esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Simulation Model and Parameters
### Modeling and Parameterization of Tower Grounding Grids and Natural Gas Pipelines  
In the actual project of power overhead line tower grounding grid structure, various types are used. The most commonly used type is the box with a ray type grounding grid. For this study, a typical grounding grid is selected, with a diameter of \(\Phi\) = 16 mm and a buried depth of h\({}_{1}\) = 0.8 m, using galvanized round steel as the material. As shown in Figure 2, the length of grounding grid box is 15 m and the length of the extended ray is 20 m. The angle between the extension ray and the box extension line is \(\alpha\) = 45\({}^{\circ}\). When calculating the distribution of lightning current, it is sufficient to model it as a grounding resistance.  
The selection of the natural gas pipeline is based on a standard, exemplified by the cross-section of the pipeline shown in Figure 3. The pipe insulation layer material used is 3 PE, with the specific parameters provided in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Numbers** & **Outside Diameter/mm** & **DC Resistance/(\(\Omega\cdot\)km\({}^{-1}\))** & **Fission Number** \\ \hline LGJ-400/35 & 26.82 & 0.07389 & 4 \\ \hline JLB20A-80 & 11.4 & 1.0788 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of conductors and lightning conductors.  
Figure 1: The tower model and parameters used in ATP-EMTP software are calculated. (**a**) Tower model. (**b**) Electric power tower structure.  
Figure 2: Tower grounding grid structure. (**a**) Lightning current dispersion path. (**b**) Tower grounding grid structure.  
To investigate the effects of overvoltage on a natural gas pipeline at a cross-crossing after a lightning strike on the conductor, causing flashover of the line insulator and resulting in ground scattering current, a simplified model of the T\({}_{0}\), T\({}_{\rm R1}\) tower grounding grid and the natural gas pipeline (as depicted in Figure 4b) is established using COMSOL Multiphysics 5.6 simulation software. The length of the natural gas pipeline (l\({}_{\rm p}\)) is 400 m, and both sides of the pipeline and the T\({}_{0}\), T\({}_{\rm R1}\) tower grounding grid have a pendant angle of (\(\beta\)). Both sides of the pipeline are treated with grounding, and the burial depth (h2) is 2 m.
## 3 Inverse Problem
### Sensitivity Analysis  
The sensitivity coefficient \(J_{ij}\) describes the sensitivity of the state variable in relation to a disturbance in parameter \(P_{j}\) and has an important function in parameter estimation. Small magnitude values of this coefficient indicate that large variations in \(P_{j}\) cause small changes in the dependent variable. The sensitivity coefficient formula is shown in Equation (19) [30].  
\[J_{ij}=\frac{\partial\theta_{i}}{\partial P_{j}} \tag{19}\]  
For situations where the analytical solution of the problem is not known, it is possible to determine the sensitivity coefficient by approximating the first-order derivative using finite differences. The forward difference approximation given by Equation (20) was used.  
\[J_{ij}\cong\frac{\theta_{i}(P_{1},P_{2},\ldots,P_{j}+\varepsilon P_{j},\ldots,P_{Npar})-\theta_{i}(P_{1},P_{2},\ldots,P_{j},\ldots,P_{Npar})}{\varepsilon P _{j}} \tag{20}\]  
However, to compare the values of the sensitivity coefficients for parameters with different units and orders of magnitude, the reduced-sensitivity coefficient is presented in Equation (21).  
\[J_{Pj}=P_{j}\frac{\partial\theta_{i}}{\partial P_{j}} \tag{21}\]
## 6 Nanopharmaceutics
### Enhancing Efficacy of Modified-Gemcitabine Nanoparticles in Pancreatic PDX Models
#### 6.2.2 Methods  
Gem was modified by linking 4-amino group of Gem and stearoyl linear acyl derivative to form 4-(N)-stearoyl-gemcitabine (Gem-stearate). Gem-stearate nanoparticle (GSN) was further prepared by mixing lecithin and labrasol solutions until homogenous mixture was formed. Gem-stearate was then added to the mixture and vortexed intermittently until homogenous solution was achieved. The bond between 4-amino group of Gem and stearoyl derivative was confirmed by nuclear magnetic resonance (NMR) and micro-elemental analysis. The particle size of GSN was determined by using a particle size analyzer. Patient-derived primary pancreatic cancer cells (CMZ and G46 Ca) and MiaPaCa-2 cells were treated with blank nanoparticles and different concentrations of free Gem and GSN for 48 hours and determined the viability by using Resazurin assay. Mice with pre-established tumors (patient-derived xenografts (PDX)) of a pancreatic model (G46 Ca) were treated with Gem and GSN.
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
###### Abstract  
The academic and professional community has recently started to develop the concept of 6G networks. The scientists have defined key performance indicators and pursued large-scale automation, ambient sensing intelligence, and pervasive artificial intelligence. They put great efforts into implementing new network access and edge computing solutions. However, further progress depends on developing a more flexible core infrastructure according to more complex QoS requirements. Our research aims to provide 5G/6G core flexibility by customizing and optimizing network slices and introducing a higher level of programability. We bind similar services in a group, manage them as a single slice, and enable a higher level of programmability as a prerequisite for dynamic QoS. The current 5G solutions primarily use predefined queues, so we have developed highly flexible, dynamic queue management software and moved it entirely to the application layer (reducing dependence on the physical network infrastructure). Further, we have emulated a testbed environment as realistically as possible to verify the proposed model capabilities. Obtained results confirm the validity of the proposed dynamic QoS management model for configuring queues' parameters according to the service management requirements. Moreover, the proposed solution can also be applied efficiently to 5G core networks to resolve complex service requirements.  
Keywords:5G/6G; dynamic QoS management; network slicing; software-defined networking; queue management +
Footnote †: journal: Sensors
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 6 Performance Verification and Error Analysis
### Comparison of High-Speed Trajectories  
In scenarios involving high-speed flight, Figures 9 and 10 offer a comprehensive comparison between the method proposed in this paper and that of reference [6] through simulation. The discrepancy between the planned and actual output thrust in reference [6] is substantial due to the quadratic increase in air resistance under high-speed conditions. This discrepancy escalates, surpassing the drone's maximum thrust limit, which results in a significant rise in the tracking error of the state trajectory during actual flight. Consequently, there is a considerable deviation between the final position and velocity states and the terminal state constraints. Despite  
Contrarily, the linearization method proposed in this study more accurately restores actual drag under high-speed conditions. This is evidenced by the significant correlation between the maximum thrust of the planning results and the actual maximum thrust. Consequently, when using the planning results derived from this method, a trajectory is deemed unfeasible, aligning more closely with real-world scenarios. This notable advantage demonstrates that our method offers superior accuracy and reliability in handling high-speed flight situations, particularly when considering intricate dynamic models and external drag effects.  
The lateral tracking error identified in the experimental results predominantly arises from control delays, attributed to the drone's physical property constraints and the inherent response time of its flight control system. This particular error exhibits a consistent trend in the simulation outcomes of both methodologies, which is not the primary focus of this study. The central emphasis of this paper lies in contrasting the trajectory planning techniques between the two methods and highlighting the performance superiority of the proposed method in high-speed flight conditions.  
In conclusion, the algorithm presented in this paper offers more effective trajectory planning outcomes within a comparable timeframe to the method referenced in [6]. This is particularly important for managing high-dynamic anti-drone swarm task scenarios, as the quality of trajectory planning significantly influences both task efficiency and safety.
# Improving Serial Detection Using MAP Algorithm for Bit-Patterned Media Recording
## 3 Simulations  
### BPMR Channel  
In Figure 1, for simplification, we did not include the modulation and demodulation blocks. However, in the simulation, we implemented the modulation of the original signal \(u[k]\in\{0,1\}\) into the signal \(a[j,k]\in\{-1,+1\}\) and the demodulation of signal \(\hat{a}[j,k]\) into the estimated original signal \(\hat{a}[k]\).  
The received signal \(y[j,k]\) is expressed as follows:  
\[y[j,k]=a[j,k]*h[j,k]+w[j,k], \tag{7}\]  
where \(j\) and \(k\) are the discrete indices in the down- and cross-track directions, respectively; \(w[j,k]\) is additive white Gaussian noise (AWGN) with zero mean, variance \(\sigma^{2}\). The BPMR channel pulse response \(h[j,k]\) is expressed as follows:  
\[h[j,k]=P\left(jT_{x},kT_{q}-\Delta_{off}\right), \tag{8}\]  
where \(P(x,q)\) is expressed as follows [14]:  
\[P(x,q)=A\exp\left(-\frac{1}{2c^{2}}\left[\left(\frac{x+\Delta_{x}}{PW_{x}} \right)^{2}+\left(\frac{q+\Delta_{q}}{PW_{q}}\right)^{2}\right]\right), \tag{9}\]  
where \(x\) and \(q\) are the down- and cross-track directions, respectively; \(\Delta_{x}\) and \(\Delta_{q}\) are the down- and cross-track bit location fluctuations, respectively; \(A=1\). The constant, \(c=1/2.3548\)[15], and \(PW_{x}\) and \(PW_{q}\) are the \(PW_{50}\) components of the down- and cross-track, respectively. TMR is defined as the ratio between the head offset size and the magnetic island period, and is expressed as follows:  
\[\text{TMR}(\%)=\frac{\Delta_{off}}{T_{q}}. \tag{10}\]  
We created a BPMR channel with an areal density of \(3\text{ Tb}/\text{in}^{2}\) (\(0.465\text{ Tb}/\text{cm}^{2}\)) (\(T_{x}=T_{q}=14.5\text{ nm}\)) [16]. The signal-to-noise ratio (SNR) was defined as SNR = \(10\text{log}_{10}(1/\sigma^{2})\)  
Figure 5: Effect of MAP algorithm on the output of vertical detection.  
To simplify, we first applied our proposed model to the BPMR channel without the TMR effect (\(\Delta_{off}=0\)). This channel has the following parameters:  
\[\mathbf{H}=\left[\begin{array}{ccc}0.0824&0.3876&0.0824\\ 0.2125&1&0.2125\\ 0.0824&0.3876&0.0824\end{array}\right]. \tag{11}\]
## 3 Proposed SECHash System Model
### SECHash System Model Methodology  
1. SECHash System Design Process: The SECHash model is proposed to solve the problem of the lack of individuals' confidence in government due to corruption and fraud. In this paper, a case study in this area has been carried out. However, it will examine a specific use case related to external invoices/bills municipalities must pay. For example, suppose there is a maintenance bill from non-governmental organisations (NGOs) that the municipality must pay. The accountant or municipal employee will manually enter the bill data into the municipality's financial system. At this stage, the billing record is drafted. After auditing, the same employee or maybe another one confirms the draft bill, a journal entry is created on the financial system, and all financial statements are reflected in the accounts. One of the problems that may arrive from the manual entry of bills is that we cannot guarantee the correctness  
Figure 1: SECHash system model.  
of the bill data. Additionally, there is no way to verify the integrity of the entered information in the case of malicious actors.  
The two main issues the SECHash model will look at are integrity and correctness. The suggested solution is to address these issues in a manner that is automated, transparent, and easy to understand in layperson's terms. Thus, promoting trust in municipalities and other similar governmental entities.
2. SECHash System Design Components: The proposal is based on three main components as follows: 1. OCR component: to capture the scanned bill's content, extract its data, and transform them into digital data that the municipal financial system can read. 2. Blockchain component: to hash the scanned bills documents and store the hashes in the network--this is a write-private network with an open-read permission model. 3. Bills explorer component: to allow the user to request and view the hash for any document.
3. Technology Selection: As a result of the recent literature review shown in Section 2, blockchain with OCR can be an excellent combination to address integrity and correctness issues. Therefore, the proposed SECHash system proposal is based on blockchain and OCR technologies in its solution.
4. Proof-of-Concept Implementation: this work builds the proposed SECHash to defend the idea. And to prove that blockchain technology in the governmental sector will preserve data integrity.
# Design of a Rotary Transformer for Installations on Large Shafts
## 3 Finite-Elements Analysis
### Two-Dimensional Analyses  
The preliminary planar finite-element analyses were intended to check the capability of the structure based on realistic geometrical dimensions and on the adopted materials. The goal was to obtain a device able to transmit a power in the order of 4 kW from primary to secondary; this solution can be used to feed electric or electronic equipment for both signal or power applications. For example, a rotary transformer such as this can be used for de-icing purposes on tail-rotors on helicopters, as shown in [11; 12], or to supply rotors in medium-size wound induction motors as well as synchronous machines (see [5; 6; 7; 8]). The switching frequency at which the transformer should operate was set in the order of 10-40 kHz to allow the use of industrial components and to avoid radiated emissions. Moreover, the converter was assumed to work in square-wave modulation, allowing us to obtain a first harmonic with peak value \(4/\pi\)\(V_{\text{DC}}\) (Figure 5). As a consequence, ferrite was chosen as the material for the core to limit eddy-current and hysteresis losses as much as possible. Then, its geometrical dimensions were roughly estimated based on the available space. For example, the inner radius of the core should be around 90 mm, whereas its height can range between 20 and 30 mm. To this aim, FEMM was exploited in view of the optimization of the core, since its files can be called, generated, edited and executed from MATLAB(r). Due to the coupling of the two aforementioned softwares, it was possible to estimate the parameters \(L_{1}\), \(L_{2}\) and \(M\) of the rotary transformer, computing the flux linkage in FEMM for the two windings while supplying them one at a time. Then, the values of the parameter were used in MATLAB(r) to solve the equivalent circuit reported in Figure 3 (a supply voltage of \(V_{\text{DC}}=270\) V was assumed, which could be obtained using a rectifier on a monophase system) and inject the computed currents \(I_{1}\) and \(I_{2}\) back into each corresponding wire in FEMM in a new simulation to check the saturation of the magnetic core. An example of the output of one of these analyses is reported in Figure 6. The problem was set to be axisymmetric, i.e., the drawn section was considered to be perfectly symmetrical over a rotation of 360\({}^{\circ}\) with respect to the shaft.  
Figure 6: Density plot of the absolute value of the induction field \(|B|\) superimposed to the field lines of the real part of the magnetic vector potential \(\hat{A}\) for a realistic geometrical configuration of the rotary transformer.  
These preliminary investigations were all static, that is, without simulating the spin of the transformer. However, they prove that a structure with an air gap of 1 mm is capable of transferring power in the range 1-6 kW (depending on the cross section of the magnetic circuit and on the number of turns of the coils) from primary to secondary without saturating the magnetic core.
# A Study of Emergency Aircraft Control During Landing
## 3 Proposed Solution
### Control System Architecture  
A shared feature among the existing emergency thrust control systems is their control path-shaping process, which is based on experimental, manual selection of control parameters. This makes it difficult to determine whether a resulting configuration is the best one available due to a presence of local minima/maxima of the non-linear system. The control law proposed in this paper is based on the idea of optimal control by deploying LQRI controllers [11; 12] in both control channels.
## 2 Related Works
### Self-Training Research  
Digital fitness can be classified in three ways depending on the participation of the user and the instructor (Figure 1). First, there is the passive approach [24], which occurs through an online assessment by a human instructor. This method involves evaluating the student's movements through real-time video calls or video submissions and providing feedback, following the model of traditional offline fitness coaching. The advantage of this approach is that it allows for expert guidance. However, it has the drawback of being dependent on human resources, as it requires the assistance of a human instructor.  
Second, the hybrid approach [25] allows users to receive help from both a human instructor and an automated system. This approach offers the advantage of unrestricted learning through an automated system and the ability to receive expert assessments. This method allows for more precise and varied perspectives on information, but it has the limitation of still requiring human expert participation in terms of cost savings.  
Lastly, the self-training approach [1; 2; 3; 4; 5; 6; 7] is based on learners following expert movements or standardized movements on their own without the intervention of a human instructor. In this approach, learners can learn on their own through expert workout videos, and there is an approach that uses automated systems to compare the postures of experts and learners, analyze the differences, and provide guidance. The advantage of this approach is that it does not require human resources, making it cost-effective and allowing for training without time constraints. However, this method heavily relies on the learner's  
Figure 1: The branches of digital fitness research. Each approach is depicted in a unique color for differentiation: passive in green, hybrid in teal, and self-training in orange. Digital fitness research is divided into passive approaches, hybrid approaches, and self-training approaches. Passive methods involve learners learning exercises through live or recorded videos. Hybrid methods involve a combination of human instructor intervention and automated system analysis. Self-training methods involve learners watching and learning from experts without human intervention.  
will and the system's performance, so the effectiveness of the exercise can vary depending on the individual.
# Developing a Building Fire Information Management System Based on 3D Object Visualization
## 3 Methodology  
This study defines an information structure in conjunction with 3D object model for fire disaster management using BIM software, and develops a BFIMS system prototype that intuitively visualizes the information requirements at building fire response sites. To achieve the purpose of this research, the following three steps were conducted. First, the information requirements to be utilized directly or indirectly by emergency responders were derived based on previous studies related to fire disasters. Based on the derived requirements, the UML was used to establish the data structure and define the relationships among the data. Second, the Sketchup software and the programming language Ruby were used to develop a BFIMS that can intuitively identify location data and detailed information of the building space as well as internal facilities. Finally, the scenario-based case study project was applied to verify the effectiveness of the proposed system, followed by in-depth interviews with experts and practitioners.
## 2 Datasets and Pre-Processing Steps
### XCO\({}_{2}\) from Model Simulations
We simulated atmospheric CO\({}_{2}\) concentration from 1 January 2009 to 31 December 2015, using GEOS-Chem, version 11.1. The model used here is driven by 3 hourly assimilated meteorological observations from GEOS at 2\({}^{\circ}\) (latitude) by 2.5\({}^{\circ}\) (longitude) horizontal resolution with 47 vertical layers. The GEOS-Chem XCO\({}_{2}\) (GEOS-XCO\({}_{2}\)) was calculated with the average kernel [60] of GOSAT XCO\({}_{2}\) within the local time around 13:00. After that, GEOS-XCO\({}_{2}\) was resampled to be 1 \(\times\) 1 degrees similar as that of CT-XCO\({}_{2}\). We emphasize that this model simulation is not intended to match observations.
# Classroom Expression Recognition Based on Deep Learning
## 4 Experiment and Analysis
### Expression Recognition Effect Display  
The expression recognition process in this paper can be divided into the following steps, the expression recognition workflow is shown in Figure 13:  
1. Image acquisition. Perform face recognition on the input image, select the image, and perform expression recognition on the detected face.
2. Face selection. According to the face detected in step (1), the O-Net network layer in the expression recognition network algorithm model is used to select the face coordinates, save the obtained coordinate data value, and pass it as a parameter to the next step.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **MAPE (\%)** & **F1-Score** & **Precision** \\ \hline IXception & 84.06\% & 5.32 & 0.843 & 0.850 \\ LDL-ALSG & 84.43\% & 4.98 & 0.847 & 0.854 \\ APM & 85.17\% & 4.75 & 0.852 & 0.860 \\ DLP-CNN & 85.14\% & 4.82 & 0.850 & 0.858 \\ Ours (MAF-ER) & 86.79\% & 4.21 & 0.867 & 0.875 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy comparison.  
Figure 12: Confusion matrix of the MAF-ER algorithm.  
Call the model. The system calls the facial expression recognition model, selects the face coordinates detected in step (2), detects the face through the model, and recognizes the facial expression.
4. Recognition result. The actual expression detected in step (3) is presented in the form of a label on the box of the selected face.
# Dynamic Dark Channel Prior Dehazing with Polarization
## 3 Experimental Setup
### Polarization Imaging System
#### 3.1.2 Mechanical Module  
The three-dimensional mechanical structure, which is designed by using of SOLIDWORKS 2018 to fix and arrange optical elements, is made of 8 mm thick aviation aluminum with high strength, corrosion resistance, and oxidation resistance characteristics [25]. This could effectively prevent the damage of the harsh external environment to the optical elements. A three-dimensional adjustment frame was configured in the mechanical structure, which could further enhance the registration degree between different polarimetric imaging units. Figure 3a shows the three-dimensional model of the mechanical module, and Figure 3b represents the structural diagram of the instrument.  
Figure 2: (**a**) Imaging principle. BS: beam splitter; PSA: polarization state analyzer. (**b**) Integrated beam splitter prism. (**c**) Traditional discrete beam splitter.
## 5 IETF ROLL Working Group
### Research Challenges
#### 5.4.1 Interaction with MAC Protocols  
First of all, the RPL interaction with different MAC protocols and duty cycles is not yet determined. Especially the behavior at startup, when lots of configuration traffic is sent out, hasn't been researched yet. Since most IoT devices are, by definition, energy constrained, this aspect requires significant additional research.