esa-sceva/satcom-chunk-collection huggingface dataset:
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 7 Discussion
### Loss Scenarios of STPA  
By implementing a current control environment and rewarding safe actions with RL, we identified self-guided loss scenarios that lead to hazardous actions. This helps in shortening the required time to meet safety requirements and avoid hazards. The context table presented in [36] is used to systematically analyze system components and interaction control structures, identify loss scenarios, and discover potential loss scenarios in the system. [PERSON] et al. [37] derived unsafe scenarios through a context table to generate software safety requirements and created software safety constraints for each context using the Boolean operators AND and OR. Also, it can look for all aspects of the system's possible risks, but developing safety requirements for all of them could be time-consuming and could be useless if the hazards do not show up in practice.  
This could be a challenge for organizations with limited resources or expertise in risk management, so to reduce time and resources for system hazard analysis, we recommend our proposed method. [PERSON] et al. [38] previously aimed to introduce the method to combine STPA and RL. Thus, we further formalized the implementation of the RL environment, providing a more standardized approach for practical utilization. Additionally, we conducted analyses concerning hazard frequencies, along with the derivation of loss scenarios. [PERSON] et al. [39] introduce athe transformation of STPA analysis into acyclic diagrams. We propose the hazard trajectory diagram to graphically indicate every sequence of steps that is heading to a loss.
# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 4 Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
### HRC Framework Overview
#### 4.1.3 Stackelberg Trust-Based HRC Framework  
The Stackelberg trust-based human-robot framework for warehouse picking is illustrated in Figure 3. It contains three parts. First, the POSG is defined to model and evaluate human trust. Specifically, trust between the human and the robot is considered a hidden human state. This hidden state is modeled and calculated in a partially observable form. Since the model involves actions, states, and environmental states of both human and robot agents, game theory is used to establish interactions between them. Human trust dynamics are modeled using a linear Gaussian system, and Bayesian posterior belief space updates are employed for the robot to evaluate human trust. Then, based on human trust levels, a sigmoid function is applied to generate the human strategy space. An iterative Stackelberg trust strategy generation (ISTSG) algorithm is designed to solve the optimal robot strategy space, which is implemented by the Stackelberg strategy and the Bellman equation. This process forms the foundation for decision-making in human-robot collaboration. Finally, the obtained human-robot collaboration decision strategies are formalized as a Partially Observable Markov Decision Process (POMDP) model. The properties of human-robot collaborative picking are described as PCTL formulae with extended rewards. The POMDP model and PCTL formulae are verified and analyzed by the probabilistic model-checking tool PRISM. This enables our framework to be checked to establish whether it meets the probabilistic requirements and achieves efficiency optimization.  
Figure 3: Stackelberg trust-based HRC framework for warehouse picking.
## Appendix A Repeatability  
As mentioned in the text, experiments have been repeated for each temperature, either in successive days or slightly changing the range of drive amplitudes. Results of repeated experiments on Berea are shown in Figure 11. Results for concrete are similar. Excellent repeatability is obtained, the scattering among data at the same temperature (different colors) being negligible when compared to the difference in behavior due to a temperature change.  
Here, the two fitting procedures are compared. Fitting data with a sigmoidal-like function (left column in Figure 11) seems slightly more accurate than fitting with a second-order polynomial fit (central column), particularly for the unloading branch of the curves (blue). The advantages of the sigmoidal-like fitting are even more pronounced when considering the baseline velocity variation (not reported here but shown in Figure 3).  
The better quality of the fit using Equation (6) could be better appreciated when calculating the residuals (absolute values of the difference between experimental and fit data). They are shown for Berea and concrete in the right column of Figure 11. Thus, the advantages of the proposed sigmoidal-like function, used to fit the data, are two-fold: the fit is slightly more accurate and the function is more universal, in the sense that it fits (with parameters with the same sign) all data well (loading/unloading, conditioned/baseline, velocity/damping).
## 3 Methods  
Dysarthria's unique communication challenges necessitate the exploration of alternative methods for interacting with SVAs. Given the variable severity of dysarthria, it was imperative to evaluate various interaction methods to determine their effectiveness across this spectrum. Each method represented a distinct mode of interaction, providing valuable insights into which modes are most accessible and user friendly for people who have varying degrees of speech impairment.  
In this study, we measured several key attributes to assess the interaction methods, including the usability, effectiveness, satisfaction, and workload:  
* The usability attribute measured the user's ease and efficiency in interacting with the system. This was measured by the system usability scale (SUS), a widely used tool for testing usability [39] that has been used across various domains, including the usability of SVAs [40; 41; 42; 43; 44]. This survey comprises 10 questions rated on a 5-point Likert scale that ranges from strongly disagree to strongly agree.
* The effectiveness attribute measured the user's ability to complete a task (task success rate) [28]. The task was considered successful if the SVA successfully replied to or performed the command requested. The success was recorded during the study and confirmed by video recordings. Although the SUS provides subjective user feedback, the effectiveness attribute offers objective concrete data on how well a system performs in achieving its intended tasks [45] and many studies have used it in combination with the SUS [45; 46; 47; 48].
* The preference for each system was evaluated through direct feedback from a post-study interview, which focused specifically on system preference as an indicator of satisfaction [49]. This approach complemented the other measures and provided a deeper understanding of the user feedback.
* The workload identified the effort required to perform a task. This was measured using the NASA Task Load Index (NASA-TLX) questionnaire [50], which contained six questions focusing on mental demand, physical demand, temporal demand, performance, effort, and frustration level. Using this measure was particularly crucial for individuals who have dysarthria and often experience rapid fatigue. By employing the NASA-TLX, we aimed to gain a deeper understanding of the workload implications for this specific user group [41; 44; 51].
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 5 Replacing Optimization Algorithms by Trained ANNs
### Performance of the BFNN for Single-Beam and Dual-Beam Cases  
The FFNN model of BFNN runs on a HP Pavilion desktop computer (model no. TG01-1008 np) with an Intel(r) CoreTM 15-10400F CPU @ 2.90 GHz and 32 GB RAM. Training of the BFNN takes less than 20 min; however, the trained network can predict the necessary phase distribution for arbitrary beam directions in a fraction of second, which is much less time-consuming than the global optimization of the phase distribution in MATLAB that can take many hours to complete on the same hardware. Moreover, the BFNN model needs much less memory space as compared to the optimization code.  
The correlation between the numerically optimized phase values and the values predicted by the trained BFNN in a single-beam scenario is quantified by the correlation coefficient, which was calculated to validate BFNN performance. These results are depicted in Figure 13. Figure 13a presents the correlation between the predicted phase values and the optimized values used for training the BFNN. As seen in Figure 13a, 87.5% of the predictions are above the accuracy average of the BFNN predictions, which reaches 0.98. As shown in Figure 13b, all of the predicted values for 30 different sets of \((\theta_{i},\varphi_{i})\) pairs from the validation set, which are not included in the dataset used for training, are predicted with more than 83% accuracy. However, the mean accuracy for predictions is decreased to 0.92 in this case, Figure 13b.  
Finally, in Figure 14 we present results for the electric field patterns of \(10\times 3\) PMS obtained with the optimized FT-based beamforming method realized in MATLAB and predicted by the BFNN in single- and dual-beam scenarios with different direction(s) of the main beam(s). The considered beam directions are as follows: For the single-beam case, \((\theta_{1},\varphi_{1})=\{(30^{\circ},45^{\circ}),(60.2^{\circ},221.1^{\circ}),(70.23^{\circ},63.46^{\circ})\}\) and for the dual-beam case, \((\theta_{1},\varphi_{1})=(30^{\circ},45^{\circ})\) and \((\theta_{2},\varphi_{2})=(30^{\circ},225^{\circ})\). In the single-beam case, the case with \((\theta_{1},\varphi_{1})=(30^{\circ},45^{\circ})\) is one of the cases included in the training dataset, Figure 14a-c. However, the beam angles \((\theta_{24},\varphi_{24})=(60.2^{\circ},221.1^{\circ})\) and \((\theta_{30},\varphi_{30})=(70.23^{\circ},63.46^{\circ})\) are not in the training dataset and are just used for validating BFNN performance for arbitrary input data, Figure 14d-i, respectively. In the dual-beam case, the pairs \((\theta_{1},\varphi_{1})=(30^{\circ},45^{\circ})\) and \((\theta_{2},\varphi_{2})=(30^{\circ},225^{\circ})\) are included in the training dataset, Figure 14j-l. The results show a very good agreement between the numerically optimized and the BFNN-predicted data.
# Hybrid Quantum Dot as Promising Tools for Theranostic Application in Cancer
## 3 Hybrid Quantum Dot as Cancer Theranostics: Contemporary Research
### Diagnostic Application
Thus, using QDs fluorescence, the in vitro and in vivo localization of nanocapsules into breast tumors was observed. Recent research related to hybrid QDs utilized for their diagnostic/imaging applicability in different types of cancers is summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of QDs** & **Type of Cancer** &
\begin{tabular}{c} **Diagnostic/Imaging** \\ **Technique** \\ \end{tabular} & **Outcome** & **Refs.** \\ \hline Lactoferrin QDs & Breast cancer & Fluorescence imaging & Intracellular uptake of QDs showed fluorescence fluorescent due to mercopropionic acid-capped cadmium telluride and was successfully used as theranostic & [89] \\ \hline Gelatin/chondroitin QDs & Breast cancer & Fluorescence imaging & Matrix metalloproteinase layer enabled tracing their internalization into cancer cells and strong non-immunogenic response used as diagnostic & [90] \\ \hline Magnetic graphene-QDs & Cancer cells & Electrochemical detection imaging & Images show high fluorescence in HeLa cells & [91] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of contemporary research carried out utilizing hybrid quantum dots for diagnostic/imaging applications in cancer.
## 3 Results and Discussion
### Calculation Function  
Relevant studies showed that temperature has a significant effect on the dielectric properties of grain. Therefore, it was necessary to analyze the interaction effects of process variation on the measured capacitance of the sensor to fully determine the variation law of capacitance in the filling layer and obtain the calculation function in the process of online moisture content measurement by capacitance method. Table 3 shows the measured corn capacitance under different moisture content, temperature and porosity by the measurement device developed in this study. Analysis of variation was used to determine the effect of process variables on the response and to fit second-order polynomial models to the experimental data. After multiple regression fitting of the experimental data, the ternary quadratic mathematical equation describing the corn capacitance and moisture content, temperature, and porosity was established, as shown in Equation (24), in which \(P\) was the porosity. The correlation coefficient (\(R^{2}\)) of the fitting function was 0.999, which indicated that the predictions were in close agreement with the measured values. \[C=10.25+0.4409 MC+0.14336 Tg-0.668P+0.001019 MC\times Tg+0.005868 MC\times Tg\] \[-0.003144 Tg\times P+0.001911 MC^{2}+0.001046 Tg^{2}+0.00895P^{2} \tag{24}\]  
The results of variance analysis of Equation (24) are shown in Table 4. The moisture content, temperature, porosity, and their interactions all significantly influenced capacitance, among which moisture content had an extremely significant influence. The \(F\)-value of the model was 49.57; however, the \(p\)-value was less than 0.0001, which proved that model was significant and the experimental results were not random. Both \(Adj\)-\(R^{2}\) and \(Pre\)-\(R^{2}\) were 0.998. These consistent results further demonstrate the effectiveness of model. Therefore, the fitting mathematical model can be used to predict corn moisture content in the drying process. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Df** & **SS** & **MS** & **F–Value** & \(p\)–Value** \\ \hline \(MC\) & 1 & 170.919 & 18.9910 & 7637.55 & \textless{}0.001 \\ \(T\) & 1 & 0.602 & 0.6022 & 242.20 & \textless{}0.001 \\ \(P\) & 1 & 0.551 & 0.5510 & 221.59 & \textless{}0.001 \\ \(MC\textasci{}T\) & 1 & 0.104 & 0.1037 & 41.72 & \textless{}0.001 \\ \(MC\textasci{}P\) & 1 & 0.151 & 0.1505 & 60.55 & \textless{}0.001 \\ \(T\textasci{}P\) & 1 & 0.284 & 0.2841 & 114.25 & \textless{}0.001 \\ \(MC^{2}\) & 1 & 0.534 & 0.5344 & 214.90 & \textless{}0.001 \\ \(T^{2}\) & 1 & 0.056 & 0.0559 & 22.48 & \textless{}0.001 \\ \(P^{2}\) & 1 & 0.726 & 0.7263 & 292.09 & \textless{}0.001 \\ \(Model\) & 9 & 0.123 & 0.1233 & 49.57 & \textless{}0.001 \\ \(Error\) & 65 & 0.162 & 0.0025 & & \\ \(Total\) & 74 & 171.080 & & & \\ \(R^{2}\) & 0.999 & & & & \\ \(Adj\)-\(R^{2}\) & 0.998 & & & & \\ \(Pre\)-\(R^{2}\) & 0.998 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variance evaluation of linear, quadratic, and interaction terms for the response and coefficient of the prediction models.
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 3 Experimental Results
### Analyzing Experimental Outcomes
#### 3.3.2 Comparison of Subjective Quality
On the one hand, as illustrated in Figure (a)a, although HM16.5 exhibited strong results as a high-efficiency algorithm for CU partition, this efficiency may come at the cost of increased computational complexity. However, compared with Figure (a)a, the CU partitioning of our scheme was very consistent with that of HM16.5, which is one of the important reasons for the high coding performance obtained by our method. On the other hand, from a subjective perspective, the HM16.5 method divides some regions that do not need CU partition, while the proposed method does not divide these areas, which better reflects the subjective feelings. Specifically, Figure (a)a,b highlights yellow regions that are not divided in our method but are divided in HM16.5. This is one of the reasons for the low complexity of our scheme. It is noteworthy that similar results were obtained for other test sequences of JCT-VT, which proves the robustness and effectiveness of the proposed algorithm. Figure 8: Comparison results of the RD curves. (**a**) Basketball Drive; (**b**)BQ Square. Figure 9: _Cont._  
Figure 10: CU partition results for Frame 3 of the BQ Mall sequences on QP = 37. (**a**) CU partition by HM 16.5 (r1–r3 are zoomed-in demonstration); (**b**) CU partition by CNN-CBAM (r4–r6 are zoomed-in demonstration). Figure 9: Subjective results for Frame 3 of four sequences on QP = 37. (**a**) Traffic; (**b**) Cactus; (**c**) BQ Mall; (**d**) Kristen & Sara.
## 7 Conclusions  
NS of the BAUV, which uses only the dead reckoning method based on the course and speed measurements, determines the position coordinates which error has quickly built up over time. In a study, after 500 s (corresponding to travelling 500 m) the average error of the position coordinates reached a value of 127.7 m (Figure 5).  
In turn, the USBL system ensures high accuracy of determining the position coordinates, though only at short distances from the transceiver. In studies, at a distance of 500 m, the average error of determining the position coordinates will reach a value of 26.2 m (Figure 3).  
Combining the EKF measurement method in the calculations carried out by devices installed on the BAUV (log, INS, hydrostatic pressure sensor) with distance and direction measurements carried out in the USBL system allows for obtaining the position coordinates significantly more accurate. This happens mainly because the position coordinates obtained are based on low accurate COG and SOG measurements and may be adjusted every now and then by more precise distance measurements of r carried out by the USBL system. This is confirmed by the full results of the three tests presented in the article.  
The presented research concerned only method of BAUV positioning during undisturbed motion along the straight line. The obtained results are based only on simulated measurements. Further research will focus on tests in real environment. Sea currents and waving of sea will be taken into consideration. Furthermore, positioning accuracy of BAUV will be assessed during movement along composed trajectories including straight sections as well as curves lines.  
The work presented in the paper was carried out in equal contribution of all authors. [PERSON] conceived the concept and idea. Authors together implemented software and carried out the experiments. The manuscript was also written as a common work.  
The authors declare no conflict of interest.
## 8 Conclusions  
In this paper, a simulation-based performance characterization of the DTN architecture and protocols over the primary and secondary cislunar relay architecture is presented. The focus was on data transmission from spacecrafts to Earth ground stations over the end-to-end cislunar communication path. The investigated DTN protocol configurations are roughly ranked according to their goodput performance. Based on the investigation results, LTP LTP TIP PRIM (i.e., running LTP over both link segments of the primary cislunar relay path) shows a significant performance advantage over any other DTN protocol configurations at all cislunar link delays (1200 ms-5000 ms) and noise levels with a BER of 0-10\({}^{-5}\). For the transmission over the secondary relay path, LTP TCP SEC (i.e., running LTP over the long link segment and TCP over the short link segment of the secondary relay path) has an overall performance advantage over LTP UDP SEC (i.e., running LTP over the long link segment and UDP over the short link segment of the secondary relay path). However, the performance advantage is significant only over less lossy cislunar channels with a BER \(\leq 10^{-6}\) accompanied by a short link delay (<2000 ms) and over lossy cislunar channels with a BER = 10\({}^{-5}\) accompanied by a long link delay (\(\geq\)2000 ms). In all other investigated cislunar communications scenarios, the goodput advantage of LTP TCP SEC over LTP UDP SEC is not considered significant.  
UDP LTP PRIM show the lowest goodput amongst all the DTN protocol configurations for link delays up to 4000 ms and a BER \(\leq 10^{-6}\). When a higher BER and/or a longer link delay are involved during transmission, LTP UDP SEC has the lowest goodput. UDP LTP PRIM shows a consistent goodput performance of around 1500 bytes/s at all the experimented link delays and channel error rates, implying that its performance is not affected by link delay and channel noise. In comparison, LTP UDP PRIM and LTP TCP SEC show obvious performance degradation with the increase in link delays at all three error rates.  
The performance differences of the protocols are mainly caused by their design and operation differences, which are also investigated.  
**Author Contributions:** Conceptualization, R.W.; methodology, R.W. and D.W.; software, D.W.; validation, D.W. and R.W.; formal analysis, D.W. and E.W.; investigation, D.W., E.W. and R.W.; writing--original draft preparation, D.W. and E.W.; writing--review and editing, E.W. and R.W.; visualization, E.W.; supervision, R.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.