esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Singular Points Detection Based on Faster-RCNN
## 2 Proposed Method
### Fine Single Point Extractor  
From Section 2.1, large numbers of proposals without orientation information are generated by the network. To generate candidate patches which are big enough for detection task, we choose \(48\times 48\) pixel patches and choose top-100 patches ranked by the probabilities (after non-maximum suppression) as the candidate patches. The number of the candidate patches is set to be larger than the number of true singular points in a single fingerprint image (at most four singular points) so that our method can ensure the efficiency of the second step. As we choose more candidate patches than true singular points in each fingerprint images and the patches are different from the gird cells in size and location, the patches also need to be classified to predict the probability whether there exists a singular point in each patch. Moreover, this network will use the shared feature map to extract corresponding features of each patch so that the running time will be reduced. Then, in the second step, candidate patches expanded by these proposals will be reclassified and their orientations will be calculated based on corresponding regions.  
As these patches are centering on the predicted coarse locations of the singular points, it is necessary to extract the features of the corresponding region for precise prediction. A RoAlign [23] operation is used to extract features of each patch, which uses bilinear interpolation to compute the exact values of features and will keep location information of each patch. In order to obtain orientation information, an orientation constraint is added in the network (see Figure 2). Then, the corresponding regions centering at proposals are used to train the network with another multi-task loss consisting of location classification, location regression and orientation regression. Finally, location information will be refined and orientation information will be calculated, while the probability (score) will be  
Figure 3: The proposed FCN architecture.  
predicted according to whether the candidate patches exist singular points. Thus, we can get the corresponding regions centering at singular points and the center coordinates of the regions are the coordinates of singular points according to the location information and probability. Combining with the orientation information, singular points will be detected. Furthermore, the same features, which are extracted from raw fingerprint images in the first step, are used from sharing feature maps; thus, singular points extracting speed could be accelerated.
## 3 Results and Discussion
### Load and Instrumental Signal Behavior in the Bending Tests
This is attributed to the initial settling of the material when the surface of the sensing area was touched by the loading nose (G1, G3, and G5) or when the sensor was turned from its initial position owing to the absence of a perfect embedment (G4 and G6). Moreover, for lateral displacements larger than 0.5 times the x\({}_{\text{o}}\) value and that exceeded 80% of the maximum load, a loss of linearity was observed. This can be attributed to the loss of friction between the loading nose and the surface of the sensor. Additionally, the variation on the slopes of the curves can be attributed to the fact that the greater the lateral displacements to which the strain gauges were subjected, the lower the load required to maintain the maximum bending moment. Finally, in Figure 5a,b, two types of behavior can be observed from the relationship between load-displacement and instrumental signal-displacement. This can be explained according to the position of the loading nose concerning the embedment of the strain gauge. The first type of behavior corresponded to an immediate response of the instrumental signal and the load on the sensor caused by the lateral displacement induced by the loading nose on those strain gauges embedded between 30% and 45% of the central axis of their sensing areas (sensors G1 and G3). The second type of behavior corresponded to a slow response of the instrumental signal and the load on the sensor when the embedment was  
Figure 5: Average load and instrumental signal curves obtained from the two-point bending tests: (a) dimensionless load–displacement curve, (b) dimensionless instrumental signal–displacement curve. located in the center or on the edge of the sensing area of the sensor (sensors G2, G4, and G6). Moreover, large lateral displacements were required to induce changes in the electrical resistance of the sensing area due to the absence of a perfect embedding of the strain gauge.
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 3 Results and Discussion
### Model Calibration
#### 3.3.3 Partial Least Squares Regression (PLSR)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phenological Stage** & **Nutrient** & **Equation** \\ \hline \multirow{4}{*}{V4} & Ca & Ca = 41.380740 - 10.38684 \(\lambda\)1683 nm - 3.63733 \(\lambda\)1405 nm + \\  & Ca & 1.344476 \(\lambda\)13 nm + 4.155283 \(\lambda\)1883 nm - 9.864286 \(\lambda\)1726 nm \\  & & \(-\) 7.38664-\(\lambda\)1714 nm - 7.014894 \(\lambda\)1031 nm + 5.896256 \(\lambda\)1904 \\  & & \(\text{nm}-\) 10.20575 \(\lambda\)1727 nm + 2.19506\(\lambda\)625 nm \\  & Zn & \(\text{Zn}=-\)607.617 - 10181.2 \(\lambda\)634 nm + 3809.383 \(\lambda\)1364 nm + 2204.323 \(\lambda\)1994 nm \(-\) 3493.65 \(\lambda\)1981 nm \\ \hline \multirow{2}{*}{R6} & \multirow{2}{*}{Zn} & Zn = \(-\)119.164400 + 70.25752 \(\lambda\)944 nm + 71.05573 \(\lambda\)951 nm + 71.56557 \(\lambda\)1890 nm + 88.90969 \(\lambda\)1864 nm + 68.28416 \(\lambda\)925 nm \\  & & \(\text{+}\) 67.71706 \(\lambda\)923 nm \\ \hline \multirow{4}{*}{R9} & K & K = \(-\)1.65418 - 80.7834 \(\lambda\)630 nm + 116.8171 \(\lambda\)1405 nm \\  & Ca & Ca = 106.346000 - 252.393 \(\lambda\)1350 nm + 3637.852 \(\lambda\)631 nm \\  & & \(\text{3048.85}\) 3613 nm \\ \hline \multirow{4}{*}{
\begin{tabular}{c} All the bands together \\ (V4. R6. R9) \\ \end{tabular} } & P & P = 6.162487 - 124.4690 \(\lambda\)685 nm + 181.6616 \(\lambda\)651 nm \\  & & 87.4141 \(\lambda\)504 nm + 299.9536 \(\lambda\)1715 nm \(-\) 293.5019 \(\lambda\)1649 nm \\  & K & 8.754299 - 427.4241 \(\lambda\)685 nm + 2012.5704 \(\lambda\)651 nm \\ \cline{1-1}  & K & 1940.1794 \(\lambda\)504 nm + 2668.3838 \(\lambda\)1715 nm \(-\) 2624.08331735 \\ \cline{1-1}  & & \(\text{nm}\) \\ \cline{1-1}  & Ca & Ca = 16.96427 + 193.3209 \(\lambda\)705 nm - 670.447 \(\lambda\)2377 nm + 458.5386 \(\lambda\)1890 nm \\ \cline{1-1}  & Zn & Zn = \(-\)92.4856 + 1803.0171 \(\lambda\)1715 nm \(-\) 2073.2190 \(\lambda\)2434 nm \\ \cline{1-1}  & & \(-\) 519.7374 \(\lambda\)751 nm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Equations used in calibrating the PLSR models. present results, [PERSON] [76] states that Ca compounds bind to plant cells that are sensitive in the NIR region. Analysing Table 4, few of the models obtained in this study selected wavelengths within the range of 680 to 720 nm, which may have contributed to the low efficiency of the models in estimating the zinc content. Zn is a micronutrient for the life cycle of plants and carries out various metabolic activities. In their research, [PERSON] [67] states that Zn is correlated with the red-edge wavelengths (680 to 720 nm); however, their results are different from those found in the present study.
# The MADAG Strategy for Fault Location Techniques
## 4 Empirical Study
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Subject Programs** & **MADAG** & **DMS** & **RAPTOR** & **FEP** & **ART-MIN** & **SEQUOIA** & \
\begin{tabular}{c} **Total-** \\ **Statement** \\ \end{tabular} &
\begin{tabular}{c} **Add-** \\ **Statement** \\ \end{tabular} \\ \hline \multirow{3}{*}{Siemens} & Och:10.0 * & \multirow{3}{*}{18} & \multirow{3}{*}{20} & \multirow{3}{*}{97} & \multirow{3}{*}{150} & \multirow{3}{*}{500\({}_{+}\)} & \multirow{3}{*}{500\({}_{+}\)} & \multirow{3}{*}{500\({}_{+}\)} \\  & Tar:10.0 * & & & & & & & \\  & Jac:7.0 * & & & & & & & \\ \hline \multirow{3}{*}{UNIX} & Och:14.4 * & \multirow{3}{*}{16} & \multirow{3}{*}{48} & \multirow{3}{*}{98} & \multirow{3}{*}{56} & \multirow{3}{*}{176} & \multirow{3}{*}{500\({}_{+}\)} & \multirow{3}{*}{150} \\  & Tar:13.9 * & & & & & & & \\  & Jac:14.6 * & & & & & & & \\ \hline \hline \end{tabular}  
* The numerical results round off to the 2 nd decimal place.  
\end{table} 
Table 5: Needed the number of test cases to SBFL techniques. In Figure 6b, the MADAG test and original test are similar in the shapes of the curves for effectiveness. However, the curve of the MADAG test is always higher than that of the original test until 15%, where they overlap. Moreover, we apply the selected test cases to the Tarantula and Jaccard to measure the effectiveness of fault localization. The Tarantula has similar effectiveness to the Ochiai for the Siemens and UNIX suites (Figure 7a,b). Among them, the effectiveness of the Ochiai is still superior to Tarantula. Jaccard has the worst efficiency compared with Tarantula and Ochiai [1,2]. In Figure 8a,b, we observe that the curve of the MADAG test is always higher than the original test because the test suite contains many redundant tests, which causes the effectiveness of the Jaccard to be poor. The selected test cases by the MADAG strategy improve the effectiveness of fault localization with the Siemens and UNIX suites for the Jaccard. This result implies that the MADAG strategy can pick up \"key test cases\" that have been applied to the Jaccard and have better effectiveness in terms of fault localization than the original test. Furthermore, by selecting test cases via the MADAG strategy and applying them to Ochiai, Tarantula, and Jaccard, their curves are always higher than those of the original test as shown in Figures 6b, 7b and 8b. Therefore, more redundant test cases  
Figure 8: (**a**) Effectiveness comparison on the Siemens suite for Jaccard; and (**b**) on the UNIX suite for Ochiai. Figure 7: (**a**) Effectiveness comparison on the Siemens suite for Tarantula; and (**b**) on the UNIX suite for Tarantula. have an essentially inverse effect on the effectiveness of fault localization. In other words, the effectiveness of SBFL is likely not improved by applying whole test cases from the test suite. A similar experimental study by [PERSON] et al. [34] applied only FOnly tests to the SBFL technique, in which the effectiveness of fault localization is better than applying whole test cases.
### Background and Literature Review
#### 1.2.1 Adaptive Video Streaming and CDN  
Every day, millions of videos are uploaded to the Internet. End-users from all around the world commonly request to watch a substantial chunk of these video files [6]. Adaptive video streaming is a method of delivering video material over the internet that modifies the video quality in real-time dependent on the viewer's internet connection and device capabilities. This ensures a seamless and uninterrupted viewing experience, even on devices with limited internet or computing capacity. HTTP Live Streaming (HLS), Common Media Application Format (CMAF), Microsoft Smooth Streaming (MSS), Dynamic Adaptive Streaming over HTTP (DASH), and Adobe HTTP Dynamic Streaming (HDS) are common adaptive bitrate streaming methods. These protocols function by dividing the video into small pieces and encoding it at multiple bitrates, leaving the player to switch between them dependent on network circumstances [7; 8; 9]. CDNs can be utilized in conjunction with adaptive video streaming to guarantee that video equipment is delivered fast and efficiently to the users. The CDN can handle the distribution of video files to multiple locations, while the adaptive streaming technology can adjust the quality of the video on the fly. In recent years, there have been several issues that can arise with cloud-based content delivery networks that can impact the delivery of content to users. CDNs rely on a large number of servers to distribute content. Moreover, CDNs use caching to store frequently accessed content on servers closer to the user. Based on the user's location, the CDN system routes users to the nearest server referred to as a Point of Presence (PoP). This is accomplished using a technique known as \"geographic routing\", which compares the user's IP address to the closest PoP and the system directs the user to the closest server for content delivery. As a result, the user experience is enhanced and latency is decreased [10]. In CDNs, a Cache Server temporarily stores frequently accessed content. A Proxy Server acts as an intermediary between the end-user and the origin server, forwarding requests from the end-user to the origin server and returning the responses back to the end-user. The cache server decides which data to preserve in its cache and which data to delete when its memory is full using a variety of methods, including Least Recently Used (LRU), Most Recently Used (MRU), and Least Frequently Used (LFU) [11; 12].
## 4 Conclusions  
The addition of graphene nanoplatelets up to contents of 8 wt.% almost did not modify the density and hardness of the epoxy resin. Therefore, the improvements in the wear behaviour of the epoxy/GNP nanocomposites observed were not due to hardness increase by the addition of GNPs.  
The wear behaviour of the epoxy resin improved with the addition of two types of graphene nanoplatelets tested. The mass loss, wear rate and friction coefficient decreased with the increasing graphene contents. This effect was more marked for nanocomposites with 5 wt.% and 8 wt.% of GNPs.  
The incorporation of high percentages of the two types of graphene nanoplatelets provoked a decrease in the width and depth of wear track surfaces. Nevertheless, the wear mechanism depended on the GNP type.  
When using non-functionalised GNPs, the improvement in the wear behaviour was due to the fact that graphene acted as a lubricant from inside the epoxy resin. This effect was observed mainly at higher percentages of graphene, since at low contents, the behaviour was similar to that observed in the neat epoxy resin. Almost no wear was observed in resin reinforced with 8 wt.% of non-functionalised GNPs, in which the wear track was smooth, justifying the lubricant effect.  
However, in the materials reinforced with f-GNPs, the improvement in the wear behaviour was due to its detachment from the material and subsequent adherence to the surface acting afterward as a lubricating layer. The smaller size and/or functionalization promoted the detachment of epoxy and f-GNPs together.  
Figure 8: SEM images of worn surfaces of nanocomposites with 3 wt.% and 8 wt.% of non-functionalised GNPs (**a,c**) and amino-functionalised GNPs (**b,d**), respectively. SEM images of the debris particles detached during test (**e,f**).  
The main conclusion is that the addition of 5-8 wt.% of the two types of graphene nanoplatelets to the epoxy resin improved its tribological properties due to the lubricant effect of graphene that decreased friction with ceramic alumina bearings.  
[PERSON] prepared the research methodology as well as the results analysis and preparation of the initial version of the manuscript. A.J.-S. contributed to the experimental manufacturing and testing, preparation of the final version and revisions of the manuscript and funding acquisition. A.U. contributed to funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Young Researchers R&D Project (Ref. M2183, SMART-MULTICOAT) financed by _Universidad Rey Juan Carlos and Comunidad de Madrid; Ministerio de Economia y Competitividad_ (project MAT2016-78825-C2-1-R) and _Comunidad de Madrid_ (ADITIMAT-CM S2018/NMT-4411).  
The authors declare no conflict of interest.
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
###### Abstract  
This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks such as Omnispace and Lyra, and an interference analysis in the L-band between NTN satellites and the mobile satellite network Immarsat. This study simulates an NTN satellite network with typical characteristics defined by 3 GPP and ITU-R for the n255 and n256 bands. Furthermore, it provides calculations illustrating the signal-to-noise ratio degradation of low-Earth-orbit (LEO), medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellite networks operating in the L-band and S-band when exposed to interference from NTN satellites.  
5G satellite; NR; 6G satellite; IoT; NTN; 3 GPP; S-band; L-band; n255; n256; interference analysis; direct-to-cellular; Immarsat; Omnispace; Echostar Article
# Study of the Effect of Laser Radiation Parameters on the Efficiency of Lithotripsy
## 3 Results
The ablation process during femtosecond laser lithotripsy, as described above, is due to the mechanism of photoionization ablation, as a result of which it is fundamentally different from the process of microsecond laser lithotripsy due to the photothermal mechanism. In microsecond laser lithotripsy, shock waves are generated by both plasma expansion and bubble collapse with an amplitude of more than 100 bar\({}^{2}\)[28]. These bubbles appear due to the local evaporation of the liquid near the sample. At the same time, the shock waves generated by microsecond radiation are strong enough to crush stones when they reach their surface. In the case of laser lithotripsy with a femtosecond pulse duration, the calculus undergoes a photoionization process and forms a plasma mixture of ions and electrons. After ionization and plasma generation, the incoming laser energy is absorbed by free electrons, providing acceleration and the production of additional free electrons [30]. The high-speed free electron number is large enough to remove sample material. It is worth noting that the photoionization mechanism of ablation provides the smallest residual fractions. In comparison, nanosecond laser lithotripsy produces large fragments that range in size from a few hundred microns to several millimeters [31]. It is necessary to emphasize that due to the short duration of exposure to radiation and the material being processed, corresponding to a pulse duration of about 250 fs, only a small part of the incident laser energy was transferred to heat the ions and create shock waves. Due to these factors, when we exposed the sample to femtosecond laser pulses, the liquid did not evaporate. It leads to the appearance of vapor bubbles near the sample, which causes retropulsion [30; 31]. At the same time, as can be seen from the graph, when using the millisecond stone processing mode, the effect of retropulsion is observed due to cavitation in a liquid medium, amounting to about 9.6 mm per 9 W of averaged power. Figure 5: Performance dependence on sample processing time with femtosecond pulses.
# Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
## 3 Simulations and Results  
### Scass-U  
SCASS-U was simulated with 14 elements, steered towards the broadside with \(\Delta=0.3^{\circ}\). Figure 2 shows the beam patterns of the SAs and their unified beam pattern. It is clear in Figure 2b that the main lobe of the SCASS-U is narrower than the main lobes of the SAs. This is because the main lobe of the overall beam pattern is, in fact, the overlapping portion of the main lobes of SA1 and SA2. It can seen from the figure that an increase in \(\Delta\) (note that the separation between the maxima of the beam pattern of SA1 and SA2 is \(2\Delta\)) will definitely reduce the overlapping region, thus consequently reducing the width and height of the main lobe of the overall beam pattern. Hence, for the selected \(\Delta\) (\(0.3^{\circ}\)), the main lobe of the SCASS-U is 0.28 dB lower than the main lobes of the SAa. This represents a power loss of 0.28 dB. It is obvious from the discussion above that an increase in \(\Delta\) will cause a narrower main lobe but a higher power loss. Therefore, \(\Delta\) needs to be kept small so that the power loss remains within a limit. This power loss also causes the PSLR to degrade. To regain the PSLR, by suppressing the side lobes, Dolph-Chebyshev weights have been employed to form an SCASS-C, discussed in the next subsection. It is also noteworthy here that in Figure 2, the grating lobes of SA1 and SA2 do not overlap. However, if \(\Delta\) is large, the grating lobes overlap, causing higher side lobes and a lower PSLR. Thus, PSLR degradation with increasing \(\Delta\) is due to two factors; power loss in the main lobe and overlapping of the grating lobes. The first factor causes the peak of the main lobe to decrease while the second causes the side lobes to rise. Power loss is always there for all non-zero values of \(\Delta\) while the grating lobes only overlap for large values of \(\Delta\).
## 4 Discussion
Background, directional effects and pixel heterogeneity could also compromise the estimation of \(f_{\mathit{PAR}}\)[96; 97]. SVI not used in this work have been proposed to minimize some of these effects, as the Soil Adjusted Vegetation Index [98] or the Enhanced Vegetation Index [99] and are prescribed for heterogeneous landscapes such as TGE [100]. In other cases, indices maximizing sensitivity to canopy chlorophyll such as the MERIS terrestrial chlorophyll index (MTCI) were reported as better estimators of \(f_{\mathit{PAR}}\)[56; 101]. However, SVI-based \(f_{\mathit{PAR}}\) modeling does not explicitly describe radiation interaction in a heterogeneous 3-D structure, and only more advanced approaches could robustly improve \(f_{\mathit{PAR}}\) representation in TGE ecosystems. In this work, error analysis reveals that \(\mathit{GPP}_{\mathrm{hh}}\) is underestimated in a wide range of sun zenith angles and at mid-low \(\mathit{PAR}\) conditions, but that overestimation is maximum at low sun zenith angles (<30\({}^{\circ}\)), and large \(\mathit{PAR}\) conditions. Tree shadows would reduce the actual amount of radiation absorbed by grass; though, this effect would be also compensated at the largest sun zenith angles by diffuse radiation. Ignoring this fact might led to estimate a light response curve that saturates quicker than it actually does when using data from sunny days (as in \"Flight\" datasets); which could explain \(\mathit{GPP}_{\mathrm{hh}}\) underestimation in cloudy days or at mid-low \(\mathit{PAR}\) levels. Overall, at footprint and mid-low RS scales, the study site behaves quite homogeneously, so that the contribution of trees and grasses to the spectroradiometric and flux measurements remains stable. Footprint climatology approaches achieved significant improvements against pixel-based approaches when different covers were separated in the footprint area, in large blocks [7; 13; 14]; which is not the case in the Majadas site. However, this approach could be more suitable when trees and grasses (or soil, [12]) are modeled separately. The authors recognize that there is a spatial mismatch between \(\mathit{NEE}\) and ecosystem respiration, and therefore in the computation of \(\mathit{GPP}\). However, the impact of this mismatch should remain low compared to other sources of uncertainty analyzed in this work.