esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 4 Experiments
### Dataset of Shoulder Bone X-ray Images  
The MURA dataset is one of the largest among the published open-source radiography datasets. It contains finger, elbow, wrist, hand, forearm, humerus, and shoulder bone X-ray images [2]. In this study, only the shoulder bone X-ray images within the MURA dataset were used mainly because it is the most balanced type in the MURA dataset in terms of distribution of the amount of data provided for both training and validation. This balanced distribution is presented in Figure 19. A balanced dataset can otherwise be obtained with data augmentation or synthetic data generation to avoid problems that may arise when working with an imbalanced dataset. Although the MURA dataset is an open-source dataset, only the training and validation datasets are publicly available. The classification models used in the first study and in studies conducted in competition conducted with  
Figure 17: A block diagram of the proposed EL2 ensemble model.  
the MURA dataset were tested using test data that are not publicly available. Due to the confidential nature of the test data and the inability to conduct testing with these data as other studies have, the validation data was used as test data in this study.  
These images, which initially had different resolutions and three channels, were first pre-processed and then converted to 320 \(\times\) 320 \(\times\) 3 pixels before being used for the deep learning model. The size 320 \(\times\) 320 \(\times\) 3 was chosen because it is the resolution most compatible with other studies using this dataset. The data formats are prng, and no alterations were made in terms of the format type. The image type of the shoulder bone X-ray images, the number of training images, the number of test images, and the original and new image sizes are provided in the Table 2.  
Figure 19: The shoulder bone X-ray dataset [2].  
Figure 18: The proposed models for classification of shoulder bone X-ray images.  
The table above shows that the image sizes were originally different but later converted to 320 \(\times\) 320 \(\times\) 3 in size. All images are in png format. The original quantity of normal images was 4211 in the training section and 285 in the test section, and that of abnormal images was 4168 in the training section and 278 in the test section.
## 4 Methodology
### Distance Metric Implementation
The consistent reduction in modified distances suggests that categorical proximity provides meaningful information beyond binary state differences, supporting the theoretical foundation for ordinal-aware distance measures in temporal trajectory analysis. **Distance Metric Theoretical Foundation:**  
The modified Hamming distance preserves ordinal structure through mathematical formalization that extends classical categorical similarity measures [15; 16]:  
**Regular Hamming Distance:**  
\[d_{H}(x,y)=\sum_{i=1}^{n}\delta(x_{i},y_{i})\quad\text{where }\delta(x_{i},y_{i})= \begin{cases}1&\text{if }x_{i}\
eq y_{i}\\ 0&\text{if }x_{i}=y_{i}\end{cases}  \tag{5}\]  
**Modified Hamming Distance (Ordinal-Aware):**  
\[d_{MH}(x,y)=\sum_{i=1}^{n}|x_{i}-y_{i}| \tag{6}\]  
**Theoretical Properties:**  
* Non-negativity: \(d_{MH}(x,y)\geq 0\);
* Symmetry: \(d_{MH}(x,y)=d_{MH}(y,x)\) ;
* Triangle inequality: \(d_{MH}(x,z)\leq d_{MH}(x,y)+d_{MH}(y,z)\);
* Ordinal preservation: \(d_{MH}\) captures magnitude of categorical differences. This formalization ensures mathematical rigor while maintaining interpretative clarity for policy applications, addressing the limitations of binary categorical distance measures that treat all category differences as equivalent [38]. \begin{table}
\begin{tabular}{l c c} \hline
**Index** & \(\mathbf{f_{1}}\) & \(\mathbf{f_{2}}\) \\ \hline \(H(X,Y)\) & 1.00 & 0.67 \\ \(H(Y,Z)\) & 0.75 & 0.92 \\ \(H(Z,X)\) & 0.75 & 0.83 \\ \(H_{W}(X,Y)\) & 1.00 & 0.71 \\ \(H_{W}(Y,Z)\) & 0.67 & 0.67 \\ \(H_{W}(Z,X)\) & 0.58 & 0.63 \\ \hline \end{tabular}
\end{table} 
Table 4: Pairwise comparison of regular and modified Hamming distance. \begin{table}
\begin{tabular}{l c c} \hline
**Index** & \(\mathbf{f_{1}}\) & \(\mathbf{f_{2}}\) \\ \hline Regular Hamming & 0.83 & 0.81 \\ Modified Hamming & 0.75 & 0.67 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of regular and modified Hamming for the features. The average Hamming distance values show how on the calculated feature the objects tend to be more similar that the independent features.
## 2 Materials and Methods
### Experiment Design and Data Processing  
The fish meal samples were measured using E-nose, based on the optimum experiment conditions. A total of 6 fish meal samples of storage grades were measured, and every sample was tested 30 times in parallel. Based We calculated the relative standard deviation (RSD, n = 30) of the relation steady-state response average value for each sensor.  
Savitzky-Golay was adopted to perform five-point filtering and smoothing processing [34], in order to eliminate the noise signals. Six features were extracted, including integral value (INV), wavelet energy value (WEV), maximum gradient value (MGV), average differential value (ADV), relation steady-state response average value (RSAV), and variance value (VARV), as characteristic parameters of the electronic nose signals of fish meal samples. A total of \(10\times 6\) characteristic parameters were extracted to form a 60-dimensional characteristic matrix. Because the dimensions of the six eigenvalues differed, the data of each eigenvalue must be normalized. In this paper, normalization was utilized to normalize the data, as shown in the formula (1). PCA and LDA analysis methods were employed to analyze the classification results of each feature. A multi-layer perceptron neural network [35; 36; 37], random forest [38], K-nearest neighbor (K = 3) [39], support vector machine [40] and Naive Bayes [41] classification methods were simultaneously employed to classify fish meal of different qualities with 6 characteristic values as inputs, and the classification accuracy rate for each characteristic value was obtained.  
\[x^{\prime}_{i}=\frac{x_{i}-x_{\min}}{x_{\max}-x_{\min}} \tag{2}\]  
where \(x^{\prime}_{i}\) is the normalized characteristic value; \(x_{i}\) is the eigenvalue of the original data; \(x_{max}\) is the maximum value of the original feature; and \(x_{min}\) is the minimum value of original features.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 5 Experiment and Result Analysis  
### Experimental Data and Configuration  
The data for this study were obtained from the AIS dataset downloaded from marinecadastre.gov. The original AIS dataset consists of dynamic and static information about the vessel, which includes multi-dimensional information such as MMSI, coordinated universal time (UTC), latitude, longitude, speed over ground (SOG), course over ground (COG), timestamps, and call signs, as shown in Table 1. This study uses the tanker data from a strait in July 2021 as the experimental data. The latitude range of the selected region is 23.00-24.5 N, and the longitude range is 79.50-84.00 W. There are 191,506 trajectory points in the statistical region range.  
Based on relevant research and experience, this study conducted experiments in a Windows 11/64-bit environment with Python 3.6.9 environment based on the PyTorch framework. The experimental data obtained after processing in Section 3 were divided into 70% for the training dataset and 30% for the test dataset. In addition, this study finalized a set of optimal hyperparameter configurations through experimental comparisons. The loss function was mean square error (MSE), the optimizer was set to Adam, the learning rate was initially set to 0.001, the learning rate adjustment multiplier was 0.9, the loss rate was 0.1, the hidden layer unit in the neural network was set to 64, the number of layers was set to 2, the number of epochs was set to 40, and the batch size was set to 64.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Static Information**} & \multicolumn{4}{c}{**Dynamic Information**} \\ \hline
**Attribute** & **Description** & **Type** & **Attribute** & **Description** & **Type** \\ \hline MMSI & Maritime mobile service identity & Text & BaseDateTime & Full UTC date and time & DataTime \\ \hline VesselName & Vessel name & Text & LON & Longitude & Double \\ \hline IMO & International Maritime Organization & Text & LAT & Latitude & Double \\ \hline CallSign & Vessel call sign & Text & SOG & Speed over ground & Float \\ \hline VesselType & Vessel type & Int & COG & Course over ground & Float \\ \hline Length & Length of vessel & Float & Heading & True heading angle & Float \\ \hline Width & Width of vessel & Float & Status & Navigation status & Int \\ \hline TransceiverClass & Class of AIS transceiver & Text & Draught & Draft depth of vessel & Float \\ \hline  & & & Cargo & Cargo type & Text \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vessel information table.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 2 Related Works
### Review of [PERSON] et al.'s Scheme
#### 2.2.2. [PERSON] et al.'s Scheme Cannot Guarantee Anonymity in Authentication Phase  
A solution that provides anonymity must ensure that no one except the server knows the user's personal information. We assume that the attacker \(U_{A}\) is a legitimate user. Hence, \(U_{A}\) will obtain \((\overline{C_{1}}=h(PID_{A}||ID_{\infty}||x)\), \(\overline{C_{2}}=h(ID_{A}||x)\), \(ID_{\infty})\) from _CS_ in the user registration phase. Once \(U_{A}\) intercepts the message \(M_{1}=\{PID_{i}\), \(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\}\) from \(U_{i}\) and uses \(PID_{i}\) as new pseudo-identity to restart an authentication session, \(U_{A}\) can obtain the \(ID_{i}\) of the user \(U_{i}\). Details of the process are as follows.  
Step 1: First, \(U_{A}\) chooses a random number \(r_{A}\) to calculate \(\overline{D_{1}}=C_{1}\)'\(\oplus r_{A}\), \(\overline{D_{2}}=h(r_{A}||PID_{A}||ID_{\infty})\oplus ID_{A}\), \(\overline{D_{3}}=C_{2}\)'\(\oplus h(ID_{A}||HP_{A})\oplus PID_{i}\oplus h(r_{u}||ID_{A})\), and \(\overline{D_{4}}=h(ID_{A}||PID_{A}||PID_{i}||P_{u}||\overline{D_{3}})\). \(U_{A}\) sends the message \(\overline{M_{1}}=\{PID_{A}\), \(\overline{D_{1}}\), \(\overline{D_{2}}\), \(\overline{D_{3}}\), \(\overline{D_{4}}\}\) to \(S_{j}\).  
Step 2: When \(U_{A}\) receives \(\overline{M_{4}}=\{\ \overline{D_{12}}\), \(\overline{D_{13}}\), \(\overline{D_{14}}\}\), \(U_{A}\) can compute \(ID_{i}=D_{2}\oplus h(D_{1}\oplus\overline{D_{12}}\oplus h(r_{u}||PID_{i})\ \ ||PID_{i}||ID_{\infty})\), where \(D_{1}=h(PID_{i}||ID_{\infty}||x)\oplus r_{u}\), \(D_{2}=h(r_{u}||PID_{i}||ID_{\infty})\oplus ID_{i}\), and \(\overline{D_{12}}=h(PID_{i}||ID_{\infty}||x)\oplus h(r_{u}||PID_{i})\).  
Therefore, [PERSON] et al.'s scheme cannot guarantee anonymity in the authentication phase.
###### Abstract  
The reliability of aircraft control surfaces, constructed from thermoplastic materials, can be affected by impacts from airborne particles. Recognizing the exact position of such impacts is essential for correctly estimating the resulting damage. This research intended to address the issue by introducing an innovative structural health monitoring solution capable of autonomously detecting and localizing impacts using acoustic emission monitoring. The objective of this research is to investigate the application of AE for the localization of impacts on aircraft elevators using machine learning techniques, specifically regression algorithms. To achieve this goal, two algorithms, linear regression, and random forest, were employed for predicting the impact locations based on AE signals. The performance of each algorithm was validated on a thermoplastic composite aircraft elevator. Results indicated that both linear regression and random forest models show high accuracy in predicting the impact locations. The random forest model, with an R\({}^{2}\) value of 0.98616 and an RMSE of 0.6778, outperformed the linear regression model, which exhibited an R\({}^{2}\) value of 0.9361 and an RMSE of 1.4614.  
thermoplastic composite; impacts; acoustic emission; structural health monitoring Article
## 4 Classification of Measurement Methods
### Chemical Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Barriers and Limitations** & **Description** & **Solution** & **Ref.** \\ \hline \multirow{3}{*}{Data and Data Labelling} & An insufficient number of labelled samples and difficulties in correcting data drift affect the performance of AI models. & Transfer learning, semi-supervised learning, advanced drift correction methods. & [69; 70; 71] \\ \cline{2-4}  & Differences in the size of training and test sets lead to inconsistent prediction models. & Balanced training sets, cross-validation, context and data distribution analysis. & [72] \\ \hline \multirow{3}{*}{Calibration and validation} & Calibration models degrade over time and need to be frequently updated. & Drift correction, standardisation of calibration, updating the calibration model. & [73] \\ \hline \multirow{3}{*}{Feature extraction} & Extracting representative features from high-dimensional data is difficult. & PCA, deep neural networks, complex signal processing. & [74; 75] \\ \cline{2-4}  & Temperature modulation and selectivity of sensor arrays affect stability. & Optimisation of temperature control algorithms, sensor architecture selection. & [76] \\ \hline \multirow{3}{*}{Cross-sensitivity and selectivity} & Sensors react to multiple substances, making classification difficult. & Machine learning for pattern analysis, sensors with lower selectivity. & [77] \\ \cline{2-4}  & & Low power sensors, optimised detection and recovery algorithms. & [77] \\ \hline \multirow{3}{*}{Power consumption and acquisition time} & The presence of noise and outliers affects classification accuracy. & Data pre-processing, signal filtering, noise cancellation. & [69; 72] \\ \cline{2-4}  & Evaluation and & Lack of consistent assessment methods to replace sensor panels. & Integrated sensor systems, AI-based validation. & [78] \\ \hline \multirow{3}{*}{Reliability of prediction models} & Models degrade over time due to variations in operating conditions. & Mechanisms for model updating, adaptation to environmental changes. & [69] \\ \cline{2-4}  & Time required for traditional analyses & Panel assessments are expensive and prone to human error. & Automation of odour assessment, AI-supported systems. & [79; 80] \\ \hline \multirow{3}{*}{Overfitting} & Overfitting makes it difficult for models to generalise to new data. & Regularisation, feature selection, extension of the data set. & [81] \\ \cline{2-4}  & & & \\ \hline \multirow{3}{*}{Classification under variable conditions} & Sensor drift and environmental changes destabilise the classification. & Dynamic classification models, stepped classifiers, advanced adaptive algorithms. & [82] \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 5: The main challenges associated with standardisation and the use of electronic noses with the proposed solutions.
## 3 Results  
### Numerical Simulations  
A total of 22 bone models were simulated (Table A2 in Appendix D). The reference sound velocities \(\mathit{Ct.v}_{11}^{Ref}\) of the porous models, as determined by transmission simulations, are summarized in Table A3 in Appendix D. The estimated \(\mathit{Ct.Th}^{MF}\) and \(\mathit{Ct.v}_{11}^{MF}\) values for all models and the relative errors are summarized in Table A4 in Appendix D. Without PAC, all deviations from the ideal flat plate geometry led to deteriorations of precision and accuracy. In most situations, PAC improved both the precision and accuracy (Tables 3 and 4), which will be described in more detail in the following sections.  
\begin{table}
\begin{tabular}{c l l l l l} \hline  & **Model** & **No PAC** & **ST** & **ST + ACF** & **ST + ACF + CC** \\ \hline \multirow{3}{*}{\(\mathit{Ct.Th}^{MF}\)} & Curved bone plate & 10.2\% & 2.2\% & 1.9\% & 1.8\% \\  & Curved tilt bone plate & 9.6\% & 10.3\% & 5.2\% (1.3\%) * & 5.2\% (1.2\%) * \\  & Material inhomogeneity & 23.2\% & 14.6\% & 6.3\% & 8.3\% (3.5\%) ** \\ \hline \multirow{3}{*}{\(\mathit{Ct.v}_{11}^{MF}\)} & Curved bone plate & 10.4\% & 2.4\% & 2.1\% & 1.9\% \\  & Curved tilt bone plate & 9.8\% & 10.1\% & 5.1\% (1.4\%) * & 5.1\% (1.2\%) * \\ \cline{1-1}  & Material inhomogeneity & 25.3\% & 3.4\% & 3.4\% & 2.8\% (3.0\%) ** \\ \hline \end{tabular}  
* Exclusion of bone models with tilt angles over 7\({}^{\circ}\). ** Exclusion of bone model with porosity 20\%.  
\end{table}
Table 4: Accuracy of \(\mathit{Ct.Th}^{MF}\) and \(\mathit{Ct.v}_{11}^{MF}\) after each PAC method.  
\begin{table}
\begin{tabular}{c l l l l l} \hline  & **Model** & **No PAC** & **ST** & **ST + ACF** & **ST + ACF + CC** \\ \hline \multirow{3}{*}{\(\mathit{Ct.Th}^{MF}\)} & Curved bone plate & 4.3\% & 1.7\% & 1.4\% & 2.0\% \\  & Curved tilt bone plate & 2.3\% & 7.3\% & 4.3\% (4.2\%) * & 4.3\% (1.1\%) * \\  & Material inhomogeneity & 18.5\% & 1.4\% & 4.7\% & 7.2\% (1.9\%) ** \\ \hline \multirow{3}{*}{\(\mathit{Ct.v}_{11}^{MF}\)} & Curved bone plate & 4.3\% & 1.6\% & 1.4\% & 2.0\% \\  & Curved tilt bone plate & 2.3\% & 7.1\% & 4.2\% (2.5\%) * & 4.3\% (0.8\%) * \\ \cline{1-1}  & Material inhomogeneity & 15.9\% & 7.9\% & 7.9\% & 8.2\% (7.9\%) ** \\ \hline \end{tabular}  
* Exclusion of bone models with tilt angles over 7\({}^{\circ}\). ** Exclusion of bone model with porosity 20\%.  
\end{table}
Table 3: Precision of \(\mathit{Ct.Th}^{MF}\) and \(\mathit{Ct.v}_{11}^{MF}\) after each PAC method.
# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 3 Results and Discussion
### Performance of the Improved Approach  
To verify the reliability and applicability of the proposed approach, the standard deviation (STD), root mean square error (RMSE) and mean absolute error (MAE) of the two approaches were analyzed in detail. The RMSE and MAE can be used to evaluate the difference between the estimated value and referenced value, while the STD is used to quantify the variation or dispersion of a set of data [34; 35; 36].  
Table 1 compares the two methods for snow depth estimation. The STD is a good indicator of the stability of the results. It can be observed that the proposed approach has higher precision than the  
Figure 8: Daily averaged snow depths based on the two methods in 2014 (a), 2015 (b), 2016 (c), 2017 (d) and 2018 (e). Red line represents the measured snow depth from field work. Green and blue lines denote the snow depths estimated with LSP and LSP after wavelet decomposition, respectively. The error bars show the daily standard deviations of the GNSS-based estimations, and the average standard deviations were \(\sim\)7.13 and 6.80 cm for LSP and LSP after wavelet decomposition, respectively, as also shown in Table 1.  
LSP method in terms of the STD, indicating that its results are more robust. Furthermore, the MAEs of the estimated and measured values are 5.72 and 5.57 cm, and the RMSEs are 7.52 and 7.37 cm, respectively, for the LSP approach and the proposed approach, which means that the proposed method is more consistent with the field measurements than the original approach. By comparing the accuracy in different years, we can see that the improvement is limited because the wavelet decomposition transform is only a denoising method and does not change the frequency characteristics contained in the original signal. When the noise is large, as shown in Figure 2, the improvement effect is obvious; however, when the noise is small, the accuracy of the two methods is roughly equivalent.  
As discussed in the method section, after estimating the snow depth from individual SNR sequences, the daily average snow depth was obtained using observations of all the satellites over the whole azimuth in one day. For the daily snow depth estimation, snow depths derived from individual satellites with random errors over three standard deviations were eliminated during data processing, so data utilization is an important indicator for evaluating the reliability of the improved method. Figure 9 represents the data utilization rates of the two methods over 5 years, and Table 2 shows the annual mean data utilization rates of the two methods. Compared with the LSP method, the improved wavelet-based method has significantly higher data utilization, especially in 2014, 2016 and 2018, and the mean data utilization rate over 5 years increased from 72.18% to 80.94% for the improved method, which means that the improved method is more robust and reliable.  
Figure 9: _Cont._
# Plant-Inspired Soft Growing Robots: A Control Approach Using Nonlinear Model Predictive Techniques
## 5 Results and Discussion
### Obstacle Avoidance  
In the second simulation scenario, the proposed NMPC was assessed against avoiding obstacles in the field. As a result, a static point obstacle with a circular structure is located at \(\mathbf{x}_{o}=[x_{o},y_{o},z_{o}]^{T}\) within the robot's path from a point of reference \(\mathbf{x}_{0}\) to the target \(\mathbf{x}_{g}=[1,1,1]^{T}\) metres from its base. At this stage, the estimation horizon is set to \(N=30\), and the sampling time is set to \(T=0.1\) s. A nonlinear inequality restriction has been added to the NMPC's optimal control problem to escape the obstacle by keeping the Euclidean distance between both the tip of the robot \((x,y,z)\) and the obstacle's location within a specified secure distance \((r_{t}+r_{o})\) as follows:  
\[-\infty\leq d_{r,\rho}+(r_{t}+r_{o})\leq 0 \tag{15}\]  
Figure 5: Results of point stabilization simulation experiment to assess the the proposed NMPC-based growth control.  
where  
\[d_{r,\rho}=\sqrt{\left(x-x_{o}\right)^{2}+\left(y-y_{o}\right)^{2}+\left(z-z_{o} \right)^{2}}\]  
is the span between both the robot's tip and the obstacle, and the robot's tip and obstacle radii are \(r_{t}=0.1\) m and \(r_{o}=0.1\) m, respectively. The NMPC has successfully found a secure way for the robot to escape the obstacle using the condition calculated from the MHE, as seen in Figure 6. The subsequent actuation shows that, during navigation, the robot must change its bending angle and curvature to escape the obstacle. It is worth noting that Equation (15) only considers the tip position. This solution to avoiding obstacles cannot promise that the vine robot's whole body will escape the obstacle. Nevertheless, this could be addressed in future work by splitting the body of the robot into segments whose positions could be predicted using the robot's shape parameters. After that, another restriction could be applied to ensure that these segments are not in the path of the obstacle.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 4 Results and Analysis
### Flooded Village  
In this scenario, a small village with 31 houses that has been affected by a flood is simulated, as shown in Figure 9. The dotted lines connected with a cloud show the nodes' association with an IP subnet and the solid lines show VoIP peers. As for the other previous two scenarios, the main scenario is experimented with six sub-scenarios. The terrain size is 300 \(\times\) 300 m. All the nodes in the sub-scenarios are stationary and the nodes' configuration parameters are shown in Table 3. The analysis of this scenario shows that FV2 is the best performing sub-scenario in which IEEE 802.11g, 802.11, G.711 and AODV are integrated. The worst performing sub-scenario is FV6, in which IEEE 802.11n, 802.11e, G.729, and AODV are integrated, as shown in Figure 10a for the VoIP initiators and Figure 10b for the receivers.  
By analyzing all sub-scenarios, we can conclude that IEEE 802.11g with AODV in the case of FV2 and FV4 without the IEEE 802.11s integration show better performance as compared to the other sub-scenarios.  
Figure 8: First responders with random waypoint mobility MOS values: (**a**) VoIP initiators; (**b**) VoIP receivers.  
Figure 9: Flooded village scenario.