esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Progress Regarding _Cordyceps militaris_, Its Metabolite Function, and Its Production
## 2 Biological Compounds of _C. militaris_
### GABA  
GABA, short for \(\gamma\)-Aminobutyric acid, has a molecular formula of C\({}_{4}\)H\({}_{9}\)NO\({}_{2}\) and a molecular weight of 103.12 g/mol. It is classified as a non-protein amino acid and is synthesized within the human body from glutamic acid. Concentrations of GABA were determined to be 68.6-180.1 mg/kg and 756.30 mg/kg dry weight in the mycelium and fruiting bodies of _C. militaris_, respectively [17]. GABA exhibits various biological activities, such as antioxidative, anti-inflammatory, anticonvulsant, antihypertensive, and antidiabetic properties.  
GABA, known for its antioxidant properties [59], helps plants cope with various environmental stresses by providing nicotinamide adenine dinucleotide (NADH) and/or succinic acid via GABA shunts in the tricarboxylic acid cycle [111]. In addition to maintaining hormone and mineral nutrition, GABA reduces lipid peroxidation and enhances stress resistance in plants [112].  
In relation to the anti-inflammatory ability of GABA, it exerts potent protective effects in models of type 1 diabetes by acting on both T cells and macrophages [60]. Additionally, GABAergic agents directly act on antigen-presenting cells (APCs), reducing MAPK signals and attenuating the following adaptive inflammatory responses to myelin proteins [113].  
GABAergic neurons and neurotransmitters modulate various brain circuits to regulate stress and anxiety responses, under both normal and pathological conditions [114]. They also modulate both rapid eye movement (REM) and non-REM, especially slow wave sleep (SWS), through corticomedullary pathways [115]. Additionally, GABAergic neurons and neurotransmitters regulate circadian rhythms by modulating the suprachiasmatic nuclei (SCN) [116].  
By activating GABA receptors, central GABA has the potential to lower blood pressure and slow down the heart rate, making it a valuable therapeutic option for preventing and treating cardiovascular diseases resulting from hypertension, particularly in patients with cardiac insufficiency [117].  
GABA, an antidiabetic factor coexisting with insulin in pancreatic \(\beta\)-cells [118], exerts its effect by acting on beta cell proliferation and the immune system. In patients with type 2 diabetes, a significant decrease in circulating GABA concentrations was observed, indicating its potential therapeutic value for diabetes [119].
# Scaling of FAPAR from the Field to the Satellite
## 4 Results and Analysis  
### Temporal Corrections  
#### 4.1.1 Sensitivities of FAPAR to Extra Skylight Proportions  
In the proposed temporal normalization and analysis, only the proportion of skylight associated with SZA was considered. However, extra changes in skylight proportion could also be integrated into FAPAR-P model, which is interesting for further considerations on temporal correction issues.  
Two cases of SZA = 0\({}^{\circ}\) and SZA = 60\({}^{\circ}\) were conducted. Thus the minimum of skylight proportion is the proportion solely determined by SZA, while the maximum is 100%, as in Figure 6. It could be inferred that the effects of extra skylight are nearly linear and are more obvious at low LAI values. Besides, the changes of FAPAR with extra skylight show increasing trend at SZA = 0\({}^{\circ}\), and a mild decreasing trend at SZA = 60\({}^{\circ}\). In both cases the FAPAR value at 100% skylight remains the same. The opposite trend could be attributed to the more significant effects of SZA than those of skylight proportion. The findings were in accordance with those revealed in [7], where a higher skylight proportion tends to increase FAPAR when SZA < 21\({}^{\circ}\) and to reduce it when SZA > 30\({}^{\circ}\).  
As shown in Figure 6, given specific SZA a change of 20% of skylight proportion affects FAPAR in a subtle way, even at low LAI values. The effect of SZA is proved to be more influential than the extra proportion of skylight.  
Figure 6: Effects of skylight proportions on FAPAR under VIS = 30 km and: (a) SZA = 0\({}^{\circ}\); and (b) SZA = 60\({}^{\circ}\).
## 4 Experimental Characterization of Si-SC Structures with SLARC
### Optical Reflectivity Characteristics
broader wavelength range. Notably, the Si\({}_{3}\)N\({}_{4}\) SLARC with a thickness of 90 nm exhibits a minimum reflectivity of 0.16% at 750 nm compared to 9% for SiO\({}_{2}\) counterpart at 650 nm. The optimal spectral region for Si-SC light absorption spans from 350 nm to 1000 nm, with peak absorption efficiency typically occurring between 450 nm and 750 nm, centered around 600 nm. Table 1 provides a quantitative comparison of reflectivity values for n-type Si with and without SLARCs at key wavelengths within this range, alongside reference data for intrinsic Si. Both intrinsic and n-type Si exhibit relatively high reflectivity across the measured spectrum, with a decreasing trend towards longer wavelengths. The introduction of SiO\({}_{2}\) and Si\({}_{3}\)N\({}_{4}\) SLARCs significantly reduces reflectivity, particularly in the visible range (450 nm to 750 nm). SLARC thickness affects reflectivity, with thinner SiO\({}_{2}\) SLARCs generally showing lower reflectivity. However, this trend is less pronounced for Si\({}_{3}\)N\({}_{4}\) SLARCs. Reflectivity varies significantly with wavelength and SLARC thickness, highlighting the importance of considering the entire solar spectrum for SLARC and MLARC optimization. These experimental results serve as a foundation for subsequent modeling and simulation studies of Si-SC structures incorporating SLARCs and MLARCs.
## 6 Conclusions and Future Scope  
This paper proposes angular inclination-based RSC and routing. The network area is considered as a circular area, and it is divided into equal-sized virtual concentric rings based on the radius of the circle. Each ring is further divided into equal-sized clusters, which increases gradually from the inner layer to the outer layer. The CH is selected based on the nearest distance to the midpoint of the cluster. Furthermore, the CH is updated based on the number of live nodes and the residual energy to ensure load balancing. The number of nodes and packet collection rates are high because RSC is implemented for large-scale networks. To identify the optimal aggregation measures for large-scale applications, the statistical measures are considered with the objective of a high packet collection rate with reduced reclustering frequency. There is a tradeoff between the transmission distance and the range when the quantity of live nodes and routing is considered. It was found that the RSC outperformed the existing FSC in network energy conservation.  
Even though the present work yielded better results compared to the existing state-of-the-art algorithms, static random deployment with the manual change of quadrants is to be implemented in future. For real-time applications, it would be more useful if the quadrant is changed dynamically. This work addresses only flat surface deployment. In future, it should be extended to address regions with sloppy surfaces to bring realistic results. In addition to this, security of sensor networks and secure data transmission has to be taken into consideration for further improvement.  
Conceptualization, L.K., R.K.D. and D.G.G.; methodology, L.K., D.G.G. and T.R.G.; software, R.K.D., T.R.G.; validation, L.K., R.K.D. and D.G.G.; investigation, L.K., R.K.D. and D.G.G.; resources, T.R.G., M.K.A., and E.A.N.; data curation, M.K.A., L.K.; writing--original draft preparation, L.K., R.K.D. and D.G.G.; writing--review and editing, M.K.A., T.R.G. and E.A.N.; project administration, D.G.G., T.R.G. and E.A.N.; funding acquisition, M.K.A. and E.A.N. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to King Saud University for funding this work through Researchers supporting project number (RSP-2020/164), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Instruments and Reagents  
The morphology of prepared Fe\({}_{3}\)O\({}_{4}\)/Au nanoparticles was observed on a transmission electron microscope (TEM, Tecnai G2 F30 S-Twin, FEI Company, Hillsboro, OR, USA) equipped with an energy dispersive X-ray (EDX) analyzer (JEOL 4000 FX), operating at an acceleration voltage of 300 keV. SERS spectra were recorded on a DeltaNu Advantage 785 Raman spectrometer equipped with a microscope and a stage-sample holder. The spectrometer has a laser power of 120 mW with an excitation wavelength at 785 nm and a spectral range of 200-2000 cm\({}^{-1}\). Fourier transform infrared spectroscopy (FTIR) analysis was conducted on a Shimadzu IRaffinity-1S spectrometer equipped with a Specac ATR attachment containing a ZnSe crystal (refractive index \(n\) = 2.43). The transmission and reflectance spectra were recorded in the range from 4000 to 600 cm\({}^{-1}\) with a resolution of 2 cm\({}^{-1}\).  
Fe\({}_{3}\)O\({}_{4}\) nanoparticles (97%, 50-100 nm) and Rhodamine B isothiocyanate (mixed isomers, C\({}_{29}\)H\({}_{30}\)C\({}_{1}\)N\({}_{3}\)O\({}_{3}\)S) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Gold chloride acid hydrate (HAuCl\({}_{4}\)-4H\({}_{2}\)O, 99%) was purchased from Shanghai Siyu Chemical Technology limited Company (Shanghai, China). Melamine (99%), 5-aminoorotic acid (AOA, 98%), \(\gamma\)-mercaptoethanol protropyl triethoxysilane (MPTES, 98%) (3-aminopropyl) triethoxysilane (APTES, 99%), tetraethoxysilane (TEOS, 99%), and hydrated sodium citrate (99%) were purchased from Aladdin. Dialysis membrane (Spectra/Por, MWCO = 1000) was bought from Spectrum Laboratories Inc. (Rancho Dominguez, CA, USA). The active ester polyethylene glycolthiol (\(\geq\)95%, NHS-PEG-SH, MW = 1000) was obtained from Shanghai Tuoyang Biological Technology Limited Company (Shanghai, China). Sodium dihydrogen phosphate (NaH\({}_{2}\)PO\({}_{4}\)-2H\({}_{2}\)O), disodium hydrogen phosphate (Na\({}_{2}\)HPO\({}_{4}\)-H\({}_{2}\)O), and all other chemicals were of analytical grade and purchased from Hangzhou Chemical Reagent limited company (Hangzhou, China).  
Figure 1: The hydrogen bonding-assisted [Fe\({}_{3}\)O\({}_{4}\)/Au–AOA\(\bullet\)\(\bullet\)\(\bullet\)\(\bullet\)melamine\(\bullet\)\(\bullet\)\(\bullet\)\(\bullet\)AOA–RhB] surface enhanced Raman spectroscopy (SERS) matrix.
# Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing
## 1 Introduction
This applies also for the analog implementation of the beamspace techniques as in [19, 20]. However, the application of the digital beamspace preprocessing requires a coherent demodulation of the receive branches to preserve the phase progression between adjacent receive channels. This can become a very challenging task, especially in massive MIMO systems, where the number of receive paths is quite large. Therefore, an extension to a sub-array architecture is explored in the context of digital beamspace preprocessing as well offering the opportunity to relax the design requirements of the LO distribution network. In summary, the contribution of this work is to combine the individual (known) concepts of noncoherent detection scheme, sub-array approach, and digital beamspace processing for a joint examination under proper channel conditions. To the authors' best knowledge, this is presented for the first time in the area of massive MIMO transmission. This paper is organized as follows. The system model is introduced in Section 2, which includes a generalized system overview when digital beamspace preprocessing is applied. After a brief recapitulation of the underlying channel model and the noncoherent detection scheme denoting the most important aspects, a detailed description of the digital beamspace preprocessing tailored to noncoherent detection is presented. Numerical results are shown in Section 3, where full- and sub-array architectures and the influence of the channel properties are discussed. Finally, Section 4 concludes the paper. _Notations:_ Bold-faced capital letters and bold-faced small letters denote matrices and column vectors, respectively. Superscripts \((\cdot)^{\mathsf{T}}\), \((\cdot)^{\mathsf{H}}\), and \((\cdot)^{-1}\) indicate the transpose, the conjugate transpose, and the inverse of a matrix, respectively. The operators \(\mathrm{E}\{\cdot\}\), \(||\cdot||_{2}\), and \(||\cdot||_{\mathrm{F}}\) denote the expectation, the Euclidean norm of a vector, and the Frobenius norm of a matrix, respectively. \(\mathbf{I}_{N}\) is the \(N\times N\) identity matrix and \(\mathbf{diag}\left(\cdot\right)\) represents a diagonal (block) matrix.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 3 Future Perspectives
### Image Processing Algorithms
Finally, the amount of training associated with each algorithm and the need to parameterize the algorithms are another major obstacle. This parametrization can be more difficult in some MLAs like SVM but, overall, past studies show that the parameters tend to be case sensitive and therefore fundamental [112; 123]. New developments on how to transfer learning from one case study to another are needed and they will definitely increase the importance and usage of MLAs and DL in mineral exploration. On the other hand, there are still a great number of potentialities to explore in Li exploration that are constantly increasing with the new models and algorithms being proposed. These include adaptations of the original algorithms (e.g., oblique RF classification [125] or transductive SVM [126]) and the use of non-traditional optimization algorithms, such as genetic algorithms and particle swarm optimization, with MLAs [127]. The application of MLAs in sub-pixel classification (e.g., [128]) could also help to decrease the impact of mixed pixels in medium spatial resolution images.
## 3 Analysis of Energy Efficiency and Network Connectivity
### Underwater Energy Consumption Model  
The attenuation or path loss that occurs in an underwater acoustic channel over a distance \(l\) for a signal of frequency \(f\) is given by  
\[A(l,f)=l^{k}\alpha(f)^{l} \tag{1}\]  
where \(k\) is the spreading factor and \(\alpha(f)\) is the absorption coefficient. The spreading factor \(k\) describes the geometry of propagation, and its commonly used values are \(k=2\) for spherical spreading, \(k=1\) for cylindrical spreading, and \(k=1.5\) for the so-called practical spreading. The counterpart of \(k\) in a radio channel is the path loss exponent whose value is usually between 2 and 4, where the former represents free-space line-of-sight propagation, and the latter represents two-ray ground-reflection model. The absorption coefficient \(a(f)\) in dB/km for \(f\) in kHz can be expressed as [13]:  
\[10\text{log}\alpha(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.7 5\times 10^{-4}f^{2}+0.003 \tag{2}\]  
The above formula is generally valid for frequencies above a few hundred Hz. For lower frequencies, it is suggested to use the following formula:  
\[10\text{log}\alpha(f)=0.002+0.11\frac{f^{2}}{1+f^{2}}+0.011f^{2} \tag{3}\]  
The power consumption (denoted as \(P_{t}(l,f)\)) for the single packet transmission with distance \(l\) and frequency \(f\) can be approximately expressed as [13]:  
\[P_{t}(l,f)=N(f)A(l,f)B(f)SNR \tag{4}\]  
where \(N(f)\), \(B(f)\) and \(SNR\) are the power spectral density of the noise at frequency \(f\), the usable bandwidth around the center frequency \(f\), and the target signal-to-noise ratio at the receiver, respectively. The conversion from acoustic power \(P_{t}(l,f)\) in dB re \(\mu\)Pa to electrical power \(\mathcal{P}_{t}^{e}(l)\) in Watt is given by [14]:  
\[\mathcal{P}_{t}^{e}(l)=P_{t}(l,f)\cdot 10^{-17.2}/\varphi \tag{5}\]where \(10^{-17.2}\) is the conversion factor and \(\varphi\) is the overall efficiency of the electric circuitry (power amplifier and transducer). Here \(f\) is omitted in \(\mathcal{P}_{t}^{\epsilon}(l)\) for a fixed frequency.  
In practice, a certain non-zero minimum level of power is always radiated for a transmission regardless of how short the distance is [15]. Thus, the total power required for communicating over a distance \(l\) is modified as:  
\[\mathcal{P}(l)=\text{max}\{\mathcal{P}_{t}^{\epsilon}(l),\mathcal{P}_{min}\}+ \mathcal{P}_{r} \tag{6}\]  
where \(\mathcal{P}_{min}\) is the minimum transmission power, \(\mathcal{P}_{r}\) is the fixed overhead for receiving data, and all of them are measured in Watt. Then, the total energy consumption for single transmission (denoted by \(\eta(l)\) in Jouel) is calculated as:  
\[\eta(l)=\mathcal{P}(l)\times\frac{L_{d}}{R} \tag{7}\]  
where \(L_{d}\) is the packet size in bit and \(R\) is the transmission rate in bps.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Zinc Nanoparticles  
Zinc is a crucial element for normal human body functioning. This element is involved in many enzymatic and physiological reactions. Zinc oxide nanoparticles (Zn-NPs) can reduce oxidative stress by eliminating free radicals and possess antidiabetic and antibacterial properties. The spectrum of biomedical use of Zn-NPs is diverse. These nanoparticles are used as carriers for drug delivery, contrast agents in biomedical imaging, or nanosensors. Due to their biocompatibility, they have been integrated into bioactive ceramics and bioactive glass [133].  
Zn-NPs exhibit osteogenic properties that are caused by Zn\({}^{2+}\) ions release. These ions can promote bone growth, mineralization, and bone formation. The mitogen-activated protein kinase pathway is activated by Zn\({}^{2+}\) ions and promotes the expression of the _OC_ gene. Zn\({}^{2+}\) ions also support bone formation by enhancing Runx2 expression by increasing Zn\({}^{2+}\) carriers such as zinc transporter protein ZIP1 [136].  
[PERSON] and [PERSON] used the electrospinning technique for the preparation of fibrous zinc oxide composite scaffolds. Polycaprolactone (PCL) with incorporated Zn-NPs provided slow degradation and dissolution of Zn\({}^{2+}\) ions over time. Zn-NPs were used in different concentrations (1-10 wt%). Biocompatibility was evaluated as a degree of MSCs differentiation. Low concentrations of Zn-NPs promoted chondrogenic differentiation, which was indicated by the highest collagen type II production and expression of cartilage-specific genes. On the other hand, high concentrations of Zn-NPs promoted osteogenic differentiation of human MSCs. This differentiation was proved by the highest ALP activity, collagen production, and expression of bone-specific genes. Results of this study have shown the potential of Zn-NPs composite scaffolds in bone tissue engineering [137].
## 2 Background and Related Work
### Virtual Reality Applications in Football  
Virtual reality (VR) is becoming a valuable tool in football training, especially in injury prevention and performance enhancement through biomechanical feedback and mixed reality simulations. While VR has been successfully applied in neurorehabilitation for stroke patients [37; 38], in football, it is primarily used to simulate real-life game scenarios, helping players improve their tactical awareness, decision-making, and physical response to various in-game situations [39]. By training players to internalize safer movement patterns in a controlled environment, VR can reduce the risk of injury caused by poor technique or bad decision-making under pressure [37; 40]. Additionally, VR has been used to enhance goalkeeping skills, allowing players to practice reflexes and decision-making without the physical risks of traditional on-field training [39]. VR also aids in technique improvement by offering a controlled space for athletes to refine their skills, reducing the likelihood of injury during actual play [37; 41]. For injured players, VR can support rehabilitation by helping them maintain cognitive sharpness and engage in biomechanical analysis and proprioceptive exercises, contributing to effective recovery and injury prevention [41]. Leading VR providers [39; 42] like ViPER, STRIVR, and REZZIL have developed specialized programs that include eye-movement tracking, heart rate monitoring, and biomechanical feedback to enhance player performance [41]. Despite its success, VR in football training has not fully addressed the specific challenge of monitoring and correcting movement patterns during injury prevention exercises for youth players. While VR tools improve decision-making and cognitive skills, they do not yet provide real-time biomechanical feedback in a way that is tailored to injury prevention, particularly in youth football contexts. Therefore, we present our conceptualization process for developing a DAid(r) smart socks-based biofeedback system to enhance injury prevention in football through real-time biomechanical monitoring and mixed reality feedback.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results
### Regional Carbon Stock Mapping  
To assess the spatial estimation capabilities of different models, we generated basin-wide carbon stock maps for the Liuchong River Basin using four representative approaches: Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Vision Transformer (ViT), and the proposed Enhanced ResNet. As shown in Figure 8, each model produces spatially continuous carbon density estimates at a resolution of 640 m \(\times\) 640 m, based on patch-wise predictions. While all models capture the general spatial trend of higher carbon densities in mountainous forested regions and lower values in valleys or agricultural zones, the level of detail and spatial coherence varies. The RF and GBDT outputs appear more fragmented and noisy, with abrupt transitions that lack ecological plausibility. ViT shows improved smoothness but tends to underestimate carbon stock in dense forest zones.  
In contrast, the Enhanced ResNet yields the most coherent and ecologically consistent spatial patterns. High-carbon areas (e.g., >150 Mg/ha) are clearly identified in the northeast  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Variant** & **RMSE (Mg/ha)** & **MAE (Mg/ha)** & \(\mathbf{R^{2}}\) \\ \hline ResNet-18 (Baseline) & 29.41 & 21.87 & 0.683 \\ +MSRB only & 25.36 & 18.72 & 0.741 \\ +MSRB + SE (Enhanced ResNet) & **23.02** & **17.15** & **0.773** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of structural components in the proposed ResNet architecture. Bold is the best result.  
Figure 8: Basin-level forest carbon stock maps (Mg/ha) across the Liuchong River Basin generated by (**a**) RF, (**b**) GBDT, (**c**) ViT, and (**d**) Enhanced ResNet. Higher values are concentrated in mountainous forest regions.  
and central highlands--regions dominated by evergreen and mixed forests. The spatial distribution aligns well with known topography and forest cover, indicating the model's superior ability to integrate spectral and spatial information. These maps demonstrate that deep learning models, particularly with architectural enhancements like multiscale and attention modules, are well-suited for large-scale carbon stock estimation in heterogeneous landscapes. The outputs are suitable for supporting applications in forest monitoring, climate policy design, and land management planning.