esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Simulation Conditions
### Tlm03e Prototype
#### 3.1.3 Controller  
A motorcycle's controller is its brain. It brings together all components of the vehicle by receiving inputs (from the battery, from speed or brake sensors, etc.) and translating them into signals that dictate the behavior of the motorcycle's components.  
The selected controller unit needs to uphold the following requirements: (1) the maximum output current needs to be at least the motor maximum current (780 \(A_{rms}\)); (2) the maximum input voltage must be at least the maximum voltage of the battery pack (126 V); (3) the communication protocol should be CAN, to be compatible with the team's electronics department's protocols; and (4) the maximum output frequency needs to be at least the motor frequency at max speed (8000 rpm).  
The chosen controller for the TLM03e was a Sevcon Gen4 size 6. This work uses an empirically observed efficiency of 95% since one is not provided in the controller manufacturer's datasheet. This paper assumes 100% efficiency in cables due to their relatively short length. The total efficiency of the system was calculated by multiplying the efficiencies of the batteries, the motor, the transmission, the controller and the cables.
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 4 Results and Discussion
### Correlation between Displacements and Water Level Variations
They added that, since those bridges lie on deep foundation piles (as the Po River Bridge), this correlation is likely not a symptom of scour phenomena. Similar results have also been obtained in [12], in which the authors studied the anomalous deformations and the subsequent collapse in 2015 of the Tadcaster Bridge (UK). In that case study, the authors analysed a dataset of SAR images acquired from March 2014 to December 2015 in the X-band by the satellite constellation TerraSAR-X in ascending geometry. They performed the Small BASeline Subset (SBAS) technique through the software SARscape to extract displacement time series. The authors highlighted a distinct movement in the bridge portion where the collapse occurred before the event. Those movements strongly correlated to the changing water level of the Wharfe River. However, unlike the bridges crossing the Po River, the Tadcaster Bridge lies on shallow foundations; indeed, the collapse occurred due to scour by eroding the soil at the base of the fifth pier of the bridge. These results highlight the importance of considering environmental factors and the geotechnical characteristics of the foundation soils in bridge monitoring. Indeed, understanding the behaviour of the underlying foundation soil under fluctuations in river water levels would be crucial for accurately interpreting the structure's displacements above the ground measured by terrestrial and satellite technologies. However, this is a challenging task due to the frequent absence of persistent on-site monitoring of the movements of the foundation soil or the displacements of the piers in the riverbed. For this reason, studying the movements of the territory around the bridge by looking at the displacements of the PSs identified in the entire case-study area can give a first idea of the problems like subsidence, differential displacements, and landslides affecting the bridge. Finally, it is important to point out that the correlation between PS displacements and temperature expresses a correlation between the response of a man-made structure and an environmental load (i.e., the temperature variation), while the correlation between PS displacements and water level expresses a possible correlation between the soil movement and an environmental load (i.e., the water level variation).
## 3 Results and Discussion
### Sensory Evaluation of Bread  
The sensory characteristics of bread, including appearance, colour, elasticity, and porosity, are crucial not only for consumer acceptance but also for evaluating functional quality. All bread samples formulated with cereal-based beverages received full consumer acceptance, with mean overall scores exceeding 3.75 points (Table 6), corresponding to an acceptability index (AI) of 75%. According to [PERSON] et al. [17], products exceeding an AI of 70% may be considered acceptable from a sensory standpoint, confirming the positive reception of all tested breads. Across all formulations--regardless of beverage type or substitution level--attributes such as appearance, crust color, taste, and smell were consistently rated highly, with mean scores ranging from 4.5 to 5.0. These values did not differ significantly from those of the control sample (CON, which served as the reference), indicating that replacing water with cereal-based beverages did not negatively impact the organoleptic properties. The use of a trained panel ensured precise and consistent sensory assessments, but may not fully reflect the diversity of consumer preferences. The small differences observed suggest that all formulations were well accepted. To improve representativeness, future studies could include untrained consumer testing. In addition, instrumental smell analysis--such as GC-MS or electronic nose techniques--may offer deeper insight into volatile compound profiles and their relationship with cereal-based beverage substitution. Unlike the findings reported by [PERSON] et al. [48], where crust browning was intensified through Maillard reactions involving lactose in milk and acid whey, no such effect was observed in this study. This is likely due to the absence of reducing sugars in the cereal-based beverages. Nevertheless, crust colour was still rated favourably, which may be attributed to the presence of natural pigments in the plant beverages. Elasticity and porosity showed greater sensitivity to formulation differences. Breads with spelt (BS) and millet (BM) beverages at substitution levels up to 75% retained excellent physical properties, with elasticity and porosity scores comparable to those of the control (mean values between 4.4 and 4.8). The BS50 and BM50 variants, in particular, demonstrated
## 3 Results and Discussion
### Preparation and Characterization of the Material
#### 3.1.4 XRPD  
The crystallinity of the materials was investigated with XRPD (Figure 3). Results obtained for bare TiO\({}_{2}\) are in agreement with JCPDS card no. 21-1272 for anatase and show reflections at 25.47, 37.97, 48.21, 54.09, 55.24, 62.95, 68.89, 70.47, 75.31\({}^{\circ}\)[18]. Additional reflections coming from admixture of Fe\({}_{3}\)O\({}_{4}\) can be noted in the composite material at 35.76\({}^{\circ}\), 57.19\({}^{\circ}\), 62.95\({}^{\circ}\). The reflection at 30.42\({}^{\circ}\) can be assigned both to Fe\({}_{3}\)O\({}_{4}\) and Fe\({}_{2}\)O\({}_{3}\), which is created as a result of Fe\({}_{3}\)O\({}_{4}\) oxidation during exposure to air [19]. In addition, the composite revealed further changes in the XRPD spectrum. It especially concerns the shifting of the peaks at 38\({}^{\circ}\) and 48\({}^{\circ}\) towards lower values, which is the evidence for the material's crystallinity being impacted as a result of functionalization with Fe\({}_{3}\)O\({}_{4}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Particle Size (Mean \(\pm\) SD)** & **PDI \({}^{\bullet}\)** \\ \hline TiO\({}_{2}\) & 190.9 \(\pm\) 103.4 nm & 0.293 \\ TiO\({}_{2}\)–Fe\({}_{3}\)O\({}_{4}\) & 369.5 \(\pm\) 232.9 nm & 0.397 \\ \hline \hline \end{tabular} SD–standard deviation; PDI–polydispersity index; \({}^{\bullet}\) calculated according to the formula (SD/Mean)\({}^{2}\).  
\end{table}
Table 1: Hydrodynamic size analysis of the composite materials with NTA.  
Figure 3: X-ray powder diffraction patterns of bare TiO\({}_{2}\), Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\)–Fe\({}_{3}\)O\({}_{4}\) composite materials. A—reflection corresponding to anatase TiO\({}_{2}\), M—reflection corresponding to magnetite Fe\({}_{3}\)O\({}_{4}\).  
Figure 2: Hydrodynamic size measurements with NTA. The graph shows particle size distribution in the range of 0–1000 nm for (**A**) bare TiO\({}_{2}\) and (**B**) TiO\({}_{2}\)–Fe\({}_{3}\)O\({}_{4}\).
## 5 Evaluation of Keypoint Matching
### Environment of the Evaluation
#### 5.2.2 Dataset  
The SEN1-2 dataset [39] was prepared from Sentinel-1 (SAR satellite) and Sentinel-2 (optical satellite) of the European Space Agency [40]. The dataset contains co-registered Sentinel-1 and Sentinel-2 image patches. Each item of the Sentinel-1 data has one 8-bit and 256 \(\times\) 256-pixel channel (C-band, VV polarization), while that of Sentinel-2 data has three 8-bit and 256 \(\times\) 256-pixel channels (red, green, and blue band). Their spatial resolution is 10 m per pixel. The \"Urban\", \"Farm\", and \"Hill\" areas were selected from the \"spring\" data in the SEN1-2 dataset due to their importance during a disaster event, and 2936, 3320, and 3224 image pairs were extracted, respectively. From these pairs, 300 sets each were selected as the test and validation data, respectively, and the remaining sets were used as training data. Figure 7 shows examples of the training, validation, and test image data for the experiment.  
Figure 7: Examples of training, validation, and test images for the experiment. The upper half of each dataset represents SAR images, and the others represent optical images.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Simulation Phase  
A trained model can be used to predict different time horizons, which is an advantage of this solution. The output of the model consists of the solar irradiance of the next day and can thus be fed repeatedly to the input of the model, making it possible to predict 2, 3, 4, 5, etc. days ahead. The problem is that the model input consists of day-of-year features, DaySin, and DayCos, in addition to solar irradiance. Thus, it is necessary to transform these features from the last timestep in the window to day of year, according to Equation (9). Then, add 1 day to this day and convert it back to the DaySin and DayCos features, according to Equations (10) and (11). The newly created DaySin and DayCos are merged with the predicted solar irradiance for the next day. The vector thus created can be fed to the model input. It does not make sense to train the model to predict DaySin and DayCos since it is designed to be fully focused on predicting only solar irradiance.  
\[day_{t}=\left\{\begin{aligned} &\text{round}(\frac{\text{atan2}(DaySin _{t},\ DayCos)*183}{\pi}+366),\ \ x\leq 0\\ &\text{round}(\frac{\text{atan2}(DaySin_{t},\ DayCos)*183}{\pi}), \ \ x>0\end{aligned}\right. \tag{9}\]  
\[DaySin_{t+1}=\sin\frac{2\pi(day_{t}+1)}{366} \tag{10}\]  
\[DayCos_{t+1}=\cos\frac{2\pi(day_{t}+1)}{366} \tag{11}\]  
In Figure 11, we can see the significance of the hyperparameters in affecting the best validation mean squared error metric and also their correlation with this metric. The hyperparameters are ranked in order of importance from most significant to least significant. The green correlation represents a positive relationship, which means that as the value of the hyperparameter increases, the error of the model increases. A red correlation represents the opposite, and thus the best mean squared error metric decreases as the value of the hyperparameter increases.
# Effect of Dry-Wet Cycle Periods on Properties of Concrete under Sulfate Attack
## 2 Experimental Program  
### Materials  
Ordinary Portland PO42.5 Cement and fly ash (FA) were adopted in this study. Their main chemical components and physical properties are provided in Table 1. The XRD spectrum of the fly ash is shown in Figure 1, and Figure 2 shows the particle size distribution of the cement fly ash. The aggregate included natural river sand with a fineness modulus of 2.87 and basalt gravel with a continuous gradation of 5-20 mm in particle size. Replacing cement with 20% fly ash had been reported to significantly improve the resistance of concrete to sulfate attack [24, 25, 26]; thus, concrete with a strength grade of C30 and 20% fly ash were selected in this study. The mixture ratios are shown in Table 2. In addition, industrial-grade sodium sulfate with a purity of 99% was bought from a market. A test erosion solution was prepared with 5% sodium sulfate (by weight).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Composition** & **Cement** & **Fly Ash** \\ \hline
**Chemical (\%)** & & \\ MgO & 3.34 & 1.5 \\ SiO\({}_{2}\) & 31.43 & 58 \\ CaO & 41.28 & 2.8 \\ Al\({}_{2}\)O\({}_{5}\) & 12.43 & 30 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.34 & 4.3 \\ K\({}_{2}\)O & 0.80 & 1.36 \\ SO\({}_{3}\) & 3.22 & 1.22 \\ Na\({}_{2}\)O & 0.43 & 0 \\ LOI & 1.09 & 0.82 \\ C\({}_{2}\)S & 18.79 & - \\ C\({}_{3}\)S & 43.38 & - \\ C\({}_{3}\)A & 6.00 & - \\ C\({}_{4}\)AF & 8.55 & - \\ \hline
**Physical properties** & & \\ Spec. surf. area (m\({}^{2}\)/g) & 332 & 287 \\ Density (kg/m\({}^{3}\)) & 3.06 & 2.34 \\ Compressive strength at 3/28 days (MPa) & 26.6/54.5 & - \\ Flexural strength at 3/28 days (MPa) & 5.42/8.74 & - \\ Time of initial/final setting (min) & 90/300 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition and physical properties of cement and fly ash (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**W/B**} & \multirow{2}{*}{
\begin{tabular}{c} **Sand Ratio** \\ **(\%)** \\ \end{tabular} } & \multicolumn{4}{c}{**The Amounts of Different Materials (kg/m\({}^{3}\))**} \\ \cline{3-6}  & & **Water** & **Cement** & **Fly Ash** & **Sand** & **Aggregate** \\ \hline
0.54 & 36 & 195 & 289 & 72 & 664 & 1180 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Designed mixture for concrete by proportion.
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 6 Experimental Results
### Occlusion Avoidance
#### 6.2.1 Optimization Using Environment Gradients  
The test cases were repeated numerous times, where we varied the initial azimuth (\(\varphi\)) value between [\(-\pi/32\); \(\pi/32\)) with a step size of \(d\varphi=\pi/208\). The results shown in Figure 5 visualize the main advantage of the pre-trained method. With objects characterized by complex geometries, such as the chairs and the tables used, the environment-supplied gradient-based method took a considerably higher amount of steps to reach an unoccluded state, especially if the initial azimuth value was set to be low, i.e., between \(-2^{\circ}\) and \(2^{\circ}\). We argue that this was caused by the environment gradients moving the camera towards local minima, which were more common in cases of objects containing structural holes and beams. This phenomenon can be seen from the variance of the steps taken in Figure 5. During the two chairs test case, when the initial azimuth value was set to 0, the direct  
Figure 4: Environments for test scenarios. Two chairs (**left**) and two tables (**right**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **Loss** & **Pixel Acc** & **IoU** \\ \hline Base UNet & 0.038 & 0.607 & 99.65 & 61.09\% \\ R-UNet & 0.038 & 0.606 & 99.65 & 61.47\% \\ SR-UNet & 0.038 & 0.61 & 99.64 & 60.34\% \\ SAR-UNet & 0.038 & 0.609 & 99.66 & 61.87\% \\ SAR-UNet-L1 & **0.005** & **0.604** & **99.67** & **62.37**\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the pre-training on the architectures investigated. We display the dice loss, pixel-wise accuracy and Intersection over Union (IoU) measures for the occlusion segmentation, and the Mean Squared Error (MSE) for the gradient prediction task. Best results are emphasized in bold.  
gradient-based agent took 2377 steps to find an unoccluded state, whereas completing the task for two tables only took 163 steps. Note that, in the figure, the maximum number of steps was limited to 2000 to enhance visibility.  
We found that the methods usually yielded somewhat symmetrical results for negative and positive initial \(\varphi\) values. This symmetry can also be observed in Figure 5. We argue that this was caused by the symmetrical structure of the used table and chair objects. The slight differences were most likely caused by the slight imperfections of the surfaces of the models, that caused the silhouette renders used for gradient calculation to become unsymmetrical. These imperfections, however, can also be expected from real-life, physical objects. Therefore, we think that this feature of the ShapeNet dataset had a positive effect on the generalization capabilities of our solution. The previously mentioned problem of the render engine causing the sporadic appearance of NaN gradients could also lead to an increased number of steps taken by the agents.
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
###### Abstract  
Combining the requirements of spectrometers for unmanned aerial vehicle platforms, a miniaturized airborne wide-angle push-broom imaging spectrometer with an Offner configuration is designed. The system comprises an objective lens and an Offner-type spectrometer with a spectral range of 400-1000 nm and a spectral resolution of 15 nm. The objective lens and Offner spectrometer are designed in isolation before integration. The front objective lens is an inverted telephoto with a focal length of 13 mm, a relative aperture of 1/4.5, and a field of view of 54\({}^{\circ}\). The frequency of the convex grating in the Offner configuration is 102 LP/mm, and the dispersion width is 2.6 mm. The modulation transfer function of the integrated system is greater than 0.4 at the Nyquist frequency in all spectral bands. To estimate the volume and weight of the system, a preliminary optical-mechanical design scheme is given in this paper. The entire spectrometer has a volume of \(130\times 80\times 120\) mm and is less than 3 kg, which realizes the miniaturization design of the imaging spectrometer with a wide field of view for unmanned aerial vehicle platforms.  
spectrometer; UAV; optical design; SNR; Offner; miniaturization +
Footnote †: journal: Applied Physics Letters
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture
#### 3.1.3 Network Bandwidth Constraints  
Centralized solutions are not suitable for smart city scenarios since sending all the data collected by IoT devices to the cloud layer will require an enormous amount of network bandwidth, which centralized solutions and Low Power Wide Area Network (LPWAN) technologies cannot support. Fog computing enables data processing and analytics operations locally, which drastically reduces the amount of data that needs to be sent to the cloud layer.
## 2 Materials and Methods
### RNA Extraction and Transcriptome Construction  
_R. glutinosa_ samples (young leaves, tuberous roots, and adventitious roots) were collected and immediately ground to powder in liquid nitrogen. Total RNA was extracted using the Trizol reagent. The RNA quality and quantity were evaluated using Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The purity of RNA was analyzed using gel electrophoresis and Agilent 2100 Bioanalyzer. The Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA) was applied to construct transcriptomes. The low-quality reads, including reads with adaptors, unknown nucleotides, or reads with more than 50% of bases with Q-value \(\leq\)20, were discarded to generate clean reads. Q20, Q30, and GC content of the clean data were calculated, and the downstream analyses were conducted based on the clean data with high quality. Transcriptome assembly was accomplished using Trinity software with min_kmer_cov set to 3 by default, and all other parameters set to default [30].
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
###### Abstract  
Industry 4.0 is moving forward under technology upgrades, utilizing information technology to improve the intelligence of the industry, whereas Industry 5.0 is value-driven, aiming to focus on essential societal needs, values, and responsibility. The manufacturing industry is currently moving towards the integration of productivity enhancements and sustainable human employment. Such a transformation has deeply changed the human-machine interaction (HMI), among which digital twin (DT) and extended reality (XR) are two cutting-edge technologies. A manufacturing DT offers an opportunity to simulate, monitor, and optimize the machine. In the meantime, XR empowers HMI in the industrial field. This paper presents an XR application framework for DT-based services within a manufacturing context. This work aims to develop a technological framework to improve the efficiency of the XR application development and the usability of the XR-based HMI systems. We first introduce four layers of the framework, including the perception layer with the physical machine and its ROS-based simulation model, the machine communication layer, the network layer containing three kinds of communication middleware, and the Unity-based service layer creating XR-based digital applications. Subsequently, we conduct the responsiveness test for the framework and describe several XR industrial applications for a DT-based smart crane. Finally, we highlight the research challenges and potential issues that should be further addressed by analyzing the performance of the whole framework.  
digital twin; extended reality; human-machine interaction; framework; crane +
Footnote †: journal: Applied Sciences