esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Case Study  
This study focuses on the Sinu River basin, encompassing a drainage area of 13,952 km\({}^{2}\) and a length of 437.97 km (see Figure 1). The basin covers territories in three Colombian departments--Antioquia, Sucre, and Cordoba--ultimately discharging into the Caribbean Sea waters in the municipality of San Bernardo del Viento. The datasets used in this research were sourced from the limigraphic station on the Sinu River known as the \"Monderia Automatic\" station. This station is located at latitude 8.751611111 and longitude \(-\)75.8924166, with the station code 13067020. It is operated by the Colombian Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) [13]. The limigraphic station includes the hydrological effects caused by the Uria I Hydropower Plant, which is situated in the northwestern region of Colombia, approximately 30 km south of the municipality of Tierralta in the department of Cordoba. The dam consists of a central core of clayey gravel, standing at a height of 73 m. Its spillway, constructed of concrete, has a maximum discharge capacity of around 9000 m\({}^{3}\)/s.
## 4 Conclusions and Discussion  
We proposed a new approach for precise and safe cell transportation with a robot-aided HOT system and a microtool fabricated with 2 PP nano printing. A control strategy combined with the imaging processing algorithm was developed for automated cell transportation. Three optical traps were employed to push cells through transporting and rotating the microtool with the control strategy and real-time visual feedback. Experiments with the yeast cells were performed to demonstrate the effectiveness of the proposed approach. Under the manipulation of the optical tweezers and microtool, the yeast cell was pushed to the desired positions by following straight paths with high precision. Experiments with fluorescence microscopy verified that indirect manipulation could induce less exposure to the cell compared with direct optical manipulation.  
Figure 13: Indirect optical manipulation induced less photobleaching. (**a**) One cell was trapped by two microtools for fluorescence imaging. (**b**) Plot of time-dependent change of the fluorescence intensity under direct and indirect optical manipulation. (**c**) Fluorescent images of the mitoTracker Red probe stained cell during the indirect optical manipulation. (**d**) Fluorescent images of the mitoTracker Red probe stained cell during the direct optical manipulation.  
A limitation of the microtool-based method is that the microtool is relatively large compared with the precious workspace of the HOT. This limitation may cause the following problems: (1) The microtool is difficult to directly apply in crowded cell manipulation environments. Particularly, when a larger microtool is used to transport mammalian cells, the microtool and cell will occupy a large part of the workspace of the HOT, and the transportation space is squeezed. (2) The large size results in difficult manipulation of non-spherical cells by controlling multiple microtools cooperatively. Our future work will address the optimization of the shape and size of the microtool and the development of additional intelligent control and path planning algorithms for transportation and rotation of more cell types. In addition, specific biological applications of our approach will be studied.  
S.H. and H.X. contributed equally to this work. Conceptualization, S.H., H.X., S.C., and D.S.; methodology, S.H., H.X., and T.W.; software, S.H. and H.X.; validation, S.H., H.X., T.W., S.C., and D.S.; formal analysis, S.C. and D.S.; investigation, S.H. and H.X.; resources, S.H. and D.S.; data curation, S.H. and H.X.; writing--original draft preparation, S.H., H.X., and S.C.; writing--review and editing, S.H. and S.C.; visualization, S.C.; supervision, D.S.; project administration, S.H.; funding acquisition, S.H. and D.S.  
This research was funded by the National Natural Science Foundation of China, grant number 61803270; Science Fund for Creative Research Groups of National Natural Science Foundation of China, grant number 51821093; Research Grants Council of the Hong Kong Special Administrative Region, China, grant number CityU 11209917.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### TG-DSC Examination  
Figure 7 shows the TG-DSC curves of the mortar specimens with different lithium mica slag contents at an age of 180 days. The temperatures of 80-120 \({}^{\circ}\)C and 390-470 \({}^{\circ}\)C correspond to the decompositions of AFt (3 CaO\(\cdot\)Al\({}_{2}\)O\({}_{3}\)-3 CaSO\({}_{4}\)-32H\({}_{2}\)O) and calcium hydroxide (Ca(OH)\({}_{2}\)), respectively. As shown in Figure 7, AFt and Ca(OH)\({}_{2}\) are present, which is consistent with the results of XRD analysis.  
Figure 8 shows the quantities of AFt and Ca(OH)\({}_{2}\) of the mortar specimens with different lithium mica slag contents at 28, 150, 180, and 240 days. The results show that the amount of AFt increased with an increase in lithium mica slag content before 150 days. The content of AFt decreased in nearly all of the mortar specimens after 150 days, but the content of AFt was the highest in the mortar specimen with 30% lithium mica slag. This demonstrates that the addition of lithium mica slag promoted the formation of AFt. The  
Figure 6: XRD analyses of mortar specimens with different lithium mica slag contents at different ages.  
Figure 7: TG–DSC analyses of the mortar specimens with different lithium mica slag contents at 180 days.  
content of Ca(OH)\({}_{2}\) was almost the same in all mortar specimens at 28 days. Additionally, mortar specimens with a lithium mica slag dosage exceeding 20% exhibited a significantly lower content of Ca(OH)\({}_{2}\) compared to those with a dosage of no more than 10% in the later stages. The presence of SO\({}_{4}\)\({}^{2-}\) in the mortar specimens doped with lithium mica slag resulted in the production of more AFt. This production, however, requires the consumption of Ca(OH)\({}_{2}\), resulting in a decrease in the amount of Ca(OH)\({}_{2}\) present in the mortar specimens. This trend was especially evident in mortar specimens doped with lithium mica at a content of 20% or higher.
## 7 Conclusions  
In this paper, we have summarized the latest research activities on the emerging field of RIS-assisted NTN-based 6G communications. By addressing critical user needs such as enhanced security, energy efficiency, expanded range, and high data rates, the integration of RISs has been demonstrated as a key enabler of future wireless communication systems. The synergy between RISs and MIMO systems, as well as advanced radio communications, highlights the importance of AI and ML in optimizing RIS-based beamforming. These technologies work together to solve pressing scientific and engineering challenges while promoting sustainability in NTN operations. The findings in this paper emphasize the pivotal role RISs will play in unlocking the full potential of NTNs, driving the evolution of next-generation wireless technologies. By shedding light on the benefits of RISs for 6G NTNs and proposing future research directions, this work offers a comprehensive understanding of how RISs can revolutionize the field and catalyze the development of highly efficient and innovative wireless communication systems.  
Conceptualization, C.E.W., F.A.K., Q.Z.A. and T.A.; Methodology, C.E.W., F.A.K. and Q.Z.A.; Software, C.E.W.; Validation, C.E.W.; Formal analysis, C.E.W.; Investigation, C.E.W.; Writing--original draft, C.E.W., F.A.K., Q.Z.A., P.S. and T.A.; Writing--review and editing, P.S. and T.A.; Supervision, F.A.K. and Q.Z.A.; Funding acquisition, P.S. and T.A. All authors have read and agreed to the published version of the manuscript.  
Part of this work is supported by the European Union through the Horizon Europe Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 101086218.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
Sixth Generation 6G Artificial Intelligence AI Base Station  BS Deep Deterministic Policy Gradient DDPG Deep Learning DL Deep Q-Network DQN Deep Reinforcement Learning DRL Deep Space Network DSM EM Extremely High Definition F Full-Duplex FB-Altitude Platform Integrated Satellite IS Internet of Things IoT Internet of Vehicles IoV Line of Sight LoS Low Earth Orbit LEO Machine Learning ML
## 2 Data and Methods
### Satellite Data
#### 2.2.4 Other Sources of Data  
Copernicus is a collaborative Earth observation initiative supported by the European Space Agency and EUMETSAT, offering comprehensive global Earth observation data that include marine data. The data are accessible to the public and scientific community through the Copernicus Marine Environment Monitoring Service (CMEMS), which provides ocean observation and prediction products for various oceanic parameters and indicators across the world's oceans. Additionally, topographic data were sourced from GEBCO2023, the General Bathymetric Chart of the Oceans, while marine disaster data for Zhejiang Province were obtained from the Department of Natural Resources of Zhejiang Province. A detailed list of the sources of oceanic and atmospheric data, along with relevant information, can be found in Table 4.
## 4 Simulation Results and Attack Test
### Time Efficiency of the Encryption Scheme  
In addition to security, time efficiency is also an important indicator for an encryption scheme. A practical encryption should strike a balance between the aspects of security effect and time efficiency, which indicates that it is expected to have both time and effect advantages. Many different criteria have been proposed in recent years; however, nearly all these time-evaluating methods show their drawbacks in diverse situations, and to make our judgment of the time efficiency more comprehensive, the encryption time, encryption throughput (ET), and number of cycles were applied into the assessment of the time efficiency of our algorithm. ET is negatively correlated with the complexity of the algorithm, while the number of cycles shows the positive correlation with the computational complexity. The calculation formula for the ET and the formula for the number of cycles is displayed below:  
\[\text{ET}=\frac{image_{size}(byte)}{encryption_{time}(second)} \tag{10}\]  
\[\text{Number of cycles per byte}=\frac{CPU_{speed}(Hertz)}{ET(byte)} \tag{11}\]  
In this paper, the Lena image with size \(256\times 256\) is adopted for the test case, and 100 times encryption is executed for calculating the average time of our algorithm.  
Table 6 shows the encryption time of our algorithm and some other algorithms in [33,37,38], and the comparison of the EF and the number of cycles is revealed in Table 7. From the table we can see that the algorithm we proposed performs with better time efficiency and consumes fewer machine cycles. Compared with the block-based scheme in [38], our scheme improved the security with the exchange of the speed, while the time efficiency is enhanced from the comparison with the pixel-level algorithm in [33] and the bit-level algorithms in [21,37]. Considering the results from the comparison, our algorithm performed excellently in both time efficiency and security aspects.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Entropy** \\ \hline Lena & 7.4486 \\ Encrypted Lena & 7.9971 \\ Cameraman & 7.1047 \\ Encrypted Cameraman & 7.9974 \\ Pepper & 7.5936 \\ Encrypted Pepper & 7.9969 \\ Baboon & 7.3739 \\ Encrypted Baboon & 7.9972 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The information entropy of the variant images.
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 4 PDS-Based Solution for IoT Privacy
### GDPR Compliance  
The PDS promotes benefits for personal data privacy. However, it also poses challenges concerning GDPR compliance, especially regarding assigning responsibilities and determining the legal bases for data processing. [PERSON] et al. [57] reviewed some available PDS platforms and determined the roles of users, platforms, and app developers.  
In Section 3.3.2, we reviewed the GDPR concepts controllers and processors. In a centralized context, their definitions are straightforward, but in a PDS environment, the boundaries between these roles are unclear, making it more challenging to assign them. For instance, in this context, users have great control over their data, deciding who can access it. Consequently, they can be considered controllers of their data. However, to fully assume this responsibility, users need to understand and manage all aspects of privacy and security and know the GDPR implications, which is unlikely. Thus, PDS platforms and app developers should probably share the controller role and be co-responsible with data users.  
Another technical challenge for PDS is related to the legal basis for data processing. The PDS relies primarily on explicit user consent for its data processing. GDPR, however, recognizes additional legal bases for processing, such as legitimate interest, legal obligations, and contractual obligations.  
[PERSON] et al. [57] understand that the advancement of PDSs depends not only on technological development but also on regulatory progress adapted to this decentralized reality, clarifying the definitions of the roles of stakeholders as well as their responsibilities and obligations in data processing.
## 5 Results and Discussion  
### The Results of the Calibration Flight  
#### 5.1.1 Results of Adaptive Interference Cancelation (AIC)  
As an important reference, the AIC method is often used in advance to eliminate the vibration interference magnetic field of a helicopter, and the results are calculated by using the method shown in Figure 8. The blue dot and black solid line represent the compensated field before and after the AIC method, respectively, and the black line is a clear offset of 1 nT. In Figure 8, the vibration interference magnetic field is weakened to a certain extent, after using the AIC algorithm. In order to display the effect of elimination to a more visible extent, the algorithm effect is displayed using a time-frequency diagram, as shown in Figure 9.  
In Figure 9, the interference fields at multi-frequency points still exist after the proposed method. Therefore, this AIC algorithm had a very limited effect on reducing the interference that was caused by the helicopter vibration.  
Figure 7: Block diagram of the improved coherent noise suppression (ICNS) method.
## 5 Experimental Results
### Performance Metrics
#### 5.2.2 Quality Ratio  
Quality ratio refers to a quantitative factor that controls how efficiently the program uses its parameters to produce high performance. The users who pass the tests on the basic cybersecurity topics are further tested on advanced cybersecurity themes. However, in these calculations, we use the following equation of the quality ratio:  
\[Q=\frac{N_{t}}{T}\times 100\% \tag{39}\]  
where \(Q\) is the quality ratio, \(N_{t}\) is the total number of steps used to solve advanced tasks, and \(T\) is the total time required to solve these tasks.  
This ratio shows the final cybersecurity awareness levels of the tested users. Consequently, we analyze the correlation between the time and the quality ratio. The result demonstrates in Figure 4b that the proposed WBCA has a 99.97% quality ratio, whereas the contending programs have a 98.08-99.38% quality ratio. N-TISM has a lower quality ratio. The main reason for not completing tasks is that the contending programs are affected due to malicious threats compared to the proposed WBCA program.
## 3 Data Analysis
### Label Sentiment  
We performed sentence-level sentiment analysis on our dataset by using an off-the-shelf sentiment model ([[https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)]([https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)) (accessed on 23 June 2023)). For each emotion, we count every positive and negative sentence labeled with that emotion. The resulting ratio is shown in Figure 5 with a summary of the most positive and negative emotions in Table 7.  
Again, the results are mostly common sense. Where they are immediately obvious, it is easier to understand in conjunction with the label correlations in Figure 4 and by keeping in mind the definitions in Table 1. In Section 3.2 we discussed the use of contrast results in low negative correlation for emotions that should be opposites by common sense reasoning. In part, this helps explain why the sentiment of emotions is generally not more extreme  
\begin{table}
\begin{tabular}{l l l c} \hline \hline
**Highest Correlation** & & \multicolumn{2}{c}{**Lowest Correlation**} \\ \hline despair & sadness & optimism & pain \\ calmness & relief & annoyance & optimism \\ fear & nervousness & approval & frustration \\ anger & annoyance & frustration & gratitude \\ excitement & joy & disappointment & optimism \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of emotion correlation: highest and lowest correlated emotions. The results are very similar to GoEmotions.  
Figure 5: Sentiment of emotions: ratio of positive and negative sentiment sentences labeled with each emotion in our dataset.  
\begin{table}
\begin{tabular}{l r l r} \hline \hline  & **Most Positive** & \multicolumn{2}{c}{**Most Negative**} \\
**Emotion** & **Positive (\%)** & **Emotion** & **Negative (\%)** \\ \hline admiration & 92 & frustration & 88 \\ approval & 91 & boredom & 86 \\ optimism & 90 & despair & 86 \\ trust & 89 & annoyance & 83 \\ joy & 89 & pain & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of emotion sentiment: most positive and most negative emotions. Most positive and most negative emotions match common sense expectations.  
(i.e., closer to 100% negative or positive). The emotion that jumps to our attention is \"envy\". At first, by common sense, it seems like it ought to be a more negative emotion, but the relatively high correlation with \"admiration\" helps explain why it is not: admirable things are enviable, but that is not necessarily negative. For example, \"Even the pattern from which she is working, the silk, the gold, the lawn, made happy by her touch, are sanctified, are envied\".
## 3 Results
### Calculation Method of Wave Crest Height and Panel Impact Force
#### 3.2.2 Calculation Method of Panel Wave Impact Force
Compared with other influencing factors, the overall change trend was weak, especially in its long period; the impact force had little change. Due to the interaction between various factors, they cannot be considered separately. Therefore, in this study, the wave steepness \(H/L\) and relative superelevation \(\Delta h/H\) take two exponential function relations, which are considered together. At the same time, the influence of the transverse and longitudinal beam structure, plate width, and water depth is calculated by introducing the coefficient calculation method. According to the analysis of the variation relationship between the above impact force and various influencing factors, and using the multiple linear regression method to determine the coefficients of various physical parameter terms, the following equations for the maximum impact force of the wave uplift force of the new barrel-pile foundation compound structure wharf, reflecting various influencing factors, can be used:  
\[\frac{P}{X\gamma H}=K_{1}\bigg{(}1-\frac{\Delta h}{\eta}\bigg{)}^{0.75}exp \left[-K_{2}\bigg{(}\frac{\Delta h}{H}-0.16\bigg{)}^{2.86}\right] \tag{12}\]  
\[X=\frac{L}{4}\bigg{(}1.3-\frac{\Delta h}{H}\bigg{)}^{2} \tag{13}\]  
Figure 6.0 \(B/H\) and \(P/\gamma H_{i}\) change relationship. Figure 6.1  
Figure 6.2  
where \(P\) is the maximum impact force of the wharf panel; \(\eta\) can be obtained from Equation (10) above; \(X\) is the representative width of the impact in the wave propagation direction, which can be obtained from Equation (13); when the calculated value of \(X\) is greater than plate width b, \(X=B\); \(k_{1}\) is the impact coefficient of the transverse longitudinal beam under the wharf, which can be obtained from Equation (14); and \(K_{2}\) is the impact coefficient of the plate width and water depth, which can be obtained from Equation (15).
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 3 Upstream Optimum ONU Operating Conditions  
In performing the numerical simulations over a 25 km SSMF, an IMDD PON theoretical model developed and verified in [6] was adopted, where the procedure detailed in [25] was used to simulate the optical OFDM signal generation, nonlinear transmission, and direct detection.  
Figure 1: (**a**) DFT-spread hybrid OFDM–DFMA PON. (**b**) A schematic example of the ONU frequency planning and digital filter frequency responses for an excess of bandwidth of \(\alpha=0\). SC: subcarrier; DFT: discrete Fourier transform; M\(\uparrow\): up-sampling factor; DAC/ADC: digital-to-analogue/analogue-to-digital converter; IM: intensity modulator; OC: optical coupler; SSMF: standard single-mode fibre; VOA: variable optical attenuator; PD: photodetector; S/P: serial-to-parallel conversion; Equal: equalization; ONU: optical network unit; OLT: optical line terminal; DSP: digital signal processing; SW: subwavelength; CH: channel.
## 3 Results
### Special Role of PRI in the SIF-GPP Relationship for Corn and Soybean
#### 3.2.2 Relationships between PRI and SIF for Corn and Soybean  
Due to the importance of SIF and NPQ as key energy dissipation pathways other than PQ, it is crucial to comprehend the potential association between SIF and NPQ in order  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Crops** & **Timescale** & **Linear Model** & **R\({}^{2}\)** & **RMSE** & **r** & \(p\) \\ \hline \multirow{3}{*}{Corn} & Half-hourly & \(GPP_{pre}=8.87\times SIF_{t}+20.29\) & 0.48 & 10.22 & 0.69 & \textless{}0.01 \\  & Daily & \(GPP_{pre}=8.38\times SIF_{t}+13.93\) & 0.49 & 6.38 & 0.70 & \textless{}0.01 \\ \cline{2-7}  & Half-hourly & \(GPP_{pre}=6.45\times SIF_{t}+8.85\) & 0.54 & 9.59 & 0.73 & \textless{}0.01 \\ \cline{2-7}  & Daily & \(GPP_{pre}=6.57\times SIF_{t}+5.23\) & 0.62 & 6.25 & 0.79 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regression relationship between \(SIF_{t}\) and GPP for corn and soybean. \(GPP_{pre}\) represents the predicted GPP.  
Figure 4: Relationships between \(SIF_{t}\) and GPP for corn ((a) for half-hourly data; (c) for daily data) and soybean ((b) for half-hourly data; (d) for daily data). Table 1 lists the corresponding regression coefficients. The color bar on the right indicates the magnitude of PRI.  
to more accurately estimate GPP. NPQ can be represented by PRI using remote sensing methods. At half-hourly and daily timescales, there is a substantial correlation between \(SIF_{t}\) and PRI for corn and soybean (Figure 5). However, PRI has a stronger correlation with \(SIF_{t}\) for soybean (0.63 for half-hourly data and 0.78 for daily data) than that for corn (0.34 for half-hourly data and 0.51 for daily data). It suggests that for soybean, SIF is more prone to be influenced by PRI.  
Additionally, \(SIF_{t}\) and GPP had a lower value at lower PRI (Figure 5) and the slopes of the \(SIF_{t}\)-GPP relationship varied with PRI (Figure 4). It indicates that the allocated energy to GPP and \(SIF_{t}\) fluctuates dynamically with NPQ and the ratio of GPP to \(SIF_{t}\) has a higher value when NPQ decreases. Therefore, the potential fluctuations in energy for NPQ should be taken into account in the \(SIF_{t}\)-based GPP estimation.