esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 4 Security Analysis  
This section verifies the informal security analysis of our proposed model to ensure that it can defend against numerous prevalent attacks that persist in sensor-based PH systems. The following scenarios assume that an imposter tries to control the whole PH network through several well-known security breaches.  
* **Impersonation and Spoofing Attack:** IoT sensors are prone to impersonation and spoofing attacks. The device, which is attached to patients, is usually verified at the beginning of any session. During the ongoing session, if the intruder inherits the device and pretends to be the user, the patient's data privacy is at risk. However, continuous authentication prevents the system from such an attack. Also, using multimodal biometric signals to authenticate instead of unimodal prevents a spoofing attack from device and user perspectives;
* **Injection and Tampering Attack:** The network that adopts the proposed authentication approach is also secure from an injection attack. If attackers want to implant a node in the patient's PH network, they will not be able to do so as the biometric trait will not match. Node tampering attack is also prevented as even if the intruders intrude on the node and implant a new biometric authentication signal, the network will reject the device;
* **Registration Phase Attack:** An imposter can register a rough device into the PH network during the registration phase. However, to mitigate this attack in our model, we use two-step registration: user registration and device registration. As a result, a device is only registered if it is attached to any previously registered legitimate user.
## 5 Conclusions  
Cavitating flows that occur in a variety of practical cases can be modeled with a wide range of methods. Based on the FBM turbulence model and level set method, the applicability of the Kunz, FCM, and Zwart cavitation models in sheet and cloud cavitation around a NACA0015 Hydrofoil were investigated. Several conclusions can be drawn as follows.  
At the condition of sheet cavitation, the Kunz model has an accurate prediction for the steady and unsteady cavitation phenomena. It can accurately capture both the adverse pressure gradient and the action of the re-entrant jet. The Zwart model can predict the structure of the attached cavity accurately, but it cannot predict the adverse pressure gradient and the formation of the re-entrant jet. Meanwhile, the full cavitation model (FCM) overpredicts the unsteady characteristics of sheet cavitation, and cannot capture the structure of the cavity bubble.  
At the condition of cloud cavitation, the full cavitation model (FCM) has a more accurate prediction for the flow field structure and reflects the unsteady phenomena that are observed in the experimental tests. Meanwhile, with the Zwart model, the vortex is generated in the tailing edge of the cavity, and expands with the forward movement of the re-entrant jet. However, it is not obvious enough for the Zwart model to simulate the vortex motion near the cavitating area. The Kunz model does not predict the shearing action of the re-entrant jet on the attached cavity. The variation of the cavity structure is inconsistent with the experimental observations. Moreover, for the cavitation around a hydrofoil, the selection of cavitation models seems to have a good general validity.  
Conceptualization, A.Y.; methodology, A.Y. and D.Q.Z.; software, Q.H.T.; validation, A.Y., Q.H.T. and D.Q.Z.; formal analysis, A.Y.; investigation, A.Y.; writing--original draft preparation, A.Y. and Q.H.T.; writing--review and editing, A.Y.; funding acquisition, A.Y.  
This work was financially supported by the National Natural Science Foundation of China (Project Nos. 51806058), and the Fundamental Research Funds for the Central Universities (Project No. 2019B15014).  
The authors declare no conflict of interest.  
Figure 10: Re-entrant jet and vortex shedding observed in the experiment (\(\sigma=0.65\), \(\alpha=60^{\circ}\)).
## 4 Discussion
From a supplementary correlation analysis, it was found in the HL and SL that the peak backswing angular velocity and the peak downswing angular velocity were correlated to each other significantly across all club conditions: \(r\) = 0.500, 0.597, and 0.591 for the driver, 5-iron, and pitching wedge, respectively, in the HL, and \(r\) = 0.339, 0.493, and 0.530, respectively, in the SL. This means faster backward rotation of the axle tends to result in faster downward rotation of the axle. The backswing-downswing association got weaker in the UL and club/wrist, and the driver condition revealed weaker associations than the iron and wedge conditions did, suggesting added complexity in the UL/club/wrist motions, particularly in the driver swing. The overall findings of this study (correlations and kinematic sequences) and those of [PERSON] and colleagues [1,2] collectively highlight the benefits of a faster backswing motion for skilled golfers. The potential benefits of active (faster) backswing include the following: (1) a faster backward rotation of the axle leads to a more body-driven backswing along with large backward angular momentums of the axle segments; (2) a body-driven backswing promotes the LBS swing pattern by increasing the backswing X-factor stretch and elongating the transition phase; (3) the continuous HL-SL torsional separation during the transition phase that comes with the LBS swing style induces full and well-spaced proximal-to-distal transition sequence; (4) a continuous proximal-to-distal transition promotes SSC-style muscle actions during the transition and early downswing phases making the swing easier with more efficient use of muscles; (5) an active and continuous backswing-to-downswing transition requires the lower body to interact with the ground more actively, resulting in large golfer-ground interaction moments (i.e., the GRF moment in the frontal plane and the pivoting moment in the horizontal plane) [1]; (6) large golfer-ground interaction moments at TB enable fast acceleration of the downward rotation of the body from the beginning of the downswing, thus shortening the early downswing phase without a waste of time; (7) early acceleration of the downward rotation allows the axle and the UL to reach their angular velocity peaks early and secure a sufficient time for the body-to-club angular momentum transfer; (8) the LBS-style (body-driven) swing also promotes delayed release of the club (i.e., wrist uncocking).
## 3 Results and Discussion
### Effect of pH on Ethanol Production  
Ethanol fermentation was evaluated at two different pH profiles to determine the effect of pH: uncontrolled pH and controlled pH at 5.5. Figure 1 shows the cell population and concentrations of glucose and ethanol in the fermentation broth with controlled pH at 5.5 and uncontrolled pH. The results clearly indicate that a higher growth rate for biomass was obtained with the controlled pH at 5.5 (0.496 log CFU/mL/h) than uncontrolled pH (0.289 log CFU/mL/h).  
Furthermore, the maximum ethanol concentration and production rates were 27.7 g/L and 5.47 g/L/h, respectively at controlled pH 5.5, whereas 22.75 g/L and 2.22 g/L/h were obtained at uncontrolled pH. Yields of the fermentations (product produced /substrate consumed) were similar; 46.23% and 44.55% for controlled and uncontrolled pH profiles, respectively. For controlled pH, glucose concentration of the fermentation medium began to increase during the first few hours of fermentation instead of reduction, which might be due to saccharification was still in progress (Figure 1). Therefore, glucose conversion continued simultaneously during earlier stage of the fermentation, and after 24 h of fermentation, glucose was completely consumed. This increase of glucose enhances ethanol yield and growth of biomass.  
Yields of the fermentations (product produced/substrate consumed) were similar; 46.23% and 44.55% for controlled and uncontrolled pH profiles, respectively. However, it was difficult to determine the exact amount of consumed glucose because saccharification was still being performed in  
Figure 1: Glucose and ethanol, and biomass concentrations at pH 5.5 (**a**) and uncontrolled pH (**b**).  
the earlier stage of fermentation. Therefore, determination of optimum pH was dependent on growth rate, production rate, and ethanol concentrations, which indicated that controlled pH at 5.5 is better for ethanol fermentation.  
It was reported that high ethanol production was obtained by using initial pH 5.0 to 6.0 [7], which was in agreement with the results of this study. It was also shown that no ethanol production exists lower than pH 4.0 [19]. During the uncontrolled pH treatment, however, the minimum observed pH was 4.1. [PERSON] _et al._[20] reported that maximum ethanol yield, maximum growth rate, and biomass concentration were obtained at pH 5.5 on carob as a medium for ethanol production. Therefore, pH 5.5 was found to be the best pH level and therefore, used for the rest of the study.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R\({}^{2}\) = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R\({}^{2}\) = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.  
Sun-induced chlorophyll fluorescence (SIF); EVI; boreal forest; MODIS 2020
# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 5 Conclusions  
In this paper, we delved into the nuanced realm of image watermarking, a technique characterized by the subtle integration and retrieval of watermarks within a cover image. The motivation for this investigation is spurred by the growing synergy between image watermarking and deep learning--a field renowned for its adeptness at unraveling intricate patterns and representations. This study stands as a comprehensive exploration, not merely retracing the trajectories of extant methodologies but exploring the historical context, current innovations, and future prospects of deep learning in image watermarking.  
Distinctive in its approach, this survey illuminates the landscape of deep learning-based image watermarking, marked by its precision and depth of analysis. It offers three primary contributions to the scholarly discourse. First, we introduce a systematic classification that segments deep learning-based image watermarking into three core categories: embedder-extractor, deep networks for feature transformation, and hybrid methods. This refined categorization is premised on the diverse roles that deep learning occupies in related studies and is crafted to infuse clarity and direction into ongoing research. Secondly, we examine emblematic methodologies and encapsulate the multifaceted directions and challenges that each category embodies. This aims to provide readers with a consolidated, insightful overview, distilled from a plethora of diverse yet interconnected research. Finally,our analysis expands to unravel prospective research trajectories, mapping out uncharted territories and emergent themes in the field of deep learning-based image watermarking.  
Conceptualization, X.Z.; methodology, X.Z., A.D., F.A. and A.T.; writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation (NSF) grant number 2104267.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Spatial Similarity Comparison Experiment  
Firstly, we determined the anchor sample A in each data set according to the typical symptoms of diabetic complications and then calculated the similarity distance between positive samples, samples enhanced with Simkg-BERT, and samples enhanced with SimBERT and anchor sample A using the SBERT algorithm.  
t-SNE (t-distributed stochastic neighbor embedding) is the most mainstream and cutting-edge dimensionality reduction algorithm, for which the main goal is to project known high-dimensional data into a low-dimensional space. In this study, we projected the EHRs in the high-dimensional vector space to the two-dimensional coordinate space so that the position of each EHR in the space with respect to the anchor point was more clearly presented. We compared each of the 40 EHR samples generated using Simkg-BERT and SimBERT in the vector space, with the dark-blue points being positive samples, the red points being the Simkg-BERT-generated chart samples, and the yellow points being the SimBERT-generated chart samples. The two-dimensional position of each sample with respect to the anchor point for each of the four diabetes complications is shown in Figure 5. It can be seen that the red points are closer to the dark-blue points of the positive samples, while the yellow points are further away from the dark-blue points.  
As reported by previous researchers, hard negatives are the closest negative samples to the anchor point or positive samples, i.e., the most similar samples in the embedding space. We calculated the average Euclidean distance from each type of sample to the anchor point, as shown in Table 4. We can see that the negative samples generated using the SimKG-BERT model are closer to the positive samples and the anchor point in the space vector compared to those generated with Sim-BERT; they are also more in line with the definition of a hard negative and able to provide more gradient information to the model during training. So, we deemed it appropriate to choose the SimKG-BERT model for the data augmentation of the misdiagnosis medical records.  
Figure 5: Similarity distance of each sample of diabetic complications.
# A BDGIM-Based Phase-Smoothed Pseudorange Algorithm for BDS-3 High-Precision Time Transfer
## 1 Introduction
Its time transfer accuracy is one order of magnitude higher than that of CV time transfer [15]. The quality of pseudorange observations has a significant impact on time transfer, as shown by the analysis above; hence, it is imperative to improve their quality. The accuracy of phase observations is about 100 times higher than that of pseudorange observations, and phase observations are less susceptible to the multipath effect. However, the carrier phase is affected by integer ambiguity. Pseudorange observations are the opposite of carrier phase observations. Therefore, smoothing the pseudorange using a high-precision carrier phase is an effective method to improve pseudorange accuracy. References [16; 17; 18] verified the feasibility of dual- and tri-frequency phase-smoothed pseudoranges. For the vast majority of civil users, pseudorange single point positioning (SPP) is sufficient to meet the needs of various application scenarios in daily life. For multi-frequency users, ionospheric delay errors may be removed by combining multi-frequency signals. However, for single-frequency (SF) users, ionospheric delay variation is the main factor contributing to the deterioration of phase-smoothed pseudoranges, so the key to improving the performance of SF phase-smoothed pseudoranges is to accurately calculate and deduct the ionospheric delay variation between adjacent epochs. Therefore, a phase-smoothed pseudorange method considering ionospheric delay variation is proposed in this paper, and it is applied to BDS-3 SF SPP time transfer, which belongs to AV time transfer. The ionospheric delay variation is calculated using the BeiDou global broadcast ionospheric delay correction model (BDGIM). This paper is structured as follows: In Section 2, we provide a brief description of the BDGIM model, a detailed derivation of a phase-smoothed pseudorange model based on the BDGIM, and a presentation of the accompanying SF SPP time transfer method. In Section 3, the datasets used for SF SPP time transfer and its processing methods are given. In Section 4, we first evaluate the BDGIM's performance compared with the Klobucharmodel. Then, the performance of SF SPP time transfer is assessed. Finally, conclusions are summarized in Section 5.
# Numerical Simulation on the Leakage-Induced Collapse of Segmental Tunnels
## 2 Model Tests
### Test Results  
It was observed in the tests that the process of soil erosion had visible stages, which is shown in Figure 3[25]. In the figure, the green arrows indicate the direction of soil displacement. The internal phenomena in the tunnel during the process are shown in Figure 4. Based on the different physical phenomena, the process of soil development and tunnel response after tunnel leakage can be divided into four stages as follows:  
1. Stage 1: After the start of water and soil rushing from the waist to the interior of the tunnel, a visible soil cave appeared on the exterior of the tunnel, and the displacement of the soil was mainly concentrated in the interior of the soil cave. The initial soil cave was temporarily stabilized, as shown in Figure 3a. At the same time, the top of the tunnel slowly deformed inward, as shown in Figure 4a.
2. Stage 2: The initial soil cave destabilized, soil erosion developed again, and the erosion area rapidly developed upward. Subsequently, a secondary soil cave formed and temporarily stabilized again, as shown in Figure 3b. For the tunnel, it was observed that the top of the tunnel showed large deformations and dislocation, as shown in Figure 4b.
3. Stage 3: After a brief period of stabilization, the secondary soil cave destabilized again and the erosion area developed upward, followed by the formation of a third soil cave. The third soil cave again entered the temporary stabilization state, as shown in Figure 3c. At the end of this stage, the top of the tunnel was impacted by falling soil, and several segments collapsed, as shown in Figure 4c.
4. Stage 4: The third soil cave destabilized, the ground subsided, and the destabilized soil fell on the top and shoulder of the tunnel, as shown in Figure 3d. Several rings of the tunnel collapsed, as shown in Figure 4d.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Soil Type** & \multicolumn{2}{c}{**Content of**} & **Cohesion** & **Friction** & **Permeability** \\  & & **Clay (\%)** & **(kPa)** & **Angle (\({}^{\circ}\))** & **Coefficient (m/s)** \\ \hline  & A & 0 & 0.34 & 40.17 & \(2.21\times 10^{-3}\) \\  & B & 5 & 1.81 & 35.56 & \(1.33\times 10^{-4}\) \\ Model soil & C & 10 & 2.88 & 35.87 & \(8.04\times 10^{-5}\) \\  & D & 20 & 4.28 & 34.16 & \(6.45\times 10^{-5}\) \\ Background case & / & 3.0 & 32.0 & \(5.34\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of soils.  
Figure 2: Model tunnel.  
Figure 4: Tunnel response during the model tests: (**a**) Deformation; (**b**) Deformation and dislocation; (**c**) Several segments collapsed; (**d**) Tunnel collapsed.  
Figure 3: Soil erosion phenomena in the model tests: (**a**) Initial soil cave; (**b**) Secondary soil cave; (**c**) Third soil cave; (**d**) Ground surface subsidence.
# A Modeling of Human Reliability Analysis on Dam Failure Caused by Extreme Weather
## 4 Bayesian Network Construction
### Accident Event Tree
Taking the example of \"unsuccessful dispatch-failed intervention\", first, due to the possibility of taking a chance, drought, and other reasons, the reservoir authorities ignored the scheduling protocols for the flood season and did not empty the reservoir in time. The lack of weather forecasts and warnings, the failure to monitor the occurrence of extreme weather in the basin, the absence of water level monitoring instrumentation or visual monitoring, resulting in the failure to open the gates in time, and the failure of the alarms to work correctly, resulting in the failure to detect the danger, are a series of reasons, as shown in Figure 11.  
The event of \"unsuccessful dispatch-failed intervention\" includes five steps: scheduling protocol, weather forecasts and warnings, water level monitoring instrument, visual monitoring, and alarm, which are listed in chronological order. Scheduling protocols are the first preventive step and are the foundation for reservoir safety through the flood season. According to the relevant regulations, reservoirs should be drained below the permissible water level in advance to ensure safe operation during the flood season. Since extreme weather is frequent nowadays, and droughts may even precede floods, reservoir authorities risk ignoring scheduling protocols by chance. The second step is weather forecasting and warning technology. If there are advanced and complete rainfall measurement and reporting technologies in the basin, they can grasp and dispatch in time before the occurrence of heavy rainfall. Failure to forecast ahead of time requires reservoir managers to keep an eye on the monitoring instrumentation during extreme weather events to keep abreast of the dam's operational behavior. If the dam does not have an effective safety monitoring system, it can only rely on manual inspection and visual monitoring, which makes it more difficult to judge the engineering safety. Managers with experience and specialized skills can make crisis predictions based on rain conditions and the rate of water level rise for emergency response. If none of the above behaviors are successful, the alarm device is the last line of defense at this point.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## Abbreviations  
* 3 DFAMIC 3-Dimensional Subsurface Flow and Fate and Transport of Microbes and Chemicals model ART3D Analytical Model for Simulating Reactive Multi-species Transport in 3-Dimensional Groundwater Systems BIOREDOX-MT3 DMS A Coupled Biodegradation-Redox Model with Modular Transport 3 Dimensional Model Simulator CDISCO Conceptual Design for In Situ Chemical Oxidation COEC Contamination Of Emerging Concern CORT3D Chemical Oxidation Reactive Transport in 3 Dimensions DFT Density Functional Theory DNAPL Dense Non-Aqueous Phase Liquid EEA European Environment Agency EPA Environmental Protection Agency ES&T Environmental Services & Technologies ESTCP Environmental Security Technology Certification Program FEFLOW Finite Element subsurface Flow system FEHM Finite Element Heat and Mass Transfer Code GMS Groundwater Modelling System HPS-PROBAN Horizontal Plane Source--Probabilistic Analysis