esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### 1D Periodic PLAs Made of W-SiN-W Membranes
It is to be stressed that, even though the structure is certainly anisotropic in the 1D periodic W-SiN-W membrane, the perfect-absorption mechanism is primarily independent of the incident polarizations and the structural anisotropy. Figure 6 displays EM-field distributions at perfect-light-absorption conditions of 1D W-SiN-W membranes with air slit \(S=750\) nm and width of the metal \(W_{d}=250\) nm. The corresponding absorptance spectra are shown with the red curves in Figure 4 and the three nearly perfect-absorption peaks are indicated by the red cones. Green lines indicate the boundaries of the 1D periodic membrane, similarly to Figure 5.  
Figure 5: EM-field distributions of the 1D W-SiN-W membrane of 5550 in Figure 4. (**a,b**) absolute values of electric- and magnetic-field distributions, \(|\mathbf{E}|\) and \(|\mathbf{H}|\), respectively; the incident polarization and wavelength are indicated by the sky-blue cone in Figure 4a; (**c,d**) \(|\mathbf{E}|\) and \(|\mathbf{H}|\) distributions, respectively; the incidence condition is indicated by the sky-blue cone in Figure 4b. The green lines commonly represent the boundaries of the 1D W-SiN-W structure. The coordinate axes are shown, in common, at the left-hand side. All the panels are shown in linear scale in the minimum-to-maximum manners. Figure 6a,b shows the \(|\mathbf{E}|\) and \(|\mathbf{H}|\) distributions, respectively, under p polarization and 1252.0 nm. The EM-field distributions are qualitatively similar to those in Figure 5a,b, irrespective of the substantial difference in air slit \(S\). Figure 6c,d shows the \(|\mathbf{E}|\) and \(|\mathbf{H}|\) distributions, respectively, under the condition of p polarization and 2201.6 nm, which correspond to nearly perfect absorption sensitive to \(S\). We note that only the scale in Figure 6c is shown in a peak-cut way; that is, although the maximum value of \(|\mathbf{E}|\) in Figure 6c is 64, the scale is set to have the maximum at 30 and the locations exceeding 30 are represented in sky blue. Qualitatively, the EM-field distributions are distinct from those in Figure 6a. The electric- and magnetic fields show strong localization around the W-SiN-W gap. It is obvious that the \(|\mathbf{H}|\) distribution takes the maximum value in the gap. These EM-field distributions are peculiar to the gap mode or plasmonic waveguide mode [12]. Thus, it is found that the plasmonic gap mode appears at a particular condition, being sensitive to the structural parameters. Figure 6e,f shows the \(|\mathbf{E}|\) and \(|\mathbf{H}|\) distributions, respectively, under s polarization and 1060.2 nm. The EM-field distributions are quite similar to those in Figure 5c,d, strongly suggesting that the perfect absorption owing to the impedance matching is quite robust to the structural modifications.
## 2 Role of Ports in the Decarbonisation of Maritime Transport
### Integration of Renewable Energy in Ports  
Ports are key enablers of the energy transition by integrating renewable energy systems such as offshore wind and solar power. These initiatives are designed to reduce operational emissions and ensure a stable, sustainable energy supply for maritime operations. By 2050, most major ports are expected to operate as energy hubs, integrating offshore renewable energy systems to supply clean energy to vessels and local grids. The development of floating offshore wind farms near ports--such as those planned in the North Sea, exemplifies this trend [19].  
In conclusion, the maritime transport sector and ports are shifting towards decarbonisation. Regulatory mandates and advancements in alternative fuels and renewable energy drive this transformation. By 2050, ports are expected to operate as self-sufficient, sustainable hubs, supporting a fleet of zero-emission vessels and contributing to global climate goals. However, achieving these ambitions will require continued investment, international cooperation, and a commitment to innovation.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 4 Method, Part 2: Open Loop GNSS-R Delay and Doppler Tracking Algorithms
### Estimating the Reflected Signal Code Phase Delay  
With a common clock hardware configuration the predicted code phase of the reflected signal is a simple function of the path difference between the direct and reflected signals and the direct signal code phase. The path difference in meters (\(dP\)) can be calculated as,  
\[dP=P_{T2S2R}-P_{T2R}=(P_{S2R}+P_{T2S})-P_{T2R}. \tag{15}\]  
where \(\bar{P}_{T2R}\) is the length of the path from the transmitter to the receiver, \(\bar{P}_{T2S2R}\) is the combined path length of the reflected signal, which is the sum of the path from the transmitter to the specular point (\(\bar{P}_{T2R}\)) plus the path from the specular point to the receiver (\(\bar{P}_{T2R}\)). Equation (15) can also be expressed as a function of the transmitter, receiver, and estimated specular point location as,  
\[dP=(\left|\vec{T}-\vec{S}\right|+\left|\vec{S}-\vec{R}\right|)-\left|\vec{T}- \vec{R}\right|\!. \tag{16}\]  
Following, the code phase of the reflected signal can be calculated relative to the navigation tracked direct signal from the same transmitting satellite as,  
\[\tau_{ref}=\tau_{dir}-dP_{chips}. \tag{17}\]  
As the GPS signal code phase is a continually repeating length of psuedo-random sequence of transitions [1], a modulo of the code phase repeat period is needed to bring the absolute code phase within the range of the transmitted sequence. Therefore, \(\tau_{ref}\) needs to be corrected based on the length of the specific code phase, for GPS L1 C/A code this can be expressed as,  
\[if(\tau_{ref}<0),\tau_{ref}=\tau_{ref}+1023. \tag{18}\]  
where \(\tau_{ref}\) is the predicted code phase of the reflected signal, \(\tau_{dir}\) is the code phase of the direct signal tracked from the zenith navigation channel for the same transmitter, and \(dP_{chips}\) is the path difference converted from meters to code phase chips, such that \(dP_{chips}=\frac{dP}{\alpha}\). Where \(\alpha\) is the number of meters per code chip, in the case of the GPS L1 C/A signal, \(\alpha=293.0522561\).  
Figure 2: Illustration of the common clock GNSS-reflection (GNSS-R) instrument configuration, where direct and reflected receive channels are processed using the same sample clock.  
The estimated value of \(\tau_{ref}\) can then be used to initialize the reflection channel correlators of the GNSS-R instrument as the reference code phase delay around which to produce a two dimensions map in delay and Doppler (DDM) suitable for surface observations.
## 6 Economic Benefits of Pre-Grouting
### Economic Rationality Analysis  
Tunnel umbrella grouting saves approximately 6.82 million yuan compared to surface pre-grouting, but it leads to an extension of the construction period by about 11 months. Moreover, advanced drilling and grouting inside the tunnel can only reinforce the tunnel crown within a 120\({}^{\circ}\) range, leaving the sides and bottom unprotected and posing a potential  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project** & **Unit** & \
\begin{tabular}{c} **Quantity/Quantity** \\ **of Works** \\ \end{tabular} &
\begin{tabular}{c} **Total Price/** \\ **10,000 RMB** \\ \end{tabular} \\ \hline Drill depth & m & 14,580 & 29.2 \\ Cement paste & m\({}^{3}\) & 132.3 & 75.4 \\ Drilling and injection integrated & & 4 & 260.32 \\ machine & & 486 & 21.87 \\ Number of boreholes & & 486 & 5.23 \\ Grouting pipe & & & 392.02 \\ Total & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: TBM pipe shed grouting investment table.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project** & **Quantity of Work** & **Unit Price/RMB** &
\begin{tabular}{c} **Total Price/** \\ **10,000 RMB** \\ \end{tabular} \\ \hline Surface cleaning & 6576/m\({}^{3}\) & 5.3 & 3.5 \\ Deep drilling & 45,055/m & 110 & 495.6 \\ Cement paste & 4546/m\({}^{3}\) & 950 & 431.9 \\ Bushing & & 212 & 13.2 \\ Drilling machine & & 325,700 & 130.28 \\ Total & & & 1074.48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Surface grouting investment table.  
risk of TBM jamming. In contrast, surface pre-grouting strengthens the formation before the TBM reaches fault zones, ensuring smooth passage and avoiding construction delays.  
TBM umbrella grouting has high costs during machine unjamming, reducing its economic efficiency. As the tunnel depth increases, the grout initially consumes a large amount of slurry, with lower pressure at the start. As the voids fill, the pressure increases. At greater depths, effectively injecting grout into deep fractures becomes a technical challenge, compromising grouting quality and prolonging the grouting period.  
Surface pre-reinforcement in guilty sections offers significant advantages in construction time. However, umbrella grouting poses risks during TBM unjamming, potentially causing ground collapse and endangering workers and equipment. Even if the unjamming is successful, TBM operations may still face jamming risks. Considering both economic and technical feasibility, surface pre-grouting should be prioritized when the tunnel depth is less than 70 m to effectively prevent TBM jamming incidents.
###### Abstract  
This paper investigates a type of linear vibration isolator which incorporates two passive devices, negative stiffness elements and an inerter, into the conventional isolator (massstiffnessdamper). Two configurations of the passive devices are considered, respectively: (1) the direct parallel connection of the inerter and the negative stiffness element; (2) the series connection of the inerter with the viscous damper, which, together, is then connected with the negative stiffness element in parallel. Numerical evaluations are presented for the isolators subject to base harmonic and shock excitation, in terms of the natural frequency, the displacement transmissibility and shock isolation performance. Compared with other corresponding linear isolators, it is found that in the majority of the excitation situations, the presented isolator with the second configuration has a better performance for some isolator parameter values, that is, the inertance-to-mass ratio, the ratio of negative to positive stiffness and the damping coefficient ratio of the two dampers.  
passive vibration isolator; negative stiffness; inerter; displacement transmissibility; shock isolation +
Footnote †: journal: Applied Sciences
# Control strategies of gecko's toe in response to reduced gravity
## 1 Introduction  
Geckos are able to run on vertical and even inverted surfaces as fast as other terrestrial animals can run on normal ground. It is widely believed that the secret of such extraordinary locomotory ability lies in the adhesive structures found on the toes. [1; 2; 3]. One fundamental property of their adhesive structures that helps to achieve a controlled, energy-efficient adhesion is the directional dependence of their adhesion. Patterns of ground reaction forces show that geckos can control attachment and detachment via shear forces, by pulling their legs towards or pushing their legs away from the body [1; 4]. Separate parts of this control system seem to be established at different levels of the hierarchical organization of this adhesive system [5; 6; 7; 8; 9; 10; 11; 12; 13]. For example, the animal can controladhesion by shifting the position of the center of gravity, by changing each footfall position (e.g., with different joint movement), by varying gait characteristics (e.g., velocity, stride length, stride frequency and duty factor) or by employing specific features of the adhesive system itself (e.g., viscoelasticity of the underlying layers or hierarchy of setal organization).  
Although the basic functional mechanisms of adhesive pads are rather well understood, the control principles of attachment and detachment are still largely unknown. Control strategies that have been investigated in terrestrial animals with perturbation suggest that both active (neuro-muscular) control and passive (mechanical) reactions play important roles in the control of rapid locomotion [14; 15; 16; 17; 18]. Patterns of single leg ground reaction forces suggest dynamic stability control in gecko locomotion [4; 5; 7]. Differences in locomotor performances [4; 5; 7; 19] suggest that a trade-off between successful adhesion and the ability to respond dynamically to locomotor perturbations may exist.  
Multiple toes differing in orientation appear to play an important role during gecko locomotion, as the animal can avoid detachment by aligning some of their toes opposite to the force vector. It is likely that employment of the adhesive system is associated with modulation of inter-digital angle and foot alignment [20; 21]. However, much less is known about adhesion force of single toes during locomotion.  
In the present study, we aimed to obtain measurements of ground reaction forces generated by the single toes of geckos running on level substrate. We used helium-filled balloons to counterbalance the gecko's body weight and to observe how the adhesive pads were applied to the ground. We addressed the following questions: what are the single leg and single toe ground reaction forces, and how do geckos employ their adhesive system in response to the reduced gravity?
## 2 Proposed Data-Driven Approaches
### Prognostic Problem  
The degradation pattern consists of multivariate time-series data obtained from condition monitoring sensors \(X_{s_{i}}=\left[x_{s_{i}}^{(1)},\ldots,x_{s_{i}}^{(m_{i})}\right]^{T}\), along with the accompanying RUL values \(Y_{i}=\left[y_{i}^{1},\ldots,y_{i}^{m_{i}}\right]^{T}\). These measurements were collected from a fleet of \(N\) units, denoted as \((i=1,\ldots,N)\). Every observation \(x_{s_{i}}^{(t)}\in R^{p}\) consists of a vector containing \(p\) raw measurements that were taken under specific operating conditions \(\omega_{i}^{(t)}\in R^{s}\). The duration of the sensory signal for the \(i^{\text{th}}\) unit is denoted as \(m_{i}\), which may vary among different units. The cumulative length of the dataset in question is \(m=\sum_{i=1}^{N}m_{i}\). In a more concise manner, the available dataset is denoted as \(D=\left\{W_{i},X_{s_{i}},Y_{i}\right\}_{i=1}^{N}\). Considering the provided scenario, the objective is to develop a predictive model \(\mathcal{G}\) that can accurately estimate the RUL \((\hat{Y})\) on a test dataset consisting of \(M\) units \(D_{T*}=\left\{X_{sj*}\right\}_{j=1}^{M}\). These units are represented by multivariate time-series data \(X_{sj*}=\left[x_{sj*}^{1},\ldots,x_{sj*}^{k_{j}}\right]\), which captures readings from various sensors [28]. The cumulative length of the test data is \(m_{*}=\sum_{j=1}^{M}k_{j}\).
# Formation of a Freezing Wall Around a Vertical Shaft Under Localized Freezing
## 5 Conclusions  
In this study, we proposed a localized freezing method on the inside of a vertical shaft. At freezing temperatures of \(-27.5\)\({}^{\circ}\)C to \(-35\)\({}^{\circ}\)C, the daily average growth rate of the freezing wall behind the slotting zone during one year was linearly negatively correlated with the freezing temperature, and the average growth rate was \(-0.00033\) m/(d\(\cdot\)\({}^{\circ}\)C). The same trend was observed between the shaft lining thickness (0.5 to 2.5 m) and the freezing temperature, with an average growth rate of \(-0.00262\) m/(d\(\cdot\)m). Using the thickness of the freezing wall behind the slotting zone reaching 1.2 m as the slotting criterion, a freezing duration of 123 days is required under typical operational parameters. During the slotting process, with a slot height ranging from 0.5 to 2.0 m, the freezing wall remained intact without thawing over a 15-day continuous excavation period and exhibited ongoing outward expansion.  
Due to the assumption in the numerical model that the formation is uniform, continuous, and isotropic, the parameters are idealized compared to the actual situation. Therefore, the simulation results have certain limitations and cannot accurately characterize freezing factors such as uneven cracks in the actual strata. In the next phase of study, we will expand the scope of physical model experiments and incorporate field practice to identify and  
Figure 23: Thickness and average temperature of freezing wall for different slotting durations at 1.5 m slot height.  
analyze the scientific and engineering problems associated with implementing the localized freezing method.  
Conceptualization, W.Y.; methodology, Y.Z. and Y.W.; formal analysis, Z.Y. and C.Z. ([PERSON]); Software, Y.Z. and Y.M.; investigation, T.H. and T.L.; project administration, W.Y.; supervision, C.Z. ([PERSON]); writing--original draft, Y.Z. and Y.W.; writing--review and editing, Y.Z. and C.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 42302273), Natural Science Foundation of Jiangsu Province (No. BK20231080), China Postdoctoral Science Foundation (No. 2022M713367), the Foundation Research Project of Xuzhou (No. KC22061), and Fundamental Research Funds for the Central Universities (No. 2022 QN1025).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Nanjing Jianye Civicism Construction Co., Ltd. Author [PERSON] was employed by the company Zhejiang Huadong Geotechnical Investigation & Design Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Design and Implementation of a Tether-Powered Hexacopter for Long Endurance Missions
## 4 Discussions  
The 421 W power consumption was measured in an outdoor environment with moderate wind, evidenced by our time-lapse experiment video [26]. According to the historical weather records [27], the averaged wind speed was 2.75 m/s at 10 m height over the ground level. Therefore, the 50 m altitude wind speed was estimated at 5 m/s according to the boundary layer theory. Our system was tested at 8 m/s wind speed, and it worked normally. In the future, we will try to equip an anemometer with the UAV to measure the in-situ wind speed. The DC-DC convertor provides 600 W, which implies a 179 W (29.8%) spare. Furthermore, a 36 Wh battery serves as a backup power which provides 4 min of flight-time to land the hexacopter safely in case of an emergency due to the malfunction of the power supply chain. That implies the parallel-connected backup battery can provide extra 540 W power (totally 1140 W), which can satisfy the power requirement due to the strong wind shear and the movement required by the mission. A 3.88% power consumption difference between the theoretical model and measurements is observed. The model inaccuracy could be caused by reasons listed below:  
1. The extra forces caused by tether: The force caused by wind and tension on the tether may cause the hexacopter to produce extra thrust in order to balance itself;
2. Imbalance of the hexacopter: Power supply and mission payload may cause an imbalance, thus requiring extra thrust for the hexacopter to maintain its altitude;
3. Side wind: Since the hexacopter is set to hover at a fixed position throughout the test, side wind may cause it to produce more thrust than estimated in order to maintain its position.
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 2 Materials and Methods
### Data Collection
#### 2.2.1 Image Preprocessing  
GEE provides a rapid analysis using Google's computing infrastructure, which offers online datasets in near-real time [10]. Landsat 5 Surface Reflectance images (1990, 2007), Landsat 7 Surface Reflectance images (2000), and Landsat 8 Surface Reflectance images (2019) were selected for Haitan Island land cover classification in GEE (Table 1).  
Images were selected and mosaicked based on the quality assessment band flags to minimize the effects of clouds and clouds' shadows. Simultaneously, the spectral bands in Landsat image, including red, green, and blue bands (RGB), near-infrared (NIR), and short-wave infrared (SWIR), were selected for classification. In addition, NDVI [19], Normalized Difference Built-up Index (NDBI) [18], Modified Normalized Difference Water Index (MNDWI) [14; 18], and tasseled cap transformation (brightness, greenness, wetness) [30; 31] were calculated and added to the bands, which are helpful for land use classification [18; 32]. All of the processing was conducted in GEE Code Editor ([[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/))).
## 2 Proposed Algorithm
### Interval Type-2 Fuzzy System
#### 2.1.2 Knowledge Base of the Type-2 Fuzzy System  
The knowledge base is composed of a set of IF-THEN clauses that bind all the inputs and the output to encompass the expert knowledge about the application. An example of a rule is as follows:  
IF \(E_{r}\) is Low AND \(N_{CHr}\) is Low AND \(E_{rCH}\) is Low AND \(R_{noCHr}\) is Low THEN output is Very Low.  
This example rule encloses a basic expected behavior: if a node has a low battery level, its relative energy is low, and it has been selected as a CH several times recently, its probability of being chosen as CH should be very low to avoid becoming again a CH and to allow a better energy balance in the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Lower Interval Limit \(o_{l}\)** & **Upper Interval Limit \(o_{h}\)** \\ \hline Very Low & 0 & 0.3 \\ Low & 0.2 & 0.4 \\ Medium & 0.3 & 0.6 \\ High & 0.6 & 0.8 \\ Very High & 0.75 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values for the output interval fuzzy set.  
Figure 3: Input Type-2 Fuzzy Set.  
Additionally, taking into account that the system is a TSK, we can freely adjust the output variable to suit the requirements of the systems (see Table 1), whereas, with a Mamdani system, the output values are not predictable or adjustable beforehand.  
For our implementation, the knowledge base is composed of 81 rules that are displayed in Table 2.
# A Stable Backup Routing Protocol for Wireless Ad Hoc Networks
## 2 Stable Backup Routing Scheme (SBR)
### Backup Route Maintenance
#### 2.3.2 Replying BRREP  
After receiving BRREQ, each neighbor looks for available paths in its own backup route table by matching destinations and inefficient node included in BRREQ. Assume that a neighbor finds an alternate path to a certain destination, it would send a unicast BRREP to the sender of BRREQ to declare the existence of a backup route. The BRREP carries the necessary information of the corresponding backup route. Since the BRREQ/BRREP exchange is fast and occurs merely within the range of one-hop, the value of waiting time \(\mathrm{T_{th}}\) only needs to be set to ensure timely receptions of all responding BRREPs.