esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Pure Gas Transport Properties
For a better understanding of the transport phenomena of gas separation membranes, the diffusion and solubility coefficients are often correlated with the molecular properties of gases [58]. Both neat polymers show a linear dependency of the diffusivity (\(D\)) on the squared gas diameter (\(d^{2}_{\mathit{eff}}\)) (Figure 7a,b), suggesting that the gas transport in neat polymers follows the diffusion solution model. In the presence of the MOF, both polymers show non-linearity of the \(D\) vs. \(d^{2}_{\mathit{eff}}\) correlation (Figure 7), which becomes more evident at higher MOF loading. A similar trend was recently observed for PIMs [59], where it was attributed to a different size-selectivity for small and large gas species due to the highly-interconnected free volume. These results suggest that the CuNi-MOF particles, with their internal void structure, have a similar effect on the gas diffusion of Pebax(r)1657 and PEEK-WC, introducing preferential diffusion pathways into the system which change the main transport mechanism, especially for the small molecules. As described above, the remarkably lower effective diffusion in Pebax(r)1657 is due to the much higher sorption capacity of the MOFs than of the neat Pebax(r)1657 [43]. Only for the CO2/N2 gas pair, there is a remarkable inversion from preferential N2 diffusion in the neat Pebax(r)1657 to preferential CO2 diffusion for the sample with the highest MOF concentration. Instead, in the glassy PEEK-WC, with much lower intrinsic diffusion coefficients in the neat polymer, the MOF has a strong positive effect on the total permeability of the mixed matrix membrane due to the far more rapid diffusion through the cages of the MOFs. In PEEK-WC, after an initial decrease, compared to the neat polymer, the solubility of all gases increases with the CuNi-MOF concentration. This is similar to what was observed in Pebax(r)1657, but only in PEEK-WC leads to an increasing permeability due to the positive effect on the diffusivity as well.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG
### Structure and Training Process of the MADDPG-VSC Algorithm  
#### 3.3.1 Multi-Agent Algorithm Model Structure  
The MADDPG-VSC algorithm is based on the DDPG framework and employs multi-agent DRL with an actor-critic architecture [35; 36; 37]. It is specifically designed to address the cooperative control challenges in variable-sweep wing UAVs operating in multi-agent environments. A key innovation of the algorithm lies in the introduction of an adaptive sweep angle control strategy, which enables precise optimization of continuous action spaces, allowing dynamic adjustments to formation strategies to meet complex mission requirements. Figure 6 illustrates the overall structure of the MADDPG-VSC algorithm, highlighting the flow of information and the interactions between various components during the training process. Each DDPG agent, such as a variable-sweep wing UAV, is equipped with its own actor and critic networks. The critic network in the MADDPG-VSC algorithm not only evaluates the state-action pairs of individual agents but also integrates  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Flight States** &
\begin{tabular}{l} **Flight Velocity** \\ **(km/h)** \\ \end{tabular} & **Flight Altitude (m)** & **Sweep Angle (\({}^{\circ}\))** \\ \hline Takeoff & 0–60 & 0–500 & 0–10 \\ Climb & 60–100 & 500–1000 & 10–20 \\ Cruise & 120–140 & 1000–2000 & 15–25 \\ Maneuver & 100–140 & 500–1500 & 20–35 \\ Dive & 120–160 & 0–1000 & 40–50 \\ Vertical Attack & 160–180 & 0–500 & 50–60 \\ Landing & 0–60 & 0–500 & 0–10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Flight states and sweep angle configurations of the L-30A UAV.  
policy information from other agents, forming a global perspective that guides overall formation decisions. This sharing and cooperation of information among multi-agent is crucial for the algorithm to effectively handle complex tasks.  
The variable-sweep wing UAV, through the rotating mechanism depicted in the figure, demonstrates how the adaptive sweep angle control strategy dynamically adjusts the flight control parameters through real-time interactions with the environment. The actor network selects optimal actions based on the current state, while stochastic noise is introduced to facilitate policy exploration, and the critic network evaluates these actions. The MADDPG-VSC algorithm leverages actor-critic networks to adapt to complex environments, optimizing UAV formations and reducing task uncertainties. Moreover, the algorithm enhances training stability through the use of an experience replay buffer and a soft update mechanism, mitigating the risks of overfitting and unstable convergence. During each training cycle, both the critic and actor networks progressively optimize their policies, and soft updates gradually align the target networks with the current networks, ensuring efficient decision making and task execution in multi-agent, complex environments.
# A Survey on Satellite Communication System Security
## 1 Introduction
Not only has KA-SAT been an attack target, but so has Starlink, the most commercialized SCS for general communication, during the ongoing conflict between Ukraine and Russia, which exposed many threats. For example, SpaceX, the company that operates Starlink, has reported instances of jamming attacks on Starlink terminals in Ukraine. In response, they have updated the system's software to counter such threats [6]. Additionally, recent research has also shown that Starlink terminals can be compromised by using a custom modchip to execute arbitrary code via voltage fault injection, which bypasses signature verification [7]. To address these emerging threats and ensure the reliability and security of global communications, numerous studies have been proposed. These efforts aim to deal with physical layer security [8; 9; 10], improve cryptographic security measures [11; 12; 13], and integrate terrestrial-space network frameworks [14; 15; 16; 17; 18]. However, there is still a lack of systematic categorization and a limited knowledge about SCSs' security issues in existing research.
## 3 The Basic of GNSS
### System Architecture
#### 3.5.1 Space Segment  
A constellation of multiple satellites, each with atomic clocks, is used to ensure that the system allows for users to continuously determine their position in all weather conditions anywhere on or near the Earth. The distance measurement method is based on measuring the difference in propagation delay of radio signals received from different satellites. The functionality of the system lies in the fact that, having signals received from precisely synchronized sources, the receiver does not need to be synchronized with the satellites as well, so no atomic clock is required for the receiver of the satellite signals. For two-dimensional positioning only, it is necessary to receive signals from at least three satellites, while at least four satellites are needed for three-dimensional positioning. It follows that to achieve global coverage, at least four satellites visible at all times from any point on Earth are required, which raises a significant design problem. The user on the ground cannot maintain strict synchronization with the satellites, and the ranges to different satellites calculated from observed delays are called pseudo-ranges. The true range must be calculated after accounting for clock error. In order to determine the receiver's location in three dimensions, latitude, longitude, and altitude, three true ranges are obviously required, hence the need for a fourth measured delay to calculate the receiver's clock error. If the clocks are synchronized and their assumed positions are correct, a single correction to the user's clock will cause all the spheres to intersect at a single point, the user's position.  
Figure 5: Basic architecture of a multi-sensor positioning system with GPS. Prepared on the basis of [46].  
Figure 6: Physical architecture of a positioning system based on GNSS and Wi-Fi and GSM-R communications. Prepared on the basis of [63].  
The orbital parameters of the satellites are shown in Figure 7 and include the great orbital half-axis, orbital eccentricity, orbital inclination (the angle between the plane of the orbit and the plane of the Earth's equator), perigee argument (the angle between perigee and the node, the point where the plane of the orbit intersects the plane of the equator) and node elevation (an astronomical term for the azimuthal angle relative to the first point of Aries), and the epoch (time) of the perigee passage [67; 68].
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Responsive Web-Based Data Acquisition Platform
#### 2.8.1 Main Dashboard  
The private web platform [[http://tairda.siteldi.mx/](http://tairda.siteldi.mx/)]([http://tairda.siteldi.mx/](http://tairda.siteldi.mx/)) integrates a dashboard to see retrieved data from all RiverCore nodes. The dashboard shows real-time data of the deployed nodes, a chart of current and previous data, and a map with markers which show the position of each node registered on the platform. Such markers change color according to the warning level of the corresponding river section. Fixed devices are registered and managed on a dedicated section of the web platform, where parameters such as the node name and sensor features are configured.  
A similar section has been created to show the data captured by the weather station nodes. On both dashboards, clickable icons can be seen on a map representing the real-time position and status of the stations. Some variables are graphed in real-time, but, importantly, all data from every node are stored and can be retrieved in the historical section.
## 1 Introduction  
Dispersion accumulation has become an inevitable problem in modern ultra-long distance optical fiber communication with ultra-high rate and ultra-wide bandwidth, and it will seriously affect the construction of an all-optical network system. To solve the problem, the cumulative dispersion generated after long distance transmission in all channels needs to be compensated by broadband dispersion compensation (BDC). What's more, the smaller the residual dispersion, the longer the transmission distance of dispersion is limited [1; 2; 3].  
In 2004, [PERSON] et al. first proposed that the air hole-quartz structure dual-concentric-core microstructure fiber (DCC-MSF), based on the mode coupling theory, can be used for realizing dispersion compensation of single wavelength [4]. Since then, the air hole-quartz structure DCC-MSF has interested researchers all over the world, and there are now more documents about it [5; 6; 7]. In 2016, [PERSON] et al. designed a DCC-MSF. The negative dispersion value varied from \(-\)1000 to \(-\)2500 ps/(nm\(\cdot\)km) within a 200 nm bandwidth. However, the residual dispersion value was not mentioned [8]. The existing problem is that the air hole easily deforms or collapses during the MSF drawing process, which changes the characteristics of MSF. What's more, the collapse of the air hole makes fusing between MSF and single-mode fiber (SMF) more difficult, which greatly limits the industrialization of such MSF in dense wavelength division multiplexing (DWDM) systems. Alternatively, all-solid MSF can be constituted by using the doped rod to replace the air hole. In addition,a doped rod with a different effective refractive index (\(n_{\text{eff}}\)) value can be acquired through reasonable doping [9; 10].  
All-solid MSF has been researched widely in recent years because it has the advantages of low drawing difficulty and can be used for easily accessing existing systems [11; 12]. In 2017, [PERSON] et al. presented an all-solid DCC-MSF, which can realize dispersion compensation of single wavelength. Its negative dispersion value at 1550 nm reached \(-\)32,620 ps/(nm\(\cdot\)km) [13]. However, only compensating the cumulative dispersion of some wavelengths makes it very difficult to satisfy the need of DWDM systems in fiber communication. In 2018, [PERSON] et al. proposed an all-solid DCC-MSF with BDC covering S + C bands. Its compensating multiple was 15.43 times standard SMF, and the residual dispersion value varied within \(\pm\)0.15 ps/(nm\(\cdot\)km) [14]. Although the variation range of residual dispersion was small, the compensation multiples and bandwidth were slightly insufficient.  
In this paper, an all-solid DCC-MSF with BDC characteristics is presented. The wide-band dispersion compensation in the range of 1385 nm-1575 nm is realized. In addition, it is demonstrated that the designed MSF is easily fabricated and can easily access existing optical network systems, which provides the basis of a theory for new optical transmission medium for the generation of a new DWDM system.
## 3 Medium Access Control Layer
### Time-Based Schemes  
Protocols based on this scheme use the complete bandwidth for a certain amount of time, so multiple transmissions have to be distributed in time by scheduling them, reserving the channel time prior to transmission or directly sending the data packet. Specifically, there are mainly two different strategies to avoid collisions:  
1. _Scheduled-based:_ a time interval or frame is divided between all nodes. With this strategy, two techniques are normally used: 1. _Fixed TDMA:_ each node is assigned a time period where it is able to transmit. 2. _Adaptive TDMA:_ time periods are assigned on demand, either by dynamically assigning the slots by some coordinator or by allowing the nodes to contend for the slots.
2. _Random-based:_ selections of the transmission start and end times are arbitrary, and nodes directly compete for channel acquisition. This group of protocols can also be sub-divided into: 1. _Direct._ Protocols under this group send data directly without performing any channel reservation. 2. _Reservation._ Prior to the transmission of the actual data packet, nodes reserve the channel using control packets.
###### Abstract  
As an important national strategy infrastructure, the Space Information Network (SIN) is a powerful platform for future information support. The architecture model of the SIN is of great significance to the construction and development of the SIN. For the problems related to the poor versatility, portability, and recombination of the existing architecture modeling methods of the SIN, in this paper, based on the Data as a Center (DaaC) modeling idea, we propose a reconfigurable model of the task-oriented architecture of the SIN. Combining with the typical characteristics of the SIN, and drawing on the advantages of activity-based flexibility, service-oriented integrity, and object-oriented reusability, we propose a DaaC modeling idea with space data. The DaaC modeling idea can solve the problems related to the poor versatility and portability of the SIN architecture. Based on the DaaC idea, we analyze the requirements of the task-oriented architecture, and define the basic concepts of SIN reorganization, including the reconfigurable target, reconfigurable scheme, and reconfigurable SIN. We establish the reconfigurable principles of loose coupling, compatibility, isolation, and deconstruction. Meanwhile, we analyze the realization mechanism and methods of the task-oriented reconfigurable model of the SIN based on DaaC. Finally, we take a typical SIN as an example, and make a case study on land-based anti-missile combat activities as the task background based on the DoDAF2.0 (Department of Defense Architectural Framework 2.0) framework and the STK (Satellite Tool Kit) simulation platform. The case results are consistent with the theoretical expectation, and it verifies the feasibility and effectiveness of our proposed method.  
Keywords:space information networks; architecture; data as a center; reconfigurable model; task-oriented; resource reorganization +
Footnote †: journal: Applied Sciences
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 3 Precoder and Equalizer Design
### Complexity Analysis  
In this section, the complexity of the proposed algorithms are evaluated. In Algorithm 1, the digital equalizer is computed using the inversion of a \(U\times U\) matrix, which results in a complexity \(O(U^{3})\). This inversion is repeated \(i_{\max}\) times, and then the total complexity of Algorithm 1 is \(O(i_{\max}U^{3})\).  
In the Algorithm 2, the metric of Equation (29) is tested for all elements of the codebook \(\mathcal{F}_{a}\), composed by the vectors of \(\mathbf{A}_{rx}\in\mathbb{C}^{N_{rx}\times N_{d}N_{ny}U}\). The product complexity of the matrix of size \(U\times N_{rx}\) by a vector of size \(N_{rx}\) is \(O(N_{rx}U)\). Then, the complexity of the metric evaluation is \(O(N_{cl}N_{ny}N_{rx}U^{2})\). As these operations are repeated \(N_{rx}^{RF}\) times, the complexity of the Algorithm 2 is \(O(N_{cl}N_{ny}N_{rx}N_{rx}^{RF}U^{2})\). Therefore, the total complexity computation of the proposed equalizer is \(O(i_{\max}U^{3}+N_{cl}N_{ny}N_{rx}N_{rx}^{RF}U^{2})\).
```
1:\(\mathbf{W}_{a,0}=\) Empty Matrix
2:\(\overline{\mathbf{W}}_{res,k,0}=\mathbf{H}_{k}^{H}\)
3:for\(r=1,\ldots,N_{rx}^{RF}\)do
4:\(\Pi_{k,r}=\overline{\mathbf{W}}_{res,k,r-1}\mathbf{A}_{rx}\)
5:\(n_{opt,r}=\operatorname*{argmax}_{i=1,\ldots,N_{cl}N_{ny}U}\sum\limits_{k=1 }^{N_{r}}\left[\Pi_{k,r}^{H}\Pi_{k,r}\right]_{IJ}\)
6:\(\mathbf{W}_{a,r}=\left[\mathbf{W}_{a,r-1}\middle|\mathbf{A}_{rx}^{(n_{opt,r})}\right]\)
7:\(\overline{\mathbf{W}}_{res,k,r-1}=\left(\mathbf{H}_{k}^{H}-\mathbf{W}_{ad,k} \right)\overline{\mathbf{R}}_{k}\)
8:endfor
9:return\(\mathbf{W}_{a}=\mathbf{W}_{a,N_{rx}^{RF}}\)
```  
**Algorithm 2** Analog part of equalizer
_Article_  
Residual Properties Analysis of Steel Reinforced Recycled Aggregate Concrete Components after Exposure to Elevated Temperature  
[PERSON] 1,2,*, [PERSON] 1, [PERSON] 1 and [PERSON] 1  
College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China; [EMAIL_ADDRESS] (R.Y.); [EMAIL_ADDRESS] (C.J.); [EMAIL_ADDRESS] (F.N.)  
Key Laboratory of Disaster Prevention and Structure Safety of Chinese Ministry of Education, Nanning 530004, China  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-138-7880-6048  
Received: 6 October 2018; Accepted: 19 November 2018; Published: 24 November 2018  
**Abstract:** The application of recycled aggregate concrete (RAC) has developed rapidly in recent years. But how to evaluate the residual properties of RAC after the fires is more beneficial to the further popularization and application of RAC. This paper presents the residual properties of RAC and steel reinforced recycled aggregate concrete (SRRAC) components after exposure to elevated temperature. A total of 176 specimens (120 rectangular prisms specimens, 24 SRRAC short columns and 32 SRRAC beams) were designed and tested after exposure to elevated temperature. The parameters were considered in the test, including replacement percentage of recycled coarse aggregate (0%, 30%, 50%, 70% and 100%) and exposure to different temperatures (20, 200, 400, 600 and 800 degrees centigrade). According to the test results, heat damage and residual properties of specimens were analyzed in detail, such as surface change, mass loss, bearing capacity degradation, stiffness degradation, ductility and energy dissipation of specimens under the elevated temperature. The results showed that a series of significant physical phenomena occurred on the surface of RAC and SRRAC components after exposure to elevated temperature, such as the color changed from green-grey to gray-white, chapped on the concrete surface after 400 degrees centigrade and the mass loss of concrete is less than 10%. The degradation of mechanical properties degenerated significantly with the increase of temperature, such as the strength of RAC, and compressive capacity, bending capacity, shear capacity and stiffness of SRRAC components, among that, the degradation of the strength of RAC was most obvious, up to 26%. The ductility and energy dissipation of SRRAC components were insignificant affected by the elevated temperature. Mass loss ratio, peak deformation and bearing capacity showed a slight increase trend with the increase of replacement percentage. But the stiffness showed significant fluctuation when replacement percentage was 70% to 100%. And the ductility and energy dissipation showed significant fluctuation when replacement percentage was 30% to 70%.  
**Keywords:** recycled aggregate concrete (RAC); steel reinforced recycled aggregate concrete (SRRAC); elevated temperature; residual properties
## 3 System Reliability Evaluation Method That Considers Multiple Failure Criteria
### Entropy Weight Method  
Entropy theory was first introduced to information theory as a measure of information disorder by [PERSON] (1954). The entropy weight method, an objective weighting method, utilizes entropy theory to determine the index weight according to the information provided by the observations of each index. The importance of the index is reflected by the degree of deviation between the observations of the same index. The greater the degree of deviation, the smaller the entropy value, and the corresponding index weight should be larger [PERSON] (1954). The objective weight calculation procedures of the indexes are as follows.  
(1) Construct the evaluation matrix \(\mathbf{M}\):  
Let the evaluation matrix \(\mathbf{M}=(x_{ij})_{\max\mathrm{cn}}\) be an information system according to the knowledge and experience of peer experts.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**n** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \(RI\) & 0 & 0 & 0.58 & 0.9 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The random consistency index (RI) [PERSON] (1954).  
Normalize the evaluation matrix M:  
In order to eliminate the impact of different index dimensions on evaluation, the evaluation matrix needs to be normalized.  
\[v_{ij}=\frac{x_{ij}-\min(x_{j})}{\max(x_{j})-\min(x_{j})},i=1,2,\ldots,m;j=1,2, \ldots,n. \tag{18}\]  
where \(v_{ij}\) is the standardized value of the \(j\)th index of the \(i\)th evaluation sample. \(m\) and \(n\) represent the number of experts and the number of indexes, respectively.  
(2) Calculate the characteristic proportion (\(p_{ij}\)) of the \(j\)th index of the \(i\)th evaluation sample:  
\[p_{ij}=\frac{v_{ij}}{\sum\limits_{i=1}^{m}v_{ij}},j=1,2,\ldots,n, \tag{19}\]  
which satisfies \(0\leq p_{ij}\leq 1\).  
(3) Calculate the entropy value (\(e_{j}\)) of the \(j\)th index:  
\[e_{j}=-\frac{1}{\ln(m)}\sum\limits_{i=1}^{m}p_{ij}\ln(p_{ij}),j=1,2,\ldots,n, \tag{20}\]  
where if \(p_{ij}=0\) or \(p_{ij}=1\), \(p_{ij}\ln(p_{ij})=0\).  
(4) Calculate the difference coefficient (\(d_{j}\)) of the \(j\)th index:  
\[d_{j}=1-e_{j},j=1,2,\ldots,n. \tag{21}\]  
(5) Calculate the entropy weight (\(u_{j}\)) of the \(j\)th index:  
\[u_{j}=\frac{d_{j}}{\sum\limits_{j=1}^{1}d_{j}},j=1,2,\ldots,n. \tag{22}\]