esa-sceva/satcom-chunk-collection huggingface dataset:
# Speed Bump and Pothole Detection Using Deep Neural Network with Images Captured through ZED Camera
### Deep Learning Proposals  
[PERSON] et al. presented a study to address road damage detection issues [9]. A large-scale road damage dataset was prepared, and images were captured using a smartphone installed on a car. Next, they used convolutional neural networks to train the damage detection system with their database and compared the accuracy and runtime speed on a GPU server and a smartphone. The accuracy reached was 75%. [PERSON] and [PERSON] [7] attempted to identify the road surface by classifying it into potholes, speed bumps, and normal road from image data. The classification of the road surface from the images, utilizing convolution neural network ResNet-50, is discussed. The images are hand-classified into the three classes, and these are used to train the neural network, achieving a true positive rate of 88.9%. [PERSON] et al. [21] proposed two speed bump-detection methods: (i) [PERSON]'s threshold and (ii) morphological operation. These processes are superior to the existing ones because they do not utilize any external information network, so they are free from GPS error, network overload, delay, and incorrect alarm. The proposed [PERSON]'s approach is straightforward, efficient, and yields 74.6% accuracy. The performance of the morphological or structural operation achieves good results, with an 85.8% detection ratio. The two proposed designs provide a higher than 90% detection rate for properly painted roads and optical illusion-type speed bumps. Another contribution is the investigation of [PERSON] et al. [22], which classified a dataset of images with potholes in various road conditions and lighting variations. They tested various deep learning architectures such as Tiny-YOLOv, YOLOv4, and YOLOv5, with accuracies of 80.04%, 85.48%, and 95%, respectively. The study found Tiny-YOLOv4 to be the best model for pothole detection, with 90% detection accuracy.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Volatile Compounds** & **RT (s)** & **Control (\%)** & **O-Lp (\%)** & **O-Lc (\%)** & **O-La (\%)** & **O-St (\%)** & **O-Lb (\%)** \\ \hline
2-Propylfuran & 377.78 & ND & 0.07 \(\pm\) 0.02 a & ND & 0.08 \(\pm\) 0.01 a & 0.06 \(\pm\) 0.01 a & 0.07 \(\pm\) 0.00 a \\
2-Butylfuran & 483.20 & 0.10 \(\pm\) 0.00 a & ND & 0.07 \(\pm\) 0.01 b & 0.11 \(\pm\) 0.01 a & 0.07 \(\pm\) 0.00 b & 0.11 \(\pm\) 0.02 a \\
2-tert-Butoxytetra- & 515.08 & 0.11 \(\pm\) 0.02 b & ND & 0.19 \(\pm\) 0.05 a & ND & ND & ND \\ Furan, 2-pentyl- & 585.34 & ND & 2.20 \(\pm\) 0.09 b & 2.74 \(\pm\) 0.13 a & 2.27 \(\pm\) 0.31 b & 2.08 \(\pm\) 0.08 b & 2.66 \(\pm\) 0.22 a \\ Furan, & 739.45 & ND & ND & 0.04 \(\pm\) 0.01 b & 0.08 \(\pm\) 0.02 b & ND & 2.48 \(\pm\) 0.27 a \\
2-Hepptylfuran & 769.09 & 0.01 \(\pm\) 0.01 a & 0.01 \(\pm\) 0.00 a & 0.000 \(\pm\) 0.00 a & 0.01 \(\pm\) 0.01 a & 0.01 \(\pm\) 0.00 a & 0.01 \(\pm\) 0.00 a \\ Furan, & 981.74 & 0.14 \(\pm\) 0.04 a & 0.11 \(\pm\) 0.03 ab & 0.05 \(\pm\) 0.01 c & 0.08 \(\pm\) 0.01 bc & 0.13 \(\pm\) 0.02 ab & 0.14 \(\pm\) 0.03 a \\
2-(methylfuran) & 1087.83 & 0.06 \(\pm\) 0.02 a & 0.07 \(\pm\) 0.01 a & 0.05 \(\pm\) 0.02 a & 0.06 \(\pm\) 0.01 a & 0.06 \(\pm\) 0.02 a \\ \hline Hydrocarbons & & & & & & & \\ Pentane & 127.57 & ND & 0.48 \(\pm\) 0.03 a & 0.45 \(\pm\) 0.03 a & 0.43 \(\pm\) 0.02 a & 0.49 \(\pm\) 0.05 a & 0.31 \(\pm\) 0.04 b \\ Heptene & 176.43 & 0.07 \(\pm\) 0.02 b & 0.41 \(\pm\) 0.12 a & ND & ND & 0.06 \(\pm\) 0.02 b & 0.09 \(\pm\) 0.01 b \\ Undeacane & 338.38 & 0.01 \(\pm\) 0.00 b & 0.01 \(\pm\) 0.00 b & 0.01 \(\pm\) 0.00 b & 0.09 \(\pm\) 0.02 a & 0.01 \(\pm\) 0.00 b & 0.01 \(\pm\) 0.00 b \\ Dodecane & 543.61 & ND & 0.25 \(\pm\) 0.04 a & ND & 0.16 \(\pm\) 0.03 b & ND & ND \\ Pentadecane & 546.45 & 0.17 \(\pm\) 0.02 ab & ND & 0.14 \(\pm\) 0.01 b & ND & 0.19 \(\pm\) 0.03 a & 0.16 \(\pm\) 0.02 ab \\ Tetradecane & 643.72 & ND & 0.36 \(\pm\) 0.03 a & 0.01 \(\pm\) 0.01 b & 0.36 \(\pm\) 0.02 a & 0.04 \(\pm\) 0.00 b & ND \\ Nonadecane & 893.62 & 0.05 \(\pm\) 0.01 a & 0.02 \(\pm\) 0.01 c & 0.05 \(\pm\) 0.00 a & 0.05 \(\pm\) 0.01 a & 0.03 \(\pm\) 0.00 bc & 0.04 \(\pm\) 0.01 ab \\ \hline Terpenes & & & & & & & \\ Limonene & 548.57 & 0.01 \(\pm\) 0.00 c & 0.03 \(\pm\) 0.01 b & 0.05 \(\pm\) 0.02 ab & 0.03 \(\pm\) 0.01 b & 0.06 \(\pm\) 0.01 a & 0.05 \(\pm\) 0.00 ab \\ \hline  & & & & & & & \\ \end{tabular} Different lowercase letters indicate significant differences between different lactic acid bacteria fermented samples (\(p<0.05\)). O-Lp, sample fermented with _L. plantarum_; O-La, sample fermented with _L. acidophilus_; O-Lc, sample fermented with _L. casei_; O-Lb, sample fermented with _L. bulgarcius_; O-St, sample fermented with _S. thermophilus_. ND, not detected; RT: retention time; h, hours.  
\end{table} 
Table 2: _Cont._
# Characterization of Chirality in Diffractive Metasurfaces by Photothermal Deflection Technique
## 3 Results
### Determination of the Induced Temperature of the Metasurface  
It is well known that the photothermal deflection technique, in the transverse configuration, is extremely sensitive to the thermal gradients induced in the air layer in contact with the metasurface (at a minimum height of \(z\approx\) 20-50 \(\upmu\)m) (see Equation (1)). The 1D map of the lateral heat flux induced by the incident pump and the diffracted beams can be obtained simply by performing a probe beam scan, as shown in Figures 3 and 5b. Moreover, by analyzing both the PTD amplitude and the phase signals in the proximity of each heat source (<0.3 mm), recent articles demonstrated that the temperature map of each individual heat source can be accurately estimated by applying inverse methods [41, 42, 43, 44]. In fact, in the case of a heat source with a cylindrical symmetry, Equation (1) can be inverted to obtain the surface temperature increase from the following inverse integral transformation [34, 45, 46]:  
\[T(r)=-\quad\frac{1}{\pi}\left(\frac{1}{n}\frac{dn}{dT}\right)^{-1}\quad\int \limits_{r}^{\infty}\frac{\Phi_{x}(x)}{\sqrt{x^{2}-r^{2}}}dx \tag{8}\]  
where \(1/n(dn/dT)\approx-10^{-6}\)\({}^{\circ}\)C\({}^{-1}\), and r is the radial distance from the heat source (the extension to elliptical heat sources is also allowed). We applied Equation (8) to process the PTD data in Figure 3, separately, for each heat source within the spatial range of 0.3 mm. As an example, Figure 6 shows a reconstructed surface temperature profile, produced by the incident pump beam of 40 mW (\(m=0\)) and by the diffracted beams with an index of \(m=2\), 3, 5, 6 for normal incidence at wavelengths 514 nm and 458 nm, and at f = 36 Hz. The temperature profiles exhibit Gaussian-like shapes and the spot-sizes are shown in Figure 6 and reported in Tables 3 and 4. It is worth noting that, for \(m=0\), the temperature profile of the incident beam is superimposed, with a secondary source produced by the beam itself when reflected by the mirror and re-absorbed at the surface with a weak lateral shift of 0.07 mm due to a small misalignment. The maximum temperature increase is estimated at around 11 \({}^{\circ}\)C, independent of the wavelength and the kind of incident pump beam chirality. Chirality has been tested on the temperature induced by the diffracted beams. Looking at Figure 6, chirality is rather irrelevant at a wavelength of 458 nm, while it becomes clearly observable at a wavelength of 514 nm for \(m\
eq 0\). In particular, a change in the sign of chirality is also visible for all the orders (see \(m=2\), 3, 5, 6). One reason for this could be the increase in the internal angle of the diffracted beams from 42 deg at 458 nm to 48 deg at
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
###### Abstract  
Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.  
Keywords:time-series forecasting; dynamic pricing; digital signage advertising; data characteristics; model selection +
Footnote †: journal:  
_Future Internet_
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
## 4 Results and Discussion  
To validate the effectiveness and accuracy of the proposed reduced-order model based on Kolmogorov-Arnold Networks, this study first describes the training procedure and the primary associated parameters of the ROM. Subsequently, the ROM is applied to predict nonlinear unsteady aerodynamics at varying amplitudes and reduced frequencies, and the results are compared against those obtained from computational fluid dynamics simulations. Furthermore, the ROM is coupled with the structural equations of motion to predict the responses of limit cycle oscillations (LCOs), and these predictions are validated by comparing them with corresponding CFD results. Finally, a comparative analysis of predictive accuracy and computational efficiency among different approaches is conducted to evaluate their overall performance.
## 3 Results and Discussion
### Environmental Benefits  
Environmental monitoring studies conducted in recent years have the effectiveness of the restoration process [24; 25]. The successive improvement of the oxygen balance observed since 2019 has enabled positive changes in the quality of the lake water. First of all, the process of internal release of nutrients from sediments to water has been limited. Before restoration, the content of total phosphorus in the interstitial waters of the upper sediment layer (0-10 cm) reached very high values, averaging 4.0-8.5 mg L\({}^{-1}\). The high concentration gradient between the sediment layer and the water column favored the migration of nutrients from the bottom sediments to the water. The observed concentrations of phosphorus in the near-bottom water (10 cm layer of water-bottom sediment interphase) during the summer stratification reached 3.5-5.0 mg L\({}^{-1}\); in the waters 1 m above the bottom, we recorded values of 2.5-3.0 mg L\({}^{-1}\); surface water was characterized by the richness of this element at the level of 0.3-0.4 mg L\({}^{-1}\). During the period of operation of oxygen reactors (2020 and 2021), phosphorus compounds were present in much lower amounts. The water column was characterized by the concentration of this nutrient at the level not exceeding 0.2 mg L\({}^{-1}\) (surface water); the over-bottom water contained phosphorus in an amount that did not excel 0.5 mg L\({}^{-1}\); the interphase waters, just above the sediment, had a phosphorus concentration below 1 mg L\({}^{-1}\) The slowdown of the phenomenon of nutrients internal loading from sediments was also evidenced by the reduction of phosphorus concentration in interstitial waters (a decrease of concentrations by about two-fold, to a level not exceeding 2 mg L\({}^{-1}\)). Reducing the supply of phosphorus in the ecosystem caused a number of beneficial changes--the biological productivity of the lake was reduced, phytoplankton biomass decreased, the process of rebuilding submerged macrophytes began, and water transparency increased [25]. The year 2021 was the first year in the history of contemporary research on Lake Lajskie in which the presence of hydrogen sulfide was not observed in the bottom layers of water.
We examined three machine learning approaches--Cubist, Support Vector Regression (SVR), and Random Forest (RF)--for downscaling of MODIS 1 km 8-day ET product (MOD16A2) using eleven 30-m Landsat 8 data-derived variables (indices), including surface albedo, LST, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Depth of Landsat 8 OLI band 6 (D\({}_{1609}\)), Normalized Difference Infrared Index--Landsat 8 OLI band 7 (NDIIb7) and Temperature Vegetation Dryness Index (TVDI). Among the eleven indices, LST has been successfully used for ET estimation due to its sensitivity to local moisture status; surface albedo has been used as an input parameter for various SEB models; vegetation greenness indices including NDVI, EVI, SAVI and MSAVI are associated with growing status of vegetation; and wetness indices including NDWI, NDMI, D\({}_{1609}\), and NDIIb7 are highly correlated with water consumption that is reflected from ET processes. The method assumed that vegetation and surface wetness conditions do not change considerably within an 8-day interval, and the growing status of vegetation is highly correlated with water consumption that is reflected from ET processes. Different from previous ET downscaling methods, the machine learning models in our study do not need to calculate Landsat-based ET and thus do not rely on the accuracy of meteorological parameters. The models were tested at four study sites within different climate zones in the United States and South Korea, and evaluated using _in situ_ flux tower measurements.
## 4 Selection of Host Species
### Geographic Origin  
Factors such as climate, altitude, and local ecosystem diversity impact the survival, reproductive success, metabolic pathways, and stress responses of the fungus, thereby affecting the quality and quantity of medicinal compounds produced. For example, optimal growth temperatures for _C. millitaris_ are between 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C with relative humidity around 90%; regions that naturally provide these conditions support better fungal development and higher yields of compounds like cordycepin and polysaccharides [64]. Additionally, higher altitudes expose the fungus to lower temperatures and increased UV radiation, inducing stress responses such as the synthesis of protective antioxidants, which enhance the therapeutic efficacy of the harvested compounds [38]. The local biodiversity and soil composition also dictate the types of hosts available, influencing nutrient uptake and the specific metabolic pathways activated in the fungus, leading to unique bioactive compound profiles [65].  
The genetic makeup of _C. millitaris_ varies across different geographic regions, impacting its adaptability, health, and productivity. Diverse genetic traits within populations from varied regions contribute to differences in resistance to pathogens, adaptation to environmental stressors, and metabolic functions. Geographic isolation can lead to genetic drift or localized adaptations, where populations evolve distinct genetic markers that confer advantages in specific environments, optimizing resistance to local climatic stressors or host utilization [66]. Studies reveal that molecular adaptations in _C. millitaris_ strains vary by geographic location. These strains may express different gene sets, particularly those involved in stress response and secondary metabolism. Such variations optimize growth and survival under diverse environmental conditions. [67].  
Advanced genomic, transcriptomic, and metabolomic technologies are critical for understanding how geographic origin influences _C. militaris_. Techniques such as genome sequencing of strains from various origins help identify genetic variations linked to phenotypic differences and local adaptations [68]. Metabolomic profiling provides insights into the metabolic pathways influenced by geographic factors and their impact on compound synthesis [69]. Field studies examining the natural habitats of _C. militaris_ enhance our understanding of the ecological dynamics that influence fungal populations, including interactions with hosts and competitors [70]. This knowledge is invaluable for optimizing cultivation practices and developing conservation strategies, enabling the selection of strains adapted to specific climates or efficient in producing desired compounds, thereby enhancing yield and quality in commercial farming and aiding in the conservation of wild populations via informed sustainable harvesting and habitat preservation efforts.
## Materials and Methods
### Preparation of MIPs and NIPs  
#### 2.2.1 Synthesis of MIP1 and NIP1 by Bulk Polymerization  
Synthesis of the MIP1 [9]: Synthesis of the MIP1 by bulk polymerization followed a reported procedure. In general, gossypol (0.083 mmol, 43 mg), DMAEMA (1.0 mmol, 157 mg), EGDMA (5.0 mmol, 990 mg), and AIBN (0.27 mmol, 44 mg) were dissolved in 4.0 mL dichloromethane in a screw-capped vial. This mixture was sonicated for 5 min and degassed for 15 min under nitrogen. The vial was sealed and then immersed in a water bath at 65 \({}^{\circ}\)C for 24 h. As the control, a non-imprinted polymer (NIP1) was synthesized following the same protocol but without adding the template. The resulting polymer monoliths were crushed and ground with a mortar and pestle to a fine powder. The powder was sieved using 200 mesh sieve. Particle size fraction below 75 um was collected.  
Removal of template molecule: For MIP1, the un-reacted species and templates were removed by Soxhlet extraction with methanol for 24 h and then washed with 10.0 mmol L\({}^{-1}\) NaOH aqueous solutions for 2 h per cycle until no template molecules were detected by a UV-Vis spectroscopy. Finally, the MIPs were washed with pure water to be neutral. For NIPs, the particles were washed by Soxhlet extraction with methanol for 24 h to remove the un-reacted substances. Finally, both MIP and NIP were dried at 60 \({}^{\circ}\)C using the vacuum drying in an oven overnight.
## 2 Methods
### Economic Analysis Framework  
The economic analysis is conducted to evaluate the different processes because it provides a comprehensive understanding of the process [46]. The cost estimation framework used in this study is illustrated in Figure 5. The assumptions considered for the economic analysis are summarized in Table 4 [47; 48; 49].  
The amortization factor (\(\alpha\)) represents the annual repayment of the total capital cost (TCC); it is computed as  
\[\alpha=\frac{i(1+i)^{n}}{\left(1+i\right)^{n}-1} \tag{33}\]  
where \(i\) represents the interest rate (8%) and \(n\) represents the plant lifetime (25 years) [51].  
To calculate the raw material cost, this study considered both the initial charge cost and the leakage makeup cost as mentioned earlier. The total raw material cost of variable cost can be calculated  
\[\text{Raw material}\;\cos\mathfrak{t}=\left(\frac{\text{initial charge}\;\cos \mathfrak{t}}{n}+\text{leakage makeup}\;\cos\mathfrak{t}\right)\times\frac{ \text{CEPCI (2020)}}{\text{CEPCI (2017)}} \tag{34}\]  
The raw material cost is adjusted based on the chemical engineering plant cost index (CEPCI) because some material costs are based on 2007 and 2020 prices [52]. The CEPCI for 2020 is 599.5; 2017 is 567.5; and 2007, 525.4 [53].  
The considered raw materials in this study are: methane and natural gas, $0.21/kg [54]; ethane, $0.534 kg [55]; propane, $0.679/kg [56]; butane, $0.702/kg [57]; and nitrogen $0.546/kg [58]. As methane  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Assumptions**} \\ \hline \(\bullet\) & All costs are presented in USD (2017). \\ \hline \(\bullet\) & Plant availability of 95\% corresponds to an operation time of 8322 h/year [49]. \\ \hline \(\bullet\) & Total equipment cost (TEC) is evaluated using Aspen process economic analyzer (2017) [49; 50]. \\ \hline \(\bullet\) & As the land and labor costs vary depending on the scenario; they are not considered in this study. \\ \hline \(\bullet\) & Total raw material costs include the initial charge and make-up costs of the material: the compressor \\  & leakage rate of 0.05 wt. \% is assumed [32; 33; 34]. \\ \hline \(\bullet\) & Electricity costs are based on the average retail electricity prices in the US ($0.1048 per kWh) in 2017 [51]. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Assumptions for economic analysis.  
Figure 5: Cost estimation framework.  
has the largest proportion in the composition of the natural gas, the price of the methane is assumed to be the price of the natural gas.  
The total annualized cost (TAC) is calculated as  
\[\text{TAC}=\frac{\text{TCC}}{n}+\text{TOC} \tag{35}\]  
Product cost is a useful index to compare \(LNG\) liquefaction plants regardless of its capacity [24]; the product cost is calculated as  
\[\text{Product cost}\left(\frac{\text{LUSD}}{\text{tpa}}\right)=\frac{\text{TCC} +\text{TOC}*n*\text{plant availability}}{\text{LNG production rate}} \tag{36}\]  
where \(n\) represents the plant lifetime and the plant availability is 95%.
## 4 Application of the Concepts: Design of an In-Line Measuring Machine
### Architecture of In-Line Measuring Station
#### 4.2.3 Robustness and Safety  
In the proposed design, the laser and the linear motors need to be protected to allow for reliable operation. For this reason, the operations of part load unload and measurement were separated from each other. The operator handles the part in front of the machine. A pneumatic actuator inserts the part into the measurement volume. This volume is kept relatively isolated from the environment by an enclosure, which prevents debris and dust from affecting the linear guides or the laser. This enclosure also prevents the operator from being exposed to potentially harmful laser light and from reaching the machine's moving elements.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 5 Results
### Execution Time  
Regarding the execution time of each method, the runtime of a hundred executions is measured, but only the median time is considered in order to avoid the effect of a non-real-time execution. The experiment was conducted on a HP-ZBook-17-G2 equipped with a 2.8 GHz Intel Core i7 4810 MQ processor, 32 GB RAM, running the Ubuntu 20.04 64-bit version. The results displayed in Figure 20 show that PCA combined with ZEROES is the most time-consuming method, taking 4.7 ms to process one image, which is approximately 5 times lower than the time between each image capture when considering a frame rate of 40 fps (1/40 = 25 ms). Therefore, the ZEROES method remains fully compliant with a real-time OCC application, considering both the gain in communication performance and the short execution time of the proposed method.