esa-sceva/satcom-chunk-collection huggingface dataset:
Computer-aided diagnostic (CADx) tools aim to supplement clinical decision-making. They combine elements of computer vision and artificial intelligence with radiological image processing for recognizing patterns [4]. Much of the published literature describes machine learning (ML) algorithms that use handcrafted feature descriptors [5] that are optimized for individual datasets and trained for specific variability in size, orientation, and position of the region of interest (ROI) [6]. In recent years, data-driven deep learning (DL) methods are shown to avoid the issues with handcrafted features through end-to-end feature extraction and classification.  
Convolutional neural networks (CNNs) belong to a class of DL models that are prominently used in computer vision [7]. These models have multiple processing layers to learn hierarchical feature representations from the input pixel data. The features in the early network layers are abstracted through the mechanisms of local receptive fields, weight sharing, and pooling to form rich feature representations toward learning and classifying the inputs to their respective classes. Due to lack of sufficiently extensive medical image data, CNNs trained on large-scale data collections such as ImageNet [8] are used to transfer the knowledge of learned representations in the form of generic image features to the current task. CNNs are also shown to deliver promising results in object detection and localization tasks [9].  
The astounding success of deep CNNs coupled with lack of explainable decision-making has resulted in a perception of doubt. This poorly understood model behavior has limited their use in routine clinical practice [10]. There aren't enough studies pertaining to the visualization and interpretation of CNNs in medical image analysis/understanding applications. In this article, we (i) detect and distinguish pneumonia types in pediatric CXRs, and (ii) explain the internal operations and predictions of CNNs applied to this challenge.  
In this study, we evaluate, visualize, and explain the predictions of CNN models in classifying pediatric CXRs to detect pneumonia and furthermore to differentiate between bacterial and viral pneumonia to facilitate swift referrals that require urgent medical intervention. We propose a novel method to visualize the class-specific ROI that is considered significant for correct predictions across all the inputs that belong to an expected class. We evaluate and statistically validate the performance of different customized CNNs that is trained end-to-end on the dataset under study to provide an accurate and timely diagnosis of the pathology. The work is organized as follows: Section 2 discusses the related work, Section 3 elaborates on the materials and methods, Section 4 discusses the results, and Section 5 concludes the study.
## 2 Fault Analysis of Load Mutation Caused by Electrical Interference
### Oscillating Load-Mutation Fault  
Not all the load mutations caused by electrical interference are of the pulsing type described in the previous section, but there is also an oscillating load-mutation fault. The timing diagrams for the AGC command, actual generated load, FDEM, and pressure of the governing stage are shown in Figure 2. The peak-to-peak value of the load is only 0.4 MW. Nevertheless, load fluctuations occurred at 1680-2000 s and the value expanded to 1.8 MW within 20 s. Compared with the actual load variation trend, the reverse action and hysteresis of the FDEM indicate the stability of the thermal system. However, frequent fluctuations in the excitation current indicate that this load oscillation was caused by electrical interference, with fluctuations peaking at approximately 10 A.  
The above analysis allows us to extract a specific fault characteristic of load oscillation caused by electrical interference. When a fault with frequent load oscillation occurs, the unit load corresponds to a sine wave signal with 20 s and an amplitude of 0.6% Pe superimposed on the stable output of the unit's thermal system.  
Figure 2: Oscillating load mutation.
## 5 Conclusions and Future Work  
A smartphone framework-based authentication technique has been designed and implemented, leveraging the TOPSIS technique-based extracted feature ranking, dynamic pattern recognition (behavioral biometrics), and different ML models. It has been proven that when RF is incorporated, the proposed technique performs better than the other two ML algorithms, with a reported accuracy of 95.2%. The system is also resilient against serious cybersecurity attacks, achieving over 93% accuracy against environmental changes and 96% detection rates against spoofing attacks. By considering only critical features, such as touch pressure, velocity, and trajectory curvature, the framework balances security and usability, enabling real-time adaptability for end users. The proposed technique provides a lightweight, robust, secure, and scalable authentication solution when the TOPSIS decision-making technique and ML algorithms are integrated into the design. Despite promising results, this work has limitations, including the use of a small dataset and the lack of testing against extreme environmental factors. In future work, we plan to expand our datasets further by considering multi-modal biometrics, such as facial recognition and fingerprints, and leveraging deep learning algorithms, such as CNN, DNN, and RNNs, to enhance performance in real-world applications and improve energy efficiency.  
Conceptualization, M.L.S.; methodology, M.L.S., Q.A. and M.M.H.; software, M.M.H. and N.S.A.; validation, M.L.S., A.A.Y. and R.A.A.; formal analysis, S.H.T. and S.K.A.; investigation, M.L.S. and Q.A.; resources, M.F. and A.H.K.; data curation, S.L.Q. and N.S.A.; writing--original draft preparation, M.L.S.; writing--review and editing, Q.A., M.M.H. and N.S.A.; visualization, R.A. and S.H.T.; supervision, M.L.S.; project administration, M.L.S. All authors have read and agreed to the published version of the manuscript.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of AP Locations
#### 4.2.2 Performance of Metric of Double Measurements  
Figure 9 shows the error bounds by using different metrics of double measurements. The relations between the error bounds and AP locations exhibited in Figure 9 are in accordance with the ones by using the metric of a single measurement. In addition, based on the results in Figures 9 and 10, the error bounds achieved by the metric of double measurements are lower than the ones by the metric of single measurement.
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 2 IP Mobility Management Protocols
### Micro Mobility Management Solutions
Each mobile node is given an IP address and home domain, however, when a mobile node moves to a foreign domain, it follows the mobile IP scheme for such interdomain mobility. The node then obtains the CoA from the foreign domain. Figure 4: Hierarchical Mobile Internet Protocol (MIP). A base station frequently sends agent advertisement messages that can be used by moving mobile nodes within the home domain to detect the new base station. Once a mobile node detects the existence of a new base station, it sends a registration request that starts the HAWAII path set up procedure. In order to maintain routing to mobile nodes in the home domain, HAWAII uses path setup update messages; power-up, refresh, and update. These messages are used to build host-based routing entries in selected routers within the domains. Mobile nodes frequently send path refresh messages to keep the host-based routing entries updated in the home base station. Aggregated refresh messages are then sent up the overlay tree to update intermediate routers and the main DRR. If there is a failure to send the frequent path refresh messages, a mobile node host-based routing entry is removed from the base station.
## 4 Results
### Analysis of Enhancing Question Diversity
#### 4.2.3 Univariate Analysis of Variance (ANOVA) Follow-Up Tests  
To further explore the specific differences in generation methods across various dimensions, we conducted separate univariate ANOVAs for each dependent variable and applied Bonferroni corrections to control the Type I error rate. Table 8 presents the results of these ANOVAs. The results indicate that the generation method significantly affects all three dimensions: diversity (F(2, 87) = 13.002, \(p<0.001\), partial \(\eta^{2}=0.309\)), challenge (F(2, 87) = 12.530, \(p<0.001\), partial \(\eta^{2}=0.301\)), and higher-order thinking (F(2, 87) = 17.724, \(p<0.001\), partial \(\eta^{2}=0.379\)).  
We further conducted [PERSON]'s HSD post-hoc tests, the results of which are shown in Table 9. The post-hoc test results indicate: (1) Multi-Examiner significantly outperforms GPT-4 across all dimensions (\(p<0.001\)). (2) There are no significant differences between Multi-Examiner and human-generated question sets across all dimensions (\(p>0.05\)). (3) Human-generated question sets significantly outperform GPT-4 across all dimensions (\(p<0.001\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Generation Method** & **Mean** & **Standard Deviation** & **N** \\ \hline Multi-Examiner & 4.23 & 0.57 & 30 \\ GPT-4 & 3.40 & 1.13 & 30 \\ Human & 4.43 & 0.57 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Descriptive Statistics of Question Set Scores by Generation Method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Effect** & **F-Value** & **Hypothesis DF** & **Error DF** & \(p\)**-Value** & **Partial \(\eta^{2}\)** \\ \hline Generation Method & 14.016 & 2 & 87 & \(<0.001\) & 0.244 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the Multivariate Analysis of Variance for Question Set Scores.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent Variable** & **Sum of Squares** & **DF** & **Mean Square** & **F-Value** & \(p\)**-Value** & **Partial \(\eta^{2}\)** \\ \hline Diversity & 8.022 & 2 & 9.011 & 14.016 & \(<0.001\) & 0.244 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Univariate Analysis of Variance Results for Each Evaluation Dimension.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 3 Circuit Design of RF DPA
### Stability Analysis  
Stability analysis plays a crucial role in DPA design, ensuring the amplifier's reliability under various conditions. The stability of a DPA is influenced by several factors, including impedance matching, load and source impedance, bias settings, and the characteristics of the power combiner. To function effectively as an RF power amplifier instead of an oscillator, a DPA must possess a stability factor (K) greater than 1 [30]. One commonly used method for assessing unconditional stability, where K > 1, is [PERSON]'s condition, which relies on the value of K. However, while the K-\(\Delta\) test provides a mathematical evaluation of unconditional stability, it does not enable straightforward comparisons between different devices due to its reliance on two separate parameters. A newer criterion, utilizing a single parameter \(\mu\)[31], has been introduced to gauge stability. When \(\mu\) surpasses 1, the device is considered unconditionally stable, and higher \(\mu\) values indicate greater stability.  
According to the stability plot in Figure 11, the device is unconditionally stable through both the K-\(\Delta\) Test and the \(\mu\)-Test by adding a 3.3-ohm resistance in the gate pin of both transistors in the frequency range of 3 to 4 GHz. This finding makes the device appropriate for the DPA design proposed in the paper.  
Figure 10: The layout of the (**a**) IMN of the peak and (**b**) OMN of the peak (dimensions in mm).  
Figure 9: The layout of the (**a**) IMN of the main and (**b**) OMN of the main (dimensions in mm).
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 4 P4-Kbr
### Use of MPLS to Create the Virtual Links  
As seen before, P4-KBR is based on the creation of a virtual space, where network elements and hosts/contents/services (from now, end-points) involved in the communication are identified by virtual identifiers (vids).  
In a theoretical way, vids are defined as L-bits strings that comprise two parts of L/2 bits. In the case of a network element, the first L/2 bits are the result of applying a hash function to the device identifier and the last L/2 are set to one. In the case of end-points, the first L/2 are the result of applying the hash function to the device identifier to which the end-point is connected and the last L/2 bits are the result of applying the hash function to its name (a flat or a hierarchical textual name, depending on the service that is going to use P4-KBR).  
According to the vid assignment scheme, location of nodes in the virtual ring is not linked to the physical topology. For this reason, although the action _send to node x_ defined at node n establishes a virtual link between the node n and the node x, it is likely that both nodes are not directly connected. On the other hand, this action has to be translated into a P4-compatible action. In particular, the action should indicate the adequate output port to forward the packet toward destination x.  
In order to translate a virtual link between two nodes not connected physically into a physical path, avoiding the appearance of loops during the forwarding process, we proposed the creation of a tunnel using the MPLS technology [23], as it is summarized by an example shown in Figure 4. This figure corresponds to the physical topology of the nodes that resulted in the virtual topology shown in Figure 3. As can be seen there, for example, the third of the wilcarded ranges defined at dp6 is associated to the action _send to node 703 (dp3)_. Due to the fact that there is not a direct link between dp6 and dp3, a MPLS tunnel is configured by the installation of adequate flow table entries in the involved nodes.  
Figure 3: Ranges of dp6 cannot be expressed as bitmasked ranges, so a larger number of bitmasked subranges are obtained using the prefix algorithm. P4 and P4 Runtime support the inserting of table entries using RANGE match as match field type. On the other hand, P4 language also allows the implementation of a solution based on the use of LPM matching. The “Translation of ranges solution” is also implemented and evaluated in this work.
## 3 Results
### Analysis of Experimental Factors  
To analyze the trend in the influence of experimental factors on the filling coefficient and particle axial velocity coefficient, response surface plots and single-factor plots were used for analysis.  
As shown in Figure 7, when the diameter elongation percentage is fixed, the filling coefficient shows a significant decrease and then a slight increase with the reduction in the spiral pitch elongation percentage at the lower part of the diameter elongation percentage and a significant increase at the higher part in the diameter elongation percentage. When the spiral pitch elongation percentage is fixed, the filling coefficient decreases with the decrease in the diameter elongation percentage, showing a trend of first decreasing and then slightly increasing when the spiral pitch elongation percentage is low and a significant increasing trend when the spiral diameter elongation percentage is high.  
The red dots in Figures 8 and 9 represent the experimental values of repeated experiments, and the lines serve as the relationship curve between the experimental factors and the experimental indicators. As shown in Figures 8 and 9, the particle axial velocity coefficient shows a decreasing trend as the spiral pitch elongation percentage increases. In contrast, as the rotational speed increases, the particle axial velocity coefficient shows an increasing trend.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Particle Axial Velocity Coefficient \(y_{1}\)/\%**} \\ \hline
**Source of** & \multirow{2}{*}{**Square SUM**} & **Degree of** & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} \\
**Variance** & & & & & \\ \hline Model & 1163.52 & 9 & 129.28 & 6.10 & 0.0019 \\ \(x_{1}\) & 905.67 & 1 & 905.67 & 42.74 & \textless{}0.0001 \\ \(x_{2}\) & 15.14 & 1 & 15.14 & 0.71 & 0.4132 \\ \(x_{3}\) & 197.42 & 1 & 197.42 & 9.32 & 0.0093 \\ \(x_{1}x_{2}\) & 7.72 & 1 & 7.72 & 0.36 & 0.5564 \\ \(x_{1}x_{3}\) & 5.09 & 1 & 5.09 & 0.24 & 0.6323 \\ \(x_{2}x_{3}\) & 0.35 & 1 & 0.35 & 0.02 & 0.8993 \\ \(x_{1}^{2}\) & 13.45 & 1 & 13.45 & 0.63 & 0.4398 \\ \(x_{2}^{2}\) & 2.78 & 1 & 2.78 & 0.13 & 0.7231 \\ \(x_{3}^{2}\) & 16.28 & 1 & 16.28 & 0.77 & 0.3967 \\ Residual & 275.45 & 13 & 21.19 & & \\ Misfit term & 128.59 & 5 & 25.72 & 1.40 & 0.3193 \\ Pure error & 146.86 & 8 & 18.36 & & \\ Total variation & 1438.97 & 22 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis of variance of the particle axial velocity coefficient.  
Figure 7: Response surface graph of the filling coefficient.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 5 Results And Discussion
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ITACIE-GP** & **ACIE-GP** & **ITIE-GP** & **ITAC-GP** \\ \hline
**Dataset** & **Mean \(\pm\) Std** & **Mean \(\pm\) Std** & **Mean \(\pm\) Std** & **Mean \(\pm\) Std** \\ \hline FEI\_1 & **94.40 \(\pm\) 2.65** & 92.40 \(\pm\) 2.33+ & 94.40 \(\pm\) 2.65= & 93.60 \(\pm\) 2.33+ \\ FEI\_1\_Blur & **93.20 \(\pm\) 2.40** & 90.80 \(\pm\) 1.60+ & 92.00 \(\pm\) 1.26+ & 93.20 \(\pm\) 3.25+ \\ FEI\_Low & **92.00 \(\pm\) 1.79** & 87.60 \(\pm\) 4.45+ & 91.20 \(\pm\) 4.31+ & 90.80 \(\pm\) 2.71+ \\ FEI\_1\_Noise & **91.60 \(\pm\) 3.44** & 89.60 \(\pm\) 4.45+ & 89.60 \(\pm\) 4.63+ & 91.60 \(\pm\) 2.71= \\ \hline
**JAFFE** & **91.23 \(\pm\) 1.86** & 85.75 \(\pm\) 3.63+ & 89.04 \(\pm\) 5.20+ & 90.41 \(\pm\) 2.29+ \\
**JAFFE\_Blur** & **84.11 \(\pm\) 2.39** & 82.47 \(\pm\) 4.62+ & 82.19 \(\pm\) 2.74+ & 83.56 \(\pm\) 3.00+ \\
**JAFFE\_Low** & **84.93 \(\pm\) 5.27** & 82.19 \(\pm\) 4.67+ & 84.66 \(\pm\) 5.44+ & 84.66 \(\pm\) 5.44+ \\ JAFFE\_Noise & **67.12 \(\pm\) 3.87** & 63.84 \(\pm\) 6.46+ & 66.30 \(\pm\) 3.93+ & 67.12 \(\pm\) 3.87= \\ \hline FLOWER & **87.89 \(\pm\) 4.88** & 85.96 \(\pm\) 3.28+ & 86.32 \(\pm\) 5.86+ & 86.84 \(\pm\) 4.99+ \\ FLOWER\_Blur & **90.00 \(\pm\) 3.87** & 85.26 \(\pm\) 3.94+ & 87.37 \(\pm\) 1.97+ & 89.47 \(\pm\) 4.40+ \\ FLOWER\_Low & 86.84 \(\pm\) 3.33 & 85.26 \(\pm\) 4.88+ & 78.95 \(\pm\) 2.35+ & **87.37 \(\pm\) 3.49-** \\ FLOWER\_Noise & **84.74 \(\pm\) 4.53** & 83.68 \(\pm\) 4.82+ & 83.68 \(\pm\) 1.97+ & 84.74 \(\pm\) 4.53= \\ \hline
**ORL** & **99.00 \(\pm\) 0.33** & 96.83 \(\pm\) 1.62+ & 98.67 \(\pm\) 0.67+ & 99.00 \(\pm\) 0.62+ \\
**ORL\_Blur** & **90.00 \(\pm\) 3.87** & 85.96 \(\pm\) 3.28+ & 87.37 \(\pm\) 1.97+ & 89.47 \(\pm\) 4.40+ \\
**ORL\_Low** & 96.83 \(\pm\) 0.97 & 95.67 \(\pm\) 1.93+ & 95.67 \(\pm\) 1.78+ & **97.00 \(\pm\) 1.00-** \\
**ORL\_Noise** & **91.83 \(\pm\) 2.26** & 88.83 \(\pm\) 2.51+ & 89.83 \(\pm\) 3.78+ & 91.83 \(\pm\) 2.26= \\ \hline
**overall** & \multicolumn{4}{c}{**41+,5=2-**} \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Ablation experiment.
## 4 Results Analysis
### Displacement Field and Crack Type Analysis  
#### 4.3.1 Description of Crack Types  
In the discrete element method, the excessive relative displacement between particles can lead to broken bonds, as reflected in the contacts within the PFC software. This process is instrumental in simulating the formation of cracks within the material. The classification of crack types is typically based on the relative normal and tangential displacements of the particles [29; 53; 54; 55; 56], and they are broadly categorised into three main types: tension, shear, and mixed failure. The types of cracks are described as follows and plotted in Figure 8:  
1. Direct tensile (DT). This type of crack forms when the displacement directions of two particles are opposite and parallel to the line connecting their centres.
2. Relative tensile (RT). The displacement directions of the two particles are aligned and parallel to the line connecting their centres; however, the magnitudes of the displacements differ.
3. Direct shear (DS). The directions of the two particles' displacement are opposite and perpendicular to the line connecting their centres.
4. Relative shear (RS). The directions of the two particles' displacement are uniform and perpendicular to the line connecting their centres, but with differing displacement magnitudes.
5. Mixed failure (MF). This mode of failure is characterised by the simultaneous presence of relative displacements in both the normal and tangential directions for the two particles.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
###### Abstract  
With the upgrading of manufacturing production lines and innovations in information technology, logistics robot technology applied in factories is maturing. Rail-mounted logistics robots are suitable for precise material distribution in large production workshops with fixed routes and over long distances. However, designing an efficient path-planning algorithm is the key to realizing high efficiency in multi-robot system operations with rail logistics. Therefore, this paper proposes an improved Dijkstra algorithm that introduces real-time node occupancy and a time window conflict judgment model for global path planning and conflict coordination in multi-robot systems. More specifically, the introduction of real-time node occupancy can determine the shortest feasible routes for each task, and the introduction of the time window conflict judgment model can avoid the route conflict problem in the execution of multiple tasks, planning the shortest route without conflict. For the robot UBW positioning module, a Chan algorithm based on TDOA is proposed to realize the accurate positioning of rail-mounted logistics robots during their operation. Compared with the traditional Dijkstra algorithm, the results show that the algorithm proposed herein can plan a conflict-free and better path and dynamically adjust the on-orbit conflict in real time to avoid track congestion and efficiently complete multiple distribution tasks.  
orbit-mounted robot; dynamic path planning; improved Dijkstra algorithm; time window; Chan algorithm +
Footnote †: journal: Applied sciences
## 4 Simulation and Evaluation  
We use MATLAB R2009a to carry out the simulation, and in this paper the simulation parameters are set as follows: 100 nodes are randomly deployed in a region of 100 m \(\times\) 100 m. The base station locates on the point (50, 50). The original energy of the nodes is 0.1 J. The speed of movement of the nodes is in the range of 0-10 m/s and the angle of movement is in the range of [0, 180\({}^{\circ}\)]. The number of cluster heads is six (the base station is regarded as a fixed cluster head). The weight coefficients are \(a_{1}=0.3\), \(a_{2}=0.3\). The length of data transmitted for each round is 4000 bit. The energy model is adopted as in [20].  
When transmitting \(K\) bit data, and the distance is \(d\), the energy communication of a node can be calculated as follows:  
\[E_{Tx}(k,d) =E_{Tx,elec}(k)+E_{Tx,amp}(k,d) \tag{7}\] \[=\left\{\begin{array}{ll}kE_{dec}+k\varepsilon_{fs}d^{2}&d{<}d _{0}\\ kE_{dec}+k\varepsilon_{mp}d^{4}&d\geq d_{0}\end{array}\right.\]  
where the \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are energy consumption coefficients of the power amplifier circuit, and \(d_{0}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}\).  
The energy consumed by receiving \(K\) bit data is calculated as follows:  
\[E_{Rx}(k)=E_{Rx,dec}(k)=kE_{dec} \tag{8}\]  
where \(E_{dec}=50\) nJ/bit, \(\varepsilon_{fs}=10\) pJ/bit/m\({}^{2}\) and \(\varepsilon_{mp}=0.00013\) pJ/bit/m\({}^{4}\). The energy consumption of data fusion is \(E_{DA}=5\) nJ/bit.