esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of a Coherent Dual-Beam Array Feed Network for Aperiodic Concentric Ring Antennas
## 2 Problem Formulation  
### Aperiodicity in Concentric Ring Arrays  
An aperiodic or non-uniform array is considered when the interelement spacing of antenna elements is not equal between them. Thus, consecutive rings conformed by antenna elements in a circular layout which share the same origin where each ring has its own radius within an aperiodic layout (different interelement distances or radii between rings) are known as aperiodic concentric ring arrays (ACRAs). In general, let us consider a concentric geometry, assuming \(N_{r}\) circular rings with \(N_{e}\) radiators for each ring which are deployed in the \(x-y\) plane, as illustrated in Figure 1. The far-field pattern considering an arbitrary complex excitation set (\(w_{mn}\)) without a radiator in the center of the array can be calculated as [22]:  
\[AF(\theta,\varphi)=\sum_{n=1}^{N_{r}}\sum_{m=1}^{N_{e}}w_{mn}\exp[jk[x_{nm}(u)+ y_{nm}(v)]], \tag{1}\]  
where \(x_{nm}=r_{n}\cos\varphi_{nm}\) and \(y_{nm}=r_{n}\sin\varphi_{nm}\) define the antenna element locations, the antenna elements' deployment around the rings is indicated by \(\varphi_{nm}=2\pi(m-1)/N_{e}\), and \(r_{n}\) denotes the radial distance per ring from the origin of the antenna array until the last ring. The \(u-v\) space considering the excitation phases is defined by \(u=\sin\theta\cos\varphi-\sin\theta_{0}\cos\varphi_{0}\) and \(v=\sin\theta\sin\varphi-\sin\theta_{0}\sin\varphi_{0}\). The steering angle for translation is denoted by (\(\theta_{0}\), \(\varphi_{0}\)); in this case, \(\theta\) is the angular direction in elevation and \(\varphi\) is the spatial angle for the azimuth. Finally, the wavelength \(\lambda\) is related to the angular wavenumber \(k=2\pi/\lambda\) in Equation (1).  
Assuming a multi-beam scenario, the array factor is simultaneously optimized with \(p\) beams, considering the aperiodicity in function of the different radius in each ring \(r\) and an isophoric array feeding [23; 24; 25], the \(AF\) can be written as follows:  
\[AF^{p}(\theta,\varphi,\vec{r})=\sum_{n=1}^{N_{r}}\sum_{m=1}^{N_{e}}\exp[jkr_{n }(u\cos\varphi_{nm}+v\sin\varphi_{nm})], \tag{2}\]  
In the following section, the theoretical model of the feeding subsystem for the ACRA is described.
# Research on the Application of THz-TDS in Coal-Rock Interface Recognition
###### Abstract  
The recognition of coal-rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal-rock interface, including the identification of coal-rock and coal-rock mixtures, as well as the accurate characterization of coal seam thickness. Terahertz detection was used to obtain the optical parameter information of pressed pellets prepared by mixing two different kinds of coal and two kinds of rock. Based on the experiment's results, a database was established for the identification of coal-rock interfaces for coal mining machines. The terahertz detection was performed on 10 different kinds of sheet anthracite with different thicknesses, and the terahertz spectra of coal seams with different thicknesses were simulated and calculated using simulation software. By comparing the two effective mining thicknesses, parameters can be provided for coal seam mining. The experiment and simulation show that the terahertz time-domain spectroscopy technology has a promising application prospect in the identification of coal-rock interface.  
coal-rock interface; terahertz detection; coal mining machines; optical parameter
## 3 Model and Methods
### Processing of Gaofen-1 Data  
For the GF-1 imagery data, the obtained data are initial digital number (DN) values, which require registration and radiometric calibration for the panchromatic data. The multispectral data also need to undergo preprocessing operations, including registration, radiometric calibration, and atmospheric correction, to derive surface reflectance data. Since there is currently a lack of targeted atmospheric correction methods for GF-1 satellites,  
Figure 2: Workflow diagram of the bathymetric mapping.  
this study employs the 6S atmospheric correction method proposed by [PERSON] et al. [31], which has good general applicability. The image-related parameters required for this method can be obtained from the accompanying XML attribute table, while aerosol optical thickness data AOT550 can be computed using Aerosol Robotic Network (AERONET) ([[https://aeronet.gsfc.nasa.gov](https://aeronet.gsfc.nasa.gov)]([https://aeronet.gsfc.nasa.gov](https://aeronet.gsfc.nasa.gov)) (accessed on 29 July 2024)) data.  
In ocean remote sensing, the mirror reflection of sunlight on the rough sea surface (sun glint) is a strong interference signal, and the presence of sun glint also introduces errors when carrying out bathymetric mapping inversion, so it must be suppressed. In this study, we employed the remote sensing correction method proposed by [PERSON] et al. [32] in 2005 to perform deglint on the multispectral data. The specific mathematical relationship is as follows:  
\[\rho_{SR}^{deg}(\lambda)=\rho_{SR}(\lambda)-b_{\lambda}(\rho_{NIR}-MIN\rho_{NIR}) \tag{1}\]  
where \(\lambda\) represents the central wavelength of each band, \(\rho_{SR}^{deg}\) is the surface reflectance after deglint, \(\rho_{SR}\) is the initial surface reflectance data corresponding to the central band, \(\rho_{NIR}\) is the surface reflectance in the near-infrared band, \(MIN\rho_{NIR}\) is the minimum value of near-infrared band reflectance within a given area, and \(b_{\lambda}\) is the undetermined linear coefficient between the visible band corresponding to the central band and the near-infrared band.  
Finally, the processed panchromatic and multispectral images were fused using the NNDiffuse algorithm proposed by [PERSON] et al. This method treats the spectrum as the minimum element, allowing for better preservation of spatial and spectral characteristics, which facilitates the detailed feature extraction of satellite images [33].
# Technology Behavior Model--Impact of Extended Reality on Patient Surgery
## 3 Materials and Framework  
### Technology Acceptance Model  
The technology acceptance model (TAM) is a behavioral representation of the study of human acceptance of new technologies and information [32]. The TAM is currently one of the most used models in new technology research [33]. The purpose of TAM research is that people experience efficiency gains of new technologies over existing ones through the influence of external factors on usage, such as perceived usefulness [34]. In addition, the way and process of use are more convenient than the existing one, perceived use. The perceived experience leads to a change in the user's usage behavior [35]. This process is the acceptance of new technologies and information by the perceived behavioral intention that the model focuses on [36]. In this study, patients used the extended reality smart glasses to experience the surgical process and the surgical operation to ease their presurgical anxiety through understanding. Subjective perceptions of the superiority of smart glasses surgery over existing surgical modalities were accepted. This leads to patients choosing smart surgery. However, previous studies have shown relatively low explanatory power and inconsistencies among the components in the research model. Mainly reflected in the following points: (1) the research components are inconsistent, some use attitudes to measure acceptance indicators, and some use behavioral intentions or actual use [37]; (2) some major relationships are inconsistent, for example, perceived ease of use only had a significant effect in studies with behavioral intent and had little effect in other studies; (3) the assessment of specific components is ambiguous, and there is no clear division standard [38].
# Improvement of Radiant Heat Efficiency of the
## 2 FE Analysis of the RT and the Strip
### Determination of the Design Variables of a Honeycomb Pattern  
FE analyses were carried out to compute radiant heat (J/s) when the RT surface printed with honeycomb pattern faces the steel strip (Figure 6a). The ratio of 200 mm (tube width in the length direction) over 300 mm (steel strip width) in the partial FE model was set to be coincident with the ratio of the length of average straight tube of the actual RT on which the honeycomb pattern was printed over the width of the actual steel strip being annealed in the actual CAL. The distance between RT and the steel strip is 305 mm, which is usually applied in actual operation. The steel strip thickness was 3 mm. The FE analyses were performed using the partial FE model to reduce the computation time.  
Three different honeycomb structures as shown in Figure 6b-d were considered in FE analysis to determine the design variables of a honeycomb structure. Figure 6b is the honeycomb structure of the smallest printable size in this study. Figure 6c,d are representative of the honeycomb structures. Other sizes of honeycomb structures were also considered.  
Figure 6e-g compares the computed radiant heat of three honeycomb patterns with varying dimensions (width, length, and thickness). The change in radiant heat was significant along the central longitudinal nodes of the strip. Hence, the radiant heat was taken as the average of the radiant heat calculated along the middle longitudinal (y-direction) nodes of the strip. The radiant heat in  
Figure 6: (**a**) Honeycomb design variables (**left**). The partial FE model with honeycomb patterns printed (**right**). (**b–d**) denotes the dimensions of the honeycomb structures used in the FE analysis. ‘\(t\)’ denotes the height of honeycomb grid in the tube radius direction. (**e–g**) indicate the contour of RADTLA, time integrated radiation heat per unit area (J/s) for three honeycomb structures right above.  
Figure 6e-g were 548.6 (J/s), 564.5 (J/s), and 527.8 (J/s), respectively. Note the radiant heat was 519.6 (J/s) when the RT surface without the honeycomb pattern faces the strip. The radiant heat increased by approximately 8.64% compared to the radiant heat of RT without the honeycomb pattern when the honeycomb design variables shown in Figure 6 were applied. Hence, in this study, the honeycomb design variables in Figure 6 were selected for DED printing.  
RT deflections induced by the weight of the honeycomb pattern were also investigated. Difference in the maximum deflection of RT induced by the addition of the honeycomb pattern was only 0.0021 mm (0.14%). Therefore, it was judged that the weight increase caused by the addition of the honeycomb pattern on the RT had a negligible effect on the deflection of RT.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
## 2 System Description
### Signal Reception Model
For the subcarrier phase correlator, assuming that the code phase in the local signal and the received signal are fully synchronized, the normalized subcarrier phase correlation function \(R^{sc}\Big{(}\tau_{1}^{sc},\tau_{2}^{sc}\Big{)}\) is:  
\[R^{sc}\Big{(}\tau_{1}^{sc},\tau_{2}^{sc}\Big{)}=\frac{1}{N_{s}}\sum_{m=0}^{N_{ s}-1}sc_{g_{m}^{sc}(\tau_{1}^{sc})}sc_{g_{m}^{sc}(\tau_{2}^{sc})}, \tag{3}\]  
where \(sc_{k}\) is the \(k\)-th symbol in the subcarrier sequence \(\{sc_{1},sc_{2},\cdots,sc_{k},\cdots\}\), \(g_{m}^{sc}(\tau^{sc})(=\text{mod}((mT_{s}-\tau^{sc})/T_{sc}))\) is the subcarrier index of the \(m\)-th sample in the signal with the initial phase \(\tau^{sc}\), \(\tau_{1}^{sc}\) and \(\tau_{2}^{sc}\) represent the subcarrier phase error of the input signal and the local signal. \(T_{sc}(=1/F_{sc})\) is the symbol width of the subcarrier and \(F_{sc}\) is the subcarrier symbol rate. The early-late subcarrier phase correlator is utilized to estimate the subcarrier phase offset of the local signal and the received signal. The normalized subcarrier phase discrimination function \(D^{sc}\Big{(}\tau_{0}^{sc},\varepsilon\Big{)}\) is:  
\[D^{sc}\Big{(}\tau_{0}^{sc},\varepsilon\Big{)}=\frac{1}{1+2d_{0}^{sc}}\Big{[}R ^{sc}\Big{(}\tau_{0}^{sc},\tau_{0}^{sc}+\varepsilon+d_{0}^{sc}\Big{)}-R^{sc} \Big{(}\tau_{0}^{sc},\tau_{0}^{sc}+\varepsilon-d_{0}^{sc}\Big{)}\Big{]}, \tag{4}\]  
where \(\tau_{0}^{sc}\) is the initial subcarrier phase of the received signal and \(2d_{0}^{sc}\) is the early late correlation interval of the subcarrier phase discriminator. Since the symbol rate of the subcarrier is higher than the spreading code rate, the subcarrier phase has a higher estimation accuracy than that of the spreading code. However, the estimation of the subcarrier phase has periodic ambiguity. Therefore, the pseudo-noise ranging of the BOC signal is usually estimated by combining the code phase and the subcarrier phase  
\[\hat{\tau}=round\bigg{(}\frac{\hat{\tau}_{0}^{c}-\hat{\tau}_{0}^{sc}}{T_{sc}} \bigg{)}\times T_{sc}+\hat{\tau}_{0}^{sc}, \tag{5}\]  
where \(round(\cdot)\) represents a rounding function, \(\hat{\tau}_{0}^{c}\) is a code phase estimation result, \(\hat{\tau}_{0}^{sc}\) is a subcarrier phase estimation value and \(T_{sc}\) is a phase period of the subcarrier discriminator.
## 4 Results
### Biophysical Variables Retrieval
It is also of interest that the SWIR bands play an important role for TOC and TOA retrieval algorithms. Finally, as a demonstration case, we applied the TOC- and TOA-trained GPR models to a cloud-free subset of a S2 L1C and L2A imagery over the Barrax region to evaluate the actual performance of the models to convert S2 data into maps. The obtained maps are shown in Figure 7. At a glance, the similarity between both L1C and L2A maps can be observed; for both data levels, reasonable retrievals are obtained. This is encouraging, as it suggests that retrievals can be directly obtained from L1C data given a cloud-free sky, but a closer inspection is necessary to evaluate the quality of both products. Clearly differences appear. For Cab, the L2A product provides a sharper contrast between vegetated and non-vegetated surfaces with probably some overestimations. The Cw map looks most similar, while LAI is generally underestimated with probably overestimation for L1C over vegetated surfaces. These differences are also reflected in the scatter plots shown underneath. They indicate that, despite some mismatch for non-vegetated surfaces, Cab and Cw perform alike, for Cab a systematic overestimation for the L2A product as compared to the L1C product. On the other hand, the LAI L1C and L2A products are more poorly correlated; particularly the L1C yielded considerably higher estimates over the green irrigated areas. Yet, it must be remarked that both LAI models require improvements. This and other limitations are discussed further on. Figure 6: GPR band sigma (\(\sigma_{b}\)) for trained models for S2 L2A TOC and L1C TOA data. The lower the \(\sigma_{b}\), the more important the band in the model development.
## 4 Objective Function and Optimization Design  
### Objective Function  
Treating both the time-delay term and the external excitation in Equation (8) as excitation terms [30], the solution of the equation can be written as  
\[\begin{split} x^{*}(t^{*})=\exp(\mathbf{A}t^{*})\mathbf{x}^{*}(0)+\int\limits_ {0}^{t^{*}}\exp[\mathbf{A}(t^{*}-s)]\mathbf{B}{x_{a}}^{*}(s-\tau^{*})ds+\int\limits_{0} ^{t^{*}}\exp[\mathbf{A}(t^{*}-s)]\mathbf{w}(s)ds.\end{split} \tag{21}\]  
Letting the time step \(\Delta t={t_{k+1}}^{*}-{t_{k}}^{*}\), if the simulation duration is \(T\), then the total number of steps is \(n=T/\Delta t\). Equation (21) can be discretized into the following stepwise integral function of the recursive form:  
\[\begin{split}\mathbf{x_{k+1}}^{*}=\exp(\mathbf{A}\Delta t)\mathbf{x_{k}}^{*}+ \int\limits_{{t_{k}}^{*}}^{{t_{k+1}}^{*}}\exp[\mathbf{A}({t_{k+1}}^{*}-s)]\mathbf{B}{x_ {a}}^{*}(s-\tau^{*})ds+\int\limits_{{t_{k}}^{*}}^{{t_{k+1}}^{*}}\exp[\mathbf{A}({t_ {k+1}}^{*}-s)]\mathbf{w}(s)ds.\end{split} \tag{22}\]  
Then, Equation (22) can be written as  
\[\begin{split}\mathbf{x_{k+1}}^{*}=\exp(\mathbf{A}\Delta t)\mathbf{x_{k}}^{*}+ \int\limits_{{k}\Delta t}^{(k+1)\Delta t}\exp\{\mathbf{A}[(k+1)\Delta t-s]\}\mathbf{w} (s)ds+\int\limits_{(k-n)\Delta t}^{[(k+1)-n]\Delta t}\exp\{\mathbf{A}[(k+1)\Delta t -s-n\Delta t]\}\mathbf{B}{x}(s)ds.\end{split} \tag{23}\]  
By solving the equation, the vibration response of the system at each time point can be derived as  
\[\begin{split}\left\{\begin{array}{l}{x_{s}}^{*}=[{x_{s1}}^{*}, \;{x_{s2}}^{*},\;\ldots,\;{x_{sk}}^{*},\;\ldots,\;{x_{sn}}^{*}]\\ {\dot{x}_{s}}^{*}=[{\dot{x}_{s1}}^{*},\;{\dot{x}_{s2}}^{*},\;\ldots,\;{\dot{x}_ {sk}}^{*},\;\ldots,\;{\dot{x}_{sn}}^{*}]\\ {\dot{x}_{t}}^{*}=[{\dot{x}_{t1}}^{*},\;{\dot{x}_{t2}}^{*},\;\ldots,\;{\dot{x} _{tk}}^{*},\;\ldots,\;{x_{tn}}^{*}]\\ {\dot{x}_{t}}^{*}=[{\dot{x}_{t1}}^{*},\;{\dot{x}_{t2}}^{*},\;\ldots,\;{\dot{x} _{tk}}^{*},\;\ldots,\;{\dot{x}_{tn}}^{*}]\\ \end{array}\right.\end{split} \tag{24}\]  
Bringing the values obtained from Equation (24) into Equation (7), the sprung mass acceleration can be calculated for each time point as  
\[\begin{split}{\ddot{x}_{s}}^{*}=[{\ddot{x}_{s1}}^{*},\;{\ddot{x}_ {s2}}^{*},\;\ldots,\;{\ddot{x}_{sk}}^{*},\;\ldots,\;{\ddot{x}_{sn}}^{*}].\end{split} \tag{25}\]  
The goal of the new vibration-absorbing wheel structure is to improve the ride comfort and handling stability of the vehicle to the maximum. Thus, SMA, SD, and DTD are selected as the optimization objective function, which can be expressed as  
\[\begin{split} J(g,\;\tau)=\frac{\text{RMS}({\ddot{x}_{s}}^{*})}{ \text{RMS}({\ddot{x}_{s}}^{*})_{\text{p}}}+\frac{\text{RMS}({x_{s}}^{*}-{x_{t}} ^{*})}{\text{RMS}({x_{s}}^{*}-{x_{t}}^{*})_{\text{p}}}+\frac{\text{RMS}({x_{t}} ^{*}-{x_{r}}^{*})}{\text{RMS}({x_{t}}^{*}-{x_{r}}^{*})_{\text{p}}},\end{split} \tag{26}\]  
where the subscript p is the conventional structure performance index.
## 3 Results and Discussions
### Characterization of SDCap
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & & & **Mass Fraction/\%** & \\ \cline{3-6}
**Component** & **Function** & **WBDF-1** & **WBDF-2** & **WBDF-3** & **WBDF-4** & **WBDF-5** \\ \hline bentonite & filtrate reducer and viscosifier & 4.0 & 4.0 & 4.0 & 4.0 & 2.0 \\ PHPA & encapsulator & & 0.25 & & & \\ FA-367 & encapsulator & 0.3 & & & & \\ SDCap & encapsulator & & & 0.45 & 0.375 & 0.35 \\ XC & viscosifier & & 0.1 & & 0.1 & 0.15 \\ PAC-LV & filtrate reducer & 0.5 & 0.6 & 0.5 & 0.6 & \\ CMC-LV & filtrate reducer & & & & 0.5 \\ starch & filtrate reducer & 1.0 & & 1.0 & & \\ SMP & filtrate reducer & & 4.0 & & 4.0 & 4.5 \\ polyamine & shale inhibitor & & & & 2.0 \\ Polyglycol & shale inhibitor & 3.0 & & 3.0 & & \\ KCl & shale inhibitor & 3.0 & 4.0 & 3.0 & 4.0 & 4.0 \\ NaCl & hydrate inhibitor & 10.0 & 10.0 & 10.0 & 10.0 & 20.0 \\ \hline \end{tabular}
\end{table} 
Table 1: Composition of the WBDFs (water-based drilling fluids). that its rheological properties were more independent on temperature under the deepwater drilling conditions, thus reducing the risk of rheology-related problems.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 2 Wet Methods for Black Silicon Fabrication
### Metal-Assisted Chemical Etching (MACE)
#### 2.1.3 Nickel-Assisted Chemical Etching  
The Ni-assisted chemical etching process was utilized by [PERSON] et al. to investigate the impact of different etching regimes on BSi reflection [33]. The BSi with a thickness of 580 nm, which was produced under illumination for 60 min, demonstrated exceptionally low reflectance measurements of 2.3% within the ultraviolet (UV) spectrum (200-400 nm), 0.5% in the visible spectrum (400-750 nm), and 0.3% in the infrared spectrum (750-1300 nm). These findings highlight the significance of the thickness and duration of treatment in determining BSi reflectivity, which has a direct impact on the detection response of the optical devices. In addition, Ni NPs can be eliminated during the etching procedure, thus saving time by reducing the cleaning steps required to eliminate them [34].
## 5 Performance Evaluation
### Comparative Analysis  
The comparative analysis was incorporated with the key objective of minimization of energy and delay in the network. Energy consumption and delay are the two challenging constraints in sensor nodes that are required to be as low as possible during the transmission of data. In a UWSN, the common processes performed by the sensor are sensing and transmitting the sensed data to the AUV and then to the sink. Here, the data collecting AUV is not subjected to limited energy, and, hence, it moves in and around the network to collect the data from sensors. The efficient solution of using an AUV for data collection is concentrated in the UWSN environment to ensure limited energy consumption when gathering data. Table 3 discusses the prior research work in which the underwater sensor data were collected from nodes in the network. The existence of demerits in each work is illustrated, and it is resolved in this proposed work, which depicts on output result. The comparative parameters that are taken in account are based on the significance of the network lifetime and the delay that exists during data transmission and data gathering. The better efficiency of this proposed system was estimated and compared with the previous research works on UWSNs that concentrated on data gathering with AUVs. The previous works on UWSNs dealt with AUVs that move underwater to collect sensed data from the sensors. In previous works, the sensed data were directly collected from individual sensor nodes or from the CHs. The supportability for event message transmission in the UWSNs was not considered in previous works, as shown in Table 4. The processes of clustering, scheduling and AUV path prediction are performed for data gathering. The main goal of this UWSN was to gather data with limited energy consumption and delay. The possible solutions that were involved to prolong network lifetime and minimize delay are:  
* To develop clusters in the network reduces energy consumption, since the data is collected by a CH, and it delivers to the AUV;
* The assignment of sleep slots for sensor nodes enables the reduction of energy consumption in sensors due to the nil processing for certain time period;
* Appropriate path planning of AUV minimizes waiting time in CHs, and that reduces the delay in gathering.
# High-Power Broadband Frequency Chirped Intensity-Modulated Single-Frequency 1064-nm Laser
###### Abstract  
Optical carried microwave radar (OCMR), combining the advantages of lidar and microwave radar, uses an intensity-modulated laser beam as the medium to detect the distance and velocity of objects. In order to achieve a high ranging resolution at long distance, a high-power frequency chirped intensity-modulated light source at a 1064-nm wavelength was developed. A low-power narrow-linewidth 1064-nm laser beam was input to a Mach-Zehnder electro-optic modulator (EOM), and the frequency of the EOM was tuned from 10 MHz to 2.1 GHz. The output from the EOM was coupled to a laser-diode-pumped ytterbium-doped fiber amplifier (YDFA). A maximum output power of 29.5 W was achieved from the YDFA. We measured the frequency characteristics of the modulated laser beams before and after amplification, respectively. The amplification process did not bring any obvious degeneration of the modulation in terms of the bandwidth, frequency stability, chirping linearity, and modulation depth. Our research presents a new approach of obtaining a high-power broad-band intensity-modulated light source for OCMR.  
Keywords:modulated laser; frequency chirped; optical carried microwave radar +
Footnote †: journal: Applied Physics Letters