esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge of Quantum Hidden Variables Enables Backwards-In-Time Signaling
## 2 The Protocol
Note that quantum complementarity forces [PERSON] to choose one of these two possibilities: she cannot perform the measurements connected to both choices since they do not commute. The accessible information of the ancient [PERSON] is one bit, which has been transferred to her from the recent [PERSON]. Indeed, the bit that the recent [PERSON] wants to transfer to the past is stored in some other degree of freedom \(f\) (see below for a detailed discussion), and the mutual information between this bit and the bit uncovered by the previous [PERSON] is equal to  
\[I(b:f)=H(b)+H(f)-H(b,f),\]  
where \(H(b)=H(f)=1\) (the bits can have both values with equal probability) and \(H(b,f)=1\) (the bits are correlated, as explained in the protocol above). The measurements in point 3 of the protocol can be performed in the following way. The joint measurement of \(\sigma_{1x}\sigma_{2x}\) and \(\sigma_{1y}\sigma_{2y}\) can be performed through a measurement that has Bell states as eigenstates. Indeed, consider \(|a\rangle,|b\rangle\) eigenstates of \(\sigma_{x}\) and \(|\pm\rangle=(|a\rangle\pm|b\rangle)/\sqrt{2}\) eigenstates of \(\sigma_{y}\), then  
\[ \begin{split}|aa\rangle+|bb\rangle&=|++\rangle+|-- \rangle:\sigma_{1x}\sigma_{2x}=+1,\;\sigma_{1y}\sigma_{2y}=+1\\ |aa\rangle-|bb\rangle&=|+-\rangle+|-+\rangle: \sigma_{1x}\sigma_{2x}=+1,\;\sigma_{1y}\sigma_{2y}=-1\\ |ab\rangle+|ba\rangle&=|++\rangle-|--\rangle: \sigma_{1x}\sigma_{2x}=-1,\;\sigma_{1y}\sigma_{2y}=+1\\ |ab\rangle-|ba\rangle&=|+-\rangle-|-+\rangle: \sigma_{1x}\sigma_{2x}=-1,\;\sigma_{1y}\sigma_{2y}=-1\\ \end{split}  \tag{2}\]  
Analogously, the joint measurement of \(\sigma_{1x}\sigma_{2y}\) and \(\sigma_{1y}\sigma_{2x}\) can be performed by the measurement that has the following two-spin maximally entangled states as eigenstates:  
\[ \begin{split}|a+\rangle+|b-\rangle&=|+a\rangle+|- b\rangle:\sigma_{1x}\sigma_{2y}=+1,\;\sigma_{1y}\sigma_{2x}=+1\\ |a+\rangle-|b-\rangle&=|+b\rangle+|-a\rangle: \sigma_{1x}\sigma_{2y}=+1,\;\sigma_{1y}\sigma_{2x}=-1\\ |a-\rangle+|b+\rangle&=|+a\rangle-|-b\rangle: \sigma_{1x}\sigma_{2y}=-1,\;\sigma_{1y}\sigma_{2x}=+1\\ |a-\rangle-|b+\rangle&=|+b\rangle-|-a\rangle: \sigma_{1x}\sigma_{2y}=-1,\;\sigma_{1y}\sigma_{2x}=-1\\ \end{split}  \tag{3}\]  
Both of these observables are incompatible (do not commute) with the measurement of the single spins, represented by the observables \(\sigma_{1x}\otimes\mathds{1}_{2}\), \(\mathds{1}_{1}\otimes\sigma_{2x}\), etc. Thus, textbook quantum mechanics does not lead to paradoxes, namely either the local values are defined _or_ the joint values are determined by the measurements defined in Equations (2) and (3). The Peres-Mermin argument shows that an apparent paradox arises if one can determine both the local values (through the spin hidden variables) and the joint values. Our protocol hinges on this.
## 4 Discussion
### Convolutional Neural Network Training Set Extension  
By comparing the expected FOM value of CNN classification with the FOM value of the PSD method, it can be seen that the CNN classification method is better than the PSD method. Additionally, the dataset can be further expanded based on the calculated expected values.  
In practical applications, payloads will encounter extreme high and low temperatures; in addition, front-end sensors operating in orbit for a long period of time will produce corresponding performance degradation, which will have an impact on the back-end data, and these are difficult to simulate in the ground calibration or test. Thus, the acquisition of in-orbit data can further expand the coverage of the training sample set and improve the robustness and generalization of the model. By constructing a waveform library of ground and in-orbit data and iteratively updating the training, the accuracy of the model will be enhanced to a new level.
## 5 Discussion and Conclusions  
Among all compression algorithms considered, the proposed BAQALC algorithm showed the highest compression performance. Although BAQALC's stability was not the best, it was not significantly different from the highest stability performers, e.g., LZW or UHT.  
An overall trend of DNA compression solutions can be inferred from this research. There was a trade-off trend between compression performance and stability performance among related research in DNA data compression. As noted in Section 4.2., the higher the stability coefficient (SD), the lower the stability.  
BAQALC displayed the best performance with lung cancer FASTQ data. This should be taken into consideration for future research, because there is a possibility to improve the performance according to disease type.  
In addition, this solution is designed to operate within a blockchain network, making it immune to hacking and unreliable uploading. The proposed solution is envisioned to contribute to providing an efficient and secure transmission and storage platform for next-generation medical informatics systems regarding DNA.  
The proposed BAQALC solution specifically counters UHT, UHTL, and MUHTL solutions provided by [PERSON] et al., which are methods based on Huffman logic. These two studies are based on the same variants of dictionary coding methods. Although Huffman is a powerful compression method when modified, our results showed that LZW modifications could better serve as compression solutions for DNA FASTQ files.  
This research proposes BAQALC, a lossless compression algorithm that efficiently and securely transmits and stores the immense amount of all DNA sequence data types. Through BAQALC, efficient and reliable transmission and storage of the immense amounts of DNA sequence data will be easier and more reliable, even for complex data such as FASTQ that are being generated by NGS for genomes.  
The limitation of this research is that the stability was low compared to widely-used algorithms. Future research should include reducing the process overload or instability of the algorithm.  
Figure 5: CR and Stability Comparison of All Solutions.  
Furthermore, more specific algorithm modes that are optimized according to disease types should be researched. Lastly, in terms of service rather than research, a reward system should be adopted by public databases in order to facilitate motivation among miners.  
[PERSON] designed the entire research, developed, analyzed, and evaluated the research. [PERSON] mainly contributed to the development of this research. [PERSON] managed the overall research as project manager.  
Korea Technology and Information Promotion Agency: S2601135, Curaum Inc.: CISKRSCGA01.  
This research was government funded by TIPA (Technology and Information Promotion Agency), grant number S2601135. This research was also funded by the Curaum Research Scholarship Grant, grant number CISKRSCGA01.  
There are no conflicts of interest.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 3 Semiflooding Location Service
### Principle  
The main idea of the semiflooding location service is as follows. All nodes in the network periodically broadcast a packet containing their location information with the time when the location was issued. Nodes do not need to be synchronized for this operation, i.e., all nodes' internal times do not need to be aligned to a single time reference, and location packets can even be broadcast at different updating rates if needed. When a node receives a location packet, it first compares the timestamp in the location packet with the one stored in the node's location table. If the incoming information is older than or equal to the locally stored one, the incoming packet is discarded. Otherwise, the new location information is saved, replacing the former information, and 1 in \(q\), the packet is forwarded to the neighbors. The way parameter \(1/q\) is used is twofold. On the one hand, \(1/q\) can be seen as a probability, in which case a random generator can be used to determine whether the packet has to be forwarded. If the value returned by the (pseudo)random generator is smaller than \(1/q\), the packet is forwarded; otherwise, it is not. On the other hand, \(1/q\) can be understood as a threshold for deterministic forwarding. For example, if \(q=3\), only one packet in three is forwarded, i.e., the first two are not forwarded, while the last one is. The value for \(q\) is not necessarily an integer and could be any real number greater than 1. The closer to 1, the higher the frequency of packet forwarding.  
Figure 2 shows how far location packets are propagated at each location update when \(q\) is equal to 2, and a deterministic algorithm is used. In this example, the longest path from the node emitting the location packets to any other nodes is 3. The first location packet (the first arrow clockwise in Figure 2) reaches all nodes in the network, i.e., those that are at a distance of up to three hops from the emitting node. The second location packet (the second arrow in the same direction) only reaches one-hop nodes. The third location packet (and the same goes for packet numbers 7, 11, 15, etc.) reaches nodes at one and two hop(s). The fourth location packet (and any other odd-ranked location packet) only reaches nodes at one hop. Location packet 5 reaches all nodes up to three hops away.  
Figure 2 also highlights the fact that the greater the distance from the node emitting the location update is, the longer the period of time between two location updates. However, for most applications (including routing), the greater the distance between two nodes is, the less accurate the knowledge of the other node's position is. Most of the time, an approximate position is more than enough, the accurate location of the destination node becoming increasingly important as a data packet gets  
Figure 2: Semiflooding location service.  
closer to the destination node. As a result, only significant changes in node locations should be reported, and this is exactly what the semifiododing location service does.
## 4 Results and Discussion
### Flow Structure and Thermal Fields
#### 4.1.2 Flow and Thermal Fields for \(\mathrm{Ra}=5\times 10^{8}\)  
In general, the structure of the flow becomes more complex compared to the case of \(Ra=5\times 10^{7}\). The turbulent regime is well established, represented by a multitude of closed cells of different sizes. Among these, two large natural convection cells (Rayleigh-Benard type) are located above the blocks occupying the entire upper half of the cavity, Figure 7a, for \(Re=10^{3}\). The jet of fresh air, still weak, bypasses these cells by tracing the long path before leaving the cavity. When the \(Re\) is increased to \(1.5\times 10^{3}\), the right convective cell is split into two. One is relocated towards the right corner of the cavity and the other is lowered into the micro-cavity, Figure 7b. The more \(Re\) increases, the more the forced jet takes over the convective cells at the top of the cavity and the more air is circulating in the micro-cavity. The competition between the natural convection cells and the forced jet driving the latter to laminate the horizontal walls of the blocks, Figure 7c,d, for \(Re=2\times 10^{3}\) and \(2.5\times 10^{3}\). It is also necessary to mention the appearance of small entrainment cells between the fresh air and the horizontal walls of the heat sinks, shielding against heat transfer for high values of \(Re\).  
The isothermal lines are generally very tight along the vertical walls of the heat sinks. The development of thermal boundary layers is observed in these areas. On the other hand, there is a distortion of the isothermal lines around the hot horizontal walls. The joint effect of the separation of the fresh air jet and the drive keys above them is, surely, the cause of poor heat exchange. For \(Re=10^{3}\), Figure 8a, it is noted that the lower zones on the left and right of the two blocks are the sites of thermal stratification presented by practically horizontal isothermal lines. The rest of the cavity is the site of distorted and distant lines testifying to a fully turbulent regime. There are also existing closed isothermal lines. They can indicate the existence of warm puffs of air moving in the cavity. As \(Re\) increases, the stratification mentioned above disappears and the lower zones of the cavity (on the side of the fresh air inlets) become isothermal cold, Figure 8b-d.
## 4 Experiment Results and Discussion
### Dataset Preparation
#### 4.1.3 Umn Dataset  
The UMN dataset [35] has a low complexity compared to the PETS2009 and UCSD datasets due to the stable indoor background scenario. The video recordings from UMN Plaza1 and Plaza2 surveillance cameras were prepared to monitor crowd density.
## 3 Results
### Validation of SSM Estimation Model  
To evaluate the robustness of the SSM estimation model, 81 in situ measurement points were selected to verify the model. The agreement between the estimated SSM values and  
Figure 5: SSM mapping in a sample in Gu’an, Hebei.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Polarization** & **Vegetation Index** & **N** & **Fitting Equation** & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{VV} & NDVI & 91 & SSM = \(0.399-0.367\mathrm{VI}+0.315\mathrm{VI}^{2}+0.855\mathrm{VI}^{4}+0.008\sigma^ {0}\sec\theta\) & 0.565 \\  & NDWI & 91 & SSM = \(0.208-1.159\mathrm{VI}-2.745\mathrm{VI}^{2}+3.67\mathrm{VI}^{4}+0.021\sigma^ {0}\sec\theta\) & 0.576 \\  & & & +0.081\(\sigma^{0}\mathrm{VI}\sec\theta+0.1\sigma^{0}\mathrm{VI}^{2}\sec\theta\) & \\  & MCARI & 91 & SSM = \(0.313+2.506\mathrm{VI}-14.59\mathrm{VI}^{2}+66.472\mathrm{VI}^{4}+0.003\sigma^ {0}\sec\theta\) & 0.588 \\  & & & +0.09\(\sigma^{0}\mathrm{VI}\sec\theta-0.381\sigma^{0}\mathrm{VI}^{2}\sec\theta\) & \\  & NDVI & 91 & SSM = \(0.939-3.45\mathrm{VI}+4.658\mathrm{VI}^{2}+0.136\mathrm{VI}^{4}+0.015\sigma^ {0}\sec\theta\) & 0.336 \\ \multirow{4}{*}{VH} &NDWI & \multirow{4}{*}{91} & SSM = \(-0.072\sigma^{0}\mathrm{VI}\sec\theta+0.102\sigma^{0}\mathrm{VI}^{2}\sec\theta\) & \\  & NDWI & 91 & SSM = \(-0.232-2.72\mathrm{VI}-3.477\mathrm{VI}^{2}+2.884\mathrm{VI}^{4}-0.0000676\sigma ^{0}\sec\theta\) & 0.329 \\ \cline{1-1}  & MCARI & 91 & SSM = \(0.427+4.289\mathrm{VI}-25.37\mathrm{VI}^{2}+89.291\mathrm{VI}^{4}+0.004\sigma ^{0}\sec\theta\) & 0.385 \\ \cline{1-1}  & & & +0.104\(\sigma^{0}\mathrm{VI}\sec\theta-0.503\sigma^{0}\mathrm{VI}^{2}\sec\theta\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimation equations and accuracy of SSM under different polarization and vegetation indices.  
in situ measured SSM values was evaluated on the basis of statistical metrics, i.e., R\({}^{2}\), MAE, and RMSE. The estimated values of the six models were compared with in situ measured SSM, and the R\({}^{2}\), MAE, and RMSE were calculated (Figure 6). In VH polarization, the predicted SSM values were higher than the in situ measured results, and the scattered points were separated from the attachment of the 1:1 line. The predicted results of VV polarization were close to the in situ measured SSM values, and most of the scattered points were located near the 1:1 line.  
By comparing the R\({}^{2}\), MAE, and RMSE of the six SSM estimation models with the measured values, it can be seen that the combined effect of VV polarization and MCARI is the best. As seen in Figure 6c, the best performance is the combination of VV polarization and MCARI, with RMSE, MAE, and R\({}^{2}\) of 0.028 cm\({}^{3}\)/cm\({}^{3}\), 0.023 cm\({}^{3}\)/cm\({}^{3}\), and 0.867, respectively. Compared to the republished research [42] using single RS data, the method of combining the S1 and S2 images acquired higher accuracy. In summary, we chose MCARI and VV polarization to estimate SSM in our study.
# Antimicrobial Volatiles of the Insect Pathogen _Metarhizium brunneum_
## 2 Materials and Methods
### Evaluation of VOC Antimicrobial Activity  
Fourteen _M. brunneum_ VOCs, validated using authentic standards (Supplementary Table S1), were tested against a range of rhizosphere and non-rhizosphere microbes. Details of the test organisms, strains, source and ecological relevance are summarised in Supplementary Table S2. The antimicrobial activity of _M. brunneum_ VOCs was determined using the disc volatilisation and radial growth assays. Pure compounds from commercial sources were used for these and subsequent studies (Supplementary Table S1).  
The disc volatilisation assay was based on the methodology described by [34]. Briefly, test bacteria and _Candida_ yeast were grown on Luria Bertani Agar (LBA; pH 7.5) and SDA, respectively. The cultures were incubated at 35 \({}^{\circ}\)C for 24 h and fresh inoculum was prepared by diluting the bacteria and yeasts in sterile PBS, pH 7, and adjusted to the 0.5 McFarland standard. Bacterial and yeast cell densities were measured in a spectrophotometer at 500 nm and 530 nm wavelengths, respectively [35; 36]. The freshly prepared bacteria and yeasts were inoculated onto LBA and SDA media, respectively. An 8 mm diameter filter paper disc was placed on a coverslip attached to the inside of a Petri dish lid and loaded with 10 \(\upmu\)L of the volatile compound before inverting the culture plate over the lid and sealing with two layers of Parafilm. After 24 h of incubation at 35 \({}^{\circ}\)C, the diameter of the clear (inhibition) zone was measured in millimetres. Blank discs served as negative controls. To determine whether the microbes could recover, the paper disc was removed and the plates were ventilated for 15 min and incubated for another 24 h at 35 \({}^{\circ}\)C. There were four replicates per treatment and the experiment was repeated two times.  
The radial growth assay was adapted from the method described by [37]. The filamentousomycete (_P. ultimum_) and fungi (_F. gramitearum_, _B. cinerea_) were grown on PDA at 27 \({}^{\circ}\)C for 7 days. Plugs (8 mm diameter) were removed from the centre of the unseeded PDA using a sterile cork borer and replaced with mycelial plugs of the actively growing test fungus. The inoculated plates were treated with the volatile compounds as described above and incubated at 27 \({}^{\circ}\)C with the colony diameters being measured (in millimetres) 5 days post-exposure. For the control, a blank paper disc was used. The percent growth inhibition (%GI) was calculated using the formula: %GI = (R1 \(-\) R2)/R1 \(\times\) 100 where R1 is the radial growth of the control and R2 is the radial growth of the treated oomycete/fungi. The inhibition of these organisms was compared with that of the EPF _Metarhizium anisopliae_ ARSEF 5469, obtained from the USDA ARS culture collection.
## 2 System Design and Configuration
### Rating and Specifications of the Solar Panel  
The solar panel is rated at \(P_{pv}\) = 420 W peak, with each module comprising 60 series-connected cells. This configuration results in an open-circuit voltage (\(V_{oc}\)) of 72 V and a rated short-circuit current (\(i_{sc}\)) of 7.84 A. The maximum power point voltage (\(V_{mp}\)) is determined to be 29 V, which corresponds to 40% of \(V_{oc}\). Similarly, the rated current (\(i_{mp}\)) at maximum power is observed to be 7.34 A, representing 93% of \(i_{sc}\). To ensure that the maximum voltage at the optimal power point (\(V_{mpp}\)) remains 58 V, the required number of modules (\(N_{s}\)) in series is calculated at \(N_{s}\) = 2 as follows:  
\[N_{s}=\frac{V_{mpp}}{V_{mp}} \tag{1}\]  
The root mean square (rms) current at the maximum power point (MPP) is calculated at \(i_{mpp}\) = 7.24 A given by Equation (2). Therefore, following Equation (3), one module in parallel is sufficient to achieve the rated \(i_{mpp}\):  
\[i_{mpp}=\frac{P_{pv}}{V_{mpp}} \tag{2}\]  
\[N_{p}=\frac{i_{mpp}}{i_{mp}} \tag{3}\]  
Using Equations (1)-(3), the design of the solar PV array is formulated by employing a perturb and observe (P&O) MPPT algorithm.
## 4 Experimental Evaluation
### Localization and timing experiment  
We have carried out an analysis of the GPS performance. The GPS selected is able to provide the current GMT time, latitude and longitude. We tested the whole GPS system for a coldstart configuration. A coldstart is when the GPS has been powered off for a long period and then it is switched on. At this moment, the GPS needs to find the satellite's position to locate itself. The first data the GPS obtains is the satellite connection and the current time. Typically, 5 to 20 seconds are needed for the timing acquisition. After this first period, the GPS obtains the satellite's positions and once different satellites have been located, the GPS is able to obtain its own location. A period of 2 minutes on average is needed for localization after a coldstart. Once located, the GPS updates its position every second.
## 4 Empirical Results
### Generating Developer Metrics
#### 4.1.2 Fusion Metrics  
RQ3: Can new metrics be obtained by combining similar properties or activities?  
In our results, we observed that some metric groups came to the forefront, especially issue related metrics. New metrics can be proposed by grouping comments, code contributions, or other common feature metrics. Fusion metrics were created from combinations of single metrics.  
1. _[PERSON]'s metric:_ is mentioned previous section. It is metrics from similar study [5].
2. _code_contributions:_ is created from the sum of all code contribution-related metrics.  
\[\textit{code\_contributions}=\textit{pr\_opened}+\textit{issue\_opened}+\textit{issue\_hasPR}+ \textit{pr\_merged}+\textit{commit\_committed} \tag{9}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Metric** & **Definition** \\ \hline
1 & issue\_opened & Number of issue opened \\
2 & issue\_commented & Number of comment to issue \\
3 & issue\_closed & Number of issue closed \\
4 & issue\_hasPR & Number of issue that has a PR \\
5 & issue\_assigned & Number of issue assigned \\
6 & commit\_commented & Number of comment to commit \\
7 & commit\_authored & Number of authorship in commit \\
8 & commit\_committed & Number of commit \\
9 & pr\_opened & Number of pull requests \\
10 & pr\_merged & Number of PR merged \\
11 & pr\_assigned & Number of PR assigned \\
12 & pr\_commented & Number of comment to PR \\ \hline \hline \end{tabular}
\end{table}
Table 8: Single developer metrics definitions.  
3. _comments_: is created from the sum of all comment-related metrics. \[\text{\emph{comments}}=\text{\emph{issue\_commented}}+\text{\emph{commit\_commented}}+ \text{\emph{pr\_commented}}\] (10) 4. _issue_related_: is created from the sum of all issue-related metrics. \[\text{\emph{issue\_related}}=\text{\emph{issue\_opened}}+\text{\emph{issue\_ hasPR}}+\text{\emph{issue\_commented}}+\text{\emph{issue\_assigned}}\] (11) 5. _pr_related_ is created from the sum of all PR-related metrics. \[\text{\emph{pr\_related}}=\text{\emph{pr\_opened}}+\text{\emph{pr\_merged}}+ \text{\emph{pr\_closed}}+\text{\emph{pr\_assigned}}\] (12) 6. _commit_related_: is created from the sum of all commit-related metrics. \[\text{\emph{commit\_related}}=\text{\emph{commit\_commented}}+\text{\emph{commit\_ authored}}+\text{\emph{commit\_committed}}\] (13) 7. _commit2 comment_ is created from the (_commit_committed_ divided by _commit_commented_) \[\text{\emph{commit\_2 comment}}=\frac{\text{\emph{commit\_committed}}}{ \text{\emph{commit\_commented}}}\] (14) 8. _issue2 comment_ is created from the (_issue_opened_ divided by _issue_commented_) \[\text{\emph{issue\_2 comment}}=\frac{\text{\emph{issue\_opened}}}{ \text{\emph{issue\_commented}}}\] (15) 9. _pr2 comment_ is created from the (_pr_opened_ divided by _pr_commented_) \[\text{\emph{pr\_2 comment}}=\frac{\text{\emph{pr\_opened}}}{\text{\emph{pr\_ conmented}}}\] (16) 10. _code2 comment_ is created from the ratio of two fusion metrics (_contribution_ divided by _comment_) \[\text{\emph{code2 comment}}=\frac{\text{\emph{contribution}}}{\text{\emph{comment}}}\] (17)