esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
###### Abstract  
Silicon photonics has drawn increasing attention in the past few decades and is a promising key technology for future daily applications due to its various merits including ultra-low cost, high integration density owing to the high refractive index of silicon, and compatibility with current semiconductor fabrication process. Optical interconnects is an important issue in silicon photonic integrated circuits for transmitting light, and fiber-to-chip optical interconnects is vital in application scenarios such as data centers and optical transmission systems. There are mainly two categories of fiber-to-chip optical coupling: off-plane coupling and in-plane coupling. Grating couplers work under the former category, while edge couplers function as in-plane coupling. In this paper, we mainly focus on edge couplers in silicon photonic integrated circuits. We deliver an introduction to the research background, operation mechanisms, and design principles of silicon photonic edge couplers. The state-of-the-art of edge couplers is reviewed according to the different structural configurations of the device, while identifying the performance, fabrication feasibility, and applications. In addition, a brief comparison between edge couplers and grating couplers is conducted. Packaging issues are also discussed, and several prospective techniques for further improvements of edge couplers are proposed.  
silicon photonics; optical interconnects; fiber-to-chip coupling; edge couplers; inverse taper +
Footnote †: journal: Applied Sciences
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
## 4 Effect of the Modification with Nanomaterials in the Mechanical Performance of Asphalt Mixtures
### Nanoclay
Regarding the 2% PNC modification, the modified mixture presented 16% increase in indirect tensile strength (Figure 8), 10% average increase in dynamic modulus, 29% increase in resilient modulus (Figure 12) and 34% reduction in permanent deformation (rut depth using APA rutting test). Only in the case of permanent deformation, the 4% PNC performed better than the 2% PNC, presenting 42% reduction in rut depth. [PERSON] et al. [37] studied a stone mastic asphalt (SMA16) mixture modified with 4% nanoclay hydrophilic bentonite. The mixtures were produced with 6.0% binder content and using 35/50 asphalt binder (control binder), granitic aggregates, and limestone filler. The mixture with 4% nanoclay presented: 30% increase in indirect tensile strength (Figure 8); improvement in indirect tensile strength ratio from 75% to 92% (Figure 9); 13% reduction in permanent deformation (Figure 11); 6.6% reduction in stiffness modulus at 20 \({}^{\circ}\)C; and better fatigue resistance (14% increase in the strain for 10\({}^{6}\) cycles). [PERSON] et al. [104] modified a SMA mixture (NMAS 9.5 mm) with 1%, 2%, 3%, and 4% cloisite-15A. For the mixture production, 6.7% binder content, limestone aggregates and limestone filer were used. In this study, two control binders were considered, the 60/70 asphalt binder and the 60/70 modified with 5% SBS. In both mixtures, 60/70 and 60/70 + SBS, the nanoclay modification enhanced the properties of the mixture, increasing the effect with the increase in dosage. Thus, the 4% dosage presented the highest gains in performance. Regarding the mixture with 60/70 control binder, the 4% modification presented 23% increase in Marshall stability, 9% increase in flow number (lower plastic deformation) and 61% reduction in permanent deformation. Regarding the mixture with 60/70 + SBS control binder,  
Figure 12: Resilient modulus of control and nanoclay modified asphalt mixtures. the 4% modification presented 11% increase in Marshall stability, 8% increase in flow number (lower plastic deformation), and 16% reduction in permanent deformation. Generally, the asphalt mixtures modified with nanoclays revealed higher Marshall stability, enhanced water sensitivity (indicated by higher indirect tensile strength ratio and higher retained Marshall stability), lower permanent deformation, and better fatigue resistance. Regarding stiffness, the nanoclay modified mixtures presented higher values, particularly at higher temperatures. The effects obtained with the modifications were dependent on the type of nanoclay being used. The raw nanoclays tend to present stronger effects according to the increase in dosage, while some of the organically modified nanoclays present a peak in performance enhancement at a relatively low dosage and a further increase worsens the performance. Although the majority of the nanoclay modifications had the effect of increasing the indirect tensile strength, some of organic modifiers caused a reduction.
## 3 Results and Discussions
### Comparison of Forming Behavior of Steel
#### 3.2.2 Comparison of Forming Response: Effective Strain  
Apparently, the effective strain (\(\textit{C}_{\textit{eff.}}\)) curves exhibited a similar phenomenon, as shown in Figure 9 for all AISI steels with given combinations of \(a,h\), and \(r\). However, meager deviations were observed in the strain paths among all the AISI steels.  
The effective strain sharply increased with billet size ratio (\(z\)) up to around 0.7 (the effective strain was highly sensitive to z variations up to about 0.7). For all specified values of \(z\), the strain increased with an increase in level settings of \(a,h\), and \(r\) for all three materials. For lower combination levels of \(a,h\), and \(r\), the strain for AISI 1045 was slightly less than for AISI 1010 and AISI 1018 materials. For high-level settings of \(a,h,r\), and \(z<0.6\), it was observed that the strain for AISI 1045 was slightly less compared to strain magnitudes for AISI 1010 and AISI 1018. However, for \(z>0.6\), the strain in the case of AISI 1045 was higher than either AISI 1010 or AISI 1018 materials.  
The volume of metal that undergoes deformation in the region \(z<0.6\) is lower; hence, there is a steep increase in the values of effective strain. Contrary to this behavior, marginal variations were observed subsequently with specified combinations of \(a,h\), and \(r\), because of higher volume (\(z>0.6\)) that marginally contributed to a rise in the values of effective strain. Though increased values in punch angle and land height readily increased resistance to the metal flow, observed with high-level combinations of \(a,h\) and \(r\), actually, the effect of reduction ratio at \(r=0.6\) (punch diameter was 19 mm) was somewhat restrained for the fixed billet diameter. A decreased carbon content also readily assisted deformation, hence,  
Figure 8: Comparison of forming response: effective stress.  
the effective strain values were witnessed to be smaller for AISI 1045 than AISI 1010 and 1018 for all combinations of \(a\), \(h\) and \(r\). On the other hand, a dominance of AISI 1018 steel in effective strain path for higher combinations of \(a\), \(h\), and \(r\) as compared to AISI 1010 and 1045 steel, which might be correlated with a strain hardening effect that has been observed with micro-hardness values among these steel grades.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 3 Decentralization of Virtual Linkage Approach
### Consensus Control  
#### 3.2.1. Implication of Consensus of Virtual Linkage's State \(\xi_{i}\)  
As mentioned above, each agent has an instantiation of virtual linkage states \(\xi_{i}=(r_{i},q_{i},\theta_{i},\lambda_{i})\) and the consensus module implement consensus strategies to ensure each instantiation converge into the desired value. In this part, the implication of \(\xi_{i}\) is illustrated.  
In the virtual linkage approach, each agent is specified with a vector \(\chi=\big{(}l_{i},\
u^{j}p_{i}^{\dagger n}\big{)}\) before task. The \(l_{i}\) is the ID number of virtual link which the \(i\)th agent belongs to, and \(\
u^{j}p_{i}^{\dagger n}\) is the \(i\)th agent's relative position in the \(j\)th unit virtual link. Meanwhile, each agent is randomly initialized with a \(\xi_{i}=(r_{i},q_{i},\Theta_{i},\lambda_{i})\). With knowing the \(\chi\) and \(\xi_{i}\), each agent now is able to calculate its global position in the world.  
Note that each \(\xi_{i}\) corresponds to a state of the virtual linkage (See Figure 4). The consensus module will ensure all the \(\xi_{i}\) converge into a common value  
\[\xi_{1}=\xi_{2}=\ldots=\xi_{n}=\
u^{j}\xi_{contr}. \tag{3}\]  
The team of agents forms virtual linkages and moves in desired formation shapes along a specified path. Note that the formation pattern can be easily reconfigured by reconfiguring the coordinate variable \(\
u^{j}\xi_{contr}=(\
u^{r}_{contr},\
u^{r}q_{contr},\
u^{r}\Theta_{contr},\
u^{r }\lambda_{contr})\).  
Figure 3: Decentralized virtual linkage architecture.
## 2 Materials and Methods
### Characterization Methods  
#### 2.5.1 Microscopy Analysis  
Microscopy analysis was performed using a Leica DM750 M Optical Microscope (Leica Microsystems, Wetzlar, Germany) to observe the surface morphology of the wet-spun filaments and braided structures. Microscopy images were used to assess the diameter of the filaments by measuring their width at 100 different points. These measurements were then analyzed to calculate the mean diameter and standard deviation, assuming a Gaussian distribution model for statistical consistency. For more detailed surface analysis, Field Emission Scanning Electron Microscopy (FESEM) was conducted using a Phenom ProX desktop microscope (Thermo Fisher Scientific, Waltham, MA, USA). Samples were mounted on aluminum stubs with conductive carbon tape and analyzed without coating under a voltage of 5 kV. Images were captured at different magnifications to assess filament uniformity, cross-sectional structure, and surface characteristics of the PCL and PCL/CNC filaments, as well as braided structures.
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 5 Multipath Channel Model  
The signal propagating through the wireless channel environment experiences physical changes such as dispersion, refraction, and scattering. Hence, the received signal disturbed by the wireless channel objects has the multiple copies of the originally transmitted signal with different phases and amplitudes. The impulse response of the multipath Rayleigh fading channel through which the OFDM signal passes can be characterized as follows:  
\[h(\tau,t)=\sum_{l=0}^{L-1}\alpha_{l}(t)\delta(t-\eta_{l}(t)) \tag{29}\]  
where \(L\) denotes the number of taps, \(\tau\) is the delay time of each direction, and \(\alpha\) is the complex coefficient of each path.
# Evaluation of Workability and Mechanical Properties of Bottom Ash Aggregate Concrete
## 3 Experimental Details
### Concrete Mixtures  
Table 3 shows the mixture proportions of concrete according to the design variables. The major variables are unit cement content based on _W_/C and the types of fine and coarse aggregates. A total of 12 designed concrete mixes are classified into the following three groups according to _W_/C: 0.45 (group L), 0.375 (group M), and 0.3 (group H). The first letter of the name of each specimen refers to the group symbol, the second letter to \(R_{S}\), and the third letter to \(R_{G}\), respectively. \(R_{S}\) and \(R_{G}\) refer to the ratios of the replacement of fine and coarse BAA with natural sand and crushed granite, respectively. For example, specimen L-100-0 indicates a mixture in L group (_W_/C = 0.45), whose designed compressive strength (\(f_{cd}\)) is relatively low, for which natural sand is used in the case of fine aggregate and BAA is used in the case of coarse aggregate. In addition, H-0-0 indicates a mixture with _W_/C of 0.3, and for which BAA is used for both fine and coarse aggregates. In all the mixtures, the fine aggregate ratio (_S_/_n_) and unit water content (_W_) were fixed at 45% and 175 kg/m\({}^{3}\), respectively. BAA was immersed in water for 48 h before use in the experiment, and then dried in a shaded environment until it entered the saturated surface-dry state. This was done because, when using LWA with high water absorption, the water needed for the hydration of the cement can be absorbed due to the high porosity inside the aggregate, which affects the mechanical properties of concrete after its hardening. A commercially available, polyacrylamide-based, water-reducing admixture was added to 0.6% of the cement mass content.  
Figure 2: Shapes and scanning electron microscopy (SEM) images of bottom ash aggregates.
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
###### Abstract  
IPv6 geolocation is necessary for many location-based Internet services. However, the accuracy of the current IPv6 geolocation methods including machine-learning-based or deep-learning-based location algorithms are unsatisfactory for users. Strong geographic correlation is observed for measurement path features close to the target IP, so previous methods focused more on stable paths in the vicinity of the probe. Based on this, this paper proposes a new IPv6 geolocation algorithm, SubvectorS_Geo, which is mainly divided into three steps: firstly, it filters geographically relevant routing feature codes layer by layer to approximate the fine-grained trusted region of the target; secondly, it extracts delay vectors into the trusted region; thirdly, it evaluates the vector similarity to determine the final target geolocation information. The final experiments show that the median error distance range is 7.025 km to 9.709 km on three real datasets (Shanghai, New York State, and Tokyo). Compared with the advanced method, the median distance error distance is reduced by at least 6.8% and the average error distance is reduced by at least 9.2%.  
IPv6 geolocation; network mapping; neural network +
Footnote †: journal: Applied sciences
## 3 Results
### Analysis between limbs in ACL and NI Groups Considering DL or NDL  
Peak actEMG analysis between limbs obtained statistical significance in ACL players, while in NI players, no statistical differences were obtained. In ACL_DL players, peak actEMG in BF was significantly higher for NDL, which was the non-injured limb group, than for DL, which was the injured limb (\(p\) = 0.041). ACL_NDL players showed significantly higher peak actEMG in GMax for NDL, the injured one, compared to DL, the non-injured one (\(p\) = 0.043). Moreover, ACL_DL players revealed strong to excellent effect sizes in peak actEMG in Gm (d = 1.035). Thus, peak actEMG in Gm was higher for DL, the injured limb, compared to NDL, the non-injured one. In addition, ACL_NDL players demonstrated strong to excellent effect sizes in peak actEMG in BF (d = 1.140). Thus, peak actEMG in BF was higher for NDL, the injured limb, compared to DL (Table 5). No statistical significance was obtained in the comparative analysis of average actEMG between limbs for any group. However, ACL_DL players showed strong to excellent effect \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**DOMINANT LIMB (DL)**} & \multicolumn{6}{c}{**NO DOMINANT LIMB (NDL)**} \\ \cline{2-13}  & **ACL** & **NI** & & & & & **Confidence** & **ACL** & **NI** & & & **Confidence** \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & & & & **Interval** & **(95\%)** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Confidence** \\ \hline H\_peak, Torque (Nm/kg) & \(1.55\pm 1.55\) & \(1.50\pm 0.50\) & 0.831 & 0.11 & (\(-\)0.47 to 0.58) & \(1.25\pm 0.91\) & \(1.47\pm 0.57\) & 0.596 & 0.29 & (\(-\)1.11 to 0.67) \\ Q\_peak, Torque (Nm/kg) & \(2.86\pm 2.86\) & \(2.69\pm 0.98\) & 0.640 & 0.23 & (\(-\)0.59 to 0.94) & \(2.54\pm 0.97\) & \(2.50\pm 1.08\) & 0.958 & 0.03 & (\(-\)1.27 to 1.34) \\ GMax\_peak, Torque (Nm/kg) & \(3.15\pm 3.15\) & \(2.93\pm 1.12\) & 0.175 & 0.22 & (\(-\)0.85 to 1.29) & \(2.71\pm 0.93\) & \(2.99\pm 1.10\) & 0.649 & 0.27 & (\(-\)1.58 to 1.03) \\ Gm\_peak, Torque (Nm/kg) & \(2.22\pm 2.22\) & \(1.61\pm 0.65\) & 0.092 & 0.94 & (\(-\)0.11 to 1.32) & \(1.78\pm 0.78\) & \(1.52\pm 0.68\) & 0.540 & 0.35 & (\(-\)0.64 to 1.16) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of peak torque of force outcomes during maximal voluntary isometric contraction between groups, ACL and NI, considering DL or NDL.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 2 Architecture Design
### Network Layer  
Inspired by the development of communication services on geosynchronous satellites, high-orbit satellite communication services are studied and derived. A satellite can cover almost half of the globe at high altitudes, which allows it to provide services in an incredibly broad range.  
Low-Orbit Satellite  
Compared with GEO satellites, a LEO satellite-based IoT has smaller path loss and propagation delay [12]. The LEO satellite-based IoT has the characteristics of high reliability, wide-coverage, and large capacity, thus it is a significant extension of the terrestrial IoT. LEO  
Figure 4: A network model for satellite-based IoT.  
Figure 3: Schematic diagram of the system.  
satellite communications cannot be affected by ground obstacles, because IoT devices can access the LEO satellites with flexible angles. Furthermore, LEO satellites use multi-beam technology to provide services for more IoT devices.  
Consequently, this experiment is based on a LEO satellite-based IoT.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 6 Network Parameters Adjustment and Performance Enhancement
### Study of Multiple Mobile Sinks  
We also studied the influence of multiple mobile sinks on the lifetime of the network. The moving radius of each sink was set to 0.25R. Simulation results illustrate that with the increasing numbers of mobile sinks, the lifetime of the network improved, as is shown in Figure 15. We also noticed a trend that with the number of mobile sinks increasing, the improvement of the lifetime becomes fewer and fewer. Since the mobile sinks are more expensive than ordinal sensors, three mobile sinks are recommended to be the most cost-effective.  
We compared the energy consumption of multiple mobile sinks when the radius of the mobile sink was 0.25R. It can clearly be seen from Figure 16 that when the number of the mobile sinks increased, the upward trend of the energy consumption of the whole network decreased. When the number of the mobile sinks exceeded three, the performance of the whole network does not change much.  
Figure 14: Different speed of the mobile sink.  
Figure 15: Different mobile sink number.
## 3 Long-Range, Low-Power IoT Communication Protocols and Their Security
### C-UNB/Sigfox
It monitors tailing dams where mine residues are deposited, using sensors such as a tensiometer and soil moisture and temperature sensor. They called it TD-DAQ or tailing dam data acquisition. This application encompasses the fields of geology, waste management, and civil engineering, which makes it very unique and important for the safety of mining operations. Moreover, this system, which was deployed in South Africa, is one more example of the global reach of IoT. When checking the security of the TD-DAQ system, it was not clear from the paper describing the TD-DAQ whether Sigfox was used in encryption mode or _clear mode_, or whether some additional application layer encryption was developed by the authors.
# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 2 Extraction of Multi-Scale Recurrence Quantification Measurements (MRQMs)
### Recurrence Quantification Analysis (RQA)
#### 2.2.2 Recurrence Quantification Measurements
It has advantages in detecting subtle changes in biological time series and can indicate the degree to which the time series repeat the same sequence. \[RPDE=-(\ln T_{\max})^{-1}\sum\limits_{l=1}^{T_{\max}}P(t)\ln P(t) \tag{15}\]  
Each point of the time series \(\{x(1),\ x(2),\dots,\ x(N)\}\) is plotted as a histogram during the period of the threshold return. \(P(t)\) is the normalized result of the histogram. Where \(T_{\max}\) is the maximum recurrence period and t is the time between returns. _Clustering coefficient (Clust)_ represents the probability that two neighbors of any state in the RP structure are also gathered together in the complex network theory. \[Clust=\sum\limits_{i=1}^{n}\frac{\sum\limits_{k=1}^{n}R_{ij}R_{jk}R_{ki}}{RR_{i}} \tag{16}\]  
\[RR_{i}=\sum\limits_{j=1}^{n}R_{ij} \tag{17}\]  
where \(RR_{i}\) indicates the local recurrence rate. _Transitivity (Trans)_ quantifies the geometric properties of the phase space trajectory and has an effective role in different dynamic systems. \[Trans=\frac{\sum\limits_{i,j,k=1}^{N}R_{ij}R_{jk}R_{ki}}{\sum\limits_{i,j,k=1} ^{N}R_{ij}R_{ki}} \tag{18}\]  
With the further development of nonlinear analysis, researchers realized that the nonlinearity of speech limits the performance of speech signal processing, for example, it is difficult to improve the accuracy of the linear prediction of speech. The speech utterance system is a nonlinear time-varying system. In this work, RQMs can quantify the recurrence characteristics of glottal signals.
## Appendix A Notation  
\begin{tabular}{l l} \(x_{m}(d)\) & the \(d\)th complex symbol modulated on the \(m\)th subcarrier \\ \(\alpha\) & frequency packing factor (FPF) \\ \(\beta\) & time packing factor (TPF) \\ \(M\) & the number of subcarriers \\ \(D\) & the number of the symbols transmitted on each subcarrier \\ \(T_{s}\) & Nyquist symbol interval \\ \(T_{f}\) & shift interval of the filter \\ \(L\) & the length of the time domain shaping filter \\ \(Q\) & the length of circular expanding \\ \(\mathbf{v}_{m}\) & the output signal vector on the \(m\)th subcarrier before \(Q\)-point IDFT operation \\ \(\mathbf{V}\) & the output signal matrix on \(M\) subcarriers before \(Q\)-point IDFT operation \\ \(\mathbf{F}_{D}\) / \(\mathbf{F}_{D}^{H}\) & \(D\times D\) Fourier/Inverse Fourier transform matrix \\ \(\mathbf{T}_{Q,D}\) & \(Q\times D\) cyclic extension matrix with \(D\)-dimensional identity matrix \(\mathbf{I}_{D}\) \\ \(\mathbf{W}\) & the frequency-domain window matrix \\ \(\mathbf{S}_{m}\) & the \(m\)th cyclic shift matrix \\ \(\mathbf{g}\) & transmitted DBT-MC-FTN signal vector \\ \(\mathbf{\Omega}\) & a matrix consists of \(M\) cyclic shift matrixes \(\mathbf{S}_{m}\) \\ \(\mathbf{\Lambda}\) & a diagonal matrix with diagonal element \(\mathbf{WT}_{Q,D}\mathbf{F}_{D}\) \\ \(\mathbf{X}\) & a signal matrix consists of signals on \(M\) subcarriers \\ \(\mathbf{\Phi}\) & the equivalent matrix of the transmitter \\ \(\mathbf{y}\) & the receiver signals vector \\ \(\mathbf{z}\) & the additive white Gaussian noise (AWGN) with zero mean and the variance of \(\sigma^{2}\) \\ \(\mathbf{H}_{c}\) & a circulant matrix constructed by \(h\) \\ \(\mathbf{\Psi}\) & a circulant matrix constructed by \(\mathbf{\Phi}\) \\ \(\mathbf{\Theta}\) & a \(Q\times Q\) circulant matrix with first column vector is \(V\)=\(\mathbf{\Phi}X\) \\ \(\mathbf{r}\) & the signal vector after demodulation \\ \(\mathbf{C}\) & the correlation matrix regardless of the effect of the channel \\ \(\mathbf{Z}_{\mathbf{\Phi}^{m}}\) & the noise matrix contaminated by the demodulation matrix \(\mathbf{\Phi}^{*}\) \\ \(\mathbf{H}_{el}\) & the equivalent channel matrix containing the transceiver and the channel \\ \(\mathbf{P}\) & \(N_{p}\)-length pilot symbol vector \\ \end{tabular}