esa-sceva/satcom-chunk-collection huggingface dataset:
### Oxidation  
PFASs exhibit greater resistance to oxidation by ozone and hydroxyl radicals compared to other micro-contaminants, primarily due to the strong C-F bond and the electron-withdrawing nature of their functional groups [24]. [PERSON] et al. [28] highlighted that F is the most electronegative element, which hinders its oxidation and helps retain its electrons. Consequently, the powerful inorganic oxidant ozone faces thermodynamic challenges in oxidizing F. This, combined with the presence of the strong C-F bond and electron-withdrawing functional groups -COOH and -SO\({}_{3}\)H in PFCASs and PFSAs structures, indicates their likely resistance to oxidation, even when exposed to molecular ozone and hydroxyl radicals. Furthermore, [PERSON] et al. [18] stated that many conventional approaches, such as thermal treatment, direct photolysis, and general hydroxyl-based chemical oxidation, are ineffective for removing aqueous PFASs. The review by [PERSON] et al. [24] revealed that advanced oxidation processes, which have a higher oxidation potential compared to most physico-chemical and biological reactions, were also ineffective in degrading PFOSs at a concentration of 20 mg/L over a 120 min period using laboratory-scale experimentation with ozone, ozone hydrogen peroxide, ozone/UV, and Fenton's reagent. Moreover, other oxidants like dichromate and potassium permanganate had no significant effect on a wide range of PFAAs, including PFOS. The review also highlighted that perfluoroalkyl sulfonates seem to be considerably more recalcitrant to chemical oxidation compared to perfluoroalkyl carboxylates.  
Advanced oxidation processes have also shown limited effectiveness for PFAS removal, even when applied at doses and contact times typical of drinking water treatment plants. These processes often result in the formation of non-selective hydroxyl radicals when combining technologies such as hydrogen peroxide plus iron (Fenton's reagent), ozone plus peroxide, UV plus titanium dioxide, UV plus ozone, UV plus chlorine, and UV plus peroxide [31; 41]. According to [PERSON] et al. [25], removal rates typically range between 0 and 15 percent under conditions observed in drinking water treatment plants. [PERSON] et al. [24] reported that advanced oxidation processes often lead to only the partial degradation of PFASs, resulting in the formation of PFASs with shorter perfluorinated alkyl chains. Even at a full-scale water reclamation plant in Queensland, Australia, multiple stages of ozonation with doses as high as 5 mg/L and a 15 min contact time did not satisfactorily remove PFASs [29].
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 4 Performance Verification and Analysis
### Real-World Scenario Test Environment  
Further comprehensive validation was conducted to assess the applicability of the model performance in real-world scenarios, aiming to deeply investigate the performance of the model under complex real-world conditions. We established an mmW frequency  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Data** & **Recorded Value (\({}^{\circ}\))** & **Theoretical Value (\({}^{\circ}\))** \\ \hline \(\theta_{\text{horizontal}}\) & 59.95 & 60 \\ \(\theta_{\text{vertical}}\) & 40.28 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: OTA sensing angle range test results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test Data** & **Results (m)** \\ \hline \(R^{+}_{\text{min1}}\)  
band 5G-A commercial demonstration network test environment. It is important to note that the data used for analysis in this subsection came from large-scale field measurements. The sample size was sufficient, and the coverage was extensive, which effectively reflected the real-world situation. Based on this, this section provides a detailed introduction to the various comparative tests conducted on the low-altitude network sensing model based on ISAC base stations. Through multidimensional comparative analysis, we accurately evaluated the performance differences of the model under different conditions, providing solid data support for the optimization and practical application of the model.  
The test area covered urban areas and towns, including residential buildings, small commercial streets, squares, etc. These areas are typical test scenarios. It was limited to the low-altitude area below 300 m and was a multi-cell networking scenario.  
As shown in Figure 11, the test area covered 6 square kilometers and included 8 ISAC base stations with 19 sectors. The average inter-station distance was approximately 1 km. International equipment manufacturers provided the test base stations. The AAU had a vertical FOV of 40\({}^{\circ}\) and a mechanical tilt angle of 0\({}^{\circ}\), with an average base station height of approximately 30 m. These parameters were incorporated into the single base station 3D sensing model, as shown in Figure 10. The sensing waveform combined a continuous wave and a pulse wave. The EIRP power of the continuous wave was 66 dBm, and the pulse wave was 75 dBm. The carrier configuration of the AAU is shown in Table 1. Except for the value of N ranging from 2 to 5 in the system, all the other parameters were the same as in Table 2.  
The test UAV was a DJI Mavic 3, with unfolded dimensions of 347.5 mm \(\times\) 283 mm \(\times\) 107.7 mm (without propellers) and a diagonal wheelbase of 380.1 mm.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### GRACE and GRACE Follow-On  
The Gravity Recovery and Climate Experiment (GRACE) [12] was launched by NASA in 2002. The project consisted of two spacecraft with orbits of 500 km and a separation of 100-150 km. The task was to measure the gravitational potential coefficient through relative distance changes in the co-orbiting spacecraft and to conduct observational research on the Earth's water quality [13], geology, and climate. The inter-satellite status is measured using a two-way K-band ranging system (KBR), with the payloads shown in Table 3. GRACE-2 satellite was retired in 2017 due to battery aging. In May 2018, NASA and the German Earth Science Research Center jointly launched GRACE Follow-On (GRACE-FO) to continue the mission. In addition to the Super STAR accelerometer, KBR system, star sensor, laser-ranging angular reflector, and BLACKJACK dual-band GPS receiver [14], a new laser rangefinder was introduced to improve accuracy.
###### Abstract  
These days, environmental pollution, notably water pollution, has increasingly caused severe human health problems. The major water pollutants are heavy metals. MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite was prepared in the current work via in situ method and tested to remove lead ion Pb\({}^{2+}\) and neutral red (NR) dye from water. The prepared nanocomposite was characterized using different techniques, including X-ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, Raman spectra, and vibrating sample magnetometer. The prepared nanocomposite showed high adsorption capacity toward Pb\({}^{2+}\) and NR dye removal according to Langmuir fitting indicating the monolayer homogeneous adsorption of pollutants over the adsorbent surface and can be separated easily with an external magnet. The effect of different factors, including contact time, pH, initial concentration, and adsorbent dose on the adsorption, were also studied. The increased concentration of pollutants led to increased adsorption capacity from 63 to 625 mg/g for Pb\({}^{2+}\) ions and from 20 to 90 mg/g for NR dye. The increased adsorbent dose led to increased removal efficiency from 39% to 98.8% and from 63% to 94% for Pb\({}^{2+}\) and NR dye, respectively. The optimum pH for the adsorption of both pollutants was found to be 6.0. The reusability of MnFe\({}_{2}\)O\({}_{4}\)/GO nanocomposite was studied for up to five cycles. The nanocomposite can keep its efficiency even after the studied cycles. So, the prepared magnetic nanocomposite is a promising material for water treatment.  
environment; water treatment; carbon-based nanomaterials; adsorption +
Footnote †: journal: Physics Letters B  
0000-0002-236X][PERSON]  
1 Chemistry Department, College of Science, Princess Nourah bint Abdulrahman University,  
Riyadh 11671, Saudi Arabia; [EMAIL_ADDRESS]
## 4 Results and Discussion
### Process Flow Efficiency
High-frequency transitions occurred between sequential stations, with the 202 \(\to\) 203 transition recording 5592 events, while alternative paths and cross-transitions (such as 202 \(\to\) 205 with 729 events) demonstrated the flexibility of the production system. The average defective rate across the system was 2.2%, with bottleneck stations showing higher rates (2.3-3.5%) compared to non-bottleneck stations (1.5-1.9%). Energy consumption and environmental metrics showed a direct correlation with production volumes and cycle times. The total daily energy consumption of the system reached 4015 kWh, resulting in 2007.5 kg of CO\({}_{2}\) emissions and 146.5 kg of waste generation. Stations with higher throughput, such as Station 207, demonstrated better energy efficiency per unit produced despite higher absolute consumption values. The inspection station (Station 209) showed the lowest throughput, but maintained moderate defective rates at 2.2%, indicating its critical role in quality control despite apparent inefficiency in processing speed. The operational and environmental constraints in Table 3 establish the boundary conditions for the optimization of the tube making process. These constraints are derived from actual production data, with operational limits that ensure equipment protection (95% utilization while maintaining minimum viable throughput (94 units/day). The environmental constraints reflect the worst-case scenarios observed, restricting energy consumption to 600 kWh/day and emissions to 300 kg CO\({}_{2}\)/day per machine. Process constraints maintain system efficiency at an 87% minimum while permitting cycle times between 564 and 960 s, based on actual station performance ranges. Resource allocation provides sufficient staff (four operators/shift) with built-in capacity buffers (15%) for bottleneck stations, while data management constraints ensure a real-time monitoring capability with 250 ms latency and 1 Hz sensor sampling rates. \begin{table}
\begin{tabular}{l l r} \hline
**Constraint** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Operational} & Machine utilization limit & 95\% \\  & Minimum throughput & 94 units/day \\  & Maximum idle time & 15\% \\  & Station availability & 98\% \\ \hline \multirow{3}{*}{Data Management} & Data transfer latency & 250 ms \\  & Sensor sampling rate & \(1\,\mathrm{Hz}\) minimum \\ \cline{1-1}  & Historical retention & 5 years \\ \hline \end{tabular}
\end{table} 
Table 3: Operational and environmental constraints for tube-making machines.
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 5 Case Study
### Simulation System
Scheme 2 considers the modularity index and the active balance index, and the number of clusters after division is four. The performance of the cluster structure is stronger; meanwhile, compared with scheme 1, each cluster has stronger power autonomy, but the frequency regulation response speed index is neglected and the residual DG capacity within each cluster is not reasonably configured in the process of cluster division, so the frequency regulation cannot be utilized fully. Scheme 3 comprehensively considers the module degree index and the frequency regulation functionality index. The number of clusters is four, the same as in scheme 2. The performance of the cluster structure is weaker than that in scheme 1, and the module \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Residual DG Flexibility** & **Remaining Energy Storage** \\  & **Capacity (kW)** & **Capacity (kWh)** \\ \hline Scheme 1 & 74,596.82 & 81,596.74 \\ Scheme 2 & 83,597.46 & 68,754.42 \\ Scheme 3 & 72,654.83 & 87,692.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sum of capacity available during system frequency regulation time. Figure 6: Comparison of the convergence speed of two genetic algorithms. degree index is smaller, but it is not much different from that in scheme 2, which is also greater than 0.5, and it has better structural performance. Compared to scheme 2, scheme 3 considers the instantaneous power balance between renewable energy units and loads, i.e., it introduces the frequency regulation response speed index. Table 2 shows that this division can promote renewable energy consumption and storage can leave more power. Comparing schemes 2 and 3, there is not much difference in the active balance degree, but scheme 3 has more remaining DG capacity and more potential for frequency regulation. The results of the analysis show that the active balance index proposed in this paper improves the power autonomy and active response capability within clusters on the basis of  
Figure 7: Results of division under three index systems: (**a**) modularity index; (**b**) modularity and active balance index; and (**c**) comprehensive index system. satisfying better source-load matching. The response speed index can accurately describe the frequency regulation response characteristics of the resource flexibility within clusters, match the response speed of resource flexibility with demand flexibility based on matching the supply and demand flexibility, maximize the cluster response speed, and improve the ability of clusters to participate in frequency adjustment, as well as provide a theoretical basis for cluster-assisted regulation of the frequency fluctuation of the system. In addition, by setting different weights for each index, cluster division under different regulation objectives can be satisfied.
## 2 Materials and Methods
### Plan Complexity: LINAC Watch  
All the calculated plans were sent to the LINAC Watch software (Qualiformed, La Rochesur-Yon, France) to evaluate their complexity. In particular, the LINAC watch software decrypts and automatically analyzes the temporal tracking files of the actual radiation by exploiting the LINAC machine dynamic log files in IMRT and VMAT treatments. It allows the real-time comparison between the LINAC performance during the treatment session and the plan scheduled by the TPS in terms of MLC leaf positions, jaw positions, delivered MUs and fluence (i.e., the MU delivered per unit of integrated surface over the total duration of the radiation session), the gantry and collimator rotation angles, and the beam-off lags at each control point [34]. In addition, the software allows the assessment of the plan complexity through the estimation of the modulation complexity score (MCS) as defined by [PERSON] et al. [35] for step-and-shop treatments and adapted for VMAT by [PERSON] et al. [36]. The MCS ranges from 0 to 1.0 (an MCS equal to 1.0 is assign to an open rectangular field) and incorporates information on variability in leaf positions, degree of irregularity in field shape, segment weight, and area. The MCS is based on the evaluation of two different parameters:  
Leaf sequence variability (LSV) considers the shape of each segment based on the change in leaf position of adjacent MLC leaves, defining a specific shape. By indicating N as the number of open leaves constituting the beam and pos as the coordinates of the single leaf position,  
\[pos_{max}=<(pos_{N\in n})-pos_{N\in n}>_{leafbank} \tag{5}\]  
represents the maximum distance between positions for each (left/right) leaf bank, considering only the leaves positioned over the jaws. Thus, the LSV can be calculated as:  
\[\begin{array}{c}LSV_{segment}=\langle\frac{\sum_{n=1}^{N}(pos_{max}-(pos_{n}- pos_{(n+1)}))}{N\;x\;pos_{max}}\rangle_{leftbank}+\\ \langle\frac{\sum_{n=1}^{N}(pos_{max}-(pos_{n}-pos_{(n+1)}))}{N\;x\;pos_{max}} \rangle_{rightbank}\end{array} \tag{6}\]  
Aperture area variability (AAV) represents the variation in segment area and shape concerning the maximum aperture defined by all the segments (segments similar to the maximum aperture have a more significant score). The mathematical form for the AAV parameter calculation is:  
\[AAV_{segment}=\frac{\sum_{n=1}^{A}<pos_{a}>_{leftbank}-<pos_{a}>_{rightbank}}{ \sum_{a=1}^{A}<max(pos_{a})>_{leftbank\in beam}-<max(pos_{a})>_{rightbank\in beam}} \tag{7}\]  
where A is the number of leaves in the leaf bank.  
The complexity score is, finally, estimated considering LSV, AAV, and the relative segment weight for each beam as:  
\[\begin{array}{c}MCS_{plan}=\sum_{j=1}^{J}MCS_{beam,j}\cdot\frac{M\text{U}_{ beam,j}}{M\text{U}_{plan}}\end{array} \tag{8}\]  
where J is the number of beams and \(MCS_{beam,j}\) is the complexity index calculated for the j-th beam and defined as:  
\[\begin{array}{c}MCS_{beam,j}=\sum_{i=1}^{I}AAV_{segment,i}\cdot LSV_{segment,i }\frac{M\text{U}_{segment,i}}{M\text{U}_{beam}}\end{array}\]  
with I number of segments of the j-th beam.
###### Abstract  
Industry 4.0 has transformed manufacturing and automation by integrating cyber-physical systems with the Industrial Internet of Things (IIoT) for real-time monitoring, intelligent control, and data-driven decision making. The IIoT increasingly relies on IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) to achieve reliable, low-latency, and energy-efficient industrial communications. The 6 TiSCH protocol stack integrates scheduling and routing to optimize transmissions for resource-constrained devices, enhancing Quality of Service (QoS) in IIoT deployments. This paper proposes an innovative adaptive and cross-layer slotframe allocation technique for 6 TiSCH networks, dynamically scheduling cells based on node hop distance, queue backlog, predicted traffic load, and link quality metrics. By dynamically adapting to these parameters, the proposed method significantly improves key QoS metrics, including end-to-end latency, packet delivery ratio, and network lifetime. The mechanism integrates real-time queue backlog monitoring, link performance analysis, and energy harvesting awareness to optimize cell scheduling decisions proactively. The results demonstrate that the proposed strategy reduces end-to-end latency by up to 32%, enhances PDR by up to 27%, and extends network lifetime by up to 10% compared to state-of-the-art adaptive scheduling solutions.  
6 TiSCH; autonomous; adaptive; scheduling technique; IIoT; latency; PDR +
Footnote †: journal:
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Rotating Disk Experiments
#### 2.4.2 Rotational Vessel Wall Displacement  
Inter-frame displacements were estimated after RF data beamforming. Both PRFs of 8 and 4 kHz were studied, resembling inter-frame displacements present in early systole and late diastole of the vessel wall dynamics (see Table 2).
# An Overview of CubeSat Missions and Applications
## 5 CubeSats as Astronomical Exploration and Research Enabling Platforms
### X-Ray Spectrometers  
Moving on to the X-ray spectrometers, they are instruments capable of qualitative measurements regarding the composition of the specimen. They are used in astronomy for studying stars and galaxies, as well as supernova events and other high-energy phenomena. One notable mission that utilized this technology is the MinXSS Satellite [46]. Developed by the University of Colorado, Southwest Research Institute, NASA GSFC, and NCAR, MinXSS was a 3U CubeSat that launched in 2016 and operated for one year. Its mission was to study the X-ray emissions of the Sun, as well as their interaction with Earth's atmosphere. The primary instrument used was Amptek X123-SDD (Amptek, Bedford, MA, USA). Weighing less than 350 g, it was capable of measuring solar spectra within the International Organization for Standardization (ISO) standard SXR range of 0.1-10 nm [218]. Another mission is HaloSat Satellite [219]. Developed by University of Iowa, HaloSat was a 6U CubeSat with the mission to constrain the mass and spatial distribution of hot gas associated with the Milky Way by mapping soft X-ray line emission from highly ionized oxygen. It utilized three identical detector units, each of them containing an X-ray detector and signal processing electronics. From the initial results, it is evident that HaloSat can advance the understanding of the hot halo of the Milky Way, and that the construction and deployment of CubeSats for astrophysics should be encouraged. In Figure 13a, HaloSat's deployment from ISS can be seen. Beyond the previously discussed missions, there are more missions that utilize X-ray spectrometers in CubeSat, such as SpIRIT, a 6U CubeSat technology demonstrator developed by University of Melbourne [220] and CubIXSS, a 6U CubeSat funded under NASA's Heliophysics Flight Opportunities for Research and Technology (H-FORT) program, which aims to study the creation of hot plasma in solar flares using soft X-ray spectroscopy [221].
## 4 Nonlinear Finite Element Analysis Considering Fire Damage
### Validation of FE Model  
Figures 16 and 17 as well as Table 3 present a comparison of the test and analysis results in terms of temperature in the cross-section and mid-span deflection of the D200-T7 and D265-T8 specimens. It was found that the FE model evaluated the temperature for each cross-sectional height according to the fire exposure time with good accuracy. Furthermore, it predicted the maximum temperature of concrete around the prestressing strands located at the bottom flange when the fire resistance time reached 120 min (=7200 s), which was similar to the test results. In addition, as shown in Figure 17a, the FE model yielded analysis results that were similar to the deflections caused by the fire load on the D200-T7 specimen, and the difference between the deflections obtained from the analysis and test was only 0.03 mm when the fire resistance time reached 120 min. As shown in Figure 17b, for the D265-T8 specimen, the FE model simulated the fire resistance behavior of the specimen accurately, and the final deflection predicted by the FE model was approximately 81.7 mm, which is similar to the deflection observed in the test (76.7 mm).  
Figure 13: Thermal boundary conditions.  
Meanwhile, to investigate the fire resistance performance of HCSs more comprehensively, many fire tests are to be conducted while considering various cross-sectional details, load conditions, and span lengths. However, this is difficult to achieve when the complexity of the experiment and the time and costs required for the fire test are considered. Therefore, in this study, numerical simulations were performed using the verified FE model, where the HCS depth, span length, hollow ratio in a section, cover thickness of concrete, and load ratio were used as variables for 21 cases (see Table 2). Subsequently, the fire resistance performance of the HCS according to the key variables was analyzed in detail.