esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal Carbides for Biomass Valorization
## 3 Application in Biomass Transformation
### Second-Generation Biomass
#### 3.2.4 Reforming Reactions: Selective C-C Bond Scission  
In contrast to the HDO reaction, where C-O cleavage is desired, in the reforming reaction the C-O bond should be maintained, while C-C bond is cleaved. The interest in selective reforming of oxygenates lies in the possibility of producing hydrogen or syngas, which are necessary for several applications. Tar, a byproduct of biomass gasification, which leads to the formation of hydrogen and synthesis gas, is undesired [74]. Tar condenses at reduced temperature, thus blocking and fouling process equipment, such as engines and turbines. Hydrogen finds many applications as an energy carrier, for example in fuel cells for power generation.  
Mo\({}_{2}\)C supported biomass char (Mo\({}_{2}\)C/BC) was utilized as a catalyst for the steam-reforming of heavy tar into hydrogen between 550 and 750 \({}^{\circ}\)C [75]. In order to obtain H\({}_{2}\) and CO\({}_{2}\), the authors proposed that the biomass tar is first decomposed to H\({}_{2}\) and CO on the catalyst. The resulting CO reacts further with the excess steam through water gas shift reaction, leading finally to the formation of the desired H\({}_{2}\) and CO\({}_{2}\).  
High yields were observed with an optimized Mo loading of 20 wt % at a reaction temperature of 650 \({}^{\circ}\)C. At higher temperatures, Mo\({}_{2}\)C is oxidized to MoO\({}_{2}\), as reported previously by [PERSON] et al. [76]. MoO\({}_{2}\), known not to be active in the steam reforming reaction, was identified in the XRD pattern of the spent catalyst at the expense of the Mo\({}_{2}\)C phase. Carburization at 900 \({}^{\circ}\)C allows the full recovery of the Mo\({}_{2}\)C phase.
## 1 Introduction
Despite a number of experimental and modeling studies of anomalous climate and weather events, there are still limited studies on the regional and global synthesis of the terrestrial ecosystem responses to atmospheric impacts. Given the diversity of possible feedbacks between terrestrial ecosystems and the atmosphere, we may suppose that new comprehensive knowledge of the plant-atmosphere interaction is essential for a better understanding of the changes in ecosystem functioning, productivity, and stability, as well as for predicting future climate change. Another very important issue is the different response of ecosystems to atmospheric anomalies of different intensities and durations. There are several studies discussing the extreme threshold definition in regard to terrestrial ecosystem response to weather and climate anomalies [3]. [PERSON] [35] proposed to consider events that simultaneously experience abnormal climatic conditions and natural ecosystems experience strong impacts that exceed normal variability. [PERSON] et al. [3] introduced the term \"extreme impact\", when a resilience threshold (\"extreme response threshold\") is passed, placing the ecosystem and associated carbon cycling into an unusual or rare state. Threshold values are usually exceeded when a stressor dose (i.e., cumulative amount defined by stress intensity multiplied by stress duration) reaches a critical level (e.g., during waterlogging, drought, and/or extended periods of exceptionally high or low temperatures), or when the intensity of an extreme climatic event is critically high (e.g., during a storm). Thresholds can be overcome at a plant organ, individual plant, or entire plant community level, resulting in changes in the carbon and water budgets of the entire ecosystem. However, in the above-mentioned study, the specific threshold values for meteorological parameters are not defined. Meanwhile, the threshold values of meteorological parameters associated with the abrupt changes in the rates of evapotranspiration, GPP, ER, and NEE of CO2 between ecosystems and the atmosphere may differ significantly between geographical regions. In this context, the question of identifying weather extremes in terms of their impact on plant communities, taking into account regional conditions and using different methods for extreme event assessment, needs further investigation. The main goal of our study is to assess the differences in the response of daily net CO2 fluxes between different tropical ecosystems and the atmosphere to extreme weather conditions (such as extreme temperatures and precipitation). As a working hypothesis, the study suggests that the daily CO2 flux response of tropical vegetation can differ depending on local geographical conditions and intensity of temperature and precipitation anomalies. To solve this scientific problem, we are going to use available reanalysis data, as well as information about CO2 fluxes from the global FLUXNET database [24].
## 2 Field Test Preparation  
A group of field tests were conducted in a test site in Ningbo, a coastal city in Southeast China, to study the bearing capacity of driven PHC nodular piles embedded in soft soil layers. In engineering projects, the 350 (400) mm PHC nodular pile (the diameter of nodule is 400 mm, while the diameter of pile shaft is 350 mm) and 450 (500) mm and 550 (600) mm PHC nodular piles are normally adopted in ground treatment projects. The diameter of the nodule is 50 mm larger than the diameter of the pile shaft to reduce the disturbance induced by the installation effect. For the PHC nodular pile adopted in PGP pile, the 600 (800) mm and 500 (650) mm PHC nodular piles are mainly used as the core pile, and the diameter of the nodule is 200 mm and 150 mm larger than the diameter of the pile shaft, respectively. This is because in the PGP pile installation process, the PHC nodular pile is planted into the pile hole under gravity, and the disturbance induced by the PHC nodular pile installation is very limited [18]. In this research, the 450 (500) mm PHC nodular pile was adopted in the field tests, as shown in Figure 1. The diameter of pile shaft was 450 mm, and the nodule diameter increased to 500 mm. The thickness of pile shaft wall was 95 mm. Moreover, it can also be seen in Figure 1 that the height of nodule was 25 mm, and the inclination angle was 26.6\({}^{\circ}\). The distance of the adjacent nodule of 450 (500) mm PHC nodular pile was 1 m, and the ratio of nodular spacing/nodular diameter was 2. The entire length of the test PHC nodular pile was 40 m, and the pile segments were 13 m, 13 m and 14 m long, respectively. The test PHC nodular pile was driven into the ground directly by the piling rig, as shown in Figure 2.  
Figure 1: Photograph and schematic of 450 (500) mm PHC nodular pile. The soil profiles and properties of the test site are shown in Table 1, in which \(H\) is the thickness of each soil layer, \(\gamma\) is unit weight, \(w\) is water content, \(e\) is void ratio, \(E_{\text{s}}\) is elastic modulus, which is measured in triaxial tests, \(c\) and \(\varphi\) are the cohesion and internal friction angle, respectively, which are measured by consolidated-undrained (CU) triaxial tests, and \(q_{\text{sa}}\) and \(q_{\text{pa}}\) are the ultimate skin friction and unit base resistance for driven PHC pipe pile, which are estimated based on the soil properties according to the local specification [19]. It can be seen in Table 1 that the test site consists of deep soft soil, clay and silty clay layers with poor engineering properties. The thickness of two surficial soft soil layers reached 30.3 m, and the cohesion and internal friction angle of two soft soil layers were only around 10 kPa and 10\({}^{\circ}\), respectively. The thickness of the clay layer was 14.2 m, and the cohesion and internal friction angle of the clay layer increased to 27.3 kPa and 15.0\({}^{\circ}\), respectively. The test PHC nodular pile base was located at the clay layer. Moreover, the groundwater level of the test site was about 0.5-1.0 m below the ground surface.
# Pullout Response of Ultra-High-Performance Concrete with Twisted Steel Fibers
## 3 Experimental and Verification
### Experimental Results  
#### 3.3.1 Image Analysis  
Figure 5 shows the XCT images of the typical cross-sections of sample with fiber (before pullout, white color) and without fiber (after pullout, black color). As expected, due to the twisting geometry, the 2D images of twisted fibers show the uneven width of fiber channel. It is interesting to observe that the fiber-matrix interface is relatively smooth without considerable damage. The fiber channel area in the image with steel fiber before the pullout test is 12.154 mm\({}^{2}\), which is rather close to that without fiber after the pullout test (12.239 mm\({}^{2}\)). Note that the scanning resolution is approximately 30 \(\upmu\)m, so that any micro-damage with features less than 30 \(\upmu\)m is out of sight in the obtained images. As short microcracks less than 30 \(\upmu\)m in concrete are related to concrete durability performance instead of mechanical property performance [23; 24], the matrix-fiber interface could be regarded to be not damaged within the mechanical analysis scope of this study. While future study can be performed to understand the influence of microcracks on the property of twisted steel fiber such as the crack-induced steel corrosion after the mass transport channel is formed.  
During the axial pulling process of TSF from the matrix, the fiber was gradually moving out of the solid matrix. No metal piece was peeled off during the pullout process. The untwisting phenomenon was found after around 1-3 min of pulling and the whole twisting shape disappeared completely after pullout, as shown in Figure 6. This untwisting phenomenon is consistent with the existing numerical studies from researchers such as [PERSON] et al. [14] who numerically investigated the pullout response of the single twisted fiber with a triangular cross-section. As the untwisting behavior inside the concrete is invisible, we then conduct a numerical study in Section Numerical analysis to understand the untwisting characteristics inside the concrete.  
Figure 5: Typical XCT section scanning images of concrete matrix before and after twisted steel fiber pullout experiment.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 2 Related Work
### Cooperative Caching in Satellite-Terrestrial Networks  
Satellite-terrestrial networks with cooperative caching can enhance the performance of user service experience and network running utility [38; 39]. Initially, a two-layer content caching and delivery approach is proposed to minimize the satellite bandwidth consumption [40], a caching placement optimization problem is investigated to maximize the quality of experience [41], and a coding-based collaborative caching scheme is also studied to jointly optimize the strategy of caching placement and power allocation [42]. However, the above existing works just discuss the problems in satellite-terrestrial networks without inter-satellite communication, which can result in performance degradation.  
Then, satellite-terrestrial networks with inter-satellite communication are further investigated for cooperative caching, where a cooperative multi-layer edge caching approach for base stations, satellites, and gateways is proposed to minimize the content service delay of users [43], and a simple yet effective cooperative content retrieval scheme is studied to reduce the traffic load [44]. Moreover, a content popularity-aware caching placement approach is discussed to improve the cache hit-rate and content service delay [45]. Note that the joint optimization problems of data routing, network bandwidth, and storage resources are not fully considered. To this end, this paper comprehensively considers all of the routing path, storage and bandwidth resource allocation, and caching placement and proposes a neighborhood search approach to address the problem effectively.
## 6 Conclusions  
Based on the presented research and calculations, the following conclusions can be drawn:  
* The error difference according to Formula (5) of determination of characteristics when using different numbers of samples (9, 21 and 30) is 2.5% and 1.3%.
* The characteristic obtained from 9 samples falls within the scatter of the error of estimation of characteristic for 30 samples and can be adopted for estimating fatigue life when taking the standard deviation according to the recommendations of the International Institute of Welding [40], i.e., \(\sigma\) = 0.25. However, fatigue life at 5% probability for 9 specimens without correction of standard deviation is larger than fatigue life at 5% probability for 30 specimens (discussed in Chapter 5 and presented in Figure 11) and could not be used for engineering purposes.
* The power of the test \(M\) increased more than two times between the number of samples (9 and 30).
* The number of samples should be determined based on consideration of the required accuracy of fatigue life/strength estimation and economic justification.
* The determination of the accuracy of the estimation should be preceded by an analysis of the damage caused after damage to a given structural element, i.e., loss of health, property damage, risk of environmental contamination, etc.
* The value of the standard deviation has a significant effect on the power of the statistical test \(M\) and the error \(\delta\) of fatigue life estimation, according to Formula (5).
* Recommendations according to ISO 12107:2003 [16] should be considered reasonable, but the final number of samples should be determined after conducting preliminary  
Figure 12: Relationship between the number of samples and the power of the statistical test.  
tests (min. eight specimens [16]) and verifying the estimating error \(\delta\) according to Equation (5), or the power of the statistical test \(M\) according to Equation (7). If the obtained error \(\delta\) or power of the statistical test \(M\) is lower than required, the number of specimens must be increased by one sample on the stress level. Then the error \(\delta\) is calculated according to Equation (5) or the power of the statistical test \(M\) is calculated according to Equation (7). The obtained value is checked. When the error \(\delta\) or the power of the statistical test \(M\) meets the requirements, the test is stopped.  
Conceptualization, P.S. and J.S.; methodology, P.S.; software, P.S.; validation, P.S. and J.S.; formal analysis, P.S.; investigation, P.S.; writing--original draft preparation, P.S.; writing--review and editing, P.S. and J.S.; visualization, P.S.; supervision, J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 4 Photon Diagnostics Systems
### Invasive Photon Diagnostics Systems
#### 4.2.1 MCP Based Detector  
When all undulator segments are inserted to establish the SASE condition, this detector measures intensities from the initial signs of lasing up to saturation [57]. Two horizontal manipulators insert either 15 mm diameter MCP discs for integral intensity monitoring with 1% rel. accuracy over a large pulse energy range (1 nJ-10 mJ), a photodiode (Hamamatsu, 10 \(\times\) 10 mm\({}^{2}\), 300 \(\upmu\)m thick), or a larger MCP-intensified phosphor screen providing an intensified beam image with 30 \(\upmu\)m resolution via an optical camera setup.
## 4 Results
### Simulation Parameters  
The underwater acoustic modem has been tested at sea trails, as shown in Figure 5. The design indexes of the communication distance and communication rate between point to point have been verified. Based on this, simulation parameters with the shallow sea should be set as the simulation scenario, as shown in Table 4.  
We use four performance metrics, which are route discovery success ratio, packet delivery ratio, energy consumption and protocol overhead of nodes. RFAODV, AODV and DSAODV are compared for their performance at different traffic load conditions, number of nodes and move speed.  
1. Route discovery success ratio is the ratio of the number of successful route establishment to the total number of route discovery attempts;
2. Packet Delivery Ratio is the ratio of the packets succeed to deliver at the destinations to those generated by the sources;
3. Consumption energy is defined as the total energy consumption divided by the number of received packets;
4. Protocol overhead is the total number of routing control packet;
5. Traffic load is the ratio of the total number of application layer data bits transmitted to the upper limit of the theoretical value of network transmission.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline Routing protocol & RFAODV/ AODV [13]/DSAODV [21] \\ \hline Scene size & 3000 m \(\times\) 3000 \(\times\) 2000 m\({}^{3}\) \\ \hline Propagation model & Underwater propagation \\ \hline Channel & Underwater channel \\ \hline MAC protocol & Aloha \\ \hline Antenna & Omni Antenna \\ \hline Transmission power & 20 W \\ \hline Receiving power & 2 W \\ \hline Idle power & 20 mW \\ \hline acoustic velocity & 1500 m/s \\ \hline Transmission range & 1000 m \\ \hline Packet size & 512 Byte \\ \hline Communication rate & 10 kbps \\ \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 5: Sea trials of underwater acoustic modem.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 4 Proposed Algorithm
### Balancing the Energy Consumption Ratio  
Figure 13 shows the energy consumption ratio of CHs during different time periods in the CCWSN with four coronas and four sectors. In the CMS2V2 algorithm, MSs move along the predetermined paths based on the determined velocities and sojourn at the clusters for the limited time obtained from Equations (29) and (30). MSs compensate the extra energy consumption of CHs based on their waiting time calculated from Equation (24) to (27). As shown in Figure 13, the energy consumption ratio of CHs in the same sector and different coronas is balanced every time that MSs sojourn in the clusters in each sector. This feature is considered a solution to mitigate the energy holes, which increases the network lifetime.  
Figure 12: Network lifetime with different velocities of the innermost MS.  
Figure 13: Energy consumption ratio of CHs.
## 4 Global Navigation Satellite System (GNSS) Measurements  
GNSS measurements use ground stations to observe the 3D surface displacement field. Nowadays, continuous GNSS observations are widely applied for the (near) real-time monitoring of active volcanoes, for example, at Piton de la Fournaise, La Reunion [32] and in Central Africa (KivuGNet [33]). The more labor intensive campaign-style GNSS measurements are used to enable tracking of 3D surface deformation over a wider area, albeit with lower temporal resolution. Compared to GPS measurements, GNSS measurements take advantage of signals broadcast by a diversity of satellite navigation systems, such as the American GPS, Russian GLONASS, European Galileo, and Chinese BeiDou systems, thus improving their accuracy, redundancy, and availability.  
In the framework of the Continuously Operating Caribbean GPS Observational Network (COCONet), 61 continuous GNSS stations were installed in the Caribbean by 2014. The aim of the project is to create a large-scale geodetic and atmospheric infrastructure to improve understanding of geohazards in the region, such as earthquakes, hurricanes, flooding, volcanoes, and landslides, but also, to improve the understanding of the kinematics of the regional tectonic plates [34].  
In 1998, ten markers for campaign-style surveys were installed on Saba and St. Eustitius [35]. GPS campaign surveys were conducted at these sites in 1998, 2000, 2001, 2004, and 2009, and the data were used to study the complicated kinematics of the Caribbean subduction zone [35; 36; 37]. According to [PERSON] [35], velocity azimuths with respect to a fixed Caribbean plate are NW for Saba and St. Eustitius, sub-parallel to the arc, supportive of a NW translation of a forearc sliver.
# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 3 Review of DT in HRC
### Benefits of DT-HRC
#### 3.5.3 Task Planning and Optimization  
The contemporary nature of customer demand requires shop-floor environments to be more flexible and customizable. HRC robots are highly suitable for reconfigurable low-volume production facilities [164]. The rise in the number of HRC tasks within manufacturing facilities where humans and robots share a fenceless environment demands methods to mediate tasks between them. There are various algorithms and decision support systems for task planning and allocation in hybrid and collaborative work cells [114]. The DT of HRC combined with information from the virtual shop floor, virtual world model and the data from the physical world can be utilized to adapt the behaviour of HRC to cope with varying production volumes [98]. The distribution of tasks in HRC will depend on the shop-floor resources. Resource allocation can be formulated as a search problem where an intelligent decision-making system distributes tasks to humans and robots separately [143]. An assembly planning framework that creates a DT of a product from its digital description can perform the virtual assembly of the product and allocate resources accordingly [165]. This approach, coupled with task planning algorithms, can be used to automate the DT-HRC process to efficiently allocate resources and tasks separately to the human and the robot working in a shared space. As the DT-HRC model receives data from various IoT devices, it can continuously update its state and track changes. Furthermore, data can be analyzed efficiently to improve the performance of the physical model [166]. The bottleneck of the process, cycle time and idle time for each task needs to be identified for the adaptation of DT in process planning [167]. A discussion of planning and optimisation of commissioning scenarios where a DT of a process is created is presented in [119].  
A DT-HRC testbed implemented to analyze human-robot coordination shows how information gathered across various modules that track human arm trajectory is used with robotic reasoning to identify the tasks that the robot can do without colliding with the operator [168]. Another example of DT-HRC uses an event-based simulation to model workload balancing, task distribution between the robot and the human, and trajectory optimization as per ISO15066 [48]. It also shows the generation of an on-the-fly robotic program to carry out the assigned tasks. Therefore, DT in HRC can be useful for accomplishing complex tasks and building a more robust, safe HRC environment. In addition, DT-HRC enables simulation of the range of states of the robot and the operators as well as the production scenarios that involve various resources to find an optimal solution [22].
# Exploiting Outage and Error Probability
## 4 Simulation Results and Discussions
### Network Performance Parameters--Definitions  
Subject to validation of the proposed work, we conduct simulations in terms of the following metrics.  
1. _Network lifetime:_ It is the time duration from the start of the network till the death of the last node.  
_Total energy consumption:_ It is the total energy consumed by all the nodes during transmission, reception, and idle time. It is measured in Joules.
* _Throughput:_ It is defined as the total number of successfully received packets at the sink and is measured in packets/time slot.
* _Packet drop:_ It is the number of packets that are not successfully received at the sink.
* _Packet acceptance ratio:_ It is the ratio of packets received at sink to the total number of packets sent towards sink.