esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 5 Conclusions and Future Work  
In this paper, we proposed a trusted routing scheme based on the blockchain and reinforcement learning to provide a trusted routing environment and improve the performance of the routing network. As a decentralized system, the blockchain network provides a feasible scheme for routing information management and a platform for reinforcement learning of routing scheduling. We use the blockchain token to represent the routing packets, and each routing transaction is released to the blockchain network through the confirmation of the validator nodes. By making every routing transaction recorder traceable and tamper-proof, routing nodes can obtain dynamic and trusted routing information on the blockchain network. We also describe the detailed reinforcement learning model to adaptively choose the best routing path and avoid the routing links with malicious nodes. Finally, we carry out simulation experiments, the experimental results show that our system can effectively suppress the attacks of malicious nodes, and the system's latency and throughput performance are excellent.  
In the future, we plan to use our system for experiments in more routing scheduling algorithms besides BP algorithm to verify the effectiveness and portability of our system. We also plan to incorporate the blockchain-based data validation technology into our work [39; 40; 41; 42]. In addition, we  
Figure 10: Token transaction throughput of PoA and PoW blockchain systems.  
intend to put the reinforcement learning model into the blockchain smart contract to simplify the operation complexity and reduce the running burden and expense of the server side.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and recorded the experimental data; [PERSON] analyzed the experimental data; [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] reviewed the paper.  
This work is supported in part by the National Science Foundation of China (Grant No. 61471120 and No. 61702562) and by the 111 Project (Grant No. B18059).  
The authors declare no conflict of interest.
## 1 Introduction
However, in practical situations, SAR platforms may deviate from the nominal linear trajectory due to factors such as changes in airflow. The echo signal received by nonlinear-trajectory SAR will be imaged with motion error compensation [29]. When jamming nonlinear-trajectory SAR, these algorithms cannot construct the azimuth time-varying trajectory deviation phase of the jamming signal pulse by pulse, which will lead to defocusing and shifting of deceptive jamming imaging [30; 31; 32]. Currently, the deceptive jamming for nonlinear-trajectory SAR can only use the inefficient point-by-point superposition direct calculation algorithm (SA) [11]. To sum up, for nonlinear-trajectory SAR with trajectory deviation in actual combat, how to consider the calculation efficiency and the ability to construct trajectory deviation pulse by pulse is a challenge for SAR deceptive jamming in practical applications. This paper proposes a nonlinear-trajectory SAR deceptive jamming algorithm based on a hybrid domain efficient modulation (HDE). The HDE algorithm simulates the SAR jammer's frequency response function with trajectory deviation in the time-frequency hybrid domain. It decomposes the coupling term of the trajectory deviation and JFR function with the assumption of center beam approximation and trajectory deviation constraints. The frequency response function of the deceptive jamming template is computed in the azimuth frequency domain, and the real-time trajectory deviation term is constructed in the azimuth time domain. The HDE algorithm combines the efficiency of azimuth frequency-domain processing with the flexibility of azimuth time-domain processing, allowing for efficient and accurate generation of deceptive signals for nonlinear-trajectory SAR. It solves the problem of real-time deceptive jamming against nonlinear-trajectory SAR and has the potential for practical application in SAR jamming. The paper is organized as follows. Section 2 provides a detailed derivation of the HDE algorithm. Section 3 gives the workflow and validity constraints of the HDE algorithm implementation. Section 4 describes the simulation with experimental validation of the HDE algorithm and analyzes the algorithm's computational complexity. Section 5 discusses the HDE algorithm through effectiveness analysis and experimental results. Section 6 concludes this paper.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 8 Future Research Challenges  
De-identification and anonymization of data is a crucial aspect for protecting individual privacy in the era of smart cities. However, current methods are far from perfect and there are several research challenges that must be addressed to achieve greater privacy protection.  
One of the key challenges is to develop de-identification methods that ensure unlinkability, which is the ability to prevent the re-identification of an individual from multiple data sources. This can be done by using advanced techniques such as differential privacy or \(k\)-anonymity.  
Another challenge is to improve selective disclosure, which involves allowing access to only the minimum amount of information necessary to perform a specific task, while maintaining privacy. This requires the development of new methods for data masking, data sharing and data access control.  
The concept of self-sovereign identity is also gaining momentum, especially using Blockchain technologies. This concept gives individuals control over their own data, allowing them to choose what information they want to share and with whom.  
There is also a need to improve the scalability and interoperability of de-identification methods across multiple data sources. This will enable the integration of data from different sources to support data analysis and decision-making without compromising privacy.  
The security and reliability of de-identification methods is also a major challenge, as the risk of human error and security breaches must be reduced. This requires the development of secure protocols for data sharing and the implementation of robust security measures, such as encryption and secure key management.  
Finally, harmonizing privacy regulations and data protection standards across countries is another important challenge, as privacy laws vary widely from one country to another. This requires international cooperation and a shared understanding of the importance of privacy protection in smart city data.
## 1 Introduction  
Bismuth trioxide (Bi\({}_{2}\)O\({}_{3}\)), possessing six polymorphic modification forms of \(\alpha\)-Bi\({}_{2}\)O\({}_{3}\), \(\beta\)-Bi\({}_{2}\)O\({}_{3}\), etc., is regarded as a promising semiconductor, which has attracted growing interests because of its narrower band gap of 2.5-2.8 eV (compared with traditional photocatalyst-TiO\({}_{2}\) of \(>\)3.0 eV) and thermal stability, high dielectric permittivity, and refractive index [1; 2; 3; 4], resulting in Bi\({}_{2}\)O\({}_{3}\) and its corresponding films' wide numerous technological application in fields of the fuel cell layer, catalysis, sensors, microelectronics, electro-optics, and so on [5; 6; 7; 8; 9; 10].  
Recently, various methods, such as magnetron sputtering and liquid deposition, have been used for synthesis Bi\({}_{2}\)O\({}_{3}\), as described in literature [11; 12; 13; 14]. For example, [PERSON] et al. proved that Bi\({}_{2}\)O\({}_{3}\) films consisting of nanowires with an average diameter of ~7 nm were fabricated using the Bi high temperature evaporation on an Al foil substrate, which wasfollowed by oxidative nucleation and growth at 350 \({}^{\circ}\)C [15]. The Bi\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\) photocatalytic film coated on floated glass balls was used for efficient removal of an organic pollutant reported by [PERSON] et al., by using a sol-gel method assisted with a double-solvent system [16]. [PERSON] et al. designed a novel hetero-structured Bi\({}_{2}\)O\({}_{3}\)-TiO\({}_{2}\) nanotube array composite film for photo-electrochemical cathode protection applications [17]. The thin films of bismuth oxide and yttrium-stabilized Bi\({}_{2}\)O\({}_{3}\) were synthesized by a sol-gel/ink-jet printing method [18] and a pulsed laser deposition [19], respectively, which is complex in operation. However, the film-formation of Bi\({}_{2}\)O\({}_{3}\) powders by a facile and low-cost technique is still a challenge.  
In addition, the nano-Bi\({}_{2}\)O\({}_{3}\) particles and their films are hydrophilic, leading to the destruction of structures in the humidity environment or under water conditions. Furthermore, instruction for designing the water-resistant Bi\({}_{2}\)O\({}_{3}\) films are rarely reported. Thus, in this study, a novel superhydrophobic/functionalized Bi\({}_{2}\)O\({}_{3}\) coating has been successfully prepared by a facile and low-cost technique using the electrophoretic assembly method (EAD) and surface modification, learning from the 'lotus effect' of surfaces capable of separating from water completely [20; 21; 22] and the facile and EAD conveniently film-forming design process based on low-cost equipment [23; 24; 25; 26]. Furthermore, the microstructure and hydrophobic properties of the target film are investigated, which follows in detail.
## 3 Results  
### Simulated Microgravity Influences the TCam-2 Cell Membrane Surface at Cell-to-Cell Contacts  
As previously described, the 24 h exposure to simulated microgravity caused significant TCam-2 cell membrane modifications since the mechano-sensing microvilli-like structures, present on the surface of the cell membrane, collapsed when cultured in the random positioning machine (RPM) [2 ]. Herein, we focus on the effects induced by simulated microgravity on the surface membrane at the cell-to-cell contacts. In Figure 17, we report representative images of SEM analyses on sub-confluent (A,B,C,D) or confluent (E,F,G,H) cells cultured at 1 g (CTR) or under simulated microgravity (RPM). SEM images showed that sub-confluent cells began to contact each other forming mainly larger sites of interaction when cultured in the RPM condition (Figure 17C,D). This occurs if compared with cells cultured at 1 g, in which the cell-to-cell contacts were characterized by membrane protrusions that are much more thin and similar to filopodia (Figure 17A,B). Even in confluent cells, which appeared as an epithelial-like sheet, the RPM condition caused an evident modification of the cell-to-cell contact area (Figure 17G) that appeared, at high magnification, to be formed by complex membrane interdigitations (Figure 17H), whereas in cells cultured at 1 g the cell-to-cell interface appeared mostly linear (Figure 17E,F).  
Figure 1: Scanning electron microscopy analyses of TCam-2 seminoma cells exposed for 24 h to 1 g (CTR: **A,B,E,F**) and simulated microgravity (RPM: **C,D,G,H**). In (**A–D**), the images were captured at the periphery of the culture dish where cells are sub-confluent. In (**E–H**), the images were captured in the middle part of the dish where cells are confluent. The arrows in all the figures indicate the regions of plasma membrane, in which cells interact with each other. In (**A,C,E,G**), lower magnifications (from 2000 to 5500\(\times\)) and in (**B,D,F,H**) higher magnifications (from 15,000 to 38,500\(\times\)) are shown.
# Mining Pool Selection under Block WithHolding Attack +
## 3 Evolutionary Game for Mining Pool Selection
### Game-Theoretic Analysis
#### 3.3.2 Nash Equilibria (NE)  
The population states \(\mathbf{x}=[x_{1},\dots,x_{M}]^{\top}\in\mathcal{X}\) can be interpreted as mixed strategies that each miner may choose, where each \(x_{i}\) denotes the probability of selecting pool \(i\). An NE is then a mixed strategy \(\mathbf{x}^{*}\) that is a best reply to itself. That means under the condition that other miners choose \(\mathbf{x}^{*}\), a miner cannot gain more revenue by choosing any other \(\mathbf{x}\in\mathcal{X}\) rather than \(\mathbf{x}^{*}\). Formally, an NE satisfies the following inequality [46]:  
\[(\mathbf{x}^{*}-\mathbf{x})^{\top}Y(\mathbf{x}^{*})\geq 0,\quad\forall\mathbf{x}\in\mathcal{X}, \tag{11}\]  
where \(Y(\mathbf{x})=[y_{1}(\mathbf{x}),\dots,y_{M}(\mathbf{x})]^{\top}\) with \(y_{i}(\mathbf{x})\) given by (1). According to [47], an NE is actually a rest point of the following equation system of the ODEs:  
\[\dot{x}_{i}(t)=x_{i}(t)\big{(}y_{i}(\mathbf{x}(t),\mathbf{\omega},s_{i},\mathbf{a})- \overline{y}(\mathbf{x}(t),\mathbf{\omega},\mathbf{s},\mathbf{a})\big{)}=0, \tag{12}\]for \(i=1,2,\cdots,M\). This means that, in an NE, the temporal growth rate of the population fraction of each pool \(i\) is zero. That is, \(\forall i\in\mathcal{M},\dot{x}_{i}(t)=0\) holds [48]. By solving the above equation system, we can find the NEs of the game.
# Effect of Multiple Reclosing Time Intervals on Axial Vibration of Winding
## 4 Evaluation of the Effect of Reclosing Time Interval on Short-Circuit Vibration
### Acceleration Response of Transformer Winding at Different Reclosing Time Intervals  
Two parameters describe the intensity of vibration at the transformer secondary short circuit: the vibration acceleration and the vibration displacement. In Figure 7, the variation in vibration displacement in two short circuits is shown. The vibration-displacement peak value of the secondary short circuit close to the permanent short circuit is much greater than that of the vibration-displacement peak value in the case of the primary short circuit, but the vibration displacement requires an additional cycle to reach the peak value. In Figure 8, the variation in vibration acceleration in two short circuits is shown. The acceleration amplitude of the primary short circuit and that of the secondary short circuit are very different, and the change in acceleration is more obvious than the change in displacement. The vibration-acceleration amplitude rises faster when there is damped forced vibration and decreases more significantly when there is damped free attenuation. However, in the same way, the vibration acceleration reaches the peak after a delay of one cycle.  
In Figure 9, the interval corresponding to 0-180\({}^{\circ}\) is about 10 ms, and the change law of vibration displacement and acceleration in a cycle is nearly identical, with an initial decrease and then an increase within 10 ms. The variation in the range of vibration acceleration at different times is more significant than that of displacement. Therefore, it is easier to obtain clear conclusions by analyzing the acceleration resulting from permanent short-circuit closing. The following analysis considers only the vibration acceleration.  
After the transformer short-circuit fault is removed and before closing, the windings are free-vibrating, so closing under different free-vibration intensities will result in different degrees of superposition effect. Figure 10 shows the maximum response of vibration acceleration of the windings during secondary short-circuit impact under different closing waiting times within 0-1000 ms after fault removal.  
According to the vibration-acceleration-amplitude curves obtained in Figure 10 in the short-circuit interval from 0 to 1000 ms, the following characteristics of vibration response can be obtained: firstly, due to the superposition effect, the peak of the secondary vibration acceleration is higher than the peak of the primary vibration, but it continues to decay with interval increasing; secondly, the closing waiting time is 0-600 ms and the amplitude of the secondary vibration gradually decays. The amplitude of the secondary vibration becomes essentially stable within 600-1000 ms; the superposition effect is small, and the influence is not obvious.
# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 4 Preventive and Corrective Lost Circulation Measures
### Preventive Measures to Avoid Lost Circulation during Drilling
#### 4.1.1 Underbalanced Drilling (UBD)  
During conventional drilling, the pressure of the mud column in the wellbore is higher than the pore pressure which represents overbalance conditions. In some cases, especially during drilling through highly depleted reservoirs, naturally fractured, vugular, and highly permeable formations, due to the low pore pressure, losses to the formation can occur. Therefore, mud pressure in the wellbore can be held below the pore pressure (underbalanced drilling) which allows the formation fluid to flow into the wellbore [58; 59].  
To achieve circulating densities lower than the pore pressure, the use of lighter fluids is required, such as air, mist, foams, and aerated mud foam with back pressure (see Figure 2) [15; 16; 17].  
In some cases, underbalanced drilling is the only available drilling technology [16], and it is more complex than conventional drilling or managed pressure drilling due to the presence of multi-phase fluid flows (see Figure 2). The pressure window in Overbalanced Drilling (OBD) is between the pore pressure and the fracture pressure gradient, while in underbalanced drilling (UBD) it is between the borehole stability pressure and the pore pressure (Figure 3) so, in some situations, the size of the windows will determine the appropriate drilling technology [60].  
A significant benefit of underbalanced drilling is that depleted or fractured reservoirs can be drilled with minimizing loss of circulation which has an impact on mud cost, and thus increases the ROP, bit life, well productivity, etc. [61]. However, during underbalanced drilling there is a need for additional equipment and procedures (higher pressure rotating  
Figure 1: Preventive and corrective measures hierarchy (**a**) and wellbore strengthening (**b**).  
control heads, four-phase separators etc. [16]), such as the lack of customized rigs and additional crew, so sometimes it is not technically feasible [62].  
According to [PERSON] et al. (2020), recent attempts to drill new wells in the Pakistan gas field Qadirpur were extremely unsuccessful because of the massive lost circulation resulting in severe reservoir damage and zero production. By applying UBD, the re-entry well-X through highly depleted and fractured reservoir formation was drilled without problems and the high cost of complete lost circulation was eliminated [15].
## 4 Simulation Experiments and Results
### Simulation Results and Analysis  
To investigate its effectiveness, we compared simulation results of a-HPDB to those obtained from (HPDB) [10] and R-HPD [11] under different system configurations.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Smart Grid High-Voltage Transmission Line State Data Acquisition Technology
#### 2.2.3 Data Collection Based on Orthogonal Discrete Multiwavelet Transform Algorithm  
With the help of multiresolution analysis, it is possible to analyze the multiwavelet function motion space by means of the following equation:  
\[ \begin{array}{rl}V_{0}&=V_{1}\oplus W_{1}=V_{2}\oplus W_{2}\oplus W_{1}\\ &=V_{L}\oplus W_{L}\oplus W_{L-1}\oplus\cdots\oplus W_{1}\end{array} , \tag{1}\]  
where, \(L\) is an integer that indicates the number of spatial bases. According to the above equation, power edge calculation input data conforming to the multiwavelet algorithm can  
Figure 2: Edge server running framework. be represented by the orthogonality of the scale function and multiwavelet space function as follows:  
\[ \begin{array}{ll}f&=f_{1}+g_{1}=f_{2}+g_{2}+g_{1}\\ &=f_{L}+g_{L}+g_{L-1}+\cdots+g_{1}\end{array} , \tag{2}\]  
where \(f_{1}\) and \(g_{1}\) denote the initial and terminal functions of the multiwavelet function moving space in the multiresolution analysis, respectively. The two are expanded on the data, and the expressions are:  
\[f_{J}(x)=\sum_{k}C_{J,k}^{T}\varphi_{J,k} \tag{3}\]  
and  
\[g_{J}(x)=\sum_{k}C_{J,k}^{T}\psi_{J,k}. \tag{4}\]  
In Equations (3) and (4), the multiwavelet space shifting initial function is called the scale function in the calculation application process, and the end function that will appear in the application process is called the wavelet function in this algorithm. The following functional relationship equations describe the output with the help of the orthogonal principle:  
\[C_{J,k}^{T}=\big{\langle}f(x),\varphi_{J,k}\big{\rangle} \tag{5}\]  
and  
\[D_{J,k}^{T}=\big{\langle}f(x),\psi_{J,k}\big{\rangle}. \tag{6}\]  
It can be seen through Equations (5) and (6) that the following problems remain in the relationship between the two: the scale function and the wavelet function can only respond to the movement of the space through the beginning and end functions, and it is also necessary to solve for the decomposition coefficients of the space function, which need to be decomposed to obtain the space function in the method proposed in this study.
## 4 Discussion
### The Impact of Tree Size and Forest Structure on Forest Productivity  
Simulation results in this study show an increase of productivity with tree size from small- to medium-size trees, while the productivity of large trees (>60 cm) decreases. These results fit to previous studies, which reported a hump-shaped relationship of growth with tree diameter [50; 51] as well as an age-related decline in forest productivity after maturity [11; 52]. The age-related decline of productivity might be a result of decreased hydraulic conductance with tree height [11; 53]. Therefore, large trees respond with an earlier stomatal closure to prevent a breakdown of the hydraulic system, which reduces daily net photosynthesis [54]. This phenomenon is reported for several tree species [55; 56; 57; 58]. To compensate the higher hydraulic resistance, large trees allocate more carbon into fine roots and stem growth than in leaves, which results in reduced growth efficiency with age [53; 59]. Other studies show that large trees can overcome this decline in growth efficiency by an increase of leaf area [60]. Fostering trees with a probable high productivity, e.g., through selective management, does not automatically result in increasing stand productivity because competition and age-related mortality might increase carbon losses. However, small trees with diameters less than 30 cm tend to be relatively less productive (in relation to their leaf area), especially if they are shaded by larger trees [51]. These shaded trees might not receive enough light to grow into the canopy and remain small or even die. Therefore, stand productivity is dependent on vertical stand structure and light conditions. When large trees were excluded from the simulation (virtual experiment), productivity of medium size trees increased. Due to their high productivity, medium size trees play thus an important role for climate mitigation strategies.