esa-sceva/satcom-chunk-collection huggingface dataset:
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Multicast-Emulation Framework
#### 3.2.1 Introduction to Key Elements  
The architecture involves a name resolution system (NRS), Designated Router (DR)s, Rendezvous Points (RPs), data sources, data receivers, multicast processing nodes and so on. NRS consists of multiple switches with the name resolution function. NRS uses globally unique names to identify multicast services and maintains the mapping between identifiers and network addresses (NAs) of one or more multicast processing nodes. DR is responsible for processing requests from data sources and receivers and performs RP and GP-selection methods to determine the locations of RP and GP for the multicast tree.  
The RP in this framework is similar to the RP in PIM-SM. It is a set of pre-arranged nodes that can receive data from DRs and provide the data distribution service as the root node of the multicast tree. All multicast processing nodes maintain an MFT. Each entry in the MFT corresponds to a forwarding rule of a multicast group. The key of the entry is the ID of the multicast service, and the value is the NA list of the next-hops. Each node in the multicast distribution tree can register the mapping between the multicast service ID and its NA in NRS. Emu-nodes are the gateway of the emulation network and the real network, which is used to complete the data exchange between the real network and the emulation network.
## 4 Precise Orbit Determination (POD)
### Orbit Validation
#### 4.2.2 SLR Validation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PRN** & **STD** & **MEAN** & **RMS** & **NP\#** & **NP(U)** & **\%** \\ \hline C01 & 20.0 & \(-18.9\) & 27.5 & 305 & 305 & 100 \\ C08 & 9.8 & \(-0.3\) & 9.8 & 345 & 331 & 96 \\ C10 & 8.3 & 0.8 & 8.4 & 270 & 256 & 95 \\ C11 & 6.8 & \(-0.6\) & 6.8 & 499 & 484 & 97 \\ C13 & 6.2 & 0.7 & 6.2 & 293 & 293 & 100 \\ C32 & 9.6 & \(-9.5\) & 13.9 & 85 & 85 & 100 \\ C33 & 3.8 & 8.1 & 8.9 & 33 & 33 & 100 \\ C34 & 4.2 & 8.6 & 9.5 & 58 & 58 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SLR validation of BeiDou-2 and BeiDou-3e orbits. STD, MEAN, and RMS represent the standard deviation, mean value, and root mean square of the two-way SLR residual, NP# and NP(U) represent the number of tracking points and points from which the data were used, and % represents the data utilization percentage (units: cm vs. adimensional). may be caused by inaccuracies in the PCO or LRA offset supplied by the OCC. C33 and C34 displayed a bias of about 8 cm, and their STDs were both about 4 cm, as shown in Figure 6.
## 3 The Effect of RF Exposure on _in Vitro_ Models: Finding the Pathways
### Intracellular Pathways: The Usual Suspects
In 2004, the REFLEX study, Quality of Life and Management of Living Recourses, funded by the European Union for the evaluation of risk of potential environmental hazards of _in vitro_ electromagnetic field exposure, stated that RF-EMF increased microtubular damage and the number of DNA strand breaks in HL-60 cells. Microtubules are macromolecular structuresconsisting of tubulin heterodimers and are present in almost every eukaryotic cell. It has been demonstrated that 900 MHz interferes with the polymerization process _in vitro_ to alter microtubules structure [34]. Recently, however, [PERSON] [35] failed to reproduce genotoxic effects of RF-EMF in HL-60, adding more uncertainty about the real effect of EMF on cell viability.
## 2 Materials and Methods
### MELCOR Models  
#### 2.6.1 Single-Channel Model  
The MELCOR 1.8.6 single-channel model was developed with the aim to reproduce the thermal-hydraulic behavior of a single FWMU channel during the Pressure Drop Characterization Tests. Starting from a geometry-based model, a \"doubling\" procedure was applied with the purpose to find a discretization having a sufficient low influence on the computed results. The number of control volumes (CVs) was increased progressively until the difference among the pressure drops in two subsequent discretizations was below 1/1000 of the pressure at the outlet. The additional L/D \(\geq\) 1 requirement was applied to avoid too-small Courant time-steps.  
The obtained model consisted in 16 volumes for each nozzle and 28 volumes for the square tube (Figure 3). An additional time-independent volume was employed to impose fluid conditions at the inlet, whilst a time-dependent junction was applied at the outlet to set the mass flow rate. Since, in MELCOR, it is possible to specify the time-dependent flow path only in terms of velocities, an appropriate control function (CF) was created to calculate the velocities considering the fluid properties and the mass flow rates at the outlet.  
Heat structures (HSs) were added to represent the horizontal and vertical nozzle tubes and the FWMU steel structure. In particular, a total of 97 HSs were employed to simulate the top and bottom plates, the lateral wall and the inner wall separating the straight parts of the square tube. For all the structures, a constant temperature (20 \({}^{\circ}\)C) was imposed on the external surface facing the \"environment\", whereas a convective boundary condition (with the heat transfer coefficient calculated by MELCOR itself) was applied to the inner surface. Since the P-92 steel is not included in the MELCOR 1.8.6 database for materials, its properties (specific heat, conductivity, and density) were defined as a function of temperature through three different tabular functions (TFs). All the values in the tables are the ones proposed in [4].
## 3 Outage Probability of RIS-ONOMA  
In this section, the closed-form probability of outage expressions of RIS-ONOMA are derived for SR and AP configurations. In order to maximize the DL sum capacity, the optimal power fractions to allocate for _SU_ and _WU_ are also determined.
# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 6 Performance Evaluation
This result proves the efficiency of SDN solutions, except that the consumption of the bandwidth is not optimal and does not even arrive at half level, as shown in Figure 9. The efficiency of a routing protocol lies in the network's stability in terms of communications with the least possible interruption. Jitter is the performance metric that allows us to distinguish the most reliable routing solutions with the most stable routes. In Figure 10, the protocols using ML algorithms, i.e., Q-  
Figures 11 and 12 show the end-to-end delay of the four protocols. Mainly our solution presents the shortest end-to-end delay, whether for 250 m or 500 m transmission range. Moreover, when we vary the size of the transmitted packets, we notice that always our protocol presents the best end-to-end delay compared to Q-learning, multipath, and OLSR protocols. Except that for the case of 500 m transmission range, the Q-learning learning solution shows the best performance in term of the end to end delay. We explain this to the fact that the supervised learning analyzes a massive amount of data to form a system and produces a generalized formula. The quality of the training data influences this ML method's prediction results, so the results obtained are approximated. Whereas in Q-learning solution which uses reinforcement learning model, the reinforcement core is defined in the Markov model decision process. Reinforcement learning is trained as a learning agent where it functions as a reward and action system. The learning system itself creates data by interacting with the environment to observe its basic behaviour in discrete steps. The approach investigates every time the background and receives a reward for each observation. Finally, the goal is to collect as many bonuses as possible for doing more observations. Furthermore, this type of learning machine uses the Markov decision process that provides a mathematical framework for modeling. Sequential decision-making occurs, and the next entry depends on the learner's decision or the system. Based on the mathematical formulation, the reinforcement learning model generates an exact result available from the initial state. Moreover, in this scenario, our system's size is relatively small, which allows Q-learning to make several observations in a short  
Figure 10: Packet Jitter Derivation (Jitter) vs. number of vehicles (**a**) range 250 m, (**b**) range 500 m.  
Figure 9: Throughput vs. number of vehicles (**a**) range 250 m, (**b**) range 500 m.  
time, which improves its performance. In contrast, if the network becomes large enough and complex, the Q-learning response time will become tedious, influencing, and deteriorating the end to end delay.
## 3 Results and Discussion  
### Mechanism Performance  
#### 3.1.1 Crystal Phase of Absorbed Mineral Powder  
In order to explore the changes after adsorbing the asphalt binder, the crystal phase was considered in the powder samples analyzed with XRD. Figure 2 shows the XRD spectra for three samples: A, B and C. The obtained peak (104\({}^{\circ}\)) belongs to calcite Ca\({}_{6}\)C\({}_{6}\)O\({}_{18}\). All the specimens had a peak in correspondence of the same degree value, as well as a similar flow trend. This means the crystal phase of applied mineral powder is calcite Ca\({}_{6}\)C\({}_{6}\)O\({}_{18}\). Moreover, the same regularities of three XRD spectra in Figure 2 indicate that after adding in pure gasoline (sample B) and adsorbing the asphalt binder from the solution (sample C), the mineral powder did not comprise any changes regarding the crystal phase.
## 4 Algorithm Design
### Adaptive Fuzzy PID for Coupling System
#### 4.2.3 Fuzzy Rules Design  
The fuzzy rules for the design output quantities \(\Delta K_{p}\), \(\Delta K_{i}\) and \(\Delta K_{d}\) are summarized based on expert experience [27; 28], as shown in Table 2.  
The fuzzy inference surface of each output quantity is shown in Figure 10. It can be concluded that, when \(|\!|\!|\) is large, \(\Delta K_{p}\) should be considered larger, \(\Delta K_{d}\) as smaller, and \(\Delta K_{i}\) needs no adjustment. When \(|\!|\!|\) is medium, \(\Delta K_{p}\) should be considered smaller, and \(\Delta K_{i}\), and \(\Delta K_{d}\) should be considered medium. When \(|\!|\!|\) is small, \(\Delta K_{p}\) and \(\Delta K_{i}\) should be increased appropriately and \(\Delta K_{d}\) should be kept as a medium.  
Figure 10: _Cont._  
Figure 9: (**a**) The affiliation function and distribution of \(e\) and \(e_{c}\); (**b**) the affiliation function and distribution of \(\Delta K_{p}\); (**c**) the affiliation function and distribution of \(\Delta K_{i}\) and \(\Delta K_{d}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{e_{c}}\) & **NB** & **NM** & **NS** & **ZO** & **PS** & **PM** & **PB** \\ \hline NB & PB/NB/PS & PB/NB/NS & PM/NM/NB & PM/NM/NB & PS/NS/NB & ZO/ZO/NM & ZO/ZO/PS \\ NM & PB/NB/PS & PB/NB/NS & PM/NM/NB & PS/NS/NM & PS/NS/NM & ZO/ZO/NS & NS/ZO/ZO \\ NS & PM/NB/ZO & PM/NM/NS & PM/NS/NM & PS/NS/NM & ZO/ZO/NS & NS/PS/NS & NS/PS/NS \\ ZO & PM/NM/ZO & PM/NM/NS & PS/NS/NS & ZO/ZO/NS & NS/PS/NS & NM/PM/NS & NM/PM/ZO \\ PS & PS/NM/ZO & PS/NS/ZO & ZO/ZO/ZO & NS/PS/ZO & NM/PS/ZO & NM/PM/ZO & NM/PB/ZO \\ PM & PS/ZO/PS & ZO/ZO/PS & NS/PS/PS & NM/PS/PS & NM/PM/PS & NM/PB/PS & NB/PB/PB \\ PB & ZO/ZO/PB & ZO/ZO/PM & NM/PS/PM & NM/PM/PM & NM/PM/PS & NB/PB/PS & NB/PB/PB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy rules of \(\Delta K_{p}\), \(\Delta K_{i}\) and \(\Delta K_{d}\).  
According to the affiliation function of each fuzzy variable and fuzzy rule table, we designed the PID parameter adjustment algorithm using the fuzzy inference theory to obtain more accurate control parameters and improve the performance of the fuzzy control system. If \(\Delta K_{p}\), \(\Delta K_{i}\) and \(\Delta K_{d}\) are the pre-tuned values of the PID controller's three parameters, then the formulae for calculating the three are:  
\[K_{p}=K_{p}^{\prime}+\Delta K_{p} \tag{14}\]  
\[K_{i}=K_{i}^{\prime}+\Delta K_{i} \tag{15}\]  
\[K_{d}=K_{d}^{\prime}+\Delta K_{d} \tag{16}\]
## 4 Application Examples
### Maximum Concentration of Expired CO\({}_{2}\)  
The CO\({}_{2}\) concentration of the exhaled air, measured with capnography, is an important parameter in medicine and basic physiological research. The waveform of CO\({}_{2}\) concentration changes during the respiratory cycle comprises four phases (I: inspiratory baseline, II: expiratory upstroke, III: expiratory plateau, and IV: inspiratory downstroke) where the phase IV marks the peak of the whole waveform. This peak value is called end-tidal CO\({}_{2}\) (P\({}_{\mathrm{ET}}\)CO\({}_{2}\)) and is a best approximation of the alveolar CO\({}_{2}\) level, _i.e._, the partial pressure of CO\({}_{2}\) in the arterial blood (PaCO\({}_{2}\)) [51]. A precise and robust measurement of the phase IV peak is indispensable to correctly determine the P\({}_{\mathrm{ET}}\)CO\({}_{2}\) values.  
The time series analyzed comprised a 500 s long CO\({}_{2}\) time series measured on a resting subject (sampling frequency: 5 Hz, device: Nellcor N1000 gas analyzer, Nellcor. Inc, Hayward, USA). The results of the analysis are given in Figure 7. All 133 peaks were correctly detected (no false negative errors) and no false detection occurred (no false positive errors).  
Figure 6: Results of applying the AMPD algorithm to a [O\({}_{2}\)Hb] time series with BVP-peaks.
## 3 Methodology
### Model Parameterization
#### 3.1.2 Specifying Transition Rules
Constraint Rules  
_Constraint_ rules prevent transitions related to a specific cell state from occurring within restricted areas. Rules of this type require a potential next state (\(S_{T+1}\)) and a binary raster map indicating where cells are barred from becoming \(S_{T+1}\)[48]. With _Constraint_ rules, users can easily add, swap, or delete areal restrictions to explore different scenarios or spatial policies [56]. Allocation and Quantity Rules  
_Allocation and Quantity_ rules guide the location and amount of specific from-to transitions through suitability maps and quantity limits. With this rule type, prospective changes can be limited to the most suitable or susceptible locations identified using analyses like MCE [79], or those corresponding to locations with higher probabilities outputted by ML classifiers [55]. _Allocation and Quantity_ rules require users to specify a cell's current state (\(S_{T}\)) and the potential next cell state (\(S_{T+1}\)). Unlike other rule types, _Allocation and Quantity_ rules  
Figure 4: UML diagram of supported transition rule types. have three modes (Table 1). Optional fields for each mode include a minimum suitability threshold (\(min_{m}\)), a path to a suitability, susceptibility, or probability map (\(M\)), and an integer (\(Q\)) indicating the maximum quantity of cells permitted to transition at each discrete timestep or model iteration (\(\Delta T\)). Rule Priority Scheme  
With the _Rule Priority_ field, users can override the default order of generative rule execution at each model iteration (Figure 5). The purpose is to support prioritization of change mechanisms and to maintain explainable model behavior, especially in multi-class model implementations. For example, a less common but important transition occurring in specific situations could be eliminated by competing rules that propagate more widespread or probable change behaviors. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Rule Mode** & **Description** & **Use Case Example** \\ \hline \multirow{4}{*}{(1) Allocation Limit} & The potential change must meet or exceed a minimum suitability/susceptibility/probability threshold. & Limiting insect infestation propagation to locations with MCE-derived susceptibility values exceeding 0.6 [60]. \\ \cline{1-1} \cline{2-3}  & The potential change locations are limited to \(Q\) random locations. & Refining insect infestation rates or area using historical averages [55]. \\ \hline \multirow{4}{*}{(3) Allocation and Quantity Limits} & The potential change location must be in the top \(Q\) suitable locations based on the suitability map. If specified, the location must also meet or exceed a minimum suitability threshold. & Limiting urban growth transitions to the top \(Q\) locations based on transition probability maps generated by ML algorithms [20; 50]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of _Allocation and Quantity_ rule modes. Figure 5: Default order of transition rule application if no rule priorities are set.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 5 Control Algorithms Used for Different Semi-Active Control Systems  
Semi-active control systems employ various control algorithms to achieve their intended purposes. These control algorithms are designed to adaptively regulate the damping or stiffness properties of structural components, thereby mitigating vibrations and enhancing the overall performance of structures. Creating an accurate mathematical model of the structure-controller system for real-world structures, while accounting for the various uncertainties associated with process parameters, is a challenging endeavor. Nonetheless, these issues must be addressed to create and develop controllers that operate effectively in these complex systems. Various control strategies have been documented in the literature, including fuzzy logic, neural networks, instantaneous optimal control, the sliding mode technique, linear quadratic Gaussian (LQG) controllers, and linear quadratic regulators (LQRs). [PERSON] and [PERSON] [29] examined and assessed the effectiveness of different semi-active control techniques for MR dampers, including the Lyapunov controller, decentralized bang-bang controller, modulated homogeneous friction algorithm, and a clipped optimal controller. Numerous recognized algorithms in control engineering have been applied to manage systems, including optimal control, LQR or LQG, pole placement, sliding mode control, the H\({}_{2}\) and H\({}_{1}\) methods, fuzzy control, and various additional techniques. [PERSON] [4] and [PERSON] et al. [159] detail the most appropriate algorithms for structural applications and the practical factors that need consideration. The selection of a control algorithm relies on the unique features of the semi-active control system, the type of structural vibrations, and the goals of control. Engineers and researchers select the most suitable algorithm based on the application and desired performance outcomes.
# Intelligent Reflective Surfaces for Wireless Networks:
## 5 Practical Challenges, Open Issues and Future Research Lines
### Open Issues and Future Research Directions
#### 5.2.1 Near-Field Region and Spherical Wave-Front Model  
Most papers about IRSs assume the far-field operation region. However, the combination of large-scale antennas with high frequencies often results in communications in the near-field region. The operation of IRS-assisted systems in this region and the design particularities by considering the spherical wave-front model have not been thoroughly studied in the literature.  
The authors in [94] provide a detailed analysis on the design and use of IRSs by considering both the near-field and far-field radiation characteristics of such surfaces, diverging from the commonly adopted far-field implementation of these apertures in the literature. Some important issues are described in this work which can be considered for design particularities and even for user scheduling in communication systems.
## 4 Experimental Evaluation--RIOTA's Implementation and Cork as a UNO
### Results  
The illumination device was tested in two different ways. First, the necessity of using the illuminator ring was tested. As aforementioned, the photos were captured with the illuminator ring turned on and off. The results of the number of matched descriptors are shown in Figure 3.  
The second test was devised with the aim of studying the influence of the illuminator device on capturing stable and repeatable features across different camera sensors. The results are presented in the bar chart of Figure 4.  
The discussion of tests and results shown on Figures 3 and 4 are presented in Section 4.4.1. Beyond the tests performed on the illuminator device, RIOTA's implementation was tested against different databases. Three different approaches were used to evaluate RIOTA's implementation on  
Figure 3: Bar chart of the light test performed on the illumination setup. Each individual bar corresponds to the matching result obtained using Recognition of Individual Objects using Tagless Approaches (RIOTA) implementation.  
the cork database: (i) accuracy, EER, FAR, and FRR are exhibited on Table 1; (ii) the equal error rate plot, often used to evaluate a biometric recognition system, is shown in Figure 5; (iii) in Figure 6 the inter- and intra-distances, commonly used to evaluate UNOs and/or PUFs, are represented by their histograms, whereby the occurrences are replaced by its normalized value such that the maximum frequency equals 1.0, and the number of matched descriptors is normalized to a score (%) ranging from \([0,100]\), such that the maximum number of matched descriptors equals to the score of 100%. The value of statistical mean and standard deviations of the inter- and intra-normalized distance scores are calculated as \((\mu_{intra},\sigma_{intra})=(2  
To infer how well the RIOTA's implementation behaves in a different domain, three public iris databases were used. The results are presented using the EER measure and compared with state of the art methods. Table 2 presents the results obtained using the CASIA-IrisV1 database, on Table 3 the results achieved on the IITD iris database are shown, and in Table 4 the EER are displayed for the MMUv1 iris database.  
Additionally, the time needed for each task of the RIOTA's implementation was measured for each database and the results are exhibited on Table 5.
## 2 Methodology
### Listening Test Preparation  
#### 2.3.1 Speech Material and Speech Intelligibility Assessment  
Listening tests were aimed at assessing speech intelligibility under realistic acoustic conditions using speech material described by [PERSON] et al. [28], particularly the open-set format. This material, already used in research studies as it is based on the extended version of the test that has been optimized for the Italian language [29] and for other languages of the world [30] to provide accurate results, allows for the reduction of the duration period of the tests and the decrease of deconcentration bias. In summary, the test consists of the administration of syntactically correct but semantically unpredictable words and sentences randomly built from a 7 \(\times\) 3 matrix of words (\"number, object, adjective\"). Such sentences, which were uttered in anechoic conditions, were then presented under realistic and significant signal-to-noise ratios (SNRs) to evaluate speech intelligibility in terms of the percentage of correctly understood words overall. The words of the sentences were simple, of common use, and easy to understand even by young children, who may not have a huge vocabulary yet. An example of a sentence is \"_Sette sedie utili_\" meaning \"Seven useful chairs\".
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 4 Spatiotemporal Traffic Prediction Models
### Bayesian Inference Approach
The results show that the BHM-GEV approach performs better when the traffic conflict observations are distributed over different intersections. However, the BHM-GEV approach may provide inefficient performance when there is a limited number of traffic conflict observations. A number of limitations are discussed in the study, where there are still some challenges in predicting traffic accidents at intersections, such as having short traffic observations at intersections, which are not preferable for modeling. However, the authors recommended collecting data over a longer period of time, with temporal dimensions such as days, weeks, and months. The limited number of traffic observations that are collected at intersections restricts other researchers from tackling this important topic in traffic modeling.