esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Strain Distribution  
The strain distribution curves of the concrete UHPC and reinforcement for each specimen under different loading levels are shown in Figure 7.  
Figure 7: Strain distribution curves for concrete UHPC and reinforcing steel. **(a)** Z1; **(b)** Z2-1; **(c)** Z-2; **(d)** Z2-3; **(e)** Z3; **(f)** Z4.  
As shown in Figure 7a, for comparison specimen Z1, the concrete at the edge of the compression zone was crushed when the load reached the peak load. Its ultimate compressive strain was 2127.462 \(\mu\epsilon\). The compressive strain of the reinforcement in the compression zone was 2083.488 \(\mu\epsilon\), and the reinforcement on the compression side reached yield, and the strain in the reinforcement in the tensile zone was 408.126 \(\mu\epsilon\), and the reinforcement in the tensile zone did not reach yield. These results show that the strain development pattern of the Z1 cross-section of the comparison specimen is consistent with the characteristics of low-eccentric-compression damage.  
As shown in Figure 7c, for reinforced specimen Z2-2, when the load reached 84% of the peak load, the strain in the tensile reinforcement was 2011.51 \(\mu\epsilon\), the tensile reinforcement yielded, and the strain in the concrete in the compression zone was 2566.47 \(\mu\epsilon\). When the load reached the peak load, the concrete strain at the edge of the compression zone was 3640.15 \(\mu\epsilon\), reaching the ultimate compressive strain. These results show that the reinforcement in the tension zone yielded before the concrete at the edge of the compression zone of the specimen reached the ultimate compressive strain. The strain development pattern in the cross-section of reinforced specimen Z2-2 was consistent with the characteristics of high-eccentric-compression damage.
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model
### Specific Attenuation Model for Rain, Fog, Snow, and Gases
#### 2.3.4 Gas  
Considering the presence of oxygen, nitrogen, rare gases, and water vapor in the air, which absorb radio waves and cause atmospheric attenuation, it is essential to incorporate  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Regions** & **Frequency** & \(\eta_{LoS}\) & \(\eta_{NLoS1}\) & \(\eta_{NLoS2}\) & \(\eta_{NLoS3}\) \\ \hline Region 1 & 28 GHz & \(-\)0.7108 & 7.4100 & 14.4860 & 20.6935 \\ \hline Region 1 & 71 GHz & \(-\)0.7102 & 7.4154 & 14.4935  
this effect into the free-space path loss model to determine the propagation attenuation of electromagnetic waves in a standard atmosphere. According to [37], the atmospheric attenuation \(\beta\) can be calculated as  
\[\begin{split}\beta&=\beta_{o}+\beta_{w}\\ &=0.1820f_{\mathrm{GHz}}\Big{(}N_{\mathrm{Oxygen}}^{\prime\prime} \left(f_{\mathrm{GHz}}\right)+N_{\mathrm{Water\,Vapor}}^{\prime\prime}(f_{ \mathrm{GHz}})\Big{)},\end{split} \tag{9}\]  
where \(\beta_{o}\) and \(\beta_{w}\) represent the specific attenuation (dB/km) due to dry air (oxygen and pressure-induced nitrogen) and water vapor, respectively. Furthermore, \(N_{\mathrm{Oxygen}}^{\prime\prime}\left(f_{\mathrm{GHz}}\right)\) and \(N_{\mathrm{Water\,Vapor}}^{\prime\prime}(f_{\mathrm{GHz}})\) denote the imaginary components of the frequency-dependent complex refractivities for oxygen and water vapor, respectively [37].
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 4 Discussion
For clarity, we will calculate the information content (12) of cylindrical and linear interferograms for different types and levels of aberrations with a variation of the parameter \(\alpha\), which shows how many times one type of interferogram is more effective for recognizing aberrations from interferograms using convolutional neural networks. With an average value of aberrations, \(0.3<c_{nm}<0.7\) cylindrical interferograms are more sensitive than linear ones, regardless of the parameter \(\alpha\). At high levels of aberrations, \(c_{nm}>0.7\) cylindrical interferograms are also consistently more sensitive than linear ones. Figure 15 shows diagrams of information content (12) \(E\) of cylindrical and linear interferograms for different types and levels of aberrations with variations in the parameter \(\alpha\). Thus, the maximum information content of cylindrical interferograms is achieved when detecting medium (\(\geq\)0.5) and strong aberrations (>0.7). In this case, the cylindrical interferogram is 15% more informative than the linear one. The average information content in detecting medium and strong aberrations, regardless of the frequency of the interferogram, is 9%. A 15% increase in information content parameter (Figure 16) for certain wave aberrations is shown (defocusing, astigmatism, spherical aberration, and 2 nd order astigmatism) when used as a reference beam, a cubic cylindrical wavefront instead of a flat wavefront. Moreover, the use of cylindrical interferograms makes it possible to improve the results of neural network recognition (Table 6). The average absolute error decreases in the range of 20% to 30%. It is worth noting that, among the limitations of the proposed approach, one can single out the influence of noise and other artifacts that affect the accuracy of the results of the recognition of aberrations. In addition, a reference plane beam can be easily created experimentally, and any structural reference beams (including those with a cylindrical wavefront) require additional optical elements, which is also a limitation of the interference method. Thus, we can talk about some correlations between sensitivity and MAE recognition by the neural network. This makes it possible to predict which types of reference beams will give the best result before training the neural network. Figure 14: Averaged sensitivities of cylindrical and linear interferograms to wavefront aberrations. Figure 15: The information content parameter of cylindrical and linear interferograms to wavefront aberrations.
## 2 Materials and Methods
### In Vitro Drug Release and Mathematical Model Fitting  
Using the dialysis bag method at pH 1.2, the in vitro drug release behavior of the pure drug suspension and optimized NSs were evaluated. Gastrointestinal tract acidic pH was reported to be most appropriate for absorption of orally administered ITB [35]. The samples pure ITB suspension and optimized nanosponge IBNS4 (equivalent of 10 mg ITB) were dispersed in 5.0 mL (pH 1.2) and filled in a dialysis bag (molecular weight: 14 kDa) that was closed on both ends, then suspended into a beaker with 50 mL of media (pH 1.2) maintained at 37 \(\pm\) 2 \({}^{\circ}\)C while being stirred magnetically (50 rpm). To keep the medium's sink condition, 0.5 mL of sample was withdrawn and replenished at predetermined intervals. The drug concentration in the aliquots was evaluated using a Jasco UV/Visible Spectrophotometer V-630, Tokyo, Japan. The in vitro drug release data were obtained by plotting cumulative percent drug release [36; 37].  
By putting the data in the following equation and performing a regression analysis, the zero-order, first-order, Higuchi, and Korsmeyer-Peppas kinetics models were calculated [23; 38]. The corresponding equation for each model is shown as follows:  
Zero-order: Q\({}_{t}\) = Q\({}_{0}\) + k\({}_{0}\)t  
First-order: logQ\({}_{t}\) = logQ\({}_{0}\) - k\({}_{1}\)t/2.303  
Higuchi: Q\({}_{t}\) = k\({}_{H}\)t\({}^{1/2}\)  
Korsmeyer-Peppas: M\({}_{t}\)/M\({}_{\infty}\) = k\({}_{n}\)  
where, Q\({}_{t}\) is the amount of drug dissolved during time t, Q\({}_{0}\) is the amount of drug initially dissolved in diffusion medium, k\({}_{0}\), k\({}_{1}\) and kH\({}_{1/2}\) are the zero-order, first order and Higuchi model rate constants, respectively. M\({}_{t}\) and M\({}_{\infty}\) are cumulative drug release at time t and infinite time, respectively. The t stands for release time, n for the diffusional exponent indicating the release mechanism, and k stands for the rate constant of I-NS structural and geometric features. Additionally, appropriate parameters were plotted in each of the aforementioned models, and the release kinetic behavior of ITB was assessed using the R\({}^{2}\) value (coefficient of multiple determination; 0 \(\leq\) R\({}^{2}\)\(\leq\) 1). The release mechanisms were also detailed [39] using the values of n = 0.45 (Case I or Fickian diffusion), 0.45 < n < 0.89 (anomalous behavior or non-Fickian transport), n = 0.89 (Case II transport), and n > 0.89 (Super Case II).
## 2 Hybrid Source Localization Based on TOA and TDOA
### Hybrid Source Localization Algorithm Model
The nonlinear least squares estimator can be used to continuously approximate the position vector \(\mathbf{x}\) through iterative operations, and the regular iterative algorithms are the Newton-Raphson method, the Gauss-Newton method, and the steepest descent method [13], among which the steepest descent method is highly stable but slow to converge, and the Newton-Raphson method and Gauss-Newton method are fast to converge, but have poor stability, and require matrix inversion with a large amount of calculation. Levenberg-Marquardt algorithm combines the advantages of the Gauss-Newton method and steepest descent method, and by adding the judgment condition of first-order approximation and weighting the two algorithms, it can improve the stability of the algorithm and accelerate the convergence speed of the algorithm at the same time. The DOG-LEG algorithm based on the steepest descent method, which adjusts the step according to the gradient change of \(\mathbf{f}(\mathbf{x})\), can improve the convergence speed. Let \(\mathbf{F}(\mathbf{x})=\tilde{\mathbf{\tau}}-\mathbf{f}(\mathbf{x})\), the nonlinear least squares estimation can be used to find the \(\hat{\mathbf{x}}\), that minimizes the sum-of-squares function of the nonlinear error function \(\mathbf{J}(\mathbf{x})\)  
\[\mathbf{J}(\mathbf{x})=\parallel\mathbf{F}(\mathbf{x})^{2}\parallel=\parallel\tilde{\mathbf{\tau} }-\mathbf{f}(\mathbf{x})^{2}\parallel=\left(\tilde{\mathbf{\tau}}-\mathbf{f}(\mathbf{x})\right)^{ T}(\tilde{\mathbf{\tau}}-\mathbf{f}(\mathbf{x})) \tag{6}\]  
\[\hat{\mathbf{x}}=\text{arg}\,\min_{\mathbf{x}}\mathbf{J}(\mathbf{x}) \tag{7}\]  
The Gauss-Newton method is employed to obtain the iterative equation for the estimation of
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 5 Experiment
### Application Suggestions  
The signals separated by the Stacked-LSTM network in this article are 10 mixed signals composed of five modulated signals. In fact, we can construct a training set with more modulation signals, which is expandable in the types of separated signals, and is not limited to the data set we use. The network structure and scale can be adjusted according to the amount of data, and the network can be trained to separate more kinds of signals.
# A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
## 5 Discussion  
The key factor in the recommendation task is the embedding of users and items. The CF-based method has the disadvantage of insufficient features due to sparse data. We found that the semantic information in the knowledge graph can effectively improve the effect of recommendations. Therefore, we propose KANR, an end-to-end recommendation method that uses semantic information in the user-item interaction knowledge graph to enhance the attention. It contains three parts, namely, a recommendation model, a knowledge graph embedding model, and an information interaction unit. The recommendation model uses the attention aggregation network to calculate the weights of historical interactive items and aggregate the product vectors to capture the user's interest preferences. The knowledge graph embedding model takes the item to be recommended as the head entity and the user as the tail entity, and uses the Semantic Matching Energy Model (SME) to obtain their semantic vector. Both the recommendation model and the knowledge graph embedding model share vector features through the information interaction unit. The item vector to be recommended and the head entity vector share features, and the user-item aggregation vector and the tail entity vector share features. It is worth mentioning that KANR changes the traditional attention mechanism and uses the information interaction model to share user vectors and historical interaction item vectors. The advantage of this is  
Figure 5: Performance analysis of the model in a cold start scenario.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**d** & **8** & **16** & **32** & **64** & **128** \\ \hline Movielens-1M & 0.887 & 0.902 & 0.915 & **0.929** & 0.923 \\ \hline Last.FM & 0.808 & **0.817** & 0.812 & 0.796 & 0.773 \\ \hline Book-Crossing & **0.731** & 0.744 & 0.729 & 0.722 & 0.718 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The impact of different embedding dimensions on AUC indicators. The best results are in bold.  
that it can introduce knowledge learned during information interaction into the attention mechanism. We conducted a series of experiments to prove that the semantic information in the knowledge graph can effectively enhance the recommendation model through the information interaction model.
## 3 Experimental Results and Validation
### Effect of Charging Strategies of Fleet of EVs on PAR and Cost Reduction  
The experiments aimed to evaluate the performance of different EV charging strategies in minimizing peak load and energy costs over a three-day horizon. The energy costs were calculated using two pricing schemes: the province of Ontario's TOU rate structure (Table 2), and fixed rates for all other provinces. These schemes influence the energy cost based on the timing and pattern of power consumption. The charging strategies were compared with the predicted values in terms of aggregated peak power (kW), Peak-to-Average Ratio (PAR), PAR reduction, and cost reduction (%) in Tables 3 and 4 for spring and winter, respectively. The PAR results show that the greatest ratio of Peak-to-Average load was 1.45, with 1.35 for the reference case for spring and winter. The lowest ratio (PAR) was 0.73 and 0.69 for each of the proposed strategies, CS1 \(\times\) CS4, for spring and winter. [PERSON] et al. (2021) [13] obtained similar results by evaluating the overnight charging strategy with a PAR reduction, demonstrating the relief for the grid with lower implementation costs. [PERSON] et al. (2022) [29], Proposed an aggregated coordination \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Charging Strategies**} & **Aggregated Peak** & **Peak-to-Average** & \
\begin{tabular}{c} **PAR Reduction (\%)** \\ **Ratio (PAR)** \\ \end{tabular} &
\begin{tabular}{c} **Cost Reduction (\%)** \\ **Compared to the** \\ **Reference Case** \\ \end{tabular} \\ \hline ML Prediction & & 745.24 & 1.35 & - & - \\ \hline \multirow{3}{*}{Overnight} & CS1 & 520.49 & 0.97 & 28 & 29 \\  & CS2 & 576.68 & 1.05 & 22 & 22 \\  & CS3 & 632.87 & 1.13 & 16 & 15 \\ \hline \multirow{3}{*}{Workplace/Other Charging Sites} & CS4 & 579.84 & 1.05 & 22 & 23 \\  & CS5 & 604.91 & 1.10 & 18 & 19 \\ \cline{1-1}  & CS6 & 650.33 & 1.18 & 12 & 13 \\ \hline \multirow{3}{*}{Overnight \(\times\) Workplace/Other Charging Sites} & CS1 \(\times\) CS4 & 391.92 & 0.69 & 49 & 56 \\  & CS1 \(\times\) CS5 & 424.06 & 0.75 & 45 & 53 \\ \cline{1-1}  & CS1 \(\times\) CS6 & 456.21 & 0.80 & 41 & 51 \\ \cline{1-1}  & CS2 \(\times\) CS4 & 438.90 & 0.77 & 43 & 52 \\ \cline{1-1}  & CS2 \(\times\) CS5 & 473.34 & 0.83 & 39 & 49 \\ \cline{1-1}  & CS2 \(\times\) CS6 & 507.79 & 0.89 & 34 & 46 \\ \cline{1-1}  & CS3 \(\times\) CS4 & 485.88 & 0.84 & 38 & 47 \\ \cline{1-1}  & CS3 \(\times\) CS5 & 522.63 & 0.91 & 33 & 44 \\ \cline{1-1}  & CS3 \(\times\) CS6 & 559.37 & 0.97 & 28 & 41 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Peak power in kW, Peak-to-Average ratio (PAR), PAR reduction (%) compared to the reference case, and cost reduction (%) compared to the prediction for winter Canada-wide. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Charging Strategies**} & **Aggregated Peak** & **Peak-to-Average** & \ \begin{tabular}{c} **PAR Reduction (\%)** \\ **Compared to the** \\ **Prediction** \\ \end{tabular}  & \begin{tabular}{c} **Cost Reduction (\%)** \\ **Compared to the** \\ **Prediction** \\ \end{tabular}
## 2 Materials and Methods
### Gps L5  
GPS L5 shares the same CNAV navigation data than L2C, although it is broadcasted twice as fast, allowing for the same frame synchronization scheme to be applied [41]. GPS L5 transmits 10,230 chips every 1 ms period; the chipping rate must be 10 times faster than for GPS L1 C/A, _i.e._, 10.23 Michip/s. Considering a 60 MHz sampling frequency, the acquisition is performed with a \(\pm 3\) samples correlator spacing.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## Appendix E Seasonal Variation of NDVI According to the Dominant Land Cover
## 7 Discussion
### Feedback from the Demonstration Phase
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Feedback Regarding** & \multicolumn{2}{c}{**Comments from Participants**} \\ \hline \multirow{6}{*}{Usefulness of the MM} & \multicolumn{2}{c}{_“Helps to focus on the right things”;_} \\  & _“Useful for making management more aware of the need to invest in BI”;_ \\  & \multicolumn{2}{c}{_“Great help for discussion (such that one does not forget important aspects)”;_} \\ \cline{2-3}  & \multicolumn{2}{c}{_“Useful as a reference maturity model and very complete”;_} \\  & \multicolumn{2}{c}{_“Great tool for benchmarking. Will help HELs to strategically implement BIA and to plan activities”;_} \\  & It needs to be improved, solving some ambiguities ( clarification of some terms). & \\ \hline \multirow{6}{*}{Dimensions} & Performance management dimension received the largest number of comments; the description was unclear to most participants; & \\  & Cloud computing infrastructure was considered not relevant and not always an advantage for HELs. This dimension should be replaced by an IT infrastructure dimension; & \\ \cline{2-3}  & User capabilities: comments regarding the need to distinguish different leadership levels (program committees, faculty, recroy); & \\ \cline{2-3}  & User engagement: clarification required in the definition of the dimension and on the term “consistently use” (levels 3 and 4); & \\ \cline{2-3}  & Analytical culture: name change proposition to Analytical and decision-making culture; & \\  & BIA strategy: clarification required in the definition of the dimension, to emphasize that refers to how the BIA strategy supports the university goals; & \\  & Change management: generated comments, particularly from participants with a technical background, that questioned if the dimension was related to IT change management and ITIL. & \\ \cline{2-3}  & Levels & \multicolumn{2}{c}{_“How to represent a partial match in two or three adjacent levels? Should the best- or the worst-case scenario be selected to decide the correct level?”_} \\ \cline{2-3}  & The distance between consecutive levels is not always the same; & \\ \cline{2-3}  & \multicolumn{2}{c}{_“Is Level 5 really always “better”?”._} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Qualitative feedback received on the EUNIS BI SIG 2019 workshop. Figure 5: Major dimension revisions on HE-BI MM v1.0.
###### Abstract  
As radio frequency (RF) hardware continues to improve, two-way ranging (TWR) has become a viable approach for high-precision ranging applications. The precision of a TWR system is fundamentally limited by estimates of the time offset \(T\) between two platforms and the time delay \(\tau\) of a signal propagating between them. In previous work, we derived a family of optimal \"one-shot\" joint delay-offset estimators and demonstrated that they reduce to a system of linear equations under reasonable assumptions. These estimators are simple and computationally efficient but are also susceptible to channel impairments that obstruct one or more measurements. In this work, we formulate an extended Kalman filter (EKF) for this class of estimators that specifically addresses this limitation. Unlike a generic KF approach, the proposed solution specifically integrates the estimation process to minimize the computational complexity. We benchmark the proposed first- and second-order EKF solutions against the existing one-shot estimators in a MATLAB Monte Carlo simulation environment. We demonstrate that the proposed solution achieves comparable estimation performance and, in the case of the second-order solution, reduces the computation time by an order of magnitude.  
wireless sensor networks; two-way ranging; distributed coherence; internet of things; positioning, navigation, and timing; signal processing; spectrum sharing; spectral convergence +
Footnote †: journal:  
considered immature and insufficient for safety-critical applications. To address these limitations, we have developed a family of novel extended Kalman filter (EKF) extensions that enhance and make robust this system. These tracking filters improve the positioning performance by assuming more realistic first- and second-order Markov models and reduce the susceptibility to outages and cycle slips by estimating the parameters over multiple frames rather than the previous \"one-shot\" approach. We specifically evaluate the proposed methods against our previous results [9] using a simple MATLAB simulation platform and demonstrate notable improvements in performance and processing speed.
## 4 Results and Experiments
### Dataset Description and Preprocessing Overview
#### 4.1.1 CICIDS2017 Dataset  
The CICIDS2017 dataset, as described in Section 3.1, provides a comprehensive representation of network activities, encompassing both legitimate and malicious traffic across diverse attack types, making it a critical resource for IDS research. Despite its value, the dataset poses challenges such as incomplete records, duplicate data, and significant class imbalances. These issues were systematically addressed through the preprocessing techniques outlined in Section 3.2. The process involved managing missing values, eliminating duplicates, and employing sophisticated methods for detecting and removing outliers, including LOF. Additionally, numerical features were scaled using MinMaxScaler to ensure uniformity. To address class imbalance, advanced resampling techniques were integrated into the model training pipeline, including enhanced hybrid ADASYN-SMOTE for oversampling in binary classification, advanced SMOTE for oversampling in multi-class classification, and ENN for undersampling. These comprehensive preprocessing steps  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Binary Classifier** & **Multi-Class Classifier** \\ \hline Batch size & 128 & 128 \\ \hline Learning rate & 0.001 & 0.001 \\ \hline Optimizer & Adam & Adam \\ \hline Loss function & Binary cross-entropy & Categorical cross-entropy \\ \hline Metric & Accuracy & Accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 29: Hyperparameters of the Transformer-DNN model.  
significantly improved the dataset's robustness and reliability for binary and multi-class classification tasks.