esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
## 3 Experimental Results
### Critical Region Segmentation Performance  
To evaluate the performance of the proposed segmentation, we used the dice similarity coefficient (DSC) between the resultant segmentation mask and the ground truth region. Denoting the target foreground voxels in the segmentation mask as \(F\) and the voxels in the corresponding ground truth region as \(G\), the DSC was calculated as \(2|F\cap G|/(|F|+|G|)\).  
The ground truth region was manually obtained by using an interactive segmentation tool called ITK-SNAP [15]. Both the outer air and airway segmentation are important because they play a major role in determining the needle route search space. We present the segmentation performance in Table 4.  
For the outer air segmentation, the standard geodesic distance-based method gave a DSC of 97.74%, whereas the proposed method gave a DSC of 99.54%. Similarly, for the airway segmentation, the standard distance resulted in a DSC of 96.50%, whereas the proposed reformulation resulted in a DSC of 97.98%. The improvement made by the proposed geodesic distance was significant showing a \(p\)-value \(<\) 0.02. In Figure 4, we present two examples of major improvement. Because the optimal distance threshold varies for different patients, the standard geodesic distance revealed under-segmentation and/or over-segmentation cases. On the other hand, the proposed reformulation allows us to determine the segmentation label based on zero-distance conditions consistently across all patients. Thus, it could generally provide a noteworthy improvement over the standard method.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Standard GD \({}^{*}\)**} & **Proposed GD \({}^{*}\)** \\ \hline Outer air & 97.74\% & **99.54\%** \\ Airway & 96.50\% & **97.98\%** \\ \hline \hline \end{tabular} \({}^{*}\)GD: geodesic distance.  
\end{table}
Table 4: DSC of the segmentation result with respect to ground truth.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Vocal Fold** & **Observer 1** & **Observer 2** \\ \hline Right & 94.12\% & 95.59\% \\ Left & 91.92\% & 92.65\% \\ Right (paresis) & 93.10\% & 96.55\% \\ Left (paresis) & 89.66\% & 89.66\% \\ \hline \hline \end{tabular} Inter-observer agreement: 96.69\%.  
\end{table}
Table 2: Vocal fold localization accuracy.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Localization Accuracy**} \\
**Method** & **Right Fold** & **Left Fold** \\ \hline RL (proposed) & 94.12\% & 91.92\% \\ Deep Coordinate Regression [9] & 79.41\% & 80.88\% \\ Deep Heatmap Regression [10] & 85.29\% & 84.56\% \\ \hline \hline \end{tabular} The above results are based on observer 1’s evaluation.  
\end{table}
Table 3: Vocal fold localization performance of different methods.
## 4 Result Analysis and Discussion
### Limitation  
This study's limitation is that it is difficult to find an appropriate dataset for the classification problem. Obtaining current medical information is tough; however, databases such as Kaggle effectively maintain study data. On the other hand, computational complexity is present. Leveraging cloud-based technology, such as the complimentary edition of Google Colab, could simplify these tasks. Buying a pro version and optimizing time and resources is possible; however, conducting this study required a high-performance workplace. Also,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author and Year** & **Dataset** & **Method** & **Accuracy (\%)** \\ \hline
[51] & Kaggle & CNN & 96 \\ \hline
[52] & Kaggle & CNN & 95.7 \\ \hline
[53] & Br35H & CNN & 99.3 \\ \hline
[32] & Br35H & CNN+GoogleNet & 99.5 \\ \hline
[54] & Br35H &
\begin{tabular}{c} Efficientnetb0, \\ VGG-19 \\ \end{tabular} & 99.7 \\ \hline
[55] & Fighshare SARTAJ & InceptionV3 & 97.12 \\  & Br35H & CNN & 99.3 \\ \hline
**This Study** &
\begin{tabular}{c} Br35H \\ Kaggle \\ \end{tabular} & SwT+Resnet50V2 & 99.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with related studies.  
future work may address the latest transformer methodologies in the event of a higher GPU due to the high computational complexity required.
# Emotional Dynamics in Social Interactions: A Multi-Strategy Synergy Model
## 3 Stability Analysis
### Global Stability Analysis  
**Theorem 2**.: _When the control strategies fail (\(\mu=\
u=\xi=0\)) and the parameters satisfy \(\beta,\kappa,\gamma>0\), the unique equilibrium point \(E^{*}\in(0,1)\) of the system (5) is globally asymptotically stable. That is, for any initial emotional value \(E(0)\in[0,1]\), the system trajectory satisfies:_  
\[\lim_{t\to\infty}E(t)=E^{*}\]  
Proof.: Define the candidate Lyapunov function:  
\[V(E)=\frac{1}{2}(E-E^{*})^{2}\quad\text{which satisfies:}\begin{cases}V(E)>0 &\text{if }E\
eq E^{*}\\ V(E^{*})=0&\text{if }E=E^{*}\end{cases} \tag{11}\]  
Computing the derivative of \(V(E)\) along the system trajectory (9), we obtain  
\[\begin{split}\frac{dV}{dt}&=(E-E^{*})\frac{dE}{dt}\\ &=(E-E^{*})\left[\beta\kappa(1-E)^{2}-\gamma E\right]\end{split} \tag{12}\]  
Using the equilibrium Equation (8) to substitute \(\gamma E^{*}=\beta\kappa(1-E^{*})^{2}\):  
\[\begin{split}\frac{dV}{dt}&=(E-E^{*})\left[\beta \kappa(1-E)^{2}-\beta\kappa(1-E^{*})^{2}-\gamma(E-E^{*})\right]\\ &=\beta\kappa(E-E^{*})\left[(1-E)^{2}-(1-E^{*})^{2}\right]-\gamma (E-E^{*})^{2}\end{split}\]  
Expanding the difference of squares,  
\[(1-E)^{2}-(1-E^{*})^{2}=(E^{*}-E)(2-E-E^{*})\]  
Substituting this, we obtain  
\[\begin{split}\frac{dV}{dt}&=\beta\kappa(E-E^{*})[( E^{*}-E)(2-E-E^{*})]-\gamma(E-E^{*})^{2}\\ &=-\beta\kappa(E-E^{*})^{2}(2-E-E^{*})-\gamma(E-E^{*})^{2}\\ &=-(E-E^{*})^{2}[\beta\kappa(2-E-E^{*})+\gamma]\end{split}\]  
Analyzing the signs of each term,  
* Square term: \[(E-E^{*})^{2}\geq 0\]and equals zero only when \(E=E^{*}\).
* Combined coefficient: \[\beta\kappa(2-E-E^{*})+\gamma>\beta\kappa(2-1-1)+\gamma=\gamma>0\] Therefore, \[\frac{dV}{dt}=-\underbrace{(E-E^{*})^{2}}_{\geq 0}\cdot\underbrace{[\beta \kappa(2-E-E^{*})+\gamma]}_{>0}\leq 0\] and \(\frac{dV}{dt}=0\) if and only if \(E=E^{*}\). Using [PERSON]'s stability theorem, the equilibrium point \(E^{*}\) is globally asymptotically stable.
# On a Framework for Federated Cluster Analysis
## 3 Results
### Experiments
#### 3.2.3 Evaluation on Real-World Data
The three centers all have similar values in feature direction \"balance\" and are very different in the other dimensions \"education\" and \"age\". Intuitively, this makes sense because the vast majority of data have relatively low balance, and the other dimensions are key discriminators. The fourth center is the center of wealthy customers (very high account balance) in the federated and the nonfederated settings. However, in the federated setting, the center has a higher value for \"balance\" (3.1) compared to the central clustering (2.4). For a visualization of the centers, please refer to Figure 7. Hence, the center is further from the other centers, which is is the reason for a lower FedFuzzDB index. In the central clustering case, the center does not move as far in the balance direction, because the mass of all points has a value close to zero. Recall that in fuzzy clustering all points are considered for the calculation of the center. Hence, many points (even though with low weight for further points) still have a noticeable effect. The key difference is that the federated clustering algorithm computes global centers based on the local cluster centers, which changes the relative importance in this case. To illustrate this, seven of 40 local-only cluster centers have a balance of \(>\)2.4, which is 15.9% of all local-only cluster centers. In contrast, in the central dataset, only 3.9% of all points have a balance of \(>\)2.4. This leads to higher cluster center dissimilarity \(M_{ij}\)  
Figure 5: Local-only Davies–Bouldin index for different (but not all) clients. According to the index and the elbow method, each client has a different number clusters locally (management, \(K=5\); technician, \(K=4\); student, \(K=3\)). Figure 6: Federated fuzzy Davies–Bouldin index for different number of clusters. The lower FedFuzzDB index lets us conclude that the effect on the spread (and, hence, the assignments \(u_{ij}\)) is small. In summary, we demonstrate how our cluster analysis framework can be applied to gain insights from real-world datasets. We see that there is a good consistency between the federated and the nonfederated cluster results, and the federated algorithm produces even better results in terms of (federated) fuzzy Davies-Bouldin index.
## 2 Materials and Methods
### Data Sets
#### 2.3.3 Flux Tower Data  
The eddy covariance (EC) flux tower data were used to validate the ET and GPP retrievals derived from the models. Here, 8 flux tower sites were selected in northeast Asia, which represent various major regional biomes (Table S1). For a detailed validation of GPP and ET, refer to Supplementary Materials.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 4 Reliable ICT
### Definition: Reliability  
Similarly to the term sustainability, the notion of reliability has been the subject of many different definitions which are overlapping to a large extent and mostly reflect similar aspects in different domains. Furthermore, reliability can also be seen as an attribute of dependability as we are going to briefly discuss in the next subsection.  
According to the Cambridge online dictionary, the term reliability is defined as follows: _\"The quality of being able to be trusted or believed because of working or behaving well\"_[43]. With respect to information and communication systems, which are at the heart of a smart city, the following definition is preferred: _\"Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time, or will operate in a defined environment without failure\"_[44]. This definition originates from the American Society for Quality (ASQ) and has a more technical perspective. Hence, by applying this definition to the ICT infrastructure of a smart city, we put the reliability requirements in context.
## Nomenclature  
\begin{tabular}{l l} \(a_{z}\) & normal accelerations, g \\ \(b\) & wing span, m \\ \(C_{x}\), \(C_{z}\), \(C_{m}\) & longitudinal aerodynamic force and moment coefficients \\ \(C_{y}\), \(C_{l}\), \(C_{n}\) & lateral-directional aerodynamic force and moment coefficients \\ \(\overline{c}\) & mean aerodynamic chord, m \\ \(h\) & altitude, m \\ \(l_{xx}\), \(l_{yy}\), \(l_{zz}\) & moments of inertia about \(x\)-, \(y\)-, and \(z\)-axes, respectively, kg\(\cdot\)m\({}^{2}\) \\ \(l_{xy}\), \(l_{xz}\), \(l_{yz}\) & products of inertia, kg\(\cdot\)m\({}^{2}\) \\ \(k_{1}\), \(k_{2}\) & longitudinal and lateral-directional reduced frequencies, respectively \\ \(M\) & Mach number \\ \(m\) & aircraft mass, kg \\ \(m_{f}\) & fuel flow rate, kg/hour \\ \(N_{1}\) & the rpm of the compressor, rpm \\ \(p\), \(q\), \(r\) & Body axis roll rate, pitch rate, and yaw rate, deg\(\cdot\)/s \\ \(\overline{q}\) & dynamic pressure, kpa \\ \(S\) & wing reference area, m\({}^{2}\) \\ \(T\), \(W\) & thrust and aircraft weight in flight, N, respectively \\ \(T_{2}\) & time to double or halve the amplitude, s \\ \(t\) & time, s \\ \(X\), \(Y\), \(Z\) & forces acting on the aircraft body-fixed axes about \(x\)-, \(y\)-, and \(z\)-axes, respectively, N \\ \(a\), \(\dot{\alpha}\) & angle of attack, deg., and time rate of angle of attack, deg./s, respectively \\ \(\beta\),\(\dot{\beta}\) & sideslip angle, deg., and time rate of sideslip angle, deg./s, respectively \\ \(\delta_{a}\), \(\delta_{c}\), \(\delta_{r}\) & control deflection angles of anileron, elevator, and rudder, respectively, deg. \\ \(\phi\), \(\theta\), \(\psi\) & Euler angles in roll, pitch, and yaw, respectively, deg. \\ \(\lambda_{r}\), \(\lambda_{i}\) & eigenvalue in real (i.e., in-phase) and imaginary (i.e., out-of-phase) parts, \\ \(\zeta\) & damping ratio \\ \(\omega_{n}\) & natural frequency \\
**Abbreviations** & \\ AAA & Advanced Aircraft Analysis \\ A/P & autopilot \\ DOF & degree of freedom \\ FDR & flight data recorder \\ FLM & Fuzzy logic modeling \\ IATA & International Air Transport Association \\ \end{tabular}
## 3 Results and Discussion
### Performance Evaluation of Different Steps of the Proposed Method  
The primary goal of spectral updates was to generate stable and reliable spectral ranks. The experimental results have shown that the inclusion of spectral updates helped to stabilize the feature spectra and increased the classification accuracy significantly. This is because a relatively significant spectrum in spectral rank may not be ranked as significant; however, the update process brings back this spectrum into a significant list. The spectral refinement finds the strong aflatoxin B1-correlated spectra using the SVM fitness score and  
Figure 4: Illustration of binary aflatoxin B1 classification accuracy comparison for dataset 3. **(a)** Raw data, **(b)** pre-processed data.  
IG score and finds those spectra significant that are found strong in both fitness scores. The experiment results have shown that spectral refinement improves the spectral rank and corresponding classification accuracy significantly. The comparison of aflatoxin B1 classification of different combinations of the developed model is illustrated in Figure 5. It is observed that the refinement pass with rank update requires higher iteration than other combinations to reach 4 spectra. However, at the initial stage, the spectral refinement does not improve the classification accuracy. This is due to a higher number of irrelevant spectra in the acquired hyperspectral images. In each iteration, the proposed algorithm finds the least significant spectra and removes them from the spectra set. Therefore, when the amount of aflatoxin B1-correlated spectra becomes dominant, the classification accuracy increases. So, the classification accuracy reached its highest peak when the spectra set contained the highest aflatoxin B1-sensitive spectral bands. Then, the reduction in the spectral band accounted for losses of relevant aflatoxin B1-sensitive information. The removal of the most significant spectra results in a reduction in information, making it more difficult to distinguish aflatoxin B1-contaminated almonds from uncontaminated ones. Conversely, the inclusion of relevant spectral bands improves model performance by capturing subtle variations in the data that are essential for accurate classification. Therefore, the refinement pass becomes crucial at a later stage when all selected spectra show some sensitivity to aflatoxin B1, and it helps retain more relevant spectra over less significant ones. For this phenomenon, the aflatoxin B1 classification accuracy decreases with a decrease in the number of significant spectral bands. A lower number of spectra increases the speed of aflatoxin B1 detection. Selecting the most significant spectra helps achieve optimal classification accuracy for industrial applications.  
Figure 5: Illustration of achieved binary classification accuracy of different phases of developed feature selection algorithm for three different datasets.
## 2. Mature Technologies for Water and Wastewater Remediation  
### Chlorination  
#### 2.1.1. Applications of Chlorination  
Chlorination is the most widely used disinfection technique globally for purifying drinking water [9]. South African tertiary water treatment plants utilise this technology, adopting chlorine and chloramine as the primary and secondary disinfectants, respectively [10].
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 4 Performance Evaluation
### Simulation Scenario Construction for the Network Games
#### 4.1.2 Performance Evaluation of UDP Scenario  
The analysis results of the UDP performance evaluation scenario using traffic statistics are as follows: traffic speed (bits/s): the message transmission/reception rate was the same as the traffic speed (300 bytes/s) which has been set initially and the entire performance analysis result is shown in Figure 9, which shows that all the messages have been received normally without any packet losses. This, and the absence of any background noises, also shows that all the messages have been received correctly. The average end-to-end delay during the packet transmission/reception was 0.0047 s.  
Figure 8: Results of TCP performance evaluation scenario.  
Figure 9: Results of UDP performance evaluation scenario.
# Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
## 2 Vibration Experiments with a Vibro-Ripper
### Frequency Domain Analysis of TPA  
The TPA can be calculated by using the frequency domain or the time domain, depending on the calculation method for analyzing the measured signal. The TPA method for load estimation and quantification varies with the type of experiment and analysis method (e.g., methods for matrix inversion, dynamic stiffness, and intensity). The FRF data obtained from excitation tests and modal impact experiments are commonly used. Equation (1) can be summarized into Equation (2) when you exclude the effect of acoustic. The simplified equation is a matrix inversion method that can predict the transmitted load using the frequency response of the structure.  
\[P_{\mathrm{f}}(\omega)=\sum_{i=1}^{n}VTF_{i}(\omega)\cdot F_{i}(\omega) \tag{2}\]  
This study employed the FRF obtained through modal experiments, involving a matrix inversion method that uses the vibrational characteristics during operation of the transfer function and the vibro-ripper. The load was analyzed using matrix inversion approaches by substituting the mass term (i.e., [PERSON]'s second law of motion), with the transfer function. This approach has a range of uses in engineering [23]. The matrix inversion approach can be expressed using vibration characteristics (i.e., the acceleration value) acquired by operational vibration experiments and the inverse matrix of the transfer function, as given in Equations (2) and (3).  
\[\begin{bmatrix}F_{1}\\ \vdots\\ F_{N}\end{bmatrix}=\begin{bmatrix}\mathrm{H}_{1}&\cdots&\mathrm{H}_{\mathrm{ IN}}\\ \vdots&\ddots&\vdots\\ \mathrm{H}_{\mathrm{M1}}&\cdots&\mathrm{H}_{\mathrm{MN}}\end{bmatrix}\cdot \begin{bmatrix}\bar{x}_{1}\\ \vdots\\ \bar{x}_{M}\end{bmatrix} \tag{3}\]  
\[\begin{bmatrix}F_{1}\\ \vdots\\ F_{N}\end{bmatrix}=\begin{bmatrix}\mathrm{H}_{1}&\cdots&\mathrm{H}_{\mathrm{ IN}}\\ \vdots&\ddots&\vdots\\ \mathrm{H}_{\mathrm{M1}}&\cdots&\mathrm{H}_{\mathrm{MN}}\end{bmatrix}^{-1} \cdot\begin{bmatrix}\bar{x}_{1}\\ \vdots\\ \bar{x}_{M}\end{bmatrix} \tag{4}\]  
Here, \([F_{N}]\) is the excitation force, \([\mathrm{H}_{\mathrm{MN}}]\) is an \(m\times n\) transfer function matrix obtained through the modal impact experiments that describes the characteristics of the structure, and \(\left[\bar{x}\right]\) is the vibration characteristics of the structure. For the details of the TPA technique, refer to [PERSON] et al. [12].
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 2 The Proposed Deep CNN Architecture
### The Proposed Network  
The proposed deep convolution neural network is shown in Figure 5. The depth of the network is determined by the convolution-pooling layer or convolution layer. The pooling layer is used to reduce the dimensions and does not need to perform complex matrix operations or learn any parameters. The size of the kernel in the pooling layer is \(2\times 1\), and the stride is \(2\times 1\). The pooling method is 'VALID' max pooling and the size of input data will be halved in the time dimension through the pooling layer. Finally, the fully-connected layer maps the features learned by the CNN to the output through converting three-dimensional data into one-dimensional.  
The input data is three-dimensional and is expressed by \(T\times S\times F\), where \(T\) is the length of the TW, \(S\) is the sensor data and \(F\) is the number of feature map (the input data contains only one feature map). The normalized data for \(T\) consecutive time cycles is directly used as input to the CNN. As the depth of the model increases, the number of convolution kernels after the \(1\times 1\) kernels in each layer is  
Figure 5: The proposed network.  
doubled until the input of the \(L\)th layer is less than six in the time dimension, and the pooling layer will no longer be included in the layer and subsequent layers. If the pooling layer for the input data less than six is used again, only the \(1\times 1\) convolution kernel can be used in the next layer, which is only an increase in the number of feature maps rather than feature extraction. The number and size of the convolution kernels in the subsequent layers will be the same as the \(L\)th layer. Before other kernels, the number of \(1\times 1\) kernels is half that of other kernels to satisfy Equation (3), while the first layer is different. This is because the number of the initial \(1\times 1\) kernels before the first convolution-pooling layer is half of other kernels in the first layer, so the number of the \(1\times 1\) kernels before other kernels is set to one quarter of other kernels in the first layer as the bottleneck to reduce the parameters. Note that, the maximum size of the kernel in each layer should be less than or equal to the time dimension of the input data in that layer.  
Here is a concrete example, when the TW size was set to 30, the number of different kernels is presented in Table 1. Due to the dimensionality reduction of the pooling layer, the input size of the third layer was \(7\times\mathrm{S}\times 160\), so the kernel module in this layer removed the \(9\times 1\) kernels. Similarly, only \(1\times 1\) and \(3\times 1\) convolution kernels were used in the fourth and subsequent last layers, and no pooling layers were added.
## Abbreviations  
The following are the abbreviations used in the manuscript.  
ITS Intelligent Transportation Systems.  
VSL Variable Speed Limits.  
LCC Lane Change Control.  
Dec-POMDP Decentralized Partially Observable Markov Decision Process.  
CTM Cell Transmission Model.  
MARL Multi-Agent Reinforcement Learning.  
FACMAC Factory Multi-Agent Centralized Policy Gradients.  
FM-ITC Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control.  
FM-ITC-Darea Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control in Dynamic Areas.  
MPC Model Predictive Control.  
RM Ramp Metering.  
VSL-RM Variable Speed Limits with Ramp Metering.  
V2X Vehicle-to-Everything.  
RL Reinforcement Learning.  
CTDE Centralized Training and Decentralized Execution.  
MADDPG Multi-Agent Deep Deterministic Policy Gradient.  
OU Ornstein-Uhlenbeck.  
TTS Total Travel Spend.  
SPSC Simple Proportional Speed Controller.  
TVT Total Vehicle Turnover.