esa-sceva/satcom-chunk-collection huggingface dataset:
# From 5G to 6G--Challenges, Technologies, and Applications
## 2 Fifth-Generation Network's Shortcomings
### B. Link Latency  
Currently, many real-time services have emerged to be an integrated part of the network for many years to come. The services can range from helping in the creation of smart cities, such as autonomous vehicles and factories, to identifying new ways to interact with the environment, such as _virtual reality_ (VR) and exoskeletons or prosthetic limbs [22]. Most real-time services are time sensitive and have stringent latency requirements (10 ms and below) in order to ensure an effective operation mode. Moreover, some technology-related factors can cause latency degradation, such as the length of the cyclic prefix (CP) in OFDM systems or using dedicated channels for machine communications, which require constant dynamic scheduling due to their sporadic nature of transmission [23].  
In the latest industrial revolution, _Industry 4.0_, many applications require simultaneous support for URLLC (Figure 3) to achieve fully autonomous operation without human supervision or intervention. This has been considered in the latest release of the 5G standards; however, this support is limited for basic motion control at 1 ms latency at best [24]. In many applications, such as aircraft or vehicle control and intra-vehicle communication for suspension and engine control, the required latency is sub-ms (0.1-1 ms) [25].  
Many of the previous applications have multiple rigorous requirements simultaneously for optimal operation. An example of this can be found in autonomous systems, where simultaneous support for super-URLLC is needed combined with high data rates for some scenarios. For instance, this can be translated to latencies down to a 250 us (some papers even suggest 100 us) round-trip time combined with a link reliability of \(10^{-9}\) at  
Figure 3: The fifth-generation network’s pillars with examples of each of them.  
10 Gbps for applications such as operating factories using virtual presence [26; 27]. This requires 10-fold and 50-fold improvements in latency and reliability, respectively, over current 5G standards [25]. Furthermore, 5G promises to offer low latency for short packets only. Customizing the data rate, latency, and link reliability for the different applications is not fully considered in 5G and has not yet been achieved efficiently. Thus, it is debatable if 5G holds the full prerequisites to construct smart cities with the support of the different machine communication requirements [19]. This leaves room for improvements in the next generation, such as securing better random access (RA) methods for machine communications, efficiently managing the more sophisticated industrial control schemes, and achieving sub-_ms_ link latency.
## 2 Random Access Procedure (RAP) and Access Class Barring (ACB) Scheme
Since up to three uplink grants can be sent per subframe in a RAR message, the number of available uplink grants per RAR window depends on its length. **Connection request and contention resolution:** After receiving the corresponding uplink grant, the UEs schedule the transmission of the connection request message (Msg3) to the BS through dedicated resources. Then, the BS responds to each connection request with a contention resolution message (Msg4). The transmission of both of these messages is protected by a hybrid automatic repeat request (HARQ) mechanism. The maximum number of allowed preamble transmissions for each UE is broadcast by the BS through the SIB2 [1]. Access failure can occur, for example, due to a preamble collision or to wireless channel errors during contention resolution. Whenever an access failure occurs, and if the maximum number of preamble transmissions has not been reached, the UE waits for a random backoff time (determined by the backoff indicator); then, it randomly selects and transmits a new preamble at the next RAO.
## 3 Results
### Correlations  
#### 3.2.1 Whole Population Correlation  
Table 2 illustrates the correlation between the pairs of parameters across the whole population. A very strong or strong correlation was identified between measurements of the lateral and line-1 longitudinal (\(p\leq 0.05\); \(r=0.92\)) (Figure 3) and medial and line-1 longitudinal (\(p\leq 0.05\); \(r=0.9\)) (Figure 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Mean** & **Median** & **SD** \\ \hline lateral [mm] & 18.28 & 18.81 & 3.38 \\ medial [mm] & 18.59 & 19.01 & 3.04 \\ rostral [mm] & 6.02 & 5.87 & 0.91 \\ caudal [mm] & 8.66 & 8.36 & 1.36 \\ area [mm\({}^{2}\)] & 150.99 & 151.52 & 39.76 \\ line-1 longitudinal [mm] & 19.9 & 20.14 & 3.25 \\ line-2 transverse [mm] & 7.58 & 7.63 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The descriptive analysis of obtained results in the whole population.  
Figure 2: The schematic illustration of the seven measurements (rostral (from point **A** to point **B**); lateral (from point **B** to point **C**); caudal (from point **C** to point **D**); medial (from point **D** to point **A**); line-1 longitudinal (from point **E** to point **F**); line-2 transverse (from point **G** to point **H**); area (green color)) situated on one side of the 3D printed model (CT). The orientation of each measurement was appointed (rostral, caudal, medial, lateral). Point **E** = 1/2 **AB** rostral length; point **F** = 1/2 **CD** caudal length; point **G** = 1/2 **DA** medial length; point **H** = 1/2 **BC** lateral length.  
Figure 4: Correlation between the pairs of parameters (medial and line-1 longitudinal) in whole population.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Lateral** & **Medial** & **Rostral** & **Caudal** & **Area** & \
\begin{tabular}{c} **Line-1** \\ **Longitudinal** \\ \end{tabular} &
\begin{tabular}{c} **Line-2** \\ **Transverse** \\ \end{tabular} \\ \hline lateral & & 0.75 & & 0.48 & 0.62 & 0.92 & 0.51 \\ medial & 0.75 & & 0.51 & 0.59 & 0.90 & 0.41 \\ rostral & & & & & 0.59 \\ caudal & 0.48 & 0.51 & & 0.60 & 0.42 & 0.76 \\ area & 0.62 & 0.59 & & 0.60 & 0.65 & 0.62 \\ line-1 longitudinal & 0.92 & 0.90 & & 0.42 & 0.65 & 0.46 \\ line-2 transverse & 0.51 & 0.41 & 0.59 & 0.76 & 0.62 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The \(r\)-value for the whole population correlations (\(r\)-value presented only for pairs showing statistically significant correlation, \(p\leq 0.05\)).  
Figure 3: Correlation between the pairs of parameters (lateral and line-1 longitudinal) in whole population.
# FY3E GNOS II GNSS Reflectometry: Mission Review and First Results
## 2 Instrument Overview
### Raw Sampling Data  
GNOS II can also provide raw intermediate frequency (IF) sampling data for scientific applications. The data are in a limited number of collections due to their large volume, but contain more information (carrier phase, multi-GNSS signals, alterable sampling resolution, etc.) than the L1 product described in the next section. Twelve hot points were selected over the globe, among which 10 points are over the ocean (near buoys from the National Data Buoy Center), 1 point is over ice (near the Chinese Arctic Huanghe Station) and 1 point is over land (at the central plain of China). Figure 7 shows the distribution of the hot points. The receiver will switch to raw sampling mode when the distance between the specular point and hot points is less than the operational limit, which is set to 50 km. The length of sampling time is set to 10 s. Note that the locations of hot points, sampling length and operational limit can all be reset onboard.  
Figure 5: The number of tracks for each GNSS satellite in one day (25 July 2021). The left one is for BDS and right one is for GPS.  
Figure 6: Number of tracks for each day in August 2021.
## 3 Results
### Spatiotemporal Characteristics of TSS Concentration  
#### 3.2.1 Analysis of Optical Characteristics of the Water Body in Hedi Reservoir  
The maximum spectral reflectances measured in the field are all less than 0.1 (Figure 4). The spectral curve of the water body of Hedi Reservoir shows a significant four-peak trend different from coastal and estuarine waters [18]. The spectral curve corresponding to the change in the concentration of TSS and other watercolor components also shows different changes at each peak. In the range of 560-590 nm, the first strong reflection peak appeared due to the weak light absorption and cell scattering of chlorophyll in phytoplankton [48]. The reflectance corresponding to the peak area gradually increases as the concentration of TSS increases. At the same time, since cyanobacteria are the absolute dominant species in the water body of Hedi Reservoir [30; 49; 50], the cyanobacteria have an absorption peak near 620 nm, therefore reflectance appears as an absorption valley or shoulder-shaped near 630 nm [48; 51; 52]. Similarly, a reflection valley appears near 675 nm due to the strong absorption of algae chlorophyll in the red light band [51]. At the same time, a clear reflection peak was generated between the two reflection valleys (near 620 and 675 nm), and the peak was located near 650 nm, which is one of the important optical signals to characterize whether the water body contains phycocyanin [26; 53].  
Figure 3: Estimated TSS concentrations based on the three-band model from Landsat OLI imagery in Hedi Reservoir on 16 October 2015, where the different color lines stand for towns over the reservoir basin (**a**), and comparison between the in situ measured and OLI imagery-retrieved TSS concentrations (**b**). The color scale is the legend of the TSS concentrations, in mg/L.  
Besides, the reflectance of the water body corresponding to this peak will increase with the increase in the TSS concentration, and its position will also move along the direction of the longwave, which will appear as a \"red shift\" phenomenon [48; 54], which will result in a wide range of the peak wavelength coverage. The third reflection peak caused by the Raman effect appears around 700 nm [55], which is the most prominent spectral feature of algae-containing water and is considered to be an important basis for determining whether the water contains algae chlorophyll. Finally, the reflection peak around 810 nm is an important spectral feature of the presence of suspended inorganic matter in water, and it can be used to distinguish lower and higher TSS concentrations. The water body of Hedi Reservoir has mainly suspended particles, showing strong backscattering. With the increase in the concentration of TSS, the characteristic of the above four bands are more prominent, resulting in higher reflectance of reservoir water. However, in the near-infrared region, the backscattering coefficient of pure water gradually decreases with the increasing wavelength [56; 57]. On the other hand, the strong absorption of water in the near-infrared region [56] leads to a generally low reflectance of water.
# Temperature Effects in Packaged RF MEMS Switches with Optimized Gold Electroplating Process
## 4 Measurements and Discussion
### Temperature Effect on RF Performance  
Figure 13 shows the insertion loss and isolation curves of the packaged switch measured at room temperature. The measured insertion losses of the switch are \(-0.3\) dB @ \(10\) GHz and \(-0.6\) dB @ \(30\) GHz; the measured isolations are \(-25.2\) dB @ \(10\) GHz and \(-20.1\) dB @ \(30\) GHz.  
The RF performance variation of the switches working at different temperature environments is tested and shown in Figure 14. The working temperature is raised from \(5\) to \(75\)\({}^{\circ}\)C in steps of \(10\)\({}^{\circ}\)C, and the RF performance of the switches is read after each temperature point has been stabilized for \(1\) h. The insertion losses of three switch samples versus working temperature are shown in Figure 14a. It can be seen that the insertion losses of the switches decrease with the increasing temperature. The reason may be that higher temperature softens the contact material and improves the contact effect. The isolation of three switch samples versus working temperature is shown in Figure 14b. The isolation of the switch is almost unaffected by the working temperature. Since the isolation of the switch is related to the position of the movable beam, the results in Figure 6 indicate that temperature has almost no effect on the position of the movable beam. This is consistent with the test results shown in Figure 14b.  
Finally, Figure 14c,d show the temperature effects on insertion loss and isolation of the switch at different operating frequencies, respectively. It can be seen that the trend of RF performance changing with temperature is consistent at different operating frequencies.  
Figure 11: Electric field distribution of the switch under DC bias voltage.  
Figure 12: The lifetime of the switches at different temperatures.
## 3 Results
### Spatiotemporal Characteristics of Vegetation Change  
Figure 6 illustrates the trend in NDVI and LAI in the HLS from 2000 to 2018, both of which surpassed the 95% significance threshold. Throughout the study period, most areas in the HLS experienced an upward trend in NDVI and LAI, with very few areas showing a decline. Figure 6c,d indicate that 92.03% of the NDVI in the HLS exhibited a significant increase, while 78.92% of the LAI showed a significant rise, closely linked to the implementation of the project aimed at converting farmland back to forests and grasslands. Grassland is the predominant land use type in the HLS, accounting for 91.82% of the area. The project focusing on the conversion of farmland to forest and grassland places a strong emphasis on the preservation and development of forested areas and grasslands, which has greatly contributed to the marked increase in NDVI and LAI. Specifically, this project not only enhances soil stability through the restoration of vegetation cover but also improves local microclimate conditions and bolsters the carbon sink function of the ecosystem. At the same time, this project also effectively reduces soil erosion and promotes the circulation and utilization of water resources. In addition, the restoration of grasslands and woodlands provides a good habitat for wildlife and further enhances regional biodiversity. The combined effects indicate that the initiative to convert farmland into forests and grasslands has been pivotal in enhancing the ecological environment of the HLS, leading to a substantial increase in NDVI and LAI. From the broken lines in the diagrams in Figure 6e,f, it can be seen that from 2000 to 2018, the annual NDVI and LAI in the HLS were in a fluctuating upward state, in which NDVI increased from 0.38 to 0.62. The linear fitting results showed that R\({}^{2}\) = 0.79, and the average increase rate was 0.009/a; the annual LAI value increased from 1.02 to 1.58, and the linear fitting result was good (R\({}^{2}\) = 0.87), with an average increase rate of 0.035/a.  
Figure 5: The annual verification results for ET and MODIS ET simulated by the PT-JPL model are verified year by year (including Bias, RMSE, and linear fitting R\({}^{2}\) as indicators).
## 3 Results and Discussion
### Optimization and Parametric Analysis  
The optimization of the S\({}_{11}\) parameters for the connector-integrated antenna structure was performed using CST Studio Suite. To facilitate an efficient optimization process, the antenna model was restructured with a focus on key performance factors. The trust region framework (TRF) algorithm was selected due to its proven success in similar optimization studies reported in the literature. This optimization focused on fine-tuning the critical geometric parameters, including the top, side, and bottom gap dimensions, patch arm placement, and middle gap measurements. Constraints were applied to each of these parameters (u, d, e, f, g, h, i, j, k, l, m7, n, o, p, r, s, t, u, v, y) to ensure precision.  
After multiple iterations, the resulting S\({}_{11}\) values for the optimized designs are presented in Figure 5. Each curve represents a distinct optimized configuration (Opt-1, Opt-2, Opt-3, Opt-4), revealing how the parameter adjustments influence the return loss at the targeted 28 GHz and 38 GHz frequency bands. These results clearly demonstrate the efficacy of the parametric optimization process, leading to improved impedance matching and overall performance enhancement across the desired frequency bands.  
After determining the optimized dimensions, further parametric analysis was conducted to refine the design and address potential challenges related to manufacturing tolerances. In this context, the spacing between the arms and the upper gap dimensions were identified as critical parameters due to their significant influence on S\({}_{11}\) and gain characteristics. The width (g) and height (h) of the arm spacing, along with the width (r) and height (s) of the upper gap, were systematically varied, starting at approximately 10% of the optimized step interval and progressively decreasing. These analyses were performed both independently and in combination to assess their impact on the overall design parameters. To account for manufacturing tolerances, the arm spacing parameters (g, h) were analyzed within the ranges of 0.4 mm-1.5 mm and 4 mm-8.5 mm, using step sizes between 0.15 mm and 0.5 mm. Similarly, the upper gap parameters (r, s) on the patch were examined with a step size of 0.1 mm across the intervals of 1.3 mm-2.1 mm  
Figure 5: Comparison of optimization results for the S\({}_{11}\) parameter.  
and 1.9 mm-2.8 mm. The effects of these parametric variations on S\({}_{11}\) performance are illustrated in Figure 6. The simulation results indicate that the spacing between the arms has a more pronounced impact on the S\({}_{11}\) values, whereas the influence of the upper gap remains relatively limited.  
Figure 7a,b show the S\({}_{11}\) results for the arm spacing (g and h) and upper gap (r and s) configurations. These investigations focused on fine-tuning the slot dimensions to minimize performance deviations due to fabrication inaccuracies, ensuring the antenna's robustness and consistency throughout the specified frequency range.
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 3 Fragility Curve Based on Incremental Dynamic Analysis  
### Intensity Measure (IM) and Damage Measure (DM) Selection  
Since the building models employed in this study are first-mode-dominated structures, Sa(T\({}_{1}\)) is a better choice as the IM. On the other hand, the story drift ratio \(\theta\)max was used as the DM index. The \(\theta\)max is employed as a quantitative measure to distinguish performance levels during IDA-based seismic fragility assessments. The performance-based guidelines established by JSCA [18] were used as a reference to select the structural damage from three different levels as shown in Table 6. The selection of damage levels such as 1/200 shows minor damage, while 1/100 and 1/50 represent significant damage and collapse respectively.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 2 Star Pattern and Catalog
### Star Pattern Establishment  
In star identification algorithms, the construction of star pattern features typically involves selecting the navigation star to be identified as the center, with the surrounding stars as neighboring stars for feature construction. As shown in Figure 2, when constructing star pattern features, the navigation star serves as the center of a circle, and half of the field of view \(FOV/2\) is used as the radius. This circular area represents the observable region of the celestial sphere that the star sensor can capture when the navigation star is the central axis of view.  
Figure 1: The imaging principle of a star tracker.  
Figure 2: A sketch of feature extraction.  
In this paper, the distance \(r_{i}\) between the navigation star \(R\) and its neighboring stars, along with the angles \(\theta_{i}\) between adjacent neighboring stars, are used as the two fundamental features of the navigation star. These features exhibit rotational invariance, making them robust for star identification.  
In this region, all stars except the navigation star \(R\) are considered neighboring stars. These neighboring stars are labeled in counterclockwise order as \(\{S_{1},S_{2},\ldots,S_{n}\}\), where \(n\) represents the total number of neighboring stars. Connecting each neighboring star \(\{S_{1},S_{2},\ldots,S_{n}\}\) to the navigation star \(R\), the following Equations (2) and (3) can be used to calculate the distance \(\{r_{1},r_{2},\ldots,r_{n}\}\) and angles \(\{\theta_{1},\theta_{2},\ldots,\theta_{n}\}\):  
\[r_{i}=\sqrt{(x_{i}-x_{c})^{2}+(y_{i}-y_{c})^{2}} \tag{2}\]  
\[\theta_{i}=\left\{\begin{array}{cc}\arctan(\frac{y_{i1}-y_{i2}}{x_{i1}-x_{c }})-\arctan(\frac{y_{i1}-y_{c}}{x_{i1}-x_{c}})&\text{for}&i=1,2,\ldots,n-1\\ 360-\arctan(\frac{y_{i2}-y_{c}}{x_{n}-x_{c}})+\arctan(\frac{y_{i1}-y_{c}}{x_{ 1}-x_{c}})&\text{for}&i=n\end{array}\right. \tag{3}\]  
where \(x_{i}\) and \(y_{i}\) are the \(i\)-th neighbor star coordinates in the image coordinate system, respectively; \(n\) is the total neighbor stars in the FOV; and \(x_{c}\) and \(y_{c}\) are the coordinates of the navigation star.  
The above features are arranged in a counterclockwise manner. Different starting points \(s_{i}\) correspond to feature vectors \(v_{r}\) and \(v_{\theta}\) that are merely cyclically shifted. For example, for navigation star \(R\), the feature vectors \(v_{r}=\{r_{1},r_{2},\ldots,r_{n}\}\), \(v_{\theta}=\{\theta_{1},\theta_{2},\ldots,\theta_{n}\}\) obtained from starting point \(s_{1}\) and the feature vectors \(v_{r}=\{r_{6},r_{7},\ldots,r_{n},\ldots,r_{5}\}\), \(v_{\theta}=\{\theta_{6},\theta_{7},\ldots,\theta_{n},\ldots,\theta_{5}\}\) obtained from another starting point \(s_{6}\) differ only by one cyclic shift. Therefore, using these two features as star pattern features has rotational invariance.
## 2 Materials and Methods
### Zebrafish Embryo Toxicity Test  
The embryonic toxicity tests on zebrafish (_Danio rerio_), a widely used fish model organism, were performed using a modified version of the Fish Embryo Acute Toxicity Test (FET)--OECD guidline 236 [58] in accreditated laboratories for laboratory animals of the Department of Animal Protection and Welfare and Veterinary Public Health (University of Veterinary Sciences Brno, Czech Republic).  
The fertilized eggs were purchased from a certified commercial producer (Mendel University in Brno, Czech Republic) [1; 2]. At first, the quality and stage of the zebrafish embryos were examined using a stereomicroscope. The fertilized eggs at the 16-cell stage without any obvious abnormalities were selected for the embryonic toxicity test, in which they were exposed to the individual antidepressants and to their mixtures for 96 h. Detailed information about the tested concentrations is highlighted in Section 2.1. Chemicals and Materials. The eggs were randomly distributed on microwell plates, one embryo in each well. A total number of 36 fertilized eggs was used for each tested concentration and for the control group as well. The tested solutions were changed every 24 h by gently draining each well and adding a new working solution slowly to avoid any disturbance. The plates were stored in the growth chamber with constant parameters (26 \(\pm\) 1 \({}^{\circ}\)C, photoperiod 12 h/12 h). The embryo monitoring was performed using a stereomicroscope at 24, 48, 72, and 96 h post-fertilization (hpf). Next, the lethal and sublethal endpoints were recorded--mortality, hatching, the occurrence of malformations (e.g., oedema, changes in pigmentation, deformities) [59]. In addition to this, the heart rate monitoring was performed using a stereomicroscope at 48 hpf. The heart rate observations were performed manually by visual inspection and repeated three times for each embryo. The number of beats was counted for 30 s and the results were expressed as the number of beats per minute. The embryo monitoring was performed by experienced examiners who were blind to the treatments.