esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 5G Technology Enablers for V2X Use Cases
### The Evolution of V2X Communications
#### 3.1.8 Differences in the Communication Methods  
As depicted in Table 2, the new modulation was the most significant improvement of Release 16. The most important difference between the C-V2X and DSRC technologies is the way they access the channel. The DSRC is based on WiFi technology. Thus, it uses CSMA, which implies that the transmitted frames are not aligned. The radio checks if a transmission is in progress, and if it is not, it tries to transmit the requested data. This a simple yet robust operation mode; however, it might not be optimal. Contrary to this, C-V2X technology uses radio resource blocks, which are assigned in a distributed way (in Mode 2 or Mode 4). The resource blocks require tight time scheduling, which is solved via GNSS signals (in Mode 2 or Mode 4) [61,65].
# In Silico SwissADME Analysis of Antibacterial NHC-Silver Acetates and Halides Complexes
## 3 Results and Discussion
### Stability vs. LUMO Density  
Even at the first glance, the highly active antibacterial NHC-complexes **1**, **2** and **5** substantially differ in terms of their lipophilic profiles--the parameters that condition the pharmacokinetics and pharmacodynamics of drugs. The missing puzzle in the structure--activity relationship is the stability of the NHC-silver complexes, which to a greater extent than lipophilicity, conditions antibacterial potency. Hence, the degradation of silver-NHC complexes arises from hydrolysis or interaction with chloride ions present in the bloodstream, resulting in silver chloride precipitation. [PERSON] and coworkers demonstrated that the caffeine complex, despite being fairly hydrophilic, proves remarkable stability in water, compared to dimethylimidazole counterparts (Figure 15) [26]. To illustrate the problem, we have performed quantum chemical calculations using density functional theory (EDF2/6-31G*) theoretical approach [70]. The obtained results clearly indicate that stabilities of the studied complexes correlate with the corresponding LUMO absolute values mapped on the isodensity surfaces. In other words, the stable complexes feature smaller unoccupied orbitals, which are involved in the electrophilic hydrolysis (by OH- or Cl- ions) of C Ag bonds. Since all four compounds proved significant antibacterial potencies in vitro, the impact of their lipophilicity parameters within this context should be considered unknown. This statement can be further supported by the fact that introduction of hydroxylgly groups greatly (**5**, **53**, **55**) enhances the water solubilities of the resulting silver carbene complexes and retains single digit \(\mu\)g/mL MICs against CF pathogens tested in vitro [52; 53; 72; 73].  
Figure 14: The BOILED\(-\)Egg diagram for complexes **A** (top) and **B** (bottom).
# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 5 Implementation and Evaluation Using an Example  
### Formalization  
The proposed SWARMs ontology is developed using Protege ([[http://protege.stanford.edu/](http://protege.stanford.edu/)]([http://protege.stanford.edu/](http://protege.stanford.edu/))) and formalized in OWL. The formalization of the SWARMs ontology in OWL allows a great level of expressivity while producing a model that can be easily shared through the web and thus be open to third party extensions. The proposed SWARMs ontology has been continuously inspected and evaluated by marine experts and ontology engineers along the development process. In addition, the reasoning result using the Pellet ([[https://www.w3.org/2001/sw/wiki/Pellet](https://www.w3.org/2001/sw/wiki/Pellet)]([https://www.w3.org/2001/sw/wiki/Pellet](https://www.w3.org/2001/sw/wiki/Pellet))) reasoner has shown that the proposed SWARMs ontology is consistent. The hierarchy of the proposed SWARMs ontology is shown in Figure 9.  
It is worth mentioning that the Semantic Web Rule Language (SWRL) ([[https://www.w3.org/Submission/SWRL/](https://www.w3.org/Submission/SWRL/)]([https://www.w3.org/Submission/SWRL/](https://www.w3.org/Submission/SWRL/))) is adopted in the SWARMs ontology to compensate the inability of OWL to express complex rule formations and relations. Essentially, SWRL rules are defined in an antecedent-and-consequent implication. By adding SWRL rules, the SWARMs ontology can be enhanced in terms of logicality, human-readability, expressivity, and completeness. Operators or marine experts can define rules through the MMT. The Rules and Policies Creator can translate those rules into the SWRL format and store them in the ontology model. Thus, the SWRL rules along with ontology instances in the ontology model can be important inputs for rule-based reasoning. Here, an example rule is provided by using the syntax of SWRL math built-ins.  
Example rule:  
_WaterSurface(?x),hasWind(?x,?y),Wind(?y),WindSpeed(?y,?z),surlb:greaterThan(?z,?20)-StrongWind(?y)_  
The rule above uses the SWRL syntax to express the definition of the concept StrongWind. It indicates that the wind speed, above the investigated water surface, which is faster than 20 m/s, can be classified as strong wind. Based on this SWRL rule, the rule-based reasoning can deduce if the wind above the water surface can be regarded as strong or not.  
Figure 9: A snapshot of the hierarchy of the SWARMs ontology.
## 3 Methods
### Implementation of the LSTM Framework  
The SM value at time \(t\) (\(Y_{t}\)) was predicted by using \(n\) number of input features with previous \(w\) sequential days (window size) as \([X_{t-1}^{n}\ldots X_{t-w}^{n}]\). After preparing the dataset, we divided it temporally into 60% for training, 10% for validation, and 30% for testing purposes. The temporal split corresponds to 658 days used to train the model starting from 31 December 2017 until 20 October 2019, 109 days used to validate the model training between 21 October 2019 and 6 February 2020, and 330 days used to evaluate the trained model from 7 February 2020 until 1 January 2021. Whereas the LSTM model was built with training data, the hyperparameter tuning was carried out by using a validation dataset. After the optimum hyperparameter set was determined, independent evaluation of the model was conducted based on testing data.  
Before starting the training, we normalized all the input features via the MinMaxScaler function of the sklearn Python package to ensure numerical stability. For the normalization, we followed different strategies for static and dynamic features. By their nature, the static features have global minimum and maximum values; therefore, we normalized them together. On the other hand, dynamic features have local variations that change each station's minimum and maximum values, leading to a station-based normalization.  
One of the primary flexibility features involved in the use of time series data is the varying length of past data to make future predictions. In such a structure, the number of previous timesteps is called the window size. The window size parameter must be selected carefully because it impacts forecast accuracy. For its determination in the SM forecast, we reformed the original dataset according to different window sizes: last one day, five days, ten days, and thirty days.  
The LSTM networks were created by using TensorFlow back-end with GPU processing integration in the conda environment. We used the the gridSearchCV function of the sklearn Python library, to determine the LSTM model's hyperparameters. In addition, in the LSTM architecture, all models started with an LSTM layer, followed by a one-dimensional dense layer as an output.
## 6 Results and Discussion
### Scenario 1 (Constant Speed Cruise with Constant Power Supply)  
When the SOC of the battery is kept at 95% and the SC is not providing any power to the battery, then the boost converter boosts the power to meet the motor requirement. The speed of the motor is uniformly increased because the DC input to the BLDC motor is constant. The speed of the BLDC motor can be observed in Figure 17.  
The battery's SOC is 95% and it can be seen in the figure below that the battery is discharged. Since the starting current of the BLDC motor is high, as a result, it will draw  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Control Strategies**} & **When the Battery’s SOC Is 45\%** & **When the Battery’s SOC Is 95\%** \\ \cline{2-3}  & **ISE IAE ITAE** & **ISE IAE ITAE** \\ \hline PI & 0.55 1.16 1.49 & 0.52 1.13 1.45 \\ FLC & 0.41 0.97 1.32 & 0.45 1.07 1.37 \\ \hline \hline \end{tabular}
\end{table}
Table 9: ISE, IAE, ITAE of PI and FLC.  
more current from the battery in the beginning. It can be observed that in beginning there is a sudden increase in the battery discharge rate. This is because of the high starting current. Similarly, the FLC also responds to the high starting current by adjusting the duty cycle of the boost converter to regulate the voltage. The battery's SOC and response of the FLC can be shown in Figures 18 and 19, respectively.  
The purpose of the boost converter in the system is to regulate the voltage from the battery. The boost converter minimizes any fluctuation at its input and provides a constant output. This regulation can be seen in Figure 20 where it can be observed that the output voltage remains constant regardless of battery's SOC, since in the beginning, the current drawn by the BLDC motor is high, which causes a sudden spike in the output voltages and current of the boost converter.
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
## 3 Proposed Approach
### Description of the Colorization Procedure  
The proposed method of thermographic colorization seeks to calculate P polynomials of different degrees for each one that approximate the original thermal sequence. The information contained in these approximations are then synthesized in a reduced number of sequences using dimensionality reduction techniques. In this study, several reduction tech  
Figure 1: Methodology followed in the proposed quaternionic NDT thermography.  
niques have been analyzed, including PCA, Kernel principal component analysis (KPCA), independent component analysis (ICA) and minimum noise fraction (MNF).  
The dimensionality reduction process is applied to each group of P images that is obtained by selecting the frame in the same position from the P calculated polynomial approximations. This process is carried out for all the frame positions so that a sequence of N color images is available after the reduction procedure, where each color image synthesizes in three channels the information contained in the P approaches initially generated for each frame (Figure 2).  
Each of the dimensionality reduction techniques reconstructs the original data in a new space based on a different principle. The PCA and KPCA techniques optimize the variance of the input data, the MNF technique optimizes the SNR and the ICA technique uses the principle of independence (measured by the entropy parameter in this study) to project the original data into the new space. After the application of each of these techniques, a new sequence is obtained with the same number of images as the initial sequence but with new features characteristic of the applied reduction technique.  
To complete the proposed colorization process and produce a color image from each frame, three images must be selected from each reconstructed sequence. The most intuitive criterion for selecting the three images with the greatest relevance and informative content is to choose the three images that maximize the dimensional reduction principles applied by each technique, i.e., the variance, the SNR and the entropy. According to this criterion, for the cases of PCA, KPCA and MNF, the first three components are selected, since they are techniques that sort the results in decreasing order of the parameter used, while in the case of ICA, the entropy parameter must be evaluated in the results obtained and then select the three components with higher values, since this technique does not sort the results in the application process.
## 4 Discussion
This marks the third phase (Figure 6C), where the applied voltage reaches its minimum value and a substantial number of electrons and negative ions are deposited on the dielectric surface, electrostatically held by the positive bound charge in the dielectric. As the voltage begins to increase, the potential of the discharge electrode becomes positive with respect to this surface charge. When the difference between these potentials reaches a critical level, the fourth phase of positive streamer formation begins (Figure 6D). The positive streamer is a thin channel of conducting plasma. It consists of a positively charged front and a highly ionized but electrically neutral channel. The positive streamers develop in the opposite  
Figure 6: The electrical state of the SDBD plasma reactor at different stages of the discharge evolution. (**A**) Maximum voltage, (**B**) voltage decreases, (**C**) minimum voltage, and (**D**) voltage increases. The arrows show the order of phases. direction to the electron drift, consuming the free electrons along the way as they enter the streamer channel and move toward the discharge electrode. Typically, free electrons at the streamer head are generated by ionizing radiation. However, in the SDBD, a pre-existing population of electrons on the dielectric surface supports streamer development. As these streamers grow and extend away from the discharge electrode, the electric field at their heads weakens, and when it becomes too weak to support gas ionization, the streamers extinguish. As before, the irregularities along the edge of a planar electrode create points of high electric field that are the onsets of the streamer channels and promote their formation. On the other hand, the uniform electric field generated around the cylindrical electrode suppresses their formation. Instead, the electrons are attracted more evenly by the electrode, producing a characteristic glow along its entire length, which is visible in the final stage of this phase (Figure 2B). Meanwhile, the polarization of the dielectric barrier shifts, and its upper barrier layer becomes negatively polarized, completing the cycle.
## 2 Experiment
### The Development of Solid-Phase Microextraction for Trace Arsenic(III) Preconcentration  
A sample solution of 40 mL of arsenic(III) was taken in a 50 mL polypropylene tube, and the pH was adjusted to 7 using 2 mL of a phosphate buffer. The solution was then optimized by adding drops of HCl (0.01 M) or NaOH (0.01 M). Then, 0.05 g of the fabricated SSC-MOFs was added, and the mixture was continuously shaken at 150 rpm for 10 min to allow for the adsorption process and the mass transfer of the arsenic(III) ions from the aqueous solution to the surfaces and pores of the SSC-MOFs. The second step for the separation of the arsenic(III)-loaded SSC-MOFs from the aqueous liquid phase was achieved via the centrifugation of the mixture at 5000\(\times\)\(g\) rpm for 2 min to separate the aqueous phase from the SSC-MOFs. After discarding the aqueous phase, the arsenic(III) was recovered from the SSC-MOFs via elution with 1.0 mL of 0.05 M nitric acid. The preconcentrated arsenic(III) trace content was then detected via ICP-MS. The efficiency of the developed solid-phase microextraction procedure was evaluated by calculating the recovery%, using Equation (1) as follows:  
\[Recovery\%=\left(\frac{C_{f}}{C_{0}}\right)*100, \tag{1}\]  
where \(C_{f}\) is the final concentration of extracted arsenic(III) detected, and \(C_{0}\) is the initial concentration detected in the primary arsenic(III) sample solution.  
The adsorption kinetic of arsenic onto SSC-MOFs is studied as described in the Supplementary Materials. The developed solid-phase microextraction procedure using SSC-MOFs was repeated to study the influence of the most controlling parameters, such as the pH, eluent type and concentration, sample volume, shaking time and co-existing ions, for optimizing the microextraction process. In addition, the intra-day precision and inter-day precision were evaluated by using 0.01 mg\(\cdot\)L\({}^{-1}\), 0.03 mg\(\cdot\)L\({}^{-1}\) and 0.05 mg\(\cdot\)L\({}^{-1}\) of arsenic(III) with 8 replicates for different time intervals over three consecutive days [57]. For an investigation of the ability to recycle the applied SSC-MOFs after their use, the adsorbent was washed twice with ethanol and once with water and then dried in oven at 105 \({}^{\circ}\)C for 10 h. The adsorbent was then reused for subsequent solid-phase microextraction procedures. The preconcentration and recycling processes were repeated 8 times. Scheme 1 represents the experimental steps for preparing SSC-MOFs for solid-phase microextraction prior to ICP-MS detection.
# Adhesion Behavior between Multilayer Graphene and Semiconductor Substrates
## 2 Method  
### MD Model  
Figure 1 is the MD exfoliation model of three-layer graphene on a silicon substrate. Each graphene layer contained 1764 C atoms with the size of 102 A \(\times\) 43.3 A. The material of the substrate was silicon (100) with the lattice constant of 0.543 nm. In the preparation process of the graphene-based MEMS sensor, due to the weak physical bonding between graphene and the substrate by van der Waals forces, we had to subject the Si substrate surface to oxygen plasma treatment to improve the graphene film performance. After that, the Si surface roughness decreased. Additionally, the manufacturing was operated in the ultra-clean room. After the oxygen plasma treatment, the silicon substrate was dealt with by ultrasonic cleaning in acetone and deionize purified water rinsing, then it was dried for at least 30 min at 100 \({}^{\circ}\)C, so the trapped charge and trapped molecules were not the main influencing factors to the adhesion behavior of graphene-silicon. As a result, in this study, the silicon surface was approximated to the natural Si (100) lattice plane. In order to ignore the effect of substrate size and save calculation time, the silicon substrate size should be 40 A larger than the size of the graphene, according to our previous studies. So, the Si substrate size was 190 A \(\times\) 130 A \(\times\) 16 A (containing 22,257 Si atoms). A series of constant vertical upward velocities were applied to the graphene to simulate the direct vertical exfoliation process. In order to study the effect of the number of graphene layers on the bonding mechanism, ten different models with one to ten layers of graphene were set up.
# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 4 Experiment
### Datasets and Results
#### 4.2.1 Ablation Studies before and after Network Improvement  
In this section, in order to verify the effectiveness of our method, we compare the network before and after improvement. We adopted the U-Net with VGG19 pre-trained model as our framework. The experiments were mainly carried out on the CVC-ClinicDB dataset. The key objectives of the experiments were as follows:  
1. Examining whether feature re-extraction enhances the network's segmentation performance on medical images.
2. Evaluating whether the improved skip-connection can effectively filter out interfering information and further improve the performance of the network. The specific experimental results are shown in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **U-Net (Baseline)** & **U-Net+Process1** & **EFEN** \\ \hline DSC (\%) & 87.81 & 91.52 & 93.04 \\ mIoU (\%) & 78.81 & 86.90 & 87.25 \\ Recall (\%) & 78.65 & 85.43 & 86.99 \\ Precision (\%) & 93.29 & 95.12 & 95.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation studies before and after network improvement on CVC-ClinicDB dataset.  
Figure 3: Images augmented by 25 kinds of methods.  
From Table 1, it is shown that adding the feature re-extraction process and improved skip-connection can improve the performance of the baseline network continuously. Compared with the baseline U-Net, employing feature re-extraction yields a result of 91.52% and 86.9% in DSC and mean IOU, which brings 3.71% and 8.09% improvement, respectively. This is because the feature re-extraction process can further calibrate the fuzzy information on the basis of the initial feature extraction, so the feature extraction ability can be improved. Based on U-Net+Process1, using improved skip-connection can further improve the performance of the network. Because skip-connection in U-Net adds the features extracted by the encoder to the output of the corresponding layer of the decoder indiscriminately, it will inevitably introduce noise information into it, thus interfering with the subsequent segmentation results. The improved skip-connection designed in this paper uses the cross-attention mechanism on the input feature map from the encoder, because the supervision information comes from deep semantic information, so it can greatly reduce the noise. Compared with the baseline U-Net, EFEN has a DSC of 93.04%, which is superior to U-Net (87.81%) by 5.23%. This experiment proves that our method is effective.
## 4 Evaluation Metrics and Experimental Results
### Experimental Setup and Parameters  
The parameter settings are as follows: batch size is set to 32, number of training epochs is 100, optimizer is Adam, loss function is CRF Loss, base learning rate is \(5\times 10^{-6}\), dropout rate is 0.5, and the maximum length of input text is 50. To prevent overfitting during the training process, enhance training efficiency, and avoid excessive training, we implemented the EarlyStop mechanism with an early_stop_ratio set to 20.  
The software and hardware facilities mainly used in this study are listed in Table 2.
# Single-Baseline RTK Positioning Using Dual-Frequency GNSS Receivers Inside Smartphones
## 4 Test Setup  
As discussed in the Introduction, one of the problems encountered using smartphone GNSS devices is to be able to know if the GNSS measurements are pre-filtered from the internal chipset. For this reason, a specific tool has been developed by the authors in order to get raw measurements directly from the device without any other pre-filtering operation. A dedicated code developed by the authors has been considered for processing all data collected in real-time, both from the master and rover receivers, in order to perform the single-base RTK positioning. As described in the previous section, two different receivers have been settled on a known point as a master station: the first one is a multi-frequency and multi-constellation GNSS receiver (TORI permanent station) that is part of the EUREF permanent network (www.epncb.oma.be/). The second one is a multi-frequency and multi-constellation smartphone device (Xiaomi Mi8). The main characteristics of these devices are summarized in Table 1. The rover device considered in these tests has the same characteristics of the smartphone used as the master station: in this case, the rover receiver (about 1 km far from the master station) has been settled on a known point in order to compare the estimated results with the reference coordinates. These reference results have been obtained using a geodetic GNSS receiver settled for 12 hours on the same point and post-processing. These observations followed a network adjustment computed with the Bernese GPS 5.0 software in the same reference frame of the master stations: ETRF2000(2008.0) [26; 27]. The GNSS positioning has been performed considering different sessions with a length of about 10 min during November and December 2018, repeated in different days and hours (in order to make all results have independent atmospheric conditions and satellite geometry distributions), and with a sampling rate of 1 Hz. The entire conditions around the smartphones have been considered, paying attention to avoid multipath effects, electromagnetic disturbances, and building obstructions.