esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Predictive Current Control Strategy
### The Predictive Current Control Scheme of the T-Type Inverter
#### 4.2.3 Predictive Current Control Method  
The T-type inverter control requirements are as follows:  
* A reference for grid current tracking;
* DC link capacitor voltage neutral point balancing;
* Reducing the switching frequency.  
These requirements can be expressed in the form of a cost function so that the cost function can be minimized. The T-type inverter has the following components:  
\[g=\left|i_{\alpha}^{*}-i_{\alpha}^{p}\right|+\left|i_{\beta}^{*}-i_{\beta}^{ p}\right|+\lambda_{dc}\left|v_{c1}^{p}-v_{c2}^{p}\right|+\lambda_{n}n_{c} \tag{25}\]  
The first two terms are the load current errors in orthogonal coordinates in which the real part and the imaginary part of the predicted current vector \(i^{p}\) are \(i_{\alpha}^{p}\) and \(i_{\beta}^{p}\), respectively, and the real part and the imaginary part of the reference current vector \(i^{*}\) are \(i_{\alpha}^{*}\) and \(i_{\beta}^{*}\), respectively. The third cost function measures the difference between the capacitors' voltages of the predicted DC link, which are calculated from Equations (19) and (20). Then, by minimizing this term, the voltages of the capacitors will tend to be equal. The last item is the number of transitions needed to switch from the current switch state to the switch state being evaluated. Switching states mean fewer switching power semiconductors, which is preferred.  
In this way, the use of these terms will directly affect the switching frequency of the converter [21]. The relationship between the weighting factors \(\lambda_{dc}\) and \(\lambda_{n}\) is used for reference tracking, the voltage balance, and the cost function in the reduction of the switching frequency.  
Certain \(\lambda\) values mean higher priorities to that objective. The predictive current control method for the T-type inverter is shown in Figure 7.
## 3 Results and Discussion
### Influence of Ester Gunn Addition on Ostwald Ripening
These effects may have been due to differences in the packing and interactions of the surfactant molecules at the droplet surfaces, which could influence the swelling or shrinking of the oil droplets, as well as the transport of oil molecules across the surfactant monolayer. For instance, ester gum inhibited Ostwald ripening more effectively in the emulsions stabilized by surfactants with a small hydrophilic group (P10 SE) than those stabilized with surfactants having a large hydrophilic group (P20 SE). This may have been because the surfactants with a smaller hydrophilic group were packed together more tightly at the droplet surfaces, which made it more difficult for oil molecules to diffuse through. According to the previous study [28], the type and concentration of emulsifiers determine whether coalescence or Ostwald ripening mainly contributes to the growth of the droplets in emulsions containing relatively polar oils. The ability of emulsifiers to solidify the interfacial layer adsorbing them is also one of important factors controlling coalescence and/or Ostwald ripening. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [28] reported that Ostwald ripening in emulsions prepared from limonene was not observed. Therefore, it should be noted that droplet coalescence could also contribute to the droplet growth within the homogenizer and during storage in this study. However, we believe that our results are better explained by the Ostwald ripening mechanism. First, since limonene, the major component (94-96%, depending on biological origin) in orange oil, could be dissolved in water to 13.8 mg/L [29], orange oil is known to be relatively polar oil [13; 24], which makes it highly susceptible to Ostwald ripening. Ostwald ripening tends to occur more rapidly when the droplet size is small, which is a possible reason why Ostwald ripening occurred rapidly in our study, unlike the previous finding [28] (droplets prepared in this study were approximately 10 times smaller than those reported in the previous study). Second, the ability of ester gum to inhibit droplet growth is also consistent with this mechanism due to the entropy of mixing effect mentioned earlier.
## 2 Data
### Modis  
From the Moderate Resolution Imaging Spectroradiometer (MODIS) IWV products, the daytime IWV products based on near-infrared (NIR) measurements [24] are obtained from the National Aeronautics and Space Administration (NASA) online database [32]. The IWV is retrieved by using the ratio of NIR water vapor absorbing channels and atmospheric window channels. From this water vapor transmittance, the IWV is derived with an accuracy of 5-10%, making use of theoretical radiative transfer calculations and a look-up-table procedure. The IWV products from both MYD05 and MOD05 collection 6, based on the MODIS instruments onboard the afternoon and morning polar-orbiting satellites Aqua and Terra, respectively, are used. Combined, they provide a near-global coverage twice a day. The spatial resolution of MODIS is 1 km at nadir, and it has a swath width of 2330 km. Next to a quality control, for which the indicator \"Quality_Assurance_Near_Infrared\" must be set to _useful_, the accompanying cloud mask is used to mask out cloudy pixels for which only the IWV above the cloudy layer can be retrieved.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 2 Antenna Design  
The flowchart in Figure 1 describes the methodology used for optimizing the MIMO antenna parameters. The approach includes a number of phases, such as the choice of operating frequency for the chosen antenna structure, the development of an optimized antenna design that fulfils the set objectives, and experiments with the improved manufactured prototype to confirm the efficiency and effectiveness of the new design. It starts with understanding how the 'design parameters', operating 'frequency', and the 'performance' of a MIMO antenna pair correlate with each other. Subsequently, the analysis also determines the best MIMO antenna pair to optimize its ten-element MIMO array, which is designed, simulated, and fabricated for testing. Data collection and design verification were performed using the ANSYS Electronics Desktop 2023 R2. The systematic approach to the MIMO antenna design employed by this methodology guarantees a one-stop solution for antenna parameter selection and ultimate validation on a test bed. The approximate X-shaped MIMO antenna is presented in Figure 2 with a fabricated prototype in Figure 3. The overall size of the MIMO antenna is 158 mm \(\times\) 60 mm, while the dielectric material used for this purpose was Fr-4 with 1.6 mm thickness. The single antenna is fed by a 50 \(\Omega\) feed line and the spacing between any two adjacent elements is approximately half wavelength at 5 GHz.  
Figure 1: Flowchart of a proposed ten-element MIMO array.
## 5 Advanced MPPT Techniques  
Advanced MPPT approaches are mainly characterized by their ability to learn and adapt to complex and dynamic environmental conditions, providing improved accuracy and efficiency in tracking the MPP compared to traditional techniques. They can be categorized into three main types: smart techniques, metaheuristic techniques, and hybrid techniques. Each category offers unique advantages and can be selected based on the specific requirements.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 5 Experiments
### General Setup  
SmartNoise is developed by the OpenDP initiative, which includes Microsoft as well as the Institute for Quantitative Social Science (IQSS) and the School of Engineering and Applied Sciences (SEAS) at Harvard University [24]. A broad application of the tools in research, industry, and by governmental institutions is the declared goal of the project participants. The open source framework is one of the most popular in the field of DP and comprises several components, including SmartNoise SQL and SmartNoise Synthesizers ([[https://github.com/opendp/smartnoise-sdk/tree/main/synth](https://github.com/opendp/smartnoise-sdk/tree/main/synth)]([https://github.com/opendp/smartnoise-sdk/tree/main/synth](https://github.com/opendp/smartnoise-sdk/tree/main/synth)) (accessed on 18 August 2022)). The former is used in the interactive scenario to privatize SQL queries against, for example, PostgreSQL databases or Pandas data frames. As the name suggests, the latter allows generating synthetic data with three different synthesizers (MWEM, DP-CTGAN, and PATE-CTGAN), which we already described in Section 3.3. The models of DP-CTGAN and PATE-CTGAN are trained with PyTorch, and for the discriminator of DP-CTGAN, the Opacus library is used. We increased the default value of max_bin_count of MWEM to 1440 (number of minutes in a day) for our experiments, which turned out to be the best value for time series data such as those in our use case after a few trials. For PATE-CTGAN and DP-CTGAN, the default parameters were used.  
We also made some adjustments to the Fitbit data set (heartrate_seconds_merged.csv) for our scenario. First, we only considered four individuals and shortened the examination time to one day, shrinking the dataset from nearly 2.5 million entries to approximately 35,000. In addition, the timestamps were changed to one-minute granularity and converted to the number of minutes since the beginning of Unix time to simplify later evaluations. After the entries were randomly sorted, this adjusted dataset (tachycardia.csv, hereafter referred to as raw data) finally served as the input for the synthetic data generators. The first five entries can be seen in Table 1.  
Before calculating the metrics, both the synthetic and raw data were downsampled into one-minute bins by taking the mean to smooth out strong outliers and facilitate comparability. To determine the TP metric, the raw data as well as the generated data were first interpolated with cubic splines. Subsequently, the derivatives of the cubic splines were evaluated for each minute according to the previously presented definition of the metric. Afterwards, these values were added up and divided by the number of minutes multiplied by the PIF.  
To calculate the TU, we simply counted the number of tachycardia minutes in both the raw and synthetic datasets. For all experiments, 20 runs were performed. The computations were executed on a machine with 2x Intel Xeon 4210 2.2 GHz CPUs, 8x NVIDIA GeForce RTX 2080 TI GPUs and 144 GiB RAM running CentOS 7 and Python 3.8.12.
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 4 Experiments
### Datasets  
The three HSI datasets in our experiments are TeaFarm, Salinas, and Indian Pines, whose pseudo-color and ground-truth images are shown in Figures 3-5.  
Salinas: The data have a size of 512 \(\times\) 217 with a spatial resolution of 3.7 m. The dataset contains 224 contiguous spectral bands. After removing 20 water absorption bands (108-112, 154-167, 224), 204 bands are actually used for training. The dataset includes 16 types of land cover classes.  
Indian Pines: Imaged by the AVIRIS spectrometer, with a wavelength range of 0.4-2.5 \(\upmu\)m, the data size is 145 \(\times\) 145, and there are 224 spectral bands, of which 200 are used for experiments. The dataset contains 16 types of land cover classes.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 2 Uplink and Downlink Channels in Satellite-Ground FSO Communications
### Absorption and Scattering Loss
According to the Kruse model [18], atmospheric attenuation coefficient \(\beta_{\lambda}\) due to fog can be described in terms of meteorological visibility V (in kilometers) and transmittance threshold (T\({}_{\text{th}}\)) as follows:  
\[\beta_{\lambda}=\frac{10\log_{10}\text{T}_{\text{th}}}{\text{V}(\text{km})} \left(\frac{\lambda}{\lambda_{0}}\right)^{-\delta} \tag{2}\]  
where \(\lambda_{0}\) is the maximum spectrum of the solar band and \(\delta\) is given by  
\[\delta=\left\{ \begin{array}{ll}1.6&\text{for V }>\;50\;\text{km}\\ 1.3&\text{for }6\;\text{km }<\;\text{V }<\;50\;\text{km}\\ 0.585\text{V}^{1/3}&\text{for }0\;\text{km }<\;\text{V }<\;6\;\text{km}\end{array} \right. \tag{3}\]The most popular \(T_{\text{th}}\) values are 2% and 5% [18]. A popular empirical model for calculating the attenuation coefficient is the [PERSON] model [18]. The [PERSON] model's coefficient can be changed in accordance with [PERSON]'s theory as follows:  
\[\delta= \begin{cases}\begin{array}{c}1.6&\text{for V }>\;50\;\text{km}\\ 1.3&\text{for }6\;\text{km }<\;\text{V }<\;50\;\text{km}\\ 0.16\text{V}+0.34&\text{for }1\;\text{km }<\;\text{V }<\;6\;\text{km}\\ \text{V }-\;0.5&\text{for }0.5\;\text{km }<\;\text{V }<\;1\;\text{km}\\ 0&\text{for V }<\;0.5\text{km}\end{array}\end{cases}  \tag{4}\]
## 4 Development of ViPER+ for Construction Vehicles and Equipment Pose Estimation
### Data Collection Subsystem
#### 4.1.3 Reception Timestamp Correction  
The last step in the data collection sub-system is correcting the timestamps that are received from anchors. Every time an anchor reports a time-sync message, the server calculates the clock difference and the clock skew of that specific anchor based on the data of the time-sync report. Upon the reception of beacon packets, the server uses the last clock skew value to adjust the anchor's clock with the clock of the time sync anchor.
## 3 Novel Taxonomy for SSNs
### SSNs Using Hybrid Level Virtualization
#### 3.3.4 An Insight on the Application of Hybrid Level Virtualization  
The hybrid solutions benefit from both LLV and HLV features, thus it takes much attention in recent running research.  
For the discussed Hybrid Level Virtualization, the following is concluded:  
* MW/Cluster-based Sensomax: At LLV Java Sun Spot devices are used to exploit their ability to run concurrent application tasks. Each application is implemented as an agent. The base station can assign multiple agents to one node, using one-hop broadcast. For NLV it uses clustering concept, where it divides the network into clusters, each with its own cluster head. Multi-set Architecture for Multi-applications Running on WSN: It uses TinyOS to provide LLV, thus, it inherits its drawbacks. While, for NLV, the network is divided into subsets, each subset groups nodes as a cluster and each has its own cluster head. Each subset is dedicated to one application. When SSNs is deployed, only one application begins its execution, according to the pre-defined sequence. While nodes in other subsets sleep to save energy until their turn comes.
* MW and Virtual link/overlay
* SenShare: It uses TinyOS to provide LLV, thus, it inherits its drawbacks. Nevertheless, it uses CTP for creating dynamic clusters for HLV.
* A framework for service providing in VSNs: it provides LLV using MW in each node, while the HLV is achieved by gateway nodes that provide VSNs creation and management.
* MW/Hierarchical clustering
* Melete system: At LLV, Melete improves on Mate, but, since application tasks have their own data and execution space, only a limited number of application tasks can run concurrently. The programming model is based on the event-driven approach of TinyOS. The application programs are written in TinyScript. While HLV is achieved by a dynamic grouping scheme. Finally, the programmer needs to be aware of many situations that may arise in the network and program the responses, and this approach is not flexible at all.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 4 Communication in Distributed Systems
### Inexact Parallel-in-Time Integrators
The pronounced dependence on smaller bandwidth shown in Figure 3, left, however, makes this approach interesting for a growing imbalance of compute power and bandwidth. Situations where this is already the case is in compute clusters with commodity network hardware and HPC systems where the communication network is nearly saturated due to concurrent communication going on, for example due to the use of spatial domain decomposition. Indeed, using the cheap transform coding discussed in Section 4.2 below, an overall run time reduction of 10% has been observed on contemporary compute nodes connected by gigabit Ethernet [65]. Figure 3: Theoretically estimated parallel efficiency \(E=T_{\text{seq}}/(NT_{\text{par}})\) for variation of different parameters around the nominal scenario (marked by *). _Left:_ varying communication bandwidth in terms of the communication time for uncompressed data. _Right:_ varying requested tolerance.
## 3 Processing and Extraction
### Other Alternative Methods of Extraction
#### 3.6.1 Pressurized Liquid Extraction
However, antioxidant activity determined in ABTS and DPPH assays assumes higher values when the residue after SFE-CO\({}_{2}\) is extracted again using PLE with ethanol as a solvent. [PERSON] et al. [140] used pressurized liquid extraction with 50% ethanol of raspberry pomace remaining after SFE-CO\({}_{2}\) extraction to obtain antioxidative compounds which were used to prevent beef burger spoilage during prolonged storage. The extraction yield was 19.3% and the obtained extracts were characterized by the following antioxidant capacities: 936 \(\mu\)mol TE/g (extract) and 123 \(\mu\)mol TE/g in ORAC and ABTS assays, respectively. The TPC of the extract was 208.3 mg GAE/g. It was concluded that 1% PLE extract application helped to prevent oxidation in the burgers.
# Analysis of Video Transmission Capabilities in a Simulated OFDM-Based Supplementary BPL-PLC System
## 3 Materials and Methods
### Type of Coupling  
In order to develop a reliable simulation model of a dedicated power cable, it was necessary to perform a series of theoretical analyses. The analyses of the transmission properties of the cable, including its attenuation, characteristic impedance, group delay [11], etc., showed that in the frequency range from 2 MHz to approx. 7.5 MHz, the measured SNR values are within the regression error, regardless of the cable model adopted for the analysis. Obtained results for inductive-inductive and mixed capacitive-inductive coupling are shown in Figures 4 and 5, respectively.  
The methodology for performing verification measurements is shown in Figure 6. As observed, the Gwinstek frequency generator was connected to the input coupler (in this case, the inductive coupler) on one side, and the USB-SA44B SignalHound spectrum analyzer was connected to the output coupler (in this case, the capacitive coupler) on the other side.  
Figure 4: Relation between SNR and frequency for different cable models for inductive–inductive coupling.  
Figure 5: Relation between SNR and frequency for different cable models for capacitive–inductive coupling.  
By measuring the signal levels at both input and output, it was possible to determine the relation between attenuation and frequency. Next, by evaluating the noise level, the SNR value for a given frequency could be determined.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
###### Abstract  
Cavitation is a major concern in liquid rocket engine turbopumps, and as an effective measure to improve cavitation quality, an inducer with helical static blades has attracted attention in recent years. In order to study the effect of the radial height of helical static blades on the cavitation performance of the inducer, CFD methods based on the Reynolds-averaged N-S equation, the standard \(k\)-\(\epsilon\) turbulent model, and the Schmerr and Sauer cavitation model are employed to analyze the cavitation flow characteristics of a certain inducer with different helical static blades. The results show that with the increase in radial height, the backflow in the flow field is enhanced. Affected by this situation, the head is improved, the efficiency is reduced, and the low-pressure zone on the suction surface at entrance is enlarged. The helical static blade can delay the channel blocking of cavitation by providing an extra channel for the extension of bubbles. However, the effectiveness is restricted because the cavitation area enlarges with the radial height of the helical static blade. Although the effect of radial height on the head and the cavitation performance is opposite, there is an optimal radial height from 0.05 to 0.125 that improves both at the same time.
## 3 System Model Design  
In opportunistic social networks, there are many algorithms that select the appropriate relay nodes based on the similarity between the nodes. However, when considering the similarity between nodes, these algorithms usually only consider a single factor such as location, common neighbor node, and social attribute. Since these algorithms tend to ignore a lot of valuable information between nodes, the relay nodes obtained by these algorithms are often not so reliable. Therefore, this paper proposes a data transmission strategy that utilizes the mobile similarity and social similarity between neighbor nodes and destination nodes to select more reliable relay nodes. Next, we will introduce the algorithm step by step in detail.
## 3 Case Study Results and Discussion
Both pulses used in the experiment [7] had exactly the same wavelength, and the differential reflectance peak (caused by the Pauli blocking of the probe photons' absorption) should occur at zero delay, when the concentration of non-thermalized photocarriers would reach its highest value, unless the pump and probe spots were shifted with respect to each other (as shown in the inset of Figure 2), in which case it would take some time for photocarriers to reach the center of the probe spot via diffusion. However, the spots in the experiments of Ref. [7] were not too small, and the spatial diffusion should be of little importance there (in contrast with, e.g., Ref. [40]). Nevertheless, the experimental results of [7] exhibited a peak that was shifted by \(\approx\)100 fs, which possibly could be attributed to an error in controlling the delay time between the pulses during the experiment because the pump and probe had exactly the same central wavelength and the generation of photocarriers by the pump should have an _immediate_ effect on the probe pulse absorption. The fitting also yields a spatial shift of \(0.62w_{0}\) between the centers of the pump and probe spots on the surface. It may look too big, but it is partially because we assumed both spots have the same size, which probably was not the case in the experiment (we found no information regarding this in the article). Moreover, the pump beam could be attenuated with respect to the nominal fluence value provided in the article because of several natural reasons. Therefore, we can conclude that our theoretical model explains the experimental data very well.
## 3 Results and Discussion
### Influence of the Polyp Morphology in the Results  
Figure 4 shows the Jaccard index for the four analysed models, when polyps in the test set of the PICCOLO dataset are classified based on their morphology accordingly to the Paris classification. It can be clearly seen that flat polyps (0-IIb) obtain the lowest results in all models, and that pedunculated and sessile polyps (0-Ip, 0-Ips and 0-Is) outperform the flat ones (0-IIa, 0-IIa/c and 0-IIb). This result is well aligned with the clinical findings shown by clinical works [8; 9]. Furthermore, it is important to remark that the best results for flat polyps are obtained with models trained with PICCOLO dataset in three out of the four considered models. Regretfully, this analysis cannot be done with models trained using CVC-EndoSceneStill and Kvasir-SEG, as polyp morphology is not available for those datasets. Similarly, [PERSON] et al. [41] employed a proprietary dataset to train the polyp detection method based on YOLOv2, and they also found that DL methods show lower sensitivity for flat polyps.