esa-sceva/satcom-chunk-collection huggingface dataset:
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
###### Abstract  
Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, \(LESS\), which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate \(LESS\) in both real WSN systems and a large-scale simulation, and the results show that \(LESS\) can reduce energy and bandwidth consumption by up to 50% while still achieving more than 90% link quality estimation accuracy.  
link quality reconstruction; topology construction; compressive sensing; wireless sensor networks; environmental monitoring +
Footnote †: journal: Sensors
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 3 The Radial Gradient Seismic Metamaterial Structure
### Band Gap and Mechanism Analysis  
The finite element software COMSOL Multiphysics 5.5 was used to simulate the RGSM structure, and the calculation results are shown in the band structure of Figure 2a. It can be observed from the figure that there are three complete band gaps below 20 Hz, and the frequency ranges of the band gaps are 5.3484-13.882 Hz, 15.016-15.274 Hz, and 18.488-20 Hz, respectively. In order to further verify the shielding effect of triangular cross-section radial seismic metamaterials on Lamb waves, we calculated the incident wave frequency response curve of the nine-cycle radial periodic structure, as shown in Figure 2b. The attenuation domain in the frequency response curve is basically consistent with the band gap range in the energy band curve.  
Figure 1: Triangular cross–section radial seismic metamaterial structure. (**a**) Lattice structure and BZ wave vector in reciprocal space; (**b**) three–dimensional periodic structure; (**c**) the formation of radial seismic metamaterial structure; (**d**) periodic array model; (**e**) arrangement of radial seismic metamaterials in soil.  
In order to explore the generation mechanism of the band gap, the vibration modes at several special points in the band structure diagram are calculated, as shown in Figure 3. It can be seen from the figure that the vibration mode of point A1 is mainly the axial vibration of the steel ring in the unit, and the soil acts as the matrix. The mode of A2 is mainly manifested as the vibration of the soil in the unit cell. The vibration of the upper soil is wavy from the outer soil to the inner soil, and the steel layer remains stationary at this time. The A3 mode is mainly manifested as bottom and inner vibration, and the steel layer is still relatively static. A4 and A3 have the same vibration mode. Compared to the vibration state under the A3 mode, the vibration intensity at the bottom of the unit cell under the A4 mode is weaker.  
Figure 3: The RGSM modal diagram of the unit cell at a special point; (**a**) the vibration mode at point A1; (**b**) vibration mode at point A2; (**c**) vibration mode at point A3; (**d**) vibration mode at point A4.  
Figure 2: (**a**) Dispersion curve of triangular cross\(-\)section unit cell structure; (**b**) Attenuation spectrum.
# Multipath Data Storage by Third-Order Nonlinear Optical Properties in Zinc Oxide Nanostructures
## 4 Conclusions  
Polarization-selectable nonlinear optical absorption and nonlinear refraction were analyzed for developing a multipath load balancing technique by nanosecond pulses in a pump-probe configuration. The inherent modification of a third-order nonlinear optical response exhibited by optically irradiated ZnO nanostructures was studied by a detuning approximation. A two-wave mixing allowed us to identify two-photon absorption processes together with the optical Kerr effect as the third-order optical nonlinearities exhibited in different regions of the ZnO films. The systematic reading and recording of data as the real and imaginary part of the third-order nonlinear optical in the samples was obtained by nanosecond pulses featuring logic gate functions. The impact of nonlinear optical phenomena in low-dimensional materials seems to be suitable for designing platforms and dynamic tools for sensing and processing multiscale signals.  
Figure 6: Third-order nonlinear optical susceptibility vs. population loss factor \(T_{1}\)/\(T_{2}\) in the laser annealed ZnO nanostructures.  
**Author Contributions:** [PERSON] carried out nonlinear optical experiments; [PERSON] is responsible for the preparation and structure characterization of the samples; [PERSON] analyzed photodamage effects; [PERSON] analyzed nonlinear optical functions; [PERSON] elucidated the application of the nonlinear optical properties and designed the paper. The manuscript was written through contribution of all authors. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Consejo Nacional de Ciencia y Tecnologia (CONACyT) (grant No. CB-2015-251201), Comision de Operacion y Fomento de Actividades Academicas, Instituto Politecnico (COFAA) (2020) and Instituto Politecnico Nacional (IPN) (SIP-2020).  
**Acknowledgments:** The authors kindly acknowledge the financial support from the Instituto Politecnico Nacional, COFAA-IPN, Universidad Nacional Autonoma de Mexico, Universidad Politecnica del Valle de Mexico and Consejo Nacional de Ciencia y Tecnologia. The authors are also thankful to the Central Microscopy facilities of the Centro de Nanociencias y Micro y Nanotecnologia del Instituto Politecnico Nacional.  
**Conflicts of Interest:** The authors confirm that there are no conflicts of interest related to this research.  
**Data Availability:** The authors declare that the data can be obtained upon request by emailing the corresponding author.
## 3 Results
### Annual Change Rate of Degraded Rangeland  
To further evaluate the change patterns of rangeland degradation, the annual change rate of degraded rangeland was obtained for the three study intervals (Table 4).  
Figure 5: Rangeland degradation from 1996 to 2011 for the Linxia rangeland.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Degraded** & \multicolumn{2}{c}{**1996**} & \multicolumn{2}{c}{**2001**} & \multicolumn{2}{c}{**2006**} & \multicolumn{2}{c}{**2011**} \\ \cline{2-9}
**Rangeland Type** & **Area (km\({}^{2}\))** & **\%** & **Area (km\({}^{2}\))** & **\%** & **Area (km\({}^{2}\))** & **\%** & **Area (km\({}^{2}\))** & **\%** \\ \hline Non-DR & 602.74 & 17.10 & 437.39 & 12.55 & 764.52 & 21.44 & 710.01 & 19.62 \\ Sl-DR & 1629.37 & 46.23 & 1141.79 & 32.75 & 1353.82 & 37.96 & 1613.04 & 44.56 \\ M-DR & 1039.97 & 29.50 & 1524.82 & 43.75 & 1153.42 & 32.34 & 1014.44 & 28.02 \\ Se-DR & 252.67 & 7.17 & 381.87 & 10.95 & 294.42 & 8.26 & 282.49 & 7.80 \\ Total & 3524.15 & 100.00 & 3485.87 & 100.00 & 3566.18 & 100.00 & 3619.98 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of degraded rangeland area statistics in 1996, 2001, 2006, and 2011 for Linxia rangeland.  
During the 1996-2001 interval, the area of SI-DR decreased with an annual decrease rate of 5.98%, and the annual increase rate of M-DR and Se-DR was 9.32% and 10.22%, respectively. However, the opposite situation was illustrated after 2001, namely, the area of the SI-DR began to increase, and the annual change rates of M-DR and Se-DR turned to be negative. The increase rate of SI-DR for 2001-2006 and 2006-2011 intervals was 3.71% and 6.38%, respectively, which means that the increase pace gradually accelerated. On the other hand, the decrease rate of M-DR and Se-DR gradually slowed down from 2001 to 2011; the annual M-DR reduction rate was 4.77% from 2001 to 2006 and 2.41% during 2006-2011; similarly, the decreasing rate of Se-DR for the two intervals was 4.58% and 0.81%, respectively. The decrease in the area of M-DR and Se-DR suggested that the \"Grain for Green\" and grazing ban policies implemented since 2000 in the study region played important roles in the recovery of rangeland vegetation and the reversal of the rangeland degradation trend.
## 5 Analog Domain Processing
### Modulated Symbol Structure  
Data sent through underwater channels undergo severe attenuation and distortion by multipath propagation and time-domain channel variation. Thus, it is very difficult to acquire the exact phase of each symbol at a receiving modem in such a harsh environment. Thus, since acoustic micro-modems should possess the properties of small size, low power consumption and low cost, amplitude shift keying with non-coherent detection which does not require the phase of signal for detection is chosen for the modulation and demodulation of this research.  
Meanwhile, in order to mitigate the intersymbol interference caused by multipath propagation, each modulation symbol is comprised of an emitting period and a silent period. The silent period is a kind of symbol-level guard time where no signals are transmitted. That is, for the transmission of '1', sinusoidal electric signals are applied to a transducer during the emitting period whereas no signals are generated for the silent period as illustrated in Figure 7. For the transmission of '0', no signals are generated for the entire symbol duration since amplitude shift keying is utilized.  
Figure 5: Functional diagram of the transmitter.  
Figure 6: Functional diagram of receiver.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 3 Ocean Applications  
Remote sensing of the ocean surface has traditionally been an application of active (e.g., scatterometers, altimeters) or passive instruments (e.g., radiometers) [59]. GNSS-R operates somewhere in between active and passive modes as the transmitted GNSS signals are used opportunistically for remote sensing, in addition to their intended use in navigation. In GNSS-R, signals that reflect off the Earth's surface are processed to gain insight on the roughness or height of the surface. Depending on the science application intended and signal processing applied, GNSS-R receivers are operated as bistatic scatterometers or altimeters [60]. This section discusses the oceanic applications of GNSS-R remote sensing.
## 1 Introduction
Recently, pre-signal control methods for other vehicles have been studied in signalized intersections. [PERSON], [PERSON], [PERSON]. [17; 18] proposed a pre-signal system that employs sorting strategies for generic intersections, illustrated the potential benefits of the proposed system, and demonstrated that the pre-signal control method can increase intersection capacity effectively by a natural experiment. [PERSON], [PERSON]. [19] developed an optimization model of the lane assignment and signal timing at the tandem intersection with pre-signal to minimize the delay. [PERSON] et al. [20; 21] established a signal control model for an intersection entrance with a double-stop line. [PERSON] et al. [22] and [PERSON] et al. [23] studied the method of setting the pre-signal at variable lanes of an intersection, with the aim of solving the problem of traffic jams and idle lanes caused by the uneven distribution of traffic flow. As a special and complex traffic control mode, integrated waiting area (IWA) has been proposed recently as a solution to increase the capacity of the signalized intersection without flaring up the approaches. Although IWAs have been put into practice in several cities in China and decreased approach delays at some intersections in these cities which suffer from intersection space shortages, the application is still in its early stage, compared with other traffic management measures. An understanding of when and where the concept of IWA is desirable is important for its application. More studies are needed to optimize the traffic control method and quantitatively assess the impacts of IWA on the intersection capacity and travel times. To increase the utilization ratio of the approaches at signalized intersections, the pre-signal control is introduced into the integrated waiting area and a traffic control method combining the variable lanes and waiting areas is proposed in this paper.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Compared Algorithms
#### 5.2.1 Oapf  
In OAPF, transmitters periodically broadcast a beacon signal and an ID message that carries sender information, and then they wait for an ACK from neighboring nodes. Let us define \(t_{B}\), \(t_{C}\), and \(t_{A}\) as the period for sending the beacon, the ID, and the ACK, respectively. Transmitters repeat this beacon-ID-ACK procedure until they receive ACKs. After a node receives a beacon, it awakes to receive the ID message to check whether it is in the forwarding set of the transmitter. If the node (i.e., receiver) is on the list of forwarders, it responds with an ACK to the sender. If the transmitter receives multiple ACKs, it selects one node based on the priority of nodes. Then, it transmits data packets. Let \(t_{D}\) denote the transmission time for data packets. After receiving the data packets, the receiver becomes a transmitter and follows the beacon-ID-ACK procedure to find the next-hop relay for the data packets.  
In OAPF, when a node wakes up, it can be selected as a forwarder for several transmitters. Therefore, there are potential collisions when transmitters attempt to forward packets to this node. In addition, since OAPF aims to reduce E2E delay, the transmission times for data packets should not be large, and there is no packet recovery mechanism after packet collisions. We set time periods for beacon signaling, ID signaling, ACK wait time, and data transmission to 0.01 s, 0.01 s, 0.02 s, and 0.03 s, respectively.  
The duty cycle interval calculated by DDS was applied to OAPF, since OAPF does not have a mechanism to determine the duty cycle interval. In addition, the wake-up rate of nodes in OAPF was set to the same as that of DDS, which is \(\frac{1}{T}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Range of Value** & **Default Value** \\ \hline Delay bound (seconds) & \(\{10,20,30,40,50\}\) & 30 \\ Required DSR (\%) & \(\{80,85,90,95,97\}\) & 95 \\ Event rate (packets/s) & \(\{0.1,0.5,1.0,2.0\}\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Sensing area & 40 \(\times\) 50 m \\ Transmission range & 10 m \\ Bandwidth & 2 Mbps \\ Number of nodes in area & 78 \\ Active period & 0.4 s \\ Data packet size & 46 bytes \\ Data transmission phase duration & 50 min \\ Initial energy of nodes & 500 J \\ Radio propagation model & TwoRayGround \\ Antenna model & OmniAntenna \\ Interface queue type & PriQueue \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
## 4 Monitoring Results and Discussion
### Tree Impedance Change after Probe Insertion  
Figure 10 shows the change in the impedance over 14 days immediately after inserting the probe into the zelkova tree. The x-axis represents the date and the y-axis represents the tree impedance measured using the four-point measurement method. Immediately after inserting the probe on 31 May, the impedance value was at the 1100 \(\Omega\) level, and after the overall impedance value decreased for about a week, the impedance value was stable at the 900 \(\Omega\) level. The tree impedance begins to decrease for 3 to 4 days after the probe is inserted, and the regular pattern of impedance variation that repeats on a daily basis does not appear. Afterwards, a pattern of impedance variation that repeats on a daily basis gradually begins to appear, and after 9 June, 10 days after the insertion of the probe, an impedance variation pattern that repeats on a daily basis is visible. At this time, the tree impedance has a value between approximately 800 \(\Omega\) and 950 \(\Omega\). This tendency is presumed to be a phenomenon that occurs as the tissue around the probe heals over time after the tree tissue is damaged when the probe is inserted into the tree [19]. The closure effect resulting from the healing process can facilitate proper contact between the probe and the tree, ensuring effective connectivity. Considering these observations, it can be seen that the tree impedance information is only reliable if the data are used about a week after probe  
Figure 9: Long-term observed data: (**a**) ambient temperature, (**b**) light intensity, (**c**) impedance (four-point measurement method).  
insertion. Therefore, it is questionable whether a one-time tree impedance measurement using a conventional Shigometer provides appropriate data.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 4 Hybrid Simulated Annealing Algorithm
### Neighbor Generation  
A neighbor of \(P\) is a new placement of the wireless communication towers that can be obtained by randomly changing the location of one randomly selected wireless communication tower in \(P\).
## 2 Dataset Establishment and Preprocessing
### Key Drilling-Parameter Selection  
Different drilling parameters contain different information and exhibit different correlations with the surrounding rock classes. To reduce the feature dimensions and improve the analysis efficiency, it is necessary to retain the parameters exhibiting high correlations and filter the parameters exhibiting low correlations with the surrounding rock classes. Because the surrounding rock classes and drilling parameters are related to categorical variables and do not belong to the bivariate normal distribution, the Kendall coefficient [28] was used to evaluate the correlations between drilling parameters and the surrounding rock classes.  
The Kendall coefficient evaluates the correlation between the two random parameters. In this study, the Tau\({}_{b}\) in [PERSON]'s method was used to evaluate the correlations between the parameters while drilling and the surrounding rock classes; the parameters while drilling that exhibited low correlations with the surrounding rock classes were excluded  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drilling Parameter** & **Unit** & **Drilling Parameter** & **Unit** \\ \hline Depth & m & Water supply pressure & MPa \\ Torque & kN-m & Water supply rate & L/min \\ Rotation Speed & rpm & Drainage pressure & MPa \\ Percussive force & kN & Drainage rate & L/min \\ Beat per minute & bpm & EV & J/m\({}^{3}\) \\ Thrust & kN & ELT & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major drilling parameters.  
Figure 1: Typical variation of some drilling parameters. (**a**) Penetration rate of drilling, Torque, and Thrust variation and (**b**) Penetration energy, EV energy, and ELT variation.  
from the features. Assuming that \(X\) represents a parameter while drilling variable and \(Y\) represents a rock-class variable, the calculation of \(\text{Taub}_{\text{b}}\) is as follows:  
\[\text{Taub}_{\text{b}}(X,Y) =\frac{N_{c}-N_{d}}{\sqrt{(n_{0}-n_{1})(n_{0}-n_{2})}} \tag{1}\] \[n_{0} =\frac{n(n-1)}{2}\] (2) \[n_{1} =\sum_{i}\frac{t_{i}(t_{i}-1)}{2}\] (3) \[n_{2} =\sum_{j}\frac{u_{j}\big{(}u_{j}-1\big{)}}{2} \tag{4}\]  
where \(n\) is the number of elements contained in the variable, \(n_{0}\) is the total number of element pairs, \(N_{c}\) is the number of element pairs in the same order, \(N_{d}\) is the number of element pairs in the reverse order, and \(n_{1}\) and \(n_{2}\) are the number of the same element pairs in \(X\) and \(Y\), respectively.  
The \(\text{Taub}_{\text{b}}\) value ranges between \(-1\) and \(1\): The closer it is to \(1\), the greater the positive correlation between \(X\) and \(Y\); the closer it is to \(-1\), the greater the negative correlation between \(X\) and \(Y\); and the closer it is to \(0\), the weaker the correlation between \(X\) and \(Y\). Simultaneously, the \(p\) value is calculated with \(\text{Taub}_{\text{b}}\), reflecting the significance level of the correlation; as shown in Table 2, if \(p>0.05\), \(\text{Taub}_{\text{b}}\) is unreliable. If \(p<0.05\) or \(p<0.01\), \(\text{Taub}_{\text{b}}\) is reliable.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 5 Numerical Results  
This section presents the numerical results of the proposed association scheme. In particular, the behavior of the new association policy is investigated either considering the optimal association or the proposed heuristic. To derive numerical results we have considered the parameters listed in Table 2. For what concerns the number of cells and users, we refer to the values of the PPP distribution (i.e., \(\mathcal{N}_{C}=\lambda_{S}A\) and \(\mathcal{N}_{U}=\lambda_{U}A\)). In order to have results not depending on the specific cells deployment, the numerical results have been averaged on several realizations of a given scenario. More in detail, for a given scenario the values of \(\lambda_{S}\) and \(\lambda_{U}\) are fixed, but in each realization of the scenario users and cells are randomly placed. Consequently, performance of a particular scenario realization is dependent on the particular position of cells and users. In order to avoid this, we have generated 500 different realizations of the same scenario and we have averaged the obtained results.  
In order to verify the effectiveness of the proposed association metric and heuristic, different benchmarks have been considered. In particular, the optimal \(\mathbf{\Phi}\) vector and \(\mathbf{\Gamma}\) matrix are derived for:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Area (\(A\))** & \(\mathbf{100\times 100\,\mathrm{mt}}\) \\ \hline \(\mathcal{N}_{C}\) & [1–15] \\ \(\mathcal{N}_{U}\) & [10–50] \\ \((\alpha_{1}\),\(\alpha_{2}\)) & \((2,4)\) \\ \(\bar{d}\) & 25\(\,\mathrm{mt}\) \\ No. of Macro BS & \(m=0/1\) \\ No. of Micro BS & 1 \\ No. of Pico BS & 50\% (\(\mathcal{N}_{C}-1-m\)) \\ No. of Femto BS & 50\% (\(\mathcal{N}_{C}-1-m\)) \\ \(p_{macro}^{[TX]}\) & 20\(\,\mathrm{W}\) \\ \(p_{micro}^{[TX]}\) & 5\(\,\mathrm{W}\) \\ \(p_{pico}^{[TX]}\) & 0.13\(\,\mathrm{W}\) \\ \(p_{femto}^{[TX]}\) & 0.05\(\,\mathrm{W}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used to produce the numerical results.  
* **Max-SINR** association--each user is associated with the active BS that provides the highest SINR with the goal of maximizing the mean secure throughput per user;
* **Max-Secure Area (SA)** association [29]--each user is associated to the active BS that provides the highest secure area with the goal of maximizing the mean secure area per user;
* **Max-SINR AIIOn** association--all the BSs are active, and each user is associated with the BS that provides the highest SINR.  
For what concerns the newly defined association metric, we have considered the optimal association (Max-\(E^{[sec]}\)) obtained with the ES and the proposed heuristic (Proposed-Heur). The optimal solution allows to verify the accuracy of the proposed approach, that results to be needed in high dimension scenarios when the ES presents an excessive complexity.  
The metrics that are considered here are the secure area and secure throughput averaged per user, the secure EE (i.e., the proposed utility) and the total consumed power.