esa-sceva/satcom-chunk-collection huggingface dataset:
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 5 Determination of Safe Vibration Thresholds and Development of Mitigation Strategies
### Determination of Safe Vibration Thresholds
#### 5.1.1 Validation Through Field Measurements and Numerical Simulations  
During the field measurement phase, vibration monitoring points were installed on the central pillar and diaphragm wall to record the PPVs at different locations during blasting. FLAC3D software was used to construct a three-dimensional tunnel structure model to simulate the effects of blasting vibrations on key structural components. A comparison of simulation results with field data is as follows: The simulation results indicate that the PPVs are 1.482 cm/s at the bottom of the central pillar, 1.424 cm/s for the diaphragm wall, and 1.537 cm/s for the roof. The maximum deviation between simulated and measured data is 16%, confirming the reliability of the model.
## 3 Results and Discussion  
### Thermal Measurements  
In Table 4 the main data related to the thermal conductivity measurements are reported. Tests are all related to a hot side set-up temperature of 45 \({}^{\circ}\)C because tests at 50 \({}^{\circ}\)C showed higher uncertainties. A preliminary test was carried out in order to measure the thermal conductivity of the only plaster board support, found equal to 0.21 W/mK (total thickness of the board equal to 12 mm). After determining the total thermal resistances of the composed samples (plasterboard support, waste glued panel, and plasterboard support), the thermal resistance and the thermal conductivity of the only insulating panel were calculated, as shown in Equation (2). Table 4 shows data obtained for each test and the final value of the recycled insulating panels thermal conductivity.  
These values should be analyzed considering the thermal conductivity of the waste-based panels composed of only one single material: the \(\lambda\)-values were measured by means of the same methodology in previous works [12; 16] and they are resumed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Description of the Panels**} & \multirow{2}{*}{**\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{   }}}}}}}}}}}}}\)**} & \multirow{2}{*}{**\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}}\)} & \multirow{2}{*}{**\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}\)}} & \multirow{2}{*}{**\(\mathbf{  
It is possible to observe that the best solution from the thermal point of view is obtained for the panel type 1, composed of 70% of cork and 30% of rice husk, which are the materials with the lowest thermal conductivity values (Table 5). Panels type 3, 5, and 6 have the same thermal insulation behavior (0.07 W/mK), showing the worst performance, whereas intermediate values were found for samples 2 and 4 (0.064 W/mK). All the measurements showed uncertainty u-values in the 4-5% range, except for panel 3 (cork and end-life granulated tires), with a slightly higher value (5.7%), due to a higher variation of the heat flux during the test (differences of about 4.5 W/m\({}^{2}\)): this is probably due to the configuration of the panel 3, that is not very compact and regular in shape.
## 2 Materials and Methods
### LC-MS/MS System and Conditions for Quantification of the 4 Markers  
Quantification of the 4 markers in the DGJT sample was performed using an Acquity UPLC H-Class system (Waters, Milford, MA, USA) with a TQ-XS system controlled by MassLynx software (version 4.1, Waters). A Waters Acquity UPLC BEH C\({}_{18}\) column (2.1 mm \(\times\) 100 mm, 1.7 \(\upmu\)m) was used to separate the marker compounds with a distilled water containing 0.1% (\(v\)/\(v\)) formic acid and 5 mM ammonium formate (solvent A)-acetonitrile (solvent B) system. The mobile phase was eluted at 0.3 mL/min. The gradient conditions were the following: 20% solvent B at initial to 0.1 min, 20-95% solvent B at 0.1 to 14.0 min, 95-100% solvent B at 14.0 to 15.0 min, 100-20% solvent B at 15.0 to 15.1 min, and 20% solvent B at 15.1-18.0 min. The temperature of the column and sample trap were maintained at 45 and 5 \({}^{\circ}\)C, respectively. Quantification was conducted using the MRM mode. Capillary voltage, source temperature, desolvation temperature, desolvation gas flow, and cone gas flow were set at 3.0 kV, 150 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, 700 L/h, and 50 L/h, respectively. Other parameters such as collision energy, cone voltage, and transition for LC-MS/MS MRM analysis of each marker are shown in Table S3.
## 2 Data and Methods
### Vertical TEC from the International GNSS Service and Empirical Models  
#### 2.4.1 IGS Global Ionospheric Maps  
The final IGS GIMs of vTEC [1] were used, which are provided at a temporal resolution of 2 h and at a spatial resolution of 2.5\({}^{\circ}\) by 5\({}^{\circ}\) in latitude and longitude, respectively. The maps are available in the IONEX (ionosphere exchange) format at the Crustal Dynamics Data Information System (CDDIS) Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA, Greenbelt, DC, USA) website ([[https://cddis.nasa.gov/index.html](https://cddis.nasa.gov/index.html)]([https://cddis.nasa.gov/index.html](https://cddis.nasa.gov/index.html)), accessed date: 24 April 2019).  
GIM vTEC is used to estimate the total number of electrons in Earth's ionosphere, namely the global electron content (GEC) index [56]. The units of GEC are GECU, where 1 GECU = 10\({}^{32}\) electron. Magnetic storms influence GEC in different ways (e.g., regional variations) [57], and GEC reflects the dynamics of the ionosphere as a whole response.
## 3 Results and Discussion
### Effect of IC Design
#### 3.4.2 Reading Errors
There were two coarse errors on the digital speedometers IC06 and IC07 in the 1 st session that cannot be correlated to any other information at those ICs. The number of accurate readings on ICs with a classic speedometer is relatively low compared to a digital speedometer. The design of analog speedometers from the sample is not suitable for precise reading in a short time. The graduation of the scale and the pointer that conceals the numbers can be the causes of small errors, i.e., within the major division of the scale. There is no obvious advantage of minor marks on the IC01 and IC02 scales. Even so, the accuracy of the reading is usually quite sufficient for controlling the speed of the vehicle, as an absolutely accurate value is not necessary.
## 2 Seismic Response Calculation Method for Soil Layers Based on the Modal Superposition Method  
### Theoretical Background  
Figure 1 shows an arbitrary two-dimensional irregular site. After intercepting a certain range of soil, a finite element model with \(n\) degrees of freedom (DoF) can be formed. The equation of motion for the soil layer under seismic excitation can be expressed as [25]:  
\[[M]\big{\{}\tilde{u}\big{\}}+[C]\big{\{}\dot{u}\big{\}}+[K]\big{\{}u\big{\}}=-[M] \big{\{}\dot{I}\big{\}}\tilde{u}_{g} \tag{1}\]  
where \([M]\), \([C]\), and \([K]\) are the mass matrix, damping matrix, and stiffness matrix, respectively; \(\{u\}\), \(\{\dot{u}\}\), and \(\{\ddot{u}\}\) are the displacement vector, velocity vector, and acceleration vector; and \(\{I\}\) is the distribution vector of ground motion; \(\ddot{u}_{g}(t)\) is the time history of seismic excitation. The characteristic equation of the undamped system corresponding to Equation (1) is  
\[\Big{(}[K]-\omega^{2}[M]\Big{)}\big{\{}\dot{\phi}\big{\}}=0 \tag{2}\]  
The first \(N\)-th natural frequencies \(\omega_{i}\) and modes \(\{\phi\}\) (\(i=1,2,\ldots,N\)) of the soil layer can be obtained by solving Equation (2). By expanding the displacement vector into the modal space, the dynamic response of the system can be expressed as  
\[\{u\}=\sum_{i=1}^{N}\{\phi_{i}\}q_{i} \tag{3}\]  
where \(q_{i}\) is the generalized coordinates. For a proportional damping system, equation governing the motion of generalized coordinates can be expressed as  
\[\ddot{q}_{i}+2\zeta_{i}\omega_{i}\dot{q}_{i}+\omega_{i}^{2}q_{i}=-\gamma_{i} \ddot{u}_{g}(t) \tag{4}\]  
where \(\gamma_{i}=\{\phi_{i}\}^{T}[M]\big{\{}\dot{I}\big{\}}/\{\phi_{i}\}^{T}[M]\big{\{} \phi_{i}\}\) is the modal participation factor, \(\zeta_{i}\) is modal damping ratio. After obtaining the \(q_{i}(t)\), \(\dot{q}_{i}(t)\), and \(\ddot{q}_{i}(t)\) of the generalized coordinates, the \(u_{j}(t)\), \(\dot{u}_{j}(t)\), and \(\ddot{u}_{j}(t)\) of the \(j\)-th DoF can be expressed as  
\[u_{j}(t)=\sum_{i=1}^{N}\phi_{ji}q_{i}(t) \tag{5}\]  
\[\dot{u}_{j}(t)=\sum_{i=1}^{N}\gamma_{i}\phi_{ji}\ddot{q}_{i}(t) \tag{6}\]  
\[\ddot{u}_{j}(t)=\sum_{i=1}^{N}\gamma_{i}\phi_{ji}\ddot{q}_{i}(t) \tag{7}\]
## 3 Proposed Methods  
### Dataset for Reference CAD Models  
Pascal3D+ [22] has been commonly used in many 6-DoF pose estimation studies. This study also uses Pascal3D+ to train the proposed pose estimation network. In addition, we use Pascal3D+, Pix3D [23], and ObjectNet3D [24] as the test datasets. The Pascal3D+ dataset consists of 12 categories, including airplanes, bicycles, chairs, and automobiles, and consists of approximately 36,000 images. Among these, 28,100 images are used for training, and 2113 images are used for testing. The Pix3D dataset contains 9 indoor object categories, generally categorized for IKEA household items, with about 10,000 images. Among them, the chair category contains the largest portion, with around 4000 images. ObjectNet3D includes 100 categories and consists of approximately 90,000 images. Unlike the previous two datasets, ObjectNet3D encompasses categories for relatively small objects such as keys, pencils, and pens.  
For CAD model retrieval, the 3D CAD dataset from ShapeNetCore is used. It is a subset of the ShapeNet [12] database. ShapeNetCore provides 3D CAD models across 55 upper categories for indoor and outdoor objects, with various subcategories. In this study, 10 upper categories (_bag, bed, bottle, bench, car, chair, clock, motorcycle, stove_, and _table_) and 10 subcategories within each category are selected, resulting in a total of 100 CAD models. The selection criteria for these categories are based on the following principles, and the 100 CAD models are shown in Figure 3.  
* The upper category should have a matching category in the Microsoft COCO dataset [25] because the object detection network is trained on this dataset.
* The appearance of the CAD models in a subcategory should be different enough to facilitate similarity measurement.
* The appearance and shape of the CAD models across all categories should be general so that they appear in a sufficient amount of RGB image data.  
Figure 2: Representation of 3-DoF rotation from the reference to the camera coordinate systems.
# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
###### Abstract  
Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly to e-health data encounters hurdles, including limitations in data size, redundancy, and capacity, particularly in open-channel patient data transmissions. As a result, the unique characteristics of images, marked by their risk of data loss and the need for confidentiality, make preserving the privacy of data contents a complex task. This underscores the pressing need for innovative approaches to ensure the security and confidentiality of sensitive healthcare information within medical images. The proposed algorithm outperforms referenced algorithms in both image fidelity and steganographic capacity across diverse medical imaging modalities. It consistently achieves higher Peak Signal-to-Noise Ratio (PSNR) values, indicating superior image fidelity, reduced noise, and preserved signal quality in CT, MRI, ultrasound, and X-ray modalities. The experimental results demonstrate a considerable improvement in both the Peak Signal-to-Noise Ratio (PSNR) and maximum embedding capacity. Specifically, the average PSNR value for the X-ray modality reached a notable 73 dB, signifying superior image quality. Moreover, the CT modality exhibited the highest maximum embedding capacity, measured at 0.52, showcasing its efficiency in accommodating data within the images. Moreover, the algorithm consistently offers increased steganographic data hiding capacity in these images without perceptibly degrading their quality or integrity.  
medical image security; reversibility; encrypted domain; watermarking; embedding capacity +
Footnote †: journal: Medical Imaging Security, 40(1), 2020
## 2 Related Works
### Attention Mechanisms  
Attention mechanisms aim to suppress semantic noise and highlight effective information by building various long-range dependencies. Numerous attention-based methods have contributed to the success of deep learning in the field of computer vision. These methods can be broadly categorized into two categories according to the scale structure they use as the basis of the attention mechanism: single-scale attention and multi-scale attention.  
**Single-scale attention**: Here, a single original scale feature map is used to generate the attention map. In an early version of this, SENet [3] adaptively recalibrated channel-wise feature responses by explicitly modeling single scale feature interdependencies between channels. Working more broadly from a spatial dimension, BAM [4] and CBAM [5] similarly used both channel-wise interdependencies and spatial locations for the purposes of reweighting. This can yield a better performance while using the same parameters. Other approaches, such as SRM [6], have sought to model style information dependencies and incorporate them into the attention block. However, single-scale attention methods  
Figure 2: Structural diagram of the proposed Calibrated Convolution with Gaussian of Difference.  
are generally limited by their inability to make full use of self-similarity information and structural information at different levels, leading to sub-optimal results.  
**Multi-scale attention**: Here, a multi-scale feature set is used to generate the attention map. Thus, SKNet [9] has a built-in dynamic kernel selection mechanism that is guided by a combination of features from different multi-scale convolutions (\(3\times 3\) and \(5\times 5\)). This enhances network performance by having a smaller number of additional parameters and a lower computational complexity. SPA [10] introduces structural regularization in the form of spatial pyramid blocks that can also incorporate structural information. This is then used to build a more comprehensive multi-scale attention map. The recently developed SCNet [11] adaptively builds multi-scale spatial and inter-channel dependencies around each spatial location by means of a novel self-calibration operation. HMSA [25] uses a multi-scale attention module that learns to predict a dense mask that can combine multi-scale predictions. This not only improves performance in comparison to average-pooling but also allows the network to diagnostically visualize the importance of different scales for classes and scenes.  
Most existing multi-scale attention methods have evolved from multi-scale inference for computer vision. As a result, they can effectively collect self-similarity information and structural information contained in features at different scales. However, most methods combine the multi-scale information by using simple addition operations, which introduces redundancy. Furthermore, the scale-invariant features contained in the multi-scale information are overlooked despite their importance to the feature calibration process.
# HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network
## 3 Numerical Experiments
### Assessing Image Quality  
Two metrics were used to assess image quality: the structural similarity index (SSIM) and the correlation coefficient (CC). The original \(O(x,y)\) and reconstructed \(R(x,y)\) object planes were compared.  
CC shows how strongly two variables are linearly related to each other. For images, this corresponds to how many pixels in the two images correlate with each other. However,  
Figure 4: Examples of objects used in training datasets. (**a**,**b**)—digits (objects 1), (**c**,**d**)—circles (objects 2).  
the correlation coefficient does not take into account other factors that can affect image quality, such as contrast and brightness. The CC is defined as follows [74]:  
\[\text{CC}=\frac{\sum\limits_{x=1}^{M}\sum\limits_{y=1}^{N}\left(O(x,y)-\mu_{O} \right)\cdot\left(R(x,y)-\mu_{R}\right)}{\sqrt{\left(\sum\limits_{x=1}^{M}\sum \limits_{y=1}^{N}\left(O(x,y)-\mu_{O}\right)^{2}\right)\left(\sum\limits_{x=1} ^{M}\sum\limits_{y=1}^{N}\left(R(x,y)-\mu_{R}\right)^{2}\right)}}, \tag{4}\]  
where \(\mu_{O}\) and \(\mu_{R}\) are the mean values for the images \(O(x,y)\) and \(R(x,y)\). If the CC value is 1, the images are identical. The closer the CC is to zero, the lower the similarity of the images.  
SSIM considers image structure and contrast as well as brightness. It compares not only pixel brightness values but also their location and structure. The structural similarity index can more accurately assess image quality, especially when there are distortions in the image structure, such as blurring. SSIM [75] was also used as a measure of image quality. In a simple form, it is defined as follows:  
\[\text{SSIM}=\frac{(2\mu_{O}\mu_{R}+c_{1})(2\sigma_{OR}+c_{2})}{(\mu_{O}{}^{2}+ \mu_{R}{}^{2}+c_{1})(\sigma_{O}{}^{2}+\sigma_{R}{}^{2}+c_{2})}, \tag{5}\]  
where \(\sigma_{O}\), \(\sigma_{R}\) and \(\sigma_{OR}\) are standard deviations and cross-covariance for the images \(O(x,y)\) and \(R(x,y)\), and \(c1\), \(c2\) are constants. The SSIM value is equal to 1 when the images are identical.  
The average values of the metrics were estimated. For a better understanding of how the neural network works, it is necessary to understand the possible variation in values, so the standard deviation (STD) was also determined.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 3 Case Study: Zhongma Landslide
### K-Means Clustering  
The GNSS-4 with more obvious deformation and less noisy data and the rainfall station was selected for the study. From 23 April 2019 to 7 December 2021, 13,670 monitoring data were collected, including 12,724 GNSS-4 data and 946 daily rainfall data.  
Notably, in real landslide displacement monitoring, missing values may be caused by various factors, so they may appear at any time. Before being used for training and classification, the displacement data are processed by methods such as one-time sample difference to obtain an isochronous order. We used the slide window with the size of 48 and step size of 2 to construct the landslide displacement subseries, so each subseries has a time span of 2 days and a data length of 48 items. Finally, 23,050 data items were generated.  
The velocity feature was extracted from each subseries using least squares fitting. The max-min normalization was applied to the two features, see Equation (10).  
\[X_{norm}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{10}\]  
K-means clustering was performed and the K values were determined by the elbow method. In Figure 6, the K value is 4.  
Figure 5: Deformation data and rainfall data of the Zhongma landslide.  
We select K = 4 for K-means clustering. The clustering centroids are shown in Table 1, and the clustering results are shown in Figure 7. We divide the warning into three levels. Level 1: The medium speed state. At this level, the deformation speeds up. The landslide has the possibility of occurrence. Level 2: The high speed state. In this state, the deformation velocity is high. The landslide is in a more dangerous state. Level 3: The super high speed state, in which the displacement velocity is large. In this state, the landslide is likely to fail.
# A Machine Learning Framework for Student Retention Policy Development: A Case Study
## 3 Methodology
### Proposed Framework
#### 3.2.2 Machine Learning Models Used
* The Decision Tree (DT) algorithm is a supervised learning algorithm used frequently for classification problems [42]. The decision tree method creates a model in the form of a tree structure consisting of a root, inner nodes that are decision or feature nodes, and leaf nodes that represent the target class. The root and inner nodes contain predicates, i.e., they are labeled with a question. Branches leaving these nodes indicate possible answers to the predicate. Thus, each decision node represents a prediction for the solution of the problem [43].
## 3 System Model
### Network Model  
The system model is considered three-dimensional with random location of the nodes due to its impact on many important parameters on underwater sensor environment. The surface buoys reside at the water surface and the anchor nodes are connected to these buoys using a rope or cable. In order to place the sensor nodes at intermediate depths, the sensor nodes can be attached to the surface buoy with length adjusted wire or rope. An instance of a possible scenario is shown in Figure 1, where the deployment of nodes is based on random locations including a gateway or sink node that is at the surface. A minimum distance between two nodes is assumed to avoid undesired overlaps.
## 2 Methodology
### Ensemble Model (Benefits)
#### 2.4.2 Less Overfitting  
Overfitting arises when a model acquires the ability to perfectly match the random fluctuations included in the training data, leading to inadequate performance when applied to new, unseen data. Ensemble methods can mitigate overfitting by combining the predictions of numerous models in a way that achieves a more accurate final prediction. Each individual model may exhibit its own method of overfitting the training data. However, when the predictions of the ensemble are combined, the ensemble demonstrates superior generalization performance. Ensembling, which combines multiple models together, effectively mitigates any overfitting tendencies exhibited by individual models [46].  
To address overfitting, dropout layers were incorporated into the SynthSecureNet architecture. This technique randomly drops units during training, helping to generalize the model to unseen data. However, other advanced methods for preventing overfitting, such as early stopping and advanced regularization techniques (e.g., L2 regularization, weight decay), were not utilized in this study. Additionally, ensemble diversity metrics, which measure the uniqueness and complementarity of the models combined in an ensemble, could further enhance the robustness of SynthSecureNet. These methods will be explored in future work to improve the model's performance and generalization.