esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Methyl Orange Degradation with Laser-Generated ZnO and Ce-Doped ZnO Nanoparticles
## 1 Introduction
On the other hand, ZnO and Ce-doped ZnO materials grown on glass substrates were also fabricated using the sol-gel method, spin coating, and water bath growth techniques. Subsequently, to achieve a rapid, facile, and high-yield production of Ce-doped ZnO NPs for photocatalytic degradation, the solution combustion approach was employed. Remarkably, the optimal Ce concentration (3.28%) demonstrated significant photocatalytic degradation efficiency (99.5%) after a reaction time of 70 min [14]. The investigation of Ce-doped ZnO nanomaterials for their photocatalytic capacity in degrading organic pollutants has been relatively limited, as evident from the available literature. Furthermore, the existing preparation methods often entail elevated temperatures, prolonged reaction durations, and toxic organic solvents and chemicals, posing environmental risks. Therefore, there is a critical need to explore alternative methodologies that are cost-effective, environmentally benign, non-toxic, and devoid of chemical hazards [15]. This study presents a novel investigation of 1% Ce-doped ZnO as a photocatalytic system fabricated utilizing a high-power CW CO\({}_{2}\) laser. Notably, this laser-based technique is particularly intriguing for its energy efficiency, ease of handling, cost-effectiveness, and capacity to operate at room temperature to prepare Ce:ZnO NPs. The advantages of this approach lie in its rapid, straightforward, and facile synthesis process, yielding well-crystalline products with excellent structural and optical characteristics. The produced Ce-doped ZnO NPs were thoroughly characterized using various techniques, and their potential application as photocatalysts for degrading MO in aqueous wastewater solutions was investigated under both UV and solar-light irradiation. Consequently, the study delvedinto assessing degradation effectiveness, photothermal and decomposition effects, and the influence of laser energy. Additionally, the investigation encompassed a comprehensive analysis of the process kinetics and isotherms associated with the degradation process.
# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 4 Result
### Comparison with Human Inspection  
To systematically evaluate the practical performance of the proposed model, this study invited a radiographic testing expert with advanced certification from the State Administration for Market Regulation to independently conduct manual inspections on 140 radiographic films containing seven types of defects. The evaluation focused solely on defect detection performance, disregarding localization accuracy. The experiment compared the model and manual inspection in three key aspects: defect sensitivity, noise robustness, and efficiency.  
Figure 5b,d,f shows that the proposed model significantly enhances sensitivity to low-contrast defects through frequency domain decomposition. In the detection of defects such as slag inclusion (Precision: 0.6529 vs. 0.5400), porosity (0.7112 vs. 0.6038), and undercut (0.6347 vs. 0.4600), WT-YOLO's precision exceeded that of manual inspection, with the largest difference reaching 0.1747 (for undercut). This indicates that WTConv's frequency-domain filtering effectively suppresses artifacts and background textures in radiographic films, reducing false positives. Additionally, for a total of 140 defect images, the model's inference time is only 10 s, averaging approximately 0.07 s per image, which is nearly 300 times faster than manual inspection, which takes over 80 min.  
Figure 5: Comparison of YOLOv11n, WT-YOLO and manual performance metrics for each class.  
However, the model exhibits relatively lower sensitivity and recall rates for high-risk defects, such as cracks (Recall: 0.5277 vs. 0.8800), lack of fusion (0.5692 vs. 0.8235), and incomplete penetration (0.6225 vs. 0.8750). Additionally, the model's F1-score for complex edge defects, such as incomplete penetration (0.5933 vs. 0.9333), still indicates room for improvement. The comparative examples are shown in Figure 6.
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 3 System Model
### Energy Model  
As the research [15; 27; 28; 29; 30] has discussed, the energy used for transmission accounts for the majority of the total energy consumption. Therefore, energy consumption used for transmission is only considered in this paper. The energy used for transmission is generally divided into two parts, sending and receiving units, as shown in Figure 2. In the sending unit, the digital signal is transformed into an analog signal by the transmit electronics and then the analog signal is strengthened by the amplifier. The power of the amplifier is adjustable and it uses different power according to the communication distance. A threshold value \(d_{0}\) is calculated to adjust the power of amplifier. If the communication distance exceeds the threshold value \(d_{0}\), free space model is used, otherwise, a multi-path fading model is used. In the receiving unit, the analog signal is transformed into digital signal again and the energy used in this part only depends on the amount of data.  
The total energy \(E_{Tx}\) used for sending unit can be calculated using the Formula (1).  
\[E_{Tx}(L,d)=E_{dec}\cdot L+\varepsilon_{amp}\cdot L \tag{1}\]  
where \(d\) represents the communication distance between the source node and the target node. \(L\) denotes the length of data package. \(E_{dec}\) represents the energy consumed by transmitting one-bit data between two sensors. \(\varepsilon_{amp}\) is the energy consumption for the amplifier and it can be calculated by Formula (2).  
Figure 1: Network model.  
Figure 2: Network model.  
\[\varepsilon_{amp}=\left\{\begin{array}{ll}\varepsilon_{fs}d^{2},\textit{when}\ d \leq d_{0}\\ \varepsilon_{mp}d^{4},\textit{when}\ d>d_{0}\end{array}\right. \tag{2}\]  
where \(\varepsilon_{fs}\) represents the energy consumption for free space model and \(\varepsilon_{mp}\) represents the energy consumption for multi-path fading model. Additionally, \(d_{0}\) is the threshold value for amplifier to adjust its power. \(d_{0}\) can be calculated by Formula (3).  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{3}\]  
The total energy \(E_{Rx}\) used for receiving unit can be calculated using the Formula (4).  
\[E_{Rx}(L)=E_{\textit{elce}}\cdot L \tag{4}\]
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method
### Improved Adaptive Region Correction  
The gamma function is widely used for correcting the differences between bright and dark images, proving effective in various image enhancement algorithms. However, in traditional non-uniform-illumination image enhancement research, the exponent of the gamma function is often set as a constant based on the specific image scene. The drawback of this fixed setting is its limited capability to enhance only one or a certain type of image scene, lacking adaptability to multiple scenarios [19]. Given that the pixel mean value of a uniformly illuminated image should be about 1/2 after normalization [20], the correction algorithm in this paper introduces an idea of processing illumination components in blocks to realize image low-light enhancement and high-light suppression.  
In this section, a quantitative analysis of the pixel average value of the illumination images obtained in Section 2.3 is carried out first, and the specific steps are as follows: firstly, the luminance component map is divided into areas [21] with the size of MxN on the right side in Figure 3; then, the segmented areas are classified into medium-high luminance area and low-luminance area on the basis of pixel mean values.  
The coordinate model for the segmented area image is as follows:  
As shown in the coordinate model in Figure 4, the square \(\overline{ABCD}\) is the location of the image segmentation area, and (a) and (b) in Figure 4 are the corresponding grayscale histograms of these areas, respectively. It is evident that the majority of the pixel intensities in area (a) are clustered in a narrow area, resulting in a darker image visually. On the other hand, the histogram distribution in area (b) is relatively uniform, leading to a brighter image perceived by the human eye. In Figure 4, the center point (\(x_{0}\), \(y_{0}\)) in the area is used to calculate the average pixel value of each block area [22]. The formula for calculating the average brightness value of the block area is expressed as:  
\[\mu=\frac{\sum_{x=a}^{x=a}\sum_{y=0}^{y=a}l_{x,y}}{(a+1)\times(a+1)} \tag{10}\]  
Figure 3: Blocking of light component maps.  
The implementation steps of the regional enhancement algorithm for non-uniform-illumination images studied in this section are shown in Figure 5:
# A Study on the Remaining Reserves of the Weiyuan Shale Gas Block in Three-Dimensional Development
###### Abstract  
After 5 different plans of development, the Weiyuan block is struggling to maintain stable production and urgently needs a new method to improve the recovery rate. Practice at home and abroad shows that three-dimensional development can effectively improve the degree of vertical shale gas utilization. Therefore, this paper first analyzes the distribution of reservoirs in the Weiyuan block and calculates the distribution of remaining reserves through the methods of fine description of gas reservoirs and establishment of a three-dimensional geological model. The results show that the recovery degree of the platform in the main production area of Weiyuan is 35-60%, and the average remaining reserves of the wells are (1.2-3) \(\times\) 10\({}^{8}\) m\({}^{3}\), and there is three-dimensional development potential in the local area. Secondly, through the establishment of a fracture and rock mechanics model, the target layer system for three-dimensional development of the Weiyuan block is preliminary selected from five aspects, namely, remaining reserves, reservoir characteristics, fracture density, mechanical characteristics, and the comparison of the height of the fracture network. Finally, in order to check the correctness of the target layer for three-dimensional development and evaluate the effect of the three-dimensional development, four scenarios for the lower branch of the HS platform are designed and numerically simulated using Petrel RE software. The results of the numerical simulation show that Option 2 is the most effective, which can increase the production of the platform by 1.76 \(\times\) 10\({}^{8}\) m\({}^{3}\), and the recovery rate grows from 16.4% to 31.0%, with an overall increase of 14.6%. The relevant understanding can provide a reference for the three-dimensional development strategy of the Weiyuan shale gas block.  
s 2024 14 9738 1 [1]Applied Sciences
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 4 Validation Analyses  
Validation analyses were carried out both for flaring sites discrimination and flared volumes computation.
# Cell Therapy in the Treatment of Coronary Heart Disease
## 2 Non-Cardiac Origin Cells for Cardiac Cell Therapy
### Hematopoietic Stem Cells  
One common type of stem cell utilized in cell therapy is HSCs, which comprise no more than 0.1% of unfractionated mononuclear cells found in bone marrow [31]. HSCs have the ability to self-renew and generate multipotent progenitors that differentiate in a sequential fashion to produce various specialized cells, such as lymphocytes, dendritic and natural killer cells, megakaryocytes, erythrocytes, granulocytes, and macrophages [32]. During the process of differentiation, HSCs demonstrate distinct antigenic characteristics that are associated with their properties and functions. These antigens aid in the identification of stem cell subpopulations and enhance the outcomes of HSCs transplantation by increasing the purity of the cell product utilized in allografts [33]. The surface antigen CD34 is expressed by human HSCs and is commonly used as a marker in clinical settings to identify and quantify the population of progenitor cells to be infused [34]. Additional surface markers such as CD90, CD38, c-Kit, CD105, and HLA-DR are used for evaluation of cell maturity [33]. The combined use of these markers provides valuable information for the isolation and purification of HSCs subpopulations. However, phenotypic characterization of HSCs provides only a partial picture of their functional activity (especially after ex vivo expansion), so that the best subset of HSCs for the treatment of CHD remains to be identified. [31; 33].  
In 2001, a study evaluated the effect of intramyocardial administration of HSCs during modeling of MI in mice [35]. The researchers observed differentiation of the transplanted cells into cardiomyocytes and endothelial cells, resulting in a relative enhancement of cardiac performance in the animals. Subsequent studies on the use of HSCs for cell therapy in CHD failed to show a beneficial effect of cell transplantation and demonstrated the inability of HSCs to differentiate into cardiomyocytes [36; 37]. The different results of these studies have been attributed to the use of different cell isolation and culture protocols.  
Several clinical studies on the use of HSCs in the treatment of patients with CHD (mainly ischemic cardiomyopathy) have shown that HSC transplantation is completely safe and does not cause any complications, but no significant improvement in cardiac function was observed [15; 16; 38]. Currently, it is speculated that HSCs may exert a paracrine effect on cardiac tissue by releasing growth factors that promote angiogenesis [21].
## 3 Results and Discussion
### BMP--Co-Digestion Scenarios
Generally, the produced gas yields for SE significantly increased as the concentrations of the two food processing by-products decreased. The same increasing trend was observed for WS; however, the difference in the produced yields from the different co-digestion trials was not always significant. As an example, the methane production from WS, after subtracting the methane production from RM (70:30 ratio), was 229 mL CH\({}_{4}\)/g VS, reaching 230 mL CH\({}_{4}\)/g VS when a 90:10 ratio with RM  
Figure 3: Cumulative methane production examining the co-digestion of (**A**) untreated wheat straw with DDGS; (**B**) untreated wheat straw with RM; (**C**) steam-exploded wheat straw with DDGS and (**D**) steam-exploded straw with RM. was applied. Based on the results from the first BMP experiment, the addition of a nitrogen source affected, to a higher extent, the digestion of SE compared to WS. As reported in previous studies [38], the steam explosion pre-treatment can partially disrupt the structure of straw, and as a consequence, the carbohydrates present in the lignocellulosic biomass become more accessible to the microorganisms of AD. It is possible that this increased availability of degradable components of straw along with the increased availability of nitrogen resulted in an increase in the microbial population of the system and, as a consequence, the biotransformation of the feedstock to biogas was also enhanced. Another possible explanation for the results presented in Figure 4 is the selective consumption of the feedstock by AD microorganisms. It is already proven that when different feedstocks or feedstock components co-exist inside a bioreactor, the microorganisms will first consume the more easily degradable material [39]. As shown earlier, the digestion of DDGS or RM offered higher biomethane yields compared to WS and SE, without any signs of inhibition or lag phase occurring. It is likely that DDGS and RM (especially in cases of 50:50 ratio or higher) were preferable substrates for AD microorganisms and their depletion coincided with micronutrient depletion. As such, WS and SE digestion was limited in these cases. On the other hand, when significantly lower amounts of DDGS and RM were present in the AD system, they offered a balanced C/N ratio, enhanced the metabolic activity and were quickly depleted, allowing for a further breakdown of WS and SE. According to these results, small amounts of either DDGS or RM can be used as an AD supplement to stimulate gas production and improve straw biodegradability.
# Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN
## 2 Materials and Methods  
Since the training process of CNN optimization has a relationship with the initialization conditions, the initialization of the convolution kernel is very important in CNN training. The \"Xavier initialization value\" and the \"He initialization value\" are applied to initialize the convolution kernel. Because these initial values are independent of training samples, that the convolution kernel and the local pattern of the training samples match is a very small probability event. That the network needs multiple iterations of training to match the local pattern of the training samples increases the training time of the CNN and slows down the convergence speed of the network. In order to speed up the convergence of the network and improve the recognition accuracy of the network, the representative local patterns in sample images are extracted, and they are applied to become the initial value of the convolution kernel. Therefore, the convolution kernels are able to match the local pattern of the images better in the initial training of the network and make the network appear near the convergence zone with a great probability. The network will obtain the global optimal solution quickly and preferable discrimination accuracy.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 2 Materials and Methods
### GNSS-R-Like System  
#### 2.1.1 Transmission System  
During the preparatory activities of the experiment, a GPS-like signal was generated at L1-band (1575.42 MHz) using a Rohde & Schwarz SMU-200A vector signal generator at UPC premises. The signal included the Coarse/Acquisition (C/A) Pseudo-Random Noise (PRN) codes 16, 20, 29, and 31 simultaneously, and the navigation information, which allowed to test the signal transmission in situ using a commercial GNSS receiver. Its power level was \(-\)81 dBm so that, with such a high SNR, the GNSS-R observables could be measured with very short integration times (coherent integration time T\({}_{\text{coh}}\) = 1 ms, and no incoherent averaging: N\({}_{\text{incoh}}\) = 1). This signal was recorded, and then played back using an ADALM-PLUTO (rev B) commercial off-the-shelf (COTS) software-defined radio (SDR) from analog devices [7].  
Moreover, to track any frequency offset and drifts, at the beginning and at the end of each GPS-like signal, a CW beacon centered at L1-band + 500 kHz was also transmitted. Thus, the final synthesized transmitted signal consisted of 20 s of a synchronization CWbeacon, followed by 40 s of an L1-band GPS signal, and finally 20 s of the synchronization CW beacon.  
The signal was transmitted separately, and sequentially, by 6 different antennas. Three of them were located at an azimuth angle of 0\({}^{\circ}\) with respect to the receiving antennas, at specific heights in order to have elevation angles of 60\({}^{\circ}\), 45\({}^{\circ}\), and 30\({}^{\circ}\). The other three transmitting antennas were located at the same height, and at different azimuths of 60\({}^{\circ}\), 45\({}^{\circ}\), and 30\({}^{\circ}\) between the transmitters and the receivers (Figure 1). As will become evident, this is an important fact since, due to the small area that the system is getting the reflections, at a given time the footprints on the water surface are different. It is also worth noting that all the antennas were hanging from the 8.5 m height corridors, to avoid placing the masts and poles in the middle of the flume, which would have distorted the wave field.  
A RF switching matrix controlled by a Raspberry Pi was used to perform the sequential sweep of the transmitting antennas. The transmitted signal was conditioned by means of a variable attenuator, and a fixed amplifier (Figure 2), to compensate the different signal path losses from each transmitting antenna, and to ensure a proper reception of the GPS signal. The variable attenuator was adjusted for each transmitter to achieve a CN0 from 35 dBHz to 50 dBHz, as measured with a commercial off the shelf (COTS) GPS receiver connected to the receiver's side.
# Feasibility of a Helium Closed-Cycle Gas Turbine for UAV Propulsion
## 3 The Helium Closed-Cycle Gas Turbine UAV Propulsion
### Engine Model Development
#### 3.1.2 Precooler Heat Exchanger Model  
The precooler is modelled as a heat exchanger using the \(E\)-NTU method, and a counterflow shell and tube configuration was assumed. The \(\varepsilon\)-NTU method was used since the inlet condition (temperature and pressure) of the fluid stream can be easily obtained and simplifies the iteration involved in predicting the performance of the flow arrangement. This method is fully described in references [34; 35]. The approach also assumes that the heat exchanger effectiveness is known, and the pressure losses are given.  
Therefore, the effectiveness of the heat exchanger is the ratio of the actual heat transfer rate to the thermodynamically limited maximum heat transfer rate available in a counter flow arrangement.  
\[\varepsilon=\ \frac{Q_{actual}}{Q_{max}}=\frac{C_{hot}(T_{hotin}-T_{hotout})}{C_{ min}\ (T_{hotin}-T_{coldin})}=\ \frac{C_{cold}(T_{coldout}-T_{coldin})}{C_{min}\ (T_{hotin}-T_{coldin})} \tag{17}\]  
where \(C_{min}\) and \(C_{max}\) are the smaller and larger of the two magnitudes of \(C_{hot}\) and \(C_{cold}\)  
\[C_{hot}=(WC_{P})hot\ fluid\ Stream,\ \ C_{cold}=(WC_{P})cold\ fluid\ Stream \tag{18}\]  
\[C_{min}\ =\ \left\{\begin{array}{l}C_{hot}\ for\ C_{hot}<C_{cold}\\ C_{cold}\ for\ C_{cold}<C_{hot}\end{array}\right. \tag{19}\]  
For counterflow shell and tube heat exchangers, number of transfer unit (\(NTU\)) is given by:  
\[NTU=\frac{LOG_{e}\left[\frac{2-\varepsilon(1+C^{*}-\eta_{Hex})}{2-\varepsilon (1+C^{*}+\eta_{Hex})}\right]}{\eta_{Hex}} \tag{20}\]  
where  
\[C^{*}=Capacity\ rate\ ratio=\frac{C_{min}}{C_{max}} \tag{21}\]  
\[\eta_{Hex}=\ \left(C^{*2}+1\right)^{0.5} \tag{22}\]  
Assuming pressure losses, then the exit pressure at design point is given as:  
\[P_{out}=P_{in}(1-\Delta P) \tag{23}\]  
where \(\Delta P\) is the percentage of pressure loss specified as the input for each component. It is important to emphasis that the precooler utilizes air cooling. The code was modelled to have the working fluid temperature after pre-cooling process equal to the compressor entry temperature; although, in a real scenario, the temperature may vary a little.