esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Analytical Methods  
Soil active reaction (pH\({}_{\text{H2O}}\)) was potentiometrically determined in a soil to deionized water suspension of 1:2.5 (\(w/v\)) after shaking for 15 min followed by a 30 min equilibration period. Potential acidity (pH\({}_{\text{KCl}}\)) was determined by measuring soil pH in a 1.0 M KCl solution using the same protocol. The difference between pH\({}_{\text{(H2O)}}\) and pH\({}_{\text{(KCl)}}\) values gives a measurement of exchangeable acidity.  
Mineralogical analysis was performed by X-ray diffraction (XRD) on a Bruker AXS D8-Advance diffractometer using CuK\(\alpha\) radiation at 40 kV and 30 mA. Randomly oriented powders were scanned with a step size of 0.02\({}^{\circ}\) and a counting time of 0.6 s per step. The XRD patterns were processed using DIFFRAC plus software linked with a reference database, and relative mineral abundance was estimated by empirical intensity factors weighting the integrated peak areas of diagnostic reflections [19].  
Selected soil samples and all the efflorescent salt samples were examined by environmental scanning electron microscopy (ESEM) using a FEI-Quanta 200 instrument operated at 20 kV and equipped with an energy-dispersive spectrometer (EDS), combining back-scattered electron (BSE) imaging with EDS microanalysis to assist in the mineral identification.  
Total concentrations of major and trace elements were analysed by inductively coupled plasma optical spectroscopy (Agilent 5110 ICP-OES) and inductively coupled plasma mass  
Figure 3: Aerial view (Google Earth) of the degraded wetland showing the location of the soil cores collected along two linear transects spanning from the abandoned sulphide heaps to the salt marshes. Figure inset shows the soil core sampling method.  
spectrometry (Agilent 7900 ICP-MS), respectively, following multi-acid digestion (HClO\({}_{4}\)-HNO\({}_{3}\)-HCl-HF). Analytical quality control was monitored by the use of international certified reference materials (AGW-1 and SARM-4), blanks and duplicates to check the accuracy and precision of the data. The relative standard deviations of the analyses were typically below 5% for major elements and better than 10% for trace elements.
## 3 Results
### Impact of MDIs on Quality of Life and Satisfaction  
Out of 11 includes articles, only 3 studies assessed the quality of life/degree of satisfaction of patients post prosthetic therapy with MDIs [8; 38; 43]. The indices described by these studies are as follow (1) Oral Health Impact Profile (OHIP-14); (2) Denture Satisfaction Questionnaire (DSQ); (3) Visual Analogue Scale Satisfaction (VAS); and (4) Oral Health-Related Quality of Life (OHRQoL). The chewing efficacy was reportedly unchanged using immediate loaded MDIs to retain mandibular overdentures at 1-year follow-up, however, it was enhanced at 5-year follow-up. Moreover, a constant increase in the maximum voluntary bite force (MBF) was observed over time. Additionally, a high level of OHRQoL and satisfaction was expressed by the patients receiving this treatment modality.
## 2 Materials and Methods
### WEave Blockchain Framework Overview (Original Design)
#### 2.1.3 Traceability and Audit Functions  
In addition to production events, WEave included audit functions in the chaincode to retrieve history or verify constraints. Any participant, including the third-party auditor (AU), could query an asset's provenance via a series of functions that would traverse the chain of references (using Fabric's ledger query by history or by linked IDs in state) to output the lineage of that item. For example, for a given finished garment ID, the chaincode could fetch which fabric roll(s) were used, and in turn, which raw material lots composed that fabric, thereby constructing the assembly tree. This forms the basis for transparency: every finished product is linked to verifiable data on its components and origins. Mass balancing, too, could be achieved by a skeptical auditor through functions like GetContentWeight (content []string), requiring a list of asset IDs as input and, from there, comparing the outputs against the stated assets of the relevant assembly tree(s). Moreover, immutability of the ledger guarantees that this history cannot be tampered with without detection. WEave's design objectives explicitly included compliance with traceability standards, like ISO 9000:2015 SS3.6.13's requirement to \"trace the origin of materials and parts\" and providing real-time information sharing to all stakeholders who have permission [25].  
To ensure data integrity, endorsement policies in the Fabric network are set such that all organizations on a channel must endorse chaincode deployment (preventing untrusted code). Each transaction must similarly be endorsed by the relevant subset of participating organizations. For example, a transfer of goods might require that both sender and receiver organizations endorse the transaction, ensuring consensus on the handoff. Additionally, chaincode included programmatic checks on the invoker's role (using Fabric's MSP ID feature) to enforce role-based permissions beyond the endorsement rules. This combination of blockchain consensus and smart contract logic makes the WEave framework robust against data manipulation and unauthorized actions, supporting a high level of trust and accountability.
# Evaluation and Prediction of Ecological Benefits in Song-Liao River Basin
## 2 Materials and Methods  
### Study Area  
Song-Liao River Basin (115\({}^{\circ}\)31\({}^{\prime}\)E-135\({}^{\circ}\)9\({}^{\prime}\)E, 38\({}^{\circ}\)35\({}^{\prime}\)N-53\({}^{\circ}\)35\({}^{\prime}\)N) (Figure 1), located in the northeastern region of China, has a total area of 1,249,200 km\({}^{2}\). Its administrative divisions comprise four leagues east of the Inner Mongolia Autonomous Region, a portion of Chengde City in Hebei Province, and the provinces of Liaoning, Jilin, and Heilongjiang. Mountains encircle SRB on the west, north, and east. It borders the Bohai Sea and Yellow Sea on the south, forms the expansive Songnen Plain and Liaohe Plain in the middle and south, and forms the northeastern Sanjiang Plain. The Song-Liao River Basin is situated in the westerly belt, which is predominant at high altitudes in the north latitude and has more westerly belt weather and climate features. The northeast region has a temperate continental monsoon climate zone, clearly identifiable as a continental climate. There are several regions with a cold temperate climate, humid and rainy summers, and long, frigid winters. SRB is one of the three temperate forest belts in the northern hemisphere and is home to the largest natural forest in China. In China, there are several swamp and forest wetlands. The Songhua and Nenjiang river basins are the country's most concentrated wetland distribution areas. Furthermore, most of the land in SRB is composed of black soil, a significant grain-producing region in China. The primary vegetation is _Armeniaca sibirica_, _Hemipelea davidii_, _Ephedra distachya_, _Carangana microphylla_, _Phragmites australis_, and Securinega suffruticosa_. The main land use types in the Song-Liao River Basin are Cropland and Woodland, and their proportions have been stable at 30% and 40% for many years. Followed by Grassland, accounting for about 20%, and Residence accounts for about 2.5%.
## 4 Results
### Ranges of LRM GR Variation  
To estimate the range of the decrease in the GR of the LRMs, the premixed and dry LRM results were first combined to complement the values with conservative percentages, as shown in Table 12; that is, when the LRMs were used, the GR was reduced by a larger percentage. In addition, the effect of the LRMs did not follow a linear or logarithmic relationship with the resistivity or the GS design. Similarly, different LRMs (types and brands) were used.  
Table 12 outlines the range of reduction in the GR value when the 1.8-m vertical rods were covered with the LRMs and a #2 AWG bare grounding wire was used. For example, the one-rod GS design covered with an LRM (1 LRMV) showed a 30% reduction in the GR value in soils with resistivities ranging from 1 to 10 \(\Omega\)-m and a 60% reduction in soils with resistivities ranging from 241 to 250 \(\Omega\)-m.  
When the 1.8-m rods were placed in the soil with a resistivity of 10 \(\Omega\)-m, the GR was 5.13 \(\Omega\); when the rods were covered with the LRMs, the GR decreased by 1.54 \(\Omega\) or 30% to 3.59 \(\Omega\). In turn, when the 1.8-m rods were placed in the soil with a resistivity of 250 \(\Omega\)-m,  
\begin{table} \begin{  
+++ ==WARNING: Truncated because of repetitions==
tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\rho\) (\(\Omega\)-m)} & **1 LRMR** & **2 LRMRP** & **3 LRMRP** & **3 LRMRT** & **4 LRMRS** & **6 LRMRR** \\ \cline{2-7}  & \(\Delta\)**GR (\%)** & \(\Delta\)**GR (\%)** & \(\Delta\)**GR (\%)** & \(\Delta\)**GR (\%)** & \(\Delta\)**GR (\%)** & \(\Delta\)**GR (\%)** \\ \hline  
++  
the GR was 128.26 \(\Omega\); when the rods were covered with an LRM, the GR decreased by 76.96 \(\Omega\) or 60% to 51.30 \(\Omega\).
## 3 Results
### Validation of Irrigated Landcover  
As stated earlier, validation of the irrigated land cover classification was carried out at the farm level. Farms \(\geq\) 10 ha in size were selected for validation. A farm was considered as 'irrigated' ('Reference') based on a substantive amount of water delivery (\(\geq\)0.1 ML per ha) to that farm during the timeframe of the summer season (December to February). No or a negligible amount of water delivery (<0.1 ML per ha) was taken as the absence of irrigation on a farm. These reference farms were compared with the classification results for accuracy.  
As per the classification, a farm was considered 'irrigated' provided a substantial proportion of pixels (\(\geq\)25%) within that farm belonged to Sector 4 (wet and active vegetation; refer to Figures 3 and 6). This criterion was uniformly applied to both the optical and thermal approaches.  
Tables 4 and 5 provide the classification accuracy for the two approaches. The calculation of the confusion matrix resulting in the producer's and user's accuracies followed  
Figure 8: Maps of irrigated landcover classes (thermal approach). **(A)** Pixel classes, **(B)** irrigated landcover.  
Figure 7: Maps of irrigated landcover classes (optical approach). **(A)** Pixel classes, **(B)** irrigated landcover.  
the standard procedure [49; 50]. Although the overall accuracy was high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). The user's accuracy for irrigated landcover was noticeably higher in the case of the optical approach (98.3%) as compared to that of the thermal approach (88%). However, there was very little difference between the two approaches as per the producer's accuracy for irrigated landcover (96.2% for the optical and 97.1 for the thermal approach).
# ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries
## 1 Introduction
African stock exchanges have undergone several reforms in order to attract more portfolio flows over the years. By and large, they all have made ICT adoption one of the major developmental factors of the reforms they experienced over the period of their existence. The adoption of ICT technologies has mainly included the adoption of automated trading systems by the stock exchanges. Some of the stock exchanges have also become integrated, in a sense allowing the dual or multiple listing of shares. On the demand side, there has been an adoption of technologies such as mobile phones, Internet and broadband by consumers. Arguably, this has led to an increase in stock market transactions, as consumers are now able to transact in the comfort of their homes and at the click of a button. In essence, ICT adoption by consumers confers convenience, which make them purchase shares easily. Therefore, the above foregoing demonstrates the importance of ICT to the sustenance of a stock exchange. Notwithstanding, extant studies have focused on the stock market development and economic growth nexus. There is dearth in literature that has examined the role of ICT in fostering stock market development. The theoretical foundations of the study are anchored on the Schumpeterian growth models (which focuses on the finance-growth linkage) and the ICT adoption theories (the technology acceptance model, diffusion of innovations, the unified theory of acceptance and use of technology, the model of the IT implementation process and the information systems success model). Against this backdrop, the present study sought to examine the impact of ICT adoption on stock market development in Africa. The following hypotheses were empirically tested in this study:  
H\({}_{0}\): ICT adoption has no significant impact on stock market development in Africa  
H\({}_{\text{A}}\): ICT adoption does not have a significant impact on stock market development in Africa  
The rest of the paper is organised as follows: Section 2 reviews the related literature of this study. Section 3 presents an overview of the stock markets in Africa. Section 4 describes the research methodology employed in this study. Section 5 presents and discusses the research findings and Section 6 concludes the paper.
## 2 Materials and Methods
### Material Characterization  
Scanning electron microscopy (LEO 1530, Zeiss, Oberkochen, Germany) images were taken to determine the shape, size, and distribution of NdFeB particles in the polymer matrix. The average particle size was determined by measuring the diameter of 100 particles visible on the fracture surface. To this purpose, a filament was broken manually and the fracture surface coated with 5 nm of Gold (sputter coater, CCU-010, Safematic, Bad Ragaz, Switzerland). The gravimetric fraction of NdFeB particles was determined with thermogravimetric analysis (STA 449C, Netzsch GmbH, Selb, Germany) in an argon atmosphere by heating to 700 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min (TGA). The filament density was determined according to Archimedes' principle with water as the immersible phase.  
To evaluate the remanence and intrinsic coercivity of printed parts and their variation, the demagnetization curves of 10 printed cylinders (\(\varnothing\) 15 mm, h = 5 mm) were measured using a permagraph (Permagraph L, Magnet-Physik, Dr. Steingroever GmbH, Cologne, Germany). Furthermore, the surface roughness of the 10 cylinders was determined using a laser scanning microscope (VK-X260K, Keyence, Neu-Isenburg, Germany). The roughness was determined separately for vertical and horizontal surfaces. Ten profile lines per sample with a length of 700 \(\upmu\)m were used. The profile lines were aligned parallel to the building direction for vertical surfaces and angularly distributed for horizontal surfaces.
# Sloping Farmlands Conversion to Mixed Forest Improves Soil Carbon Pool on the Loess Plateau
## 4 Discussion
### Factor Effects on SOC since Vegetation Restoration  
Soil physical-chemical properties have been extensively used to evaluate SOC, however, these properties usually change slowly, and thoroughly reflecting soil changes using these properties is impossible [13]. Soil microbial properties rapidly respond to soil changes caused by both natural and anthropogenic factors, and some enzymes are closely related to soil energy flow and nutrient cycles [13]. For instance, soil microbial biomass is considered to be a transformation agent of soil organic matter (SOM) and a labile pool for plant nutrients [40]. Soil quality indicators have been developed because of the complex nature of soils and the exceptionally large number of soil properties that must be determined. Selection of indicators that appropriately reflect the overall change in soil quality is important.  
Soil N dynamics are a key parameter in the regulation of long-term terrestrial C sequestration [41]. This study also found that SOC had significant correlations with AN, TN, AP and TP (Figures 2 and 3). In fact, SOC was closely coupled with TN [9], and SOC showed the same dynamics as soil TP during vegetation restoration [5,6]. In addition, SOC was significantly positively correlated with C/N (\(p<0.05\)). [PERSON] et al. [42] reported that SOC was significantly positively correlated with the soil TN and C/N ratio following vegetation restoration. The study also found that SOC was significantly positively correlated with LOC (\(p<0.01\)) and NLOC (\(p<0.01\)) (Table 3) because LOC and NLSOC are two components of SOC. NLOC is a relatively stable form of soil carbon, and LOC is mainly input into soils by higher plants, which will increase the SOC content even though priming accelerates the decomposition of native SOC [43].  
Soil enzyme activities were significantly correlated with the SOC content [15; 44], because the transformations of important organic elements are facilitated by microorganisms [45]. However, one study reported polyphenol oxidase was closely related to soil humus decomposition and was not significantly correlated with SOC [13]. This may be related to the different components of the litter and the pathway of humus decomposition in the soils of different species [13]. SOC also had a positive correlation with MBC [46; 47]. MBC reflects the size of microbial populations and includes both metabolically active and resting-state microorganisms [6], whereas parameters such as BR reflect the actual and potential microbial activities in the soil [13]. Indeed, soils with more SOC also had higher BR [48], because a large part of the SOC is dedicated to sustaining microbial respiration.
###### Abstract  
Wetlands are essential carbon sinks in the global ecosystem, absorbing CO\({}_{2}\) in their biomass and soils and mitigating global warming. Accurate aboveground biomass (AGB) and organic carbon (Corg) estimation are crucial for wetland carbon sink research. Remote sensing (RS) data effectively estimate and map AGB and Corg in wetlands using various techniques, but there is still room to improve the efficiency of machine learning (ML)-based approaches. This study examined how different sample data treatments and plot sizes impact a random forest model's performance based on RS for AGB and Corg prediction. The model was trained with samples of emergent vegetation collected in a palustrine wetland in southern Brazil and spectral variables (single bands and vegetation indices--VIs) from medium- and high-resolution optical images from Sentinel-2 and PlanetScope, respectively. The treatments involved AGB and Corg values dimensioned for three different plot sizes (G1) and the same subjected to normalized natural logarithmic transformation--NL (G2). Therefore, six AGB and Corg models were created for each sensor. Models and sensor performance and spectral variable importance were compared. In our results, NL sample data RF models proved more accurate. Larger plots produced smaller prediction errors with S2 models, indicating the influence of plot size on the reliability of the estimate. S2 surpassed PS in AGB/Corg prediction, respectively--S2 (R\({}^{2}\) 0.87; 0.89, RMSE OOB: between 19.7% and 22.7%); PS (R\({}^{2}\) 0.86; 0.86, RMSE OOB: between 21% and 35.9%)--but PS was superior in mapping spatial variability. The VI CO\({}_{2}\)Flux and S2's SWIR, blue, green, and RE bands 6 and 7 were more important for AGB/Corg prediction. The contribution of this study is the finding that in addition to optimizing RF model parameters, optimizing the AGB and Corg dataset collected in the field, i.e., evaluating normalization and plot sizes, is crucial to obtain more accurate estimates with RS- and ML-based models. This approach enhances AGB/Corg stock estimation in wetlands, and the highlighted predictors can act as spectral indicators of these ecological functions. These results have the potential to guide standardization in the collection and processing of input data for predictive models of AGB/Corg in wetlands, with the aim of ensuring consistent predictions in inventories and monitoring.  
improve prediction models; random forest regression; AGB spectral indicator; carbon stocks; mars
## 1 Introduction  
### Motivation  
Multi-robot systems have a wide range of applications in everyday life, such as target tracking [1, 2], search and rescue [3], task sequencing and assignment [4], and collaborative monitoring [5]. One of the most prominent research topics on multi-robot systems is the flocking control [6, 7], and its goal is to achieve velocity consistency and avoid collisions using only local information. Inspired by flocking behaviors such as bird migration [8, 9] and fish predation [10, 11], researchers have realized coherent and efficient movement of robot swarms, which is crucial for improving the survival rates, reducing energy consumption, and improving the quality and efficiency of completing tasks.  
For sparse swarms with a dozen or fewer robots, a new problem has arisen. Sparse robot swarms, especially swarms with sparsely available communications, usually have poor robustness, as evidenced by the frequent splitting of individuals during flocking. Sparisely available communications are specified by limited communication distance and limited communication bandwidth. Due to the lack of redundancy of nodes and links in the swarms, the destruction of a single node or link can easily lead to swarm splitting. Meanwhile, the random initial states coupled with the locally available information makes the swarm converge to at best a locally optimal stable form. In summary, the poor swarm robustness and restricted communications limit further improvements in consistency performance and the application of distributed control strategies to physical robots.  
We believe that the poor swarm robustness is a result of the unbalanced distribution of node and link importance. The local information structure can easily lead to the existence of a few critical robot nodes or communication links. These nodes or links clearly occupy more critical positions in the communication topology, and their destruction will cause more damage to the structure of the swarms and even lead to the swarm splitting due to the disrupted communication path. From an internal perspective, the formation of the key nodes or links will increase the overall risk of swarm splitting due to the failure of an individual node or link, and from an external perspective, these critical nodes or links are more likely to be targeted. In conclusion, the formation of the key nodes or links, i.e., the unbalanced distribution of node and link importance, will have a significant impact on causing the swarm to exhibit poor robustness, especially in sparse communication environments.  
Therefore, this paper is devoted to generating a distributed flocking control strategy, of which the goal is to achieve better swarm consistency and improve the swarm robustness with sparsely available communications. Meanwhile, we believe that achieving a balanced distribution of node importance and link importance is a key way to improve the swarm robustness.