esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Local Wind Conditions Analysis  
This research incorporates a meteorological study with data collected in the last 7 years in weather station #76628 SMN-CONAGUA, geographically located at 20\({}^{\circ}\)37\({}^{\prime}\)24.1\({}^{\prime\prime}\) N and 100\({}^{\circ}\) 22\({}^{\prime}\)06.0\({}^{\prime\prime}\) W at an altitude of 1969 m a.s.l. The data were collected at a ground level height of 18 m. A statistical analysis of meteorological variables was carried out, to analyze the factors that affect the wind behavior and operation of the wind turbine. A summary of this statistical analysis is shown in Table 2.  
In the relative frequency distribution, the data are grouped by each unit and the range of wind speeds that occur on more occasions is determined. The results obtained indicate that speeds between 3 and 4 m/s occurred 19.44% of the time. Values greater than 10 m/s had a recurrence of less than 1%, so they will not be considered for decision-making, and the controller will consider them as the cut-off wind speed. The average wind speed was 4.44 m/s with a standard deviation of 1.98 m/s, so wind speeds between the range of 2.46 and 6.42 m/s will be considered stable winds for controller decision-making. A histogram of wind speed with relative frequencies for each unit of magnitude of speed is shown in Figure 6.  
Information on the wind direction at the installation site is also obtained; a direction mode of 83.3\({}^{\circ}\) (east to west) was obtained, which is interpreted as prevailing winds, where the range between 70\({}^{\circ}\) and 110\({}^{\circ}\) represents 72% of the recurrence in the wind direction. A summary of the wind direction data is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Meteorological** & **Wind Speed (m/s)** & **Temperature (\({}^{\circ}\)C)** & **Relative Humidity (\%)** & **Atmospheric Pressure (mbar)** \\ \hline Mean & 4.44 & 17.72 & 58.26 & 806.99 \\ Standard Error & 0.01 & 0.03 & 0.13 & 0.01 \\ Median & 4.24 & 16.88 & 57.00 & 807.05 \\ Mode & 3.62 & 14.25 & 98.67 & 807.27 \\ Std. Deviation & 1.98 & 6.27 & 26.20 & 2.27 \\ Variance & 2.72 & 39.52 & 688.25 & 5.15 \\ Range & 15.00 & 37.57 & 95.50 & 15.70 \\ Minimum & 0.00 & 0.23 & 4.50 & 798.85 \\ Maximum & 15.00 & 37.80 & 100.00 & 814.55 \\ \hline \end{tabular}
\end{table}
Table 2: Statistical analysis of meteorological variables.  
Figure 6: Histogram of wind speed with relative frequencies for each unit of magnitude of speed.
# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
###### Abstract  
Object detection is a classic image processing problem. For instance, in autonomous driving applications, targets such as cars and pedestrians are detected in the road scene video. Many image-based object detection methods utilizing hand-crafted features have been proposed. Recently, more research has adopted a deep learning approach. Object detectors rely on useful features, such as the object's boundary, which are extracted via analyzing the image pixels. However, the images captured, for instance, in an outdoor environment, may be degraded due to bad weather such as haze and fog. One possible remedy is to recover the image radiance through the use of a pre-processing method such as image dehazing. We propose a dehazing model for image enhancement. The framework was based on the conditional generative adversarial network (cGAN). Our proposed model was improved with two modifications. Various image dehazing datasets were employed for comparative analysis. Our proposed model outperformed other hand-crafted and deep learning-based image dehazing methods by 2 dB or more in PSNR. Moreover, we utilized the dehazed images for target detection using the object detector YOLO. In the experimentations, images were degraded by two weather conditions--rain and fog. We demonstrated that the objects detected in images enhanced by our proposed dehazing model were significantly improved over those detected in the degraded images.  
object detection; road scene; fog; rain; image dehazing; generative adversarial network; conditional generative adversarial network +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON] Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model. _Appl. Sci._**2023, _13_, 5326. [[https://doi.org/10.3390/app13095326](https://doi.org/10.3390/app13095326)]([https://doi.org/10.3390/app13095326](https://doi.org/10.3390/app13095326))
## 3 Results and Discussions  
In this study, the optimal double-junction cell design is investigated to obtain higher solar cell efficiency. Indeed, this study focuses mainly on the optimization of the solar cell structure presented in Figure 1, which is based on the incorporation of a buffer layer and a double back surface field (BSF) layer, with the main goal of enhancing its performance, mainly the efficiency.  
The methodology used in this investigation systematically characterizes the properties of \(GalnP\) and \(GaAs\) that allow establishing a foundation for optimizing their respective single-junction cells. Whereas the modeling process focuses on assessing the impact of different BSF configurations, the lattice mismatches in \(GaAs\) structures are taken into account by the integration of variable pad thicknesses. To achieve accurate design optimization through the used software, the doping concentrations and BSF layer thickness of the double-junction cell are carefully adjusted. This meticulous optimization process unveils the advantages of the main idea proposed in this study, which allows for the performance of a robust tool for designing high-efficiency tandem solar cells. The key to this performance boost lies in the synergistic interplay between the buffer layer and the upper double-layer BSF.
## 3 Hierarchical Control System
### Optimization Process  
The complexity of the presented plant and the number of various choices which have to be taken 'online' during the work cycle impose implementing more advanced control solutions in such systems. This is why the authors of this paper decided to implement a supervisory controller responsible for adjusting the control system settings during its work to increase the efficiency and to minimize the maintenance costs. Most of them (almost 60%) are generated by the aeration system which is the main part of the whole system taken under consideration in the optimization process in this paper. Designing such a controller, all the restrictions concerning the plant and water-law permit for Swarzewo WWTP have to be considered. To solve the optimization problem described in the further part of this paper,  
Figure 4: Block diagram of the work cycle: \(t_{f}\) is the duration of the current filling [min]; \(t_{f,max}\) is a time limit on filling [min]; \(t_{s}\) is the duration of sedimentation [min]; \(t_{s,max}\) is a time limit on sedimentation [min]; \(t_{w}\) is the duration of excess sludge wasting [min]; \(t_{w,max}\) is a time limit on excess sludge wasting [min]; \(V\) is the current volume of the wastewater-sludge mixture in the tank [m\({}^{3}\)]; \(V_{max}\) is the maximum capacity of the tank [m\({}^{3}\)]; \(V_{min}\) is the minimum volume of the mixture in the tank [m\({}^{3}\)]; \(V_{f}\) is the volume of the last filling [m\({}^{3}\)]; \(TSS_{1}\) is the concentration of the TSS in the top layer of the tank and \(TSS_{1 min}\) is its limit [g/m\({}^{3}\)]; \(TSS_{e}\) is the concentration of the TSS in the effluent and \(TSS_{max}\) is its limit [g/m\({}^{3}\)]; \(dTSS_{2}\)—derivative of the TSS concentration in the middle layer of the reactor [g/m\({}^{3}\)].  
the authors decided to use the general approach, which combines deterministic and non-deterministic algorithms. As a result, the mentioned controller delivers proper information regarding the work cycle (quantity and length of aerobic/anaerobic phases) to the lower level parts of the control system for each refill of the SBR. Details of operating principles are described in further sections.
## 3 Result
### The Film Shape at the Initial Stage
The higher \(V_{\text{G}}\) is, the stronger is the gas resistance to the spreading of liquid over this narrow surface. Figure 3: Maximum film thickness near the inlet versus gas velocity: (**a**) Water; (**b**) WGS2. In downward flow, gravity prevents the accumulation of liquid downstream from the slot outlet, and no strong local thickening of the liquid film occurs. \(H_{\text{max}}\) is slightly (10-20%) larger than the slot thickness and it does not show any strong dependence on \(V_{\text{G}}\). Moreover, in both upward and downward flow, \(H_{\text{max}}\) is independent on the liquid flow rate. This film thickness behavior is quite different from that of stabilized gas-sheared liquid films, where film thickness increases with liquid flow rate and decreases with gas velocity in either upward or downward flow. However, the gas and liquid flow rates strongly affect the length of the stabilization zone, \(X\). The results of the measurements are presented in Figure 4. With increasing \(V_{\text{G}}\), \(X\) decreases approximately as \(V_{\text{G}}^{-2}\) (see Figure 4a,b). With an increasing liquid flow rate, \(X\) increases. In the same subfigures, a comparison between the liquids with different viscosities is presented. Despite almost threefold viscosity difference, the length of the stabilization zone is almost the same at the same volumetric flow rate of the liquid. If the same liquid Reynolds numbers were compared, the liquid flow rate would have been almost three times larger for WGS2, along with the \(X\) value. This implies the independence of \(X\) on liquid viscosity at a fixed \(Q\), which is quite surprising. Figure 4: Length of the stabilization zone. (**a**,**b**) The effect of liquid viscosity at a fixed liquid flow rate for downward (**a**) and upward (**b**) flows. (**c**) The effect of flow orientation. (**d**) The effect of the liquid flow rate for a fixed gas velocity, \(V_{\text{G}}=29\) m/s. In downward flow, the \(X\) values are larger than in the upward flow if other conditions are the same. The quantitative effect of flow orientation is shown in Figure 4c, where the direct comparison is given. Obviously, the largest values correspond to the lowest \(V_{\text{G}}\) and the largest \(Q_{\text{L}}\), and vice versa; the values \(X>40\) mm (i.e., the cases when stabilization is not reached within the interrogation area) are not shown. In general, \(X\) is about 20% lower in the upward flow; at the largest \(V_{\text{G}}\), the difference nearly disappears. The same trend can be seen in Figure 4d, where the dependence of \(X\) on the liquid flow rate at a fixed gas velocity is shown. It includes all the experimental points with \(V_{\text{G}}\) = 29 m/s in an upward/downward flow for all three working liquids. All the points lie around the same line, further supporting the idea of independence of \(X\) on viscosity.
# An Energy-Efficient Routing Algorithm
###### Abstract  
Compressed sensing (CS) has become a powerful tool to process data that is correlated in underwater sensor networks (USNs). Based on CS, certain signals can be recovered from a relatively small number of random linear projections. Since the battery-driven sensor nodes work in adverse environments, energy-efficient routing well-matched with CS is needed to realize data gathering in USNs. In this paper, a clustering, uneven-layered, and multi-hop routing based on CS (CS-CULM) is proposed. The inter-cluster transmission and fusion are fulfilled by an improved LEACH protocol, then the uneven-layered, multi-hop routing is adopted to forward the packets fused to sink node for data reconstruction. Simulation results show that CS-CULM can achieve better performances in energy saving and data reconstruction.  
underwater sensor networks; compressed sensing; energy consumption; multi-hop routing; data collection 0.00.  
is mainly depending on distance, but with a different calculating formula compared with other wireless media; (4) noise and interference--the large background noise and interference will cause low receiving signal-to-noise ratio and increasing error probability. These problems jointly decide that we should transmit information as few as possible in USNs. As a result, we can adopt CS for data compression at the transmitting node and data reconstruction with less data at the receiving node. In addition, another fact we cannot ignore is that all nodes in USNs are driven by batteries. Battery energy is limited and the battery replacement is also with low operability. Then, we need to design a high energy efficiency transmitting method when attempting to make efficient data acquisition and transmission.  
In order to achieve efficient data gathering in three-dimensional USNs, we specifically combine CS, clustering, and multi-hop routing together and design an energy-efficient data gathering scheme based on CS (CS-clustering uneven-layered and multi-hop routing, CS-CULM) to reduce the amount of data required to reconstruct the original network map.  
The paper is organized as follows: in Section 2, we make an introduction to CS. The system model is described briefly in Section 3. Section 4 gives the packet format and energy consumption model used in this paper. The CS-CULM algorithm is proposed in Section 5, followed by numerical simulations and discussions in Section 6. Finally, we provide concluding remarks in Section 7.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 6 Results and Analyses  
Figure 6 shows the results of PNC using the RF pilot tone in a 2000 km coherent communication system, where the CPR is realized using the NLMS algorithm. In Figure 6a, the effective linewidth is 170 MHz and there is no EEPN, since the linewidth of the transmitter laser is 170 MHz and the linewidth of the LO laser is 0 Hz. It is found that both the Tx and the LO LPN can be entirely suppressed. In Figure 6b, the linewidths of both the Tx and the LO lasers are 5 MHz. According to Equations (1) and (4), the EEPN is quite significant in such a case, and the effective linewidth is the same (170 MHz) as that in Figure 6a. It is found in Figure 6b that the BER performance improves considerably (around half an order of magnitude in the BER floor, from \(8\times 10^{-4}\) to \(3\times 10^{-4}\)) using the RF pilot tone, compared to the case of NLMS CPR only. The results in Figure 6a,b demonstrate the efficiency of the RF pilot tone in compensating for both LPN and EEPN. It can be found that the use of RF pilot tone can entirely mitigate the LPN from both the Tx and the LO lasers. However, it cannot fully compensate for the EEPN in optical fiber transmission systems since the EEPN actually represents a complicated integration of the phase, the amplitude, and the time jitter noise [10; 12; 16; 29].  
When the CPR is implemented using DPD, the results of the PNC using the RF pilot tone in the same 2000 km coherent system are illustrated in Figure 7. In Figure 7a, the linewidth of the transmitter laser is 170 MHz and the linewidth of the LO laser is 0 Hz, representing an ab  
Figure 5: High-speed coherent communication system with an orthogonally-polarized RF pilot tone.  
Figure 6: Phase noise cancellation (PNC) results of the 2000 km coherent transmission system (the one-tap NLMS CPR). Ideal: linewidth of both Tx and LO lasers is 0 Hz. w/o: without, w/: with. (a) Transmitter: 170 MHz, local oscillator: 0 Hz; (b) Transmitter = local oscillator: 5 MHz.  
that the full suppression of LPN can be performed with the use of the RF pilot tone, compared to the scheme of DPD only. In Figure 7b, the linewidths of both the transmitter and local oscillator lasers are 5 MHz, so that the EEPN is significant again (with an effective linewidth of 170 MHz). Similar to Figure 6b, a considerable improvement of BER performance is also achieved with half an order of magnitude in the BER floor (from \(6.5\times 10^{-4}\) to \(1.5\times 10^{-4}\)), when the RF pilot tone scheme is applied prior to the DPD.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### TinyML
#### 2.2.2 Characteristics of Large-Scale IoT Systems  
The characteristics of large-scale IoT systems and the enhancements introduced by TinyML are effectively outlined in Table 3. In these systems, a distributed topology with devices spread across various locations results in data decentralization and increased latency; TinyML tackles this by facilitating edge computation, enabling local data processing to reduce latency and provide real-time insights. The voluminous data streams generated continuously can burden storage and transmission channels, but TinyML assists by prioritizing, compressing, and filtering data at the device level, managing storage needs and reducing data transmission demands.  
The varied landscape of IoT devices, each with different data formats and communication protocols, is harmonized by TinyML, which standardizes data processing and extraction at the source, ensuring consistent data representation across diverse device types. Power and resource constraints, especially in battery-operated devices, pose significant challenges in IoT systems. TinyML models are designed for optimal computational efficiency, performing tasks effectively without draining device resources. Finally, in applications that require real-time processing, such as health monitoring or predictive maintenance, delays in processing can be critical. TinyML enables rapid on-device processing, allowing immediate responses to changing data patterns, thus enhancing the overall functionality and effectiveness of large-scale IoT systems.
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 3 Handover Mechanism
On the other hand, if the train speed is 2700 km/h, the coverage distance for BS1 will be: uncovered area BS1C is 1500 m, this is the nearest point to the BS which is (C), when the train front transceiver is at that point; the same distance will not be covered by BS2 when BS1 needs to be closed or at the closest point (C, 2 s closing procedure). In addition, the train length should not be considered as it affects the final length, which is assumed to be a 100 m train. Therefore, uncovered area will be \(|\)BS1C\(|\) + \(|\)BS2C\(|\) + train length, and equals 3.1 km. This distance should be subtracted from the total area between BS1 and the start point \(|\)BS1T\(|\), which is 14.2 km, so, the BS for speed 2700 km/h actually covers a distance of 11.1 km. Compared with the scenario of [1], this is a significant improvement as the base station in [1] covered 200 m, and therefore, the distance between a BS and the next one was 200 m in [1]. Figure 6: Flow chart of the handover process.
# Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
## 2 Materials and Methods
### Difference Scheme of the Poisson Equation
However, the modified version of the HHL algorithm employs CRZ rotations, enabling the operator to be represented as a sequence of one-qubit rotations and controlled rotations. This simplifies the implementation of the algorithm on a quantum computer and substantially reduces resource requirements. Thus, the use of CRZ rotations in the modified version of the HHL algorithm is an innovative approach that expands our knowledge and abilities in the field of applying quantum computing to solve linear algebra and optimization problems. This could lead to new opportunities in various areas, such as quantum systems modeling, portfolio optimization, machine learning, and other data-intensive applications. The HHL algorithm is a quantum algorithm that can be used to solve systems of linear equations [18]. It is based on the use of the Fourier transform on a quantum computer [19]. The Quantum Lab program based on Jupyter technology in IBM Quantum was used to implement this algorithm. It allows one to execute the code on real quantum systems, and the results are stored in the cloud. In addition, various available quantum simulators can be used [20]. In this article, quantum simulators are used to implement and test the HHL algorithm. Using the HHL algorithm, it is possible to solve a system of linear Equation (5) transformed into a 2D Poisson equation, where A is a matrix of coefficients and b is a vector of the right-hand side. In the case of solving the Poisson equation on a 2D grid, the matrix \(A\) can be formed by approximating the second derivative with respect to the variables \(x\) and \(y\) using difference expressions.
## 2 Methodology  
### Test Fuels  
In this study, palm oil biodiesel was selected among many biodiesel. Palm has the highest production rate among raw materials because it has the highest oil content among raw materials for biodiesel production, and the process of converting to biodiesel is the same as using other raw materials [42]. [PERSON] et al. [43] compared several biodiesel and reported that palm oil biodiesel has a high potential for production to meet future demand because of high oil productivity about 13 times better than soybean oil. Moreover, a life cycle analysis (LCA) revealed that palm oil biodiesel could reduce greenhouse gases (GHG) emissions by 62% compared to others (soybean oil 40%, rapeseed oil 45%, and sunflower oil 58%). It is known that the physical properties such as viscosity, cetane number, and heating value of palm oil biodiesel are better than other types of biodiesel. [PERSON] et al. [44] reported that the physio-chemical properties of palm oil biodiesel met the requirement of diesel engines compared with other biodiesel such as soybean and rapeseed oil.  
Here, the name of the test fuels of palm oil biodiesel and its blends is expressed as PD, which is the abbreviation for palm oil biodiesel. PD0 means 100% petroleum diesel and PD100 means 100% palm oil biodiesel. PD10 and PD30 have blended 10% and 30% proportions of palm oil biodiesel by volume with pure petroleum diesel. The properties of petroleum diesel and palm oil biodiesel are shown in Table 1. Normally, it is known that the viscosity of biodiesel is higher than that of diesel and has a high surface tension which can affect the atomization of injected fuel in the cylinder at the same injection pressure [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Diesel** & **Palm Oil Biodiesel** & **Test Method** \\ \hline Density at 15 \({}^{\circ}\)C (kg/m\({}^{3}\)) & 836.8 & 877 & ASTM D941 \\ Viscosity at 40 \({}^{\circ}\)C (mm\({}^{2}\)/s) & 2.719 & 4.56 & ASTM D445 \\ Lower heating value (MIJ/kg) & 43.96 & 39.72 & ASTM D4809 \\ Cetane number & 55.8 & 57.3 & ASTM D4737 \\ Flash point (\({}^{\circ}\)C) & 55 & 196.0 & ASTM D93 \\ Pour point (\({}^{\circ}\)C) & \(-\)21 & 12.0 & ASTM D97 \\ Oxygen content (wt.\%) & 0 & 11.26 & - \\ Hydrogen content (wt.\%) & 13.06 & 12.35 & ASTM D5453 \\ Carbon content (wt.\%) & 85.73 & 79.03 & ASTM D5291 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of test fuels.