esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Strategy Hybrid Whale Optimization Algorithm Improvement
## 3 Multi-Strategy Hybrid Improved Whale Optimization Algorithm
### Deflection and Decentralized Search Strategies  
The individuals in the swarm-based search algorithm possess three attributes: position, angle, and direction. As shown in Figure 2, it is assumed that the front represents the current forward direction of an individual, while the left and right represent the directions after randomly turning an angle \(r\) to the left or right, which can be expressed by Equation (18).  
\[\left\{\begin{array}{c}\text{Search ahead }X=X(t)+r_{1}l_{max}D_{p}(\varphi)\\ \text{Search to left }X=X(t)+r_{1}l_{max}D_{p}(\varphi+r_{2}\theta_{max}/2)\\ \text{Search to right }X=X(t)+r_{1}l_{max}D_{p}(\varphi(\varphi-r_{2}\theta_{max}/2) \end{array}\right. \tag{18}\]  
where \(X(t)\) represents the current position of an individual, \(r_{1}\) is a random number following the standard normal distribution, \(r_{2}\) is a random number that is uniformly distributed in the interval [0, 1], \(l_{max}\) denotes the maximum search step length, and \(\theta_{max}\) represents the maximum turning angle. \(D_{p}(\varphi)\) represents a direction vector, controlled by a Gaussian distribution, \(D_{p}(\varphi)\sim N(0,S^{2})\). If these three positions are better than the previous position, the individual moves to the best among the three positions; otherwise, it remains stationary.  
Based on the modified parameters, to further enhance the search efficiency and convergence of WOA, this study introduces a new dispersal search strategy. In order to expand the search range of WOA, the improved WOA employs a central deflection and random perturbation strategy for the first 60% of the population. For cases where the random value is less than 0.9, the new position will be updated based on a certain deflection coefficient applied to the current position's forward path, as shown in Equation (19).  
\[X(\overset{\rightarrow}{t+1})=\overset{\rightarrow}{X(t)}+k\overset{ \rightarrow}{X(t)} \tag{19}\]  
where \(k\) represents the search deflection coefficient; in this case, the value of \(k\) is the linear increase in [0.5, 2].  
For the remaining cases where the random value exceeds the threshold, the new position undergoes a random perturbation around the current best individual, thereby increasing the search diversity and enhancing the algorithm's global search capability, as shown in Equation (20).  
\[X(\overset{\rightarrow}{t+1})=\overset{\rightarrow}{X(t)}+N(0,S^{2}) \tag{20}\]  
where \(S\) denotes a constant. To balance development and exploration, it takes a value of 0.5. \(N\) indicates the current iteration population.  
Figure 2: Position deflection diagram.
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 2 Objectives and Motivation  
The core of the project is employment of multi-point distributed measurement system synchronized to the precision clock with high accuracy. This measurement configuration allows the evaluation of power network response at different nodes to time-varying harmonic current injections. The research project advances the knowledge in the field of power quality and introduces new approaches and measurement techniques for assessment of harmonic distortion and its impact on wide networks. Additionally, the results of this study are expected to further facilitate integration of devices with global synchronization of the samples and their utilization for power quality assessment.  
The objectives of the research comprise investigation of the phenomena of harmonic interaction between background voltage harmonic distortion and harmonic current injections of the nonlinear sources under the study [11; 12; 13]. Furthermore, with provision of experimental measurements a diversity of harmonic sources is evaluated. The diversity of phase angles of different harmonic-producing sources is what leads to cancellation effects between harmonic currents [14; 15]. According to the IEC 61,000 family of standards, these effects are accounted by applying summation exponents to magnitudes of harmonic currents. To improve the evaluation of harmonic current components, a grid measurement technique which relies on time synchronization is necessary. This takes into consideration time variation of current components and preserves current phase angles. The latter factor results in vectorial summation of currents emitted by different harmonic sources. At this stage, the accuracy of the measurement instrumentation and time synchronization are vital for accurate evaluation of harmonic emission. This type of analysis and configuration of measurement system provides an advantage over conventional power quality assessment methods. Therefore, a voltage harmonic distortion can be correlated in time with harmonic current injections at different locations.  
Moreover, an additional objective is to investigate the influence of network impedance on voltage distortion levels. The outcome of a harmonic analysis can differ given the situation when absolutely identical harmonic producing loads operating at identical power levels are connected to the system with dynamically changing impedance. The spectral components extracted out of synchronized data are anticipated to provide a good insight into the consequences of varying system impedance.
###### Abstract  
The position between BeiDou geostationary Earth orbit (GEO) satellites and ground-based receiving stations can roughly be considered to be constant with negligible fluctuations; thus, the total electron content (TEC) data over a fixed ionospheric piercing point (IPP) can be continuously acquired, which is advantageous for monitoring ionospheric disturbances. Focused on the Jiuzhaigou Ms7.0 earthquake that occurred on 8 August 2017, the TEC data inverted by the BeiDou GEO satellite were analyzed to extract ionospheric disturbances potentially associated with the earthquake. It was found that significant anomalies in ionospheric TEC occurred 10-11 days, 6-7 days, and 1-9 h prior to the earthquake, which was mainly located in the southeast and southwest directions within about 2500 km distance from the epicenter. Comparing the spatial and temporal characteristics between the ionospheric disturbance and the radon gas near the surface, the atmospheric electric field, and the spectrum of TEC data, it was considered that the chemical and acoustic-gravity wave pathway may play an important role in the lithosphere-atmosphere-ionosphere coupling (LAIC) mechanism.  
[PERSON], [PERSON], [PERSON], X. The Analysis of Ionospheric TEC Anomalies Prior to the Jiuzhaigou Ms7.0 Earthquake Based on BeiDou GEO Satellite Data. _Remote Sens._**2024**, _16_, 660. [[https://doi.org/10.3390/rs16040660](https://doi.org/10.3390/rs16040660)]([https://doi.org/10.3390/rs16040660](https://doi.org/10.3390/rs16040660))
## 3 System Model  
The system model consists of a source (S), a destination (D), and one relay (R). Communication between S and D is achieved with the aid of a relay node operating in half-duplex mode, which employs a DF-based algorithm. It is assumed that S, D, and R have a single antenna for transmission and reception. The power spectral density (PSD) of additive white Gaussian noise (AWGN) at the receiver is assumed to be \(N_{0}\) with a mean of zero, \(n\sim CN(0,N_{0})\). The system model is shown in Figure 1.  
Figure 1: System model of DF relaying system.  
Let the instantaneous SNR between S-R and R-D be \(\gamma_{1}\) and \(\gamma_{2}\), respectively, defined as \(\gamma_{1}=\frac{\|h_{1}\|^{2}P_{1}}{N_{0}}\) and \(\gamma_{2}=\frac{\|h_{2}\|^{2}P_{2}}{N_{0}}\), where \(h_{1}\) and \(h_{2}\) are the channel coefficients between the source-relay and relay-destination links. \(P_{1}\) and \(P_{2}\) are the power transmitted by the source and the relay, respectively. Furthermore, let the PDF of \(\gamma_{1}\) and \(\gamma_{2}\) be \(p_{\gamma_{1}}(\gamma_{1})\) and \(p_{\gamma_{2}}(\gamma_{2})\), respectively. In order to support the unified approach of this study, it is assumed that the fading channel between S-R and R-D can belong to any family of PDFs. Furthermore, it is also assumed that \(p_{\gamma_{1}}(\gamma_{1})\) and \(p_{\gamma_{2}}(\gamma_{2})\) can acquire different fading envelopes. Furthermore, \(p_{\gamma_{1}}(\gamma_{1})\) and \(p_{\gamma_{2}}(\gamma_{2})\) are assumed to be independent, non-identical distributed (INID) variables.
## 1 Introduction
### Cost Model  
The total power system \(C_{Total}\) involves one investment cost of submarine cables and the energy loss cost of power system, consisting of four parts, as shown in Equation (5).  
\[C_{Total}=C_{Cable}+C_{Const}+C_{Loss}+C_{OS} \tag{5}\]  
Cable cost \(C_{Cable}\) is divided into high-voltage and low-voltage cable costs, which are functions of cable length and cross-sectional area, and is given in Equation (6).  
\[C_{Cable}=\sum_{i=1}^{N_{M}}3\cdot L_{i,j}^{M}\cdot c_{j}^{M}+\sum_{i=1}^{N_{ H}}3\cdot L_{i,j}^{H}\cdot c_{j}^{H} \tag{6}\]  
where \(N_{M}\) and \(N_{H}\) are the number of medium and high-voltage cable segments, respectively; \(L_{i,j}^{M}\) and \(c_{j}^{M}\) are the length of the \(i\)th medium-voltage cable section and the unit length price of the correspondingcable, respectively; and \(L_{i,j}^{H}\) and \(c_{j}^{H}\) are the length of the high-voltage cable section \(i\) and the unit length price of the corresponding cable, respectively.  
The construction cost \(C_{Const}\) represents the cost of trench construction for subsea medium- and high-voltage cables, and is shown in Equation (7).  
\[C_{Const}=\left(\sum_{i=1}^{N_{M}}L_{i,j}^{M}+\sum_{i=1}^{N_{H}}L_{i,j}^{H} \right)\cdot c^{T} \tag{7}\]  
where, \(c^{T}\) is the construction cost per unit length, and its value is 21,000 USD/km according to engineering experience [7].  
The energy loss cost \(C_{Loss}\) is the sum of the energy loss costs of the power network topology lifetime, and is shown in Equation (8).  
\[C_{Loss}=\sum_{i=0}^{\tau-1}{(1+q)^{i}E_{AL}c_{EP}} \tag{8}\]  
where \(\tau\) is the design life of the wind farm, taken as 20 years; \(q\) is the discount factor, taken as 0.02; \(E_{AL}\) is the annual power loss of the power network; and \(c_{EP}\) is the unit price of the wind farm sold to the grid, taken as 0.05 USD/kWh.  
Substation cost \(C_{OS}\) is an empirical formula related to rated power and is fitted based on a large amount of data [38], shown in Equation (9).  
\[C_{OS}=A_{p}+B_{p}P_{SR}^{\delta} \tag{9}\]  
where \(P_{SR}\) is the rated power of offshore substations. \(A_{p}\), \(B_{p}\), and \(\beta\) are constant coefficients, taken as \(-\)205,000, 364.6, and 0.4473, respectively.
## 6 Influence of ABHs on the Vibration Characteristics of the Support Plate
To better analyze the influence of the damping ABHs on the vibration acceleration of the support plate in the whole frequency band, three monitoring points were established at the left, middle and right positions of the support plate, as shown in Figure 16.  
Figure 15: The vibration acceleration cloud diagram of the original support plate and the damping ABH support plate: (**a**) damping ABH support plate, 500 Hz; (**b**) original support plate, 500 Hz; (**c**) damping ABH support plate, 1000 Hz; (**d**) original support plate, 1000 Hz; (**e**) damping ABH support plate, 2000 Hz; (**f**) original support plate, 2000 Hz; (**g**) damping ABH support plate, 3000 Hz; (**h**) original support plate, 3000 Hz; (**i**) damping ABH support plate, 4000 Hz; (**j**) original support plate, 4000 Hz; (**k**) damping ABH support plate, 5000 Hz; (**l**) original support plate, 5000 Hz. Figures 17-19 are the vibration acceleration response spectrograms of the three monitoring points. It can be seen from Figure 17 that for the left monitoring point, the 2-D damping ABHs can significantly reduce the vibration acceleration of the refrigerator support plate in the range of 3 kHz to 5 kHz, and the maximum attenuation value can reach 6.69 dB (with a reference of 0.1 m/s\({}^{2}\)); in the 0-3 kHz frequency band, the acceleration peak values of the left monitoring point are reduced at some frequencies but increased at other frequencies. It can be concluded from Figure 18 that for the middle monitoring point, the 2-D damping ABHs have no obvious suppression effect on the acceleration peak values of the support plate; however, the acceleration peak values are increased at some frequencies. It can be seen from Figure 19 that for the right monitoring point, the 2-D damping ABHs show a good vibration suppression effect in almost the whole frequency range, and the maximum acceleration attenuation can reach more than 10 dB, indicating that the 2-D damping ABHs have an obvious vibration suppression effect on the right side of the support plate. Considering that the left and right sides of the support plate are connected to the refrigerator body, and the mass and volume of the refrigerator body are much larger than the mass and volume of the support plate, it can be considered that the true boundary conditions of the support plate are basically the same as in the simulation. Combined with the sound and vibration measurement in Section 2, it can be concluded that the 2-D damping ABHs can suppress the vibrations generated by the compressor at 1390.6 Hz, 2087.5 Hz, and 2260.9 Hz from being transmitted to the refrigerator body through the support plate, thereby suppressing the radiated sound of the back wall of the refrigerator at frequencies of 1250 Hz, 2000 Hz, and 2500 Hz. Figure 16: Schematic diagram of the location of the vibration response monitoring points. Figure 17: Acceleration response spectrum of the left monitoring point.
## 3 Data and Methods
### Data Preprocessing  
After the raw data were collected, data preprocessing needed to be performed. The collected datasets unavoidably involve some missing values due to machine failure, routine maintenance, human error, insufficient sampling, and other factors. The missing values normally are required to be removed or filled to ensure the performance of modeling [35].The missing rate in this experiment was relatively small, at 2.65%. We implemented the linear interpolation methods following [36] to fill the empty values. After this procedure, each station resulted in 17,544 records of PM2.5 concentrations.  
Furthermore, to mitigate the impact of dimension and speed up the model training, min-max normalization was adopted to normalize the data. Calculation of the normalization can be formulated as Equation (1)  
\[\mathrm{x^{*}}=\frac{\mathrm{x-x_{min}}}{\mathrm{x_{max}-x_{min}}} \tag{1}\]  
where \(\mathrm{x_{max}}\) represents the maximum value in the dataset and \(\mathrm{x_{min}}\) represents the minimum value.  
Figure 1: Research framework.
## 5 Discussion
### The Role of Reverberation on Perceived Externalization  
The experimental results shown in Figure 9 confirmed the findings of our previous study in [14], i.e., the reverberation at the contralateral ear had more influence on perceived externalization than that at the ipsilateral ear. During this experiment, most listeners reported that the sound images were perceived as unnatural for the \"RL\" and \"RR\" conditions since the energy of the lateralizedreverberation was strongly changed. As mentioned before, listeners should ignore the naturalness of the stimuli, and they were only asked to evaluate perceived externalization of stimuli.  
For a frontal sound source, the degree of externalization decreased significantly by removing the reverberation in either left or right ear signals. The contribution of reverberation at the contralateral ear to perceived externalization increased as the sound source moved from frontal to lateral incidence angles. For azimuth angles larger than 30\({}^{\circ}\), the reverberation at the ipsilateral ear had only a slight contribution to the externalization of sound images, and the reverberation at the contralateral ear dominated the judgment of perceived externalization. This finding may be utilized in the design of binaural rendering systems. In the case of lateral sound sources (azimuth angles larger than 30\({}^{\circ}\)), the amount of reverberation at the ipsilateral ear can be reduced appropriately to reduce the computational complexity, while taking into account the perceptual attributes such as listener envelopment, naturalness, etc.
# Source Rock Prediction Using Well Log and Seismic Data:
## 2 Regional Geological Overview
### Development of Source Rocks  
Previous studies have suggested that the Cote d'Ivoire Basin is mainly composed of three sets of source rocks: Aptian, Albian, and Cenomanian-Turonian.  
The Aptian-stage source rocks are mainly lacustrine shale, and the kerogen type is mainly type III, with an average TOC of 0.6 wt.%-2.6 wt.%. The parent material source is mainly terrestrial, with a small amount of marine organic matter, and the thin oil source interval and thick gas generation interval are interbedded. At present, it has only been drilled in Ghana and is a minor source rock, which may be found in deep-water areas in the future [21].  
The Albian-stage source rocks can be divided into middle- and upper-Albian source rocks. The middle-Albian source rocks are lacustrine shale [22], which is the oldest source rock found in the Cote d'Ivoire Basin and has entered the gas generation stage [23]. The kerogen types are mainly type II and type III, with an average TOC of about 1.2 wt.%. The source rocks of the Upper Albian stage are marine shale and marl, and the kerogen type is mainly type II, with an average TOC of about 2.1 wt.%, which is mainly used for oil generation.  
The main source rocks of the Cenomanian-Turonian stage are marine shale, and the kerogen type is mainly type II. The average TOC of the Cenomanian-stage source rocks is about 0.75 wt.%-2.6 wt.%, and the average TOC of the Turonian stage is about 5.5 wt.%. This set of source rocks extends in the deep-water direction, and the oil and gas in the Jubilee oilfield come from this set of source rocks [15].
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 2 Generation Algorithm
### Sensors and Attack Subnetwork  
The real connection strategy determines the attack and sensor nodes; that is, the outermost C2 nodes of the C2 network belong to the unit of the combat formation, which is not separated from other attributes of the formation, such as attack or sensor attributes.  
Figure 2: The generation of the combat network.  
Therefore, the leaf nodes of the C2 network are connected with at most one attack and sensor node. Nodes in the attack and sensors subnetwork are shown in Table 14.  
Specify the number of sensor nodes as \(S_{n}\) and satisfy \(S_{n}<N_{L}\). We randomly select \(S_{n}\) sensor nodes from \(N_{L}\) C2 nodes, and each C2 node \(D_{i}\) generates a corresponding sensor node \(S_{i}\), where \(i<S_{n}\). The sensor node degree is distributed according to the power law. The maximum weight of the sensor node is defined as \(W\). The Poisson distribution curve is fitted according to the weight parameter, and the connection probability \(P\) of each sensor node is generated. The sensor node self-connects according to the probability \(P\) to form a sensor subnetwork. Similarity, we can generate the attack subnetwork. Specify the number of attack nodes as \(F_{n}\), which satisfy \(F_{n}<N_{L}\). We randomly select \(F_{n}\) attack nodes from \(N_{L}\) C2 nodes, and each C2 node \(D_{i}\) generates a corresponding attack node \(F_{i}\), among which \(i<F_{n}\).  
The attack node is at the edge of the combat SoS and belongs to the leaf node, receiving the information and instructions from sensor nodes and C2 nodes to complete relevant combat tasks. Targeted information and C2 information flow through the attack node, comprehensively using various types of weapons and equipment or methods of fighting to attack enemy targets. The attack node is at the edge of the combat network; as a combat unit, it usually has the attributes and functions of sensors, communication, C2, and attack, such as the air patrol formation and ground attack formation. Therefore, here, we are no longer able to distinguish the sub-attributes of the attack network separately but rather, we intend to define the granularity of the study as a combat unit and analyze the relationship between the fusion of its internal multiple attributes and other types of nodes in the network.  
Here, the attack node is the soft kill and hard kill weapon equipment (platform or system) that affects the enemy, which is collectively called the attack node. Therefore, the attack node can understand weapons and equipment entities that can destroy and interfere with the enemy. Destruction refers to a means to cause damage or loss of combat capability to the enemy within a certain time and scope, while interference is a means to instruct the enemy's electronic systems and equipment losing their original capability.
## 4 Results
Lower RMS error values generally indicate a better model fit, with errors between 0.2 and 0.5 being considered highly accurate in many statistical engineering applications. The high \(R^{2}\) value confirms that the models capture the key trends in the data, while the low RMS error indicates that the models make highly accurate predictions within the specified range of crack dimensions. Figures 9 and 10 illustrate the regression models for the stress intensity factor and plastic limit pressure comparing finite element analysis (FEA) results with statistically obtained expressions (1 and 2). The scatter plots represent the observed FEA values, while the regression lines demonstrate the relationship modeled by the statistical analysis. The high degree of alignment between the FEA data and the regression models suggests a strong correlation between them. This indicates that the proposed expressions accurately represent the relationship between the crack dimensions within the tested range. The tight grouping of data points around the regression lines confirms the validity of the statistical models for predicting stress intensity factor and plastic limit pressure. This demonstrates that generated expressions are reliable for practical engineering calculations. Although the regression models have shown a strong fit within the tested range, future work could explore the models' behavior beyond the tested crack sizes and include parameters such as pipe thickness and radius, which may further increase their accuracy and applicability. It is also essential to validate these models against experimental data or other analytical solutions to ensure their robustness in different scenarios. Figure 9: Stress intensity factor scatter plot.
## 3 Analysis and Discussion
### Analysis of AFEM Setting  
A different setting of internal parameters for AFEM can significantly affect the model's performance in making predictions, including the weight constraint method, normalization method, activation function, and weight initialization method. In this subsection, the \(M_{1}\) model is used to analyse the effect of AFEM parameter settings on model performance in detail. Here, classification network hyperparameters are set with: a learning rate of 0.001, a dropout ratio of 0.3, and a preprocessing truncation duration of 30.73 s based on the studies in the above subsections. Meanwhile, five network structures are selected for training: 1 layer 256 cells, 2 layers 64 cells, 2 layers 128 cells, 3 layers 32 cells, and 4 layers 32 cells. For simplicity, since the other groups follow a similar trend, only the results on the 3 layers 32 cells are shown.