esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos
## 2 Materials and Methods  
An overview of our proposed approach is presented in Figure 1. We used a backbone CNN model to extract visual features from static images and a GCN classification network to learn the relationship between the labels. The LSTM structure was used to model the temporal association of consecutive frames and focus on the invariant target features in the spatio-temporal information to obtain more accurate recognition.  
To better exploit the correlation between labels, recurrent neural networks [29; 30], attention mechanisms [31], and probabilistic graphical models [32; 33] are widely used.  
[PERSON] et al. [30] used RNNs to convert labels into embedding vectors to model the correlation between each label. [PERSON] et al. [34] proposed a spatial regularization network (SRN) with only image-level supervised learning of the spatial regularization between labels. Recently, [PERSON] et al. [35] proposed a multi-label image recognition model based on GCN which can capture global correlations between labels and infer knowledge from beyond a single image, and achieved good results. Inspired by [PERSON]'s work, we use graph structures to explore the dependencies between labels. Specifically, GCNs are used to disperse information from multiple labels so as to learn associative and dependent classifiers for each anatomically located label. These classifiers are further fused to image features to predict the correct outcome with label associations.  
In the work of incorporating time series into deep learning models, many approaches based on dynamic time warping [36], conditional random fields [37], and hidden Markov models (HMMs) [38] have been proposed. However, the existing methods have some problems and challenges. For example, when exploring temporal correlation, these methods mostly focus on linear statistical models, which cannot accurately represent the complex temporal information during endoscopy. Second, it is difficult for these methods to accurately analyze transitional video frames where multiple targets are present at the same times, which are important for the accurate identification of anatomical locations. Several methods have been proposed to process sequential data by nonlinear modeling of temporal dependencies, such as LSTM, and have been successfully applied to many challenging tasks [28; 39; 40]. To address the problem of surgical procedure identification similar to EGD inspection, [PERSON] et al. [28] introduced LSTM to learn temporal dependencies, and trained it in combination with convolutional neural networks. The learned temporal features are very sensitive to the changes of the surgical procedure, and can accurately identify the phase transition frames. Receiving inspiration from this approach, we proposed an LSTM fused with a GCN-based multi-label classification model for end-to-end training.
## 1 Introduction
Since vehicles have constrained mobility patterns due to the road layout, they can be naturally grouped [29]. The structure of groups can be used as a virtual backbone consisting of the group headers [30], and specific data need to be collected and distributed in a given group [31]. However, most existing works focus on minimizing the low-tier message overhead instead of the high-tier communication cost. In addition, the mobility patterns of a fleet following a common route are not explored to efficiently collect and distribute sensing data [32]. The fleet management and fleet communication problems have to be considered for sensing data collection and distribution in two-tier MLTNs, where all fleet members need to connect to a backend server, transmit individual sensing information, and receive global status information. Compared with the 3G/LTE network, the IEEE 802.11 network has high bandwidth with no charging fee [33]. In addition, the IEEE 802.11 channel can be reused by different ad-hoc groups in the same 3G/LTE base station. Furthermore, the broadcasting nature of the IEEE 802.11 network is efficient for transmitting global fleet information to multiple neighboring cyclists by only using a single broadcast message [34]. Therefore, a two-tier network is built to minimize the 3G/LTE cost. To reduce the amount of 3G/LTE data transmitted and the number of 3G/LTE connections, we propose a cooperative framework for sensing data collection and distribution with packet collision avoidance in two-tier MLTNs to exploit ad-hoc low-tier communication as much as possible. Cyclists are divided into groups, and each group has a gateway to serve as the sensing data aggregator. Distributed grouping of cyclists is applied to maintain the network topology. Sensing data aggregation considers cyclists' mobility patterns to minimize high-tier communication, which leads to an efficient solution for two-tier MLTNs. Simulation results show that the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmitted and the number of 3G/LTE connections. The rest of this paper is organized as follows. Section 2 discusses existing works. Section 3 defines the problem of cooperative sensing data collection and distribution. Section 4 describes the proposed framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. We analyze the high-tier communication cost in Section 5. Simulation results are presented in Section 6. Finally, Section 7 concludes the paper.
## 6 The Need to Digitalize, Automate, Integrate Oil and Gas Data
#### Digitalization of Oil and Gas Using Machine Learning  
Reviews on digitalization were conducted using ML on recent developments in oil and gas. One of the findings revealed that oil and gas should leverage the new technological developments with focus, agility, and collaborative teams of big data [41]. Another study was conducted on evaluating the status of the data-driven approach in the oil and gas industry, where historical data is used [45]. It was found that the data-driven approach provides huge advantages in the industry over the conventional approach under certain conditions. Unfortunately, it was also discovered that the approach for many industry professionals remains fuzzy. Thus, it is clear that there is a need for further study of ML in the oil and gas domain. Figure 5 shows a real-time dashboard illustration for web application necessaries, functionality, analytics library, and web-based programming.  
[PERSON] et al. [67] attempted to solve issues of reciprocating compressors in the petroleum industry. The findings suggested a system for fault diagnosis for reciprocating compressors using ML techniques based on a learned dictionary. The system evaluated 5-year operations collected from offshore oil corporations in the cloud environment. Significantly, their proposed system showed better results and indicated 80% accuracy, which can effectively diagnose potential faults in compressors. Similarly, research was conducted by applying a machine learning approach for big data, sizing of metal-loss defects, and failure risk analysis in oil and gas pipelines [68; 69; 70]. They adopted [PERSON]'s back-propagation learning algorithm [71]. They found a promising result with an estimated accuracy of 86% (\(\pm\)10% error tolerance) and 89% (\(\pm\)15% error tolerance). A recent work conducted by [PERSON] et al. attempts to provide an elastically scalable cloud-based system to solve big data issues for the upstream oil and gas industry with high performance. [PERSON] et al. [66] used machine learning and processed complex datasets in real time or near real time effectively. Figure 6 presents the thermal analysis based on distributed temperature sensing (DTS) measurements on total production. The proposed integrated system contains several services, including legacy oil and gas data. However, this work has not been integrated with meteorological or oceanographic data. There is no review  
Figure 5: Data communication and flow between field device, cloud server, and customer [66].  
and approval process in the system, which reduces the quality of the proposed monitoring. Hence, a system that will incorporate digitalization, monitoring, and data integration of meteorological and oceanographic or Metocean data is necessary.
## 5 Experimental Verification  
### Experimental Setup  
In this section, the experimental setup shown in Figure 5a was prepared to verify the natural frequencies obtained analytically. The beam with different masses at both ends was suspended from each end by an inextensible rope to ensure the free-free boundary condition. The physical parameters related to beam and end masses used in the experimental study are given in Table 3.  
The first five natural frequencies of free-free beams with different end masses were obtained by using the standard modal analysis method [38]. In this method, the structure is impacted with an impact hammer and the response of the structure is obtained by means of an accelerometer. In order to obtain the response of the structure under the standards, the structure is divided into certain points, and an impact is applied to these points with the impact hammer. For this reason, in our study, nine points were marked on the beam. The equipment used in the experimental study is shown in Figure 5c. A single-axis DYTRAN accelerometer with 101.25 mV/g sensitivity was used to obtain the acceleration data precisely. The accelerometer was mounted on the heavier mass so that the  
Figure 5: (a) Free–free beam with unequal end masses, (b) 3D section view of the beam and notations of dimensions (\(M_{2}\)), (c) data acquisition system: (1) computer including RT Pro Photon data analysis software; (2) impact hammer; (3) photon+ data acquisition card.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Properties** & **Beam** & **Tip Mass (\(M_{1}\))** & **Tip Mass (\(M_{2}\))** \\ \hline Modulus of elasticity, \(E\) (GPa) & 210 & 210 & 210 \\ Density, \(\rho\) (kg/m\({}^{3}\)) & 7800 & 7800 & 7800 \\ Length, \(L\) or \(L_{M}\), (mm) & 323.5 & 25.555 & 15.35 \\ Width, \(w\) (mm) & 30 & 30 & 30 \\ Thickness, \(b\), or \(t_{M_{1}}\) (mm) & 1.185 & 21.185 & 21.185 \\ Mass, \(M_{b}\) or \(M_{tip}\), (gr) & 87.520 & 126.660 & 76.090 \\ Distances, \(d\), (mm) & - & 12.777 & 7.675 \\ Rotary inertia (according to the attachment point), \(J_{z}\), (kg.m\({}^{2}\)) & - & \(32.30\times 10^{-6}\) & \(8.822\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The physical properties of beam and tip masses.  
vibration data would not be affected by the weight of the accelerometer. A Bruel & Kjaer impact hammer with a sensitivity of 2.24 mV/N was used to impact the beam with tip masses. The accelerometer and impact hammer were connected to a Bruel & Kjaer Photon+ model data analyzer. The beam was divided into nine points and acceleration data from the beam-end mass system were obtained by applying an impact to these points on the beam with the impact hammer. Inappropriate impacts were eliminated using software and not included in the average. The fast Fourier transform (FFT) analysis of the obtained vibration data was performed using RT Pro Photon data analysis software. The tests were similarly repeated 10 times and the natural frequencies of the beam-end mass system were obtained by taking the average of these tests.
## 4 Conclusions and Future Research  
This paper provides a review of recent literature covering three main areas: additive manufacturing, SC, and spare parts. The review, which followed a systematic review methodology and content analysis, shows that most research on AM and SC for spare parts is recent, and AM is increasingly being considered in SC configurations. The review shows that although the adoption of AM is growing, it is not considered a replacement for conventional spare parts supply and conventional SCs. It is considered a complementary measure to support spare parts management. Considering the service level, the capability of AM technology, the turnaround time, and the quality of spare parts with AM, the development of technology and the utilization of AM for smaller and low-value parts with higher demand variability can be an option to increase SC efficiency. There are also challenges related to investments. As investments need to be made for a specific type of technology using a specific type of material, and as it is difficult to convert manufacturing processes to digital processes, it seems that it takes a bit of time for the industry to adopt AM. Nevertheless, opportunities exist, methods are being developed, quality aspects are being sorted out, and analysis is becoming stronger to support the decision-making process. It should be noted that AM has added value to various stages and echelons of SCs. The review shows that there are opportunities for AM in terms of technical and economic feasibility.
# Developing a Digital Twin and Digital Thread Framework for an 'Industry 4.0' Shipyard
## 6 Industry 4.0 Shipyard  
The differences between implementing a DTW in the manufacturing and maritime domains have been recently studied [56]. The study showed that very few implementation frameworks for the maritime domain have been developed but found one promising framework with the basic requirements for a DTH solution. The study concluded that both domains are developing open platforms for DTW implementation and present some useful real-world implementation examples of DTW [56].  
DTW has also been proposed as a natural step from MBSE, with great possibilities of improvement in the production of highly complex products such as cruise ships. Some of the advantages highlighted are the ease in collaboration amongst all teams involved in the process of ship design [62, 63, 64, 65]. Also, the possibility to access information and manage it efficiently using an advanced interface could help develop efficient maintenance and training programs that, in time, can lead to higher operational performance levels [66, 67].  
Additionally, DTH has been identified as a different way from the traditional 2D drawings for shipbuilders to design and build their ships faster and better. DTH offers shipbuilders the possibility of having their employees and suppliers connected to and synchronized with their shipyard, production planning, customer orders and requirements, 3D models and every aspect of design [68].  
However, the current DTW models applied in the ship building industry show that only some of the components of the ship are being represented in the DTW, which is understandable due to the considerable number of sub-assets included in a modern ocean vessel. Including such a huge number of parts and their properties, interactions and performances in a model imposes great challenges. As DTH technology continues to mature, it will help the industry improve several aspects of their production processes through collaboration and constant communication of information [69].
## 3 Results
### Size of SBO-NLCs  
The predicted size of center point STDs was 184.62 nm, whereas the actual SBO-NLC sizes varied (198.43, 167.23, and 163.10 nm). The optimum-sized SBO-NLCs (i.e., the smallest size: 105.26 nm) were generated with a combination of 2.5% wax, 7.5% surfactant, and 30% PEG400 in the surfactant (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**STD**} & \multicolumn{2}{c}{**Size**} & \multicolumn{2}{c}{**Polydispersity Index**} & \multicolumn{2}{c}{**Zeta Potential**} & \multicolumn{2}{c}{**Entrapment Efficiency**} \\ \cline{2-9}  & **Actual** & **Predicted** & **Actual** & **Predicted** & **Actual** & **Predicted** & **Actual** & **Predicted** \\ \hline  
0.28 & \(-\)32.63 & \(-\)27.07 & 81.42 & 82.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The predicted and observed values for size, polydispersity index, zeta potential, and entrapment efficiency. * Excluded from the analysis.  
The reduced quadratic model was adapted for the SBO-NLC size analysis. The model was significant (_p_ = 0.0010), and the lack-of-fit value was non-significant (_p_ = 0.6268). Acceptable ranges of adjusted R\({}^{2}\) (0.7681) and predicted R\({}^{2}\) (0.5924) values were detected in the reduced quadratic model (Table 3).  
The values suggested that the reduced quadratic model was appropriate for predicting SBO-NLC size. The CCD-generated reduced quadratic equation for SBO-NLC size was as follows (Formula (3)):  
\[\text{Size}=73.2372+38.8639\text{A}+1.8463\text{B}+0.8235\text{C}-1.1797\text {AB}-0.2393\text{BC}-2.2088\text{A}^{2} \tag{3}\]  
where A = the percentage of wax in the formulation (2.5 to 7.5%), B = the percentage of surfactant in the formulation (2.5 to 7.5%), and C = the percentage of PEG400 in the surfactant (10 to 30%).  
The concentrations of wax and surfactant greatly influence the size of NLCs. The minimum concentration of wax and a high concentration of surfactant yielded smaller NLCs. The percentage (%) of PEG in the surfactant was not an influencing factor with respect to the size of NLCs (Figure 1).
## 4 Discussion
### Spatial Distribution Characteristics of HMs in Sediments
The texture of sediments and their organic matter content also play important roles in the adsorption and desorption processes of HMs [73]. Fine-grained sediments, which are rich in organic matter, have a stronger adsorption capacity for HMs, and therefore, higher concentrations of HMs are found in these areas [74]. In addition, the chemical properties of water, such as pH and redox potential, affect the speciation and mobility of HMs, thereby influencing their bioavailability and potential environmental impacts [75].
# Experimental and Numerical Studies on the Impact Energy Absorption of Cutting Shear Rings
## 2 Design of Energy Absorption Device  
At present, most energy absorption devices utilize metal damage behavior to achieve energy absorption, and irregular chip spatter can be observed in operation. In addition, energy absorption devices taking up too much space is also a common problem. In this paper, an impact energy absorption device based on CSR is designed. The CSR system absorbs energy in a variety of different ways, such as metal shearing, extrusion, and upsetting, improving metal utilization.  
The CSR system consists of a pedestal and energy absorption rods. The rods are installed in the pedestal, as shown in Figure 1a. The device is connected to the external device through the pedestal. The form and fixing method of the pedestal can be determined according to the actual environment. The energy absorption rod is composed of shear rings and a guide bar and absorbs collision energy through the outer shear rings. For the CSR system, the number of energy absorption rods and the number, thickness, and spacing of external shear rings are adjusted to achieve different energy absorption sizes. At the same time, by adjusting the pedestal to reduce the thickness of the first shear ring, the initial peak force can be reduced, thus avoiding damaging the structure in the first place.  
The working principle of the CSR system is shown in Figure 2, which is briefly described as follows: when the impactor hits the energy absorption rods at a certain speed, there is a trend of relative movement between the energy absorption rods and the pedestal, and the shear energy absorption process starts. By cutting off the shear rings outside of the energy absorption rods, the collision energy can be absorbed quickly.
## 3 Results
### Experimental Considerations
#### 3.3.2 Dependence of Beam Profile on Distance from Detector  
The validity of the angular profile measurements in this study is based on the assumption that the emitter produces a divergent beam, such that the angular profiles are distance invariant. In order to verify this assumption, angular profiles at 600 GHz were recorded at different distances from the emitter for both emitter orientations. The frequency of 600 GHz was chosen for this test and those in the following sections because at 600 GHz there is a single peak in both E-plane and H-plane, and there is sufficient THz power to provide good SNR (signal-to-noise ratio). The profiles shown in Figure 6a,b confirm good consistency, apart from minor variations in the width and height. These small features can be explained by the differences in angular resolution caused by varying the distance between the emitter and detector. The angular resolution will be discussed in detail in the next section.
## 2 Materials and Methods
### Experimental Procedure  
During the athlete's serve landing phase, the motion dynamics and kinematics of their lower extremities were recorded by the Vicon motion-tracking system (Oxford Metrics Ltd., Oxford, UK) operating at 200 Hz, along with a Kistler force plate (Kistler Instrumente AG, Winterthur, Switzerland) recording at 1000 Hz. The marker model included 49 points, which were as follows: thoracic vertebrae 10, scapular--inferior angles, scapula--acromial edge, deltoid tuberosity, humeral lateral epicondyle, styloid tips of the radius and ulna, base of the index finger, anterior superior iliac spine, iliac crest, posterior superior iliac spine, three points of the thigh, lateral and medial epicondyles of the femur, fibular head, tibial tuberosity, calf triad, lateral and medial projections of malleoli, heel bone, dorsal margins of the first and fifth metatarasal heads, and dorsal side of the second metatarasal head.  
Initially, the resting state of each athlete was individually recorded, which was defined as a period with no significant joint or muscle activity. This resting state was then used as the baseline for analyzing the athlete's dynamic serving motion. After the athlete had completed a full dynamic warm-up (DWU) [31], consisting of 4-5 min of self-directed general warm-up, 6-8 min of dynamic stretching, 10 min of tennis-specific high-intensity  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & \(n\) & **Age (years)** & **Height (cm)** & **Weight (kg)** & **Years of Training** \\ \hline Amateur & 15 & 21 \(\pm\) 1.94 & 176 \(\pm\) 2.67 & 70 \(\pm\) 3.65 & 6.67 \(\pm\) 1.29 \\ Professional & 15 & 20 \(\pm\) 1.05 & 177 \(\pm\) 2.69 & 71 \(\pm\) 4.15 & 11.73 \(\pm\) 0.96 \\ \hline \hline \end{tabular} The values are presented as the mean \(\pm\) SD.  
\end{table}
Table 1: Participants’ demographic data.  
exercises, and 3 rounds of explosive movements with a 15 s rest between each round, a platform-style serve was simulated on the force platform, ensuring that the subject applied the same magnitude of force for each serve as they had for the first one. As shown in Figure 1, data analysis was conducted using the first five successful serves [32]. This study selected kinematic and dynamic indicators for the three joints of the lower limb in the sagittal, coronal, and horizontal planes [33], including ROM in degrees (\({}^{\circ}\)), moment and peak moment in Nm/kg, and stiffness in Nm/kg\({}^{\circ}\), which was calculated using the formula shown below [20]:  
\[\text{K}=\frac{\Delta\text{M}}{\Delta\theta} \tag{1}\]