esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Prototype Implementation  
We implemented the prototype of the proposed system utilizing the Python Qt5 library version 5.15.9. The implemented system includes simple login functionality, video upload, and guide video creation processes. Through this system, users can upload their golf swing videos and visually compare which parts differ from a professional's motion. This allows users to self-learn to achieve a swing more similar to that of a professional. Figure 7 illustrates the prototype where the proposed method is applied.  
Users can create an account using basic information (account details, gender, etc.). Upon logging in with the created account, the main page for golf swing diagnosis is displayed. On the main page, users can diagnose their golf swing, view past records, and set options. Options allow users to decide whether they want to strictly view the swing differences between the pro and the learner, view them in a balanced manner, or view them conventionally. Additionally, settings related to the pose estimation model and coordinate interpolation can be adjusted.  
Figure 6: An example of a generated golf guide video. The image on the left is the generated text that will appear as the intro to your guide video. The text includes a brief greeting and provides a brief summary of the overall similarity score, the most similar joints, and the joints that need correction. The image on the right is a visual representation of the pro’s movement and the learner’s movement, showing where improvement is needed. The red circles in the image are automatically generated by the guide system to indicate areas that need to be corrected.  
After setting the options and pressing the swing diagnosis button, users are directed to the video upload page. Upon setting the file path for the user's swing video, swing analysis is initiated. In this process, the proposed method identifies the swing of a professional golfer that is most similar to the user's swing and demonstrates, through video and text generation, which parts need correction to achieve a more similar swing. The text generation offers a template-based presentation of an overall evaluation of the swing, which actions were most similar to those of the pro golfer, and which actions need improvement. The generated video aids the user by visually marking which joints differ, helping users easily understand the required corrections. The created guide videos are accessible through the history page, allowing users to easily access and review past records in the future.  
Figure 7: Prototype of proposed system. This figure begins with the top-left image as the first and concludes with the bottom-right image as the last. The first and second figures show the sign-in and main page view. The third figure shows the option of swing guide system. The fourth and fifth figures indicate the swing guide video and generated text. The last figure is the user history view that shows the user’s entire swing guide history.
# Unsupervised Offline Changepoint Detection Ensembles
## 3 Results
### CPD and CPDE Procedures vs. SOTA Changepoint Detection Algorithms  
We should note that in our work CPD procedures from [22] are sometimes considered as state-of-the-art (SOTA) for changepoint detection problem. Moreover, existing algorithms and approaches in the CPDE field are not systematized, and it is questionable to consider any of them as SOTA. Quite noteworthy is that all of the added-to-compare algorithms except for unsupervised ARIMAFD [46] are semi-supervised since they need a training set with the healthy operation mode, while all algorithms tested in our work are unsupervised. Therefore, compared algorithms are applicable in different situations and are not competing.  
In Table 4, we compared our results with some other SOTA results achieved on TEP and SKAB benchmarks. We have found only one work [47] where CPD algorithms were applied to TEP benchmark with NAB metric. We provide here the results from this work. It is worth noting that the TEP benchmark in this work differs from ours since the authors synthesized datasets from the TEP model themselves, while we used the most common previously generated benchmark. This fact makes the comparison for the TEP benchmark not fully reliable. For the details about the dataset and differences from our version of the TEP benchmark, an interested reader can refer to [47]. For SKAB, we used the results presented in the related repository [[https://github.com/waico/SKAB](https://github.com/waico/SKAB)]([https://github.com/waico/SKAB](https://github.com/waico/SKAB)) (accessed on 8 May 2021). Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED) from [48]is positioned as the state-of-the-art in technical diagnostics. Under the LSTM, anomaly detection algorithm based on the LSTM-based forecasting algorithm is meant.  
It is clear from the table that CPD and proposed CPDE procedures show slightly worse results than Neural Networks-based SOTA approaches while confidentially overperforming classical ([PERSON]'s \(T^{2}\)) and situational (ARIMAFD) algorithms.
# T-Splines for Isogeometric Analysis of the Large Deformation of Elastoplastic Kirchhoff-Love Shells
## 6 Conclusions  
We have extended the isogeometric Kirchhoff-Love shell computational model based on T-splines to large deformation elastoplastic problems, with the advantages of representing the trimmed model with one single patch and local refinement capability. The proposed formulation is able to deal with large elastoplastic strains as well as finite rotations, which is validated by various benchmarks. The results are compared with those from references and FEA (ABAQUS), confirming the accuracy and efficiency of the method we implemented.  
In our paper, five Gaussian integration points are used in the thickness direction, which is a time-consuming calculation process. [PERSON] and [PERSON] [PERSON] and [PERSON] (2013) advocated an approach based on directly expressing moment versus curvature relations for elastoplastic shells. What is more, [PERSON] et al. [PERSON] et al. (2016) proposed a novel implicit rate type response relation, bypassing the integration of elastoplastic stress-strain relationships across the thickness of the beam at each time step. These methods may bring efficiency and accuracy to the isogeometric analysis of elastoplastic Kirchhoff-Love shells, and will be considered in our future study.  
In our current adaptive analysis framework, if the relative error is greater than the prescribed error, the local refinement will be performed. Similarly, if the relative error is less than another specified value, a mesh recovery should be performed, which can reduce the number of equations to be solved and improve computational efficiency. The refinement of T-mesh is based on the knot insertion algorithm of the B-spline curve so that the geometry can remain unchanged. However, in the process of mesh recovery, geometric errors will be caused by knot removal. Multiresolution techniques and a suitable geometric error tolerance should be taken into account. The mesh recovery of T-splines for adaptive analysis will be a challenging topic in future research.  
Conceptualization, M.G., G.Z. and W.W.; methodology, X.D. and M.G.; software, M.G.; validation, M.G., R.Z. and J.Y.; formal analysis, M.G.; investigation, M.G., R.Z. and J.Y.; resources, G.Z. and W.W.; data curation, M.G.; writing--original draft preparation, M.G.; writing--review and editing, W.W. and X.D.; visualization, X.D. and M.G.; supervision, G.Z.; project administration, G.Z.; funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
The work is supported by National Natural Science Foundation of China (Project Nos. 61972011, 62102012, 62002004, and 52175213) and the Project is funded by China Postdoctoral Science Foundation (Project No. 2021M690294).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 35: The z-displacement of the rectangular shell at various deformation stages: (**a–c**) result from IGA, rotation angle 60\({}^{\circ}\), 120\({}^{\circ}\), 180\({}^{\circ}\); (**d–f**) result from FEA, rotation angle 60\({}^{\circ}\), 120\({}^{\circ}\), 180\({}^{\circ}\).
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Technological Evolution of DES in Healthcare
#### 4.5.3 Digital Twin Development  
The concept of digital twins has emerged as a transformative advancement in healthcare DES. [PERSON] et al. [58] provide a comprehensive review of digital twin applications, demonstrating their role in enabling continuous system monitoring and real-time decision support. Their analysis highlights the potential of digital twins for healthcare process optimization and resource management. [PERSON] et al. [59] further demonstrated the practical application of digital twins in healthcare settings, particularly in improving operational efficiency and resource utilization.
# Quantum Circuit Design of Toom 3-Way Multiplication
## 4 Cost Analysis  
In this section, we analyze the complexity of the quantum Toom 3-way multiplication circuit. In particular, the metrics used are the Toffoli count, qubit count, and Toffoli depth of the multiplication, in the form of space-time complexity trade off when performed recursively. In this study, we focus on Toffoli calculation since it incurs higher cost than other classical reversible gates counterparts such as NOT and CNOT [41; 42]. Furthermore, Toffoli gates correspond directly to the depth of the T gate (often referred to as \(T\)-depth) in the Clifford + T library, which is currently the most favorable fault-tolerant implementation for a quantum algorithm, that directly affects run times [12; 43]. Hence, this gate has been commonly used as the primary metric for assessing the cost of quantum circuitsand algorithms, such as used in [4; 5; 8; 11; 30] to name a few. Note that the discussion concentrates on the asymptotic performance rather than for small-number multiplication.
###### Abstract  
To investigate the combustion characteristics in multi-point lean direct injection (LDI) combustors with hydrogen/air, two swirl-venturi 2 \(\times\) 2 array four-point LDI combustors were designed. The four-point LDI combustor consists of injector assembly, swirl-venturi array and combustion chamber. The injector, swirler and venturi together govern the rapid mixing of hydrogen and air to form the mixture for combustion. Using clockwise swirlers and anticlockwise swirlers, the co-swirling and count-swirling swirler arrays LDI combustors were achieved. Using Reynolds-Averaged Navier-Stokes (RANS) code for steady-state reacting flow computations, the four-point LDI combustors with hydrogen/air were simulated with an 11 species and 23 lumped reaction steps H\({}_{2}\)/Air reaction mechanism. The axial velocity, turbulence kinetic energy, total pressure drop coefficient, outlet temperature, mass fraction of OH and emission of pollutant NO of four-point LDI combustors, with different equivalence ratios, are here presented and discussed. As the equivalence ratios increased, the total pressure drop coefficient became higher because of increasing heat loss. Increasing equivalence ratios also corresponded with the rise in outlet temperature of the four-point LDI combustors, as well as an increase in the emission index of NO \(\text{EI}_{\text{NO}}\) in the four-point LDI combustors. Along the axial distance, the \(\text{EI}_{\text{NO}}\) always increased and was at maximum at the exit of the dump. Along the chamber, the \(\text{EI}_{\text{NO}}\) gradually increased, maximizing at the exit of chamber. The total temperature of four-point LDI combustors with different equivalence ratios was identical to the theoretical equilibrium temperature. The \(\text{EI}_{\text{NO}}\) was an exponential function of the equivalence ratio.  
gas turbine engine; lean direct injection; four-point; low emissions combustion +
Footnote †: journal: Applied Sciences
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 2 Materials and Methods
### Generative Adversarial Networks  
Deep-learning models require a large number of data samples to train the models, and there are few publicly available high-quality datasets in areas such as conveyor-belt-detection inspection, and it is difficult to obtain a sufficient number of samples from field collection. In 2014, [PERSON] et al. [17] proposed GANs (generative adversarial networks), a new solution to the problem of insufficient samples in the dataset. GANs can generate more samples, different from the existing data, to enhance the generalization performance of the model.  
As shown in Figure 1, during training, a set of randomly generated noise vectors were first input to the generator to generate a set of false images. Then, the generated false images were input to the discriminator, together with the real images, to judge the authenticity of the images. After iteration, the discriminator could eventually not distinguish whether the images were from the real training samples or generated by the generator, at which time the GAN reached the optimal state. The objective function of the GAN is as follows:  
\[\underset{G}{\text{\emph{min}}}\underset{D}{\text{\emph{max}}}V(D,G)=\mathbb{E }_{\mathbf{x}\sim p_{\text{data }}(\mathbf{x})}[\log D(\mathbf{x})]+\mathbb{E}_{\mathbf{z}\sim p_{ \mathbf{z}}(\mathbf{z})}[\log(1-D(G(\mathbf{z})))] \tag{1}\]  
where \(G\) is the generator and \(D\) is the discriminator.
## 3 Results and Discussion
### Optimization of SPE Procedures
#### 3.2.3 Sample Breakthrough Volume  
Large volume water analysis is beneficial for a larger preconcentration factor and thus higher detection sensitivity. To determine the sample breakthrough volume, 50, 100, 200, 500, and 1000 mL volumes of ultrapure water containing 0.5 \(\upmu\)g/L of NAP and 5 \(\upmu\)g/L of KEP, DIF, MEF, DIC, and NIF were percolated through the column. The results in Figure 7 demonstrate excellent recoveries of all pharmaceuticals above 88% with a sample volume up to 1000 mL, implying that a theoretically 1000-fold preconcentration factor was achieved. However, due to an increased back pressure as a result of possible polymer swelling and particulate plugging, a 500 mL volume for real environmental samples was selected to avoid the complete block of SPE process.  
Figure 6: Effects of elution volume on the recoveries (%) of selected pharmaceuticals, \(n=3\).
## 1 Introduction
The approach is designed to work in real-time and makes no prior assumptions regarding noise, number of participants or spatial configuration. This is because it was developed also with the intent of being used in parallel projects, where it is required to recognize speakers and the moments they intervene in. The newly designed modality fusion layer performs successful integration of multi-source uni-sensory information through feedback stimulation of spatially proximal neural regions. The obtained results were highly positive, having exceeded expectations in terms of current state-of-the-art standards. The presented paper is structured as follows. Initially, examination and insight into previous research is shown in Section 2, introducing the reader into the topic and contextualizing our approach. The proposed technique is described in Section 3, followed by an overview of the experiments carried out in Section 4 and the obtained results. These are then examined and critically discussed in Section 5. Finally, a conclusion is provided in Section 6. Figure 1: Exemplary output of an multi-speaker active speaker detection (ASD) system where an intervening speaker is denoted in green and a passive speaker is encircled in red.
# Thermal Runaway Diagnosis of Lithium-Ion Cells Using Data-Driven Method
## 1 Introduction  
The lithium-ion battery (LIB) has become the essential energy storage solution across various applications, including transportation, electric grid, and consumer electronics, since it has considerably improved efficiency and reduced costs over the past decade [1, 2, 3]. However, battery failures cause serious problems such as explosion and fire accidents in aircraft, robots, and smartphones [4, 5, 6].  
Mechanical, electrical, thermal, and electrochemical abuse are the critical factors that trigger and cause severe battery failure. Thermal runaway is an uncontrollable chemical process within a battery cell that causes a catastrophic fire. Chain reactions can occur between heat, temperature, and chemical reactions if a battery exceeds specific temperature thresholds [7]. This is especially dangerous for cylindrical cells that could explode due to their pressure build-up design. Conventional approaches have developed the thermal runaway prediction model based on the internal mechanism of Li-ion cells to support early warning [8, 9]. However, the behaviors of Li-ion cells depend on various factors, including the manufacturer and model during thermal runaway [10]. Furthermore, repeatedly tested identical cells under the same abuse conditions still show considerably different heat output and temperatures [11]. Thus, deterministic analytical models are insufficient for capturing the complicated behaviors of cells undergoing failure, even for the same cell.  
Most previous works on machine learning (ML) techniques focus on lifetime predictions [12, 13] and the development of optimal charging protocols for LIB cells [14, 15]. Although some ML models are developed for fault diagnosis, most works consider the system level rather than the cell level of LIBs [4]. Recent research combines the ML model with varying levels of simulation model complexity to investigate the vast parameter spaces in abuse testing [16, 17]. However, the experimental validation of these models is costly and complex, making the validation process especially challenging.  
We use different ML techniques to estimate the most likely cause of the thermal runaway by using the experimental measurements of open-source battery failure data [18]. Bayesian optimization techniques are applied to optimize the hyperparameter of various ML techniques such as support vector machine (SVM), naive Bayes (NB), decision tree ensemble (DTE), and multi-layer perceptron (MLP) models. We analyze different evaluation metrics, including prediction accuracy, confusion metrics, receiver operating characteristic (ROC) curves, and the feature importance of ML models.
###### Abstract  
Network on chip (NoC) is the main solution to the communication bandwidth of a multiprocessor system on chip (MPSoC). NoC also brings more route requirements and is highly prone to errors caused by crosstalk. Crosstalk has become a major design problem in deep-submicron NoC communication design. Hence, a crosstalk error model and corresponding reliable system with error correction code (ECC) are required to make NoC communication reliable. In this paper, a reliability system evaluation model (RSE) of NoC communication with analysis from backend to frontend has been proposed. In the backend, a crosstalk error rate model (CER) is established with a three-wire RLC coupling model and timing constraints. The CER is used to establish functional relations between interconnect spacing, length and signal frequency, and test system reliability. In the frontend, a reliability system performance model (RSP) is established with a CER, reliability method cost and bandwidth. The RSE summarizes the frontend and backend model. In order to verify the RSE model, we propose a reliability system with a hybrid automatic repeat request technique (RSHARQ). Simulation demonstrates that the CER model is close to real circuit design. Through the CER and RSP model, the performance of RSHARQ could be simulated.  
network on chip; interconnects; crosstalk; hybrid automatic repeat request +
Footnote †: journal:  
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# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
###### Abstract  
The field of cloud computing has witnessed tremendous progress, with commercial cloud providers offering powerful distributed infrastructures to small and medium enterprises (SMEs) through their revolutionary pay-as-you-go model. Simultaneously, the rise of containers has empowered virtualisation, providing orchestration technologies for the deployment and management of large-scale distributed systems across different geolocations and providers. Big data is another research area which has developed at an extraordinary pace as industries endeavour to discover innovative and effective ways of processing large volumes of structured, semi-structured, and unstructured data. This research aims to integrate the latest advances within the fields of cloud computing, virtualisation, and big data for a systematic approach to stream processing. The novel contributions of this research are: (1) MC-BDP, a reference architecture for big data stream processing in a containerised, multi-cloud environment; (2) a case study conducted with the Estates and Sustainability departments at Leeds Beckett University to evaluate an MC-BDP prototype within the context of energy efficiency for smart buildings. The study found that MC-BDP is scalable and fault-tolerant across cloud environments, key attributes for SMEs managing resources under budgetary constraints. Additionally, our experiments on technology agnosticism and container co-location provide new insights into resource utilisation, cost implications, and optimal deployment strategies in cloud-based big data streaming, offering valuable guidelines for practitioners in the field.  
multi-cloud; big data; containers; reference architecture; stream; vendor lock-in +
Footnote †: journal: _Applied_  
_aciences_
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
###### Abstract  
High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size = 0.7 m) stereoscopic satellite imagery to retrieve a DEM on sandy coastline. A 40-km coastal stretch in the Southwest of France was selected as a pilot-site to compare topographic measurements obtained from Pleiades satellite imagery, Real Time Kinematic GPS (RTK-GPS) and airborne Light Detection and Ranging System (LiDAR). The derived 2-m Pleiades DEM shows an overall good agreement with concurrent methods (RTK-GPS and LiDAR; correlation coefficient of 0.9), with a vertical Root Mean Squared Error (RMS error) that ranges from 0.35 to 0.48 m, after absolute coregistration to the LiDAR dataset. The largest errors (RMS error > 0.5 m) occurred in the steep dune faces, particularly at shadowed areas. This work shows that DEMs derived from sub-meter satellite imagery capture local morphological features (e.g., berm or dune shape) on a sandy beach, over a large spatial domain.  
Pleiades; photogrammetry; LiDAR; RTK-GPS; beach topography 2019 11 590; doi:10.3390/rs11050590