esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory  
In geophysical applications, the displacement current is typically ignored when the frequency of the electromagnetic field is low. [PERSON]'s equations can then be simplified as follows [24]:  
\[\
abla\times\mathbf{E}=i\omega\mu_{0}\mathbf{H} \tag{1}\]  
\[\
abla\times\mathbf{H}=\sigma\mathbf{E}+\mathbf{J}_{\mathrm{s}} \tag{2}\]  
where the space magnetic permeability is denoted by \(\mu_{0}\), angular frequency is denoted by \(\omega\), source current is denoted by \(\mathbf{J}_{\mathrm{s}}\), and conductivity tensor is denoted by \(\sigma\):  
\[\mathbf{\sigma}=\begin{pmatrix}\sigma_{x}&0&0\\ 0&\sigma_{y}&0\\ 0&0&\sigma_{z}\end{pmatrix} \tag{3}\]  
where \(\sigma_{x}\), \(\sigma_{y}\) and \(\sigma_{z}\) are principal conductivities. In this study, we discuss only the principal axis anisotropy case. However, our formulation also works in the case of general anisotropy.  
Calculate the curl at the left and right ends of Equation (1), and use Equation (2) to eliminate the magnetic field:  
\[\
abla\times\
abla\times\mathbf{E}-i\omega\mu_{0}\mathbf{\sigma}\mathbf{E}=i\omega\mu_{0} \mathbf{J}_{\mathrm{s}} \tag{4}\]  
The total and secondary field formulations are two types of formulations that are typically used for solving the 3D MCSEM modeling problem. In contrast to the total field formulation, the secondary field formulation's source term comprises a primary electric field. This type of source term is smoother than the current source used in the total-field formulation. Therefore, in this study, we use a secondary field formulation to address the marine CSEM modeling problem in an anisotropic medium. In this formulation, the total field can be decomposed into two parts: the background (primary field, \(\mathbf{E}_{\mathrm{p}}\)) and the anomaly fields (secondary field, \(\mathbf{E}_{\mathrm{s}}\)):  
\[\mathbf{E}=\mathbf{E}_{\mathrm{s}}+\mathbf{E}_{\mathrm{p}} \tag{5}\]Similarly, the conductivity tensor can be decomposed into background (\(\sigma_{\rm b}\)) and anomalous conductivities (\(\Delta\sigma\)):  
\[\sigma=\sigma_{\rm b}+\Delta\sigma \tag{6}\]  
With substitution of Equations (5) and (6) into Equation (4), the Helmholtz equation with the electric component of the secondary field can be written as:  
\[\
abla\times\
abla\times E_{\rm s}-i\omega\mu_{0}\sigma\vec{E}_{\rm s}=i\omega \mu_{0}\Delta\sigma\vec{E}_{\rm P} \tag{7}\]  
The secondary magnetic component can easily be calculated using Equation (8) once the secondary electric component in Equation (7) can be solved:  
\[H_{\rm s}=\frac{1}{i\omega\mu_{0}}\
abla\times E_{\rm s} \tag{8}\]
# A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype
## 4 Proposed HS (MOHSg)  
The effectiveness of metaheuristic algorithms is driven by two fundamental components: exploration and exploitation. The proper balance between these two componentsgreatly influences the algorithm efficiency. Highly exploit-focused algorithms explore only a fraction of the search space and tend to become stuck in the local optimum. On the other hand, highly scan-focused algorithms converge very slowly, and the solution time can be very long.  
HS proposals with a fixed exploration-exploitation balance are unable to adjust their behavior as required by the problem. On the other hand, the proposals with variable parameters do not contemplate the distribution of solutions in the search space, which, as seen above, can have different configurations for the formation of the Pareto-optimal front. Instead, the proposal presented in this paper is capable of balancing the exploration and exploitation by adjusting the pitch, based on the distribution of the solutions in the search space. Therefore, it does not require parameters such as the static bandwidth used in MOHS2, nor dynamic parameters such as those required by MOHS3.
# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 3 Results Analysis
### Canopy Interception Evaporation
Figure 5 shows the seasonal distribution of the difference of Ec  
Figure 3: Spatial distribution of global Ec (units: W/m\({}^{2}\)): (**a–d**) NOAA reanalysis data and (**e–h**) simulated by CLM4.5. between the simulated results of CLM4.5 and reanalysis data. It can be seen from Figure 5 that, except for southwestern Eurasia, Greenland, northern Australia, part of South America, and the high latitude region of the northern hemisphere in April, the simulated Ec is smaller than the reanalysis data over much of the globe. Through the analysis of the canopy interception parameterization scheme, the findings indicate that the physical variables related to the canopy interception process (e.g., throughfall, canopy drip) are considered in the model design principle [15]. However, the existing interception calculations do not directly consider the influence of rainfall intensities and wind speeds. For example, when raindrops reach the leaf surface at a certain speed, splash droplet evaporation (SDE) is produced. Similar research findings propose that rainfall intensity plays a decisive role in the process of canopy interception and evaporation and that 10-40% of precipitation will be consumed in the process of SDE under heavy rainfall [34; 35]. Wind can influence canopy interception precipitation by shaking branches [36; 37; 38; 39]. Besides, an interception by vegetation does not distinguish between liquid and solid phases, namely rain and snow are treated the same, which may have an influence on Ec in the middle and high latitudes in winter. These factors may act as the possible causes of the canopy interception simulation bias. Therefore, accounting for these factors is one possible path to further improve the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mean (Units: W/m\({}^{2}\))**} \\ \cline{2-5}  & **RMSE (Units: W/m\({}^{2}\))** & **CLM4.5** & **NOAA** & **SCC** \\ \hline January & 7.6 & 3.3 & 5.4 & 0.91 \\ April & 7.1 & 4.3 & 6.6 & 0.91 \\ July & 10.1 & 6.1 & 10.6 & 0.85 \\ October & 7.6 & 3.3 & 6.4 & 0.89 \\ Annual mean & 6.4 & 4.3 & 7.3 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The root mean square errors (RMSEs), means and spatial correlation coefficients (SCC) for global Ec in January, April, July, and October and annual mean. Figure 4: Spatial distribution of global precipitation (units: mm/d) in January (**a**), April (**b**), July (**c**) and October (**d**) derived from Qian atmospheric forcing data.
# Quantum Synchronization and Entanglement of Dissipative Qubits Coupled to a Resonator
## 3 Numerical and Semi-Analytical Results  
In the previous section, we described how the time-dependent dissipative quantum problem of a driven cavity coupled to qubits can be transformed into a stationary problem by moving to the rotating frame. Finding the steady-state density matrix of the system is then reduced to finding the kernel of the RWA Lindblad operator \(\mathcal{L}(\hat{\rho})\). We then presented several approaches to finding this steady state. An exact numerical approach is based on iterating solutions to Sylvester equations, a weak damping perturbation theory series, which can be interpreted as population dynamics of the quantum eigenstates of the system, and a semiclassical approximation which we expect to be valid at stronger damping where quantum coherence is quickly destroyed.  
Here, we first present some numerical results that confirm the convergence of the system to its RWA steady state and then investigate the different regimes of this model, where our two semi-analytical approximations apply. For most parameters, we find that either one of these approximations can be used but we also identify a regime where they are not able to describe the entangled dissipative state of the system.
## 1 Introduction  
This work has the following two main goals: First, to compare the advantages and the limitations of the recently developed Ultrasonic Guided Wave Leakage (UGWL) approach and the better-known Half-Cell Potential (HCP) method in the detection of corrosion in reinforced concrete (RC) bridge decks. Secondly, to establish a quantitative correlation between UGWL data and chloride content.  
In prior work, the UGWL method, a new approach to non-destructive testing (NDT) of reinforced concrete members using ultrasound, demonstrated the ability to detect the _onset_ of corrosion and delaminations between rebar and concrete [1; 2; 3]. It was demonstrated that the UGWL method can be used effectively on epoxy-coated bars and the method's field readiness was tested through two field pilot studies. On the other hand, HCP is a well-known and standardized method used to detect corrosion in RC systems and is commonly used on bridge deck inspections. To establish the goals of the project, an experimental program was conducted. The two methods are compared using data from lab specimens submerged in 10% NaCl over a period of six months. In addition, the chloride content in the concrete was determined at the end of the period and correlated to the data from UGWL and HCP methods.
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion
### Study on the Influencing Factors of Single-Tooth Broken Rock
#### 3.2.1 Spherical Tooth into Rock Displacement  
As shown in Figures 5 and 6, the 16 mm and 22 mm tungsten carbide buttons impact the rock and penetrate into its interior under the application of impact energy. At approximately 0.0005 s, the buttons reach their maximum penetration displacement. For a 16 mm button diameter, the maximum single penetration displacement ranges from 0.66 mm to 0.79 mm, while for a 22 mm button diameter, it ranges from 0.59 mm to 0.72 mm. The reason is that with a smaller button diameter, the localized pressure between the button and the rock surface increases during impact, resulting in greater penetration depth. As the impact energy increases, the maximum penetration depth also increases. After penetrating the rock, the reaction force exerted by the rock on the button exceeds the drilling pressure,  
Figure 4: The comparison diagram of mathematical model and simulation stress curve.  
causing the button to rebound and move upward. Around 0.006 s to 0.007 s, the button reaches its maximum upward displacement, which is approximately 1.05 mm to 1.6 mm. Subsequently, as the drilling pressure fully counteracts the reaction force from the rock, the button begins to move downward again. Finally, the button reaches a state of dynamic equilibrium as the drilling pressure is balanced by the reaction force from the fractured pit and the button itself.  
Figure 6 illustrates a positive correlation between drilling pressure and penetration displacement. For 16 mm buttons, displacement increased from 0.66 mm (1 kN) to 0.79 mm (1.8 kN), while 22 mm buttons showed a rise from 0.59 mm to 0.72 mm. This trend is attributed to enhanced contact force under higher pressures, enabling deeper rock penetration. This is because an increase in drilling pressure strengthens the contact force between the drill bit and the rock. Consequently, the contact force between the tungsten carbide button and the rock surface also increases, enhancing the energy generated by each impact. This results in a stronger impact and cutting force from the button on the rock, effectively penetrating the rock.  
Figure 5: The displacement curve of the spherical tooth under 1.4 kN drilling pressure.  
Figure 6: The maximum rock-entry displacement of 16 mm and 22 mm spherical teeth under different bit weights and impact energy.
# Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots
## 2 The Probabilistic BR Loss Model
### An Iterative Algorithm for the Determination of \(P(i,n)\)  
Initially, we express \(P(i,n)\) via the boundary states \(P(0,0)\) and \(P(1,0)\) as follows:  
\[P(i,n)=S^{0}_{i,n}P(0,0)+S^{1}_{i,n}P(1,0) \tag{4}\]  
At this point, all terms in (4) (including the coefficients \(S^{0}_{i,n}\) and \(S^{1}_{i,n}\)) are unknown. A formula for the derivation of \(S^{0}_{i,n}\) and \(S^{1}_{i,n}\) can be obtained if we substitute (4) in (2), to have:  
\[\begin{array}{ll}S^{0}_{i,n+1}P(0,0)+S^{1}_{i,n+1}P(1,0)&=\frac{\lambda_{i}( n)+n\mu_{i}+\theta_{i}}{(n+1)\mu_{i}}\left(S^{0}_{i,n}P(0,0)+S^{1}_{i,n}P(1,0) \right)-\frac{\lambda_{i}(n-1)}{(n+1)\mu_{i}}\left(S^{0}_{i,n-1}P(0,0)+S^{1}_{For a system of capacity \(C\) b.u., the determination of \(S^{0}_{i,n}\) and \(S^{1}_{i,n}\) requires 2C Equations of (6) and (7), respectively.  
Up to this point, the coefficients \(S^{0}_{i,n}\) and \(S^{1}_{i,n}\) have been determined (via (6) and (7), respectively). The next step is to calculate the values of \(P\)(0,0) and \(P\)(1,0). To this end, a system of two equations is necessary. The first equation is based on (3), where the case of \(i=0\) is considered:  
\[(C\mu_{s}+\theta_{s})P(0,C)-\lambda_{0}(C-1)P(0,C-1)-\theta_{m}P(1,C)=0 \tag{8}\]  
Equation (8), via (4), can be written as:  
\[(C\mu_{s}+\theta_{s})(S^{0}_{0,C}P(0,0)+S^{1}_{0,C}P(1,0))-\lambda_{0}(C-1)(S^ {0}_{0,C-1}P(0,0)+S^{1}_{0,C-1}P(1,0))-\theta_{m}(S^{0}_{1,C}P(0,0)+S^{1}_{1,C}P (1,0))=0\]  
or  
\[P(0,0)\Big{(}S^{0}_{0,C}\ (C\mu_{s}+\theta_{s})-S^{0}_{0,C-1}\lambda_{0}(C-1)-S^ {0}_{1,C}\theta_{m}\Big{)}+P(1,0)\Big{(}S^{1}_{0,C}\ (C\mu_{s}+\theta_{s})-S^{1}_{0,C-1}\lambda_{0}(C-1)-S^{1}_{1,C}\theta_{m} \Big{)}=0 \tag{9}\]  
Equation (9) is the first equation to be used for the determination of \(P\)(0,0) and \(P\)(1,0).  
The second formula is based on the fact that:  
\[\sum_{i=0}^{1}\sum_{n=0}^{C}P(i,n)=1 \tag{10}\]  
Equation (10), via (4), can be written as:  
\[P(0,0)\sum_{i=0}^{1}\sum_{n=0}^{C}S^{0}_{i,n}+P(1,0)\sum_{i=0}^{1}\sum_{n=0}^ {C}S^{1}_{i,n}=1 \tag{11}\]  
Equation (11) is the second equation to be used for the calculation of \(P\)(0,0) and \(P\)(1,0). Having determined the values of \(P\)(0,0) and \(P\)(1,0) (via (9) and (11), respectively) and the values of \(S^{0}_{i,n}\) and \(S^{1}_{i,n}\)(via (6) and (7), respectively), we can calculate the values of \(P\)(\(i\),\(n\)) via (4) and, consequently the CBP of new and handovers calls together with the link utilization, according to the next subsection.
# Lightning-Ignited Wildfires beyond the Polar Circle
## 4 Discussion
### Potential Influence of Wildfires on the Carbon Balance in the Arctic  
The pattern of post-fire recovery is varied within different sectors. Thus, in East Siberia, wildfires sharply decreased productivity almost to zero, whereas in West Siberia and Scandinavia, changes in productivity smooth (Figure 12g,c,h). These differences are likely caused by the different fuel deposits and moisture and, consequently, by the level of fire severity. However, the recovery pattern for the entire Arctic indicated a severe fire impact on the GPP vegetation (Figure 12a). Nevertheless, within all sectors, productivity recovered to the pre-fire level within 5-10 years (Figure 12).  
Moreover, positive post-fire GPP trends are observed within ca. 100% of burns in the Arctic (Figure 13a), which should be attributed to the positive influence of current warming on Arctic vegetation productivity. That is supported by countervailing GPP trends within burns throughout the entire Arctic (Figure 13a,b), coincided with the earlier described phenomenon of \"tundra greening\" (e.g., [25; 58; 61]) and indicated increased carbon fixation throughout the Arctic. On-ground observations in the Siberian Arctic also indicated increased growth of dominant woody species (larch, spruce, and birch), leading to an increase in woody biomass [30; 31; 62]. A tall woody shrub (_Alnus fruticosa_) was reported to replace low-growing moss and lichen vegetation after tundra fires in Western Siberia [17; 60]. Although quantitative analysis of the balance between fire-driven carbon loss and carbon accumulation is still poor, these observations support the status of the Siberian Arctic as a carbon sink [62].  
Similar findings were reported for the Arctic forests of North America, where net ecosystem biomass production and black spruce growth increased [26; 29]. In Alaskan forests, [PERSON] et al. [63] found that severe burning of organic soils shifted tree dominance from slow-growing black spruce to fast-growing broadleaf trees, resulting in a net increase in carbon storage by a factor of five. Increased dominance of deciduous species in the Arcticcould increase the carbon residence time and mitigate the release of stored soil carbon into the atmosphere.  
Although the circumpolar Arctic is a remote area, it is increasingly the focus for resource extraction. The implications of increased fire, particularly in Western Siberia and Alaska, will pose additional challenges for gas and petroleum exploration.
## 4 Discussion
### Clinical Needs in Robotic Rehabilitation of Upper limbs  
Besides the efficacy/effectiveness aspect [67] mentioned in Section 4.2, other needs of the clinical practice should be considered by the researchers when designing rehabilitation robots. These clinical needs can be grouped into four aspects [68]:  
1. Psychological: The patient should not be afraid of robotic rehabilitation devices. They should not replace the therapist but make their job easier.
2. Medical: The robot should be adaptable to the human limb in terms of segment lengths, range of motion, and degrees of freedom (DoFs) [68; 69]. To adapt the robot to different human bodies (sizes and weights), the segments of exoskeletons should be adjustable. A robot with a high degree of freedom (DoF) offers a wide variety of movements with many anatomical joint axes [68]. Modularity would be welcome in the medical aspects because the modular prototype, as the one previously proposed [12], can offer single-joint rehabilitation training, as well as multi-joint composite training. This feature increases the flexibility of exoskeleton robot applications in rehabilitation [70].
3. Ergonomic: The design of an upper limb exoskeleton robot must allow some additional space for the patient.
4. Control: The robot should be back-drivable. The weight of the robot should not be felt by the patient, and they should be able to move the robotic device. Assist-as-needed control is the most common approach used in robotic rehabilitation devices [25; 56; 71]. This method allows the patients to initiate the movement on their own and to be partially assisted when they start regaining their lost motor function (active therapy) [70; 72]. The need here is control sharing, which is the ability to control the forces applied by the robot [68]. A previously proposed prototype [43] is back-drivable (instrumented compliance). There are two kinds of back-drivability [73], the most advantageous of which, mechanical back-drivability, is strongly dependent on the transmission mechanism of the exoskeleton [73]. Consequently, back-drivability is an important clinical need that must be considered by researchers.  
The spasticity measurement is a potential clinical need because spasticity is the most common symptoms of stroke patients [74].
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 4 Conclusions  
As long as a spoofing detection could be depicted as a hypothesis testing, it can be fused with others by the method that is proposed in this paper. A key point of the fusion method is to simplify the discernment frame in order to assign the belief functions for spoofing detection results. Most of the spoofing detections are equivalent to hypothesis testings. The deviation degree of the test statistic and threshold represents the reliability of the measurement, and it is the basis of the belief function of the spoofing detection result. The performance evaluation and simulations based on two detections are provided in the paper. The proposed method improves the performance of spoofing detection and makes the results more reliable. The root cause of improvement is the increase of the detection data. However, there are some questions to be solved; for example, the evidence from different detections should be independent, but this may not be true in spoofing detections because some measurements are related in the GNSS receiver, such as \(C/N_{0}\) and correlation values, while some measurements are independent, such as \(C/N_{0}\) and Doppler shift. Besides, the selection of detectors to be fused in a specific scenario and the assignment of weightings for them are problems to be studied.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61571255).  
[PERSON] and [PERSON] discussed this problem and got the ideas to fuse multiple detections together. [PERSON] wrote the paper and completed the performance analysis. [PERSON] and [PERSON] coordinated the study and reviewed the manuscript.  
The authors declare no conflict of interest.
## 4 Case Study
### Annual Vehicle Tax Electronic Service  
Each car owner is required to pay an _Annual Vehicle Tax_ (AVT) for every vehicle registered under his ownership. Overall tax amount is calculated based on vehicle's CO2 emissions, engine size, and production date and is paid separately from annual income tax. Electronic service is provided by the General Secretary of Information Systems (GSIS) which operates under the authority of the Ministerial Department of Finance. The applicable hierarchy is presented below in Figure 3. For the successful completion of the electronic service user is required to submit her National Taxation Number along with a vehicle's license plate. GSIS validates data accuracy and generates a receipt which contains owners'first and last name, vehicle's license plate, and the tax amount to be paid along to a unique payment identifier, which will be utilized by the bank institution, where payment will be made.
## 1 Introduction
The effectiveness of the EACH-COA strategy was tested using various metrics: throughput, latency, and network longevity. * EACH-COA performs CH selection and cluster formation. Using the coati optimization method, the best CH is selected, and clusters are grouped together using the nodes that are closest to each other. * Existing optimization techniques are compared with the proposed EACH-COA methodology to demonstrate that EACH-COA outperforms them in network longevity. * The fitness function is computed using residential energy (RER) and distance parameters in the CH selection process. * The proposed EACH-COA technique was simulated using MATLAB 2019a. The overall network lifetime and throughput were improved by 8-15% and 5-10%, respectively. The remainder of this article is organized as follows: Section 2 discusses existing works on CH selection-based optimization algorithms. Section 3 presents the system model, including the COA-network and COA-energy models. Section 4 describes the CH selection process using the coati optimization algorithm. Section 5 discusses the simulation, results from the discussion, and a comparison with existing optimization techniques. Section 6 presents conclusions and directions for future work.