esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Routing Algorithm
## 7 Conclusions  
In this paper, we take the advantages of CS and combine it with an appropriate routing algorithm in three-dimensional USNs; an energy-efficient algorithm (CS-CULM) based on CS is proposed, aiming at effectively extending the lifetime of USNs, while ensuring the accuracy of data reconstruction. The simulation results show that in USNs, when basic transmission length \(r\) is 3.5 km, CS-CULM can prolong the network lifetime by up to 66% compared with CS-CLM while achieving effective data reconstruction.  
Figure 6: Reconstruction error \(\varepsilon\) vs. number of sensor nodes: comparison among CS-3 DIAIR and different probabilities of becoming a cluster head: \(q=0.15\), 0.35, 0.55 in CS-CULM.  
Figure 7: The original temperature and reconstruction data in three-dimensional underwater sensor networks (USNs). (**a**) The original data; (**b**) The reconstruction data.  
**Acknowledgments:** This work is partly supported by the National Natural Science Foundation of China (No. 61401118, No. 61371100 and No. 61671184), Natural Science Foundation of Shandong Province (No. ZR2014 FP016 and BS2012 DX001), the Fundamental Research Funds for the Central Universities (No. HIT.NSRIF.2016100 and 201720) and the Scientific Research Foundation of Harbin Institute of Technology at Weihai (No. HIT(WH)201409 and No. HIT(WH)201410).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] contributed simulation tools; [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
For instance, one study investigated hardware-based shading mitigation methods in PV grid-connected energy systems, focusing on module and array reconfiguration to enhance performance [17]. Another review examined the theoretical aspects of reverse breakdown phenomena in PV cells and explored multiple approaches to mitigate partial shading effects [18]. Implementing hardware-based solutions often necessitates extra components and intricate system modifications. While these approaches can effectively address shading issues, soft-computing approaches are increasingly preferred because of their superior ability to track the GPP under partial shading with higher accuracy and adaptability [16]. Collectively, the literature underscores the challenges encountered due to partial shading circumstances and the diverse mitigation strategies employed to optimize PV system performance. Soft-computing algorithms offer a dynamic and efficient alternative, complementing hardware-based methods in enhancing energy extraction in PV applications. Given the critical issue for robust shading mitigation techniques, this study is structured around four key objectives, detailed as follows:  
* Model and design the power and control circuits of the photovoltaic energy conversion system under static and dynamic environments. * Apply and compare two advanced metaheuristics-based MPPT techniques, Cuckoo Search Optimizer and Particle Swarm Optimization, to optimize, simulate, and follow the global maximum power output of the PV energy system in both static and dynamic environments. These algorithms are selected for their proven efficiency and robustness in handling the challenges of non-linear and multi-modal optimization problems such as those arising under partial shading in PV energy systems. CSO is known for its superior convergence speed and ability to escape local optima, while PSO excels in simplicity, adaptability, and achieving high accuracy. By leveraging the strengths of these methods, this study aims to provide a detailed comparative analysis of their performance in maximizing PV system efficiency. * Investigate the performance of the proposed photovoltaic energy conversion system under both time-variant and -invariant PSCs. * Analyze and compare the effectiveness of the selected metaheuristic-based MPPT techniques in terms of global power tracking, convergence time, and oscillations around the steady state.
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 2 Materials and Methods  
In rescue operations, the safety of both rescuers and victims relies on having accurate and up-to-date information about the environment. The unpredictable and rapidly changing conditions of a disaster scene make it essential for rescuers to understand the physical and geographical features of the area, as well as any human-made structures or infrastructures present. However, lighting deficiencies and scene degradation can present significant challenges for rescuers in identifying key features, such as pathways, buildings, and other landmarks. To address these challenges, we propose a pipeline that integrates both DL and IP techniques specifically designed to handle extreme lighting conditions. In the following section, the pipeline graphics are depicted using the TM-DIED: The Most Difficult Image Enhancement Dataset [21], which showcases images in various lighting scenarios featuring diverse intensity shifts between regions that are underexposed, overexposed, and correctly exposed.
## 4 Performance Analysis  
The procedure presented in Section 3 was used to synthesize the IKMs of the PUMA 560 robot and a leg of the BH3-R walking hexapod, both presented in Figure 2. As was previously explained in Section 3, the output of the developed procedure is the calculated IKM, both in C++ and in MATLAB\({}^{\textregistered}\) script.  
The performance of the two IKMs was simulated in MATLAB\({}^{\textregistered}\), testing their ability to solve the Inverse Kinematic Problem in all the workspace of their corresponding robotic systems. All the obtained solutions for both IKMs were compared with their corresponding reference models that are the kinematic models calculated by traditional methods (see Section 2).
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 2 System Model  
In VANETs, according to the type of message sender and receiver, the communication fashions can be divided into two categories: (1). Vehicle to Vehicle (V2V), which means the sender and receiver are all vehicles; 2. Vehicle to infrastructure (V2I). In (V2I), the types of terminals include Base Station (BS),wireless access point (AP) and other infrastructure. Without loss of generality, in this paper, we use \(V\) to indicate vehicle and \(I\) as BS, AP, etc.
# A Circularly Polarized mmWave Dielectric-Resonator-Antenna Array for Off-Body Communications
## 2 Design Methodology  
A single DRA element was developed first to operate at 28 GHz. The parameters of the proposed DRA array were set based on mmWave off-body-communication requirements, such as low profile, wideband and high gain. The proposed antenna was simulated in free space as well as on top of a three-layer model of human tissue to investigate the effects of the human body on performance.
# Improved Safety Analysis Integration in a Systems Engineering Approach
## 5 Improved System Behavior Based on DFT Recommendation
### Step 4: Describe the System Behavior Using the DFT Information
#### 5.2.1 Qualitative and Quantitative Analysis  
The quantitative analysis aims at computing the probability of a top level event failure. In this analysis type, two different approaches are defined, which are the Continuous-Time Quantitative Analysis (CTQA) and the Discrete-Time Quantitative Analysis (DTQA). The CTQA is an approach that considers the evolution of the failure of the system over time, while the DTQA is an approach that considers the whole lifespan of the system as a single event. The qualitative analysis aims at identifying the high-level event which is the necessary and sufficient combination of the basic events. The techniques that are used most of the times in the qualitative analysis are \"minimal cut sets\", \"minimal cut sequences\", and \"common cause failures\". The Cut Set (CS) is composed of component failures that cause the failure of the system. The Minimal Cut Set (MCS) is a cut set which, when any element is removed from it, what remains is no longer defined as a cut set [36]. The Minimal Cut SEQuence (MCSEQ) is a cut sequence which, when any element is removed from it, what remains is no longer a cut sequence. It causes the occurrence of the top level event of the Dynamic Fault Tree. The difference between the MCS and the MCSEQ is that the MCSEQ considers the occurrence order of the failure of the basic events.  
Figure 4: Subtree of a standby redundancy with switch component.
## 3 Performance Characterization and Reliability Assessment
### Thermal Shock Test  
Figure 13 provides comprehensive documentation of the thermal shock testing methodology and results for all three resonator configurations. Part (a) displays the detailed temperature cycling profile used during testing, showing the temperature excursions between extreme conditions of \(-50\)\({}^{\circ}\)C and \(+145\)\({}^{\circ}\)C. The graph plots temperature (\({}^{\circ}\)C) against time, illustrating the complete thermal shock protocol where samples were maintained at each temperature extreme for 30 min before transitioning to the opposite extreme, completing a total of 10 full cycles. This rigorous testing follows industry-standard reliability assessment protocols designed to evaluate the robustness of MEMS packaging under severe thermal stress conditions. Part (b) presents a comparative bar chart showing insertion loss measurements (in dB) for all five samples of each configuration (L1, L2, and L3) both before and after the thermal shock testing. The consistent performance values between pre-test and post-test measurements conclusively demonstrate that no bonding interface failure occurred during the thermal cycling process for any of the three configurations. This thermal stability verification confirms that the Au-Au thermocompression bonding technique employed in this study provides excellent thermal resilience across all resonator width configurations, even under extreme temperature variations. These results further validate the reliability of the L3 design (100% width matching), which not only maintains superior RF performance but also exhibits robust thermal stability comparable to the other configurations, making it particularly suitable for applications in harsh environmental conditions.
## 1 Introduction
Given the high computational cost demand for the optimization problem, the selected wake model needs to facilitate the optimization to progress smoothly while maintaining a high level of computational accuracy. In this regard, [PERSON] et al. [12; 13] proposed a mechanistic semi-empirical wake model and an integrated Computational Fluid Dynamics (CFD) wake model for solving the two-dimensional and three-dimensional wind farm layout optimization problems, respectively. [PERSON] et al. [14] evaluated the performance of a new Gaussian-based wake model by comparison to the results presented in the typical wind farm layout optimization studies. Moreover, the evaluation of wind farm power output under realistic wind condition by Weibull distribution is a problem to be solved for accurately calculating the fitness of objective function in layout optimization study. [PERSON] et al. [15] initially studied the wind farm layout optimization under the Weibull distribution wind condition. The means of calculating the total wind farm power output under the Weibull distribution were formulated using the wind speed discretization method. Followed by the work, [PERSON] et al. [16] and [PERSON] et al. [17] applied the same discretization method to study the wind farm layout optimization problem with more advanced optimization algorithms. The idea of the discretization method was to divide the wind speed region into a certain number of bins with constant interval, and then the power output was calculated as an integral of wind speed using the approximate average wind speeds of different bins [18; 19]. In this paper, a real offshore wind farm layout optimization using the unrestricted coordinate method was conducted under different wind scenarios, including the simplified wind conditions and a realistic Weibull distribution wind condition. A recently developed analytical wake model, which has a great balance of the computation cost and calculation accuracy, was employed for the optimization study. The means of handling irregular boundary of real offshore wind farm were proposed along with the discretization method applied for calculating the wind farm power output under Weibull distribution wind condition. The remainder of this paper has been organized as follows. Section 2 presents the modelling and methodology applied in the study of wind farm layout optimization, which includes the wind turbine model, wind farm model, wind condition models, wake model, wind farm boundary handling, and objective function fitness evaluation techniques. Section 3 discusses the optimization results and Section 4 concludes the research work.
## 3 Results and Discussion  
### Results of the Model  
A numerical simulation was conducted based on the aforementioned conditions, and the resulting temperature distribution at depth is shown in Figure 8; the temperature measurement hole data are used as validation data to compare with the simulated calculation of the temperature measurement hole data, and the simulated temperature measurement data have a good correspondence with the actual temperature measurement data (Figure 9). Figure 10 shows that the tunnel temperature rises as the burial depth increases. The temperature gradient is higher at 0-50 km, and it decreases after the tunnel passes through the fracture of the south edge of Laji Mountain. The tunnel crosses the high-temperature zone from 0 to 50 km. Between 0 and 28 km, the surrounding rock formations along the tunnel route are primarily composed of granite, and due to the high thermal conductivity of granite and the heat-conducting channel formed by the fracture zone near the area, the tunnel reaches the maximum temperature of 51.1 \({}^{\circ}\)C in the surrounding rocks at 22.5 km, which is an abnormally high-temperature zone. Between 50 and 74 km, the temperature of the surrounding rock along the tunnel route drops significantly after crossing the fracture at the southern edge of Laji Mountain, with the overall tunnel temperature not exceeding 26.1 \({}^{\circ}\)C.  
Figure 8: Cloud diagram of numerical simulation field of tunnel. 1—Qinghai Nanshan North Marginal Fracture; 2—Datong River Fracture; 3—Hacheng Fracture; 4—Laji Mountain South Marginal Fracture; 5—Laji Mountain North Marginal Fracture.  
Figure 9: Comparison of actual and simulated temperatures of probe holes.
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 3 Result and Discussion
### Verification of Generalization Proficiency  
In addition to testing the algorithm on the dataset, we chose conveyor belt damage data from the Refs. [25; 26; 29; 30; 32] for validation of the model's generalization ability, and the results are shown in Figure 8. Generalization ability refers to the ability of the neural network model to adapt to fresh samples, and we expect that the model we obtain through training on the dataset will still give reasonable output when faced with data outside the dataset, i.e. fresh samples. The generalization ability of a model is the third common measure of the superiority of a neural network model, besides the mean Average Precision (mAP), and the prediction speed (FPS).  
In Figure 8, the first row show the original image of the conveyor belt damage, figures shown in the second row are the detection results using the method in this paper, and the third row are the detection results given in Refs. [25; 26; 29; 30; 32] respectively. Among them, (c) shows the tear detection method based on image processing, (f) shows the detection method based on infrared spectral analysis, which integrates the problem of local temperature increase due to sliding friction during the tearing process of the conveyor belt, and (l) shows the method assisted by a line laser, which transforms the detection problem of tears into the detection of corner points in a continuous smooth curve with the help of a line laser generator, (m,n) are the damage form of the conveyor belt proposed in Ref. [32], (m) shows scratch, and (n) shows tear, but the recognition results in this paper are all tears, because the annotations in our dataset are different from that. As shown in Figure 8, the algorithm in this paper achieves good detection results in these fresh samples, proving the strong model generalization capability of the algorithmic model in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\alpha\) & **mAP/\%** & **FPS** \\ \hline MobilenetV1-Yolov4 & 0.25 & 74.49 & 70.26 \\ MobilenetV1-Yolov4 & 0.5 & 85.07 & 66.54 \\ MobilenetV1-Yolov4 & 0.75 & 91.12 & 63.28 \\ MobilenetV1-Yolov4 & 1 & 89.95 & 51.12 \\ \hline MobilenetV2-Yolov4 & 0.5 & 89.32 & 45.87 \\ MobilenetV2-Yolov4 & 0.75 & 90.67 & 42.47 \\ MobilenetV2-Yolov4 & 1 & 88.60 & 42.00 \\ MobilenetV2-Yolov4 & 1.3 & 93.22 & 35.62 \\ \hline MobilenetV3-Yolov4 & 0.75 & 91.30 & 49.14 \\ MobilenetV3-Yolov4 & 1 & 92.41 & 44.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The influence of different adjustment factors on the test results.  
Figure 8: _Cont._
## 3 Results and Discussion
### Temperature Susceptibility  
Temperature susceptibility of modified asphalt binders was assessed using the penetration index (_PI_) and penetration viscosity number (_PVN_) based on penetration and softening point at 25 \({}^{\circ}\)C and viscosity at 135 \({}^{\circ}\)C. Higher _PI_ and _PVN_ values indicated reduced temperature susceptibility. _PI_ classified asphalt binder type and suitability for highway construction, while _PVN_ gauged temperature susceptibility. The findings in Table 4 indicate that both _PI_ and _PVN_ values increased with crumb rubber addition, signifying lower temperature susceptibility in CR asphalt binders. Except for Sasobit, WMA additives displayed similar behavior in raising temperature susceptibility values. Asphal binders modified with WMA additives and crumb rubber exhibited temperature susceptibility akin to virgin asphalt binders. The utilization of Sasobit in the rubberized asphalt binder leads to an elevation in the PI value, accompanied by a reduction in the _PVN_ value. This aligns with the outcomes observed in the present study [48]. Table 4 shows the outcome of the _PVN_ and _PI_ of all asphalt binders in the study.
# Research on Testing Method of Oil Characteristic Based on Quartz Tuning Fork Sensor
## 3 Oil Ageing Processes and Sensors for Fluid Property Detection
### Incorrect Fluid Detection  
Filling with incorrect fluids can be dangerous for the engine. Indeed, engines are designed to operate with determined oil characteristics. Use of the wrong oil may degrade engine performance and could induce risk of failure.  
Fluid property sensor measurements of four different oils are presented in Figure 20: three engine oils, 0W40, 10W30 and 15W40, and one bio ISO32 hydraulic oil were studied. Each oil sample was stabilized and tested at different temperatures, ranging from 50 \({}^{\circ}\)C to 120 \({}^{\circ}\)C. The results demonstrate the capability of the fluid property sensor to detect the presence of an incorrect fluid with viscosity, density, dielectric, and resistivity measurements.  
A fluid property sensor is capable of differentiating any type of engine oil or hydraulic oil, mainly with viscosity measurements. Indeed, the viscosity of each sample is clearly identified by fitting with viscosity grade standard SAE J300: 15W40 oil has the highest viscosity; 0W40 oil has a viscosity lower than those of the other engine oils at low temperature and has a viscosity similar to that of 15W40 oil at high temperatures; 10W30 oil has lower viscosity at high temperature than the two other engine oils. ISO32 hydraulic oil has a lower viscosity than the three engine oils. Density depends on oil composition and cannot be predicted with the SAE J300 grade standard. Fluid property sensors clearly differentiate the different oils by using density measurements. The three tested engine oils have similar dielectric constants, \(\varepsilon=2.2\) at 100 \({}^{\circ}\)C and can easily be differentiated from the tested biohydraulic oil with \(\varepsilon=2.7\) at 100 \({}^{\circ}\)C, although ISO32 hydraulic oils typically have dielectric constants ranging between \(\varepsilon=2\) and \(\varepsilon=2.4\). Some differences between the fluids can also be observed from resistivity values [48; 49].  
Figure 19: Correlation between Abs (TBN-TAN) and Rp measurements at 100 \({}^{\circ}\)C.  
Effective detection of incorrect fluid is made possible by modeling the desired fluid for the considered application and fixing thresholds for each physical parameter. The threshold levels will depend on the application and the different incorrect fluids that may be encountered.
# Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset
## 3 Results
### Results of Illumination Level on Detection Scores  
Table 12 shows the results of various illumination angles and levels. The average is taken from the three best detection scores for all the three different lamp angles (180, 135 and 90). Each angle has two levels of illumination--a lux level less than 150 and a lux level in between 150 and 300. The lux measurements are taken to ensure that the detection occurs during the specified illumination level, as per the JIS specification. The threshold for detection is set at 30% and, hence, if there is any detection less than 30%, it will be shown as 'Nil' below. Table 13 shows the average detection score of workpieces and the variance. The 135-degree level 1 has the highest average detection score (81.17%), which is followed by the 135-degree level 0 at 80.50%. The 180-degree level 1 has the lowest detection scores of 77.94%. The lowest variance is 1.65% at the 135-degree level, and the highest variance is 25.08% for the 90-degree level 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Average Precision** & **Control Dataset** & **Alpha1** & **Alpha2** & **Alpha3** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Yellow cube & 78.9 & 78.1 & 78.9 & 76.2 \\ Yellow cylinder & 76.3 & 72.2 & 82.7 & 83.3 \\ Red cube & 73.6 & 85.1 & 45.0 (\(-\)28.6\%) & 29.0 \\ Red cylinder & 73.4 & 65.9 & 55.0 & 66.7 \\ Blue cube & 65.8 & 66.1 & 77.6 & 90.40 (+24.6\%) \\ Blue cylinder & 71.9 & 77.3 & 73.8 & 78.4 \\ \hline Overall APs & 73.3 & 74.1 (+0.8\%) & 69.5 & 65.8 \\ Variance of APs (\%) & 16.4 & 0.47 & 1.924 & 3.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average Precision with Alpha datasets. Figure 9: Average Precision with Alpha datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **180\({}^{\circ}\) Level 0** & **180\({}^{\circ}\) Level 1** & **135\({}^{\circ}\) Level 0** & **135\({}^{\circ}\) Level 1** & **90\({}^{\circ}\) Level 0** & **90\({}^{\circ}\) Level 1** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Yellow cube & 79.00 & 80.00 & 82.33 & 81.67 & 76.33 & 80 \\ Yellow cylinder & 77.33 & 69.00 & 82.00 & 85.00 & 86.33 & 84 \\ Red cube & 80.00 & 83.33 & 80.33 & 78.00 & 71.00 & 73.67 \\ Red cylinder & 84.33 & 78.33 & 80.00 & 84.00 & 75.67 & 81 \\ Blue cube & 76.67 & 77.00 & 78.67 & 77.00 & 71.67 & 73 \\ Blue cylinder & 75.00 & 80.00 & 79.67 & 81.33 & 76.00 & 77.67 \\ \hline Average & 78.72 & 77.94 & 80.50 & 81.17 & 76.17 & 78.22 \\ Variance & 8.86 & 19.75 & 1.65 & 8.40 & 25.08 & 15.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Detection scores and Lux level for 180-degree level 1 lamp.