esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
## 3 Details of Damst
### Damst Point List and Packet Format
#### 3.2.2 Packet Format  
Once the mobile sink needs the sensing data of the interested region, it sends a data query to the RA and gets a reply via DAMST points on the trajectory from the RA. Both the query packets and the reply packets follow the packet format defined in Table 1. In the packet format, packet type represents that a packet is a query packet or a reply packet. The RA ID points out which RA the mobile sink requests to. Data type, collection period and node collection radius of the region are designated by the mobile sink to indicate what kind of data it needs, how often to update data and how large of the interested region. The mobile sink can require the RA to send data of a specific node through Node ID.  
In a scenario with multiple mobile sinks, the Sink ID informs the RA which one needs the interested data. The most important two parameters in the packet format are DAMST point list and route list, which determine how to make routing decisions in forwarding the query packets and reply packets, the detailed parts are explained in Section 3.3.2. Before delving into other details of DAMST, we define some important notations in Table 2.
## 2 Materials and Methods
### Experimental Setup  
The experimental setup is depicted in Figure 4. Ultrashort laser pulses from a titanium sapphire CPA system (80 fs pulse duration, 0.9 mJ pulse energy, 1 kHz repetition rate) are spectrally broadened in an argon-filled hollow-core fiber. These spectrally broadened pulses are then sent into a prism  
Figure 3: Characterization of different surfaces by a broadband XUV spectrum. An input XUV signal (**a**) is reflected from a surface (**b**) with either constant reflectivity (red curve) or decreasing reflectivity with increasing photon energy (blue curve). The corresponding XUV signal after reflection is shown spectrally resolved in (**c**) and spectrally integrated in (**d**). The ratio of the integrated signal of both surfaces is only determined by their reflectivity. The ratio between these two signals is then sensitive to a variation of the incident spectrum.  
compressor with a deformable mirror as a symmetry mirror for pulse-shaping. The shaped pulses are focused into an argon-filled capillary for HHG. Further details about the pulse-shaping device and the HHG setup can be found in [23]. The generated XUV radiation is separated from the laser beam by an aluminum filter foil (thickness of 300 nm), focused by a toroidal gold mirror into the interaction region, and characterized by an XUV spectrometer (LHT 30, HORIBA Jobin Yvon, Bensheim, Germany) equipped with a PI-SX 400 back-illuminated XUV-CCD, Roper Scientific, Martinsried, Germany).  
Various gaseous, liquid, or solid targets can be investigated by the focused XUV beam and charged particles can be detected, e.g., by time-of-flight electron or by ion spectrometers [24; 25]. Here, we use the reflection from a metal surface to direct the XUV beam onto a Channeltron (CEM045 JR-1, PHOTONIS, Sturbridge, MA, USA) at a distance of 25 cm. It should be noted that the Channeltron used in our experiments is able to detect photons in a wavelength range from 0.1 nm to about 150 nm, and is regarded as a solar blind detector. The detector is supplied with a voltage of 2200 V along the tube axis. To measure the arrival time of the signals, the output of the detector is amplified, discriminated (ORTEC Model 9327, AMETEK, Meerbusch, Germany), and measured with a multiscale (P7887, FASTComTec, Oberhaching, Germany).  
It should be mentioned that the steel surface is not of optical quality in terms of XUV mirrors, which can cause scattering and reduced reflectivity due to surface roughness. In addition, the XUV beam being focused at the steel surface has a large beam size at the position of the detector. This becomes important when analyzing the temporally resolved signal from the Channeltron. Due to its cone shape, various spatial areas are illuminated and secondary electrons experience different accelerating potentials. Hence, the detected signal from the XUV photons is temporally smeared out to cover a range of approximately 30 ns.
## 3 Example Analysis
### Reliability Analysis of Network Attack Detection Model  
According to the actual structure of the CPPS, the abstract topology of the micro cyber-physical fusion system is drawn as shown in Figure 11. This experiment adopts the following assumptions: (1) There is only physical flow interaction between physical nodes, so there is no vulnerability to network attacks, so the interaction risk is 0%; (2) Information nodes will not be attacked by the network when there is collaborative analysis.  
In the figure, \(\{\text{N1},\text{N2},\text{N3},\text{N4}\}\) stands for four physical nodes, \(\{\text{G1},\text{G2}\}\) stands for two generators, \(\{\text{U1},\text{U2},\text{U3},\text{U4}\}\) stands for four data acquisition devices, \(\{\text{I1},\text{I2},\text{I3},\text{I4}\}\) stands for information nodes, \(\{\text{C1},\text{C2},\text{C3},\text{C4}\}\) stands for controllers, the direction of the arrow stands for the direction of data flow transmission, and the weight of the edge stands for the safety factor in the process of data transmission. The figure contains four closed data circulation loops, representing the closed-loop control of the four physical nodes from the measurement data acquisition to the control signal transmission. The process is as follows: \(N1\to U1\to I1\to N1\), \(N2\to U2\to I2\to N2\), \(N3\to U3\to I3\to N3\), \(N4\to U4\to I4\to N4\). Based on the arithmetic average method, the safety factors of the four roads are 0.9509, 0.9571, 0.9452, and 0.9708. Further calculation shows that the safety factor of the whole cyber-physical system is 0.956, that is, the reliability of the network attack detection model is 95.6%. It can help to protect the CPPS from network attacks to a certain extent.  
Figure 11: Complex network topology of a CPPS.
## 4 Study Case System  
The proposed BESS participation model is simulated in the small island grid presented in Figure 6, consisting of (a) a thermal power station comprising 6 \(\times\) 1 MW diesel units (DUs), (b) a 0.5 MW/0.5 MWh BESS, (c) a 0.9 MW wind turbine (WT), (d) a 1 MW\({}_{\rm P}\) PV plant and (e) the system load. The six identical diesel units are modeled according to [63], including the non-linearities of the turbocharged prime movers, and a typical DU governor model (DEGOV1) [64; 65], to capture the frequency response characteristics of the autonomous system. The 0.5 MW/0.5 MWh Li-ion BESS allows for a 1 h discharge time at rated power within its permissible SOC range (\(SOC_{b,min-}-SOC_{b,max+}\)). Details on the battery model used can be found in [66]. The battery cycles are used as a simple degradation indicator in time-domain simulations, calculated based on the energy throughput of the batteries, as in [56]. A SOC variation from \(SOC_{b,max+}\) (90%) to \(SOC_{b,min-}\) (20%) and back is equivalent to one full cycle.  
A pitch controlled, full-power converter WT is considered, whose MPPT module is based on a static optimum power-speed curve and a PI speed controller [67; 68; 69]. The PV system is simulated as in [22] and operated in MPPT mode. The load damping is considered negligible, due to the small scale of the electrical system [70]. The system parameters used for the simulations are summarized in Appendix A (Tables A1 and A2).
## 3 Application to Immersed Tunnel
### Boundary Conditions and Input Load
#### 3.3.3 Hydrodynamic Pressure  
When an immersed tunnel is vibrated by an earthquake, there is an interaction between the seawater and immersed tunnel. At this time, the seawater generates dynamic water pressure on the immersed tunnel; thus, the seismic response analysis of the immersed tunnel is actually a 3D fluid-solid coupling dynamic calculation problem of the structure-soil-fluid interaction system, but the calculation scale considering fluid-solid interaction is huge. In this paper, the additional mass method is used instead of the fluid FEM to simulate the hydrodynamic action of the overlying water. The hydrodynamic pressure of the fluid acting on the surface of the site or immersed tube is approximately equal to the product of the fluid mass per unit area and the acceleration at the active surface, i.e., the additional mass is equal to the mass of the water column above the active area [42]. The direction of the additional mass is the normal direction of the acting surface. As shown in Figure 10, the additional mass acting on the \(i\)-node on the horizontal plane is the \(z\)-direction, and the additional mass acting on the \(j\)-node on the slope is the normal direction of the slope.  
ABAQUS uses the additional mass element to simulate the hydrodynamic pressure through the additional mass method. Firstly, the additional mass on the action surface is calculated. The surface pressure of the additional mass field distribution is applied on the action surface, and the reaction force of each node on the action surface is inversely calculated. Then, the mass matrix is assembled by the node reaction force, and the additional mass element is customized by combining the user element provided in ABAQUS. Finally, the additional mass element is coupled with each node of the action surface, and the simulation of additional mass in ABAQUS can be realized.
## 3 Methods and Fault-Slip Data
### FSA and Win-Tensor  
The driving best-fit stress tensors, determined by [32], were calculated with the aid of the FSA software ([42], and subsequent modifications) and the Win-Tensor software [43], by taking into account MA \(\leq 30^{\circ}\) in SSs shown in Figure 1b. The FSA software first calculates many reduced stress tensors through a random grid search, following the Monte Carlo search method of [1]. The Win-Tensor software initially estimates the parameters of a reduced stress tensor through an improved version of the Right Dihedra method. Afterwards, it defines the optimal stress tensor through an iterative rotational stress optimization process that further minimizes the slip deviations and favors slip on the fault planes [77].  
Regarding the application of the Win-Tensor software on their database, the parameters for assessing the stress state's quality are the same as the FSA software, i.e., the MA and its distribution (ideally unimodal) and the high-shear/normal stress ratio. We have to mention that the ratio of shear stress (\(\tau\)) to normal stress (\(\sigma_{\rm n}\)) on the fault plane is critical for its (re)activation, and it is called slip tendency T\({}_{8}\)[78].
## 4 Discussion  
This work is the first demonstration of CT and MRI quantitative imaging application combined to radio-immunotherapy treatments in the treatment of naive breast tumors, where the outcome is evaluated by histopathology. The use of a syngeneic animal model allows correlation with the actual number of surviving tumor cells rather than a surrogate for treatment response such as tumor volume. This critical point is illustrated by our findings that tumor volumes do not correlate with Ki-67 proliferation marker.  
The superior MRI soft tissue contrast allows better delineation of tumors when fused to CT imaging studies. MRI results corroborated with the CT results, as opposed to current clinical radiomics publications on immunotherapy, where only CT-based features were utilized [11; 12; 13; 14]. Besides the improved visualization capabilities, the presented results indicate that more radiomics features from the MRI studies correlate with the post-treatment immunohistochemistry in comparison to the CT-derived radiomics features from the same tumors. Therefore, MRI complements CT not only for visualization purposes, but also for quantitative imaging investigations. The MRI imaging of the five animals was performed in two consecutive sessions, each of approximately \(\sim\)30 min. The CT imaging followed immediately after the MRI image acquisition. The same hardware (scanners) with the same imaging protocols were used for both MRI and CT imaging of the two batches of animals. Therefore, the variability of the imaging features due to the acquisition technique and the processing equipment is very minimal.  
Since subcutaneous tumors were derived from the same cell line, the observed variations in the imaging features cannot be attributed to tumor genotype, which existence affects treatment outcomes in clinical studies. Instead, the variations in the radiomics features presented herein depict the changes in the tumor phenotype and in particular the tumor heterogeneity, which are probably due to the effects of the tumor microenvironment as well as the specific host traits [22].
## 3 Results and Discussion
### Eor
#### 3.4.4 Static Adsorption Experiments  
Adsorption of AOT alone or combined with the polymer, as a function of initial surfactant concentration, is shown in Figure 6. It can be observed that the adsorption density increased with the initial surfactant concentration. This behavior is in agreement with other adsorption studies on sandstone rocks [24; 25; 41].  
Experimental data of adsorption isotherms were fit to Langmuir and Freundlich models. The fit parameters and standard deviations are shown in Table 5.  
According to the standard deviations (Table 5), the Freundlich model describes the adsorption equilibrium much better than the Langmuir model. Consequently, multilayer adsorption is assumed for both AOT and the AOT + PEG-600 eutectic. Adsorption data are presented as a function of equilibrium concentrations in Figure 7 to show the fit of the Freundlich model to the experimental data. Interestingly, Figures 6 and 7 show that the amount of adsorbed surfactant was significantly lower when the surfactant was used as a part of the eutectic mixture. This phenomenon has been previously reported in the literature for flooding with combinations of surfactant and polymer [42]. When the polymer flows in front of the surfactant slug, the rocky formation is covered by the polymer and fewer sites are available for the surfactant adsorption to occur (competitive adsorption).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Standard Deviation (mg/g)** \\ \hline Pseudo-first order & K1 (h\(-1\)) = 0.220 & 0.182 \\ Pseudo-second order & K2 (h\(-1\)) = 0.337 & 0.240 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters and standard deviation of kinetics models for adsorption of the optimized formulation on sandstone rock.  
Figure 6: Adsorption of the eutectic mixture (red, shaded columns) and of pure AOT (black columns) in crushed sandstone as a function of the initial surfactant concentration.  
adsorption of ionic surfactants is mainly dependent on the nature of the rock surface and the chemical structure of the surfactant. Sandstone rock is composed of quartz and silicate-like minerals, which are negatively charged components. AOT is an anionic, negatively charged surfactant, so repulsion between the rock surface and the surfactant prevents adsorption from occurring [43]. Equilibrium adsorption values ranged from 1.7 to 4.8 mg of surfactant adsorbed per gram of rock (Figure 6), values relatively high but expected, due to the small size of the sandstone particles used in the study. Similar values have been previously reported [44].
## 4 Decision Support System
### Expected Annual Damage (EAD)  
Risk-based assessment methods assist decision-makers in envisaging the spatial and temporal risk distributions of floodplains. A probabilistic event [57] is flood damage, and flood intensity varies greatly over the floodplain. To date, the spatial distribution of risks and the benefits of flood mitigation measures have seldom been taken into account [58]. The average level of damage that could be expected annually due to the probabilistic nature of floods and flood losses is the expected annual damage (\(EAD\)). [PERSON] et al. [59] used an approach to evaluate the feasibility of life cycle cost (LCC) and return on investment (ROI) analysis strategies and found that by reducing expected annual damage, the projects produced net welfare gains. Damage curves and maps of \(EAD\) distribution provide an extensive illustration of the distribution of risk. [PERSON], [PERSON] et al. [60] used risk curves on a basin and subbasin basis to represent the temporal variability of flood risk for decision making. To discover the lateral trends in risk along the river, \(EAD\) can also be calculated across the flow. Therefore, decisions based on \(EAD\) have benefits over conventional approaches. \(EAD\) provides the risk estimate and shows how far from achieving the optimal risk point (\"ORP\") the current flood management practice is.  
In the 1990s, to develop a hydroeconomic model for \(EAD\) estimation [57], the US Army Corps of Engineers used the relationships between damage, frequency, discharge, and stage. If flow conditions and societal vulnerability remain unchanged, these correlations function well. Structural measures substantially alter the flow regime and flood behavior. Once the flow regime is changed, relationshipspreviously developed are no longer applicable. Therefore, a hydroeconomic model cannot be used under such conditions. In order to handle such changes appropriately, detailed analysis using GIS data and a 2D hydrodynamic model is required. Based on the actual conditions of the floodplain, the method supports _EAD_ calculation. In addition to their potential benefits and proposed uses, the concepts of _EAD_, damage curve, and _EAD_ distribution map have been introduced (see Equations (8) and (9)).  
\[EAD=\sum_{i=0}^{i=\infty}D_{i}\times\Delta P_{i} \tag{8}\]  
\[D_{i}=\frac{D_{P_{i-1}}+D_{P_{i}}}{2} \tag{9}\]  
It is important to note that the term \"expected\" is used instead of \"average\". The reason is that the damages or losses are probabilistic in nature and are calculated using the exceedance of probability fittings to estimate the hazard and the resulting damage [61].
# Influence of Perforated Soils on Installation of New Piles
## 1 Introduction  
Many cities in Japan and Southeast Asia are located on soft soil, and thus, many of the structures built on this type of soil have piles. In recent years, there has been an increasing demand to replace ancient structures that were built during the period of high economic growth in Japan. The removal of such structures also involves the removal of the piles (the existing piles) that supported the structures, and the backfilling of the pulling-out holes formed during the removal.  
In Japan, these existing piles are categorized as industrial waste and must be removed from the soil in a reliable manner. For the same reason, it is not appropriate to break or to leave the existing piles or any part of them in the soil. The main method for removing an existing pile, without crushing or leaving it in the soil, is to pull it out. However, when an existing pile is removed by pulling it out, a pulling-out hole is formed [1; 2; 3; 4]. If this pulling-out hole is left hollow, settlement will occur in the surrounding soil. On the other hand, it has been reported in past research that the settlement in the surrounding soil can be suppressed by filling in the pulling-out hole. In other words, as the soil in which a pulling-out hole remains may deteriorate, the backfilling of the pulling-out hole is essential [5; 6; 7].  
However, there are no standard guidelines for the backfilling of pulling-out holes [1; 2; 3; 4]. At present, therefore, each owner or contractor must determine the material and the construction method to use for backfilling. This results in a difference between the mechanicalproperties of the original soil and those of the soil that has been backfilled, namely, the soil on which a newly built structure will be constructed.  
In this study, it is assumed that a new pile will be installed on a perforated soil, (that is, the soil left after removing the existing piles) where the mechanical properties differ between the original soil and the backfilled soil. More specifically, the behavior of a new pile, when installed on the boundary of a soil between two types of mechanical properties, is evaluated by a three-dimensional linear elastic analysis.
## 4 Discussion
### Implications of the Study  
To our knowledge, this study presents the first attempt to explore the mangrove dynamics in a prograding delta setting by integrating UAV and multiple sources of satellite imagery (L7, L8, S1, and S2) in GEE. Studies to classify mangrove forest have been conducted in the last two decades [8; 10]. They were based not only on one source but also on the combination of multiple satellite sources [12; 14; 103; 104] and became more popular with the advent of GEE. The resulting three-monthly classification maps are more frequent than the commonly produced annual mangrove maps. The frequent monitoring is deemed necessary in this environmental setting, since the sediment influx continues at an unprecedented rate, promoting rapid delta development and mangrove belt expansion. The results of the UAV and the entire methodology presented here shows the advantage of using an off-the-shelf drone. The CHM and individual tree locations present important structural attributes to characterise the mangrove forest. It is essential as there is likely a much closer linkage between diameter at breast height (dbh) and height rather than between crown radii and dbh. Accurate information of mangrove biophysical structure is critical for the ecologist, coastal manager, or policymaker.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
###### Abstract  
The transportation industries forecast that by 2050 more than 50% of vehicles on the road will be autonomous vehicles, and automotive services will dynamically support all vehicles. All of them will be serviced using the latest technology, which includes the Software Defined Network (SDN) and available new generations (5G+ or 6G) at the time. Although many transportation services and rapid facilities are achievable dynamically, transportation services with automation and intelligent actions are still not mature because the legacy of transport services cannot be corporate with the 5G+. These expected problems can be improved through the following possible and manageable approaches: flexible framework of 5G automotive services from the legacy systems, designing energy-efficient and intelligent infrastructures with SDN, and managing security solutions that evolve with the emerging technology. An efficient model (flexible framework) is proposed to secure smart transportation services with a secure and intelligent connected system and security solutions based on the 5G concept. Although 5G is considered in this framework, the method and steps of design and solution phases will be adaptable to the 5G+ framework. Furthermore, the basic properties of SDN allowed us to design a novel approach for measuring data traffic related to transport services and transport management, such as the priority of the transportation services. With the emergence of 5G+ capabilities, transportation services expect more challenges through future user requirements, including dynamic security solutions, minimum latency, maximum energy efficiency (EE), etc. Future automotive services depend on many sensors and their messages received through secure communication systems with 5G+ capabilities. As a result, this theoretical model will prove that 5G capabilities provide security facilities, better latency, and EE within the transportation system. Moreover, this model can be extendable to improve the 5G+ transportation services.  
transportation; SDN; autonomous vehicles; 5G network; traffic services Article