esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods Used
### Geotechnical and Geophysical Data in the Durres Region
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Lithology**} & \multicolumn{6}{c}{**Granulometry**} & \multirow{2}{*}{**Sensitivity**} \\ \cline{3-15}  & & & & & & & & & & & & & & \\ \cline{2-15}  0–3 m & \multicolumn{2}{c}{Marshy-lagoon sub} & \multirow{2}{*}{4.0} & \multirow{2}{*}{24} & \multirow{2}{*}{72} & \multirow{2}{*}{2.64} & \multirow{2}{*}{1.84} & \multirow{2}{*}{1.31} & \multirow{2}{*}{38} & \multirow{2}{*}{39} & \multirow{2}{*}{0.8} & \multirow{2}{*}{24} & \multirow{2}{*}{0.05} & \multirow{2}{*}{-} \\  & \multicolumn{2}{c}{sand with peat} & & & & & & & & & & & \\ \hline \multirow{3}{*}{3–15 m} & \multicolumn{2}{c}{Powdery} & \multirow{3}{*}{3.0} & \multirow{3}{*}{15} & \multirow{3}{*}{82} & \multirow{3}{*}{2.66} & \multirow{3}{*}{1.86} & \multirow{3}{*}{1.48} & \multirow{3}{*}{35} & \multirow{3}{*}{48} & \multirow{3}{*}{0.89} & \multirow{3}{*}{26} & \multirow{3}{*}{0.05} & \multirow{3}{*}{1.0} \\  & \multicolumn{2}{c}{sand} & & & & & & & & & & & \\ \hline \multirow{2}{*}{15–28 m} & \multirow{2}{*}{Sub-clay} & \multirow{2}{*}{17} & \multirow{2}{*}{60} & \multirow{2}{*}{23} & \multirow{2}{*}{2.68} & \multirow{2}{*}{1.89} & \multirow{2}{*}{1.5} & \multirow{2}{*}{35} & \multirow{2}{*}{48} & \multirow{2}{*}{0.92} & \multirow{2}{*}{14} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.05} \\  & \multicolumn{2}{c}{Clay} & \multirow{2}{*}{30} & \multirow{2}{*}{60} & \multirow{2}{*}{10} & \multirow{2}{*}{2.72} & \multirow{2}{*}{1.9} & \multirow{2}{*}{1.6} & \multirow{2}{*}{38} & \multirow{2}{*}{49} & \multirow{2}{*}{0.95} & \multirow{2}{*}{14} & \multirow{2}{*}{0.25} & \multirow{2}{*}{-} \\ \hline \multirow{2}{*}{>130 m} & \multicolumn{2}{c}{Clayey-sand} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{2.75} & \multirow{2}{*}{2.08} & \multirow{2}{*}{1.9} & \multirow{2}{*}{11.13} & \multirow{2}{*}{40} & \multirow{2}{*}{0.68} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & \multicolumn{2}{c}{basement rock} & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of physical-mechanical parameters: \(\gamma\) (T/m3)—specific weight; \(\Delta\) (T/m3)—volume weight; \(\delta\) (T/m3)—skeleton volume weight; W %—humidity; in %—porosity; \(\phi\)—porosity coefficient; \(\Phi\) (\({}^{\circ}\))—internal friction angle; C (kg/cm2)—cohesion; \(\mu\) (m/24 h)—permeability. Figure 6: Geologic cross-section IV—IV [30]. Figure 7: Location of the cross section IV-IV, 6.0 km long (**top part**); The position of DURR station (red triangle) and several boreholes (SH-xx) are also presented (**bottom part**). The white arrow indicates the MASW-Vs measurement profile-P39 carried out close to the DURR accelerometric station.
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 4 Sensor Experiment and Discussion
### Symmetry and Sensitivity Evaluation  
Then, the symmetry of the developed current sensor was the main concern in this work, and measured as shown in Figure 15. It is obviously that the gathered sensor signal decreases monotonically with increasing current intensity from 0 to 10 A, meanwhile, it increases with decreasing current intensity, and reaches to the initial value. It means that good symmetry in sensor response was observed for the FeNi coated current sensor.  
Also, for comparison, the symmetry of the developed FeCo coated current sensor [12] was measured as shown in Figure 15. The obvious asymmetry phenomenon was observed in the sensor response at increasing and decreasing current intensity, which is caused by its larger remanence and hysteresis effect over the FeNi described in Figure 3.  
Usually, the hysteresis error (\(\epsilon_{\mathrm{H}}\)) was defined by \(\epsilon_{H}=\frac{(\Delta y)_{\mathrm{max}}}{2y_{FS}}\times 100\%\), \(\left(\Delta y\right)_{\mathrm{max}}=\mathrm{max}(y_{ui}-y_{di})\), where \(y_{ui}\) and \(y_{di}\) are the response at the same current input when the current increases and decreases, \(y_{FS}\) is the full-scale output of the current sensor. Hence, a hysteresis error of 0.97% was obtained from the FeNi coated sensor, which is substantially lower than the FeCo coated sensor (8.72%).  
The corresponding sensitivity was measured to be 10.7 KHz/A, which agrees well with the predicted value of 11 KHz/A, as shown in Figure 16, and it corresponds to a magnetic field sensitivity of 5.35 KHz/Oe. Usually, the detection limit in current sensing closely relates to the frequency stability of the oscillator. Considering the measured short-term stability of \(\pm\)0.5 Hz/s, and 10.8 KHz in response to current of 1 A, the threshold detection limit of the developed sensor was expected to be \(\sim\)0.2 mA. From these results, we suggest that this prototype FeNi coated SAW sensor is very promising for current sensing with high sensitivity, excellent repeatability, good symmetry and linearity.  
Figure 16: The sensitivity evaluation of the developed FeNi coated sensor.  
Figure 15: The symmetry test of the FeNi coated sensor and in comparison to FeCo coated sensor.
## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.3 RV Segmentation Stage  
The training, validation, and test sets used in this stage consist of the same ROIs from the previous experiment. However, the ground truth for all sets includes only RV annotations.  
The proposed method uses the IRAX-Net to perform the RV segmentation. This network was trained for 300 epochs using the Jaccard Loss function, Adam optimizer, learning rate of 0.001, and decay factor of 0.1.  
It has approximately 73.3 million trainable parameters. For comparison, other networks besides IRAX-Net were tested. The results achieved can be seen in Table 5.  
The experiments with U-Net and EAIS-Net were initially performed to segment the three structures. However, those networks presented promising results only for the MYO and LVC segmentation. In the case of the RV, less expressive results were obtained, which motivated the execution of tests with other networks.  
Tests were carried out with X-Net, but this network performed poorly. Other adaptations were made to this architecture, such as using the EfficientNet B3 and Inception-ResNet-v2 (IRv2) networks as backbones. The latter, in turn, presents better results. Given that, the last adaptation proposed included the Attention blocks in the second decoder path of the modified X-Net (with the IRv2 backbone), thus developing the IRAX-Net. In  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & **Methods** & **Dice** & **IoU** \\ \hline \multirow{3}{*}{LVC} & **U-Net** & **0.9236** & **0.8851** \\  & U-Net + AIS Blocks & 0.9203 & 0.8782 \\  & EAIS-Net & 0.9047 & 0.8640 \\ \hline \multirow{3}{*}{MYO} & **U-Net** & **0.8547** & **0.7708** \\  & U-Net + AIS Blocks & 0.8393 & 0.7491 \\ \cline{1-1}  & EAIS-Net & 0.8356 & 0.7498 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between the results obtained in the experiments for the refined MYO and LVC segmentation substage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Dice** & **IoU** \\ \hline U-Net & 0.7025 & 0.6249 \\ EAIS-Net & 0.7649 & 0.7013 \\ X-Net & 0.6864 & 0.6150 \\ X-Net (EfficientNet B3) & 0.5835 & 0.4826 \\ X-Net (IRv2) & 0.7208 & 0.6595 \\
**IRAX-Net** & **0.8213** & **0.7623** \\
**(IRv2 + Attention Blocks)** & **0.8213** & **0.7623** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis between the networks used in the RV segmentation process.  
the experiments performed, this network achieved the best results for RV segmentation, obtaining Dice of 0.8213 and IoU of 0.7623, which, on average, represent an increase of 5.8% over the results of EAIS-Net.  
It is noted that IRv2's feature extraction process is more effective than both the standard architecture of X-Net and its version with EfficientNet B3 as a backbone. In addition, Attention blocks proved important in obtaining more expressive results. Therefore, IRAX-Net was the network chosen to integrate the RV segmentation stage.
## 5 Development of CNN Model
### Performance Metrics  
The proposed network is simulated using the Keras library of Python and TensorFlow backend. In this work, we analyze CNN performance considering two major variables described in Section 3, using datasets based on four presented scenarios (see Section 5.1). Table 6 shows the accuracy results of training the network. The influence of using the data augmentation technique is examined and is presented in Table 6. This technique improves the accuracy of classification by expanding the training dataset.  
In the worst scenario (unstable environments and the same inputs), the accuracy of classification is 92.29%, which can achieve 97.79% in the case of applying different input patterns in a stable condition.  
Additionally, for a classification problem with N challenges, the number of samples for each label (M) is an influential parameter to achieve better accuracy. In this work, the authors have accomplished the classification process using 90 samples for each class, which is a reasonable decision for a PUF-based mechanism leading to a cost-effective enrollment procedure. However, it is functional to add more samples to each class in order to generate a more comprehensive dataset and achieve satisfactory accuracy depending on the application. Figure 7 illustrates the average accuracy of classification after 60-epoch training as a function of the number of samples in each class, considering the different number of classes. The experiment is performed by writing the same input pattern into various memory blocks at room temperature. The results indicate that it is feasible to achieve an error less than \(10^{-1}\) and even near \(10^{-2}\) by adjusting the number of measurements during the enrollment phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experimental Conditions**} & \multicolumn{4}{c}{**Accuracy of Classification (\%)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Same Input Pattern for All Blocks**} & \multicolumn{2}{c}{**Different Input Patterns**} \\ \cline{2-5}  & **Augmented Data** & **Original Data** & **Augmented Data** & **Original Data** \\ \hline Stable temperature & 96.12 & 94.66 & 97.79 & 97.15 \\ Various temperatures & 92.29 & 91.03 & 94.9 & 94.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of classification considering different scenarios (N = 100, M = 90).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Dimension** \\ \hline Convolution 2D & (222, 222, 128) \\ Convolution 2D & (220, 220, 32) \\ Max pooling & (109, 109, 32) \\ Convolution 2D & (107, 107, 16) \\ Convolution 2D & (105, 105, 32) \\ Max pooling & (52, 52, 32) \\ Convolution 2D & (50, 50, 16) \\ Max pooling & (24, 24, 16) \\ Flatten & 9216 \\ Dense & 145 \\ Dense & 75 \\ Dense & Number of classes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Architecture of the convolutional neural network (CNN) used for classification.
## 2 Materials and Methods
### Evaluation Metrics  
An efficient technique for performance evaluation is cross-validation (k-fold). Cross-validation randomly divides the database into K parts and trains the model K times using one part as a test and the remaining parts for training [10]. It is essential to compare different algorithms to find the most suitable one for the problem [6]. A 10-fold cross-validation technique was used with the Test and Score widget [42].  
The trained model evaluates the algorithm's performance using the test dataset [5]. Then, predictions can be made using a new dataset. This study randomly divided the database into 20% for testing and 80% for algorithm training.  
Regarding the evaluation metrics for the algorithms [9; 10; 11], the following were used:  
* MAE (Mean Absolute Error);
* MSE (Mean Squared Error);
* RMSE (Root Mean Squared Error);
* R\({}^{2}\) (Coefficient of Determination).  
The metrics can be expressed according to the following equations [9; 10; 11]:  
\[\text{MAE}=\frac{1}{\text{N}}{\sum_{\text{i=1}}^{\text{N}}}|y_{\text{i}}-x_{ \text{i}}| \tag{1}\]  
\[\text{MSE}=\frac{1}{\text{N}}{\sum_{\text{i=1}}^{\text{N}}}(y_{\text{i}}-x_{ \text{i}})^{2} \tag{2}\]  
\[\text{RMSE}=\sqrt{\text{MSE}}=\sqrt{\frac{1}{\text{N}}{\sum_{\text{i=1}}^{ \text{N}}}(y_{\text{i}}-x_{\text{i}})^{2}} \tag{3}\]  
\[\text{R}^{2}=1-\frac{\sum(y_{\text{i}}-x_{\text{i}})^{2}}{\sum(y_{\text{i}}- y^{*})^{2}} \tag{4}\]  
where the following applies:  
* \(y\) is the real value
* \(x\) is the predicted value
* \(y^{*}\) is the mean of the real values  
Figure 3 shows the model scheme created in Orange. In this workflow, the following used widgets are visible: Data Sampler (which randomly divides the database in a chosen proportion: 20% for testing and 80% for training), RF and SVM (the algorithms used), Test and Score (used for cross-validation), Prediction (used for predicting porosity), and Feature Importance (used to evaluate the importance of features in the prediction).
# An Approach for Modelling Harnesses in the Extreme near Field for Low Frequencies
###### Abstract  
A key part of every space science mission, in the system-level approach, is the detailed study and modeling of the emissions from transmission lines. Harnesses usually emit electromagnetic fields due to the currents (of common and/or differential modes) that flow on their shields. These fields can be identified via conducted emissions measurements. Relying on the operating frequency, any cable can be considered as a dipole or a traveling-wave antenna. Limited work can be found in the literature regarding modeling methodologies for cable topologies, especially in the low frequency (ELF, SLE, VLF, LF) domain. This work intends to provide perceptions for the precise estimation of harness radiated emissions, consider a mission-specific measurement point (where the sensors are placed), and follow ESA's recent science mission studies for electromagnetic cleanliness applications. For the low frequencies considered herein, any linear cable path is considered as a point source (infinitesimal dipole) and we evaluate its effect on the calculated electric field extremely close to the source. For such distances, it is shown that the dipole representation is not accurate. To remedy this phenomenon, this article proposes a methodology, which can be easily expanded to complex cable geometry cases.  
low-frequency harness model; Hertzian dipole; near-field approximation; source segmentation 20221230202022022022022022022022022022022022022022022022202202202220220222022202220222022202220222022202220222022202220222022202220222202220222022220222202  
the current flow upon the shield as the electromagnetic radiation source. Following the aforementioned approach, the common-mode (CM) and differential-mode (DM) currents, identified during the measurement campaign of conducted emissions, can be treated separately as individual sets of various dipole sources. Consequently, conducted emissions can be used to predict radiated emissions, since the only parameter needed is the CM or DM current distribution.
# Deep-Learning Software Reliability Model Using SRGM as Activation Function
## 5 Sensitivity Analysis  
### Results of Variation for Changes in Parameters  
The software reliability model estimates the number of failures \(m(t)\) by estimating a parameter that contains mathematical assumptions. Whenever the parameters change, the \(m(t)\) in the software reliability model also changes; therefore, it is extremely sensitive to small changes in the estimated parameters [66; 67]. To evaluate the sensitivity of the model to changes in parameters, the parameters of the proposed model were varied by \(-10\%\), \(-5\%\), \(5\%\), and \(10\%\). Figure 5a shows the estimates of the time-varying parameters for Dataset 1. From \(-10\%\) to \(10\%\), the change in parameter \(b\) did not cause much change in \(m(t)\), so the last eight time points are presented with a zoomed-in graph. Figure 5b shows the estimates of the time-varying parameters for Dataset 2, with the results for the last nine time points zoomed in because they are similar to those for Dataset 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**No.** & **Model** & **MSE** & **PRR** & **PP** & **R2** & **RMSPE** & **MAE** & **MEOP** & **TS** & **PC** & **preSSE** \\ \hline  
roposed model & 34.9817 & 0.1325 & 0.2242 & 0.9995 & 5.9145 & 5.9145 & 5.807 & 0.9571 & 96.9617 & 174.911 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of all criteria from Dataset 2.  
Figure 5: (a) Predicted change in parameter \(b\) for Dataset 1 and (b) predicted change in parameter \(b\) for Dataset 2.  
Table 8 and Figure 6 show the results of comparing the MSE values based on the changed estimates for Datasets 1 and 2 with the MSE values of the proposed deep-learning software reliability model for a numerical comparison. The comparison criterion was the MSE of the proposed deep-learning software reliability model, and the MSE of the changed parameters was divided by that of the proposed deep-learning software reliability model. The results for Dataset 1 show that the MSE increases by factors of 3.378 (\(-\)10%) and 2.459 (\(-\)5%) as \(b\) becomes smaller, and by factors of 1.670 (5%) and 2.054 (10%) as \(b\) becomes larger compared with the previously estimated values. The results for Dataset 2 show that the MSE increases by factors of 1.587 (\(-\)10%) and 1.258 (\(-\)5%) as \(b\) becomes smaller, and by factors of 1.119 (5%) and 1.350 (10%) as \(b\) becomes larger compared with the original estimate. This demonstrates that the MSE increases more for smaller than for larger values of the estimated fault detection rate \(b\), indicating a poor estimate. This is because Datasets 1 and 2 have a high percentage of failures that either decrease in number or recur, even as the number of time points increases.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 5 Aviation Big Data Multi-Layer Security Sharing Framework
### Publish-Subscribe-Based Secure Data-Sharing Scheme for ATM  
To meet the demand for large-scale direct data sharing among ATM participants while ensuring secure sharing policies, this scheme addresses challenges such as difficult data sharing and inefficient access control. By leveraging blockchain for managing data service publishing, querying, subscribing, and deregistration, along with attribute-based encryption (ABE) and integrity verification, it enables secure ATM data sharing and fine-grained access control. These strategies enhance both data security and sharing efficiency.  
The data provider (DP) encrypts data using attribute-based encryption (ABE) and stores the ciphertext in cloud storage. The DP generates metadata (recording data keys, verification credentials, etc.) and publishes it on the blockchain to complete data registration.  
Figure 3: Airborne node block deployment diagram.  
Beyond storing metadata, the blockchain also maintains data-sharing records and uses smart contracts to handle requests from participants, including data registration, data querying, data subscription, data updates, and data deregistration, while data consumers (DCs) search for target data via data query contracts. If a DC meets the DP's access control requirements, it retrieves the ciphertext from cloud storage and decrypts it. DCs can also subscribe to data via data subscription contracts. Data owners can push updates periodically to subscribers via data update contracts. Data owners can terminate data distribution via data deregistration contracts.  
The specific process of the data-sharing scheme is illustrated in Figure 4.
# Mass Collaboration and Learning: Opportunities, Challenges, and Influential Factors
## 2 Base Concepts  
There is no universally agreed single definition of mass collaboration as the concept can be addressed from a variety of perspectives. Mass collaboration, as a new and emerging area, overlaps in some ways with a number of other collective action-related terms, including collective intelligence, crowd-sourcing, crowd wisdom, peer production, user-powered systems, community systems, social systems, social collaboration, Wikinomics, smart mobs, and human computation [1]. Even though certain general characteristics of mass collaboration are addressed in most proposed definitions (a large number of participants who act collectively), some particular characteristics and features still remain controversial and confusing, e.g., What kind of problems are tackled? Should technology always be used? If yes, what kind of technology is exactly needed? Whether or not all forms of mass collaboration use a textual medium? etc. Furthermore, the current literature introduces the idea of mass collaboration from different perspectives, such as knowledge construction [4], innovation [1], organizational sustainability [5], knowledge management, and collaborative learning [1; 6]. Table 1 presents some partial definitions in order to gain a better insight into this concept. The closest notion to mass collaboration among the various overlapping concepts is collective intelligence. These two terms are even used as synonymous by some authors [3]. They claim that principles such as openness, peering, sharing, and acting globally make it possible to apply them interchangeably. In order to better discern between mass collaboration and collective intelligence some related definitions and statements on collective intelligence are summarized in Table 2.  
The fact is that it is controversial whether mass collaboration and collective intelligence are similar concepts or not. If not, what differences and similarities are there between these two collective actions? Existing examples show that mass collaboration and collective intelligence have in fact many things in common. In some examples, they are particularly the same, but in some instances, they can be clearly distinguished. Based on: (A) our findings from the literature review, (B) comparison of these two concepts and their suggested definitions (some of them are addressed in Tables 1 and 2), and also (C) our understanding of some application examples of mass collaboration (listed in Table 3), a number of similar characteristics and different features can be seen between mass collaboration and collective intelligence, including:  
Some major similarities: