esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 2 Evaporites and Bedded Evaporite Deposits
### Bedded Enaporite Deposits and Caverns in the U.S. and Canada  
Salt deposits used for constructing salt caverns can be either salt domes or salt beds. Initially, all salt deposits are formed as salt beds; over time, salt domes develop when salt flows through overburden layers [106]. Figure 9 shows caverns developed in both salt domes and bedded salt. As seen in the figure, solution mining methods, cavern shape, and the presence of insoluble interlayers are the primary differences between caverns in bedded salt and those in salt domes.  
Figure 9: Salt caverns in **(a)** salt domes and **(b)** bedded salts [107].  
Salt domes are geographically limited in distribution. The locations of salt domes and salt beds in the U.S. are shown in Figure 10. With the increasing importance of underground energy storage, investigating and developing caverns in bedded salts is crucial.
# Configuration Design and Gait Planning of a Six-Bar Tensegrity Robot
## 4 Gait Planning of the Six-Bar Tensegrity Robot
### Rolling Principle Analysis  
The six-bar tensegrity robot is composed of rigid rods and elastic cables. When the length of the rod member changes, the cable member will change so that the entire structure is deformed and the center of gravity of the robot is offset. When the projection of the center of gravity leaves the bottom landing triangle, the robot rolls over to another triangle landing state along the direction of the center of gravity offset.  
First, the center of gravity (CG) is projected onto the open triangular plane at the bottom of the structure, and for each projected surface, the distance between the projected point of the center of gravity and the three sides of a closed triangle is measured, as shown in Figure 7.  
If the projected point of the center of gravity can move beyond a side to the outside of the triangle, the distance is measured as a negative value, and this distance also becomes the gravitational moment, which can be calculated through Equation (22):  
\[\mathbf{T}_{i,j}=\mathbf{G}\frac{\mathbf{L}_{i,j}\times\mathbf{L}_{\text{G},i }}{\mathbf{L}_{i,j}}\;(i,j)\in[1,12] \tag{22}\]  
where \(\mathbf{T}_{i,j}\) is the gravitational moment projected from the center of gravity to the \((i,j)\) side; \(\mathbf{G}\) is the robot gravity; \(\mathbf{L}_{i,j}\) is the \((i,j)\) side vector; \(\mathbf{L}_{\text{G},i}\) is the center of gravity line vector with \(i\), and \(\mid\mathbf{L}_{i,j}\mid\;\) is the length of the \((i,j)\) side.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node Number** & \(\boldsymbol{x}\)**Coordinate/m** & \(\boldsymbol{y}\)**Coordinate/m** & \(\boldsymbol{z}\)**Coordinate/m** \\ \hline
1 & 0.100 & 0.000 & 0.000 \\
2 & \(-\)0.500 & 0.087 & 0.000 \\
3 & \(-\)0.050 & \(-\)0.087 & 0.000 \\
4 & \(-\)0.017 & 0.099 & 0.200 \\
5 & \(-\)0.077 & \(-\)0.064 & 0.200 \\
6 & 0.094 & \(-\)0.034 & 0.200 \\
7 &According to the above analysis, it can be concluded that when the gravity moment of the robot is greater than 0, the center of gravity of the robot is located in the bottom triangle and cannot roll; when the gravity moment of the robot is equal to 0, the center of gravity is on the edge of the triangle and is in a critical state; when the gravitational moment of the robot is less than 0, the center of gravity of the robot deviates from the bottom triangle to achieve rollover, and the smaller the value of the gravitational moment is, the more easily the robot rolls.
## 5 Experiments and Evaluation
### Experimental Setup
The Theil-Sen Regressor algorithm estimates the slope and intercept of the linear relationship between the input features (attention vector) and the target variable. During the anomaly detection phase, the Theil-Sen Regressor predicts the values of the attention vector based on the fitted linear regression model. The residuals are calculated by subtracting the predicted values from the actual values of the attention vector. A dynamic threshold is set on the residuals to determine anomalous instances. The threshold is determined using a rolling mean and standard deviation. Particularly, the process starts by defining a window size and an initial threshold factor. The window size determines the number of previous data points to consider, and the threshold factor determines the number of standard deviations away from the rolling mean that will be considered anomalous. Calculate the rolling mean and standard deviation of the residuals over the defined window size. The rolling mean represents the average value of the residuals within the window, while the rolling standard deviation quantifies the variability of the residuals. Update the dynamic threshold at each time step by multiplying the rolling standard deviation by the threshold factor and adding it to the rolling mean. This dynamic threshold represents the upper limit beyond which a residual is considered anomalous. Compare the absolute value of each residual to the dynamic threshold. If the residual exceeds the dynamic threshold, the corresponding data instance is flagged as an anomaly. Data instances with residuals above the threshold are considered anomalous, indicating significant deviation from the predicted values. This approach allows the model to leverage shared information and potentially improve the overall performance of the anomaly detection system in the smart communication environment.
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 2 Problem Statement and System Model
### Energy Consumption Model  
In 3D wireless self-organizing networks, most of the energy consumed by nodes is used for communication, so the energy used by nodes for computation, storage, and sleep is negligible. Since many factors can interfere with node communication in practical application scenarios, a threshold \(r_{d0}\) was introduced in the energy consumption model to run different models, which is calculated as shown in Equation (4):  
\[r_{d0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{4}\]  
where \(\varepsilon_{fs}\) denotes the free-space channel model signal amplifier power consumption and \(\varepsilon_{mp}\) denotes the multi-path fading channel model signal amplifier power consumption, which are used in this paper to determine the selection thresholds for different channel models, taking the values of 10 \(\times\) 10\({}^{-12}\) J and 1.3 \(\times\) 10\({}^{-15}\) J, respectively. The energy consumed by a node in sending \(k\) bit data to a node \(r_{d}\) apart is given in Equation (5):  
\[E_{TX}(k,r_{d})=\left\{\begin{array}{l}E\times k+\varepsilon_{fs}\times k \times d^{2},r_{d}<r_{d0}\\ E\times k+\varepsilon_{mp}\times k\times d^{4},r_{d}>r_{d0}\end{array}\right. \tag{5}\]  
When the distance \(r_{d}\) between two nodes is less than the threshold \(r_{d0}\), the free-space propagation model is run, and vice versa, the multi-path fading channel model is run. The energy consumed by a node when it receives \(k\) bit data is shown in Equation (6):  
\[E_{RX}(k)=E\times k \tag{6}\]  
where \(E\) is the energy the node requires to send/receive a unit bit of data.
## 2 Methods
### Schur-Based Substructuring Methods
#### 2.2.3 One Schur Complement Approach  
We can improve the former approach by imposing the boundary conditions after the condensation of the problem since we only need to compute (and store) one Schur complement. The procedure that we follow in this approach is:  
1. Generate the unknowns following the same hierarchy as in Section 2.2.1.
2. Compute the integral terms related to \(\Omega\) in each element from (9), i.e., without including any boundary term.
3. Extract the subset of unknowns that belongs to any boundary (not only the interfaces between cells but also those of the boundaries of the original problem domain \(\Omega\)).
4. Condense the problem by computing the Schur complement of this generic unit cell.  
Once we have the matrix \(S\), we assign the different boundary conditions for each cell. Let us follow the three-step procedure detailed in Section 2.2, being \(g_{2}\) all the unknowns extracted in the third step of the former procedure. We have to distinguish between the three boundary conditions considered in Section 2.1:  
* For Dirichlet boundary conditions in (5a), we directly remove the unknowns.
* For Neumann boundary conditions in (5b), we do not need to add anything since it is a natural boundary condition.
* For Cauchy boundary conditions in (5c), we can use the fact that the Schur complement is computed as \(S=L_{2,2}-L_{2,1}L_{1,2}^{-1}L_{1,2}\). Since all the unknowns related to the Cauchy boundary condition belongs to \(g_{2}\), the only block that changes in the original matrix  
Figure 3: Illustration of the approach followed in Section 2.2.2. Each color means a different Schur complement, showing 9 different complements.  
is \(L_{2,2}\). If we call \(C\) to this change, we would obtain that the block \(L_{2,2}^{C}\) (with Cauchy boundary conditions assigned) is \(L_{2,2}^{C}=L_{2,2}+C\), so the difference when adding the Cauchy boundary condition to the Schur matrix is that same contribution, i.e., \(S^{C}=S+C\). Analogously, on the right-hand side, the only term that changes is \(f_{2}\) from (13), so we compute the sixth term in (9) and obtain the reduced right-hand side \(y_{2}\).  
Thus, we only need to condense the problem once, and then, we particularize this condensed problem to each unit cell. If we take the same example as Section 2.2.2, we would obtain the same \(S\) for all the domains, which would be valid for cells 5 and 6, whereas for cell 1 we would compute the terms related to the absorbing boundary condition on the south face and add it to that generic \(S\).  
This approach improves the procedure explained in Section 2.2.2 since we store only one Schur complement and we add the boundary conditions on the reduced set of unknowns, which is computationally cheap. In addition, we can tune the phase of each excitation in every unit cell without needing to compute a different Schur complement, as would be the case in Section 2.2.2. Here, we only need to include the unknowns associated to the waveport to the subset of unknowns \(g_{2}\). This is extremely useful when having an array of antennas since we can steer the main beam of radiation.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 1 Introduction
### Related Works
The classical job-shop-scheduling problem (JSP) is one of the most well-known scheduling problems. It consists of \(m\) machines and \(n\) jobs. A job contains several operations to be processed in a fixed order. In the JSP, each operation can be processed by one specific machine [23; 24]. The flexible job shop scheduling problem (FJSP) is an extension of the JSP, which allows an operation to be processed by one of two or more machines. In other words, an operation is processed by one of the alternative machines in the FJSP [25]. Since a machine is predetermined for a specific operation, the JSP can be solved by specifying the priority of given operations such that a high-priority operation precedes others with a lower priority in the queue for given machines. Thus, the FJSP can be regarded as a sequencing problem, suitable for intelligent algorithms [26; 27]. The genetic algorithm based on candidate sequence (COGA) is adopted to solve the FJSP [28]. Developed by [PERSON] and [PERSON] in 1995, particle-swarm optimization (PSO) is a population-based stochastic optimization technique inspired by the swarming behavior characteristic of bird flocks or fish schools [29]. PSO has recently been applied to COPs, such as shop scheduling [30; 31], the traveling-salesman problem [32], quality-of-service multicast routing [33], and vehicle routing [34; 35]. PSO is usually used to solve continuous problems, and discretization is required when solving COPs. [PERSON] et al. use PSO to solve the assembly JSP, design a discrete particle swarm optimization algorithm (DPSO) to realize the discretization of the position-update process through operations such as insertion and exchange. The Metropolis criterion in the simulated annealing algorithm (SA) is used to set the acceptance probability of the location update [36]. [PERSON] et al. used GAs and DPSOs to manage the complexity of the problem, compute feasible and quasi-optimal trajectories for mobile sensors, and determine the demand for movement among nodes [37]. [PERSON] et al. used DPSOs combined with the genetic operators of GAs to compute feasible and quasi-optimal schedules for directional sensors and to determine the sensing orientations among the directional sensors [38]. The above studies use other intelligent algorithms such as the SA and GA to hybrid with PSO, replacing particle updates with intelligent-algorithm operations. We need to make targeted improvements to the DPSO for specific problems.
Sensors_**2013**, _13_, 7308-7322; doi:10.3390/s130607308  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Optimized Handover Scheme with Movement Trend Awareness for Body Sensor Networks**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Electrical and Computer Engineering, National University of Singapore,  
Singapore 119613, Singapore; E-Mail: [PERSON]S]  
\({}^{2}\) Department of Computing, Imperial College, London SW7 2 AZ, UK;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Graduate University of Chinese Academy of Sciences, Beijing 100049, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +65-6516-1076.  
_Received: 28 March 2013; in revised form: 26 May 2013 / Accepted: 29 May 2013 / Published: 3 June 2013_  
**Abstract:** When a body sensor network (BSN) that is linked to the backbone via a wireless network interface moves from one coverage zone to another, a handover is required to maintain network connectivity. This paper presents an optimized handover scheme with movement trend awareness for BSNs. The proposed scheme predicts the future position of a BSN user using the movement trend extracted from the historical position, and adjusts the handover decision accordingly. Handover initiation time is optimized when the unnecessary handover rate is estimated to meet the requirement and the outage probability is minimized. The proposed handover scheme is simulated in a BSN deployment area in a hospital environment in UK. Simulation results show that the proposed scheme reduces the outage probability by 22% as compared with the existing hysteresis-based handover scheme under the constraint of acceptable handover rate.  
**Keywords:** localization; handover; body sensor network
## 2 Materials and Methods
### Cultivation Medium  
A synthetic must MS300 was used for the fermentations, adapted from [PERSON] and colleagues [23]. The base medium, adjusted at pH 3.3, thus contained the following components (expressed per liter): glucose 100 g, fructose 100 g, DL malic acid 6 g, citric acid 6 g; mineral salts: KH\({}_{2}\)PO\({}_{4}\) 750 mg, KH\({}_{2}\)SO\({}_{4}\) 500 mg, MgSO\({}_{4}\) 7H\({}_{2}\)O 250 mg, CaCl\({}_{2}\) 2H\({}_{2}\)O 155 mg, NaCl 200 mg; nitrogen compounds: NH\({}_{4}\)Cl 460 mg, L-tyrosine 18 mg, L-tryptophane 179 mg, L-isoleucine 33 mg, L-aspartic acid 45 mg, L-glutamic acid 120 mg, L-arginine 374 mg, L-leucine 48 mg, L-threonine 76 mg, L-glycine 18 mg, L-glutamine 505 mg, L-alanine 145 mg, L-valine 45 mg, L-methionine 31 mg, L-phenylalanine 38 mg, L-serine 79 mg, L-histidine 33 mg, L-lysine 17 mg, L-cysteine 13 mg; trace elements: MnSO\({}_{4}\) 7H\({}_{2}\)O 4 mg, ZnSO\({}_{4}\) 7H\({}_{2}\)O 4 mg, CuSO\({}_{4}\) 5H\({}_{2}\)O 1 mg, KI 1 mg, CoCl\({}_{2}\) 6H\({}_{2}\)O 0.4 mg, H\({}_{3}\)BO\({}_{3}\) 1 mg, (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\) 1 mg; anaerobic growth factors: ergosterol 3 mg, oleic acid 1 mg, Tween 80 100 mg; vitamins: myo-inositol 20 mg, pantothenic acid hemi-calcium salt 1.5 mg, nicotinic acid 2 mg, pyridoxine 0.25 mg.  
In order to evaluate the impact of both thiamine and biotin on the production of volatile compounds, this base medium was declined under nine different variations, relying on three different concentration modalities for each of the vitamins: a total absence of the given vitamin, a concentration fit to trigger deficiencies in yeasts [24; 25], and the original concentration of the MS300 as described by [PERSON] et al. (2013). The nine different cultivation medium variations are described in Table 1.
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 5 Simulation Results  
In this section, four UAVs are considered to locate the static and movable emitter to verify the sensor-emitter geometries, respectively. MATLAB simulations are implemented with a 2.7 GHz Intel core processor with 8 GB of memory. The initial UAVs state vectors are \(\chi_{1}(1)=[-9600,-5000]^{T}\), \(\chi_{2}(1)=[-10000,\ -5000]^{T}\), \(\chi_{3}(1)=[-10000,\ -5400]^{T}\) and \(\chi_{4}(1)=[-9600,\ -5400]^{T}\). The headings for UAVs are all equal to \(\pi/2\)(north) at the initial moment and other key parameters are listed in Table 1.  
Figure 3: UAV path planning for localization based on CRLB (TDOA: time-difference-of-arrival; SDP: semidefinite programming; EKF: extended Kalman filter).
###### Abstract  
To solve the problem of location services in harsh environments, we propose an integrated navigation algorithm based on broadband low-earth-orbit (LEO) satellite communication and navigation integration with 3-satellite alternate switch ranging. First, we describe the algorithm principle and processing flow in detail; next, we analyze and model the ranging error source and propose a combined multipath and non-line-of-sight (NLOS) error analysis model, which avoids discussing the complex multipath number of paths and its modeling process; in addition, we also propose a multimodal Gaussian noise-based interference model and analyze and model the LEO satellite orbital disturbance. The final simulation results show that our proposed algorithm can not only effectively overcome inertial navigation system (INS) divergence, but also achieve high positioning accuracy, especially when continuous ranging values are used. It can still ensure good anti-interference performance and robustness in terms of path and noise interference and by alternately switching ranging, there are other potential advantages. Compared to some of the existing representative advanced algorithms, it has higher accuracy, stronger stability and lower cost. Furthermore, it can be used as a location reference solution for real-time location services and life search and rescue in harsh environments with incomplete visual satellites and can also be used as a technical reference design solution for the future integration of communication and navigation (ICN).  
3 -satellite; integrated navigation; LEO; INS; switching +
Footnote †: journal: Advances in Neural Information Processing Systems
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 7 Conclusions  
In this article, we reviewed the existing QL-based position-aware routing protocols for FANETs along with their advantages and limitations according to the characteristics of the dynamic topology. We found that incorporating QL with position-based routing protocols significantly improves the routing performance in terms of energy consumption, end-to-end delay, local minimum avoidance, and routing loop avoidance. Additionally, the QL technique improves the PDR, minimizes the control overhead, and provides tolerance to localization error. The surveyed protocols were qualitatively compared in terms of their objectives, innovation features, and several important performance metrics. In addition, we discussed important performance improvement criteria such as precise SINR, delay calculation, multi-objective reward function, self-healing, and robustness. From our comparative discussion, it was inferred that researchers or engineers can make a choice of an appropriate routing protocol by taking not only their target applications but also their primary performance metrics. Our comparative results and relevant discussion will help them to make such a choice more effectively. In addition, we discussed several important open issues and research challenges along with their potential research directions for improving the routing performance in FANETs. We also found that making the routing decision jointly consider the path stability, PTS, and residual energy level of UAVs enhances the routing protocol performance in highly dynamic FANETs.  
The individual contributions of the authors are as follows. [PERSON] reviewed, analyzed, and summarized the Q-learning-based routing protocols for FANETs. [PERSON] directed the research and contributed to the review and analysis of the routing protocols. The paper was drafted by [PERSON] and subsequently revised and approved by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by a research fund from Chosun University (2021).  
Not applicable.
## 2 The Overall Strategy of D-PMU Placement
### The Overall Strategy of D-PMU Placement in Distribution Network  
In order to realize the large-scale application and placement of D-PMU in the distribution network, the OPP method should not only take the effect of state estimation and economic efficiency into account, but it should also integrate with present measurement systems, such as SCADA and AMI. The existing measurement system is shown in Figure 1. D-PMU is generally installed on the bus of a medium-voltage distribution network. Different from SCADA and AMI, it can provide phasor information of bus voltage and branch current at high frequency. Therefore, the optimal placement of D-PMUs can realize dynamic observation of distribution networks. The key problem of OPP is studying which buses should be placed with a D-PMU to realize reliable and dynamic observations of the distribution network.  
In the optimization process, injection and power flow measurements provided by the SCADA system should be further applied to the OPP model, which can improve network observability [21]. AMI provides measurement data in the distribution network, such as node injection power pseudo-measurements. However, the sampling frequency of AMI measurements is low, and these measurements are usually used in the observability analysis of some non-essential areas of the distribution network.  
With the continuous development of DGs and demand side management, the distribution network adjusts its network structure by operating segment switches and tie switches. This can reduce the cost of the system's operation and improve the consumption capacity of renewable energy. Therefore, the frequency of topological changes continues to increase. It is necessary to consider the impact of multiple topology observability on D-PMU placement.  
Considering the factors above, the main process of D-PMU placement is shown in Figure 2. Firstly, some D-PMUs are preferentially placed on important buses. Secondly, an OPP model that can adapt to topology changes, and which is compatible with other measurement systems and can achieve multiple objectives, is established. Then, a corresponding intelligent algorithm is selected to solve the model. Finally, the most appropriate D-PMU placement order is calculated to guide the phased construction.  
Figure 1: The structure of the existing measurement system.  
Figure 2: The main process of D-PMU placement.