esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DNN-Based Synchronization and the Proposed Receiver
### Input/Output of the DNN Model and Its Training Method  
The coarse timing offset was estimated by cross-correlation between the received LFM and the local one. However, its bias was significant because of the uncompensated random movement. The delay bias, \(\Delta\), of the LFM is a determined function of the Doppler scaler, \(\gamma\), written as follows:  
\[\Delta=-\frac{T_{\text{Sync}}\gamma}{(f_{1}-f_{0})(1-\gamma)}\bigg{(}\frac{f_ {1}}{1-\gamma}-\frac{f_{0}\gamma}{1+\gamma}\bigg{)}. \tag{6}\]  
The derivative of \(d(t)\) is the time-varying speed, written as \(v(t)=\partial d(t)/\partial t\). The timing offset and the Doppler scaler are written as \(\tau(t)=d(t)/c\) and \(\gamma(t)=v(t)/c\), respectively.  
According to the packet structure, the timing offsets \(\{\tau_{\text{B}}(kT_{\text{Frm}})\}_{k=0}^{N_{\text{Frm}}}\) at the start position of the synchronization signals are estimated by the correlation; however, they have the Doppler-induced bias, written as:  
\[\tau_{\text{B}}(kT_{\text{Frm}})=\tau(kT_{\text{Frm}})+\Delta(kT_{\text{Frm}}), \qquad 0\leq k\leq N_{\text{Frm}}, \tag{7}\]  
where, for a small Doppler scaler, \(\gamma(kT_{\text{Frm}})\ll 1\), the bias is approximated by:  
\[\Delta(kT_{\text{Frm}})=-\rho\gamma(kT_{\text{Frm}}), \tag{8}\]  
and the delay-Doppler ratio is written as:  
\[\rho=-\frac{f_{1}}{f_{1}-f_{0}}T_{\text{Sync}}. \tag{9}\]  
To learn the correlation among the timing offsets, the DNN was fed with the biased timing offsets in one packet with a fixed input scaler. Furthermore, to improve the numerical efficiency, the desired output of the DNN was the scaled bias vector rather than the direct timing offsets. The training diagram for the DNN timing bias estimation is shown in Figure 1. The biases generated by Equation (7) with the scaler and the DNN output were compared in the DNN optimizer.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 5 Conclusions  
In this paper, we proposed an open-source-based high-speed distributed purification platform, SPinDP, which supported efficient sampling and filtering in constantly generated stream environments. To design SPinDP, we first identified three problems with existing purification technologies and derived major components to address these problems. We then subdivided the roles of each component and designed their functions over Apache Storm and Apache Kafka. SPinDP featured RDMA-Storm and locality-aware dynamic grouping and consisted of four major components: Data Stream Processing Engine, Purification Library, Plan Manager, and Shared Storage. In particular, we aimed to increase the platform's usability with novel purification algorithms for both expert and non-expert users. Finally, we confirmed the functionality and efficiency of the proposed SPinDP through experiments on a real distributed cluster. The results demonstrated that SPinDP correctly performed purification functions for sampling and filtering and significantly improved the performance compared to not only conventional Ethernet but also recent IPoIB. Based on these results, we believe that SPinDP is a new attempt to integrate and expand existing sampling and filtering algorithms and provides stable and high-level purification of large-scale streams. As future work, we plan to enhance SPinDP to not only support real-time streaming but also to handle large-scale bulk data processing. Additionally, we will explore the utilization of the improved SPinDP within the Ray framework instead of the Storm framework for more effective handling of large-sized stream data.  
Conceptualization, formal analysis, software, and writing--original draft preparation and editing, M.-S.G.; conceptualization, formal analysis, writing--review and editing, and validation, Y.-S.M. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-00859, Development of A Distributed Graph DBMS for Intelligent Processing of Big Graphs) and a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1A2C1003067).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], for their technical research and development contributions to this project.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
# Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging
## 3 Methods
### Pre-Processing Techniques  
Some of the most challenging aspects of OCT volumetric data are its low signal-to-noise ratio (SNR), which may compromise the registration performance, and the subtle structural changes in the retinal tissue, usually caused by the early manifestation of pathologies. To mitigate these issues and to ensure the registration is focused on the parts of the image that need to be corrected, pre-processing is often used. Among the reviewed works, two main groups of pre-processing can be considered: image quality enhancement and segmentation of structures of interest.  
Figure 5: Schematic of motion correction based on multimodal acquisition. The upper line denotes a misaligned volume whereas the bottom row shows an aligned volume. The SLO image is used as reference to guide the en face alignment of the OCT volume.  
_Enhancement_: A common pre-processing technique is to smooth the intensity so that the noise and small structural changes are blurred. Filtering, intensity normalization, and background removal have been widely used to enhance the OCT volumes.  
_Segmentation_: The most common targets in segmentation are retinal vasculature and retinal layers. For the latter, only a few layers are usually segmented (e.g., retinal boundaries and the retinal pigment epithelium (RPE) layer).  
For further details on other pre-processing techniques applied to OCT retinal imaging, we refer to the work by [PERSON] et al. [51].
## 3 Results
### Quantitative Results
#### 3.2.1 Image Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Original**} & \multicolumn{3}{c}{**One-Channel**} & \multicolumn{3}{c}{**Three-Channel Grayscale**} & \multicolumn{3}{c}{**Three-Channel Color**} \\ \cline{3-10}  & & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Augmented & accuracy & 0.956 & 0.029 & 0.988 & 0.012 & 0.998 & 0.002 & 0.998 & 0.003 \\ \cline{2-10} dataset & F1 score & 0.960 & 0.025 & 0.988 & 0.011 & 0.998 & 0.002 & 0.998 & 0.003 \\ \hline Original & accuracy & 0.956 & 0.029 & 0.995 & 0.011 & 0.993 & 0.002 & 0.997 & 0.005 \\ \cline{2-10} dataset & F1 score & 0.960 & 0.025 & 0.995 & 0.010 & 0.993 & 0.006 & 0.997 & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average and standard deviation of Resnet18 validation result. The validation results of Resnet18 also show that the networks trained with the three-channel augmented color dataset have high average accuracy. As a result, the proposed data augmentation method was effective for the image classification task, which uses the grayscale image captured by mono cameras for surface inspection.
## 2 Model and Method  
### Operational Process and Structure  
The structure of the DES and the separate energy system are shown in Figure 1. Typical, a DES is mainly composed of a power generation unit, a heat recovery steam generator (HRSG), a heat exchanger, an absorption refrigeration unit, and an electric refrigeration unit [18; 30]. The DES uses the gas turbine as the power generation unit. The natural gas enters the gas turbine combustion chamber for combustion, producing high-temperature flue gas, which drives the turbine to generate power. The high-temperature flue gas discharged from the gas turbine can be recycled by the heat recovery steam generator and heat exchanger to recover waste heat in the winter and passes into the absorption refrigeration unit for refrigeration in the summer. If the energy generated by the gas turbine cannot meet the load of the building, the deficit energy is provided by the municipal power grid, the electric refrigerator unit, and the gas-fire boiler. The supply of cold, heat,and power can be made more reliable and flexible by coupling the various energy units mentioned above. In a typical separate energy system, the load demands are provided by the local utility grid, the gas boiler, and the electric cooling system, respectively [28].
## 3 The Spectral Method Algorithm for a Fluid-Filled Elastic Tube  
### The Spectral Method Theory  
The advantage of the spectral method is that it does not require calculation of the root of the Bessel functions, but instead uses the differential matrix to solve the eigenvalue problem. The core idea is to approximate unknown functions to be solved in the differential equation through global interpolation using high-order orthogonal polynomials. As for the eigenvalue problem of a fluid-filled elastic tube, the differentiation occurs on a bounded interval, \(0\leq r\leq r_{2}\); the appropriate choice is to use Chebyshev polynomials to construct the differential matrix according to [PERSON] (1983). The choice of polynomials for different unknown function \(f(x)\) is listed in Appendix A, Table A1.  
The differential matrix \(D_{x}^{(n)}\) is defined as follows according to [PERSON] (1984):  
\[\begin{pmatrix}f_{1}^{(n)}\\ f_{2}^{(n)}\\ \vdots\\ f_{N}^{(n)}\end{pmatrix}\approx\underbrace{\begin{pmatrix}D_{11}^{(n)}&D_{12}^ {(n)}&\cdots&D_{1N}^{(n)}\\ D_{21}^{(n)}&\ddots&&\vdots\\ \vdots&&\ddots&\vdots\\ D_{N1}^{(n)}&\cdots&\cdots&D_{NN}^{(n)}\end{pmatrix}}_{D_{x}^{(n)}}\begin{pmatrix} f_{1}\\ f_{2}\\ \vdots\\ f_{N}\end{pmatrix} \tag{40}\]  
where \(f_{i}\) is the value of the function \(f(x)\) evaluated at the interpolation point \(x_{i}\), \(i=1,2,...N\), \(f_{i}^{(n)}\) is the \(n\)th derivative of \(f(x_{i})\), and the \(N\times N\) matrix \(D_{x}^{(n)}\) is the differential matrix.  
The MATLAB program chebdif [PERSON] (1984) generates interpolation points on the interval \(-1\leq x\leq 1\):  
\[x_{i}=\cos\biggl{(}\frac{(i-1)\pi}{N-1}\biggr{)} \tag{41}\]  
Here, interpolation points for \(r\) are \[r_{i}=\frac{(r_{2}-r_{1})x_{i}+r_{1}+r_{2}}{2} \tag{42}\]  
where \(r_{2}\) denotes the outer radius, \(r_{1}\) denotes the inner radius.  
\(D_{x}^{(n)}\) generated by chebdiff [29] approximates \(d^{m}/dx^{m}\), but here requires the approximation of \(d^{m}/dr^{m}\), which is given by  
\[D_{r}^{(n)}=\left(\frac{2}{r_{2}-r_{1}}\right)D_{x}^{(n)} \tag{43}\]  
Wave equations and boundary conditions only deal with \(r\) derivatives, so the spectral method algorithm in the next section uses \(D^{(n)}\) to represent \(D_{r}^{(n)}\) for simplicity.  
[PERSON] [23] gave a heuristic argument about the theoretical accuracy of the spectral method. For \(f(x)\) on the Chebyshev interval \(-1\leq x\leq 1\), it requires at least two interpolation points per wavelength to accurately resolve \(f(x)\). If \(N\) is large enough, the interpoint spacing is asymptotically \(\frac{\pi}{N}\) according to Equation (41). Assuming the wavelength of \(f(x)\) is \(\lambda\), and it is roughly constant across the domain, then the condition for accurate resolution is  
\[\frac{\pi}{N}\leq\frac{\lambda}{2} \tag{44}\]  
A more formal demonstration based on the convergence rates of Chebyshev series can be found in the study of [PERSON] and [PERSON] [30].
## 2 Materials and Methods
### Data Pretreatment Methods  
To harmonize the spatial datasets originating from diverse sources and utilizing different coordinate projections, all the data were standardized to the European Terrestrial Reference System 1989 Lambert Azimuthal Equal Area (ETRS89-LAEA) coordinate system via reprojection and transformation. We aggregated the 30 m high-resolution European forest disturbance maps to 0.05\({}^{\circ}\) resolution (-5 km) from 1986 to 2016. For each 0.05\({}^{\circ}\) grid cell, the pixel-level values quantifying disturbance severity and relevant ancillary variables were extracted employing the Python function \"Extract_tifvalue\". Subsequently, zonal statistics were implemented to compute country-level aggregates by averaging the pixelvalues within each nation's boundaries. This procedure yielded annual, national-scale time series datasets for the forest disturbance and the associated variables [43].
## 5 Description of the Heuristics
### First Phase: Time Schedule  
In the standard fat-tree topology, each switch has the same number of inputs and outputs; therefore, in principle, every incoming flow will have an outgoing link. With these assumptions, the flows are parallelizable, that is, they can be transmitted simultaneously, as long as they do not share the same origin and/or destination. Based on this, two strategies were proposed for this first stage, where all the flows of the system are scheduled, giving priorities to those flows with the shortest period or nearest deadline.  
Between the methods proposed for this stage, the first one consists of generating the scheduling for all the instances of each flow sorted from smallest to largest period. This fixed priority scheduling strategy ensures that flows with smaller periods are more likely to be transmitted. Algorithm 1 shows the pseudo-code of the procedure.  
For the second alternative, whose pseudo-code is shown in Algorithm 2, the decision of which flow to send is performed on each slot of time, and flows are sorted from nearest to farthest deadline. This strategy has the advantage that it can be used as an on line scheduling policy, since the method performs a single pass and schedules only based on the deadline. The disadvantage is that, in the case of a periodic flow system, incomplete instances can occur for both low and high period flows.  
The complexity of this heuristic is polynomial and is given mainly by the process of sorting the set of flows by deadline on every slot. For this reason, the complexity of the process is \(\mathcal{O}(H\times|F|\times\log|F|)\).  
The output of this first stage is a list of flows to be allocated in paths for each slot ordered by priority. The priority is assigned according to periods or deadlines depending on the approach selected. If \(\pi\) indicates the priority, when assigned by periods \(\pi_{j}<\pi_{i}\) if \(p_{i}<p_{j}\). This assignment is fixed and does not change with time, it is named Rate Monotonic. If priorities are related to deadlines, they are dynamic as the time remaining to reach a deadline decreases continuously. In this case, \(\pi_{j}<\pi_{i}\) if \(p_{i}<v_{j}\).  
Figure 3: Stage scheme.
# Train Speed Estimation from Track Structure Vibration Measurements
## 3 Procedures for Train Speed Estimation
### Train Speed Estimation from Displacement Dynamic Response Signals  
Figures 1b and 8 shows the typical time domain and frequency domain signals of vertical displacement dynamic response, respectively. It can be seen from Figure 8 that the signal contains a lot of noise. Furthermore, the true dynamic response signals and the disturbance signals are in different frequency bands.  
Figure 7: The geometric dimension of the train.  
Low-pass filtering (LPF) is thus chosen to suppress disturbance signals. To retain the characteristics of the signals, the cutoff frequency of the LPF should be greater than the frequency of the bogies passing through the sensors. Supposing that the train speed is \(v\) and the minimum spacing of adjacent bogies is \(l_{\rm min}\), the cutoff frequency \(f_{\rm c}\) should satisfy the condition of \(f_{\rm c}\geq v/l_{\rm min}\). Figure 9 shows the low-pass filtered displacement signal. Compared to the original signal in Figure 1a, the noise and disturbance in vertical displacement-time waveform signals can be well suppressed, which meets the requirement of further signal feature extraction. The subsequent treatment is the same approach as that of the acceleration signals.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## Appendix C Required Precision for Calculating the Optimal Bias for DCO-OFDM  
As explained in Section 6.8, although the optimized DC bias provides the best energy efficiency, its calculation requires high accuracy, and equivalently entails a high computational cost. This appendix clarifies this issue and shows why in practice the use of optimal DC bias could be of little interest.  
Remember from Section 5.5 and Appendix A that the optimal bias \(B_{\mathrm{cc}}\) is calculated so as to minimize the mean square error \(\mathcal{E}\). Figure 11 shows the partial derivation of \(\partial\mathcal{E}/\partial B_{\mathrm{cc}}\) as a function of \(B_{\mathrm{cc}}\) considering a transmit power of \(P_{\mathrm{Tx,e}}=50\,\mathrm{mW}\).  
Notice that the derivate is very close to zero for a large range of \(B_{\mathrm{cc}}\) (between 3 and 3.8); notice also the magnified part in the figure. This signifies that the calculation of the exact optimal bias needs a high numerical precision. For instance, for the considered case study, we have calculated the optimal bias using a relatively low precision of \(10^{-2}\) (the corresponding \(B_{\mathrm{DC}}\) is \(\sim\)2.9406, that we will refer to asLP: Low Precision), where the resulting BER performance was worse than that with a clipping factor of 7 dB. With a higher numerical precision of \(10^{-5}\) (the corresponding \(B_{\text{DC}}\) is \(\sim\)2.932, which we will refer to as HP: High Precision), we could attain a better performance, as can be seen from Figure A3. Although precisely calculating the bias is rather straightforward for a theoretical study, it would not be the case for a practical hardware implementation of the system with limited accuracy (e.g., using pre-registered look-up tables for a set of transmit powers \(P_{\text{Tx,e}}\)). Therefore, in this sense, the use of optimal biasing for DCO-OFDM signaling would rather have little interest in practice.
## 2 Description of the Observation Setup
### GPS Observations
The GPS data of the campaigns were processed using default parameters of a commercial GPS data processing software (Leica Geo Office--LGO) and in the results the relative accuracy of GPS-derived positions of the deformation points is calculated better than \(\pm 3\) millimeters. In deformation analysis of the ISTA pillar using the GPS technique, the baseline solutions are considered between two continuous reference stations, namely, the ISTA and KANT pillars. The KANT geodetic pillar is located 6 km southeast of ISTA on geologically-stable hard ground (see the map in Figure 3). Both geodetic monuments are located on the same tectonic plate [24-26,33]. In the processing strategy, the coordinates of ISTA (Easting and Northing) were derived from the hourly GPS data of the stations with 30-second sampling intervals. The sequential solutions (24 solutions per day for approximately 30 days) of both measurement campaigns constituted the time series. Then, the periods of movement were determined using LSSA, and the results were compared with the output from the LSSA for inclination sensors data.
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 4 Discussion
In summary, it can be seen that the enhancement of eddy detection capability by HY-2 series satellites varies with regions. On the one hand, the orbital coverage is latitudinal and the enhancement is not equitable. For regions with similar eddy field complexity, such as the high latitudes and low latitudes in this paper, although they are both dominated by smaller mesoscale eddies, the probability of eddies being detected is enhanced by the increased orbital coverage. On the other hand, SSH field variability affects the accuracy of the eddy identification results. Under the same orbital coverage, such as the middle latitudes and low latitudes, smaller mesoscale eddies may more easily be distorted into larger structures by the mapping procedure [43], while the detection of larger mesoscale eddies is likely to be correctly captured. Thus, the accuracy of eddy detection seems like the result of a combination of the level of orbital coverage and the complexity of the eddy field.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 4 Results and Analysis  
### Throughput (bits/s)  
Figure 4a-f illustrates the performance of a network in terms of throughput, by varying the number of UAVs, speed, and area dimensions. The \(X\)-axis represents simulation time and the \(Y\)-axis indicates the throughput in bits per second. When the number of UAVs increased, the throughput also increased, and the performance of the network rose. When comparing the throughput by each of these protocols, DSR had high throughput when the number of UAVs was lower (i.e., 30) in both cases, when the speed of the UAVs was 25 m/s and 60 m/s as shown in Figure 4a,b respectively. This is due to their reactive nature, which renders DSR more suitable for FANETs in a highly mobile network, and also due to the use of a route cache and overhearing features. The AODV outperformed DSR, OLSR and ZRP, with the number of UAVs increasing to 100 in both scenarios, when the speed of the UAVs was set to be 25 m/s and 60 m/s, respectively, as shown in Figure 4c-f. This is due to the fact that AODV assigns dedicated time slots for packet transmissions in order to avoid network congestion. Moreover, by decreasing the area dimensions (i.e., 1 km \(\times\) 1 km), high throughput was achieved, as shown in Figure 4c,f. OLSR had average performance in all scenarios, whereas ZRP had the worst throughput because of extra routing overhead.  
Figure 4: _Cont._
## 4 Communication Standards for Wireless Sensor Networks
### WirelessHART  
WirelessHART protocol is designed for industrial automation. It is a real-time, centralized and multi-hop mesh network developed for industrial monitoring and control applications [60; 61; 62]. It is based on IEEE 802.15.4 compatible radio and operates on 2.4 GHz ISM band. It uses direct sequence spread spectrum (DSSS) technology. Besides DSSS, it uses Time Division Multiple Access (TDMA) technology in which 10 ms time slots are allocated to nodes. The range of this technology is up to 200 m. Security of communications is maintained using 128 bit AES encryption [62]. Individual session keys as well as common network encryption keys are shared among all nodes for broadcast services. WirelessHART overcomes all the shortfalls of Zigbee in terms of robustness, reliability, security and message delivery [60; 61; 62; 63; 64].
## 6 Implementation and Evaluation  
Our solution described above enables the use of unicast messages as an alternative to using multicasts for realizing CoAP group communication. In order to evaluate our solution and to show how it can be used in a real-world scenario we have implemented it and built a demo box for demonstration purposes. In this section we present the implementation of our solution and a basic description of the demo box followed by functional and performance evaluation.