esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
QZS is very promising for low-frequency vibration isolation due to the nonlinear HSLD stiffness, and a large-amplitude negative stiffness is needed for heavy-load applications. Thanks to the advantage of nocontact, magnetic springs have been widely utilized to obtain negative stiffness. However, the negative stiffness of existing magnetic springs is often not enough for heavy-load applications. To overcome this issue, this paper investigated novel magnetic springs with large-amplitude negative stiffness by introducing Halbach arrays.  
Figure 17: The structures of **(a)** 5-3 Type I (each magnetic ring has the same height); **(b)** 5-3 Type II (the outer and inner Halbach cylinders have the same height); **(c)** 3-5 Type I (each magnetic ring has the same height); and **(d)** 3-5 Type II (the outer and inner Halbach cylinders have the same height) HCMSs.  
Figure 18: Stiffness curves of **(a)** 5-3-type and **(b)** 3-5-typeHCMSs.  
The key outcome of this paper is to present a \"building block\" methodology for designing magnetic springs with specific requirements. Based on this, many variants of HCMSs can be easily analyzed and optimized. The main highlights of this paper include the following:  
(1) The analytical stiffness formula of the HMCS is built based on the Amperian current model and the Biot-Savart law.  
(2) HCMSs can generate negative stiffness with different equilibrium positions, and the amplitude of negative stiffness of HCMSs is much larger than that of existing single-layer magnetic springs.  
(3) It is difficult to increase the amplitude and the uniformity of negative stiffness simultaneously, so a trade-off strategy is needed to design the HCMS.  
(4) Increasing the layer number of Halbach cylinders can result in larger-amplitude and wider-range negative stiffness.  
In future, novel HCMS configurations consisting of an outer cylinder and multiple inner cylinders can be investigated to deal with the conflict between the amplitude and the uniformity of negative stiffness. In addition, HCMS-integrated QZS will be designed and tested for heavy-load applications.  
Conceptualization, Z.C. and Y.X.; methodology, Z.C.; validation, Z.C., Y.Z. and A.W.; writing--original draft preparation, Z.C.; writing--review and editing, Y.X.; project administration, Z.C.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52377204) and the Research Startup Fund Program for High-Level Talents of Shandong Xiehe University (grant number SDXHQD2025003). Data will be made available upon request to the corresponding author.  
The authors thank all reviewers for their comments to improve this work.  
The authors declare no conflicts of interest.
# Agricultural Waste-Based Biochar for Agronomic Applications
## 3 Biochar as a Soil Amendment
### Impacts on Water Retention  
Many investigations reported that biochar amendment improves soil water retention capacity [98; 99; 100] and reduces the water stress of plants during prolonged dry periods caused by global climate changes in the future [101; 102; 103; 104]. The highly porous structure of biochar improves soil physical properties and interstitial spaces within the biochar-soil matrix and allows the reorganization of pore size distribution, which enhances the soil's water retention capacity [24; 69]. Biochar has a hydrophilic surface because of the presence of oxygen-containing functional groups on its surfaces [105], which enables water to infiltrate into its intraproes [106]. An increase in the number of oxygen-containing functional groups on the biochar surfaces induces an enhancement ofthe biochar's hydrophilicity [105]. Another consideration that must be examined during biochar amendment is its effect on plant-available water content. Generally, biochar improves plant-available water, but the improvement significance is quite dependent on soil texture; the water uptake by plants increases by 45% for coarse-textured soils, by 21% for the medium-textured soils, and by only 14% for fine-textured soils, while others reported different values between 4 and 130% [26] and an average of 15% [107]. Although a high number of investigations reportedthe beneficial effect of biochar on water content in soils, other studies reported contrary results. [PERSON] et al. (2018) applied biochar produced from herbaceous plant cuttings to a sandy soil at a rate of 1-50 t/ha and reported no significant enhancement in soil water retention ability, even with the increase in its porosity following biochar addition. The authors attributed that to the high hydrophobicity of biochar, which allowed for preventing the infiltration of water into soil intraproes and then negatively affected its water retention ability. Additionally, not all studies reported the favorable effect of biochar addition on plant growth and yield. In the work performed by [PERSON] et al. (2019), the application of biochar derived from maize stover at a rate of 11.3 Mg/ha for 295 days resulted in no effect on available water capacity; this tendency is probably associated with the blockage of soil micropores by some components contained in the applied biochar, such as ash.
## 3 Results
### Analysis of Elemental Composition of Discharge Electrode Material  
The external surface of the corroded discharge electrode and a clean cut of the electrode were analyzed using the EDS method. Some specimens, approximately 5 mm thick and 18 mm in diameter, were cut from the pipe supporting the spiked blades of the discharge electrode, perpendicularly to its axis (cf. Figure 1a) in order to analyze the chemical composition of the original electrode material. The EDS spectrum showing the elemental analysis of the material of the discharge electrode in various places is shown in Figure 6.  
The electrode material was cleaned and analyzed in selected, non-corroded places. The elemental composition of the discharge electrode in various places is presented in Table 1.  
Figure 5: EDS spectrum of fly ash collected from discharge electrode in electrostatic precipitator after combustion of industrial solid waste. Inset: investigated sample of ash.  
Figure 6: EDS spectrum of clean surface of discharge electrode material after cutting (inset: image of cross-section of test sample).  
The results obtained for the cut surfaces indicate that the discharge electrode was made of austenitic chromium-nickel steel, probably type 316 Ti, with the following composition: Fe (\(\leq\)73%); Si (\(\leq\)1%); S (\(\leq\)0.01%); P (\(\leq\)0.015%); Ni (\(\leq\)20.5 \(\div\) 13.5%); Mo (\(\leq\)3%); Mn (\(\leq\)2%); Cr (\(\leq\)16.5 \(\div\) 18.5%) Al (\(\leq\)1.0%); and C (\(\leq\)0.05%) (cf. Figure 6). This type of steel is generally used for the construction of electrodes of electrostatic precipitators due to its high hardness, flexibility, and modulus of elasticity.  
This composition of the electrode material can be changed at its surface due to chemical interaction with exhaust gasses (NO\({}_{\rm X}\), SO\({}_{\rm X}\)) and the radicals produced in the electric discharge generated by a high voltage applied to the discharge electrode, and interaction with fly ash particles. The changes in the elemental composition of these characteristic places will be discussed in the following sections.
###### Abstract
constructed a feature set by spectral and texture features and then used nighttime lighting information and spectral information to select positive and negative samples of impervious surfaces to complete the extraction of impervious surfaces [25], which effectively reduces the influence of bare soil on the extraction of impervious surfaces. For this purpose, the luminance information of DMSP-OLS-like data [26] is used to obtain the vector map of built-up areas using the spatio-temporal normalized thresholding method based on pseudo-invariant features. Classification samples of water bodies, vegetation, and bare soil were established in the complete image using the corresponding indices, impervious surface classification samples were established in the built-up area using the Enhanced Impervious Surface Index, and the extraction of impervious surfaces was accomplished using the maximum likelihood classification method. Surface temperature inversion was performed using Landsat OLI/TIRS C2L2 imagery and surface temperatures were categorized into high-temperature, sub-high-temperature, medium-temperature, sub-low-temperature, and low-temperature zones based on the mean covariance method. The following analyses were performed to investigate the effect of impervious surfaces on surface temperature: (1) The overlay analysis of the impervious surface extraction results from 2013 to 2022 and the results after surface temperature grading in Xuzhou City showed that more than 50% of the impervious surface area is a high-temperature zone or a sub-high-temperature zone. (2) Based on the density of impervious surfaces, Xuzhou City is divided into three parts: impervious surface aggregation area, permeable surface area, and other impervious surface area. The average temperatures of the above three areas were calculated separately, and it was found that the average temperature was the highest in the impervious surface aggregation area. (3) A certain number of samples were selected in and around the built-up area, and the impervious surface density and average temperature of the selected sample areas were calculated and analyzed by linear regression. The results show that surface temperature and impervious surface density are directly proportional. In conclusion, the increase in impervious surface is one of the reasons for the increase in surface temperature, so the monitoring of impervious surfaces is of great significance for the management of the urban environment.
## 2 Materials and Methods
### Statistical Analysis  
The data were analysed using the Stat graphics Centurion XVI.I program (Manugistics, Inc., Rockville, MD, USA). Differences between the treatments were determined using one-way analysis of variance (ANOVA). The obtained values were separated using Tukey's test and considered significant at \(p<0.05\).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Active Compound**} & \multicolumn{2}{c}{**lons Scanned**} & \multicolumn{2}{c}{**Linearity**} & \multirow{2}{*}{**R2**} & \multirow{2}{*}{**LOD**} & \multirow{2}{*}{**LOQ**} & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**Recovery**} & \multirow{2}{*}{**RSD**} & \multirow{2}{*}{**U\({}_{t}\)**} & \multirow{2}{*}{**U\({}_{t}\)**} & \multirow{2}{*}{**U\({}_{t}\)**} & \multirow{2}{*}{**U\({}_{t}\)**} & \multirow{2}{*}{**U\({}_{t}\)**} \\  & \multicolumn{2}{c}{**Range**} & & & & & & & & & & & \\ \hline
1-Pentent-3-olololololololumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\
2-Hepunnoen & 58,991 & 114 & 0.414-219.6 & 0.975 & 0.124 & 0.414 & 89 & 89.3 & 58 & 90 & 12 & 13.5 \\
4-rephenol & 99,121 & 136 & 0.377-187.0 & 0.999 & 0.113 & 0.377 & 403 & 1048.2 & 2.3 & 24 & 6.0 & 29 & 7.2 \\
4-rephenol & 57,82 & 67 & 4.240-101.9 & 0.988 & 1.25 & 4.2 & 423 & 92.2 & 52 & 46 & 10.9 & 65 & 15.4 \\ Ethyl-2-methylbutyrate & 102, 74, 87 & 0.039-15.6 & 0.995 & 0.012 & 0.039 & 42 & 84.3 & 5.2 & 2 & 4.8 & 4 & 9.5 \\ Ethyl-isovalerate & 88, 56 & 0.039-16.7 & 0.993 & 0.012 & 0.039 & 45 & 82.8 & 5.2 & 3 & 6.7 & 5 & 11.1 \\ General & 69, 68, 312 & 0.799-194.0 & 0.979 & 0.228 & 0.759 & 79 & 81.0 & 20.8 & 9 & 11.4 & 11 & 13.9 \\ Hexanal & 56, 72, 82 & 39.78-1982.0 & 0.959 & 11.9 & 39.7 & 807 & 81.8 & 5.3 & 92 & 11.4 & 117 & 14.5 \\ Methyl benzoate & 74, 87, 101 & 1.457-57.5 & 0.990 & 0.425 & 1.4 & 1528 & 87.1 & 5.9 & 77 & 5.0 & 15.2 & 82.2 \\ Methyl octanoate & 74, 87, 127 & 0.416-208.5 & 0.994 & 0.125 & 0.416 & 449 & 88.3 & 9.9 & 42 & 9.4 & 45 & 10.0 \\ Methyl-2-methylbutyrate & 88, 57, 101 & 0.078-15.5 & 0.991 & 0.023 & 0.078 & 84 & 80.0 & 4.6 & 8 & 9.5 & 11 & 13.1 \\ Nordidid & 69, 93, 107 & 1.5-764.0 & 0.934 & 0.46 & 1.5 & 1646 & 87.6 & 32.9 & 642 & 39.0 & 684 & 41.6 \\ crypt acetate & 70, 84, 112 & 2.095-95.6 & 0.993 & 0.6 & 2.0 & 404 & 89.3 & 8.5 & 47 & 11.6 & 49 & 12.1 \\ trans-2-Hexan-1-ol & 55, 69, 63 & 28.14-1407.2 & 0.999 & 8.7 & 28.8 & 418 & 87.1 & 6.9 & 87 & 20.8 & 95 & 22.7 \\ trans-2-Hexan-1-ol & 57, 82, 67 & 19.7-986.2 & 0.960 & 5.9 & 19.7 & 400 & 80.3 & 3.6 & 34 & 8.5 & 63 & 15.8 \\ \hline \hline \end{tabular} LOO - Limit of detection, LOQ - Limit of quantification, U\({}_{t}\) = Uncertainty of repeatability, U\({}_{t}\) = Uncertainty of reproducibility.  
\end{table}
Table 1: Validation parameters for CC-MS determination.
## 4 Methodology
### Network Bandwidth-Learning Dataset  
In a wireless network environment, the LTE dataset provided by Zeondo was used to obtain network bandwidth data that fluctuate with time and mobility [27; 28]. The dataset reflects user mobility, with data collection locations varying over time. To measure performance under different network bandwidth variances, user mobility is measured for pedestrian, bus, and train. From November 2017 to February 2018, University College Cork examined 4G (LTE-A) network parameters such as download and uplink rates based on pedestrian, bus, and train mobility patterns. Two operators walked multiple routes across Cork City Center, Ireland, to measure throughput in the pedestrian scenario. The two operators' average movement speeds were 2.4 kph and 1.5 kph, respectively, with a minimum speed of 0 and maximum speeds of 4.0 and 3.0. The bus scenario was gathered in the urban and suburban locations, using public transportation. The train scenario was collected using trains traveling 240 km between Cork City and Dublin City and 75 km between Cork City and Farranfore City in Ireland. In the bus and train scenarios, throughput was measured by two operators. The average bus speeds were 17.2 kph and 10.7 kph, respectively. The average train speeds were 60.6 and 53.9 kph. The minimum speeds for both the bus and the train were zero. The maximum bus speeds were 34 and 30 kph, whereas the maximum train speeds were 109.4 and 114 kph. To eliminate bias in the bandwidth prediction model, we employed seven scenarios for each of the three mobility patterns. The entire dataset consisted of approximately 8 h of network bandwidth measurement data. The data were randomly split into learning and testing sets at a 7:3 ratio, with the learning set representing approximately 5 h and the testing set representing approximately 2 h. Figure 4 presents the network bandwidth statistics for the pedestrian, bus, and train datasets.  
Figure 4 presents the measurement results for pedestrians. The average network bandwidth measured in the pedestrian dataset was 1373 KB/s with a maximum of 9910 KB/s and minimum of 0 KB/s. In the bus dataset, the average bandwidth was 1250 KB/s with a maximum of 10,984 KB/s and minimum of 0 KB/s. For the train dataset, the average bandwidth was 623 KB/s with a maximum of 21,627 KB/s and minimum of 0 KB/s. As mobility increased, the average network bandwidth decreased. Even when walking, there were instances of intermittent network loss; however, as shown in Figure 4,c, the higher speeds of buses and trains led to more frequent interruptions in network access. Figure 5 presents the proportion of times network bandwidth was measured at 0 KB/s in each of the pedestrian, bus, and train datasets relative to the total time. Network stability decreased and bandwidth fluctuations became more pronounced as movement speed increased.
## 3 Results
### Effect of LE on AMPK Signaling  
Because previous studies have suggested that the AMPK pathway is activated by H\({}_{2}\)O\({}_{2}\)-induced oxidative stress in C2C12 murine myoblasts [14], the role of the AMPK signaling pathway in the cytotoxic protective effect of LE was evaluated. LE treatment did not affect AMPK\(\kappa\)\(\alpha\)1/2 expression or acetyl-CoA carboxylase (ACC) levels but significantly increased p-AMPK\(\kappa\)\(\alpha\) (Thr172) and p-ACC (Ser79) levels, in a concentration-dependent manner (Figure 5a). In addition, neither AMPK\(\kappa\)\(\alpha\)1/2 nor p-AMPK\(\alpha\) (Thr172) expression was affected by H\({}_{2}\)O\({}_{2}\) treatment, and p-AMPK\(\kappa\) (Thr172) expression, but not AMPK\(\kappa\)\(\alpha\)1/2 expression, was upregulated by LE (Figure 5b).  
Figure 4: Effect of LE on H\({}_{2}\)O\({}_{2}\)-induced mitochondrial dysfunction in C2C12 cells. Cells were treated, with or without LE, for 1 h before treatment with 0.8 mM H\({}_{2}\)O\({}_{2}\) for 24 h. (**a**) and (**b**) MMP was stained using 5,5′,6,6′-tetrachloro-1,1′,3′-tetraethyl-benzimidazoleylcarbocyanine iodide (JC-1) and then analyzed using flow cytometry: (**a**) Typical results of three independent experiments of flow cytometry are shown; (**b**) Ratio of JC-1 aggregates to monomers. Data indicate mean \(\pm\) SD values (\(n\) = 3; *** \(p\) < 0.001 vs. control cells; **\(\mu\)**\(p\) < 0.01 vs. H\({}_{2}\)O\({}_{2}\)-treated cells); (**c**) Cytochrome \(c\) expression in mitochondrial and cytoplasmic fractions. Western blot analysis was performed using cytochrome \(c\) oxidase subunit IV (COX IV) and \(\beta\)-actin were as loading controls for mitochondria and cytoplasm, respectively. Representative results of three independent experiments are shown. Mito, mitochondrial fraction; Cyto, cytoplasmic fraction.  
Next, the AMPK inhibitor compound C was used to determine whether AMPK signaling was required for the protective effect of LE against H\({}_{2}\)O\({}_{2}\)-mediated cytotoxicity. The protective effects of LE against H\({}_{2}\)O\({}_{2}\)-induced growth inhibition and morphological changes were entirely abolished by compound C (Figure 5c,d). In addition, compound C significantly reduced the LE-mediated upregulation of p-AMPK\(\alpha\) (Thr172) but had no significant effect on AMPK\(\alpha\)1/2 expression. Thus, the protective activity of LE against oxidative damage involved the AMPK pathway (Figure 5e).
# Delegation-Based Personal Data Processing Request
## 3 Delegation-Based Personal Data Processing Request Notarization Framework
### Notarized Data Structure  
The main notarized data required for a GDPR compliance audit related to the processing of personal data comprises a request for data processing and a response to the request. The request, which is one of the primary data in the proposed framework, should have the request number, ID of the DS, URL of the SP, e-mail address of the SP, data processing type, and the information of the request. These are stored in blocks as a transaction and following the creation of a ledger block, the framework adds it to the BC to provide security and integrity. However, it is important to ensure controlled access and management of notarized data only by network entities who have the authority to access the framework. To realize this, a data structure should be defined for authorization control consisting of the authorization number, authorization type, authorization ID, SP URL, grantor's ID, and permitted authorization period. Details of the processing request, response on request, and authorization-related data structure for configuration of the BC ledger are as follows:  
* Request: A structure requests for the processing of personal data sent by the DS to the DC.
* Response: A structure for a response to a request sent by the DC to the DS.
* Authorization: A structure of data access rights granted by the DS or DC.
* Parameters of data structures for transactions are as follows:
* Parameters of Request: The number of requests, DS's ID, SP's URL, SP e-mail, processing type, the content of the request and timestamp.
* Parameters of Response: The number of responses, the number of requests, DS's ID, data SP's URL, subject's e-mail, processing type, content of request, content of response, and timestamp.
* Parameters of Authorization: The number of grants, grantor type, auditor's ID, SP's URL, grantor's ID, and authority expiration date.  
The value of the processing type, which is a parameter of the structure request and structure response, is pre-defined as reading, correction, deletion, processing suspension, and transmission, which are defined as the right of the DS to own data in the GDPR. The DS selects the type of processing, and thereafter, the request is delegated and notarized for the processing.
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results and Discussions
### Thermogravimetric Analysis  
The degree of hydration at 28 days of hydration was measured via Thermogravimetric analysis. Figure 6 presents the results of TGA analysis for various specimens cured for 28 days. Three discrete phases were seen in the TG curves for all the specimens. Following Ref [38], major weight loss observed at 420-540 \({}^{\circ}\)C; corresponding to the decomposition of portlandite was used to measure the degree of hydration. Particularly, the weight loss in the range of 50-200 \({}^{\circ}\)C; can also be caused due to unbound  
Figure 5: Heat release profiles of paste specimens.  
water, hence the first phase may not be a correct indication of C-S-H amount [38]. The amount of CH was assessed according to the following formula [38]:  
\[CH\left(\%\right)=\left(\frac{MW_{CH}}{MW_{H2O}}\right)\times MW_{CH}(\%)\]  
where \(MW_{CH}(\%)\), \(MW_{CH}\), and \(MW_{H2O}\) represents the weight loss during the dehydration of CH as a percentage of the ignited weight (%), the molecular weight of CH, and the molecular weight of H\({}_{2}\)O, respectively.  
The tabulated data in Figure 6 shows the CH content calculated for various specimens. It is obvious that the control (REF) specimen demonstrated the highest content of CH (20%) amongst all the specimens. The specimens prepared with W3 ash particles depicted the least content (13.9%) of calcium hydroxide followed by those with W1 ash particles (14.9%). Contrary to this, a CH content of 18.2% was noticed for the specimens prepared with W2 ash particles. The results of TGA analysis suggest that the addition of WSA generated extra hydration products (C-S-H) by reacting with CH. A prominent reduction in CH amount due to the incorporation of finer WSA particles indicates the nucleation effect. Also, the inner amorphous silica was made available for pozzolanic reactions due to the grinding of the particles of WSA [24]. Summarizing, the pozzolanic, filler and heterogeneous nucleation effects were responsible for the enhanced performance of the finer ash particles [24; 37].