esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Fatigue Life  
Four-point bending fatigue tests were performed for each mix. These experiments were carried out in accordance with Austroads standard AG:PT/T233 [27]. The results are shown in Table 10.  
Figure 4: (**a**) Marshall stability and (**b**) flow values of the mixtures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mix** & \
\begin{tabular}{c} **Average Maximum** \\ **Density (t/m\({}^{3}\))** \\ \end{tabular} & **Average VMA (\%)** &
\begin{tabular}{c} **Average Air Voids** \\ **(\%)** \\ \end{tabular} \\ \hline Control (1.5\% HL) & 2.464 & 15.49 & 4.79 \\ \hline
1 (1.5\% GGBFS) & 2.470 & 15.21 & 4.44 \\ \hline
2 (3\% GGBFS) & 2.476 & 14.98 & 4.15 \\ \hline
3 (5\% GGBFS) & 2.476 & 14.87 & 4.02 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Volumetric characteristics of the mixtures.  
72,348 cycles. The sample with 3% GGBFS failed at 92,524 cycles, which was the highest value. The sample with 5% GGBFS failed at 78,940 cycles. The initial flexural stiffnesses were recorded for all samples and were: 1.5% HL = 6650 MPa, 1.5% GGBFS = 6687 MPa. The highest value of initial flexural stiffness showed 6718 MPa. Once again, the sample with 5% GGBFS showed the initial flexural stiffness of 6702 MPa.  
According to Table 10, when 1.5% GGBFS was added to the asphalt mixture, the average number of cycles to failure was 1.42% lower than that of the control mixture with 1.5% HL. The average initial flexural remained relatively constant.  
With 3% GGBFS addition, the average number of cycles to failure increased by 26%, while the average initial flexural remained relatively constant.  
However, when the concentration of GGBFS was increased to 5%, the average number of cycles to failure decreased significantly, by 14.68%, compared to that of Mixture 2 (3% GGBFS).  
These results support the Marshall stability and flow test results. The addition of GGBFS to the asphalt mixture improves fatigue life because it reduces air voids. When the concentration of GGBFS was 3%, the fatigue life decreased due to an excess of filler in the asphalt mixture, which compromises its strength and durability. As a result, there is an optimal concentration of GGBFS for asphalt mixtures. Mixture 2 (3% GGBFS) had the best fatigue behaviour of all the tested mixtures.
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 2 Related Work
### Yolo11  
Object detection algorithms play a critical role in the field of computer vision. As the requirements for object detection in real-world applications continue to increase, the limitations of conventional approaches that primarily rely on handcrafted features have become more apparent. Consequently, algorithms based on deep learning models are increasingly gaining prominence. Within this domain, the You Only Look Once (YOLO) frameworks stand out as a significant contribution. YOLO11, released by Ultralytics in September 2024, introduces several architectural optimizations and innovations that further enhance feature extraction and inference speed.  
In the backbone and neck of the YOLO11 architecture, a novel C3K2 module is introduced [33]. It optimizes the computation process, reduces redundant parameters, and enhances feature representation simultaneously. Following the Spatial Pyramid Pooling Fast (SPPF) module [34], a Cross Stage Partial Self-Attention (C2 PSA) block [35] is integrated to absorb the model into the target regions and increase the granularity of feature extraction. In the head of the network, YOLO11 adopts an anchor-free decoupled head, which further delineates the processes of box regression and classification. There is no doubt that these modifications significantly enhance efficiency. Meanwhile, these updating strategies enable the model to maintain high throughput even when processing high-resolution inputs.  
Collectively, these enhancements enable YOLO11 to achieve more remarkable real-time detection performance. They can also provide us with a faster and more accurate solution for computer vision applications. The enhanced modules of YOLO11 are illustrated below in Figure 1.
## 3 Results
### Sensitivity of the Container Dynamics to the Material Characteristics Parameters  
A sensitivity analysis, as already introduced in Section 2.5, was performed for \(f_{c}\) for the container and \(K\) to better investigate the effects of these material characteristics on the container dynamics, allowing to choose the best performing set of parameters for the case studied. When varying one of the two parameters the other was kept constant to exclude combined effects, i.e., \(f_{c}=0.3\) and \(K=0.7\) were chosen for this purpose.  
In every test involving the effect of \(f_{c}\) only the value for the container was varied, maintaining \(f_{c}=0.3\) for the flume floor. This choice was due to preliminary tests showing the flume kinematic friction coefficient not affecting the container dynamics, as expected. Figure 4 shows the difference in coordinate in \(x\)-direction (\(\Delta x\)) and \(y\)-direction (\(\Delta y\)) with respect to the container initial position for both investigated variables. This representation was chosen to better highlight the small differences between different values of the parameters that would not be otherwise as clear when using global coordinates.  
The effect of \(f_{c}\) in \(x\)- and \(y\)-directions is shown in Figure 4a,b, respectively, where only the value for the container was varied and a constant value of \(f_{c}=0.3\) was used for the flume floor. Increasing values of \(f_{c}\) corresponds to a progressive, although small, decrease in distance travelled over time along the test area (Figure 4a). The general behaviour of \(\Delta y\) with increasing \(f_{c}\) is less clear due to cumulative effects at later stages of the container movement. A zoom for 1.10 s \(\leq t\leq\) 1.40 s is included in the figure to better visualise the effect of friction at the early stage of the container motion, showing that the container meandering motion starts sooner with larger values of \(f_{c}\).  
Figure 4: Comparison of \(\Delta x\) and \(\Delta y\) to analyse the effects of (**a,b**) \(f_{c}\) and (**c,d**) \(K\) on container dynamics.  
Figure 4c,d show the effect of \(K\) on the container trajectory where a constant value of the \(K\) is given each time to both the container and the flume bottom. \(K\) is seen to affect the container trajectory less than \(f_{c}\) for this study. This is due to impacts between the container and the flume floor only occurring at the onset of the container motion with only sliding between the two surfaces occurring for the rest of the motion. Note that the effect on the \(x\) evolution can be considered negligible in this case.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
## 5 Discussion
Considering the implementation of both applications with NMR-MPar running with the supervisor scenario, the reliability of the TSP is 0.9998 at 50,000 h, while that corresponding to the MM case is 0.9964. Only for reference, typical required reliability for space mission applications is \(>\)0.9995 [41]. In order to have a first insight into the impact on power consumption of the implementation of the  
Since the overhead of the implementation of the NMR-MPar is almost fixed to aroun one Giga cycles independently of the application, for short applications, the impact is greater. From the results presented in the above table, it is possible to observe in the case of TSP that, when the application runs at 400 MHz, there is an increase of about 1.7 times in the power average and energy consumption when executing the application with or without redundancy. This relation is reduced to 1.4-times when running the application at 100 MHz. In the case of MM, operating at 400 MHz, there is an increase of two-times the power average and 2.5-times the energy consumption; while at 100 MHz, there is an increase of 1.8-times the power average and 1.9-times the energy consumption. It is also important to analyze the behavior of the system when decreasing the operating frequency four times. For all the scenarios, there was a 40-54% reduction of the power, but an increase of 1.3-2.1-times in the energy consumption. Consequently, by reducing the operating frequency, the power dissipation and performance are reduced, while the energy consumption increases. Regarding future directions, this work opens up various possibilities to continue the investigation in evaluating the NMR-MPar approach under particle radiation to confirm the results obtained from fault-injection, and further tests must be run to appraise its overhead better in terms of energy and power consumption. Furthermore, it is interesting to conjugate this approach with other Software-Implemented Fault Tolerance (SIFT) techniques to better improve the reliability of the system.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 6 Conclusions  
This paper optimizes a reactive jamming utilization technique combined with deep learning and, on this basis, constructs the Intelligent Multi-Sequence Frequency Hopping (IMSFH) communication framework. Compared with existing jamming utilization methods, this framework analyzes training signals during the training stage to learn a reactive jamming rule from the spectrum waterfall and leverages this learned rule to improve frequency-detection performance during the communication stage, ultimately achieving effective utilization of reactive jamming. Finally, a simulation study of the IMSFH framework under different jamming parameters was conducted, and its performance was compared with IDFH communication frameworks. The simulation results demonstrate that as the reactive jamming power increases in complex jamming environments, the performance of IMSFH significantly improves. Compared with the MSFH communication framework and other improved MSFH frameworks, IMSFH exhibits superior performance. The IMSFH communication framework does not rely on specific preconditions, simplifying the communication signal processing flow and ensuring communication quality in low-SNR environments. Moreover, in complex jamming environments, IMSFH outperforms the IDFH communication framework by making better use of intelligent reactive jamming, demonstrating enhanced anti-jamming capabilities.  
Conceptualization, T.H. and X.L.; formal analysis, T.H. and Y.L.; funding acquisition, X.L. and M.W.; software, T.H.; writing--original draft, T.H.; writing--review and editing, T.H. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 62071135, the Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (Guilin University of Electronic Technology) under Grant No. CRKL200204, No. CRKL220204, RZ18103102, and the 'Ba Gui Scholars' program of the provincial government of Guangxi.  
All of the grants for this manuscript are still in the research phase, and some research data or key codes are currently limited to disclosure within the project team. However, the datasets used and/or analyzed during the current study are available via email ([EMAIL_ADDRESS]) on reasonable request.  
We are very grateful to volunteers from GLUT for their assistance in the experimental part of this manuscript.  
The authors declare no conflicts of interest.  
Figure 11: Error rate performance of IDFH and IMSFH in complex jamming environments.
## 4 Real Ship Experimental Verification
### Experimental System Layout and Parameter Setting  
#### 4.2.1 Experimental Point Layout  
The experiment in this study took the propulsion shaft system of a 16000 TEU container ship's low-speed diesel engine as the experimental subject and conducted sea trials in the East China Sea, where the sea conditions were between two and three levels, with the overall wind and wave direction from the northwest. To verify the dynamic impact of the shaft-driven generator without a support bearing on the propulsion shaft system, three measurement points were arranged in this experiment. They were located at the midpoint of the rear intermediate shaft, the midpoint behind the motor on the motor shaft, and the midpoint in front of the motor on the motor shaft, respectively, to measure their vibration torque and vibration stress, allowing for a comparative analysis of the data from the three measurement points. The schematic diagram of the measuring point position and the actual measuring point arrangement are shown in Figure 8.  
Figure 7: Experimental system diagram: (**a**) schematic diagram of the experimental system; and (**b**) physical image of the experimental system.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 2 Related Work
This gap underscores the necessity for specialized models that incorporate phase drift data to enhance AoA accuracy. The integration of time series data and positioning is crucial for accurate indoor localization, yet models designed specifically for precise angle estimation remain limited. The reviewed studies highlight the potential of using techniques such as GRU [39] and LSTM [40] to develop advanced models that leverage their time series handling capabilities, thereby improving AoA estimation accuracy.
## 5 Computational Analysis
### Conclusions and Recommendations  
Three distributor plates and various operational parameters have been studied to investigate the hydrodynamic characteristics of bubbling fluidized bed gasifiers. After extensive experimental and computational studies, many conclusions have been made to optimize the fluidization phenomena.  
* The perforated distributor plate exhibits the lowest minimum fluidization velocity compared to the other two.
* Pressure drop is found to be maximum for a 45\({}^{\circ}\) slotted plate.
* The hybrid plate shows the lowest pressure drop due to its highest open area ratio. Also, using this plate, minimum fluidization velocity remains between perforated and 45\({}^{\circ}\) slotted plate cases.
* Furthermore, a lower portion swirling flow is observed in a 45\({}^{\circ}\) slotted plate for better lateral mixing of particles. The initial Bed height rise ratio is also highest for a 45\({}^{\circ}\) slotted plate and lowest for a perforated distributor plate due to many smaller-sized bubbles initially rupturing the bed surface.
* It is also revealed that the bubble rise velocity ratio (F.I.) is highest for a perforated distributor plate, whereas it becomes lowest for a 45\({}^{\circ}\) slotted plate.  
Figure 22: Graph and contours of mean velocity magnitude of sand particles [m/s] with hybrid plate.  
* The 45\({}^{\circ}\) slotted plate improves inside mixing and lateral flow in the fluidized bed's lower portion.
* The hybrid plate improves mixing by combining smaller holes with 45\({}^{\circ}\) slots.
* For applications requiring critical lower portion solid mixing and dead zone removal, a 45\({}^{\circ}\) slotted plate is recommended.
* The hybrid plate suits various operational conditions and improves gasifier operational efficiency.
* Table 7 of the article summarizes the conclusions on the hydrodynamics of bubbling fluidized bed gasifiers, detailing the findings and recommendations for various operational and geometric parameters.  
**Author Contributions:** [PERSON] and [PERSON] proposed the main idea and methodology. [PERSON] and [PERSON] performed experimental research work. [PERSON] performed the CFD analysis and wrote the manuscript. [PERSON], [PERSON] and [PERSON] provided suggestions to improve the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project is supported by the National University of Sciences and Technology (NUST) under NUST Research Grant No. 21839.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of hydrodynamic characteristics of a cold model of gasifiers to a geometric and operational parameter.
# Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
###### Abstract  
The increase in mining depth necessitates higher strength requirements for hard rock pillars, making mine pillar stability analysis crucial for pillar design and underground safety operations. To enhance the accuracy of predicting the stability state of mine pillars, a prediction model based on the subtraction-average-based optimizer (SABO) for hyperparameter optimization of the least-squares support vector machine (LSSVM) is proposed. First, by analyzing the redundancy of features in the mine pillar dataset and conducting feature selection, five parameter combinations were constructed to examine their effects on the performance of different models. Second, the SABO-LSSVM prediction model was compared vertically with classic models and horizontally with other optimized models to ensure comprehensive and objective evaluation. Finally, two data sampling methods and a combined sampling method were used to correct the bias of the optimized model for different categories of mine pillars. The results demonstrated that the SABO-LSSVM model exhibited good accuracy and comprehensive performance, thereby providing valuable insights for mine pillar stability prediction.  
pillar stability prediction; SABO-LSSVM; ensemble algorithms; oversampling algorithm +
Footnote †: journal: Applied Sciences
## 2 Description of Experiments
### Struvite Production  
The tests were carried out both to identify the dosages of phosphorus and magnesium reagents and the modality of reactant addition to maximize the ammonium abatement. MgCl\({}_{2}\cdot\)6H\({}_{2}\)O solution (50 g/50 mL) and phosphoric acid (75% \(w/v\)) were used as magnesium and phosphorus sources. In a first set of experiments, \(n\)(Mg):\(n\)(N) and \(n\)(P):\(n\)(N), molar ratios between 1 and 1.5, were tested.  
In these tests, the solutions of Mg and P were added to leachate samples until the established values of _n_(Mg):_n_(N) and _n_(P):_n_(N) molar ratios were reached. Afterwards, the mixture pH was set to 9, identified as the most favorable for struvite precipitation [3; 31], by means of instantaneous additions of NaOH (40% \(w/v\)). The tests were carried out on leachate volumes of 200 mL and mixed at 300 rpm for 15 min by means of magnetic stirrers. In a second set of experiments, the solutions of magnesium, phosphorus, and NaOH were contextually dosed. Specifically, the chemicals were gradually fed to leachate so as to maintain the pH constantly around to 9. These tests were conducted on leachate volumes of 1.5 L by testing _n_(Mg):_n_(N) and _n_(P):_n_(N) ratios of 1.1, 1.2, and 1.3. For this purpose, beakers of 3 L, equipped with mechanical stirrers, were used. The reaction mixtures were continuously mixed (300 rpm) for an additional 15 min after addition of the reactants was completed.  
All the experiments were conducted at room temperature (20 \(\pm\) 2 \({}^{\circ}\)C) and pressure. After the reaction time, the mixtures were left to settle for about 30 min to promote the struvite sedimentation. The liquid phase was then sampled for analytic determinations. The precipitate, produced applying the more favorable conditions identified, was recovered and directly used for decomposition tests.
## 6 Experiment and Analyses
The test result is shown in Figure 23, where Figure 23a,b are the current of the DC motor when driving the circle roller mechanism at voltage of 2 V and 2.5 V. Figure 23c,d are the current of the DC motor when driving the cam roller mechanism at a voltage of 2 V and 2.5 V. From an intuitive perspective, Figure 23a,b, show that the current of the motor driving circular roller has greater fluctuation, while Figure 23c,d show that the current of the motor driving cam roller is smoother (which can also be proved by data analysis). Table 5 shows the effective values of the current passed through the DC motor driving circular roller and the cam roller under different voltages, and the calculation equation of effective value is Equation (27). \[I_{e}=\sqrt{\frac{\sum\limits_{i=1}^{n}{{(I_{i})}^{2}}}{n}} \tag{27}\]  
where \(I_{e}\) is the effective current, and \(I_{i}\) is the transient current that is measured by an oscilloscope with a total data count of \(n\). Figure 22: Comparison chart of the simulated flapping trajectory and actual flapping trajectory. Compared to a circular roller, a motor driving cam mechanism has a smaller effective current and lower energy consumption under the same voltage. At the same time, the current waveform was smoother, which proves that the cam mechanism reduced the phenomenon of the motor torque increase, which was caused by the excessive tension of the string. This also proved, from an experimental perspective, that the cam mechanism that was designed according to parameter analysis had more advantages than the circular roller mechanism. It is worth mentioning that our experiments were conducted at a relatively low flapping speed with a load of carbon fiber rods of which the mass was almost negligible. At that point, the measured flapping trajectory and maximum amplitude showed a good similarity with the simulation data, which validated the correctness of our kinematic design. However, as the flapping frequency increased and the wings were installed, the aerodynamic and inertial effects gradually increased. Due to the elasticity of the rope and the tensile properties of the structural components, the measured flapping amplitude would be higher than the designed amplitude. Factors such as these are difficult to consider in the design of structural morphology, so they are not within the scope of experimental verification in this article. Studies related to this aspect can be included in future research plans.
# Assessing the Security and Privacy of Baby Monitor Apps
## 4 Evaluation of the Privacy Policies
### Data Security  
Article 32 of the GDPR requires data controllers and processors to take appropriate data protection measures to ensure the security of collecting and processing personal data based on existing risks [35]. Dormi and Babyphone Mobile state to take concrete security measures by encrypting transmitted audiovisual data. More concretely, the Babyphone mobile app offers end-to-end encryption as an additional option by asking the user to set a password as the encryption key. The Dormi app offers end-to-end encryption by default. The Saby app generically states to take security measures and apply commercial security products to protect collected personal data. The BabyCam app does not provide any explicit statements about security measures.