esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Field Verification of the Pothole Area  
Upon the successful characterization of the pothole surface using original and calibrated spectral channels, the area covered by each pothole was determined and compared with those measured on field. Figure 9 provides a comparison of the pothole area predicted via original and calibrated spectral channels.  
From Figure 8, the area of the measured potholes 1 (Figure 8a), pothole 2 (Figure 8b) and pothole 3 (Figure 8c) were to be 3.11 m\({}^{2}\), 1.68 m\({}^{2}\) and 0.83 m\({}^{2}\), respectively. From Figure 8a, both the original and calibrated spectral channels overestimated the size of the pothole. Overestimation of pothole 1 can be attributed to the presence of soils adjacent to the pothole. However, calibrated red channel (Figure 8b, c) estimated pothole 2 and pothole 3 accurately, with a slight area deviation from the measured potholes. By implications, the sigmoid calibration function demonstrated the ability to reduce noise in UAV imagery, leading to improved accuracy in the recognition of potholes.  
Figure 8: Performance evaluation results of sigmoid calibration function on (**a**) blue, (**b**) green, (**c**) red, (**d**) NIR and (**e**) red edge spectral channels.
## 2 Motivation and Background
### The Importance of Upper Air Water Vapour, Clouds and Aerosols
The net effect is still uncertain although it is generally believed that cirrus has a net warming effect [18]. Other high altitude clouds are also likely to have a net warming effect. A colder stratosphere in combination with a higher water vapour content will increase the probability of the formation of ice clouds in the lower stratosphere: polar stratospheric clouds. These clouds play a major role in the heterogeneous ozone chemistry and the decrease in ozone in polar winter, and require continued observation and better characterization for model validation [19, 20]. Polar mesospheric clouds (noctilucent clouds) are thin layers of nanometer-sized ice particles that occur at even higher altitudes: between 82 and 87 km in the high-latitude summer mesosphere [21]. Their formation is very sensitive to the mesospheric environment, and changes in their frequency of occurrence, brightness and altitude could be related to climate change [22], so continued observation of these phenomena is necessary. In contrast to the tropospheric water vapour increase, which is well simulated in global climate models, modelled past and projected future stratospheric water vapour variations significantly disagree between different GCMs [23, 24]. Bias correction is also a challenge for the assimilation of observations of humidity [25]; accurate observations of stratospheric water vapour at high vertical resolution would allow more stringent testing of the models, and better exploitation of existing satellite and radiosonde data in data assimilation systems. All these uncertainties must be understood in order to make better observations and predictions of climate change.
## 6 Experimental Results and Analysis
### Real Ultrasound Image Experiment  
In our research, actual ultrasound images collected by a Healson U20 detect genre ultrasound diagnostic device are used. The liver B-mode ultrasound images of 256 \(\times\) 256 and 472 \(\times\) 256 are tested, respectively. The dataset is objectively evaluated by three noise evaluation metrics: _MSSIM_, _FSIM_, and _EPI_. The data analysis in Table 6 reveals that the refined Neighbor2 Neighbor approach introduced in this research substantially surpasses alternative algorithms in preserving structural similarity and edge retention, as indicated by the _MSSIM_, _FSIM_, and _EPI_. The proposed method achieves a mean increase of 0.01 in _MSSIM_, 0.02 in _FSIM_, and 0.06 in _EPI_ when compared to the original Neighbor2 Neighbor technique. Furthermore, against the BM3D algorithm, the improvements are even more significant, with average enhancements of 0.10 in _MSSIM_, 0.15 in _FSIM_, and 0.51 in _EPI_. These results underscore the method's efficacy in noise reduction while concurrently maintaining the structural integrity and original edge details of the images.  
Additionally, Table 6 presents the processing times for denoising a single image using different denoising methods. It is evident that our approach demonstrates a significantly higher implementation efficiency compared to traditional algorithms. Although the proposed method exhibits a slightly higher runtime than the Noise2 Noise, Noise2 Void, and Neighbor2 Neighbor speckle reduction methods, it only requires an average of 7.76 milliseconds to process speckle noise images. This is advantageous for the utilization of end-to-end network architectures as well as GPU and multi-threading capabilities.
## 7 Discussion  
Four flights were conducted on two segments of a 1.2 km long dike and about 1100 images were acquired. In this study, unlike what most commercially available photogrammetric softwares do, we performed an estimation of multiple lever-arms within the BBA procedure. It liberates us from the physical constraint between the camera and the GNSS antenna and makes it possible to merge image blocks of different configurations. For camera calibration strategy, instead of performing laboratory calibration in a close range configuration, we proposed an in-flight calibration which describes the true acquisition condition and gives more accurate camera calibration. Despite the poor geometry of the dike, our approach obtained a centimetric accuracy with one GCP and an accuracy of 3.9 cm with 0 GCP when in-flight calibration of camera and lever-arm is provided. The results outperform that of the majority of literature works.  
Based on the experiments presented in the article, we suggest that the best acquisition geometry for corridor mapping is the inclusion of nadir and oblique images of different flight heights. The mapping accuracy can reach 1 cm when good GNSS data and at least one GCP are given. For the acquisitions with lower accuracy demands and limited budget, one nadir flight is sufficient for achieving an accuracy of 4 cm with a well-calibrated camera and lever-arm. Under the condition where well-calibrated camera and lever-arm are unavailable, the combination of one nadir and one oblique flight can reach an accuracy of 8-10 cm without any GCPs.  
**Author Contributions:** [PERSON] and [PERSON] provided the initial idea for this study; [PERSON] conceived and designed the experiments; [PERSON] analyzed the data; [PERSON] wrote the main manuscript; and [PERSON] reviewed the manuscript; [PERSON] provided the funding for acquisitions.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This study is part of Ph.D. thesis work that is funded by the Compagnie Nationale du Rhone.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Influence of Lubrication Systems on the Fatigue Strength of Bolted Joints
## 5 Conclusions  
For bolted joints, this study shows an important gain in knowledge, since lubricants systems are given no consideration as factors influencing fatigue strength in the design process.  
* Lubrication has a positive effect regarding crack initiation and crack growth on bolted joints under fatigue load. Depending on the lubricant properties, lifetime and fatigue strength improvements can be observed, e.g., oiled bolts did not break at all at a fatigue load amplitude of 2.9 kN in five out of five cases, whereas unlubricated bolts failed by fracture at an average of approx. 470,000 cycles.
* Based on the results, which show large differences in lifetime depending on the lubricant, two hypotheses were derived and validated. Hypothesis I: reduced thread friction facilitates relative movement in thread contact and reduces local stress. Hypothesis II: a mobile creep lubricant settles at the crack front and expands the crack growth phase.
* A method to determine the crack initiation has been derived from detailed frequency analysis. By means of stopped tests at different drops in frequency and marking of the already damaged area by heat-fitting, a critical frequency change rate, e.g., of \(-2\times 10^{-6}\) Hz/Cycle for 5 kN fatigue load, has been identified as the crack initiation criterion. This criterion can be determined load-dependently.
* The resistance and creep properties of the lubricants determine the onset of damage due to cracking and the progress of cracking until failure due to fracture. It could be observed that greased bolts crack later and the crack growth is slower compared to unlubricated bolts. For oiled bolts, the crack initiation is earlier than for greased bolts, but the crack growth phase is significantly expanded and leads to a pronounced improvement in the stress resistance under fatigue loading
* In order to take the findings into account in the design process, the preload dependence must be investigated. Based on numerical studies [3], the preload influences the relative movement in the thread contact, and thus the crack initiation is affected. The stability of the lubrication properties and the load transmission at the contact area under fatigue load is difficult to represent numerically. Experimental investigations should follow to evaluate these influences.
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 3 Methodology
### Crowd Counting Module
#### 3.2.1 Distant View Crowd Counting  
In view of different head sizes in the image, the traditional convolution could cause the loss of spatial feature information. A small-sized crowd of an image often cannot be accurately identified, thus greatly reducing the accuracy of crowd density. To tackle the above problems, a deconvolution-based crowd counting network is proposed for distant-view scenarios, called DVNet. First, motivated by the feature map extracted by VGG-16, the information loss is reduced by deconvolution operation, which can estimate the small  
Figure 2: Segmentation Based on Scene Adaption. (**a**): input image samples; (**b**): distant view image; (**c**): close-up view image. Two dense scene images are divided adaptively with the division ratio of 0.34 and 0.52, respectively.  
size of the head. Then, it is processed by the dilated convolution module, and the dilated convolution rate is set to 2 to enhance the information extraction of crowd characteristics in dense areas. Finally, we adopt three convolutions and one dilated convolution to filter out the complex background elements, to improve the accuracy of density analysis.
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 6 Conclusions  
To balance the trajectory tracking accuracy and stability of heavy-duty AGVs during operation, this paper takes outdoor heavy-duty AGVs transporting liquid-filled tonne and small drums as the research object. A trajectory tracking control strategy with load transfer control is proposed to reduce load transfer effects and improve driving stability and tracking accuracy. The main contributions are as follows:  
1. A 3-DOF kinematic model of the heavy-duty AGV is established. Through analysis of lateral and longitudinal load transfer under complex pavement conditions, the laws governing load transfer are derived;
2. A control strategy considering both lateral and longitudinal load transfer is proposed. A lateral load-improved MPC controller based on lateral acceleration is designed alongside an adaptive torque distribution strategy according to longitudinal load transfer;
3. Finally, a MATLAB/Simulink and CarSim co-simulation platform is built to conduct simulations on pavements with high and low adhesion coefficients. The results show that on both pavement types, the proposed control strategy effectively reduces vehicle lateral acceleration and lateral load transfer while maintaining tracking accuracy, decreases maximum tire adhesion rate, and improves driving stability, achieving ideal control performance for heavy-duty AGV.  
Figure 18: Adhesion rate curve of each wheel: (**a**) left front wheel; (**b**) right front wheel; (**c**) left rear wheel; (**d**) right rear wheel.  
**Author Contributions:** Conceptualization, X.L.; methodology, X.L. and S.C.; software, S.C. and X.C.; validation, X.L., B.L. and Y.S.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, X.L., X.C., B.L., C.W. and Y.S.; supervision, X.L., X.C., B.L., C.W. and Y.S.; project administration, X.L., B.L. and C.W.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Discussion
### Socio-Economic Barriers to Remote Sensing Adoption in West Africa  
The adoption of RS technologies in West Africa faces substantial socio-economic barriers, including high costs of advanced RS technology, limited technical expertise, and low educational levels, which hinder widespread implementation. Regional research institutions are further constrained by high publication fees, inadequate funding opportunities, and limited institutional capacity to support RS-based initiatives. Restricted access to digital datasets, inadequate or lack of capacity-building programs, and a general lack of RS awareness among stakeholders exacerbate these challenges [13; 20; 204; 205]. To overcome these barriers and ensure the sustainability of measures the following elements revolve: (a) design and adaptation, (b) user and policy orientation, (c) education and training, (d) outreach and communication, (e) monitoring and evaluation, and (f) funding [206]. More specifically, this involves deploying low-cost sensors, promoting open datasets, and leveraging open-source software supported by cloud computing to lower financial en try barriers and enhance accessibility [204; 205]. Strengthening technical, financial, and management skills through capacity-building programs is critical for empowering rural populations and fostering expertise. Partnerships, cooperative research projects, and mutual funding opportunities can facilitate knowledge sharing and resource pooling across institutions [21; 205; 206]. Connecting RS applications to climate change mitigation funds and embedding them into frameworks such as ISFM or System of Rice Intensification (SRI) can enhance their financial viability and effectiveness. Integrating these efforts into a holistic strategy will improve agricultural resilience, enable access to crucial funds, and ensure sustainable development in West Africa [20; 21].
## 3 Results
### Plasma Excitation Temperature  
The excitation-temperature is a significant plasma parameter that could be measured using the Saha-Boltzmann-plot-method including Boltzmann-plot-method. In the Boltzmann plot approach, only the atomic emission spectral lines are essential while in the Saha-Boltzmann plot technique, numerous spectral emission lines such as ionic as well as atomic lines of the corresponding elements are required. To plot both species such as atomic and ionic on the same Boltzmann-type plot, Equation (1) can be utilized. To place both species (ions and neutrals) on the similar plot, the change in abscissas and ordinates of ion points is desired. We adjust the abscissa a measurements of the ionic energy-levels \(\left(E_{J}^{z*}\right)\) only by inserting modified ionization energy such as \(E_{J}^{z*}=E_{J}^{z}+\sum_{k=0}^{z-1}\left(E_{\infty}^{k}-\Delta E_{\infty}^{ k}\right)\)[47] to the ion energy levels and, finally, we modify the Saha-Boltzmann expression for the line emissivity of an ingredient element [47]:  
\[\ln\left(\frac{I_{1}}{g_{1}}\frac{\lambda_{1}}{A_{1}}\right)^{*}=\ln\left( \frac{I_{1}}{g_{1}}\frac{\lambda_{1}}{A_{1}}\right)-\ln\left(6.04\times 10^{21} \times\frac{T^{\frac{3}{2}}}{n_{e}}\right) \tag{1}\]  
Now, we can make plots similar to Boltzmann plots for each species such as atomic as well as ionic using Equation (1). The accurate determination of electron temperature depends on ionic and atomic lines availability having large differences in upper-level excitation energies. Here, the average electron temperature was achieved from the Saha-Boltzmann-plots of Ca (I), and Ca (II) emission lines. The Saha-Boltzmann-plot achieved from sample-(\(\alpha\)), the measured data-points were observed to be less dispersed (\(R^{2}\geq 0.98\)) and the maximum of the Ca emission lines with a linear-trend as shown in Figure 3. The estimated temperature was 9000 \(\pm\) 500 K.  
Figure 2: The emission spectrum of the doped P. harmala seed sample-(\(\delta\)) within the wavelength varies from 240 nm to 700 nm.
## 4 Numerical Simulation Results
### Elastic Modulus  
#### 4.2.1 Elastic Modulus Under Different RFCAs and Confining Stresses  
The elastic modulus represents the ability of the material to resist elastic deformation and is a key parameter in geo-mechanical model tests. Figure 7 plots the flow rule of the elastic modulus under different RFCAs and confining stresses.  
Under uniaxial compression, the elastic modulus of the numerical specimen increases by 20.1% from 378.34 MPa for \(H\) = 0.1 to 453.83 MPa for \(H\) = 0.7. This is similar to the laboratory UCT results, by which the elastic modulus of the specimen increases from 371.7 MPa to 418.80 MPa when \(H\) grows from 0.1 to 0.5. This indicates that the numerical result agrees well with the laboratory UCT results.  
Figure 6: Stress–strain curves of numerical specimens with different RFCAs. (**a**) Confining stress of 0.2 MPa; (**b**) Confining stress of 0.6 MPa.  
For the uniaxial compression condition, the elastic modulus escalates as the RFCA grows both from numerical and laboratory results, which is consistent with the conclusion drawn by [PERSON] et al. [11]. The reason is that as the value of \(H\) increases, resulting in a higher percentage of fine aggregates, the specimen material becomes denser due to the filling of pores between coarse aggregates. This consequently leads to a noticeable increase in the elastic modulus.  
In comparison, when the confining stress is at 0.8 MPa, the elastic modulus of the numerical specimen is 399.82 MPa at \(H\) = 0.1 and increases to 461.68 MPa at \(H\) = 0.7, a rise of 15.5%. Compared to the uniaxial compressive condition, the influence of RFCA on the elastic modulus weakens under higher confining stresses. This is because, under the exertion of high confining stress, the pores in the numerical specimen compress. Under elevated stress conditions, the sample becomes denser, effectively reducing the pore spaces available for the filling of fine aggregate. Consequently, this diminishes the sensitivity of the sample's elastic modulus to RFCA.
## 2 Materials and Methods
### Statistical Analysis  
The median error was used to determine the trueness, whilst the interquartile range (IQR) was used to assess the precision. The Mann-Whitney U test (Table 3) and Kruskal -Wallis test (Table 4) were used to compare dimensional error for arch location, resin type, and washing technique. All statistical analysis was performed using IBM SPSS statistics software (Version 24; IBM, Armonk, NY, USA) with a significance level of 0.05. This allowed for a comparison of the parameters of resin type, use of nitrogen, and post-processing technique.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Resin Types** & **Pre-Wash** & **Post-Wash** & **Post-Curing** \\ \hline A & Upper DentaGuide (\(n=5\)) & Hand wash for 5 min & Hand wash for 5 min & 8000 flashes without nitrogen \\ B & Upper DentaClear (\(n=5\)) & Hand wash for 2.5 min & Hand wash for 2.5 min & 4000 flashes with nitrogen \\ C & Upper DentaClear (\(n=5\)) & Hand wash for 5 min & Hand wash for 5 min & 4000 flashes with nitrogen \\  & Upper DentaClear (\(n=5\)) & & & \\ D & Upper DentaGuide (\(n=10\)) & Hand wash for 5 min & Ultrasonic bath for 5 min & 4000 flashes with nitrogen \\  & Lower DentaGuide (\(n=10\)) & & & \\ E & Upper DentaClear (\(n=5\)) & Ultrasonic bath for 5 min & Ultrasonic bath for 5 min & 4000 flashes with nitrogen \\  & Upper DentaGuide (\(n=5\)) & & & \\ \hline \end{tabular}
\end{table}
Table 2: Breakdown of specific post-processing techniques used for resin and arch type per group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processing Type** & **Median** & **IQR** & **Pairwise Comparison** & \(p\) **Value \({}^{*}\)** \\ \hline HH\_wo\_N (A) & \(-\)0.042 & \(-\)0.068—0.039 & A \textless{} B,C & \\ HH2.5 (B) & 0.015 & \(-\)0.015–0.026 & B \textgreater{} E & \\ HH5.0 (C) & 0.0007 & \(-\)0.022–0.032 & C \textgreater{} E & 0.008 \({}^{*}\) \\ H\_US (D) & \(-\)0.005 & \(-\)0.046–0.035 & & \\ US\_US (E) & \(-\)0.025 & \(-\)0.054—0.007 & & \\ \hline \hline \end{tabular}  
* Groups A–E explained in Table 2. Only significant pairwise comparisons are listed in the table. \({}^{*}\)—significant.  
\end{table}
Table 4: Results from Kruskal–Wallis test for pairwise comparisons using median (trueness) and interquartile range (IQR) (precision) for various processing types. All measurements are in millimetres.  
Figure 4: Measurement set-up using the coordinate measuring machine (CMM). Hole 1 is the datum where X, Y, and Z will be zero. X-axis is defined along the line centre to centre of hole 1–6 for consistency. Datum plane is defined as the surface of the guide around hole 1, describing the X–Y plane.
## 3 Results and Discussion
### Thermodynamic Analysis of Hydrogen Dissociative Adsorption on Pipeline Steel Surfaces
#### 3.3.3 Changes in Gibbs Free Energy and Chemical Potential
The trend of the negative value of \(\Delta\)G decreasing as the pressure increases is consistent with previous predictions, indicating that under high-pressure conditions, the adsorption process of hydrogen tends to occur more spontaneously. A more negative value of free energy means an increase in the thermodynamic stability of the adsorption process. The trend of the chemical potential increasing with pressure further validates that under high-pressure conditions, the possibility of hydrogen molecules adsorbing to the steel surface increases, aligning with the trend of increased spontaneity in the adsorption process. \begin{table}
\begin{tabular}{c c c} \hline
**P (MPa)** & **\(\Delta\)G (kJ/mol)** & **\(\mu\) (kJ/mol)** \\ \hline
2 & \(-\)112.595 & 7.426 \\
4 & \(-\)114.313 & 9.144 \\
6 & \(-\)115.318 & 10.149 \\
8 & \(-\)116.031 & 10.862 \\
10 & \(-\)116.584 & 11.415 \\
12 & \(-\)117.036 & 11.867 \\
14 & \(-\)117.418 & 12.249 \\
16 & \(-\)117.749 & 12.580 \\ \hline \end{tabular}
\end{table} 
Table 4: Chemical Potential (\(\mu\)) and Gibbs Free Energy (\(\Delta\)G) at Different Pressures at a Temperature of 25 \({}^{\circ}\)C.  
Figure 11: Variation of Gibbs free energy \(\Delta\)G and chemical potential \(\mu\) with temperature.