esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 3 Framework and Methodology
### Underwater Human Tracking
The overall algorithm flow is shown in Figure 3. In addition, KCF uses the nature of diagonalization in the frequency domain of the circular matrix to convert the calculation from the time domain to the frequency domain, and by combining fast Fourier transform, the tracking speed is greatly improved. At the same time, due to the peculiarities of the underwater environment, the UAR needs to maintain a very close formation with the target. To ensure that the UAR can effectively track the target, the overall visual center of the tracked target needs to be kept as close to the image center as possible. To this end, we designed a target tracking rule called \"Active Control\" to adjust the UAR's position and attitude in time. The principle of the \"Active Control\" algorithm is to always keep the distance between the target center and the frame center within a certain range, as shown in Figure 4. A limited box \(LBOX\) is constructed with the frame center point \(FCP\) and the side length is a default value _three_. The target center point \(TCP\) needs to be in that square at all times. In addition, the area outside the \(LBOX\) is divided into 8 blocks, which are \(Upper\ Left\), \(Upper\ Right\), \(Left\), \(Right\), \(Lower\ Left\), \(Lower\), and \(Lower\ Right\). When the target is out of the restriction square, the next control command is sent to the STM32 control board according the area and position of the \(TCP\). Finally, according to the overall flow, in the underwater tracking process of this project, we used the target position obtained by SSDLite to initialize the KCF tracker when tracking starts and when tracking fails. The overall algorithm flow pseudo-code is shown in Algorithm 1, where cmd denotes the control command passed to the STM32 control board. Figure 4: Positive control schematic. The UAR movement is controlled according to the position of the target center \(TCP\), and the \(TCP\) is kept in the center of the frame \(LBOX\). Figure 3: Overall flow of KCF tracking algorithm. ```
0: Video stream after image enhancement \(I\)
0: Control command \(cntd\)  Load trained SSDLite model, init \(thre\), set \(tracking\) = False while (get frame \(F\) from \(I\))!= None do if not \(tracking\)then  Detect person in frame \(F\), init the \(tracker\), set \(tracking\) = True \(tracker\) update, get confidence score \(conf\), person position \(rect\) if\(conf>thre\)then  according to the \(ActiveControl\) and \(rect\), calculate \(cmd\) else \(tracking\) = False endif endif endwhile
```  
**Algorithm 1** Underwater human tracking algorithm  
We tested the proposed human tracking algorithm on the underwater dataset, and the results are shown in Figure 5. Column 1 of the figure is the initial frame, where the human position information is obtained using underwater target detection, which is used to initialize the tracker, and columns 2 to 4 show the subsequent tracking results of the tracker.
# Flow Characteristics and Loss Models of the Transonic Radial Turbine
## 5 Conclusions  
In this paper, the flow characteristics and loss models of transonic turbines are studied. With the increase in turbine expansion ratios, an oblique shock wave near the trailing edge will gradually strengthen and move downstream, and finally, the oblique shock wave will turn into a positive shock wave, causing strong shock wave-induced loss. Previous turbine loss models are mainly aimed at subsonic conditions, including incidence loss, passage loss, leakage loss and trailing edge loss without a shock wave-induced loss model of a transonic turbine. In order to predict the performance of transonic turbines more accurately, a shock wave-induced loss model is proposed in this paper. The new combinations of the loss models can make the largest predicted efficiency error decrease from about 10% to 3.5%.  
The passage loss is the dominating loss of the turbine, which ranges from 8% to 10%. The tip clearance loss will decrease with the increase in the expansion ratio. The trailing edge loss is about 2% and it will increase first and then decrease as the expansion ratio increases. The closer to the design rotation speed, the lower the incidence loss. The shock wave-induced loss increases rapidly when the turbine works in transonic conditions because of the increase in the Mach number.  
Conceptualization, C.Z., W.Z. and Y.Z.; methodology, C.Z.; formal analysis, C.Z.;investigation, C.Z.; resources, W.Z.; writing--original draft preparation, C.Z.; writing--review and editing, C.Z.; visualization, C.Z.; supervision, Y.Z.; project administration, C.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China(No. 2020 YFB1901702).  
Not applicable.  
The authors thank [PERSON] and [PERSON] for the guidance of the work.  
The authors declare no conflict of interest.  
Figure 12: The distribution of different losses as fraction. (**a**) 70,000 rpm. (**b**) 80,000 rpm. (**c**) 90,000 rpm. (**d**) 100,000 rpm.
## 2 Materials and Methods
### Model Development
#### 2.2.1 Bagged Decision Tree  
Overfitting is common in individual decision trees. Bootstrap aggregation, often known as bagging, is an ensemble technique that lessens the impacts of overfitting and enhances generalization. There are many strategies to protect the model from overfitting, including good data collection for the model, test-train split, k-fold cross validation, feature selection, regularization, pruning algorithms, and so on. In this study, 'good data' were ensured by collecting high-quality data, cleaning, and processing the data, and verifying the data via a statistical summary. For the bagged decision tree, the model can be protected from overfitting by increasing the number of trees and number of samples per leaf and limiting the tree depth. Multiple subsets can be produced from the training data with the aid of bootstrapping. After that, several trees are constructed using the bootstrapped samples. Bootstrapping uses sampling with replacements to produce various subsets by selecting repeated data points at random from the training set and can be written as [49]:  
\[fbg(x)=\frac{1}{B_{g}}{\sum_{b=1}^{B_{g}}}F^{*b}(x) \tag{2}\]  
where \(B_{g}\) = total bootstrap samples and \(F^{*b}(x)\) = forecasted fitted regression model.  
The generalized structure of the bagged decision tree is shown in Figure 4. The bagged decision tree offers two key benefits, including lower model variance and the ability to train numerous trees at once.  
Figure 3: Decision tree model.
_Applied_  
_sciences_  
_Article_  
**High-Speed Visual Analysis of Fluid Flow and Heat Transfer in Oscillating Heat Pipes with Different Diameters**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2,* and [PERSON] 1**  
Footnote 1: School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, China; [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (Q.S.); [EMAIL_ADDRESS] (L.W.)  
Footnote 2: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-25-8379-2483  
Academic Editor: [PERSON]  
Received: 1 September 2016; Accepted: 20 October 2016; Published: 26 October 2016  
**Abstract:** The oscillating heat pipe (OHP) is a new member in the family of heat pipes, and it has great potential applications in energy conservation. However, the fluid flow and heat transfer in the OHP as well as the fundamental effects of inner diameter on them have not been fully understood, which are essential to the design and optimization of the OHP in real applications. Therefore, by combining the high-speed visualization method and infrared thermal imaging technique, the fluid flow and thermal performance in the OHPs with inner diameters of 1, 2 and 3 mm are presented and analyzed. The results indicate that three fluid flow motions, including small oscillation, bulk oscillation and circulation, coexist or, respectively, exist alone with the increasing heating load under different inner diameters, with three flow patterns occurring in the OHPs, viz. bubbly flow, slug flow and annular flow. These fluid flow motions are closely correlated with the heat and mass transfer performance in the OHPs, which can be reflected by the characteristics of infrared thermal images of condensers. The decrease in the inner diameter increases the frictional flow resistance and capillary instability while restricting the nucleate boiling in OHPs, which leads to a smaller proportion of bubbly flow, a larger proportion of short slug flow, a poorer thermal performance, and easier dry-out of working fluid. In addition, when compared with the 2 mm OHP, the increasing role of gravity induces the thermosyphon effect and weakens the 'bubble pumping' action, which results in a little smaller and bigger thermal resistances of 3 mm OHP under small and bulk oscillation of working fluid, respectively.  
**Keywords:** oscillating heat pipe; fluid flow motion; flow pattern; thermal performance; inner diameter
# Advanced Hysteretic Model of a Prototype Seismic Isolation System Made of Polymeric Bearings
## 1 Introduction and Motivation
These mathematical models, however, are evaluated for different base isolators, which are characterized by their own specific load-displacement behaviour, and thus, may not be fully appropriate for detailed numerical analysis of structures isolated with different types of base isolation systems. Moreover, most of these models refer to either elastomeric bearings (see, for example, [21; 22; 23; 24]), or HDRBs (see, for example, [25; 26]), which are also susceptible to vertical loads. Motivated by the preceding discussion, the present paper was designed to verify the effectiveness of an advanced hysteretic model in simulating complex force-deformation behaviour of a prototype seismic isolation system made of PBs, which are constructed with the use of a specially prepared for this purpose polymeric material with improved damping properties [27]. High effectiveness of PBs in reducing structural vibrations due to seismic excitations was confirmed during previously conducted extensive shaking table investigation and the results have already been published (see [28] for details). In order to achieve the above-mentioned aim of the present investigation, the following specific objectives were established:  
1. Perform dynamic oscillatory test in order to determine the energy dissipation properties of the base isolation system made of PBs based on the experimentally obtained hysteresis loops. 2. Propose a general form of an advanced mathematical model, which defines the lateral force as a nonlinear function of shear displacement and deformation velocity, in order to accurately capture the nonlinear mechanical behaviour of PBs in numerical analysis of base-isolated structures. 3. Evaluate the function parameters by fitting the general form of the proposed mathematical model into the experimentally obtained hysteresis loops using the least squares optimisation method. 4. Implement the proposed mathematical model into the computational analysis in order to perform numerical evaluation of the seismic response of the previously examined experimental modelswith dynamic characteristics typical for low-rise and medium-rise buildings, both fixed-base and base-isolated with the use of PBs, to a number of different ground motions and a mining tremor. 5. Verify the effectiveness of the proposed mathematical model in simulating nonlinear behaviour of PBs by comparing the results of the detailed lumped-parameter analysis with the experimentally obtained shaking table data.
# Effect of Terahertz Antenna Radiation in Hypersonic Plasma Sheaths with Different Vehicle Shapes
## 2 The Plasma Sheath and the THz Array Antenna  
The models consist of two parts, the plasma sheath and the 0.14 THz array antenna. In order to investigate the impact of vehicle shape on onboard antenna performance, plasma flow fields corresponding to two common shapes are simulated. The two shapes are blunt-cloned and sharp-coned.
# Comparative Analysis of Exemplar-Based Approaches for Students' Learning Style Diagnosis Purposes
## 2 Methods  
### Combining Case-Based Reasoning and Bayesian Approach for Students' Learning Style Modelling  
As the aim of this scientific research is to explore possibilities and issues in the area of applying combined Bayesian and case-based reasoning approaches to model student learning styles, papers and video material on the theme were reviewed. A literature-based approach has been used. The target goal of the review of papers about Bayesian networks, mixture and mixed membership modelling (Latent Dirichlet allocation, topic models), case-based reasoning (CBR), rule-based reasoning (RBR) methods and their combination (Bayesian Case model (BCM) and interactive BCM (iBCM) [6], CBR + BN, CBR + RBR) was to comparatively analyze the main trends existing in the area of modelling, which integrates CBR methods for prediction, diagnosis, and reasoning. The potential to predict student learning styles based on his/her behavioral factors (learner's behavioral activities in automated hypermedia learning systems) that determine learning style was discussed. As a result of the research, the Bayesian case model is proposed for dynamic prediction of a student's learning style. BCM is a generative model, also known as \"latent variable model\", which models uncertainty using probability and provides a way of modeling how a set of observed data arises from a set of underlying causes. As Bayesian probability theory is used for modelling aleatoric uncertainty, BCM combines a Bayesian approach with the case-based reasoning method, which models epistemic uncertainty arising due to limited knowledge or data. Using case-based reasoning, new problems are solved by retrieving cases describing similar prior problems from memory and adapting their solutions to the new case. In this way, BCM mimics the natural human decision-making process.
# The Role of Blockchain in Improving the Processes and Workflows in Construction Projects
## 3 Methodology
### Research Questions Definition
#### 3.1.1 What Are the Cases of Blockchain Technology in the Construction Industry?
The analysis revealed that there has been an increase in the application of blockchain technology over time, which reflects the growing interest in blockchain technology in the construction industry. As shown in Figure 3, the publications trended dramatically across all databases, reflecting the current sentiment toward blockchain in the construction industry. This demonstrates that blockchain in the construction industry is a relatively new area of research. Figure 4 depicts the types of papers reviewed. As shown in Figure 4, four main types of publications were included in this research: journals (86%), trade journals (2%), books (3%), and other forms (8%). Furthermore, the geographical distribution of the reviewed papers from various countries is depicted in Figure 5. The geographical distribution of the chosen papers demonstrates that blockchain technology in the construction industry has gained the attention of researchers all over the world. The analysis was performed based on the disclosed country of the author. If not disclosed, the publication location was used instead. In the case of multiple authors, the location of the first author or the principal author was considered. It is clear that the United States dominates other countries in relation to the authorship and publication of blockchain and smart contracts in the construction industry. The United States' publications represent 24% of the total, followed by China which scored 17% as per the analysis globally. South Korea and India were close to each other in the  
Figure 4: Publication sources of the matching 692 publications from database searches. Figure 3: Publication analysis by year in the four databases. ranking, with 10% and 9% of the papers, respectively. These statistics show that blockchain is receiving a lot of attention in construction projects over the world.
## 2 Liquefaction Fragility  
### Seismic Fragility Assessment and TELES  
Fragility evaluation and associated curves are crucial in seismic risk assessment as they quantify the probability that a structure or ground will reach or exceed certain damage states under varying earthquake intensities. These curves can be derived from probabilistic analyses and used to predict potential damage, guide retrofitting efforts, and inform emergency response plans. In practice, they enable engineers and decision-makers to identify vulnerable structures/ground, prioritize investments in seismic reinforcement, and develop strategies to mitigate risks to life and property during seismic events. In this study, ground damage caused by liquefaction is focused on.  
In Taiwan, the Taiwan Earthquake Loss Estimation System (TELES) [10] is an advanced tool developed by the National Center for Research on Earthquake Engineering (NCREE) to assess and manage earthquake risks and damages. It integrates seismic hazard analysis, structural damage assessments, and socio-economic impacts to provide a comprehensive evaluation of potential earthquake outcomes in Taiwan. Fragility curves are crucial to TELES as they play a vital role in the TELES framework by quantifying the vulnerability of structures, infrastructure, and ground to seismic events, thus enabling more accurate loss estimations and risk assessments. Evaluation of liquefaction fragility is one of the important tasks that needs to be refined in TELES.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 4 Methodology
### Generating Dataset for Sentiment Cue Extraction
#### 4.2.3 Mask Sequence Interpretation Score
```
0: instance \(X\), the sentiment category of the instance \(C\), length of the instance \(n\), well trained sentiment classification model \(f_{\text{sc}}\), candidate sequence \(Y^{c}=\{y^{c}_{1},y^{c}_{2},\cdots,y^{c}_{n}\}\)
1:\(\bar{Y}^{c}\leftarrow\emptyset\)
2:for\(i\in[1,2,\ldots,n]\)do
3: Add \(1-y^{c}_{i}\) to \(\bar{Y}^{c}\)
4:endfor
5:\((p^{Y^{c}}_{\alpha 0},p^{Y^{c}}_{\alpha 1})=f_{\text{sc}}(X,Y^{c})\), \((p^{Y^{c}}_{\alpha 0},p^{Y^{c}}_{\alpha 1})=f_{\text{sc}}(X,\bar{Y}^{c})\)
6:\(C^{Y^{c}}=\arg\max(p^{Y^{c}}_{\alpha 0},p^{Y^{c}}_{\alpha 1})\), \(C^{Y^{c}}=\arg\max(p^{Y^{c}}_{\alpha 0},p^{Y^{c}}_{\alpha 1})\)
7:\(\Delta P^{Y^{c}}=|p^{Y^{c}}_{\alpha 0}-p^{Y^{c}}_{\alpha 1}|\), \(\Delta P^{Y^{c}}=|p^{Y^{c}}_{\alpha 0}-p^{Y^{c}}_{\alpha 1}|\)
8:\(RCT_{Y^{c}}\gets RCT(Y^{c},n)\)\(\triangleright\) Refer to Equation (13)
9:\(MSIS_{Y^{c}}\gets MSIS(C,C^{Y},C^{\bar{Y}^{c}},\Delta P^{Y^{c}},\Delta P ^{Y^{c}})\)\(\triangleright\) Refer to Equation (14)
10:return\(MSIS_{Y^{c}}\)
```  
**Algorithm 2** Evaluating the candidate sequence \(Y^{c}\)  
The process begins by creating an inverse sequence \(\bar{Y}^{c}\), which serves as a complement to \(Y^{c}\) by inverting the binary values. This step ensures that we can compare the effects of including versus excluding specific tokens identified as sentiment cues on the predictions of the sentiment classification model. Next, the algorithm employs \(f_{\text{sc}}\) to calculate the probabilities of \(X\) belonging to each sentiment category, both with and without the sentiment cues as indicated by \(Y^{c}\) and \(\bar{Y}^{c}\), respectively. These probabilities allow the computation of the Probability Discrepancy (\(\Delta P\)) for both sequences, offering insight into the decisiveness of the sentiment classification under different conditions. The RCT for \(Y^{c}\) is then calculated, providing a measure of the proportion of tokens in \(X\) identified as sentiment cues by \(Y^{c}\). This ratio is crucial for ensuring that the selection of sentiment cues is both significant and minimal, avoiding over-representation of cues. Finally, the MSIS for \(Y^{c}\) is determined based on the sentiment category predictions and Probability Discrepancies for both \(Y^{c}\) and its inverse.
## 1 Introduction
In summary, to address the challenges of bulky dimensions, high loss, and thermal management in high-power, MHz-range MRI applications, this study proposes a capacitor-loaded microstrip hybrid structure by integrating transmission line theory with lumped-element capacitor principles. The compact 3.0T RF combiner was designed by optimizing the microstrip width for a fixed substrate thickness, achieving high power density (8 kW) and low insertion loss (0.4 dB) at the operation bandwidth and minimizing its footprint (320 \(\times\) 200 \(\times\) 10 mm). Its high-power capability was analyzed through thermal management. The implemented combiner was successfully integrated into a 3.0T MRI RFPA system and reduced the RFPA's total dimensions, further improving imaging validation to achieve good quality. Although this approach narrows the bandwidth, the optimized combiner remains fully compatible with MRI systems due to their inherent narrowband operation.
# Optimization of Glass Edge Sealing Process Using Microwaves for Fabrication of Vacuum Glazing
## 3 Deriving Optimal Process Conditions Using Design of Experiments (DOE)
### Analysis of Experimental Results  
The analysis based on the design condition tables were carried out to assess the significance of each factor and the significance of the interaction terms. Based on a 95% confidence, if the \(p\)-value was lower than 0.05, it was pooled as an error term and only the significant terms were verified. The glass sealing temperature and the holding time were found to affect the step height. Moreover, it was determined that the squared holding time, heating rate, and sealing temperature exhibited an interactive behavior. The \(\mathbb{R}^{2}\) value was the coefficient of determination and it showed the effectiveness of the model. If the \(\mathbb{R}^{2}\) value was close to 100%, it indicated that the model was representative of the observed values. An \(\mathbb{R}^{2}\) value of 66.80% was obtained using the regression equation based on the three process variables, indicating a significance within 5%. Table 3 lists the analyzed results. Equation (1) shows the obtained regression equation.  
\[s_{h}=-107.8+12.46h_{r}+0.1846s_{t}+0.1942h_{t}-0.002266h_{t}^{2}-0.02200h_{r}s _{t} \tag{1}\]  
where:  
\(\mathbf{s_{h}}=Step\)\(height\)\((\)mm\()\)  
\(\mathbf{h_{r}}=Heating\)\(rate\)\((/\)min\()\)  
\(\mathbf{s_{t}}=Sealing\)\(temperature\)\(()\)  
\(h_{t}=Holding\)\(time\)\((min)\)  
Figures 7 and 8 show the contour and surface plots of the step height based on the process variables. The step height showed a curvature effect depending on the holding time. It was also found that the step height of the sealed edge was less affected by the heating rate, and that the step height was proportional to the temperature of the sealed edge.