esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Situ Nanoindentation Surface Topography of Lead-Free Piezoelectric Thin Films
## 3 Results
### Relationship Between Mechanical Properties by Nanoindentation and Roughness Parameters
It should be noted that the hardness and the modulus show small differences between the four surfaces, about 1 GPa for the hardness and 10 GPa for the modulus (Figure 11). These results put in evidence some findings. With regard to the hardness, it was observed that as the valley regime assumes greater significance for cut-off lengths exceeding 500 nm (low-pass filter), the hardness values tend to increase. The surface designated as B (Brownian surface of fractal dimension 2.5) exhibited a higher hardness value. Thus, it seems that the fractal nature of the surface is responsible for the observed increase in hardness, which is attributed to an increase in the proportion of large valleys. With regard to the elastic modulus, there is an observable increase in relation to the void volume of the roughness core, Vvc. Hence, suggesting that the presence of voids related to the surface roughness produces an increment in the stiffness of the surfaces. It can be noted that while the methodology reliably distinguishes between the four samples in this study, the reproducibility of these results may vary with different deposition conditions or material compositions. As such, further studies could extend this analysis across a broader range of samples and processing techniques to validate the generality of these correlations.
# Selected Aspects of Designing Modular PEMFC Stacks as Power Sources for Unmanned Aerial Vehicles
## 2 Experimental Parts
### Method of Measurement and Measuring Equipment Used
The BCH Energy 2000 (BCH) was also constructed from Nafion-based membranes; metallic bipolar plates were used. This unit was designed for direct application in drones [41]. In the case of the H2000 PEMFC stack, graphite-bipolar plates (HG) were used, but the electrical U-I and P-I characteristics are also similar to those of an Aerostak series fuel cell stack recommended for drone applications [42]. In subsequent investigations, the elaborated two-module 2-kW PEMFC stack was investigated using laboratory electrical propulsion systems such as the one shown in Figure 4.  
Figure 3: The 1-kW PEMFC module (1) or (2) during EIS investigations performed online with temperature distribution measured with a thermographic camera. A brushless direct-current motor (BLDC) was used for these stationary laboratory tests. The electric motor was connected first to a propeller (30 \(\times\) 12) and then to the constructed 2-kW PEMFC stack as a power source. During the measurement, the rotational speed of the electrical motor was gradually increased while the electric power drawn from power source (I) (two PEMFC fuel cell modules (1) and (2) connected in parallel) was being recorded. In the next stage, the 500-W PEMFC stack was tested as an auxiliary power source to supply servomechanisms, such as the one shown in Figure 5. To investigate this device in conditions approximating those of an actual application, an experimental servo setup was constructed, consisting of an L 13 BLANIK (BH135) model airplane with installed servos. A BH135 aircraft was equipped with an Aeronaut CAM carbon 16 \(\times\) 8 mm propeller, an AXi 4130/20 electrical engine, a remote-control system, five servos, and two mini-servos. The servos were installed, enabling control of the hatches, rudder, engine, aileron, and elevator. The performance of the servo operations supplied by the 500-W PEMFC stack was tested using an E-Sky 000537 Servo Tester. This device imitated the output impulses of the remote controller, enabling the servos to be tested without installing a remote-control system each time. Figure 4: Design of an electrical setup to test the constructed 2000-W PEMFC stack connected with a propulsion unit. Figure 5: Diagram of a 500-W modular fuel cell stack integrated with UAV on-board flight control devices.
## 3 Proposed System Design
### BLDC Motor Controlled by Electronic Commutation  
The BLDC motor is an electronically commutated device and requires data regarding the rotor position for the proper commutation of the stator current. The BLDC motor needs a quasi-rectangular shaped input current drawn into the machine. The back emf of each phase is trapezoidal and 120 electrical degrees-displaced. The BLDC motor is rotated in a sequence to energize the stator winding. Hall sensors sense the rotor position, and it is embedded into the stator. Whenever the magnetic poles of the rotor are nearer to the hall sensors, the sensors feed a low or high signal. The complete commutation sequence can be found by integrating the three-hall sensor output. Another important key factor of electronic commutation is high-frequency switching loss, which can be eliminated by providing the fundamental switching frequency of VSI. The switching sequences for the electronic commutation of the BLDC motor are the same as those employed in [18].
## 3 Results and Discussion
### Simulations vs. Experiments for Single Shovels
#### 3.1.2 Simulations of the Interactions between Soil and a Single YS  
The ANOVA in Table 6 and the response surfaces in Figure 10 indicate that the draught force of YS is also sensitive to the cutting speed \(v\), the digging depth \(D\) and the mounting angle \(\gamma\). The regression equation with the value of R\({}^{2}\) being 0.9610 is expressed as Equation (9), and it is also highly significant at \(p\) = 0.0004.  
\[\begin{array}{c}F_{D}=1.75+0.43v+29.50D-0.32\gamma+3.70 vD-\\ 0.07v\gamma+0.19D\gamma+0.79v^{2}-61.50D^{2}+6.81\gamma^{2}\times 10^{-3}\end{array} \tag{9}\]  
In addition, the contributions of these factors follow the same order with that of DZ: \(D>v>\gamma\). The \(p\) values in Table 6 indicate that the effects of \(v\), \(D\), \(\gamma\) and \(v\gamma\) are highly significant and the effect of \(\gamma^{2}\) is significant, while the effects of the other sources (\(vD\), \(D\gamma\), \(v^{2}\) and \(D^{2}\)) are not.  
Compared with DZ, the draught force of YS also increases as \(v\) and \(D\) increase, but the relation between the draught force of YS and \(\gamma\) is quite different. As it can be seen from Figure 10b, at fixed \(D\) and low \(v\), the draught force of YS decreases but then increases as \(\gamma\) increases, whereas the draught force of YS at fixed \(D\) and high \(v\) decreases and tends to be stable with the increase in \(\gamma\). This similar trend was also reported by [PERSON] et al. [44]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & \multirow{2}{*}{**DF**} & **Mean** & \multirow{2}{*}{**F Value**} & \multirow{2}{*}{**\(p\) Value**} & \multirow{2}{*}{**Significance**} \\  & **Squares** & & & & & \\ \hline Model & 7.34 & 9 & 0.82 & 19.19 & 0.0004 & **\\ \(v\) & 2.26 & 1 & 2.26 & 53.09 & 0.0002 & **\\ \(D\) & 3.32 & 1 & 3.32 & 77.96 & \textless{}0.0001 & **\\ \(\gamma\) & 0.73 & 1 & 0.73 & 17.21 & 0.0043 & **\\ \(vD\) & 0.03 & 1 & 0.03 & 0.80 & 0.3995 & \\ \(v\gamma\) & 0.26 & 1 & 0.26 & 6.12 & 0.0426 & **\\ \(D\gamma\) & 0.02 & 1 & 0.02 & 0.40 & 0.5484 & \\ \(\gamma^{2}\) & 0.16 & 1 & 0.16 & 3.81 & 0.0918 & \\ \(D^{2}\) & 0.10 & 1 & 0.10 & 2.34 & 0.1699 & \\ \(\gamma^{2}\) & 0.47 & 1 & 0.47 & 11.03 & 0.0127 & * \\ \hline Residual & 0.30 & 7 & 0.04 & & & \\ Lack of fit & 0.30 & 3 & 0.10 & & & \\ Pure error & 0 & 4 & 0 & & \\ \hline Cor total & 7.64 & 16 & & & & \\ \hline \end{tabular} Notes: ** means highly significant (\(p<0.01\)), * means significant (\(0.01<p<0.05\)).  
\end{table}
Table 6: ANOVA of the simulated draught force of YS.  
and [PERSON] et al. [45]. In addition, at fixed \(v\) (Figure 10c), regardless of whether \(D\) is high or low, the draught force of YS decreases and then increases as \(\gamma\) increases, but the variation trend is tamer than that at fixed \(D\) as shown in Figure 10b. Indications mentioned here demonstrate that the interaction between \(v\) and \(\gamma\) is worthy of note for YS, which is different to DZ and can be confirmed by Tables 5 and 6.
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 3 Results  
### Reconstruction Parameter Determination  
For the determination of \(\tau\), the AMI method was used in this study. Based on the split frame lengths of 5 s, 8 s, 10 s, 15 s, MI functions of 60 split frames were calculated individually; when the MI function of each split frame reached the first to the fifth local minima, the corresponding \(\tau\) values were recorded. Figure 5 illustrates the distribution of \(\tau\) values of each local minimum for the four split frame lengths with box plot, including its upper and lower edges, median, and outliers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Equation** \\ \hline \(MNN\) & \(\sum\limits_{i=1}^{\text{N}}\text{Y}(i)/\text{N}\) \\ \(SDNN\) & \(\sqrt{\sum\limits_{i=1}^{\text{N}}\left(\text{Y}(i)-MNN\right)^{2}/\text{N}}\) \\ \(RMSSD\) & \(\sqrt{\sum\limits_{i=1}^{\text{N}}\left(\text{Y}(i+1)-\text{Y}(i)\right)^{2}/ \left(\text{N}-1\right)}\) \\ \(NN50\) & The number of RRI sequences where the difference between adjacent terms is greater than 50 ms \\ \(PNN50\) & The ratio of the number of adjacent terms in RRI sequence whose difference is greater than 50 ms to the sequence length \\ _skewness_ & \(\text{M}_{3}\sqrt{\left(\text{M}_{2}\right)^{3}}\) \\ _kurtosis_ & \(\frac{\text{M}_{4}}{\left(\text{M}_{2}\right)^{2}}-3\) \\ \hline \hline \end{tabular} Where Y is an RRI sequence, N is the number of data points in the RRI sequence, M\({}_{k}\) is the \(k\) statistical moment of RRI sequence.  
\end{table}
Table 3: Seven HRV features.  
As shown in Figure 5, the box plots demonstrate a widening trend from the first to the fifth local minimum, indicating that MI is becoming dispersed. To achieve the minimum value of MI, the median of the first local minimum was selected as the appropriate value of \(\tau\). For the split frame lengths of 5 s, 8 s, 10 s, 15 s, the range of \(\tau\) was 10.5-12.5, so the integer 11 or 12 was chosen as the optimal value of \(\tau\).  
For the calculation of \(m\), the FNN method was used in this study. After selecting \(\tau\) as 11 and 12, their respective probability density curves of \(m\) were plotted (Figure 6). The two curves are tangent at \(m=6\), and 83% of the data are sufficient for embedding. Considering the limitation of the computational effort, six was taken as the value of \(m\).
# Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy
## 3 Methodology and Materials
### Detection Metrics  
This subsection describes the metrics used to evaluate the performance of the approach proposed in this paper and the alternative approaches used in literature.  
The main metric is the Error Rate (ER), which is 1-Accuracy and it is defined as:  
\[\text{ER}=1-\frac{\text{TP}+\text{TN}}{\left(\text{TP}+\text{FP}+\text{FN}+ \text{TN}\right)} \tag{5}\]  
where TP is the number of True Positives, TN is the number of True Negatives, FP is the number of False Positives and FN is the number of False Negatives.  
To complement the accuracy metric, the True Positive Rate (TPR) and the False Positive Rate (FPR) are used, which are defined in the following equations:  
\[\text{TPR}=\frac{\text{TP}}{\left(\text{TP}+\text{FN}\right)} \tag{6}\]  
\[\text{FPR}=\frac{\text{FP}}{\left(\text{FP}+\text{TN}\right)} \tag{7}\]  
Another method to evaluate the performance of the approach proposed in this paper, is the Receiver Operative Characteristics (ROC) curve which is created by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold settings. A metric based on the ROC curve is the Equal Error Rate (EER), which is the point on the ROC curve that corresponds to have an equal probability of miss-classifying a positive or negative sample.
# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
## 3 Model
### Attention Mechanism
#### 3.2.2 Spatial Attention Mechanism  
Spatial attention models are able to pinpoint key regions in the network that need processing and utilize the spatial relationships within feature maps to generate spatial attention feature maps. First, for an input feature map \(F\) with dimensions \(H\times W\times C\), global max pooling and global average pooling are performed along the channel dimension, resulting in two \(H\times W\times 1\) feature maps. These maps are then merged to form an effective feature representation. Convolution is applied to the merged feature map to generate the spatial attention feature map \(M_{S}(F)\in R^{H\times W}\). Channel pooling helps reduce the channel size, facilitating subsequent learning of spatial features. Next, the outcomes of global max pooling and global average pooling are concatenated along the channel, yielding two-dimensional feature maps \(F_{avg}^{S}\in R^{1\times H\times W}\) and \(F_{max}^{S}\in R^{1\times H\times W}\). After concatenation, the combined feature map is convolved through a standard convolutional layer to generate a two-dimensional spatial attention map. Specifically, a \(7\times 7\) convolution operation is applied to the concatenated result, producing a feature map of size \(H\times W\times 1\). This feature map is then passed through the sigmoid activation function to obtain the spatial attention weight matrix \(M_{S}\in R^{1\times H\times W}\). To sum up, the calculation formula of spatial attention is as follows:  
\[\begin{array}{ll}M_{s}(F)&=\sigma(f^{7\times 7}([Avgpool(F);MaxPool(F)]))\\ &=\sigma(f^{7\times 7}[F_{avg}^{s};F_{max}^{s}])\end{array} \tag{12}\]In Equation (12), \(f^{\gamma\times 7}\) represents a convolution operation with a \(7\times 7\) convolution kernel.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Performance Metrics for Classification
#### 3.3.4 Matthews Correlation Coefficient (MCC)  
The MCC, named after biochemist [PERSON], is a robust statistical rate used to evaluate the quality of binary classifications in machine learning. This coefficient effectively incorporates TPs, TNs, FPs, and FNs into its calculation, making it a balanced metric suitable for datasets with imbalanced classes. The MCC is essentially a correlation coefficient between the observed and predicted binary classifications, yielding a value between \(-1\) and \(+1\). A coefficient of \(+1\) indicates perfect prediction accuracy, 0 suggests a performance equivalent to random guessing, and \(-1\) denotes complete disagreement between prediction and observation. It is important to note that values of the MCC that do not reach the extremes of its range (neither \(-1,0\), nor \(+1\)) still provide useful insights into the predictive accuracy and are not indicative of mere random guessing, contrary to some interpretations; rather, they reflect the degree of correlation based on the dataset's specific characteristics [27]. While there is no perfect way of describing the confusion matrix of True and FPs and Negatives by a single number, the MCC is generally regarded as being one of the best such measures [28]. Other measures, such as the proportion of correct predictions (also termed accuracy), are not useful when the two classes are of very different sizes. For example, assigning every object to the larger set achieves a high proportion of correct predictions, but is not generally a useful classification. Some scientists claim the MCC to be the most informative single score to establish the quality of a binary classifier prediction in a confusion matrix context [29]. The MCC can be calculated directly from the confusion matrix using the following formula:  
\[MCC=\frac{TP\cdot TN-FP\cdot FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}. \tag{9}\]  
Selecting the most appropriate metrics for our lung nodule classification project presented significant challenges, due to the inherent advantages and disadvantages of each metric. Extensive research was conducted, including experiments with various augmentation techniques, to determine which metrics were most effective for this specific application.
## 3 Problem Formulation and Resource Allocation  
Allocating the limited power and channel resources for each cell to maximize the overall performance of the NOMA system is a significant challenge in NOMA research and is proven to be an NP-hard problem [11, 12]. This paper takes the energy efficiency as the optimization objective for the multi-cell NOMA system. Specifically, the constrained optimization problem for resource allocation in this multi-cell NOMA system is formulated as follows:  
\[\begin{split}(\text{P1}):&\max\limits_{G,P}\text{ E}_{H}[E_{sum}]\\ s.t.&\text{C}_{1}:\sum\limits_{u=1}^{K_{m}}G_{m,u}^{n}= 2,G_{m,u}^{n}\in\{0,1\}\\ &\text{C}_{2}:p_{m}^{\max}\geq p_{tot,m}\geq 0\\ &\text{C}_{3}:p_{m}^{n}\geq 0,p_{tot,m}=\sum\limits_{n=1}^{N}p_{m}^{ n}\\ &\text{C}_{4}:p_{m,u}^{n}\geq 0,p_{m}^{n}=\sum\limits_{n=1}^{K_{m}}p_{m,u} ^{n}\end{split} \tag{7}\]In (6), \(G\) and \(P\) are the allocation schemes of channel and power; the \(C_{1}\) constraint indicates that the base station \(m\) allocates the channel \(n\) to two users; the \(C_{2}\) constraint indicates that the total transmission power \(p_{tot,m}\) of the base station \(m\) does not exceed the maximum allowable power \(p_{m}^{\max}\); the \(C_{3}\) constraint indicates that the power of the base station \(m\) on the subchannel \(n\)\(p_{m}^{n}\) is non-negative and the power of the base station is the sum of the subchannel powers; the \(C_{4}\) constraint indicates that the power \(p_{m,u}^{n}\) allocated by the base station \(m\) to the user \(u\) on the subchannel \(n\) is non-negative, and the sum of \(p_{m,u}^{n}\) is equal to the power of the subchannel \(n\)\(p_{m}^{n}\).  
For the above constrained optimization problem P1, we need to find the near-optimal channel and power allocation schemes to maximize the energy efficiency of the NOMA system. Assuming that there are \(K_{m}\) users in the cell \(m\), then the NOMA system has \(\prod_{m=1}^{M}\left(C_{K_{m}}^{2}\right)^{N}\) channel allocation schemes. This implies that the number of channel allocation schemes increases exponentially with the number of users and channels. Thus, it is extremely difficult to find the near-optimal channel allocation scheme by traditional methods. Moreover, since the channel gain is time-varying, it imposes the requirement of the timeliness of the resource allocation. Taking into account the above considerations, this paper proposes a resource allocation framework based on the multi-agent deep reinforcement learning and the unsupervised learning for the downlink multi-cell NOMA system.
## 4 Determination of Full Anchor Cable Support Parameters  
### Roadway Support Scheme Design  
Based on field support experience and suspension theory [27], a set of support parameters within a reasonable range were selected and combined to determine the best plan.  
The length of the anchor cable is determined by the following equation:  
\[L=L_{1}+L_{2}+L_{3} \tag{1}\]  
In the equation, \(L_{1}\) represents the length of the prestressed anchor cable in the anchored section in meters (m), \(L_{2}\) represents the length of the prestressed anchor cable in the free section in meters (m), and \(L_{3}\) represents the exposed tension length, which is typically taken as 0.2 m.  
The required spacing of the roof anchor supports to withstand the weight of the pressure arch can be determined by the following equation:  
\[a=\sqrt{\frac{R_{T}}{K\gamma h}} \tag{2}\]  
In the above equation, \(a\) represents the spacing of the roof anchor bolts measured in meters. \(R_{\mathrm{T}}\) represents the anchoring force of the anchor bolts measured in kilonewtons (kN). \(K\) represents the safety factor of the prestressed anchor bolts. \(\gamma\) represents the average unit weight of the overlying rock strata, expressed in kg/m\({}^{3}\). \(h\) represents the effective  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Distance from Working Face**} & \multicolumn{2}{c}{**Roof Displacement Amount/mm**} & \multicolumn{2}{c}{**Roof Plastic Zone Range/m**} \\ \cline{2-5}  & **I** & **II** & **I** & **II** \\ \hline
5 m & 261 & 247 & 3.2 & 2.75 \\
10 m & 253 & 208 & 3.2 & 2.3 \\
15 m & 220 & 173 & 2.75 & 2.3 \\
20 m & 186 & 153 & 2.75 & 1.85 \\
25 m & 167 & 131 & 2.3 & 1.4 \\
30 m & 143 & 116 & 1.85 & 1.4 \\
35 m & 122 & 99 & 1.4 & 0.95 \\
40 m & 113 & 91 & 1.4 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Displacement and plastic range of roadway roof.  
length, which refers to the actual working length or the effective supporting length of the prestressed anchor cable. It is typically measured in meters (m).  
These parameters are shown in Table 3. A 17.8 mm diameter steel strand was used as the anchor cable, and corresponding anchoring agents were used. The preload for each support scheme was set at 150 kN.  
The support parameters of the adjacent working faces were used as a reference to determine the parameters of the two schemes, with only the row spacing of the anchor bolts adjusted accordingly. In both schemes, four bolts were arranged on two ribs of the roadway, with a distance of 1000 mm between bolts and an angle of 70\({}^{\circ}\) between the surrounding rock and the upper and lower bolts.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 3 The Key Management Scheme for Cluster-Based WSNs
### Mobile Sensors Subscheme for Hierarchical WSNs  
#### 3.4.1 Mutual Authentication and Key Distribution Process  
Since sensor nodes have a high probability of moving between different clusters of the network, the dynamic subscheme for hierarchical architecture is more complicated. In Figure 5, \(S_{0}\) moves from the cluster \(C_{0}\) into another cluster named \(C_{2}\). As the location of each CH is assumed to be unchanged, the process of authentication and key distribution  
Figure 4: Specific steps for phase II in an example.  
Figure 3: Specific steps for phase I in an example.  
between CH and BS is the same as that of the static subscheme. The main difference between the static subscheme and the mobile subscheme lies in the key distribution process.  
The key distribution process of the mobile scene includes six steps.  
1. When \(S_{0}\) moves into cluster2, it will send a cluster-entry request to CH\({}_{2}\). The cluster forming and cluster head detection process is not described in this paper. For more information, please refer to [24].
2. CH\({}_{2}\) detects and receives this message. Next, CH\({}_{2}\) replies to \(S_{0}\) with a message including its identification code \(ID\_CH2\).
3. \(S_{0}\) updates the identification of the present cluster, replacing \(ID\_CH0\) with \(ID\_CH2\).
4. \(S_{0}\) applies for the latest session key \(SK_{2}\) via the base station using the cipher6 denoted as follows: \[\text{Cipher6}=E(PUK,ID\_CH2\|ID\_50\|timestamp\|SK\_50\|R)\] (6)
5. The BS decrypts cipher6 with the \(PVK\) and finds \(ID\_CH2\), \(SK\_50\), and \(ID\_50\) via Plain6 = \(D(PVK,\) Cipher6\()\) = \(D(PVK,\)\(E(PUK,ID\_CH2\|ID\_50\|timestamp\|SK\_50\|R))\) = \(ID\_CH2\|ID\_50\|SK\_50\|R\). The latest session key \(SK_{2}\) can be picked out in terms of \(ID\_CH2\), and the \(S_{0}\) is authenticated via BS according to \(ID\_50\). Next, the cipher7 will be sent to \(S_{0}\). The cipher7 is built as follows: \[\text{Cipher7}=E(SK\_50,SK_{2}\|R).\] (7)
6. \(S_{0}\) decrypts the cipher7 with the symmetric key \(SK\_50\) and successfully finds \(SK_{2}\).  
Thus, the mobile sensor can achieve the latest session key of the present cluster and send encrypted traffic to the corresponding cluster head. The detailed key agreement process in mobile subscheme is depicted in Figure 6.  
Figure 5: Sensor \(S_{0}\) moves from Cluster0 to Cluster2.