esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Catalytic Performance of CuFeNPs@TWM for the Elimination of CECs
This decrease could be explained by the inefficient interaction of the catalyst with the contaminant, produced by different phenomena such as active site saturation, agglomeration, or competition between molecules for the same adsorption sites [32; 85]. Alike, during the EF process, the oxidation rate was slower, achieving a complete elimination of ASA and NAP at 20 min of the treatment, but 8.7% of IBU remained after 30 min of the process. This effect could be attributed to the non-desired reactions, such as the interaction of Fe\({}^{2+}\) with \({}^{\bullet}\)OH to generate Fe\({}^{3+}\) and OH\({}^{-}\). This process reduces Fe\({}^{2+}\) and \({}^{\bullet}\)OH available, thereby decreasing the oxidation efficiency of the CECs [86; 87]. The pH of the solution is an important parameter in the EF process. To investigate its influence, the degradation efficiency at varying pH levels was studied using CuFeNPs@TWM (Figure S7). Results show that the contaminant removal by adsorption and degradation is strongly dependent on the initial solution pH. At pH 5 (Figure S7a), adsorption removal was below 8% for all contaminants, while degradation efficiencies after 30 min of the EF process were 14.0% for ASA, 33.6% for NAP, and 18.0% for IBU. At a pH of 7 (Figure S7b), no removal by adsorption was observed, and degradation efficiencies dropped to 8.8% for ASA, 14.6% for NAP, and 4.3% for IBU. As can be seen, the degradation efficiency at pH 3 >>> pH 5 > pH 7. This trend could be explained by the pH\({}_{\text{pzc}}\) of the catalyst (pH\({}_{\text{pzc}}\) 2.3). When the solution pH is 5 and 7 (i.e., pH >> pH\({}_{\text{pzc}}\)), there was a negative charge due to the deprotonation. This negatively charged surface reduces interactions with the contaminants, which are also negatively charged at their respective pH higher than their pKa values (ASA: 3.0, NAP: 4.2, and IBU: 4.9) [88; 89; 90]. Furthermore, at higher pH values, the generation of \({}^{\bullet}\)OH is reduced, contributing to the reduced degradation efficiency. It is noteworthy that the catalytic activity of the prepared material significantly surpasses that of previously reported biochar-based catalysts used in Fenton-based processes for the removal of CECs. A detailed comparison is provided in Table 4. The prepared biochar composite demonstrates significantly higher catalytic efficiency compared to similar biochar composites synthesized using analogous procedures but with different biomass and nanoparticles. This superior performance in terms of the applied current and required treatment time highlights the potential of our biochar composite to set a new benchmark for developing highly efficient Fenton-based catalysts for CECs removal.
###### Abstract  
Technological advancements have led to increased confidence in the design of large-scale wireless networks that comprise small energy constraint devices. Despite the boost in technological advancements, energy dissipation and fault tolerance are amongst the key deciding factors while designing and deploying wireless sensor networks. This paper proposes a Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm (FheCA) for wireless sensor networks (WSNs), which demonstrates energy-efficient clustering and fault-tolerant operation of cluster heads (CHs). It treats CHs as no special node but equally prone to faults as normal sensing nodes of the cluster. The proposed scheme addresses some of the limitations of prominent hierarchical clustering algorithms, such as the randomized election of the cluster heads after each round, which results in significant energy dissipation; non-consideration of the residual energy of the sensing nodes while selecting cluster heads, etc. It utilizes the capability of vector quantization to partition the deployed sensors into an optimal number of clusters and ensures that almost the entire area to be monitored is alive for most of the network's lifetime. This supports better decision-making compared to decisions made on the basis of limited area sensing data after a few rounds of communication. The scheme is implemented for both friendly as well as hostile deployments. The simulation results are encouraging and validate the proposed algorithm.  
WSN; energy efficiency; hierarchical clustering; fault tolerance 2021  
24 June 2021  
Published: 2 July 2021
## 3 Results and Discussion
### The Influence of a Template Agent on the Pore Structure of POPs  
We have reported in our previous research [12] that the template agent is a useful tool to tune the pore structure, bulk density, and morphology of the prepared POPs. In the prepared PDVB-based POPs with dual functional comonomers HEMA and styrene, iron oxide (II, III) aggregates of 20 nm were used as a template to tune their pore structure and morphology. As seen in Table 1, the iron oxide (II, III) template could help to improve the specific surface area and the total volume in the HEMA/St/DVB system (Samples 8-2 and 9), increasing the SSA from 183 to 215 m\({}^{2}\)/g and the PV from 0.296 to 0.301 cm\({}^{3}\)/g. Furthermore, the bulk density of the prepared poly(HEMA-co-St-co-DVB) particles increased obviously from 0.22 to 0.29 g/cm\({}^{3}\). As we explained, the particle-forming mechanism using a template agent in our previous work [12], when a hydrophilic metal oxide was used as a template agent, the hydrophilic functional comonomers would adsorb on the interfaces of nanoaggregates of the iron oxide (II, III). After a free-radical initiator initialized the copolymerization on their interfaces, the poly(HEMA-co-St-co-DVB) particles were prepared around the nanoaggregates of iron oxide (II, III). As the HEMA, styrene, and DVB monomers continued to diffuse and polymerize on the surface of the metal oxide, the POP particles continuingly grew larger and finally the metal oxide or nanoaggregates were dispersed in the matrix of the prepared POPs. After acid etching, the metal oxide was removed and the final POPs were obtained. From Figure 6, we can observe an overall increase of SSA and PV of Sample 8-2 when the nanoaggregates of iron oxide (II, III) in Sample 8-1 was removed, while the pore-size-distribution curves of the three samples (8-1/8-2/9) kept similar. These results could be reasonably explained by this proposed particle-forming mechanism of the template agent. Similar results are also observed when the nanoaggregates of iron oxide (II, III) were used as a template agent in the synthesis of poly(HPMA-co-DVB) particles (see Samples 7-1/7-2/4).  
Figure 5: NLDFT simulation of pore structure of POPs (Samples 12/13/14) from dual functional comonomers. (**a**) Pore-size-distribution curves of dS(d) vs. d and (**b**) dV(d) vs. d.
# Distributed Bragg Reflectors for GaN-Based Vertical-Cavity Surface-Emitting Lasers
## 4 Non-Epitaxial DBRs for GaN VCSELs
### Non-Epitaxial DBRs through Substrate Thinning and Curved Dielectric Mirrors  
Given the difficulty presented in 4.1 and 4.2 in forming all dielectric mirrors, Sony recently demonstrated yet another version of VCSEL with dielectric mirrors [93] using the concept of \"external cavity\" where a curved dielectric mirror is coated on the substrate side after substrate thinning [94; 95; 96]. This implementation, sometimes called thin-disk laser, is possibly the least complicated in terms of both epitaxial growth and device fabrication compared to all other approaches (Figure 10). The use of a very long vertical cavity (>100 \(\lambda\)) is useful for high optical output power [97]. The use of external curved mirrors with controlled curvature offers tunability in engineering the in-plane optical confinement which is typically lacking in III-nitride VCSELs.
## 3 Results  
Dataiku automatically ranks the best-performing interpretable model based on the set performance metric (AUC in this case). The characteristics of the sample are presented in Table 1. It presents an overview of a cohort and only includes gender as well as the top 10 predictors of PSYPROB that were selected as the most essential based on the Shapley values, to keep it concise. The cohort predominantly resides in states such as New York, Colorado, and Illinois. Notably, around 30% had no prior treatment episodes, reflecting a significant proportion seeking treatment for the first time, while diverse referral sources: individuals, legal systems, and community referrals highlight the multifaceted pathways to treatment. In terms of race, the majority of the individuals classified themselves as Black or African American (73.9%), and a range of substance use patterns emerged, encompassing various substances across primary, secondary, and tertiary categories. The significant unemployment rate of 52.3% among the cohort highlights the possible socioeconomic factors at play. The substances encompass alcohol, cocaine/crack, marijuana/hashish, prescription opiates/synthetics, methamphetamine/speed, and various other substances. The diagnostic data indicated a significant occurrence of opioid dependence, with a prevalence rate of 33.8%. Additionally, around 26.3% of individuals received medication-assisted opioid therapy. \begin{table}
\begin{tabular}{l l c} \hline \hline \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Value**} & **Number (\%)** \\ \hline GENDER & Female & 132,128 (100) \\ \hline \multirow{8}{*}{State (STFIPS)} & New York & 35,662 (27) \\  & Colorado & 11,247 (8.5) \\ \cline{1-1}  & Illinois & 9351 (7.1) \\ \cline{1-1}  & Michigan & 9161 (6.9) \\ \cline{1-1}  & North Carolina & 8410 (6.4) \\ \cline{1-1}  & New Jersey & 7520 (5.7) \\ \cline{1-1}  & Indiana & 6806 (5.2) \\ \cline{1-1}  & Connecticut & 5502 (4.2) \\ \cline{1-1}  & Kentucky & 4954 (3.7) \\ \cline{1-1}  & Tennessee & 4052 (3.1) \\ \cline{1-1}  & Missouri & 3593 (2.7) \\ \cline{1-1}  & Pennsylvania & 3236 (2.4) \\ \cline{1-1}  & Ohio & 2771 (2.1) \\ \cline{1-1}  & Other & 19,863 (15) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline characteristics of the cohort (n = 132,128). Table 2 shows the performance matrices for each algorithm applied for binary classification in the test set (N = 26,368). The primary evaluation criterion in this study was the AUC. All AUC values were between 0.631 and 0.817. This range signifies the probability that any of the algorithms would correctly rank a randomly selected woman with co-occurring mental and substance use disorders higher than one without such disorders. As hypothesised, the super learning model showed the largest AUC of 0.817, demonstrating robust predictive capability. The performance of the super learning model is closely
## 3 Results and Discussion
### Freeze-thaw Damage Prediction Model Based on Grey Model by GM (1,N)
#### 3.2.1 Two-Variable Grey Model GM (1,2)  
Based on the damage degrees of _VA_, _STS_, and _ITSM_, three two-variable grey models could be established, i.e., GM (1,2) of _VA_ and _STS_ (as show in Figure 6), _VA_ and _ITSM_ (as show in Figure 7), _STS_ and _ITSM_ (as show in Figure 8). [PERSON] found that the more samples do not necessarily mean the more accurate prediction of a grey model [50; 51]. Accordingly, these GM (1,2) were established in terms of the experimental data under 0\(\sim\)9 freeze-thaw cycles, and the experimental data under 12 and 15 freeze-thaw cycles were used to verify the established GM (1,2).  
Figure 6: Comparison between predictive and experimental values for asphalt mixtures without basalt fiber (AM) and asphalt mixtures with basalt fiber (BFAM) based on GM (1,2) of _VA_ and _STS_ tests: (**a**) Damage degree of _VA_ for AM; (**b**) Damage degree of _VA_ for BFAM; (**c**) Damage degree of _STS_ for AM; (**d**) Damage degree of _STS_ for BFAM.  
Figure 8: _Cont_.  
Figure 7: Comparison between predictive and experimental values for AM and BFAM based on GM (1,2) of _VA_ and _ITSM_ tests: (**a**) Damage degree of _VA_ for AM; (**b**) Damage degree of _VA_ for BFAM; (**c**) Damage degree of _ITSM_ for AM; (**d**) Damage degree of _ITSM_ for BFAM.  
The predictive values of _VA_ and _STS_ compared with the experimental results are presented in Figure 6, based on the grey model GM (1,2) of _VA_ and _STS_ tests. It is apparent that the relative errors for _VA_ of AM and BFAM are within 6%, and the relative errors of _STS_ of AM and BFAM are around 10%.  
The predictive values of _VA_ and _ITSM_ compared with the experimental results are presented in Figure 7, based on the grey model GM (1,2) of _VA_ and _ITSM_ tests. It is apparent that the relative errors for _VA_ of AM and BFAM are around 6%, and the relative errors of _ITSM_ of AM and BFAM are within 17%.  
The predictive values of _STS_ and _ITSM_ compared with the experimental results are presented in Figure 8, based on the grey model GM (1,2) of _STS_ and _ITSM_ tests. It is apparent that the relative errors for _STS_ of AM and BFAM are within 9%, and the relative errors of _ITSM_ of AM and BFAM are around 13%.
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 1 Introduction
Along with a combination of a time-averaging process and a _M of N_ detection strategy, three geometry-based algorithms, namely measurement subset testing, consecutive exclusion, and iterative change of measurement weights, are evaluated. In Section 5, a combination of monitoring techniques is investigated based on two cascading methods and for more reliable multipath mitigation. Finally, the methods are tested in Section 6 using three sets of GPS L1, L2C and L5 measurements in static and kinematic multipath scenarios. In addition to analytical evaluation and practical implementation of the different monitoring techniques, the main contribution of this research includes: (a) Adaption of the GF metric to the case of multipath detection given its advantages over the conventional C/N\({}_{0}\)-based detection metrics, (b) investigation of new geometry-based exclusion and de-weighting approaches to address multiple simultaneous multipath scenarios and (c) cascading the different monitoring techniques to improve positioning performance.
## 3 Results and Discussion
### Experiments in Flight Conditions
#### 3.2.1 Uncertainty of Altitude Measurements. Change of Sensory Information Sources  
The aim of the experiment was to verify how imprecise and non-stationary the altitude measurements of the UAV flight are in the building, based on its basic on-board avionics only. The motion capture system was used as a ground truth. The results are shown in Figure 10. The task for the UAV was to fly to a fixed altitude of 1 m and hover in the air.  
As the average error from registered trials is only 0.80%, the range of actual/instantaneous values ranges from 0.85 m to 1.14 m and increases with the passage of time. Such a dispersion of measurements is a problem and major difficulty in the proposed machine learning procedure used in real-world conditions for altitude controller tuning. Therefore, the motion capture system was used for further estimation of the UAV flight altitude. This eliminates the measurement error as a source of additional errors during the \(J\) calculation.
## 4 Core Insights and Critical Reflections
### Evolution and Breakthroughs of Key Technologies
#### 4.1.3 Advancements in Flight Control and Navigation Systems  
Early UAV flight control mainly relied on manual remote control or simple autopilots. With the development of Global Navigation Satellite System (GNSS, such as GPS, GLONASS, Beidou) and Inertial Measurement Unit (IM)U technology, the positioning and attitude control accuracy of UAVs has been greatly improved. RTK (Real-Time Kinematic) technology can provide centimeter-level and even millimeter-level real-time dynamic positioning, which significantly improves the geometric accuracy of UAV-RS data. Flight control systems are also becoming increasingly intelligent, with functions such as autonomous path planning, obstacle avoidance, and adaptive flight. For example, the construction of a general physical model for UAV-RS systems has transformed the multi-rigid-body stitching of imaging payloads into a single rigid-body imaging method. This has achieved simple automatic control of the payload and, based on this, an integrated UAV-RS system has been built to realize automated dynamic remote sensing control and observation [157]. The progress of path planning algorithms, especially path planning methods based on machine learning and deep learning, such as Deep Q-Network (DQN), Convolutional Neural Network (CNN) path planning, and Generative Adversarial Network (GAN) path planning, enables UAVs to better adapt to dynamic environments, optimize flight paths, and improve the efficiency and safety of mission execution.  
In the future, flight control and navigation systems will place greater emphasis on autonomy, robustness, and collaboration, such as multi-UAV collaborative operations and autonomous obstacle avoidance path and planning in complex environments. For example, an AI-assisted Terrain Aided Navigation (TAN) system can combine high-performance terrain servers and digital elevation models to achieve low-altitude navigation in complex terrain [158]. In addition, UAV swarm technology has also made significant progress. Through advanced Guidance, Navigation, and Control (GNC) technology and swarm intelligence software, multiple UAVs can autonomously collaborate under manned aircraft command to perform complex tasks such as real-time surveillance and threat detection, demonstrating great potential in Manned-Unmanned Teaming (MUM-T) [159].
## 3 Aeroacoustic Model
### Engine Acoustic Source Model
#### 3.1.4 Computation of Boundary Conditions  
The coupling of the in-duct acoustic propagation model with the external one is achieved through a dedicated boundary conditions written in terms of acoustic pressure \(p(r,\theta)\) and its normal derivative \(\partial p(r,\theta)/\partial n\). Once the modal solution is known at the engines inlet and outlet plane, the sound pressure field \(p(r,\theta)\) and its normal derivative \(\partial p(r,\theta)/\partial n\) in frequency domain can be easily computed by Equation (6):  
\[p^{\omega}(x,r,\theta)=\sum\limits_{m=-\infty}^{\infty}\sum\limits_{n=0}^{ \infty}A^{\pm}_{mn}\frac{J_{m}(k_{r}r)+Q_{mn}Y_{m}(k_{r}r)}{\sqrt{F_{mn}}}\exp (ik_{x}^{\pm}x+im\theta) \tag{6}\]  
As the normal vector points either in the x-direction (engine outlet plane) or against the x-direction (engine inlet plane), the normal derivative of the sound pressure field is given by Equation (7):  
\[\begin{array}{ll}\frac{\partial p^{\omega}(x,r,\theta)}{\partial n}&=\pm \frac{\partial p^{\omega}(x,r,\theta)}{\partial x}\\ &=\pm ik_{x}^{\pm}\sum\limits_{m=-\infty}^{\infty}\sum\limits_{n=0}^{\infty}A^ {\pm}_{mn}\frac{I_{m}(k_{r}r)+Q_{mn}Y_{m}(k_{r}r)}{\sqrt{F_{mn}}}\exp(ik_{x}^ {\pm}x+im\theta)\end{array} \tag{7}\]  
It should be noted that Equations (6) and (7) have to be evaluated for \(x=0\), as the modal amplitudes \(A^{\pm}_{mn}\) already include the effect of propagation from a location 1 (source) to a location 2 (inlet/outlet plane) according to Equation (5).
# Hazard Assessment for a Glacier Lake Outburst Flood in the Mo Chu River Basin, Bhutan
## 4 Results
### Comparison with SRTM  
From the dam to Punakha, the worst case inundation area on the SRTM DEM is 16.86 km\({}^{2}\); this is 26% larger than on the 5-m DEM. It is obvious that the coarser DEM delivers a poorer representation of the riverbed topography (see Figure 2), especially steep banks and terraces that constrain lateral water flow and the widening of the river. On the  
Figure 6: Inundation extent (red: worst case, orange: average GLOF, yellow: best case) around Gasa Hot Springs. Background map: World Imagery (Source: Esri, Maxar, Earthstar Geographics, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community).  
Figure 7: Maximum flood depth for the worst case scenario (see text for explanations) in Punakha, based on EM3D elevation data. Letters a-c (marked by yellow circles) indicate the location of affected buildings. Background map: World Imagery (Source: Esri, Maxar, Earthstar Geographics, USDA FSA, USGS, Aerogrid, IGN, IGP, and the GIS User Community).  
smoother SRTM DEM, the water can run more easily onto floodplains and is less restricted to its minor bed. This effect and thus the difference between the two DEMs is largest in river sections where a mild slope follows a steeper one. In the very first river section, the mean slope is 4.7%. After 23 km, where the influent enters the main stream, the mean slope is suddenly reduced to 2.5% along the following 7 km. In this section, the inundation area on the SRTM DEM is three times larger than on EM3D.  
Inundation depths for the Punakha region are displayed in Figure 8.  
Although the velocity tends to be higher on a smoother terrain model, the flood is slowed down at the same time, because larger areas are flooded along the route. In other words, the water flows more rapidly, but also more onto floodplains, which again acts decelerating. In our case study, the latter effects overcompensates the first and, as a result, the flood arrives 15 min later in Punakha when the coarser DEM is used.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background
### Computational Infrastructure for Resilience  
Ensuring the operation of services, networks, and computing infrastructure in the face of events threatening resilience is challenging. Modern solutions such as those based on microservices architecture [27], which create a simplified structure for applications by separating them into essential and mostly reusable functions, can help to improve application performance, scalability, and fault tolerance. Technologies such as software containers support the packaging, deployment, and orchestration of services, and comply with resiliency requirements such as high availability, scalability on demand, load balancing, and traffic limitations. In addition, the computing and network infrastructure must be allocated efficiently and dynamically and adapt to changing conditions; this is supportedby network virtualization technologies, distributed storage systems, and resource allocation solutions in heterogeneous environments.