esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Power Load Prediction of VMD-LSTM Based on ISSA Optimization
## 2 Fundamentals of the Model
### Sparrow Search Algorithm
\(\varepsilon\) is the smallest constant introduced to prevent the denominator from being zero. When \(f_{i}>f_{g}\), this signifies that the sparrow is positioned at the population periphery, where it faces a higher vulnerability to predators. When \(f_{i}=f_{g}\), sparrows located in the population interior perceive the danger and need to approach other individuals to mitigate predation risks. \(K\) represents the direction in which the sparrows are moving and is also the step size control parameter. Algorithm 1 is the pseudo-code of the SSA. ```
Input: \(G\): the maximum iterations \(PD\): the number of the producers \(SD\): the number of the sparrows who perceive the danger Establish an objective function \(F(X)\),where variable \(X=(X_{1},X_{2},\cdots,X_{d})\)  Initialize a population of N sparrows and define its relevant parameters Output: \(X_{best}\),\(f_{g}\) 1: when the maximum iterations \(G\) is not met do 2: Rank the fitness values and find the current best individual and the current worst individual 3: \(R_{2}=rand(1)\) 4: for\(i=1:PD\) 5: Using Equations (3) and (4) update the sparrow's location 6: endfor 7: for\(i=(PD+1):D\) 8: Using Equations (3)-(5) update the sparrow's location 9: endfor 10: for\(i=1:SD\) 11: Using Equations (3)-(6) update the sparrow's location 12: endfor 13: Obtain the current new location 14: If the new location is better than before, update it 15: \(t=t+1\) 16: endwhile 17: return\(X_{best}\),\(f_{g}\)
```  
**Algorithm 1**The framework of the SSA  
As mathematically demonstrated in Equations (12)-(17), the LSTM network inherently exhibits strongly nonlinear mapping properties between the input-output sequences, which poses significant challenges for long-term load forecasting involving complex temporal patterns (e.g., non-stationarity, seasonality, and multi-scale dependencies). For such scenarios, the hyperparameter configuration, including the learning rate \(l_{r}\), hidden layer neuron counts \(L\), and training batch size \(N\), plays a decisive role in the model performance. Conventionally, heuristic-based tuning relying on subjective experience and a priori knowledge leads to suboptimal configurations and computational inefficiency, particularly for deep LSTM architectures with multiple hidden layers. To address this, this study proposes a bio-inspired optimization framework that leverages the Sparrow Search Algorithm (SSA) to systematically optimize these critical hyperparameters. By mimicking avian foraging behavior, the SSA balances global exploration and local exploitation to identify optimal values for the learning rate \(l_{r}\), hidden layer neuron counts \(L\), and training batch size \(N\). This data-driven approach minimizes human bias, reduces overfitting risks, and enhances both the forecasting accuracy and computational efficiency for complex load prediction tasks.
# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 4 Experimental Results and Analysis
### Comparison with Other Algorithms  
To evaluate and compare the denoising performance of the DnCNN model against other prevalent denoising techniques, this study employed the DnCNN model alongside Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and convolutional auto-encoders to process seismic signals across a range of signal-to-noise ratios (SNRs). The test subjects, depicted in Figure 8, consist of seismic signals adulterated with noise to create signals with SNRs spanning from \(-6\) dB to \(10\) dB in increments of \(2\) dB. These synthesized signals, which incorporate both noise and seismic components, served as the basis for the comparative analysis. The comparative denoising outcomes, as documented in Table 1, offer insights into the efficacy of each algorithm at varying SNR levels, providing a comprehensive assessment of their respective denoising capabilities.  
An analysis of Table 1 reveals that while both the WT and EMD techniques provided some enhancement in the seismic signal SNR, their effectiveness was constrained. In contrast, the DnCNN model surpassed these methods by learning noise patterns from the signals during training, resulting in a more substantial increase in the SNR for the denoised seismic signals. The DnCNN outperformed the WT and EMD across various SNR levels, demonstrating its superiority. Although the DnCNN model showed a slightly higher SNR compared to the convolutional auto-encoder, the difference is not markedly significant.  
Figure 8: Waveform diagram of the seismic signal with high SNR and noise signal; (**a**) seismic signal; (**b**) noise waveform.  
Figure 7: Test result example of noise with lower frequency: (**a**) shows the original seismic event signal, (**b**) shows the noise-added seismic signal, and (**c**) shows the denoised seismic signal.  
Overall, the DnCNN model significantly outshined the traditional methods in terms of SNR improvement and denoising performance, highlighting the advantages of its residual learning approach. The DnCNN model achieved an average SNR improvement of 2.63 dB over the WT method and 2.34 dB over the EMD method. Additionally, the average improvement in the correlation coefficient for the DnCNN compared to the WT and EMD was 0.0476 and 0.0259, respectively. These enhancements underscore the DnCNN model's optimized capability for seismic signal denoising.
## 3 Methodology
### Compound Span Representation Layer: Dense + Recurrent Positional Encoding  
To handle nested and overlapping entities effectively, we propose a Compound Span Representation Layer that integrates Dense Layers with Recurrent Positional Encoding (RoPE). This layer combines local span features with sequential dependencies for robust representation.  
For a span \(s_{i,j}\) between tokens \(c_{i}\) and \(c_{j}\), the representation is computed as:  
\[s_{i,j}=W_{1}h_{i}\oplus W_{2}h_{j}+R\ (_{i,j}).\]  
where \(W_{1}\) and \(W_{2}\) are dense layers and \(R\ (_{i,j})\) is the positional encoding for the span. To further enhance span representation, we introduce a lightweight **Bidirectional GRU** that processes sequential spans:  
\[\hat{s}_{i,j}=BiGRU\big{(}[S_{i,j}]\big{)}.\]  
This hybrid approach enables the model to capture both local and global span dependencies, addressing challenges posed by Arabic's morphological richness and entity nesting.
## 3 Results and Discussion
### Electrostatic Potential Mapping (EPM)  
Another parameter for examining a molecule's chemical reactivity is electrostatic potential mapping (EPM). In hydrogen bond interactions, it is a crucial tool for locating reactive regions for nucleophilic or electrolytic attacks. It is also important for understanding the biological recognition process 2. Compared to the energy gap between the HOMO and LUMO orbitals, which only considers the size of the gap, EPM clearly shows the regions of the molecule most prone to positive or negative potentials. Figure 4 shows the electrostatic potential maps for the four molecules, highlighting the hydrophilic (negative and positive potential) and hydrophobic (neutral) regions.  
In all polymers studied here, the negative potentials are located on the double-bonded oxygen atom (negative potential sites are found on electronegativ  
Figure 4: Electrostatic potential map (EPM) of (**A**) PMMA, (**B**) PCL, (**C**) PLGA with 6 repeating units, and (**D**) QPhNO\({}_{2}\), displayed in ball-and-stick representation.  
potential sites are around the hydrogen atoms). These graphs provide information about the regions from which the compound may engage in non-covalent interactions [49], presenting the lowest value of \(-155.40\) kJ/mol and the highest value of 128.30 kJ/mol for PMMA. A similar behavior was observed in the PCL molecule, with a minimum negative value of \(-175.40\) kJ/mol and a positive electrostatic potential reaching a maximum value of \(127.50\) kJ/mol. PLGA has the lowest electrostatic potential value near the carbon atom, with a double bond of \(-192.30\) kJ/mol and the highest positive value of 116.90 kJ/mol. Finally, the most negative value of \(-177.20\) kJ/mol and the most positive value of 198.40 kJ/mol were found for the QPhNO\({}_{2}\) compound.  
Thus, we can conclude that the three polymer molecules used in this study exhibit hydrophilic regions (positive and negative electrostatic potential) and hydrophobic regions (regions with neutral electrostatic potential), as does the QPhNO\({}_{2}\) molecule. The negative EPM regions, in red, correspond to electrophilic reactivity, while the positive region, shown in blue, corresponds to nucleophilic reactivity. This is because the electrostatic force plays an important role in the interaction between QPhNO\({}_{2}\) and the polymers.
## 4 Methodology  
The methodology applied in this study is based on five main steps: (i) spatial database construction; (ii) multi-collinearity analysis; (iii) implementing the maximum entropy (MaxEnt) method for mapping gully erosion susceptibility; (iv) assessing the accuracy and robustness of the MaxEnt method and related gully erosion susceptibility map; and (v) sensitivity analysis of predisposing factors. More details of the methodology used are explained below.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 5 Mining of Complex Relationship Weights in Dynamic Vehicle Clusters
### Vehicle Cluster Representation Method Based on Hypergraph Model  
To address the multidimensional, dynamic, and complex interaction relationships between vehicle nodes, the hypergraph model is employed to represent the connections between pairs of vehicle nodes and hyperedges [23; 24]. A hyperedge can connect any number of nodes, and a node can appear in multiple hyperedges, as shown in Figure 4, where different colors represent different vehicle clusters.  
This section constructs a relationship mining model based on hypergraph theory for the multidimensional dynamic vehicle cluster characteristics. This model quantifies the impact weights between vehicle nodes, vehicle clusters, and vehicle node behaviors. In dynamic vehicle clusters, relationship mining is divided into three categories: relationships between vehicle nodes, relationships between vehicle nodes and vehicle clusters, and relationships between vehicle clusters.  
The dynamic vehicle clusters defined by the hypergraph model represent a ternary relationship \(HG=(V,E,\omega)\). Here, \(V\) represents the set of hypergraph vertices, with each node representing a vehicle node \(v\) in the cluster. \(E\) represents the collection of hyperedges in the hypergraph, and \(\omega\) represents the collection of weights for each hyperedge. Unlike general graphs, hypergraphs have clustering characteristics because a hyperedge can connect to any number of vertices. Thus, a hyperedge \(e_{1-i}\) is a collection of vertices \((v_{1},v_{2},\ldots,v_{n})\). If a hyperedge \(e_{ij}\), which represents the connection between \(v_{i}\) and \(v_{j}\), contains two vertices, then \(v_{i}\) and \(v_{j}\) are considered adjacent.  
If a vertex \(v_{g}\) belongs to two hyperedges \(e_{i}\) and \(e_{j}\), then \(e_{i}\) and \(e_{j}\) are adjacent. The weight \(\omega\) of each hyperedge represents the importance or confidence level of the relationship between nodes. Specifically, the relationship equations of hypergraphs, nodes, and hyperedges in the hypergraph model can be expressed as follows.  
\[\begin{array}{l}h(v_{i},e_{k})=\left\{\begin{array}{ll}1,\ v_{i}\in e_{k} \\ 0,\ v_{i}\
otin e_{k}\end{array}\right.\\ \delta(e_{k})=\sum_{v_{i}\in V}h(v_{i},e_{k})=\left|e_{k}\right|\\ d(v_{i})=\sum_{e_{i}\in E}h(v_{i},e_{k})\omega(e_{k})=\sum_{e_{i}\in E|v\in e }\omega(e_{k})\end{array} \tag{2}\]  
where \(h(v,e)\) represents the node-hyperedge function, \(\delta(e_{k})\) denotes the degree of hyperedge \(e_{k}\), i.e., the number of nodes contained in the hyperedge. \(d(v_{i})\) represents the degree of vertex \(v_{i}\), i.e., the number of edges the vertex is included in.
# Failure Warning at the End of Service-Life of Lead-Acid Batteries for Backup Applications
## 2 Linear Superposition-Voltage Aging Model
### Linear Aging Filter of Parameters to Counter Hysteresis
\(E\hat{\Omega}C_{j,k}\) corresponds to the end of terminal voltage (1.80 V). If \(E\hat{\Omega}C_{j,k}\) is below 80%, filter loop is halted, and the battery is removed out of stack. The battery's state of health can be further verified by deep-discharge and EIS:  
\[\Phi_{x,k}=\left[ \begin{array}{cc}1&1\\ 0&1\end{array} \right] \tag{47}\]  
\[\left[ \begin{array}{c}\hat{\xi}_{k}^{-}\\ \hat{\beta}_{k}^{-}\end{array} \right]=\;\Phi_{x,k}\left[ \begin{array}{c}\hat{ x}_{(k-1)}\\ \hat{\beta}_{(k-1)}\end{array} \right] \tag{48}\]  
\[P_{x,k}^{-}=\;\Phi_{x,k}P_{x,(k-1)}\Phi_{x,k}^{T} \tag{49}\]  
\[K_{x,k}=\;P_{x,k}^{-}H_{x}^{T}\Big{[}H_{x}P_{x,k}^{-}H_{x}^{T}+M_{x}\Big{]}^{-1} \tag{50}\]  
\[1.80\;=\;\hat{V}_{OCP,k}-\frac{Ah_{o}E\hat{\Omega}C_{j,k}}{\hat{c}_{bulk,k}}-I_ {29.4}\left(\frac{\hat{r}_{\Omega,k}}{1-E\hat{\Omega}C_{j,k}^{\hat{\xi}_{ \Omega,k}}}\right) \tag{51}\]  
Step 3. Updating estimation with measurement of deep-discharge:  
After measurement of deep-discharge, \(x_{k}\) and \(\beta_{k}\) is calculated in Equation (52). In Equation (53), state vector estimation (\(\hat{x}_{k}\) and \(\hat{\beta}_{k}\)) is updated with measurement (\(x_{k}\) and \(\beta_{k}\)). In Equation (54), \(P_{k}\) is updated before jumping back to Step 2. \[\beta_{k}=\min\left\{\frac{x_{k}-x_{o}}{k-o},\frac{x_{k}-x_{(k-1)}}{1}\right\} \tag{52}\]  
\[\left[ \begin{array}{c}\hat{x}_{k}\\ \hat{\beta}_{k}\end{array} \right]=\left[ \begin{array}{c}\hat{\xi}_{k}^{-}\\ \hat{\beta}_{k}^{-}\end{array} \right]+K_{k}\left[\left[ \begin{array}{c}x_{k} \\ \beta_{k}\end{array} \right]-H_{x}\left[ \begin{array}{c}\hat{\xi}_{k}^{-}\\ \hat{\beta}_{k}^{-}\end{array} \right]\right] \tag{53}\]  
\[P_{x,k}=\left[I-K_{x,k}H_{x}\right]P_{x,k}^{-} \tag{54}\]  
Step 4. Increment (\(k+1\)) and go back to Step 2. Until battery failure, algorithm returns to Step 2. In filtering loop, sampling interval between \(k\) and (\(k+1\)) is limited by accelerated aging period.
## 4 Data Acquisition and Analysis
### Dimensionality and Strike Analysis
Short periods represent shallow depth while longer periods show characteristics at greater depth. Figure 5: The rose diagram plot for strike direction derived from impedance invariants and phase tensor at all frequencies (**a,c**) and at decades frequency segments (**b,d**). The 1D inversion was performed using IPI2 WIN_MT, which is designed for automatic MT-sounding interpretation of a single profile using data amplitude and/or phase curves. The 2D inversion was carried out using the open-source Occam2D code. Occam2D is an extension of the Occam 1D inversion scheme, aiming to generate a smooth model from EM data while maintaining a specified misfit tolerance [50,51]. Considering the amount of our data, all data were included for the inversion, but any noisy data were smoothed to reduce the noise effect.
## 2 Basic Theory  
### FCF of Intershaft Bearing  
When the working surface of a bearing has defects, these defects will collide with its contact component and generate impact vibration, causing changes in the different components of the bearing. In practice, the impact vibration frequency is known as the FCF. When bearing components have defects, such a vibration frequency will be evident, making it possible to determine whether bearing parts suffer from faults. Equations (1)-(3) are the FCF equations for a traditional bearing's inner race, outer race, and rolling element, respectively [23]. The traditional bearing mentioned here refers to a bearing with a fixed outer ring.  
\[\text{Ball Pass Frequency on Inner ring}(BPFI)=\frac{1}{2}Zf_{r}\bigg{(}1+ \frac{d}{D}\cos(\alpha)\bigg{)} \tag{1}\] \[\text{Ball Pass Frequency on Outer Ring}(BPFO)=\frac{1}{2}Zf_{r} \bigg{(}1-\frac{d}{D}\cos(\alpha)\bigg{)}\] (2) \[\text{Ball Spin Frequency}(BSF)=\frac{f_{r}}{2}\frac{D}{d}\bigg{(}1 -\bigg{(}\frac{d}{D}\cos(\alpha)\bigg{)}^{2}\bigg{)} \tag{3}\]  
where \(f_{i}\) (Hz) represents the rotating frequency of the bearing's inner race, \(d\) (mm) is the diameter of the rolling element, \(D\) (mm) is the pitch circle diameter of the REB, and \(\alpha\) is the contact angle.  
Unlike traditional bearings, an intershaft bearing's inner and outer rings both rotate. Therefore, the FCF equations for a traditional bearing cannot be directly applied to intershaft bearings. Assuming that a sensor is installed in the outer ring (in fact it is impossible), namely, the sensor rotates synchronously with the outer ring. As for the sensor, the outer ring is relatively motionless because it is in the relative coordinate system. In this coordinate system, the outer ring is motionless and the rotating frequency of the inner ring is \(|f_{0}-f_{i}|\). This coordinate system is defined as rotating speed difference domain. In this domain, Equations (1)-(3) can be applied to intershaft bearings. No matter in which coordinate system, the FCFs are constant. Therefore, the FCF equations for intershaft bearings with inner and outer rings have the same rotating directions as shown in Equations (4)-(6)  
\[BPFI=\frac{1}{2}Z|f_{0}-f_{i}|\bigg{(}1+\frac{d}{D}\cos(\alpha)\bigg{)} \tag{4}\]\[BPFO=\frac{1}{2}Z|f_{0}-f_{i}|\left(1-\frac{d}{D}\cos(\alpha)\right) \tag{5}\]  
\[BSF=\frac{1}{2}|f_{0}-f_{i}|\frac{D}{d}\left(1-\left(\frac{d}{D}\cos(\alpha) \right)^{2}\right) \tag{6}\]  
where \(f_{0}\) (Hz) represents the rotating frequency of the bearing's outer race, and the other parameters have the same meanings as defined for Equations (1)-(3).  
There are two main frequencies that greatly influence the envelope demodulation analysis of bearing fault diagnosis: (1) the bearing FCF, which is associated with the rotating speed; and (2) the carrier frequency, which determines the demodulation bandwidth. This is irrelevant to the rotating speed but relevant to the resonance frequency of the bearing components and signal transmission path.
## 7 Challenges and Future Directions
### Scalability  
There are a remarkable amount of devices that make up the IoT. Instead of being networked as a loop, such devices are typically connected to each other in hierarchy subdomains. As a result, there are substantially more connected devices than there are on the present internet. Scalability is hampered by the architecture's complexities. In order to address complex networks and maintain extremely simple techniques in the network, this problem should be solved. A system that supports and scales with a wide range of sensors recognizes and addresses this. Moreover, authentication frameworks should be designed in such a way that they can be deployed in a system with a large number of resource-constrained devices.
# Regional Traffic Event Detection Using Data Crowdsourcing
Geotagging involves tagging posts with geographically identifiable data accessible to other users. However, event detection techniques that rely on geotags are limited by poor accuracy because only 2% of social media users actually use geotags. In this paper, we propose a traffic event detection technique that uses social media-based machine learning. Traffic events are identified via machine learning, and the keywords in the text are used to extract the locations where the events occurred. This study provides the following contributions:  
* The ratio of irrelevant data is reduced by the data collected via crowdsourcing;
* Through the use of self-learned word embedding, performing event detection is more effective and flexible regardless of the social media service used;
* We compared several models and selected the model with the best performance;
* Geographic locations can be determined through text mining rather than social media functions. This paper is organized as follows: Section 2 analyzes and describes the problems of existing techniques. Section 3 introduces the processes and content of the proposed traffic event detection technique. Section 4 discusses the performance results of the proposed method. Finally, Section 5 presents the conclusions of this paper and suggestions for follow-up research.
###### Abstract  
Synthetic polymers offer control over composition, architecture, mechanical properties and degradation kinetics. Predictable sterilization of synthetic polymeric scaffolds made from low temperature melting polymers, remains a challenge to clinical translation. We previously demonstrated successful room temperature sterilization of electrospun polycaprolactone scaffolds (ePCL) using peracetic acid (PA). The current paper investigates the effects of PA sterilization on two different scaffolds types--ePCL and commercially available porous polystyrene (Alvetex(r)) scaffolds using mouse calvarial osteoblasts cell line (MC3T3) and Live-Dead Assay. We report cytotoxicity in PA-treated ePCL scaffolds (PA-ePCL), while control scaffolds strongly supported cell survival. Treatment of PA-ePCL scaffolds with known methods of PA residual elimination (sodium thiosulfate, catalase, washing and aeration) had minimal effect on MC3T3 survival. However, incubation in 80% ethanol for 30 min successfully eliminated the toxic PA residuals and restored scaffold cytocompatibility. On the other hand, PA treatment of Alvetex(r) scaffolds induced diametrically opposite effects: cell survival and proliferation was enhanced after PA exposure and these responses were reversed following ethanol wash. These results suggest that PA treatment can induce different biological effects based on polymer chemistry and scaffold architecture and presents interesting opportunities to modulate biological properties of tissue engineering scaffolds.  
peracetic acid; sterilization; electrospinning; polymeric scaffolds; cytotoxicity; polycaprolactone; polystyrene; quenching; Alvetex