esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison Between the Proposed Model and Current Mainstream Models  
This model was based on the existing six-degrees-of-freedom ROV model, with optimizations made to the resistance matrix and including the three-dimensional ocean current interference on the underwater robot's motion path. Additionally, the plotting functions of the original model were modified to enable the simulation of the underwater robot's three-dimensional path.  
To objectively highlight the advantages and innovations of the proposed model, a comparative simulation was conducted against a baseline model. To emphasize the differences in behavior under dynamic flow conditions, we intentionally used an intensified ocean current velocity of \(v=7\,\mathrm{m/s}\), not to represent typical marine conditions but to amplify the response characteristics of the models for clearer observation. In this scenario, the ROV started from position \([0,\,0,\,-40]\) and aimed for a target at \([-15,\,-15,\,-60]\). The water depth was set to 30 m, with the ROV initially located 12 m below the still water level. The baseline model results are presented in Figure 12, while the simulation outputs from the proposed model are shown in Figures 9-11.  
The results were as follows: In the previous model, the ROV finally reached \([-14.96,\,-14.77,\,-60.34]\), which was considered to have no ocean currents and wake flow because it reached the target with minor inaccuracy, which is not based on real-life. Compared with Figure 13, where a considerable gap was found between the target and final positions, the ocean current successfully influenced the path of the ROV. Thus, the proposed model successfully figured out how ocean currents influenced the path of an ROV's track with a 3D visualization.  
Figure 12: The simulation result of the previous model.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 2 Materials for Microthruster Fabrication
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fabrication** & **Target Thrust \({}^{\ddagger}\) (mN)** & **Reference** & **Type** & **Propellant** & **Catalyst** \\ \hline Stainless steel & 850 & [27] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 90\% & Ag \\ (Machining) & 500 & [28] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 80–87\% & MnO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) \\  & 100 & [29] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 92\% & Ag/flake \\ \hline  & 13.5 & [30] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 87\% & FeCl\({}_{2}\) liquid \\  & 1 & [23] & Monopropellant & Hydrazine & Ir/Ti, SiOX, Si \\ Silicon & 1 & [10] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 50\% & MnO\({}_{2}\) nanowire \\  & 0.5 & [9] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 90\% & Ag \\  & N/A & [21] & Monopropellant & Hydrazine & Metallic substrate \\ \hline  & 3.78 & [31] & Cold gas & Nitrogen gas & N/A \\ Silicon (w/Glass) & \(\sim\)1 & [22] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 60, 90\% & Pt \\ \hline  & N/A & [25] & Monopropellant & HAN & N/A \\ HTCC & 0.96 & [11] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 31\% & Pt \\  & 360 & [19] & Monopropellant & HAN based & Electrolytic ignition \\ \hline  & 150 & [32] & Monopropellant & HAN based & Electrolytic ignition \\ LTCC & 3 & [33] & Monopropellant & H\({}_{2}\)O\({}_{2}\) & Ag \\  & 1 & [34] & Bipropellant & Ethylene/ argon–oxygen & Spark ignition \\ \hline  & 100 & [17] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 90\% & Pt \\ Glass & 50 & [20] & Monopropellant & H\({}_{2}\)O\({}_{2}\) 90\% & Pt \\  & 35 & [12] & Monopropellant & ADN/H\({}_{2}\)O\({}_{2}\) & Pt/La/Al\({}_{2}\)O\({}_{3}\) \\ \hline \hline \end{tabular} \({}^{\dagger}\) maximum value.  
\end{table} 
Table 1: Summary of the fabrication materials used in previous micro-propulsion studies. In this research, different materials used in previous studies have been considered and compared in order to address propulsion performance, heat loss, and propellant flow characteristics through a micronozzle depending on the structural materials, such as silicon, LTCC, HTCC, and glass for MEMS-based propulsion and stainless steel and copper for conventional machining-based propulsion. Additionally, the effects of propellant enthalpy on heat loss and material sensitivity have been examined with respect to propulsion performance degradation for different fabrication materials. As there are numerous potential thruster profiles that could be incorporated into a microthruster design depending on material type and fabrication process, an appropriate design reference should be considered for the investigation. We chose a planar design for a monopropellant microthruster, as reported in [20], which detailed the successful operation of a microthruster using hydrogen peroxide as a propellant to produce 50 mN thrust. The designed profile of the microthruster is shown in Figure 1, which has five different layers regarding the design profiles of a microinjector, microchamber, and micronozzle.
###### Abstract  
Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users' polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a \(k\)-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF-IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of \(k\) features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naive Bayes (NB) model with the RF method.  
sentiment analysis; machine learning; cross-validation; vectorization; feature importance
###### Abstract  
Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (<50 mm\({}^{2}\) per pixel; \(\tau^{2}\) = 0.78) to medium-resolution PlanetScope Dove-R (3 m\({}^{2}\) per pixel; \(\tau^{2}\) = 0.51) and low-resolution simulated Sentinel-2 (10 m\({}^{2}\) per pixel; \(\tau^{2}\) = 0.23). A fractionated water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, 52: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors.  
m 2023157661 [1]1 Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn TR10 9 FE, UK 12 Cambrome School of Mines, University of Exeter, Penryn Campus, Penryn TR10 9 FE, UK 2  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
###### Abstract  
Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations.  
Internet of Things (IoT); low earth orbit (LEO) satellite communication; cognitive radio; resource allocation 2023233875  
1
## 5 Numeric Integration
### Truncation Error and Mesh Width  
There are three sources of errors: Floating-point cancellation, discretization, and truncation. A bound for the floating-point error \(\Delta_{\text{fp}}\tilde{Y}\) can be estimated as in Equation (34). The discretization error will be controlled by iterative refinement of the grid (Section 5.5).  
Truncation errors arise from the introduction of finite summation limits in Equation (50). The truncation error at the lower summation limit is  
\[\Delta_{\text{tr}}^{-}\tilde{Y}=\left|\int_{-\infty}^{-N}\!\mathrm{d}k\,\phi^{ \prime}(k)\cos(\ldots)g(\ldots)\right|<\int_{-\infty}^{-N}\!\mathrm{d}k\,|\phi ^{\prime}(k)| \tag{59}\]  
Provided \(\phi^{\prime}(k)\) does not change its sign for \(k<-N\), the absolute-value operator can be omitted, and the integral becomes trivial, yielding  
\[\Delta_{\text{tr}}^{-}\tilde{Y}<\phi(-N)\lesssim N\mathrm{e}^{\eta(-N)} \tag{60}\]  
To obtain a bound for the upper truncation error, we start from the trapezoidal sum:  
\[\Delta_{\text{tr}}^{+}\tilde{Y}\simeq\left|\sum_{k=N+1}^{\infty}b_{k}g(\ldots)\right| \tag{61}\]  
Using Equation (52),  
\[\Delta_{\text{tr}}^{+}\tilde{Y}<\sum_{k=N+1+\
u}^{\infty}\phi^{\prime}(k) \frac{\pi N}{\mathrm{e}^{\eta(k)}-1}\lesssim\sum_{k=N+1}^{\infty}\pi N \mathrm{e}^{-\eta(k)} \tag{62}\]  
The summands decay faster than in a geometric series so that  
\[\Delta_{\text{tr}}^{+}\tilde{Y}<\pi N\mathrm{e}^{-\eta(+N)} \tag{63}\]  
similar to Equation (60). Altogether, the truncation error decreases double exponentially with increasing \(N\). Therefore we can request at very little cost a safety factor of \(m=10\) or more in the error bound,  
\[\Delta_{\text{tr}}\tilde{Y}/\tilde{Y}<\delta/m \tag{64}\]  
which ensures that truncation contributes almost nothing to the overall error. For the same reason we must not care about the non-strict (\(\lesssim\)) estimates in Equations (60) and (62).  
To make use of Equation (64), we need a lower bound for \(\tilde{Y}\). From the data shown in Figure 2, we can infer that the lowest \(\tilde{Y}\) that needs to be computed numerically is at \(\beta=0.1\), \(Y=V\); its value is little above \(\tilde{Y}_{0}:=2\cdot 10^{-20}\). The choice Equation (58) ensures the asymptotic behavior \(\eta(x)\to\pm q\mathrm{e}^{hx}\) for \(x\to\pm\infty\). Thence Equation (64) is satisfied by  
\[h=\frac{1}{N}\ln\left(\frac{1}{q}\ln\frac{(\pi+1)mN}{\delta\tilde{Y}_{0}}\right) \tag{65}\]
## 4 System Modelling Methodology
### Channel Model  
As mentioned in Section 4.1 and illustrated in Figure 1, the utilisation of the standard T/R RIS in its present state inevitably leads to a 50% loss in signal power, reducing the coverage area during transmission. This is in contrast to the novel HRIS-DMA technique discussed in this research. In order to provide a clear and concise explanation of the new HRIS, the channel model must be clarified first. This channel model adjustment is crucial for assessing the effectiveness of the communication network supported by the HRIS-DMA.  
Let \(H\) be the channel matrix between the BS and the users and \([H]_{n,p}\triangleq h^{(n,p)}\) be the gain from the BS \(p^{th}\) antenna to the user \(n\). Path loss, fast fading, and HRIS responses are considered when modelling the channel. Then, the single channel model between the \(m^{th}\) HRIS element, and the BS \(p^{th}\) antenna and user \(n\) is given by  
\[\hat{h}_{m}^{(n,p)}=\sqrt{\alpha_{m}^{(n,p)}}g_{m}^{(n,p)}\Gamma_{m}^{n}, \tag{4}\]  
where \(\alpha_{m}^{(n,p)}\) represents path loss, \(g_{m}^{(n,p)}\) is the fading coefficient with zero mean and independent unit variance for different HRIS channels, and \(\Gamma_{m}^{n}\) represents the HRIS element response. The path loss, on the other hand, is modelled as in 5:  
\[\alpha_{m}^{(n,p)}=\frac{\lambda^{2}Gl_{M}^{2}}{M}\frac{G_{HRIS}F_{m}^{p}F_{m} ^{n}}{(D_{m}^{p}D_{m}^{n})\gamma} \tag{5}\]  
where \(\lambda\) is the wavelength, \(G\) represents BS antenna gain, \(\gamma\) is the path loss exponent, \(F_{m}^{p}\) is the radiation power at the \(m^{th}\) HRIS signal received from the \(p^{th}\) BS antenna, \(F_{m}^{n}\) is the signal power radiation from the \(m^{th}\) HRIS antenna to the user \(n\), \(G_{HRIS}\) is the HRIS antenna gain, and \(D_{m}^{p}\) and \(D_{m}^{n}\) are the distance between the base station and HRIS element antenna and the distance between the HRIS element and users, respectively.  
The effect of user location and angle with respect to the BS and HRIS was elucidated by modelling and measuring the signal power radiation as in (6) and (7), respectively. In (7), the determination of the \(\varepsilon\) value depends on the user's location in the transmission area or reflection area. Thus, (4) can be expressed as in (8), with an average path loss value.  
\[F_{m}^{p}=\cos^{2}\theta_{m}^{p}G_{HRIS} \tag{6}\]  
\[F_{m}^{n}=\cos^{2}\theta_{m}^{n}\varepsilon_{(t,r)} \tag{7}\]  
\[h^{(n,p)}=\sum_{m=1}^{M}\hbar_{m}^{(n,p)}=\sqrt{\hat{\pi}^{n}}\sum_{m=1}^{M} \delta_{m}^{(n,p)}\Gamma_{m}^{n} \tag{8}\]
# On the Use of PEDOT as a Catalytic Counter Electrode Material in Dye-Sensitized Solar Cells
## 3 PEDOT in DSSCs
### PEDOT Based Alternative Counter Electrodes in DSSCs
#### 3.2.1. Iodide-Iodine Electrolyte
The improved electrocatalytic behavior of PEDOT with incorporated conductive carbon black was explained by a lower charge transfer resistance at CE \(\mid\) electrolyte interface, compared to regular PEDOT [57]. Vapor phase polymerization is another potential technique commonly used to prepare PEDOT-based CEs. Very recently, conversion efficiency of 8.4% was reported for PEDOT-based DSSCs, in conjunction with an \(\text{I}_{3}-/\text{I}^{-}\) redox mediator [58]. The authors first deposited a thin layer of Fe\({}_{2}\)O\({}_{3}\) on FTO, and subsequently, they obtained PEDOT CEs by in situ treatment with EDOT and HCl: Fe\({}^{3+}\), which generated by dissolution of Fe\({}_{2}\)O\({}_{3}\) upon under acidic conditions, acts as the oxidizing agent of EDOT.
## 4 Challenges and Future Directions
### Energy Consumption  
Energy consumption in a blockchain-implemented IoUT network is different when compared to terrestrial environments. Both of these technologies consume enormous energy for providing their specific services [105]. In the IoUT, all deployed underwater sensors communicate to the central sink sensor for delivering the information that is gathered from different devices. All successfully established IoT networks have faced various challenges when deployed underwater. This includes short-range communication, high attenuation, enormous ultra-violet radiation, the high expense for sensor deployment, and so on. In addition, the underwater environment has its unique characteristics. The terrestrial signal cannot be used for underwater communication. Instead, acoustic signals are preferred. Providing efficient underwater communication through acoustic signals is a challenging task because the signals suffer from high attenuation, long propagation delays, and high bit rates. Due to this, more communication failures may occur between sensors, which can lead to an increased number of data re-transmissions. An IoUT sensor has limited power, and it is not so easy to recharge or replace the battery in the IoUT. Extensive research has been performed to solve the blockchain energy issues, but, still, it remains as a key challenge. When the blockchain services are implemented with the IoUT network, each service requires high energy per transaction. Long delays can also occur before the transaction becomes confirmed. Therefore, the energy consumption of blockchain-enabled IoUT networks is a complex research problem that needs more attention from the research community.
## 3 Results
### Biological Potential  
#### 3.3.1 Antioxidant Potential  
CBG's recognized biological effects include antioxidant, anti-inflammatory, and antibacterial properties [3; 28]. However, understanding the potential impact of encapsulation on these bioactivities is crucial to determining the viability of the delivery system. In this context, the present study examines the antioxidant capacity of the CNC-CTAB delivery system encapsulating CBG, alongside the unencapsulated biomolecule and the carrier itself. This evaluation employs distinct yet complementary methodologies. Initially, the samples underwent assessments to determine their inherent scavenging and reducing abilities via radical scavenging assays (DPPH and ORAC) with ascorbic acid serving as the antioxidant reference. The results are presented in Figure 4.  
CBG exhibited promising antioxidant activity in both the DPPH assay (591.99 umol TE/g) and the ORAC assay (2827.36 umol TE/g), with Trolox equivalent values superior to those of ascorbic acid (\(p<\) 0.05), and DPPH inhibition close to 40%, similar to the antioxidant control (\(p>\) 0.05). Literature studies have also shown that the antioxidant activities of cannabinoids, in  
Figure 4: Antioxidant activity of CNC-CTAB encapsulating CBG, free CBG, and the carrier (CNC-CTAB) assessed using DPPH and ORAC assays compared to ascorbic acid. The results for the ORAC and DPPH assays expressed in umol TE/g are represented by the columns (left \(y\)-axis). The green dot indicates the DPPH assay results expressed as a percentage of inhibition (right \(y\)-axis). Statistically significant differences (\(p<\) 0.05) between the various samples for each assay are represented by different letters.  
cluding CBG, are stronger than Trolox in DPPH and ORAC methods, which has been attributed to the presence of phenolic groups, OH groups, and double bonds in the CBG molecule [29, 30]. In fact, the authors observed a decrease in the antioxidant activity among cannabinoids in the order CBG, CBD, and THC, suggesting that it results from the decrease in the number of OH groups and double bonds in individual molecules [30]. While CBG demonstrates notable antioxidant activity across DPPH and ORAC assays, its radical-scavenging effectiveness measured lower than that of some polyphenols, particularly curcumin (4675.64 umol TE/g through DPPH and 76.69% DPPH inhibition) [31] Curcumin's higher performance in these assays can be attributed to its polyphenic structure, which provides additional phenolic groups that actively neutralize free radicals, lending it superior chemical antioxidant capacity in comparison to CBG. CBG antioxidant activity decreased significantly (\(p\) < 0.05) after encapsulation, with the CNC-CTAB delivery system encapsulating CBG showing relatively low antioxidant activity in the DPPH assay (8.33 umol TE/g, 0.10% DPPH inhibition) and the ORAC assay (21.05 umol TE/g) (Figure 4). The free carrier material, CNC-CTAB, did not exhibit relevant antioxidant activity in these assays.
# Analysis of the Sugarane Biomass Use to Produce Green Hydrogen: Brazilian Case Study
###### Abstract  
Conventional hydrogen production processes, which often involve fossil raw materials, emit significant amounts of carbon dioxide into the atmosphere. This study critically evaluates the feasibility of using sugarcane biomass as an energy source to produce green hydrogen. In the 2023/2024 harvest, Brazil, the world's largest sugarcane producer, processed approximately 713.2 million metric tons of sugarcane. This yielded 45.68 million metric tons of sugar and 29.69 billion liters of first-generation ethanol, equivalent to approximately 0.0416 liters of ethanol per kilogram of sugarcane. A systematic literature review was conducted using Scopus and Clarivate Analytics Web of Science, resulting in the assessment of 335 articles. The study has identified seven potential biohydrogen production methods, including two direct approaches from second-generation ethanol and five from integrated bioenergy systems. Experimental data indicate that second-generation ethanol can yield 594 MJ per metric ton of biomass, with additional energy recovery from lignin combustion (1705 MJ per metric ton). Moreover, advances in electrocatalytic reforming and plasma-driven hydrogen production have demonstrated high conversion efficiencies, addressing key technical barriers. The results highlight Brazil's strategic potential to integrate biohydrogen production within its existing bioenergy infrastructure. By leveraging sugarcane biomass for green hydrogen, the country can contribute significantly to the global transition to sustainable energy while enhancing its energy security.  
biohydrogen production; lignocellulosic feedstock; sustainable biofuels; renewable energies; Brazilian energy transition 2025