esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Market Approach and Discussion
### Ethics Concerns  
Designers of implantable medical devices have balanced safety, complexity, power consumption, and cost. However, today there are new concerns to take into consideration: security and data privacy [1]. As biosensors monitoring involves collection of data about vital body parameters from different parts of the body and making decisions based on it, the information is of a personal nature and is required to be secure [5]. The reason is to protect patients from acts of theft or malice, especially as medical technology becomes increasingly connected with other systems via wireless communications or the Internet. Implantable medical devices, including pacemakers, cardiac defibrillators, insulin pumps, and neurostimulators feature wireless communication [129].  
Susceptibility to security breaches could compromise performance safety and the privacy of patients [2]. [PERSON] _et al_. [1] stated that there are two types of vulnerability: privacy, in which patient data is exposed to an unauthorized party, and control, in which an unauthorized person gains control of the device's operation or even disables its therapeutic services.  
There is a need to ensure the privacy and security of medical data [4]. Recent analyses of implantable medical devices have revealed several security and privacy vulnerabilities [1]. For example, wireless connectivity could compromise the confidentiality of transmitted data or send unauthorized commands to the device [129]. Privacy specifications seem to be vague [4], in fact medical devices vary widely with regard to security features because no specific security guidance or requirements have been promulgated by the FDA [2].  
Privacy-preserving methods should be developed for the comfort of the people monitored [3] and ensure reliable, secure communication and continued functionality while preserving patients' safety, confidentiality, and data integrity. There is nearly universal agreement on the importance of security for personal health information and electronic health records, but there is still a disagreement over the security requirements for medical devices [2].  
Security must be considered in early design phases [1]. Some approaches have explored the feasibility of protecting an implantable device from privacy attacks by implementing security mechanisms entirely on an external device [129] or by encrypting data [1; 2; 3]. Moreover, in an effort to ensure security, personal authorizations and authentication have been proposed [20]. Therefore, Information and Communication Technologies (ICT) systems must facilitate the re-design of the current processes of care and follow up through the provision of services that enable the correct management of the patients within the healthcare organizations [130].  
New and emerging technologies upset established moral norms by bringing to surface issues which were not previously open for discussion [131]. Argumentative patterns in this field are now known as NEST-Ethics (New & Emerging Science and Technology Ethics) [132].
## 3 Results of the Signal Analysis before Classification
### Order to Move Forward and to the Right  
Since it is a pilot study, EEG signal measurements presenting evoking several dozen attempts of each type of robot movements, i.e., move forward, backward, to the right and to the left based on motor imagery were collected from each of the two study members. The measurements at rest were also collected. The persons tested did not perform any additional movements which might cause the appearance of biological artefacts in the archived signal, i.e., eye blinking, eyelid raising, lip movement. This paper was written based on over a dozen examples of collected EEG signal measurements and their further classification together with the resulting mobile robot movement.  
Filtered EEG signals with the values obtained by integrating one-second measurement duration periods can be read from the characteristics shown in Figures 5 and 6. Figure 5 contains a set of measurements pertaining to evoking potential at rest and also imitating a move forward and to the right. These are the measurements taken with the participation of the first person tested. The investigations concerning the second person are presented in the same way. The results are shown in Figure 6. In this case the person tested was asked again to perform the sequence of movements: _at rest, move forward, at rest, move to the right_. Exactly the same course of orders took place in the case of the first person tested (See Figure 6).  
Analyzing the characteristics in Figures 5 and 6, a full repeatability may be observed. For the first person tested (Figure 5), two filtered signal measurements together with the integrated values obtained based on them were presented. To present the situation more clearly, only more significant channels taking part in the investigation were presented in the legend. They were extrapolated mainly by using the expert method, based on numerous tests. The same procedure was applied for testing the second person (Figure 6). At this stage of testing, the biggest participation of the following channels was observed: F3, F, F8, T8, FT10, FC6, C4, FC2, F4. The integrated values obtained marked as _Integrated signals_ will be used for signal classification (Section 4 and 5).
## 4 Analysis for Network Throughput and Average Age of Information  
Based on the analyses presented in Section 3, we now further provide analyses for the network throughput and the AoI. The analysis for network throughput follows from Section 3.1, while the analysis for the AoI follows from Section 3.2.
## 4 Experimental Results and Analysis
### Experimental Result of the Bias Ratio  
In this experiment, three labels of Prince rising (Up), Prince falling (Down) and flat (Flat) are set for 6, 12 and 20 BIAS according to Formula (17). The experimental results are shown in Figures 7 and 8 and Table 2. Figure 7a demonstrates the variation of model loss at 100 epochs. Figure 7b shows the accuracy variation at 100 epochs. Table 2 shows the Recall, Precision, F1 measure and average Accuracy of the 6, 12 and 20 BIAS. Figure 8a-c demonstrates the Confusion Matrices of the 6, 12 and 20 BIAS, respectively. In this experiment, the loss of the training data decreased rapidly within 10 epochs, and then showed a steady downward trend. The loss of the validation data converged at about 10-20 epochs. In Figure 8a-c, 6 BIAS has higher prediction accuracy than 12 BIAS and 20 BIAS. However, similar to RSI, most of the misjudgments of 'Up' and 'Down' are misjudged as 'Flat'. The reason is probably that 'Flat' is the most common case in stock market trading, and its data volume is also greater than the sum of the 'Up' and 'Down' categories. In summary, the forecast accuracy of 6 days BIAS is 75.6%, which is slightly higher than BIAS based on 12 days, and a large extent better than 20 days BIAS. The average Recall and Precision of 6 BIAS in the three categories are higher than 12 BIAS and 20 BIAS. Twenty days is roughly equivalent to one month trading. Considering the adaptability of the deep network, the results show that long-term BIAS statistics did not increase the accuracy of predictions. Similar to RSI, BIAS's estimates are mostly correct for the 'Flat' label, with Precision and Recall reaching up to 87.2% and 81.4%, and 84.2% in F1 Measure.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Trend** & **20\_80\_RSI** & **30\_70\_RSI** \\ \hline \multirow{3}{*}{Precision} & Up & 60.3\% & 57.4\% \\  & Flat & 91.3\% & 87.2\% \\  & Down & 49.4\% & 44.2\% \\ \hline \multirow{3}{*}{Recall} & Up & 64.2\% & 57.0\% \\  & Flat & 83.2\% & 82.4\% \\  & Down & 67.6\% & 58.3\% \\ \hline \multirow{3}{*}{F1 Measure} & Up & 62.2\% & 57.2\% \\  & Flat & 87.1\% & 84.7\% \\ \cline{1-1}  & Down & 57.1\% & 50.3\% \\ \hline Accuracy & & 79.2\% & 76.4\% \\ \hline \end{tabular}
\end{table}
Table 1: Precision, Recall, F1 Measure and Accuracy of the RSI TI.  
Figure 6: The Confusion Matrices of (**a**) \(20\leq\text{RSI}\leq 80\), and (**b**) \(30\leq\text{RSI}\leq 70\).
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
### Satellite Mission Scenario  
To figure out more concretely what could bring such a kind of hybrid atomic-electrostatic instrument, we consider one particular scenario that has been clearly identified for a future space geodesy mission. This scenario is based on a low-low satellite-to-satellite tracking (SST-LL) configuration [61; 62]. SST-LL missions involve the measurement of the differences in satellite orbital perturbations over baselines of a few hundred kilometers. These measurements are typically implemented with two satellites on the same orbit in the Earth's gravity field, one satellite tracking the other. The gravity field is recovered by determining the inter-satellite distance variation produced exclusively by the geopotential. The distance variation between the two satellites is measured either by a microwave link, adopted on GRACE and GRACE-FO, or by laser interferometry, which has been also adopted on GRACE-FO as a technology demonstrator [63] and is the foreseen method for the NGGM mission since it should allow higher resolution than a microwave link. The contribution of non-gravitational effects, produced for example by atmospheric drag, are separately measured by accelerometers on each satellite and accounted for in data processing. We thus consider a payload allowing retrieving the gravity field that is mainly composed of a ranging system and accelerometers.
## 4 Conclusions  
A novel multi-trace variational mode decomposition-based seismic spectral decomposition method is presented for the purpose of better discrimination of the subtle seismic geological channels. The conclusions from this study are that:  
1. The variational mode decomposition method has the advantages of high resolution, complete decomposition, and avoidance of the mode stacking effect, which is suitable for enhancing the extraction of the target signal in seismic interpretation;
2. Multi-trace variational mode decomposition provides an improved lateral consistency and resolution for recognizing the subtle geological features of channels, which enhances the quality and reliability of seismic interpretation;
3. Tested by a 3D seismic dataset, the results have proved that, on data-driven decomposition approaches, a higher spectral-spatial resolution and clearer stratigraphy and lithology boundaries were obtained, compared to the traditional short-time Fourier transform method.  
Although the computational efficiency was improved, a multi-core parallel computing strategy should be considered when processing 3D datasets by using the MTVMD method. With more big data input, the alternating direction method of multipliers for MVMD in the frequency domain may have high computation complexity. Data segmentation is also an effective strategy that divides the input data into a series of smaller ones, which can be considered for processing. In addition, subsequent research should also concentrate on the optimal selection of decomposition parameters.  
[PERSON] wrote the paper and performed the experiments; [PERSON] provided the original idea and wrote the program code; [PERSON] contributed to the conceptualization of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Science Foundation of China under Grant 42072125 and the National Science and Technology Major Project of China under Grant 2017 ZX05008-001.  
Not applicable.  
The calculations are supported by the High-performance Computing Platform of Peking University.  
The authors declare no conflict of interest.
## 4 Integrated Energy System Optimization Scheduling
### Optimal Scheduling Model without Accounting for Energy Storage Lifetime Decay
#### 4.1.2 Constraints  
(1) Coupling device constraint:  
\[\begin{array}{l}P_{i}^{\min}\leq P_{i}\leq P_{i}^{\max}\\ Q_{i}^{\min}\leq Q_{i}\leq Q_{i}^{\max}\end{array} \tag{15}\]  
where \(P_{i}\) and \(Q_{i}\) are the electric and thermal power of the coupled equipment of the electric-gas integrated energy system, respectively.  
(2) Energy storage constraint:  
\[\begin{array}{l}0\leq P_{ES,C}\leq P_{ES,C}^{\max}\\ 0\leq P_{ES,D}\leq P_{ES,D}^{\max}\\ SOC_{ES}^{\min}\leq SOC_{ES}\leq SOC_{ES}^{\max}\end{array} \tag{16}\]  
where the superscripts max and min correspond to the upper and lower limits of the physical quantity, respectively.  
(3) Energy balance constraint:  
a. Electrical balance constraint:  
\[\begin{array}{l}P_{grial}(t)+P_{PV}(t)+P_{WT}(t)+P_{mt}(t)=P_{cc}(t)+P_{eb}(t) +P_{ES}(t)+P_{P2G}(t)+L_{e}(t)\\ \end{array} \tag{17}\]  
where \(L_{e}(t)\) is the electrical load.  
b. Natural gas balance constraint:  
\[q_{grid}(t)+q_{P2G}(t)=q_{mt}(t)+L_{q}(t) \tag{18}\]  
where \(L_{q}(t)\) is the gas load.  
c. Thermal equilibrium constraint:  
\[Q_{eb}(t)+Q_{he}(t)+Q_{P2G}(t)=L_{h}(t) \tag{19}\]  
where \(L_{h}(t)\) is the heat load.  
d. Cold equilibrium constraint:  
\[Q_{ac}(t)+Q_{cc}(t)=L_{c}(t) \tag{20}\]  
where \(L_{c}(t)\) is the cold load.  
e. Balance of waste heat output from gas turbines and heat input from heat exchangers and absorption chillers.  
\[Q_{ac,in}(t)+Q_{he,in}(t)=Q_{mt}(t) \tag{21}\]
## 4 WSN Clustering
### Cluster Head Election Using Fuzzy Logic  
Each round in Cluster Head Election using Fuzzy Logic (CHEF) used a random number to select temporary CHs [81]. The selected CH makes use of two fuzzy parameters: power level and proximity. Proximity in terms of distance is mainly the distances between all nodes. Each CH calculates and broadcasts its probability value using fuzzy conditional rules. The CH with the highest probability value is chosen as CH and advertised for the addition of the remaining member nodes. When compared to previous methods, CHEF extends network life but introduces overheads such as traffic and network overheads due to periodic notifications. Furthermore, the process of cluster head election is costly in terms of energy consumption because it is performed across the entire network, resulting in significant energy consumption.
## 3 Results and Discussion
### Analysis of CO\({}_{2}\) Emission and Energy Cost  
The estimated remediation energy cost is shown in Figure 8a based on the test results at 600 \({}^{\circ}\)C. Conventional thermal desorption technology costs 51.9 USD/ton (per metric ton) to remediate the oil-contaminated soil based on the burning of fossil fuel, while indirect-heated microwave thermal desorption technology without fossil fuels reduces the remediation energy cost by 75% based on an effective microwave heating mechanism.  
Figure 8b shows CO\({}_{2}\) emissions resulting from this study. Conventional thermal desorption technology shows 0.01 ton-CO\({}_{2}\) emissions from burning fossil fuels to clean up the soil. However, fossil-fuel-free indirect-heated microwave thermal desorption technology can reduce CO\({}_{2}\) emission by 25%. These calculation proce  
Figure 8: Comparison analysis of (**a**) Remediation energy cost (USD) and (**b**) Carbon dioxide emissions (ton-CO\({}_{2}\)).  
Figure 7: Results of the (**a**) Direct shear test and (**b**) Compaction test.
# Forces in the Interaction of Light with Matter
## 5 Optical Binding  
When more than one particle is trapped in a Gaussian laser beam, in an element of the focal region over which the beam has approximately constant intensity, optically induced inter-particle forces arise in addition to the individual trapping forces. Their effect is to establish a potential energy landscape that can hold the particles in a stable, non-contact configuration. First observed by [PERSON] et al. [126], the effect now commonly known as optical binding was first predicted through quantum electrodynamical methods by [PERSON] [125]. The effect was later more fully formulated by [PERSON] and [PERSON] [107], first applied to carbon nanotubes [153] and then extended to multiparticle arrays [98]. Modelling the corresponding Mie scattering effect for larger particles in a trapping cavity leads to results of similar form [127]. Further studies have also identified the additional, optically induced torques that arise when the trapped particles are anisotropic, markedly so in the case of nanowires [128]. The optically induced pair potential is a damped, slowly oscillating function of distance, as illustrated in Figure 2.  
Pair trapping usually occurs when the separation between the two particles is at the closest absolute minimum--near to, but not exactly equal to, the optical wavelength. Displacements from this equilibrium position are subject to restoring forces that depend on whether the particles align along the axis (longitudinal binding) or in a plane perpendicular to it (transverse binding). For particles of polarisability \(\alpha\) trapped by a laser of wavenumber \(k\) and degree of first order coherence\({}_{12}^{(1)}\) across the interparticle divide, [35] the dependence of the forces on their separation \(R\) emerges from the energy expressions as follows:  
\[F_{binding}^{||}(R)=\frac{\alpha^{2}|\mathbf{E}^{2}|S_{12}^{(1)}}{4 \pi\epsilon_{0}R^{4}}\{3\cos(2 kR)- \tag{26}\] \[kR\sin(2 kR)-3k^{2}R^{2}\cos(2 kR)-2k^{3}R^{3}\sin(2 kR)\},\]  
\[F_{binding}^{\perp}=\frac{\alpha^{2}|\mathbf{E}^{2}|S_{12}^{(1)}}{2\pi \epsilon_{0}R^{4}}\Big{\{}3\cos(kR)+3 kR\sin(kR)-k^{2}R^{2}\cos(kR)\Big{\}}. \tag{27}\]  
The pair potential itself can be controllably cancelled by another beam entering a trapping cavity from the side [154]--a technique that also enables the cancellation of dipole forces in Bose-Einstein concentrates [130] and cold atoms [131]. The wider capacity to collectively control optically trapped particle arrays is also a topic of great current interest [100,132].
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
###### Abstract  
The Crabster CR200 is an underwater walking robot inspired by crabs and lobsters, designed for precise seabed inspection and manipulation. It maintains stability and position on the seafloor, even in strong currents, by adjusting its posture through six legs, each with four degrees of freedom. The key advantage of the CR200 lies in its ability to resist drifting in strong currents by adapting its posture to maintain its position on the seafloor. However, information is still lacking on which specific posture generates the maximum downforce to ensure optimal stability in the presence of currents and the seabed. This study aims to determine the fluid forces acting on the CR200 in various postures using Computational Fluid Dynamics (CFD) and identify the posture that generates the maximum downforce. The posture is defined by two parameters: angle of attack and seafloor clearance, represented by the combination of the robot's pitch angle and distance to the seabed. By varying these parameters, we identified the posture that produces the greatest downforce. Through a series of analyses, we identified two main fluid dynamic principles affecting the downforce on a robot close to the seabed. First, an optimal pitch angle exists that generates the maximum downward lift on the robot's body. Secondly, there is an ideal distance from the seabed that produces maximum suction on the bottom surface, thereby creating a strong Venturi effect. Based on these principles, we determined the optimal robot posture to achieve maximum downforce in strong current conditions. The optimal underwater robot posture identified in this study could be applied to similar robots operating on the seafloor. Furthermore, the methodology adopted in this study for determining the optimal posture can serve as a reference for establishing operational postures for similar underwater robots.  
Crabster; CR200; underwater walking robot; computational fluid dynamics; optimal attitude; downforce; maximum lift; venturi effect; ground effect; incompressible flow; Reynolds-averaged Navier-Stokes +
Footnote †: journal: Applied sciences
## 6 Simulation Results and Analysis
For \(\lambda=0\), T\({}_{\text{m}}=\) Low, the wireless energy transmitters are outside the coverage of the nodes and the inter-meeting time between the senor-nodes and the energy transmitters is very small. This is known as the energy starving moment of the sensor-nodes because only minimum energy is transmitted to the nodes within this shortest time. This may be because the mobile power chargers have not yet arrived within the vicinity of the nodes or that they are moving at a high speed within the coverage area. At high speeds, inter-meeting times are reduced and only minimum energy can be transmitted to the nodes and nodes will be power-hungry. Hence, WPT efficiency is reduced as the sensor-nodes are unable to efficiently charge their batteries. This is designated as \"without charging\" in Figure 6.  
Figure 7 shows the effect of varying the number of sensor-nodes on the average energy charged for MCBR-Flooding and IEEABR protocols by considering different trade-offs (\(\lambda\)). Figure 6: WPT efficiency at varying times of the wireless mobile battery charger (MBC) for MCBR-Flooding and IEEABR protocols. Again, IEEABR (an ant-based protocol) outperformed MCBR-Flooding protocol. Here, a given number of IoT sensor-nodes (Ns = 200), sparsely distributed across the network, was varied in accordance with the energy received from the mobile power chargers. And it is observed that the energy charged by the sensor-nodes initially increased proportionally with the number of sensor-nodes in the network and then stabilized. Let us consider the different trade-off factors. For \(\mathbf{\lambda}=0\), the energy transmitters are not optimally placed within the vicinity and coverage area of the sensor-nodes and only little or no energy is transmitted to the sensor-nodes. Hence, sensor-nodes receive minimum or no energy to charge their batteries because the energy transmitted is not fairly and uniformly distributed across the network. The sensor-nodes are assumed not to be charging at maximum level and this is therefore designated as \"without charging\" as shown in Figure 7. However, the total energy charged is observed to increase with the number of sensor nodes in the network. For \(\mathbf{\lambda}=1\), the mobile battery chargers (energy transmitters) are optimally placed within the vicinity of the sensor-nodes and the energy transmitted in the network is maximized. Here, energy is fairly and uniformly distributed across the network as long as the energy transmitters are within the coverage area. As shown in Figure 7, the energy transmitted (i.e., average energy charged) initially increased proportionally with the number of sensor-nodes and then stabilized. This is designated as \"battery charging\" in the diagram. From the graph, we can see that when Ns > 80 (for IEEABR) and Ns > 140 (for MCBR-Flooding), the change in the total energy received (average energy charged by the sensors) is insignificant. This may be connected with the fact that each sensor-node in the vicinity or coverage area receives a uniform charging from the power chargers (wireless energy transmitters).