esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 4 ANSYS Workbench Implementation
### Blade and Protection Implementation  
As briefly discussed in Sections 2 and 3, the wind turbine is a grounded structure, hence, the lightning current as a result of return stroke will then be passed safely to the ground through hub, nacelle, tower, and tower footing at the ground level. Hence, when lightning strike on the lightning receptor installed on a blade, the ground is elevated to the highest tip at the time of strike due to the blade tip being at its highest point at a time. Thus, this assumption is also used by many other lightning researchers around the world [1; 3; 4; 8; 10; 14; 15; 19; 20; 26] and also for this study. Owing to that, single blade was examined without any attachment to rotor and nacelle. The ANSYS Workbench version 18.2 was used to carry out the simulation of the lightning protection of blade. The available software license was for Academic Research, which restricts the meshing node number to 300,000 which corresponds to around 40,000 elements depending on the meshing algorithm chosen. As it was mentioned earlier, the size base was taken from an existing model (Vestas V164-9.5 MW. The turbine had approximately 80 m long blades; in the model it was extended slightly to represent a potential future size. As shown in Figure 1, the hollow blade design can be seen from what was modelled in ANSYS DesignModeler. The model measurements were 85 m long, 5 m wide, 2.6 m depth, 10 times base-to-tip ratio, and 0.015 m wall thickness. The current was applied at point A, meanwhile, points B and C were specified as 0 V.  
Figure 1: Wind turbine blade for simulation.  
The blade material was chosen to be E-glass fiber-reinforced polyester with the necessary values set manually [30; 31] to serve as an insulator-type blade, the lightning conductor was set to the copper parameters taken from standards [12] with a 50 mm\({}^{2}\) round cross-section as the minimal specified area. For evaluation, one of the recommended method by IEC 61400-24 [7] was considered where this method was used previously for smaller turbines, although in this project, it was examined for larger turbine blade.
## 3 Results and Discussion
### Water Vapor Transmission Rate  
Water plays an important role in food spoilage; therefore, an important property of edible films is their ability to prevent the exchange of moisture between the medium and the food matrix. WVTR is the measure of the moisture that goes through a unit space of material per unit time [42]. When the papaya content of the composite film increased from 8 to 10%, the WVTR decreased significantly because of its easy dispersion in the porous film, as presented in Table 4 and Figure 9. The value of WVTR for all edible films increased when compared with the taro starch in addition to glycerol, a result from [PERSON] et al. [55]. As a result, the denser membrane structure created more tortuous pathways for the diffusion of water molecules and thus reduced the membrane's WVTR. On other hand, when soy protein was added to the papaya and gelatin films, the increased carboxyl group yielded increased WVTR values. The film surface became coarser and thicker due to the high concentration of soy protein, with increased pores in the membrane matrix resulting in bigger openings in the film grids [56]. The results also showed that the WVTR of the edible gelatinous film was slightly higher than that of papaya, which may be due to the water-induced swelling of the membrane [57].  
Figure 8: Nanoindentation graphs of the films: (**a**) Penetration depth vs. time curves of the edible thin films, with gelatin (G-2) film indicated by blue line, papaya (G/PP-2) film indicated by red line, and soy protein (G/PP/SP-2) film indicated by black line. (**b**) Nanoindentation curve of edible films plotted against force vs. displacement. Microscopic images of nanoindentation impressions of (**c**) G-2 films, (**d**) G/PP-2 films, and (**e**) G/PP/SP-2 films.  
Plasticizer likewise plays a significant part in WVTR. The concentration of glycerol plasticizer can increase the flexibility and water vapor transmission rate (WVTR) of edible films. The use of plasticizers increases the physical and functional properties of edible films, (1) flexibility, sensitivity, and moisture, and changes (2) functional properties. Plasticizer decrease influences: (1) the biopolymer chains adjacent to each other, (2) the sensitivity that occurs out of the water, and (3) the flexibility of the material [58].  
At room temperature, minimal weight loss was observed with 10 g papaya edible film (3.6 gm), and maximum weight loss was observed with 9 gm gelatin (5.76 gm). In general, the results show that weight loss can be reduced by adding various content to slow the penetration of water into the environment, where the content itself acts as a barrier to the edible surface [59].
## 4 Results and Discussions
### Results of ATPA  
Figure 8 compares the experimentally measured noise level at Target 1 with the noise level computed using the ATPA methodology at the same location. Additionally, the figure delineates the contributions of structure-borne and air-borne noise components separately.  
As shown in this figure, the noise level computed using ATPA closely aligns with the measured noise pressure. The figure also illustrates that air-borne noise is the main contributor to the overall noise. The structure-borne noise component is higher in the range of 160-1000 Hz compared with the rest of the frequency range.  
The noise pressure at Target 2 is calculated with the same method. Figure 9 illustrates a comparison between the sound pressure levels obtained using ATPA and direct measurements. The measured noise level is well synthesized with the ATPA method, except in the range between 200 and 300 Hz, where a gap between the measurement and synthesis is observed. This might be due to a modal behavior that cannot be reconstructed with the ATPA energetic approach used in this work.  
As in Target 1, the results show that air-borne noise significantly outweighs structural-borne noise, across the whole frequency range.  
Figure 8: Synthesized sound pressure level calculated using ATPA method at Target 1.  
Figure 9: Synthesized sound pressure level calculated by ATPA method at Target 2.
## 2 Materials and Methods
### SERS Measurements  
Raman spectra were measured using a _Princeton Instruments TriVista CRS (777)_ spectrometer combined with an optical microscope (20\(\times\) objective). One monochromator was used with gratings of 1800 mm\({}^{-1}\) and 600 mm\({}^{-1}\). The excitation source used was a _Cobalt Samba 150_ 532 nm YAG diode-pumped laser with a power of 2.55 mW at the sample. Measurements were conducted using various exposure times and accumulations. Each sample was measured at multiple points in order to obtain a more complete dataset of spectra characterising the average performance of the substrate.  
Before every measurement, the system was calibrated using a crystalline silicon plate to eliminate any spectral inaccuracies of the Raman signal, as well as to obtain the maximum possible intensity (peak position at 520 cm\({}^{-1}\)). Regarding data comparison, the signal of the different samples was measured under the same conditions with constant parameters--exposure time and laser power--using filters. The spectra were averaged from around 15-20 measurements at various points on the substrates. In this way, signal fluctuations (which can be large in the case of SERS) tend to be minimised and a better overall characterisation for each substrate is obtained. The fluorescence backgrounds were removed using a spline, manually approximating the shape of the fluorescence spectrum. The (analytical) enhancement factor was calculated by taking the intensity ratio of a Raman peak between a normal Raman measurement and a SERS measurement, normalised by the respective concentrations of the analyte [2].  
\[AEF=\frac{I_{SERS}}{I_{Raman}}\frac{C_{Raman}}{C_{SERS}} \tag{1}\]
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 2 Background and Algorithm Overview
### Overview of GNSS-R Open Loop Tracker Algorithm  
The methodology of the GNSS-R open loop tracking algorithm presented here consists of two main parts: defining the reflection geometry and predicting the delay and Doppler of the reflected signal.
## 5 Future Trends, Challenges, and Opportunities  
RISs have been considered as a key enabling technology for 5G and 6G systems to aid wireless communications. A higher data rate, low latency, spectral efficiency, and support from a massive number of users are some of the benefits RISs provide. According to the listed ML applications in this survey, the future directions that authors pointed out are the following:  
* The extension or scalability of systems from MISO to MIMO;
* The use of multiple RISs;
* The use of larger datasets;
* Improvements in the ML-trained model to avoid overfitting;
* Hyperparameter tuning for DL and DRL approaches.  
Additionally, since the challenges for 6G consist of having self-planned and self-organized networks, recent studies have applied ML algorithms to some challenges of RIS-assisted communication. Despite the significant advances in ML approaches for RIS communications, it is crucial to understand that this research area is relatively new, and there are still some open research problems and issues to address.
# Onboard Processing in Satellite Communications Using AI Accelerators
## 4 AI-Capable Commercial Chipsets
Working on onboard device requirements, AMD announced the release of the first space-grade XILINX Versal adaptive SoCs, enabling onboard AI processing in space [30; 45; 48]. The actual information about this device is predicted to be available in early 2023 (according to the information available in January 2023 [30]), and will be  
Figure 11: Performance related to the power consumption of the most remarkable families in byte operations (INT8), half-precision floating point operations (FP16), and simple-precision floating point operations (FP32). based on the Versal AI Core VC1902, a family not analyzed for its high power consumption (87 mW reported in [49]), including Class B qualification, radiation tolerance, and 45 \(\times\) 45 mm\({}^{2}\) packing [48]. There is no information yet about the possible power consumption, the principal inconvenience of its predecessor, and the computer capacity of the chip.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IIoT & Industrial Internet of Things. \\ UL-AoA & Uplink Angle of Arrival. \\ UL-TDOA & Uplink Time Difference of Arrival. \\ Multi-RTT & Multi-round Trip Time. \\ DL-AoD & Downlink Angle of Departure. \\ DL-TDOA & Downlink Time Difference of Arrival. \\ MIMO & Multiple Input Multiple Output. \\ PRS & Positioning Reference Signal. \\ SRS-Pos & Sounding Reference Signal for Positioning. \\ UE & User Equipment. \\ NR & New Radio. \\
3 GPP & Third-Generation Partnership Project. \\ LPHAP & Low Power devices with High Accuracy Positioning. \\ GNSS & Global Navigation Satellite System. \\ RSTD & Reference Signal Time Difference. \\ A-AoA & Azimuth Angle of Arrival. \\ Z-AoA & Zenith Angles of Arrival. \\ TRP & Transmission/Reception Point. \\ RSRP & Reference Signal Received Power. \\ OFDM & Orthogonal frequency-division multiplexing. \\ PAPR & Peak to Average Power Ratio. \\ PRB & Physical Resource Block. \\ RE & Resource Element. \\ MUSIC & Multiple Signal Classification. \\ SCS & Subcarrier Spacing. \\ \end{tabular}
## 4 Experiments  
### Experimental Setting  
In this section, we demonstrate the denoising performance of our proposed method by comparing it with state-of-the-art methods, such as BM3D [1], WNNM [3], DnCNN [4], FFDNet [5], DAGL [25], DeamNet [26], DRUNet [27], IRCNN [28], DIP [23], N2V [16], and SwinIR [11]. Three datasets were utilized for the performance comparison: Set5 [29], Set12 [4], and BSD68 [30]. The Set12 dataset consists of 12 grayscale images that are widely used for image denoising tests. The BSD68 dataset contains 68 grayscale images randomly selected from the BSD500 dataset. The Set5 dataset comprises traditionally used color images for comparing image denoising methods. The images in the three databases are corrupted by AWGN with noise levels \(\sigma=15,25,50\). We used PSNR as an evaluation metric. A better method is expected to yield a higher PSNR value.
## 3 Methodology
### Preprocessing  
Data preprocessing is an essential step for several NLP applications. This step varies slightly from one NLP application to another based on the application requirements and goals. The need for data preprocessing increases in morphologically rich languages such as Arabic and Turkish languages. Arabic text has several characteristics that make it more challenging for NLP systems such as diacritics and mixture of dialect, modern, and classical texts. These challenges can be observed obviously in social media texts [46]. Preprocessing the text before feeding it to machine learning algorithms is important and can improve the accuracy of these models sharply [47].  
Several preprocessing steps have been applied to the Arabic microblogs used to train and evaluate the proposed models, as shown in Figure 1. These steps involve text cleaning, normalization, removing diacritics, removing or replacing emojis, tokenization, and removing stop words. These steps are widely used in Arabic sentiment analysis and have already shown their efficiency for several NLP tasks [48,49]. To ensure a fair comparison between the proposed models, the same preprocessing steps were applied for each model with all datatsets.  
The first step of the data preprocessing stage is text cleaning, which includes removing the URLs, mentions (@username), HTML, line breaks, and extra white spaces. This step is followed by normalizing Arabic letters to unify the different forms of Arabic letters such as!!  
After normalizing the Arabic tweets, we proceed with text tokenization step and stop-words removal. Text tokenization is the process of segmenting the text into tokens to make it more convenient for producing words vectors. This step is followed by stop-words removal. Stop-words that do not express any emotion, such as preposition, were removed. During our experiments, we observed that removing stop-words in static-embedding based models improves the model performance. However, removing stop-words is not performed in neural models that rely on contextual embeddings, since these models consider all stop-words to provide enough context information. In addition, stop-words receive as much attention as non-stop-words in BERT-based models [50]. Lastly, to study the effect of emojis in learning process, we removed the emojis for one experiment and replaced them with their representation in another experiment, as will be discussed in Section 3.2.
## 4 Experimental Setup
### Sentence Significance Factors Determination
#### 4.4.2 Sentence-to-Sentence Cohesion Assessment  
Sentence-to-sentence cohesion is computed using the TextRank algorithm [17], which assumes that a sentence similar to other sentences is a significant sentence.  
To apply the TextRank algorithm, there is a need to build a graph using bug reports. The algorithm considers each sentence as a vertex and takes the similarity between two sentences as the weight of an edge. The values of all nodes are computed using the TextRank algorithm; however, the TextRank algorithm has limitations in that it cannot capture the entire meaning of a sentence because it computes a value only through the word frequencies.  
Our method uses a TextRank algorithm based on sentence embedding [19] to capture the meaning of a sentence. First, the method computes the cosine similarity between two sentence vectors created in Step 2. The cosine similarity between two sentence vectors becomes the weight of an edge. Second, the TextRank algorithm is applied to calculate the value of each sentence. The TextRank value of sentence \(i\) is denoted as \(Cscore_{i}\) and is calculated as shown in Equation (8)  
\[Cscore_{i}=(1-d)+d\times\sum\limits_{V_{j}\in In(V_{i})}\frac{w_{ji}}{\sum_{V _{k}\in Out(V_{j})}}\times Cscore_{j} \tag{8}\]  
For a given vertex \(V_{i}\), \(In(V_{i})\) is the set of vertices that point to vertex \(V_{i}\), and \(Out(V_{i})\) is the set of vertices to which vertex \(V_{i}\) points. The weight of the edge between two vertices \(V_{i}\) and \(V_{j}\) is denoted \(w_{ij}\). A damping factor, which is denoted as \(d\), is the probability of jumping from a given vertex to another random vertex in the graph. This factor is usually set to 0.85 [20]; we also use this value in our implementation.  
The sentence is given an initial value of 1. The TextRank algorithm is run on the graph for several iterations until the TextRank value converges. The larger the number of sentences is with a similar meaning, the more important a sentence is, and the higher the TextRank value of the sentence.
## 2 Samples and Experimental Procedure
### Experimental System and Testing Method
#### 2.2.3 AE Monitoring  
AE tests were performed on the AE system of Physical Acoustics Beijing Office (China) using a threshold value of 40 dB. The main amplifier was set to 40 dB, and the sampling frequency was 1 MHz. The frequency range of the sound-emission probe was 20-400 kHz. Six Nano30 sensors were uniformly fixed on both sides of the sample, apart from the DIC observation front and back, and an appropriate amount of couplant was applied between the sensor and sample to enhance the sample's adhesiveness and reduce the material's AE signal depletion of the material.  
Figure 2: Labeled image of the loading system and DIC.  
The AE and high-definition cameras were synchronized during the sample single-axis compression process, ensuring that the digital images, mechanical parameters, and sound-emission-related parameters were recorded simultaneously in real time (Figure 3).
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Graph Coloring MAC (GC-MAC)  
Another energy-saving graph coloring MAC technique (GC-MAC) has just been suggested, similar to ST-MAC [87]. Due to the fact that ST-MAC uses a centralized scheduling method. It just necessitates a global understanding of the network nodes. Due to the high latency and limited data transfer rates in UWSNs, it is costly [88], GC-MAC, in comparison to ST-MAC, can execute collision-free communication in a distributed manner without knowing the exact location of a node, which is required in terrestrial networking [8].
## 4 Forecasters Development
### Development of the Artificial Intelligence Models
#### 4.2.2 Models Hyper-Parameters Adjustment
Therefore, looking at the results obtained, it can be concluded that the model that better forecasts the PV production is obtained by adding three neurons in the hidden layer and setting 1-time delay step in the input and 2 in the feedback. Hyper-Parameters Adjustment of SVR  
\(\varepsilon\)-SVR with RBF kernel has three hyper-parameters that can be adjusted: \(C\), \(\varepsilon\), and \(\gamma\). \(C\) is the regularization parameter, \(\varepsilon\) determines the margin of the bounding decision, and \(\gamma\) determines so far, the influence of a single training example reaches. If \(C\) has a large value, the decision function is better at estimating training points, and if \(\gamma\) is too large, the influence of support vectors is more localized. In both cases, overfitting will happen, and as a result, a trained model with bad generalization capabilities will be obtained. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**N\({}^{o}\) of Neurons**} & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline
2 & 143.60 & 253.23 & 0.8849 \\
4 & 131.62 & 237.86 & 0.8917 \\
5 & 130.07 & 235.69 & 0.8931 \\
6 & 132.53 & 239.13 & 0.8911 \\
8 & 131.75 & 238.03 & 0.8923 \\
10 & 131.91 & 238.61 & 0.8910 \\
15 & 132.04 & 241.99 & 0.8872 \\
20 & 133.02 & 243.59 & 0.8853 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of tests for hyper-parameter adjustment. The selection of each hyper-parameter was made through Bayesian optimization, an automatic search algorithm based on the Gaussian process. Bayesian optimization has become a successful tool for hyper-parameter tuning that can achieve great forecast accuracy in a few samples [35]. Cross-validation (CV) is used as a validation method during the optimization. CV is a data re-sampling method to assess the generalization ability of predictive models and to prevent overfitting [36] because time series data are used to train the model, the CV was employed on a rolling basis. The Bayesian optimization was made with a 10-day TW for training, using a CV to measure the performance of each hyper-parameter combination and the model hyper-parameters \((C,\ \epsilon,\ \gamma)\) were sampled using a log-uniform distribution as specified in Table 4. The results from the hyper-parameter optimization are shown in Table 5.
# New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
## 5 Conclusions  
Computers, communications and consumer products go hand in hand with our life. Suppliers of various brands need to continuously carry out new product research and development. If the demand can be accurately predicted, it will be of great help to enterprise decision makers and production equipment suppliers.  
To forecast the production and demand of downstream customers, this research reveals a new grey model which uses boxplots to estimate the trend of data and, if this is combined with FGM, it is known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM), which improves the accuracy of predictors by setting the coefficient sets of \(\alpha\) in traditional grey model, even if there is no large amount of past data [17; 18].  
In the experiment, we try to observe the demand pattern of customers through the new grey prediction model BP-FGM (1,1). The difference between FGM (1,1) and BP-FGM (1,1) is that the improvement of FGM (1,1) aims to explore the suitable background value to make the prediction value more accurate. BP-FGM (1,1) not only uses the advantages of the fractional cumulative grey prediction model, FGM (1,1), but adopts the concept of the fuzzy sequence set that introduced box-and-whisker plots combined with triangular membership functions to estimate the trend of data in order to fuzzify the time-series data, and then, through the GM modeling procedure, improves the prediction accuracy, as is illustrate in Figure 6.  
For modeling, MAPE is established to be acting as the objective function of the optimization model, the results from the three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.  
In this study, we focused on the new product demand of the semiconductor industry only, experiments for different research, such as weather and temperature forecasts, grain yield or short-term electricity demand, may be studied in future work.  
Conceptualization, D.-C.L. and Y.-S.L.; methodology, W.-K.H.; software, W.-K.H.; validation, Y.-S.L. and W.-K.H.; writing--original draft preparation, W.-K.H.; writing--review and editing, Y.-S.L.; supervision, D.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, grant number MOST-110-2221-E-006-194.  
Figure 9: Representing the absolute percentage error for Case C by using the FGM and BP-FGM model.