esa-sceva/satcom-chunk-collection huggingface dataset:
# Globally Conditioned Conditional FLOW (GCC-FLOW) for Sea Clutter Data Augmentation
## 5 Conclusions  
A novel work, Globally Conditioned Conditional FLOW (GCC-FLOW), has been proposed for sea clutter data augmentation due to the expensive and time-consuming nature of acquiring the desired sea clutter data. Experimental results show that GCC-FLOW successfully generates sequences of arbitrary length with the same statistical properties of the given sea clutter data, in contrast to the existing block-by-block methods. Moreover, by introducing a global state variable, the proposed GCC-FLOW can generate sea clutter with different sea states by feature interpolation without requiring the training data with a target feature. Subsequent studies will concentrate on the interpolation of not only the sea state but also other marine environmental conditions, such as wind speed and the average height of waves, in accordance with global condition variables.  
Software, S.L.; Writing--original draft, S.L.; Writing--review and editing, W.C.; Supervision, W.C.; Project administration, W.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Agency for Defense Development of Korea, grant number UI220077J.  
**Institutional Review Board Statement:** Not applicable.  
Figure 12: Interpolated distribution by the global condition variable \(\alpha=0.2\), \(0.5\), \(0.8\): beginning (**a**), middle (**b**), end (**c**).  
Figure 13: Interpolated autocorrelation by the global condition variable \(\alpha=0.2\) (**1**), \(0.5\) (**2**), \(0.8\) (**3**): beginning (**a**), middle (**b**), end (**c**).
# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 2 AI in SSC
### General Picture of AI in SSCs  
AI is playing an increasingly important role in the development of SSCs, supporting resource management, energy efficiency improvements and environmental monitoring. AI enables the optimisation of urban processes such as public transport, waste management or air quality control, contributing to the sustainability of cities. AI-based systems also support real-time data analysis, enabling faster responses to problems such as traffic jams or infrastructure failures. The developing AI technologies in SSCs are helping to integrate different city systems, leading to a more holistic approach to city management. At the same time, AI is becoming crucial in areas such as public health, where it can monitor and predict the spread of disease or support the care of seniors. As cities become smarter, AI is helping to drive data-driven decision-making, leading to more efficient use of resources. However, the development of AI in SSCs brings challenges, such as the need to protect privacy, transparency of algorithms and regulatory compliance. It is also important that AI technologies are accessible and understandable to all residents to avoid digital exclusion. As technology advances, AI has the potential to become a cornerstone of sustainable urban development, but this requires further research and innovation to maximise benefits and minimise risks. Currently, SC goals are generally focused on striving for  
* Better education and work/career opportunities and achieving work-life balance (as part of the so-called individual success);  
Figure 1: Framework of smart city evolution and AI integration (own version based on [4; 5]). * Higher ecological performance (including natural diversity);
* Support of the health of residents, including support based on preventive medicine, faster and more accurate diagnoses and timely healthcare services, and the well-being of residents;
* Support of the social activity of residents as a community. This means that SC 'in the background' supports economic, social and health activities while balancing budget, risk and technological upgrades. By monitoring, analysing and responding (both to correct operations and to anomalies such as failures) in real or near real time, smart city processes can be streamlined and optimised. This will become increasingly easier as platforms develop, standardise and the price of sensors, effectors and other IoT components (including cloud services and edge computing) falls [6]. An increase in available computing power at a much lower cost is expected, which is very beneficial for the broader use of AI [7]. Detailed visualisations of predictions, such as paraconsistent analysis, are needed, creating a framework for the use of other systems, both collecting data and carrying out the resulting analysis in SC [7]. The challenge here is the technological heterogeneity of SCs, which use very different approaches, protocols, methods and communication technologies (also depending on their manufacturers and SC implementers) (Table 1) [8; 9].
## 3 C/N Penalty of RoF Link
### C/N Penalty of RoF Link with OSSB Modulation  
One of the methods used to overcome DPF is OSSB modulation. Based on Equation (14), DD-MZM can generate OSSB modulation by setting the parameters \(\gamma=1/2\) and \(\theta=90^{\circ}\). The effectiveness of OSSB modulation in overcoming DPF can be seen through the C/N penalty curve of the link. The calculation of the C/N penalty of the RoF link with OSSB modulation can be completed using steps similar to ODSB modulation but using \(\theta=90^{\circ}\). The results of the calculation are shown in Figure 4,b.  
Figure 3: C/N penalty of the RoF link with ODSB modulation for (**a**) \(m\leq 2\) and (**b**) \(m>2\).  
Figure 4a illustrates the curve of the C/N penalty of the RoF link with OSSB modulation at \(m\leq 2\). Figure 4b shows the curve of the C/N penalty at \(m>2\). There was no deep fade on the RoF link with OSSB modulation. There was almost no difference between the C/N penalty max and C/N penalty min (\(\Delta\) C/N penalty) at \(m=0.1\) (only 0.029 dB). The \(\Delta\) C/N penalty at \(m=0.5\) is 0.560 dB, and it increased to 2.507 at \(m=1\). This shows that OSSB modulation could overcome DPF effectively at \(m=0.1\), and its effectiveness was reduced by increasing \(m\).  
A comparison of the value of the \(\Delta\) C/N penalty and DF between ODSB and OSSB modulation is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \multicolumn{2}{c}{**ODSB Modulation**} & \multicolumn{2}{c}{**OSSB Modulation**} \\ \cline{2-4}  & \(\Delta\)**C/N Penalty (dB)** & **DF** & \(\Delta\)**C/N Penalty (dB)** & **DF** \\ \hline
0.1 & 30.111 & 6.8 & 0.022 & 0.008 \\
0.5 & 29.046 & 6.5 & 0.560 & 0.202 \\
1 & 25.455 & 5.5 & 2.507 & 0.930 \\
1.5 & 21.935 & 4.2 & 7.566 & 2.764 \\
2 & 40.013 & 10.5 & 19.264 & 4.383 \\
2.5 & 32.922 & 6.9 & 11.517 & 3.064 \\
3 & 25.762 & 5.6 & 27.229 & 5.629 \\
3.5 & 41.870 & 10.8 & 26.272 & 5.207 \\
4 & 34.368 & 7.4 & 11.525 & 3.773 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison value of the \(\Delta\) C/N Penalty and DF between the ODSB and OSSB Modulation.  
Figure 4: C/N penalty of the RoF link with OSSB modulation for (**a**) \(m\leq 2\) and (**b**) \(m>2\).
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 2 Materials and Methods
### Subjective Evaluation Experiment and EEG Signal Observation Experiment under LFS Stimulation
#### 2.1.3 Experimental Method of Brain Wave Observation  
The composition of subjects, the equipment used for audiovisual signal presentation, and the experimental setting were identical to those in the subjective evaluation experiment. The EMOTIV EEG device, as illustrated in Figure 2, is utilized for recording the brain wave signals. Figure 3 illustrates the proper placement of the EEG device on the subject's scalp, with electrode positions AF3, AF4, F3, and F4 based on the Brodmann functional brain regions known to process emotion stimuli [29]. The video signals and LFS signals used in the experiment remained the same as those in the subjective evaluation experiment in the same order. The duration of the video and audiovisual signals was adjusted to 10 s, with a 5 s interval between them, and a 15 s interval between each type of emotion, during which gray pictures were displayed without accompanying sound. Subjects were instructed to maintain a relaxed state, minimize blinking and swallowing, and avoid head movement during the signal presentation. Initially, a 15 s baseline reference state was recorded without any visual or auditory stimulation. Subsequently, brain wave signals were recorded during the presentation of video signals depicting happy, sad, and shocked emotions without audio stimulation, and then, with the addition of LFS stimulation, following the experimental conditions in order.  
Figure 3: Wearing of wireless portable EEG devices.  
Figure 2: EMOTIV wireless portable EEG device.
## 2 System Model
### Transmission Schemes  
We studied the following three strategies under cooperation.  
* Silencing: CRAN silences some of the interfering RRHs, thus mitigating interference to enhance the system performance of the typical link. At a silenced RRH, the transmission signals are completely turned off, which helps to boost the SIR in the victim cell, and it has been proposed for 5G [38].
* TAS with silencing: CRAN selects the best channel for transmission among the typical link and cooperating RRHs. After selecting the best channel, it forces all the cooperating RRHs to remain silent. This scheme presents some diversity gain and has optimal reception reliability at the UE.
* MRT: CRAN jointly transmits from the typical link and cooperating RRHs to serve the UE. This scheme provides high optimal reception reliability and significant diversity gain to cope with very stringent reliability constraints and fading channel impairments.  
We assume that during transmission, a proper amount of pilot signal accompanies all transmission schemes [23]. Hence, TAS and MRT require 1-bit feedback to select among the best link or jointly transmit to the desired UE. However, in silencing, CRAN sends a decision to the cooperating RRH to remain silent during the beginning of every downlink transmission slot, which avoids the use of precise control and extra signalling. Thus, proposed schemes do not incur heavy signalling overhead burden and hence are feasible for upcoming 5G systems [39; 40]. MRT is considered as a reference scheme to provide optimal performance with higher transmit diversity gain compared to TAS and silencing [41]. However, MRT can be costly compared to TAS and silencing at the implementation side, but this scheme is being implemented in practice [25]. Meanwhile, in the case of a limited feedback system avoiding the complexity compared to MRT, TAS provides significant performance gains [42; 43].
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Communication
With minimal feedbacks, multiple access and inter-symbol interferences (MAI/ISI) may be handled using a low-cost time-domain hybrid BF (HBF) approach in simultaneous multiple access (SDMA). This technique makes use of the spatial variety offered by several baseband processing units and the directivity offered by a PAA in RF beam patterns. A powerful beamformer is necessary to minimize the signal-to-interference-plus-noise ratio (SINR) for each user while utilizing the least amount of overall transmit power in order to provide stable signal quality in wireless multimedia streaming over indoor 60 GHz radio [98].
## 2 State of the Art
### Researches about the Security Culture
#### Proposal of [PERSON] [48] proposes the construction of an ISMS that is oriented towards SMEs and whose central point is the security culture. This is done by analyzing the state of SMEs in Australia, after which the conclusion is reached that that the information security risks for Australian SMEs have increased over the last 15 years as the result of greater access to the Internet, but that the level of information security and awareness in SMEs has not maintained a good rhythm and continues to be low.
* Security culture: Procedures with which to respond to new events (such as security violations) will help to underline the importance of information security for workers [27]. Incentives may also be useful as regards modifying employees' behavior. However, [PERSON] and [PERSON] [14] warn that management controls should be based on ethical values. * Behavior: The management of initiatives whose intention is to develop desirable behavioral features with regard to the personnel's responsibility, integrity, trust and ethics [20]. Strong values are, however, necessary to support management initiatives [14]. Information security is strengthened when strong values are disseminated among collaborating entities and other interested parties [42]. The development of intrinsic motivation is important [38], and can be supported by promoting those personnel who appropriately fulfil the security regulations [45]. * National ethics and organizational culture: According to [PERSON] [42], the creation of security forums at a national level may favor the creation of an information security culture. According to the research carried out by [PERSON] [48], the principal challenges as regards developing the information security culture in SMEs include:  
* Motivating company owners to allocate appropriate funding to information security. * Convincing company owners to carry out a formal risk analysis. * Ensuring that company owners develop information security policies and procedures and assign responsibilities. * Developing a proactive attitude towards information security. * Identifying and establishing a series of awareness-raising activities that can be adapted to SME environments. The principal conclusions reached after applying the framework were that, although it valuable on an individual basis as regards identifying the elements that an SME and security culture-oriented ISMS should contain, it is not a complete and usable model.
## 3 Energy and Information Beamforming
### Relation between Two Beamforming Schemes  
According to (20) and (27) and Theorem 1, we can see that the energy beamformer of the statistical max-SINR scheme converges to that of the strongest-path beamforming in the large \(\left\{N_{x},N_{y}\right\}\) limit. Moreover, if Conditions (a) and/or (b) are satisfied after the MUI-aware sensor scheduling, we can neglect the term incurred by MUI, i.e., \(\sum_{k=1,k\
eq l}^{L}\left(\frac{\eta T}{1-\tau}p_{k}\frac{\beta_{k}K_{l}}{k _{k}+1}N_{x}N_{y}\right.\left.-\frac{E_{k}^{p}}{\left(1-\tau\right)T}\right) \mathbf{C}_{k}\), in the information beamformer given by (24), and rewrite (24) as:  
\[\mathbf{w}_{D,l}=\mathbf{u}_{\max}\left\{\mathbf{C}_{l}\right\}\overset{N_{x},N_{y}\rightarrow\infty}{=}\frac{1}{\sqrt{N_{x}N_{y}}}\mathbf{b}\left(\rho_{x, l}\right)\otimes\mathbf{a}\left(\rho_{y,l}\right). \tag{32}\]  
Therefore, we can conclude that, under the MUI-aware sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP antennas tends to infinity.
# Laser Technology in Photonic Applications for Space
## 3 Lasers in Space
### Optoproretchics in Propulsion  
\(\bullet\)Indirect Ignition System by Detonation of Pyrotechnics Using Short Laser Pulses  
Optical pyrotechnics exhibit improved safety and testability when compared to traditional pyrotechnic devices. Opto-pyrotechnics use intense laser pulses distributed by laser fibers to start the pyrotechnic chain reaction; instead of the standard electrical impulses used by traditional technologies. Optical pyrotechnics are already being used in spatial applications, for example, in the Ariane 5 launcher and satellites, such as Demeter [122].  
\(\bullet\)Direct Laser Ignition  
Laser ignition technology was initially thought as a more efficient and cleaner combustion alternative to the common electrical spark plugs. The technology has also taken its first steps towards implementation in the next generation of space engines. Laser ignition offers several advantages compared to the indirect ignition by detonation of pyrotechnics using laser sources technique discussed in the previous subsection. The main advantage of direct laser ignition compared to solid propellant pyrotechnics lies in the possibility of device re-ignition thanks to the combustion chamber design. Another clear advantage over electrical igniters is the fact that electrodes do not have to be inserted inside the combustion chamber, allowing in this case a more controlled combustion propagation. Laser ignition devices have been reported, such as the HiPoLas(\(\Re\))by [PERSON] et al. [123], which is based on a DPSSL Nd:Cr:YAG laser and delivers 30 mJ, 2.5 ns pulses at 1064 nm. Although the miniaturized laser reported seems to emit less than the energy required to efficiently ignite propulsion systems, the technology offers several advantages, such as minimal ignition delay, increased ignition probability for a wider range of mixture ratios, and controlled initial chamber conditions [123]; moreover, laser ignition can be provoked at any point inside the combustion chamber or at various points simultaneously [124]. The test results and the demonstration of the feasibility of using this technology for the Ariane 6 launchers have motivated Airbus to claim that it will push forward the development of this technology for its forthcoming Safran launchers [125; 126].
# Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization
## 2 Hybrid Intelligent Algorithm Modeling
### IPSO-BP Prediction Model  
The IPSO-BP hybrid intelligent algorithm model encodes the parameters, initializes the population, and calculates the initial global extremum, and then decodes and calculates the fitness function. Finally, if the calculation result meets the end condition, the network parameters are output. If not, one can update the particle speed and position, control the random variation of the genes, update the extreme value, and return to decoding. The specific prediction process is displayed in Figure 3.  
Figure 2: Flow chart of the PSO algorithm.  
The IPSO-BP model improves the speed update formula on the original basis, and it can be written as:  
\[V_{id}(t+1)=wV_{id}(t)+c_{1}r_{1}[P_{id}(t)-X_{id}(t)]+c_{2}r_{2}[P_{id}-X_{id}(t)] \tag{4}\]  
where \(w\) is the inertia weight; \(c_{1}\), \(c_{2}\) are the learning factors; \(r_{1}\), \(r_{2}\) are the random numbers; \(P_{id}(t)\) is the individual extremum; \(P_{id}\) is the global extremum; \(X_{id}(t)\) is the weight vector.  
In Equation (4), first and foremost, the first term is related to the particle's last modified increment, which can balance the global search and the local search. In addition, the second term is the part that the particle learns from its own optimal value, called the self-learning part, which can keep particles with a strong global search ability and avoid falling into the local minima. Finally, the third term is the part that the particles learn from the global optimal value, called the mutual learning part, which can speed up the search.  
The proposed algorithm tends to solve the global search problem when the inertia weight is high and the particle velocity is fast. When the weight value is small, the speed of the particles is low, which is conducive to the local search. When evaluating the wind resources of the wind farms in this study, the fitting accuracy of the overall data needs to be considered, so the inertia weight \(w\) is set as a large constant.
## 2 Problem Description and Formulation
### The RC-HVRPTW Model  
In multi-satellite ground station scheduling, the ground station communication time is scheduled as cargo to multiple satellites with the shortest task waiting time. The relationship between satellite orbits and the geographic location distribution of ground stations determines that there are different visible windows between satellites and ground stations, which means that each satellite is associated with a specific ground station set. As shown  
Figure 1: Example of MGSS problem.  
Figure 2: Schedule example.  
in Figure 1, the ground station set for SAT-3 is \(\{GS-1,GS-2\}\), and the ground station set for SAT-1 is \(\{GS-1\}\). According to the urgency of the data, it is necessary to reduce the waiting time of the downlink task as much as possible. In order to facilitate downlink and reduce waiting time, the communication time of the ground station should be allocated to the corresponding downlink request. An alternative representation can be used to represent customer-based graphics and road networks. A customer-based multiple graph, which contains multiple links between two vertexes, which represent different paths in the graph based on different vehicle. In other words, there is no longer a single path between two vertices, but there are multiple alternative paths provided by different vehicles. There are always different routes between nodes, and each route means a different travel cost. The choice of a different route for each node will affect its next route.  
RC-HVRPTW arising in the multi-satellite ground station scheduling can be defined on the directed graph \(G=(V,A)\) where \(V=(0,1,\cdots,n)\) is the vertex set, \(A=\{(i,j)|i,j\in V,\)\(i\
eq j\}\) is the arc set. Downlink requests are regarded as customers, and ground stations are regarded as vehicle resources for providing kinds of services. The downlink request \(i\) has a service time \(t\)_Dur_ and open window \([b_{i},e_{i}]\) and the arrival time \(t\)_Arrival_. In order to ensure the integrity of information, each downlink request is executed only once. Customers with different requests should be served by different kind of vehicles. \(Arc\ (i,j)_{k}\) contains the cost of task transformation from \(i\) to \(j\) using vehicle \(k\). A fleet \(G=(1,2,\cdots,g)\) of heterogeneous vehicles are used to deliver variety of products to the customers. The objective of cost minimization takes into account customer waiting cost and travel cost. The decision variable \(x_{ijk}\) is a binary variable. It is set to 1 when vehicle \(k\) provide service \(j\) before \(i\), 0 for others. \(t_{i}\) is the service start time of customer i.
# _Skin Imaging_: A Digital Twin for Geometric Deviations on Manufactured Surfaces
## 3 Skin Imaging of Manufactured Cylindrical Surface
### Deviation-Plane Generation  
The idea is to unfold the fitted cylinder into a flat rectangle as Deviation-Plane, and then localize all the surface's image-points on it. The image-point's coordinate in SG coordinate system has been obtained as \(\left(x_{SG}\quad y_{SG}\quad z_{SG}\right)\). The aim is to find its corresponding point \(\left(x_{DP}\quad y_{DP}\right)\) on the Deviation-Plane. Figure 10 illustrates Deviation-Plane definition and localization of the Image-Point on it. The length of the deviation plane is equal to the perimeter of the circular base of the cylinder, and the width of the deviation plane is the same as the cylinder axis length. Considering the SG coordinate system, the intersection of the positive direction of \(Y_{SG}\) axis and the fitted cylinder is defined as the origin of the Deviation-Plane coordinate system (\(\mathrm{O}_{\mathrm{DP}}\)). The line passing from this point and parallel with the cylinder axis defines the \(Y_{\mathrm{DP}}\) axis; in other words, \(x_{SG}\) of each image-point is substituted for \(y_{DP}\) of its corresponding point on the deviation plane. Furthermore, the \(X_{\mathrm{DP}}\) axis corresponds to the perimeter of the cylinder's circular base at \(\mathrm{O}_{\mathrm{SG}}\). Moreover, in order to determine the value of \(x_{DP}\) on the Deviation-Plane for each point, the circular base at \(x_{SG}\) is considered, then the length of the circle's arc between that image-point and the intersection point of \(Y_{\mathrm{DP}}\) axis on the considered circle is the desired value for \(x_{DP}\).