esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Auger Electron Microscopy (AES) Surface Characterization
#### 3.1.2 Auger Electron Microscopy (AES) Two Dimensional Mapping Analysis  
Once the Auger electron spectra were obtained for the four elements C, O, Si, and F, the two-dimensional (2D) mapping information could be reconstructed for a large area with FDTS SAM film patterns. As shown in Figure 3a, the reconstructed 2D heat image matched very well with the secondary electron image of Figure 2a. The dark area was free of fluorine elements, while the red area was filled with dense FDTS SAM molecules. Shown in Figure 3b, carbon KLL Auger electrons were detected in all three areas, while two kinds of areas were clearly differentiated. In the silicon dioxide area, the amplitude of the carbon KLL Auger electron area was higher than that of carbon KLL Auger electrons from FDTS SAM film patterns, thus matching the measurement result from Figure 2c. Theoretically, more carbon elements should exist on the FDTS SAM film pattern surface than on the silicon dioxide surface due to the carbon chain in the FDTS SAM molecule. We assumed the observation was highly likely since the FDTS SAM film pattern had smaller surface energy than the silicon oxide film, with lower absorption of organic contaminants on the FDTS SAM film pattern surface than on the silicon dioxide surface. For oxygen KLL Auger electrons, the amplitude of KLL Auger electrons was smaller on the FDTS SAM surface than that of KLL Auger electrons on a silicon surface. This observation is plausible because more oxygen element and more organic contamination were on the silicon dioxide surface than on the FDTS SAM surface, also matching the measurement result from Figure 2c. For silicon KLL Auger electrons, the amplitude of KLL Auger electrons on the silicon dioxide surface should be a little bit higher than that of KLL Auger electrons on the FDTS SAM film pattern since the detection depth of AES is about 7.0 nm for this tool (ULVAC-PHI 710, Japan).  
Figure 2: (**a**) Secondary electron SEM image of an area of 50 μm by 50 μm. Area1 (red rectangle) and Area3 (blue rectangle) were selected in dark areas containing FDTS SAM film patterns, while Area2 (green rectangle) was selected in light areas containing silicon dioxide film patterns. (**b**) Auger electron spectra collected from Areas1 (red line), Area2 (blue line) and Area3 (green line) shown in (**a**). (**c**) Atomic concentration (AC%) of four elements’ KLL Auger electrons on Area1, Area2, and Area3.  
Indeed, the boundary between the two kinds of areas could barely be distinguished due to the superior sensitivity of Auger electron microscopy (AES). The smallest SAM patterns fabricated were 10 microns thick and could be precisely reconstructed with AES with the lateral resolution of 7.0 nm of this tool (ULVAC-PHI 710, Japan). Highly surface-sensitive information with a smaller spatial resolution of element information could be obtained for the precise FDTS SAM film patterns using by Auger electron microscopy.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 2 Preliminaries
### Reverse Proxies  
A reverse proxy is a server that acts as an intermediary between client devices and backend servers. It forwards client requests to the appropriate backend server and returns the server's response to the client. Unlike a forward proxy, which accesses resources on behalf of clients, a reverse proxy operates on behalf of servers, simplifying and managing server interactions for the client. Reverse proxies are a key component in modern web architectures, offering benefits such as enhanced scalability, security, and performance.  
One of the primary functions of a reverse proxy is to serve as a Layer 7 load balancer that distributes the incoming traffic across multiple backend servers. This ensures optimal resource utilization, prevents server overload, and improves overall system responsiveness. Additionally, reverse proxies can cache responses from backend servers, speeding up client interactions and reducing redundant processing. Security is another critical advantage, as reverse proxies mask the identity and IP addresses of backend servers, minimizing the attack surface and protecting against direct cyberattacks.  
Reverse proxies are extensively used in content delivery networks (CDNs) to cache static content closer to users, facilitating faster delivery. They also centralize SSL/TLS termination, offloading encryption and decryption tasks from individual servers, which simplifies certificate management and enhances performance. Moreover, reverse proxies support advanced functionalities such as URL rewriting, request filtering, and authentication enforcement, making them indispensable for managing and optimizing web traffic in complex network infrastructures.
## 3 Results
### Changes in ET and GPP  
The changes in ET and GPP were examined from 2000 to 2019 based on the DTVGM simulation using historical observations in the six catchments (Table 6). The ET and GPP in the region show a significant increase (\(\alpha\) = 0.01) of 2.40 mm/a and 6.00 gC/m\({}^{2}\)/a, respectively. Figure 8 shows simulated annual ET in the six catchments during the past years. The ET increased at the rate of 6.92 mm/a, 3.49 mm/a, 0.44 mm/a, 1.59 mm/a, 1.54 mm/a and 1.20 mm/a for GBC, JMC, BSC, THC, DSC and CHC, respectively. The Mann-Kendall test reveals that the ET increased significantly in the BCC, JMC, DSC (\(\alpha\) = 0.01) and THC (\(\alpha\) = 0.05), while it is not obvious for the BSC and CHC. Figure 9 displays the modeled annual GPP in the six catchments. The GPP shows significant increasing trends in all of the six catchments at a significance level of 0.01. Additionally, the changing trends are 14.30 gC/m\({}^{2}\)/a, 7.73 gC/m\({}^{2}\)/a, 6.41 gC/m\({}^{2}\)/a, 4.25 gC/m\({}^{2}\)/a, 4.38 gC/m\({}^{2}\)/a, 5.28 gC/m\({}^{2}\)/a for GBC, JMC, BSC, THC, DSC and CHC, respectively. Furthermore, the variations in ET within the year were investigated as shown in Figure 10a. In the BCC, the ET increases in all the months, especially in the spring and summer season from April to September. However, the ET shows decreasing trends in some months in the autumn and winter seasons and increased mostly in spring and summer seasons in the other five catchments. Particularly, the ET decreased from autumn to early spring in the following year in the BSC. Figure 10b displays the changing pattern in GPP within the year. The GPP increased in nearly all the months except for February. The major increase in monthly GPP mainly occurred in the spring and summer seasons, from April to September. Figure 10c summarizes the mean changes in ET and GPP in the water source region, and both ET and GPP increased mainly in spring and summer, while the uptrends were relatively smaller in autumn and winter. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Catchment**} & \multicolumn{3}{c}{**ET**} & \multicolumn{3}{c}{**GPP**} \\ \cline{2-7}  & **Annual ET (mm)** & **Slope (mm/a)** & **Z\({}_{\text{c}}\)** & **Annual GPP (gC/m\({}^{2}\))** & **Slope (gC/m\({}^{2}\)/a)** & **Z\({}_{\text{c}}\)** \\ \hline BGC & 506.64 & 6.92 & 3.15 *** & 861.45 & 14.30 & 3.15 *** \\ JMC & 516.66 & 3.49 & 3.15 *** & 802.87 & 7.73 & 4.51 *** \\ BSC & 460.44 & 0.44 & 0.55 & 820.69 & 6.41 & 3.28 *** \\ THC & 390.01 & 1.59 & 2.30 *** & 562.55 & 4.25 & 4.32 *** \\ DSC & 415.01 & 1.54 & 2.37 *** & 675.00 & 4.38 & 4.25 *** \\ CHC & 398.59 & 1.20 & 1.14 & 818.46 & 5.28 & 3.21 *** \\ Mean & 446.67 & 2.40 & 2.50 *** & 702.57 & 6.00 & 4.24 *** \\ \hline \hline \end{tabular}  
*** and ** indicate significance levels of 0.01, 0.05, respectively.  
\end{table} 
Table 6: Annual changes of the ET and GPP in the six catchments.
## 5 BDS Data Description
### Data Labeling  
To validate the proposed CDAENet algorithm, it is necessary to collect satellite signal data from a real environment. Inspired by the literature [42], signal types were labeled using a method of projecting satellite positions from images taken by an eye camera. For each satellite measurement provided by the receiver, the elevation and azimuth angles of the corresponding satellite were calculated. The elevation and azimuth angles are projected onto a skylpot of the satellite to determine the satellite signal type. The detailed methodology can be found in Figure 10. The results of the labeling of the data are shown in Figure 11, where the green color in the skylpot represents trees, and the satellites falling in them are NLOS signals. The white color represents the sky area, and the satellites falling in it are LOS signals.  
Figure 10: Flow of determining the type of BDS signal using fisheye images.  
Figure 9: Data-collection environment of D1, D2, D3 and D4.
# An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems
## 4 Software Design  
### Software Design of the MCU  
To reduce power consumption, in addition to selecting low-power devices in the hardware design, the key modules also adopted an efficient power management algorithm in the software design of the MCU. The working voltage of the soil moisture sensor device, which was one of the main energy consumption components of the system, was high. However, in practical applications, the sensor device does not need to work continuously for a long time. In a data acquisition and transmission cycle, it would be idle most of the time. Therefore, a 12 V power supply that enabled control was designed for the hardware. When the sensor device worked and effectively outputted data, the MCU controlled the MOS tube to be in the cut-off state and turned off the power supply of the sensor device to avoid continuous power consumption after data acquisition. The LoRa RF communication module was also a main energy-consuming unit, and the working currents corresponding to 5 dB and 20 dB transmission powers were 75 ma and 130 mA, respectively. When the module did not need to work, the MCU would put it to sleep.  
The working flow of the MCU software is shown in Figure 4. After the initialization of the MCU and each module, the MCU controlled the MOS tube to be in the cut-off state, turned off the sensor power supply to reduce energy consumption, enabled the MCU to interrupt, and then entered the low power consumption mode. The timer interrupt function was used to realize the acquisition of sensor data, and the MCU timer could automatically overload the time constant, thereby accurately controlling each module to complete different tasks during different timer cycles (250 ms).
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 4 Numerical Simulation Results and Analysis
### Influence of Rear Guide Vane and Swirl Guide Vane on Performance of Dust Removal Fan
performance of the dust removal fan, consideration can be given to canceling the rear guide vane structure. At the same time, from the numerical simulation results, adding the swirl guide vane structure will reduce the total pressure efficiency and static pressure efficiency of the dust removal fan, but the dust removal efficiency will be significantly improved. According to the dust concentration cloud image of the exit plane and axial section of the dust removal fan, when there is no swirl guide vane, the dust in the exit section of the dust removal fan is distributed throughout the entire flow channel and it is far from the side wall of the dust removal fan. This is because the swirl guide vane of the dust removal fan causes the gas in the outlet section to swirl, increasing the centrifugal force on the dust particles and bringing their movement trajectory closer to the side wall of the dust removal fan. This makes it easier for the dust particles to be captured by the water film on the side wall of the dust removal fan, thereby improving the dust removal efficiency. Therefore, the swirl guide vane is highly beneficial for enhancing the dust removal performance of the dust removal fan.
## 2 Dynamic Model of a Teleoperator System
: _The time-varying delay \(T_{i}(t)\) is positive or zero, and it is bounded as well as its first and second derivatives, \(\hat{T}_{i}(t)\) and \(\hat{T}_{i}(t)\), respectively, i.e., \(0\leq T_{i}(t)\leq\overline{T}_{i}<\infty\), \(0\leq|\hat{T}_{i}(t)|\leq T_{i}^{*}<\infty\), and \(0\leq|\hat{T}_{i}(t)|\leq T_{i}^{**}<\infty\), for \(i=1,\mathsf{r}\). Furthermore, the upper bound \(T_{i}^{*}\) is known._  
**Remark 1**. : _Higher frequencies in the time delays result in larger bounds for their derivatives, which may, in turn, affect the stability of the closed-loop system by necessitating higher control gains to ensure stability. Nevertheless, it is typically assumed that \(T_{i}^{*}<1\)[38]._  
The following notation is used throughout this work. The symbol \(\|\mathbf{x}\|\) represents the standard Euclidean norm when \(\mathbf{x}\) is a vector, whereas \(\|\mathbf{A}\|\) denotes the induced 2-norm for a matrix \(\mathbf{A}\). For any function \(\mathbf{f}(t):[t_{0},\infty)\rightarrow\mathbb{R}^{n}\), the \(\mathcal{L}_{\infty}\)-norm is defined as  
\[\|f(t)\|_{\infty}=\sup\limits_{t\geq t_{0}}\|\mathbf{f}(t)\|, \tag{3}\]  
and the \(\mathcal{L}_{2}\)-norm is defined as  
\[\|\mathbf{f}(t)\|_{2}=\bigg{(}\int_{t_{0}}^{\infty}\|\mathbf{f}(t)\|^{2}\mathrm{d}t \bigg{)}^{\frac{1}{2}}. \tag{4}\]
## 3 Results and Discussion
### Melting Profile  
The composition of triacylglycerlycerols and the location of fatty acids influence the melting profiles of oils. In the melting curve of apricot kernel oil A1 (Figure 2a), only one well-defined peak was observed at \(-11.69\)\({}^{\circ}\)C. Apricot kernel oil A2 was characterized by the presence of three peaks on the melting curve (Figure 2a) at \(-32.00\), \(-22.37\), and \(-15.22\)\({}^{\circ}\)C. The melting curves of the peach kernel oils were similar (Figure 2b). Two peaks were found: the first one at a temperature of \(-26.70\)\({}^{\circ}\)C in the melting curve of the P1 oil and \(-24.88\)\({}^{\circ}\)C in the melting curve of the P2 oil; the second one was a well-defined peak at temperatures of \(-15.77\)\({}^{\circ}\)C and \(-13.36\)\({}^{\circ}\)C for the P1 and P2 oils, respectively. [PERSON] et al. [36] also reported that peach kernel oils were characterized by melting at low temperatures: \(-12.2\) and \(-12.1\)\({}^{\circ}\)C. Generally, the occurrence of endothermic peaks at low temperatures indicates the content of triacylglycerlycerols rich in poly- and monounsaturated fatty acids [29]. This was reflected in the fatty acid profiles; the analyzed A1, A2, P1, and P2 oils contained approximately 90% of unsaturated fatty acids. Differences in the melting profile of apricot kernel oils may result from the relative proportions of mono- and polyunsaturated acids. Oil A2 with a higher share of polyunsaturated acids and a lower share of monounsaturated acids was characterized by the presence of peaks on the melting curve at lower temperatures compared to oil A1. The berry seeds' lipid fraction was also characterized by the presence of endothermic peaks in the melting curve at negative temperatures, which the authors associated with the melting of individual fractions of triacylglycerlycerols containing unsaturated acids [42].  
Figure 2: DSC melting profiles of apricot kernel oils (**a**) and peach kernel oils (**b**).
## 2 Materials and Methods
### Volatile Metabolites Analysis by Headspace Solid-Phase Microextraction (HS-SPME)  
Automated headspace solid-phase microextraction (HS-SPME) was performed using an auto-sampler CTC PAL (CTC Analytics, Zwingen, Switzerland). This was equipped with an SPME fiber/syringe holder, a temperature-controlled six-vial agitator tray, and a temperature-controlled chamber for the conditioning of the fiber. The SPME fiber material was polydimethylsiloxane/divinylbenzene (PDMS/DVB) (Supelco Inc., Bellefonte, PA, USA). Before the extraction of volatile metabolites, the fiber was conditioned according to manufacturer instructions. For each extraction, a working volume of 7 mL of fermented stool must was placed into a 20 mL clear glass vial fitted with polypropylene hole screw caps and PTFE/silicone septum. The samples were equilibrated for 30 min at 50 \({}^{\circ}\)C in the temperature-controlled agitator tray. Afterwards, the SPME device was automatically inserted in the sealed vial through the septum, and the fibber was exposed to the volatiles of the headspace in the vial. Following this, the SPME fiber was immediately inserted into the gas chromatography (GC) injector, and the fibber was thermally desorbed for 20 min at 220 \({}^{\circ}\)C. The volatiles of fermented stool must were identified using an Agilent 7890B gas chromatograph coupled to an Agilent 5975C mass selective detector (Palo Alto, CA, USA). Volatile metabolites were separated on a HP-INNOWAX MS capillary column (30 m \(\times\) 0.25 mm ID; 1.0 \(\upmu\)m film thickness, Agilent Inc., Santa Clara, CA, USA). The oven temperature program began with 3 min at 40 \({}^{\circ}\)C, followed by a 3 \({}^{\circ}\)C/min-rise to 120 \({}^{\circ}\)C, then a temperature ramp at 6 \({}^{\circ}\)C/min-rise to 200 \({}^{\circ}\)C and held at 200 \({}^{\circ}\)C for 20 min. Helium was used as the carrier gas at a flow rate of 1.2 mL/min. The quadrupole mass filter for mass spectrometric detection was operated at 150 \({}^{\circ}\)C in the electron impact mode (EI 70 eV). The ion source temperature was set at 220 \({}^{\circ}\)C, and the transfer line was set at 210 \({}^{\circ}\)C. The mass acquisition range was 40-400 \(m\)/z. The peaks were identified based on their fragmentation patterns using the NIST Mass Spectral Search Program version 2.0 (NIST, Washington, DC, USA). To perform the quantitative determination of volatile metabolites in fermented stool at different days, samples were run in triplicate, and the integrated areas based on total ion chromatograms were normalized to areas of the internal standard and averaged. For this, 100 \(\upmu\)L of 1-butanol (500 mg/L) was used as an internal standard.
## 3 Results
### Systematic Repositioning of Drugs/Molecules
In vitro technologies have demonstrated that ET and DOX increase apoptosis, demonstrating their synergistic effect with TRAIL. These results were confirmed with the binding energy for the ternary complexes TRAIL-DR5-ET and TRAIL-DR5-DOX [21]. Thionine (TH), an organic dye, and its derivatives have been proposed as promising drugs for the photodynamic therapy of cancer [24]. Previous studies reported that TH shows possible genotoxic and cytotoxic activity in prokaryotic cells [25]. Since there is a wide range of applications of TH, it is important to evaluate the site-specific interaction of TH with human serum albumin (HSA), the mainprotein in plasma, which is responsible for the maintenance and regulation of the colloidal osmotic pressure of blood [24]. HSA has several binding sites to which various ligands can bind. The binding of ligands to this protein can influence drug distribution, as HSA plays a crucial role in the transport of endogenous/exogenous compounds. Therefore, HSA is widely used in clinical applications as a drug delivery system. [PERSON] et al. revealed that TH interacts with the hydrophobic cavity of subdomain IIA of HSA (PDB ID:1 AO6) and that the complex shows a good level of cytotoxicity in cancer cells through in vitro studies [24]. Previous studies have shown the anticancer and antimetastatic effects of the microbial polyketide 2,4-diacetylphloroglucinol (DAPG) through the regulation of NF-\(\kappa\)B activity [26]. The induction of NF-\(\kappa\)B also mediates the expression of other antiapoptotic proteins and protein kinases in cancer cells. However, the mechanism of action of DAPG acting on metastatic proteins such as Matrix metalloproteinase 9 (MMP9), MMP2, NF-\(\kappa\)B, and the antiapoptotic Bcl-2 family proteins are not yet known. In a recent study [27], binding energies, computed by molecular docking, revealed that MMP2, MMP9, and NF-\(\kappa\)B achieved a higher interaction with DAPG. In vitro studies have confirmed that DAPG compounds are able to inhibit cancer cells in several cancer cell lines, including MDA-MB-231 [27]. Drug repositioning offers new clinical indications for known drugs/molecules using an efficient, low-cost and riskless strategy. However, despite encouraging results, drug repositioning is a complex process that involves different elements, such as the use of big medical data, to develop an appropriate approach for drug repositioning and a new framework for the integration of available resources.
# How _Pseudomonas nitroreducens_ Passivates Cadmium to Inhib Plant Uptake
## 3 Results and Discussions
### 11830 Passivation Cadmium to Inhibit Adsorption of H. cordata  
_H. cordata_ has been demonstrated as a heavy metal hyper-enriched plant [48]. Microorganisms play an important role in enhancing the accumulation of cadmium from soil by _H. cordata_, and this study has been frequently reported [30; 49; 50]. But, few studies have been reported on the inhibition of heavy metal uptake by _H. cordata_ or other hyper-enriched plants. [PERSON] et al. [51] reported a composite bacterial agent that produces the phytohormone abscisic acid (ABA). After adsorption, it inhibited the enrichment of Cd in field vegetable crops. The microbial inhibition of cadmium accumulation in cereals was reported by [PERSON] et al. [52]. This result indicated that 11830 is suitable for the biosorption of Cd in soil under conditions of high concentrations, and this adsorbent can inhibit the bioaccumulation of hyper-enriched plants, which has great potential in the bioremediation of cadmium-contaminated soil.  
In summary, Figure 7 illustrates that 11830 treatment suppressed the pathway of cadmium enrichment in Houttuynia cordata (arrows indicate the possible path direction of cadmium in plants). 11830 passivated cadmium mainly through extracellular mineralization. In the presence of 11830, cadmium is mainly trapped in the soil by passivation, inhibiting its translocation process in the plant. Under the stimulation of cadmium, the functional proteins, metal transporters and related energy supply systems of 11830 were enhanced to passivate cadmium in the soil from being absorbed and accumulated by _H. cordata_.  
Figure 6: Uptake of Cd by _H. cordata_ after bioremediation by 11830.
## 6 Keystroke Recognition
### Deep Learning-Based Line Evaluation
#### 6.6.3 Comparison with Traditional Models  
We then benchmarked the proposed method with other models, as summarised in Table 6. Our findings revealed that deep learning models, particularly 1D CNNs, provided superior performance and robustness over traditional signal processing pipelines for keystroke classification using WiFi CSI. The CNN benefited from its ability to learn hierarchical temporal features directly from raw input, reducing dependency on manual preprocessing. Although more computationally intensive, these models show strong potential in scenarios where robustness to noise is essential. However, interpretability and real-time deployment challenges remain. Future work may consider lightweight architectures or hybrid approaches that combine handcrafted features with neural networks for improved efficiency and interpretability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & \(f_{1}\)**Score** & **Avg AUC** \\ \hline PCA + DTW + KNN & 89 & 0.884 & 0.923 \\ \hline PCA + SVM & 87.2 & 0.865 & 0.905 \\ \hline DWT + RF & 86.5 & 0.849 & 0.891 \\ \hline Raw CSI + CNN & 92.1 & 0.917 & 0.964 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different ML algorithms.  
Figure 19: ROC curves for CNN keystroke classifier.