esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Experimental Verification  
To further validate the proposed conclusions, a hardware platform based on controller hardware-in-the-loop (CHIL) was set up, as shown in Figure 14. This hardware platform consists of two parts: a control section based on TMS320F28335+FPGA, and a main circuit section simulated by Typhoon602+. The control section is responsible for sampling, computation, control, and waveform generation, while Typhoon602+ simulates the main circuit of a hybrid DC transmission system with wind power integration. The control board inputs the collected voltage and current data into the DSP. After executing the relevant calculations and controls, the DSP sends the waveform signals to the FPGA, which processes these signals and then sends them to Typhoon602+.  
Figure 13: Variation of droop coefficients with coordinated control strategy: (**a**) VSC2- droop coefficient; (**b**) VSC3- droop coefficient.  
Figure 15 presents the frequency variation output waveforms under continuous and intense wind speed fluctuations. The yellow waveform represents the scenario without additional frequency control, while the green waveform depicts the scenario using the proposed control strategy. Figure 16 shows the frequency variation output waveforms after introducing load disturbances, with the yellow waveform again representing the scenario without additional control and the green waveform using the proposed strategy. By comparing these experimental results with the simulation results in Sections 5.3 and 5.4, it can be observed that the experimental waveforms closely match the simulation waveforms. The introduction of auxiliary frequency control to participate in system frequency regulation enhances the frequency stability of the hybrid DC transmission system with wind power integration, further verifying the effectiveness of the proposed strategy.  
Figure 14: Hardware platform of CHIL experiment.  
Figure 15: Case 1 experimental results: comparison of system frequency changes.
# A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems
## 2 The Proposed Mechanism  
### Overview  
In this section, we describe the overview of the our proposed scheme through Figure 1. As the group moves collectively, our mechanism puts data in a ring-based movable area of the group. In order to define the delivery distance, we construct a grid-based virtual structure in the movable area. Based on the virtual structure, our proposed scheme is largely divided into two forwarding steps: main forwarding and branch forwarding. In the main forwarding process, the data are forwarded along a straight line between the source node and center point of the group sinks (the solid line in Figure 1). The branch forwarding is performed at each branch point in the process of main forwarding. When any branch point receives data through the main forwarding process, each branch point forwards the data through the line to the previously anticipated boundary of the movable area of mobile sinks (the dotted line in Figure 1). In addition, the proposed mechanism calculates the maximum (farthest) distance for the real-time communication based on the virtual structure. Then, each destination and the progress toward the destination could be provided according to either main forwarding or branch forwarding.
# A Comparative Study of Weighting Methods for Local Reference Frame
## 2 Overview of Five Weighting Methods
By using this method, a CA-based LRF method may obtain the more repeatable and robust LRF, and the information  
Figure 1: The process diagram of building a local reference frame. obtained by the comparison also has some reference value for the GA-based methods. The weights are calculated as  
\[w_{q_{i}}=(R-\|\mathbf{q}_{i}-\mathbf{p}\|)^{2}. \tag{5}\]  
This weighting strategy is designed to improve the robustness of the LRF against clutter, occlusion, and missing regions because the distant points contribute less to the covariance matrix. **Gaussian function (GF)**[27]: [PERSON] used the GF in the moving least-squares approach to approximate the local surface. Here, it is introduced to determine the weights for the LRF, because better performance may be obtained by this method. The weights are calculated by  
\[w_{q_{i}}=e^{-\left(\frac{\|q_{i}-\mathbf{p}\|}{h}\right)^{2}}, \tag{6}\]  
where \(h=\sigma\cdot R\), and \(\sigma\) is the Gaussian parameter. It can be seen from Equation (6) that the weights decrease as the distance increases, so the distant points are assigned smaller weights, similar to SHOT, BSC, and TOLDI. Therefore, this weighting strategy is also useful to improve the robustness of the LRF against clutter, occlusion, and missing regions. However, the introduction of \(\sigma\) provides flexibility in the weight assignment. The big value of \(\sigma\) slightly weakens the contributions of the distant points, while the small value of \(\sigma\) greatly weakens the contributions of the distant points. That is, a better weight assignment would be achieved if the value \(\sigma\) is properly selected. In Section 4, five different values of \(\sigma\) (i.e., 0.2, 0.4, 0.6, 0.8, and 1) are selected to perform the comparison.
## 4 Evaluation of Quality and Efficiency  
In this section, the previously described methods for determining load-notch-strain curves with the help of FEA were examined to determine their quality and efficiency. For this purpose, quality and efficiency criteria were first defined, and a database with different component geometries, load types, and material parameters was created. Then, using the methods presented in Section 3, load-notch-strain curves were generated for the components from the database and evaluated with the quality and efficiency criteria.
# Robust Control for a Slung-Mass Quadcopter Under Abrupt Velocity Changes
## 5 Experimental Results  
In order to verify the proposed control strategy in real-time, experimental tests were realized to show the performance in outdoor environments. One of the challenges in the transport of suspended loads is the strong induced disturbance when abrupt changes occur in translational velocities. The slung-mass oscillatory behavior has been shown in both numerical simulations and experimental tests. The slung-load position and velocity angle could be used to reduce the oscillations. From the simulation results the slung-mass oscillations presented in the three cases are worst under abrupt velocity changes. Due to these considerations, the experimental results were conducted only considering abrupt velocity changes.  
The velocity reference is similar to the sine function but creates a sawtooth wave with peaks of 0 and 8 m/s, this function was programmed on the quadcopter autopilot  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline Wind & 10 m/s & Gust & 20 m/s & Cloud Cover & 73\% & Visibility & 16 km \\ Wind Dir. & East & Temperature & 24 °C & Precip Prob & 4\% & Weather & Cloudy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Environmental conditions during outdoor experimental test.  
Figure 6: Experimental setup.  
Figure 7: Quadrotor with slung-mass outdoor flight test.  
increasing the forward speed and then returning to hover flight. This forward and stop routine is repeated in the quadrotor flight. The forward navigation velocity of the slung-mass quadrotor \(v_{x}\) and the slung-mass angular position \(\alpha\) response during the outdoor flight tests are shown as follows:
# Modelling and Measuring Trust in Human-Robot Collaboration
## 3 Materials and Methods  
The study of the dynamics that determine trust is a topic of great interest, although there is limited collected data available for analysis to draw conclusions from. In our case, the objective of the experimentation is to design and implement a process that can identify in real time if there has been a breach of trust between the operator and their robot companion (cobot) and to understand the factors that cause these variations in trust and identify the biological reactions related to it.  
To minimize the inclusion of excessive variables that could occlude the true nature of trust, we imply that the cobot is a non-humanoid robotic arm with limited interaction capabilities. Thus, the only interaction with the human counterpart is reduced to the effectivity and performance of the tasks under its control. Additionally, in order to avoid contextual randomness, we decided that every participant should interact with the system in a similar environment, and thus, we determined that the experimental study would focus solely on the dispositional and learned dimensions of trust.  
Consequently, in the context of this research, trust is defined as the predictability of the human that the robot will exhibit appropriate behaviour, following the established guidelines for the performance of its work in an orderly manner, without failure or errors that could be interpreted as dangerous to the person, the robot itself, or its work environment.  
Our strategic planning for the experimental process aimed to achieve the following objectives:  
* Systematically collect a diverse array of relevant psychophysiological signals, emphasizing signal cleanliness and minimizing signal randomness;
* Investigate the influence of human traits on various aspects of human-machine trust, specifically in the context of dispositional and learned trust dimensions;
* Examine the role of the system's capabilities, especially predictability and reliability, in shaping the evolutionary process of trust.  
In the following subsections, the conceptual design of the experimentation, as well as the equipment and method used for this research, are described in detail.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 5 Results and Discussion  
In this section, we present comprehensive results to verify and compare the performance of NR-V2X Mode 2 with the LTE-V2X Mode 4 communication protocol in a platooning scenario. A variety of propagation scenarios and MAC configurations are tested to validate the analytical model and the proposed approximation. The shadowing components in the propagation environment are identically distributed. Additionally, we refer to the shadowing spatial correlation model from [37], where the decorrelation distance in urban settings is equal to 10 m. Therefore, the shadowing components are practically uncorrelated in the platooning scenario under consideration, where the distance between vehicles is greater than 13 m.  
The packet generation process is implemented by enforcing the model introduced in [37], which represents an abstraction of the packet exchange dynamics specifically tailored for vehicle networks. Depending on the outcome of the SB-SPS selection, the approximated SINR is calculated for each generated packet, and the PER is determined by sampling the BLER-SINR curves that were reproduced from [29] for LTE-V2X and [38] for NR-V2X, respectively.  
In our investigation, we consider a single 10 MHz channel reserved specifically for V2V communications. Additionally, we assume perfect synchronization, and we set the transmission power \(P_{tx,i}\) to 30 dBm for each vehicle. All the other relevant parameters considered for the evaluation are listed in Table 2 and the considered values are set to be compliant with the 3 GPP specifications.  
To verify the accuracy of the proposed approximation rationale as described in Section 4, extensive Monte Carlo simulations using MatLab are also performed for both NR-V2X and LTE-V2X, generating \(10^{6}\) independent channel realizations for each vehicle and packet transmission attempt. Performance is evaluated in terms of packet success probability by changing the number of interfering vehicles, the number of resources (i.e., the number of transport blocks in a selection window), and the separation between the transmitter and receiver. At each data point for the simulations, the standard deviation is also displayed as a vertical error bar.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value (or Range)** \\ \hline Transmission power \((P_{t})\) & 30 dBm \\ Reference path gain \((c_{0})\) & \(-\)42.5 dB \\ Path loss exponent \((\alpha)\) & 3.68 \\ Noise power \((\sigma_{w}^{2})\) & \(-\)95 dBm \\ Channel bandwidth \((W)\) & 10 MHz \\ Fading spread \((\sigma_{i})\) & 3 \\ Number of resources in selection window \((N)\) & up to 15 \\ Number of vehicles \((M)\) & up to 16 \\ Distance \(d_{ij}\) & [10–200] m \\ Channel sensing time \((T_{s})\) & LTE-V2X: 1 s; NR-V2X: 0.5 s \\ Power level threshold \((P_{th})\) & [\(-\)128, \(-\)2] dBm \\ Probability of keeping the resources \((p_{k})\) & [0 80\%] \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters.
## 2 Materials and Methods
### Extraction Procedure  
For the conventional extraction process (ST), mixtures (comprising 1 g of plant material and 20 mL of solvent) were stirred at 500 rpm under varying temperature and time conditions. Prior to the ST extraction, some samples underwent additional pretreatment with green extraction methods (PEF or US, or both). For the PEF treatment, a pulse period of 1 ms (frequency: 1 kHz), a pulse duration of 10 us, and an electric field strength of 1.0 kV/cm were employed. This setup involved a mode/arbitrary waveform generator (UPG100, ELV Elektronik AG, Leer, Germany), a digital oscilloscope (Rigol DS1052E, Beaverton, OR, USA), a high-voltage power generator (Leybold, LD Didactic GmbH, Huerth, Germany), and custom stainless-steel chambers (Val-Electronic, Athens, Greece). The US pretreatment was conducted using an Elmasonic P70H machine, 180 W (Elma Schmidbauer GmbH, Singen, Germany), at 30 \({}^{\circ}\)C and 37 kHz. The dried material was pre-soaked in the solvent for 10 min before any treatment. Following extraction, the mixture was centrifuged at 4500 rpm for 10 min, and the supernatant was collected for subsequent analyses.
## 3 Construction and Training of Local PINN Models with Limited Data  
### The Framework of Local PINN Models  
Unlike conventional neural networks that depend purely on data-driven training, PINNs integrate prior physical knowledge as governing constraints within the model. This method guarantees that the neural network's predictions align with the underlying physical principles, thus lessening the reliance on extensive experimental data and enhancing model interpretability. In terms of structure, a PINN consists of two essential components: (1) a fully connected neural network for function approximation and (2) embedded physical information that imposes constraints specific to the domain. The network architecture is depicted in Figure 3.  
Figure 2: Comparisons between analytical and numerical results for \(w\) under the parameters given in Equation (25) are as follows: (**a**) the curves of \(g_{\mu_{0}}(c,v)=0\) in the \(c-v\) parameter plane (defined in Equation (19)), with \(g_{\mu_{0}}(c,v)<0\) in the shaded region; (**b**) \(w\) for point \(\psi_{1}\); (**c**) \(w\) for point \(\psi_{2}\); and (**d**) \(w\) for point \(\psi_{3}\).  
In this study, the local PINN models receive as input the chosen coordinate y-values and their associated beam deflection measurements. The models then output arbitrary coordinates along with their predicted deflection values. The governing equation for the vibration of the beam under a moving load is denoted as  
\[f\left(y;\frac{d^{4}w}{dy^{4}},\frac{d^{2}w}{dy^{2}},\frac{dw}{dy};\beta_{i} \right)=0,\quad i=1,2,3,4. \tag{29}\]  
The full expression for the function \(f(\cdot)\) is given explicitly in Equation (5). The loss function used in the PINN framework typically consists of the following two main components:  
\[\mathcal{L}_{\text{PINN}}=\underbrace{\lambda\cdot||w_{p}-w^{*}||}_{\text{ Data}}+\underbrace{\mu\cdot||f||}_{\text{Physical laws}} \tag{30}\]  
in which  
\[||w_{p}-w^{*}||=\frac{1}{N_{d}}\sum_{i=1}^{N_{d}}|w_{p}(y_{i})-w^{ *}(y_{i})|^{2},\] \[||f||=\frac{1}{N_{g}}\sum_{i=1}^{N_{g}}|f(y_{i})|^{2}=\frac{1}{N _{g}}\sum_{i=1}^{N_{g}}|\frac{\text{d}^{4}w_{p}}{\text{d}y^{4}}+\beta_{1} \frac{\text{d}^{2}w_{p}}{\text{d}y^{2}}+\beta_{2}\frac{\text{d}w_{p}}{\text{ d}y}+\beta_{3}w_{p}-\beta_{4}\delta(y)|^{2},\]  
where \(w_{p}\) represents the predicted deflection of the beam, while \(w^{*}\) corresponds to its respective true value. The weighting parameters \(\lambda\) and \(\mu\) balance the contributions of different terms in the loss function. \(||\cdot||\) denotes the norm, while \(N_{d}\) and \(N_{g}\) indicate the number of training samples for each data type. The PINN models are optimized by minimizing the combined loss function (30), with tanh employed as the activation function.
# Removal of Phosphate from Aqueous Solution by
## 3 Results and Discussion
### Adsorption Mechanisms and Analysis
#### 1 Electrostatic attraction  
Adsorption of phosphate onto the adsorbent was mainly achieved by the attraction of opposite charges by electrostatic induction [71]. Commonly, phosphate in solution exists mainly as H\({}_{2}\)PO\({}_{4}\)\({}^{-}\), HPO\({}_{4}\)\({}^{2-}\), and PO\({}_{4}\)\({}^{3-}\). Zeolite-biochar composites contain a large amount of calcium and magnesium oxides that should be partially protonated in an aqueous solution to be positively charged [25; 54]. When the pH of a solution is smaller than the pH of the metal oxides, also known as a zero-point charge, the positively charged zeolite-biochar composite has the capability to absorb the negatively charged phosphate via the process of electrostatic gravity.  
\[\text{Ca(Mg)O}+\text{H}_{2}\text{O}\rightarrow\text{Ca(Mg)-OH}^{+}+\text{OH}^{-}\]  
\[\text{Ca(Mg)-OH}^{+}+\text{H}_{2}\text{PO}_{4}\text{${}^{-}$}/\text{HPO}_{4} \text{${}^{2-}$}/\text{PO}_{4}\text{${}^{3-}$}\rightarrow\text{Ca(Mg)-OH}^{+} \text{-H}_{2}\text{PO}_{4}\text{${}^{-}$}\]
## 3 Results and Discussion
### Hierarchical Cluster Analysis (HCA) and Volatile Profiles Development
#### 3.3.1 Changes in the Concentration Profiles of Aldehydes  
As shown in Figure 4, the highest aldehyde concentration was found in S-240 (45.29 \(\mathrm{\SIUnitSymbolMicro g}\)/g), followed by S-0 (23.01 \(\mathrm{\SIUnitSymbolMicro g}\)/g) and S-210 (22.73 \(\mathrm{\SIUnitSymbolMicro g}\)/g). A significant variation of the aldehyde profiles was found with S-0 and S-240 (Figure 3), whereas 2-methyl-butanal (S-240, 12.07 \(\mathrm{\SIUnitSymbolMicro g}\)/g), 3-methyl-butanal (S-240, 14.42 \(\mathrm{\SIUnitSymbolMicro g}\)/g) with a typical chocolate and bready odor, exist in both S-0 and S-240 with their concentration developed \(\sim\)100 times throughout the dehydration process. Further, 5-methyl-2-furancarboxaldehyde (caramel odor, 9.46 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240), 5-hydroxymethylfurral (buttery and caramel odor, 1.52 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240), 2-methyl-propanal (fresh aldehyde odor, 3.55 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240), benzeneacetaldehyde (sweet odor, 3.47 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240), \(\alpha\)-ethylidene-benzeneacetaldehyde (powdery and cocoa odor, 0.62 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240), 3-ethyl-benzaldehyde (bitter almond odor, 0.51 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240), methional (bready odor, 0.38 \(\mathrm{\SIUnitSymbolMicro g}\)/g in S-240), emerged in the post stage of the dehydration process. Aldehydes could be generated from unsaturated fatty acids [25] and could also be derived from amino acids via Strecker degradation at a higher temperature, such as 3-methyl-butanal, 2-methyl-butanal, and 2-methyl-propanal, which could be derived from leucine, isoleucine, and valine [26]. Methional could be derived from methionine [17], and benzaldehyde could be derived from phenylalanine [27]. Meanwhile, 5-methyl-2-furancarboxaaldehyde could be derived from glucose through a series of reactions i.e., Amadori rearrangement, enolization, and cyclization [17]. Aldehydes are aroma-active compounds in both cane juice and NCS, and could also participate in the aldol condensation reactions and contribute to the formation melanoidin polymers that impact on the colour of the final product [6].
## 2 Materials and Methods
### Data and Field Work
#### 2.2.3 Meteorological Data  
Data from the SASSCAL (Southern African Science Service Centre for Climate Change and Adaptive Land Management) WeatherNet [28] were used as input for the soil water balance model. The station nearest to the test area is Wako-Kungo (11 deg24''40''S, 15 deg07''45''E, 1331 m in altitude), approximately 20 km southeast of the IIA weather station. However, because data related to air temperature and humidity are lacking until 20 May, data from Kibala (Catofe) station (10 deg44''10''S, 14 deg59''04''E, 1272 m in altitude, approximately 60 km north of the IIA weather station) were also downloaded. The location of each station is displayed in Figure 1.  
The weather parameters available to calculate the crop water requirements and that were common to both stations were air temperature, precipitation, air humidity and solar radiation. Wind speed, required for the soil water balance model, was only available for Wako-Kungo from 1 March to 18 June. Monthly average air temperature, precipitation and solar radiation values are listed in Table 3 for the Kibala and Wako-Kungo stations.
###### Abstract  
Delays in transportation due to congestion generated by public and private transportation are common in many urban areas of the world. To make transportation systems more efficient, intelligent transportation systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. However, most existing approaches only react once the traffic jam has occurred and, therefore, the delay has already spread to more areas of the traffic network. We propose a vehicle redirection system to avoid congestion that uses a model based on deep learning to predict the future state of the traffic network. The model uses the information obtained from the previous step to determine the zones with possible congestion, and redirects the vehicles that are about to cross them. Alternative routes are generated using the entropy-balanced k Shortest Path algorithm (EBkSP). The proposal uses information obtained in real time by a set of probe cars to detect non-recurrent congestion. The results obtained from simulations in various scenarios have shown that the proposal is capable of reducing the average travel time (ATT) by up to 19%, benefiting a maximum of 38% of the vehicles.  
traffic congestion detection; minimizing traffic congestion; traffic prediction; deep learning; urban mobility; ITS; Vehicle-to-Infrastructure +
Footnote †: journal: Applied Soft 2019
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 4 Challenges and Future Directions
### Advanced Manufacturing and Integration Techniques  
Advances in the manufacturing techniques used to create nuclear sensors are critical for developing compact, scalable, and cost-effective nuclear sensors. Recent work has focused on innovative fabrication methods, such as the use of printed melt wire chips for temperature sensing in nuclear reactors. For instance, melt wire chips--fabricated by printing silver melt wires onto grooved steel substrates--demonstrate sensor dimensions as small as 2 mm (see Figure 4) [2], which is particularly advantageous for environments with limited space, such as nuclear power plants.  
Techniques like roll-to-roll printing [37; 98], microfabrication [99], and additive manufacturing [2; 3; 4] are being optimized to produce high-performance sensor components at a reduced cost and with enhanced reproducibility. These methods help in minimizing the overall size and weight of the sensors. As manufacturing processes continue to mature, the combined effect of material innovations and advanced fabrication will drive the widespread deployment of nuclear sensor technologies, making them more accessible, reliable, and effective in a wide range of applications.