esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Bridge Displacement Monitoring Algorithms Based on Multi-Targets Tracking
## 1 Introduction
Initially, many scholars set the target board as a rectangular board with special square black-and white geometric patterns and obtain the center of a window by a special algorithm. This method has high monitoring accuracy of sub-millimeters under ideal conditions. However, the target has a directional characteristic that is, when using the target as the photographed object, each of its sides must be horizontal or vertical. If it is tilted, the precision of displacement monitoring will be reduced dramatically [25]. Another disadvantage of this method is that its calibration of the visual monitoring system designed is complex, leading to large calculation. To overcome this problem, [PERSON] et al. [26] changed the pattern on the target board into a circle so that the precision would not be affected even if the target board was tilted. Moreover, the computation complexity of this method is much less than the above method. In [26], the method's cost of erecting detection equipment is too large for bridges with a large span that need to locate more multi-targets because of its tracking only one target on a camera. In order to overcome the limitations of DIC, target tracking technology and other existing approaches, a novel algorithm based on specific target tracking technology is proposed in this article that can measure displacements of multi-targets simultaneously and be free of complex data processing. Experiments have been conducted to test the algorithm's various properties with images obtained by Photoshop (Adobe Photoshop CS6, Adobe Systems Incorporated, San Jose, California, USA). Results show that this method realizes accuracy, robustness, real-time, low-cost, simplicity, and self-adaptability, which sufficiently adapts the bridge displacement monitoring in theory. The remainder of the paper is organized as follows. Section 2 discusses the key fundamentals of target tracking technology and DIC. The proposed algorithm is described in Section 3. The experimental results are analyzed in Section 4. Section 5 draws conclusions.
## 5 Modification of the HBFT Algorithm
### Discussion  
Based on the simulation experiment results mentioned above, our mHBFT algorithm demonstrates significant improvements over the HBFT algorithm in various aspects. By adopting the mHBFT algorithm, it becomes feasible to utilize the proposed hierarchical Byzantine fault-tolerant consensus mechanism to manage internal affairs within real-world  
Figure 11: Delay of PBFT, HBFT, and mHBFT relative to the number of nodes.  
companies, which holds tremendous significance for the practical adoption of blockchain technology in everyday life.  
Compared to HBFT, the mHBFT algorithm exhibits outstanding performance in terms of throughput, latency, and consensus efficiency. The combination of the RAFT election process and flexible leader node selection enhances the stability of the algorithm. Additionally, replacing the RSA algorithm with the ECC algorithm for cryptographic procedures in our HBFT algorithm accelerates the consensus process, reducing latency and improving throughput while maintaining system security and efficiency. Furthermore, the introduction of batching in our HBFT algorithm further enhances consensus efficiency and mitigates the impact of increasing node numbers on latency.  
With these enhancements, our mHBFT algorithm has demonstrated better performance in TPS, latency, and consensus efficiency. It opens up possibilities for the real-world application of blockchain networks in various large-scale companies to efficiently manage transactions and accomplish tasks.
## 7 Discussion and Results
### Receiver Operating Characteristic (ROC) Curve Testing  
As observed in Figure 23, receiver operating characteristic (ROC) datasets have shown an organisation of the samples, even the random and noise samples were strongly mixed into the cleaned set. The larger the area under the curve (AUC), the better the classifier methodology for the true positive rate (sensitivity) vs. true negative rate (\(1-\) specificity) diagram, as seen in Figure 24. Ideally, the objective is the perfect classifier; nevertheless, a result above the random classifier line would allow us to conclude that the methodology is acceptable.  
Given all of the datasets, the cancer patients dataset is acceptable but by not as much as the other datasets; it has a smaller AUC.
## 4 Conclusions  
The tests performed evidenced that FRP-LVL modifies and, in general, improves LVL's physical and mechanical properties.  
* In general, FRP-LVL increases their physical properties in 48 h water immersion when glued with PVAc adhesive. The sample PVAc-CF had the best performance, with a decrease in thickness swelling by 19%. These results introduce a good option for reinforced-LVL panels, as PVAc is a more environmentally friendly adhesive than epoxy resin and, in its case, evidenced good dimensional stability;
* FRP-LVL increase their flexural stiffness and strength properties when glued with ER adhesive; the samples ER-CF and ER-BF increased the MOE by 31% (FW), 36% (EW), and the MOR by 38% (FW) and 29% (EW), respectively;
* LVL panels, when reinforced with ER-CF, increase their tensile strength by 56% and their hardness by 41% in comparison to the control panel.  
Figure 9: Results of the thermal conductivity test of each sample (error bars indicate standard deviations).  
**Author Contributions:** Conceptualization, M.N.-D., B.S.-V. and C.O.-C.; methodology, M.N.-D., B.S.-V. and B.M.-R.; software, M.N.-D. and C.O.-C.; validation, M.N.-D., B.S.-V., C.O.-C. and M.V.-V.; formal analysis, M.N.-D., B.S.-V., C.O.-C., B.M.-R., M.V.-V. and A.O.-V.; investigation, M.N.-D., B.S.-V., C.O.-C., B.M.-R., M.V.-V. and A.O.-V.; resources, M.N.-D. and B.S.-V.; data curation, M.N.-D., B.S.-V. and C.O.-C.; writing--original draft preparation, M.N.-D., B.S.-V. and C.O.-C.; writing--review and editing, M.N.-D., B.S.-V. and C.O.-C.; visualization, M.N.-D., B.S.-V. and C.O.-C.; supervision, M.N.-D.; project administration, M.N.-D.; funding acquisition, M.N.-D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding, and the APC was funded by Centro Nacional de Excelencia para la Industria de la Madera (CENAMAD).  
Not applicable.  
**Data Availability Statement:** Data is unavailable due to privacy restrictions because this work is part of a current investigation project.  
We extend our gratitude for the collaboration, equipment, and infrastructure provided by the Wood and Adhesive Engineering Products Laboratory (PRODIMA-LAB) at the Department of Civil and Environmental Engineering, Universidad del Bio-Bio. We also would like to thank the ANID BASAL FB210015.  
The authors declare no conflict of interest.
## 4 Evaluation and Analysis
### Experimental Results
#### 4.3.1 Qualitative and Quantitative Comparisons
This demonstrates that the parameters \(\omega\) and \(\alpha\) both have a significant impact on loop closure detection, and different \(\omega_{i}\) for each \(M_{ii}\) makes the method more robust in distinguishing positive and negative pairs. Besides, compared with Pairwise, MetricNet achieves better average precision of up to 47.20%, 23.60%, and 29.3% on Gardens Point, Nordland, and St. Lucia datasets, respectively.
## 5 Experimental Nanomechanical Measurements  
It is virtually impossible to measure the elastic constants of a single layer graphene via a direct tensile test experiment. One has to proceed indirectly, e.g., by measuring the response of graphene to bending or to torsion.  
Reference [42] reports on the frequency shift of a torsional eigenmode of a mechanical oscillator upon deposition of a graphene monolayer on an area called \"the neck\". The frequency shift traduces a modification of the torsional rigidity of the oscillator due to the addition of graphene. The torsional  
Figure 1: Geometry of buckling for a graphene ribbon of width \(l\) physisorbed on a substrate at the equilibrium distance \(z_{0}\). The two edges of the ribbon, supposed to be clamped, are submitted to a normal tension \(\gamma\) uniformly distributed along the length.  
rigidity depends on the shear modulus \(G\) of the material (Si) the oscillator is made of and on the shear stiffness \(g\) of the graphene overlayer. The relative change of resonance frequency is [42]  
\[\frac{\Delta f}{f}=\frac{3g\theta}{2 Gt} \tag{13}\]  
where \(t\) is the thickness of the device (0.3 mm), \(\theta\) is the fraction of the neck surface covered by graphene and determined by microscopy. Although of the order of \(10^{-5}\), \(\Delta f/f\) can be measured, which leads to the value of \(g\) given in Table 1.  
Almost all the published experimental measurements of the Young modulus of graphene are based on AFM nano-indentation realized at the center of graphene suspended above a trench [60,61] or a hole [43,62]. Measuring the slope of the load-displacement curve gives access to the elastic constants of graphene. However, there is always some in-plane residual tension \(\gamma_{r}\) in the deposited graphene layer, which introduces an additional parameter to be fitted along with the elastic constants [60]. For the case of a clamped circular membrane of radius \(R\) loaded by a central point force \(F\), [PERSON] _et al_. [62] proposed the following load-displacement behavior  
\[F=\pi\gamma_{r}\delta+1.06C\delta^{3}/R^{2} \tag{14}\]  
where \(C\) is the in-plane stiffness constant (Equation (4)) and \(\delta\) is the AFM tip displacement. Unfortunately, the numerical constant 1.06 depends on the Poisson ratio \(\
u\), making the determination of \(C\) dependent on the adopted value of \(\
u\) (taken as 0.165 in Reference [62]). The most recent measured stiffness constant \(C\) of graphene is \(C\) = 330 \(\pm\) 15 N/m, irrespective of whether the sample is pristine (exfoliated), large-grain or small grain CVD materials [43]. It has been confirmed by molecular dynamics that the presence of grain boundaries does not reduce significantly the value of \(C\) compared to perfect graphene [63, 64, 65]. However, the mechanical strength (mechanical tension leading to fracture) of samples with average grain sizes 1-5 m is smaller that that of single-crystal graphene, but still retains 85% of the strength of a defect-free sample (33 N/m against 34.5 N/m) [43].
###### Abstract  
Stress management is an adaptive advantage for survival in adverse environments. Pathogens face this challenge during host colonization, requiring an appropriate stress response to establish infection. The fungal pathogen _Cryptococcus neoformans_ undergoes thermal, oxidative, and osmotic stresses in the environment and animal host. Signaling systems controlled by Ras1, Hog1, and calcineurin respond to high temperatures and osmotic stress. Cationic stress caused by Na+, K+, and Li+ can be overcome with glycerol, the preferred osmolyte. Deleting the glycerol phosphate phosphatase gene (_GPP2_) prevents cells from accumulating glycerol due to a block in the last step of its biosynthetic pathway. Gpp2 accumulates in a phosphorylated form in a _cnn_A strain, and a physical interaction between Gpp2 and Cna1 was found; moreover, the _sprp_\(\Delta\) strain undergoes slow growth and has attenuated virulence in animal models of infection. We provide biochemical evidence that growth in 1 M NaCl increases glycerol content in the wild type, whereas _sprp_2\(\Delta\)_, _cnn_A, and _cnb_1\(\Delta\) mutants fail to accumulate it. The deletion of _cnb_1\(\Delta\)_ or _cnn_A renders yeast cells sensitive to cationic stress, and the Gfp-Gpp2 protein assumes an abnormal localization. We suggest a mechanism in which calcineurin controls Gpp2 at the post-translational level, affecting its localization and activity, leading to glycerol biosynthesis. Also, we showed the transcriptional profile of glycerol-deficient mutants and established the cationic stress response mediated by calcineurin; among the biological processes differentially expressed are carbon utilization, translation, transmembrane transport, glutathione metabolism, oxidative stress response, and transcription regulation. To our knowledge, this is the first time that this transcriptional profile has been described. These results have implications for pathogen stress adaptability.  
Footnote †: journal: Fungi  
Cryptococcus neoformans; calcineurin; glycerol phosphate phosphatase; osmotic stress; cationic stress; GPP2
## 4 Results and Discussion
### Tests Carried Out against Beacons Arranged in a Triangle
The DR and EKF methods are only better at the beginning of the test and in a very short time frame \(\langle 0\,\mathrm{s};18\,\mathrm{s}\rangle\). Figure 16: Distance to reference position from estimated position during a single SV crossing as a function of time. Figure 17: Arithmetic mean distance to the reference position from the estimated position during 100 SV crossings as a function of time. Figure 18: Moving average distance to the reference position from the estimated position during a single SV crossing as a function of time (logarithmic scale—log base = 10). Figures 20 and 21 show plots of moving RMS to the reference position from the estimated position during a single and 100 SV crossings as a function of time. Comparing Figures 20 and 21 and Figures 11 and 12 with each other, it is clear that the graphs relating to the DR and EKF methods have the same course and that the graphs relating to the GLSA and GRA methods are also parallel to each other. However, it is different for the GRA method. In Figures 11 and 12, it obtains the smallest moving RMS distance values (smallest scatter around the moving average distance) over a larger time interval \(\langle 200\) s;\(1000\) s\(\rangle\). Figures 22 and 23 present the percentage of distance to the reference position from the estimated position with values falling within the ranges: \(\langle 0\) m; \(1\) m\(\rangle\), \(\langle 1\) m; \(2\) m\(\rangle\), \(\langle 2\) m; \(3\) m\(\rangle\), and \(\langle 3\) m; \(4\) m\(\rangle\). The bar charts in Figures 22 and 23 show that the largest distance percentages were obtained by the GSLA and GRA methods in the interval \(\langle 0\) m; \(1\) m\(\rangle\), the GRA method in the interval \(\langle 1\) m; \(2\) m\(\rangle\) (similar to Figure 13 and 14), and the GRA method in the interval \(\langle 1\) m; \(3\) m\(\rangle\). Figure 19: Moving average distance to reference position from the estimated position during 100 SV crossings as a function of time (logarithmic scale—log base = 10). Figure 20: Moving RMS of the distance to the reference position from the estimated position during a single SV crossing as a function of time (logarithmic scale—log base = 10).
###### Abstract
Congestion control is particularly important and regarded as one of the most significant challenges [11; 12]. Congestion not only reduces network throughput but may also lead to packet loss, affecting the reliability and stability of the network. Frequent retransmissions consume valuable energy from nodes, accelerating their energy depletion and shortening the network's lifespan. Therefore, it is necessary to introduce reasonable congestion control mechanisms, dynamically adjust data transmission, balance network loads, and avoid or reduce congestion. This paper proposes a mixed packet forwarding mechanism with a priority schedule based on dynamic access probability and wakeup threshold. The wakeup threshold controls the system's state switching and saves system energy consumption. The dynamic access probability controls packet access based on the system's implementation status, reducing system congestion. The following are the primary contributions of this paper. 1. A mixed packet forwarding mechanism is suggested to satisfy the diversity needs of data transmission while balancing average latency and network energy consumption. 2. A three\(-\)dimensional Markov\(-\)chain (3 DMC) model with preemptive priority is established to evaluate the performance of the proposed mechanism. 3. Numerical results show how various parameters affect system performance and how effective the proposed method is. The remainder of the paper is structured as below. In Section 2, we review the relevant literature. We provide a mechanism description and modeling analysis of the forwarding mechanism with priority scheduling based on dynamic access probability and wakeup threshold in Section 3. The performance expressions for two different priority packets and the CH are given in Section 4. In Section 5, we analyze how various parameters affect system performance through numerical results and evaluate the effectiveness of the proposed mechanism in balancing average latency and energy consumption. In Section 6, we summarize our work. The complementary probability event is expressed using the overbar mark, and Table 1 displays the symbols used in this paper along with their meanings.
Early studies primarily relied on satellite multispectral sensors or field-measured spectral data to derive the X, Y, and Z tristulums values of the CIE system through various processing methods. These values were then used to calculate chromaticity parameters and assess water quality status [16, 17, 18]. In recent years, CIE-based color parameters--such as hue angle, chromaticity coordinates, and water color indices--have been increasingly employed in remote sensing analyses of aquatic ecosystems. These include investigations of lake eutrophication [19, 20], water color constituents (e.g., chlorophyll-a, suspended matter, and colored dissolved organic matter) [21, 22], nearshore algal blooms [23, 24, 25], and green tides [26, 27]. Notably, _Suada salsa_ exhibits a distinct reddish-brown hue during its mature stage, which contrasts sharply with the surrounding land cover [4]. This pronounced color difference makes hue angle-derived metrics from remote sensing imagery particularly effective for its identification. Despite the clear potential and broad applicability of this approach, to our knowledge, no prior studies have reported the use of hue angle to detect and quantify _Suada salsa_. Recent advancements in UAV remote sensing technology have enabled high-resolution (centimeter-level) imagery, making it a powerful tool for ecological and environmental monitoring [8, 28, 29, 30, 31]. Previous studies [32] have placed high performance requirements on UAV imaging payloads, typically necessitating the use of multispectral or hyperspectral sensors for aerial photography and subsequent information extraction. This has partially limited the broader application and scalability of UAV-based remote sensing techniques for _Suada salsa_. Moreover, most extraction methods have relied primarily on vegetation indices, which still exhibit certain limitations in accurately distinguishing _Suada salsa_ from other vegetation types. This study aims to leverage UAV-based visible-light imagery and hue angle analysis to (1) accurately extract _Suada salsa_ from complex backgrounds, which include various land cover types such as needs and bare land in the target area, and (2) develop a robust remote sensing inversion model to estimate _Suada salsa_ biomass.
## 3 Methodology
### Machine Learning Models
#### 3.4.5 Model's Differences  
The LG is a linear classification model that estimates the probability of belonging to a particular class. Unlike the other mentioned models, LG assumes a linear relationship between the predictor variables and the target variable. However, this limits its ability to capture nonlinear patterns in the data. In contrast, RF is an ensemble of decision trees that combines the individual predictions of each tree to obtain a final prediction. Unlike LG, RF can capture nonlinear relationships and perform well on complex datasets. ANNs are highly flexible and powerful models that consist of interconnected layers of nodes. Unlike the previous models, ANN can learn complex nonlinear representations of the data, enabling them to adapt to a wide range of problems. As for the SVM, they come in two variants: a linear kernel and a radial kernel. The SVM with a linear kernel (SVML) uses a linear function to map the data to a higher-dimensional space, whereas the SVM with a radial kernel (SVMR) uses a nonlinear function that allows for classification in more complex feature spaces.  
In summary, the differences between all these models lie in their ability to capture nonlinear relationships, the flexibility in data representation, and the complexity of the problems to address. While LG is limited to linear relationships, RF and ANN are better suited for nonlinear relationships. Within SVMs, a linear kernel is used in cases of linear separation, whereas a radial kernel is more flexible and can address more complex problems.
# Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression
## 2 Data Preparation  
Longwu Road, a Shanghai urban arterial road, was utilized as the study area. In China, arterial roads are the road grade second only to expressways in terms of traffic flow. The study area was set as ten sections in Longwu Road, which is a key road connecting downtown in Xuhui District, which means that its daily average traffic flow is at the forefront of the arterial roads. In this context, a section is defined as the road between two neighboring intersections. Longwu Road is oriented north to south and originates from West Longhua Road, and its destination is Huaii Road, with a total of 6.7 km in length.  
Longwu Road is a typical urban road. As shown in Figure 1, the traffic flow statistics of a typical urban road intersection in Shanghai, its vehicle structure is usually comprised of minibuses and large buses. At the same time, there are a small number of small trucks and large trucks. The average daily traffic flow is relatively stable, except for the weekend traffic flow being less than that of weekdays. The daily traffic flow will not show large changes, so it can be assumed that the average daily traffic flow of Longwu Road will not produce too many sudden-change situations.  
The pavement structure of Longwu Road is semi-rigid pavement. Specifically, there is a 4 cm SMA-13 (SBS modified) layer, 6 cm AC-20C layer, 7 cm AC-25C layer, 0.6 cm slurry seal layer, 48 cm cement-stabilized macadam layer, and 15 cm gravel sand layer. The pavement structure mentioned above is the most commonly utilized in Shanghai urban arterial road construction. Therefore, it can be inferred that the daily variation of pavement distress on Longwu Road is less affected by the variation of the average daily traffic volume and the variation of the pavement structure.  
The study period was set from 21 January to 30 June 2021, totaling 161 days. During the study period, Shanghai underwent a transition from winter to summer, which means that the low-temperature and high-temperature conditions of the road are covered. Moreover, in the spring, the rainfall in Shanghai is intensive. So, the study period also covers the road surface conditions for the entire rainy season.  
There were three data sources collected for this study: (1) pavement surface photographs data; (2) roadway geometric data; and (3) meteorological data. The pavement surface photographs data contain raw pavement surface images and corresponding raw GPS data. Then, combined with roadway geometric data and meteorological data, the pavement distress initiation dataset was established, shown in Figure 2.