esa-sceva/satcom-chunk-collection huggingface dataset:
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 4 Migration Management of VM over SDVC
### SDN Controller Assistance for VM Migration over SDVC
During the trip, vehicle A accelerates at the point to break the ad-hoc connection. Vehicle C will access the radio access because vehicle D still hosts a VC with enough computing and network resources. The active VM migration on micro-data center-2 will then update routing rules to SDN devices on vehicle D so that vehicle C would continue hosting the dedicated VM of the current cloud-based vehicular service. In this scenario, the active VM in micro-data center-1 would continue control the routing rules of vehicle A. The SDN communication infrastructure controller would assume that vehicles D and C connect to the access radio deployed at MEC micro-data center-2. The SDN communication infrastructure controller in proximity would then allow the active VM controller-2 to establish forwarding rules to the rest of vehicles in the ad-hoc network established. This VM migration scenario is shown in Figure 7c. The other case is shown in Figure 7d and concerns across MEC and central cloud. This case is similar to the previous but that, after a while, most of the vehicles leave the existing ad-hoc network. Therefore, there is, for instance, no radio access network to connect vehicle C. In fact, vehicle C establishes the connection with the central cloud. This means that the dedicated VM is migrated at the central cloud. In this case, the SDN OpenFlow controller updates routing rules to both SDN communications infrastructure controller and the active VM controller according to the location of vehicle C.  
Figure 7: Active Controller migration scenario in the Software cloud vehicular network: (**a**) inter-MEC migration case; (**b**) intra-MEC VM migration case; (**c**) across MEC micro-data computing; and (**d**) across MEC and central cloud.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 3 Results
### Forecasters Feedback to NUCAPS Developers
Thus, NUCAPS could also benefit from alerting forecasters to regions of high CAPE or large gradients in the lapse rate.
## 4 Conclusions  
In summary, we designed and fabricated a square IJJ stack consisting of about 1200 junctions with a lateral size of 240\(\times\)240 \(\upmu\)m\({}^{2}\). The goal was to improve the emission properties of the BSCCO emitters on the low-frequency end of emission, making use of low-lying geometric resonances of the stack. We studied the current-voltage characteristics and achieved continuous terahertz emission with frequency from 0.15 to 1.01 THz at bath temperatures ranging from 5 to 85 K. We observed the most powerful emission in the frequency range between 0.2 and 0.52 THz. A record-low emission frequency of 0.15 THz was observed at \(T_{\rm b}\) = 85 K. The geometry studied can be used to provide the applicability for BSCCO terahertz emitters also at frequencies well below 0.4 THz.  
Conceptualization, H.S. and H.W.; methodology, H.S., S.C. and H.W.; validation, Y.-L.W., G.S., J.C. and H.W.; formal analysis, H.S., S.C. and H.W.; investigation, H.S., S.C. and H.W; resources, T.H., Y.-L.W., G.S., J.C. and H.W.; data curation, H.S. and S.C.; writing--original draft preparation, H.S. and S.C.; writing--review and editing, H.S., H.W., P.W., Y.-L.W., G.S., J.C., T.H., V.P.K., D.K. and R.K.; visualization, H.S. and S.C.; supervision, H.W. and P.W.; project administration, H.W. and P.W.; funding acquisition, H.W. and P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 61727805, 11961141002, 62288101, 62201396); the National Key R&D Program of China (Grant Nos. 2021 YFA0718802, 2018 YFA0209002); Jiangsu Key Laboratory of Advanced Techniques for Manipulating Electromagnetic Waves; the COST Action CA21144 superqmap; the USU \"Cryointegral\" that is supported by a grant from the Ministry of Science and Higher Education of the Russian Federation, agreement No. 075-15-2021-667; and the Innovation and Entrepreneurship Program of Jiangsu Province (Grant No. JSCSBS20211334).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
Figure 4: (**a**) The detected radiation power versus the measured frequency at the color-coded \(T_{\rm b}\) values. Four radiation peaks are marked by dashed lines. (**b**) The measured radiation frequency versus \(T_{\rm b}\) from 5 to 85 K. The radiation power is indicated by the colors.  
**Acknowledgments:** We thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for the valuable discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest. H.C.S. and S.X.C. contributed equally to this work.
###### Abstract  
Terpenes represent a structurally diverse class of natural compounds with increasing scientific interest due to their potential anti-inflammatory properties. This study investigates the in silico binding behavior of six plant-derived terpenes--a-pinene, \(\beta\)-pinene, menthol, camphor, limonene, and linalool--against four key enzymes in the arachidonic acid (AA) metabolic pathway: cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2), 5-lipoxygenase (5-LOX), and phospholipase A2 (PLA\({}_{2}\)). AA serves as a reference for binding energy comparison. Blind rigid-body molecular docking is performed using AutoDock 4.2 and the Lamarckian Genetic Algorithm, with 100 runs per ligand-enzyme pair and the energy-based selection of optimal poses. The analysis includes binding energy (AG), inhibition constants (Ki), root-mean-square deviation (RMSD), and residue-level interactions. Several terpenes exhibit favorable binding energies and inhibition constants across the evaluated enzymes. For COX-1 and COX-2, menthol and camphor show low Ki values, indicating stable binding. Menthol and limonene also show the strongest affinities for PLA\({}_{2}\), exceeding AA. The focus is on compounds with potential to modulate arachidonic acid metabolism. In this context, \(\beta\)-pinene engages the catalytic site of PLA\({}_{2}\), linalool forms multiple contacts within key regions of 5-LOX, and menthol, \(\alpha\)-pinene, and \(\beta\)-pinene align with functionally important regions in both COX isoforms. These targeted interactions suggest that the highlighted compounds may selectively interfere with enzymatic activity in inflammation-related pathways. By modulating key steps in AA metabolism, these terpenes may influence the biosynthesis of pro-inflammatory mediators, offering a promising avenue for the development of safer, plant-derived anti-inflammatory agents. The findings lay the groundwork for further experimental validation and the structure-based optimization of terpene-derived modulators.  
terpenes; arachidonic acid; docking; anti-inflammatory effects
# Information Relaying Methods in VANET: Algorithms, Standards and Tests
## 8 Conclusions  
To address the issues found in the real-world test, we propose a cluster-based relaying vehicle selection scheme with a restricted seeding procedure. Taking cellular-based network architecture into account, Veins-based simulation is facilitated to recover the situation in the test, and the relaying based approach is nominated and compared. From the simulation results, the performance gain in terms of system throughput, latency, and packet received rate could be achieved by the comparison with the state-of-the-art and the no relaying case.  
Conceptualization, T.C. and C.S.; methodology, T.C.; software, T.C. and L.L.; validation, T.C., C.S. and L.L.; formal analysis, T.C.; investigation, T.C. and L.L.; resources, C.S.; data curation, T.C. and L.L.; writing--original draft preparation, T.C. and L.L.; writing--review and editing, C.S.; visualization, T.C.; supervision, C.S.; project administration, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
Special thanks to Sony RDC Beijing Lab head [PERSON] for his consistent support of our V2X for autonomous driving project. Meanwhile, we are also gradeful for the real-world opportunity of China C-V2X large scale test organized by IMT-2020 (5G) promotion group and CAICT.  
The authors declare no conflict of interest.  
Samples of the compounds are available from the authors.  
The following abbreviations are used in this manuscript:  
C-V2X Cellular-based Vehicle to Everything  
VANET Vehicular Ad-Hoc Network  
NLOS Non Line-of-Sight  
MSSP Maximum Stable Set Problem  
CHN Continuous Hopefield Network  
MSE Mean Square Error  
RSU Road Side Unit  
NWDAF Network Data Analytics Function  
QoS Quality of Service  
LMF Location Management Function  
CAICT China Academy of Information and Communications  
Figure 7: Data reception rate performance improvement with relation to transmission distance.
## 3 Results and Discussion
### Simulation for Data Accuracy in Randomly Placed Non-Hierarchical Bidirectional Graph
#### 3.3.2 Evaluation of Complexity of Information Fusion  
In this section, we will generalize the complexity of information fusion in an unstructured network which retransmissions are not allowed. Let \"\(j\)\" be the average number of neighbors of the unstructured network. In chain and tree-based networks, the value of \(j\) was approximately 1 and 2, respectively. However, for unstructured network \(j\) can be any value depending on the instance of the network. For instance, the network that we used for simulation in Figure 3 has \(j=3.2\). j can be obtained using Equation (44).  
\[j=\sum_{i=1}^{N}\frac{(degree-1)}{N} \tag{44}\]  
where \(i\) is the \(i\)th node and \(N\) is the total number of nodes. In other words, each node of the network in average needs to fuse \(j\) number of frames/packets received from neighbors per data gathering cycle for AVG and proposed methods whereas EBDKF will receive \(3j\) frames/packets. Table 5 summarizes the complexity of information fusion when \(j\) varies in an ad hoc-type Network.  
As it can be observed from the generalized results in Table 5, the average fusion complexity will be in decreasing order of EBDKF, proposed method and AVG.  
Figure 20: Accuracy comparison of 3 different fusion techniques for an ad hoc-type network.
# Qualitative Assessment of Hazardous Gas Emissions from Sewage Sludge-Derived Biochar
## 2 Materials and Methods
### Experimental Setup  
A conceptual drawing of the experimental setup is shown in Figure 2. Air from the compressed cylinder was routed to a filter to remove moisture and VOCs. The mass flow controller directed air to the distribution vessel, which evenly delivered airflow monitored by rotameters to reactors simulating the storage of biochar.  
Figure 2: Laboratory-scale experimental stand for testing the effects of storage headspace-to-biochar volume ratio and stored biochar headspace ventilation on hazardous gas emissions from sewage sludge-derived biochar.  
Four 250 mL glass reactors were filled with biochar to create two headspace-to-biochar volume ratios. Biochar amounts of 6.25 g or 75.5 g resulted in a headspace-to-biochar volume ratio of 24:1 or 1:1, respectively. Experiments were conducted at two airflow rates set to either 250 (setup 1) or 500 mL-min\({}^{-1}\) (setup 2), resulting in four air exchanges (AE) per min of biochar headspace, i.e., 1.04, 2.00, 2.08, and 4.00 (air exchanges per min) (Figure 3).
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Microsatellites
#### 3.3.3 Capella X-SAR (48 kg)  
This SAR constellation, developed by Capella Space, will have 36 satellites by 2023, 6 of which are already in orbit [115]. The first satellite, Capella 1 or Denali, has a launch mass of less than 50 kg with an origami-like antenna shown in Figure 20 (left). The second satellite, Capella 2 or Sequoia, is about twice as large as the predecessor, accommodates a 600 W transmitter and a mesh-based 8 m\({}^{2}\) reflector that is shown in Figure 20 (right) to achieve sub-meter ground resolution [116]. Following the launch of Capella 1 in December 2018 and Capella 2 in March 2020, three satellites (Capella 3, 4, 5) will be launched with further design improvements, constituting a \"Whitney sub-constellation. Another satellite called Capella X was launched into an inclined orbit with 45\({}^{\circ}\) inclination in May 2020. The Whitney sub-constellation and others will constitute the \"Capella 36\" constellation, and the coverage performance of possible configurations in between is summarized in Table 7. Capella Space is using the satellite ground station service provided by Amazon Web Services (AWS) for the rapid delivery of satellite data to its customers [117].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Total \# Satellites \(\rightarrow\)** & **6** & **12** & **24** & **36** \\ \hline \# orbit planes & 2 & 4 & 8 & 12 \\ Average revisit (h) & \textless{}4 & \textless{}2 & 1 & \textless{}1 \\ Maximum revisit (h) & 12 & 6 & 4 & \textless{}2 \\ InSAR revisit (h) & 24 & 12 & 6 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Capella X-SAR observation modes and 1-satellite/3-satellite revisit performance.  
Figure 19: MicroSAR: (**left**) Artist’s rendition. (**right**) Simulated revisit times in the Norwegian Sea and the Greenland Sea. Source: Kongsberg Satellite Services.  
Figure 20: Capella series: (**left**) Capella 1 (Denali). (**right**) Capella 2 (Sequoia). Source: Capella Space.
## 3 System Model and Problem Description
### Problem Definition  
In the UAV-assisted MEC system, the limited airborne energy makes the balance between UAV energy consumption and computing task allocation a key issue. We aim to maximize the energy efficiency of UAVs by jointly optimizing their 3D flight trajectories and user device task offloading rates while meeting UAV maneuverability constraints and other communication resource limitations. The energy efficiency \(\gamma\) is defined as the ratio of the total number of data bits successfully processed by the UAV to the total energy consumed. The expression for \(\gamma\) is as follows:  
\[\gamma=\frac{\sum_{t=1}^{N}\sum_{u=1}^{Ul}I_{u}(t)}{\sum_{t=1}^{N}\sum_{u=1}^{Ul} \left(E_{u}^{rec}(t)+E_{u}^{comp}(t)\right)+\sum_{t=1}^{N}E^{fly}(t)} \tag{19}\]  
where \(I_{u}(t)\) is the number of task bits offloaded from user device u to the UAV in time slot \(t\), and the total energy consumption of the UAV \(E_{u}^{comp}(t)\) includes three parts: flight energy consumption \(E^{fly}(t)\), task receiving energy consumption \(E_{u}^{rec}(t)\), and computation and processing energy consumption. Formula (19) quantifies the relationship between task processing volume and total energy consumption, directly reflecting the energy efficiency performance of UAVs through \(\gamma\). Therefore, a joint optimization strategy with maximized energy efficiency (OPMEE) for the UAV flight trajectory and user device task offloading rate problem can be modeled as  
\[\begin{array}{ll}\text{P1:}&\binom{max}{c(t),\eta_{u}(t)}\gamma_{CSE}\\ &\text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
\[\text{P2}: (\underset{(c(t),\eta_{u}(t))}{max})\Big{\{}\sum_{t=1}^{N}\sum_{u=1}^{ U}I_{u}(t)-\gamma\Big{[}\sum_{t=1}^{N}\sum_{u=1}^{U}\Big{(}E_{u}^{rec}(t)+E_{u}^{ comp}(t)\Big{)}+\sum_{t=1}^{N}E^{fly}(t)\Big{]}\Big{\}} \tag{22}\] \[\text{s.f. }\text{C1}\sim\text{C6}\]  
where \(\gamma\) is a non-negative parameter. When solving P2, we first fix a value of \(\gamma\), then optimize the variables and \(\eta_{u}(t)\) update \(\gamma\) based on the current solution, and iterate until the algorithm converges to obtain the optimal solution \(\gamma^{\star}\).  
For the second difficulty, an alternating optimization strategy can be adopted to decouple and block the optimization variables, decomposing the original problem into two more easily solvable sub-problems: (1) Optimizing the user offloading rate under a fixed UAV trajectory. (2) Optimizing the UAV trajectory under a given offloading rate. The definitions and solutions for these two sub-problems are given below.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 3 Methods
### Hierarchical Thresholding Approach  
The last step in the classification process chain (Figure 4) is the hierarchical thresholding approach, which is divided into three consecutive steps. First of all, the permanent open water surfaces are identified using the SWBD mask (see Section 2) and excluded from further analysis. In addition, GUF and HAND masks (see Section 2) were used as exclusion layers before the next step. The other two steps are based on the remaining unclassified image elements using the derived time series features (see Section 3.2). The threshold values for the corresponding time series features are automatically generated by a decision tree classifier [62] using reference-based training data (see Section 3.2). The automatic threshold definition represents a great advantage, since manually determining thresholds, especially for separation of TFV to other classes, can be a complex process [6]. With the aid of the aforementioned time series features and corresponding threshold values, the class TOW was derived firstly. Equivalent to TOW, the remaining unclassified image elements were separated into TFV and Dry Land in the last step.
# Advanced Automatic Welding System for Offshore Pipeline System with Seam Tracking Function
###### Abstract  
Automatic welding technology is a solution to increase welding productivity and improve welding quality in offshore pipe welding. To increase welding productivity, it is necessary to save time during the assembly/disassembly of the guide track from the welding carriage and pipe to move the next station. The guide track consists of a pneumatic system that does not separate the welding carriage, and two welding carriages operate on a half-pipe joint to increase productivity. These welding carriages automatically operate under the controller command. An automatic welding system consists of a DC motor module, a step motor module, a welding control module, a welding monitoring module, and a central control module. The control systems incorporate control modules and transmit commands to each module for an automatic welding system. In order to minimize the inevitable misalignment between the centerline of the welding seam and the welding torch for each welding pass, a moving average algorithm for seam tracking is proposed, which was proven to be suitable for the root pass, filling pass, and cap pass. Welding experiments were also carried out to verify the validity of the weld seam tracking system.  
automatic welding system; pneumatic system; guide track; seam tracking; offshore pipe welding +
Footnote †: journal: Applied Sciences  
_Applied Sciences_  
_Technical Note_  
_Advanced Automatic Welding System for Offshore Pipeline System with Seam Tracking Function_  
_J.-Hyeong Park, 10, 324; doi:10.3390/app10010324_
## 4 The Proposed Reliable Transport Scheme
### Steps of the Scheme  
The transport scheme of this paper involved the following parameters: (1) The number of coded packets injected into the network by source nodes per cycle \(M\); (2) coded packets' diffusion time \(T_{s}\); (3) time period waiting for ACK return \(T_{w}\). Each parameter is a function of \(r\), which indicates the number of packets that the destination node still needs to decode source file. \(r\) was provided by the index of ACK received by the source node at the end of the last cycle, which may have differed from the actual packet collection of the destination node. The proposed reliable transport scheme was summarized in Algorithm 1.  
```
1.Initialize:\(r=K\);
2.While r > 0
3.Start a new cycle \(r\). The source node sends \(M_{r}\) coded packets to the network, and each time it encounters a relay node, the source gives it a new packet until all \(M_{r}\) packets are sent;
4.Packets diffusion phase: Each packet is disseminated and routed in an epidemic fashion last for time \(M_{r}/\lambda+T_{s,r}\), where \(M_{r}/\lambda\) is the time required for the source to send out \(M_{r}\) packets in step 3;
5.Waiting phase for ACKs: At the end of the diffusion phase, the source node waits for ACKs (there may be multiple) from the destination node, and the waiting period is: \([M_{r}/\lambda+T_{s,r},M_{r}/\lambda+T_{s,r}+T_{w,r}]\). During this phase, coded packets are no longer copied to relays; they are only delivered to the destination node. The reason for setting the waiting time is that in this intermittently connected network, it takes time for an ACK to propagate from the destination to the source. To sum up, the length of a cycle is: \(M_{r}/\lambda+T_{s,r}+T_{w,r}\);
6.At the end of cycle \(r\): All relays clear the coded packets in the cache. The source node takes \(q\), which is the minimum index value of the received ACKs, as an argument for the next cycle;
7.Update the value of \(r\): \(r=q\).
8.End While
```  
**Algorithm 1** File transport scheme