esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### iEEG Acquisition and Data Selection  
The iEEG was recorded with subdural grids and strips and/or depth electrodes. Data were recorded using XLTEK NeuroWorks (Natus Inc., Golden, CO, USA). As part of our institution's clinical practice, pediatric epileptologists review each patient's iEEG on a daily basis and extract multiple 5 min segments that contain considerable interictal epileptiform activity [26; 27; 28]. We retrospectively reviewed these segments and selected the first 2 min of data for each patient without any major artifacts, technical disruptions, or clinical events marked on EEG annotations. Channels with continuous artifacts were excluded.
## 4 Application of HS to VM Consolidation
### Harmony Search for VM Consolidation
#### 4.2.3 Procedure of HS for VM Consolidation  
The proposed VM consolidation model aims to achieve its original purpose of reducing the DC power consumption while deriving stable operation by searching for combinations of stable VM and reducing unnecessary VM migrations. To explore the stable workload combinations hereafter, an environment that can predict the workload is assumed based on the VM workload collected. Workload prediction is an essential process for resource allocation, capacity planning, and energy savings in dynamic cloud computing environments [39]. VM consolidation utilizes a separate monitoring tool to detect overload or underload of the PM workload. When certain conditions are met, VM placement is implemented based on the prediction data and resource information of the VM workload, and HS is utilized to find the optimal PM-VM mapping information. The pseudo code representing the proposed VM consolidation model is shown in Table 1.  
To implement VM placement, the initial value for the HS parameters (HMS, HMCR, PAR, and BW) are set at the beginning of the algorithm (line 3), PM-VM mapping information for consolidation is entered and duplicated for the same number of times as the HMS (line 4). The HS method uses the MC, PA, and RS to generate a new HM with improved objective value through repeated iterations (line 7-37). The new HM is generated sequentially through HS operators for the length of HM (number of VMs) (line 9-28). The first number is randomly generated between 0 and 1 to determine the existence of the MC and randomization collectively (line 10-15). Then, the second random number is generated to determine the existence of PA (line 22-28). If the second random number is smaller than PAR, then the neighborhood solution is generated using PA (line 29-30). The method of generating the neighborhood solution and the conditions of determining the PM that is migrated to the VM are described in Section 4.2.2. If the new HM generated has a better objective value than the worst HM from the existing HMs, the new HM is replaced with the worst HM (line 34-36). Although the objective value is worse, even when the stability of the PM is improved, the worst HM is replaced with the new HM. When the stopping condition is met, the HS terminates and VM migration occurs with the newly generated PM-VM mapping result (\(x^{\prime}\)) (line 38).
## 7 Experiment 2: Comparison of SER Methods for Amharic Cross-Lingual SER
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training** & **Testing** & **Accuracy** & **F1-Score** \\ \hline AlexNet & ASED & EMO-DB & 65.80 & 56.85 \\  & EMO-DB & ASED & 62.39 & 58.53 \\  & ASED & RAVDESS & 66.00 & 53.17 \\  & RAVDESS & ASED & 65.87 & 55.57 \\  & ASED & URDU & 60.00 & 56.28 \\  & URDU & ASED & 50.67 & 48.45 \\ \hline
**Average** & & & 61.79\% & 54.81\% \\ \hline VGGE & ASED & EMO-DB & 66.67 & 52.55 \\  & EMO-DB & ASED & 64.22 & 58.53 \\  & ASED & RAVDESS & 59.25 & 51.85 \\  & RAVDESS & ASED & 61.43 & 62.75 \\  & ASED & URDU & 59.69 & 56.34 \\  & URDU & ASED & 60.00 & 53.94 \\ \hline
**Average** & & & 61.88\% & 55.99\% \\ \hline ResNet50 & ASED & EMO-DB & 64.06 & 50.42 \\  & EMO-DB & ASED & 58.72 & 45.94 \\  & ASED & RAVDESS & 61.75 & 48.68 \\  & RAVDESS & ASED & 64.16 & 52.66 \\  & ASED & URDU & 61.56 & 62.06 \\  & URDU & ASED & 61.33 & 60.03 \\ \hline
**Average** & & & 61.93\% & 53.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experiment 2: Cross-lingual SER results (train in one language, test in another language). The languages are Amharic (ASED), German (EMO-DB), English (RAVDESS), and Urdu (URDU). ResNet50 (61.79%, 61.88%, 61.93%, respectively) which are also very close (only 0.14% from the smallest to the biggest). The results in the table also show that the average F1-score performance for VGGE (55.99%) is higher than that for AlexNet (54.81%, 1.18% lower) and ResNet50 (53.30%, 2.69% lower). Hence, it is concluded from these results that the prediction performance of VGGE was best, closely followed by AlexNet and then ResNet50. However, the range of F1-scores is small, only 2.69% from the smallest to the biggest, indicating only a slight difference in performance between different scenarios. Regarding the results as a whole, two points can be made. First, the accuracy obtained by training on one language and testing on another is surprisingly good. Second, the best language to train on when testing on Amharic seems to vary by model. For AlexNet, it is RAVDESS (65.87%); for VGGE, it is EMO-DB (64.22%); and for ResNet50, it is RAVDESS again (64.16%). Finally, we can compare our results for this experiment (Table 7) with those given for previous cross-lingual studies in Section 2. Generally, they seem comparable. Our average results are around 62%. In the previous studies, we see 56.8% [9], 57.87% [17], 65.3% [19], and 62.5% [22]. The highest is 71.62% [14]. In looking at these figures, we must remember that the exact methods and evaluation criteria used in previous experiments vary, so exact comparisons are not possible. Many different languages and datasets are used, emotion labels may need to be combined or transformed in different ways, and so on. Please refer to Section 2 for the details regarding these figures.
# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 3 System Model
### Formation of the Voronoi-Cluster  
During the process of cluster formation, each cluster head records the information near the other cluster heads, including the remaining energy and the distance between nodes. The specific steps are described as follows:  
1. There are \(N\) sensor nodes and a sink node which are deployed on 2-D plane, and \(k\) cluster heads are selected by sink according to the residual energy and geographical position;
2. The monitoring area is divided into \(k\) Voronoi cells in terms of the cluster heads' position;
3. Cluster head \(CH_{i}\) broadcasts the message, including its residual energy and ID, to adjacent nodes. The other cluster heads which can receive the information will restore to the memory and generate their own node list, including the residual energy of the source node, and the distance between them.
4. Next, find out the center node among all cluster heads in a single Voronoi cell. The center node sends _Center Declare Message_ (CDM) to other cluster heads, and adds the nodes to the set by \(\mathit{SendMessage}(ID_{u},CDM)\).
5. After working for some time, if \(\mathit{ResEnergy}(u)<\mathit{ResEnergy}(v)\),\(v\in\mathit{nodelist}_{u}\), node \(u\) will give up the role of center node and notify all cluster heads of the set.
6. Calculate the distance between all adjacent cluster heads. If \(\min\left\{d(u,v)\right\}<d_{0}\), combine the Voronoi cells with adjacent cluster heads and constitute the links for all member nodes.
7. All cluster heads send _Active Dynamic Information_ (ADI) message to others periodically. Once a cluster head failures, other cluster heads can perceive quickly and then output the backup set. The partition of the link caused by cluster head failure can be fixed as far as possible, so as to reduce the loss of data and ensure the reliability of transmission.
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 5 Virtual Resource Load Forecasting  
As mentioned in Section 3, workload prediction in cloud computing can be conducted at the application level or the infrastructure level. In this study, we focus on utilizing infrastructure-level techniques, which rely on predicting one or more resource usage metrics, such as CPU, memory, disk, or network utilization. Specifically, we employ and compare different time-series forecasting methods to predict virtual resource CPU usage.  
To accomplish this, it was imperative to gather historical data on CPU usage from various virtual resources over a specified time period. For each virtual resource trace, the historical data were divided into two datasets: one for training and another for testing. The training data were then employed to train various time-series forecasting models, while the test data were used to evaluate the accuracy of the predictions using appropriate error metrics.
## 5 Results and Discussion
For the HSMO, the majority of counts fall within the lower end of this range, indicating that the HSMO tends to favor a sparse selection of features by focusing on those that significantly contribute to the model's prediction. The HSMO optimizer demonstrates a more defined feature density distribution, due to its inherent properties to balance exploration and exploitation. On the other hand, the HPSO demonstrates a more active exploration approach. The presence of higher counts beyond the primary peak range reflects HPSO's tendency toward broader exploration, driven by its selection of pressure mechanisms. The aggregated results across 60 optimizer runs for each extractor, as illustrated in Table 3, indicate that the HSMO achieves a leading performance in accuracy while maintaining a similar reduction in features compared to the HPSO. Notably, the HSMO-based HOG achieved a maximum geometric mean of 85.40%, an accuracy of 98.27%, and F1-score of 98.15%, with only 25.78% of features. In comparison, the HPSO achieved a slightly lower accuracy of 95.02% and an F1-score of 94.71%. Although the HPSO provides a balanced outcome, it tends to a more aggressive feature reduction and still achieves competitive accuracy, for example, 95.89% for LBP with only 23.44% of features. The lower accuracy observed with the BPSO and BSMO compared to the proposed hybrid approach of HPSO and HSMO stems from the fact that these methods are restricted to their fixed search space and limited dynamic capability, which results in suboptimal solutions, limited exploration, and an inability to handle continuous variables effectively. For instance, the best accuracy achieved for LBP-BSMO is 93.94% with a higher feature retention of 28.91%. This result underscores that binary optimization methods lack the flexibility needed to adapt continuously and refine solutions effectively. Figure 10: _Cont._  
Figure 10: Resulting feature histograms and optimized feature mapping for LBP, HOG, edge extractors, and Gabor.
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 2 Proposed Design of FSS Configuration
### Dual-Band Single-Layer Structure

Table 3: Comparison of surface design with operational parameters. only thing that is needed for the simplest FSS to work. If admittance (\(\gamma\)) is a collection of patches that represent first-order filters, it indicates the behavior of a series resonant LC circuit. If \(\gamma\) is an aperture-type first-order FSS, it denotes a parallel resonant LC circuit. It is possible to make more complicated dispersion characteristics, such as multiband filters or high-order filters with narrow or widened bands, by combining unit cells that have varying capacitances and inductances. Since FSSs are made of a single metal sheet, they still respond with an electric field. Let us assume that an incoming EM wave is impinging on the array and is transmitting in a particular \(\hat{s}\) direction [13], as indicated in Equation (5). \[\hat{s}=\hat{s}s_{x}+\hat{y}s_{y}+\hat{z}s_{z} \tag{5}\]  
Throughout the element, the currents will be of the same amplitude, and the incident EM field's phase will coincide with the currents' phase. The currents in the \(m\)th column and \(n\)th row can be expressed as per [PERSON]'s theorem; the expression is written in (6). \[I_{mn}=I_{0,0}e^{-j\beta mD_{x}S_{x}}e^{-j\beta mD_{z}S_{z}} \tag{6}\]  
Applying [PERSON]'s law to reference element 0, 0 gets (7):  
\[V^{0,0}=\left[Z_{L}+\sum\
olimits_{m=-\infty}^{\infty}\sum\
olimits_{n=-\infty }^{\infty}Z_{0,mn}e^{-j\beta mD_{x}S_{x}}e^{-j\beta mD_{z}S_{z}}\right]I_{0,0} \tag{7}\]  
Array impedance can be examined by (8):  
\[Z^{0,0}= \sum\
olimits_{m=-\infty}^{\infty}\sum\
olimits_{n=-\infty}^{\infty}Z_ {0,mn}e^{-j\beta mD_{z}S_{x}}e^{-j\beta mD_{z}S_{z}} \tag{8}\]  
An overall effective impedance can be expressed by (9):  
\[Z_{eff}=\frac{Z_{unit}}{1+\Gamma_{array}} \tag{9}\]  
Because of the intercellular coupling, the inductance and capacitance of an array of unit cells in an FSS differ from those of a single unit cell. The coupling modifies the combined resonant frequency and impedance of the array, hence impacting its capacity to selectively filter certain frequencies of electromagnetic waves. The resonant frequency will be calculated from the formula in (10). \[F_{resonance,array}=\frac{1}{2\pi\sqrt{L_{eff}C_{eff}}} \tag{10}\]
# Research on the Vehicle Steering and Braking Stability Region
## 3 Solution of Equivalent Equilibrium Point
### Analysis of Bifurcation Characteristics of Equilibrium Point  
In this section, the equilibrium point of the vehicle dynamics equivalent system is solved by an improved hybrid algorithm based on the genetic algorithm (GA) and sequential quadratic programming (SQP) method [32]. The specific solution flow chart is shown in Figure 5.  
The solution conditions are: initial longitudinal velocity \(v_{x}\) = 30 m/s, front wheel angle \(\delta_{f}\) = 0.015 rad, and the search range of braking torque \(T_{b}\) is [0, 504] N\(\cdot\)m, with value intervals  
Figure 5: The hybrid solving process. Figure 6a shows the variation of values of all equilibrium points obtained by the hybrid algorithm. It can be seen that the longitudinal velocity \(v_{x}\) of the equilibrium points is around 30 m/s. Figure 6 shows the bifurcation diagrams of equilibrium points from different perspectives, with red \"\(\Circuit\)\" indicating stable equilibrium points, and blue and black \"\(\Circuit\)\" indicating unstable equilibrium points. Through the analysis of the equivalent equilibrium point of the system, it can be concluded that when the value of the braking torque \(T_{b}\) is less than 272.16 N\(\cdot\)m, there is only one unstable equilibrium point in the system. As the braking torque increases, the closer the system approaches the stable equilibrium point, the easier it is to stabilize. When the value of braking torque \(T_{b}\) exceeds 272.16 N\(\cdot\)m, another unstable equilibrium point and stable equilibrium point of the system appear. Based on the above analysis, it can be confirmed that the change in the number of equilibrium points of system with the change of braking torque \(T_{b}\) is a typical static bifurcation phenomenon. For this phenomenon, we can visually observe the change in the number of equilibrium points through the phase plane diagram, as shown in Figure 7. The initial conditions for the multi-initial points simulation are: longitudinal velocity \(v_{x}=30\) m/s, front wheel steering angle \(\delta_{f}=0.015\) rad, lateral velocity range of \(-5\) m/s to 5 m/s, with intervals of 1 m/s, and range of yaw rate values \(\omega\) from \(-0.5\) rad/s to 0.5 rad/s, with intervals of 0.2 rad/s. When the braking torque is \(T_{b}=262.08\) N\(\cdot\)m, as shown in Figure 7a, the system only has one unstable saddle point (which can be confirmed as a saddle point based on the phase trajectory manifold around the equilibrium point), and from the phase trajectories in the lower left corner, it can be seen that the phase trajectories in the area are connected. When the braking torque \(T_{b}=272.16\) N\(\cdot\)m, the phase trajectory in the small graph in the lower left corner separates, resulting in the stable focus point and another unstable saddle point. The change in the number of equilibrium points before and after the increase of braking torque indicates that the system has undergone static bifurcation, which is consistent with the equilibrium point bifurcation diagram in Figure 7.
## 3 Methods and Data  
### Methods  
The vegetation vulnerability analysis was constructed by principal component analysis based on natural and human factors. Then Moran's I was used to measure the spatial cluster of vegetation vulnerability for the base period, near future period, and distinct future period. Finally, the stochastic matrix was used to quantitatively analyze the transition of vegetation vulnerability from the base period to the near future period and the distinct future period.
## 3 Experiment and Results Analysis
### Network Model Evaluation Metrics  
When evaluating the performance of Named Entity Recognition models for marine coral reef ecosystems, this study utilizes three core metrics: Precision (P), Recall (R), and the F1 Score. These metrics collectively form a comprehensive evaluation system to quantify the model's performance. Precision (P) reflects the proportion of named entities correctly identified by the model out of all the entities it identified. Recall (R) measures the proportion  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Entity Category** & **Number of Annotated Entities** \\ \hline Coral species & 1946 \\ \hline Biological species & 1459 \\ \hline Geographic locations & 973 \\ \hline Environmental factors & 487 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the number of annotated entities in marine coral reef ecosystems.  
Figure 7: Annotation format of coral reef ecosystem Named Entity Recognition experimental dataset (part).  
of named entities correctly identified by the model out of all actually named entities. The F1 Score is the harmonic mean of Precision and Recall, providing a balanced reflection of the model's accuracy and robustness. The formulas for these three metrics are shown in Equations (9)-(11):  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\% \tag{9}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{10}\]  
\[F1=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}}\times 100\% \tag{11}\]  
Here, \"TP\" (True Positive) refers to the number of samples correctly predicted as positive, \"FP\" (False Positive) refers to the number of samples incorrectly predicted as positive, and \"FN\" (False Negative) refers to the number of samples incorrectly predicted as negative.  
Through the comprehensive application of these three metrics, the performance of the Named Entity Recognition model for marine coral reef ecosystems can be thoroughly evaluated, ensuring both comprehensive and precise model assessment.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 3 Parameterized Modeling of Optimal DES Planning in Active Distribution Networks (ADNs)
### System Power Flow Constraint  
\[\sum\
olimits_{i\in\Omega_{\text{b}}}P_{s,t,i}=\sum\
olimits_{j \in\Omega_{\text{b}}}\left(P_{s,t,ji}-r_{j\text{i}}l_{s,t,ji}^{2}\right)+P_{s, t,i} \tag{6}\] \[\sum\
olimits_{i\in\Omega_{\text{b}}}Q_{s,t,i}=\sum\
olimits_{j \in\Omega_{\text{b}}}\left(Q_{s,t,i}-x_{j\text{i}}l_{s,t,ji}^{2}\right)+Q_{s, t,i}\] (7) \[P_{s,t,i}=P_{s,t,i}^{\text{DG}}+\sum\
olimits_{m\in\Omega_{ \text{type}}}P_{s,t,i,m}^{\text{DES}}-P_{s,t,i}^{\text{LOAD}}\] (8) \[Q_{s,t,i}=Q_{s,t,i}^{\text{DG}}+\sum\
olimits_{m\in\Omega_{ \text{type}}}Q_{s,t,i,m}^{\text{DES}}-Q_{s,t,i}^{\text{LOAD}}\] (9) \[P_{s,t}^{\text{SUB}}=\sum\
olimits_{i\in\Omega_{\text{b}}}P_{s, t,i,j},\;\;i\in\Omega_{\text{sub}}\] (10) \[U_{s,t,i}^{2}-U_{s,t,j}^{2}-2\left(r_{i}P_{s,t,ij}+x_{i}Q_{s,t, ij}\right)+\left(r_{i}^{2}+x_{ij}^{2}\right)l_{s,t,ij}^{2}=0\] (11) \[l_{s,t,ij}^{2}=\frac{P_{s,t,ij}^{2}+Q_{s,t,ij}^{2}}{U_{s,t,i}^{2}} \tag{12}\]where \(\Omega_{\rm b}\) is the set of all branches and \(\Omega_{\rm sub}\) is the set of substation nodes. \(P_{s,t,ij}\) and \(Q_{s,t,ij}\) are the active and reactive power flow of branch \(ij\) in the \(s\)th scenario. \(P_{s,t,i}\) and \(Q_{s,t,i}\) are the total active and reactive power injection at node \(i\) in the \(s\)th scenario. \(I_{t,ij}\) is the branch current magnitude and \(U_{t,i}\) is the node voltage magnitude. \(r_{ij}\) and \(x_{ij}\) are the resistance and reactance of branch \(ij\). \(P_{s,t,i}^{\rm LOAD}\) and \(Q_{s,t,i}^{\rm LOAD}\) is the active and reactive power load at node \(i\) in the \(s\)th scenario.  
Constraints (6) and (7) represent the power balance of node \(i\) in period \(t\) at the \(s\)th scenario. The power injection of node \(i\) in period \(t\) in the \(s\)th scenario can be described as (8) and (9). Constraint (10) represents the active power balance at the substation node. The voltage drop equation over each branch is expressed as (11). The current magnitude of each branch can be determined by using (12).
## 2 Materials and Methods
### Stability Studies  
To evaluate NEs colloidal stability over time, both empty and loaded NEs were stored for up to 90 days at two different temperatures (room temperature and 4 \({}^{\circ}\)C). Samples were analyzed at specific time points (1, 30, 60, and 90 days) to observe any changes in hydrodynamic diameter and \(\zeta\)-potential.