esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Flexural Tests on IL- Samples  
Three-point bending tests were conducted on the IL- samples, and the bending stresses were then compared instead of the strengths, owing to irregular shape and the same geometry pattern. The central loading was directly onto the middle strip and parallel to the strip interface, as shown in Figure 7c. The average flexural stresses were improved by 46% from 1975 (IL-0) to 2876 N (IL-1), and a 120% improvement from 891 (IL-2) to 1963 N (IL-3). The IL-0/2 samples broke into two pieces with a neat breaking interface, as shown in Figure 15a, and the IL-1/3 samples broke with a rough surface as shown in Figure 15b, proving the staples contributed to the pulling resistance, and softened the post-peak behavior of the samples, as well as in the reference [50].  
Figure 14: Failure photos of the IR- series samples: (**a**) IR-2, (**b**) IR-6, and (**c**) IR-1.  
Figure 15: Failure of the IL- samples: (**a**) a neat break of IL-0 samples without staples, (**b**) regular breaks of the IL-1/3 samples.  
The extruded mortar could be adjusted more (IL-0) or less (IL-2) than the geometry requires by changing the extrusion speed; as mentioned in Section 3.1, the adjacent strips could still join and bond. Compared to IL-0, low material extrusion speed for IL-2 resulted in a lower interface bonding strength since the contact area in the strip interface was less than IL-0 due to less material extruded than in IL-0. Thus, IL-3 showed a relatively significant improvement over IL-2, which was complicated based on the poor interface bonding. We suggest that the 46% improvement in the staple reinforcement of IL-0 was modest.
## 6 Conclusions
### Application of the Model  
The predictive capabilities of the integrated model utilizing RF-GA-LSSVM presented in this paper surpass those of other algorithms. The findings serve as a valuable reference for real-world applications. This model offers an effective approach to enhance environmental management, integrate production data, and mitigate dust hazard risks in opencast mining operations, thereby reducing the environmental, production, and occupational risks associated with dust levels in these areas. This is primarily evident in the following two key areas:  
1. An effective model for predicting dust concentration can assess the level of dust pollution in opencast mines. This offers a new approach for managing and preventing dust, allowing production teams and on-site workers to proactively develop strategies, adjust mining operations promptly, and mitigate the negative effects of dust pollution on the mine's ecological environment. It also helps prevent damage to mining machinery and enhances the precision and effectiveness of dust control measures, thereby ensuring safer mining operations.
2. Dust pollution in opencast mines not only harms the ecological environment but also impacts production efficiency and safety, posing serious health risks to workers. By implementing a prediction model, it is possible to thoroughly investigate the factors contributing to dust concentration in these mines, providing a theoretical framework for assessing dust-related risks and helping to manage or reduce the incidence of occupational diseases like pneumoconiosis.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 1 Introduction
RQ2: How can the relevance and adequacy of the identified requirements for the AIRDROP platform be ensured to meet the complex dynamics of HL operations? Addressing this literature gap, this paper makes a significant contribution to the field of HL and disaster management technology integration. Specifically, it uniquely provides the first comprehensive framework for the elicitation, specification and validation of the requirements necessary for the development and operational success of a holistic HL platform, such as AIRDROP. Through expert engagement and rigorous analysis, this work not only identifies and prioritizes key functional and non-functional requirements but also aligns them with the complex needs of humanitarian operations. Such a systematic requirements engineering process is pivotal for bridging theoretical concepts with practical applications, thereby offering a robust foundation for the effective design and implementation of technology-driven HL solutions. The paper is organized as follows: Section 2 presents a comprehensive literature review of previous research efforts in the domain of humanitarian drone applications, with the aim of elucidating their areas of focus, including the technologies used, the disaster phases of application, and the range of services offered, as well as highlighting the research gap AIRDROP attempts to address. Section 3 provides a meticulous exploration of AIRDROP's diverse array of proposed services and functionalities. Section 4 details the research design employed in this study, explaining the adoption of an abductive research approach and the integration of design science research (DSR) methodology alongside survey research. In Section 5, a structured methodology for requirement elicitation and specification is presented, followed by requirement validation, which is conducted through a structured questionnaire. Section 6 delves into the presentation of questionnaire analysis results, including descriptive statistics and the outcomes of non-parametric tests. Central to the validation process is the MoSCoW (Must Have, Should Have, Could Have, Would Have) methodology, which is employed to prioritize these requirements. The significance of differences in participant responses is assessed through the Kruskal-Wallis H test and Mann-Whitney U test. Section 7 offers an in-depth discussion of the implications drawn from the results, focusing on their significance for the design of AIRDROP. Finally, the paper concludes in Sections 8 and 9, summarizing the key findings and insights obtained throughout the study and addressing research limitations.
## 4 Analysis of Influencing Factors
### Effect of the Support Structure Insertion Ratio  
The insertion ratio k of the supporting structure refers to the ratio of the length below the bottom of the foundation pit to the length above it. According to the research of [PERSON] et al. [38], the insertion ratios considered here are 0.2, 0.4, 0.6, 0.8 and 1.0.
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 2 Rough Surfaces Parametrization  
This section is devoted to mathematical methods used to characterize randomly rough surfaces. A rough surface is described using its height deviation from a mean smooth surface. Hereafter are presented the statistical distributions (height and correlation functions) and associated parameters usually employed to properly define the rough surface properties. We will here take the example of Gaussian height distributions since most of the used models of rough surface scattering assume such a distribution. However, although many surfaces (both natural and engineered) exhibit Gaussian height distributions, there are also many others that do not [23; 24]. Examples of generating non-Gaussian rough surfaces can be found in the literature [25; 26].
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 3 Finite Element Method
### Stabilization of the Convection-Diffusion Term  
The first terms in the Oseen problem (7) represent a convection-diffusion operator of the form \(-\imath\Delta\mathbf{u}_{h}+\mathbf{w}\cdot\
abla\mathbf{u}_{h}\), where the wind \(\mathbf{w}\) is given by the solution of the previous iteration. In the numerical solution of this kind of problems, one important quantity to estimate the stability of the method is the Peclet number, defined as the ratio between convective and diffusive forces, i.e., \(Pe=\frac{Lw}{\
u}\), where \(L\) is a characteristic length and \(w\) is the local wind velocity. When discretizing a convection-diffusion problem, this number is used when considering as characteristic length the dimension of the elements. In this way, this parameter is able to tell whether the solution will be stable or not: small values of \(Pe\) assure a good solution, while a large \(Pe\) leads to instability.  
This can be solved using a finer discretization, but when the viscosity is very low, the elements would need to be so small that the computational cost would become enormous. Another option is to use a stabilization technique: instead of solving the unstable problem, one can solve a slight modification of the problem that is stable. This is achieved by adding some artificial diffusion to the problem, which assures that the Peclet number decreases. Of course, the solution will be slightly different from the real one, but it will be at least stable. If the stabilization is done correctly, then the solution will not be affected too much.  
There exist various methods of stabilization; in this work, the simplest one will be used, called streamline diffusion (SD), which adds diffusion only in the direction of the wind. Other approaches include the Streamline upwind Petrov-Galerkin (SUPG), the Algebraic subgrid scale (ASGS) stabilized Finite Elements, and the Galerking Least-Square (GLS) methods, but their theory and implementation are far more complicated, and they are beyond the scope of this paper (see (15) for more details). The SD method introduces another bilinear form in the formulation of the Navier-Stokes equations, \(s(\cdot,\cdot)\colon\ V_{h}\ \times\ V_{h}\ \mapsto\ \mathbb{R}\) defined as  
\[s(\mathbf{u}_{h},\mathbf{v}_{h})=\int_{\Omega}\tau_{SD}(\mathbf{w}\cdot \mathbf{u}_{h})(\mathbf{w}\cdot\mathbf{v}_{h})\,d\Omega,\]  
where \(\tau_{SD}\) is defined as follows: call \(Pe_{h}=\frac{lw}{2\
u}\) the mesh Peclet number, with \(h\) the local dimension of the element. Subsequently, if \(Pe_{h}<1\), \(\tau_{SD}\) is set to zero, i.e., no stabilization is performed on those elements where \(Pe_{h}\) is sufficiently small. Instead, where \(Pe_{h}\geq 1\)  
\[\tau_{SD}=\frac{h}{2w}\Big{(}1-\frac{1}{Pe_{h}}\Big{)}.\]  
This choice of the stabilization parameter is taken from (2).  
This stabilization technique will be useful when solving the Navier-Stokes equation with low values of viscosity.
## 2 Materials and Methods
### Detection of Caspase Multiplex Activity  
The protease activity of caspases-3, -8, and -9 in HeLa cells after treatment was assessed using a caspase multiplex assay kit (fluorometric) from Abcam (ab219915), and the assay was performed as per the kit's protocol. Intrinsic apoptosis pathway triggers the activation of Caspase-9, whereas extrinsic pathway activates Caspase-8. Both extrinsic and intrinsic pathways lead to activation of Caspase-3 (executioner). To determine the effect of kaempfer treatment on caspase activation, 1 \(\times\) 10\({}^{4}\) cells/well were seeded, followed by kaempfer treatment at 20, 30, 40 and 50 uM. Following treatment, respective caspase substrates were added (100 uL/well prepared in assay buffer) in each well and kept for 1 h in dark at RT. Fluorescence reading was measured at Ex/Em = 535/620 nm (Caspase 3), Ex/Em = 490/525 nm (Caspase 8) and Ex/Em = 370/450 nm (Caspase 9) to evaluate the alteration in caspase activity.
## 3 Results and Discussion
### Fatigue Resistance  
Figure 6a shows the elliptical crack fronts developed on the fracture surfaces of FC1-FC5 specimens under the action of the different steps of cyclic loading to which they were subjected. The heat tinting at 250 \({}^{\circ}\)C applied for 15 min at the end of each fatigue step allowed identification and measurement of each front on the fractured surface of all five analyzed specimens. The results are given in Figure 6b and illustrate the dependence of the crack depth, expressed as a fraction a/2R of the specimen diameter, on the number of fatigue cycles. The plots indicate the tendency of the crack to initiate and grow when more than 10,000 loading cycles are exceeded. In addition, the correlation between the crack depth a and the cracked area A\({}_{\text{f}}\) is shown in dimensionless terms in Figure 7a, expressed as respective fractions a/R and A\({}_{\text{f}}\)/A\({}_{\text{0}}\) of the specimen radius R and the uncracked area A\({}_{\text{0}}\). These results allow the mean crack growth rate da/\(\Delta\)N and the mean stress intensity factor range \(\Delta\)K at the deepest point of the crack front to be computed for each fatigue step and then matched in a logarithmic plot to determine whether they obey the Paris-Erdogan Law:  
\[\frac{\text{da}}{\text{dN}}=\text{C}(\Delta\text{K})^{\text{m}} \tag{5}\]  
given that in this equation \(\Delta\)K and da/dN are, respectively, the applied stress intensity range at each fatigue cycle and the crack growth per fatigue cycle.  
According to the stress intensity factor K given by Equations (3) and (4) for the tested crack configuration, \(\Delta\)K becomes:  
\[\text{K}=\text{Y}_{\text{A}}\Big{(}\frac{\text{a}}{\text{P}},\frac{\text{a}}{ 2\text{R}}\Big{)}\frac{\Delta\text{F}}{\pi\text{R}^{2}}\sqrt{\pi\text{a}} \tag{6}\]  
where R is the specimen radius and a and c are the semi-axes of the crack front for the applied load range, \(\Delta\)F. Then, Equation (5) was particularized for the load range applied during each loading step and for the mean values of the elliptic crack semi-axes measured at the initial and final crack fronts of the step. The resulting pairs of values (\(\Delta\)K, da/dN) are plotted as blue dots in Figure 5b and confirm that the fatigue cracking of the tested steel follows the Paris-Erdogan Law with constants: m = 3 and C = 4.45 \(\cdot\) 10\({}^{-12}\) MPa\({}^{-3}\)m\({}^{-1/2}\)/cycle. The values of these constants reflect a typical fatigue resistance of ferrite-pearlite structural steels, significantly higher than that of martensitic steels [28].  
Figure 6: (**a**) Macroscopic images showing the fracture surfaces of the fatigue-tested notched cylindrical specimens with marked fatigue loading steps; (**b**) Relative crack depth a/2R as a function of specimen diameter vs. number of fatigue cycles, N.
## 4 Classification of MAC Protocols Based on Channel
### Centralized Single-Channel MAC Protocols
#### 4.4.1 VAT MAC  
According to VAT-MAC (novel adaptive TDMA-based MAC) protocols, RSU is used to provide efficient access management for more efficient network performance [85].  
In the VAT-MAC time management period (TMP), the RSU broadcasts a time management frame (TMF), noting the length of the free transmission period (FTP) and contention period (CP) depicted in Figure 9. The FTP protocol is used for packet transmissions of allocated time-slots, and unidentified vehicles can compete for idle slots in the CP. The RSU can identify the assigned slot in the upcoming FTP if the vehicle successfully accesses the CP without colliding. The RSU further calculates the average vehicle density based on the collision probability in order to anticipate the number of newly entering vehicles. By doing so, it is able to predict the collision probability. By adjusting the frame length accordingly, VAT-MAC is capable of improving network scalability and of ensuring the efficient use of time slots. The mathematical analysis and simulation experiments indicate that VAT-MAC can significantly enhance system scalability and throughput. The performance can be enhance with incorporation of CSMA/CA along with TDMA mechanism.
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
## 5 Conclusions  
### Two Advantages of STPA-SN  
1. Higher efficiency.  
The formalization of the STPA is the foundation of the automation. After the STPA-SN is defined, a prototype tool was developed, and the example in Section 3 can be demonstrated through the tool (see the video at [[https://youtu.be/LQVuW0X8](https://youtu.be/LQVuW0X8) tJY]([https://youtu.be/LQVuW0X8](https://youtu.be/LQVuW0X8) tJY), accessed on 16 January 2022. Slight variations exist).  
According to the tool's demonstration, the following features could be observed:  
* PUCCAs could be generated automatically.  
* The loss scenario can be generated automatically.
* Hence, it can be concluded that STPA-SN has higher efficiency than HSTPA.  
2. Better collaboration with SysML-based systems engineering.  
It could be found from the tool that the SysML model is the main interface for users and the NuSMV model is transparent to the users. This feature is attributed to the automatic conversion between the SysML model and the NuSMV model.  
If a system already has artifacts in SysML, it is more possible for STPA-SN to reuse these artifacts to construct the analytical model than HSTPA, which uses non-standard language. Therefore, it can be concluded that STPA-SN is more convenient to be incorporated into SysML-based systems engineering than HSTPA.
## 5 Discussion
To summarise, the detailed analysis contributing to the diagnosis of the city in terms of urban retention and hydrological risk should include:  
* Analysis of archival data regarding past severe floods and local flooding, including the extent or coordinates of events if available;
* Analysis of the modelled extent of flooding based on risk scenario maps;
* Assessment of soil sealing and depression areas that are not naturally drained or connected to the rainwater drainage system;
* Evaluation of several GIS-calculated and satellite-based indicators, such as NDVI, NDBI, green area resource index, and biologically active index;
* Multi-criteria analysis. The presented methodology was implemented in several cities with varying landscapes, populations, sizes, and hydrography, either as part of the City with Climate project or adaptation plans. It helped policymakers identify the most vulnerable areas of the cities in relation to the urban water cycle, pinpointing locations for interventions aimed at modernising grey infrastructure or implementing blue-green infrastructure.
## 2 Background Work
### Scheduling the Dispatch of the Ferry  
The scheduling of when exactly to send the ferry to collect sensed data from nodes is a complicated task. In [23, 24] the authors studied the scheduling problem where the path of the mobile sink was optimized to visit each node in the WSN before its buffer gets full. Buffer overflow was used as a trigger to send the ferry to collect data to avoid data loss.  
In [19, 20] the authors suggested the mobile sink to visit exact locations (rendezvous points) based on a predetermined schedule to collect data. The rendezvous points buffer and aggregate data originated from the source nodes through multi-hoping and transfer it to the mobile sink upon its arrival.  
The authors in [25] used a ferry to help in collecting data in partitioned wireless sensor networks and transfer the collected data stored locally back to the base station. The authors classified scheduling of a ferry visit into three categories: time-based scheduling, location-based scheduling and dynamic-based scheduling. Time-based scheduling happened when a node dies and its death leads to partitioned WSNs.  
This node will have a higher priority for ferry visits. Location-based scheduling assigns the nodes closer to the base station a higher priority for the ferry visit. Dynamic-based scheduling is based on calculating the distances between the current location of the ferry and the locations of the partitioned wireless sensor networks that have not been visited by the ferry, and selects the shortest distance for the next visit.  
In [26] the authors considered on-demand data collection where sensor nodes broadcast data collection requests when their buffers are about to be full. On receiving such requests, the ferry moves toward the sensor nodes to collect data and transfer the data to the sink.  
The authors in [27] proposed a mobile node to help disseminate data to the sink. It was used to move back and forth along the linear network, and collect data from the individual sensors when they come within its communication range. The mobile node will then transfer the collected data to a base station. The mobile node was also used to perform other functions, such as data processing, and aggregation, and can also transport messages from the sink to the sensor nodes.
## 5 Conclusions  
In this work, we have tackled the problem of quantifying the in-common information expressed from HbO\({}_{2}\) and HHb functional connectivity (FC) networks. This is achieved by our proposed Differential Symmetry Index or DSI.  
We found that performing a symmetry analysis between the HbO\({}_{2}\) and HHb FC networks can help in deciding if it is more appropriate to use one Hb signal (either HbO\({}_{2}\) or HHb) or both when computing FC. This becomes particularly relevant when we deal with fNIRS experiments with low-density FC networks or with naturally sparse functional networks--as in typical commercially-available fNIRS devices. Simultaneously, it is possible to infer that having high-density data makes such contrast likely redundant as a high symmetry between HbO\({}_{2}\) and HHb networks may be expected. From a data set including six healthy subjects performing a series of cognitive tasks in an ecological environment while walking, we recovered the task-related FC networks. We found that the FC networks of HbO\({}_{2}\) and HHb retrieved from the social and non-social prospective memory tasks are more symmetric than the networks from the other tasks. Furthermore, we investigated the segregational and integrational impact on the networks when including walk-related breathing rate and heart rate systemic factors. By including these non-neuronal factors, the information expressed by the functional networks was elucidated. A set of spurious associations vanished (conjectured from the increase in path length and a decrease in global efficiency), and the distribution of functional links changed (inferred from the change in modularity). In summary, the resulting symmetry values were significantly different from the expected ones as shown by the random networks analysis. Therefore, the FC analysis must include a subsequent integration-segregation measures analysis and the addition of a symmetry analysis between HbO\({}_{2}\) and HHb FC networks is highly recommended. The DSI distribution suggested that values over 0.2 indicate that Hb networks are symmetric.  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceived the idea of this work and conducted the definition of the mathematical model. [PERSON] defined the experimental design and carried out the experiment. [PERSON] and [PERSON] contributed and strength the interpretation of the findings. [PERSON] and [PERSON] wrote and prepared the Matlab codes to be available online. All authors contributed to the redaction of the manuscript.  
**Acknowledgments:** Research funded by EU Marie curie FP7-PEOPLE-2012-IRSES-316337 and CONACYT CB-2014-01-237251. [PERSON] and [PERSON] are supported by the Wellcome Trust (104580/Z/14/Z).  
**Conflicts of Interest:** The authors declare no conflict of interest.