esa-sceva/satcom-chunk-collection huggingface dataset:
# SHPB Experiment and MPM Simulation on Dynamic Properties of Unsaturated Clay Under Impact Loading
## 3 MPM Simulation on SHPB Test of Unsaturated Clay
### Constitutive Law for Unsaturated Clay
#### 3.2.2 Mechanical Constitutive Model Considering Strain Rate Effect  
On the basis of elastic-plastic theory, two types of strain increment were considered in the establishment of the mechanical constitutive model. The resumable elastic strain and its increment were represented by Hooke's law. The unresumable plastic strain was described using a modified D-P yield function to define the direction and degree of plastic deformation, in which the strain rate enhancement effect was taken into account. As the  
Figure 7: SWRC of lateritic clay and prediction of VG model.  
yield surface expands or shrinks, its shape and size change accordingly. And this change contributes to the strain rate strengthening effect in the modified DP model. However, the central part for describing the rate-dependent behavior with the DP criterion is determining the appropriate hardening parameter from the view of material constitutive model. The specific expression of the modified DP model is given in Equation (8).  
\[f(I_{1},J_{2})=\alpha I_{1}+\sqrt{I_{2}}-\beta\cdot R(\dot{\varepsilon}) \tag{8}\]  
where \(I_{1}\) is the first invariant of the stress tensor; \(J_{2}\) is the second invariant of the stress deviation tensor; \(\alpha=0.01\) is a constant of the D-P function; \(\beta\) denotes the hardening parameter; \(R(\dot{\varepsilon})\) represents the dynamic strength increase factor, whose expression is shown in Equation (9).  
\[R(\dot{\varepsilon})=1+\left(\frac{\dot{\varepsilon}}{C}\right)^{1/P} \tag{9}\]  
where \(C\) and \(P\) are calibrated CS coefficients based on static shear test and dynamic SHPB test, as given in Figure 4.  
Moreover, a damage parameter \(D\), as defined in Equation (10), was introduced to account for the failure or fracture process. When the plastic strain \(\varepsilon^{p}\) reaches the value of \(\dot{\varepsilon}_{0}^{p}\) (the strain corresponding to the peak of the stress-strain curve), \(D=0\), indicating that the soil begins to undergo damage. When the plastic strain \(\varepsilon^{p}\) reaches the value of \(\dot{\varepsilon}_{f}^{p}\) (the maximum strain of the stress-strain curve), \(D=1\), indicating that the soil becomes fully damaged.  
\[D=\frac{\dot{\varepsilon}^{p}}{\dot{\varepsilon}_{f}^{p}},\ \varepsilon^{p}\in \left[\dot{\varepsilon}_{0}^{p},\ \dot{\varepsilon}_{f}^{p}\right] \tag{10}\]  
where \(D\) is the damage parameter; \(\dot{\varepsilon}^{p}\) is the plastic strain; \(\dot{\varepsilon}_{0}^{p}\) equals the strain corresponding to the peak of the stress-strain curve; \(\dot{\varepsilon}_{f}^{p}\) equals the maximum strain of the stress-strain curve.
###### Abstract
Applied these recent techniques, coupled with geomorphological and hydrological data is very effective in assessing, categorizing, and analyzing the collected data in order to assess the different flash floods [9]. The work of [9] studied the flood risk assessment in a metropolitan urban city in Greece using GIS technology, and the authors modeled the distribution of the hazard spots. Morphometric and hydrological parameters are widely used in exploring the catchments' geomorphic history and development of their drainage network [5,10]. For example, [PERSON] et al. [8], examined the safety of one of the most important monument roads in Egypt. They applied GIS on remotely sensed data to estimate the flash flood hazard level and developed models to trace the flood hazards in highway areas. Morphometric and hydrological parameters aid in understanding the variation in the hydrological characteristics of the related sub-catchments [20,21]. The main objective of this study is to expand the current literature to include flood mitigation strategies between the El-Qussier and Marsa-Alam coastal region, which has no sufficient flooding studies. The data found in this study were processed and analyzed to calculate the effective morphometric parameters along the main catchments and sub-catchments in order to assess the risk of flash floods that periodically occur. For these objectives, we applied remote sensing and geospatial technologies for morphometric landforms extraction and analysis. In addition, it aims to evaluate two major flooding events to quantify surface water using the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) and Soil Conservation Service (SCS) unit hydrograph model. Generally, this study aims to fill a geo-hazard studies gap along an important coastal zone of the Egyptian Red Sea coast.
# Research on the Influence of Turbulent Flow Induced by Dunes on AUVs
## 2 Materials and Methods
### Numerical Verification Method  
The accuracy of the numerical method was verified using the test data of [PERSON] et al. [24]. Specifically, the experimental data from their T6 test case were selected for comparison. In that experiment, an artificial dune with a length of approximately 1.6 m and a height of about 0.08 m was used. The shape of the dune was the same as that described in Section 2.3 and shown in Figure 5. The flow was in the positive direction of the x-axis. The experiment provided information about the flow field along vertical lines at different downstream distances from the crest of the dune. In this paper, the longitudinal average velocity distributions along vertical lines located at distances of 0.13 m, 0.48 m, 0.82 m, and 1.27 m downstream from the dune crest were selected for verification, as illustrated in Figure 5. Based on the experimental data, a dune model with the same scale as the experiment was established for numerical simulation. To closely match the experimental conditions while ensuring computational efficiency, the length of the calculation domain was set to 1.6 m, the height to 0.37 m, and the width to 1 m. The boundary conditions were set the same way as described in Section 2.3, based on the flow velocity information provided in the experiment at the location closest to the inlet. The test was carried out by changing the flow velocity of the velocity inlet in the simulation and finally setting the flow velocity of the velocity inlet in the simulation to 0.68 m/s to match the experiment.  
Figure 6 shows a comparison between the velocity distributions obtained from numerical simulations and experimental results at different positions along the vertical line. The sampling interval is 150 s, with a sampling frequency of 0.01 s. It can be observed that near the leeward side of the dune, the flow velocity distribution along the vertical line is uneven due to the influence of vortices, and the flow velocity on the vertical line away from the leeward side of the dune tends to be uniform. At the locations where \(x=0.48\) m and \(x=1.27\) m, there are certain errors between the flow velocity distribution obtained using the numerical simulation and experiment. This may be attributed to the numerical simulation's inability to account for the actual characteristics of the dune material and the possibility that the boundary conditions were not entirely consistent with real-world conditions. Overall, the results obtained using the numerical method show good agreement with the experimental values, indicating a certain degree of reliability in the numerical approach.  
Figure 4: Grid generation (\(H/D=5.5\)).  
Figure 5: Diagram showing the vertical line position for recording flow velocity.
###### Abstract  
Underwater wireless communication technology plays a key role in the field of marine equipment technology. In this paper, we experimentally demonstrate an underwater optical-acoustic cooperative communication platform for an underwater wireless data transmission system. The system utilizes an underwater Remote-Operated Vehicle (ROV) as a carrier, equipped with LEDbased optical communication and acoustic communication modems. In particular, the system applies optical communication to transmit large-scale data and applies acoustic communication to provide acoustic-assisted signaling exchange before optical transmission and Automatic Repeat Request (ARQ) during optical transmission. By experimentally measuring the transmission distance under different water quality conditions, we found that the system can achieve a data rate of up to 5 Mb/s over a laboratory underwater channel of 7.6 m. By comparing the attenuation coefficients for the outdoor underwater environment with that in clear seawater, we estimate that the achievable link distance in clear seawater can reach 11 m with a data rate of 3.125 MB/s. The proposed system takes both implementation complexity and cost into consideration and also provides significant guidance for future real-time high-speed underwater optical-acoustic communications.  
ROV; optical communication; acoustic communication; cooperative Article
## 4 Results and Discussion
### Comparison of the Flow Evolution: Experimental Visualization versus Numerical Computation
#### 4.1.2 Glycerine Case \(Re_{g}\sim O(10^{2})\)
In this second stage during the interval 2-3 s, the water free surface continues to be clearly fragmented, while the glycerine seems to be more compact with a more smoothed free surface. After 3 s, the water still impacts the ceiling and wave breaking is observed for the water, while no top impacts are observed and only a combination of standing waves are detected for the glycerine. An interesting difference appears during the last part of the experiment. In the case of the water, the tank movement is more damped and the oscillations are lowered sooner, but a remaining standing wave can be observed for a long time as the viscosity action takes a long time to dissipate its energy. In the case of the glycerine, the tank oscillates a longer time, but the liquid remains a flat block during the whole final part as the viscosity dissipates the standing wave quickly. Figure 6: Experimental snapshots (**top**) of a vertical sloshing experiment with glycerine at a 39% filling level and 10 g of maximum acceleration. Numerical counterparts (**bottom**) simulated the \(\delta\)-SPH method. In Figure 8, the tank accelerations of both cases are shown for the same mass ratio and natural frequency. First of all, it can be observed that the matching between the experimental and computational results is very accurate. This validation was already performed in [26] with an older version of the AQUAgpusph code. In Table 2, the damping ratio \(\zeta\) defined in Section 3.1.1 and the time necessary to decrease the tank acceleration to 10% of its initial value \(t(a=0.1\cdot a_{0})\) are quantified for the water and glycerine. It is confirmed again that the damping of the water is larger than the glycerine. Figure 8: Comparison between \(\delta\)-SPH simulations and experimental data of the acceleration of the tank over time for water and glycerine decay tests at 10 g of maximum acceleration. Figure 7: Normalized amplitude of the relative kinetic energy of the liquid. Comparison between water and glycerine. In Figure 9, the vertical sloshing force is plotted for both fluids non-dimensionalized with \(F_{0}=m_{l}\omega_{0}^{2}y_{0}\), where \(m_{l}\) is the liquid mass, \(\omega_{0}\) is the characteristic angular velocity and \(y_{0}\) the initial displacement of the tank. Despite a similar plot being shown in previous publications [26], this new and improved version of the AQUAgpusph code has been able to produce more accurate results than the previous ones. The agreement between the experimental and computational is accurate in both cases. It is interesting to confirm some physical findings that appear in this plot. First, the initial impacts of both cases are quite similar with \(\sim\) 20 N as the mass ratio, and the fluid densities are similar as well. Second, the time required for the water to produce impacts lower that 10% of the initial impact is 2.29 s, while the glycerine needs 3.19 s to decrease the vertical impacts.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### Comparative Analysis
#### 4.2.4 Legal  
In the legal domain, where clarity and transparency are essential for justifiable decisions, the selection of suitable XAI frameworks is critical. The Anchors Approach, CEM, and LOREs emerge as the top contenders, offering tailored solutions to meet the unique challenges faced in legal reasoning and decision-making. The Anchors Approach's succinct IF-THEN rules provide concise explanations for individual predictions, aligning seamlessly with the legal precedents and decision-making frameworks. The CEM's focus on identifying the necessary features and minimal alterations for class prediction offers a systematic approach to understanding the legal outcomes and implications. Additionally, LOREs' localized explanations and counterfactual rules contribute to nuanced interpretations of legal decisions, aiding in the exploration of alternative scenarios and legal arguments. By leveraging these frameworks, legal practitioners can navigate complex legal landscapes with clarity and confidence, ensuring fair and just outcomes. Ref. [83] investigates the application of XAI in the legal domain, an area of interest within the AI and law community. It highlights the gap in the user experience studies concerning XAI methods and the overall concept of explainability. The study evaluates the effectiveness of various explainability methods (Grad-CAM, LIMEs, and SHAPs) in explaining the predictions for legal text classification, with legal professionals assessing the accuracy of these explanations. Additionally, the respondents provide insights into the desired qualities of AI legal decision systems and their general understanding of XAI. This research serves as a preliminary study to explore lawyers' perspectives on AI and XAI, paving the way for more in-depth investigations in the field.  
Ref. [84] discusses the role of XAI in ensuring legal compliance, particularly in the context of automated decision-making (ADM) systems governed by regulations such as the GDPR. It introduces the \"Explanation Dialogues\" study, aiming to understand how legal experts perceive and assess the explanations generated by ADM systems. By focusing on GDPR provisions and expert interviews, the research sheds light on how XAI can facilitate transparency and accountability in legal processes. This interdisciplinary approach underscores the importance of integrating XAI principles within regulatory frameworks to enhance compliance and uphold legal standards.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 4 Discussion
### Structure of _Almus_ Ectomycorrhizal Communities  
Our results support a growing literature indicating that _Almus_ associates with a relatively low diversity of host-specific ectomycorrhizal fungi of approximately 50-60 species [24]. Moreover, our NMDS/PerMANOVA analyses support the notion that _Almus_ EMF communities are highly distinct relative to other host species [22; 46; 47]. We identified 38 fungal phylobins on _Almus_, the majority (55%) in the Phylum Basidiomycota. Of these _Almus_ phylobins, four were found more than four times: _Alnicola luteolofibmilosa_ 2 (26 times), _Tomentella_ sp. 6 (18 times), _Ascomycete_ sp. 3 (six times), and _Philaccephala fortinii_ 4 (five times) (Appendix C). Ten rarer types were found between two and three times; three of these 10 were found in more than one stage. The remaining 23 phylobins were detected only once across all sites combined. _Alnicola_ and _Tomentella_, the two dominant genera on alder, have been recognized as some of the most common _Almus_-specific EMF taxa globally [17; 22; 48]. Individuals from these two genera made up 43% of the total community on _Almus_ across our sites. We also detected species from other EMF genera known to be selective towards _Almus_, including _Alpova_[22; 49; 50], _Inocyte_[23], _Tuber_[22], _Lactarius_[49], and _Cortinarius_[16], but did not find other common _Almus_-specific EMF lineages including/peziza michelii-peziza succosa/genea-humaria/pachyphloeus-amylascus, and/tarzetta-geopyxis [22; 49].  
Although the strong host specificity of EMF on _Almus_ has been repeatedly confirmed, the reasons behind this are less clear. The specificity of host-EMF relationships may result from geographic isolation; however, some argue that such specialization derives from spatial heterogeneity in soil physical/chemical parameters, and specialization to those conditions [25]. It may be that the unique physiology of N-fixing host species requires EMF partners well suited to facilitating their growth. This could include specialization for P mobilization and uptake, as mentioned above, given the capacity of EMF to aid in P acquisition [51; 52; 53]. However, specialization could also be tied to micronutrient acquisition; for instance, Mo, which is required by _Almus_ as a co-factor in the protein complex that reduces atmospheric N\({}_{2}\)[54]. Finally, given that _Almus_ appears to shift _Frankia_ partners across successional stages based on their carbon cost relative to N-fixing capacity [9; 33], similar market economies may be at play in _Almus_-EMF communities. [PERSON] et al. suggested that the up-regulation of modulation and N-fixation by _Almus_ in P-fertilized plots was enabled by lower carbon partitioning to EMF [9]. The combined costs of having to support both N-fixing bacterial partners and mycorrhizal partners place unique carbon demands on N-fixing plants and suggest that hosts may closely regulate carbon partitioning to EMF mutualists relative to the benefits they afford.
## 2 Materials and Methods
### Concept of Remodelling CAD Data to GIS Environment  
The transformation scheme from CAD to GIS also has two parts (see Figure 8):  
1. Conversion of vector graphics from CAD to GIS, where the problem of displaying (mapping) layers from CAD to GIS layers is solved. This view is not generally compatible. In CAD it is possible to insert elements of different geometry types into the same layer, in GIS each vector layer must have its own geometry (point, line, area). The layer mapping generally has an M:N cardinality--see Figure 8.
2. In the case of converting non-graphical information (attributes), the mapping is 1:1, but a restructuring of the attribute tables is required. Attributes need to be mapped to features according to layers in the GIS.  
At this stage, the reasoning was based on the fact that the existing CAD documentation of the railway infrastructure is in 3D, the BIM should be a model in 3D and the DTM CZ should be in GIS in 2D or as 2.5D as a surface description. The basic step of converting data into a GIS environment is its remodeling in GIS software. CAD drawings have data stored in layers, the GIS model is an object-oriented database with 2D geometric objects to which descriptive information can be linked. ESRI's ArcMap software was used to create the  
Figure 8: Conversion from CAD to GIS.  
Figure 7: Model deviations after generalization (exaggerated).  
model. The CAD 3D drawing was imported into the ArcMap environment with subsequent redrawing into a 2D plan drawing of the enclosed GIS objects (surfaces). Subsequently, descriptive information was added to the objects in the database.  
The first step is to import the DGN into the GIS environment, during which the content of the drawing is sorted according to object types--point, line, area. The goal is to create data content that conforms to the required data standard. Two approaches are possible for creating objects in GIS. The first one is the principle of manual digitization, i.e., the creation of the corresponding objects in layers also with the resolution of the object type (point, line, area). The second approach is data analysis using scripting.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 2 System Model
### Connectivity and Coverage Model  
Assume a rectangular area R, within which \(L\) target deployment points are located, denoted by the set \(L=\{1,2\ldots l\}\), \(l_{i}=\left(lx_{l},ly_{l}\right)\) representing the horizontal coordinates of the target deployment points. Let N UAVs be distributed within this area, \(u_{i}=\left(x_{i},y_{i}\right)\) representing the horizontal coordinates of the \(i_{th}\) UAV. \(R_{i}\) represent the communication radius of the \(i_{th}\) UAV. Let \(Z_{i}=\left(zx_{i},zy_{i}\right)\) represent the coordinates of any point in the rectangular area. The distance between any two UAVs must not be less than the UAVs safe distance, nor exceed the maximum communication distance. The distance between UAVs can be expressed as follows:  
\[d_{i,k}=\sqrt{\left(h_{sta}-h_{k}\right)^{2}+\left(u_{i}-u_{k}\right)^{2}}\geq d _{min} \tag{4}\]  
where \(d_{min}\) represents the UAVs safe distance.  
Modeling the connectivity rate as a function of Euclid distance \(R_{u_{i},l_{i}}\) between UAVs \(u_{i}\) and target deployment points \(l_{i}\): max  
\[P_{u_{i},l_{i}}=\left\{\begin{array}{l}1,0\leq R_{u_{i},l_{i}}\leq d_{max}\\ 0,R_{u_{i},l_{i}}>d_{max}\end{array}\right. \tag{5}\]  
\[R_{u_{i},l_{i}}=\sqrt{h_{sta}{}^{2}+\left(lx-xi\right)^{2}+\left( lyi-yi\right)^{2}} \tag{6}\]  
where \(d_{max}\) represented the maximum communication distance of UAVs.  
Figure 2: Plot of path loss versus horizontal distance.  
The connectivity fitness function is:  
\[f_{1}=\sum_{l_{i}\in R}P_{u_{i},l_{i}} \tag{7}\]  
Modeling coverage as the Euclid distance \(R_{u_{i},l_{i}}\) between a UAV \(u_{i}\) and any point \(Z_{i}\) within a given region:  
\[P_{u_{i},Z_{i}}=\left\{\begin{array}{l}1,0\leq R_{u_{i},Z_{i}}\leq d_{max}\\ 0,R_{u_{i},Z_{i}}>d_{max}\end{array}\right. \tag{8}\]  
\[R_{u_{i},Z_{i}}=\sqrt{\left(zxi-xi\right)^{2}+\left(zyi-yi\right)^{2}} \tag{9}\]  
The coverage fitness function is:  
\[f_{2}=\sum_{Z_{i}\in R}P_{u_{i},Z_{i}} \tag{10}\]  
The connectivity fitness function and the coverage fitness function are specified as follows:  
\[Maximize:fit_{final}=\left[\vartheta\times\frac{f_{1}}{L\times L}+(1- \vartheta)\times\frac{f_{2}}{S}\right],\vartheta\in(0,1) \tag{11}\]
## 3 Results
### Most Significant Model Parameters  
The results of PRCC based sensitivity analysis are illustrated in Figure 11. Cardiac output (Figure 11A) is positively modulated by the intrinsic heart rate and systolic elastance of the right ventricle under all four conditions (baseline, HD, TH, and HD&TH), while it is negatively modulated by the diastolic right ventricle elastance, upper body resistance, and the baroreflex mechanism parameter, G. Additionally, HD, TH, as well as TH&HD markedly alter the sensitivity of cardiac output to the right ventricle elastance as well as the baroreflex control mechanism. The heart rate (Figure 11B) is maximally modulated by the intrinsic heart rate and the baroreflex mechanism, while resistances (splanchnic and lower body) and right ventricle diastolic elastance play a secondary but significant role. While heart rate depends positively on the intrinsic heart rate and diastolic right ventricle elastance, it is modulated negatively by the baroreflex and resistance mechanisms. The systemic artery's systolic (Figure 11C) as well as diastolic (Figure 11D) pressures are maximally modulated by the baroreflex. Resistances and elastances play an important role in modulating both. Further, the systemic artery's compliance (aortic compliance) negatively modulates the systolic pressure while it positively modulates diastolic pressure (Figure 11C,D). Blood vessel shear was seen to be modulated by intrinsic heart rate, baroreflex, elastances, and microvascular resistances. The ranked PRCC sensitivities to shear are shown in Figure 11E,F.