esa-sceva/satcom-chunk-collection huggingface dataset:
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
## 5 Results
### Position Results
Figure 13: Interference #2 (fast chirp): 10 MHz bandwidth and chirp repetition rate of 10 μs. (**a**) Horizontal error over time. (**b**) Horizontal error CDF. 100% availability) improved the availability in some cases, e.g., it provided 100% availability for the pulsed and slow chirp signals for both HE and LC. However, in other cases, it degraded the availability, e.g., CW, 4 MHz noise and 35 MHz noise. The most extreme case is with the CW tone signal, but it is known that the HDDM-AGC has issues with CWs [13]. However, the degradation is expected due to the loss of 6 dB \(C/N_{0}\) over the up-converter for the two noise interference, making the comparison unfair. The LC \"HDDM\" (48% to 100% availability) significantly increased the performance compared to LC \"No IM\" (51% to 83% availability). However, for the CW and 4 MHz noise interference it degrades, similar to what is observed for the HC. In some cases, e.g., slow frequency hopper and pulsed interference, the HDDM increased the availability of more than 20%. Table 3 shows the horizontal error probability below 95% R-95. The R-95 is only calculated over the output PVT values. Therefore, if a receiver stays longer in track with degraded tracking conditions, it would have inferior performance to a receiver that stops PVT reporting earlier. Consequently, the results are biased from a scientific perspective but are valid for a practical receiver evaluation. The HC \"Road antenna\" had a 95 % error between 26 cm and 55 cm, which is approximately three to eight times better than the LC (1.39 m to 3.48 m). This is expected as it is a geodetic grade receiver with superior processing capabilities but it is also higher size, weight, and power (SWAP). The HC \"No IM\" had a R-95 error between 0.42 m and 3.80 m, which is better than \"With IM\" (0.58 m to 5.12 m) and \"HDDM\" (1.10 m to 5.63 m). It is unexpected, as it is assumed that mitigation improves performance. A possibility is that the IM degrades the PVT, similar as shown by [PERSON] and [PERSON] [29]. Another possibility is that the IM results in a larger fraction of PVT solutions being provided at lower \(C/N_{0}\)s, resulting in larger position errors [26]. A similar observation is found with the LC where the \"No IM\" (1.70 m to 5.01 m) has superior results to the HDDM (1.75 m to 8.22 m). The HC HDDM had inferior R-95 to the HC \"with IM\", and is mainly contributed to the 6 dB loss of the 1-bit DAC: a 6 dB loss in \(C/N_{0}\) results in a factor four reduction in PVT precision [26].
# An Efficient Routing Protocol for Quantum Key Distribution Networks
## 3 Simulation Results and Analysis
### Comparative Study of AODV, DSDV, and OLSR
Results of the experiment show that OLSR is more suitable for QKD networks than the other two protocols. However, the quantum secure key utilization of OLSR is only 38.81% when the communication level is 1, indicating that there remains significant room for improvement. As a result, in this paper, we attempt to propose a more efficient routing protocol based on OLSR. Figure 6: Topology of the USNET network [32]. The numbers on each edge represent the length of the optical fiber link (in km). Figure 7: Comparison of AODV, DSDV, and OLSR in real-time PDR. (**a**) Communication level = 0.2. (**b**) Communication level = 0.4. (**c**) Communication level = 0.6. (**d**) Communication level = 0.8.
## 3 Test Results and Analysis
### Analysis of Influencing Factors and Failure Characteristics of Shear Strength  
#### 3.2.1 Effect of Normal Stress on Shear Strength  
As can be seen from Figure 6, the shear strength of the rock discontinuity is jointly affected by normal stress, undulating angle, and undulating height. When other factors remain unchanged, the shear strength of the rock discontinuity increases with the increase of normal stress. The shear strength decreases with the increase of the angle of relief when the height of relief of the rock discontinuity remains constant. The shear strength of the rock discontinuity is also affected by the height of the relief of the rock discontinuity. The shear strength increases with the increase of the height of the relief.  
Figure 6: Shear strength curves under different normal stresses at 40\({}^{\circ}\) undulating angles.
## 6 Conclusions  
This paper proposes a fast, efficient, accurate, and flexible approach to convert the noisy sensory depth image information of the soil into a surface and volume representation. It is shown that the proposed method is less computationally heavy in comparison with conventional methods by more than 300 times. Also, it provides an efficient method to calculate the metrics such as surface area, volume, and mass of the soil which are important metrics in the earth moving applications. The proposed method efficiently reconstructs surfaces by leveraging connectivity data from neighboring points in the depth image, eliminating the need for additional optimization. Furthermore, it extensively utilizes parallel computation during the curve approximation and marching cubes to further enhance performance.  
In addition, the nature of the proposed algorithm can make benefit of parallel computations by accelerating the computational speed by using a GPU. The low computation time also make the proposed algorithm suitable for simulation and machine learning applications.  
Methodology, F.H.S.; simulations, F.H.S.; experiments, F.H.S.; writing--original draft preparation, F.H.S.; writing--review and editing, F.H.S. and J.S.; supervision, J.S.; project administration, J.S. and H.L.; funding acquisition, J.S. and H.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data No.** & **Proposed** & **Poisson** & **Crust** \\ \hline Data 1 & 0.08 s & 25.27 s & 3.2 s \\ Data 2 & 0.08 s & 25.24 s & 3.3 s \\ Data 3 & 0.07 s & 24.72 s & 3.2 s \\ \hline \end{tabular}
\end{table}
Table 2: Surface reconstruction computation time.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Digging No.** & **Estimated Mass** & **Actual Mass** & **Relative Error** &
\begin{tabular}{c} **Marching Cubes** \\ **Computation Time** \\ \end{tabular} \\ \hline Cycle 1 & 2.752 (kg) & 2.888 (kg) & \%4.92 & 0.05 (s) \\ \hline Cycle 2 & 4.791 (kg) & 4.723 (kg) & \%1.42 & 0.05 (s) \\ \hline \end{tabular}
\end{table}
Table 3: Estimated and actual mass, relative error, and computation time of excavated soil.  
**Funding:** The Korea Evaluation Institute of Industrial Technology (KEIT) Industrial Technology Innovation Program (20023824) and the NSERC Discovery Grant funded this research.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not available due to confidentiality arising from the nature of the contracted project.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 2 Materials and Methods
### Instrumentation and Chromatographic Separation  
HPLC analyses were performed on a 1100 Series HPLC Value System from Agilent (Santa Clara, CA, USA) equipped with a 1100 Series diode array detector (DAD). Analyte separation was performed with an Excel 5 C18 analytical column from ACE (Aberdeen, UK) with the dimensions 150 \(\times\) 4.6 mm. The mobile phase consisted of 0.1 vol.% TFA in water (eluent A) and MeOH (eluent B). A gradient program with the following settings was applied (min/vol.% of eluent B): 0/10, 14/80, 14.1/99, 16/99, 16.1/10, 20/10. The injection temperature was set to 10 \({}^{\circ}\)C and the injection volume was 5 \(\mu\)L with a needle-wash in MeOH. The mobile-phase flow-rate was 700 \(\mu\)L min\({}^{-1}\) and the temperature of the column oven was set to 40 \({}^{\circ}\)C. The detection of analytes was performed at 218 nm.  
DSPE experiments were conducted on a ThermoMixer(r) C and the evaporation of solvents was performed using a Concentrator plus, both from Eppendorf AG (Hamburg, Germany). Drying of the synthesized crosslinker and polymers was accomplished in a Vacutherm vacuum drying oven from Thermo Fisher Scientific. The pH value of the phosphate buffer solutions was checked with a pH meter SevenEasyTM from Mettler Toledo (Columbus, OH, USA). BET measurements were performed utilizing a NOVA 2000e Surface Area and Pore Size Analyzer from Quantachrome Instruments (Boynton Beach, FL, USA) and the software, Quantachrome NovaWin. Prior to the measurement, the sample was heated to 120 \({}^{\circ}\)C in vacuum for 30 min followed by N\({}_{2}\) adsorption at 77 K (five points from 0.05 to 0.30 p/p\({}_{0}\)). Images of the polymer surface were made by scanning electron microscopy (SEM) on a JSM-6010 LV from JEOL (Freising, Germany). Prior to measurements the samples were sputtered with gold and measurements were conducted at high vacuum with an excitation voltage of 15 kV. NMR spectra were recorded with avance DPX 300 MHz spectrometer from Bruker (Billerica, MA, USA) equipped with a 5 mm broadband probe at 25 \({}^{\circ}\)C. ATR-FTIR measurements were performed using a Spectrum 100 equipped with a Universal ATR Sampling Accessory with the software Spectrum (version 2.0.0.0) from PerkinElmer (Waltham, MA, USA). Prior to measurements, the samples were dried in a vacuum centrifuge for 24 h with the mode setting V-AQ. Six scans were carried out with a wavelength range of 4000-650 cm\({}^{-1}\) and a resolution of 4 cm\({}^{-1}\).
## 2 Materials and Methods
### Atmospheric Data Measurements  
A 32 m tall flux tower was installed at geographic coordinates 25\({}^{\circ}\)41\({}^{\prime}\)21.1\({}^{\prime\prime}\) S and 50\({}^{\circ}\)09\({}^{\prime}\)47.1\({}^{\prime\prime}\) W at 780 m above sea level, with a minimum fetch of 250 m from any direction. Meteorological and eddy covariance data were collected between 1 November 2009 and 10 April 2012 at various heights. At 11 m and 32 m, air temperature and wind components were measured using a CSAT3 3-D Sonic Anemometer (Campbell Scientific Inc., Logan, UT, USA), and H\({}_{2}\)O/CO\({}_{2}\) concentrations were measured using an open-path infrared gas analyzer (LI-7500; LI-COR Inc., Lincoln, NE, USA), both at a frequency of 10 Hz. At 29 m, air temperature (Tair) and relative humidity (RH) were measured using a temperature and relative humidity probe (HMP45C Campbell Scientific Inc., Logan, UT, USA), and global radiation (Rg) was measured using the pyranometer (LI-200, LI-COR Inc., Lincoln, NE,USA), both at one-minute intervals. More details about data collection can be found in [PERSON] et al. [31].  
Precipitation (Prec) data and gaps in the meteorological measurements (Rg, Tair, and RH) due to lack of energy or sensor maintenance were filled using data collected at a nearby station (OMM83836) of the Brazilian National Meteorological Service (INMET), located approximately 50 km from the experimental site in Iriati (latitude: \(-25.50^{\circ}\); longitude: \(-50.64^{\circ}\)). Remaining gaps in the dataset were filled using the ERA5 reanalysis [32]. Climatic Normals for precipitation and air temperature were obtained from the same INMET station from 1991 to 2020 dataset.
## 2 Material and Methods
### Area of Study and Sampling Procedure  
The experiments were conducted at Shalom farm situated in Patrocinio, Minas Gerais state, Brazil (18\({}^{\circ}\)56\({}^{\prime}\)38\({}^{\prime}\) S 46\({}^{\circ}\)59\({}^{\prime}\)34\({}^{\prime}\) O). The environmental samples were collected in triplicate and are composed of: (i) 100 g of soil collected at 10 cm depth in an area of 1 m\({}^{2}\) in the treetops; (ii) 10 g of coffee leaves collected from the soil surface; (iii) 20 fruits collected from the soil surface; (iv) 10 g of leaves collected from the branches 30 cm from the apical region; (v) 20 cherries collected from the coffee tree; (vi) 20 cherries before de-pulping; (vii) 20 over-ripe fruits collected from the coffee tree; (viii) 100 g depulped beans; (ix) 50 mL water used for de-pulping beans. The samples were stored in falcon sterile tubes (50 mL) and transported to the Center of Agroindustrial Biotechnology of Parana (CENBAPAR, Curitiba, Brazil) under refrigeration and maintained at \(-\)20 \({}^{\circ}\)C until further analysis.  
Later, coffee cherries (_Coffea arabica_ var. Catuai Amarelo) were harvested manually and mechanically pulped. Approximately 100 kg of pulped coffee beans were deposited in a 1 m\({}^{3}\) concrete tank containing about 50 L of water, in accordance with the local wet processing method. Before inoculation, the cell viability of the starter cultures was determined by spread plate and the results were expressed in CFU/mL. Thus, the appropriate inoculum concentration of _P. fermentatus_ YC5.2 and _P. acidilactici_ LPBC161 were adjusted to achieve an initial concentration of 10\({}^{7}\) CFU/mL and inoculated simultaneously in the fermentation tanks. About 100 g of coffee beans plus the liquid fraction of the coffee pulp were collected at 0, 8, 19, and 24 h. The liquid fraction was frozen in Falcon tubes sterilized at \(-\)20 \({}^{\circ}\)C for further analysis. Finally, the coffee beans sampled from each point were sun-dried to 12% moisture. Fermentation and sample collection were performed in triplicates.
## 3 Results and Discussion
### Seismic Analysis on the Existing RC Building  
The performance of the numerical analysis on the unreinforced building is crucial not only for assessing the seismic safety and identifying vulnerabilities but also for designing the cold-formed exoskeleton tailored to the building's capacity and specific needs. The RC building was subjected to pushover analysis in both primary directions, which generated capacity curves for each direction. Figure 9 presents the pushover curves under the most severe loading conditions for each thrust direction. The red square on the curves marks the point at which the first structural damage occurred, which is a key parameter for seismic design.  
The main parameters of the analysis are summarized in Table 6, which highlights that the structure exhibited greater ductility in the transverse direction (Y) and increased stiffness and strength in the longitudinal direction (X). The initial damage in the structure occurred in the longitudinal direction at a displacement of 0.61 cm, whereas in the transverse direction, the first point of damage corresponded to a displacement of 1.75 cm. In both cases it was represented by the formation of plastic hinges on the columns and beams.  
Figure 8: FEM model of the retrofitted structure: 3D view (**a**), plan layout (**b**), longitudinal sections (**c**,**e**) and transversal sections (**d**,**f**).
## 2 Materials and Methods
### Data Collection and Monitoring Campaign
The technical specifications of the sensors are presented in Table 3.  
Meteorological input data for ENVI-met calibration were collected using a Davis Vantage Pro 2 weather station (Davis Instruments Corp., Hayward, CA, USA) installed on the rooftop of a campus building. The station recorded air temperature, relative humidity, wind speed, and wind direction every 5 min, providing consistent and localized climatic data. The technical characteristics of the station are presented in Table 4. In addition, solar radiation values (global, direct, and diffuse irradiance) were retrieved from the SIAS station (Sicilian Agrometeorological Information Service), located near the study area, to improve the accuracy of radiation modeling. Finally, to ensure the external validity of the data collected, a comparison was made with reference values from the nearest ARPA (Regional Environmental Protection Agency) monitoring station. The consistency between datasets confirmed the reliability and representativeness of the locally collected information, which was used in the subsequent modeling phases.
###### Abstract  
A dual-broadband and high-efficiency reflective linear polarization converter based on an anisotropic metasurface is presented. The device consists of two symmetrical, double-slotted metallic split-rings and one criss-cross structure, a dielectric layer, and a completely reflective metallic ground. The converter exhibits four resonances and can near-perfectly convert \(x\)- or \(y\)-polarized incident waves into cross-polarized waves in the frequency ranges of 9.38-13.36 GHz and 14.84-20.36 GHz. The polarization conversion ratios (PCRs) of the two bands are 98.21% and 99.32%, respectively. The energy conversion ratio (ECR) for energy loss measurement is almost 100% in these frequency bands. The polarization conversion principle is studied. The bandwidths and PCRs of the two bands are determined by varying the dielectric layer thickness. The simulation results are consistent with experimental observations. The designed dual-broadband and high-efficiency metasurface has great potential in the application of electromagnetic polarization control.  
Keywords:dual-frequency; polarization converter; metasurface; high-efficiency +
Footnote †: journal: Applied Sciences
## 2 Simulation and Experimental Procedure
### Spectroscopic Characterization  
Among the used values of the spectroscopic parameters, some have been measured experimentally on the FUT, while others have been taken from the literature. Table 3 reports the values used in the simulation code.  
The Er\({}^{3+}\) absorption cross section was calculated from the absorption spectrum of the tested EDF, obtained through the cutback technique [38; 39] and using the following formula:  
\[\sigma_{a}^{Er}(v)=\frac{att^{Er}(v)}{10\log(e)N_{Er}\Gamma(v)} \tag{11}\]  
where \(N_{Er}\) is the Er\({}^{3+}\) ion concentration, reported in Table 2. On the basis of the McCumber theory, the Er\({}^{3+}\) emission cross section was calculated by the relationship  
\[\sigma_{e}^{Er}(v)=\sigma_{a}^{Er}(v)e^{\frac{E-hv}{e^{2}}} \tag{12}\]  
where \(E\) is the energy difference between the lower lying state of the multiplet forming the \({}^{4}\)I\({}_{13/2}\) energy level and lower lying state of the multiplet forming the ground state \({}^{4}\)I\({}_{15/2}\); \(T\) is the temperature; and \(\kappa\) and \(h\) are the Boltzmann and Planck constants, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spectroscopic Parameter** & **Value** \\ \hline \({}^{4}\)I\({}_{13/2}\) Lifetime \(\tau_{21}\) & 8.72 ms (\(\square\)) \\ \({}^{4}\)I\({}_{11/2}\) Lifetime \(\tau_{32}\) & 10 \(\upmu\)s [23] \\ \({}^{4}\)I\({}_{9/2}\) Lifetime \(\tau_{43}\) & 0.1 \(\upmu\  
The lifetime measurements were performed using the TRL technique. The set-up description was reported in [PERSON] et al. [29, 40]. The radiation-induced variations in the Er\({}^{3+}\) absorption and emission cross sections and the lifetime of the \({}^{4}\)I\({}_{13/2}\) energy level were also measured, to evaluate their influence on the gain degradation.
# Initial Positioning Assessment of BDS New Satellites and New Signals
## 1 Introduction
[19] first gave a 4-day SISRE result of eight BDS-3 satellites comparing broadcast ephemeris and POD results from International GNSS Monitoring and Assessment System (iGMAS), which shows that the root mean square (RMS) error of the satellite orbit in three-dimensions (3D) is about 1.07 m, while the average RMS of the satellite clock error is 1.12 ns and the average SISRE is 0.44 m. However, there is still little research on positioning performance using the current BDS-2 and BDS-3 constellation. This paper aims to present an initial assessment of the positioning performance of the current BDS constellation with new satellites and new signals. Section 2 investigates the global satellite visibility and position dilution of precision (PDOP) of the current BDS constellation. To verify the accuracy of the satellite segment, Section 3 presents the SISRE of BDS with over 60 days of data. Then, in Section 4 we assess the SPP performance of BDS using 54 globally distributed stations which is also compared with that of the GPS and BDS-2 systems. Positioning performance of the new BDS signals including B1c and B2a is also analyzed and compared. In Section 5, RTK performance of the current BDS is assessed in comparison with the BDS-2 system. Finally, conclusions are presented.
###### Abstract  
Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.  
electronic health records; clinical text; natural language processing; named entity recognition; relationship extraction; machine learning +
Footnote †: journal: Medical  
Extraction (RE) is used for detecting and classifying the annotated semantic relationships between the recognized entities. Significant research on NER and RE has been carried out in the past both on clinical narratives and other types of text. For example, in the sentence, \"**Her white count** remained **elevated** despite discontinuing **her G-CSF**\", the words in bold are the various entities in the sentence. After the entities are recognized, the relationship between two or more entities is extracted. In this case, \"**her white count**\" and \"**elevated**\" are found to be related to each other in a manner dissimilar to the nature of the relationship between \"**elevated**\" and \"**her G-CSF**\". In the sentence \"**Atorvastatin** is found to have therapeutic effects in **breast cancer** although no clinical trials are performed at present\", the NE of interest includes the name of the drug (atorvastatin) and the disease name (breast cancer), whereas the drug-disease relation (atorvastatin-breast cancer) is the relationship of interest. Figure 1 shows a pictorial representation of the association between NER and RE.