esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 2 Conventional Process of Machine Learning  
The process of creating ML prediction models mainly involves three steps: data collection, model establishment, and model evaluation.  
1. In the data collection phase, data quality checks ensure data integrity by addressing missing values, outliers, and duplicates. Missing values can be removed or imputed using statistical methods; extreme outliers may be excluded if they significantly impact results. Data are partitioned into training, validation, and testing sets to facilitate effective model training, tuning, and evaluation. For certain cases, specific values such as 0 or \(-1\) can be used to fill missing values.
2. In the model establishment phase, ML algorithms are chosen based on problem type and data. Common algorithms include supervised, unsupervised, and reinforcement learning. Model structure and parameters are defined, and the model is trained to minimize loss. Feature selection narrows down the feature set for high-precision and actionable insights. Classic feature selection methods include genetic algorithms, evolutionary algorithms like particle swarm optimization, and stochastic methods like Monte Carlo.
3. The final step involves interpreting, testing, and evaluating the model. Model evaluation in ML is the process of measuring and analyzing the performance of the trained model. It aims to assess the model's performance and accuracy in solving problems and provides metrics to evaluate its performance. Model evaluation measures performance using metrics like root mean square (RMSE), mean absolute error (MAE), mean absolute percentage (MAPE), and correlation coefficient (R\({}^{2}\)) on the testing set to assess accuracy and problem-solving capabilities.  
The general sequence of applying ML in materials is illustrated in Figures 3 and 4.  
Figure 3: Machine learning rough flowchart.
## 3 Results
### Control Specim Analysis and Experimental Data Processing
#### 3.2.3 Type 3 Test Specimens
The graph compares the mechanical behavior of the materials, showing the variation in maximum tensile strength (MPa) over time. Source: Authors’ own elaboration, DorART Project, 2025. However, in formulations M1 and M9, the initial moisture loss appears to be partially offset by the formation of new -OH groups, likely due to mild oxidation, bond cleavage, or exposure of previously hidden functional groups. The oxirane ring vibration band at \(\sim\)910 cm\({}^{-1}\) is also consistently present and suggests curing reactions or the loss of unreacted epoxide groups, a phenomenon that is particularly evident for the samples containing epoxy components. The carbonyl group (C=O) at \(\sim\)1730 cm\({}^{-1}\) shows a slight increase in M1, M4, and M9, indicating the onset of oxidative processes or thermal degradation of the organic matrix. In the case of inorganically filled materials, such as M5, M6, and M8, spectral variations are observed in the 1100-900 cm\({}^{-1}\) region, which are attributed to internal structural reorganizations that are likely related to sulfate- or silicate-based phases. By contrast, M3, M7, and M9 show minimal chemical or mineralogical changes, suggesting that they had greater structural and chemical stability under conditions of moisture and temperature fluctuations. In conclusion, thermo-hygrometric aging cycles affected all samples to varying degrees; however, the extent and nature of the observed changes clearly depended on the specific composition of each formulation.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
###### Abstract  
In Underwater Sensor Networks (UWSNs), the energy sources of sensor nodes are limited and difficult to recharge and solar energy cannot be used in that environment. The power issue is one of the most significant constraints in underwater sensor networks and energy balancing is essential to prolong the network lifetime. The MAC/routing protocols that are used in other types of networks may not be suitable for UWSNs due to their unique characteristics. This paper aims to overcome the energy problem by developing a new MAC/routing protocol for UWSNs called the Energy-Efficient protocol for UWSNs (EE-UWSNs). It is based on five principles to save sensor energy and to prolong the lifetime of UWSNs. These principles are using finite levels of power, applying the multi-hops transmission, narrowing the scope of transmission, applying inactivation mode, and balancing energy consumption. Using the AUVNetSim simulator, which is a Python project developed by the Massachusetts Institute of Technology (MIT), the proposed EE-UWSNs protocol was compared with well-known protocols. Simulation results proved that the proposed protocol reduces the average energy consumption of sensors by up to 68.49% compared with the other protocols. Furthermore, the average number of collisions and the end-to-end delay are enhanced.  
a 2022
# Estimating Carrot Gross Primary Production Using UAV-Based Multispectral Imagery
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vegetation Index** & **Model** & **RMSE** & **R\({}^{2}\)** & **_p-Value_** \\ \hline NIRv & GPP \(=-0.28(0.16)+24.12(0.99)\times\)NIRv & 1.6589 & 0.7823 & 0.06 & \(7.8\times 10^{-10}\) \\ MSAVI & GPP \(=-1.37(0.21)+16.96(0.68)\times\) MSAVI & 1.7780 & 0.7667 & \(4.7\times 10^{-5}\) & \(6.5\times 10^{-10}\) \\ MSAVI2 & GPP \(=-1.28(0.20)+15.71(0.64)\times\) MSAVI2 & 1.7898 & 0.7652 & \(7.2\times 10^{-5}\) & \(7.6\times 10^{-10}\) \\ SAVI & GPP \(=-2.09(0.24)+17.55(0.72)\times\) SAVI & 1.8446 & 0.7580 & \(6.3\times 10^{-6}\) & \(7.7\times 10^{-10}\) \\ EVI2 & GPP \(=-1.45(0.22)+15.45(0.67)\times\) EVI2 & 1.8730 & 0.7543 & \(4.6\times 10^{-5}\) & \(1.3\times 10^{-9}\) \\ WDVI & GPP \(=-0.56(0.16)+20.38(0.67)\times\) WDVI & 1.8091 & 0.7626 & \(3\times 10^{-3}\) & \(1.1\times 10^{-10}\) \\ DVI & GPP \(=-0.58(0.15)+20.06(0.64)\times\) DVI & 1.8468 & 0.7577 & \(2.4\times 10^{-3}\) & \(7.9\times 10^{-11}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Models of the selected VIs to predict the daily GPP with their cross-validation statistics. Figure 3: (a) RMSE of each VI; (b) R\({}^{2}\) score of each VI. Figure 5 shows the temporal trajectory of the estimated GPP using simple interpolation for all the indexes selected, with their respective RMSE and R\({}^{2}\) score (No BSE can be computed). The accumulated GPP is an important measure that is used to predict yield and efficiency in the use of resources (water, carbon, and nutrients). The accumulated GPP-EC is 580.4 g C m\({}^{-2}\), and the estimated accumulated GPP using NIRv is 520.4 g C m\({}^{-2}\) (10.3% error); using MSAVI it is 516.7 g C m\({}^{-2}\) (11.0% error); using MSAVI2 it is 515.3 g C m\({}^{-2}\) (11.2% error); using SAVI it is 514.9 g C m\({}^{-2}\) (11.3% error); using EVI2 it is 513.6 g C m\({}^{-2}\) (11.5% error); using WDVI it is 521.5 g C m\({}^{-2}\) (10.1% error); and using DVI it is 522.1 g C m\({}^{-2}\) (10.0% error). Surprisingly, the estimated accumulated GPP is a bit better using WDVI and DVI even though the other indexes outperform them. As indicated in [10], using a more  
Figure 4: The temporal trajectory of the estimated GPP using cross-validated NIRv. Figure 5: The temporal trajectory of the daily GPP estimated with VIs and GPP-EC. complex LUE model with an R\({}^{2}\) score of 0.96, the error in the accumulated GPP is around 6%, so the 10% error found here can be expected from a much simpler model.
## 3 Results and Discussion
### Differential Scanning Calorimetry (DSC)  
Table 2 presents the phase transition temperatures of salicylic acid samples following exposure to various electromagnetic fields, compared to the untreated control. Individual DSC thermograms for each sample are provided in the Supplementary Material (Figure S4a-p).  
Only slight differences in phase transition temperatures were observed between field-exposed samples and the control. The onset temperature ranged from 158.80 \({}^{\circ}\)C (SPM \(-\)15 V) to 159.27 \({}^{\circ}\)C (PPM 10/10), while the peak melting temperature was lowest (161.86 \({}^{\circ}\)C) for samples exposed to a static magnetic field with positive polarity at 25 V (SMF +25 V) and a rotating magnetic field at 30 Hz (RMF 30 Hz).  
More pronounced differences were observed in the melting enthalpies (\(\Delta\)H\({}_{\text{m}}\)), which ranged from 138.98 J/g (PMF 10/10) to 233.70 J/g (SMF \(-\)15 V). These discrepancies are partly due to the known phenomenon that salicylic acid undergoes partial decomposition during melting, complicating the precise determination of the enthalpy values.  
It is hypothesized that the small shifts in melting temperature may reflect minor modifications in the crystalline form of salicylic acid caused by exposure to various electromagnetic fields. However, all values remain within a narrow range, suggesting that the thermal behavior of the compound remains largely consistent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **T\({}_{\text{DSConset}}\)**[\({}^{\circ}\)**C]** & **T\({}_{\text{DSCmax}}\)**[\({}^{\circ}\)**C]** & **\(\Delta\)H\({}_{\text{m}}\) [J/g]** \\ \hline Control & 158.95 & 162.69 & 222.90 \\ OMF 45 Hz & 159.13 & 162.62 & 171.23 \\ OMF 55 Hz & 158.97 & 162.56 & 195.13 \\ OMF 65 Hz & 159.08 & 162.90 & 179.19 \\ PMF 1/1 & 159.16 & 162.25 & 139.75 \\ PMF 5/5 & 158.84 & 162.31 & 198.56 \\ PMF 10/10 & 159.27 & 162.37 & 138.98 \\ SMF +5 V & 158.93 & 162.28 & 180.19 \\ SMF +15 V & 158.87 & 161.93 & 184.21 \\ SMF +25 V & 158.89 & 161.86 & 179.45 \\ SMF \(-\)5 V & 159.09 & 162.34 & 140.41 \\ SMF \(-\)15 V & 158.80 & 162.25 & 233.70 \\ SMF \(-\)25 V & 158.90 & 162.10 & 189.29 \\ RMF 10 Hz & 158.82 & 162.12 & 204.53 \\ RMF 30 Hz & 158.86 & 161.86 & 186.17 \\ RMF 50 Hz & 158.88 & 162.16 & - \\ \hline \hline \end{tabular} \(T_{\text{DSConset}}\)—melting onset temperature; \(T_{\text{DSCmax}}\)—peak melting temperature; \(\Delta\)H—melting enthalpy.  
\end{table}
Table 2: Phase transition temperatures of salicylic acid before and after exposure to an electromagnetic field.
# Push Attack: Binding Virtual and Real Identities Using Mobile Push Notifications
## 3 Attack Description
### On-Line Attack  
The proposed attack consists of actively triggering a push notification message to the victim's personal device with the aim of capturing the related data packet sent by the OSN platform to the targeted device. The temporal correlation between the triggering and the sniffed data marks the presence of the victim on the observed network.  
The attack scenario in Figure 2 involves the following parties:  
* Adversary and victim: The attacker and the targeted virtual identity should be on the same network, for example served by the same WiFi access point or connected to the same Ethernet network.
* Online platform: This usually is the OSN where the victim is registered and represented by the so-called virtual identity. The platform gives the attacker the functionality to trigger push notifications on the user's devices.
* Push notification infrastructure: This is the framework needed to deliver the push notification. It is either platform-specific (e.g., Facebook notification system for Android devices) in the sense that the platform manages push notifications through its own infrastructure or OS-specific when the OS provider manages the notification infrastructure (mandatory for Apple iOS and a common choice for Google Android).  
The full attack comprises the following steps:  
1. Fill a list of possible profile candidates: the attacker targets victims related to a specific location or habits associated with a physical location providing the network access
2. Trigger push notifications to all candidates: the attacker starts to trigger notifications to victims' devices by soliciting online platform events with a pool of bots. This produces push notifications directed to victims' devices that the attacker tries to capture where the targeted user could be connected.
3. Inspect the data over the air, searching for a well-known data pattern: the attacker collects packets related to push notifications from known platforms (e.g., Facebook, Twitter, WhatsApp, etc.) to detect specific size and time patterns that clearly identify the targeted profile.  
Figure 2: Attack scenario.  
Victims often cannot detect the attack since attackers can use specific events triggered by the online platform that erase the previous action. An example is requesting the victim's friendship as a first step and later erasing the notification (hopefully before the OS displays it) on the victim's device by removing the friendship request.  
Finally, knowing the victim's device's MAC address enables user position estimation by passive triangulation or even by active searching. One can repetitively send ICMP echo requests or ARP requests to the device's MAC address, read the RSSI of the incoming packets and then progressively approach the target.
# Modeling and Control Simulation of Power Converters in Automotive Applications
## 2 Electothermal Modeling and Control Algorithm
### Switching Device Thermal Modeling and Power Losses  
The transition from the physical device of a power MOSFET to its equivalent circuit model, based on the Foster model for thermal modeling, involves the representation of the thermal layers that extend from the junction (J) to the surrounding environment (A) (as schematized in Figure 11). Imagine the MOSFET as a system of stacked thermal layers. The junction (J) is the internal region where heat is generated due to power losses. Surrounding it is the case layer (C), which is the body of the MOSFET that wraps around the junction. The case is connected to the heat sink (S), an external component that absorbs heat from the case. Finally, the heat sink is in contact with the environment (A), which is the surrounding temperature influencing the heat dissipation process. The Foster model simplifies this thermal complexity by using thermal resistors to connect the layers. Thermal resistances, such as \(R_{thJc}\), \(R_{thCS}\), and \(R_{thSA}\), act as thermal bridges between layers, allowing heat to flow through the model, analogous to the flow of current in an electrical circuit. The conceptual step then involves assigning thermal resistances to the different thermal layers, reflecting the propagation of heat through the MOSFET during its operation. The Foster model equations describe the change in temperature in each of the layers in response to heat flow. The utilization of this equivalent circuit representation enables simulations to comprehend the thermal behavior of the MOSFET under diverse operating conditions. In summary, [PERSON]'s model simplifies the intricate thermal behavior of the MOSFET, facilitating the analysis and simulation of device heating in real-world scenarios.
## 3 Maritime Cognitive Radio Network and Channel Modeling
### Sea Motion  
A fundamental of propagation analysis for MCRNs is the generation of a random sea surface. The general condition of the sea surface over a large area is called the sea state. The sea state is divided into 10 levels, as defined by [PERSON] [15]. The levels of the sea state are defined by considering the wave height, wave period and power spectrum. Sea states 0-3 are generally considered calm sea conditions. Moderate sea conditions are in sea states 4-5. Sea states \(6\) and above are the worst sea conditions, having high waves, which cause severe degradation of communication by affecting the movement of the antenna.
# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 2 Literature Review
### Spatiotemporal Urban Traffic Forecasting Methodology
These specifications required the explicit specification of the spatial or spatiotemporal graph of dependencies. Later, deep learning models were utilized to learn spatiotemporal relationships in a data-driven way. [PERSON] et al. [40] proposed the deep belief network for spatiotemporal feature learning; [PERSON] [41] utilized the tensor-based convolution for model temporal and spatial relationships; and [PERSON] et al. [42] applied the popular GAN architecture. Recently developed graph-based convolutional ANN architecture quickly found its application in spatiotemporal traffic forecasting. [PERSON] et al. [43] proposed GCNN with a higher order spatial graphconvolution and tested its scalability for a citywide road network. [PERSON] et al. (2018) applied GCNN with spectral and spatial graph-based convolutions. [PERSON] et al. (2018) extended GCNN by the learning of several kernels for every road segment and weighting them for construction of sparse locally connected networks. Summarizing the development of spatiotemporal urban traffic forecasting methodologies, we state that the two most popular approaches, multivariate time series and machine learning models, have come to the data-driven learning of spatiotemporal graphs of dependencies, and are used for better forecasting performance and interpretability of solutions.
###### Abstract  
The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.  
wireless sensor networks; DV-Hop; differential evolution; node localization +
Footnote †: journal: Sensors
# Computer Vision System: Measuring Displacement and Bending Angle of Ionic Polymer-Metal Composites
## 5 Conclusions  
A computer vision system was proposed to measure the displacement and bending angle of ionic polymer-metal composite (IPMC) utilising Python in conjunction with OpenCV. Coding functions and the mathematical formulas used were described. IPMC contour detection was discussed in detail. Extreme points detection of the IPMC contour was used to obtain the tip-to-tip displacement values. Appropriate camera settings were discussed, mainly focusing on four settings: resolution, frame rate, shutter speed and field of view. A linear displacement calculation method was used for this study. Measurements generated from the vision system were experimentally compared to approximated values via the manual calculation method. Good agreement was found between the results produced by the two methods. However, the system is not without its limitations. Main issues include cessation of the system as it approaches close to the maximum bending angle of 90\({}^{\circ}\) and requiring specific conditions for effective detection of IPMCs. Furthermore, the linear displacement calculation method used in this study cannot accurately describe the IPMC bending as compared to the arc length between IPMC tips. Due to the off-axis deformation and non-uniform curvature of IPMCs, the later calculation method cannot be used in this vision system unless an effective method of detecting the bending centre has been developed. The authors conclude that the proposed vision system can accurately approximate the displacement and bending angle of IPMCs. A GitHub link to the open-access code can be found in the Supplementary Materials section.  
The open-access code is available at the following link; [[https://github.com/[PERSON]/IPMC-Displacement-Vision-System.git](https://github.com/[PERSON]/IPMC-Displacement-Vision-System.git)]([https://github.com/[PERSON]/IPMC-Displacement-Vision-System.git](https://github.com/[PERSON]/IPMC-Displacement-Vision-System.git)). Full license statement on the code usage is available on the GitHub repository.  
Conceptualization, E.M. and J.G.L.; methodology, E.M.; software, E.M. and K.F.; validation, E.M., J.G.L. and K.F.; formal analysis, E.M.; investigation, E.M.; resources, J.G.L. and C.L.H.; data curation, E.M.; writing--original draft preparation, E.M.; writing--review and editing, E.M., J.G.L. and K.F.; visualization, E.M.; supervision, J.G.L. and C.L.H.; project administration, E.M. and J.G.L.; funding acquisition, J.G.L. and C.L.H. All authors have read and agreed to the published version of the manuscript.  
This publication has emanated from research conducted with the financial support of the President's Doctoral Scholarship scheme from the Technological University of the Shannon, Enterprise Ireland funding under the Technology Gateway program, grant number TG-2017-0114, and Science Foundation Ireland (SFI) under grant number 16/RC/3918.