esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Opportunistic Routing Protocol for Sensor Network in Emergency Applications
## 3 Proposed Opportunistic Routing Protocol
### Routing  
Once clusters are constructed, nodes start to collect data and, hence, data transmission or routing starts. If a node becomes isolated from a cluster due to mobility (known as a noise node) cluster restoration is required before the node can transmit data.  
Scenario 1: Whenever a node \(x\) has some data to transmit, if it is a member node of a cluster, it transmits data to its CH. If node \(x\) is isolated or border node of a cluster it first tries to restore the connectivity (Section 3.4) and then transmits data. Otherwise, it opportunistically forwards data to a neighboring node.  
Scenario 2: Whenever a node \(x\) receives a data packet from other nodes, (1) if \(x\) is a member node of a cluster it communicates with the CH and transmits data to the CH. Then, the CH transmits data to the next hop towards the destination (2) if the node \(x\) is a border node, it is a noise, which is either a member of a cluster or not. If \(x\) is a member of a cluster it transmits data to the CH. If \(x\) is not a member of a cluster it must be within the communication range of two neighboring clusters. Then, the node \(x\) broadcasts a message to find out the closest neighboring cluster and joins a cluster by restoring connectivity (presented in Section 3.4).  
Scenario 3: The node might also loose contact with other nodes and become isolated. Figures 3-5 illustrate this scenario. In Figure 3, the node \(z\) is not a member of a cluster. Whenever it senses an event of interest or receives data from other nodes, it forwards data to the closest neighboring/forwarder node \(y\). However, the node \(y\) is isolated and has no contact with other nodes. As the node \(y\) moves due to mobility and reaches the communication range of node \(x\), it immediately forwards data to node \(x\) (Figure 4). Then, node \(x\), being a noise (border node trying to join to a cluster), forwards data to the member nodes and CH of a cluster towards the destination, as illustrated in Figure 5.  
Figure 4: Opportunistic message forwarding.  
Figure 3: Disaster network topology.  
Forwarder node selection: During routing, a node may need to select a forwarder node. To do so, each node keeps the MPC value with all of its neighboring nodes and opportunistically forwards data to the neighboring node with the highest MPC. As this protocol is applicable to a disaster situation where all nodes are moving almost at the same speed towards the location of the emergency or disaster, they are more likely to keep communicating with a number of nodes. Thus, the source node that is the initiator of data transmission should be opportunistically able to transmit data to the destination by communicating with those nodes.
###### Abstract  
The recent development of maritime transport has resulted in the demand for a wider communication bandwidth being more intense. Cognitive radios can dynamically manage resources in a spectrum. Thus, building a new type of maritime cognitive radio communication system (MCRCS) is an effective solution. In this paper, the enhanced dynamic spectrum allocation algorithm (EDSAA) is proposed, which is based on the Cournot game model. In EDSAA, the decision-making center (DC) sets the weights according to the detection capability of the secondary user (SU), before adding these weighting coefficients in the price function. Furthermore, the willingness of the SU will reduce after meeting their basic communication needs when it continues to increase the feasable spectrum by adding the elastic model in the SU's revenue function. On this basis, the profit function is established. The simulation results show that the EDSAA has Nash equilibrium and conforms to the actual situation. It shows that the results of spectrum allocation are fair, efficient and reasonable.  
c pacs: 14.20.-c, 11.30.-  
a game method that uses the terminal power of SUs to establish a trust mechanism, but this method is mainly applied on handheld devices, which require low power consumption.  
Marine wireless channels change quickly and there are frequent disturbances, which are due to the typical complex electromagnetic environment [12]. Therefore, a single SU's detection of PU involves a certain degree of misjudgment probability. Therefore, we propose the enhanced dynamic spectrum allocation algorithm (EDSAA) based on the Cournot game.  
Considering that the maritime wireless communication system is a short-range regional communication system, we first construct a regional multi-user cooperative spectrum allocation system model based on the decision-making center (DC). The DC can evaluate the accuracy of each SU by a new mechanism in order to adaptively adjust the weight of the SU in the game process. In addition, the SU's income is usually non-linear in the game process. Thus, we use the elastic model to construct the corresponding profit function to make it more suitable for the actual situation. Finally, the simulation results show that the EDSAA exists in a Nash equilibrium. Furthermore, the algorithm can filter out the SUs with relatively low detection capabilities in the whole region. This is conducted according to the priority coefficients obtained by the adaptive adjustment of the algorithm, which provides the basis for system optimization.  
The rest of this paper is organized as follows. The system model is introduced in Section 2. In Section 3, the Game theory model is introduced, the new profit function is established with the modified revenue function and price function in addition to the Nash equilibrium being solved. Section 4 demonstrates the simulation results and conclusions are given in Section 5.
## 2 Modeling of Residual Life Prediction for Electrical Connector
### Intermitt Failure Dynamics of Electrical Connectors  
In practical engineering applications, the \"severity\" of intermittent failure will grow with the degradation of connectors. For example, the frequency, duration and magnitude of intermittent failure increase. The typical evolution of intermittent failure dynamics is shown in Figure 3.  
The whole life of electrical connectors is simply divided into three stages. The four-wire method is introduced to test the contact resistance under three stages, respectively. The curves of resistance are shown in Figure 4.  
Traditionally, only one feature is selected to characterize the \"severity\" of intermittent failure. However, a single feature is not sufficient for intermittent failure characterization. Therefore, we are committed to extract a more effective feature from contact resistance data for intermittent failure characterization.  
Considering the characteristics of intermittent failure, such as frequency, amplitude and duration, we use the area enclosed by the contact resistance curve and the fault threshold for intermittent failure characterization--called the area of intermittent failure in this paper. From the previous literature and measured results, it is found that intermittent failure is closely related to the degradation of the electrical connector. With the deterioration of the electrical connector, the area of intermittent failure will increase in general; the process is shown in Figure 5.  
Figure 4: The curve of contact resistance under different states of the electrical connector.  
Figure 5: The “severity” of intermittent failure increases with the degradation of the using state.  
Figure 3: Evolution of intermittent failure dynamics [11].  
Simply extracting the area of intermittent failure at any time to predict the residual life of the electrical connector, the prediction result may have a great deviation from the real residual life, so a statistical model needs to be used for modeling the using state of the electrical connector. The Hidden Markov Model (HMM) is an important statistical method, which is especially suitable for describing stochastic processes with state transitions. Similar to HMM, with the degradation of the using state, the area of intermittent failure shows a specific distribution characteristic. To facilitate the prediction of the residual life of electrical connectors, the Hidden Semi-Markov Model (HSMM) is introduced for modeling the using state of electrical connectors.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 1 Introduction
In this paper, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the borehole data, especially in the transition areas of the geological surface of coal seam to ensure the integrity of the transition data. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated data. Our method can be used to generate the coal-rock interface and predict the coal seam thickness, where the data will be used to guide the shearer drum to automatically adjust the coal cutting height in automatically and intelligently coal mining.
## 2 Materials and Methods
### Prediction Models and Accuracy Assessment
#### 2.6.4 Model Accuracy  
The prediction performance of models was evaluated using R\({}^{2}\), RMSE, the ratio of performance to deviation (RPD), and the ratio of performance to the interquartile distance (RPIQ) developed by [PERSON] et al. [86]. Higher R\({}^{2}\) and lower RMSE indicate a  
Figure 4: Schematic diagram: (**a**) The Random Forest model structure (adapted from [PERSON] et al. (2022) [101]); (**b**) The DNN model architecture (adapted from [PERSON] et al. (2017) [103]).  
higher model prediction accuracy. As suggested by [PERSON] et al. (2019), predictions of soil property models were considered poor for RPD \(<\) 1.4, moderate for 1.4 \(<\) RPD \(<\) 2, and accurate for RPD \(>\) 2. Similarly, [PERSON] and [PERSON] (2010) considered model predictions very poor for RPIQ \(<\) 1.4, fair for 1.4 \(<\) RPIQ \(<\) 1.7, good for 1.7 \(<\) RPIQ \(<\) 2, very good for 2 \(<\) RPIQ \(<\) 2.5, and excellent for RPIQ \(>\) 2.5. The formulas for the performance parameters are as follows:  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(\hat{y}_{i}\ -\ \overline{y}\right)^{2}}{\sum_{i=1}^{ n}\left(y_{i}\ -\ \overline{y}\right)^{2}},\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(\hat{y}_{i}\ -\ y_{i}\right)^{2}}{n}},\]  
\[RPD=\frac{STD}{RMSE},\]  
\[RPIQ=\frac{Q_{3}\ -\ Q_{1}}{RMSE}\]  
where \(y_{i}\) indicates the measured value of SOC content of the ith sample, \(\hat{y}_{i}\) the predicted value of SOC content of the ith sample, \(\overline{y}\) the average value of measured SOC content, n the number of samples, (i = 1, 2, 3, \(\ldots\), n), and \(Q_{3}\ -\ Q_{1}\) indicates the interquartile distance. \(Q_{3}\) is the value below which we find 75% of the samples and \(Q_{1}\) is the value below which we find 25% of the samples.
# An Interference Cancellation Scheme for High Reliability Based on MIMO Systems
## 1 Introduction
The main reason for high complexity of the conventional QRD-\(M\) is that it calculates many unnecessary survival paths at all layers. To reduce the number of unnecessary survival paths, [13] eliminates unnecessary survival paths which have larger accumulated squared Euclidean distance (ASED) than adaptively calculated threshold at each layer.
# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
###### Abstract  
Constant detection and monitoring of archaeological sites and objects have always been an important national goal for many countries. The early identification of changes is crucial to preventive conservation. Archaeologists have always considered using service drones to automate collecting data on and below the ground surface of archaeological sites, with cost and technical barriers being the main hurdles against the wide-scale deployment. Advances in thermal imaging, depth imaging, drones, and artificial intelligence have driven the cost down and improved the quality and volume of data collected and processed. This paper proposes an end-to-end framework for archaeological sites detection and monitoring using autonomous service drones. We mount RGB, depth, and thermal cameras on an autonomous drone for low-altitude data acquisition. To align and aggregate collected images, we propose two-stage multimodal depth-to-RGB and thermal-to-RGB mosaicking algorithms. We then apply detection algorithms to the stitched images to identify change regions and design a user interface to monitor these regions over time. Our results show we can create overlays of aligned thermal and depth data on RGB mosaics of archaeological sites. We tested our change detection algorithm and found it has a root mean square error of 0.04. To validate the proposed framework, we tested our thermal image stitching pipeline against state-of-the-art commercial software. We cost-effectively replicated its functionality while adding a new depth-based modality and created a user interface for temporally monitoring changes in multimodal views of archaeological sites.  
a +
Footnote †: footnote]  
**Keywords:** aerial monitoring; unmanned aerial vehicles; mosaicking; cultural heritage detection; photogrammetry; open-source software
# Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks
## 5 Case Studies
Figure 10 shows the angle profile restored via the proposed method using the generated angular acceleration and time data. The red, green, and blue lines represent the roll, pitch, and yaw angles in the LVLH frame, respectively. The error of each angle was maintained at less than 0.007 at the beginning of each target pointing, and no significant discontinuities were observed in the figure. In addition, when the difference in the rate between the end of one mission and the beginning of the next was slight (i.e., there was an RMS difference of less than 0.1\({}^{\circ}\)/s) or significant (i.e., there was an RMS difference of more than 0.1\({}^{\circ}\)/s), both were generated within a similar error range. An additional case analysis was performed to evaluate the performance under various maneuvering conditions. The test set used in the analysis considered a range of ground observation values with maneuver angles ranging from 2 to 68\({}^{\circ}\). The angular rate range at the start and end of the maneuvers was set to a constant range of 0.01-0.25\({}^{\circ}\)/s for all maneuvers. As mentioned in Section 3, a total of 200 cases in the verification dataset were used for analysis, and the proposed technique was applied. The results are shown in Figures 11-14. Figure 11: Discontinuous quaternion errors at initial position of target pointing generated by proposed technique as a function of maneuver angle. Figure 14: Discontinuous angular rate errors at initial position of target pointing generated by proposed technique as a function of satellite rate difference between target points. Figure 12: Discontinuous angular rate errors at initial position of target pointing generated by proposed technique as a function of maneuver angle. Figure 13: Discontinuous quaternion errors at initial position of target pointing generated by proposed technique as a function of satellite rate difference between target points. Figures 15 and 16 show that the overall error generated by the zero-to-zero analysis is more significant than that generated by the other techniques. Additionally, the performance of the profiles generated solely by the DNN and optimization using the zero-to-zero analysis to obtain an initial guess was better than that of the zero-to-zero analysis. However, some sections using the DNN technique solely exhibited worse performances. The optimization using the zero-to-zero analysis to obtain an initial guess had an error similar to that of only the DNN technique, but the performance was poor compared to that of the proposed technique. The proposed technique exhibited an overall smaller error range than the other techniques and had consistent performance under various maneuver conditions.
# Evaluation of Linearization Methods for Control of the Pendubot
## 2 Model  
The Pendubot is a connection of \(N=2\) rigid bodies coupled in a tree structure, supported on ground via an actuated frictionless revolute joint. Both links have non-zero mass and the revolute joint connecting them is unactuated. As a result, the system has one degree of under-actuation (2 DOF with 1 independent actuator).  
In Figure 1, a standard pendulum structure is depicted. The reference frame is attached at the pivot point, and coordinates are indicated as \(\theta=\left[\theta_{1}\,\theta_{2}\right]^{\top}\in S^{1}\times S^{1}\). In order to establish system dynamics one can define Lagrangian \(L=K-V\), while \(K=\frac{1}{2}\theta^{T}D(\theta)\dot{\theta}\) denotes the kinetic energy, with \(D\) being a positive definite inertia matrix, and
###### Abstract  
A novel high-speed 3D shape measurement technology called temporal Fourier transform profilometry (TFTP for short) is proposed by combining the merits of Fourier transform profilometry (FTP) and phase-measuring profilometry (PMP). Instead of using the digital light projector, a mechanical projector is employed to generate multi-period phase-shifting fringe patterns sequentially. During the reconstruction process, the phase value of each pixel is calculated independently along the temporal axis and no spectrum filtering operation is performed in a spatial domain. Therefore, high-frequency components containing the detailed information of the measured object effectively remain. The proposed method is suitable for measuring isolated dynamic objects. Only one frame of deformed fringe pattern is required to retrieve one 3D shape of the measured object, so it has the obvious advantage if measuring the dynamic scene at a high speed. A low-cost self-made mechanical projector with fast projection speed is developed to execute the principle-proof experiments, whose results demonstrate the feasibility of measuring isolated dynamic objects.  
high speed 3D shape measurement; dynamic measurement; fringe projection; temporal Fourier transform profilometry (TFTP) +
Footnote †: journal: Applied Physics Letters
###### Abstract  
The development of low-orbit satellite communication networks marks the beginning of a new era in global communication. However, in the context of large-scale LEO satellite communication scenarios, the traditional adjacent connection transmission method limits the advantages of low latency in optical communication. Multi-hop transmission increases the number of hops and propagation distance, thereby affecting time-sensitive business transmissions. Therefore, based on the design of optical interconnect parallel subnetworks, this paper proposes a scheduling strategy for time-sensitive business transmissions between LEO satellites. Firstly, this strategy integrates the gate control scheduling mechanism from Time-Sensitive Networking (TSN) transmission in the interconnect parallel subnetwork scenario. Secondly, considering issues like queuing after subnetwork division, excessive burden, and algorithm complexity, mathematical problem abstraction modeling is applied to subsequent route scheduling, with reinforcement learning used to solve the problem. Through simulation experiments, it has been observed that compared to SPF (Shortest Path First) and ELB (Equal Load Balance), this approach can effectively enhance the control capability of end-to-end latency for TSN services in long-distance transmissions within Low Earth Orbit mega-constellations. The integration of reinforcement learning decision algorithms also reduces the complexity compared to traditional constraint-solving algorithms, ensuring a certain level of practicality. Overall, this solution can enhance the communication efficiency and performance of time-sensitive services between satellite constellations. By integrating time-sensitive network transmission technologies into optically interconnected subnets, further exploration and realization of low-latency and controllable latency satellite communication networks can be pursued.  
Low Earth Orbit constellation; parallel subnetwork; laser communication; time-sensitive network; gate scheduling transmission; reinforcement learning
# Tribological Behavior of Biomass Fast Pyrolysis Fuel and Diesel Blends
## 2 Experimental
### Structure and Principle of Tribometer  
The experiment was carried out on a multifunctional piston ring-cylinder liner friction and wear simulation tester. Its structural diagram is shown in Figure 1. The device was composed of a driving part, loading part, friction measurement part, real-time temper ature measurement part, etc. The model of the temperature sensor was PT100/PT1000 (\(-\)200-650 \({}^{\circ}\)C). The driving part of the testing machine was realized by the crank slider mechanism. A long rod and fixture were fixed on the piston to drive the upper and lower piston ring specimens to slide relative to the upper and lower cylinder liner specimens. The crank length was 57 mm, the connecting rod length was 175 mm, and the stroke of the testing machine was 80 mm. The piston ring sample was cut from the actual piston ring by wire cutting, with a diameter of 110 mm, a circumferential width of 10 mm and a piston ring height of 3 mm. The cylinder liner (122 mm \(\times\) 15.6 mm \(\times\) 6.3 mm) sample was cut from the actual cylinder liner by wire cutting. The measurement of friction force was realized by a tension and pressure sensor, TJL-1 tension pressure sensor, the comprehensive precision was 0.02-0.05% F.S, and the wear loss was obtained by weighing with an electronic balance. A high-quality boron alloy cylinder liner was provided by Yangzhou Wuqiao Co., Yangzhou, China. The piston ring (double chromium ductile iron) was from Nanjing Feiyan Piston Ring Co., Nanjing, China.  
According to the actual contact state between the piston ring and cylinder liner of diesel engine, the test load was selected to 210 N [13], test stroke 80 mm, test time 6 h, and the oil supply method was oil dropping lubrication (oil dropping speed: 25 mL/h). In order to ensure the reliability of test results, the friction and wear test was repeated three times according to the same specification under the same conditions. In this experiment, the friction and wear characteristics of microemulsion biomass fuel at different crank speeds (300 r/min, 450 r/min, and 600 r/min), i.e., reciprocating frequency of 5 Hz, 7.5 Hz, and 10 Hz, were investigated and compared with commercial 0\({}^{\#}\) diesel under a load of 210 N and reciprocating frequency of 10 Hz.
###### Abstract  
The prediction of the support pressure (\(P_{i}\)) and the development of the ground reaction curve (GRC) are crucial elements of the convergence-confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the \(P_{i}\) of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material's strength in the plastic zone. The analysis also presents separate solutions for the Mohr-Coulomb and Hoek-Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr-Coulomb criteria and 17-5-15-1 for the Hoek-Brown criteria appeared optimum for the prediction of the \(P_{i}\). On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr-Coulomb criterion and 17-3-12-1 for the Hoek-Brown criterion were found to be the optimum for the prediction of the \(P_{i}\).  
support pressure; radial displacement; ground reaction curve; circular tunnel; rock mass; elasto-plasticity; strain-softening; artificial neural network