esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Attempts to Quantify GSI with an Explicit Focus on the Vertical Axis of the V-GSI Chart
### Key Equations to Quantify VFC (P30) and P32 Estimates  
Provided that potential failure mechanisms associated with longer joints or faults are understood and considered in the geological model, it is contended that scaled VFC (P30\({}_{\text{UNIT VOLUME}}\)) and P32\({}_{\text{UNIT VOLUME}}\) parameters as used in the V-GSI chart do have their place in engineering design. Equation (1) can be used to estimate VFC contributions for individual joint sets in a rock mass. This is calculated as the inverse of the mean 'true' spacings (_Sti_) for each joint set present in a rock mass  
\[VFC_{i}\left(contribution\right)=\frac{1}{\
abla St_{i}} \tag{1}\]  
and the 'orientation' bias-free \(VFC\) for a rock mass is then simply the sum of the individual VFC contributions for each discontinuity set present in the rock mass, but, in general, is calculated with the use of Equation (2), irrespective of the number of _ith_ discontinuity sets (_NDS_)  
\[VFC=\sum_{i=1}^{i=NDS}\left(\frac{1}{\
abla St_{i}}\right) \tag{2}\]where \(\
abla St_{i}\) implies that the distribution of the joint spacing data is log normal or exponential and therefore geometric means are used to calculate _Sti._  
VFC, which requires correction of 'apparent' spacing data to 'true' spacing data (described in Equation (5)) per set, can also provide reasonable estimates of P32 UNIT VOLUME using Equation (3)  
\[P30\;(VFC)\to P32=\sum_{i=1}^{i=NDS}\frac{1}{St_{i}}*\pi r^{2}_{i}=\sum_{i=1}^{ i=NDS}VFCi*\pi r^{2}_{i} \tag{3}\]  
The 95% confidence limits on the mean VFC can also be calculated using Equation (4)  
\[VFC_{limits}=\sum_{i=1}^{i=NDS}\left(\frac{1}{\overline{S}_{t_{i}}\pm 1.96\times \frac{\partial S_{t}}{\sqrt{n}}}\right) \tag{4}\]  
where \(n\) is the number of 'true' spacing observations recorded for the _ith_ set, respectively. One of the other advantages of the VFC parameter is that it does not require the running of advanced 3D software to stochastically generate discrete fracture networks (DFNs) for rock masses to determine volumetric fracture counts (VFCs or P30) or P32. Field-collected apparent joint set spacing (Sa) data is corrected to true spacing (St) data, using real or virtual measurement scanline vectors on actual exposed faces or on scaled underground maps, outcrop maps, or foundation maps. St data can be used to estimate reliable 'orientation' bias-free mean VFCs and the associated confidence limits. These in turn can then be plotted on the vertical axis of V-GSI chart. The VFC mean and limits are then linked to correlated ratings on the vertical axis [3] (Figure 6). Once the joint conditions are assessed the overall V-GSI ratings can be assessed and H-B strength and deformability parameters can be determined. As a reminder, St data require the use of a correction factor (CF) and the calculation of CFs is possible because the respective angular relationships between, e.g., a tunnel face, a rock cut, or foundation surface, and measurement scanlines (which are vectors when directions are measured) and discontinuities are recorded by the mapping process.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Self-Organizing Control Using Artificial Potential Functions  
In this section, the scale-independent relative orbital elements (SIROEs), as well as the corresponding bound formulations, are introduced first. The relative motion bound denotes the maximum or minimum value of the relative distance between two satellites.  
Figure 4: Street-of-coverage geometry of the perturbed coplanar satellites. D and E are two sub-satellite points. A and C are two intersections of two coverage circle. Point A, B, C form a right triangle. Point D, E, F form a right triangle.  
Based on SIROEs, the artificial potential functions for single satellite control are designed under the continuous coverage constraint. The self-organizing control rules for the coplanar satellites are presented.
## 4 Proposed Architectural Approach  
### Architectural Components  
The proposed architectural approach for an O-RAN-based, 5G Standalone (SA) network for NPN deployments is shown in Figure 2. The infrastructure layer consists of the core and edge/regional network functions virtualization infrastructures (NFVIs), the cell site platform, transport network segments, and synchronization elements. NFVI is a key component of the network functions virtualization (NFV) architecture that describes the hardware and software components on which virtual networks are built. The cell site platform includes proprietary infrastructures, where the software is coupled with the hardware and supports the deployment of physical network functions (PNFs).  
The network function layer consists of all NFs, related either to the O-RAN or the 5G core (5 GC). The O-RAN architecture is based on the decomposition of elements into NFs. To additionally support vertical disaggregation, the 5G SA core network (CN) combines the necessary NFs for the user plane (UP) and the control plane (CP).  
At the MOA layer, various modules have been included providing intelligence and automation to the network and service management and orchestration. The modules are responsible to provide the following main functions:  
* Resource and service orchestration
* Network slicing
* Element management
* System telemetry data collection
* Management data analytics
* ML model design, training, and placement
* Non real time O-RAN control.  
The main functionalities at the MOA Layer are described in detail in the following subsections.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 6 Results
### DRX Power Saving with Adaptive SPS  
The simulation results for DRX power savings with adaptive SPS algorithm for 4 traffic classes are discussed henceforth. For demonstration purposes, we deploy 2 example values of \(f_{S}\) in our model, \(f_{S}=0.4\) and \(f_{S}=0.8\). Figure 19 shows the percentage of packets meeting their respective delay budget values as total number of UEs increase from 40 to 120. Initially, the dynamic scheduler performs well but as the number of UEs increased beyond 60, more packets fail to be delivered within their delay budgets. For the adaptive SPS algorithm, the delay performance is satisfactory with both configurations. For \(f_{S}=0.4\)  
Figure 18: Mean control channel utilization with increasing number of UEs.  
98.8% packets meet their delay budget for 120 UEs, while for \(f_{S}=0.8\), the value is 93%. The reason for performance degradation in case of \(f_{S}=0.8\) is due to the PRB ceiling restriction which causes some packets in the UE buffer to wait for the next SPS opportunity.  
As shown in Figure 20, the UE power consumption values are averaged over 1000 TTIs for 4 classes of traffic. It is clear from the results that the adaptive SPS with DRX functionality saves significant amount of UE power for all classes of traffic. The high power consumption with the dynamic scheduler is due to the UEs remaining mostly in active state. For, \(f_{S}=0.8\), more power saving is achieved because of longer sleep periods. Class A UEs are the most power consuming due to their higher frequency of packet arrival.  
Figure 21 compares the channel utilization values which shows the dynamic scheduler having high control signaling load with 100% PDCCH saturation for 120 UEs. The PDCCH saturation with dynamic scheduler causes the delay performance degradation as seen in Figure 19. The adaptive SPS schedulers have very low PDCCH utilization, only requiring control signaling for initial grants and occasional updates of MCS and re-transmissions.  
Figure 21 also shows the PUSCH utilization comparison where the three schedulers have similar utilization values. The adaptive SPS schedulers have slightly higher data channel consumption because of semi-persistently allocating minimal resources where occasionally the UE might have no data to be sent. The predictive allocation scheme for \(f_{S}=0.8\) might also over-allocate sometimes which make the PUSCH utilization higher for it. This is a small trade-off for the SPS schedulers to save on control overhead and power consumption. If there is data channel resource constraint, then the conservative/reactive allocation scheme is more suitable than the predictive scheme for adaptive SPS.  
Figure 19: Percentage of packets meeting delay budget with DRX power saving.
## 3 Chitosan Modification and Functionalization
### Chitooligosacharide (COS) and Low Molecular Weight (LMW) Chitosan  
High molecular weight CS is very difficult to use in commercial applications due to its high viscosity. Reducing the molecular weight of CS is a good way of decreasing viscosity issues and enhancing biological properties thanks to the production of chitoligosacharides (COS) and low molecular weight CS (LMW). Generally, oligosaccharides are defined as oligomers with a degree of polymerization ranging from 2 and 10, but some higher DPs from 11 up to 30 are described as LMW in the literature [55]. The production of COS and LMW CSs is mainly achieved by physical, chemical, and enzymatic methods [55]. Table 1 gives an overview of these possible strategies, including the associated conditions for efficiently producing LMW CS or COS. The reduction of molecular weight by chemical, physical, or enzymatic processes has also been related to improving the solubilization of CS in water or acetic acid solutions [4, 55]. Depolymerization of CS is principally performed by acid chitosanolysis, which is the most reported method for producing COS and LMW CSs [4]. Overall, chemical processes include chitosanolysis with HCl [56], HNO\({}_{2}\)[57], H\({}_{2}\)O\({}_{2}\)[58], and potassium persulfate [59].  
Physical processes include depolymerization with sonication [26], electromagnetic irradiation, gamma irradiation [60, 61], microwave irradiation, or a thermal procedure [62]. Finally, enzymatic processes use specific enzymes like chitinase [63] and chitosanase [64], but also non-specific enzymes, such as pepsin [65], cellulase [66], lipase, pronase, protease [67], lysozyme, papain, glucanase, hemicellulose, or pectinase. However, the main issues of enzymatic depolymerization are probably the cost of making it redhibitory for bulk use in commercial applications and the relative slowness of reactions. In contrast, the main drawbacks of chemical methods involve the use of non-green chemicals, the need of their removal, and the heterogeneity of final products [4]. New methods for reducing the molecular mass of CS have been described, e.g., high-pressure homogenization (HPH) [68], plasma [69], or using zeolites adsorbents [70] to purify acid hydrolysis COS and LMW CS. Additionally, electrochemical processes have also been developed to efficiently depolymerize CS [71].  
Figure 5: The mains cross-linking reactions using chitosan.
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 3 Results and Discussion
### Inhibition of the Biofilm Development  
ZnO-NPs were tested for biofilm inhibition against _E. coli_, _S. aureus_, and _P. aeruginosa_. First, the MIC of ZnO-NPs was obtained with broth dilution assay. The MIC of ZnO-NPs against _E. coli_, _P. aeruginosa_, and _S. aureus_ was recorded to be 200, 400, and 200 \(\mathrm{\SIUnitSymbolMicro mL}\), respectively. The quantitative analysis of biofilms inhibition was performed and then qualitative examination of biofilms on glass surface was tested. The findings are discussed below.
## Biome-Scale Forest Properties in Amazonia Based on Field and Satellite Observations  
**[PERSON] 1,2**  
1 Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford OX1 3 QY, UK  
2 National Institute for Space Research (INPE), Sao Jose dos Campos, SP 12227-010, Brazil;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +55-12-3208-6439; Fax: +55-12-3208-6488  
_Received: 20 February 2012; in revised form: 26 April 2012 / Accepted: 27 April 2012 / Published: 4 May 2012_  
**Abstract:** Amazonian forests are extremely heterogeneous at different spatial scales. This review intends to present the large-scale patterns of the ecosystem properties of Amazonia, and focuses on two parts of the main components of the net primary production: the long-lived carbon pools (wood) and short-lived pools (leaves). First, the focus is on forest biophysical properties, and secondly, on the macro-scale leaf phenological patterns of these forests, looking at field measurements and bringing into discussion the recent findings derived from remote sensing dataset. Finally, I discuss the results of the three major droughts that hit Amazonia in the last 15 years. The panorama that emerges from this review suggests that slow growing forests in central and eastern Amazonia, where soils are poorer, have significantly higher above ground biomass and higher wood density, trees are higher and present lower proportions of large-leaved species than stands in northwest and southwest Amazonia. However, the opposite pattern is observed in relation to forest productivity and dynamism, which is higher in western Amazonia than in central and eastern forests. The spatial patterns on leaf phenology across Amazonia are less marked. Field data from different forest formations showed that new leaf production can be unrelated to climate seasonality, timed with radiation, timed with rainfall and/or river levels. Oppositely, satellite images exhibited a large-scale synchronized peak in new leaf production during the dry season. Satellite data and field measurements bring contrasting results for the 2005 drought. Discussions on data processing and filtering, aerosols effects and a combined analysis with field and satellite images are presented. It is suggested that to improve the understanding of the large-scale patterns on Amazonian forests, integrative analyses that combine new technologies in remote sensing and long-term field ecological data are imperative.  
**Keywords:** Amazonia; above ground biomass; fire; drought; field data; forest productivity; monitoring; net primary production; rain forest; satellite data; wood density; remote sensing; MODIS
## 3 Results and Discussion
### Total Efficiency  
The total efficiency of an antenna is one performance factor that defines the general performance of an antenna in any communication system. In fact, it measures the portion of power that is actually emitting from the antenna to the power fed to it, combining the effects of the radiation efficiency and the impedance mismatch. In the proposed ten-port MIMO antenna system, the total efficiency values varied from 76.12% to 91.57% for all the operating bands of interest, emphasizing the upper mid-band region performance of the proposed design, as shown in Figure 10. This high efficiency translates to low power losses, a factor that makes the antenna very appropriate for next-generation wireless applications that require efficient, low-power systems. Various aspects such as effective design of the stacked geometry of the PCB, effective design of the feed lines, and efficient decoupling play a critical role in attaining such efficiency. The efficiency of all the bands under the LTE was considerably constant; the LTE 46, LTE 47, and n102 all performed efficiently, making the product reliable and apt for multi-national compact devices such as smartphones that function in multi-bands.  
Figure 10: _Cont._  
Figure 9: The realized gain of a proposed MIMO antenna array throughout the obtained frequency band.
# Upmix B-Format Ambisonic Room Impulse Responses Using a Generative Model
## 5 Experiments Results  
To showcase the efficacy of the generative model in addressing the FOA upmixing problem, we conduct a comprehensive analysis of the spatial characteristics exhibited by the model's output. Additionally, we explore the potential advantages that arise from employing such RIRs in binaural rendering applications.  
We conducted an experiment to estimate RIRs at 32 positions on the surface of a spherical microphone array with a radius of 0.042 m, corresponding to the Eigenmike microphone layout. Three methods were utilized for RIR estimation:  
1. First-order Ambisonics (FOA) reconstruction;
2. Upmixed RIRs using ASDM;
3. Upmixed RIRs using HiFi-GAN.  
The simulation procedure for ASDM can be referred to in the work by [PERSON] et al. [8]. The pseudo-intensity vector is utilized for estimating the DOA of the FOA RIRs, which are further used to re-encode the RIRs for the generation of the upmixed HOA RIRs. Additionally, a spectral decay correction is applied. Further details regarding the ASDM simulation procedure are not elaborated in this study.  
Figure 5 shows the time-domain signal and spectrum of the RIRs for the tenth channel in a test set. We can observe that HiFi-GAN effectively compensates for the high-frequency roll-off in low-order signals. Conversely, ASDM, which enhances the directionality of waves in each time window, equalizes the spectral characteristics based on the first-order signals and does not directly address the issue of high-frequency roll-off.  
To conduct a quantitative comparison of upmixing errors across different methods, the NMSE values are computed between the spectra of the three considered cases above the aliased frequency and the spectra of the ground truth. These results are averaged over the frequency range. The analysis reveals that the HiFi-GAN method exhibits the lowest error, with an average NMSE of \(-10.3\) dB. The FOA reconstruction approach demonstrates an average estimation error of \(-7.9\) dB. In contrast, the upmixed RIRs generated using the ASDM method exhibit a higher error of \(-5.3\) dB.  
The inferior high-frequency reconstruction in ASDM upmixed RIRs can be attributed to the lack of explicit spectral modeling during the upmixing process. ASDM upmixing relies solely on directional decompositions derived from the omnidirectional W-channel of the FOA RIRs. Consequently, the ASDM algorithm does not have a mechanism to directly optimize the high-frequency fidelity of the original FOA signals. The omnidirectional W-channel lacks adequate directional information to properly calibrate the high-frequency components during upmixing. This shortcoming results in the suboptimal high-frequency reconstruction observed in ASDM upmixed RIRs. Our proposed technique addressesthis by incorporating multi-resolution spectrogram losses during HiFi-GAN training to explicitly retain high-frequency details from the original FOA RIRs.
###### Abstract
The main reason is that it lacks the ability to handle sharp fluctuations in the load profile, and noisy data have a large negative impact on load forecasting. Another reason is that it lacks the treatment of the strong fluctuation of the load curve. Undecomposed load noise is large, and the direct prediction error is large. Reference [14] integrates empirical mode decomposition, particle swarm optimization, and an adaptive network-based fuzzy inference system. This method decomposes the load and reduces the noise to a certain extent, but it does not introduce an attention mechanism based on the neural network. As a result, it obtains the same prediction feature weights and can still be further improved. To summarize, the previous methods have three problems in the daily load forecasting of group residents. Firstly, the load data of group residents have the characteristics of high dimensionality and large amount of data. Existing research tends to predict the load of individual residents, and there is a lack of research on the data characteristics of group residents' load. Secondly, there is a lack of treatment for the fluctuation of the load curve. The traditional method is fixed in the noise reduction time base function, and there is no adaptive matching signal which leads to noisy forecast data. Thirdly, the time series information is assigned the same weight, and the important information of the load data is not fully utilized in the prediction. Therefore, to solve the above problems, we propose a group resident daily load forecasting method. This method successfully reduces the forecast error and realizes the different daily load forecasts of group residents. The structure of this paper is as follows: Section 2 introduces the theoretical basis of the daily load forecasting method. Section 3 introduces the experimental analysis and experimental results, and Section 4 is the conclusion.
###### Abstract  
The aims of this study were to analyze the muscle injury incidence in an elite Spanish soccer academy during three consecutive seasons attending to different chronological age groups (i.e., U14, U16, U19 and senior) and to examine the hamstring injury incidence in this elite soccer academy. Elite male youth soccer players (227) participated in this study. A total of 207 time-loss injuries (i.e., injuries that involve at least one absence day) were observed during this study period. The overall incidence rate of muscle injury was 1.74 muscle injuries/1000 h. In addition, higher muscle injury incidence was observed during match play in comparison to training sessions (6.78 vs. 3.20 muscle injuries/1000 h, \(p<0.05\)). The oldest age group presented the highest injury rate (2.73 muscle injuries/1000 h, \(p<0.05\)), with the burden (i.e., number of absence days per 1000 h of exposure) peak values recorded in the U16 age group (26.45 absence days/1000 h). In addition, muscle tears accounted for the greatest percentage of muscle injuries (43.5%), and the most frequent anatomical site of injury was the hamstring (30.4%). Muscle tear was the most common type of hamstring injury (49.2%), with the biceps femoris the most commonly injured muscle of the hamstring complex (39.7%). Fullbacks (FB), wide midfielders (WM) and forwards (F) suffered a greater number of hamstring injuries. Hamstring injury incidence showed a seasonal variation, as indicated by peaks in August and October. Specifically, the highest injury incidence was observed in the final part of each period during match play. These results reinforce the necessity to implement individual preventive strategies according to each specific injury profile across the youth soccer development phase.  
Keywords:injury and prevention; team sports; health; youth +
Footnote †: journal: Applied Sciences