esa-sceva/satcom-chunk-collection huggingface dataset:
# Social Milieu Oriented Routing: A New Dimension to Enhance Network Security in WSNs
## 4 Experimental Results
### Performance Analysis  
Figure 3 plots the utilities of the extracted social trust paths with different network scales and different weights of QoT constraints. The weights of QoT parameters are set as \(w_{T}\) = 0.5, 0.75, 0.75, 0.6, \(w_{R}\) = 0.5, 0.25, 0.25, 0.4, and each group corresponds to Weight IDs1, 2, 3, 4, respectively. From the figure, we could see that our QA_OSTP can always find utilities, each of which is not worse than that of MFPB_HOSTP and that of T-MONTE-K. This is because in QA_OSTP, quantum fluctuation is adopted to avoid the local minima and quantum mechanics works with wave functions that can sample different regions of phase space equally well. While for MFPB_HOSTP and T-MONTE-K, although sub-networks are extracted through exhaustive searching before the algorithm execution and Backward_Search scheme is used to estimate whether there exists a feasible solution in a sub-network, due to the intrinsic characteristic of heuristic algorithms, it does not guarantee that the best social trust path will be found. Moreover, with the network scale growing larger, when the social trust path with the maximal utility is not a feasible solution, the heuristic search can hardly find a near optimal solution and usually returns an infeasible one even when a feasible solution exists (e.g., case S3). Thus, in any case, QA_OSTP shows better performance. Specifically, we find that the mean value of utilities of QA_OSTP is 34.06% more than that of MFPB_HOSTP and 66.43% more than that of T-MONTE-K in Figure 3a; 50.65% and 78.10% more in Figure 3b; 50.05% and 89.14% more in Figure 3c; 35.82% and 62.32% more in Figure 3d.  
Figure 4 shows the average execution time of algorithms with different weights of QoT parameters and different network scales. Note that, for MFPB_HOSTP and T-MONTE-K, execution time should include the exhaustive searching time for extracting sub-networks, which is not taken into consideration in [PERSON]. _et al._'s work. From Figure 4, we can see that when the network scale is not large, MFPB_HOSTP, T-MONTE-K, and QA_OSTP perform well and the difference is trivial because the searching space is relatively small. Nevertheless, with the network scale expanding, we can observe that QA_OSTP can outperform MFPB_HOSTP and T-MONTE-K in terms of execution time. This is an interesting result since annealing is a very slow physical process and may be a little time-consuming in statistical mechanics. Since the quantum Hamiltonian in QA_OSTP is approximated with the aid of a Suzuki-Trotter transformation in CPIMC-based QA, quantum tunneling can avoid some unnecessary  
Figure 3: Comparison of path utilities of networks.  
searching in MFPB_HOSTP and T-MONTE-K, which can accelerate the annealing process. Specifically, the average execution time of our proposed algorithm is only 35.90% of that of MFPB_HOSTP and 47.38% of that of T-MONTE-K in Figure 4a; 27.80% and 33.23% in Figure 4b; 28.10% and 37.87% in Figure 4c; 33.19% and 43.16% in Figure 4d.
## Materials and Methods
### Mod17A2 GPP Data  
Remotely sensed GPP data obtained from the MOD17A2 version 6.0 product [37], derived from the MODIS sensor onboard the Terra satellite were used. This product provides 8-day composites data at a 500 m spatial resolution and is available for download in [[https://modis.gsfc.nasa.gov/data/dataprod/mod17.php](https://modis.gsfc.nasa.gov/data/dataprod/mod17.php)]([https://modis.gsfc.nasa.gov/data/dataprod/mod17.php](https://modis.gsfc.nasa.gov/data/dataprod/mod17.php)) accessed on 22 April 2022. These data are calculated based on the concept of light use efficiency [38], through the relation between incident photosynthetically active radiation (PAR), the fraction of photosynthetically active radiation absorbed by plants (FPAR), and the actual light use efficiency (\(\varepsilon\)) of vegetation [38].  
\[\text{GPP}=\varepsilon\times\text{ APAR} \tag{5}\]  
where APAR is the absorbed photosynthetically active radiation, which is calculated as the product between the FPAR--derived from the MOD15A2H product [39]--and the PAR. PAR values are obtained from the Global Modeling Assimilation Office (GMAO) reanalysis [40], set and corresponding to 45% of the total incident solar energy in the visible spectrum (0.4 to 0.7 \(\upmu\)m). In the MOD17A2 product, \(\varepsilon\) values are derived from the attenuation of its maximum value (\(\varepsilon\)max) due to two environmental stresses: (1) minimum temperature, which can inhibit photosynthesis, reducing enzymatic activity, and (2) VPD, because high VPD can reduce stomatal conductance [41].
## 3 Results and Discussion  
### Fuel Characterization of Treatment Samples  
R. PSD and R. Kenaf contained moisture content of approximately 10%. After the ashless and torrefraction processes, all samples showed low moisture content of approximately 1% (Table 1). After torrefraction in all samples, the volatile matter (VM) decreased and the fixed carbon (FC) increased. These results are in line with findings from a previous study [21]. Torrefraction led to a decrease in VMcontent and an increase in FC content. Therefore, the proximate constituents of the torrefied samples became closer to those of the coal samples. In contrast to torrefaction, A. PSD and A. Kenaf produced by the ashless process showed increase in VM and decrease in FC compared to R. PSD and R. Kenaf because of reduced ash contents and impact of ash rejection process. Torrefaction also caused an overall increase in the ash content, although the rates for Kenaf were higher than those for PSD. This could have been due to a loss of organic matter content in the form of VM released during torrefaction. This could result in retention of ash in the solid products, which would lead to an increase in the ash content in torrefied samples [22]. In the ashless samples, the results confirmed that more ash was removed compared to the raw sample before pre-treatment. In coal-fired power plants, ash is a major source of slagging and fouling; therefore, the ashless sample was found to be more stable than the torrefied or raw samples in terms of combustion. However, because the increase in the calorific value was small, the combined ashless-torrefied pre-treatment is determined to be the most effective process. The results demonstrate the alterations in the elemental composition of the pretreated biomass. As expected, there was an increasing trend in carbon content after torrefaction, while oxygen and hydrogen contents decreased. The reduction in the hydroxyl groups with the formation and evolution of water vapor can explain these significant losses [23]. Further illustrations of the changes in the elemental compositions of the samples can be observed in a van Krevelen diagram (Figure 1). R. PSD and A. PSD showed similar values at higher positions, while T. PSD and T.A. PSD exhibited similar values lower than that of R. PSD. This indicates that the ashless process had a negligible impact on PSD, while the torrefaction process had significant impact on the elemental constituents. R. Kenaf had the highest H/C and O/C values, which indicated that it contained the biomass of the lowest quality. In case of kenaf, the ashless process led to improved elemental composition, resulting in A. Kenaf being similar to existing R. PSD. However, A. Kenaf and T.A. Kenaf had lower values compared to A. PSD and T. A PSD. This suggests that when Kenaf is pretreated, carbonization can be active unlike that in pretreated PSD. The elemental analysis of ash indicated that in the case of R.P and T.P, the carbon content increased owing to the reduction in the oxygen and moisture contents in the torrefaction process. However, in the
###### Abstract  
Aroma plays a pivotal role in defining tea quality and distinctiveness, and tea producers have often observed that specific drought conditions are closely associated with the formation and accumulation of characteristic aroma compounds in tea leaves. However, there is still limited understanding of the differential strategies employed by various tea cultivars in response to drought stress for the accumulation of key volatile aroma compounds in fresh tea leaves, as well as the associated metabolic pathways involved in aroma formation. In this study, two widely cultivated tea cultivars in China, Fuding Dabai (FD) and Wuniuzao (WNZ), were examined to assess the impact of mild field drought stress on the composition and accumulation of key volatile aroma compounds in fresh leaves using headspace gas chromatography-ion mobility spectrometry (HS-GC-MS) and headspace solid phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) technologies. Results revealed that drought stress led to a substantial increase in the diversity of volatile compounds (VOCs) in FD, while WNL exhibited a notable rise in low-threshold VOC concentrations, amplifying sweet, floral, fruity, and earthy aroma profiles in post-drought fresh leaves. Through partial least squares discriminant analysis (PLS-DA) of HS-GC-IMS and HS-SPME-GC-MS data, integrating variable importance projection (VIP) scores and odor activity values (OAVs) above 1, 9, and 13, key odor-active compounds were identified as potential markers distinguishing the drought responses in the two cultivars. These compounds serve as crucial indicators of the aromatic profile shifts induced by drought, providing insights into the differential metabolic strategies of the cultivars. Additionally, KEGG enrichment analysis revealed 12 metabolic pathways, such as terpenoid biosynthesis, fatty acid synthesis, cutin, suberine, and wax biosynthesis, and phenylalanine metabolism, which may play crucial roles in the formation and accumulation of VOCs in tea leaves under drought stress. These findings provide a comprehensive framework for understanding the cultivar-specific mechanisms of aroma formation and accumulation in tea leaves under mild drought conditions.  
Camellia sinensis cultivar; drought stress; fresh tea leaves; gas chromatography-ion mobility spectrometry (GC-IMS); headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS); aroma profile; odor activity values (OAVs); odor-active aroma compounds +
Footnote †: journal:  
_Keywords_: _Camellia sinensis_ cultivar; drought stress; fresh tea leaves; gas chromatography-ion mobility spectrometry (GC-IMS); headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC-MS); aroma profile; odor activity values (OAVs); odor-active aroma compounds
## 6 Future Directions
Additionally, new protocols need to be developed, while the existing ones need to be updated to suit the current requirements of smart grid applications [48; 116]. To quantify the quality of a smart grid, several key metrics need to be considered as smart grids are complex systems that integrate information technology, communications, and automation with traditional electrical grids. These metrics can be categorized into different aspects like reliability, efficiency, sustainability, security, and resilience. Reliability is one of the most important aspects of a smart grid since it ensures that the grid can meet demand consistently without interruptions. Additionally, smart grids aim to improve both operational and energy efficiency through real-time data, automation, and demand-side management [119; 120]. Given the increased reliance on digital systems and IoT devices, smart grid security is paramount. This includes the ability to track and lower the number of cybersecurity incidents, measure how well the grid can continue to operate or recover after a cyberattack, and ensure that the communication infrastructure that supports smart grid operations remains secure and operational. The resilience metric also plays a significant role in understanding the complex interplay between the behaviors and operational characteristics of interdependent critical infrastructures such as smart grids [121]. * Digital Twin (DT): With the expansion of contemporary cities, several energy layers--such as microgrids, smart grids, and transportation systems--have appeared, presenting a variety of difficulties for a multidimensional energy management system. As an example, traffic is a major problem in transportation systems that requires real-time planning, monitoring, and management. Remote data transfer within the electric grid and various analyses requiring real-time data are only a few of the current issues in this sector. These issues can be resolved by presenting and analyzing a genuine digital twin DT framework in each area. As a result, it is vital to consider numerous applications of DT in the development of various areas of energy management, such as smart grids, transportation systems, and microgrids [53; 122]. Digital twins have the potential to significantly improve smart grid security against cyberattacks while also addressing the CIA triad. Digital twins generate real-time virtual replicas of the smart grid, allowing for the continuous monitoring of the system's operation and security. In the event of an attack, digital twins can enable operators to simulate reactions and assess consequences before acting on the actual grid. Operators can use digital twins to test cyberattack scenarios in a secure, virtual environment, assessing the grid's resilience and developing effective remedies. During an attack, digital twins can provide backup simulations, allowing for operational awareness even if parts of the smart grid are affected, reducing grid downtime and ensuring system availability [123].
# Drilling Condition Identification Method for Imbalanced Datasets
## 4 BiLSTM-GRU-Based Classification Model for Drilling Conditions  
To balance modeling performance with computational efficiency and reduce the risk of overfitting, this paper explores the application of the BiLSTM-GRU concatenated model in drilling condition classification. The model combines BiLSTM's ability to capture global context with GRU's efficient feature compression, demonstrating significant advantages in handling complex, multivariate drilling time-series classification tasks. By adjusting parameters such as the number of network layers and hidden units, the BiLSTM-GRU model can adapt to data characteristics of various scales, enhancing the model's generalization ability and ensuring robustness and reliability in real-world drilling condition identification. Additionally, its efficient architecture strikes a balance between classification accuracy and computational efficiency, making it suitable for complex drilling scenarios.
## 1 Introduction
### How Can We Model Formally the Given Quantum Protocol?
If the observed outcome is \(\lambda_{1}\), then the program applies the Hadamard super-operator \(\mathcal{G}\) to \(q\) and its outcome is measured again in line \(\ell_{1}\). The application of \(\mathcal{G}\) is repeated until the outcome \(\lambda_{0}\) is observed: the program just terminates at line \(\ell_{3}\). The corresponding quantum Markov chain is depicted on the right of Figure 1: the single lines of the program are taken as the classical states of the quantum Markov chain, while the instruction performed on each line is used to decorate the corresponding transition; note that we split the projective measurement \(M\) into the two super-operators \(\mathcal{E}_{0}=\left\{\left|0\right\rangle\left\langle 0\right|\right\}\) and \(\mathcal{E}_{1}=\left\{\left|1\right\rangle\left\langle 1\right|\right\}\), since there are two possible outcomes \(\lambda_{0}\) and \(\lambda_{1}\)respectively, leading to two different states/program lines. This simple loop program cannot be represented in the model given by [PERSON] et al., since the \(\frac{1}{2}\)-amplitude damping channel \(\mathcal{A}\) is not an operation available in the Clifford group. In this paper we use the model of quantum Markov chains, as introduced by [44], to which we refer to for a more detailed comparison with other types of super-operator weighted Markov chains available in literature. This model is well suited to model quantum programs and protocols: as we have seen for the loop program above, representing the given program or protocol as a quantum Markov chain is not so complicated, since the classical states correspond to the single lines/steps of the quantum system, and the transitions are decorated with the (components of the) super-operators. The main aspect to remember while modelling a quantum protocol in this framework is that the classical states _do not_ represent the effect of the quantum operations on the quantum states, but only the current step in the algorithm. For instance, consider the quantum Markov chain shown in Figure 1: state \(\ell_{1}\) represents the fact that we applied the operation \(\mathcal{A}\) to the input quantum state \(q\) and that the next operation is its measurement, with two possible outcomes \(\mathcal{E}_{0}(\mathcal{A}(q))\) and \(\mathcal{E}_{1}(\mathcal{A}(q))\). However, state \(\ell_{1}\) does not represent the actual quantum state \(\mathcal{A}(q)\) and how it is modified by the different operations, or whether successive visits to \(\ell_{1}\) correspond to the same state of the quantum system.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 3 Proposed Method
### Key Generation Stage
#### 3.2.1 Initialization Process  
The key selection procedure is thought to produce a new key size as a result of this initialization of prime numbers.  
\[Key_{1,2,...n}=\{x_{1},x_{2},\ldots x_{n}\} \tag{2}\]
## 3 Parametric Study
### The Effect of Horizontal Length of Inverted L-Shaped Radiating Patch (\(L_{4}\))  
Figure 15 illustrates the simulated RLBW and ARBW under the variable horizontal length of the inverted L-shaped radiating patch (\(L_{4}\)): 24, 29, and 34 mm. As \(L_{4}\) became excessively large (34 mm), the reflection coefficient (\(\left|\mathrm{S}_{11}\right|\)) was greater than \(-10\) dB (\(\left|\mathrm{S}_{11}\right|>-10\) dB), resulting in an impedance mismatch. As \(L_{4}\) increased (from 29 to 34 mm), AR became smaller but ARBW became narrower. The optimal \(L_{4}\) was 29 mm.
# HyperIO: A Hypervisor-Based Framework for Secure IO
## 6 Applications  
We demonstrate the usefulness of HyperIO by presenting two use cases. Both use cases demonstrate the ability to display or receive sensitive information without making it vulnerable to theft by malware.  
These applications provide an example to the main use cases of the HyperIO environment.  
* Trustworthy network paths;
* Trustworthy human interface devices;
* Trustworthy screen output.  
The first use case is an extension for the Firefox web browser, which allows a remote webserver to securely input and output sensitive user information. This application demonstrates secure network channel and trustworthy network interfaces as well as trustworthy  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Category** & **Thin Hypervisor** & **HyperIO** & **VirtualBox** \\ \hline App start-up & 2.29 & 6.01 & 44.92 \\ Video conferencing & \(-\)8.36 & \(-\)8.02 & 43.78 \\ Web browsing & 2.66 & 2.75 & 32.75 \\ Spreadsheet & 1.18 & 2.62 & 39.91 \\ Writing & 1.54 & 2.69 & 42.66 \\ Photo editing & \(-\)0.24 & 0.38 & 38.35 \\ Video editing & 1.77 & 5.7 & 28.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance degradation in percentage concerning the configuration without a hypervisor.  
\begin{table}
\begin{tabular}{l l} \hline \hline Host CPU & Intel(R) Core(TM) i7-10610U \\ Host memory & 16 GB \\ Host OS & Ubuntu 20.04.1 LTS \\ VMM & VMware Workstation 15.5.6 \\ Guest CPU & Intel(R) Core(TM) i7-10610U \\ Guest memory & 8 GB \\ Guest OS & Windows 10 (19041) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testing environment configuration.  
human interface devices (keyboard). The second use case allows user-mode applications written for the Windows operating system to display sensitive information or receive sensitive input from the user. This application demonstrate trustworthy screen output and trustworthy human interface devices. In the following sections, we describe the implementation of these two applications.
## 2 The Rise of the Federated Learning Approach
### Delving into Decentralized Federated Learning (DFL)
#### 2.3.3 Fairness  
It is worth highlighting that not all nodes participating in the global training have the same resources (asymmetry), and hence, the produced outcome cannot be of the same importance regarding all nodes. In their paper [60], [PERSON] et al. promoted fairness in decentralized federated machine learning by maintaining uniformity in accuracy distribution across cooperating devices. The algorithm called q-FFL is an alternative algorithm to FedAvg that incorporates a parameter q in the loss function to achieve balanced weight updates. For example, the higher the loss value, the larger the weights, with the aim of rewarding the most contributing nodes and thus reach a consensus. Compared to FedAvg, q-FFL reduces approximately 45% of the average accuracy fluctuations among devices, while q-FedAvg performs better regarding accuracy metrics on different types of data (synthetic non-IID, synthetic IID, hybrid-synthetic). The authors also ran experiments with parameterized FedSGD (q-FedSGD) and concluded that its performance behaviour was proven to be superior to q-FedAvg when dealing with heterogeneous data (non-IID) and is even better, unlike the FedSGD variant that tunes the step size of the algorithm.
## Conclusions  
The extraction conditions for the HS-SPME with PA, PDMS, CAR/PDMS, and DVB/CAR/PDM fibers were optimized, and the different SPME fibers had consistent optimal conditions for detection. A total of 64 volatile compounds and 14 aroma-active compounds were identified from the sample using SPME with different fibers. Ethyl benzeneacetate, phenylethyl alcohol, and benzaldehyde, which all had concentrations greater than 1000 \(\mathrm{\SIUnitSymbolMicro g}\)/L in each fiber coating, were the main volatile compounds in the samples. The OAV results indicated that seven odor-active compounds, i.e., phenylethyl alcohol, styrene, 1-methyl-naphthalene, 2-methyl-naphthalene, benzaldehyde, benzeneacetaldehyde, and 2-methoxy-phenol, were the prominent odor-active compounds that had a high OAV. PCA showed a strong correlation between the 64 volatile compounds and 14 odor-active compounds and four SPME fibers. PA was efficient in the extraction of polar acids and alcohols; PMDS was efficient in the extraction of nonpolar hydrocarbons; and DVB/CAR/PDMS was efficient in the extraction of medium polar esters and benzene derivatives. The distribution of the intensities of odor characteristics with different SPME fibers was also affected.  
This research was supported by the Special Fund for Modern Agro-Industry Technology Research System (CARS-06-13.5-A29) and the Hebei Province Innovation of Modern Agricultural Project (F17R02). We would like to thank LetPub (www.letpub.com) for providing linguistic assistance during the preparation of this manuscript.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.