esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications
### Analysis and Synthesis of Diffractive Optical Elements  
Diffractive elements are of nature to be treated with a diffraction theory. However, when several diffractive elements are involved, the application of a diffraction theory becomes forbidding, since we do not have a mechanism at our disposal, such as the compound system in geometrical optics. Our approach offers such a mechanism. The diffractive optical elements are replaced by an equivalent system that is characterized by two principal planes, i.e., two focal and two nodal points.  
For the synthesis of diffractive optical elements, we optimize the position of the principal, focal, and nodal planes. The optimization may be proceeded analytically or through an iterative process. Then, we design diffractive elements that converge the wavefront towards the focal points and redress the beam (make them parallel) at the principal planes.
## 3 Results
### Adaptability to Specific Scenarios
#### 3.4.1 Identification of Relevant Acoustic Characteristics  
To evaluate the algorithm's ability to detect an infant crying, we used a data set composed of 500 recordings, of which 250 were crying episodes and 250 were general environmental sounds. Table 8 shows the results obtained by evaluating the ability of the algorithm to detect a baby's crying. The precision achieved is exceptionally high, with a value of 98.5%, indicating that the algorithm has classified the recordings accurately between crying episodes and other environmental sounds. Sensitivity, representing the proportion of correctly identified crying episodes versus all actual crying episodes, is also notable at 97.8%. This suggests that the method rarely misses a crying episode. Furthermore, the specificity of 99.2% denotes that the algorithm is highly efficient in discarding sounds that do not correspond to a baby's cry, significantly reducing false positives.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 1 Introduction
### Challenges to Domain Adaptation in Satellite-Borne Machine Learning  
One way to counter the negative effects of the domain gap is to apply _domain adaptation_ methods. These methods aim to make a model more robust to changes in data distribution between the source domain (where the model was trained) and the target domain (where the model is deployed). Section 2.2 provides a survey of these methods. However, there are major challenges to the application of domain adaptation to satellite-borne machine learning:  
* Edge compute devices for satellite-borne machine learning are still much more limited in terms of compute capability relative to their desktop counterparts. For example, the EoT AI board [4], which features an Intel Myriad 2 vision processing unit (VPU), is targeted for accelerating machine learning inference. Furthermore, the onboard central processing unit (CPU) is typically catered for data acquisition and processing activities [6], not for training machine learning systems or using domain adaptation techniques that are computationally costly to run.
* The operational constraints of a space mission, particularly limited, unreliable and/or asymmetrical downlink/uplink bandwidths, lead to obstacles in data communication that affects domain adaptation, e.g., difficulties in procuring labelled target-domain data and remote updating of the model deployed in space.  
Section 3 further discusses the challenges in the context of a concrete EO mission. We stress that the existing works on domain adaptation in EO or remote sensing applications (see Section 2.2) have not addressed the challenges above.
# Energy Efficiency Perspectives of PMR Networks +
## 4 Energy Efficiency Strategies for PMR Systems
### BCCH Carrier Power Control  
Power control is a software based solution that introduces an energy saving mode on the BCCH transceiver. Such a solution reduces the overall power consumption by transmitting dummy bursts on the idle channels, i.e., on the time slots that are not allocated to a TCH, with a power level lower than the maximum transmission power of the BCCH channel. The behaviour of power control is shown in Figure 8: note that, in our analysis, a 2 dB power reduction has been considered. In order to keep the cell range unaltered, the BCCH channel is always transmitted at full power. The introduction of power control allows the power consumption to vary according to the served traffic. Figure 7 shows the behavior of a TETRA base station power consumption when power control is applied. The largest energy saving is achievable in the case of low traffic load, while for higher traffic load values, the performance of power control converges to the TETRA baseline case. We observe that the gain due to power control on the BCCH carrier is smaller with respect to the impact of carrier sleep mode. Therefore the combined adoption of the two solutions should be supported. In this regard, the proposed power consumption model has been evaluated by statistical analysis, considering a weekly traffic data set of a deployed TETRA regional network and a 95% confidence interval.  
Figure 8: Transmitted signals for power control (**a**) and no power control (**b**) cases.  
Figure 9: Daily average energy consumption of a single TETRA base station.  
Figure 9 shows how the considered strategies perform in terms of daily average power consumption. As expected, the application of the carrier sleep mode brings significant energy savings, resulting in a 30% lower power consumption, considering the daily average traffic load of a single TETRA base station. Even if the energy gains achieved by power control on the BCCH carrier are smaller, employing this strategy in combination with the carrier sleep mode results in an additional 10% saving, showing that the common techniques proposed for commercial cellular standards could actually improve the energy efficiency of PMR systems.
## 2 Design Issues of Drone Routing for Drone-Based Delivery Systems
### Designing Optimal Trajectory to Reduce Delivery Time  
Trajectory design is one of the major challenges in drone-based delivery systems as it helps reducing the total travel time. Due to diverse delivery locations and charging points, it is often challenging to find the optimal trajectory that can minimize the overall delivery time and enable the drone to successfully fly back to the depot [31]. Analyzing the drone's trajectory data and optimizing it is an NP-hard problem, where NP stands for non-deterministic polynomial-time. As the number of depots and customer points increases, the problem of searching for optimal trajectory becomes extremely complex [6]. Thus, significant efforts should be made to avoid such shortcomings.
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 3 Results
### Models Application
Such information is only visible with 10 and 20 m resolution, and thus, the METRIC model, due to the use of thermal images, can not show. Spatially detailed information, as seen in the Sentinel-2 images of Figure 11, has great value for field professionals. Coinciding dates between Sentinel-2 and Landsat-8 show high similarities in the spatial ET\({}_{\text{a}}\) between approaches 1 and 2 with METRIC. Approach 2 stands out for having larger spatial proximity. Such proximity is evidenced in Table 5, in which the averages ET\({}_{\text{as}}\) in approach 2 have the smallest differences in the estimated averages by the METRIC model. In addition, the standard deviation for approach 2 was also smaller than approach 1 but larger than the METRIC model. This fact might be attached to the more detailed spatial resolutions of approaches 1 and 2 when compared to the method using the METRIC model. After calculating the integral of ET\({}_{\text{a}}\), it was found that the sugar cane total water demand along the crop cycle was 1417.77 mm for approach 1, 1474.26 mm for approach 2, and 1544.11 mm for METRIC. It is perceptible that total evapotranspiration estimated for approaches 1 and 2 were close to the total evapotranspiration by METRIC, where percentual differences were 8.18 for approach 1 and 4.52 for approach 2 when compared to METRIC. Approach 2 had a closer value to the one estimated by METRIC, going against the values found for dates indicated in Figure 11. Sugar cane total evapotranspiration during its cycle, found by [PERSON] and [PERSON] [35], was 1388 mm. The one found by [PERSON] et al. [38] for the Brazilian Northeast conditions was 1600 mm. Thus, it can be noticed that the total evapotranspiration found in this work is close to the values found in Brazil.
# Simulation and Design of Three 5G Antennas
## 3 Design of 5G Microstrip Array Antennas
### Design and Simulation of 4 \(\times\) 4 Antenna Units
#### 3.2.1 Analysis and Simulation of Element Spacing  
After selecting the antenna elements and considering the mutual coupling effects between these elements, the key factor influencing the performance of the array antenna is the element spacing 'd' [23]. To simplify the simulation and analysis process, a linear polarization mode is chosen for this design, with all antenna elements excited with equal amplitude and fixed directional electric field vectors. When \(u=kdcos\beta+\alpha=0\), the array factor S(u) has its maximum beamforming value S\({}_{\text{max}}\) and the corresponding maximum radiation direction angle \(\beta_{\text{max}}\), which represents the main lobe in the array antenna's radiation pattern. When u = \(\pm\) 2\(\pi\), sidelobes will appear, and to suppress the sidelobes' impact on the antenna performance, it is recommended to choose \(|u|<2\pi\), as shown in Equation (24)  
\[\text{d}<\frac{\lambda}{|\text{cos}\beta-\text{cos}\beta_{\text{max}}|_{\text{ max}}}, \tag{24}\]  
Given that \(0\leq\beta\leq\pi\), and considering Equation (25).  
\[|\text{cos}\beta-\text{cos}\beta_{\text{max}}|_{\text{max}}=1+|\text{cos} \beta_{\text{max}}|, \tag{25}\]  
Therefore, we can derive Equation (26).  
\[\text{d}<\frac{\lambda}{1+|\text{cos}\beta_{\text{max}}|}, \tag{26}\]  
For a uniform planar array with edge radiation, \(\beta\text{max}=\pi/2\), thereby calculating \(\text{d}<\lambda\), which means that, in general array antenna designs, the element spacing is typically less than one wavelength. According to [PERSON] antenna theory book, array element spacing must exceed 0.5 wavelengths to avoid overlap. When the number of elements is fixed, smaller spacing results in a narrower main lobe, but excessively small spacing can cause strong mutual coupling between elements. In this design, the resonant frequency  
Figure 11: The gain pattern of the microstrip antenna unit at a frequency of 3.5 GHz. (**a**) Three-dimensional gain pattern of the antenna unit and (**b**) polar gain pattern of the antenna unit.  
is 3.5 GHz, with a spatial wavelength of \(\lambda\) = 85 mm, implying an element spacing in the range 42.5 mm \(<\) d \(<\) 85 mm. For simulation and analysis, we chose d = 50 mm, d = 60 mm, and d = 70 mm. Figure 12 illustrates the simulated radiation pattern in the xoz plane at 3.5 GHz.  
From Figure 12, it can be observed that when d = 50 mm, the antenna elements are relatively close, resulting in stronger mutual coupling, appearance of sidelobes, and lower gain at the maximum radiation direction. However, as d increases from 50 mm to 60 mm, the main lobe width of the antenna array decreases while the gain increases. On the other hand, as d increases from 60 mm to 70 mm, the gain at the maximum radiation direction decreases. In summary, an optimal choice for the element spacing should be d = 60 mm.
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
## 1 Introduction  
In the past decades, the thermal conductivity of nanofluids as the essential heat transfer property has been given much attention. They have been proposed as promising heat transfer fluids for numerous applications, especially in solar collectors [1]. In fact, nanofluids are fluids which consist of a base fluid with suspended nano-sized particles (1-100 nm) [1; 2]. Researchers have focused on the thermal conductivity of different types of nanofluids, such as oxide, nitride, carbon nanotubes, carbon fiber, carbon black, graphene oxide, graphene sheet and hybrid materials of different shapes, i.e., particles, disks, tubes, flakes, fibers, etc. [3; 4]. In recent years, many carbon-based nanofluids have been investigated. Their thermal properties were measured experimentally and analyzed theoretically. These carbon-based nanoparticles include single-layer carbon nanotubes, multilayer carbon nanotubes, graphene, graphene oxide, graphite nanoplates, etc. Compared with metal or metal oxide, carbon nanostructures have higher intrinsic thermal conductivity and lower density, and carbon nanostructures have strong c-c covalent bond and phonon scattering, so their thermal conductivity is higher than other nanoparticles [5]. In addition, some studies showed that carbon-based nanomaterials, such as carbon nanotubes, graphite nanoparticles and flaked graphite nanofibers, are also good substrate for the preparation of hybrid nanofluids. It is worth noting that the axial thermal conductivity of carbon nanotubes is 3000 w/mk [6] but that of graphene monolayer is 5000 w/mk [7]. This means that Graphene and its oxides have good thermal conductivity and are very beneficial to their application in nanofluids for enhanced heat transfer. Graphene oxide nanofluidsare one of most promising nanofluids widely investigated by researchers, and the precise and reliable correlations for prediction of their thermophysical properties are still highly desired although there are already many correlations available from the literature. On the other hand, there are many numerical studies on the flow and heat transfer properties of the various kinds of nanofluids, which are important for the in-depth understanding and prediction of the performance of nanofluids for application in practice [8; 9; 10; 11; 12; 13; 14].  
The aim of this study is to evaluate the influence of various parameters on the heat transfer performance of graphene oxide nanofluids and establish a theoretical and experimental correlation for the precise prediction of the thermal conductivity of such nanofluids. Firstly, five kinds of graphene oxide nanofluids were prepared, with a mass fraction of 0.002% to 0.010% at the same concentration gradient. Then, the stability, thermal conductivity and dynamic viscosity of each nanofluid were measured. Finally, the empirical correlation of the thermal conductivity versus the temperature and nanoparticle concentration is presented.
# Experimental and Numerical Study of the Jib Connection Frame of a Wheeled Crane
## 2 Finite Element Analysis
### Finite Element Analysis  
The beam model comprises BEAM188, consisting of 3035 elements, with a total mass of 680.3 kg. The solid model comprises SOLID185, consisting of 157,287 elements, with a total mass of 681.7 kg. The discrepancy in mass between the two models is 0.2%. In the actual force, the lifting weight is transferred to the jib through the pulley block at the top of the jib. The force is transmitted along the jib structure to the connecting joint. The axial load ratios transferred to the four chords of the connecting section in the actual lifting of the weight are \(-0.55\), \(-1\), 0.29, and 0.78. The narrow end is a fixed constraint, and loads of \(-0.55\), \(-1\), 0.29, and 0.78 are applied at chord 1, chord 2, chord 3, and chord 4, respectively, at the stressed end. Additionally, certain lateral loads are applied to chord 1 and chord 4. The boundary conditions and forces are shown in Figure 4. Where chord 1 exerts a pressure of 431.8 kN and a lateral force of 39.3 kN, chord 2 exerts a pressure of 785 kN, chord 3 exerts a tension of 612.3, and chord 4 exerts a tension of 227.7 kN and a lateral force of 39.3 kN. The results of the static analysis of the model, which demonstrate the displacement changes obtained, are presented in Figure 5. It can be observed that the maximum displacement obtained with the beam model is 16.15 mm, and the maximum displacement obtained with the solid model is 15.96 mm. Notably, the maximum displacement occurs at the same location, with an error of approximately 1.2%.  
A buckling analysis was conducted on the model, and the results are presented in Figure 6. The ultimate load error is 3.98%. According to the analysis results, it can be observed that chord 1 and the transverse connection may be unstable in the plane of amplitude variation, with chord 2 identified as the primary destabilizing member. Accordingly, chord 2 should be the primary focus of the new structural design experimental program for this project. This will facilitate an analysis of the strain gauge arrangement location and extraction of the stress at this arrangement. The results of the comparison demonstrate that the beam model is an effective substitute for the solid model.  
Figure 3: Bilinear elastic–plastic behavior.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Reh/MPa** & **Rm/MPa** & **A\%** & **E/MPa** & \(\mu\) \\ \hline S890 & \(\geq\)890 & 940–1100 & \(\geq\)11 & \(2.1\times 10^{5}\) & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of S890.
## 3 Methodology
### Work Environment  
In this work, we use the simulation environment fully described in [28]. We rely on the freeware Player/Stage environment [42] to run our control system. Player/Stage supports multiple concurrent client connections, so it can simulate every robot in a MRS. The main advantage of this environment is that physical robots can be replaced by simulated ones in a straight way [43]. This feature allowed us to use a few physical robots to fix the heuristic parameters of the proposed algorithm (Table 1) and then use the same software in large networks' simulation. Our real robots are based on off-the-shelf Hexbug robot toys (Figure 5). Every robot control board circuitry includes an H-bridge for motor control, plus feeding and protection electronics. The board is connected to a EZ430-RF2500 from Texas Instruments Inc. (Dallas, TX, USA), including an MSP430F2274 microcontroller and a CC2500 RF chip. Figure 6 presents the power consumption vs. distance plot for CC2500 RF according to its datasheet [44]. The system uses the Texas SimpliciTI Low Power RF open protocol to operate 2.4-GHz wireless networks. Real robots estimate distances using RSS-based trilateration, but they also include a SHARP infrared range sensor for collision avoidance (robots may avoid collisions with each other using RSS, but they need a sensor to detect dead nodes and other obstacles in the environment).  
Robots in simulation always know their location with respect to the others and to the coverage area boundaries. We have only used real robots for parameter setting. In these cases, we relied uniquely on RSS-based trilateration for localization, so localization errors appeared. We did not implement any statistical robotic localization method in our robots for several reasons: (i) the computational load of such processes is high, and our microcontroller memory was mostly dedicated to the communication protocol stack implementation; (ii) our robots are legged, and their body structure does not allow any simple odometry mechanism; (iii) the onboard SHARP sensor has a very limited detection range. Since our deployment algorithm is fully reactive, it would work in real environments despite localization errors. However, if localization precision were required, our robots would need to be replaced to support more complex localization mechanisms.  
After the parameters are heuristically fixed (see Section 2.3), a large number of these robots is simulated in Player/Stage to test the behavior of large MWSN during deployment and self-healing. Environments in our simulations are 150 \(\times\) 150 m\({}^{2}\). These environments can be fully covered with 100 of our robots.
## 4 Experiments
### Comparative Experiments on Rice Grain Dataset
#### 4.7.2 Qualitative Experiments  
To further validate the reconstruction performance of our model on various rice images, we selected several typical rice samples for visual comparison analysis. As illustrated in Figure 10, our model is capable of restoring the contours of rice grains more clearly while preserving more texture details. In comparison with other lightweight methods, our model exhibits a distinct advantage in reconstructing rice grain details. For example, as shown in Figure 11, the rice grain edges reconstructed by CARN, IMDN, and ShuffleMixer appear rather blurry, whereas our model accurately restores the surface texture of the rice grains, yielding superior overall visual quality. Our model accurately reconstructs surface textures, providing superior visual fidelity. These high-quality reconstructions establish a reliable feature foundation for downstream tasks such as rice variety identification and processing quality inspection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scale** & **Methods** & **Params (K)** & **PSNR** & **Inference** \\  & & & **(dB)/SSIM** & **Time (ms)** \\ \hline \multirow{8}{*}{\(\times\)4} & CARN [7] & 1592 & 34.91/0.8370 & 57.7 \\  & IMDN [8] & 715 & 36.54/0.8944 & 26.0 \\ \cline{1-1}  & SAFMN [32] & 240 & 37.65/0.9122 & 8.9 \\ \cline{1-1}  & ShuffleMixer [52] & 411 & 37.86/0.9137 & 17.8 \\ \cline{1-1}  & SwinIR-S [12] & 897 & 37.90/0.9150 & 31.5 \\ \cline{1-1}  & DRFAN (ours) & 1057 & 38.09/0.9165 & 41.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparisons on the rice dataset under a \(\times\)4 scale factor, with the best and second-best results indicated in red and blue, respectively. FLOPs and inference time are calculated with a 1280 \(\times\) 720 GT image.
# A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises
### Enabling Digital Twin Technology  
[PERSON] et al. [20] summarised the enabling technologies that can address these challenges into five major categories, namely: 1. physics-based modelling, 2. data-driven modelling, 3. big data cybernetics, 4. infrastructure and platforms, and 5. human-machine interface. For example, data management, safety, and security can be addressed by data-driven modelling and infrastructure and platforms, specifically cryptography, blockchain and big data technologies. Signal processing, real time communication and latency are addressed by data compression and communication technologies such as 5G and Internet of Things (IoT). Connectivity issues and data collection after the sale of the product can be improved with the infrastructures and IoT technologies used for environmental monitoring.  
As such, information and communications technology (ICT) is essential to support the DT transformation. ICT emphasises the unified communication of information through the integration of telecommunication, computers and devices, enterprise software, and storage, enabling all stakeholders (both inside and outside the companies) to access, transmit and use the required information.