esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Motivation
### Motivation for PAPR Reduction  
This section emphasizes the importance of PAPR reduction. Because HPA is the primary source of nonlinear distortion in OFDM-based systems, an adequate HPA model must be considered. Different models exist for memoryless HPAs, such as Rapp model, Ghorbani model, and [PERSON] model [38]. Generally, AM/AM characteristics of any memoryless HPA have three regions, namely linear region, compression region, and saturation region, as illustrated in Figure 1. Nonlinear distortion severity relies on the value of the input back-off (IBO), which is given by [16];  
\[IBO=10\log_{10}\biggl{(}\frac{P_{sat}}{P_{avg}}\biggr{)}=10\log_{10}\biggl{(} \frac{s_{sat}^{2}}{\mathbf{E}\{|s(t)|^{2}\}}\biggr{)} \tag{6}\]  
Or  
\[IBO=P_{sat}\ \ [dB]-P_{avg}\ \ [dB] \tag{7}\]  
where \(P_{sat}\) is the saturation level of input power, \(P_{avg}\) is the input signal average power, and \(s_{sat}\) is the saturation level. Predistortion is usually used to convert the compression region into a linear region to reduce the required back-off. AM/AM characteristics of linearized HPA (also called soft Limiter (SL)) is expressed as [3];  
\[\Lambda[|s(t)|]=\begin{cases}s(t)&|s(t)|\leq s_{sat}\\ s_{sat}e^{-j\angle s(t)}&|s(t)|>s_{sat}\end{cases} \tag{8}\]  
Theoretically, IBO must be equal to the PAPR of an input signal (i.e., IBO = PAPR) to avoid nonlinear distortion. However, this degrades the HPA efficiency, which is related to the back-off and PAPR as follows [13; 16];  
\[\eta=\frac{\eta_{max}}{IBO}=\frac{\eta_{max}}{OBO}=\frac{\eta_{max}}{PAPR} \tag{9}\]  
where \(\eta_{max}\) is the maximum efficiency depending on the HPA class, \(\eta_{max}=0.5\) for class A and 0.785 for class B. It is worth mentioning that in the case of linearized HPA (i.e., SL), output back-off (OBO) is equal to IBO. Due to this inverse relationship between linearity and HPA efficiency, we have to reduce the PAPR of the OFDM signal [13]. Figure 1 shows that HPA efficiency has its maximum value at the saturation level. In this figure, the original OFDM signal (red) has to work with high IBO (\(IBO_{1}\)) to avoid distortion. Modified OFDM signal (green) after PAPR reduction requires low IBO (\(IBO_{2}\)). There are different selection criteria exits to choose from among the several PAPR reduction techniques. The following section discusses different selection criteria.
## 3 Results
### Case Study: December 2018  
Mount Etna volcano erupted again between 24 and 27 December 2018 from a fissure that opened on the high south-eastern flank, generating a lava flow spreading in Valle del Bove. The closest pre-eruptive and post-eruptive MSI data available and not affected by clouds are the ones recorded on 27 September 2018 (pre) and 26-29 December 2018 (post), respectively. The size of the area of interest is zoomed to the region of size \(266\times 401\) pixels, i.e., the input matrix is 67,084 \(\times\) 13. In this case, one post-eruptive dataset was recorded when the lava eruption was still taking place, and the hot portion of the lava flow is easily identified with a simple threshold on MSI Band 12. Therefore,  
Figure 6: Lava flow map resulting from the BNN and the post-processing stages (red) and the contour of the real lava flow (white) observed during the Etna eruption occurred in August 2018. The northern flow is the one emitted on 24 August, while the southern one was erupted on 27 August.  
we build our model with only the cooling lava flow portion; then, in order to get the whole lava flow field, we merge our results with the hot lava flow portion resulting from the thresholding. Filtering out the hot lava flow pixels, a 53,443 \(\times\) 13 input matrix is given as input to the k-medoids stage that clusters the pixels in 50 classes, like in the other cases. For the training stage, only 15 of the 150 training pixels belong to the lava flow field, i.e., only 0.028% of the full dataset is labelled as belonging to the cooling lava flow. In order to test the trained BNN, the remaining 53,293 \(\times\) 13 pixels are used. After the post-processing step is performed, the hot lava flow portion is added to the cooling lava flow previously obtained, producing as a result the 2D map shown in Figure 7.
## 3 Background
### Security Requirements
#### 3.3.8 Real-Time Guarantees  
Applications used in IoV are designed in such a way that they are time sensitive. For this reason, they have to disseminate information when needed and at the right time. The failure to deliver information in a timely manner can lead to accidents and unnecessary delays.
# Foams in Wastewater Treatment Plants: From Causes to Control Methods
## 4 Evaluation Method
### Direct Methods
#### 4.1.6 Foaming Scum Index (FSI)  
[PERSON] and [PERSON] [62] proposed a global approach specifically for the characterization of CAS foaming through the foaming scum index (FSI). FSI has been formulated as an algorithm using data produced from a series of individual tests normally employed to investigate foaming such as foam potential, foam stability and filamentous bacteria. Seven parameters were used in FSI formulation: foam color, bubble size, foam stability, foam potential, coverage and filament abundance within foams. To increase the robustness of the model, foam potential (FP2) has been measured using the glass column sintered disc method following procedures described by [PERSON] et al. [20]. Hypotheses have been put forward on the influence of each variable on the others and field studies have been conducted in 10 different WWTPs, which employed activated sludge as the main treatment process. A preliminary weighting scheme was proposed in order to convert measured values into standardized scores so that parameters were comparable on the same scale: lower scores reflect a smaller contribution of each parameter to activated sludge foaming. Multivariate analysis and structural equation modeling (SEM) have been used to weight each parameter within the FSI, considering the relevance of each to foaming in CAS plants. FSI is calculated as follows in Equation (6) [62]:  
\[FSI=\left(\sum_{n=7}^{n=1}p_{i}w_{i}\right)^{2} \tag{6}\]  
where \(FSI\) is foaming scum index (from 0 to 25), \(p_{i}\) is the score value of the variable from 1 to 5, \(w_{i}\) is the weighting attributed to every parameter. [PERSON] and [PERSON] [62] declare that high FSI values indicate the presence in the plant of persistent and stable foams that can cover the entire aeration basin. Instead, low FSI values refer to foams that collapse quickly and occupy only a small part of the surface of the basins.  
The equipment and the operative conditions of foams direct evaluation methods are summarized in Table 1. Figure 3 shows the pilot plant used by the researchers for aeration tests on foaming described above.
## 4 Characterization of the Fiber Compound-Ring Lasers
### Optical Signal-To-Noise Ratio  
The measured peak signals from the optical spectrum analyzer for the above wavelength spectrum (see Figure 8) were used to determine the optical signal-to-noise ratio of the proposed fiber compound-ring laser by subtracting the peak power value at each center wavelength (i.e., 1535, 1540, 1545, 1550, 1555, 1560 and 1565 nm) from the background noise level of each wavelength spectrum (i.e., OSNR (dB) = P\({}_{\mathrm{Signal}}-\mathrm{P}_{\mathrm{Noise}}\), where both P\({}_{\mathrm{Signal}}\) and P\({}_{\mathrm{Noise}}\) level are expressed in dBm), as demonstrated in Figure 9. The OSNR remained well above +39 dB over the whole wavelength tuning range, where the maximum OSNR of +44.6 dB was obtained at 1565 nm.  
The obtained OSNR spectrum is in the range that is comparable to the OSNR levels for fiber lasers used in remote sensing [69; 70], fiber sensors that utilize wavelength-peak measurement method [62] and optical communication systems [71].
## 5 Experiment Verification and Analysis
### Experiment on Datasets of Rolling Bearing in Case Western Reserve University
#### 5.1.3 Data Enhancement  
The network model used in this paper is 1 DCNN, and the input reconstructed signal is a one-dimensional time series signal. The prerequisite for this kind of deep learning model is to use a lot of data to train the model in order to enhance the generalization ability of the model. However, in the actual operation of the machine, due to the fault, it is very difficult for the machine to run for a long time, and a large amount of data can rarely be collected. Therefore, data enhancement technology is also widely used in convolution neural networks, and higher classification accuracy can be obtained in the case of fewer fault samples. Among them, through the overlapping method to intercept the training samples, it is easier to obtain a large amount of data. By setting the offset and sample length, the vibration signals under different fault types are overlapped and sampled by the method of equal interval offset sliding, so as to increase the number of training samples. The schematic diagram is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Layer** & **Kernel Number** & **Kernel Size** & **Kernel Stride** \\ \hline Convolution layer 1 & 16 & 16 & 2 \\ Pooling layer 1 & 16 & 2 & 2 \\ Convolution layer 2 & 32 & 3 & 1 \\ Pooling layer 2 & 32 & 2 & 2 \\ Convolution layer 3 & 64 & 3 & 1 \\ Pooling layer 3 & 64 & 2 & 2 \\ Convolution layer 4 & 64 & 3 & 1 \\ Pooling layer 4 & 64 & 2 & 2 \\ Convolution layer 5 & 64 & 3 & 1 \\ Pooling layer 5 & 64 & 2 & 2 \\ Fully connection & 1 & 100 & — \\ Softmax & 1 & 10 & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of the constructed 1 DCNN.  
Figure 6: Relationship between sparsity and reconstructed signal envelope spectral kurtosis.
## 2 Methods  
### Foudamentals of Polarimetric SAR  
The scattering characteristics of the observed target can be described by matrix, \(\mathbf{S}\); which links the scattered and incident electromagnet field, in the backscattered coordinate system:  
\[\mathbf{E}^{5}=\frac{e^{-jkr}}{r}\mathbf{S}\mathbf{E}^{i} \tag{1}\]  
where \(k\) is the wavenumber of the EM wave, \(r\) is the distance.  
Fully polarimetric SAR observations can be achieved by quad-polarimetric mode, in which both horizontal and vertical polarized signals are transmitted alternatively and received coherently. The \(2\times 2\) scattering matrix is used to represent the single look complex quad-pol SAR data:  
\[\mathbf{S}=\left(\begin{array}{cc}S_{hh}&S_{tr}\\ S_{vh}&S_{vv}\end{array}\right) \tag{2}\]  
where \(S_{ij}\) describes the transmitted and received polarization, respectively, with \(h\) denoting the horizontal direction and \(v\) denoting the vertical direction.  
To take advantage of statistical properties and reduce the effect of speckle noise of SAR data, covariance matrix is often derived from the scattering matrix by multilook its second order products:  
\[\mathbf{C}=\left(\begin{array}{cc}\left\langle S_{hh}^{2}\right\rangle& \left\langle\sqrt{2}S_{hh}S_{bv}^{*}\right\rangle&\left\langle S_{hh}S_{vv}^ {*}\right\rangle\\ \left\langle\sqrt{2}S_{hv}S_{hh}^{*}\right\rangle&\left\langle 2S_{bv}^{2} \right\rangle&\left\langle\sqrt{2}S_{hv}S_{vv}^{*}\right\rangle\\ \left\langle S_{vv}S_{hh}^{*}\right\rangle&\left\langle\sqrt{2}S_{vv}S_{hv}^{* }\right\rangle&\left\langle S_{vv}^{2}\right\rangle\end{array}\right) \tag{3}\]where \"**\" is the symbol of conjugate, and \"\(<\) >\" stands for multilook by using an averaging window. Multilook is applied as a standard procedure to obtain the second order statistics (covariance matrix, coherence matrix) of the SAR data, an average window of \(5\times 5\) is normally used for balancing the multilook result and maintaining the spatial resolution.
## 4 Network Performance  
The mechanisms to evaluate the network performance and inform end-users about the network status at any time are important aspects of WSN deployments, in addition to the objective of the main application. For this purpose, a methodology for network analysis is examined based on one of the main concerns of end users for WSN deployments: data quality.  
Data quality is often evaluated based on statistical methods that consider the amount of samples available and the precision of instruments. When WSNs are used as data gathering mechanisms, samples collected by more sensors are forwarded as packets towards the base station. Major factors that may affect the quality of the data include packet duplications and packet losses, which should be considered before any domain-specific analysis. Most protocol stacks available for WSNs, including XMesh and CTP, provide different mechanisms for addressing, though only partially, these problems.  
In long-term deployments, such as the ASWP testbed, all motes will eventually consume out their energy resources and stop working. Therefore, it is necessary to make periodic visits for replacing more batteries. Additionally, these visits may include replacement of broken motes, node relocations, and other miscellaneous replacements and/or repairs (e.g., external sensors). However, the battery replacement at each node introduces a perturbation in the network for routing to adapt and eventually stabilize, which presents additional tradeoffs for maximizing the network performance and, at the same time, minimizing the frequency of these visits (_i.e._, maintenance costs).
# An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis
## 4 Experiment
### Results
#### 4.2.1 Six Stored Patterns

Table 2: The overall accuracy of each model on the synthetic data. Figure 9: Synthetic data of the \(11\) template patterns. The first 4 samples of 7 templates are shown. From Figure 11, we can see that the performance of DTW and scaling PIP barely changed with the noise level, and the accuracy of each pattern is very balanced. It indicates these two models would not have a preference on some specific pattern. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & **ED** & **DTW** & **PIP** & **PIP-TG** & **N-Equal-Part** & **Scaling PIP** \\ \hline \multirow{4}{*}{\(\sigma=0.15\)} & H\&S & 0.565 & 0.960 & 1.000 & 1.000 & 0.550 & 0.990 \\  & Tria-A & 0.550 & 0.955 & 0.985 & 0.945 & 0.785 & 0.925 \\  & CWH & 0.715 & 0.995 & 0.895 & 0.895 & 0.935 & 0.915 \\  & Reverse CWH & 0.725 & 0.995 & 0.810 & 0.850 & 0.975 & 0.900 \\  & Trip-B & 0.245 & 0.920 & 0.995 & 0.995 & 0.845 & 0.990 \\  & Doub-T & 0.760 & 0.990 & 0.610 & 0.550 & 0.940 & 0.960 \\ \hline \multirow{4}{*}{\(\sigma=0.20\)} & Overall & 0.593 & 0.969 & 0.883 & 0.873 & 0.838 & 0.947 \\ \cline{2-8}  & H\&S & 0.535 & 0.940 & 0.975 & 0.975 & 0.555 & 0.890 \\  & Tria-A & 0.445 & 0.930 & 0.970 & 0.930 & 0.630 & 0.875 \\  & CWH & 0.665 & 0.955 & 0.800 & 0.755 & 0.905 & 0.915 \\  & Reverse CWH & 0.590 & 0.980 & 0.690 & 0.710 & 0.940 & 0.895 \\  & Trip-B & 0.225 & 0.885 & 0.975 & 0.995 & 0.735 & 0.940 \\  & Doub-T & 0.660 & 0.895 & 0.515 & 0.500 & 0.935 & 0.925 \\ \hline \multirow{4}{*}{\(\sigma=0.25\)} & Overall & 0.520 & 0.931 & 0.821 & 0.811 & 0.783 & 0.907 \\ \cline{2-8}  & H\&S & 0.495 & 0.845 & 0.930 & 0.930 & 0.630 & 0.785 \\ \cline{2-8}  & Tria-A & 0.410 & 0.855 & 0.960 & 0.905 & 0.715 & 0.795 \\ \cline{2-8}  & CWH & 0.645 & 0.915 & 0.625 & 0.685 & 0.865 & 0.835 \\ \cline{2-8}  & Reverse CWH & 0.665 & 0.925 & 0.540 & 0.540 & 0.930 & 0.760 \\ \cline{2-8}  & Trip-B & 0.165 & 0.790 & 0.955 & 0.905 & 0.715 & 0.945 \\ \cline{2-8}  & Doub-T & 0.715 & 0.825 & 0.530 & 0.505 & 0.910 & 0.890 \\ \hline \multirow{4}{*}{\(\sigma=0.25\)} & Overall & 0.516 & 0.859 & 0.757 & 0.745 & 0.794 & 0.835 \\ \cline{2-8}  & \multicolumn{1}{c}{} & & & & & & \\ \end{tabular}
\end{table} 
Table 3: The accuracy of each template pattern with different standard deviation. Figure 10: Processing time of each model.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 4 Performance Analysis
### Control Overhead
#### Two-Way Handshake Process  
In the two-way handshake route discovery process, the source transmits only one PREQ, and other nodes, except the destination, re-transmit the PREQ when they receive it for the first time. Upon receiving copies of the PREQ, the destination selects two node-disjoint paths and then sends two PREPs back to the source. Like MP-AODV, NDMR discards duplicates of the PREQ received at intermediate nodes.  
The total numbers of PREQ and PREP copies, denoted by \(numPREQ^{NDMR}\) and \(numPREP^{NDMR}\), respectively, are determined as follows:  
\[numPREQ^{NDMR}=N-1, \tag{9}\]  
\[numPREP^{NDMR}=n_{1}^{NDMR}+n_{2}^{NDMR}, \tag{10}\]  
where \(n_{1}^{NDMR}\) and \(n_{2}^{NDMR}\) are the hop counts of the first and second paths, respectively.  
Three-Way Handshake Process  
In the three-way handshake discovery process, the source first transmits a PREQ. Upon receiving this PREQ, the destination sends a PREP back to the source and transmits an SPREQ. Intermediate nodes belonging to the first path discard the SPREQ. When the source receives the first copy of the SPREQ, it sends back a PREP to the destination.  
The total numbers of request and reply messages transmitted and re-transmitted in the three-way handshake route discovery process are calculated as follows:  
\[numPREQ^{NDMR}=2N-n_{1}^{MP-AODV}-1, \tag{11}\]  
\[numPREP^{NDMR}=n_{1}^{NDMR}+n_{2}^{NDMR}. \tag{12}\]  
For the sample network in Figure 5, the number of RREQs and RREPs are as follows.  
* Case 1: The route discovery in this case is the two-way handshake process: \[numRREQ^{MP-AODV}=29\] \[numRREP^{MP-AODV}=8\]
* Case 2: The route discovery in this case is the three-way handshake process: \[numRREQ^{MP-AODV}=55\] \[numRREP^{MP-AODV}=8\]  
It can be seen from the results that the control overhead of the three-way handshake process-based NDMR (NDMR-3 HS) is similar to that of MP-AODV, whereas the two-way handshake process-based NDMR (NDMR-2 HS) significantly reduces the control overhead when compared with MP-AODV.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 4 Experiments
### Experimental Sequences  
Figure 12 illustrates how the robot performed its mission. First, the robot, located at the _corridor1_, received a mission from an administrator to inspect _kdiHall1_ on the first floor of the convention center and come back _base1_. Then the SMF, shown in a blue box, generated an on-demand database that contained only the database needed in the first-floor database, considering the robot's characteristics and missions. In addition to those defined in Table 1, classes expressed in the database were used by adding object and space classes, such as _Column_ and _InformationCenter_, in consideration of the convention center environment. The SMF also built and saved an occupancy map for estimating the robot's pose and generating waypoints.  
Second, the SAN module set goals as \"inspected _kdiHall1_\" and \"robot_at _cir752 base1_\" to divide the mission into six modular behaviors using the ROSPlan, which moved the robot to the working area, realized the inspection, and returned to the robot base; each behavior was described on Listings 1 and 3.  
Figure 11: The TOSM-based database for experiments.  
Figure 12: The TOSM-based autonomous navigation framework sequence diagram.  
Each waypoint following behavior can be expanded, as shown in Figure 12, where the PRM algorithm used randomly sampled points to generate a 2D graph. This graph was traversed to generate the shortest path to the goal. Afterward, each desired point in the graph was sent sequentially to the Action planner, which then used the trained policy to navigate safely.  
```
(:actioninspect:parameters(?v-robot?wp-waypoint?fl-floor):precondition(and(robot_at?v?wp)(waypoint_at_floor?wp?fl)):effect(inspected?wp))
```  
Listing 3: Inspect action.  
Figure 12 describes the moment of executing second behavior \"goto_waypoint _cir752 c2 kdjHall1_\", which meant _cir752_ should go to _kdjHall1_ from _c2_ inside _floor1_. When the robot started executing the behavior, it got sensory inputs to recognize the environment. The SIP module recognized objects and places using sensory inputs based on the CNN model with semantic knowledge in the on-demand database. In this case, it could infer \"_cir752 isLookingAt column12_, column16_, and _informationCenter1_\", \"_cir752 isLocatedAt corridor2_ that _isInsideOf floor1_\", and _column12 isNextTo column16_\". In addition, it got datatype properties of each instance. Finally, if the robot executed the final behavior successfully, which was goto_waypoint _base1_, it accomplished the mission.
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 3 RARZ Protocol
### RARZ Protocol Phases
#### 3.2.1 Network Configuration Phase  
The network configuration or setup phase splits the whole sensing region into different concentric static rings around the BS. For ring formation, the BS broadcasts messages with various transmission powers to the sensing field. The BS first broadcast a message covering the 100 m field, then each time increases its transmission power to cover the 50 m range (i.e., Ring1 \(\rightarrow\) 100 m, Ring2 \(\rightarrow\) 150 m, Ring3 \(\rightarrow\) 200 m,......, Ring10 \(\rightarrow\) 600 m) until it covers the whole field. Each message from the BS contains a ringID and each node, upon first receiving the message, will set its ringID to the one contained in the message received. For covering the whole sensing field, the BS progressively broadcasts messages with various communication ranges comprising different ringIDs. Each node, upon receiving a ring i message from BS, recognizes that the latter belongs to ring i unless they have already set a ring with lower ringID. Figure 2 elaborates the network setup phase of RARZ.  
All the nodes in a particular ring have the same ringID (i.e., network address). At the end of this phase, a route discovery process is formulated such that nodes belonging to the smallest ring will relay the packet to the BS and any other node in the same ring and nodes located in the higher ring will discard the packets. A distributed coordination function, as used in IEEE 802.11, is utilized in RARZ for medium access with two major distinctions. First, it does not devise pre-assigned MAC addresses; secondly, there is no strict per-node addressing for communication.
###### Abstract  
In this work, a multi-hop string network with a single sink node is analyzed. A periodic optimal scheduling for TDMA operation that considers the characteristic long propagation delay of the underwater acoustic channel is presented. This planning of transmissions is obtained with the help of a new geometrical method based on a 2D lattice in the space-time domain. In order to evaluate the performance of this optimal scheduling, two service policies have been compared: FIFO and Round-Robin. Simulation results, including achievable throughput, packet delay, and queue length, are shown. The network fairness has also been quantified with the Gini index.  
Keywords:optimal scheduling; time-division multiple access; Gini index; string network; underwater sensor networks +
Footnote †: journal: