esa-sceva/satcom-chunk-collection huggingface dataset:
# Lacase in Biorefinery of Lignocellulosic Biomass
## 4 Bioprocesses for Lacase Production
### Solid-State Fermentation  
Solid-state fermentation (SSF) is a fermentation method that involves the growth of microorganisms on the moist solid substrate or on the surface of a solid support material, which does not act as carbon source without the presence of free water. The solid support material used for fungal growth can be either inert or biomaterial, typically lignocellulosic. SSF is regarded as superior bioprocess by many researchers for enzyme production via filamentous fungi, as it closely imitates its natural environment [44; 45]. It can also be more environmentally friendly, as it can use agricultural waste as a substrate, which can reduce waste and improve sustainability.  
Agricultural waste, being lignocellulosic in nature, can be used for lacase production, such as rice straw. Rice straw is produced in large quantities globally and could be a potential substrate for laccase production. Studies have shown that rice straw can be effectively used as a substrate for laccase production by fungi, which can be used for various industrial applications. When lignocellulosic material is used primarily as a carbon source and then as support, the fungal mycelium grows through the biomaterial, inducing laccase production due to the presence of lignin, and a slow nutrient release effect makes it better. Water alone can be used as a moisturizing agent, but solutions containing carbon and nitrogen stimulants may also be used to accelerate the initial stages of material colonization to improve laccase productivity [46]. The properties of the lignocellulosic material, such as particle size and composition, as well as the type of moisturizing agent, are critical factors that affect both the cost and efficiency of the production process [44].  
Other lignocellulosic agricultural wastes that have been studied for laccase production include wheat straw, corn stover, and sugarcane bagasse. Limitations and challenges are also associated with SSF, such as difficulty to control and online-monitoring, which makes optimization difficult as compared to SmF [47]. There are several factors that can affect the production of laccase from lignocellulosic agricultural wastes, such as particle size of the substrate, moisture content or water activity, including the type of waste used and the type of fungus used. Optimizing these factors can improve the efficiency and yield of laccase production from lignocellulosic agricultural wastes [48].
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 2 Materials and Methods
### Data Model
Based on that, the following measurement model can be proposed:  
\[Y_{k}=A(\theta)Q+V_{k}, \tag{2}\]  
where \(A(\theta)=[A_{1}(\mathbf{r}_{1})\ \cdots A_{L}(\mathbf{r}_{L})]\) is the \(M\times 3L\)_lead field_ matrix containing the individual \(A_{l}(\mathbf{r}_{l})\) responses to each dipole source, \(\theta=[\mathbf{r}_{1},\ldots,\mathbf{r}_{L}]^{T}\) represents the positions of the sources (parameter to be estimated), \(Q=[\mathbf{q}_{1},\ldots,\mathbf{q}_{L}]^{T}\) is the \(3L\times N\) matrix of dipole moments, and \(V_{k}\) is the \(M\times N\) matrix of measurement noise given by  
\[V_{k}=\left[ \begin{array}{cccc}v_{1}(1)&v_{1}(2)&\cdots&v_{1}(N)\\ v_{2}(1)&v_{2}(2)&\cdots&v_{2}(N)\\ \vdots&\vdots&&\vdots\\ v_{M}(1)&v_{M}(2)&\cdots&v_{M}(N)\end{array} \right], \tag{3}\]  
where \(v_{m}(t)\) is a zero mean Gaussian noise realization, uncorrelated in time and space, and with variance \(\sigma_{v}^{2}\) independent of time. In (2), the lead field matrix \(A(\theta)\) allows for the calculation of the distributions of the electric potentials given a source configuration. The calculation of those distributions implies solving [PERSON]'s equations for the physical model of source, realistic head and measurements, with the associated boundary values. There is no known closed-form analytic solution to this _forward problem_, but here we consider the case of the boundary element method (BEM) as it is known to converge to the true solution under certain conditions when the tessellated head model is sufficiently refined [19].
## 3 Evaluation of Flow Performance
### Tests of Flow Performance  
Using the test bench described in Section 3.1, tests of flow performance were conducted on three feasible prototypes, namely: a. UP-PRESS, b. FIX-ROLLER and c. AIR-BAG, of which the measurement results are shown in Figure 10a-c. The left ordinate corresponds to the discharge flow rate, whereas the right ordinate corresponds to the amount of discharged liquid. In the performance test, liquid was discharged from drip bags with 500 mL content over a time period of about 2 to 3 h, at a flow rate adjusted between 3 and 4 g/min. Apparently, prototype AIR-BAG (see Figure 10c) showed a relatively high flow stability, compared with prototypes UP-PRESS and FIX-ROLLER (see Figure 10a,b). In terms of flow variation, UP-PRESS showed a relatively large up-and-down, while FIX-ROLLER demonstrated small zigzagging of flow fluctuation. In a general sense, the inconsistent contact surface interaction between the pressurization mechanism and the drip bag in the two prototypes accounted for these flow behaviors.  
Figure 10: Flow rate measurement results of the prototypes. (**a**) Prototype UP-PRESS. (**b**) Prototype FIX-ROLLER. (**c**) Prototype AIR-BAG. (**d–f**) Pictures of prototypes under tests corresponding to (**a–c**).
## 3 Results
### Biomarker Results  
#### 3.3.1 Acetylcholinesterase Activity  
In the negative control, in the absence of GLY, the activity of AChE was similar in the 0, 5, and 10% amended controls but significantly higher in the 15% biochar-amended control (Figure 3). The addition of GLY at a rate of 2 mg/kg did not change the AChE levels observed in the controls; a significantly higher AChE activity was present in the 15% biochar-amended treatment, whereas AChE activity was similar in the 0, 5 and 10% biochar-amended treatments.  
In the two highest concentrations of GLY (4 and 8 mg/kg), the pattern of AChE activity observed in the controls and lowest GLY concentration was different; the activity of AChE was now similar in all treatments and biochar amendments, including in the 15% amendment. Overall, as the concentration of GLY increased, the initially high AChE activity in the 15% amendment decreased to the point that no difference in activity could be found at 4 and 8 mg/kg.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ECs (mg/kg)** & **0\% Biochar** & **5\% Biochar** & **10\% Biochar** & **15\% Biochar** \\ \hline EC10 & 5.238 (2.168–12.657) & \(>\)8 & \(>\)8 & \(>\)8 \\ EC20 & 6.848 (2.643–18.317) & \(>\)8 & \(>\)8 & \(>\)8 \\ EC50 & \(>\)8 & \(>\)8 & \(>\)8 & \(>\)8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effective concentrations (ECs) of glyphosate on _Eisenia fetida_ biomass calculated after 96-h exposure in biochar-amended (5, 10 and 15%) and non-amended OECD soil.  
Figure 2: Change in biomass (%) of adults _Eisenia fetida_ after 96-h exposure to glyphosate in biochar-amended (5, 10 and 15%) and non-biochar amended OECD artificial soil. Data represented as mean of 3 replicates (\(n=30\) per treatment). Error bars represent standard deviations and different letters represent significant differences.
## 10 Common Use of RQD for Rock Mass Classification
### RMR Value  
The Rock Mass Rate (RMR) system was introduced by [PERSON] in 1976 [4] and modified several times. The 1989 version [5] is analyzed here, as this is the most widely used version. This method offers significant advantages due to its reliance on a limited number of essential parameters related to the mechanical and geometric properties of rocky slopes. These key requirements ensure the method's effectiveness and adaptability in various geological settings.  
According to the definition of RMR, it depends on the following parameters:  
\[\text{RMR}=\text{R1}+\text{R2}+\text{R3}+\text{R4}+\text{R5} \tag{50}\]  
where  
1. Uniaxial compressive strength (0-15);
2. RQD value (0-20);
3. average joint space (5-20);
4. joint wall conditions (0-30);
5. water (0-15).  
In the field, sites can be classified using this method. Structural regions are characterized by the presence of similar rock types or discontinuities, allowing each section to be distinctly identified and categorized [68].  
The RMR method offers guidelines for implementing tunnel rock reinforcement, with adjustments based on factors like subsurface depth, tunnel size and shape, and the excavation method used. Furthermore, RMR is applied to predict the behavior of dredged material and estimate stand-up time. The physical characteristics of a rock mass are also ascertained using it [5; 68; 69].
## 4 Discussion
### Multi-Domain Assessment
#### 4.1.3 EEG  
The EEG desynchronization is noticeable in both ipsi- and contra-lateral hemispheres at T0 and T1 for the beta band; in three cases (patients 1, 3 and 4) it is also recognizable for the alpha band at T0. Furthermore, all patients show an ERD post-treatment in the alpha band as well. The literature suggests that the lack of ERD may imply a severe impairment causing a baseline so low that no desynchronization can be identified [44]. The fact that, conversely, all patients show a desynchronization at T1 can be the first indicator that the treatment obtained some positive effect at a cortical level as well. Moreover, a decreasing trend can be outlined for ERDc both in beta and alpha band while for ERDi the same consideration applies to the alpha band only. Such finding agrees with previous studies suggesting that an enhancement of the desynchronization (especially in the contralateral hemisphere) may reflect a motor recovery [45]. The fact that the desynchronization does not occur symmetrically in the ipsilateral and contralateral side can be thought as another indicator, not only of the effect of the treatment, but also of the recovery strategy adopted by the specific patient at the cortical level. In fact, in the literature, it was shown that healthy subjects often show larger desynchronization in the contralateral side with respect to the limb performing the movement [46; 47], therefore a shift toward this physiological condition may suggest an improvement. Concurrently, the ipsilateral region can be used to compensate the lesioned hemisphere [28], possibly due to a reduced interhemispheric inhibition [48]. This can also be seen analyzing the behavior of the LC index (beta) which increases from T0 to T1, moving from a negative (ERDi! ERDc) to a positive (ERDi? ERDc) value in 3 patients out of 5. The only subject showing an LC decrease is Patient 4, who also shows the highest motor functionality according to the FMA at T0, and almost a complete recovery of the motor function at T1. The unexpected LC decrease might be associated to an automatization process, which can occur in skilled subjects when repeating a known gesture that requires a reduced effort and consequently a reduced activation of the contralateral side [22; 49].
###### Abstract  
Forest structural data are essential for assessing biophysical processes and changes, and promoting sustainable forest management. For 18+ years, the Multi-Angle Imaging SpectroRadiometer (MISR) instrument has been observing the land surface reflectance anisotropy, which is known to be related to vegetation structure. This study sought to determine the performance of a new MISR-High Resolution (HR) dataset, recently produced at a full 275 m spatial resolution, and consisting of 36 Bidirectional Reflectance Factors (BRF) and 12 Rahman-Pinty-Verstraete (RPV) parameters, to estimate the mean tree height (\(\mathrm{H_{mean}}\)) and canopy cover (CC) across structurally diverse, heterogeneous, and fragmented forest types in South Africa. Airborne LiDAR data were used to train and validate Random Forest models which were tested across various MISR-HR scenarios. The combination of MISR multi-angular and multispectral data was consistently effective in improving the estimation of structural parameters, and produced the lowest relative root mean square error (rRMSE) (33.14% and 38.58%), for \(\mathrm{H_{mean}}\) and CC respectively. The combined RPV parameters for all four bands yielded the best results in comparison to the models of the RPV parameters separately: \(\mathrm{H_{mean}}\) (R\({}^{2}\) = 0.71, rRMSE = 34.84%) and CC (R\({}^{2}\) = 0.60, rRMSE = 40.96%). However, the combined RPV parameters for all four bands in comparison to the MISR-HR BRF 36 band model it performed poorer (rRMSE of 5.1% and 6.2% higher for \(\mathrm{H_{mean}}\) and CC, respectively). When considered separately, savanna forest type had greater improvement when adding multi-angular data, with the highest accuracies obtained for the \(\mathrm{H_{mean}}\) parameter (R\({}^{2}\) of 0.67, rRMSE of 31.28%). The findings demonstrate the potential of the optical multi-spectral and multi-directional newly processed data (MISR-HR) for estimating forest structure across Southern African forest types.  
vegetation structure; LiDAR; multi-spectral and multi-angular measurements; MISR; MISR-HR; Random Forest 201810, 1537 ; doi:10.3390/rs10101537 www.mdpi.com/journal/remotesensing
###### Abstract  
Compared to conventional single-aperture infrared cameras, the bio-inspired infrared compound eye camera integrates the advantages of infrared imaging technology with the benefits of multi-aperture systems, enabling simultaneous information acquisition from multiple perspectives. This enhanced detection capability demonstrates unique performance in applications such as autonomous driving, surveillance, and unmanned aerial vehicle reconnaissance. Current single-aperture small target detection algorithms fail to exploit the spatial relationships among compound eye apertures, thereby underutilizing the inherent advantages of compound eye imaging systems. This paper proposes a low-rank and sparse decomposition method based on bio-inspired infrared compound eye image features for small target detection. Initially, a compound eye structural weighting operator is designed according to image characteristics, which enhances the sparsity of target points when combined with the reweighted \(l_{1}\)-norm. Furthermore, to improve detection speed, the structural tensor of the effective imaging region in infrared compound eye images is reconstructed, and the Representative Coefficient Total Variation method is employed to avoid complex singular value decomposition and regularization optimization computations. Our model is efficiently solved using the Alternating Direction Method of Multipliers (ADMM). Experimental results demonstrate that the proposed model can rapidly and accurately detect small infrared targets in bio-inspired compound eye image sequences, outperforming other comparative algorithms.  
bio-inspired infrared compound eye; infrared small targets; low-rank sparse decomposition; target detection 2025
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 3 Proposed Scheme
### Data Transmission Model
\begin{array}{l}\mathbf{y}_{k}=\mathbf{H}_{k}\mathbf{W}_{k}\mathbf{s}_{k}+ \mathbf{n}_{k}=\mathbf{H}_{eff,k}\mathbf{s}_{k}+\mathbf{n}_{k}\\ =\mathbf{H}_{eff,k}\left[\begin{array}{c}s_{1}\left(k\right)\\ s_{2}\left(k\right)\\ \vdots\\ s_{N_{r}}\left(k\right)\end{array}\right]+\left[\begin{array}{c}n_{1}\left( k\right)\\ n_{2}\left(k\right)\\ \vdots\\ n_{N_{r}}\left(k\right)\end{array}\right]\\ =\mathbf{H}_{eff,k}\left[\begin{array}{c}\sqrt{\mathbf{p}_{1}}\mathbf{s}_{1,1}\left(k\right)+\sqrt{\mathbf{p}_{2}}\mathbf{s}_{1,2}\left(k\right)+\cdots+ \sqrt{\mathbf{p}_{N}}\mathbf{s}_{1,N}\left(k\right)\\ \sqrt{\mathbf{p}_{1}}\mathbf{s}_{2,1}\left(k\right)+\sqrt{\mathbf{p}_{2}} \mathbf{s}_{2,2}\left(k\right)+\cdots+\sqrt{\mathbf{p}_{N}}\mathbf{s}_{2,N} \left(k\right)\\ \vdots\\ \sqrt{\mathbf{p}_{1}}\mathbf{s}_{N_{r},1}\left(k\right)+\sqrt{\mathbf{p}_{2}} \mathbf{s}_{N_{r},2}\left(k\right)+\cdots+\sqrt{\mathbf{p}_{N}}\mathbf{s}_{N_{ r},N}\left(k\right)\end{array}\right]+\left[\begin{array}{c}n_{1}\left(k\right)\\ n_{2}\left(k\right)\\ \vdots\\ n_{N_{r}}\left(k\right)\end{array}\right].\end{array}  \tag{8}\]  
where \(\mathbf{H}_{eff,k}\) denotes the effective channel of the \(k\)-th user. According to the Equation (8), the MUI is perfectly eliminated and the \(k\)-th user receives its own data. Finally, the users can be considered as point-to-point MIMO. As a result, by exploiting both the spatial and power domains additionally, more data symbols can be transmitted in the same resource (frequency/time). In the proposed scheme, as the number of symbols at each superposed signal is increased, sum throughput at each user is linearly improved. Although the allocated power to each symbol is reduced, the total throughput is improved since the number of transmit symbols is increased. Unlike other techniques that use only one dimension, such as space or power domains, the proposed scheme can achieve significant gains in overall system throughput by using additional domains.
## 2 Materials and Methods
### Experimental Design  
The experimental period spanned a total of 20 months with sampling in November 2017 (T1), June 2018 (T2; 3 sites only--TAU-T, TAU-O, RAG) and November 2018 (T3), corresponding to 8, 15 and 20 months of sediment nutrient enrichment (Table 1). Two November sampling periods  
Figure 1: Location of the four estuaries within the North Island of New Zealand (**A**), and sampling sites within (**B**) Manukau (MNK), (**C**) Raglan (RAG), (**D**) Tauranga (TAU) and (**E**) Whangateau (WTA) estuaries. Panel (**F**) depicts an example of the allocation of nutrient enrichment treatments (control = 0, medium = 150 and high = 600 g N m\({}^{-2}\)) to experimental plots within the Whangateau estuary. For site-specific GPS locations see Table A1.  
were chosen to correspond with warmer late spring temperatures and higher light availability, and allowed the incorporation of potentially longer-term responses to nutrient enrichment (e.g., alterations of macrofaunal communities). An additional winter sampling (June 2018) at three sites was conducted to examine the potential influence of reduced temperatures and light conditions on primary production measures.  
At each site, nine 9 m\({}^{2}\) experimental plots were divided into three nutrient enrichment groups, either control 0 g N m\({}^{-2}\), medium 150 g N m\({}^{-2}\) or high nutrient treatment 600 g N m\({}^{-2}\) (Figure 1F). To achieve enrichment, a slow-release urea fertiliser (Nutricote 40:0:0 N:P:K) was added at two known quantities to a series of 180 evenly spaced holes at a depth of 15 cm by extracting a sediment plug (3 cm diameter) with a handheld corer, adding a known volume of fertiliser and then replacing the sediment plug. Nitrogen-only fertiliser was used since New Zealand estuaries are typically N limited and because the urea in Nutricote quickly hydrolyses to ammonium (NH4\({}^{+}\)), a form of nitrogen associated with eutrophication and the remineralisation of organic matter ([PERSON], 2017). This method results in an even elevation of porewater NH4\({}^{+}\) throughout the experimental plots ([PERSON], 2017). To maintain enrichment throughout the experimental period, fertiliser was first applied in March 2017 and reapplied following sampling in November 2017 and June 2018 (Table 1). Sampling, therefore, took place between 5 and 8 months after each application of fertiliser.
## 2 Experimental Setup
The lines of sight (LOS) of these two spectrometers are illustrated in Figure 2.  
In this work, we used the polarimeter-interferometer (POINT) system to measure and provide real-time feedback control of the line averaged electron density, n\({}_{\text{e}}\)[21]. We also employed a 32-channel heterodyne radiometer (HRS) system to observe the intensity of thermal electron cyclotron emission (ECE) in the frequency range of 104-168 GHz [22]. Positions of ECE measurement at Bt = 2.25 T in the discharges studied in this work are illustrated in Figure 2. In addition, we used multi-channel hard X-ray diagnostics systems placed around the EAST machine to detect hard X-ray emission resulting from the thick target bremsstrahlung of runaway electrons that were lost from the plasma and impinging on the vessel walls or plasma facing components, covering the energy range of 0.1-7.0 MeV [7; 23; 24]. Figure 1: Top view of optical layout of (**a**) EUV_Short and (**b**) EUV_Long spectrometer. Figure 2: EAST poloidal cross-section, lines of sight of EUV spectrometers (EUV_Short: red line and EUV_Long: blue line) and position of ECE measurement (purple points, HRS01-HRS32), channels of 01–32 are from low field side (LFS) to high field side (HFS). The red rectangles indicate the cross section of magnetic coils.
# Ising-Based Kernel Clustering +
## 1 Introduction
Since such approximated methods fail to find the exact solution in many cases, Ising-based combinatorial clustering that potentially solves the exact solution has to be expanded to handle any distribution of data. This paper suggests a solution for achieving combinatorial clustering capable of handling any irregular data. Specifically, the proposed method involves performing Ising-based kernel clustering by employing the kernel method on Ising machines to execute combinatorial clustering. The proposed method transforms irregular data onto the high-dimensional feature space by using an appropriate kernel function [22]. Here, it takes a lot of time to calculate the transformed data from the irregular input data. To accelerate this preprocess, this paper also discusses an efficient implementation of Ising-based kernel clustering for numerical libraries. Efficient representation to utilize numerical libraries is defined by matrix-matrix calculations. The remainder of this paper is structured as follows: Section 2 provides an overview of Ising machines and combinatorial clustering. Section 3 proposes Ising-based kernel clustering and efficient implementation to utilize numerical libraries. Section 4 presents an evaluation of the proposed method with respect to quality and execution time. Section 5 describes the related work of the proposed method. Finally, Section 6 summarizes the main contributions of this paper.