esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### IDCP Simulation Results  
Figure 5a-c shows that IDCP outperforms the other three algorithms significantly, under all three types of applications. It, in turn, verifies that IDCP can adapt to multiple types of applications. The performance of Infocom 2018 and TC 2017 is quite close when applied to our problem. In addition, it can be observed that the more tasks needed to be offloaded, the higher the application latency, which is consistent with intuition.  
IDCP also has significant advantages with varying number of local processors, as shown in Figure 6a-c. When the number of local processors varies from 3 to 15, IDCP always achieves the best performance. In particular, when working on workflow DAG, the application latency of IDCP is less than 0.085 on average, but the lowest application latency of the other three algorithms is over 0.13, which means that IDCP improves the performance by at least 150%.  
Figure 7a-c shows that when the delay factor varies between 1 and 3, the application latency of IDCP changes rapidly from high to low, and then continues to decrease slowly after the delay factor reaches 3. It indicates that IDCP is quite sensitive to the situation of the network, yet it still achieves greatest performance under all situations. In random DAG situation, when delay factor reaches 5, the application latency of IDCP is less than one-half of the work in TPDS2012. Hence, IDCP reduces application latency by over 200%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **IDCP** & **Infocom-2018 [8]** & **TC-2017 [36]** & **TPDS-2012 [32]** \\ \hline Objective & Total time & Energy & Total time & Total time \\ Type & Iterative & List scheduling & List scheduling & List scheduling \\ Online & No & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the algorithms evaluated.  
Figure 5: Time cost of different topologies under the number of nodes.  
Figure 6: Time cost of different topologies under the number of processors.  
Figure 7: Time cost of different topologies under communication Computation Ratio (CCR).  
In conclusion, the simulation results show that under different experiment settings, our proposed approach outperforms the three offloading algorithms Infocom-2018, TC-2017, and TPDS-2012 by a clear margin. This is due to the fact that these algorithms are constructive list-scheduling heuristics, which make only limited efforts to search for efficient solutions, while the proposed IDCP algorithm is an iterative algorithm which repeatedly migrates tasks to keep shortening the dynamic critical path such that the total processing time is finally minimized.  
The proposed heuristic is fast and flexible, but it is still only an offline algorithm. Accordingly, in future work, we can extend it to an online algorithm so that it can adapt to various dynamic scenarios. In addition, the communication models in this paper do not consider complicated 5G communications. In this case, another future direction is to integrate complex 5G communication models in our algorithm such that the algorithm would better fit future 5G scenarios.
## 3 Results and Discussion  
### Chemical Safety Assessment  
Figure 4 depicts the contributions of substances to the highest values of flammability, explosiveness, and toxicity parameters. The parameter estimations for each compound in the process stages are listed in the Appendix A (see Table A1). The flammability parameter reached the highest value for ethanol at 70.44, followed by L-alanine at 36.74. The values for astaxanthin, phenylalanine, and lysine were less than 1, suggesting that they are safer in terms of fire hazards. Ethanol is also responsible for the explosiveness parameter calculated at 0.82. For toxicity parameters, substances involved in the demineralization stage such as carbonates and chlorides have significant contributions. Calcium phosphate, calcium chloride, phosphoric acid, magnesium chloride, magnesium carbonate, and sodium hydroxide showed similar hazards when exposed by inhalation, ingestion, or dermal absorption (\(\mathrm{S_{TOX}}\) = 99.75). The maximum CSTS was estimated at 171.01 for large-scale chitosan production. This result was compared with those found in the literature for energy systems such as palm oil production and petrochemical industry. According to [PERSON] et al. [14], the methyl methacrylate manufacturing process under six different routes reached a CSTS of around 240-342, which are significantly higher than those obtained in this work. For the recovery of palm oil using different solvents [18], the CSTS values ranged 191-198 when handling chemical substances such as cyclohexane, n-hexane, and 2-pentanone.  
Figure 5 shows the ranking of most hazardous chemicals according to the values of CSTS (\(>\)99). The compounds at the top of the ranking must receive special attention when implementing safeguards at the design phase. Results reported that ethanol is the most hazardous chemical in the large-scale production of chitosan from shrimp wastes with a CSTS value of 170.65 followed by calcium carbonate, and calcium phosphate with 102.87 and 99.75, respectively. The hydrochloric acid and magnesium chloride showed CSTS values of 99.74. The presence of hazards associated with chemical handling is similar for compounds in rank position 1-7 because of the similarity in the toxicity parameter. Based on the chemical safety assessment, the use of ethanol in the depigmentation stage presents the highest risks for chitosan production from shrimp wastes. Alternative solvents such as vegetable oils (e.g., sunflower oil, coconut oil, or palm oil) must be considered in future for astaxanthin removal [24]. This corresponds to the green extraction methods addressed at lab-scale [25; 26].
###### Abstract
As the final step, these short-chain prenyl diphosphate intermediates, such as GPP, FPP and GGPP, are catalyzed by specific terpene synthases (TPS) to synthesize diverse terpenes [10]. Sesquiterpene synthase catalyzes FPP to form sesquiterpenes, while monoterpene and diterpene synthases catalyze the formation of monoterpenes and dGTP, respectively [10]. After the formation of these basic terpene units, monoterpenes, diterpenes, and sesquiterpenes can be subject to further modifications, including reduction, fusion, hydroxylation, glycosylation, cyclization, and prenylation reactions to transform into other metabolites [1; 10; 15; 16]. Many key genes in the MEP pathway are highly associated with oleoresin production, including _4-hydroxy-3-methylbut-2-enyl diphosphate reductase_ (_HDR_), _DXS_, and _geranylgeranyl pyrophosphate synthase_(_GGPS_) [17]. Moreover, (-)-\(\alpha\)/\(\beta\)-pinene synthase and ethylene-responsive transcription factors (ERFs) are implicated in regulating the yield of oleoresin in Masson pine [18; 19]. Additionally, previous studies demonstrated that the biosynthesis of terpenoid in conifers is also affected by diverse biotic and abiotic stresses, such as senescence, wounding, hormones, pathogens, and pests [20]. Slash pine is an important timber resource and resin-producing species are native to southeastern United States. With the characterization of fast growth, strong adaptability, long fiber, and high yield of secondary metabolites, such as oleoresin via the ducts, slash pine is widely planted in many countries for its economic value [21; 22; 23; 24]. In Brazil and China, slash pine is the major conifer species for the production of pine resin [25]. Generally, the oleoresin of slash pine is annually tapped through the bark-chipping method. Although it is labor intensive, this traditional manual harvesting way is still the common method to harvest resin [26]. Over the last two decades, numerous studies were conducted to explore the possibilities in increasing the resin yield from slash pine, such as developing new resin tapping methods [26; 27], exploring efficient resin stimulant paste during resinosis [28], as well as screening for high resin-yielding pine individuals [29]. However, the lack of knowledge about the genetic regulation network of oleoresin biosynthesis in slash pine severely impedes us in developing new breeding method. In the present study, the overall aim was to explore the terpenoid composition of oleoresin produced by high- or low-yielding slash pines, and through transcriptomic analysis and WCGNA analysis to identify the key genes and gene modules in relation to the terpenoid biosynthesis of high and low resin-yielding slash pines. The ultimate goal was to establish a gene regulation network controlling the terpenoid biosynthesis of slash pine.
## 4 Experimental Process and Result Analysis
### Preprocessing the Experimental Dataset  
The original satellite observation and navigation message files contain many parameters. However, not all of these parameters are directly related to the positioning error estimation. Therefore, the data in these files need to be preprocessed. Data preprocessing includes unifying the data value range and preliminary feature screening. Since the value ranges of different data are different, it is necessary to standardize the data. The standardization operation is to subtract the mean of the same type of data from the data to be standardized and divide it by the standard deviation, so as to achieve the purpose of scaling different data in proportion.  
The main features in the dataset and their corresponding descriptions are listed in Table 1. These features are utilized in the training and testing processes of the improved MLP model. The quality of the feature selection strategy will directly affect the GNSS positioning error estimation accuracy of the presented method. The improved MLP model mainly selects features through MHA and gating operation. Then, the weights are updated according to the gradient, thereby gradually optimizing the performance of the model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline Satellite clock difference & The difference in time between an atomic clock on a satellite and a reference clock on Earth. \\ Satellite clock deviation & The offset of an atomic clock on a satellite from its theoretically precise time. \\ Satellite elevation & The angle between the satellite and the horizon where the receiver is located. \\  & The ionospheric delay means that when a satellite signal passes through the ionosphere, \\ Ionospheric delay & the free electrons in the ionosphere will scatter and reflect the satellite signal, thereby \\  & changing the propagation path of the satellite signal. The ionospheric delay is obtained by \\  & the Klobuchar model. \\ Satellite azimuth & The angle between the line from the ground observation point to the satellite and the due \\  & The tropospheric delay means that when a satellite signal passes through the atmosphere, \\  & the water vapor in it will cause the satellite signal to refract, thereby changing the \\  & propagation path of the satellite signal. The tropospheric delay is obtained by the \\  & Saastamoinen model. \\ Carrier-to-noise ratio & The ratio of the carrier power of the received signal to the noise power in the \\  & surrounding environment. \\ Satellite coordinate & The satellite position information in the ECEF coordinate system is calculated using the \\  & satellite broadcast ephemeris. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main features in the dataset and their corresponding descriptions.  
Figure 6: Positioning error results are obtained through the Kalman filter method.
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery
### Preferential, Differentiated MTC using CAT-M1 in LTE and 5G  
The requirement for reliability of the local MAS communications during a blackout stated in Section 2 means that the mobile access network of the PLMN must be able to distinguish the MAS MTC devices from other MTC and H2H users and give them preferential handling during network emergencies. Preferential, differentiated MTC is supported in the 3 GPP standardisation since 3 GPP Release 8 through the EAB Override [14] and application-specific congestion control for data communication (ACDC) [15] functions. However, these functions are currently not implemented in commercially available PLMNs and UEs. EAB Override allows UEs to access the network under EAB conditions by requesting a packet data network (PDN) connection for which EAB does not apply [15]. To ensure that MAS MTC devices can communicate during EAB conditions, MAS MTC devices could be configured with multiple PDNs, one PDN for general communication and connectivity, and second PDN specific to the blackout scenario.  
ACDC applies for UEs with AC < 10 and allows or prevents connection attempts from UEs on a per-application basis. The MAS MTC devices could be defined as an ACDC application whose devices are always allowed to connect, fulfilling the microgrid black start communication reliability requirement.  
Hence, either of the CAT-M1 EAB Override and ACDC functions could ensure that MAS MTC devices can communicate reliably during emergencies. ACDC has the advantage over EAB Override of not requiring multiple PDNs. Both ACDC and EAB allow reliable MTC communication during blackout to be achieved while using radio resources more efficiently than the PLMN-based Access Barring described in the next Section 3.5, as they allow the microgrid MTC devices to use the same PLMN as other MTC and H2H devices. In the long-term, MAS use cases, including autarkic microgrid black start, can be run on CAT-M1 (massive IoT) networks, which are part of both the LTE and 5G standards.
## 3 Results and Discussion
### Tensile Mechanical Properties and Hardness  
Figure 5a, after cold rolling, the yield strength (YS) of the specimens increased from 115 to 220 MPa, and the ultimate tensile strength (UTS) increased from 169 MPa to 240 MPa. Figure 5b exhibited the uniform elongation (UE) and total elongation (TE), which decreased from 11.8% and 20.2% to 3.2% and 16.7% after cold rolling, respectively. Compared to the literature [34], the CCDR Al-Si alloy displayed mechanical properties superior to that of the traditional processes. The aforementioned ductility cannot satisfy further cold workings to form fasteners, thinner wires, and screws; therefore, the heat treatment is necessary for ductile improvement.  
Figure 4: The microstructure evolution of the CCDR Al-Si alloy after cold rolling and T6 heat treatment after cold rolling in the axial section and the transverse section: (**a**) F, transverse, (**b**) F, axial, (**c**) FR, transverse, (**d**) FR, axial, (**e**) FRH, transverse, and (**f**) FRH, axial. BSE image of (**g**) F, axial, (**h**) FR, axial, and (**i**) FRH F, axial.  
As shown in Figure 5a, after the heat treatment, (FR\(\rightarrow\)FRH), both YS and UTS were reduced (Table 3). Figure 5b indicated that the ductility was significantly improved in both the UE and TE after the heat treatment (Table 3). The ductility can correspond to the industrial need; on the other hand, the strength dramatically decreased. Conclusively, there are two main reasons for the substantial increase in ductility: (1) the dendritic structure partial fracture recrystallized and equiaxed after the heat treatment, and (2) the Si particles become finer, round, and blunt after the heat treatment, which can alleviate the tensile crack propagation rate [28; 35; 36].  
The hardness (Figure 5c) indicated that cold rolling can increase the HRF hardness to approximately 70 and decrease it to approximately 30 after the heat treatment. Since the  
Figure 5: Tensile properties: (**a**) strength, (**b**) elongation, and (**c**) HRF hardness of the CCDR Al–Si alloy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **YS (MPa)** & **UTS (MPa)** & **UE (\%)** & **TE (\%)** & **HRF** \\ \hline F & 115 & 169 & 11.8 & 20.2 & 50 \\ \hline FR & 220 & 240 & 3.2 & 16.7 & 73 \\ \hline FRH & 38 & 77 & 29.7 & 55.3 & 28 \\ \hline  & SD of YS (MPa) & SD of UTS (MPa) & SD of UE (\%) & SD of HE (\%) & SD of HRF \\ \hline F & 13.60 & 17.64 & 1.07 & 2.88 & 1.00 \\ \hline FR & 3.87 & 1.75 & 0.53 & 0.80 & 0.80 \\ \hline FRH & 4.12 & 4.97 & 1.52 & 2.16 & 1.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of the Al–Si alloy.  
Si content exceeded the solid solubility in the Al matrix, the solid solution strengthening effect achieved the limit and was difficult to increase by the heat treatment. Although the rounding of the Si particles caused a decrease in the hardness, the heat treatment reduced the brittleness effect for better workability of the CCRD Al-Si alloy. Nevertheless, for the purpose of CCRD Al-Si alloy manufacturing, we should consider not only mechanical properties but, also, the wear resistance [3; 37].
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 5 Computational Experiments
### Algorithm Performance Test
#### 5.1.3 Stability Analysis  
To further test the stability of the algorithm under the same parameter configurations, examples R101, R105, C101, C105, RC101, and RC105 are tested 10 times each; the results are shown in Figure 9.  
Figure 9 shows that during the 10 tests, the maximum value of the objective function of R101 is 558.2347, the minimum value is 527.6039, the average value is 541.8093, and  
Figure 8: Comparative iteration for solving RC101_25 using different strategies.  
Figure 9: Algorithm stability test results. The red dots represent the numerical results of ten tests, while the blue dots indicate the mapping positions of these red dots on the Z-axis.  
the average error rate relative to the minimum value is 2.69%. The maximum value of the objective function of R105 is 535.0197, the minimum value is 507.3721, the average value is 516.1252, and the average error rate relative to the minimum value is 1.73%. The maximum value of C101 is 343.7847, the minimum value is 337.7465, the average value is 342.3882, and the average error rate relative to the minimum value is 1.37%. The maximum value of C105 is 301.5938, the minimum value is 297.6879, the average value is 300.4842, and the average error rate relative to the minimum value is 0.94%. The maximum value of RC101 is 370.7237, the minimum value is 349.6057, the average value is 363.3692, and the average error rate relative to the minimum value is 3.94%. The maximum value of RC105 is 348.8725, the minimum value is 341.6005, the average value is 344.94, and the average error rate relative to the minimum value is 0.98%. Of the six tests, R101 had the highest average error rate while C105 had the smallest. Overall, due to the limitations of the heuristic algorithm, the results of each solution fluctuate to a certain extent and the relative average error rate of the 10 tests is controlled within an acceptable range of 5%, indicating the stability of the designed algorithm and showing that it can be used to solve VRPDTW problems.
## 5 Feature Extraction
### Time-Frequency Domain Features
[8] use the wavelet transform based-technique for denoising purposes in the context of road anomaly recognition. As can be appreciated, time-frequency analysis could positively impact the detection and characterization of road surface anomalies that are not only limited to feature extraction or signal representation but also for denoising applications. However, the setting of the parameters of this transfor \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Method** & **Parameters** \\ \hline \multirow{4}{*}{[PERSON] et al. [40]} & \multirow{4}{*}{STFT} & Variation of window type. \\  & & Variation of window length. \\  & & Variation of overlapping between windows. \\ \cline{2-3}  & \multirow{2}{*}{CWT} & Morse wavelet used as mother wavelet \\  & & Variation of frequency scales \\ \hline [PERSON] et al. [31] & \multirow{2}{*}{CWT} & Daubechies 3 wavelet (DB3) \\  & & as the mother wavelet \\ \hline [PERSON] et al. [18] & \multirow{2}{*}{DWT} & Five level decomposition with a \\  & & Daubechies 2 wavelet (DB2) \\ \hline [PERSON] et al. [26] & \multirow{2}{*}{DWT} & 3 levels Reverse \\  & & Biorthogonal 3.1 wavelet \\ \hline [PERSON] et al. [42] & \multirow{2}{*}{DWT} & Tested 3 wavelets at scales 4 and 5. \\  & & Mortlet, Daubechies 6 and Daubechies 10 wavelets \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Time-frequency methods used to extract features from inertial sensors for the detection and classification of road surface anomalies. mation and the adequate selection of a kernel (i.e., mother wavelet or window function) have not been fully explored or tested.
## 3 Design of Segmented Mirror with a GRoC Actuation System
### Actuation Point's Position Design of Segmented Mirror
#### 3.3.2 Procedure of the Actuation Point's Position Optimization  
With the help of Figure 8, the procedure of the actuation point's position optimization is outlined in the following steps.  
1. Generate 20 training points and 10 testing points of design variables by LHS.
2. Take the sampled points into the numerical simulation. In the numerical simulation, a geometric model is built at a sampled point by computer-aided design (CAD) software, Solidworks. Then analyze the geometric model to get figure deformation and the maximum local stress of the mirror with the finite element analysis (FEA) software Patran and Nastran. Finally, the surface shape error RMS and the amount of GRoC actuation are evaluated by using MATLAB.
3. Divide the surface shape error RMS and the maximum local stress by the amount of GRoC actuation to get \(Y_{\text{RMS}}\) and \(\delta_{\text{MAX}}\). Build RBFNN, PRS, and KRG, respectively, using \(Y_{\text{RMS}}\), \(\delta_{\text{MAX}}\), and training points, then construct multiple surrogates according to Section 2.1.
4. Solve the determination coefficient \(R^{2}\) of multiple surrogates and check whether it is greater than 0.9. If not, add 5 training points and return to step 2.
5. Search the optimum of the multiple surrogates through MIGA (population number is 5, island number is 4, evolution number is 10).
6. Verify whether the optimum meets boundary conditions. If not, add 5 training points and return to step 2.
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 2 Related Work
### DRL-Based Approaches to Minimize Energy Consumption
The mixed-integer non-linear problem (MINLP) is decomposed into two subproblems and solved using convex optimization, double deep Q-network (DDQN), and DDPG algorithms. [PERSON] et al. [18] proposed a joint optimization problem to determine user offloading strategies and UAV trajectories. They used a DDPG-based iterative optimization of the energy consumption algorithm to minimize vehicle energy consumption and determine optimal offloading while ensuring the maximization of the task transmission rate. [PERSON] et al. [16] considered a task offloading scenario in a hybrid UAV-assisted MEC network with the objective of minimizing system energy consumption subject to maximum tolerable latency and computing limitations. They proposed a DRL-combined successive convex approximation (SCA) algorithm to obtain a near-optimal solution with low complexity. The multiagent DDPG (MADDPG) algorithm is used to provide a suitable service assignment and task splitting, while the SCA algorithm solves trajectory planning and resource scheduling. [PERSON] et al. [19] investigated the computation offloading and resource allocation in a satellite-terrestrial integrated network. They minimized energy consumption while guaranteeing latency tolerance by jointly optimizing the offloading decision and resource allocation. They formulated the minimization problem as an MDP and designed a TD3 algorithm to determine offloading decision and resource allocation mechanisms.