esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
DRL can also be applied to different function blocks in communication networks, such as end-to-end design, slice management [38], mobile edge computing [39], etc. In [40], the authors construct a DNN-based end-to-end system optimization model to reduce the data at the transmitter end while improving the decoding accuracy. For the joint optimization of different blocks, the DL approach can utilize a data-driven model based on expert knowledge and a big data system [41; 42]. Furthermore, the authors [43; 44] provide model-based optimization approaches in the physical layer. DL can also be integrated with different iterative estimation approaches to train the required DRL parameters and full automation of the system [45]. However, optimizing DRL parameters is a key issue in evaluating the agent's final performance metrics [46]. Bayesian optimization [47] has recently achieved significant success in solving a hyperparameter optimization problem. Furthermore, the neural architecture assistant search framework was proposed in [48; 49] for optimizing hyperparameters and predicting the accuracy of the final learning outcome. The work in [25] uses the generalized Nash equilibrium problem (GNEP) algorithm to optimize transmitted power control while assigning the same RB in multiple cells. The formulated GNEP analyzes the optimization problem of small cell networks via variational inequality theory. Lagrange multipliers are proposed to evaluate the actions of the Q function while ensuring global QoS requirements. The proposed approach in this paper uses both the game theoretical formulation and the DQL algorithm to address the optimization of downlink power allocation in multi-cell networks. In particular, the DQL-based distributive algorithm is used to evaluate the actions of the Q function while avoiding the same RB allocation to users in neighboring cells that are located close to the cell edge. Because, in such situations, the users involved in the neighboring transmitter-receiver pairs cannot obtain essential SIR to work correctly regardless of the transmitting power of interfering BSs.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 4 Using ECADLL Information for Spoofing Detection
### Detecting a Generic Impairment  
The first thing the algorithm should focus on is detecting a generic impairment. Just by having this information, the receiver is put into an alert stage, and it does not trust the channels that are flagged as impaired.  
In order to detect that an undefined impairment signal is present, either multipath or spoofing, an initial decision can be made using the basic information of Unit1. If the DLL in Unit1 is locked, thus having a low variance of \(\hat{\tau}_{1}\), and the relative amplitude \(\hat{a}_{1}/\hat{a}_{0}\) is greater than a certain threshold \(\hat{a}_{th}\), the channel can be declared as impaired, and the error mitigation for multipath starts working.  
\[D_{i}(t_{k})=1\text{ if }\sigma_{\hat{\tau}_{1}}<\sigma_{th}\text{ and }\frac{ \hat{a}_{1}}{\hat{a}_{0}}>a_{th} \tag{9}\]  
where \(\sigma_{\hat{\tau}_{1}}\) is the variance of the estimated delay \(\hat{\tau}_{1}\) in a 1-s window. A window of 1-s duration was used, as a tradeoff providing enough data points to have a good estimation of the variance \(\sigma_{\hat{\tau}_{1}}\), but keeping the capability to make a decision at a good rate. Thresholds \(\sigma_{th}\) and \(a_{th}\) are defined according to the DLL bandwidth and the expected level of noise. In order to assess the threshold \(\sigma_{th}\), we observed the relationship between the variance of the DLL when tracking a simulated interference signal and the variance when no additional signal was present. Using these values, we were able to obtain an appropriate threshold for the specific configuration. For threshold \(a_{th}\), we empirically chose to flag any signal with at least 10% of the Unit0 signal power, since any signal stronger than that is likely to be an interference signal.  
In Figure 5, we can observe how the top-most decision is the discrimination between classifying the signal as impairment and no impairment. Summarizing, the channel will be flagged as impairment if the tracking of Unit1 is considered locked to a signal.
## 6 Conclusions  
In this study, we trained and predicted the LSTM and EMD-LSTM models by observing and comparing the exact Dst index and selecting appropriate data segments. The factors affecting the prediction accuracy of the LSTM model were first tested, and the parameters used in the LSTM model were determined. Then, the two models were used to predict the Dst index for the quiet and active periods. After analyzing the prediction results, the related conclusions are reached as follows:  
1. In predicting the Dst index using the LSTM model, a decrease in the learning rate and an increase in the number of training times have no significant influences on improving the prediction accuracy. When using 90-day length data for 7-day prediction, the change in the RMSE is within 0.1 nT and the CC varies around 0.01 under different learning rates. The change in the RMSE is within 0.05 nT and the CC does not change when using 1096-day data, whereas increasing the training data length can improve the prediction accuracy. The prediction accuracy can be improved by increasing the length of the training data: the RMSE decreases from an average of 3.25 nT to 1.95 nT and the CC improves from 0.91 to 0.94 when using 1096 days of data, which suggests that the base dataset is a critical factor in controlling the prediction.
2. The LSTM and EMD-LSTM models have better Dst prediction for the quiet period than the active period. The reason is that the fluctuation in the Dst index during the quiet period is slight (\(\pm\)50 nT), so the resulting model based on higher temporal resolution (time-averaged) is robust. The interferences from other factors, especially the solar activity, are less in the quiet period; combining the appropriate training rate and training times can predict the Dst changes better. During geomagnetic storms, the fluctuation amplitude in Dst caused by the solar wind can reach several hundred nT or more. Compared with the use of the LSTM model, the error is significantly reduced when training data are decomposed using the EMD algorithm and then put into the LSTM training, particularly during the big magnetic storm, but the overall prediction accuracy is lower than that of LSTM.
3. Although the overall prediction accuracy of the LSTM model is slightly higher than the EMD-LSTM model (the RMSE is reduced by about 1.5 nT and the CC is improved by about 0.03), there is a noticeable lag in the prediction results of the former. EMD-LSTM significantly improves the lag in the prediction results of LSTM. In addition, the prediction accuracy of EMD-LSTM during magnetic storms is better than that of LSTM. In practical applications, it is appropriate to select a suitable model by referencing the intensity of solar activity, such as the sunspot number, and if its value is high, the EMD-LSTM model can be chosen, or the LSTM model can be chosen if the  
Figure 20: Predictions of the geomagnetic storm that occurred on 24 April 2023.  
situation is opposite. If the error requirement is not strict, using the LSTM method with a higher learning rate is economical to reduce the computation amount and satisfy the accuracy requirement.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 5 Results
### Sensitivity Analysis ODTS and Process Noise  
To understand what will be the ultimate achievable accuracy when considering the improved ODTS accuracy (i.e., 15 m position uncertainty 3-sigma) and a navigation-grade IMU (i.e., low process noise), both elements are combined in a single configuration. Figure 15a shows the horizontal covariance (3-sigma) for both the baseline scenario and the scenario considering the improved ODTS and navigation-grade IMU. It can be seen that the horizontal covariance (3-sigma) improves by almost one order of magnitude. Furthermore, note that the 3-sigma horizontal covariance is below 10 m for the complete duration of the traverse (except when the EKF is initialized). Figure 15b shows the DEM height uncertainty. As the horizontal covariance for the improved scenario is below 10 m, the uncertainty of the DEM measurement is almost uniquely defined by a data component.  
Finally, Table 14 shows the statistics in case of an improved ODTS and navigation-grade IMU. In line with Figure 15a, a substantial improvement is shown for this configuration, resulting in a 3-sigma horizontal covariance that is below 10 m for (almost) the full duration of the traverse.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scenario** & **68 th Percentile [m]** & **95 th Percentile [m]** & **99.7 th Percentile [m]** \\ \hline
1. Baseline & 37.2 & 80.3 & 81.0 \\
2. Improved & 8.3 & 17.9 & 18.1 \\ (Tactical-Grade IMU) & & & \\
3. Improved & & & \\ (Navigation-Grade IMU) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 13: Statistics highlighting the gain in performance when improving the EKF process noise (by changing the type of IMU). The percentiles refer to the time series of the horizontal uncertainty (3-sigma).  
Figure 15: Performance of the LCNS position solution. (**a**) shows the horizontal (2D) covariance of for the baseline and improved scenarios (considering an improved ODTS accuracy and navigation-grade IMU, while (**b**) shows the 1-sigma uncertainty of the DEM measurement for the same scenarios as defined in (**a**).
# Multi-Objective Configuration Optimization of
## 2 Multi-Objective Optimization Model for Allocating HESS
### Evaluation Index of Wind Output Power Compensation  
Function of HESS is realized through compensating the fluctuation of wind power, \(P_{WD}\), with its real-time output power, \(P_{SC}+P_{B}\). The compensation objective is to make the compensated wind power output, \(P_{WDCom}\), approach the reference power, \(P_{WDInf}\), as closely as possible. Hence, Equation (8) can be used as compensation evaluation index. Apparently, \(\eta\) is less than or equal to 1 and the less the better compensation effect.  
\[\eta^{t}=\frac{{{P^{t}}_{WD}}^{*}}{{{P^{t}}_{WDref}}}=\frac{{{P^{t}}_{WD}+{{P^{t }}_{SC}}+{{P_{B}}^{t}}-{{P^{t}}_{WDref}}}}{{{P^{t}}_{WDref}}}. \tag{8}\]  
From Figure 2, it is evident that \(\triangle P_{WD}=P_{WD}-P_{WDInf}\). It is \(\triangle P_{WD}\) according to which HESS carries out power allocation. Consequently, a reasonable \(P_{WDref}\) needs to be set for minimizing the wind power output fluctuation. For this purpose, [PERSON] et al. [9] proposed adjusting the time constant of the LPF, \(\tau_{t}\), based on the remaining energy in HESS, thus realizing \(P_{WDref}\) regulating. To be more detailed, \(\tau_{t}\) is properly increased if HESS remains adequate energy, so that \({{P^{t}}_{WDref}}\) approaches \({{P^{t}}^{-1}}_{WD}\). Accordingly, change rate of the compensated wind power output is decreased. \({{P^{t}}_{WDref}}\) can be formulated as Equation (9).  
\[{{P^{t}}_{WDref}}=\left({1-\lambda^{t}}\right){{P^{t}}^{-1}}_{WD}{}^{*}+ \lambda^{t}{{P^{t}}_{WD}} \tag{9}\]  
where \(\lambda^{t}\) is the filtering coefficient, and \(\Delta t\) and \(\lambda^{t  
\(\alpha^{t}\) can be either 0 or 1, which is formulated as Equation (14). \(\delta\) is the threshold level of wind power compensation rate.  
\[\alpha^{t}=\left\{\begin{array}{ll}1&\left|\eta^{t}\right|\leq\delta\\ 0&\left|\eta^{t}\right|>\delta.\end{array}\right. \tag{14}\]
# Parasitic Parameters Extraction of High-Speed Vertical-Cavity Surface-Emitting Lasers
## 2 Materials and Methods
### Parasitic Extraction Method  
Although the theoretical model is the same equivalent circuit model [14; 21], the extracted parameters were determined by the fit method. In [15; 16; 17], the approach of  
Figure 4: Equivalent electrical circuit model of an oxide-confined top-emitting VCSEL.  
extracting data using a first-order low-pass filter transfer function, which is the simplest circuit model that contains only \(C_{a}\), \(R_{a}\), and \(R_{m}\), is oversimplified. In addition, in [14,18], the equation of \(C_{p}\) and \(R_{p}\) used in the device parasitic value extraction is based on [23]. In [23], capacitors corresponding to the probe pads of VCSELs were fabricated above conducting ground planes on undoped GaAs substrates [23], this approach has not taken into account the effect of the device on pad capacitation and resistance. In this work, we extract the parasitic data employing the nonlinear least squares method that is based on the equivalent circuit model, which allows for avoiding inappropriate formulations and extracting various parasitic parameters directly simultaneously. The basic least squares approach to solving curve fitting problems is given as [24]:  
\[f(x)=a_{1}\varphi_{1}(x)+a_{2}\varphi_{2}(x)+\cdots+a_{m}\varphi_{m}(x) \tag{9}\]  
where \(a_{m}\) (\(m\) = 1,2,3,4,5... \(m\) \(<\) \(n\)) is the undetermined coefficient and \(\varphi_{m}(x)\) is a group of selected linearly independent functions.  
The values of \(L\), \(R_{m}\), \(R_{a}\), \(C_{a}\), \(R_{p}\), and \(C_{p}\) can be easily obtained through this approach. Owing to the relationship between the values, the fitting result is multiple groups of values. The most reasonable group of data is the one that simultaneously satisfies the \(S_{11}\) curve fitting minimum error; all values obey the rules of physics and demands of the judgement requirement \(\left\lvert{H_{\text{ext}}(f)}\right\rvert^{2}/\left\lvert{H_{\text{ext}}(0)} \right\rvert^{2}\) = 0.5 [24].
## 6 The Case (Xiong'an New Area, China)  
### The Concept of Development  
The concept of UUS development in the Xiong'an New Area emphasizes the principle of \"first underground, then above ground,\" highlighting the importance of rational development and utilization of UUS in the planning and construction of the area. The \"Outline of Xiong'an new area planning in Hebei province\" explicitly states the need to \"actively utilize shallow layers, sub-shallow space, and, if feasible, make use of sub-deep space while flexibly reserving deep space.\" In constructing the UUS structure of the Xiong'an New Area, a comprehensive consideration of the engineering geological structure and hydrogeological conditions was undertaken. Consequently, the UUS development and utilization was categorized into four layers: shallow layer (L1: 0 to \(-\)30 m), sub-shallow layer (L2: \(-\)30 to \(-\)50 m), sub-deep layer (L3: \(-\)50 to \(-\)70 m), and deep layer (L4: \(-\)70 to \(-\)100 m) [2] (Figure 9). The selection of elements thoroughly assesses the characteristics of UUS, encompassing resources, ecological variables, and disaster-related factors. It mainly considers indicators, such as topography, engineering geological structures, foundational soil conditions, hydrological conditions, environmental geological issues, and dynamic geological impacts.
## 3 System and Problem Definition for Scheduling Problem
### Proposed Approach Based on Reinforcement Learning
#### 3.4.1 Adapted Multi-Armed Bandit Model  
The MAB is a common model in reinforcement learning [40]. The model is of a game played in rounds from time \(t=1,...,T\). At each round, the player must select a single action from a known set of actions \(a_{t}\in A\). The environment then generates a reward \(r_{t}\sim R^{a_{t}}\) where \(R^{a_{t}}\) is an unknown probability distribution. The goal of the game is to maximize the cumulative reward \(\sum_{t=1}^{T}r_{t}\) by selecting actions which are likely to give high rewards. Due to the unknown nature of the reward distributions, players are required to first \"explore\" the possible actions to figure out the distributions before they can \"exploit\" the actions with the highest average probability.  
For this paper, the problem is modeled as an adaptation of the MAB (AMAB) model. The possible actions are the different possible allocations of single jobs currently in the RMS queue, i.e., for each job there exists an action for each possible number of cores available for it to be allocated to  
Figure 2: An example value curve of a job.  
There may be multiple jobs of the same type in the queue which will create duplicate allocation possibilities, however, they will likely have different schedule delays. The rounds are the allocation stages, which occur at every change in the job queue or available resources. Therefore, the time step between each round varies significantly. The rewards are \(Val_{j}/Energy_{j}\) for each job after computation is complete.  
The MAB model does not perfectly fit the described problem due to a number of assumptions: rounds are at discrete time steps, every action is available at every round, exactly one action is selected per round, and rewards are received instantly after selecting an action. As such, the problem model differs in the following ways: only a subset of all possible actions are available per round, multiple or no actions may be selected per round, and rewards at the start of some round in the future after selecting an action. To fit the model, this paper considers the use of the Upper Confidence Bound (UCB) algorithm [41], which is described in the next subsection.
# Chemical, Nutritional and Antioxidant Characteristics of Different Food Seeds
## 4 Conclusions  
Ten samples of each seed type (sunflower, poppy, hemp, flax and sesame) were analyzed in order to determine the content of SFAs, MUFAs and PUFAs regarding FAs content from lipids; the mineral, protein content and antioxidant capacity were also determined. Low amounts of stearic and palmitic acids (SFAs) were determined in the analyzed samples. The main FA found in poppy (67.2% \(\pm\) 0.80%), sunflower (61.0% \(\pm\) 0.70%), sesame (60.2% \(\pm\) 0.50%) and hemp (55.9% \(\pm\) 0.30%) seed oils was linoleic acid. Due to its high content of two essential PUFAs (linoleic acid and linolenic acid 3:1), hemp seeds can be used as functional ingredients in the food industry. The main FA identified in flaxseed oil (73.2% \(\pm\) 0.40%) was \(\alpha\)-linolenic (C18:3, n-3) acid. In addition, in all the analyzed samples, PUFA were found in high amounts. A high amount of omega-3 (\(\omega\)-3) FAs was found in flaxseed oil and a small amount in hempseed oil. All studied seeds are rich in minerals (Na, K, Ca and Mg), which are co-factors for FAs and protein metabolism. The obtained results add valuable information to the knowledge on the chemical compositions of food seeds and will be useful and of interest in nutrition.  
[PERSON] conceived and designed the experiments, O.C. performed minerals and protein analysis, E.N. performed oil extraction, M.H.K. performed fatty acids analysis, A.B. performed antioxidant capacity and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Component analysis. (**a**) Projection of components 1 and 2, and (**b**) projection of components 1 and 3.  
**Funding:** This research was funded by Competitiveness Operational Programme of the Ministry of European Funds, Contract No. 7/01.09.2016, code MY SMIS 105654 and the article processing charge (APC) was funded by Romanian Research and Innovation Ministry under PROINSTITUTIO project, Contract no. 19 PFE/17.10.2018.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction  
Technological developments in dentistry have delivered an offspring of digital devices. There has been a global convergence in avant-garde digital impression systems via use of intraoral scanners (IOS) [1, 2]. Intraoral scanners are digital devices for capturing direct optical impressions with the use of optical laser scanning or structured light [3, 4] based on the principle of parallel confocal scanning. They collect information of the dental arches (or projection, object of interest), and obtain three-dimensional representations, employing a scanning software. After processing the captured images, scanning software generates point clouds [4, 5]. Then, a triangulation is obtained to estimate the teeth surface by connecting points with each other, ending up with a three-dimensional (3D) surface model (mesh). An IOS possesses many inherent advantages over a conventional physical detection of impression because of its ability [6; 7] to quickly and accurately produce meshed models of the patient's mouth. It also possesses great flexibility acquisition, and is beneficial, particularly for examinations in sensitive patients [8; 9]. The optical detection with IOS allows to acquire an immediate visualization and determination of the areas of impression. Numerous studies have assessed the accuracy of intraoral scanners (IOS), but there is a paucity of data derived from analysis comparing the depiction ability of the critical finish line and the finish line accuracy between different devices [3; 10; 11]. Few studies have shown the limits of these scanners in subgingival preparations and in the presence of biological fluids and secretions (saliva, blood, crevicular fluid) [7; 8; 9; 10; 11]. In subgingival preparations, the light can hardly penetrate the groove and correctly detect the marginal areas [12; 13; 14; 15; 16]. Using a rubber dam can avoid such a problem. It allows isolating the dental surfaces, but only in extra gingival preparations [10]. Our study was aimed to compare the accuracy of the finishing line among three intraoral scanners in subgingival preparations with and without saliva, to identify the possible clinical acceptability in wetness condition. The null hypothesis of this study was that oral environment would not affect the scan data precision of the intraoral scanners, and no significant difference would be found in accuracy between the oral condition, with and without saliva.
## 4 Results
### Statistics and Trends
#### 4.5.1 Overview of the Trends by Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & **LR** & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**NB**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**All Models**} \\ \hline  & **DS1** & **DS2** & **DS3** & **DS1** & **DS2** & **DS3** & **DS1** & **DS2** & **DS3** & **DS1** & **DS2** & **DS3** & **DS1** & **DS2** & **DS3** \\ \hline
**LIWC** & 78.02 & 74.62 & 74.4 & 77.24 & 73.32 & 74.6 & 76.47 & 74.58 & 72.20 & 78.05 & 69.98 & 74.20 & 77.45 & 73.13 & 73.85 \\ \hline
**n-grams** & 76.46 & 67.93 & 70.47 & 74.82 & 67.12 & 69.47 & 77.61 & 69.24 & 71.47 & 72.18 & 65.06 & 67.80 & 75.27 & 67.34 & 69.80 \\ \hline
**n-grams** + LIWC** & 78.79 & 74.29 & 75.30 & 76.69 & 74.35 & 75.13 & 78.15 & 71.34 & 72.87 & 74.52 & 71.75 & 73.93 & 77.04 & 73.33 & 74.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Overview of trends by model for the 3 datasets collected (yellow color: maximum average accuracy obtained for each individual model and dataset; red color: maximum average accuracy obtained using all models on each individual dataset).