esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement Quality Analysis
### MQA Conclusions  
The measurement stability and error were analyzed for both systems. The measurement stability for BDS code-phase measurements is better than that for GPS and the carrier-phase measurements have almost the same performance. However, the BDS satellite orbit and clock errors are larger in the BDS pseudorange. This result confirms the DQA results. Referring to DQA1 and 2, the error from satellite clock is larger than the satellite orbit error for the general case. Applying extra satellite clock correction estimation to have a more accurate pseudorange increases the compatibility and interoperability between GPS and BDS.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 1 Introduction
Aiming at the problems existing in the abovementioned two strategies, our paper improves SSP and proposes a parameter communication optimization strategy based on the Dynamic Finite Fault Tolerance (DFFT), named Dynamic Synchronous Parallel Strategy (DSP). DSP dynamically adjusts the parameter synchronization strategy between worker nodes and PS based on the performance monitoring model, which effectively reduces the influence of the different performance of worker nodes and ensures the accuracy as well as the convergence rate.
## 5 Kramers-Kronig Detection Scheme
### Related Impact on the KK Scheme
#### 5.2.4 IQ Imbalance  
The effective implementation of the KK algorithm depends mainly on the SSB-modulated signal. A usual method of generating an optical SSB signal is by the \(IQM\), as discussed in Subsection 4.1. In an \(IQM\), for the perfect generation of an SSB signal, the \(I\) port and \(Q\) port are driven via the DSB upconverted signal and its related Hilbert transform, respectively. In this regard, an SSB signal will be generated through the seamless cancellation of one sideband by the combination of the Hilbert transform pairs. Furthermore, to fulfill the minimum-phase condition, it is imperative to add a strong CW optical carrier to the signal [127].  
It is noteworthy that the \(IQM\) is most efficient when biased at null. This means better electrical spectrum efficiency as it generates two sidebands. This implies an improved power efficiency. Therefore, any configuration that deviates from this will suffer either electrical spectrum efficiency or power efficiency degradation. Furthermore, the practical challenges of the \(IQM\) implementation to generate the optical SSB signal are timing misalignment, amplitude imbalance, and \(IQ\) phase mismatch between the \(I\) and the \(Q\) components of the signal. Likewise, these factors will significantly affect the suppression ratio of the optical sideband. Consequently, the resulting \(IQ\) imbalance of the modulator causes the minimum-phase condition violation and generates interfering DSB distortion components in the optical spectrum. These result in the performance degradation of the KK system. Moreover, the subsequent limitation of the \(IQ\) imbalance on the back-to-back performance is owed to the minimum-phase condition, while its associated degradation in the transmission performance is due to dispersion-induced RF power fading [127].
## 5 Limitations  
The application of 3 MA is limited to only ferromagnetic materials. Due to the skin effect, the analyzation depth of the 3 MA application is always restricted. As already described, this maximum electromagnetic interaction depth is dependent on probe head design and on applied magnetic field strength and excitation frequency. Moreover, the material properties (conductivity and magnetic hysteresis), as well as the size and the shape of the inspected specimen, will have an influence. Usually, the maximum interaction depth is not previously known, leading to an uncertainty in depth-dependent measurements. Fortunately, powerful simulation tools have been developed at Fraunhofer IZFP, allowing for a realistic prediction of the maximum interaction depth [74].  
In general, 3 MA calibration can be challenging. Usually, a series of calibration samples with graded target values is required. Even if the 3 MA calibration measurements only represents little effort, the production of these calibration parts and their (destructive) testing with reference methods  
\begin{table}
\begin{tabular}{l l c c} \hline \hline FA0-II & No grinding damage & FB1-II & \begin{tabular}{c} Light tempered zones \\ at \(<10\%\) of the flank \\ \end{tabular} \\ \hline FB2-II & \
\begin{tabular}{c} Light tempered zones \\ at 10 to 25\% of tooth flank \\ \end{tabular} & FB3-II &
\begin{tabular}{c} Light tempered zones \\ at \(>25\%\) of tooth flank \\ \end{tabular} \\ \hline FD3-II & Strong tempered zones & FE3 & Re-hardening \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of different types of calibration—without and with US parameters.  
Figure 27: 3 MA valve spring scanner system: (**a**) measuring system and (**b**) application for depth-dependent determination of hardness (Hd) and residual stress (RS).  
could be expensive in time and costs. However, these costs must be faced with the enormous savings potential of the application of the calibrated 3 MA system.  
Current research activities aim to reduce the experimental effort for 3 MA calibration by developing generalized material databases and by using simulation models that are combined with machine learning algorithms (see Section 3.6).  
Further optimization of the 3 MA probe head should be dedicated to the increase of its robustness and wear resistance in harsh environments, because, today, probe head damage due to its intensive usage cannot be avoided. Furthermore, there is a need for the realization of large probe heads allowing for lift-offs higher than 5 mm. On the other side, smaller probe heads with a sensitive area not bigger than 0.5 \(\times\) 0.5 mm\({}^{2}\) are required for testing parts with restricting geometry (for example, in the radius of crankshafts). Additional measuring information could result from a deeper analysis of BN and IP double peak signals and IP droplet curves [75; 76].
###### Abstract  
Surface deformations were observed in Bolvadin, located in the province of Afyon (western Turkey) in the last decade which occurred without any destructive earthquakes. In this study, ground deformation of the Bolvadin region is analyzed by means of multi-temporal interferometric synthetic aperture radar (InSAR) technique with Sentinel-1 synthetic aperture radar (SAR) data. Sentinel-1 data acquired in ascending and descending orbits between October 2014 and October 2018 are processed with the Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) open source software tools. Deformation velocity maps and line-of-sight (LOS) displacement time series are produced and compared with geology, groundwater level and the water surface area of Eber Lake nearby. Deformation velocity maps reveal significant subsidence in most of the town and surrounding regions, which is confirmed by field observations that show severe damage to the settlements and infrastructure of the town. The most severe subsidence is observed to be in the southern part of Bolvadin with rates up to 35 mm/year, which is characterized by the presence of soft alluvial deposits. Composed of slope debris/talus and conglomerate, the northeastern part of the deforming region experiences a relatively lower rate of subsidence. A strong correlation between LOS displacement time series and groundwater level exists both in the short and long term. Moreover, short term variations in LOS displacement time series also show high similarity with seasonal variations in the water surface area of Eber Lake located a few km southeast of the town. We conclude that the primary cause of subsidence is most probably the overexploitation of groundwater and hydrological changes because of (1) the strong correlation of subsidence with lithological units, (2) the similarity between deformation rate and groundwater level changes, (3) the correspondence of seasonal variations in water surface area and short-term deformation rate oscillations, and (4) the absence of InSAR velocity contrast across the active faults.  
multi-temporal InSAR; time series analysis; sentinel-1; land subsidence; Bolvadin 2019
# Impact of Peri-Implant Inflammation on Metabolic Syndrome Factors: A Systematic Review
## 3 Results
### Peri-Implantitis and Dyslipidemia  
[PERSON] et al. (2018) and [PERSON] et al. (2012) evaluated the association between peri-implantitis and dyslipidemia. Although they both found that peri-implantitis may alter the lipid profile in patients, their findings were not completely consistent.  
In [PERSON]'s work (2018), peri-implantitis was associated with a significantly higher total cholesterol level (mean 212 mg/dL in peri-implantitis group versus mean 156 mg/dL in controls, \(p\) < 0.001) and LDL cholesterol level (mean 120 mg/dL in peri-implantitis group versus mean 91 mg/dL in controls, \(p\) < 0.001) after adjusting for history of periodontitis. Together with other findings on proinflammatory markers, they concluded that patients with peri-implantitis exhibited increased low-grade systemic inflammation and dyslipidemia.  
Nonetheless, [PERSON]'s study (2018), where peri-implant mucositis was additionally included as an individual group, suggested that total cholesterol and LDL cholesterol levels were not significantly different in patients diagnosed with peri-implant mucositis and peri-implantitis relative to healthy individuals (Table 2). Their results showed that level of triglycerides was significantly higher in patients with peri-implantitis than in the peri-implant health group (median 148 mg/dL versus median 95 mg/dL, \(p\) < 0.001). However, as an intermediate stage, levels of triglycerides in patients diagnosed with peri-implant mucositis (median 125 mg/dL) were not significantly different to either peri-implantitis or peri-implant health group.
# Theoretical Description of Carbon Felt Electrical Properties Affected by Compression
###### Abstract  
Electro-conductive carbon felt (CF) material is composed by bonding together different lengths of carbon filaments resulting in a porous structure with a significant internal surface that facilitates enhanced electrochemical reactions. Owing to its excellent electrical properties, CF is found in numerous electrochemical applications, such as electrodes in redox flow batteries, fuel cells, and electrochemical desalination apparatus. CF electro-conductivity mostly arises from the close contact between the surface of two electrodes and the long carbon fibers located between them. Electrical conductivity can be improved by a moderate pressing of the CF between conducting electrodes. There exist large amounts of experimental data regarding CF electro-conductivity. However, there is a lack of analytical theoretical models explaining the CF electrical characteristics and the effects of compression. Moreover, CF electrodes in electrochemical cells are immersed in different electrolytes that affect the interconnections of fibers and their contacts with electrodes, which in turn influence conductivity. In this paper, we investigated both the role of CF compression, as well as the impact of electrolyte characteristics on electro-conductivity. The article presents results of measurements, mathematical analysis of CF electrical properties, and a theoretical analytical explanation of the CF electrical conductivity which was done by a stochastic description of carbon filaments disposition inside a CF frame.  
Carbon felt; electrical conductivity; theoretical model Article
## 3 Results and Discussion
### Method Validation  
#### 3.2.1 Chromatographic System Suitability  
Metformin, glipizide and gliclazide were repeatedly retained at 1.675 min \(\pm\) 0.002, 2.895 min \(\pm\) 0.004 and 4.329 min \(\pm\) 0.010, respectively. The metformin peak exhibited a tailing factor of 1.200 \(\pm\) 0.002, the glipizide peak had a tailing factor of 1.055 \(\pm\) 0.002, while that for gliclazide was 1.058 \(\pm\) 0.004, indicating sharp and symmetric drug peaks. The three analyte peaks were well separated, where the resolution between the metformin and glipizide peaks was 6.153 \(\pm\) 0.020, and the resolution between the glipizide and gliclazide peaks was 5.658 \(\pm\) 0.006. In all reported results, % RSD never exceeded 0.006%, indicating the suitability of the chromatographic conditions and the HPLC system for analysis of metformin, glipizide and gliclazide. Results are recorded in Table 3 and presented in Figure 2.
# Research on the Performance of an Aircraft Landing
## 2 System Modeling  
### Research Object  
The hook lock mechanism is used to achieve smooth locking of the landing gear in the up position after retraction into the aircraft cabin. In Figure 1, the schematics of a particular landing gear hook lock are presented. The lock mechanism is composed of linkage 1, linkage 2, lock hook 3, a lock bracket (including an unlocking actuator), a spring, etc. It operates on the following principle. In the locking process, the landing gear retracts slowly, driven by the retraction actuator, and when the lock is almost closed, the ulpock roller on the gear strikes the lock hook by the action of retraction actuator. Through the impact force \(F_{Impact}\), a counterclockwise moment \(M_{Impact}\) is formed to rotate the hook, which also drives the corresponding rotation of linkages 1 and 2 until they cross the centerline by the action of the spring to reach a stable locked state. At this point, the pressure inside the retraction actuator chamber is relieved. After the landing gear loses support from the retraction driving force, the ulpock roller, which originally impacted the upper hook edge during locking, turns to hang on the lower hook edge due to the action of gravity on the landing gear. This ultimately completes the locking process of the landing gear upper lock. During the unlocking of the hook lock, the hook is subjected to a moment \(M_{\omega}\), that is opposite to the direction of the locking moment \(M_{Impact}\) since the entire landing gear hangs on it. Linkages 1 and 2 are opened by overcoming the spring force \(F_{s}\) and moment \(M_{\omega}\) of the lock with the unlocking driving force \(F_{Ulnlock}\), so that the lock mechanism leaves the stable overcenter state, and the roller separates itself from the hook by the action of gravity on the landing gear to complete unlocking.
## 6 Comparison of LW-CCA with ContikiMAC
### Average of Percentage for Listen and Transmit Duty Cycle in the Network  
This section analyzes the simulation results for a network (20 sender nodes) in terms of listen and transmit duty cycle that refers to the percentage of the duty cycle used to listen and transmit to the radio. Table 9 shows the percentage of listen and transmit duty cycles based on the low send rate and high send rate for LW-CCA and ContikiMAC nodes. The simulation outputs show that in both scenarios, percentage of listen duty cycle in LW-CCA nodes are significantly lower than ContikiMAC nodes. LW-CCA decreases average of listen duty cycle about 0.2 in both of low high send rate scenarios that it results in a reduction of approximately 18% in the average total listen duty cycle. The average percentage of transmit duty cycle in LW-CCA does not make much difference to ContikiMAC. The experiment results show that the proposed LW-CCA method can reduce the time of listen duty cycle by considering a dynamic RSSI check time so that it tries to reduce listen time in false WakeUps and idle listenings. How to calculate the average percentage of listen and transmit duty cycle is according to Equations (4) and (5). Rx_time, Tx_time, CPU_time, and LPM_time are equal with numbers of ticks available in Table 8.  
\[\text{Rx DutyCycle}(\%)=100\star(\frac{\text{Rx\_time}}{\text{CPU\_time}+ \text{LPM\_time}}) \tag{4}\]  
\[\text{Tx DutyCycle}(\%)=100\star(\frac{\text{Tx\_time}}{\text{CPU\_time}+ \text{LPM\_time}}) \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RDC** & **CPU\_time** & **LPM\_time** & **Rx\_time** & **Tx\_time** \\ \hline ContikiMAC (Low Rate) & 4498.73 & 46,354.15 & 511.15 & 145.71 \\ ContikiMAC (High Rate) & 4834.95 & 47,584.65 & 719.45 & 273.50 \\ LW-CCA (Low Rate) & 4537.25 & 47,478.90 & 425.40 & 147.52 \\ LW-CCA (High Rate) & 5066.35 & 48,036.60 & 601.05 & 281.30 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average of CPU ticks in SKY nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RDC** & **Rx Duty Cycle (\%)** & **Tx Duty Cycle (\%)** \\ \hline ContikiMAC (low rate) & 1.005 & 0.286 \\ ContikiMAC (high rate) & 1.372 & 0.521 \\ LW-CCA (low rate) & 0.817 & 0.283 \\ LW-CCA (high rate) & 1.131 & 0.529 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average of Rx and Tx duty cycles (%).
# Unveiling the Core Patterns of High-Latitude Electron Density Distribution at Swarm Altitude
## 5 Discussion
During periods of high geomagnetic activity, various phenomena contribute to the dynamic nature of the high-latitude mesoscale ionosphere. Precipitating electrons propagate towards the pole, and localized structures of electron density known as polar cap patches can be observed moving into and across the polar cap region; additionally, flux transfer events (FTEs) occur, in which photoionized plasma from closed field lines on the day side is transported into the polar cap across the boundary between open and closed field lines. These processes lead to plasma redistribution and changes in plasma density structures in the surrounding regions. The third mode effectively captures the overall results of electron motion, showing an evident increase in electron density along the two convection cells in the polar cap region and subsequent accumulation on the night side at high latitudes. The outcome of this dynamic is significantly less pronounced during periods of low geomagnetic activity, likely due to the influence of a predominantly positive IMF z-component (see Table 1), which affects the configuration of the convection cells, as well as to lower overall dynamics in this region compared to the geomagnetic disturbance period.
# Transient Responses of Repeated Transverse Impacts on Beams
## 5 Results and Discussion
### Transient Impact Behaviors
#### 5.3.5 Separation Velocity  
As shown in Figure 15a,b, the separation velocity of the sphere of the last impact for every impact velocity are analyzed. The separation velocity of the sphere predicted by eigenfunction expansion method and spring-mass method are compared with the experimental results. As shown, large differences could be found between the eigenfunction expansion method and spring-mass method. It means that the wave propagation effect has obvious influence on the separation velocity of the sphere under repeated impacts. The separation velocity of the sphere is known directly related to the kinetic energy loss of the sphere during the impacts. For spring-mass method, the smaller kinetic energy loss results in the larger absolute separation velocity of the sphere, as shown in Figure 15a,b. The comparison results show that eigenfunction expansion method is suitable for the transient responses of repeated transverse impacts on beams.  
Figure 14: Comparison of coefficient of restitution (COR): (**a**) [PERSON] model; (**b**) Thornton model.  
Figure 15: Comparison of separation velocity of the sphere: (**a**) KK model; (**b**) KE model.  
Moreover, the positive separation velocity of the sphere has been observed in the experimental results and the simulated results of eigenfunction expansion method. However, spring-mass method predicts negative separation velocity of the sphere. The separation velocity of the sphere either positive or negative determines the subsequent motion of the sphere, which determines the occurrence of sub-impact phenomenon [4]. The positive separation velocity of the sphere indicates that the sphere keeps moving in the dropping direction and will impact the beam again after finally separation upward and sub-impact can take place. The negative separation velocity of the sphere predicted by using the spring-mass means that the sphere reverses the direction of movement after impact and sub-impact phenomenon can rarely be observed.
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
## 2 Materials and Methods
### Reservoir Sediment  
The reservoir sediments (RS) were collected from the reservoir of Wadi Jezzi Dam (Figure 1b) situated a few kilometers to the west of the port city of Sohar, Oman. The sediments were in an intact form at the time of collection and easily transported to the experimental site. The study requires the reconstituted technique of loose specimen preparation, so the sample disturbance was not of much interest. The photomicrograph of the RS is shown in Figure 1d which confirms with the grain size distribution of the sediments presented in Figure 2. Before employing in the experimental phase for this study, the sediments were dried in an oven for 24 h at 105 \({}^{\circ}\)C. The elemental composition of the inorganic sediments is presented in Table 1 which shows mainly the presence of oxygen, carbon, silicon and a minor proportion of metals, but does not include the heavy metals which are harmful. The sediments are classified as high plasticity clay (CH) according to USCS [30] with a specific gravity (G\({}_{\text{s}}\)) of 2.38 and fine contents of 74.1%. The standard proctor compaction test gave a maximum dry density (\(\gamma_{\text{dmax}}\)) of 1.52 g/cm\({}^{3}\) at an optimum water content of 22.5%.