esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Enabled Smart Cities: Evolution and Outlook
## 2 IoT-Enabled Smart Cities Landscape
### Sunshine Coast Region  
The Sunshine Coast Region in Australia has started to implement a smart city program in 2016 with the goal to \"build a stronger economy and a safer community, and improve service delivery to our residents, businesses and visitors.\" [30]. The expected benefits include reduced carbon emissions, traffic congestion and consumption of resources, improved standard of services and quality of lifestyle for residents, reduced costs of service delivery, increased safety and data-driven design and planning. The Smart City Framework was developed together with Cisco and Telstra.  
Specific projects that have been implemented are the following [31]:  
* **Sunshine Coast Council (SCC) App:** provides information about facilities, events, guided tours, but also serves as a disaster hub with emergency contacts.
* **Smart Street:** with free public WiFi collecting data to understand how public spaces are used. Overall, more than 200 access points have been deployed.
* **Pedestrian and cyclist counters:** monitoring how many people use public areas like parks and walking trails, allowing better planning, maintenance and cleaning.
* **Networked LED street lighting:** is turned on and off automatically during twilight periods to reduce energy consumption and thus electricity costs and CO2 emissions.
* **Waste bin sensors:** to measure waste levels and send alerts if the bin is full or still empty to help deliver more cost-effective waste management services.
* **Networked irrigation system:** monitors the soil and sends alerts when watering is required. Additionally, flow meters identify problems and leaks to reduce the waste of water.
* **Networked flood sensors:** installed on roads and bridges, which are susceptible to flooding.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 3 Dataset  
The dataset consisted of over 600 high-resolution satellite scans in the X-Band. The dataset was collected from TerraSAR-X working in ScanSAR, Spotlight, and Stripmap modes, made available by the European Space Agency. The orbit height was 514 km at the equator, at an inclination of 97.44\({}^{\circ}\). The Open Data program imagery from Capella Space was also used. The resolution range was between 1 m \(\times\) 1 m and 20 m \(\times\) 20 m, and image sizes covered over 10,000 \(\times\) 20,000 pixels. The scans were gathered from nadir or close to nadir angles.  
Figure 5: Two pairs of the images before (X—input) and after filtering (Y—labels).  
For training purposes, the images were compressed into 8-bit png images. For Neural Network training, sub-images were extracted with a size of \(1024\times 1024\) pixels with the scale N:1 where N \(\in\) <1, 10>. This was performed as an extraction of a sub-image of size \(1024\,k\times 1024\,k\) pix and zooming out according to the description in the Section 2.1 (where \(k\) is scaling coefficient, \(k\in N\)). Images with a black area of bigger than 20% of the image were deleted from the dataset.  
A train-val-test split was performed in the ratio (75%-20%-5%). It is important to mention that the test dataset consisted of all sub-images with 5% of the high-resolution images, so that they did not occur in the training or validation data, while the train-val split was performed in a per-sub-image manner.
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 3 Fuzzy Approaches for Energy Performance of Buildings Forecasting
### Fuzzy Models Identification  
In this section the development of ANFIS and FIR models is described. As mentioned before, in this application we have two output variables: HL and CL. We want to study if it is possible to estimate each output by using the eight input variables that represent different building parameters (see Table 1). Both ANFIS and FIR methodologies allow developing models that have a single output, that is, SISO (single input single output) or MISO (multiple inputs single output) models. Therefore, for each methodology two sets of models are obtained, one for each possible output. We refer to sets of models because for each methodology and each output we obtain a model for each of the 10-fold CV,and this is repeated 100 times. Therefore, 1000 models are obtained and validated for each of the two methodologies and outputs studied.  
Both fuzzy approaches need to discretize the quantitative data into qualitative data. To this end, it becomes necessary to define at least two parameters: the number of classes (also called granularity) chosen for each input variable (and also for the output variables in the case of the FIR models), and the shape of the membership functions of the input variables (and also for the output variables in the case of the FIR models).  
In this research we decided to discretize the input variables relative compactness (RC), surface area (SA), wall area (WA), and glazing area distribution (GAD) into three classes and roof area (RA), overall height (OH), orientation (O), and glazing area (GA) into two classes. The output variables are discretized into three classes in the case of the FIR models. Remember that ANFIS does not have fuzzy consequents, that is, the rules' output is a function (see Figure 5). These discretization parameters have been chosen based on the analysis of the data. The variable OH can take only two possible values and is, therefore, represented in two classes. Variables RA, O, and GA can take four different values, and some of these values appear only a few times. A discretization with more than two classes does not enhance the model's prediction power; instead, a higher number of classes can lead to a curse of dimensionality problem. Therefore, it was found that two classes are enough for these variables to have a good representation and to obtain efficient models. The variables that are discretized into three classes have a uniform distribution in their dimensionality space; therefore, an odd number of classes seem more reasonable. Three is the lowest number of classes that give good results for these variables. A Gaussian shape has been used to discretize all variables, since slightly better results are obtained.
## 4 IoT for Geospatial Research
### Scope of Analysis  
An interesting research question was whether the analysis performed was local (i.e., at the specific location where the IoT sensors were physically placed) or more global (i.e., to detect events ata certain regional scale). Only 15 papers (27%) used measurements at the spot, without generalizing. Examples include tourism recommendations [20; 51]; monitoring water levels [21], tsunami [27], landslides [26] or even human diseases [29] at specific locations; or travel planning [52]. Table 2 summarizes the cases where IoT-based sensors and their measurements were used to generalize and make larger-scale reasoning from the limited number of point measurements. These cases were categorized according to the IoT method used. Six different methods were defined, while the seventh one is a combination of the other six. Participatory sensing, fixed IoT sensor installations, satellite imagery and ground sensor sampling constitute the most popular IoT-based methods for generalizations. It was also observed that the method of ground sensor sampling was associated with the geospatial analysis techniques of interpolation and kriging. Relevant to the scope of analysis is the question of whether there have been observed temporal trends and findings in the use of IoT in geospatial context for longer term. An interesting time-related challenge observed was about how the nature and composition of fish communities varies in time daily and seasonally around artificial reefs [67]. Furthermore, time series data allowed creating maps of potholes in streets, and pollution and noise around a city [25], with classification as day vs. night, or in different seasons, to observe seasonal changes [22]. It was also possible to monitor travel and activity patterns to understand daily routines of commuters, predicting traffic [12; 56; 42] and estimating the impact of climate change, i.e., how the change of season varies geographically affecting different ecosystems [24; 70]. Continuous measurements allowed studying phenomena such as the temporal variations of landslides [26], the evolution of disease outbreaks [29; 48] and spatial risk prediction [65], the activity and behaviour of wild animals [59; 60], the behaviour of invasive species relative to native species in biodiversity-related projects [49], and the seasonal impact of livestock agriculture on the physical
## 2 Materials and Methods
### Statistical Analysis  
All data are presented as means \(\pm\) standard deviations (SDs). The normality of the data was assessed using the Shapiro-Wilk test. Correlation analysis was conducted to examine the relationship between the AmAt ratio and ME, with correlation strength interpreted as follows based on Hopkins [31]: 0-0.1 (small), 0.1-0.3 (moderate), 0.3-0.5 (large), 0.5-0.7 (very large), and 0.9-1 (extremely large). The corresponding 95% confidence interval was calculated as well. A one-way repeated-measures ANOVA was used to evaluate the effect of different SSC conditions on mechanical and EMG parameters, followed by Bonferroni post-hoc tests when significant effects were detected. The alpha level was set at 0.05. All statistical analyses were conducted using the GraphPad software (GraphPad Software, Inc., Boston, MA, USA, v. 8).
# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 3 Exhibition of Recent Literature
### Literature Examining Other Imaging Technologies from 2024
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Reference** & **Date** & **Picture** & **Model** & **Model** & **Damage** \\  & & & & & **Application** & **Type** \\ \hline [PERSON] and & [18] & 2024 & IR & custom CNN & \begin{tabular}{c} anomaly detection, \\ classification (12 classes) \\ \end{tabular} & \begin{tabular}{c} cracking, hotspot, \\ shadowing, diode fault, \\ soiling, vegetation, \\ offline module, etc. \\ \end{tabular} \\ \hline [PERSON] & [2019] & 2024 & IR & NASNet + LSTM & \begin{tabular}{c} anomaly detection, \\ classification (12 classes) \\ \end{tabular} & \begin{tabular}{c} hotspot, cracking, \\ shadowing, soiling, \\ offline, vegetation, etc. \\ \end{tabular} \\ \hline [PERSON] & [2019] & 2024 & IR & \begin{tabular}{c} CNN + SVM \\ (hybrid) \\ VGG, ResNet, ViT \\ \end{tabular} & \begin{tabular}{c} hotspot, cracking, \\ shadowing, soiling, \\ diode fault, offline \\ module, etc. \\ (12 classes) \\ \end{tabular} \\ \hline [PERSON] et al. & [2019] & 2024 & RGB (UAV) & \begin{tabular}{c} AlexNet + \\ ensemble (SVM, \\ KNN, J48) \\ \end{tabular} & \begin{tabular}{c} classification \\ (visual failures) \\ \end{tabular} & \begin{tabular}{c} snail trail, fractures, \\ discoloration, burn \\ marks \\ \end{tabular} \\ \hline [PERSON]- & [2019] & 2024 & RGB & \begin{tabular}{c} CenterNet-based \\ keypoint detection \\ \end{tabular} & \begin{tabular}{c} real-time solar \\ panel detection \\ with UAV \\ \end{tabular} & \begin{tabular}{c} panel level localization \\ \end{tabular} \\ \hline [PERSON] et al. & [2019] & 2024 & RGB & \begin{tabular}{c} CNN (VGG16 \\ based) \\ \end{tabular} & \
\begin{tabular}{c} anomaly \\ classification, \\ failure detection \\ \end{tabular} &
\begin{tabular}{c} dust, dirt, bird \\ droppings, shading \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of research publications examining other imaging technologies with CNN in 2024.
# Influence of Thin Fluorine Resin Film on DUV LED Packaging Devices
###### Abstract  
Amorphous fluorine resin is a promising material that can be used for the encapsulation of deep-ultraviolet light-emitting diodes (DUV LEDs) to promote the light output, due to its light characteristics which mean it shows no absorption in the DUV wavelength region. However, obvious decay and high cost are the problems faced by fluorine resin. In this study, thin-fluorine-resin-film-plm-packaged DUV LEDs were fabricated by the drip-coating method and the characteristics were tested. The results show that the light output power increased from 4.95 mW to 5.44 mW at 40 mA, and the cost of fluorine resin can be reduced to \(\sim\)10%. In addition, no degradation during the aging was observed. However, when the light output power reached 12 mW or higher, \(>\)10% decay was observed after aging for 1000 h. In conclusion, thin-fluorine-resin-film-packaged DUV LEDs can achieve 10% light output power enhancement by using less fluorine resin material, and the material is more applicable to low-power DUV LEDs.  
deep-ultraviolet light-emitting diode; fluorine resin; light output power; reliability
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 4 System Optimization Scheme Based on A Multi-Constraint Multi-Objective Genetic Algorithm
### Problem Formulation
#### Load balancing
The number of non-dominated solutions in this dominated layer can be expressed as  
\[c_{m}=\sum_{m=1}^{\mathrm{b}_{\mathrm{n}}}\mathrm{s}(\mathrm{d}(\mathrm{m}, \mathrm{n})), \tag{32}\]  
The shared fitness of the non-dominated solution \((F_{m}^{\prime},S_{m})\) can be expressed as  
\[h_{m}=\frac{\mathrm{s}(\mathrm{d}(\mathrm{m},\mathrm{n}))}{c_{m}}, \tag{33}\]  
A selection strategy based on the k-means algorithm and proportional distribution is used in each level of the dominance layers to select the optimal solution from the elite pool so that individuals are distributed along the Pareto front. The results in this dominance layer can be expressed as  
\[KNum_{m}=sgn(h_{m}-\sqrt{\sum_{k=1}^{3}\left[\frac{f_{k}((F_{m}^{\prime},S_{m}) )-f_{k}((F_{m}^{\prime},S_{m}))}{f_{k}^{max}(F,S)-f_{k}^{min}(F,S)}\right]^{2} }), \tag{34}\]  
where \(f_{k}((F_{m}^{\prime},S_{m}))\) is the solution of the kth objective function. \(f_{k}^{max}(F,S)\) and \(f_{k}^{min}(F,S)\) are the maximum and minimum values of the kth objective function, respectively. \(sgn\) is the sign function, which means that when the unit ball contains more dominated solutions, \(KNum_{m}\) will be larger.
## 2 Materials and Methods
### Model Calibration  
First of all, the boundary conditions and the recharge amount for a steady state model were fixed. Successively, the calibration of the model was carried out based on hydraulic head data available for the P1 borehole, varying the input parameter of the hydraulic conductivity until similar values were found for the simulated (output parameter) and observed P1 hydraulic heads.  
According to some specific procedures, such as the _trial and error method_ and _automatic calibration techniques_, the calibration phase represents the process through which the hydraulic parameters provided in the input for the model are modified, so that they are matched with the observed data. This can be considered a benchmark phase for groundwater modelling because it is preparatory for the implementation of the simulations having a predictive purpose [22], also allowing one to provide for the lack of hydraulic and hydrogeological data. As some physical and hydraulic parameters are not available, the simulated hydraulic conductivity was calibrated on the hydraulic head data of the P1 borehole detected during the time period of March 2012-March 2019; this well is located within the catchment area, about 400 m upstream of the spring outlet.  
Figure 8 shows different simulations of the model considering different thicknesses of the aquifer's saturated zone (TSZ), taking into account the narrow values of the hydraulic conductivity, \(K\), in particular, using the constant recharge amount \(R\) = 1.06 m/y, and considering the mean value of the hydraulic head detected in the borehole P1, \(h\) = 422.16 m a.s.l., the \(K\) value was found to range from a maximum of 0.01145 m/s, (TSZ = 100 m) to a minimum value of 0.000831 m/s ascribable to a TSZ = 2000 m. The calibration phase highlights the relationship occurring between the TSZ of the aquifer and the hydraulic conductivity of the medium for 3D groundwater, using the EPM approach.  
In particular, the _trial and error method_, used to match the simulated hydraulic head with the available observed data, highlights that the hydraulic conductivity of the medium drops with the increase of the aquifer TSZ, in accordance with the hydraulic assumptions followed to build the conceptual and numerical model (_Dirichlet_ and _Neumann conditions_). The hydraulic conductivity range found for different scenarios is in agreement with the K values estimated for other karst systems in southern Italy [41; 42].
## 2 Materials and Methods
### Large Eddy Simulations  
A higher spatial resolution in a numerical weather model can follow the terrain variations and capture large eddies in the atmospheric flow. In WRF, large eddy simulations (LESs) are applied by turning off the planetary boundary layer scheme, and the terrain height of the inner D05 domain enclosing the Fakken wind power plant is shown as a black rectangular box in Figure 3. The LES model domain is set up with a 3d Smagorinsky diagnostic K for sub-grid turbulence (km_opt = 3), the recommended diffusion option for complex terrain (diff_opt = 2), and sound-wave damping to allow for steep hills (epssm = 0.8).  
The horizontal resolution in D05 is 90.9 m, a nesting factor of 11 from the D04 1 km resolution, avoiding the \"terra incognita\" with spatial resolution in the range of 0.1-1 km where neither PBL parameterisation nor LES is optimal for modelling turbulence [39; 40]. The LES domain has 254 \(\times\) 154 cells, covering approximately 23 \(\times\) 14 km. The 30-arcsecond resolution in GMTED corresponds to approximately 900 m along the latitude lines, which is suitable for the D03-D04 WRF domains. For the D05 domain, the Norwegian Digital Terrain Model (NDTM) with a 10 m resolution is used as input. The 70 vertical levels used in the WRF domains are the pressure-based eta levels, where vertical stretching results in the lowest layers with a thickness below the 90.9 m horizontal resolution.  
Figure 3: Terrain height in WRF domain D04 using Norwegian Digital Terrain Model with 10 m horizontal resolution. Black rectangular box corresponds to D05 LES domain.  
LESs are generally computationally intensive, and special care must be taken to avoid numerical instabilities from the steep mountains in complex terrain. In this research, we have restricted the LESs to cover the ten longest high-wind-speed events found in the wind measurements during 2017. This allows us to study high-wind-speed measurements whilst at the same time ensuring that the measurements belong to a continuous event. Such events are easier to simulate and analyse than spread-out strong-wind measurements over a longer duration.
# Dynamics Analysis of Rotating Cantilever Beams with Free End Mass
###### Abstract  
An improved modeling method for the dynamics analysis of rotating cantilever beams with free end mass is introduced. The proposed improved modeling method is based on the nonlinear Green strain theory in this study. By using the Hamilton principle and the [PERSON] method, the discrete dynamic equations of the axial and chordwise motions are obtained. Meanwhile, the equations for free vibrations analysis of the rotating cantilever beam with free end mass are derived by introducing the equilibrium axial deformation into both axial and chordwise motions. Numerical simulations are performed to validate the proposed model, and the results are compared and discussed with the other modeling methods. The comparison shows that even a small free end mass has an important influence on the dynamic characteristics of the rotating beam. Furthermore, the influences of various parameters on the vibration frequencies and axial deformations of the rotating cantilever beams are analyzed, such as the angular speed, the free end mass to beam mass ratio, and the center body radius to beam length ratio. Under the same conditions, the influence of the free end mass on the variation in the first- and high-order frequencies with the increasing angular speed are different.  
rotating beam; free end mass; dynamic characteristics; dynamic stiffening; free vibration Article
## 3 Results  
### Model Simulation  
In this study, simulation tools called WindSim and WRF were used, and WindSim was used to analyze wind resources by HHWS and REWS, and calculate predicted power generation. WRF is used to calculate the thermal atmospheric stability, which affects the amount of wind power generation. Temperature, sea surface temperature, or heat flux values are required to obtain the thermal atmospheric stability. The only temperature observed from marine LiDAR were data for the month of December 2014 out of two years (a few percent of data acquisition rate, compared to the entire period). Therefore, ERA5 re-analysis data were used for MCP from the temperature point of view, and correlation with the observed values was analyzed with temperature data in December 2014 to verify ERA5 (Figure 5).  
For correlation analysis, root mean square error (_RMSE_), mean bias error (_MOE_), and index of agreement (_IOA_) indicators were used, and the equations for each indicator are as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(M_{i}-O_{i})^{2}}{n}} \tag{11}\]  
\[MBE=\frac{\sum_{i=1}^{n}(M_{i}-O_{i})}{n} \tag{12}\]  
\[IOA=1-\left[\frac{\sum_{i=1}^{n}(O_{i}-M_{i})^{2}}{\sum_{i=1}^{n}\left(\left|M _{i}-\overline{O}\right|+\left|O_{i}-\overline{O}\right|\right|^{2}}\right] \tag{13}\]  
Figure 4: Introduction to our research procedures and methodologies.  
As a result of analyzing the one-hour average temperature data of 10 m for the month of December 2014, RMSE, MOE, and IOA were (0.57, 0.08, and 0.98), respectively, showing a significant level of correlation. Although LiDAR observation data are used for wind resource analysis, wind speed correlation was also analyzed to further improve the reliability of the ERA5 temperature data, compared to the actual observation value. Since it is clear that temperature-related changes in the thermal properties in the atmospheric boundary layer will affect the changes in wind resources, the wind speed data of ERA5 were also compared with the observed values. Similarly, we compared only the month of December 2014, which was the same as the period for securing temperature data, and the RMSE, MOE, and IOA of wind speed data at 65 m of (1.46, \(-\)  
WindSim also showed CFD analysis results that converge in all 12 sector analyses at 30-degree intervals. It seems that no problems occurred in the process of calculating wind speed, wind direction, Weibull distribution, turbulence intensity, turbulent kinetic energy, and wake loss rate (Figure 7).