esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The PFTs and Seasonal Contribution to CUE Extremes  
Different PFTs had different contributions to CUE extremes. PFTs also contribute similarly to the positive and negative extremes (Figure 3). Grasslands contributed the largest to CUE extremes, with the contribution being approximately 32.4% to positive extremes and 30.2% to negative extremes, suggesting grassland has a large potential to increase global carbon absorption. Grasslands were followed by shrubs and croplands, contributing approximately 28.0% and 21.0% to positive extremes and 24.5% and 22.8% to negative extremes, respectively. In contrast, forests contributed less to CUE extremes, and the sum of all forests only contributed 17.4% to positive CUE extremes and 22.5% to negative CUE extremes. These results indicate that grasslands have more potential to enhance carbon uptake, and forests have more stability in carbon transformation.  
We analyzed the seasonal contributions to CUE extremes (Figure 4). The results indicated that MAM contributed the largest to positive CUE extremes, with a contribution of 30.5%. JJA, SON, and DJF were followed by contributions of 26.2%, 24.4%, and 18.9%. The contribution to negative extremes had some differences from positive extremes. JJA contributed most to negative CUE extremes, with the contribution being 29.7%. Followed by MAM, SON, and DJF, the contributions were 28.4%, 22.4%, and 19.6%. These results suggest that MAM has a larger potential to increase carbon absorption, and JJA is more susceptible to carbon transformation proficiency.  
Figure 4: The seasonal contribution to CUE extremes. “MAM” contains the month of March, April, and May; “JJA” contains the month of June, July, and August; “SON” contains the month of September, October, and November; “DJF” contains the month of December, January, and February.  
Figure 3: The contribution of vegetation types to CUE extremes. “MAM” contains the month of March, April, May; “JJA” contains the month of June, July, and August; “SON” contains the month of September, October, and November; “DJF” contains the month of December, January, February. “Pos” represent the positive extremes, and “Neg” represent negative CUE extremes. “ENF” is evergreen needleleaf forest, “EBF” is evergreen broadleaf forest, “DNF” is deciduous needleleaf forest, “DBF” is deciduous broadleaf forest, “MF” is mixed forest, “SHR” is a shrub, “GRA” is grass, and “CRO” is cropland.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects  
One of the major challenges of designing UAV-based communication systems is to determine the proper horizontal and/or vertical placement of the UAVs as well as the trajectory of the UAVs with regard to other ground or flying objects in order to achieve optimal or near-optimal performance. Moreover, in critical and forensics applications, where public safety and security are of high importance, the rapid and efficient physical detection, localization, and identification of the type of noncompliant UAVs is indispensable [87]. Next, we survey works related to detection/localization as well as placement and trajectory design.
###### Abstract  
In the digital world, the demand for better interactions between subscribers and companies is growing, creating the need for personalized and individualized experiences. With the exponential growth of email usage over the years, broad flows of campaigns are sent and received by subscribers, which reveals itself to be a problem for both companies and subscribers. In this work, subscribers are segmented by their behaviors and profiles, such as (i) open rates, (ii) click-through rates, (iii) frequency, and (iv) period of interactions with the companies. Different regressions are used: (i) Random Forest Regressor, (ii) Multiple Linear Regression, (iii) K-Neighbors Regressor, and (iv) Support Vector Regressor. All these regressions' results were aggregated into a final prediction achieved by an ensemble approach, which uses averaging and stacking methods. The use of Long Short-Term Memory is also considered in the presented case. The stacking model obtained the best performance, with an R\({}^{2}\) score of 0.91 and a Mean Absolute Error of 0.204. This allows us to estimate the week's days with a half-day error difference. This work presents promising results for subscriber segmentation based on profile information for predicting the best period for email marketing. In the future, subscribers can be segmented using the Recency, Frequency and Monetary value, the Lifetime Value, or Stream Clustering approaches that allow more personalized and tailored experiences for subscribers. The latter tracks segments over time without costly recalculations and handles continuous streams of new observations without the necessity to recompile the entire model.  
email marketing; email frequency; email optimization; customer segmentation; machine learning; ensemble learning; deep learning +
Footnote †: journal: Applied  
Accepted: 10 August 2022  
Published: 19 August 2022  
**Published:** **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Results and Discussion  
### Numerical Simulation Results  
By simulating the formation processes of dam-break debris flow in different interfacial models, a series of experiments was completed. For each of these experiments, multiple parameters associated with the movement of the fluid were measured (surface heights, fluid velocities, pressures, and shear deformations). Figure 7 represent the free surface values for each of the tests conducted in Table 1. The \(x\)-axis represents the length L of the debris flow, and the \(y\)-axis represents the height H of the debris flow. There are three models: Figure 7a,d show model no. 1, the interfacial force only contains the viscous force; Figure 7b,e show model no. 2, the interphase force includes the viscous force and the drag force; Figure 7c,f show model no. 3, and this model takes into account the synergy of the viscous force, the drag force, and the virtual mass force.  
By comparing the free-surface flow, it is found that models no. 1 and no. 2 have similar free surface heights in the whole study area. The free surface slowly declines in the front section of the dam body, and begins to decline rapidly from x = 2.5 m. However, model no. 3 begins to decline rapidly from x = 2.15 m, showing more fluctuation characteristics in the area of x > 2.5 m compared to no. 1 and no. 2.  
Figure 6: Initial state. The water level upstream maintained constant at h = 0.2 m.  
In addition, after 0.2 s of fluid start-up, particle initiation occurs in three different inter phase force models of dam-break debris flow. However, by comparing the particle initiation positions, it is found that the positions where most particle moved were x = 2.09 m, x = 2.10 m, and x = 2.91 m, respectively. Comparing model no. 1 and no. 2 in Table 1, the simulated initiation positions associated with the additional mass force are obviously further upstream.  
The above phenomena show that the numerical model considering the general virtual mass force presents a difference in heights of the free surface flow. However, due to the inherent characteristics of the SPH method, it is also difficult for particles in different layers to fully mix. Therefore, it can be considered that this difference may be caused by the interaction between sediment particles and water particles at the interface. Through observations of the region between 2.5 and 3.0 m, it can be noticed that there is a certain correlation between the free surface and the particle initiation phenomenon, which indicates that there is a certain coupling effect between the particle initiation and the flow. The flow magnitude causes the erosion and the particle to move, and the shape of the dam body changes after particles starts to move producing some kind of response to the subsequent flow.
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion  
### Simulation Correctness Verification  
The simulation takes the average stress curve at several points on a hemispherical surface located 36 mm from the impact source and compares it with the results of the tungsten carbide button impact model simulation. As shown in Figure 4, the trends of the two curves are the same. However, when the stress stabilizes, the simulated stress curve is lower and dynamically stable. This phenomenon is attributed to the simulation model considering the effect of stress waves on rock fragmentation, which weakens the energy of the stress waves. Additionally, since the rock does not have infinite boundaries, stress waves reflect upon reaching the boundaries. Nevertheless, the rock model is designed to be sufficiently large, making the impact of this reflection minimal.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 4 Conclusions  
In this paper, GNSS-R DDM measurements collected by the SGR-ReSI on board the TDS-1 satellite are used for distinguishing between sea ice and water. A dDDM observable is proposed for the first time and two methods (PS-D and PN-D) based on power-summation and pixel-number of dDDMs are proposed to detect sea ice. Meanwhile, several data processing schemes including noise subtraction, incoherent averaging and overall normalization are employed to improve significantly the performance of proposed sea ice detection approaches. The feasibility of PS-D and PN-D methods is validated with ground-truth sea ice edge data provided by OSI SAF and the results indicate the good performance of PS-D and PN-D with a detection probability of 99.72% and 99.69% respectively and a probability of false detection of 0.28% and 0.31% respectively. The proposed methods considerably outperform the existing methods with an improved detection probability of more than 1.5% and significantly reduced probability of false detection.  
The dDDMs of water-water transition show larger spreading characteristics than those of ice-ice transition due to the larger variability of reflected surface. This characteristic allows us to distinguish water-water from ice-ice transition, while it does not allow us to model the relationship between received signals and the roughness as the TDS-1 data was not calibrated and the instrument onboard the TDS-1 satellite was not optimized for altimetry purposes.  
In the future, it is desirable to further reduce the sea ice false detection probability to obtain more accurate sea ice coverage. Open ice affects sea ice detection and new techniques are needed to handle this issue. Moreover, data coverage rate of TDS-1 is rather limited but CYGNSS launched in 2016 and the potential GEROS-ISS [50] mission will provide much wider coverage.  
This work was supported in part by the National Natural Science Foundation of China under Grant 41574031. The authors would like to thank the China Scholarship Council (CSC) for funding and UK TechDemoSat-1 team for providing TDS-1 DDM data. All authors have made significant contributions to this manuscript. [PERSON] and [PERSON] proposed the idea, analyzed the data and wrote the paper. [PERSON] and [PERSON] provided valuable advices and revised the manuscript. The authors declare no conflict of interest.
## 8 Future Opportunities and Research Directions
### Integration with 5G, 6G, and Beyond  
The rollout of 5G networks and the development of 6G technologies present unprecedented opportunities for RF energy harvesting. With the proliferation of small cells, massive MIMO antennas, and dense heterogeneous networks operating at millimetre-wave (mmWave) and terahertz (THz) frequencies, ambient RF energy will become more abundant and diverse. Future RFEH systems must be designed to exploit the high-frequency, high-density RF environment of 5G and 6G networks. Research has highlighted the potential of harvesting from mmWave bands using specialized rectennas optimized for high-frequency capture [123]. Furthermore, 6G systems are expected to embrace SWIPT more fully, where devices will simultaneously receive data and energy from the same carrier waves [124; 125]. Future research should prioritize the co-design of high-frequency rectennas and beamforming protocols tailored to mmWave and THz energy-harvesting scenarios, where dynamic energy beam steering from base stations can be synchronized with user equipment for optimal power reception [40]. Moreover, the detailed modelling and prototyping of SWIPT-enabled transceivers using power-splitting or time-switching architectures will be crucial in determining optimal trade-offs between energy-harvesting efficiency and communication performance in real-world 6G deployments. A phased roadmap for implementation could involve the initial lab-scale demonstration of SWIPT at sub-6 GHz frequencies, followed by mmWave integration in small-cell testbeds, and eventual deployment in 6G pilot networks featuring edge intelligence for real-time energy scheduling [85]. Integrating RF energy harvesting into 5G/6G base stations, IoT devices, and even user terminals could dramatically reduce the energy footprint of future networks, aligning with sustainability goals such as net-zero emissions.
# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 7 Trust Model Realism Furthermore, Limitations  
For scalability purposes, the IoT system is aggregated into FCDs. The trust model is designed with the scalability factor in mind. The trust model elements such as DTT, RTT, and ITT are designed to operate and evolve trust in a purely distributed manner. There is no FCD that is omniscient. Rather each FCD: (a) has its own view of how trustworthy other FCDs are and keeps this information in DTT (b) controls the monitoring process of its own transactions using its FC, (c) maintains RTT using its own FC.  
The size of RTT is small since it contains information about the FCD recommenders which is a small set of the total number of FCDs. On the other hand, DTT contains one entry for each of the FCDs that this particular FCD directly interacted with. Hence, as the direct interaction with different NCDs increase, the size of DTT increases in a linear fashion.  
A potential implementation problem with our trust model is ensuring that the identities of FCDs cannot be created trivially. This is crucial since reputation can get erased if an FCD changes its identity. Hence, an untrustworthy FCD can use this trick to start fresh every time it builds up a bad reputation history. Further, FCDs may expect their recommendation requests to be fulfilled by others. However, when it comes to others asking for recommendations, this particular FCD chooses not to give recommendations by simply returning \(-1\) (i.e., do not know) or basically ignoring the request. That is, FCDs may refuse to give recommendations for various reasons. Further, by isolating dishonest  
Figure 7: Evolution of internal trust of members, (**a**) Trustworthy Members (**b**) Untrustworthy Members.  
recommenders and routing the recommendation requests to only honest recommenders, a tedious task can be potentially created by bombarding these honest recommenders with a rather huge volume of recommendation requests. As a result, an honest recommender might be inclined to refuse giving recommendations to avoid the extra work. To remedy this problem, incentives should be provided to encourage and reward these cooperative FCDs. As such, more explicit incentives should be provided by our trust model. For example, cooperative FCDs should be given reduced cost when using resources or should have a higher priority when submitting tasks.  
Another limitation of our trust model that we foresee is the potential creation of performance bottlenecks. Because every FCD wants to keep honest recommenders, the majority, if not all, of the recommendation requests will be routed to the honest recommenders. This not only creates a tedious task that can be bothersome for these honest recommenders, but also can create a potential performance bottleneck. Therefore, responses to recommendation requests can experience a longer delay.
# De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction
## 2 Principle of Spectral Subtraction
### Basic Spectral Subtraction
In using the algorithm to de-noise the RIR, the noise floors were reduced by approximately 5 dB. As a result, the de-noised EDCs go below the noisy EDCs, leading to about 6 dB in the dynamic range improvement for noise levels of \(-60\) dB. The results suggest that the SS algorithm has the possibility to reduce the noise presented in the corrupted RIRs. Comparing with the dynamic range improvement (up to 18 dB), achieved using generalized spectral subtraction with the optimal factors presented in Section 4.1, the SS algorithm shows a weaker ability to improve the EDCs dynamic range. The algorithm requires access to future enhanced spectra that may not be amenable to real-time implementation [27]. Therefore, the GBSS algorithm was explored to lower the overall noise levels in the RIRs and increase the dynamic range of the EDCs because of its ability to eliminate the music noise to further reduce the background noise.
# Implications of Rigid Gripping Constraints on Clubhead Dynamics in Steel Golf Shafts
## 2 Materials and Methods
### Theoretical Propagation Speeds
#### 2.1.3 Bending Strain  
The strain created by bending waves is more complex than torsional or longitudinal strain, and is made up of a combination of compressive, tensile and shear strains. Unlike longitudinal and torsional waves, the phase speed of bending waves (\(c_{B}\)) does depend on wave frequency (\(f\)) and can be expressed as a proportion of \(c_{L}\) (see Equation (1)) as shown in Equation (4),  
\[c_{B}=\sqrt{2\pi fc_{L}}. \tag{4}\]  
Frequency testing of the shaft used in the experimental stage of this investigation was used to generate an approximation of the frequency of the first bending mode (\(f_{1}\approx 5.4\) Hz) under a fixed-free condition. Equation (5) [18] can be used to estimate the frequency of the nth bending mode (\(f_{n}\)),  
\[f_{n}\approx 2.81\bigg{(}n-\frac{1}{2}\bigg{)}^{2}f_{1}\, \tag{5}\]  
for which \(f_{2}\), \(f_{3}\) and \(f_{4}\), were calculated as 34.1, 94.8 and 185.9 Hz respectively. Equation (4) was then used to calculate the corresponding wave speeds (\(f_{1}\approx 414\) m\(\cdot\)s\({}^{-1}\), \(f_{2}\approx 1040\) m\(\cdot\)s\({}^{-1}\), \(f_{3}\approx 1734\) m\(\cdot\)s\({}^{-1}\) and \(f_{4}\approx 2428\) m\(\cdot\)s\({}^{-1}\)). Given that the reduction in energy content with increasing \(n\) assumes a Gaussian profile, such that energy \(\propto n^{2}\), higher order modes were not of concern as their potential effect was considered negligible.
## 3 System Dynamics Analysis  
### Flexible Space Robot and Satellite Dynamics Modeling  
The kinetic energy of the flexible space robot is mainly composed of the kinetic energy of the base, links and motor rotors [27], as follows:  
\[T=\frac{1}{2}m_{0}\dot{r}_{0}^{2}+\frac{1}{2}J_{0}\dot{\theta}_{0}^{2}+\sum_{ j=1}^{2}\frac{1}{2}J_{0}^{l_{j}}\rho_{j}\dot{r}_{\rm d}x_{j}+\sum_{j=1}^{2} \frac{1}{2}J_{mj}\dot{\theta}_{mj}^{2} \tag{4}\]  
where \(J_{0}\) and \(J_{mj}\) are the inertia moment of the base and the \(j\)-th motor rotor, respectively, and \(\rho_{j}\) is the linear density of link \(B_{j}(j=1,2)\).  
The potential energy of a flexible space robot is mainly composed of a flexible base, flexible joints, and flexible links, as follows:  
\[U=\frac{1}{2}k_{\rm b}x_{\rm b}^{2}+\frac{1}{2}\sum_{j=1}^{2}\sum_{k=1}^{2}k_ {\delta jk}\delta_{jk}^{2}+\sum_{j=1}^{2}\frac{1}{2}k_{mj}(\theta_{mj}-\theta_ {j})^{2} \tag{5}\]  
where \(k_{\delta jk}=El_{j}\int_{0}^{l_{j}}\left(\dot{\phi}_{jk}^{2}(x_{j})\right){ \rm d}x_{j}\), \((j,k=1,2)\).  
Substituting Equations (4) and (5) into the Lagrange equation yields the space robot dynamic model first equation of Equation (6). Using the Newton-Euler method to calculate the satellite dynamics model the second equation of Equation (6), we have:  
\[\left\{\begin{array}{l}\mathbf{D}_{\rm N}\ddot{\mathbf{q}}_{\rm z}+\mathbf{H}_{\rm N} \dot{\mathbf{q}}_{\rm z}+\left[\begin{array}{c}\mathbf{0}_{2\times 1}\\ k_{\rm b}x_{\rm b}\\ 0\\ -\mathbf{K}_{\rm m}\mathbf{\sigma}\\ \mathbf{K}_{\rm\delta}\\ \mathbf{K}_{\rm m}\mathbf{\sigma}\end{array}\right]=\left[\begin{array}{c}\mathbf{0}_{3 \times 1}\\ \tau_{0}\\ \mathbf{0}_{6\times 1}\\ \mathbf{\tau}_{\rm m}\end{array}\right]+\mathbf{J}^{\rm T}\mathbf{F}_{\rm P}\\ \mathbf{D}_{3}\ddot{\mathbf{q}}_{3}=\mathbf{J}_{3}^{\rm T}\mathbf{F}_{\rm P^{\prime}}\end{array}\right. \tag{6}\]  
where \(\mathbf{K}_{\rm m}=\text{diag}(k_{\rm m1},k_{\rm m2})\), \(\mathbf{\sigma}=\mathbf{q}_{\rm m}-\mathbf{q}_{\rm j}\), \(\mathbf{q}_{\rm j}=[\theta_{1},\theta_{2}]^{\rm T}\), \(\mathbf{q}_{\rm m}=[q_{\rm m1},q_{\rm m2}]^{\rm T}\), \(\mathbf{K}_{\rm\delta}=\text{diag}(k_{\rm 511},k_{\rm 512},k_{\rm 521},k_{\rm 522})\), \(\delta=[\delta_{11},\delta_{12},\delta_{21},\delta_{22}]^{\rm T}\), \(\mathbf{D}_{N}=\left[\begin{array}{cc}\mathbf{D}_{10\times 10}&\mathbf{0}_{10\times 2}\\ \mathbf{0}_{2\times 10}&\mathbf{J}_{\rm m}\end{array}\right]\), \(\mathbf{D}\) is a symmetric positive definite mass matrix, \(\mathbf{J}_{\rm m}=\text{diag}(J_{\rm m1},J_{\rm m2})\), \(\mathbf{H}_{N}=\left[\begin{array}{cc}\mathbf{H}_{10\times 10}&\mathbf{0}_{10\times 2}\\ \mathbf{0}_{2\times 10}&\mathbf{0}_{2\times 2}\end{array}\right]\), \(\mathbf{H}\) is the matrix of centrifugal force, \(\tau_{0}\) and \(\mathbf{\tau}_{\rm m}\) are the control torque of the base and motor rotor, \(\mathbf{F}_{\rm P}\in\mathbb{R}^{3\times 1}\) is the force on the end point \(P\) of the robot, \(\mathbf{F}_{\rm P^{\prime}}\in\mathbb{R}^{3\times 1}\) is the force on the satellite point \(P^{\prime}\), and \(\mathbf{D}_{3}=\text{diag}(m_{3},m_{3},J_{3})\).