esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Spatio-Temporal Variations of CI  
Temporally, the overall interannual variation of CI in the Sanjiang Plain from 2001 to 2015 exhibited a slight upward trend with a slow growth rate of 0.021\(\cdot\)10 a\({}^{-1}\) (\(p<0.05\)). However, the seasonal variation was obvious, with the strongest clumping effects in the summer and the weakest clumping effects in the winter. [PERSON] [63] investigated the temporal  
Figure 13: Statistical significance of driving factors.  
characteristics of CI for major vegetation types in China from 2000 to 2013, finding that the mean values of CI in the growing season for all vegetation types exhibited relative stability, and the seasonal variation of CI was significant. The results in this study were relatively consistent with the abovementioned conclusions. Areas with significant improvement for CI\({}_{\text{GS}}\) in the Sanjiang Plain were mainly distributed in plain areas, with flat terrains and gentle slopes, and the variation of land use type was mainly from forestland to cultivated land. Therefore, CI\({}_{\text{GS}}\) showed a significant upward trend. Most areas with significant degradation were located in hills and small relief mountains that had gentle slopes, with elevations greater than 200 m, and the major land use type was forestland. Thus, the increase of clumping effects in the Sanjiang Plain could be related to improvements in vegetation growth.  
The spatial distribution of the multiyear mean CI in each season was related to the spatial distribution of the land use type; this finding was consistent with the conclusion reached by [PERSON] et al. [64] in the Daxing'an Mountains, Northeast China. Moreover, the multiyear mean CI of each season exhibited a slight downward trend with increasing elevation; the reason for this is that the main land use types of the plain with elevations less than 200 m and greater than 200 m were cultivated land and forestland, respectively. Thus, the overall multiyear mean CI in each season decreased with increasing elevation.
## 4 Discussion
### Simulating Soil Gas Fluxes and Measurement Uncertainty  
Soil chamber measurements are known to be subject to errors and both spatial and temporal variability are expected [62; 63]. Measurements during this study were made once every 2-4 weeks; therefore, estimated annual fluxes may have compounded errors resulting from diurnal and short-term temporal variations. Overall, the results show that PSIM-simulated soil CO\({}_{2}\) effluxes matched the measured effluxes more closely than those simulated by PnET. PSIM gave a mean ratio of soil CO\({}_{2}\):TER of 0.67 for 2008-2011 with and without thinning, which is similar to the mean ratio of 0.61 and 0.60 in measurements by [PERSON] and [PERSON] [50] and [PERSON] et al. [64], respectively. The equivalent ratio for the PnET simulations was 0.47 for 2013-2014. PSIM simulated a greater annual maximum total root mass of 0.9 kg DW m\({}^{-2}\) than did PnET (0.25 kg DW m\({}^{-2}\)) due to the addition of understorey vegetation. We suggest that the differences in total CO\({}_{2}\) efflux are largely due to the simulation of root exudates that occurs in PSIM, generating extra substrates for heterotrophic respiration, but is not modelled in PnET.  
Chamber measurements of soil N\({}_{2}\)O fluxes are subject to similar measurement errors as soil CO\({}_{2}\) effluxes [65; 66]. The overestimation of N\({}_{2}\)O peak fluxes by the model has been discussed above as an effect of the simplification of rainfall simulation and resulting moisture content. The presence of the understorey affects the intensity of simulated fluxes, rather than seasonal pattern, and is linked to simulated N uptake. A constant N deposition was assumed in the simulation, but [PERSON] et al. [67] report decreases in NH\({}_{4}\)-N throughfall at Alice Holt from 1995 to 2006 of approximately 0.7 NH\({}_{4}\)-N eq ha\({}^{-1}\) year\({}^{-1}\), which would result in a decrease in N\({}_{2}\)O fluxes if continued during the period simulated here and may also have contributed to the mismatch between measured and modelled data.  
The inability to model the uptake of N\({}_{2}\)O is a further simplification that contributes to poor agreement with measurements.
## 6 Case Study of a Wind Power Plant Connected to a Weak Grid
### Stability Analysis  
By obtaining the equivalent wind power plant impedance and the equivalent grid impedance, Figure 8 can be used for stability analysis. Based on this figure, the transfer function for the current can be obtained as  
\[I(s)=\frac{V_{wt}-V_{grid}}{Z_{wt}+Z_{grid}}=(\frac{V_{wt}-V_{grid}}{Z_{wt}}) (\frac{1}{1+Z_{grid}/Z_{wt}}) \tag{13}\]  
For the stability analysis, the poles of the system can be obtained by solving the following equation:  
\[Z_{wt}+Z_{grid}=0 \tag{14}\]  
As the wind power plant is stable when connected to a strong grid, i.e., \(V_{wt}\)/\(Z_{wt}\) has no right-half plane poles, an alternative option for the stability analysis is to assess the Nyquist stability criterion for \(Z_{grid}\)/\(Z_{wt}\).  
A weak grid model is considered here including inductive reactance (\(X_{g}\)), resistance (\(R\)), and series capacitive reactance (\(X_{c}\)) for compensation. To illustrate sensitivity to variation in the network, short-circuit level, \(X_{g}\)/\(R\) ratio and line series compensation, three scenarios, are studied, all based on a fictitious wind turbine converter controller, the parameters of which are listed in [21]. The wind turbine has been tuned for a strong grid. In Appendix B, all the variables and parameters used in the figures are summarized.  
Figure 8: Aggregated wind power plant model connected to weak grid.  
Figure 7: Aggregated model of the 108 MW wind power plant shown in Figure 8, (**a**) aggregated model on each string as one 36 MW wind turbine, (**b**) aggregated model of three strings into one 108 MW wind turbine.
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 2 Background and Related Work
### Error Correction Codes for Flash Memory  
The first-generation NAND flash memory was much simpler than the current generation. However, over time, the storage capacity of NAND flash memory has increased by moving to smaller geometries, and also storing more bits per cell. This required a much better error-correction algorithm to ensure data integrity for NAND flash devices. The P/E cycle value is about 100,000 for SLCs, 10,000 or less for MLCs, and 1000 or less for TLCs. As error rates increase rapidly, the usage of flash devices is emerging as a major issue. The advent of TLC flash and even smaller geometries will further increase the error-correction requirements in NAND flash. Figure 2 shows the raw bit error rate (RBER) for the P/E cycle of the TLC NAND flash memory [24]. As shown in the figure, when there are thousands of P/E cycles, the RBER is so high that it is known to reach the lifecycle limit.  
Figure 1: Basic architecture for NAND flash-based storage devices.  
As a method for recovering such errors, ECC methods [3; 4; 25; 26; 27; 28], such as BCH and LDPC, are embedded in the NAND flash memory controller, which can cover page-level errors for programming and retrieving data. The BCH codes form a class of cyclic error-correcting codes that are constructed using polynomials over a finite field. An arbitrary level of error correction is possible, and it includes efficient code for un-correlated error patterns. Low-density parity check (LDPC) codes are considered one of the best choices for current flash memory controllers owing to their excellent error-correction capabilities. LDPC code decoding is an iterative process with the decoding input as certain probability information, which leads to a dependency of LDPC error correction on the accuracy of the input information [4]. ECCs for flash memory storage systems have been widely studied in both BCH and LDPC. Ref. [3] addresses applications using BCH ECC coding driver on a Linux platform, and many other chip designers have presented BCH bit error coding implementations. In contrast, LDPC receives attention from the industry, as there have been many presentations on LDPC in flash summits in the recent past [25; 26]. In [27], a LDPC decoding strategy optimized for flash memory was proposed. Tanakamaru et al., have shown that LDPC codes can improve SSD lifetimes by 10 [28]. K. Zhao presents three techniques for mitigating an LDPC-induced response time delay [4].
## 3 Results  
### Physical and Structural Characterization  
Figure 2a presents the DSC traces of the ball-milled Al and B+GNP powders conducted at a heating rate of 5 \({}^{\circ}\)C/min under an argon atmosphere. An exothermic peak is observed beyond the melting point of Al (660 \({}^{\circ}\)C) around 680 \({}^{\circ}\)C [41], signifying the possible chemical reaction between Al, B, and GNPs initiating solid-solid reactions [21]. No (endothermic/exothermic) peaks are observed beyond 1000 \({}^{\circ}\)C. This suggests that there is a simultaneous reaction of Al, B, and GNPs and the in-situ synthesis of Al\({}_{3}\)BC. The XRD patterns (Figure 2b) of (B+GNPs)/Al milled powders reveal the presence of crystalline _fcc_-Al phase [space group: Fm-3m; PDF-2-2023#00-004-0787]. The peak broadening with increasing milling time indicates the possible structural refinement. Furthermore, a decrease in intensity of the _fcc_-Al peak (111) (w.r.t. (200) peak) demonstrates the dissolution of the _fcc_ phase, which indicates successful alloying [42]. Figure 2c shows the XRD pattern of the as-fabricated Al\({}_{3}\)BC/Al composite. The diffraction peaks show the presence of _fcc_-Al, _hcp_-Al\({}_{3}\)BC [space group: P63/mmc; PDF-2-2023#00-047-1628], and _hcp_-AlB\({}_{2}\) [space group: P6/mmm; PDF-2-2023#03-65-3381] phases. Importantly, no unwanted Al\({}_{4}\)C\({}_{3}\), a common by-product during solid-liquid or liquid-liquid state reaction(s), was detected. In addition, a higher intensity of Al and Al\({}_{3}\)BC phases than AlB\({}_{2}\) phases indicates their dominant behavior. The lattice parameters (\(a\)), and the relative amounts of the phases in terms of mass fraction/phase volume fractions, \(V_{f}\), were determined using the XRD peak profile analysis (refer to Table 1 for details) [42]. It shows the volume fraction of the Al matrix (\(V_{f}^{Al}\)) accounts for 25 \(\pm\) 2 wt.%, primary reinforcement Al\({}_{3}\)BC (\(V_{f}^{Al_{3}BC}\)) corresponds to 65 \(\pm\) 2 wt.%, and AlB\({}_{2}\) (\(V_{f}^{AlB_{2}}\)) volume fraction is calculated to be 10 \(\pm\) 1 wt.%. This suggests that the addition of GNPs and B promotes the Al\({}_{3}\)BC formation preferentially reacting with Al over forming AlB\({}_{2}\).
## 2 Materials and Methods  
### pr.IBA as the Primary Investigated Sample  
The sample investigated in the present study originated from processed IBA (pr.IBA) that has already been through sequences of separation processes, both commonly conducted by IBA processing companies, followed by an extended separation process involving a selective milling operation and an additional classification process. The total material stream from raw IBA to the fractions investigated in the current study is provided in Figure 1. In this instance, details regarding the applicability of both the selective milling process and extended separation steps by employing additional sequences of magnetic, eddy current, and density separations are provided in other dedicated reports [19, 20].  
Specifically, considering the standard process carried out by IBA processing companies in Germany, wet raw IBA (approximate annual material volume of 6 Mt) that was naturally weathered for around two to three months was first classified using a sequence of screening processes. Approximately 0.4% of the oversized (>32 mm) metals were discarded as scrap for another recycling process. In addition, incompletely incinerated waste was separated from the mainstream unprocessed IBA (96.2%) to be charged back into the incinerator. The unprocessed IBA subsequently underwent a sequence of magnetic and eddy current separations and generated three products: around 7% ferrous metals, 2.8% non-ferrous metals, and approximately 77.5% pr.IBA.  
The three products mentioned above are the typical end outputs of IBA processing plants operating in Germany. Considering the pr.IBA, this fraction can be further classified into two distinct particle size distributions: pr.IBA 0-10 mm and 10-32 mm. In the boundary of this study, the investigated sample originated specifically from pr.IBA 0-10 mm upon additional treatment steps. The treatment began with a selective milling process, where three fractions were yielded: the fine (FF010), middle (MF010), and coarse (CF010) fractions. Furthermore, MF010 and CF010 underwent additional magnetic and eddy current separation, yielding three additional products: output magnetic, mineral, and non-ferrous. Given several circumstances, including the particle sizes, mass fraction to total mass streams, results from previous studies, and prospective applications of the mineral fraction, only the output mineral (MF010-Min) from MF010 was taken as a considerable representative and will be washed in this research.
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 5 Results and Discussion
### Implementation on the High-Performance Aws-FPGA  
The AWS-FPGA platform significantly outperforms Ultra96 in technology and hardware resources. Xilinx Virtex UltraScale+ VU9P on the high performance platform has 16 times more logic cells and 18 times more DSPs than Ultra96. However, the exploration of the design space allowed scaling the architecture to obtain a higher number of cores on the AWS-FPGA. In this way, we perform the algorithm's scaling up to 20 cores of the KNN and the modified KNN. The results in terms of hardware resource consumption and processing time are shown in Table 3.  
The elapsed time is for processing 100 K symbols i.e., 17,182 symbols per second on our multi-core KNN architecture. The architecture for our modified KNN algorithm processes 32,573 symbols per second, improving performance by 1.9 times. The implementations used a 32-bit floating-point format so that the results were compared with those obtained by the authors in Matlab, achieving 1.04 Mb/s for the modified KNN. Nevertheless, the number of bits per symbol from an ADC for optical communications applications is usually much lower, so internal operations in CuPUs could be performed using a fixed-point format with a lower number of bits. The percentage of hardware resources over the AWS-FPGA is low for the 20-core design, so it is possible to explore a larger design that processes more input symbols in parallel.
## 5 Discussion  
This paper presents a dynamic rockfall detection algorithm that integrates a background subtraction algorithm with neural networks, based on existing railway slope scenarios, aimed at addressing the early warning needs for potential rockfall hazards in target scenes. Compared to traditional target detection algorithms, it offers higher precision and target matching, effectively leveraging the advantages of the background subtraction algorithm to filter out stationary, lower-risk targets. However, there are areas for improvement in the overall effectiveness:  
1. The Gaussian mixture model method has limited interference resistance to background changes. In practical applications, it is still susceptible to weather, lighting, and terrain fluctuations, lacking stability. The background subtraction algorithm itself does not have autonomous learning capabilities; it merely delineates target areas and relies on the neural network of the YOLO model for identification. Future developments are anticipated to achieve autonomous learning capabilities.  
Figure 11: The final detection results of the introduction of the monocular vision algorithm: the width of the detection target is shown in the upper right corner of the inspection frame.  
2. The target information is singular. Even though rockfalls are the most common risk factor in railway slope scenarios, other potential threats to railway safety from foreign object intrusions still exist. Future work aims to aggregate other risk targets and expand the range of detectable foreign objects.
3. The dataset's scene diversity is limited. The dataset in this paper primarily relies on imagery from a certain railway slope in Shandong, China, which cannot broadly represent the diverse geomorphological and climatic railway operation scenarios nationwide. A dataset lacking in diversity will limit the extension to more scenarios.
## 4 Results and Discussion  
### Evaluation of Results  
The ANN and SVR models developed in the present study were used to predict the coefficient of moment redistribution for statically indeterminate structures. The predicted values and experimental results of the two models for the training and testing sets are presented and compared in Figures 12 and 13. It can be seen intuitively that the predicted values for the both models show good agreement with the experimental values. To further evaluate the performance of the developed models, four indices of accuracy were calculated for the training and testing sets: mean squared error (MSE), determination coefficient (\(R^{2}\)), mean absolute percentage error (MAPE) and integral absolute error (IAE). MSE and \(R^{2}\) are computed using Equations (3) and (4), respectively, and MAPE and IAE are correspondingly expressed as follows:  
\[\text{MAPE}=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left|\frac{t_{i}-c_{i }}{t_{i}}\right| \tag{13}\]  
\[\text{IAE}=\frac{\underset{i=1}{\overset{n}{\sum}}\left[\left(c_{i}-t_{i} \right)^{2}\right]^{1/2}}{\underset{i=1}{\overset{n}{\sum}}t_{i}}\times 100\% \tag{14}\]  
Figure 11: Flow chart of the grid search algorithm.  
All performance indices for the proposed ANN and SVR models are listed in Table 7, which indicates that both models have high-precision prediction ability for the coefficient of moment redistribution. Moreover, the SVR model showed slightly better results than the ANN model.
## 3 Results and Discussion
### MIMO Performance Analysis  
The MIMO performance of the proposed design was analyzed using envelop correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL) and mean effective gain (MEG). The detailed analyses for each of the parameters are reported in the relevant sections below.  
Figure 14: Radiation pattern for the mid-band MIMO antenna: (**a**) measured and simulated radiation pattern for phi = 90\({}^{\circ}\) at 3.8 GHz; (**b**) measured and simulated radiation pattern for theta = 90\({}^{\circ}\) at 3.8 GHz.  
Figure 15: Radiation pattern for the high-band MIMO antenna: (**a**) measured and simulated radiation pattern for phi = 90\({}^{\circ}\) at 27.5 GHz; (**b**) measured and simulated radiation pattern for theta = 90\({}^{\circ}\) at 27.5 GHz.
# Microbiological Safety and Health Properties of Markleted Fermented Root Vegetables
## 3 Results and Discussion
### Total Phenols' Content
The order of TPC levels in the analyzed fermented vegetables was as follows: beetroot > celeriac > carrot > radish > white radish. By comparison, the reported TPC levels for their fresh counterparts follow a similar order: beetroot (164-211 mg GAE/100 g f.w.) [2; 28; 57], celeriac (59 mg GAE/100 g f.w.) [57], carrot (35.19 mg GAE/100 g f.w.) [58], and radish (29.45 mg GAE/100 g f.w.) [59]. The antioxidant potential and phenolic content of vegetables are strongly influenced by the raw material and cultivation conditions [60; 61]. Data on root vegetables cultivated in Poland indicate relatively low antioxidant potential, with carrots containing 18.0 mg GAE/100 g fresh weight (f.w.) [62] and celeriac containing 32.2 mg of phenolic compounds per 100 g f.w. [63]. These findings are different to other studies, which indicated that TPC increased in fermented vegetables compared to their fresh counterparts [64; 65; 66]. However, there was research that reported a decrease in phenols amount after fermentation. Moreover, the percentage decrease in TPC after the mentioned process was dependent on the specific bacteria type [66]. Nevertheless, the vegetables analyzed in our study were industrially fermented and purchased in this form. Therefore, there are no data about the TPC of fresh samples of analyzed vegetables. That is why we cannot unequivocally state what effect the fermentation process had on the antioxidant potential of the analyzed vegetables. Nevertheless, the analyses performed indicate that the pickled roots available on the Polish market are characterized by a decreased TPC compared to the fresh vegetables. There are a notable lack of data on the TPC of fermented root vegetables in the scientific literature. Most available studies have focused on by-products of these pickles, such as fermented carrot juice (Salgam). For example, one study reported TPC values for Salgam ranging from 185.93 to 257.10 mg GAE/L [67], while another study indicated values between 627.0 and 748.5 mg GAE/L [68]. However, comparisons between these findings and the present study are challenging due to differences in sample matrices (juice versus whole vegetables). Based on recommendations concerning consumption of phenols (500 mg GAE/day) [69], it was concluded that pickled roots constitute a moderate dietary source of the mentioned compounds. The consumption of 100 g of the analyzed fermented vegetables covered these requirements in a range of 2.8% for white radish, 3.36% for carrot, 3.89% for radish, 5.77% for celeriac, and 11.34% for beetroot.
###### Abstract  
The utilization of autonomous maritime vehicles is becoming widespread in operations that are deemed too hazardous for humans to be directly involved in them. One of the ways to increase the productivity of the tools used during missions is the deployment of several vehicles with the same objective regarding data collection and transfer, both for the benefit of human staff and policy makers. However, the interchange of data in such an environment poses major challenges, such as a low bandwidth and the unreliability of the environment where transmissions take place. Furthermore, the relevant information that must be sent, as well as the exact size that will allow understanding it, is usually not clearly established, as standardization works are scarce in this domain. Under these conditions, establishing a way to interchange information at the data level among autonomous maritime vehicles becomes of critical importance since the needed information, along with the size of the transferred data, will have to be defined. This manuscript puts forward the Maritime Data Transfer Protocol, (MDTP) a way to interchange standardized pieces of information at the data level for maritime autonomous maritime vehicles, as well as the procedures that are required for information interchange.  
autonomous maritime vehicles; protocol; protocol data unit +
Footnote †: journal: Sensors  
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