esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
## 4 Experimental Data Set  
### GF-3 Satellite ATI Experimental Mode  
The 1 m resolution multi-polarization SAR satellites 01 and 02, also known as GaoFen-3 B and C (hereinafter referred to as GF\(-\)3B and GF\(-\)3C), are two follow-up operational satellites of China's first polarization SAR\(-\)GF\(-\)3 satellite. The mission of the two satellites is to provide quantitative and stable remote sensing data for marine targets, marine dynamic environmental factor monitoring, and for emergency management, land, geology, water conservancy, environmental protection, agriculture, meteorology, and other applications through in-orbit operation of the GF\(-\)3 satellite network.  
The GF\(-\)3B and GF\(-\)3C satellites inherited the mature technologies of the GF\(-\)3 satellites, except for the multi-polarization SAR and dual-frequency GPS. Compared with the GF\(-\)3 satellite, the two satellites have received system optimization and improvements, while preserving the main technical state. Specifically, the Automatic Identification System (AIS) signal-receiving system was added, to improve the monitoring capability for the main offshore monitoring target: offshore ships; the traditional scanning mode was changed to the TOPSAR mode; the spatial resolution and observation breadth of the wave mode were enhanced; the daily observation time was increased, to further improve the efficiency and availability of satellite usage; and the satellites carry an onboard real-time processor. Functions such as imaging processing, region of interest extraction, and target detection can be realized on the satellite, to enhance the capabilities for early detection, early prevention, and early response to maritime emergencies and marine and land natural disasters.  
The ATI mode of the GF\(-\)3 satellite utilizes a similar principle to the DRA mode of the TerraSAR-X satellite [4], where the full aperture sends a signal, and the two sub-apertures simultaneously receive the echo. As shown in Figure 2, the full aperture phase center is at \(a_{0}\), and the range between \(a_{1}\) and \(a_{2}\) is \(2B\). The signal received by apertures 1 and 2 can form an interferometric baseline in azimuth, with a length of \(B=3.75\) m [18]. The radar parameters of the GF\(-\)3 satellite ATI mode are shown in Table 2.  
Figure 1: GF-3 satellite observation area (red) and HFSWR measurement area (blue) in the experiment conducted in Qing Dao in 2022.
## 4 Results and Discussion  
### Two-Dimensional Thermal Image  
Heat was provided to the surface of the specimen with an excitation coil, and a 2D thermal image, measured with an IR camera, was acquired. The images for total frames were loaded into MATLAB software using LSM. After acquiring a sequence image based on the scanning line, only the image area of the specimen was cropped using the 'crop' function. If the crop was not performed, even coils appeared in the image and became an obstacle to image processing.  
As the moving speed of the specimen was slower, the amount of heat source supplied increased, and it can be seen in Figure 8a that the temperature was relatively high. When there was a large supply of a heat source, the temperature of the whole specimen improved. Therefore, the thermal profile of each row was analyzed to compare the thermal contrast between the defective area and the sound area, as shown in Figure 9.  
The slower the moving speed of the specimen, the greater the input amount of the heat source supplied to the surface. This increased the overall temperature of the specimen. However, as the input amount of the heat source increased, the thermal contrast between the defective area and the sound area did not increase. Therefore, the process of determining the optimal thermal contrast (a high thermal contrast between the defective area and the sound area means that the detectability is improved) was performed. Two ROIs of \(5\times 5\) pixels (one at the center of the defective area and another in the adjacent sound area) were set for the defective area and the sound area. The thermal contrast equation is as follows [36]:  
\[\Delta\mathrm{c}=\mathrm{DROI}_{\mathrm{mean}}-\mathrm{SROI}_{\mathrm{mean}} \tag{4}\]  
where \(\mathrm{DROI}_{\mathrm{mean}}\) and \(\mathrm{SROI}_{\mathrm{mean}}\) mean the average temperatures of the defective area and sound area, respectively.  
Figure 8: Temperature images from the induction thermography based on LSM: (a) 5 mm/s, (b) 10 mm/s, (c) 15 mm/s, and (d) 20 mm/s.  
Table 3 shows the average thermal contrast values for the four defects in the four rows for each moving speed. It can be seen that the largest thermal contrast occurred at 5 mm/s. Although it was not linearly proportional, it can be considered that the slower the moving speed, the greater the amount of heat supplied so that the thermal contrast is larger.
## 2 Thematic Review and Analysis
### Novel Concepts
#### 2.7.3 Advanced Thermo-Hydraulic Fluids  
While not a novel approach, the use of additives in the heat carrier medium or other make-up fluids to enhance the overall efficiency of DH systems merits attention, particularly in the context of reducing return temperatures. A prominent example involves drag-reducing additives, which aim to decrease friction losses in DH distribution piping. These additives can potentially yield significant savings in pumping energy and allow for the reduction in pipe diameters. However, it is important to note that such additives may adversely affect the heat transfer properties of the heat carrier medium, depending on their specific type. Studies have indicated that while there may be a minor reduction in heat loss within the DH network, there can be a significant decrease in heat transfer efficiency through heat exchangers due to these additives [145]. Additionally, references suggest the potential of advanced fluids, such as high-temperature phase-change slurries, in improving heat transfer, provided that the DH system and its components are redesigned accordingly [145; 146; 147; 148; 149; 150; 151].
# Hiding Stealth Optical CDMA Signals in Public BPSK Channels for Optical Wireless Communication
## The Theories and Principles Background of the Proposed Stealth Communication Approach
### Chirped Fiber Bragg Gratings  
As described in Section 2.1, dispersion leads to the pulse broadening of optical pulses propagating along a fiber and becomes a limiting factor for optical communication systems operating at high bit rates. Herein, we use the characteristics of dispersion to stretch the pulse; this transforms the stealth signal into a noise-like signal so that it can be buried in a public channel.  
Figure 3 shows the structure of a CFBG [26]. A fiber Bragg grating is a periodic perturbation of the refractive index along the fiber length. The core of the fiber is exposed to an intense optical interference pattern to fabricate the Bragg gratings. When the incident light passes through the periodic structure, a narrow band of the optical field is reflected by continuous, coherent scattering from the variations in the refractive index. The strongest interaction occurs at the Bragg wavelength \(\lambda_{\text{B}}\), which is given by  
\[\lambda_{\text{B}}=2n_{eff}\Lambda, \tag{9}\]  
where \(n_{eff}\) is the effective refractive index of the core and \(\Lambda\) is the grating period. The chirping of a fiber Bragg grating indicates changes in the period of the grating with distance.  
Figure 2: Structure of SPC-OCDMA encoder.  
As depicted in Figure 3a, the grating period increases along the fiber's length so that shorter wavelengths are reflected nearer the input of the fiber, whereas longer wavelengths are reflected nearer the output. Thus, light with longer wavelengths is more delayed than light with shorter wavelengths. This CFBG structure produces positive dispersion. In contrast to positive dispersion, the structure of the CFBG in Figure 3b produces negative dispersion. CFBGs can provide a large dispersion with a small size. To provide the same dispersion in a standard single-mode fiber, the required length must be increased by a factor of 2000 [27], which can make the system more compact.
## 2 Materials and Methods  
### Data Collection  
This study collected pest images from three locations in Yunnan Province, China: the He Kai Base of Yuecheng Technology Co., Ltd. (Menghai County, 100.46\({}^{\circ}\) N, 21.78\({}^{\circ}\) E,China); the tea gardens in Sanjia Village (Nanhua County, 100.76\({}^{\circ}\) N, 24.78\({}^{\circ}\) E, China); and the teaching and research base of the Tea Science College at Yunnan Agricultural University (Kunming, 102.75\({}^{\circ}\) N, 25.13\({}^{\circ}\) E, China).  
During the image collection phase, a specific approach was employed to capture images of fast-moving insects. Sticky traps were placed on tea trees to attract a large number of insects for photography under supplementary lighting. On the other hand, slow-moving insects were directly photographed above the leaves. A mobile device with the macro lens APL-MS002 BK (APEXEL, Shenzhen, China) was used to capture magnified images of the pests at a 200\(\times\) zoom level. The lens had a focal length of 40.6 mm, an exit pupil diameter of 1.45 mm, a focusing distance of 0.3 mm, and a brightness range of 85 to 150 lm. The color temperature was set between 6000 and 6500 k. After capturing the photos, they were transmitted to the terminal for subsequent image processing. The data collection process is illustrated in Figure 1.  
For the four types of pests, _Toxoptera aurantii_, _Xyleborus fornicatus Eichhoff_, _Empoasca priisuga Matumura_, and _Malthodes discicollis Baudi [PERSON]_, this study collected 133, 141, 128, and 143 original images, respectively, totaling 545 tea garden pest images, which served as training samples for the model recognition. _Toxoptera aurantii_ is among the most detrimental pests to tea plants, severely impacting their health by sucking phloem sap and excreting honeydew that increases the risk of sooty mold fungi [34]. _Xyleborus fornicatus Eichhoff_, primarily parasitic on tea and rubber trees, inflicts substantial branch damage, with infestation rates that can reach 50-65%, sometimes leading to complete plant desiccation [35]. _Empoasca priisuga Matsumura_ affects tea plant growth by piercing and sucking the sap from buds and leaves, resulting in a decline in tea quality. Its small size and rapid reproduction rate render control measures challenging [36]. _Mathhodes discicollis Baudi di Selve_ exhibits over-predation behavior in tea gardens, which includes consuming beneficial insects like spiders [37; 38; 39]. Figure 2 showcases some of the collected original image samples.  
Figure 1: Collection of original images of pests.
## Theory and Model
### Finite Element Model
#### 2.2.2 Finite Element Mesh and Time Step  
In order to accurately invert the propagation of sound wave in medium and take into account the amount of simulation calculation, based on previous research and reference literature [22; 25], we deduce that a single wavelength in medium contains at least 10 element meshes. Therefore, the medium meshing needs to satisfy the conditions given by Equation (4).  
\[\mathrm{L_{mesh}}\leq\frac{1}{10}\frac{C}{f} \tag{4}\]  
where \(C\) is sound velocity in medium, and \(f\) is frequency of sound wave. In the actual experiment, a relatively high-frequency air-coupled transducer (400 kHz) was used to improve the resolution. Therefore, during the simulation, the working frequency of loaded excitation signal is 400 kHz.  
And, taking into account the actual conditions of the layers of the honeycomb sandwich composite sample, different mesh sizes are selected for different medium layers. The mesh type is free triangular mesh. The size of largest unit in free triangular of each medium layer is shown in Table 1.  
Figure 4: The two dimensional model of honeycomb sandwich composite embedded with debonding defect.  
Figure 3: The two-dimensional model of honeycomb sandwich composite.  
In order to fully display the details of propagation of sound wave, time step was set to 30 ns.  
The macroscopic properties of GFRP materials are found to be orthogonal anisotropy, and the physical properties are shown in Table 2. The linear elastic material is used in other solid domain (Aluminum honeycomb core and epoxy resin adhesive layer), and the material properties are shown in Table 3.
## 3 Proposed Methodology
### Particle Swarm Optimization  
PSO is a nature inspired optimization technique based on the movement and intelligence of the swarm. In PSO, each particle continues to update its position under the previous experience and neighbours. A particle is composed of three vectors. The first is the x-vector, which records the current situation. The second one is the p-vector which records the location of the best solution in search space, and the third one is v-vector which contains the gradient for which particles will travel if undisturbed. The working of PSO can be explained in the following four steps as illustrated in Figure 3.  
Step 1: Initialization: Random particles initialize the PSO algorithm, and each particle is a solution. Each particle searches for the optimum value by updating the generation. In each iteration, every particle is updated. After finding the best value, the particle updates its velocity and position. Particles can update their position by  
\[x_{i}^{(t+1)}=x_{i}^{(t)}+v_{i}^{(t)}*t \tag{19}\]  
Particle can update its velocity by the following equation  
\[x_{i}^{(t+1)}=w*v_{i}^{(t)}+c_{1}*v_{1}(xbesti^{t}-x_{i}^{t})+c_{2}*r_{2}(gbesti ^{t}-x_{i}^{t}) \tag{20}\]  
Step 2: Fitness investigation: The fitness size is analyzed for every particle. Choose the particle with the best fitness value.  
Step 3: For each particle calculate velocity and position from Equations (1) and (2)  
Step 4: Evaluate Fitness \(f(x_{i}^{t})\) Find current best  
Step 5: Update t = t + 1  
Step 6: Output gbest and \(x_{i}(t)\)  
The process will be repeated until the condition is met.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 5 Industrial Application Perspective
### Intelligent Logistics and Transportation  
Logistics and transportation are two essential foundations of the anticipated IoT development over the coming years. Most applications aim to increase efficiency in sectors like cargo or public transportation. However, while some specific applications can tolerate jitter, delay, or unreliability, other applications cannot [31]. Due to the enormous quantity of data generated by sensors deployed on cars or roadside units (RSUs), intelligent transportation systems (ITSs) may have extra communication overhead, high bandwidth consumption, and more significant reaction delays while transmitting data to cloud servers. The cellular LPWAN architectures LTE-M and NB-IoT have been used by ITS applications to meet the challenges mentioned earlier. Also, LTE-M and NB-IoT are appropriate for providing backhaul infrastructure for ITS applications [95]. NB-IoT and LTE-M are the best suited for these applications because of their diversity, range, QoS, and low latency [95]. In particular, a smart parking management system was developed using NB-IoT to mitigate the high power consumption and high deployment costs of wireless networks. The proposed system  
Figure 8: Applications of Industrial Internet of Things based on LPWAN protocols.  
has been deployed in two cities in Zhejiang Province, China, to effectively improve the utilization of existing parking facilities [96].
## 2 Methods, Study Area and Data Analysis
### Satellite Image Data Analysis
data were subjected to a leave-one-out cross-validation regression procedure [58] to determine independent estimates of LAI for each date. This method involved leaving out the data for one date and computing the regression relationship using all other dates; the process was repeated for all dates to give nine independent LAI estimates.
# Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition
## 1 Introduction
### Contributions and Structure of the Paper  
This paper designs an ASL recognition system for translation applications in the hearing-impaired community, so the goal of this paper is to help hearing-impaired people communicate better with others. The proposed ensemble-learning model for sign language recognition is proposed, using multiple CNNs with accuracy-based weighted voting (AWV) to increase the ASL recognition performance. The contributions of the paper can be summarized as follows:  
* An ASL Recognition System using Multiple deep CNNs and Accuracy-based weighted voting (ARS-MA) is proposed, which consists of data preprocessing, feature extraction with multiple deep CNNs, and classification.
* Multiple deep CNNs are designed for feature extraction, and an AWV algorithm is proposed for classification.
* Three new datasets for classification are created from the feature extraction results, with two hyperparameters, \(\alpha\) and \(\lambda\), introduced to optimize the accuracy of the AWV algorithm.
* The proposed model recognizes 29 gestures with accuracies of 98.83% for the ASL Alphabet dataset and 98.79% for the ASLA dataset with complex backgrounds.  
The remainder of this paper is organized as follows: Section 2 introduces the details of the methods and models. Section 3 presents the results and comparison of our method to other methods. Finally, Section 4 summarizes the conclusions.
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 4 Experimental Setup  
### Validation in Gridless Nyquist-WDM System Affected by ICI  
We validate the KNN-based demodulator in a Gridless Nyquist-WDM system affected by Inter-Channel Interference (ICI) [15]. The authors used this experimental setup to collect real data in gridless scenarios with ICI. Then, the authors process the data offline in MATLAB to find the optimal KNN parameters. The authors plotted K-neighbors against the bit error rate (BER), finding a value of 15 for K. Also, using BER, they found a value of 3240 for the training data set.  
In the experimental setup, three lasers with 100 kHz linewidths generated 3 \(\times\) 16 Gbaud 16-QAM Nyquist WDM system. Different DACs operated at 64 Gsamp/s generated the center and side channels to produce uncorrelated signals, see Figure 6. The authors used a random bit sequence and mapped it to generate 16-QAM symbols using a root raised cosine filter with a roll-off factor of 0.1. They added optical noise to yield an OSNR from 19.3 dB to 31.3 dB. The receiver used a wideband coherent receiver with an electrical bandwidth of 35 GHz. The signal was digitized by an 80 Gsamp/s real-time oscilloscope, followed by offline post-detection DSP processing in MATLAB.  
Figure 5: Hardware architecture for the implementation of the modified KNN algorithm. New blocks were added to the original KNN architecture.
## 2 Methodology
### Fenton Experiments  
To evaluate the influence and predict the optimal conditions of Fenton variables for caffeine degradation, an experimental design was developed based on the second-order response surface methodology. In the experimental design, the \(C_{0}\), the pH of the reaction medium of 3, and the room temperature were considered constant. The initial concentrations of Fe ions and H\({}_{2}\)O\({}_{2}\) were considered independent parameters and were defined at three levels. The reagent concentration ranges, as shown in Table 2, were defined based on studies of pharmaceutical compound degradation [10; 21; 38; 46; 47; 48]. The Fenton reaction was evaluated at 5, 15, 30, and 60 min.  
Fenton experiments were conducted at room temperature in volumes of 0.5 L of a 20 mg L\({}^{-1}\) caffeine solution. The homogenization of the reaction medium was carried out using a magnetic stirrer (New Lab NL 01-03 A). Initially, the pH of the caffeine solution was adjusted to 3, and then, a pre-defined amount of Fe\({}^{+2}\) was added. Next, the required volume of H\({}_{2}\)O\({}_{2}\) was added and the testing time began. After starting the reaction, aliquots of the solution were removed at 5, 15, 30, and 60 min. All aliquots removed from the solution were filtered through a 0.22 \(\upmu\)m hydrophobic polytetrafluoroethylene (PTFE) membrane and sent to analysis by using a Carbon Analyzer (Shimadzu TOC-VCSH, Sao Paulo, Brazil) and High-Performance Liquid Chromatography (HPLC) (Agilent 1200 Infinity). The degradation percentage was calculated using Equation (2) and mineralization was calculated using Equation (3).  
\[\text{Degradation }(\%)=\left(1-\frac{A-HPLC_{F}}{A-HPLC_{0}}\right)\cdot 100 \tag{2}\]  
where \(A\)-\(HPLC_{0}\) and \(A\)-\(HPLC_{F}\) are the values of the CAF areas in the chromatograms of the initial and final HPLC analysis, respectively.  
\[\text{Mineralization }(\%)=\left(1-\frac{TOC_{F}}{TOC_{0}}\right)\cdot 100 \tag{3}\]  
where \(TOC_{0}\) and \(TOC_{F}\) are the initial and final CAF Total Organic Carbon values, respectively.
## 4 Analysis and Results
### Model Interpretation
#### 4.4.1 Local Interpretable Model-Agnostic Explanations (LIME)  
Figure 8 shows the local explanations of the Random Forest classifier for the first sample and the second sample using the LIME technique. According to Figure 8, the five most significant factors in Random-Forest-based churn detection for the first customer are the 'Contract', 'Number of Dependents', 'Online Security', 'Premium Tech Support', and 'Number of Referrals'. Furthermore, an increase in the value of these five factors will drive the prediction toward not churning, while an increase in the value of 'Payment Method' and 'Monthly Charge' will increase the likelihood of churning. For the second customer, 'Monthly Charge' was more important than 'Premium Tech Support' and was one of the top five factors that influenced a customer's subscription decision. In addition, contrary to the first customer, LIME indicates that an increase in the value of 'Contract', 'Number of Dependents', and 'Number of Referrals' values can increase the likelihood of the second customer's churn. See Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Sensitivity** & **Specificity** \\ \hline
**Logistic Regression** & 0.7553 & 0.84 & 0.7400 & 0.7704 \\ \hline
**KNN** & 0.7129 & 0.81 & 0.8467 & 0.5805 \\ \hline
**Naïve Bayes** & 0.7984 & 0.88 & 0.8227 & 0.7744 \\ \hline
**Decision Tree** & 0.8004 & 0.80 & 0.7907 & 0.8100 \\ \hline
**Random Forest** & 0.8694 & 0.95 & 0.8547 & 0.8839 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of descriptive statistics.  
Figure 7: Model Evaluation.  
However, the LIME explanation only predicts how the present prediction will change in response to minute changes in the input values. Therefore, this study also employs another explanation technique, SHAP, to show the overall importance of the factors. The results are shown in the following subsection.