esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanisms for Generating Shaft Frequency Signals of Vessels
### System Design and Construction  
This section aims to study the characteristics of shaft frequency signals from different types of vessels. To achieve this, scaled-down model experiments are conducted based on the shaft frequency signal generation principle. The experiments simulate far-field measurement scenarios for typical vessels. The experimental system uses titanium-based oxide electrodes as the anode signal source to simulate the shaft frequency electric field generated by the vessel. Ag/AgCl electrodes are used as the electric field acquisition system at different underwater depths. Assuming a typical vessel with a length of 178.8 m, a width of 28 m, and a maximum speed of 18 knots, three anodes are used. An equivalent electric dipole source scaling model is established for different targets according to the ratio of 1:100, as shown in Figure 3.  
In Figure 3, anodes 1, 2, and 3 are all made of titanium-based oxide and are equivalent to different dipoles simulating different types of vessels based on different power connections. In Figure 3a, anode 2 is connected to the positive pole of the power source, while  
Figure 3: Scaled model of equivalent dipole sources for different types of vessels: (**a**) equivalent dipole for vessel A; (**b**) equivalent dipole for vessel B; and (**c**) equivalent dipole for vessel C.  
Figure 2: Shaft frequency electric field signals generated by the passage of a passenger vessel: (**a**) electric field strength in the Ex direction; (**b**) electric field strength in the Ey direction; and (**c**) electric field strength in the Ez direction.  
anodes 1 and 3 are connected to the negative pole. In Figure 3b, anode 1 is connected to the positive pole of the power source, and anodes 2 and 3 are connected to the negative pole. In Figure 3c, anode 3 is connected to the positive pole of the power source, and anodes 1 and 2 are connected to the negative pole. The arrangement of sensors and simulated sources is shown in Figure 4. The electric field measurement system uses Ag/AgCl electrodes, with electrode distances set at 5 cm and measurement depths at 14 cm, 28 cm, and 42 cm. The sampling frequency is 200 Hz. Standard simulated sources are used as target source signals, fixed on the towing device, and moved at a speed of 5 cm/s. The water depth in the test area is approximately 0.9 m, the conductivity is 4.58 S/m, and the power source is a GS610 programmable current source with a signal fundamental frequency set at 4.73 Hz and a signal amplitude of 0.7 A.
## 3 Results and Discussion
### Color Measurement
positive a* value suggesting a tendency to red color hues and positive b* values indicating a yellowish hue color [59]. Whereas [PERSON] et al. [60] microencapsulated _Bifidobacterium_ Bb-12 by spray-drying using whey as a carrier material. They received the high-value L* (85.96 \(\pm\) 1.81), indicating that the powder showed light color, the high parameters of a* (0.95 \(\pm\) 0.05) and b* (11.42 \(\pm\) 0.44), indicating shades tending to red and yellow, respectively. This may be because of the Maillard reaction between reducing sugars and proteins [60]. Also provided in Table 5 are the \(\Delta\)E (DeltaE or dE) values, which are a measure of the change in visual perception between two colors when comparing a reference sample with a given sample [61]. The color difference between two colors is described as the distance between the involved locations (L1, a1, and b1 as well as L2, a2, and b2) and is calculated using the Euclidean distance Formulas [62]:  
\[\Delta\mathrm{E}_{\mathrm{ab}}=\sqrt{\left(\mathrm{L1-L2}\right)^{2}+\left( \mathrm{a1-a2}\right)^{2}}+\left(\mathrm{b1-b2}\right)^{2}\]  
The delta E values are described in the legend of Table 5 [63]. The difference in color of just one powder (with an additional 20% of hemp oil) compared to the control sample is unnoticeable. The delta E value of most samples is in the range of 1-2 (The difference is only noticed by an experienced observer) or slightly above 2 (the difference is also noticed by an inexperienced observer). Only the powder with 30% borage oil added gives the impression of two different colors compared to the control sample (\(\Delta\)E = 9.59).
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method
### Double Deep Q-Network
#### 4.3.4 Network Architecture  
The following subsection describes the architecture of the double deep Q-Network model. The double deep Q-Network model used in this study is shown in Figure 4.  
As shown in Figure 5, the double deep Q-Network network has the same architecture as the DQN network. As mentioned earlier, by separating action selection and evaluation, we avoid estimating the value of the current state through direct max Q. This can be expressed as sending \(\underset{a^{\prime}}{\operatorname{argmax}}Q(s^{\prime},\ a^{\prime};\theta)\) from the Q-Network to the target network.  
Figure 5: Architecture of DDQN model.  
Additionally, the structure of the deep neural network utilizes a total of 3 dense layers and 1 flattened layer. The agent's state serves as the input for the first layer, and the action is derived from the final layer. Two dense layers are employed as hidden layers in the neural network, with the rectifier linear unit (ReLU) applied as the activation function for each layer.
# Verification of Industrial Worker Walking Efficiency with Wearable Hip Exoskeleton
## 4 Discussion
### Ascending Stairways
In this study, when wearing a WIM, EEM was reduced by 7.4% when walking on a treadmill, but the energy efficiency was higher at 12.9% when climbing stairs. The net EEM showed that treadmill walking and ascending stairs had higher energy efficiencies of 10.6% and 15.7%, respectively. The results of this study were similar to those of [PERSON] et al. (2018) [14], although there were differences in the reduction rates of energy consumption. [PERSON] et al. [14] reported that when climbing stairs, an exoskeleton (GEMS) significantly reduced net oxygen consumption and net metabolism by 8.6% and 10.2%, respectively. In addition, it was reported that the net metabolic power with a walking-assistive exoskeleton (GEMS) was significantly reduced even though it was 2 kg heavier than a general soft robot, which is a very meaningful result [14]. Stair walking is a difficult activity that requires approximately 30-40% more cardiopulmonary metabolic energy than walking on level ground [39, 40]. The results of this study are encouraging because the EEM and net EEM decreased more when climbing stairs than when walking on level ground. It is believed that metabolic energy can be used more efficiently if workers wear WIMs while climbing stairs at industrial sites. Although many products cannot be compared because the types, driving methods, and hardware types of exoskeletons are diverse, we compared the energy efficiency of our product with that of a hip joint exoskeleton with similar design and weight (Table 6). Use of a lightweight (2.31 kg), portable hip-flexion-assistive exosuit with a similar form-factor location as our device showed a metabolic reduction of 7.2 \(\pm\) 2.9% when walking on level ground compared to walking without the exosuit [9]. In addition, in the rigid-type GEMS, weighing 2.8 kg, the net metabolic rate decreased by 6.6% when walking on level ground and by 10.16% when walking up stairs [13, 14]. In the case of our product (WIM), energy metabolism decreased by 7.4% when walking on level ground (treadmill), and by 12.9% when walking up stairs. Both GEMS and our product showed a greater decrease in energy metabolism when walking up stairs than when walking on level ground. Our product uses a single actuator, which can be compared to the existing product, further reducing the weight compared to the existing product. Nevertheless, it is noteworthy that metabolic energy decreased by 12.9% during stair assistance. Reducing the weight of assistive devices worn on the human body at industrial sites is a very important issue, and our product is expected to be useful at industrial sites because it is the lightest and has the greatest energy consumption reduction effect.
## 2 Design of the Solar-Powered ECG Device
### Hardware Design
#### 2.1.2 Design of Low Power Analog Front-End and Bluetooth Module  
The structure of the ECG monitoring device is shown in Figure 3. It mainly consists of four analog modules that collect ECG signals from the human body and the processor interfaces with Bluetooth 4.1 through an enhanced universal synchronous asynchronous receiver transmitter (EUART) [18]. The serial peripheral interface (SPI) module is used for communication with an ADS1293 chip with the frequency of the SPI module (2 MHz is used in this design) [17; 18]. The interrupt-on-changed module is used to detect the time of data in the ADS1293 chip that is ready to read. The timer's module is designed with a resolution of 16-bit. For the ECG analog front-end module, we used an ADS1293 chip, which has an operating voltage from 1.65 V-3.6 V with a digital module and 2.7 V-5.5 V [17] with the analog module, and was specially designed for ECG applications with similar three channels with 24-bit integration to measure biological potential [17; 24]. Each channel can be set for a specific sampling rate and bandwidth, with the AC/DC detection electrode dropping out. In this design, we configure the sampling frequency of the chip from 100 Hz to 2133 Hz depending on the data accuracy and data processing capability [17].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vin (V)** & **Vsuper Cap (V)** & **Charging Time (s)** \\ \hline
0.7 & 4.2 & 405 \\
1.2 & 4.2 & 137 \\
2.4 & 4.2 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The time of solar energy harvesting module to fully charge 0.47 F-5.5 V the supercapacitor with different input voltages.  
Figure 2: Solar energy harvesting and power module.  
The Bluetooth module uses the RN4020 chip, which is a microchip product with an operating voltage from 1.8 V to 3.6 V and a maximum data transfer rate of up to 1 Mbps. Additionally, the module also supports microchip low energy data profile (MLDP) [19] mode, which has been exclusively developed by microchip for its Bluetooth modules, with transfer speeds of up to 115,200 bps. To ensure data transfer speeds with the Bluetooth module and the speed of communication with ADS1293, the MCU is configured to operate at 32 MHz crystal. We used an external crystal of 8 MHz with the PLL mode (\(\times\)4) because this will consume less current than using the internal crystal [18]. Table 3 describes the voltage supply for all components. The real ECG prototype is shown in Figure 4 and the hardware solar cell and electrode are shown in Figure 5.
# Study on Slump and Compressive Strength of Gangue Based on Aggregate Size Gradation
## 3. Experimental  
### Experimental Design  
#### 3.1.1. Talbot Theory  
One of the ways to optimize the grain size gradation of gangue is to apply Talbot theory, which provides a method for calculating and designing aggregate gradation. By means of Talbot theory, the appropriate proportion of particles of different sizes can be determined to achieve maximum density and minimum porosity, thereby improving the compaction degree and compressive strength of the backfill. In practical application, this method can simplify the grading design process by drawing the maximum density curve in semi-logarithmic coordinates and determining the content proportion of each particle size according to this curve. In this way, the physical properties of backfill can be effectively improved and its application in mine engineering can be enhanced.  
[PERSON]'s formula:  
\[T_{x}=\left[d/D\right]^{K} \tag{1}\]  
where \(T_{x}\) is the passing rate of aggregate particle size \(d\), %; \(d\) is the current aggregate particle size; \(D\) is the maximum aggregate particle size, mm; \(K\) is the Talbot coefficient.  
According to [PERSON]'s formula, the particle size composition of gangue aggregate with different K values (0.3, 0.4, 0.5, 0.6) is taken, and the passing rate of gangue aggregate in each screen is shown in Table 3. Therefore, four different aggregate gradations were selected in this paper; namely, the ratio of coarse aggregate to fine aggregate was 6:4, 5:5, 4:6, and 7:3.
# Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video
## 2 Preliminaries
### Neural Networks  
Once the features are extracted separately from both the visible and IR images, they are combined together and fed to a neural network classifier. In order to accommodate temporal changes, the neural network should be fast in learning and be plastic to accommodate changes in the data. The unsupervised Kohonen Self Organizing Feature Map (SOM) [18] and the supervised Probability neural network (PNN) [19] are chosen and their effectiveness are examined for this problem.  
On one hand, Kohonen SOM is one of the most popular unsupervised networks which exploits the natural structure of the input feature space without using any priori class information. This method is suitable for the cloud classification tasks where ground truth is often not available or reliable and a huge amount of satellite data is available. The training process of the SOM is based on the competitive learning rule. When a new input is present, the neurons will compete with each other by comparing the distance of the input and the weight vector of that neuron. For the winning neuron, winner-takes-all weight adjustment rule is applied. The neurons, which are close enough to the winning neuron in a neighborhood, will also participate in learning. As the learning progresses, the neighborhood shrinks to declare only one winning neuron. In this way, the SOM represents the feature space by a set of well organized neurons which corresponds to the centroid of each cluster. These neurons later are mapped into physical classes.  
On the other hand, PNN is a kind of supervised network which is closely related to Bayes classification rule and Parzen non-parametric probability density function (PDF) estimation. Comparing with the well-known back-propagation (BP) network, PNN has a very fast one-pass learning scheme while it has a comparable generalization ability [19].
# Fast Path Planning of Autonomous Vehicles in 3D Environments
## 1 Introduction
This inspired us to develop a path planner that is fast in generating a near-optimal path considering a 3D workspace without node points. This paper considers the problem of generating a safe path to the goal in a 3D cluttered workspace without node points. This paper introduces a 3D path planner with novel concepts, such as a virtual agent and virtual sensors. In order to generate a 3D path to the goal as fast as possible, we let the virtual agent deploy virtual sensors iteratively, such that the connected sensor network can be formed. If there is no obstacle between a generated virtual sensor, say \(n_{g}\), and the goal, then our path planner ends. The constructed sensor network serves as a topological map for the AV, and we find a shortest path from the start to \(n_{g}\) utilizing the network. As the AV reaches \(n_{g}\), it moves directly towards the goal, since there is no obstacle along the line segment connecting \(n_{g}\) and the goal. This direct maneuver is utilized for decreasing the computational load of generating a path to the goal. As far as we know, this direct maneuver towards the goal was not utilized in other path planners, such as RRT-star or A-star planners. The proposed path planner is fast in generating a near-optimal path considering a cluttered 3D workspace without node points. The virtual agent's maneuver is biased towards the goal, in order to find a path to the goal as fast as possible. Moreover, the size of the agent is set considering the safety margin of the generated path. This paper proves that our path planner is assured to find a near-optimal path to the goal in finite time. Through MATLAB simulations, we demonstrate the outperformance (low computational load and short path length) of our 3D path planner by comparing it with the 3D RRT-star planner in [24]. The organization of this paper is as follows: Section 2 introduces assumptions and definitions. Section 3 addresses a fast path planner in three dimensions. Section 4 shows simulation results of our path planner. Section 5 presents the discussion of our paper. Section 6 provides conclusions.
## 2 Materials and Methods
### Data  
#### 2.2.1 Satellite-Based Time-Series Data: NDVI, LAI, and FPAR  
The latest version of the NDVI dataset generated from the AVHRR sensor onboard a series of NOAA satellites (Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g) from January 1982 to December 2011 was used in this study [37,38]. The NDVI data were used as a proxy for terrestrial GPP [39] to compare with the model outputs. The datasets were provided with a spatial resolution of 8 km \(\times\) 8 km and a 15-day temporal resolution over the period from July 1981 to December 2011. To create spatially and temporally consistent data with the terrestrial biosphere model simulations, we averaged the GIMMS NDVI3g data to create quarter-degree spatial resolution and monthly temporal resolution data.  
The third generation data of the LAI and Fraction of Photosynthetically Active Radiation absorbed by a canopy (FPAR) datasets (LAI3g and FPAR3g) [40] were used as inputs for the BEAMS model [18,41]. These datasets are integrated products of GIMMS NDVI3g data [37,38] and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products [42] that include updated algorithms for data screening [43]. In generating the LAI3g and FPAR3g data, a set of neural networks were first trained on best-quality and post-processed MODIS LAI and FPAR products as well as GIMMSNDVI3g data for the overlapping period from 2000 to 2009. The trained neural networks were then used to produce the LAI3g and FPAR3g datasets using GIMMS NDVI3g data for the remaining period. The original data were provided at 15-day temporal intervals and 8-km spatial resolutions. We converted these data into monthly temporal resolution and quarter-degree spatial resolution by averaging that data using the same procedure used for the NDVI dataset.
## 2 Literature Review
### Social Internet of Vehicles (SIoV)  
The SIoV integrates vehicular networks with the social behavior of users, enabling enhanced vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communication [41]. By combining vehicular communication technologies with the dynamics of social networking, the SIoV aims to transform the driving experience through improved road safety, traffic coordination, travel efficiency, and real-time infotainment services [42]. Despite the potential benefits, existing architectures primarily rely on centralized cloud and semi-centralized edge computing systems, which pose limitations in termsof latency, scalability, and security, particularly when managing the vast amount of data generated by connected vehicles [43].  
Holochain can effectively overcome these challenges by enabling secure, P2P communication among vehicles without relying on a central server. For instance, it supports decentralized trust management by allowing each vehicle to maintain and validate its own interaction history and reputation, enabling autonomous trust assessments [44]. Malicious behaviors, such as false data injection or identity spoofing, are mitigated through peer validation and application-specific rules--invalid or harmful data is rejected before dissemination. Repeated misbehavior leads to exclusion from the network, not merely a reduced trust score.  
Holochain supports real-time, privacy-preserving data exchange, allowing vehicles to share critical information like traffic congestion or hazardous road conditions without exposing sensitive user data. Its architecture enables secure, context-aware social services such as dynamic carpooling, collaborative trip planning, and decentralized incentives. In emergency scenarios, vehicles can form resilient ad hoc networks, ensuring continuity of communication.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 5 Results and Discussion
### Results in Network Adjustments
#### 5.3.1 No Outlier Case  
For scenario package 01, the results confirmed the statements in the literature. The conventional Least Squares method (LS) was the best linear unbiased estimator when the observations were free of outliers.  
The solution classifications by detecting outliers in the residuals are shown in Figure 12. LS had excellent performance, with no false positives, getting an \"A\" classification in all scenarios. Most REs showed similar results, with about half of the classifications as \"A\".  
This means that all REs presented false positives in some scenarios. Figure 13 shows the false positive counts for each estimator. Most estimators achieved similar results, with almost one false positive per scenario.  
Figure 12: Classifications achieved in scenarios without outliers by estimator. “A” stands for no false positive, and “B” one or more false positive(s).  
Figure 13: Number of false positives for the scenarios without outliers, grouped by estimator.  
To check the solutions of each estimator, we elaborated the box-plot chart shown in Figure 14. The LS exceeded all other estimators, showing a lower variance and a lower variation between the scenarios. As expected, the LS estimation was superior to any other RE, when tested in the conditions of no outliers. In addition, LS required no MH to estimate the parameters and presented low computational cost, when compared with estimators that need MHs. It is important to point out that the points will not reach zero, due to the random errors present in all observation vectors.  
Both for outlier identification and co-ordinate estimation, LS proved to be the best method for scenarios with no outliers. The high number of false positives in the other estimators solutions led to discarding good observations and greater deviations in the estimation.