esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of the Proposed Methodology
### MCD Simulations  
This is the next step of the proposed methodology (see H on Figure 2). In our case, the Siemens NX-MCD software (Version 2000) tool was used only on the first level (see Figure 1). On this first level, the machine control system was not used and the model of the system was not created according to the SIL or HIL concept (SIMIT program was not used). The MCD tool allowed to simulate the sequential movements of the virtual model of the machine (including the weights of specific parts and inertial mass). The mechatronic model included the behaviour of actuators (position, speed, acceleration, and jerks). In the case of the portal centre, movements in directions X, Y, and Z were simulated, with the corresponding acceleration and with travel to the end positions of the machine. Furthermore, the change of machine accessories (milling and boring heads) was simulated. MCD simulations were performed continuously throughout all phases of the machine design. The created model and obtained results could be further used for possible virtual commissioning of the machine (see Section 6).  
Machining Simulations of the Digital Twin of the CNC Machine Tool in Create MyVirtual Machine Software  
This is the next step of the proposed methodology (see I on Figure 2). The main objective was to simulate the machining technology and prevent possible problems in the production of a specific part. In this case, machining simulations were used in the pre-production phase not only of the machined part itself but uniquely in the pre-production phase (design and development) of the entire production machine--the multifunctional portal centre. For the machining simulations, Create MyVirtual Machine 1.4.1.0 (CMVM) software was used. As input to the simulations, the NC code obtained from the postprocessor was used. The CMV software allowed to virtually prepare the production on a digital twin of the machine with the same interface and interpolation as the real machine with the integration of the control system, namely, SINUMERIK ONE. In order to verify the technological characteristics (especially the dimensions) of the proposed machine, the following steps were necessary:* The kinematic model of the machine was developed in Create MyVirtual Machine 3D Builder 1.4.1.0 software.
* In CMVM software, the real control system was linked to the machine kinematics.
* The control system (SINUMERIK ONE) was configured for a given kinematic chain.
* Tool exchange and tool management were implemented.
* The machine kinematics were virtually compensated.
* A 3D model of the workpiece and tools was imported.
* The NC code generated by the postprocessor from the CAM tool was imported.  
The utilisation of CMVM software is shown in Figure 10.  
The virtual machine model of the multifunctional portal centre included accessories in the form of milling and turning heads, see Figures 11 and 12.  
Machining simulations (CMVM) were performed continuously throughout all phases of machine design.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 4 Evaluation
### Simulation Setup
After 360 min, the simulation is stopped. Besides the passive list in Setup C.1, we consider a public list of potential contacts in Setup C.2 that is frequently updated. To reduce the quantity of messages sent by the merging algorithms each node only starts a merger if it picks a random value less or equals \(\alpha/size_{c}\), where \(size_{c}\) is the estimated number of nodes in the current construct. In Setups C.3-5, we choose values of 1, 5, 10, and 100 for \(\alpha\), i.e., the number of aimed ongoing merge activities in a network. Similar to the previous scenario a Chord ring with 1024 participants is formed from minute 0 to minute 150. After 180 min a group of 400 nodes is isolated, after 240 min, it is connected again to the remaining groups, and after 360 min, the simulation is finished. Setups C.3-5 help in finding suitable values for the parameter \(\alpha\), which relates to the probability with which a node initiates a merging approach. In Setup D.1, we evaluate the Ring Reunion Algorithm with four parallel instances and ideal \(\alpha=10\) (estimated in Setups C.3-5) against the simple and Gossip-based Ring-Unification Algorithm in a complex and more realistic scenario in which multiple regions are isolated. We examine this scenario with \(\alpha=1\) and \(\alpha=10\) to compare a poor value for \(\alpha\) (1) to a fair one (10). As described in the previous scenario setup, 1024 nodes join a common Chord ring during the first 150 min. A group of 400 nodes is then isolated from the 180 th minute to the 240 th minute. In addition, another group of 50 nodes is isolated from the 200 th minute to the 240 th minute. Finally, from minute 240 to 300, a third group of 100 nodes is isolated from the other regions. Finally, in Setups E.1-2, we consider churn; a group of 400 nodes is separated from the rest of the Chord ring in minute 180, when all nodes have joined the network successfully. The duration of the isolation lasts 60 min. From minute 240 to 360, when the simulation finishes, the nodes unify the partitioned network again. In the previous scenarios, we did not consider churn in order to focus on the fundamental behavior of each tested algorithm. This time, churn is enabled throughout the whole simulation to show that our Ring Reunion Algorithm is able to handle it without loss of performance. In addition, with this last simulation setup, we examine the influence of different parameter settings on the Ring Reunion Algorithm in the presence of churn. The supplementary parameters we examine in this scenario are the parameter that controls the number of parallelized merger instances and the interval within which the public list is iterated.
## 3 Setup
### Signal Generation and Measurement Unit  
Figure 8 takes a closer look to the signal generation. The sine voltage \(V_{DAC1}\) is the digital analog converter output of the microcontroller which oscillates between 0 V and 3.3 V. The left amplifier is the voltage controlled gain amplifier LMH6503. It amplifies its differential input voltage. Therefore, \(R_{pot1}\) sets the negative input voltage to 1.65 V so that the differential input voltage is a symmetrical sine wave. The amplitude of the output signal \(V_{amp}\) can be controlled by \(V_{Gain}\). The components on the right-hand side implement a difference amplifier. As all resistors that belong to this circuit have an equal resistance,  
\[V_{out}=V_{offset}-V_{amp}\]  
applies to the output voltage. Accordingly, voltage \(V_{amp}\) is inverted and shifted by an offset. As \(V_{amp}\) is a symmetrical sine wave, and only the phase difference between the applied current and the voltage response is evaluated for determining the impedance, an inversion of \(V_{amp}\) does not have any impact on the measurement.  
Figure 7: Schematic of developed circuit for measuring impedance spectra.  
\(V_{Gain}\) and \(V_{Offset}\) are generated by a single digital analog converter output using a sample and hold circuit shown in Figure 9. The first amplifier implements a voltage buffer that decreases the current of the digital analog converter. The following voltage divider shifts the digital analog converter output range to symmetrical \(\pm 1\,\mathrm{V}\) range by referencing the lower pin of \(R_{2}\) to a negative voltage. Thereby, the offset voltage of \(V_{out}\) can correspond to either a constant charging or a constant discharging current. The actual sampling is implemented by a digitally controlled analog switch \(S_{1}\) while the capacitors hold the sampled voltages. The circuit is controlled by the microcontroller that uses a timer with three output compare channels that cyclically generate interruptions at three different time points. During the first interruption, the switch is set to middle position that is not connected. The second interruption changes the output of the digital analog converter output voltage to the value of the next clamp. Finally, the switch is switched to this clamp charging the corresponding capacitor. The final voltage buffers output the voltage that is stored in the capacity. As the input current of an operational amplifier is rather small, the voltage of the capacity is almost constant, even when the digital analog converter signal is removed when switching to the other path.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 4 Results and Discussions
### Optimization for EH
As a conclusion, considering the AEH results shown in Figure 7 and the number of BSs covered by the UAV's receiver described above, the total AEH that can be obtained for considered scenario in very clear air conditions with a target BER of \(5\times 10^{-5}\) and assuming realistic transmitted powers between 30 and 35 dBm is in the range between 134 and 450 mJ/s for a UAV height of 300 m and between 164 and 558 mJ/s for a UAV height of 1000 m. This free energy collected from the information-carrying FSO signals complements the energy harvested from the terrestrial optical and RF wireless power transfer (WPT) charging stations and contributes to extend the battery life of UAVs.
## 6 Conclusions  
Some applications for IoT require disruption-tolerant communication, especially in sparse and intermittent networks where there is no continuous end-to-end communication between the data source and its destinations. The design of DTN multicast routing protocols in such contexts becomes more complex due to the reliability constraints in ensuring that intended receivers receive messages without compromising the overall performance. The current state of multicast routing approaches in DTN was outlined and classified based on their design concepts: social-awareness, buffer-efficient, energy-awareness, and geography. Although the surveyed protocols are classified into specific categories, some of them exhibit the characteristics of another category due to the hybrid approach incorporating multiple design concepts.  
The usage of buffer management schemes is highest among the discussed schemes due to their non-protocol dependent nature. The rest of the described protocols are rather scenario-specific, as the authors intend to solve some specific problems using DTN multicast services. The social-aware multicast routing protocols have evolved from merely using the encounter history to including the dynamic social features of the users. MSN continues to evolve due to the inseparable relationship between humans and devices. Socially-aided protocols will deserve more attention than ever in the future of DTN research. However, we should not neglect the importance of energy-aware protocols, since IoT devices are usually resource-constrained, and energy conservation is essential for maintaining longer network lifetimes. The exploitation of social patterns to minimize delivery costs in combination with efficient buffer management schemes will definitely help in providing energy-efficient protocols in the future.  
Open issues in DTN were also discussed both in general and in terms of multicast limitations. Node integrity and group authentication in DTN have always been major concerns, especially with the increasing involvement of humans and their devices. It is critical to adopt security measures and trust management mechanisms. However, the increase in computation costs and maintenance overhead is unavoidable, and efficient mechanisms need to be devised to improve the overall performance of security mechanisms. The focus of our future work is to address some of the performance-related issues in designing a reliable multicast for DTN to support IoT applications.  
The contributions of authors are as follows: Conceptualization, K-S.W. and T-C.W.; formal analysis, K-S.W.; investigation, K-S.W.; resources: K-S.W.; supervision, T-C.W.; validation: T-C.W.; writing--original draft: K.S.W.; writing--review and editing, K-S.W. and T-C.W.  
This work is supported in part by Universiti Sains Malaysia through RUI Grant No. 1001/PKOMP/811336.
## 1 Introduction
Monitoring of rSCo\({}_{2}\) using NIRS devices, cerebral oximeters, have been widely used during cardiac surgery, especially during cardiac bypass operations, as cardiac surgery patients often suffer from perioperative neurological complications [5]. Numerous studies regarding NIRS in cardiac surgery show positive correlation between intraoperative cerebral desaturation and postoperative cognitive disturbances, stroke, prolonged hospital stay [6]. Lately, monitoring of cerebral oxygen saturation has gained increasing popularity also in non- cardiac surgeries. During thoracic surgery--thoracotomy or thoracoscopy where at least one measurement lower than 80% from baseline value has been reported [3]. During shoulder surgery  
Figure 3: Cerebral oxygenation values appearance on the INVOS (in vivo optical spectroscopy) monitor (L—left cerebral hemisphere, R—right cerebral hemisphere; picture taken from the INVOS Cerebral/Somatic Obimeter Operations Manual, Covidien 2010). Figure 2: INVOS sensor with one light diode and two light detectors (picture taken from The INVOS Cerebral/Somatic Obimeter Operations Manual, Covidien 2010). in the beach chair position, where cerebral desaturation episodes can be 1 minute up to 1 hour long [3]. In gynecological procedures, where the Trendelenburg position is used, as well as urological operations with hemodilution [3]. Furthermore, vascular surgery, carotid endarterectomy, where rSco2 decreases during clamping of internal carotid artery or with the blood loss during abdominal aortic aneurysm repair [3]. There is a very limited amount of studies available regarding rSco2 intraoperative monitoring during spinal operations, although special attention should be paid due to prone position and possible blood loss. Prone position during spinal surgery might be associated with several physiological challenges, which influences cerebral blood flow and oxygen supply creating conditions for cerebral desaturation. Increased abdominal pressure due to various etiologies might decrease vena cava inferior transvascular pressure and reduce venous return to the heart. Moreover, increased external pressure on the chest, resulting from prone positioning, also reduces cardiac index, in addition to increasing the peak airway pressure, thereby decreasing lung compliance causing a further decrease in cardiac output [10]. The aim of study was to evaluate the usefulness of monitoring intraoperative cerebral oxygenation during spinal surgery in order to discover cerebral desaturation.
# Stabilization Methodology in Foundation Soils by ERT-3D Application in Estepona, South Spain
## 4 Results and Discussion  
The interpretation and adaptation of this paper's proposed methodology were satisfactory and successful. The building, on the verge of collapse, recovered its stability and was ready for use again. This procedure is promising for buildings that have reached their ultimate limit state but must be maintained either because they could not be rebuilt due to urban regulations (as in this case), or because they are listed as historic and cultural heritage buildings to be preserved at all costs.  
The advantage of this proposed method is that ERT-3D allows researchers to determine if the foundation settlement and subsidence of the subsoil has been permanently eliminated after consolidation injections. Consolidating foundations through grout injections is inherently problematic because researchers do not know how much injection grout or how many injections are necessary, and they are unable to control or physically see the result, as it is underground. Therefore, an intervention methodology, such as the one presented here uniting ERT-3D monitoring with the consolidation injection process, is essential for checking the results. The advantage of this ERT-3D methodology, as well as consolidation grout injection compared to other reclining systems (e.g., micropyles), is that it is a non-invasive method for an already damaged building. This makes the system useful in extreme cases, like the one analyzed here.  
In the application of our methodology, two electrical profiles were made, E1 and E2 (Figure 6), an upper part of approximately 3.90 m thick was defined for E2 (wall B) and  
Figure 10: Results of 3D inversion of electrical resistivity tomography over the whole survey area. On the left, view from the south, and, on the right, view from the north. Contrast tomography after the consolidation injections in the subsoil.  
2.00 m for E1 (wall A) with high resistivity, ranging from 500 to up to 3000 \(\Omega\) m. This section corresponded to the colluvial material (porous) that covers the metamorphic rock (phyllites). Under this lithology was the rocky matrix, formed by altered phyllites with less resistivity, of the order of 55 \(\Omega\) m. The upper colluvial material, which was in contact with the underside of the building's footings, showed a significant degree of alteration, which means a high resistivity (from 1000 to up to 3000 \(\Omega\) m). However, the resistivity values were quite variable within this mass of colluvial material, and this is linked to two causes: (1) lack of compaction and (2) presence of gaps and cavities in the subsoil where the foundation of the building supported, which coincided with the levels I and II altered. These data confirm the instability results of the shallow foundation of the building. The building's final state, after the application of this methodology, is shown in Figure 11.
## 3 Methodology  
### Point Features Extraction  
A set of point features could be extracted for each LiDAR point. These point features are divided into three categories, namely elevation-based features including height variation (\(HV\)) and height standard deviation (\(HSD\)); intensity-based features including intensity coefficient of variation (\(ICOV\)) and intensity density (\(ID\)); and geometry-based features including point density (\(PD\)) and number of returns (\(NOR\)). Figure 3 shows the workflow of land/water discrimination using different point features.  
Figure 3: Workflow of land/water discrimination based on LiDAR point features and seeded region growing algorithm. \(HV\): Height Variation; \(HSD\): Height Standard Deviation; \(ICOV\): Intensity Coefficient of Variation; \(ID\): Intensity Density; \(PD\): Point Density; and \(NOR\): Number of Returns.  
Figure 2: Light Detection and Ranging (LiDAR) point clouds from the three channels for a subset of the study area colored by elevation; intensity; and number of returns.  
The water points were labeled based on the following assumptions. First, the water points were assumed to have the lowest intensity in the scene [16; 20]. Second, the intensity variation was higher in water bodies than in the land areas [18; 19]. This is attributed to the laser pulse angle of incidence with respect to the water surface, which relies on the scan angles, water surface roughness (i.e., waves), the and aircraft's attitude. Third, the C1 and C2 were used to acquire LiDAR data from water surfaces, while the green wavelength was used to acquire LiDAR data from both water surfaces and benthic layers due to its ability to penetrate water bodies. Thus, these characteristics increased the usefulness of the LiDAR data in land/water discrimination, when the C3 was combined with C1 or C2.  
The LiDAR point features were extracted based on a local neighborhood of each point, where neighboring points were considered using a fixed searching radius in 2D space. A searching radius of 1 m was used in order to define the land/water interface with high resolution and to ensure a sufficient number of points in the neighborhood [44]. The details of those point features are presented in the following subsections, assuming that a LiDAR point \(p_{i}\) and its neighboring points are \(j\), where \(j=1,2,3,...,n\), and \(n\) is the total number of neighboring points of \(p_{i}\).
# Granular Description of Uncertain Data for Classification Rules in Three-Way Decision
## 5 Experiment and Discussion
### Synthetic One-Dimensional Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \([x_{0}-\tau_{1},x_{0}+\tau_{2}]\) & \(Acc\) & \(R\) & \(P\) & \(E_{mis}\) \\ \hline \(E_{ur}=0\%\) & / & 0.9830 & 0.9830 & 0.9830 & 0.0170 \\ \(E_{ur}=5\%\) & [0.4437, 0.5237] & 0.9937 & 0.9937 & 0.9937 & 0.0063 \\ \(E_{ur}=10\%\) & [0.4237, 0.5337] & 0.9957 & 0.9957 & 0.9957 & 0.0043 \\ \(E_{ur}=15\%\) & [0.3737, 0.5637] & 0.9988 & 0.9988 & 0.9988 & 0.0012 \\ \(E_{ur}=20\%\) & [0.3637, 0.5837] & 0.9988 & 0.9987 & 0.9988 & 0.0012 \\ \hline \multicolumn{7}{c}{Conventional way} & 0.9942 & 0.9942 & 0.9942 & 0.0058 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of three-way decision in one-dimensional synthetic data. On the other hand, it is noted from Table 2 that there is no uncertain region considered when \(E_{ur}=0\%\). Actually, the method degenerates to a traditional two-way decision under this case. Moreover, to compare with the conventional three-way decision, we can choose data within the membership range of [0.45, 0.55] consisting in the uncertain region; the performance is also added to Table 2. It is seen that the conventional method can outperform \(E_{ur}=5\%\) under the given fuzzy set describing uncertainty, which the proposed method can have a flexible operation, and it can outperform the conventional way with better parameters describing uncertain regions.
## 2 Materials and Methods
### Confocal Laser Scanning Microscope  
Confocal laser scanning microscopy (CLSM) images were acquired with a Nikon A1R confocal laser scanning microscope to understand the 3D structure and thickness of the biofilms. The 24-h-grown biofilms, rinsed twice with 2 mL of PBS, were stained with 1 mL of a mixture containing the fluorescent dyes. The protocol used for staining is a simplified protocol adapted from [PERSON] et al. [PERSON] et al. (2016). The dyes mixture contained the (i) calcofluor white stain solution (CFW, Sigma Aldrich, St. Louis, Missouri) and the (ii) SYTO9 and propidium iodide (PI) solution (LIVE/DEAD\({}^{\text{TM}}\) BacLight\({}^{\text{TM}}\) Bacterial Viability Kit, Life Technologies, Carlsbad, CA) with proportion 1:1 in volume. The CFW solution was prepared with a concentration 10 mg/mL in water, while SYTO9-PI solution was prepared with a concentration 3 uL/mL of each dye in PBS. Biofilms were stained with 1.2 mL of the dyes' mixture solution for 20 min. During staining, the biofilms were kept in darkness to avoid fluorescent dyes' photobleaching. Afterwards, the staining mixture was removed, and the biofilms were rinsed with 2 mL of distilled water to discard the excess of dyes. Biofilms were sealed with a cover slide (35 mm, no 1.5 glass thickness; MatTek in vitro life science, Bratislava, Slovakia) using nail polish. The wavelengths of excitation (\(\lambda_{\text{exc}}\)) of CFW, SYTO9 and PI were 405, 425 and 575 nm, respectively, while the emission was captured using pass band filters (\(\Delta\lambda_{\text{em}}\)) of 425-475, 500-550 and 570-620 nm, respectively. The 60\(\times\) oil lens was mounted for the acquisitions. NIS Elements software was used to capture the images and ImageJ to analyse them.