esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 3 Reviewing Positioning Systems and Their Capabilities
### Categorizing Systems by Their Capabilities  
In Table 1, we categorize the various technologies based on their respective advantages and disadvantages. For example, LIDAR (Light Detection and Ranging) is widely regarded as one of the most effective technologies for acquiring 3D data and mapping surface or shallow-water environments [75]. However, its primary limitation is depth, which significantly affects its performance beyond certain thresholds. Moreover, water conditions, which are influenced by a variety of factors, remain unpredictable, making it difficult to maintain accuracy. Murky waters can interfere with data collection and image processing, which are critical for the proper operation of all Unmanned Underwater Vehicles (UUVs).  
While it is essential to understand the strengths and weaknesses of each technology, a more comprehensive approach is required. In addition to categorizing their advantages and disadvantages, we should also include other key factors, such as cost, depth limitations, specific applications, range capabilities, and maintenance requirements. Creating a table with this expanded set of information will provide a clearer and more practical understanding of how each technology can be utilized effectively in different underwater environments.
However, a comprehensive comparison between multiple AI models for forest applications has rarely been reported. The FluxCom (Jena, Germany) provides an AI-based operative GPP product with a spatial resolution of 10 km and a temporal resolution of 8 days. The spatial resolution is, however, not sufficient to investigate the GPP of locally inhomogeneous forest ecosystems for the countries with complex terrains such as South Korea. This study examines the application of AI methods to improve the reliability of the daily GPP predictions for the forest in South Korea using in-situ observations from an EC flux tower and the satellite remote sensing data such as solar radiation, temperature, humidity, and vegetation index. The purposes of this study are (1) to objectively compare the performance of multiple AI models such as SVM, RF, ANN, and DNN for the daily forest GPP in South Korea and (2) to examine the accuracy characteristics according to AI models in relation to different seasons and unexpected weather conditions. We focused on the Gwangneung site from the Korea Regional Flux Network (KoFlux) in South Korea, 2006-2015. Furthermore, this study suggests the possibility of overcoming the spatial limitation of point-based observations and applying the AI model over South Korea.
## 2 Theoretical Background
### Peridynamics Theory and Peridynamics Differential Operator
Derivatives of the discrete (PD grid) PD field, even in the presence of discontinuities, can be found by applying the peridynamic differential operator (PDDO) [29,30]. According to the PD theory [26,43], the variation of a scalar field \(f=f(\mathbf{x})\) at the point \(x\) is influenced by its interaction with other points \(x^{\prime}\) in the horizon \(H_{x}\) (Figure 4). Moreover, the degree of interaction between the PD points is specified by a nondimensional weight function \(\omega(|\mathbf{\xi}|)\) which reflects the degree of non-locality among the points of the domain. The weight function \(\omega\) is chosen such that it captures the physical characteristics of the material behaviour, namely decreasing degree of interaction with increasing distance \(|\mathbf{\xi}|\) between the points. The PDDO allows for the calculation of the spatial derivatives of the field function \(f=f(\mathbf{x})\) based on integral equations. For the 3D case (Figure 4), the expression of the PDDO for the first order derivatives of the function \(f\) can be derived as [29,30]:  
\[\frac{\delta f}{\delta x_{i}}=A^{-1}\int_{H_{x}}\omega(|\mathbf{\xi}|)f(\mathbf{x}+ \mathbf{\xi})\xi_{i}dV,\;i=1,\;2,\;3, \tag{3}\]  
where the quantity \(A\) is given by \(A=\int_{H_{x}}\omega(|\mathbf{\xi}|) \begin{bmatrix}\xi_{1}^{2}&\xi_{1}\xi_{2}&\xi _{1}\xi_{3}\\ \xi_{1}\xi_{2}&\xi_{2}^{2}&\xi_{2}\xi_{3}\\ \xi_{1}\xi_{3}&\xi_{2}\xi_{3}&\xi_{3}^{2}\end{bmatrix} dV\), and \(dV\) is the volume of the PD point. The effect of the weight function \(\omega\) is analyzed in [38,39]. [PERSON] et al. [39] concludes that a linear function (e.g., \(\omega(|\mathbf{\xi}|)=\delta/|\mathbf{\xi}|\)) is enough for 3D problems and a cubic order function is well suitable for 2D case. In this way Equation (3) allows for an integral-based calculation of the derivatives of displacements \(u_{1}\), \(u_{2}\), \(u_{3}\) at point \(x\) (by considering the function \(f\) in Equation (3) as \(u_{1}\), \(u_{2}\), \(u_{3}\), respectively): \(\
abla_{\mathbf{o}}\mathbf{u}=\frac{\phi_{\mathbf{u}}}{\delta\mathbf{x}}\); which allows for the calculation of the conventional continuum mechanics Lagrangian strain tensor at point \(x\):  
\[\mathbf{E}=\frac{1}{2}(\
abla_{o}\mathbf{u}+(\
abla_{o}\mathbf{u})^{\mathrm{T}}+\
abla_{o} \mathbf{u}\cdot(\
abla_{o}\mathbf{u})^{\mathrm{T}}). \tag{4}\]  
Figure 4: The basic principles of the PD theory. In the discretized form of Equation (3) (i.e., replacing the integral over the horizon \(H_{x}\) with summation over the PD points inside of the horizon), the function \(f\) in Equation (3) becomes the discrete values of the function at the discrete PD points inside of the horizon \(H_{x}\).
## 3 Information-Theoretic Results
### Realism Constraints
#### 3.1.1 Weak and Strong Realism Constraints  
The notion of weak realism is inspired by machine learning practices, such as GAN-based image reconstruction schemes [4]. In information theory, weak realism has been studied within the framework of empirical coordination [41; 42] using the empirical distribution (type). In contrast, strong realism compares joint distributions over blocks of symbols, thereby imposing stricter constraints. [PERSON] [44; 45] investigated the RDP trade-off for general information sources using fixed-length and variable-length coding, suggesting the need for channel resolvability codes. Single-letter expressions can be derived under the assumption of i.i.d. output [31; 43].
## 3 Methodology  
In this section, we detail the methodology for the identification of low-latency Internet traffic. Our approach combines ANN with the application of wavelet transform to create a powerful system for distinguishing a unique form of 'noise' from the'signal' of network with precision and consistency. The proposed framework, illustrated in Figure 1, integrates three core components to enable accurate low-latency traffic classification:  
* Temporal Feature Extraction: This stage extracts time-domain metrics from raw network traffic, including throughput (total data volume per interval), slope (rate of packet change over time), downlink-to-uplink ratio (asymmetry in traffic direction), and moving averages (smoothed trends of throughput). These features capture dynamic traffic behavior, such as burstiness and periodicity, which are critical for distinguishing low-latency traffic (e.g., rapid bidirectional packet exchanges in video conferencing) from bulk transfers (e.g., FTP). Equations (2)-(6) formalize these metrics.
* Wavelet Transform (Frequency-Domain Enhancement): The temporal features are processed using a continuous wavelet transform (CWT) with a Ricker (Mexican hat) wavelet kernel. The CWT decomposes each feature into multi-scale frequency components, isolating high-frequency patterns (e.g., microbursts in low-latency traffic) and low-frequency trends (e.g., steady streaming flows). This stage transforms raw time-series data into wavelet coefficients, effectively separating structured \"noise\" (low-latency traffic) from the broader network \"signal\".
* ANN Classifier (Dual-Domain Analysis): An MLP artificial neural network receives concatenated time-domain and wavelet-transformed features as input. The MLP comprises an input layer (temporal + wavelet features), two hidden layers with ReLU activation for nonlinear pattern recognition, and a softmax output layer for probabilistic classification.  
Figure 1: System architecture.
# Performance Evaluation of a Novel Propulsion System for the Spherical Underwater Robot (SURIII)
## 4 CFD Simulation
### Pre-Processor of the CFD Simulation  
The pre-processor of the hydrodynamic model includes many contents. Firstly, in the design process, some details need to be added in the 3D model of the robot to make it closer to the prototype. However, it is necessary to simplify the 3D model for CFD simulation, so that it can get effective results and reduce the computational time. The simplified parts are as follows:  
1. The thruster has some complicated surfaces and their area are very small, so these surfaces are pre-processed as regular surfaces;
2. Some irregular solids have been changed to cylinder or cuboid shape;
3. Some parts such as screws and nuts have been omitted. And the simulation models are shown in Figure 12.  
Secondly, for CFD simulation, another important model is the flow field. The flow field will affect the hydrodynamic characteristic of the robot, so it needs to be established base on the robot. The boundary of the flow field should be large enough, so it will avoid influence the domain around the robot. A cylinder with the diameter of 2 m and the height of 4 m is chosen as the flow field and the robot is put in the center of it. However, in hydrodynamic analysis, it generally needs a computational domain. The computational domain can be obtained by using Boolean operation between 3D model and flow field. The complete hydrodynamic model is shown in Figure 13.  
Thirdly, the boundary conditions of the hydrodynamic model are set in the pre-processor. In this research, boundaries make up of inlet, outlet and wall which are shown in Figure 14. For different motions, it only needs to adjust the relationship between the robot and the direction of the inlet. The inlet velocity of the forward motion is 0.15 m/s and the inlet velocity of up and down motion is 0.083 m/s. The flow field is the turbulence handled by the k \(-\)\(\varepsilon\) model. The mesh is also the crucial step of hydrodynamic analysis. It is generated by using the curvature method. This method can refine the element size of the edge and curve surface. In this hydrodynamic model, the robot is considered as a suspended status. At the present step, the gravity and buoyancy are both ignored. The environment condition is set as the isothermal temperature of 20 \({}^{\circ}\)C.  
Finally, the convergence criterion is set as \(1\times 10^{-4}\) in the solver control. Three hydrodynamic models (up, down and forward) are calculated for the CFD simulation.
## 4 Results
### Hydrogeological Analysis  
The groundwater level evolution in the study area during the period of 1992-2003 is depicted in Figure 7. Due to strong groundwater withdrawal from the plain aquifer, the water table suffered a general piezometric lowering. According to [72], the maximum values (up to \(-5\) m) of the lowering of the water table level were observed in the northwestern and south-western sectors of the study area and the urban area of Sarno village.  
Figure 6: Geological map of the Sarno Mountains with plots showing the spatial arrangement of bedding and fault data throughout the map area.  
The groundwater lowering rate varied between 0.45 and 0.09 m/yr. The highest rates, occurring between 1992 and 2003, were concentrated in the central portion of the study area, around the town of Sarno and in the sector between the F1 and F2 faults.  
As the study period was characterised by relatively stable amounts of monthly rainfall (Figure 8), the groundwater lowering likely resulted from other causes. The most probable of these could be intense groundwater pumping from the plain aquifer, which is equivalent to about 30 \(\times\) 10\({}^{6}\) m\({}^{3}\)/yr [72], for agricultural, domestic, and industrial purposes.  
Figure 8: Monthly rainfall (mm) at Sarno rain gauge station (Figure 1B for location) in the period of 1992–2003. Dotted red line indicates the mean long-term trend.  
Figure 7: Piezometric level variation (m) in the period of 1992–2003 for the study area ([38], integrated). Dashed black lines indicate faults mapped in Figure 4.
# A Cyber-Physical System for Wildfire Detection and Firefighting
###### Abstract  
The increasing frequency and severity of forest fires necessitate early detection and rapid response to mitigate their impact. This project aims to design a cyber-physical system for early detection and rapid response to forest fires using advanced technologies. The system incorporates Internet of Things sensors and autonomous unmanned aerial and ground vehicles controlled by the robot operating system. An IoT-based wildfire detection node continuously monitors environmental conditions, enabling early fire detection. Upon fire detection, a UAV autonomously surveys the area to precisely locate the fire and can deploy an extinguishing payload or provide data for decision-making. The UAV communicates the fire's precise location to a collaborative UGV, which autonomously reaches the designated area to support ground-based firefighters. The CPS includes a ground control station with web-based dashboards for real-time monitoring of system parameters and telemetry data from UAVs and UGVs. The article demonstrates the real-time fire detection capabilities of the proposed system using simulated forest fire scenarios. The objective is to provide a practical approach using open-source technologies for early detection and extinguishing of forest fires, with potential applications in various industries, surveillance, and precision agriculture.  
C 2023, 15, 237. [[https://doi.org/10.3390/fi15070237](https://doi.org/10.3390/fi15070237)]([https://doi.org/10.3390/fi15070237](https://doi.org/10.3390/fi15070237))
## 5 Conclusions  
In this study, we proposed a method for improving serial detection using the MAP algorithm. First, the 2D interference from the HDS channel was estimated using the 2D GPR target used by [PERSON] and [PERSON] [26]. These interferences are then supplied to the serial MAP detection. The first detection (horizontal detection) estimates the conditional probabilities of the six symbols. These probabilities are then input to the second detection (vertical detection) to exploit the probabilities of bits 0 or 1. Simultaneously, vertical detection extracts extrinsic information to provide feedback on horizontal detection. Based on this feedback, we designed an iterative algorithm for serial MAP detection. The results showed that the MAP algorithm improved the gain by 3.5 dB at a BER of \(10^{-3}\). Furthermore, the iterative algorithm improved performance by up to 1 dB over the proposed model without the feedback line. The proposed model can withstand up to 3% of the blur effect. Finally, in the case of misalignment, the proposed model achieved the best performance.  
Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government. (MSIT) (2021R1A2C1011154).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Mul/Div** & **Add/Sub** & **Log/Exp** \\ \hline Our model & 1201 & 733 & 336 \\ Serial detection in [26] & 252 & 700 & 0 \\
1D GPR [15] & 33 & 48 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity of the proposed model and previous studies.  
Figure 10: BER performance of the proposed model with blur = 1.85 and 10% misalignment.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### Generation of Input Data for Mobility Prediction  
Mobility profiles are generated by collecting input data and reducing the size by removing the outliers. The reduced data are transmitted to each base station which in turn predicts the next cell.  
Figure 1: Block Diagram of Proposed System.
## 3 Results and Discussion  
### Characterization of E-POSS  
The active carbon-carbon double bonds of Ov-POSS cannot participate in the curing reaction of E51, so they were converted into epoxy groups (E-POSS) by oxidation method with m-CPBA. E-POSS can act as a crosslinker to increase the crosslinking degree and mechanical properties, owing to their epoxy groups and the inorganic core of POSS. The FTIR spectrum of E-POSS is shown in Figure 3. The absorption band at around 910 cm\({}^{-1}\) was assigned to the epoxy group stretching vibration, the absorption bands at around2875 cm\({}^{-1}\) and 2940 cm\({}^{-1}\) were assigned to C-H stretching vibration, the absorption bands at around 1205 cm\({}^{-1}\) were assigned to the antisymmetric bridge C-O-C stretching, and the absorption band centered at around 1120 cm\({}^{-1}\) was assigned to the Si-O-Si stretching vibration [27; 28]. The vibrational absorption band of the carbon-carbon double bond of Ov-POSS completely disappears in the FTIR spectrum of the obtained product, indicating that C=C groups had been oxidized to epoxy groups.  
As shown in Figure 4, the \({}^{1}\)H NMR spectrum of E-POSS also showed that the carbon-carbon double bonds of Ov-POSS converted into epoxy groups as the absorption peaks of three protons of the epoxy group appeared at 2.26 ppm, 2.76 ppm, and 2.98 ppm, respectively, while the protons of the carbon-carbon double bond disappeared (the absorption peaks of three protons of the ethylene group of Ov-POSS were located at 5.93 ppm, 6.05 ppm, and 6.12 ppm [29]). The results of \({}^{1}\)H NMR and FTIR were consistent, indicating that all the C=C bonds of Ov-POSS had been converted to epoxy groups by the oxidation with m-CPBA. Therefore, E-POSS can be successfully synthesized by this method.  
Figure 4: \({}^{1}\)H NMR spectrum of E-POSS.  
Figure 3: FTIR spectrum of E-POSS.
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 3 Common Challenges in Autonomous Driving
### Limited Computation  
Operations such as creating environment understanding, sensory data fusion, finding patterns based on data analytics for behavioral planning, and other operations of this nature are extremely computationally intensive and require rich infrastructure in the vehicle. The edge and Roadside Units (RSUs) may have computing-rich infrastructure owing to the recently new idea of extended sensors and distributed decision-making for attaining the goals of a higher level of autonomous driving. This entails significant expenses, which led to an increase in vehicle pricing. Therefore, sharing the computation, i.e., creating the perception on the road and sharing it in real-time with the vehicles, may be one approach to reduce such high costs. More processing will be directed toward the RSUs, and little is kept inside the vehicle. Although it looks appealing, it might be challenging to implement solutions that are reliable, real-time, and highly available. Some of the obvious challenges of this category are discussed below:  
* **CH 1:** What perception information is to be provided, and to which vehicle? It is crucial since the RSU may cover a large geographic area with numerous vehicles, which creates the perception. It goes without saying that not all vehicles need to have access to every piece of information.
* **CH 2:** Can on-road deployed sensors generate environmental understanding as excellent, as it is produced by on-board sensors?
* **CH 3:** Can demand estimation for computation in the vehicle or at RSUs be conducted using artificial intelligence (AI) approaches? This is significant because new ideas could include virtualized network functions, machine learning as a service (MLaaS), data analytics as a service (DAaaS), information as a service (IaaS), and others that depend on the availability of computing infrastructure at regular times and locations.
# Porcelain Tile Polishing Residue in Concrete as an Additive or Replacement for Portland Cement
###### Abstract  
In this study, 10-50% of porcelain tile polishing residue (PPR) was used as an additive or as partial replacement of cement in concrete. The cement consumption was kept constant by correcting the amount of sand for each mixture. Concrete workability (slump) was reduced by up to 88.72% when PPR replaced the cement by up to 30%, while it was reduced by only 4.10% when PPR was added to the concrete at the same levels. Compressive strength at 28 days increased up to 92.22% with 50% PPR as additive, reducing the equivalent emission of CO\({}_{2}\) per m\({}^{3}\) of concrete up to 38.18%. PPR incorporation reduced the water permeability of concrete by up to 30.70% and 17.54% when used in addition and in cement replacement, respectively. Overall, PPR as an additive up to 50% and in cement with substitution levels up to 10-40% presented themselves as viable solutions for developing more resistant and durable concretes than the reference mixture (without incorporation of PPR).  
concrete; residue; porcelain tile polishing; additive; supplementary cementitious material Article
## 2 Field Sites
### Dahra, Senegal  
Dahra (15.402\({}^{\circ}\)N, 15.432\({}^{\circ}\)W) is located in the Louga province of northern Senegal. Long-term mean annual air temperature is 29 \({}^{\circ}\)C and mean annual precipitation is 416 mm (Table 1). The vegetation around the site is grazed by livestock and dominated by annual grasses, particularly _Aristida ascensionis_, _Zornia latifolia_, _Eragrostis tremula_, _Dactyloctenium aegyptium_, and _Cendrus biforus_. The canopy cover is approximately 3% and comprises _Acacia Senegal_, _Acacia tortilis_, and _Balantites aegyptiaca_. The soils are sandy with low water holding capacity and organic matter content. A complete description of Dahra is provided in [PERSON] et al. [38].
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 3 Preliminary  
### Background of Open-World Learning  
In real-world recognition/classification tasks, it is usually difficult to collect training data for all classes when training a recognizer or classifier due to various objective factors. The dataset is typically closed-set in deep learning, with the training and test data having the same annotations and feature space. Based on this assumption, traditional recognition and classification algorithms have achieved remarkable success in various machine learning tasks. However, real-world scenarios are often open-set and non-smooth, such as log fault detection, where the emergence of invisible situations significantly weakens the robustness of these existing approaches. To address this challenge, this requires a model that can both recognize known normal log sequences and reject unknown log faults.  
Previously, researchers have proposed open-set recognition (OSR), which cannot fully learn all data knowledge during training, but the model can recognize unknown category data during testing. This requires the classifier to be able to accurately recognize not only the visible categories but also the unseen ones. Due to the small number of abnormal log sequences, previous researchers have usually used semi-supervised log anomaly detection methods, where only normal log sequences are targeted during training and abnormal log sequences need to be found during testing. A general framework for open-set recognition learning algorithms can help us to continuously recognize unknown category data.