esa-sceva/satcom-chunk-collection huggingface dataset:
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 2 Related Work
In the area of traffic forecasting and trajectory prediction, data-driven approaches tend to outperform the parametric approaches. In [38], a fuzzy neural network is employed to increase the prediction accuracy of the traffic flow. The support vector machine [39] also has been introduced for trajectory prediction. The machine learning algorithms have the advantage of dealing with high dimensional and nonlinear relationships, which is especially suitable for establishing train dynamic model and train speed prediction on account of the dynamic and nonlinear nature [40]. Among the machine learning algorithms, the recurrent neural network (such as the LSTM network) is designed to seize the features of the temporal and spatial evolution process. Several studies [41; 42; 43] focused on the use of LSTM neural networks for traffic forecasting and demonstrated the advantages of this kind of algorithm. Encouraged by the successful applications of LSTM based algorithms in the domain of transportation, we propose to employ LSTM networks for train dynamic model construction and train speed prediction, since the train operation process can be regarded as a time sequence problem.
# TRSDL: Tag-Aware Recommender System Based on Deep Learning-Intelligent Computing Systems
## 2 Related Work
### Recommender Systems Based on Deep Learning  
[PERSON] et al. introduced the two-layer restricted Boltzmann machines (RBM) [29] for modeling ratings that slightly outperform carefully tuned SVD models but that still face cold start problems. To address such problem, collaborative deep learning (CDL), proposed in [30], integrates a Bayesian stack denoise auto-encoder (SDAE) [31] and a collaborative topic regression (CTR) [32] to learn items' and users' latent vectors, respectively. In terms of the ability of generating ranked recommendation lists, Collaborative Deep Ranking (CDR) [33], an improved pair-wise model, performs better than the point-wise CDL. To solve the sparsity problem of ratings, [PERSON] et al. proposed convolutional matrix factorization (ConvMF) [34] which integrates CNN into probabilistic matrix factorization (PMF) to enhance the prediction accuracy.  
[PERSON] et al. presented AutoRec [35], a novel model based on AutoEncoder. AutoRec takes user or item vectors as input and reconstructs them in the output layer. The values in the reconstructed vectors are the predicted ratings of the corresponding position. Along this direction, many improved models based on AutoEncoder were proposed [36; 37; 38]. Besides computer vision, convolutional neural network is also widely used in natural language processing and other fields such as clickbait detection [39] and so on. [PERSON] et al. [40] proposed a model that applies convolutional neural networks (CNN) in music recommendations to capture advanced features from music signals. In [41], RNN was applied in a session-based recommendation system to predict the next-step behavior based on the user's behavior records. Subsequently, a series of variants of the session-based recommendation were proposed [42; 43; 44]. Then, recommendation systems for modeling the dynamic preferences of users were put forward [45; 46]. [PERSON] et al. proposed a Recurrent Recommender Networks (RRN) [47] based on the RNN, which was able to model the seasonal evolutions of items and changes of user preferences over time. Moreover, DeepFM [48] was proposed to integrate factorization machine(FM) and deep neural networks. It was able to model the high-order feature interactions via DNNs and low-order interactions via FM. IRGAN [49] is the original model which applies GAN to three information retrieval tasks: web search, item recommendation and question answering. Google proposed a general framework of wide and deep learning [32]--jointly trained wide linear models and deep neural networks--that combines the benefits of memorization and generalization for recommender systems.  
The studies above are about recommender systems based on deep learning. They have achieved excellent results in their respective subdivision research areas. However, these models have not introduced tags into recommender systems. We believe that tags provide additional information for reflecting user preferences and the item characteristics, which can of further enhance the RS performance.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results
### Visualization Example
#### 3.1.4 Weight Strategy  
The RMSE improvement results, based on scintillation and standard parameters, through the weight strategy, are shown in Figure 10. It can be observed that using the standard parameters improved the RMSE slightly more than the scintillation parameters. The improvement rates in Figure 10a-d are 71.6%, 72.7%, 73.1% and 73.4%, respectively, which are comparable to those in Figures 7a-d and 8a-d. Though the best improvements were larger when applying the observation removal strategy, this led to high GDOP values. High GDOP values typically indicate close distances between satellites and a low confidence level of observations from these satellites, which may lead to large position uncertainty [42]. As weighting observations have no effect on the satellite availability, the GDOP remains unchanged after applying the weight strategy.  
Figure 9: Comparison between the GDOP before and after the observation was removed based on the MT of (**a**) MP1, (**b**) MP2, (**c**) ROTI, (**d**) MP1, MP2 and ROTI.
## 2 Modeling of Dynamic Rotational Error for Spindle-Rolling Bearing System
### Dynamic Model of the SRB System
The least-square method is used for evaluating the synchronous error motion value. The solution of the synchronous error motion value is transformed into a nonlinear least squares problem. The distances \(\mathrm{x_{LSC}}\) and \(\mathrm{y_{LSC}}\) of the least squares center from the polar chart center can be calculated as follows:  
\[ \begin{split}\mathrm{x_{LSC}}&=\frac{2}{n}\sum \limits_{i=1}^{n}\mathrm{x_{i}}\\ \mathrm{y_{LSC}}&=\frac{2}{n}\sum\limits_{i=1}^{n} \mathrm{y_{i}}\end{split}  \tag{32}\]  
where \(\mathrm{x_{i}}\) and \(\mathrm{y_{i}}\) are the values of the ith data points in the X and Y direction, respectively. \(\mathrm{R_{LSC}}\) is the radius the least squares circle and can be calculated as follows:  
\[\mathrm{R_{LSC}}=\frac{2}{n}\sum\limits_{i=1}^{n}\sqrt{\left(\mathrm{x_{i}}- \mathrm{x_{LSC}}\right)^{2}+\left(\mathrm{y_{i}}-\mathrm{y_{LSC}}\right)^{2}} \tag{33}\]  
Error motion polar plots and corresponding error motion values are depicted in the Figure 7.
## 3 Results
### Wine Sensorial Characteristics  
After conducting a comprehensive sensory evaluation of 18 experimental wine samples and a control group (Figure 8), it was found that the control group exhibited a notably astringent and harsh mouthfleel, with a marked deficiency in aroma. In contrast, wine samples treated with varying degrees of ultra-high pressure displayed diverse sensory characteristics.  
In terms of mouthfleel, when the pressure was kept below 200 MPa for a duration of 10 min, the treated wine samples showed a slight reduction in bitterness and less harshness compared to the control group, indicating a modest improvement overall. This suggests that pressures below 200 MPa for a short duration primarily facilitated reactions among certain chemical components in the wine, with minimal impact on the overall structure and composition of the wine. However, at 300 MPa, the effect of different processing durations on mouthfleel was more pronounced. Specifically, a 20 min duration softened the mouthfleel significantly and reduced harshness, whereas a 30 min duration, while further diminishing bitterness, could lead to over-reaction, resulting in an unbalanced and bitter taste [25]. Increasing pressure does not always correlate with quality enhancement; excessive pressure can disrupt the balance between the structural and compositional elements of the wine [33].  
Figure 8: Average sensory scores of wine samples under Different HHP treatments: (**a**) appearance and taste; (**b**) aroma.  
For instance, wine samples processed at 400 MPa for 10 min had a slightly inferior mouthfeel compared to those treated at 300 MPa for 20 min; extending the duration to 30 min at pressures of 500 MPa and 600 MPa resulted in an unbalanced and bitter mouthfeel.  
Regarding aroma, the aroma profiles of these treated Marselan wines showed similarities, mainly their herbal, fruity, and floral aromas. The intensity of fruit and floral aromas was the most significant, followed by herbal, fatty, and chemical aromas, whereas the baked aroma was relatively weak. Compared to the control group, the HHP-treated grapes showed a significant improvement in all types of aromas, with the increase in fruit and floral aromas being the most notable, without generating off-flavors. This phenomenon is mainly related to an increase in the content of compounds such as ethyl acetate, hexyl acetate, ethyl butyrate, and ethyl caprylate during the treatment process. Based on these results, we inferred that HHP treatment can add rich fruit and floral aromas to Marselan wine.
# Evaluating the Quality of Light Emitted by Smartphone Displays
## 3 Results  
### Color Gamut Performance  
Based on the measured x and y values for the red, green, and blue colors, graphs were created to illustrate the color gamut of the selected smartphone screens (Figure 3). The fill of the sRGB triangle was also calculated (Table 2). It was performed by comparing the area of the sRGB triangle with the area obtained from experimental measurements. A value above 100% indicates that the gamut of a given screen exceeds the sRGB standard.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Red**} & \multicolumn{2}{c}{**Green**} & \multicolumn{2}{c}{**Blue**} & \multirow{2}{*}{
\begin{tabular}{c} **sRGB** \\ **Filling** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**Shift**} \\ \cline{2-2} \cline{6-9}  & & & & & & & & \\ \hline
1 & 0.6666 & 0.3333 & 0.2530 & 0.7046 & 0.1414 & 0.0433 & 140.5 & G \\ \hline
2 & 0.6816 & 0.3184 & 0.2  
Based on the results, it can be stated that 8 out of 9 samples exceed the sRGB standard, as the calculated fill values of the triangle are well above 100%. Only sample 9 does not meet these requirements. Its color gamut fills only about 73% of the sRGB triangle. It is the oldest smartphone on the set, featuring an LCD TFT display. In comparison, sample 8 also premiered in 2014. Still, it has a significantly larger available color gamut, which could be attributed to the AMOLED screen technology, and it competes closely with much newer models.  
The smartphone that meets the sRGB color triangle standards but performed the worst compared to the other models is sample 6. It is one of the best smartphone models available from 2017. Its color gamut exceeds the standard by only 20%. A competing flagship model with an AMOLED display, released a month earlier (sample 4), has a broader color gamut and ranks first among all tested samples. Its color gamut exceeds the sRGB standard by more than 50%.  
All AMOLED display smartphones selected for the tests achieved similar values for the color gamut area (ranging from 140% to 150% of the sRGB triangle). However, the smartphones with LCDs (TFT and IPS) tested have a significantly smaller available color gamut than those with AMOLED displays. This fact occurs regardless of the device's price or release year.  
Figure 3: The fillings of the sRGB standard color gamut (red line) by the color gamut of the particular sample (blue line).  
The color gamut of smartphones most frequently showed a noticeable shift of one color triangle vertex toward the green colors. It was most evident for phones with AMOLED displays. These smartphones had chromatic coordinates for this color that extended well beyond the range defined by the standard. For many models, the color space was also shifted beyond the required range for the red color (samples 2-8). The most significant shift toward the red color was observed for samples 2, 4, and 7. All phones (except for the non-compliant sRGB sample 9) had the closest values to the sRGB standard for the blue color.
# Ecotoxicological Evaluation of Methiocarb Electrochemical Oxidation
## 2 Materials and Methods
### Electrodegradation Experiments  
The EO experiments aimed to study the influence of the composition of the supporting electrolyte, of the applied current density and of the electrolysis duration/applied electric charge on the toxicity reduction towards _Daphnia magma_ and on the removal rate of methiocarb, total organic carbon (TOC) and total nitrogen (TN). Thus, the EO assays were conducted at room temperature (22-25 \({}^{\circ}\)C) in batch mode with stirring (250 rpm), using an open, undivided and cylindrical glass cell (250 mL of capacity) and 200 mL of solution. A commercial Si/BDD anode purchased from Neocoat and a stainless-steel cathode, each one with an immersed area of 10 cm\({}^{2}\), were utilized as electrodes. They were placed in parallel with an inter-electrode gap of 0.3 cm and were centered in the electrochemical cell. The experiments, utilizing the solutions described in Table 1, MC + Na\({}_{2}\)SO\({}_{4}\) and MC + NaCl, were performed at two different applied current densities, 100 and 300 A m\({}^{-2}\), which was the electric current supplied by a GW, Lab DC, model GPS-3030D (0-30 V, 0-3 A) power source. For the applied current density of 100 A m\({}^{-2}\), 5 h and 6 h duration assays were performed, corresponding to applied charges of 1.80 and 2.16 kC, respectively. For the experiments run at 300 A m\({}^{-2}\), the assays had 3 h duration, corresponding to 3.24 kC of applied charge. Samples were collected each hour for physicochemical determinations to monitor the experiments. Additionally, in the experiments run at 300 A m\({}^{-2}\), a sample was collected after 20 min of starting the assay corresponding to an applied charge of 0.36 kC, which was the same reached after 1 h by the experiments run at 100 A m\({}^{-2}\). Toxicological assays were only performed for the initial (MC + Na\({}_{2}\)SO\({}_{4}\) and MC + NaCl) and final samples (MC + Na\({}_{2}\)SO\({}_{4}\)-100 A m\({}^{-2}\)-1.8 kC (5 h), MC + Na\({}_{2}\)SO\({}_{4}\)-100 A m\({}^{-2}\)-2.16 kC (6 h), MC + Na\({}_{2}\)SO\({}_{4}\)-300 A m\({}^{-2}\)-3.24 kC (3 h), MC + NaCl-100 A m\({}^{-2}\)-1.8 kC (5 h), MC + NaCl-100 A m\({}^{-2}\)-2.16 kC (6 h) and MC + NaCl-300 A m\({}^{-2}\)-3.24 kC (3 h)). For the kinetic study, assays with 1 h duration were run with samples being collected every 15 min.  
All of the EO assays were performed at least in duplicate and results presented are mean values of all of the determinations performed.
## 3 Solving QTL by Multi-Criteria Optimization
### QTL Solution Representation  
A model solution is a subset of single loci and pairs of epigenetic loci along with their corresponding PLS weight parameters. The set of single loci solution for additive influence is represented as a set of model parameters \(B_{a}\in\mathcal{P}(B^{1})\), where \(B^{1}=\{\beta_{j}:1\leq j\leq k\}\). The set of epistatic solution loci is \(B_{e}\in\mathcal{P}(B^{2})\), where \(B^{2}=\{\beta_{u,v}:1\leq u\leq k,u<v<k\}\). The overall solution is the union of these two sets \(B_{s}=B_{a}\cup B_{e}\). Given that \(B_{\diamond}\in\mathcal{P}(B^{1})\) then for additive genes \(B_{a}\) is defined:  
\[B_{a}=\operatorname*{arg\,max}_{B_{\diamond}}\left\{\sum_{i=1}^{n}\left(t_{i}- \beta_{0}-\sum_{\beta_{j}\in B_{\diamond}}\beta_{j}M_{i,j}\right)^{2}\right\} \tag{3}\]such that the maximization criteria is defined as:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(t_{i}-\beta_{0}-\sum_{\beta_{j}\in B_{a}}\beta_ {j}M_{i,j}\right)^{2}}{\sum_{i=1}^{n}\left(t_{i}-\bar{t}\right)^{2}} \tag{4}\]  
where \(\bar{t}=\frac{1}{n}\sum_{i=1}^{n}t_{i}\), the mean trait outcome and \(\sum_{i=1}^{n}\left(t_{i}-\bar{t}\right)^{2}\) is the total outcome variation. \(R^{2}\) lies between 0 and 1, when \(R^{2}=1\) the model is perfectly fitted.  
Given that \(B_{*}\in\mathcal{P}(B^{2})\), then when we include epistatic genes, the overall solution \(B_{s}\) is defined:  
\[B_{s}=\operatorname*{arg\,max}_{B_{\circ},B_{*}}\left\{\sum_{i=1}^{n}\left(t_{ i}-\beta_{0}-\sum_{\beta_{j}\in B_{\circ}}\beta_{j}M_{i,j}-\sum_{\beta_{u,v}\in B _{*}}\beta_{u,v}M_{i,u}M_{i,v}\right)^{2}\right\} \tag{5}\]  
such that the maximization criteria is:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(t_{i}-\beta_{0}-\sum_{\beta_{j}\in B_{\circ }}\beta_{j}M_{i,j}-\sum_{\beta_{u,v}\in B_{*}}\beta_{u,v}M_{i,u}M_{i,v} \right)^{2}}{\sum_{i=1}^{n}\left(t_{i}-\bar{t}\right)^{2}} \tag{6}\]  
The genetic algorithm described below searches the space of \(\mathcal{P}(B^{1})\cup\mathcal{P}(B^{2})\), which contains \(O(2^{k^{2}})\) possible solutions when considering epistatic solutions, or \(O(2^{k})\) when only additive solutions are considered. The aim is to identify a solution that maximizes \(R^{2}\) in Equation (6) and minimizes the complexity of the solution \(|B_{s}|\). These two optimization criteria are in conflict because the more parameters employed in the model, the better the fit to the data. However, too many parameters result in fitting the inevitable environmental variation that exists in the data and produces biologically meaningless results. It is this problem of over fitting and parameter selection that the work here is designed to solve. There follows a review of multi-objective optimization approaches and the specific genetic algorithm applied to solve the QTL problem.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Architecture
#### 2.4.2 Path Prediction Task  
During full path prediction, the graph embeddings \(\mathcal{H}\) are used to generate the attack path matrix \(\hat{Y}\). Specifically, these embeddings are processed by a multilayer perceptron (MLP) [64] with \(k=4\) fully connected hidden layers, each with learnable parameters. Each hidden layer comprises 512 neurons, followed by a ReLU activation function. The final hidden layer is fully connected to an output layer with \(|V|\) neurons, followed by a normalization step. Normalization is applied using a logistic function that scales the logits from the preceding layer. The logistic function, denoted \(\sigma(\cdot)\) is a standard non-linear function which can be used for normalization. The function is defined as \(\sigma(x)=\frac{1}{1+e^{-x}}\). This transformation produces the matrix \(\hat{Y}\), where each element is constrained to the range \([0,1]\). The matrix \(\hat{Y}\) serves as the final adjacency matrix encoding the attack paths, where \(\hat{Y}_{ij}=y\) indicates the probability that an edge \(i\to j\) belongs to the attack path. To convert this probability to categorical (0 or 1) values, we compute the model's Receiver Operating Characteristic (ROC) curve (see Appendix D) and determine the optimal threshold using [PERSON]'s J index [99]. The threshold value is provided in Section 3. Figure 4 illustrates the path prediction block.
## 5 Decoding the Intricacies of Blind Source Separation (BSS): A Deep Dive into Core Principles
### Mastering Artifact Removal: A Comparative Analysis of BSS, ICA, and CCA Techniques  
Blind Source Separation (BSS), Independent Component Analysis (ICA), and Canonical Correlation Analysis (CCA) stand as pivotal methods in the identification and elimination of EEG artifacts, each bringing its set of strengths, limitations, and challenges to the forefront of neuroscience research. BSS excels in flexibility, adapting seamlessly to various signal types, yet it often struggles with determining the optimal number of sources and handling complex artifacts. ICA is particularly effective against specific artifacts, such as ocular disturbances, although it is constrained by its assumption of statistical independence among sources. CCA shines in studies that correlate EEG signals with external stimuli, yet its reliance on correlated datasets narrows its applicability to particular experimental conditions.  
Despite their inherent potentials, the effectiveness of these techniques is intricately linked to the application context, the characteristics of the signals involved, and the precision in their implementation. The strategic integration of these methodologies could pave the way for a more robust and adaptable artifact removal approach, underscoring the need for a multidimensional strategy that transcends the individual limitations of each method while maximizing their collective benefits. This integrative approach not only enhances the understanding of complex EEG data but also propels the field toward more accurate and reliable interpretations of neural activity.  
A summary of this SWOT analysis is shown in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{3}{*}{**Strengths**} & **BSS** & **ICA** & **CCA** \\ \hline \multirow{3}{*}{**Strengths**} & Explore a versatile methodology suitable for a wide array of signals and artifacts, designed for unsupervised contexts and requiring no prior source information. This tool extends research possibilities across diverse data environments. & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & Ability to identify relationships between two sets of variables. Applicability in multi-disciplinary studies where the correlation between different types of data is crucial. \\ \cline{2-3}  & Difficult to identify the sources to be separated. Performance dependent on the type of data. Difficulty in processing data affected by uncertainties and/or inaccuracies. & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{1-1} \cline{4-4}  & & & \\ \cline{1
# Capabilities and Limitations of Using Desktop 3-D Printers in the Laser Sintering Process
## Materials and Methods
### Particle Size Distribution  
The powder's morphology is an important factor regarding the raw material for every powder bed fusion process. Generally, a smaller particle size with a more regular shape and narrow particle size distribution is more favourable when it comes to improving the manufacturing accuracy [22].  
The powder's particle size and its distribution were measured using dry laser diffraction spectroscopy (Sympatec, HELOS/BR 4470 C, RODOS/T4, R4, Clausthal-Zellerfeld, Germany) with a measurement range of \(0.1\div 875\)\(\upmu\)m according to the ISO 13220-1 standard. The dispersing method was set at 2 bar and used a vibration material feeder (feed rate was set to 100%, gap width was 3.5 mm). Particle D\({}_{50}\) is considered to be the average particle diameter by volume (50%). D\({}_{10}\) and D\({}_{90}\) were also determined, as well as the percentage participation of particles below 10 and 90 \(\upmu\)m. Additionally, the span was calculated based on Equation (2):  
\[Span=\frac{D_{90-D_{10}}}{D_{50}} \tag{2}\]
## 3 Results  
### Cell Viability  
The percentages of cell viability are presented in Figure 5. Starting from day 4, all test groups showed a statistically reduced viability compared to the control (\(p<0.05\)). However, the cell viability of the IAC was lower than that of the UI (\(p<0.05\)). The platform-matched groups were significantly lower in cell viability on day 4 compared to their counterparts, CS and TS groups (\(p<0.001\)). On day 7, all the test groups continued to show less viability compared to the control (\(p<0.005\)). The CM and CSW had also lower cell viability than the UI, at (\(p<0.001\)) and (\(p<0.01\)), respectively. On day 10, all the implant-abutment couplings had lower cell viabilitycompared to the control (REF) (\(p<0.001\)), while the UI group was not different from the control but was statistically different from the TM, CM and CSW groups (\(p<0.001\)). At later incubation periods of 14 and 21 days, all IAC continued to show less viability than the control and the UI group (\(p<0.001\)). It was also noted on day 21 that CM and TM had less cell viability than their counterparts, the CS and TS groups (\(p<0.05\) and \(p<0.005\) respectively).