esa-sceva/satcom-chunk-collection huggingface dataset:
# Naphthalene-Based Polymers as Catalytic Supports for Suzuki Cross-Coupling
## 4 Materials and Methods
### Reaction Procedure and Analysis of the Reaction Mixture  
The Suzuki cross-coupling was carried in a temperature-controlled glass batch reactor with a magnetic stirrer at the following conditions: 900 rpm, 1 mmol of BrAN, 1.5 mmol of PBA, 2.0 mmol of NaOH, 0.2 mol.% of Pd (with respect to BrA) at 60 \({}^{\circ}\)C. All experiments were carried out in air using an EtOH-H\({}_{2}\)O mixture (volumetric ratio 5:1) as a solvent. The total volume of the liquid phase was 30 mL. The choice of the EtOH-H\({}_{2}\)O mixture and NaOH was based on our preliminarily studies [46].  
Before the catalyst addition into the reactor, in each experiment, a blank test (duration of 60 min) was carried out in order to ensure that there is no reaction without the catalyst since even small amounts of leached palladium, if remaining on the stir bar or on the reactor walls, can catalyze the cross-coupling [47]. In all experiments, PBA was used in excess with respect to BrAN due to the possible non-selective PBA homo-coupling with the formation of BP.  
In each catalytic experiment, samples of the reaction mixture were periodically taken and analyzed via GC-MS (Shimadzu GCMS-QP2010S) equipped with a capillary column HP-1 MS (100 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness). Helium was used as a carrier gas at a pressure of 74.8 kPa and linear velocity of 36.3 cm/s. The oven temperature was programmed: 120 \({}^{\circ}\)C (0 min) \(\rightarrow\) 10 \({}^{\circ}\)C/min (160 \({}^{\circ}\)C) \(\rightarrow\) 25 \({}^{\circ}\)C/min (300 \({}^{\circ}\)C) \(\rightarrow\) 300 \({}^{\circ}\)C (2.4 min). The temperature of the injector, interface, and ion source was at 260 \({}^{\circ}\)C, ranging from 10 up to 500 \(m/z\). The concentrations of the reaction mixture components were calculated using the internal standard calibration method (DPA was used as an internal standard). It is noteworthy that during the GS-MS analysis, PBA underwent dehydration and trimerization, which affected the signal intensity. Due to the low level of confidence in the quantitative analysis of PBA, its consumption was not monitored.  
Catalytic activity was characterized by the initial transformation rate, \(R_{0}\), defined as the tangent of the slope of the initial linear part on the kinetic curves of BrAN consumption, and related to the amount of palladium in the system: \(R_{0}=(N_{BrAN,0}-N_{BrAN,j})\times\tau_{i}{}^{-1}\times N_{Pd}{}^{-1}\), where \(N_{BrAN}\) is the number of moles of BrAN transformed by the reaction time \(\tau\); \(N_{Pd}\) is the overall number of moles of Pd; and \(\tau\) is the reaction time, in minutes.  
The conversion (\(X\),%) of BrAN was defined as \(X_{\%}\)\(\%\) = \((N_{BrAN,0}-N_{BrAN,j})\times N_{BrAN,0}{}^{-1}\times 100\).  
The selectivity(\(S\),%) with respect to MBP was defined as a share of MBP among the reaction products (BP and MBP): \(S\),\(\%\) = \(N_{MBP,i}\)\(\times\)\((N_{MBP,i}\)+\(N_{BP,i})^{-1}\)\(\times\) 100.  
The yield (\(\gamma\),%) of MBP was defined as \(Y\),% = \(S\),% \(\times\)\(X\),% \(\times\) 0.01.
# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 4 Results
### Layer-Wise Evaluation Results
#### 4.1.1 Average Metric Values by Layer per Database and PTM  
The results in Figures 3 and 4 reveal distinct layer-wise performance trends among the evaluated models. Models like WavLM-L, W2V2-L-XLSR53-ES, and W2V2-L-R-Libri960h, each with 24 layers, achieve their highest F1-scores and UAR in the earlier layers (e.g., layers 3-6), demonstrating early-layer dominance. In contrast, Whisper-L-v3 and Whisper-L-v3-ES (32 layers) exhibit peak performance in intermediate layers, with optimal F1-scores observed in layers 13-15 and UAR in layers 12-14. Similarly, the 48-layer W2V2-XLSR-ES achieves its best F1-scores in layers 9-11, while UAR performance shifts slightly to layers 5, 10, and 11, indicating metric-specific nuances. Models like TRILsson and L-CLAP-G deviate from this trend, providing fixed embeddings rather than layer-specific outputs, represented as constant values in the figures.  
For all models, the F1-score decreases significantly in the final layers. This suggests that as the network depth increases, the representations become more specialized and might be less effective for emotion classification tasks. Also, the fact that certain specific layers optimize classification indicates a sweet spot where the representation balances both abstraction and discrimination of emotional features.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Database** & **Metric** & **PTM** & **Classifier** & **Score (Layer)** \\ \hline INTERISP & Accuracy & HuBERT-L-1160k & SVM & 99.83\% (6) \\  & F1-Score & HuBERT-L-1160k & SVM & 99.83\% (6) \\  & UAR & HuBERT-L-1160k & SVM & 99.88\% (6) \\ \hline EmoFilm\({}_{ES}\) & Accuracy & Whisper-L-v3-ES & MLP & 91.67\% (22) \\  & F1-Score & Whisper-L-v3-ES & MLP & 91.52\% (22) \\  & UAR & Whisper-L-v3-ES & MLP & 91.83\% (22) \\ \hline EM-SpDB & Accuracy & W2V2-L-XLSR53-ES & MLP & 88.28\% (6) \\  & F1-Score & W2V2-L-XLSR53-ES & MLP & 88.32\% (6) \\  & UAR & W2V2-L-XLSR53-ES & MLP & 88.02\% (6) \\ \hline EmoWisconsin & Accuracy & W2V2-XLSR-ES & MLP & 62.50\% (15) \\  & F1-Score & Whisper-L-v3-ES & MLP & 58.92\% (31) \\  & UAR & W2V2-XLSR-ES & SVM & 40.0\% (14) \\ \hline MESD & Accuracy & W2V2-XLSR-ES & MLP & 95.38\% (6) \\  & F1-Score & W2V2-XLSR-ES & MLP & 95.40\% (6) \\  & UAR & W2V2-XLSR-ES & MLP & 95.38\% (6) \\ \hline MEACorpus & Accuracy & W2V2-XLSR-ES & MLP & 92.69\% (5) \\  & F1-Score & W2V2-XLSR-ES & MLP & 92.53\% (5) \\  & UAR & W2V2-XLSR-ES & SVM & 90.82\% (3) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of best results by database, PTM, and classifier.
## 3 Results and Discussion
### Evaluation of the Self-Healing Ability of the Coatings  
#### 3.7.1 Salt Spray Tests  
The results of the scratched coatings after 72 h of salt spray corrosion tests are presented in Figure 7. The control panel and the panel without capsules showed severe corrosion after exposure to the salt solution (Figure 7a). It can be seen in Figure 7 that with increasing the capsule content in the coatings, the corrosion in the scratch area was considerably decreased. This can be due to the healing reaction occurring when capsules rupture in the crack area. However, it can be seen that by increasing capsule content to 4 wt.%, the number of dark spots representing the porosity has increased in the matrix, due to capsule agglomeration [12]. According to Figure 7, the coating containing 2 wt.% of capsules showed the most efficient corrosion protection of the coating after scratch and exposure in the corrosive environment (5 wt.% NaCl solution). The anticorrosive property of this sample is because of the efficient healing reaction between epoxy and released CAR from capsules and the formation of a new film on the substrate [44].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Code** & **Nanocapsules Content (wt.\%)** & **Adhesion Strength (MPa)** & **Total Bending Elongation (\%)** \\ \hline A & 0 & 2.59 & 24.8 \\ B & 1 & 2.11 & 19.8 \\ C & 2 & 2.10 & 19.6 \\ D & 4 & 1.43 & 18.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact of nanocapsules content on the properties of the coatings.  
Figure 7: Images of the scratched coatings after salt spray test: (**a**) Sample A; (**b**) Sample B; (**c**) Sample C; (**d**) Sample D.
## 3 Results and Discussion  
### Wavelength Selection  
The purpose of wavelength selection is to enable classification with a limited number of wavelengths (3-7) that can be created using optical filters, LEDs, etc. to produce a simple, low-cost classification device. The robustness of the proposed simulated annealing approach was evaluated by running 10 iterations of the algorithm with the VNIR data for the \(k=7\) cases and examining the variation in the resulting selected wavelengths and the associated WKNN classification accuracies. Figure 6a shows the wavelengths selected for each of the 10 iterations, with each row of similarly colored dots representing a single iteration. Although some variability in the selected wavelengths is noticeable, the plot of multiclass classification accuracies for these iterations in Figure 6b shows little variability in the resulting accuracy. The standard deviation over these 10 accuracy values was 0.13%.  
Figure 7 shows the average VNIR reflectance spectrum for a red snapper fillet with the \(k=3\), 4, 5, 6, and 7 optimal wavelengths selected by the simulated annealing algorithm. For all \(k\) values, the selected wavelengths correspond to interesting peaks, valleys, and inflection points of the spectrum. Clearly, the region of wavelengths <600 nm is favored along with the trough near 950 nm.  
The wavelength selections for the fluorescence data in relation to the average spectrum for one of the red snapper fillets are shown in Figure 8. For this mode, the initial wavelength selections are concentrated at the minima of the spectrum with no wavelengths near the large peak around 670 nm selected until the \(k=6\) case.  
Figure 5: Average spectra for each of the 25 fish species. (**a**) VNIR reflectance; (**b**) scaled fluorescence; (**c**) SWIR reflectance. Error bars correspond to half of a standard deviation over all voxels for each species.  
Figure 9 shows the wavelength selections for the SWIR reflectance data. The selections for each of the \(k\) values are concentrated near the trough around 1000 nm and the inflection point near 1160 nm. No wavelengths above 1200 nm are selected.  
Table 2 shows the results of the comparison between the proposed simulated annealing-based wavelength selections method and the three alternative methods. For each combination of spectral mode and number of selected wavelengths, the simulated annealing method yields the set of wavelengths that produces the highest 4-fold cross validated classification accuracy with the WKNN classifier.  
Figure 6: Results of the wavelength selection robustness study. (**a**) Scatter plot showing selected wavelengths for 10 iterations of the \(k\) = 7 VNIR study. (**b**) Plot of final accuracies for each of the 10 iterations.  
Figure 7: Average VNIR reflectance spectrum for one of the red snapper fillets with the optimal \(k\) = 3, 4, 5, 6, 7 wavelength selections.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 2 Materials and Methods
### Discounted Cash Flow Analysis
#### 2.4.1 Capital Expenditure Estimation  
The capital investment of the hard carbon production facility was evaluated using a bottom-up approach, integrating literature-derived unit costs with standard cost multipliers widely adopted in chemical process engineering [26]. The equipment list includes biomass handling, drying, pyrolysis, acid washing, carbonization, graphitization, and ball milling. While baseline costs for the thermal units (pyrolysis, feedstock handling, waste treatment) were adapted from a U.S. DOE techno-economic analysis of lignocellulosic biorefineries [27]. The cost of the graphitization step was estimated independently based on commercial quotations for silicon carbide vacuum sintering furnaces, with a unit price of approximately 80,000 USD per unit and a throughput of 3 tons per day per furnace [28]. This price was scaled to match a production capacity of 2000 tons per day, consistent with the design basis of the reference system. A total graphitization investment of 384 million CNY was assumed based on this match, in alignment with the capital intensity of comparable high-temperature carbon treatment systems. All equipment costs reported in Table 2 are benchmarked to a reference year of 2021.  
The total purchased equipment cost was then scaled up based on the production capacity and number of batches and augmented using standard installation factors as shown in Table 1 in Appendix A. Specifically, installation (40%), instrumentation (20%), piping (15%), electrical systems (15%), buildings (30%), yar  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Materials/kg**} & \multicolumn{4}{c}{**Power/kWh**} \\ \hline Specific input & BR & H\({}_{2}\)SO\({}_{4}\) & Water & Stirring & Washing & Drying & Pyrolysis & Carbonization & Milling \\ Value & 4.45 & 3.56 & 10.43 & 0.00040 & 0.037 & 0.64 & 3.35 & 1.98 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific material and energy inputs for producing 1 kg of hard carbon [17].  
service facilities (20%) were all computed as percentages of the delivered equipment cost. These constitute the direct capital cost.  
Indirect costs include engineering and supervision (25%), contingency (30%), contractor fees (10%), and startup expenses (10%). The Fixed Capital Investment (FCI) is the sum of the direct and indirect costs. A working capital equivalent to 15% of the FCI was also considered, leading to the Total Capital Investment (TCI).  
The land cost was assumed to be 6% of the delivered equipment's value. All cost estimates were adjusted to 2024 values using the Chemical Engineering Plant Cost Index (CEPCI). This capital cost structure aligns with established methods used in biorefinery evaluations and reflects current commercial estimates for high-temperature processing of battery materials.
## 3 System Design  
### Physical Model and Operation Principle  
Based on the analysis of the atmospheric conditions, we designed the standalone renewable energy system. As shown in Figure 3, the proposed renewable energy system in this study is equipped with a power generator, an energy storage system, an end-user and a control station. The power generator consists of the PV arrays and wind turbines (WT), which can complete the conversion of wind energy and solar energy to electric energy. The energy storage system includes low-temperature batteries. The end-user is various loads at Zhongshan Station, which includes instruments for scientific research, electricity for daily use, heating, etc. The control station includes a control system, which has functions of controlling the process of charging and discharging of hybrid wind-solar power system. The rotation of wind turbines can produce AC currents. A three-phase rectifier circuit in the control system is designed to convert the three-phase alternating currents into stable direct currents. A DC chopper circuit is also designed to implement the control strategy for the renewable energy system  
Figure 2: The monthly meteorological data of Zhongshan Station. (**a**) wind speed, (**b**) solar radiation and day length, (**c**) air temperature.  
to complete maximum power output. The control system is used to monitor voltage and current of PV and WT, battery voltage and charging current. This proposed system would be a reliable and sustainable energy supply and guarantee the load demand of Zhongshan Station for 24 h a day.
## 4 Discussion
### Influence of Modulus of Elasticity  
#### 4.2.1 Stress Field  
The change in Young's modulus directly affects the magnitude of the stress at the ice-rock interface. From Equation (17), it can be seen that the stress at the interface increases with an increase in the modulus of the ice. To further investigate the influence of the modulus of elasticity on the stress at each position, the model used for the study has a lateral pressure coefficient of 1 and elastic moduli of ice and rock of 0.5 and 2, respectively. The stress field distribution obtained from this model is shown in Figure 10.  
Figure 9: Crack extension pattern under different lateral pressure coefficients: (**a**) \(\lambda=0\), (**b**) \(\lambda=1\).  
It can be seen from the figure that the patterns of change in radial and circumferential stresses are always opposite, regardless of the elastic modulus ratio. When the Young's modulus of the ice is lower than that of the rock, the radial stress near the edge of the hole is lower than that in the interior of the rock. Inside the rock, the radial stress is always lower than the circumferential stress. When the modulus of elasticity of ice is higher than that of rock, the original law of change is exactly the opposite. This shows that the increase in the modulus of elasticity of the ice contributes to stability at the orifice.
# Designing an Adaptive Underwater Visible Light Communication System +
## 6 Receiver Design
### Adaptive Reception  
The adaptive receiver design is shown in Figures 10-12. The receiver first receives the preamble and calculates the power at three positions, labeled \(P1\), \(P2\), and \(P3\) as shown in Step 1 of Figure 10. These values of power are stored in an array for counter \(n\), where \(n=1,2,3\) correspond to angles \(a1,a2,a3\) in Figure 11. Once the counter is greater than 3 (\(n>3\)), the receiver compares the power values, adjusts its position to align with the angle that has the maximum power, and then transmits the ACK signal.  
The transmitter subsequently compares the power of the received ACK signal with a predefined threshold to determine whether to proceed with sending the actual message. If the message is transmitted, the received signal is recovered, demodulated, and evaluated for the BER as shown in Step 2 of Figure 10. A BER of zero indicates no data loss, while any non-zero BER indicates that some data were lost. Sections 6.2-6.4 provide a detailed explanation of each step at the receiving end.  
Figure 9: Angular position of the receiver with regard to transmitter.
## 7 Simulation Results and Discussion  
In this section, three experimental scenarios are presented to verify the Quality of Service (QoS) performance of the proposed AFB-GPSR. Each scenario is designed to assess the impact of various MANET environmental settings on the AFB-GPSR performance in comparison to the GPSR and AODV routing protocols. The first scenario examines the performance under various node densities at a speed of 10 mps. The second scenario is tested by varying the speed of nodes for a density of 50 nodes. The third scenario examines the impact of transmission power upon GPSR in fixed and dynamic BIT statuses. In the proposed scenario, the QoS metrics in terms of packet-delivery ratio (PDR), end-to-end-delay (E2 ED) and throughput are evaluated for the proposed AFB-GPSR with regard to the standard GPSR and AODV protocols.
## 6 Performance Analysis
### Results and Discussion
#### 6.3.3 Experiment III  
Figure 7 shows the measured error rate for the GetBallot and CastVote functions at varying transaction send rates and transaction totals. The results indicate that the number of transactions and transaction send rate impact the error rate in conjunction. For a total number of transactions below 10,000, neither tested function ever dropped or rejected a transaction for every send rate tested. At 10,000 transactions submitted, the error rate for the GetBallot function rose minimally when the send rate passed the scheme's peak throughput measured in Experiment I. For 10,000 total transactions, the error rate for the CastVote function rose quickly once the send rate passed the scheme's peak throughput measured in Experiment I. The results are generalized as follows:  
* Errors are unlikely to occur when transactions are submitted to the scheme at/or below its peak throughput.
* Error rate follows the same trend as latency, rising only when the transaction send rates eclipse the scheme's peak throughput.
* Error rate increases when large numbers of transactions are being processed (i.e., 10,000).  
Figure 6: Varying the number of total transactions’ impact on throughput and latency: GetBallot and CastBallot. (**a**) GetBallot (Throughput); (**b**) GetBallot (Latency); (**c**) CastVote (Throughput); (**d**) CastVote (Latency). (Author’s own processing).  
We have concluded that our scheme is scalable up to 10,000 voters as long as user interaction is capped on a transaction per second basis. For usages such as fetching election results, if user interaction is capped to 200 transactions per second, users will experience little to no latency with our scheme, and no queries are likely to be dropped. For casting votes, if user interaction is capped to 75 transactions per second, users will experience minimal latency, and no votes are likely to be lost. These results also prove the feasibility of using the IBM Cloud to host our Hyperledger Fabric blockchain. As discussed in [33], Hyperledger Fabric itself is scalable, but now we can conclude that hosting Hyperledger Fabric on the IBM Cloud is also scalable, even when constrained by operating cost. As noted previously in this paper, we leveraged the free tier Kubernetes service on the IBM Cloud. This gave us minimal operating capacity, and with a larger budget, more scalability could be expected. To determine the exact scalability of a higher-tier service, however, further performance testing would be required.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 8 Conclusions  
IoT is evolving in several application areas, resulting in billions of devices being interconnected. These devices are resource-constrained by nature and are unable to implement conventional security techniques of machine learning and IDS. RPL-routing protocol is adopted by most IoT applications and is prone to numerous attacks. To address these attacks, a trust-based approach is a viable option. This research proposes a trust- and mobility-based secure routing protocol, which relies on critically chosen trust metrics, including mobility metrics. The results indicate that the proposed solution outperforms the existing techniques with enhanced network performance for static and dynamic scenarios in topology stability, packet loss rate, and throughput. Moreover, it has been proven to fulfil the routing protocol requirements.  
For future work, the proposed protocol shall be enhanced for power consumption. It will also be evaluated for colluding attacks. We plan to evaluate the results with an increased number of nodes in the network as well as with the testbed experiments considering additional performance parameters and improved experiments for real-world scenarios.  
Conceptualization, S.M.M., N.J., R.K.M., M.S.H. and A.Y.; methodology, S.M.M., N.J., R.K.M., M.S.H. and A.Y.; software, S.M.M., N.J., R.K.M., M.S.H. and A.Y.; validation, S.M.M., N.J., R.K.M., M.S.H. and A.Y.; investigation, S.M.M., N.J., R.K.M., M.S.H. and A.Y.; writing--original draft preparation, S.M.M., N.J., R.K.M., M.S.H. and A.Y.; writing--review and editing, S.M.M., N.J., R.K.M., M.S.H. and A.Y.; visualization, S.M.M.; supervision, R.K.M. and N.J.; funding acquisition, S.M.M., N.J., R.K.M., M.S.H. and A.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Researchers Supporting Project number (RSP-2021/32), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.