esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Computer Vision and AI in Parking Space Allocation in a Seaport
## 1 Introduction
**Narrow Lanes Between Container Stacks:** One of the primary challenges faced within seaports is the limited space available for the movement of heavy-duty trucks and cargo transport vehicles. The narrow lanes between towering stacks of containers present a logistical bottleneck [9]. Maneuvering through these confined spaces not only consumes time but also elevates the risk of accidents and damage to valuable cargo. 2. **Heavy-Duty Truck Congestion:** Seaports are bustling hubs of activity, with an incssant flow of heavy-duty trucks ferrying containers to and from cargo ships [10]. This constant stream of vehicular traffic often leads to congestion, causing delays in cargo handling and impacting the overall efficiency of seaport operations. 3. **Global Significance of Seaports:** Seaports are the lifeblood of international trade, playing a pivotal role in the transportation of goods across borders. As globalization continues to expand, the importance of seaports in sustaining the global economy cannot be overstated. Any disruption or inefficiency in seaport operations reverberates worldwide, affecting trade, economies, and supply chains on a global scale. Understanding the unique challenges faced by seaports in parking space allocation and traffic management is essential to appreciate the critical role that AI and CV technologies can play in mitigating these issues. By addressing these challenges effectively, our research aims to contribute to the enhancement of seaport efficiency, sustainability, and their role as a key driver of global economic growth. Beginning with a survey of the pertinent literature on the use of AI and CV in parking management and smart cities, the research sets forth precise objectives to achieve this goal. The second objective is to determine the problems with traffic control and geographical distribution at seaports. The third objective is to investigate how AI and CV might be used to optimize space usage and traffic flow at seaports. Reviewing previous studies and case studies on the use of AI and CV in managing seaport parking is the fourth objective. The article concludes by outlining potential research areas and applications for AI and CV in managing seaport parking. Additionally, this paper provides a thorough examination of the advantages of AI and CV in designing sustainable and efficient parking systems. Data gathering and quality evaluation, an investigation of CV and AI in smart parking management, and a discussion of parking spot distribution are all part of this. The findings are analyzed and prospects for future studies are emphasized by summarizing the studies included in this research. Finally, this article contends that AI and CV have the potential to greatly enhance traffic management and space allocation in seaports, resulting in large efficiency and sustainability gains for the global economy.
## 6 Conclusions  
To investigate the influence of train vibration on over-track buildings in the throat area of metro depots, a simulation model of over-track buildings in the throat area of metro depots was established using a finite element method. The influence of train vibration on over-track buildings in the throat area of metro depots under different parameters was quantitatively studied, and the main conclusions are as follows:  
1. The \(VL_{\rm{Zmax}}\) at the centre of each floor of an over-track building periodically increases with the rise of storey, and the maximal value appears on the top floor. The \(VL_{\rm{Zmax}}\) of the top floor is 3.8-11.1 dB higher than that of the first floor in over-track buildings with 4-20 storeys. The \(VL_{\rm{Zmax}}\) of each floor of a frame building is 1.4-6.0 dB higher than that of a shear wall building.
2. When \(D\) increases by 10 m, the \(VL_{\rm{Zmax}}\) of each floor will decrease by 0.5-4.6 dB. When \(H\) increases by 1 m, the \(VL_{\rm{Zmax}}\) of each floor will decrease by 0.7-2.3 dB.
3. When \(T\) increases by 0.05 m, the \(VL_{\rm{Zmax}}\) of each floor will decrease by 0.5-3.5 dB. When \(S\) increases by 0.1 m, the \(VL_{\rm{Zmax}}\) of each floor will decrease by 0.4-4.5 dB.
4. According to results of the quantitative simulation, a prediction model was developed to analyse the vibration impact of train vibration on over-track buildings in the throat area of metro depots. The maximal impact of train vibration on over-track buildings of metro depots can be predicted on the basis of this model.  
In this study, under the condition that the ground projected areas of the over-track platform and over-track building are fixed, the single factor method is used to quantitatively study the influence of changing the main parameters of the over-track platform and over-track building on vibration transmission through simulation. If the ground projected area of the over-track platform and over-track building changes, the absolute value of simulation results will also change but the relative value will basically remain unchanged. Therefore, the model established based on the relative values of simulation results has certain universality.  
Due to the fact that there are not enough over-track buildings of metro depots in operation in China and that vibration sources such as the operation of fixed equipment and the movement of people inside over-track buildings cause significant interference with train vibration tests, this study did not obtain actual measurement data to directly verify the rationality of the model. The effectiveness and generalization of this model deserves further verification.  
Conceptualization and methodology, G.D.; data curation and software, J.G.; writing--original draft preparation, H.L.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results  
In this section, we present a comprehensive analysis of the experimental results. Section 5.1 focuses on the Average Fade Rating (AFR) analysis of watermark channels and its impact on the OFDM receiver's performance. Section 5.2 explores the receiver's behavior when trained and tested on the same channel (where each channel is split into training and testing sets). Section 5.3 evaluates the model's robustness in challenging UWA environments, addressing environmental mismatches. Section 5.4 investigates the effectiveness of pre-trained models, emphasizing their ability to generalize across diverse scenarios. Section 5.5 delves into the performance of transfer learning models, testing adaptability to real-world data from Qingdao Lake. Finally, computational complexity is analyzed in Section 5.6.
## 4 Conclusions  
In this study, we comprehensively analyzed the aroma profiles of HY and GF EOs using sensory analysis, e-nose, GC-MS and GC-O combined with chemometric approaches. The extraction method had an effect on the flavor of HY and GF EOs, with CP EOs showing a stronger citrus characteristic aroma, MADH EOs richer in alcohols, ketones and oxides and SCC extracts containing a greater variety of volatiles. This may have implications for the production of citrus EOs. Additionally, HY EO exhibited a stronger woody, green and fatty aroma compared to GF EO, with a weaker floral, fruity and sweet aroma. The GC-O results demonstrated that this was due to the fact that the represented odorants in HY EO were germacrene D, germacrene B and nootkatone, whereas geraniol, linalool and octanal contributed significantly to GF EO. In addition, \(\gamma\)-terpinene, \(D\)-limonene, germacrene D, nootkatone, germacrene B and terpinolene were identified as aroma markers using VIP \(>\) 1 to distinguish HY from GF. Our study provides data support for establishing the correlation between the bioactivity and flavor compounds of HY in subsequent research.  
H.C.: Writing--review and editing, project administration. F.L.: Writing--original draft, formal analysis. Y.Z.: Conceptualization, validation. Z.Y.: Formal analysis, data curation. J.C.:  
Figure 5: OPLS-DA model of aroma-active compounds in HYEO and GFEO. The score plot (**A**), loading plot (**B**), permutation test at 200 times (**C**) and VIP values (**D**). Numbers of compounds correspond to Table 2. HY, Huyou; GF, grapefruit; CP, cold pressing.  
Methodology, validation. S.C.: Methodology, investigation. X.Y.: Methodology, funding acquisition, project administration. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (Grant No. 32172334), the Science and Technology Cooperation Program of Zhejiang Province (2022 SNJF083) and the Fundamental Research Funds for the Central Universities (226-2023-00098).  
Not applicable.  
The dataset is available from the first author on reasonable request.  
The authors declare no conflict of interest.
## 3 Results
### Multivariate Prediction Models
#### 3.2.3 Random Forest  
Random forests consist of an ensemble of classification trees, where each tree is trained using a unique bootstrap sample and a different combination of variables. This approach ensures diversity among the trees, resulting in a more robust model. For our analysis, we utilized a sample size of 222, which corresponds to approximately 60% of the total data, to build each tree. The splitting rule employed was Gini, and we considered 10 random split points. Furthermore, we added a total of 300 trees to the ensemble, as this number was found to be reasonable based on the reduction in prediction error depicted in Figure 8. The error rate was evaluated for the non-acidotic cases (0), acidotic cases (1), and all data, using the out-of-bag (OOB) error estimation. The data not used for building each tree were used to estimate prediction error, and an average prediction error was estimated.  
Figure 6: Classification tree. Class 1: acidotic, class 0: non-acidotic.  
Figure 7: Variable importance in classification tree. Dec_area: deceleration area; Rep_time: reperfusion time; FHR: fetal heart rate.  
To optimize the terminal node size of the forest and the number of variables used to train each tree, we employed a tuning optimization parameter. The results of this optimization are depicted in Figure 9. The best-performing model was achieved with a minimum size of three for the terminal node and by training each tree with six variables.  
Figure 10 illustrates the variable importance in the random forest, highlighting the most significant variables. It is noteworthy that in a more robust model like the random forest model, the importance of the slope in the last deceleration window becomes increasingly prominent.
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 3 Results and Discussion
### Spectral Reflectance
During crop development, there were hardly any alterations in this region because irrigation continued throughout the cycle and was suspended only in phase R9; nevertheless, no alterations in reflectance were observed in this phenological stage, indicating that the reading was taken prior to the plant showing signs of water deficit. P plays a part in the process of producing energy for photosynthesis [62]. A reduction in the levels of this nutrient leads to a reduction in chlorophyll efficiency, resulting in increased reflectance in the visible region, as occurs in plants in the process of senescence. Like P, K is included in the process of energy production and has a role in activating the ATPase enzyme, forming ATP [63]. [PERSON] [64] found that wavelengths, which are important for predicting this nutrient in leaves, are located in the visible and red-edge regions. Ca participates in the transfer of electrons from photosystem II to photosystem I, i.e., it helps to increase leaf chlorophyll, and as a result, increases absorption in the visible region [65]. Zn is involved in the activity of the superoxide dismutase enzyme that increases photosynthetic activity in the plant [66]. This nutrient is correlated with wavelengths between 680 and 720 nm, located in the red-edge region, as discussed in [67].
## 4 Validation Results  
In this section the comparison between the numerical simulations (totally 45 models) and Equation (20) are compared in order to validate and discuss the proposed models.  
Figure 9 shows the comparison between the results from Finite Element Model (FEM) and the results from Equation (20) for the 45 performed models.  
First or all, it is worth noting that the fundamental periods increase with the depth of the soil layer, as expected, since the system period depends on both \(\alpha\) and \(\beta\) that are proportional to H\({}^{-2}\) (thickness of soil layer), and thus T is proportional to H.  
In addition, for all the models, the smallest values of vs. (100 m/s) give the maximum values of fundamental period, meaning that soil deformability is important in elongating the periods. This is also expected by Equation (20), since \(\alpha\) is proportional to the shear wave velocity and thus T is inversely proportional to Vs.  
Moreover, the results show that the values provided by the proposed formulation are close to those derived from FEM models. The maximum difference occurs for Model 1 (H = 10 m), for the most rigid soils (Vs = 500 m/s) with +6.71%, 6.92% and 7.34% for S1, S2 and S3, respectively. Therefore, it is possible to see that (1) the proposed formulation is validated by the numerical simulations even if realistic soil mechanisms such as non-linear deformations and plasticity reduce period elongation. (2) The proposed formulation gives small discrepancies of the periods (not necessarily conservative) for the system.  
Figure 9: (**a**). Soil S1: comparison (Model1, Model2 and Model3). (**b**). Soil S2: comparison (Model1, Model2 and Model3). (**c**). Soil S3: comparison (Model1, Model2 and Model3).
## 6 Conclusions  
To analyze three-dimensional crack stress thresholds and deformation characteristics of cement stone subjected to sulfuric acid corrosion, a series of true triaxial tests were carried out on cement stone under different acid treatment times (0, 7, 14, and 28 days).  
Figure 12: Ratio of unstable and stable crack growth stages.  
The changes in cement stone mechanical properties after corrosion by sulfuric acid were revealed, which provides an important reference for wellbore stability and underground engineering construction. The primary conclusions are summarized as follows:  
1. The effect of sulfuric acid on cement stone can be divided into the damaging effect of acid and the strengthening effect of CaSO\({}_{4}\). In the range of 0 to 14 days, the damaging effect of acid dominates. The structural integrity of cement stone is damaged by sulfuric acid. However, in the range of 14 to 28 days, the strengthening effect of CaSO\({}_{4}\) dominates. The structural integrity of cement stone is slightly enhanced by sulfuric CaSO\({}_{4}\).
2. The cement stone exhibits anisotropic deformation characteristics under true triaxial stress states. With increasing \(b\), \(\varepsilon_{2}\) gradually shifts from \(\varepsilon_{3}\) to \(\varepsilon_{1}\). The deformability of cement stone increases with increasing \(\sigma_{3}\). Under the coupling effect of acid and CaSO\({}_{4}\), the deformation modulus of cement stone first decreases and then increases slightly with increasing acid treatment time.
3. The crack stress thresholds first increase and then decrease with increasing \(b\). With the increase in \(\sigma_{3}\), the crack stress thresholds increase dramatically. Additionally, the crack stress thresholds first decrease and then increase with increasing acid treatment time.
4. The proportion of unstable crack growth stage and crack growth stage increases with \(\sigma_{3}\). The proportion of unstable crack growth stage and crack growth stage first increases and then decreases with increasing acid treatment time. Additionally, the ratio of unstable crack growth and stable crack growth stages has the same variation law as that of the unstable crack growth stage and crack growth stage. However, the proportion of stable crack growth stage has the opposite variation law to that of the unstable crack growth stage and crack growth stage.  
Methodology, Z.C.; Validation, Z.C. and Y.X.; Formal analysis, Z.C.; Writing--original draft, Z.C.; Writing--review & editing, Y.X.; Project administration, Y.X. All authors have read and agreed to the published version of the manuscript.  
This work is sponsored by CNPC Innovation Found (#2022 DQ02-0610) and Fundamental Research Funds for the Central Universities (CUGB).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that this study received funding from CNPC Innovation Found. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## 1 Introduction
One consideration of this indicates that the combination of light with the correct overall dose of a photosensitizer (PDT) can eliminate microorganisms, cancers and unwanted tissues. However, a second consideration, which represents the \"other side of 'Janus' face\", is that light alone delivered at a low irradiance and fluence (PBM) can heal and regenerate damaged tissues. Therefore, it can be suggested that photodynamic therapy is the \"killer\", and that photobiomodulation serves as the \"healer\" [21]. Hence, examining the optical properties of the photosensitizer and the target tissue is of the utmost importance in order to achieve a predictable treatment, and this aspect should be borne in mind when determining the photodynamic effects of these treatments [22]. An incident light beam impacting on matter will undergo reflection, refraction, absorption and scattering before exiting it as transmitted light. Both scattering and absorption reduce the light energy, leading to beam attenuation, and therefore the optical penetration depth (distance at which the light intensity reduces to 37% of its initial value) will be affected [22; 23]. Knowledge of the light distribution is vital for the calculation of laser parameters applied during aPDT in order to maximize the favourable effects both on the target (photosensitizer) and surrounding tissues. The transmitted light passing through the photosensitizer may lead to beneficial PBM effects. In general, spectrophotometric measurement of the transmitted light through samples is widely considered to be a reliable method commonly performed in different studies [24; 25]. To the best of our knowledge, to date, there has not been any study evaluating the light attenuation through various photosensitizers. Hence, in this study, the light intensity losses of five photosensitizers (curcumin, methylene blue, toluidine blue, indocyanine green and a methylene blue derivative) illuminated by their corresponding laser wavelengths (445 nm, 635 nm, 660 nm and 808 nm) were examined, using a high-resolution spectrophotometric analysis. The applied wavelength range (445-808 nm) falls within the aforementioned limits of PBM [15]. Following this analysis, calculated fluences are proposed in order to achieve secondary beneficial PBM effects on the surrounding tissues in clinical practice.
## 4 Materials and Methods
### Ligand-Receptor Molecular Docking  
AutoDock Tools 4.2.6 was employed to conduct molecular docking, aligning ligands' energy-minimized three-dimensional structures with target proteins' energy-minimized structures [67]. The prepared ligands in.pdb format were converted to pdbqt format using the Python script prepare_ligand4.py version 3.13.1. from AutoDock Tools 4.2.6. Similarly, the energy-minimized structure of Keap1, Nrf2, and the ligands (apigenin, genistein, gossypetin, kaempfer, luteolin, phloem, and quercetin) in.pdb format were converted to.pdbqt format using the Python script prepare_receptor4.py from AutoDock Tools 4.2.6. In this molecular docking study, comparators such as bardoxolone methyl (PubChemChem CID: 400769), dimethyl fumarate (PubChemChem CID: 637568), omaveloxolone (PubChem CID: 71811910), and sulforaphane (PubChem CID: 5350) were used to evaluate the binding effectiveness of the test ligands (Figure 10) [7; 69]. For protein\(-\)ligand docking, the appropriate grid box was manually determined based on the search space center and size for both the protein and ligand, considering key residues crucial for the function and specificity of the proteases. For Keap1, the grid box was defined with a search space center of (\(-\)47.843, 4.168, \(-\)12.971) and a search space size of 64 A \(\times\) 60 A \(\times\) 60 A. The molecular docking of the prepared ligands with the target protein was conducted using AutoDock Tools 4.2.6, employing the Lamarckian Genetic Algorithm (LGA) with 100 conformation searches. The best-docked pose, characterized by the lowest binding energy, was selected from the output of AutoDock Tools 4.2.6. Finally, the combined structure file in.pdb format of the docked protein\(-\)ligand complex, with the ligand in the best-docked pose, was analyzed using the Discovery Studio 2019 program [70; 71].  
Figure 9: The three-dimensional structures of the Keap1 macromolecule bound to the Cpd16 inhibitor (**left**) and to Nrf2 (**right**).