esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Details
### Dataset  
In this study, we aim to predict the near-wake transverse velocity in laminar flow over a circular cylinder by using a neural network with instantaneous wall pressures on the cylinder surface as the input variables. The transverse velocity in the wake of a circular cylinder has been considered a good indicator for the state of Karman vortex shedding [27; 28; 29; 30; 31]. In this regard, one of the motivations for constructing neural networks to predict the transverse velocity is to further integrate them, in future studies, with active feedback control methods such as proportional-integral-derivative (PID) control [27; 29], whose control purpose is to mitigate the strength of the Karman vortex shedding. Figure 1 shows learning dataset domains for neural networks at \(Re=60\) and \(300\). As shown in Figure 1a for the two-dimensional flow at \(Re=60\), the wall pressures \(p_{wall}\) at \(44\) points on the cylinder surface, ranging from \(\theta=-90^{\circ}\) to \(90^{\circ}\), constitute the input variables for the learning dataset. Here, \(\theta\) represents an angle measured from the base (\((x/d,y/d)=(0.5,0)\)) of the cylinder. The output variables for the learning dataset consist of the transverse velocities \(v\) in the ranges of \(-0.8\leq x/d\leq 2.7\) and \(-0.8\leq y/d\leq 0.8\), encompassing a total of 8192 data points. On the other hand, Figure 1b shows learning dataset domains for input and output variables for the three-dimensional flow at \(Re=300\). As shown, the number of input variables for \(p_{wall}\) is \(44(\theta)\times 128(z)\) on the \(\theta\)-\(z\) plane of the cylinder surface. Here, the locations for \(\theta\) and \(z\) on the \(\theta\)-\(z\) plane range from \(\theta=-90^{\circ}\) to \(90^{\circ}\) and from \(z/d=0\) to \(12\), respectively. The number of output variables for the transverse velocity \(v\) is \(44(x)\times 128(z)\) on the finite center plane (\(y=0\)) of \(0.5\leq x/d\leq 1.6\) and \(0\leq z/d\leq 12\) in the wake of the cylinder. For both \(Re=60\) and \(300\), we note that the wall pressures for the input variables are instantaneous ones.  
As described in Section 2.1, input and output datasets for training neural networks are obtained from numerical simulations of flow over a circular cylinder at \(Re=60\) and \(300\). Consistent with the governing equations in Equations (1) and (2), we adopt dimensionless input and output variables for neural networks, which are non-dimensionalized with the freestream velocity and fluid density. From the numerical simulation of the two-dimensional flow at \(Re=60\), we collect \(3000\) and \(1000\) instantaneous flow fields as training and testing data, respectively. On the other hand, from the numerical simulation of the three-dimensional flow at \(Re=300\), we collect \(40{,}000\) and \(5000\) instantaneous flow fields as training and testing data, respectively. As flow structures become more complex with increasing Reynolds numbers, we use a larger number of instantaneous flow fields for constructing neural networks at \(Re=300\) compared to that at \(Re=60\).
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks
#### 4.1.7 CN-SFC Framework  
[PERSON] et al. [76] presented an SDNLess SFC microservice architecture for Cloud-Native NFV, a framework for cloud-native SFC creation which uses an advanced version of network service mesh (NSM) [[https://networkservercemesh.io/](https://networkservercemesh.io/)]([https://networkservercemesh.io/](https://networkservercemesh.io/)) (accessed on 27 December 2021) and Kubernetes ([[https://kubernetes.io/](https://kubernetes.io/)]([https://kubernetes.io/](https://kubernetes.io/))) (accessed on 2 January 2022) for chaining cloud-native elements to form a service chain. They considered the use of SFCs in microservice architectures and addressed the shortcomings of the NSM architecture, such as its inability to handle L2 or L3 traffic and the lack of support for advanced routing algorithms.  
The cloud-native framework, CN-SFC, was also used to achieve traffic steering by efficiently load-balancing traffic across the Cloud-native functions (CNFs). An approach was introduced, which uses the weighted round robin algorithm by maintaining the weights of Kubernetes pods and distributing traffic based on pods with high weight values. TS-CNF, which is a traffic steering problem, was modelled and solved as an integer programming (IP) problem.  
In terms of the evaluation of CN-SFC, a Kubernetes cluster was used, which consists of an NSM control plane, a master node, and two worker nodes. The Docker and the Kind [[https://kind.sigs.k8s.io/](https://kind.sigs.k8s.io/)]([https://kind.sigs.k8s.io/](https://kind.sigs.k8s.io/)) (accessed on 28 December 2021) framework were used to run the cluster. The proposed network-aware traffic steering scheme (NA-TS) was evaluated by varying the number of flows that need access to the network service in the cluster. The number of replicas that were used for deploying the VPN and firewall services were also varied, while UDP and ICMP (to evaluate reachability) packets were generated and evaluated (jitter and packet loss in the service chain) between the two assigned pods and those deployed in the cluster. Additional metrics such as the latency of the network and service instantiation time were also evaluated.  
Figure 10: Metron framework overview.
## 3 Results and Discussion
### Differences in TPC, TFC, and TPAC  
As shown in Table 1, the TPC, TFC, and TAPC showed a similar trend, decreasing in the order of UL > BTL > GTL. Polyphenols, specifically flavonoids and flavonoid glycosides, are the main active components in _P. chinense_ Pursh leaves. UL had the highest polyphenol (179.34 mg/g DW), flavonoid (71.93 mg/g DW), and proanthocyanidin content (65.91 mg/g DW) among the three types of _P. chinense_ Pursh leaves. From fresh leaves to green and black tea, the total polyphenol contents decreased by 22.55% and 45.84%, the total proanthocyanidin contents decreased by 16.75% and 17.87%, and the total flavonoid contents decreased by 12.39% and 44.29%, respectively. During the processing of tea, high-temperature roasting or fermenting accelerated the transformation of polyphenols and flavonoids, resulting in a sharp decrease in content, consistent with previous research [8]. In addition, the TPC and TPC of GTL were significantly higher than those of BTL. Green tea is processed at high temperatures to inhibit enzymatic oxidation and prevent the oxidation of polyphenols. However, in the fermentation of BTL, phenolic substances are continuously consumed due to the oxidation, polymerization, and condensation of polyphenols. Therefore, BTL had a lower polyphenol content than GTL. Studies also indicated that the bitterness and astringency of tea decreased with the polyphenol content after high-temperature treatment [21], making BTL more suitable as a tea drink.
# SACT: A New Model of Covert Communication Based on SDN
## 6 Discussion  
Through the experimental results, we can know that in the experimental environment we set up, the SACT system operates stably and efficiently as we expected. In methodology, we took into account the actual environment and made corresponding analysis of theoretical. Our experimental environment also simulates the real environment as much as possible, including network jitter, delay distribution and so on. However, there are still some aspects that we need to think about and discuss:  
1. NTP synchronization is simple and efficient with mature processing method clock skew. However, the security issues of NTP servers have not been fully resolved. We are considering finding more secure and efficient synchronization methods in the future, or making improvements to NTP servers, such as adding a blockchain mechanism.
2. Algorithms of convolutional encoding and decoding can be further optimized. Furthermore, we will promote the system in the future.
## 5 Experiments  
The proposed concepts and algorithms were verified with experiments performed in one of the Warsaw University of Technology laboratories and a typical fully furnished flat. The experiment consisted of two phases. First, an exemplary ranging sensor available on the market--VL53L1X by ST Microelectronics was tested to verify whether its properties allow for use in the proposed hybrid localization scheme. This part was conducted in laboratory rooms. The second part of the experiment consisted of using the aforementioned sensors and a BLE-based localization system developed within the IONIS project [37] to verify the proposed concept in an apartment.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 7 End-User Study
### Observations  
We have derived additional findings from the observations and notes made by the study leader:  
1. When the study participants from both groups G1 and G2 were modeling the provided IoT-driven BP with BPMN 2.0, the study leader learned that the frustration level had been very high. This is noticeable as most study participants expressed their displeasure or disappointment about modeling the IoT-specific behavior with BPMN 2.0, which also affected their performance (cf. Figure 21). Most participants struggled to find proper workarounds to capture the involved IoT devices and IoT-specific process behavior. Moreover, they captured IoT involvement in terms of a black or white box pool. For communication purpose, the study participants used message events or tasks. While they applied service, script and message tasks for sensors and actuators, they either used conditional events or loops via XOR gateways for modeling the IoT conditions. Regarding the BPMNE4 IoT modeling framework, it is noteworthy that none of the study participants used pools when modeling IoT-driven BPs, as unique modeling element exists for capturing sensor and actuator behaviors (cf. Section 5), meaning IoT involvement no longer needs to be represented by pools.
2. Prior knowledge of BPMN 2.0 and the IoT domain was advantageous. Study participants with knowledge of IoT-driven BPs found it easier to model the given IoT-driven process according to the textual instructions. They also found it more accessible and required less time. Moreover, these study participants experienced a lower workload than others (cf. Table 6--BPMN Modeler _Best_ value). This can be explained with the fact that study participants with prior knowledge in IoT-driven BPs already knew which BPMN 2.0 modeling elements can be used to express IoT participation (service, script, business rule and message tasks or events and pools) and IoT-specific behavior (e.g., continuous pull interaction and IoT conditions). Finally, study participants with prior knowledge of IoT-driven BPs in BPMN 2.0 reported a lower task load when using BPMNEAIoT than when using BPMN 2.0 despite their non-familiarity with BPMNE4 IoT.
## 2 Preliminaries
### Distance-Hereditary Graphs
: _Let \(G_{1}\) and \(G_{2}\) be disjoint distance-hereditary graphs. If \(G=G_{1}\bigoplus G_{2}\), then_  
1. \(C(G[TS(G)])=C(G_{1}[TS(G_{1})])\)_;_
2. \(C(G)=C_{\overline{T}}(G_{1})\cup C_{\overline{T}}(G_{2})\cup C_{X}(G)\)_;_
3. \(C_{T}(G)=C(G[TS(G)])\)_; and_
4. \(C_{\overline{T}}(G)=C_{\overline{T}}(G_{1})\cup C_{\overline{T}}(G_{2})\)_._  
**Lemma 4** ([22]). : _Let \(G_{1}\) and \(G_{2}\) be disjoint distance-hereditary graphs. If \(G=G_{1}\bigoplus G_{2}\), then_  
1. \(C(G[TS(G)])=C(G_{1}[TS(G_{1})])\)_;_
2. \(C(G)=C_{\overline{T}}(G_{1})\cup C_{\overline{T}}(G_{2})\cup C_{X}(G)\)_;_
3. \(C_{T}(G)=\emptyset\)_; and_
4. \(C_{\overline{T}}(G)=C(G)\)_._  
**3. The \((p,d,\ell,b)\)-Clique Transversal Problem**  
Let \(G\) be a graph and \(b\in\mathbb{Z}\). Suppose that \(p\in\{0,-1\}\) and \(d,\ell\in\mathbb{Z}^{+}\) are fixed. A function \(f:V(G)\to\{p,p+d,\ldots,p+(\ell-1)\cdot d\}\) is a \((p,d,\ell,b)\)-clique transversal function of \(G\) if \(f(C)\geq b\) for every \(C\in C(G)\). The minimum weight of a \((p,d,\ell,b)\)-clique transversal function of \(G\) is defined as \(\tau_{C}(p,d,\ell,b)(G)\). The \((p,d,\ell,b)\)-clique transversal problem is to find a \((p,d,\ell,b)\)-clique transversal function of \(G\) with minimum weight. **Remark 1**. : _Suppose that \(G\) is a graph and \(b\) is an integer. Let \(p\in\{0,-1\}\) and \(d,\ell\in\mathbb{Z}^{+}\) be fixed. If \(b>p+(\ell-1)\cdot d)\cdot\omega(G)\), then \(G\) has no \((p,d,\ell,b)\)-clique transversal function. If \(b<p\cdot\omega(G)\), then \(\tau_{C}(p,d,\ell,b)(G)=p\cdot|V(G))|\)._  
**Lemma 5**. : _Let \(G\) be a graph. Then,_  
1. \(\tau_{C}^{k}(G)=\tau_{C}(0,1,2,k)(G)\)_;_
2. \(\tau_{C}^{\{k\}}(G)=\tau_{C}(0,1,k+1,k)(G)\)_;_
3. \(\tau_{C}^{\{\infty\}}(G)=\tau_{C}(-1,2,2,1)(G)\)_; and_
4. \(\tau_{C}^{\{\infty\}}(G)=\tau_{C}(-1,1,3,1)(G)\)_._  
Proof. : The statements can be easily verified according to their definitions. Lemma 5 demonstrates that the \(k\)-fold clique, \(\{k\}\)-clique, minus clique, and signed clique transversal problems are particular cases of the \((p,d,\ell,b)\)-clique transversal problem. The formulation provides a unified approach to solving these four clique transversal problems. Additionally, following Remark 1, we focus the problem on a distance-hereditary graph \(G\) for \(b\) within the range \(p\cdot\omega(G)\leq b\leq(p+(\ell-1)\cdot d)\cdot\omega(G)\).
## 2 Materials and Methods
### Methods  
#### 2.3.1 Tests on Natural Materials  
The mineralogical analysis was carried out on air-dried samples which were crushed into 63 \(\upmu\)m fine powder. X-ray diffraction analysis was carried out using Brucker D8-Eco diffractometer emitting Cu K-alpha radiation wavelength (\(\lambda\)), 1.54 A, energy of 40 kV and 45 mA, and 20 from 0\({}^{\circ}\) to 70\({}^{\circ}\) at the AGES (Argiles, Geochimie et Environnement sedimentaire) laboratory of the University of Liege, Belgium. The mineral proportions were quantified from X-ray diffraction patterns using the Rietveld refinement method [55]. The geotechnical tests were carried out at the National Civil Engineering Laboratory of Cameroon (LABOGENIE), Yaounde, Cameroon. Measuring the natural moisture content, which consists of recording successive weights of the soil samples before and after drying in an oven at 105 \({}^{\circ}\)C was carried out in accordance with the NF P 94-050 standard [56]. The specific gravity (ys) was obtained using the helium gas pycnometer method in accordance  
Figure 2: Clay-rich lateritic soil profiles in the Bangangte study area. A, humorious horizon; B, mineral-rich horizon.  
with the NF P94-054 standard [57]. The bulk density (\(\gamma\)) was determined using the hydrostatic method following the NF EN 1936 standard [58]. The particle size distribution was carried out using the sieve and hydrometer analysis based on the NF P 94-056 and NF P 94-057 standards [59; 60]. The determination of the Atterberg limits was conducted to measure the plasticity of soil materials. It was carried out according to the NF P 94-051 standard [61]. The Atterberg limits test is represented by liquid limit (LL), plastic limit (PL), and plasticity index (PI). The obtained parameters allowed the classification of the studied materials according to the chart developed by the Highway Research Board (HRB) and the Casagrande chart [7]. The methylene blue test (MBT) was carried out in accordance with the NF P 94-068 standard [62]. The MBT enabled the quantification of the ionic absorption capacity of a soil by measuring the quantity of methylene blue necessary to cover the external and internal surfaces of the clay particles contained in the soil. The modified Proctor test was carried out in accordance with the NF P94-093 standards [63], which enabled the determination of the maximum dry density (MDD) and optimum moisture content (OMC) of the investigated soils.  
The soaked CBR (SCBR) test was conducted on the soil sample according to the NF P 94-078 standard [64]. This test measures the resistance of soil materials under controlled moisture content and density by the penetration of a cylindrical plunger with a cross-sectional area of 1935 mm\({}^{2}\). The SCBR was calculated as the ratio of the force required to push the plunger to a certain depth into soil materials and the force required to push the same plunger at the same depth into a standard sample of compacted crushed stone (2.5 mm and 5 mm of penetration) and calculated as shown in Equation (1).  
\[CBR=\frac{Applied\ load}{Standard\ load}\times 100 \tag{1}\]
## 5 Solving Strategy Based on NSGA3  
The above model is a nonlinear, multiperiod, and multiobjective optimization problem. In a multiobjective optimization problem, the relationship between optimal solutions is usually nondominated, and there are a few cases where one optimal solution dominates all other feasible solutions. Therefore, the optimal solution of the optimization problem is usually a set of solutions, called the nondominated solution set or the Pareto optimal solution set.  
In the two-objective optimization problem, the nondominated sorting genetic algorithm 2 (NSGA2) with the crowding distance strategy is usually adopted [42; 43; 44]. However, in the face of multiobjective optimization problems of three or more objectives, if we continue to use the crowding distance of NSGA2, the convergence and diversity of the algorithm will be problematic, such as an uneven distribution of the Pareto solution on the nondominated layer, resulting in the algorithm falling into a local optimum. Therefore, the reference point-based many-objective evolutionary algorithm (NSGA3) is proposed. The framework of the NSGA3 is basically the same as that of the NSGA2, except that the selection mechanism is different. The NSGA2 uses crowding distances to select individuals with the same nondominated level, while the NSGA3 uses a reference point-based approach [45; 46] to select individuals.
## 5 Overall Strategy and Readiness Level
### Overall Land Cal/Val Strategy
* Database and tools--The final stage of an operational Cal/Val system consists of providing the users with a consistent and fully automated tool, including satellitesub-settings, and embedded analytics for satellite products comparison with reference data using standardised procedures. This is the framework originally developed by CEOS-LPV with the OLIVE tool [175], which is now being replicated to other terrestrial ECVs [178]. A centralised database with associated tools is key to enhance uptake of Cal/Val data within the community and for fostering the adoption of community-agreed standardised procedures.
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 3 Error Transmission Theory
### Error Transfer from Geographic to Space Rectangular Coordinate Systems  
For the geographic coordinate system coordinates \(A_{n}=\left(x_{n},y_{n},z_{n}\right)^{T}\), the UAV geodetic coordinates \(A_{gfc}=\left(B,L,H\right)^{T}\sim N(\mu_{gfc},P_{gfc})\), where \(\mu_{gfc}=\left(\overline{B},\overline{L},\overline{H}\right)^{T}\); the diagonal matrix \(P_{gfc}=diag(\rho_{\overline{B}}^{2},\rho_{L}^{2},\rho_{H}^{2})\) can be obtained by transforming the geographic coordinate system \(A_{e}=\left(x_{e},y_{e},z_{e}\right)^{T}\).  
The coordinate transformation is represented as follows:  
\[\begin{bmatrix}x_{e}\\ y_{e}\\ z_{e}\end{bmatrix}=\begin{bmatrix}\cos(L)&\sin(L)&0\\ -\sin(L)&\cos(L)&0\\ 0&0&1\end{bmatrix}^{-1}\begin{bmatrix}-\sin(B)&0&\cos(B)\\ 0&1&0\\ -\cos(B)&0&-\sin(B)\end{bmatrix}^{-1}\begin{bmatrix}x_{n}\\ y_{n}\\ z_{n}\end{bmatrix}_{\mu_{gfc}=\left[\overline{B},\overline{L},\overline{H} \right]}+\begin{bmatrix}x_{\sigma_{0}}\\ y_{\sigma_{0}}\\ z_{\sigma_{0}}\end{bmatrix} \tag{10}\]  
\[P_{e}=(I_{n}^{e})P_{gfc}(I_{n}^{e})^{T} \tag{11}\]  
where \(\begin{bmatrix}x_{\sigma_{0}}&y_{\sigma_{0}}&z_{\sigma_{0}}\end{bmatrix}^{T}\) represents the translation distance from the spatial Cartesian coordinate system to the origin of the geographic coordinate system.  
\[J_{n}^{e}=\begin{bmatrix}\frac{\partial x_{e}}{\partial B}&\frac{\partial x_{ e}}{\partial L}&\frac{\partial x_{e}}{\partial H}\\ \frac{\partial y_{e}}{\partial B}&\frac{\partial y_{\sigma}}{\partial L}&\frac {\partial y_{\sigma}}{\partial H}\\ \frac{\partial z_{\sigma}}{\partial B}&\frac{\partial z_{\sigma}}{\partial L}& \frac{\partial z_{\sigma}}{\partial H}\end{bmatrix}_{A_{gfc}=\mu_{gfc}} \tag{12}\]
## 3 Data and Methodology
### Methodology
#### 3.2.2 Dynamic FSM
Consequently, the dynamic FSM could largely solve the problem of an incomplete collection of keywords. In addition, because of the uncertainty of the threshold setting of the bidirectional FSM, the dynamic FSM designed in this study discarded the threshold setting and sorted all types of entity labels in an address according to the global FSM set, even when the address was missing or disordered. Therefore, an FSM set corresponding to this address was obtained.
## 4 Proposed PSO Variant
### VAINDIWPSO Algorithm
#### 4.1.1 Improvements Nonlinear Dynamic Inertial Weights  
Because the PSO algorithm has the trait of fast convergence, the algorithm effortlessly falls into the local optimum in the later stage of iteration, so the optimal solution cannot be obtained. The linear inertia weight update approach cannot efficiently resolve this problem [39], so the nonlinear dynamic inertia weight is delivered [40], as (17) shows:  
\[\omega(k+1)=(\omega_{\max}-\omega_{\min})\times(\frac{\omega(k)}{\omega_{\max }})^{\frac{k}{\max}} \tag{17}\]  
It can be seen from (17) that \(\omega(k)\) is descending alongside the nonlinear curve, which reduces the probability of the algorithm falling into the local optimum. However, due to the complicated terrain environment throughout path planning, an improved nonlinear dynamic inertia weight is introduced, such as (18):  
\[\omega(k+1)=(\omega(k)-\omega_{\min})\times(\frac{\omega_{\min}}{\omega_{\max }})^{\frac{k^{2}}{\max^{2}}} \tag{18}\]  
Compared with (17), in (18), the variable \(\omega(k)\) is placed in the coefficient term, and the \(\frac{k}{\max}\) term is squared so that the inertia weight can rapidly converge to meet the real-timenecessities of the algorithm for the system. For further optimization, a random disturbance term is delivered based on (18), and (19) is introduced again:  
\[\omega(k{+}1)=(\omega(k)-\omega_{\text{min}})\times(\frac{\omega_{\text{min}}}{ \omega_{\text{max}}})^{\frac{1}{\tau_{\text{max}}^{2}}}+R(k) \tag{19}\]  
Among them, \(R(k)\) is a disturbance term whose range is a uniformly distributed random number \([-\omega_{\text{min}}/2,\omega_{\text{min}}/2]\).  
In the algorithm solution process, the inertia weight suggests a general downward trend. It cannot be proven that the inertia weight in the subsequent iteration ought to be smaller than the preceding one, and the algorithm solely explores locally in the later stage and cannot precisely resolve the optimal solution. Therefore, the inertia weight is improved to make the nonlinearity decrease, and random disturbances are added, as shown in (19), so that tiny oscillations take place during the iterative update process, and when the algorithm falls into a local optimum, it can rely on this tiny oscillation to leap out of the local exploration state.