esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### fNIRS Data Acquisition and Analysis  
A portable LIGHTNIRS system manufactured by Shimadzu Corp. in Kyoto, Japan, was utilized to record \(\Delta\)HbO and \(\Delta\)HbR in the PFC, as illustrated in Figure 3.  
Figure 2: The schema of the experimental paradigm.  
In this experiment, eight optode emitters and eight detectors were strategically placed to cover the PFC, resulting in 22 measurement channels, as depicted in Figure 4. Specifically, channel 19 was aligned with the Fpz point according to the 10-20 international system. The sampling frequency for signals was set at 13.33 Hz. Additionally, a 3D Fastrak digitizer was utilized to accurately determine the spatial coordinates of optical probes using four anchor points (NZ, CZ, AL, and RL) as references [46].  
The fNIRS data underwent processing using the NIRS_KIT toolbox v3.0 [47] within MATLAB (R2021a, MathWorks, Inc., Natick, MA, USA). Processing steps encompassed detrending, motion correction based on Temporal Derivative Distribution Repair (TDDR) [48], and filtering with a bandpass filter set between 0.01 and 0.08 Hz. The decision to use 0.08 Hz as the low-pass cutoff was based on common practices in fNIRS and neuroimaging studies, where this frequency range is typically selected to remove low-frequency noise and physiological artifacts, such as respiration and heart rate, while preserving the neural activity signals of interest [47]. Then, the \(\Delta\)HbO and \(\Delta\)HbR signals of each channel were averaged across the entire task period of 6 s for each stimulus presentation. Based on the estimation results in Montreal Neurological Institute (MNI) spatial coordinates to obtain the corresponding Brodmann areas (BAs) [49], two regions of interest were delineated, comprising the left and right DLPFC, as outlined in Table 2. The \(\Delta\)HbO and \(\Delta\)HbR of the left and the right DLPFC were computed by averaging the \(\Delta\)HbO and \(\Delta\)HbR data from these channels.  
Figure 4: Schematic diagram of optode emitters (red), detector (blue), and channel (yellow).  
Figure 3: Portable functional near-infrared spectral imaging equipment.
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 2 Materials and Methods  
### Research Objective and Problem Statement  
Given a deep-learning-based automatic segmentation technique comprising convolutional filters, as well as the original medical images and their adversarial counterparts, the primary objective of this research is two-fold: (1) vulnerability identification: to pinpoint the most susceptible filter-wise components within the automatic segmentation process. By understanding the weak links, this research aims to fortify the segmentation method against adversarial perturbations. (2) Visualizing critical features of attacks for physicians: to deliver a comprehensive visual examination of the adversarial samples. This will aid physicians in understanding the subtle alterations introduced by adversarial attacks.  
Specifically, the vulnerability identification can be rigorously defined as follows. Let a clean image be denoted by \(x\) and its corresponding adversarial counterpart, crafted to mislead the target classification model \(f\), be denoted by \(x_{adv}\). The features are extracted from a hidden layer \(h_{l}\) within the model \(f\), where \(l\) denotes the layer index. The primary objective is to employ an attack detection mechanism \(C\), leveraging the feature representations \(h_{l}(x)\) and \(h_{l}(x_{adv})\), to accurately distinguish between clean and adversarial samples. Given that the attack detection distinguishes between clean and adversarial images, the objective function is formalized using binary cross-entropy as follows:  
\[\min_{C}\ -\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(C(h_{l}(x_{i})))+(1-y_{i})\log(1-C( h_{l}(x_{i})))] \tag{1}\]  
where \(y\) denotes the true label of the \(i\)-th sample, with 0 indicating a clean image and 1 indicating an adversarial sample.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 2 MANET Routing Protocols: Background and a Review of the Literature
### MANET Classification
Their findings suggest that adopting AODV to consider node capabilities, such as the transmission rates and mobility patterns, can lead to reduced end-to-end delay and increased throughput. * Proactive (table-driven) routing protocol: Every node in the network has one or more routes to any possible destination in its routing table at any given time. Each node maintains routing information concerning every other node (or nodes located in a specific part) in the network. The emphasis behind proactive protocols is that control messages are exchanged between nodes periodically [28; 29]. Messages can be sent to inform and enable nodes to know their local network, exchanging knowledge of the network topology amongst all the nodes within a network. The authors of [30] conducted a comprehensive survey of the routing protocols in MANETs, highlighting the inherent challenges and solutions associated with different protocols. Their study outlines how the dynamic nature of MANETs, characterized by node mobility and varying transmission capabilities, complicates routing decisions. They emphasize that a robust understanding of the operational environment and the characteristics of the nodes is critical to optimizing the protocol performance. The advantage of proactive protocols is that the required routes are immediately available. Still, the downside of this is that the bandwidth suffers due to the flooding of control messages and periodic updates of the network topology amongst the nodes locally. * Hybrid routing protocols: Hybrid routing protocols are a combination of both distance-vector routing and link-state routing. Hybrid protocols aim to work out the kinks that both distance-vector and link-state protocols have by having the ability to choose which protocol is most suitable for use [31; 32]. Every node acts reactively in the region close to its proximity and proactively outside that region or zone. Hybrid protocols are a new generation of protocols that are both proactive and reactive in nature. This protocol is designed to increase the scalability by allowing the nodes near each other work together to form a backbone to reduce the route discovery overheads.
# Superhydrophobic Surfaces Enabled by Femtosecond Fiber Laser-Written Nanostructures
## Materials & Methods
### Characterization of Surface Structure  
The morphology of the laser-structured surface was characterized by a scanning electron microscope (SEM, FEI QUANTA FEG 600). The surface morphology and wettability of laser-processed surfaces were evaluated as functions of PRR, laser pulse fluence (a ratio of laser pulse energy and beam area), scanning speed, and pitch.  
To further characterize the surface morphology, the wavelength-dependent reflectance in the visible spectral range (i.e., 360-740 nm) was measured by a spectrophotometer (CM-2600d, Konica Minolta, Tokyo, Japan). The reflectance value was obtained for each experimental condition and correlated with the SEM measurements and the degree of hydrophobicity.  
The hydrophobicity of the surface was evaluated by measuring the contact angle and the rolling angle of water droplets deposited on the structured metal surface. The contact angle is defined as the angle of a droplet where the water-air interface intersects the metal surface, characterizing the tendency of a liquid to spread on a solid surface; the rolling angle represents the inclination of a surface from which a drop rolls off. Superhydrophobicity is generally accepted as when the contact angle exceeds 150\({}^{\circ}\) and the rolling angle is less than 10\({}^{\circ}\)[27].  
We used a method similar to the static sessile method and we captured the water droplet's contact with the metal surface using a high-resolution CCD and a macro-lens camera [28]. Then, we used image processing to calculate the contact angle as defined previously. To measure the rolling angle, we gradually tilted the substrate to determine when the droplet started to move.  
Figure 1: The schematic of the experiment setup for the fabrication of nanostructures on a metal surface. AOM: acoustic optical modulator. The devices in red text were assembled to allow for the adjustment of the process parameters.
## 5 Performance Evaluation
### Result Analysis and Discussion
#### 5.2.4 Energy Consumption Analysis  
The amount of energy consumed by the network, as a whole, while using the routing protocol, as detailed in Figure 14 a, b, is related to as the network's energy consumption. Due to less control, the overhead packets are used, as well as the proper sink advertisements only, to the cluster head only, and none of the nodes are involved in knowing the sink position. Thus, less energy is consumed in the limited nodes, as compared to the involvement of the node's energy utility in the other routing protocols, than the modified ring routing. So much less energy is consumed by the modified ring, in contrast to the other protocols, as shown in Figure 14 a, even though the network density increases. As the sink  
Figure 12: End-to-end delay analysis; (**a**) variation in the network density; (**b**) variation in the sink speed.  
Figure 13: Control overhead analysis; (**a**) variation in the network density; (**b**) variation in the sink speed.  
node travels, relatively few nodes are also engaged, making the modified ring more energy efficient, as seen in Figure 14b. It aids to prolong the entire network life, which enhances its appropriateness for the remote monitoring applications.  
Thus, the overall analysis of the proposed model is summarized as the complexity analysis in Table 3.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 3 Improved Hybrid A* Algorithm
### Reeds-Shepp Curves  
The Reeds-Shepp curve is a tool used for the path planning of nonholonomic constrained vehicles, capable of generating the shortest feasible paths that allow both forward and backward motion. They extend Dubins curves (which permit only forward movement) and are applicable to vehicles with minimum turning radius constraints. The calculation of the Reeds-Shepp curve is as shown in Equation (23):  
\[d=\text{min}\sum d_{i} \tag{23}\]  
where \(d\) represents the total path length; \(d_{i}\) denotes the segment length of the path, where each segment may consist of straight-line driving, maximum-turning-radius curves, and reversing (backward motion).  
In Reeds-Shepp path calculation, the shortest feasible path is composed of five segments combining forward motion, backward motion, and turns. The fundamental constraints are as follows in Equation (24):  
\[\left\{\begin{array}{l}\frac{dx}{dt}=V\cos(\theta)\\ \frac{dy}{dt}=V\sin(\theta)\\ \frac{dy}{dt}=\frac{V}{R}\end{array}\right. \tag{24}\]  
where \(V\) denotes the velocity (positive values indicate forward motion, and negative values indicate reverse motion); \(R\) represents the turning radius; and \(\theta\) is the heading angle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Punishment** & **Effect** & **Technical Implementation** \\ \hline Incomplete constraint & \begin{tabular}{c} Ensures that the path \\ complies with the vehicle \\ kinematics \\ \end{tabular} & \begin{tabular}{c} Reeds–Shepp curve generation \\ Hermit satisfaction \\ \end{tabular} \\ Obstacle collision & \
\begin{tabular}{c} Avoid paths that cross \\ obstacles \\ \end{tabular} &
\begin{tabular}{c} Grid map (sign) and continuous \\ monitoring (isPathClear) \\ Fitness function \\ (length\_penalty) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: The role of penalty functions in hybrid A*.  
The Reeds-Shepp algorithm, as a path planning method based on kinematic constraints, has broad application in the fields of autonomous driving and robotics. Its specific applications and technical implementation details in different scenarios are shown in Table 4.  
Therefore, in this work, the Reeds-Shepp algorithm is added, as it can generate paths that meet the minimum turning radius requirements when moving forward or reversing. By prioritizing paths with fewer reversals, the number of unnecessary reversals is effectively reduced, thereby optimizing the quality of the path.
## 4 Results
### Comparison of Serial, OTA, and Dynamic (VM) Software Updates
This finding implies that VM-based updates may be particularly advantageous in applications where real-time  
Figure 22: Comparison of update times for dynamic, OTA, and serial firmware updates (authors’ contribution). responsiveness is critical, as they avoid the substantial overhead associated with traditional OTA or serial firmware updates. The comparative analysis of firmware update mechanisms highlights the limitations and advantages of each approach. Serial updates, while straightforward, are severely limited by the baud rate, making them impractical for large firmware files in time-sensitive applications. OTA updates offer a feasible alternative with moderate performance, though network-induced delays may reduce predictability in environments with unstable Wi-Fi connectivity. In contrast, VM-based updates demonstrate high efficiency and low latency, offering a robust solution for scenarios requiring rapid reconfiguration and minimal down-time. Consequently, in applications where frequent or real-time updates are essential, the VM-based approach appears optimal, leveraging in-memory operations and minimizing reliance on external transfer speeds [29]. Future research could explore optimizing the deserialization and garbage collection processes further to reduce the already minimal update latency in VM-based configurations, potentially enabling even faster response times in dynamic system environments.
## 4 Results and Analysis
### Results and Analysis of Vibration Monitoring  
After measurement, the horizontal distance between the entrance of Longmen Tunnel and the Great Wall is 85 m and the vertical distance is 93 m. The site location of the entrance of Longmen Tunnel and the Great Wall is shown in Figure 14.  
In order to study the vibration impact of high-pressure gas expansion rock mass cracking excavation on the surrounding buildings and structures, two monitoring points were arranged on the flat ground surface outside the tunnel at 60 m and 80 m away from the tunnel face. The specific site arrangement of monitoring points and site photos are shown in Figures 15 and 16. In order to avoid mistakenly picking up the vibration data caused by the falling rock blocks, the trigger level of the two blast vibration loggers was set to 0.10 cm/s in accordance with the Blasting Safety Regulations (GB6722-2014) [33] for the safe allowable vibration speed of general ancient buildings and historic sites.  
After the excavation of high-pressure gas expansion rock mass cracking, it was found that two blast vibration loggers were not triggered, indicating that the vibration caused by the high-pressure gas expansion rock mass cracking test at the monitoring point was less than 0.10 cm/s. The vibration was less than 0.1 cm/s at 60 m, and the vibration continued to decay with the increase of distance. The horizontal distance between the entrance of Longmen Tunnel and the Great Wall is 85 m and the vertical distance is 93 m. The distance between the protected point of the Great Wall and the high-pressure gas expansion rock mass cracking point in the tunnel is always greater than 60 m--from 126 m at the beginning to 93 m when the rock mass cracking point is directly underneath the Great Wall. Therefore, during the construction of the tunnel under the Great Wall, the vibration caused by high-pressure gas expansion rock mass cracking will not cause damage to the Great Wall and  
Figure 14: The site location of the entrance of Longmen Tunnel and the Great Wall.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Number** & **Network Parameter** & **Quantity of Expansion** & **Mass of Gas- Generating** & **Stepping** & **Unit Con-** & **Rock Kingdom** \\  & **(m \(\times\) m)** & **Pipe** & **Agent (kg)** & & **Volume (m\({}^{3}\))** & **(kg\(\cdot\)m\({}^{-3}\))** & **(m)** \\ \hline
3 & 1.0 \(\times\) 0.9 & 62 & 67.4 & 1.4 & 22 & 3.06 & 0.25–0.6 \\
4 & 0.8 \(\times\) 0.7 & 68 & 73.4 & 1.5 & 39 & 1.88 & 0.2–0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of upper step excavation tests.  
meets the safe allowable vibration speed of general ancient buildings and historic sites. The high-pressure gas expansion rock-cracking technology has the advantage of little vibration.
# Coordination of Macro Base Stations for 5G Network with User Clustering
## 3 Problem Reformulation with User Clustering and Benders Decomposition
### Benders Decomposition for (\(P_{2}\))  
Because the continuity equations for indoor temperature (Equation (32)) and backup batteries (Equation (38)) are both coupling constraints between adjacent time steps, (\(\mathbf{P}_{2}\)) is a large-scale MILP of a multi-device, multi-step, jointly optimal scheduling, which cannot be decomposed into \(\mid\mathbf{T}\mid\) single-step optimality problems in the form of (\(\mathbf{P}_{1}^{t}\)) and is difficult to solve directly.  
The Benders decomposition algorithm [35] is extensively applied to decompose large-scale MILPs. By selecting complex variables from all variables in (\(\mathbf{P}_{2}\)), the problem is decomposed to a master problem (MP), a feasibility subproblem (SP\({}_{1}\)), and an optimality subproblem (SP\({}_{2}\)) to decrease the complexity of the original problem. In this study, all integer variables (\(\left\{U_{air}^{m,t}\right\}\), \(\left\{U_{ch}^{m,t}\right\}\), and \(\left\{U_{dis}^{m,t}\right\}\)), and all AC-related continuous variables (\(\left\{P_{air}^{m,t}\right\}\), \(\left\{P_{cool}^{m,t}\right\}\), and \(\left\{T_{in}^{m,t}\right\}\)) in (**P\({}_{2}\)**) are included in the MP, and the other continuous variables are included in the SPs. The flow chart of the Benders decomposition algorithm applied to (**P\({}_{2}\)**) is shown in Figure 5. The specific application steps are as follows:  
1. Input the model parameters, initialize the lower and upper bounds of (**P\({}_{2}\)**)** \(LB=0\) and \(UB=\infty\), and initialize the number of iterations \(k=1\);
2. Check whether the condition pertaining to the number of iterations \(k\leq k_{max}\) is established. If it is, then go to step (3); if it is not, then the loop ends, and the optimal solution of (**P\({}_{2}\)**) fails to be found in \(k_{max}\) iterations;
3. Solve the MP to obtain the optimal function value \(\hat{\theta}\) and optimal variable values (\(\left\{\hat{U}_{air}^{m,t}\right\}\), \(\left\{\hat{U}_{ch}^{m,t}\right\}\), \(\left\{\hat{U}_{dis}^{m,t}\right\}\), \(\left\{\hat{P}_{cool}^{m,t}\right\}\), and \(\left\{\hat{\mathcal{T}}_{in}^{m,t}\right\}\); let \(LB=\hat{\theta}\); Input the value of the complex variables into \(\text{SP}_{1}\) and then solve the optimal function value \(\hat{F}_{SP_{1}}\) of \(\text{SP}_{1}\) and check whether \(\hat{F}_{SP_{1}}\geq 0\) is established; if it is, then add a Benders feasibility cut into the MP, let \(k=k+1\); if it is not, then go to step (5);
5. Input the value of the complex variables into \(\text{SP}_{2}\), and then solve the optimal function value \(\hat{F}_{SP_{2}}\) of \(\text{SP}_{2}\); let \(UB=\hat{F}_{SP_{2}}\);
6. Check whether the convergence criterion \(\left|UB-LB\right|/LB\leq\epsilon\) is established. If it is, then the optimal solution of (**P\({}_{2}\)**) is found, and the loop ends; if it is not, add a Benders optimality cut to MP, let \(k=k+1\), and go to step (2).
## 3 Results
### Pathogen Spillover
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Sampling** & **Green Area** & **Bee Species** & **Real-Time** & **Real-Time** & **Real-Time** & **One-Step** & **One-Step** & **One-Step** & **One-Step** & **Nosema sp.** & **Electron** \\
**Date** & **Green Area** & **Bee Species** & **PCR (CBPV)** & **PCR (ABPV)** & **PCR (DWW)** & **PCR (DWW)** & **PCR (CBPV)** & **PCR (CBPV)** & **PCR (CBPV)** & **Process (CBPV)** & **Process (CBPV)** & **Process (CBPV)** & **Process (CBPV)** \\ \hline
8 July 2020 & Modern park E & _A. mellifera_ & D (1.72 \(\times\) 10\({}^{8}\)) & D (1.7 \(\times\) 10\({}^{9}\)) & N & N & N & N & N & N & N \\
8 July 2020 & Modern park E & _B. terrorits_ & N & D (8.3 \(\times\) 10\({}^{7}\)) & N & N & N & N & N & N & N \\
8 July 2020 & Modern park E & _At._ & _Intermittum_ & D (5.3 \(\times\) 10\({}^{8}\)) & D (7.4 \(\times\) 10\({}^{9}\)) & D (6.6 \(\times\) 10\({}^{9}\)) & N & N & D & N & N & N \\
10 July 2020 & Modern park E & _B. piscurrent_ & N & D (2.1 \(\times\) 10\({}^{9}\)) & N & N & D & N & N & N & \\
17 July 2020 & Modern park D & _A. mellifera_ & D (1.3 \(\times\) 10\({}^{9}\)) & D (4.8 \(\times\) 10\({}^{9}\)) & D (1.4 \(\times\) 10\({}^{11}\)) & N & N & D & N & & ++++ \\
17 July 2020 & Modern park D & _B. terrorits_ & D (9 \(\times\) 10\({}^{9}\)) & D (1.55 \(\times\) 10\({}^{13}\)) & D (7.6 \(\times\) 10\({}^{9}\)) & N & N & D & N & N & (++++) \\
17 July 2020 & Modern park D & _An._ & N & D (7.3 \(\times\) 10\({}^{9}\)) & D (9.1 \(\times\) 10\({}^{9}\)) & N & N & D & N & N & Proarrowius-like (+) \\
23 July 2020 & Modern park C & _A. mellifera_ & D (1.37 \(\times\) 10\({}^{9}\)) & D (3.1 \(\times\) 10\({}^{12}\)) & N & N & N & D & N & N & (EMARPV) \\
23 July 2020 & Modern park C & _An._ & N & D (1.14 \(\times\) 10\({}^{9}\)) & N & N & N & D & N & N & N \\
23 July 2020 & Modern park C & _B. furcits_ & N & D (2.7 \(\times\) 10\({}^{9}\)) & N & N & N & N & N & N & N \\
30 July 2020 & Historic city & _A. mellifera_ & N & D (2.7 \(\times\) 10\({}^{9}\)) & N & N & N & N & N & N & N \\
30 July 2020 & Historic city & _B. terrorits_ & N & D (9.1 \(\times\) 10\({}^{9}\)) & N & N & N & N & N & N & N \\
30 July 2020 & Modern park A & _A. mellifera_ & N & D (2.7 \(\times\) 10\({}^{9}\)) & N & N & N & N & N & N & N \\
14 September 2020 & Modern park E & _A. mellifera_ & N & D (2.7 \(\times\) 10\({}^{9}\)) & N & N & N & D & N & N & N \\ \hline \end{tabular}  
* N = Not detected/No sources detected/No viral particles observed by EKM for DWV and/or ABPV, D = Detection. Viral load expressed in genomic copies/individual and species in the case of Nosema spp.  
\end{table} 
Table 7: Analytical details of pathogen detection in 2020. presence/absence, viral load, spores, and results of EM observation (identification of DWV/ABPV by IEM or presence of any other virus) [ABPV = Acute Bee Paralysis Virus; CBPV = Chronic Bee Paralysis Virus; DWV = Deformed Wing Virus; QCV = Black Queen Cell Virus; SBV = Sac: Broad Virus; KBV = Kashmir Bee Virus; IAV = Israeli Acute Paralysis Virus).
# PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature
## 1 Introduction
The SDPPG waveform in Figure 3 is the same as Figure 2b, and waves c and d are missing; however, the LCFs can also be obtained from this waveform. Further, in our previous study, the correlation between the systolic blood pressure (SBP) and the LCFs was confirmed [16]. However, research on blood pressure estimation by LCF has not been done yet. The object of this study is to confirm the effectiveness of the SBP estimation method using LCFs. Even though we can extract the LCFs from these contour lines, the features extracted from adjacent contour lines tend to show similar trends; therefore, there is a strong correlation between these features. This issue is known as multicollinearity, which will hinder accurate estimation using conventional multiple regression analysis (MRA). Partial least-squares (PLS) regression [17] is one method that can be used to address this issue in multivariate estimation models, and it has been successfully used in various disciplines. Thus, in this study, we used PLS regression to establish a BP estimation model based on LCFs, which are prone to multicollinearity.