esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
SUAS platforms can be custom integrated with a range of sensors, including digital, multispectral, hyperspectral, thermal infrared, and Lidar sensing and imaging devices for crop characterization at a very broad spectrum [2]. SUAS imaging systems have been successfully used for numerous mapping and phenotyping applications [13] drought resistance [19], crop growth [2], yield [12], and grain moisture estimation [20], among others. Such applications have demonstrated significant improvement in yield estimations for crops including corn [12], wheat [21], and cotton [22], among others. For example, studies by [PERSON] et al. [23], [PERSON] et al. [1], and [PERSON] et al. [15] noted relative accuracies in the form of R\({}^{2}\) = 0.72-0.77 and rRMSE% = 15.9%, using aerial multi-modal spectral data through deep neural networks and machine learning. Recently, with the advancements in precision agriculture, large aerial imagery datasets have been processed to model crop traits [2] using supervised machine learning (ML) models [24; 25; 26] such as random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN), among others [1,2,26; 27; 28]. These ML models could often be affected in their performance due to dataset sizes, requirement of substantial computational resources and time to handle large datasets [23]. For example, [PERSON] et al. [27] combined satellite remote sensing and ML models to forecast corn grain yield. Similarly, [PERSON] et al. [28] utilized remote sensing data and ensemble learning algorithms to estimate wheat yield using VIs. [PERSON] [29] fused MODIS imagery-derived VIs and environmental variables using RF to estimate SY and obtained errors of 0.206 t/ha [30]. ML models are capable of capturing key complexities and distributions of data to reduce the effects of an unknown variability, and they thereby tend to become robust. While studies have utilized SUAS remote sensing and ML to estimate crop traits and yield [31], limited studies exist which have quantified the impact of SCN on SY and crop physiology. Moreover, evaluating the impact of data size on such modeling is a critical factor to study. This gap is being addressed in this study by evaluating the impact of SCN on soybean yield and physiology followed by evaluating ML models for SY estimation under varied dataset sizes. The specific objectives are to (1) evaluate the impact of SCN infestation on crop vigor and yield, (2) quantify the impact of SCN infestation on soybean physiology through aerial multispectral imagery, and (3) estimate SY using aerial imagery-derived reflectance (REFs) and VIs inputs with statistical and ML models at various training data sizes.
# Optimized Sonar Broadband Focused Beamforming Algorithm
## 3 Simulation and Performance Analysis  
It is assumed that the incident signal is a wideband signal with a frequency range of 2250 Hz-3000 Hz. The bandwidth of this broadband signal is 750 Hz. A uniform circular array is built from 24 isotropic elements, and its radius is 0.5 m.
# Ultrafast Optical Signal Processing with Bragg Structures
## 3 Optical Signal Processing in Resonantly Absorbing Bragg Reflector (RABR)
### Theoretical Considerations
By colliding with another input pulse, the stored pulse can be released; both pulses can be released from the RABR with an efficiency of up to 96% (Figure 3) [13]. The nonlinear frequency conversion of zero-velocity (standing) short light pulses, based on the stimulated Raman scattering (SRS), has also been studied [7]. To reduce the walk-off effects, both the pump and Stokes waves must be phase-matched, traveling with equal speeds. This can be realized in a 1D doubly resonant Bragg reflector (DRBR), where \"doubly\" implies supporting the resonant interactions at two different wavelengths simultaneously, as specified below [7]. The DRBR structure consists of 1D periodically arranged layers of two-level atoms and a passive BG. The period of the array of the two-level-atom layers is equals to the half-wavelength of the pump pulse. The bandgap center of the passive BG is equal to the wavelength of the Stokes pulse. In the DRBR, the pump pulse can be decelerated and stopped by thin atomic layers [41]. On the other hand, the Stokes pulse can be generated as a standing or slowly oscillating soliton by the passive Bragg  
Figure 3: A contour plot illustrating the release of a laser pulse stored in the RABR by dint of its collision with an additional pulse injected into the structure [13]. The pulse width is \(\tau_{0}=0.5\tau_{c}\), and the injected amplitude is \(\Sigma_{0}^{+}=3.5\), for the stopped pulse, and \(\Sigma_{0}^{+}=3.84\) for the incident one. Both input pulses have a standard sech profile. Figure 2: **(a)** The evolution of the population inversion, \(W\), illustrating the creation of an oscillatory standing soliton in the RABR from a sech-shaped input. The pulse width is \(\tau_{0}=0.5\tau_{c}\), and the injected amplitude is \(\Sigma_{0}^{+}=4.3\); **(b)** The generation of two standing oscillatory solitons, with the pulse widths \(\tau_{0}=0.5\tau_{c}\) and injected amplitude \(\Sigma_{0}^{+}=3.6\)[12]. reflector [14]. Due to the interaction of these two kinds of stopped light pulses, the energy of the pump pulse can be efficiently transferred to the Stokes pulse. Furthermore, the energy of the Stokes pulse will eventually leak out from both edges of the finite-length of DRBR in the form of Raman solitons.
## 3 Proposed Method
### Boundary Object Detection Module  
In videos, the positions of the out-of-view targets returning from outside to inside the shots are uncertain, meaning that the target can reappear from almost any location at the shot boundary. Therefore, the YOLOv5-DeepSORT model, with excellent accuracy of object detection and inference speed, becomes a suitable tool for detecting the target in the boundary object detection module.  
[PERSON] et al. [52] utilized the power transformations to existing IoU loss functions, such as GIoU and CIoU, to develop a new IoU loss function, alpha-IoU. The \(\alpha\) in the alpha-IoU represents a power parameter that provides greater flexibility for the detector to achieve different bounding box regression accuracy. Abundant experimental results reveal that the alpha-IoU can realize more accurate object detection by weighting the gradient and loss of objects with high IoU values in object detection models [53].  
The original loss function of YOLOv5 is replaced by the alpha-IoU in the boundary object detection module of TRTrack to enhance the object detection accuracy and flexibility  
Figure 4: Feature maps obtained in the tracking network of the preliminary tracking module. It can be seen that as the number of network layers increases, the feature map becomes more and more focused on the target.  
of the boundary object detection module. Based on the idea of alpha-IoU, we set the loss function only takes effect when the parameter \(\alpha\) is greater than \(1\). The formula is as follows  
\[L_{\alpha-IoU}=\frac{1-IoU^{\alpha}}{\alpha},\ \alpha>1 \tag{5}\]  
where \(IoU\) means the original Jaccard overlap calculation representing the intersection ratio of the prediction bounding box and the ground truth. In this case, the whole loss value of YOLOv5 in TRTrack is as follows  
\[L_{total}=L_{conf}+L_{class}+L_{\alpha-IoU} \tag{6}\]  
where \(L_{conf}\), \(L_{class}\), and \(L_{\alpha-IoU}\) indicate the confidence loss, the classification loss, and the actual alpha-IoU loss, respectively. The specific alpha-IoU calculation equation is shown as  
\[L_{\alpha-IoU}=\sum_{i=0}^{S^{2}}\sum_{j=0}^{B}(1-L_{\alpha-IoU}) \tag{7}\]  
where \(S^{2}\) represents the number of image grids, and \(B\) is the number of every grid anchor box in object detection.  
On this basis, after the method in Section 3.1 is used to judge whether the target leaves the shot, the object detection is conducted at the shot boundary, and the detection range function is \(at\_bdy\) mentioned in Section 3.1. Then, the results of object detection are shown as  
\[det=\{d_{1},\ldots,d_{n}\}\ \in\ at\_bdy(b_{d}) \tag{8}\]  
which represents a collection of detection bounding boxes for objects at the shot boundary, and \(b_{d}=(x,y,w,h)\) are the whole object detection bounding boxes.
# Single-Shot Compressed Imaging via Random Phase Modulation
## 4 Compressed Imaging Using Random Phase Modulation
### Single-Shot Compressed Imaging with Fourier-Domain Single Random Phase Mask (CI-FSRPM)
The test environment is a 32-bit window7 operating system, the processor  
Figure 7: Super-resolution imaging tests of CI-FSRPM. **(a)** Original Lena image. **(b)** Original Phantom image. **(c)** Compressed measurement of Lena image. **(d)** Compressed measurement of Phantom image. **(e,f)** IST and GPSR reconstructions from **(c)** with SNR 29.84 dB and 29.68 dB. **(g,h)** IST and GPSR reconstructions from **(d)** with SNR 39.87 dB and 39.62 dB. is Intel (R) Core (TM) i5-2320 quad-core, the frequency is 3.00 GHz, available memory is 3.0 G, and we used Matlab2010a software. Numerical results are listed in Table 2.  
As depicted in Table 2, the CI-DRPM method proposed by [PERSON] with a random demodulator (RD) for subsampling using tvqc_logbarrier solver can only deal with 1280 \(\times\) 1280 pixels; larger is impossible. Our proposed CI-FSRPM is superior to CI-DRPM in terms of image size, reconstruction time, and reconstruction quality. The reason is that our proposed CI-FSRPM performs in 2D form, which is more effective than CI-DRPM performs in 1D form.
# Numerical Study on Temperature Distribution of Steel Truss
###### Abstract  
Aqueduc, one kind of bridge structure overpassing a long space, is a significant structure for water delivery for the purpose of agricultural or domestic usage. Aqueduc has quite different loads from other forms of bridges, of which temperature effects due to the environment temperature change, such as seasonal weather or radiation from sunshine, are of great importance. With water flowing inside, the temperature boundary of aqueducs, especially for steel aqueducs, is much more complicated, and relevant researches are limited. In this paper, a 3D Finite Element Method (FEM) simulation process is presented to analyze temperature distribution on the cross-section of a new-type steel truss aqueduct, which belongs to the Water Transfer Project from Yangtze River to Huai River in China. ASHRAE clear-sky model is used to calculate the solar-radiation variation, including direct radiation, diffuse sky radiation, and ground reflected radiation on steel surfaces. The time-dependent sunshine radiation angle of incidence and shielding effect of steel trusses are considered. The water inside the aqueduc is also included in this model, which significantly influences the temperatures of the inner surfaces of the aqueduct. Several temperature distributions under critical conditions of winter and summer are shown in this study, and results of the empty aqueduct under the same circumstances are also provided as a comparison. The effects of wind speed, geographic latitude, and direction of the aqueduc are examined. The conclusions and approach provided by this study could serve as significant references for thermal design and control of similar steel truss aqueducs.  
steel truss; aqueduct; thermal analysis; solar radiation; shadow effect +
Footnote †: journal: Applied Sciences
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 3 Evolutionary Algorithms
### Differential Evolution  
Differential Evolution (DE) has been introduced in [27] for solving continuous optimization problems with the aim to handling non-differentiable, non-linear, and multimodal cost functions. Moreover, it has been designed to be easy for the user (robustness of the parameters choice) and suitable for parallelization. The algorithm has been applied to a wide range of problems, obtaining very good results [28].  
The algorithm is based on the vector-based mutation: this operator has the objective to create the new population starting from the existing one. As a first step, two individuals of the population are selected (all the selection possibilities can be used) and the difference vector is calculated. Then a third individual is selected, and it is moved in the search space by a quantity that is proportional to the difference vector:  
\[\mathbf{x}_{i}(t+1)=\mathbf{x}_{r_{1}}(t)+F\cdot(\mathbf{x}_{r_{2}}(t)-\mathbf{ x}_{r_{3}}(t)) \tag{18}\]  
where \(F\) is an user defined parameter and it has been set equal to 0.5.  
This algorithm is native for real-valued problems, like the reflectarray antenna optimization. The algorithm has been implemented with an elitism-based replacement of the individuals, i.e., each element of the population is replaced with a new one only if the cost value is improved.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
###### Abstract
[PERSON] [9; 10] conducted sequential studies on the axial compression performance of circular CFT stub columns, first analyzing their fundamental mechanical behavior and then developing a theoretical model to predict the axial yield capacity of CFRP-confined specimens. [PERSON] [11] used ANSYS to simulate the axial compression test of concrete-filled FRP-steel stub columns and obtained the ultimate bearing capacity, which is in line with the test results. By using the ABAQUS, the load-strain curve and stress figures of concrete-filled CFRP-steel stub columns under axial load are simulated, which are consistent with the test results of Z.-X. [PERSON] [12]. [PERSON] [13] carried out experimental research and finite element analysis on concrete-filled CFRP-steel stub columns under axial compression. [PERSON] [14] defined a \"confined concrete-filled steel tube\" by the CFRP hoop coefficient, carried out experimental research, and established a theoretical analysis model. However, most of the above studies aimed at research on concrete-filled CFRP-steel stub columns and long columns. The study of concrete-filled CFRP-steel middle long columns is relatively limited. Especially the comprehensive research on the numerical simulation analysis of concrete-filled circular CFRP-steel middle long columns. In this paper, the axial compression test of ten different concrete-filled circular CFRP-steel middle long columns was carried out. Combined with theoretical analysis, the calculation method of bearing capacity of it was found. The influence of different concrete strength grades, layers of CFRP, and wall thicknesses of steel tubes on the ultimate bearing capacity of samples is discussed. Then, the load midspan deflection curves and ultimate bearing capacity were obtained by finite element analysis software ANSYS2021R1. Compared with the test results, the correctness and practicability of the simulation analysis of concrete-filled circular CFRP-steel middle long columns are further verified, which are of great significance for analyzing the ultimate bearing capacity and theoretical derivation of it.
# Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems
## 2 Theoretical Background and Proposed Control Law
### Selection of the Quality Functions
Taking a closer look at \(h_{\rm s}(\underline{\xi}(t),\underline{\xi}_{\rm ref}(t))\), where all \(\underline{\underline{M}}(t)\) are specified by (16) and defining  
\[W_{\rm s}(\underline{\xi}_{\rm ref,s}(t))=\underline{\xi}_{\rm ref,s}^{T}(t) \underline{M}\,\underline{\xi}_{\rm ref,s}(t), \tag{17}\]  
the quality function becomes  
\[h_{\rm s}(\underline{\xi}(t),\underline{\xi}_{\rm ref}(t))=\frac{V_{\rm s}( \underline{\xi}_{\rm eq,s}(t))}{W_{\rm s}(\underline{\xi}_{\rm ref,s}(t))}. \tag{18}\]  
Applying the quotient rule, the time derivative of the quality functions follows  
\[\dot{h}_{\rm s}(\underline{\xi}(t),\underline{\xi}_{\rm ref}(t))=\frac{\dot{ V}_{\rm s}(\underline{\xi}_{\rm eq,s}(t))W_{\rm s}(\underline{\xi}_{\rm ref,s}(t))- \dot{W}_{\rm s}(\underline{\xi}_{\rm ref,s}(t))V_{\rm s}(\underline{\xi}_{ \rm eq,s}(t))}{\big{(}W_{\rm s}(\underline{\xi}_{\rm ref,s}(t))\big{)}^{2}}. \tag{19}\]  
Thus, condition (14) is fulfilled as long as the inequality  
\[\frac{\dot{W}_{\rm s}(\underline{\xi}_{\rm ref,s}(t))}{W_{\rm s}(\underline{ \xi}_{\rm ref,s}(t))}>\frac{\dot{V}_{\rm s}(\underline{\xi}_{\rm eq,s}(t))}{V _{\rm s}(\underline{\xi}_{\rm eq,s}(t))} \tag{20}\]  
holds. It can be noticed that both quotients in (20) are a measure for the convergence to the subsystem equlibria \(\underline{x}_{\rm eq,s}\). When \(V_{\rm s}(\underline{\xi}_{\rm eq,s}(t))\) is a candidate Lyapunov function and a constant reference point \(\underline{x}_{\rm ref}(t)=\text{const. }\) as well as constant input \(\underline{u}(t)=\text{const. }\) are given, (20) simplifies to the classical Lyapunov stability criterion, where the time derivative of \(V_{\rm s}(\underline{\xi}_{\rm eq,s}(t))\) must satisfy \(V_{\rm s}(\underline{\xi}_{\rm eq,s}(t))<0\), \(\forall\underline{\xi}_{\rm eq,s}(t)\).
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Obtaining Pure Oxides and Metals
#### 3.7.4 Other Methods  
The metals obtained with the methods described above are usually too contaminated to be used directly for certain purposes, especially for the production of semiconductors. Therefore, it is necessary to carry out additional operations to further purify them.  
Gallium can be purified in six independent ways [96]:  
1. [leftmargin=*]
2. Filtration. A dense filter is used, onto which raw gallium is poured at an elevated temperature. The method takes advantage of the easy fusibility of gallium and the poor solubility of many impurities in the molten gallium;Chlorination. Raw gallium is subjected to a reaction with chlorine, and the resulting gallium(III) chloride is distilled from the chloride solutions containing impurities (except aluminum chloride);
* Amalgamation. The solubility of gallium in mercury is high and increases with increasing temperature; therefore, saturating mercury with raw gallium at 400 \({}^{\circ}\)C, filtering out impurities, and then slowly cooling the amalgam allows for the isolation of purified gallium;
* Zone melting method, i.e., local melting of the charge. The molten zone moves along its axis. It is mainly used for further purification of previously purified gallium or its chlorides;
* Vacuum heating. It allows the removal of mercury from gallium by heating it under reduced pressure and at a high temperature;
* Czochralski method. Slow extraction of a single crystal of gallium at a temperature close to solidification. Indium can be purified with the following methods [96]:
* Melting under a layer of NaOH + NaCN. It allows for the removal of residues of copper, zinc, cadmium, and lead (which is usually removed before electrolysis via coprecipitation of lead(II) sulfate with barium sulfate);
* Distillation of impurities with a boiling point lower than indium (2080 \({}^{\circ}\)C);
* Amalgamation. It is repeated several times and combined with the distillation of mercury under reduced pressure;
* Zone melting method. It is described above;
* Extraction of a single crystal from a molten indium in an inert atmosphere.  
Refined gallium may contain \(10^{-4}\%\) to \(10^{-8}\%\) of impurities, while refined indium may contain \(10^{-6}\%\) to \(10^{-7}\%\) of the impurities in the final product [96].
# Space Object and Background Polarization Models and Detectability Analysis
## 4 Imaging Contrast Simulation Analysis
At this time, the target pixel value in the polarization image acquired by the system is larger than the value of the bright sky background, which effectively reflects and uses the ability of polarization imaging technology to \"weaken strong light\" and \"strengthen weak light\", so that the weak space object signal is highlighted in the strong background noise of the daytime sky. In summary, the space target polarization imaging technology can greatly improve the target-background contrast index under certain angles. The difference between space target and sky background polarization characteristics can be used to capture space targets more efficiently in the stable sky background polarization distribution, which can effectively solve the problem of low imaging contrast caused by strong background noise interference during daytime, and can become an important technical component of the all-day space target ground-based observation network.
## 4 Discussion and Conclusions  
Our analysis shows that the there is a significant difference between the performance evaluated by simple Beer's law and Monte Carlo simulation, as the latter comprehensively considers the contribution of scattered photons and the system transceiver parameters. Monte Carlo simulation is a flexible and effective approach for studying the characteristics of underwater channels. Nevertheless, there is still a discrepancy between the results obtained by Monte Carlo and the actual implementation. Firstly, in this paper we only consider a perfectly aligned link performed in deep sea without transmitter-receiver misalignment, water turbulence, and sea surface reflection, which may affect the performance of the system. Secondly, despite the high flexibility and accurate solution, the Monte Carlo method also suffers from random statistical errors and low simulation efficiency. It is only a simulation which cannot be completely equal to real performance. Therefore, this discrepancy is worthy of further investigation and demonstration with experiments.  
Figure 6: (**a**) Secret key rate of four types of water obtained by Beer’s law and Monte Carlo simulation. The solid lines correspond to the results obtained by Monte Carlo with considering the receiver parameters, the dashed lines correspond to the results obtained by Beer’s law, and the dot-dashed lines correspond to the results obtained by Monte Carlo without considering the receiver parameters. From left to right, the curves correspond, respectively, to the turbid harbor water case, the coastal ocean water case, the clear ocean water case and the pure see water case. (**b**) Secret key rate for different beam width \(\omega_{0}\) and maximum initial divergence angle \(\theta_{0,max}\).  
In conclusion, we proposed a realistic model to evaluate the performance of a CVQKD system over an underwater link by using Monte Carlo simulation, which took different water types, link distances, and transceiver characteristics into consideration to estimate the transmittance and detection efficiency of the system. Simulation results shown that the secret key rate and transmission distance of the underwater CVQKD system obtained by Monte Carlo simulation are distinctly different from those obtained by simple Beer's law. This research is beneficial for building a realistic underwater CVQKD system to achieve the necessary secure distance in different oceanic types, and also enables system designer to select the appropriate light source and transceiver devices for practical underwater CVQKD systems.  
Conceptualization, Y.W. and Y.G.; Methodology, X.W. and Q.L.; Validation, W.H. and Y.M.; Formal analysis, Y.M.; Investigation, W.H. and H.D.; Writing--original draft preparation, Y.M.; writing--review and editing, W.H.; Visualization, Y.M.; Supervision, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (61972418, 61977062, 61871407, and 61801522).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript: