esa-sceva/satcom-chunk-collection huggingface dataset:
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
### Spatial Skyline Query  
Figure 2 and Table 2 demonstrate an example of spatial skyline queries. In Figure 2, we use square symbols, triangle symbols and star symbols to indicate the location of facilities. Notice that \"\(+\)\" mark on the facility symbol represent preferable facilities such as bus/train stations, while \"\(-\)\" mark on the facility symbol for unpreferable ones such as open landfills. We represent the star symbols and triangle symbols as the preferable facilities, while the square symbols for unpreferable facilities. The star symbols and the triangle symbols are denoted as \(F1^{+}=\{f1_{1}^{+},f1_{2}^{+},f1_{3}^{+}\}\) and \(F2^{+}=\{f2_{1}^{+},f2_{2}^{+},f2_{3}^{+}\}\), while the square symbols are denoted as \(F3^{-}=\{f3_{1}^{-},f3_{2}^{-},f3_{3}^{-}\}\).  
In Table 2, there are three candidate points \(p_{1},p_{2},p_{3}\). The values represent the distance of the candidate point \(p_{i}(i=1,2,3)\) to the nearest facilities. For example, the nearest facilities of the candidate point \(p_{1}\) are \(f1_{1}^{+},f2_{1}^{+},f3_{3}^{-}\) for facility type \(F1,F2\) and \(F3\). The distances are \(3,5\) and \(10\) respectively. We assume that the smaller value is better, so that we multiply -1 to each distance value of unpreferable  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Price** & **Distance** \\ \hline \(h_{1}\) & 3 & 8 \\ \(h_{2}\) & 5 & 4 \\ \(h_{3}\) & 4 & 3 \\ \(h_{4}\) & 9 & 2 \\ \(h_{5}\) & 7 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A Hotel Example.  
Figure 1: A Conventional Skyline Example.  
Figure 2: Some Facilities in a Map.  
facilities \(F^{-}\). In this example, point \(p_{1}\) dominates point \(p_{2}\), since the values of \(F1^{+},F2^{+}\) and \(F3^{-}\) of point \(p_{1}\) are smaller than the values of point \(p_{2}\). \(p_{3}\) is not dominated by \(p_{1}\) and \(p_{2}\), since \(p_{3}\) is closer to \(F2^{+}\) than \(p_{1},p_{2}\) but farther to \(F3^{-}\) than \(p_{2}\). So the skyline objects are \(p_{1}\) and \(p_{3}\). In general, we can address the spatial skyline problem by conventional skyline queries after calculating the data table such as Table 2.  
However, in some real-world scenarios, the candidate points like \(p_{1},...,p_{n}\) are not given for the location recommendation problem. In this situation, we need to find a two-dimensional area with more preferable facilities around it and away from the unpreferable facilities. To address such problem, we proposed the \"Area Skyline Query\" in our previous work [5], which is a new selection method of areas in a map.
## 6 Methodology
### Experimental Setup  
Two different scenarios were defined in order to assess the impact of the Packet Injection Rate (PIR) and node distance on BER, PLR, and End-to-end delay.
# An Improved Bi-LSTM-Based Missing Value Imputation Approach for Pregnancy Examination Data
## 3 Results and Analysis
### Analysis of Experimental Results  
In this paper, we propose an improved bi-LSTM-based missing value imputation approach for pregnancy examination data. This model, compared with the traditional LSTM model, has forward and backward factors to jointly determine the results of filling miss data, which makes the accuracy of filling missing data effectively improved. This paper selects the results on the validation set after training on 70% of the preprocessed data as the training set. Table 8 shows the comparison of different filling methods. This paper uses SMAPE to evaluate the missing values. It is shown that the bidirectional LSTM model interpolation method has the best filling effect regarding missing data compared with other methods. The experiment found that ST-MVL is not good for filling data columns with serious missing data, so we used data with a missing rate of less than 50% for ST-MVL experiments.  
In addition, the lightgBM disease prediction algorithm [32] was used to compare the training model before and after data filling. As is shown in Figure 10, the blue curve represents the training results using the complete data of pregnancy weeks filled by LSTM, and the orange curve represents the training results using unfilled pregnancy week data. It can be seen that the accuracy of the prediction results after filling is higher. In addition, it can be seen that the prediction accuracy is positively correlated with the number of pregnancy weeks. From the figure above, it can be seen that the model has a good prediction effect at just 25-26 pregnancy weeks, and the AUC of the HDP prediction effect can reach more than 0.75. From 25 weeks on, forecasts continuously improve. Data from week 25 to week 40 are more important to model predictions than data before week 25.  
\begin{table}
\begin{tabular}{c c} \hline \hline Optimizer & [PERSON] method \\ Loss function & Cross-entropy and L2 regulation = \\ Parameter initialization & Set value to zero. \\ Dimension of word vectors & 32 \\ Dimension of position vectors & 5 \\ Batch & 128 \\ [PERSON] learning rate & \(1.00\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hyperparameter Settings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Filling Methods**} & **Cubic Spline Interpolation** & **KNN Filling** & **LSTM Model** & **ST-MVL** &
\begin{tabular}{c} **Improved Bidirectional** \\ **LSTM Model** \\ \end{tabular} \\ \hline SMAPE(\%) & 8.745 & 6.746 & 8.796 & 6.734 & 6.569 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of different filling methods.  
Figure 9: Bidirectional LSTM structure.
# Risk-Based Selection of Inspection Method for External Post-Tensioning System of Bridges
## 4 Application of RBSIM for Selection of Inspection Method, a Case Study
### Selection of Inspection Method, Application, and Results  
Based on the results of the test hypothesis in Table 4, since the \(p\)-Value is less than 0.1, the change in the condition of the post-tensioning system points to significant deterioration. Therefore, the spot inspection results \(\left(\overline{\text{C}}\right)\) shall be considered as the current condition of the post-tensioning system, and Equation (3) shall be used. In other words, the last inspection report \(\left(\hat{\text{C}}\right)\) of post-tensioning system was deemed invalid; hence, it was replaced by the spot inspection result. Furthermore, a Kolmogorov-Smirnov test was conducted on the spot inspection results. It was verified that the distribution of condition is normal and confirms prior analysis assumption. Available NDE methods in the region where the bridge is located included visual inspection, sounding, impulse response, ultrasonic\(\backslash\)Pulse-Echo, vibration response, passive infrared thermography, Residual Magnetic Flux, X-Ray and  
Figure 13: Spot inspection activities (case study) to verify the current condition of the bridge; (**a**) one of defected external tendons, (**b**) inspectors during spot inspection.  
2 two-stage inspection methods which are sounding-borescope and sounding-radiography. These methods are listed in Table 5 with the corresponding basic estimate.  
To apply the proposed risk-based selection of the inspection method, accuracy of the available NDE methods from Table 2 and the results of spot inspection \(\left(\overline{\mathbb{C}}\right)\) from Table 4 were used. The joint probability density function according to Equation (4) was calculated for available NDE methods and is reflected as \"\(\text{P}_{\text{error}}\)\" in Table 5. In this table, \"\(\text{C}_{\text{pi}}\)\", \"\(\text{C}_{\text{si}}\)\", and \"\(\text{C}_{\text{n}}\)\" represent the cost of primary, secondary, and total inspection, respectively. The column under \"Risk Error\" in this table introduces the risk of false negative inspection reports corresponding to each NDE method based on the probability and the consequences. The total risk in Table 5 is calculated as the sum of the \"Risk Error\" and the total cost for each NDE method. The two-stage inspection that includes sounding-borescope was identified with the minimum total risk, based on the analysis. The results of the study were presented to the bridge owners and a conclusion for using the sounding-borescope method was approved. Accordingly, all external post-tensioning elements were inspected first using the sounding method. The elements in which defects were detected were included for further investigation and suspect locations along the post-tensioning elements were marked for further investigation (See Figure 14). The suspect locations were dissected for application of the second stage of inspection, i.e., the use of a
## 2 Basic Principles of Diagnostic Models
### Multi-Head Attention  
The multi-head attention (MA) mechanism is the cornerstone of transformer construction [23; 24]. Compared with conventional attention mechanisms, the MA mechanism can make the output of the attention layer contain representation information from different subspaces, thereby enhancing the expression ability of the model. It uses different query vectors to focus on different parts of the input information in order to analyze the current input information from different perspectives.  
In the field of transformer fault diagnosis based on vibration signals, the multi-head attention mechanism has demonstrated significant advantages over traditional standard attention mechanisms in various aspects. Through the parallel processing of multiple heads, the multi-head attention mechanism can more comprehensively and deeply capture the multidimensional features contained in vibration signals, which is crucial for accurately determining the type, location, and severity of faults. At the same time, this mechanism also has stronger robustness and can cope with possible noise and interference in vibration signals, ensuring the stability and reliability of diagnostic results.  
It mainly consists of three steps: Firstly, input the extracted features, then perform linear transformation, and map the results to the query space \(Q\), key space \(K\), and value space \(V\), respectively. Secondly, the scaled dot product and Softmax function are used to calculate each attention distribution, and the attention distributions are weighted and summed to obtain the corresponding output. Finally, use concatenation to concatenate multiple output results. The formula is shown in Equations (8)-(12).  
\[Q=HW_{\text{Q}}=[\beta_{1},\beta_{2},\cdots,\beta_{\lambda}] \tag{8}\]  
\[K=HW_{\text{K}}=[k_{1},k_{2},\cdots,k_{\mu}] \tag{9}\]  
\[V=HW_{\text{V}}=[v_{1},v_{2},\cdots,v_{\mu}] \tag{10}\]\[Y_{i}=\sum_{j=1}^{H}f_{\text{Softmax}}[s_{3}(\beta_{i},k_{j})]\cdot v_{j}=f_{ \text{Softmax}}(\frac{QK^{\text{T}}}{\sqrt{D_{k}}})\cdot V \tag{11}\]  
\[Y=f_{\text{Concat}}(Y_{1},Y_{2},\cdots,Y_{\lambda}) \tag{12}\]  
where \(W_{Q}\), \(W_{K}\), and \(W_{V}\) are linear transformation parameters for the query space, key space, and value space.  
\(D_{k}\) is a matrix composed of the number of dimensions for each key, \(k\) is the element vector in the key space, \(v\) is an element vector in the value space, \(\lambda\) is the number of query vectors, \(f_{\text{Concat}}(\cdot)\) is the feature concatenation function, and \(\mu\) is the number of dimensions after linear transformation.
## 3 Proposed Methodology
### The Optimization of Threshold Value  
For single fault diagnosis, the failure mode with the highest probability of the output probability vector \([p_{1},p_{2},\,\ldots,p_{m}]\) obtained from diagnostic mode is the predicted result. However, for simultaneous fault diagnosis, the number of single failure modes occurring simultaneously is uncertain. Based on this, a suitable discriminative threshold is crucial to acquire multiple failure modes simultaneously occurring according to the output \([p_{1},p_{2},\,\ldots,p_{m}]\).  
Usually, many classification algorithms use 0.5 as the universal threshold, and the probability of greater than 0.5 indicates that some fault occurs, otherwise, the failure does not appear [31; 32]. Without considering any apriori information, the universal threshold is not appropriate for some specific fields. In particular, the performance of the universal threshold decreases when the training sample size distribution is heterogeneous.  
In this paper, in order to generate a suitable threshold for identifying simultaneous failure modes, single failure samples and simultaneous failure samples are utilized. The range of discriminative threshold \(\varepsilon^{*}\) is set to (0, 1). By using the optimal discriminative threshold, \([p_{1},p_{2},\,\ldots,p_{m}]\) is converted into final result vector \([\![f_{1},f_{2},\,\ldots,f_{m}]\!]\):  
\[f_{i}=\left\{\begin{array}{ll}1\;if\;p_{i}\;\geq\;\varepsilon^{*}\\ 0\;if\;p_{i}<\varepsilon^{*}\end{array},\;i\;=1,\ldots,m\right. \tag{20}\]  
in which simultaneous fault modes are the corresponding failure mode with the element 1 in the result vector \([\![f_{1},f_{2},\,\ldots,f_{m}]\!]\). Based on the global optimization ability and the advantages of small calculation of particle swarm optimization (PSO) [33; 34], this paper uses the following formula as the objective function to obtain the optimal value of the decision threshold:  
\[\min(1-\mathrm{F}) \tag{21}\]  
where F represents the F1-measure indicator.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 2 Related Works
### Traditional Image Processing Techniques  
Traditional image processing techniques for time classification have employed mainly histograms of images [20; 21; 22]. [PERSON] et al. classified daytime and nighttime using thresholding HSV histogram. This method determined the first parameter, representing an amount of red or yellow pixels [20]. In addition, they determined the second parameter of light. This method used brightness and color components, which are important for classifying time. Similarly, [PERSON] et al. used a HSV histogram and discriminated daytime and nighttime scenes employing thresholding H-histogram and V-histogram [21]. In addition, [PERSON] et al. proposed a time classification method that used intensity, chromaticity in RGB component, and \(k\)-means segmentation [22].  
However, these methods can only classify binary class. Because the difference between day and night is clear, it is easy to separate. However, it is still challenging to classify day, dawn (dusk), and night. We use a RGB color histogram representing the difference of time period and successfully classify them with high accuracy.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Carotenoids
#### 4.2.2 Lutein  
Lutein [(3R,3'R',6'R)-beta, epsilon-carotene-3,3'-diol] (Figure 3) is a naturally occurring, heat-sensitive oxycarotenoid. Its structure consists of a C40 isoprenoid backbone (with conjugated double bonds) and oxygen-containing rings at both ends. This structure endows lutein and its metabolite, zeaxanthin, with potent antioxidative activity. Lutein has been reported as a powerful antioxidant effective against hydroxyl radicals (HO\(\bullet\)), peroxyl radicals (ROO\(\bullet\)), superoxide anions (O\({}_{2}\bullet^{-}\)), and hypochlorous acid (HOCI) [60,61].  
Lutein serves as a preventive measure against neoplastic changes and circulatory system diseases, while also reducing the risk of atherosclerosis, coronary heart disease, and various eye disorders [49,56].  
Moreover, lutein exerts significant protective effects on ocular health, cardiovascular function, skin integrity, and pathological conditions such as cancer. Numerous studies have demonstrated lutein's efficacy in shielding the skin and eyes from photodamage. In the retina, lutein protects retinal pigment epithelial (RPE) cells from oxidative stress by converting into the zeaxanthin radical ion. Its role as a photoprotective agent is particularly noteworthy, as it efficiently filters blue light within the visible spectrum. Chronic exposure to ultraviolet (UV) radiation accelerates skin aging and increases the risk of developing cellular carcinomas. Due to its lipophilic nature, lutein is embedded within the lipid bilayer of cell membranes, where it scavenges free radicals generated during biochemical processes. It interrupts the chain reaction of lipid peroxidation, thereby mitigating oxidative damage. Topical and systemic formulations containing lutein have demonstrated potential in repairing radiation-induced skin damage and preventing photoaging. These properties underscore lutein's value as a key component in dermatological and photoprotective therapies [60].  
Currently, lutein used in cosmetics is primarily derived from margold extract or microbial fermentation (_Chlorella zofingiensis_, _Muriella zofingiensis_). Synthetic production is less common, as consumers increasingly prefer natural and eco-friendly ingredients. Natural cosmetics often utilize margold-derived lutein due to its high bioavailability and potent antioxidant properties. The biotechnological production of lutein through microbial fermentation is gaining popularity, as it enables the production of a stable, pure ingredient with consistent quality, aligning with vegan and ecological trends in the cosmetics industry [62].
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 4 Experimental Study
### Optimization Model
#### 4.3.1 Transformation (Normalization) of Production Objectives  
When applying a weighted sum approach or other types of scalarization, it is reasonable to normalize individual objectives _Fi_ entering the scalar multi-objective function to avoid the scaling effects and thus ensure their comparable impact in the objective function expression. We utilized the transformation which represented a robust min-max type of normalization. It can also be referred to as the upper-lower-bound transformation in the literature [47]. This transformation and many other potential ones are provided in a survey by [PERSON] and [PERSON] [24] in a comprehensive overview. The applied robust transformation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Production Goal** & **Constraint** \\ \hline Average flow time (min) & \textless{}180 \\ Average machine utilization (\%) & \textgreater{}80 \\ Average number of products (pcs) & \textgreater{}400 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Constraints for optimization experiments.  
was in this study implemented in a form (5) involving two so-called reference points as maximal and minimal values of the individual objective functions \(F_{i}\).  
\[F_{i}^{transform}=\frac{F_{i}-F_{i}^{W}}{F_{i}^{U}-F_{i}^{W}} \tag{5}\]  
Here, the transformation (5) includes a component of an ideal point [38] vector \(F_{i}^{U}\) (maybe replaced the point, referred also as a utopia point [23]) and a component of the worst point vector \(F_{i}^{W}\). They represent the unreachable solution for individual production objectives as the best (ideal), and as the worst one. The ideal point vector \(\mathbf{F^{U}}\) is determined as the optimum of the single-criterion function according to the optimization goal regardless of other objectives. Vector \(\mathbf{F^{W}}\) is composed of the worst values of production goals, obtained from single-optimization experiments. Value \(F_{i}\) in the expression (5) represents the individual objective value, resulted from the simulation run. The transformation scheme ensures the same range and magnitude for all objectives within the range between 0 and 1. Comparing to works [24; 47] where the distance between solution \(F_{i}\) and utopia point is minimized, we modified the transformation scheme to (5) and maximized the distance between solution \(F_{i}\) and the worst point.
## 1 Introduction
The task-based description logic presented in Section 2.4 can be used for the strategic factory planning process at the beginning of the factory lifecycle and for operational steering and control during a factory's operational phases. Furthermore, the database can be easily maintained and expanded due to the lack of fixed connections between machines and products. Furthermore, due to the feature and competence-based allocation principle, identifying potential matches between machines and products can be partly automated, saving both money and time. If connected with smart machines and CPSs, the logic also holds the possibility to update itself automatically, should improvements or deteriorations in machines within the production system occur. However, to present the logic and potential benefits, it is first necessary to examine the relevant literature in the relevant fields of research. Following this argument, the next chapter will explore the factory planning process (Section 2.1) and the dependencies between factory planning and operation control (Section 2.2). In Section 2.3, we will discuss the idea of service-oriented architectures (SOAs) as a means for a more decentralized and flexible production planning solution. Section 2.4 will bring these elaborations together and present a task-based view both on factory planning and flexible operational management. This aims at helping to facilitate a better and more efficient design of both factories and production management systems. Section 2.5 will summarize the potential benefits of the proposed basic task logic and highlight the contributions. The main contributions of this paper can be considered as follows:  
* The proposed design of a task-based allocation principle for machines and products in manufacturing systems;
* The implications of how such a new way of allocation can impact both factory and technology planning as well as operational control;
* The implications of such a design on manufacturing data consistency and maintenance compared to conventional work plans. Furthermore, examples and potentials for applying the task-based logic will be presented in Section 3 and discussed in Section 4.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 2 Methodology  
A broad systematic research of CMC materials literature was undertaken to reveal the use of the existing oxide and non-oxide CMCs in aero-engine applications. Keywords or phrases included \"oxide CMC\", \"non-oxide CMCs\", \"material classification\", \"alumina oxide\", \"aluminosilicates\", \"carbon, silicon carbide\", \"titanium carbide\", \"tungsten carbide ceramics\". Oxide and non-oxide CMC material searches were conducted, focusing on evidence-based research articles published between 1986 and 2022.  
Titles and/or abstracts of articles identified through searching online literature databases, such as Scopus, Web of Science, and Google Scholar, were reviewed and evaluated for information relevance. Relevant articles were subjected to inclusion or exclusion criteria according to the research needs. Alumina oxide, aluminosilicate, carbon, silicon carbide, titanium carbide, and tungsten carbide material classification studies and application usage in high-temperature and pressure application studies were reviewed. Articles that did not focus exclusively on the classification of oxide and non-oxide CMC materials were excluded from further evaluation. Finally, data supporting the aerospace applications in which CMC materials have been used were sought and incorporated in the classification of the materials.  
Figure 2 illustrates the growing interest in CMCs from the academic community. As can be seen, research on CMCs has been of constant interest since the 90s. However, it is obvious from Figure 3 that the research community has slightly moved its interest from non-oxide to oxide CMCs. In general, specific achievements, such as increasing the operating temperatures, introducing easier and more cost-effective manufacturing processes, and improving the thermal properties of the CMCs, resulted in more researchers being active on CMCs.  
Figure 2: CMC materials research published since 1986.