esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 LEFM-Based Analytical Model
### Model Definition  
In order to check this assumption, the study of the fracture behaviour of BiR connection is proposed within the framework of equivalent LEFM, which is well known to be useful to characterize the quasibrittle failure of load-bearing components [21].  
The reference mechanical model is schematized in Figure 13, with evidence of the required geometrical and mechanical parameters in the detailed view.  
Considering that the joint acts as a fibre in the matrix, where the fibre represents a steel glued-in bar and the matrix consists of the wood log as in Figure 13, the following model of shear stresses distribution along the joint is proposed in this study:  
\[\tau(x)=\frac{A_{s}}{b_{b}}\omega\bigg{(}(\sigma_{s,max}-q)\frac{cosh(\omega x )}{sinh(\omega L)}+q\frac{cosh(\omega(L-x))}{sinh(\omega L)}\bigg{)}\bigg{)} \tag{10}\]  
Figure 13: Mechanical model for the analysis of force transmission and deformation behaviour in BiR connections.  
where \(A_{0}\) is cross-area of the wooden part; \(b_{k}\) is the mean width of the adhesive layer; \(\sigma_{0,max}\) represents the maximum normal stress in the BiR; \(x\) is the length coordinate; \(L\) denotes the length of the adhesive joint (anchorage length); \(\omega\) is a correction factor that can be estimated from:  
\[\omega^{2}=\frac{1}{p} \tag{11}\]  
and:  
\[p=\left(\frac{E_{s}A_{s}E_{w}A_{w}}{E_{w}A_{w}+E_{s}A_{s}}\right)\frac{h_{b}}{ G_{b}b_{b}}\quad\left(\text{in mm}^{2}\right) \tag{12}\]  
\[q=\left(\frac{E_{s}}{E_{w}A_{w}+E_{s}A_{s}}\right)F_{ax}\quad\left(\text{in MPa}\right) \tag{13}\]  
By integrating Equation (10) to get a strain energy release rate, the J-integral method could be implemented directly. Further, the problem analysis and the definition of an accurate behaviour model for BiR connections should be necessarily based on local stress distribution which has been obtained in the framework of DIC system (i.e., Figure 7).  
According to the known maximum shear strength of wood, it is possible to predict the bearing capacity of the BiR connection, i.e., maximum pull-out force, as:  
\[F_{ax}=\frac{\tau(x)\cdot\frac{b_{b}}{\omega}\frac{sinh(\omega\cdot L)}{cosh (\omega\cdot x)}}{1+\left(\frac{E_{s}\cdot A_{s}}{E_{w}\cdot A_{w}+E_{s}\cdot A _{s}}\right)\cdot\left(\frac{cosh(\omega\cdot(L-x))-cosh(\omega\cdot x)}{cosh (\omega\cdot x)}\right)} \tag{14}\]  
From the developed analytical model, the variation of the shear force, stress and strain in BiR connections can be thus predicted along the bonding length \(L\). Selected examples are proposed in Figure 14.
## 3 Numerical Model
### Environmental Conditions  
Simultaneous random-wave and seismic excitations are considered for global performance analysis. The same wave and seismic time histories are inputted in both programs for cross-checking. JONSWAP wave spectrum is used to generate time histories of random waves. Significant wave height and peak period for the 100-year-storm condition are 11.7 m and 13.0 s. Enhancement parameter is 2.14 that is the average value in Korea [45]. Random waves are generated by superposing 100 component waves with randomly perturbed frequency intervals to avoid signal repetition. The lowest and highest cut-off frequencies of input spectrum is 0.3 rad/s and 2.3 rad/s, respectively. The wave direction is perpendicular to a longitudinal direction of the tunnel. A 3-h simulation is carried out to analyze the statistics of dynamic behaviors and mooring tensions under the storm condition. Figure 3 shows theoretical JONSWAP wave spectrum and the reproduced spectrum from the time histories of wave elevation. It also shows the time histories of wave elevation produced by the JONSWAP wave spectrum.  
Regular (sinusoidal) and recorded irregular seismic excitations data are also employed. The amplitude of regular seismic motion in the vertical direction is 0.01 m at diverse frequencies from 0.781 rad/s to 7.805 rad/s. Figure 4 shows the time histories of seismic displacements and corresponding spectra for recorded irregular seismic excitations in three directions, which are obtained by USGS [46]. The earthquake occurred in 78 km WNW of Ferndale, California, USA in 2014, and the magnitude of this earthquake is 6.8 in Richter scale. Seismic displacements in three directions are inputted for each anchor point of mooring lines and two ends of the tunnel fixture at every time step. Hydrodynamic force from the seaquake effect is also computed for the tunnel and mooring lines.  
Figure 3: Wave time histories produced by JONSWAP wave spectrum (**a**) and theoretical JONSWAP wave spectrum and reproduced spectrum from wave time histories using FFT (fast Fourier transform) for validation (**b**).
## 3 Methods and Datasets
### Spatiotemporal Distribution of Mountain Glacier Records in the EAM Region
#### 3.2.2 Taiwan Mountains  
Whether or not there is any geomorphic evidence of Late Quaternary glaciation in Taiwan has been controversial due to the lack of chronological evidence [57]. However, it has been established that Late Quaternary glaciers in Taiwan were distributed in the high mountains of the island's eastern central sector. Using the geomorphic properties of these mountain glaciers, Taiwanese scientists have subdivided them into two categories: a \"direct melt type\" and a \"retreat melt type\" [58,59]. [PERSON] et al. [39] used TL dating to date glacial sediments from Mount Shesan in Taiwan, and divided them into the Shanzhuang glacial stage (corresponding to 55 \(\pm\) 4 ka \(-\)44.25 \(\pm\) 3.72 ka) during the early LGP (MIS 4), the Shuiyuan glacial stage (corresponding to 18.26 \(\pm\) 1.52 ka) during the LGM, and the Shesan glacial stage (corresponding to the YD event during the LGP). Scholars used \({}^{10}\)Be and OSL dating techniques to measure glacial sediments and boulders on Mount Yusan, Mount Nanhuta, Mount Shesan, and other peaks, giving results that ranged between 55 \(\pm\) 4 ka and 3.3 \(\pm\) 1.1 ka [38; 39; 40; 41]. These dating results supported previous subdivisions of the glaciation sequences of Taiwan's mountain ranges [39; 60]. [PERSON] et al. [61] speculated that the large-scale, two-stage cirques evident on Mount Yusan may be older than the early LGP.
###### Abstract  
This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessive energy usage. Employing a combination of 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) methods within the framework of the Internet of Things (IoT), the study encompasses multiple components, including hardware, network, database, data analysis, and software. The research outcomes encompass a real-time web application for monitoring and control, temperature distribution visualizations within the incubator, a prototype incubator, and a predictive energy consumption model. Testing the LSTM method resulted in an RMSE of 42.650 and an MAE of 33.575, while the CNN method exhibited an RMSE of 37.675 and an MAE of 30.082. Combining CNN and LSTM yielded an RMSE of 32.436 and an MAE of 25.382, demonstrating the potential for significantly improving neonatal care.  
neonatal incubator; energy consumption prediction; CNN-LSTM; Internet of Things; artificial intelligence Article
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 4 Testbed Description
### Experimental Setups  
The hardware testbed is built using 10 dual-homed wireless nodes (five sender-receiver pairs) installed with Linux kernel implementation of MPTCP (version-0.89). The nodes concurrently send data packets using two real wireless networks; one is a 4G/LTE network and the other is a Wi-Fi network. The dual-homed wireless nodes access the wireless medium based on the technology used by each network. In Wi-Fi networks, the access to the medium is done using the IEEE 802.11 distributed coordination function (DCF), which follows the carrier-sense multiple access with collision avoidance (CSMA/CA) mechanism, as described in the IEEE 802.11 standard [35]. This mechanism is based on a random backoff procedure that is triggered when the channel changes its state from busy to idle. In this procedure, a backoff timer is randomly chosen from a contention window, which is doubled after each unsuccessful transmission. The LTE network follows the access mechanism defined by the 3 GPP standard Release 8 [5]. It uses single-carrier frequency division multiple access (SC-FDMA) to access the uplink channel, whereas it uses orthogonal frequency division multiple access (OFDMA) to access the downlink one. Each network technology uses a different frequency band--5 GHz for Wi-Fi and 2.6 GHz for LTE.  
We consider two topologies in this research: a setup without a bottleneck link (Setup 1) and a setup with such as a link (Setup 2). Figure 1 shows the architecture of Setup 1, which containsdual-homed wireless sender nodes. Each node hosts two wireless network interfaces. One interface is connected to a Wi-Fi network via Router 1, and the other is connected to the LTE network via a real eNodeB. However, in the case of the receiving nodes, one interface is a wired interface and the other is a wireless interface. The wired interface is connected to the LTE backbone network via an Ethernet switch. The wireless interface of the receiving nodes is connected to another Wi-Fi network via Router 2, which uses a different radio channel than the Wi-Fi network of the sending nodes. The LTE backbone network consists of an evolved packet core (EPC), which includes the mobility management entity (MME), serving gateway (SGW), and packet data network gateway (PGW). The configuration of Setup 1 makes the subflows of the same MPTCP connection independent of each other (no common bottleneck link between them).  
Figure 2 reveals a scenario where the subflows of one MPTCP connection share a bottleneck link. The interfaces of the dual-homed sender nodes in Setup 2 are connected the same way as in Setup 1. However, the receiver nodes connect to both the LTE backbone network and the Wi-Fi network provided by Router 1 via an Ethernet switch. As revealed in Figures 1 and 2, the testbed includes two real eNodeBs to realize S1 LTE handover.  
Figure 1: Testbed Setup 1.  
Figure 2: Testbed Setup 2.
## 3 Results and Discussion
### Numerical Results  
#### 3.2.1 Model Validation and CPC Thermal Performance  
Figure 11a shows a comparison of the predicted outlet temperature (using the coupled FEM-CFD simulation) and the experimentally measured outlet temperature \(T_{o}\) for both CPC collectors (absorber without fins and with segmented fins) from 6 a.m. to 6 p.m. on 1 March 2020. The predicted \(T_{o}\) is in agreement with the measured \(T_{o}\) with a difference of less than 0.5 \({}^{\circ}\)C. It is noted that the predicted curves in Figure 11a were obtained with inlet velocities of 0.0075 and 0.007 m/s for the absorber without fins and with segmented fins, respectively. This difference could be attributed to small variations in the mass flow rate between each CPC system evaluated experimentally; however, it is noted that the difference between the value of the inlet velocities used in the simulation and the experimental value is less than 10%. Figure 11b shows the predicted \(T_{o}\) for both CPCs using either the mapped heat flux load on the absorber surface (as describe in Section 2.6.1) or a homogeneous heat flux load based on the total energy on the absorber surface. It can be seen in Figure 11b that for the case of the absorber without fins, the difference between the predicted \(T_{o}\) when using the mapped heat flux and the homogeneous flux is negligible; however, for the finned absorbers, there is a difference of up to 1 \({}^{\circ}\)C. This demonstrates the advantage of using the mapped heat flux on the surface of the absorber with fins to obtain more accurate predictions.  
Figure 11: (a) Comparison between experimental and predicted outlet temperatures for both CPC with absorber without fins and with absorber with segmented fins on 1 March 2020; (b) predicted outlet temperatures for CPCs with absorbers without fins, with segmented fins and with continuous fins using either mapped heat flux load or homogenous heat flux load.  
Figure 10: Thermal efficiency \(\eta_{t}\) for the CPC collectors with absorber without fins and with segmented fins on 1 March 2020.  
Figure 12 shows contour plots of the predicted temperatures of the absorbers using the FEM-CFD simulations at 12 p.m. (noon) on the 1 March 2020. Contour plots of the temperatures of the cross section at the outlet are also shown in Figure 12. It can be seen that the temperature increases from the inlet to the outlet in the direction of the flow, as expected, due to the transfer of heat from the absorber to the water. Cross sections show that the temperature of the water is higher in the zones closer to the absorber tube wall and colder in the center of tube. Figure 12 shows that higher temperatures were obtained with the finned absorbers due to the additional energy received on the absorber surface (see Section 3.2.2) that is consequently transferred to the water.
# Multi-Modal Sonar Mapping of Offshore Cable Lines with an Autonomous Surface Vehicle
## 4 Experiments
### Waypoint Tracking Results  
The pre-planned waypoints and the actual vehicle trajectories are shown in Figure 8. The ocean current velocity during the survey was approximately 2.1 m/s and its direction was opposite to that of the vehicle course when the waypoint tracking started (the current forecast was acquired by observing the buoys). This caused the vehicle to move slowly until it reached a large turning point at waypoint 9. Then, it quickly returned to the starting point (waypoint 17). The average speed over ground (SOG) of the vehicle between waypoints 1 and 9 was 1.5 m/s; the average SOG during the homing journey from waypoint 9 was  
Figure 8: Waypoint tracking performance under the harsh ocean environment. Survey paths and desired waypoints are shown in white circles and dotted lines. Actual vehicle trajectories are shown in solid lines with cyan and blue circles.  
Figure 9: ASV software components. Manufacturer-provided and in-house software blocks are shown in white and gray boxes, respectively.  
5.8 m/s. This indicates that the vehicle was under the influence of a high current. We can also observe this in Figure 8, where the density of the ASV positions is much higher until it changes its heading at waypoint 9.  
Another difficulty in waypoint tracking is the reduction in CT errors. As the current pushes the vehicle on the lateral side from the northeast direction, it is difficult for the vehicle to overcome the CT errors with constrained thrust force and maneuverability, especially when it follows the north-heading paths. Moreover, the CT error was more significant when the angle between the path segment and the current direction became perpendicular, for example, paths WP 1-2, WP 3-4, and WP 8-9. Due to this, the ASV could not observe part of the power lines in the WP 8-9 segment, both with MBES and SSS measurements.  
In future studies, through validation with additional field tests, a more dedicated controller design to overcome the harsh ocean currents may be realized.
## 4 Summary and Conclusions  
This study developed an evaluation method for deriving the leakage reduction factors of WDSs in the case of Korean small and medium cities. To develop the evaluation method for deriving leakage reduction factors, water loss management efficiency evaluation and formulation of water loss were derived based on improving the revenue water ratio data according to the water loss activity of WDSs in local small and medium cities. For this goal, this study performed three steps. First, data from the study area were collected, and the significant factors selected based on the information characteristic (e.g., revenue water ratio, water loss management activity, variation of the revenue water ratio according to the leakage management activity, leakage status, spatiotemporal characteristics). Secondly, the collected data underwent pre-processing for the selected factors. In the data pre-processing, independent and dependent variables were determined, and correlation analyses were performed between these independent and dependent variables. Through T-score conversion, the data were then standardized to be matched up with the units of various factors. Finally, the efficiency of leakage management actions was determined by the formulation of leakage using various data analysis approaches using MRA analysis and DNN. To apply the proposed approach, the revenue water ratio and the leakage ratio variation data according to the leakage management activity (PR, LR, LD, MR) for 22 local small and medium cities in South Korea were used. Among the four leakage reduction activities, PR was highlighted as an essential activity for decreasing the leakage ratio and increasing the revenue water ratio in most of the data classifications, excluding Group 6. In addition, in relatively well-managed WDSs (i.e., the leakage ratio is less than 10% and the revenue water ratio is higher than 80%), the LD was presented as an effective activity to improve WDS network conditions. However, the first analysis did not consider the temporal correlation of each water loss management activity; pipe and meter replacement reflected the time-sequential characteristics of the revenue water ratio and the leakage ratio after recovery. It is necessary to consider the spatiotemporal characteristics when classifying the data. Therefore, in the second analysis, the data were classified by considering the characteristics of time and location, and the effect of water loss management activity was analyzed. According to the second analysis results, the annual water loss management action of small cities (e.g., BH and JE) was more actively performed. It is thought that this is because a city with a small water supply population is more likely to be more efficient than other regions in distributing support as it is more likely to improve leakage according to the
## 1 Introduction
[26] studied the effects of the cathode inlet temperature and relative humidity on performance, and found that the residual heat in the fuel cell could affect the fuel cell performance, and that lower temperature and higher relative humidity resulted in better fuel cell performance. The relative humidity influence on PEMFC vehicle operation has also caused [PERSON] et al. [27] great interests. They concluded that low humidity was beneficial in reducing costs and the overall weight of the humidifier in the external cell cathode, according to serious flooding phenomena. As mentioned in the above research, the relative humidity significant effects on the PEMFC water management system and performance has been confirmed. However, with the fast development of PEMFC, the emerging stack combined by many single fuel cells satisfied the need of high power; meanwhile, homogeneity problems have arisen. This paper takes into account the pressure drop about the fastest power attenuation single cell in the stack, and indicates the saturation pressure by the pressure drop, and comprehensively analyzes the fuel cell performance from two aspects of calculation and experiment. Firstly, a three-dimensional anode relative humidity (ARH) model is put forth. Then, the geometric model and grid computing are established, and the ARH model is coupled into computational fluid dynamics software for calculation. Next, tests are conducted on the basis of an experimental platform under these conditions: a cell working temperature of 70 \({}^{\circ}\)C and an inlet pressure of 0.1 MPa with a relative humidity of 40%, 55%, 70%, 85%, and 100%. Finally, the experimental results of the Fluent model and the ARH model are compared. The counters of fuel cell species are displayed and analyzed.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 2 Autonomous Driving Based on Dead Reckoning
### Error Measurement Based on Image Processing  
To accurately measure the tracking accuracy of each position during the trajectory tracking process, a GoPro recording camera was installed on the side of the vehicle for shooting the lane line on the left side of the vehicle and calculating the distance between the vehicle and the lane line based on the lane line position in the video, as shown in Figure 5. The actual distance is compared with the reference trajectory distance as a means of calculating the tracking error for each position.  
According to the differential drive model that was introduced earlier, the vehicle position is calculated based on the position of the center of the rear wheel, so the GoPro was installed directly above this position.
## 2 Materials and Methods
### Gas Treatment  
From 26 April to 22 November 2012 and from 29 March to 14 November 2013, the seedlings were exposed to charcoal-filtered (CF) air or O\({}_{3}\) at 1.0, 1.5 or 2.0 times the ambient concentration in the OTCs. For each treatment, three chamber replications, for a total of 12 chambers, were used. The ambient O\({}_{3}\) concentration was independently monitored by a UV absorption O\({}_{3}\) analyzer (ML9810, Teledyne Monitor Labs, Englewood, CO, USA) at the experimental site. Ozone was generated from oxygen-enriched dry air by an electrical discharge O\({}_{3}\) generator (Oz-24-UA, Ebara Corporation, Japan) and injected into the OTCs through a water trap that removed the nitrogen byproducts produced by the O\({}_{3}\) generator [34]. The O\({}_{3}\) concentrations at 90 cm above the OTC floors were monitored using the UV absorption O\({}_{3}\) analyzer. The O\({}_{3}\) concentration accumulated over a threshold of 40 nmol mol\({}^{-1}\) (ppb) (AOT40) is used to investigate the concentration-based critical levels for O\({}_{3}\) impact on vegetation. Thus, whether the O\({}_{3}\) concentration in the present study was above the level having negative impact on vegetation, could be investigated, even though the AOT40 is biologically less relevant than POD in O\({}_{3}\) impact assessments on vegetation [18]. Consequently, we calculated AOT40 as the sum of the differences between the hourly mean O\({}_{3}\) concentrations and 40 ppb when the former exceeded 40 ppb during the daylight hours and the solar irradiation was greater than 50 W m\({}^{-2}\).