esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiment Evaluation  
This section evaluates the performance of each classifier on the basis of results achieved in the continuous authentication phase and in the anomaly detection phase. The performance of each method is measured using three common metrics: false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER). FAR is the probability that a user who should be rejected is accepted by the system. FRR is the probability that a user who should be accepted is rejected by the system. EER is a threshold value between the false acceptance rate and the false rejection rate. Another important evaluation tool to examine the classifiers and show the performance of the biometric system is to plot the receiver operating characteristic (ROC) curves. It is important to interpret a classifier with the structure of its ROC curve because they show the prediction success of the models on several operating points corresponding to all possible thresholds. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) [38]. The following expressions were used for performance evaluation purposes [39]: TP: true positive, TN: true negative, FP: false positive, FN: false negative, FAR: false acceptance rate, FRR: false rejection rate, and EER: equal error rate:  
\[A\text{CC}=\frac{TP+TN}{TP+TN+FP+FN} \tag{25}\]  
\[TPR=\frac{TP}{TP+FN} \tag{26}\]  
\[TNR=\frac{TN}{TN+FP} \tag{27}\]  
\[FPR=\frac{FP}{FP+TN} \tag{28}\]  
\[FNR=\frac{FN}{FN+TP} \tag{29}\]  
\[FAR=\frac{Number\ of\ accepted\ impostersers}{Total\ number\ of\ imposters} \tag{30}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **KNN** & **DT** & **RF** & **CNN** \\ \hline Accuracy & 98.2 & 92.2 & 98.0 & 98.5 \\ Recall & 97.8 & 90.7 & 96.2 & 97.3 \\ Precision & 93.9 & 90.8 & 97.0 & 97.1 \\ F1-score & 95.6 & 91.7 & 95.4 & 95.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Phase 2: Anomaly detection results in Scenario A: single mouse movement action.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **KNN** & **DT** & **RF** & **CNN** \\ \hline Accuracy & 93.1 & 83.5 & 95.4 & 88.6 \\ Recall & 90.2 & 80.7 & 93.1 & 85.2 \\ Precession & 89.1 & 81.6 & 92.8 & 86.8 \\ F1-score & 91.5 & 81.4 & 93.6 & 82.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Phase 2: Anomaly detection results in Scenario B: single point-and-click action.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **KNN** & **DT** & **RF** & **CNN** \\ \hline Accuracy & 89.4 & 74.1 & 92.1 & 85.7 \\ Recall & 87.2 & 72.2 & 91.1 & 85.4 \\ Precision & 86.7 & 71.7 & 92.4 & 84.2 \\ F1-score & 84.3 & 72.6 & 90.2 & 83.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Phase 2: Anomaly detection results in Scenario C: Set of mouse movement and point-and-click actions.  
\[FRR=\frac{Number\ of\ rejected\ genuines}{Total\ number\ of\ genuines} \tag{31}\]  
\[EER=\frac{FAR+FRR}{2} \tag{32}\]
# Communication Rate Increase in Drill Strings of Oil and Gas Wells Using Multiple Actuators
## 4 Experimental Results on Communication and Data Detection
### Two Actuators Simultaneously Transmitting Two Data Streams
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Receiving Sensor Pair** & **BER** & **SNR (dB)** \\ \hline Strain and \(x\)-acceleration & \(7.1\times 10^{-04}\) & 3.0 \\ Strain and \(y\)-acceleration & \(2.9\times 10^{-04}\) & 3.1 \\ Strain and \(z\)-acceleration & \(1.3\times 10^{-02}\) & 3.6 \\ \(x\)- and \(y\)-acceleration & \(3.1\times 10^{-02}\) & 4.3 \\ \(x\)- and \(z\)-acceleration & \(4.5\times 10^{-02}\) & 4.7 \\ \(y\)- and \(z\)-acceleration & \(3.6\times 10^{-02}\) & 4.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average BERs and SNRs of various receiving sensor pairs, with two actuators simultaneously transmitting two data streams. Figure 7: BERs (**top**) and SNRs (**bottom**) at different receiver positions, with two actuators transmitting two data streams simultaneously. The receivers were a strain sensor and the \(y\)-channel of a triaxial accelerometer. The piecewise linear graphs represent average BERs and SNRs for each of the two data streams versus the receiver position. Figure 8: BERs (**top**) and SNRs (**bottom**) at different receiver positions, with two actuators transmitting two data streams simultaneously. The receivers were a strain sensor and the \(z\)-channel of a triaxial accelerometer. The piecewise linear graphs represent the average BERs and SNRs for each of the two data streams versus the receiver position. Figure 9: BERs (**top**) and SNRs (**bottom**) at different receiver positions, with two actuators transmitting two data streams simultaneously. The receivers were the \(x\)-channel and \(y\)-channel of a triaxial accelerometer. The piecewise linear graphs represent average BERs and SNRs for each of the two data streams versus the receiver position.
## 4 Discussion
### Analysis of Influencing Factors of Methane Flux in Rice Paddies  
Accurate prediction of methane flux (FCH4) for rice paddies is crucial for understanding the greenhouse gas budget of rice paddy ecosystems and achieving environmental sustainability. [PERSON] et al. [40] identified the XGBoost model as the most suitable model with outstanding efficiency and accuracy for predicting FCH4. However, [PERSON] et al. have not studied the environmental factors influencing FCH4 emissions. Understanding the causes of FCH4 emissions contributes to more sustainable agricultural development. This study investigated the ability of the XGBoost and RF models to predict FCH4 and its causes.  
The importance of methane prediction input characteristics at each site is shown in Figure 7. The dominant factors of methane prediction were also changing with the location of the site. Among them, RECO performed best at IT-Cas and JP-Mse sites, while at the KR-CRK and US-Twt sites, NDVI had the greatest impact on the model. At the remaining three sites, SWC obtained the largest gain value.  
[PERSON] et al. [41] found that RECO was a powerful factor that is necessary to explain methane emissions. The importance of RECO in paddy field CH4 prediction was also well reflected in this study. RECO played the most important role in the IT-Cas and JP-Mse sites. Although the gain value obtained by RECO was no more than 0.1, some sites also had the importance of the top 3. RECO had such a good performance, which is attributed to the fact that it may represent the final result of complex nonlinear processes that also affect CH4 exchange and are the direct drivers of CH4 production and methane transport in paddy fields.  
[PERSON] et al. [42] indicated that vegetation indexes could be used as input factors for determining CH4 flux in rice paddies. NDVI was one of the indicators of vegetation change and performed best when predicting CH4 at KR-CRK and US-Twt sites. In addition, it also had a good performance at IT-Cas and PH-RiF sites, with a gain value better than other variables. These results can prove the possibility of NDVI for FCH4 prediction.  
[PERSON] et al. [43] indicated that TS and SWC were the most important factors controlling CH4 emissions from rice paddies on seasonal timescales through the partial F tests.  
[PERSON] et al. [44] showed that the average annual soil temperature was the strongest predictor of annual CH\({}_{4}\) flux across wetland sites globally. This study found that TS and SWC were major factors in CH\({}_{4}\) prediction at the half-hour scale. For instance, TS ranked second in importance among two sites and third in one site; SWC ranked first in three sites, while the rest also performed in the top four. The main reason is that TS and SWC are important regulators of soil reduction conditions and enzymatic processes. Higher soil temperatures can enhance methane production, molecular diffusion, and transport within plants [45; 46]. SWC regulates the balance between CH\({}_{4}\) production and oxidation by influencing the depth of anaerobic and aerobic zones in the soil [47].
## 2 Problem Statement  
Figure 1 shows the action loop for the path following control task considered in this work. First, suitable sensors should detect the path boundaries, and the planned path should be closely followed. Afterwards, the path is forwarded to the reinforcement learning-based path-following controller, which is trained in simulation with the vehicle model of the target vehicle.  
Reinforcement learning agents in simulation-based environments are usually trained with fixed model parameter values. In autonomous driving, however, some vehicle dynamics parameters might be uncertain or might change over time due to environmental influence. This might negatively affect the agents' performance during their execution if the parameters cannot be determined beforehand or change after training. Figure 2 qualitatively shows this for a path-following motion control task. Let \(\xi\) represent the dynamics parameter of the vehicle model. Furthermore, assume that \(\xi_{\text{train}}\) is a fixed value for \(\xi\) that is applied to the vehicle learning model during training. On the left side of Figure 2, it can be seen that the agent performs well after training and during execution if the actual parameter \(\xi_{1}\) of the vehicle equals the parameter \(\xi_{\text{train}}\). However, on the right side, the agent is not able to provide a satisfying control performance during execution and drives off the road since the true parameter \(\xi_{2}\) of the vehicle differs from the fixed value \(\xi_{\text{train}}\) used during training. The possibility of such an unrobust control behavior poses a major security risk.  
To overcome this issue and train robust agents for a path-following control task in the presence of uncertain and changing dynamics parameters, we apply dynamics randomization during the reinforcement learning process in this work. The underlying path following control problem considered in this paper is based on our previous work in [14] and is introduced in detail in Appendix A. We assume that the path boundaries can be detected in each time step and that a path planning module, such as in [15], is given. Furthermore, the learning model of the controlled vehicle is based on the extended non-linear single-track model of the ROboMObil [12] (c.f. Appendix B). We assume that  
Figure 1: The action loop for the considered path following control task.  
a nonlinear observer, such as in [16], estimates the necessary states for the reinforcement learning controller.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 3 Hydrogen Storage Technologies
### Physical-Based Storage Technologies  
Hydrogen storage technologies are classified as physical-based or material-based technologies. Physical-based storage technologies consist of changing the hydrogen state, by increasing the pressure, decreasing the temperature, or changing both simultaneously, to reduce the volume and, consequently, increase the volumetric energy density. Compressed hydrogen, liquid hydrogen, and cryo-compressed hydrogen are the most common technologies used to physically store hydrogen [66].  
In compressed gas storage, hydrogen is compressed (up to 700 bar) to increase its volumetric density and energy content per unit volume, and then stored in aboveground or underground containers [69]. In aboveground compressed gas hydrogen storage, hydrogen is stored in cylindrical vessels [66; 70; 71]. It is the most established way to store hydrogen; nonetheless, this process presents high development and manufacturing costs, especially in producing carbon fiber composite containers, and it requires a great deal of energy [66]. In addition, due to its small molecular size and high diffusivity, hydrogen leakage from high-pressure steel or aluminum vessels is a significant concern, making the development of materials resistant to hydrogen leakage and the integration of advanced hydrogen detection sensors essential for safe storage [72]. In underground compressed gas hydrogen storage, hydrogen can be stored in natural caverns, aquifers, underground containers, or underground pipes [73].  
In liquid hydrogen storage, hydrogen is cooled below its boiling point (\(-253\)\({}^{\circ}\)C at atmospheric pressure). Liquid hydrogen presents a greater volumetric density than that  
Figure 13: Overview of hydrogen storage methods [66].  
achieved by compressing it to a pressure of 700 bar (70.9 kg/m\({}^{3}\) vs. 39.1 kg/m\({}^{3}\)) and has a gravimetric density of up to 20%wt [74; 75]. Nonetheless, this process presents the big disadvantage of consuming a great amount of energy, equivalent to about 30 to 35% of the energy content of the stored hydrogen, as well as high boil-off losses [66; 70].  
Cryo-compressed hydrogen storage technology combines compression with cryogenic technologies. This storage approach is based on the optimization of the pressure and temperature conditions simultaneously [66].
## 1 Introduction  
### Aquaculture  
The current state of aquaculture plays a critical role in providing food and employment to millions of people, many of whom live and depend on aquaculture for subsistence. For this reason, aquaculture has been identified as the food industry with the highest annual growth rate with 5.3 percent per year in the period 2001-2018 [1]. Aquaculture in Portugal reached 14,336 mts in 2019 corresponding to an increase of 2.5% compared to the previous year [2]. Most of the Portuguese aquaculture is developed in old salt pans in the central and southern areas of Portugal, while freshwater aquaculture is scattered throughout the northern and central regions. In any case, land-based production in aquaculture recirculating systems (RAS) or in natural systems can be an option to increase the aquaculture production and can be appropriate for some species such as flat fishes, e.g., turbid (_Scophthalmus maximus_), Senegalese sole (_Solea senegalensis_), or to produce new species such as the sea urchin _Paracentrotus lividus_[3, 4] and the sea cucumber _Holothuria manmata_[5]. However, the limited areas for aquaculture production mean that offshore aquaculture may be seen as a solution for other species rearing. Offshore aquaculture has been accepted as a sustainable, economical, and viable form of food production in many coastal nations [6, 7, 8]. This is mainly due to the problems related to coastal pollution, the use of resources [9], and the lack of suitable sites for production. To ensure the correct choice for new aquaculture facilities offshore, it is necessary to understand all factors that influence the well-being of the organisms [10]. It is required to ensure adequate physico-chemical conditions, such as nitrogenous compounds (ammonia, nitrite, and nitrate), phosphate, silicate, dissolved oxygen, pH, temperature, and chlorophyll \(a\). There is a very close relationship between the water quality and the survival and health of organisms, where poor water quality can cause mortality, slow growth, and can induce stress, reducing the farmed animals' health and welfare leading to increased susceptibility to diseases. The effort required by fishes, bivalves, or other aquatic organisms exposed to suboptimal water quality creates impacts and diverts energy from growth. Furthermore, survival declines as the water quality parameters diverge from the optimum for a species. According to [PERSON] [11], most of the problems with water quality in aquaculture result from the less-than-optimal quality of the water. Some of the major water-quality stressors are inappropriate values of water temperature, salinity, pH, or suboptimal concentrations of dissolved oxygen, ammonia nitrogen, and nitrite among others. From the standpoint of aquaculture implementation, location is the most relevant step as it is the broadest criterium. Knowledge of hydrographic conditions, such as swells and currents, is fundamental to deploying offshore aquaculture cages and other structures. Adverse marine conditions can cause damage to the equipment and lead to stock losses.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 5 Experiments and Results  
Extensive experiments were conducted to evaluate the performance of the proposed superscalar pipelined data storage exchange method and hardware architecture. To ensure the completeness and comprehensiveness of the experimentation and evaluation, the superscalar pipeline data storage and exchange engine was embedded as an intellectual property (IP) into the \"CPU + FPGA\" heterogeneous SAR imaging processing system platform for testing. The experimental result evaluation is divided into two parts. The first part involves a performance evaluation of the superscalar pipeline data storage and exchange engine, from which the bandwidth of storage access in both the range and azimuth directions, as well as the matrix conversion time, are obtained. Furthermore, a comparison of the performance metrics is conducted with reference to multiple relevant papers. In addition, the throughput rate, efficiency, and speedup ratio of the data storage exchange engine with a super-scalar pipeline are presented and compared with non-pipeline and scalar pipeline approaches in order to analyze the performance enhancement achieved through the super-scalar pipeline design. The second part entails the performance analysis of the imaging results obtained from the \"CPU + FPGA\" heterogeneous SAR imaging processing system platform. In this part, according to the complete imaging process of SAR, the SAR raw echo data are processed using the \"CPU + FPGA\" heterogeneous SAR imaging processing system platform to obtain SAR imaging results, which are then compared to the imaging results obtained from MATLAB. In addition, a description of the hardware resource utilization of the entire heterogeneous system is provided. Finally, the imaging performance of SAR is evaluated and compared with other SAR imaging processing systems. In the following subsections, the experimental environment configuration and detailed experimental results will be described.
## 3 Novel Vehicle Terminal Based on Acoustic Communication Technology
### Acoustic Demodulation Module
#### 3.2.1 \"ASK + FSK\" Compound Modulation Mode  
The modulation and demodulation of the signal will affect its response speed, noise immunity, and other properties. According to the difference of the signal control sinusoidal carrier parameters of digital baseband, the modulation methods of acoustic wave can be divided into three ways, such as  
Figure 3: Configuration of time-sharing rental vehicle terminal for an electric vehicle based on acoustic communication technology.  
amplitude-shift keying (ASK), phase-shift keying (PSK), and frequency-shift keying (FSK). For example, ASK is used in AliPay to encode or decode, while chirp is the same.  
In order to enhance the anti-noise ability of the acoustic communication technology basically, this paper combined the advantages of ASK and FSK modulation methods, and proposed the \"ASK + FSK\" compound modulation, that is, the frequency hopping was used to represent the information of the transmitted signal.  
When the sine wave of frequency \(f_{1}\) jumps to a sine wave of \(f_{2}\) (\(f_{1}<\)\(f_{2}\)), it indicates the state of the code element being \"1\". When the sine wave of frequency \(f_{2}\) jumps to a sine wave of \(f_{1}\) (\(f_{1}<\)\(f_{2}\)), it indicates the state of the code element being \"0\". The signal modulation waveform of the compound modulated mode is shown in Figure 4. After filtering, the condition of triggering code element change is judged according to the amplitude and duration of the jump component.  
By using this method, the effects of noise, jitter, and other false triggering can be reduced, while anti-noise ability can be improved. Combining with the traditional data transmission verification measurement, the complete data transmission can be realized. Thus, the acoustic communication can be more secure and better user experience can be realized.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 3 Applications and Deployments
### Deployments
The propagation performance measurement results were benchmarked to a number of propagation models (ITU model, multi-wall model and ray tracing model) and it was found that the best model that predicts indoor LoRa propagation is the multi-wall model. Another LoRaWAN indoor deployment was presented in [39], in an industrial environment. The gateway was installed indoor and measurement points included, in addition to indoor positions, also points at the outdoor vicinity of the industrial environment. It was shown that a total industrial indoor area of 34,000 m\({}^{2}\) could be covered by single gateway using SF7, while outdoor vicinity area still could be reached using SF12.
# Optimal Disintegration Strategy in Multiplex Networks under Layer Node-Based Attack
## 3 Experimental Analysis
### The Existing Disintegration Strategies
#### 3.1.1 Multiplex Node-Based Strategies  
For multiplex node-based strategies, we order the multiplex nodes according to the global information of the multiplex network and remove the layer nodes included by the multiplex nodes in each layer consecutively. The high degree strategy is the easiest method for disintegrating a network. We remove the layer nodes successively according to the multiplex degree of the multiplex nodes they belong to and name this method as high multiplex degree (HMD) strategy. If we recalculate the multiplex degree after removing all the layer nodes of the multiplex node with the highest multiplex degree at each step, this is referred to the high multiplex degree adaptive (HMDA) strategy.  
Another strategy is a heuristic strategy named effective multiplex degree (EMD) [26], which is designed for attacking interdependent networks. The weight of the multiplex nodes \(w_{i}\) are calculated iteratively according to the following formula:  
\[w_{i}^{(t+1)}=\sum_{l=1}^{M}\sum_{j\in\mathcal{N}^{a}}\frac{1}{M_{j}}\frac{w_ {j}^{(t)}}{k_{j}^{(a)}} \tag{7}\]where \(\mathcal{N}^{\alpha}\) is the set of neighbors of multiplex node \(v_{i}\) in layer \(\alpha\) and \(k_{j}^{(\alpha)}\) is the layer degree of multiplex node \(v_{j}\) on layer \(\alpha\), \(M_{j}\) is the number of layers in which multiplex node \(v_{j}\) participates. We make all the \(M_{j}=M\) in this paper and the initial value of the \(w_{i}\) is the multiplex degree \(k_{i}\) of node \(v_{i}\). It is worth noting that the EMD strategy is also \"adaptive\", meaning that we recalculate all the weights of the remaining multiplex nodes after removing all the layer nodes of a multiplex node. The iterations \(t\) in each calculation is 10.