esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.3 Phase Behavior Experiments  
Phase behavior experiments, mainly water stability experiments and salinity experiments, are performed as a way to study the adsorption capacity of surfactant solutions at the oil/saline water interface. Water stability experiments are mainly to determine the stability behavior of water under different conditions, which can be carried out by methods such as physical tests and chemical tests. Physical tests usually involve observing the stability behavior of water at different temperatures, pressures, and pH values, e.g., by adding different chemicals and examining the rheological properties of the water. Chemical tests, on the other hand, focus on the stability of water through a series of experiments to detect the reactions between water molecules and other chemicals. These experiments can help us to understand the stability of water in different environments and thus speculate on the adsorption capacity of surfactant solutions at the oil/saltwater interface. Salinity experiments, on the other hand, are used to study the adsorption capacity of surfactant  
\begin{table}
\begin{tabular}{c c} \hline
**Ionic** & **Concentration (mg/L)** \\ \hline chloride ion & 39,540 \\ Sulfate ion & 230 \\ bicarbonate ion & 66 \\ Magnesium ion & 1800 \\ Calcium ion & 3680 \\ Sodium ion & 26,000 \\ Iron ions & 50 \\ Potassium ion & 200 \\ \hline \end{tabular}
\end{table}
Table 4: Formation water characteristics.  
solutions at the oil/brine interface by measuring salinity. Salinity is one of the important factors affecting the properties of surfactant solutions, so the adsorption at the oil/brine interface can be understood by measuring the changes in the properties of surfactant solutions at different salinities. Measurements can be carried out using equipment such as a digital salinometer, and attention needs to be paid to sample handling and measurement techniques to ensure the accuracy of the results.  
In these experiments, varying amounts of surfactants were incorporated into the solutions to achieve surfactant concentrations ranging from 0.7 to 7 mg/L. Subsequently, 5 mL of crude oil and the prepared surfactant solution were combined in a graduated pipette and agitated for 3 h at 50 \({}^{\circ}\)C to simulate reservoir conditions. Following this, the pipettes were maintained at 50 \({}^{\circ}\)C to allow for the attainment of phase equilibrium. Ultimately, the volume of microemulsion produced in each pipette was quantified. This measurement is critical as systems exhibiting lower interfacial tensions are capable of generating larger volumes of stable microemulsion through the enhanced dissolution of oil and water.
## 3 Results and Discussion
### Development of MW-SPM
#### 3.1.4. Molar Ratios and Mechanisms of the Reactions  
The molar ratio of DUV and SEL to DDQ was calculated by a spectrophotometric titration method [47]. The ratio of DUV: DDQ was 1:1, and that of SEL: DDQ was 1:2 (Figure 7). These ratios indicated that only one electron-donating site on the DUV molecule contributed to the yielding of the CT complex with one molecule of DDQ; however, two sites on SEL molecule contributed to the reaction with two molecules of DDQ. Energy minimization for both DUV and SEL molecules was carried out, and the density of electron on each atom was computed for DUV and SEL molecules (Figure 1). Utilizing molecular orbital computation software (MOPAC), molecular dynamics computation software (MM2 and MMF94), and CS Chem3D Ultra, version 16.0 (CambridgeSoft Corporation, Cambridge, MA, USA), the energy minimization and charge calculation were carried out. For DUV molecules, the highest electron density was located on the nitrogen atom of -NH-group (amine nitrogen). This density was -0.9 (the negative signs indicate the negative electron density. For SEL, the maximum electron densities were positioned on the two nitrogen atoms of the -NH- groups (amine nitrogens); both nitrogen atoms have the same electron density (-0.9). These data, along with the results of molar ratio, suggested that one nitrogen atom of -NH- group of DUV and two nitrogen atoms of -NH- group of SEL had contributed to the formation of CTC with DDQ.  
These findings led to the hypothesis that the CT interaction of DUV and SEL with DDQ would progress as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Studied Range** & **Optimum Valuea ** \\ \hline DDQ conc. (\%, \(w/v\)) & 0.125–0.8 & 0.5 \\ Solvent & Differentb  & Methanol \\ Reaction time (min) & 0–30 & 5 \\ Temperature (\({}^{\mathrm{c}}\)C) & 25–50 & 25 \\ \(\lambda_{\mathrm{max}}\) (nm) & 400–800 & 460  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization of conditions for the MW-SPM for DUV and SEL based on their formation of colored CT complexes with DDQ.  
Figure 6: Effect of solvent on the reaction of DDQ (0.5%, \(w/v\)) with DUV and SEL. Concentrations of DUV and SEL in their mixtures were 150 \(\upmu\)g/well for both.  
Figure 8: Schemes for the CT reaction of DUV (**A**) and SEL (**B**) with DDQ reagent.  
Figure 7: Plots of absorbance versus molar ratio of [DDQ]/[Drug] obtained from reaction mixtures of fixed concentrations of DUV (\(\blacktriangle\)) and SEL (\(\bullet\)) with varying concentration folds of DDQ. The mole ratio was assigned as the point of intersection of the tangents of straight-line portions of the plots.
## 3 Results
### Imaging Methods  
Imaging played a crucial role in both the diagnosis and intra-operative guidance of endoscopic procedures for spondyloscitis across the included studies. Pre-operative imaging most commonly involved magnetic resonance imaging (MRI), which was used to confirm the diagnosis, assess the extent of infection, and plan the surgical approach. MRI was particularly valuable in identifying abscesses, vertebral destruction, and spinal cord involvement. Intra-operatively, most studies employed fluoroscopy to guide the placement of instruments during endoscopic debridement or discectomy. Some studies also reported the use of computed tomography (CT) scans, either pre-operatively or post-operatively, to further evaluate bone integrity and ensure the completeness of infection removal [9]. Overall, the combination of MRI and fluoroscopy was the most frequently used imaging protocol, providing accurate visualization while minimizing radiation exposure. This imaging strategy contributed significantly to the precision and safety of endoscopic procedures, especially in complex or multi-level infections.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## Appendix A
Therefore,  
\[t^{(2)}\big{|}_{t_{0}=-X_{s}/(2v)}\in\left[-\frac{r_{s}^{\epsilon}}{r}\frac{ X_{s}}{2v}-\frac{1}{2}T_{w}\left|\frac{r_{s}^{\epsilon}}{r}-1\right|,-\frac{r_{s}^{ \epsilon}}{r}\frac{X_{s}}{2v}+\frac{1}{2}T_{w}\left|\frac{r_{s}^{\epsilon}}{ r}-1\right|\right], \tag{33}\]4  
\[t_{l}^{(1)}-t_{l}^{(2)}=\left\{ \begin{array}{cc}\frac{1}{a(r)}\left(\frac{r_{s} ^{e}}{r}-1\right)\left(\frac{X_{s}}{2v}-t_{0}\right)&\text{Sliding Spotlight}\\ \frac{1}{a(r)}\left(1-\frac{r_{s}^{e}}{r}\right)\left(\frac{X_{s}}{2v}+t_{0} \right)&\text{TOPS}\end{array} \right. \tag{100}\]  
Combine (101) multi-channel sliding spotlight SAR for and (100) for multi-channel TOPSAR as  
\[t_{r}^{(1)}-t_{r}^{(2)}=\left\{ \begin{array}{cc}\frac{1}{a(r)}\left(1-\frac{ r_{s}^{e}}{r}\right)\left(\frac{X_{s}}{2v}+t_{0}\right)&\text{Sliding Spotlight}\\ \frac{1}{a(r)}\left(\frac{r_{s}^{e}}{r}-1\right)\left(\frac{X_{s}}{2v}-t_{0} \right)&\text{TOPS}\end{array} \right. \tag{102}\]  
Since \(\alpha\left(r\right)\) is positive, \(r_{s}^{e}>r>0\) in multi-channel sliding spotlight SAR, \(r_{s}^{e}<0<r\) in multi-channel TOPSAR and \(-\frac{X_{s}}{2v}\leq t_{d}\leq\frac{X_{s}}{2v}\), then \(t_{l}^{(1)}-t_{l}^{(2)}>0\) and \(t_{r}^{(1)}-t_{r}^{(2)}<0\).
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 7 Discussion  
In this article, our scheme relies on the reliability of DC, which may lead to the single-point failure problem. With the development of blockchain technology, the cost of hardware and software is gradually reduced, and the access volume of smart meters in AMI system is also increasing year by year. The average technical cost per user is also getting lower and lower. We are designing new solutions to solve the potential single-point failure problem by introducing decentralized blockchain into AMI.  
In the traditional centralized structure, the resources of a single device are limited and the ability to resist attacks is weak, which may affect the entire network in the event of failure or in the face of new security threats. The introduction of blockchain can not only solve the problem of single point of failure, but also enhance the flexibility and anti-attack ability of the scheme, such as enhancing the system's ability to resist flooding attacks. With the increase in the number of blockchain nodes, the total computing power is also growing, and the entire AMI network will become more and more difficult to be submerged or even paralyzed. In addition, the dynamic addition and revocation of devices are also allowed.  
In our scheme, different entities of AMIs adopt the same cryptosystem. However, in the actual AMI system, due to the complexity of the communication network and the differences in resource capacity, different entities may adopt different cryptosystems. This provides ideas for our further research. In future research, we will consider the design and application of heterogeneous cryptosystems in the AMI to enhance the applicability of the AMI with different configurations.  
In addition, we will further conduct testing on larger AMI networks to better evaluate the effectiveness of the proposed scheme, which will help us to identify actual security challenges and compatibility issues and make corresponding adjustments.  
Figure 7: Energy consumption comparison of [2; 13; 17; 18; 20].
# Using a Pair of Different-Sized Spheres to Calibrate Radar Data in the Microwave Anechoic Chamber
## 4 Discussions
If the location of any antenna changes, the above verification should be repeated. In this kind of anechoic chamber, a small movement of the antenna could cause a considerable change in the signal amplitude of a target. Thus, we need to adjust the positions of the antennas to ensure that the test area is in the pure area. This calibration is conducted using the pair of spheres in the optic region. The difference between the RCS values of the pair of spheres provides calibrations for the general radar calibration using only one calibration sphere, including (1) verifying whether the scattering regions are in the same scattering region, and (2) checking whether the test region is in the pure area of the microwave anechoic chamber. In addition to the above two benefits brought by the method using a pair of spheres, the locations of the two spheres also provide the location of the target area. However, sometimes in the microwave anechoic chamber, the background clutter is still too high, meaning that the radar signals from a target with a small RCS value may be not distinguished from background clutter. In this situation, the locations of the two spheres can be useful. Taking the results in Figure 4 as an example, both spheres were located at approximately 11.6 m, which indicates the target area's approximate region. Since the distance between the table and the antennas is the same, the drone's radar data and the seagull are also located at approximately 11.6 m. Since the centers of the spheres were at the same location, the difference between the spheres was 10 cm, which is equal to the difference (i.e., 10 cm = 0.15-0.05 m) between the radii of the two spheres. Therefore, this value confirms the location of the target area. In conclusion, the calibration procedure of the primary measurement setup using a pair of different-sized spheres is described in Figure 5.  
Step (1) Calibrate the VNA to remove the cable effect before connecting the VNA and the antennas. This calibration can be conducted using the standard cable components provided by the VNA. Step (2) Calibrate the antenna to ensure that the target is in the pure area after connecting the VNA and the antennas with cables. Step (3) Extract the signals of targets with the range profiles, based on the position located by the spheres, and then process them for the goal of the test. Figure 5: Radar calibration procedure using a pair of spheres.
## 5 Conclusions  
The Ria of Huelva was selected as a case study to test the industrial fluid deprivation efficiency of the ASEC technology due to its strong mining contamination originating from the Odiel-Tinto River basin. Sediment from the port of Huelva derived from dredging activities and two heavily acid/metal polluted sites of the upstream rivers was collected and treated with the active system to obtain pure water and dried solids. The current study has demonstrated the complete efficacy of the removal of dissolved elements and the neutralization of pH from both residues (dredged materials and mining residues) located in the Huelva province: from pH ~3 to pH 6.5-7. The complete recovery of a high-quality water volume represents an environmental milestone in the restoration of aquatic ecosystems. On the other hand, the obtained dried solids represent, based on the characterization results, a new source of critical raw materials and other elements of commercial interest for re-exploitation (especially in terms of mining residue treatment by-products) providing important potential revenues (~1.2 million USD), as well as a reduction in transport and management of dredged material costs. A new alternative has been presented here, which incorporates dredged materials into the circular economy by reutilizing the high-quality water obtained in the process, along with the proper management of the solid phase (moisture < 1%), obtaining potentially high revenues (~700 kUSD, data with 70% discount) and offering traditional construction material alternatives.  
[PERSON] and [PERSON] were involved in all aspects of the manuscript such as conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing, reviewing and editing, visualization, supervision, project administration and funding acquisition. [PERSON] was involved in conceptualization, methodology, software, validation, formal analysis, investigation, resources, writing, reviewing and editing, visualization, supervision, project administration and funding acquisition. [PERSON] and N.O.N. were involved in validation, investigation, writing, reviewing and editing, visualization and supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ASEC Grant #190180588 through EU EIC/Accelerator Horizon Europe 2022 call.  
Not applicable.  
Data Availability Statement: Data are unavailable due to privacy or ethical restrictions.  
Acknowledgments: The authors thank the Water Challenge S.L. technical team for all the activities conducted during the study in the ASEC plant and the technical and laboratory staff from the ICMAN and ICMS, both belonging to CSIC.  
Conflicts of Interest: Author [PERSON] was employed by the company Water Challenge S.L. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 5 Discussion
### Limitations of the Study
#### 5.1.3 Centralized Learning Approach  
The study adopted a traditional centralized approach to ML, where IoT device data were collected in a single central repository. While this approach enables the efficient processing of large datasets, it poses a number of challenges that are particularly relevant in the context of IoT.  
This centralized approach poses a significant threat to user privacy. Sending large quantities of data, potentially containing sensitive information, to a central server increases the risk of interception or unauthorized use. Second, transmitting data from multiple IoT devices can be burdensome to the network, especially for devices with limited bandwidths. Delays in data transmission can lead to reduced system performance and impede real-time operation.
# Experimental Study on the Condensation Heat Transfer on a Wettability-Interval Grooved Surface
## 2 Description of Experiment
### Wettability  
Surface wettability is an important factor affecting the condensate dynamic behaviors and heat transfer performance. The contact angles of various functional surfaces including hydrophilic pure copper grooved surface, hydrophobic grooved surface, and wettability-interval grooved surface were measured using the sessile drop method to characterize the surface wettability. Previous studies have suggested that the wettability on the surface with grooves shows anisotropic characteristics [29]. Therefore, the contact angles perpendicular and parallel to the groove direction were measured in this study and are shown in Figure 2. The results indicated that due to the extrusion of the groove, the spreading of droplets perpendicular to the groove direction was restricted so that the contact angle perpendicular to the groove direction was higher than that parallel to the groove direction. For example, on the hydrophilic pure copper grooved surface, the contact angles perpendicular and parallel to the groove direction were \(90^{\circ}\) and \(80^{\circ}\), respectively. This is strongly associated with the hydrophilic and hydrophobic surface wetting modes. Droplets on hydrophilic surfaces exhibit an infiltrating Wenzel state [30], where the droplet edges are pinned to the surface and difficult to spread, so the external groove structure has a strong influence on the apparent contact angle. On the other hand, the droplets on hydrophobic surfaces show a suspended Cassie state, in which the droplets are constrained by the solid boundary in a relatively small way. The surface anisotropy on the wettability of the hydrophobic grooved surface was comparatively weak. The contact angles were perpendicular and parallel to the groove direction of the hydrophobic grooved surface were \(142^{\circ}\) and \(138^{\circ}\), respectively. Related studies have demonstrated that the increase in hydrophobicity can weaken the wetting anisotropy of the surface with grooves [31]. On the wettability-interval grooved surface, the contact angle perpendicular to the groove direction was \(125^{\circ}\), and that parallel to the groove direction was only \(74^{\circ}\). This is due to the combined effect of the wettability difference between hydrophilic and hydrophobic surfaces and the geometric structure of grooves. The hydrophobic surface hinders the spreading of droplets so that droplets tend to spread on the hydrophilic surface; meanwhile, the ridge structure of the grooves acts as a restriction on the spreading of droplets in the vertical ridge direction, so that droplets are easily stretched out in the direction of the parallel grooves. Meanwhile, the ridge structure of the grooves acts as a restriction on the spreading of droplets in the vertical ridge direction, so that droplets are easily stretched out in the direction of the parallel grooves.
## 3 Results and Discussion
### Characterization of the Materials
#### 3.1.4 XRD  
The crystallographic structure of the XDA-1G resin was investigated using XRD, and the resulting pattern is shown in Figure 5. The XRD spectrum displays a broad, low-intensity diffraction peak centered around 20 = 20-25\({}^{\circ}\), which is characteristic of amorphous carbonaceous materials [36]. In addition to this diffuse region, the pattern reveals subtle, broad peaks at 20 = 21.36\({}^{\circ}\), 25.68\({}^{\circ}\), 41.68\({}^{\circ}\), and 68.52\({}^{\circ}\), which correspond to the (110), (121), (331), and (520) planes, respectively. These peaks are not sharp or intense, reinforcing the conclusion that the XDA-1G resin lacks long-range crystalline order and is predominantly amorphous.  
Such a disordered structure is typical of organic polymer-based materials, especially those synthesized from styrene-divinylbenzene (S-DVB) copolymers. This amorphous  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**BET** & **t-Plot Micropore** & **BJH Adsorption** & **BJH Desorption** & **DFT** & **DFT** \\
**Surface Areas** & **Surface Areas** & **Surface Areas** & **Surface Areas** & **Pore Volume** & **Surface Areas** \\
**(m\({}^{2}\) g\({}^{-1}\))** & **(m\({}^{2}\) g\({}^{-1}\))** & **(m\({}^{2}\) g\({}^{-1}\))** & **(m\({}^{2}\) g\({}^{-1}\))** & **(m\({}^{2}\) g\({}^{-1}\))** & **(m\({}^{2}\) g\({}^{-1}\))** \\ \hline
1439 & 611.0 & 177.6 & 10 configuration is often advantageous for adsorption applications, as it provides greater structural flexibility and a high density of accessible adsorption sites. These features facilitate the efficient accommodation and interaction of adsorbate molecules like m-cresol, enabling fast mass transport and high adsorption capacity.  
Importantly, the XRD pattern shows no distinct diffraction peaks associated with crystalline inorganic phases, such as silica, alumina, or metal oxides. This absence confirms that the XDA-1G resin is composed primarily of organic matter and free from significant inorganic impurities, consistent with the manufacturer's formulation.  
In combination with the large surface area and hierarchical porosity observed in nitrogen adsorption-desorption studies, the amorphous nature of the XDA-1G resin contributes significantly to its high adsorption capacity and diffusion efficiency, making the XDA-1G resin well-suited for environmental remediation of organic pollutants.
# Dynamic Routings in Satellite Networks: An Overview
## 2 Related Works
Therefore, similar to SO-DNN, the algorithm uses a multi-objective deep neural network (MO-DNN) to learn. Experimental results show that the integrated network achieves better network coverage, lower latency, higher throughput, and a longer path life. Due to the highly dynamic and time-varying nature of the topology of satellite networks. Based on the characteristics of satellite networks, academia and industry must develop new routing algorithms to improve the stability of satellite routing algorithms and satellite communication performance. However, the above overview works did not focus on the uniqueness and new key issues of dynamic routing in satellite networks in timely manner, especially for the requirements, challenges, and solutions of dynamic routings in large-scale multi-layer satellite networks. The comparison of related works is shown in Table 2. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Network Type** & **Reference** & **Feature** & **Main Contributions** \\ \hline SAGISN & [14] & & Reviewed the architecture and key technologies of SAGINs and summarized potential technical challenges of SAGIM \\ \hline SAGIN & [8] & & Studied traffic-based algorithms and quality of service (QoS)-based IP routing algorithms \\ \cline{3-4}  & & Integrated & Revised several critical factors of the genetic algorithm \\ \cline{3-4}  & [13] & & Reviewed the architecture and key technologies of SAGINs and summarized potential technical challenges of SAGIM \\ \cline{3-4}  & [17] & & Designed a SAGIN framework and a deep-reinforcement-learning-assisted routing algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of related works.