esa-sceva/satcom-chunk-collection huggingface dataset:
# From NFT 1.0 to NFT 2.0: A Review of the Evolution of Non-Fungible Tokens
## 2 Background and Preliminaries
### NFT Standards  
NFT standards describe how to build non-fungible tokens on a particular blockchain protocol. Ethereum was the first blockchain protocol to create and launch NFTs, but it is not the only one [39]. We can divide the NFT standards into Ethereum standards and non-Ethereum standards.  
The two main pieces of an NFT are the smart contract, which contains the logic and decentralised storage, such as the InterPlanetary File System (IPFS) [40], to store the metadata and assets that the smart contract points to. Traditionally, there are two ways NFT smart contracts such as the ERC-721 and ERC-1155 variants can store information:  
* The contract stores a universal resource identifier (URI) that is accessible via the tokenURI() function. This URI points to a decentralised storage location such as IPFS or Arvweave [41], and applications such as Opensea can query these data directly from the source.
* The smart contract stores the data needed to recreate the asset. Generally speaking, the data are composed of a few bytes per token, and the smart contract also has a function that recreates the asset. This approach is more commonly used in generative art NFTs.  
The metadata for most existing NFTs contain names, descriptions, and content identifiers (CIDs) that point to media files. However, due to the flexibility of the metadata described above, metadata can be used to specify many things: expiration date, the underlying asset, strike price, yield, fixed interest rate, depreciation rate, accessibility, etc. Metadata can also contain functional attributes, such as specifying in-game character levels, recording the number of scans of a QR code on a membership pass, or specifying irreplaceable details in financial information. As an example, Uniswap uses NFTs to keep track of liquidity provider (LP) positions. When someone adds liquidity to a liquidity pool, it receives an NFT that contains the pool identifier, the number of tokens added to the pool, the earned fees, and other information in its metadata.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 1 Introduction
### Physical Layer Latency Probing  
Providing sufficient latency containment mechanisms for tomorrow's real-time applications is not a trivial task. Every layer in the communication stack has its own level of flexibility. Sometimes, the latency accumulated in a single layer is also dependent on other layers. Starting from the bottom up, the present work studies latency accumulation from the perspective of the PHY. It considers both transmitter (TX) and receiver (RX)-based latency management mechanisms to determine the resulting range of expected latency values. The PHY component performance figures are derived from research efforts concerning integrated circuit (IC) design and are implemented in a tailor-made 802.11 ad PHY simulation framework. Alongside the time delay results, the effects of latency mitigation on data integrity are evaluated. The three-fold contribution of the present work is summarized below.  
1. PHY latency analysis: The dependency of time delays on PHY protocol data unit (PPDU) payload length, PPDU aggregation, the selected MCS, the employed demapping algorithm, and the number of LDPC decoding iterations is established.
2. Latency management mechanisms: Data transmission over an additive white Gaussian noise (AWGN) channel is carried out to study the trade-offs between the incurred latency and the resulting BER. The lowest achievable latency is determined in relation to PHY tuning parameters and using \(10^{-5}\) as the BER constraint. Moreover, Pareto optimality in achieving minimal BER is addressed in light of different latency thresholds.
3. Simulation framework: An open-source IEEE 802.11 ad PHY latency and BER simulation framework has been designed during the course of the study. It closely complies with the WiGig standard, offers flexibility for future studies, and is shared in open access.  
The target latency performance metric and the derivation of the ideal case time delay, assuming infinite processing speed, is presented in Section 2. Associating the PHY's components with realistic performance figures sourced from state-of-the-art literature is described in Section 3, while Section 4 touches upon the inner workings of the simulation environment and outlines the workflow adopted for the purpose of generating the latency and BER results. The simulation results are contained within Section 5, whereas their inter-dependencies are discussed in Section 6. Finally, Section 7 summarizes the findings and highlights potential future research prospects.
## 3 System Model
### Reward Function
#### 3.4.1 Reward
\[\hat{d}_{stop} =1-\text{max}\bigg{(}0,\text{min}\bigg{(}1,\frac{D_{stop}}{D_{ maxstop}}\bigg{)}\bigg{)}, \tag{24}\] \[\hat{o}_{ego} =\text{max}\bigg{(}0,\text{min}\bigg{(}1,\frac{1}{1+V_{ego}} \bigg{)}\bigg{)}, \tag{25}\]  
where \(D_{maxstop}\) denotes the maximum allowable distance from the current vehicle position to the stop line, which was set to 30 m in this experiment. In other situations, specifically, when the traffic light is yellow, the vehicle is designed to drive defensively at a minimum speed \(V_{min}\). To implement this behavior, the Speed Reward \(R_{speed}^{Y}\) is defined according to Equation (26):  
\[R_{speed}^{Y}=1-\frac{V_{ego}}{V_{min}}. \tag{26}\]  
To encourage the vehicle to maintain lane alignment during driving, the reward function is designed to assign higher rewards when the vehicle remains close to the center of the road. Accordingly, the Centerline Distance Reward \(R_{center}\) is defined as in Equation (27):  
\[R_{center}=\text{max}\bigg{(}1-\frac{D_{center}}{D_{max}},\;0\bigg{)}, \tag{27}\]  
where \(D_{center}\) denotes the Euclidean distance between the current position of the vehicle and the center of the road. \(D_{max}\) represents the maximum allowable value of \(D_{center}\), which was set to 3 m.  
To further promote stable driving along the road centerline, the Centerline Standard Deviation Reward, based on the standard deviation of the centerline distance, \(R_{std}\), is defined as shown in Equation (28):  
\[R_{std}=\max\left(1-\frac{|\sigma_{center}|}{\sigma_{max}},\;0\right) \tag{28}\]  
where \(\sigma_{center}\) denotes the standard deviation of the distance between the ego vehicle and the road centerline during driving, \(\sigma_{max}\) represents the maximum allowable standard deviation, which was set to 0.4 in this experiment. The Vehicle Heading Reward \(R_{L}\) is designed to prevent abnormal directional deviations, such that higher rewards are given when the vehicle's heading aligns closely with the road direction. This reward is defined by Equation (29):  
\[R_{L}=\max\left(1-\frac{|\theta_{ego}|}{\theta_{max}},\;0\right), \tag{29}\]  
where \(\theta_{ego}\) represents the heading angle between the vehicle's current orientation and the road direction, while \(\theta_{max}\) denotes the maximum allowable heading angle, which was set to \(90^{\circ}\) in this experiment.
## 1 Introduction and Literature Review
In summary, to address the specific requirements for surface defect detection in particleboard, this study proposes the PBD-YOLO algorithm, which integrates multi-scale feature fusion and weak texture enhancement. The main contributions of this paper are as follows:  
1. Based on the YOLOv10 algorithm architecture, an improved algorithm, PBD-YOLO, is proposed for particleboard surface defect detection. PBD-YOLO improves the mAP and the recall while guaranteeing the original network's real-time performance. 2. The PBD-YOLO algorithm introduces the Space to Depth and Difference Enhance Convolution (SPDDEConv) module, which utilizes spatial partitioning, multi-branch difference convolution, and a channel fusion strategy [21, 22]. This approach aims to retain more information during the down-sampling process and sharply enhance the edge texture characteristics of defects on the surface of particleboard. As a result, it improves the model's ability to detect defects, particularly those with weak features. 3. The PBD-YOLO algorithm also introduces the Switchable Atrous Convolution (SAC) within the C2f feature extraction module. SAC looks twice at the input features with different atrous rates, and the outputs are combined via switches. Additionally, the ShareSepHead module is designed to merge feature maps of varying sizes from the neck by sharing weights. This design enhances the model's adaptability to multi-scale defects on the particleboard surface and improves its robustness when simultaneously detecting multiple targets of different scales.
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Wi-Fi RSSI Characteristics
#### 3.1.1 Nonlinearity and Uncertainty  
According to a propagation model of Wi-Fi RSSIs [43; 44; 45], the RSSI value decreases exponentially when the distance between a transmitter and a receiver increases linearly. In reality, however, the interruption of the multipath generates uncertain Wi-Fi RSSIs because of the existence of a number of walls and the interference from other radio signals.  
For validation, we record Wi-Fi RSSI values on an office floor shown in Figure 2a. Figure 2b shows the RSSI values according to the distance from Wi-Fi AP2; one graph shows the RSSI curve when a user moves along the corridor and the other is when the path is interrupted by walls. Because this study does not assume the that a map is available, it is impossible to predict the change in RSSI patterns with respect to the distance.
# Transverse Spin Hall Effect and Twisted Polarization Ribbons at the Sharp Focus
## 1 Introduction  
Previously, an experimental observation of a polarization Mobius ribbon at the sharp focus of a vector laser beam generated with a q-plate has been reported [1]. The polarization Mobius ribbon was shown to make 3 half-twists at the tight focus of an initial optical vortex with topological charge \(n=1\), making 5 half-twists at \(n=3\). A technique for generating exotic polarization Mobius ribbons has been numerically demonstrated [2], and the generation of polarization Mobius ribbons upon focusing tilted vector beams has been studied [3]. Alongside one-sided polarization Mobius ribbons, it has been found that paraxial and non-paraxial vector beams can also form two-sided twist bands with an even number of twists [4; 5; 6; 7].  
On the other hand, as far as the spin Hall effect at the focus is concerned [8; 9; 10], it has been shown that with increasing topological charge of vector vortex beams, the number offocal regions with alternating spin signs also increases. Such opposite-sign focal regions occur in pairs, so the general spin of the beams remains zero, meaning that the number of opposite-sign regions lying on an axis-centered circle is always even. Therefore, by making a full circle in the focal plane, the polarization ellipse makes an even number of semi-turns, i.e., an integer number of full turns. Meanwhile, a surface generated by the major/minor-axis vector of the polarization ellipse is not a one-sided Mobius ribbon, as was the case in Refs. [1; 2], but a two-sided surface with an even number of semi-turns [4]. Such polarization ribbons have been given the name twisted ribbons [4].  
The arrangement of polarization ellipses in the focal plane can be derived from an equation for the coordinates of the major and minor axes of the polarization ellipse proposed by [PERSON] [11]. As a rule, the major and minor axes of polarization ellipses are calculated numerically. In the meantime, in this work we discuss two examples of vector fields (a cylindrical _n-th_ order vector field and an azimuthally polarized optical vortex with topological charge _n_), for which we derive exact analytical relationships that describe coordinate distributions of the major and minor axes of polarization ellipses in the tight focus plane, based on a Richards-Wolf formalism [12].
## 2 System Model
### Low-Temperature Loop  
In Figure 3, two cases are identified for the LTL. If \(T_{3^{\prime}}+T_{\rm PP2}\leq T_{\rm jw1}\), then Case 1 occurs. In Figure 3a, the LTL working fluid is preheated by the jacket cooling water to a two-phase state.  
When the PPTD of an LTL preheater is at State point \(3^{\prime}\), the LTL working fluid mass flow rate can be expressed as follows:  
\[m_{\rm L}=\frac{m_{\rm H}(h_{6}-h_{1})+m_{\rm jw}c_{\rm pw}\big{(}T_{\rm jw1}- T_{\rm jw2}\big{)}}{(h_{4^{\prime}}-h_{3^{\prime}})} \tag{8}\]  
\[T_{\rm jw2}=T_{3^{\prime}}+\Delta T_{\rm PP2} \tag{9}\]When the PPTD of the preheater occurs at State point \(2^{\prime}\), the mass flow rate is expressed as follows:  
\[m_{\rm L}=\frac{m_{\rm H}(h_{6}-h_{1})+m_{\rm jw}c_{\rm pw}\big{(}T_{\rm jw1}-T_{ \rm jw3}\big{)}}{(h_{4^{\prime}}-h_{2^{\prime}})} \tag{10}\]  
\[T_{\rm jw3}=T_{2^{\prime}}+\Delta T_{\rm pp2} \tag{11}\]  
where \(c_{\rm pw}\) is the specific heat capacity of the jacket cooling water at constant pressure, which is set to 4.18 kJ/(kg\(\cdot\)K); \(m_{\rm jw}\) is the jacket cooling water mass flow rate; \(T_{\rm jw}\) is the jacket cooling water temperature; and \(\Delta T_{\rm pp2}\) is the LTL preheater PPTD.  
If \(T_{3^{\prime}}+T_{\rm pp2}>T_{\rm jw1}\), then Case 2 occurs (Figure 3b). The LTL working fluid is only preheated to State point \(7^{\prime}\). When the PPTD of the preheater is at State point \(7^{\prime}\), \(m_{\rm L}\) is calculated as follows:  
\[m_{\rm L}=\frac{m_{\rm H}(h_{6}-h_{1})}{(h_{4^{\prime}}-h_{7^{\prime}})} \tag{12}\]  
\[h_{7^{\prime}}=f(p_{\rm Le},T_{7^{\prime}}) \tag{13}\]  
\[T_{7^{\prime}}=T_{\rm jw1}-\Delta T_{\rm pp2} \tag{14}\]  
where \(p_{\rm Le}\) is the LTL evaporation pressure.  
When the PPTD of the preheater occurs at State point \(2^{\prime}\), \(m_{\rm L}\) is obtained using Equations (10) and (11).  
The cooling water mass flow rate is calculated as follows:  
\[m_{\rm w}=\frac{m_{\rm L}(h_{6^{\prime}}-h_{1^{\prime}})}{c_{\rm pw}(T_{\rm w 2}-T_{\rm w1})} \tag{15}\]  
where \(T_{\rm w}\) is the cooling water temperature.  
The power consumed by a cooling water pump is  
\[P_{\rm pw}=\frac{m_{\rm w}gH}{\eta_{\rm pw}} \tag{16}\]  
where \(H\) is the cooling water pump head, and \(g\) is the gravitational acceleration, which is set to 9.8 m/s\({}^{2}\).  
The power produced by the LTL turbine is calculated as  
\[P_{\rm L}=m_{\rm L}(h_{4^{\prime}}-h_{5^{\prime}s})\eta_{\rm t}=m_{\rm L}(h_{ 4^{\prime}}-h_{5^{\prime}}) \tag{17}\]  
The power consumed by the LTL working fluid pump can be expressed as  
\[P_{\rm LP}=\frac{m_{\rm L}(h_{2^{\prime}s}-h_{1^{\prime}})}{\eta_{\rm P}}=m_ {\rm L}(h_{2^{\prime}}-h_{1^{\prime}}) \tag{18}\]  
The LTL net power output is  
\[P_{\rm Lnet}=P_{\rm L}-P_{\rm LP}-P_{\rm pw} \tag{19}\]
# Autonomous Navigation of Robots: Optimization with DQN
## 5 Design and Execution Strategy for the Control System: A Comprehensive Overview
### Robot's Interaction with the Environment: Actions of the DQN Agent  
The DQN agent's actions refer to the movements performed by the Turtlebot3 Burger as it interacts with the environment. These actions are determined based on the output produced by the last layer of the neural network. The Turtlebot3 Burger is designed to have a set of five actions that dictate its movement, depending on the observed state. A visual representation of these actions can be found in Figure 8.  
By leveraging the power of the neural network, the DQN agent is able to make informed decisions regarding its movement, taking into account the information it has gathered from the environment. These actions play a crucial role in navigating obstacles and accomplishing tasks effectively and efficiently.  
Additionally, the Turtlebot3 robot maintains a constant linear speed of 0.15 m/s. However, its angular speed varies depending on the action taken by the robot, as outlined in Table 1.  
Table 1 provides the corresponding angular velocities for each action performed by Turtlebot3. These angular velocities, measured in radians per second, determine the rotational movement of the robot. The values in the table allow the robot to effectively navigate its environment by adjusting its orientation based on the desired action.  
By associating specific angular velocities with each action, the control system ensures that the Turtlebot3 responds appropriately to achieve its intended trajectory and successfully avoid obstacles.
# Activation Analyses of Disposal Options for Irradiated Be\({}_{12}\)Ti
## 4 Results of Activation Calculations
### Activity of the Be\({}_{12}\)Ti in the HCPB Blankets
The breakdown of the total activity of the Be\({}_{12}\)Ti (\"average\") in the central outboard blanket (OBC) is shown in Figure 7. Assuming that a remote handling of the \"hot\" blankets after the reactor shutdown might be possible after a specific cooling time, isotopes dominating the total activity at the cooling time longer than 1 month were determined and the breakdown of the total activity is presented in Figure 7. For the cooling time from 1 month to 1 year, \({}^{45}\)Ca (T\({}_{1/2}\) = 162.6 days) and \({}^{46}\)Sc (T\({}_{1/2}\) = 83.8 days) define the total activity. These isotopes are formed during the neutron irradiation of Ti due to (n,p) and (n,\(\alpha\)) reactions. \({}^{60}\)Co (T\({}_{1/2}\) = 5.27 years) dominates the activity 10 years after the shutdown of the reactor; it is formed from the Co impurity in the Be\({}_{12}\)Ti due to \({}^{59}\)Co(n,\(\gamma\))\({}^{60}\)Co reaction. The decay of \({}^{63}\)Ni defines the activity for the cooling time of 100 years. The significant concentration of \({}^{63}\)Ni(T\({}_{1/2}\) = 100 years) in the Be\({}_{12}\)Ti results from the accumulation of this isotope due to the reaction on the Ni impurity, \({}^{62}\)Ni(n,\(\gamma\))\({}^{63}\)Ni. A time evolution of the specific activity in the irradiated Be\({}_{12}\)Ti of the different newly generated radioactive nuclides in the central OB blanket grouped according to atomic numbers is shown in Figure 8. The tritium removal bypass is switched off in the FISPACT activation simulation but tritium generation is not accounted for in the final result. Using the data presented in Figure 8, several groups of activated products responsible for the Be\({}_{12}\)Ti activity can be identified according to their atomic numbers. Right after the reactor shutdown, a variety of radionuclides with a wide spread of atomic numbers are generated: light ones with A \(<\) 14, 18 \(<\) A \(<\) 30, 30 \(<\) A \(<\) 65 including fission products, 71 \(<\) A \(<\) 76, 80 \(<\) A \(<\) 82 and fissile nuclides with 90 \(<\) A\(<\) 97. One year after the shutdown, the main contributors to the activity are still nuclides with 18 \(<\) A \(<\) 30, some elements within the wide  
Figure 7: The dominant radionuclides in the irradiated Be12 Ti in the OB central blanket at the cooling time longer than 1 month. group of the fission products, Ta-W-Re, Tl and the group of the trans uranium elements: U, Pu and Am. After 100 years of decay, the most essential elements are N, Ni, Cd, U, Pu and Am. The chemical impurities available in the Be\({}_{12}\)Ti affect and form its activity for all cooling times.
## 2 Materials and Methods
### ROS Measurement  
ROS production was quantified using an CM-H2 DCFDA probe (ThermoFisher Scientific, Milan, Italy) according to the manufacturer's instructions. Cells were plated in black 96-well plates and treated with the indicated compounds. After 24 h, the supernatant was removed and cells were incubated with medium containing 1 \(\upmu\)M ROS probe for 30 min at 37 \({}^{\circ}\)C protected from light. The medium containing the probe was then removed and replaced with fresh medium. The fluorescence was monitored using the EnSight Multimode plate reader (PerkinElmer, Waltham, MA, USA) at 492 nm excitation/517 nm emission wavelengths. Cells were then fixed in paraformaldehyde (PFA) solution (3% PFA in PBS) for 20 min at 4 \({}^{\circ}\)C and processed for the crystal violet assay used as normalization. Briefly, cells were stained with crystal violet solution (0.2% crystal violet in PBS with 20% methanol) for 10 min at room temperature (RT) and washed with deionized water, then the dye was solubilized in sodium dodecyl sulfate (SDS) solution (1% SDS in PBS) by shaking the plates until complete dissolution was achieved. Absorbance was measured by reading the plate at 595 nm emission wavelength. Results were expressed as the ratio of ROS fluorescence/crystal violet absorbance and reported as the fold change, setting control to 1.
###### Abstract  
Once an active distribution network of a cyber-physical system is in alert state, it is vulnerable to cross-domain cascading failures. It is necessary to transit the state of an active distribution network of cyber-physical system from an alert state to a normal state using a preventive control policy against cross-domain cascading failures. In fact, it is difficult to construct and analyze a preventive control policy via theoretical analysis methods or physical experimental methods. The theoretical analysis methods may not be accurate due to approximated models, and the physical experimental methods are expensive and time consuming for building prototypes. This paper presents a preventive control policy construction method based on a deep deterministic policy gradient idea (shorted as PCMD) to generate and optimize a preventive control policy with Artificial Intelligence (AI) technologies. It adopts the reinforcement learning technique to make full use of the available historical data to overcome the problems of high cost and low accuracy. Firstly, a preventive control model is designed based on the finite automaton theory, which can guide the data collection and learning policy selection. The control model considers the voltage stability, frequency stability, current overload prevention, and the control cost reduction as a feedback variable, without the specific power flow equations and differential equations. Then, after enough training, a local optimal preventive control policy can be constructed under the comparability condition among a fitted action-value function and a fitted policy function. The constructed preventive control policy contains some control actions to achieve a low cost and in accord with the principle of shortening a cross-domain cascading failures propagation sequence as far as possible. The PCMD is more flexible and closer to reality than the theoretical analysis methods and has a lower cost than the physical experimental methods. To evaluate the performance of the proposed method, an experimental case study, China Electric Power Research-Cyber-Physical System (shorted as CEPR-CPS), which comes from China Electric Power Research Institute, is carried out. The result shows that the effectiveness of preventive control policy construction with the PCMD is better than most current methods, such as the multi-agent method in terms of reducing the number of failure nodes and avoiding the state space explosion.  
active distribution network; preventive control policy; reinforcement learning; cross-domain cascading failures; cyber-physical system +
Footnote †: journal: Applied Sciences
## 3 Method
### PreLU  
In this paper, the activation function after the convolution layer uses a parametric rectified linear unit (PReLU) instead of the traditional ReLU [13]. This is because PReLU can successfully avoid causing dead zone. The formula for PReLU is as follows.  
\[\sigma(x_{i})=\max(x_{i},0)+a_{i}\min(x_{i},0), \tag{5}\]  
where \(x_{i}\) is the input signal of the \(i\)th convolutional layer and \(a_{i}\) is the coefficient of the negative part. However, in ReLU, \(a_{i}\) is 0.