esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Estimating Source Depth Based on the Adjacent Mode Group Acoustic Pressure Field
## 2 Theory
### Design of Source Depth Estimation Method
In general, the termination temperature is 0 \({}^{\circ}\)C, but this increases the time complexity and requires more iterations. As long as the termination temperature approaches 0 \({}^{\circ}\)C, the end state is basically the same, so only an acceptably low temperature is required. The initial temperature was 2000 \({}^{\circ}\)C, the end temperature was 10\({}^{-14}\)\({}^{\circ}\)C, the temperature coefficient was 90%, the length of the Markov chain was 100 (number of internal cycles) and the tolerance was a dynamic value. The equation is as follows:  
\[\Theta(\Omega)= \begin{cases}e^{\frac{\text{Cost}(\Omega)-\text{Cost}(\Omega)}{ \Omega}}&(\text{Cost}(\Omega^{\prime})<\text{Cost}(\Omega))\\ 1&\left(\text{Cost}(\Omega^{\prime})\ \geq\ \text{Cost}(\Omega)\right)\end{cases}  \tag{10}\]  
where \(\Omega\) represents the current source depth. If the cost of internal circulation at the current temperature is lower than the cost of the previous internal circulation, the current procedural source depth is accepted; if the cost of the internal circulation at the current temperature is higher than that of the last internal circulation, the exponential function is used to calculate the current tolerance to choose the source depth. The direction change of depth after the current internal circulation is determined by a Gaussian random number, and the range of change is determined by the combination of the current temperature and the random number. Algorithm 1 summarizes the basic process of estimating the depth of the source through the simulated annealing algorithm, starting from the distribution of the AMG acoustic pressure field. In the simulation environment of Table 1, the estimated source depth obtained by setting the source depth to 100 m was \(\Omega_{0}=97.26\) m.  
```
Input: Comparison parameters (AMG acoustic pressure field). Initialization: Set the initial temperature, Markov chain length, and temperature coefficient and randomly set the initial source depth. Process:
1. Obtain the latest iteration number of the inner loop and check whether it reaches the Markov chain length;
2. If the number of internal circulations reaches the length of the Markov chain after cooling down, calculate the cost of this internal circulation depth according to Equation (4) and skip to Step 4;
3. If the number of internal circulations does not reach the length of Markov chain, calculate the cost under the depth of this internal circulation according to Equation (4);
4. Calculate the tolerance according to Equation (5);
5. If the tolerance is 1, accept the current depth and skip to Step 9;
6. If the tolerance is not 1, use probability values to select the depth;
7. Accept Step 6 and skip to Step 9;
8. Abandon Step 6;
9. Determine whether the threshold temperature has been reached and terminate if the conditions are met; otherwise, return to Step 1. 0. Over Output:\(\Omega\)
```  
**Algorithm 1**A method for estimating source depth based on the AMG acoustic pressure field.
## 3 Results
### Parameter Optimization Results  
The lower the particle axial velocity coefficient, the faster the axial velocity of fertilizer particles, and the less likely it is to block the fertilizer applicator cavity. The higher the filling coefficient, the more stable the filling coefficient. Based on practical work experience in the market, the particle axial velocity coefficient should be less than 10%, and the filling coefficient should be more significant than 95%. The results from Figure 1 show that the filling coefficient of the spiral-pushing fertilizer applicator is lower at low rotational speeds. Therefore, the fixed spiral-pushing fertilizer applicator rotational speed is 30 r/min, and \(x_{1}\) and \(x_{2}\) are optimized. The results are shown in Figure 10.  
The yellow shaded area in Figure 6 is the optimal combination area for \(x_{1}\) and \(x_{2}\). The results from Figure 10 show that the yellow shaded area in the figure represents the optimal combination of the spiral pitch elongation percentage \(x_{1}\) and the spiral diameter elongation percentage \(x_{2}\). When the spiral pitch elongation percentage is within a range of 189.82-200%, and the spiral diameter elongation percentage is within a range of 102.75-106.76%, the filling coefficient is more significant than 95%, and the particle axial velocity coefficient is less than 10%. When selecting the spiral pitch elongation percentage of 190% within the optimization interval, the spiral diameter elongation percentage is 105%. The accumulation fertilizer section's calculated spiral pitch and diameter are 38 mm and 42 mm. Through simulation verification, it was determined that the filling coefficient of the optimized spiral-pushing fertilizer applicator was 97.24%, and the particle axial velocity coefficient was 7.61%, which met an optimization range and proved the correctness of the parameter optimization.  
Figure 8: Analysis of the influence of spiral pitch elongation percentage on the particle axial velocity coefficient.  
Figure 9: Analysis of the influence of rotational speed on the particle axial velocity coefficient.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Adaptability
#### 2.3.1 Fault Tolerance  
As an indication of system reliability, after a fault is detected, diagnosed and isolated, the inverter and consequently the whole system are required to continue normal operation. The diagnostics and isolation procedure is crucial to prevent the problem from propagating with possibly catastrophic consequences. To be able to continue to work under faulty conditions several methods are proposed, some of them are based on the implementation of extra hardware and modification in the topology and/or the modulation procedure [74; 75]. These modifications are implemented on switch level, leg level, module level or device level. At all these levels, the fault tolerance characteristic is achieved by means of redundancy. Implementation of redundant hardware at leg level is a proposed solution. In case of a switch fault, the method proposed in [76] can replace the bidirectional switch traditionally installed in a matrix converter with any of the other nine switches. The principles of the leg level criteria are simple. When the fault occurs, and it has been diagnosed the switching signals of the semiconductors of that leg are removed and transferred to the redundant leg after the bidirectional switch connecting the leg to the main circuit is triggered. The timing of these switching and replacements are also important and require precision [77; 78]. In [79], the implementation of extra hardware is not included, and the methods proposed used the inherent redundancy of multi-level inverter to achieve fault tolerance characteristic, this is an example of module level. Generally, the concept of modular level fault tolerance is to bypass the faulty semiconductor switch or switches, and the resulting decreased and often unbalanced output voltage is regulated by modifying the modulation method considering a phase shift in the voltage reference to provide a balanced line to line output voltages [80; 81]. The device level, which is normally implemented at industrial applications, is to use a parallel redundant inverter.
# Bayesian Binary Search
## 2 Methodology
### Search Space Probability Density Function Estimation
#### 2.7.1 Supervised Learning Approaches  
* **Gaussian Process Regression (GPR):** GPR provides a non-parametric way to model the PDF, offering both a mean prediction and uncertainty estimates.
* **Bayesian Neural Networks (BNNs):** BNNs combine the flexibility of neural networks with Bayesian inference, allowing for uncertainty quantification in the predictions.
* **Quantile Regression:** Quantile Regression estimates the conditional quantiles of a response variable, providing a more comprehensive view of the relationship between variables across different parts of the distribution, without assuming a particular parametric form for the underlying distribution.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 4 Experimental Results and Analysis
### Cluster Building Time  
The algorithm's computational complexity is measured by its cluster construction time during the clustering process. Clustering algorithms often use nodes with accompanying fitness values as input to choose the CH and associate members. Cluster building time refers to the time it takes an algorithm to go from receiving inputs to creating outputs. The performance of an unmanned aerial vehicle (UAV) would be negatively impacted if the time it takes to assemble a cluster is too long. The longer it takes to create a cluster, the more energy is consumed, resulting in a shorter lifespan for UAVs in a network. Unlike ACO and GWO, which start with various solutions and converge to the best solution, our EECP-MFO quickly connects to the ideal solution. This makes it outperform ACO, PSO, and GWO. A shorter cluster building time has been achieved to shorten the energy and time it takes for the UAVs to pick a path. It can be noted from Figures 9-11 that the greater the grid size, the more UAVs in a network will take more time to execute the clustering algorithm, which increases cluster building time. In Figure 11, we do not see any massive difference in clustering time for the cluster building by our proposed algorithm, making it superior to the other algorithms.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Values**} \\ \hline Grid Size & 1000 \(\times\) 1000 m\({}^{2}\), 2000 \(\times\) 2000 m\({}^{2}\) and 3000 \(\times\) 3000 m\({}^{2}\) \\ \hline Density of Connected Nodes & 20, 30, 40, 50, 60 \\ \hline Minimum Distance Between Nodes & 5 m \\ \hline Mobility Model & Reference Point Mobility Model \\ \hline Simulation Runs & 10 \\ \hline Simulation Time & 120 s \\ \hline Position Exchange Interval & 2 s \\ \hline Node Energy Level at Start Time & 80-Watt Hour \\ \hline Transmission Range & Dynamic \\ \hline Transmission Frequency & 2.45 GHz \\ \hline Constant Bit Rate & 100 kbps \\ \hline Receiver Sensitivity & \(-\)90 dBm \\ \hline W1 +W2 + W3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
Figure 11: Time for clustering Vs number of UAVs (2000 \(\times\)2000 m\({}^{2}\)).  
Figure 10: Time for clustering vs. number of UAVs (3000 \(\times\)3000 m\({}^{2}\)).  
Figure 9: Time for clustering vs. number of UAVs (1000 \(\times\) 1000 m\({}^{2}\)).
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 4 Proposed Congestion-Aware RPL  
This section describes the proposed CoAR. First, we formulate the routing metrics. Then, a novel congestion-aware objective function named CoA-OF is defined. For this, we utilize a technique for order preference by similarity to ideal solution (TOPSIS) of MCDM method which has been proposed by [PERSON] and [PERSON] in [PERSON] and [PERSON] (2018). TOPSIS is one of the widely used methods that has been successfully applied to solving different problems in various applications ([PERSON] and [PERSON], 2018). The method combines multiple metrics to develop an objective function. It suggests that the best alternative should have the shortest distance from the positive ideal solutions and the farthest distance from the negative ideal solutions.
## 5 Bolt Looseness Detection Results and Discussion  
### Training and Testing Results  
The CNN-LSTM-MHSA network was built for estimating bolt looseness conditions based on the framework in Figure 6. The structure and detailed parameters of each layer for the neural network are listed in Table 3. A mini-batch size of 128 was used in conjunction with the Adam optimization algorithm. The training loss gradually stabilized after 30 epochs. The local bolt looseness severity estimation results for each row in the testing cases were shown in Figure 14. The estimated values coincide well with the labels, and no false, alarming, or missed detections exist. The local and overall relative error indices were defined for quantitatively accessing the bolt looseness detection accuracy as  
\[Error_{i}^{local}=\sqrt{\sum_{j=1}^{96}\left(r_{ij}-f_{ij}\right)^{2}/\sum_{j=1 }^{96}\left(1-f_{ij}\right)^{2}} \tag{8}\]  
\[Error^{overall}=\sqrt{\sum_{j=1}^{96}\left(R_{j}-F_{j}\right)^{2}/\sum_{j=1}^{96 }\left(1-F_{j}\right)^{2}} \tag{9}\]  
where \(R_{j}=\frac{1}{4}\sum_{i=1}^{4}r_{ij}\) and \(F_{j}=\frac{1}{4}\sum_{i=1}^{4}f_{ij}\) are the predicted and real looseness severities of all the bolt, respectively. The calculated \(Error_{i}^{local}\) was 3.33%, 4.0%, 5.05%, and 3.48% for four rows, with an average value of 3.96%. The \(Error^{overall}\) was 2.09%, with the overall bolt looseness estimation result shown in Figure 15.  
Figure 16 shows the bolt looseness detection results for four individual cases, indicating the excellent performance of the proposed CNN-LSTM-MHSA method. The bolt is assumed to be loosed when the estimated value exceeds 0.15. Based on this threshold, the bolt looseness localization results are categorized into four classes: TP (true positive), TN (true negative), FP (false positive), and FN (false negative). The accuracy of bolt looseness positioning was represented by the Acc value, calculated using Equation (10). Figure 17 gives the confusion matrix, and the Acc value was 100%.  
\[Acc=\frac{TP+TN}{TP+FP+FN+TN} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Active Function** & **Size** & **Remarks** \\ \hline Conv1 & ReLU & 16 \(\times\)[3, 4] & Padding: same \\ Conv2 & ReLU & 32 \(\times\)[3, 4] & Padding: same \\ Max-pooling & ReLU & [2, 2] & Padding: same \\ Flatten & - & - & - \\ LSTM & - & 24 & Number of hidden \\ MHSA & - & 8 \(\times\) 24 & - \\ Fully connected1 & ReLU & 128 & - \\ Fully connected2 & Sigmoid & 4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the CNN-LSTM-MHSA network.  
Figure 16: Quantitative detection results for individual cases. (**a**) [0.35,0.65,0,0]; (**b**) [0.45,0,0.55,0]; (**c**) [0,0.35,0.65,0]; (**d**) [0,0.65,0,0.45].  
Figure 17: Damage localization result (0 means intact).  
Figure 15: Overall bolt looseness severity estimation results.
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
###### Abstract  
The competition of speech recognition technology related to smartphones is now getting into full swing with the widespread internet of thing (IoT) devices. For robust speech recognition, it is necessary to detect speech signals in various acoustic environments. Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, we propose a new technique to improve robustness of a speech/music classifier for an enhanced voice service (EVS) codec adopted as a voice-over-LTE (VoLTE) speech codec using long short-term memory (LSTM). For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. To overcome the diversity of music data, a large scale of data is used for learning. Experiments show that LSTM-based speech/music classification provides better results than the conventional EVS speech/music classification algorithm in various conditions and types of speech/music data, especially at lower signal-to-noise ratio (SNR) than conventional EVS algorithm.  
speech/music classification; Enhanced Voice Service; long short-term memory; big data 10.3390/sym10110605
## 4 System Model
### Establishing the Link with Neighbors  
Sensor nodes are deployed in an underwater environment and each sensor generates a hello message (control message) to find its neighbors. This hello message is a tuple of several things: source-id, destination-id, the status of a node (dead or alive), type of node (source node or sink node), coordinates node and residual energy of current node. This structure is presented in Figure 5.  
The estimated and the actual distances of each neighbor are calculated on the basis of coordinates.
## 2 Antenna Design  
### Dual-Band Dielectric Resonator Antenna  
The antenna is matched to the rectifier, and both of them are designed for 50 \(\Omega\) characteristic input impedance. In Figure 1, a compact DRA operating at two frequency bands is designed in ANSYS High-Frequency Structure Simulator (HFSS). The two resonance modes are excited by the mode of the dielectric cylinder. The proposed DRA is composed of a dielectric cylinder (\(\epsilon_{\rm d}\) = 19.0, \(\delta_{\rm d}\) = 0.007) and a square dielectric substrate (\(\epsilon_{\rm r}\) = 2.65, \(\delta_{\rm r}\) = 0.003). The dielectric cylinder is on the top of the square dielectric substrate. A coupled slot is etched on the top metal layers of the substrate and a 50 \(\Omega\) microstrip line is on the bottom metal layers of the substrate. The microstrip line is used to excite the coupled slot, and thus excite the DRA. The sizes of the dielectric cylinder and the feeding structure are related to the impedance matching.  
The simulated S11 of the proposed DRA is shown in Figure 2. The measured impedance bandwidths with S11 \(\leq\) -10 dB are 2.41-2.51 GHz and 5.63-5.88 GHz, respectively, for lower and higher bands that fully covers the impedance bandwidth of the 2.45 GHz and 5.8 GHz.  
Figure 1: Configuration of the proposed DRA: (**a**) top view; (**b**) back view; (**c**) side view. \(L\) = 60 mm; \(h\) = 0.8 mm; \(D\) = 30.2 mm; \(h_{d}\) = 15 mm; \(W_{f}\) = 2.18 mm; \(W_{S}\) = 3.5 mm; \(L_{f}\) = 4 mm; \(Ls\) = 20 mm.  
Figure 2: Simulated S11 of the proposed DRA.  
The electric field distributions shown in Figure 3 verify that the proposed DRA resonates at its TEM111 mode at 2.45 GHz, and at 5.8 GHz it resonates at its TEM211 mode. Figure 4 shows the simulated radiation patterns of the proposed DRA at the operating frequency bands. At 2.45 GHz, the cross-polarization of the x-z plane is less than \(-40\) dB, and that of the y-z plane is less than \(-30\) dB. At 5.8 GHz, the cross-polarization of the E plane is also less than \(-40\) dB, and that of the H plane is less than \(-25\) dB. A broad beamwidth can be observed. For 2.45 GHz, the half-power beamwidth (HPBW) is about \(120^{\circ}\) (E plane) and \(90^{\circ}\) (H plane). For 5.8 GHz, the HPBW is about \(60^{\circ}\) (E plane) and \(40^{\circ}\) (H plane), which can achieve a better performance of energy harvesting.
# Recycled Carbon Fibers with Improved Physical Properties Recovered from CFRP by Nitric Acid
## 2 Materials and Methods
### Materials
#### 2.1.2 Epoxy Resin Decomposition and Recovery of Recycled Carbon Fibers and Resins  
The CFRP specimen was fixed using a PTFE tube (inner diameter = 6 mm; outer diameter = 8 mm) and immersed in a test tube containing an aqueous solution of nitric acid (Kanto Chemical Co., Inc., Tokyo, Japan, special grade). The nitric acid aqueous solution  
Figure 1: (**a**) CFRP automotive prototype waste manufactured from virgin carbon fibers and fully cured epoxy resin; (**b**) CFRP specimen used in this research, (**c**) decomposition and recovery process of carbon fiber and resin from CFRP immersed in HNO\({}_{3}\).  
was adjusted to 8 M because a sufficient concentration of nitric acid was necessary to proceed with the resin decomposition reaction, as shown in the previous report [15]. The volume of the nitric acid aqueous solution was adjusted to 16 mL per 1 g of CFRP specimen. The test tube was placed in a bath with a constant temperature of 80 \({}^{\circ}\)C. After immersion for 2 h-120 h, the solid residue containing the carbon fibers was collected from the test tube. The recycled carbon fibers (rCFs) were fully rinsed with deionized water, and the residual moisture in the carbon fibers was removed using a dryer (at 80 \({}^{\circ}\)C for 48 h) (Figure 1c).  
The resin dissolved in the nitric acid aqueous solution was recovered through a liquid-liquid extraction performed at room temperature using ethyl acetate (Kanto Chemical Co., Inc., Tokyo, Japan >99.3%) [15]. In this study, we report the results focused only on the rCFs, and an evaluation of the recovered decomposed resin component will be described in further research. However, it was confirmed that this method could recover both the carbon fibers and the decomposed resin component from CFRP molded products.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 4 Experiments and Results
### Determining Similarity Threshold  
A similarity threshold is a cut-off point to classify whether they are similar or not. The similarity threshold behaves differently based on the vector space model and system domain structure. In our method (Jupyter notebook is available at [[https://anaconda.org/korawit/similarity_threshold/notebook](https://anaconda.org/korawit/similarity_threshold/notebook)]([https://anaconda.org/korawit/similarity_threshold/notebook](https://anaconda.org/korawit/similarity_threshold/notebook))), we approximately select the similarity threshold based on a predictive model validated by a human with the following steps: (1) Randomly select a small sample of questions (i.e., pairs of context and signature.) (2) Compute cosine similarity score for each question using wsdw2v. (3) Each question is given a binary score (1 if relevant, 0 if not relevant) whether it is relevant between context and signature by a human without seeing its cosine similarity score. (4) Using a logistic regression model [39] by input cosine similarity scores and binary scores as true labels, then fitting the model by minimizing differences between true labels and predicted labels (we used scikit-learn library to create and fit the logistic regression model.) (5) The logistic regression model can now predict a probability of relevance given a cosine similarity score. Input all cosine similarity scores again into the model and predict probabilities of relevance, sort all cosine similarity scores and corresponding probabilities of relevance by descending order, then select a cosine similarity score at 0.5 of the probability of relevance as an empirical similarity threshold. In our case, we found that 0.37 was the empirical similarity threshold of our method. We will use 0.37 as the similarity threshold throughout the paper. If the cosine similarity score below this threshold means that the contextual similarity might fail to determine the correct word sense, the score will be calculated from the probability of sense distribution instead. Set too high similarity threshold only the word sense that has almost identical context and signature, can be scored. The score is more likely to compute from the probability of sense distribution. On the other hand, set too low similarity threshold allows the word sense that has less similar context and signature, can be scored. The score is more likely to be computed from cosine similarity rather than the probability of sense distribution.