esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Analysis of Different Tunneling Parameters
### Effect of Penetration on Rock Breaking  
Penetration is a critical parameter that significantly influences rock-breaking efficiency. Figure 15 illustrates the effect of penetration on crack formation. As penetration increased, the depth of cutter entry into the rock mass grew, generating more cracks and a larger fragmentation volume. Increased penetration also reduced the spacing between ridges in the cutting paths, allowing cracks to interconnect more effectively, further enhancing fragmentation.  
The transition from uncoordinated to coordinated rock-breaking occurred as penetration increased. This coordination improved energy usage efficiency, as crack propagation accelerated and the volume of fragmented rock increased. Consequently, specific energy decreased, while rolling force steadily increased due to greater friction and resistance between the cutter and rock.  
Figures 16 and 17 show the effects of penetration on rolling force and specific energy, respectively. Rolling force increased with penetration as deeper cuts required greater energy to overcome friction and resistance. Specific energy decreased with penetration because the rate of increase in the rock fragmentation volume exceeded that of rolling force.  
When penetration ranged between 2 and 10 mm, the rock-breaking efficiency improved consistently. This was attributed to enhanced crack propagation, increased cutter synergy, and the generation of a greater volume of rock fragments. Thus, appropriately increasing penetration significantly enhances rock-breaking efficiency.  
Figure 15: Effect of penetration on rock cracks.  
[PERSON] et al. [11] developed a finite element model using LS-DYNA to study rock breaking by disc cutters, focusing on the rolling process and fracture patterns. Our study builds on their work by using the discrete element method to conduct a more detailed investigation into the influence of penetration depth on rock-breaking efficiency. [PERSON] et al. [14] used MatDEM to investigate the rock-breaking mechanisms of disc cutters in soil-rock composite formations. We complement their findings by analyzing the effects of varying penetration depths on rock-breaking efficiency through simulations, offering more specific guidance for engineering applications. By systematically analyzing the impact of penetration depth on rock-breaking efficiency, our research provides deeper insights and tailored recommendations for engineering practices.  
Figure 16: Effect of penetration on rolling force.  
Figure 17: Effect of penetration on rock-breaking specific energy.
## 4 Two-Time Scale Control  
### Fast Subsystem and the Corresponding Controller  
In order to actively suppress the flexible vibration of the joint caused by the RSEA device, based on singular perturbation theory, the post-capture hybrid system was transformed into two subsystems, a slow rigid motion subsystem, and a fast flexible-joint subsystem. This controller consists of a slow sub-controller and a fast flexible-joint sub-controller:  
\[\mathbf{\tau}_{m}=\mathbf{\tau}_{\rm f}+\mathbf{\tau}_{\rm s} \tag{21}\]  
where \(\mathbf{\tau}_{\rm f}\in\mathbf{R}^{2\times 1}\) is the fast flexible-joint sub-controller, \(\mathbf{\tau}_{\rm s}\in\mathbf{R}^{2\times 1}\) is the slow sub-controller.  
Defining the positive proportional factor \(\varepsilon\) and the positive definite diagonal matrix \(\mathbf{K}_{1}\), it satisfies:  
\[\mathbf{K}=\frac{\mathbf{K}_{1}}{\varepsilon^{2}} \tag{22}\]  
Invoking Equation (22), the flexible-joint fast subsystem is:  
\[\varepsilon^{2}\tilde{\mathbf{\tau}}_{\theta}=\mathbf{I}_{m}^{-1}\mathbf{K}_{1}(\mathbf{\tau} _{m}-\dot{\mathbf{\theta}}-\dot{\mathbf{\tau}}_{\theta}) \tag{23}\]  
In order to suppress the elastic vibration of the system, the following speed difference feedback controller is designed to control the fast subsystem:  
\[\mathbf{\tau}_{\rm f}=-\mathbf{K}_{\rm f}(\dot{\mathbf{\theta}}_{m}-\dot{\mathbf{\theta}}) \tag{24}\]  
where \(\mathbf{K}_{\rm f}=\mathbf{K}_{2}/\varepsilon\), \(\mathbf{K}_{2}\in\mathbf{R}^{2\times 2}\) is a positive definite diagonal matrix.  
Substituting Equations (21), (24) into Equation (23), we have:  
\[\varepsilon^{2}\mathbf{I}_{m}\tilde{\mathbf{\tau}}_{\theta}=\mathbf{K}_{1}(\mathbf{\tau}_{ \rm s}-\mathbf{I}_{m}\ddot{\mathbf{\theta}}-\mathbf{\tau}_{\theta})-\varepsilon\mathbf{K}_{2 }\dot{\mathbf{\tau}}_{\theta} \tag{25}\]  
It can be shown that while \(\varepsilon\to 0\), the equivalent stiffness of joints \(\mathbf{K}\rightarrow\infty\). At this point, the hybrid system is equivalent to a rigid model. Then the dynamic equation of the slow subsystem can be obtained from first item of Equations (20) and (21)  
\[\mathbf{D}_{x\theta}\dot{\mathbf{\theta}}_{\theta}+\mathbf{C}_{x\theta}\dot{\mathbf{\theta}}_ {\theta}=\mathbf{\tau}_{x\theta} \tag{26}\]  
where \(\mathbf{D}_{x\theta}=\mathbf{D}_{x}+\mathbf{I}_{x}\), \(\mathbf{I}_{x}=\text{diag}(0,I_{1m},I_{2m})\). \(\mathbf{C}_{x\theta}\) is the corresponding matrix of \(\mathbf{C}_{x}\) when \(\dot{\mathbf{\theta}}=\dot{\mathbf{\theta}}_{m}\), \(\mathbf{\tau}_{x\theta}=\left[\tau_{0},\mathbf{\tau}_{\rm s}^{\rm T}\right]^{\rm T}\).
## 1 Introduction
This could be due to overfitting, which is a common issue in medical image classification with a limited numberof discriminatory image features [7; 13; 42; 43; 44]. Prominent CNN models typically encompass over ten layers, boasting 60+ million trained parameters, and are trained on expansive datasets like ImageJ, which houses 1000 categories. In contrast, medical images have limited features, and their differences are often imperceptible to the human eye. This could be why the features/filters/convolutional layers trained on ImageJ may differ from those of medical images, resulting in lower testing performance [45]. In transfer learning, the adapted filters may retain irrelevant features, contaminating the classification process. In summary, researchers have achieved accuracy levels exceeding 95% for COVID-19 detection through the selection and refinement of appropriate CNN models. Yet, systems based on these CNN models were often developed using data specific to certain clinics and acquired using their unique imaging modalities. Most proposed models or systems have not been tested on other datasets. It is also noted that even though the COVID-19 datasets have become more available, most have not been validated and can be subject to mislabeling, noise, incompleteness, corruption, or low quality [46; 47; 48; 49]. One relevant question is how these models or systems that have been trained on one training database will perform on other datasets that inevitably have certain disparities. This question is even more pronounced when the training dataset contains a limited number of images. In this scenario, will the high accuracy of trained models be maintained when tested on larger data or images from other clinics? If not, how much lower accuracy will be expected and/or tolerated? Will adding new images to the original training dataset always improve the model's performance, and what level of improvement can be expected via re-training the model with extended data? To achieve a desirable classification accuracy, how many new images should be added to the original training dataset? The objective of this study was to evaluate the performance of CNN models in diagnosing COVID-19 and pneumonia in the lungs based on X-ray images from different data sources, as well as to assess the performance variation from multi-level training. Specific aims include the following:  
* Comparing the three-class classification performances of four CNN models: AlexNet, ResNet-50, MobileNet, and VGG-19. * For a given model, evaluating the model's performance on X-ray images from different sources (i.e., inside and outside the training space). * Quantifying the benefits of multi-round training with extended data on the model's performance. * Evaluating the model's interpretability via heatmaps and intermediate activation features.
## 1 Introduction
[18] used floor-segmentation and road-segmentation to identify safe indoor walking paths for visually impaired people. Paper [19] developed a navigation assistant for the visually impaired to effectively avoid obstacles when walking outdoors using the CNN-based object detection, tracking, and recognition methods. However, it does not guide the visually impaired user to walk on either the sidewalk or the crosswalks. In the paper [20], a visual localizer, composed of a ConvNet descriptor and a global optimizer, was used to achieve robust visual localization to assist visually impaired people's navigation. To guide the visually impaired through the intersection, [21] introduced a wearable system with a CNN-based object detection model to recognize crosswalks. In addition, it uses iAPS (intelligent, accessible pedestrian signal) mechanisms to receive the traffic status. So far, developing an improved assistive system to help the visually impaired walk safely is still an active topic for current society. This article presents a wearable assistive system that can safely help visually impaired people walk on sidewalks and crosswalks. The organization of this article is as follows. Section 2 introduces the materials and methods, including the main objective, system configuration, and the main functions required by the visually impaired in the proposed assistive system. This section has two neural network-based models to be trained and used in the walking guide strategy for the visually impaired. Section 3 presents the practical experiment by a visually impaired user. Section 4, there are some discussions about the experiments. At last, the conclusions and future works are drawn in the final section.
# Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO
## 2 Representative Methods in Antenna Selection
### Greedy Selection Algorithm  
The greedy algorithm is a heuristic algorithm commonly employed for solving combinatorial optimization problems [27]. In antenna selection problems, the fundamental principle of the greedy algorithm involves iteratively selecting the currently optimal antenna to maximize specific system performance metrics. The typical implementation steps of the greedy algorithm are as follows:  
1. Initialization: Set the selected antenna subset \(S=\emptyset\).
2. Iterative selection: At each step, iterate over all remaining unselected antennas \(k\in\{1,2,\ldots,N_{r}\}\backslash S\), evaluate the performance gain after incorporating antenna \(k\); select the antenna \(k^{*}\) that maximizes the contribution to a specific system performance metric (e.g., channel capacity), then update \(S=S\cup\{k^{*}\}\). Repeat Step 2.
3. Termination: When \(\left\lvert S\right\rvert=M_{r}\), cease selection and output the current antenna subset \(S\) as \(S_{opt}\).  
Compared with the exhaustive search algorithm, the greedy algorithm significantly reduces computational complexity and demonstrates strong applicability in large-scale MIMO systems, enabling rapid generation of approximate optimal solutions under scenarios with limited computational resources or time-constrained conditions. However, its stepwise decision-making mechanism that iteratively selects the locally optimal choice may lead to sub-optimal global solutions. Furthermore, the algorithm's optimality guarantee relies on idealized channel condition assumptions. When confronted with practical channel impairments such as spatial correlation, hardware imperfections, and dynamic interference, the greedy approach may fail to achieve optimal performance due to its myopic optimization strategy and sensitivity to channel model inaccuracies.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 3 TestBed Materials and Machine Learning Model
### Light Detection and Ranging (LIDAR) Sensor Description  
LIDAR is used to locate the remote worker by collecting the distance and angle data of the remote or isolated worker. In this work, RP-LIDAR A1 by RoboPeak was used. This is a low cost 360 degree 2D laser solution capable of 360 degree scan within 6 to 100 m range depending on the settings. It contains a range scanner system and motor system, as shown in Figure 5. It is connected to the host computer via a Universal Asynchronous Receiver-Transmitter(UART)/USB communication interface. The data output is made of four types: distance (mm), current heading angle of the measurement (degree), quality of the measurement (level), and start flag (Boolean). The meaningful data relevant to the testbed is the distance and current heading angle of the measurement to locate the isolated factory worker. Details of the sensor is found in Reference [51]. To visualize data, there are several options, such as RoboStudio, development SDK available for Windows, x86 Linux, and Arm Linux. However, since we have used windows for other sensors, we maintained same for RP-Light Detection and Ranging (LIDAR) by using RoboStudio application in windows. Figure 6 is a sample of how the data is displayed using RoboStudio application.  
Figure 4: Visualizing the UWB data output showing the movement and breathing data. The visualization is made possible using the RealTerm Serial capture program 2.0.0.070 version installed in the host computer. The unit of the breathing data is breaths per minute (bpm).  
Figure 5: RP-LIDAR System Connection showing the specification of the motor, scanner, and interface.
# Scalability Analyses of Premium Connections Characterized by a Surface Fractal Function
###### Abstract  
With the development of high-pressure, high-temperature wells, the scalability of premium connections is being threatened by heavy loads. For the sake of researching the scalability of premium connections at the microlevel on the sealing surface, a rough profile was characterized according to a fractal function, and the influence of the fractal parameters of contact behavior on the rough surface was analyzed. A full-size premium connection model with a fractal surface FEM was established using ABAQUS software to analyze the Von Mises stress and contact pressure, and the scalability was then analyzed using contact strength theory. It was found that utilizing fractal functions to describe the rough morphology on the sealing surface of premium connections could reveal the contact behavior of the sealing surface more realistically. The premium connection's fractal FEM was closer to the actual situation, considering the effect of the asperities' elastic-plastic deformation on the sealability. The fractal dimension \(D\) had a greater influence on the contact area and contact pressure than the scale coefficient \(G\). When the fractal dimension \(D\) was less than 2.5, the maximum Von Mises stress and maximum contact pressure on the sealing surface were 8.81 \(\times\) 10\({}^{8}\) Pa and 1.20 \(\times\) 10\({}^{9}\) Pa, respectively, making the surface prone to gluing. The sealability of the premium connection was primarily affected by the axial tension. When the axial tension was 1.2 \(\times\) 10\({}^{6}\) N, the sealing surface experienced significant displacement along the axial direction, the effective contact length reduced from 2.72 \(\times\) 10\({}^{-3}\) m to 2.24 \(\times\) 10\({}^{-3}\) m, and the maximum contact pressure reduced from 8.10 \(\times\) 10\({}^{8}\) Pa to 6.39 \(\times\) 10\({}^{8}\) Pa, which led to a 30% decrease in sealing strength, which may pose a high risk of sealing failure. When the internal pressure reached 1 \(\times\) 10\({}^{8}\) Pa, the effective contact length increased from 9.08 \(\times\) 10\({}^{-3}\) m to 1.06 \(\times\) 10\({}^{-2}\) m, the maximum contact pressure increased from 8.67 \(\times\) 10\({}^{8}\) Pa to 1.37 \(\times\) 10\({}^{9}\) Pa, and the sealing strength increased by 23%.  
contact analysis; finite element; premium connection; sealability; surface fractal Article
## 3 Results
### Prediction of VMD Components
#### 3.2.1 Rolling Prediction of High-Frequency Components Based on DBN Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Hidden Layer RBM1**} \\ \hline
**Number of hidden nodes** & 4 & 8 & 12 & 16 & 20 & 24 & 28 & 32 \\
**Output error/\%** & 2.7427 & 2.1418 & 1.6717 & 1.2419 & 1.0242 & 1.3237 & 1.5517 & 1.8152 \\
**Training time/s** & 13.27 & 13.36 & 13.21 & 13.53 & 13.47 & 14.18 & 19.97 & 24.41 \\ \hline  & \multicolumn{4}{c}{**Hidden Layer RBM2**} \\ \hline
**Number of hidden nodes** & 4 & 8 & 12 & 16 & 20 & 24 & 28 & 32 \\
**Output error/\%** & 1.5024 & 1.3024 & 1.2124 & 1.9122 & 2.0038 & 2.1522 & 2.0155 & 2.0014 \\
**Training time/s** & 15.18 & 15.94 & 16.47 & 17.08 & 21.78 & 28.15 & 30.32 & 37.77 \\ \hline  & \multicolumn{4}{c}{**Hidden Layer RBM3**} \\ \hline
**Number of hidden nodes** & 4 & 8 & 12 & 16 & 20 & 24 & 28 & 32 \\
**Output error/\%** & 4.5174 & 4.2415 & 3.7791 & 2.8172 & 3.4014 & 3.1179 & 3.1563 & 4.7791 \\
**Training time/s** & 22.47 & 25.78 & 30.11 & 33.91 & 55.78 & 80.14 & 117.35 & 135.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training times and output errors of IMF1 hidden layer nodes. IMF1: Intrinsic Mode Function 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**VMD Decomposition High-Frequency** & **IMF1** & **IMF2** & **IMF3** \\ \hline
**DBN structure** & 8-20-12-1 & 8-16-12-4-1 & 8-12-8-1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variational Mode Decomposition (VMD) decomposition high-frequency components’ Deep Belief Network (DBN) structure. Through the analysis above, the IMF1-IMF3 components are predicted using the trained model, as shown in Figure 7.  
Figure 7 shows that the high-frequency components IMF1 and IMF2 have strong volatility, their prediction errors are larger than other components, the IMF3 components fluctuate less than the previous ones, and the error is reduced. Overall, the high self-learning and adaptive capabilities of the DBN model are suitable for predicting high-frequency components with strong volatility and short periods.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 2 Internet of Healthcare Things (IoHT)
### IoHT Components
#### 2.2.5 Feedback Layer  
The physicians and the emergency response teams (ERT) provide feedback in any emergency and play a significant role in the healthcare system. At this layer, doctors and ERT respond to the system when they receive an alert from it.  
Physicians are an integral part of the healthcare system and can observe their patients anytime, anywhere, and change treatment when required. An IoT device for a physician gives real-time information about the patient under observation. The IoT gateway device gathers information from different healthcare devices and forwards it to PDA devices held by physicians.  
Emergency Response Team (ERT): In case of any medical emergency, the responsibility of the ERT is to provide medical attention to patients on-premises or remotely. In the context of IoHT, medical devices monitor patients' conditions and generate alerts in case of a medical emergency. Emergency care is a critical part of medical services and is influenced by the contextual information's time, availability, and accuracy [58].
# Integrating Classical Preprocessing into an Optical Encryption Scheme
## 1 Introduction
### Our Contribution  
A protocol is proposed which takes advantage of the physical security guarantee offered from the AlphaEta setup and builds on this using classical constructions. In Section 2, we review the AlphaEta protocol and some definitions and results about _all-or-nothing transforms_ (AONTs), a tool that has already proved to be useful in [PERSON] et al.'s work [19]. To be able to work conveniently with individual bits when discussing security, we introduce the notion of a _restricted_ AONT and present a way of constructing these type of transformations. We propose a security model for encryption schemes using \"hybrid ciphertexts\", invoking both an optical channel (like AlphaEta does) and a classical communication channel. We present an (efficient) construction, building on AlphaEta, offering security in our model.  
The security guarantee we establish is information-theoretic: we leverage the optical channel so that (physical) guarantees should prevent the adversary from learning any bit of the payload. Standalone, this may not be satisfying for applications yet, but we can integrate classical (high-speed) authenticated encryption. Then, with a heuristic argument, we create a situation where an adversary is unlikely to even intercept the correct _ciphertext_, offering a conceptually interesting additional layer of security: in traditional attack models, knowledge of the ciphertext is commonly considered as granted. It seems fair to say that we offer the first formalized proposal dealing with such hybrid ciphertexts, provably establishing the aforementioned advantage. It adds the physical security guarantee, but is easier/more flexible to implement since not the whole ciphertext needs to be transmitted through the optical channel.
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
## 3 Results and Discussion  
### Comparative Analysis of Main Parameters  
The proposed antenna's performance was first examined through simulations and analyses conducted with Ansys HFSS 2021, utilizing both a seven-layer homogeneous human head model and an actual human head model. To corroborate the simulation findings, the fabricated antenna underwent testing in minced pork. Figure 3(a)-c present the \(\left.\right|S_{11}\left.\right|\), \(\left.\right|S_{21}\left.\right|\), and AR parameter results derived from simulations with the seven-layer homogeneous human head model and the real human head model, as well as from the measurements conducted in minced pork. Figure 3(a) clearly shows that the antenna operates at a resonant frequency of 2.45 GHz, which is within the ISM band (2.4-2.48 GHz). Specifically, in the seven-layer homogeneous human head model, the real human head model, and minced pork, the antenna exhibited resonant frequencies of 2.45 GHz, 2.5 GHz, and 2.4 GHz, respectively. These correspond to FBWs of 28.16% (2.14-2.83 GHz), 26.12% (2.13-2.77 GHz), and 28.16% (1.92-2.61 GHz).  
Figure 4: _Cont._Figure 4b illustrates that, at 2.45 GHz, the \(\mid\)S\({}_{21}\mid\) value from simulations within the seven-layer homogeneous human head model was \(-\)27.5 dB. Conversely, the measured \(\mid\)S\({}_{21}\mid\) values were consistently below \(-\)26.5 dB across the operational frequency range. The minor discrepancy between the simulated and measured results may be due to factors such as the effect of soldering coaxial cables, which can influence the antenna's surface wave propagation and the reflected field. This could cause surface waves to propagate from one port to the other, thereby affecting the antenna's performance. It should be noted that, since the two antenna radiating units have similar geometries--\(\mid\)S\({}_{11}\mid\) = \(\mid\)S\({}_{22}\mid\) and \(\mid\)S\({}_{21}\mid\) = \(\mid\)S\({}_{12}\mid\)--only the results for \(\mid\)S\({}_{11}\mid\) and \(\mid\)S\({}_{21}\mid\) are presented in the figure, while \(\mid\)S\({}_{22}\mid\) and \(\mid\)S\({}_{12}\mid\) are ignored.  
The simulated and measured ARs are illustrated in Figure 4c, where the 3 dB ARBW values obtained from simulation in the seven-layer human head model and the real human head model are close to each other, at 16.3% (2.23-2.63 GHz) and 15.9% (2.25-2.64 GHz), respectively. Additionally, the measured 3 dB ARBW in minced pork reaches 12.2% (2.34-2.64 GHz). It can be noted from the figure that the ARBW of the proposed antenna completely covers the desired ISM band.