esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Modeling Specific Energy Requirements Using ANN  
As shown in Figure 8b, the best validation performance of ANN was obtained at the fourth epoch that yielded the lowest \(MSE\). The weights and biases associated with this performance were selected as the final model weights and biases. Although the model training continued until the 10 th epoch, as long as the validation error decreased, the training phase was terminated after six consecutive iterations due to an increase in the validation error after the fourth epoch. This was implemented to prevent overfitting. However, the training performance curve did not indicate any overfitting as long as the test and validation curves showed similar trends, which indicates that the training process was robust. The ANN model training exhibited a remarkably high level of regression (\(R^{2}\) train = 0.9735, Table 2), indicating a strong agreement between the observed and predicted specific energy values. Those values suggest accounting for at least 97% of the variability in specific energy values by the developed ANN model. Similarly, high agreement evident in the testing dataset (\(R^{2}\) value of 0.9114) suggests that the developed ANN model could account for approximately 91% of the variations at low error and high efficiency in measured specific energy values, even when applied to new independent data (Table 2). This also indicates the generalizability of the developed ANN model. Other studies that have deployed ANN for modeling complex soil-tool interactions [53; 54] have also reported ANN to be advantageous for offering fast, precise, and reliable computation of multivariable, nonlinear, and complex relationships relative to mathematical and other conventional numerical methods. This is very well performed in Figure 10 that illustrates very close alignment between specific energy values measured during experiments and those predicted by the ANN model for each trial in this study. The study by [PERSON] et al. [29] predicted draft forces on a tillage implement using a 5-9-1 ANN model where prediction errors were below 6.5% compared to the standard mathematical equation that estimated draft forces at errors over 30%. Similarly, a 4-6-2 ANN model was used for predicting traction performance and the MSE error was 2.55 \(\times\) 10\({}^{-12}\)[53]. Although the dataset size was relatively smaller, ANN has been well reported to perform reliably and robustly with smaller datasets [55; 56; 57; 58; 59; 60]. The next step will be the implementation of the SPE prediction model under various field and vegetation conditions. The developed model and approachcould be instrumental for the manufacturers and design engineers of machinery systems in simulating the designs of soil-interacting tools for operations such as tillage and weeding, among others. Most importantly, such simulation will guide manufacturers to develop matching implements for the tractors to ensure high fuel efficiency, optimal performance, and high crop productivity.
## 3 Numerical Simulation Computation
### Parameter Configuration  
In order to simplify the calculations, all formations with less than 15% mud content are uniformly treated as salt rock formations in the numerical model. The physical and mechanical parameters of the surrounding rock are shown in Table 1, which are measured and calculated in laboratory measurements.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Salt Rock** \\ \hline Density (kg/m\({}^{3}\)) & 2100 \\ Elastic modulus (GPa) & 3.6 \\ Bulk modulus (GPa) & 2.75 \\ Poisson's ratio & 0.282 \\ Initial permeability (m\({}^{2}\)) & \(5.47\times 10^{-21}\) \\ Initial porosity & 0.01 \\ Biot’s coefficient & 0.12 \\ Cohesion (MPa) & 3.69 \\ Internal friction angle (\({}^{\circ}\)) & 38.76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physical and mechanical parameters of the surrounding rock.  
Figure 1: Shape of the salt cavern J106 and the grid partitioning.  
Hydrogen and natural gas (over 95% is methane) exhibit distinct physical and mechanical properties. The relevant parameters selected for numerical simulations are presented in Table 2.
## 4 Results
### Performance Analysis: Resolution vs. Frame Rate
#### 4.1.2 GPU Memory Access Analysis  
To complement the FPS-based performance analysis, GPU memory access behavior was analyzed across five cloth resolutions (8 \(\times\) 8 to 128 \(\times\) 128). We measured the peak GPU memory bandwidth for both read and write operations under the same static cloth simulation conditions used in the FPS test. Measurements were taken using the Meta Quest Developer Hub over a 5 s sampling window, and the maximum observed bandwidth values were recorded.  
Figure 5 presents the comparison of memory read and write bandwidth across three systems: Unity Cloth, GPU-PBD (Single-Sided), and GPU-PBD (Double-Sided). As expected, the GPU-PBD systems, especially the double-sided configuration, exhibited significantly higher read bandwidth usage due to increased vertex access and constraint computations. Write bandwidth was less consistent across resolutions, reflecting the asynchronous and conditional nature of constraint projection updates.  
Despite higher memory access, the GPU-PBD system remains efficient for real-time XR deployment up to 64 \(\times\) 64 resolution. These findings reinforce the trade-off between physical realism and GPU resource consumption, offering additional insight into performance scalability.  
Figure 4: FPS performance comparison across different cloth resolutions.  
Figure 5: Peak GPU memory bandwidth usage (Read/Write) across different cloth resolutions for Unity Cloth and GPU-PBD systems.
## 1 Introduction
To overcome these limitations, we propose an enhanced consensus algorithm called RE-BPFT, which integrates a sophisticated node reputation evaluation framework. This model comprehensively captures multiple aspects of node behavior, including direct interactions, indirect feedback, and historical performance, and leverages the ID3 decision tree approach to dynamically classify nodes based on their reliability. Moreover, we introduce a reputation-based, weighted-random mechanism for electing primary nodes, significantly reducing centralization risks and improving fault tolerance. The main contributions of our work can be summarized as follows:  
1. Development of a multi-faceted node reputation evaluation model enabling dynamic assessment and effective management of nodes. 2. Application of an optimized ID3 decision tree algorithm for precise and automated node classification. 3. Introduction of a novel weighted-random primary node election strategy to prevent centralization issues and enhance network robustness. 4. Empirical validation through extensive experiments demonstrating RE-BPFT's superior performance over traditional algorithms (PBFT, RBFT, and PPoR) in terms of reduced communication overhead, lower consensus latency, and increased throughput. The remainder of this paper is structured as follows. In Section 2, we critically review existing literature related to consensus mechanisms and node reputation models. Section 3 elaborates on the design principles and implementation details of our proposed RE-BPFT algorithm, highlighting both the reputation evaluation framework and the node classification methodology. Section 4 presents comprehensive experimental results, thoroughly analyzing the performance of RE-BPFT under varying conditions. Finally, Section 5 summarizes our findings and outlines potential directions for future research.
###### Abstract  
This paper provides a technical review regarding the latest progress on multi-input multi-output (MIMO) digital signal processing (DSP) equalization techniques for high-capacity fiber-optic communication networks. Space division multiplexing (SDM) technology was initially developed to improve the demanding capacity of optic-interconnect links through mode-division multiplexing (MDM) using few-mode fibers (FMF), or core-multiplexing exploiting multicore fibers (MCF). Primarily, adaptive MIMO filtering techniques were proposed to de-multiplex the signals upon different modes or cores, and to dynamically compensate for the differential mode group delays (DMGD) plus mode-dependent loss (MDL) via DSP. Particularly, the frequency-domain equalization (FDE) techniques suggestively lessen the algorithmic complexity, compared with time-domain equalization (TDE), while holding comparable performance, amongst which the least mean squares (LMS) and recursive least squares (RLS) algorithms are most ubiquitous and, hence, extensively premeditated. In this paper, we not only enclose the state of the art of MIMO equalizers, predominantly focusing on the advantage of implementing the space-time block-coding (STBC)-assisted MIMO technique, but we also cover the performance evaluation for different MIMO-FDE schemes of DMGD and MDL for adaptive coherent receivers. Moreover, the hardware complexity optimization for MIMO-DSP is discussed, and a joint-compensation scheme is deliberated for chromatic dispersion (CD) and DMGD, along with a number of recent experimental demonstrations using MIMO-DSP.  
digital signal processing; multi-input multi-output; mode-division multiplexing; least mean squares; frequency-domain equalization; recursive least squares; space-time block-coding; mode-dependent loss +
Footnote †: journal: Applied Physics Letters
## 4 Numerical Calculation of Slopes Based on Weibull Parameter Random Distribution Model
### Slope Stabilization Measures
#### 4.4.2 Rock Energy Damage Analysis  
As shown in Figure 14a,b, the system energy loss variable cloud maps at excavation steps 4 and 13 under unsupported conditions are presented, while Figure 14c,d show the system energy loss variable cloud maps at the same excavation steps under supported conditions. In the unsupported slope, as excavation progresses, the maximum damage variable (ds) of 0.82 is observed in the 1195-1240 m platform area, specifically at the weak interlayer chlorite position, where the unit body's damage variable is larger. After support is applied, with continued excavation, the damage volume significantly decreases, and ds reduces to 0.48. This suggests that under supported conditions, the slope remains in a stable state. From the perspective of energy damage evolution, the support system effectively meets the actual engineering requirements.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Evaluation** & **u** & **v** & **\(\Delta\)** & **Fos** \\
**Indicators** & \(-\)105.7 & \(-\)1116.7 & 777,566,612.7 & 1.31 \\ Displacement & \(-\)0.5798 & 0.2726 & 0.447 & 1.31 \\ Plastic zone & \(-\)14.5 & 215.7 & 1,231,826.23 & 1.31 \\ \hline \end{tabular}
\end{table}
Table 6: Correspondence table of safety factor and cusp catastrophe theory eigenvalues.  
Figure 13: Fitted curves of plastic zone, displacement, and energy dissipation under support. (**a**) Plastic zone fitting curve schematic diagram; (**b**) dissipated energy fitting curve schematic diagram; (**c**) displacement fitting curve schematic diagram.
# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
## 7 Conclusions  
The relentless rise in global demands for communication infrastructures has driven the continuous expansion of wireless communication mobile networks and an exponential growth in their traffic rates. This has led to a sharp increase of energy consumed by such systems, which has given rise to the concept of a green communication scheme. The latter aims to reduce the overall power needed to operate the future mobile communications systems. MIMO decodes are going to be an integral part of the 5G wireless communication networks. Therefore, in this work we introduced a novel energy efficient MIMO decoder which needs a fraction of the overall power requirement compared with recently published designs in 5G networks. The proposed design is based on an improved LR-aided K-Best MIMO detection algorithm. We have implemented the proposed decoder in 32 nm standard CMOS technology and verified its hardware implementation. In addition we have presented a framework to estimate the overall power costs of a MIMO system, which includes the power consumed by the decoder itself and the minimum transmission power required for a reliable operation. Our results indicate the proposed decoder reduces the overall power costs by four-fold on average compared to existing schemes for 5G communication network, thanks to its high BER performance. Our analysis also showed that the transmission power is going to be far more significant than that consumed by the MIMO decoder in future wireless networks. This is because of the continuous rise of transmission frequency (i.e., more transmission power), combined with scaling of semiconductor technology (i.e., a reduction in hardware dynamic power consumption), this observation should be noted by designers of future energy-efficient MIMO decoders.  
Figure 7: Comparative power costs of MIMO decoders for different transmission carrier frequencies.  
**Author Contributions:** [PERSON]: Main author and hardware development; [PERSON]: Co-author and software development; [PERSON]: Software implementation; [PERSON]: Hardware implementation and software development; [PERSON]: Software implementation; [PERSON]: Hardware implementation  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Fruit Quality
The TSS content of the tomato fruits also showed significant differences between treatments (Table 2). The Control presented the highest value with 5.4\({}^{\circ}\) Brix, surpassing the 500 mg L\({}^{-1}\) of Cu NPs treatment, which presented a decrease of 5.55%. [PERSON] et al. [16] reported that the application of Cu NPs + chitosan did not generate differences in the TSS in tomato fruits. The decrease in TSS can be caused by the use of simple sugars in cellular respiration [41]. A decrease in the titratable acidity (TA) was observed in the 125 mg L\({}^{-1}\) Cu NPs treatment. With respect to the Control, it decreased by 16.33% (Table 2). [PERSON] et al. [16] reported an increase in the TA in tomato fruits with the application of Cu NPs + chitosan. [PERSON] et al. [17] reported that the application of 0.2 and 2.0 mg of Cu NPs increased the TA in jalapeno peppers by 25 and 35.42%, respectively. [PERSON] et al. [42] mentioned that the accumulation of citric acid is due to the decrease of malic and fumaric acids derived from changes in glycolysis and the Krebs cycle since they are the predominant carbon fluxes during the fruit ripening process. An increase in respiration during maturation, characteristic of the climacteric fruit, will determine the increase in the content of some intermediate products of the Krebs cycle. Therefore, it is likely that the application of Cu NPs influenced the respiration of the fruit, which translates into a delay in the maturation and, as a consequence, a decrease in the titratable acidity. However, this effect depends on the dose, as well as on the species under study. With respect to the oxidation-reduction potential (ORP), the 250 mg L\({}^{-1}\) of Cu NPs treatment was 5.7% lower than that of the Control. [PERSON] et al. [16] reported a decrease in ORP with the application of Cu NPs + chitosan. An ORP with low values indicates better fruit quality, which can be translated into greater antioxidant potential [16]. This indicates a beneficial effect of the Cu NPs application (250 mg L\({}^{-1}\)) on fruit quality.
# GC- and UHPLC-MS Profiles as a Tool to Valorize the Red Alga Asparagopsis armata+
## 2 Materials and Methods
### GC-MS Analysis of A. armata Dichlorethan Extract  
Before GC-MS analysis, four replicates of dried dichloromethane extract of _A. armata_ (extract A, nearly 20 mg each), was dissolved in 1 mL of dichloromethane, added the internal standard (octadecane) and was silylated using 250 \(\upmu\)L of pyridine, 250 \(\upmu\)L of BSTFA, and 50 \(\upmu\)L of TMSCI in a screw glass tube at 70 \({}^{\circ}\)C during 30 min. This derivatization procedure is well known [25; 26] and allows the hydroxyl groups that may exist in the extract constituents to be transformed into sillyoxyl groups. Thus, compounds with -OH substituents will be detected by GC-MS analysis as silylated derivatives which are more volatile, stable, and less polar [25; 26].  
Each replicate was analyzed by GC-MS twice using a QP2010 Ultra Shimadzu apparatus with Xcalibur software, equipped with a DB-5-1 &W capillary column (30 m \(\times\) 0.25 mm i.d. and a film thickness of 0.25 um). The injection was in split mode. The temperature of the column was 70 \({}^{\circ}\)C during 2 min, at 3.5\({}^{\circ}\)/min until 120 \({}^{\circ}\)C, at 9\({}^{\circ}\)/min until 170 \({}^{\circ}\)C, 170 \({}^{\circ}\)C during 10 min, at 9\({}^{\circ}\)/min until 300 \({}^{\circ}\)C, which was maintained for 6 min. Injector temperature was at 320 \({}^{\circ}\)C, and the transfer-line temperature was at 200 \({}^{\circ}\)C. The mass spectrometer was operated in the EI mode with energy of 70 eV, and data were collected at a rate of 1 scan s\({}^{-1}\) over a range of _m/z_ 50-1000. The carrier gas was helium (purity of 99.995%) at 1.2 mL/min.  
The compounds were identified, from total ion chromatogram, by comparing the retention times and by comparing their mass spectra with (i) those existing in the MS databases of Wiley229, NIST14, and Shimadzu Pesticide Library; (ii) those published in the literature and by analyzing the fragmentation pattern; (iii) those obtained in the same experimental conditions to standard compounds.  
Quantitative analysis was carried out using internal standard method (octadecane, 28.68 min), by intrapolation on the calibration curves obtained with at least 4 distinct but known concentrations of representative standards of each family of organic compounds identified in the dichloromethane extract (namely, palmitic acid, stearic acid, (_Z_,_Z_) 9,12-octadecationoic acid, 1-octadecanol, monopalmitin, cholesterol, sitosterol, phenyl acetic acid, 1,8-dibromooctane, 1-bromonane) relative to octadecane. The correlation coefficient (r\({}^{2}\)) of each calibration curve was greater than 0.99. All standards with hydroxyl substituents were subjected to the silylation procedure described above.
## 4 Materials and Methods
### Yeast Heterologous Expression  
The full-length 2,3-oxidosqualene cyclase _PgbAS1_ gene was inserted into pESC-URA vector, and the full-length of _PgCYP716_ genes were inserted into the pESC-TRP vector. The engineered yeast strain Saccharomyces cerevisiae WAT11, which has been modified to incorporate the P450 reductase from _A. thaliana_, serves as an optimal heterologous host for the expression of P450 enzymes. This strain is equipped with the mevalonate (MVA) metabolic pathway, enabling it to synthesize the triterpenoid precursor 2,3-oxidosqualene [56]. To validate the function of the _PgCYP716s_, the pESC-_PgbAS1_-URA vector was used to transform WAT11, after which the cyclization of 2, 3-oxidosqualene was catalyzed to generate \(\beta\)-amyrin [57]. Further, the pESC-_PgCYP716s_-TRP vector was used to transform the WAT11 containing pESC-_PgbAS1_-URA as described previously [10]. pESC-_PgbAS1_-URA and pESC-TRP (the empty vector) were also co-transformed into WAT11 as the control. The transformed yeast was inoculated in 5 mL SD/-TRP-URA liquid medium and grown till the OD\({}_{600}\) reached 1.5. Further, the yeast cells were harvested by centrifugation at 1000 rpm for 1 min, washed two times using sterile water, resuspended in 100 mL SC/-TRP-URA liquid medium, which added 2 g galactose, and cultured for 3 days in the dark at 28 \({}^{\circ}\)C to induce metabolite biosynthesis in yeast. Further, we use a mixture of acetone and ethyl acetate (1:1 \(v/v\)) to extract yeast metabolites as described previously [56]. The liquid was evaporated using a low-temperature concentration method, and the remaining residue was dissolved in 1.5 mL methanol for the next step of analysis. The metabolites isolated from yeast culture were detected using the vanillin-perchloric acid method and high-performance liquid chromatography (HPLC) [58; 59]
## 2 Radio Propagation Measurement Method
### Radio Propagation Measurement Environment  
In this study, to clarify the propagation characteristics of A2G communication in different environments, we selected two propagation environments on the campus of the University of Toyama. Figure 2 shows two environments using aerial imagery from Google Earth [43]. The simple propagation environment (S environment) is an area with a building but almost no obstructions in the surrounding area. In contrast, the complex propagation environment (C environment) is an area surrounded by buildings that have balconies and windows in the wall facing the propagation environment. These two environments were chosen to clearly evaluate the differences in propagation characteristics between S and C environments.  
To accurately understand the propagation characteristics, measurement settings were made according to each environment as detailed in Table 2. Radio wave propagation measurements are performed in a static environment without people or other moving objects, which ensures that the propagation paths are generally consistent. Changes in UAV attitude and position due to wind will cause changes in the scattered path power, so the received signal may fluctuate slightly, but drastic changes such as fading are not expected. In such cases, the Rician K-factor cannot be accurately estimated from the measurement data. To cause fading at these points, either the transmitting or receiving station would have to be moved, so to simulate this, the UAV was flow  
Figure 1: Radio propagation measurement setup: (**a**) UAV equipped with transmitter and antenna and (**b**) receiver system on the ground.  
Figure 2: Radio propagation measurement environment from Google earth [43].  
distance between the transmitting antenna mounted on the UAV and the nearest building was defined as \(D_{t}\), whereas the distance between the receiving antenna on the ground and the nearest building was defined as \(D_{r}\).
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 2 System Model  
This section describes the model considered for the time-varying MIMO system using one-bit ADCs. The transmitter is equipped with \(N_{t}\) antennas, and the receiver has \(N_{r}\) antennas. The transmission frame is first explained, followed by the received signal. Finally, the considered detector is described using the likelihood.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Descriptions** \\ \hline \((\cdot)^{H}\) & conjugate transpose \\ \((\cdot)^{T}\) & transpose \\ \(\mathbb{R}\) & set of real number \\ \(\mathbb{C}\) & set of complex numbers \\ \(\text{Re}(\cdot)\) & real component of the complex number \\ \(\text{Im}(\cdot)\) & imaginary component of the complex number \\ \(\text{Sign}(x)\) & function whose output is 1 for \(x\geq 0\) and \(-1\) otherwise \\ \(\Phi(\cdot)\) & cumulative distribution of the standard normal random variable \\ \(Q(\cdot)\) & standard Q-function \\ \(\mid\cdot\mid\) & cardinality of the set \\ \(\mathbb{1}\{\mathcal{E}\}\) & one if the event \(\mathcal{E}\) is true; otherwise, it is 0 \\ \(\mathbb{P}(\cdot)\) & probability operation \\ \(\mathbb{E}(\cdot)\) & expectation operation \\ \(J_{0}(\cdot)\) & the 0-th order Bessel function of the first kind \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key notations in this study.