esa-sceva/satcom-chunk-collection huggingface dataset:
# Genesis and Magmatic Evolution of the Gejiu Complex in Southeastern Yunnan, China
## 5 Discussion
### Integrated Model of Magmatic Evolution in the Gejuu Region  
The widespread magmatic activity in the Gejuu region necessitates the occurrence of an active tectonic event. From the perspective of regional tectonic history, this magmatic activity cannot be attributed to continental collision orogen, as the collision between the Indian Plate and the Eurasian continent only began around 65 Ma [66; 67]. The presence of the Laojunshan metamorphic core complex [68], rift basins [69; 70], emplacement of the Kunlun Pass alkaline granite in Guangxi [71], and the discovery of late Yanshanianian-to-early Himalayan mafic-alkaline rocks near the Gejuu granites are inconsistent with a compressional environment typical of intracontinental collisional orogen. In the Y-Ce-Nb discrimination diagram (Figure 11), A1 represents magmas related to mantle plumes or rifts, while A2 indicates magmas associated with crustal extension at continental margins [38; 72]. All samples from the Gejuu complex plot are located within the A1 field, suggesting that the Gejuu complex formed through the delamination of the thickened crust and lithosphere. This process involved the upwelling of magmas due to the intrusion of the asthenosphere into the lithospheric mantle, with the thickened crust likely being juvenile lower crust formed by earlier oceanic plate subduction. Combined with the earlier discussion on the non-compressional tectonic environment, these findings indicate that the lithosphere in this region experienced extension and thinning due to asthenospheric upwelling. Therefore, the Geju complex formed in a dynamic setting of lithospheric extension and thinning.  
Lithospheric extension triggered the partial melting of the enriched lithospheric mantle, generating alkaline mafic magmas through mixing with the lower crust [73]. These mantle-derived magmas ascended, providing the heat necessary for partial melting of the lower crust [57; 62]. The mixing of these two magma types formed the Gejuu granitic bodies, which is consistent with the genetic studies discussed earlier. The granitic melts were emplaced as plutons in the upper crust. The gabbro and diorite-monzonite originatedfrom early mantle-derived basaltic magmas, which were subsequently contaminated by hydrous crustal magmas, forming diorite-monzonite similar to that found in the Cortlandt complex [59]. The granitic enclaves represent intermediate products of the mixing between basaltic mafic melts and granitic magmas formed during the emplacement of upper crustal plutons. Through mechanisms such as physical mixing, mineral crystallization, and elemental diffusion, the diverse rock types of the Geju complex were ultimately formed (Figure 12).
## 2 Materials and Methods  
### Microcosm Preparation  
Anaerobic washwater digestion microcosms were prepared using mixed sludge provided from an operating food production washwater anaerobic digestor. Microcosms were prepared inside a LABstar Glove Box Workstation (MBraun, Stratham, United States) under a 100% argon atmosphere in order to prevent the mixed sludge from oxygen exposure. In addition, 15 mL of mixed sludge were placed into 24 individual 40 mL glass bottles with 24 mm miniert valves (Supelco, Bellefonte, United States). The bottles were then divided into 4 sets of 6 bottles for tests of exposure to 0, 16.7, 33.3, and 66.7 mg/L benzalkonium chloride (BAC) (Sigma Aldrich, Oakville, Canada). These concentrations were chosen to elicit a wide range of metabolomic response from BAC exposure rather than to explicitly identify the inhibitory limits for BAC. BAC was added to each bottle using 15 mL of a 0.01 M pH 7 phosphate buffer (Sodium phosphate monobasic dihydrate/Sodium phosphate dibasic, Sigma Aldrich) containing the appropriate amount of BAC to achieve the desired concentrations. A buffered solution was used to control the pH to help improve the analysis of water from these microcosms by NMR.  
Microcosm bottles were placed into an Innova 3100 water bath shaker (New Brunswick Scientific, Stevenage, United Kingdom) and left to incubate at a temperature of 35\({}^{\circ}\)C and a shake rate of 75 rpm until being tested on days 1, 2, 5, 9, 12, and 18. Samples were vented through Miniert lid valves daily to alleviate built up biogas. The CO\({}_{2}\) and CH\({}_{4}\) composition of the biogas produced was measured at specified time points by GC FID. The headspace of each bottle was analyzed using a syringe through the Miniert valve in order to limit the introduction of oxygen into the bottle. Each treatment and timepoint in the study is represented by a single bottle that was sacrificed to extract the water sample for NMR analysis. This approach was chosen as the removal of water from the microcosms needed for analysis would disrupt the equilibrium and negate differences in metabolite levels observed at different points in the study.
## 4 Simulations and Results
### Security Features of Proposed System
#### 4.6.4 Single Point of Failure  
In our proposed system, a distributed file storage system, i.e., IPFS, is used. The file is divided into different chunks, which are stored at each node of the network. IPFS acts as a P2P network, which overcomes the problem of single point of failure. Hence, our proposed system is robust and achieves high throughput.
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 3 Alternative Processes for Increased H\({}_{2}\)/CO Ratio
### Methane Tri-Reforming (TRM)
[145], the modulation on the H\({}_{2}\)/CO ratio as a function of the feed composition is due to competitive CH\({}_{4}\) reforming reactions that take place as a result of the presence of multiple oxidants (CO\({}_{2}\), H\({}_{2}\)O and O\({}_{2}\)) on the TRM process. [PERSON] et al. [149] investigated the influence of different support materials for 5 wt% Ni-based catalysts. The authors synthesized four different catalysts supported over Al\({}_{2}\)O\({}_{3}\), yttria-stabilized-zirconia (YSZ), CeO\({}_{2}\) and silicon carbide (\(\beta\)-SiC). The high thermal conductivity, mechanical strength and chemical inertness of \(\beta\)-SiC proved to be excellent catalytic features for TRM. The 5 wt% Ni/\(\beta\)-SiC presented the highest H\({}_{2}\)/CO ratio of 2.0 on the catalytic tests. Moreover, both 5 wt% Ni/\(\beta\)-SiC and 5 wt% Ni/CeO\({}_{2}\) catalysts showed high CH\({}_{4}\) conversion and the highest reaction rates (11.70 \(\times\) 10\({}^{4}\) mol s\({}^{-1}\) gNi\({}^{-1}\) and 11.13 \(\times\) 10\({}^{4}\) mol s\({}^{-1}\) gNi\({}^{-1}\), for 5 wt% Ni/CeO\({}_{2}\) and 5 wt% Ni/\(\beta\)-SiC, respectively).
# Underwater Electromagnetic Sensor Networks, Part II: Localization and Network Simulations
## 5 Conclusions  
We intend that this publication serves as proof of concept for a prototype U-WSN deployment in shallow waters with complete communication capabilities. In order to accomplish that, we took real measurements of the underwater channel and characterized an EM channel model in the first part of this paper, i.e., [9]. From these measurements and channel model, we fitted a parametric path loss model for narrow-band communications and low noise levels.  
We studied and proposed algorithms for self-localization techniques with available neighboring communication. We took into account packet loss considerations due to channel changes and also low connectivity between nodes, as well as low data rate packet limits. We proposed Algorithm 1 for self-localization, which can be solved in a distributed manner, and Algorithm 2 for UUV target localization, which is solved optimally.  
We also presented preliminary simulation results of U-WSN in Castalia, a realistic, flexible and fast simulator, the precursor of a real deployment. We considered routing, as well as diffusion scenarios and presented some results that are useful for designing centralized and distributed signal processing algorithms, which are motivated by the previous localization proposals. In order to perform the simulations, we extended Castalia, implementing the proposed path loss model, including coherent receiver and channel coding to the radio and adding diffusion capabilities to CTP.  
The authors would like to thank [PERSON] (ULPGC) for all of the help provided while making the underwater measurements. Likewise, the authors would like to thank [PERSON] and [PERSON] (PLOCAN) for setting up the measurement testbed in the pier and into the sea. Work partially supported by the Spanish Ministry of Science and Innovation grant TEC2013-46011-C3-1-R, the Foundations and methodologies for future communications and sensor networks (COMONSENS) Network of Excellence TEC2015-69648-REDC and by a doctoral grant Formacion de Profesorado Universitario (FPU) to [PERSON].  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed the transceiver and performed the underwater measurements. [PERSON] and [PERSON] coordinated the project. [PERSON], [PERSON] and [PERSON] proposed the algorithms, performed the simulations and wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  
Figure 15: Latency histogram for a packet to be received by the sink.
## 4 Simulations and Results
### Propositions  
This subsection provides four different propositions along with their proofs.  
**Proposition 1**.: _A malicious node can intercept communication between vehicles. However, it cannot get significant information about the plain text from the ciphertext._  
**Proof.** In the proposed system, communication between vehicles is secured using encryption algorithms. Even if the malicious node can get the ciphertext, it is almost impossible for it to retrieve the plain text from the ciphertext. A private key is required for decrypting the data, which is unique and quite difficult to guess. Another way to get the plain text is to generate a new key at every iteration. However, in this case, different plain text is obtained at each iteration. Therefore, it is still quite impossible to get original plain text.  
**Proposition 2**.: _A malicious node can send transactions to other vehicles in a network. However, it is impossible to get the signature of the authorized vehicle._  
**Proof.** In the proposed system, each vehicle has a unique crypto ID, which acts as a unique identification of the vehicle. IVTP is responsible for providing secure crypto IDs to vehicles. In V2V communication, vehicles communicate with each other using the crypto IDs. The complete information of the vehicle is stored in IPFS, using the unique crypto ID. The identity of the vehicle is not used by a malicious node because it does not know the original crypto ID of the vehicle.  
**Proposition 3**.: _Suppose that a malicious node can access the data stored in IPFS. Still, it cannot tamper the data or cause the issue of single point of failure._  
**Proof.** In our proposed system, we used a distributed file system (IPFS) for data storage; so, it is quite impossible for a malicious node to cause the issue of single point of failure. Even if the malicious node can access IPFS, it still cannot tamper the data. All the data stored in IPFS is encrypted using symmetric key encryption. To retrieve or read the stored data, a private key is required, which is unique and cannot be guessed easily by a malicious node. Therefore, it is very difficult for a malicious node to tamper the data or to perform any malicious activity.  
**Proposition 4**.: _PoA consensus mechanism causes privacy leakage problem. However, our system ensures privacy of the system._  
**Proof.** In our proposed system, we used PoA consensus mechanism; in which, a miner is selected for validation of the transactions. A group of nodes having large number of computational resources are considered as miners. Winner among this group is selected by considering its reputation value. So, there is no privacy leakage problem in our proposed system.
## 5 Discussion
### Future Research Possibilities  
The procedures of the BT-based mass deployment introduced in this study are based on the partition and logical volume (LV). For example, if there are two or more partitions in a hard drive, the first partition must be deployed first; then, the deployment of the second partition will be initiated. In the implementation, the modified tracker for the BT service, the ocs-bttrack, uses the parameter  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Program**} & \multirow{2}{*}{
\begin{tabular}{c} **Software** \\ **License** \\ \end{tabular} } & \multicolumn{5}{c}{**Case(*)**} & \multirow{2}{*}{**Notes**} \\ \cline{3-4} \cline{6-7}  & & **1** & & **2** & & **3** & **4** \\ \hline Clonezilla live & GPL & Yes & Yes & Yes & Yes & Open architecture, supports most of the mainstream \\  & & & & & & OSs deployment. \\ \hline ESR [56] & N/A & U(*) & U(*) & U(*) & U(*) & Yes & Dynamic module loading technique makes hardware \\  & & & & & & independent deployment. \\  & & & & & & & Provides a set of tools for cloning, configuring \\  & & & & & & & (post installation), and managing cluster nodes. \\ Work by [PERSON] and & & & & & & & Proposed system architecture utilizes P2P \\  & & & & & & & communication between nodes that leads \\  & & & & & & & to increasing throughput. \\ \hline \hline \end{tabular}  
* 1. Book from a live system (run without installation); 2. Support unicast solution; 3. Support multicast solution; 4. Support BT solution. ** Not mentioned in the paper; nor was a source code or binary program available for verification.  
\end{table}
Table 7: Comparisons of bare-metal provisioning solutions that include the BT solution. CeCILL: CEA CNRS INRIA Logiciel Libre, ESIR: efficiency, scalability, independence, and reliability; GPL: General Public License, OS: operation system.  
\"-tracker_timeout\" or \"-tracker_max_completed\" to control whether the deployment of the first partition for all of the client machines has been completed or not. However, such a procedure has limited the flexibility of BT-based deployment, because the simultaneous read/write features of the BT-based solution for all of the partitions and LVs have not been fully utilized. For the multicast-based solution, all of the client machines should be synchronized when the used blocks are written to partitions. However, such limitations do not exist in the BT solution. Furthermore, the BT solution should allow a client to join the deployment at any time before all of the BT-related services have been terminated. In general, the simultaneous read/write feature and the inclusion of a client at any time in the deployment process can be included in the future. The flexibility of the BT-based deployment solution can greatly be improved.
## 3 Results
### Ion Dynamics  
The ion dynamics in the fibers F1, F2 are shown in Figure 3. In the two models, the initial concentration of Na\({}^{+}\) uniformly distributed in the fiber is 1 ppm, and the initial concentration of H\({}_{3}\)O\({}^{+}\) in the fiber is 0. At 250 s, Na\({}^{+}\) near the anodes in the fibers F1, F2 begins to migrate (dark blue indicates concentration zero). At 1000 s, Na\({}^{+}\) in the fibers F1, F2 has migrated to the core center. However, the cation-depleted region occupies the core area in F2 is less than F1. At this time, H\({}_{3}\)O\({}^{+}\) is ionized from the air into the fiber and accumulates at the edge of the hole (the concentration of H\({}_{3}\)O\({}^{+}\) in the deep red region is highest). At 3000 s, in the fiber F1, Na\({}^{+}\) have crossed the core center, and the negatively cation-depleted region left by Na\({}^{+}\) migration covers the core center. H\({}_{3}\)O\({}^{+}\) migrates to the edge of the core under the driving of the external electric field, neutralizing the cation-depleted region near the edge of the core. However, the negatively cation-depleted region does not cover the fiber F2 core center. At 5600 s, the H\({}_{3}\)O\({}^{+}\) has reached the core center, and the second-order nonlinearity layer covers the fiber F1 core center. At this point, Na\({}^{+}\) has diffused to the outer surface of the fiber. The second-order nonlinearity layer almost completely covers the fiber F2 core at 6220 s.  
Figure 3: Concentration distribution of Na\({}^{+}\) and H\({}_{3}\)O\({}^{+}\) in cross section of the optical fibers F1 (first two rows) and F2 (last two rows) with both electrodes biased at positive high-voltage at different poling times.
## 3 Materials and Methods  
This section describes the predictive models used for landslide susceptibility mapping, with a particular focus on two main models: cellular automata and the random forest model. It is divided into several subsections that deal with the following topics:  
1. Cellular automata: we describe the modeling of complex phenomena with simple local rules, applied in various fields, and simulating terrain evolution and susceptibility to landslides.  
2. Random forest model: a supervised learning algorithm is used for regression and classification, which creates a forest of decision trees and improves the robustness of the model through random selection of characteristics.  
3. Self-normalizing neural network (SNN) model: It is a type of neural network designed to automatically maintain the activations of neurons with zero mean and unit variance during training. It is particularly effective for tasks that require deep networks and are competitive with traditional methods on structured data.  
4. Model integration: this is a combination of cellular automata and random forest to improve landslide risk prediction, creating a feedback loop that continuously improves model accuracy.  
5. Methodology: we describe the methodological process, from the definition of the cell grid to the prediction of the landslide susceptibility index, including the search for the optimal parameters for the model.  
The most widely used models in landslide susceptibility maps (LSMs) are random forest models, logistic regression, support vector machine, and artificial neural networks.  
Despite progress, there is still significant potential to integrate machine learning algorithms with other landslide prediction techniques effectively. These events' increasing frequency and intensity necessitate an innovative approach to spatial planning. Our study aims to tackle these challenges by combining machine learning techniques with a cellular automata model to enhance landslide risk prediction. This approach seeks to provide more accurate and reliable forecasts. By integrating advanced technologies such as machine learning and cellular automata, we can improve the identification of risk areas and support more informed land management decisions. The model's ability to provide data with high spatial accuracy is crucial for identifying the most vulnerable areas and implementing preventive measures. The proposed system can be a valuable tool for local authorities, helping them to make informed decisions about land use and urban planning. We chose the random forest model for machine learning because of its proven accuracy and robustness compared to other models. Although more advanced options exist, such as deep neural networks, the random forest model balances interpretability, training speed, and predictive performance. Additionally, it is less prone to overfitting than other models, making it particularly well suited for our application.
## Materials and Methods
### Algorithms
#### 2.4.1 SA and CA Coarse Filtering  
In order to provide a pre-selection of the traffic, due to the ADS-B IN equipment's capability of detecting traffic located very far away and therefore a priori not representing a risk for the ownship, a suitable coarse filtering function is needed to select the surrounding aircraft to be processed by the CA and SA functionalities, thus reducing the computational burden. The basic principle proposed for coarse filtering consists of excluding the aircraft whose distance from the ownship is greater than a specified threshold from the conflict/collision detection. The thresholds set for conflict and collision detection should be different due to the different natures of risk associated with SA and CA functionalities. Therefore, two different coarse filtering systems were implemented, one for each functionality.
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 3 Implementing Collaborative UVs for the OWT IMR Operations  
Collaboration between similar or different types of unmanned vehicles, aerial, surface, or underwater, to perform IMR tasks for OWT can be established through various strategies. Currently, the level of feasibility needed to implement collaborative UVs for OWT IMR operations is only between two different UVs. Even though the state-of-the-art multi-drone platform has a single control interface to support a UUV and a UAV [63], however, the collaboration is either between a UAV and a USV or between a UUV and a USV. Therefore, to successfully implement collaborative UVs for the OWT IMR operations, one must establish sound strategies between any two UVs in the first instance. The important strategies that can be devised are in performing complex tasks, solving localization and navigation issues, prolonging operation time, and establishing effective communication.