esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Joint Optimization Algorithm Design
### MAHPPO Algorithm
#### 3.1.3 Reward Function
In order to prevent excessive differences in the policy updates of the actor networks, a clipped surrogate loss function was adopted [30], which is expressed as  
\[L^{\text{CLIP}}(\theta_{u})=\mathbb{E}\big{[}\min\big{(}r_{\theta_{u}}\hat{A}_ {k}\big{)},clip(r_{\theta_{u}},1-\Delta,1+\Delta)\hat{A}_{k}\big{]}, \tag{22}\]  
where \(r_{\theta_{u}}=\frac{\pi_{\theta_{u}}(A_{\mathbf{k}_{u}}|S_{k})}{\pi_{\theta \text{old}}^{\text{old}}(A_{\mathbf{k}_{u}}|S_{k})}\) is the ratio of the current policy to the old policy and \(\pi_{\theta_{old}}(A_{k}|S_{k})\) represents the old policy. \(\Delta\) is a hyperparameter. \(\hat{A}_{k}\) is the advantage function estimated by the critic network, which is used to measure the advantage of action \(A_{k}\) in state \(S_{k}\) compared with the average action. The detailed definition of \(\hat{A}_{k}\) is given by  
\[\hat{A}_{k}=R_{k}-V_{\phi}(S_{k}). \tag{23}\]In order to avoid falling into a suboptimal policy and encourage agents to explore, an entropy coefficient \(\mathcal{H}(\pi_{\theta_{u}}(A_{k_{u}}|S_{k}))\) is introduced into the loss function of the actor networks. Then, the loss function of the actor network is expressed as  
\[L(\theta_{u})=\sum_{u=1}^{U}\Big{[}L^{\text{CLIP}}(\theta_{u})+i \mathbb{E}[\mathcal{H}(\pi_{\theta_{u}}(A_{k_{u}}|S_{k}))]\Big{]}, \tag{24}\]  
where \(i\) is a hyperparameter. The detailed flow of the MAHPPO algorithm is shown in Algorithm 1. In order to enhance the robustness of the algorithm, we normalized \(\mathcal{S}\) and \(\mathcal{A}\) to the range \([-1,1]\). Moreover, the reward scaling technique was used for improving algorithm performance [31].
## 2 Context, Hypotheses and Segmentation Protocol  
In this paper, large diameter PMS machines with a high number of poles and with surface mounted radially magnetized magnet are considered. This kind of machines are widely used in naval propulsion application or associated with tidal or wind turbines for renewable power generation [7; 8]. In these kinds of applications, as shown in [5], introducing segmentation can lead to significant improvements in terms of performances and/or reliability. The segmentation consists of removing some active parts (in the stator or in the rotor), like coils, permanent magnets and/or yoke. For the stator segmentation, coils and the corresponding part of the stator magnetic circuit are removed and replaced by an magnetic material (magnetic gaps). For the rotor segmentation: the magnets and  
Figure 1: Structures of permanent magnet synchronous machines with (**a**) stator and (**b**) rotor segmentations.  
the corresponding part of the magnetic circuit are removed and replaced by magnetic materials (amagnetic gaps).  
Large segmentations with gaps larger than one pole pair are considered. In fact, to keep the windings and flux balanced, entire number of pole pairs (for rotor segmentation) and entire number of winding periods (for stator segmentation) are removed. This is why the considered hypothesis and proposed models fit well with large diameter machines with a high number of pole pairs. Indeed, a high number of pole pairs allows a large number of segmentation possibilities. The segmentation topology can be defined by two parameters: the number of introduced gaps in the circumference \(n_{gap}\), and the gap ratio \(p_{gap}\). The considered structures comprise \(n_{gap}\) gaps with the same angular width. These \(n_{gap}\) gaps are regularly distributed in the structure circumference. The gap ratio \(p_{gap}\) corresponds to the proportion of the gap angular parts in regards to the entire circumference of the machine. So \(p_{gap}=0\) corresponds to the classical machine without angular gaps, and \(p_{gap}=1\) would correspond to a hypothetical machine without stator (or rotor).  
Figure 2 presents a part of a stator-segmented machine (with concentrated windings) where the different parts of the machine are described. Figure 3 shows the different structures which are studied in this paper: non-segmented machine Figure 3a, stator-segmented machine with magnetic gaps Figure 3b and rotor-segmented machine with magnetic gaps Figure 3c.  
Figure 3: Magnetic circuits of the studied machines: (**a**) non-segmented machine, (**b**) stator-segmented machine with magnetic gaps, (**c**) rotor-segmented machine with magnetic gaps.  
Figure 2: Stator-segmented structure.
# On Correlated Equilibria in Marinatto-Weber Type Quantum Games
## 1 Introduction  
Technological progress that occurred in the last few years made controllable manipulations of single quantum objects possible. This resulted in the emergence and intensive development of a new branch of science placed on the borders of mathematics, computer science, and quantum physics--quantum information theory. The playing of a game is connected to a transfer of information between players and possibly an arbiter. If a carrier of this information is a quantum object, we deal with so called quantum games, the theory of which has been intensively developed during the last twenty years.  
Quantum game theory begun with considering a simple extensive form game in [1]. [PERSON] showed that a player equipped with unitary strategies has a winning strategy. Other fundamental papers on quantum games include [2]. The scheme defined by [PERSON], [PERSON], and [PERSON] was the first formal protocol of playing quantum games. This scheme uses quantum computing formalism in describing \(2\times 2\) bimatrix games. According to [2], players' strategies are unitary operators that depend on two parameters and act on maximally entangled two-qubit states. The scheme gives the possiblity of obtaining more efficient results in comparison with the results that may be obtained in games played classically. This feature is well illustrated by the prisoner's dilemma game that in a classical version has a unique, inefficient Nash equilibrium. The Eisert-Wilkens-Lewenstein (EWL) scheme enables players to obtain a Pareto optimal Nash equilibrium. [PERSON] and [PERSON] [3] introduced an alternative model of playing a quantum game by applying quantum formalism to classical game theory in a more straightforward way. In the general case of \(m\times n\) bimatrix games, players' strategies are identified with permutation matrices which are performed on a \(mn\)-level quantum system [4], and then measurements are done. This simple model has found applications in many branches of game theory: from evolutionary games [5, 6] to games in extensive form [7] and duopoly problems [8, 9].  
In general, a large part of noncooperative quantum game theory is devoted to studying results of a quantum game by simply applying nonclassical moves, seeking rational strategy profiles among quantum strategies, and pointing out differences between classical and nonclassical solutions [10, 11, 12, 13, 14, 15].  
This paper presents a completely different approach. Our goal is to identify elements of a quantum scheme that can be described by classical terms. This new approach may provide for further developments of quantum game theory. By identifying solution concepts from classical game theory in quantum games, we can modify existing quantum schemes or construct new ones. We found that there is a strict connection between correlated equilibria of a game and Nash equilibria in the corresponding quantum game. On this basis, we formulated a new scheme for bimatrix games.
## 3 The Image-Based Model
### Session Volumes
#### 3.1.1. Quantization Error  
The extents of \(V\) and \(b\) have clearly a huge impact on voxels' size, and thus on location quantization when encoded as RGB color. Given \(E_{x}(V)\), \(E_{y}(V)\) and \(E_{z}(V)\) as extents of the volume \(V\) along x, y and z axes respectively, we retrieve the single voxel extents (\(\Delta x\), \(\Delta y\) and \(\Delta z\)):  
\[\Delta x=\frac{E_{x}(V)}{2^{b}}\quad\Delta y=\frac{E_{y}(V)}{2^{b}}\quad \Delta z=\frac{E_{z}(V)}{2^{b}} \tag{3}\]  
Figure 1: (**Left**) A single session volume arranged in a sample immersive virtual environment (IVE). (**Right**) A sample visualization of color-coded locations of the same IVE using the spatial quantizer (Definition 2). Each location inside the volume can thus be translated into a color and vice versa.  
Figure 2: Decoding 3D location (\(p\)) from a quantized RGB value (voxel \(<i,j,k>\in V\)).  
Given a lossless RGB color \(c\) (corresponding to a voxel \(<i,j,k>\)), we know the original 3D location was quantized somewhere inside the voxel extents (\(\Delta x\), \(\Delta y\) and \(\Delta z\)). The decoding routine approximates the returned location \(p\in\mathbb{R}^{3}\) to the voxel center (see Figure 2), introducing a _quantization error_. The maximum quantization error \(\epsilon\) (worst case) for each axis can be defined by:  
\[\epsilon=<\pm\frac{\Delta x}{2},\pm\frac{\Delta y}{2},\pm\frac{\Delta z}{2}> \tag{4}\]  
Multiple session volumes \(\{V_{0},V_{1},...\}\) can be deployed in the same 3D scene, each capturing state attributes in different portions of the IVE, each operating individually, providing great scalability for the encoding model.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### CPS Conceptual Overview
#### 3.1.3 Communication Network  
Interactions between different networks (sensors and actuator networks) in a CPS is achieved by wireless communication technology. Just like IoT technology, wireless communication may range from short to long range. The short-range communication technologies may include radio frequency identification (RFID), ultra wideband (UWB), fireless fidelity (Wi-Fi), Bluetooth, near-field communication (NFC), Zigbee, Z-wave, IPv6 over low power wireless personal area networks (6 LoWPAN), etc. Amongst others, the long-range communication technologies include GSM/3G/4G, long-range (LoRa), long-range wide area network (LoRaWAN), SigFox, DASH 7, narrow band Internet of Things (NB-IoT), Weightless, etc. Each of these technologies has its own application requirements and challenges. In most CPS applications, short-range communications such as UWB, Bluetooth, Wi-Fi, and ZigBee are mostly used. In this section, we give a short overview and also compare the characteristics of some commonly used wireless communication standards.  
Figure 3: A sensor network design challenge for pipeline health monitoring.  
RFID  
Radio frequency identification (RFID) utilizes electromagnetic fields (near field or far field) to locate and trace tags connected to an object. It is specified in the standards of the ISO/IEC 18000 series. It operates at different frequencies ranging from low to microwave frequencies with different data rates and communication ranges. At the low frequency (LF) band, it operates at 125 kHz with small data rate and communication range below 1 kbps and 1 m, respectively. At the high frequency (HF) band, RFID operates at 13.56 MHz with data rates close to 25 kbps and a covering distance of around 1.5 m. At ultra-high frequency (UHF), its frequency of operation is in the 860-960 MHz range, covering up to 10 m with data rates up to about 100 kbps, while at the microwave frequency band, RFID operates at 2.45 GHz with good communication range and data rates [49].
## 7 Shortcomings and Prospects  
The qualitative and quantitative analysis of IVOCs is gradually becoming a research hotspot in the field of environmental chemistry. Currently, numerous analyses of IVOCs have been conducted internationally. However, there are still some shortcomings in the research, mainly reflected in regard to the following three aspects. (1) Many IVOC components in environmental samples remain uncharacterized, especially due to the extensive presence of isomers and substituents, which significantly increases the detection difficulty. Existing analytical techniques (such as GC-MS, CIMS, and GC\(\times\)GC-ToF-MS [77]) are still insufficient in terms of separation efficiency and detection sensitivity to meet the precise detection needs of IVOCs in complex environments. This results in limitations in regard to species identification rates and quantitative accuracy. (2) Although the macroscopic laws of IVOCs generating SOAs have been preliminarily revealed through field observations, smog chamber simulations, and model simulations, there is still a knowledge gap at the molecular level. For example, the atmospheric oxidation pathways of IVOC species, SOA tracer information, and the relevant physicochemical properties are not yet fully understood. (3) Current toxicological studies mainly focus on PAHs among IVOCs, while research on the exposure pathways, metabolic mechanisms, and combined toxic effects of emerging pollutants, such as oxygen-containing compounds (e.g., aldehydes and ketones) and heterocyclic compounds, is significantly lagging.  
The qualitative and quantitative analysis of IVOCs is a complex and continuously evolving field, facing many challenges in the future. On the one hand, to achieve the successful quantitative analysis of ambient IVOCs and reduce uncertainties in IVOCs measurement, it is necessary to develop instruments with higher separation and resolution capabilities, especially online measurement instruments with a high temporal resolution. On the other hand, to further explore the qualitative characteristics of IVOC oxidation products and the health risks, and to reduce the uncertainties in model simulations, more comprehensive and in-depth research on their reaction mechanisms are needed, especially for aliphatic aldehydes, ketones, carboxylic acids, esters, ethers, and heterocyclic compounds in IVOCs.
## 4 Model Performance Analysis
### Clustering Analysis for Datasets  
It is important to estimate the number of K of clustering analysis in order to develop a robust re-generated supervised learning model [35]. We performed an analysis to find the optimal number of K from three clustering validity algorithms (Figure 10). First, using a connectivity index, it was found that the optimal number of k was two values for 3 cluster analysis. For K-means (C-index: 8.2) and PAM (C-index: 3.6) cluster algorithms, C-index looks similar on the graph, but more than twice as different from PAM, which shows better performance. Second, in the algorithm for calculating the number of K through the Dunn index value, the highest slope index was found in two. Finally, the optimal number of K was also calculated from the silhouette index value.  
Accordingly, best performance clustering is characterized by a lower C-index or a higher Dunn and Silhouette index values. It is understood that the PAM clustering provides the most efficient clustering compared to Hierarchical and K-means clustering analysis. Figure 11 shows the 9 input variables with two principal components (PC), the x-axis being PC 1 and the y-axis is PC 2. It represents the quantitative position coordinates of the yield values of whole production wells calculated by K-means clustering. The cluster 1 model of the blue circular point is distributed in the northwest direction from the center point, and the cluster 2 model is distributed in the southeast direction from the center point.  
Figure 9: Data partitioning scheme with K-fold validation.
## 3 Results
### DPSC Proliferation Ability  
The cell proliferation capacity and Ki67 expression in the DPSCs after 3, 5, 15, and 26 passages are shown in Figure 6. After 3P and 5P, the cells exhibited similar proliferation rates for up to 4 days, whereas after 15P, the cells demonstrated a significantly reduced proliferative capacity, and after 26P, there was growth arrest. No Ki67\({}^{+}\) cells were observed after 26P. Notably, the number of Ki67\({}^{+}\) cells did not substantially decrease until 15P, whereas the area of Ki67 in the nucleus gradually diminished.  
Figure 2: Expression patterns of surface antigens on rat DPSCs after passaging. Flow cytometric analysis used to detect surface antigens: CD34 for hematopoietic stem cells and endothelial cells marker, CD45 as leucocyte marker, and CD90 for mesenchymal stem cell marker. 3P, 5P, 15P, and 26P indicate 3, 5, 15, and 26 passages.  
Figure 4: Changes in F-actin in rat DPSCs. Staining of actin filament using fluorescent phalloidin.  
Figure 3: Changes in rat DPSC shapes and nuclei morphologies. Microscopic observations of cell shapes (**A**) and nuclei (**B**) after 3, 5, 15, and 26 passages (3P, 5P, 15P, and 26P, respectively) and their semi-quantified data (\(n=50\), violin plot) on cell and nuclear size. (**C**) Orthogonal views (X–Z planes) from three-dimensional reconstruction images on nuclei and quantitative data of those volumes (\(n=20\), violin plot). * \(p<0.05\), *** \(p<0.001\), and *** \(p<0.0001\).
## 4 The Proposed ECPSO-MTP
### Complexity Analysis  
The complexity of ECPSO-MTP for solving clustering problems is discussed and analyzed in this subsection. Firstly, some clarifications are given as follows: \(M\) represents the totality of data points in the dataset and \(D\) represents the dimension of data points. \(K\) represents the totality of clusters in the dataset, usually \(D\ll M\), and \(K\ll M\).  
The computation of the proposed ECPSO-MTP consists of three main steps, including initialization, evolution, and communication phases. In the initialization phase, the computation time is mostly determined by the calculation cost of the fitness function, and the time needed for distance computation for one object is \(KMD\), which can be simplified to \(M\). The computation time for all objects in the system is \(nM\), and the complexity for the initialized state of ECPSO-MTP is \(O(nM)\). In the evolution phase, the time of evolution rules needed by executing once in \(\sigma_{o}\) is \(nM\). The computation time of the evolution phase for \(\sigma_{1}\) to \(\sigma_{m}\) under the distributed and parallel processing of P systems is \(nM\). And the computation time of the evolution phase for \(\sigma_{m+1}\) is \(mM\). Then the complexity of the evolution state of ECPSO-MTP is \(O((n+m)M)\). In the communication phase, the number of communication rules needed to be executed once in ECPSO-MTP is set to 1. The communication time from\(\sigma_{o}\) to \(\sigma_{m+1}\), \(\sigma_{m+1}\) to \(\sigma_{m+2}\), \(\sigma_{o}\) to \(\sigma_{m+2}\), is equal to 3, and the complexity of the communication state of ECPSO-MTP is \(O(3)\). Therefore, the time of the whole system by executing one iteration is \((n+m)M+3\), and the cumulative time of the system is \(nM+((n+m)M+3)t_{\text{max}}\). Then the complexity of the proposed ECPSO-MTP is \(O(nMt_{\text{max}})\).
# Coexistence in Wireless Networks: Challenges and Opportunities
## 6 Challenges, Opportunities, and Limitations of Coexistence
To avoid coexistence and ensure efficient and reliable wireless communication, several opportunities exist, including the following:  
* Spectrum allocation: One of the primary ways to avoid coexistence is to allocate specific frequency bands to different wireless systems. This approach ensures that each wireless system operates in its own frequency band, thereby reducing the likelihood of interference [121]. Governments and regulatory bodies often play a significant role in spectrum allocation, and they allocate frequency bands based on factors such as the type of wireless application, geographical location, and licensing requirements [122]. * Frequency agility: Another opportunity to avoid coexistence is frequency agility, where wireless systems are designed to switch between multiple frequency bands [123]. This approach allows wireless systems to avoid congested frequency bands and operate in less crowded areas, thereby reducing the likelihood of interference. Frequency agility is commonly used in wireless networks such as Wi-Fi, where multiple frequency bands are available for use [124]. * Power control: Wireless systems can employ power control to mitigate interference. This involves dynamically adjusting the transmission power of the devices to avoid disrupting nearby wireless networks [125]. For instance, in cellular networks, base stations regulate their power output based on the mobile device's distance and signal strength, thereby reducing the potential for interference with neighboring cellular systems [126]. * Antenna design: Antenna design plays a critical role in avoiding coexistence. Antennas can be designed to focus their energy on specific directions, thereby reducing the likelihood of interference with nearby wireless systems. Additionally, antenna diversity techniques such as MIMO (Multiple Input Multiple Output) can be used to improve the reliability of wireless communication by using multiple antennas to transmit and receive signals [127]
* Protocol design: Protocol design is also an essential aspect of avoiding coexistence. Wireless protocols can be designed to detect and avoid interference, such as using collision avoidance techniques to avoid packet collisions in Wi-Fi networks [128]. In addition, protocols can be designed to prioritize critical data and ensure that it is transmitted without interference [129].
## 2 Materials and Methods
### LET\({}_{D}\) and \(y_{\rm D}\) from MCDS  
The MCDS was originally developed to estimate the clustering of DNA lesions to form SSB, DSB and other complex types of DNA damage [46; 47]. Version 3.10A of the MCDS introduced the capability of calculating microdosimetric quantities (e.g., lineal energy and specific energy) for ions passing through a water (or a water-equivalent) medium via a deterministic algorithm that accounts for changes in energy loss and finite ion range within micron-scale targets [17; 48]. The irradiation geometry used in the MCDS corresponds to protons (or other ions) randomly entering a spherical target surrounded by a vacuum with options to consider a spherical layer of water (e.g., cell cytoplasm) or a water-equivalent layer representing the bottom of a cell culture dish. The protons are assumed to travel in a straight line until their kinetic energy reaches zero. The algorithm accounts for both crossers and stoppers, i.e., protons that spend either part or all of their kinetic energy within the target volume, respectively, as well as for changes in proton stopping power within the target volume. However, MCDS neglects energy-loss straggling, so, for monoenergetic beams, the equality LET\({}_{\rm F}\) = LET\({}_{\rm D}\) holds. For polyenergetic beams, the MCDS computes the dose-averaged LET averaged over the incident ion fluence. The MCDS algorithm does not account for \(\delta\)-rays produced by ions within or external to the target. For conditions in which particle equilibrium holds, \(y_{\rm F}=\rm LET_{F}\) and \(y_{\rm D}=9/8\ y_{\rm F}\) hold. When stoppers become significant (the range is less than the diameter of the target), which the MCDS accounts for, \(y_{\rm F}\ \leq\ \rm LET_{F}\) and \(y_{\rm D}\leq 9/8\ y_{\rm F}\).
## 4 Experimental Results and Discussion
### Experimental Results  
The purpose of this study was to examine the usage of S-HAR in conjunction with DL models to detect transitional behaviors. We utilized two HAR datasets, including fundamental and transitional actions, HAPT and MobiAct v2.0. Preprocessed accelerometer and gyroscope data were utilized for training and assessing the DL models using the five-fold cross-validation methodology. The average accuracy and Standard Deviation (SD) obtained from the experiments are considered to assess model performance. The experimental findings are exhibited as follows:  
Following the specification of prototype hyperparameters in the preceding step, the hybrid deep residual network was trained on the two datasets (HAPT and MobiAct v2.0). Tables 4 and 5 provide the exploratory outcomes.