esa-sceva/satcom-chunk-collection huggingface dataset:
# Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset
## 2 Material and Methods
### Evaluation  
The evaluation metrics used were the _DSC_, given by Equation (1), Hausdorff distance (HD) and average symmetric surface distance (ASSD) [24]. The HD metric is given by Equation (3) in which \(H(A,B)\) is the Hausdorff distance, \(A\) and \(B\) are two distinct objects, \(h(A,B)\) is the maximum distance of any point of A to its nearest point in B, and vice versa for \(h(B,A)\).  
\[H(A,B)=max(h(A,B),h(B,A)) \tag{3}\]  
The ASSD metric is given by Equation (4), in which \(ASD(A,B)\) is the average of distances between the points of the borders of the ground truth, \(A\), and the predicted mask, \(B\), \(S(A)\) is the set of border points belonging to \(A\), \(S(B)\) is the set of border points belonging to \(B\), \(\sum_{s_{A}\in S(A)}(ds_{A},S(B))\) is the sum of distances of all border points of \(A\) to \(B\)--vice versa for \(\sum_{g_{B}\in S(B)}(ds_{B},S(A))\)--and \(S(A)+S(B)\) is the sum of all border points of \(A\) and \(B\).  
\[ASD(A,B)=\frac{\sum_{s_{A}\in S(A)}(ds_{A},S(B))+\sum_{g_{B}\in S(B)}(ds_{B},S( A))}{\mid S(A)+S(B)\mid} \tag{4}\]  
The HD and ASSD are metrics that take into account pixel spacing and each scan can present its own value for this image property. Therefore, the preliminary results of these two metrics obtained for each image were multiplied by the correspondent image pixel spacing, in order to produce normalized metrics. The three metrics were determined  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline Optimizer & Adam \\ \hline Learning rate & 0.00001, 0.0001, 0.001 \\ \hline Batch size & 4, 8, 16, 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variable hyper-parameters and their values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Dataset** & **\# CT Scans** & **\# CT Images** \\ \hline Training & & 34,969 \\ \hline Validation & & 14,986 \\ \hline \multirow{4}{*}{Test} & 24-LCTSC & 24 & 3675 \\ \cline{2-3}  & HUG-ILD & 112 & 2978 \\ \cline{1-1} \cline{2-3}  & VESSEL12 & 10 & 4279 \\ \cline{1-1} \cline{2-3}  & CHUSJ & 27 & 3340 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of the data per training, validation, and test set.  
between lung masks from the datasets (used as ground truth) and the segmentation mask produced by the model developed.  
Additionally, a clinical assessment of the results was performed by two experienced radiologists. They performed a visual analysis of the 40 randomly selected cases and they evaluated and discussed the cases for the learning models that failed the segmentation.
## 3 Methodology  
The overall process for the recognition of the summer maize cultivated area in Huanghuaihai Plain is displayed in Figure 3. The main process includes: (1) The support vector machine (SVM) algorithm was adopted to distinguish the spatial distribution of summer maize in the reference area. The summer maize MODIS EVI time series curve was acquired through masking multiple periods of MODIS EVI images and the distribution of summer maize in the reference area, which was smoothed with the Savizky-Golay method [52; 53; 54; 55]. (2) Through the stepwise regression method to analyze the relationship between summer maize phenological observation data and environmental factors from 32 agrometeorological stations, multiple linear regression equations were constructed to simulate the summer maize phenology. The simulated phenology acted as the corrected parameter for the MODIS EVI time series curve to obtain the equations of the standard summer maize EVI time series curve in the study area. (3) By calculating the mean absolute distances (MAD) map between standard summer maize EVI time series and actual MODIS EVI time series images, and setting the appropriate thresholds with the p-tile method, the summer maize planting area was estimated in Huanghuaihai Plain.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 5 IoT Middleware Security
### Overview of the Name Resolution Framework in MobilityFirst-Based IoT  
As discussed in Section 4.1, the GNRS and the NCRS center on the concept of the GUID, which serves as the identifier and the public key. Within the scope of the IoT, however, the identifier application/assignment operation and relevant cryptographic computations may be beyond the capability of most IoT devices. For the remainder of the paper, we are not worried about devices with strong computational capability and sufficient resources, such as smartphones, laptops and tablets. Instead, we focus on low-end IoT devices, such as small sensors and actuators, which will account for the majority of IoT devices. These low-end devices, unfortunately, have very limited computational capabilities and strict resource constraints in terms of storage, power, and bandwidth. Hence, they are unable to perform many heavy duty tasks, including the computation operations of public/private cryptography. To enable secure communication, we propose a lightweight approach by using symmetric keys to replace the GUID in the scope of local IoT systems. In other words, an IoT device's identification associated with membership within a local IoT system is associated with a symmetric membership key shared between the device and the local group authority, i.e., the LSG. Symmetric cryptography is chosen to make the membership key because it is lightweight and has much less computational requirements.  
On the other side, in order to smoothly integrate the local IoT system into the global MobilityFirst network and take advantage of the rich network services provided by MobilityFirst, it is necessary to maintain use of the GUID in the scope of the broader global network so that data consumers can contact IoT devices or retrieve/subscribe IoT data with the corresponding GUIDs. In order to reconcile the conflicts of the two different cryptographic schemes (used in two different scopes), integrating an IoT name resolution service (shown in Figure 7) in the middleware to handle naming-related issues is the best solution as it keeps a clean separation between the underlying network infrastructure and the IoT system. The IoT name resolution service (IoT-NRS) bridges the gap between different cryptographic schemes used in the local IoT network and the global Internet. This design makes IoT systems flexible, extensible and easy to manage.
## 4 Simulation Results and Discussion
### _The Sum Rate of the System with Average SNR from 5 dB to 25 dB_  
Figure 7 shows the sum rate _versus_ system average SNR. BER = \(10^{-6}\), the number of users is 16. The average SNR is defined as \(P_{tot}/N_{0}B\). The \(N_{0}\) is changed to make the average SNR varying from 5 dB to 25 dB. From Figure 7, it can been seen that the sum rate for all methods increases as the average SNR increases. In fact, with the increase of average SNR, the AWGN noise power density declines, and, in accordance with the optimization objective Equation (4), the sum rate increases. In the simulation result in Figure 7, in the same SNR from 5 dB to 25 dB, the proposed method ACO-SPA achieves a relatively higher sum rate than the other two methods.
## 5 Conclusions  
The multipath effect is one of the numerous interferences impairing GNSS positioning accuracy. Multipath-induced errors severely restrict the performance of high-precision GNSS receivers. Several estimation techniques were investigated for high-accuracy positioning systems since multipath mitigation is essential for enhancing positioning accuracy.  
The KF algorithm was first constructed based on the minimal mean square error criterion, which is only optimal when the system is assumed to be linear and the noise to be Gaussian. However, the original EKF-based state estimation issue lacks adequate resilience when the system experiences non-Gaussian errors, making it impossible to estimate the state vector accurately. Therefore, alternative optimization criteria were proposed to replace MMSE to reduce the impact of non-Gaussianity and successfully solve the estimation issue when the system is subjected to such conditions. Using an ITL approach for non-Gaussian signal processing in GNSS navigation state estimation, MCC was devised because correntropy contains the second- and higher-order moments of errors. To address the estimating challenges in non-Gaussian noise situations, MCC was created to replace MMSE in a robust EKF called MCEKF.  
This study proposed an extended Kalman filter with maximum correntropy criteria for processing GPS navigation, which is particularly helpful for minimizing the outlier type of multipath interference. Due to the alteration in observation data, the typical EKF cannot adjust the covariance parameters in the measurement model. Therefore, to further increase the accuracy in navigation state estimation, IMM was utilized to alter the covariance parameters of the measurement noises used in each of the parallel filters in the IMM architecture. The suggested IMM-MCEKF algorithm demonstrated a notable improvement  
Figure 11: Comparison of the position accuracies in terms of RMSE for the four approaches.  
Figure 10: Comparison between the pdfs of three-dimensional position errors for the four approaches: (a) east; (b) north; and (c) altitude.  
in estimation accuracy and can act as a viable replacement for GNSS navigational processing based on performance comparisons with EKF, IMM-EKF, and MCEKF.  
Conceptualization, D.-J.J.; methodology, D.-J.J.; software, D.-J.J. and J.-H.L.; validation, D.-J.J., J.-H.L. and Y.C.; writing--original draft preparation, D.-J.J. and J.-H.L.; writing--review and editing, D.-J.J.; supervision, D.-J.J. All authors have read and agreed to the published version of the manuscript.  
The author gratefully acknowledges the support of the National Science and Technology Council, Taiwan under the grant numbers NSTC 110-2221-E-019-042 and NSTC 111-2221-E-019-047.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Methodology
### Autoencoder Implementation
#### 4.2.3 Decoder Function  
The decoder reconstructs the engine input data from \(Z\) in accordance with Equation (2).  
\[\hat{X}=f_{d}(Z;W_{d})=\sigma(W_{d}Z+b_{d}) \tag{2}\]  
How was the autoencoder architecture designed? At the entrance of the autoencoder layer was the sequence input layer with the size of the engine input data. The encoder consists of two fully connected layers (128 units) with ReLU activation. The following layer was the bottleneck layer: the 32-unit layer. The decoder layer was designed symmetrically to the encoder. Following the decoder was the output layer, which was the fully connected layer matched to the size of the data. The last layer was the regression layer.  
To train the autoencoder neural network, various training options were tested to achieve the best results. Hyperparameters (layer sizes, dropout rate, epochs) were selected via grid search and performance analysis. For the autoencoder, we evaluated bottleneck dimensions [16; 32; 64], dropout rates {0.1, 0.2, 0.3}, and epochs [200, 300, 400] on a validation set (10% of training data). The configuration minimizing reconstruction loss (RMSE) and RUL prediction error was adopted: a 32-unit bottleneck, a dropout of 0.2, and 400 epochs. As a training optimizer, [PERSON] was selected with a learning rate of \(10^{-3}\). To prevent overfitting, regularization with a 0.2 dropout rate was set. The mini-batch sequence size was set at 8.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 5 Building the Cloud Detector
### Training Cloud Detectors  
Following [5], a two-stage supervised training procedure was performed on the cloud detector \(f(\cdot\,;\theta)\) parameterised by \(\theta=\{\theta_{\text{ext}},\theta_{\text{cls}}\}\), where \(\theta_{\text{ext}}\) denotes the weights in the feature extraction layer and \(\theta_{\text{cls}}\) denotes the weights in the classification layer. Training commenced by optimising \(\theta_{\text{ext}}\) and \(\theta_{\text{cls}}\) on the training set of \(\mathcal{D}^{s}_{\text{TH30}}\) to allow the feature extraction layer to recognise \"cloud shapes\":  
\[\theta_{\text{ext}}^{s}=\operatorname*{arg\,min}_{\theta_{\text{ext}},\theta_ {\text{cls}}}\;\sum_{(x_{i}^{s},y_{i}^{s})\in\mathcal{D}^{s}_{\text{TH30}}}L(f (x_{i}^{s}\,;\theta_{\text{ext}},\theta_{\text{cls}}),y_{i}^{s}), \tag{2}\]  
where \(L\) is the binary cross-entropy loss function. Then, \(\theta_{\text{cls}}\) was optimised on the training set of \(\mathcal{D}^{s}_{\text{TH70}}\) to fine-tune the classification layer while freezing the weights in the feature extraction layer:  
\[\theta_{\text{cls}}^{s}=\operatorname*{arg\,min}_{\theta_{\text{cls}}}\;\sum_ {(x_{i}^{s},y_{i}^{s})\in\mathcal{D}^{s}_{\text{TH70}}}L(f(x_{i}^{s}\,;\, \theta_{\text{ext}}^{s},\theta_{\text{cls}}),y_{i}^{s}). \tag{3}\]  
Other training specifications such as learning rate and its decay schedule, as well as loss function modifications, followed [5]. Once trained, a source cloud detector \(f(\cdot\,;\,\theta^{s})\) was obtained, where \(\theta^{s}=\{\theta_{\text{ext}}^{s},\theta_{\text{cls}}^{s}\}\). In Section 7, we describe how the performance of \(f(\cdot\,;\,\theta^{s})\) was evaluated.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Cloud Detectors** & \begin{tabular}{c} **Memory** \\ **Footprint** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **Bands** \\ \end{tabular} & \begin{tabular}{c} **CONV** \\ \end{tabular} & \begin{tabular}{c} **BN** \\ \end{tabular} & \
\begin{tabular}{c} **FC** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ \end{tabular} \\ \hline CloudScout-3 & 5.20 & 3 & 1,026,560 & 2304 & 263,682 & 1,292,546 \\ CloudScout-8 & 5.20 & 8 & 1,042,560 & 2304 & 263,682 & 1,308,546 \\ ResNet50-3 & 94.00 & 3 & 23,454,912 & 53,120 & 4098 & 23,512,130 \\ ResNet50-8 & 94.00 & 8 & 23,470,592 & 53,120 & 4098 & 23,527,810 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing cloud detectors in terms of (i) memory footprint (in MB using FP32 weights), (ii) number of input bands, (iii) number of weights in the convolutional (CONV) layer, (iv) number of weights in the BN layer and (v) number of weights in the fully connected (FC) layer.  
Figure 4: The CloudScout [5] architecture.
## 3 Experimental Apparatus
### Experiment 1: OES Wavelength Selection  
The experiment is categorized into a preliminary experiment for OES data analysis for wavelength selection and a main experiment for heat flux calculation. In the preliminary experiment, a 6-inch dummy wafer was placed on the chuck, and then Ar gas was injected into the chamber to discharge plasma. To select an appropriate wavelength, a wavelength value with a certain trend should be determined and then the line ratio should be analyzed without a dramatic difference in intensity tendency despite changes in process parameters. Therefore, the Ar flow rate was fixed, and the pressure and source power were selected for the process parameter to select the wavelength. This is because light emission is caused by excitation and de-excitation reactions that require specific energy. At this point, the reaction is mainly caused by the mean free-path value. The pressure and source power parameters affect the density of plasma, which in turn affects the mean free path. Therefore, gas density was set, and the source power and pressure parameters were chosen to determine the amount of energy transfer that generates a reaction. Pressure was split in units of 10 mTorr from 30 mTorr to 70 mTorr, and source power was split in the incremental step of 100 W  
Figure 2: Schematic of 6-inch ICP-RIE.  
from 100 W to 300 W. Wavelength data collection through the OES sensor was conducted and analyzed.
# Estimation of the Noise Source Level of a Commercial Ship Using On-Board Pressure Sensors
###### Abstract  
The dominant underwater noise source of a ship is known to be propeller cavitation. Recently, attempts have been made to quantify the source strength using on-board pressure sensors near the propeller, as this has advantages over conventional noise measurement. In this study, a beamforming method was used to estimate the source strength of a cavitating propeller. The method was validated against a model-scale measurement in a cavitation tunnel, which showed good agreement between the measured and estimated source levels. The method was also applied to a full-scale measurement, in which the source level was measured using an external hydrophone array. The estimated source level using the hull pressure sensors showed good agreement with the measured one above 400 Hz, which shows potential for noise monitoring using on-board sensors. A parametric study was carried out to check the practicality of the method. From the results, it was shown that a sufficient recording time is required to obtain a consistent level at high frequencies. Changing the frequency resolution had little effect on the result, as long as enough data were provided for the one-third octave band conversion. The number of sensors affected the mid- to low-frequency data.  
propeller cavitation; hull pressure; noise monitoring; underwater radiated noise +
Footnote †: journal: Applied  
**Keywords:** propeller cavitation; hull pressure; noise monitoring; underwater radiated noise
## 2 The Proposed Fusion Approach
### Modified Principal Component Analysis (MPCA)  
MPCA is used to turn uncorrelated variables into correlated variables. This method is useful for analyzing data and determining the optimal features for data collection. The first principal component represents data with the greatest variance. The others are just as much of what is left. The data is well represented by the first principal component, which also illustrates the direction of maximum variation. In this paper, the MPCA approach is used to determine the best-represented value of each subband of source images after implementing the LP-based qshiftN DTCWT method. These values are then multiplied by matched source image subbands. MPCA's goal is to transfer data from the original space to the Eigen space. By saving the components with the largest eigenvector, the variance of the data is enhanced, and the covariance is lowered.  
Specifically, this method removes redundant data from source images and extracts the most significant components. Furthermore, MPCA prioritizes components with the greatest impact and resistance to noise. As a result, the MPCA decreases blurring and spatial distortions. The steps of the MPCA algorithm are as follows:  
1. Create a vector from the data
2. Determine the covariance matrix of the given vector \[(i.e.,cov([im1(:)]))\]
3. Calculate the eigenvalues and eigenvectors of the covariance matrices \[(i.e.,[V,D]=eig(C))\]
4. Choose the first principal component in the order of the eigenvectors \[(i.e.,[max,ind]=sort(diag(D),^{\prime}\textit{descend}^{\prime}))\] \[a=V(;ind(1))./sum\Big{(}V(;ind(1))\Big{)}\]
5. Finally, to get the features extracted image \((i.e.,F\_E\_image=a(1)*im1)\)  
Figure 3: The qshiftN DTCWT filter’s structure.
# Developmental Screening System for Patient Vibration Signals with Knee Disorder
## 2 Methods
### Signal Measurement  
According to pedestrian walking speed data, the normal walking pace of adults is 3-5 km/h, and the step distance is 50-70 cm [35]; the step cycle is 2 s. For a patient with an injured limb, in which fast swinging of the limb is not possible, the step cycle increases to 4 s. To maintain consistency during the test, the study participant assumed a sitting posture to free the lower limbs. The examiner then moved the participant's limb. The participant then swung the lower limb passively and cyclically five times from 0\({}^{\circ}\) to 90\({}^{\circ}\) in 4 s. The resulting vibration sound signals were recorded and analyzed. For each cycle, knee joint vibration signals were collected by nursing staff after the participant rested for 30 s.  
For the analysis, we attempted to identify an appropriate base of wavelets and wavelet coefficients from the sound signals that best-represented the knee joint ligament. Symlet-5, Symlet-6, Symlet-7, Daubechies-8, Daubechies-9, and Daubechies-10 are commonly applied wavelet basis functions that are characterized by sudden drastic variations. These six wavelet functions were applied in the study; signals below 30 Hz were filtered out. If the signal energy of a specific band was much lower than those of the other bands, the band was not processed. Wavelet coefficients from 1 to 7 were used to execute dynamic time warping (DTW) to calculate the difference between parameter values.
## 3 Results and Discussion  
### Infrared Spectroscopic Characterisation of Extracts  
Figure 1 shows the FTIR spectra of the three solvent single-stage extracts of coal tar residue. The absorption peaks around 3440 cm\({}^{-1}\) for the three solvent extracts are the characteristic absorption peaks of joined hydroxyl groups or the absorption peaks of stretching vibrations of intramolecular hydrogen bonds. The peaks of some intensity at 3043 cm\({}^{-1}\) for the three solvent extracts are the absorption peaks of C-H stretching vibrations on the benzene ring. Increasing aromatic ring condensation and higher aromatic ring substitution reduces the C-H structure and thus makes the absorption peak weaker. The peak near 1594 cm\({}^{-1}\) is the absorption peak of the C=C backbone stretching vibration of the aromatic ring. The multiple absorption peaks between 2850-2920 cm\({}^{-1}\) represent the aliphatic hydrogen absorption peaks of the -CH\({}_{2}\)- structure, while the absorption peak near 1435 cm\({}^{-1}\) is the characteristic aliphatic hydrogen peak of the -CH\({}_{3}\)- structure, both of which indicate a high level of aliphatic structure and aromatic ring oligomerisation in the three extracts. 910-650 cm\({}^{-1}\) is the characteristic absorption peak of the aromatic compound. The absorption peaks at 910-850 cm\({}^{-1}\) are usually the out-of-plane bending vibration absorption peaks of a single (i.e., isolated) hydrogen atom by a substituted benzene ring; at 815-790 cm\({}^{-1}\) they are caused by the out-of-plane bending vibration of two neighbouring hydrogen atoms by a substituted benzene ring; at 750 cm\({}^{-1}\) they are caused by the out-of-plane bending vibration of three neighbouring hydrogen atoms by a substituted benzene ring; and at 710 cm\({}^{-1}\) they are the out-of-plane bending vibration absorption peaks of aromatic ring skeletons. The absorption peak at 710 cm\({}^{-1}\) is an out-of-plane bending vibration of the aromatic ring backbone. All of these indicate that the extract contains a high concentration of aromatic compounds [14].  
Figure 1: Infrared spectra of three solvent extracts from coal tar residues.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 5 Methodology
### Structural Analysis
#### 5.2.4 Thermal Analysis  
A thermal analysis is conducted to evaluate temperature distributions across structural components, considering heat transfer mechanisms such as conduction, convection, and radiation. Finite element software, such as SAFIR, is commonly employed to simulate thermal and mechanical behavior under elevated temperatures [49; 51; 52].