esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection
## 6 Conclusions  
In this study, the application of three YOLO models for detecting the pothole spots on images from road surfaces is investigated. Given the set of 665 images dataset used to train the models in this study, the research findings provide admissible evidence that the YOLOv4-tiny model achieves the purpose of the pothole detection application because it has the highest mean average precision of 78.7% compared to those of YOLOv4 (77.7%) and YOLOv5s (74.8%). It would be desirable to advance the model by further extending the network architecture of the backbone in-depth for higher accuracy in detecting potholes. In addition, the efficiency of the YOLO model runtime may be enhanced by automating the labeling strategy for potholes in a future study. This paper directly applies the YOLO series for pothole detection to mine new knowledge along with experimental analysis for comparison, hence, identifying the best model for the pothole detection issue. Indeed, the fittest model identified by this study may contribute to improve the prediction accuracy in future studies. In addition, small potholes located at long distances have low accuracy. Neither bad weather conditions nor lack of light has not been studied. These limitations may be covered by future study.  
Conceptualization, S.-S.P. and V.-T.T.; methodology, S.-S.P.; software, V.-T.T.; validation, S.-S.P., V.-T.T. and D.-E.L.; formal analysis, S.-S.P.; investigation, S.-S.P.; resources, S.-S.P.; data curation, S.-S.P.; writing--original draft preparation, V.-T.T.; writing--review and editing, D.-E.L.; visualization, V.-T.T.; supervision, S.-S.P.; project administration, D.-E.L.; funding acquisition, S.-S.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1A5A1025137).  
Not applicable.  
Figure 11: Detection results of YOLOv5s.
## 4 Discussion
### Time-Dependent Response
As an alternative to diffusion, the heat transport could be dominated by ballistic phonons: excited electronic states relax and emit incoherent phonons locally heating the lattice. At sufficiently high heating levels, the incommensurate modulation eventually melts in the entire probed volume. The speed of such transport will be an average of the group velocity of phonons, weighted by their occupation [42]. Since most vibrational modes exist in relatively low dispersion areas of the Brillouin zone, it is reasonable to expect that the heat transport velocity is much smaller than the velocity of long-wavelength acoustic phonons (speed of sound). This weighted average was calculated based on the phonon band structure of the paraelectric phase, ignoring contributions to the heat transport from excitations from the incommensurate phase such as phasons or amplitudons or topological defects. The ballistic heat transport velocity along the inward surface normal was found to be 125 m/s, which exceeds the observed velocity by more than one order of magnitude. This indicates that the heat propagates faster into the material than the phase transition and is not the limiting factor. We thus interpret our data such that the probed volume is in a superheated state and that the incommensurate phase is initially reduced only at the surface by electron-phonon interactions. This is followed by a subsonic melt front that propagates from the surface region into the bulk. This signifies that there is a fast component of the system that couples to the incommensurate modulation that can reduce the order parameter instantly; however, that process is bound to the pumped volume close to the surface. An intensity reduction to 70 % corresponds to the disappearance of the incommensurate phase over the first 20 nm, given a probing depth of 130 nm. While the heat gets redistributed over the sample, the seed of the high-temperature phase at the surface then grows at the expense of the superheated incommensurate phase. This picture of melt-front growth is typically indicative of first-order phase transitions [43], which is perplexing since most equilibrium data appear to show that the incommensurate melting phase transition at equilibrium is second order [12; 13]. This may indicate that the laser-induced phase transition has a stronger first-order character, possibly due to the inability of the structure to expand or contract laterally on these time scales. The response may also be strongly affected by competition between the incommensurate order parameter with a component of the internal strain and/or pinning to defects, a phenomenon seen in kinetic studies of other incommensurately ordered systems [44; 45].
# Nucleotides as Bacterial Second Messengers
## 5 The c-di-AMP Nucleotide
### Cellular Processes Regulated by c-di-AMP
#### 5.2.2 Sensing of DNA Damage  
When DNA is damaged, detection and repair mechanisms are activated. DisA is a sensor protein that can scan the chromosome to detect alterations on DNA; when DisA performs this function, it simultaneously synthesizes c-di-AMP [73]. The DisA movement on DNA depends on the synthesis of c-di-AMP [73]. For example, when DNA strands are at a stalled replication fork or Holliday junction, c-di-AMP synthesis and DisA movement stop, which decreases the level of c-di-AMP. This decrease in c-di-AMP levels is a likely signal to stop replication and initiate the DNA repair mechanism by the RadA protein. In the case of bacterial sporulation, DNA damage is detected by DisA, resulting in the inhibition of sporulation initiation. Under these circumstances, c-di-AMP levels fall, and restoration of sporulation initiation can be triggered by the exogenous addition of c-di-AMP [74], suggesting that c-di-GMP levels are a signal to control DNA damage. Furthermore, it has been observed that in _B. subtilis_ cells, the GdpP expression increases when DNA is exposed to an agent that damages this molecule, resulting in low c-di-AMP levels and sporulation rate [74]. Therefore, c-di-AMP levels indicate DNA integrity and help ensure that only undamaged DNA is packaged within the _B. subtilis_ spore.
## 3 Results and Discussion
### Enzyme and Genes Involved in the Cr(VI) Reduction  
According to the literature, chromate reductase is divided into the following categories: (1) Cytochrome C: cyt c\({}_{7}\), mtrC, and omcA have been identified in _Desulfuromonas_ and _Shewanella_. (2) Flavin protein: ChrR, nfsA, and yieF have been found in _E. coli_, _Pseudomonas_, _Gluconacetobacter_, _Shewanella_, and _Bacillus_. (3) Old yellow enzymes: CrS has been discovered in _Thermus scotoductus_. (4) Hydrogenases: mreG and hydC have been found in _Desulfovibrio_[7]. The chromate reductase of _Orthobacterium_ sp. CUST210-1 was found at approximately 25 kDa on the SDS-PAGE gel, suggesting that the chromate reductase was the ChrR protein; similar results were reported for _S. maltophilia_ and _Alishewanella_ sp. induced in the presence of Cr(VI) [14; 40]. To confirm this speculation, the 11 primer pairs of known chromate reductase genes were used to synthesize corresponding genes involved in Cr(VI) reduction by the CUST210-1 strain [8]. Results indicate that the _chrR_ gene was successfully synthesized. The BLAST analysis of the sequence indicated 99.2%, 97.2%, 96.8%, 94.8%,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cr** & **Cr(VI) by Surface** & **Cr(III) by Surface** & **Cr(III) in** & **Cr(VI) in** & **Cr(OH)\({}_{360}\)** & **Cr(VI)** & **Cr(OH)\({}_{360}\)** \\
**Distribution** & **Adsorption** & **Adsorption** & **Solution** & **Solution** & **Outside Cell** & **Inside Cell** & **Inside Cell** \\ \hline Relative amount (\%) & 3.05\% & 8.49\% & 3.12\% & 0\% & 26.70\% & 0\% & 58.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of chromium (VI) reduction products.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Cr(III) in** & **Cr(III)** & **Cr(III)** & **Cr(III)** & **Cr(III)** & **Cr(OH)\({}_{360}\)** & **Cr(OH)\({}_{360}\)** \\
**Solution** & **Exchangeable Form** & **Adsorbed Form** & **Organically Bound** & **Carbonate Form** & **Outside Cell** & **Inside Cell** \\ \hline Relative amount of Cr(III) species (\%) & 3.22\% & 0.21\% & 2.90\% & 5.53\% & 0.13\% & 27.53\% & 60.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of Cr(III) species in Cr(VI) reduction products.  
and 92.6% similarity with the ChrR sequences from _E. coli_, _P. putida_, _Shewanella_ sp., _Gluconacetobacter_ sp., and _S. maltophilia_, respectively.  
Theoretical ChrR protein sequences derived from the nucleotide sequence revealed an identical amino acid sequence for _Oclrobactrum_ sp. CUST210-1. Figure 2 displays the crystal structure of ChrR in _Ochrobactrum_ sp. CUST210-1. The ChrR includes three \(\beta\)-sheets, 14 \(\alpha\)-helices, and five loops; BLASTP analysis revealed 98.5%, 96.8%, 95.2%, 91.5%, and 86.8% homology to the ChrR of _E. coli_, _P. putida_, _Shewanella_ sp., _Gluconacetobacter_ sp., and _S. maltophilia_.
## 4 Design Optimization  
### Formulation of Design Problems  
The larger the numbers of holes and ventilation blades, the better the heat dissipation performance is. However, as the heat dissipation area increases due to the holes and ventilation blades, various problems occur such as concentrated stresses and, consequently, crack formation. Optimization was aided to design a shape and pattern of the holes and ventilation blades leading to the minimum concentrated stress and disc temperature.  
Four design elements in the shape of the brake disc that affect the objective function were the shape of the holes, the number of holes, the diameter of the holes, and the diameter of the ventilation blades. The optimal design problem can be formulated as  
Figure 8: Temperature distribution of disc.  
shown in Equation (7) below. In addition, there are three shapes of apertures: holes, slits, and a mixture of holes and slits, as shown in Table 4. The values of three variables (except for the hole types) were defined in the form of continuous variables with lower and upper limit values. After the formulation was carried out, the brake disc model was transformed by dimension within the ranges of design variables to carry out brake-disc modeling and analysis.  
\[\begin{array}{l}\text{Find Hole-Shape, Number, Diameter,}\\ \text{Ventilation Blade-Diameter}\\ \text{To minimize Stress, Temperature}\\ \text{Shape = [Holes, Slits, and Holes and Slits]}\\ \text{10 \leq Number of Hole \leq 30}\\ \text{4 mm \leq Diameter of Hole \leq 8 mm}\\ \text{4 mm \leq Diameter of Ventilation Blade \leq 8 mm}\\ \end{array} \tag{7}\]
## 5 Conclusions  
By combining EKF, PF, and AGA, a novel EKF-GAPF algorithm is proposed in this paper to improve the estimation performance of navigation signals. This algorithm effectively overcomes the particle degradation problem in PF as well as the limitations of EKF when dealing with highly nonlinear distributions. First, EKF-GAPF provides more accurate state estimation and transition models for particles in PF through the state prediction and update mechanisms of EKF. This approach fully utilizes the local linearization ability of EKF and the nonparametric advantages of PF, thereby enhancing the accuracy of state prediction, reducing dependence on the number of particles, and improving the distribution of particle weights in complex multipath environments. To address the particle degradation problem, the algorithm introduces AGA to optimize the importance sampling process, which enhances global search capability and avoids the risk of falling into local optima by dynamically adjusting the weights of particles and the state update strategy. Experimental results show that EKF-GAPF outperforms traditional EKF, EPF, and PF algorithms in both the magnitude and delay estimation of multipath parameters.  
Future research will focus on real-time applications, including tests in dynamic environments to evaluate the performance of EKF-GAPF in real navigation systems. Additionally, a real-time multipath estimation system based on EKF-GAPF will be developed to validate its effectiveness in complex scenarios.  
Conceptualization, J.L. ([PERSON]) and X.S.; methodology, Y.J.; software, J.L. ([PERSON]) and J.L. ([PERSON]); validation, X.S., J.L. ([PERSON]) and L.L.; formal analysis, [PERSON] ([PERSON]); investigation, Y.J.; resources, [PERSON] ([PERSON]); data curation, L.L.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, L.L. and J.L. ([PERSON]); visualization, Y.J.; supervision, [PERSON] ([PERSON]); funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Science and Technology Project (AA24263010, AA23062038, ZY23055048, AB23026120, ZY22096026, AA24206043, AB24010085), the National Natural Science Foundation of China (U23A2080, 62462019, 62161007, 62471153), the Guangdong Basic and Applied Basic Research Foundation (2023A1515012846), the Guangxi Key Laboratory of Precision Navigation Technology and Application (DH202230), the Basic Scientific Research Ability Improvement Project for Young and Middle-aged Teachers of Guangxi Higher Education Institutions (2024 KY0233, 2025 KY0243), the Guangxi Major Talent Project, and the Project of the Collaborative Innovation Center for BeiDou Location Service and Border and Coastal Defense Security Application.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the GUET-Nanning E-Tech Research Institute Co., Ltd., The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Workflow-Based Access Control with Authorization Policies
### Policy Encapsulation with Feature Toggles and Circuit Breakers  
To protect access to the data that each microservice stores and processes, the REST end-points are augmented with authorization policies to grant or deny access to the underlying data and functionality. These authorization policies evaluate decisions based on various attributes (e.g., the role of the subject requesting access). For these attributes, the following circumstances may arise:  
* The values of all attributes used in the conditions of a policy are available in the data store of the microservice and require no further processing. Typical examples are attributes related to the local data resource itself (e.g., name of patient in medical records) or environment attributes (e.g., time and location of the request).
* Some attribute values are not available, but can be derived or computed from the data available in the data store (e.g., compute a risk score based on the anomalous nature of the request). The attribute may be computed on demand or cached if the processing is too resource intensive.
* Some policies depend on attributes whose values are being managed by other microservices, such as in the need-to-know example (granting access to medical records depends on the existence of a patient appointment; see Figure 10). However, requests for these attributes will also be subject to the authorization policies put in place for these third party microservices.  
Our framework relies on a feature toggle per policy to determine whether (1) attributes used in the conditions should be retrieved and/or computed on demand or whether cached results should be return and (2) whether a microservice accepts requests from other microservices to help resolve attributes. These feature togles are put in place to avoid a given microservice being overloaded with authorization related requests, leaving no resources available for its core functionality.  
The state of the feature togles is updated at runtime by a single circuit breaker per microservice that monitors (1) the outcome of the authorization decision and (2) the latency incurred for computing the authorization decision. As partially depicted in Figures 3 and 4, the circuit breaker may return default deny decisions or cached decisions depending on the state in which it is. With this particular configuration, we aim for a fail fast scenario such that our framework is not flooded with access requests that would otherwise be denied if evaluated.  
Figure 10: Policy evaluation depending on third party microservices.
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 4 Experiments and Results  
### Model Training  
The dataset used for model training included 26,200 Dunhuang mural images that were all 256 \(\times\) 256 pixels in size. The learning rate for the model training was 0.0002, and the batch size was 1. We selected 50 original mural images to test the model's performance. We manually re-cropped these images to create a test set. The test involved both central regular and random irregular defect images, and Figure 3 shows some of the test results.  
Quantitative Evaluation. The Structural Similarity Index Measure (SSIM) is used to evaluate the similarity between two images, which can measure the authenticity of the restored images generated by the model. The SSIM value ranges from [0, 1], with higher values indicating better image quality. Table 1 presents the SSIM values calculated for the restored images and the original images shown in Figure 3. The results demonstrate that the  
Figure 3: Partial test results of the image restoration model: (a1,a2) large regular defects; (b1,b2) random irregular defects.  
model performs well in restoring random irregular defects, with the restored images closely resembling the originals. However, in cases of large-area defects in the central region, the model is only capable of correctly filling in color blocks, resulting in restored images that lack texture details and have indistinct boundaries between lines and color blocks. Overall, large-scale pattern loss remains a significant challenge for image restoration tasks.
## 3 Results
### Treatment Effects on MPB Biomass and Primary Production  
MPB biomass varied with site, sample date and sediment enrichment level, but not in a consistent manner (e.g., high treatments having greater biomass than other treatments, or consistent relationships within a site through time) (Table 10). Any deviations in MPB biomass from control plots primarily occurred at sites with the lowest water column turbidity (TAU-O, TAU-T and WTA) while no difference in chl \(a\) was observed at the most turbid sites (MNK-L and MNK-R during T1 and RAG throughout the experimental period) (Table 10).  
Preliminary analysis of nutrient enrichment effects on primary production measures revealed high variability between sites and sample dates (Figure 10, Table 11), and so site-specific data were control normalised to see whether this would reduce variability and reveal effects of nutrient enrichment on primary production (Figure 3). Nutrient enrichment did not have a significant effect on submerged NPP\({}_{\text{CN}}\) or GPP\({}_{\text{CN}}\) after 8 (T1) and 20 (T3) months of enrichment regardless of treatment level (medium or high) (Table 3). While it was not possible to distinguish long-term treatment effects (between T1 and T3) for emerged production measures, nutrient enrichment effects on NPP\({}_{\text{CN}}\) and GPP\({}_{\text{CN}}\) were not observed after 20 months of enrichment (T3). Therefore, nutrient enrichment treatment data were pooled for all subsequent analysis.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Difference from Control**} & \multicolumn{2}{c}{**Difference between Treatment Means**} & \multicolumn{2}{c}{**Difference between T1 and T3 Means**} \\ \cline{3-7}  & **Mean** & \(\mathbf{\updownarrow}\) & \(\mathbf{p}\) & \(\mathbf{\updownarrow}\) & \(\mathbf{p}\) & \(\mathbf{\updownarrow}\) & \(\mathbf{p}\) \\ \hline
**Submerged** & & & & & & \\ NTP\({}_{\text{CN}}\) & 1.41 & 1.72 & 0.104 & \(-\)1.47 & 0.150 & & \\ T1 High & 0.91 & \(-\)0.37 & 0.715 & & & & \\ T3 Medium & 1.38 & 1.38 & 0.187 & \(-\)1.08 & 0.289 & & \\ T3 High & 1.00 & \(-\)0.00 & 0.999 & & & 0.08 & 0.935 \\ Medium & & & & & & \(-\)0.31 & 0.757 \\ High & & & & & & \\ GTP\({}_{\text{CN}}\) & & & & & & \\ T1 Medium & 0.97 & \(-\)0.22 & 0.830 & \(-\)1.01 & 0.322 & & \\ T1 High & 0.74 & \(-\)1.43 & 0.171 & & & \\ T3 Medium & 1.10 & 0.77 & 0.451 & \(-\)1.03 & 0.313 & & \\ T3 High & 0.90 & \(-\)0.69 & 0.502 & & & & \\ Medium & & & & & & \(-\)0.74 & 0.470 \\ High & & & & & & \(-\)0.65 & 0.528 \\ \hline
**Energed** & & & & & & \\ NTP\({}_{\text{CN}}\) & 1.01 & 0.13 & 0.901 & \(-\)1.23 & 0.228 & & \\ T3 High & 1.56 & 1.30 & 0.207 & & & & \\ GTP\({}_{\text{CN}}\) & 1.20 & 1.51 & 0.144 & \(-\)1.01 & 0.321 & & \\ T3 High & 1.68 & 1.47 & 0.154 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(t\)-test results examining nutrient treatment effects on control normalised (CN) primary production measures.
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 2 System Design and Methodology
### Positioning of the Watering Point
Since the vertical unit vector can be represented in two directions, the system watering is set to water from the target with a smaller X-coordinate to the one with a larger X-coordinate, in order to reduce the total moving distance of the motor. Then, the distance between the new watering and central points for the seedling plant is set and multiplied by the selected unit vector to obtain the corrected distance in the X- and Y-directions. According to the size of the seedling plant, the new targeted watering point will be 1.5 to 2.5 cm far from the center of the seedling plant, which is on the straight line of the leaf growth, as illustrated in Figure 5:0  
In addition to the corrected distance in the direction of X- and Y- directions, since the height of the stem-root junction area will be slightly lower than the center of the seedling plant, an additional offset for the Z-direction height will be taken into account. After including the tri-axial correction values to relocate the seedling, the original 3D coordinates can be transferred to the real 3D coordinates for accurately targeting the watering point. If the leaf vector cannot be found after selecting the prediction box of YOLOv3, the distances in the X- and Y-directions shall not be corrected. However, for the Z-direction, the collision between the sprinkler and the center of the seedling shall be prevented by increasingly adjusting the height for watering via the customized functions. To make clear, the whole correction process for 3D coordinates of the seedling is described in Figure 5:1