esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.4 Hierarchical-Based  
The development of network clustering methods and mobile edge computing has led to the development of hierarchical-based routing protocols. Hierarchical routing algorithms rely to a certain extent on the formation of clusters. Since each layer has different configuration requirements, routing incompatibilities may arise [44; 45]. Combining the characteristics of hierarchical routing, some researchers have improved the protocol for FANETs.  
MPCA is a classical hierarchical routing protocol proposed by [PERSON] et al. [46]. MPCA is essentially a predictive clustering algorithm. Because of the time-varying nature of the FANET, UAV networks can be joined or lost at every moment. This study, therefore, predicts the movement of UAVs by the relative position between every pair of UAV nodes, their speed, and the probability of this node staying in this self-assembled network, and uses this to select the node for the next hop of the packet. The advantages of MPCA in terms of reliability and stability are also demonstrated through simulations. [PERSON] et al. [47] investigated the problem of optimizing the placement of drone relay nodes for SDN-FANET and proposed an SD-UAVNet architecture. This architecture includes a FANET controller that considers global drone context information, preventing drone collisions, optimizing UAV movement, and establishing routing paths to determine relay node deployment to provide QoE-enabled video transmission. Based on this research, [PERSON] et al. [48] considered the impact of control information on the network and proposed a cluster-based control plane management protocol, CAPONE. This protocol reduces network overhead while ensuring drone management and control message delivery in SDN-FANET.  
Hierarchical-based routing protocols can better overcome the uncertainty of topology and movement in FANETs. However, they may make the SDN central controller or cluster head nodes so crucial that their failure may cause the entire network to go down or become inefficient. Currently, FANETs face highly variable task topologies, unstable network link connections, weak network security protection, and application heterogeneity. Therefore, further improvements to the SDN-FANET architecture are necessary to address issues such as limited user computing resources, high network latency, and fragile security protection in harsh environments.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 2 GNSS Methodologies
### Tomography
Voxel-based methodologies, despite being quite popular, have some drawbacks: (1) the rank deficiency in the coefficient matrix, leading to the need of applying constraints; (2) the assumption that water vapor is constant in the space inside each voxel; and (3) the  
Figure 2: Voxel-based representation of tomography, with signal rays crossing the voxels. Source: [PERSON] and [PERSON] [42]. number of unknown parameters is high compared to the number of observations. In order to avoid these drawbacks, some methodologies avoiding the use of voxels have been developed. One of then considered a function-based tomography, as Figure 3 shows. This approach uses B-spline functions and a distribution of the atmosphere in layers as the base to build the system of equations. A prior constraint of the 3D structure of the water vapor needs to be applied in this vector, based on radiosonde data. A test [43] with a 17-station network in California was carried out, comparing the conventional, voxel-based method, and this new B-spline function method, both validated against radiosonde observations. RMSE was reported to reduce by about 0.3 g m\({}^{-3}\). Instead of B-spline functions, [PERSON] et al. [44] proposed another method based on nodes rather than in voxels, with the aim of reducing the discretization effects. Also, a piece-wise linear fitting was used, instead of the standard (one-time) one. In an experiment to compare the standard method and this node-based method against reference radiosonde profiles, RMSE was found to decrease from 1.5 g m\({}^{-3}\) to 0.83 g m\({}^{-3}\). The improvement was stronger in rainy days. Whatever the tomography method proposed, another way to increase the number of observations is to include several GNSS [42]. The multi-GNSS tomography approach was validated using the Continuously Operating Reference Stations (CORS) network in Wuhan, China, against radiosonde and ERA5, improving in a 5% GPS-only tomography. The comparison of GPS-only, GPS+GLONASS (Globalnaya navigatsionnaya sputnikovaya sistema, Russian GNSS) and GPS+GLONASS+BDS (BeilDou Navigation Satellite System, Chinese GNSS), showed that BDS did not improve much the tomography. The improvements were associated to the increase of signals available for tomography (1.6 times more for 2-GNSS, and 2.2 for the three GNSS). Tomography of water vapor using GNSS networks is an emerging field that, although it has room for improvement, will provide very useful information about the 3D water vapor distribution. The use of multi-GNSS approaches and the densification of station networks will help improving tomography. This will improve NWP analysis and forecasts, and the general knowledge of atmospheric water vapor.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework
### Reinforcement Learning Decision and Strategy Mechanism
#### 3.1.2 Reinforcement Learning Strategy  
A Deep Q-Network (DQN) is used as the migration strategy agent. For each type of state Sc, DQN outputs an action set, \(a_{c}\in\{0,1\}\), where 1 means executing migration and 0 means skipping migration. The action selection strategy adopts an \(\varepsilon\)-greedy policy,  
Figure 2: The framework of cloud-edge collaborative model adaptation.  
where the agent selects a random action (exploration) with probability \(\varepsilon\) and chooses the action with the highest Q-value in the current Q-network (exploitation) with probability \(1-\varepsilon\). The value of \(\varepsilon\) gradually decays during training, allowing the strategy to shift from early-stage exploration to convergence towards the optimal policy. For the Reinforcement Learning Experience Pool \((s_{c},a_{c},r_{c},s^{\prime}_{c})\), trained by replaying the experience pool, the goal is to minimize the Bellman residual:  
\[L_{Q}=(Q(s_{c},a_{c})-[r_{c}+\gamma\cdot\underset{a^{\prime}}{\max}Q(s^{ \prime}_{c},a^{\prime})])^{2} \tag{5}\]  
where \(\gamma\) is the discount factor used to balance long-term and short-term benefits. The strategy training goal is to learn the optimal Q function:  
\[\pi^{*}(s)=\underset{\text{a}}{\operatorname{argmax}}Q(s,a) \tag{6}\]  
This mechanism can dynamically adjust the migration strategy of the edge model according to the performance feedback of the cloud and the edge in each iteration.  
The structure of the Q-network is shown in Table 1.
## 3 Metabolic Engineering to Improve Bio-Butanol
### Iso-Butanol  
Iso-butanol is one of the advanced biofuels which has gained importance recently. It is because unlike ethanol, iso-butanol is more aqueous, miscible, and less corrosive [66; 67; 68; 69]. Like n-butanol, iso-butanol takes its origin from the keto-acid pathway (Figure 1) [70]. Enhanced iso-butanol production in _E. coli_ was achieved when a set of new genes were overexpressed like kivD, alsS, and adh2 from _Lactobacillus lactis_, _Bacillus subtilis_, and _S. cerevisiae_, respectively [53]. Moreover, it was observed that high levels of iso-butanol were lethal for a host's (_E. coli_) survival. So, a strain which can tolerate the elevated levels of iso-butanol was engineered by altering cAMP receptor protein. Even at high levels of iso-butanol, cells of this strain were mostly viable [71]. Recently, [PERSON] et al. have engineered _E. coli_ with deletions in the Embden-Meyerhof pathway, along with a few alterations in the Entner-Doudoroff pathway. These alterations have improved the production of iso-butanol in _E. coli_[72]. _Klebsiella pneumoniae_ already has a pathway for iso-butanol production but in a quiescent state. [PERSON] et al. have turned off a few genes related to the lactate pathway, leading to the activation of the iso-butanol pathway in _K. pneumoniae_. Apart from _K. pneumoniae_, _Enterobacter aerogenes_ was also engineered for producing iso-butanol [73]. In _E. aerogenes_, nitrate and formate pathways (anoxic pathways) produced iso-butanol. In _E. aerogenes_, iso-butanol pathway-related genes (ilvD, kivD, budB, ilv C, and adhA) were expressed along with the knockout of a few genes (budA, pHB, and ldhA) to improve the yield of iso-butanol [74]. _S. cerevisiae_ has the capability of producing lower amounts  
Figure 1: Keto-acid pathway in _E. coli_ for iso-butanol as well as n-butanol production.  
of iso-butanol as a side-effect of the Ehrlich pathway for amino acid synthesis [75; 76]. _S. cerevisiae_ was engineered by knocking out the threonine pathway [75] and overexpressing valine pathway-related genes [75; 76]. Commercial iso-butanol is being produced by companies like Gevo, Butamax, and BASF SE. Iso-butanol is used only when blended with gasoline [65]. Though iso-butanol has good features, its blending with gasoline is costly. Production of iso-butanol needs to be optimized and has to be made cost-effective by advanced engineering to use it a substitute for fossil fuel.
# Improving Tire Specification Character Recognition in the YOLOv5 Network
## 1 Introduction
[PERSON] [11] proposed an end-to-end, efficient, and independent automatic LP (license plate) recognition system, based on the YOLO model, which included a unified LP detection and layout classification method. The system achieved a balance between accuracy and speed, but the CNNs (convolutional neural networks) used in this method required a large amount of labeled data for training and were less effective in some scenarios. [PERSON] [12] proposed YOLO-Z, an improved YOLOv5 method, for detecting small objects in autonomous driving, which simplified Pan-Net into FPN and replaced it with biFPN. However, this method's ability to process scale changes was limited, making it difficult to detect objects with large scale differences in the same image. [PERSON] et al. [13] proposed a method for traffic sign detection, based on the YOLOv5 network model, which used a balanced feature pyramid structure and a global context block to enhance feature fusion and feature extraction capabilities. However, in some cases, the balanced feature pyramid structure resulted in information loss and could lead to decreased detection performance when there were too few low-resolution features. To improve the accuracy and precision of recognizing characters on automobile tires, this paper proposes an improved automobile tire character recognition model based on YOLOv5. The model added a decoupled head, replaced the C3 module with the C3-Faster module, and replaced the CIOU with WIOU in the original YOLOv5 network, which enhanced the accuracy and precision of the network in recognizing the characters on automobile tires.
## 3 Results
### The Results of Reliability Methods
#### 3.2.2 FORM and Simplified FORM  
The average reliability index error calculated by the FORM method is 0.008. Its value depends on the accuracy of the iterative procedure of searching for the MPP location. The procedure is stopped when the criteria related to \(\epsilon_{1},\epsilon_{2},\epsilon_{3}\) (Figure 5) values are satisfied. Regarding this, the criteria were defined as follows: \(\epsilon_{1},\epsilon_{2},\epsilon_{3}<10^{-5}\). Meeting them took performing 8-9 iterations per analysis. According to expectations, the FORM curve is slightly below the control curve (Figure 10), which is a consequence of the performance function approximation by the first-order curve.  
The Simplified FORM method curve also matches well with the control method, with an average error of 0.03. High accuracy was achieved due to the strict conditions which were set regarding the error of the two iterative procedures performed within the method. These procedures aim to calculate the reliability index for which FS equals one. The stopping criteria is set as follows: \(|\mathrm{FS}-1<10^{-4}|\). It took approximately 2000 iterations in a single analysis to meet the criteria.
## 4 Discussion
### Dimensionality and Spectral Endmembers  
Another important finding of this analysis is the broad agreement with previous studies in terms of both spectral dimensionality [31; 44; 45; 46; 47] and the generality of S,V,D endmember spectra and associated mixture model [27; 31; 38; 46; 48]. The prominence of the NPV EM is more evident than in some prior studies, which may be due to the focus of the study on late summer agricultural landscapes with spatially extensive monocultures of dense vegetation at various stages in its life cycle. In this study, the clear differentiation of S and N EMs along PC3 is especially interesting given the dichotomy of (1) the broad similarity of their spectral continua versus (2) the important differences in cellulose/lignin absorption features at SWIR wavelengths [49].
## 1 Introduction  
Myocardial infarction (MI) is a clinical syndrome in which myocardial cell death and dysfunction occur due to ischemia [1, 2]. It affects 4-10% of the world's population and ranks among the leading causes of heart failure and death, resulting in a 30-day mortality of 15% despite advances in coronary revascularization treatments [3; 4; 5]. Post-MI morbidity and mortality are driven largely by cardiogenic shock, a state in which cardiac dysfunction results in systemic hypoperfusion and end-organ damage [6]. Cardiogenic shock is treated with indotropes, medications that augment the contractility of viable myocytes primarily via beta-adrenergic agonism and phosphodiesterase-3 inhibition [6; 7]. While these agents can be lifesaving when used for short durations, their shared mechanism of increasing intracellular cyclic AMP (cAMP) and calcium concentrations results in several adverse effects, including life-threatening arrhythmias, induced myocardial ischemia, maladaptive ventricular remodeling, and tachyphylaxis [8; 9; 10; 11].  
[PERSON] et al. recently characterized a novel pathway by which myocyte contractility is mediated independent of intracellular cAMP and calcium concentrations: the microtubule affinity regulating kinase 4 (MARK4) pathway [12]. MARK4, a protein elevated in heart tissue after MI, reduces contractility via the post-translational modification of cardiomyocyte microtubules [13]. More specifically, MARK4 phosphorylates microtubule-associated protein 4, enabling vasobibin 2 to detyrosinate \(\alpha\)-tubulin, which in turn stabilizes microtubules and stiffens the myocardium. In addition to characterizing this pathway, [PERSON] et al. found that mice with genetically knocked out MARK4 had a preserved cardiac ejection fraction following MI owing to reductions in \(\alpha\)-tubulin detyrosination and improvements in myocyte contractility [12]. While genetically knocking out MARK4 is not a translatable solution for humans, these findings highlight the potential of using MARK4 inhibition as a strategy to improve hemodynamics and mitigate cardiogenic shock after MI.  
We hypothesized that naringenin, a small-molecule inhibitor of MARK4 [14], could be used to reduce cardiac \(\alpha\)-tubulin detyrosination, thereby improving cardiomyocyte contractility and augmenting hemodynamics post-myocardial infarction. Second, we aimed to assess the effects of naringenin on end-organ toxicity in the kidneys, liver, and heart following MI. We further hypothesized that delivery within a micellized carrier would increase therapeutic concentrations of naringenin, thereby enhancing its therapeutic benefit.
# Development of Automatic Berthing Support Program for Autonomous Ships
###### Abstract  
Research on autonomous ships has primarily focused on developing response technologies for navigation from pilot station to pilot station. This study developed an automatic berthing support program that calculates the necessary thruster output values for the bow and stern to achieve the desired berthing speed under varying external force conditions, requiring only essential ship information as input. The program determines the thruster output by analyzing the forces and moments acting on the hull during the berthing process. An experimental setup equipped with the automatic berthing support program was installed on a ship. The outputs of the bow thruster (Thruster(F)) and stern tug (Tug(A)) were 300-544 hp on average, whereas the values calculated by the automatic berthing program (Program(F), Program(A)) were 105-131 hp. The calculation results of the automatic berthing support program of the ship were approximately 3-5 times greater than the horsepower values of the thruster and tug used during actual berthing, probably because the actual berthing speed was 0.25-1.13 m/s, which is more than five times higher than the set speed of 0.05-0.15 m/s. The results indicate that the automatic berthing support program is promising for future applications in automatic berthing systems for autonomous ships.  
autonomous ship; automatic berthing; external force; thruster; turning moment +
Footnote †: journal: Applied sciences  
1
## 3 Results
### Impermeable Surface Extraction
#### 3.1.2 Impermeable Surface Density Extraction Results  
Next, the impervious surface extraction accuracy at different Impervious Surface Density (ISD) values for the three nighttime lighting data are compared. This is carried out by classifying the extraction results for cities with different geomorphological types in the study area into five categories based on the ISD values: 0 \(\leq\) ISD < 0.2 (low density), 0.2 \(\leq\) ISD < 0.4 (low to medium density), 0.4 \(\leq\) ISD < 0.6 (medium density), 0.6 \(\leq\) ISD < 0.8 (medium to high density), and 0.8 \(\leq\) ISD \(\leq\) 1 (high density). Figure 5 shows the results of the impervious surface density distribution obtained from the three data types. It can be observed that the results tend to be consistent in terms of the spatial distribution of the high-density impervious surface distribution. Furthermore, all the results form a high-density impervious surface concentration area centered on the central city. The high-density impervious surface area extracted from the Flint data in Karst gorge and crested depressions is significantly higher than that extracted from the NPP-VIIRS and Luojia1-01 data. Meanwhile, the impervious surface density extracted from the NPP-VIIRS and Flint data in this area is significantly higher than that extracted from the Luojia1-01 data. The impervious surface density obtained from the Luojia1-01 depressions and Karst plateau is considerably lower than that obtained from the NPP-VIIRS and Flint data. The impervious surface densities extracted from the Karst trough valley and non-Karst landscapes nighttime light data are similar, indicating that the impervious surface densities in flat and contiguous terrain are less affected by the resolution.
## 3 Numerical Experiments
### Field Example
(**a**) Red crosses indicate the locations of the selected seismic traces in the interpolated seismic volume. (**b**) Interpolated seismic data of the crosswell profile. To validate the proposed model, we compared the predicted Vp and density of FCRN, MFCRN, MFCRGRU, and M2S-MFCRGRU across the entire crosswell profile and six wells. Figure 10 presents the visual results of the Vp inversion alongside the observed well data. It is apparent that the inversion results predicted by FCRN (Figure 10a) obviously contain non geological structures. In general, the inversion result of M2S-MFCRGRU (Figure 10d) is stable and geologically makes sense compared to the other three models. Specifically, it exhibits superior horizontal continuity in the regions marked by red rectangles and red arrows and shows the clearest stratigraphic information in the yellow circles. Regarding Figure 11, the visual comparison of density inversions is showed. The density inversion profiles predicted by FCRN (Figure 11a) and MFCRN (Figure 11b) display unreasonable inversion details, as indicated by black arrows. Accordingly, the density inversion achieved by M2S-MFCRGRU (Figure 11d) exhibits the clearest horizontal continuity and is visually superior in the regions marked by red rectangles and white arrows. Furthermore, in the area highlighted by black circles, the M2S-MFCRGRU successfully reproduces inversion results that exhibit greater consistency with the stratigraphic structure displayed by seismic data. Overall, Figures 10 and 11 show that the Vp and density profiles predicted by M2S-MFCRGRU exhibit increased transverse continuity and improved longitudinal resolution compared to the other three networks. Table 4 shows the NRMSEs and PCCs of the six wells. On the one hand, the comparison of MFCRN and MFCRGRU illustrates that the GRU component can effectively improve the accuracy of the inversion. On the other hand, we can see that the numerical evaluation of the M2S-MFCRGRU inversion results is not the best, but from the overall crosswell profile, we can see that M2S-MFCRGRU has the best generalization ability. Figure 10: Inversion results of Vp on field data. (**a**) FCRN. (**b**) MFCRN. (**c**) MFCRGRU. (**d**) M2S-MFCRGRU. To further verify the generality of M2S-MFCRGRU, a blind well test is performed in this paper. In this test, we choose w5 as the blind well and only use five wells (w1 to w4, and w6) to augment the training and validation dataset. We compare the accuracy of the network inversion by a single trace comparison at the blind well, the predicted profiles, and the two quantitative metrics. The Vp prediction and density prediction at w5 are presented in Figure 11(a),b, respectively. The overall trend of the well-logging data shows that the MS2-MFCRGRU inversion results are more close to the labeled data. Moreover, we invert the seismic data of the inline profile where w5 is located, as displayed in Figure 13. Figures 14 and 15 show the inline profile predictions of Vp and density, respectively. It can be seen that the Vp inversion
## 2 Background and Related Work
### Related Work
#### 2.2.3 Application of Transformer and BERT in Recommendation Systems  
The introduction of transformer-based models, notably BERT (Bidirectional Encoder Representations from Transformers) [6], has revolutionized the field of natural language processing (NLP). Their success in NLP has prompted researchers to explore their application in recommendation systems, with studies demonstrating their ability to capture complex user-item interactions and preferences [23; 24; 25]. The work of [25] is particularly relevant, as they utilized BERT for sequential recommendation tasks. However, the application of BERT and similar models specifically tailored to model concurrent purchase data in e-commerce recommendation systems remains relatively uncharted territory.