esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 UAV-Assisted IoV and Blockchain Solutions for Smart Cities
### Blockchain-Based UAV Network for Mobile Edge Computing  
Edge computing is a \"counterpart\" for the costs and issues of cloud computing. Physical proximity to devices can ensure lower latency and higher bandwidth. Likewise, it opens a new layer of privacy policy enforcement via the edge server before the data is released into the cloud service or storage.  
Mobile Edge Computing (MEC) is similar but uses any sort of device that can be connected to 5G or other wireless networks and can be moved locally or geographically in a predetermined or even random manner.  
UAVs seem uniquely tailored to such networks once they can use the air and not only a surface to move, and can carry out wireless connection, sensors, data, and more. They can be especially helpful in emergencies such as natural disasters, where the stationary or ground infrastructure is destroyed or incapable of responding. Therefore, in such situations, Blockchain can be used in MEC architectures if it can ensure that UAVs will trust each other, and it will be possible to build a flat architecture where other services can rely on with lesser concerns.  
Lastly, Blockchain, along with smart contracts and transaction models, can be used to facilitate, but not only, the following: data deliverance, content sharing, and casting, and caching between the users connected to edge server nodes or caching servers (which can be a UAV network above in the air).
###### Abstract  
A sound radiation calculation method by using dominant modes is proposed to predict the sound radiation from a cylindrical shell. This method can provide an effective way to quickly predict the sound radiation of the structure by using as few displacement monitoring points as possible on the structure surface. In this paper, modal analyses of a submerged cylindrical shell are carried out by taking the vibration mode of a cylindrical shell in a vacuum, as a set of orthogonal bases. The modal sound radiation efficiency and modal contributions to sound radiation power are presented, and comparison results show that a few modes dominantly contribute to the sound radiation power at low frequencies. These modes, called dominantly radiated structural modes in this paper, are applied to predict the sound radiation power of submerged cylindrical shells by obtaining the modal participant coefficients and sound radiation efficiency of these dominant modes. Aside from the orthogonal decomposition method, a method of solving displacement modal superposition equations is proposed to extract the modal participant coefficients, because few modes contribute to the vibration displacement near the resonant frequencies. Some simulations of cylindrical shells with different boundaries are conducted, and the number of measuring points required are examined. Results show that this method, based on dominant modes, can well predict the low-frequency sound radiation power of submerged cylindrical shells. In addition, compared with the boundary element method, this method can better reduce the number of required measuring points significantly. The data of these important modes can be saved, which can help to predict the low-frequency sound radiation of the same structure faster in the future.  
submerged cylindrical shell; sound radiation power; dominantly radiated structural modes +
Footnote †: journal: Applied Soft 2020, 10, 3073; doi:10.3390/app10093073
# A Compact Printed Monopole Antenna for WiMAX/WLAN and UWB Applications
## 4 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Frequency** & **2.45(GHz)** & **4.95(GHz)** & **5.80 (GHz)** & **Relative Bandwidth** \\ \hline
[5] & Gain (dB) & \(-0.90\) & 1.58 & 2.20 & 4.80\% \\  & Efficiency & 52.00\% & 75.00\% & 89.00\% & and 18.96\% \\
[19] & Gain (dB) & 3.08 & \(-0.5\) & 0.4 & 23.3\% \\  & Efficiency & 91.07\% & 22\% & 45\% & and 14.03\% \\
[20] & Gain (dB) & \(-0.70\) & 1.60 & 1.65 & \# \\  & Efficiency & \# & \# & \# & \# \\ Proposed & Gain (dB) & \(-3.57\) & 1.61 & 0.95 & 4.08\% \\ antenna & Efficiency & 47.27\% & 95.72\% & 95.58\% & and 64.31\% \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison between the proposed and some existing antennas.
## 5 Discussion
### Causes of Pit Slope Deformation and Failure
#### 5.1.2 Stratigraphic Lithological Distribution  
The eastern pit slope of the Wolong Coal Mine constitutes an anti-inclined layered rock slope, covered with 3-15 m loose Quaternary alluvial pebble-gravel soil layer at the top. The lower strata exhibit significant inclinations, facilitating the percolation of rainwater along the bedding surfaces. Figure 19 reveals that the upper part of the pit slope predominantly comprises medium- and coarse-grained sandstone, characterized by favorable geological properties. In contrast, the lower part consists mainly of sandy mudstone and mudstone interlayered with coal seams, which serve as softer and weaker interlayers. Therefore, the eastern pit slope as a whole constitutes a stratigraphic structure of an \"upper-hard-and-lower-soft\" two-layered configuration. The presence of weak interlayers reduces the strength of the surrounding structure [46]. Initially, the weaker middle and lower strata experience gradual deformation under the influence of gravity, with subsequent deformation extending to the upper strata as it intensifies.  
Figure 30: Deformation and failure mechanism of the eastern pit slope in Wolong Coal Mine. (**a**) Factors influencing slope deformation and failure; (**b**) Freeze–thaw cycles; (**c**) Variation in the shear strain increment; (**d**) Clay minerals expand and soften when exposed to water.
## 2 Dynamic Modeling of Trans-Medium Process
### Dynamic Modeling
#### 2.3.1 Added Mass  
Since the coaxial HAUV can be considered symmetric, both the mass matrix and the inertia matrix of the HAUV are simplified to their principal diagonal matrices based on related literature [16]. \(m=diag\begin{bmatrix}m&m&m\end{bmatrix}\) and \(I=\begin{bmatrix}I_{x}&I_{y}&I_{z}\end{bmatrix}\) are the HAUV's body mass and the moments of inertia, respectively. The fluid around the HAUV is moved by the HAUV as it navigates in the water. The mass of this attached fluid is called added mass (inertia), and it is proportional to the acceleration and angular acceleration of the object. According to Assumption 1, the added mass matrix is also diagonal and can be described as \(m_{a}=diag\begin{bmatrix}m_{i}&m_{\dot{v}}&m_{\dot{w}}\end{bmatrix}\), \(I_{a}=\begin{bmatrix}I_{\dot{p}}&I_{\dot{q}}&I_{\dot{r}}\end{bmatrix}\). \(m_{w}\) and \(I_{w}\) are the total mass and inertia of the vehicle, respectively.  
\[\begin{array}{l}m_{w}=m+k_{s}m_{a}\\ I_{w}=I+k_{s}I_{a}\end{array} \tag{4}\]  
**Remark 1**.: _Based on the design in reference [15], we introduce a linearization coefficient to approximate the variation trend of additional variables. The significance of setting a linear switching coefficient is to avoid the jump mutation of the medium density in the cross-media stage, which approximately represents the variation trend of additional variables and links the HAUV's underwater maneuvering and aerial maneuvering processes together. The linearization coefficient \(k_{s}\) can be expressed as follows:_  
\[k_{s}=\left\{\begin{aligned} 0,z\geq\ h/2\\ (1/2\ -z/h),-h/2<z<\ h/2\\ 1,z\leq-h/2\end{aligned}\right. \tag{5}\]  
_where \(h\) is the height of the vehicle. Obviously, \(k_{s}\) represents the approximate variation law of the hydrodynamic force of the vehicle, and the aerodynamic variation trend is represented by \(k_{sa}\), which is expressed as \(k_{sa}=1-k_{s}\)._
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 4 Quantum Statistical Model with Outliers
### Homodyne Measurement on the Noisy Quantum Gaussian States  
Denote the standard quadrature operators by:  
\[\hat{X}=\frac{1}{\sqrt{2}}\left(\hat{a}+\hat{a}^{\dagger}\right)\text{ and }\hat{P}=\frac{1}{\sqrt{2}i}\left(\hat{a}-\hat{a}^{\dagger}\right).\]  
The homodyne measurement at \(\phi\) is defined by a projection measurement of an observable:  
\[\hat{X}_{\phi}=\hat{X}\cos\phi+\hat{P}\sin\phi. \tag{31}\]  
As is well known in quantum optics, a homodyne measurement at \(\phi\) on the coherent state \(|\alpha\rangle\) gives statistics of the normal distribution whose probability density function is:  
\[\text{P}_{\alpha}(x|\phi)=\sqrt{\frac{2}{\pi}}\exp\left[-2\left(x-\text{Re} \,\left(\alpha e^{-i\phi}\right)\right)^{2}\right]. \tag{32}\]Therefore, denoting \(\theta_{1}=\text{Re}\,\alpha\) and \(\theta_{2}=\text{Im}\,\alpha\), respectively, it is equivalent to the normal distribution:  
\[\mathcal{N}\left(\theta_{1}\cos\phi+\theta_{2}\sin\phi,\frac{1}{2}\right),\]  
where \(\mathcal{N}(\mu,\sigma)\) denotes the normal distribution with the expectation value \(\mu\) and the standard deviation \(\sigma\). More generally, statistics of measurement outcomes of homodyne measurement on the quantum gaussian state \(\rho_{\alpha,\kappa}\) (25) are:  
\[\text{P}_{\alpha,\kappa}(x|\phi)=\frac{1}{\sqrt{2\pi\left(\kappa^{2}+1\right) }}\exp\left[-\frac{1}{2\left(\kappa^{2}+\frac{1}{4}\right)}\left(x-\text{Re} \,\left(\alpha e^{-i\phi}\right)\right)^{2}\right]. \tag{33}\]  
This identifies statistics of the normal distribution:  
\[\mathcal{N}\left(\theta_{1}\cos\phi+\theta_{2}\sin\phi,\sqrt{\kappa^{2}+\frac {1}{4}}\right).\]  
Combining our quantum contaminated model (28) and measurement statistics (33), we obtain statistics for homodyne measurement at \(\phi\) on the noisy quantum gaussian states as:  
\[\text{P}_{\alpha,\varepsilon}(x|\phi)=\left(1-\varepsilon\right)\text{P}_{ \alpha}(x|\phi)+\varepsilon\int p_{z_{0},\alpha_{0}}(z,\kappa)\,\text{P}_{z, \kappa}(x|\phi)d^{2}z\,d\kappa. \tag{34}\]  
This is a convex mixture of normal distributions. For the case of a single outlier quantum state (29), we have:  
\[\text{P}_{\alpha,\varepsilon}(x|\phi)=\left(1-\varepsilon\right)\text{P}_{ \alpha}(x|\phi)+\varepsilon\,\text{P}_{z_{0},\kappa_{0}}(x|\phi). \tag{35}\]  
When we consider homodyne measurement on a more general quantum contaminated model (30), measurement statistics are given by:  
\[\text{P}_{\alpha,\varepsilon}(x|\phi)=\left(1-\varepsilon\right)\text{P}_{ \alpha}(x|\phi)+\varepsilon\int f^{\mathcal{N}}_{\mu_{1},\sigma_{1}}(z_{\text {R}})f^{\mathcal{N}}_{\mu_{2},\sigma_{2}}(z_{1})\,\text{P}_{z,\kappa_{0}}(x| \phi)d^{2}z. \tag{36}\]  
We note that the second term can be further simplified by the use of a convolution formula of the normal distribution. For our purpose, the above expression suffices to implement our numerical simulation, which will be discussed in the next section.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 2 Congestion and Awareness Control
### Integration of Congestion and Awareness Control  
The independent operation of congestion and awareness control processes can have a negative impact on the capacity to satisfy the application requirements or the channel load (see the example illustrated in Figure 2). A methodology to avoid such impact is the design of a new control process that integrates the operation and objectives of congestion and awareness control. An example is the INTERN scheme [27] that integrates MINT with a control process inspired by A-DCC. Other integrated examples are [28; 29]. INTERN has been designed to guarantee that the application requirements of all vehicles are satisfied while controlling the channel load below \(\mathbb{C}BR_{max}\); contrary to MINT, INTERN does not try minimizing the channel load. To this aim, INTERN introduces the MINT approach into a control process inspired by LIMERIC and that exploits DCC_NET's piggybacking scheme. Each vehicle using INTERN configures its packet transmission frequency \(T_{f}\) as the minimum packet reception frequency required by its application plus a margin \(\Delta T_{f}\geq 1\):  
\[T_{f}(t)=R(t)+\Delta T_{f}(t). \tag{5}\]\(\Delta T_{f}\) is dynamically adapted by each vehicle using a control process that tries to utilize, and distribute among vehicles, the available bandwidth. The available bandwidth is limited by the fact that \(CBR_{max}\) cannot be exceeded. This process is followed in order to minimize the negative effects of unexpected variations of the radio link quality that could prevent satisfying the application requirements. The \(\Delta T_{f}\) parameter of a vehicle at a given time instant depends on the minimum \(\Delta T_{f}\) of all its neighboring vehicles, and a quantity proportional to the ratio between the maximum \(CBR\) experienced within 2 hops and \(CBR_{max}\). More specifically, each vehicle adapts its \(\Delta T_{f}\) parameter using the following equation:  
\[\Delta T_{f}=\Delta T_{f}^{T}+\frac{\Delta T_{f}^{T}}{CBR_{2 hops}}\Big{(}CBR_{max}-CBR_{2 hops}\Big{)}=\Delta T_{f}^{T}\frac{CBR_{max}}{CBR_{2 hops}}, \tag{6}\]  
where \(\Delta T_{f}^{T}\) represents the minimum \(\Delta T_{f}\) of neighboring vehicles. The information piggybacked by each vehicle implementing INTERN is then the locally experienced \(CBR\), its current \(\Delta T_{f}\), and the maximum \(CBR\) and the minimum \(\Delta T_{f}\) received from its neighboring vehicles.
## 5 Implementation of Transient Thermal Analysis  
The FE simulation software ANSYS [42] is used to simulate the heat conduction and thermal boundary condition equations described in the previous section. Time-dependent temperature distributions and thermal gradients for steel I-girders are developed using a transient thermal module [42]. To conduct thermal analysis and to apply the total solar radiation heat flux accurately, the steel girder model was divided into three categories depending on the nature of the surface:  
1. The top surface of the top flange and the top surfaces of the bottom flange,
2. The bottom surface of the bottom flange and the subsurface of the overhangs, and
3. Vertical webs considering the shadowing effect that changes with time.  
The solar radiation heat flux components described in Equation (4) were applied on the ANSYS model surfaces after considering the different solar angles and their directions in addition to the solar incidence angles at each model surface [43]. The heat transfer convection coefficient presented in Equation (8) was calculated based on the wind speed, and the air temperature data were applied to the model surfaces to govern the convection thermal boundary condition. The longwave radiation was applied on the ANSYS thermal model surfaces as cooling thermal irradiation as presented in Equation (9). The meshing was conducted using the quadratic and triangle elements to simulate an accurate thermal finite element model as shown in Figure 3. The Solid90 element is used to simulate the steel web and flanges. Surf152 is the thermal element used to simulate the thermal boundary conditions (convection, solar radiation and longwave radiation). A diagram of the thermal boundary conditions (boundary environmental thermal loads) applied to the thermal FE steel girder model is illustrated in Figure 4. The thermal material properties significantly control the heat transfer conduction and the thermal boundary conditions described in the previous section. The thermal properties of steel were accurately applied to the ANSYS thermal model as previously used by [PERSON] [37], with the following parameters: density = 7850 kg/m\({}^{3}\), thermal conductivity = 44.5 W/mK, specific heat = 475 J/(kg\(\cdot\)K), surface emissivity of 0.9 and absorption coefficient of 0.7. The initial time to initiate the thermal analysis simulation was taken at midnight due to the approximate temperature uniformity across the exposed steel girder. To further reduce the effect of this assumption, the thermal analysis was started three days before the day of interest that was used for the thermal validation. For thermal model verification, the steel I-girder's air temperature, global solar radiation and wind speed data, recorded between 11 June 2015 and 14 June 2015, as shown in Figure 5, were utilized as the thermal loads to apply the thermal boundary conditions described in Equation (3).  
Figure 4: The thermal boundary conditions applied to the FE steel girder model.  
Figure 3: The mesh of the 3-D finite element model.
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 2 Related Work  
Reinforcement learning (RL) can be seen as a means to explore the optimal action strategy in a continuous decision-making process. In this process, there is an agent. The agent performs the action in the state space by the size of the reward and then feeds back the step reward to the agent after the action is performed. By observing the impact of actions on a physics-informed environment (rock hardness mapped from the sonic logging data of Well A in Shengli Oilfield, formation pressure gradients following the depth curve, and borehole stability calculated via the Mohr-Coulomb criterion, agents learn to adjust strategies. The ultimate goal is to maximize the cumulative reward [28, 29, 30, 31]. As illustrated in Figure 1, the reinforcement learning framework consists of an agent, environment, and reward system. At a certain moment \(t\), the intelligent experience observes the current environmental state \(S_{t}\). According to the reward situation of the known strategy, the agent selects an action \(A_{t}\) that is most likely to increase the long-term cumulative reward in this state. When the action is completed, at the next moment \(t+1\), the environment changes, according to the current state and the action taken, into a new state \(S_{t+1}\), and gives the corresponding reward \(R_{t+1}\). This process is repeated continuously until the learning strategy of the agent tends to be stable or the preset optimization goal is achieved.  
Deep reinforcement learning (DRL) is a combination of reinforcement learning and deep learning. The core idea of DRL is to use the representation learning ability and end-to-end learning characteristics of deep neural networks to solve the limitations of traditional reinforcement learning in high-dimensional state spaces and complex tasks.
## 2 Proposed Single Source Three-Phase Topology
### Working Principle  
The operating principle of the PT can be depicted with the assistance of per phase leg (say phase R), as shown in Figure 2. Since each phase leg of the PT is identical, the comparative analysis for the other two phases can be extended. As a result, the idea of using onlyone phase leg is considered to be more illuminating. The activity of the single-phase leg is clarified in the accompanying mode. In every mode of activity, the inverter synthesizes a specific voltage level with the assistance of the desired switching combination mentioned in Table 1. The sequence of the voltage generation path and charging path of the capacitor are set apart by dotted blue and green lines separately.  
\[\text{Mode }\delta_{1}\text{: }v_{Rn}(t)=0\]  
In this mode of activity, the controlled switches \(S_{R2}\), and \(S_{R4}\) are in on state, and \(S_{R1^{\prime}}\) and \(S_{R3}\) are in off condition to accomplish zero volts across the load terminals. The capacitor (\(C_{R}\)) is energized to a voltage of magnitude \(V_{DC}\) through the forward-biased diode (\(D_{R}\)) because of the activation of switch \(S_{R2}\). The leading way for pole voltage is shown in Figure 3a.  
\[\text{Mode }\delta_{2}\text{: }v_{Rn}(t)=+1V_{DC}\]  
Figure 3: Different modes of operation of pole voltage generation. (**a**) \(v_{Rn}(t)=0\); (**b**) \(v_{Rn}(t)=1V_{DC}\); (**c**) \(v_{Rn}(t)=2V_{DC}\).  
Figure 2: Circuit diagram for per phase leg R.  
To achieve the targeted output voltage \(V_{DC}\), this mode of operation simultaneously activates the switches \(S_{R2}\) and \(S_{R3}\) while simultaneously deactivating the switches \(S_{R4}\) and \(S_{R1}\). Due to the activation of the switch \(S_{R2}\), the capacitor is connected in shunt with the isolated dc voltage source and energized to a voltage of magnitude \(V_{DC}\). The leading way for pole voltage is shown in Figure 3b.  
\[\text{Mode }\delta_{3}\text{: }v_{Rn}(t)=+2V_{DC}\]  
This voltage is achieved by simultaneously activating \(S_{R1}\) and \(S_{R3}\), and deactivating \(S_{R2}\), and \(S_{R4}\). Due to the activation of the switch \(S_{R1}\), the capacitor (\(C_{R}\)) is placed in cascade to the load through switch \(S_{R3}\) and releases its stored energy to the output. Hence, the output level becomes \(2V_{DC}\). The leading way for pole voltage is shown in Figure 3c.
## 3 Results
### Effects of Different Land Use Patterns on \(K_{os}\)  
As shown in Table 4, the depth of the humus layers correlated with varying increases in \(K_{\text{OS}}\) across all treatments, except for the lower layer of the FB treatment. The most significant increase was observed in SC (110.01%), while the smallest increase was noted in FB (25.86%). Tillage treatments generally resulted in a reduction of \(K_{\text{OS}}\) in the surface layer (0-20 cm) across all treatments, with notable differences among different land use patterns. The non-tillage treatments exhibited significantly higher \(K_{\text{OS}}\) than the tillage treatments. No significant difference was observed between GB and FB, nor among SC, MC, and MSR. The \(K_{\text{OS}}\) ranking was as follows: FB > GB > MSR > MC > SC. In the 20-40 cm soil layer, the \(K_{\text{OS}}\) of the SC was significantly lower than that of the other four treatments, with the order being MC > FB > MSR > GB > SC, showing increases of 49.55%, 33.66%, 29.75%, and 25.35%, respectively, compared to the SC treatment. Furthermore, cultivation also enhanced the \(K_{\text{OS}}\) in the 40-60 cm soil layer, with the ranking being SC > MC > MSR > FB > GB. Notably, SC was significantly higher than both FB and GB, while no significant differences were found among the other treatments, including between MC and MSR. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{3}{c}{**Depth (cm)**} \\ \cline{2-4}  & **0–20** & **20–40** & **40–60** \\ \hline SC & 9.29 \(\pm\) 1.7 b & 12.27 \(\pm\) 0.41 b & 19.51 \(\pm\) 1.9 a \\ MC & 9.88 \(\pm\) 1.18 b & 18.35 \(\pm\) 3.12 a & 18.7 \(\pm\) 1.94 ab \\ MSR & 9.9 \(\pm\) 2.56 b & 15.92 \(\pm\) 1.21 a & 17.97 \(\pm\) 1.66 ab \\ GB & 10.86 \(\pm\) 0.16 ab & 15.38 \(\pm\) 0.34 a & 16.07 \(\pm\) 1.04 b \\ FB & 13.03 \(\pm\) 1.05 a & 16.4 \(\pm\) 0.7 a & 16.3 \(\pm\) 1.27 b \\ \hline \hline \end{tabular} Note: The experiment treatments included continuous soybean cultivation (SC), continuous maize cultivation (MC), maize–soybean rotation (MSR), grass belt (GB), and forest belt (FB). The lowercase letters represent significant differences among different treatments (\(p<0.05\)). \(n=3\). The same is below.  
\end{table} 
Table 4: Effects of different land use patterns on \(K_{OS}\).