esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Feasibility of Interhemispheric Patch Detection Using Ground-Based GNSS Measurements
## 4 Results and Discussion  
The analysis presented below consists of two parts, and involves three consecutive days (17-19 March 2015). The first part is particularly aimed at the impact of the spatial distribution of the permanent receivers on the feasibility of interhemispheric patch detection. It also demonstrates the comparison of GNSS results with Polar Cap Products based on SWARM in-situ measurements. The second one provides an analysis of the temporal variations of patch occurrence and its dependence on IMF Bz. Furthermore, it includes also a comparison of the STEC enhancement that was observed for both hemispheres. All of the computations were performed with the use of multi-purpose ionosphere monitoring software (IONMON), which was written in Fortran and developed at UWM.
## 4 Mechanisms of Tensile Anisotropic Difference  
As mentioned above, the microstructural features of the HWMM-produced magnesium alloy sample are similar to those of aluminum alloy. The AZ31B magnesium alloy studied in this paper has a fine-grained equiaxed structure similar to that reported in the literature [14; 17]. In the HWMM, the AZ31B magnesium alloy alloyed with Al can easily form fine and equiaxed grains [34]. Due to the difference in thermal boundary conditions at different positions, the solidification rate is different so that an obvious layered structure is formed. Therefore, based on the key factor in the formation of a fine equiaxed microstructure according to the interdependence theory proposed by [PERSON], this morphology usually leads to tensile anisotropy [35].  
From the analysis of the tensile fracture mechanism of the HWMM aluminum alloy, it can be seen that the fusion line area of the sample is prone to fracture during the tensile process due to the obvious uneven structure [32]. The physical parameters of the AZ31B magnesium alloy and the Al5 Si aluminum alloy are shown in Table 3. From the table, it can be found that the thermal conductivity of the AZ31B magnesium alloy is 0.39 times that of the Al5 Si aluminum alloy, and the solidus temperature of the AZ31B magnesium alloy is 29.8% lower than that of the Al5 Si aluminum alloy. These differences lead to a more obvious uneven microstructure near the fusion line of the AZ31B magnesium alloy, and the grain size difference between adjacent areas reaches 40%. At the same time, the chemical activity of the AZ31B magnesium alloy is much higher than that of the Al5 Si aluminum alloy.  
During the forming process of magnesium alloy, it is easy to observe oxides formed on the surface, resulting in some defects such as incomplete fusion and porosity. It leads to the fracture of the magnesium alloy near the fusion line, which occurs more easily than in aluminum alloy during the tensile process, as shown in Figure 11. Therefore, the tensile property decrease in magnesium alloy along the deposition direction is larger than that in aluminum alloy. The tensile properties along the feed direction are less affected by the uneven structure near the fusion line. As a result, the tensile strength and elongation anisotropy of the AZ31B magnesium alloy samples is larger than those of the Al5 Si aluminum alloy.  
Figure 11: Schematic diagram of the HWMM-produced AZ31B magnesium sample fracture mechanisms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Parameters** & **Mg** & **AI [36]** \\ \hline Density, kg/m\({}^{3}\) & 1770 [37] & 2700 \\ \hline Poisson ratio & 0.35 [37] & 0.33 \\ \hline Young’s modulus, GPa & 45.0 [37] & 69.4 \\ \hline Solidus temperature, \({}^{\circ}\)C & 566 [38] & 806.4 \\ \hline Thermal conductivity, W/(m:k) & 96 [3] & 247 \\ \hline Specific heat capacity, J/(kg:K) & 1000 [38] & 963 \\ \hline Coefficient of linear expansion, m/m\({}^{\circ}\)C & 2.6 \(\times\) 10\({}^{-5}\)[3] & 2.4 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical parameters of magnesium alloy and aluminum alloy.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 3 Calibration Mission Impact over Systematic Errors
### Pointing  
Pointing errors can also produce TB, EB and BB spurious signals. For instance, focal plane misalignment can lead to differential pointing errors between the detectors, resulting  
Figure 3: Systematic errors in the TB (**a**) and EB (**b**) angular power spectra per logarithmic interval produced by polarization angle errors. The spurious signal produced by 30, 10, 3 and 1 arc-minutes of polarization rotation are represented by the blue curves. The same assumptions of Figure 2 have been adopted.  
Figure 2: Systematic errors in the BB angular power spectra per logarithmic interval produced by polarization rotation. The spurious signal produced by 30, 10, 3 and 1 arc-minutes of rotation are represented by the blue curves. L2-CalSat would allow the suppression of any polarization angle errors to the region below the 1 arc-minute blue curve. Primordial B-mode for two values of \(r\) (solid black lines), gravitational lensing B-mode (dashed black) and the EE (solid green) spectra are plotted for reference. A Gaussian beam of FWHM = 30 arcmin has been adopted.  
in T to B leakage, especially on small angular scales. Figures 4 and 5 shows the simulated leakage signals for random pointing errors of 1, 5 and 10 arc-minutes. This effect is simulated by remapping each pixel of the CMB maps (T, Q and U) into a new pixel at a distance randomly Gaussian distributed, with a dispersion given by the above-mentioned pointing errors, and towards a direction randomly selected according to a uniform distribution. In Figure 4, it can be observed that this kind of errors are specially problematic in the case of BB signals at very high \(\ell\). In the TB and EB spectra of Figure 5 it can be observed that random pointing errors affect mainly at multipoles above \(\ell\sim\) 100. As for the case of the systematic associated to the polarization angle discussed in the previous subsection, this effects, if not properly accounted for, biases the B-mode detection, in particular, at the recombination peak.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 6 Conclusions
**Funding:** This work was supported by Science Foundation of Donghai Laboratory [grant numbers DH-2022 KF01019]; the National Natural Science Foundation [grant numbers 41906152, 42176012, 42130402, and 41976174]; the special fund for Natural Resources Development (Innovation Project of Marine Science and Technology) of Jiangsu Province [grant numbers HY2019-1, JSZRHYKJ202001, JSZRHYKJ202008, JSZRHYKJ202102, and JSZRHYKJ202119]; the open funding of the Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport [grant number Yn918003]; the Natural Science Foundation of Jiangsu Province [grant numbers BK20180803 and BK20170871]; the National Natural Science Foundation of China [grant number 42130405]; and the National Key Research and Development Program of China [grant number. 2021 YFC3101702]. **Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[[http://36.112.130.153:7777](http://36.112.130.153:7777)]([http://36.112.130.153:7777](http://36.112.130.153:7777)), accessed on 20 July 2022; [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 20 July 2022; [[https://osdds.nsoas.org.cn/](https://osdds.nsoas.org.cn/)]([https://osdds.nsoas.org.cn/](https://osdds.nsoas.org.cn/)), accessed on 20 July 2022; [[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)), accessed on 20 July 2022. **Acknowledgments:** The authors thank the Ocean Satellite Data Distribution website, hosted by the Computer Network Information Center of the Chinese Academy of Sciences, for the HY-1C satellite data. We also thank the United States Geological Survey for the Landsat5 TM and Landsat@OL_TIRS satellite data, the China Centre For Resources Satellite Data and Application for the HJ-1A/B and GF-1 satellite data, and NASA for the MODIS satellite data. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Study Area and Datasets
### In Situ Measurements from Long-Term Monitoring  
#### 2.2.1 Meteorological Station and Borehole Data  
The air temperature and precipitation data were obtained from the station Mare Sale which contributes to WMO (World Meteorological Organization). It is located approximately at 100 km distance, located on the coast but has been shown to be representative for conditions on central Yamal [21]. MAAT is about \(-8\)\({}^{\circ}\)C [26].  
Summer 2016 has been distinct regarding air temperature [22]. Mid-July (day of year 190-200) air temperatures have been above average what was followed by a second warm period in August (after day of year 210, Figure 2c). All other analyzed years in this study show lower deviations.  
Air temperature records are usually converted to degree-days of thawing (DDT) for purposes of permafrost studies. The daily mean temperatures are summed up for all cases where they exceed 0 \({}^{\circ}\)C. The overall annual sum of a year provides a thaw index. It is commonly used for long-term studies and in the context of ground temperature modelling as well as assessment of subsidence, (e.g., [29]). A continuous increase of DDT since the start of measurements in 1947 has been reported for Mare Sale [22]. The seasonal evolution of DDT (see Figure 2a) is used for the analyses and offset correction of satellite derived displacements in our study.  
Figure 1: Overview of the study site: Locations of active-layer measurement sites (black and white dots) and of the satellite data coverage (rectangles) over land cover for central Yamal (source: [28], grey—no data, contains modified Copernicus data; projection and grid UTM Zone 42N) and on overview map for the entire peninsula (projection UTM Zone 42N, grid geographic).  
Ground temperature measurements at VD were initiated in 1993 [21]. Boreholes are equipped with HOBO data loggers and measure ground temperature at different depths. We use borehole 53 (number in the data base of the Global Terrestrial Network on Pernafrost--GTN-P) which is located close to the CALM grid. It represents a similar maximum thawing depth as the average of the CALM grid and includes a sensor at 100 cm depth.  
Figure 2: Air temperature records from station Mare Sale). **(a)** Cumulative degree-days of thawing (DDT) for years with satellite records and 2012. **(b–e)** Time series of daily mean air temperature (colored lines, color correspond to years in (**a**); black line—average 1999–2018), dates of satellite acquisitions and in situ data collection (vertical dotted lines—COSMO-Skymed, vertical dashed lines—Sentinel-1, vertical solid lines—dates of in situ subsidence measurements).
## 3 Results and Discussion
### Baseline Case Study of the Solar ORC System Performance
#### 3.1.3 Volume Ratio  
The volume flow ratio is termed as the ratio of specific volume value at the expander exit and expander entry; it is a parameter that indicates what extent the fluid volume rises during the process of expansion. Ideally, a lower volume ratio means cheaper expanders are required to operate in the ORC. The normal trend that can be seen in Figure 8 is that the mixtures have a lower volume ratio in comparison to the pure fluids, and this certainly signifies that mixtures are less expensive than pure fluids. In particular, R245 fa/Propane (0.8/0.2) has a significantly lower volume ratio, in comparison to other mixtures of R245 fa/R152a, isobutane/propane, and butane/propane that are being operated at the hot source temperature of 85 \({}^{\circ}\)C. For mixtures containing heptane and hexane, their volume ratios are the lowest amongst all other fluids. However, one must also keep in mind that the net work output for these mixtures is significantly low and that they are being operated at higher temperatures that can affect the costing of solar equipment within the ORC system.
## 3 Materials and Methods
### Testing of Object Detection Algorithms  
Bearing in mind the proposed approach and various scenarios, a comparison of suitable object detection algorithms was conducted. Each frame is to be resized to 1280 pixels in width and 720 pixels in height. The model has to consider the accuracy of the bounding box, confidence score, count of bounding boxes, and performance.  
Following testing on the CPU, YOLOv3-tiny is not suitable for expected scenarios due to zero detected cars in the first scenario despite the highest performance up to 30.46 FPS in the third scenario. Other models are similar to each other, but YOLOv4 shows the highest confidence score at almost 99%, and precisely located bounding boxes that guarantee stability and successful tracking. SSD MobileNet model has potential due to its well-demonstrated results, yet tests have proved to have an insufficient ability to accurately locate a real object.  
However, further testing on GPU shows that the RetinaNet model has a low performance of around 4 FPS and a confidence score of around 85%. Faster R-CNN ResNet-50 FPN surpasses other models of the same type in the majority of criteria. However, the performance around 4 FPS is considerably slow. This weakness can be largely eliminated via motion detection. YOLOv4 surpasses all models, especially in terms of performance up to 18 FPS, and stability of detection in terms of the real location of bounding boxes, which are their dominant features. The performance of YOLOv4 is at least three times higher in contrast to the top Faster R-CNN model. In the research, the YOLOv4 algorithm is used for the detection of vehicles.  
Figure 3: Candidate situations. (**a**) View above the road level; (**b**) bird’s eye view from the side; (**c**) bird’s eye view; (**d**) road level view. Source of the input video: [26].
## 7 Conclusions and Further Developments
The proposed method, once completely tested and refined on the case study building, could be extended to large building stock, supporting the decision-making process of building owners and building managers. Potential applications of the system would entail the integration of other kind of sensors to monitor Indoor Air Quality (IAQ), carbon dioxide, temperature, humidity, and Volatile Organic Compounds (VOC) levels, resulting in a more complete evaluation of the building conditions and Indoor Environmental Quality (IEQ). Sensors could play an important role for safety management purposes. The combined use of the system with Smart Contract and Blockchain technology could ensure increased network security, reliable data storage, traceability of data, and the possible automation of payments for cleaning activities. Cleaning contracts could in fact be customized based on the actual use of spaces, detected by the proposed system. Conceptualization: E.S., M.L., and L.P.; methodology: E.S., M.L., and L.P.; software: L.P. and G.P. ; validation: E.S, M.L., L.P., G.P., G.M.D.G., L.C.T., and G.B. ; formal analysis: E.S., M.L., and L.P.; investigation: M.L., L.P., and G.P. ; resources: G.M.D.G. ; data curation: M.L. and L.P.; writing--original draft preparation: E.S., M.L., and L.P.; writing--review and editing: E.S., M.L., L.P., G.P., G.M.D.G., L.C.T., and G.B. ; visualization: E.S., M.L., and L.P.; supervision: G.M.D.G., L.C.T., and G.B. ; project administration, G.M.D.G. ; funding acquisition, G.M.D.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Restrictions apply to the availability of these data. Data was obtained from Department of Architecture, built environment and construction engineering and are available from the authors with the permission of Department of Architecture, built environment and construction engineering party. The authors thank Eng. [PERSON] and Eng. [PERSON], and the Department of Architecture, Built Environment, and Construction Engineering for the support in the ongoing research. This research is developed in collaboration with Laser Navigation srl. The authors declare no conflict of interest.
## 4 Conclusions  
This paper introduces a four-layer Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) film system which has been successfully applied to develop an RSOA chip with a wide-spectrum, low-reflectance output facet. The RSOA, utilizing this film system, exhibits a 3 dB spectral width of 79.4 nm in its spontaneous emission spectrum, covering a wavelength range from 1497.2 nm to 1576.6 nm. Within this range, the ripple is kept below 1 dB, with the ripple near 1550 nm reaching as low as 0.5 dB, which represents the best reported performance to date. To assess the practical performance of this RSOA chip, it is packaged into a narrow-linewidth external cavity laser. Under the test conditions of 25 \({}^{\circ}\)C and 180 mA, the external cavity laser demonstrates a linewidth of 299.8 Hz and an output power of 12.6 mW. By adjusting the FP standards, filters, and other parameters of the external cavity, the lasing wavelength ranges from 1535.83 nm to 1561.42 nm. This research significantly advances the field of external cavity lasers, offering new possibilities for wide-tuning and narrow-linewidth applications.  
Conceptualization, S.L., Z.L. and Y.C.; methodology, S.L., M.R. and H.B.; validation, S.L., Z.L., Y.C. and H.Y.; writing--original draft preparation, S.L.; writing--review and editing, S.L., Z.L., Y.C. and H.Y.; project administration, J.L. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, funding number 62035009.  
Figure 10: The spectra of the narrow-linewidth laser.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors are grateful to the Nanometer Optoelectronics Lab, Institute of Semiconductors, Chinese Academy of Sciences; and the School of Integrated Circuits, University of Chinese Academy of Sciences.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Evaluation and Analysis
### Experimental Results
#### 4.3.3 Evaluation of Similarity Measurement Component
It gets slightly higher mean average precision than the original version on the Oxford5K and Paris6K datasets. We attribute this to the version that splits images into more patches, enabling the network to focus on relations between more refined feature sub-regions, thereby enhancing the model's robustness to significant viewpoint variations. Although our network is able to produce relatively reliable detection results to some extent, it is still not comparable to professional methods for image retrieval tasks. We believe this limitation arises because the algorithm relies solely on a single image without considering additional perceptual information like semantics, which limits the validity of  
Figure 14: The dramatic viewpoint variations of sample images from Oxford5K and Paris6K datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{2-3}  & **Oxford5K** & **Paris6K** \\ \hline CBE [13] & 0.5829 & 0.6989 \\ SGH [14] & 0.4726 & 0.5647 \\ ITQ [12] & 0.6006 & 0.5980 \\ LPM [18] & 0.6065 & - \\ UBEF [15] & 0.6249 & 0.6642 \\ SMVF-CVT [19] & **0.6514** & **0.7281** \\ \hline \hline \multicolumn{3}{c}{**Proposed methods**} \\ \hline
**MetricNet** & 0.5908 & 0.5732 \\
**MetricNet_3x3** & 0.6007 & 0.5767 \\ \hline \hline - means that the corresponding method did not experiment on the open dataset. The best performance is in **bold**, and the second best is underlined.  
\end{table} 
Table 5: The mAP Comparison of Different Networks. the proposed method facing viewpoint variations. Moreover, the patch-to-patch design of MetricNet is not adequate for the task of filtering and selecting the most relevant regions.
## 2 Data and Pre-Processing
### Study Spectral Bands and Indices  
The Sentinel-2A MSI has 13 spectral bands covering the spectral range 440-2180 nm, with 10 m, 20 m and 60 m pixels (Table 2). In this study the three 60 m bands (b01, b09 and b10) were not used due to their coarse spatial resolution and because they are specific to atmospheric characterization and not land surface monitoring. The remaining ten bands were selected for the analysis.  
In addition, six spectral indices were considered (Table 3). The indices were based on the conventional normalized vegetation index (NDVI) that is computed as the difference between NIR and red reflectance divided by their sum, and the normalized burn ratio (NBR) that is computed as the difference between NIR and SWIR reflectance divided by their sum. Four NDVI variants derived at 10 m and at 20 m using the four different possible combinations and spatial resolutions of the red, red edge, and NIR bands were considered. Similarly, two NBR variants derived at 20 m using the 20 m NIR band and the two 20 m SWIR bands were considered.
## 5 Conclusions  
In this paper, we analyze and study the application of a TSN circular queue forwarding mechanism and propose an optimization mechanism of queue analysis for large-scale LEO satellite communication networks. This mechanism uses the Particle Swarm Optimization algorithm to optimize the maximum time-sensitive traffic load and calculates the optimal solution of the queue length and number of outgoing ports of each node. The simulation results demonstrate that, with the optimization proposed in this paper, the forwarding of time-sensitive flows in the network experiences lower packet loss rates, shorter transmission delays, and higher throughput. This optimization algorithm decreases the effect of microbursts with lower computational complexity. Therefore, this mechanism can improve the effectiveness and reliability of satellite communication and achieve the high-quality and high-efficiency transmission of time-sensitive traffic.  
Conceptualization, Z.Z. and J.Y.; methodology, J.Y.; software, Z.Z. and H.J.; validation, Z.Z. and J.Y.; formal analysis, T.D.; investigation, T.D.; resources, Z.L.; data curation, Y.X.; writing--original draft preparation, Z.Z.; writing--review and editing, J.Y. and J.W.; visualization, Y.X.; supervision, T.D.; project administration, T.D.; funding acquisition, T.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China (2022 YFB2902504).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
There is no conflict of interest between any of the authors and the company Space Star Technology Co., Ltd.