esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Artificial Neural Network  
The principle of ANNs is grounded in simulating the structure and functions of the human brain to process information. ANNs belong to the domain of artificial intelligence, alongside other methods such as case-based reasoning, expert systems, and genetic algorithms. While classical statistics, fuzzy logic, and chaos theory comprise related fields, ANNs stand out for their ability to perform massively parallel computation for data processing and knowledge representation.  
At its core, an ANN comprises interconnected processing elements known as neurons, organized into distinct layers within the network structure. The most effective type of ANN, the multi-layer perceptron (MLP), typically consists of three layers: an input layer, an output layer, and one or more hidden layers. Neurons within each layer are interconnected but not within the same layer. Information processing occurs as signals are transmitted between neurons through connection links, each with an associated weight that modulates the signal. The sum of weighted input signals to each neuron is transformed by an activation function, typically nonlinear in nature.  
The performance of an ANN is influenced by the network architecture, the activation function, and the learning algorithm. For network architecture, this involves the arrangement of neurons and layers within the network. The number of hidden layers and neurons is determined by the complexity of the problem being addressed. In addition, the activation function determines how the weighted sum of inputs is transformed into an output signal. It introduces nonlinearity into the network, allowing it to model complex relationships between inputs and outputs. The learning algorithm governs how the network adjusts its connection weights during training to minimize prediction errors. One commonly used algorithm is the feedforward backpropagation algorithm, which updates weights based on the difference between the predicted and actual outputs.  
During training, the network is exposed to a sufficient number of input-output patterns, known as training pairs, to learn the underlying relationships between inputs and outputs. The training process involves iteratively adjusting the connection weights until a specified error goal, such as root mean square error (RMSE), is reached. Once trained, the network can be used to predict outputs (e.g., blast-induced ground vibration) for new input data. Figure 1 illustrates an ANN model for predicting blast-induced ground vibration.
# Omnidirectional Haptic Guidance for the Hearing Impaired to Track Sound Sources
## 3 System Configuration and Procedure
### Driving the Vibrator Using the Interaural Time Difference  
As shown in Figure 3, if the sign of the time difference between the two ears is positive, the sound source is located on the left side of the user, and if it is negative, it is located on the right side. The larger the time difference, the larger the angle of the sound source. How should we convey this information to the user by vibrating the vibrator? Figure 11 shows the relationship of the time difference between the two ears and the X-shaped voltage output from the FPGA module to the left and right vibrators. Different voltages are output to the two vibrators depending on the time difference between the two ears. When the time difference between the ears is 0 us, the output voltage to the vibrators is equal. When the time difference between the ears is +150 us, the sound source is located on the left side of the subject, so the vibration intensity of the left vibrator is increased so that the output voltage to the right and left vibrators is 1:3. In other words, when the sound source is located on the right side, the right vibrator is vibrated more strongly, and when it is located on the left side, the left vibrator is vibrated more strongly. Note that both vibrators are always vibrating during the sound. In Figure 11, the output voltage to the vibrator on the left side is increased proportional to the time difference between the ears, while it is decreased inversely on the right side. In this way, the subject can know the direction of the sound source through the vibrations of both vibrators because the ratio of the vibration intensity of the two vibrators changes continuously according to the value of the interaural  
Figure 10: Relationship between input voltage of vibrator and vibration amplitude of plastic plate glued to speaker driver.  
Figure 9: (**a**) Appearance of a vibrator and (**b**) placement of vibrators attached to the back of a vest.  
time difference. The output voltage to the vibrators was assumed to change linearly in the range of \(-300\) to \(+300\)\(\upmu\)s in the time difference between the ears and to be constant otherwise. The narrower the range of the linearly varying time difference, the more the ratio of vibration intensity of the vibrators changes, so further study of the user's reaction speed may be necessary.
# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 4 Three-Dimensional Coordinated Control Strategies for DDEV Chassis
In this hierarchical architecture, the upper-level vehicle body control layer employs a Nonlinear Sliding Mode Control (NSMC) algorithm to calculate the total control forces and moments required by the vehicle based on higher-level commands (such as desired path, stability objectives). The introduction of SMC aims to enhance systems' robustness against model uncertainties and external disturbances. The middle layer is designed as a tire force allocation layer, which, through advanced optimization algorithms, optimally allocates the generalized control forces and moments calculated by the upper layer to the longitudinal and lateral forces of each wheel. This allocation process also indirectly influences the dynamic distribution and effective utilization of vertical loads. The lower-level actuator control layer is responsible for precisely driving the corresponding actuators to accurately achieve the optimal tire force output determined by the middle layer. This study aims to comprehensively enhance vehicles' driving safety, handling stability, ride comfort, and road tracking capability. [PERSON] et al. [121] proposed a Unified Chassis Control (UCC) strategy. This strategy, through an ingenious fixed-point control allocation algorithm, effectively allocates the generalized control forces/moments calculated by the upper-layer controller to the target slip ratio and slip angle for each tire. It is noteworthy that for driving conditions on uneven roads, this study designed an H\(\infty\) robust controller based on a modified quarter-vehicle model to ensure the performance and stability of vertical control. Furthermore, to address the issue of the instantaneous loss of tire contact force that may be caused by emergency situations such as uneven roads or tire blowouts, the strategy also introduced a Reconfigurable Control Allocation (RCA) method, significantly enhancing the system's fault tolerance and robustness. The core concept of UCC lies in implementing a unified coordinated management and optimal allocation for all key chassis actuators, with a particular focus on the importance of maintaining tire contact stability under vertical disturbances and enhancing the system's robust adaptability. The ultimate goal is to comprehensively improve vehicles' driving stability and ride comfort. Table 5 lists common longitudinal, lateral, and vertical coordinated control strategies. In summary, research on coordinated chassis control for DDEVs has entered a new stage of the integrated coordination of lateral, longitudinal, and vertical (three-dimensional) aspects, aiming to achieve the comprehensive optimization of overall vehicle dynamic performance. To achieve this goal, researchers have explored various advanced coordi
# Slotted E-Shaped Meta-Material Decoupling Slab for Densely Packed MIMO Antenna Arrays
## 3 Radiation Pattern of the Antenna Arrays  
The simulated 2D polar plots of the three types of the array of antenna: MTM without DS, MTM-DS, and slotted MTM-DS at 9.5 GHz are shown in Figure 12. A good pattern correlation in 2D is observed for the three types of the antenna array. Although the electric plane for the three types is slightly the same, the magnetic plane for the slotted MTM-DS is minimal, and the gain is improved at the selected frequency 9.5 GHz.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Operation** & **BW (GHz)** & **Fraction** & \multicolumn{3}{c}{**Mutual Coupling Suppression**} \\  & **BW (GHz)** & **S11 \textless{} 10 dB** & **BW (\%)** & \multicolumn{3}{c}{**between Antenna Array(dB)**} \\ \cline{5-7}  & & & & **Min** & **Ave** & **Max** \\ \hline Without DS & 9.22–10.5 & 1.28 & 12.98 & \(-14.5\) & \(-18\) & \(-23.7\) \\ With MTM-DS & 9.3–10.5 & 1.2 & 12.12 & \(-20.2\) & \(-23.8\) & \(-27.5\) \\ With slotted & 8.67–10.18 & 1.51 & 16.08 & \(-28.5\) & \(-36\) & \(-43.7\) \\ MTM-DS & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurements resulted from the three isolating techniques.  
Figure 11: Simulated and measured S-parameters results of the fabricated \(2\times 1\) antenna microstrip patch antenna array with slotted MTM-DS.
## 3 Background and Overview of LEACH
### LEACH Overview
#### 3.3.1 Setup Phase  
LEACH completes its operation in rounds, each round starts with a setup phase followed by a steady state phase. Initially, a cluster organizes itself in the setup phase and completes its advertisement operation. Then, in the steady state phase the CMs transmit data to the CH and this latter transfers the aggregated data to the BS [4,18].  
When the clusters are created, each node individually decides whether to participate in CH selection operation in the current round or not. Equation (8) shows the formula for calculating \(T(N_{i})\) threshold value where \(N_{i}\) is a node and \(p\) is the probability of node \(N_{i}\) to become the CH in round \(r\). \(G\) is a set of nodes that are not selected as CH in previous \(1/p\) rounds. Based on these parameters the threshold value is calculated for further setup operations [7].  
Regardless of the network density, the decision is made up on the initial probability (i.e., the probability \(p\) used in Equation (8) of the nodes that will become the CH. Afterwards, those nodes get a chance to become the CH for the current \(1/p\) rounds. CHs broadcast an advertisement (ADV) message to all nodes. Upon receiving an ADV message, CMs send a response message to their chosen CH individually based on the measured RSSI. Each CH creates a TDMA schedule and sends it to its CMs to avoid the collision while transmitting data to CH. Each CM receives the TDMA schedule and accordingly switches to sleep state until its scheduled time slot is reached. At this instant only, this CM can send the data. The operation of TDMA schedule creation is illustrated in Figure 2, where \(TS_{i}\) refers to Time Slot \(i\).  
Figure 1: Example of a WSN composed of a number of clusters.
## 4 Experiments  
### Datasets  
The proposed model was verified on two highway traffic datasets, PeMSD4 and PeMSD7, collected by Caltrans Performance Measurement System (PeMS) [47] at 30-s intervals. The traffic speed data were aggregated from the raw data into 5-min windows. This system deploys 39,000 detectors in major cities in California. Geographic information of sensors is recorded in datasets with corresponding interval. The details of the datasets in our experiments are:  
**PeMSD7:** It contains the traffic information from the sensors on the highways of Los Angeles County. We selected 204 sensors and collected three months of data from 1 January 2018 to 31 March 2018 for the experiment.  
**PeMSD4:** It refers to the traffic data from the sensors in San Francisco Bay Area. We chose 325 sensors and extracted the data from 1 January 2017 to 31 March 2017 for the experiment.  
During the experiment, both datasets were divided into chronological order, with 70% used for training, 10% for validation, and the remaining 20% for testing. The sensors distribution of the two datasets is displayed in Figure 7. In the data preprocessing stage, we discarded traffic speed outliers less than 0 and used the tensor decomposition method to complete the missing values in the traffic speed data. Then, we encoded the non-numeric features in external factors using a one-hot encoding scheme. Later, we used Min-Max normalization to map its value into \([0,1]\) and the original speed into \([-1,1]\). During the evaluation phase, we re-projected the speed back to the original range as the final prediction result.
## 4 Discussion
Nrf2 decreases oxidative stress by regulating the expression of HO-1 and other antioxidant genes, while ADMA, MDA, and NF-kB increase oxidative stress and low-grade inflammation by contributing to ED, inhibiting the nitric oxide synthase pathway [43]. Endothelial damage caused by atherosclerosis risk factors occurs early, before measurable changes in the vascular wall, such as CIMT [44]. Our study assesses, for the first time, the relationship between CIMT, ED biomarkers, and oxidative stress in MetS. As ED and increased IMT are interrelated, indicative of different aspects of the atherosclerotic process, their early detection may be important for cardiovascular prevention. Based on the present and previous findings, we suggest that measuring ADMA levels, together with oxidative stress markers (MDA, HO-1, and Nrf2), may be a promising step in developing an effective model to monitor the severity of CIMT and atherosclerosis in patients with MetS. Carotid intima-media thickness is an important tool for early screening to assess subclinical manifestations of cardiovascular and metabolic diseases and a strong predictor of future cerebral and cardiovascular complications [45]. There are convincing data that the protective effect of some natural agents (phenylpropanoids, flavonoids, terpenoids, alkaloids, stilbenes, and quinones) on endothelial cells against oxidative stress-induced injury is mainly due to the activation of the Nrf2/HO-1 signalling pathway [19]. A limitation of the study is the size of the cohort. This is a pilot study to check the hypothesis that expression of oxidative stress- and inflammation-related genes in PBMC may reflect pathologies. Particularly, we found that in MetS, expression is dependent on IMT. In addition, age appears to be a factor, defining the expression in PBMC in MetS. Increasing the size of the study population would provide more detailed data about the molecular mechanisms of MetS impairments. Furthermore, the applicability of molecular biomarkers, including the PBMC expression levels of the selected genes in personalized and translational medicine, can be assessed. In addition, in this cohort, there was a gender imbalance. Improving the study in this direction will elucidate whether there are gender-dependent differences in MetS.
# Intelligent Monitoring Systems for Electric Vehicle Charging
## 4 Intelligent EV Charging Monitoring System
### Optimization Framework
#### 4.3.1 Problem Formulation  
The core multi-objective optimization problem for \(N\) charging stations over \(T\) time intervals is formulated as follows:  
\[\min_{\mathbf{P}}\sum_{k=1}^{4}w_{k}\hat{f}_{k}(\mathbf{P})\quad\text{subject to}\quad\eqref{eq:constraint}-\eqref{eq:constraint}, \tag{1}\]  
where \(\mathbf{P}\in\mathbb{R}_{+}^{N\times T}\) is the power allocation matrix, \(w_{k}\in[0,1]\) are normalized weights (\(\sum w_{k}=1\)), and \(\hat{f}_{k}=(f_{k}-f_{k}^{min})/(f_{k}^{max}-f_{k}^{min})\) are normalized objectives. The original objectives are as follows:  
Objective 1 (Peak Shaving):  
\[f_{1}=\max_{t\in T}\sum_{i=1}^{N}P_{i,t}+\lambda\sum_{t=1}^{T-1}\left|\sum_{i =1}^{N}(P_{i,t+1}-P_{i,t})\right|, \tag{2}\]  
where \(\lambda>0\) (kW\({}^{-1}\)) penalizes power gradient norms.  
Objective 2 (Economic Efficiency):  
\[f_{2}=\sum_{t=1}^{T}\sum_{i=1}^{N}\Big{[}c_{t}P_{i,t}+\alpha(P_{i,t}-P_{i}^{ \text{target}})^{2}+\beta\max(0,P_{i,t}-P_{i}^{\text{rated}})\Big{]}, \tag{3}\]  
with \(c_{t}\) ($/kWh) as the time-varying electricity price, \(P_{i}^{\text{target}}\) (kW) as station-specific power targets, and \(P_{i}^{\text{rated}}\) (kW) as the maximum rated power.  
Objective 3 (User Satisfaction):  
\[f_{3}=-\sum_{i=1}^{N}\sum_{j\in\mathcal{J}_{i}}\Bigg{[}\beta_{1}\frac{P_{i,t} }{P_{j}^{\text{req}}}+\beta_{2}\eta_{i,t}+\beta_{3}\exp\Bigg{(}-\frac{|P_{i,t }-P_{j}^{\text{pref}}|}{P_{j}^{\text{tol}}}\Bigg{)}\Bigg{]}, \tag{4}\]  
where \(\mathcal{J}_{i}\) denotes users at station \(i\), \(P_{j}^{\text{req}}\) (kW) is the required charging power, \(P_{j}^{\text{pref}}\) (kW) is the preferred power level, and \(P_{j}^{\text{tol}}\) (kW) is the tolerance threshold.  
Objective 4 (Grid Stability):  
\[f_{4}=\sum_{t=1}^{T}\Big{[}\gamma_{1}(\Delta f_{t})^{2}+\gamma_{2}(\Delta V_{t})^{ 2}+\gamma_{3}\sum_{i=1}^{N}|Q_{i,t}|+\gamma_{4}\text{THD}_{t}^{2}\Big{]}, \tag{5}\]  
where \(\Delta f_{t}=f_{t}-f_{0}\) (Hz) and \(\Delta V_{t}=V_{t}-V_{0}\) (V) are frequency/voltage deviations from nominal values, \(Q_{i,t}\) (kVAR) is the reactive power, and \(\text{THD}_{t}\) (%) is the total harmonic distortion.
# Assessing the Effect of Drivers' Gender on Their Intention to Use Fully Automated Vehicles
## 3 Results
### Structural Analyses
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Group A: Male Drivers**} \\ \hline  & **Path** & **S.P.C. \({}^{\text{a}}\)** & **S.E. \({}^{\text{b}}\)** & **C.R. \({}^{\text{c}}\)** & \(p\)\({}^{\text{d}}\)** \\ \hline Intention & \(\leftarrow\) & Interaction with ICTs & 0.086 & 0.04 & 2.102 & 0.036 \({}^{\text{*}}\) \\ Intention & \(\leftarrow\) & Greater connectivity & \(-\)0.106 & 0.096 & \(-\)2.191 & 0.028 \({}^{\text{*}}\) \\ Intention & \(\leftarrow\) & Reduction of driving demands & 0.058 & 0.118 & 1.201 & 0.230 N\({}^{\text{/S}}\) \\ Intention & \(\leftarrow\) & Fuel/energy consumption saving & 0.104 & 0.045 & 2.229 & 0.026 \({}^{\text{*}}\) \\ Intention & \(\leftarrow\) & Travel efficiency & 0.231 & 0.013 & 4.751 & \textless{}0.001 *** \\ Intention & \(\leftarrow\) & Increased safety & 0.301 & 0.05 & 6.128 & \textless{}0.001 *** \\ \hline  & \multicolumn{4}{c}{**Group B: Female Drivers**} \\ \hline  & \multicolumn{2}{c}{**Path**} & **S.P.C.** & **S.E.** & **C.R.** & \(p\)\({}^{\text{d}}\) \\ \hline Intention & \(\leftarrow\) & Interaction with ICTs & 0.017 & 0.045 & 0.417 & 0.677 N\({}^{\text{/S}}\) \\ Intention & \(\leftarrow\) & Greater connectivity & 0.048 & 0.099 & 1.02 & 0.308 N\({}^{\text{/S}}\) \\ Intention & \(\leftarrow\) & Reduction of driving demands & 0.157 & 0.112 & 3.315 & \textless{}0.001 *** \\ Intention & \(\leftarrow\) & Fuel/energy consumption saving & 0.056 & 0.049 & 1.173 & 0.241 N\({}^{\text{/S}}\) \\ Intention & \(\leftarrow\) & Travel efficiency & 0.166 & 0.014 & 3.241 & 0.002 \({}^{\text{*}}\) \\ Intention & \(\leftarrow\) & Increased safety & 0.397 & 0.056 & 7.941 & \textless{}0.001 *** \\ \hline  & \multicolumn{4}{c}{Notes for the Table: \({}^{\text{a}}\) S.P.C. = Standardized path coefficient; \({}^{\text{b}}\) S.E. = standard error; \({}^{\text{c}}\) C.R. = critical ratio; \({}^{\text{d}}\) bias-corrected \(p\)-value; \({}^{\text{*}}\) = path is significant at the level \(p\)\(<\) 0.050; \({}^{\text{*}}\) = path is significant at the level \(p\)\(<\) 0.010; \({}^{\text{*}}\) = path is significant at the level \(p\)\(<\) 0.001; \({}^{\text{N}}\)/S\({}^{\text{*}}\) non-significant path.} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multi-group SEM model to predict drivers’ intention to use automated vehicles.
## 4 Advanced Machine Health Diagnosis Framework
### Fault Severity Assessment  
The predicted degradation trend is projected onto a predefined health scale that classifies fault severity into distinct levels (e.g., incipient, moderate, critical). This classification is based on the amplitude of the fault frequency component. As the amplitude increases and crosses specific thresholds, the system assigns the fault to a corresponding severity class.  
These thresholds are established either from historical data patterns or expert-defined criteria and reflect the typical progression of the fault under real operating conditions.  
Figure 2: Advanced machine health diagnosis framework.  
For example, low amplitude values indicate normal degradation (incipient), moderate amplitudes reflect a progressing fault that may soon impact performance, and high amplitudes correspond to a critical condition requiring immediate attention.  
This data-driven classification supports maintenance teams by providing a clear, risk-based view of asset health, enabling the prioritization of interventions based on severity and estimated time-to-failure.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Biopreservatives  
With the increasing awareness of the potential health risks posed by food additives, the concept of clean-label foods gained more attention. Natural preservatives not only extend the shelf life of food but also offer additional health benefits [112]. It was reported that the global sales of natural food preservatives are estimated to be 612 million by 2024, and it might increase up to 2-fold ten years later ([[https://www.futuremarkeinsights.com/reports/natural-food-preservatives-market](https://www.futuremarkeinsights.com/reports/natural-food-preservatives-market)]([https://www.futuremarkeinsights.com/reports/natural-food-preservatives-market](https://www.futuremarkeinsights.com/reports/natural-food-preservatives-market)) (accessed on 13 February 2025)).  
Due to the hydrophobic skeleton, terpenoids are often lipophilic so they can disrupt cell membranes and inhibit enzymes related to energy metabolism and DNA synthesis, leading to impaired cell viability [5]. For instance, \(\alpha\)-pinene and limonene can inhibit the activity of _E. coli_ and _Staphylococcus aureus_. On the other hand, some terpenoids act as antioxidants by scavenging free radicals and protecting food from oxidative damage [14]. For example, carotenoids are widely used as antioxidants due to their conjugated double-bond system. In this scene, terpenoids with antibacterial and antioxidant bioactivities have great potential as food preservatives [113]. In fact, terpenoids have already been commonly used as food preservatives in fruits, meat products, seafood, and dairy products, in the form of natural extracts or essential oils [114; 112]. However, since extracts and essential oils contain multiple components, achieving their minimum inhibitory concentration (MIC) often requires high-level supplementation. Additional chemicals might affect the taste of the food or introduce undesirable flavors [112]. Terpenoid cell factories through metabolic engineering may meet demands for food preservation and food safety.