esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Sectoral Applications and Monitoring of Critical Failures
#### 4.2.2 Advancements in Vibration Analysis for Predictive Maintenance  
Vibration analysis remains one of the most widely adopted techniques for predictive maintenance, providing a robust foundation for early fault detection in critical components. With the integration of artificial intelligence (AI) and machine learning, this methodology has evolved into more precise and efficient diagnostic frameworks, improving fault identification across various industrial applications [55; 56].  
Deep learning methods have enhanced fault detection accuracy in motors, bearings, and gears [51; 56], significantly reducing false positives and improving reliability [56]. In mining, vibration analysis has been successfully applied for real-time monitoring of conveyor belts and crushers, minimizing unplanned downtime and extending equipment lifespan [54; 72]. Similarly, in hydroelectric plants, these algorithms have optimized turbine and transmission operations, contributing to overall energy efficiency improvements [55; 60].  
Recent studies highlight how novel wavelet-based feature extraction techniques and multi-kernel convolutional neural network (CNN) architectures effectively process variable-speed machinery data, overcoming the limitations of traditional stationary-state assumptions [14; 30]. Comparative assessments indicate that Extreme Gradient Boosting (XGBoost), when combined with robust preprocessing techniques, achieves high accuracy in vibration fault diagnosis [5; 23]. Furthermore, multi-sensor fusion strategies, integrating acoustic emissions, infrared thermography, and vibration signals, have resulted in more comprehensive monitoring solutions, particularly in safety-critical environments such as nuclear reactors [67; 77].  
Advancements in multiscale signal decomposition have enabled more effective feature extraction for advanced classification models [21; 22; 23], while Bayesian updating frameworks [24; 25; 26] have facilitated adaptation to time-evolving baseline conditions in large rotating machinery. Additionally, wavelet-based analysis combined with XGBoost has significantly reduced detection latency in heavy-haul trains, enhancing predictive accuracy [66; 78].  
Improvements in sensor technology and the integration of Internet of Things (IoT) architectures have further enhanced real-time data collection and analysis, enabling more precise fault detection and predictive capabilities [18; 56; 59; 68]. As a cost-effective and non-intrusive technique, vibration analysis continues to play a critical role in reducing reactive maintenance expenses, particularly in demanding sectors such as energy and manufacturing [23; 58].
## 4 Application of Fe-MOF in Advanced Oxidation Processes
### Electrochemical AOP
#### 4.3.1 Electro-Fenton
The high porosity of this material minimizes the limitations of mass transport and its ferromagnetic characteristic improves the catalyst recovery process at the end of the process. In comparison with MIL-88B and NH\({}_{2}\)-MIL(Fe)-88 at natural pH 5.5, nano-ZVI@C obtained from NH\({}_{2}\)-MIL(Fe)-88 with a total abatement after 1 h, this catalyst demonstrated its superiority. It was determined that the ferric/ferrous iron took place on the catalyst surface and the presence of amino group improves the degradation process due the decreasing the carbon bandgap energy. Following this line, recently [PERSON] et al. [204] obtained an efficient bimetallic catalyst Fe-Mo by calcination MIL-53(Fe)@MoO\({}_{3}\) (FeMo@PC). In this bimetallic material is highlighted the electron transfer between them and the possibility of continuous regeneration on the cathode increases the hydrogen peroxide decomposition. Thus, using this bimetallic catalyst, the effect of Mo co-catalysis was confirmed, in which \(\equiv\)Mo\({}^{4+}\) reduced \(\equiv\)Fe\({}^{3+}\) to \(\equiv\)Fe\({}^{2+}\) that could follow the Fenton reaction. It is outstanding the high degradation level achieved for several persistent pollutants (SMT, phenol, 2,4-dichlorophenoxyoxyacetic acid and CBZ) with a catalyst load of 25 mg/L and current intensity of 5 mA. Based on the excellent results of electro-Fenton and photo-based AOPs the development of the photo-electro-Fenton process is an upgraded electro-Fenton process.
## 4 Fast Fourier Transform (FFT) Drawbacks under Low Levels of Dynamic Eccentricity  
In order to guarantee the accuracy of the simulation results using the finite element method, the time step must be sufficiently small. Besides that, the simulation time needs to be ensured in order to obtain suitable frequency resolution [13]. A high sampling frequency and a long-time acquisition are mandatory in order to obtain good results using the FEA. However, this processing obviously increases the computational complexity for simulation study. To extract the fault information, the feature extraction method is based on the low characteristic frequency of airgap eccentricity.  
To highlight the FFT drawbacks under low levels of DE, the simulation tests are implemented on a three-phase squirrel induction motor with 11-kW output power, 4 poles, and 26 rotor bars. The stator windings are coupled with delta connection. Appendix A listed the parameter of the induction motor.  
Both the sampling frequency of single current and the corresponding voltage signal are 1000 Hz, while the number of sampling points \(N\) is 1000. The calculated FFT spectrums of the apparent power signature with five types of purely DE levels are shown in Figure 4a-e. The spectrums shown in Figure 4a-c confirm that the characteristic harmonic component with over 6% of DE appears clearly at 48.34 Hz. However, due to the weak signal under low levels of DE, the diagnostic method based on the FFT becomes more difficult. The simulation results show that the faulty signature at \(2f_{r}\) is interfered with another noisy signal due to the FFT limitations. It is obvious that the characteristic frequency component-based fault location and amplitude are difficult to extract under lower levels of DE. Hence, this process may lead to erroneous eccentricity fault diagnosis.  
Figure 4: FFT spectrum of apparent power with six different levels of dynamic eccentricity under full load (simulation): (**a**) 10% DE, (**b**) 8% DE, (**c**) 6% DE, (**d**) 4% DE, (**e**) 2% DE, and (**f**) healthy.
## 3 Results and Discussion
### Characterization of MGONCs
#### 3.1.2 TEM  
The TEM images of the Fe\({}_{3}\)O\({}_{4}\) nanoparticles are presented in Figure 4a. Approximately 200 Fe\({}_{3}\)O\({}_{4}\) nanoparticles were randomly selected, and their particle diameters were measured, resulting in the histogram displayed in Figure 4b. The analysis revealed that 78% of the nanoparticles exhibited a particle size distribution ranging from 6 to 8 nm. The average size of the prepared Fe\({}_{3}\)O\({}_{4}\) nanoparticles was calculated to be 7.32 \(\pm\) 1.12 nm.  
Figure 4c shows a captured TEM image of a single Fe\({}_{3}\)O\({}_{4}\) nanoparticle, clearly demonstrating the nearly round shape. Furthermore, a high-resolution image was obtained, which indicated a lattice spacing of approximately 0.26 A, corresponding to the (220) lattice. This observation confirmed that the Fe\({}_{3}\)O\({}_{4}\) nanoparticles possessed good crystallization properties.  
Figure 5a displays the TEM image of MGONCs, confirming the homogeneous distribution of Fe\({}_{3}\)O\({}_{4}\) nanoparticles supported on the surface of GO. The electron-selected area diffractions, depicted by the circular patterns in Figure 5b, further supported this observation. The positions of the electron diffraction peaks were categorized as (220), (311), (400), (422), (511), and (440), which was consistent with the results obtained from X-ray diffraction (XRD) [38]. These diffraction patterns indicated that the MGONCs obtained the face-centered cubic structure.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 4 Experimental Works and Results
### Experimental Setup
#### 4.1.2 Average Residual Energy  
Figure 5 shows the average residual energy for 30, 60, 90, or 120 nodes in millijoules. Again, MMTM-RPL is compared with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The number of nodes is shown on the \(x\)-axis and the average residual energy is shown on the \(y\)-axis for the three types of attack.  
Observe that our mechanism has a higher average residual energy for the different numbers of nodes. For a rank attack, the average residual energies were 1.52, 1.16, and 1.34 mJ for MMTM-RPL, DCTM-IoT, and RBAM-IoT, respectively. The main reason is that all calculations of trust parameters and trust values are handled by the fog layer rather than the nodes. Thus, the overhead for the nodes is minimized by transferring the work to the control layer. Nodes gather only messages and parameters and send them to the control layer. In contrast, for the other techniques, all computations were performed by the node layer. In addition, note that the average residual energy is a maximum for sinkhole and Sybil attacks.  
The OF also directly influences the overall energy consumption of the network. Although the other mechanisms spend less energy than MMTM-RPL initially, over time, MMTM-RPL consumes less energy overall. Note that the trust computation considers contextual information and quality of service and influences network survivability and node failure. For peer-to-peer connections and identifying rogue nodes, the suggested technique has a longer network lifetime and lower energy consumption.  
Figure 4: Comparison of network lifetime for different attacks: (a) Rank attack, (b) Sybil attack, and (c) Sinkhole attack.  
Figure 5: _Cont._
## 5 Protocol Comparison
\begin{table}
\begin{tabular}{l|l l l l l l l} \hline \multirow{3}{*}{**Protocol**} & \multicolumn{6}{c}{**Metrics of Comparison**} \\ \cline{2-9}  & **Lifetime** & & & **Location** & **Network** & **Proposed** & **Application** & \multirow{3}{*}{**Mobility**} \\  & **Improvement** & **Power Adjustment Technique** & & **Awareness** & **Structure** & **Routing** & **Type** & \\ \hline
**BOU-WA [17]** & Power Control & By configuration packet & Yes & Multi-Hop & Not & Event-driven & No \\  & & & & Sensor-to- & & Specified & & \\  & & & & & Sink Network & & & \\
**ATPC [18]** & Power Control & Feedback-Based (RSSI/LQI) & No & Flat & RPAR [55] & Time-driven & No \\
**ODTPC [19]** & Power Control & Feedback-Based (RSSI/Margin Field) & No & Flat & DD [29] & Time-driven & No \\
**EECA [16]** & Power Control \& Feedback-Based (RSSI/Margin Field) & Yes & Geographic & EECA [16] & Event-driven & No \\  & Multi-path & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of power control schemes. The bio-inspired mechanisms as discussed in Section 4.5 are compared in Table 4. These algorithms are mainly aimed to enhance the network lifetime by finding optimum path. These protocols are compared together according to the following criteria:  
* _Network structure_: This field is discussed before (in Table 1). The first protocol [20] in Table 4 is hierarchical while the last one [54] use location-based structure accompany with bio-inspired mechanism to prolong network lifetime. * _Lifetime improvement mechanism_: This field shows that which kind of mechanism for lifetime improvement is used in each protocol. As shown in Table 4, all protocols only use bio-inspired mechanism. * _Application Type_: This field is discussed before (in Table 1). Most of the algorithms [47, 49, 51] are used for event-driven applications and only [51] could be applied for Time and Event-driven applications simultaneously. * _Multi-path_: Multi-path methods can avoid network partitioning by distributing traffic loads on most of the sensor nodes through several paths. In this table only [49] use this method. * _Data aggregation_: This field is discussed before (in Table 1). The cluster heads (CHs) in hierarchical networks [20] usually employ data aggregation through the cluster members. * _Mobility_: None of the protocols in Table 4 proposed for mobile environments. * _Location awareness_: This field is discussed before (in Table 1). The protocols proposed in [20], [51] and [54] are location aware.
## 8 Discussion and Future Work
### Practical Implications  
Practically, the enhanced predictive accuracy afforded by our methodologies holds significant promise for the field of drug safety monitoring. The potential to streamline FDA data processes and improve the identification of adverse drug events could markedly benefit public health by enabling more timely and accurate safety interventions. However, the implementation of these advanced models in real-world pharmacovigilance systems necessitates careful consideration of operational challenges, including the integration with existing IT infrastructures and the ongoing maintenance and updating of the models in response to new data.
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 5 Proposed VDTN Solution
### Glowworm-Based SCF Node Selection  
Similarly to the FA-based approach, the probabilistic selection of GSO is used to optimize the selection of next-SCF node. The GSO seeks completing FA when the latter fails in detecting better SCF relay nodes.  
The mapping between the GSO elements and VDTN routing components is illustrated in Table 2:  
The predefined SCF fitness formula is applied to update the luciferin evolution from the bundle's host \(\mathbf{t}\) to all candidate SCF vehicles \(\mathbf{t+1}\). The luciferin update formula is upgraded to the SCF node selection in Equation (14):  
\[L(candidate)=(1-\rho)L(host)+(\gamma\times Swarm_{fitness}(host,candidate)) \tag{14}\]  
Considering:  
* \(L(candidate)\): the candidate node's luciferin value.
* \(L(host)\): the bundle carrier node's luciferin value.
* \( Swarm_{fitness}(host,candidate)\): the fitness function.
* \(\rho\): the luciferin decay constant.
* \(\gamma\): the luciferin enhancement constant.  
The luciferin update formula by replacing the constants \(\rho\) and \(\gamma\) is calculated in Equation (15):  
\[L(candidate)=(1-0.1)L(host)+(0.1\times Swarm_{fitness}(host,candidate)) \tag{15}\]  
According to the GSO process, the selection probability of passing a bundle from host to a candidate SCF vehicle (\(Prob_{host,candidate}\)) is applied to extract the optimum SCF vehicle according to the probability values of all candidate nodes as following in Equation (16):  
\[Prob_{host,candidate}(t)=\frac{L_{candidate}(t)-L_{host}(t)}{\sum_{k=1}^{ Cand\_List}(L_{k}(t)-L_{host}(t))} \tag{16}\]  
Considering:  
* \(Prob_{host,candidate}(t)\): the selection probability of a candidate vehicle from the bundle's at period \(\mathbf{t}\).
* \(L_{host}(t)\): the luciferin value of the bundle's host vehicle at period \(\mathbf{t}\).
* \(L_{candidate}(t)\): the luciferin value of a candidate vehicle relatively to the bundle's destination at period \(\mathbf{t}\).
* \(Cand\_List\): the luciferin value of a candidate vehicle at period \(\mathbf{t}\).  
The candidate with the highest value is more likely to be selected as next-SCF vehicle of the bundle.
# Metal Recovery from Wastes: A Review of Recent Advances in the Use of Bioelectrochemical Systems
## 3 BES Coupling with Other Technologies for Metal Recovery
### Coupling MFC with Photocatalytic Fuel Cells  
Despite the significant advancements in MFCs, considerable constraints in terms of power generation and bacterial start-up time still remain. Given the slow metal recovery rates at the cathode due to slow microbial kinetics at the anode, research trends are currently focused on the incorporation of photocatalysts into the BES configuration. The (bio)anode is either supplemented with a photocatalyst or complemented with an additional photoanode, a semiconductor material such as TiO2, WO3, or CdS, which generates electron-hole pairs upon exposure to light [102]. The positive holes facilitate the decomposition of organic waste or water oxidation at the anode (in parallel with the bioanodic reactions), while the photogenerated electrons are transferred to the cathode, as displayed in Figure 4b.  
The synergy of bio- and photo-generated electrons effectively overcomes the kinetic limitations of the cathodic process, significantly enhancing the system's overall performance [103; 104]. Additionally, the use of micro- and macro-algae for bioremediation of toxic heavy metals is being explored to further improve these systems [105]. Algae offer advantages such as low operational and maintenance costs, natural abundance, and high efficiency in metal removal via biosorption and bioaccumulation. The integration of these biological and photocatalytic components not only optimizes wastewater management but also generates substantial environmental benefits, promoting a more sustainable and cost-effective approach to resource recovery and pollution mitigation [103; 106; 107; 108].
## 6 Numerical Example  
We handle a real-world construction material selection problem in this part by using the q-ROFAAWA and q-ROFAAWA aggregation operations. An explanation of the problem is as follows.  
A sound product is possible only with sound materials; materials are the key to everything. The selection of the best building material is essential for the long life of the building. This article discusses the case study for selecting cement companies from the list of companies as cement is one of the essential constituents of construction material. In the global market, competition between cement companies is increasing day by day, and all companies are trying to produce high-quality cement. The selection of the best cement company is a challenging problem that can be accomplished with the help of the MADM procedure by keeping in mind the expert's opinion under uncertain situations.  
**Example 4**.: _Consider we are selecting the cement company from the list of five companies like \(\mathfrak{a}_{i}=(i=1,\ 2,\ \ldots,\ 5)\). We have the following attributes considered such as: \(G_{1}\) is the life of the cement, \(G_{2}\) is the fineness of the cement, \(G_{3}\) is the handling storage of cement, \(G_{4}\) is the effect of climate on cement. The attribute weight as \(\zeta=(0.34,\ 0.26,\ 0.24,\ 0.16)\) distributed by the DMs. The DMs will evaluate the five cement companies \(\mathfrak{a}_{i}=(i=1,\ 2,\ \ldots,\ 5)\) in ambiguity with \(q\)-ROF data under the following four attributes \(G_{i}=(i=1,\ 2,\ 3,\ 4)\) as presented in Table 2. It is noted that initially, we take parameters \(q=3\) and \(\mathcal{N}=1\) for \(q\)-ROFAAWA and \(q\)-ROFAAWG AOS. Furthermore, we also discuss the effect of the changeability in parameters \(q\) and \(\mathcal{N}\)._* First, we construct the decision matrix by collecting the data from the five cement companies and provide all the data collection in the form of a matrix to experts for DM. We have considered four attributes with weight as follows: \(G_{1}\) is the life of the cement 0.34, \(G_{2}\) is the fineness of the cement 0.26, \(G_{3}\) is the handling storage of cement 0.24, \(G_{4}\) is the effect of climate on cement 0.16. The collection of the data is represented in Table 2.
* In this step, we aggregate the information the DMs provide by using the q-ROFAAWA and q-ROFAAWG AOs. The aggregation findings are presented in Table 3. (Note that at the start, we take parameters \(q=3\) and \(\mathcal{N}=1\) during the aggregation.)
* To apply the score values formula discussed in Definition 3. to check the best option from five companies. The score values are shown in Table 4. For better understanding, the findings of SF are represented geometrically in Figure 1.  
For clarity and better understanding, we depict the score values in Figure 1.  
The results obtained by utilizing the q-OFAWA and q-OFAAWG operators show that \(\mathfrak{a}_{5}\) is the suitable alternative. Additionally, aggregated results from WA and WG operators do not always need to give the same rankings. However, it depends upon the DMs which WA and WG operators they select for the data aggregation.
## 3 Results
### Neutron/X-ray Diffraction (ND/XRD)
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Comp.**} & \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Unit Cell Volume/Å**} & \multicolumn{2}{c}{**Occupancy at Al**} & \multicolumn{2}{c}{**Phase Ratio**} & \multicolumn{2}{c}{**Reliability**} \\ \cline{4-9}  & & & **Al** & **Ti** & **NaAlO\({}_{4}\)** & **NaAlO\({}_{4}\)** & **NaCl** & **Al** & **R\({}_{\text{exp}}\)/\%** \\ \hline \multirow{2}{*}{NaAlO\({}_{4}\)} & before & neutron & 285.608 (4) & 1.0 & 0 & 0.9723 (10) & 0.0276 (3) & - & 0.0000 (19) & 2.36 \\  & \multirow{2}{*}{dope} & X-ray & 284.864 (9) & 1.0 & 0 & 0.9753 (12) & 0.0063 (4) & - & 0.0183 (14) & 2.51 \\ \hline \multirow{4}{*}{NaAlO\({}_{\text{T}}\)= 0.02 TiCl\({}_{3}\)} & before & neutron & 285.640 (3) & 0.9786 (5) & 0.0213 (5) & 0.9019 (5) & - & 0.0665 (19) & 0.0314 (12) & 4.24 \\  & 1 st des. & X-ray & 284.385 (5) & 0.97 (2) & 0.02 (2) & 0.862 (4) & - & 0.061 (2) & 0.076 (3) & 1.98 \\ \cline{1-1} \cline{2-9}  & after & neutron & 285.629 (16) & 0.9945 (5) & 0.0054 (5) & 0.8663 (4) & 0.0391 (16) & 0.07328 (18) & 0.0210 (8) & 5.64 \\ \cline{1-1}  & 1 st re-abs. & X-ray & 284.703 (6) & 0.99 (2) & 0.00 (2) & 0.777 (3) & 0.00179 (18) & 0.0581 (13) & 0.162 (3) & 2.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Rietveld refinement of NaAlO\({}_{4}\) and NaAlO\({}_{4}\)–0.02 TiCl\({}_{3}\) before the desorption and after the re-absorption of deuterium. Figure 1: Hydrogen pressure–composition (\(p\)–\(c\)) isotherm of deuterium desorption of NaAlO\({}_{4}\)–0.02 TiCl\({}_{3}\) at 398, 403, and 408 K.