esa-sceva/satcom-chunk-collection huggingface dataset:
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 4 Photonic Bandgap (Photonic Crystal) Fibers  
The theory of light guidance in HC-PBGF and ARF fibers can be found in [18; 19]. The key loss mechanisms in HC-PBGFs are presented in [11]. The history of HCF development until 2020, with a focus primarily on loss reduction, and compact descriptions of each guidance mechanism can be found in paper [10].  
Technical data, fiber photos, and results of measurements included in Sections 3-5 are collected from the referenced literature, product data sheets, and patent descriptions.  
Figure 3: HCF with a Bragg reflector made of 74 alternating layers of PEI polymer, seen as grey (each 0.47 μm thick), and arsenic triselenide, seen as white (each 0.27 μm thick). The cladding had a 684 μm outer diameter and was made of PES polymer. (**A**) complete fiber, (**B**) Bragg reflector, (**C**) layers of Bragg reflector. This fiber had a fundamental photonic bandgap at 2.28 μm. Reproduced from [15].  
Graphs were drawn by the authors from data in the indicated references to ensure uniform appearance and/or present different fibers or parameters together. In a few cases, data are estimated in the absence of published information and indicated as such.
## 2 Description the PV System Based on the NEC Boost Converter
### Comparison with the Classical Boost Converter
In conclusion, the proposed PV system based on the NEC boost converter introduces lower current harmonics in comparison with a PV system based on the classical boost converter, thus providing a better power quality to the DC-link or gird-connected inverter. Moreover, the NEC boost PV system can be designed to exhibit the same PV voltage ripple provided by the classical boost solution with an equivalent inductive energy storage, which provides a fair comparison between both solutions. Other DC/DC converters can also be used to provide continuous input and output currents in a PV system, but with different voltage conversion ratio, voltage polarity, or stress in the elements. For example, the Cuk converter provides continuous current at both the input and output ports [23], but in a PV system application, the input inductor of the Cuk converter must support all the PV current, while the inductors of the NEC boost converter only support part of the PV current. Similarly, the internal capacitor voltage of the Cuk converter is higher than the output voltage (\(v_{p}/d\)); in fact, for the condition used in this comparison section (\(d=0.5\)), the internal capacitor of the Cuk converter must support 50% more voltage than the internal capacitor of the NEC boost converter (which only supports the output voltage). Moreover, the output voltage of the Cuk converter has inverted polarity, which introduces additional complexity to the sensor circuits with respect to the classical boost converter. However, the output of the NEC boost converter has a voltage with a different reference with respect to the PV source, which requires an additional differential voltage sensor, similar to the Cuk converter, as depicted in Figure 2.  
Finally, the advantage of reaching the MPP condition when the output voltage is near or lower than the PV voltage, provided by buck/boost-type converters [24], does not apply to microinverter applications since the input voltage required by the inverter of the second stage is much higher than the PV voltage provided by a small PV source. For example, a BP585 PV panel has a maximum MPP voltage equal to 18 V, while some boost inverters require a DC-link voltage equal to 48 V; more traditional buck inverters require input voltages higher than 160 V for connection to 110 VAC grids and input voltages higher than  
Figure 6: Comparison of the AC components introduced into the second stage by both the classical and NEC boost solutions (\(d=0.5\)). 312 V for connection to 220 VAC grids. Therefore, for microinverter applications, the NEC boost converter provides lower component stress and simpler sensing conditions than the Cuk converter without a significant disadvantage.
## 2 Proposed Radio Link Model for Underground Mines
### Propagation Zone Model
#### 2.2.1 Free Space Dividing Point  
The Dividing Point 1 (\(DP_{1}\)), also called the free space dividing point, refers to the distance from Tx at which the boundary of the first Fresnel zone can be defined for the nearest walls of the tunnel. At this point, the signal starts to encounter significant attenuation due to the presence of the tunnel walls, which is determined using \(R_{max}\) and \(D_{min}\).  
\[R_{max}=\frac{1}{2}\sqrt{\lambda d} \tag{9}\]  
\[D_{min}=\Bigg{[}\bigg{(}x_{z}(f_{i})-\frac{x_{t}+x_{r}}{2}\bigg{)}^{2}+\bigg{(} y_{z}(f_{i})-\frac{y_{t}+y_{r}}{2}\bigg{)}^{2}+\bigg{(}z_{z}(f_{i})-\frac{z_{t}+z_{ r}}{2}\bigg{)}^{2}\Bigg{]} \tag{10}\]  
\[DP_{1}=Min\big{\{}Z_{r}\big{|}(R_{max}=D_{min}),Z_{r}\varepsilon R^{+}\big{\}} \tag{11}\]  
where \(z_{r}\) is the Rx antenna height, \(R_{max}\) denotes the radius of the maximum first Fresnel zone, and \(D_{min}\) denotes the minimal distance between the middle point on the line-of-sight (LOS) between the Tx and receiver (Rx) and the intersection between the maximum Fresnel  
Figure 2: Theory of dividing Point 1 for tunnel cross-sections.  
zone plane and the tunnel surface. The details and simplified formulas of the Dividing Point 1 in arbitrary cross-sectional tunnels are given by [11].
## 4 Results
Additionally, it is expected that not all flows should match between an LCI source and LCIA method, as there are some flows that will not result in an impact for the categories assessed in the selected LCIA method. By adhering to the strict definition of an EF used by the FEDEFL, not all the original source EFs in USLCI are considered EFs. For example, 'volume occupied, reservoir' or 'process effluent' from the USLCI are not mapped to the FEDEFL since the FEDEFL does not consider space occupation an EF and process effluent is also not a specific substance, but rather represents a mix of unknown substances. In the case of 'volume occupied, reservoir', this also does not correspond to impact assessment categories in the covered LCIA methods, so exclusion is not expected to affect the robustness of study results. In the case of 'process effluent,' the original LCI developer did not provide sufficient information on the composition of the flow to allow for incorporation in an LCIA method. While tools such as the FEDEFL improve the interoperability and structure of LCA models, model results remain dependent on the quality of the underlying source data. As the GLAD critical review and Table 5 revealed, there currently exists a gap between LCA sources' EF structures and LCIA methods' EF development [10]. The use of a standard list, such as the FEDEFL can be a vital step forward to closing the gap between LCIA method development and LCI databases. Figure 1: Analysis of improved matching using FEDEFL as the common list.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 4 BB84 Simulation Model
### Validation  
Finally, QS provides a potential subsequent step, namely a message exchange, as depicted in Figure 4. This is not a component of the BB84 algorithm; rather, it is a validation process to show that the generated key can be employed to transmit encrypted messages in a practical setting.  
The newly created and verified shared secret key is used by both [PERSON] and [PERSON] to generate a One-Time Pad (OTP). [PERSON] encodes the original input string by XOR-ing it with the OTP and sends the encrypted message to [PERSON]. [PERSON] XORs the message once more with his own OTP to obtain the decrypted message. The program then checks whether the message that [PERSON] decrypted differs from the original that was sent by [PERSON]. If [PERSON]'s resulting message differs from that originally sent by [PERSON], the cause may be the undetected interception of [PERSON] or random noise. This step is feasible only in a testing-simulative scenario, as in the real world, [PERSON] and [PERSON] would have to publicly share the decrypted message to ascertain that they are equal.
## 4 Experiments  
The experimental portion of our work consisted of two distinct parts: (1) Data preparation, given how the condition of the available data was not suitable for proper immediate testing; (2) modality experimentation, where each modality was tested first individually and then fused together. The ultimate goal of each experiment was to recognize, out of \(N\) conversation participants, which \(M\leq N\) were actively speaking at any moment.
## 3 Results  
### Petri Dish Experiment  
#### 3.1.1 Germination Percentage (G%) and Germination Speed Index (GSI) Calculations  
Germination as a test parameter in the Petri dish experiment was calculated for three LDPE MP concentrations: 0.01%, 1%, and control with 0% LDPE MP. After seven days of exposure, the germination percentage was 92% for Control, 94% for LDPE 0.01%, and 90% for 1% LDPE concentration (Figure 5).  
The germination speed index (GSI) expresses the percentage of germination on each day during the seven-day germination period. Higher GSI values indicate higher and faster germination of the seeds [52]. Calculated germination rate indexes of _S. alba_ in the Petri dish experiment were 3.74 \(\pm\) 0.22 for Control, 3.43 \(\pm\) 0.11 for 0.01% LDPE MP, and 3.25 \(\pm\) 0.24 for 1% LDPE MP.  
Figure 5: Effect on germination percentage of _S. alba_ exposed to different LDPE MP concentrations in the substrate-free Petri dish experiment exposure after 7 days. Values are presented as mean \(\pm\) standard error (SE).
## 2 Background
### Knowledge-Defined Networking  
In 2003, [PERSON] et al. [58] suggested the addition of a Knowledge Plane (KP) to the traditional computers network architecture formed by the Control Plane and Data Plane [33]. KP adopts Artificial Intelligence to perform tasks that are human intelligence characteristic, i.e., systems with the ability to reason, discover meaning, generalise, or learn from past experiences [36]. To achieve these abilities, KP proposed the use of ML techniques that offer advantages to networking, such as automation processes (recognise-act), recommendation systems (recognise-explain-suggest), and data prediction. These advantages bring the possibility of having a smart network operation and management [34].  
Nowadays, the possibility of improving the way to operate, optimise and troubleshoot computer networks by using KP is nearer than fifteen years ago because of two main reasons. Firstly, SDN offers full network view via a logically centralised Controller. Furthermore, SDN improves the network control functions that facilitate the gathering of information about the network state in real-time [34]. Secondly, the capabilities of network devices have significantly improved, facilitating the gathering of information in real-time about packets, processing time, and flow-granularity [33].  
The addition of KP to the traditional SDN architecture is called KDN [34]. It comprises four planes: Data Plane, Control Plane, Management Plane, and KP. The Data Plane is responsible for generating metadata by the forwarding network devices. The Control Plane provides the interfaces to receive the instructions from the KP; then the Controller transmits the instructions to forwarding devices. Furthermore, the Control Plane sends metadata to the Management Plane about the network state. In the Management Plane, the metadata from Control and Data Planes are collected and stored. The Management Plane provides a basic analysis of statistics per-flow and per-forwarding devicesto the KP. Finally, KP sends to the Controller one or a set of instructions about what the network is supposed to do [59].  
KDN works by employing a control loop. Formally, a control loop can be described as a system that is used to maintain the desired output, in spite of environmental disturbances. Overall, the components of a control loop include a Data Acquisition Module (DAM), Data Analyser Module (DANM), and APplication Module (APM). In KDN, DAM comprises the Data and Control Planes, DANM contains both the Management Plane and the ML Module from KP, and the APM includes the Decision Module from KP [59].
## 2 NB-PLC and RF Characterization
### RF Characterization  
In smart metering applications, the RF channel model describes the behavior of wireless communication channels between smart meters and data concentrators or other points within a smart grid system. This channel model provides a mathematical representation of RF signal propagation and interaction with the environment, besides the various impairments and effects during transmission. Understanding and modeling the attenuation and the noise of the RF channel is crucial for designing and optimizing communication systems to ensure reliable data transmission and reception in smart metering networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Region** & **Percentage** \\ \hline R1 & 70\% \\ R2 & 20\% \\ R3 & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time percentage of noise regions.  
Figure 6: Flowchart of periodic impulsive noise generation.  
Figure 7: Cyclostationary noise wave.
## 3 Numerical Simulation Verification of Bubble Morphology in Tubes  
Due to the experimental structure setting in this paper, the bubble generation bracket blocks the light and shooting angle, and the overall structural image of the bubble cannot be obtained. Therefore, the supplement of numerical simulation is particularly important in the study of bubble morphology. Numerical simulation technology is an effective method for analyzing structural reliability and has been widely used in structural design [29; 30; 31]. The images in [PERSON]'s paper [17] are compared with the numerical simulation results. Figure 4a shows the morphology and jet state of the bubble after the electric spark bubble is generated at the center of the tube when the length is 200 mm, the diameter is 20 mm, and the maximum radius of the bubble \(R_{max}\) is 13.6 mm. The dimensionless parameters are \(\eta_{b}=0.5,\gamma_{t}=1.47,\gamma_{L}=14.7\). The morphological evolution of the bubble is shown in Figure 4a, and the subscript is the time corresponding to the experimental image. Figure 4b shows some results of simulation using Abaqus2024 commercial software. The simulation in this paper adopts the CEL method. The domain, mesh, and boundary conditions are all included in the model, which has been published on Github. Link is in the Appendix A. The figure shows an image of the cross-section of the bubble in the tube in the \(y\)-axis direction. It can be seen from the figure that the simulation image corresponds well to some forms of the experimental image. Due to the action of the wall in the cylinder, the bubble expands in an ellipsoidal shape until the bubble volume is the largest. At this time, the velocity in the middle of the bubble is zero; then, the bubble enters the collapse cycle. During the collapse process, the short axis of the ellipsoidal bubble begins to collapse first, and the collapse direction is to move toward the middle along the axis, forming two jets in opposite directions. With the collapse of the bubble and the continuous movement of the jet, the bubble is penetrated and cut into a ring until the first bubble cycle ends.  
Both the experiment and the numerical simulation experienced the above-mentioned changes in bubble morphology, such as an ellipsoidal shape during expansion, a \"counterjet\" during collapse, and an annular bubble in the late stage of collapse. Overall, within the allowable error range, the numerical solution is consistent with the experimental value, especially for the stage before the bubble burst. The good agreement between the two not only reveals the dynamic changes of bubbles under the action of the annular boundary but also verifies the feasibility of the experimental model and the numerical algorithm. In the next tube experiment, this simulation method is used to supplement the experimental conditions, such as the central shape of the obscured bubble and the water flow velocity.  
Figure 4: (**a**) Experimental results in a tube; (**b**) Abaqus numerical simulation results.
## 1 Introduction
The signal processing capability of wave-mixing technique, which could be used to replace the nonlinear resonance method, is insensitive to the nonlinearity of the measurement system and has a multi-selective frequency mode. Nonlinear Lamb wave frequency-mixing technique relies on the interaction characteristics between two incident waves of different frequencies in the structure, thus achieving a considerable detection capability for inner defects. For an undamaged isotropic solid plate with pure elastic behaviors, no new frequency components will be generated when the two waves meet. On the contrary, in a structure that contains micro-cracks, interactions will occur in the associated regions when the two waves meet in the nonlinear region, resulting in coupling of the two waves. New frequency components will be generated in the frequency domain, including sum and difference frequencies. Nonlinear Lamb wave frequency-mixing detection has attracted increased research attention because of its great potential to detect micro-cracks and damage and has gradually been applied in NDT. [PERSON] et al. [21] derived a set of necessary and sufficient conditions for the generation of resonance waves by two time-harmonic plane waves and obtained closed analytical solutions for the generation of resonance waves by two linearly propagating sinusoidal pulses. [PERSON] et al. [22] conducted an exploratory study using nonlinear Lamb wave for damage detection in plates and investigated various aspects of nonlinear features by the frequency-mixing technique. [PERSON] et al. [23] analyzed the feasibility of the proposed acoustic nonlinear parameters and applied nonlinear Lamb wave-mixing technique to detect the degree of damage of micro-cracks in sheet metal, and the obtainedresults demonstrated that the consistence between the theoretical analysis and the experimental study. However, few studies have been carried out on nonlinear Lamb wave frequency-mixing to measure the direction of micro-cracks. This paper presents a nonlinear Lamb wave frequency-mixing technique to characterize the direction of micro-cracks by analyzing the scattering features around them. The paper is organized as follows: Section 2 introduces the basic theoretical background of nonlinear Lamb wave-mixing technique. The fundamental theory of pulse inversion and its use to extract the sideband at the sum or difference frequency signal are also described. Section 3 describes in detail the types of 3D finite-element model of micro-cracks and the process of micro-crack direction identification, and Section 4 draws conclusions.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 6 Simulation Experiments and Performance Evaluation
### Node Activation Heuristic  
Simple topologies with one server attached to the switch at the lowest level were considered to investigate the node activation heuristics. The constant flows of 8 MB were generated. The solution obtained for the ECMP algorithm without energy management heuristics was used as a reference point. It defines 100% energy consumption level in Figure 5. The tested heuristic algorithm activates additional switches if exceeding a given port load threshold. It allowed for a significant reduction of device activation energy costs. However, the costs of handling flows remained unchanged in most test cases. The cause for this is that, assuming the same data transfer costs through all devices, the path determined by the ECMP algorithm itself has the lowest transmission cost. Thus, the only source of savings may be placing all unused modes into sleeping mode. The reduced transmission cost, recorded for larger threshold values and butterfly topology, is not an actual profit but is the side effect of data losses presented in Figure 6. Significantly, the energy usage was also lower than for smaller values of the threshold and lower than the optimal solution found with the MIP solver. This testifies that the number of active switches was, in this case, insufficient.  
The proposed heuristic allows a greater energy gain for a butterfly topology than a fat-tree one. In particular, it is caused by a higher number of devices placed in the additional layer of the network. This resulted in higher energy consumption compared to that of the fat-tree topology. Analysis of individual cost components for ECMP routing without energy-saving heuristic and both topologies are presented in Table 2. Setting a high port load threshold can result in packet loss, especially at higher levels of hierarchy, as the switches there located aggregate flows gathered by lower level nodes. Passing a path through such a switch can redirect flows to an excessively loaded part of the network, which results in a high loss rate. However, even conservative settings of port load threshold--in the range of 40-50%--allow visible savings and provide the headroom for burst traffic.  
Figure 4: Packet loss for butterfly and varying flow size.
## 4 System Methodology
### Preprocessing, Feature Extraction and Feature Ranking
#### 4.2.3 Nonlinear Features  
To increase the robustness of features extracted for this classification, nonlinear features were extracted using linear analysis, such as sample entropy, fuzzy entropy, and approximate entropy, as summarized in Table 2.
# The Impact of Thermal Treatment on Structural Changes of Teak and Iroko Wood Lignins
## 3 Results and Discussion
### FTIR Spectra Analysis
Thus, the decrease of absorbance in iroko was more pronounced than in teak (this may be due to a more notable cleavage of methoxyl groups in the iroko). According to [PERSON] et al. [65], the decrease absorbance may be due to a decrease in the methoxyl groups in lignin. The peak absorbance around 1327 cm\({}^{-1}\) (C\(-\)O vibration in syringyl derivatives-condensed structures in lignin) [38; 62] increased permanently, except for the iroko at the highest temperature, where a decreased absorbance was observed. This suggests the condensation reactions in lignin structure (more pronounced in teak than iroko wood) as observed by [PERSON] et al. (2015) [58] for teak wood. A similar trend was also found for the band absorbances around 1265 cm\({}^{-1}\) and 1220 cm\({}^{-1}\) (C\(-\)O stretching of guaiacyl units) [63; 66]. Considering the absorbance values at 1265-1267 cm\({}^{-1}\), it can be assumed that the proportion of guaiacyl units in the teak wood lignin is higher than in iroko. In contrast, the NBO results show a higher S/G ratio in teak lignin compared to iroko lignin. This difference may be because NBO provides the results only for non-condensed lignin substructure, while FTIR and SEC analyses show the properties of the whole dioxane lignin. Larger amounts of guiacyl units in teak lignin according to the FTIR method indicate their greater condensation, resulting in lower yields of NBO products and higher S/G ratio relative to iroko lignin. The band at 1120 cm\({}^{-1}\) (C\(-\)H vibration in lignin) is typical of syringyl units [67; 68]. This band markedly decreased with an increasing temperature in both wood species. Compared to the band near 1220 cm\({}^{-1}\), there is a notable decrease in absorbance, which may be due to higher thermal stability of guaiacyl structures against syringyl ones, as reported by [PERSON] et al. [69].