esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 TVWS-DTV Coexistence: Field Measurements
### Field Tests Considering Long Distances
#### 3.2.4 Results from the First Stage of Field Tests
It can be observed that even though it is the closest location to the BS where the tests were conducted, the DTV signal did not suffer interference, even for the conditions of higher \(P_{\text{TX}}\) TVWS and LNA\({}_{\text{RX}}\) on, which allowed amplifying the received interfering signal from the antenna by approximately 23 dB. Additional test results for the coexistence of DTV and TVWS  
The complementary tests were executed to reach the operational limits of DTV and TVWS coexistence, since the initially proposed parameters did not allow achieving \"Intermittent\" or \"Not OK\" DTV STB Status. Therefore, analyzing only the location closest to the BS (MS_Loteamento), the transmission power of the 5G-RANGE system was increased above 4 Wp. Tests were conducted with \(P_{\text{TX}}\) TVWS up to 10 Wp delivered to the transmission antennas. Expanding to powers above this value, an increase in OOBE was observed, and due to these new values of \(P_{\text{TX}}\) TVWS not being previously linearized, it would not be possible to conclude whether the reason for incorrect demodulation in the STBs was due to the protection ratio reaching the limit or the lack of linearization to reduce OOBE. Under these power conditions and additionally considering the reception LNA in the on state, it was possible to obtain a PR of \(-30.2\) dB, resulting in \"OK\" DTV STB Status for all three STBs after the MS's setup. However, with lower PR values, it was possible to obtain \"Not OK\" or \"Intermittent\" DTV STB Status in two situations, as evidenced in
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
## 3 Features and Applications of 300 GHz Band Communication  
Practical application of terahertz communication depends on communication characteristics, including communication distance. Therefore, we first consider its characteristics and suitable applications. Then, we introduce recently standardized channel allocation in the 300 GHz band, and finally consider the possibility of application to space.
## 4 Experiments and Results
### Implementation in Carla Simulator
From the visual observation, the agent will quickly collide with the obstacles and start the next episodes in the early stage. After more training, the agent can gradually keep driving normally for a long time without collision damage. Figure 5 shows the eval average reward graph generated after training. The light orange line is the raw evaluation average reward, which fluctuates significantly due to the stochastic nature of the environment and exploration. The dark red line is a smoothed version of the reward, computed using a moving average, to highlight the overall trend of improvement. It reflects the average cumulative reward of the agent. In an episode, the cumulative reward is the total reward obtained by the agent from the initial state to the termination state. Eval average reward is the average of the cumulative rewards of multiple episodes. As shown in the figure, the rewards gradually increase as the training progresses, which indicates a successful implementation of reinforcement learning. Figure 6 shows the loss curves of the experimental vehicle after DDPG training. The green curve represents the critic loss, and the red curve represents the actor loss. These two main loss curves are commonly monitored during DDPG training. The loss curves of the critic network and actor network reflect the optimization states of different networks. The critic network is used to evaluate the value of an action under the current strategy. The critic loss uses the mean square error (MSE) to calculate the difference between the current estimated \(Q\)-value and the target \(Q\)-value. In the early stages of training, the critic loss tends to be higher because the agent has limited understanding of the environment. As training progresses, the critic loss should gradually decrease and stabilize. The actor network loss is calculated differently, using feedback from the critic network to optimize the strategy so that it selects actions that maximize the critic's estimated \(Q\)-value. The actor loss should be analyzed in conjunction with critic loss. If the actor loss does not converge, the target value of the critic may always be biased. In this experiment, both the critic loss and the actor loss curves demonstrate clear convergence. This proves that the training is effective, and the algorithm can successfully implement the target task in the simulator. In addition, Figure 7 shows the noise curve generated during DDPG training in this experiment. The light orange line represents the raw value, and the dark red line is a smoothed version of the curve, showing the overall trend of decreasing noise. The downward trend of the curve indicates that the noise gradually decays throughout the training process, reflecting the agent's transition from exploration-focused behavior to exploitation of the learned optimal policy. Figure 5: Eval avg reward diagram of the experiment. Figure 6: Loss diagram of the experiment.
# Dynamic Programming Ring for Point Target Detection
## 3 MFF-DPR-Based Point Target Detection
### Mff-Dpr
To mitigate the target identification errors, this paper proposes an algorithm on any two trajectories as follows:

Assume that the trajectory coordinate sets of two trajectories are denoted by

\(\Big{\{}\boldsymbol{p}_{1}^{(t)}\big{|}1\leq t\leq N\Big{\}}\) and \(\Big{\{}\boldsymbol{p}_{2}^{(t)}\big{|}1\leq t\leq N\Big{\}}\); then, if

\[\text{min}\Big{\{}\|\boldsymbol{p}_{1}^{(t)}-\boldsymbol{p}_{2}^{(t)}\|_{2} \Big{|}1\leq t\leq N\Big{\}}\geq 5, \tag{12}\]

then, the two trajectories do not intersect, and the trajectory regularization of the two trajectories terminates; otherwise, the time of intersecting of the two trajectories is obtained by:

\[t_{c}=\text{argmin}_{1\leq t\leq N}\Big{\{}\|\boldsymbol{p}_{1}^{(t)}- \boldsymbol{p}_{2}^{(t)}\|_{2}\Big{\}}, \tag{13}\]

and trajectory regularization is performed as follows:

Suppose \(\boldsymbol{p}_{1}^{(t_{c}-t)}\), \(\boldsymbol{p}_{1}^{(t_{c}+t)}\), \(\boldsymbol{p}_{2}^{(t_{c}-t)}\), and \(\ \boldsymbol{p}_{2}^{(t_{c}+t)}\) denote local trajectories at time \(t_{c}\), and \(\boldsymbol{\hat{p}}_{1}^{(t_{c}-t)}\), \(\boldsymbol{\hat{p}}_{1}^{(t_{c}+t)}\)\(\boldsymbol{\hat{p}}_{2}^{(t_{c}-t)}\) and \(\boldsymbol{\hat{p}}_{2}^{(t_{c}+t)}\) are the predicted trajectories obtained by the straight-line fitting of \(\boldsymbol{p}_{1}^{(t_{c}+t)}\)\(\boldsymbol{p}_{1}^{(t_{c}-t)}\)\(\boldsymbol{p}_{2}^{(t_{c}+t)}\), and \(\boldsymbol{p}_{2}^{(t_{c}-t)}\), respectively, as shown in Figure 4a; where, \(1\leq t\leq T_{L}\). The fitting errors of the two types of trajectories can be calculated by:

\[\left\{ \begin{array}{l}E_{1}=\sum_{1\leq t\leq T_{L}}\|\boldsymbol{\Delta p }_{11}^{(-t)}\|_{2}^{2}+\|\boldsymbol{\Delta p}_{22}^{(-t)}\|_{2}^{2}+\| \boldsymbol{\Delta p}_{11}^{(+t)}\|_{2}^{2}+\|\boldsymbol{\Delta p}_{22}^{(+t)} \|_{2}^{2}\\ E_{2}=\sum_{1\leq t\leq T_{L}}\|\boldsymbol{\Delta p}_{12}^{(-t)}\|_{2}^{2}+\| \boldsymbol{\Delta p}_{21}^{(-t)}\|_{2}^{2}+\|\boldsymbol{\Delta p}_{12}^{(+t )}\|_{2}^{2}+\|\boldsymbol{\Delta p}_{21}^{(+t)}\|_{2}^{2}\end{array} ,\right. \tag{14}\]

where \(\Delta\boldsymbol{p}_{ij}^{(\pm t)}=\boldsymbol{p}_{i}^{(t,\pm t)}-\boldsymbol {\hat{p}}_{j}^{(t,\pm t)}\), and \(T_{L}=10\) based on experience. If \(E_{1}<E_{2}\), the trajectory correlation is performed using the method shown in Figure 4b; otherwise, the method shown in Figure 4c is employed. ```  
**Algorithm 2**_Cont_. The probability of target identification errors can be reduced by regularization but cannot be eliminated completely. For instance, if two trajectories that intersect at time \(t_{c}\) have the same three-dimensional tangent vector, errors of the two types of trajectory fitting methods will be very close (\(E_{1}\approx E_{2}\)), making it impossible to perform the trajectory correlation in the absence of other prior information about the target.
## 3 Materials and Methods
### Absorption Measurements  
Absorption spectra were recorded using a Cary 100-Varian UV-Vis equipped with thermostatted cells. Liquid mixtures were placed in rectangular quartz cells of 1 cm path length, and absorption spectra were recorded at 20.0 \(\pm\) 0.5 \({}^{\circ}\)C in the 200-800 nm wavelength region.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 3 Existing Methods for Vbda  
In this section, we will delve into the two main Vbda methods. We will highlight how each method works, how it is implemented, and its pros and cons. With a thorough examination of these methods, readers will be able to better understand the two main Vbda methods and choose the technique that best suits their application scenarios.
## 3 Results
### Calculation of Reference Values  
After the visual analysis of the results of different segmentation thresholds, the upper and lower values of the grid search were determined. Concretely, the grid search was performed between 14% and 22% and increasing the segmentation thresholds by 1% in each iteration. The optimal segmentation thresholds for the ventilation and perfusion images and for each lung are shown in Table 3. The table also includes the reference values of volume differences. These correspond to the segmentation of the images of the reference group using the pairs of segmentation thresholds shown in the same table.  
An example of the segmented SPECT ventilation and perfusion images is shown in Figure 2. These correspond to a 58-year-old woman of the reference group who presents normal lung function. Both images show relative homogeneous uptake patterns. The complete lung volume was segmented in both images and visually they are similar. The difference between ventilation and perfusion volumes in both lungs was less than 200 cm\({}^{3}\).
## 1 Introduction
_Integrity_ Phishing, fraud and exploitation of software vulnerabilities, traffic hijacking can eavesdrop activities and transactions, manipulate data, return falsified information and redirect clients to illegitimate sites. Similarly, weak interfaces and Application Program Interface APIs cannot protect users from accidental or malicious attempts [3]. For example, Hewlett-Packard ( Palo Alto, California, CA, USA) proposes an Integrity Virtual Machines Architecture [7]. 4. _Isolation_ Cloud Computing must have a level of isolation among all the VM data and the hypervisor [8; 9]. In Infrastructure as a Service (IaaS), it means isolating VMs' storage, processing memory and access path networks. In Platform as a Service (PaaS): running services and API calls must be isolated. Moreover, in Software as a Service (SaaS): isolation amongst transactions must be achieved. 5. _Availability_ Illegitimate users consume much of the victim's processing power, memory, disk space or network bandwidth. It also causes system slowdowns, which prevents legitimate users from using the service. Consequently, the VM becomes unavailable, causing a Denial of Service (DoS) or Distributed Denial of Service (DDoS). For example, a DDoS attack with compromised Internet of Things devices happen on Dyn (DNS infrastructure) [10] and paralysed some cloud computing-based sites such as GitHub and Airbnb [11]. This survey focuses on DoS and DDoS attacks and defenses applied to Cloud Computing availability. It will demonstrate that DoS and DDoS attacks (specifically XML-DoS and HTTP-DoS) present a serious threat to Cloud Computing, with many vulnerabilities, originating from various types of attacks and attackers, the latter originating from various types of attacks and attackers. This paper presents the design experiment and the metrics used to evaluate DoS and DDoS defenses. Our contributions to this survey are to present the following:  
* DoS and DDoS attacks targeting Cloud availability;
* a description of the specific XML-DoS and HTTP-DoS attacks;
* the defenses applied to DoS and DDoS attacks in Cloud Computing;
* the specific defenses against XML-DoS and HTTP-DoS attacks;
* a summary of how to evaluate such defenses. So far, the features of Cloud Computing and certain security aspects were presented. The remainder of this paper is organized as follows: Section 2 describes the work related to the classification of security issues, Section 3 identifies the possible attacks and attackers, Section 4 focuses more specifically on DoS and DDoS applied to Cloud Computing and Section 5 examines possible detections and mitigations of DoS and DDoS attacks. Furthermore, Section 6 identifies the experimentation and the metrics to evaluate the defenses and Section 7 offers conclusions.
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 3 Simulation Verifications  
### Construction of Variable Wing Leading-Edge Model  
The swept-wing leading-edge structure was composed of skin, a spar, and an internally embedded leading-edge deformation drive mechanism [32], as illustrated in Figure 7. The chordwise direction of the leading-edge structure was defined as the \(X\)-direction, the vertical height direction of the structure was defined as the \(Z\)-direction, and the lateral direction of the skin was defined as the \(Y\)-direction. In the simulation model of the wing leading edge, the unfolded length of the leading-edge skin was 957.6 mm, with a width of 350 mm and a thickness of 3 mm.  
The internal driving mechanism of the variable leading edge comprised a rocker arm and four connecting rods. The leftmost endpoint of the rocker's arm was hinged at the loading point O. One end of each of the four connecting rods was connected to the rocker's arm through hinge points 1, 2, 3, and 4, respectively, while the other end was connected to the four spars through brackets. In the finite element simulation analysis, the root of the leading edge was set as a fixed support. By applying a bending moment \(M_{Y}\) at the loading point O, the rocker's arm drove the four connecting rods to deflect, thereby achieving control over the deformation of the wing's leading edge.  
Under the action of the leading-edge deformation drive mechanism, the wing leading edge can rotate clockwise around the fixed point O. During the rotation process, the aerodynamic performance of the aircraft, including lift, drag, and stability, will correspondingly change. This design facilitates maintaining stable flight performance to the maximum extent possible during different mission modes.  
A simulation validation of the morphological reconstruction method for the skin structure of the variable wing leading edge was conducted for four different leading-edge deformation states, with deflection angles of \(2^{\circ}\), \(4^{\circ}\), \(8^{\circ}\), and \(10^{\circ}\), respectively.
## 4 Data Preprocessing  
In this study, the labeled Packet Capture (PCAP) traces provided by the UPC's Broadband Communications Research Group [33] is used for training and testing the deep learning models. The PCAP trace file capture and store network packets using programs such as Wireshark and tcpdump. The size of the original PCAP trace file provided is around 59 GB, with a total of 769,507 flows in the file. An additional information file provided with the PCAP trace file includes the labels (application name) of the traffic data and provides the ground truth for the prediction based on deep learning (To collect and accurately label the traffic flows, a volunteer-based system was used. For the details of it, refer to Reference [33]).  
For the preprocessing of the supplied data, based on the number of flows, eight types of applications with more than 1000 flows were selected. The eight applications' label names are the Remote Desktop Protocol (RDP), Skype, SSH, BitTorrent, HTTP-Facebook, HTTP-Google, HTTP-Wikipedia,  
Figure 1: Proposed architecture in software defined network (SDN)-based Network.  
and HTTP-Yahoo. The application layer payload data of the selected applications are filtered and extracted, and the learning data are generated using the extracted payload data.
# From NFT 1.0 to NFT 2.0: A Review of the Evolution of Non-Fungible Tokens
## 3 Blockchain and Solutions for NFT  
During the last few years, several blockchain platforms have been proposed. The choice of the right platform to develop an NFT application can affect the functionalities of the application itself. First of all, we identify the principal criteria to consider before developing an NFT platform:  
* **Transaction speed and confirmation time.** Transaction speed plays an important role in the success of an NFT project. It is not feasible for blockchains with lower transaction speeds to perform a bigger number of transactions per second [44]. Additionally, transaction speed has also a big impact on transaction costs. Indeed, if a blockchain has a low throughput, users have to pay higher fees to make sure that miners prioritize their transactions over others [45].
* **Transaction fee.** To speed up the adoption of NFTs, the market needs lower fees. Indeed, low transaction costs are crucial to the wider adoption of NFTs. The high fees for the minting process, but also for buying and selling NFTs, means that people can easily lose money. For this reason, the chosen blockchain used to implement the NFT project should have no fee or a small fee, such as in Algorand [46].
* **Smart contract functionality.** The functionalities of an NFT are based on the underlying smart contracts. Indeed, all NFT platforms rely on smart contracts for verifying ownership and handling the transfer of tokens. For this reason, NFT smart contracts are important, and a current NFT project should be implemented by using a blockchain with robust and reliable smart contract functionality. Furthermore, the blockchain's smart contract programming language is important because it impacts the time and cost of confirming a transaction.
* **Consensus algorithm.** A consensus algorithm is a basis for a blockchain to achieve an agreement. The chosen blockchain technology and its consensus algorithm can have an impact on the NFT environment. Blockchains based on the PoW consensus mechanisms normally have to deal with numerous issues related to scalability and the time required to reach a consensus. A good alternative is projects based on PoS consensus algorithms, such as the Ethereum blockchain. Another way to reduce the issues, in particular those related to energy consumption, is to use layer 2 solutions that help reduce the number of transactions.  
In the rest of the Section, we analyse the most used Blockchain platforms for NFT applications, as well as new solutions such as layer 2 and other protocols, by taking into account the features mentioned.
## 3 Reliability Analysis
### Formulation of the RBTO Problem Considering the Self-Weight  
The objective of analyzing strut-and-tie models using the TO strategy is to find a reinforcement arrangement within the design domain that minimizes the maximum von Mises stress of the structure for given loading and boundary conditions. Mathematically, the problem can be stated as  
\[\begin{split}\text{Minimize}& V=\sum_{e=1}^{ne}x_{e}V_{e}\\ \text{subject to}& K(\mathbf{x})U(\mathbf{x})=F(\mathbf{x})\\ & F(\mathbf{x})=f^{ext}(\mathbf{x})+f^{grav}(\mathbf{x})\\ &\sigma_{e}^{vm}-\sigma^{*}\leq 0\\ & V(\mathbf{x})=\sum_{i=1}^{NE}x_{i}V_{i}-\text{V}\leq 0\\ &\beta(\mathbf{u})=\beta_{t}\\ &\mathbf{x}=\{x_{1}\ x_{2}\ x_{3}\ \dots\ x_{n}\},\ \ x_{i}=1\ or\ \ x_{i}=10^{-9}\end{split} \tag{8}\]  
where \(\beta_{t}\) is the target reliability index and \(\sigma_{e}^{vm}\) the von Mises stress on each element is calculated using Equation (9).  
\[\sigma_{e}^{vm}=\Big{[}\sigma_{x}^{2}+\sigma_{y}^{2}+\sigma_{z}^{2}-\sigma_{ x}\sigma_{y}-\sigma_{x}\sigma_{z}-\sigma_{y}\sigma_{z}+3\tau_{xy}^{2}+3\tau_{xz}^{2} +3\tau_{yz}^{2}\Big{]}^{1/2} \tag{9}\]  
where \(V\) is the volume of the whole structure, \(V_{e}\) is the volume of the e-th element, \(K\) is the stiffness matrix of the structure, \(U\) is the displacements vector, \(F\) is the force vector, ne is the total number of finite elements of the structure, \(\sigma_{e}^{vm}\) is the von Mises stress of element e, \(\sigma^{*}\) is an admissible stress, \(x_{e}=0\) denotes empty material, and \(x_{e}=1\) denotes solid material. This formulation shows that the optimization procedure aims to minimize the number of elements and therefore minimize the volume of the structure. This structure is subject to the equilibrium equations as well as a stress constraint for each element that must be less than or equal to the permissible stress.
## 2 Numerical Model  
The numerical model presented in this manuscript was developed within the commercial software platform COMSOL. The finite element method (FEM) was employed to perform the calculations. The modeling of microwave infrared thermography (MIRT) necessitates the examination of two phenomena in a multi-physics mode, namely, electromagnetic wave propagation (EMW) and heat transfer (HT). The calculations were performed in the frequency (for EMW) and time domains (HT) (for continuous excitation) and in the time domain (for modulated excitation). The subsequent subsections present the specifics regarding the numerical modeling. First, the geometric characteristics of the model and the utilized mesh are demonstrated. Subsequently, the boundary conditions used are explicated, in conjunction with the material properties. Lastly, the outcomes of the model are illustrated and analyzed.