esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Morphological Characterisation
#### 2.1.2 Descriptive Analysis of Quantitative Morphological Traits Studied
This gives us a cumulative variability of 94.12%. The high representativeness of axis 1 indicates a strong morphological organisation of the two varieties studied. The projection of the quantitative characteristics onto the plane defined by axes 1 and 2 (Figure 2) shows the formation of two groups of characteristics. Group 1, located on the positive side of axis 1, consists of the characters relating to the variety _A.P_ var. _pyrethrum_, whereas the second group contains the characters relating to the variety _A.P_ var. _depressus_. This subdivision shows that the grouped characters probably represent two morphologically different taxa, at least for the characteristics studied. The results are similar using either qualitative or quantitative characterisation, as can be seen by the similarity in Figures 1 and 2. Correlations between the quantitative characteristics show a strong relationship between the characteristics describing the same variety, whether it is _A.P_ var. _pyrethrum_ or _A.P_ var. _depressus_. The dimensions of the floral parts (capitula, ligulate flowers, and tubular flowers) are strongly related to each other; the wider the capitula, the longer it will be, and the lower the number of ligulate and tubular flowers, the wider and longer these flowers will be. A significant positive correlation was observed between the number of branches and the number of capitula per plant, which can be considered an indicator of fruit yield per plant. These results are in agreement with those of other studies [78; 79; 80], which showed a positive correlation between plant height, number of branches, number of fruits per plant, and leaf length and width. Factorial correspondence analysis indicates that the five most discriminating qualitative characters are petal back colour, root colour, wing shape, seed colour, and leaf base aspect. Analysis of qualitative and quantitative morphological characteristics shows that there is a difference between the two varieties studied, which is in line with previous work by [PERSON] and [PERSON] (2016); [PERSON] and [PERSON] (2016), who showed morphological differences in leaves, flowers, roots, and seeds between the two varieties. The evaluation of the two varieties for quantitative and qualitative morphological characters of the flowers, roots, seeds, or leaves is a good means for the differentiation of the taxa. In fact, the whole set of examined characters allows us to separate the studied varieties into two different taxa.
## 5 Hopf Bifurcation Analysis
### Hopf Bifurcation for the Constant Delay Model  
Figure 12 shows the change of the Hopf bifurcation to the stability lobes of the constant delay model; the blue line represents the sub-critical Hopf bifurcation area. It can be seen that the system only has the subcritical Hopf bifurcation in the case of the constant delay model because it is mainly related to the dependence of the non-linear cutting force on the cutting thickness.  
To verify the Hopf bifurcation feature of the constant delay model, the variation of amplitude \(a\) and axial dimensionless cutting depth \(\psi_{CD}\) were plotted at \(\Omega=1.2\), \(2\), \(4\); \(\rho=11\) (see Figures 13-15). It can be seen that the amplitude of the drilling system will continue to increase until it loses stability (sharp point) in the stable cutting area. Therefore, the linear stability region of the drilling system is unstable and may be self-excited.  
Figure 11: Sketch of subcritical and supercritical Hopf bifurcations.  
Figure 12: The change of Hopf bifurcation to stability lobes of the CD model.
## 5 Conclusions  
Because early bearing faults are masked by signal noise, they are difficult to detect. Noise reduction is necessary for successful early bearing fault detection. The traditional methods of eliminating noise require prior knowledge of the signal and a lot of parameters to be set, meanwhile, a complicated iteration in the process of optimization may cause a decrease in real-time fault detection. Noise reduction based on dimensional reduction, such as SVD, is a better strategy for discovering the principle components of the useful signal.  
The key to successful separation of noise and useful signal based on SVD is to choose the effective singular value that represents the useful signal, which can be used to reconstruct the de-noised signal. Seeking the effective singular value based on a traditional method such as maximum peaks of difference or the curvature spectrum often causes the distortion of the reconstructed signal, because the number of effective singular values determined by these older methods is small, which causes the loss of some information.  
This paper uses the variation of difference of curvature peaks to accurately find the mutational site of the spectrum of incremental singular entropy, which is regarded as the number of effective singular values for SVD. The experiment results for dealing with real bearing data shows that the proposed method successfully extracted fault signatures under industrial noise and solved the problem of reduction of excessive noise caused by an unappropriated selected number of singular values. The results verified that SVD is a powerful tool that can offer available fault information for the later rolling element bearing fault diagnosis. However, this paper only discusses the circumstances of Gaussian white noise. In the next step, extracting the faulty signal under other noises such as color noise will be focused on to increase the reliability and universality of our method.  
This work received financial support from the National Natural Science Foundation of China (No. 61202027), the Beijing Natural Science Foundation of China (No. 4122015), and the Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality (No. IDHT20150507).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents,  
Figure 11: The envelope spectrum of serious outer race faults in bearing 1: (**a**) Original faulty signal; (**b**) De-noised faulty signal based on MPODSOISE and MPOCSOISE; (**c**) De-noised faulty signal by MMBDSOICE; (**d**) De-noised faulty signal by MMBCSOICE.  
materials and analysis tools; [PERSON] wrote the paper. All authors analyzed the data and contributed to the completion of the manuscript.  
The authors declare no conflict of interest.
## 4 Results and Analysis
### Results of Classifiers without Hyperparameter Optimization  
The performance metrics of the machine learning classifiers with 10-fold cross-validation on the complete set of 18 principal components (PCs) and six optimal PCs selected from CFS with the Best First search method are provided in Table 7.  
Table 7 shows that, on a complete set of PCs, the KNN classifier produced the best accuracy of 96.34% at a cost of 21 instances in 0 ms while the RTF classifier delivered an accuracy of 96.16% and the highest AUC of 0.996 at a cost of 22 observations in 800 ms. In comparison, on optimal PCs, the accuracy of J48, AB, and BG classifiers has been improved while that of SVM, KNN, and RTF classifiers has been decreased. RTF attained the best accuracy of 95.99% and an AUC of 0.992 at a cost of 23 samples in 120 ms. The accuracy of KNN has dropped to 95.81%, and its AUC increased to 0.963 in 0 ms. The other metrics such as sensitivity, specificity, precision, and F-measure are equivalent to the accuracy value since they have been computed as the weighted average of both target class 0 and 1. The ROC plots of the Rotation Forest ensemble on complete and optimal  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{2-3}  & \multicolumn{3}{c}{**Low Risk (0)**} & **High Risk (1)** \\ \hline \multirow{2}{*}{True class} & Low risk (0) & True Negative (TN) & False Positive (FP) \\ \cline{2-3}  & High risk (1) & False Negative (FN) & True Positive (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion Matrix.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**On a Complete Set of PCs**} & \multicolumn{10}{c}{**On Optimal PCs**} \\ \hline
**Model** & **Acc.** & **Sen.** & **Spe.** & **Pre.** & **F-meas.** & **AUC** & **Cost** & **Time (ms)** & **Acc.** & **Sen.** & **Spe.** & **Pre.** & **F-meas.** & **AUC** & **Cost** & **Time (ms)** \\ \hline SVM & 84.82 & 84.82 & 83.62 & 85.00 & 84.91 & 0.842 & 87 & 100 & 84.64 & 84.64 & 83.00 & 85.14 &  
Figure 6a shows the ROC plot, that is, the true positive rate versus the false positive rate of the Rotation Forest ensemble with the J48 base classifier on 18 principal components of the combined dataset with an AUC of 0.996. Figure 6b shows the ROC plots of the Rotation Forest on six optimal components selected from the CFS method with an AUC of 0.992. Overall, with the dimensionality reduction, the accuracy of SVM, KNN, and RTF has declined, and that of J48, AB, and BG has increased, the AUC of all the classifiers has improved except for SVM, and the training time of all the classifiers has dropped to more than 50%. The KNN classifier stood as the best algorithm on full PCs with the cost of just 21 instances out of 573 in 0 ms of training time. The comparison of the model's training time is shown in Figure 7.  
Figure 7 shows that the training time of the models on an optimal set of principal components by removing the irrelevant ones using the CFS method has significantly dropped, especially for the Rotation Forest ensemble, compared to that on a complete set of components.
# Binary Mixes of Self-Compacting Concrete with Municipal Solid Waste Incinerator Bottom Ash
## 3 Results and Discussion
### Mechanical Performance
#### 3.4.5. Shrinkage  
Figure 7 (left) shows the evolution of shrinkage over 91 days. [PERSON] et al. [14] described the total length of shrinkage in ternary mixes of SCC with FA and MIBA as having rapid initial evolution. In this study, all mixes between 14 to 28 days reached around 50% of their corresponding 91-day shrinkage value. All mixes containing MIBA presented greater shrinkage when compared to the RC and more so with increasing ash content. Other authors witnessed similar results [13; 44; 53]. Mixes C20 BA, C30 BA, C40 BA and C50 BA showed 91-day shrinkage values of \(\sim\)580 \(\upmu\)m/m, \(\sim\)670 \(\upmu\)m/m, \(\sim\)840 \(\upmu\)m/m and \(\sim\)930 \(\upmu\)m/m, respectively, whilst that of the RC was of \(\sim\)500 \(\upmu\)m/m. Up to two-three days after demoubling, mixes C20 BA and C30 BA presented slight expansion (\(\sim\)100 \(\upmu\)m/m), which was then negated with high shrinkage. It is possible that Al particles may have continued to oxidize after setting, thereby prompting an internal expansion of the already hardened specimen.  
Similar behaviour was observed by [PERSON] [13], having witnessed an accelerated development of shrinkage in the first 28 days, evolving progressively until 91 days. In the same sense, [PERSON] et al. [52], when studying the effect of MIBA on the production of mortars, observed an increase in shrinkage in mixes with higher replacement levels. According to the authors, this increase was partly due to the higher \(w/c\) ratio that resulted from the ashes' use.  
Figure 7 (right) presents the estimate of the total shrinkage values, for conventional concrete, according to the expressions proposed by Eurocode 2 [46], for the ages previously considered. Comparing the calculated vs. measured values, it appears that the EC2 model tends to overestimate shrinkage at early ages. However, over time, shrinkage evolves significantly more in actual specimens, translating into an overlap between the relationship and the line of equality and progressing towards underestimation of the model. After 91 days, RC and C20 BA mixes showed an absolute shrinkage value lower than that predicted by EC2, corresponding to approximately 97% and 92%, respectively, of the final value indicated by the standard.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation
### Inter-Packet Arrival Time  
Inter-packet arrival time refers to the time that elapses from the receipt of a packet at the data sink to the instant at which the next packet is received.  
According to the operation of the SES _reservation-based method_, it can be expected that the time between the receipt of two consecutive packets at the destination node is determined by the time between slots that one particular node has available for transmission. This time is set by the length of the _wakeup interval_, which, in turn, depends on the WO parameter. The theoretical inter-packet arrival time is:  
\[W1=meshBaseActiveDuration\times 2^{\text{WO}}=5\ ms\times\ 2^{5}=160\ ms,\ if\ WO=5 \tag{3}\]  
Figure 9: Packet latency.  
\[WI = meshBaseActiveDuration\times 2^{WO}=5\ ms\times 2^{6}=320\ ms,\ if\ WO=6 \tag{4}\] \[WI = meshBaseActiveDuration\times 2^{WO}=5\ ms\times 2^{7}=640\ ms,\ if\ WO=7\] (5) \[\overline{it}_{P_{n}}=\left\{\begin{array}{l}160\ ms,\ WO=5\\ 320\ ms,\ WO=6\\ 640\ ms,\ WO=7\end{array}\right. \tag{6}\]  
Increasing the WO by one doubles the inter-packet arrival time, causing a decrease in throughput. This has been tested through simulations of a 6 \(\times\) 6 network with WO-AO configurations in Figure 10.  
It can be observed that the obtained measurements are consistent with the theoretical values, with values around 0.16 s for WO = 5, 0.32 s for WO = 6 and 0.64 s for WO = 7. There are, however, small variations in the inter-packet arrival times, also known as jitter, which will be discussed in the next section. These variations are mainly due to the imperfect synchronization process performed in the SES mechanism and the CSMA-CA mechanism. In addition, larger variations, also caused by the CSMA-CA mechanism, may indicate packet delays through several _wakeup intervals_ and, when packets arrive out of order, negative values may be found.
## 2 Study Goal and Site Description  
The goal of this paper is to introduce methodologies for the remote satellite monitoring of the location and movement of subsurface thermal events within landfills, such as smolders and fires. As a case study, these methods were applied to the Bridgeton Sanitary Landfill in Bridgeton, Missouri, U.S., where abnormal subsurface thermal activity has been ongoing since 2010 [18; 19]. The Bridgeton Sanitary Landfill in Bridgeton, MO (GPS: 38.767\({}^{\circ}\), \(-\)90.443\({}^{\circ}\)), roughly 20 miles from downtown St. Louis, MO, is part of a larger site containing five distinct waste areas, a transfer station, and a maintenance building (Figure 1).  
This landfill is divided into the North (adjacent to nuclear waste disposal area) and South Quarries, which are connected by a narrow band called the \"Neck.\" The North (upper) and South (lower) Quarries cover an area of approximately 52 acres. Waste placement at the Bridgeton landfill commenced around 1985 and ceased in December 2004. The total waste thickness at the end of the waste acceptance period was reported to be approximately 320 feet, with 80 feet of waste above the ground surface [21]. The landfill accepted approximately 17,000,000 in-place cubic yards of waste, including commercial, industrial, and municipal solid wastes. The Bridgeton landfill has a leachate collection system and is equipped with 200 temperature and gas well monitoring units. In our analysis, measurements from these sources were used as ground truth for comparison with the remote sensing results. In late 2010, a subsurface elevated temperature event was confirmed in the southern half of the landfill. This SSE, also known as a \"subsurface fire (SF),\" later expanded in all directions. [PERSON] (2013) estimated the expansion rate of underground combustion in the South Quarry area to be 2.8-3.0 feet per day. This expansion rate estimate was later adjusted to 1-2 feet per day [21; 22]. The smoldering event appears to be contained in the South Quarry [23].
# Rapid Measurement and Identification Method for the Geometric Errors of CNC Machine Tools
###### Abstract  
Error compensation technology offers a significant means for improving the geometric accuracy of CNC machine tools (MTs) as well as extending their service life. Measurement and identification are important prerequisites for error compensation. In this study, a measurement system, mainly composed of a self-developed micro-angle sensor and an L-shape standard piece, is proposed. Meanwhile, a stepwise identification method, based on an integrated error model, is established. In one measurement, four degrees-of-freedom errors, including two-dimensional displacement and two-dimensional angle of a linear guideway, can be obtained. Furthermore, in accordance with the stepwise identification method, the L-shape standard piece is placed in three different planes, so that the measurement and identification of all 21 geometric errors can be implemented. An experiment is carried out on a coordinate measuring machine (CMM) to verify the system. The residual error of the angle error, translation error and squareness error are 1.5\", 2 \(\upmu\)m and 3.37\", respectively, and these are compared to the values detected by a Renishaw laser interferometer.  
CNC machine tools; geometric error; L-shape standard piece; integrated error model; stepwise identification +
Footnote †: journal: Applied Sciences
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 3 Related Foundation
### Tools Comparison  
The research object of this paper is the security mechanism in the protocol. The analysis focuses on the security verification of the security protocol to find the protocol security vulnerabilities, so it is assumed that the communication is reliable. Therefore, network simulation tools, such as GNS and NS3 [20] are not used to study any communication protocol or communication mechanism.  
The current mainstream protocol formal modeling tools, such as Scyther [21], Tamarin [22], ProVerif [23] and CPN Tools [24]. Among them, the Scyther tool is based on the SPDL language to model the protocol; the Tamarin tool describes the protocol process based on multiple sets of rewriting rules, and uses first-order logic to quantify protocol messages and time nodes, thereby realizing the description of the protocol attributes; the Proverif tool models the protocol based on the rule form of the logic programming language Prolog, and infers whether an event will occur according to some rules; the CPN Tools models the protocol based on the colored Petri net. Although they can both model protocols in different ways, CPN has certain advantages compared with other automatic protocol security verification tools. The tool comparison is as follows:  
The Scyther tool is insufficient in the expansion of algebraic properties, and cannot detect attacks containing algebraic properties. The attack model of the Tamarin tool requires manual input, which is difficult to operate. The ProVerif tool cannot discover the loopholes in the protocol, but can only prove the security properties that the protocol satisfies. CPN Tools calculates the state space and generates a state space report by creating a hierarchical CPN reachability graph. At the same time, the high degree of freedom in the CPN modeling process has become one of its advantages. The state space is completely controlled by the modeler himself, and exclusive modeling and analysis methods can be implemented for different protocols. This is why CPN protocol verification is often more effective than automated protocol verification tools, so we use CPN Tools to simulate the protocol.
## 4 Discussion
### The Influence of Met-Ocean Conditions  
One of the constraints within SAR satellite systems is the influence of sea conditions, which introduce speckle noise, thereby compromising the image quality and the accuracy of the SLs derived from SAR. The issue arises from the inherent roughness of sea surfaces, causing radar signal to scatter in various directions [49]. This scattering behavior hinges on several factors, such as ocean wave conditions and wind speed. Consequently, the received radar signal's strength fluctuates due to scattering, leading to the characteristic speckle pattern seen in SAR images. Moreover, sea conditions are not static; they can rapidly change due to factors like wind, tides, and currents. These fluctuations introduce temporal variability in how radar waves interact with the sea surface. Since an SAR system collects multiple radar readings over time, these changing sea conditions across different acquisition instances can contribute to speckle noise. This occurs because they create varying interference patterns in the radar returns. This is the case of Salinas beach, for example. This beach often experiences high-energy sea conditions, and it is exposed to strong winds, resulting in a high amount of speckle. Conversely, both Bull Island and Start Bay are only slightly impacted by such met-oceanographic conditions, resulting in less speckle.  
To face this problem, some works have suggested the use of met-ocean data to select those scenes that provide less speckle noise [50]. However, such selection would reduce significantly the number of SAR-SLs obtained in sites where this issue occurs frequently, such as Salinas. In the methodology presented here, the filter based on the heatmap of shorelines allowed for the identifying and selecting of data from areas with high probability of occurrence of shorelines, avoiding the disturbance in areas far from typical SAR-SL detection, such as those where sea surface roughness is strongly affected by waves and wind.
## 6 Experimental Studies
### Vision-Based Tracking
#### 6.2.2 Control Accuracy  
In contrast to very good agreement of the visual measurements with the incremental ones as stated above, control accuracy unfortunately is not yet satisfactory for this scenario. This becomes obvious considering the end-effector error  
\[e_{\text{ee,vis,enc}}=\|\mathbf{r}_{\text{ee,vis,enc}}-\mathbf{r}_{\text{ee,des}}\|\, \tag{36}\]  
where \(\mathbf{r}_{\text{ee,vis,enc}}=\mathbf{r}_{\text{ee}}(\alpha_{\text{vis,enc}},\beta_{ \text{vis,enc}})\) is the end-effector position computed by the forward kinematics with the generalized coordinates measured by encoders, while the control loop is closed with visual feedback. The maximum value of this end-effector tracking error is about 90 mm, which is way too big for most applications. This is also one reason why such a simple trajectory has to be chosen in this scenario. The large tracking error is caused by the long sampling interval of \(\Delta T=200\) ms required by image processing. It results in long prediction times, which negatively affects the validity of the linearization used for the prediction, since a change of operating points is not considered in the LTV MPC scheme for simplicity. Also, modeling errors like, for example, in the motor unit's frictional torque have a bigger effect the longer the time step size is.  
Thus, improvements in control accuracy can be achieved in different ways: One option is using shorter sampling times as shown in the first study, see Section 6.1. This is achieved either in terms of software improvement, for example, by optimizing the presented image processing algorithm, or using more powerful hardware, like a faster processor, or by a combination of both aspects as in ready-to-use motion capture systems like, for example, OptiTrack, see References [43; 44]. Another option is considering the change of operating points in the prediction, since neglecting it is only appropriate for small prediction horizons as stated in Section 4.2. Being well aware of the necessity for improvement, further effort is spent on these topics in on-going research.