esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
## 3 Methodology
### Improved Network
#### 3.2.4 Focaler-IOU  
Focaler-IoU is a loss function designed for bounding box regression in object detection. Primarily, it focuses on diverse regression samples to enhance detector performance across various detection tasks. By reconstructing the IoU loss through linear interval mapping, Focaler-IoU strategically prioritizes samples based on their regression difficulty. Furthermore, it explicitly addresses the distributional imbalance between hard and easy samples during bounding box regression, which is a critical aspect often overlooked by traditional IoU-based losses. This is achieved by dynamically adjusting gradient contributions according to sample difficulty, thereby optimizing both training stability and model generalizability [29].  
As defined in Equation (13), Focaler-IoU dynamically adjusts the loss based on the Intersection over the Union (IoU) value. When the IoU is below a lower threshold d, the loss is set to 0; when the IoU exceeds an upper threshold u, the loss is fixed at 1. For IoU values between d and u, the loss follows a linearly increasing function proportional to the IoU. This design ensures that the loss function exhibits controlled sensitivity within the intermediate IoU range, thereby prioritizing samples where the predicted and ground-truth bounding boxes exhibit moderate overlap. By focusing on these \"moderate difficulty\" samples--neither excessively challenging nor overly straightforward--the model is incentivized to learn discriminative features from scenarios requiring nuanced localization rather than overemphasizing trivial or outlier cases. The expression is shown in Equation (13).  
\[IoU_{focaler}=\begin{cases}0,&IoU<d\\ \frac{IoU-d}{u-d},&d\ll IoU\ll u\\ 1,&IoU>u\end{cases} \tag{13}\]  
In sonar image object detection tasks, where detection difficulty varies significantly across target categories, we propose replacing the original Complete IoU (C-IoU) with Focaler-IoU to address the distributional imbalance between hard and easy samples, thereby enhancing detection precision. This adaptation specifically targets scenarios where conventional IoU losses inadequately handle ambiguous boundaries and partial occlusions common in underwater sonar data.
## 5 Challenges and Future Perspectives  
Technical and Operational Challenges:  
1. Process Control Precision: Achieving consistent and uniform outcomes with FS and UHS is complicated by the need for precise control over electric fields, temperature, and sintering atmosphere. This variability often results in non-uniform material properties, underscoring the need for advanced mechanistic insights and optimization strategies. Future research should aim to understand the physicochemical interactions and phase transformations during sintering, focusing on the dynamics of fast heating and cooling and the impact on material defects and microstructures under non-equilibrium conditions.
2. Material Compatibility and Diversity: Both FS and UHS encounter limitations with specific ceramics and composites, necessitating broader exploration of materials that are amenable to fast sintering processes without sacrificing structural integrity. Addressing this challenge involves developing new ceramic composites and coatings optimized for these sintering technologies, with enhanced ionic conductivities, mechanical properties, and thermal stabilities.
3. Scalability for Industrial Production: Scaling FS and UHS to industrial levels presents significant challenges, particularly in ensuring uniform treatment of larger or complex-shaped components and maintaining process stability. Investigating scalable sintering solutions that can adapt FS and UHS for large-scale manufacturing is crucial. Collaborative efforts between academia, industry, and equipment manufacturers are pivotal in this regard.
4. Specialized Sintering Equipment: The requirement for advanced sintering systems capable of managing ultrafast heating rates and achieving high temperatures contributes to the cost and complexity of FS and UHS. Future pathways include the integration of these sintering techniques with additive manufacturing technologies, which could revolutionize SOFC component design and fabrication by enabling complex geometries and tailored microstructures for enhanced performance.
5. Cost-Effectiveness and Energy Efficiency: The economic viability of FS and UHS in SOFC production hinges on balancing the initial investment in specialized equipment against the potential energy savings and improved efficiency from reduced sintering times. Emphasizing sustainable manufacturing practices that reduce the energy footprint of SOFC production aligns with global sustainability goals, urging further quantification of the environmental and economic impacts of these technologies. Innovative Pathways for Technological Integration and Process Innovation:
6. Synergy with other processes and hybrid sintering techniques: Exploring FS and UHS in conjunction with other emerging sintering technologies could provide balanced solutions to existing challenges. This approach promises uniform material properties, reduced thermal gradients, and improved densification outcomes, marking a transformative era for SOFC fabrication.
## 4 Feature Extraction and Control Methods
### Feature Extraction  
The active balancing system operates to reduce the unbalance vibration. If some errors occur in the extraction of the unbalance vibration, then several parameters of the balancing system will go into effect, such as balancing accuracy, balancing speed, and action time. Even when the error is large, the balancing operation will increase the vibration of the rotor and cause a misalignment. Therefore, it is particularly important to accurately extract the phase and amplitude of the unbalance vibration of the rotor system.
## 4 Results  
### Antenna Input Characteristics  
We first characterized the balun by measuring its scattering parameters on a standard, 50 ohm normalizing impedance. Based on that, we found the insertion loss of the balun terminated on the folded dipole (Figure 7), and the input impedance both at the antenna input and at the balun input.  
Figure 8 shows the variation as a function of frequency of the resistance, reactance, and VSWR at the antenna input. Losses in the antenna wire and balun make it possible to obtain a VSWR mostly below five over the frequency range of interest, i.e., between 7 and 30 MHz. The efficiency ranges between \(-36\) and \(-7.8\) dB; however, our gain measurements yield stable results for transmission between two such antennas placed at distances of up to 2.5 m one from the other, and for an input power of 0 dBm.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 8 Conclusions  
In this paper, we have proposed a network and security architecture for the disaster surveillance UAV system. The network architecture of the system is based on IEEE 802.11 ah standard where non-relaying and relaying nodes are separated using different RAW slots which provide low access contention for sensed and relayed data. This system provides a high degree of accuracy and availability of the collected information from the disaster area and consequently assures the quality of the rescue and evacuation operations. The proposed scheme preserves the integrity and availability of the collected information by utilizing fingerprint features and data redundancy techniques; it also deploys the lightweight Ring-LWE crypto-system to further protect the confidentiality of the transmitted messages with a low computation burden.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] designed the security scheme; [PERSON], [PERSON], [PERSON], and [PERSON] designed the communication scheme and the performance evaluation experiments. [PERSON] and [PERSON] wrote the final version of the paper.  
**Funding:** The work of [PERSON] and [PERSON] was supported through their respective NSERC Discovery Grants. The work of [PERSON] was partially supported by the Faculty of Science, Ryerson University.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Energy consumption pattern for drone trip.
## 5 ACTOR Design
### The Building Blocks
```
1: Upon receiving a DIO, measure RSSI
2: Initialize the Action Values-Based Equation (5)
3:for each packet \(p\) sent through neighbor \(Nbr\)do
4:if\(Nbr\) not in the neighbor table AND Received ACK then
5: Add \(Nbr\) to neighbor table
6: set \(N_{t}(a)\) and \(\bar{X}_{t}(a)\) to zero for all transmission power settings
7:endif
8:if Parent Switch happened then
9: Reset the learned Action Values
10:endif
11: Calculate UCB index and select transmission power based on based on Equation (8)
12: Select UCB index based on Equation (9)
13: Transmit packet with selected transmission power
14: Increment the counter for transmission power \(a\): \(N_{t}(a)=N_{t}(a)+1\)
15: Update \(demand_{Nbr}\) from
16:if packet sent successfully then
17: the Action Values based on Equation (7)
18:endif
19:if packet sent failed then
20: the Action Values based on Equation (7)
21:endif
22: Set the transmission power to \(\max(UCB,demand_{i})\)
23:if\(A_{t}\
eq A_{t-1}\) (if optimal transmission power changes) then
24: signals the demand (\(A_{t}\)) to the parent
25:endif
26:endfor
```  
**Algorithm 1** ACTOR  
We also help the UCB by filtering the transmission power settings that can be classified as insufficient. If a transmission power has been used at least three times and the action value is zero, we avoid using that action and lower power levels in the future. It is also worth mentioning that, due to the lack of comprehensive support for floating point operation in the target IoT micro-controllers, we perform the mathematical operations in the integer domain.
# Global Regulatory Challenges for Medical Devices: Impact on Innovation and Market Access
## CE Marking
### European Database on Medical Devices
#### 4.1.3 Post-Market Surveillance  
Article 83 of Regulation (EU) 2017/745 outlines the requirements for the post-market surveillance system [11]. Manufacturers must establish and maintain a system proportional to the device's risk class, integrated into their QMS. This system actively collects, records and analyzes relevant data on device quality, performance and safety throughout its lifecycle. The data collected is used for various purposes, including updating benefit-risk assessments, improving risk management and identifying preventive or corrective actions when necessary. Manufacturers must inform relevant authorities when corrective actions are needed [43; 52].  
Article 85 of Regulation (EU) 2017/745 mandates that manufacturers of Class I devices produce a post-market surveillance report summarizing results and conclusions, including details of any preventive or corrective actions taken [11]. Manufacturers of Class IIa, IIb and III devices must also produce periodic safety reports, updating them annually for Class IIb and III devices and as needed for Class IIa devices. For Class III and implantable devices, manufacturers must submit safety reports to the NBs participating in the conformity assessments, while for other devices, reports are provided to the relevant NBs and competent authorities upon request [43; 52].  
This comprehensive framework ensures the ongoing monitoring of medical devices' safety and performance post-commercialization, facilitating the timely identification and management of any issues that may arise [11]. Table 1 provides an overview of the classification and regulatory requirements pre- and post-market for medical devices in the EU.  
Figure 6: Modules of EUDAMED [51].
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 3 Reputation-Based Honest Blockchain Pool Mining
### Reputation Estimation Based on Probability Interval  
When \((n+1)\)-th mining round starts, the minimum criterion for joining the mining is the entry threshold \(h^{(i)}(i\in\mathcal{L})\) for the pool \(i\), and we have \(\mathrm{h}=\Big{[}h^{(1)},h^{(2)},\ldots,h^{(\mathcal{L})}\Big{]}^{T}\). If \(R_{j}(n)<h^{(i)}\), they are not allowed to enter pool \(i\).  
While \(n\rightarrow\infty\), the reputation distribution of miner \(j\) obeys the normal distribution of \(N\bigg{(}\mu_{j},\frac{\sigma_{j}^{2}}{n}\bigg{)}\), where we have the expectation \(\mu_{j}\), variance \(\sigma_{j}^{2}\) and arithmetic mean \(\overline{R}_{j}\). Based on the confidence interval estimate, the reputation interval at the confidence level of \(1-a\) could be obtained as [24]:  
\[B_{j}(,n)=\big{(}\overline{R}_{j}(n)-\frac{\sigma_{j}}{\sqrt{n}}t_{\frac{ \alpha}{2}(n-1)},\overline{R}_{j}(n)+\frac{\sigma_{j}}{\sqrt{n}}t_{\frac{ \alpha}{2}(n-1)}\big{)} \tag{10}\]  
where \(\sigma_{j}=\frac{1}{n-1}\sum\limits_{n=1}^{N}\big{(}R_{j}(n)-\overline{R}_{j} \big{)}^{2}\).  
That is, the reputation level of miner \(j\) after \(n\)-th mining is in the reputation interval \(B_{j}(a,n)\). It can reflect the level of creditworthiness in a certain period, while the stability of the reputation is depicted by reputation fluctuation \(f_{j}\). We propose a hypothesis test with confidence level of \(\alpha\) to calculate the rejection interval, then the miner \(j\) is rejected as being dishonest within the rejection interval, i.e.,  
\[\sigma_{j}^{2}\geq\frac{f_{j}^{2}X_{a}^{2}(n-1)}{n-1} \tag{11}\]
## 1 Introduction
### Article Selection Criteria  
We searched for articles for the survey in Google Scholar, Institute of Electrical and Electronics Engineers (IEEE) Xplore, ScienceDirect, and PubMed. Then we applied certain criteria to select articles to include in the survey. Figure 8 shows the flow chart of the selection criteria.  
The use of DT and FL is increasing in the IIoT, IoV, and IoD. In this survey, we analyzed several applications of DTs and FL to the IIoT, IoV and IoD. The survey is organized as follows: Section two presents the discussion about the existing surveys on DT and FL. Section three presents the application of DT and FL to the IIoT, IoV, IoD. In section Four, the open research issues and future directions are explained. Section Five concludes the survey. The organization of the survey is shown in Figure 9.  
Figure 8: Flowchart of the article selection criteria.  
Figure 7: Number of articles on FL on Google Scholar versus year (accessed on 21 May 2022).
## 4 CO2 Emissions Analysis and Indicators in Sub-Projects
### Shield Segment  
For segmental lining projects, CO2 emissions are primarily influenced by the length and thickness of the segments, the type of segment, and the reinforcement ratio of the concrete. However, as shown in Table 4, the CO2 emissions per unit of length and per unit of project volume remain relatively consistent. Therefore, this analysis focuses on CO2 emissions per unit of length and per unit of project volume.  
The results of the correlation analysis are presented in Table 5. The CO2 emissions per unit of length for Tunnels 1, 2, 3, and 4 are quite similar, fluctuating around 35,000 kgCO2e/m, with a maximum difference of only 0.44%. Likewise, the CO2 emissions per unit of volume for Tunnels 1, 2, 3, 4, and 6 remain stable, averaging approximately 1180 kgCO2e/m3, with a maximum variation of 1.6%. The higher unit of volume CO2 emissions in Tunnel 3 can be attributed to the partial use of steel segments.  
From the perspective of segmental lining sub-projects, the CO2 emission metrics per unit of length and per unit of volume remain stable, with minimal variation. From a CO2 reduction standpoint, achieving significant reductions during the design phase is difficult unless more carbon-efficient materials are utilized and the use of steel segments is minimized in the design process.
## 3 Results and Discussion  
### HPLC-DAD Optimization  
In the present contribution, two UV filters (PBS and BZ4, Table 1) were studied as target analytes. From the very beginning, the HPLC-DAD instrumental conditions were evaluated, such as UV/vis absorbance spectra, retention times, and resolution. A wavelength at 300 nm (\(\lambda_{\mathrm{max}}\)) was selected as it maximized the DAD signal for both target compounds. Since the ionization equilibrium is temperature dependent and the two UV filters are very polar and ionizable compounds, temperature control can improve the HPLC selectivity [14]. Therefore, the column temperature was maintained constant at 50 \(\mathrm{\SIUnitSymbolMicro}\)C, once better symmetrical peak shapes were achieved. Therefore, optimized HPLC-DAD conditions resulted in good resolution in a suitable analytical time (11.0 min). Instrumental sensitivity was evaluated through the limits of detection (LODs) and quantification (LOQs), obtained after the injection of diluted standard mixtures and calculated with a signal-to-noise ratio (S/N) of 3 and 10, respectively. From the data obtained, instrumental LODs and LOQs of 20 and 100 \(\mathrm{\SIUnitSymbolMicro}\) L\({}^{-1}\), as well as 60.0 and 300.0 \(\mathrm{\SIUnitSymbolMicro}\) L\({}^{-1}\) were achieved, respectively. The instrumental calibration was assessed with standard mixtures ranging from 60.0 to 10,000 \(\mathrm{\SIUnitSymbolMicro}\) L\({}^{-1}\) for PBS and 300.0 to 25,000 \(\mathrm{\SIUnitSymbolMicro}\) L\({}^{-1}\) for BZ4, using six levels. The regression plots achieved showed good linearity responses (\(r^{2}\) = 0.9982 for PBS and 0.9996 for BZ4).
## 5 Conclusions  
In this article, a novel statistical approach, which is capable of reflecting various noises affecting the SI/PI of the high-speed channel, was proposed and verified. Probability density functions (PDFs) of output step-responses and steady-state responses affected by the various noises were derived using HSPICE. Output responses were derived from repetitive step-response simulations using HSPICE. Occurrence probability of each output response was calculated to formulate the PDFs. During this step, it is possible to consider the bit pattern and data-coding impacts mathematically. In this article, uncorrelated cases (randomly switching) and DBI coding were considered as representatives. From the derived output response PDFs, main-cursor (MC) PDFs and inter-symbol interference (ISI) PDFs were defined. After recursive convolution between the MC-PDFs and ISI-PDFs, four final statistical output responses are derived. Finally, the statistical eye-diagram can be estimated by superposing the derived statistical output responses and expanding them to two-unit intervals (UIs). Accuracy and computational efficiency of the proposed method were validated. The proposed method enables extremely low BER analysis where conventional methods fail to estimate noises with low occurrence probabilities.  
The proposed method was applied for SI/PI co-design and co-analysis of the high bandwidth memory (HBM) interposer channel. In the study, various design parameters affecting the SI/PI are considered. Specifically, impacts of the decoupling capacitor on the hierarchical PDN impedance and the statistical eye-diagram were focused on. From the derived statistical eye-diagrams of the HBM interposer channel, BER eye-diagrams and bathtub curves enabling timing and voltage analysis are derived. It is verified that the proposed method enables efficient SI/PI co-design and analysis by simultaneously plotting various results affecting the SI/PI of the target channel.
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 2 Process for Fatigue Life Estimation Based on the Local Stress-Strain Method  
The fatigue life prediction method consists of three aspects: fatigue performance of the material, transformation from nominal stress and strain into local stress and strain, and fatigue cumulative damage theory. A modified Neuber equation was used to transform nominal stress-strain to local stress-strain. The Miner theory and Manson-Coffin equation have been widely used to characterize cumulative fatigue damage. Therefore, the cyclic stress-strain curve and strain-life curve can be combined as the local stress-strain method to estimate the fatigue crack life.  
The raw data needed for calculation includes the elastic modulus, the steady cyclic stress-strain curve, the strain-life curve, the theoretical stress concentration factor, and load history.  
Steps for fatigue life estimation, based on the local stress-strain method, are shown in Figure 1.
## 4 Simulation Results and Analyses
### Comparison between Different Equalizations
This finding is because the bidirectional structure of the IFDTE obtains bidirectional diversity, and the performance of the Bi-IFDTE outperforms the IFDTE. Therefore, the IFDTE outperforms the conventional turbo equalization, and the bidirectional structure improves the performance of the IFDTE. Table 4 summarizes the running time of different equalization schemes during numerical simulations. The time-domain equalization runs longer than the frequency domain  
In fact, our research work has included a rather comprehensive comparison with typical existing equalization schemes in UWACs to highlight the novelty and superiority of the proposed approach. And the comprehensive comparison can be seen from the simulations in Figures 5, 6, 10 and 11 with well-established methods, including the TDTE and FDTE and their extensions. In addition, the simulation of the MSE performance curve in Figure 4 and the EXIT charts in Figures 8 and 9 have further verified the good performance of the proposed scheme. Therefore, there are adequate data to verify the significant performance of the proposed scheme. However, there are also potential challenges or scenarios where the proposed method may not be as effective. This mainly relates to the Cramer's Rao criterion, which all estimation algorithms must obey. If the UWAC channel is time variance, and the channel parameters change within one block length of the channel codes employed in the turbo equalization, the estimation cannot be obtained and the proposed algorithm is not effective either.