esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum 3D FFT in Tomography
###### Abstract  
The Radon transform constitutes the conventional tool for tomosynthesis, i.e., the composition of cross-sections of an object from its projections. It is actually a version of the Fourier Transform, which is accompanied by the appropriate digital high pass filters for correct distribution of energy among the reconstructed frequency components. The Radon transform and its inverse are employed in their 2D and 3D versions, respectively, and the whole procedure is verified by the a priori known cross-sections to be reconstructed (known fandom). Usually, 3D medical image cubes, which are to be reconstructed, require powerful computational tools since the 2D projections are of high-resolution containing millions of pixels. Although the 3D FFT is very fast, the large number of projections will result in a 3D spectrum of very large dimensions. Inverting this spectrum with the inverse 3D FFT is extremely time consuming. In this work, the implementation of the 2D Radon transform using the 2D Quantum Fourier Transform is analytically presented. Simultaneously, its inverse version is realized by means of the Quantum inverse 3D FFT. For this purpose, a review of the necessary quantum computational units is presented for the implementation of the quantum 3D FFT and simultaneously simple examples of tomosynthesis are given by means of the quantum version of the 2D Radon transform and its inverse 3D counterpart. The whole procedure of the quantum tomosynthesis is analytically described.  
Quantum Fourier Transform; quantum circuits; tomography; radon transform; quantum 3D back projection Article
###### Abstract  
With the accelerating growth of infrastructure construction, carbon emission and environmental pollution problems have become increasingly severe. In order to promote the sustainable development of the construction industry, using rice husk ash (RHA) in recycled aggregate concrete has aroused extensive interest. This study aims to investigate the impact of the partial replacement (0%, 10%, 20%, 30% of binder) of ordinary Portland cement (OPC) with RHA by equal mass on recycled concrete's mechanical characteristics and freeze-thaw resistance. The workability, compressive strength, mass loss and dynamic elastic modulus of recycled concrete were tested, and the hydration products and microstructure were analyzed using scanning electron microscope (SEM) tests. The mechanism of the freeze-thaw damage deterioration of RHA recycled aggregate concrete was revealed. The results indicate that the incorporation of RHA has an adverse effect on the workability of fresh concrete. Its high specific surface area will provide a large number of nucleation sites for the hydration reaction, refining the pore structure in the paste and improving the weak bonding of the interfacial transition zone (ITZ) by enhancing the matrix's pozzolanic reaction effect and filling effect, thus improving the compressive strength of concrete. Furthermore, the porous structure of the recycled aggregate attached mortar and mesoporous RHA will absorb a lot of water during the freeze-thaw cycles. With the continuous accumulation of expansion pressure, the interior pores and cracks will gradually expand and extend, leading to more severe damage to the concrete, and the degree of freeze-thaw damage deterioration grows as the RHA replacement ratios increase.  
rice husk ash; recycled aggregate concrete; mechanical characteristics; freeze-thaw resistance; deterioration mechanism +
Footnote †: journal: Applied Sciences
# Research Progress on the Mechanisms and Control Methods of Rockbursts under Water-Rock Interactions
###### Abstract
[34] used a true triaxial test system of unloaded minimum principal stress to investigate the mechanical strength of sandstone specimens subjected to various water contents and intermediate principal stresses. The crack extension behavior of red sandstone affected by various water contents and intermediate principal stresses was investigated by [PERSON] et al. [35], who investigated the mechanical properties and energy dissipation characteristics of sandstone with different water contents under dynamic loading by means of the split Hopkinson press bar (SHPB) test. [PERSON] et al. [3] conducted a series of uniaxial compression tests on sandstone samples with different water contents. The effect of water content on the energy evolution characteristics during the loading process was analyzed, revealing the fundamental mechanism of rock explosion inhibition by water injection in rocks. Additionally, studies have indicated that rockbursts are less likely to occur in underground engineering within rock masses that have high moisture content [28; 36]. [PERSON] et al. [37] observed that the presence of water and slurry in tunnels significantly decreases the probability of rockburst occurrences. [PERSON] et al. [38] studied rockburst incidents across different hard rock mines and discovered that rockbursts occurred less frequently in rock masses with higher moisture content. This is because in deep underground environments, rock masses are exposed to high geological stress and complex groundwater conditions [39; 40]. Therefore, to better understand the mechanisms of strain rockbursts under the influence of moisture, considering the impact of water on strain-induced rockbursts is crucial. In this paper, the recent progress in understanding the mechanism of water influence on rock burst occurrence at home and abroad is made from several aspects, such as the mechanism of water influence on rock burst occurrence, the study of rock burst tendency under different water content conditions, the weakening effect of water on hard rock and the evolution law of microcracks in rock, the mechanical properties of rock under wet and dry conditions, and rock burst prevention considering the water effect. In this paper, the typical engineering examples are introduced, and the next research work is introduced and prospected.
## 3 Meta-PPO Anti-Jamming Intelligent Decision Algorithm
### Meta-Learner
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Network Background** & **Core Algorithm** \\ \hline
[1] & Internet of Things (IoT) & Machine Learning \\
[2] & Cognitive Radio (CR) & Machine Learning \\  & & Supervised Learning \\
[3] & Wireless Network & Unsupervised Learning \\  & & Reinforcement Learning \\  & & Deep Learning \\
[4] & Internet of Things (IoT) & Deep Reinforcement Learning \\  & Unmanned Aerial Vehicles (UAVs) & Stackelberg Game \\
[5] & Internet of Satellites (IoS) & Reinforcement Learning \\
[6] & Maritime Communication & Deep Reinforcement Learning \\
[7] & Unmanned Aerial Vehicles (UAVs) & Reinforcement Learning \\
[8] & Unmanned Aerial Vehicles (UAVs) & Deep Reinforcement Learning \\
[9] & Wireless Sensor Network & Stochastic Game Framework \\
[10] & Cellular System & Deep Reinforcement Learning \\
[11] & Wireless Network & Transfer Learning \\
[12] & Unmanned Aerial Vehicles (UAVs) & Dyna-Q-Learning \\
[13] & Unmanned Aerial Vehicles (UAVs) & Distributed Reinforcement Learning \\
[14] & Wireless Network & Reinforcement Learning \\
[15] & Underwater Sensor Networks & Reinforcement Learning \\
[16] & Few-shot Image Classification & Meta-Learning \\
[17] & Few-shot Learning & Model Agnostic Meta-Learning \\
[19] & Face Recognition System & Meta-Learning \\
[21] & Markov Decision Task & Meta-Reinforcement Learning \\
[22] & Non-linear Control Task & Meta Q-Learning \\
[23] & Continuous-control Benchmark & Meta-Reinforcement Learning \\
[23] & Artificial Intelligence & Meta Q-Learning of Multi-task \\
[24] & Artificial Intelligence & (MQMT) \\
[25] & Vision-based Control Task & Meta-imitation Learning \\
[26] & Pendulum Benchmark Problem & Proximal Policy Optimization (PPO) \\
[28] & Atari Games and Control Tasks & PPO with Policy Feedback (PPO-PF) \\
[30] & Matrix Games and Grid-based Games & Fast-PPO \\
[31] & Sensing and Communication in & Proximal Policy Optimization (PPO) \\
[32] & Wireless Network & Deep Reinforcement Learning \\
[33] & Wireless Network & Double-Q Learning \\
[34] & Wireless Network & Machine Learning \\
[35] & Wireless Network & Machine Learning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of references. Figure 7: Intelligent anti-jamming model based on Meta-PPO algorithm.
## 4 Results of Experimental Investigations  
### Crack Pattern and Failure Crack at Ultimate Load  
For a detailed comparison between the experimental results and SCPT analysis, it is important to have detailed information not only on the final crack pattern but even more on the crack propagation during the loading process. The root of the shear crack serves as an input parameter for the SCPT algorithm and is therefore of major importance.  
Both subtests B1-1 and B1-2 showed a similar crack pattern regarding the major flexural shear cracks (Figure 5). In both cases, the first flexural cracks spread from the middle of the beam to the supports. The resulting flexural shear cracks propagated nearly vertically up to the mid-depth of the beam. Then, the inclination was significantly reduced from this height and the shear cracks became flatter. With the increasing load and thus decreasing shear crack inclination, the formation of a decisive bending shear crack was observed. Whereas the root of the failure crack in subtest B1-1 was identified at a distance of 2.67 \(\times\) d from the support, subtest B1-2 revealed an increased distance of 3.54 \(\times\) d (Figure 5).  
For both subtests, a brittle failure after reaching the maximum load, indicated by a significant load drop, was observed revealing a flexural shear failure. The failure loads are given in Table 2 and reveal a similar level of ultimate capacity for B1-1 and B1-2.  
Figure 4: External measuring instrumentation.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 6 Experiment Results and Analysis
### Ablation Experiment  
To investigate the impact of the aforementioned improvements in the model's detection accuracy, this paper conducted ablation experiments on the bearing image dataset using YOLOv5 as the base model. Firstly, the CA mechanism was added to the C3 module in the backbone network. Secondly, the ASFF adaptive spatial feature fusion structure was introduced to the neck network. Lastly, the original GIoU bounding box loss function in the network was replaced with WIoU, and the experimental results were recorded. The results are presented in Table 3:  
Analyzing the experimental results from Table 3, it can be observed that the original YOLOv5 network achieved a mAP of 94.2% on the bearing image dataset. After adding the CA mechanism to the C3 module of the backbone network, precision and recall increased by 2% and 2.3%, respectively, and mAP increased by 1.9%. The significant improvement in accuracy demonstrates that the CA mechanism can make the network focus more on the target regions in the images, thereby enhancing the model's detection performance.  
Further, by adding the adaptive spatial feature fusion module (ASFF) to the end of the neck network, precision and recall increased by 1.5% and 2.3%, respectively, and mAP increased by 1.2%. The experimental results confirm that ASFF can eliminate feature inconsistencies at different levels, leading to improvements in all detection metrics.  
Finally, replacing the bounding box loss function GIoU with WIoU resulted in precision and recall increasing by 0.6% and 0.7%, respectively, and mAP increased by 1.1%. The use of WIoU in this study, compared to GIoU, shows better fitting of the bounding boxes, thereby improving the model's detection performance.  
In the end, the improved YOLOv5 achieved precision and recall of 97.5% and 96.5%, respectively, and a mAP of 98.4%. Compared to the original YOLOv5, these metrics improved by 4.1%, 5.3%, and 4.2%, respectively. Figure 19a shows the test results of the original YOLOv5, and Figure 19b shows the test results of the improved YOLOv5. The experiments demonstrate that, through the three improvements mentioned, the enhanced model exhibits effective improvement in detection accuracy, enhancing the precision of bearing defect detection and providing technical support for automated detection in bearing manufacturing.  
Figure 18: mAP comparison map.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Experiments** & **YOLOv5** & **CA** & **ASFF** & **WIoU** & **Precision/\%** & **Recall/\%** & **mAP/\%** \\ \hline Experiments 1 & \(\surd\) & & & & 93.4 & 91.2 & 94.2 \\ Experiments 2 & \(\surd\) & \(\surd\) & & & 95.4 & 93.5 & 96.1 \\ Experiments 3 & \(\surd\) & \(\surd\) & \(\surd\) & & 96.9 & 95.8 & 97.3 \\ Experiments 4 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 97.5 &
## 3 Model Development
### Entropy-Unscertained Measure Theory
#### 3.2.1 Definition of Unascertained Measure  
Suppose that the assessment object (that is, the research object that needs to be assessed) \(X=\{X_{1},X_{2},\cdots,X_{n}\}\) and the assessment index set (that is, the set of established assessment indexes) \(I=\{I_{1},I_{2},\cdots,I_{n}\}\). If \(x_{ij}\) denotes the measured value of the i-th assessment object \(X_{i}\) with respect to the j-th assessment index \(I_{j}\), then \(X_{i}\) can be expressed as an m-dimensional vector named \(\{x_{i1},x_{i2},\cdots,x_{im}\}\). Suppose that the assessment grade space (that is, the set of all assessment grades) \(U=\left\{C_{1},C_{2},\cdots,C_{p}\right\}\), \(C_{k}(k=1,2,\cdots,p)\) is the \(k\) assessment grade. Furthermore, suppose that the \(k\) grade is higher than the \(k+1\) grade in the safety risk, that is \(C_{k}>C_{k+1}\). If \(C_{1}>C_{2}>\cdots>C_{p}\) or \(C_{1}<C_{2}<\cdots<C_{p}\) is satisfied, \(\left\{C_{1},C_{2},\cdots,C_{p}\right\}\) is called an ordered segmentation class of assessment space \(U\).  
If \(\mu_{ijk}=\mu(x_{ij}\in C_{k})\) denotes the degree that the measured value \(x_{ij}\) belongs to the k-th assessment grade class \(C_{k}\).  
\[0\leq\mu\Big{(}x_{ij}\in C_{k}\Big{)}\leq 1\ (i=1,2,\cdots,n;j=1,2,\cdots,m;k=1,2, \cdots,p) \tag{1}\]  
\[\mu\Big{(}x_{ij}\in U\Big{)}=1(i=1,2,\cdots,n;j=1,2,\cdots,m) \tag{2}\]  
\[\mu\Big{|}x_{ij}\in\cup\limits_{I=1}^{k}C_{I}\Big{|}=\sum\limits_{l=1}^{k}\mu \Big{(}x_{ij}\in C_{I}\Big{)}(k=1,2,\cdots,p) \tag{3}\]  
Equation (1) is called as non-negative boundedness, Equation (2) is called as normalization, and Equation (3) is called as additivity. If \(\mu\) satisfies Equations (1)-(3), and then \(\mu\) is called as unascertained measure, abbreviated as measure.
## 3 System Architecture: Edge-FLGuard
### Security and Resilience Enhancements  
Edge-FLGuard incorporates multiple strategies to preserve security, privacy, and robustness, as follows:  
* Privacy Strategies:
* Optional integration of differential privacy during local model updates (via Gaussian noise injection).
* Gradient clipping and noise injection to obfuscate sensitive patterns.
* Use of TLS 1.3 for transport-layer security during model transmissions.
* Model Poisoning Mitigation:Byzantine-resilient aggregation (e.g., trimmed mean or Krum) can be substituted for FedAvg in hostile environments.
* Update filtering: Discarding outlier updates based on gradient similarity metrics or historical contribution scores.
* Fallback mode: Edge nodes can revert to previous model versions or enforce anomaly threshold tightening if global models are suspected to be corrupted.
* On-Device Verification:
* Edge nodes retain autonomy to override global model decisions if local rules are violated (e.g., zero-day traffic spikes).  
Together, these mechanisms allow Edge-FLGuard to operate under zero-trust assumptions, ensuring graceful degradation in adversarial or disconnected conditions.
# Measurement-Driven Synthesis of Female Digital Mannequin Using Convex Sub-Volumes
## 3 Materials and Methods
### Construction of Reference Mannequins Based on the Base Mannequin  
In order to obtain parameterization, a set \(RM\) of five Reference Mannequins with **known** geometry and measurements is created. Each one of the mannequins in \(RM\) are instances of the \(BM\) that preserve topology but differ in geometry. The **geometry** and **measurements** of each mannequin in \(RM\) are the **parameters** of the set of functions \(F\) (see  
Figure 6: Example of cushion \(C_{E_{1}E_{2}}\), arbitrarily oriented and positioned ellipsoids (\(E_{1}\) and \(E_{2}\)) in space with convex hull operation between \(E_{1}\) and \(E_{2}\). **(a)** Ellipsoids \(E_{1}\) and \(E_{2}\). **(b)** Cushion \(C_{E_{1}E_{2}}\) between ellipsoids \(E_{1}\) and \(E_{2}\).  
Figure 7: Mannequin approximated with cushions and ellipsoids. Ellipsoid mannequin **(a)** for comparison. Cushions computed according to Figure 5. Small chubby mannequin (see Section 3.3) as **(a)** Ellipsoid-based Mannequin and **(b)** Cushion-based Mannequin.  
Equation (1)) that synthesizes the measurement-driven (parameterized) mannequins. Each \(RM_{i}\) satisfies a different set of five tailor measurements \(x\in A\) presented in Table 3. The measurements that define each of the models are intended to provide both extreme and average reference geometries and measurements for the interpolation process.  
**Creation of the Reference Mannequin Geometries**  
The process for creating the geometry of each Reference Mannequin \(RM_{i}\in RM\) consists of instancing \(BM\) and modifying its geometry. The position, orientation, and size (principal semi-axes dimensions) of each ellipsoid in \(RM_{i}\) is modified such thatthe measurements in Table 3. The step-by-step process performed for every mannequin is presented next (see also Figure 8), and the mannequins resulting from this process can be seen in Figure 9.  
1. The Base Mannequin (see Figure 4b) is scaled in the vertical direction to comply with the specific height \(He\in x_{i}\) (see Table 3).
2. Each ellipsoid is then manually modified (scaled, rotated, and translated) to obtain a mannequin that visually approximates the tailor measurements \(x_{i}\in A\).
3. Function \(m_{T}(h_{i})\) is applied to obtain the current measurements (\(x_{t}\)) of the mannequin. See Section 3.5.
4. If some cross section does not satisfy the desired measurement \(x_{i}\), the ellipsoids that influence it are manually modified (scaled, translated, and rotated) again.
5. Steps 3 and 4 are repeated until the the measurements in Table 3 are satisfied.
## 2 Materials and Methods
### Quantitative PCR Analysis of hao, nirS, nirK, and 16S rDNA Genes  
To determine the gene abundances in 1 ng of extracted DNA, quantitative PCR (qPCR) was performed in a Corbett Real-Time PCR Machine with the Rotor-Gene 6000 series software 1.7 (Qiagen, Netherlands), using the SYBR Green. The abundances of nitrifiers and denitrifiers were measured by quantifying the _hao_, _nirS_, and _nirK_ genes. The abundances of microbial communities were determined by quantifying the 16S rRNA gene. Standard curves were constructed for the _hao_, _nirS_, and _nirK_ genes using the positive recombinant plasmids Hhao-05/pMD19, HnirS-4/pMD19, and VnirK-15/pMD19 as standards, respectively. For the 16S rRNA gene, the positive recombinant plasmid W16S-1/pMD19 was used as the standard. The samples and standards were analyzed with qPCR in triplicate, and the specificity of the qPCR was confirmed with a melting curve analysis, agarose gel electrophoresis, and DNA sequencing. The efficiencies of the PCRs were 86.9-98.7%, with \(R^{2}\) values \(>\)0.993 for all the calibration curves.
# KRDroid: Ransomware-Oriented Detector for Mobile Devices Based on Behaviors
## 5 Evaluation
### Experiments
#### 5.3.2 RQ2: Will KRDroid misjudge other malware applications as ransomware?  
Since a ransomware application is a kind of malware, we still need to test that the accuracy of KRDroid is independent of malware classification. We randomly sampled 1000 ransomware in \(D_{1}\) and randomly sampled 1000 goodware in \(D_{3}\). These samples are collected from reference [24; 26]. We used these samples and 1000 malware in \(D_{2}\) as the dataset for test in this experiment. As mentioned above, all the applications in \(D_{2}\) are malware, which is different from ransomware.  
As shown in Figure 6, we found that there are 981 samples that can be correctly identified as ransomware, and only 19 ransomware misjudged as non-ransomware. There are 1986 samples that can be correctly identified as non-ransomware, and only 14 non-ransomware misjudged as ransomware. The false-positive rate of KRDroid is 1.94%, and the false negative rate is 0.7%.  
**Insight**. KRDroid is a ransomware-oriented detector rather than a malware detector. It does not misjudge other malware applications as ransomware because other malware applications do not have typical ransom behaviors.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Accurate Channel Estimation for mmWave Systems
#### 4.2.3 RSSI-Based Association Strategy for Channel Selection
The integration of the reflection gain from the IRS to the user in a specific beam direction accounts for the effectiveness of the IRS in enhancing the signal. Factors in the user's position, the IRS's placement, and the reflection angles reflect a realistic evaluation of the network performance. The noise component and interference are modeled considering the typical high-density user scenarios in 5G mmWave networks. Unlike traditional approaches that focus solely on the closest IRS for user association, Equation 18 evaluates the overall network geometry and channel conditions. This approach acknowledges the practical limitations of IRSs, such as restricted reflection angle adjustments and incomplete channel state information. It emphasizes the importance of optimized IRS deployment to maximize signal quality and reduce interference. Overall, this formulation offers a more accurate and holistic view of network performance in IRS-assisted mmWave systems, facilitating effective resource allocation and communication strategies. This assumption is not suitable for distributed systems. IRS-equipped mmWave mobile networks are favored due to their disregard for the variability in signal attenuation experienced by the user being served, which are typically implemented through LOS/NLOS mmWave communication. An example would be that an IRS that is near an NLOS connection might offer lower signal quality compared to an additional IRS with an LOS connection, albeit closeness typically leads to an increased likelihood of experiencing LOS. Furthermore, the predetermined reflection angles of IRSs also contribute to the variability in the reflection gain received by the user, a factor that cannot be explained by simply considering the closest proximity. Our goal is to assess the downlink performance of distributed mmWave networks with IRS implementation. Our main focus is on the downlink coverage probability, represented by \(P(T)\), which is the likelihood that the average user encounters an _SINR_ above the threshold \(T\), shown as follows:  
\[P(T)=P[SINR>T] \tag{25}\]By observing Equations (23) and (24), it is evident that varying _SINRs_ exist for various user connection statuses. To obtain them or to have them determine coverage probability, it is essential to gather statistical data on the signal power and interference power of the desired signal in various connection scenarios. These abilities are closely connected to the distance distributions and the line-of-sight/non-line-of-sight propagation condition. As a consequence, we have obtained precise findings for channel estimation through optimization. We proceed to process this channel estimation using hybrid beamforming.