esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Model Improvements Expected in the Future  
This research is the preliminary modeling of the green tide that happened in Jinmeng Bay to determine its migration characteristics and the influencing factors; the physico-chemical variables affecting the germination, reproduction, and extinction of the green macroalgae are not taken into consideration. It has been proven by many previous studies that the eutrophication of waters results in green tides due to the massive amount of nutrients derived from the discharge of effluent, agriculture, and aquaculture [2, 15, 60, 61, 18]. In fact, in addition to nutrients, some other pressure variables, such as temperature, irradiance, salinity, oxygen, density of sea water, distribution of chlorophyll, and so on, influence the characteristics of green tides in terms of their spatiotemporal distribution, habitats, and origins, as shown by the cruise surveys and cultivation experiments carried out by other scholars [19, 40, 62, 63, 64, 65, 66]. Hence, a comprehensive coupled model involving a hydrodynamic model and an ecological model (including a water quality model and a growth model for green macroalgae) should be established to simulate the physical-chemical-biological processes in green tides. Such coupled models have been developed and applied successfully by several researchers; however, they are suitable for single floating macroalgae types, such as _Ulva prolifera_[38, 39] and _Sargassum_[59], and, therefore, a new coupled model with a growth model designed for the three dominant species (_Ulva pertussis_, _Bryopsis plumosa_, and _Ulva prolifera_) in Jinmeng Bay is expected to be developed in following studies, and it should also account for the vertical distribution of the green macroalgae in the water column. Additionally, a 3D computational mesh should be applied in the model to consider the vertical dispersion of macroalgae particles. In fact, the dispersion of the macroalgae is mostly induced by turbulence, which is expressed by a simplified equation (Equation (10)) in this research for the purpose of a rapid pre-warning of green tides in Jinmeng Bay. For more accurate modeling of the dispersion of macroalgae, an adequate turbulence model with a high-order numerical scheme [67] is expected to supplement the hydrodynamic model. It is known that the density difference can cause the formation of density currents, which is due to a property of the fluid (e.g., temperature or salinity) or by the sediment in suspension [68]. The excess of biomass in the water column can also change the water density, and thus induces density currents which should be considered in the improvement of the model in future work.  
Figure 10: Wind rose derived from the analysis of wind data during the third quarter of 2021.
# Development and Evaluation of Doppler Ultrasound Training Phantom for Human Vessel Simulation
## 2 Materials and Methods
### Blood-Mimicking Fluid  
The blood-mimicking fluid was made using glycerin. To prepare the fluid, 50 g of 99% glycerin was mixed with 500 g of deionized water, and stirred at 650 rpm for 30 min, using a stirrer. Glycerin was chosen due to its ability to adjust acoustic properties based on concentration, providing flexibility in controlling the characteristics of the fluid. A glycerin concentration of 10% was selected, to create a blood-mimicking fluid with similar acoustic properties, while minimizing the influence of backscattering [31; 32].
# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 3 Proposed Framework: SVIoT
### AES-128 Encryption and Data Shifting  
The security of secret data is a priority in cases of sensitive applications, including healthcare. Various data breaches are noted each year in the healthcare sector, demanding the development of novel mechanisms to enhance security and privacy. In the proposed  
Figure 3: (**a**) Original image block; (**b**) Generation of the cover image using ODNMI.  
Figure 2: Block diagram of the proposed scheme.  
scheme, we encrypted the secret data using an AES-128 encryption scheme to enhance its security. It is a symmetric cipher that involves ten rounds, pertaining to substitution and transposition of the secret information. In this work, we considered a key length of 128 bits, due to the advantage of faster encryption. After encrypting the data, Left Data Shifting (LDS) was used to embed the data into the cover image. It is a novel approach to hiding more data while observing less deterioration of the image. The data to be embedded is first converted into 3-bit (000 through 111) chunks, representing a decimal value ranging from zero through seven. The data, including the entire range of digits that were converted to 3-bit chunks, are then shifted four steps towards the negative axis of the number line; i.e., zero through seven yields negative four through three (100 through 011). Hence, the digits \"0, 1, 2, 3, 4, 5, 6, and 7\", after a four-step shift towards negative axis of the number line, are substituted by \"\(-4\), \(-3\), \(-2\), \(-1\), 0, 1, 2, and 3\", respectively. A higher number of bits are needed to represent the magnitude of digits zero through seven, in comparison to the magnitude of digits negative four through three. Thus, embedding data in the resultant format would yield a less deteriorated cover image when compared to conventional methods. The undesirable pixel values (out of range pixel values due to the addition of decimal values corresponding to 3-bit message chunks) are taken care of by setting different boundary conditions, so that the value of resultant pixels falls within the grayscale range of 0 to 255.
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 5 Design of UAV Cyber Range Risk Assessment Method
The overall security risk value formula of UAV is as follows:  
\[R_{all}=\sum\limits_{i=1}^{n}R_{i}W_{i} \tag{7}\]  
The flow of Algorithm 1 is as follows. ```
Input: UAV \(-\)\(ADM\) Output: UAV security risk value BEGIN
1. Initialize()
2. Establish a set of attack and defense types \(T=(T_{A},T_{D})\)
3. Establish both offensive and defensive action sets \(B=(B_{A},B_{D})\)
4. For all \(S_{i}^{A}(t_{A})\in S_{A}(t_{A})\)
5. Calculate attack income \(APF\) and defense income \(DPF\), respectively
6.\(\sum\limits_{t_{D}\in T_{D}}P_{A}(t_{D}|t_{A})U\big{(}F_{A}^{*}(t_{A}),F_{D}^{* }(t_{D}),t_{A}\big{)}\geq\sum\limits_{t_{D}\in T_{D}}P_{A}(t_{D}|t_{A})U\big{(} F_{A}(t_{A}),F_{D}^{*}(t_{D}),t_{A}\big{)}\)
7.\(\sum\limits_{t_{A}\in T_{A}}P_{D}(t_{A}|t_{D})U\big{(}F_{A}^{*}(t_{A}),F_{D}^{* }(t_{D}),t_{D}\big{)}\geq\sum\limits_{t_{A}\in T_{A}}P_{D}(t_{A}|t_{D})U\big{(} F_{A}(t_{A}),F_{D}^{*}(t_{D}),t_{D}\big{)}\)
8. Get a mixed Bayesian equilibrium, \(F_{A}^{*}(t_{A})=\big{\{}f_{i}^{A^{*}}(t_{A}),f_{2}^{A^{*}}(t_{A}),\cdots,f_{ n1}^{A^{*}}(t_{A})\big{\}}\)
9.\(R_{i}=\sum\limits_{i=1}^{n}P(t_{A}|t_{D})\sum\limits_{i=1}^{m}f_{i}^{A^{*}}(t_ {A})\dfrac{U_{D}(b_{A},b_{D},t_{D})}{U_{D}^{\max}(b_{A},b_{D},t_{D})}\)
10. Invoke algorithm 2 to calculate the weight of the UAV subsystem. 11.\(R_{all}=\sum\limits_{i=1}^{n}R_{i}W_{i}\)
12. Return \(R_{all}\) END
```  
**Algorithm 1** Static game risk evaluation algorithm of incomplete information  
The weight of the UAV subsystem can be calculated by various methods. In this paper, the fuzzy Analytic Hierarchy Process is used to calculate the subsystem weight. A number of experts assign importance to each subsystem, construct a judgment matrix, and according to \(r_{i}\) and \(f_{ij}\), complete the consistency transformation; the judgment matrix is transformed into a fuzzy judgment matrix that meets the consistency conditions, and finally the subsystem weight \(W_{i}\) is obtained. \[r_{i}=\sum\limits_{m=1}^{n}r_{im} \tag{8}\]  
\[f_{ij}=\dfrac{r_{i}-r_{j}}{2}+0.5 \tag{9}\]  
\[W_{i}=\dfrac{1}{n}-\dfrac{1}{2\alpha}+\dfrac{1}{n\alpha}\times\sum\limits_{j=1 }^{n}f_{ij} \tag{10}\]  
The flow of Algorithm 2 is as follows.
###### Abstract
Moreover, cryptography-based account storage and sharing methods are proposed to prevent password leakage, even if the system is compromised. As attackers may also try to read authentication credentials in the system or application codes, this system adopts hard-coded elimination and code obfuscation technology to securely store all of the credentials. We implemented the prototype of SA-UBA and conducted extensive experiments with SA-UBA in real scenarios. Our experimental results demonstrate that SA-UBA introduces a small overhead. For example, it incurs less than 6% band-width usage and about 5% time usage for users with privileged accounts to access cloud resources. Moreover, SA-UBA can detect various texts from complex images with a 22% improvement compared to existing work, and achieve a 94% accuracy in extracting text information. Based on the extracted information, SA-UBA can recognize fine-grained user behaviors with 75% accuracy. In summary, our paper makes the following contributions:  
1. We propose a system named SA-UBA to provide automated and fine-grained user behavior audit method for cloud platforms, especially for graphical operating systems;
2. To prevent users from bypassing the audit system or tampering with behavior logs, we propose a secure privileged accounts management method with a cryptography mechanism;
3. We implement the prototype of SA-UBA and conduct extensive experiments in real scenarios to demonstrate the effectiveness of automated auditing and analyze the security of SA-UBA. We also analyze in detail the introduction of overhead as a supplementary experiment. The rest of this paper is organized as follows. Section 2 reviews the related studies and compares SA-UBA with existing solutions. Section 3 introduces the overall structure of the SA-UBA. Sections 4 and 5 present the detailed design of this system. Section 6 evaluates the overhead and effectiveness of the system. Section 7 concludes our paper.
## 2 Materials and Methods
### RNA Extraction and qPCR Analysis  
The fungus was cultured for 7 days as previously described (t0) and was subjected to osmotic shock following the same procedure as for the determination of compatible solutes. The mycelium was harvested by centrifugation, frozen with liquid nitrogen, grinded using a mortar and pestle, and 100 mg were used to isolate total RNA using the TRI-reagent method (Sigma-Aldrich). cDNA was synthesized from 2 \(\upmu\)g of DNase-treated total RNA, using the RevertAid(tm) H Minus First Strand cDNA synthesis kit with a dT18 primer (Thermo Fisher Scientific).  
Primers for qPCR analysis were designed using Primer3 Plus [42] and their physico-chemical properties and amplicon structures were evaluated using DINAMelt [43] and Mfold [44], respectively. All primers used are listed in Supplementary Table S1.  
Two-step qPCR reaction conditions (temperature, primer concentration and efficiency) were optimized for each primer pair (see Supplementary Table S1 for reaction conditions). qPCR reactions contained 5 \(\upmu\)L of QuantiiNOVA SYBR GREEN Master Mix (QIAgen, Hilden, Germany) and 1 \(\upmu\)L of a 1:8 dilution of cDNA in a final volume of 10 \(\upmu\)L. All reactions were quantified in duplicate using a Rotorgene apparatus (QIAgen, Hilden, Germany). A melting curve and a polyacrylamide gel electrophoresis were used to verify the specificity of the amplified product.  
Relative expression levels were calculated with the Pfaffl method using the REST software [45; 46]. In cases where the randomization test performed by REST confirmed statistical significance of the fold change, a binary logarithm (logFC) over 2 was considered as biologically significant up- or down-regulation. The genes _sarA_ and _cox5_ were used as reference genes for normalization [47].
## 5 Conclusions  
In conclusion, this research adopted an integrated approach combining finite element analysis with in vitro mechanical experiments, which not only validated the high accuracy of finite element models derived from CT scan data but also confirmed the practical mechanical support efficacy of the designed tibial implant. The novel hybrid-porous implant developed in this study demonstrated superior mechanical support efficacy compared to conventional square- and circular-porous designs. At 73% medium-high porosity, implantation of hybrid-porous devices resulted in 19.97% and 15.33% reductions in tibial maximum stress relative to traditional square-porous and circular-porous implants, respectively, with concomitant reductions of 31.60% and 11.83% in implant maximum stress, alongside 4.09% and 4.24% decreases in marginal displacement. This advantageous performance profile remained consistent across the full porosity spectrum. Biomechanical testing revealed that hybrid-porous tibial implants demonstrated superior performance across the full porosity range (53-83%), maintaining minimal post-implantation displacement values (1.67-1.87 mm) which were 7.0-12.0% lower than those observed with conventional mono-porous implant configurations. This study demonstrates that variations in structural configuration and porosity significantly influence postoperative tibial stress distribution, which contributes to improved mechanical environments in high tibial osteotomy treatment of knee osteoarthritis.  
Conceptualization, Z.X. and Y.X.; methodology, Z.X. and C.M.; software, Z.X.; validation, Z.X. and C.M.; formal analysis, Z.X.; investigation, C.M.; resources, Y.X.; data curation, Y.X. and Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, Y.X. and C.M.; visualization, Z.X.; supervision, Y.X.; project administration, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Hebei Province of China, grant number C2023202010; Full-time Talents Program of Hebei Province of China, grant number HY2024050012; National Natural Science Foundation of China, grant number U23A6008.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
###### Abstract  
High rainfall environmental conditions can easily cause erosion or collapse of the granite residual soil slope. However, traditional slope reinforcement methods have drawbacks such as poor landscape effect, high energy consumption of raw materials, and environmental pollution. This study studied the application of microbial-induced calcite precipitation (MICP) in the reinforcement of granite residual soil. The consolidation effect of various methods was investigated, and the influence of cementing liquid concentration and pH value on consolidation under optimal curing conditions was explored. The results showed that the bacteria concentration reached OD\({}_{600}\) = 3.0 and urease activity was 31.64 mM/min, which positively impact the production of calcium carbonate and the stability of crystal morphology. In addition, the soaking method was found to have the most effective consolidation effect on the surface soil samples, with the lowest disintegration rate. On the other hand, the peristaltic pump grouting method is the most effective in strengthening depth. This method resulted in a 513.65% increase in unconfined compressive strength (UCS), a 297.98% increase in cohesion, and a 101.75% increase in internal friction angle. This study also found that after seven rounds of grouting, the highest UCS was achieved in consolidated soil samples with a 0.5 mol/L cementing solution concentration, reaching 1.602 MPa. The UCS of soil samples increases as the pH value of the cementing fluid increases within the range of 6-8. As the pH value reaches 8-9, the strength increases and stabilizes gradually. These research findings can serve as an experimental basis for strengthening granite residual soil slopes and a guide for improving microbial geotechnical strengthening methods.  
MICP; granite residual soil; strengthen; urease activity; cementing solution concentration
## 4 A Case Study
### Evaluation and Discussion
If two components of a subsystem were spatially connected to each other but the model assessed the connection incorrectly, the two components were distinguished as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subsystem Name**} & \multirow{2}{*}{**Number of Components**} & \multicolumn{2}{c}{**Generate Connection Table Process**} & \multicolumn{2}{c}{**Generate Logic Model Process**} \\ \cline{3-6}  & & **Omissions (Pairs)** & **Accuracy (\%)** & **Errors (Pairs)** & **Accuracy (\%)** \\ \hline Supply air 6 & 26 & 0 & 100 & 0 & 100 \\ Supply air & 188 & 3 & 98.4 & 0 & 100 \\ Intake air 1 & 21 & 1 & 95 & 0 & 100 \\ Intake air 2 & 19 & 0 & 100 & 0 & 100 \\ Intake air 3 & 29 & 5 & 82.1 & 2 & 92.9 \\ Intake air 4 & 24 & 2 & 91.3 & 1 & 95.7 \\ Intake air 5 & 32 & 0 & 100 & 0 & 100 \\ Intake air 6 & 35 & 0 & 100 & 0 & 100 \\ Intake air & 160 & 8 & 95 & 3 & 98.1 \\ Wet pipe 1 & 23 & 0 & 100 & 0 & 100 \\ Wet pipe 2 & 28 & 0 & 100 & 0 & 100 \\ Wet pipe 3 & 40 & 1 & 97.5 & 2 & 100 \\ Wet pipe 4 & 34 & 1 & 97.1 & 1 & 100 \\ Wet pipe 5 & 23 & 0 & 100 & 0 & 100 \\ Wet pipe 6 & 30 & 0 & 100 & 0 & 100 \\ Wet pipe & 178 & 2 & 98.9 & 3 & 98.3 \\ Dry pipe 1 & 20 & 0 & 100 & 0 & 100 \\ Dry pipe 2 & 28 & 0 & 100 & 0 & 100 \\ Dry pipe 3 & 14 & 0 & 100 & 2 & 85.7 \\ Dry pipe 4 & 33 & 1 & 97.0 & 0 & 100 \\ Dry pipe 5 & 28 & 0 & 100 & 0 & 100 \\ Dry pipe 6 & 44 & 2 & 95.5 & 2 & 95.5 \\ Dry pipe & 167 & 3 & 98.2 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._shown as Figure 14. In addition, the connection table could be manually added with two adjacent components to correct such omissions so that a complete connected path could be obtained. 3. There were four subsystems with less than two errors when generating logic models and the accuracy rates remained higher than 90%. An analysis of the results demonstrated that the values of properties were ambiguous in terms of their semantic meanings. For example, the fire damper was one of the valves in the HVAC system. However, if users used \"valve\" to represent the damper instead of using \"damper\", the algorithm was unable to match the valve description with the fire damper component. Such problems were corrected by adding and deleting upstream and downstream components manually. The results also indicated that this approach could achieve the automatic completion of logic chain. As discussed above, one limitation of this approach is because there was no unified modeling standard for MEP systems, the omission of certain necessary data in the model would deduce the accuracy.
## 4 Interference-Aware Clipping Scheme
### Clipping Noise Analysis  
As shown in [43], the clipped time-domain OFDM signal in Equation (5) can be expressed as the output of the memoryless nonlinear transformation of the Gaussian random variables as follows:\[\widehat{x}_{t}(n)=\alpha_{t}x_{t}(n)+d_{t}^{\prime}(n),\ \ 0\leq n\leq N-1, \tag{7}\]  
where \(\alpha_{t}\) is the attenuation factor, and \(d_{t}^{\prime}(n)\) is the in-band clipping noise.  
The frequency representation of the clipping noise \(d_{t}^{\prime}(n)\) can be written as  
\[D_{t}^{\prime}[k]=\frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}d_{t}^{\prime}(n)e^{-2 \pi mk/N}. \tag{8}\]  
As the number of subcarriers increases, \(D_{t}^{\prime}[k]\) approaches a complex Gaussian random variable with zero mean due to the central limit theorem. According to [PERSON]'s theorem, the expectation of the \(|D_{t}^{\prime}[k]|^{2}\), \(E(|D_{t}^{\prime}[k]|^{2})\) can also be calculated in the time domain. From Equation (5), the distortion \(|d_{t}^{\prime}(n)|\) can be given by  
\[|d_{t}^{\prime}(n)|=|x_{t}(n
## 2 Material and Methods
### Road Network Information Model Coupled with Flood Information Construction  
The issue of personnel evacuation in dam breach accidents is inherently a complex systems engineering problem, influenced by the evolution of flood dynamics and constrained by the road network structure. To describe the relationships between the nodes and edges in the road network more precisely, this study employs graph theory. This effectively analyzes critical issues such as node connectivity and path selection and constructs a road network information model [26; 27; 28]. By integrating flood evolution data, the road network structure is digitally represented, leading to the development of a coupled flood-road network coupling model.  
\[\left\{\begin{array}{l}G=(P(G),E(G),\psi_{G},\varphi_{G})\\ P(G)=\left\{p_{i}|i=1,2,\ldots,n\right\}\\ E(G)=\left\{\langle p_{i},p_{j}\rangle\right\rvert p_{i},p_{j}\in P\right\}\\ \mathbf{\psi_{G}}=\left\{\langle t_{i},s_{i},v_{i}\rangle|p_{i}\in P\right\}\\ \mathbf{\varphi_{G}}=\left\{e_{ij}|\big{\langle}p_{i},p_{j}\big{\rangle}\in E \right\}\end{array}\right. \tag{4}\]  
\[\mathbf{\psi_{G}}=\left(\begin{matrix}t_{1}&s_{1}&v_{1}\\ t_{2}&s_{2}&v_{2}\\ \vdots&\vdots&\vdots\\ t_{n}&s_{n}&v_{n}\end{matrix}\right) \tag{5}\]  
In this equation, \(G\) represents the road network information model. Specifically, \(P(G)\) denotes the set of road network nodes \(p_{i}\), which correspond to the intersections in the actual road network, where \(n\) is the total number of nodes. Additionally, \(E(G)\) represents the set of road segments (\(p_{i}\), \(p_{j}\)) within the network, with the travel cost (\(e_{ij}\)) determined by Equation (6). Furthermore, \(\psi_{G}\) is the parameter matrix, which includes the flood arrival time (\(t_{i}\)), flood inundation depth (\(s_{i}\)), flood flow velocity (\(v_{i}\)), and other relevant factors at each road network node. Lastly, \(\varphi_{G}\) is the adjacency matrix for road weights, which characterizes the travel cost and connectivity relationships between nodes.  
\[e_{ij}=\left\{\begin{array}{l}\frac{d_{ij}}{\overline{v}_{0}}&\text{Segments }\big{\langle}p_{i},p_{j}\big{\rangle}\text{ connected, }h=0\\ \frac{d_{ij}}{\overline{\eta}(v_{0}-3.1h)}&\text{Segments }\big{\langle}p_{i},p_{j}\big{\rangle}\text{ connected, }0<h\leq 0.2\\ \frac{d_{ij}}{\overline{\upsilon}_{0}\overline{\upsilon}_{0}}&\text{Segments }\big{\langle}p_{i},p_{j}\big{\rangle}\text{ connected, }0.2\leq h\leq 0.8\\ \inf&\text{Segments }\big{\langle}p_{i},p_{j}\big{\rangle}\text{ disconnected, }h>0.8\end{array}\right. \tag{6}\]  
In this context, \(d_{ij}\) represents the length of the arc \(\langle p_{i},p_{j}\rangle\), \(d_{ij}\
eq 0\) with the calculation formula provided in Equation (7). Additionally, \(v_{0}\) denotes the normal walking speed of individuals unaffected by the flood, while \(\eta\) is the reduction coefficient, set at 0.9 to ensure pedestrian safety. \(h\) represents the flood depth (m) of the segment, and inf indicates infinity.  
\[d_{ij}=\left\|p_{i}-p_{j}\right\|_{2}=\sqrt{\left(x_{j}-x_{i}\right)^{2}+\left(y _{j}-y_{i}\right)^{2}} \tag{7}\]