esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overload in SAG Mills
### Overload Classification vs. Time-Series Overload Forecasting  
In SAG mill operations, overload management relies on two primary approaches: classification in time and forecasting. Classification in time identifies overload events as they occur or immediately afterward, enabling reactive responses to minimize damage or inefficiencies. Forecasting, however, uses historical data and predictive modeling to anticipate overloads before they happen, enabling proactive adjustments. Forecasting with a lead time of at least 10 min is especially critical, given the 8-min operational reaction time required to adjust parameters effectively. This window allows operators and systems to optimize feed rate, water addition, or rotational speed, preventing the predicted overload and improving overall mill performance.  
This proactive approach to overload management has several key benefits:  
* Reduced Wear and Tear: By avoiding overloads, the mill experiences less mechanical stress, reducing wear on critical components and extending their lifespan.
* Optimized Energy Consumption: Preventing overloads helps maintain the mill at its optimal efficiency point, avoiding the excessive energy consumption typically associated with recovery from overload conditions.
* Increased Throughput: Maintaining stable mill operations without the interruptions caused by overloads can lead to a more consistent and higher throughput.  
While classification in time is essential for immediate overload response, the ability to forecast overloads with sufficient lead time shifts the paradigm from reactive to proactive management, enabling operators to optimize performance, reduce operational risks, and enhance the overall efficiency of SAG mill operations.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
###### Abstract  
As part of the contribution to flood disaster risk reduction, it is important to identify and characterize flood areas, locations, and durations. Multiple satellite-based flood mapping and monitoring are an imperative process and the fundamental part of risk assessment in disaster risk management. In this paper, the MODIS-derived synchronized floodwater index (SfWi) was used to detect the maximum extent of a nationwide flood based on annual time-series data of 2015 in order to maximize the application of optical satellite data. The selected three major rivers--i.e., Ganges, Brahmaputra, and Meghna (GBM), transboundary rivers running through the great floodplain delta lying between Bangladesh and eastern India--show that a propensity of flood risk was revealed by the temporal and spatial dynamics of the maximum flood extent during the 2015 monsoon season. Resultant flood maps showed that SfWi-indicated flood areas were small but more accurate than those derived from the single use of the MODIS-derived water index. The return period of SfWi-indicated maximum flood extent was confirmed to be about 20 years based on historical flood records.  
flood risk; nationwide flood mapping; synchronized floodwater index (SfWi); MODIS; risk reduction +
Footnote †: journal: Geo-Inform
# A Wideband Digital Pre-Distortion Algorithm Based on Edge Signal Correction
###### Abstract
Reference [11] employed a low-speed Analog-to-Digital Converter (ADC) to sample the feedback signal. Subsequently, some signal processing techniques are used to reconstruct the power amplifier's output signal near the original high sampling rate. However, the DPD model derived from this method suffers from poor fitting performance and requires specific comb-shaped integrators with stringent implementation conditions. Reference [12] considered edge signals outside the center frequency to estimate model parameters, enabling the algorithm to linearize higher bandwidth signals. Nevertheless, this algorithm does not directly process signals near the center frequency, resulting in low model fitting accuracy and insufficient linearization performance in highly non-linear environments. Reference [13] eliminated the band-pass filter and utilized a resampling module to continuously adjust the sampling rate to achieve the optimal rate suitable for the model. However, due to neglecting the issue of power leakage, non-linear effects are exacerbated in multi-channel communication systems. In summary, current DPD methods for linearizing wideband signals mostly employ down-sampling techniques to reduce the amount of data processed. While this approach somewhat decreases computational complexity, it overlooks the impact of unsampled signals on the model, leading to poor model fitting, reduced stability, and lower communication transmission quality. To address the aforementioned issues, this paper proposes a wideband DPD algorithm based on edge signal correction. By optimizing the design of traditional feedback loops, the system architecture is simplified. Concurrently, the algorithm samples signals near the center frequency in the frequency domain, thereby reducing data volume and processing complexity. It calculates the DPD model parameters by comparing the sampled signals with the source signals. Additionally, dynamic non-linear cross-terms are added to the pre-distortion model, and sampled edge frequency signals are incorporated into the calculation of cross-term parameters to fine-tune the entire model. The aim is to compensate for the non-linear and memory effects of the power amplifier to enhance the accuracy and efficiency of the pre-distorter. The proposed scheme is analyzed through simulations using MATLAB R2024a, employing Adjacent Channel Power Ratio (ACPR) and Normalized Mean Squared Error (NMSE) as evaluation metrics. Finally, hardware experiments are conducted to validate the simulation results. The structure of this paper is as follows: Section 2 introduces the proposed wideband DPD model based on edge signal correction and the method for extracting model parameters. Section 3 presents simulation analyses of the proposed wideband DPD algorithm introduced in Section 2. Section 4 details the design of hardware experiments and analyzes the experimental results. Section 5 concludes the paper by summarizing the work and innovations presented and outlines future research directions.
## 3 Results
### Aggregate Contact Characteristics  
Table 4 presents the quantitative analysis of coarse aggregate contact points. Figure 12 illustrates the longitudinal distribution patterns of contact points within CPAM specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group Number** & **Average Particle Size** & **Contact Threshold** & **Longitudinal Average Contact** \\  & **(mm)** & **Point** \\ \hline CPAM1 & 4.52 & 1.04 & 41 \\ CPAM2 & 9.09 & 2.09 & 79 \\ CPAM3 & 9.00 & 2.07 & 78 \\ CPAM4 & 6.74 & 1.55 & 10 \\ CPAM5 & 4.48 & 1.03 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation results of contact for CPAM specimens.  
Figure 11: Fractal dimension of CPAM specimens.  
Figure 12: Distribution of CPAM contact points.  
The analysis shows clear differences in contact point distribution across formulations. CPAM2 and CPAM3 display optimal contact characteristics, with average longitudinal contact points near 80, indicating excellent aggregate packing density. In comparison, CPAM4 and CPAM5 show substantially fewer contact points, primarily in the 10-20 range, demonstrating poor aggregate interlock. CPAM1 exhibits intermediate performance, with approximately 40 contact points.  
These differences directly relate to aggregate gradation characteristics. CPAM2 and CPAM3's superior performance results from their well-balanced coarse aggregate distribution, which enhances dense packing and inter-particle contact. The greater number of contact points improves load transfer efficiency, leading to higher material strength. By contrast, CPAM4 and CPAM5's limited contact points stem from their unbalanced gradation, especially the inadequate 2.36 mm aggregate content, which weakens aggregate interlock and structural integrity.
## 3 Results
### Neural Network Models and SHAP Analysis
#### 3.1.3 Neural Network Model and SHAP Analysis for Case 3
In case 2, the force \(F_{p}\) had a small \(R^{2}\), and since the insert radius \(r_{i}\) was included, the \(r_{i}\) became the most important variable according to the SHAP analysis, and the \(R^{2}\) for \(F_{p}\) in case 2 was higher. It could be seen in the SHAP analysis that for all four outputs, the value of the insert radius of 0.4 mm impacted the value of average roughness \(R_{a}\), whereby the value of \(R_{a}\) decreased, and the values of all three forces were also decreased. Feed rate \(f\) was the second most important feature for \(R_{a}\), \(F_{c}\), and \(F_{p}\), while for \(F_{f}\), it was the third most important feature. Depth of cut \(a_{p}\) was the most important feature for \(F_{c}\) and \(F_{f}\) and the third most important feature for \(R_{a}\) and \(F_{p}\). Higher values of both \(f\) and \(a_{p}\) increased the values of all outputs. Cutting speed \(v_{c}\) was, as in almost all cases, the variable with the lowest importance. The higher values of \(v_{c}\) increased the values of \(R_{a}\) and \(F_{p}\) and decreased the values of \(F_{c}\) and \(F_{f}\).
## 4 Monitoring System Testing and Analysis
### System Simulation Experiment  
#### 4.1.1 Experimental Parameter Settings  
Python is used to simulate the algorithm ODMT mentioned in this paper. In total, 50 to 100 wireless rechargeable sensors are randomly placed within a 400 m \(\times\) 400 m mobile sensor network. The sensors have a transmission range of 50 m. The MS acts as a mobile receiver and moves through the network collecting data without stopping. The battery rating of each sensor is 1 J, and the cache rating is 250 KB. In addition, the sensor nodes randomly generate 256 bits of burst data with the specific simulation parameters shown in Table 3.
## 4 Discussion
### Testing and Validation of a New Design  
As emphasized in the literature, the immersive nature of VR encourages more creative design solutions in the automotive context [16; 17]. Particularly in the V-Cockpit scenario, VR enables users to experiment with different configurations and immediately view the results. The possibility to breathe life into a new cockpit design within hours (if not even minutes) from its initial conception is a strong argument in favor of our platform. Thanks to different simulation scenarios, it is possible to identify possible flaws and inconsistencies early on with the current design. For example, it is possible to discover that some specific interactions with the IVI system are particularly distracting, diverting the user attention from driving to interacting with the car equipment for too long, or that the visual look and feel adopted by the software graphical user interface screeches against the colors and materials surrounding the many IVI displays.  
User feedback from designers and engineers highlights the superior experience provided by the VR-based approach. The immersive nature of VR and the possibility to interact with a living system allows for a more intuitive and engaging interaction with prototypes, surpassing the limitations of 2D drawings and static physical models.  
The V-Cockpit platform's ability to run IVI system software on its native platform within the VR environment ensures compatibility with embedded hardware constraints. This feature is crucial for accurately simulating the final product's performance and ensuring that all design elements function as intended. The flexibility of the system to adapt to different car models and manufacturer-specific requirements enhances its applicability across the automotive industry, which is particularly valuable in an industry characterized by rapid technological advancements and mutable consumer demands.
## Appendix A Description of VGT Flow Oriented Model
### Efficiency Model  
The efficiency model presented in [30] is based on the use of the Euler equation of turbo-machinery for radial gas turbines and assuming constant meridional component velocities. By this, both assumptions, and doing some simplifications, the definition of the total to static adiabatic efficiency of the turbine (Equation (A8)) can be expressed as Equation (A9).  
\[\eta_{t/s}=\frac{T_{0t}-T_{4t}}{T_{0t}-T_{4 ts}}\] (A8)  
\[\eta_{t/s}=\frac{u_{3}c_{0}\;tan\;\alpha_{3}-\left[u_{3}\left(\frac{r_{3}}{r_{ 3}}\right)-c_{0}\;tan\;\beta_{4}\right]u_{3}\left(\frac{r_{3}}{r_{3}}\right)} {c_{p}T_{0t}\left(1-\left(\frac{1}{\Pi_{0 rA}}\right)^{\frac{\gamma-1}{\gamma} }\right)}\] (A9)  
Based on the definition of blade to jet speed ratio (\(\sigma\)) in Equation (A5), Equation (A10) is obtained for a constant tip speed maps.  
\[\eta_{t/s}=-2\cdot\left(\frac{r_{4}}{r_{3}}\right)^{2}\cdot\sigma^{2}+2\cdot \frac{A_{\text{eff}}}{A_{0}^{\text{geom}}}\cdot\left(tan\;\alpha_{3}+\frac{r_{ 4}}{r_{3}}\cdot tan\;\beta_{4}\right)\left[\frac{1}{\Pi_{0 rA}}\right]^{\frac{1 }{\gamma}}\cdot\sigma\] (A10)  
It is worth highlighting that the above equation is dependent on \(A_{\text{eff}}\), which can be calculated using Equation (A3). The rotor inlet flow angle (\(\alpha_{3}\)) is estimated as a function of stator outlet flow angle (\(\sigma_{2}^{metal}\)), as shown in Equation (A11) [13,30]  
\[tan\;\alpha_{3}=z_{3}^{\text{geom}}\cdot sin\;\varphi_{2}^{metal}\] (A11)  
Coefficient \(z_{3}^{\text{geom}}\) is based on the geometry of VGT stator vanes and was obtained theoretically, as described in [30]. The final turbine efficiency equation is lumped into \(K_{i}\) terms, as shown in Equation (A12). A fitting parameter \"\(z\)\" is introduced into \(K_{2}^{\star}\) and it has six coefficients that mainly depend on the reduced turbine speed, blade to jet speed ratio, and VGT position, as shown in Equation (A15). Equations (A13) and (A16) are related to physical values from the turbine geometry and turbine map, as described in [30].  
\[\eta_{t/s}=-K_{1}\cdot\sigma^{2}+K_{2}^{\star}\cdot\left(1-\frac{K_{3}}{ \sigma^{2}}\right)^{\frac{1}{\gamma-1}}\cdot\sigma\] (A12)  
\[K_{1}=2\left(\frac{r_{4}}{r_{3}}\right)^{2}\] (A13)  
\[K_{2}^{\star}=2\frac{A_{\text{eff}}}{A_{0}^{\text{geom}}}\left(z\cdot z_{3}^{ \text{geom}}sin(\varphi_{2}^{metal})+\sqrt{\frac{K_{1}}{2}}tan(\beta_{4}^{metal })\right)\] (A14)  
\[z=-(a^{\prime}\cdot n_{\text{red}}+b^{\prime})\cdot\sigma+(c^{\prime}\cdot n _{\text{red}}+d^{\prime}\cdot VGT^{2}+e^{\prime}\cdot VGT+f^{\prime})\] (A15)  
\[K_{3}=\frac{u_{3,red}^{2}}{2c_{p}}\] (A16)
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Positioning Techniques
#### 4.2.1 RSS
If the position of the receiver is closer to a LED lamp, the correlation will be larger. Note that this is not sufficient to determine the exact position. For more accurate localization, a training phase similar to the Wi-Fi fingerprint method is provided to improve performance. Unlike the systems mentioned above, the study presented in [57] considers the scenario where the receiver is not parallel to the ceiling but tilted, which is more realistic in practical use. As a result, many of the existing solutions are considered insufficient to provide high accuracy in situations involving user mobility or general cases. This paper provides a comprehensive analysis of the performance of the system with a tilted receiver. It also proposes the use of orientation information, e.g., from a gyroscope, to further improve the accuracy of the system. It is important to note that a comprehensive theoretical accuracy evaluation of indoor positioning methods based on VLC using RSS is presented in [58], which also provides additional relevant references. Under typical parameter settings, the expected achievable accuracy can reach the centimeter level.
# Cellular-V2X Communications for Platooning: Design and Evaluation
###### Abstract  
Platooning is a cooperative driving application where autonomous/semi-autonomous vehicles move on the same lane in a train-like manner, keeping a small constant inter-vehicle distance, in order to reduce fuel consumption and gas emissions and to achieve safe and efficient transport. To this aim, they may exploit multiple on-board sensors (e.g., radars, LiDARs, positioning systems) and direct vehicle-to-vehicle communications to synchronize their manoeuvres. The main objective of this paper is to discuss the design choices and factors that determine the performance of a platooning application, when exploiting the emerging cellular vehicle-to-everything (C-V2X) communication technology and considering the scheduled mode, specified by 3 GPP for communications over the sidelink assisted by the eNodeB. Since no resource management algorithm is currently mandated by 3 GPP for this new challenging context, we focus on analyzing the feasibility and performance of the dynamic scheduling approach, with platoon members asking for radio resources on a per-packet basis. We consider two ways of implementing dynamic scheduling, currently unspecified by 3 GPP: the sequential mode, that is somehow reminiscent of time division multiple access solutions based on IEEE 802.11p--till now the only investigated access technology for platooning--and the simultaneous mode with spatial frequency reuse enabled by the eNodeB. The evaluation conducted through system-level simulations provides helpful insights about the proposed configurations and C-V2X parameter settings that mainly affect the reliability and latency performance of data exchange in platoons, under different load settings. Achieved results show that the proposed simultaneous mode succeeds in reducing the latency in the update cycle in each vehicle's controller, thus enabling future high-density platooning scenarios.  
V2X; cellular V2X; VANETs; cooperative driving; 3 GPP; platooning +
Footnote †: journal: Sensors
## 3 Interference Mitigation Schemes
### Cognitive Radio Approach
#### 3.3.3 Cognitive-Receiver Initiated CyclEd Receiver  
A hospital may be the environment with the highest level of interference for WBASNs. Many people wearing WBASNs may be staying or walking about a hospital at any time. In [40], a cognitive framework is applied to WBASNs for avoiding interference in hospitals. The Cognitive-Receiver Initiated CyclEd Receiver (C-RICER) MAC protocol includes three main tasks: The first task is channel sensing, which senses the channel and builds a full interference map including the frequency channels. The second task is power adaptation, which reduces transmission power to reduce interference. The final task is channel adaptation, which enables the coordinator to switch to another channel.
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 6 Discussion
### Effects of Soil Friction Angle on Change in Pile Capacity after Excavation  
Figure 14 shows the relationship between pile uplift capacity loss and the soil effective friction angle \(\varphi^{\prime}\). It can be seen in this figure that the pile uplift capacity loss decreases  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mathbf{H_{\rm e}/\mathbf{L_{\rm e}}}\) & \(\mathbf{\chi}\) & \(\mathbf{G_{\rm s}}\) (MPa) & \(\mathbf{\varphi^{\prime}}\) (\({}^{\circ}\)) \\ \hline
0.5, 1, 1.5 & 0.2, 0.3 0.4 0.5 & 5, 10, 20, 40 & 20, 25, 30, 35 \\ \hline \end{tabular} Note: The unloading modulus of soil can be determined as: \(E_{\rm air}=2(1+\
u)G_{\rm s}\).  
\end{table}
Table 4: Variables in the parametric study.  
Figure 12: Calculation model for the parametric study.  
Figure 13: Schematic of the determination of the pile uplift capacity.  
linearly with the soil effective friction angle increases. When the effective pile length is equal to the excavation depth, i.e., \(H_{\text{e}}=L_{\text{e}}\), the pile capacity loss decreases from 52.5% to 38.9%, with the soil effective angle increasing from 20\({}^{\circ}\) to 35\({}^{\circ}\). The ratio of the excavation depth to the effective pile length, i.e., \(H_{\text{e}}/L_{\text{e}}\), also has a significant effect on pile capacity loss. When the soil effective friction angle is 30\({}^{\circ}\), the pile capacity loss increases from 30.3% to 50.7%, with the ratio of the excavation depth to the effective pile length decreasing from 1.5 to 0.5.
## 4 Experimental Results and Analysis
### VLSI Analysis  
Table 2 compares the hardware area required for different methods of the syndrome generator ([20] and the proposed). Since the GPUs were employed for key-equation and Chien search, the hardware was only compared for the syndrome generator unit for multi-bit error correction for different \(GF\) dimensions. This is a fair comparison since the area of the GPUs was already accounted for systems with graphical display. The hardware implementations were targeted for 28-nm and met a frequency  
Figure 7: Syndrome computation time for different arches.  
of 1 GHz. In order to support different finite field (\(m=12,13,14,15\)) and 40-bit error correction, the hardware method [20] consumed 30,247 \(\upmu\)m\({}^{2}\), whereas our proposed hybrid architecture consumed 10,633 \(\upmu\)m\({}^{2}\), thus saving two-fold of the area. This area savings was due to the splitting of the syndrome generation into two units (SRU and syndrome kernel). When there were no errors in the page, the total time taken by the proposed decoder was less than 5 \(\upmu\)s (Figure 7), which was less than the average read latency of 100 \(\upmu\)s. Table 3 compares the power consumed by the conventional method [20] and our proposed method. The last entry in the table provides the power consumption required to support error correction over different fields (\(m=12,13,14,15\)) and until 40-bit error correction. There was a savings of 4 mW in our proposed method.