esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 1 Introduction
### Related Work  
Many studies show that satellite systems based on beam-hopping technology has better performance than others. In [8], [PERSON] analyzed the throughput of the downlink segment of a LEO global broadband satellite network and presented upper and lower bounds on the downlink throughput as a function of the number of spot beams, the interference constraints, and the coverage area. In [10], the author focused on studying the forward link beam-hopping transmission scheme, and their simulation results showed that the beam-hopping satellite system outperforms traditional systems in terms of matching throughput with ground business requirements, as well as effective utilization of available resources. In [11], [PERSON] studied the flexibility of beam-hopping systems and showed that its capacity was higher than that of non-hopping systems. In [12], [PERSON] provided a detailed analysis of the performance improvement brought about by the beam-hopping technology from the perspectives of flexible allocation of on-board resources and satellite load performance.  
The design of resource allocation based on beam-hopping technology is another research hotspot. In [13], the author improved the total throughput of the system by optimizing beam allocation. In [14], [PERSON] proposed a greedy algorithm in the LEO satellite system to allocate frequency and power to beams. In [15], the author considered power optimization based on demand and channel quality, which aimed at overall system performance. The study [16] introduced joint power and frequency allocation optimization, focusing on maximizing the minimum ratio between user requests and the signal-to-interference-plus-noise ratio (SINR) provided, but flexibility is limited by orthogonal carrier allocation and binary power allocation assumptions within the beam cluster. At the same time, multi-beam satellite resource allocation is developing from fixed allocation to dynamic joint allocation. In the literature, refs. [17; 18] conducted a series of research works to verify the advantages of the highly flexible dynamic beam-hopping method in multi-beam satellite systems. In addition, the DVB-S2X standard has proposed several superframe specifications [19] to support beam-hopping transmission in future multi-beam satellites, which also means that beam-hopping technology has enormous potential.  
Some studies have proposed joint research between beam-hopping technology and other technologies. In [20], the author studied the synergistic effects of non-orthogonal multiple access and beam-hopping in multi-beam satellite systems and proposed a greedy algorithm. The authors of [21] proposed a method of combining precoding and beam-hopping, which added precoding to balance inter-beam interference. Reference [22] studied a possible method to improve system performance through the interaction between onboard switching fabric and BH.
# GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III
## 5 Conclusions  
Through the long-term monitoring and evaluation of GPS signals, it was observed that GPS III signals improved significantly as compared to GPS II signals. This work established a signal distortion model according to the characteristics of the navigation load components. Furthermore, all GPS satellite digital distortions and analog impulse responses were estimated simultaneously using the joint estimation method. The analysis revealed that, compared with those of GPS II, the digital distortions of GPS III were negligible. With regard to the analog impulse response, GPS III satellites exhibited better amplitude and group delay consistency than GPS II. Lastly, the ranging bias caused by the navigation payload channel improved by approximately 0.02 m in GPS III satellites, as compared with that of GPS II satellites.  
Figure 15: BIII-2 L5-Cd SCB.  
**Author Contributions:** [PERSON] conceptualized and wrote the paper; [PERSON] and [PERSON] supervised the paper and suggested modifications; [PERSON] analyzed the data. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported by the Light of the West of the Chinese Academy of Sciences under Grant No 29Y817 YR000109.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors sincerely appreciate the support from NTSC of the Chinese Academy of Sciences.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
In this article, a novel multi-mobility UAV channel model, based on different 3D dynamic trajectories, is proposed. For this study, a Markov chain was introduced, to realize the randomness of the Tx, and an ST mobility model was used for generation of the Rx trajectory, to simulate the actual situation of UAV-assisted emergency communications. Based on the multi-mobility multi-trajectory model, the typical statistical properties of the proposed channel model--such as time-variant PDP, temporal ACF, spatial CCF, stationary interval, and RMS DS--were investigated. Moreover, the RMS DS of our proposed model was validated by the measurement data. The results proved that the trajectories of both UAVs and ground vehicles, and the trajectories of moving vehicles around the Rx, had an impact on the PDPs. For the same trajectory of the Tx, the time-variant ACFs presented different changing trends according to different Rx trajectories. For the same trajectories of the Tx and the Rx, the time-variant ACF had a faster decline with the increasing of UAV velocity. The variation of the Rx trajectory had little effect on the CCFs. Frequent  
Figure 10: The CDF of the RMS DS with simulation and measurement data (\(v_{UAV}\) = 5 m/s, \(v_{vehicle}\) = 5 m/s, \(f_{c}\) = 3.6 GHz): (**a**) satellite cloud image of the measurement site; (**b**) CDF of the RMS DS, based on measurement.  
movements of UAVs, as well as ground vehicles, can affect the stationarity of the channel in time as well as in space domains. Our future work will increase the scope of application of the trajectory at the ground Rx and UAV Tx ends, and will make the model more consistent with real scenarios.  
Conceptualization, Y.L.; formal analysis, J.Z., Y.L. and H.C.; funding acquisition, Y.L.; investigation, J.Z., Z.Z. and J.L.; methodology, J.Z., H.C. and J.H.; resources, Y.L.; software, J.Z., H.C. and J.H.; supervision, [PERSON]; validation, [PERSON], [PERSON], [PERSON] and H.C.; writing--original draft, J.Z.; writing--review and editing, [PERSON], [PERSON], H.C., [PERSON] and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 62001269, by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2022K009), and by the Future Plan Program for Young Scholars of Shandong University.  
Not applicable.
# Adaptive Algorithm for Fast 3D Characterization of Magnetic Sensors
## 3 Results
### 2D Estimation, Threshold Analysis, and Adaptation Behavior
As the expected bandwidth of the sensor extends to 1 kHz and the estimate cannot be expected to be accurate above 2 kHz (cf. Section 2.1), we only show the frequency up to around 1.4 kHz on the y-axis. The response is then encoded by a color for the magnitude of a specific bin and is plotted for each of the 360\({}^{\circ}\) (x-axis). The low-pass characteristic is clear in and around the sensitive axis (\(0^{\circ}\), \(180^{\circ}\), and 360\({}^{\circ}\)), as is the cosine-like behavior with insensitive areas around 90\({}^{\circ}\) and 270\({}^{\circ}\). The directivity behavior is clearly visible when showing singular frequency bins only over the angular directions. This leads to the polar representation plotted in Figure 11. Here, the frequency responses are based on a shorter, 512-sized direct implementation of a magnitude discrete Fourier transform (DFT) that is directly calculated at runtime with the algorithm to provide visual feedback during experiments. Estimated responses from every second bin were chosen and are depicted in the figure, this time being encoded by lighter colors. This shows the directivity pattern in a more tangible fashion, while also providing the low-pass information due to the shrinking radial dimensions at higher frequency bins. Figure 9: Visualization of the adaptation behavior over the starting period of a 2D adaptation measurement. The continuing, beneficial model adaptation is visible, as is the change in the system triggered by a sufficiently good estimate or a time-based threshold. A new direction’s short starting time with a step size of 1 can also be discerned from the progression of the adaptive step size in the lower plot controlled by the CEA. Figure 11: Alternative visualization in a polar plot of the 2D estimation results. The radial component corresponds to the magnitude at the respective angular direction. The color encodes different frequency bins in the estimated frequency response. The expected cosine behavior is clearly apparent and the low-pass characteristic is discernible from the much smaller magnitudes of higher frequency bins. Figure 10: Spectrogram-like visualization of the results of a 2D estimation run. The frequency response is plotted over the 360\({}^{\circ}\) measured on the x-axis with the y-axis depicting the frequency bins up to around 1.4 kHz. The color encodes the respective magnitude derived via FFT from the impulse response estimations. The expected behavior with a low-pass characteristic at around 1 kHz and insensitive directions at 90\({}^{\circ}\) and 270\({}^{\circ}\) is clearly visible.
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 4 Results
### Direct EEG Prediction  
We begin the discussion of the results by an overall evaluation of all available electrodes' data gathered and employed for predictions as described in Section 3. The subject-based results are illustrated in Figure 4.  
We initially observed that the overall average obtained for the \(R^{2}\) score of all sensors' data was just below \(R^{2}=0.64\). This level indicates an overall approach of fair prediction performance. We furthermore observed that this average level was accompanied by a significant level of variability (\(SD=0.43\)). Indeed, a closer inspection of the individual subject prediction performance readily reveals considerable differences. The first subject in this set, as an extreme example case, exhibited even a negative \(R^{2}\) score.  
Next, we considered the prediction errors indicated as MAE, also illustrated in Figure 4. We first observed that the QoE prediction yields MAE values that indicate significant deviations from the original (\(M=1.85,SD=0.16\)). The small level of variability here results from only a small spread on the high end for individual subjects (ranging between 1.57 and 2.13). Additional evaluations of a potential reduction in the amount of sensors, either to the left two, right two, outside two or center, two indicated that the prediction performance would decrease even further.  
Figure 4: Overview of the EEG data-based results for individual users and electrode positions for the QoE/QoS predictions based on SVR.  
Shifting the view to an approximation of the QoS (set image quality level), we continue to illustrate our results in Figure 4. A first view at the overall level of the \(R^{2}\) score obtained is similar to the one we derived for the QoE predictions, resulting in an average \(R^{2}=0.68\). The performance increased for some users (including the outlier observed for QoE values) and only partially decreased for some of the others, which subsequently was reflected in the slightly reduced variability (\(SD=0.34\)). Comparing the different scenarios for reduced amounts of sensors, we furthermore observed this trend continuing, with an exception for the reliance on the outside sensor data only, where the QoS prediction performance was below that for the QoE.  
We observed a significant reduction of the MAE (\(M=0.26\), \(SD=0.24\)) that came with the increase in the \(R^{2}\) performance, which was also reflected in the MAE range (ranging from 0-0.83). Performing a similar evaluation of the possibility for complexity reductions as before, we noticed that the decrease of EEG measurement data had an overall damaging effect on the results. While better prediction outcomes were attained for individual subjects in some constellations, the generally observable trend for different electrode placement scenarios was one of an overall reduced prediction accuracy indicated by lower \(R^{2}\) scores paired with higher MAE levels.
## 3 Results
### Effects of GPP and VSCM on Gene Expression  
Figure 4 shows the expression level of different molecular makers in hG-MSCs cultivated on GPP, VSCM, and TCP. In all cases, the gene expression of all investigated proteins was higher in the cells growing on the biomaterials compared to TCP, and in many cases, these differences were statistically significant. The differences between different materials were rather marginal; hG-MSCs growing on GPP showed minorly but significantly higher  
Figure 3: The effects of GPP, VSCM, and TCP on the metabolic activity of hG-MSCs. The amount of metabolized resazurin produced by cells was measured after \(3,7\), and \(14\) days. Cells seeded on TCP served as control. The data from five individual donors are represented as the mean \(\pm\) SD. * and **—significantly different between cells seeded on different surfaces of materials at the same time point of measurement with \(p<0.05\) and \(p<0.01\), while the distinctions between days \(3\) and \(7\) and \(14\) for the same material are indicated by # (\(p<0.01\)) and \(\dagger\) (\(p<0.05\)), respectively. Compared to VSCM, GPP promoted the metabolic activity of hG-MSCs.  
Figure 2: The proliferation/viability of hG-MSCs on GPP, VSCM, and TCP. The proliferation/viability of cells was measured with CCK-8 after \(3\), \(7\), and \(14\) days. Cells seeded on TCP served as control. The data are shown as the mean \(\pm\) SD (\(n=5\)). Differences between groups at the same time point are indicated by * (\(p<0.05\)) and ** (\(p<0.01\)), whereas those between days \(3\) and \(7\) and days \(7\) and \(14\) for the same material are indicated with # (\(p<0.01\)) and + (\(p<0.01\)), respectively. GPP is more effective than VSCM in facilitating the proliferation/viability of hG-MSCs.  
expression of VEGF-A, \(\alpha\)-SMA, and TGF-\(\beta\)1 after 3 days and COL1A1 after 14 days of culture compared to VSCM.  
Figure 4: Gene expression of various proteins in BG-MSCs grown on different materials. Gene expression levels of COL1A1 (**a**), VEGF-A (**b**), ACAT2 (**c**), TGF-\(\beta\)1 (**d**), FAK (**e**), FGF-2 (**f**), ITG-\(\beta\)1 (**g**), and IL-8 (**h**) were assessed by RT-qPCR after 3, 7, and 14 days of culture, calculated using the \(2^{-\Delta\Delta\text{CI}}\) method and GAPDH as a reference gene. Y-axes show the n-fold expression levels compared to the level of day 3 of TCP control (n-fold expression = 1). The data are displayed as the mean \(\pm\) SD of five different donors. Differences between groups at the same time point are indicated by * and ** for \(p<0.05\) and \(p<0.01\), respectively. hCG-MSCs cultured on biomaterials exhibited increased gene expression of the investigated biomarkers compared to those on TCP. Cells cultured on GPP demonstrated slightly yet significantly increased expression of VEGF-A, \(\alpha\)-SMA, and TGF-\(\beta\)1 after 3 days, and COL1A1 after 14 days, compared to those grown on VSCM.
## 3 Artificial Audio Effect Technologies
### Sound Recording and Reproduction Technology
With the new possibilities of multitracking technology, delay-based effects emerged, such as _delay_ (echo), _flanging_, and _slapback_. These effects are closely related to each other. The main difference lies in the delay time ranges in which they operate. Table 1 shows the approximate delay time ranges for the members of this family of effects. While flangers usually apply periodic modulation, many chorus implementations perform random modulation of the delayed signal. Furthermore, as opposed to the chorus and flanger effects, echo effects (and resonator and slapback effects) typically do not employ any modulation of the delay time. While [PERSON] is considered the first to use the flanger effect in 1952 [16]; its first use with automatic double tracking (ADT) goes back to [PERSON] experimenting as a recording engineer at Abbey Road Studios in the late 1960s. [PERSON] is credited with coining the term \"flanging\" for this particular effect while dynamically changing the speed of the second tape machine manually [81; 82]. Another standard audio effect in a music production studio is the chorus. With the arrival of the first digital delay effect unit, the Lexicon Delta T, in 1973, it was possible to achieve shorter delays than with the previously used tape delays [6]. Although the effect relies on multiple tracks of the same recording, it may still be classified as a timbre-effect [83]. It is a delay-based audio transformation where the output is a linear sum of the dry input signal and the dynamically delayed input signal. The delay time range for this effect is relatively short (under 30 ms) to make the output sounds perceived as one. Modulation of the delay time results in some deviation in pitch, depending on the modulation depth. Specifically, reducing the delay time by reading from the delay line at a faster rate increases the pitch while slowing down reading from the delay line lowers the pitch. The typical application of the chorus in music production is to take a single source sound and to emulate multiple sources playing in unison to represent the natural effect occurring when several performers play or sing the same music. The effect takes into account the fact that it is virtually impossible for musicians to play in perfect synchronicity. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Delay Range (ms)** & **Modulation** & **Effect Name** \\ \hline
0–20 & - & Resonator \\
0–15 & sinusoidal & Flanger \\
10–25 & random/sinusoidal & Chorus \\
25–50 & - & Slapback \\ \textgreater{}50 & - & Echo \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Delay time range approximates for delay-based effects according to [PERSON] [80]: The modulation sources may vary depending on the implementation and musical application. Figure 5: With a black control box (the _[PERSON]_) mounted on a guitar, [PERSON] was able to control tape machines during live performances. Photo © 2010 by [PERSON] (Zap Ltd. Music), used with permission.
# Health Risk from Toxic Metals in Wild Rice Grown in Copper Mining-Impacted Sediments
## 3 Results and Discussion  
### Distribution of Toxic Metals in the Water Column, Porewater, Sediment, and Wild Rice  
The toxic element content of the sediment, water, porewater, and wild rice plant samples are shown in Table 1. The results for the Sand Point sediment samples are broadly in agreement with previous reports of stamp sand and sediment analysis reported for the L'Anse and Keweenaw Bay areas [6; 7; 8]. The stamp sand discharged by Mass Mills in Keweenaw Bay during the first two decades of the 20 th century has continued to migrate south toward L'Anse Bay, the largest Indian reservation in Michigan [5]. The Sand Point site is located at the entrance to L'Anse Bay and is expected to contain stamp sand deposits in the sediments. As seen in Table 1, the levels of toxic metals were below sedimentquality guidelines (SQGs), except for Cu. SQGs for the protection of sediment-dwelling organisms in freshwater ecosystems include a variety of measurements that are grouped under threshold effect concentration (TEC), below which adverse effects are not expected to occur [19]. TECs include threshold effect levels (TELs), effect range low values (ERLs), or lowest effect levels (LELs). While the TEL value for Cu is 35.7 ppm, the Sand Point sediment samples had a mean value of 43.7 ppm of Cu. However, the values for all other toxic metals in the Sand Point sediments were below TELs [16]. Since Lake Plumbago is an inland lake with no direct connection to Lake Superior, high levels of toxic metals were not expected in the sediments. Interestingly, the average Cd levels (1.38 ppm) exceeded the TEL value of 0.596 ppm in Lake Plumbago. In addition, As levels were 3.7 fold, Cr 4.3 fold, Mn 8.6 fold, Ni 5.2 fold, and Pb 6.7 fold higher compared to those of Sand Point sediments. Because of the legacy of mining in the area, most of the studies on sediment and water contamination reported so far were carried out on Keweenaw and L'Anse Bays. To our knowledge, no such study has been carried out on other inland lakes where wild rice restoration is practiced.  
On examination of the toxic element levels in porewater, the Probable Effect Quotient (PEQ) values (calculated as total element concentrations/Probable Effect Concentration of element) were all well below one for both Sand Point and Lake Plumbago samples. This indicates that the probability of toxic effects on benthic organisms in the lab from these samples would be negligible [20].
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 4 Factors Influencing the Production of Microbial Biopolymers
### Extrinsic Factors  
(i) Downstream processing: The overall production of biopolymers is influenced by various downstream processes; filtration, precipitation, and extraction directly affect the quality of biopolymers [247]. The extraction of a biopolymer from agro-industrial waste provides less yield as compared to extraction from commercially utilized materials. Extraction of biopolymer by solvents and multiple steps in downstream processing raise the end cost of biopolymer [248]. For example, [PERSON] et al. [249] and [PERSON] [250] concluded in their studies that there are several processes in the manufacture of PHAs, and downstream processing can make up half of those steps' production costs. Periodically, the temperature step involved in downstream processing leads to a deteriorating effect on the attributes of some biopolymers.  
(ii) Process design: The final attributes of fabricated biopolymers depend upon various process factors such as agitation techniques, the type of bioreactor employed, inoculum density, feeding methods, mass flow, enthalpy of stream, and processing time [251]. Simple batch reactors, continuous or fed-batch systems, and semi-continuous are possible types of bioreactors employed for the production of microbial biopolymers. In terms of the layout, scalability, ease of inspection, control, and the particular needs of the biopolymer being produced must be considered [9; 252].  
(iii) Scale-up considerations: There are complications in repositioning lab-scale biopolymer production on an industrial scale. For prosperous large-scale production, components like conserving constant requirements, precluding contamination, and improving nutrient and oxygen transportation are paramount [7].  
(iv) Ageing of biopolymers: After the fabrication of the biopolymer, it experiences changes in its structural and rheological properties over time. This process is known as the aging of biopolymers. Changes in crystallinity and physicochemical attributes are directed towards the weakening of biopolymers. [PERSON] et al. [253], [PERSON] et al. [254], [PERSON] et al. [255], [PERSON] et al. [256], [PERSON] et al. [182] and [PERSON] et al. [257] observed the impact of aging on poly(3-hydroxybutyrate-co-3-hydroxyvalerate, alginate, chitosan, bacterial cellulose and polylactic acid, respectively.