esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The main contributions of this article are as follows:  
* Synthesis and implementation of control algorithms, an observer for rotor speed, electromagnetic torque and stator flux, along with Software-In-the-Loop (SIL) implementation for performance comparison of the proposed PMSM-SCSs based on DTC strategy, using high-end controllers: PI, PI-EOA-EOA, FOPI, TID and FO-Lead-Lag in the constant flux case, and SMC and FOSMC in the variable-flux case;
* Calculation of PMSM speed signal FD for high-end controllers: PI, PI-EOA, FOPI, TID and FO-Lead-Lag for constant flux, and SMC and FOSMC for variable flux;
* Demonstration of the parametric robustness of the PMSM-SCS based on the DTC strategy by maintaining the control performance, even in the case of a 50% increase in the \(J\) parameter, which represents the combined inertia of rotor and load;
* Real-time Hardware-In-the-Loop (HIL) implementation in an embedded system of the proposed PMSM-SCS based on a DTC strategy in the case of constant flux and variable flux, using FOPI-, SMC- and FOSMC-type controllers. The rest of the paper is presented and structured as follows: Section 2 presents the proposed PMSM-SCS using the DTC strategy, while the numerical simulations performed in Matlab/Simulink for the proposed PMSM-SCS using the DTC strategy in the case of constant flux and variable flux are presented in Section 3. The real-time implementation in the embedded system in the HIL stage and experimental setup are presented in Section 4, and some conclusions and suggestions for future research are presented in Section 5.
## 2 Materials and Methods  
### Laboratory Scale Experiment  
The instruments and experimental procedures are described in detail herein to supplement those briefly introduced in previous work [20]. Fixed bed filtration experiments were performed using a laboratory-scale filtration unit with a 900 mm deep sedimentation chamber and an 800 mm deep media chamber with a 100 \(\times\) 100 mm\({}^{2}\) base and a media depth of 600 mm. The granular media in this study were provided by C & C Inc. (Siheung, Korea) and were prepared from shale via grinding, sintering, expanding, and molding into granular shapes. The average size, porosity, specific gravity, and water permeability were 2.92 mm, 60.7%, 1.55, and 0.14 cm/s, respectively. Schematic diagrams and pictures of the laboratory scale instruments and the granular media are provided in Figure 1.  
The road deposited sediment (RDS) on local highways was collected by road sweepers and used as SS in the present study to simulate the actual conditions experienced by stormwater runoff filtration devices. The RDS was provided by the Korea Expressway Corporation and was dried at 105 \({}^{\circ}\)C overnight. The fraction passed through the #60 sieve was used to match the size distribution of the SS in stormwater runoff. The average particle size was 125.6 \(\upmu\)m, the median size was 122.1, and the 10%, 60%, and 90% fines (d\({}_{10}\), d\({}_{60}\), and d\({}_{90}\)) were 13.2, 122.1, and 246.2 \(\upmu\)m, respectively [20]. This is consistent with previous studies showing that 96.2% of stormwater runoff particles were <200 \(\upmu\)m in size [29], and 90% were <250 \(\upmu\)m [30]. Similarly, in the Republic of Korea, the d\({}_{10}\), d\({}_{60}\), and d\({}_{90}\) of the SS in stormwater runoff samples from the 39 highway were 4.2, 57.6, and 276.5 \(\upmu\)m, respectively [31], while those from residential and industrial areas were 0.87\(-\)5.78, 4.11\(-\)61.37, and 8.02\(-\)166.86 \(\upmu\)m, respectively [32], and those from a residential area were 9.82, 38.99, and 159.61 \(\upmu\)m, respectively [33].  
The SS-free tap water and 10,000 mg/L aqueous suspensions of SS were introduced simultaneously to the sedimentation chamber so that the SS concentration of the influent was 168.4 \(\pm\) 4.4 mg/L. The influent was pumped upward through the sedimentation chamber and then the media chamber. Filtration velocities of 20, 30, and 40 m/h were used, giving influent flow rates of 0.2, 0.3, and 0.4 m\({}^{3}\)/h. The effluents of both chambers were collected periodically during the 300 min of operation, and the SS concentration was measured using standard methods [34]. The influent SS concentration, filtration velocity, and operation period were determined considering the protocols for the evaluation of the performance of stormwater filtration devices of the Korea Ministry of Environment [35], the New Jersey Department of Environmental Protection [36], British Water [29], and Auckland Council [30].  
Figure 1: A schematic diagram of the laboratory-scale filtration system with inset photographic images of the granular media (left) and filtration chamber (right).
# Green Ultrasound-Assisted Extraction of Podophyllotoxin from _Juniperus scopulorum_ Needles
## 2 Materials and Methods
### High-Performance Liquid Chromatography (HPLC) Analysis  
The podophyllotoxin quantification was carried out on an Agilent liquid chromatographic system including an Agilent 1260 Infinity G5611A Quaternary Pump (with an include degasser), an Agilent 1260 Infinity II G7129A vial sampler, and an Agilent 1260 Infinity II G7117C diode array detector (DAD). The entire HPLC chain was controlled by HPLC 1260 Infinity software. The separation was performed at 35 \({}^{\circ}\)C on a KINETEX (Phenomenex) F5 column (100 \(\times\) 3.0 mm i.d; 2.6 \(\upmu\)m), with 5 \(\upmu\)L as the injection volume. The mobile phase was composed of HPLC-grade solvents: water (solvent A) and methanol (solvent B). With a flow rate of 0.7 mL/min, a nonlinear gradient was applied for the mobile phase variation as follows: from 0 to 2 min of A-B: 70:30 (\(v/v\)) to 38:62 (\(v/v\)), from 2 to 9 min of A-B: 38:62 (\(v/v\)) to 33:67 (\(v/v\)), and from 9 to 12 min of A-B: 33:67 (\(v/v\)) to 5:95 (\(v/v\)). Then, the column was rinsed with absolute methanol for 10 min. Detection was performed at 295 nm. Podophyllotoxin was identified due to its retention time and UV spectrum in comparison to the reference standard. Quantification was performed using a calibration curve of podophyllotoxin ranging from 125 \(\upmu\)g/mL to 1 mg/mL, with a correlation coefficient higher than 0.999, LOD = 35.73 \(\upmu\)g/mL, and LOQ = 108.27 \(\upmu\)g/mL.
## 5 Results and Analysis
### Multi-Constellation Scenarios
#### 5.2.1 Narrowband Interference  
The use of multi-constellation significantly enhanced the solution availability to be 100% in both scenarios. However, none of the constellations were available all the time due to the presence of jamming, which targeted each band individually. The chart shown in Figure 8 tells that the GPS constellation was unavailable for 39% of the trajectory in scenario #2, in which the jamming signal was centered in the GPS band. Similarly, the GLONASS constellation was lost for 14% of the time of the trajectory in scenario #3, as the jamming was centered at the GLONASS band. Tables 5 and 6 show that the receiver was tracking at least 7 and 9 satellites at the maximum jamming power of \(-\)74.8 dBm for scenarios #2 and #3, respectively.  
Figure 11 shows the DOP values for both scenarios. It can be illustrated from the figure that the confidence of the position solution was reasonably good throughout the whole trajectory in both scenarios.  
Figure 10: Average carrier-to-noise ratio of scenario #3. (**Top figure**)—GPS measurements; (**bottom figure**)— GLONASS measurements.  
Figure 9: Average carrier-to-noise ratio of scenario #2. (**Top figure**)—GPS measurements; (**bottom figure**)— GLONASS measurements.  
scenarios. However, the DOP values were better in scenario #3, which is intuitively understandable due to the availability of more GPS satellites and their better geometry over GLONASS.
# Mining Pool Selection under Block WithHolding Attack +
## 3 Evolutionary Game for Mining Pool Selection
### Game-Theoretic Analysis
#### 3.3.1 Replicator Dynamics  
The growth rate of the miner population in a pool can be described by the replicator dynamics [44]. Based on the pairwise proportional imitation protocol [45], the replicator dynamics of pool \(i\) is given by the following Ordinary Differential Equation (ODE):  
\[\dot{x}_{i}(t) =\frac{dx_{i}}{dt}=f_{i}(\mathbf{x}(t),\mathbf{\omega},s_{i},\mathbf{a})\] \[=x_{i}(t)\big{(}y_{i}(\mathbf{x}(t),\mathbf{\omega},s_{i},\mathbf{a})-\overline {y}(\mathbf{x}(t),\mathbf{\omega},\mathbf{s},\mathbf{a})\big{)}, \tag{10}\]  
where \(x_{i}(t)\) is the population fraction of pool \(i\) at time \(t\), \(y_{i}(\mathbf{x}(t),\mathbf{\omega},s_{i},\mathbf{a})\) is the expected payoff of miners in pool \(i\) at time \(t\) and \(\overline{y}(\mathbf{x}(t),\mathbf{\omega},\mathbf{s},\mathbf{a})=\sum_{i=1}^{M}y_{i}(\mathbf{x}, \mathbf{\omega},s_{i},\mathbf{a})x_{i}\) is the average expected payoff of all the miners at time \(t\), i.e., network average payoff. We can see that the replicator dynamics \(\dot{x}_{i}(t)\) represents the growth rate of the population of pool \(i\) at time \(t\), which is related to the difference between the miner payoff of pool \(i\) and the network average payoff. If the payoff of pool \(i\) is larger than the network payoff, its population will increase. Otherwise, its population will decrease.
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 2 Study Area and Datasets
### Sentinel-1 Data  
Sentinel-1 is the first of the Copernicus program satellite constellation conducted by the European space agency (ESA). It provides continuous all-weather, day-and-night radar imagery at the C-band. This mission is composed of a constellation of two satellites, Sentinel-1A and Sentinel-1B. Sentinel-1A was launched on 3 April 2014, and Sentinel-1B on 25 April 2016. They share the same orbital plane, with a repeat cycle of 12 days. Thus, after 25 April 2016, the combined repeat cycle is approximately 6 days, with almost daily revisit in the polar regions. These SAR instruments may acquire data in four exclusive modes: stripmap (SM), interferometric wide swath (IW), extra-wide swath (EW), and wave (WV). Among them, the modes EW and WV mainly operate over the ocean. For every mode, there are three kinds of products: level-0, level-1, and level-2. Level-0 is the SAR raw data. Level-1 products form a baseline product from which level-2 products are derived and consist of two types: SLC (single look complex) and GRD (ground range detected).  
In this study, the level-1 GRD products of EW are used to detect coastal polynyas in the Ross Sea near the Ross Ice Shelf. The EW mode is used because of its wide swath (400 km) which results in larger areal coverage than the other modes. The spatial resolution of EW images is 25 m along-track by 100 m cross track.  
The EW GRD SAR data are available in the ESA Open Access Hub ([[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home))). ESA provides software for processing Sentinel data called SNAP. In this study, the SNAP is used to process the EW GRD level-1 data, including radiometric calibration, speckle filtering, geometric ellipsoid correction, conversion to decibel value (dB), and mosaic data (if multiple images in one day). Radiometric calibration is used to convert the original number to the backscatter coefficient. The module \"single product speckle filter\" in SNAP is used to remove speckle noises in images with the Lee Sigma filter at a target window size of \(3\times 3\). The final projection of dB images is set to the  
Figure 1: Sentinel-1 SAR image of the Ross Sea with the coastal boundary and named locations of the Ross Ice Shelf, Ross Island, Drygalski Glacier Tongue, McMurdo Sound, Terra Nova Bay, and the Ross Sea.  
stereographic South Pole, with a central meridian of 169 degW and a standard parallel of -75 degS, and a grid size of 40 m \(\times\) 40 m.  
All the sentinel-1 EW GRD data covering the RISP and MSP for the periods of 1 March-30 November 2017 and 2018 are downloaded and processed as explained above. Finally, a total of 143 images (days) in 2017 and 215 images (days) in 2018 are obtained to analyze the polynya ice production of these two periods.
## 3 Methodology
### Application of Lexicons
For instance, in fraud detection for mobile applications, a good F1-score is greater than 0.7 [57]. In medical contexts, benchmark studies on annotated colon cancer pathology reports have reported F1-scores ranging from 0.82 to 0.93 for primary tumors and 0.65 for metastatic tumors [58]. In emotion analysis, studies have achieved macro F1-scores of 0.53, 0.62, and 0.64 [36; 37; 59] using techniques such as BERT, which employs transformer-based models pretrained and fine-tuned to achieve state-of-the-art results [60]. Comparing against a baseline model helps determine what constitutes an acceptable F1-score [61]. For this experiment, we will consider the F1-score--micro, macro, or weighted--of 0.53 achieved in the original paper from the XED dataset as our benchmark for a good F1-score. It is important to note that while BERT considers contextual information, the lexicons used in our experiment only consist of unigrams [36; 60]. Precision-recall (PR) curves were also generated for each emotion category, reflecting the varying thresholds at which the lexicons were generated. Figure 4 illustrates the theoretical behavior of precision-recall curves under different scenarios. It could serve as a general reference for understanding curve shapes. The optimal curve would be one that is closer to the top-right corner, indicating a higher area under the curve and better performance in terms of precision and recall. Figure 3: Confusion matrix structure ([PERSON] et al., 2009) [55].
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 1 Introduction  
_Candidia albicans_ belongs to our normal mucosal surface's microbiota, from where it may emerge as a pathogen causing local infections, such as inflammation in the oral cavity and _Candidia_ vagnitis [1, 2]. _Candidia_ species are still the most common cause of fungal diseases worldwide: these yeasts cause infections that range from superficial mucosal membranes to life-threatening invasive diseases, entailing extensive medical or surgical treatment [3, 4, 5]. _Candidia_ spp. exist as commensals, and as opportunist pathogens, being able to compromise various organs and cause diseases in immunocompromised or critically ill patients [6, 7, 8, 9]. Therefore, for clinical purposes, the identification of _Candidia albicans_ per se does not have clinical relevance, especially in colonized environments such as the mouth.  
_Candidia_ can lead to infection due to changes in the host environment. The infection process is dependent on the host tissue integrity and its ability to maintain normal microbiota, as well as on a healthy immune system. A change in the balance of the resident microbiota, such as the placement of dental implants [10, 11], can result in favorable environmental conditions for the proliferation of organisms with the potential for host invasion [12].  
Candidia, a bloodstream infection, is a serious hazard to hospitalized patients, being considered the most clinically relevant form of _Candidia_ infection [13]. Candidatei is themost common invasive infection, with mortality rates reported in clinical settings ranging from 30 to 60% [14; 15]. The species responsible for the infection has different susceptibility patterns to antifungal drugs, representing a serious challenge for patient treatment. Although oral candidiasis is also an important form of _Candida_ infection occurring in the oral cavity, the amount of information available on candidemia is overwhelmingly more abundant.  
Despite the clinical relevance of _Candida_ spp., the distinction between different species causing oral candidiasis is often difficult. Although there are several molecular techniques currently available for the identification of _Candida_ spp., the different strengths and weaknesses associated with these techniques mean it is difficult to reach a consensus on the adoption of an optimal identification method. Any diagnostic tool needs to combine a certain set of attributes in terms of accuracy, specificity, and cost, and at the same time, it must be user-friendly and as non-time-consuming as possible. Therefore, the purpose of this review is to analyze the molecular methods currently used for the detection of _Candida_ spp. with a special focus on _Candida_ involved in oral infections. A comparative analysis in terms of each method's accuracy, specificity, cost, time, and complexity will be formed. At the same time, we also describe currently used molecular targets as well as others with the potential to improve oral candidiasis diagnosis.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 4 Simulation
### Simulation Results and Discussion
#### 4.2.1 The Network System Architecture in the Simulation  
As shown in Figure 11, 100 nodes (circles of various colors) are randomly deployed in the triangular prism area. The mobile anchor node starts at \((0,0,100)\), moves along trajectory 1 (blue solid line) and trajectory 2 (blue dotted line), and ends at \((600,0,100)\). After the mobile anchor node finishes moving, underwater sensors use receiving messages to locate themselves. The localization process needs to run five times. In the five localization processes, the interval between adjacent broadcast points \(\Delta L\) is set to 1 m, 5 m, 10 m, 15 m, and 20 m, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(L\) & 600 m & \(H\) & 100 m \\ \(N\) & 100 & \(\Delta L\) & \(1,5,10,15,20\) m \\ \(\Delta L_{ext}\) & 0.5 m & \(\Delta d\) & 1 m \\ \(SL\) & 100 db & \(f\) & 24 KHz \\ \(k\) & 2 & \(v\) & 5 m/s \\ \(cd_{s}\) & 100 m & \(cd_{a}\) & 600 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the simulation.  
Figure 11: The network system architecture in the simulation.
## 5 Hardware Deployment
### Wireless IP Camera  
The wireless cameras selected transmit MPEG-4 standard video compression, which has higher compression and quality compared to other standards. It also consumes low bandwidth. MPEG-4 is commonly used in video streaming over IP environments. The video is streamed using the HTTP protocol (we have chosen this protocol to facilitate the video visualization) with very good results. Chosen cameras are able to stream video with a resolution of 320 \(\times\) 240 using 25 fps (PAL system) and they are able to transmit audio in both directions (from the camera and to the camera). Their working temperature is between \(-5.5^{\circ}\)C and \(75^{\circ}\)C.  
Figure 7: Integrated Circuit used to provide two serial ports.  
Wireless camera video streaming is transmitted from the camera to the server directly. The person sited in the server (a fire fighter) can choose by software the camera to be watched and the software opens a connection with that camera and receives the video streams sent via the camera.
# Study on the Deployment of Ultra-Wideband Positioning Base Stations in Pig Farms
## 3 Positioning Results Assessment  
The DOP, HDOP, VDOP, PDOP are an important basis for evaluating positioning results [17; 18; 25; 26] GDOP is defined as the ratio of the root-mean-square (RMS) position error to the RMS ranging error. Similarly, Horizontal DOP (HDOP), Vertical DOP (VDOP), and Position DOP (PDOP) can be defined.  
DOP (Dilution Of Precision) defined as:  
\[DOP=\frac{\sigma_{x}}{\sigma_{r}} \tag{16}\]  
HDOP (Horizontal Dilution Of Precision)  
\[HDOP=\sqrt{\Big{(}\big{(}H^{T}H\big{)}^{-1}\Big{)}_{1,1}+\Big{(}\big{(}H^{T}H \big{)}^{-1}\Big{)}_{2,2}}=\sqrt{l_{11}+l_{22}} \tag{17}\]  
VDOP (Vertical Dilution Of Precision)  
\[VDOP=\sqrt{\Big{(}\big{(}H^{T}H\big{)}^{-1}\Big{)}_{2,2}}=\sqrt{l_{33}} \tag{18}\]  
PDOP (Position Dilution Of Precision)  
\[PDOP=\sqrt{\Big{(}\big{(}H^{T}H\big{)}^{-1}\Big{)}_{1,1}+\Big{(}\big{(}H^{T}H \big{)}^{-1}\Big{)}_{2,2}+\Big{(}\big{(}H^{T}H\big{)}^{-1}\Big{)}_{3,3}}= \sqrt{l_{11}+i_{22}+i_{33}} \tag{19}\]  
GDOP (Geometric Dilution Of Precision)  
\[GDOP=\sigma_{r}^{2}(i_{11}+i_{22}+i_{33})=q_{11}+q_{22}+q_{33} \tag{20}\]
## 5 Experiment
### Discussion  
To explore the intrinsic reason and find how the FMAG achieves a higher hopping height than the DAG, the experiment results combining the variation of the equivalent lever length are discussed in detail. According to the bionic leg structure in Figure 1, the angular velocity of the knee joint in this paper can be expressed as  
\[\omega_{knee}=\frac{2v_{body}}{(l_{1}+l_{2}+l_{3})\cos(\theta_{knee}/2)} \tag{19}\]  
where \(v_{body}\) is the vertical velocity of the robot body during the take-off phase.  
From Equation (19), with the increments of the knee joint angle and the body velocity, the angular velocity of the knee joint continues to increase during the take-off phase. The cylinder velocity \(v_{cy}\) at the knee joint can be expressed as the product of the knee join angular velocity and the equivalent lever length  
\[v_{cy}=\omega_{knee}r \tag{20}\]  
Based on Equation (20), the equivalent lever length can be divided into two stages. In the first stage, both of the angular velocity \(\omega_{knee}\) and the equivalent lever length \(r\) increase. And the speed of the increment of \(v_{cy}\) will accelerate. In the second stage, the equivalent lever length \(r\) begins to decrease continuously. The decline of \(r\) can partially offset the increment of \(\omega_{knee}\). And the speed of the increment of \(v_{cy}\) will slow down gradually. Based on the upper optimization results, the equivalent lever lengths of the DAG and FMAG are shown in Figure 8a. The first stage in the DAG accounts for 38.4% of the range of the knee joint, while it is only 16.6% in the FMAG. Moreover, in the second stage, the variation of the equivalent lever length of the FMAG (from 0.0479 m to 0.0171 m) is larger than that of the DAG (from 0.0462 m to 0.0251 m).  
The experiment results of the cylinder velocity and the corresponding equivalent lever length of the DAG and FMAG are shown in Figure 8b,c. The larger initial equivalent lever length under the FMAG causes a quicker increment of the cylinder velocity at the beginning. Benefitting from the low percentage of the first stage, the increment of the cylinder velocity under the FMAG begins to slow down (at about 0.16 s and 0.3 m/s) earlier than that under the DAG (at about 0.23 s and 0.36 m/s). With the decrease of the equivalent lever length, the speed of the cylinder velocity increment decreases continuously. The cylinder velocities both under the DAG and FMAG almost maintain constant at the end of the take-off stage. Due to the larger proportion of the second stage of the equivalent lever length, the final cylinder velocity under the FMAG (0.43 m/s) is smaller than that of the DAG (0.49 m/s). Therefore, the pressure drop under the FMAG is smaller than that under the DAG. And more energy is supplied to the cylinder, resulting in a higher hopping height of the robot body under the FMAG. The robot hopping energy as the sum of the kinetic and potential energy increment is shown in Figure 8d. The final hopping energy under the FMAG (about 93 J) is larger than that of the DAG (about 81 J). Thus, the robot can receive 14.8% more energy under the FMAG.