esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs +
## 4 Reinforcement Learning
### The MDP Framework for Solving Floorplanning Problems  
In floorplanning problems, the agent in reinforcement learning interacts with the environment by selecting a perturbation to iteratively generate new floorplan solutions. The objective is to minimize the total area and total wirelength, which serve as rewards to encourage the agent to learn better strategies and ultimately find an optimal floorplan solution. To explore better floorplan solutions, the following MDP is defined:  
1. State space \(S\): for the floorplanning problem, a state \(s\in S\) represents a floorplan solution, including a complete sequence of gates (\(\Gamma_{+}\), \(\Gamma_{-}\)), and the orientation of each module.
2. Action space \(A\): A neighboring solution of a floorplan is generated by predefined perturbations in the action space. The following five perturbations are defined: 1. Swap any two modules in the \(\Gamma_{+}\) sequence. 2. Swap any two modules in the \(\Gamma_{-}\) sequence. 3. Swap one module from the \(\Gamma_{+}\) sequence with one module from the \(\Gamma_{-}\) sequence. 4. Randomly move a module to a new position in both \(\Gamma_{+}\) and \(\Gamma_{-}\). 5. Rotate any module in the sequence pair by \(90^{\circ}\).
3. State transition \(P\): given a state, applying any of the above perturbations will result in the agent transitioning to another state, simplifying the probabilistic setting in the MDP.
4. Reward \(R\): Allocating rewards for actions taken in a state is crucial in reinforcement learning. In this floorplanning problem, the objective is to minimize the area and wirelength. Thus, the reward is assigned as the reduction in the objective cost. A positive reward is assigned whenever the agent discovers a better solution, while no reward is assigned otherwise. The reward function is defined as follows: \[r=\left\{\begin{matrix}F(s)-F(s^{\prime}),&F(s^{\prime})<F(s)\\ 0,&F(s^{\prime})\geq F(s)\end{matrix}\right.\] (7)  
Figure 3: A typical framework for MDPs.  
The reward \(r\) in this context refers to a local reward, representing the reward value obtained when the current floorplan transitions from state \(s\) to state \(s^{\prime}\) through a perturbation. Here, \(F\) represents the optimization objective function defined in Equation (3).
# Design and Performance Analysis of BDS-3 Integrity Concept
## 2 BDS Integrity Concept
### \(P_{sat}\) and \(P_{const}\) Calculations  
#### 2.3.1 \(P_{sat}\) Calculation for B1I  
For any satellite at any time \(t\), the B1I SIS health status is determined as following:  
\[\text{SatH1}=0\ (\text{Health}) \tag{1}\]  
where SatH1 is the integrity status parameter of B1I.  
Using the precision satellite orbit product to calculate the along-track error (denoted as \(\Delta T\)), cross-track error (denoted as \(\Delta N\)), and radial error (denoted as \(\Delta R\)) of the satellite orbit in the broadcast NAV message, and using the precision satellite clock product to calculate the satellite clock error (denoted as \(\Delta clk\)) in the broadcast NAV message. Their projection to the worst user location (WUL) position can be obtained by Equation (2) [26; 27].  
\[\text{SISURE}=\left|(\Delta R-\Delta clk)+C_{1}\cdot sign(\Delta R-\Delta clk )\cdot\sqrt{(\Delta T^{2}+\Delta N^{2})}\right| \tag{2}\]  
where \(C_{1}\) is the projection factor at the WUL results from the approximation of the ratio \(R_{earth}/a_{satellite}\), with \(R_{earth}\) is the mean earth's radius and \(a_{satellite}\) is the semi major axis of the BDS satellites orbit. \(C_{1}\) is given in Table 4 for MEOs and GEO/IGSOs, respectively.  
The satellite service failure condition of the B1I signal is determined by the following logical function over the hour interval:  
\[\text{F}_{\text{P}_{\text{sat}},\ \text{B1I}}=\left\{\begin{array}{ll}1,( \text{SISURE}\geq 4.42\ \text{URA},&\text{without a timely alert})\ \text{and}\ (\text{SatH1}=0)\\ &0,\text{other}\end{array}\right. \tag{3}\]  
where the URA indicator of the B1I signal can be obtained via the user range accuracy index (URAI) broadcast in the D1 NAV message of B1I [23].  
Faults have a finite duration before they are either corrected or before the user is notified. Here, we use the term mean time to notify (MTTN) in hours to denote the expected average fault duration [27; 28]. On this basis, \(\text{P}_{\text{sat}}\) of a year is defined as the ratio of the MTTN duration of satellite failure conditions to the total hours of all satellites in the constellation, which can be expressed as follows:  
\[\text{P}_{\text{sat}}=\frac{\sum_{\text{N}_{\text{sat}}}\sum_{\text{MTTN}}( \text{F}_{\text{Psat,SV}})}{\text{N}_{\text{hours}}\cdot\text{N}_{\text{SV}}} \tag{4}\]  
where \(\text{N}_{\text{sat}}\) is the total number of failure satellites of a year, \(\text{N}_{\text{hours}}\) is the total hours of a year, and \(\text{N}_{\text{SV}}\) is the nominal number of satellites in the constellation (for BDS is 27 in ICAO SAPRs).
## 3 Integration of the Dual-Mode Converter and the Conical Horn Antenna
### Full-Wave Simulations of the DMC and the DM-CHA  
The S-parameters of different simulations are represented in Figure 9. The six lines represent the reflection coefficient for the \(TE_{10}{}^{rec}\) mode in the rectangular waveguide, but for different structures and excitations. The first and second lines in the figure legend are associated to the reflection coefficient of the two mode converters (\(TE_{10}{}^{rec}-TE_{11}{}^{cir}\) and \(TE_{10}{}^{rec}-TE_{01}{}^{cir}\)) working independently, as in Figures 4 and 6. The third and fourth lines in the legend are the reflection coefficients of the DMC, when it is excited by either \(port\)\(1,\Sigma\) or by \(port\)\(2,\Delta\), respectively. Finally, the fourth and fifth lines in the legend are the reflection coefficients of the DM-CHA (the DMC connected to the conical horn), when it is excited by either \(port\)\(1,\Sigma\) or by \(port\)\(2,\Delta\), respectively.  
The return loss for the DMC and the mode converters working independently show good values. Nevertheless, some deterioration is observed in the DMC, especially in the difference pattern response (\(\Delta\)), due to the mutual interaction of the two mode converters when they are combined. The return loss for the DM-CHA system in Figure 9 is better than 20 dB for excitation by \(port\)\(1\) (\(\Sigma\)), and better than 9 dB for excitation by \(port\)\(2\) (\(\Delta\)), across the whole frequency band from 37.5 to 39 GHz. This is a 0.5 GHz reduction compared to the desired 37 to 39 GHz band.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 7 Conclusions
**Abbreviations**  
The following abbreviations are used in this manuscript:  
ACNs  
Actuator Nodes  
ADAS  
Advanced Driver Assistance Systems  
ADS  
Automated Driving Systems  
API  
Application Programming Interface  
AS
Application Servers  
AUTOSAR  
Automotive Open System Architecture  
AV  
Autonomous Vehicle  
BS  
Base Station  
CaaS  
Cooperation as a Service  
C-ITS  
Cooperative Intelligent Transport Systems  
IDS  
Data Distribution Service  
FOSS  
Free and Open Source Software  
GSM  
Global System for Mobile Communications  
ICN  
Information-Centric Networking  
ICT  
Information and Communication Technologies  
IMS  
IP Multimedia Subsystem  
IoT  
Internet of Things  
ITL  
Intelligent Traffic Light  
ITS  
Intelligent Transportation System  
LCM  
Lightweight Communication and Marshaling  
MITS  
Maritime Intelligent Transportation System  
MOM  
Message-Oriented Middleware  
MQTT  
Message Queuing Telemetry Transport  
MSN  
Marine Sensor Networks  
NDN  
Named Data Networking  
OPC UA  
Open Platform Communications Unified Architecture  
OS  
Operating System  
P2P  
Peer-to-Peer  
PuDZ  
Pop-up Drone Zones  
PX4  
PX4  
QuoS  
Quality of Service  
RLS  
Resource List Server
# Generalized Chirp Spread Spectrum for Underwater Acoustic Communications
## 1 Introduction
In contrast, the proposed G-CSS enables flexible utilization of frequency resources by independently configuring different modulation orders at the start and end points of the symbol. The proposed G-CSS configures frequency bins with different modulation orders at the start and end points of the symbol. Then, the proposed method modulates the signal by assigning chirp signals with starting and ending frequencies determined by the bits to be transmitted. As the number of frequency bins at the start and end points of the symbol can be flexibly configured, the proposed G-CSS allows for design with various modulation orders. Consequently, it enables the design and transmission of signals tailored to the transmission rate required by the system. Through computer simulations and ocean experiments, we compare and analyze the BER performance of the proposed G-CSS with conventional up/down-CSS and LoRa-CSS, demonstrating that the proposed G-CSS exhibits a superior BER performance at the same transmission rate.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 5 Analysis and Evaluation
### The Goal of Experiments  
To evaluate the performance of our proposed scheme, we compare _IPPNN_ with the Baseline and NPMML [11] algorithms.  
_(i)_ Precision measurement: To measure the model precision of our method and the influence of using Taylor approximation in neural network model, an experiment with _IPPNN_ and Baseline (a centralized version, non-privacy-preserving setting) and Baseline_app (with Taylor polynomial approximation) is designed to compare the classification accuracy.  
_(ii)_ Efficiency measurement: For the purpose of evaluating he efficiency of our method, we compare the execution time of _IPPNN_ and NPMML training different image datasets. We use the NPMML proposed in [11] as the comparative object because it is the closest state-of-the-art approach to ours. In NPMML, the neural network model is built using the combination of Paillier cryptosystem and a CCA-2 secure public-key encryption scheme.
# The BCL Method for DSSC: Basis and Applications +
## 7 The Role of Lithium on the Photo Conversion Efficiency of Ruthenium Dyes  
A few decades ago, researchers showed that the presence of lithium in the electrolyte enhances the performance of the cell [19]. This effect was attributed to the intercalation of the atom in the surface of titanium dioxide, which lowers the semiconductor energy band gap. On the other hand, the interaction of the lithium cation with ruthenium complexes containing dicarboxibipiridine ligands has been used as evidence in a series of studies employing different spectroscopic techniques. It has been confirmed [20] that the presence of the cation causes a red shift of the MLCT absorption band, a quenching of the fluorescence and the increase in the Ru(II)/Ru(III) oxidation potential.  
Another series of studies employing photoinduced and transient absorption spectroscopy on ruthenium dyes anchored to a titanium dioxide surface showed that the lithium  
Figure 29: Correlation between GEI and PCE.  
cation is able to annihilate the electric field created by the charge injected on the conduction band of titanium dioxide, a behavior identified with a Stark effect.  
A theoretical model [21] employing molecular simulation of the TiO\({}_{2}\) surface attributed the origin of this effect to an electric field generated by a charge in the semiconductor and/or to the presence of the oxidized complex.  
A study intending to examine these phenomena by means of a theoretical simulation and TDDFT calculations was performed [22]. It seeked to show that this behavior can be attributed to a dye-lithium adduct that is photochemically generated (Figure 30), and also to look how this adduct influences the photo conversion efficiency of the DSSC. To achieve this goal, the use of BCL methodology and DoLAc architecture is proposed to build a simulation model and calculate its efficiency through the Global Efficiency Index (GEI) with and without Li\({}^{+}\).
# Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
## 1 Introduction
In this study, we aim to improve the accuracy of the guide tree, especially for highly diverged sequences, by using three-way alignment to measure the distance between sequences. The three-way alignment is expected to improve the three constituents' pairwise alignments and distance estimations, leading to more accurate distance estimates and the resulting guide trees. We assess the performance of MAFFT [12] with two types of guide trees, one generated internally by MAFFT and the other based on three-way alignment.
## 4 Simulation and Analysis of EKF-GAPF
### Comparison of Particle Diversity  
To verify the advantage of EKF-GAPF in particle weight distribution, this paper conducted a comparative experiment between PF and EKF-GAPF, and statistically analyzed the particle weight distribution. Figure 5 shows the weight distribution of the two algorithms after 500 iterations, where Figure 5a shows the particle weight distribution of PF, and Figure 5b shows the particle weight distribution of EKF-GAPF.  
In Figure 5a, the particle weight distribution is extremely uneven, with a few particles having very high weights and most particles having weights close to zero. This phenomenon indicates that the diversity of particles decreases, that is, a serious particle degradation problem occurs, which will make only a small number of particles able to effectively contribute to the posterior probability distribution, while most particles have minimal impact on the estimation results. In other words, the filtering process mainly relies on a small number of high-weight particles. As the number of iterations increases,  
Figure 4: Performance comparison between AGA and traditional GA: (**a**) Results for test function 1; (**b**) results for test function 2.  
errors may gradually accumulate, ultimately reducing filtering accuracy and robustness. In contrast, the weight distribution in Figure 5b is relatively uniform, and the difference in particle weights is not significant, indicating that EKF-GAPF significantly alleviates particle degradation through global optimization of GA during the particle resampling stage and maintains particle diversity. This results in most particles making a relatively balanced contribution to the posterior probability distribution, thereby improving the accuracy and stability of filtering.
## 3 Methods
### Water Environment Evaluation Method  
To evaluate the quality of the water environment, this study employs a combination of the single-factor water quality index method and the comprehensive water quality index method.  
The formula for the single-factor water quality labeling index is expressed as follows:  
\[P_{i}=K_{i}+\frac{C_{i}-S_{ik}^{lower}}{S_{ik}^{upper}-S_{ik}^{lower}} \tag{1}\]  
\[P_{DO}=K_{DO}+\frac{S_{DO,K}^{upper}-C_{DO}}{S_{DO,K}^{upper}-S_{DO,K}^{lower}} \tag{2}\]  
In the formula, \(P_{i}\) represents the single-factor water quality index of the \(i\)-th parameter; \(C_{i}\) is the measured concentration of the \(i\)-th water quality parameter; and \(K_{i}\) denotes the water quality category of the \(i\)-th parameter, determined by comparing the measured \(C_{i}\) with the \"Environmental Quality Standards for Surface Water\" (GB 3838-2002) [42]. The possible values for \(K_{i}\) are 1, 2,..., 6. \(S_{ik}^{upper}\) and \(S_{ik}^{lower}\) indicate the upper and lower limits of the concentration of the \(i\)th index in the water quality of the \(K_{i}\) category of the Environmental Quality Standard for Surface Water (GB 3838-2002) [42], respectively. Taking dissolved oxygen as an example, when the measured concentration of \(C_{i}\) = 5.2 mg/L, its \(K_{i}\) category = III (\(S_{ik}^{lower}\) = 5.0, \(S_{ik}^{upper}\) = 6.0); at this time, \(P_{i}\) = 3 + (5.2 - 5.0)/(6.0 - 5.0) = 3.2, which characterizes the water quality in the middle of Class III.  
When the water body is classified as worse than Class V, the increasing trends of water quality parameters and DO (dissolved oxygen) are calculated using the following formulas:  
\[P_{i}=6+\frac{C_{i}-S_{i5}^{upper}}{S_{i5}^{upper}} \tag{3}\]  
Figure 2: Technical flowchart of the proposed method.  
\[P_{DO}=6+\frac{\frac{S_{DO,S}^{lower}-C_{DO}}{S_{DO,S}^{lower}}}{S_{DO,S}^{lower}} \tag{4}\]  
The formula for the composite water quality labeling index is as follows:  
\[P=\theta\overline{P}+(1-\theta)P_{max} \tag{5}\]  
\[\overline{P}=\frac{\sum\limits_{i=1}^{n}P_{i}}{n} \tag{6}\]  
In the formula, \(P\) represents the comprehensive water quality index; \(\overline{P}\) is the average value of the single-factor water quality indices; and \(\theta\) is the weight controlling the influence of each water quality index, with a range of [0, 1]. In this study, \(\theta\) is set to 0.5; \(P_{max}\) is the maximum value among the \(n\) single-factor water quality indices. The value of \(P\) can be used to determine the water quality category and compare pollution levels within the same category. According to the classification criteria, when \(1.0\leq P\leq 2\), the water body is classified as Class I; when \(2.0<P\leq 3.0\), the water body is classified as Class II; when \(3.0<P\leq 4.0\), the water body is classified as Class III; when \(4.0<P\leq\)5.0, the water body is classified as Class IV; when \(5.0<P\leq 6.0\), the water body is classified as Class V; and when \(P>6.0\), the water body is classified as worse than Class V.
## 3 Methodology
### UHR Implementation  
The spatial resolution of the UHR product is a factor of about 2 less than the standard L2W product, therefore additional tuning of the GF algorithm [23] was performed. To note, both primary and secondary thresholds are set an order of magnitude less than in the standard product. However, this is not a simple matching of the thresholds to the wind features. The reconstruction of the full resolution ASCAT data is a trade-off between higher spatial resolution and noise, which becomes important over small-scale and localized changes in the wind speed and direction, as well as in the presence of rain. Therefore, the gradient features associated with the UHR might not always correspond to the features associated with the L2 wind product due to the higher sensitivity of the former in capturing changes in the wind vectors. This is prevalent for high quality wind retrievals: on a 12.5-km (or 25 km) grid, wind variability at a sub-scale pixel might be hindered due to smoothness over a larger area, whereas the 3.125 km grid product is more challenged in detecting the respective variability over the same domain. Although the order of magnitude of the gradient wind field is the same (\(10^{-4}\) s\({}^{-1}\)) between ASCAT wind vectors of different resolutions, the thresholds that capture transitions within the field associated with the outflow boundaries are not the same. This indicates that the thresholds reflect to different scales of ocean wind, associated in this case with the evolution of an outflow boundary. The sensitivity of the UHR in response to the ocean state induces wind variability on a case-by-case basis, which is modulated according to the characteristics of the weather system. To capture this natural variability in the gradient wind field, additional small adjustments in the thresholds need to be applied. Due to the lack of quality control and appropriate rain flagging, the interpretation of UHR-derived GFs needs to be addressed with caution. For example, small features near precipitation may be related to rain-afflicted retrievals of low quality, which however may lead to gradient wind magnitudes that exceed the applied thresholds and ultimately to the generation of gradient wind features.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 2 Description of Flywheel Energy Storage System
### Structure and Components of FESS
#### 2.2.1 Flywheel Rotor
Low speed flywheels are usually made of heavier metallic material and are supported by either mechanical or magnetic bearings. High speed flywheels generally use lighter but strong composite materials and typically require magnetic bearings. The price of high speed flywheels can be up to five times higher than the cost of low speed flywheels, according to [18; 23]. The authors note that the cost of a flywheel system is governed by the design of the whole system, not the rotor, although this core element may dictate the design of the other elements in the system, hence the total cost. A new class of intermediate speed flywheels, benefiting from the low cost of steel materials but a sufficiently high energy density, is also being developed, based on the use of laminated steel. This has the potential to offer low cost, but also compact, options [36].
## 4 Lattice Composite Tower and Damping System
### Finite Element Model of Lattice Composite Tower and Damping System  
Traditional towers in a super-long-span suspension bridge usually have no space for the installation of energy dissipation devices due to their slenderness along the direction of the tower height. To allow space for the installation of energy dissipation devices, an alternative scheme with four separated sub-towers along the tower height as well as connecting beams at the tower top was proposed, as demonstrated in Figure 10. The term \"lattice composite tower\" refers to the whole of the four separated sub-towers connected through beams and dampers in a lattice form. A finite element model of a super-long-span suspension bridge with lattice composite towers and dampers was then established, as shown in Figure 11.  
Figure 10: FEM of a super-long-span suspension bridge with lattice composite towers.  
Figure 9: Time–frequency analysis of tower top displacement using Chi-Chi earthquake data.  
The frequencies of the original main tower, lattice composite tower and lattice composite tower with a friction damper were compared, as shown in Table 3. The frequency of the lattice composite tower was higher than that of the original main tower, indicating that the lattice composite tower effectively improved the lateral stiffness of the main tower in the super-long-span suspension bridge. The friction damper also enhanced the lateral stiffness of the main tower, resulting in a small increment in the frequencies.