esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Metal Ions in Spin-on Hardmask Using Functionalized Porous Silica Adsorbents
## 4 Conclusions  
In this study, two types of mesoporous silica materials with different pore sizes--MCM-41 and SBA-15--were functionalized with various surface groups and evaluated as adsorbents for metal ion removal from SOH solutions under different chemical environments. Adsorption experiments were conducted using solvent-only, monomer-containing, and polymer-containing solutions to reflect relevant conditions in semiconductor manufacturing processes. Among the tested functional groups, amine-modified mesoporous silica (-NH\({}_{2}\)) exhibited the highest metal ion removal efficiency. MCM-41-NH\({}_{2}\), in particular, achieved over 99% removal in solvent-only conditions.  
Amine-modified mesoporous silica showed better adsorption performance in polymer-containing solutions than in monomer-containing solutions. In particular, SBA-15-NH\({}_{2}\) successfully removed all target metal ions in polymer-containing solutions with relatively  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Al3+** & **Cu2+** & **Fe3+** & **Ni2+** \\ \hline q\({}_{\text{m}}\) (mmol g\({}^{-1}\)) & 0.6206 & 0.8672 & 0.5179 & 0.3653 \\ b & 12.515 & 1691.2 & 736.92 & 221.56 \\ \(\Delta\)G (J mol\({}^{-1}\)) & \(-\)6260.67 & \(-\)18,416.3 & \(-\)16,358.1 & \(-\)13,379.5 \\ \hline \end{tabular}
\end{table}
Table 2: Langmuir isotherm parameters including maximum adsorption capacity (q\({}_{\text{m}}\)), Langmuir constant (b), and Gibbs free energy (\(\Delta\)G) for the adsorption of metal ions onto amine-functionalized MCM-41 at 298 K.  
uniform and high efficiency. Adsorption isotherm analysis showed that all tested ions fit the Langmuir model most closely, indicating monolayer adsorption behavior governed primarily by chemical interactions between the metal ions and the functionalized surface. These results underline the critical influence of surface functional groups in determining the adsorption efficiency of mesoporous materials.  
This work provides meaningful insight for the development of next-generation adsorbents tailored for advanced lithography processes requiring precise contamination control and highlights the promise of functionalized mesoporous silica as a viable solution for trace metal removal in semiconductor manufacturing.  
W.K.: investigation, data curation, formal analysis, writing--original draft; K.L.: Data curation, visualization, writing--original draft; H.K.: Data curation, investigation, formal analysis; M.C.: investigation, resources; S.-K.H.: conceptualization, resources, funding acquisition; J.E.L.: conceptualization, supervision, validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Samsung Electronics Co., LTd. (IO240810-10698-01).  
Not applicable.  
The data presented in this study are available on request.  
This study was funded by Samsung Electronics Co., Ltd., and [PERSON], [PERSON] are an employees of the company. This does not alter the authors' adherence to journal policies on data sharing and publication ethics.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results
### Root Mean Square  
Figure 6 presents the RMS analysis for BDCOA with different population sizes i.e., 20, 30, 40, and 50. The evaluation of RMS with different optimization methods is shown in Figure 7. This evaluation confirms that BDCOA with population 30 has enhanced performance when compared to the other variations. Additionally, BDCOA has a lower RMS than MOA, POA and COA due to its effectiveness when searching for optimum control signals. The control signals of DM are used by BDCOA to reduce the aberrations, thereby facilitating the minimization of RMS.  
Figure 5: Convergence analysis.  
Figure 6: RMS for different population sizes.
## 5 Results and Discussion
### Pilot Plant Analyses
#### 5.2.2 Power Profile  
Figure 7 shows the power released/absorbed by the HTF and the PCM and HTF average temperature profiles during the charging and discharging processes at an HTF mass flow rate of 1500 kg/h. At the beginning of both processes, higher values of power were achieved because the heat was mainly transferred to the metallic tubes bundle, which has a high thermal conductivity, and at the same time the heat transfer between the HTF and the PCM located around the HTF pipes was at a maximum because the PCM thermal resistance was low and the thermal gradient between the HTF and the PCM was at a maximum. Immediately after, when the PCM started the phase change, the power drastically decreased due to the increase of the PCM thermal resistance. The reason lies in the fact that the PCM melting front moved away from the HTF as the phase change continued and therefore increased the distance between the HTF and the PCM melting. Focused on the charging process (Figure 7a), a liquid ring was created around the HTF pipes, and, therefore, the natural convection became the predominant heat transfer mechanism. As the process continued, the liquid fraction of PCM increased and, consequently, the thermal conductivity associated, until all the heat transfer was aimed to increase the temperature of the PCM located at the corners and the central part of the tank. During the discharging process (Figure 7b), a more sharp decrease could be observed. The reason lies in the fact that a solid PCM ring was created around the HTF pipes, which limited the heat transfer since the thermal conductivity became the dominant heat transfer mechanism.  
Figure 6: Comparison of the temperature profiles of two representative PCM temperature sensors with three different mass flow rates. (**a**) charging process and (**b**) discharging process. Shaded area indicates the melting and the solidification temperature range.  
Figure 8 shows the comparison between the three evaluated mass flow rates (500, 1500 and 2500 kg/h) of the power released/recovered by/from the HTF during a charging and a discharging process. It can be observed that at the beginning of both processes, the higher the mass flow rate, the higher the power as previously detailed. However, as the processes continued, the thermal gradient between HTF and PCM decreased and the influence of the mass flow did not become as relevant.
## 1 Introduction
The authors in [17] developed a lightweight user verification system where the client in the IoD environments needs to directly access information from a drone to show that the client is authorized for accessing the dataset from that drone. The formal security validation with the generally recognized automatic confirmation of Internet security protocol and application tool alongside informal security investigation shows that the suggested technique is secured against well-known attacks. In [18], the authors developed a Drone-map organizer, viz., a service-related fog-based drone managing scheme that monitors, communicates, and controls with drones through the networks. The suggested technique enables interaction with various drones through the internet that allows drones to be controlled anyplace and anywhere, with no long-distance restrictions. This model offers drones access to fog computational resources for drones to perform heavyweight load computation. Though existing works have focused on the IoD environment, it is still necessary to design energy-efficient and security-based solutions for the IoD environment with the inclusion of multiple input parameters of the drones. This study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy (RE), distance, and trust degree. The experimental validation of the ESRO-SRP technique is carried out under several aspects. In short, the major contributions are summarized as follows. * To the best of our knowledge, the ESRO-SRP technique for the IoD environment does not exist in the literature. * Develop a new ESRO-SRP technique for a secure route selection process in the IoD environment. * Derive an ESRO-SRP algorithm using the combination of QOBL with the traditional SRO algorithm and derive a fitness function involving multiple input parameters. * Simulate the performance of the ESRO-SRP technique under varying levels of energy consumption in the IoD environment. The rest of the paper is organized as follows. Section 2 offers a detailed discussion of the proposed model and Section 3 validates the experimental results of the proposed model. Finally, Section 4 concludes the study with key findings and possible future enhancements.
## 3 Hydrological Applications
### Mapping of watersheds and hydrologic features
#### 3.1.1 Flood Mapping of Devils Lake  
Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images aboard the Landsat-5 and 7 satellites, respectively, for the years 1991- 2003 were used to delineate flooding map and also compute lake surface area. A mosaic of three Landsat scenes (Path/Row: 31/27, 32/26, 32/27) was used to cover the entire study area.  
The lake surface area of the chain of Devils Lakes excluding Stump Lake mapped from Landsat for 1993 and 2003 is indicated in Figure 3. Between 1993 and 2003, the surface area of the lake was lowest in 1993 and highest in 2003 (Figures 3).The lake surface area from Landsat images was classified and only lake areas were computed for each respective year. As depicted from the images, the areal increase of the lake is greater in recent years. The white line in Figure 3 marks the lake boundary line in 1992. The corresponding surface areas for 1993 and 2003 are also shown in the figure indicating an increase in the flooded area by 117% between 1993 and 2003.  
Figure 4, lake surface area by and year (without Stump Lake) derived from Landsat images, shows that each meter rise of lake inundates a progressively greater area resulting in increased flood volume. It is shown that a 1.3 m increase between 1994 and 1995 at an elevation of 435.8 m adds about 39.5 km\({}^{2}\) to the lake's surface area, whereas a 1.3 m increase between 1996 and 1997 at an elevation of 438  
m adds nearly 49 km\({}^{2}\) ([PERSON] et al. 2006a, 2006b). This indicates the severity of the flooding problem at higher lake levels attributed partly due to the flat topography of the contributing watershed.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 3 Energy-Efficient Reliable Multipath Routing Protocol Using Opportunistic Routing
### Multiple Path Bifurcation Decision Process
However, during this process the issue of whether a neighbor node not realizing a packet transmission of the other neighbor nodes may arise. That is, if the distance between a pair of neighbor nodes of the intermediate node is longer than the transmission radius of the sensor, they may not recognize the transmission of the other, causing excessive bifurcation without timer termination. Figure 2 shows the node selection problem. When the intermediate node, \(S\), broadcasts the packet to its neighbor nodes, the neighbor nodes start the timer to broadcast the packet. Suppose that a timer of node 1 expires when the number of calculated paths is one. Node 1 tries to broadcast the packet, and the other neighbor nodes terminate their timers when they receive the packet of node 1. However, nodes 4 and 5 are located outside the transmission range of node 1. Thus, nodes 4 and 5 cannot overhear the packet of node 1 and the timer does not terminate. Finally, nodes 4 or 5 broadcast the packet when their timers have expired. This phenomenon causes excessive bifurcation. The transmission of node 1 cannot reach to node 4 and 5  
Figure 2: An example of the node selection problem. To solve this problem, the intermediate node calculates a neighbor set that selects the maximum number of neighbors. The nodes in the set should be located within a communication radius of each other. That is, the node can communicate with any node in the set. To calculate the neighbor set, the intermediate node calculates the center point of all of the neighbor nodes and excludes the farthest node from the center point. The intermediate node then measures the distance between the farthest pair among the neighbor nodes. If the measured distance is less than the transmission radius, the neighbor set calculation is completed. However, if the distance is greater than the radius, the abovementioned process is repeated until the proper neighbor set is found. Through this process, the intermediate node can find the proper neighbor set in which all of the nodes are separated by the communication radius. As shown in Figure 2, the source \(S\) calculates the center point \(C\) of all of the neighbor nodes. The source node excludes node 1 that is the farthest from \(C\) among the neighbor set. The source node measures whether the two most distant neighbors of the remaining neighbors, nodes 2 and 5, are within the communication radius of each other. As the distance is shorter than the transmission radius \(r\), nodes 2-5 are located within one communication radius of each other. The source node \(S\) forms a neighbor set consisting of nodes 2-5. Finally, as the node \(S\) broadcasts the packet to the formed neighbor set, opportunistic routing could avoid the abovementioned problem.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
### Obstacle Detection
This makes it the preferred option for applications that require reliable and high-precision measurements, especially in dynamic environments where obstacles can vary.
## 6 Discussion
### Emission Reduction by Region
#### 6.1.2 Pause & Resume  
When evaluating the Pause & Resume strategies for durations up to 100% of the duration of the original experiment, [PERSON] et al. [9] find the opposite of the Flexible Start result: short experiments like DenseNet 201 only see emissions reductions around 2-3%, while the 6.1B parameter transformer training run actually sees the largest decrease in emissions (11.4%). This observation remains valid in our experiment: Pause & Resume on HF-SCA achieves a 9.47% carbon saving, while shorter workloads are around 1-2%. Particularly, results are comparable between HF-SCA and the 6.1B transformers even if the workload sizes are vastly different (with the latter being more than 10 times longer than the former). What we have found is that the greener the region, the more effective Pause & Resume is (the opposite of Flexible Start).
## 3 Simulation
### Influence Ratio of Each Optical Component  
The simulation scheme could also be used to analyze the influence ratio by disintegrating the pixel offset of the whole optical system to each optical component. The influence ratio could substantially improve the structural design of optical components and arrange a reasonable isolation. The influence ratio of each optical component at different speeds are shown in Figure 8 by extracting the top three peaks. Perturbation had the greatest influence on the primary mirror in this opto-mechanical system when the pixel offset fell below 500 Hz, wherein the influence ratio was over 95%. In contrast, perturbation was independent on the secondary mirror and image surface. Particularly, the sum of two components accounted for no more than 2%.
## 3 Results
### Comparison of the Two Representations
A second-order polynomial model is used for the mean curve on a logarithmic scale, as given by  
\[S=exp\Big{(}p_{1}\ln E^{2}+p_{2}\ln E+p_{3}\Big{)} \tag{3}\]  
Figure 9:0 Fitting parameters of the entropy-energy dispersions in the two periods for a building complex near San Giovanni station: (**a**) parameters of the mean model; (**b**) parameters of the standard deviation model. Figure 9:1 Fitting parameters of the energy-velocity dispersions in the two periods for a building complex near San Giovanni station: (**a**) parameters of the mean model; (**b**) parameters of the standard deviation model. The value of \(p_{3}\) represents the limit of the entropy, on the logarithmic scale, for an energy value that tends toward zero, so \(S\) tends toward \(exp(p_{3})\). The sign of \(p_{1}\) expresses the concavity of the quadratic curve, while the ratio \(-p_{2}/2p_{1}\) expresses its minimum. As mentioned above, a positive value of \(p_{1}\) is enforced, in accordance with the physical meaning of the interpolated data. The decrease in the third parameter leads to a downward curve shift toward lower entropy values from the first period to the second. The module of the second parameter also decreases, leading to an increase in the curve slope on the logarithmic scale. Instead, the increase in the first parameter reduces the model's curvature; therefore, the entropy decreases faster with the energy increase. The curve tends to lower and shift toward higher energy values. The effect given by the first and third parameters is to obtain an approximately constant stretch at low entropy. A first-order polynomial curve models the standard deviation adopting the least-squares method. The parameters are shown in Figure 15(b). The first parameter increases in modulus, representing a more rapid reduction in variance with increasing energy. In addition, the effect of the second parameter is added, causing an upward shift. Thus, for low energy values, a similar standard deviation is obtained in the two periods, whereas for lower entropy values, a lower deviation is obtained. The points result closer to the mean model. Hence, in the second period (the one affected by excavations) there is an evident variation of the dispersions, underlined through the analysis of the fitting parameters. Moreover, the three dispersions analyzed display a reduction of the signal deviation from the mean model, which can be interpreted as lower uncertainty in the measures.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies
#### 3.2.4 Current-Fed Cascaded Buck and Full-Bridge Converter  
A current-fed converter based on the cascaded buck and full-bridge topology was investigated in [96]. The converter is shoot-through tolerant and has a wide output voltage range. However, its high component count, limitation in operating frequency, and  
Figure 13: (**a**) Full-bridge converter. (**b**) Current-fed Weinberg converter.  
lower efficiency due to additional switching stages are the disadvantages of this topology. Figure 14a shows the circuit topology of a current-fed cascaded buck and full-bridge converter.
## 5 Evaluation
### Comparing with IO-Detectors  
It is not evident how to accomplish directly comparing earlier proposals on IO-detection [10; 11; 12] in terms of detection accuracy, as we offer a much finer granularity in context profiling than just classification into three scenarios (indoor, outdoor, semi-outdoor), and our location context does not only include scenarios (see our definition in Section 3.1). Earlier GPS-free proposals [10; 11] complain about the excessive delay incurred by a GPS-based approach, but the performance of commercial GPS modules has drastically improved since, and we only retrieve raw GNSS measurements. Consequently, we do not experience any excessive delay in detecting context changes during our experiments. As a result, the only meaningful comparative metric left is energy efficiency.  
To this end, we compare the energy consumption between our system and GPS-free IO-detection proposals [10; 11], which rely mainly on a magnetometer, cellular receiver, and light sensor. Therefore, we develop an Android application to collect magnetic field strength, cellular signal, and light intensity at a frequency of 100 Hz and install it on a Huawei P10, in order to emulate the systems used in [10; 11]. By contrast, our application only collects raw GNSS measurements at a frequency of 1 Hz. We measure the battery life under the following three conditions: (i) the phone stays with a user who either conducts data collection or acts normally in his/her daily life; (ii) each application keeps running until the battery level depletes to 30%; and (iii) each screen is set to the minimum brightness for checking results. As shown in Table 2, our system consumes about 20% more energy, largely comparable to the GPS-free solutions. Nonetheless, as our method achieves a much finer granularity in profiling location context compared with those earlier proposals; we believe that the slight disadvantage of our system, in terms of energy consumption, is acceptable. In fact, there is still a room to further improve the energy efficiency of GNSS sensing, given that this is only a preliminary attempt to make use of this recently available sensing ability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Set** & **Battery Life (Hours)** \\ \hline magnetometer + cellular + light & 29.2 \\ \hline raw GNSS measurement listener & 24.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Battery consumption comparison between two different sensor sets.  
Figure 10: Trace samples with context label results.
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 2 Design of a Single-Axis Controller Based on Sliding-Mode Disturbance Observer
### Traditional Sliding-Mode Control (SMC)  
#### 2.1.1 Introduction to Traditional SMC  
SMC is a nonlinear control method with a variable structure, which refers to the controller's output changing according to the system's current state, causing the state vector to move along the designed state trajectory. In Figure 1, the AB part is in the approaching stage, where the state outside the sliding surface will continuously approach the sliding surface and reach the sliding surface within a limited time. The BC part is in the sliding stage, where the system state moves along the designed sliding surface to the equilibrium point O. Therefore, the design of sliding-mode controller includes the selection of sliding surface and the design of the reaching law. The design of these two parts is independent of the parameters of the controlled system and external disturbances; therefore, SMC has strong robustness.