esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements in Phytoremediation Research in South Africa (1997-2022)
###### Abstract  
Several mining-related pollutions, industrial waste, and soil deterioration define South Africa's environmental landscape. These have led to the consumption of unhealthy food, contaminated agricultural products, and polluted water. The polluted environment has been linked to numerous diseases among the populace, thus making environmental remediation an important issue in South Africa. Phytoremediation has been identified as a biological method for the restoration of polluted environments naturally and holistically. Therefore, it is vital to evaluate the level of phytoremediation-related research in South Africa in pursuit of a way out of environmental pollution. Thus, the purpose of this study was to map phytoremediation-related research in South Africa from inception to 2022. Statistical records from the Web of Science Core Collection were analyzed with the bibliometric package in RStudio, while mapping was performed via VOSviewer. Our study showed a low annual growth rate of publication (4.49%). The analysis uncovered that the 39 documents analyzed were written by 112 authors, and the first document was featured in the Journal of Geochemical Exploration in 1997. [PERSON] and [PERSON], [PERSON] are the most relevant authors. USA has the strongest collaboration with South Africa, while the _International Journal of Phytoremediation_, the _South African Journal of Botany_, and _Water SA_ are the most relevant journals. The result of this study can guide upcoming researchers and policymakers, together with essential facts for enhancing the restoration of the polluted environment in the country.  
bibliometrics; phytoremediation; phytoextraction; phytostabilization; South Africa; VOSviewer 1
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 3 Corpora Description
### Training Corpora  
#### 3.1.1 Acoustic Corpus  
The acoustic corpus was composed by annotated audio contents from seven different datasets, summing up a total of 743 h and 35 min. The following Table 1 presents the final number of hours containing only speech in each of the datasets.  
The RTVE2018 dataset [23] was released by RTVE and comprises a collection of TV shows drawn from diverse genres and broadcast by the public Spanish National Television (RTVE) from 2015 to 2018. This dataset was originally composed by 569 h and 22 m of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Duration** & **License** \\ \hline RTVE2018 & 112 h 30 min & Non-Commercial \\ SAVAS & 160 h 58 min & Commercial/Research \\ IDAZLE & 137 h 8 min & Non-Commercial \\ A la Carta & 168 h 29 min & Non-Commercial \\ Common Voice & 158 h 9 min & Mozilla Public License 2.0 \\ Albayzin & 5 h 33 min & Commercial/Academic \\ Multext & 0 h 47 min & Commercial/Academic \\ \hline Total & 743 h 35 min & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Duration of the speech segments for each dataset with their corresponding licenses.  
audio with a high portion of imperfect transcriptions and, thus, they could not be used as such for training. Therefore, a forced-alignment was applied in order to recover only the segments transcribed with a high literality, obtaining a total of 112 h and 30 min of nearly correctly transcribed speech segments.  
The _SAVAS_ corpus [24] is composed of broadcast news contents in Spanish from 2011 to 2014 of the Basque Country's public broadcast corporation EiTB (Euskal Irrati Telebitsia), and includes annotated and transcribed audios in both clear (studio) and noisy (outside) conditions. The _IDAZLE_ corpus is integrated by TV shows from the EiTB broadcaster as well, and it comprises a more varied and rich collection of programs of different genres and styles. TV shows are also the contents which compose the _A la Carta_ ([[https://www.rtve.es/alacarta/](https://www.rtve.es/alacarta/)]([https://www.rtve.es/alacarta/](https://www.rtve.es/alacarta/)), accessed on 28 December 2021) the acoustic corpus, including 265 contents broadcasted between 2018 and 2019 by RTVE.  
The _Common Voice_ dataset [25] is a crowdsourcing project started by Mozilla to create a free and massively-multilingual speech corpus to train speech recognition systems. Finally, the well-known and clean _Albuyzin_[26] and _Multext_[27] datasets were also included, mainly to favour the initial training steps and alignments of the systems.
## 3 Results and Discussion
### X-ray Diffraction Phase Analyses  
The phase analyses of the surfaces of the samples of the C84400 copper alloy, thermally oxidized in the N\({}_{2}\)-O\({}_{2}\) gas mixtures at 650 \({}^{\circ}\)C, showed also the presence of various diffraction planes of the oxides formed, as presented in Figure 4. The diffraction peaks corresponded to the (002), (111), (022), (220), (-202), (202), (-113) and (-311) planes of CuO; the (100), (002), (110),(101), (200) and (112) planes of ZnO; and the (010), (020), (201), (012), (022), (202), (131), (111), (311), (222), (032) and (040) planes of PbO. These identified planes confirmed the presence of monoclinic CuO, hexagonal ZnO and orthorhombic PbO on the oxidized surfaces, based on Powder Diffraction File (PDF) cards numbers 80-1917, 36-1451 and 38-1477 for PbO, CuO and ZnO, respectively.  
From the X-ray analyses presented in (Figure 4a,b), it is observed that a lesser number of the oxide planes diffracted the X-rays in samples oxidized in N\({}_{2}\)-5 wt.% O\({}_{2}\) compared to those oxidized in N\({}_{2}\)-0.75 wt.% O\({}_{2}\) over the same time period. The X-ray diffraction peaks were broad, due to the very small sizes of the oxides formed. Based on the XRD data, no Cu\({}_{2}\)O was formed on the oxide layers in both N\({}_{2}\)-O\({}_{2}\) gas mixtures used. All the copper oxide exists in the form of CuO. The miller-indexed (002) and (111) reflections for CuO have the highest intensities, indicating that these were the preferred crystal planes of the oxides in the samples that were thermally oxidized in the N\({}_{2}\)-0.75 wt.% O\({}_{2}\) gas mixture. The preferred planes for the PbO and ZnO equally formed during the thermal oxidation of the samples in N\({}_{2}\) 0.75 wt.% O\({}_{2}\) gas mixture were (111) and (110) Miller-indexed reflections, respectively. In the N\({}_{2}\) 5 wt.% O\({}_{2}\), the faster oxidation rate resulted in a faster nucleation and growth of the oxide layer, causing lesser oxide planes to occur.  
The Gibb's free energy of formation, \(\Delta G\), of ZnO, CuO and PbO shows that the formation of ZnO is the most energetically favorable at the process temperature of 650\({}^{\circ}\). In copper alloys, there is a critical zinc concentration above which only the oxidation of the zinc will occur. This critical concentration estimated by [PERSON] (1984) is 15 at.% Zn. The concentration of zinc in the C84400 copper alloy is 8.9 at.% (9 wt.%), which is lower than the critical concentration for the formation of only ZnO from the oxidation of zinc. Hence, CuO was equally oxidized from Cu\({}_{2}\)O during the thermal oxidation. Lead has a very low melting point (327.3 \({}^{\circ}\)C) [PERSON] (1984). To this extent, the lead particles in the alloy microstructure melted quickly in the process of thermal oxidation and segregated to the surface, where it was easily oxidized to PbO, as noted by the X-ray diffraction analysis.
## 3 Results
### Early Earthquake Magnitude Estimation as a Classification Task  
Ref. [47] explored the impact of input data length on a deep-learning-based magnitude classifier, evaluating durations from 1 to 30 s of P-wave data. The model employed a combination of CNNs and Bi-LSTMs for feature extraction and classification, distinguishing between noise, low-magnitude, and high-magnitude events (\(M\geq 5.0\)). Data augmentation techniques, including polarity inversion and sliding windows, were applied specifically to high-magnitude events to address class imbalance, improving classification performance. Despite testing various data lengths, the results indicate that extending the input window beyond 3 s does not significantly enhance classification accuracy, suggesting that early waveform information carries sufficient discriminative features for magnitude classification. The model consistently achieved an accuracy range of 90.04% to 93.86%, demonstrating robustness across different hypocentral distances and signal-to-noise ratio (SNR) conditions. However, classification errors were concentrated around the \(M5.0\) decision threshold, with 65% of events between \(M4.5\) and \(M5.0\) misclassified as high-magnitude. This trend suggests a tendency toward conservative magnitude estimation, prioritizing the reduction of false negatives over false positives. The study highlights the potential for integrating such classifiers into EBV systems, as rapid magnitude classification using minimal waveform data is crucial for timely alerts. However, the misclassification near the decision boundary raises questions about whether a probabilistic approach or a regression-based framework could improve classification confidence and mitigate the overestimation of events near \(M5.0\). Future work could explore hybrid approaches combining classification and regression methods to enhance both speed and reliability in real-time seismic applications. Ref. [55] proposed a CNN model to estimate earthquake severity. Instead of predicting the exact magnitude, the model classifies earthquakes as medium-to-large (\(M_{L}\geq 5\)) or small (\(M_{L}<5\)), using only the first 4 s of the P-wave. The CNN architecture consists of blocks with convolutional layers, Max-pooling, and \(1\times 1\) convolutional layers to enhance feature abstraction. The training uses binary cross-entropy loss and the Adam optimizer
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 3 IoT Technology Evolution
proposed IoT satellite connectivity using ZigBee architecture to monitor the power generation system where they used the Iridium satellite transceiver for the backhaul connection over satellite [20]. Although this research provides a solution of using ZigBee over satellite, this approach does not satisfy the DtS. In addition, the backhaul link using the Iridium satellite transceiver significantly increases the cost of the system because of the transceiver cost as well as monthly billing. According to the LPWAN standard, the 3 GPP created NB-IoT, also known as cellular LPWAN. It is currently a component of both 4G and 5G networks. When operating within the licensed spectrum for this technology, the modulation method is Quadrature Phase Shift Keying (QPSK). The maximum transmission power is 23 dBm with a depth sensitivity of \(-125\) dBm. In addition, the data transfer rate from the base station to the end devices may reach 250 kbps at its peak but drop as low as 26 kbps at its lowest. Delay and Doppler are critical concerns for the NB-IoT signals and its physical layer. Therefore, this protocol will need to be modified to make use of it in Satellite-to-Earth communications. Mobile Network Operators (MNO) are also responsible for base station installation. The importance of IoT technology over satellite connectivity was highlighted by [PERSON] et al. in their article [21]. Although [PERSON] et al. addressed only military application in which they proposed the idea to monitor different aspects of military surveillance over NB-IoT, it shows the significance of IoT technology over satellite links. [PERSON] et al. suggested a Doppler correction solution for NB-IoT user end devices, considering it to connect the base station over satellite link [22]. Their research is valuable and adds to past concepts of NB-IoT improvement; however, the main focus was on the ground data relay architecture, which does not meet the problem addressed in this research. Semtech pioneered the LoRa wireless technology that is still in use today. This technology can transmit up to 22 dBm of output power with a sensitivity of up to \(-139\) dBm and a data rate of up to 27 kbps. The Chirp Spread-Spectrum (CSS) modulation technique is utilized, which is more resistant to interference and jamming than other modulation techniques. Furthermore, it communicates through unlicensed ISM bands that are less crowded than licensed channels. In LoRa operation, various settings of the transceivers need to be tuned, including the transmit power, bandwidth, code rate, and spreading factor. LoRa may be utilized with a number of different MAC layer protocols, the most generally used being LoRaWAN. LoRaWAN is a free, open-source wireless network protocol optimized for battery-powered end devices. Numerous suppliers provide commercial off-the-shelf (COTS) LoRa nodes and gateways. As a result, it is a good approach to propose a LoRaWAN satellite built-in gateway in the future.
## 5 Discussion and Open Research Issues
### QoS in I2O WBANs
#### 5.1.2 QoS Metrics for I2O WBANs  
Despite significant developments in wireless communication technology, QoS handling for different healthcare applications in I2O WBANs remains a challenging issue [39; 40]. The general QoS metrics from the I2O WBAN network perspective involve network throughput, reliability transmission, energy efficiency, network lifetime and so on. It should be noticed that applications may request target-specific QoS support by specifying their requirements in terms of one or more of the QoS metrics. In general, an I2O WBAN is required to analyze the application requirements and deploy various QoS mechanisms [35]. Table 9 illustrates the QoS mechanism solutions based on requirements [4; 41].
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 3 Materials and Methods
### The Black-Scholes Model  
The Black-Scholes model, introduced by [PERSON] and [PERSON] in 1973 [2], is a seminal framework in financial mathematics for option pricing. The model assumes that markets are frictionless, with no transaction costs, taxes, or arbitrage opportunities, and that the underlying asset prices follow a geometric Brownian motion with constant volatility. Under these assumptions, the price of a European option satisfies the following partial differential equation (PDE):  
\[\frac{\delta V}{\delta t}+\frac{1}{2}\sigma^{2}S^{2}\frac{\delta^{2}V}{\delta^{ 2}S}+rS\frac{\delta V}{\delta S}-rV=0 \tag{1}\]  
where:  
* V(t, S): Option price as a function of time t and the underlying asset price S;
* \(\sigma\): Volatility of the underlying asset;
* r: Risk-free interest rate;
* T: Time to maturity.  
For a European put option, the terminal condition is V (T, S) = max(K - S0), where K is the strike price and S is the spot price of the underlying asset at maturity.
# Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
## 4 Numerical Examples
### Nguyen-Dupuis Network  
The Nguyen-Dupuis network, as shown in Figure 2, serves as our first case study. The free-flow travel time and capacities of the road segments in the Nguyen-Dupuis network are presented in Table 1. To validate our approach, we assume that three road segments--the red segments 5-9, 7-8, and 9-10--require maintenance. Given that only one road segment can be maintained at each stage, we solve the single-level model using the CPLEX solver.  
Table 1 provides the free-flow travel times and capacities of the road segments in the Nguyen-Dupuis network. The results are summarized in Table 2, which presents the feasible solutions for the first stage of the optimal maintenance scheme. The third column in Table 2 displays the expenses of the maintenance plans, indicating that the third scheme has the lowest cost. Therefore, we recommend maintaining road segments (5-9) first and then continuing with the suggested approach until all three road segments are included in the maintenance plan. The overall cost of the recommended maintenance plan is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Road Segment** & **Road Segment Serial** & \(\mathbf{c_{a}}\)** **veh/h** & \(\mathbf{t_{a,0}}\) & **Road Segment** & **Road Segment Serial** & \(\mathbf{c_{a}}\)**veh/h** & \(\mathbf{t_{a,0}}\) \\ \hline
1–5 & 1 & 900 & 7 & 8–2 & 11 & 700 & 10 \\
1–12 & 2 & 700 & 8 & 9–10 & 12 & 700 & 10 \\
4–5 & 3 & 700 & 9 & 9–13 & 13 & 600 & 9 \\
4–9 & 4 & 900 & 14 & 10–11 & 14 & 700 & 8 \\
5–6 & 5 & 800 & 5 & 11–2 & 15 & 700 & 9 \\
5–9 & 6 & 600 & 9 & 11–3 & 16 & 700 & 8 \\
6–7 & 7 & 900 & 5 & 12–6 & 17 & 300 & 7 \\
6–10 & 8 & 500 & 13 & 12–8 & 18 & 700 & 15 \\
7–8 & 9 & 300 & 5 & 13–3 & 19 & 700 & 11 \\
7–11 & 10 & 400 & 9 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Free-flow travel times and capacities of the road segments in the Nguyen–Dupuis network.  
Figure 2: Nguyen–Dupuis network with its OD pairs and capacities.
## 4 Results
Figure 7: Mean values with ranges of physical-chemical properties of littoral waters of sectors of the Lake Piaseczno catchment in the 2015 and 2018 summer seasons: O\({}_{2}\), oxygen concentration (mg O\({}_{2}\cdot\)dm\({}^{-3}\)); temperature (\({}^{\circ}\)C); EC, electrolytic conductivity (\(\upmu\)S\(\cdot\)cm\({}^{-1}\)); pH, reaction. Research supplementing the values of the physical-chemical properties of the pelagial waters in Lake Piaseczno also showed no considerable differences for both study seasons, and were in ranges that are typical of water bodies with a poor trophic status, as additionally suggested by the value of the indicator TSI SD being \(<\)40 (compare Figure 9). The limnological status of a water body is determined by the quantity and quality of allochthonic substances that are supplied from the catchment, depending on the character of the occurring ecosystems and their anthropogenic transformations. An analysis of the unitary coefficients of the export of surface loads of biogenic substances from the catchment area of Lake Piaseczno with variable forms of land use showed that the load of biogenic substances generated annually to the lake is 1151.1 kg N and 42.49 kg P. The highest contribution in the balance of biogas is reached by recreational areas and loose rural building development: 689.4 kg N and 22.98 kg P\(\cdot\)year\({}^{-1}\). Due to their phytogeochemical property of the bioretention of biogenic substances, forests, tree stands and shrubs generate relatively small amounts of N and P (123.57 and 8.24 kg\(\cdot\)year\({}^{-1}\), respectively). Currently, extensively used arable land introduces approximately 338.13 kg N and 11.27 kg P\(\cdot\)year\({}^{-1}\) to the lake waters annually. Tourism and unorganised recreation related to beaches and bathing areas generate variable amounts of biogenic substances, depending on the number of persons participating in recreation in the shore zone, which in turn depends on appropriate weather conditions and the day of the week (weekday or weekend, [20; 21]). Based on the adopted methodical assumptions, it was determined that the maximum amount of biogenic substances from this type of human pressure could potentially reach approximately 170.49 kg N\(\cdot\)year\({}^{-1}\) and 77.91 kg P\(\cdot\)year\({}^{-1}\) (arbitrarily assuming 30 days in a year with the maximum pressure of beach tourism, which is currently doubtful due to the decreasing number of tourists). Figure 8: Mean values with ranges of biological indicators of the trophic status of pelagial waters in Lake Piaseczno in the 2015 and 2018 summer seasons. GPP, Gross Primary Production (mg C\({}_{\rm ass}\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\)); CHLa, chlorophyll \(a\) concentration (mg Ch\(\cdot\)_a\(\cdot\)m\({}^{-2}\)); TSI CHLa, Carlson’s Trophic State Index.
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 6 Encryption Evaluation
### Number of Pixels Change Rate  
The NPCR [9] is a test used to measure the avalanche effect in image encryption. The avalanche effect is utilized to test the diffusion mechanism efficiency. A single bit change can be made in image \(P_{1}\) to give a modified image, \(P_{2}\). Both \(P_{1}\) and \(P_{2}\) are encrypted to give \(C_{1}\) and \(C_{2}\), respectively. The NPCR is defined as follows:  
\[NPCR=\frac{1}{M\times N}\sum_{i,j}D(i,j)\times 100\ (\%) \tag{5}\]  
\[D(i,j)=\left\{\begin{array}{ll}0,&if\ \ C_{1}(i,j)=C_{2}(i,j)\\ 1,&if\ \ C_{1}(i,j)\
eq C_{2}(i,j)\end{array}\right. \tag{6}\]  
where \(D(i,j)\) is defined in Equation (6). The ciphertext images before and after 1 px change in a plaintext image are defined as \(C_{1}\) and \(C_{2}\). The pixel values at grid \((i,j)\) in \(C_{1}\) and \(C_{2}\) are described as \(C_{1}(i,j)\) and \(C_{2}(i,j)\), respectively. M and N are the dimensions of the image to be encrypted.
## 2 Materials and Methods
### Satellite Observations  
SIF observations are taken from the Global Ozone Monitoring Experiment-2 (GOME-2) on-board EUMETSAT's Meteorological Operational Satellite-A (MetOp-A) for the time period 2007-2014. These retrievals are based on the method by [PERSON] et al. (2017), and are obtained from the band 4 using a spectral window of 720-758 nm. Data is available globally at 0.5\({}^{\circ}\) spatial resolution and up to 1.5-day temporal resolution in the absence of clouds. SIF data is taken from GOME-2 rather than newer sensors, such as the Orbiting Carbon Observatory-2 (OCO-2) or the TROPOspheric Monitoring Instrument (TROPOMI), as GOME-2 has global coverage and the longest record length. PAR data from the Clouds and Earth's Radiant Energy System (CERES) (66) are selected based on the closest overpass time to GOME-2 (near 9:30 AM), and resampled from 1\({}^{\circ}\) to 0.5\({}^{\circ}\). SIF and PAR retrievals are cross-masked and temporally gap-filled prior to the calculation of their ratio using a 15-day moving window; when necessary, estimates are subsequently spatially gap-filled by weighting the eight neighboring pixels based on distance. Time steps with PAR \(<\) 40 W/m\({}^{2}\) are masked to exclude potential nighttime conditions in high latitudes. Finally, GLOBSNOW snow water equivalent data [67] is used to mask for snow conditions.
# Model Mediation to Overcome Light Limitations--Toward a Secure Tactile Internet System
## 4 Model Mediation
### Model Mediated Systems
#### 4.2.3 Recent Systems
The master side deploys the common Hunt-Crossley nonlinear predictor, which receives the force and position signals from the communication channel and from the end user as well and produces an output signal that represents the force feedback. The stability of the proposed system has checked by analyzing the dissipation energy. The master device has been considered as a cascaded three systems (i.e., human, master device, and Hunt-Crossley predictor) and decoupled from the slave domain. The human and the master devices have a passive impedances and authors proved that the approximation of Hunt-Crossley predictor also possess a passive impedance. The slave device has been proved to have passive impedance as well and thus, the stability has been ensured for the overall system. The system has been simulated for both constant and varying environments and simulation results have indicated that the system has achieved faithful force interaction and the matching between perceived and real environment dynamics was accurate. The system did not consider the DoF of haptic devices employed in master and slave sides, which represents the main weakness.