esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 5 Preprocessing of Datasets  
Normally, data derived from the wind farm contain many outliers, noisy data, and missing values. These unreasonable data decrease the prediction accuracy. Data preprocessing can provide a strong preliminary interpretation of the dataset; consequently, data preprocess is the first stage in a typical forecasting algorithm.  
* Data cleaning: original data have many outliers, and these data are unreasonable and need to be cleaned.  
Figure 3: Schematic structure of an autoencoder with three fully connected hidden layers.  
Filling missing data: It is necessary to fill missing data while some measured wind data collected from the wind farm were lost. In this study, missing data are filled by linear interpolation. Thus, the accuracy of the forecasting model can be significantly improved.
* Data normalization: Different variables have different units; that is, the process of data normalization is important. The following formula was used to transform all data values within the range 0 I: \[D_{i,n}=\frac{D_{i}-\text{min}(D_{train})}{\text{max}(D_{train})-\text{min}(D_{ train})}.\] (16)  
Every country has developed their own NWP systems, including global and regional models. This work utilizes historical wind power data recorded at Taiwan's wind farms and the NWP wind-speed forecasts by Taiwan's central weather bureau. The state-of-the-art NWP system is essential for the weather forecasts and climate applications. In particular, NWP also plays an important role in the application for renewable energy. The Taiwan CWB has developed the global and regional NWP systems for 30 years [21]. In this paper, three CWB NWP systems were used to evaluate the potential for forecasting wind power, which are called WRFD, RWRF, and WEPS. It consists of the complex model of WRF. In a WEPS model, it generates ensemble wind speeds (20 different NWP outputs) every three hours. An example of a 20-WEPS member of wind speeds is shown in Figure 4.  
The proposed dimensional reduction module is called the autoencoder-based deep learning method. The lower-bound and upper-bound NWP values are obtained from 20 members of ensemble wind speeds by dimensional reduction. It converts 20 NWP ensemble values to two values (lower and upper bound) to enhance the forecasting accuracy. The autoencoder is trained using both encoder and decoder. To obtain the dimension reduction, the layer between the encoder and decoder is set to two. Then, the middle layer is used as an output layer, as shown in Figure 5. Figure 6 shows the overall results after the preprocessing process, which points out the accuracy and stability of the new dataset. In this study, high dataset stability is defined if the data go through the processes of removing noises, filling missing data, and using the algorithm of the autoencoder. These processes are critical to improve the performance of a forecasting model.  
Figure 4: Example of a 20-WEPS member of ensemble wind speeds. WEPS: Weather Research and Forecasting (WRF) ensemble prediction system.
## 2 Materials and Methods
### Statistical Analysis  
To bridge subjective risk assessments from the survey with the objective and numerical measures described in the previous sections, this study performed a series of statistical analyses. First, the study investigated whether participants' risk ratings differed significantly among four categories of sidewalk environments based on the presence (1) or absence (0) of obstructions (O) and barriers (B). These were labeled O0_B0, O0_B1, O1_B0, and O1_B1. For example, all 14 images in the control dataset, which represent scenes with neither obstructions nor barriers, were classified under the O0_B0 category. Because the dataset did not meet normality assumptions, the non-parametric Kruskal-Wallis test was employed to evaluate whether there was a statistically significant difference in the perceived risk among these four groups. Whenever the Kruskal-Wallis test indicated a significant effect (\(p<0.05\)), [PERSON]'s post hoc test was carried out to identify which specific pairs of groups differed. Next, the study examined the impact of the proportion of the sidewalk area occupied by obstructions and barriers denoted by the obstruction-sidewalk ratio and the barrier-sidewalk ratio, respectively. To investigate how increases in these ratios relate to perceived risk, both [PERSON]'s rank correlation and ordinary least squares regression analyses were performed. Lastly, considering that some visible characteristics in images may not directly influence the perceived risk but might shape how participants interpret the built environment, this study explored the mediating effects of candidate variables on the relationship between the obstruction-sidewalk ratio and participants' perceived risk using a non-parametric, bootstrap-based approach.
## 2 Materials and Methods  
In total, 27 samples (9 commercial labels \(\times\) 3 bottles per label) from different commercial whisky brands were used. The whisky samples were sourced from commercial labels having different alcohol contents (range between 37 and 40% _v_/_v_) and stored during different years (aging) depending on the commercial brand. Commercial labels used in this study included Canadian Club (Canada), Chivas (Scotland), Jameson (Ireland), Crown Royal (Canada), Johnnie Walker (Black Label) (Scotland), Johnnie Walker (Red Label) (Scotland), Jim Beam (Black Label) (USA), Jim Beam (White Label) (USA), and Kilbeggan (Ireland). Prior to analysis, samples were diluted in milliQ water (1/10) to optimise the UV-VIS spectra of the sample (maximum absorbance of 2.5 a.u.). Samples were scanned in transmission mode using a 1 mm path length cuvette before being equilibrated in the instrument and then analysed at 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, and 55 \({}^{\circ}\)C (\(\pm\)1 \({}^{\circ}\)C). Both milliQ (distilled water) water and 40% ethanol were also scanned as controls. The spectrum of each sample was collected in an Agilent Cary 3500 UV-VISspectrophotometer in the range 200 nm and 800 nm in progressive steps of 1 nm (Agilent Technologies, Asia-Pacific, Mulgrave, Australia, 2019). Cary 3500 UV workstation software was used to obtain graphs and adjust experiment settings (Agilent Technologies, Asia-Pacific, Australia, 2019). Overall, the data set comprised 108 samples (9 commercial labels \(\times\) 3 bottles per label \(\times\) 4 scanning temperatures).  
The UV-VIS spectra data were exported in comma-separated values (CSV) format into The Unscrambler (version X, CAMO ASA, Oslo, Norway) for chemometric analysis. Second derivative was calculated using Savitzky-Golay transformation (10 point smoothing and second order filtering) before principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Discrimination models were achieved using PLS-DA (PLS1 algorithm), where the cut-off was set to 0.5 [25; 26]. Cross validation (leave one out) was used as the validation method in both PCA and PLS-DA [25; 26].
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 2 Electrification of Marine Vessels
### Artificial Intelligence and Applications in Marine Vessels
In hybrid vessels, DL models such as convolutional neural network (CNN)-long short-term memory (LSTM) structures are utilized to instantaneously assess battery load profiles and switch between diesel and electric propulsion in response to sudden environmental changes, which ensures both performance continuity and fuel savings. Advanced safety and reliability: With constant monitoring of the health and different status conditions of batteries and other energy systems, AI and ML approaches can dramatically improve the stability, safety, and reliability of naval systems. These approaches can predict anomalies pointing to various safety risks, such as overheating batteries or electrical errors, and this way, they can enhance the trigger automatic safety protocols to alert human operators beforehand [52; 59; 60]. Integration with renewable energy sources: AI and ML approaches can integrate renewable energy sources like solar and wind into EMSs and BMSs for naval environments. These approaches can forecast renewable energy availability, handle its integration with traditional power sources, and consequently utilize stable and efficient energy supply even in varying environmental conditions and factors [53]. Economic processes: These approaches can conduct cost-benefit analyses in real time to decide the most economically efficient direction for managing various systems onboard, including energy and battery resources. Hence, this provides remarkable cost savings in fuel and maintenance for EMSs and BMSs in naval environments [58]. Adaptation and learning: These methods can learn from new incoming data and adapt their learning strategies over time. This ability enables EMSs and BMSs to become much more efficient and effective. Because of that, they collect more operational data over time from the naval environment, and they can continuously improve energy and battery management strategies in maritime operations [59]. In summary, applications based on AI, ML, and DL approaches in EMSs and BMSs have the potential to drastically advance efficiency, stability, and safety and leverage naval platforms and ships to operate more autonomously and robustly in challenging environments. Studies [60; 61; 62; 63; 64; 45; 65] have shown that we can divide the AI and ML- and DL-based approaches for EMSs and BMSs into three main branches, shown below (Figure 6). Additionally, these approaches are especially utilized for accurate estimation of critical battery parameters such as state of energy (SoE), state of charge (SoC), and remaining useful life (RUL) for BMSs.
## 2 Related Work
### The Shelf Space Allocation Problem  
A related issue is the shelf space allocation problem [39; 40], which is also linked to the optimal selection of products to be displayed on shelves with limited space. In this problem, however, several items of the same product can be selected. According to [PERSON] and [PERSON] [13], there are two different questions associated with the planning: assortment planning and shelf space planning. The first relates to listing decisions based on consumer choice, whereas the second deals with the limited shelf space. Our work considers both aspects, that is, which products should be shown in a limited space. Retail stores, especially brick-and-mortar ones, need to make decisions on what stock should be displayed in order to increase the customer's attention. Despite the proliferation of numerous software applications in shelf space management, which make use of historical data, new algorithms--as the ones introduced in this paper--are needed to deal with multi-period and dynamic versions of the product selection problem. Most of the literature about the shelf space allocation problem refers to supermarket products [41; 42]. Other areas, such as fashion stores, have not received much attention. [PERSON] [43] analyzed how the atmosphere in fashion stores influences sales. [PERSON] et al. [44] investigated how pre-packs are used in retail distribution and how this reduces handling costs. Notice that fashion and apparel customers do not approach the store with a clear purchase objective, as it would be the case for food in supermarkets or electronic equipment. Instead, the customer enters the store just to look and see if something attracts his / her attention. It is the store's function to appeal to them to make them buy from the store and not from another shopping channel, e.g., a mobile device, tablet, or personal computer.  
Table 1 summarizes the literature on product recommendation, product assortment, and shelf space allocation problems according to the solving methodology employed. Among them, we highlight the use of dynamic programming methods, heuristics/metaheuristics, and data mining/machine learning strategies such as collaborative filtering, association rules, and sequential pattern analysis. From the literature review, one can notice that approximate methodologies have gained more popularity over the years. This fact can be explained by the continuous growth in the size of realistic instances.  
\begin{table}
\begin{tabular}{c
# Development of a Natural Matrix Hybrid Hydrogel Patch and Evaluation of Its Efficacy against
## 3 Results and Discussion
### Biocompatibility Evaluation  
After removing the eluted medium, L929 cell growth in 96-well plates was observed using an optical microscope. Compared to untreated cells, cell shape was consistent, and cell viability was increased by the application of CAE-loaded HA-Dex hybrid hydrogel patches. However, when the CAE content was above a certain concentration, the number of cells decreased, and the size of the granules did not increase. Moreover, the shape of the cells was unstable, so the cells could not adhere, indicating decreased cell survival (Figure 8A). Cell viability was tested after obtaining the medium eluted from the L929 cells. Cell viability was similar to that of the untreated cells, but cell viability decreased above a 0.8 wt% CAE concentration (Figure 8B).  
Figure 7: _Centella asiatic_ extract (CAE) release profile of CAE-loaded hyaluronic acid-dextran (HA-Dex) hybrid hydrogel patches. Control: HA-Dex hybrid hydrogel patches with no CAE; (a) 9.8:0.2 HA-Dex: CAE; (b) 9.6:0.4 HA-Dex: CAE; (c) 9.4:0.6 HA-Dex: CAE; (d) 9.2:0.8 HA-Dex: CAE; (e) 9:1 HA-Dex: CAE.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 2 Antenna Design
### The Proposed Microstrip Monopole Antenna Array  
The next step was to construct a two-element array using the optimized single element, as depicted in Figure 9a. These two elements were allocated on a single substrate while maintaining the minimum required spacing of \(\uplambda\)/2 between them. The width of the substrate was incremented to be \(20.64\times 20\) mm\({}^{2}\), while other dimensions of the patch and ground remained unchanged. Two L-shaped slots were embedded at the top corners of the ground plane with optimized parameters, as shown in Figure 9b. In these arrangements, the elements could be fed by a single line or by multiple lines, depending on their feeding method. This feed network was chosen for designing the two-element array network, and the dimensions of this network are given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline Wp & 8.4 & D2 & 3 & Wcp & 4 & R2 & 0.04 \\ LL1 & 1.52 & D3 & 0.2 & Rcp & 1.12 & R3 & 0.4 \\ LL2 & 2.6 & D4 & 1.6 & S & 0.5 & R4 & 1.2 \\ WL1 & 0.27 & LF1 & 1.4 & Wst & 0.1 & X1 & 3.4 \\ WL2 & 1.04 & LF2 & 3.48 & Ls & 12 & X2 & 1.74 \\ LT & 1.52 & WFI & 0.4 & Ws & 9.6 & - & - \\ WT & 0.3 & WF2 & 0.8 & G & 0.5 & - & - \\ D1 & 1.26 & Lcp & 3.48 & R1 & 0.1 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal geometrical parameters of the proposed monopole antenna.  
Figure 8: Simulated return loss for the proposed antenna with various radii of the ground structure ring (unit in mm).  
Figure 9: Configuration and parameters of the proposed antenna array and ground plane (**a**) Front view; (**b**) Back view.  
To investigate the performance of the proposed array, the model was simulated using the HFSS software. It could be seen from Figure 10 that the antenna array succeeded to resonate at dual frequencies (58.8 GHz and 61.75 GHz), and it could achieve \(-\)10 dB impedance bandwidth of 6.6 GHz from 57.2 GHz to 63.8 GHz. Moreover, the achieved return loss at both frequencies was \(-\)31 dB, confirming the suitability of the proposed array for 5G applications.
## 5 Discussion
### Palaeodepositional Environments
#### 5.1.4 Paleoredox Characterization  
It is generally accepted that trace elements such as V, Ni, Cd, Sc, Cr, Th, and U are sensitive to the redox properties of the environment and that the elemental abundances and ratios can be used as indicative parameters of redox conditions. However, trace elements in sediments are strongly influenced by inputs from land-based sources of detritus. Therefore, the trace elements in this study must be autochthonous in the oceans, and therefore the influence of terrestrial sources of detrital material must also be considered when discussing the recovery of paleoredox environments by trace elements. Al in shale is mainly derived from clay minerals formed by weathering of terrestrial source clastic materials, so the element Al serves as a signature element indicative of terrestrial source clastic materials. In this next experiment, it was found that the element Al has a strong correlation with U and Th, suggesting that the U and Th in the samples mainly originate from the contribution of terrestrial source detrital material. While V, Ni, Cd, and Cr have no significant correlation with Al, the elements V, Ni, and Cr were adopted as indicative parameters of the palaeoredox environment. A V/Cr value of less than 2 is an oxidising environment, between 2.00 and 4.25 is a moderately reducing environment, and a V/Cr ratio greater than 4.25 is a strongly reducing environment. The V/(V + Ni) value is also an effective indicator of redox conditions, with a ratio of less than 0.46 for an oxidising environment, between 0.46 and 0.57 for a weakly oxidising environment, between 0.57 and 0.83 for an anoxic environment, and between 0.83 and 1.00 for an extremely anoxic environment [60; 61; 62; 63; 64].  
The trace element results in Figure 6d indicate that the Dalong Formation is a weakly oxidised environment, the Wujiaping Formation is an anoxic environment, and the Maokou Formation is an anoxic environment. This corresponds to the low TOC values of the Dalong Formation compared to those of the Wujiaping and Maokou Formations.
## 4 Modeling the Target Tracking Process Using Kalman Filters
Based on the algorithm in Figure 1, and following the analytical expressions (2-9) for calculating the equivalent W-function of radar operation, the distribution function, \(\Phi(z)\), and the probability distribution density, \(\varphi(x)\), of the \"interaction\" time with the tracking object, and also, using the features of the GERT model branches (\(\lambda_{1}>\lambda_{2},\ \lambda_{2}\approx\lambda_{3}\approx\lambda_{4} \approx\lambda_{5},\lambda_{6}\approx\lambda_{7}\)) characteristic of this process, we study the probability-time characteristics (PTC) of the process. Let us find the distribution density, \(\varphi(x)\), probabilities of the message transmission time, provided that z are chosen as the roots of Equation (10) conditional probabilities and intensities in the GERT network branches have values: \(p_{1}=0.9\), \(q_{1}=0.4\), \(p_{2}=p_{3}=0.6\), \(q_{2}=q_{3}=0.4\), \(\lambda_{2}=7\), \(\lambda_{3}=\lambda_{4}=\lambda_{5}=6\), \(\lambda_{6}=\lambda_{8}=4\). Intensity, \(\lambda_{1}\), takes the values:  
1. \(\lambda_{1}=9\);
2. \(\lambda_{1}=5\);
3. \(\lambda_{1}=2\). Taking into account the given GERT network features, along with in accordance the expression (9), we obtain:  
\[ \begin{array}{rl}\varphi(x)=\sum\limits_{k=1}^{8}&\text{Res}[\text{e}^{x \text{x}}\Phi(z)]=\\ &\quad\quad=\sum\limits_{k=1}^{8}\frac{e^{(a+bi)x}\Big{(}\big{(}a+bi \big{)}^{4}-y(a+bi)^{3}+u(a+bi)^{2}-x(a+bi)+c\Big{)}}{8(a+bi)^{7}-7w_{72}(a+bi)^ {6}-6w_{6}(a+bi)^{5}-5w_{5}(a+bi)^{4}}\\ &\quad\quad\quad-4w_{4}(a+bi)^{3}-3w_{3}(a+bi)^{2}-2w_{2}(a+bi)-w_{1}\\ &\quad\quad-\frac{e^{(a+bi)x}\Big{(}(a-bi)^{4}-y(a-bi)^{3}+u(a-bi)^{2}-x(a-bi) +c\Big{)}}{8(a-bi)^{7}-7w_{72}(a-bi)^{6}-6w_{6}(a-bi)^{5}-5w_{5}(a-bi)^{4}}\\ &\quad\quad\quad-4w_{4}(a-bi)^{3}-3w_{3}(a-bi)^{2}-2w_{2}(a-bi)-w_{1}\end{array}  \tag{11}\]  
Figure 6: The probability graphs of interaction completion within specified time intervals. Papers [22; 23] show that the sum of the any fractional rational function values  
\[\text{f}(z)=\frac{d_{m}z^{m}+d_{m-1}z^{m-1}+\ldots+d_{1}z+d_{0}}{l_{m}z^{m}+l_{m -1}z^{m-1}+\ldots+l_{1}z+l_{0}},\ d_{m}\
eq 0,\ l_{m}\
eq 0 \tag{12}\]  
investigated for values of complex conjugate arguments can be represented in the form:  
\[\frac{\tau+i\beta}{\gamma+i\beta}+\frac{\tau-i\delta}{\gamma-i\delta} \tag{13}\]  
where \(\tau,\beta,\gamma,\delta\) are some coefficients.
# A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication
## 2 Related Work  
### The Construction Method for the Semantic Knowledge Base  
SKBs originated as computational knowledge graphs, designed to formally represent entity concepts and their relational networks. Recent advancements in semantic communication have driven the evolution of four distinct SKB construction methodologies.  
The first is to construct a semantic knowledge base based on a knowledge graph. It is mainly oriented to text transmission and speech transmission, and constructs a semantic knowledge base with multi-level structure by extracting ternary groups [2; 10; 11]. The second is to use the labeled training dataset as a knowledge base, which describes the knowledge through the distribution of statistical properties of the training dataset. This method can dynamically update the coding and decoding scheme through the domain adaptive technique in migration learning. It solves the problem of mismatch between the transmitted data information and the training dataset [2]. The third is to use the statistical properties of features extracted from deep learning models as a semantic knowledge base. This method is mainly oriented to tasks such as image categorization, which extracts the correlation between the feature maps of image datasets with category labels and object categories as a semantic knowledge base [12; 13]. The last one uses LLMs to replace the knowledge base, and semantic extraction and recovery through LLMs effectively solves the semantic ambiguity problem with good generalization [14; 15].  
Currently, various semantic knowledge base construction schemes have been applied to end-to-end semantic communication systems. Particularly in task-oriented semantic communication, the integration of task knowledge into semantic encoding and decoding processes achieves higher compression rates. However, existing studies embed knowledge into semantic encoding/decoding algorithms in the form of algorithmic parameters, which hinders knowledge updating during communication. This approach struggles to adapt to dynamic changes in task knowledge in task-oriented semantic communication. In contrast, entity knowledge bases represented by knowledge graphs can address this limitation [16]. Therefore, it is a critical step to realize the efficient and accurate construction of task knowledge bases in enhancing task-driven image semantic communication technologies.