esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Studies
### Simulation of the DWTA Problem
#### 4.2.4 Performance Metrics  
In this paper, the inverted generational distance (\(IGD\)) is used to measure the performance of multiobjective optimization algorithms in solving DWTA problem [41]. \(IGD\) is a variant of \(GD\), which can not only evaluate the convergence of multiobjective solutions, but also evaluate the distribution characteristics of solution set to a certain extent. The calculation formula is as follows:  
\[IGD(P,P^{*})\triangleq\frac{\sum_{i=1}^{|P^{*}|}\mathit{dist}\big{(}P^{*}_{i}, P\big{)}}{|P^{*}|} \tag{21}\]  
where \(P^{*}\) is set of uniformly distributed points in the objective space along the true Pareto front (PF) or nearly true PF when it is hard or impossible to get the true PF, \(P\) is an approximate PF of the true PF, \(|P^{*}|\) represents the number of points in \(P^{*}\), and \(\mathit{dist}\big{(}P^{*}_{i},P\big{)}\) is the minimum Euclidean distance between \(P^{*}_{i}\) and elements in \(P\). The smaller the value of \(IGD\), the better the convergence and distribution of the searched solution.  
As to multiobjective DWTA problems, it is hard to obtain the true PF. To address this issue, we run each instance for 30 times, execute the non-dominated sorting on these solutions obtained earlier and select 500 evenly distributed points in Pareto front as the true Pareto front \(P^{*}\).
## 3 Materials and Methods
### Classification Methods
#### 3.2.9 Decision Tree  
The decision tree (DT) is a type of model used for both classification and regression. Trees answer sequential questions, which are sent down a certain route of the tree given the answer. They are intuitive and provide one of the simplest portrayals for classification purposes. Tree depth represents how many questions are asked before reaching the predicted classification [36].
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 4 Concept
### P2P Network  
To agree on a common set of TSAs, a communication channel is needed through which all participants can share their trusted TSAs. Since there might not be a central instance for the communication that is trusted by all, a P2P network is used allowing the participants to interact directly with each other without an intermediary. As already described, the P2P network is established dynamically when a critical traffic incident occurs between the involved parties as soon as the defined trigger is released. To be able to set up such a network dynamically, all participants' IPs must be known by everyone. Depending on the application, this may not be the case, so the IP-addresses must first be exchanged with each other. In practice, this can be done in every conceivable way. In the following, we have outlined an exemplary possibility in more detail.  
Let \(P\) be the set of all participants, so one of the participants \(a\in P\) publishes its IP addresses publicly so that the other participants from the set \(P\setminus a\) can obtain it. Subsequently, each participant from \(P\setminus a\) sends its own IP-address to participant \(a\). In this way, participant \(a\) knows the IP addresses of all participants, enabling him to forward the addresses of all participants to everyone. In this way, each participant receives the IP-addresses of all other participants, which means that each participant is now able to communicate with one another.  
Usually, the initiating participant should be responsible for publishing the primary IP address. The IP address can be transmitted via various channels, via a website or AIS messages. It is also conceivable to publish the address as a service in a public Maritime Service Registry (MSR) of the MCP. The service registry acts as a directory where services of maritime stakeholders can be found by entering various parameters such as keywords or geographical regions. For example, a port operator could publish its IP address as a service in the Maritime Service Registry. Vessels wishing to enter the port would then have the opportunity to search the MSR for a corresponding service, in order to find the stored IP-address that is required for the initial contact. After all, the parties have already contacted the port operator; therefore, the latter can set up the P2P network as described above. However, in some cases, it might not be possible for every participant to directly expose an IP-port for communication (e.g., due to some ship-related IT security regulations). Also, as IP connectivity is not always available, it is conceivable to use other technologies (such as VDES) as a backup solution for communication. Another possible solution is an architecture, as proposed by IEC 63173-2 [42], where a service at the shore-side exposesa publicly available interface via IP and the communication to the ship (\"last mile\") is realized with a more secure communication channel.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 5 Experimental Results
### Results for Generation Tasks
#### 5.2.1 Achievable Rates Under Different Distance Measures  
Figure (a)a illustrates the simulation semantic rate-distortion curves under Wasserstein distance and reverse KL divergence, denoted by \(R_{WD}\) and \(R_{RKL}\), respectively. We observe a three-way trade-off among transmission rate, MSE distortion, and semantic distance: For a fixed distortion level, increasing the transmission rate leads to smaller semantic distance. Conversely, under a fixed rate, stricter semantic distance constraints result in higher bit-wise distortion. To obtain a more intuitive understanding of the semantic rate-distortion  
tradeoff, we also demonstrate a 3D surf plot of the semantic rate-distortion function in Figure 3b.  
Moreover, we also note that the rate under Wasserstein distance to achieve the same levels of distortion and semantic distance is always higher than that under reverse KL divergence. This confirms that Wasserstein distance imposes a more stringent measure of statistical dissimilarity, as noted in Section 4.3. The above observations are aligned with the theoretical predictions for Gaussian sources in Theorems 1 and 2.
## 3 System Architecture
### Line Coding  
Line coding is the process of converting digital data to digital signals that convert a sequence of bits into a digital signal. At the sender's side, the digital data are encoded into a digital signal. At the receiver's side, the digital data are recreated by decoding the digital signal.  
Run-length limited (RLL) coding is a common/useful line-coding technique for a limited bandwidth communication channel. The RLL codes include four parameters: m, n, d, and k. The rate of the code was calculated using the two parameters m and n. However, the two parameters d and k specify the minimum and the maximum number of zeros between consecutive codes. Special RLL codes are used in light communication to provide dc balance because flicker mitigation is required in VLC (except in some OCC modes). In the IEEE 802.15.7-2018 standard, the Manchester codes 4B6B and 8B610 are  
Figure 1: Reference architecture of the multiple-input multiple-output–camera on–off keying (MIMO–COOK) scheme.  
recommended for deployment in the OCC system [25]. Table 1 presents three line codes, which are recommended in IEEE 802.15.7-2018.
## 3 Bispectrum Analysis of EEG Signals for an Emotion Recognition System
### EEG Database and Its Acquisition  
Currently, the existing EEG databases available for research purposes mostly contain the EEG signals of motor imagination ([PERSON] et al., 2018; [PERSON] et al., 2018), sleep stages ([PERSON] et al., 2018) and epilepsy ([PERSON] et al., 2018), and only a few EEG databasesrelated to the emotional states are available, such as Mahnob-HCI [42], the SJTU Emotion EEG Dataset (SEED) database [43] and the DEAP database [4]. This work utilized the DEAP database [4] that provided the most EEG data from participants; emotional stimuli came from video music clips, and the emotional states of the participants were defined in 2D emotion models (i.e., the arousal and the valence levels [4]), producing four quadrants of emotion: the high arousal, high valence (HAHV); the low arousal, high valence (LAHV); the low arousal, low valence (LALV); and the high arousal, low valence (HALV) emotion classes.  
In this database, the emotional stimuli for each quadrant of the 2D emotion model were portioned equally with 10 music videos, and the excited EEG signals from the brain activity were recorded using 32 channels of an EEG Biosemi ActiveTwo with a sampling rate of 512 Hz. The DEAP database also provided a list of the videos and their YouTube URLs, the basic statistics of participants' ratings, the physiological signals, the facial expression videos and the preprocessed data. EEG signals were recorded from 32 people (balance between women and men) between the ages of 19 and 37 years. During the recording process, each participant was asked to rate, on a scale of 1-9, his or her emotional response when watching the video in terms of arousal, valence, likes/dislikes, the dominance and the familiarity through the Self-Assessment Manikin (SAM) questionnaire [4].  
As EEG signals are vulnerable to noises, several noise filtering methods, such as to remove power line noise, eye and muscle artefact removal [44,45], are usually applied. In DEAP, several pre-processing steps that consist of a band-pass filtering at 50 Hz and 60 Hz, a high-pass filtering at 2 Hz, a down-sampling process to 128 Hz and the removal of artefacts corresponding to electrooculography (EOG) have been conducted [4]. The DEAP database then provides 32 matrices of 40 \(\times\) 40 \(\times\) 8064, representing video/trial \(\times\) physiology channels \(\times\) duration of the signals, respectively; however, for our research purposes, these original 32 matrices were divided into a 1280 person-video data matrix (32 people \(\times\) 40 videos). In this work, we only considered the EEG signals; thus, only 32 out of 40 physiology signals are used, and we eliminated the first three-second baseline from the 63-second duration of the signals, so the resulting size of each matrix was 32 \(\times\) 7680 (EEG channels \(\times\) duration of the signals). The 1280 matrices were further divided into 320 matrices (32 people \(\times\) 10 videos) for each class of emotion.
## 3 Results
### Effects of Injection at the Valve and Reactivity Fuels on the Emission Attributes
#### 3.3.2 CO Emission  
As also observed in Figure 16a, the CO emission increased from 4.5 to 5.5 bar IMEP, then decreased as the load increased during the combustion of the B5 in reactivity with the biogas injected at the valve. The burning of the B5, along with the biogas injected at the valve, demonstrated the highest capability in reducing the CO emission. When the biogas was premixed, the CO emission rises, especially at 4.5-5.5 bar IMEP and shows otherwise at full 6.5 bar IMEP. The reduction in the CO emission manifested more significantly at low to mid charges, compared to the combustion of conventional diesel and premixed biogas. The less CO emission could be due to the higher in-cylinder temperature developed when B5 was used, hence improved the rate of the CO oxidation, as shown in Figure 6c under Section 3.1.1. Generally, Figure 6c indicated the less in-cylinder temperature at a load of 5.5 bar IMEP, which could likely be the reason for a high CO emission with this particular load. The use of B5 and biogas injected at the valve reduced the CO emission averagely by 20.33%, at moderate IMEP, compared to the combustion of conventional diesel and premixed biogas. However, the trend of the CO emission was found comparable to that of the usual diesel RCCI combustion for both premixed and biogas injected at the valve. The in-cylinder mixing of biogas minimized the persistent challenge of CO emission compared to the traditional premixed way, as clearly shown in Figure 16b. The analysis of the reduction, relative to the biogas injection approaches, revealed that injection of the biogas at the valve was able to cut down the CO emission on average by 9.87%.  
According to Figure 16c, the CO emission was found less at 1600 rpm and increased as the speed progressed to 1800 rpm then decreased as the engine speed reached 2000 rpm and maintained the pattern at 4.5-5.5 bar IMEP. The difference in the CO emission reduced as the load increased and remained virtually similar at medium load capacity for all the engine speeds. However, the CO emission tended to decrease with the increase in the engine speed from 5.5 to 6.5 bar IMEP. Besides, as observed from Figure 16c, the pattern of the CO emission at an engine speed of 1600 rpm differed from that of other speed levels; thus, generating the highest CO emission at 6-6.5 bar IMEP. More of the production of the CO happened at 6-6.5 bar IMEP because of the increase in the amount of biogas.  
Figure 16: Engine-out CO emission for (**a**) B5/biogas RCCI for premixed and port injection at the valve, (**b**) diesel/biogas in a premixed and port injection at the valve, and (**c**) B5 and premixed biogas RCCI combustion at various engine speed.  
However, these trends could be because, at this load range, most of the CO generated gets emitted at 1600 rpm while oxidized and gets emitted as CO\({}_{2}\) when the engine speed increases to higher levels.
## 3 Methodology
### Activity Identification Phase  
First, the activities of the resources from the construction site are identified using sensors and deep learning algorithms. Motion data (e.g., acceleration, orientation, etc.) from different articulated body parts of the equipment are captured using IMUs. These data contained noises and inconsistencies. A noise filtering technique, Median filtering is used to eliminate the noises and the linear interpolation method is used to interpolate the missing data points. Data augmentation techniques are used to generate synthetic data capturing the inter-class dependencies among different activities (e.g., loading, hauling, etc.). Then a deep learning technique, long short-term memory (LSTM)-based recurrent neural network (RNN) is trained and tested to identify activities of the equipment. Details of this activity identification framework are presented in [PERSON] & [PERSON] [29]. Four major activities are identified in this study, which are essential to developing the DES model, and they are _Loading_, _Hauling_, _Dumping_, and _Returning_ dump trucks. After the activities are  
Figure 3: Framework for data-driven decision making.  
identified, further steps can be categorized into two phases: the discovery phase, and the update phase. In the discovery phase, a baseline discrete-event simulation model is created from the activities using the process mining technique. Once the baseline model is created, the cycle time of each activity is calculated and fitted to the distribution using Chi-square goodness-of-fit. As the project progresses, the prior distributions are updated using the Bayesian updating technique and these updated distributions are used as a new input to the baseline simulation model. An updated estimation is generated from the model and the plan is updated based on the new prediction. The discovery and update phases are explained in detail in the following sections.
## 1 Introduction
In recent years, frequent forest fires have posed significant threats not only to forests, lives, and property within affected areas but also to the safe operation of transmission lines traversing forested regions. Transmission lines are usually located outdoors and are subjected to wind loads year-round. When a fire occurs, transmission towers are exposed to the combined effects of fire and wind loads. Therefore, it is necessary to study the multi-hazard response of transmission tower-line systems under fire and wind loads. The paper investigates the impact of high temperatures on the bearing capacity of transmission tower-line systems under wind load and explores the effects of uneven horizontal spacing distribution and changes in the elevation of the target tower on the bearing capacity of the tower-line system. The failure criteria for transmission tower components at high temperatures were determined by considering the constitutive relationship of steel at ambient temperature and the variation patterns in material strength and elastic modulus with temperature. A finite element model of the transmission tower-line system was established using ABAQUS software. The paper studied the effects of temperature, uneven horizontal spacing distribution, and changes in the elevation of the target tower on the response of the transmission tower-line system by comparing collapse-resisting wind speeds and collapse processes under various conditions.
## 5 Conclusions  
Based on the IDDES method and sliding grid technique, the effects of three factors-- such as the porosity of the wind barrier, the crosswind speed, and the running speed of the HST--on the sudden change in the aerodynamic load of the HST induced by a wind barrier are analyzed, and the reasons for the sudden change from a flow field perspective are given. The influences of the buffer structure with the three different lengths of 45 m, 90 m, and 135 m on the sudden change in aerodynamic load of a HST are investigated. The main conclusions are as follows:  
1. The lower the porosity of the wind barrier, the higher the crosswind speed, and the lower the running speed of the HST, leading to a larger sudden change in the aerodynamic load of the HST. Comparatively, the degree of the sudden change in the aerodynamic load is greater for the HST entering the wind barrier area of the bridge than exiting.
2. When a HST traverses the wind barrier area of a bridge, there are noticeable changes in the wind speed within the running area and the flow field around the HST. These changes lead to significant fluctuations in aerodynamic pressure on surfaces of the HST, leading to a drastic variation in the pressure difference between the windward and leeward surfaces, as well as between the top and bottom of the train. As a consequence, the HST experiences a sudden change in its aerodynamic load.
3. A buffer structure measuring 45 m in length demonstrates limited effectiveness in reducing the sudden change. However, increasing the length of the buffer structure to 90 m significantly improves its ability to mitigate these fluctuations. This results in a 10.04% reduction in the amplitude of the change and a 55.59% reduction in the amplitude of the rate of change in the lateral force coefficient of the head car. On the other hand, a further increase in the length of the buffer structure to 135 m leads to a negligible enhancement in the buffering effect. Therefore, a 90 m buffer structure is considered the most suitable option.
4. The installation of a 90 m buffer structure significantly enhances the safety of HST operations. It decreases the WLRR and DC of the head car by 14.30% and 5.11%, respectively.  
[PERSON] and [PERSON] conceived this research; [PERSON] completed a series of numerical simulations and analyzed the data; [PERSON] provided suggestions for the writing and revision of the paper; [PERSON] and [PERSON] jointly wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper was supported by the Science and Technology Research and Development Program Project of China National Railway Group Co., Ltd. (grant number L2021G002).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.2 Crowding
```
1 Select parent 1 and 2 randomly without replacement;
2 Generate offspring \(c_{1}\) and \(c_{2}\);
3 if\(d(p_{1},c_{1})+d(p_{2},c_{2})\leq d(p_{1},c_{2})+d(p_{2},c_{1})\)then
4 if\(f(c_{1})>f(p_{1})\)then replace \(p_{1}\) with \(c_{1}\);
5 if\(f(c_{2})>f(p_{2})\)then replace \(p_{2}\) with \(c_{2}\);
6 else
7 if\(f(c_{2})>f(p_{1})\)then replace \(p_{1}\) with \(c_{2}\);
8 if\(f(c_{1})>f(p_{2})\)then replace \(p_{2}\) with \(c_{1}\);
9 end while
```  
**Algorithm 3**Deterministic crowding. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[18] & 2003 & DDC-GA &
\begin{tabular}{c} Improved crowding genetic algorithm using an additional dispersion feature to encourage dispersion of search agents and discovery of newer optima. \\ \end{tabular} & Crowding \\ \hline
[43] & 2004 & CDE & Crowding adapted to differential evolutio.n & Crowding \\ \hline
[45] & 2008 & GPCGA &
\begin{tabular}{c} Genetic algorithms incorporate crowding to replace individuals with similar ones, maintaining \\ diversity by preventing premature convergence. \\ \end{tabular} & Crowding \\ \hline
[46] & 2010 & DMC-CROWDING &
\begin{tabular}{c} Distance measure comparison to improve crowding examines the performance of Mahalanobis and Euclidean distances in crowding techniques, finding that the Mahalanobis distance enhances the identification of multiple global optima. \\ \end{tabular} & Crowding \\ \hline
[47] & 2012 & AIWC &
\begin{tabular}{c} Artificial weed colonies use neighbourhood crowding schemes to maintain diversity in multimodal optimisation. \\ \end{tabular} & Crowding \\ \hline
[47] & 2012 & AWC-NC &
\begin{tabular}{c} Artificial weed colonies with a neighbourhood crowding scheme use crowding mechanisms to maintain diversity and prevent premature convergence in multimodal optimisation. \\ \end{tabular} & Crowding \\ \hline
[11] & 2012 & N-HS &
\begin{tabular}{c} Niching harmony search method integrates deterministic crowding with harmony search to maintain diversity and locate multiple optima in multimodal optimisation problems. \\ \end{tabular} & Crowding \\ \hline
[48] & 2013 & AMC &
\begin{tabular}{c} Adaptive mutation in crowding niching incorporates crowding mechanisms with adaptive mutation to balance exploration and exploitation, improving hierarchical multi-label gene function prediction by maintaining population diversity and enhancing convergence rates. \\ \end{tabular} & Crowding \\ \hline
[49] & 2013 & SNBDE & Spatially neighbors best search differential evolution uses crowding techniques to restrict competition among neighbouring solutions, maintaining multiple optima and enhancing global search capabilities by balancing exploration and exploitation. \\ \end{tabular}
\end{table} 
Table 3: Summary of niching algorithms: crowding.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 4 Results and Analysis
### Fiber-Based Signal Transmission Experiment
#### 4.1.2 Range-Doppler Imaging
To underscore the speed advantage of the novel DRC method, we recorded the runtime required for the DRC and PLE methods to compensate for the same dataset. Table 3 indicates that both methods produce comparable compensation accuracy and image quality. However, the DRC method offers a clear advantage in speed compensation. Analysis of running time on a PC shows that the DRC method requires only one tenth of the time needed by the linear estimation method. Thus, it is evident that the DRC-based phase noise compensation approach can significantly improve compensation speed while maintaining accuracy, enhancing the real-time performance of this technique in practical applications. Figure 11: Evaluation metrics for the center point of the image under varying levels of compensation: (a) without compensation; (b) short delay coarse compensation only; (c1) coarse compensation with (a) and (b). Figure 10: (**A**) Range and (**B**) azimuth direction profiles across different compensation strategies. \(N_{1}=31\); (c2) fine compensation with dual reference channel (DRC) and \(N_{1}=31\); (c3) fine compensation with phase linear estimation (PLE) and \(N_{1}=31\); (d1) coarse compensation with \(N_{1}=30\); (d2) fine compensation with DRC and \(N_{1}=30\); (d3) fine compensation with PLE and \(N_{1}=30\).