esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Processing
#### 2.4.3 Masking  
A shapefile of the Murrumbidgege Irrigation supply channel network was obtained, and uploaded as a Google Fusion Table. This was then imported to GEE as a Feature Collection. To avoid problems with registration errors between the geometries in the supply channel features and the UAV and satellite images, the supply channel geometries were buffered by 10 m. These buffered geometries were then used to mask the images, so the resulting images only included the supply channels and a narrow region of land around the channels, with a total width of 20 m. This masking results in a reduced number of pixels to be processed. It also makes land area classification more accurate, as there is less variation in the reflectance characteristics of the land areas on either sides of the supply channels than that of the total land area of the unmasked images.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 1 Introduction
In Section 2, a brief description of the current high spectral efficiency techniques proposed in the UWOC context is presented. Next, Section 3 presents the main assumptions and the mathematical model for the Tx/Rx. A brief presentation of the O-OFDM techniques is presented in Section 4, where the adequacy of the most recent techniques to our application is also discussed. Afterwards, the considered O-OFDM schemes in this work are described briefly in Section 5. Next, a set of numerical results is presented in Section 6 to study the performance of an SiPM-based system. Lastly, Section 7 concludes the paper.
## 2 Data
### Crop Mask  
A binary crop mask was derived from the Corine Land Cover version 2018 classification, freely distributed by the European Environmental Agency (EEA) at [[https://land.copernicus.eu/pan-european/corine-land-cover/clc2018](https://land.copernicus.eu/pan-european/corine-land-cover/clc2018)]([https://land.copernicus.eu/pan-european/corine-land-cover/clc2018](https://land.copernicus.eu/pan-european/corine-land-cover/clc2018)) (accessed on 20 November 2021), by extracting 2.1.1 (non-irrigated arable land) and 2.4.2 (complex cultivation patterns) classes as arable land. Further, the binary mask was used to generate fractional arable land products at spatial resolutions matching the Sentinel-3, MODIS, and ERA-5 products. These fractional estimates were used as weights to spatially aggregate satellite and agro-meteorological variables for the administrative units. An example of fractional arable land over Poland is presented in Figure 1.
## 4 Simulation Results  
The fully integrated power amplifier in this design is manufactured using the SMIC 55 nm process, with a 3.3 V supply voltage. Occupying an area of 0.57 mm\({}^{2}\), as shown in Figure 18a, multiple ground pads are strategically placed to reduce the inductance of the bonding wires. The chip occupies an area of 1.45 mm\({}^{2}\), as shown in Figure 18. In addition to the power amplifier, the chip also includes the SPI module, a bandgap, and other test modules. The simulation software used in this design is Cadence IC 617.  
The simulated S-parameter results, shown in Figure 19, indicate that the small-signal gain of the PA is approximately 9.46 dB/9.98 dB-11.48 dB at both 2.4 GHz and 5 GHz. Within the 5 GHz frequency range, the gain remains relatively flat across the entire bandwidth.  
To characterize the linearity and efficiency of the PA, continuous wave simulations were employed. The simulation results displayed in Figures 20 and 21 illustrate the single-tone linearity of the PA. The saturation power, power gain, and power-added efficiency (PAE) are presented in the operating frequency range. It is seen that at 2.4 GHz and 5 GHz, the saturation power is 28.03 dBm/27.5-28.2 dBm, PAE is 33.25/24.5-31.1%, and the power gain is 9.37/11.5-10.03 dB. Additionally, the saturation power of the PA remains above 27.5 dBm.  
The results for two-tone linearity are depicted in Figure 22. In the 2.4 GHz and 5 GHz frequency bands, the IMD3 is within \(-\)30 dBc when the output power is 15 dBm, meeting the requirements of WLAN standards.  
Figure 18: (**a**) Layout of proposed PA. (**b**) Layout of test chip.  
Figure 21: Simulated \(P_{sat}\) and PAE at different frequencies.  
Figure 19: Simulated S-parameters. (**a**) 2.4 G band. (**b**) 5 G band.  
Figure 20: Simulated power gain and PAE versus output power. At (**a**) 2.4 and (**b**) 5.5 GHz modes.
## 2 Microstructure of Laminated Shale Specim and Micro-Constitutive Models  
### Geometric Model and Loading Parameters  
A uniaxial compression model for a laminated shale specimen was established. This shale model is 50 mm in diameter and 100 mm in height, which is identical to the suggestion of International Society for Rock Mechanics (ISRM) [29]. Its porosity is set as 5%, which is within the typical range of 2-10% [30; 31]. Its density is 2330 kg/m\({}^{3}\), which remains unchanged in simulations [32; 33]. This two-dimensional model is composed of 9,223 particles (the particles are discs in PFC\({}^{\text{2D}}\) and spheres in PFC\({}^{\text{3D}}\)) and approximately 20,000 contacts. These discs distribute uniformly in a range from 0.6 mm to 1.0 mm in radius.  
Figure 1 shows the established shale model under uniaxial compression. This model has two components: rock matrix and beddings. The rock matrix is presented in gray and the beddings are presented in black. The dip angle of beddings is defined as the angle between the bedding and horizontal plane. The bedding is horizontal if the dip angle is 0\({}^{\circ}\). In order to avoid the interference of other factors such as interlocking problems in PFC [34], the perpendicular distance of adjacent beddings maintains a constant at different dip angles of beddings. Based on previous modeling experiences [35], the perpendicular distance between adjacent beddings is set to 16 mm, which is approximately 10 times the disc diameter. The quasi-static loading rate in these numerical compression tests is set as 0.025 m/s, which refers to an investigation of loading rate effects on the cracking behavior of a rock specimen [36]. The loading process will be terminated only if the residual strength decreases to 70% of peak strength.  
Figure 1: A numerical model established for shale specimen in uniaxial compression loading.
## 3 Data Preprocessing and Evaluation Metric  
### Data Preprocessing  
Training and testing the ARDU model require a large amount of data, so the Stanford Global Seismic dataset _STEAD_ (Stanford Earthquake Dataset) [30], which contains 1 million three-component seismic signals, was used in this paper. These 1 million three-component seismic signals come from 450,000 earthquakes from January 1984 to August 2018, providing reliable seismic signals and noise labels for supervised learning algorithms.  
In this paper, 130,000 seismic signals and noise signals were selected from STEAD and the randomly selected data were cropped. The first 3-5 s of the arrival time of the P wave given by the dataset labels were randomly taken as the initial point of cropping, and the 30 s backward from that point were cropped because the 30 s include the main waveform of the event, and the signal sampling rate was set to 100 Hz. To prevent the model from overfitting, the cropped seismic and noise signals were normalized with Formula (7), and finally, the cropped seismic signals and noise were randomly synthesized into 130,000 seismic signals with a low SNR and noise, which were randomly divided into a training set, a validation set, and a test set according to the ratio of 6:2:2 for model training, validation, and testing. In Formula (7), \(v_{i}\) represents the amplitude value of the seismic event signal.  
\[v_{i}=\frac{v_{i}}{\max|v_{i}|} \tag{7}\]  
A CNN has a stronger feature extraction ability for 2D data, so to fully use the denoising performance of the model, short-time Fourier transform (STFT) was applied to the synthetic seismic signals with noise to obtain 2D time-frequency domain signals. The STFT is shown in Formula (8), where \(z(k)\) is the source signal at time \(k\) and \(g^{*}(kT-mT)\) is the window function. In Formula (8), \(m\) represents the translation of the time window, \(n\) represents the number of sampling points in the time window, \(T\) represents the sampling period in the time domain, and \(F\) represents the sampling period in the frequency domain.  
\[STFT_{Z}(m,n)=\sum\limits_{k=-\infty}^{\infty}z(k)g^{*}(kT-mT)e^{-j2\pi(nF)k} \tag{8}\]
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 2 Related Work
### Transformer-Based Methods  
Recently, the revolutionary introduction of transformer [30] architectures has propelled Vision Transformers (ViTs) [31] to exhibit outstanding performance across a range of visual tasks. This has facilitated the proliferation of transformer-based methods in the field of image enhancement. These methods harness the powerful self-attention mechanisms of transformers to address complex spatial relationships and nonlinear degradation patterns, ultimately enhancing image quality and detail [32; 33]. [PERSON] et al. [34] introduced a transformer-based network named LLFormer, specifically for enhancing low-light images. This network utilizes a transformer block designed around axes to execute self-attention operations on the spatial dimensions of height and width within a defined window. [PERSON] et al. [35] integrated Retinex theory with the transformer architecture to propose Retinexformer, a model designed for low-light image enhancement. [PERSON] et al. [36] proposed a lightweight and effective network based on the proposed pixel-wise and patch-wise cross-attention mechanism PPformer. [PERSON] et al. [37] proposed a novel luminance and chrominance dual-branch network, termed LCDBNet, for low-light image enhancement. It divides low-light image enhancement into two sub-tasks: luminance adjustment and chrominance restoration. [PERSON] et al. [38] introduced LYT-Net, a low-light image enhancement model based on lightweight transformers in the YUV color space. Complementary to these efforts, [PERSON] et al. [39] designed ERNet with parallel multiview attention branches, effectively enhancing and preserving discriminative defect features from multiple perspectives, which is particularly beneficial for detail-sensitive enhancement tasks.  
The related methods are developing towards multi-branch tasks and combinations of multiple methods. The above methods have rarely been applied in the study of hydraulic concrete image enhancement. Our study analyzes the characteristics of hydraulic concrete images and designs a targeted hydraulic concrete image enhancement method.
# The Influence of Immersive and Collaborative Virtual Environments in Improving Spatial Skills
## 3 Materials and Methods  
Our investigation centers around an experimental analysis that compares students' scores on spatial tests before and after performing an activity in which they construct geometric solids in a virtual immersive collaborative environment. We worked with an initial group of 41 student volunteers from a first-year industrial engineering course. As these students had completed technical coursework in high school, they were expected to have acquired some skills in spatial visualization through their past training in technical drawing.  
The students' baseline spatial skills were measured with two well-known tests, which were administered online on the students' own time. Twelve students were removed from the study at this point since they did not complete the tests. The twenty-nine remaining students were divided randomly into nine groups of three students, plus one group of two students, for the completion of the collaborative virtual activity. Twelve of the twenty-nine participants were women, and seventeen were men.  
The virtual activity was carried out with a software application we developed specifically for this study, which centered on the creation of a geometric model constructed from primitive solids. The students participated simultaneously with their group members in the virtual space and interacted together to build the model. Since this was a new software application and the students did not know how to use it, before each group of students began the modeling process, they first built a model in a shared virtual space with an instructor. The instructor helped the students to understand the functionality of the software and the modeling method.  
At the end of the modeling activity, the students again took the two spatial tests under the same conditions as they had conducted previously. By comparing their performance on the pre- and post-tests, we draw some conclusions about the activity's ability to improve spatial skills. In order to assess the design of the novel software we created for this study, the students also completed two questionnaires about the usability and functionality of the software after they finished the modeling activity.
###### Abstract  
In recent years, the vehicle-based indirect Structural Health Monitoring (iSHM) method has been increasingly used to identify the dynamic characteristics of railway bridges during train crossings, and it has been shown that this method has several advantages compared to traditional SHM methods. A major advantage is that sensors are just mounted on the vehicle, and no sensors or data acquisition systems need to be installed on the railway bridge. In this paper, the application of the vehicle-based iSHM method is demonstrated numerically and experimentally for determining the natural frequencies of railway steel bridges during train crossing. The coupled linear equations of motion of the train-bridge multi-body system are derived, and train crossing simulations are conducted numerically, considering different train speeds. Three different railway bridges are considered, and the train-induced vibration responses are calculated for both the train multi-body system and the railway bridge models. Different representative evaluation points are chosen for the wheelsets, bogies, and car bodies of the considered vehicle. To calibrate the numerical model, the resonance frequencies of an existing single-span steel bridge are measured in situ by the application of forced vibration tests. Besides the executed in situ measurements of the bridge, the considered crossing vehicle is also instrumented with several accelerometers at the wheelsets, bogies, and car bodies, and the vibration responses of both the bridge and the crossing vehicle are measured simultaneously during the duration of several train crossings with different train speeds. The recorded vibration responses are analyzed in the frequency domain and compared with numerical simulation results. It is shown that the first bending frequency of the considered railway bridge can be clearly identified from the computed frequency response spectra and that the vehicle-based iSHM method provides a promising tool for identifying the dynamic characteristics of railway bridges.  
a Article  
Application of Vehicle-Based Indirect Structural Health Monitoring Monitoring Method to Railway Bridges--Simulation and In Situ Test  
1  
1 Research Unit of Mechanics and Structural Dynamics, Institute of Structural Engineering, Faculty of Civil and Environmental Engineering, TU Wien, Karlsplatz 13/212-03, 1040 Vienna, Austria 1  
1 Research Unit of Steel Structures, Institute of Structural Engineering, Faculty of Civil and Environmental Engineering, TU Wien, Karlsplatz 13/212-01, 1040 Vienna, Austria 1  
[EMAIL_ADDRESS]  
1
# Influence of Hot Carrier and Thermal Components on Photovoltage Formation across the p-n Junction
###### Abstract  
In the present work we reveal the existence of the hot carrier photovoltage induced across a p-n junction in addition to the classical carrier generation-induced and thermalization-caused photovoltages. On the basis of the solution of the differential equation of the first-order linear time-invariant system, we propose a model enabling to disclose the pure value of each photovoltage component. The hot carrier photovoltage is fast since it is determined by the free carrier energy relaxation time (which is of the order of \(10^{-12}\) s), while the thermal one, being conditioned by the junction temperature change, is relatively slow; and both of them have a sign opposite to that of the electron-hole pair generation-induced component. Simultaneous coexistence of the components is evidenced experimentally in GaAs p-n junction exposed to pulsed 1.06 \(\upmu\)m laser light. The work is remarkable in two ways: first, it shows that creation of conditions unfavorable for the rise of hot carrier photovoltage might improve the efficiency of a single junction solar cell, and second, it should inspire the photovoltaic society to revise the Shockley-Queisser limit by taking into account the damaging impact of the hot carrier photovoltage.  
hot carriers; photovoltage; p-n junction; GaAs; solar cell +
Footnote †: journal: Applied Sciences
## 1 Introduction
This can not only reduce the overhead but also provides flexibility in resource allocation by allowing multiplexing multicast and unicast traffic at the same time interval. In addition, uplink feedback is available in the form of a channel quality indicator (CQI) and/or HARQ. Despite the overhead increase in the uplink, uplink feedback is a crucial function to meet the stringent reliability requirement in many use cases such as V2X and unmanned aerial vehicle (UAV). A low-overhead feedback scheme was proposed in Reference [12] to reduce the overhead while mitigating the limitation of the conventional multicast service. And HARQ-based feedback schemes were investigated for V2X and UAV cases in References [5; 13], respectively. Furthermore, owing to uplink feedback, the bandwidth can be adaptively allocated based on the channel quality of the worst user. When the number of users in a multicast service, that is, the _multicast group size_, is 1, its operation is the same as unicast. As the multicast group size increases, more users with good channel quality suffer a disadvantage, since they have to use a multicast channel where bandwidth is allocated based on the worst channel quality. This is wasteful for them since a smaller bandwidth is enough to provide the target multicast rate. To reduce this resource waste, [PERSON] et al. proposed providing multicast transmission with two multicast channels, that is, a supplemental multicast channel in addition to the basic multicast channel [14]. The performance of the proposed multicast scheme was analyzed through simulations in terms of spectral efficiency. However, two multicast channels were not utilizedoptimally due to _partial channel quality feedback_, where only the channel quality of the worst user is available at the base station. In this paper, the channel quality information of individual users is assumed to be available at the base station, called _full channel quality feedback_. An optimal resource allocation method is proposed to minimize the required bandwidth with the target multicast rate, \(R_{T}\), which is found to be a convex problem depending on the channel quality of the basic multicast channel (BMCH) and the supplementary multicast channel (SMCH) of the users in a multicast service. Also, a computationally efficient algorithm is proposed to solve the convex problem, which is a pointwise minimum of a family of affine functions. A fluid model is used to take into account other cell interference. Performance is evaluated in terms of the required bandwidth and is compared with existing multicast schemes by varying the multicast group size and the number of users assigned to the BMCH.
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
## 5 Conclusions  
Compositional analysis of chalcopyrite, a primary ore of copper, of local origin, was performed using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) techniques. Plasma parameters such as electron temperature and number density were calculated, and conditions of optically thin plasma and local thermodynamic equilibrium were verified, which were perquisites of the CF-LIBS technique. The LIBS spectrum revealed the presence of four elements viz., Cu, Fe, Ca, and Na. Quantitative analysis gave the relative concentrations of Cu, Fe, and Ca as 58.9%, 40.2%, and 0.9% by weight respectively. X-ray fluorescence (XRF) showed the presence of five compositional elements viz. Cu, Fe, Si, Se, and Ag, with weight percentages of 58.1%, 35.4%, 5.7%, 0.7%, and 0.1% respectively. A comparison of the results obtained using CF-LIBS and XRF techniques displayed that quantitative results of major elements of Cu and Fe were in close agreement, whereas the techniques differed in qualitative and quantitative results for minor elements. This result shows the effectiveness of CF-LIBS methodology in the quantification of at least major elements of multi-elemental geological samples without the requirement of reference material and resorting to the tedious and time-consuming calibration method. In general, this application of the CF-LIBS technique can be extended for quantitative analysis of a range of multi-elemental samples. This research work might be useful for scientists and engineers working in the areas of geology, mineralogy, chemistry, and spectroscopy.  
**Author Contributions**: Conceptualization, M.H., S.A.A. and A.A.; methodology, A.A.; validation, T.M.K.; formal analysis, M.H., S.A.A. and A.A.; investigation, M.H., S.A.A. and A.A.; resources, M.H. and M.R.I.F; data curation, A.A., N.H.; writing--original draft preparation, M.H., S.A.A. and A.A.A.; writing--review and editing, M.R., T.M.K., P.A., M.U.K.; visualization, P.A., M.U.K.; supervision, M.H. and S.A.A.A.; project administration, M.H., S.A.A. and M.R.I.F.; funding acquisition, M.R.I.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Research Universiti Grant, Universiti Kebangsaan Malaysia, Gearan Universiti Penyelidikan (GUP), code: 2018-134.  
**Acknowledgments**: We are extremely thankful to [PERSON], Quaid-i-Azam University Islamabad for providing laboratory facility at Atomic and molecular Physics Laboratory to perform this experiment. This work was supported by the Research Universiti Grant, Universiti Kebangsaan Malaysia, Gearan Universiti Penyelidikan (GUP), code: 2018-134.  
**Conflicts of Interest**: The authors declare that they have no conflict of interest.