esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Results
The laser energy on the screen was calibrated by taking full energy shots without a target in place (to determine the light level corresponding to 100% of the laser energy delivered to target) and the linearity of the response was calibrated by taking shots with reduced energy. Figure 2 shows examples of the measured 1D spatially-resolved spectrum of the back-reflected second harmonic radiation for \(l\) = 10, 40, 100 and 200 nm targets. The spectral width of the peaks shown in the line-outs is \(\sim\)5 nm (FWHM). Whilst this is in relatively good agreement with the expected pulse duration-bandwidth product for a 40 fs pulse of 400 nm light (5.9 nm assuming a Gaussian profile and 4.3 nm for Sech-squared), there may be additional mechanisms occurring during the interaction which lead to this narrow spectral width. This aspect will be investigated in follow-on work. For the \(l\) = 100 nm and \(l\) = 200 nm cases, the measured red shift (due to the surface recession as it is driven forward by the laser pressure) is small and occurs over a wide radius compared to the thinner targets. The measurements for the \(l\) = 10 nm and \(l\) = 40 nm cases show a significantly larger degree of red-shift, as well as some blue shift, which is likely to be produced by significant expansion of the ultrathin foils at the beginning of the interaction. Both are present because the spectral measurements are time-integrated. We also note the presence of spectral structure (or'stripes') with wavelengths corresponding to lower degrees of red-shift. We believe this to result from the excessive deformation of the target critical surface in the thinnest foils, coupled with the variation in PCDS velocity with radius, which produces a complex interference pattern in the back-reflected light. This aspect will be investigated in more detail in future work. The largest red-shift is produced by the region of highest velocity (i.e., the peak of the spatial intensity distribution) and is not affected by the interference at the slightly lower wavelengths. Thus, the maximum PCDS velocity is obtained by measuring the cut-off wavelength corresponding to the largest degree of red-shift (i.e., the rightmost stripe) and using this in the calculation based on Equation (6). Figure 2: Representative experimental measurements of back-reflected second harmonic light along a radial direction for: (**a**) \(l\) = 10 nm; (**b**) \(l\) = 40 nm; (**c**) \(l\) = 100 nm; (**d**) \(l\) = 200 nm targets. The spatial scale refers to the spectrometer plane. The line-outs correspond to the signal sampled along the centre axis. The degree of red and blue shifting of the reflected light, corresponding to the plasma surface receding and expanding towards the laser, respectively, increases with decreasing target thickness.
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 5 Experiment and Numerical Results
### Results
#### 5.2.4 Evaluation of the Unexpected Rate  
Finally, we evaluated the unexpected rates of drug refillings during 30 timeslots by using our proposed method and three baselines, as shown in Figure 7. We measured this aspect to illustrate the efficiency of our system for drug refillings and maintaining the volume of drugs at a stable level during a finite horizon. A high unexpected rate result is often considered as a low performance feature in inventory management. On average, over-provisioning has the highest rate by 1.675% and reaches the peak rate by 2.808%. It is agnostic of the user demand, and so, it is not able to prevent the system from a shortage or an overstock situation. If we increase the amount of the refilling volume, we can reduce the shortage, but it will proportionally increase the refilling cost and the number of redundant drugs. The ski-rental approach obtains an average rate of 1.54%, higher than that of the DRLD with 1.03%. This rate can be reduced by adjusting the \(c\)-competitive value. In practice, it is impossible to obtain the correct value for all drugs with an unknown usage demand. In Figure 7, the rate of the ski-rental approach fluctuates, with a large magnitude from the lowest rate of 0.1718% to the highest of 2.54%. With the awareness ability employed in the exploration and exploitation steps of the DRL, DRLD outperforms the other methods in this evaluation. The results illustrated in this figure reveal the efficiency of the DRLD at reducing the number of unexpected drugs in the refilling decision.
## 1 Introduction
In [9], a circular dumbbell-Shaped DGS was implemented to increase the bandwidth of all three bands, but at the same time, the gain was slightly decreased. In [10], two different but complimentary DGS structures were implemented which lead to increased bandwidth as well as reduced return loss. In [11; 12], rectangular and H slots respectively were used to achieve improved parameters. Both produced enhanced bandwidth and gain in all the three bands, but the efficiency dropped. In [13], good patch miniaturization was achieved using a shorting post, and a U shaped DGS; however, the lower band had poor gain and efficiency. In [14], a tri-band antenna was presented where miniaturization was achieved by implementing an E-type unit cell in conjunction with an F shaped slot as a DGS but resulted in a narrow bandwidth. Other design approaches where multiband operations are achieved but with narrow bandwidth characteristics also exist. In [15], a dual-band antenna was designed where miniaturization was achieved by etching a square slot in the middle of the patch, and a pair of mirror image L-shaped slots with a pair of slits were cut on the radiating element to achieve the dual-band operation but produced a narrow bandwidth. In [16], a U-shaped feeding method was used to increase the gain but the bandwidth was not so promising. In [17], a rounded corner rectangle radiating patch was used to achieve a dual-band characteristic and high gain but with a narrow bandwidth. DGS can also be used to achieve band notch characteristics. A pair of DGS resonators have been used to reject the downlink of X-band satellite communications (7.0-7.40 GHz) [18]. This rejection can be controlled by the gap width of the resonator. On the other hand, by introducing an open ring DGS [19] on the metasurface unit, a high lower-stopband suppression level of 20 dB is achieved while the in-band antenna performance remains unaffected. However, the antenna size is bigger for the 5G IoT applications. DGS has also proved its importance in reducing mutual coupling between array antennae. An octagonal ring slot [20] with cross lines has been implemented as a DGS unit to enhance the isolation of a \(2\times 2\) multiple-input and multiple-output (MIMO) antenna, but the impedance bandwidth is a narrow bandwidth. This paper provides an analysis of the effect of loading defected metallic ground planes with a simple stub-slot configuration. The defects are etched symmetrically but not underneath the feed line and are also totally isolated from each other, i.e., they are not connected by a split line to form a conventional dumbbell-shaped DGS, so, the structure is similar to a 2-D multiperiod electronic structure. However, the radiating element of the patch is modified by the combination of stubs and slots. This combination is responsible for the dual band nature in the desired frequency spectrum. Further, the addition of DGS results in a wider bandwidth with little effect on the position of the passband frequencies.
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 4 Analysis and Experiments of Visual-Inertial Odometry Optimization
### KITTI Dataset Test Experiment  
As shown in the table, the official evaluation metrics of the KITTI dataset are used to validate the quantitative results of our method, which are the average translation RMSE drift rate (%) and the average rotation RMSE drift rate (%/20 m). The table shows that our proposed method outperforms VINS-Fusion in both translation and rotation. Compared with VINS-Fusion, our method significantly reduces the translation drift by 36% and the rotation drift by 9%. Due to the less accurate calibration and time synchronization of the camera and IMU on the KITTI dataset, the integration of visual and inertial features becomes more effective after external memory attention, which is beneficial for deep mining and the utilization of features between data. Details can be found in Table 4. The deep learning model adaptively adjusts the feature weights for different positions based on the training data. Meanwhile, the algorithm continuously processes and optimizes the pose, IMU data, and odometer data through polling and updates these three data based on virtual IMU data, improving the calibration of the temporal information and achieving lower drift.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 1 Introduction
Data rate optimization is investigated in Section 4 while physical network optimization is studied in Section 5. Finally, simulation and numerical results are presented and discussed in Section 6 followed by the Section 7.
# Wavelet Time-Scale Modeling of Brand Sales and Prices
## 4 Development of the Mathematical Modeling System  
In the literature there are few models relating to marketing. We firstly recall that our aim in the present work is not purely the development of new models but instead the proof of the influence of the time-scale factor on marketing models, as shown in many other contexts in financial and economic studies, thus involving wavelets in the improvement of these models. Our contribution may be seen as the wavelet time-scale generalization and/or extension of existing models by considering scale-by-scale versions. A recent models proposed by [PERSON] et al. [44] was adopted to describe the link between brand sales, prices, competitors, and promotions. It states that  
\[S_{t}=\beta_{0}+\beta_{1}P_{t}+\beta_{2}CP_{t}+\beta_{3}PR_{t}+\beta_{4}D_{t}+ \epsilon_{t}, \tag{8}\]  
where:  
* \(S_{t}\) stands for the sales value at time \(t\);
* \(P_{t}\) stands for the price at time \(t\);
* \(CP_{t}\) stands for the competitor's price at time \(t\);
* \(PR_{t}\) is the PROMO variable at time \(t\);
* \(D_{t}\) refers to percentage distribution of the main brand;
* \(\epsilon_{t}\) is an error term.  
Using wavelets, we consider a multi-resolution or multi-horizons or levels model, where the variables must be replaced by their projections on the multi-resolution and/or detail spaces, as in (7). Therefore, for any level \(j\), we obtain a \(j\)-level model  
\[S_{t,j}=\beta_{0,j}+\beta_{1,j}P_{t,j}+\beta_{2j}CP_{t,j}+\beta_{3,j}PR_{t,j} +\beta_{4,j}D_{t,j}+\epsilon_{t,j}, \tag{9}\]  
where:  
* \(S_{t,j}\) reflects the sales value at time \(t\) and the level or horizon \(j\);
* \(P_{t,j}\) stands for the price at level \(j\) and time \(t\);
* \(CP_{t,j}\) stands for the competitor's price at level \(j\) and time \(t\);
* \(PR_{t,j}\) is the PROMO variable at horizon \(j\) and time \(t\);
* \(D_{t,j}\) stands for the percentage distribution at horizon \(j\) and time \(t\);
* \(\epsilon_{t,j}\) is an error term already relative to level \(j\) and time \(t\).  
Considering this wavelet decomposition leads to horizon-specific models that permit us to explore more efficiently the behavior of brand sales and prices, according to the time scale.
# Deep Data Assimilation: Integrating Deep Learning with Data Assimilation
###### Abstract  
In this paper, we propose Deep Data Assimilation (DDA), an integration of Data Assimilation (DA) with Machine Learning (ML). DA is the Bayesian approximation of the true state of some physical system at a given time by combining time-distributed observations with a dynamic model in an optimal way. We use a ML model in order to learn the assimilation process. In particular, a recurrent neural network, trained with the state of the dynamical system and the results of the DA process, is applied for this purpose. At each iteration, we learn a function that accumulates the misfit between the results of the forecasting model and the results of the DA. Subsequently, we compose this function with the dynamic model. This resulting composition is a dynamic model that includes the features of the DA process and that can be used for future prediction without the necessity of the DA. In fact, we prove that the DDA approach implies a reduction of the model error, which decreases at each iteration; this is achieved thanks to the use of DA in the training process. DDA is very useful in that cases when observations are not available for some time steps and DA cannot be applied to reduce the model error. The effectiveness of this method is validated by examples and a sensitivity study. In this paper, the DDA technology is applied to two different applications: the Double integral mass dot system and the Lorenz system. However, the algorithm and numerical methods that are proposed in this work can be applied to other physics problems that involve other equations and/or state variables.  
data assimilation; deep learning; neural network +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Analysis of Temporal and Spatial Variation and Correlation of Tourism Carbon Emissions
#### 3.1.2 Correlation Analysis of Tourism Carbon Emissions  
To further explore the impact of the COVID-19 pandemic on tourism carbon emissions, Pearson correlation analysis was conducted using IBM SPSS Statistics 26 on carbon emissions data during the two years under investigation (Table 4). In 2019, there was a significant correlation (0.464) between tourism purposes (sightseeing, leisure vacation, business travel, visiting relatives and friends, and others) and total tourism carbon emissions. However, the analysis results for 2020 showed a weak and insignificant correlation (\(-\)0.03) between tourism purposes and total tourism carbon emissions. This indicates that the occurrence of the COVID-19 pandemic has had a significant impact on people's willingness to travel.  
In 2019, there was a highly significant correlation (0.768) between tourism accommodation and total tourism carbon emissions. In 2020, the correlation remained significant but decreased to 0.502. Compared to tourism purposes, accommodation, as an essential activity in travel, showed a relatively minor decrease in correlation. However, its impact on total tourism carbon emissions remained significant.  
In 2019, all modes of tourism transportation (railway, road, water transport, civil aviation) showed significant correlations. Civil aviation had the highest correlation (0.783), followed by road (0.685), water transport (0.491), and railway (0.393), with decreasing levels of correlation. This indicates that before the occurrence of the pandemic, tourism transportation in China was highly active and characterized by diverse travel modes. In 2020, due to the impact of the COVID-19 pandemic, the number of people choosing railway travel decreased. The correlation between railway travel carbon emissions and total tourism carbon emissions decreased and became insignificant (0.15). However, civil aviation (0.982), water transport (0.361), and road (0.432) transportation remained significant. The increased correlation between civil aviation carbon emissions and tourism carbon emissions suggests that during unexpected public events like the COVID-19 pandemic, most people, for risk avoidance purposes, choose short-duration and higher-priced civil aviation for travel. Traditional and relatively lower-risk travel modes, water transport and the road, still showed significant correlations.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 2 Problem Analysis
### Security Threat Modeling Using STRIDE  
Table 1 presents the IoT vulnerabilities, security threats, and countermeasures classified by STRIDE [21]. Most of the security vulnerabilities stem from insecure public key encryption, which uses RSA, DH, and ECC in an internet-exposed SSI network platform. To mitigate these security risks, it is crucial to apply data encryption using PQC and to enhance network access control in the SSI network. Moreover, Table 1 identifies specific vulnerabilities, such as authentication and insecure public key encryption in quantum computing. These vulnerabilities expose the network to several security threats, e.g., unauthorized access, response to false information, authentication failure, eavesdropping, data interception in home IoT setups, and replay attacks across IoT systems.  
For each category of threats identified by STRIDE, Table 1 also lists the countermeasures, with a strong emphasis on data encryption using PQC as the primary method of safeguarding against these vulnerabilities. Additionally, for DoS threats, network access  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
\begin{tabular}{l} Insecure public key \\ encryption in quantum \\ computing \\ \end{tabular} & Unauthorized access & Data Encryption with PQC \\ \hline \hline \end{tabular}
\end{table}
Table 1: STRIDE security threat Analysis: The SSI network platform has exposed authentication and session connection information for L2 TP tunnel connections. However, vulnerabilities and security threats associated with SSI network platform can be effectively countered through data encryption using PQC.  
control is listed as a countermeasure. The primary reason for implementing robust security measures is to protect the SSI network platform against various vulnerabilities and threats.
## 2 Material and Methods
### Classifier and Feature Validation  
On statistical verification, all the features, including time segments and complexity of time segments of PR, QT, RR, and ST, were given to six supervised models [30], as shown in Figure 6. The initial parameters that were assigned for the models are as given in Table 2. The steps of the classification are given below that were implemented in Orange (version 3.31.1), which is a widely used open-source toolkit for data mining, data visualization, and machine learning [31]:  
1. File upload: Consisting of ECG signal of one subject, split at every 5 th interval. In each signal, approximately 44 PR, QT, RR, and ST segments were extracted and given to the classifier as separate instances (inputs). A total of 120 instances (30 signals \(\times\) 4 intervals) were classified based on 180 features (4 time segments \(\times\) 44 and 4 FD values).
2. Data sampler: A total of 70% of data were used for training and 30% for testing, with 10-fold cross validation performed to identify the accuracy. Data sample \(\rightarrow\) Data: indicates the data given to the classifier model. Remaining data \(\rightarrow\) Data: indicates the test data given to the prediction. Model \(\rightarrow\) Predictors: indicates the trained data from model given to prediction.
3. Different supervised models, including random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), naive Bayes (NB), AdaBoost (AB), and neural network (NN) [32], were used to classify.
4. Prediction: All the models are linked to the prediction for identifying the classification accuracy. The remaining data mentioned in the model are used as the test data for computing the classification accuracy.  
Figure 5: (**a**) An instant of a time segment (QT) of normal and early CAN signal; (**b**) log N vs. log r for a normal signal for FD computation.  
Ranking using Relieff estimator [33] was analyzed, which ranks the best contributing feature for classification.
## 4 Reliability Analysis of High-Pressure Tunnel
### Reliability Analysis of Tunnel System Based on ISSA-Kriging-MCS Model  
By using direct shear tests and deformation tests on rock masses to obtain relevant mechanical parameter data, the average value and other statistical values are estimated through a statistical analysis. The characteristic values of each parameter are finally determined as shown in Table 5, and each random variable follows a standard normal distribution.  
Under three failure modes, combined with the three-dimensional numerical model, the constructed ISSA-Kriging-MCS model was used to calculate the relevant reliability parame  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(\mathbf{E_{1}}\) & \(\mathbf{c}\) & \(\mathbf{\varphi}\) & \(\mathbf{E_{2}}\) \\  & **Gpa** & **Mpa** & \(\circ\) & **Gpa** \\ \hline Mean value & 11.25 & 1.1 & 42.5 & 33 \\ Standard deviation & 1.35 & 0.165 & 1.955 & 2.805 \\ COV & 0.12 & 0.15 & 0.046 & 0.085 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical characteristic values of each parameter.  
Figure 7: The relationship between axial stress and mechanical parameters.  
ters. The failure probability and reliability index of the high-pressure tunnel under the coordinated bearing of surrounding rock and lining are shown in Table 6. Based on a general series system, the reliability of the series system was calculated using \(G(X)=min(g_{1},g_{2},g_{3})\) as the limit state function. The response surface of each random variable parameter to the results is shown in Figure 8. By analyzing the response surface, it is possible to understand which parameters have the greatest impact on the response value, which can help optimize the design scheme and improve the performance and reliability of the system. By using the constructed reliability analysis model to analyze the three failure modes, it can be seen that the failure probabilities corresponding to the three failure modes were all very small, and the reliability indexes were all greater than 4.2, ranked as \(g_{3}(X)>g_{1}(X)>g_{2}(X)\). According to the Unified Standard for Reliability Design of Hydraulic Engineering Structures (GB50199-94) [34], it can be concluded that the surrounding rock-reinforced concrete lining structure of the high-pressure tunnel was stable and reliable, and the support system met the design requirements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Limiting Function** & \(g_{1}(X)\) & \(g_{2}(X)\) & \(g_{3}(X)\) \\ \hline Reliability index & 4.4046 & 4.2316 & 4.4999 \\ Failure probability & \(5.3\times 10^{-6}\) & \(1.2\times 10^{-5}\) & \(4.0\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reliability index and failure probability under different failure modes.  
Figure 8: The response surface of each random variable to the calculation results. The change in color gradient of all response surface plots (from bottom to top) represents the increasing reliability of the system.