esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 5 Experimental Results
### Performance Measures  
Various metrics can be used to measure the performance of classifiers. In a binary case, important metrics to measure the performance of the classifier are precision and recall. **Precision** is the number of true positives divided by the sum of true positives and false positives. **Recall** is the number of true positives divided by the sum of true positives and false negatives. Frequently, **accuracy** is used to measure the percentage of overall agreements between predictions and correct answers.  
In multi-class problems, the accuracy can show skewed performance because of an imbalance of classes. For example, if the percentage of S2 in the testing set is up to 45%, then just classifying all data as S2 archives an accuracy of 45% with bias in the data. In the case of multi-class, precision and recall can be measured for each class. An F1 score can be used by employing these two measures. The **F1 score** calculates the harmonic mean of the precision and recall per class. The F1 scores are not biased due to class imbalance. To measure the overall performance, the accuracy and weighted F1 score were used in our research. The **weighted F1 score** was calculated as the weighted average of the F1 score for each class. The closer the accuracy, precision, recall, F1 score, and weighted F1 score values were to 1, the more reliable the classifier was. Using the EarlyStopping function of TensorFlow [31], the training was stopped when the accuracy of the validation data did not improve more than six epochs. Then, we measured the performance values for the testing data using the Scikit-learn package [34].  
All the experiments were conducted on a server with an Intel i9-9920X CPU @3.50 GHz, 128 GB RAM, four NVidia GeForce RTX 2080, and CUDA 10.1. It took approximately 20 s to perform one epoch in training.
## 4 Implementation
### Monitoring Model
#### 4.3.1 Information Gathering  
Initially, while the path-message \(M\) is in the information gathering state, an agent receives it, adds traffic information to it and then sends it to another agent. After agent \(a_{i}\) adds information to \(M\) another agent \(a_{j}\) is randomly chosen and the message \(M\) is send to it. The length of a path-message \(M\), denoted as \(|P|\), is limited by a percentage of the population of the agents in the network. This option controls the life-time of path-messages and thus prevents the flooding of the agents' network by significant traffic related to path-messages relaying. Formally, the length of the path-message (and its corresponding life span) shall always be \(|P|\leq c|A|\), where \(c\in(0,1]\).
## 2 Related Works
### EH-Based Clustering Routing Protocols
#### 2.2.1 Clustering Routing Protocols for EH-WSNs  
Energy potential LEACH protocol [29] enhances the LEACH protocol by incorporating an energy potential function to assess nodes' EH capabilities. This enhancement has led to increased network lifespan and throughput compared to the original LEACH protocol. In the multi-hop energy-neutral clustering (MENC) routing protocol [30] for WSNs, the network is segmented into rings of equal size, and within each ring, CHs are selected based on nodes' residual energy to form clusters. MENC defines energy neutrality constraints from the energy expenditure of data transfers within and between clusters, enabling nodes to function in an energy-neutral mode. It maximizes the network information rate by optimizing the number of rings, clusters per ring, and the minimal data transmission interval, thus achieving network perpetuity. Adaptive energy harvesting aware clustering routing protocol [31] for EH-WSNs selects CHs based on the EH rate and the nodes' remaining energy, exhibiting improvements in node survival and network throughput over conventional WSNs clustering algorithms. Clustering protocols for EH-WSNs often choose CHs based on nodes' residual energy without accounting for dynamic spectrum accessibility. In contrast, EH-CRSNs clustering protocols require intra-cluster nodes to comply with a groupwise constraint, ensuring at least one common channel is available for use. Consequently, clustering routing protocols designed for EH-WSNs are not suitable for EH-CRSNs.
## 4 Discussion
The current work confirmed the effect that LLLT has in promoting the odontogenic differentiation of GMSCs by showing that the genes DSPP and DMP1 were highly elevated in both experimental groups. In addition, a laser with a lower energy (\(1.5\) J/cm\({}^{2}\)) exhibited considerably greater gene expression of DMP1 and DSPP than a laser with a higher energy (\(3\) J/cm\({}^{2}\)), which suggests that lasers with lower energy can have a more significant influence in encouraging the odontogenic differentiation of GMSCs. The stimulation of the odontogenic differentiation of GMSCs by LLLT may be inferred from the elevated expression of DSPP and DMP1, both of which are markers of this process. Despite this, more study is required to ascertain the histological structure of the newly created tissue. Additionally, some recently introduced compounds have been demonstrated to have a significant influence on the oral environment. Probiotics [75], lysates [76], and postbiotics [77] can modify clinical and microbiological parameters in periodontal patients, so these products should also be considered in future clinical trials, as adjuvants and in combination with LLLT. We recognize that individual variability plays a crucial role in assessing treatment outcomes, and we intend to explore this aspect in our subsequent research. By analyzing individual cells, we aim to identify any variations in responses that may contribute to different treatment outcomes among individuals. This approach will provide a more comprehensive understanding of LLLT's efficacy and its potential implications. On the other hand, this study was an in vitro experiment, which limited its ability to completely replicate the complex in vivo environment. Results obtained in a controlled laboratory environment may not necessarily translate to the same clinical outcomes.
## 7 Results and Discussion
### Efficiency  
We measured the electrical and mechanical power for one minute to determine the efficiency, the ratio between the electrical power demanded from the batteries, and the mechanical power delivered to the wheels. So for this test, we followed the same procedure in several gearbox speed changes to observe the efficiency variation and how it behaves. The vehicle can go at 20 km/h in first, second, and third gears. At 40 km/h in second, third, and fourth gears. At 60 km/h in third, fourth, and fifth gears. At 80 km/h in fourth and fifth gears. At 90 km/h in fourth and fifth gears. At 100 km/h in fifth gear and 110 km/h in fifth gear.  
The efficiency is shown in Figure 15; at low speeds, the efficiency is higher with the final ratio of 4.3, but once the speed increases, the final ratio of 3.54 still increases its efficiency. Efficiencies above 60% are obtained when exceeding 80 km/h. While at speeds below 60 km/h, the efficiencies do not exceed 30% at 110 km/h, the efficiency of the entire powertrain is 75%, which is the maximum efficiency found in the vehicle.
# Headspace Volatiles and Endogenous Extracts of _Prunus mume_ Cultivars with Different Aroma Types
## 2 Results
### Analysis of Differential Headspace Volatiles of P. mume Cultivars with Four Aroma Types  
Based on the relative content of headspace volatiles in eight _P. mume_ cultivars, an OPLS-DA model was established, which allowed identifying the differential headspace volatiles of four aroma types as volatile biomarkers. As shown in Figure 4, eight _P. mume_ cultivars were divided into four types, and the cross-validated predictive capability (Q2 = 0.776) manifested the model's feasibility. Moreover, the volatile biomarkers were selected based on the VIP value. As shown in Table 1, 10 differential headspace volatiles with a VIP value greater than 2 were selected as volatile biomarkers to distinguish the four aroma types. The strong and light-scented types were comprised of (_E_)-cinnamaldehyde and camphene. Similarly, the strong and sweet-scented, fresh and sweet-scented types included (_E_)-cinnamaldehyde, (_Z_)-3-hexen-1-ol, and (_Z_)-2-hexenol. Methyl salicate and camphene were included in fresh and light-scented types. The fresh and strong-scented types were  
Figure 3: Volatilization rate of main compounds of _P. mume_ cultivars with four aroma types. The vertical axis showed the natural logarithms (Ln) of the ratios of the endogenous extracts content to headspace volatiles content.  
(_E_)-cinnamaldehyde, cinnamyl alcohol. The sweet and light-scented types included benzyl butyrate, pentyl acetate, m-cresol, and (_Z_)-3-hexen-1-ol.  
Meanwhile, OAVs were calculated to further decode the relative contribution of volatile biomarkers to the four aroma types. It was found that the OAVs of volatile biomarkers were all greater than 1, including pentyl acetate, (_Z_)-2-hexenol, (_E_)-cinnamaldehyde, methyl salicate, and their aroma characteristics were mainly fruity, green, spicy, cinnamon (Table S4). Finally, it can be known that the content of (_Z_)-2-hexen-1-ol in the 'JM' of the fresh-scented type was higher than in the other aroma types. Similarly, (_E_)-cinnamaldehyde in strong-scented type was higher than that in other aroma types, and methyl salicate and pentyl acetate in light-scented type was significantly higher than the ones observed in other aroma types (Table S5).
## 4 Results and Discussion
### The Fe-V Sigma-Phase  
The formation probability of the \(\sigma\)-phase in the Fe-V alloy is many times larger than in the Fe-Cr system due to a much wider temperature and compositional ranges, where this phase is stable [32]. Despite the fact that the \(\sigma\)-phase in the Fe-V system was found many years ago, the concentration and temperature ranges of its existence are still not precisely known. In general it is difficult to accurately determine the phase boundaries due to several factors such as: the slow kinetics of the transformation, the dependence on the purity of the alloys, the mechanical state (strained or strain-free state) and the annealing temperature. According to the phase diagrams analyzed in [32] the upper temperature limit for the \(\sigma\)-phase stability equals to \(1,321\,^{\circ}\mathrm{C}\) for an alloy composition \(x_{V}=46.5\%\), and it depends rather strongly on composition: \(\sim 1,000\,^{\circ}\mathrm{C}\) for \(x_{V}=34\%\) and \(\sim 1,050\,^{\circ}\mathrm{C}\) for \(x_{V}=63\%\). On the other hand, the lower temperature limit of formation does not depend too much on the concentration and is around 647-650 \({}^{\circ}\mathrm{C}\).
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 4 Results
### Diagenetic Processes
#### 4.2.1 Compaction  
Compaction includes mechanical compaction and chemical compaction, acting throughout the entire diagenetic process. It is a significant diagenetic process in clastic rock reservoirs, influencing the entirety of pore formation, evolution, and preservation in sandstone reservoirs. Among them, mechanical compaction is explained by the fact that sandstones become compacted under the weight of the overlying strata by grain displacement and deformation so that porosity is reduced relatively and even leads to their densification [50; 51]. The intensity of mechanical compaction is related to the rock composition, reservoir depth, geothermal gradient, and tectonic stress in the study area. The inequigranular sandstone has a high mud matrix content and is less sortable, while the compaction effect is high, so the mud matrix and finer grains are squeezed into the space among coarser grains, reducing the porosity (Figure 6A,B). Line contact among grains is dominant, and some grains are f  
Figure 5: Pore types in Enping Formation in Huizhou Sag.
## 1 Introduction
Several studies have explored the chemical composition and nutritional properties of different carob varieties in both the eastern and western parts of the Mediterranean Basin. The chemical composition of carob seeds, owing to their industrial and economic significance, has been extensively studied in various Mediterranean countries [19; 20; 21; 22; 23]. However, the chemical composition of carob pulp has received limited attention. Specifically, in the eastern part of the Mediterranean, the chemical composition of carob pulp has been assessed for Lebanese, Cypriot, and Turkish cultivars [24; 25; 26]. Conversely, carob pulp cultivars in the western part have been investigated in Sicily, Morocco, and Portugal [27; 28; 29]. To date, no study has comprehensively assessed and compared the pulp chemical composition of wild carob trees in both the eastern and western Mediterranean basins. The current study aimed to assess the primary chemical constituents, including nutritional components, polyphenol content, and antioxidant activity, of carob pulp collected from wild trees present in the eastern (Lebanon) and western (Spain and Morocco) parts of the Mediterranean area. The assessment includes lipid content, protein content, moisture ash content, percentage of fiber, total polyphenols, antioxidant activity, and sugar constituents.
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 1 Introduction
While the earlier studies employed manual annotation [16] or rule-based [17] methods for crowd grouping, these approaches suffered from inherent flaws, and the resulting group interactions remained unquantified post-grouping. To better predict pedestrian trajectories and ensure safety while enhancing vehicle-road coordination, we need to improve the accuracy of pedestrian trajectory prediction. To more accurately model interactions in real-world scenarios, we constrained the pedestrian observation area and designed a directed social attention function. This function effectively quantifies pedestrian interaction relationships by simultaneously considering the relative positions, relative velocities, and walking directions. A directed adjacency matrix was then constructed to encode the individual pedestrian features. Additionally, we proposed a spatiotemporal weighted graph attention network to process directed weighted graphs. For group characteristics, we designed a group attention function to refine the crowd grouping criteria and quantify the interaction intensities among group members. By employing this function, the pedestrians within a crowd were systematically categorized into groups, a group adjacency matrix was constructed, and the resulting data were processed using a spatiotemporal graph convolutional neural network. Finally, we integrated social interaction features and performed trajectory prediction using the Time Extrapolator Convolutional Neural Network (TXP-CNN). The above contributions can be summarized as follows:  
* A novel dual social graph attention network is proposed for pedestrian trajectory prediction, capable of comprehensively quantifying individual and group pedestrian features, fully harnessing dynamic interaction patterns, and substantially improving the model performance. * A directed social attention function was developed, introducing the concept of directed interaction relationships, explicitly incorporating factors such as vision, position, and distance to quantify directed pedestrian interactions. Furthermore, a spatiotemporal weighted graph attention network was proposed to process these graphs. * A group attention function was designed, the group division rules were improved, groups were effectively divided from the crowd, and the interaction intensity of groups was quantified. * Experimental evaluations conducted on the ETH/UCY pedestrian trajectory prediction dataset demonstrate that the proposed dual social graph attention network outperforms the existing methods in terms of both Average Displacement Error (ADE) and Final Displacement Error (FDE).
## 2 Thermal Evaluation of Overhead Electrical Lines
### Dynamic Line Rating
#### 2.2.1 Indirect Dynamic Line Rating  
DLR, with indirect methods, considers that the ampacity of transmission lines dynamically changes depending on environmental conditions. Ambient conditions encompass all climate-related factors, including ambient temperature, wind speed and direction, solar radiation intensity, etc. [7; 10; 11]. The evaluation of lines is indirectly calculated using meteorological data recorded or forecasted along the transmission line. This method is also known as weather-dependent line rating. Meteorological data, whether measured or predicted, are considered primary inputs into weather-dependent line rating systems. Weather sensors may be positioned along the transmission line to gather weather data for implementing DLR. Assessing the conductor's thermal balance equation forms the primary basis for weather-dependent line rating computations [11].
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### \(\mathcal{F}\)-Deletion Problems
#### 4.6.2 Subgraph Deletion  
Let \(H\) be a fixed pattern graph with \(k\) vertices. Given a host graph \(G\), deciding whether \(H\) is a subgraph of \(G\) (in the usual sense) is known as Subgraph Isomorphism, whose parameterized complexity with various parameters (e.g., \(k\), \(\mathbf{tw}(H)\), \(\mathbf{genus}(G)\), etc.) was studied by Marx and Pilipczuk [263].  
Guruswami and Lee [115] studied the corresponding vertex deletion problem \(H\)-Subgraph Deletion (called \(H\)-Transversal in the paper), which is a special case of \(\mathcal{F}\)-Deletion where \(\mathcal{F}\) is the set of graphs that do not have \(H\) as a subgraph. Note that the problem admits a simple \(k\)-approximation algorithm that runs in time \(O(n\cdot f(n,H))\), where \(f(n,H)\) denotes time to solve Subgraph Isomorphism with the pattern graph \(H\) and a host graph with \(n\) vertices. Their main hardness result states that assuming the Unique Games Conjecture, whenever \(H\) is 2-vertex connected, for any \(\varepsilon>0\), no polynomial time algorithm (including algorithms running in time \(n^{f(k)}\) for any \(f\)) can achieve a \((k-\varepsilon)\)-approximation. (Without the UGC, they still ruled out a \((k-1-\varepsilon)\)-approximation.)  
Among \(H\) that are not 2-vertex-connected, there is an \(O(\log k)\)-approximation algorithm when \(H\) is a star (in time \(n^{O(1)}\)) or a path (in time \(f(k)n^{O(1)}\)) [115; 116; 264]. The algorithm for \(k\)-path follows from the result for Treewidth \(k\)-Deletion, because any graph without a \(k\)-path has treewidth at most \(k\). Whenever \(H\) is a tree with \(k\) vertices, detecting a copy of \(H\) in \(G\) with \(n\) vertices can be done in \(2^{O(k)}n^{O(1)}\) time [265], and it is open whether there is an \(O(\log k)\)-approximation algorithm for \(H\)-Subgraph Deletion in time \(f(k)\cdot n^{O(1)}\).
## 4 Feature Extraction Techniques
### Principal Component Analysis (PCA)
#### 4.1.2 Covariance Matrix Computation, \(\sum\)  
The covariance matrix \(\sum\) is computed to identify the variation between each variable in \(\bar{S_{i}}\) and the mean of \(\bar{S_{i}}\). This step is used to eliminate redundant points or information since the variables are presumed to be highly correlated. \(\sum\) is a symmetric \(d\times d\) matrix, where its entries are covariances linked to all the possible pairs of the initial variables. For example, given a \(d\)-dimensional matrix of the form:  
\[G=\Big{(}\mathbf{G}_{1}\quad\mathbf{G}_{2}\quad\ldots\quad\mathbf{G}_{d}\Big{)}, \tag{2}\]  
where \(\mathbf{G}_{1}\), \(\mathbf{G}_{2}\), and \(\mathbf{G}_{d}\) are defined as:  
\[G_{1}=\begin{pmatrix}\bar{s}_{1}\\ \bar{s}_{1}\\ \vdots\\ \bar{s}_{1\ast}\end{pmatrix},\qquad\quad G_{2}=\begin{pmatrix}\bar{s}_{21}\\ \bar{s}_{2}\\ \vdots\\ \bar{s}_{2\ast}\end{pmatrix},\qquad\quad\cdots\qquad G_{d}=\begin{pmatrix}\bar {s}_{d_{1}}\\ \bar{s}_{d_{2}}\\ \vdots\\ \bar{s}_{d_{\ast}}\end{pmatrix}.\]  
Then, the covariance of matrix \(G\) can be computed as:  
\[G=cov(G)=\left(\begin{array}{cccc}\sum\frac{\mathbf{G}_{1}^{2}}{a}&\sum\frac {\mathbf{G}_{1}\mathbf{G}_{2}}{a}&\ldots&\sum\frac{\mathbf{G}_{1}\mathbf{G}_{ d}}{a}\\ \sum\frac{\mathbf{G}_{2}\mathbf{G}_{1}}{a}&\sum\frac{\mathbf{G}_{2}^{2}}{a}& \ldots&\sum\frac{\mathbf{G}_{2}\mathbf{G}_{d}}{a}\\ \vdots&\vdots&\ddots&\vdots\\ \sum\frac{\mathbf{G}_{d}\mathbf{G}_{1}}{a}&\sum\frac{\mathbf{G}_{d}\mathbf{G}_ {2}}{a}&\ldots&\sum\frac{\mathbf{G}_{d}^{2}}{a}\end{array}\right), \tag{3}\]  
where the variance of \(\mathbf{G}_{1}\), \(\mathbf{G}_{2}\), and \(\mathbf{G}_{d}\) is the diagonal of Equation (3). Also, it should be noted from Equation (3) that \(\sum\frac{\mathbf{G}_{1}\mathbf{G}_{2}}{a}=\sum\frac{\mathbf{G}_{2}\mathbf{G}_ {1}}{a}\) is the covariance of two vectors, which is defined as:  
\[cov(\mathbf{G}_{1},\mathbf{G}_{2})=cov(\mathbf{G}_{2},\mathbf{G}_{1})=\frac{ \sum(\mathbf{G}_{1}-M(\mathbf{G}_{1}))(\mathbf{G}_{2}-M(\mathbf{G}_{2}))}{a-1}. \tag{4}\]
### Significance  
It is valuable to present quantitative measurements that highlight the benefits and efficiency improvements achieved through such return temperature reductions for DH systems. By examining specific data points and empirical studies, we can more clearly understand the positive impact on system performance, energy savings, and overall operational efficiency.  
[PERSON] et al., at the International Energy Agency (IEA) report, [11] present the economic savings achieved in a combined heat and power plant at a capacity of 12 MW\({}_{\text{e}}\) and 40 MW\({}_{\text{th}}\) with a reduction of 10 \({}^{\circ}\)C at the return temperature by DH. The obtained savings are given in detail with 71,785 EUR/year as a saving in the heat loss, 27,446 EUR/year as a saving in the pumping cost, and 101,236 EUR/year for the excess production of electricity achieved (assumed price rates in this case study are given as 23 EUR/MWhNCV for fuel, 110 EUR/MWh\({}_{\text{el}}\) for electricity, and 40 EUR/MWh\({}_{\text{th}}\) for heat).  
[PERSON] and [PERSON] [1] provide a representative cost reduction gradient of approximately 0.16 EUR/MWh\({}_{\text{th}}\)-\({}^{\circ}\)C based on an analysis of 27 Swedish DH systems. For example, a DH system producing 500 GWh\({}_{\text{th}}\)/year could achieve savings of 800 kEUR with a 10 \({}^{\circ}\)C reduction in return temperature, given a currency exchange rate of SEK/EUR = 0.106. Additionally, considering the benefits of flue-gas condensation, a return temperature reduction of 5 \({}^{\circ}\)C can increase the heat utilization rate by 1-5% compared to normal conditions [30]. According to [30], the average annual return temperature in Swedish DH systems was 47 \({}^{\circ}\)C, which could be reduced to 32 \({}^{\circ}\)C with the available technological know-how, as detailed in a licentiate thesis from 1998 [31].  
[PERSON] et al. [11] emphasize the issue of high return temperatures in many existing DH systems, based on the authors' experiences. The report estimates a potential saving of approximately USD 100 million for Sweden for each degree reduction in return temperature, as per a personal communication with Dr. [PERSON] in 1999. An illustrative case study within the same IEA report describes a district with a 135 km distribution network supplying 1650 detached houses, with an annual heat demand of 327 GWh\({}_{\text{th}}\) and a heat loss of 52 GWh\({}_{\text{th}}\). The study indicates that a 5 \({}^{\circ}\)C reduction in return temperature can lead to a decrease in heat loss by 2080 MWh\({}_{\text{th}}\), a 14% reduction in flow rate, and a 36% reduction in pumping electricity consumption (assuming pump electricity consumption is proportional to the cube of the flow rate). Additionally, the case study notes that a 10% increase in temperature difference results in a 10% reduction in flow for a DN800 pipe over 5 km. This increased temperature difference allows for pipe dimension reduction to DN700, yielding a saving of 440 EUR/m (with an exchange rate of EUR/USD = 0.8 in 2005).