esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 5 Conclusions  
In this paper, to address the issue of reduced accuracy of onboard sensors of CUAVs due to hazing weather conditions, leading to unreliable and unsafe execution of landing tasks, we propose a dehazing algorithm and a runway detection and localization algorithm to tackle the aforementioned problem.  
In the dehazing network model, we propose a U-shaped network structure, introduce the lightweight fused attention mechanism module combining simple spatial attention and simple channel attention and utilize a Transformer module with linear computational complexity. This implementation ensures both enhanced dehazing capability of the network and low computational cost during forward propagation. Considering real-time requirements, we select the UFLD algorithm as the runway detection algorithm. By filtering and localization the detected points, we can obtain highly accurate localization runway line equations.  
Finally, we conducted extensive experiments on our entire algorithm framework. We compared the performance of our dehazing model with other state-of-the-art models, as well as the localization results obtained from our dehazing algorithm and localization method. The experiments demonstrate that our dehazing algorithm achieves the best dehazing effect while significantly improving runway detection performance under hazed conditions.  
Additionally, to further ensure the safe and reliable landing of carrier-based UAVs, it is important to note the following: First, our image dehazing model may not address other types of image degradation, such as rainy images or motion-blurred images. Second, due to the limitations of the dataset used for training, the optimization effect on images in heavy fog conditions may not be satisfactory. Third, this paper does not address the control decision methods for carrier-based UAVs after detecting the runway lines. Our future work will focus on designing better algorithms to address these issues.  
Conceptualization, C.L.; methodology, C.L. and Y.W.; software, C.L. and Y.W.; validation, Y.Z., R.M. and Y.W.; formal analysis, R.M., and C.Y.; investigation, P.L.; resources, P.L.; data curation, R.M.; writing--original draft preparation, C.L.; writing--review and editing, P.L. and C.Y.; visualization, C.L.; supervision, P.L.; project administration, P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62273178, and in part by the National Key Research and Development Program of China U2233215 and in part by Phase VI 333 Engineering Training Support Project.  
Not applicable.  
Under reasonable conditions, all datasets used in this paper are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
## 3 Basic Assumptions
### Network Model
Additionally, the RSUs contain tamper resistance hardware (TRH), which acts as a memory device to store private information in a secure manner, and it also evaluates the quality of the messages during the process of communication between the SVs to RSUs. Static/Mobile Devices: The portable computing device are able to communicate with the vehicles, such as wireless sensors, mobile phones, wearables, tablets, etc., using 3G/4G/5G or Wi-Fi [30]. Each device consists of a smart card (smart chip) to perform certain operations such as computation management and storage. That smart chip controls all the operations during the process of communication between the SVs/mobile devices to RSUs. Trusted Authority (TA): The major functionalities of TA are to generate and transmit the public system parameters and additionally monitor the SVs and the RSUs. TA helps to protect the data transmission by assigning pseudo-identities to SVs, which leads to generating the private keys in the SVs. It maintains the database and stores the information on the SVs' pseudo-identities. TA is assigned with high energy and high storage when compared with the other sections. Remote Cloud (RC): RC acts as the central registration center for the TAs of several regions. Through TAs, the RC will control all the devices, such as SVs, RSUs, and static/mobile devices. The localization process of RSUs is controlled directly by the RC according to the registration of the end users, such as SVs and static/mobile devices. It  
Figure 4: Proposed Network Architecture. maintains a private, secure database that consists of all the information of the system. The symbols and descriptions used in the research are given in Table 2.
## 3 Field Investigations  
### Refraction Microtremor Surveys  
A Refraction Microtremor survey (ReMi) is a common method for acquiring shear wave velocity profiles, developed by [PERSON] in 2001 [15]. This survey uses a portable seismograph connected to an array of evenly spaced geophones to record surface waves or Rayleigh waves generated by background acoustic sources. In this study, ReMi data were used as a validation dataset to evaluate the accuracy of the estimation performed by HVSR inversion via GA.  
For GMMA, surveys were conducted in schools located in Coastal Lowlands as part of a previous project [5]. For Leyte Province, survey site selection was based on several factors, including accessibility, logistical feasibility, and the availability of a dry, solid, and flat  
Figure 2: **(A)** Geological and **(B)** geomorphologic map of Leyte, as well as the location of the ReMi and 3 CMT survey sites depicted by green triangles and red points, respectively. Geologic map based on an unpublished map from Mines and Geosciences Bureau Regional Office VIII (2023) [31]. Geomorphologic map generated by the Ground Shaking Component of the PHIVOLCS Accelerated Earthquake Multi-hazards and Risk Assessment Program in the Philippines (ACER).  
stretch of ground free from underground structures such as culverts or drainage systems. Additionally, sites were chosen to ensure even distribution across the geomorphological units within the study area.  
In the GMMA and Leyte surveys, 12 geophones with a frequency of 4.5 Hz were used. The array length was set to at least twice the depth of interest. Due to space constraints, GMMA schools allowed for a geophone spacing of 4 to 8 m, whereas Leyte sites had a spacing of 8 to 10 m. The geophones were connected to either a DAQLink II or DAQLink III seismograph, recording a total of thirty recordings of at least 30 continuous seconds each. The first ten recordings captured ambient noise (e.g., vehicles, pedestrians, wind, construction). Additional recordings were made with an active noise source--a sledgehammer striking a steel plate--at each end of the array. The recorded distance-time traces were processed using SeisOpt ReMi version 4.0 software modules VSpect and Disper, following the data processing guidelines by [PERSON] et al. (2022) [16] and the SeisOpt ReMi User's Manual [32]. In VSpect, recordings were converted into a slowness-frequency (p-f) spectrum. The minimum velocity envelope was determined from this spectrum by selecting picks along the lower bounds, which were then used in Disper to produce a dispersion curve and a 1-D shear wave velocity profile through inversion. Disper also automatically calculates the 100 ft (approximately 30 m) average velocity, which is used as the Vs30 value for the processed site.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 6 Conclusions  
In the present work a study of the accuracy of broadcast orbits has been presented, taking into account a time span of one week. In this phase, the offset between broadcast reference frames and ITRF has been evaluated, for homogeneous blocks of satellites. The results of an analysis carried out starting from 1 January 2014 and still active were presented, aimed at the evaluation of the misalignment between timescales of the various GNSS's. In the next sections the main results obtained are summarized and discussed.
# Perspectives of Hydrogen Generation in Cavitation-Jet Hydrodynamic Reactor
###### Abstract  
The article investigates the potential for producing hydrogen by combining the methods of water splitting under cavitation and the chemical activation of aluminum in a high-speed cavitation-jet flow generated by a specialized hydrodynamic reactor. The process of cavitation and water spraying causes the liquid heating itself until it reaches saturated vapor pressure, resulting in the creation of vapor-gaseous products from the splitting of water molecules. The producing of vapor-gaseous products can be explained through the theory of non-equilibrium low-temperature plasma formation within a high-speed cavitation-jet flow of fluid. Special focus is also given to the interactions occurring at the interface boundary phase of aluminum and liquid under cavitation condition. The primary solid products formed on aluminum surfaces are layerite, copper oxides (I and II), iron carbide, and a compound of magnesium oxides and aluminum hydroxide. A high hydrogen yield of 60% was achieved when using a 0.1% sodium hydroxide solution as a working liquid compared to demineralized water. Moreover, hydrogen methane was also detected in the volume of the vapor-gas mixture, which could be utilized to address the challenges of decarbonization and the recycling of aluminum-containing solid industrial and domestic waste. This work provides a contribution to the study of the mechanism of hydrogen generation by cavitation-jet processing of water and aqueous alkali solutions, in which conditions are created for double cavitation in the cavitation-jet chamber of the hydrodynamic reactor.  
cavitation; cavitation-jet reactor; Venturi tube; Laval nozzle; hydrogen generation; plasma formation; cavitation-jet chamber +
Footnote †: journal: Physics Letters A
## 2 Materials and Methods
### 2.3.2.2.3.1. Sample Preparation for SEM
#### 2.3.2.3.2. SEM Analysis  
The samples were examined under a scanning electron microscope (LEO-1430 VP, Carl Zeiss, Oberkochen, Germany) with a 20 kV beam at 500-4000\(\times\) magnification. To obtain the smear layer and dentin erosion scores, microphotomicrographs were randomly coded and evaluated independently by two blinded and calibrated (Cohen's Kappa > 80%) researchers ([PERSON] and [PERSON]). The inconsistencies were resolved by a third party ([PERSON]).  
The images were assessed for the presence of a smear layer (at \(\times\)2000 magnification) and dentin erosion (at \(\times\)4000 magnification) at the coronal, middle, and apical thirds of the root canal walls.  
The smear layer was graded on a scale from 1 to 5 using the modification of a scoring system proposed by [PERSON] et al. [69]. The criteria for the scoring were as follows:  
1--no smear layer; all tubules are completely open;  
2--small amount of smear layer; most tubules are completely open, some partially open;  
3--moderate amount of smear layer; some tubules are completely open, some are partially open, some are closed;  
4--moderate amount of smear layer; the majority of dentinal tubules are closed or partially open;  
5--severe homogeneous smear layer covering the dentin surface with few or no dentin tubules open.  
The scores for dentin erosion were attributed using a modified rating system proposed by [PERSON] et al. (2003) [70] as follows:  
1--no erosion (all tubules look normal in appearance and size);  
2--moderate etching/erosion (peritubular dentin is eroded, dental tubules are slightly enlarged, \(<\)10% of tubules are connected);  
3--severe etching/erosion (intertubular dentin is destroyed, and \(>\)10% of tubules are connected to one another) [Figure 2].
## 6 Evaluation Experiment
### Overall Comparison and Discussion  
#### 6.7.1 Cityscapes  
Table 21 presents the result of a comparison of mIoU scores in each Cityscapes experiment. Experiment B produced the highest mIoU score. This result demonstrates the effectiveness of using augmented images obtained using the conditional GAN. By contrast, the mIoU score obtained from Experiment C was lower than that of the baseline. We infer that the augmented images obtained using the conditional and unconditional GANs provided no contribution to improvement of the accuracy. Experiment D results demonstrated that, using all images, including the original, improved the mIoU score.  
Figure 22 depicts representative images of comparative semantic segmentation results in each Cityscapes experiment. The road, car, vegetation, and building classes were segmented correctly. False segmentation pixels occurred at the sidewalks on the left and right sides of the images. Based on these results, we infer that segmentation between fence and vegetation is a challenging task for this dataset.  
Figure 23 depicts results obtained from comparison of the respective experiments for the lower and upper five classes ordered by the IoU scores in Table 4. The lower five classes are traffic light, pole, rider, ground, and traffic sign in the training subset images and are traffic light, train, motorcycle, terrain, and rider in the test subset images. Two classes of traffic light and rider are common in both datasets. Results obtained from Experiment B improve accuracy in the respective classes. Although the test subset showed remarkable improvement in accuracy for the _ground_ class, it dropped considerably for the rider class. The upper five classes are building, vegetation, car, sky, and road. They are car, unlabeled, building, vegetation, and road for the test subset. Three classes of building, vegetation, and road are common to both. The accuracy for the training subset exhibits no remarkable difference. For the test subset, the argument data combinations in Experiments B and D were slightly predominant. We consider that Experiments A and C contributed no accuracy improvement. However, the improved accuracy results in Experiments B and D verify a tendency that is similar to the overall results, as shown in Table 21.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & **Baseline** & **A** & **B** & **C** & **D** \\ \hline Training & 0.48 & 0.49 & **0.51** & 0.47 & 0.50 \\ Test & 0.27 & 0.20 & **0.27** & 0.25 & **0.27** \\ \hline \hline \end{tabular}
\end{table}
Table 21: Comparison results of mIoU scores for the respective Cityscapes experiments (Bold values show maximum scores).  
Figure 22: Comparison results of semantic segmentation in each Cityscapes experiment.
## 3 Results
### The Effect of the Orientation on the Pressure under the Helmet
The results of the quantification of the four characteristics of the pressure waveforms: (**A**) peak overpressure, (**B**) rise time, (**C**) impulse, and (**D**) duration are presented. The data collected at 70 kPa incident blast overpressure (BOP) (~10 psi, denoted as T5) are expressed as average \(\pm\) standard deviation. The red horizontal lines behind the data indicate average incident shock wave characteristics and are plotted as a reference to visualize the trends in the data. orientations are the most distinct. The 0\({}^{\circ}\) and 180\({}^{\circ}\) are on the opposite sides of the PC1 values (i.e., \(-\)2.5 to 4.0 and 3.2 to 4.5, respectively). On the contrary, the clusters for the 90\({}^{\circ}\) and 270\({}^{\circ}\) orientations data are grouped with significant overlap. It is an expected outcome, considering exposure at both of these orientations are equivalent and observed differences stem from asymmetry in circumferential sensor distribution, i.e., two sensors are located on the right side of the headform (H6 and H7), while only one (H10) was placed on the left side (Figure 2B). Moreover, the aggregate peak overpressure and impulse values for the 90\({}^{\circ}\) and 270\({}^{\circ}\) are very similar (Table 1), unlike the other two orientations tested. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{1}{c}{**Peak**} & \multicolumn{1}{c}{**Rise Time, ms**} & \multicolumn{1}{c}{**Duration, ms**} & \multicolumn{1}{c}{**Impulse, kPa-ms**} \\  & **Overpressure, kPa** & & & \\ \hline Helmet orientation & & & & \\
0\({}^{\circ}\) & 1856 & 1.70 & 42.2 & 1344 \\
90\({}^{\circ}\) & 1045 & 2.79 & 42.8 & 1224 \\
180\({}^{\circ}\) & 842 & 3.30 & 45.1 & 1281 \\
270\({}^{\circ}\) & 1021 & 3.36 & 47.0 & 1219 \\ Helmet type & & & & \\ Headform & 1610 & 0.17 & 36.3 & 1175 \\ Ops-Core & 1938 & 1.23 & 34.3 & 1226 \\ Airframe & 1910 & 1.90 & 43.7 & 1331 \\ ECH & 2180 & 1.24 & 36.1 & 1297 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The aggregate values (sum of averages) obtained from the quantification of the peak overpressure, rise time, duration, and impulse in the: (1) helmet orientation (ACH), and (2) helmet type (Ops-Core, Airframe, ECH) blast tests. The unprotected headform was used as a reference in the helmet type tests. Figure 4: Biplot showing first and second principal components of the surface pressure parameters of the headform equipped with an ACH helmet. The scores plot illustrates the clustering in the space of two principal components, PC1 and PC2. The data were recorded at 70 kPa (\(\sim\)10 psi) nominal shock wave intensity, and the effect of the headform orientation is presented. The shaded areas surrounding the data points are 95% confidence ellipses.
## 3 Finite Element Formulation  
The numerical modelling was conducted using COMSOL software version 6. COMSOL uses the finite element method, and the segregated solver was used. The fluid was assumed to be Newtonian, and a steady-state condition was adopted. The reason for assuming Newtonian flow for the nanofluid was the low concentration of metallic nanoparticles. Table 1 presents the physical properties of the two fluids under consideration. The flow was assumed to be incompressible, and with the flow rate used, the flow was laminar.  
The full Navier-Stokes equations combined with the continuity and energy equations were solved numerically. The TPMS was modelled as a solid structure in the flow domain. This approach made the model very complex. The reason is that one may observe channels with closed ends inside the structure. Thus, this requires the flow to be redirected to the place with lower pressure. One may observe some high flow inside the structure, then the flow decreases. This non-uniform flow allows the fluid to circulate longer inside the structure, removing additional heat. Regarding the solving of the equations, it makes solving this set of equations more challenging to converge. To overcome this situation, initially assuming zero flow, the fluid equations are solved first. The model is solved again upon achieving convergence using the flow field results as an initial condition. The new set of equations consists of the flow field formulation and the energy equation. This approach allows a fast and accurate convergence. The convergence criteria are set when the residue-containing variables, such as the three velocities, the pressure, and the temperature, are below \(10^{-6}\). In addition, the refined mesh can help to solve this problem accurately but at the expense of the processing time.  
The formulations used in the current analysis were the following:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fluids and Materials** & \(\rho\) (**kg/m3**) & \(\mu\) (kg/m.s)** & **Cp (J/kg.K)** & **k (W/m.K)** \\ \hline Distilled Water & 998.2 & 0.001001 & 4128 & 0.613 \\ \hline Nanofluid (0.6\%vol Al\({}_{2}\)O\({}_{3}\) + 99.4\%vol water) & 1013.811 & 0.00104 & 4109.198 & 0.6242 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of the materials and fluids used [17].
## 2 Genome Representation  
In this section, we present the main idea of using segments to represent a genome and contrast the efficiency of this approach with a traditional representation as vectors of genomic marker values. One of the most common biological distance measures is the Morgan: one Morgan is the genomic distance over which, on average, one crossover occurs per meiosis [10]. Let \(L\) be the length of the chromosome, in Morgans; the expected number of crossovers per meiosis is \(L\). Let \(S\) denote the total number of segments per individual and \(S(\cdot)\), the number of segments per haploid. Founder \(A\) haploids, \(A_{1}\) and \(A_{2}\), consist of \(S=S(A_{1})+S(A_{2})\) segments. Initially, each haploid consists of one segment, \(S(A_{1})=S(A_{2})=1\). Each crossover introduces one additional segment per haploid; thus, the progeny of \(A\times A\) (\(A\) crossed, e.g., pollinated, with \(A\)) consists of, on average, \(S+2L\) segments. In addition to the segment boundaries, the parental origin (in this case, label \(A_{1}\) or \(A_{2}\)) is known for each segment in the simulation context. Using the segment boundary and label data, we are able to perfectly _reconstruct_ each progeny from the known genomes of the parents. Thus, the segment starts, and labels define a necessary and sufficient representation of the progeny genomes.  
Each progeny can be represented with a dynamic data structure that allows efficiently iterating over the existing segments and adding new segments. An example of such a data structure is a balanced search tree, where the _key_ of each node represents the location where the corresponding segment begins. The advantages of a balanced tree include logarithmic time search for the segment overlapping a given genome position. An alternative representation is a linked list, with the caveat that searching for the segment overlapping a given position takes linear time. However, since a common application of segment representation is to compute the phenotype using all segments and markers, in increasing order of genome location, a linked list may be a sufficient representation. In our experiments, we used a balanced search tree (C++ standard container map) to demonstrate that even this more complex data structure with some overhead has negligible time and space requirements compared with the vector representation. Additional data for each segment (node) will include the label of its parental origin.  
The key ideas of the genome representation are illustrated with the examples in Figure 2. Figure 2(a) shows the representation of a haploid obtained after the meiosis between two founder haploids. Figure 2(b) shows the haploid representation when the meiosis operation is performed on the haploid obtained in Figure 2(a) and the haploid of another founder.  
Figure 2: Illustration of the genome segment representation. (**a**) Representation of founder genome \(A\) haploids, \(A_{1}\), \(A_{2}\), and the result of meiosis with a crossover at \(x\) are shown; (**b**) Output from (a) and founder haploid \(B\) are shown going through meiosis, with a new crossover introduced at \(y\).