esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 4 Experimental Evaluation
### Normal Bearing  
To illustrate the effectiveness of the proposed bearing fault diagnosis method, a baseline case is first studied, in which both bearings are healthy, as shown in Figure 17. Due to relative motion, bearing components generate vibrator signals in operation, as shown in Figure 18a. The shaft rotational speed is shown in Figure 18b. Figure 18c shows the frequency spectra of a healthy bearing as the shaft accelerates from 20 Hz to 25 Hz within 3.347 s, i.e., the acceleration a equals 3/2 Hz/s. The time-frequency representation (TFR) of the signal is obtained via STFT, as shown in Figure 18d. In the TFR, several suspected resonance frequency bands, in which the energy is most concentrated, are adaptively removed using different indexes in Figure 19. Based on the maximum of each index, an optimal frequency band is selected for further analysis. In Figure 19a, the optimal frequency band is (4375, 4687.5) Hz in the FK. The WPTK is shown in Figure 19b, and the corresponding optimal frequency band is (9376, 10,000) Hz at the 4 th decomposition level. The Protrugram with BW = 400 Hz and step = 50 Hz is shown in Figure 19c, and the maximum kurtosis is calculated at 4974 Hz. In Figure 19d, the FOSCK, the optimal frequency band corresponding to the maximum CK is calculated at the 5 th decomposition level, and its frequency band is (3125, 3437.5) Hz. The original signal is filtered by using different band-pass filters corresponding to these optimal frequency bands. The envelope of each filtered signal is calculated by using the Hilbert transform and resampled into the angular domain by using COT. The envelope order spectrum analysis result is shown in Figure 20. In all the figures, the dominant order components are related to the shaft rotational order (SRO) and its harmonics. These  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Fault Severity** & **Bearing** & **Number** & **Contact** & **Pitch** & **Ball** & **BPFO** & **BPFI** \\  & **Type** & **of Balls** & **Angle** & **Diameter** & **Diameter** & **BPFO** & **BPFI** \\ \hline \(3/4^{n}\)Rotor bearing & ER-12K & 8 & 0 & 1.318 in & 0.3125 in & 3.048 & 4.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the bearings.  
Figure 17: The test bench for bearing fault detection.  
results imply that both bearings are healthy. It is worth mentioning that the SRO and its harmonics appear due to rotor disk machining error and installation error.  
Figure 19: The optimal frequency band obtained by different methods: (**a**) FK, (**b**) WPTK, (**c**) Protrugram and (**d**) FOSCK.  
Figure 18: The signal measured from a normal bearing: (**a**) time-domain signal, (**b**) the shaft rotational frequency from 20 Hz to 25 Hz, (**c**) the frequency spectrum of (**a**,**d**) TFR by using STFT.
## 2 Materials and Methods
### Evaluation of Mean Absorbed Doses and Relative Uncertainties  
For voxel dosimetry approaches, the \(\overline{AD}\) was computed in the total lungs region as follows:  
\[\overline{AD}=\frac{1}{N_{Lungs}}\sum_{x,y,z}AD\ (x,y,z) \tag{4}\]  
where the sum runs for all the voxels of the lung volume; \(N_{Lungs}\) is the total number of lung voxels; and \(AD(x,y,z)\) is the voxel absorbed dose for the voxel in the \((x,y,z)\) position. In this study, \(AD\) values, and consequently \(\overline{AD}\), are always expressed as Gy per GBq of administered activity (Gy/GBq).  
The relative uncertainty, Equation (5), of the \(\overline{AD}\) evaluated by \(MC\) has been computed [30]:  
\[U_{ROI,rel}=\sqrt{\frac{1}{n}\sum_{k=1}^{n}u_{k,rel}^{2}} \tag{5}\]  
where \(n\) is the number of voxels in the ROI and \(u_{k,rel}\) is the relative statistical uncertainty in the k-th voxel. The relative uncertainty maps were obtained together with the \(AD\) maps as output from the \(MC\) simulations.  
The \(\overline{AD}\) obtained from each dosimetric method was compared with respect to the \(MC\) using correlation plots, with the MC's \(\overline{AD}\) on the x-axis (data obtained with the Reference phantom and the \(LS\) conditions) and the \(\overline{AD}\) obtained from each classical approach on the y-axis. Also, relative differences, Equation (6), in \(\overline{AD}\) between the tested method and \(MC\) were calculated.  
\[RD(\%)=\frac{\textit{Tested}-\textit{MC}}{\textit{MC}}\times 100 \tag{6}\]
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 4 Experimental Results and Discussion
### Geolocation Experiment Result  
Following the settings in Table 1, we collected 5610 landmarks for traceroute measurements in the three regions around the world. We trained the measurements for 60% (3365) of the landmarks in Table 2. Of these 3365 landmark measurements, 20% (1122) were used as the validation set, and another 20% (1123) were selected as the test set. Table 3 shows the geolocation errors that can be geolocated in the three regions for the method proposed in this paper, as well as for the other three IP positioning algorithms. We compared three different IP geolocation algorithms with the geolocation algorithm proposed in this paper under the same landmarks and measurement environments in the three regions. The cumulative distribution of the geolocation errors of the four algorithms for locatable entities is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Device** & **Region** \\ \hline Probe deployment & China: Zhengzhou and Hong Kong \\  & USA: Virginia \\ Landmark deployment & Shanghai, Tokyo, and New York State \\ Detection protocol & ICMPv6 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental device.  
Figure 4: Landmark distribution in the three regions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Landmark Deployment** & **Quantity** \\ \hline China & Shanghai & 2195 \\ America & New York State & 1826 \\ Japan & Tokyo & 1589 \\ total & 5610 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset details.  
Figure 5: Error distance CDF of the three regions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Shanghai, China**} & \multicolumn{2}{c}{**New York State, USA**} & \multicolumn{2}{c}{**Tokyo, Japan**} \\ \cline{2-10}  & **Max** & **Ave** & **Med** & **Max** & **Ave** & **Med** & **Max** & **Ave** & **Med** \\ \hline IPv-CBG 1 & 59.892 & 31.802 & 29.254 & 486.729 & 43.603 & 16.105 & 53.766 & 27.587 & 28.088 \\ Corr-SLG 1 & 55.427 & 15.337 & 13.916 & 481.127 & 37.303 & 7.501 & 46.881 & 12.019 & 9.856 \\ TNN 2 & 48.881 & 15.139 & 13.617 & 44.095 & 34.39 & 10.381 & 47.874 & 14.475 & 12.249 \\ MLP-GeoE 2 & 46.273 & 13.809 & 11.614 & 428.376 & 27.361 & 8.047 & 43.972 & 12.118 & 10.618 \\ Our Proposed & 44.895 & 11.194 & 9.709 & 421.527 & 24.854 & 7.025 & 42.018 & 10.564 & 8.751 \\ \hline \end{tabular}
\end{table}
Table 3: Performance (kilometers) comparison of baselines and SubvectorS_Geo.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 3 Results and Analysis
### Comparison with CHM Method  
In the CHM-based segmentation method, the value of recall varies from 76.92% to 86.96%, with a total value of 81.32% (Table 2). The value of precision varies from 90.70% to 93.10%, with a total value of 91.74%. The value of F1-score changes from 0.83 to 0.90, with a total value of 0.86. The recall, precision and F1-score values of Plot 3 are the highest. In this paper, the results of individual tree segmentation based on the improved DBSCAN method are compared with the CHM-based method. Individual tree segmentation of TLS based on the improved DBSCAN method improves the results of recall, precision, and F1-score (Figure 4). Compared with the segmentation method based on CHM, the total recall, precision, and F1-score of our method improved by 9.52%, 3.64% and 0.07, respectively. The individual tree segmentation method based on CHM has difficultly detecting small trees under tall tree crowns, especially when small trees are completely blocked. The distance between the breast height of small tree and tall tree in the red box in Figure 9 is 1.09 m, which is much larger than the \(\varepsilon\) value of the proposed method in Plot 4 of 0.78 m. However, the small tree in the red box is not correctly detected by the CHM-based segmentation (Figure 9).
## 3 Results and Discussion
### Batch Adsorption Experiment  
The percentage removal of all anions increased as the adsorbent dosage was increased due to the availability of more adsorption sites (Figure 3). The removal efficiencies were 99%, 90% and 60% for sulphate, phosphate and nitrate, respectively at an adsorbent dose of 3 g/L. The corresponding values of removal efficiencies at an adsorbent dose of 10 g/L were 99%, 96% and 91%, respectively. The reason for the higher adsorption of sulphate than the other anions was that sulphate has two negative charges whereas phosphate has between one and two charges (H\({}_{2}\)PO\({}_{4}\)\({}^{-}\), HPO\({}_{4}\)\({}^{2-}\)) and nitrate has only one charge at pH 7.0-7.5 of the MBR water. Increased number of negative charge of the anion is expected to increase the electrostatic adsorption of the anions on the positively charged Dowex 21K XLT ion exchanger.  
Figure 3: Effect of Dowex 21K XLT resin dose on the percentage removal of phosphate, nitrate and sulphate from MBR treated solution (I is initial concentration).  
Figure 2: Effect of pH on zeta potential and phosphate removal efficiency of Dowex 21K XLT.
## 4 Data Collection  
In this work, we tested our proposed method using a WSN traffic dataset associated with DoS and DDoS attacks. There are different types of network traffic datasets such as CICDDoS2019 [8], BoT-IoT [68], and WSN-DS [29]. The CICDDoS2019 and BoT-IoT datasets were collected from various device types (servers, sensors, routers, and switches) that were originally classified as IoT networks and not WSNs, whereas the WSN-DS was collected from WSNs by using the LEACH protocol. The Label feature in WSN-DS data categorizes different types of DoS attacks (Blackhole, Grayhole, Flooding, and Scheduling). Moreover, it contains 18 features with approximately 325,000 records. The features disclosed are: WSN node ID, occurrence time, whether the WSN node is CH, identity of the CH for WSN nodes that are not CH, distance between WSN node and CH, advertisement messages sent from CH to WSN nodes, advertisement messages received from CHs, join request messages sent from WSN nodes to CH, join request messages received by CH from WSN nodes, TDMA  
messages received by CHs from WSN nodes, rank, number of data packets sent from WSN nodes to CH, number of data packets received from CH, number of data packets sent to the AP, distance between CH and AP, send code, and label. These features and their sequence are illustrated in Table 3.  
As with our goals to reduce node power consumption and move the defense decision to edge AP, we converted these four types of DoS attacks into one class labelled \"1\"; the label \"0\" indicates normal traffic. The amount of attack data is 88.6% less than that of normal data. Furthermore, the attack data is distributed as 33% Blackhole, 11% Flooding, 35% Grayhole, and 21% TDMA.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.17 MinTax  
MinTax [71] uses _in-situ_ compilation of a high-level language, and dynamic linking, to reduce update sizes. The high-level language allows functionality to be coded very efficiently, and in a heterogeneous network the same update can be sent to all nodes, and then compiled on each node for its own architecture (rather than disseminating multi-architecture updates). Lookup tables are used to provide for direct access to hardware I/O ports on different platforms. Code updates are stored in EEPROM, and after compilation is written to Flash memory. Significant energy reductions are shown for a simple _Blink_ application when compared to Deluge under TinyOS (from 560 mJ to 1 mJ).  
MinTax itself provides no specific features for autonomous updates, but the energy reductions reported are significant.
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 2 Exploring the Options  
Different integrity concepts can be envisaged depending on the LEO constellation's capabilities. We explore different system configurations that lead to different LEO constellations capabilities, which are laid out here.
# Compaction Characteristics and Minimum Void Ratio Prediction Model for Gap-Graded Soil-Rock Mixture
## 2 Vibrating Compaction Test  
### Test Programs  
In this study, the gap gradation is generated by removing particles within a gap interval of a continuous gradation (called basic gradation), as shown in Figure 1. The mass of the missing particles within a gap interval is transferred into the remaining particles in proportion to their mass ratios. In a gap gradation, particles are divided into two groups at two sides of the gap interval, fine-grained group and coarse-grained group. The mass content of particles in the fine-grained group is defined as \(f_{c}\). The basic gradation is characterized using the fractal dimension \(D\), defined as the gradient in a double logarithmic plot of mass of particles against particle size [25].  
\[\frac{M(d<R)}{M_{T}}=(\frac{R}{R_{L}})^{3-D} \tag{1}\]  
in which \(M(d<R)\) is the mass of particles with diameters finer than \(R\), \(M_{T}\) is the total mass, \(R_{L}\) is the maximum diameter. In this test, \(R_{L}=60\) mm.  
The lithology of the test SRM is slightly-weathered dolomite, with a saturated uniaxial compression strength of 30-60 MPa (medium hard rock), which was taken from a rockfill dam site in China. The specific gravity of the SRM is measured at 2.65 and its shape is subangular. Five basic gradations with different fractal dimension \(D\) were tested. For each basic gradation, four gap gradations were generated, with gap intervals of 5-20 mm, 2-5 mm, 0.5-2 mm and 0.25-0.5 mm, which correspond to medium gravel, fine gravel, coarse sand and medium sand, respectively, according to Chinese standards on soil classification. Furthermore, for the gap gradation with the gap interval of 5-20 mm, seven different values of \(f_{c}\) were considered. The detailed test program is given in Table 1.  
Figure 1: Definition of a gap gradation generated from a continuous gradation.  
From these tests, we attempt to explore the effects of different basic gradations, gap intervals and mass contents of the fine-grained group \(\langle f_{c}\rangle\) on the compaction characteristics of SRMs.  
The vibrating compaction tests were carried out on a steel-made compaction cylinder with an inner diameter of 300 mm and a height of 500 mm. Each SRM sample has 75 kg in mass and was divided into three equal parts. Then, each part was dropped carefully into the cylinder. As shown in Figure 2, a vibrating compactor (Frequency: 45 Hz; Exciting force: 5.4 kN) was placed on the top of the sample and compacted the sample for eight minutes. It was found that the height of the sample would hardly change after a five-minute vibrating compaction. Therefore, the sample was considered to be in the densest state after the eight-minute vibrating compaction. After each test, the total height of the sample was measured, and then its void ratio was calculated. Also, grain-size analysis was performed and the particle breakage during vibrating compaction was analyzed.
# Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming
## 4 Space Lower Bounds for Dynamic Programming
The arrays of ordered lists \(L,L^{\prime}\) contain at most \(d^{2}\) elements and all entries are \(\leq\)\(d\) or \(\infty\): If the additional cost (compared to the baseline cost) of dominating a block \(S_{i}\) of the current partition from some subtree \(T_{y}\) exceeds \(d\), we disregard this possibility--it would be cheaper to just take all vertices in \(S_{i}\), a possibility explored in a different branch. To find a minimal solution from the table we need to avoid using the same subtree to dominate more than one element of the partition; however, at any given moment we only need to distinguish at most \(d^{2}\) subtrees. Thus, the size of the arrays \(L\) and \(L^{\prime}\) is bounded by \(O(d^{2}\log d)\). The only other space consumption is caused by a constant number of variables (_result_, _baseline_, _baseline_\({}^{\prime}\), \(b\), \(b^{\prime}\) and \(x\)) all of them \(\leq n\). Thus, the space consumption of a single call is bounded by \(O(d^{2}\log d+\log n)\) and the lemma follows. ```
Input: A graph \(G\), a treedepth decomposition \(T\) of \(G\), a node \(x\) of \(T\) and sets \(P,D\subseteq V(G)\). Output: The size of a minimum Dominating Set. 1 if\(x\) is a leaf in \(T\)then
2 if\(x\
otin N_{G}[D]\) or \(P\
ot\subseteq N_{G}[D]\)then\(D:=D\cup\{x\}\) ;
3 if\(P\
ot\subseteq N_{G}[D]\)thenreturn\(\infty\) ;
4 elseif\(x\in D\)thenreturn\(1\) ;
5 elsereturn\(0\) ;
6
7 end if
8
9 result \(:=\infty\);
10\(P:=P\cup\{x\}\);
11 foreach partition \(S_{1}\uplus\cdots\uplus S_{\ell}\) of \(P\)do
12\(L:=|P|\)-element array of ordered lists;
13\(L^{\prime}:=|P|\)-element array of ordered lists;
14\(baseline:=0\);
15\(baseline^{\prime}:=0\);
16 foreach child \(y\) of \(x\) in \(T\)do
17\(b:=\text{\it domset}(G,T,y,\varnothing,D)\);
18\(baseline:=baseline+b\);
19\(b^{\prime}:=\text{\it domset}(G,T,y,\varnothing,D\cup\{x\})\);
20\(baseline^{\prime}:=baseline+b^{\prime}\);
21 for\(S_{i}\in\{S_{1},\ldots,S_{\ell}\}\)do
22\(c:=\text{\it domset}(G,T,y,S_{i},D)-b\);
23\(c^{\prime}:=\text{\it domset}(G,T,y,S_{i},D\cup\{x\})-b^{\prime}\);
24 Insert \((c,y)\) into ordered list \(L[i]\) and keep only smallest \(\ell\) elements;
25 Insert \((c^{\prime},y)\) into ordered list \(L^{\prime}[i]\) and keep only smallest \(\ell\) elements;
26
27 end if
28
29 end for
30/* Find minimal cost of dominating \(\{S_{1},\ldots,S_{\ell}\}\) from \(L\) and \(L^{\prime}\) by solving appropriate matching problems (see proof of Lemma 2 for details). */
31\(result:=min(result,\text{\it find\_min\_solution}(L)+baseline)\);
32\(result:=min(result,\text{\it find\_min\_solution}(L^{\prime})+baseline^{\prime}+1)\);
33
34 end for
35 returnresult;
```  
**Algorithm 1**Computing dominating sets with very little space.
## 3 Methodology
### Wind Resource Assessment  
#### 3.1.1 Weibull Density Function  
The Weibull distribution model is applied for describing and analyzing wind data. This function is used to predict the characteristics of prevailing wind profile precisely [36]. To use the Weibull probability distribution, it is necessary to calculate two parameters, the scale parameter (\(k\)) and the dimensionless shape factor (\(c\)). Indeed, the two parameter Weibull distribution \(f(v)\) of measured wind speed \(v\) (m/s) and the cumulative distribution of Weibull are defined by the following equation [37; 38]:  
\[f(v)=\left(\frac{k}{c}\right)\left(\frac{v}{c}\right)^{k-1}exp\left[-\frac{v}{ c}\right]^{k} \tag{1}\]  
\[F(v)=1-exp\left[-\frac{v}{c}\right]^{k} \tag{2}\]  
In the literature, several methods to estimate the scale and shape parameters of the Weibull distribution function are reported [39]. In this study, four methods available are selected to estimate the Weibull parameters, namely the maximum likelihood method, WASP method, moment method, and empirical method of Jesus. These methods have been detailed in the Supplementary Material. Root mean square error, determination of coefficient, and mean bias error are computed to validate and compare the computed results.
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 2 Materials and Methods  
### Study Area  
The study area chosen was Macao in the Santarem district, as shown in Figure 1. This area has been hit several times by fires and has been managing FMZs for several years. The area is situated in the central part of continental Portugal (39 deg32'54.708'' N-8 deg0'17.9964'' W), and its FMZs cover an area of about 4263 ha, with 1120 ha being FMZs around roads. Along with geographic information, in vector format, about this municipality FMZs, a collaboration was established between NOVA LINCS (a Computer Science research laboratory located at the Faculty of Science and Technology of the NOVA University of Lisbon) and the city hall of Macao which provided information about the FMZs that were treated in 2018. In Figure 2 some FMZs that have been treated (white) and others that have not (red) are represented. This type of information is essential to validate the created models.
## 5 Results and Discussion
### Energy Cost  
Simulation results for energy cost per packet are shown in Figure 8, the proposed routing scheme attains lower energy cost due to the node selection mechanism it employs which select only the nodes with highest energy level. Displacement of intermediate nodes in the path causes the variation of energy levels of nodes whilst increasing mobility. The MRLAM scheme also selects nodes with better link quality and lower speed to decrease energy consumption and packet loss ratio, this awareness is not used in the other routing protocols. The increase in a node's movement in the network increases the complexity of network topology for sensing and route computation process. However, the proposed MRLAM routing scheme takes advantage of utilizing Q-learning algorithm along with minimum energy consumption node for transmission packets that helps to reduce the energy cost as well as complexity of the network. The Q-learning algorithm periodically updates the state-action value with learning rate \(\alpha\) based on energy consumption of intermediate nodes as well as the number of packets transmitted and received. Moreover, there is reduced network packet flooding and energy cost of packets forwarded to the destination in MRLAM scheme compare with the other schemes. From the perspective of the overall node speeds, critical analysis of the simulation results shows a decrease in energy cost by 33% and 23% as compared to the MP-OLSR and MP-OLSRv2 routing schemes, respectively.  
Figure 7: Node energy consumption with different speed of nodes.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 2 Analysis and Research of Blood Pressure Measurement Methods
### An Improved Arterial Tonometry Blood Pressure Measurement Method  
To address the issues of the arterial tonometry method, this paper proposes an improved arterial tonometry blood pressure measurement method. This method combines the preliminary blood pressure values calculated using arterial tonometry with other extracted pulse wave parameters to form a new feature set. Through machine learning algorithms, more accurate blood pressure measurement values are obtained.  
The steps of the proposed blood pressure measurement method are as follows: (1) by adjusting the tightness of the cuff to control the magnitude of the external pressure and observing the pulse wave amplitude to adjust the external pressure, the vessel is considered flat when the pulse wave amplitude is maximized; (2) after the pulse wave amplitude reaches its maximum, the pulse signal is collected, and the pulse wave parameters are extracted. Based on the extracted pulse wave parameters such as peak and trough values, the preliminary blood pressure value is calculated using the arterial tonometry method. The obtained preliminary blood pressure value is then fused with other pulse wave parameters such as peak and trough values, tidal wave anterior valley value, pre-rebound wave value, descent and inflection valley value, rebound wave peak value, systolic period \(St\), diastolic period \(Dt\), etc., to form a new feature set. The machine learning algorithm is then used to achieve more accurate blood pressure measurements.