esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Natural Language Analysis Subgroup  
Natural language analysis can be divided into categories: quantitative methods, based on the simpler principle of a set of words, and more complex methods of understanding natural language. \"Bag of words\" is a simplified model used in NLP and information retrieval. In this model, the text is presented as an unordered set of words without regard to grammar or even word order. The simplifying assumption that these variables are independent is considered. Since these methods are mainly based on word and/or feature counts, these problems with medical annotation text become extremely relevant since all different representations of a feature must be counted as the same feature [52]. NLP allows you to parse text and extract metadata from content such as concepts, entities, keywords, categories, moods, emotions, relationships, semantic roles, and syntax. This will require the creation of a pipeline for text cleaning, automatic detection and correction of errors in fuzzy words, work with abbreviations, normalization of units of measure, normalization of date and time formats, text blurring, and text extraction. Several tools are used: Tokenizers [53], Segmenters [54], Tree Analyzer [55], and Dependency Analyzers [56]. In _Understanding Text from the Ground Up_, [PERSON] and [PERSON] demonstrate that Convolutional Neural Network (CNN) can achieve outstanding performance without knowledge of words, phrases, sentences, or any other syntactic or semantic structures in relation to human language [57]. [PERSON] and colleagues in _Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network_ presented a model for POS (Part-of-Speech) tagging [58].  
In [59], the effectiveness of the use of NLP methods for extracting knowledge about new chemical compounds, which are crucial for the drug discovery process, is shown. Despite the complexity of the task due to the large volume of existing patents and the complex linguistic properties of patents for chemicals, the use of NLP methods demonstrates high efficiency.  
In [60], [PERSON] and colleagues found an approach to NLP based on the embedding of DNNs. This method aims to convert the format of the Simplified Molecular Input System (SMILES) string into word embedding vectors to represent compound semantics. These vectors are fed into supervised machine learning algorithms such as LSTM, Support Vector Machine (SVM), and Random Forest (RF) to build quantitative models of structure and activity relationships on toxicity datasets.
## 3 Performance Analysis of NOMA-Enabled HSTRNs
### Outage Probability  
Outage performance is defined as the maximum rate that can be guaranteed for considered system. Outage probability is one of the critical metrics for determining the performance of emergency services [16]. In other words, the minimum outage probability is closely related to the capacity. Therefore, it is important to characterize the outage behavior of the energy harvesting HSTRNs.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 6 Conclusions  
This article proposes a centralized approach for organizing Internet of Things communication entities into an unequal hierarchy of hybrid clusters with the goal of preserving the network from rising hotspot issues and extending the lifetime of the network under the 5G environment. In particular, the suggested protocol has unique characteristics such as chain topology formation, clusters of different communication topology, balancing energy consumption among cluster heads, preserving energy consumption of nodes within the cluster, selecting and implementing suitable communication interface of the IoT application system. These features were achieved through the following implementations. First, a vector projection approach that takes into account the central station position for generating shorter concentric chains was used. In addition, a probability sub-optimal and multi-hop routing mechanism were implemented to lessen the load on the cluster heads and to extend the lifespan of the network. Finally, a ranking method was used to select proper communication interface. Numerical results showed that unequal clustering mechanism optimized the existence of network and balanced energy depletion through cluster heads benchmarked against the state-of-the-art techniques.  
In addition to the numerical simulations, as a future research direction, the proposed energy-efficient routing protocol can be augmented with a real-world off scenario-based framework. For instance, a co-simulation of the wireless sensor node deployment can be  
Figure 14: Performance results for 300 nodes in a 200 m \(\times\) 200 m network.  
Figure 13: Performance results for 150 nodes in a 200 m \(\times\) 200 m network.  
conducted by incorporating their geographic coordinates assuming the sensor nodes are GPS-enabled.  
M.B.; methodology, M.B. and H.T.R.; software, M.B. and A.A.; validation, M.B., N.C. and H.T.R.; formal analysis, H.T.R and M.B.; investigation, M.B., H.T.R., N.C. and A.A.; resources, M.B. and H.T.R.; data curation, M.B. and H.T.R.; writing--original draft preparation, M.B., H.T.R., N.C. and A.A.; writing--review and editing, M.B. and H.T.R.; visualization, M.B. and N.C.; supervision, M.B. and H.T.R.; project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflicts of interest.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
###### Abstract
The Pearson coefficient is used to assess the degree of correlation between two variables for each IMF (Intrinsic Mode Functions) component of the decomposition. The IMF component that can characterize the fault is accurately selected by combining the alignment entropy. The TEO algorithm calculates the time at which the captured wave head reaches the end of the line. In terms of fault location, the \(\alpha\) mode and \(\beta\) mode components are used to calculate the fault distance at the end of the line, effectively eliminating the influence of wave speed and fault time on the location results. Furthermore, according to the distance difference between each end and node, a fault branch judgment matrix is constructed to achieve accurate selection and location of fault segments. Finally, ATP/EMTP is used to further verify the method.
###### Abstract  
Fractional Fourier transform (FrFT) is a useful tool to detect linear frequency modulated (LFM) signal. However, the detection performance of the FrFT-based method will deteriorate drastically in underwater multi-path environment. This paper proposes a novel method based on time-reversal and fractional Fourier transform (TR-FrFT) to solve this problem. We make use of the focusing ability of time-reversal to mitigate the influence of multi-path, and then improve the detection performance of FrFT. Simulated results show that, compared to FrFT, the difference between peak value and maximum pseudo-peak value of the signal processed by TR-FrFT is improved by 8.75 dB. Lake experiments results indicate that, the difference between peak value and maximum pseudo-peak value of the signal processed by TR-FrFT is improved by 7.6 dB. The detection performance curves of FrFT and TR-FrFT detectors with simulated data and lake experiments data verify the effectiveness of proposed method.  
Keywords:linear frequency modulation; time-reversal; fractional Fourier transform; detection method; underwater multi-path environment +
Footnote †: journal: Applied Sciences
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 1 Introduction
The quantification of breaks and trends in the forest EVI time series and their link with climatic and environmental drivers can help, therefore, to determine the current and future state of forested landscapes. A forest that is greening as opposed to one that is browning is likely healthier and hence, provides more ecosystem services. To quantify EVI changes and assess forest health in Algonquin Provincial Park and the surrounding area, we use remote sensing data and change detection algorithms. Specifically, we focus on the following objectives: (1) detect and quantify significant breaks in the forest EVI, (2) detect and quantify significant trends in the forest EVI, and (3) explain variations in the magnitudes of forest EVI breaks through the forest- and climate-related predictors. We predict that (1) protected forests will show more positive breaks and greening trends (but less browning) compared to non-protected forests as they experience less anthropogenic pressures; (2) negative abrupt changes in the EVI are more related to disturbances and climate stress compared to positive changes, which are likely more related to favorable climate conditions; and, (3) forest age and composition are important for understanding EVI abrupt change as drivers affect trees differently depending on these factors [34; 35].
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 4 Results
### TVOC Content Prediction (SGP30 Sensor Response)  
Figure 8 compares the performance of MLTs in predicting TVOC content (evaluated using an SGP30 sensor) in the living room based on IAQ monitoring in the kitchen. The following performance metrics were used: R2 (Figure 8a), MAPE (Figure 8b), and RMSE (Figure 8c). Their median values are shown in Table 3.  
As shown in Figure 8a, linear MLTs offered the best quality in predicting TVOC content, evaluated using the SGP30 sensor. Coefficients of determination were highest in the case of MLR (R\({}^{2}\) = 0.75) and MLP2 (R\({}^{2}\) = 0.75). The naive approach (R\({}^{2}\) = 0.55) showed lower performance. The quality of nonlinear MLTs (DT, RFR, KNN, and MLP1) was very low, as shown by small R\({}^{2}\) (0.18-0.41). Based on Figure 8b, linear methods MLR (MAPE = 0.93%) and MLP2 (MAPE = 0.91%) yielded the lowest percentage error of TVOC content prediction. In the case of the naive approach, the error was higher (MAPE = 1.65%). The lowest MAPE achievable using a nonlinear technique was offered by RFR (MAPE =1.53%). Regarding the RMSE of TVOC content prediction (Figure 8c), all the MLTs (RMSE = 195-289 s.r.u.) offered lower errors than the naive approach (RMSE = 302 s.r.u). In addition, the errors attained using linear MLTs were the smallest (RMSE = 195-197 s.r.u). In conclusion, it is possible to predict TVOC content, evaluated using an SGP30 sensor, in the living room based on IAQ monitoring in the kitchen with a percentage error of less than 1%, which corresponds to about 196 s.r.u. using linear models. Similar to T and RH, it is crucial to note that this result was achieved by prediction models trained on IAQ monitoring data collected in different apartments and not where the prediction was realized. The best prediction was achieved using MLR, and the quality of this model was the highest. Non-linear models, despite their poor quality, outperformed the naive approach in terms of prediction error.
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 2 Materials and Methods
### Parameters  
The relevant parameters were divided into two categories: those that pertain to the water and surrounding environment and those that pertain to the farmed species and the farm itself (Figure 1b). Parameters that affect the water, which is the natural habitat of the farmed species and thus has a direct or indirect impact on all organisms, are considered the most crucial. Among these, dissolved oxygen is considered the most important, but other parameters such as water temperature and chlorophyll-a can also have an indirect effect on the survival and well-being of the farmed species. The parameters are estimated to a large extent using satellite data and are validated through _in-situ_ measurements. Parameters related to the farm include the feeding rate, the oxygen consumption rate, and the increase in fish weight and biomass.
# Fungi-Templated Silver Nanoparticle Composite: Synthesis, Characterization, and Its Applications
## Materials and Methods
### Degradation Studies  
The catalytic activity of the FH-AgNPs was assessed by degradation of methylene blue (MB) at room temperature. The degradation studies were carried out in a quartz tube using a visible photoreactor. A tungsten halogen bulb of 364 nm and 250 W was used as the light source. In order to gain the absorption-desorption equilibrium of the dye, the dye sample (20 ppm) was bubbled with the catalyst (30 mg) for 30 min in the dark before irradiation. Approximately 3 mL of the solution was taken out at definite time intervals during the reaction and the catalyst particles were removed by centrifugation. A UV-Visible spectrometer was used to find out the percentage degradation of the dye with respect to time.  
The catalytic efficiency was calculated using the formula  
\[\%\ of\ degradation=\frac{C_{0}-C_{t}}{C_{0}}\times 100\]  
where \(C_{0}\) is the initial concentration and \(C_{t}\) is the concentration at a particular time interval.
## 6 Conclusions  
We have conducted a study on the impact of jamming in an RIS-assisted Dual-Hop FSO Communication system in the presence of a malicious jammer. A novel PDF for the S-IRS-D, UAV-IRS-D, and UAV-D links under the combined influence of AT, PE, and AoA fluctuations has been developed. Based on the jamming scenario, closed-form expressions for the end-to-end ABER have been derived for SISO-FSO systems. To mitigate the impact of jamming in an RIS-assisted Dual-Hop FSO Communication system, a SIMO FSO system has been implemented. We have utilized the Mellin transform to derive the PDF of the legitimate channel and the jamming channel in the SIMO FSO system and the end-to-end ABER have been derived for SIMO-FSO systems under different jamming Scenarios. The simulation results are compared with the analytical results to validate the accuracy of the derived expression. It has been observed that as the number of receiving apertures increases, the system shows a significant improvement in bit error performance. For the SISO and \(1\times 2\) FSO systems, we conducted separate analyses on the impact of different jamming probabilities. In Scenario 1, the jamming occurs at D, while in Scenario 2, the jamming occurs at the RIS, affecting the system's ABER. In regions with low SJR regions, it was found that ABER is inversely proportional to the jamming probability \(\rho\); conversely, in relatively high SJR regions, ABER exhibits a direct proportionality with \(\rho\). Simultaneously, the jamming in Scenario 1 has a greater impact on the system's ABER compared to the interference in Scenario 2. The impact of different positions of the RIS on the overall system performance has also been investigated. The system achieves optimal performance when the RIS is situated in closer proximity to the source.  
Conceptualization, J.W. and D.G.; methodology, J.W.; software, J.L.; validation, J.W., L.H. and H.D.; formal analysis, S.Z.; investigation, J.W.; resources, D.G.; data curation, J.L.; writing--original draft preparation, J.W.; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (U21A20511) and Natural Science Foundation of China, grant number (62371457).  
Data Availability Statement: Data will be made available on request.  
Conflicts of Interest: The authors declare no conflict of interest.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 3 Resource Allocation
### Power Allocation  
Power allocation is next performed for all users. Firstly, we apply the DL algorithm to allocate the power among subchannels, and then the power of the multiplexed users is given according to constraints. The specific power allocation process is as follows.
## 3 Experimental Study
### Experimental Setup  
#### 3.1.1 Dataset Overview  
This study adopted the publicly available C-MAPSS turbofan engine sub-dataset FD001 generated by the Commercial Modular Aero-Propulsion System Simulation software (version 2) created by NASA [22]. The dataset consists of simulated sensor signals of hundreds of motors operating until system failure due to varying degrees of deterioration. In this study, the FD001 sub-dataset was selected to verify the effectiveness of the proposed approach.  
Detailed information on the FD001 sub-dataset is presented in Table 1. While each engine was set to operate under stationary conditions, the FD001 sub-dataset was intentionally contaminated with noise.  
Figure 4: Proposed network structure.  
The time series data were acquired from 21 sensors for each engine in the dataset. The description of the 21 sensors in the dataset is presented in Table 2. Each engine unit had a certain level of initial wear that was considered normal. Furthermore, engines were healthy in the early stages of operations and tended to degrade over time until a failure occurred.  
The FD001 sub-dataset included training and test sets. In the training set, the time series of 21 sensor values, which were collected periodically at each operation cycle, included the whole lifetime of the engines. In contrast, in the test set, engine sensor readings were terminated at an arbitrary operation cycle before the system failure. The FD001 sub-dataset had 20,631 cycles for the training dataset and 13,096 for the test dataset. The last point of the cycles in the test dataset was the point at which the RUL was predicted. The primary purpose of RUL prediction is to create a model based on the training dataset and to predict the RUL for the endpoint in the test dataset using the proposed model.
# Prestack Seismic Inversion via Nonconvex L\({}_{1-2}\) Regularization
## 3 Synthetic Examples
### Analysis of Factors Influencing the Inversion Results  
[PERSON] and [PERSON] et al. both argued that high-quality far-angle gathers are the key to generating accurate density results when performing a prestack AVO inversion [4; 15; 16]. To study this problem in-depth, we divided the angle gathers (3\({}^{\circ}\) to 45\({}^{\circ}\)) into five groups of small, medium, and large angles (Table 1). The inversion results corresponding to the five sets of angle gathers are shown in Figures 7-11.  
Figure 8: The inversion results for angle gathers of 6\({}^{\circ}\), 21\({}^{\circ}\), and 36\({}^{\circ}\) using **(a)** our L\({}_{1-2}\) regularization inversion method and **(b)** the BP inversion method. The red, blue, and black curves represent the inverted, model, and initial values, respectively.  
Figure 7: The inversion results for angle gathers of 3\({}^{\circ}\), 18\({}^{\circ}\), and 33\({}^{\circ}\) using **(a)** our L\({}_{1-2}\) regularization inversion method and **(b)** the BP inversion method. The red, blue, and black curves represent the inverted, model, and initial values, respectively.  
Figure 9: The inversion results for angle gathers of 9\({}^{\circ}\), 24\({}^{\circ}\), and 39\({}^{\circ}\) using **(a)** our L\({}_{1-2}\) regularization inversion method and **(b)** the BP inversion method. The red, blue, and black curves represent the inverted, model, and initial values, respectively.  
Comparison of the five groups of experimental results shows that the P/S impedance results for the L\({}_{1-2}\) and BP inversion methods are not sensitive to the angle gathers. However, per the Fatti approximation formula, errors in the density calculations decrease as the degree of the angle gather increases. Even in the absence of far-angle gather seismic data, the P/S impedance and S impedance results generated by the L\({}_{1-2}\) inversion method are more accurate than those produced by the BP inversion method.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 4 Metal Telluride Thin Films
### Lanthanum Telluride Films  
The properties of lanthanum telluride (La\({}_{2}\)Te\({}_{3}\)) films were studied by using various tools. The obtained films have hydrophilic surfaces and showed specific surface areas of about 51 m\({}^{2}\)/g [126]. The power density, energy density, and specific capacitance were 7.22 kW/kg, 60 Wh/kg, and 194 F/g, respectively. Researchers concluded that these materials indicated capacitive retention at about 8% over 1000 deposition cycles when the scan rate was 100 mV/s.
## 3 Results and Discussion
### Histograms of Pore Distribution and Pore-Volume Distribution (In Length)  
As an alternative to the two previous subsections, it is possible to classify pores according to their length instead of their volume. In this way, firstly, histograms of pore distribution (in length) and, secondly, histograms of pore-volume distribution (in length) can be obtained.  
In the first case, the histograms of pore distribution represent, for each length range, the number of pores whose length is within that range. Analogous to the previous case, it is often interesting to represent nested histograms. The histograms of the specimens considered in this work are shown below (Figure 21).  
The results show that smaller pores are again predominant. In this case, about 50% of all pores have a length between 0.1 and 0.3 mm. In a more in-depth analysis, it is observed that the most common length is in the range of 0.20 to 0.25 mm. Differences are observed among the specimens, although they do not seem to be very significant.  
It should be recalled that, due to the limitations of the scanning equipment, all those pores with a length of less than 0.1 mm were discarded.  
In the second case, the histograms of pore-volume distribution (depending on pore length) represent, for each length range, the total volume of those pores whose length is within that range (Figure 22).  
Figure 21: Histograms of pore distribution (in length) for different ranges, from the largest (**a**) to the smallest (**b**).  
As can be seen in Figure 21, the largest percentage of the porosity of the specimens is in the pores whose length ranges from 0.7 to 0.9 mm. Although this range of pores is not the most numerous, it is the one with the largest volume. In this case, a greater dispersion of results is observed among the specimens.  
Again, the aim of these analyses is to know as precisely as possible the concrete microstructure, in order to try to explain the dispersion observed in macroscopic tests. This aspect is very interesting for the study of mechanical fatigue in concrete, whose intrinsic dispersion is very wide.