esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Vibration sensitivity and zero-expansion temperature of a 100 mm cubic reinforced-support FP cavity were analyzed. Firstly, vibration sensitivity of the FP cavity was modeled and simulated. To simulate the real situation of the FP cavity, two different constraints were used for the analysis. The analysis results of both constraint methods show that the vibration sensitivity of the FP cavity is lower than 1.5 \(\times\) 10\({}^{-10}\)/g. Secondly, zero-expansion temperature of the FP cavity was simulated. The analysis results show that the zero-expansion temperature of the FP cavity with reinforced support is affected by about 1 \({}^{\circ}\)C compared to the zero-expansion temperature of the FP cavity itself.  
Vibration sensitivity and zero-expansion point temperature of the FP cavity were tested experimentally. The experimental results show that vibration sensitivities of the reinforced-support FP cavities are 1.73 \(\times\) 10\({}^{-10}\)/g, 2.09 \(\times\) 10\({}^{-11}\)/g, and 2.13 \(\times\) 10\({}^{-11}\)/g in the Z, X and Y directions. This is consistent with the results of the simulation. Compared with the four-point support FP cavity, the reinforced-support FP cavity has a similar vibration sensitivity. The zero-expansion temperature of the reinforced-supported FP cavity is about 29.5 \({}^{\circ}\)C. According to the experimental and simulation results for zero-expansion temperature, the reinforced-support method has limited effect on the zero-expansion temperature of the FP cavity.  
The reinforced-support FP cavity offers performance consistent with a four-point support. The reinforced-support FP cavity can remain stable in the environment of mechanical vibrations such as transportation or rocket launches. Therefore, this reinforced-support may expand the application of ultra-stable lasers with cubic FP cavity in transportable or space applications.  
Conceptualization, L.C. and J.J.; methodology, L.C. and F.M.; software, F.M.; validation, L.C., L.M. and J.J.; formal analysis, F.M.; investigation, L.C., L.M. and F.M.; resources, L.C., L.M. and J.J.; data curation, L.C. and F.M.; writing--original draft preparation, F.M.; writing--review and editing, L.C. and L.M.; visualization, F.M.; supervision, L.C., L.M. and J.J.; project administration, J.J.; funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (2021 YFC2201804).  
Not applicable.  
Dataset is available upon request from the authors.  
The authors declare no conflicts of interest.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction
### Spatiotemporal Data Refinement and Categorization
#### 4.1.5 Trip Distribution Analysis  
Trip distribution analysis plays a vital role in transportation planning by uncovering the spatial and temporal patterns of origin-destination trips. This analysis helps identify areas with high travel demand, allocate resources efficiently, and make informed decisions about transportation infrastructure and services [42].  
Due to the spatial and temporal sparsity of our three-year dataset, obtaining sufficient trip coverage across the study area was challenging. To overcome this issue, we generated heatmaps of pickup and drop-off locations, which revealed densely concentrated areas of trip activity. Figure 7 shows the focused area with trip patterns, despite the overall sparsity of the data.  
Furthermore, ensuring the deployment of accurate prediction models for autonomous vehicles in urban areas is of utmost importance. Failure to account for data sparsity can result in the limited availability of data, compromising the performance of the model.  
Figure 6: Temporal clustering results at different time scales.  
Consequently, this can lead to suboptimal navigation and an inability to generate optimized route plans.  
The accurate boundaries of the chosen areas were determined using latitude and longitude coordinates. The northern and southern coordinates were found to be 40.888558 and 40.647952, respectively. Similarly, the western and eastern coordinates were identified as \(-\)74.008347 and \(-\)73.823495, respectively. To focus our analysis on specific areas, we employed a box-constrained approach. This approach allowed us to filter out trips that had pickup and drop-off locations outside the specified boundaries.
## 1 Introduction  
In recent decades, advances in the field of biomaterials has grown so much, leading to concentration on the synthesis of alternative biocompatible materials or to improving the characters of these present materials. Chitosan, as a mucopolysaccharideside having structural characteristics similar to glycosamines, seems to be an ideal biopolymer with a wide variety of biomedical and industrial applications [1]. It is one of the most abundant polysaccharides on Earth and a natural cationic copolymer. Chitosan is the alkaline deacetylated product of chitin, derived from the exoskeleton of crustaceans, insects and fungal cell walls. chitin consists of a sugar backbone with \(\beta\)-1,4-linked glucosamine units and a high degree of acetylation (see Figure 1; all figures have been drawn by the authors) [2]. It is also a derivative of cellulose with the hydroxyl groups replaced by amine groups, thereby making it a polycation. Chitosan is the major derivative of chitin, which is composed of randomly distributed \(N\)-acetyl glucosamine and p-glucosamine, varying in composition, sequence and molecular chain length [3, 4].  
Figure 1: Chitosan chemical structure.  
Many beneficial pharmacological properties have been suggested for chitosan due to its biocompatibility, non-toxicity, biodegradability, antibacterial activity, antioxidant activity and muco-adhesive properties [5]. Furthermore, it has been introduced extensively in the pharmaceutical industry including the formation of tablets as a controlled release dosage form [6], a gel absorption enhancer [7], for drug dissolution in wound-healing products and in developing micro/nanoparticles. Chitosan is a weak polybase with a pKa around 6.5, implying that its charge density varies in the pH range of 6-6.5. This imparts pH-responsiveness, which is beneficial for various therapeutic applications. For this reason, chitosan-soluble and -insoluble transition occurs at pH between 6 and 6.5, which is a convenient range for biological applications (see Figure 2).  
The high charge density of chitosan at pH levels below the pK a results in polyelectrolyte formation, whereas a low charge density at neutral pH contributes to its low cytotoxicity and facilitates the intracellular release of biomolecules. It is reported that the low charge density leads to low solubility, and aggregation. Thus, the poor stability of chitosan-based formulations depends on the type of chitosan applied [8]. The degree of de-acetylation and molecular weight might alter the cationic properties of chitosan by varying the positive charge density and affect its cell-dependent transfection efficiency [4]. The cationic nature of chitosan enables the formation of polyelectrolyte complexes with the negatively charged biomolecules, allowing for the interaction with cell membranes and more efficient transfection.
## 3 Background and Related Works
### Overview of 6 LoWPAN Compression Solutions
It also includes compression of the UDP header to 4 bytes but does not allow compression of the Checksum, authorized only when upper layer use the tunneling (as IPsee Encapsulating Security Payload tunnel mode). In addition, it restricts the range of UDP ports from 61,616 to 61,631 to compress this value to 4 bits. This IPv6 header compression can only be applied to local link addresses. To overcome this problem, an IETF draft LOWPAN_HC1g has been published. LOWPAN_HC1g applies to global addresses for IP multi-hop communications. These two compression mechanisms (LOWPAN_HC1 and LOWPAN_HC1g) are complementary. It is, therefore, necessary to implement both of them. Today, the 6 LoWPAN group proposes to use LOWPAN_IPHC. It allows to replace LOWPAN_HC1 and LOWPAN_HC1g. IPHC bytes result from compression of the IPv6 header. They mainly integrate quality of service information, next headers, the number of hops, and the source/destination addresses compressed. With LOWPAN_IPHC the compression rate depends on the type of communication:* For communications over a local link, the IPv6 header can be reduced to 2 bytes (1-byte Dispatch and 1-byte LOWPAN_IPHC);
* For communications requiring multiple IP hops, the header can be compressed to 7 bytes (1-byte Dispatch, 1-byte LOWPAN_IPHC, 1-byte Hop Limit, 2-bytes Source Address, and 2-bytes Destination Address). 6 LoWPAN does not concern only the IPv6 header, but also concerns the UDP header. However, UDP does not dispose of a next header field, that is why it was necessary to compress the header of the upper-layer protocol. This 6 LoWPAN compression variant is named 6 LoWPAN-GHC [31].
# Optical Injection Locking for Generation of Tunable Low-Noise Millimeter Wave and THz Signals
## 4 Results and Discussion
### Injection Locking with a Single Laser
Figure 6a shows the variation of the PN of the locked Laser 3 due to a decrease in the optical power from the master injected into the cavity of the SL for zero offset of the master tone. For all different injected ratios, we can see identical behavior of the PN at frequency offsets up to 100 kHz. At these offsets, the PN of the beating between the locked laser and the master tone follow the PN profile of the RF signal generator with a constant offset, as expected from a frequency doubling two-tone system with suppressed carrier [43], suggesting that the slave laser accurately matches the master tone. However, starting at 100 kHz, PN behavior starts to differ from the master PN profile, and this difference increases with the decrease in the injected power. Such phenomena may be due to the fact that more power is required to ensure the SL is fully locked to the master and, consequently, PN contributions of the laser at larger offsets are also suppressed. Further, comparing Figures 5c and 6a, it can be noticed that PN has a similar shape at frequencies below 100 kHz, but has a different profile at larger frequency offsets. This behavior of the PN can indicate the different performance of the lasers due to the divergent designs or fabrication variations, while at the same time, the fact that PN starts to increase at the same frequency offset can tell us that injection locking cannot suppress all fluctuations in the slave laser. Finally, it should be noted that the difference of the noise floor at higher frequency offsets was determined by different power levels of RF beat tone, resulting from reduced power from the secondary master tone.
## 4 Simulation and Evaluation
### Comparison with other algorithms  
In this section, we compare Multi-MAF to other algorithms, namely the Push Diffusion and One-Phase-Pull (1 PP) Diffusion algorithms [13]. We use the same scenarios described before, and set Push and 1 PP Diffusion parameters--such as data rate, control messages (interests and reinforcements) periods and packet sizes--to be fair with the Multi-MAF parameters. The simulation results are presented in Figures 11 and 12.  
Figure 11 shows that Push Diffusion and 1 PP Diffusion behave similarly SID and EF-Tree, respectively. A wider comparison among these protocols is performed by [PERSON] _et al._[26]. The reason is that Push Diffusion and SID are similar proposals, infrastructure construction and maintenance; and so are 1 PP Diffusion and EF-Tree. The main difference between SID and Push Diffusion is that in Push Diffusion, exploratory packets are sent in longer fixed periods, while in SID data are broadcast until paths  
Figure 11: Comparison with Diffusion versions in the correlated detection scenario.  
Figure 10: Multi-MAF’s performance under different probabilities of failure.  
are defined. This difference makes SID more appropriated for event-driven scenarios and Push Diffusion more vulnerable to eventual packet losses, as the delivery ratio graphics show. Obviously, when fewer packets are delivered, the absolute energy consumption is lower, which is not an advantage in this case. In summary, compared to these classical solutions, Multi-MAF achieves some benefits, since it adapts to different dynamic conditions.  
The previous hybrid adaptive proposal [8] uses a threshold for the number of sources as an adaptation model. This model does not properly represent the event occurrence characteristics. For example,  
Figure 12: Comparison with Diffusion versions in the uncorrelated detection scenario.  
Figure 13: Comparison with threshold version.  
occasional distribution of event detections can lead to a concentrated measurement higher than the established threshold. In this case, a proactive behavior that may not correspond to event occurrences, which are better captured using MAF. We also compared the Multi-MAF with the original version in the Uniform detection scenario, which better presents the variance in detections. We use thresholds of 1 (Multi-1) and 3 (Multi-3) for Multi, because they lead to the best performance in the lower and high traffic cases, respectively. As we can see in Figure 13, Multi-MAF results, with the new adaptive model, are closer to the best performance of the other algorithms.
## 4 Conclusions  
Incorporation of Kakadu plum extract as reducing and stabilizing agents enabled the green synthesis of flower-shaped KKD-ZnO-NPs. The UV-Vis, FTIR, XRD, DLS, TEM, and SEM structural and optical analysis confirmed the formation of efficient KKD-ZnO-NPs. In vitro cytotoxicity testing revealed that KKD-ZnO-NPs had anticancer efficacy on human lung cancer cell lines (A549) as well as the production of ROS. On human lung carcinoma cells, KKD-ZnO-NPs induced higher ROS levels than Kakadu plum extract only. Moreover, KKD-ZnO-NPs upregulated _p53_ and suppressed _bcl2_ gene expression as regulators of cancer cell apoptosis. On the other hand, chronic or recurrent acute inflammation caused by infectious agents or other sources has the potential to promote tumor formation, growth, emergence, and malignant transformation. Furthermore, cancer and inflammatory tissues offer a number of vascular mediators, including NO, and their most notable physiological effect is increased vascular permeability. Moreover, our results showed that KKD-ZnO-NPs possess a similar capacity of reducing NO production compared to the L-NMMA as positive control. Based on the enhanced permeability and retention (EPR) effect of macromolecular drugs, enhanced vascular permeability of tumor and inflammatory tissue is the key to future drug development of highly specific targeting of the desired tumors or inflammatory lesions. However, further biological studies are needed to characterize the molecular mechanisms of KKD-ZnO-NPs and to clarify the cellular pathways involved. This approach may be an effective cancer treatment that breaks the link between inflammation and cancer. Taken together, our findings suggest that KKD-ZnO-NPs at limited dosage (10 \(\mu\)g/mL) could be a promising candidate for the development of a pharmaceutically effective cancer therapy drug with minimum toxicity.  
Conceptualization, J.C.A., D.U.Y. and D.C.Y.; data curation, R.M.; Formal analysis, Z.M.R. and G.-Y.K.; Funding acquisition, G.-Y.K., Methodology, Z.M.R., J.N. and E.J.R.; Resources, B.M.K., J.C.A. and S.C.K.; Supervision, D.C.Y., S.C.K. and G.-Y.K.; Validation, J.C.A. and E.J.R.; Visualization, D.W.L., R.A., and J.H.K.; Writing--original draft, Z.M.R. and J.N. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korean Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the High Value-added Food Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (317041-5).  
Data is contained within the article.  
Kakadu plum sample was supported by Happiness sales and C&S Bank.  
The authors declare no conflict of interest.
## 2 Failure Recovery in SFC and Challenges
### Limitations of Conventional Failure Recovery Mechanisms in SFC  
Three kind of failures can occur in SFC: (1) the failure of a link or a network entity in a path from one SFF to the next SFF; (2) the failure of the SFF; and (3) the failure of the SF [15]. We can replace the case of SF failure with the link to the SF failure, because a fully functional SF is useless if it is not accessible. SFF failure is more serious, as it can disrupt many of the SFC due to the relatively high number of links connected to it. However, network link failures are 155% more likely to happen than network node failures, as per [17]. Therefore, this study focused on protection from the failure of the link between two SFFs (Type 1) and the link between the SFF and SF (Type 3).  
Traditional local and global failure recovery methods can be applied to failure cases in SFC. The local detouring techniques set a backup path for each individual link in the network. In the underlay network, the connection between two consecutive SFFs may consist of a single hop or multiple hops. It is sufficient to provide a local detouring path in the case of a single hop; however, in the case of multiple hops, the cost of setting up individual local detouring paths for the link in each hop is very high in terms of initial setup and idle resource occupancy. Switches at the either end of the link store a separate backup path for every link to which it connects. Moreover, local failure recovery techniques cannot recover the failure of the SFF to SF link, as a single link connects the SFF to SF and has to detour traffic to backup the SF. In summary, local failure recovery techniques cannot cover all types of failures in SFC and cause resource under-utilization with a high initial setup cost.  
Global failure recovery is simpler than local failure recovery, as a shortest disjoint backup path is installed at the time of the initial path setup. This causes computational overhead at the controller at the time of initialization along with high idle resource occupancy, but shows better performance in terms of end-to-end transmission delay. In SFC, global recovery can be applied in multiple ways. Either a global backup SFC can be deployed with all backup SFs or a backup path for each segment can be installed. However, none of these approaches are efficient at recovering from all types of failures in SFC. The limitations of local and global failure recovery mechanisms for Type 1 and Type 3 failures in SFC are further explained through Figure 1,b, respectively. SFC in Figure 1 is given as \(\{SF1,SF2\}\) with backups \(\{SF1^{{}^{\prime}},SF2\}\), respectively. The proposed HP-SFC utilizes the strengths of local and global recovery mechanisms in a hybrid approach and reduces their weaknesses.
## 3 Case Study  
OAKA is supplied by three (3) high/medium voltage (HV/MV) substations (D1, D2 and D3) and there are twenty-two (22) medium/low voltage MV/LV substations. The power limits of the HV/MV substations are \(P_{limit}^{D1}\) = 6 MVA, \(P_{limit}^{D2}\) = 8 MVA and \(P_{limit}^{D3}\) = 6 MVA. The simplified sketch of the considered microgrid is depicted in Figure 5.  
To select the appropriate data for the calculations of the next section, the sport center's load for the years 2018-2020, is analyzed. Even though the load of the year 2020 was significantly low because of the regulations that were taken during COVID-19 pandemic, the demand peak was higher than the other two years. The total load, the average seasonal load and the load time series of OAKA for the years 2018-2020 are depicted in Figures 6-8. Year 2020 was not considered in the calculations because of its extended deviations due to the pandemic. Hence, the load of the OAKA microgrid in the first year of the investment period was estimated as the average demand of the year 2018 and 2019. To incorporate the stochastic behaviour of the consumption of the microgrid, a raise of 1% of the consumption of the initial year for every next year is added, and a random value between \(-20\) and \(20\) kW is added, too.  
Figure 5: Topology of the OAKA HV/MV substations.  
Figure 6: Load per substation for the years 2018–2020.  
According to the power limits of D1, D2 and D3 and the predicted peak power at the end of the investment period, the maximum number of PEV chargers is calculated. The proportion of AC and DC charger was considered 80% and 20%, respectively. The majority of the PEV drivers that use DC chargers are those whose do not have adequate time to charge their PEV at an AC charger in order to reach their next charging point. In addition, most people stay long enough at the sport center and the AC chargers could cover the charging needs. Furthermore, the DC chargers are more expensive. Hence, a small number of PEVs would use DC charger. The maximum number of PEV chargers is 308 AC chargers and 77 DC chargers. In case a PEV arrives, and all chargers are occupied, it will not charge.  
Finally, for simplification purposes the sport center is considered as one unit as illustrated in Figure 9. The arrows show the energy flow direction. The optimal PV and PEV charger distribution among the H/V substations is not considered in this study.  
Figure 8: Total load time series for the years 2018–2020.  
Figure 7: Average seasonal total load for the years 2018–2020.
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 2 Methods
### The Transfer Learning Framework for RUL Prediction
#### 2.3.2 Input Data and Parameter Settings  
All training and test datasets are shown in Table 1. Single-cycle data in the C-MAPSS dataset is a 30-dimensional feature vector, consisting of 3 operational settings, 21 sensor values and 6 one-hot encoding values. Each input sample in our networks contains 30 single-cycle data which are extracted from every multivariate time series of trajectories as shown in Figure 3. The step size of the sliding window is 1. The true RUL of a sample is determined by the true RUL of the last single-cycle data. For training data, 40% of trajectories are randomly selected as validation data. For testing data, the last slide window of every trajectory is selected as the input data.