esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study describes, for the first time, the development and validation of a novel MW-FLR for the determination of DUV in its capsules and plasma samples. The method relied on measuring the native fluorescence of DUV in an aqueous solution. The proposed method offered many advantages such as the simplicity of the procedures, the use of low volumes of samples, short analysis time, high sensitivity, and selectivity. Also, the method did not involve any time-consuming liquid-liquid or solid-phase extraction procedures for the plasma samples or chemical derivatization prior to the analysis. Besides, the proposed method had high throughput which enabled the convenient processing of many samples in quality control and/or clinical laboratories. Furthermore, the method is characterized by its greenness because it uses water as a solvent.  
Figure 7: Correlations between the nominated concentrations of DUV taken from copiktra capsules (**A**) and spiked in plasma samples (**B**) measured by the proposed MW-FLR method with those measured by reference methods. The reference methods were microwell-spectrophotometric assay (MW-SPA) and high-performance liquid chromatography with fluorescence detection (HPLC-FD) for measuring DUV concentrations in copiktra capsules and plasma samples, respectively.  
**Author Contributions:** A.M.A.-H.: Investigation, Visualization, Data curation, Writing--review & editing, H.W.D.; Investigation, Data curation, Formal analysis, A.Y.S.; Investigation, Visualization, Data curation, N.Y.K.; Investigation, Writing--review & editing, R.A.-S.; Resources, Writing--review & editing, I.A.D.; Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing--review & editing, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, grant number RSP-2021/353.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of King Saud University (protocol code KSU-SE-20-51 on 8 November 2020).  
Not applicable  
**Data Availability Statement:** All data are in the article  
The authors extend their appreciation to the Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia for funding this work through grant No. RSP-2021/353.  
The authors declare no conflict of interest.
###### Abstract  
This study reports the synthesis and evaluation of iron molybdate (Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\)) and iron tungstate (FeWO\({}_{4}\)) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet-visible (UV-vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV-vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\) presented well-defined exposed (001) and (110) surfaces, while FeWO\({}_{4}\) showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps--2.11 eV for Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\) and 2.03 eV for FeWO\({}_{4}\). PL analysis revealed deep defects in Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\) and shallow defects in FeWO\({}_{4}\), which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\) and 45.3% for FeWO\({}_{4}\) after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications.  
iron-based oxides; photocatalysis; real industrial effluent 2025
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Synthesis of Fiber Bragg Gratings from a Targeted Transmission Specifications
#### 2.4.2 Non Minimum-Phase Transmissive Transfer Function  
In principle, signal processors whose transfer functions are not a minimum-phase function, \(H_{NMP}(f)\), would not benefit from the possibility of being implemented by an FBG operating in transmission. However, few years ago, a general approach was presented, capable of synthesizing any arbitrary linear optical pulse processor, including those requiring a non-MP filtering response, using a MP transfer function \(H_{MP}(f)\)[42]. This approach relies on the property described and demonstrated in [24] that establishes that \"any causal temporal function with a dominant peak around or close to the origin will be either a MP function or close to one\". Hence, the presented design scheme starts from the target non-MP transmissive temporal impulse response, \(h_{NMP}(t)=\Im^{-1}[H_{NMP}(f)]\), and converts it into a MP response by just introducing an instantaneous component, e.g., a Dirac delta function \(\delta(t)\)[42], in the following fashion  
\[h_{MP}(t)=K_{1}\cdot\delta(t)+K_{2}\cdot h_{NMP}(t-\tau_{G}) \tag{36}\]  
where \(\tau_{G}\) is the time delay between the two terms and \(K_{i},i=1,2\), are their amplitudes, which regulate the distribution of the input energy at the grating output. The corresponding spectral transfer function will be MP and can be expressed as  
\[H_{MP}(f)=K_{1}+K_{2}\cdot H_{NMP}(f)\cdot\exp(-2\pi f\tau_{G}) \tag{37}\]  
Note that Equation (37) defines an interferometric response where the phase of \(H_{NMP}(f)\) is encoded in the phase of the resulting cosine-like spectral shape (a particular example of the described spectral profile is shown later in Section 3.4). \(H_{MP}(f)\) can be readily synthesized by following the algorithm presented in previous Section 2.4.1. Hence, \(H_{T}(f)\) must approximate \(H_{NMP}(f)\) over the grating operation bandwidth. The selection of the values of \(K_{1}\) and \(K_{2}\) is restrained by the grating physical constraints. First, the maximum transmissivity is limited to \(T_{\max}=1\) and hence [42]  
\[|H_{T}(f)|\leq\sqrt{T_{\max}}\to K_{1}+K_{2}\leq 1 \tag{38}\]  
Besides, the maximum reflectivity \(R_{\max}\) attained imposes that  
\[|H_{T}(f)|\geq\sqrt{1-R_{\max}}\to K_{1}-K_{2}\leq\sqrt{1-R_{\max}} \tag{39}\]  
This inequality becomes strict when \(R_{\max}=1\) to avoid singular points in \(H_{MP}(f)\). However, this situation is not usually attained in practice (\(R_{\max}<1\)), and hence Equations (38) and (39) can be solved using the equal signs. This fact enables optimizing the energy transfer to the non-MP component of the output signal. Note that the maximum signal energy transferred to the non-MP portion will be of about 50%, attained when \(R_{\max}\) approaches 1. The described approach can be applied provided that the target impulse response is restricted to a well-defined, finite temporal window.
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 4 Experimental Verification
### Experimental Results
#### 4.3.3 Dynamic Performance of the Proposed Three-Coil PT-WPT System  
Figure 16 presents the dynamic waveform of the proposed system. Figure 16a illustrates the system's start-up waveform for a coupling coefficient of 0.1 for adjacent coils. When the total transmission distance was fixed at 15 cm, a step change in the load resistor was executed, depicted in Figure 16b. With a sudden change in the DC load from 12 to 16 \(\Omega\) in constant power mode, the waveforms show a reduction in load voltage from 7.35 to 3.96 V and an increase in load current from 2 to 3.85 A.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 4 Results and Discussion
### Results for Only Voice Streams
#### 4.1.4 Results during the TCP FIN Attack  
During this attack, Asterisk was flooded with TCP FIN packets [60]. Figure 14 represents the number of different TCP packets. As can be seen from the graphs, again, there were no TCP SYN and the TCPSYNACK packets because Asterisk was not being accessed at that moment. There is a huge number of TCP FIN packets because the Asterisk web server generated them as the other side was also sending TCP FIN packets. The Asterisk web server generated these TCP FIN packets because it assumed that the TCP FIN packets received from the other side were generated to terminate the TCP session. Asterisk was successfully accessed through a browser.  
Figure 14: Number of different TCP packets sent during the TCP FIN attack.  
Figure 13: Instantaneous values of the jitter for the voice stream between VM_4 and Asterisk (**a**) and VM_3 and Asterisk (**b**) during the TCP RST attack.  
Figure 15 presents the summarized results for the voice traffic that was exchanged in both directions between VM_4 and VM_3 during the attack. Figure 15a shows the summarized results for the voice stream that was exchanged between VM_4 and Asterisk, and Figure 15b shows the summarized results for the voice stream that was exchanged between VM_3 and Asterisk. As can be seen, the jitter values were close to the results from when Asterisk was not being attacked. There was no packet loss.  
Figure 16a,b shows the variation in jitter for the voice streams between VM_4/VM_3 and Asterisk in both directions.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 2 Simulation Environment
### User Navigation Equipment
#### 2.2.1 Radiofrequency-Based Measurements
The standard deviations associated with \(\varepsilon_{\rho}\) and \(\varepsilon_{\dot{\rho}}\) are \(\sigma_{\rho}=$10\,\mathrm{m}$\) and \(\sigma_{\rho}=$0.1\,\mathrm{m}$\,\mathrm{s}^{-1}$\), values assumed in accordance with the ongoing studies [11; 13]. The receiver clock bias and drift, which represent \(c\delta t_{u}\) and \(c\delta t_{u}\), respectively, are modelled using the two-state clock model presented in [26], in which the frequency deviation (i.e., the clock drift) originates from two types of noise, a White Frequency Modulation (WFM) and a Random Walk Frequency Modulation (RWFM). The resulting clock bias will then be represented by a Wiener noise plus an integrated Wiener noise. The dynamical system that simulates the evolution of these quantities is summarised in Equations (12) and (13), in which \(\varepsilon_{b}\) and \(\varepsilon_{d}\) are Gaussian white noises, whose standard deviation can be properly tuned to match the Allan variance of the desired type of receiver clock. \[\dot{b}_{c} =d_{c}+\varepsilon_{b_{c}} \tag{12}\] \[\tilde{p}_{i}=c\left(t_{u}+\delta t_{u}-t_{s,i}-\delta t_{s,i}\right)=\rho_{i}+c \delta t_{u}+\varepsilon_{\delta t_{u,i}} \tag{6}\]0  
The servicers' ephemerides are used inside the navigation filter to predict the pseudorange and pseudorange-rate quantities. In this work, the residual errors of the LNS predictions coming from the Orbit Determination and Time Synchronisation (ODTS) process performed on the ground, also known as Signal In Space Error (SISE), are modelled as simple three-dimensional position and velocity additive Gaussian white noises, with standard deviations of \(\sigma_{r}\) and \(\sigma_{v}\), respectively, here assumed as \(15\,\mathrm{m}\) and \(0.15\,\mathrm{m}\,\mathrm{s}^{-1}\), re spectively, in line with the Galileo Service Definition Document [27] and the expected Moonlight performance [11]. A good reference to further investigate the details of GNSS systems can be found in [28].
## 3 Results
### Implant Description and Distribution
#### 3.1.1 One-Stage Surgery  
Fifty-two individuals, comprised of 23 (44%) men and 29 (56%) women, underwent one-stage surgery that involved a total of 140 implants. Their age range was 22-74 years (mean 54 \(\pm\) 14.8), and 8 (15%) were current smokers. Of the 140 implants, 110 (78%) were in the lower jaw and 30 (22%) in the upper jaw. The implant diameter ranged from 3.5 mm to 6 mm (mean 4.3 \(\pm\) 0.5 mm), and the implant length ranged from 10 mm to 16 mm (mean 12 \(\pm\) 1.4 mm). The follow-up ranged from 1-132 months (mean 27 \(\pm\) 24 months). The gingival index at the last follow-up was 0 for 100 (71%) implants and 1 for 39 (28%) implants (one implant failed). Crestal bone loss ranged from 0 mm to 8 mm: the mean mesial crestal bone loss was 1.09 \(\pm\) 0.93 mm, and the mean distal bone loss was 1.07 \(\pm\) 1 mm (\(p>\) 0.05). The follow-up period was divided into implants with short (i.e., \(\leq\)36 months) and long (>36 months) follow-ups. The mesial crestal bone loss was 1.1 \(\pm\) 0.7 mm for the short follow-up period and increased slightly to 1.2 \(\pm\) 1.4 mm, for the long follow-up period (\(p>\) 0.05). The distal crestal bone loss was 1 \(\pm\) 0.8 mm for the short follow-up period and increased slightly to 1.3 \(\pm\) 1.5 mm for the long follow-up period (\(p>\) 0.05).
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### RSU Registration  
CA generates an identity \(ID_{u}\) and a sequence number \(n_{u}\) for a RSU and sends them to RSU securely. Additionally, the RSU generates an initial CRP \((C_{u},R_{u})\) by PUF chips employed on RSU, where \(R_{u}=PUF(C_{u})\). It is processed by the \(Gen\) function of FE, \((P_{Fu},R_{Fu})=Gen(R_{u})\), Then, the RSU calculates its symmetric encryption key \(K_{u}=H(R_{Fu})\) as the authentication key, and CRP \((C_{u},R_{Fu})\) and \(K_{u}\) both need to be updated periodically. Next, the RSU sends the \(K_{u}\) to CA via a secure way. Last, \(\{ID_{u},K_{u}\}\) is stored by CA, and \(\{ID_{u},C_{u},P_{Fu}\}\) is held by the RSU.
## 4 Results and Discussion
### Discussion  
#### 4.5.1 Key Innovations and Contributions  
Our experimental results validate the effectiveness of AttenFlow's core innovations. RCCF addresses fundamental limitations in retrieval fusion by introducing consensus-aware weighting and confidence estimation, leading to more reliable and accurate document retrieval. AM-HDGAT revolutionizes document classification by modeling inter-document relationships while combining complementary feature types through mutual-attention, resulting in superior classification performance and adversarial robustness.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 5 Impact of Water Vapor Variability on Meteorology and Climate
### Circulation  
As pointed out in Section 3, water vapor is greatly affected by circulation patterns, which can transport moisture or dry air to different regions. For this reason, GNSS can provide information about these circulation patterns in certain regions. [PERSON] et al. (2018) used GNSS IWV data to show that important differences in IWV can occur between the Swiss plains and nearby Alps during some thunderstorms events, attributable to regional airflow convergence. They presented a method to acquire information about the amount and change of total IWV over time for the mesoscale in the Bernese Oberland area using the GNSS IWV data. An anti-correlation pattern was found between Payeme and Saanen, which suggested a mountain-plain circulation pattern. Moreover, [PERSON] et al. (2018) and [PERSON] et al. (2018) used GNSS IWV products among other remote-sensing information about the state of the atmosphere to study the northern California Valley, which has sierra barrier jets and land-falling atmospheric rivers (ARs), modulators of the rainfall in this region. The study links these two phenomena with the orographic configuration of the Valley, and the concurrence of both phenomena at the same time, or just one of them, affects the spatio-temporal rainfall pattern.  
GNSS has been used to study water vapor during El Nino events in the coast of the South China Sea (Kasahara, 2018). During these events, IWV drops, reaching a minimum about 10 weeks later, while the opposite happens during La Nina events. Therefore, GNSS IWV is suggested to be a useful instrument for studying ENSO activity.
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 2 Materials and Methods
### Kullback-Leibler Divergence Based Blind Interleaver Parameters Estimation  
When the constructed \(\Lambda\) is synchronized to real interleaver period, \(\rho_{C}\) follows a different distribution from that given in Table 1 owing to the linear dependence among codewords. As a measure of probability matching, KLD was used to find the most different distribution from the value of random matrices for blind interleaver parameters estimation [5]. The KLD value is given by  
\[\sum|P(i)\log\frac{P(i)}{Q(i)}| \tag{2}\]  
where \(P(i)\) is the probability distribution calculated from the captured matrices and \(Q(i)\) is that of random square matrices.  
Equation (2) converges to 0 when the predicted interleaver period does not equal the actual one. If the greatest value of KLD is higher than a preset threshold, \(l\) is declared to be the interleaver period.
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
## 2 PEMFC System Model and Verification
### PEMFC Model
#### 2.1.2 Fuel Supply System  
In the fuel supply system, the amount of fuel ejected is determined by a control algorithm. Before the fuel goes into the anode for the chemical reaction, the mass flow rate of the fuel is also regulated by the fuel flow valve. The fuel flow valve is a normally closed electrical proportional valve that controls the mass flow rate of fuel. Not enough fuel flows to the anode when the coil is burned. Therefore, the valve performance is defined as noted below, which is similar to the valve of an air supply system.  
\[\phi_{H_{2},flow} =\left(1-f_{value,H_{2}}\right) \tag{11}\] \[\times\left[1+\left((1-d_{2})\times\left(u_{value,H_{2}}-1\right) +d_{1}\times\left(u_{value,H_{2}}-1\right)^{2}\right)\right]\] \[W_{H_{2}} =W_{an,IN}\times\phi_{H_{2},flow} \tag{12}\]  
Modeling of the hydrogen inlet pressure is determined and modeled based on a study by [PERSON] et al., which focuses on hydrogen inlet pressure parameter analysis [19].
# Perspective on Double Pulsed Gas Metal Arc Welding
## 5 Conclusions  
The process characteristics of DP-GMAW and their effect on solidification structure are systematically discussed. Advantages of DP-GMAW and their origin are revealed. At last, previous evolution process and future trends of DP-GMAW are summarized. Below are the specific findings of DP-GMAW:  
1. A periodical welding current generates an oscillation of arc force and droplet pressure, which produces a weld pool stir effect.
2. Periodical heat input generates expansion and shrinkage of the weld pool, which causes an unusual remelting and resolidification of the previously solidified metal. DP-GMAW has an increased solidification growth rate and cooling rate compared with conventional pulsed welding at the same heat input.
3. Microstructural refinement of fusion metal by DP-GMAW is reported by several researchers. The mechanism of microstructural refinement is revealed as combined effect of the weld pool stir effect and an increased cooling rate. The numerical and experimental results indicate that a refined microstructure can be achieved by adjusting the pulsing parameters instead of changing the heat input. DP-GMAW features diverse grain orientation due to the expansion and shrinkage of the weld pool.
4. The weld pool stir effect improves the fluidity of liquid metal in the molten pool. Therefore, a larger number of hydrogen bubbles have a chance to float out and release from the molten pool, and fewer hydrogen bubbles are trapped in the solidification wall and form pores.
5. Improved solidification cracking susceptibility is achieved due to refined solidification structures by DP-GMAW.  
The authors are grateful for the financial support for this research from the High-level Leading Talent Introduction Program of GDAS (2016 GDASRC-0106) and Industry-university-research of Guangdong Province and Ministry of Education (2013B090600098).  
[PERSON] and [PERSON] conceived the idea. [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 4 Discussion
### Potential Impacts of Observable Biases on Geophysical Retrievals  
Large-magnitude deviations are seen in both L1 observables at average ocean wind speed (U10 \(\sim\)7 m/s), primarily related to changes in viewing geometry, as well as to the GPS transmitter (Figures 4-6). However, when looking at global climatologies, the effect of these residual dependencies is likely to be partly obscured due to averaging across the large variability of ocean surface conditions, the number of GPS transmitters, and the number of CyGNSS receivers. However, significant biases are to be expected on shorter spatiotemporal scales, also depending on the transmitter-receiver pair considered. Such L1 deviations are likely to have a significant impact on ocean wind speed retrieval. In particular, given the known inverse relationship between NBRCS and U10 [24], the 3 dB range in NBRCS across geometries is expected to introduce a bias of several m/s already at low-to-moderate wind speeds, with an increasingly stronger impact at higher speeds [21]. The impact on LES is expected to be similar. As such, it is important that steps are taken towards the mitigation of these residual calibration inaccuracies ahead of the release of future versions of the CyGNSS products.
# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 2 Datasets and Methodology
### Cross-Calibrated Multi-Platform (CCMP) Wind Analysis  
The Cross-Calibrated Multi-Platform (CCMP) ocean vector wind product uses a variational method to combine satellite retrievals of ocean winds with a background wind field from a numerical weather prediction model. The result is a spatially complete estimate of ocean vector winds at six-hour intervals that is closely tied to satellite measurements and has been available from 1988 until the present. CCMP was originally developed by [PERSON] et al. [46] at NASA's GSFC. Processing has been transferred to RSS [47], where the product is undergoing further validation and development [48]. In the tropical oceans, conventional satellite winds are often unavailable due the presence of moderate to heavy rain, leaving the CCMP winds more independent from satellite measurements and closer to the background fields.