esa-sceva/satcom-chunk-collection huggingface dataset:
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 8 Simulation and Analysis  
This section describes the evaluation of the performance of TERI-MAC via simulations. The simulation platform was Aqua-Sim [34], an NS-2 based underwater network simulator. Three performance metrics were used as follows:  
* **Energy Efficiency:** The energy efficiency was evaluated in terms of the relative control message overhead, defined as the overhead of control messages divided by that of data packets in one round of data communication.
* **Channel Utilization:** The ratio of the time slots utilized for data transmission in the data communication phase (Phase 3 in Figure 4) over the total amount assigned by the receiver.
* **Hop-by-Hop Communication Delay:** The average queuing delay for the data transmission, i.e., the time period between when the original data packet is generated or the relay data is received and when the packet is transmitted to the next-hop receiver.  
The simulation results verified the enhanced energy efficiency, delivery delay, and channel utilization of TERI-MAC with the adaptive data polling mechanism. We also compared TERI-MAC with RIPT [15] which is briefly introduced in Section 3.1. All results presented in this section are the average outcomes of multiple tests.  
Figure 8: Adaptive slot assignment in TERI-MAC with a varying traffic load.  
We evaluated the performance of TERI-MAC in a tree network topology, a typical topology for underwater sensing data collection. In this topology, 20 sub-sea nodes collect and forward data to the sink node on the surface of the ocean, as illustrated in Figure 9. For each branch, we let four intended senders transmit to a common receiver. In addition, the average distance between neighboring nodes was 1 km (uniformly distributed between 800 m and 1200 m). The maximum transmission range and the maximum transmission power of each node were set as 1.5 km and 20 watts, respectively. The size of data packets was 250 bytes.  
The overall available channel bandwidth in the simulation was 5 kHz (from 20 kHz to 25 kHz). We implemented the classic frequency-dependent absorption model and the shallow water noise model presented in [35]. Aqua-Sim [34] includes a default bit error model where each bit in a receiving packet has an independent chance of having an error. Whether a packet can be successfully decoded depends not only on the bit error rate (BER) but also on the error correction coding, which is not implemented in the current version of Aqua-Sim. Therefore, the BER in the simulation was set to zero in the evaluation of both TERI-MAC and RIPT MAC. We expected a performance degradation for both protocols if the BER was remarkable. Although the BER was not considered, the channel in our simulation was not error-free. Strong attenuation strong or high noise in the environment would cause the signal-to-noise ratio (SNR) of the receiving packet to be weak, leading to a packet error. This, to some extent, allowed us to simulate the effect of physical error (even if not colliding) on the performance of the MAC protocols.
# Finding Patterns in Signals Using Lossy Text Compression
## 7 Conclusions  
Novel algorithms for lossy text compression with provable guarantees on running time and optimality were provided. We demonstrated them by providing an open-source, home-made system for automatic reverse-engineering of toy robots, with experimental results on synthetic data and real communication signals. Clearly, there are many other applications of our algorithms such as compressing and recovering XML/Http or other protocols that are made of repeated similar blocks, or finding similar scenes in video/GPS streams.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Throttle** & **Role** & **Pitch** & **Input to Algorithm 2 (Semantic Package)** & **Output (RRLE)** \\ \hline
25\% & 0\% & 0\% & 01**100100100000011010101111** \\
50\% & 0\% & 0\% & 01**0011110001000000110101010101** \\
75\% & 0\% & 0\% & 001**00011100010000011010101010** & 07??????1000100000011010101**????? \\
100\% & 0\% & 001**00011100100000011010101010** & 07??????1000100000011010101**???? \\ \hline
100\% & \(-100\%\) & 0\% & 011001001**11110000001101010101010** & 0110010001000110100 \\
100\% & \(-50\%\) & 0\% & 011001000001100000011010101000** & 0110010010000110101001**??? \\
100\% & \(50\%\) & 0\% & 01100101010100000110101010101010** & 01100100100????1000001101010101**???? \\
100\% & \(100\%\) & 0\% & 0110010000**1010000011010010101001** & 011001001000????00110101001**??? \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example experimental results on a toy helicopter. The three leftmost columns tells which RC button was pressed. The input is the recorded communication bits from the RC for a long repeated signal. The output on the rightmost column is the output of our algorithm. It is the common “intersection” of input signals. Wildcards represent unstable character due to the different throttle, role or pitch values (in bold). From the output we can conclude the format of the message, including constant bits and the sub-string that is responsible for each button. See Section 6.4 for details.  
We focused on IR signals, but similar results were obtained from radio (PWM) signals that used similar protocols; see [1]. Further work includes turning our off-line system into a real-time system that can be used to get control over dangerous quadcopters, e.g., in airports, by learning their protocols. Core-sets seems like a promising way to do this, maybe using a network (\"cloud\").  
Conceptualization, L.R. and D.F; Investigation, L.R., S.L. and D.F; Software, S.L.; Supervision, D.F.; Writing--original draft, L.R., S.L. and D.F. Writing--review and editing, L.R. and D.F.  
This research received no external funding.  
The authors thank the anonymous reviewers whose comments have greatly improved this paper.  
The authors declare no conflict of interest.
# Investigation of Electromagnetic Angle Sensor Integrated in FR4-Based Scanning Micromirror
## 4 Conclusions  
In this work, an electromagnetic angle sensor integrated in the FR4-based micromirror is investigated in detail. An accurate theoretical model was developed to predict the sensor output, especially considering the coupling effect between the driving and sensing coils. The coupling parameters (i.e., inductances of the driving coil and sensing coil, mutual inductance, and coupling  
Figure 8: Closed-loop control: (**a**) Block diagram of the closed-loop control for the FR4 micromirror. (**b**) Comparison of long-term stabilities for the FR4 micromirror operated with closed-loop control and without closed-loop control.  
Figure 9: Comparison of temperature stabilities for the FR4 micromirror operated with closed-loop control and without closed-loop control.  
coefficient) in the model were obtained by electromagnetic field FEA simulation. Test results of the coupling voltage in the sensing coil and the sensor output are in good agreement with the theoretical results, which indicates the high accuracy of the theoretical model. Although the coupling effect of the driving coil on the sensing coil affects the linearity of the angle sensor, it can still reach 0.9995. Moreover, the integrated angle sensor has a sensitivity of 55.0 mV\({}_{\text{p}}\)/\({}^{\circ}\) and a high SNR of 71.9 dB. Additionally, the frequency response and phase-frequency characteristics of the angle sensor are also measured and discussed. Finally, the sensor's good feasibility for feedback control has been verified through a simple closed-loop control circuit. Experimental results show that the micromirror operated with closed-loop control possesses better long-term stability and temperature stability than that operated without closed-loop control. In the future, the long-term stability and temperature stability of the micromirror can be further improved by developing a more precise digital closed-loop control circuit.  
[PERSON], [PERSON] and [PERSON] conceived and designed the integrated angle sensor; [PERSON], [PERSON] and [PERSON] deduced the theory; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] conceived and developed the closed-loop control circuit; [PERSON] and [PERSON] wrote the paper; [PERSON] revised it.  
This research was funded by the National Key R&D Program of China (Grant No. 2018 YFF01011200), the Special Funds of the National Natural Science Foundation of China (Grant No. 61327002), the National Natural Science Foundation of China (Grant No. 61804016) and the Fundamental Research Funds for Central Universities (Grant No. 106112017 CDJXY120006).  
The authors want to thank [PERSON], [PERSON] and [PERSON] for their valuable advice about the experiments.  
The authors declare no conflict of interest.
# Possibilities of Detecting Damage Due to Osmosis of GFRP Composites Used in Marine Applications
## 5 Terahertz Test  
As an alternative and future-proof method of non-destructive testing that can be used for damage testing of boat hulls, we present the terahertz transmission method.  
As part of the work on the use of terahertz radiation in non-destructive testing of composites, experimental tests of the composite reinforced with glass fiber were also carried out. A sample of the composite approximately 10 mm thick with circular incomplete holes milled to various depths and diameters of different sizes was made at the Air Force Institute of Technology.  
The transmission technique was chosen as the test method. The source of terahertz radiation and the scanner were stationary, and the test sample moved at a constant speed. Figure 11 shows a diagram of the stand.  
Terasense Development Labs equipment was used in the study. A line scanner with the following parameters was used as a detector of terahertz radiation: image resolution 512 \(\times\) 1 pixel with a pixel pitch of 0.5 mm and a frequency of \(\sim\)300 GHz. The IMPATT THz generator, with the parameters of frequency 292 GHz \(\pm\) 5 GHz and power \(\sim\)10 mW, was the source of terahertz radiation. This source incorporates innovative THz reflective optics based on a specially configured high-gain horn antenna in combination with a metallic mirror. The distance between the radiation source antenna and the center of the mirror was 280 mm, and the distance between the center of the mirror and the scanner was 310 mm. The scanner was located about 5 mm from the sample surface.  
Due to the adopted solution, the amount of power reaching the linear sensor matrix increases significantly. This has a decisive influence on the improvement of THz imaging. The data were recorded using dedicated software.  
As can be seen in Figure 12, the dimensions and shapes of the incomplete holes are clearly visible. The outlines of the circles visible outside the periphery of the holes may be the result of terahertz radiation scattering through the walls of the holes or the detected damage resulting from hole milling. This phenomenon will be explained in the course of our further research.  
In the THz image (Figure 12), disturbances (darker spots) are visible on the surface of the sample, apart from defects. These disturbances can be reduced by carefully selecting the appropriate sample speed.  
Figure 11: Schematic representation of experimental setup operated via Terahertz method in transmission mode [28].
## 2 Kalman Filter Design for Real-Time Estimation of Electric Power Characteristic Of Fuel Cells
### Fundamental Current-Voltage and Current-Power Characteristics of SOFCs
Suitable references for such approaches can be found in [23]. However, without extensions to a stochastic framework, as presented in the following subsection by means of a Kalman filter synthesis, classical recursive least-squares estimators do not provide information about the accuracy of the coefficient vector \(\mathbf{a}\). This issue is solved in this paper by computing the respective (co-)variances of all estimated parameters as described in the following subsection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(a_{0}\) & \(-1.38\cdot 10^{4}\) \\ \(a_{1}\) & \(-1.13\cdot 10^{3}\) \\ \(a_{2}\) & \(1.603\cdot 10^{1}\) \\ \(a_{3}\) & \(9.977\cdot 10^{3}\) \\ \(a_{4}\) & \(1.926\cdot 10^{7}\) \\ \(a_{5}\) & \(-5.475\cdot 10^{5}\) \\ \(a_{6}\) & \(5.198\cdot 10^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Offline identified parameters of the current-voltage and current-power characteristics as a function of the stack current \(l_{k}\) and the hydrogen mass flow \(\dot{m}_{\mathrm{H}_{\mathrm{H}_{2}},\mathrm{in},k}\).
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 2 Related Work
Experiments were conducted in a \(5\times 5\) m\({}^{2}\) office environment with the presence of metallic objects, with 42 tags deployed on the floor and arranged as a grid with spacing of 60 cm. The obtained median position error was about 6 cm. It is worth mentioning the work presented in [18], where an indoor simultaneous localisation and mapping (SLAM) problem is considered. A set of passive tags was deployed at the ceiling in unknown locations. RFID phase measurements are fused with the robot odometry to determine both the robot pose and the tag coordinates through a Rao-Blackwellized Particle Filtering approach. Experimental results show a global mapping error of a few centimetres in a \(3.5\times 2\) m\({}^{2}\) laboratory environment with six tags. In [36], [PERSON] et al. showed that the localisation problem can be theoretically solved locally, i.e., the a priori starting position estimate is closer to the initial actual position of the agent, and considering unicycle-like robots, which are a commonplace in industrial environments for warehouse management or for transportation in production lines. Successively, it has been shown that the problem can be actually solved also globally [37], provided that some mild assumptions on the uncertainties are satisfied. Given that the localisation problem is well-posed when RFID tags are used and fused with odometry for unicycle-like vehicles [37], the authors of this paper recently proposed in [24] a solution for industrial IoT applications based on an Extended Kalman Filter. The method has been conceived for industrial scenarios and account for specific position constraints. As an example, the issue related to pallet handling in warehouse applications [38] requires a pallet-placing uncertainty in the order of a few centimetres with a high level of confidence [39]. Hence, the main objective of the proposed approach is to robustly reduce to the maximum extent the localisation uncertainty and the vehicle trajectory estimation uncertainty to meet the industrial requirements.
# Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models
## 2 Methodology
### Anisotropic Effective Elastic Properties for 2D Fractured Models  
#### 2.1.1. Simulation Equations  
We have to use numerical methods because the TMM for predicting the elasticity of multi-scale real intersecting cracks is limited by its own assumptions. We here apply [PERSON]'s 2D numeric upscaling procedure in an attempt to pattern natural cracks. [PERSON]'s consolidation system of equations in the space-frequency domain is expressed as [26].  
\[\
abla\cdot\mathbf{\sigma}=\mathbf{0}, \tag{1}\]  
\[\
abla P_{f}=-i\omega\frac{\eta}{\kappa}\mathbf{W}. \tag{2}\]  
where \(\
abla\) represents the spatial derivatives; \(\mathbf{\sigma}\equiv\left(\sigma_{ij}\right)\) is total stress tensor; \(P_{f}\) denotes the fluid pressure, \(\kappa\) the permeability and \(\omega\) the angular frequency; and \(\mathbf{W}\) denotes average relative fluid-solid displacement tensor. \(\eta\) indicates the viscosity of pore fluid. Thus, the stress-strain relationships [32] state that  
\[\sigma_{ij}=2\mu e_{ij}+\delta_{ij}(\lambda_{u}\
abla\cdot\mathbf{U}-\alpha N \xi), \tag{3}\]  
\[e_{ij}=\frac{1}{2}\big{(}\partial U_{i}/\partial x_{j}+\partial U_{j}/\partial x _{x}\big{)}, \tag{4}\]  
\[P_{f}=-\alpha N\
abla\cdot\mathbf{U}+N\xi, \tag{5}\]  
where \(\delta_{ij}\) is the Kronecker delta, and \(\xi=-\
abla\cdot\mathbf{W}\) denotes the measurement of the local variations in the fluid content. \(\mathbf{U}\) is the solid displacement vector; \(e_{ij}\) denotes strain tensor; and \(\mu\) denotes the shear modulus of the rock with saturated water, which is equivalent to that of a dry frame \(\mu_{m}\). The Biot constant \(\alpha\), fluid storage coefficient \(N\), and Lame parameter \(\lambda_{u}\) are expressed as  
\[N=\left(\frac{\alpha-\phi}{K_{s}}+\frac{\phi}{K_{f}}\right)^{-1}, \tag{6}\]  
\[\lambda_{u}=K_{m}+\alpha^{2}N-\frac{2}{3}\mu_{m}, \tag{7}\]  
\[\alpha=1-\frac{K_{m}}{K_{s}}, \tag{8}\]  
where \(\phi\) is the porosity; \(K_{s}\) denotes the bulk moduli of solid grains; \(K_{m}\) is the bulk moduli of dry frame; and \(K_{f}\) denotes the bulk moduli of fluid phase.  
Substituting Equation (7) to Equation (3) and taking Equation (5), we have a new form of 2D/3D total stress tensor in porous fractured medium  
\[\sigma_{ij}=2\mu e_{ij}+\delta_{ij}\Big{(}\lambda\
abla\cdot\mathbf{U}-\alpha P _{f}\Big{)}, \tag{9}\]  
Figure 1: Flowchart of the modeling methodology.  
where \(\lambda=K_{m}-2\mu_{m}/3\). When \(i,j=x,y\), it is the 2D case. When \(i,j=x,y,z\), it is the 3D case. Substituting \(\xi=-\
abla\cdot\mathbf{W}\) into Equation (5), we have  
\[P_{f}=-\alpha N\
abla\cdot\mathbf{U}-N\
abla\cdot\mathbf{W}, \tag{10}\]  
Taking Equations (2) and (10), the \(P_{f}\) is given by  
\[P_{f}=-\alpha N\
abla\cdot\mathbf{U}-N\
abla\cdot\bigg{(}\frac{ik}{\eta\omega} \
abla P_{f}\bigg{)}. \tag{11}\]  
In order to obtain the solid displacement fields, we conduct relaxation tests and solve Equations (1) and (11) with the corresponding boundary conditions applying the finite-element method (FEM).
## 4 Numerical Results
### Throughput and CO\({}_{2}\) Emissions Trade-Off  
In Figure 4, we present the trade-off plot of sum-capacity and total transmission power(or CO\({}_{2}\) emissions). The trade-off is calculated between the green communication and without green communication. Trade-off is presented as the percentage decrease in sum-capacity and percentage decrease in power consumption. The decrease in sum-capacity and decrease in power consumption is calculated using the expressions:  
\[\begin{split}\text{Decrease in power}=E^{CO_{2}}\left(w_{1}=1,w_{2}=0 \right)-\\ E^{CO_{2}}\left(w_{1}=x,w_{2}=1-x\right)\end{split} \tag{13}\]  
\[\begin{split}\text{Decrease in sum-capacity}=\sum_{k}C_{k}\left(w_{1 }=1,w_{2}=0\right)-\\ \sum_{k}C_{k}\left(w_{1}=x,w_{2}=1-x\right)\end{split} \tag{14}\]  
where \(0<x<1\). Figure 4 shows the effect of green communication by changing the values of weights \(w_{1}\) and \(w_{2}\). The results show that when \(w_{2}\) is more than \(w_{1}\), there is more reduction in CO\({}_{2}\) emissions (percentage decrease in power). The reduction in CO\({}_{2}\) emissions comes at the cost of throughput reduction. From the results, we can observe that CO\({}_{2}\) emissions will decrease by 50- to 70-percent at the cost of 10- to 30-percent loss of throughput when \(w_{2}\geq w_{1}\). This means that with little sacrifices in throughput, there is a big gain in power. The different weight settings are suitable for different geographical conditions and regulatory policies. The results also show that the performance of EDA is better than GA.
## 2 Results
### The Changes in VOCs between the IFS and FFS in \(C\). mollissima  
Differential metabolites between the IFS and FFS stages were determined by VIP (VIP \(>\) 1) and absolute Log2(Fold Change) (\(\mid\) Log2 FC\(\mid\)\(\geq\) 1.0). A total of 325 significant (\(p<\) 0.05) differential VOCs (DEVs) were screened in the two flowering stages of chestnut (Figure 2A, Table S2). Among them, 248 substances were up-regulated and 77 substances were down-regulated during the flowering process. The up-regulated volatile substances were mainly terpen, while the down-regulated substances included esters and terpen (Table S2). The 325 differential metabolites were enriched in 47 pathways (Table S3), mainly including metabolic pathways (ko01100, 19 DEVs), microbial metabolism in diverse environments (ko01120, 19 DEVs), biosynthesis of secondary metabolites (ko01110, 18 DEVs), degradation of aromatic compounds (ko01220, 14 DEVs), and sesquiterpenenes and triterpenoids pathways (ko00909, 10 DEVs). The sesquiterpene and triterpenoid pathways and ethylbenzene degradation pathway were significantly enriched (\(p<\) 0.05), which may be the main pathways for the changes between the IFS and FFS (Figure 2B).  
Among all detected VOCs, 29 had a content greater than 1% (Table S4). These 29 substances accounted for over 50% of the total substance content (IFS 56.16%, FFS 54.40%). Among them, 15 substances had contents greater than 1% in both the IFS and FFS periods; 11 substances mainly existed in IFS chestnut flowers, including benzene, 1-methoxy-4-propyl, pyrazine, 2-methoxy-3-(2-methylpropyl), 2-propenoic acid, and 2-methoxyethyl ester, while acetophenone, styrene, and ethanone 1-(2-pyridinyl) mainly existed in FFS chestnut flowers (Figure 2C). Among the 29 main VOCs, only acetophenone and styrene showed significant differences between the IFS and FFS (Table S2, Figure 2D). IFS chestnut flowers contained almost no styrene and acetophenone. During the FFS, the contents of acetophenone and styrene in chestnut flowers increased sharply, to 130.31 \(\upmu\)g/g and 67.06 \(\upmu\)g/g, accounting for 10.25% and 5.14%, respectively, of the total VOC content (Figure 2D, Table S2).  
Figure 1: **(A)** The flowers of \(C\). _mollissima_ in the initial flowering (IFS, inflorescence 5–15% open) and full flowering (FFS, inflorescence 75–100% open) stages; **(B)** the proportion of the number of volatile substances in each category to the total number of volatile substances; and **(C)** the contents of various volatile compounds. Data represent the mean \(\pm\) S.D. (\(n=3\)). * \(p<\) 0.05, **+ \(p<\)**0.01.
## 6 Performance Evaluation
### Reliability in the Network
#### 6.5.2 Data Delivery Ratio in Case of Lossy Network
As can be concluded from the first experiment, applying the multipath routing scheme in D-MSR, as a management scheme with a distributed nature, will provide more reliability in data delivery in case of link failures. In Figure 28, we evaluate the repairing mechanism applied in D-MSR (D-MSR s2) and in WirelessHART (WH s2) to show which mechanism achieves a stable data delivery ratio the fastest. We consider five measuring points in each step to have more detailed view of data delivery ratio  
Figure 27: Data delivery ratio differences in five scenarios. Figure 28: Data delivery ratio difference in two scenarios. changes between interference steps. That way, we can show the behavior of each scheme in recovering the data delivery ratio after applying the interference in each step. As Figure 28 shows, the data delivery ratio suddenly drops each time the interference is applied in the network. As expected, D-MSR requires less time to reach the stable data delivery ratio value in comparison with WirelessHART. For instance, after applying the second interference step it takes more than 900 s for WirelessHART and around 500 s for D-MSR to reach a stable state value. This is because D-MSR needs less time to re-construct the new routes, to release the previous resources along the interfered route and to reserve new resources along the new path. In this figure, the AVG value in each step represents the average of the data delivery ratio in that step.