esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Characterisation and Modelling of Elastomeric Shockpads
## 2 Shockpad Characterisation
### AAA Methodology  
The AAA test device is designed to measure and record the shock absorption and deformation behaviour of 3G turf surfaces under human impact conditions to assess whether they are suitable for sporting use [21]. The device consists of a convex test foot mounted to a 20 kg mass via a spiral steel spring (Figure 3). A remote controlled electromagnet releases the mass from a height of 55 mm and the convex test foot with diameter 70 mm acts as an impact face with the surface. An accelerometer is attached to the base of the falling mass and records the acceleration throughout the drop, impact and rebound. Shock absorption, energy restitution and vertical deformation are all calculated from the accelerometer output for each drop.  
Shockpad samples of at least 30 \(\times\) 30 cm were laid beneath the impact foot and were subjected to drops from multiple heights from 55 mm down to 5 mm in 10 mm increment steps. Three drops were used for each drop height as per the FIFA test standard [21]. The different drop heights allowed the response of the shockpads under different impact energies to be examined. Raw accelerometer data was extracted from each of the test drops and processed using Matlab (Version 2017a, Mathworks, Natick, MA, USA). Velocity and displacement were calculated through integration using the trapezium rule for the duration of each drop. Shock absorption, energy restitution and vertical deformation were calculated from the first drop at each drop height using methods set out in the FIFA handbook of test methods [21] with the exception of the shock absorption which was altered to ensure the reference value for the solid floor was adjusted for each drop height. Contact time was taken as the time between the minimum vertical velocity (z-axis; Figure 3) that occurred as the impactor first contacted the surface, and the maximum vertical velocity which occurred as the impactor left the surface. The force and displacement data, obtained from the acceleration as detailed above, were converted into engineering stress and strain. Curvature of the test foot was neglected within the engineering stress calculations; a fixed cross-sectional area (of 38.5 cm\({}^{2}\)) was used throughout contact. Unloaded shockpad thickness, the average over 15 measurements (Table 1), was used to calculate the engineering strain.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Material**} & **Granule** & **Binder** & **Density** & **Thickness** & **Relative** \\  & & **Size (mm)** & & **(kg/m\({}^{3}\))** & **(mm)** & **Density** \\ \hline Berleburger & Rubber shreds & 1–2 & Polyurethane & 557 & \(15.0\pm 0.1\) & 0.49 \\ Holywell & Rubber granules & 2–6 & Polyurethane & 575–600 & \(22.4\pm 0.9\) & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shockpad properties overview.  
Figure 3: Advanced Artificial Athlete (AAA) schematic: 1. support frame; 2. electric magnet; 3. 20 kg falling mass; 4. accelerometer; 5. linear stiffness spring; 6. 70 mm diameter test foot [21].
## 2 A Proposed ASPC Approach for OCRs in a DG-Integrated Distribution Network
### Coordination Constraints of OCRs
#### 2.3.2 Boundary on Relay Operating Time  
The operating time of OC relays has a practical boundary by two limits, \(T_{ik}^{\text{min}}\) and \(T_{ik}^{\text{max}}\), which can be formulated as the following:  
\[T_{ik}^{\text{min}}\leq T_{ik}\leq T_{ik}^{\text{max}},\forall i=1,2,\ldots n \tag{7}\]  
where \(T_{ik}^{\text{min}}\) and \(T_{ik}^{\text{max}}\) are the minimum and maximum operation times of the \(i\)-th relay \(R_{i}\) when it operates as primary protection for a faulted location \(k\) in the DN, respectively. The value of \(T_{ik}^{\text{min}}\) depends on relay manufacturers, usually being higher than 0.01 s, while \(T_{ik}^{\text{max}}\) is the critical fault clearing time to avoid the damage of equipment as well as maintain the stability of power system, usually being lower than one second [32; 33].  
The Time-Dial Setting (TDS) is bounded by an available range of settings supplied by the relay manufacturer, which can be expressed as follows:  
\[TDS_{i}^{\text{min}}\leq TDS_{i}\leq TDS_{i}^{\text{max}},\forall i=1,2, \ldots n \tag{8}\]  
where \(TDS_{i}^{\text{min}}\) and \(TDS_{i}^{\text{max}}\) are the minimum and maximum TDS values of the \(i\)-th relay \(R_{i}\), respectively. The minimum and maximum available TDS values are usually in a range of 0.025 to 1.2 [32; 33]. This study selects an available range of 0.01 to 3.2, respectively, for \(TDS_{i}^{\text{min}}\) and \(TDS_{i}^{\text{max}}\).  
Similarly, the boundary of \(A\), \(B\), and \(C\) coefficients of inverse-time protection curve can be formulated by:  
\[\left\{\begin{array}{l}A_{i}^{\text{min}}\leq A_{i}\leq A_{i}^{\text{max}}\\ B_{i}^{\text{min}}\leq B_{i}\leq B_{i}^{\text{max}},\forall i=1,2,\ldots n\\ C_{i}^{\text{min}}\leq C_{i}\leq C_{i}^{\text{max}}\end{array}\right. \tag{9}\]  
where \(A_{i}^{\text{min}}\) and \(A_{i}^{\text{max}}\) are 0.009 and 150, respectively; \(B_{i}^{\text{min}}\) and \(B_{i}^{\text{max}}\) are 0.02 and 2.5, respectively; \(C_{i}^{\text{min}}\) and \(C_{i}^{\text{max}}\) are zero and 1.5, respectively, in this study. The number of intervals in a pre-determined range of \(A_{i}\), \(B_{i}\), and \(C_{i}\) is 50.
## 3 Results and Discussions
### Energy Capture Properties of C-TENG
#### 3.2.1 Electric Potential Distribution  
The potential distribution is obtained through simulation. Since the two contact materials are insulators, the designed charge is evenly distributed on the contact surface. Figure 6a-d in one cycle shows the potential distribution in different stages. When the electrode is at central position, the potential is 0. For electrode sliding to the upper edge, the potential difference between the two electrodes reaches the positive maximum value. Then, the potential becomes reverse maximum value with the electrode moving to the bottom, and finally returns to 0. When moving up or down to the limit position, the potential difference between the two electrodes reaches the maximum value. Therefore, the finite element simulation shows that the slider will generate a periodic electrical signal in each movement cycle. The results of such potential changes also verify the power generation principle of the C-TENG. Then we try to change the resistance, the gap between friction layers, and surface charge density to obtain open-circuit voltage and short-circuit current of different situations.
## 1 Introduction
### Scenarios for DCS  
There are two kinds of sensor networks where DCS could be applied. On one hand, standard WSNs have a central node named sink, base station, gateway, etc, that connects the WSN with the external world. In this kind of networks, sensor nodes gather information from the environment and store it either locally, in a rendezvous node (if DCS is being applied) or push it to the sink that stores all data. In such a network, usually the consumers of the WSN data are external users that query the network through the sink.  
On the other hand, nowadays not only sensors but actuators are becoming more relevant in the WSN community, and Wireless Sensor Networks are becoming Wireless Sensor and Actuator Networks (WSANs) [10; 11]. An actuator is a node that, based on the information retrieved from one or more sensors, performs some action. For instance a node controlling a water valve in a plantation opens it depending on the humidity and temperature conditions obtained from the sensors in the field. It must be noted that a node could perform both roles in the network, being a sensor and an actuator at the same time.  
The use of WSANs opens up the possibility of autonomous WSANs that, in a distributed way, measure the environment, process the collected data and perform some actions to get a common goal without any external intervention. Therefore, in this case, DCS seems to be a suitable storage and information retrieval system because there is no a clear central, powerful node as in the sink case.
## 2 Results and Discussion
### Discussion  
Recently, interest in the biosynthesis of triterpenes has gradually increased because of their economical and scientific importance. A number of OSCs involved in the formation of triterpene carbon skeletons have been identified and characterized [39]. While monofunctional \(\beta\)-amyrin synthases and lupeol synthases were found, all the \(\alpha\)-amyrin synthases identified so far are multifunctional and yield more than one product [33]. Many species of the _Ilex_ genus of plants are rich in triterpen saponins, mostly of the \(\alpha\)-amyrin type. The transcriptomic analysis of _I. asprella_ has revealed a few AS candidate genes, and a close investigation into these candidates and their comparison with previously characterised AS genes would provide important knowledge of this gene family.  
Unlike OSCs, the identification of new CYP450s and UGTs involved in the biosynthesis of triterpen saponins is beset with difficulties owing to the poor relationship between gene homology and functions of these two gene families. In this study, gene annotation provided a great number of putative CYP450s and UGTs. The candidate number was narrowed down to a few homologous unigenes by applying direct, homology-based screening of characterised genes. However, it is unknown whether these candidates are actually involved in the biosynthesis of triterpenoid saponins.  
Therefore, additional strategies should be engaged to identify credible candidate CYP450s and UGTs. The combination of exificutor-induced expression regulation and co-expression analysis with OSC [33,37,40] would contribute to identification of the targeting CYP450s and UGTs in _I. aprella_.  
Triterpenoid saponins were isolated from various tissues of _I. aprella_ like roots and leaves, but this study is restricted to root tissue. A more thorough analysis of different plant tissues coupled with metabolomic data would help in building a global picture of _Ilex_ triterpenoid biosynthesis and perhaps find novel candidate genes which would otherwise be difficult with sequence-homology-based searches.
## 3 Materials and Methods
### Model Evaluation
#### 3.2.4 Training Enhancements  
All hyperparameters were optimized using the Optuna framework [20]. Optuna was chosen for hyperparameter optimization due to its efficiency and ability to automate the search process, reducing the need for time-consuming manual tuning. Data augmentation techniques were implemented to all experiments including resizing to 224 \(\times\) 224, normalizing to ImageNet statistics. Also, all images were subjected to random horizontal flipping and \(\pm\)15\({}^{\circ}\) in-plane rotation.  
Two loss functions were evaluated: binary cross-entropy (BCE) and focal loss (FL) [21; 22]. BCE remains the go-to choice for multi-label recognition and has been typi  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **Description** \\ \hline CNN-SE-FFM-BiLSTM & Three SE blocks within the CNN layers followed by FFM and a BiLSTM layer to capture temporal dependencies. \\ \hline CNN-FFM-SE-BiLSTM & A single SE block after the FFM, followed by the BiLSTM layer. \\ \hline CNN-FFM-SE & A single-label approach consisting of the DenseNet-121 architecture followed by FFM and a single SE block \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the evaluated approaches.  
cally used in previous studies. On the other hand, the focal loss is designed to optimize overall misclassified examples while reducing the loss contribution from well-classified samples. This can be particularly useful in this study due to the imbalanced dataset, where the model may otherwise become overly confident in the majority classes, ignoring the minority classes.  
For learning rate adaptation and model fine-tuning during the training process, the cosine-annealing learning-rate (LR) scheduler and the reduced learning rate on plateau (ReduceLROnPlateau) scheduler were investigated [23,24]. The cosine learning-rate scheduler adjusts the learning rate following a cosine curve, gradually reducing the rate over each cycle which is particularly useful in fine-tuning the model more carefully as it approaches convergence. On the other hand, the ReduceLROnPlateau scheduler adaptively adjusts the learning rate based on the validation performance, ensuring optimal convergence. The ReduceLROnPlateau scheduler decreases the learning rate by a factor of 0.1 if the validation loss plateaued for more than five epochs.
## 1 Introduction
Although some researchers have successfully employed TLS or HLS to capture spatial information, there are still several non-trivial challenges in multi-platform point cloud registration for underground mines, such as the spatial reference, observation viewpoint, and data quality of multi-platform point clouds being so different that it is difficult to strictly establish point-to-point correspondence. In our study, the TLS and HLS point cloud registration method based on the coarse-to-fine strategy is proposed, the main contributions of which are as follows. (1) Coarse and fine registration algorithms based on control point extraction, identical area identification, and improved 3D normal distribution transform (3D-NDT) are presented, which achieves the coordinate datum unification of the TLS and HLS point cloud. (2) To provide accurate data sources for subsequent 3D modeling and deformation analysis, the roughness is calculated to remove and downsample the low-accuracy point cloud data. The article is organized as follows. An introduction to multiple techniques for spatial information collection from underground mines is presented in Section 1. The methodology for the multi-platform (HLS and TLS system) point cloud registration is proposed in Section 2. In Section 3, a case study is presented, and the results are evaluated to determine the effectiveness and applicability of the proposed assessment procedure. Section 4 summarizes the main conclusions of this paper.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 3 Materials and Methods
### Dataset  
This study utilised machine learning models to predict the average time spent online, measured in minutes, by employing sixteen independent variables. The dependent variable was the average internet usage time for the period from 2014 to 2024, as sourced from the World Bank. The analysis incorporated the following independent variables to explore these relationships:  
* Total population;
* Number of active internet users;
* Number of active social media users;
* Cellular mobile network users;
* Proportion of women by population;
* Proportion of men by population;
* Proportion of population living in rural areas;
* Proportion of population living in urban areas;
* Average (median) age of the population;
* Average mobile internet connection speed;
* Average literacy rate of the population;
* Female literacy rate;
* Male literacy rate;
* Mobile (cell) phone ownership rate of the population;
* Proportion of the population owning a smartphone or mobile phone;
* Share of mobile phones in total web traffic.  
Table 1 presents the statistical metrics, including the mean, minimum, maximum, and standard deviations of the variables utilised in the dataset for this study.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Average** & **Minimum** & **Maximum** & **SD** \\ \hline Number of active internet users & 56.81 & 35.40 & 74.41 & 12.90 \\ \hline Number of active social media users & 52.17 & 38 & 68.90 & 9.302 \\ \hline Cellular mobile network users & 69.44 & 51.45 & 77.39 & 7.920 \\ \hline Total population & 82.08 & 76.70 & 85.59 & 2.734 \\ \hline Proportion of women by population & 50.28 & 49.80 & 51.00 & 0.004 \\ \hline Proportion of men by population & 49.72 & 49.00 & 50.20 & 0.004 \\ \hline Proportion of population living in rural areas & 24.68 & 22.40 & 26.92 & 0.014 \\ \hline Proportion of population living in urban areas & 75.34 & 73.08 & 77.60 & 0.014 \\ \hline Average (median) age of the population & 31.52 & 30.70 & 32.20 & 0.397 \\ \hline Average mobile internet connection speed & 31.89 & 25.47 & 34.90 & 2.949 \\ \hline Average literacy rate of the population & 96.04 & 95.00 & 96.70 & 0.005 \\ \hline Female literacy rate & 93.42 & 92.00 & 94.70 & 0.008 \\ \hline Male literacy rate & 98.79 & 98.00 & 99.10 & 0.003 \\ \hline Mobile (cell) phone ownership rate of the population & 95.42 & 86.00 & 98.90 & 0.041 \\ \hline Proportion of the population owning a smartphone or mobile phone & 76.37 & 34.00 & 98.80 & 0.222 \\ \hline Share of mobile phones in total web traffic & 6.38 & 4.14 & 8 & 1.343 \\ \hline Average time spent online & 62.59 & 29.43 & 74.80 & 0.135 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistical properties of the variables.  
Based on the statistical analysis of the dependent and independent variables presented in Table 1, the data show an even distribution with no missing values, outliers, or noise. Consequently, the entire dataset was incorporated into the model.
## 4 Experimental Environment for Truck Platooning
### Selection of Driving Simulator and Construction of Experimental Environment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Stage** & **Details** \\ \hline \multirow{2}{*}{1} & General & The following vehicle accelerates to reduce the distance to \\  & Driving & the leading vehicle, thereby initiating the platooning process. \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Platoon} & When the distance between following and leading vehicles \\  & & decreases to approximately 50 m, enabling V2V communication, \\  & & the following vehicle requests to join the platoon. Upon approval \\  & & from the leading vehicle, longitudinal control (pedal release) \\  & & and lateral control (steering release) are automated sequentially. \\ \hline \multirow{2}{*}{3} & Platoon & The platoon maintains a constant speed and intervehicle \\  & Maintenance & distance for 120 s, as specified in the experimental scenario. \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{Platoon} & After the scenario is terminated, the following vehicle \\  & & sequentially switches to manual lateral (steering operation) \\  & & and longitudinal (pedal operation) control upon its \\  & & own request and the leading vehicle’s approval. \\ \hline \multirow{2}{*}{5} & General & The following vehicle increases its distance from the leading \\  & Driving & vehicle, dissolves the platoon, and returns to general driving. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Process of truck platooning formation and dissolution. Figure 1: Driving simulator used in the experiment and the experimental environment.
# High Speed Decoding for High-Rate and Short-Length Reed-Muller Code Using Auto-Decoder
## 2 RM Decoder Based on AD
### RM Decoding Model  
The RM code of \(n=2^{m}\) bits with the minimum Hamming distance of \(2^{m-r}\) used to encode \(k=\sum_{i}^{r}\binom{m}{i}\) bits of message is denoted as \(\mathfrak{R}(r,m)\)[15, 18]. To illustrate the proposed decoding model, we consider two cases of RM code, \(\mathfrak{R}(1,4)\) and \(\mathfrak{R}(2,4)\) of code length \(16\), because the model training for longer code consumes more time. Figure 3 shows the proposed RM decoder structure constructed in such a way that the AD of Figure 2 is followed by one fully connected (FC) layer with the number of nodes defined as \(N=2^{k}\), and, thus, \(N=2^{5}\) for \(\mathfrak{R}(1,4)\) and \(N=2^{11}\) for \(\mathfrak{R}(2,4)\). Because \(N=2^{k}\) is equivalent to the number of all possible messages transmitted, we use the FC layer as the output layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Number of Nodes** \\ \hline input & \(n\) \\
1 st hidden & \(2n\) \\
2 nd hidden & \(4n\) \\
3 rd hidden & \(2n\) \\ output & \(n\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of nodes for layers in AD.  
Figure 1: Structure of (**a**) auto-encoder and (**b**) auto-decoder.  
Figure 2: Structure of auto-decoder with 3 hidden layers.  
Moreover, a softmax activation function is used to normalize the output of the model to a probability distribution over all possible transmitted messages. Figure 4 shows the method used to estimate the transmitted message bits of the (16,11) code of \(\mathfrak{R}(2,4)\) from the output layer. The output node index of the FC layer is the decimal value corresponding to the message bits, and the transmitted message bits are estimated by converting the index of the maximum output node value into \(k=11\) binary bits.
## 3 Sensor Node Position Estimation
### The Position Packet Validation Algorithm
#### 3.2.3 Hybrid Algorithm  
The position distance algorithm improves the probability of a quadrant containing a single position packet but does not guarantee it. The circle limit algorithm, on the other hand, guarantees that a quadrant will have only one position packet, but reduces the chances of validating and accepting new position packets. This means that a sensor node may take longer to estimate an accurate position. Therefore, we propose a hybrid algorithm that combines both position distance and circle limit algorithms. Initially, a sensor node will use position distance until it has received and validated three position packets. Once the node is considered qualified, it switches to the circle limit algorithm to improve its position estimation. In this way, a node can quickly estimate an acceptable position initially and then improve it over time.
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 4 Experimental Setup and Results
### Dataset Collection and Experiment Setup
The loss function was a combination of reconstruction loss (Mean Squared Error) and KL divergence, designed to encourage accurate reconstruction of normal data while regularizing the latent space. The model was trained with a batch size of 32 for 100 epochs to ensure convergence. An early stopping mechanism was incorporated to prevent overfitting, where training was stopped if the validation loss showed no improvement for 20 consecutive epochs. These parameters were selected after repeated experimentation to optimize the trade-off between training efficiency and anomaly detection accuracy, making the model suitable for real-time V2X applications.
## 3 Measurement Setup
### Rolling Element Test Rig  
A simple customised test rig shown in Figure 2 was used for bearing test at the University of Birmingham. The test rig consists of an AC motor rotating a shaft via pulley and belt. A controlled load was applied to the rolling element under investigation. The speed of the bearing was monitored using a tachometer. The sensor which was used to record the vibration signal was a high frequency 712F accelerometer by Wilcoxon Corporation with a nominal range of 3 Hz to 25 KHz.