esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology of Simulations
### Methodology of the Simulation of Interference to MSS  
It should be noted that most MSS systems use a transparent payload. Users transmit within the frequency range of 1980-2010 MHz, subsequently, the frequency is converted to the 6700-7075 MHz range before retransmission to the Earth station gateway, which actively tracks the MSS system. Since there is no onboard processing, it is required to calculate the composite C/(N + I) of the transparent payload to estimate end-to-end performance of the link. The following expression can be used to calculate end-to-end performance [15; 16]:  
\[C/\left(N+I\right)_{total}=-10\log\left(10^{-0.1C/\left(N+I\right)_{up}}+10^{- 0.1C/\left(N+I\right)_{down}}\right)\]  
where \(C/\left(N+I\right)_{up}\) is the signal-to-noise ratio plus interference level in the Earth-to-space link, and \(C/\left(N+I\right)_{down}\) is the signal-to-noise ratio plus interference level in the space-to-Earth link [18].  
Figure 3 shows the simulation of the end-to-end performance of the MSS system while the interfering Starlink satellite is in proximity to it.  
After C/N and C/(N + I) were calculated, it is possible to calculate the Eb/No. Eb/No is directly related to the C/N value, and can be expressed as follows [19]:  
\[E_{b}/N_{0}=C/N-10\;\log(R/B)\]  
where \(E_{b}/N_{0}\) represents the ratio of energy per bit to spectral power density (dB); \(N\) is the noise level in a reference bandwidth (dBW); \(I\) represents the interference level in a reference bandwidth (dBW); \(R\) is the data rate (kbit/s); \(B\) is the reference bandwidth (kHz); and \(C\) is the carrier bandwidth [20].  
The total noise level under interference conditions should be presented as a sum of the spectral density of the receiver's noise and external noise, \(\mathrm{N_{\Sigma}}=\mathrm{N_{o}}+\mathrm{I_{o}}\). The levels of \(\mathrm{E_{b}/(N_{o}+I_{o})}\) can be checked according to the curves to calculate the BER levels depending on the modem implementation. In our study we have considered the most commonly used modulation scheme QPSK. Since there are different modem implementations for MSS no code rate was considered and raw BER levels were obtained. The threshold BER levels depend on the system and usually varies; however, the most common threshold BER level is \(10^{-6}\). A lot of MSS systems employ this threshold in their specifications, including the Globalstar satellite system. Figure 5 shows a simulation of interference from Starlink to the MSS system.
## 3 Results and Discussion
### Bubble Departure Diameters and Frequencies  
The applied potential impacts the bubble departure frequency and the bubble departure diameter. Using high-speed video recordings, we compared the bubble departure frequency measured using a potentiostat and the departure frequency obtained from the high-speed camera footage. Average values together with the corresponding standard deviation are presented in Figure 9. Higher potential results in a greater detachment diameter with a notable jump at 3.3 V, which was previously determined to be a regime-transition region. The scatter of the bubble departure diameter values is up to 3% for coalesced bubbles formed at higher potentials and up to 15% for measurements at lower potentials with limited coalescence and microbubble emission. At 3.0 V, a higher scatter of 29% was measured since the detachment of bubbles was nonperiodic at this specific potential, resulting in a greater scatter of measured bubble diameters.  
Figure 8: Efficiency of hydrogen volume production at different applied potentials.  
Figure 7: Theoretically calculated total produced volume of hydrogen according to [PERSON]’s law.  
In Figure 10, the bubble departure frequency is presented for a set of applied potentials using a logarithmic scale. Several bubbles were evaluated using high-speed video recording, and the scatter of the measured diameters from each recording is presented with vertical error bars. The scatter of bubble departure frequencies is approximately constant for the detachment of coalesced bubbles, representing up to 5% of the total calculated frequency. Again, a much higher scatter was observed at 3.0 V, pointing to irregular bubble detachment with a value of 60% compared to the average calculated frequency. For potentials ranging from 2.8 V downwards, frequency was not evaluated as bubbles did not coalesce into a single bubble before detaching from the surface.
# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones
## 2 Observation Data  
### CYGNSS Data  
The CYGNSS mission provides five levels of data products, and four of them are available to the public, i.e., Level 1 to 4 data products. The Level 2 data products consist of the mean square slope product and the ungridded wind speed product over a 25 km \(\times\) 25 km region centered at the specular point. The Level 2 wind speed products are estimated by using the delay-Doppler map (DDM) average and leading-edge slope observables that are derived from the Level 1B DDMs of bistatic radar cross section and the DDMs of effective scattering area [29]. More details about the CYGNSS data products can be found in [29]. In this study, version 2.1 of CYGNSS Level 2 data products (downloaded from [[https://podaac-tools.jpl.nasa.gov/drive/files/allData/cygnss/L2/V2.1](https://podaac-tools.jpl.nasa.gov/drive/files/allData/cygnss/L2/V2.1)]([https://podaac-tools.jpl.nasa.gov/drive/files/allData/cygnss/L2/V2.1](https://podaac-tools.jpl.nasa.gov/drive/files/allData/cygnss/L2/V2.1))) were used to observe the TCs of interest.  
It is worth noting that since the relationship between surface roughness and wind speed is not unique under different sea state conditions, two geophysical model functions (GMFs) were used for the wind speed retrieval to obtain the CYGNSS Level 2 data products. One of them is the fully developed seas (FDS) model, which is suited for the general sea state condition, and the other is the young seas with limited fetch (YSLF) model, which is suited for the sea state near or in a TC. More details about the above two GMFs can be found in [30]. In this study, the Level 2 data products derived by the FDS GMF were used to analyze the central position of TCs because the wind products derived by the FDS GMF highlight the central position of TCs by contrasting low values near the center with surrounding higher values. However, the wind products derived by the FDS GMF were not used to estimate the wind speed around the inner core of TCs. This is because the FDS GMF is restricted at high wind speeds due to the limitations in the dynamic range of wind speeds included in the training dataset [30]. Therefore, we used the Level 2 data products derived by the YSLF GMF to estimate the 10-m-referenced ocean surface wind speed (U10) around the inner core of TCs. This is because the YSLF GMF is based on matchups between measurements made by CYGNSS and the stepped frequency microwave radiometer on hurricane hunter aircraft. These matchups show that the CYGNSS L1 observables are fairly and consistently sensitive to changes in high wind speeds. As a result, the YSLF GMF performs better in estimating wind speed around the inner core of TCs than the FDS GMF [30]. Wind speeds retrieved from version 2.1 of CYGNSS Level 2 data products on 10 September 2018 are shown in Figure 1.
###### Abstract  
To address the challenge of solving aircraft's visual navigation results using scene matching, this paper introduces the spherical EPnP positioning posture-solving method, which incorporates the threshold value for the central angle and the construction of a measurement model. The detailed steps are as follows: Firstly, the positioning coordinate model of the Earth's surface is constructed to ensure the expression of the three-dimensional coordinates of the Earth's surface. The positioning is then solved by employing the EPnP positioning posture-solving algorithm on the constructed data model. Secondly, by comparing and analyzing the positioning posture values of approximate plane coordinates, the critical value is determined, which serves as a reference for plane calculations. Lastly, a theoretical measurement model for visual height and central angle is constructed, taking into account the decided central angle threshold value. The simulation experiment demonstrates that using spherical coordinates as input results in an average positioning precision that is 16.42 percent higher compared to using plane coordinates as input. When the central angle is less than 0.5 degrees and the surface area is smaller than 558502 square meters, the positioning precision of plane coordinates is comparable to that of spherical coordinates. In such instances, the sphere can be approximated as flat. The findings of this study provide important theoretical guidance for further research on scene-matching positioning posture solving. These results hold significant implications for both theoretical research and engineering applications.  
Keywords:scene matching: EPnP; vision navigation; positioning solution; measurement models +
Footnote †: journal:
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 3 Experimental Design
### Evaluation Metrics  
The performance of dangerous-object detection is evaluated using the following metrics: precision (P), recall (R), mean average precision (mAP), frames per second (FPS), and the number of model parameters [9]. These evaluation metrics are widely used in the field of object detection and are defined as follows:  
* Precision (_P_): The proportion of true positive detections among all positive predictions. It is defined as: \[\text{P}=\frac{TP}{TP+FP}\] (4) where _TP_ (true positive) represents the number of correctly detected samples, and _FP_ (false positive) denotes the number of incorrectly detected samples.
* Recall (_R_): The proportion of true positive detections among all actual positives. It is defined as: \[R=\frac{TP}{TP+FN}\] (5) where _FN_ (false negative) represents the number of missed detections.
* Mean average precision (_mAP_): The mean of the average precision (_AP_) across all categories. It provides an overall measure of the model's accuracy across different classes. It is calculated as: \[mAP=\frac{1}{N}\sum_{1}^{N}AP_{i}\] (6) where \(N\) is the number of classes and _APi_ is the average precision for the iii-th class.
* Average precision (_AP_): The precision calculated at each recall level, integrated over all recall levels. It is defined as: \[\text{AP}=\int_{0}^{1}P(R)d(R)\] (7) where \(P\) is precision and \(R\) is recall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Parameter Value** & **Parameter Name** & **Parameter Value** \\ \hline imgsz & 640 & optimizer & AdamW \\ rect & False & lr0 & 0.00125 \\ epochs & 200 & momentum & 0.9 \\ batch & 16 & close\_mosaic & 10 \\ workers & 8 & patience & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters.  
* Frames per second (_FPS_): the number of frames processed by the model per second, reflecting the real-time performance of the detection system.
* Number of parameters: the total number of learnable parameters in the model, indicating its complexity and computational requirements.  
These metrics offer a comprehensive assessment of the model's detection performance, balancing accuracy, efficiency, and computational cost.
## 5 Conclusions  
A SCOPE modeling study was conducted to examine how well OCO-2 SIF observations and flux tower GPP data could constrain uncertain parameters and thereby improve the capability of the model in simulating both SIF and GPP at two temperate forest sites. The sensitivity of GPP simulation to V\({}_{\text{cmax}}\) was greater than that of SIF simulation in SCOPE. The performances of SIF and GPP estimation were improved with the inverse estimation of key parameters in the model. Incorporating the OCO-2 SIF data constrained key uncertain parameters related to SIF simulation, but did not significantly improve the GPP simulation, suggesting that SIF observations have low capability in constraining photosynthetic capacity. The concurrent use of satellite SIF observations and flux tower GPP data improved the simulations of both SIF and GPP. The optimized model can be used to simulate SIF and GPP continuously for the past, present, and future. The simulated SIF can also potentially be used to fill the data gaps for satellite SIF products like OCO-2 to produce spatially and temporally continuous SIF products and to improve the diagnosis of carbon cycle at large scales.  
H.W.: Conceptualization, Methodology, Formal analysis, Investigation, Writing--original draft. J.X.: Conceptualization, Formal analysis, Investigation, Writing--review & editing, Supervision. Both authors have read and agreed to the published version of the manuscript.  
[PERSON] was funded by the Major Project of China High-resolution Earth Observation System (grant no. 21-Y20B01-9001-19/22), and the National Natural Science Foundation of China (grant nos. 41871250 and 41401412), and [PERSON] was funded by the University of New Hampshire.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not Applicable.  
**Acknowledgments:** We thank [PERSON] for making the SCOPE model available, the OCO-2 and MODIS teams for making the OCO-2 and MODIS products available, [PERSON] for sharing the flux tower data with us, [PERSON] for sharing the surrogate models, and [PERSON] for helping with the density-based plots. We thank the two anonymous reviewers for their constructive comments on the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Overview of the Beiyi of Shilu Iron Mine  
### Geographic Location and Geohydrological Environment  
Shilu Iron Mine, owned by Hainan Mining Co., Ltd., is located in Shilu Town, Changjiang County, western Hainan Province, China. The mining area is situated in the northwest branch of Wuzhi Mountain and belongs to a low mountainous terrain. The eastern, southern, and western sides are characterized by medium-to-low mountains, mainly composed of slate and quartzite rocks. The northern part consists of undulating granite lowlands, with the terrain gradually sloping from south to north, as shown in Figure 1.  
The iron, cobalt, and copper ore bodies in the Shilu iron ore mine area are sur-rounded by hard state rocks, mainly dolomite, tremolite, and silicified dolomite, with a local distribution of schist. The geological conditions of the mine area are simple, and the geological exploration type is Type III (medium type of laminated rocks). There are several faults in the tectonic mine area, but, in the Beiyi mining area, the faults have little influence on the overall rock stability. The hydrogeological conditions of Beiyi quarry are of the simple type; the groundwater is mainly weathered fracture water and tectonic fracture water, and the dynamic and static reserves are not abundant, and so the water volume changes are directly related to atmospheric rainfall, and there is no connection with surface water. The main water-bearing layers are the seventh and sixth layers of the Shilu Group, and the fifth layer is the water barrier of the mine area, with poor overall permeability. The water filling type of the deposit is mostly tectonic fissure filling, which is not influenced by the surface water body.  
Figure 1: Topography of Beiyi mining area.
## 3 Results
### SICL Incorporating the Proposed Design Methodology
#### 3.1.2 Improvement in Impedance Matching  
The improvement in impedance matching is evident from the reduction in return loss. To evaluate the impact of the nonuniform-width inner conductor, the S11 parameters of two interconnects--both incorporating air cavities--are compared: one featuring the nonuniform-width inner conductor and the other a uniform-width conductor. As illustrated in Figure 7, the proposed interconnect achieves a reduction in S11 of approximately 5 dB compared to the configuration with air cavities alone, underscoring the effectiveness of the design methodology in enhancing impedance matching.  
Figure 6: Attenuation constant comparison for the SICL.  
Figure 7: Return loss comparison for the SICL.
## 2 Literature Review  
### Development Status of SOAs  
A SOA is a device that amplifies an optical signal directly, without the need to first convert it to an electrical signal [17]. SOAs are made from semiconductor materials and have the same working principles as semiconductor lasers. In other words, the stimulation phenomenon of jumps between energy levels is used for amplification. SOAs have a small volume, simple structure, low power consumption, long life, and low cost. They can be easily integrated with other optical components and circuits, are suitable for mass production, and can achieve the amplification and switching functions. SOAs are widely emphasized in the applications of optical wavelength conversion and light exchange. SOAs are basically laser diodes which can amplify any optical signal coming from either side of the fiber-end and send the amplified version of the signal out of the fiber attached through the other end. They are constructed in a small package and transmit bidirectional reducing the size of a device. However, it has some limitations which include high coupling loss, dependence of polarization,and a high noise figure. It requires very high quality of anti-reflective coatings [18]. Consequently, a future research direction is to improve these limitations.  
Because optical signals are always attenuated during propagation in optical systems, signals cannot be transmitted for hundreds or thousands of kilometers without optical amplifiers. Thus, optical amplifiers are indispensable components in long-range transmission systems. The technologies related to SOAs include optics [2; 3], semiconductor [1], material [19], communication [20], and surface treatment [21] technologies. Thus, SOAs constitute an interdisciplinary research topic. In addition, with the development of 5G, mobile communication, and light communication technologies, the governments of different countries currently highly value the development potential of SOAs and believe that adequate resources should be allocated to facilitate the development of this technology [5]. Therefore, this study used SOAs as the main subject of analysis and determined the critical SOA technologies through patent analysis. The exploration of critical technologies was conducted through network analysis, which is explained in detail in the following text.
# Do We Get What We Need from Clinical Acoustic Voice Measurements?
## 2 Background: Current State of the Art and Challenges in Clinical Acoustic Measurements
### Technical Challenges  
Acoustic voice measures are objective in nature and will provide a measurement of any input signal with a given algorithm, usually regardless of signal quality or suitability. Fundamental frequency has been shown to be the most robust measure, even under sub-optimal recording conditions, allowing data comparability between different hard- and software equipment types [10; 11; 12] and audio file formats [13]. However, voice intensity and quality measurements are especially significantly influenced by characteristics of the equipment and environment, including the microphone, signal-to-noise ratio (SNR) of recording systems, data processing steps, analysis algorithms, room acoustics, and background noise [5; 6; 11; 14; 15]. Consequently, most acoustic parameters are not easily comparable to measures from other clinics, centers, practices, or research studies. Thus, without reporting of salient technical characteristics, results lack the basis for data interpretation and transfer. However, comprehensive reporting is not always included. During the COVID-19 pandemic, new aspects challenged the application of instrumental analysis. Wearing a mask, as well as the type of mask (N95, surgical, cloth), have been shown to affect speech signal quality [16; 17]. These effects may be partially mitigated by adapting voice recording strategies, such as placing the microphone differently or using additional amplification [16]. Other issues relate to remote assessments, with unspecified background noise and the availability of _high quality_ recording hardware and software in the patients' home environments. In general, studies agree that is possible to use mobile communication devices (MCDs) for voice analysis. However, identical characteristics to those in clinical settings must be considered to achieve basic data comparability [18; 19; 20]. This includes reporting of the use and type of mask during a recording session, and the specification of equipment and procedures. Further research must be conducted to system atize the procedures, establish minimal requirements, and propose calibration solutions for more reliable voice measurements with MCDs to support the broad use of these tools.
###### Abstract  
Findings on sleep bruxism (SB) in patients with obstructive sleep apnea syndrome (OSAS) are controversial, and some of these findings have relied on in-laboratory polysomnography (PSG). We aimed to identify the factors associated with SB episodes in 100 patients with OSAS using in-laboratory PSG records. Subjects with OSAS were divided into those with and without SB episodes. We analyzed the differences in patient characteristics and PSG indices. Age, gender, height, weight, body mass index, neck, waist, and hip circumferences, and the rates of hypertension and diabetes mellitus were not significantly different between the two groups. A greater proportion of stage N2 sleep in the total sleep time, longer total sleep time, longer sleep time in a supine position, shorter sleep time in a nonsupine position, lower apnea-hypopnea index (AHI), lower AHI regardless of sleeping position, lower AHI during nonrapid eye movement sleep, and higher mean oxygen saturation level were associated with SB episodes in patients with OSAS. Among these factors, longer sleep time in a supine position remained a statistically significant factor in multivariate analysis. We conclude that longer sleep time in a supine position (especially >280 min) might be associated with SB episodes in patients with OSAS.  
Keywords: obstructive; polysomnography; sleep apnea; sleep bruxism +
Footnote †: journal: Applied Sciences