esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### ResNet Layer Number Settings  
In order to improve the model performance, this study aimed to select the optimal number of network layers for ResNet. Figure 6 shows the results of experiments with 20-fold cross-validation on the sleep-EDF dataset with different network layers (18, 34, 50, 101, and 152). When the number of network layers is 34, the accuracy, F1 score, and Cohen's Kappa (\(\kappa\)) are significantly higher than the other network models. Therefore, the number of layers of ResNet is chosen to be 34 in this paper to ensure the depth features that can be extracted effectively without degrading the network performance.
## 3 Enabling Technologies for Autonomous Navigation
### Deep Sensor Fusion  
Data Fusion, or sensory information, is a skill constantly used by human beings in their day-to-day activities that mainly requires the physical understanding of the objects around them. An example of data fusion is the union of the visual senses with touch (the ability to touch) for dexterity and object recognition.  
According to [4], sensory fusion is the task of managing and coupling data and information obtained through different types of sensors to improve a specific criterion or decision making in a given process. The fusion technique normally occurs computationally through an algorithm that performs this task. The merged sensory data are enriched, improved, and more reliable than that generated by individual sensors separately.  
In the field of sensing and perception in robotics, there are classic approaches of sensor fusion through probabilistic methods such as the Bayesian inference and Kalman Filter.  
In [11], it is highlighted that with the growth of the technology of multimodal sensors, that is, sensors that provide data in different dimensions, sizes, and particularities, the application of Bayesian methods began to present difficulties. These methods normally work with small dimensions or data with homogeneous characteristics. To overcome this problem, the field of Machine Learning and Artificial Intelligence has been useful and the target of current research in sensory fusion.  
There are currently several modern sensor fusion schemes listed in the literature [4,11]. The three main fusion schemes, represented in Figure 1, are: (i) Early Fusion, (ii) Feature Fusion, and (iii) Late Fusion. In the Early Fusion technique, the data from different sensors are unified in their initial form--in their raw form (raw level). Theoretically, this fusion occurs after the immediate reading of the sensors. Feature Fusion, as its name implies, is a feature extractor (usually a shallow neural network) used after reading the raw sensor data to merge the main properties of that particular data. In Late Fusion, multiple classifiers (usually a deep neural network) are used before data union, which means that this technique is as close as possible to a decision level.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 5 Conclusions  
This paper reviews and evaluates the traditional survivability technologies for optical sensing and optical communication in OSNs. The existing survivability technologies can be divided into two categories: protection and restoration. The protection schemes such as the fiber bus protection scheme, the self-healing scheme, \(1+1\) protection scheme, the p-cycle scheme, the p-polyhedron scheme, and the multipath protection scheme have low time consumption but high spectrum resource consumption. Compared with the protection scheme, the restoration scheme has longer recovery time and cannot guarantee 100% recovery. Moreover, since the RSA problem has been proved to be an NP-complete problem, various heuristic algorithms and biological intelligence algorithms have been applied to spectrum allocation for backup light paths. Since the traditional survivability technologies are based on network connectivity, it is difficult to achieve disaster-resilience by merely relying on an optical network itself. The photonic millimeter-wave bridge scheme uses millimeter-wave technology to recover the interrupted traffic. This scheme does not rely on an optical network itself and thus provides a promising method to achieve disaster-resilience. In addition, the concept of \(k\)-node (edge) sensing connectivity is introduced. Based on \(k\)-node (edge) sensing connectivity, disaster-resilient survivability technologies are elaborated for OSNs. Recently, AI has obtained rapid development. It provides a pre-requisite for traditional survivability technologies and can help improve the survivability of optical sensing and optical communication in OSNs. AI has been applied in many fields of optical communication and optical sensing including learning, reasoning, and self-correction. In the future, other fields such as sensor networking, management and operation of OSN, can also be addressed by AI.  
**Author Contributions:** Conceptualization, Y.Z. and J.X.; methodology, Y.Z. and J.X.; supervision, Y.Z.; writing--original draft, J.X.; writing--review and editing, Y.Z.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant numbers 61701039, and the National Science Foundation for Outstanding Youth Scholars of China, grant number 61622102.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experiment and Results
### Ablation Experiment Analysis  
To evaluate the efficacy of the three proposed enhancement strategies for the IMOWAO, ablation experiments were conducted for each improvement strategy. Following the execution of the algorithm 30 times for each strategy, the mean and standard deviation of the results were calculated and analyzed. The findings of the analysis are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **1** & **2** & **3** & **4** \\ \hline Endpoint P (\%) & 0.0108 & 0.0109 & 0.0110 & 0.0109 \\ Production cost (CNY/t) & 3213.10 & 3215.97 & 3213.88 & 3210.48 \\ \hline \end{tabular}
\end{table}
Table 4: Algorithm validation results.  
Figure 4: Pareto feasible solution.  
The impact of each strategic approach on the endpoint phosphorus content and the production cost per tonne of steel has been systematically analyzed, as detailed in Table 5. This analysis comprehensively evaluates how the different strategies influence both key performance metrics, thereby providing insights into their respective effectiveness in optimizing the steelmaking parameter process. The enhanced strategy that incorporates the Sobol sequence and reward function demonstrates notable superiority regarding endpoint phosphorus content, achieving the mean value reduction to 0.0129% with a standard deviation of merely 0.0002%. Additionally, this strategy results in a slight decrease in the production cost per tonne of steel, characterized by a standard deviation of 1.54 CNY/t, indicating its high stability and efficiency. In contrast, the improved strategy that integrates the Sobol sequence with the MOGWO algorithm results in a 0.0005% decrease in the mean endpoint phosphorus content. However, this approach led to an increase in production cost, rising to 3215.81 CNY/t with a corresponding standard deviation of 8.52 CNY/t. This observation suggests a relatively weak stability in cost control when adopting this particular strategy. When combining the reward function with the enhanced MOGWO algorithm, a significant reduction in the endpoint phosphorus content is achieved, reducing to 0.0123%. This improvement is realized without significant sacrifice in the production cost, showcasing a more favorable overall optimization effect. The IMOWOA proves particularly effective in reducing the endpoint phosphorus content, with an average value of only 0.0110%. Simultaneously, it maintains effective cost control over steel production, with an average cost noted at 3214.51 CNY/t, demonstrating a relatively small standard deviation, and indicating good stability. In summary, the IMOWOA successfully establishes an optimal balance between the endpoint phosphorus content and the production cost per tonne of steel, thereby validating its effectiveness and reasonableness in optimizing phosphorus removal during the converter steelmaking process.
# Forest Fire Detection Using New Routing Protocol
## 7 Evaluation Measurements  
There are many measurements that can be used to evaluate the network performance. The researchers in [37] and [38] presented various evolution measurements such as the percentage of acquired packets and the delay. In spite of these, a simulation is required with regard to the node mobility pattern, as stated by [39] who proposed that an arbitrary waypoint mobility model can be used to ascertain the delay. Furthermore, we use different evaluation measurements such as End-to-End Delay (E2E delay), Packet Delivery Ratio (PDR), Energy Consumption (EC), and Routing Overhead (RO). These measurements are explained and calculated in the following subsections.
# An Elastic Interface Model for the Delamination of Bending-Extension Coupled Laminates
## 6 Conclusions  
An elastic interface model was introduced for the analysis of delamination in bending-extension coupled laminated beams with through-the-width delamination cracks. The analytical solution obtained applies to a wide class of multi-directional fiber-reinforced composite laminates, including cross-ply laminates, provided that their stacking sequences allow them to be modeled as plane beams. Examples illustrated the application of the model to the analysis of the double cantilever beam test in I/II mixed-mode fracture conditions. Furthermore, the effects of the interface stiffness on the model predictions were discussed with reference to the DCB specimen loaded with uneven bending moments.  
By suitably adapting the boundary conditions, the outlined solution can be easily used to analyze different delamination toughness test configurations, as well as delamination behavior in real structural elements and debonding of adhesively-bonded joints.  
The proposed model is potentially capable of highly-accurate prediction and interpretation of experimental results, provided that the appropriate interface stiffness is defined. In this respect, it will be useful to compare the model predictions with the results of experimental tests available in the literature. Future developments also include the extension of the model to fully-coupled laminates, for which plate theory must be used, and I/II/III mixed-mode fracture conditions are expected.  
Conceptualization, S.B., P.F., L.T., and P.S.V.; methodology, S.B., P.F., L.T., and P.S.V.; software, P.F. and P.S.V.; validation, P.S.V.; formal analysis, P.F. and L.T.; investigation, P.F. and L.T.; resources, S.B. and P.S.V.; data curation, P.F. and P.S.V.; writing, original draft preparation, P.F., L.T. and P.S.V.; writing, review and editing, S.B.; visualization, P.F. and P.S.V.; supervision, S.B.; project administration, P.S.V.; funding acquisition, S.B. and P.S.V.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CTE & crack-tip element \\ CP & cross-ply \\ DCB & double cantilever beam \\ DCB-UBM & double cantilever beam with uneven bending moments \\ LEFM & linear elastic fracture mechanics \\ MD & multi-directional \\ UD & uni-directional \\ \end{tabular}
## 4 Distribution Problems
### Crop Problem  
In plant production, an important task is to find a method of sowing the land with agricultural crops (cultures) in such a way that, given the expected yield of crops on the land and the profit from the sale of individual crops, the total profit is maximised.  
Assume the following notation:  
\(p_{i},\ i=1,\,\ldots,\,m\)... grounds,  
\(r_{i},\ j=1,\,\ldots,\,m\)... area of grounds (plays the role of available capacities),  
\(k_{j},\ j=1,\,\ldots,\,n\)... agricultural crops (cultures),  
\(c_{ij},\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n\)... profit from \(1\) ha of ground \(p_{i}\), sown with culture \(k_{j}\)  
\(x_{ij},\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n\)...number of hectares of ground \(p_{i}\) sown with crop \(k_{j}\).  
The mathematical model of the Crop Problem is similar to the basic version of the transportation problem with unbalanced capacities (the ground areas may not be fully used), but it lacks a set of constraints corresponding to the fulfillment of the requirements with the difference that the problem being a maximization one. It takes the following form:  
\[z=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}x_{ij}\rightarrow\max \tag{76}\]  
subject to  
\[\sum_{j=1}^{n}x_{ij}\leq r_{i},\ i=1,\,\ldots,\,m \tag{77}\]  
\[x_{ij}\geq 0,\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n \tag{78}\]  
Equation (77) expresses the use of grounds, which corresponds to the drawdown of supplier stocks in the distribution problem.  
If we required each crop \(j\) to be sown on some minimum area \(d_{j}\), then the problem would become an example of the maximization version of the _generalized distribution problem_ and the model would be modified as follows:  
\[z=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}x_{ij}\rightarrow\max \tag{79}\]  
subject to  
\[\sum_{j=1}^{n}x_{ij}\leq r_{i},\ i=1,\,\ldots,\,m \tag{80}\]  
\[\sum_{i=1}^{m}x_{ij}\geq d_{j},\ j=1,\,\ldots,\,n \tag{81}\]  
\[x_{ij}\geq 0,\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n \tag{82}\]
## 2 Modeling and Computational Approach
### Computational Approach
The latter allowed for the reflection of pressure waves, including shock waves. The initial time-step of the simulation was set at \(10^{-3}c/U_{\infty}\) for the \(G_{1}\) grid, ensuring a consistent CFL number of 0.4 in all simulations. The total sampling time of the CES-KOS simulation on both grids was 14 convective units (\(c/U_{\infty}\)). Nine (9) of these units were utilized to achieve a steady state of the simulation, while the remaining five (5) units were dedicated to the collection of turbulence statistics. Due to the interaction between the shock and turbulent boundary layer, the flow separates over the bump, and subsequently reattaches at a location further downstream on the cylinder surface [43]. Figure 2 illustrates the characteristic shock wave and the flow detachment following the shock on the bump. To numerically solve the discretized equations, we used an algebraic multi-grid solver with the van Leer scheme. The latter is a second-order upwind-based discretization scheme. The solution was considered to be converged when the relative error magnitudes of the computed flow variables reached \(10^{-6}\). The flow dynamics and corresponding shock-wave interactions were studied at two main Reynolds numbers: \(Re=\) (2.763 M, 5.5 M). Measurements for comparison are only available for the \(Re=\) 2.763 M case. The grid and \(Re\) variations in our simulations cover resolutions ranging from well-resolved LES to almost RANS modeling. In particular, the \(Re=\) 2.763 M (\(G_{2}\) case) approximates a well-resolved LES, and the \(Re=\) 5.5 M (\(G_{1}\) case) represents RANS-type modeling. By covering LES to RANS regimes, our simulations enable the investigation of resolution effects on separation predictions.
## 4 Materials and Methods
### Performance Comparison of Different Methods  
In this subsection, we conducted a systematic comparison of the fault diagnosis performance of various methods on the CWRU dataset (for detailed results, see Table 2). The findings indicate that the WPD-CSSOA-DBN method proposed by [PERSON] et al. [17] achieved a fault diagnosis accuracy of 98.24%. The CNN-LSTM-GRU method introduced by [PERSON] et al. [19] further enhanced performance, achieving a diagnostic accuracy exceeding 99%. However, this approach necessitates a complex multi-parameter tuning process, which complicates practical applications. Additionally, studies by [PERSON] et al. [20] and [PERSON] and [PERSON] [18] demonstrated exceptional performance on the CWRU dataset. Nevertheless, CNN-based methods may face challenges such as difficulties in collecting sufficient training data and limited edge computing resources in industrial environments. In contrast, the CEEMDAN method proposed by [PERSON] et al. [26] exhibits strong adaptability to different scenarios, with a fault diagnosis accuracy reaching 99.42%. This paper innovatively proposes an intelligent bearing fault diagnosis method that integrates Hilbert envelope demodulation with the energy distribution characteristics of Ensemble Empirical mode Decomposition (EEMD). By employing a normalization strategy based on the EEMD decomposition sequence to calculate the energy distribution characteristics of each IMF component layer by layer, this method ultimately achieved a fault diagnosis accuracy of 100%.  
In addition, we showed that the computational time complexity (CTC) of EMD does not exceed \(n_{s}\cdot(41\cdot n\cdot m)\), where \(n_{s}\) represents the shifting time, \(n\) denotes the IMF dimension, and \(m\) corresponds to the signal length [28]. Furthermore, the CTC of EEMD is bounded by \(NE\cdot n_{s}\cdot(41\cdot n\cdot m)\), which scales with both the EMD CTC and the ensemble time \(n_{e}\).
## 3 Classifier Design
### Modified Classifier  
We propose the stacking multiple classifiers for the fast processing within 1 s and improving classification accuracy. Figure 8 shows the structure of the modified stacking method. The modified stacking method stores and reuses the multiclass classifier's outputs to reflect continuous variability of micro-Doppler effect over time. The multiclass classifier produces \(feature(t)\) and the blender combines three consecutive features as \(feature_{blender}(t)=\{feature(t),feature(t-p),feature(t-2p)\}\), where \(p\) is the time shift (0.25 s, 50% of the window size) and the total processing time is 1 s. The sampling rate is 1024 samples/s, the window size is 512 samples and the sliding interval is 256 samples (50%). The combined \(feature_{blender}\) is used as the input feature for the blender classifier.  
Figure 7: The designed stacking method structure: The blender combines the binary classifier with the multiclass classifier that has already been trained.  
Increasing the window size can improve accuracy performance but these lead to increasing the processing time. If only a sliding interval is reduced, classification performance can be degraded due to the decreasing of the amount of input information. Thus, the window time, sliding interval and the number of features should be taken into consideration together for designing a classifier. Increasing the number of features did not make a significant performance difference, because the change of micro-Doppler signals is not much within a short time of 1 s. On the other hand, increasing the sample size of the Fourier transform requires more samples in the time domain and this comes with an increase in window time and processing time. More importantly, since the number of nodes in the input and hidden layers of DNN should be increased, the training and processing computation of the DNN get exponentially complicated. We selected 512 samples, 50% sliding interval and three \(\{feature(t),feature(t-p),feature(t-2p)\}\) within 1 s through repeated experiments.
# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
## 3 Cost Model Methodology, Selection of Cost Modelling Approach
### Development of a Resource-Based, Technical Cost Model  
The model is a linked series of Excel spreadsheets developed from a framework for pre-selecting manufacturing processes. Manufacturing cost (mc) is the sum of resource costs, i.e., material cost for one spar cap, tooling cost, capital cost, and overheads (labor and utilities).  
Spar cap cost = material + tooling + capital + labor + utility resource costs  
\[C_{mc}\ =\ \frac{mC_{m}}{(1-f)}\ +\ \frac{C_{t}}{n}\Big{(}1+\frac{n_{r}}{n_{t}} \Big{)}+\frac{1}{n_{r}}\left(\frac{C_{c}}{Lt_{200}}\ +\ C_{oth}\right) \tag{1}\]  
Figure 1: Integrated cost model for financial and engineering assessment of spar cap retrofits.  
Each of five spreadsheets corresponds to a resource cost. Two other spreadsheets correspond to production rate and cost per part. From this data, a cost curve was derived for the 35 to 75 m spar cap range, from which cost data for retrofit spar caps was taken for LCOE calculations. The last spreadsheet calculates levelized cost of energy (LCOE) from retrofit spar cap costs, turbine investment costs, discounted operational and maintenance costs (O and M), and power generation for each blade length from 35 to 75 m, including longer length CFRP retrofit blades. Levelized Cost of Energy (LCOE) is defined as:  
\[\text{LCOE}=\sum_{t=1}^{n}\frac{I_{t}+M_{t}+F_{t}}{\left(1+r\right)^{t}}\Big{/} \sum_{t=1}^{n}\frac{E_{t}}{\left(1+r\right)^{t}} \tag{2}\]  
where:  
\(I_{t}=\text{Investment expenditures in year }t\) (including financing);  
\(M_{t}=\text{Operations and maintenance expenditures in year }t\);  
\(F_{t}=\text{Fuel expenditures in year t}\), \(E_{t}=\text{Electrical power generation in year t}\);  
\(r=\text{Discount rate}\).  
LCOE is a lifetime cost per kWh in which costs and energy generation are discounted to today's values or net present value. Fuel costs are zero for wind generated energy, so \(F_{t}=0\). Initial investment \(I_{t}\) is the turbine capital cost in year 0 and additional investment cost can be a turbine retrofit in a given year \(t\). Electrical power generated from a wind turbine blade was calculated from Equation (3) and a wind speed distribution plot (Figure 2).  
\[\text{Power}=1/2\ CA\rho v^{3} \tag{3}\]  
where:  
\(C=\text{Capacity Factor}\), \(A=\text{area swept by turbine blade}=\pi v^{2}\);  
\(r=\text{radius/turbine blade length}\), \(\rho=\text{air density}\), \(v=\text{wind speed}\).
## 3 Results and Discussion
### Mathematical Empirical Models
#### 3.2.2 Model Description for Vertical Force, \(F_{y}\)
The tool movement speed, \(v\), had a negative effect on the value of the vertical force, suggesting that higher working speeds led to a reduction in vertical resistance. Variable interactions, particularly those related to soil moisture, also significantly impacted the vertical force value, indicating the complexity of the soil-tool interaction process. This model allows for more accurate prediction of vertical forces, which is essential from the point of view of optimising the operation of agricultural tools and minimising their negative impact on the soil.