esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of Atomically Thick Graphene and Its Derivatives in Electromagnetic Absorbers
## 3 Graphene-Based Metasurface Absorbers
### Multiband and Broadband Operation for Graphene Metasurface Absorbers
[111]; (**c**) schematic of a dual electric LC (i.e., inductance (L) and capacitance (C)) unit cell; (**d**) the wave absorption vs. frequency and incident angles for transverse magnetic (TM) polarized waves; (**d**) the wave absorption vs. frequency and incident angles for transverse electric (TE) polarized waves, reprinted with permission from Ref. [95]. In addition, a couple of research groups have also designed broadband graphene metasurface absorbers by stacking multiple metasurfaces resonating at different frequencies on the top of each other [116; 117; 118; 119; 120; 121]. In 2016, [PERSON] et al. presented their experimental results on the metasurface printed on a flexible substrate with graphene nano-flakes ink [122]. As shown in Figure 9a-e, the flexible absorber can be easily coated on the surface of a cylindrical object. The measurement results demonstrate effective microwave absorption from 10.4 GHz to 19.7 GHz, corresponding to a fractional bandwidth of 62%. Recently, [PERSON] et al. proposed a frequency selective surface (FSS) based metasurface absorber with a patterned multilayer rGO sandwiched between two glass fiber layers, as shown in Figure 10a [123]. Two absorption peaks are generated in their device, demonstrating broadband wave absorption at microwave frequencies. The measured RL exhibits a minimum of \(-46\) dB at 9.1 GHz for \(d_{1}=1.4\) mm and \(d_{2}=2.2\) mm. Meanwhile, the device demonstrates excellent microwave absorption (RL \(<10\) dB)  
Figure 8: (**a**) schematic of graphene metasurface absorber with buried metallic helices; (**b**) the simulated absorption spectra for left- and right-circular polarized waves, reprinted with permission from Ref. [115]. Figure 9: (**a**) schematic of the unit cell; (**b**) image of the fabricated absorber; (**c**) absorber coated on the surface of metallic cylinder; (**d**) SEM image of the printed graphene; (**e**) measurement results of the graphene metasurface absorber, reprinted with permission from Ref. [122]. over the whole measurement range (8-18 GHz), as shown in Figure 10b. The authors attribute the advantage of using rGO-FSS to the polarization and conduction losses induced by abundant micro-defects and hopping of charge carriers in their multilayer rGO, respectively. Although the operation of broadband graphene metasurface absorbers has been experimentally demonstrated, the tunability of graphene conductivity has not been investigated in these experiments. Due to the tunable sheet resistance of graphene, it is interesting to have a look at the explorations on reconfigurable graphene metasurface absorbers as well.
# Mobile Robot Path Planning Considering Obstacle Gap Features
## 2 Path Planning Method with Clearance Characteristics
### Global Path Planning Based on Gap Map
#### 2.1.2 The Search Point Selection Strategy Based on Gap Optimization  
The traditional A* algorithm expands the current node's eight adjacent grid cells, represented by n1 to n8. However, in the selection of target points, although there are eight neighborhood grids to choose from, it leads to unnecessary grids, which wastes computing time and storage space. In order to further improve the search efficiency, an optimized search point selection strategy is proposed in this study. If there is a gap between the target point and the current point in the current search region, the three nearest neighbor search directions that are in line with the gap node concentration point are abandoned. By implementing the above optimization measures, the number of search nodes can be reduced, and computing efficiency can be improved. Figure 2 illustrates the search point selection strategy in this study path planning method. The left grid map shows the traditional search strategy, where the search area is limited and does not effectively consider obstacle gaps. The right grid map demonstrates our improved strategy, where the search points (markedas green squares) are selected based on gap optimization. This strategy expands the search area to better navigate through small gaps. Obstacle areas are marked in orange, and the optimal path is highlighted in yellow. This figure highlights how strategy enhances path planning by optimizing search points based on obstacle gap features.
# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 5 Acquisition of Fleet- and Depot-Specific Maintenance Knowledge
### RailMain-KG to Inform Scheduling Processes  
The knowledge captured in RailMain-KG has great potential in extending the capabilities of scheduling procedures, hence further advancing automation in the field of train maintenance. While the idea of resource-aware scheduling was briefly touched upon in the previous section, mainly focusing on the part of information retrieval, Section 8 provides an elaborate real-like example that better outlines the benefits of resource-aware scheduling.  
Resource management within a railway maintenance depot encompasses the consistent operational efficiency of the depot but also it should embrace the perspective of resource replenishment. The resource-aware approach can transcend conventional timetabling practices to active monitoring of the status of the available resource stock. For instance, activities that focus on proactive reordering of depleted or constrained re  
Figure 6: Aggregation of requirements.  
sources can be integrated into traditional scheduling frameworks. To this end, infusing scheduling procedures with knowledge graphs about the assets of a maintenance depot not only improves the operational stability of the facility but also ensures that maintenance activities in the depot will continue to run smoothly.  
In live scheduling scenarios, such as rescheduling, conducting a resource availability check is essential, as unpredictable changes might lead to reactive resource reordering. Consequently, new schedules must be constrained by the number of units participating in maintenance interventions, as well as the time that is needed to acquire the necessary resources, if there are any shortages. This further supports resource-aware scheduling, as it facilitates effective management in terms of which vehicles should undergo the entire cycle of maintenance tasks while competing over limited resources during restocking windows.  
In the following section, step three of the proposed approach is discussed.
### Evaluation in a Real Environment  
To explore the practical performance of the SOOA, we collaborated with an IT company and conducted a real evaluation in a wired CIDN including 26 nodes. Figure 6 shows the high-level network deployment. Each node could access the Internet by connecting to a server, which acted similarly to a firewall and provided various computing resources.  
To validate the results obtained in the simulated environment, we adopted the same environmental settings and observed the network to become stable; that is, the trust values turned out to be stable. We then set up the same scenarios and randomly selected one expert node to launch our attack. Figure 7 depicts the trust value of the malicious node under both Scenario 1 and Scenario 2.  
Figure 6: The high-level architecture of a real collaborative intrusion detection network (CIDN) environment.  
* _Scenario 1_. In this scenario, it was found that the trust value of the malicious node could decrease much faster under T3U3 than T4U2. More specifically, the trust value under T4U2 could deviate around the threshold, while decreasing nearly directly under T3U3. The observations are in-line with the results obtained in the simulated environment. While we identified that the trust value of the malicious node decreased faster in a real network environment under T3U3, an attacker should make a better strategy to launch the SOOA, that is, behave normally to more than half the nodes.
* _Scenario 2_. Figure 7 shows that the trust value of the malicious node under T3U3 could maintain a downtrend for the first 10 days but would deviate around the threshold later; that is, it was above the threshold for a total of 10 days. Under T4U2, it was found that the trust value of the malicious node would not decrease below the threshold. Similarly, the observations are in-line with the results obtained in the simulated evaluation.  
On the whole, the experimental results in the real CIDN environment validated that our SOOA has the potential to compromise the robustness of the challenge mechanism by slowing the detection speed of malicious nodes. In Scenario 2, malicious nodes may have a greater chance to bypass the detection than in Scenario 1, as they can pretend to be normal to the target node directly. Furthermore, our attack could help to maintain the trust values of malicious nodes by employing a good strategy, for example, T4U2, behaving normally to most partners of the target node.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 6 Conclusions  
A remote health monitoring system is designed in this study to monitor the health of acute heart failure. IoT technology is used to design health monitoring systems in order to access patients' records without a physical appearance in medical clinics. The proposed smart healthcare framework is used to improve the odds of survival for critically ill patients and to provide easy, economical, and dependable monitoring for cardiac patients. The proposed system collects data and delivers them to the cloud, where they are further analyzed.  
In addition, an optimized CNN model is presented for the accurate detection of heart patients, and its performance is analyzed against machine learning, deep learning, and transfer learning approaches. The experimental results indicate that the proposed models achieve better results than all the employed models with a 0.9398 accuracy score. Its performance is further validated using 10-fold cross-validation and performance comparison with existing studies using the same dataset for experiments; both prove the superior results from the proposed model. The accuracy and training time of the proposed technique are also better than those of the other models.  
Conceptualization, M.U. and T.A.; Data curation, N.A.A., T.A., Z.B.F. and K.A.; Formal analysis, M.U., Z.B.F. and N.A.; Investigation, K.A. and O.S.; Methodology, N.A.A. and Z.B.F.; Resources, N.A.A. and O.S.; Software, N.A.; Supervision, I.A.; Validation, O.S. and I.A.; Visualization, N.A. and K.A.; Writing--original draft, M.U. and T.A.; Writing--review and editing, N.A.A. and I.A. All authors have read and agreed to the published version of the manuscript.  
The funding of this work was provided by [PERSON] Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R410), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The dataset and code are available from the corresponding author on reasonable request.  
The authors are thankful to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R410), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
## 5 Sensitivity of Pile Head Displacement under Scour  
The increase of pile diameter, embedment length, and wall thickness can reduce the displacement of monopiles, but the variation characteristics under scour are not clear. For example, the horizontal displacement can be reduced by altering a certain pile parameter, but its effect may be greatly reduced under scour, which decreases the economy and safety of monopile foundations. Therefore, in this section, by discussing the relationships between pile head displacement, pile parameters, and scour depth, the sensitivity of displacement under scour is studied. This is how the analysis is realized: keeping the scour depth fixed to study the relationship between pile head displacement and pile parameters; and keeping the pile parameters fixed to study the displacement before and after scouring. To quantify the variation extent of horizontal displacement before and after scouring, the concept of scour sensitivity \(S\) is defined, which takes displacement as reference:  
\[S=\frac{d_{t}}{d_{0}} \tag{13}\]  
The value of \(S\) represents the susceptibility to scour. A higher value of \(S\) indicates a larger ratio of displacement after scour than that before scour, and that the parameter studied is more susceptible to scour. In the same way, a lower value of \(S\) indicates a smaller ratio of displacement after scour to that before scour, and that the parameter studied is less susceptible to scour.  
According to Section 3, the average scour depth is 1.03D, and in order to facilitate the comparison, all scour depths in this section are taken as 1D. The initial pile diameter, embedment length, and wall thickness are 4 m, 40 m, and 100 mm, respectively. The value of pile parameters increases by 50% from the initial value, and the changes of displacement and scour sensitivity of pile head are compared. The load is applied to the top of the pile, with the amplitude of 600 kN and the direction from left to right.
# _molecules_
## 9 Curing Behavior of High-Vinyl SSBR/High-Cis BR Without DPG  
It was shown in the previous described studies that a direct coupling of a sulfur radical towards a vinyl group can take place, even in the absence of ZnO as well as stearic acid. If the assumed direct coupling mechanism shown in Figure 8 on the right side is correct, it can be considered that also DPG can be left out.  
DPG plays a manifold role inside a silica/silane compound (Figure 20) [26]. It adsorbs at the silica surface and blocks it for the adsorption of the primary accelerators, and it speeds up the silanization reaction and it can react directly with the silane.  
DPG has, for quite some time, been under discussion to be banned from use due to the release of harmful aniline [27]; therefore, it is desirable to find a curing method without DPG, especially in silica/silane compounds.  
In order to prove if the curing of 80 phr high-vinyl SSBR and 20 phr high-cis BR compounds can occur in the absence of DPG, a silica-filled high-vinyl SSBR-based model green tire tread compound without activators and without DPG was investigated and compared with the same compound but without activators. As a reference, the compound with DPG with or without activators was studied (Figure 21). A model green tire tread compound with 2.0 phr TBBS and 1.4 phr sulfur was selected as the basis for this study.  
Figure 19: Curing behavior of the silica/silane (**left**) as well as the CB-filled (**right**) high-vinyl SSBR/high-cis BR compounds; “reference” is referring to the compounds with activators.  
The curing of high-vinyl SSBR/high-cis BR with activators (ZnO/stearic acid) as well as without activators and DPG starts earlier and leads to the same (slightly higher) final torque. When DPG is left out in addition to the activators, the scorch time is slightly increased, but is still much shorter than the reference. Amines are known for their accelerating effect on the breakage of the sulfur ring, with TBBS having a higher potential than DPG [28]. When DPG is left out, the more active TBBS, or the reaction product from TBBS, the amine, is assumed to be partly adsorbed at the silica surface, being in this way partly immobilized to support the opening of the sulfur ring. The result is the observed longer scorch time.  
This means that in a high-vinyl compound not only the accelerators but also DPG can be left out to reach a sufficient crosslink density.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 2 Experimental Setups
### BRIR Measurements  
Two pairs of BRIRs were measured with a dummy head KU-100 at azimuth angles of 0\({}^{\circ}\) (front) and 45\({}^{\circ}\) (front right) in a listening room (ITU-R BS.1116, 6.7 m\(\times\) 4.8 m \(\times\) 3.2 m) and a computer room (5.9 m\(\times\) 6.5 m \(\times\) 2.7 m). The broadband reverberation time (RT\({}_{60}\)) for these two rooms is about 0.25 s (listening room) and 0.6 s (computer room), respectively. In each room, two Neumann KH 120A loudspeakers located at these two incidence angles relative to the dummy head, were used as sound sources for the recording of non-individual BRIRs. The distance between loudspeakers and the dummy head was 2 m. A 5 s long exponential sweep from 20 Hz to 20 kHz was used as an excitation signal [11], and the measurement was repeated five times. The BRIRs recorded were then windowed to 1 s with a 5 ms long half raised-cosine fall time. Non-individual BRIRs are often used in commercially available binaural rendering systems due to the difficulty in obtaining individual BRIRs for headphone users [11]. Since this paper aimed to investigate externalization of binaural sounds in common use cases, generic BRIRs were used in the experiments. Compared to the use of individual BRIRs, the localization errors of binaural sounds might be increased [12].  
The main differences between these impulse responses were the reverberant components. Slight differences (e.g., minor differences in magnitude spectra at high frequencies) were observed in direct parts of BRIRs measured in the listening room and in the computer room due to the misalignment of the measurement setups. To avoid the differences in the direct parts, the direct parts of BRIRs measured in the computer room were replaced by those measured in the listening room, and such modified BRIRs were used in the experiments. In this way, the potential influence of differences in direct sound components on externalization can be excluded. The difference in reverberant parts of BRIRs was the main factor that could lead to different externalization results.  
The extraction of the direct parts was performed by windowing the BRIRs to 2.5 ms (after the onset delay) with a half raised-cosine fall time of 0.5 ms. No audible difference was found between the original and modified BRIRs through informal listening tests. It is well know that the direct parts of BRIRs can be approximated as HRIRs, which do not contain room information. In this study, the extracted direct sound components were treated as BRIRs/HRIRs measured in an anechoic chamber (an approximation of the free-field).  
For convenience, the listening room, the computer room, and the anechoic chamber are referred to as room A, B, and C, respectively. In addition, their corresponding impulse responses are denoted as BRIR-A, BRIR-B, and BRIR-C, respectively.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 9 Manufacturing of Hollow-Core Fibers
### Scaling of Fiber  
To prevent the entry of moist air, the infiltration of liquid water when the outdoor cable is cut--up to 9 m when water pressure at typical cable laying depth of 0.6-1.2 m is considered (Section 6.4)--or the collapse of cladding during fusion splicing (Section 10.2), HCF shall be specially treated within minutes after drawing by following the steps below:  
* Sealed at both ends by fusion for storage before next steps;
* Pumped with dry inert gas, like nitrogen or argon to elevated pressure;
* Tested, e.g., with OTDR, within minutes;
* Sealed at both ends by fusion for storage and shipping.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 5 Experimental Validation
### Results and Analysis  
#### 5.4.1 Impact of Input Layer Modifications  
To investigate the impact of input data types on the performance of motor fault diagnosis, the CNN is trained using two types of input data for vibration data: raw signals and envelope spectra. Each type of input data was trained 30 times. Figure 8 presents the comparison results of the two different inputs. The accuracy of using envelope spectra as input is 97.5%, whereas the accuracy of using raw signals as input is 94.2%. Additionally, in terms of stability and speed, the performance of envelope spectra as input is better than that of raw signals. Figure 9 shows the confusion matrices for training the CNN with raw signals and envelope spectra as inputs. Figure 10 shows the feature visualization results using t-SNE. It can be seen from the figure that the method using envelope spectra as input achieves better classification. Therefore, the envelope spectrum is considered a better input type and is used in further experiments.  
labels, the other labels exhibit varying degrees of misdiagnosis. Figure 11b shows the optimized diagnostic results with an accuracy of 99.71%, which is an improvement of 2.35% compared to before optimization. Based on the analysis of the results in the figure, the optimized method shows a significant improvement in diagnosing BPFI, BPFO, FTF, and BSF. The reason for this result is that the dual-modal data combine information from different sources (vibration signals and temperature signals), providing more dimensions and more comprehensive information for fault diagnosis. By complementing each other, data from different modalities can reduce information loss and misjudgment that might occur with single-modal data, thereby enhancing the overall accuracy of the diagnosis. Therefore, the CNN-DS algorithm proposed in this study is considered effective.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 2 Communication Signal Description and Dataset Generation
### Signal Time-Frequency Description  
For multi-signals detection task, we use the wideband signal time-frequency spectrum as the neural network input. To prove the feasibility of this method, we theoretically prove the time-frequency visual characteristic of each modulation. Here, we use the short-time Fourier transform [38] (STFT) to analyze the signal time-frequency characteristic.
## 3 Simulation Results  
### Transmitting Constellation Constitution  
To evaluate performance of our NOMA downlink setup, we considered several different cases: two-, three-, and four 4-QAM/QPSK users, which form 16, 64, and 256-symbol irregular quadrature constellations, respectively, as depicted in Figure 9, showing the constellations measured at test point TP1 along with the decision regions. Observe the asymmetry in the different noise margins around each constellation point, depending on associated power coefficient. Figure 10 presents the measurements at the second test point,  
Figure 8: NOMA SIC QAM receiver processing.  
TP2. We observe the effect of pulse shaping after upsampling, excluding transitions (by taking every \(L\)-th sample in the sequence), as well as the impact of the oversampling factor \(L\) and the LO leakage effect.