esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G-MEC Testbeds for V2X Applications
## 1 Introduction
### Contributions of this Paper  
This paper is inspired and motivated by the development and deployment strategies conducted for 5G and MEC systems to support V2X applications. The survey in [9] demonstrates MEC integration with new technologies, including 5G and beyond, and presents open source activities, testbeds, and implementations. MEC deployments for vehicular network applications and services are provided in [10]. Some 5G use case scenarios, along with corresponding traffic models, that align with the requirements of the Standard Development Organizations (SDOs) and stakeholders are presented in [11]. In [12], the authors discuss the MEC deployment of resources, its migration abilities, and use cases for industrial verticals, whereas in [13], the authors present the capabilities of MEC orchestration, deployment, and open source tools. In [14], the authors demonstrate 5G testbeds for network slicing applications.  
In summary, the contributions of the paper are as follows:  
1. To develop a 5G-MEC testbed for V2X application experimentation, we investigate the literature on the 5G-MEC system and study its architecture. Based on the generalized idea proposed by standardization bodies and commercial vendors, as well as the requirements of Mobile Network Operators (MNOs), we suggest an overall system view, where the hardware and software components are considered. The software modules, such as the MEC platform (MEP), orchestration, and network flow, are placed as per the industry standards and their responsibilities.
2. This paper presents some existing state-of-the-art testbeds, shows how the 5G-MEC system is implemented, and demonstrates some use case scenarios. In the case of the V2X application testbeds, we present a summary of the Vehicle User Equipment (VUE) and Road-Side Unit (RSU) types of equipment.
3. The core part of this paper is the study of the usage complexity of the surveyed testbeds and whether the testbeds are open source and accessible. We investigate the difficulties of using the testbed resources, replicating the testbeds, and their limitations.  
This paper summarizes the available tools used in the surveyed testbeds. In addition, we present other open source tools that are used to deploy the functionalities of the testbed. Since most functionalities can be deployed using open source tools, we discuss their compatibility issues and ways to integrate them with the support of the open source community.
# Superior Control of Spacecraft Re-Entry Trajectory
## 3 Numerical Simulation Results and Analysis  
### Numerical Simulation Background  
The research object of this article is the trajectory of the re-entry flight phase of the lunar return spacecraft, which will undergo re-entry flight in the airspace with wind fields. Use superior control algorithms and weighted optimal control algorithms to optimize the trajectory of the lunar return spacecraft to reach the determined destination. The starting point for trajectory optimization is located at an altitude of 120 km above the ground, with an initial flight speed of 10,800 m/s. The lunar return spacecraft is required to meet the minimum requirements of multiple indexes during flight in airspace with wind fields.  
The simulation involves comparing the various parameters of trajectories optimized by two control algorithms. One of them is the superior control algorithm designed in this article, and the other is to use traditional optimal control algorithms for trajectory optimization. The performance index calculation method is shown in Equation (21).  
\[J_{0}=\underset{(\vec{l})(t)}{min}\int_{l_{0}}^{t_{J}}(a\hat{Q}_{S}+m_{Y}+cq)dt, \tag{21}\]  
Among them, \(\mathrm{a},\mathrm{b}\), and \(\mathrm{c}\) are the weight coefficients of their corresponding performance indicators, which are manually set and can be modified according to the situation.  
During the simulation process, various parameters of the lunar return spacecraft were inputted, and numerical simulations were conducted using C++ programming in the WIN11 system to obtain different re-entry trajectory data. The basic parameter settings for the mathematical model of the lunar return spacecraft are as follows: lift-to-drag ratio \(\lambda\) = 0.35, aerodynamic reference area \(S\) = 95 m\({}^{2}\), and mass M = 20,000 kg. The heating rate is required to at least meet the condition \(\hat{Q}_{S}\leq\hat{Q}_{SMax}\) = 450 kw/m\({}^{2}\). Overload should at least meet the requirement \(n_{Y}\leq n_{YMax}\) = 2 g.  
The horizontal wind field is shown in Figure 1. The starting point of each blue small arrow represents the coordinates of the point, the length of the arrow indicates the wind speed at the point, and the direction of the arrow is the direction of the wind at the point. This article is a fixed wind field simulation, and the specific wind speed can be set and modified as needed in the simulation. In practice, the trajectory can be re-optimized based on the real-time detected changes in the wind field situation.
## 2 Algorithm and Implementation
### LED-64 Block Cipher  
An LED-64 block cipher [38] is a lightweight symmetric security primitive, the structure and mathematical operations of which are based on the substitution permutation network (SPN). It has a 64-bit input block, 32 permutation rounds, a 64-bit encryption key, and a 64-bit output ciphertext. The arrangement of the input in the state is organized in a 4 \(\times\) 4 row-based matrix. Each cell of the matrix has a 4-bit nibble within the Galois Field (GF) (24) with the irreducible polynomial function of X4 + X + 1 (10011). The round permutation function of the LED is an AES-like function, where there are four operations in every round; these are processed sequentially, as shown in Figure 2.  
The architecture of the LED is designed in a round-based mode. Figure 3 shows the architecture of LED-64 block cipher. P refers to the original input message--plaintext. K is the encryption key, STR is the state register, AC is the AddConstant block, SC is the SubCells block, SR is the ShiftRows block, MC is the MixColumns block, RC is the round constants block, and \(C\) is the output ciphertext. The input message is XORed with the encryption key after every 3 rounds for 32 rounds. Figure 4 illustrates the control flow of the encryption operation. In the first round, the input is XORed with the encryption key, and, in rounds 1, 5, 9, 13, 17, 21, 25 and 29, the output of the MC block is XORed with the key. Meanwhile, in the other rounds, the output of the MC is loaded directly to the STR register. The \(STR\) register is updated every round to hold the updated state matrix. The round constants are generated by the RC block where the RC feeds the AC and controls the inputs to STR.  
Figure 3: Architecture of the LED-64 block cipher.  
Figure 2: LED permutation blocks.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 4 Communication  
For the communication process we distinguish between intra-cell or intra-cluster communication and inter-cell or inter-cluster communication. The first one refers to the single-hop communication between sensors or CH members and its CH. It deals with the information captured by sensors and transmitted to their CH by means of a RAP; in our work we have chosen the FSA for its simplicity [8]. On the other hand, inter-cluster communications refers to the communication from CHs located at ring \(r_{p}\) to neighboring CHs located at ring \(r_{p-1}\), i.e., hop-to-hop. As a consequence, the closer a CH of the sink is, the more traffic it will carry and, therefore, a deterministic protocol like TDMA seems more appropriate for this case, [3].
## 2 Materials and Methods
### Data Acquisition and Image Processing  
For the retinal layer thickness measurement, 3D raster scan volume data were taken at the optic nerve head, nasal, dorsal, temporal, and ventral quadrants. For the retina layer thickness time-course change measurement, the B-scan in the center location of a dorsal quadrant was measured. For the measurement of the HRL thickness distribution, the retina was divided into 8 parts. In each part, the center B-scan was selected and segmented for the HRL thickness measurement. For the retinal inner-layer structure analysis, the dorsal quadrant was imaged with a field of view of 600 \(\upmu\)m \(\times\) 600 \(\upmu\)m. Each OCT volume contained 500 B-scans, and each B-scan consisted of 500 A-scans. Two hundred repetitive 3D OCT volumes were collected with an A-scan rate of 80 kHz. The total recording time was 13.5 min.  
For image processing, the standard OCT image reconstruction and post-processing procedures were applied. Briefly, the workflow was k-sampling, interferogram extraction, apodization, fast Fourier transform (FFT), and image registration. Thereafter, the retinal layer thickness was manually measured using MATLAB. For inner retinal structure analysis, frame registration was applied [21]. Briefly, each OCT volume was considered as a rigid cubic, four degrees of freedom were registered to adjust the direction and location of the OCT volumes. The procedure was as follows: first, the enface image of each OCT volume was produced to obtain an image stack, and then the stack was registered using a plugin function in ImageJ named MultiStackReg. During the registration, the transformation matrix was recorded. Second, the enface registration transformation matrix was applied to all the data sets in MATLAB. Third, an image stack with the center B-scans of each volume was extracted. The stack was then registered with MultiStackReg, and the transformation matrix was recorded. Fourth, the B-scan registration transformation matrix was applied to all the data sets as well. The 200 OCT volumes were averaged to get a clear OCT 3D image [19; 21].  
Figure 1: Schematic diagram of the custom-designed optical coherence tomography (OCT). PC, polarization controller.
## 2 Creation of Signals Database Measurement Conditions
### Wheel Position on the Rail  
For the preparation of the test data, the signals are acquired also for different lateral positions of the wheel. The TSI says only that the wheel flange may be located at some distance from the rail head, but it does not define it in millimeters. The various positions result from the tolerances for the dimensions of rails, tracks (gauge), wheels and wheelbases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wheel Profile** & **Diameter D [mm]** & \
\begin{tabular}{c} **Flange Height** \\ **S\({}_{\text{h}}\) [mm]** \\ \end{tabular} &
\begin{tabular}{c} **Flange** \\ **Thickness Sd [mm]** \\ \end{tabular} \\ \hline k2 & 330 & 32 & 27.5 \\ k4 & 600 & 32 & 27.5 \\ k5 & 600 & 36 & 33 \\ k6 * & 632 & 29 & 14.8 \\ k8 * & 640 & 25 & 23.9 \\ k10 & 760 & 32 & 27.5 \\ k11 & 760 & 36 & 33 \\ k13 & 820 & 36 & 33 \\ k15 & 840 & 36 & 33 \\ k16 & 820 & 26 & 27.5 \\ \hline \hline \end{tabular}  
* *  
The maximum lateral distance of a flange **p** (Figure 5), equal to 60 mm is defined in [17], which specifies the parameters of axle counter systems for interoperability in Europe.  
where \(\textbf{p}\ \in\ \{\) P0, P1, P2):  
* p0, nominal position (0 mm); the flange of the wheel is as close as possible to the head of the rail;
* P1, moved laterally; the flange is moved aside from the rail head up to 60 mm (as required by [17]);
* P2, maximum; the flange is moved aside from the rail head up to the maximum feasible position (for the safety margin, see Table 2):
## 3 Optimization Methodology
### Integer Search Algorithm  
Most of the stochastic algorithms are based on the real numbers, which are used in all engineering applications. However, the variables of the PMMS problem are integer numbers, where these stochastic algorithms need adaptation to be suitable for discrete problems. Therefore, this paper has proposed a new discrete algorithm suitable for the discrete problem, which is called integer search algorithm (ISA). The ISA algorithm is proposed based on the random generation of integer numbers. The exploration and exploitation of the ISA algorithm are controlled using parameters a and A, as in (1) and (2). The ISA algorithm updates the integer variables \(X(i)\), as in (3). The main merits of the ISA algorithm are simplicity and speed.  
\[a=round\bigg{(}b-b\times\frac{Iter}{Max\_iter}\bigg{)} \tag{1}\]  
\[A=Integer\ Random\ \in[-1,1]\times a \tag{2}\]  
\[X(i)=X_{best}(i)+A \tag{3}\]  
where \(b\) is a constant \(\geq\)2, \(X_{best}(i)\) is the best position of variable \(X(i)\), \(Iter\) is the iteration number, and \(Max\_iter\) is the maximum number of iterations. the flowchart of the ISA algorithm is depicted in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**PCR**} & \multicolumn{5}{c}{**PCR after Maintenance**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** \\ \hline \multirow{3}{*}{PCR before maintenance} & 0 & M-01 & M-02 & M-03 & M-03 \\ \cline{2-6}  & 1 & — & M-01 & M-02 & M-03 \\ \cline{2-6}  & 2 & — & — & M-01 & M-02 \\ \cline{2-6}  & 3 & — & — & — & M-01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influence of repair decisions on PCR  
Figure 1: Flowchart of the integer search algorithm.
## 3 Fuzzy Logic Applications  
Fuzzy logic and fuzzy sets are powerful mathematical tools [33-35]. They are used for modeling and controlling uncertain systems in industry, humanity and nature. They are the assistants for approximate reasoning in decision making in the absence of complete and precise information. A classic set is defined in such a way that all the members in a certain universe can be partitioned into two classes: those who belong to the set, and those who do not belong to the set. In addition, classic sets are called _crisp sets_ to distinguish them from fuzzy sets. When a set A is assumed to be a crisp set defined over the universe U, it is defined as a subset of the universe U. In fuzzy logic, this feature is generalized. Thus, it is not required that \(x\) is either a full member or a non-member of the set, because \(x\) can be defined as a partial member of the set. In this logic, the characteristic function can be set to a value between zero and one. As an example, Figure 1 shows the fuzzy value for a room temperature Figure.  
A Fuzzy Logic System (FLS) is a system that is defined as the nonlinear mapping of an input data set to a scalar output data. A FLS involves four main sections: _fuzzification_, _rules_, _inference engine_, and _defuzzification_. The general architecture and major components of a FLS are shown in Figure 2.  
The procedures of the fuzzy logic are described in Algorithm 1: first, a crisp set of input data is received and then is converted to a fuzzy set using fuzzy linguistic variables, fuzzy linguistic terms, and membership functions. This phase is called _fuzzification_. Thereafter, an inference is made using a set of rules. Finally, the resulted fuzzy output is mapped to a crisp output by the membership functions. This phase is called _defuzzification_.  
Figure 1: The fuzzy value for a room temperature.  
The linguistic variables are the input or output variables of the fuzzy system. These variables are words or sentences of a natural language, instead of numerical values. The membership functions are used to quantify the linguistic terms. The common types of the membership functions are triangular, trapezoidal, and Gaussian shapes. A rule base is constructed to control the output variables in the FLS. A simple IF-THEN statement with a condition and a conclusion constructs a fuzzy rule. As an example, sample fuzzy rules for an air conditioning system are listed in Table 1. In this system, the fuzzy engine compares the room temperature and the target temperature periodically, and produces a command to heat or cool the room.  
\begin{table}
\begin{tabular}{|l l|} \hline \multicolumn{2}{|c|}{**Fuzzy Rules**} \\ \hline
1. & IF (temperature is cold OR cool) AND (target is warm) THEN command is heat \\
2. & IF (temperature is warm OR hot) AND (target is warm) THEN command is cool \\
3. & IF (temperature is warm) AND (target is warm) THEN command is no-change \\ \hline \end{tabular}
\end{table}
Table 1: Sample fuzzy rules for air conditioner system.  
Figure 2: A fuzzy logic system.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 2 Fundamentals of Dense Video Captioning  
### Definition and Objectives of DVC  
Dense Video Captioning (DVC) represents an advanced multimedia task that strives to automatically produce a series of temporally localized descriptions for events unfolding within a video [15]. Unlike conventional video captioning, which usually offers a solitary summary or caption per video, DVC demands the identification of multiple events within a video, the determination of their exact temporal boundaries, and the generation of a natural language description for each event. The core objective of DVC is to furnish a thorough, detailed, and human-readable comprehension of video content, thereby enhancing video retrieval, indexing, and browsing experiences.
# A Filter-Based and Parallel Unknown Tag Identification Protocol in Open RFID Systems
## 5 Performance Analysis  
### Fingerprint Size  
First, the influence of fingerprint size \(l_{fp}\) is discussed. In our filter, each tag has a unique fingerprint in its \(run\). To achieve a higher separation accuracy, different hash functions can be chosen for each \(run\) by transmitting multiple random seeds to tags. For saving the communication overhead, the fingerprint size was set to \(\lceil log_{2}runsize\rceil\) for each \(run\). To simplify the calculation, \(l_{fp}\) is defined as the average fingerprint size, and it is discussed below. The probability \(p_{u}\) of a slot not being occupied in the _occupied_s vector is:  
\[p_{u}=(1-\frac{1}{2^{q}})^{N}=e^{-\rho_{1}}, \tag{1}\]  
where \(\rho_{1}\) is \(\frac{N}{2^{q}}\). The number \(n_{o}\) of occupied slots in the _occupied_s vector is:  
\[n_{o}=2^{q}(1-e^{-\rho_{1}}). \tag{2}\]  
Therefore, the average \(runsize\) can be calculated as:  
\[runsize_{average}=\frac{N}{n_{o}}=\frac{N}{2^{q}(1-e^{-\rho_{1}})}=\frac{ \rho_{1}}{(1-e^{-\rho_{1}})}. \tag{3}\]  
The average fingerprint is:  
\[l_{fp}=\lceil log_{2}runsize_{average}\rceil=\lceil log_{2}\frac{\rho_{1}}{( 1-e^{-\rho_{1}})}\rceil. \tag{4}\]
# Thermometric Characterization of Fluorescent Nanodiamonds Suitable for Biomedical Applications
## 2 Materials and Methods
### HeLa Images and Cytotoxicity Assays  
HeLa cells were seeded on a sterile 96 well plate and cultured in DMEM (Dulbecco's modified eagle medium), supplemented with FBS (10%), sodium pyruvate (1 mM), penicillin (10 ug/mL), streptomycin (0.1 mg/mL) and incubated at 37 \({}^{\circ}\)C, CO\({}_{2}\) (5%) and 80% humidity; until reaching 80% of a monolayer.  
For nanodiamonds-cells interaction, the cells were washed with phosphate-buffered saline (PBS) solution to remove non-adherents, dead and rest of metabolized medium. The 100 nm nanodiamonds solution concentrated to 0.01% _w/o_ was diluted in sterile DMEM at 0.01% _w/v_ and 200 ul of the solution was added to each well, incubated for 1 h under the same conditions described previously. The cells were stirred in the dark, then the solution was removed, and they were subjected to 3 washing steps with PBS before observation at the microscope. The images of the interaction between nanodiamond and HeLa cells were obtained with a confocal microscope Nikon Eclipse Ti (Japan). The 532 nm laser beam of 5-6 mW was focused onto the samples by a 20x objective (air, NA 0.95) and fluorescence images (1024 \(\times\) 1024 pixels) were processed with the imaging software NIS-Elements (Nikon, Japan).  
The viability of HeLa cells exposed to nanodiamonds was measured using the well-known 3-(4,5-dimethylthiazol2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay. For this assay, serial dilutions of nanodiamonds were prepared in a sterile saline solution of DMEM (\(10^{-2}\), \(10^{-3}\), \(10^{-4}\), \(10^{-5}\), \(10^{-6}\), \(10^{-7}\) and 0% as a viability control). The culture medium was removed and replaced with each dispersion of nanodiamonds solutions (four replicates for each concentration per assay). The protocol was previously reported by [PERSON] in 2019 [55]. MTT was diluted in PBS (5 mg/mL) and filtered through a 0.22 um sterile filter. After 24 h of incubation, the stimulus was removed. MTT diluted in DMEM (100 ul, 12 mM) was added to the wells and incubated at 37 \({}^{\circ}\)C (5% CO\({}_{2}\)) for 2 h. MTT reactive was carefully removed, and a 100 ul of Dimethyl sulfoxide (DMSO) was added to solubilize the formazan crystals formed. Then, the absorbance was measured at 570 nm in a UV spectrophotometer (Thermo Scientific Multiskan GO), and the results were compiled using SkanIt software (Thermo Fisher Scientific, Waltham, MA, USA). Unstimulated-negative control cells were incubated with the respective complete medium. Positive control cells were treated with 10% DMSO to cause cell death. Cell viability is expressed as a percentage of the negative control, given by Equation (2).  
\[\text{Cell Viability (\%)}=\text{(A}_{\text{Sample}}/\text{A}_{\text{Control}} \text{)(100\%)} \tag{2}\]  
where A\({}_{\text{Sample}}\) represents the measurement from wells treated with nanodiamonds solutions, and A\({}_{\text{Control}}\) represents the value obtained from the wells treated with complete medium.