esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling and Stability Analysis of Articulated Vehicles
## 3 Stability Analysis Model for Articulated Vehicles
### Effects of Tire Cornering Stiffness  
Tyres are the component of the vehicle that directly contacts the ground and their condition has a crucial impact on the stability of the vehicle. In addition, pressure is one of the key parameters of a type. It is difficult or even impossible to establish a universal model to accurately calculate the relationship between tire pressure and cornering stiffness for different tires. Several related studies [25] showed that the relationship between the cornering stiffness and the tire pressure is approximately linear under heavy load. As articulated vehicles are mostly heavy construction vehicles which means the tires are always under heavy load, so the cornering stiffness is very likely to increases as the tire pressure increase linearly. Therefore, the effect of tires pressure on stability can be analysed implicitly by analysing the effect of cornering stiffness on stability.  
The stability characteristics of articulated vehicles under different tire cornering stiffness conditions are studied, and the values are shown in Figure 10. All eigenvalues are negative, and the articulated vehicle is in a stable state. According to the eigenvalue curve, increasing tire stiffness will increase the stability of the vehicle. Therefore, in terms of stability, tires with high rigidity should be chosen as much as possible when designing vehicle matching.  
Figure 10: Variation of the real parts of the eigenvalues with the tire stiffness: (**a**) real parts of \(\lambda_{1}\) and \(\lambda_{2}\) and (**b**) real parts of \(\lambda_{3}\) and \(\lambda_{4}\).  
Figure 9: Variation of the real parts of the eigenvalues with the moment of inertia: (**a**) real parts of \(\lambda_{1}\) and \(\lambda_{2}\) and (**b**) real parts of \(\lambda_{3}\) and \(\lambda_{4}\).
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs
#### 3.2.6 Energy Dispersive X-ray Spectroscopy (EDS)  
EDS is used in conjunction with Transmission Electron Microscopy (TEM) to perform elemental analysis. This approach is crucial for the identification of specific elements that have been absorbed onto the surface of MNPs, hence facilitating the study of surface coatings and alterations.
## 6 Experiments
### Experimental Setup
#### 6.2.1 Importance of Last Maintenance  
The time elapsed since the last maintenance event is a critical factor influencing mill performance and overload risk. In collaboration with mine experts, maintenance activities were meticulously annotated, enabling the creation of a novel feature, _Last Maintenance_. This feature quantifies the time since the most recent maintenance and has proven essential for enhancing overload prediction. Incorporating the _Last Maintenance_ feature into the model enriches its explanatory power by capturing the relationship between maintenance schedules and operational stability. This integration exemplifies a data-informed approach that leverages expert domain knowledge, improving predictive accuracy. By synthesizing empirical data with expert insight, our model effectively addresses the complex interdependencies in SAG mill operations, leading to more robust and reliable overload forecasting. Following the conditional mutual information (CMI) application [10], we identified the top 10 feature pairs pivotal for overload prediction. These are:  
1. Fine Granulometry and Coarse Granulometry,
2. Mill Power and Time since Last Maintenance,
3. Throughput and Time since Last Maintenance,
4. Fine Granulometry and Intermediate Granulometry,
5. Coarse Granulometry and Intermediate Granulometry,
6. Mill Pressure and Mill Speed,
7. Mill Power and Mill Speed,
8. Mill Power and Mill Pressure,
9. Mill Pressure and Time since Last Maintenance,
10. Mill Internal Pressure and Mill Pressure.  
This selection underscores the diversity of operational parameters and maintenance schedules critical for anticipating overload scenarios in SAG mills. After applying conditional mutual information (CMI) to our dataset, we constructed representative pair matrices that our predictive model would utilize. It is crucial to underscore that our business rules loss function was predicated on the interplay among mill features. Hence, the selection of pertinent pairs was aligned to ensure the inclusion of essential data, thereby facilitating feeding this section with relevant input data.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 3 Proposed Approach
### Object Classification and Thresholding  
Once vials are detected, they are categorized based on their label and structural features. A thresholding function is applied to enhance detection accuracy by eliminating false positive detections. The final decision function is defined as \(D(B)\) in Equation (3):  
\[D(B)=\begin{cases}1,&\text{if }C\geq T,\\ 0,&\text{otherwise},\end{cases} \tag{3}\]  
where \(T\) represents the confidence threshold. If the computed confidence score \(C\) is greater than or equal to \(T\), the vial is considered correctly identified and classified.
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 3 Methodology
### Data Augmentation and Enhancement  
Resized images were rotated or transformed using the affine transformation method. Different angles with a 360 rotation were used with an angle difference of 45\({}^{\circ}\). There were 9 angles in total in each sample instance applied for all classes, namely 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), 315\({}^{\circ}\), and 360\({}^{\circ}\). Affine transformation was calculated as given in Equation (1), and the applied augmentation sample for each category is shown in Figure 3.  
\[Rotation=\left[\begin{array}{ccc}cos(a)&sin(a)&0\\ -sin(a)&cos(a)&0\\ 0&0&1\end{array}\right] \tag{1}\]  
The angle of rotation according to an affine rotation is shown in Equation (1). A is the angle value that was changed nine times for an image to obtain the new rotated image.  
Data enhancement was applied with a combination of unsharp masking and color histogram equalization, and both of these methods were applied and calculated using Equations (2) and (3).  
\[f\left(I\right)=\alpha f-\beta f_{l} \tag{2}\]  
The output enhanced image was calculated in _f(I)_, where \(\alpha\) and \(\beta\) are constant, to be the input image that is multiplied where the original image is processed and subtracted via low-pass filter process mask \(f_{l}\). Histogram equalization was used in the image processing to enhance a given RGB image, and the three channels were individually evaluated using Equation (3).  
\[T_{k}=(L-1)cdf(P) \tag{3}\]  
The cumulative distribution of the given intensity was calculated over the probability of occurrences, as calculated in Equation (4)  
\[Cdf\left(P\right)=\sum_{k=-\infty}^{P}Prob(k) \tag{4}\]  
This calculated accumulative distributive value was then multiplied with maximal value intensity, and the newly calculated transformed intensity was calculated and mapped to the corresponding pixels throughout the given image.  
Rotated images covered a different aspect of the actual time occurrence, which could be any of the orientations for the user. The training and predictions of rotated augmented data offered promising results.  
Figure 3: Five types of guava species image samples with their enhanced and rotated 9 angle images in circular view in 4 circles.
## 4 Simulation Study
### Effect of the Camera Resolution  
This section presents an analysis of the performance of surveillance cameras based on the number of cameras deployed with different resolutions. The resolutions considered  
Figure 8: Performance analysis of the surveillance system according to various sensor focal lengths in terms of (**a**) detection time and (**b**) surveillance resolution.  
in this analysis are 720 p, 1080 p, 1440 p, and 2160 p. The objective of this analysis is to determine the impact of camera resolution on detection time.  
The results of this analysis are presented in Figure 9a, which illustrates the performance in terms of detection time \(T_{\text{suc}}\). For instance, when the resolution is 720 p, the detection time increases from 8.0 s to 259.1 s as the number of surveillance cameras increases from 10 to 500. Similarly, for 1080 p resolution, as the number of surveillance cameras increases from 10 to 500, the detection time increases from 8.2 s to 257.1 s. At 1440 p resolution, the detection time increases from 8.4 s to 258.4 s, while at 2160 p resolution, the detection time increases from 8.1 s to 257.7 s. The detection time values are comparable regardless of the resolution in question, and the difference in the detection time among various resolutions only ranged from 0.1 to 1.3 s. That is, only the number of surveillance cameras was a dominant factor in this simulation study. This is because the resolution is not a mandatory configuration for the surveillance area.  
In terms of measuring the quality of surveillance performance, the surveillance resolution per ms demonstrates a clear relationship with surveillance camera resolution. The performance of surveillance camera systems with different resolutions is displayed in Figure 9b, and the results indicate that as the number of surveillance cameras increases from 10 to 500, the surveillance resolution per ms increases from 24.79 to 38.78 kilopixels/ms at a resolution of 720 p.  
When the resolution is increased to 1080 p, the surveillance resolution per ms increases from 55.98 to 87.32 kilopixels/ms. An even greater increase is observed at a resolution of 1440 p, where the surveillance resolution per ms increases from 99.79 to 155.45 kilopixels/ms. Finally, at a resolution of 2160 p, the surveillance resolution per ms increases from 220.69 to 349.23 kilopixels/ms.  
Figure 9: Performance analysis of the surveillance system according to various resolutions in terms of (**a**) detection time and (**b**) surveillance resolution.  
In summary, increasing the resolution from 720 p to 1080 p, 1440 p, and 2160 p results in 2.3-, 4.0-, and 9.0-fold increases, respectively, in surveillance resolution per ms when compared to 720 p. Therefore, it can be inferred that the higher the resolution of a surveillance camera, the better the surveillance performance, as this allows for more accurate information to be obtained. These results have practical implications for the design and implementation of surveillance systems, as they highlight the importance of selecting high-resolution cameras for optimal performance.
# Mycelium-Based Thermal Insulation for Domestic Cooling Footprint Reduction: A Review
## 2 Methodology  
We conducted a comprehensive literature review using search engines like Google, Google Scholar, Scopus, and ScienceDirect databases. Our search strategy involved employing a set of keywords including \"mycelium\", \"fungal insulation\", \"mycelium insulation\", \"mycelium thermal insulation\", \"mycelium acoustic insulation\", \"particulate matter\", \"indoor temperature regulation\", \"bioremediation\", \"thermal conductivity\", \"cooling\", \"heating\", \"carbon footprint\", and \"embodied energy\". The inclusion criteria for our search encompassed papers published between 2017 and 2023, primarily focusing on the utilization of mycelium as a foundational material for thermal insulation in residential buildings. This involved exploring the multifaceted aspects of mycelium and its diverse applications. Additionally, we aimed to examine fungal adaptations and ecological roles, particularly concerning nutrient exchange, decomposition, and bioremediation. Our scope extended to investigating mycelium-based composites' mechanical, physical, and chemical attributes while evaluating conventional organic and inorganic insulation materials. The ultimate objective was to uncover the promising potential of mycelium as an eco-friendly solution for indoor temperature regulation, particulate matter absorption, biodegradability, and environmental impact. In the initial screening phase, we reviewed titles, abstracts, and various types of research content, including research articles, review articles, books, book chapters, theses, and reports. Exclusion criteria were applied to papers not written in English and those that did not pertain to the examination of mycelium for thermal insulation purposes or other environmental purposes, as indicated earlier. A total of 200 relevant sources were identified through our search engines, and the study selection process of our review is depicted in Figure 1. References that did not directly align with the scope of our review, focusing mainly on mycelium as a construction material rather than investigating its performance as a sustainable thermal insulation material, were also excluded from our analysis.
## 3 Results
### Model Selection Results
* **LightGBM** closely followed with an RMSE of 902.17, also showing excellent performance. * **Decision Tree** achieved an RMSE of 1299.57, outperforming more complex models like k-NN and neural networks. * **Random Forest** achieved a high RMSE of 1405.59. * **Neural networks** and **k-NN** showed higher RMSE values of 1671.12 and 1723.41, respectively. Figure 3: Model comparison based on mean RMSE values with 95% confidence intervals shown as error bars. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline k-NN & 0.8081 & 1723.41291 \\ RF & 0.8728 & 1405.59474 \\ NN & 0.8193 & 167.111765 \\ DT & 0.8909 & 1299.57351 \\ XGBoost & 0.9490 & 888.29617 \\ LightGBM & 0.9474 & 902.16747 \\ SVM & 0.7544 & 1949.72586 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of performance metrics for each model on validation set. * **SVM** had the highest RMSE of 1949.73, indicating the weakest performance in this context. In conclusion, only XGBoost and LightGBM outperformed the baseline, with XGBoost showing the highest mean \(R^{2}\) of 0.9490, followed closely by LightGBM with a mean \(R^{2}\) of 0.9474. All other models exhibited lower predictive performance compared to the baseline. Computational efficiency constitutes a critical aspect in the practical deployment of machine learning algorithms, particularly in environments with constrained processing resources such as onboard ship systems. To account for this, we incorporated a systematic assessment of execution times, separately evaluating training and inference durations (Table 6). All models were evaluated under a CPU-only setting to ensure consistency and eliminate variability due to hardware acceleration. This analysis is intended to complement the accuracy-based evaluation by providing performance indicators that are directly relevant to real-world deployment scenarios.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 3 Sensor Operating Modes and Energy Consumption Measurements
### Consumption Profiles in Active and Idle Mode
#### 3.3.2 Message-Processing Event  
Each time an ECG packet buffer in the MCU becomes full, the MCU transfers the Application Controller Interface (ACI) packet to the radio for transmission. The message-processing event looks very similar to a BLE connection event, except there is no RF communication involved during the event. A communication controller inside the BLE radio processes the incoming ACI packet and updates the on-board BLE services accordingly to prepare for information exchange during the next connection event. The transmitter and the receiver stay in an idle mode during the ACI packet processing event. All the activity in the nRF8001 radio is concerned with the BLE stack message processing and preparation for transmission during the next nearest BLE connection event. The measured mean message-processing event energy consumption \(e_{msg}\) is 79.21 \(\upmu\)s with 0.64 \(\upmu\)s standard deviation. An example of a message-processing event's current consumption profile is presented in Figure 5. The BLE message-processing event begins with the BLE radio wake-up procedure seen from 0.0 ms to about 0.3 ms, followed by a BLE stack processing between 0.3 ms and 3.5 ms, and ends with transition of the BLE radio back to sleep mode.
# Kinematic In Situ Self-Calibration of a Backpack-Based Multi-Beam LiDAR System
###### Abstract  
Light Detection and Ranging (LiDAR) remote sensing technology provides a more efficient means to acquire accurate 3D information from large-scale environments. Among the variety of LiDAR sensors, Multi-Beam LiDAR (MBL) sensors are one of the most extensively applied scanner types for mobile applications. Despite the efficiency of these sensors, their observation accuracy is relatively low for effective use in mobile mapping applications, which require measurements at a higher level of accuracy. In addition, measurement instability of MBL demonstrates that frequent re-calibration is necessary to maintain a high level of accuracy. Therefore, frequent in situ calibration prior to data acquisition is an essential step in order to meet the accuracy-level requirements and to implement these scanners for precise mobile applications. In this study, kinematic in situ self-calibration of a backpack-based MBL system was investigated to develop an accurate backpack-based mobile mapping system. First, simulated datasets were generated for the experiments and tested in a controlled environment to inspect the minimum network configuration for self-calibration. For this purpose, our own-developed simulator program was first utilized to generate simulation datasets with various observation settings, network configurations, test sites, and targets. Afterwards, self-calibration was carried out using the simulation datasets. Second, real datasets were captured in a kinematic situation so as to compare the calibration results with the simulation experiments. The results demonstrate that the kinematic self-calibration of the backpack-based MBL system could improve the point cloud accuracy with Root Mean Square Error (RMSE) of planar misclosure up to 81%. Conclusively, in situ self-calibration of the backpack-based MBL system can be performed using on-site datasets, reaching the higher accuracy of point cloud. In addition, this method, by performing automatic calibration using the scan data, has the potential to be adapted to on-line re-calibration.  
multi-beam LiDAR; in situ self-calibration; mobile mapping system; 3D point cloud; backpack-based mapping +
Footnote †: journal: Applied Sciences
## 1 Introduction
In a large network, this feature makes the scalability and robustness of localization algorithm better than the center of the algorithm execution throughout the global structure. At the same time, clustering technology is also very effective for broadcasting news and querying data, as the cluster head nodes can help broadcast messages and collect the data of users' interest within the cluster. At present, many domestic and foreign experts have proposed numerous different distributed clustering algorithms. The widely adopted algorithms refer to isomorphic or heterogeneous networks, which can be divided into two categories, namely those with the same composition and different composition clustering algorithms. Because of the complexity and energy configuration of network evolution, it is very difficult to design a suitable heterogeneous network energy efficient clustering algorithm. Many algorithms under the isomorphism network environment find it difficult to make full use of the heterogeneous characteristics of energy, thus low energy nodes will die earlier than high energy nodes. The proposed protocol is designed for a heterogeneous network [5, 6], which lets the cluster head node and the node residual energy be directly related to the cluster head election, in the meantime retains the advantages of distributed algorithms. In this paper, we focus on and explore in depth the nature of heterogeneous wireless sensor networks, and finally propose a novel energy-aware distributed clustering algorithm for heterogeneous wireless sensor networks (EDCA-H), which addresses the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation while the network is running and the degree of candidate nodes' effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. The rest of this paper is organized as follows: a brief introduction of works related to the topic of energy saving in heterogeneous wireless sensor networks is presented in Section 2. The energy consumption model is proposed and the optimization problems are presented in Section 3. In Section 4, the design of our novel proposed protocol is described in detail. Simulation results are discussed in Section 5. Finally, conclusions are made in Section 6.
## 3 Results
### EMG Pattern Analysis  
The EMG patterns of the left and right muscles analyzed using the CIMAP algorithm are shown in Figures 4 and 5, respectively. The pattern changes in the left GMax and VMed and those in the right TAnt indicate that, as the GF level decreased, the number of clustering patterns increased, with the highest pattern clustering observed at 80% GF. In particular, in the left GMax muscle, the number of activation patterns increased from 28 patterns with one representative pattern observed during the 1 st session (with 100% GF) to 93 patterns with two representative patterns during the session with 80% GF. Similarly, in the VMed muscle, the number of activation patterns increased from 37 patterns with one representative pattern at 100% GF to 51 patterns with two representative patterns at 80% GF. Regarding the left TAnt and right GMax, the number of clustering patterns continued to increase up to the 70% GF level. Therefore, muscle use patterns gradually became uniform during RAGT. Furthermore, both sides of the TAnt changed to use the muscle throughout the swing phase of walking motion during RAGT.  
However, in the post-assessment and follow-up sessions, the pattern became blurred when the robot was not utilized. The left GMax and GMed showed a decrease in the number of clustering patterns during the follow-up session, implying that the RAGT effect did not persist over the 4-week period. While the clinical assessments confirmed that walking quality improved during RAGT, robot assistance and training may be considered necessary, given that muscle use patterns become blurred if the exoskeleton robot is not worn.  
Figure 4: Electromyography patterns of left lower limb muscles using the Clustering for Identification of Muscle Activation Patterns algorithm. The blue lines represent the electromyography activation patterns valid for the clustering, and the black lines represent the representative pattern that clustered the blue lines. A blank table indicates that no more than 20 patterns valid for clustering were observed. GF, guidance force.