esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, that study only focused on the feasibility of load emulation where no MiL vs. HiL EMS verification was involved. In another similar study using an identical testbed, an ICE load emulation feasibility study was carried out to emulate the engine's dynamical torque characteristic for a limited time section of a driving cycle [45]. The complexities due to the electromagnetic features of an EVT makes reliable modeling and simulation of EVT-based powertrains a challenging task. Thus, for such HEV systems, performing MiL versus HiL investigations is of great importance to validate the applicability of a design, specifically from dynamic controllability aspects over the different conditions of a complete driving cycle. Compared to the literature thoroughly reviewed in the current section, the main contribution of the present work can be described as follows:  
* It focuses on both MiL and real-time HiL examinations for a less-extensively studied topology, EVT-based HEVs. * It involves real components of the topology prepared in an innovative HiL test bench for performing reliable verification tests of the employed EMSs. * It validates the real-time actuation of the components over the existing dynamics of a full driving cycle. To these ends, the whole vehicle and corresponding optimized EMS subsystems were first modeled and simulated in the MATLAB/Simulink(r) environment. Thereafter, associated HiL tests were experimentally performed in the experimental setup. The dynamic agreements of torque and speed achieved from the MiL and HiL tests were compared and statistically analyzed to validate the EMSs' applicability and power split competencies. Accordingly, the remainder of the paper is organized as follows. Section 2 presents the EVT-based drivetrain architecture for a passenger HEV. In Section 3, the individual mathematical modeling of the vehicle components and corresponding descriptions are elaborated. Section 4 provides the formulation and explanations of the employed EMSs and their incorporation into the model for off-line optimization. Section 5 presents the specifications of the prepared experimental testbed and its featuring advantages. In Section 6, the study procedure is provided, and the statistical performance indices required for the agreement and error analyses are briefly expressed. Section 7 presents and discusses the obtained verification results, and finally, Section 8 recapitulates the achieved outcomes and provides the conclusions and future work directions.
## 2 Materials and Methods
### System Components
#### 2.4.6 Converter  
In HOMER, a converter facilitates the bidirectional energy flow by inverting DC power to AC or rectifying AC power to DC. Its performance is defined by efficiency and rated capacity, determining the maximum power it can handle while accounting for conversion losses. When acting as an inverter, the output power is given by Equation (17).  
\[P_{AC}=\eta_{inv}\times P_{DC} \tag{17}\]  
where \(\eta_{inv}\) is the inverter efficiency. Conversely, in rectifier mode, the relationship is given by Equation (18).  
\[P_{DC}=\eta_{rec}\times P_{AC} \tag{18}\]  
where \(\eta_{rec}\) is the rectifier efficiency. Rated capacity defines the maximum power the converter can process in either direction, ensuring compatibility and flexibility in energy management.  
The system analyzed here includes a generic 1 kW inverter/rectifier with 95% efficiency for both modes. It operates in parallel with an AC generator and seamlessly integrates renewable energy sources, storage systems, and the grid, optimizing the overall performance of the hybrid system.
# Timed-Release Silica Microcapsules for Consistent Fragrance Release in Topical Formulations
## 3 Results and Discussion
### Synthesis of Sol-Gel Microcapsules with Cationic Emulsifier Suitable for Skin
#### 3.1.2 Effect of DPDAC Concentration on Microcapsule Size  
Since DPDAC is not a conventional emulsifier for the preparation of sol-gel microcapsules [10], its appropriate concentration for microcapsule synthesis was determined  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ingredient * (INCI Name)** & **Function of Ingredient** & **Formulation A (wt.\%)** & **Formulation B (wt.\%)** \\ \hline Water & Dispersant & 64.80 & 63.00 \\ \hline Glycerin & Moisturizer & 10.00 & 10.00 \\ \hline Polyglycerol-3 & & & \\ methylglucose distearate & Surfactant & 1.00 & 1.00 \\ \hline Glyceryl stearate & Surfactant & 1.50 & 1.50 \\ \hline Cetaeryl alcohol & Co-surfactant & 2.50 & 2.50 \\ \hline Dicaprylyl carbonate & Dispersant of oil phase & 6.00 & 6.00 \\ \hline Squalane & Dispersant of oil phase & 6.00 & 6.00 \\ \hline Caprylic/capric triglyceride & Dispersant of oil phase & 8.00 & 8.00 \\ \hline Fragrance & Fragrance & 0.20 & \\ \hline Fragrance/silica & Fragrance & & 2.00 \\ \hline Total & & 100.00 & 100.00 \\ \hline \hline \end{tabular}  
* INCI (International Nomenclature of Cosmetic Ingredients) name: systematic names internationally recognized to identify cosmetic ingredients.  
\end{table}
Table 1: Model cream formulations for quantitative analysis of timed-release of fragrance.  
by fixing the TEOS concentration at 4 wt.%. As shown in Figure 1A, the microcapsule size determined by particle size analysis followed a decreasing trend of 1.57, 1.44, 1.19, 1.05, 0.771, and 0.016 \(\upmu\)m with increasing emulsifier concentrations (0.025, 0.05, 0.1, 0.2, 0.4, and 0.8 wt.%, respectively). Figure S1 reveals no agglomeration in the formulations containing 0.025-0.4 wt.% emulsifier, with particle size decreasing as the emulsifier content increased, which agreed with the particle size analysis results. The capsules synthesized using 0.8% emulsifier could not be imaged using optical microscopy owing to their small size (-50 nm). To evaluate the effect of microcapsule size on fragrance release, four samples with \(D_{50}\) values of 0.016, 0.771, 1.19, and 1.57 \(\upmu\)m were selected (emulsifier concentrations of 0.8 wt.%, 0.4 wt.%, 0.1 wt.%, and 0.025 wt.%, respectively), and the corresponding encapsulation efficiencies were 98.014 \(\pm\) 0.194, 98.679 \(\pm\) 0.034, 99.051 \(\pm\) 0.15, and 98.152 \(\pm\) 0.05%, respectively.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 3 Rateless Coded Uplink Transmission Scheme
### Iterative Detecting and Decoding at the BBUI Pool
_Step 2_: The message from an LDPC check node \(c\) to an input node \(i\) is given by:  
\[\tanh\left(\frac{m_{ci}^{(l)}}{2}\right)=\prod\limits_{j^{\prime}\
eq i}\tanh \left(\frac{m_{rc}^{(l)}}{2}\right), \tag{9}\]  
where \(m_{rc}^{(l)}\) is the message from the input nodes which are connected to the LDPC check node \(c\) (except the input node \(i\) itself). _Step 3_: The message from an input node \(i\) to an LT output node \(o\) is:  
\[m_{io}^{(l)}=\sum\
olimits_{o^{\prime}\
eq o}m_{o^{\prime}i}^{(l-1)}+\sum \
olimits_{c}m_{ci}^{(l)}, \tag{10}\]  
where the first summation is over all LT output nodes adjacent to \(i\) other than \(o\) itself, and the second summation is over all LDPC check nodes connected to \(i\). _Step 4_: The message from an LT output node \(o\) to an input node \(i\) is given by:  
\[\tanh\left(\frac{m_{oi}^{(l)}}{2}\right)=\tanh\left(\frac{z}{2}\right)\prod \limits_{i^{\prime}\
eq i}\tanh\left(\frac{m_{i^{\prime}o}^{(l)}}{2}\right), \tag{11}\]where \(z=LLR^{a}[c_{1}]\) is the message from the MU detector and is calculated by (6), and the product is over all input symbols adjacent to \(o\) other than \(i\) itself. _Step 5: \(LLR^{c}[c_{1}]\)_ transferred to the MU detector is given by:  
\[LLR^{c}[c_{1}]=\sum_{a}m_{oi}^{(l-1)}+\sum_{c}m_{oi}^{(l)}. \tag{12}\]  
_Step 6:_ Update the message at the input code:  
\[m_{i}^{(l)}=\sum_{a}m_{oi}^{(l)}. \tag{13}\]  
Then, a similar decoding procedure is performed on the Raptor decoding graph of user 2, and after that, one decoder iteration is completed. When the mean LLR of the input nodes of both users (as given in (13)) exceeds \(m_{th}\), the second stage starts and message exchange will be individually performed on the LDPC decoding graph of each user. The specific process of this stage is similar to Step 1 to Step 2, and is omitted here. Figure 3 is the flow chart of the decoding procedure.
## 3 The Proposed Method
### Expansion of the First Constrained Polygonal Space  
Although \(S^{3}_{1}\) always finds path \(P\), it does not guarantee the quality of \(P\) in each scenario due to higher complexity in the obstacles. To circumvent this issue and ensure consistent quality for \(P\) in each scenario, the scenarios that require a bigger space are identified carefully through the CPS complexity analysis utilizing the six different parameters. With the assistance of the complexity analysis of \(S^{3}_{1}\), for a good-quality \(P\), we can accurately identify the cases that require relatively more space than already in \(S^{3}_{1}\). Having sufficient information about the obstacles connected with the boundary of \(S^{3}_{1}\) enables us to flexibly expand the space to the next level. We adopted this method to expand the space, since it yields less computing overhead and significantly enhances path quality. Hence, by processing obstacles that are penetrated by the boundary of \(S^{3}_{1}\), and by marking a polygonal boundary in an analogous way, the first CPS formation emanates into a second CPS of a relatively bigger size, compared to the \(S^{3}_{1}\) as visually depicted in Figure 6b.  
We denote this expanded space with \(S^{3}_{2}\), and it encompasses \(S^{3}_{1}\) fully. The \(S^{3}_{2}\) includes pertinent obstacles fully, and it provides greater opportunities for \(P\) to be determined from \(S^{3}_{2}\) solely. The utility of the \(S^{3}_{2}\) is that it is highly desirable space for producing \(P\) of shortest lengths. The space can be extended to nested levels in identical manner. Meanwhile, we expand the spaces only up to 2-levels because an optimal/quasi-optimal \(P\) tends to lie in \(S^{3}_{1}\) and \(S^{3}_{2}\) with acceptable probability. After the selection of appropriate highest priority space, an ESWG is constructed for pathfinding.  
Figure 5: Drawing of the complexity parameters employed to analyze the complexity of the \(S^{3}_{1}\).
## 4 Related Work in the ML and DL Domain
### MRI-Based Diagnosis of ASD
[103] employed ABIDE datasets containing sMRI and rs-fMRI scans. Seven ML models were tested for their ability to detect early signs of ASD in children between the ages of five and ten. Combining Grey Wolf Optimizer (GWO) with support vector machines (SVM) yielded the most significant results. The assessment was conducted using 10-fold cross-validation to ensure robustness, and the models were trained using an 80/20 training-validation split. Additionally, [PERSON] et al. [104] employed a unique framework, LD-MILCT, for analyzing spatial and morphological data across different brain areas. It utilized a two-stage multi-instance learning technique. The multi-instance learning head (MIL head) of a Vision Transformer is integrated into the framework to optimize the use of essential characteristics for categorization. The study used the ABIDE dataset for ASD, which included 342 typically developing controls and 213 ASD patients, and the ADNI dataset for Alzheimer's, which included 399 controls and 308 AD patients. In addition, also in 2024, [PERSON] et al. [105] employed fMRI data from 264 participants, which consisted of 134 with ASD and 130 TD children. The study utilized cutting-edge techniques for detecting ASD using graph attention networks (GATs). The model considered every brain region of interest (ROI) as a node and used wavelet decomposition to extract features from the BOLD signal. The adjacency matrix is an optimized functional connectivity (FC)matrix. Self-attention methods capture long-range relationships between features, and node-selection pooling layers ascertain the importance of each ROI for prediction. Finally, [PERSON] et al. [106] presented a multisite fMRI investigation using the ABIDE dataset. The study used a class-consistency and site-independence multiview hyperedge-aware hypergraph embedding learning (CcSi-MHAHGEL) framework to integrate functional connectivity networks (FCNs) from different brain atlases. A multiview hyperedge-aware hypergraph convolutional network (HGCN) trained adaptive hyperedge weights, developing a multi-atlas-based FCN embedding. The framework includes a site-independence module to minimize site-related disparities resulting from disparate scanning procedures and a class-consistency element to preserve intra-class compression with inter-class separation. Afterward, fully connected layers and a softmax classifier are used to facilitate diagnosis and multi-atlas-based FCN embeddings. Table 3 summarizes the studies related to the MRI image data.
## 3 Reinforcement Corrosion Monitoring Techniques
### Corrosion Assessment with Electrochemical Sensors
#### 3.1.2 Concrete resistivity (\(\rho\))  
Resistivity is an intrinsic property dependent on the ability of the concrete to carry electrical current [129]. Two different techniques, namely AC and DC, can be used for the determination of electrical resistivity [130]. These measurements apply both to external probes and to probes embedded  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Risk of Corrosion**} & \multicolumn{3}{c}{**OCP Values**} \\ \cline{2-3}  & **mV vs. SCE** & **mV vs. CSE** \\ \hline Severe & \(<\!\!-\!426\) & \(<\!\!-\!500\) \\ High & \(<\!\!-\!276\) & \(<\!\!-\!350\) \\ Intermediate & \(-126\) to \(-275\) & \(-350\) to \(-200\) \\ Low & \(>\!\!-\!125\) & \(>\!\!-\!200\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Risk of corrosion according to OCP values [50; 117].  
Figure 1: Schematic representation of OCP measurements (reproduced and adapted from [50; 73; 116]).  
in concrete. It has been used, indirectly, to assess concrete properties such as ionic diffusion of chloride ions and degree of saturation. It is a parameter strongly dependent on the quality of the concrete and the exposure conditions, such as relative humidity and temperature, which affect the degree of saturation of the concrete and therefore the \(\rho\) values.  
Several authors have correlated the risk of corrosion occur and \(\rho\)[43; 46; 50; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 130; 131; 132; 133; 134; 135], \(\rho\) measurement is a useful and additional measure in identifying problems related to concrete corrosion and in assessing the quality of concrete. However, these measurements should never be performed in isolation. The \(\rho\) assesses the corrosion risk of steel reinforcement in concrete considering the relation between the \(\rho\) and the \(i_{corr}\) of steel reinforcement [69; 132; 136]. According to some authors [137; 138], the \(\rho\) is inversely proportional to the corrosion rate of steel reinforcement in concrete. Others stated that the under dry conditions the corrosion rate is mainly controlled by the \(\rho\)[43]. In 2007, [PERSON] et al. [139] found that the \(i_{corr}\) of steel reinforcement in concrete is essentially affected by the oxygen content in the concrete and by the thickness of concrete cover when the \(\rho\) is low. Nevertheless, the authors also showed that the \(i_{corr}\) of steel reinforcement is predominantly affected by the \(\rho\) when concrete resistivity is high. Later, in 2014 [PERSON] et al. [140], found that when the limit current density is high, the corrosion process of the steel reinforcement in concrete is under the resistance control and the corrosion rate generally decreases with the increase of \(\rho\). Table 3 shows the risk of corrosion occur according to the resistivity values.  
\(\rho\) values do not indicate if the steel in concrete is in passive or active state of corrosion. Instead, they provide additional information, such as indicating where in RCS the risk of corrosion is higher [130].
# Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock
## 3 Mf Signal Impairments
### Transmitter Instability  
MF radio beacons were not initially designed to be used as a stand-alone positioning system. Therefore, some adaptations and modifications are needed for the hardware on the transmitter side. In principle, these adjustments, needed to enable R-Mode signals transmission, could be easily implemented at the sites. Figure 4 presents the generalized difference in architecture between a standard DGNSS beacon and an R-Mode-enabled radio beacon. First of all, the MSK modulator must be replaced with an R-Mode modulator, then, two additional components are required: an R-Mode message generator and a stable and accurate timing source synchronized with the R-Mode system time. The last component required is an R-Mode signal monitor. This solution is extremely cost-efficient when compared to the deployment of a new GNSS satellite.  
Nevertheless, the hardware components in the standard radio beacons are not identical. They differ depending on the country. In general, different manufacturer and different hardware specifications might be used in different countries. For this reason, transmitter-side instability effects can be observed for some radio beacons but not for others, even in the same country. As an example, we show in Figure 5 an issue that was identified at the  
Figure 4: Difference between a standard MF radio beacon architecture (**above**) and the R-Mode-enabled radio beacon (**below**).  
Gross Mohrdorf station. In detail, a phase jump in the order of 40 m is visible for Gross Mohrdorf, lasting approximately 25 min. It is interesting to observe that the jump affects one of the signals, the CW2, but not the other. This behaviour was observed from time to time, with varying severity--a clear cause has not yet been identified. In the future, these instabilities can be avoided by identifying their source or by using monitor stations with correction information sent to the R-Mode users by specific R-Mode messages.  
Part of the analysis and characterization of the transmitter chain has already started, as presented in [27]. In future projects, a deeper and more accurate analysis of the transmitter chain will be carried out in order to identify the source of such instabilities and implement a solution. Alternatively, on the receiver side, a detection mechanism in combination with a correction or exclusion technique can be applied to solve this issue.
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 2 Methodology
### Improved DeeplabV3+
#### 2.2.1 Improvement of Backbone  
The deep residual network was proposed by [PERSON] et al. [30]. In general, the number of neural network layers can be increased to make the network's prediction better, but in practice, it has been found that deepening the number of a neural network layer creates the problem of gradient disappearance or gradient explosion. This can cause the network to saturate or decline in accuracy on the training set, resulting in a degradation in model performance. The advent of deep residual networks has enabled these problems to be solved. A residual block was added to a deep residual network, as shown in Figure 3, where the network obtains an expected value of \(H(x)\) when the input is \(x\). Using the characteristics of residual learning, the residual \(F(x)=H(x)-x\) is defined, so the original expected value becomes \(F(x)+x\), making the network easier to optimize.  
The original DeeplabV3+ model has a complex and diverse dataset. Thus, the adopted backbone feature extraction network required a more complex feature extraction model, such as the structure of the original backbone network Xception, which uses a residual mechanism to connect the depthwise separable convolutions. This network has a complex structure with a number of parameters, which is not suitable for the detection task of rebar. To address this problem, ResNet50, which also has a residual mechanism, was chosen as the part of the backbone extraction network for the DeeplabV3+ model. More simple direct connections were added to the network, which had the advantages of having fewer parameters, being easier to train, and faster convergence in comparison with Xception, to fully take into account the feature scale matching problem and to improve the model inference speed.
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 5 Experiments
### Results  
First of all, a step master is employed to evaluate the performance of the system designed, which is widely used in the evaluation for surface metrology. The step master used in the experiment is 516-499 Cera Step Master 300 C, Mitutoyo, Kanagawa 213-8533, Japan, which has 5 steps designed and the nominal steps are 20, 50, 100, 300 (\(\upmu\)m), respectively. The uncertainty of the nominal steps is 0.20 \(\upmu\)m while the variation for each single step is within 0.05 \(\upmu\)m. The measured result for the step master is shown in Figure 11 as a depth map. Besides, the measured data for steps are utilized to fit planes with a robust estimation, and the results are listed in Table 2.  
Some other samples are tested with the proposed telecentric surface metrology system, including two pieces of the circuit board and both sides of a coin. The results are shown in Figure 12, and the ROI is about \(20\times 40\ \mathrm{mm}^{2}(L\times W)\) in the circuit board test while the design diameter of the coin is 20.5 mm. From the left to right in every row, the images are practicality pictures, results of the ROI and the partial detail region. The ROI and partial detail region are labeled with a blue and red rectangle in the practicality picture, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Step** & **1** & **2** & **3** & **4** & **5** \\ \hline Jump truth & & 20 & 50 & 100 & 300 \\ Measured value & & 20.0 \(\pm\) 0.6 & 49.5 \(\pm\) 0.6 & 100.4 \(\pm\) 0.6 & 299.1 \(\pm\) 0.6 \\ Mean error & 4.9 & 5.0 & 4.7 & 4.9 & 6.2 \\ RMS & 6.3 & 6.5 & 6.1 & 6.2 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured result of step master (\(\upmu\)m).  
Figure 11: The picture of the step master and the display region (**a**). Measured depth map of the step master (**b**) and corresponding off-surface error (**c**) (unit: mm).
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 2 Materials and Methods
### Data
#### 2.1.2 Meteorological data  
Downward shortwave radiation, air temperature, specific humidity and air pressure were obtained from the CRU-NCEPV6.1 dataset with a spatial resolution of 0.5\({}^{\circ}\) and a 6-h time interval. We used gross primary productivity (GPP) data from the FLUXNET2015 dataset to compare flux site measurement of GPP and LUE GPP ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)) (accessed on 1 December 2020)).