esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion  
### Numbers of Studies  
The two deep learning approaches, classification and segmentation, were found in 10 studies on ultrasound muscle images. In 2018, there were two studies on segmentation and one study on ultrasound muscle classification. In 2019, there was only one study on segmentation. Moreover, in 2020, muscle images on ultrasound using deep learning increased to seven studies (five studies on segmentation and two studies on classification). As a result, in deep learning studies on ultrasound muscle images, studies on segmentation dominated with seven studies compared to studies on classification with three studies (Figure 3).
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### DL Methodology
#### 3.3.1 Model
In [72], a 1D CNN is exploited for local attention embedding, and the encoder of the transformer is used for further feature extraction. In [23], shallow-domain knowledge-injection attention is first to extract the ECG signal feature. Then the attention outputs from the original and smoothed ECG data are regarded as the multivariate input for the 2D classification CNN. More works considering combining CNNs with transformers can refer to [35; 73]. The CNNs are also combined with attention mechanisms in [74; 75; 76]. In the selected studies, 82 studies take advantage of hybrid models, which assemble different types of DL models to classify ECG arrhythmia. The top 3 hybrid models include CNN+LSTM (24 studies), CNN+BiLSTM (15 studies), and CNN+RNN (8 studies). In most hybrid models, the CNNs often serve as feature extractors, followed by other models which perform further feature extraction. As shown in Figure 4a, the proportions of different DL models used in the selected studies are presented. Overall, the CNN (58.7%, 216 out of 368), RNN/LSTM/BiLSTM (9%, 33 out of 368), and hybrid (22.3%, 82 out of 368) are the most popular DL models for arrhythmia classification. Each year, there are more selected works considering CNN models than those considering other models each year but the number of works considering the hybrid model has been increasing in recent years.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 2 Autonomous DDboats
### Tracking Control by Means of an Artificial Potential Field Method
The influence of uncertainty and external disturbances on the actual system dynamics is investigated in detail by the estimation schemes presented in Sections 3 and 4. In future work, the corresponding estimates cannot only be employed for an a posteriori analysis as shown in this paper, but also in a closed-loop control structure to enhance the accuracy of tracking the reference trajectories \(\mathbf{y}_{\mathrm{d}}^{(i)}\) in terms of a real-time capable model predictive control approach.
## 2 Related Work
### Low Power and Lossy Networks  
Over the last few years, researchers have explicitly studied the numerous security issues associated with LLNs. In the VeRA [23], a version number and rank authentication security scheme based on one-way hash chains are proposed to secure the IPv6 routing protocol (RPL) [24] in LLN, where the misbehaving nodes illegitimately increase the version number of directed acyclic graph information object (DIO) message and compromise illegal rank values. To protect against the attackers that send DIO messages with higher version number values or that publish a high rank value, the version numbers are bound with authentication data and signature. In [25], a rank attack that aims at the rank property in RPL and its performance impact are investigated in WSNs, where the adversary can compromise the rank rule to downgrade the RPL performance. Four adversarial scenarios motivated by violating rank rule permanently and non-permanently and their potential performance impact are analyzed. In the Dodge-Jam [26], a lightweight anti-jamming technique suitable for LLN environments is proposed to address the stealthy jamming attacks with small overhead. [PERSON] et al. [27] investigated the DODAG Information Object suppression attack, which can severely degrade the routing service in RPL. The CMD [28] proposes a monitor-based approach to mitigate the forwarding misbehaviors in LLNs, where each node monitors the forwarding behaviors of the preferred parent node to observe the packet loss rate, compares the observation result with the collected packet loss rate from one-hop neighbor nodes, and detects the forwarding misbehaviors of the preferred parent node. In [29], a dynamic threshold mechanism is proposed to mitigate the destination advertisement object (DAO) inconsistency attack in RPL-based LLNs, where a malicious node intentionally drops the received data packet and replies the forwarding error packet to cause the parent node to discard valid downward routes in the routing table. The [30] identified and investigated a new type of Denial-of-Service attack, called hatchetman attack, in LLNs, where a malicious node manipulates the source route header of the received packet, and then generates and sends the invalid packets with error route to legitimate nodes.
###### Abstract  
This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin's development trajectory, ultimately fostering more sustainable and resilient urban landscapes.  
analytic hierarchy process; remote sensing; geographic information systems; land use suitability analysis; urban growth simulation +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion
### Effects of Using Different Numbers of PHCMs  
For the purpose of evaluating the effect of using Pyramid Hierarchical Convolution Modules (PHCMs), we conducted an experiment.  
As shown in Table 6, we could see that using PHCMs in our proposed PCHNet would affect the final segmentation accuracy. Compared with the use of traditional convolution layers, our proposed PHCMs could increase the segmentation performance. As PHCMs contains three convolution layers with different kernel sizes, the features extracted by PHCMs could adapt varying shapes and sizes of cracks. However, the traditional convolution layers only using one kernel size could not adapt varying shapes and sizes of cracks, meaning that some crack features of different scales were missed.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Modules** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline Traditional Convolution & 0.921 & 0.812 & 0.979 & 0.793 \\ Layers & 0.929 & 0.823 & 0.989 & 0.801 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effects of using PHCMs or traditional convolution layer.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Numbers of MGAMs** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline
2 & 0.903 & 0.791 & 0.974 & 0.735 \\
3 & 0.912 & 0.815 & 0.982 & 0.786 \\
4 & 0.929 & 0.823 & 0.989 & 0.801 \\
5 & 0.925 & 0.812 & 0.981 & 0.788 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effects of using different numbers of MGAMs.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Numbers of SAMs** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline
2 & 0.901 & 0.786 & 0.972 & 0.773 \\
3 & 0.929 & 0.823 & 0.989 & 0.801 \\
4 & 0.919 & 0.817 & 0.981 & 0.789 \\
5 & 0.906 & 0.805 & 0.972 & 0.776 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of using different numbers of SAMs.
# FREDY: Federated Resilience Enhanced with Differential Privacy
## 3 Proposed Framework
### Student Model Training  
At the final round of the FL training process, the teacher models abstain from uploading their weights to the server. Instead, these models are leveraged for making predictions on the student's publicly available data. Subsequently, the server employs unlabeled public data to engage the teachers in a querying process, followed by the execution of a noisy aggregation method to consolidate the predictions offered by the teachers. The privacy guarantees come from the aggregation mechanism that the server performs on the predictions of the teacher models. Given an unlabeled public sample of data, the server queries the trained teachers on this data point and then it counts the votes (i.e., predictions of the teachers) for each class. Finally, the server injects Laplacian noise to these vote counts and the final aggregated label of the data point for the student's training is the class with the most counts.  
Particularly, suppose we have a \(C\) number of classes in our task. For a given class \(c\in C\) and an input \(x\), the label count is the number of teachers that assign class \(c\) to input \(x\): \(Votes_{c}(x):|\{i:i\in N,T_{i}(x)=c\}|\), where \(T_{i}\) is the \(i\)th teacher and \(N\) illustrates the indices of the teacher model set. The final aggregation of teacher model predictions is as follows:  
\[NoisyAgg(x)=\arg\max_{c}\big{\{}Votes_{c}(x)+Lap\big{(}\frac{1}{\epsilon} \big{)}\big{\}}, \tag{4}\]  
where \(Votes_{c}(x)\) is the label count, \(\epsilon\) is a privacy parameter and \(Lap(\beta)\) is the Laplacian distribution with location zero and scale \(\beta\). The value of the parameter \(\epsilon\) dictates the level of privacy assurance we can establish. A larger \(\epsilon\) entails a robust privacy guarantee but comes with the trade-off of potential label accuracy degradation.  
Finally, the server employs these predictions to assign labels to the student's training dataset and then proceeds to train the student model through supervised learning. This approach facilitates the transfer of knowledge from the teachers to the student. Throughout this entire process, only the student model is publicly accessible, with the teachers remaining beyond a user's reach. The resultant student model trained on public data exhibits heightened resilience against membership inference and model inversion attacks. This robustness stems from the fact that the final training occurs with public data, thus averting direct exposure of the teachers' private information. Nonetheless, the student model acquires insights from the private data of each teacher, given that its training involves labels derived from the predictions made by the teacher models on the unlabeled data.
# Error Averaging Effect in Parallel Mechanism Coordinate Measuring Machine
## 4 Property of Slider Motion Errors in PCMM
### Straightness Motion Error
Thus, only one series of data on a slider was selected to establish the error model for the subsequent error calculation. A quadratic function was used to establish the model for straightness motion error in the \(Z\) direction. The straightness motion error in the \(Y\) direction is relatively complex, where the error model needs to be constructed with the third-order spline curve function. As shown in Table 3, 3 groups (\(x=750\), \(1000\), \(1250\)) in the measurement space were selected initially, and 9 points were selected as feature points in each group. Thus, a total of 27 points were  
Figure 8: Straightness motion error experiment. Figure 9: Straightness motion error of slider 1.  
distributed in the measurement space. Corresponding to each point is a slider position coordinate that can be determined through the inverse kinematics of PCMM. Putting the measured straightness errors of the three sliders into Equation (7) and solving the actual probe position in the measurement space by Equation (3), the contributions of straightness motion errors to the probe's positional error are listed in Table 3.  
As shown in Table 3, the straightness motion errors of the sliders contribute the smallest measuring error in the \(X\) direction and produce the largest error in the \(Z\) direction. This means that, if the purpose is to promote the \(Z\) direction measuring accuracy, the slider straightness motion error needs to be improved significantly. The error curves in three directions are shown in Figure 10. The total variation in three directions ranges from \(-0.03\) to \(-0.02\) mm. Compared with the original slider motion straightness error data, the final influence of the slider motion straightness error on the measurement accuracy is relatively decreased [9; 22]. This also demonstrates the error averaging effect for the PCMM.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 7 Conclusions  
In this paper, the joint problem of MEC server selection by the end-users, along with their optimal data offloading and the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology, enables the realistic implementation of the proposed framework. In particular, the MEC server selection part of the framework is based on a reinforcement learning technique adopting the theory of the stochastic learning automata. The end-users optimal data offloading and the MEC servers' optimal pricing of their computing services is formulated as a two-layer optimization problem. At the first layer, a non-cooperative game among the end-users of each server is formulated towards maximizing the perceived satisfaction of each end-user, as expressed by an appropriately formulated utility function. The existence and uniqueness of the game's NE point is shown, thus concluding  
Figure 8: Average end-users’ utility vs. time slots for different offloading mechanisms.  
Figure 7: Average MEC servers’ profit vs. time slots for different offloading mechanisms.  
to the end-users' optimal data offloading strategy. At the second layer of the proposed framework, an optimization problem of each MEC server's profit is formulated and the corresponding optimal price of its computing services is determined. A low-complexity Data Offloading and MEC Server Selection (DO-MECS) algorithm is introduced to realize the overall framework. The operation and performance of the proposed framework was extensively evaluated through modeling and simulation, while the presented detailed numerical results demonstrate its performance and benefits in the examined setting.  
Our current and future work contains the testing of the proposed framework in a realistic testbed environment, while the proposed framework will be extended to include additional socio-physical parameters in the MEC server's reputation score, e.g., trust level of the MEC server, security and privacy preserving characteristics, and others.  
All authors contributed extensively to the work presented in this paper. [PERSON] contributed to the design of the algorithm, developed the code of the overall framework, executed the evaluation experiment and contributed to the discussions and analysis of the comparative evaluation results. [PERSON] had the original idea on which we have based our current work and contributed to the design of the proposed framework. [PERSON] and [PERSON] were responsible for the overall orchestration of the performance evaluation work and had the overall coordination in the writing of the article.  
The research of [PERSON] was conducted as part of the UNM Research Allocation Committee award and the UNM Women in STEM Faculty Development Fund.  
The authors declare no conflict of interest.
## 4 Simulation Results and Analysis  
In this section, we present the results of our simulation, focusing on the relative error and identification of critical areas in a sample system with real sampling times. Our initial testing revealed that the timing error was extremely low, so this error source was discarded from the analysis. To validate and evaluate the performance of the system, we performed a simulation comparing the localization accuracy across various scenarios. We introduced normalized errors in the sample times, speed of sound, and measurement accuracy based on data collected during our testing. This approach allowed us to more accurately model real-world conditions and assess the system's robustness in the face of typical variations and uncertainties.
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 3 Factors Affecting the Yaw Motion
### Controllable Parameters: Design, Manufacturing Conditions  
#### 3.2.1 Parallelism between LM Guides  
Ideally, a pair of LM Guide rails should be perfectly parallel. In reality, this cannot be the case due to an assembly error found in LM Guides and the machining error on the installation surface itself.  
As shown in Figure 15, LM rail number one is an ideal straight line, and rail number two represents the straightness error in the \(y\)-direction by \(\delta_{st}\) from the block number four position, which indicates the application of an external force of \(\delta_{st}\times k_{4}\) to the number four location in the \(y\)-direction. The yaw angle was calculated by reflecting the additional external force in the yaw motion in Equation (10).  
The driving force was applied to the four types of platforms at an \(FO\) of 80% with the trapezoidal motion profile mentioned earlier. The convergence values of the yaw angle according to the parallelism error (\(PA\)) are shown in Figure 16. The yaw angle at zero parallelism is equivalent to the value of an \(FO\) of 80% in Figure 11. With parallelism, the yaw angle is increased by several times or more regardless of \(l/w\). Parallelism is the most dominant factor affecting the yaw motion, regardless of \(l/w\) ratio or \(FO\).  
Figure 15: Parallelism error between LM Guides.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Damping** & & & & \\
**Coefficient** & **0.01** & **0.1** & **10** & **100** \\
**(Nsec/mm)** & & & & \\ \hline Settling time & & & & \\ (ms) & 32.02 & 32.02 & 31.92 & 31.87 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Settling time according to damping coefficient.
# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## 3 Methodology
### Classification of COVID-19, Normal and Pneumonia Using Deep Convolutional Neural Networks
#### 3.3.2 Approach 2: Deep Learning with Image Augmentation on an Unbalanced Dataset  
Another simple and effective approach to handling the unbalanced class distribution is image augmentation. It is used to increase the number of images of the minority classes for training a model. Deep CNN models need a sufficient number of images with an evenly balanced dataset to make reliable predictions. Data augmentation can prevent the overfitting of CNNs as well. Figure 9 shows the flowchart for deep learning with image augmentation on an unbalanced dataset. The number of pneumonia and COVID-19 CXRs are relatively small compared to normal CXRs; therefore, we proportionally applied simple image augmentation methods such as rotation, flipping, shearing, and shifting to increase the number of images in the minority class. The augmented image examples are given in Figure 10. Only the images in the training dataset are augmented. Using the augmented training dataset, 11 CNNs are fine-tuned using categorical cross entropy loss as described in Section 3.3.
## 3 Results and Discussion
### Bactericidal Efficacy of Tb-PAW on Different Microorganisms
In contrast to the previous studies in the present experimental setup the bacteria were exposed to Tb-PAW and BSA at the same time which might explain the different effects between the present and other studies. However, the present setup seems more logical. For example, if a PAL is applied to the skin of living animals, the PAL is likely to come into contact with the microorganisms as well as with proteins and other (probably disturbing) substances simultaneously. Other studies indirectly support the present results. If liquids containing bacteria were treated with plasma to obtain a PAL, the proteins and other components in the media had no impact on the bacterial inactivation capacity [42; 43]. For example, [PERSON] et al. [42] inactivated microbial pathogens, such as _E. coli_ and _Campylobacter jejuni_ in chilled poultry wash water, achieving an increased reduction compared to samples in distilled water. [PERSON] et al. [43] showed in their study that after treating different types of milk (whole, semi skimmed and skimmed milk) with low pressure plasma, the inactivation of _E. coli_ decreased by 54% after 3 min. In conclusion, the protective effect of proteins against microbial inactivation by PAL seems to depend on several parameters, including the protein source, the amount of protein, the experimental setup (treatment time and treatment procedure), as well as the plasma source and its settings, as evidenced by these various study results.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Test Bed  
The test bed for this research was implemented on a system running Ubuntu 24.0.7 on a 2.3 GHz Intel Core i5 processor with 4 GB DDR3 RAM. The software stack included Docker version 24.0.7, Docker Compose 1.29.2, Java 11.0.4 (utilized for application and code chain), HLF version 2.5.9, and Fabric CA version 1.5.12. The default HLF architecture consisted of a consortium network with two organizations, each containing a single peer node and a single orderer shared by both organizations. Additional orderers or peers were introduced only in scenarios where the attack logic or mitigation strategies required a larger fault domain.  
The selected test bed configuration was designed to balance the experimental control with realism in enterprise deployments of permissioned blockchains. The choice of a two-organization consortium with one peer and a shared orderer is a common minimal configuration used in HLF deployments, e.g., in finance, where limited consortia are standard. This setup also made it easier to observe the effects of various attacks on specific nodes and the network without excessive network complexity. Furthermore, the selected versions of software components represented stable and widely used releases at the time of the study, which ensured the relevance and reproducibility of the results.