esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Conclusions and Future Perspective
### Advanced Fabrication Techniques  
The solution casting method is a cost-effective method and is commonly used at the laboratory level but it possesses some drawbacks such as difficulty in adjusting structure tuning, uniform thickness, and particle dispersion in case of composite membrane. Moreover, its durability and mechanical strength are also challenging to achieve. Research focuses on advanced techniques in the field of fuel cells to develop membranes such as the electrospinning method, layer-by-layer method, and 3D printing method. The layer-by-layer method allows for membrane preparation on a nanoscale level and it is based on the formation of membrane by deposing different charged layers on each other. This method has the flexibility of creating membranes with designed ionic pathways. The electrospinning method also allows the researcher to form membranes with multiple functional groups with a high possibility of water retention and enhanced ion conduction [131]. Three-dimensional printing is another method to develop membrane in which different patterns can be achieved within membranes which facilitate ion conduction, enhance water management, favors IEC, and enables high mechanical strength and power density [132]. The attachment of block copolymers with membranes is another efficient method to improve the ion conduction in the membrane. The crosslinking of two polymers in the membrane improves the mechanical strength of the membrane and generate self-healing properties in the membrane to adjust in the chemical polluted environment. Despite these techniques, the recent research focuses on using AI techniques to optimize the property of materials used in the membrane of fuel cells.
# PC12 and THP-1 Cell Lines as Neuronal and Microglia Model in Neurobiological Research
## 3 Results and Discussion
### PC12 Cells and Lipopolysaccharide  
In all tests on PC12 cells, a dose-dependent adverse effect of LPS was observed regardless of cell differentiation. The only exception was the MTT assay with a 1 uM LPS, where mitochondrial activity was higher than in control (by 29.0% in undifferentiated and 8.2% in differentiated cells).  
Figure 3: Exemplary micrographs of nuclear halo in undifferentiated (**A**–**C**) and differentiated (**D**–**F**) PC12 cells taken in the FHA assay: (**A**) control culture of undifferentiated cells; (**B**) undifferentiated cells incubated with 5 uM A\(\beta_{25\text{-}35}\); (**C**) undifferentiated cells incubated with 100 uM LPS; (**D**) control culture of differentiated cells; (**E**) differentiated cells incubated with 5 uM A\(\beta_{25\text{-}35}\); (**F**) differentiated cells incubated with 100 uM LPS.  
According to MTT assay results, differentiated PC12 cells treated with LPS at concentrations of 1-50 uM showed significantly lower (by 9.2-16.1%) mitochondrial metabolic activity compared to undifferentiated cells (Figure 4A).  
A statistically significant increase of cytotoxicity (LDH release) in the culture of undifferentiated cells occurred only at the 100 uM LPS concentration (19.6%), while for differentiated PC12 cells at each LPS concentration administered (32.8-94.2%; Figure 4B). A substantially higher LDH release (by 31.6-62.3%) was observed in differentiated PC12 cultures. Moreover, these differences between the effects of LPS on differentiated and undifferentiated cells were significant for each LPS concentration used.  
DCF-DA assay showed the expected increase in the level of free radicals dependent on the LPS concentration (Figure 4C). A slightly higher ROS production level was observed in differentiated PC12 cells (compared to undifferentiated cells) incubated with LPS at concentrations of 5-100 uM (by 7.1-17.0%; without statistical significance but with a clear trend in this concentration range).  
In the concentration range of 5-100 uM LPS, the increase in DNA damage level was statistically significant compared to negative control in both cell types and in differentiated cells at each LPS concentration used. The differences depending on the differentiation of PC12 cells were statistically significant at each LPS concentration and ranged from 15.3% to 42.4% (Figures 3 and 4D).
# Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
## 2 Model of Intelligent City Bus
### Battery Model  
For the battery pack, the relationships between open-circuit voltage, charging resistance, and state of charge (SoC) are complicated. In this paper, we don't consider the effect of temperature and other factors, the relationships between them are shown in Figure 3.  
The battery works safely and efficiently when SoC is between 0.5 and 0.75 and in this range, the value of internal resistance changes gently, so we can take it as a constant. For convenience, a simplified battery model with appropriate precision needs to be built, which simplify the battery as a voltage source with an internal resistance. Then, the gradient of SoC with time can be written as follows [32]:  
\[\frac{\text{d}SoC}{\text{d}t}=-\frac{U_{\text{OC}}-\sqrt{U_{\text{OC}}^{2}-4 RP_{bat}}}{2 RQ_{b}} \tag{3}\]  
where the \(U_{\text{OC}}\), \(R\), \(Q_{b}\), and \(P_{bat}\) are open-circuit voltage, internal resistance, the capacity of battery, and output power of battery.  
Figure 3: The battery parameters.  
Figure 2: The MAP chart for EM efficiency.  
When the bus drives, EM is a motor and it consumes battery energy. When the bus brakes, EM becomes a generator and it can provide energy for the battery by the regenerative braking system. Therefore, \(P_{bat}\) can defined as follows:  
\[P_{bat}=\left\{\begin{array}{ll}T_{EM}\omega_{EM}\eta_{EM}&\omega_{EM}\geq 0 \\ \frac{T_{REG}\omega_{EM}}{\eta_{EM}}&\omega_{EM}<0\end{array}\right. \tag{4}\]  
where \(\eta_{EM}\), \(\omega_{EM}\), and \(T_{REG}\) are the efficiency of EM, the output speed of EM, and the regenerative torque of EM. In order to improve energy regenerative efficiency, the mechanical braking force of rear axle should be limited so that the EM should be involved in braking as much as possible. When EM cannot provide enough braking force, the mechanical brake then participates. For safety, the following restrictions are imposed on the regenerative braking force:  
1. When \(SoC\geq 0.8\), for the safety of battery, EM is no longer engaged in braking and the mechanical braking system provides the brake torque. Thus, \(T_{REG}=0\);
2. When the brake severity \(Z\) is in the range of 0 to 0.1, EM provides the brake torque for the vehicle and \(T_{REG}=T_{EM}\);
3. When \(Z\) is in the range of 0.1 to 0.7 or EM cannot provide enough brake torque, EM and mechanical braking system provides power for together. Thus, \(T_{REG}=T_{EM}\);
4. When \(Z>0.7\), EM quits from working and the braking power is provided by mechanical braking system. Thus, \(T_{REG}=0\);
5. The EM braking effect is not obvious when its output speed below 500 r/min and the braking torque of the rear axle is provided by traditional mechanical braking system.
## 2 Problem Formulation
### Multi-Robot Task Allocation Problem--MRTA  
Following the classification proposed in [10], the _MRTA_ problem to be tackled is described as a _single-task robots (ST)_, _single-robot tasks (SR)_, and _instantaneous assignment (IA)_ problem. _ST_ means that each _robot_ is able to visit at most one _task_ at a time. _SR_ means that each _task_ requires only one _robot_ to be explored. _IA_ means that the available information about the robots, the tasks, and the environment permits only an instantaneous allocation of tasks to robots, preventing the possibility to plan future allocations. Additionally, an _ST-SR-IA_ can be formulated as an instance of the well known _Optimal Assignment Problem (OAP)_ as follows. Given \(M\) robots, \(N\) tasks, and utility estimates \(U\) for each _MN_ possible robot-task pair, the goal is to assign tasks to robots so as to maximise overall expected utility. Finally, from an _Integer Linear Programming_ perspective, the problem can be formalised as: Find the _MN_ non-negative integers \(\alpha_{ij}\) that maximise (2).  
\[\sum_{i=1}^{M}\sum_{j=1}^{N}\alpha_{ij}L_{ij} \tag{2}\]  
s.t.  
\[\sum_{i=1}^{M}\alpha_{ij}=1,1\leq j\leq N\] \[\sum_{j=1}^{N}\alpha_{ij}=1,1\leq i\leq M\]
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 2 Robust Estimation Strategy for Vehicle Motion States  
### Vehicle Model  
As the comprehensive consideration of accuracy and simplicity, a commonly used dual-track vehicle dynamic model is established in Figure 1 involving vehicle's longitudinal, lateral, and yaw motions, where the influence of suspension and vehicle roll motion is ignored.  
The dual-track vehicle can be modeled as:  
\[\dot{r} =\frac{M_{z}}{I_{z}} \tag{1}\] \[\dot{v}_{x} =a_{x}+v_{y}r\] \[\dot{v}_{y} =a_{y}-v_{x}r\]  
where \(r\) is the vehicle's yaw rate, \(v_{x}\) and \(v_{y}\) are the longitudinal and lateral velocities in vehicle's mass center, \(I_{z}\) is the vehicle's moment of inertia. The vehile's yaw moment \(M_{z}\), the longitudinal acceleration \(a_{x}\) and lateral acceleration \(a_{y}\) in vehicle's mass center in Equation (1) are expressed as:  
Figure 1: Dual-track vehicle dynamic model.  
\[a_{x} =\frac{(F_{x1}+F_{x2})\cos\delta-(F_{y1}+F_{y2})\sin\delta+F_{x3}+F_{x4} }{m} \tag{2}\] \[a_{y} =\frac{(F_{x1}+F_{x2})\sin\delta+(F_{y1}+F_{y2})\cos\delta+F_{y3}+F_{ y4}}{m}\] \[M_{z} =a\cdot((F_{x1}+F_{x2})\sin\delta+(F_{y1}+F_{y2})\cos\delta)-b \cdot(F_{y3}+F_{y4})\] \[\quad-\frac{t_{f}}{2}.((F_{x1}-F_{x2})\cos\delta-(F_{y1}-F_{y2}) \sin\delta)-\frac{t_{r}}{2}\cdot(F_{x3}-F_{x4})\]  
where \(\delta\) is the steering angle of front tire, \(m\) is the total weight of vehicle, \(a\) and \(b\) are the distances from vehicle's mass center to front axle and rear axle, \(t_{f}\) and \(t_{r}\) are the wheelbases of front tires and rear tires, \(F_{x1}\), \(F_{x2}\), \(F_{x3}\) and \(F_{x4}\) represent the longitudinal forces of left front tire, right front tire, left rear tire and right rear tire, and \(F_{y1}\), \(F_{y2}\), \(F_{y3}\) and \(F_{y4}\) represent the lateral forces of left front tire, right front tire, left rear tire and right rear tire, respectively.
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 5 System Overview
### System Architecture
#### 5.2.1 Certificate Issuance  
In order to request a certificate, the client is required to generate a Certificate Signing Request (CSR) and digitally sign the information using the client's private key. Subsequently, the entire package, along with the corresponding public key, is transmitted to the blockchain network for initial verification. The CSR contains essential details such as the domain name, country code, state/locality, organization name, department name, email address, and optional trusted users. Notably, the domain name serves as the subject name, while the email address must be an official organization email for domain verification and future communication with the requester. Following this, the CSR information and the associated public key undergo initial verification within the blockchain network. Figure 2 depicts a high-level overview of creating a signed CSR using the requester's public key and integrating the request into the network for the initial verification process.  
This validation process includes confirming the digital signature using the provided public key to ensure the integrity of the information, thereby verifying that the CSR information remains unaltered. Furthermore, an automatic validation process is conducted on the domain name and other CSR details. Upon successful validation, an X.509 certificate is generated and shared with the key-ring network for subsequent validation.
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Data Processing/Weighting
In the observation phase, the gNB performs training of the scheduled datasets based on the user input (a,b) or complementary datasets recorded by itself {a}*, according to the equivalent metrics available in the gNB itself, as per [17]. Model training based on the ANN principle enables the application of different weighting conditions and importance on data subsets, so that particular components (a,b) are collected with a varying periodicity or varying evaluation target. In the assumed scenario, the applied ANN-based algorithm, in the gNB, requires user input for the _eMBB_ service (\(a_{MBB}\)) and coverage optimization (\(b_{Coverage}\)). The algorithm plays a determinant role to set appropriate weights for the parameters representing these input data (e.g, \(w_{1}=1\) and \(w_{4}=1\)). Since the parameters that measure _eMBB_ service can be observed by the gNB itself, the measurement results for the _eMBB_ service (\(a_{MBB}\)) can be replaced by the input from the gNB [\(a_{eMBB}\)]*. The user input can be superseded, with no meaningful difference in the context of the parameter origin (user input or network measure). For simplicity, equal weights can denote the equal importance of the parameters from different data sets. However, hidden layers in the ANN model enable the discovery of more complex dependencies among each and every subset of the parameters and features, by assigning different importance and different weights to each of them. The shadowed step is used to indicate an incremental step for the measurement configuration modification (change the data set or prolong observation time). The process guarded by certain observation rules (e.g., time), let the gNB collect the data sets and observe how/if the collected values of parameters from datasets meet the target values of the requested performance indicators and what the relationship between observed parameters is. Figure 9: The model for training data collection for coverage optimization for _eMBB_ traffic.
# Estimating Airway Resistance from Forced Expiration in Spirometry
## 2 Materials and Methods
### Airway Resistance from Spirometry
#### 2.4.2 Estimation of \(\zeta\)  
Lung volumes decrease from TLC to RV during FE, unless noisy artifacts such as cough are present which we neglect in this study. So, we can consider the system in Equation (8) as an overdamped system, which implies \(\zeta>1\)[24]. In an overdamped system, the solution to Equation (8) is as follows:  
\[\mathrm{x(t)}\ =\ \mathrm{C_{1}e^{s_{1}\ (t\ -t_{1})}+C_{2}e^{s_{2}\ (t\ -t_{1})}} \tag{9}\]  
with  
\[s_{1,2}\ =\ \left(-\zeta\pm\ \sqrt{\zeta^{2}-1}\right)\omega \tag{10}\]  
There are four unknown terms, \(\mathrm{C_{1}}\), \(\mathrm{C_{2}}\), \(\zeta\), and \(\omega\) in solution 9, which can theoretically be determined using the two initial conditions (Equation (4)) and the two final conditions (Equation (3)). Applying the initial conditions (Equation (4)), we can rewrite \(\mathrm{C_{1}}\) and \(\mathrm{C_{2}}\) in terms of \(\zeta\) and \(\omega\) as follows (see Appendix A):  
\[\mathrm{C_{1}}\ =\ \frac{\dot{x}(t_{1})-s_{2}x(t_{1})}{s_{3}} \tag{11a}\] \[\mathrm{C_{2}}\ =\ \frac{-\dot{x}(t_{1})+s_{1}x(t_{1})}{s_{3}} \tag{11b}\]  
with  
\[s_{3}\ =\ 2\omega\sqrt{\zeta^{2}-1} \tag{12}\]  
We now use an optimization strategy to obtain the unknown parameters, \(\zeta\) and \(\omega\). There are two reasons for this step; the first is that final conditions (Equation (3)) could have anyway been solved numerically and the second, the estimate of the parameter \(\zeta\) may not be robust if only a single data point at time \(t_{n}\) is considered. Therefore, we consider the flow and volume of the entire manoeuver from \(t_{1}\) until \(t_{n}\) and express our cost function as follows:  
\[\mathrm{J}(\zeta,\omega)\ =\ \sum_{t\ =\ t_{1}}^{t_{n}}\left(V(t)-x(t)\right)^{2 }+\left(F(t)-\dot{x}(t)\right)^{2} \tag{13}\]where V(t) and F(t) are measured flow and volume of air during forced expiration. Then, we formulate the optimization as a minimization problem as follows:  
\[\min_{\zeta,\omega}J(\zeta,\omega)\ s.t.\ \zeta>1\ and\ \omega>0 \tag{14}\]  
We can solve the above optimization problem for \(\zeta\) and \(\omega\) using any suitable method. In our study, we used differential evolution as implemented in Python by the original authors [25]. We defined a search space of [1,5] and (0, 5] for \(\zeta\) and \(\omega\), respectively. We wrote a Python script to automate the calculation of \(\zeta\) and \(\omega\) from flow-volume measurements of all subjects [26].
# GPON PLOAMd Message Analysis Using Supervised Neural Networks
## 3 Data Characteristics
### GPON Frame Header  
The frame structure sent by the OLT consists of a physical control block downstream (PCBd) containing information for specific ONUs/ONTs and a GPON transmission container (GTC) payload carrying transferred data. The frame structure is shown in Figure 1. The most interesting part is the PLOAMd field, which is used during the ONU/ONT activation process. The other parts are irrelevant in this paper. The PLOAMd field is 13 bytes long and contains the following parts:  
* **ONU ID** specifies the receiving ONU.
* **Message ID** defines the type of message.
* **DATA** are unique data for specific message.
* **Cyclic redundancy check (CRC)** is used to verify the correctness of field data.
## 2 Analysis of Signal Power and Noise
### Signal Power  
It is well known that the observational noise is basically related to the power of the satellite signal at the receiver, and the stochastic properties of GNSS data can be described as a function of this indicator [43; 44]. Furthermore, it was also confirmed that the carrier to noise density ratio (C/N0) for BDS-IGSO and GEO satellites, which are characterised by a much higher altitude, is 3-4 dB-Hz lower than common BDS-MEO satellites [45; 46]. Thus, the starting point of the investigations given in this section is the analysis of the carrier to noise density ratio for E14 and E18 satellites registered at different altitudes. For this purpose, we computed 5-min mean values of C/N0 at two frequencies, E1 and E5a, for all FOC Galileo satellites. Subsequently, the reference level of C/N0 for common ones was determined through averaging in 10\({}^{\circ}\) bins of elevation angle. The summary of these computations for two selected stations, IAWA and OLSZ (two top and two bottom panels, respectively), is given in Figure 2. According to the results, it can be confirmed that registered C/N0 values depend on the varying radius of highly eccentric orbits. At high elevations, where satellites were practically at minimal altitudes (~17,000-18,000 km), C/N0 increased from 1 and 5 dB-Hz. Interestingly, at the first frequency (Figure 2, left column), strong discrepancies between results for satellites E14 and E18 are observed, whereas at E5a (right column) they are consistent to each other and equal about 2.5 dB-Hz. This divergence was detected at all stations and seems to be related to the different power of E1 signal emitted by both satellites. At low elevations the enhancement of C/N0 did not exceed 2 dB-Hz in all cases. Furthermore, the signal strength values registered at maximal height, which was similar to the nominal one of Galileo orbit, are coincident with average C/N0 pattern obtained for remaining satellites. The common difference between levels of C/N0 for both stations (~3 dB-Hz) seems to be connected with different antenna type. The results for sites BYCE and CHLE, equipped the same as IAWA, not included here, are generally consistent with the latter.  
Figure 1: Baselines and stations processed in the experiment.  
Figure 2: _Cont._
## 3 Results and Discussion
### Finite Element Analysis  
After each DCLT \(\pm\)\(\alpha\) sample was tested under the simulations, the deflection results from the four-point bending loading were recorded. As shown in Figure 12, the maximum deflection occurs in the center of the panel between the two loading areas. To demonstrate this process, the load-deflection curve for each sample model was recorded as shown in Figure 13, and slopes of the curves were used for measuring the bending stiffness of DCLT panels.  
Figure 11: Load–deflection curves of DCLT \(\pm\)\(\alpha\) representative samples tested under four-point bending tests.  
Figure 12: FEM test deflection results for a DCLT sample under applied load.  
The simulations were repeated one more time with the material properties of black locust and eastern white pine being considered with the measured MoE value instead of the published handbook values. As the results show, the simulated DCLT samples with the measured MoE have approximately 12-14% higher apparent bending stiffness than the results using the published E\({}_{\text{L}}\).  
Regarding the data presented in Table 5, the FEM validates the experimental and theoretical results indicating that decreasing the angle of the grain orientation in the inner layers of the panels increases the bending stiffness. The finite element method data show that DCLT \(\pm\) 10\({}^{\circ}\) has an improved apparent bending stiffness by approximately 35% (compared to 90\({}^{\circ}\) lay-up) when using either the USDA Wood Handbook [20] values or the measured MoE of the outer layers as the input for simulated models. Table 5 also summarizes the percent difference (%) between the bending stiffness properties of DCLT panels with different grain orientations in inner layers.
###### Abstract  
The use of existing measurements from a commercial wireless communication system as virtual sensors for environmental monitoring has recently gained increasing attention. In particular, measurements of the signal level of commercial microwave links (CMLs) used in the backhaul communication network of cellular systems are considered as opportunistic sensors for precipitation monitoring. Research results have demonstrated the feasibility of the suggested technique for the estimating and mapping of rain, as well as for monitoring other-than-rain phenomena. However, further advancement toward implementation and commercial use are heavily dependent on multidisciplinary collaborations: Communication and network engineers are needed to enable access to the existing measurements; signal processing experts can utilize the different data for improving the accuracy and the tempo-spatial resolution of the estimates; atmospheric scientists are responsible for the physical modeling; hydrologists, meteorologists, and others can contribute to the end uses; economists can indicate the potential benefits; etc. In this paper I will review state-of-the-art results and the open challenges, demonstrating the benefit to the public good from utilizing the opportunistic-sensing approach. I will also analyze the various obstacles on the way there.  
opportunistic sensing; rain monitoring; commercial microwave links +
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