esa-sceva/satcom-chunk-collection huggingface dataset:
# PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation
## 1 Introduction
The results of this semantic information are utilized for various applications such as robot navigation [13], image retrieval [14], and question and answer functions [15]. However, the application of heterogeneous multi-robot cooperation planning is not considered. On the one hand, deep learning outperforms extraction of semantic information from an unseen environment, but it is difficult to learn high-level processes that require causal reasoning, analogical reasoning, or planning using data [16]. On the other hand, symbolic planning that uses a logic model can guarantee solution optimality, but it can only be applied to a predefined environment. To combine deep learning and classical planning, [PERSON] and [PERSON] [17] encoded images as latent vectors with a variational autoencoder and applied PDDL planning. [PERSON] et al. [18] proposed a neuro-symbolic concept learner that learns visual scenes using a neural network and expresses them in an executable form in symbolic programs. In this study, symbolic planning and deep learning techniques are integrated to propose a cooperation planning architecture with natural language scene understanding for a heterogeneous robot team, as shown in Figure 1. Convolutional neural networks (CNNs), GCNs, and RNNs are used for natural language description and scene graph generation. JENA-TDB is used to share the semantic representation of the environment among the robots. The planning phase of ROSPlan [19] is used for generating plans. The proposed method is verified by a simulation using one unmanned aerial vehicle (UAV) and three unmanned ground vehicles (UGVs).
## 2 Materials and Methods
### SIF Retrieval and Vegetation Indices Calculation  
In this study, the SIF at 760 nm (F760) was used to quantify the emitted canopy fluorescence according to the Spectral Fitting Methods (SFM) proposed by [PERSON] et al. (2010) ([PERSON] et al., 2010). A wavelength range of 759-762 nm was applied in our study, with the assumption that the canopy reflectance and SIF are linear functions of wavelength. We only used the data when the fitting algorithm R\({}^{2}\) > 0.99. As a result, only 8 SIF values (4 values/day/crop) were successfully retrieved, although 10 measurements were collected.  
A canopy reflectance of between 200 and 1000 nm was used to calculate the vegetation index. In this study, a vegetation index named the normalized difference vegetation index (NDVI) at Red edge (RNDVI) was used to approximate the fraction of absorbed photosynthetically active radiation (fAPAR) in PAR ([PERSON] et al., 2017). The RNDVI was calculated using the following Equation (1).  
\[RNDVI=(R_{750\sim 757.5}-R_{703.75\sim 713.75})/(R_{750\sim 757.5}+R_{703.75\sim 713.75}). \tag{1}\]  
Based on the F760 results, the SIF yield at 760 nm (Fy760) was calculated as Fy760 = F760/APAR. Without the direct measurements of APAR, it was calculated as the product of PAR and fAPARgreen. The PAR was not directly measured due to the damaged probe of the meteorological station. The value of the solar irradiance between 761\(\sim\)763 nm was selected as a proxy of the PAR because of their significantly and perfectly linear relationship (R\({}^{2}\) = 0.92, \(P\) < 0.0001; Figure 1). The value of fAPARgreen can be represented by the Rededge NDVI. The detailed calculation of APAR is described in the following Equation (2). Furthermore, the apparent SIF yield at 760 nm (AF760) was also calculated as AF760 = F760/PAR using the solar irradiance between 761\(\sim\)763 nm as a proxy of PAR.  
\[\left\{\begin{array}{c}APAR=PAR\times fPAR_{green}\\ PAR=232.47+0.52\times Solar\ irradiance\ (761\sim 763\ nm)\\ fPAR_{green}=1.33\times RNDVI-0.15\end{array}\right., \tag{2}\]  
Figure 1: The relationship between the received photosynthetically active radiation (PAR) and the accumulated irradiance from 761 to 763 nm. All the measurements were collected for wheat from 20 April to 19 May 2017.
## 2 Advanced Oxidation Processes
### Sonochemical Methods
#### 3.1.1 Parabens Abatement by Photolysis  
Photolysis only uses radiation for contaminants degradation. UVA, UVC, and solar light (natural and simulated) have already been used in parabens abatement. Regarding the parabens abatement, the photolysis performed by [PERSON] et al. [91] showed negligible results (lower than 4%), while the use of catalyst and radiation led to better results. [PERSON] et al. [7] removed MP using photolysis with UV radiation, achieving less than 5% after 120 min in a pilot plant, while [PERSON] et al. [32] removed 10% of BeP in a laboratory installation. [PERSON] et al. [35] used photolysis with UVC radiation, achieving 10% removal for MP, EP, PP, and BuP and about 60% degradation for BeP in 120 min for individual parabens and about 20% for MP and BuP, 30% for EP and PP in 120 min, and 80% for BuP in 20 min, when the parabens mixture was considered. Complete mixture removal was achieved at 480 min of UVC photolysis. In general, several studies such as those of [PERSON] et al. [58], [PERSON] et al. [7], [PERSON] et al. [32], and [PERSON] et al. [91] present low performance when UVA photolysis was used in parabens abatement. Solar light studies by [PERSON] et al. [58] led to 16% of removal of MP, EP, and PP in 60 min, and the study by [PERSON] et al. [3] led to 30% and 35% of MP and EP removal in 240 min using real wastewater effluent as water matrix, suggesting an interesting behavior of solar light by itself, although the study by [PERSON] et al. [92] led to poor EP abatement. The study by [PERSON] et al. [93] led to almost complete removal of PP and BuP using UV-Vis radiation, and the study by [PERSON] et al. [94] achieved 80-99.8% of single degradation for MP, EP, or BuP in 120 min using UV (254 nm) radiation. Both studies performed good results regarding the parabens degradation, but the study by [PERSON] et al. [35] using UVC radiation only led to about 20-30% mixture degradation for MP, EP, PP, and BuP removal in 120 min (BeP was 80% in 20 min), and the total removal was only achieved after 480 min. In fact, [PERSON] et al. [35] used a higher concentration of parabens (10 mg/L of MP, EP, PP, BuP, and BeP each and 10 mg/L of p-HBA), while [PERSON] et al. [93] only applied 10 mg/L of PP and BuP each (20 mg/L total). Moreover, [PERSON] et al. [94] only used about 45.65 mg/L of MP, 49.85 mg/L of EP, and 58.29 mg/L of BuP. This should be responsible for the differences in the results, and, the presence of more compounds may make the degradation of each compound difficult. In general, it seems that UVC must be preferred to UVA light when used alone to achieve better results. However, this implies higher energy costs, besides the danger associated with such strongly energetic radiation. Table 2 presents several studies' results using photolysis in parabens abatement.
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
## 3 Results and Discussion
### Shading Characteristics of Shingled PV Module
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **No** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\  & **Shading** & **Shading** & **Shading** & **Shading** & **Shading** & **Shading** \\ \hline Size (cm\({}^{2}\)) & 20,763.6 & 20,763.6 & 20,763.6 & 20,763.6 & 20,763.6 & 20,763.6 \\ I\({}_{\rm sc}\) (A) & 10.73 & 10.66 & 10.65 & 10.7 & 10.7 & 10.66 \\ V\({}_{\rm oc}\) (V) & 44.85 & 44.61 & 44.3 & 43.9 & 43.17 & 22.65 \\ FF (\%) & 80.07 & 67.44 & 51.6 & 39.44 & 39.96 & 76.2 \\ Eff (\%) & 18.57 & 15.45 & 11.73 & 8.93 & 8.89 & 8.87 \\ P\({}_{\rm m}\) (W) & 385.58 & 320.8 & 243.67 & 185.44 & 184.7 & 184.18 \\ Power ratio (\%) & 100 & 83.19 & 63.19 & 48.09 & 47.9 & 47.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Dependence of the measured shading characteristics on the amount of shading of shingled PV modules. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **No** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\  & **Shading** & **Shading** & **Shading** & **Shading** & **Shading** & **Shading** \\ \hline Size (cm\({}^{2}\)) & 20,763.6 & 20,763.6 & 20,763.6 & 20,763.6 & 20,763.6 & 20,763.6 \\ I\({}_{\rm sc}\) (A) & 11.03 & 11.02 & 11.02 & 11.02 & 11.02 & 11.02 \\ V\({}_{\rm oc}\) (V) & 45.44 & 45.20 & 44.89 & 44.43 & 43.59 & 22.39 \\ FF (\%) & 77.75 & 65.32 & 49.33 & 38.64 & 39.29 & 76.37 \\ Eff (\%) & 18.77 & 15.67 & 11.89 & 9.10 & 9.09 & 9.09 \\ P\({}_{\rm m}\) (W) & 389.869 & 325.5 & 247 & 189.11 & 188.9 & 188.74 \\ Power ratio (\%) & 100 & 83.48 & 63.35 & 48.50 & 48.45 & 48.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Dependence of the simulated shading characteristics of a shingled PV module on the amount of shading. shown in Figure 16. This makes it possible to predict the shadow loss of a shingled PV module by analyzing the size and shape of the shadow that occurs in a specific area in the future. In addition, simulation of the shading characteristics of the shingled PV module according to the design of the circuit and bypass diode is expected to verify the circuit design. This would enable an optimal shading loss reduction circuit to be constructed accordingly.
## 4 Case Study
### Cross-Validation  
To further validate the robustness and generalization ability of the WACNN, we used a time series k-fold cross-validation. Choosing 6-fold here meant dividing the original dataset into six cycles of five years. We selected four years of data as the training set and one year of data as the testing set for each cycle, as shown in Table 10.  
According to the results of the six-fold cross-validation, it can be seen that the model has good prediction results for the six different datasets, and the errors on the training set and testing machine are within an acceptable range, proving that the proposed model has good stability and generalization ability. Meanwhile, the cross-validation results also demonstrate that the size and volatility of the data have a significant impact on the learning of the model.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 3 Spoofing Detection Method
### Msc-Mmp
Calculate the average gray values \(m_{1}\) and \(m_{2}\) for all the pixels in \(\varphi_{1}\) and \(\varphi_{2}\) \[m=AVERAGE\{\varphi(x,y)\}\] (10) 4. Calculate the new threshold \[T=\frac{m_{1}+m_{2}}{2}\] (11) 5. Repeat steps b to d, iterating \(T\) until it is less than the custom parameter \(\Delta T\), which is set to 0.1 in this example. This value was obtained empirically through experimentation and provided the necessary accuracy without excessive calculation. 4. Normalize the threshold \[T_{0}=\frac{T-1}{255}\] (12) where \(T_{0}\) represents the calculated optimal high threshold (Th) for the Canny algorithm. In general, the ratio of the high threshold to the low threshold for the Canny algorithm is 2:1; therefore, the algorithm utilizes a low threshold (TL) of \(0.5T_{0}\). Once the above steps have been completed, Canny edge detection was performed on images \(G_{0}(x,y)\), \(G_{1}(x,y)\), and \(G_{2}(x,y)\) of the three-layer Gaussian pyramid. This process yields three edge images, \(g_{0}(x,y)\), \(g_{1}(x,y)\), and \(g_{2}(x,y)\), which represent different scales. To obtain an integrated overall image, the cross-scale addition of the edge images from the three aforementioned sources was performed. Specifically, the small-scale image was resized to match the large-scale image via linear interpolation. A pixel-to-pixel addition operation was subsequently applied. In particular, the zeroth-layer image \(g_{0}(x,y)\) was taken as the reference, with the first-layer image \(g_{1}(x,y)\) and the second-layer image \(g_{2}(x,y)\) both interpolated and normalized to the size of \(g_{0}(x,y)\). Ultimately, the cross-scale addition method was employed to effectively fuse the edge details and contour information of the images at different scales, thereby enhancing the overall image quality. \[F(x,y)=g_{0}(x,y)\oplus g_{1}(x,y)\oplus g_{2}(x,y) \tag{13}\]  
where \(F(x,y)\) represents the image after cross-size fusion and where \(\oplus\) represents cross-size addition.
## 3 Case Study  
### Research Areas and Data Sources  
Yangloushi Town is located in Pingjiang County, Yueyang City, Hunan Province, China, with geographic coordinates spanning 29.2-29.6\({}^{\circ}\) N latitude and 113.5-113.7\({}^{\circ}\) E longitude. Occupying an area of approximately 156 square kilometers, the town features elevations ranging from 32 m to 1260 m, with an overall slope gradient of 0-62\({}^{\circ}\). Geologically, it lies at the northern edge of the southeastern extension of the Nanjing Mountains and the southern margin of the Yangtze Craton. The region has experienced complex tectonic evolution, including the Caledonian, Hercynian, and Yanshanian movements, which have created a diverse landscape of mountains, hills, and plains through intensive faulting and folding. This tectonic activity has rendered the area geologically unstable. This study utilizes high-resolution DEM data provided by the Hunan Provincial Geological Disaster Survey Institute (Figure 4), with a 10 m spatial resolution, to analyze the topographic characteristics and geological risks in the region.  
Figure 4: DEM of Yangloushi Town.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 4 The Immersive Video ME App
### The i-EVS ME App
#### 4.1.3 The dB VNFC  
The dB VNFC collects and monitors relevant information about users and groups, which have registered to the i-EVS system.  
When a user connects to the local access network for the first time, it can download the end-user app. The next step is registration to the system, through which the user is permanently identified by a name and/or nickname. The interaction between the end-user app and the dB VNFC allows users to create a group where video contents can be shared. For each user, the database stores all the relevant information about identity, group belonging, connectivity, and Service Level Agreement, so as to provide the required type of service. The dB component also provides basic security functionalities, such as user password management.
## 3 Experimental Data  
To verify the availability of the KNN-BILSTM model proposed in this paper, we experimentally tested the algorithm using traffic flow data from British motorways. The dataset includes real-time recordings of time, flow, and speed from all detection points on all motorways in the UK. Fifteen detection points in the interchange area of the M25 and the M23 near WARWICKWOLD in London, England, were selected as the research object. The corresponding positions of each detection point are shown in Figure 3. The selected detection point is the traffic flow in the same direction, and since the traffic flow on and off the overpass is affected, 3310 B is used as the target detection point. Due to the periodicity of the traffic flow data, the traffic flow data from 1 January 2021 to 30 April 2021  
Figure 2: Traffic flow prediction flow chart.  
was selected as the experimental data, the time interval was recorded every 15 min, and the number of samples of the current traffic flow sequence was 4 \(\times\) 24 \(\times\) 31. Eightly percent of all the data was used as training data, and the rest of the data was used as test data for the experiments.  
To evaluate the performance of the model in predicting traffic flow, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as prediction evaluation indicators, and the definition formula was as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|\mathcal{y}_{i}-\mathcal{y}_{i}\right| \tag{13}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\mathcal{y}_{i}-\mathcal{y}_{i} \right)^{2}} \tag{14}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\mathcal{y}_{i}-\mathcal{y}_{i}}{ \mathcal{y}_{i}}\right| \tag{15}\]  
where \(n\) is the number of samples, \(\mathcal{y}_{i}\) is the real value of the data, and \(\mathcal{y}_{i}\) is the predicted value of the data sample.
## 3 Results and Discussion
### Ground Truth Estimation  
Since it is not possible to analytically determine the optimal arrangement of sensors and multiple sensor configurations could be optimal, we decided to use a single metric with which to compare the optimization algorithms. If there are multiple optimal sensor configurations, they result in the same coverage value \(C\). Therefore, we chose the coverage value \(C\) as the criterion for determining the accuracy of sensor placement and used it as the ground truth representation instead of the sensor configuration.  
The time per evaluation \(TPE\) mainly depends on the number of voxels in the environment and the number and type of sensors placed. On this basis, \(TPE\) and, extrapolated from it, the average computation time of an optimization function are constant. Both the ES and RES approaches are parallelized and are compared based on the number of evaluations of the optimization function and not on the computation time.  
Both ES and RES were evaluated and compared on test cases involving the placement of a single omnidirectional sensor in two-dimensional and three-dimensional models of all three environments with a rasterization value of \(r_{v}=0.1\,\mathrm{m}\). ES was conducted with a step size of \(r_{e}=0.01\,\mathrm{m}\), while for RES, the initial value was \(r_{e}=1\,\mathrm{m}\).  
Table 2 shows the measured time per evaluation \(TPE\) in seconds, the achieved coverage values \(C\) and the number of evaluations \(E\) of the optimization function for both approaches. For two-dimensional environments, the computation time for ES was between 4 and 7.5 min, while for RES it was an order of magnitude lower, between 1 and 4 s. For three-dimensional environments, the difference is even more pronounced, ranging from 22 to 45 min for RES. Measuring the computation time for ES would be possible, but not feasible, as it is estimated to be between 80 and 204 days for the same test cases. The computation time between ES and RES ranged from a factor of 100 for two-dimensional environments to a factor of 5000 for three-dimensional environments.  
From the data obtained, it is evident that there is no difference in the coverage achieved between the two approaches for two-dimensional environments. However, for three-dimensional environments, the achieved space coverage results for the ES approach are not shown since the computation time is extended beyond practical limits.
## 4 Results and Discussion
### Performance Analysis
When employing k-means clustering, more sophisticated regions were calculated, as shown in Figure 4c, and the low power constellation points were then correctly classified. For symbols in the constellation periphery, the straight boundaries of k-means, however, were not optimal. This point can be appreciated in the zoom-in. Finally, if we applied HBC, we obtained the decision regions represented in Figure 4d. At first glance, the decision regions were similar to those using k-means. For low power symbols, where SPM did not significantly affect the shape, but caused only a rotation (this can be corroborated in the histogram of Figure 4a), the boundaries were still straight lines. For higher power symbols, in contrast, the boundaries found by HBC were not straight lines any more. We can see a clear example of the curved boundaries in the detailed view, appreciating that the curved line matched better the cluster boundary expected from the histogram.
# Effect of Marine Coolant Additives on Cavitation Erosion-Corrosion of Diesel Engine Cylinder Liner
###### Abstract  
In this study, cavitation erosion tests were conducted to investigate the effects of the presence of coolant additives and chlorides on the corrosion and cavitation erosion of cylinder liners in marine diesel engines. Electrochemical experiments were conducted to evaluate the corrosion characteristics of ductile cast iron (DCI), and the corrosion potential and corrosion current density were measured. In addition, weight loss, surface roughness, and maximum surface damage depth were quantified as a function of cavitation exposure time. Furthermore, to investigate the erosion and erosion-corrosion characteristics induced by cavitation attack, the damaged surface morphology was closely examined using a scanning electron microscope (SEM) after the cavitation erosion tests. The results revealed that the coolant additive effectively protected the DCI from corrosion caused by aggressive chlorides. In particular, when an appropriate amount of additive was added to a coolant containing 100 ppm of chloride, the corrosion current density of DCI was reduced by approximately 31.7 times, significantly improving corrosion resistance. Therefore, different surface damage mechanisms corresponding to cavitation erosion and cavitation erosion-corrosion were identified depending on the presence or absence of the coolant additive during the cavitation erosion tests.  
cobalt additive; ductile cast iron; cavitation erosion; corrosion; cylinder liner +
Footnote †: journal: Applied sciences  
1 Graduate School, Department of Marine Engineering, Mokpo National Maritime University, Mokpo 58628, Republic of Korea; [EMAIL_ADDRESS]  
2