esa-sceva/satcom-chunk-collection huggingface dataset:
# Anticipatory Troubleshooting
## 6 Evaluation
### Benchmark Systems  
All the experiments were performed over two systems, modeled using a Bayesian network (BN) following the standard use of BN for diagnosis [4].  
The BN nodes are partitioned into three disjoint sets: control nodes--their values depend only on the user's actions; sensor nodes--representing the data that can be observed from the system sensors; and health nodes--representing the potential faulty components we want to infer and repair. A health node with a value of 0 indicates that the component it represents is healthy. Other health node values represent states in which the component is in an unhealthy state.  
The first system, denoted \(S1\), represents a small part of a real world electrical power system. The BN was generated automatically from formal design [18] and is publicly available (reasoning.cs.ucla.edu/samiam/tutorial/system.net). It has 26 nodes, of which 6 are health nodes.  
The second system, denoted \(S2\), is the \"CAR_DIAGNOSIS_2\" network from the library of benchmark BN made available by Norsys (www.norsys.com/netlib/Car_Diagnosis_2.dnet). The system represents a network for diagnosing a car that does not start, based on spark plugs, headlights, main fuse, etc. It contains 18 nodes. From these nodes, seven nodes are health nodes (namely, the nodes labeled Main fuse, Alternator, Distributer, Starter motor, Air filter, Spark plugs, and Battery age). Figure 3 shows the graphical representation of S2.  
Unfortunately, we did not have real data for the above systems from which to generate survival curves. The modeling of the components survival curves will be discussed in the next sections.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication
### Underwater RF Communication
These models are essential for comprehendrehending RF signal behavior under varying conditions such as water salinity and temperature, and for designing adaptive systems [11; 54]. Moreover, multicarrier modulation techniques like OFDM can alleviate multipath propagation effects and enhance data rates by splitting the signal into multiple subcarriers for simultaneous transmission [55]. In terms of hardware improvements, advancements in miniaturized, low-power antennas and transceivers are addressing the size and energy constraints of underwater systems, particularly for AUVs and small underwater robots [11; 53]. [PERSON] et al. [56] recommend a multi-layer clustered network design that boosts data throughput and cuts energy use via multi-hop transmission. [PERSON] et al. [57] suggests integrating RF with optical and acoustic technologies to form multimodal systems, overcoming RF's range limitations. These hybrid networks can support real-time video transmission, complementing RF's short-range capabilities [58]. Overall, although RF communication has certain limitations in deep-sea or high-conductivity settings, it is still a valuable approach for high-speed, short-range applications in shallow waters. Future research should center on three main directions. First, expanding RF communication applications in freshwater and shallow water scenarios. This includes both theoretical exploration and practical application research, aiming to fully leverage the high data rate advantages of RF communication in these environments. Second, improving channel models and hardware design. More accurate channel models can help us better understand the propagation characteristics of RF signals in water, while optimized hardware design can enhance the performance and reliability of communication systems. Third, integrating RF systems with optical and acoustic technologies in multimodal networks, which can combine the strengths of different communication methods to achieve more efficient and reliable underwater communication. These improvements can help tackle challenges in marine exploration, environmental monitoring, and disaster early-warning systems [11; 54]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & **Frequency Range** & \
\begin{tabular}{c} **Wavelength** \\ **(Free Space)** \\ \end{tabular} &
\begin{tabular}{c} **Distance in** \\ **Sewater** \\ \end{tabular} & **Data Rate** \\ \hline ELF & 3 Hz–300 Hz & 10\({}^{6}\) km–10\({}^{3}\) km & Hundreds of km & \textless{}10 bps \\ SLF/ULF & 300 Hz–3 kHz & 10\({}^{3}\) km–100 km & Tens of km & 1–10 bps \\ VLF & 3 kHz–30 kHz & 100 km–10 km & 10–100 m & 100–500 bps \\ LF & 30 kHz–300 kHz & 10 km–1 km & 1–10 m & \textasci{}kpps \\ MF/HF & 300 kHz–30 MHz & 1 km–10 m & 0.01–1 m & \textasci{}Mbps \\ UHF/SHF & 300 MHz–300 GHz & 1 m–1 mm & \textless{}0.1 m & Mbps–Gbps \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Electromagnetic Wave Bands for Underwater Communication in Seawater
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 2 Static Buckling Criteria  
### Static Buckling Criteria  
From the introduction above, the buckling critical pressure of the spherical shell under a uniform external pressure load based only on the elastic material can be written as Equation (1) [45]:  
\[q_{c}=\frac{2E}{\sqrt{3(1-v^{2})}}{(\frac{t}{R})}^{2}, \tag{1}\]  
where \(E\), \(v\) are the elastic modulus and the Poisson ratio of the materials, respectively. \(t\) and \(R\) are the thickness and the radius of the shell, respectively. However, surprisingly, it is important to note that there is no buckling critical force for the spherical shell under concentrated load only.  
\[p^{*}=p\cdot R/Et^{3} \tag{2}\]  
By defining \(p^{*}\) in Equation (2), where \(p\) is the concentrated load, it can be found [45] that the slope of reaction force-deflection curve changed rapidly at around \(p^{*}=2.0\). Thus, there is no analytical buckling critical load for the spherical shell under both external pressure and concentrated load [46; 47; 48; 49]. From the report [45] mentioned above, the critical force \(p_{c}\) as a critical point load situation can be defined by \(p^{*}=2.2\). Clearly, the critical point load varies with different pressure, so that the buckling criteria is the function of both point load \(p\) and external uniform pressure \(q\). In this paper, the critical formulation can be expressed as Equation (3), satisfying two limitations:  
\[F(p,q)=0,\ p\big{|}_{q->0}=N,\ q\big{|}_{p->0}=q_{c} \tag{3}\]  
Considering that there is no buckling when only the shell is subjected to the point load, it should be noted that the \(N\) should be a very large or infinite value. The function can be determined by experimental or numerical simulation data via regression analysis [50; 51].
## 6 Discussion  
This paper explores the possibility to exploit data compression, in order to effectively tackle the wireless data transmission requirements of Epilepsy monitoring by typical WSN platforms. Adequate and objective study of Epilepsy requires continuously acquisition of respective modalities at high rates, thus creating excessive volumes of data. Following a multifaceted research and development approach, in this paper, firstly the problem is addressed algorithmically by the comparison of software-based compression algorithms targeting at optimum trade-off between compression rate and compression delay. In that respect, as analytically presented in [12] the proposed algorithm highly competitive performance. Secondly, going a step further towards practical solutions, the proposed algorithm is implemented at hardware level in the context of a typical FPGA platform. The implemented hardware module is presented in detail in this paper, while performance wise it reduced the compression delay by a factor of 1/1000. At the same time, dynamic power consumption ranges between 15 and 98 mW representing a viable solution for contemporary WSN platforms. The latter is further emphasized in the third part of this work, where the anticipated effect of integrating such a component with real prominent WSN platforms is evaluated.  
Figure 8: Compression module performance effect wrt ECG_EkgMove/shimmer BT setup.  
In this aspect two well-known wireless interfaces are considered, characterized by difference features and capabilities. Furthermore, the effect of running the proposed module at different clock frequencies is also taken into consideration. In all cases the benefits of using the proposed solution are quiet apparent. Specifically, considering Bluetooth wireless transmission, end-to-end delay reduction ranges between 11% and 65%, while energy is respective reduction ranges between 40% and 66%. Performance enhancement is even higher when considering IEEE 802.15.4 wireless communication since both transmission delay and energy consumption are reduced by from 5% to 70%.  
Without a doubt, such performance and resource conservation enhancements offer convincing arguments that hardware-based data compression comprises a viable solution towards network/node lifetime increase and wireless communication bandwidth optimum management.
## 3 Results  
### Evaluation of the Accuracy of the Age Estimation Model  
Accuracy evaluations for the ALL_Model, Male_Model, and Female_Model age estimation models are shown in Figures 5-8. As for the R and R\({}^{2}\) value, which indicate the degree of correlation, the accuracy of both age estimation models improved before and after data augmentation. Specifically, the R and R\({}^{2}\) value were 0.9643 and 0.9299, respectively, for ALL_Model, and 0.94582 and 0.93945, respectively, for Male_Model and Female_Model, and 0.89513 and 0.88257, respectively, for R\({}^{2}\). As for MAE and RMSE, which indicate the error between the actual age and estimated age, the accuracy improved before and after data augmentation for ALL_Model and Female_Model, but the results for Male_Model were almost the same before and after data augmentation. Specifically, the MAE and RMSE of the ALL_Model were 5.251 and 6.422 years, respectively, whereas the MAE and RMSE of the Male_Model and Female_Model were 6.323 and 5.766 years, respectively, and 7.734 and 7.006 years, respectively. Figure 9 shows examples of age estimation from brain MR images using this method. The upper two rows show examples of highly accurate age estimation, and the lower two rows show examples with estimation errors of about MAE or larger.  
Figure 4: The process of the statistical analysis on the accuracy of age estimation before and after the removal of the corpus callosum (CC) region.  
Figure 5: Results for correlation coefficient (R). (DA: Data Augmentation).  
Figure 6: Results for the coefficient of determination (R\({}^{2}\)). (DA: Data Augmentation).  
Figure 8: Results for root mean squared error (RMSE). (DA: Data Augmentation).  
Figure 7: Results for mean absolute error (MAE). (DA: Data Augmentation).
## 3 Construction of the Model
### Level 1 Evaluation: Working Environment  
At Level 1, the system is first evaluated for the presence of interference between the transmitter and receiver. According to the working frequency, whether there is a fundamental interference margin (FIM), transmitter interference margin (TIM), receiver interference margin (RIM), or spurious interference margin (SIM) between the transmitter and receiver are considered in turn. The IM criterion equations are as follows.  
\[|f_{T}-f_{R}|\leq 0.2f_{R} \tag{5}\]  
If Equation (5) is satisfied, FIM needs to be considered. Otherwise, FIM does not need to be considered.  
\[\begin{cases}f_{T}<10f_{R}\\ f_{T}>0.1f_{R}\end{cases} \tag{6}\]  
If Equation (6) is satisfied, TIM needs to be considered. Otherwise, TIM does not need to be considered.  
\[\begin{cases}f_{R}<10f_{T}\\ f_{R}>0.1f_{T}\end{cases} \tag{7}\]If Equation (7) is satisfied, RIM needs to be considered. Otherwise, RIM does not need to be considered.  
\[\begin{cases}0.1f_{T}<10f_{R}\\ 10f_{T}>0.1f_{R}\end{cases} \tag{8}\]  
If Equation (8) is satisfied, SIM needs to be considered. Otherwise, SIM does not need to be considered. In Equations (6) to (8), \(f_{T}\) and \(f_{R}\) denote the working frequencies of the transmitter and receiver, respectively.  
The first-level EMC evaluation is performed using the above equations. If any equation is satisfied, then the first-level EMC evaluation result will be \(\text{IM}>0\), which indicates that a potential EMI is present and a second-level evaluation is required.
## 4 An Exploration of the Advantages of Genetic Algorithms
### The Mathematical Mechanism of the Primitive GA  
The original GA is rooted in [PERSON]'s theory of natural selection, incorporating three fundamental elements: heredity, variability, and the competition for survival. This approach emulates the processes of natural selection and genetics to seek out an optimal solution [57].  
In the algorithm, each possible solution is encoded as an individual that forms a population. Initially, a group of individuals is randomly generated. Based on the fitness function, we evaluate the advantages and disadvantages, and perform selection, crossover, and mutation operations based on fitness. The selection operation reflects the principle of \"survival of the fittest\", the crossover operation combines good characteristics, and the mutation operation increases diversity. Through iteration, the population gradually evolves and approaches the optimal solution. GAs involve coding, fitness functions, crossover, and mutation operators, etc. Selection and design are critical to performance. Although it sometimes needs to be improved, its evolutionary mechanism still has significant advantages in solving complex optimization problems.  
To achieve the most effective control parameters for the EMS, the GA is utilized to delve into the optimal values of individual parameters within a constrained solution space. Its search methodology relies primarily on the evaluation of the fitness function, as expressed mathematically in Equation (1) below [178].  
\[\left\{\begin{array}{r@{\quad}l}min\ J=f(x_{1},x_{2},\cdots,x_{n}) \quad&s\cdot t\cdot g(x)\leq 0\quad&\end{array}\right. \tag{1}\]  
In this equation, \((x_{1},x_{2},\cdots,x_{n})\) represent the parameters that require optimization. The fitness value is denoted by (\(J\)), which is determined by the fitness function (\(f(x)\)). Additionally, (\(g(x)\)) represents the constraint function that needs to be considered. Through calculation, a set of potential solution vectors can be generated. Subsequently, a new population is formed by screening and retaining well-adapted individuals through reproduction, hybridization, and genetic variation. The process by which a new generation inherits genetic information from its predecessors while simultaneously increasing the likelihood of transmitting advantageous genes exemplifies the potential for adaptive enhancement within a population. This iterative mechanism continues until a predetermined termination condition is met, resulting in progressively improved adaptability. Consequently, this procedure highlights the effectiveness and superiority of GAs in addressing and solving optimization problems. Figure 11 illustrates the fundamental process of a traditional GA.  
Figure 11: The basic flow of a GA [57].
# Acoustic Improvement of Stator-Rotor Interaction with Nonuniform Trailing Edge Blowing
## 5 Experimental Results
### Acoustic Results
#### 5.2.3 OASPL Analysis  
Section 5.2.1 has shown that NTEB results in beneficial effects of noise reduction on the first and second-order BPF of forward radiation when the SPL was found to be reduced for increasing levels of NTEB mass flow. However, for the first-order BPF of backward radiation in Section 5.2.2, noise-reduction effects were not obvious, and it was even observed that NTEB can have detrimental effects. These trends are the same as those for the overall sound pressure level (OASPL) analysis of the first two orders, as presented in Table 2, where F-OASPL is the overall sound pressure level of forward radiation noise, and B-OASPL is the overall sound pressure level of backward radiation noise.  
Figure 9: SPL of the first and second-order BPF.  
The biggest OASPL reduction was more than 9 dB, which is a little smaller than the SPL reduction of the maximum acoustic mode.  
In order to present the NTEB effect on every channel in the compressor duct, the OASPL of every channel is calculated by a linear weighted sum. The OASPL reduction of the whole compressor duct is computed in comparison to the values obtained for measurements with no added mass flow. The data obtained for the forward and backward radiation modes are displayed in Table 3, which is illustrated according to the Parseval theory.  
Table 3 reveals that most of the channels displayed a reduction in the OASPL for both the forward and backward cases. While there exists an OASPL increase for some channels, at lower levels of TEB flow, the increments of the increase are closer to zero. Nevertheless, the increase of noise levels at some of the channels for NTEB do have an overall noise reduction, as revealed by the data in the last column of Table 3. The maximum value of noise reduction observed at any of the channels is about 3 dB. Therefore, in summary, NTEB has been shown to have a noise reduction effect on the OASPL in the forward as well as backward radiation modes.
## 3 Empirical Analysis
### Supplementary Experiment  
In order to better verify the applicability and generalization of the proposed model for China's power grid investment forecasting, the data of the power grid in S province, China from 1990 to 2016 is collected. The data from 1990 to 2009 are taken as the training set, and the test set is the data from 2010 to 2016. DE-GWO-SVM, GWO-SVM, SVM, and BP neural networks are used to forecast the power grid investment for comparison test. The result of error analysis is shown in Table 5.  
As shown in Table 5, \(MAPE\), \(RMSE\), and \(MAE\) of the DE-GWO-SVM model is the smallest in all models. The ranking of the prediction accuracy of the four models for China's S province is consistent with the test result of the case for China (DE-GWO-SVM > GWO-SVM > SVM > BP), which proves the applicability and generalization of the DE-GWO-SVM model for power grid investment forecasting in China.
# Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk
## 1 Introduction
Decision makers require comprehensive risk assessments, providing a full distribution of profitability outcomes before making a decision [20]. By using two risk formulas, the rolling horizon control was used to explore the risk aversion of VPPs, which can efficiently solve the risk problem of VPPs [21]. In studies [22; 23], the operating cost was minimized while maintaining the power equality of the system in question. [PERSON] et al. [24] used the robust stochastic method to analyze uncertain factors and solve the optimal scheduling of multi-energy VPPs. For the uncertainty of electricity pricing, a risk-averse stochastic stage [25; 26] was introduced by using CVAR. [PERSON] et al. [27] considered both risk-neutral and risk-averse formulations that depend on CVAR to solve the optimal operation of a VPP. Value-at-risk (VAR) is a viable measure for the risk analysis and is generally used by financial institutions to measure the minimum loss expected in a given portfolio within an assigned period [28; 29]. Conditional value-at-risk is the extended risk measure of VAR that quantifies the average loss over a specified time period of unlikely scenarios beyond the confidence level. It works similarly to a market risk measure for capital calculation. This paper used CVAR to model the risk associated with uncertainties in the objective function and constructed a CVAR-based risk aversion model [25; 26]. Uncertainties that are specific to companies can be acquired and built from operation data. An enhanced bacterial foraging algorithm (EBFA) is proposed to solve the risk dispatch problem of a VPP. In the EBFA procedure, stochastic weight trade-off (SWT) is embedded in bacterial foraging to improve the behavior patterns of each bacterium to enhance its searching efficiency in high-dimension solution space. Various moving patterns of EBFA were also proposed to improve the efficiency and effectiveness, which were demonstrated by using a VPP system on Penghu island, Taiwan. Many scenarios were created, including various seasons, power rebate pricings, and confidence levels, so the maximal risk and return of VPPs could be simulated and analyzed. Simulation results show a positive direction for a VPP to perform the power dispatch by maximizing risk income. This paper also provides a guideline for planning a VPP and includes risk management.
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 2 Data
### Emissivity
#### 2.1.5 Long Term Infrared Atmospheric Sounding Interferometer Dataset  
Monthly, 8-year averages of IASI emissivity derived from measurements are available at 0.25\({}^{\circ}\) spatial resolution. For the months of January through May, emissivity is averaged over the years 2007-2014 and for the months of June through December it is averaged over 2008-2015. An inversion scheme which uses both cloudy and cloud-free radiances is used to simultaneously retrieve atmospheric thermodynamic and surface or cloud microphysical parameters [5; 6].
## 2 System Arrangement
### Formulation of System Arrangement
#### 2.4.1 Symbol Statement  
The optimal arrangement scenario consists of \(N\) transmission towers. The average span and communication range in length are \(s\) and \(r\), respectively, thus \(m=\lfloor r/s\rfloor\) represents the number of towers that a communication range can cover. Since the communication range covers several towers, then there are many available routes, e.g., from 1 to 3 or from 1 to 2 then to 3. To describe the route of data packets, binary variables \(x_{i,k}\), \(l_{ij,k}\) are used. \(x_{i,k}\) is 1 if the end-to-end route uses the \(i\)th tower for relay in the \(k\)th route. \(l_{ij,k}\) is 1 if the data route uses a link from the \(i\)th tower to the \(j\)th tower in the \(k\)th route, and the delay of the link is described by latency \(t_{ij}\). \(D\) represents the end-to-end delay constraints for designing. The reliability of nodes is denoted by \(p\), indicating the contingency of the nodes' survivability under disasters. Specifically speaking, in Figure 3, if node \(j\) fails with probability of \(p\), then \(x_{i,k}\) is 0 and the links related to node \(j\) are broken, that is, \(l_{ij,k}=0\) and \(l_{ij,k}=0\). As a result of the self-healing characteristic of routing protocol, the route between node \(i\) and \(h\) is rebuilt, which is described by \(l_{ih,k}=1\). The number of possible routes satisfying connectivity is \(NR\) and the number of _active_ towers through the \(k\)th route is \(n_{k}\), where _active_ indicates that the tower is adopted in the \(k\)th route.