esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Step Synthesis of Cross-Linked Esterified Starch and Its Properties
## 1 Introduction  
Waxy maize, which has a strong resistance to aging and outstanding pasting property due to the high content of amylopectin (over 95%), has been widely used in the food industry. However, the low thermal stability and acid tolerance of the unprocessed waxy maize starch restricted its commercial application in larger scales [1]. Therefore, efforts have been made to modify wavy maize starch for the improvement of these properties using physical and biological methods [2, 3].  
With the development of the food industry and the need for the special foods, modified starches with enhanced performances (e.g., freeze-thaw stability, shearing, and acid tolerance) are becoming more and more popular as food additives. For example, thermal stability is essential in sterilization during food packaging, and tolerance to shear force is prerequisite for mechanical agitation [4]. Moreover, for food preservation processes, the freeze-thaw tolerance of starch is crucial for food flavor [5, 6]. Specifically, food in acid condition requires starch tolerant to acid [7, 8]. Therefore, appropriate modification of wavy maize starch is aimed to extend its applications [9]. Up to now, several modified starches have been reported to obtain special properties, such as water-retention, emulsification, and film formation. These modifications significantly simplified food processing and increased the quality of the food [10, 11, 12].  
Cross-linked esterified starch (CES) is a kind of modified starch produced via cross-linking polymerization and acetylation. CES has exceptional properties of thermal stability, along with acid and shearing tolerance. Therefore, CES was regarded as a promising material for the preparation of hydrogels. In the food industry, CES imparts the thickening needed in food applications, and it has been used for its stability and resistance to retrogradation [12]. More importantly, it exhibits a lower gelatinization temperature and a high grade of transparency [13,14]. Currently, the production of CES from potato, tapioca, ipomoea, and maize have been reported, while its one-step production from waxy maize starch is rarely investigated [15,16]. Compared to the conventional methods for CES production, the one-step synthesis of CES is a faster process which requires fewer reaction steps, which indicates that it is an economical method for industrial production. Therefore, the aim of this study was to produce CES from waxy maize starch though a one-step reaction. Moreover, assessments of CES's properties at various aspects were conducted. This study provided an important foundation for the industrial production of CES.
## 5 Conclusions  
The conclusion of this paper relates to the pure argon mixed welding of AH36 steel with thickness of 5 mm, as follows:  
1. The results show that the weld forming effect is better when the laser power is 7.5 kW in the range of sensitive laser power. When the laser power is 7 kW, the weld bottom is poorly formed; when the laser power is 8 kW, the bottom of the welding seam collapses.
2. Within the sensitive laser power range, when the laser power increases from 7 kW to 7.5 kW, the width of the molten pool increases, and the temperature rises. When the laser power increases from 7.5 kW to 8 kW, the width of the arc pool decreases, the width of the laser zone increases, and the temperature decreases.
3. After the initial formation of the keyhole, its depth gradually entered the stage of linear growth, oscillation growth, and oscillation balance. Increasing the laser power can effectively shorten the time of the two growth stages and allow the keyhole to enter the balance stage earlier.
4. In the longitudinal section, the melt pool fluid directly below the arc flowed downward and formed reflux in the neck zone. As the laser power increased, the backflow gradually decreased until it disappeared. Part of the molten metal flowed to the melt pool tail and another part flowed to the keyhole.
5. During the oscillation balance state of the keyhole, the molten metal at the bottom of the keyhole flowed to the molten pool root in the reverse direction of welding, and it can also promote weld penetration.
6. The heat-affected zone is dominated by martensite, where the average microhardness is the largest; it is 421 HV. The composition of the weld zone is mainly bainite and ferrite, and its average microhardness is 372 HV. The average microhardness of base metal is the lowest; it is 231 HV.  
Conceptualization, H.J., X.Y. and P.Z.; methodology, H.J. and X.Y.; validation, Z.Y., X.Y. and P.Z.; formal analysis, H.J.; investigation, H.J., D.W., X.Q. and X.H.; writing--original draft preparation, H.J.; writing--review and editing, X.Y.; project administration, X.Y. and K.F.; funding acquisition, P.Z., Z.Y. and K.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant number 52075317, grant number 51905333), Shanghai Science and Technology Committee Innovation Grant (grant number 19511106402), Shanghai Sailing Program (grant number 19 YF1418100), State Key Laboratory of Metal Material for Marine Equipment and Application (grant number SKLMEA-K201906), a Karamay Science and Technology Major Project (grant number 2018 ZD002B), and an Aid for Xinjiang Science and Technology Project (grant number 2019E0235).  
Not applicable.  
The authors declare no conflict of interest.
## 9 Samples of the Results
### Enhancing Sample Efficiency with Conservative Model-Based Actor-Critic  
[PERSON] et al. [86] introduced the Conservative Model-Based Actor-Critic (CMBAC), a reinforcement learning framework designed to address the challenge of model inaccuracies in complex environments. CMBAC employs an ensemble of probabilistic neural networks to approximate the posterior distribution of Q-values, using the average of the bottom-k estimates to optimize policies conservatively. This approach ensures that the agent avoids unreliable promising actions that arise from overestimation biases. The experiments were conducted on a range of MuJoCo environments, including Walker2d, HalfCheetah, and Humanoid, to evaluate sample efficiency and robustness. Metrics such as cumulative reward and learning curves were used to compare CMBAC with state-of-the-art algorithms like MBPO, MOPO, and SAC.  
A notable result is presented in Figure 19, which illustrates the average returns of CMBAC and baseline methods across six benchmark tasks. The figure demonstrates that CMBAC consistently outperforms competing algorithms in sample efficiency, achieving comparable or superior performance with fewer environment interactions. The shaded regions in the figure represent the standard deviation across multiple random seeds, highlighting the robustness of CMBAC even in noisy scenarios.  
Figure 18: Smoothed learning curves for AsDDPG and DDPG under different network hyperparameters, in (**a**) an empty world and (**b**) a simple world. The figure illustrates AsDDPG’s superior performance in terms of training efficiency and stability across all configurations (Adapted from [27]).
# Compact Sub 6 GHz Dual Band Twelve-Element MIMO Antenna for 5G Metal-Rimmed Smartphone Applications
## 6 Analysis of the MIMO Antenna System and Advantages  
To further investigate the isolation performance of the MIMO twelve-antenna unit, the surface current distribution between the two sets of six-antenna units and their ground planes is analyzed. The two sets of six-antenna units are configured in a diagonal mirror-image configuration. For clarity, only the surface current distribution of the first set of six-antenna when excited is presented. Figure 19 presents the surface current distribution of the two groups of six-antenna units and their ground planes at 3450 MHz and 4900 MHz. The results reveal that regardless of which antenna is excited, no noticeable ground current flows to other antennas are observed. The surface current distribution is smaller for antennas further away from the excited antenna. Therefore, this twelve-antenna unit has favorable isolation performance.  
Figure 19: Current distribution of the twelve-antenna system and ground plane at **(a)** 3450 MHz and (b) 4900 MHz.  
Figure 18: Calculated ergodic channel capacities of 5G antenna array prototype in 12 \(\times\) 12 MIMO system.  
To emphasize the benefits of our suggested MIMO antennas, we have incorporated a comparative analysis of MIMO antennas from the existing literature. Table 1 presents this comparison, encompassing various aspects such as dimensions, spacing between two-antenna units, isolation, and radiation performance for 5G sub-6 GHz band. Upon reviewing the table, it becomes apparent that our proposed MIMO antennas exhibit a more compact design in comparison to those in the existing literature, particularly with regard to multi-band frequencies. The distance between elements of our proposed antenna system is significantly lower (only 3 mm) compared to most existing state-of-the-art solutions. The presence of short-circuit branches can generate ground plane currents, effectively reducing the current flow to adjacent borehole antennas. This design achieves greater than 10 dB isolation performance (no additional antenna isolation components required) and sufficient radiation efficiency and ECC performance.
## 2 Materials and Methods
### Barrier Integrity Assay  
The barrier integrity assay was performed to measure the barrier function and lumen formation of endothelial vessels grown in the microfluidic device. Perfusion channels were washed once for 5 min with 25 \(\upmu\)L EGM-2 complete media to confirm the appropriate flow profile during successive barrier integrity assays [23]. Afterward, all media were aspirated from the chips. A fluorescein isothiocyanate FITC-dextran (MW: 40 kDa; Chondrex) solution was made by dissolving 0.5 mg/mL solution in complete EGM-2 media. Then, 20 \(\upmu\)L of EGM-2 media without fluorescent dye was added to the gel inlet (basal side of the chip). A total of 40 \(\upmu\)L and 30 \(\upmu\)L of FITC solution was added to the media inlet and outlet which contained endothelial microvessels, and the image acquisition was started. Image acquisition was performed on an EVOS M5000 multichannel fluorescence microscope coupled with a stage incubator using the time-lapse function (10-min, 20-s intervals). The apparent permeability was quantified using ImageJ (Version 1.54i, March 2024) [27]. Data were plotted using GraphPad Prism 8.0 (GraphPad Software, San Diego, CA, USA). We used the following formula to get the apparent permeability (\(\mathbf{P}_{app}\)) value [28]:  
\[\mathbf{P}_{app}=\frac{\Delta\mathbf{C}_{receiver}\times\mathbf{V}_{receiver}}{\Delta t \times\mathbf{A}_{barrier}\times\mathbf{C}_{donor}}\Big{(}\frac{\text{cm}}{\text{s}} \Big{)}\]  
The fluorescence intensity measured in the ECM channel between \(\mathbf{t}=0\) and \(\mathbf{t}=10\) min is represented by \(\Delta\mathbf{C}_{receiver}\). The volume of the measured region in the ECM channel (h \(\times\) w \(\times\) l) is represented by \(\mathbf{V}_{receiver}\). \(\Delta t\) is the duration between start and finish (10 min). The surface of the ECM interface with the medium channel is represented by \(\mathbf{A}_{barrier}\), and the fluorescence intensity measured in the top perfusion channel is represented by \(\mathbf{C}_{donor}\). This formula assumes that the curve is linear, often maintained when the receiver's concentration is less than 10% of the donor's [28].
# Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
## 3 The Proposed Model  
In the real world, most of the motions are continuous and sequential, especially for the videos captured by a monocular camera. However, current methods about NRSfM do not utilize the sequential or ordered information embedded in the non-rigid motion data. As for this problem, the OSC [16] and the QOSC [17] give a proper way to model the sequential property. Motivated by OSC and QOSC, we introduce a penalty term to penalize the similarity between consecutive columns of the low rank representation \(Z\) from reconstructed 3D motion data \(X\), thus we obtain the following NRSfM model:  
\[\begin{split}\min_{X,Z,E}&\frac{1}{2}\|X-XZ\|_{F}^{2 }+\frac{\lambda}{2}\|Z\|_{*}+\lambda_{1}\|ZS\|_{2,1}+\lambda_{2}\|X\|_{*}+ \lambda_{3}\|E\|_{1}\\ &\text{s.t. }W=RX^{\#}+E,\end{split} \tag{4}\]  
where \(S\) is a triangular matrix only consisting of \(-1\), \(1\), and \(0\) values, with the diagonal elements being \(-1\) and the second being \(1\),  
\[\mathbf{S}=\left(\begin{array}{ccccc}-1&0&0&\cdots&0\\ 1&-1&0&\cdots&0\\ 0&1&-1&\cdots&0\\ \vdots&\vdots&\ddots&\ddots&\vdots\\ 0&0&0&\ddots&-1\\ 0&0&0&\cdots&1\end{array}\right)_{n\times(n-1)},\]  
which can make consecutive columns of \(Z\) alike. Thus \(\|ZS\|_{2,1}\) seeks to preserve the sequential property of \(Z\) for penalty, which is determined by the inherent sequential property of the the motion data \(X\). Additionally, the norm \(\|\cdot\|_{2,1}\) is used to maintain the sparsity. It is also denoted that the rigid constraint of \(X=XZ\), the low rank self-representation, is regarded as a reconstruction error in the objective function.  
To further obtain good clustering performance, we tried to improve the current methods by modeling the structure of the affinity matrix of the LRR representation \(Z\). In subspace clustering, the ideal result of representation coefficients of inter-subspace items are all zeros and only items from the same subspace are non-zeros. Thus its certain permutation and affinity matrix drawn from different subspaces are block-diagonal. Therefore the clustering performance is improved by utilizing the block-diagonal prior. One reprehensive method is the subspace segmentation via quadratic programming (SSQP) [18], which introduces a quadratic term to force the block-diagonal feature for clustering, and it has been proved that SSQP satisfies the block-diagonal feature on the assumption of orthogonal linear subspaces [18]. Following this way, we revise the model in (4) by replacing the low rank constraint of the \(Z\) with a a quadratic term to obtain an affinity matrix with the block-diagonal feature,  
\[\begin{split}\min_{X,Z,E}&\frac{1}{2}\|X-XZ\|_{F}^{2 }+\frac{\lambda}{2}\|Z^{T}Z\|_{1}+\lambda_{1}\|ZS\|_{2,1}+\lambda_{2}\|X\|_{*} +\lambda_{3}\|E\|_{1}\\ &\text{s.t. }W=QX^{\#}+E,Z\geq 0,diag(Z)=0,\end{split} \tag{5}\]  
where \(Z\geq 0,diag(Z)=0\) is set to get a feasible solution for \(Z\) as in [18].
# Bandwidth-Enhanced Circularly Polarized Crescent-Shaped Slot Antenna via Circular-Patch Loading
## 2 Antenna Design and Parametric Studies
### Parametric Studies
This indicates that the proposed antenna is able to realize left-handed circular polarization (LHCP) in the \(+z\)-direction, whereas right-handed circular polarization (RHCP) is generated in the \(-z\)-direction. The same outcome was observed at 3.5 GHz, as shown in Figure 5b.
# A Machine Learning Method for Modeling Wind Farm Fatigue Load
## 4 Case Study
### GP Prediction Results
#### 4.3.2 Three-Turbine Case
The damage equivalent load of turbine 1 under zero yaw offset case equals 1. The same axis scale is used in the four sub-pictures. Figure 12: Blade root flapwise moment samples of normalized damage equivalent load under 9 m/s, wind speed and 5% turbulence intensity. The damage equivalent load of turbine 1 under zero yaw offset case equals 1. The same axis scale is used in the four sub-pictures. Figure 13: Max–min range of normalized fatigue damage of turbines. Figures 16 and 17 show the errors of LUT and GP methods in three turbines. In both pictures, the prediction error increases from WT1 to WT3. Specifically, \(RMSE\) increased from 0 to 21.12% in Figures 16 and rose from 0 to 43.96% in Figures 17. A similar trend can be seen in \(MAE\) error. However, there are differences between the two pictures. For example, WT1 and WT2 have minor prediction errors lower than 1.2% on \(RMSE\) and 0.2% on \(MAE\) in Figures 16. In contrast, Figure 17 shows a more significant error of up to 22.46% on \(RMSE\) in WT2. The reason is that WT1 has weak nonlinear characteristics on BREM-DEL and BRFM-DEL, and WT2 only has weak nonlinear characteristics on BREM-DEL. LUT is more suitable for mapping the weak nonlinear characteristics between the fatigue load and yaw angles. For example, Figures 16 and 17 show that LUT has a zero error in WT1, compared to a slight error in GP, with nearly 1% \(RMSE\) and 0.1 \(MAE\). Compared to LUT, GP is suitable for the nonlinear dataset. As shown in Figure 16, GP has a 5.18% \(RMSE\) and 0.99% \(MAE\) error, compared to 21.12% and 1.63% in LUT. Similarly, GP shows lower errors in Figure 17. The \(RMSE\) in GP is 6.99% (WT2) and 6.48% (WT3), compared to 22.46% (WT2) and 43.96%(WT3) in LUT. The same trend can be seen in \(MAE\). In summary, the prediction accuracy of GP improved by 13.99% (\(RMSE\)) and 0.54% (\(MAE\)) at the blade root edgewise moment and 51.87% (\(RMSE\)) and 1.78% (\(MAE\)) at the blade root flapwise moment. Figure 15: Prediction of normalized damage equivalent load at blade root flapwise moment under 9 m/s wind speed and 5% turbulence intensity.
## 6 Quality Analysis of the Reconstructed Image
### Objective Quality Analysis
_VIF_ (Visual Information Fidelity) [54] quantifies the information divided between the reference image and the distorted image relative to the information contained in the reference image. It uses _NSS_ (Natural Scene Statistic) together with the distortion model of the image and the model of the human visual system, \(HVS\). The result can range from 0 (no similarity) to 1 (identical images).
## 2 Related Work
This technology is based on the \"Internet of Things (IoT)\" paradigm via the physical layer, which assists visually impaired people in perceiving their surroundings by implementing sensors in their environment. The limited communication range means that this RFID technology cannot be applied to the spatial extent of the surrounding landscape. An RFID-based indoor navigation solution for the elderly and visually impaired [58] assists people with disabilities by enabling them to navigate independently in enclosed spaces. The aim of developing this concept was to address the challenges of indoor navigation, considering the accuracy and dynamics of different environments. The system consists of two localization and navigation components, a server and a wearable module containing a built-in microcontroller, ultrasonic sensor, RFID, Wi-Fi, and voice control modules. The system takes 0.6 s to find an obstacle. A different RFID-based map-reading system [59] provides solutions for visually impaired people to navigate in public places using a grid of RFID tags, Bluetooth, a cane RFID scanner, and a smart personal digital assistant. The map-reading system incurs relatively high costs due to its hardware components, and its reliability is compromised in high-traffic areas. Wireless network-based solutions for indoor navigation and positioning encompass diverse approaches, including communication networks, Bluetooth, UWB sensors, Wi-Fi networks, etc. [60; 61; 62]. [PERSON] and [PERSON] [63] proposed a 3D navigation system utilizing Bluetooth technology. Another approach, outlined in [64], combines Bluetooth beacons with Google Tango. UWB and Bluetooth Low Energy share many common features, such as the ability to track assets. However, UWB is known for its greater accuracy than BLE. This factor is due to the difference in the method used in individual technologies; while UWB determines the location of an object using ToF, BLE locates devices using RSSI [65], which estimates the location based on the strength of the signal emitted by the device. However, in versions greater than or equal to BLE 5.1, this level of accuracy can also be achieved using algorithms such as angle of arrival (AoA) [66; 67; 68]. The above-described technologies are particularly beneficial for blind and visually impaired people, helping them to navigate their environments with greater confidence and independence. Technology is evolving and enhancing how BVI people communicate with the world more confidently. There are no absolute advantages or disadvantages to technology. The key is to look at usage scenarios and specific support needs. Whether it is UWB, Bluetooth AoA, geomagnetism, Wi-Fi, or even GNSS, they only provide a solution to the set requirements. Which solution to choose cannot be determined by simply looking at certain technical indicators; therefore, more detailed analyses regarding measurement accuracy in different environments and different scenarios are required.
## 6 Challenges and Future Research Directions  
### Virtual Inertia as an Ancillary Service  
Many research works have proposed the possibility of using virtual inertia as an ancillary service to improve frequency stability of large power grids. In [93], a control scheme to integrate DC microgrids as virtual inertia emulating units in the traditional AC grid has been presented. With the control scheme, the resources within the DC microgrid can be dispatched as an ancillary service for inertial response. Another major source of under-utilized energy lies in modern data centers. Data centers need a high degree of reliability, and as a result large amounts of backup energy storage which are unused during normal operating conditions. Research work in [94; 95] have shown methods to utilize these resources using demand response techniques. This concept can be extended to use data center resources for virtual inertia implementation. Virtual inertia based interfaces, as mentioned in [93], can be integrated with data center resources for frequency regulation. A unit commitment model that combines system inertia from the conventional plants, and the virtual inertia from wind plants into system scheduling has been presented in [96], which allows for an economic analysis of the virtual inertia system.  
Modern wind farms are already obligated through various laws and regulations to provide inertial ancillary services [97; 98; 99; 100]. The uncaptured inertia in wind turbines, referred to as \"hidden inertia\", can be captured through the techniques described in previous sections. Commercial wind turbine manufacturers, like WindINERTIA [101] and ENERCON [102], already provide virtual inertia response. Moreover, leading inverter manufacturers like FREQCON, Schneider Electric, and ABB already provide out-of-the-box inertial response capabilities. Using electric vehicles (EVs) to provide ancillary services has become a popular research topic [103]. Typically the control algorithm of the bidirectional converters in EVs can be modified for virtual inertia implementation [104; 105].