esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Convolutional Neural Networks to Spectrum Sensing
#### 3.1.4 Application of Residual Neural Networks to Spectrum Sensing
Experiments were conducted to compare the performance of the model proposed in this paper with traditional ED, BCED, CNN, and STFT-CNN, and the results show that the extraction of signal features by wavelet transform can effectively improve the Pd, and the detection probability of WT-ResNet is higher than that of the other four methods for SNRs ranging from \(-20\) dB to \(-10\) dB, with the largest gap at \(-20\) dB. The works reviewed above are summarized in Table 5, from which it can be seen that ResNet-based spectrum-sensing methods typically learn the time-frequency information in the signal and, in particular, extract deep features from 2D spectral data. The introduction of ResNet enables deeper networks by efficiently solving the gradient vanishing and gradient explosion problems in deep networks. When there is a large amount of training data, similar to LSTM, both can have excellent adaptive ability to learn and adapt to various spectrum environments. However, the choice of whether to use ResNet in practical applications depends on the specific application scenario, available data, and computational resources.
# Dynamic Task Scheduling Scheme for Processing Real-Time Stream Data in Storm Environments
## 5 Conclusions  
In this paper, we have proposed a dynamic task scheduling scheme in which task deadline and node resources in Storm are considered. The proposed scheme considers the CPU usage, free memory size, and network load to calculate the load of each worker node and redistributes the tasks by assigning priorities based on the task deadline and the available resources. In this study, the loads were categorized into three types--CPU, memory, and CPU and memory loads--and dynamic task scheduling was performed with respect to the load type. Moreover, the throughput of each worker node was calculated by considering the state of a heterogeneous cluster, and the tasks with closer deadlines were distributed to the worker nodes with good performance to improve the QoS. The proposed scheme exhibited an approximately 15% more outstanding performance in terms of processing a complex query compared to existing schemes. Furthermore, both simple and complex queries were processed without passing the deadline in real-time processing. In future research, we will perform additional performance evaluations with the most recent existing dynamic task scheduling scheme. In addition, static task allocation and simple query processing performance will be improved by applying machine learning techniques.  
Conceptualization, D.C., H.J., J.L., K.B. and J.Y.; methodology, D.C., H.J., J.L., K.B. and J.Y.; validation, D.C., H.J., J.L. and K.B.; formal analysis, D.C., H.J., J.L. and K.B.; writing--original draft preparation, D.C., H.J., J.L. and K.B.; writing--review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT). (No. 2019R1A2C2084257), by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2021-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation), by \"Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01624701)\" Rural Development Administration, Republic of Korea, and by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2014-3-00123, Development of High Performance Visual BigData Discovery Platform for Large-Scale Realtime Data Analysis).  
Not applicable.  
The authors declare no conflict of interest.
# Exploring the Role of the Processing Body in Plant Abiotic Stress Response
## 4 P-Body and Plant Stress Responses
### P-Body Involving in Ethylene Signaling Pathway  
Ethylene is a major signaling molecule in plants responding to biotic and abiotic stresses [66]. The key components of the P-Body, such as EIN5 and PAB2/4/8, contribute to the accumulation of critical transcription factors EIN3/EIL1 in the ethylene response pathway [66, 67]. The overexpression of _EBF1 3\({}^{\prime}\)_ UTR region (_1U_) in _Arabidopsis_ promotes the translation of _EBF1/2_ mRNAs and reduces the sensitivity of the plant responding to ethylene [66]. With ethylene present, the CEND region of EIN2 binds to _1U_-containing mRNA and directs it to P-Body to interact with EIN5 and PAB2/4/8 to inhibit its translation. Consequently, the translation of _EBF1/2_ mRNAs was inhibited, and the accumulation of EIN3/EIL1, which is responsive to ethylene, was promoted [66].  
Additionally, in _Oryza sativa_, the MHZ9 protein is involved in the ethylene signaling pathway positively at the translation level [68]. In the presence of ethylene, MHZ9 together with OsEIN2-C is co-localized with the P-Body proteins OsDCP2 and OsEIN5. The binding of the N-terminal domain of MHZ9 to the 3\({}^{\prime}\)UTR region of OsEBF1/2 mRNAs inhibits its translation, thereby promoting the expressions of the _OsEIL1_ to activate the downstream ethylene signaling pathway [68]. Furthermore, the knockout mutants of genes encoding P-Body components (_EIN5_, _UPF1_, or _PAB2/8_) exhibit decreased sensitivity to ethylene. This insensitivity is especially pronounced in the _ein5 upf1 pab2 pab8_ quadruple mutant. All these results indicate that P-Bodies play a critical role in the ethylene signaling pathway. Ethylene quickly shuts down _EBF1/2_ gene expression through the mechanism of translation inhibition in the P-Body to regulate the stress responses [66].  
In addition to participating in the ethylene signaling pathway, P-Bodies can also enhance stress resistance and regulate plant growth by interacting with stress-responsive proteins in the hormonal pathways or by promoting the expression of genes associated with other hormonal pathways. For example, in _Brassica napus_, BnRH6 in the P-Body upregulates the expression of ABA signaling pathway genes, including _CYP07A1_, _MAPKKK18_, _NCD9_, _AHG1_, etc., under salt stress [69]. In the P-Body, AtTZF4/5/6 could interact with Responsive to Dehydration 21A (RD21A) and the Mediator of ABA-Regulated Dormancy 1 (MARD1) to exert post-transcriptional level regulatory function in plants' growth and responses to stress [70].
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 7 Multi-User Substring Position Searchable Symmetric Encryption
### Algorithm Definitions
It provides the security against an adaptive adversary: the cloud server does not learn anything about the document collection and search queries beyond the access, search and path patterns. However, with the addition of the access privilege property, we expand our security definitions towards the Remove functionality (Algorithm 4). We define the Rev algorithm as follows:  
**Definition 24**. : **(Revocation). ** Let MSSP-SSE = (KeyGen, BuildTree, Encrypt, Join, GroupSetup, Remove, ConstructQuery, Search, Decrypt) be a group SSP-SSE scheme, \(k\) be a security parameter and \(\mathbb{A}=(\mathbb{A}_{1},\mathbb{A}_{2},\mathbb{A}_{3})\) be an adversary. We use the following probabilistic experiment \(Rev_{MSSP-SSE,\mathbb{A}}(k)\):  
```
\((st_{A},D)\leftarrow\mathbb{A}_{1}(1^{k})\) \((sk_{A},(z_{A},ek_{A}))\gets Join(\lambda,g)\) \(c_{r}\gets SetupGroup((z_{A},ek_{A}),sk)\) \((\Lambda)\gets BuildTree(D)\) \((I,C)\leftarrow Encrypt(K,\Lambda,D)\) \(st_{A}\leftarrow\mathbb{A}_{2}^{O(I,C,st_{S^{\prime}})}(st_{A},sk_{A},(z_{A},ek_{A}),c_{r})\) \(c^{\prime}_{r}\gets Remove((z_{A},ek_{A}),sk)\) \(Q\leftarrow\mathbb{A}_{3}(st_{A})\) \(L\gets Search(st_{S},I,Q,c^{\prime}_{r})\) if \(L\
eq\bot\), output one, otherwise output zero
```  
**Algorithm 4**\(Rev_{MSSP-SSE,\mathbb{A}}(k)\). where \(O(I,C,st_{\mathbb{S}},\cdot)\) is an oracle that inputs a search query \(Q\) and outputs ciphertexts \(C\) indexed by \(L\gets Search(I,\Omega,c^{\prime}_{r})\) if \(L\
eq\bot\) and \(\bot\) otherwise. We say that the Remove algorithm achieves user revocation if for all polynomial-size adversaries \(\mathbb{A}=(\mathbb{A}_{1},\mathbb{A}_{2},\mathbb{A}_{3})\), the following is correct:  
\[Pr[Rev_{MSSP-SSE,\mathbb{A}}(k)=1]\leq negl(k), \tag{7}\]  
where the probability is over the coins of KeyGen, Join, GroupSetup, Remove and Encrypt.
## 3 Objectives and Methodology  
This paper contributes to the state of the art by offering new insights into the design of cloud edge architectures for collaborative AR systems, validating different architectural models through detailed case studies, and presenting empirical data based on simulations. The proposed solutions are tested in various real-world scenarios, showcasing their applicability and benefits in practical settings. So, the main objective of this work is to define different and valid reference architectures for deploying VR and AR applications on cloud continuum infrastructures. These architectures will be structured in such a way as to carry the computation load from the core to the edge. This work was carried out as follows to achieve the previously described objectives. First, in Section 4, we defined three different architectures using UML diagrams [17]; in particular, we used component diagrams to define the single components and sequence diagrams to show their interoperability. The next step was to determine the technologies that would implement the individual components of all architectures. After defining and implementing the different architectures to validate them, as mentioned in the introduction, ad hoc applications were developed for each architecture, which we will see in detail in the Section 5. This was carried out to validate the operation of the aforementioned architectures. Finally, the last phase involved simulation, using Cloud Analyst, to validate these architectures as users scaled up. In detail, we simulated, for the same computational capacity of edge and server components, how response times vary as users vary up to a maximum of 1000 users.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 4 Meteorological Modeling
### Weather Models
The algorithm can effectively approximate future thunderstorm dynamics [49], with particularly good performance for predictions up to 30 min. Distance differences of less than 4 km between the centres of the observed and predicted areas were observed for more than 85% of cells in the first 5 min nowcasts and between 2 km and 16 km for most 30 min nowcasts. Beyond this time, the results are more unreliable but can still provide valuable information to end users regarding storm propagation direction and possible impacts in future scenarios.
## 2 Materials and Methods
### Kinetic Evaluation and Data Analysis  
Two kinetic models were used to fit the experimental data of the BMP of the silages. These models were the dual pool first-order kinetic model (DPKM) given in Equation (1) and the modified Gompertz model given in Equation (2).  
\[\text{G}_{(t)}=\text{G}_{(0)}\left[(1-\alpha)\text{e}^{-\text{k}_{1}t}-\ (1- \alpha)\text{e}^{-\text{k}_{2}t}\right] \tag{1}\]  
\[\text{G}_{(t)}=\text{G}_{(0)}.\exp\left\{-\exp\left[\frac{\text{R}_{\text{ max}}.\text{e}}{\text{G}_{(0)}}(\lambda-t)\ +\ 1\right]\right\} \tag{2}\]  
where G\({}_{(t)}\) = cumulative methane produced (mL/gVS), G\({}_{(0)}\) = the ultimate methane produced (mL/gVS), k\({}_{1}\) = the first-order degradation constant of fast degrading component(1/d), k\({}_{2}\) = the first-order degradation constant of slow degrading component (1/d), \(\alpha\) = fraction of degradable material, t = duration of digestion (d), R\({}_{\text{max}}\) = maximum rate of methane production (mL/gVS/d), \(\lambda\) = lag phase (d), and e = [PERSON]'s constant (2.71828).  
The time, \(t_{max}\), at which the maximum rate of methane production is achieved was estimated using Equation (3).  
\[t_{max}=\lambda+\frac{C_{0}}{R_{max}e} \tag{3}\]  
Parameters from the models were estimated using a Solver add-in program in Microsoft Excel. The statistical indicators that were used to determine the more appropriate model that predicted the experimental BMP were the coefficient of determination (R\({}^{2}\)), the root mean square error (RMSE) shown in Equation (4), the Bayesian information criterion (BIC) given in Equation (5), and the Akaike information criterion (AIC) given in Equation (6).  
\[RMSE=\sqrt{\frac{SS}{n}} \tag{4}\]  
\[BIC=nln\Big{(}\frac{ss}{n}\Big{)}+kln(n) \tag{5}\]  
\[AIC=nln\Big{(}\frac{ss}{n}\Big{)}+\ 2k \tag{6}\]  
where n is the number of datasets, SS is the squared sum of residuals, and k is the number of parameters in the model.  
Data on fermentation characteristics in silages were evaluated by a two-way ANOVA with the main effects of storage period and inoculant by first-level interaction using the general linear model procedure \"Im\" of the statistical package R (R core team 2018). [PERSON]'s post hoc test was adopted to compare the difference at the 0.05 significant level. Pearson correlation analysis was used to evaluate the interdependence of fermentation products.
## 3 Experiments and Discussion
### Performance of Collision Avoidance Method
Figure 16 shows the maximum accelerations during collision avoidance under our method and under the method presented in [8]. It can be seen that the maximum acceleration of our method is mainly distributed in the range of \(-\)2 to 2 m/s\({}^{2}\), whereas the maximum acceleration of the method  
Figure 14: Collision detection distance on winding roads. The detection distance of the IEMMB method on a winding road is longer than the method used in [8]. Figure 15: Maximum yaw rate during collision avoidance. used in [8] often exceeds this range. This indicates that the evasive actions in [8] are fiercer and that our method results in more comfortable collision avoidance actions.
## 7 Conclusions  
In this article, we have focused on future networks: the Next Generation Networks and Future Generation Internet. NGN has been born in a telco environment where telco's challenge is to provide service continuity and QoS management over IP. FGI has been born in the internet world as a response to the challenges from existing IP networks. FGI has used a revolutionary approach known as the \"clean slate\" approach in which the existing internet architecture and protocols should be replaced by new ones.  
We have described how NGN has managed to solve selected challenges of the FGI, like mobility, identity, security and privacy, and QoS management. The NGN, of course, cannot respond easily to all the FGI challenges, but it is important to gain inspiration from a more strict, managed world than the open internetas it has been until now.  
We can conclude that convergence of both approaches can contribute to improve both worlds and that they should probably merge into just one approach called Future Network. Future Network should provide all expected technological advantages for technology or service providers but also offer a new improved experience for the end users.
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 3 Problem Formulation
### Reinforcement Learning Formulation
#### 3.2.3 Action Space (A)  
The action space \(A\) of our reinforcement learning model is directly defined by the agent's current node and the accessible links. It is structured as follows:  
\[A(x)=\{x^{\prime}\mid(x,x^{\prime})\in L\}\]  
where  
* \(x\) represents the current node position of the agent.
* \(x^{\prime}\) denotes a potential next node to which the agent can move.
* \(L\) is the set of all the connections in the network, where each link is a tuple \((x,x^{\prime})\) that indicates a direct connection from node \(x\) to node \(x^{\prime}\).  
This definition ensures that the action space is dynamically adjusted based on the agent's current position, reflecting the physical and network connectivity constraints.  
The agent selects from the set of possible next nodes to which it can move based on the links available at its current node. This allows for a flexible yet precise mechanism to navigate through the network, making decisions that are optimal with respect to both route efficiency and data transmission needs.
## 3 Results
### Time Distribution of Construction Accidents
#### 3.2.2 Construction Accident Number per Day of Week  
Correlation between the number of accidents and the day of the week was tested using ANOVA using data for accidents per day of the week. To establish correlation between day of the week and the number of accidents, accident data were tabulated into seven groups by day of the week. ANOVA testing was performed to establish the significance of the day of week as a factor of time to the number of construction accidents. However, a significant value of 0.767 (greater than 0.05) was obtained, indicating that there is no correlation between the day of week and the number of accidents. Figure 6 shows a scatter plot between day of week and construction accident number, in which points are distributed discretely and there is no linear correlation between the two variables.  
Although previous studies have proposed a theory of \"Monday effect\" in which more occupational accidents occur on Monday since it is hard for workers to pay attention to hazard in work on the first day after finishing weekend breaks [34], a different result based on collected data was found. This may be explained by the working habits of construction workers. Generally, some construction workers in China may work on weekends because current construction activities in China still rely on manual labor. According to annual reports of the National Bureau of Statistics (for example [35]), the average working days per month for rural migrant workers are approximately 25.2 (more than 20) during the period from 2013 to 2016. Interviews that were conducted with ten construction employees who worked in construction companies in different regions in China revealed that these construction companies allow their workers to work seven days per week. Furthermore, the daily workload for them is constant. These working conditions might have affected their behavior, where they would have been exhausted and losing focus on the safety aspects of construction operations. This might have led to the occurrence of construction accidents on any day of the week. Therefore, no correlation is observed between the day of week and the number of accidents, and there is no significant day of the week that points to a higher risk for construction accidents.  
Figure 6: Scatter plot between day of week and construction accident number.
## 4 Numerical Examples and Problem Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(k\)} & \multicolumn{5}{c}{**Dimensionless Critical Axial Velocity**} \\ \cline{2-6}  & \(v_{\text{DI}}\) & \(v_{\text{s}}\) & \(v_{\text{FI}}\) & \(v_{\text{D2}}\) & \(v_{\text{FE2}}\) \\ \hline
11 & 6.87328355 & 9.83110253 & 10.70751000 & 13.7482254 & 15.0233000 \\
12 & 6.83891402 & 9.78194257 & 10.65410000 & 13.6794780 & 14.9480000 \\
13 & 6.80838910 & 9.73828169 & 10.60660000 & 13.6184208 & 14.88120000 \\
14 & 6.78108283 & 9.69922455 & 10.56400000 & 13.5638017 & 14.82182000 \\
15 & 6.75650231 & 9.66406616 & 10.52533000 & 13.5146347 & 14.76813600 \\ \hline \end{tabular}
\end{table} 
Table 7: The effects of gradient parameter \(k\) of material on the dimensionless critical axial velocities of FCM beam (CC). \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(k\)} & \multicolumn{5}{c}{**Dimensionless Critical Axial Velocity**} \\ \cline{2-6}  & \(v_{\text{DI}}\) & \(v_{\text{s}}\) & \(v_{\text{FI}}\) & \(v_{\text{D2}}\) & \(v_{\text{FE2}}\) \\ \hline
11 & 3.43661589 & 6.87328355 & 7.065000000 & 10.3102620 & 11.19784000 \\
12 & 3.41943125 & 6.83891402 & 7.030000000 & 10.2587060 & 11.14182000 \\
13 & 3.40416891 & 6.80838910 & 6.998000000 & 10.2129172 & 11.09200000 \\
14 & 3.39051587 & 6.78108283 & 6.970000000 & 10.1719564 & 11.04750000 \\
15 & 3.37822571 & 6.75650231 & 6.94490000 & 10.1350844 & 11.00740000 \\ \hline \end{tabular}
\end{table} 
Table 5: The effects of gradient parameter \(k\) of material on the dimensionless critical axial velocities of FCM beam (SS).
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 6 Discussion
The RTPPP accuracies based on the proposed clock model are superior to 0.2 m and have improved about 16%, 14%, and 38% in N, E, and U directions, respectively. This work was supported by the Programs of the National Natural Science Foundation of China (41731066, 41774025), the Natural Science Foundation of Shaanxi Province (2016 JQ4011), the Special Fund for Basic Scientific Research of Central Colleges (grant no. CHD310826171004, CHD310826165014, Chang'an University), and the Grand Projects of the Beidou-2 System (GFZX0301040308). We also thank the GBM and iGMAS authorities for providing the data for this study. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed to discussions and revisions, providing important feedback and suggestions; and [PERSON] wrote the paper. The authors declare no conflict of interest.
## 2 Materials and Methods
### SPME-GC-MS Volatile Analysis  
Dried, finely powdered fruits were placed in SPME screw-cap vials (1.5 mL) spiked with 10 \(\upmu\)g (Z)-3-hexenyl acetate with fibers inserted manually above and placed in an oven (Yamato Scientific DGS400 Oven, QTE TECHNOLOGIES, Hanoi, Vietnam) kept at 50 \({}^{\circ}\)C for 30 min. HS-SPME analysis of the volatile compounds was performed as reported in [2; 26]. The fiber was subsequently withdrawn into the needle and then injected manually into the injection port of a gas chromatography-mass spectrometer (GC-MS). GC-MS analysis was adopted on an Agilent 5977B GC/MSD (Santa Clara, CA, USA) equipped with a DB-5 column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; Supelco, Bellefonte, PA, USA) and coupled to a quadrupole mass spectrometer. The interface and the injector temperatures were both set at 220 \({}^{\circ}\)C. Volatiles elution was carried out using the following gradient temperature program: oven was set at 40 \({}^{\circ}\)C for 3 min, then increased to 180 \({}^{\circ}\)C at a rate of 12 \({}^{\circ}\)C/min, kept at 180 \({}^{\circ}\)C for 5 min, finally increased at a rate of 40 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C and kept at this temperature for 5 min. Helium was utilized as a carrier gas with a total flow rate of 0.9 mL/min. For ensuring complete elution of volatiles, SPME fiber was prepared for the next analysis by placing it in the injection port at 220 \({}^{\circ}\)C for 2 min. For assessment of biological replicates, three different samples for each _P. armeniaca_ fruits and seed kernels were analyzed under the same conditions. Blank runs were made during sample analyses. The mass spectrometer was adjusted to EI mode at 70 eV with a scan range set at \(m/z\) 40-500.