esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The present study investigated the effect of microwave, blanching, and ultrasonic pretreatments on drying time and kinetics, effective moisture diffusion coefficient, specific energy consumption, color, rehydration rate, total phenol content, and total flavonoid content of terephthal dried by infrared (IR) and convective (CV) dryers. Comparing the two drying methods (IR and CV), the IR method had a shorter drying time and lower specific energy consumption (SEC), higher energy efficiency, and diffusion coefficient, while the CV method offered products with higher phenol and flavonoid contents, better color quality, and higher rehydration rate. Different pretreatments had different effects on these parameters. Moreover, ultrasound could better preserve the color, total phenol content, and total flavonoid content, and its rehydration rate was higher than the other pretreatments. The drying time and SEC were the lowest in the microwave pretreatment. The highest rehydration rate, phenol, and flavonoid contents and the lowest color variations were observed in the samples undergoing ultrasonic pretreatment at 60 \({}^{\circ}\)C. Meanwhile, the lowest drying time and SEC and the highest drying efficiency and rehydration rate were recorded in the microwave pretreatment upon using an infrared dryer at 750 W. Noteworthy, the ultrasonic pretreatment can provide a unique opportunity to produce high-quality products rich in phenol and flavonoids contents.
# Auralization of High-Order Directional Sources from First-Order RIR Measurements
## 2 Auralization of Arbitrary Source Directivity from Measurements
### Theory behind Source and Receiver Directional (SRD) Rits
\begin{split} h\left(\theta_{\rm R},t,\theta_{\rm S}\right)& =\sum_{n^{\prime},m^{\prime}}\sum_{n,m}Y_{n^{\prime}}^{m^{\prime}} \left(\theta_{\rm R}\right)\ h_{nm}^{n^{\prime}m^{\prime}}\left(t\right)\ Y_{n}^{m} \left(\theta_{\rm S}\right),\\ \text{with }h_{nm}^{n^{\prime}m^{\prime}}\left(t\right)& =\sum_{i}\frac{Y_{n^{\prime}}^{m^{\prime}}\left(\theta_{\rm R, \it i}\right)\ a_{\rm i}\delta\left(t-\tau_{i}\right)Y_{n}^{m}\left(\theta_{ \rm S,\it i}\right)}{\tau_{i}},\end{split}  \tag{3}\]  
where \(Y_{n}^{m}(\theta)\) are the SH of order \(n\) and degree \(m\), and the expression \(h_{nm}^{n^{\prime}m^{\prime}}(t)\) denotes a modeled source-and-receiver-directional room impulse response in Ambisonics (SRD ARIR), which we actually measure later on (see Section 2.2). The directivities \(g_{A}(\theta_{A})\), \(A\in\{\rm S,R\}\) can be represented by SH expansion:  
\[g_{A}(\theta_{\rm A})=\sum_{n=0}^{N_{\rm A}}\sum_{m=-n}^{n}\gamma_{nm}^{\rm A }Y_{n}^{m}(\theta_{A}), \tag{4}\]  
where \(N^{\prime}\) and \(N\) are the maximum orders used to represent the receiver and source directivity, respectively. By inserting Equations (3) and (4) into Equation (2), the integrals in Equation (2) invoke the orthogonality property \(\int_{\mathbb{S}^{2}}Y_{n}^{m}(\theta_{A})Y_{n^{\prime}}^{m^{\prime}*}( \theta_{A})\mathrm{d}\theta_{A}=\delta_{nm^{\prime}}^{mm^{\prime}}\) for both source and receiver, yielding a neat sum for the RIR  
\[h(t)=\sum_{n^{\prime}=0}^{N^{\prime}}\sum_{m^{\prime}=-n^{\prime}}^{n^{\prime }}\sum_{n=0}^{N}\sum_{m=-n}^{n}\gamma_{n^{\prime}m^{\prime}}^{\rm R}h_{nm}^{n^ {\prime}m^{\prime}}(t)\gamma_{nm}^{\rm S}. \tag{5}\]  
For natural, frequency-dependent directivities, multiplication by the spherical-harmonic coefficients of the source and receiver directivity \(\gamma_{nm}^{\rm S}\) and \(\gamma_{n^{\prime}m^{\prime}}^{\rm R}\) is replaced by convolution with the coefficients of their directional impulse responses \(\gamma_{nm}^{\rm S}\to*\gamma_{nm}^{\rm S}(t)\) and \(\gamma_{n^{\prime}m^{\prime}}^{\rm R}\to\gamma_{n^{\prime}m^{\prime}}^{\rm R}( t)*\), now in the SH domain.
## 4 Calculation Example
### Analysis Results of the Construction Technology Application
#### 4.2.2 Analysis of Tie-Down Tension  
After the cable net traction lifting is completed, the tie-downs are installed and tensioned in the subsequent construction to ensure the accuracy of the tension ring elevation, which is also the key to non-bracket construction. Due to the different tension sequences of the tie-downs, the cable force distribution in the cable net varies accordingly. Moreover, the change in cable forces in the cable net tends to increase the in-plane bending moment in the CR, and finally increase its deformation. Therefore, in this section synchronous equal proportion tension is first carried out to study the variation in the CR configuration, the cable forces of the tie-downs, and the elevation of the tension ring clamps during the tie-down tension process, so as to determine the key factors affecting those changes. Then, the tension sequence of the tie-downs is improved and analyzed to determine a reasonable sequence.
## 2 Theoretical Background for Spherical Localization Based on Attenuation of EM Waves
### Spherical Localization  
Consider \(N\) beacons at known positions that emit EM waves of constant magnitude, with the \(i\)-th beacon located at position \(\mathbf{r}_{i}\). The receiver has the unknown position \(\mathbf{p}\).  
The spherical self-localization problem can be decomposed into two parts: (1) range estimation from receiver node to transceiver beacons; (2) fusion of estimated ranges to obtain the position of the receiver. Spherical localization can be passive and is, hence, scalable within an increasing number of mobile receivers.  
In spherical localization, the receiver node determines ranges from the receiver to beacons by measuring either the time of flight or the received strength of the signal that is emitted by the beacons. In the presented approach, the received signal strength (RSS) is measured. The range between the receiver and the \(i\)-th beacon can be expressed by:  
\[R_{i}=\left\|\mathbf{p}-\mathbf{r}_{i}\right\|, \tag{1}\]  
which is the equation for a sphere. Hence, the ranges define spheres that are centered at the beacon locations. The intersection of all spheres is the receiver position. Consequently, the position of the receiver can be derived from geometrical relationships if the beacon locations are known as illustrated in Figure 3. Due to uncertainty in the measurement model and noise, the spheres do not intersect in general, and the intersection point needs to be estimated, e.g., with an EKF. In order for a receiver unit to self-localize in \(n\) dimensions, range estimations to at least \(n+1\) beacons are required.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### Meta-Regression Conforms a Dose-Response Relationship Between Sprint Speed and Muscle Activation  
The meta-regression model (R\({}^{2}\) = 0.881, \(p<0.001\)) demonstrated a strong linear relationship between sprint velocity and neuromuscular activation. For every 1 m/s increase in sprint speed, muscle activation rises by approximately 6.32% MVC, confirming a pro gressive neuromuscular demand with higher velocities. Statistical tests (Shapiro-Wilk, Breusch-Pagan, Durbin-Watson) confirmed that the model met all necessary assumptions, ensuring the validity and reliability of the findings.  
While the positive relationship between sprint speed and muscle activation may appear intuitive, this meta-regression provides valuable quantitative insight into the magnitude and practical implications of this link. The observed dose-response effect (~6.32% MVC increase per 1 m/s) offers a precise estimate of neuromuscular load escalation as athletes approach maximal sprinting velocity. This has practical relevance for training design, particularly in periodizing sprint intensity and managing athlete workload. For instance, understanding the nonlinear rise in activation burden supports the strategic planning of sprint volumes and intensities across training phases to minimize overload and fatigue-related injury risk. Moreover, this quantification bridges the gap between descriptive EMG studies and actionable coaching applications, enabling data-driven decisions in hamstring and gluteus-focused conditioning. From a research perspective, the model extends prior literature by not only confirming the trend but also by formalizing it with predictive capacity, strengthening its translational relevance to sport science and injury prevention strategies.
## 6 From Single-Point to Multi-Point Equalization
### Other Low-Frequency RRE Approaches  
At very low frequencies, instead of a plane wave, it is much more efficient to use a pressure-field chamber approach [170]. This approach is obtained by sending the same signal to all loudspeakers. This generates a standing wave pattern inside the room, which is homogeneous at wavelengths considerably larger than the room. For this reason, in [170] a hybrid-field playback approach is proposed which combines the efficiency of the pressure-field playback at the very low frequencies with the homogeneous sound-field obtained with the plane wave approach at higher frequencies.  
In [79], the problem of multiple-loudspeaker low-frequency RRE for a wide listening area, with the equalized loudspeaker supported by the remaining ones, is addressed as a multipoint error minimization problem between the desired response and the synthesized magnitude response. The cost function is minimized, imposing physical and psychoacoustical criteria. In particular, to obtain short equalization filters, a temporal masking constraint is imposed on the equalization filters. To avoid perceivable echoes, a combination of delay and gain relative to the main loudspeaker is considered, with the auxiliary loudspeaker signals that should fall below the echo threshold [171]. To avoid modifications in the spatial perception, the delay of the auxiliary loudspeaker signals is enforced to be at least of 1 ms in order to exploit the precedence effect. To avoid boosting the notches, a maximum-gainis imposed on the equalizers. The room equalization filters are computed considering a convex optimization framework that takes all these constraints into account.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 3 Theoretical Background
### Deep Neural Network  
The DNN is one of the neural networks with an input layer, an output layer, and many hidden layers (more than two). It includes an input layer (xi), an output layer (yi), and more than two hidden layers (hi), as shown in Figure 1. It trains as a feed-forward network to generate the corresponding output values through all hidden layers and neuron nodes in forward propagation. The non-linear activation function f takes a weighted sum of input X values and returns an output Y value, as indicated in Equation (1), where \(W_{jk}^{l}\) = the weight between node j in the layer (l\(-\)1) and node k in layer l.  
\[Y_{i}=f\Big{(}\sum_{i=1}^{t}W_{jk}^{l}X_{i}\Big{)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Methods** & **Results** & **MAPE** & **References** \\ \hline
2009–2013 & OLS & The model’s prediction accuracy has increased with the inclusion of interaction variables. & \textgreater{}4\% & [37] \\ \hline
2009–2013 & OLS and Bayesian estimation & The forecast accuracy has increased by 20\% & \multirow{2}{*}{2\% to 3\%} & [35] \\  & OLS estimation & & because of the model’s adding a temperature variable. & & \\ \hline
2009–2013 & OLS, GLSAR, FF-ANN & Compared to FF-ANN, OLS, and GLSAR, models exhibited greater forecasting accuracy. & 1.74\% to 2.95\% & [2] \\ \hline
2009–2013 & MLR with AR (2) &
\begin{tabular}{c} In comparison to OLS estimation, Bayesian estimation offers better and more reliable results. \\ \end{tabular} & 1\% to 5\% & [39] \\ \hline
2009–2013 &
\begin{tabular}{c} Ensemble for regression and ML \\ \end{tabular} & It reduces the test MAPE when implementing the blocked cross validation method. & 2.6\% & [41] \\ \hline
2013 & PSO with ANN &
\begin{tabular}{c} To train the ANN for STLF, PSO outperformed the backpropagation training technique. \\ \end{tabular} & 3.44\% & [19] \\ \hline
2013 & PSO + GA with ANN &
\begin{tabular}{c} The backpropagation and PSO training algorithms are outperformed by PSO + GA. \\ \end{tabular} & \textgreater{}3\% & [42] \\ \hline
2013 & ANN &
\begin{tabular}{c} The model’s prediction accuracy has increased with the addition of a temperature variable. \\ \end{tabular} & 3.27\% to 4.61\% & [36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of articles that were published using the EGAT dataset [41].
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 1 Introduction
### Related Works
#### 1.2.5 Graph Correlation  
Graph correlation presents the alert sequences in an acyclic graph for ease of visualization. The nodes of the graph represent the alerts, while the vertices indicate the temporal relationship between the alerts. Events are modeled as a graph \(G=(V,E)\) where the vertex \(V\) represents a set of nodes, and each node \((v\in V)\) represents the low-level n-tuple event with \(n\) mined features or attributes. The edge \(e=(vi,vj)\in E\) represents the connection between the two nodes \(v_{i}\), \(v_{j}\), showing that there is a correlation between the nodes \(v_{i}\) and \(v_{j}\). Typically, this relationship also shows that event \(v_{i}\) precedes event \(v_{j}\). The graph is weighted, where the weight of each edge corresponds to the correlation strength between the event nodes. Consider the events recorded in Table 1.  
Figure 1 below illustrates the resulting correlation graph constructed from Table 1. The nodes are labeled with the event IDs for ease of reference. The edge weights correspond  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Event ID** & **Source IP** & **Destination IP** & **Port** & **Timestamp** \\ \hline
1 & 192.168.202.103 & 192.168.207.4 & 53 & 2012-03-16 12:40:35 \\ \hline
2 & 192.168.202.89 & 192.168.207.4 & 53 & 2012-03-16 12:40:41 \\ \hline
3 & 192.168.202.95 & 192.168.207.4 & 53 & 2012-03-16 12:40:48 \\ \hline
4 & 192.168.202.61 & 192.168.207.4 & 53 & 2012-03-16 12:41:25 \\ \hline
5 & 192.168.202.89 & 192.168.207.4 & 53 & 2012-03-16 12:40:41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample network event log entries.  
to the number of features that have matched the similarity rule, while the arrow direction shows the temporal sequence of the correlated events.  
Graphs have been used in prior research to construct attack scenarios from intrusion alerts. Some of the works include showing causal relationships among alerts [27], and uncovering multistep attack scenarios [28]. Proximity graphs are used in [29] to produce data points atop of which a modified PageRank algorithm is used to detect anomalies. A vehicle security model is proposed in [30] that uses graph neural network (GNN). The model has spatial and temporal components, and it tries to bring a semantic dimension to the graph correlation to improve the correlation efficiency.
# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 4 Discussion
### Interpretability Discussion
Consequently, the station MGQ becomes the least important neighbor. Empirically, we remove the data of station MGQ and then evaluate the performance. Before eliminating station MGQ, the average RMSE, R\({}^{2}\) and SMAPE in station CCM were 34.83, 0.59 and 48.55, respectively. After removing station MGQ, the average RMSE, R\({}^{2}\) and SMAPE were 35.18, 0.59 and 49.09, respectively. Thus, the performance attenuation was slight. Figure 6: Weights that were assigned to each past moment by the temporal attention layer in PK. The \(x\) axis represents the number of each past moment and the \(y\) axis represents the prediction step (specifically, while the present moment is 0, +1 to +24 represents the forecasting moment and \(-1\) to \(-24\) represents the history to the current moment). Note that the weight 0 does not mean unrelated; it means relatively unimportant because of the min-max normalization. In summary, on the one hand, the interpretable results are consistent with the actual findings, which investigated the reliability of our proposed model; on the other hand, analyzing the inner mechanism of the model continuously deepened and strengthened the presence of spatiality. Concretely, distance is not the only important factor influencing pollutant transmission; the results also show that the wind direction matters. Thus, future work concerning spatial prediction should pay more attention to utilizing additional spatial information, like wind direction, etc.
## 6 Discussion
### Methodological Advantages over Baseline Families  
Compared with (i) univariate time-series predictors (ARIMA/ARIMAX), (ii) kernel-based regressors (SVR), and (iii) hybrid recommender systems, SE-TFF offers three methodological improvements:  
1. Multi-scale feature fusion. By nesting channel recalibration inside convolutional windows of different receptive fields, SE-TFF fuses micro-trend cues (hour-level weather fluctuations) with macro-trend information (quarterly GDP changes) in a single pass.
2. Task-aware optimization. Multi-task heads allow the model to share representations across correlated outputs--visitor volume, revenue, accommodation demand--while preventing negative transfer through RL-controlled Weighting.  
Figure 7: Overall comparison of actual vs. SE-TFF (w/o RL) predicted daily flow for 2022, 2023, and 2024.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Improvement (\%)**}} & \multicolumn{1}{c}{\multirow{2}{*}{**T4\%**}} & \multicolumn{1}{c}{**Matrix Density \(\boldsymbol{md}\)**} & \multirow{2}{*}{**Avg.**} & \multirow{2}{*}{**\(\boldsymbol{p}\)-Value**} & \multirow{2}{*}{**Cliff’s \(\boldsymbol{\delta}\)**} \\ \cline{2-2} \cline{6-13}  & & **14\%** & & **15\%** & & **16\%** & & **17\%** & & **18\%** & **20\%** & **21\%** & **22\%** & **23\%** \\ \hline MAE & SEFTF-RL (\%) & 45.3\% & 43.7\% & 44.2\% & 43.1\% & 41.3\% & 37.2\% & 37.0\% & 31.2\% & 30.5\% & 30.2\% & 38.5\% & 0.005 & \(-1.0\) \\ \hline RMSE & SEFTF-RL (\%) & 43.0\% & 42.4\% & 41.0\% & 41.7\% & 37.3\% & 36.8\% & 35.6\% & 32.5\% & 29.1\% & 28.8\% & 36.82\% & 0.005 & \(-1.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of removing RL module on performance improvement across different matrix densities.  
* Sparse-matrix robustness. Experiments reveal graceful degradation even when more than four-fifths of entries are missing. The result suggests that channel attention implicitly performs localized matrix completion, an ability absent from purely statistical methods.  
These qualities jointly reposition tourism demand forecasting from a \"single-objective curve-fitting exercise\" to a _multi-objective decision-support problem_.
## 5 Discussion
### Comparison of Model Results with Other Studies  
In order to verify the accuracy of the model calculations in this paper, comparisons with the results of other studies are still needed. Therefore, we selected the results of a geophysical survey and the results of traditional GIS-based landslide analysis methods to compare and analyze with the conclusions of this paper.  
In the 1980s, a comprehensive, integrated geophysical survey was conducted in the Yellow River Estuary waters [19]. The results of the survey showed that a large number of microslides on the leading edge of the delta existed at a water depth between 4 and 12 m in the study area. It can be seen from Figure 5 that the classification of landslides as hazard results using the spectral clustering algorithm shows that the areas with very high hazards are concentrated at a water depth range of 5-12 m. The simulation results are highly consistent with the actual survey results, indicating the accuracy and reliability of the algorithm in this paper.  
As for the conventional approach used to analyze submarine landslide hazards, the GIS-based analyzing method was also used to study the stability of submarine landslides in the Yellow River Estuary [27]. Results show that the most prone landslide areas in the study area are located at a water depth between 8 and 13 m, and the more prone landslide areas are located at a water depth between 5 and 15 m, with an average water depth of 10 m. The range of landslide-hazard areas derived from GIS results is consistent with the overall distribution range and trend compared with those derived in this paper, and the range of landslide hazards is slightly smaller. The model calculation results in this paper are closer to the results of the actual geophysical survey compared with the GIS results.  
Therefore, it is clear that the unsupervised machine learning method used in this paper has high reliability and stability after being compared with the traditional methods of submarine-landslide-hazard analysis and the actual geophysical survey results in the field.  
Although just one research area was used for the study of submarine landslide hazards, the general geological formation conditions and triggering mechanisms of submarine landslides in all study regions are comparable, despite the differences in causes and triggering variables. As a result, this paper's research technique and research hypothesis can serve as a guide for the global study of submarine landslide hazards.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 2 Basic Principles of NoN Models
### Network of Networks  
#### 2.1.1 Variables in NoN Models  
The existing NoN model is characterized by modeling the node states to express the NoN availability. The state of an arbitrary node in the NoN is determined by those of its neighbors. A node can be in one of four states, each of which is characterized by three variables (Table 1), and state transitions occur in three steps (Figure 1). The variable \(n_{i}\) indicates the existence of node \(i\), and its value is predetermined as shown by Figure 1a. The value of \(\sigma_{i}\), which expresses the _local effectiveness_ of node \(i\), is then determined based on the values \(n_{j}\) of all nodes \(j\) that are connected via internetwork links, and shifts to the states shown in Figure 1b. \(\rho_{i}\) is the _global effectiveness_ of node \(i\) and is determined by whether the node is included in the giant component (GC) composed of locally effective nodes, which is shown in Figure 1c.  
The B-NoN and C-NoN models define the local effectiveness \(\sigma_{i}\) in the same way but the GC in different ways. This results in different definitions of the global effectiveness \(\rho_{i}\), leading to a large difference in reliability between the two NoN models. In the following sections, we describe the definitions of \(\sigma_{i}\) and \(\rho_{i}\) in detail, along with the state-transition mechanisms shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol** & **Node State** & \(\mathbf{n_{i}}\) & \(\mathbf{\sigma_{i}}\) & \(\mathbf{\rho_{i}}\) \\ \hline \multirow{4}{*}{} & removed & 0 & 0 & 0 \\  & exists & 1 & 0 & 0 \\ \cline{1-1}  & locally available & 1 & 1 & 0 \\ \cline{1-1}  & globally available & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of network-of-networks (NoN) node states.  
Figure 1: Example of state transition in Brain NoN (B-NoN) model [13]. The node symbols correspond to those defined in Table 1. The black lines indicate intranetwork links and the blue dashed lines indicate internetwork links.