esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The _TPS_ genes could also been classified into different classes according to their genomic structure, including class I (13-15 exons), class II (10 exons), and class III (7 exons), which appear to have evolved sequentially from class I to class III [16]. Class I TPSs consist primarily ofiterpene synthases found in gymnosperms (secondary metabolism) and angiosperms (primary metabolism). Class II TPSs evolved from class I by loss of the conifer dicrpene internal sequence domain. Class III TPSs consist of angiosperm monoterpene, sesquiterpene, and dicrpene synthases involved in the secondary metabolism, which evolved from Class II by loss of introns [16]. There are differences in gene structure between different subfamilies, while members of the same subfamily show minor differences. _TPS-a_, _TPS-b_, and _TPS-g_ subfamilies with 6 to 8 exons belong to class III _TPS_, while _TPS-c_, _TPS-e_ and _TPS-f_ with 13 to 15 exons belong to class I _TPS_. In _M. longifolia_ genome sequence, the gene structure of _TPS_ is basically consistent with the subfamily classification, except for _TPS-e_. By comparing _TPS-e_ genes with other plants, it was observed that some _M. longifolia_\(\textit{TPS-e}\) genes have a loss of exons in the 5\({}^{\prime}\)-terminal. It has been suggested that during the evolutionary process, class I _TPS_ genes will loss exons and introns successively to form a new class, so we speculate that these exon-losing _TPS_ genes may be involved in this evolutionary process. Whether this exon deletion affects its function remains unclear. The main components of essential oils of _Mentha_ plants are monoterpenoids, which are mainly catalyzed by the TPS-b subfamily. In this study, we selected the _MlongTPS29_, a putative limonene synthase encoding genes belonged to the TPS-b subfamily, for catalytic activity analysis. Limonene is the most important precursor of the essential oil components of the genus _Mentha_, which is catalyzed by limonene synthase. In peppermint and spearmint (two widely cultivated _Mentha_ plants), the limonene synthase has been identified and shown to catalyze the synthesis of limonene from GPP [52]. The results of our study indicate that MlongTPS29 could also catalyze the production of limonene from GPP in vitro.
# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 3 Methodology  
This study is devoted to the validation of the concept of transfer learning from video prediction to spatiotemporal urban traffic forecasting areas. We arbitrarily selected popular methods of image and video processing and tested their performance for traffic flow data against state-of-the-art models.  
Let us have \(k\) spatial locations and \(Y_{t}\) as a \(k\times 1\) vector \(\left(y_{1,t},y_{2,t},\ldots,y_{k,t}\right)\) of traffic values at the spatial location \(i=1,\,\ldots\,\), \(k\) during the time period \(t\). We assumed that the one-period spatial structure is provided in the form of the weighted directed graph, where spatial locations are coded into vertices and relationships between them into edges. There is a wide set of approaches to the definition of the spatial structure [8]; in this study, we utilized travel time in uncongested traffic conditions as a primary relationship measure. In addition, we utilized cross-correlation definition of the spatial structure for the state-of-the-art regularized VAR model. We limited the complexity of the spatial structure by defining a local neighborhood for every spatial location \(i\) as  
\[NB(i,r)=\left\{j:d_{ij}<r\;and\;i\
eq j\right\}, \tag{1}\]  
where \(d_{ij}\) is a travel time between spatial locations \(i\) and \(j\), \(r\) is an arbitrary selected radius of the neighborhood. The radius is normally defined as a maximum travel time of a direct flow within the road network (without loops and forward-backward movements). For some model specifications, we also simultaneously considered two neighborhoods of different radii.  
There are several approaches for the calculation of spatial weights on the basis of observed distances. In this study, we tested two options, the negative exponential decay function \(w_{e}\) and the inverse decay function \(w_{1/d}\):  
\[w_{e}=w_{ij,e}(\sigma,r)=\left\{\begin{array}{c}exp\left(-d_{ij}^{2}/\sigma ^{2}\right)if\;j\in NB(i,r),\\ 0,\;otherwise,\end{array}\right. \tag{2}\]  
\[w_{1/d}=w_{ij,1/d}(\sigma,r)=\left\{\begin{array}{c}\frac{1}{d_{ij}},if\;j \in NB(i,r),\\ 0,\;otherwise,\end{array}\right. \tag{3}\]  
where \(\sigma^{2}\) is a decay parameter of \(w_{e}\). The negative exponential decay function \(w_{e}\) corresponds to Gaussian smoothing, widely used for image processing.
## 5 Conclusions  
This study bridges critical gaps in cold patch asphalt mixture (CPAM) evaluation by establishing an integrated framework combining theoretical analysis, laboratory testing, and advanced CT digital image processing. The systematic assessment of five widely used CPAM formulations has quantified structure-property relationships, offering scientific support for pavement maintenance material selection. Key findings include:  
1. Development of a comprehensive performance evaluation system incorporating construction workability, molding strength, cohesion, and moisture stability. Results show that excessive asphalt content (>4.7%) markedly reduces cohesion and moisture resistance, while optimal coarse aggregate content (9.5 mm: 10.0-15.0%) improves molding strength without compromising overall performance.
2. CT-based 3D reconstruction allowed accurate void characterization and performance correlation. The 2.36 mm aggregates effectively decrease the void ratio (<5%) and boost moisture stability, whereas excessive 13.2 mm aggregates expand the void area by 15-20%, substantially diminishing durability.
3. Aggregate contact analysis yielded predictive models connecting the coordination number to performance. Findings reveal that 2.36 mm aggregate content (30.0-40.0%) optimizes skeleton contact, enhancing molding strength (>0.8 MPa) and workability. Conversely, excessive 4.75 mm and 9.5 mm aggregates lower the coordination number by 20-30%, negatively impacting performance.
4. Experimental and microstructural analysis identified optimal mix parameters: asphalt content (4.5-4.7%) with critical aggregate fractions of 13.2 mm (1.0-1.2%), 9.5 mm (10.0-15.0%), 4.75 mm (35.0-45.0%), and 2.36 mm (30.0-40.0%).  
Conceptualization and methodology, L.L.; data curation and investigation, model building and calculation, W.X. and R.Y.; formal analysis and writing--original draft preparation, W.X.; writing--review and editing, L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to acknowledge the support and facilities provided by the School of Mechanics and Engineering Science at Shanghai University and Shanghai Urban Operation (Group) Co., Ltd. to carry out the research. Special thanks are extended to [PERSON] and [PERSON] for their contributions to road section investigation and test material provision in this research.  
[PERSON] is an employee of Shanghai Urban Operation (Group) 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.
## 6 Experiment and Results
### Target = Detection Experiment  
Experiment 1: The purpose of this experiment was to verify the target-detection performance of the FDA-SSD model. Specifically, 1200 images of the same type with depth information that had not been used for training were randomly selected from OTMP and put into the trained model. The test diagram is illustrated in Figure 12.  
Figure 11: The type of testing targets: (**a**) Rectangle target with grid pattern, (**b**) circular target with grid pattern, (**c**) rectangle monochrome target, and (**d**) circular monochrome target. The size of various targets was about 40 cm.  
Similarly, the 1000 test charts selected from OTMP were put into SSD, YOLO v1, and YOLO v2 models separately. The training and detection experiments were conducted without depth information. We compared the performance of the original SSD, YOLO v1, YOLO v2, and the FDA-SSD proposed in this study for the performance of parameters such as the operation time, the number of boxes, and the input resolution. The advantages of dynamic boxes brought by the dynamic selection of detectors in different scales in FDA-SSD were analyzed. The following Table 1 lists their performance comparison:  
The experimental-result table suggests that the FDA-SSD can effectively match the detector under the appropriate feature scale for target detection due to the use of the depth information of the target during detection. The actual number of anchor frames used can dynamically change according to the normalized depth value provided by the 3D-positioning algorithm. Therefore, compared with the original SSD method that detects 8732 anchor frames each time, unnecessary detection processes were screened to effectively reduce the amount of calculation. This algorithm achieved an 18.1% increase in speed compared to the SSD while maintaining the same high level of detection accuracy as the original SSD.  
Besides, comparing the performance of FDA-SDD and YOLO v2 on the OTMP dataset, it can be seen that they are very close in speed and accuracy. Considering that YOLO v2 uses Darknet-19 to achieve a smaller computational load, FDA-SSD uses the spatial information of samples to help the original SSD model to reduce the computational load of the entire detection model from another perspective and thus saves computational resources.
## Materials and Methods
### Catalytic Activity  
The catalytic performance of each Pd-based catalyst was evaluated in the carbon-coupling reaction of I-Ph and MA to produce MCIN. Molar ratios of I-Ph:Pd = 2000:1 and I-Ph:MA:TEA = 1:1.25:2 were employed. The reaction was carried out in a Parr type semi-batch reactor with a reactant pre-chamber (Scheme S1) at 120 \({}^{\circ}\)C under an Ar pressure of 5.5 bar using DMF as solvent. The total reaction mixture volume was 50 mL, and magnetic stirring was sustained throughout at a rate of 770 rpm. The PdAS(x)-MA catalyst was mixed with I-Ph, DMF, and dodecane then heated at 120 \({}^{\circ}\)C under an inert atmosphere, after which time MA and TEA were added from the pre-chamber. The conversion of I-Ph was followed by GC (Perkin Elmer Clarus 680 GC equipped with an Elite-MS5 capillary column, Waltham, MA, USA) using dodecane as an internal standard (908 \(\mu\)L dodecane, 0.8 mmol). Sample aliquots were collected at the desired reaction times, mixed with water (1 mL), extracted with Et\({}_{2}\)O (1 mL), and washed with brine (1 mL). The organic layer was then analyzed by GC. For all catalytic tests the first aliquot was taken at the moment of MA and TEA addition. The retention time of the product was confirmed with a commercial standard product. The total conversion and selectivity were calculated as  
\[\%\textit{Conversion}=\ \frac{\left[I-Ph\right]_{i}-\left[I-Ph\right]_{t}}{ \left[I-Ph\right]_{i}}\times 100 \tag{1}\]  
\[\%\textit{Selectivity}\ (\textit{MCIN})=\ \frac{\left[MCIN\right]_{t}}{ \left[I-Ph\right]_{i}-\left[I-Ph\right]_{t}}\times 100 \tag{2}\]  
Hence, turnover frequency (TOF) was calculated at 5% of conversion level as  
\[TOF\left(h^{-1}\right)=\ \frac{\textit{mol}\ \textit{I}-Ph\ \textit{consumed}}{ \textit{mol}\ \textit{Pd-time}} \tag{3}\]  
Reusability and leaching tests were also performed for the most active, selective, and operationally stable catalyst. For the reusability test, following the initial catalytic run, the catalyst was collected by direct filtration from the reactor, and the recovered catalyst was subjected to the subsequent catalytic cycle under identical conditions. The leaching test was carried out using the Sheldon test with fresh and reused (after 5 cycles) catalysts [44; 46; 47]. For the fresh catalyst, the reaction mixture was maintained for 5 min, the catalyst was separated by hot filtration, the homogeneous solution was recharged with volatile reactants (MA and TEA), and the reaction was carried out under identical reaction conditions. For the reused catalyst (after 5 cycles), the same methodology was employed after 240 min of reaction. In both experiments, the quantity of palladium leached from the catalysts to the reaction medium was analyzed by AAS after catalyst separation.
## 6 Nonlinear Fitting of Indoor Test Results  
The preceding analysis demonstrates that the slurry diffusion radius follows a power-law relationship with grouting pressure, grouting time, and slurry viscosity. In our laboratory experiments, the investigated independent variables were fine particle ratio, grouting pressure, and water-cement ratio. Given that water-cement ratio fundamentally determines slurry viscosity, we propose that the slurry diffusion radius (equivalent to anchor body diameter) similarly obeys a power-law relationship with these three parameters, as expressed by the following function:  
\[R=Ek^{F}p^{G}a^{H} \tag{13}\]  
In the above equation, \(R\) is the diameter of anchor body, m; \(k\) is the proportion of fine particles, %; \(p\) is grouting pressure, MPa; \(a\) is water-cement ratio; \(E\), \(F\), \(G\), \(H\) is the undetermined coefficients.  
After the linear transformation of the nonlinear Equation (13), the multiple linear regression analysis of the indoor test data is carried out, and the standardized regression coefficient \(\beta\)i is obtained, as shown in Equation (14):  
\[\begin{cases}R=1.11k^{-1.77}p^{0.302}a^{-0.123}\\ \beta_{k}=0.933,\beta_{p}=0.234\\ \beta_{a}=-0.185,r^{2}=0.981\end{cases} \tag{14}\]  
In the above equation, \(\beta k\), \(\beta p\), \(\beta a\) is the standardized regression coefficient of grouting diffusion radius to grouting pressure, drilling speed and slurry viscosity; \(r^{2}\) is the multiple correlation coefficient.  
The analysis reveals that the diameter of the anchorage body is most significantly influenced by the fine particle ratio, followed by grouting pressure, while the water-cement ratio exhibits the least effect. These findings are consistent with both range and variance analyses from previous laboratory tests, thereby validating the reliability of the proposed equation.  
Figure 14: Slurry diffusion at different slurry viscosity level: (**a**) 833.3 mPa-s; (**b**) 167 mPa-s; (**c**) 83.3 mPa-s.
## 4 Conclusions  
CH\({}_{4}\) plasma produced in an ECR-CVD reactor provided a coating of highly crystallized and ordered carbon structures on the steel engine piston rings. The combination of high-density plasma, N\({}_{2}\) pre-treatment on the substrates, custom-made rotating mechanism, and thermal annealing of the sample surfaces yielded precise deposition of carbon-based nanostructures on very rough substrates, leading to the filling of the crevices with regular graphene oxide and carbon nano-clusters. By this means, the advantages of the ECR-CVD method were further enhanced. Carbon deposition along with N\({}_{2}\) gas facilitated a considerable improvement in vital parameters regarding the service life of piston rings, such as a reduction of roughly 66% in the average wear rate, an improvement of 14% in the average hardness, an enhancement of 40% in the COF, and a decrease of 48% in the average R\({}_{\text{a}}\). Considering the results procured from the modified ECR-CVD coating method, this study will be a good guide in terms of providing superior features to the moving parts of mechanical systems such as internal combustion engines, pneumatic-hydraulic systems, etc. Improved fuel efficiency and overall engine performance due to reduced friction between contact regions of piston ring-cylinder liner can be achieved. The increase in surface hardness suggests the improved wear resistance of piston rings, potentially leading to longer service intervals and reduced maintenance costs for internal combustion engines. Reduced friction may lead to diminished exhaust emissions aligned with stricter environmental regulations, making this technology attractive for manufacturers aiming to produce cleaner engines. Apart from piston rings, this crystalline carbon deposition technique could be applied to other engine components subject to high wear and friction, such as cylinder liners and valve trains, further enhancing engine durability and performance. On the other hand, comprehensive evaluations encompassing long-term durability, manufacturing feasibility, broader environmental impacts, and compatibility with other engine components are essential and could be future work for assessing the practicality of implementing such coatings in commercial engines. Conceptualization, M.E. and A.C.Y. ; methodology, M.E. and A.C.Y. ; software, M.E. ; validation, M.E., A.C.Y. and H.K. ; formal analysis, M.E. and A.C.Y. ; investigation, M.E. and A.C.Y. ; resources, M.E. and A.C.Y. ; data curation, M.E. ; writing--original draft preparation, A.C.Y. ; writing--review and editing, M.E., A.C.Y. and H.K. ; visualization, M.E. and A.C.Y. ; supervision, H.K. ; project administration, M.E. and A.C.Y. ; funding acquisition, M.E. and A.C.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by Cukurova University, Department of Scientific Research Projects, grant numbers FBA-2024-16686 and FBA-2024-16928. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request.
## 3 Results
### Explainability of the AI Models  
We asked a radiologist to highlight the findings in four CXRs from the external validation dataset. We compared his findings with the features extracted by the algorithms. Figure 16 provides gradient-weighted class activation mapping heat maps (Grad-CAM) of feature importance for the most representative images from each class of the algorithm's predictions, thus helping to interpret and explain how each of the AI models performed their predictions. Figure 16a shows the heat maps for the CXR of a male patient, 73 years. It is a true-positive situation for both algorithms. The image label is suggestive of COVID-19. The bounding box highlights infiltrates, and both algorithms classified the image correctly as positive for COVID-19. Figure 16b is a false-positive situation for a 75 years old female patient. Both algorithms incorrectly identified COVID-19 findings in a patient with a moderate bacterial infection. Bounding boxes highlight infiltrates, cardiomegaly,  
Figure 15: AUC ROC comparison using [PERSON]’s test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics for \({}^{*}\)** & \begin{tabular}{c} **Cimatec\_XCOV19** \\ **Performance on the** \\ **External Validation** \\ **Dataset** \\ \end{tabular} & \begin{tabular}{c} **DeepCOVID-XR** \\ **Cimatec\_XCOV19** \\ **Previous Performance** \\ **Previous Performance** \\ **Dataset** \\ \end{tabular} & \
\begin{tabular}{c} **DeepCOVID-XR** \\ **Performance on the** \\ **External Validation** \\ **Dataset** \\ \end{tabular} &
\begin{tabular}{c} **DeepCOVID-XR** \\ **Previous Performance** \\ **Previous Performance** \\ **Dataset** \\ \end{tabular} \\ \hline Sensitivity & 0.85 & 0.93 & 0.77 & 0.75 \\ Specificity & 0.82 & 0.96 & 0.94 & 0.93 \\ Accuracy & 0.82 & 0.94 & 0.94 & 0.83 \\ AUC ROC & 0.93 & 0.98 & 0.97 & 0.90 \\ AUC PRC & 0.48 & 0.96 & 0.7 & NA \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of metrics between the Cimatec_XCOV19 and DeepCOVID-XR algorithms.  
and atelectasis. In Figure 16c, both algorithms correctly did not identify COVID-19 in a normal examination. The patient is female, 58 years old; Figure 16d shows a false-negative situation. Both algorithms failed to identify infiltrates characteristic of viral infection. Bounding boxes highlight cardiomegaly and infiltrates. The patient is female, 83 years old. There are differences in the images' background color and size because the two AI algorithms use different image pre-processing algorithms.  
Figure 16: Heat maps for (**a**) True positive. Bounding box highlights infiltrates. The patient is male, 73 years old; (**b**) False positive. Bounding boxes highlight infiltrates, cardiomegaly, and atelectasis. The patient is female, 75 years old; (**c**) True negative. Bounding box highlights infiltrates. The patient is female, 58 years old; (**d**) False negative. Bounding boxes highlight cardiomegaly and infiltrates. The patient is female, 83 years old.
## 6 Limitation and Future Scope  
Acknowledging and addressing certain limitations within this research is crucial in developing an object-detection model for recognizing endangered and hazardous animals in crop fields. There are some limitations identified in this research. The first one is a constraint with sample size. The study focuses extensively on eight species of animals that are primarily on the verge of extinction and cause agricultural damage. Still, many species could make the model more effective at detecting different animals. In addition, the experiments did not consider the influence of environmental factors, such as temperature, lighting, terrain, etc. Furthermore, the experiments were conducted exclusively with RGB images.  
To address these limitations, we proposed future works that will increase the prediction accuracy of the proposed system. In particular, providing detailed explanations and exploring possible paths for further development and research might yield significant insights. The inclusion of a broader range of animal species in the dataset will enhance the model's efficacy across various geographical locations. Moreover, the application of multi-domain learning will be utilized to improve the capacity for generalization. Incorporating weather and seasonal data, such as rainfall and temperature, will provide valuable insights into the growth stages of crops and the frequency of animal intervention within a specific time frame. The use of hyperspectral, multispectral or satellite images will increase the sustainability of this system, as it will enable the detection of animals using UAVs or other compatible devices. This research will focus on integrating video input to conduct temporal analysis to utilize motion and temporal patterns observed in video footage to enhance detection capabilities and minimize false positive rates. Additionally, the implementation of edge computing enables efficient execution on embedded devices such as Raspberry Pi deployed throughout the farm, hence facilitating the generation of alarms with little delay.