esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Behavior of Distributed versus Concentrated Volt/var Local Control Strategy
### Distributed versus Concentrated Var-Sinks
#### 2.5.3 Voltage Profile  
Figure 8 shows the theoretical voltage profile for the distributed and concentrated var-sink cases. Although the voltages at fhb and feb are equal in both cases, the voltage values in the distributed case are lower than in the concentrated one, especially in the middle of the feeder. In the case of distributed var-sinks, the voltage profile behaves convexly, while in the concentrated var-sink case, it shapes linear.  
To understand the shape of the voltage profiles, the impact of distributed and concentrated var-sink cases on their behavior is explicitly shown in Figure 9. This impact is calculated by:  
\[U_{d}^{imp}(i)=U_{fhb}^{energy}-U_{d}(i), \tag{19}\]  
and:  
\[U_{c}^{imp}(i)=U_{fhb}^{energy}-U_{c}(i). \tag{20}\]  
where \(U_{d}^{imp}(i)\)--the impact of distributed var-sinks on node \(i\), \(U_{fhb}^{energy}\)-energization voltage at feeder head node, \(U_{d}(i)\)--voltage at node \(i\) in the case of distributed var-sinks, \(U_{imp}^{c}(i)\)--the impact of concentrated var-sinks on node \(i\) for the distributed and concentrated control strategies, respectively.  
The impact on voltage profile in the case of distributed var-sinks shows a concave behavior, while in the case of a concentrated var-sink, it shows a linear one. Therefore, the distributed var-sinks exert a greater impact on voltage profile than the concentrated var-sink.  
As a conclusion, to achieve the same total voltage drop, the distributed var-sinks absorb more reactive power than the concentrated var-sink. The surplus of the reactive power absorbed by the distributed var-sinks pushes down the voltage profile without need, and overloads the grid.  
Figure 8: The theoretical voltage profile for the distributed and concentrated var-sink cases.  
Figure 7: The theoretical voltage drop amplitude in every feeder segment as a function of the bus number for the distributed and concentrated var-sink cases.  
The mathematical calculations in this chapter were possible because we made many assumptions and simplifications. To get closer to a real case, we then performed relevant simulations in a test grid.
## 2 Methodology
### Selection and Reduction Process for Geometric Parameters
#### 2.3.2 Correlation Analysis  
The correlation matrix containing the linear correlation coefficients of each pair of variables is the basis for conducting principal component analysis. One of the prerequisites for conducting PCA analysis is the relationship between the original variables, and the basis for observing a set of connected variables is the correlation matrix. To determine the degree of connection between geometric parameters describing particles, Pearson correlation coefficients were primarily calculated. The Pearson correlation coefficient indicates whether there is a linear relationship between variables. A correlation coefficient value from 0 to 1 indicates positive correlation, signifying a simultaneous increase in the values of both data sets. A correlation coefficient value from 0 to \(-1\) indicates negative correlation, meaning that an increase in the value of one variable corresponds to a decrease in the value of the other variable. Complete correlations, i.e., correlation coefficient values of r = \(\pm 1\), are not characteristic of natural systems and are most commonly associated with theoretical models. When the correlation coefficient has a value of 0, it signifies the absence of a linear relationship, indicating that knowing the values of one variable does not allow us to infer anything about the values of the other.  
Correlation analysis was conducted to assess the relationship between the 14 geometric numerical parameters listed in Table 1. Parameters with a high degree of correlation were grouped together as highly correlated parameters describing similar or correlated particle characteristics. It was necessary to select only one parameter as a representative for each group. Numerical parameters with low correlation coefficients with any other parameters were identified as independent representative parameters.
## 3 Drone Routing Algorithms
### Security Algorithms Used in Drone Routing
#### 3.3.3 Blockchain-Based Drone-Enabled Delivery Scheme for Healthcare (GaRuDa)  
Secure drone communication is a crucial for ensuring reliability in drone-based delivery systems. For effective route planning from source to destination, drones need to communicate with each other within a particular coverage range. Since the parcel delivery might be in adverse conditions, the communication network may face unexpected situations such as bandwidth limitation, which can cause delay and inaccuracy in the information being exchanged. Incorrect information may cause drones to divert from the planned trajectories and enter unauthorized areas. To address the aforementioned problems, the authors in [58] proposed a secure scheme named GaRuDa, which incorporates the internet of drones and blockchain technology between different stakeholders to enable delivery scheme with a short delay and high efficiency. The proposed framework works in three phases: The first phase, also called as data dissemination phase, consists of the healthcare institutions, staffs, and pharmacies that receive requests for health products from the patients. A hashed key value is used for accessing every record to make the information secure and reliable. In the second phase, the package is delivered by the drone using a 5G-enabled internet channel if they comply with the smart contract conditions. The last phase consists of the consumers, who are the main requesters of the drugs. In particular, the proposed framework uses blockchain-based smart contracts to guarantee trusted operation. The authors consider the inclusion of payment gateway to evaluate the effectiveness of the proposed model with respect to data storage, communication, and computation. The proposed technique can reduce the communication cost when the number of users increases. However, communication interference (co-channel and cross-channel) was not considered in this study.
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 2 Materials and Methods  
To simplify CMAC development, Level 1C-processed images from Sentinel-2A and Sentinel-2B were considered together, as their responses were not significantly different. The quality and cadence of S2 data, every five days or less, were highly promotional for CMAC empirical testing and development.  
CMAC does not differentiate among causal aerosol particles, whether due to smoke, dust, thin clouds, etc., but instead corrects imagery according to the output from an atmospheric index model that is mapped directly from each image's spectral band responses. Aerosol particles are the dominant atmospheric effect resulting in the change from VNIR surface reflectance to the TOAR measured by the satellite [20]. While water vapor is another significant variable that affects atmospheric light transmission, the visible spectral bands are well known to be free of effects from water vapor influences [21], and the S2 NIR B8A in CMAC v1.1 is positioned to avoid water vapor absorption [22]. The B8A has 20 m pixel spatial resolution, while the visible bands have 10 m resolution. This difference has posed no problems for correction or for applications; however, hypothetically, the higher the resolution, the better CMAC can accurately estimate true target surface reflectance because of less reflectance mixing and finer differentiation of atmospheric effects.
## 2 Materials and Methods
### DNA Extraction  
We developed a DNA isolation protocol for complex metagenomic samples for biogas or wastewater treatment plants that is a modified and extended version of the FastDNA Spin Kit for Soil (MP Biomedicals, Solon, OH, USA). This protocol, called \"Long-read DNA preparation for Metagenomic samples\", is stored in detail as a step-by-step protocol for better experimental reproducibility at protocols.io [19]. DNA isolation was performed and tested on fresh or frozen substrate/sludge samples. DNA extraction was performed according to the manufacturer's protocol, with various exceptions and additions to improve DNA fragment length while maintaining high yield and high enough purity to avoid interferences with the nanopores inside each sequencing flow cell.  
In brief, 400 \(\upmu\)L sample (substrate or sludge) were initially centrifuged for 5 min at 20,000\(\times\)\(g\). The supernatant was removed, and the sludge/substrate was re-suspended in nuclease-free water. This step was included before DNA isolation to remove short DNA fragments from dead cells to better utilise the matrix's binding capacity. Lysing Matrix E (FastDNA Spin Kit for Soil) was added to the samples, and a MP Biomedicals FastPrep-24 Classic Instrument (Thermo Fischer Scientific, Karlsruhe, Germany) was used for homogenisation at 20 s and 6 m/s. The samples were placed immediately on ice afterwards. Increased shearing time (e.g., 2 \(\times\) 20 s) led to decreased DNA fragment length, while not visibly improving the total yield on an agarose gel. Reduced shearing force (e.g., 4 or 5 m/s) led to decreased yield, with no or minor fragment length improvements (decreased yield could also indicate incomplete DNA extraction). DNA yield was judged on the basis of electrophoresis results in order to favour high yield in longer DNA fragment ranges and measurement of double-stranded DNA (dsDNA) via Qubit.  
Before proceeding with protein precipitation, we added a 5 min RNase incubation step. Furthermore, we tested two different washing solutions for the washing steps: \"HA-wash solution\" (see [19]) and SEWS-M (FastDNA Spin Kit for Soil). Introducing the additional HA-wash solution step improved the overall DNA fragment length and total yield. More washing steps significantly reduced the total yield. After DNA retrieval, a subsequent \"pre-cleaning\" step was added using magnetic beads (either AMPure or Highprep beads) to further reduce short fragments and possible impurities from the biogas or wastewater samples before library preparation. Samples were temporarily stored at 4 \({}^{\circ}\)C before library preparation (freezing of isolated DNA was omitted as it causes DNA shearing).
## 1 Introduction
However, in many practical situations, their mathematical models undoubtedly involve errors in relation to practical systems and quite often are not exactly available. Therefore, advanced control techniques have been required to deal with these situations, such as robust control (including sliding mode control (SMC)), adaptive control, fuzzy logic control, neural-network control, learning control, etc. This paper proposes two SMC-based active force controllers to complement a class of SDOF non-linear vibration isolators. The targeted isolators can usually be characterized as a class of non-linear systems that contain unknown, bounded uncertainties/disturbances, and unmodelled dynamics. These characteristics are the main reasons for choosing SMC to cope with this class of isolator [26; 27; 28; 29; 30; 31]. The SMC methods also have other advantages: computational simplicity and easy implementation with respect to other robust control approaches. The main disadvantage of the conventional SMC is the chattering phenomena of the system output and control input, which can induce damages to control systems. Nowadays, the super-twisting SMC algorithm has become preferable over the conventional SMC, since it greatly alleviates the chattering phenomena [30; 32; 33]. The rest of this paper is organized as follows. The description of the non-linear vibration isolators and the proposed SMC methods are presented in Section 2. Section 3 gives a specific non-linear isolator with QZS and numerical simulations and result analyses of the proposed SMC methods for this isolator are provided. Finally, the concluding remarks are drawn in Section 4.
###### Abstract  
Background: The aims of this study are to retrospectively compare the clinical, radiographic and esthetic outcomes of platform switching (PS) and platform matching (PM) dental implants in the anterior maxilla after \(\geq\) 10 years of functional loading. Methods: Marginal bone loss (MBL) levels were recorded; furthermore, peri-implant clinical parameters (PPD, BOP, PI) were collected and the Pink and White Esthetic scores (PES/WES) were used to evaluate the esthetic outcomes. Wilcoxon signed rank tests were performed to compare collected parameters among the two groups, with a \(p\)-value < 0.05. Results: A final sample of 58 patients was enrolled in this study (PM implants = 29; PS implants = 29). PS implants showed lower MBL levels (1.02 \(\pm\) 0.81 mm vs. 1.67 \(\pm\) 0.99 mm, \(p\) = 0.028) and PPD values (3.69 \(\pm\) 1.1 vs. 5.16 \(\pm\) 1.09 mm, \(p\) < 0.001) compared to PM implants. Mean PES values were higher in the PS group compared to the PM group (8.46 \(\pm\) 0.69 vs. 7.89 \(\pm\) 0.78, \(p\) < 0.005), while there were no differences for WES values (7.82 \(\pm\) 1.09 vs. 7.71 \(\pm\) 0.85, \(p\) > 0.05) and peri-implant diseases' prevalence (\(p\) > 0.05). Conclusions: After 10 years, PS implants showed statistically significant lower MBL and PPD values and higher PES values compared to PM implants.  
Keywords:dental implants; esthetics; implant-supported crown; platform matching implants; platform switching implants +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Persistence Analysis of NPP Variation  
This study was based on the use of annual and seasonal NPP raster data from 2000 to 2015. The Hurst index estimation method was used to interpolate the future trends of NPP. The persistent changes in NPP showed significant differences on different time scales for future predictions (Figure 7). During spring, the NPP persisted over \(95.38\times 10^{4}\) km\({}^{2}\) (25.67% of the study area). This was mainly distributed in Qinghai, Eastern Gansu, Ningxia, northeastern Inner Mongolia, and the eastern/central parts of Hebei and Shanxi. An area of \(49.78\times 10^{4}\) km\({}^{2}\) (13.40%) showed continued persistence of NPP in the summer. NPP showed continued persistence into the future and was mainly distributed in two regions: (1) Henan and Shandong and (2) the border between Inner Mongolia and Liaoning. In autumn, NPP persisted over an area of \(185.52\times 10^{4}\) km\({}^{2}\) (49.92%) and was mainly distributed in Qinghai, southeastern Tibet, eastern Gansu, western Ningxia and Shaanxi, and most of northeastern Inner Mongolia. The NPP persisted over an area of \(25.09\times 10^{4}\) km\({}^{2}\) (6.75% of the total study area) during winter. This was mainly distributed in Shandong, northern Henan, southern Shaanxi, and northern Shanxi. Additionally, there were a few areas in the southeastern corner of Tibet in which the NPP was more persistent. On an annual scale, the Hurst index estimate was between 0.11 and 1, with a mean of 0.497 and a standard deviation of 0.10. Fifty-four percent (\(159.54\times 10^{4}\) km\({}^{2}\)) of the study area had a Hurst index of \(<\)0.5, indicating that the NPP will persist into the future. The regions with a Hurst index of \(>\)0.5 were mainly distributed in the eastern provinces that have good hydrothermal conditions; these regions include Inner Mongolia, northern Shaanxi, the border of Liaoning and Hebei, and the border of Shanxi, Henan, and Hebei. The NPP in the eastern part of Inner Mongolia and the northern part of Shaanxi showed significant persistence, consistent with the findings of [PERSON] et al. [53], who interpolated future trends of persistence related to the vegetation growth in the Mongolian Plateau. The strong persistence of NPP around Beijing, Tianjin, and Hebei indicates that the vegetation in these areas continuously improved [48; 54]. Additionally, the southern and northern parts of Ningxia also showed a trend of persistence of NPP. This is consistent with the conclusion by [PERSON] et al. [55].
## 6 Experiments and Results  
Instead of using only a pure simulation testbed, the proposed strategy was tested with a real UAV navigating in a two-dimensional simulated polluted environment. The physical UAV in the simulated environment added constraints related to autonomy, velocity and inaccurate movements of the UAV, the last one producing irregular measurements of the pollutant. In a later stage of this research, we intend to test the algorithms with UAVs flying in real polluted scenarios.  
In order to measure the performance of the algorithm, the distance from the real source location to the source location position estimated by the algorithm is considered.
# Narrowband-Internet of Things Device-to-Device Simulation: An Open-Sourced Framework
## 1 Introduction
In each physical subchannel, the Signal to Interference plus Noise Ratio (SINR) for the received signal is computed by considering the noise, transmit power, path loss, and interference in terms of cell scenarios (i.e., microcell, macrocell of a rural area, and macrocell of an urban area) [19; 20]. The system assumes Base Stations (BSs) are in vertices at the centre of cells, and that \(N\) nodes are distributed randomly in the cells. The Media Access Control (MAC) and networking layers are implemented in conjunction with the LEACH protocol to reduce queuing delays, minimize power consumption, and increase the battery life of devices (i.e., enhance the State of Health (SOH) of the device's battery). Furthermore, it prevents possible selective forwarding attacks, insider attacks, and node compromise [21; 22; 23]. The LEACH protocol organizes the networking nodes into clusters and elects some of them as Cluster Heads (CHs). Non-Cluster Head nodes (NCHs) transmit the collected data to the CH using D2D communications. Then, each CH aggregates and prepares the data and sends all to the BS. LEACH initially uses a random selection process for the embedded devices that are chosen to be CHs. This distributes power consumption evenly in the wireless network [21; 24]. Intra- and inter-cluster collisions are decreased by the MAC layer, depending on the transmission channel. Orthogonal Frequency Division Multiple Access (OFDMA) is used for uplinking [25] with Binary or Quadrature Phase Shift Keying (BPSK or QPSK) modulation, and Single Carrier Frequency Division Multiple Access (SC-FDMA) is employed for downlinking with QPSK data change [17; 26]. Continued monitoring of nodes is necessary when data are initially centralized at the CHs. This makes the D2D clustering method the optimal option. The Key Performance Indicators (KPIs) for this analytical study are the lifetime, throughput, stability period, instability period, abilities sensing range, computation power, and field distribution of a heterogeneous network where the sensor nodes have different energy levels [21]. NB-IoT uses a bandwidth of 180 to 200 kHz [27]. This guarantees efficient communication, and it classifies the data transmitted by the CH into a set of coverage classes. This set connects to a number of signal repeats, which are allocated to users based on path loss during their contact with the BSs [15]. The rest of this paper is arranged as follows: Section 2 gives an overview of D2D communication in cellular networks. Section 3 describes the NB-IoT D2D simulation, modeling, and the node distribution, including the mathematical propagation models, the coverage class definition based on the path loss, the LEACH protocol, the main purpose of the clustering protocol, a few preliminaries on the uplink/downlink queuing server, the latency, the energy consumption model with power profiles for user and BS, and the overall system performance. Section 4 shows the simulation outputs and discusses the obtained results. Finally, we offer concluding remarks and discuss future works in Section 5.
# Metamaterial Based Design of Compact UWB/MIMO Monopoles Antenna with Characteristic Mode Analysis
## 3 Topology and Analysis  
The basic concept of the antenna designed methodology has been presented in Reference [37]. The layout and fabricated models of the proposed UWB/MIMO antenna are presented in Figure 2. The framework consists of two printed circular patches on top of the plane, and, at a ground plane, four slots were employed in both the upper and bottom layers to achieve wideband matching. Four rows of metamaterial structures were set around the two antenna components, which fill in as a decoupling network to combat the surface waves. Each row has three metamaterial components, which are adequate for perfect inductance. The total design space was 30 mm \(\times\) 60 mm\({}^{2}\). Two perturbed stubs were also utilized in the middle of the ground plane, which likewise fills in as a decoupling structure for isolation improvement. The decoupling network acts as a band-stop filter that generates transmission zeros within the transmitting components that upset the flow of current, surface waves, and near fields. Equivalent circuit model of the proposed antenna structure. Series left-handed capacitance (\(C_{L}\)), shunt left-handed inductance (\(L_{L}\)), shunt right-handed capacitance (\(C_{R}\)) and series right-handed inductance(\(L_{R}\)) are realized by slots, unit-cells, gap distances between the ground-plane and top surface, and unwanted currents flowing on the surface, respectively. The circuit model is shown in Figure 3. The dimension of the MIMO antenna and associated parameters are shown in Table 1.
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 2 Study Area and Datasets
### Datasets  
#### 2.2.1 Rain Gauge Observation Data  
Hourly precipitation observations were obtained from a ground-based network of 2177 rain gauge stations maintained by the China Meteorological Administration. The majority of these measurements are from tipping-bucket rainfall recorders which have a receiver diameter of 20 cm, a receiver height of 70 cm, and a signal output for every 0.1 mm of precipitation collected. Each observation is flagged by a quality control code (Table 1), according to which invalid (missing or low-quality) measurements are removed before evaluation (see Section 3.1 for details). Restricted by permission for data access, we were only able to obtain gauge observations for the one year of 2018. This 2018 dataset is used as a reference to evaluate and compare the IMERG precipitation products in the study area. Due to the high density of rain gauge stations, 68.3% of the IMERG grids contain at least one station. Grids with no stations are mainly located at sea. The rain gauge observations are helpful in understanding the performance of the IMERG products along the coast.