esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Connection Issue of a Wind Farm to the Electrical Grid  
In this paper, the proposed wind farm in a highland region with average power is considered as a decentralized generator unit, which is most often connected to the distribution network and that differs from centralized generator units. In Algeria, the electrical distribution grids are the most important infrastructure of the whole power system, which is considered as the final interface that leads to most industrial and domestic customers. These distribution grids are operated in ranges of voltages below 50 kV, which is the voltage level of the Medium Voltage (MV) and Low Voltage (LV) ranges. Moreover, in the Algerian distribution grid, the nominal voltage of the MV is 10 kV and 30 kV. These voltage levels allow a good compromise to limit the voltage drops, minimizing the number of source positions (connecting to High Voltage (HV)/MV power station) and reduce the inherent constraints to high voltages (investment costs, protection of property and persons). Moreover, Algerian distribution grids are, in most cases, radially networked. The map of the western Algerian electrical grid is shown in Figure 5. This figure shows the structure of the High Voltage B 220 kV transmission lines, while the substations and power plants are also shown in this figure [1]. The structure of High Voltage A 60 kV distribution lines is shown in Figure 6.  
Figure 5: Map of the western Algerian electrical grid. Reproduced from reference [1].  
Based on the structure of the electrical grid on the west side near the proposed wind farm (see Figure 6), this radial electrical grid of 60 kV can be reconfigured; it is then simulated using the Power System Analysis Toolbox (PSAT) software with actual electrical grid parameters and consumer profiles at the peak load of each bus, with a centralized generation source Tiaret City (TIARC) power plant. Simulation of the latter gives the results shown in Figures 7 and 8. More details on the overall simulation of the west Algerian grid can be found in [34].  
Figure 8: Reactive power in each bus of the 60-kV electrical grid in the Tiaret region.  
Figure 6: Western electrical grid near to the proposed wind farm.  
Figure 7: In each bus of the 60-kV electrical grid in Tiaret region: (a) voltage amplitudes; (b) active powers.  
The existence of an SNVI industrial site in the bus of N 2 between the city of Tiaret and the city of Tissemsilt can justify the presence of the voltage drop across this line as shown in Figure 7a. This resulted in demand of an excessive reactive power and active line losses due to the high-fluctuated demand of energy to the industrial site, as shown in Figures 7 and 8.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions  
We used a large set of source sequences for this set of experiments and processed them, introducing video and audio distortions. The selected distortions were based on previous studies that analyzed audio and video distortions. For each type of degradation, we generated a number of distortion levels, which were used to establish the Hypothetical Reference Circuits (HRCs) for each perceptual experiment. This section describes the different types of degradations, detailing the procedure used to treat the source contents and generate the Processed Video Sequences (PVS).
## 4 Implementation and Analysis
### Sentiment Analysis  
As presented in the related work discussion, sentiment analysis applied to open-ended student feedback is essential in automating students' feedback. Therefore, we explore sentiment analysis in our studies. We use the \"sentiment.vader.SentimentIntensityAnalyzer\"; a ready-to-use Python library to realize a simple sentiment analysis. The module was imported from the Natural Language ToolKit, a Python tool for English language processing. The sentiment analyzer based on the VADER lexicon works well for classifying social media texts, whether positive, negative, or neutral. Finally, because we had a little test set for our experiments, we manually checked the VADER-based classification to confirm that the outputs of the analyzer were correct.
## 3 Results and Discussion
### PSNR Analysis of Colorized Images  
The PSNR and SSIM values of the colorized images were analyzed to examine the algorithm's performance. Moreover, the PSNR value describes whether images are lossy formatted or the quality of the image is sharp. The measurement is described in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Tumor Images**} & \multicolumn{2}{c}{**Non Tumor Images**} \\ \hline
**Total** & **Correctly Identified** & **Accuracy** & **Total** & **Correctly Identified** & **Accuracy** \\ \hline
24 & 24 & 100\% & 11 & 11 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithmic accuracy of the system.  
Figure 10: PNSR value analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr. No** & **Image Id** & **Tumor Size (mm)** & **Image Sizing** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Tumor size calculation.  
Trivial values of PSNR in lossy image compression were between 30 and 50 DB, which depended on the image's bit depth. The PSNR value remained below 50 dB if the bit depth was 8 bits. However, in the case of 16 or 32 bits, better PSNR values were seen. However, in this 32-bit image conversion, the typical value was 50 and 80 DB. When the graph was analyzed, the PSNR values were above 60 DB. This conversion shows that the image was not lossy, and after colorization, features were preserved in the images.
## 7 Applications and Future Research Directions of RIS
### RIS-Assisted Free Space Optics (FSO) and Hybrid RF/FSO Communications  
Free space optics (FSO) may find a place in situations where fiber is too expensive or impossible to install, when high data rates are needed, and in cases when the RF spectrum becomes congested. RIS has recently received much interest as a viable technology for FSO and hybrid RF/FSO communications due to its superior performance over typical MIMO systems [110]. Furthermore, compared to multi-antenna amplification and forward relaying networks with fewer antennas, RIS as a wireless transmission technique can achieve significant performance while reducing system complexity and budget. Recently, a new wireless transmission technique based on RIS for hybrid RF/FSO communications has been presented in [111]. Two hops are used to transmit the signal. The first hop is based on FSO channel communication, whereas the second hop is based on RF throughput RIS. Compared to conventional RF/FSO communications, the proposed RIS-based RF/FSO communications offer a 25-50 dB gain. This analysis is valid when RIS is taken as a reflector or transmitter.  
The authors of [112] investigated a dual-hop mixed FSO/RF network in which the source message was forwarded to the destination via a RIS on the second hop. The majority of previous studies considered the scenario in which a RIS is utilized to replace relays. Another key situation is that the RIS, in conjunction with the RF signal generator, could be employed as part of the transmitter to assist the source in data transmissions [113]. The idea of employing a RIS as a transmitter was recently tested using a testbed platform [114]. Recently, the performance of a RIS-assisted source mixed radio frequency (RF)/FSO relay network with opportunistic source scheduling was developed and evaluated by the authors in [115].
For frequency errors induced by MIMU errors, the effect of this technique based on \(C/N_{0}\) is limited. In recent years, in challenging applications, the GNSS open-loop receiver architecture has gained much attention. A quasi-open loop receiver architecture was recently introduced to estimate the time varying carrier frequency of GNSS signals [14]. An open-loop approach combining batch and sequential signal processing has been demonstrated to improve the tracking sensitivity of a global positioning system (GPS) receiver, and it was successfully applied in the INS/GPS deep integration system [15]. This advanced open-loop receiver architecture applies batch correlation to obtain a coarse signal parameter estimation, while sequential processing is used to perform a fine signal zoom. The main drawback of the open-loop receiver architecture is the lack of computational efficiency. However, the coarse/fine tracking method provides a new perspective for us to solve the problem of large jumps in frequency errors. We present an enhanced MIMU/GNSS UTC integration system to mitigate performance degradation caused by abrupt changes in frequency tracking errors. The GPS L1 C/A code signal is used as a case study. The difference between the new architecture and conventional systems is mainly reflected in the following ways. First, each tracking channel of the enhanced UTC (EUTC) architecture contains a module for large frequency tracking error detection. It monitors the frequency tracking error by comparing the frequency discriminator outputs and the preset thresholds. Second, a serial refined frequency processor based on retuned frequency is introduced in each tracking channel. The main function of the refined frequency processor is to fix coherent integration results and prevent the tracking loop from being contaminated by large frequency errors. Finally, the channel prefilter based on the classical Kalman filter (CKF) is replaced by an adaptive prefilter with multiple fading factors, which enhances the ability to track large frequency errors.
## 2 System Model
### LEACH Protocol
#### 2.2.3 Complexity Analysis  
LEACH operates by probabilistically selecting CHs among nodes, where each node computes a threshold based on a random number and a probability threshold. The complexity is characterized by how this process scales as the network grows. Each node independently calculates a threshold based on a random number and a probability value. This operation is local to each node and involves basic arithmetic operations, typically resulting in a constant time complexity for each node, denoted as \(\mathcal{O}(1)\). Nodes are organized into clusters based on the thresholds they calculate. This clustering process involves nodes communicating and potentially establishing CHs. In a network with \(K\) nodes, each node typically interacts with a constant number of neighbors (or potential CHs), resulting in a constant overhead per node, often denoted as \(\mathcal{O}(1)\). Since the key operations--threshold calculation and cluster formation-- are repeated for each node in the network, the overall complexity of LEACH in terms of node count \(K\) is \(\mathcal{O}(K)\). This linear scalability means that as the number of nodes increases, the computational and communication load increases proportionally.
## 2 Materials and Methods
### SAR Data and Processing  
Interferometric SAR data in SLC (single look complex) format was acquired by the C-band Sentinel-1, the two-satellite SAR constellation system, using the interferometric wide (IW) mode with dual polarizations (VV and VH), for every 12 days. The imagery has a swath of 250-km and spatial resolution of \(5\times 20\) m. Except for March 7, all images between February 23 and December 8 during the 2019 growing season were acquired, resulting in a total of 24 images. Image acquisition dates are also marked in Figure 2a.  
InSAR processing includes the following steps:  
1. One scene was selected as a master and the terrain observation with progressive scans SAR (TopSAR) bursts relative time information was extracted. The study region was covered by seven bursts from the IW2 sub-swath.
2. For each scene in the data stack, the times of the seven master bursts were used to detect the corresponding bursts in each slave scene.
3. The data corresponding to the master bursts was extracted in complex format from each ESA data file into the TopSAR data format.
4. The master scene was co-registered to a 30 m SRTM (Shuttle Radar Topography Mission) Digital Elevation Model (DEM).
5. A first co-registration using amplitude correlation was applied between the master and each of the slave scenes.
6. Each co-registered slave scene was re-projected/distorted into the slant-range space of the master scene using the DEM in order to increase the spatial similarities between the scenes.
7. A second co-registration between the master and each of the re-projected slave scenes, using both amplitude and interferometric phase, was conducted iteratively until the spatial error falls below 0.001 pixels, required to eliminate phase bias induced by the Doppler effects.
8. The co-registered bursts with the phase ramps removed were mosaicked into standard SLC format.
9. Interferograms and coherence were created using a \(3\times 3\) pixels window for coherence estimation. The pixel slant-range sampling corresponds to one look (2.33 m in slant range and 13.4 m in azimuth), with a total spatial sampling of 7 m in slant range and 40 m in azimuth, equivalent to a ground sampling of \(12\times 40\) m.
10. Common band filtering and spatial phase filtering to minimize phase noise and increase interferometric coherence were applied.
11. Orthorectification and geocoding to desired projection, i.e., UTM (Universal Transverse Mercator)-17N.  
The repeat-pass InSAR processing was conducted for each pair of the temporally consecutive SAR images along the whole growth season. Through the processing, time-series InSAR coherence and SAR backscattering coefficients at VV and VH polarizations were generated. InSAR processing and a sigma-nought calibration were conducted using the Gamma software.
## 1 Introduction
### Background of an \"Estimation Procedure\"
#### 1.1.1 Soil Section and Soil Water Characteristic Curve
This phenomenon occurs because of the presence of non-uniform openings within the soil structure, which result from the varying particle sizes composing it. This trajectory variation is referred to as the hysteresis [11]. For the same maximum capillary height hc, Figure 2(d) illustrates that in the absence of variations in the capillary diameter, hc fully develops regardless of whether the process involves wetting or drying. Conversely, Figure 2(b) illustrates that, as the capillary diameters within the soil vary during the wetting process, the transition section cannot be fully saturated, thereby preventing hc from fully developing. However, Figure 2(c) shows that the section where the capillary diameter varies is already below the saturated height hc, and that the drying process does not prevent the full development of hc [15]. To obtain SWCC from the measured suction values, equations with fitting parameters were used to obtain a more accurate representation of the curve. Several fitting models can be divided based on the number of pore-size distribution series in the soil. When the soil is considered well-graded, it exhibits a single pore-size distribution series, thus displaying unimodal behavior [16]. Soils with a macrostructure and microstructure in their pore-size distribution have more than one pore-size series, and are considered multimodal (bimodal, trimodal, etc.). Their behavior changes depending on the number of pore-size distribution series [17; 18]. However, [PERSON] et al. [13] suggested that multimodal behavior could be a combination of unimodal subcurves, each associated with a different pore-size distribution series. Table 2 summarizes some of the most prominent models and parameters used. Figure 1: SWCC in the wetting and drying processes (modified from [PERSON] et al. [12]). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Author** & **Type** & **SWCC Equation** & **Parameters** \\ \hline [PERSON] [19] & Unimodal & \(\theta=\theta_{\mathrm{r}}+\frac{\left(\theta_{\mathrm{r}}-\theta_{\mathrm{r}} \right)}{\left[1+\left(\alpha\times\mathrm{h}\right)^{\mathrm{m}}\right]}\) & \(\theta_{\mathrm{r}}\): &
## 4 Classification and Features of Smartphone Apps for Solar PV Energy
### PV System Optimal Settings
\begin{table} \begin{tabular}{c c c c c c c c} \hline \hline
**Name of App** & **Developer/Company** & **Platform** & **Price \({}^{*}\)** & **Function** & \
\begin{tabular}{c} **Optimal Tit** \\ **of Module** \\ \end{tabular} &
\begin{tabular}{c} **PV System** \\ **Setting \({}^{b}\)** \\ \end{tabular} & **Reference** \\ \hline \hline Optimum Tit Angle For Solar FV Panel & Green Engineering Tips & Android & Free & \(\bullet\) (annual) & & [31] \\ Optimum Solar Panel Angle in Winter & Green Engineering Tips & Android & Free & \(\bullet\) (winter) & & [32] \\ ServerAlign & Screen Systems, Inc. & Both & Free & \(\bullet\) (sosos) & & [33] \\ Seasonal PV III Angles & Green Engineering Tips & Android & Free & \(\bullet\) (monthly) & & [34] \\ Solar Tit & [PERSON] & Android & Free & \(\bullet\) (daily) & & [35] \\ Electricity/Solar Calculator & Virol Techno & Android & Free & \(\bullet\) & [9] \\ Solar Energy Calculation & Green Engineering Tips & Android & Free & \(\bullet\) & [37] \\
4 WD Solar Calculator Plus & Tiny Parker & Android & S0.99 & Calculation & \(\bullet\) & [38] \\ \hline Solar Calculator & \begin{tabular}{c} All On One Online \\ Shopping Apps India \\ \end{tabular} & Android & Free & & \(\bullet\) & [99] \\ Solar PV System Estimator Pro & Off-Grid Online & Android & S2.00 & & \(\bullet\) & [40] \\ Off-Grid Battery Bank Calculation & Green Engineering Tips & Android & Free & & \(\bullet\) & [41] \\ Indian Solar Calculator & Prosper India & Android & Free & & \(\bullet\) & [42] \\ PV Calculator-off grid & Cristient Botes & S0.99 & & & \(\bullet\) & [43] \\ Solar Calculator & Compare All Solar & Android & Free & & \(\bullet\) & [44] \\ PV Master–Professional photovoltaic \\ solar panels & \begin{tabular}{c} [PERSON] \\ \end{tabular} & iOS & S9.99 & & & \(\bullet\) & [45] \\ PV Module Calculator & Mal-eum & Both & S4.00 & & \(\bullet\) & [46] \\ \hline \hline PV Module & \
\begin{tabular}{c} Photovoltaic Solar \\ Energy \\ \end{tabular} & [PERSON] & Both & S0.99 &
\begin{tabular}{c} Analysis \& \\ Simulation \\ \end{tabular} & I-V curve of module & [47] \\ \hline \hline \end{tabular}   
*The PV Module-Solar Photovoltaic app [47] can be used to analyze the electrical characteristics of PV modules. This app can solve simplified equations with a single exponential and five parameters of PV module from test data (Open-Circuit Volt (Voc), Maximum Power Volt (Vmp), Short-Circuit Current (Isc), and Maximum Power Current (Imp)) under standard test conditions (STC: 1000 W/m\({}^{2}\) of irradiation, cell temperature = 25 \({}^{\circ}\)C, and 1.5 g spectrum air mass) provided by the manufacturer. This app can be used to determine the parameters of a module by simulating its behavior under different conditions.
## 3 Results and Discussion
### Method Validation
#### 3.2.4 Detection and Quantification Limits of the Analytical Method  
The standard deviation of the intercept and the slope of the calibration obtained from the linear regression equation revealed good sensitivity of the analytical method, where the estimated limits of detection (**LODs**) were 59.22 \(\mathrm{ng}\)/mL, 169.48 \(\mathrm{ng}\)/mL and 151.29 \(\mathrm{ng}\)/mL for metformin, glipizide and gliclazide, respectively, while the limits of quantification (**LOQs**) were 179.47 \(\mathrm{ng}\)/mL for metformin, 513.58 \(\mathrm{ng}\)/mL for glipizide and 458.47 \(\mathrm{ng}\)/mL for gliclazide. Figure 3C represents the LODs and LOQs for the three analytes.
## 3 Study of the Pivotal Factors  
The advanced metering infrastructure is currently witnessing fervent development efforts in Europe, the United States, Japan, and various other nations. These endeavors center around the creation of smart meters with a resolute aim of energy conservation and carbon reduction. The overarching smart grid, encompassing low-power node (LPN) environments, power generation, transmission, distribution, and user terminals, is instrumental in achieving these objectives. Within the realm of natural gas management, the prevailing system architecture is demarcated into three distinct segments: smart meter (SM), communication node (CN), and device-to-device (D2D) robust architectures integrated into the smart LNGas management system (SGMS).  
Beyond the obsolescence of manual meter reading, the system boasts an intrinsic capability to accommodate diverse natural gas rates, bestowing upon the user valuable insights into their energy consumption patterns, thereby guiding them toward voluntary energy conservation, cessation, and recovery. Moreover, it streamlines gas management, augments asset management for natural gas smart meter equipment, and presents several other advantageous facets.  
Upon meticulous scrutiny, the natural gas industry anticipates prioritizing the widespread deployment of natural gas smart meters within key community edifices by the year 2020. This strategic endeavor is poised to catalyze the growth of industries closely intertwined with AMIs by capitalizing on the technological prowess within the nation's information and communication technology (ICT) sector. For low-pressure gas consumers, concomitant with the Energy Bureau's policies and a thorough benefits assessment, the proliferation of microcomputer meters among end users shall transpire progressively.  
The objectives underpinning the establishment of an AMI by natural gas operators are as follows:  
1. The facilitation of remote automated meter reading: Curtailing the need for meter-reading personnel and the associated energy consumption stemming from travel.
2. The enhancement of gas safety: By capturing and dissecting parameters from intelligent microcomputer meters, the system facilitates the issuance of pertinent event alarms.  
(3) The amplification of emergency repair efficiency:  
The management platform equips stakeholders with critical information, such as accident locations and event classifications, streamlining emergency repair operations.  
(4) The mitigation of gas consumption loss:  
In juxtaposing historical gas consumption data and records of anomalous events and integrating them with the monitoring system at pressure regulator stations, losses attributable to aging pipelines can be minimized.  
(5) The promotion of the domestic energy information and communications industry:  
This endeavor bolsters the domestic information and communication industry's entry into the natural gas domain, fostering the establishment of independent AMI technologies.