esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
## 4 Analysis of Spectral Efficiency  
To find the optimal parameter \(N\), we derive a spectral efficiency metric by considering both the downlink and the uplink. The number of bits included in a HARQ packet is expressed as  
\[N_{B}=\alpha\beta R_{d}, \tag{1}\]  
where \(\alpha\) is the modulation index, \(\beta\) is the effective code rate, and \(R_{d}\) is the resource size for the multicast packet. For example, 100 symbols generated by quadrature phase shift keying (QPSK) and 1/2 channel code includes 100 bits. To transfer a packet using the HARQ operation, HARQ subpackets which contain the same information bits are repeatedly transmitted. Assuming that when a packet is being transmitted, the number of multicast users, \(K\), to which it is being simultaneously transmitted using H-ARQ is constant, the total resource size required for both downlink and the uplink transmission is given by  
\[R_{t}=R_{d}L+KR_{u}Q, \tag{2}\]  
where \(R_{u}\) is the effective resource size needed to send an ACK message during uplink transmission, and \(L\) denotes the average number of HARQ transmissions required during downlink transmission to terminate a packet transfer, and \(Q\) denotes the average number of ACK transmissions required during uplink transmission to terminate a packet transfer. In the proposed HARQ method, \(Q\) is less than or equal to \(L\), whereas \(Q\) is equal to \(L\) in the conventional HARQ scheme. Using (1) and (2), we define a spectral efficiency metric, \(\eta\), as  
\[\begin{array}{ll}\eta&=(1-\gamma)\frac{N_{B}}{R_{t}},\\ &=\frac{(1-\gamma)\beta SR_{d}}{K_{d}L+KR_{u}Q},\end{array} \tag{3}\]  
Figure 2: Proposed HARQ scheme when \(N\) = 2.  
where \(\gamma\) is the target PER after the maximum number of HARQ transmissions has occurred. In (3), the spectral efficiency decreases as the number of multicast users decrease. This is due to the increased availability of resources for ACK feedback.
## 4 The Proposed Method: SLP-E
### Network Configuration Phase  
This work refers to the network configuration process followed in the existing similar random walk-based approaches [28; 29; 37]. The base station floods the network with a depth configuration message during initialization. The base station's neighbors are the first ones to receive the depth configuration message. The _depth_ field in this message has an initial value of zero. The nodes that receive this packet increase the depth value by one and change the value in their routing table. The packet is then rebroadcast into the network. The nodes that receive these rebroadcast packets add one to the packet's content, update their respective depth value in their data table, and rebroadcast the packet. This procedure continues until the packet arrives at the network edge. Each node receives information about its one-hop neighbors during this procedure. The neighbor of each sensor node is then divided into three categories: lower neighbors, equal neighbors, and distant or far-away neighbors. An adjacent node is referred to as a lower neighbor if its hop count, also known as the depth value, with respect to the base station is less than the node of interest, an equal neighbor if its hop count towards the base station corresponds to its own, and a distant neighbor if its hop count to the base station is higher. These details are used to route data packets coming from the source node. _Depth_ refers to the distance between a sensor node and the BS measured in terms of hops.
## 2 Materials and Methods
### Filter Design
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Operation band & 1530–1570 nm \\ Channel spacing & 100 GHz \\ Free Spectral Range & 12.8 nm \\ Insertion Loss & \textless{}1 dB \\ Suppression adjacent channels & \textgreater{}15 dB \\ Suppression non-adjacent channels & \textgreater{}20 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Desired filter characteristics. the free-spectral-range constraints reported in Table 1. Finally, the inter-resonator gap was determined, as once the racetrack shape is fixed, there is only one possible inter-resonator gap which can be used to obtain the desired coupling coefficient \(k_{R}\). The ring-based filters were initially defined so that the ring radius matched the physical length of the racetrack filters, and were then analyzed with regard to the dependence of the coupling coefficients on both the bus-to-ring gap, and the ring-waveguide width. Two principal constraints limited the range of solutions that could be considered--(i) the bus waveguide width was kept equal to 500 nm, to allow easy matching with the other on-chip components; (ii) the waveguide height was set to 100 nm, as this value was achievable without requiring additional processing steps in the fabrication of the overall chip. It is worth underlining that, while having the possibility to tune the waveguide height would have yielded an interesting degree of freedom, the design efforts were aimed at obtaining the best performance, while using only standard, and previously validated, CMOS processes, available at the CEA-Leti Si-photonics foundry. Therefore, only 220-nm and 100-nm high waveguides were considered.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## Appendix B Performance of DCO-OFDM with Optimal and Non-Optimal DC Bias  
To explain in more detail the behavior for the optimal bias with increased transmit power, we have shown in Figure 13 the color maps of \(Z\) versus \(P_{\mathrm{Tx,e}}\) for the case of \(R_{b}=20\,\mathrm{Mbps}\) with \(\eta\approx 1\,\mathrm{bps}/\mathrm{Hz}\) for the cases of optimal bias and classical bias setting with \(\mathcal{K}=7\,\mathrm{dB}\). These results require careful interpretation, as explained in the following.  
For the case of optimal \(B_{\mathrm{DC}}\), by increasing \(P_{\mathrm{Tx,e}}\), the optimal bias increases until it attains nearly the middle of the LED characteristics, i.e., \(V_{\mathrm{av}}=(V_{\max}+V_{\min})/2=3.39\,\mathrm{V}\). After this limit, if the transmit power is further increased, \(B_{\mathrm{DC}}\) remains almost unchanged, but instead the scaling factor is increased to account for increased transmit power. This results in an increased clipping noise level and consequent BER deterioration. Meanwhile, fixing the maximum \(B_{\mathrm{DC}}\) at \(V_{\mathrm{av}}\) will make the signal be clipped \"symmetrically\" from upper and lower sides.  
For the classical method, from Equation (11), \(B_{\mathrm{DC}}\) increases steadily by increasing the transmit power. For too large \(P_{\mathrm{Tx,e}}\), the signal will primarily suffer from upper clipping. Meanwhile, increasing \(P_{\mathrm{Tx,e}}\) will result mainly in increased \(B_{\mathrm{DC}}\). Hence, since \(\alpha\) is smaller, the BER will not deteriorate as one can see for the case of optimal bias. Note that this does not mean an advantage for the classical biasing: the increased \(P_{\mathrm{Tx,e}}\) does not result in an improvement of \(Z_{\max}\) or LBI. In other words, in the sense of energy efficiency, in either case, \(P_{\mathrm{Tx,e}}\) should not exceed a certain maximum transmit power.  
Overall, the interest of optimal bias setting is that it results in a larger LBI and \(Z_{\max}\).
# RUL Prediction of Rolling Bearings Based on a DCAE and CNN
## 4 Prediction of the RUL Based on 1D-CNN
The MAE refers to the average of the absolute value of the deviation between the predicted value and the true value at all times, so it can accurately reflect the magnitude of the prediction error:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert\hat{r}_{i}-r_{i}\rvert \tag{13}\]  
The RMSE refers to the square root of the ratio of the square deviation between the predicted value and the real value and the total number of predictions at all times, so it can reflect the prediction accuracy well:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\hat{r}_{i}-r_{i}\right)^{2}} \tag{14}\]  
This paper refers to the evaluation index score of the PHM2012 Data Challenge. Assume that the definition of the percentage error %\(E_{r_{i}}\) is shown in Equation (15), where ActRUL \({}_{i}\) represents the actual RUL of the \(i\)th predicted object and PredRUL \({}_{i}\) represents the predicted value of the RUL of the \(i\)th predicted object. Considering that the risk of advanced prediction of equipment in actual situations is lower than that of lagging prediction, the scoring value of the \(i\)th prediction object is defined as shown in Equation (16). The score value of the final RUL prediction result is shown in Equation (17), where  
Figure 15: The prediction effect of the model trained by three ways of adding labels: (**a**) Bearing1_1, (**b**) Bearing1_2, (**c**) Bearing1_3, (**d**) Bearing1_4, (**e**) Bearing1_5, (**f**) Bearing1_6 and (**g**) Bearing1_7. \(N\) is the number of test objects. The higher the score, the better the prediction of the remaining life:  
Figure 14.0  
\[A_{i}=\left\{ \begin{array}{ll}\exp^{-ln(0.5)\cdot(\frac{E_{r_{i}}}{20})}& \text{if }E_{r_{i}}\leq 0\\ \exp^{+ln(0.5)\cdot(\frac{E_{r_{i}}}{20})}&\text{if }E_{r_{i}}>0\end{array} \right. \tag{16}\]  
\[Score=\frac{1}{N}\sum_{i=1}^{N}(A_{i}) \tag{17}\]  
In the above indicators, \(r\) and \(\hat{r}\) are the real value and the predicted value at a certain moment, respectively. The lower the values of the MAE and RMSE, the better the prediction effect. The prediction results of Bearing1_3, Bearing1_5 and Bearing1_6 were selected for display. The prediction results of the three methods are shown in Table 5. As shown in Table 5, HI-Method was lower than the other two methods in terms of the MAE and RMSE, and the score was higher than that for the other two methods. For bearing 3 specifically, the RMSE reached 0.054, and the score reached 0.686. The average score of the seven bearings was 0.677. In general, the method proposed in this paper had the best effect and high accuracy.
## 8 Conclusions  
This paper addresses the problem of error correction in single link, relay, and broadcast systems. Specifically, it proposes techniques for optimizing the incremental redundancy (IR) bits sent by an HARQ protocol under the assumption that the feedback channel can only support a few bits of feedback per bundle of codewords (or packets). Apart from the traditional extension IR bits, consisting of a few additional bits for each codeword, this paper also considers bundle IR, consisting of encoded IR bits which the receiver can use to refine the LLRs in multiple codewords.  
The allocation of IR bits in a single link is modelled as a Markov Decision Process seeking to minimize a pre-determined cost function. The paper describes how the problem should be formulated and solved, resulting in a set of policies parameterized by the number of failures per codeword bundle, effective SNR of the received codewords, and coding rate. It then extends this single link framework to a relay scenario, where an intermediate node has to decide whether to decode (DF) or just amplify (AF) incoming bundles before forwarding them on. Finally, the paper studies a multiuser scenario where a single source broadcasts information to multiple receivers with different interests. It proposes transmitting encoded IR bits that benefit multiple receivers and formulates a convex problem to optimize their number and encoding.  
Numerical simulations show that the proposed methods provide a modest increase in throughput compared to traditional HARQ schemes with fixed-length codeword extension. The proposed policy for the relay outperforms fixed forwarding strategies and the proposed strategy for broadcast systems significantly reduces the total number of IR bits needed to guarantee a given probability of success, compared to sending individual extension bits for each codeword. The increased flexibility in requesting different numbers and types of IR bits and the ability to make decisions based on the measurement of the received signals display significant advantages in limited feedback communication systems.  
Conceptualization, M.Z.; methodology, M.Z. and B.P.; software, M.Z., A.C. and B.P.; validation, M.Z. and B.P.; formal analysis, M.Z. and B.P.; writing--original draft preparation, M.Z., A.C. and B.P.; writing--review and editing, M.Z. and B.P.; supervision, B.P.; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the CONEX-Plus Programme-Marie-Sklodowska Curie COFUND Action (H2020-MSCA-COFUND-2017- GA 801538), by AFRL and DARPA under grant 108818 and by Nokia Networks.  
The authors declare no conflict of interest.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 2 Design of Space Mirror
### Design of Mirror Configuration  
The force point of a mirror is related to the number of support points. For circular mirrors, [PERSON] et al. [10] proposed an empirical formula for calculating the number of support points of the mirror. However, there is no reasonable and feasible formula for square mirrors. In this research, we take the outer circle of the square mirror as the maximum outer envelope size, bring it into the below Formula (1) proposed by [PERSON] et al. to obtain the approximate number of support points, and then optimize it through the later finite element simulation analysis.  
In this research, the size of the mirror is 550 mm \(\times\) 450 mm, as shown in Figure 2, the diameter of its outer circle is \(\Phi\)710.6 mm, and the system index requires that the root-mean-square (RMS) of the back shape of the mirror's gravity change needs to satisfy the requirements of \(\lambda/100\) (\(\lambda\) = 632.8 nm), which is calculated according to the following equation:  
\[N=\frac{1.5r^{2}}{t}\sqrt{\frac{\rho g}{\delta E}} \tag{1}\]  
In the equation, \(r\) represents the radius, \(t\) represents the thickness, \(\rho\) represents the material density, \(g\) represents the gravitational acceleration, \(E\) stands for the modulus of elasticity, \(\delta\) stands for the maximum self-weight deformation. Calculated from Equation (2), the number of support points for a \(\Phi\)710.6 mm circular mirror is \(N\) = 3.8. Regarding a mirror in space, each support point has six degrees of freedom, translational movement along the X, Y, and \(Z\) directions, rotation around the X, Y, and \(Z\) coordinate axes as shown in Figure 3, then the mirror is unable to produce relative movement after we have completely restricted all six degrees of freedom of all the support points [11]. For the space shape, triangular is the most stable compared with other shapes, so three-point support is chosen for small- and medium-diameter (\(\leq\)\(\Phi\)350 mm) optical elements, while three-point or multiples of three are used to complete the support design for large-diameter (\(\geq\)\(\Phi\)350 mm) optical elements. In this paper, the number of support points based on the calculation is 3.8, so the three-point support design is chosen.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **Coefficient of Linear** & **Modulus of Elasticity** \\  & **g/cm\({}^{3}\)** & **Expansion** & **GPa** \\  & & \(\alpha\times 10^{-6}\)/K & **GPa** \\ \hline Fused silicon & 2.2 & 0.55 & 67 \\ Zerodur & 2.53 & 0.03 & 91 \\ Be & 1.87 & 11.3 & 287 \\ SiC & 3.12 & 2.24 & 400 \\ ULE & 2.21 & 0.03 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of mirror material parameters.  
Figure 1: Schematic diagram of mirror blank.
# Nonlinear Vibrations of Low Pressure Turbine Bladed Disks: Tests and Simulations
## 5 Comparison of Experimental and Numerical Results: Model Validation  
The comparison was made for three main parameters of interest, namely, peak amplitude, critical frequency, and damping efficiency. Damping efficiency \(d\) was defined in the same way as in Equation (2).  
The comparative analysis was performed on all of the three configurations studied herein: (a) nominally tuned (no UPDs), (b) tuned with aluminum UPDs, and (c) tuned with steel UPDs. The Tables 2-4 summarize the comparative analysis and indicates the discrepancies between numerical and experimental data.  
Figure 14: Linear and nonlinear forced responses of the tuned plus (**a**) aluminum UPD and (**b**) steel UPD configuration (ND8, 3 rd modal family).  
Figure 13: Linear forced response: Free and full-stick response with (**a**) aluminum damper and (**b**) steel damper.  
From the results obtained, we can conclude of a strong correlation between the experimental and numerical data. This is further supported by the modulus of differences, which was always less than the minimum acceptable level of 10%. This makes us conclude that the numerical model accurately reproduces the real bladed disk average behavior.  
Moreover, we can notice the greater damping capability of the steel UPDs with respect to the aluminum ones, which exhibit a 26% amplitude reduction.
## 1 Introduction
Moreover, we test the reliability mechanisms provided by each solution, considering an 802.15.4 channel under Rayleigh fading. To the best of our knowledge, this is the first work in which the behavior of the reliability mechanism of CoAP has been studied. The paper is organized as follows. First, in the following section we present the definition of CoAP and other existing proposals of application protocols for WSNs. Then, in Section 3, we present the related work. The implementation of TinyCoAP is illustrated in Section 4. The test-bed used for our experiments is described in Section 5. Results and discussion of the performance evaluation are reported in Section 6. Finally, in Section 7 we conclude the paper and give guidelines for future work.
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 2 Materials and Methods
### Bending Experiment on PVB Laminated Glass
#### 2.2.2 Displacement Measurement Technology (DIC Technology)  
Digital image correlation (DIC) is a non-contact optical method that analyzes digital images of an object's surface before and after deformation to capture deformation data. This technique typically involves a camera, image analysis software, (Software name: Match ID 2019; version: 19.2.0.0) and markers or speckles that create identifiable surface patterns. The DIC system's displacement resolution can reach sub-pixel levels, such as 0.01 pixels [37]. Unlike traditional methods that measure strain at discrete points, DIC provides a full-field view of the displacement distribution. The precision of DIC technology is exceedingly high, often within micrometer accuracy.
## 4 Techniques Used in Academia to Improve Performance Metrics
### Contention-Free (Scheduled Communication)  
Most contention-free protocols exploit the Time Division Multiple Access (TDMA) technique. In this scheme, the neighbor nodes are scheduled (either in a centralized or distributed manner) to communicate at a predefined timeslot. This mechanism provides a guaranteed and reliable hop-to-hop data transfer. Furthermore, scheduling and reserving the timeslots along the path toward the destination provides an end-to-end real-time data transfer.  
PEDAMACS [45] uses the centralized approach in scheduling the communication in the network. It supports the tree topology and provides an end-to-end bounded delay data transfer. In [46], [PERSON] and [PERSON] present the MAC protocol that uses time and frequency multiplexing. In this protocol, the star topology is considered and a contention-free scheme is utilized to restrict the hop-to-hop delivery time. They assume that the sink is equipped with multiple transceivers, which can operate at different channels selected adaptively, while the other nodes use one transceiver. Furthermore, HyMAC [47] is designed for a network in which the nodes are equipped with multiple-channel transceivers, _i.e._, a different channel can be selected in each transmission. In this protocol, the combination of TDMA and FDMA features are exploited. HyMAC uses the centralized approach to schedule the communication in the network and guarantees end-to-end bounded delivery time.
## 2 Method
### Drift Elimination
#### 2.2.5 Feedforward Layer  
Each layer of the encoder and decoder includes a feedforward network (FFN) composed of two fully connected layers, with one layer utilizing a nonlinear activation function [37]. In this paper, the activation function used is ReLU, a commonly employed activation function in neural networks. The FFN can be expressed as  
\[FFN(x)=\max(0,xW_{1}+b_{1})W_{2}+b_{2} \tag{9}\]  
where W\({}_{1}\) and W\({}_{2}\) are the parameter matrices in the two linear transformation layers, respectively, and max is the ReLU activation function.
## 4 Numerical Analysis  
The calculations were performed using the static structural module of ANSYS Workbench (Figure 13), in which the bearing was divided into three parts. The sleeve was made of bronze, while the foils were made of Inconel 618. Frictional contact was applied between the top foil and the bump foil as well as between the bump foil and the sleeve and the friction coefficient was 0.2. The remaining contact parameters had default values. The computational mesh was created using HEX20 elements. The foils consisted of two elements along their thickness. The total number of finite elements was 24,344. The geometric clearances between the bearing components did not exceed 0.001 \(\upmu\)m. All degrees of freedom on the outer surface of the bearing sleeve were fixed.  
A pressure of 8000 Pa, was applied to the inner surface of the top foil. This pressure reflects the operating conditions associated with the lubricating film of the gas bearing. The height of one of the bump foils decreased by 12 \(\upmu\)m compared to other bumps. The change in the geometry resulted in higher stresses of the remaining bumps (which increased by approximately 20 MPa). It is not possible to observe dynamic phenomena related to non-uniform bending of foil bumps using only classic displacement sensors (i.e., laser or eddy current sensors). The use of a high-speed camera in measurements enables experimental verification of phenomena that have been already analysed numerically. In the graph showing the experimental results, bigger changes in the displacement of the first bump in comparison to the second bump are marked by more red dots placed on the first bump than on the second one. In the graph showing the simulation results, bigger changes in the displacement of the first bump in comparison to the second bump correspond to higher stress values. By modelling the bumps with their proper heights, we are able to verify the experimental results.  
Another example showing how foils vibrate is shown in Figure 14. This figure shows three areas where the foil movements are particularly visible. In each of the areas, the displacements are shown at three different moments. It turns out that at individual moments the differences in displacements can be very large. Similar results were observed in experimental research.  
Figure 14: Changes of foil displacements.  
Figure 13: Model of foil bearing with unequal bumps.