esa-sceva/satcom-chunk-collection huggingface dataset:
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
###### Abstract  
With the rapid development of communication technology and the diversification and rapid growth of network application demands, the requirements for the anti-destruction capability of information networks have also increased. Therefore, an efficient and accurate metric of the survivability of satellite networks has become a hotspot of current research. Firstly, in this paper, we propose a transfer matrix-based spatial information network invulnerability evaluation algorithm. The algorithm draws the idea of a node deletion method to determine the initial importance of nodes and then establishes a formula for the importance transfer probability of the nodes. In addition, an evaluation algorithm of spatial information network invulnerability is obtained using the network structure entropy theory. Simulations show that our new evaluation algorithm based on the transfer matrix is more accurate compared to degree centricity, natural connectivity and other methods in evaluating the importance of the nodes. The proposed index can effectively reflect the change in the network topology and evaluate the network survivability.  
transfer matrix; spatial information network; invulnerability; node importance 202323266103390/s23010266
## 5 Empirical Case Study: Courier Company in Bangkok
### From a Fixed Time Instance to Time-Dependent Traffic Information Urban Logistics Problems
#### 5.5.2 From Time Instances to Time-Dependent Traffic Information with the Optimal Working Time Limit  
We have seen that transferring a route solution from a fixed time instance with 480-min working time limit led to a working time violation in the actual time routing problem. Now, instead of using the route solution obtained with the 480 min set by the company, we use the route solution obtained with the optimal working time \(\tau^{*}\) we found in Section 5.4.2. Figure 11 presents the actual vehicle time of the route solution built with the optimal working time (obtained from Table 2).  
Figure 10: Transferring route solution from a fixed time instance to actual traffic time when the working time limit in the fixed time instance was set to 480 min.  
For Scenario A, we can see that using the route solution constructed with any time instance, all retrieved vehicle times were below 480 min required by the company. For Scenario B, although some vehicle times exceeded 480 min, the amount of violations were less than 30 min, far below what we obtained previously in the unbalanced case (see Figure 10). While the evidence of Section 5.5.1 showed the impossibility of transferring the routes built with a fixed time instance to the problem with the time-dependent traffic information, the obtained results in this section confirm the transferability of the routes built from a fixed time instance to the actual traffic time problem. Eventually, the balancing property is also better retained in the actual time problem when the optimal working time \(\tau^{*}\) was used in place of 480 min when building the routes.
## 4 Results
### Assessment with High Quality Validation Data  
Figure 4A shows results for the ZTT model, with inputs of constant \(P_{bb,ST}(\psi)\) and direct measurements of \(a_{pg}\), \(b_{bp}\), and \(\widetilde{b}_{bp}\), compared to measured \(r_{rs}\) for the validation data set [29]. MAPE %\(\delta_{abs}\) was 16% for all \(\lambda\) (summarized in Table 1; spectral %\(\delta_{abs}\) provided in Table 2). Interestingly, this result is slightly more favorable than \(r_{rs}\) computed with the full radiative transfer (i.e., Hydrolight) using measured phase functions, where \({}^{\circ}\!\!\delta_{abs}\) was 17% for the same data (as reported in [PERSON] et al. [29]). Based on closure analyses for these data [29], this 17% error represented the aggregate inherent error from all sources within the data and computations, i.e., from the IOP and radiometric measurements, as well as any errors from the assumptions within the Hydrolight RT code. For the ZTT model evaluated using measured phase functions instead of the constant \(P_{bb,ST}(\psi)\), i.e., the same approach followed with the full RT computations, %\(\delta_{abs}\) was also 17% (Table 1). The different backward phase functions applied in the ZTT model are shown in Figure 5. Measured backward phase functions showed more variability at individual angles than the average \(P_{bb,ST}(\psi)\) derived from a large data set of diverse Case 1 and Case 2 water types, likely the result of small scale hydrosol patchiness along the different light paths for individual measurements at each angle. This observation is addressed further in Section 5. Moreover, the strong agreement in absolute error between full radiative transfer simulations and the ZTT analytical approximation for this diverse data set is encouraging. Excluding the effects of water Raman from the ZTT model increased absolute error by 3%, demonstrating Raman had a significant effect, especially \(>\)580 nm (results not shown). Figure 4B shows ZTT model performance with a further constraint, setting \(\widetilde{b}_{bp}\) constant at 0.006, so the only IOP inputs were measured \(a_{pg}\) and \(b_{bp}\). With respect to inversion, this approach has the same unknowns, i.e., \(a_{pg}\) and \(b_{bp}\), as current algorithms based on the simple first order approximation [16]. Resulting MAPE %\(\delta_{abs}\) was 17%, comparable to full RT computations. ZTT runs with other fixed \(\widetilde{b}_{bp}\) are shown in Table 1. Some influence from \(\widetilde{b}_{bp}\) is apparent, which has not been fully appreciated in previous models based on the first order approximation of \(r_{rs}\) to \(b_{b}/a\). ZTT model runs with Fournier-Forand phase functions for the particulate component, computed from measured \(\widetilde{b}_{bp}\) following the method of [67], resulted in \(\%\delta_{abs}\) of 19%, 3% higher than results using the constant \(P_{bb,ST}(\psi)\) (Table 1). Match ups in \(r_{rs}\) were also assessed applying the M02 model, the current standard in BRDF corrections for ocean color remote imagery (Figure 4C). The proportionality between \(r_{rs}\) and \(b_{b}/a\) is
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 2 Data and Analysis Method  
### Ionospheric Observation Data  
#### 2.1.1 Observation of GNSS from IGS  
TEC can be derived from dual-frequency measurements due to the dispersive characteristics of the ionosphere. IGS had deployed around 400 GNSS dual-frequency receivers worldwide in 2014, which can monitor the global ionosphere all day. The pseudoranges and carrier phases measured at \(f_{1}\) and \(f_{2}\) are used to calculate the slant TEC (STEC) from a satellite to a receiver as  
\[STEC_{sp}=\frac{2(f_{1}f_{2})^{2}}{k(f_{1}^{2}-f_{2}^{2})}(P_{2}-P_{1}), \tag{1}\]  
\[STEC_{sl}=\frac{2(f_{1}f_{2})^{2}}{k(f_{1}^{2}-f_{2}^{2})}(L_{1}\lambda_{1}-L_ {2}\lambda_{2}), \tag{2}\]  
where \(k=80.62\) m\({}^{3}\cdot\) s\({}^{-2}\) is ionospheric refraction, \(P_{1}\), \(P_{2}\) and \(L_{1}\), \(L_{2}\) are distances (pseudorange) and phase measurements, \(\lambda_{1}\) and \(\lambda_{2}\) are the wavelength of the signal [21].
# A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
## 3 Proposed Method
### Phase 2: Modeling  
A perceptron is the building block of Artificial Neural Network (ANN) which is a biologically inspired machine learning model. The perceptron is a mathematical representation of the neuron, the basic computational unit of the brain. In the human nervous system, a neuron receives an input signal from its dendrites and outputs a signal along its axon which is in turn branches out and connects via synapses to dendrites of other neurons. ANN is an information processing paradigm that is based on this biological structure and models complex relationships between inputs and outputs. A single perceptron mathematical model can be used as a linear classifier: it maps the input \(x\) = (\(x_{1}\),..., \(x_{n}\)), a real-valued vector, to an output value \(f(x)\) according to the following rule:\[\left\{\begin{array}{l}1,\;\;if\;\sum\limits_{i=1}^{m}\omega_{i}x_{i}\;+b>0\\ 0,\;\;otherwise\end{array}\right. \tag{9}\]  
where \(w\) = (\(\omega_{1}\),..., \(\omega_{n}\)) is a real-valued vector of weights and \(b\) is the bias. The perceptron can be generalized by applying a set of nonlinear transformations \(\varnothing_{i}\) to the input vector \(x\). The output \(y\) is given by  
\[y(x,w)=g\left(\sum\limits_{i=1}^{m}\omega_{i}\varnothing_{i}(x)\right) \tag{10}\]  
where \(m\) is the number of attributes (variables), and \(g\) is a nonlinear activation function such as sigmoid or Rectified Linear Unit (relu) functions. The Perceptron is a simple model and useful in approximating many continuous functions. In order to improve the model performance, we consider a multi-layer perceptron by adding at least one hidden layer to the perceptron model above, see phase 2 in Figure 3. The learning algorithm that is commonly used is the back-propagation algorithm [34], a generalization of the least mean squares algorithm in the linear perceptron. The sigmoid and relu function are two activation functions which are frequently used in MLP learning models.
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Research Goals  
In the explored works, we found three main goals pursued by NTA research (Figure 4a):  
* Attack detection. This is whenever the applicability of the proposal detects attacks in network traffic, meaning the identification of traffic associated with malicious behavior. An example is the work by [PERSON] and [PERSON] in [116].
* Anomaly detection. This is if the paper aims to detect anomalies in network traffic, meaning traffic that is abnormal, breaks expected patterns, or cannot be defined as following normal behaviors. Such anomalies do not have to be necessarily malicious, and the authors do not address a priori any particular attack, scheme, or traffic class. An example is the work by [PERSON] et al. in [117].
* Traffic classification. The is whenever the methods in the paper identify specific classes in network traffic. Such papers are not focused on the identification of attacks; otherwise, attack detection would be the appropriate goal. An example is the work by [PERSON] et al. in [118].
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 2 Ultra-Tight GPS/INS Integration and RISS
### RISS Motion Equations  
The 3D RISS navigation state vector consists of nine parameters as the following:  
\[X=[\varphi,\lambda,h,v_{e},v_{n},v_{u},r,p,A]^{T} \tag{1}\]  
where \(\varphi,\lambda\) and \(h\) are the position components, latitude, longitude, and altitude, respectively. \(v_{e},v_{n}\), and \(v_{u}\) are the velocity components, east velocity, north velocity, and up velocity, respectively. \(r\), \(p\), and \(A\) are the attitude components, roll, pitch, and azimuth, respectively. The calculations of the navigation solution are given in the next subsections. It should be mentioned here that the axes of the accelerometers and gyroscopes are set to match with the axes of the moving vehicle (the car in this application). Thus, a transformation operation is applied to obtain the navigation parameters with respect to a local-level frame (LLF) which represents the vehicle's attitude and velocity information on or near the Earth's surface.
# The Development of an Advanced Air Mobility Flight Testing and Simulation Infrastructure
## 4 Use Cases and Results
### The Collaborative Interface Test  
The AAM flight testing and simulation infrastructure also provides a collaborative interface that accommodates both simulated and real flights along with the commercial aviation flights for creating a communication link between the AAM vehicles and the ATC infrastructure. It allows AAM vehicles to transmit their position, altitude, and other relevant information to the ATC system. The ATC system uses this information to manage the airspace and provide guidance to the AAM vehicles, so as to avoid collisions and maintain safe separation distances. Virtual flights are tracked with simulated signals, and actual flights are tracked with telemetry information requested from USSPs by specifying operation IDs, as shown in Figure 8. Manned aviation traffic is replicated by injecting ADS-B data from the OpenSky Network.  
Figure 8: Live and virtual traffic crossover.  
Figure 7: Initial (**a**) and updated (**b**) flight plans after going through the pre-flight services within the AAM flight testing and simulation infrastructure.
## 3 Proposed Coordinated Control Method
### Strategy 3: Queue-Speed Guidance Strategy for Transition Section  
The speed guidance for the queue is based on various arrival scenarios that vehicles may encounter when traveling at normal speeds under different signal phases. Without considering secondary stopping and queuing, relevant control strategies are formulated.  
1. If the downstream intersection's coordinated phase is in the green signal state when the target queue approaches the guidance area, with sufficient remaining green time, or if it is in the red signal state with a relatively short remaining red time, and there are no vehicles ahead or any traffic that could affect the movement of the target queue, then the queue can be guided to travel at the maximum speed \(V_{max}\) allowed by the road. Refer to Equation (17) for guidance speed \(V_{ta,file}^{l}\) and Figure 7a for the illustrative scenario.  
\[V_{ta,file}^{l}=V_{max} \tag{17}\]
2. If the downstream intersection's coordinated phase is in the green signal state with sufficient remaining green time when the target queue reaches the guidance area, and there is a stable-moving queue ahead, the target queue should be guided to catch up with the trailing vehicle of the preceding queue to form a platoon of vehicles. The target queue should closely follow the preceding queue to cross the intersection stop line. Refer to Equation (18) for guidance speed \(V_{ta,file}^{l}\) and Figure 7b for the illustrative scenario.  
\[V_{ta,file}^{l}=\frac{L_{ta,file}^{hc}(t)}{L_{ah,file}^{tc}(t)\times 3.6/V_{ah,file}} \tag{18}\]  
where \(L_{ta,file}^{hc}(t)\) = distance from the front vehicle of the target queue to the intersection stop line at the moment of arrival in the guidance area, \(L_{ah,file}^{lc}(t)\) = distance from the rear vehicle of the preceding queue to the intersection stop line at the moment when the target queue is being guided, and \(V_{ah,file}\) = speed at which the preceding queue is traveling.
3. If the downstream intersection's coordinated phase is in the green signal state when the target queue arrives at the guidance area, and the queued vehicles ahead have not yet dispersed, or if it is in the red signal state with a significant remaining red time, to avoid the target queue from coming to a stop and waiting, the front vehicle of the target queue should be guided to drive to the rear of the queued vehicles in front. When the queue-starting wave reaches the last vehicle in the queue, the target queue can then closely follow the queued vehicles to cross the intersection stop line. Refer to Equation (19) for guidance speed \(V_{ta,file}^{l}\) and Figure 7c for the illustrative scenario.  
\[V_{ta,file}^{l}=\frac{L_{ta,file}^{hc}(t)-L_{t_{A},s}^{q}}{g_{l,k,s}^{b}(t)-t(n )+L_{t_{A},s}^{q}/V_{sw}} \tag{19}\]  
where \(L_{t_{A},k,s}^{q}\) = queue length of the queued vehicles in the downstream intersection's coordinated through direction with cycle \(k\) and \(t(n)\) = current moment, which is the time when the front vehicle of the target queue arrives at the guidance area.
## 2 Related Work
### Modulation and Addressing  
Some studies have explored solutions to improve the PER of WuRs through different modulation techniques, such as pulse-width modulation (PWM) [6] and pulse position modulation (PPM) [7], while other studies investigate address-coding schemes [8,9]. However, they achieve limited modulation gain and increase the power consumption by the longer wake-up sequence of 31 to 48 bits to maintain a probability of detection of 90%.  
The address-coding scheme from the work [9] maintains the desired probabilities of detection (\(P_{det}\)) of 90% by increasing the bits of the wake-up sequence. The wake-up sequence consists of a pseudo-noise (PN) code sequence of length N. The PN code supports low correlation with other sequences, which avoids a false alarm (FA) from other communicating nodes or wake-up signals for neighbor nodes. To maintain a constant \(P_{det}\) of 90%, they adjust the correlator threshold and RF input power level. If N is 31 bits, the mean time between FAs is about 10 s at the given RF input power of \(-\)56 dBm and a constant \(P_{det}\) of 90%. At the same condition, if N is seven or 15 bits, the mean time between FAs is about 0.1 s. Therefore, increasing the bits of the wake-up sequence reduces FAs in a certain time.
## 3 Numerical Simulation and Results
### Numerical Simulation Results  
#### 3.2.1 Chip Deformation Mode  
Chip deformation is an essential indicator of the cutting process and characterizes the quality of the developed numerical model. Figure 6 shows chip deformation at cutting displacements of 50, 100, and 150 mm and compares detailed deformation modes between simulation and experiment. It can be observed that both of them have continuous stripe chips with the same curl shape and the same variation pattern on the chip edges. At the beginning of the chips are both irregular edges generated by the initial unsteady cutting, followed by smoothed edges in the steady-state phase. As the chips continue to be generated and curled, the resistance to chip flow increases and the chips are squeezed, resulting in slightly thick buildups and ragged edges.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GNSS & Global Navigation Satellite System \\ HS & High Sensitivity \\ SoC-FPGA & System-on-Chip Field-Programmable Array \\ SDR & Software Defined Radio \\ \(C/N_{0}\) & Carrier to noise density ratio \\ A-GNSS & Assisted GNSS \\ CI & Coherent Integration \\ GPS & Global Positioning System \\ PRN & Pseudo Random Noise \\ SNR & Signal to Noise Ratio \\ TCXO & Temperature-Compensated Crystal Oscillator \\ OCXO & Over Controlled Crystal Oscillator \\ NCI & Non-Coherent Integration \\ PDI & Post-Detection Integration \\ FFT & Fast Fourier Transform \\ DBZP & Double BLock Zero Padding \\ LBS & Location-Based Services \\ TTFF & Time to First Fix \\ NPDI & Non-coherent Post Detection Integration \\ DPDI & Differential Post-Detection Integration \\ GPDIT & Generalized Post-Detection Integration Truncated \\ SD & Squaring Detector \\ GPDITSD & GPDIT and SD \\ \end{tabular}
# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 2 Materials and Methods
### Dynamic Selection of Path
#### 2.3.2 Global Dynamic Selection of Path  
The adaptive ant colony algorithm proposed in the literature [42; 43; 44] adaptively controls the proportion of pheromone concentration in the current optimal solution and updates the global pheromone concentration of the optimal path in real time by introducing a hyperbolic sine function as an adaptive dynamic factor \(\sigma(\sigma\in(0,1))\). In this way, the path exploration results can be effectively implemented when the performance of each path is known. In view of the real-time changes in the characteristics of road traffic network status, we apply the adaptive ant colony algorithm to solve the personalized optimal path. We add dynamic updating links of road performance to simulate the road environment realistically. The flowchart of the improved adaptive ant colony algorithm and its contribution in this study are shown in Figure 6a,b (shadow), respectively.  
Figure 6: Improved adaptive ant colony algorithm and its contribution.
# Fluoride and Pineal Gland
## 2 Pineal Gland--Anatomy and Physiology  
In humans, the pineal gland is a neuroendocrine gland weighing about 150 mg [57]. The organ, part of the epithalamus, is located between the _colleculi superiores_ of the _lamina tecti_, at the back of the posterior wall of the third brain ventricle [58] (Figure 1). The pineal gland is characterized by a very rich network of blood vessels, which ensures blood flow of 4 mL/min/g, second only to the blood supply to the kidneys [58; 59; 60]. Another unique anatomical feature of the gland is its location outside the blood-brain barrier [58; 59]. Therefore, unlike most other brain structures, the pineal gland has open access to blood and all of its components. Extremely rich vascularization and no significant restrictions in transport from the bloodstream make it possible for the pineal gland to accumulate significant amounts of various substances, mainly, calcium [58; 61; 62; 63; 64; 65; 66]; microelements such as cobalt, zinc, and selenium [67]; and fluoride [52; 53; 54].  
The basic function of the pineal gland is the production and secretion of melatonin [58; 64], a hormone found in all vertebrates [60], including humans, which regulates circadian rhythms such as the sleep-wake cycle [64] (Figure 2). It is also a strong antioxidant [68; 69; 70] and an anti-inflammatory agent [71; 72]. Although melatonin can be synthesized in almost all organs and tissues, including skin [73], intestines [74], bone marrow [75], testicles [76], ovaries [77], or the placenta [78], the proper biological response is regulated by the pineal hormone [64].  
Biosynthesis of melatonin (Figure 3) occurs in pinealocytes, which constitute about 95% of the pineal gland's volume [79]. The remaining part of the organ consists of astrocytes, microglia, vascular endothelial cells, and nerve fibers [79; 80]. The precursor of melatonin is  
Figure 1: T1-weighted midline sagittal magnetic resonance imaging (MRI) with an arrow pointing to a normal pineal gland (case courtesy of Assoc Prof [PERSON], Radiopaedia.org, rID: 10767).  
Figure 2: Chemical structure of melatonin.  
tryptophan [58], and most of the hormone is produced during sleep [81]. Its plasma concentration reaches its maximum between 2 and 3 o'clock in the morning (80-150 pg/mL) [82]. The mechanism conditioning this effect is initiated by reducing the activity of the suprachiasmatic nuclei, which occurs at night. The effect is the activation of postganglionic sympathetic fibers and the release of norepinephrine from their nerve endings. This neurotransmitter stimulates \(\beta\)-adrenergic receptors, inducing activation of the adenylate cyclase-cyclic adenosine monophosphate (AMP) system. As a result of the increased cyclic adenosine monophosphate (cAMP) concentration in pinealocyte cytosol, the activity of serotonin N-acetyltransferase increases, which leads to the stimulation of melatonin synthesis [83, 84].
## 5 Results
### Different Drift Ratio
The structure reaches its safest state when the ratio of drifts is 0.3, as the compressive stress controlling the structure's safety is minimized at this point. Figure 15 shows the distribution of the plastic zone of the surrounding rock at the end of tunnel excavation under five different drift ratios. The plastic zone exhibits an asymmetrical distribution due to topographic bias. The ratio of drifts on both sides affects the distribution of the plastic zone in the surrounding rock. The plastic zone of the surrounding rock is primarily located in the vault, left-arch waist, left-side wall, right-side wall, and left-side arch of the tunnel. As the drift ratios on both sides increase, the plastic zone at the left arch waist gradually expands, and the scope of the plastic zone of the surrounding rock also gradually increases. Based on the results above, five different excavation schemes with varying drift ratios were identified. As the drift ratio increases, there is an increasing trend in drift arch settlement, horizontal convergence, and the plastic zone of the surrounding rock. Therefore, it is recommended to control the span of the drift to reduce the disturbance of the surrounding rock, while still meeting the needs of the construction space.