esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Control Simulation of Power Converters in Automotive Applications
###### Abstract  
This research introduces a model-based approach for the analysis and control of an onboard charger (OBC) system for contemporary electrified vehicles. The primary objective is to integrate the modeling of SiC/GaN MOSFETs electrothermal behaviors into a unified simulation framework. The motivation behind this project stems from the fact that existing literature often relies on finite element method (FEM) software to examine thermal dynamics, necessitating the development of complex models through partial derivative equations. Such intricate models are computationally demanding, making it difficult to integrate them with circuit equations in the same virtual environment. As a result, lengthy wait periods and a lack of communication between the electrothermal models limit the thorough study that can be conducted during the design stage. The selected case study for examination is a modular 1\(\phi\) (single phase) onboard computer (OBC). This system comprises a dual active bridge (DAB) type DC/DC converter, which is positioned after a totem pole power factor correction (PFC) AC/DC converter. Specifically, the focus is directed toward a 7 kW onboard computer (OBC) utilizing high-voltage SiC/GaN MOSFETs to ensure optimal efficiency and performance. A systematic approach is presented for the assessment and selection of electronic components, employing circuit models for the totem pole power factor correction (PFC) and dual active bridge (DAB) converter. These models are employed in simulations closely mimicking real-world scenarios. Furthermore, rigorous testing of the generated models is conducted across a spectrum of real-world operating conditions to validate the stability of the implemented control algorithms. The validation process is bolstered by a comprehensive exploration of parametric variations relative to the nominal case. Notably, each simulation adheres to the recommended operational limits of the selected components and devices. Detailed data sheets encompassing electrothermal properties are provided for contextual reference.  
Keywords:simulation; modeling; control; AC/DC converters; DC/DC converters; automotive +
Footnote †: journal: Applied Physics Letters
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 4 Experimental Results
### Experimental Equipment and Datasets  
#### 4.1.1 Equipment  
Equipment used in the experiment includes infrared camera, LiDAR, and blackbody. Infrared cameras are used to get a 16-bit digital count according to radiation, and LiDAR is used to get the distance to an object. Furthermore, the blackbody is used to emit the exact radiant energy according to the temperature of \(T_{min}\) and \(T_{max}\).  
Infrared camera is a (Figure 12a) FLIR T620, and its main specifications are as Table 1. In particular, the spectral range is 7.8-14 \(\upmu\)m. Therefore, in STEP 1 of Figure 5, it is set as \(\lambda_{min}\) = 7.8 \(\upmu\)m, \(\lambda_{max}\) = 14 \(\upmu\)m. Therefore, \(T_{max}\) at \(T_{min}\) radiation can be calculated by integrating [PERSON]'s law over the [\(\lambda_{min},\lambda_{max}\)] interval.  
LiDAR is a (Figure 12b) Velodyne VLP-16, and its main specifications are shown in Table 2. LIDAR is a sensor that measures the distance through the reflection of a laser on an object. The distance to an object can be obtained as a distance (Equation (12)) through \(x\), \(y\), \(z\) coordinates obtained through LiDAR.  
\[distance=\sqrt{x^{2}+y^{2}+z^{2}} \tag{12}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**FLIR T620 Specifications** \\ \hline IR Resolution & 640 \(\times\) 480 pixels \\ Temperature range & \(-\)4 \({}^{\circ}\)F to 302 \({}^{\circ}\)F (\(-\)20 \({}^{\circ}\)C to 150 \({}^{\circ}\)C) or 212 \({}^{\circ}\)F to 1202 \({}^{\circ}\)F (100 \({}^{\circ}\)C \\  & to 650 \({}^{\circ}\)C) \\ Spectral range & 7.8–14 \(\upmu\)m \\ Thermal sensitivity/NETD & \(<\)50 mK @ +30 \({}^{\circ}\)C (+86 \({}^{\circ}\)F) \\ Field of view (FOV) & 25\({}^{\circ}\)\(\times\) 19\({}^{\circ}\) \\ Spatial resolution (IFOV) & 0.68 mrad \\ Image frequency & 30 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Infrared camera specifications.  
Figure 12: Experimental equipment: (**a**) Infrared camera (FLIR T620), (**b**) LiDAR (Velodyne VLP-16), (**c**) blackbody.  
The blackbody is shown in Figure 12c, and the main specifications are shown in Table 3. The blackbody emits accurate radiation at a specific temperature, and in STEP 3: Measurements of the Object, the temperature set for the blackbody is used as the ground truth to measure its performance. In practice, the emissivity of an ideal blackbody is 1, but due to hardware limitations, the emissivity of the blackbody used is about 0.96. Therefore, when calculating the radiation, it must be multiplied by 0.96. The emissivity of the skin of a person is about 0.98. Therefore, in the experiment for measuring body temperature, the radiation is multiplied by 0.98.
## 3 Results and Discussion
### Changes in the Arona Components of Cocnut Jams during Concentration  
The statistical analysis software, R, was used to form cluster heat maps to visually show the differences in the aroma compound contents at different concentration times. As shown in Figure 4, the main aroma compounds displayed various trends. All the aroma compounds in the seven groups of processed coconut jam can be divided into two clusters. R2-R27 (from 3-hydroxy-2-butanone to ethyl decanoate) were grouped into one class, and R12-R32 (from furfuryl alcohol to delta-dodeclactone) were grouped into another class. The amount of furfural and hexanal constantly increased from 0 min to post-sterilization, whereas the amounts of 2-octanone and ethyl decanoate first increased from 0 to 12 min but then gradually decreased. Conversely, the contents of pentyl alcohol, isopentyl alcohol, hexyl formate, ethyl caproate 2-octanol, 2-nonanone and ethyl dodecanoate decreased gradually. These phenomena may be due to the use of sugar as a precursor substance to generate important flavorsubstances; sugar generates a variety of aroma compounds when heated to a high temperature and produces aroma substances such as furan derivatives and ketone aldehydes [39]. In addition, during the heating process, the original heat unstable aroma substances in the coconut water were degraded, such as alcohols, ketones, and esters [40]. The Maillard reaction also causes changes in flavor, carbonyl compounds forming by oxidation of ketones and aldehydes, and reduction of sugars. Further, carbonyl compounds and amino acids undergo oxidation, decarboxylation, condensation, and cyclization to form a series of reactive intermediates [41]. However, these reactive intermediates continue to react with amino acids, ultimately causing changes in the flavor of the product.  
It was a reasonable and novel choice to use aromatic compounds as production indicators [6; 42]. As shown in Figure 5, the relative content of fur  
Figure 4: Heat map of the contents of the main volatile compounds during the concentration times and after sterilization. The content value increases with the color varying from blue to red. (TA1... 4, TB1... 4 to TG1... 4) represent samples in quadruplicate at different time points.  
Figure 5: The variation of furfural content during different concentration times.  
and the two turning points in the growth process occurred between 4 and 8 min and between 20 min and post-sterilization, which was consistent with the PCA analysis results. Moreover, the formation kinetics of the furfural in the coconut jam samples during concentration were fitted to a logistic model. We inserted the parameter values in the model equation to ultimately obtain the kinetic equation of the furfural formation (Equation (3)). The logistic model has a high degree of fit (\(r^{2}=0.97034\)).  
\[\mathrm{Y}=\frac{-5.72899}{1+\left(\frac{t}{478625}\right)^{4.90798}}+5.73277 \tag{3}\]  
Therefore, furfural can be used as a marker aroma in the production of coconut jam to monitor the degree of concentration of the product.
## 3 Approach  
In the context of automated driving, vehicle-to-vehicle (V2V) communication will be necessary for many areas. Platooning is an application for automated driving on highways. Multiple vehicles are driving in succession. By decreasing the distance between them, the cars are driving in the slipstream of the car in front of them. Hereby, the drag can be reduced to 55% on average which leads to less fuel and energy consumption and fewer emissions, according to [PERSON] et al. [18]. In such a platoon, the car ahead needs to control the car behind remotely [2] to enable the cars to accelerate and decelerate simultaneously. This wireless communication needs to be fast and secure, as there must be as little latency as possible for time-critical commands and the car behind must trust the commands it gets from the car in front. This main communication could be realized using 802.11p or 5G.  
This paper sets its focus on establishing a secure connection between two vehicles that are driving in succession on the highway before they decrease the distance between them to build a platoon. It proposes an out-of-band communication channel using the LED taillights of a car to transmit data via VLC and a camera on the succeeding car to receive it. The conceptual idea for this out-of-band channel was published in [1]. To encode the data, a modulation method proposed by [PERSON] et al. [3] called Undersampled Differential Phase Shift On-Off Keying (UDPSOOK) is used. Here the transmitting LED is modulated with a frequency that is a multiple of the frame sampling rate of the receiving camera. The modulation frequency must be higher than the CFF of the human eye to be not perceivable by a human observer; however, a camera with a very short exposure time is still able to capture the modulated light source to decode the transmitted data. This is possible by using the rolling-shutter effect of CMOS cameras.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Cross-Layer Routing
#### 3.3.5 Joint Transmission Power Control and Relay Cooperation (ATT)  
[PERSON] et al. [29] proposed a cross-layer scheme of adaptive transmission power, based on a autocorrelation coefficient ATT, a joint transmission power control, dynamic slot scheduling, and a two-hop cooperative transmission mechanism, which realized the trade-off between reliable transmission and energy. The scheme consisted of three stages--channel state prediction, adjustment of transmission power, rearrangement of slot sequence for direct transmission, and selection of relay nodes. At the beginning of the protocol, the channel states of all nodes were calculated by hub nodes. The channel state gains were subject to the Gauss random distribution. The formulae is as follows:  
\[G_{i}(S+1)\Big{|}_{G_{i}(S)}\,\sim N\Big{(}(1-\rho_{i})\mu_{i}+\rho_{i}G_{i}(S), (1-\rho_{i}^{2})\sigma_{i}^{2}\Big{)} \tag{9}\]  
In which the state of the next superframe channel \(S+1\) could be estimated by \((1-\rho_{i})\mu_{i}+\rho_{i}G_{i}(S)\).  
Hub node adaptively adjusts the transmission power, according to the estimated channel state, to achieve the purpose of energy saving. Then the slot order of DTP in direct transmission phase was rearranged, based on the channel state. The MAC superframe structure of ATT protocol is shown in Figure 11. The RAP1 phase is accessed by the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) mechanism, and the MAP phase is divided into direct transmission phase DTP and relay transmission phase RTP. Each node completes information transmission in the corresponding slot. The node with relay capability maintains the receiving state at the DTP stage, and if there is information to be forwarded, then it completes the forwarding in the corresponding slot of the RTP stage. The slots with good channel state are arranged in front, after the channel state estimation is completed. In order to avoid a node acting as a relay node, many times, the protocol regulations that the predicted channel gains, are all candidate relay nodes when they are higher than the standard value. Finally, a relay node is selected randomly from the candidate. Combined with the above methods, the protocol achieves a relatively good reliability transmission, compared with other protocols. At the same time, the energy consumption is greatly reduced through adaptive power regulation. The dynamic slot scheduling method also improves the data delivery rate, relatively.
# 5G V2X System-Level Architecture of 5 GCAR Project
## 4 Detailed Analysis of Selected Features
### Use Case-Aware Multi-RAT, Multi-Link Connectivity
However, in the case of the second scenario, the coordinator needs to compute the completion time for each multi-link/RAT connectivity based on automotive conditions as well as the intended method of data splitting between links e.g., exploiting diversity via coding or aggregating bandwidth to increase data rate. In the third step of the evaluation process, the coordinator decides which links/RATs to attach to for each of the involved vehicles/UEs. Taking into account the following parameters which areassumed to be known or predicted by the coordinator, the coordinator would be able to predict the resulting QoS of each combination of multi-link/RAT connectivity. Those parameters include:  
1. The geographical area of relevance and the related information such as buildings topology (to find the most fitting path-loss model) as well as locations and height of BSs. 2. The expected trajectory of all the vehicles in the area of relevance. 3. The data type (e.g., video, status information, warning messages, etc.) and its related properties such as packet size, arrival distribution, etc. 4. Relative priorities among different use-cases and/or vehicles. 5. Expected congestion and the number of available resources for each RAT based on the current waiting time of queues in bottlenecks and the number of connected UEs. Then, the coordinator compares the expected QoS for the whole action duration against the required QoS of the use-case and reports the promising candidates for the connection of each vehicle/UE. The final decision between those candidates is made with the aim of congestion control via traffic off-loading to the less-congested networks, while avoiding over-provisioning and waste of resources. The use case-aware multi-RAT, multi-link connectivity optimization presented by 5 GCAR can be further extended considering aspects of the trade-off between reliability and latency, as for instance considered in [15]. In this work, the Euclidean norm theory has been used to propose a reliability and latency joint function, and the relationships between reliability and latency are illustrated via Monte Carlo simulations of 5G autonomous vehicular networks. These aspects can be considered in the selection of the link/RAT to be used for V2X use cases.
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
###### Abstract  
Fruit quality assessment plays a crucial role in determining their market value, consumer acceptance, and post-harvest management. In recent years, spectroscopic techniques have gained significant attention as non-destructive methods for evaluating fruit quality. In this study, we propose a novel deep-learning network, called GCNN-LSTM-AT, for the prediction of five important parameters of tangerines using visible and near-infrared spectroscopy (Vis-NIR). The quality attributes include soluble solid content (SSC), total acidity (TA), acid-sugar ratio (A/S), firmness, and Vitamin C (VC). The proposed model combines the strengths of graph convolutional network (GCN), convolutional neural networks (CNNs), and long short-term memory (LSTM) to capture both spatial and sequential dependencies in the spectra data, and incorporates an attention mechanism to enhance the discriminative ability of the model. To investigate the effectiveness and stability of the model, comparisons with three traditional machine-learning algorithms--moving window partial least squares (MWPLS), random forest (RF), and support vector regression (SVR)--and two deep neural networks--DeepSpectra2D and CNN-AT--are provided. The results have shown that the GCNN-LSTM-AT network outperforms other algorithms and models, achieving accurate predictions for SSC (\(R^{2}\): 0.9885, RMSECV: 0.1430 \({}^{\circ}\)Brix), TA (\(R^{2}\): 0.8075, RMSECV: 0.0868%), A/S (\(R^{2}\): 0.9014, RMSECV: 1.9984), firmness (\(R^{2}\): 0.9472, RMSECV: 0.0294 kg), and VC (\(R^{2}\): 0.7386, RMSECV: 29.4104 mg/100 g) of tangerines.  
deep learning; Vis-NIR spectroscopy; food-quality assessment +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Workflow  
The data and workflow for generating cropping intensity are presented in Figure 2. First, Sentinel-2 and Landsat-8 imagery were processed and harmonized to create a high-spatiotemporal-resolution dataset. Next, we constructed a consistent EVI time series through compositing, gap-filling, and smoothing techniques. Subsequently, cropping intensity was derived by counting the number of valid EVI peaks within a year. The accuracy of the generated product was then assessed using a large number of sample points.  
Figure 1: The geographical location of Shaanxi Province in China and the distribution of agroclimatic resources within Shaanxi. **(a)** Geographical location of Shaanxi Province and its regions along with elevation distribution. **(b)** Spatial distribution of the average annual accumulated temperature above 0 \({}^{\circ}\)C during 2019–2023. **(c)** Spatial distribution of the average annual precipitation during 2019–2023.  
Figure 2: Data and workflow for generating cropping intensity.
## 4 Testbed Development
### Characterization of Starlink
#### 4.1.3 Satellite Handover  
After the previous tests, we noticed the presence of periodic sharp fluctuations when metrics were represented in linear graphs. This pattern typically corresponds to satellite handovers. Simply put, a satellite handover is the process by which either the user terminal switches its service link from one satellite to another according to certain criteria (e.g., to maintain service continuity when the current satellite contact window is coming to an end) or the satellite switches its feeder link between gateways to ensure service availability. They might be relevant for the targeted transparent architectures, so in the paragraphs below, we explain and discuss our related observations without entering into a detailed analysis.  
Starlink terminals conduct periodic handovers with the objective of preventing connectivity disruptions and ensuring performance [42]. Specifically, these handovers are managed by a global scheduler that, according to [51], assigns satellites to user terminals approximately every 15 s, thereby following a user-centric strategy. This also allows the satellites to meet optimal radio link conditions, for instance, by maintaining a high minimum elevation angle. Consequently, service performance remains balanced during short time intervals until the handover causes brief degradation.  
The fluctuations pattern is observed in Figure 6. It shows several metrics collected from the previous tests over time. We visually notice a correlation of the effect of satellite handovers in TCP metrics, although fluctuations are more clearly represented with the UDP bitrate. In TCP, the bitrate graph roughly illustrates these short intervals and the corresponding degradation peaks of handovers. The congestion window size graph oscillates accordingly, resulting in a graph shape similar to that of the bitrate. The RTT graph does not clearly depict handovers, but the existence of these time intervals during which RTT is more consistent is also visible. The retransmission graph does present strong variations in responses to significant performance drops. These metrics demonstrate the variability of service performance in satellite networks, as well as the seamless capacity of Starlink to overcome the inherent dynamism.  
Figure 5: Boxplots of the RTT and jitter achieved with Starlink in TCP. (**a**) RTT; (**b**) jitter.
## 3 Results and Discussion
### Characterization of the Materials
#### 3.1.1 FTIR  
The FTIR spectrum of the XDA-1G resin (Figure 2) was analyzed to identify the surface functional groups responsible for m-cresol adsorption. A broad and intense absorption band centered at 3440 cm\({}^{-1}\) is attributed to O-H stretching vibrations, suggesting the presence of hydroxyl groups that may arise from phenolic residues or adsorbed moisture [22]. These groups are key contributors to hydrogen bonding interactions with m-cresol [23].  
Figure 1: Schematic illustration of the batch adsorption process used to evaluate the performance of the XDA-1G resin for m-cresol removal. The initial concentration of m-cresol in all experiments was 20 mg L\({}^{-1}\), simulating high-strength industrial wastewater conditions.  
Sharp peaks at 3017 cm\({}^{-1}\) and 2926 cm\({}^{-1}\) correspond to C-H stretching vibrations from aromatic and aliphatic -CH\({}_{2}\) moieties, respectively, confirming the presence of a mixed aromatic-aliphatic backbone typical of synthetic resins [24]. The region between 1900 and 1600 cm\({}^{-1}\) features several notable bands. A shoulder around 1900 cm\({}^{-1}\) and peaks at 1705 cm\({}^{-1}\) and 1608 cm\({}^{-1}\) are assigned to C=O stretching modes, indicating the presence of carbonyl and carboxyl functionalities, as well as conjugated aromatic systems [25]. These groups are essential for \(\pi\)-\(\pi\) stacking and electron donor-acceptor interactions with m-cresol's aromatic ring [21].  
The fingerprint region (1500-1000 cm\({}^{-1}\)) displays rich structural information. Peaks at 1452 cm\({}^{-1}\) and 1376 cm\({}^{-1}\) represent C-C skeletal vibrations and C-H bending modes from aromatic rings. Additional bands at 1211 cm\({}^{-1}\), 1116 cm\({}^{-1}\), and 1019 cm\({}^{-1}\) are attributed to C-O stretching of ether linkages, supporting the presence of oxygenated functional groups that enhance polarity and chemical reactivity [26].  
Below 1000 cm\({}^{-1}\), a distinct peak at 820 cm\({}^{-1}\) is observed, corresponding to out-of-plane C-H bending in substituted aromatic rings. This feature confirms the polymeric aromatic structure of the XDA-1G resin, which favors strong \(\pi\)-\(\pi\) interactions with m-cresol molecules. Such interactions are especially important under high-concentration conditions, where hydrophobic and stacking forces dominate.  
Overall, the FTIR analysis confirms the presence of hydroxyl, carbonyl, and ether groups, along with a highly aromatic structure. These functional moieties play a critical role in the adsorption of m-cresol via hydrogen bonding, \(\pi\)-\(\pi\) stacking, and electrostatic interactions, thereby enhancing the XDA-1G resin's affinity for phenolic compounds [21; 23].