esa-sceva/satcom-chunk-collection huggingface dataset:
# Scattering Reduction and Resonant Trapping of Flexural Waves: Two Rings to Rule Them
## 2 Analytical Methods
### Trapped Vibrations in a Double-Ring Cluster of Point Resonators
#### 2.2.1 Homogenised Problem for a Double-Ring Cluster  
Formally, we look for a localised waveform (no incident field) of radian frequency \(\omega\), with the flexural displacement field \(u(\mathbf{x})\) satisfying the equation  
\[D\Delta^{2}u(\mathbf{x})-\rho h\omega^{2}u(\mathbf{x})-\omega^{2}m_{1}\sum_{j=1}^{N}u( \mathbf{A}_{j})\delta(\mathbf{x}-\mathbf{A}_{j})-\omega^{2}m_{2}\sum_{k=1}^{N}u(\mathbf{B}_{k} )\delta(\mathbf{x}-\mathbf{B}_{k})=0, \tag{9}\]  
where \(D,\rho\) and \(h\) represent the flexural stiffness, the mass density and the thickness of the elastic isotropic plate, respectively, as in Section 2.1. Here, the notations \(\Gamma_{1}\) and \(\Gamma_{2}\) are used for the concentric circles of radii \(R_{1}\) and \(R_{2}\), namely  
\[\mathbf{A}_{j}\in\Gamma_{1},\ \mathbf{B}_{k}\in\Gamma_{2},\quad j,k=1,\dots,N,\]  
and  
\[\Gamma_{j}=\{\mathbf{x}:|\mathbf{x}|=R_{j}\},\quad j=1,2.\]  
The quantity \(m_{1}\) (\(m_{2}\)) in (9) denotes the mass of the point resonator in the ring defined by \(\Gamma_{1}\) (\(\Gamma_{2}\)) (see Figure 3). It is assumed that the number of point resonators is large, i.e., \(N\gg 1\), but the combined masses of inertial clusters placed along the circles \(\Gamma_{1}\) and \(\Gamma_{2}\) are finite:  
\[M_{1}=m_{1}N,\ M_{2}=m_{2}N.\]  
For the displacement \(u(\mathbf{x})\) we use the representation (4), with the \(u_{\text{inc}}\) term being replaced by zero. At the nodal points \(\mathbf{A}_{j}\) and \(\mathbf{B}_{k}\) (\(j,k=1,\dots,N\)), Equation (8) is replaced by  
\[u(\mathbf{A}_{q}) = -D^{-1}m_{1}\omega^{2}\sum_{j=1}^{N}u(\mathbf{A}_{j})G(\beta,|\mathbf{A} _{q}-\mathbf{A}_{j}|) \tag{10}\] \[-D^{-1}m_{2}\omega^{2}\sum_{k=1}^{N}u(\mathbf{B}_{k})G(\beta,|\mathbf{A} _{q}-\mathbf{B}_{k}|),\quad q=1,\dots,N,\] \[u(\mathbf{B}_{r}) = -D^{-1}m_{1}\omega^{2}\sum_{j=1}^{N}u(\mathbf{A}_{j})G(\beta,|\mathbf{B} _{r}-\mathbf{A}_{j}|)\] (11) \[-D^{-1}m_{2}\omega^{2}\sum_{k=1}^{N}u(\mathbf{B}_{k})G(\beta,|\mathbf{B} _{r}-\mathbf{B}_{k}|),\quad r=1,\dots,N.\]  
Similar to [32], in the limit when \(N\to\infty\), the sums in the right-hand sides of (10) and (11) are approximated by the integrals. Using the notations \(\mathbf{X}^{(k)}\) for two points on the circles \(\Gamma^{(k)}\) (\(k=1,2\)), we write the system of boundary integral equations in the form  
\[ \begin{split}\frac{D}{\omega^{2}}u(\mathbf{X}^{(k)})&+ \frac{M_{1}}{2\pi R_{1}}\int_{\Gamma_{1}}u(\mathbf{Y}^{(1)})G(\beta,|\mathbf{X}^{(k)}- \mathbf{Y}^{(1)}|)\mathrm{d}\mathbf{S}_{\mathbf{Y}^{(1)}}\\ &+\frac{M_{2}}{2\pi R_{2}}\int_{\Gamma_{2}}u(\mathbf{Y}^{(2)})G(\beta,|\mathbf{X}^{(k)}-\mathbf{Y}^{(2)}|)\mathrm{d}\mathbf{S}_{\mathbf{Y}^{(2)}}=0,\quad k=1,2,\end{split}
###### Abstract  
The Wuhan Ionospheric Oblique Backscattering Sounding System with the addition of an antenna array (Viobss-AA) is the newest member of the WIOBSS family. It is a multi-channel radio system using phased-array antenna technology. The transmitting part of this radio system applies an array composed of five log-periodic antennas to form five beams that span an area to the northwest of the radar site. The hardware and the antenna array of the first multi-channel ionosonde in the WIOBSS family are introduced in detail in this paper. An ionospheric detection experiment was carried out in Chongyang, Hubei province, China on 16 March 2015 to examine the performance of WIOBSS-AA. The radio system demonstrated its ability to obtain ionospheric electron density information over a wide area. The observations indicate that during the experiment, the monitored large-area ionospheric F2-layer was calm and electron density increased with decreasing latitude.  
antenna arrays; HF radar; ionosphere; remote sensing Article
## 2 Methodology
### SF-PPP Based Regional VTEC Model  
The ionosphere is an ionized atmosphere with a height of approximately 60 km to 1000 km. To model the TEC as mathematical function, the free electrons are usually assumed to be distributed in a thin shell at a median height of 450 km [45]. The conventional method to obtain TEC from dual-frequency GPS observations is the polynomial model [46], spherical harmonic model [47], polyhedral function model [48], etc. In recent years, the Ionospheric-Constrained PPP (IC-PPP) [42] realizes the estimation of ionospheric delay on the slant signal path of each observed satellite. According to the VTEC solution from the IRC SF-PPP in this paper, the IDW method [49] can be utilized to interpolate the VTEC at the given latitude and longitude of IPP.  
The IDW method is a Shepard method [49] that provides weight for distant points by using the inverse of the distance or using a more elaborate function. Each observation is assigned to the interpolated point after weighting by the inverse of the distance. Figure 1 shows the main idea of the IDW method [49]. In the following, the index \(i\) represents an included IPP point; the index \(j\) is an interpolated point, and \(n_{j}\) is the amount of included IPP points that correlate to the interpolated point.  
In the IDW method, the weighted mean of attribute values of the discrete points is adopted to estimate the attribute value of the included IPP point [49]. Therefore, how to get a reliable weight is the key to assess the quality of interpolation results. The \(\overline{VTEC_{j}}\) at the interpolated point \(j\) could be described as  
\[\overline{VTEC_{j}}=\frac{\sum\limits_{i=1}^{n_{j}}\frac{VTEC_{ij}}{d_{ij}^{2} }}{\sum\limits_{i=1}^{n_{j}}\frac{1}{d_{ij}^{2}}} \tag{20}\]  
Figure 1: IPP points within a given radius; black points represent excluded IPP points; gray points dedicate included IPP points; white point is the interpolated point; the bold circle, which is centered on the interpolated point, separates excluded and included IPP points.  
where \(VTEC_{i,j}\) is weighted by the inverse of distance given the average VTEC at the interpolated point; \(d_{i,j}\) is the distance between the point \(i\) and the interpolated point \(j\). The IDW method usually sets the search distance within a sphere, and evaluates the estimated points by known points within the range. The weight of a known point is only determined by the distance between the known point and the one to be estimated, regardless of absolute position. This approach is convenient to implement, and the complexity in time and space is much smaller than many other methods (e.g., ordinary Kriging, co-Kriging, and empirical Bayes Kriging) [50].
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 3 Nonlinear Mobility Model
### Enhanced Gauss-Markov  
The work in [12] proposed an enhancement to GM (EGM), trying to limit the sudden stops and sharp turns and guarantee smooth paths around the boundaries. In EGM, a _direction deviation_ is calculated first using the following formula:  
\[\theta dev_{t}=\alpha\times\theta dev_{t-1}+(1-\alpha)\mu_{\theta dev}+\sqrt{ 1-\alpha^{2}}w_{\theta_{t-1}} \tag{8}\]  
where \(\theta dev_{t-1}\) is the direction deviation in previous time \(t-1\). At the beginning it is chosen randomly from the range \([-10^{\circ},+10^{\circ}]\). The \(\mu_{\theta dev}\) is the mean for direction deviation and it is chosen to be 0. \(w_{\theta_{t-1}}\) is a random variable from Gaussian distribution with mean equal to zero and variance equal to 6.2.  
These values are chosen to make the direction deviation small to guarantee gradual direction change, and this will result in more smooth paths.  
After that, the new direction is calculated as follows:  
\[\theta_{t}=\theta_{t-1}+\theta dev_{t} \tag{9}\]  
The \(x\) and \(y\) are then calculated based on the speed and direction as follows:  
\[\begin{split} x_{t}&=x_{t-1}+v_{t-1}\sin{\theta_{t }}\\ y_{t}&=y_{t-1}+v_{t-1}\cos{\theta_{t}}\\ \end{split} \tag{10}\]  
Note that EGM considers two dimensions GM, and the speed is calculated as in the regular GM. Figure 3 shows a path for a UAV using the proposed enhancement implemented in OMNeT++.  
To produce more smooth turns around the boundaries using EGM, the following changes will be applied when the drone approaches any boundary:  
* The mean of the direction deviation will be changed to \(-22.5\) or \(+22.5\) (depending on the current direction of the UAV).
* Reduction of the variance of the Gaussian distribution to 0.388 instead of 6.2.  
These changes will be applied when the drone moves close to the boundary with a specified distance (margin). The value of this margin should be selected based on the drone's speed and the value of the time step.
## 5 A New Prediction Model for Vertical DRFs
### Comparison with Seismic Codes  
The DRFs defined in various modern seismic codes rely only on the damping values alone, and the natural period is not considered in the codes, as illustrated in the table earlier. Some developments in the DRFs were adopted in seismic codes, e.g., [PERSON] and [PERSON] [41]'s equation was adopted in the ATC [57] and FEMA [58] codes, [PERSON] and [PERSON] [48]'s expression was approved in Eurocode 8 [13], and [PERSON] et al. [59]'s formulation was adopted in FEMA 450 [60]. It also worth noting that the DRF equations recommended in the seismic codes are based on the horizontal response spectrum. However, the recent Kahramanmaras earthquakes that occurred in Turkey have demonstrated that the vertical response spectrum of recorded motions can be also very high [61]. High vertical accelerations can be important for the design of seismically isolated structures since lateral movement is reduced by isolators, and non-structural elements in industrial facilities can reduce the seismic and economic losses. Assuming the DRF equations provided in the codes are valid in the vertical direction, Figure 17 compares the DRFs computed by the codes (given in Table 1) and the proposed model in this study for distinct natural vibration periods of 0.1 s, 0.25 s, 0.5 s, 1.0 s, and 2.0 s. Figure 17 indicates that the DRFs of the seismic codes [12; 13; 14; 44; 45; 46] are compatible with the proposed model results for short periods (i.e., T \(\leq\) 0.5 s). On the other hand, the seismic codes diverge from the real DRFs and underestimate the DRF values with increasing periods and damping ratios.  
Figure 16: Comparison of proposed DRFv values with equations given in Table 5 [33; 40] for different damping values.
## 3 Results and Discussion
### Performance Evaluation  
In this section, an analysis of the performance of the two AI models, ChatGPT and Gemini, over 30 diverse queries from five categories reveals notable fluctuations in their accuracy, suggesting that the effectiveness of each model depends significantly on the specific query (Figure 1). Both models demonstrated high accuracy on specific queries, such as Queries 21 and 29, for which they scored a \"5\", indicating their potential to achieve top performance. However, the results also show inconsistencies, for example, on Queries 1, 5, and 20, Gemini scored a \"4\" or above, while ChatGPT only managed below \"3\", highlighting differences in how the models process and respond to certain queries. In the category of \"biological and functional implication of LLPS\", ChatGPT performed better. In all other categories, Gemini performed superiorly. The average scores for both models were above \"3\", suggesting generally satisfactory performances. This variability in scores indicates strengths and weaknesses in each model, which could be crucial for users to consider when selecting a model for specific tasks. The evaluation criteria here show that ChatGPT excels in a broader biological context and synthesis, while Gemini is optimized for technical accuracy and specialization in LLPS principles, mechanisms, and research techniques. The identification of queries for which one model outperforms the other could lead to targeted improvements and optimization, enhancing overall efficacy in practical applications.
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 3 Materials and Methods
### Loss Calculation Method for ECG Signal Classification  
The ECG signals exhibit non-stationarity, multi-class challenges, and class imbalance. Specifically, the heart rhythm patterns fluctuate over time, with the signal exhibiting significant variability. In the presence of various arrhythmias, normal heart rhythm data typically outnumber the abnormal categories. These characteristics pose challenges to the identification of heart rhythm patterns in ECG signals. However, cross-entropy loss [21] (CEL) is a classification model with probabilistic outputs adaptable to the multi-class classification challenges posed by ECG signals. CEL excels at class distinction by optimizing the predicted probability distribution, reducing confusion between heart rhythm categories and improving classification accuracy.  
Additionally, ECG signals may contain artifacts or noise. The CEL function, optimized in the probability space, possesses inherent noise resistance, making it particularly suitable for the ECG signal classification task. Therefore, in the ECG signal classification, we employ the CEL function to quantify the discrepancy between the predicted output and the ground true classification label, evaluating the degree of deviation in the probability distribution of ECG signals, as shown in the following equation:  
\[Loss=\frac{1}{N}\sum\limits_{i=0}^{N-1}\sum\limits_{c=1}^{C}y_{i,c}log(\hat{y}_ {i,c}) \tag{9}\]  
\(N\) denotes the total number of samples used in ECG signal classification training. \(C\) is the number of categories (N, S, V, F, Q). \(y_{i,c}\) is the ground true label of ECG signal sample \(i\) belonging to category \(c\), while \(\hat{y}_{i,c}\) is the predicted probability of sample \(i\) corresponding to category \(c\), and \(\sum\limits_{c=1}^{C}y_{i,c}=1\). By minimizing the classification Loss, the predicted label \(\hat{y}_{i,c}\) is made as close to the true label \(y_{i,c}\) as possible, thereby improving the accuracy of heart rhythm pattern identification.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Bionic Type  
Structural geometry by mimicking biological systems is likewise a popular research direction for optimizing vibration isolation models [60]. They are mainly categorized as leg-mimicking structures, bird-neck-mimicking structures, and insect-mimicking structures.
## 4 Machine Learning and LoRaWAN
### Reinforcement Learning (RL) Techniques
Results indicated that throughput has been maximized for different slice types, such as Ultra High CLE and Ultra Low CLE. The study [40] addressed the problem, which concerned the dissemination of optimal network configuration parameters to end nodes in LoRa-based networks to improve per-node throughput. The authors used an RL technique to prescribe optimum distribution strategies to overcome regional restrictions on ISM band utilization. They used an RL approach, which presented a solution that formulated the optimal disseminating policies to update the parameters of LoRa-based networks. They also used deep reinforcement learning, Q-Learning, and SARSA, and the updating process has been formulated as an RL problem. The study has focused on optimizing SF and CR to improve per-node throughput. The proposed RL approach demonstrated effective optimization of SF and CR parameters and achieved up to a 147% increase in throughput. The validation and testing of models were conducted using NS-3, EDSim, and simulation modules that were integrated into MATLAB. The study [41] addressed the approach for enhancing EE in wireless underground sensor networks (WUSNs) combined with LoRaWAN technology. The authors suggested an approach that has optimized transmission topologies for underground sensors by using Multi-agent Reinforcement Learning (MARL) by considering factors and variables such as packet collisions, energy usage, and network quality. The algorithm adapts to dynamic underground environments by introducing a reward mechanism, which has enhanced its effectiveness. The authors have conducted experiments on several underground locations and network configurations, and the findings of the study showed that the MARL algorithm has optimized EE more effectively than conventional ADR techniques. The researchers have evaluated and trained the MARL algorithm on actual farm data from Ward Farm and TOSSIM, which is a simulator for TinyOS-based networks.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 3 AI-DRX for Multiple Beam Communications
### AI-DRX for Enabling Dynamic Long and Short Sleep Cycles
Our proposed algorithm keeps updating the predicted time value based on most recent received packets and their packet arrival time. Figure 6: Timing diagram of AI-DRX based dynamic short sleep cycle. DRX saves the power of UE at the cost of delay. Hence, we considered two performance parameters: energy efficiency (EE) and mean delay. The performance parameter; EE of the UE is the ratio of dynamic short sleep time and dynamic long sleep time to the sum of active time (\(T_{AC}\)), ON time (\(T_{ON}\)), beam training time, feedback time, dynamic short sleep (\(T_{DY}\)) time and dynamic long sleep time (\(T_{DY}\)). EE can be calculated by Equation (8). The beam training and feedback processes are considered during active time (\(T_{AC}\)) and dynamic short and long sleep cycles take place during (\(T_{DY}\)). \[EE=\frac{T_{DY}}{T_{AC}+T_{ON}+T_{DY}} \tag{8}\]  
Similarly, for packet inter-arrival time \(\lambda_{\kappa}\), the mean holding period of active state can be calculated as [5]:  
\[T_{AC}=\frac{1-e^{-\lambda_{\kappa}T_{IN}}}{e^{-\lambda_{\kappa}T_{IN}}(1-e^{- \lambda_{\kappa}})} \tag{9}\]  
Furthermore, the packets arrived during the sleep state and ON duration are stored in the buffer until next ON period. The packet arrival events are the random observer to the sleep period and ON state. Therefore, the mean delay is defined as the sum of mean sleep time and ON duration and is given as:  
\[\text{Mean Delay}=\frac{T_{DY}}{2}+\frac{T_{ON}}{2} \tag{10}\]
# A Lookahead Behavior Model for Multi-Agent Hybrid Simulation
## 1 Introduction
However, there are still problems that need to be addressed when developing PR-based simulations, two of which are the resynchronization interval (RI) and cyclic dependency (CD). Smaller RI brings more re-sync times, while larger RI may result in missing important interactions. CD causes an infinite loop in the synchronization of states. Aiming at tackling these two problems, this paper proposes a lookahead behavior model (LBM) for hybrid simulation. LBM uses a time window-based lookahead process to implement hybrid simulations under the PR mechanism. The goal of the method is to create a nearest next \"safe\" time window when advancing simulation time. On one hand, the interaction events that will happen in relation to two agents can be predicted in the time window, to determine when a resynchronization is needed between them. On the other hand, estimated values are introduced in a time window for an agent in the cyclic dependency so as to break the loop occurring in the state synchronization. This paper describes the lookahead modeling process in a simple military test case and compares the efficiency of the PR mechanism with those of other state update mechanisms. The results show that, compared to traditional agent behavior models (TBMs) with fixed time synchronization or variable time synchronization, LBM allows simultaneous behaviors of sense, think, and act, and skips unnecessary cycles and updates, which can lead to a performance improvement. The rest of this paper is organized as follows. Section 2 introduces related works in traditional \"sense-think-act\" paradigm, state update mechanism, and the general pattern of hybrid simulation. Further in Section 3, we present the RI and CD problems in the military context. In Section 4, the time window-based LBM is introduced. Section 5 demonstrates the realization of the lookahead process, and presents the experimental results of the proposed LBM-PR along with the comparison analysis with the existing state update mechanisms. Additionally, the paper presents a comparison between traditional behavior models and several issues for discussion in Section 6. Finally, Section 7 concludes the paper.
# Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition
## 5 Results  
### Clustering Performance  
Table 1 compares the performance of our system with that of the DBCSCAN and hierarchical algorithms, which were implemented and tested on the same dataset. The evaluation metrics used for performance comparison include the clustering correct rate (CCR), which represents the percentage of clusters identified correctly for all data, the algorithm space complexity (memory), and the processing time (PT), which only counts the processing time for clustering calculations of each algorithm for ease of comparison without considering the encoded DTS of video data and the input time of videos. Our system's computational tasks are decentralized through many agents, translating into collaborative agent behavior with less complex computational tasks per agent. Therefore, the PT per frame for our system was 0.31 s lower than that of other algorithms. Moreover, because the agents do not perform direct floating point computation operations internally but rather query the EM and use the LCS comparison algorithm, the space complexity was O (\(N^{2}\)). Through the multilayer clustering synergy, our system effectively collected common features of different regions well to achieve clustering of motion behavior, exhibiting a CCR of 97.4%.
## 5 Technique Comparison
### LASSO Method  
The Least Absolute Shrinkage and Selection Operator (LASSO) method introduces an L1 penalty to the regression model, not only shrinking the coefficients but also setting some of them to zero, thereby performing feature selection. This is particularly useful when the dataset includes many irrelevant or redundant features. LAR is commonly used in SPVSs where the objective is to identify the most critical predictors, such as solar irradiance or humidity, while ignoring less influential factors [33].  
LAR has been successfully used in SPVS modeling to simplify models by selecting only the most important environmental variables. In its study, [PERSON] and [PERSON] [23] found that LAR was particularly effective in reducing the dimensionality of SPVS data, which resulted in improved model interpretability and reduced overfitting. This ability to perform automatic feature selection makes LAR a valuable tool in optimizing PV performance predictions.  
We have another regularization/shrinkage method which can handle the same problems as RR and can address other solutions for estimating the unknown parameters of MLRM. The LASSO technique is a new regularization/shrinkage factor which is developed to overcome the problem of multicollinearity. The new formulation of the Sum of Residual (errors) Squares of LASSO [31; 32; 33] method (RSSL) becomes  
\[RSSL=\sum_{i=1}^{n}\left[y_{i}-y\right]^{2}=\sum_{i=1}^{n}\left\{[-\left(\hat{ \beta}_{0}\ +\sum_{j=1}^{p}\hat{\beta}_{j}\ x_{ji}\ )\right]^{2}+\sum_{j=1}^{p}\gamma\ \left|\hat{\beta}_{j}\right|\right\}, \tag{9}\]  
and based on Lagrangian multipliers, the LASSO estimators are given by  
\[\hat{\beta}_{R}=argmin\sum_{i=1}^{n}\left\{\left[y_{i}-\left(\beta_{0}\ +\sum_{j=1}^{p}\beta_{j}\ x_{ji}\right)\right]^{2}+\sum_{j=1}^{p}\gamma\ \left|\hat{\beta}_{j}\right|\right\},\ \gamma\geq 0. \tag{10}\]  
subject to  
\[\sum_{j=1}^{p}\ \left|\hat{\beta}_{j}\right|<t, \tag{11}\]  
It may be mentioned that the main differences between LASSO and RR are that LASSO is based on \(L_{1}\) norm and RR is based on \(L_{2}\) norm, where \(L_{1}\) norm and \(L_{2}\) norm are defined by  
\[L_{1}=\left\|x\right\|_{1}=\sum\left|x_{i}\right|\ \text{and}\ L_{2}=\left\|x \right\|_{2}=\sqrt{\sum x_{i}^{2}}. \tag{12}\]