esa-sceva/satcom-chunk-collection huggingface dataset:
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 4 Flying Ad Hoc Networks
### Routing
Furthermore, the void area means that one part of the network environment is disconnected, meaning that this area is not covered by flying nodes because there is no node in that part that connects to the outside nodes [47]. * **Delay:** Delay is the time required to transmit a data packet from the source to destination. When designing a routing algorithm, delay should be considered because real-time applications, such as monitoring, are sensitive to delay. In these applications, a high delay in the data transmission process can lead to unpleasant results [28]. * **Packet delivery rate (PDR):** It is equal to the ratio of the number of data packets delivered to the destination to the total number of packets sent by the source. Obviously, routing protocols need a higher packet delivery rate. If routing protocols are weakly designed, and the formed paths include routing loops, this has a negative effect on PDR [48]. * **Adaptability:** This means that routing protocols must quickly react to the network dynamics. For example, if a routing path is broken due to the link failure or discharging battery of nodes, the routing protocol should quickly find the alternative route. * **Load balancing:** Routing protocols must evenly distribute their operational load, including energy consumption, calculations, and communications in the network, so that no route does not consume resources faster than other routes [28]. * **Routing loops:** The routing process should be free-loop to achieve a successful packet delivery rate. * **Routing overhead:** Routing protocols must have low routing overhead, meaning that drones can communicate with each other with the least overhead in an efficient routing protocol. * **Communication stability:** High mobility of nodes and different environmental conditions such as climate changes can lead to the disconnection of communication links. Therefore, a routing technique must guarantee communication stability in the network. * **Bandwidth:** In applications such as aerial imaging, it is very important to consider bandwidth because there are restrictions such as communication channel capacity, drone speed, and sensitivity of wireless links relative to error.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 5 Experimental Validation  
### Experimental Setup  
To evaluate the designed control methods, a teleoperation assembly system was constructed at the lab (Figure 5). The UR5 6-DOF robot was employed as the remote robot equipped with a Hand-E gripper and a force/torque force sensor. A haptic feedback glove developed in our laboratory was adopted to manipulate the gripper. The Intel Realsense D455 camera was applied to acquire the video information about the remote environment. The control algorithms and the interactive command were run through PyCharm. Unlike the commonplace adoption of the ROS (Robot Operation System), the controller of the remote robot was carried out in Python under the Windows 10 system.  
For evaluating the effect of the teleoperated assembly system, a typical task called \"picking moving, target inserting\" was performed. The user controlled the remote robot  
Figure 5: The setup of the experimental system.  
by operating the wearable wireless tracking device. During the insertion assembly, the end-effector of the robot had to try to minimize contact forces. The assembly components are depicted in Figure 6. The components were designed with chamfers, and the assembly error is 2.5 mm. The left robot arm was fixed, and only the right robot arm was operated by teleoperation.
## 3 Method
### Experimental Framework
#### 3.4.3 Validation Protocols  
Validation followed ISO 13264 safety standards across three test environments: controlled laboratory (n = 10,800 trials), outdoor playgrounds (n = 12,400 trials), and obstacle-rich indoor spaces (n = 8800 trials). Environmental conditions included the following:  
* Lighting variations: 50-5000 lux;
* Temperature range: 5-40 \({}^{\circ}\)C;
* Vibration stress: 0-80 km/h equivalent (SAE J2380).  
Statistical analysis employed hierarchical linear models to account for participant-level clustering:  
* False-positive rate: 0.08% (95% CI [0.05%, 0.11%], \(\chi\)2(3) = 147.3, \(p\) < 0.001, Cramer's V = 0.72);
* Mean recognition latency: 48.3 ms (95% CI [47.1 ms, 49.5 ms], F(2,31) = 89.4, \(p\) < 0.001, \(\eta^{2}\) = 0.85);
* Emergency gesture recall: 99.1% (95% CI [98.6%, 99.6%], z = 42.7, \(p\) < 0.001, Cohen's d = 3.82).  
Baseline comparisons against conventional architectures revealed the following:  
* HMM baseline: 87.2% accuracy (\(\Delta\) = 11.9%, t(31) = 15.6, \(p\) < 0.001, 95% CI [10.3%, 13.5%]);
* CNN baseline: 93.5% accuracy (\(\Delta\) = 5.6%, t(31) = 8.9, \(p\) < 0.001, 95% CI [4.2%, 7.0%]).
# Game Theory-Inspired Evolutionary Algorithm for Global Optimization
## 4 Performance Comparison and Experimental Results  
### Test Problems and Compared Algorithms  
The C++ language was used to implement GameEA. Our program uses the.NET platform, and the PC was an AMD Phenom(tm) II X4 810 CPU (2.59 GHz) with 2 GB RAM. To evaluate the improvement of the performance of our method, the proposed algorithm was compared with four kinds of algorithms: standard genetic algorithm (StGA) [24], island-model genetic algorithms (IMGA) [25], finding multimodal solutions using restricted tournament selection (RTS) [26], and dual-population genetic algorithm (DPGA) for adaptive diversity control [27], which are famous for their unique performance with different characteristics.  
IMGA is a typical example of multi-population genetic algorithms. This type of algorithm evolves two or more subpopulations and uses periodic migration for the exchange of information among subpopulations. IMGA has multiple subpopulations that evolve separately.  
Regarding RTS, a crowding method that is slightly different from standard crowding is used. This approach randomly selects parents and allows the latest offspring to replace the most similar one using a specific strategy.  
DPGA uses two populations. Among which, the main population evolves to identify a good solution for a given problem, whereas the reserve population evolves to provide controlled diversity to the main population.  
To focus on the searching accuracy and stability, thirteen benchmark functions \(f_{1}\)-\(f_{13}\) shown in Table 2 have been considered in our experiments on real-valued representations, which are widely adopted for comparing the capabilities of evolutionary algorithms. The functions \(f_{1}\)-\(f_{7}\) are unimodal distribution functions with one peak within the entire given domain, the functions \(f_{8}\)-\(f_{13}\) are multimodal functions with a large number of local optima in the searching space. Column \(n\) in Table 2 indicates the dimensions used.
## 4 Discussion
### PTP and Jumping Ability  
The research results indicated an increase in the rate of force development (RFD) in the lower limbs of the experimental group, indicating an improvement in lower-limb explosive power. The increase in ground reaction force (GRF) suggests that augmented training led to changes in the muscle structure of the lower limbs, thereby enhancing leg strength. The increase in lower-limb strength was attributed to augmented training, which scholars have pointed out leads to changes in muscle structure due to increases in muscle fascicle angle and length [11]. Additionally, changes in the stiffness of various elastic components, such as the plantar flexor tendon complex, contribute to this improvement [37]. Another finding was that PT improves the time to muscle activation [38], which helps to enhance muscle strength and jumping ability. This indicated an increase in vertical jump height, as well as lateral and horizontal jump distances [39]. Additionally, many studies have confirmed a positive correlation between jumping ability and sports performance [40; 41; 42]. For example, a basketball player's success in rebounding, dunking, and jump shots depends on their vertical jumping ability [17]. An increase in lateral jumping ability indicates improved lateral change of direction speed and agility in basketball athletes [43]. PT, specifically squat rocket jumps, is the best way to enhance horizontal jumping ability [44].
## 3 Mathematical Equations Used  
The average pressure drop and standard deviation were calculated from the pressure drop data using the following equations:  
\[\overline{\Delta P}=\frac{1}{n}\sum_{i=1}^{n}(\Delta P_{i}), \tag{1}\]  
\[\sigma=\sqrt{\frac{1}{n-1}\sum_{1}^{n}(\Delta P_{i}-\overline{\Delta P})^{2}}, \tag{2}\]  
The pressure drop in the packed bed is described by the well-known Ergun equation:  
\[\frac{\Delta P}{\Delta L}=\frac{150 uU_{0}}{{D_{av}}^{2}}\ \frac{\left(1- \varepsilon\right)^{2}}{\varepsilon^{3}}+\frac{1.75\rho(U_{0})^{2}}{D_{av}} \ \frac{\left(1-\varepsilon\right)}{\varepsilon^{3}}, \tag{3}\]  
where '\(\varepsilon\)' is the bed void fraction, '\(U_{0}\)' is the fluid superficial velocity, '\(\mu\)' is the viscosity, and '\(D_{av}\)' is the average diameter of the agglomerates of nanoparticles.  
Figure 2: Response of the pulsed fluidized bed subjected to a flow pulsation of 0.1 Hz.  
Since the fluidized bed pressure drop is equal to the effective weight of the solid in the bed, one can, therefore, write  
\[\frac{\Delta P}{\Delta L}=\Big{(}\rho_{b}-\rho_{f}\Big{)}\text{g}=(1-\varepsilon) \Big{(}\rho_{p}-\rho_{f}\Big{)}\text{g}, \tag{4}\]  
where '\(\rho_{p}\)', '\(\rho_{f}\)', and '\(\rho_{b}\)' are true solid density, fluid density, and bulk density, respectively.  
\[\varepsilon=1-\frac{M_{bed}}{\rho_{p}V_{bed}} \tag{5}\]  
Here, '\(M_{bed}\)' is the total solid mass in the bed while '\(V_{bed}\)' is the volume of the solids in the bed.  
\[\rho_{b}=(1-\varepsilon)\rho_{p}+\varepsilon\rho_{f} \tag{6}\]  
When the fluidized bed contains a binary mixture of different-sized particles, the average diameter of the particles can be written as  
\[1/D_{av}=X_{1}/D_{1}+X_{2}/D_{2}, \tag{7}\]  
where \(D_{i}\) is the diameter of the \(i^{th}\) component and \(X_{i}\) is the fluid-free volume fraction of the \(i^{th}\) component.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Universal Feature Extraction from Dual- and Compact Polarimetric SAR Data
#### 2.4.3 Degree of Polarization and Ellipticity  
The degree of polarization and ellipticity can be similarly calculated from the Stokes vector of CP SAR mode, as introduced in Section 2.2.
## 4 Conclusions  
Prior work has documented various meshfree methods used in science and engineering problems. However, when applying these meshfree methods in computing the SCF and stress intensity factor at notch of welds, the results are sensitive to the parameters used in these methods such as the existence of notch stress singularity. It is not realistic to calculate the stress concentration factor or stress intensity factor at welds by stress solution of the conventional RPIM.  
In this study, an improved RPIM which modified the stress calculation process was presented to realize a process of obtaining consistent stress solutions at welds. The process of computing SCF and notch stress intensity factor at the weld toe based on the improved RPIM stress solution was presented. In the improved RPIM, the stress solutions in the form of membrane stress and bending stress were obtained directly from field nodes forces in order to avoid using derivatives of shape functions. Conclusions were summarized as follows.  
1. The generalized nominal stress of beam at general location without concentration effect can be computed consistently and effectively based on the stress solution of the improved RPIM
2. The SCFs and stress intensity factors results at the weld toe can be computed consistently and effectively based on stress solution of the improved RPIM.
3. Both the SCF and stress intensity factor results are insensitive to parameters such as shape parameters, dimension of the influence domain and number of field nodes.
4. In addition, we also found that EFG with the same improved technique was also able to realize the process of obtaining consistent stress solutions at welds.  
The main novelty of our studies is to provide an effective approach to apply meshfree methods in computing SCF and notch stress intensities at welds consistently. However, there are some limitations worth noting. Both RPIM and EFG are global weak-form meshfree methods. Further work should include how to apply local weak-form meshfree methods in consistently and effectively computing SCF and stress intensity factor at welds.  
Conceptualization, F.B. and B.C.; methodology, F.B.; validation, F.B., Y.Z. and X.G.; writing--original draft preparation, F.B.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We would like to thank the supports from the State Key Laboratory of Structural Analysis for Industrial Equipment of China.  
The authors declare no conflict of interest.
## Background
### ISO 19906 Life-Safety Classes, Consequences, and Exposure  
The risk-based approach employed by ISO 19906 for the design of Arctic offshore structures specifies that the reliability of a structure should reflect its exposure level with respect to life-safety, environmental, and economic consequence categories. For a given exposure level, extreme and abnormal level environmental loads corresponding to specified exceedance probabilities are determined. Calibrated action and material/resistance factors are then applied to determine the design actions corresponding to structural limit states specified in the code.  
In ISO 19906, the life-safety category of an asset takes into consideration the safety of personnel and the probability of a safe evacuation. Three life-safety categories are defined for Arctic offshore structures: S1 (manned non-evacuated), S2 (manned evacuated), S3 (unmanned).  
Similarly, the consequence category of an asset takes into consideration the potential risks in relation to the safety of personnel responding to an incident, environmental damage, and economic loss. Three consequence categories are defined in ISO 19906 for Arctic offshore structures: C1 (high consequence), C2 (medium consequence), C3 (low consequence).  
The exposure level of an asset is then determined as a function of the assessed life-safety and consequence categories. Table 1 is used to determine the exposure level as a function of life-safety categories and consequence categories [11].  
Manned non-evacuated (S1) refers to Arctic offshore structures in which there is no planned evacuation of personnel prior to a forecasted design environmental loading event. Manned evacuated (S2) refers to a platform in which evacuation of personnel prior to a forecasted design environmental loading event is planned. Unmanned (S3) refers to a platform that is not normally manned [11].  
For offshore structures, the life-safety and consequence categories and corresponding exposure levels are defined during the design process and influence the structural capacity of the design to achieve target safety levels. The application proposed here is for management of operational risk of Arctic ships. When the perceived risk of operating a vessel in ice is increased, additional operational restrictions are required to maintain an equivalent risk level.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Life-Safety** & \multicolumn{3}{c}{**Consequence Category**} \\ \cline{2-4}
**Category** & **C1 High** & **C2 Medium** & **C3 Low** \\ \hline S1: manned non-evacuated & L1 & L1 & L1 \\ S2: manned evacuated & L1 & L2 & L2 \\ S3: unmanned & L1 & L2 & L3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Determination of exposure level (based on International Organization of Standardization (ISO) 19906 [11]).
## 2 Closed Systems  
We start with a closed quantum system whose states are represented by the vectors of a Hilbert space \(\mathcal{H}\) and dynamics is given by the Schrodinger equation, \(i\hbar\partial_{t}|\psi\rangle=H|\psi\rangle\). The ket \(|\psi\rangle\in\mathcal{H}\) and bra \(\langle\psi|\in\mathcal{H}^{\dagger}\) stand for the same physical state. The sum over the basis vectors in the expectation value in a pure state,  
\[\langle\psi|A|\psi\rangle=\sum_{m,n}\langle\psi|m\rangle\langle m|A|n\rangle \langle n|\psi\rangle, \tag{1}\]  
represents the sum over quantum fluctuations, filtered by a given basis set. Since the coefficient of \(\langle m|A|n\rangle\) in the sum, the pure state density matrix, is factorized, \(\langle n|\rho|m\rangle=\langle n|\psi\rangle\langle\psi|m\rangle\), the quantum fluctuations of the bra and the ket components are independent of each other in a pure state. The bra and the ket are related by Hermitian conjugation; hence, it is sufficient to follow the time dependence only for one of them.  
We further suppose that a symmetry group \(G\) of the closed dynamics is represented by the basis transformations of \(\mathcal{H}\); there are unitary operators acting on the pure states, \(|\psi\rangle\to U(g)|\psi\rangle\), \(\langle\psi|\to\langle\psi|U^{\dagger}(g)\), and operators \(A\to U(g)AU^{\dagger}(g)\), \(g\in G\) in such a manner that they preserve the scalar product, the matrix elements, and leave the form of the Schrodinger equation invariant, \(H=U(g)HU^{\dagger}(g)\). Thus, the symmetry group of the closed dynamics on pure states is \(G\).  
The mixed states are represented by density matrices, represented by certain elements of the Liouville space, \(\mathcal{A}=\mathcal{H}\otimes\mathcal{H}^{\dagger}\), equipped by the scalar product \(\langle\langle A|A^{\prime}\rangle\rangle=\text{Tr}[A^{\dagger}A^{\prime}]\). The density matrices belong to \(\mathcal{A}_{\rho}\), a convex subset of \(\mathcal{A}\), containing the positive, Hermitian operators with unit trace. The elements of \(\mathcal{A}\) are called state components. The expectation value of the observable \(A=A^{\dagger}\) in the state \(\rho\in\mathcal{A}_{\rho}\) is given by the Liouville space scalar product, \(\text{Tr}[\rho A^{\dagger}]=\langle\langle A|\rho\rangle\rangle\).  
In the case of a symmetry, we introduce the extended basis transformations, which act with different symmetry group elements on the bra and the ket, \(|\psi\rangle\to U(g_{+})|\psi\rangle\), \(\langle\psi|\to\langle\psi^{\prime}|U^{\dagger}(g_{-})\) and transform the operators as \(A\to\mathcal{U}(g_{-},g_{+})A=U(g_{-})AU^{\dagger}(g_{+})\). It is easy to see that the scalar product is preserved by these transformations and that the Neumann equation, \(i\hbar\partial_{t}\rho=[H,\rho]\), remains invariant as well,  
\[U(g_{-})[H,\rho]U^{\dagger}(g_{+})=[H,U(g_{-})\rho U^{\dagger}(g_{+})], \tag{2}\]  
reflecting the independence of the bra and ket dynamics of closed dynamics. Hence, the symmetry group of the mixed state descriptions of a closed dynamics is \(G\otimes G\).
## 3 Results
### Salinas Beach
#### 3.2.3 Change Detection from Optical WLs in Salinas  
The available number of optical (CoastSat) WLs is significantly lower than the number of available SAR DESC SLs for the same period (see Table 6), which hampers the ability of CoastSat WLs to detect intra-annual morphological changes. No seasonal pattern is observed in the CoastSat WLs, nor in the datum-based CoastSat SLs projected onto the MLW.  
As for the inter-annual changes in the beach profile, in the inter-tidal area where the CoastSat WLs are detected, the following qualitative morphodynamic behaviors are observed in the four in situ topographic surveys (see Figure 16a, upper panel):  
* In May 2016, the beach profile was in the most advanced position, showing maximum beach accretion.
* In September 2019, the beach profile migrated inland, showing maximum beach erosion.
* In September 2021, the beach profile migrated seaward to an intermediate position.
* In August 2022, the beach profile migrated further seaward, showing maximum beach accretion, like in May 2016.  
Similar to the previous analysis, the horizontal distances from the baseline of the CoastSat WLs detected during 3 months around the date of the in situ topographic survey are represented in the heatmaps that show the concentration of CoastSat WLs in 14 sections of the inter-tidal beach profile (see Figure 16a, lower panel). Changes over time in the area with the highest concentration of CoastSat WLs in each period (over 20%) do not show any erosional or accretional behavior. The same is observed when using the CoastSat SLs corrected to the MLW datum. The correction from WLs to SLs, commonly applied to improve coastal change detection from optical images, did not reduce the dispersion of the CoastSat SLs across the beach profile in comparison to the CoastSat WLs (see Figure 16b).  
\begin{table}
\begin{tabular}{c c c} \hline
**Time Frame** & **SAR DESC SL** & **CoastSat WL** \\ \hline April–June 2016 & 9 & 7 \\ August–October 2019 & 31 & 8 \\ August–October 2021 & 29 & 3 \\ July–September 2022 & 14 & 10 \\ Total No. of records & 83 & 28 \\ \hline \end{tabular}
\end{table}
Table 6: Available SAR DESC SLs and CoastSat WLs within a 3-month time frame around the date of the in situ topographic surveys.
## 2 Related Work
### Localization-Free Routing Protocols
To overcome the issue of imbalanced data traffic, authors introduced radius competition strategy based on the residual energy and distance of the node from the sink. The cluster head is elected with highest energy among the neighbor nodes. Moreover, the number of neighbors purely depends on the distance from the sink because the smaller the distance is, higher will be the data traffic, thus smaller radius is picked to form a cluster for efficient energy consumption. Similarly, energy efficiency is achieved via discovering most reliable path in the network using the sink nodes deployed at the surface of the water [22]. This scheme advances the discovery of path by initiating the data packet from the source node to next hop node placed towards the destination. The process continues until surface is reached and gets helpful information from the surface node. Additionally, for an efficient node battery utilization, splice method is introduced to compare the energy consumption associated with each link. After comparison, the shortest path with minimal energy required to deliver data successfully is picked to resume the nodes communication.
###### Abstract  
With the increasing number of mobile subscribers worldwide, there is a need for fast and reliable algorithms for planning/optimization of mobile networks, especially because, in order to maintain a network's quality of service, an operator might need to deploy more equipment. This paper presents a quick and reliable way to automatically plan a set of frequencies in a cellular network, using both cloud technologies and linear programming. We evaluate our pattern in a realistic scenario of a Global System for Mobile communications protocol (GSM) network and compare the results to another already implemented commercial tool. Results show that even though network quality was similar, our algorithm was twelve times faster and used four times less memory. It was also able to frequency plan seventy cells simultaneously in less than three minutes. This mechanism was successfully integrated in the professional tool Metric, and is currently being used for cellular planning. Its extension for application to 3/4/5G networks is under study.  
cellular-planning; cloud-services; implementation; integer linear-programming; monitoring; optimization +
Footnote †: journal: Applied Sciences