esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials for Healthcare Biosensing Applications
## 1 Introduction
Based on structural differences, each of these nanomaterials can be further categorised into several subtypes, as shown in Figure 1.  
Figure 1: Various kinds of nanomaterials discussed in this review. Nanomaterials can be engineered by following two main approaches--top-down and bottom-up approaches. In the top-down approach, a macroscale machine is designed and controlled to fabricate an exact replica of itself, but smaller in dimension. This smaller machine in turn produces an even smaller replica and the process is repeated until nanoscale dimensions are achieved. In the bottom-up approach, larger structures are built by the assembly of individual atoms or molecules with the help of biotechnology, scanning probes, or supramolecular chemistry [16]. Although both the aforementioned approaches play a vital role in the synthesis of nanomaterial-based biosensors, the bottom-up approach finds greater application. Nanotechnological advancements have spurred the development of assays and devices for medical diagnostics which are faster, cheaper, more sensitive, and more accurate. Biosensors utilising nanomaterials bring various disciplines together, such as chemistry, molecular engineering, material science, and biotechnology [3]. They offer extremely high sensitivity, such that some biosensors are now capable of detecting as low as one parasite per microlitre of blood [17]. The recognition of disease biomarkers at extremely low abundance, with the help of nanotechnology, permits the screening of diseases at very early stages. This has the capacity to upgrade medical techniques required for follow-up procedures and routine prognosis to monitor patient diagnosis. Furthermore, blending nanotechnology with biosensing is vital for point-of-care (POC) diagnosis in countries which lack advanced medical facilities [2]. Figure 2 presents a schematic of inorganic nanoparticles in association with various kinds of biomarkers and a linear flow representing each stage of biosensing from analyte recognition through transduction, and ultimately a measurable signal that is processed and displayed. In the following sections of this paper, we provide a comprehensive review on the application of nanomaterials in healthcare sensing, covering carbon allotrope-based nanomaterials, inorganic nanomaterials, and organic nanomaterials. Figure 2: Roles of different nanomaterials in biosensing.
## 2 Related Work
### 3 DGS-Based LiDAR-Visual Fusion  
Integrating LiDAR data has greatly improved the effectiveness of 3D Gaussian Splatting (3 DGS) for large-scale scene reconstruction. 3 DGS-ReLoc [17] and Gaussian-LIC [18] leverage LiDAR point clouds along with associated images to generate 3D maps, resulting in more detailed environmental modeling. Similarly, DrivingGaussian [19] enhances the integration of features by extracting image information and projecting combined LiDAR scans. LetsGo [20] integrates Level-of-Detail (LoD) rendering into 3 DGS workflows, offering an efficient way to represent scenes using Gaussian functions at varying resolutions. LIV-GaussianMap [21] increases the efficiency of managing point clouds by utilizing a well-structured explicit octree, while TCLC-GS [22] applies a hierarchical octree of implicit feature grids and further optimizes 3 DGS through advanced color projection methods. MM-Gaussian [23] employs point cloud registration to estimate camera poses and incorporates these point clouds into maps, which improves optimization. This approach also supports progressive map visualization and leverages high-fidelity scene reconstructions to refine poses, resulting in improved positioning accuracy and mapping quality. LIV-GS [24] refines pose estimation by aligning Gaussian covariance matrices with current observations and enhances efficiency through backend corrections and Gaussian map updates. Additionally, it addresses challenges in depth continuity within unbounded environments by employing a LiDAR-based Gaussian splitting mechanism, significantly boosting performance and positioning accuracy in extensive outdoor scenarios.  
However, most of the aforementioned methods assume static environments and have limited ability to handle dynamic scenes. In dynamic scenarios, moving objects, feature matching errors, or tracking loss can significantly degrade reconstruction accuracy. To address these challenges, in this paper we propose a 3 DGS-SLAM method that resists dynamic interference, helping to achieve high-precision 3D map construction in dynamic environments.
## 5 Sink Mobility Patterns in Wireless Sensor Networks  
There are three basic mobility patterns that a mobile sink can exhibit in a sensor field [15]: random mobility, predictable/fixed-path mobility and controlled mobility. Both the random and predictable sink mobility are not in control of the network/observer.  
_Random/Unpredictable Mobility Pattern_--Random mobility pattern is exhibited if the sink device is attached to a mobile unit such as an animal which has no knowledge of its mobility (speed and direction). In this type of mobility, the sink makes the next move autonomously in terms of speed and direction. This mobility pattern is being characterized as highly unpredictable regarding the future position of the sink. For delay tolerant applications, random sink mobility results in prolonged network lifetimes [16] and is particularly applicable in situations where node deployment is not known.  
_Predictable/Fixed-Path Mobility Pattern_--This is the simplest among the three mobility patterns and is exhibited by the sink if it always follows a certain trajectory such as along the periphery of the sensing field. Using this mobility pattern, nodes along the trajectory can learn the expected time of visit of the mobile sink [15] and thus accordingly optimize their sensing and data delivery tasks. This type of mobility pattern is particularly appealing for applications such as car parks alongside roads where a mobile sink (car driver) can enquire about availability of parking slots in some region of interest.  
Controlled Mobility PatternControlled sink mobility refers to the situation when the observer of a sensor network can control the motion of the mobile sink [17]. Controlled mobility is exhibited by sinks if the sink device is attached to a mobile unit such as a robot. Controlled sink mobility is adopted based on the assumption that context-aware pervasive devices are at the disposal of network. The context-aware devices retrieve their possible future locations based on the data generated by the field sensors [18]. Depending on the application goal, the mobile sink adapts its movement (both speed and trajectory) in a deterministic way to achieve better results. For example, if the goal is to prolong network lifetime, the sink makes its next move towards energy rich areas to achieve a balanced utilization of nodes' energy reserve. Similarly, in applications where message delivery latency is critical, the sink moves towards event reporting areas to reduce multi-hop communications. However, the visiting schedule of the mobile sink must be carefully adopted as infrequently visiting particular network segments might result in large data delivery latency [19]. In terms of cost effectiveness, controlled sink mobility is not a preferred choice, but if the goal is to reduce the data delivery latency, controlled sink mobility yields better results [20] than random and/or fixed-path mobility options.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### TSN Support for Automated Driving
#### 4.6.4 Error Detection and Mitigation  
Automated Driving systems require the prompt detection of errors due to random hardware faults (e.g., traffic overload), systematic software faults, or cyber-security attacks. Measures to prevent error propagation over the network are also required.  
The TSN standard IEEE 802.1 Qci helps with per-stream metering and monitoring, error detection (e.g., babbling idiot), error mitigation, ingress policing, and filtering.  
To summarize, the TSN family of standards offers many features that match the diverse requirements of the different types of Automated Driving applications (such as sensor fusion, control, and actuation) and allows for a \"converged network\" in which the different traffic types of Automated Driving applications coexist and manage their constraints [48].
## 4 Results and Discussion
### Adhesion Stability Evaluation
#### 4.2.2 Linear Regression Line Grade  
The linear regression line was derived for each of the peel load throughout the peel load changes of the Pt (P1-P10). For each VG type, the linear regression equation was calculated and shown in the respective Figure 12a-d, and the slope of the each of the equations were used to calculate the grade. Based on Equation (2) in Section 3.3.2, it is determined that the method for deriving the slope and grade is similar (a relation of Y and X value), but the direction of the slope is not considered for the grade. Without considering the trend of decrease and/or increase of adhesion strength, and by considering only the grade (which does not consider the vector of the angle) of the linear regression line, a simpler and clearer comparison of adhesion strength stability is possible.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & & **Average** & **Standard** & **Coefficient of** \\  & & **Peel Load** & **Deviation** & **Variation** \\  & & **(N)** & **(N)** & **(\%)** \\ \hline \multirow{4}{*}{VG-1} & \multirow{2}{*}{V-2} & 205.5 & 12.25 & 6 \\  & & 203.2 & 11.37 & 6 \\  & & 204.3 & 13.28 & 6 \\  & Avg. & 204.3 & 12.30 & 6 \\ \hline \multirow{4}{*}{VG-2} & \multirow{4}{*}{V-3} & 1 & 285.6 & 32.61 & 11 \\  & & 281.1 & 9.58 & 3 \\  & & 224.2 & 73.87 & 33 \\  & Avg. & 263.6 & 38.68 & 15.9 \\ \hline \multirow{4}{*}{VG-3} & \multirow{4}{*}{V-3} & 1 & 246.6 & 49.64 & 20 \\  & & 220.2 & 19.75 & 9 \\  & & 235.7 & 11.55 & 5 \\  & Avg. & 234.1 & 26.98 & 11.3 \\ \hline \multirow{4}{*}{VG-4} & \multirow{4}{*}{V-3} & 1 & 93.1 & 4.56 & 5 \\  & & 91.2 & 12.95 & 14 \\ \cline{1-1}  & & 101.8 & 23.09 & 23 \\ \cline{1-1}  & & Avg. & 95.4 & 13.54 & 13.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The coefficient of variation of peel load data.  
Figure 12: _Cont._  
The average value of the derived slope coefficient (angle) was confirmed to be 1.19% for VG-1, 4.29% for VG-2, \(-\)5.55% for VG-3, and 2.85% for VG-4. In summary, based on the calculated slope and in turn the grade of the linear regression line, adhesion stabilization was shown in the order of VG-1 \(>\) VG-4 \(>\) VG-2 \(>\) VG-3. Table 10 shows the summarized result of the calculations.  
Figure 12: The specimen linear regression slope derivation for grade calculation; (**a**) VG 1, (**b**) VG 2, (**c**) VG 3, (**d**) VG 4.
# Digitally Chirped Multilayer Quantum Dot Lasers with Dual-Wavelength Lasing Emissions
## 3 Theoretical Simulations
After the threshold of GS2 transitions, neither saturation nor depression is observed for GS1 lasing emissions. This is because GS2 transitions do not intercept but compete for extra injection carriers in the common reservoir. The simulations in Figure 2 qualitatively reproduce very well the experiments in Figure 1.  
The spectral widths of GS1 and GS2 lasing emissions broaden with increasing current. In addition, the near-threshold single peak splits into two increasingly separated peaks (or two groups of longitudinal modes) at well above the threshold. The similar spectral broadening and splitting were also measured and simulated in Uni-MQD lasers [18]. The spectral splitting of group modes is associated with spectral hole-burning, which leads to a slightly reduced optical gain at excitonic peaks. Figure 3 shows the carrier population versus injection current at excitonic peaks of GS1, GS2, ES1 and ES2 transitions. The carrier population at the excitonic peak (\(E_{1}\) or \(E_{2}\)) is pinned but somewhat depressed above the threshold. The carriers are somewhat depleted by abundant lasing photons as can be seen in Equations (7) or (10). Moreover, the optical gain on both sides of the excitonic peak are slightly enhanced to achieve the threshold because of an increasing carrier population as well as a lower transparency carrier number. Since QD size distribution and carrier population in the experiment are not ideally symmetric, asymmetric spectral splitting results in Figure 1a.
## 3 Experimental and Simulation Setup
### Experimental Setup
#### 3.1.2 The Experiment  
The experiment focused on measuring the energy consumption of the RSU, specifically at the edge node, under varying communication ranges such as 30 m, 50 m, 70 m, and 80 m. Data packets were transmitted from the OBU in the BMW to the RSU, simulating different distances to assess the impact on energy efficiency (see Figure 3). Metrics included the recording of energy consumption and CPU load, providing insights into the performance of the edge node in a 5G communication scenario. To measure energy consumption, we managed a Power Distribution Unit (PDU) contained in the RSU, which was used to remotely power-cycle stuck components an to measure the energy consumption of the devices connected to it. We deployed a monitoring system [40] to record and monitor the CPU load. This system was encapsulated within an LXD container to ensure smooth management and monitoring of the RSU. A Prometheus server with key agents such as Node Exporter and cAdvisor was hosted within this container. These components actively gathered real-time performance metrics and health statuses, relying on a Grafana Dashboard as a centralized interface to visualize dynamic performance data and receive status updates.
## 2 Background and Related Works
### Attacks on Machine Learning Models
#### 2.2.1 Adversarial Example Attack  
Adversarial examples, also known as evasion attacks [17], are aimed at the test dataset of the victim model. In such an attack, the victim trains a well-behaved model in accordance with the usual training procedure. However, the attacker then modifies the test dataset based on some knowledge of the victim model in order to deliberately produce errors during the testing process. Taking the Fast Gradient Sign (FGSM) attack [18] as an example, the attacker calculates the gradient of the loss function for the input, and then adds this value to the original input. The resulting increase in the loss function significantly increases the probability of misclassification errors (see Figure 1). Other attacks, such as DeepFool [19], aim to make the data of a certain category close to those of another category. In such attacks, the attacker calculates the gradient and resulting values iteratively and then manipulates the testing dataset accordingly, causing the resulting decision to approach and finally cross the decision boundary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Kinds** & **Adversarial** & **Poisoning Attacks** & **Backdoor Attacks** \\
**of Attacks** & **Example Attack** & **Poisoning Attacks** & **Backdoor Attacks** \\ \hline Attack target & The test dataset & The training dataset & The test dataset \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary for the attack on ML models.  
Adversarial examples can be classified as either targeted or nontargeted, where the former types of attacks cause the final category to be misclassified as a specified (incorrect) category, and the latter attacks aim simply to make the final category be misclassified. One of the most well-known targeted attacks is the Carlini and Wagner attack [20], originally designed to trick speech recognition systems.  
Adversarial examples often have a certain degree of transferability. In other words, even though the attack is generated based on the weights, architecture, or parameters of one victim model, it may also be effective against other models. Black box attacks (i.e., attacks based on limited knowledge, see Table 1) can be achieved by training a surrogate model and then generating attack data based on the structure and parameters of this model. Furthermore, good transformability can greatly improve the attack ability and success probability of the attack, which is one of the goals pursued by many attack algorithms.
## 2 Basic Analysis of Prediction Models and Model Verification
### Prediction Models of Saturated Vapor Pressure and Saturated Density  
The common prediction models of saturated vapor pressure and density mainly include the Peng-Robinson equation of state and its modified models, the Patel-Teja equation of state and the NEOS model.
## 3 Methodology
### Modeling and Validation  
#### 3.3.1 Modeling Methods  
The flux tower footprint is typically influenced by various factors, including the instrument's height, land surface conditions, turbulence intensity, and terrain features [63]. This study determines the observation range of the flux tower through the footprint function proposed by [PERSON] et al. [64], in order to match the corresponding pixels of ET and NEE observed by remote sensing. This footprint is considered as a function of variables such as height, wind speed, wind direction, etc. For specific calculation methods, please refer to the study of [PERSON] et al. [64].  
Furthermore, to facilitate a rigorous and unbiased comparison among the four regression methods (MLR, RFR, BPNN) using the selected input variables, we randomly allocated 80% of the samples and corresponding in situ ET/NEE measurements for model training. The remaining 20% were reserved to assess model accuracy. Figure 5 provides a detailed flowchart illustrating this process.
## 3 Results and Discussion
### Inlet Cross-Flow Influence on Fan Blade Vibration
#### 3.2.2 Motion Patterns at Dominant Peaks  
The measured relative axial velocity distributions \(\bar{v}_{z,i}\) associated with the dominantly increased frequencies \(n=5\) and \(6\) are compared to the reference results in Figures 8-11.  
Figure 6: Frequency domain decomposition of cross-flow influence on measured mean surface velocity amplitude at OP1.  
Figure 7: Frequency domain decomposition of cross-flow influence on measured mean surface velocity amplitude at OP2.  
In all reference results (on the left in Figures 8-11), the distribution of \(\hat{v}_{z,i}\) forms no clear pattern, and the amplitude range is comparably low. Considering the logarithmic representation of Figures 6 and 7, the low-amplitude peaks at \(n=5\) and \(6\) are only a little above the base noise level for \(\mu=0\). In contrast to this, the right-hand graphics in Figures 8-11 show very characteristic motion patterns of the fan blades under the influence of inlet cross-flow, and decisively larger amplitudes \(v_{z,i}\).  
At the blade-pass frequency \(n=5\), both operating points OP11 (Figure 7(b)) and OP21 (Figure 8(b)) showed a synchronized blade-tip flapping motion caused by the influence of cross-flow. At the blade tips, large peak-velocity amplitudes \(\hat{v}_{z,i}(5)\) can be found. The vibration increase at the blade-pass frequency is not surprising. Because \(n=5\) is the frequency at which the fan blades passed the inlet  
Figure 8: Cross-flow influence on blade vibration at blade-pass frequency \(n=5\); OP1.  
Figure 9: Cross-flow influence on blade vibration at blade-pass frequency \(n=5\); OP2.  
cross-flow, it was also the excitation frequency of the resultant external harmonic forces, causing an in-phase motion of all the blades.  
A blade-tip flapping motion was also detected from experimental LSV data at the sixth rotation-rate harmonic \(n=6\). The pattern can be seen especially clearly for the strong excitation for OP11 in Figure 10b, but it can also be seen in Figure 11b for OP21. However, while the single-blade motion with its peak at the leading edge tip resembles the first natural mode shown in Figure 4b, the dominant blade's neighbors did not move in counter-phase to it. Instead, they have an offset \(\theta_{i,\text{ref}}\) of around \(90\lx@math@degree\), which levels the otherwise similar amplitude \(v_{z,i}\) at the bottom and upper-right blades in Figures 10b and 11b.  
Figure 11: Cross-flow influence on blade vibration at \(n=6\); OP2.  
Figure 10: Cross-flow influence on blade vibration at \(n=6\); OP1.  
For OP11, the blade root amplitudes \(\tilde{v}_{z,i}(6)\) are distinctly below zero where the blade tip is at a maximum (bottom-right blade in Figure 10b), and above zero where the blade-tip velocity is a minimum (left and top blades). This indicates a non-negligible stator-motion, that is, a motion of the entire fan at \(n=6\) for OP11.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 4 Simulations
### Simulation Parameters  
We first perform 10 experiments with different network topologies. Whenever conducting each experiment, QualNet randomly places 30 wireless nodes in a flat area of 900 m by 900 m. In this scenario, there is one source and 10 multi-receivers in a multicast tree where these nodes are randomly selected as being a receiver. For the other experiment, a different number of multi-receivers is applied under the same topology. For the entire simulation settings, each node has two network interfaces for sending and receiving data packets, so they use two channel numbers for their radio interfaces. A total of 11 channel numbers are adopted for channel assignment. The transmission range of each wireless node is 250 m and the transmission rate is 11 Mbit/s. The data packet size for all traffic is 512 bytes and the transmission rate of a source node is 100 packets/s. The traffic model is a multicast constant bit rate (MCBR) traffic generator to evaluate the multicast performance. The QualNet software provides the following multicast routing protocols: on-demand multicast routing protocol (ODMRP) [31], distance vector routing protocol (DVMRP) [32], multicast open shortest path first (MOSPF) [33], and multicast ad hoc on-demand distance vector (MAODV) [34]. We decided to use MOSPF because this protocol basically supports multiple network interfaces. Finally, we set a total simulation time to 300 s. The aforementioned simulation parameters are summarized in Table 2.
# Efficient Task Scheduling Using Constraints Programming for Enhanced Planning and Reliability
## 3 Methodology
### Justification for Selecting Constraints Programming
#### 3.4.6 Flexibility in Handling Task Variations  
Another advantage of CP is its flexibility in handling variations in task requirements. Unlike heuristic methods, which may require significant re-tuning for different task sets, CP can easily accommodate changes in the number of tasks, task durations, or resource constraints by updating the model's constraints without the need for retraining or extensive modifications to the algorithm. This adaptability makes CP a more straightforward and efficient solution for the structured, repeatable nature of MRO task scheduling.  
By selecting constraint programming for the MRO scheduling problem, this study leverages the strengths of CP in handling structured, deterministic optimization problems with well-defined constraints. This choice is validated by the experimental results, which show high scheduling efficiency and resource utilization, making CP an ideal method for the specific challenges of aircraft engine MRO scheduling.