esa-sceva/satcom-chunk-collection huggingface dataset:
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 3 Method
### Residual Feature Enhancement Module (RFEM)  
In IMDN and RFDN, a single-layer CCA module is commonly used to evaluate the channel-wise importance of distilled features and enhance them through weighted adjustment. However, this approach only optimizes in the channel dimension, failing to deeply exploit spatial detail information. As lightweight models often extract coarse-grained features, direct reliance on channel-wise adjustment tends to overlook latent detailed information, thus limiting the enhancement capability of fused features. To address these challenges, this paper proposes a residual feature enhancement module (RFEM), whose architecture is depicted in Figure 2.  
This module enhances the CCA framework by integrating a \"Batch Norm + LSKA\" structure to further exploit spatial correlations in fused features and improve the model's capability to model long-range feature dependencies. Batch normalization is employed to standardize feature distributions, mitigating instability caused by excessive activation values. To enhance feature fluidity and preserve multi-level information, the module incorporates multiple skip connections, enabling seamless transmission of features from each submodule to subsequent network layers. Additionally, by inserting convolutional layers before and after skip connections, the module allows flexible adjustment of feature channels, enhancing its applicability and efficiency. This design fully integrates spatial enhancement with feature detail extraction, providing robust support for improving overall network performance.  
Assuming that the input characteristic of RFEM is \(HS\) and the output characteristic is \(HS\), the characteristic optimization module of the \(i\)-th RLSKFDM can be depicted as  
\[F_{\text{residual1}}=CCA\left(\text{Conv}\big{(}F_{i,{RFEM}\dots in}\big{)} \right)+F_{\text{Conv}}, \tag{13}\]  
\[F_{\text{residual2}}=\text{LSKA}(BN(F_{\text{residual1}}))+F_{\text{residual1}}, \tag{14}\]  
\[F_{i,{RFEM}}=\text{Conv}(F_{residual2})+F_{i,{REFM}\dots in}, \tag{15}\]  
where \(F_{\text{residual1}}\) and \(F_{\text{residual2}}\) represent the features after two internal skip connections, and \(CCA\), \(LSKA\), and \(BN\) represent CCA, LSKA, and Batch Norm layers, respectively.  
To effectively capture long-range dependencies in remote sensing images while maintaining a lightweight structure, the LSKA module employs a decomposition strategy of large-kernel convolutions into consecutive \(K\times 1\) and \(1\times K\) depthwise convolutions. Based on empirical practices in similar large-kernel designs, we set the kernel size \(K=21\) and the dilation rate \(d=3\) to expand the receptive field without significantly increasing the parameter count. This design choice follows the general principle used in lightweight super-resolution networks like RFDN, which balances performance and efficiency.
## 4 Results and Discussion
### Parametric Analysis
#### 4.3.4 Effect of Solar Tank Capacity  
This section examines the influence of the solar tank capacity on the solar fraction. The literature recommends values of storage solar tank capacity from 20 to 100 L/m\({}^{2}\) of collector area for installations where the time delay between collection and consumption does not exceed 24 h. The solar fraction is not significantly affected by the change in storage tank capacity, as shown in Figure 12. It is clear that increasing the solar tank capacity has a slight effect on the solar fraction. A change in solar accumulator capacity from 10 to 55 L/m\({}^{2}\) of the collector area obtains an increase in solar coverage of only 60.6% to 61.1%, respectively, with this difference (0.5%) observed--that the effect of the solar tank size on solar coverage is not significantly high. The optimum capacity of the solar tank, 30 L/m\({}^{2}\), gives solar coverage of 61.6%. Figure 12 depicts that the oversized solar tank will cause a decrease in solar fraction due to increases in thermal losses. The result in Figure 12 is in alignment with that of [PERSON] [40; 41]. Therefore, it is not surprising that the optimal accumulator capacity is at the lower values of the recommended range.  
Figure 11: Solar coverage as a function of the number of collectors.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 3 Proposed Imaging Method  
In this section, an efficient imaging method is proposed. The secondary range compression (SRC) and RCM correction (RCMC) are accomplished efficiently in the 2-D frequency domain via phase multiplications. In addition, the azimuth compression is implemented in the range Doppler domain. The main steps of this imaging method are introduced below. Note that the signal of the reference channel (i.e., \(n=1\)) is taken as an example to describe the proposed method in this section.
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 6 Conclusions  
We have introduced a novel approach for designing a constant-beamwidth beamformer with null-direction constraints, utilizing uniform rectangular arrays and a generalized convolutional Kronecker product beamforming technique. By synthesizing beamformers for virtual arrays and applying our proposed technique to a full array, we successfully achieved the desired features in terms of beamwidth and null placement. Moreover, by using virtual arrays and beamformers, we demonstrated the ability to enhance the signal quality in terms of white noise gain and the directivity factor without compromising the beamwidth. The experimental results using simulated sensor arrays demonstrate that the performance of our method surpassed the performance obtained using only known techniques based on the Kronecker product for beamforming synthesis when assessing beamwidth, white noise gain, and directivity.  
Figure 2: (**a**) FNBW, (**b**) WNG, and (**c**) DF for the beamformers designed with the parameters in Table 1.  
**Author Contributions:** Conceptualization, I.C. and V.P.C.; Methodology, V.P.C.; Software, V.P.C.; Writing--original draft: V.P.C.; Writing--review and editing, I.C. and V.P.C.; Supervision, V.P.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Pazy Research Foundation and the Israel Science Foundation (grant no. 1449/23).  
**Data Availability Statement:** The source code for the simulations developed herein is available at [[https://github.com/VCuratrelli/py-cb-lcmv-rect](https://github.com/VCuratrelli/py-cb-lcmv-rect)]([https://github.com/VCuratrelli/py-cb-lcmv-rect](https://github.com/VCuratrelli/py-cb-lcmv-rect)) (accessed on 27 July 2023).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CB & Constant beamwidth \\ CKP & Convolutional KP \\ DF & Directivity factor \\ DS & Delay and sum \\ DSDI & Desired signal distortion index \\ FNBW & First-null beamwidth \\ KP & Kronecker product \\ LCKP & Linear CKP \\ LCMV & Linearly constrained minimum variance \\ MVDR & Minimum variance distortionless response \\ RCKP & Rectangular CKP \\ SD & Superdirective \\ ULA & Uniform linear array \\ URA & Uniform rectangular array \\ WNG & White noise gain \\ \end{tabular}
## 5 Conclusions  
Techniques based on the time difference of arrival (TOD) between two or more AE sensors with the cross-correlation algorithm (CCA) have been widely applied to locate the leak source using the continuous AE waveform data. In practical situations, the AE signal generated by a leak has an aperiodic waveform structure, and the exact location of the leak source cannot be determined by CCA-based methods. A mathematical model applicable to AE due to a pinhole leakage in a cylindrical vessel was summarized, in which the concentrated force-incorporated potentials responsible for the fluctuating Reynolds stress were introduced into the Navier-Lame equation. In this study, extensive experiments were carried out under different operating conditions to investigate the characteristics of the AE signals due to leakage in a gas cylinder. By validating the angular and the axial dependencies of the AE amplitudes, the proposed mathematical model was modified into two semi-empirical equations responsible for the angular and the axial properties of the AE amplitudes, making them well suited for practical applications. Application of the two equations to the data collected from two sets of the cruciform array of 12 sensors mounted on the arbitrary surface of the cylinder demonstrated that the proposed equations were able to accurately localize the leak source in the cylinder within a 1% error. The research highlights include the following: (1) the predetermination of the Green's function parameter \(\kappa_{\eta}\) through external stimuli to enhance the universality of the model; (2) the analysis of the angular/axial attenuation law of AE signals by establishing normalized simulation equations; and (3) the three-dimensional localization of the leak accurately achieved using a cruciform sensor array. The model provides a new approach to the safety monitoring of industrial storage tanks as well as gas transmission pipelines installed in the interstitial spaces of structures, especially for early warning of hazardous gas leaks, which has important practical implications.  
Conceptualization, data curation, methodology, investigation and writing--original draft, J.-G.K.; software, validation and formal analysis, K.B.K.; resources, data curation and funding acquisition, K.H.K.; writing--review and editing and visualization, K.B.K., K.H.K. and B.K.K.; supervision and project administration, B.K.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Defense Acquisition Program Administration (DAPA) of the Republic of Korea and the Korea Research Institute for Defense Technology Planning and Advancement (KRIT) (NO. R230306, November, 2024).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
All authors were employed by the company Integrity Diagnostics Korea (IDK). All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 7 Discussion  
The utilization of the 2D electrical resistivity imaging method in this study has proven highly effective in delineating subsurface features and gaining insights into groundwater occurrence within complex hydrogeological conditions. Employing forward and inverse pole-dipole configurations, with IPINV 3.2 inversion software, allowed for a comprehensive investigation of groundwater conditions across the study area. The method's success was evident as it revealed multiple faults and structural failure zones along six surveyed lines, offering valuable insights into the subsurface environment. This geophysical survey at the Nanshan Mineral Water Factory in Huarong County yielded crucial data regarding the geological composition and optimal water exploration techniques. Notably, the identification of fault zones, often associated with rock fragmentation and fissure development, emerged as a key outcome, with two structural damage areas identified at fault intersections, significantly enhancing the water supply pathways. Furthermore, the identification of a steep resistivity contact surface between argillaceous slate and sandy slate, through electrical sounding, presented an opportunity for targeted water collection and borehole placement. This information can guide the selection of verification borehole locations and aid in assessing aquifer suitability based on resistivity values. Five verification boreholes (ZK01, ZK02, ZK03, ZK04, and ZK05) were constructed in Nanshan Township, Huarong County, following the initial survey, with preliminary pumping tests revealing variable water outputs. ZK01 displayed the highest output at approximately 460 tons per day, followed by ZK03 (410 tons/day), ZK02 (170 tons/day), and ZK05 (100 tons/day). These findings confirmed both the viability of the method and the variability in groundwater availability across the study area. During the advanced verification phase, boreholes ZK05, ZK01, and ZK02 were strategically positioned in proximity to specific measuring points (No. 120, 160, and 220) (Figure 8). Results indicated that argillaceous slate with a significant clay mineral content primarily occurred at a depth of 80 m, transitioning to a greater sandy
# A Deadbeat Current and Flux Vector Control for IPMSM Drive with High Dynamic Performance
## 2 IPMSM Fundamental Model
### Stator Flux Linkage and Current Reference Calculations  
To gain high efficiency in IPMSM drive operation, the MTPA algorithm is usually considered. Concerning MTPA implemented in the conventional DTFCs or DFVCs, the calculations of the reference/command values of the stator flux linkage magnitude and load angle from the torque reference are a complicated computation [30]. Hence, the look-up-table solution is usually used instead. Moreover, it is difficult to limit the output current directly by these methods. Conversely, it would be more straightforward to conduct the MTPA computation using current reference rather than torque one.  
Therefore, in the proposed method, the current reference is chosen as input for the control loop. The current magnitude reference, \(\mathbf{i}_{\text{s}}^{*}(k)\), is determined by a PI controller for the speed loop with output limited by the rated current. The gains of the PI controller are designed based on a method presented in [31].  
According to the MTPA algorithm [32], the \(d\)-\(q\)-axis current references, \(\mathbf{i}_{\text{dq}}^{*}(k)=\left[\begin{array}{cc}\mathbf{i}_{\text{d}}^ {*}(k)&\mathbf{i}_{\text{q}}^{*}(k)\end{array}\right]^{\mathsf{T}}\), are computed as given by:  
\[\left\{\begin{array}{c}\mathbf{i}_{\text{d}}^{*}(k)=-M-\sqrt{M^{2}+\frac{ \mathbf{i}_{\text{s}}^{*}(k)}{2}}\\ \mathbf{i}_{\text{d}}^{*}(k)=\sqrt{\mathbf{i}_{\text{s}}^{*2}(k)-\mathbf{i}_{ \text{d}}^{*2}(k)}\end{array}\right. \tag{26}\]  
with  
\[M=\frac{\mathbf{\Psi}_{\text{m}}}{4\big{(}L_{\text{d}}-L_{\text{q}}\big{)}} \tag{27}\]  
Then, the magnitudes of stator flux linkage vector reference, \(\mathbf{\Psi}_{\text{s}}^{*}(k)\), and load angle reference, \(\delta^{*}(k)\), are calculated by (5), (6), and (7) using \(\mathbf{i}_{\text{d}}^{*}(k)\) and \(\mathbf{i}_{\text{q}}^{*}(k)\), respectively, as rewritten to be:  
\[\mathbf{\Psi}_{\text{s}}^{*}(k)=\sqrt{\left(\mathbf{\Psi}_{\text{m}}+L_{\text {d}}\mathbf{i}_{\text{d}}^{*}(k)\right)^{2}+\left(L_{\text{d}}\mathbf{i}_{ \text{q}}^{*}(k)\right)^{2}} \tag{28}\]  
\[\delta^{*}(k)=\text{tan}^{-1}\bigg{(}\frac{L_{\text{q}}\mathbf{i}_{\text{d}}^{* }(k)}{\mathbf{\Psi}_{\text{m}}+L_{\text{d}}\mathbf{i}_{\text{d}}^{*}(k)}\bigg{)} \tag{29}\]
## 2 Background  
ACTOR extends the standard RPL to optimize the TP using a specific reinforcement learning algorithm named UCB. So, in this section, we first review the standard RPL and its core mechanisms. Next, we present the reasoning behind choosing the UCB algorithm among all reinforcement learning schemes.  
Figure 1: ACTOR confines the channel contention among nodes in a multi-cluster topology. (**a**) The clusters interfere with each other when using the maximum transmission power. (**b**) The clusters have minimal interference with each other when using ACTOR.
## 2 Materials and Methods
### Generalized Singular Approximation Method (GSA)
In this case, we have an artificial medium where the pore space is connected and ellipsoidal particles of mineral material are enveloped by fluid with no direct contact between them. In such a case, the deformation of the fluid inclusion will make the largest contribution to the overall deformation and \(\epsilon^{\mathrm{C}}\)= \(\epsilon^{\mathrm{fl}}\). Furthermore, this situation is a representation of an unconsolidated medium, which Biot-Willis parameter \(\alpha\approx 1\)[42]. In the opposite case when \(f=0\), we get the upper Hashin-Shtrikman bound, all voids become isolated and the matrix is a connected space. Therefore, the deformation of the matrix will contribute the most to the overall deformation and \(\epsilon^{\mathrm{C}}\)= \(\epsilon^{\mathrm{M}}\). Note that all real cases met in practice correspond to parameter \(f\) between 0 and 1. A sensitivity study of effective stiffness tensor to parameter \(f\) exhibits a non-linear behavior. The elastic moduli become to decay faster as \(f\) approaches 1. A relation of parameter \(f\) with permeability is also analyzed in [43] where the authors demonstrated that this parameter is proportional to \(\ln(k/\phi)\), where \(k\) is the permeability and \(\phi\) is the porosity. In our models described in the previous section, the particles of mineral matrix, pores and cracks are considered the inclusions. For Type 1 model, \(N=3\) and the volume concentration of mineral matrix is \(v_{1}=1-(v_{2}+v_{3})\) where \(v_{2}=\phi_{\mathrm{pore}}\) and \(v_{3}=\phi_{\mathrm{crack}}\) are the volume concentrations of pores and crack, respectively. For Type 2 model, \(N=2\) and \(v_{1}=1-v_{2}\) where \(v_{2}=\phi_{\mathrm{pore}}\). The pieces of mineral matrix are considered to be spherical. We can rewrite Equation (1) in the form:  
\[ \begin{split}\mathbf{C}^{*}&=\left(\sum_{\mathrm{i }}v_{\mathrm{i}}\mathbf{C}_{\mathrm{i}}\int\mathrm{P}_{\mathrm{i}}(\theta, \varphi,\psi)[\mathrm{I}-\mathbf{g}_{i}(\mathbf{C}_{i}-\mathbf{C}^{\mathrm{c} })]^{-1}\mathrm{sin}\theta\mathrm{d}\theta\mathrm{d}\varphi\mathrm{d}\psi \right)\times\\ &\left(\sum_{\mathrm{i}}v_{\mathrm{i}}\int\mathrm{P}_{\mathrm{i}}( \theta,\varphi,\psi)[\mathrm{I}-\mathbf{g}_{i}(\mathbf{C}_{i}-\mathbf{C}^{ \mathrm{c}})]^{-1}\mathrm{sin}\theta\mathrm{d}\theta\mathrm{d}\varphi\mathrm{d} \psi\right)^{-1}\end{split}  \tag{5}\]  
where \(\mathbf{g}_{i}\) is the Green function tensor of the inclusions of type \(i\) defined by the Equation (2). The tensor \(\mathbf{C}_{i}\) is the stiffness tensor of \(i\)-th type of inclusions. The function \(P_{i}\) is the probability density function of distribution of the inclusions over their orientations; \(\theta,\ \varphi,\ \psi\) are the Eulerian angles describing the inclusion orientation in space. The tensors \(\mathbf{I}\) and \(\mathbf{C}^{\mathrm{c}}\) have the same meaning as in Equations (1) and (2).
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 2 Materials and Methods
### Field Experiment and Data Measurement
#### 2.2.2 Field Experiment  
An experiment was conducted to measure both the sensor data for the prediction model development and the axle torque for model verification, as shown in Figure 3. The location of the experimental field site is Geumam-ri, Dangjin-si 674-10 (36\({}^{\circ}\)55\({}^{\prime}\)48.1\({}^{\prime\prime}\) N, 126\({}^{\circ}\)38\({}^{\prime}\)00.3\({}^{\prime\prime}\) E). In this study, an eight-row moldboard plow, which is typically used in Korea, was selected, as shown in Table 1[8]. Table 2 shows the operating conditions of the tractor and soil environment of the field experiment site. The driving gear stages and tillage depth were set to M3 Low (7.09 km/h) and 15-20 cm, respectively, during plow tillage [6]. The soil texture, cone index, and soil moisture content of the field experiment site were referenced as representative variables of the soil environmental conditions. Soil samples were collected, taking into account the plow tillage depth (0-20 cm) at ten uniform locations on the field site. The soil texture was analyzed as silt loam (sand: 16%, silt: 62%, and clay: 22%), according to analytical methods, based on the particle distribution of sand, silt, and clay, as defined by the United States Department of Agriculture (USDA). The cone index was measured using a soil penetrometer (SC 900, Spectrum Technologies, Aurora, IL, USA), and the measurement followed the method suggested in the ASABE standard [35; 36]. The cone index value for each depth was expressed as the average cone index from 0 to 150 mm. The soil moisture content was measured using a soil moisture sensor (TDR 350, Spectrum Technologies, Aurora, IL, USA). The soil penetration resistance  
Figure 2: System installed on the tractor to collect field data for each major part: 1. Engine torque and engine speed, 2. Axle rotational speed, 3. Axle torque, 4. Travel speed, 5. Tillage depth, and 6. Data collection.  
and soil moisture were 1367 kPa and 34.17%, respectively, which are the average values measured at 100 uniform points on the field experiment site.
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 3 Performance Analysis
### Optimal Regularization Parameter  
In this section, we derive the optimal regularization parameter, \(\xi\), for the RZF precoder and study the impact of low-resolution ADCs/DACs. To derive the optimal regularization parameter, we rewrite the SINR of the private message in (23) as  
\[\mathsf{SINR}_{k,p}=\frac{\rho f(\theta,\xi)\Big{[}1+\frac{\xi}{ \theta}(1+f(\theta,\xi))^{2}\Big{]}}{\rho+(1+f(\theta,\xi))^{2}}, \tag{28}\]  
where \(\rho\) is expressed as  
\[\rho=\frac{\alpha_{D}P_{d}\lambda_{k}}{\alpha_{D}P_{d}e_{k}+(1- \alpha_{D})P\beta_{k}+1}. \tag{29}\]  
By setting the derivative of (28) with respect to \(\xi\) to zero \(\Big{(}\frac{\beta\mathsf{SINR}_{k,p}}{\delta\xi}=0\Big{)}\) and with some algebraic manipulations, the optimal regularization parameter is derived as [34]  
\[\xi_{\rm opt}=\frac{\theta}{\rho}. \tag{30}\]  
**Remark 3**.: _From the result in (30), the optimal regularization parameter is a function of the user loading ratio, and the resolutions of ADCs and DACs. As the QN due to the low-resolution DACs increases, \(\rho\) decreases. Hence, there is a corresponding increase in \(\xi_{\rm opt}\) to mitigate the effect of QN introduced by the low-resolution ADCs/DACs._