esa-sceva/satcom-chunk-collection huggingface dataset:
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Front-End  
The front-end includes a browser-based GIS application that represents the geographical area of interest and visualizes data from various sources, such as numerical series, shapes, and rasters. Users can locate meteorological stations and nearby BOLAM grid points. For each station or BOLAM node, the time series of selected meteorologicalvariables--temperature, relative humidity, and rainfall--can be retrieved from the InfluxDB database and displayed in a dedicated popup window. Users can also compare values between different points. The front-end is divided into two areas: \"Weather\", which displays maps with meteorological indicators, and \"Pests and Diseases\", which visualizes risk maps. Examples and screenshots from both areas will be shown in Section 3. Here, we mention that users can view both raster maps and time series of either measured weather variables (sampled up to 10 m) or forecasts for the next days, both at hourly intervals (see Figure 4). In the \"Pests and Diseases\" section, users can view rasters associated with the 3-day rice blast (\"brusone\") risk maps for the paddy fields at different resolution levels, along with supplementary information on the rice crops and the selected farm. An example is shown in Figure 5 for 12 July 2023. Similarly, detailed and interactive graphical outcomes are provided for locust egg hatching, as illustrated in Section 3.  
Figure 4: Temperature distribution for a selected geographic window at a specific day and time. Enabling the “Station” flag displays all stations (shown as blue dots) within the window. Detailed variations of the selected variable are shown for each station (dark popup), along with the relative trend of the value from the nearest BOLAM grid point (blue popup). The white dots represent the location of the 4 nodes of the BOLAM model closest to the selected station.  
Figure 5: Detail of the rice blast (“brusone”) risk map on a given day, highlighting the paddy field areas. The dark popup allows users to view specific information about the selected parcel, including the name of the farm, technical features like area, the rice cultivar, and other relevant details.  
On the technological side, the geographical data are exposed through GeoServer (version 2.5), and the entire application is written with the JavaScript framework MapWall (version 0.6), developed in-house at CRS4.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 5 Conclusions  
This article demonstrated the feasibility of using a gamma random variable to characterize the elevation angle PDF and CDF. The proposed methodology allowed PDF and CDF calculations of the elevation angle for LEO satellites for altitudes between 1000 km and 2000 km. The characterization of elevation angle through a gamma random variable allowed an easy computation of first and second order statistics, which, to the best of our knowledge, have not been previously addressed in the literature.  
The proposed methodology was validated with an error analysis against the empirical CDF's. The results showed that errors are low, with a mean absolute error much below 5% in most of the cases, and with around 95% of the total errors being below 5%.  
Furthermore, the proposed methodology allows an easy comparison between multiple orbits in order to determine the most suitable orbital parameters to provide coverage above a minimum elevation angle at a particular latitude.  
Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology and software, [PERSON]-[PERSON]; validation, A.A.-Z. and R.V.-H.; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON], [PERSON] and [PERSON]; project administration, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 17: CDF’s of the absolute error by elevation angle range for all the simulated orbits and ES’s.  
Figure 18: Boxplots of the absolute error by elevation angle range. Simulation results vs proposed methodology.  
**Acknowledgments:** We would like to acknowledge the support from the Smart Digital Technologies and Infrastructure Research Group, the project \"Digital Technologies to Create Adaptive Smart Cities\" as part of the Challenge-Based Research Funding Program, and the School of Engineering and Sciences at Tecnologico de Monterrey for providing the means to develop this collaborative work.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
CDF Cumulative distribution function  
CCDF Complementary cumulative distribution function  
ES Earth station  
GEO Geostationary Earth orbit  
LEO Low Earth orbit  
LMS Land mobile satellite  
LOS Line-of-sight  
NGEO Non-Geostationary Earth orbit  
NGSO Non-Geosynchronous orbit  
NLOS Non-line-of-sight  
PDF Probability density function
## 5 Research Results
### Case Study: Development of the Blockchain-Based NFT Trading Web 3.0 Application
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Metric**} & **Sprint 1** & **Sprint 2** & **Sprint 3** \\ \cline{2-4}  & **Result** & & \\ \hline Test cases executed & 50 & 40 & 45 \\ \hline Test cases passed & 48 (96\%) & 39 (97.5\%) & 44 (97.7\%) \\ \hline Max concurrent users supported & 950 & 1250 & 2500 \\ \hline Average response time (100 users) & 200 ms (UM) & 180 ms (CRUD) & 150 ms (CRUD) \\ \hline Average response time (500 users) & 350 ms (UM), 550 ms & 350 ms & 350 ms \\ \hline Error rate at 1000 users & 5\% (MS) & 4\% (some timeouts) & 4\% (some timeouts) \\ \hline Downtime during testing & 3 min (99.8\%) & N\(/\)A & N\(/\)A \\ \hline Max scaling instances & N\(/\)A & 6 instances & 8 pods \\ \hline Uptime during rolling updates & N\(/\)A & 99.98\% & 99.99\% \\ \hline Scaling trigger & N\(/\)A & N\(/\)A & 80\% CPU utilization \\ \hline Pipeline build time & N\(/\)A & N\(/\)A & 5 min \\ \hline Scaling efficiency & 25\% & 33\% & N\(/\)A \\ \hline JWT authentication success rate & 100\% & N\(/\)A & N\(/\)A \\ \hline Vulnerabilities detected & 0 critical, 1 medium, 5 low & 2 total (0 critical) & 2 total (0 critical) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Metrics and results. performance improvement when scaling up from 2 to 8 pods under high traffic, and security testing revealed no critical vulnerabilities. The final release was a fully integrated, scalable, and secure application, tested thoroughly for performance, scalability, and security, being capable of handling high traffic while maintaining the core decentralized functionality of the NFT marketplace. Image of the final release is given in Figure 4.
# Hardware-Aware Mobile Building Block Evaluation for Computer Vision
## 4 Hardware-Aware Mobile Building Blocks Evaluation
### Squeeze-and-Excitation Unit  
A squeeze-and-excitation (SE) [4] block is an architectural unit for convolutional neural networks that performs dynamic channel-wise feature recalibration to improve the representation power of a convolutional building block. Given input \(\mathbf{X}\), the SE unit first squeezes the spatial information using global average pooling:  
\[z_{c}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}x_{c}(i,j), \tag{2}\]  
This step is followed by an excitation step that recalibrates the channels:  
\[\mathbf{s}=\sigma(W_{2}(\mathrm{ReLU}(W_{1}(\mathbf{z})))). \tag{3}\]  
where \(W_{1}\) and \(W_{2}\) are two learned linear transformations and \(\sigma\) refers to the sigmoid activation function. The final output \(\mathbf{Y}\) of the SE unit is obtained by scaling the original inputs \(\mathbf{X}\):\(\mathbf{Y}=\mathbf{X}\cdot\mathbf{s}\), where \(\cdot\) refers to the channel-wise multiplication.  
MnasNet [15] brought the SE unit to mobile vision networks, as they were shown to improve model performance compared to previous state-of-the-art models with similar latency. More recently, however, squeeze-and-excitation modules have been removed from mobile vision architectures when they are deployed on embedded ML accelerators. EfficientNet-lite [32] and MobileNetEdgeTPUv2 [33], for example, remove the SE unit as they claim it is not well supported for mobile accelerators, such as the Google EdgeTPU.  
We evaluated the SE block by comparing design spaces with the depthwise separable building block, with and without an SE unit extension. Figure 8 shows that the SE block is a favorable addition for certain mobile platforms as it is very FLOPS-efficient. This translates to it being more optimal for mobile CPUs and GPUs. However, for the embedded Jetson Nano GPU, it is no longer a clear benefit to include the SE unit as it fails to exploit the optimizations for convolutional kernels and adds a significant delay. Comparing models with and without the SE unit and with equal execution time on a mobile CPU, we found that those same models without the SE unit have a running time that is up to a factor of 1.9\(\times\) longer on the embedded Jetson Nano GPU.  
Figure 7: The bottleneck structure has little influence on the latency across all tested hardware platforms. The accuracy of inverted bottlenecks significantly lags behind building blocks with standard or no bottleneck.
# Handover Management for D2D Communication in 5G Networks
## 3 System Model and Problem Formulation  
### System Model  
Assuming there are \(n\) macrocells in the network, we denote the set of eNBs as \(N=\{1,2,\cdots,n\}\). Also, we assume that there are \(d\) pairs of D2D users with \(DUE_{i}^{1}\) and \(DUE_{i}^{2}\) be the transmitter and the receiver of \(i\)-th D2D pair, respectively. For the received signal condition of each user, we use \(RSRP_{k}^{i}\) to represent the RSRP received by \(k\)-th user from \(i\)-th eNB. In addition, we define \(\varphi\) as the minimum requirement of RSRP for each device. Figure 1 shows the network environment under consideration in this paper. \(DUE_{2}^{1}\) and \(DUE_{2}^{2}\) have connected to different eNBs on their control channels. There are additional delays and overhead due to messages exchanges between these two eNBs. For simplicity and without loss of generality, we assume that each pair of D2D users is served by the same eNB in the beginning. Also, only the control plane is considered in this work; the data link between both users of the same D2D pair is out of the scope of this study ([PERSON] et al., 2020).  
In conventional trigger-based handover with the LTE A3 event, users inform the original serving eNB only when the handover conditions are met. Our method, however, let each user periodically report its measurement of received signals from its neighbor eNBs. The original serving eNB makes the handover decision for each D2D pair based on the channel conditions of both devices of the same D2D pair.  
Figure 1: Network environment under consideration.
# Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel
## 2 Materials and Methods
### Energy Balances  
#### 2.3.1 Thermal Drying of SRF from Screening Waste  
Prior to the thermochemical treatment of the SRF by gasification or combustion, the waste must be dried. In the case of combustion, moisture content is a key factor influencing the performance of alternative fuels in incineration, including combustion completeness, energy recovery, and pollutant emission [37]. Therefore, to improve the operation and mitigate corrosion, moisture should not exceed 10 wt% [38]. In the case of the gasification, SRF was considered as a residue that had been totally dried before gasification in Test #1 (air gasification of dried SRF), while it was supposed to be a partially dried residue, with a moisture content of 33.7 wt%, in Test #2 (air/steam gasification of SRF with a ratio S/C of 1 kg steam/kg C; steam is supposed to come from the moisture of the SRF fed).  
The heat requirement for drying the SRF waste (\(Q_{drying}\)) from its initial moisture (77.3 wt%) to the different final moisture contents in the range 0-10 wt% required for combustion or 0-33.7 wt% required for gasification has been calculated following an enthalpy balance, considering 100 \({}^{\circ}\)C as the exit temperature for SRF and steam:  
\[Q_{drying}=\Big{(}m_{solids\;in\;SRF}\times C_{pSRF}+m_{H_{2}O\;in\;SRF} \times C_{pH_{2}O(l)}\Big{)}\cdot\Delta T+m_{H_{2}O\;cap}\times\Delta H_{ vap,H_{2}O} \tag{5}\]  
where: \(Q_{drying}\) is the heat required for drying the SRF from its original moisture (77.3 wt%) to the desired final moisture (\(\mathrm{MJ/kg}\) of raw SRF); \(m_{solid\;in\;SRF}\) is the mass of solid contained in the raw SRF (kg); \(m_{H_{2}O\;in\;SRF}\) is the mass of water in the raw SRF before thermal drying (kg); \(C_{pSRF}\) is the specific heat capacity of the dry matter in SRF, estimated as 1.6 kJ\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\), \(C_{pH_{2}O(l)}\) is the specific heat capacity of liquid water, considered constant in the temperature interval of 298-373 K, 4.18 kJ\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\); \(m_{H_{2}O\;cap}\) is the mass of water evaporated during the thermal drying of SRF (kg); and \(\Delta H_{up,H_{2}O}\) is the enthalpy of vaporisation of water at the boiling point of 373 K (2.26 MJ/kg of H\({}_{2}\)O).
## 3 Theoretical Model  
### Numerical Simulation  
The transmission of light waves in an optical fiber can be expressed by Maxwell's equations. In general, a slowly varying envelope is used to approximate the value of the rapidly varying component in an electric field for further simplification. By introducing a delay coordinate system \(T\) for moving the group velocity of the pulse, the optical envelope is expressed using the GNLSE as follows [33]:  
\[\frac{\partial A(z,T)}{\partial z}=-\frac{\alpha}{2}A(z,T)+\sum_{k\geq 2}\frac{ \hat{k}^{+1}}{k!}\beta_{k}\frac{\partial^{k}A(z,T)}{\partial T^{k}}+i\gamma(1 +\frac{i}{\omega_{0}}\frac{\partial}{\partial T})\Bigg{[}A(z,T)\int\limits_{- \infty}^{\infty}R(T^{\prime})\big{|}A(z,T-T^{\prime})\big{|}^{2}dT^{\prime}\Bigg{]} \tag{1}\]  
where \(A(z,T)\) is the optical field envelope, and \(\alpha\) denotes the loss coefficient. The entire right-hand second term is a wavelength dispersion effect that includes the high-order dispersion. The last term includes the nonlinear effects, and \(R(t)\) is the nonlinear response function that consists of two parts \(f_{R}h_{R}\) and \((1-f_{R})\delta(t)\), which indicate instantaneous electronic and delayed Raman contributions, respectively. Here, \(h_{R}\) is the Raman response function, and \(f_{R}\) represents the Raman fractional contribution to the overall nonlinear response. In addition, \(\delta(t)\) is the Dirac delta function.
###### Abstract  
In real-world scenarios, it is common to apply a damping layer of a specific thickness to the surface of an acoustic black hole (ABH) beam to boost its energy dissipation capacity. However, it has become apparent that excessive damping layers might result in negative consequences. The present study suggests employing the backpropagation (BP) algorithm to refine the positioning, thickness, and contour of the damping layer for optimal results. This study begins with the derivation of a semi-analytical solution for the vibration characteristics of an ABH beam under a harmonic load using the Gaussian expansion method (GEM). This process results in the preliminary identification of a thickness profile for the damping layer that exhibits significant potential for energy dissipation. Subsequently, a BP neural network is trained on the data produced by the semi-analytical solution to further optimize this thickness variation function. The findings reveal that the geometry of the damping layer has a more complex influence on performance than previously recognized. The optimization guided by the BP neural network suggests that achieving a strong ABH effect does not require uniform application of the damping layer across the entire ABH section. Rather, the most effective approach is to concentrate the damping layer thickness at the ABH tip, with a rapid decrease in thickness as one moves away from this point. It is also determined that applying a damping layer in areas far from the tip is unnecessary. Additionally, an innovative strategy is proposed to enhance the system's energy dissipation capabilities without changing the truncation thickness of the ABH beam. This research contributes to a deeper understanding of how the damping layer affects the energy dissipation performance of ABH beams.  
ABH beam; damping layers; BP algorithm; optimization; Gaussian expansion method +
Footnote †: journal: Applied sciences  
Article  
[PERSON], [PERSON], [PERSON], B. Optimization of the Geometric Characteristics of Damping Layers for Acoustic Black Hole Beams Based on the Backpropagation Algorithm. _Appl. Sci._**2025**, 15227. [[https://doi.org/10.3390/app15031227](https://doi.org/10.3390/app15031227)]([https://doi.org/10.3390/app15031227](https://doi.org/10.3390/app15031227))
# Dynamic Modeling and Simulation of a Cyclic Towing System for Underwater Vehicles
## 4 Analysis of Key Parameter Influences
### Different Tension Forces  
In Case Study 2, the tensioning device applies tension forces of 1500 N, 2500 N, 3500 N, and 4500 N to the cable, while other parameters remain unchanged. At this time, the towing wheel is given the same input torque, enabling the underwater vehicle to reach the target speed of 2 m/s. Figure 7 presents the displacement and speed of the underwater vehicle, the tension and deformation of the equally divided sections of the cable between the towing wheel and the underwater vehicle, and the relationship between the input torque of the towing wheel and the speed of the underwater vehicle.  
As the tension force gradually increases, it can be observed from Figure 7a that the displacement of the underwater vehicle is 8.01 m, 8.008 m, 8.005 m, and 8.003 m, respectively. According to Figure 7b, during the constant speed phase, the speed fluctuation ranges of the underwater vehicle are 1.989 m/s to 2.056 m/s, 1.994 m/s to 2.057 m/s, 1.996 m/s to 2.051 m/s, and 1.997 m/s to 2.053 m/s, respectively. It is evident that, under the same input torque, as the tension force increases from 1500 N to 3500 N, the range of speed fluctuations gradually decrease. This indicates that, as the tension force increases, the fluctuations of forces within the towing system on the cable and the impact of external disturbances on the speed of the towed body are reduced, thereby improving the speed stability of the system. However, when the tension force is between 3500 N and 4500 N, the change in the speed fluctuation range is minimal. This is because the tension force has reached an optimal range for suppressing speed fluctuations. In practical applications, it is crucial  
Figure 7: Dynamic curves of the underwater vehicle and cable under different tension forces.  
to select an appropriate tension force to ensure speed stability while saving energy and reducing equipment wear.  
As shown in Figure 7c,d, under the tension forces of 1500 N, 2500 N, 3500 N, and 4500 N, the maximum tension in the cable occurs during the acceleration phase, with values of 4722 N, 6040 N, 7362 N, and 8686 N, respectively. At these points, the maximum deformation of the cable is 0.189 m, 0.267 m, 0.344 m, and 0.422 m, with corresponding maximum elongation rates of 0.49%, 0.69%, 0.89%, and 1.09%. The average tension in the cable during the constant speed phase is 2991 N, 4002 N, 5000 N, and 5999 N, while the average deformation of the cable is 0.088 m, 0.147 m, 0.206 m, and 0.264 m, respectively. With an increase in tensioning force applied to the system by the tensioning device, the elastic stress is intensified, resulting in the significant stretching deformation of the cable.
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 5 Results
However, the estimated current shows some oscillations that generate error peak values of almost 0.5, which will deteriorate the MPPT performance affecting the generated power. Figure 11: PV current for MPPT test. Figure 6.0 Irradiation profile based on EN50530 standard. This MPPT performance deterioration can be observed in the PV system's generated power behavior which is illustrated in Figure 6.1 The generated power using the proposed current sensorless scheme has a similar behavior than the conventional measurement based scheme, which indicates the feasibility of the proposal. Nevertheless, despite the good estimation and operation of the sensorless scheme, there are power losses caused by the current estimation error. Such losses can be reduced by improving the observer's parameters which leads to a better current estimation. Figure 15 shows the power error between the conventional INC scheme and the sensorless proposal, the power losses caused by the estimation error are evident but also small. The average value for this error is 1.15% in the range \(t=0.5\)-\(10\) s. Hence, this power losses are small enough to be a good trade-off for eliminating the cost associated with a current sensor, mainly when working with small scale domestic PV systems.
# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
## 3 UAV Design
### Preliminary Structural Design  
Based on the operational requirements, an enclosed structure must be created to protect all the electronic components from heat and combustion gases, isolate the propellers, and make a stiff, lightweight structure. The main structural elements are the top and bottom main frame panels, which support and protect all the components. Propellers also have to be protected from external impacts, while the inner volume of the UAV must be isolated from convection caused by the airflow through the propellers. For this purpose, propeller ducts were designed. Lastly, aluminum hexagonal standoffs keep the distance between the main frame panels and separate the electronic components from direct contact with the frame. A screenshot of the 3D model is shown in Figure 1. The golden reflection in the propeller ducts comes from a heat-reflective film applied to reduce the radiation absorption through these harder-to-insulate surfaces.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 1 Introduction
[37] found a reduction in the modulus of elasticity due to the increase in waste PE aggregate in mortars, which is a consequence of the reduction in speed in the ultrasonic pulse. The compressive strength was reduced due to the increase in the PE aggregate in the mixtures; a strength reduction between 29% and 69% of the reference sample was found for a 50% replacement rate of sand with PE aggregate [38]. Mixtures containing 50-60% low-density polyethylene (LDPE) were found to meet the strength criteria for masonry mortars [35]. The flexural strength is reduced as a function of an increase in the rate of PE aggregate in the mixture. A reduction in flexural strength was observed between 18% and 40% with the 50% replacement rate of sand by waste PE aggregate. Due to the presence of plastic in the mixture, increasing the deformation index of the mortar, the resulting material can absorb shock and stretch, thus becoming more ductile during mortar cracking, thus reducing cracking [34]. PE fiber provides improved durability of mortar to sulfuric acid attacks. Using ground-granulated blast-furnace slag (GGBFS) to replace cement can improve the strength of the concrete in the late period and reduce cement use and slag emissions. We expect to study the ratio of mixing these two materials to improve the engineering properties and durability of the cement mortar. The purpose of this study is to compare research trials with the previous literature using lightweight polyethylene (PE) aggregate added and ground granulated blast furnace slag (GGBFS) to replace cement in cement mortar and to learn about their performance and durability.