esa-sceva/satcom-chunk-collection huggingface dataset:
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 2 Materials and Methods
### Super-Resolution Reconstruction Algorithm for Cell Image in a Lensless System
#### 2.3.3 Reconstruction of the Focus Plane Image Algorithm  
In a non-lens system, visible light causes a diffraction phenomenon in the process of reaching the photosensitive surface of the pixel of the CMOS image sensor through the cell sample due to the absence of focus light in the lens system. As mentioned earlier, the original diffraction image is reconstructed using coaxial holography after super-resolution reconstruction. The transfer function of the diffraction model is  
\[H_{d}(\varepsilon,\eta)=\left\{\begin{array}{cc}\exp\left[id \frac{2\pi}{\lambda}\sqrt{1-\left(\lambda\varepsilon\right)^{2}-\left( \lambda\eta\right)^{2}}\right]\\ 0,&\text{otherwise}\end{array}\right.,\hskip 28.452756 pt\varepsilon^{2}+ \eta^{2}<\frac{1}{\lambda^{2}}, \tag{22}\]  
where \(d\) is the distance from the cell sample to the surface of the sensor and \(\lambda\) is the wavelength of visible light. Based on this transfer function, the relationship between the image surface (the photosensitive surface of the graphic sensor) and the image of the object surface (the cell sample plane) can be obtained as follows:  
\[Y(\varepsilon,\eta)=F(\varepsilon,\eta)\cdot H_{d}(\varepsilon,\eta), \tag{23}\]where \(H_{d}(\varepsilon,\eta)\) is the transfer function and \(Y(\varepsilon,\eta)\) is the scalar diffraction model described in Equation (1), which represents the frequency domain image of the two-dimensional intensity and phase distribution of \(y(x,y)\) on the image surface, and \(F(\varepsilon,\eta)\) represents the frequency domain image of the two-dimensional light intensity and phase distribution\(f(x,y)\) on the object surface. The lensless system obtains the image surface image \(y(x,y)\), based on Equation (23) to obtain the object surface image;  
\[f(x,y)=F^{-1}\Big{[}Y(\varepsilon,\eta)\cdot H_{d}^{-1}(\varepsilon,\eta) \Big{]}, \tag{24}\]  
Due to the missing phase, the in-line holography technique has inherent zero-level image interference. IT can be iteratively eliminated using the zero-level image method we studied earlier [33]. Through the above algorithm, a clear high-resolution image of the cells can be reconstructed.
## 4 Performance Evaluation
### Exploring the Design of Pepita  
This section introduces the methods used to investigate three features that we considered relevant in the design of Pepita: (1) the huggable aspect related to the body shape and appearance, (2) the expressiveness to convey affective states and (3) the general impression of the robot's appearance as a character. An online questionnaire using the service provided by [56] was used as a tool to collect information, which is a conventional method for comparing different types of robots [18; 19]. The complete questionnaire can be found in Appendix A. Participants were initially contacted by social network service, and after they had agreed to participate, the link for the questionnaire was sent via email. Informed consent was obtained from the participant before starting the questionnaire. Once the participants finished answering the questions, the link was disabled to avoid double responses. The selection criteria were simple; the participants had to be adults who were familiar with technology but not involved in this or any of the projects that were introduced in the questionnaire. A total of 52 participants (age \(28.0\pm 4.1\) on average, 29 males and 23 females) took part in this study. Nationalities were diverse, separated in the following groups: 4 from North America, 17 from Central America, 16 from South America, 8 from Europe and 7 from Asia.  
Figure 4: **(Top) Pressure sensor’s arrangement: each petal-shaped sensor was made of soft conductive foam and attached around the spherical body of the robot. (Bottom) Hug detection method: when a piece of the sensor is pressed with certain applied pressure, it generates a point placed in a fixed position on the Cartesian plane. When the area of the polygon formed by the generated points exceeds a threshold, the device recognizes the action as a hug.**
## 6 Knowledge Gaps and Future Trends
* _Deep Learning Applications and Challenges_: Deep learning techniques offer significant analytical capabilities for processing large data sets and detecting complex problems within collaborative robotic systems. However, to broaden their application, challenges such as high computing complexity and lengthy training times need to be addressed. Further research should focus on developing efficient deep learning algorithms, hardware accelerators, and distributed computing techniques to overcome these challenges and unlock the full potential of deep learning in industrial cobot applications. Furthermore, the dilemma of \"black box\" AI and ML models can be addressed with Explainable AI (XAI), a subset of AI and ML that supports human trustworthiness and compliance by explaining the reasoning that draws. The inclusion of XAI in Industrial DT applications would authenticate the reasoning for operators in the field. This is particularly useful for risk reduction, ethical consideration, and real-time decision making, when collaborating with operators. * _Reduced Human Involvement in Data Pre-processing and Interpretation_: Further research is needed to explore deep learning applications in the context of cobot applications with the aim of reducing the need for human involvement in data pre-processing and interpretation. Automation of these tasks through advanced algorithms and methodologies will streamline the analysis process, accelerate decision-making, and enable real-time responses in industrial collaborative robotic systems.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 4 Conclusions and Future Work  
This paper proposed an optical and SAR image registration algorithm based on the PC framework, which is novel and insensitive to scale and rotation differences between optical and SAR image pairs. The multi-scale PC-Harris is constructed based on the log-Gabor filter and the PC maximum moment. Furthermore, we constructed a discriminative descriptor based on the PC model. Experiments verify that the proposed algorithm is compatible with large-scale and rotation differences between optical and SAR images. It effectively solved the problem that the traditional PC-based registration algorithms cannot be compatible with large-scale and rotation differences between optical and SAR images. The limitations are that it has not fully covered the full-scale and full-angle differences, and the matching accuracy needs to be further improved. In the future, we will combine the local and global PC information to improve the matching performance.  
Conceptualization, Z.X. and W.Z.; methodology, Z.X. and Z.L.; software, Z.X.; validation, L.W.; formal analysis, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Model for Pressure and Strain Induced by Photoisomerization
### Strain Induced by Photo-Orientation
Indeed, when a homeotropic sample is irradiated, perpendicularly to its front surface, mesogens at that surface reorient, and the average length per molecule increases parallel to the front surface, producing expansion of that surface and the film bends away from the light source [41]. The depletion of photo-orientation of azo-polymer films near pss was reported for an optically thin self-assembled monolayer containing the azobenzene molecule [42], and reversal of bending at long irradiation times was observed for azo-polymer cantilevers [43]. In nematic liquid crystal elastomers (LCEs), photoisomerization disrupts the orientational order of the liquid crystal, and it is well known that the extent of cis isomer influences the shape of LCEs by the change in the nematic order parameter [44]. Furthermore, LCE samples irradiated with linearly polarized UV light bend in the direction of light polarization, towards the light source, indicating an appreciable strain in the direction parallel to the UV polarization (\(\epsilon_{\parallel}>\epsilon_{\perp}\)) [34]; a finding predicted by Figure 2 for intermediate irradiation times, i.e., not in the photostationary state. Indeed, it would be interesting, as suggested by the results in Figure 2, to observe the anisotropic strain near the pss to assess the effect of photo-orientation on anisotropic strain at long irradiation times or high irradiation intensities or both. The effect of photo-orientation on actuation was studied in LC polymer films, and bi-directional bending was observed by rotating the irradiation light polarization between two orthogonal directions, and it was shown that reorientation of the azo-chromophores perpendicular to light polarization results in expansion (contraction) of the front surface of the sample and its bending away from (towards) the laser source for the parallel (perpendicular) configuration [45].
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 3 Materials and Methods
### Generate the Canonical Image
#### 3.3.4 Optimization and Acquisition of Canonical Images  
The goal of generating the canonical image is to find a global static representation that can incorporate all the core content of the video frames, while ensuring semantic consistency between frames and reconstruction quality. The training of the model is achieved by minimizing the objective function \(L_{rec}\). This objective function corresponds to the \(L_{2}\) loss between the true color and the predicted color \(\mathbf{c}\) at a given coordinate \(\mathbf{x}\). Its mathematical expression is:  
\[L_{rec}=\sum_{t=1}^{T}\sum_{x\in I_{t}}|I_{t}(x)-C(D(x,t))|^{2} \tag{8}\]  
This means that during the training process, the model endeavors to make the predicted color as close as possible to the true color, thereby improving the accuracy of the reconstructed video. Specifically, here \(I_{t}(x)\) represents the color value of the pixel \(x\) in the video frame \(t\); \(C(D(x,t))\) represents the pixel value reconstructed from the canonical content field \(C\) through the temporal deformation field \(D(x,t)\).  
To prevent model overfitting and improve the generalization ability and stability of the model, additional regularization terms are introduced.Among them, the flow-guided consistency loss \(L_{flow}\) assumes a pivotal role, and the total loss is calculated as:  
\[L=L_{rec}+\lambda_{1}*L_{flow} \tag{9}\]  
where \(\lambda_{1}\) is a hyperparameter used to balance different loss terms. The value of the regularization parameter \(\lambda_{1}\) in the loss function \(L\) is determined by whether the optical flow of the video sequence is extracted during the preprocessing stage. When the optical flow data of the video sequence is extracted in the preprocessing stage, \(\lambda_{1}=1\) is selected. This approach assigns equal importance to the reconstruction loss (\(L_{\text{rec}}\)) and the flow-guided consistency loss (\(L_{\text{flow}}\)), thereby effectively preventing overfitting and enhancing generalization ability. Such balanced weighting enables the model to optimize for both accurate reconstruction and meaningful flow field generation simultaneously. Empirical verification shows that this equal-weight strategy ensures stable training, while efficiently avoiding overfitting and maintaining the model's robust generalization capability. When the optical flow data of the video sequence is not extracted during the preprocessing stage, \(\lambda_{1}=0\) is adopted.  
After completing the optimization of the content deformation field, the process of obtaining the canonical image \(I_{c}\) is relatively simple yet crucial. By setting the deformation of all points to zero, from the perspective of the deformation field, all points are mapped into a unified canonical space. The image within this space is precisely the canonical image.
# Effect of Nylon Fiber Addition on the Performance of Recycled Aggregate Concrete
## 2 Experimental Section
### Test Methods  
The compressive and split tensile strengths of concrete cylinders with dimension of 100 mm in diameter and 200 mm in length after 7, 28, and 91 days of curing were tested according to ASTM C39 [33] and ASTM C469-17 [34], respectively. For evaluating the compressive strength, the cylinders were placed under a compression testing machine of 2000 kN capacity. The test for split tensile strength was also conducted using the same compression testing machine. The mean values of the compressive and split tensile strengths of at least three samples for each concrete mix were taken, and the standard deviation from the test results was calculated. The non-destructive test, like the ultrasonic pulse velocity (UPV), was conducted according to ASTM C597-16 [35] by using PUNDIT LAB, manufactured by PCTE, Australia. The used transducers are 50 mm in diameter, and have a maximum resonant frequency of 54 kHz. A rapid chloride ion penetrability test was used to evaluate the permeability of the concrete specimens. This test was based on the standard test method of ASTM C1202-18 [36]. After having been cured for 28 days, a concrete disc, 50 mm in thickness and 100 mm in diameter, was connected to two chambers: one was filled with 3% NaCl solution and the other with 0.3M NaOH solution to form electrodes, as shown in Figure 3. An electric charge of 60 V was applied to the electrodes for 6 h. The current flowing through the concrete disc and the temperature of the solution in the chambers were recorded with an interval of 30 min. The higher level of the total charge passed represents the higher permeability of concrete, as shown in Table 4.  
The microstructures of concrete fractions after the compressive strength tests of 28-day concrete specimens, were investigated using field emission scanning electron microscopy (FE-SEM) equipped with an EDXA Falcon energy system. The concrete samples were gold-coated after drying in a desiccator for 24 h.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Concrete**} & \multicolumn{6}{c}{**Mix Proportion (kg/m\({}^{3}\))**} & \multirow{2}{*}{**Fresh Density 1**} \\ \cline{2-2} \cline{5-8}  & **Water** & **Cement** & **Sand** & **CA** & **RA** & **NF** & **SP 3** & **(kg/m\({}^{3}\))** \\ \hline CAC1 & 170 & 340 & 745 & 1015 & - & - & 2.45 & 2345 \\ CAC2 & 170 & 340 & 745 & 1015 & - & 0.6 (0.06) 2 & 2.45 & 2350 \\ CAC3 & 170 & 340 & 745 & 1015 & - & 1.2 (0.12) & 2.45 & 2386 \\ RAC1 & 170 & 340 & 745 & - & 912 & - & 2.72 & 2278 \\ RAC2 & 170 & 340 & 745 & - & 912 & 0.6 (0.06) & 2.72 & 2290 \\ RAC3 & 170 & 340 & 745 & - & 912 & 1.2 (1.12) & 2.72 & 2312 \\ \hline \hline \end{tabular}  
* Values measured according to ASTM C 138M-17a standard [32]. 2 Fiber volume fraction (v/v %). 3 Superplasticizer.  
\end{table}
Table 3: Mix proportions of concrete.
# An Efficient Parallelization of Microscopic Traffic Simulation
## 4 Discussion
### New Applications for Microsimulations  
It appears obvious that with the shortened simulation times, microsimulation models can now be employed in a series of applications where they had been too slow in the past. In addition, the ParSim simulator can run on ordinary gaming computers; no expensive hardware is necessary. Potential applications for parallelized microsimulators like ParSim include  
* Microsimulations as the main assignment method for activity/plan generation as part of agent-based demand models where traffic assignment is in a loop and needs to be repeated many times. This application may facilitate the creation of larger mobility digital twins of entire cities.
* Training AI agents, such as reinforcement learning or deep RL models, where millions of simulation episodes are needed to converge and slow microsimulations have hindered their usage in the past [11; 36].
* Traffic optimization studies, where the system evaluates control strategies, route planning, or congestion mitigation across hundreds of randomized or adaptive scenarios.
* Short-term traffic predictions based on real-time flow measurements [37].
* Crisis response simulation, enabling the fast forecasting of network behavior under incidents or disruptions and supporting real-time decision making. SUMO has al ready been used in this field [38]; an overview of the current tools and limitations is summarized by [PERSON] et al. [39].
## Control of a Stand-Alone Variable Speed Wind Energy Supply System +  
Footnote †: footnotemark:  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 4**  
Footnote 1: Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt  
Footnote 2: Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan;  
E-Mail: [EMAIL_ADDRESS]  
Footnote 3: APEARC, Aswan University, Aswan 81542, Egypt; E-Mail: [EMAIL_ADDRESS]  
Footnote 4: Faculty of Engineering, Minia University, Minia 61111, Egypt;  
E-Mails: [PERSON]] (M.A.A.W.);  
[PERSON] (M.M.H.)  
\({}^{\dagger}\) This paper is an extended version of paper published in The IEEE International Conference on Power and Energy (PECON 2012), Kota Kinabalu, Malaysia, 2-5 December 2012.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-80-3180-2995; Fax: +18-98-895-8686.  
_Received: 28 December 2012; in revised form: 13 March 2013 / Accepted: 20 March 2013 / Published: 11 April 2013_  
This paper presents a simple control strategy for the operation of a variable speed stand-alone wind turbine with a permanent magnet synchronous generator (PMSG). The PMSG is connected to a three phase resistive load through a switch mode rectifier and a voltage source inverter. Control of the generator side converter is used to achieve maximum power extraction from the available wind power. Control of the DC-DC bidirectional buck-boost converter, which is connected between batteries bank and DC-link voltage, is used to maintain the DC-link voltage at a constant value. It is also used to make the batteries bank stores the surplus of wind energy and supplies this energy to the load during a wind power shortage. The load side voltage source inverter uses a relatively complex vector control scheme to control the output load voltage in terms of amplitude and frequency. The control strategy works under wind speed variation as well as with variable load. Extensive simulation results have been performed using MATLAB/SIMULINK.  
keywords:stand-alone variable speed wind system; permanent magnet synchronous generator; DC-link voltage; controlled output voltage
###### Abstract  
For an RF system, a high-gain antenna helps to improve the equivalent isotropic radiated power (EIRP) of the transmitter and an end-fire antenna array helps to improve the directivity (D) and half power beam width (HP) of the antenna. This work presents a new and simple design method for end-fire antenna array design. The method states that when antenna elements are \(\lambda/2\) apart, a simple end-fire antenna array could be designed and constructed easily without matching networks between antenna elements. Utilizing Rogers 4350 PCB technology, three 24 GHz high-gain, compact planar two-element end-fire dipole antenna arrays are designed to verify this new design method. The achieved results are three two-element end-fire antennas with gains of 8.8, 9.9 and 9.1 dBi. These antenna arrays are characterized by high gain and simplicity in design. They are also very compact in size, with an area of about 1.9 \(\times\) 1.7 cm\({}^{2}\). The benefit of this work is that a new and simple design for end-fire antenna design is suggested, and three two-element end-fire dipole antenna arrays in planar technology which adopt the design method are presented. A utility model patent was granted for this end-fire dipole array antenna topology, ZL 202022106332.1.  
end-fire antenna array; planar technology; on-chip antenna design; half wave dipole elements 1  
Z1  
4 element 5G beam steerable array antenna with a low-frequency planar inverted-F antenna (PIFA) was presented with 9.5 dBi gain at 28 GHz. In [4], a novel 5.8 GHz planar end-fire circularly polarized (CP) complementary antenna was given with a gain of 2 dBi. In [5], a compact 7 GHz planar printed quasi-Yagi antenna with size reduction at 7 GHz with a gain of 4 dBi was demonstrated. In this work, a new and simple design method for an end-fire antenna array design is suggested; utilizing Roger 4350 PCB technology, three 24 GHz high-gain, compact planar two-element end-fire dipole antenna arrays are designed to verify this design method. The frequency is chosen at 24 GHz for the convenience of fabrication of antenna and the measurement of the antenna. It is much cheaper to fabricate, measure and verify the antenna topology at 24 GHz than on-chip mm-wave and THz antenna such as 60, 120 or 244 GHz end-fire antenna arrays. Additionally, there are also many applications at the frequency band of 24 GHz, such as 5G communication, imaging, and so on.
## 5 Discussion
### Terrigenous Material Content  
Rare earth elements are almost insoluble in sea water and come from terrigenous detritus, which can reflect the injection amount of terrigenous materials [9]. For the seven test wells in the research area, the total rare earth content \(\Sigma\)REE in the Wufeng Formation is 112.47-472.86 ug/g, with an average value of 190.39 ug/g. The total rare earth content \(\Sigma\)REE in Bed Long-1\({}_{1}\)\({}^{1}\) is 81.80\(\sim\)182.60 ug/g, with an average value of 118.80 ug/g. The total rare earth content \(\Sigma\)REE in Bed Long-1\({}_{1}\)\({}^{2}\) is 91.56-179.62 ug/g, with an average value of 122.58 ug/g. The total rare earth content \(\Sigma\)REE in Bed Long-1\({}_{1}\)\({}^{3}\) is 125.33-227.93 ug/g, with an average value of 162.38 ug/g. The total rare earth content \(\Sigma\)REE in Bed Long-1\({}_{1}\)\({}^{4}\) is 138.89-223.79 ug/g, with an average value of 181.53 ug/g.  
Continuous injection of terrigenous detrital materials would destroy the anoxic and anaerobic sedimentary environment of the bottom seawater, and it would dilute the organic matter, resulting in a decrease in TOC content. Based on the \(\Sigma\)REE content data of Wufeng Formation-Submember Long-1\({}_{1}\) in the Changning area, it is indicated that the amount of terrigenous injection in the Wufeng Formation is higher than that in Submember Long-1\({}_{1}\), and the terrigenous injection gradually increased from Bed Long-1\({}_{1}\)\({}^{1}\) upward to Bed Long-1\({}_{1}\)\({}^{4}\). It can be seen from Figure 3g that there is an obvious negative linear correlation between the terrigenous detrital injection amount and TOC content.