esa-sceva/satcom-chunk-collection huggingface dataset:
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 3 Background and Current Development of 6G
This SSN concept ensures continuous maintenance of key performance indicators (KPIs) within the intricate and dynamic environments spawned by diverse 6G application domains [75]. With regard to the 6G cellular network, the integration of connected robotics and autonomous systems (CRAS) and DLTs devices will necessitate the obligatory incorporation of SON and SSN. These mechanisms will be pivotal in governing network operations, resource allocation, and optimization. As these diverse technologies converge, sixth-generation networks will user in a new era of interconnectedness, empowering forthcoming services and societies with cutting-edge capabilities and revolutionizing how we interact, communicate, and utilize data. The journey towards 6G is set to push the boundaries of communication technology, shaping a more intelligent, efficient, and connected world. In short, 6G is poised to emerge as an authentic AI-driven communication network, imbuing the whole system with self-awareness, autonomous computation, and the capability to make independent decisions in various scenarios. The potential of 6G lies in providing network performance that surpasses the limits perceivable by human senses. Quality of experience (QoE) is inherently connected to quality of service (QoS) measurements, and the state of the network is intertwined with and influences user experience. However, a crucial challenge is to shift the perspective from considering the network as the primary determinant in assessing QoE [33].
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 2 Related Works
The structure and functional framework of the MBLCR scheme are illustrated in Figure 3. The proposed framework addresses the challenges of this research, followed by a few proposed solutions in order to attain the research objectives. Moreover, the MBLCR exploits the functionalities of other existing conventional routing protocols for the route computational mechanism. In short, the MBLCR framework is modified and improved with the integration of mobility, battery, link quality status, and contention window size of devices by employing the MCDM technique in an optimal path selection among the multiple paths.
# Maintenance of Discovered High Average-Utility Itemsets in Dynamic Databases
## 4 Proposed FUP-HAUIM Algorithm with Transaction Insertion
### The Average-Utility-List (AIL)-Structure  
The AUL-structure is utilized by the HAUI-Miner algorithm [22]. Each itemset \(X\) in the AUL-structure structure keeps three fields such as the transaction ID represented as (_tid_), the utility of an item \(X\) in a transaction \(T_{q}\) denoted as (_iu_), and the transaction-maximum-utility of an item \(X\) in transaction \(T_{q}\) denoted as (_tmu_). For generating \(k\)-itemsets (\(k\geq 2\)), an enumeration tree representing the search space is explored using a depth-first search to determine whether the superset (\(k+1\))-itemsets of \(k\)-itemsets should be generated. If it satisfies the condition, a simple join operation is recursively performed to generate the AUL-structures of \(k\)-itemsets. This process is then recursively performed until no candidates are generated. After that, a database scan is done to find the actual HAUIs. The enumeration tree for the example database is shown in Figure 2. Based on the AUL-structures, we can obtain the following properties.  
**Property 2**: _All the 1-HAUUIs of the original database are sorted by auub-ascending order, which is also used as the construction order for the AUL-structures._  
**Property 3**: _To ensure the correctness and completeness for mining HAUUIBIs and HAUIs, the AUL-structures of all items in the inserted transactions should be constructed for later maintenance. This is reasonable since the size of the inserted transactions is much smaller than that of the original database in real-life situations._  
**Property 4**: _The auub-ascending order of 1-HAUIBIs in the updated database is almost the same as the sorted order in the original database since the size of the inserted transactions is smaller than the size of the original database. Thus, this sorting order of itemsets generally does not change much when the database is updated._  
Since all 1-items of inserted transactions are kept to build the initial AUL-structures, the designed FUP-HAUIMI algorithm can efficiently find the updated HAUUBIs and HAUIs without generating candidates and is complete and correct to update HAUUBIs and HAUIs. The AUL-structures of 1-HAUUBIs obtained from the original database are shown in Figure 3.  
Figure 2: The enumeration tree used in the example.
## 3 Results  
### Model Evaluation  
#### 3.1.1 Assessing the GPP Estimation Capability of Different Models  
Figure 6, presents the performance comparison of seven machine learning models for GPP prediction against EC-based measurements. The performance metrics reveal a clear pattern of improvement from linear models to ensemble methods with RGSA hyperparameter optimization.  
The linear regression models (LASSO and Ridge) showed identical performance with \(R^{2}\) of 0.66 and \(RMSE\) of 1.74 g Cm\({}^{-2}\) d\({}^{-1}\). Their \(PSI\) values of 0.175 and 0.172 indicated moderate prediction stability. The ANN model achieved a higher \(R^{2}\) (0.70) and lower error  
Figure 5: Schematic diagram of the input variable combinations used for model development. The diagram illustrates seven different combinations (1–7) of remote sensing variables including vegetation indices (EVI, NDVI, LAI), radiation parameters (PAR, NR, SR), and climate factors (Temp, VPD). Each combination represents a unique input variable set tested in the modeling framework.  
Figure 6: Comparison of predicted versus EC-based GPP (g Cm\({}^{-2}\) d\({}^{-1}\)) using different machine learning models. (**a**) LASSO regression, (**b**) Ridge regression, (**c**) ANN, (**d**) RF base model (RF Base), (**e**) XGBoost base model (XGBoost Base), (**f**) RF with hyperparameters optimization (RF HO), and (**g**) XGBoost with hyperparameters optimization (XGB HO). The scatter plots show the density distribution of data points, with the red line representing the linear regression fit, black dashed line indicating the 1:1 line, and gray shaded area showing the 95% confidence interval.  
metrics (\(RMSE\) = 1.63 g Cm\({}^{-2}\) d\({}^{-1}\)) but exhibited the highest \(PSI\) value (0.668) among all models, suggesting poor prediction stability.  
Ensemble methods demonstrated substantial improvements over linear and ANN models. The base RF and XGBoost models both achieved an \(R^{2}\) of 0.77 with similar error metrics (\(RMSE\) \(\approx\) 1.43-1.44 g Cm\({}^{-2}\) d\({}^{-1}\)). Notably, these models showed remarkably improved stability with PSI values of 0.050 and 0.041, respectively.  
The RGSA-based hyperparameter optimization further enhanced model performance. The RF HO (hyperparameters optimization) model reached an \(R^{2}\) of 0.79 with reduced errors (\(RMSE\) = 1.37 g Cm\({}^{-2}\) d\({}^{-1}\)). The XGBoost HO model demonstrated the best overall performance with the highest \(R^{2}\) (0.81), lowest \(RMSE\) (1.29 g Cm\({}^{-2}\) d\({}^{-1}\)), nearly zero bias (0.03 g Cm\({}^{-2}\) d\({}^{-1}\)), and excellent stability (\(PSI\) = 0.040). The regression slopes for ensemble models (0.923-0.967) were consistently closer to the 1:1 line compared to linear models and ANN, indicating better capture of the GPP relationship across all observed values.  
In summary, the ensemble models, particularly XGBoost with RGSA-based hyperparameter optimization, outperformed linear regression and ANN approaches in GPP prediction, showing higher accuracy, lower errors, and substantially better prediction stability.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 3 Hybrid Acoustic-RF Approaches for Positioning Energy-Neutral Devices
### Hardware Implementation, Power Consumption and Backscatter Demodulation
#### 3.4.1 Hardware Implementation and Power Review
The total energy consumption and awake time of the chosen hardware components are shown in Table 2. Current measurements were performed with nanoampere precision on an Otii Arc precision power analyzer and used for the stable power consumption calculations. The reference voltage was fixed at 2.5 V. A Keysight DSO-X 2002A digital scope with reference resistor and precision power source was chosen to measure the transient currents of the system at startup. For the active power consumption, a wake-up time (\(\tau_{rx}\)) of only 1 ms was set. Measurements were performed in [57] to calculate the necessary power budget for RF energy harvesting. From this table, a total energy consumption below 5 uJ can be calculated to perform a single distance measurement. For complete energy-neutral operation, the current hardware should be extended with an energy harvesting circuit, e.g., E-Peas AEM 40940 and an RF wake-up detector for synchronized wake-ups. In active modus, the total power consumption is 755 uW. Without a reference wake-up and in always-active modus, this system could last for 894.04 hours or 37.25 days on a lithium CR2032 coin cell battery with a nominal voltage of 3 V and a 225 mAh capacitance. With an RF wake-up, where the wake-up time is 986 uS, the awake time is limited to 1 ms, and the measured transient energy consumption is 1.66 uJ, the lifetime of such a node could be extended to 213,646.13 hours, or 8901.92 days, or 24.38 years, without the power consumption of the awake circuit and self-discharge of the battery into account.
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
## 3 Materials and Methods
### Experimental Program
#### 3.2.1 Soil Specim Preparations  
Two methods were used to treat the soil with lignin: Method 1 involved mixing soil with lignin solutions, and Method 2 was about spraying lignin solution on the already-prepared soil samples. The rationale for using two methods was as follows: Method 1 was used in previous laboratory studies to prepare specimens for unconfined compression and triaxial tests [3,8]. In this method, the whole soil mass was mixed with lignin solutions to produce small-sized specimens. This method proved effective for a relatively small amount of soil involved, but it might be rather expensive in practice. Method 2 appeared more cost-effective in real engineering practice [57], but in this case, the lignin solution was only applied on the surface of the soil mass.  
Method 1. The lignin was mixed with distilled water to produce lignin solutions of either 1% or 3%. The lignin solutions were then mixed with the oven-dried soil to achieve the desired value of the initial water content, which was equal to the optimum water content of each soil. The solution was manually mixed with the soil for 30 min. The treated soil was sealed in a plastic bag and allowed to rest in a controlled temperature room for 24 h to ensure an even moisture distribution in the whole sample. The soil was then placed into an aluminum foil tray with a length of 31.5 cm, width of 26 cm, and height of 5 cm. Each soil was compacted in two layers in the tray to achieve the maximum dry density. However, it was challenging to achieve such high values of dry density due to technical issues during the compaction process. The following values of dry density were achieved: 1.30 g/cm\({}^{3}\) for Soil 1, 1.63 g/cm\({}^{3}\) for Soil 2, and 1.77 g/cm\({}^{3}\) for Soil 3, which were within the 95% range of the dry density.  
Method 2. The oven-dried soil was first mixed with distilled water to prepare soil specimens with the same initial water content as in Method 1. The moist soil was placed in a sealed plastic bag and allowed to rest in a controlled-temperature room for 24 h to ensure an even moisture distribution. The soil was placed in an aluminum tray of the same size as in Method 1 and compacted in two layers to achieve the same value of dry density as in Method 1. The lignin solutions of the same concentration (1% and 3%) as in Method 1 were sprayed (700 mL) on top of the soil surface.  
For each method, the soil sample in the tray was divided into three equal parts: the left part was for Couch seeds, the middle part was for Rhodes grass seeds, and the right part was for Forest blue grass seeds. The seeds were spread evenly in their designated parts, and the following seed mass was used following the government's recommendations for garden areas: 0.3 g for Coach, 0.7 g for Rhodes grass, and 0.7 g for Forest bluegrass. The seeds were covered in a thin layer of soil and watered. To help the seeds germinate and grow, 100 mL of water was sprayed on the soil surface twice per week.
## 4 The WBANs Communication Stack
### Medium Access Control Layer  
The Medium Access Control (MAC) layer plays a critical role in optimizing most of the important design cost functions. For example, MAC controls the radio activity and hence optimizes the energy consumption of the node by timely turning on and off the radio transceiver which is the most energy consuming component of the sensor node [42]. It provides medium access mechanisms such as time-based or random access to meet the time constraints (_i.e._, latency optimization), and it provides the reliability of the successful transmission of packets by controlling the medium contentions.  
In the WBAN context, the coordinator controls most of the communication and therefore TDMA is considered as an obvious choice for medium access. However, we will explore several medium access mechanisms while keeping the viewpoint of specific applications of rescue and critical operations as described earlier.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 7 Other Metal-Oxide Nanomaterials
### Wo\({}_{3}\)-Based
Excess WO\({}_{3}\) in the photocatalyst likely partially blocked TNS pores or reduced the specific surface area of WO\({}_{3}\), leading to a decrease in the overall surface area of WO\({}_{3}\)-N-TNS [120]. Apart from its visible light activity and effective pollutant degradation capabilities, WO\({}_{3}\) is a non-toxic, environmentally friendly material, making it a safe and sustainable option for air pollution control applications. It does not generate harmful byproducts during photocatalytic reactions, aligning with the principles of green chemistry. Moreover, tungsten is relatively abundant, and the cost of synthesizing WO\({}_{3}\) is lower compared to other advanced photocatalysts, making it an economically viable material for large-scale environmental applications. On the other hand, WO\({}_{3}\) photocatalytic efficiency is somehow limited under low light intensity, as it requires a certain threshold of energy to activate. Moreover, WO\({}_{3}\)-based photocatalysts can be sensitive to environmental conditions such as humidity and temperature. High humidity can lead to the formation of hydrated tungsten oxides, which can alter the material's photocatalytic properties.
## 6 Existing and Potential Industrial Applications
### Clean Conversion and Utilization of Coal-Based Fuel  
The SCHC for coal is a new combustion technology that can realize efficient and clean utilization of coal without pollutants terminal control [20]. Compared with conventional coal combustion technologies, it can restrict the formation of pollutants like NOx, Sox, and dust without desulphurization, denitrification, and dust collecting equipment, and easily realize the low-cost capture of CO\({}_{2}\). Therefore, it has an extremely superior environmental protection performance. At present, there are three main ways for its implementation: (1) coal-based fuel goes directly into HFR in the form of coal water slurry [21], (2) coal-based fuel first enters the supercritical water gasification (SCWG) reactor in the form of coal water slurry. After solid residues (mainly consisting of inorganic salts, oxides, and gravel) are removed, the products with abundant syngas and methane are injected into HFR [19], as (3) coal-based fuel first enters the SCWG reactor in the form of coal water slurry. After partial gasification and full separation for obtaining the required synthesis gas, the remaining fluid with semi-coke as the main organic matter enters HFR. The high-temperature and high-pressure products (including steam, CO\({}_{2}\), N\({}_{2}\) and other gases) directly push the steam turbine to generate electricity [119] or supply heats to the outside (Figure 11) [20].  
So far, research on SCHC mainly focuses on liquid fuels rather than solid fuels such as coal. [PERSON] studied the hydrothermal combustion characteristics of semi-coke obtained from coal gasification, and observed the SCHC process of semi-coke, included accumulating heats through heterogeneous chemical reactions and exchanging heats with the surrounding fluids [20]. Because the reaction rate was still subject to the mass transfer rate of oxygen in supercritical water, for millimeter-sized semi-coke particles, the complete burnout time was about 5 to 7 min, and for micron-sized semi-coke particles, it took about 4 to 7 s to burn out. The simulation results showed that the increase of supercritical water temperature and volatile content and the decrease of the particle-size were beneficial to the ignition of semi-coke. The critical conditions for the semi-coke ignition include 798 K of water, 7.97 wt% of oxygen concentration, 168 \(\upmu\)m of particle size and 10 wt% of volatile content. Generally speaking, influence mechanisms about the effects of the characteristics of supercritical water and pulverized coal on the ignition of coal, and the SCHC kinetics of pulverized coal may be the major concerns.
## 3 Numerical Simulation Analysis
### Reconstruction Speed  
Real-time performance is a key metric for the application of reconstruction algorithms in practical engineering, and Table 1 compares the processing times of the four algorithms (FA, ZA, PA, BA). The number of one-dimensional sampling points is set to 100 and 200 and 300 in turn. Each runtime result is the average of 20 repetitions. A comparison of the reconstruction time between the proposed reconstruction algorithm (FA) and the existing multi-directional slope-curvature hybrid reconstruction algorithm (ZA) for different sample intervals is shown in Figure 6, which shows that the reconstruction time increases with the increase in the sample points. This study reduces the reconstruction time by fifty percent by reducing the number of integration equations while ensuring accuracy as compared to the previous section.
# Antioxidant Activity of Sulfate Metabolites of Chlorogenic Acid
## 3 Results
### Characterization of Antioxidant Activity of Synthesized Metabolites

Table 2: Antioxidant capacities of phenolic acids and their sulfate metabolites measured with the CUPRAC (calculated as Trolox equivalent mM) and Folin–Ciocalteu (calculated as gallic acid equivalent ppm) assays. \begin{table}
\begin{tabular}{c c c} \hline
**Compound** & **Scavenging Effect (\%)** & **Trolox Equivalent (mM)** \\ \hline Caffeic acid & 20.99 \(\pm\) 1.05 & 0.296 \(\pm\) 0.015 \\ Caffeic acid monosulfate 1 & 2.97 \(\pm\) 0.15 & 0.042 \(\pm\) 0.002 \\ Dihydrocaffeic acid & 39.96 \(\pm\) 2.00 & 0.563 \(\pm\) 0.028 \\ Dihydrocaffeic acid monosulfate 2 & 3.61 \(\pm\) 0.18 & 0.051 \(\pm\) 0.003 \\ Ferulic acid & 13.56 \(\pm\) 0.68 & 0.191 \(\pm\) 0.010 \\ Ferulic acid-4-O-sulfate & 1.28 \(\pm\) 0.068 & 0.018 \(\pm\) 0.001 \\ Dihydroferulic acid & 23.15 \(\pm\) 1.16 & 0.326 \(\pm\) 0.055 \\ Dihydroferulic acid-4-O-sulfate & 2.76 \(\pm\) 0.14 & 0.039 \(\pm\) 0.009 \\ \hline \end{tabular} 1 Mixture of caffeic acid-3-O-sulfate and caffeic acid-4-O-sulfate at a ratio of 1:1. 2 Mixture of dihydrocaffeic acid-3-O-sulfate and dihydrocaffeic acid-4-O-sulfate at an unknown ratio.  
\end{table} 
Table 3: Scavenging effects of phenolic acid and its metabolites measured with the DPPH free radical assay. double bonds in hydroxycinnamic acid derivatives considerably enhances the antioxidant activity compared to precursor compounds in a DPPH\(\bullet\) assay. In all presented assays, \(O\)-sulfation of hydroxyl groups attached to aromatic ring resulted in a significant decrease or even inhibition of the reducing capacities of the measured samples. With regard to sulfate derivatives, the presented measurements show a significant decrease in antioxidant properties in comparison with precursor phenolic acids. In the assays based on the ET mechanism, the antioxidant properties of sulfate metabolites were significantly decreased or inhibited compared to those of precursor compounds. The same results were obtained using the DPPH\(\bullet\) assay with the SPLET mechanism. The neutralization effect of the caffeic acid monosulfate is about seven-fold lower than that of caffeic acid, while the scavenging effect of ferulic acid-4-\(O\)-sulfate is eleven-fold lower in comparison with that of precursor ferulic acid. Similar results were obtained for dihydrocaffeic monosulfate and dihydrocaffeic-4-\(O\)-sulfate, with eight-fold and eleven-fold lower values, respectively. Overall, sulfate esters of cinnamic acid derivatives exhibit a much lower antioxidant activity compared to precursor phenolic acids and are comparable to blanks.
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 5 Discussion  
In the context of the rapid development of current technologies, the method proposed in this paper exhibits strong adaptability to future architectures. With the ongoing development of ARM V9 and RISC-V architectures, which are gradually becoming mainstream choices in high-performance computing, they offer many advanced features. The ARM V9 architecture introduces enhanced security features, expanded SIMD instruction sets, and more efficient multi-core support, all of which offer great potential for algorithm optimization based on this architecture. Meanwhile, the RISC-V architecture, being open-source, provides flexible customization options, allowing optimizations tailored to specific application requirements, opening up more possibilities for lattice reduction algorithm research.  
The LLL_FP algorithm proposed in this paper, by optimizing parallel computing, memory access, and SIMD instruction utilization, can fully leverage the advanced features of the ARM V9 and RISC-V architectures. Especially in terms of multi-core and SIMD support, the optimization strategies of this algorithm can be easily ported to these new architectures for enhanced computational performance. Additionally, due to the open-source and customizable nature of ARM V9 and RISC-V, researchers can further optimize algorithms according to specific needs, further improving performance. Therefore, the method proposed in this paper is not only suitable for existing platforms but also will be adaptable to the challenges and opportunities presented by future architectures.