mathbf{x}))\) for deviating from the original input \(\mathbf{x}\).  
The following subsections provide the main principles of the aforementioned techniques. For further information on AutoEncoders, the reference [PERSON] et al. (2016) [35]is recommended. Additional details on the Shewhart Control Chart (\(T^{2}\)) can be found in [PERSON] (2009) [36].
## 5 Experiment
### Baseline Models  
To comprehensively evaluate the model performance, we compare against several state-of-the-art baselines:  
MemNN [11] is an end-to-end framework for reading comprehension and QA. All triples are stored in memory cells, and multi-hop reasoning is achieved through stacked memory layers.  
KVMemNN [12] extends MemNN with key-value memory structures. For each triple, the key slot encodes the subject-relation pair, while the value slot corresponds to the object entity.  
IRN [15] generates traceable reasoning paths by predicting intermediate entities and relations at each hop.  
PullNet [13] and GraftNet [14] both employ graph neural networks (GNNs) for subgraph construction and inference. PullNet extracts critical information to build precise subgraphs, whereas GraftNet constructs question-specific subgraphs. While effective in localized reasoning, scalability challenges arise with large-scale knowledge graph.  
Hic-KGQA [8] resolves complex queries via hypergraph construction and inference chain generation. This method excels in intricate reasoning tasks but faces computational bottlenecks with large-scale data.  
EmbedKGQA [7] performs multi-hop reasoning using pretrained entity and question embeddings. It mainly relies on static entity and question representations, which are suitable for basic reasoning tasks, but its performance is limited for more complex multi-hop reasoning tasks.  
TransferNet [9] and UNIKGQA [10] both leverage pretrained language models (LMs) for semantic alignment. TransferNet focuses on relation-question relevance, while UNIKGQA computes entity matching scores. Both benefit from LM semantic understanding but struggle with hallucination control.  
ROG [34] is a plan-retrieve-reason framework that mitigates hallucination and knowledge gaps by generating LLM-guided structured plans, ensuring faithful and interpretable reasoning.  
These baselines adopt diverse reasoning strategies, predominantly built on transformer-based [26] architectures and reliant on graph structures for multi-hop inference. Our comparisons aim to elucidate performance gaps between traditional methods and LLM-enhanced approaches, particularly when transitioning from conventional base models to more capable LLMs for handling complex reasoning tasks.
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
###### Abstract  
Accurate temperature distribution measurement during high-intensity focused ultrasound (HIFU) treatment is crucial for avoiding damage to sensitive tissues and organ systems. To surpass the constraints of conventional thermocouple temperature measurement approaches, near-infrared temperature measurement, as a non-invasive imaging method, is proposed. Using infrared glass as a temperature observation window allows for studying the temperature distribution on the surface of biological tissues under ultrasound exposure. The temperature rise in the tissues near the infrared glass under ultrasound exposure was investigated through numerical simulations. Moreover, the effects of the shear waves and thermal viscosity induced by the infrared glass were also analyzed. The results indicate that the shear wave in the glass weakens the intensity of the ultrasound pressure in the focal region while enhancing the efficiency of the acoustic thermal conversion. Thermal viscosity increases the acoustic pressure and temperature in the focal zone. Furthermore, oblique incidence facilitates the transformation of the acoustic thermal effect, caused by shear waves and thermal viscosity, resulting in an expanded temperature rise range on the tissue surface. The non-linear effects in the ultrasound field further enhance the acoustic thermal effect. Significant errors occur in the near-infrared method when utilizing infrared glass for temperature measurement, with the shear waves exerting the most substantial impact on the temperature distribution. These research findings carry substantial implications for optimizing treatment plans, enhancing treatment safety and efficacy, and offering potential application value for temperature control in HIFU treatment.  
Keywords:temperature distribution measurement; high-intensity focused ultrasound; near-infrared; shear waves; thermal viscosity +
Footnote †: journal: