esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation
### Asymmetric Topologies
#### 7.2.1 Scenario A1: ZigBee-Only, One-Hop Routes
It is important to observe that, when the node 5 generated 4 packets/s, the actual unrounded cost of the route 3,1,0 was still higher than the actual unrounded cost of the route 3,2,0; however, because  
Figure 9: The median and the 15 th to 85 th percentiles of **(a)**: the cumulative route costs of the route 3,1,0 (\(\bar{c}_{3,1,0}\)), **(b)**: the cumulative route costs of the route 3,2,0 (\(\bar{c}_{3,2,0}\)), **(c)**: the standard deviation of the estimated costs measured at node 3 for the route 3,1,0 (\(\sigma_{3,1,0}\)), **(d)**: the standard deviation of the estimated costs measured at node 3 for the route 3,2,0 (\(\sigma_{3,2,0}\)), **(e)**: the ratio of times that route 3,2,0 was chosen (\(r_{3,2,0}\)), and **(f)**: the number of times that node 3 had to retransmit per 1000 messages generated in the scenario A1. costs are rounded to the closest integer, the U-RR procedure was not able to recognize route 3,2,0 as the best route beyond this point. Note further that the U-RR procedure alternates between routes that have the same rounded cost. More specifically, as the rounded cost estimates for the route 3,2,0 became equal to 3, the U-RR procedure chose the route that had the least number of unicast transmissions in order to improve its cost estimation, resulting in \(r_{3,2,0}^{(URR)}\approx 0.5\). This behavior can be seen in Figure 9e: when node 5 generated between 3.75 and 7.5 packets/s, the U-RR procedure chose the route 3,1,0 more often than the LS-based procedure in order to improve the cost estimate of the route 3,1,0. This results in a slight increase in the number of node 3 retransmissions per 1000 messages of the U-RR procedure over the LS procedure, as can be seen in Figure 9f when node 5 generates around 4 packets/s. It is also interesting to observe that it is possible for procedures to select non-optimal routes even when the estimation has low variation. For instance, when node 5 generated 0.5 messages/s \(r_{3,2,0}^{(URR)}>0.9\), \(\sigma_{3,2,0}^{(URR)}=0\), and \(\overline{c}_{3,2,0}<\overline{c}_{3,1,0}\); however, the route 3,1,0 was still selected 10% of the time. This was probably due to node 3 missing RREQ messages from node 2 in one or more of the RREQ cycles.
## 2 Materials and Methods  
### Materials  
The clay minerals bentonite and vermiculite were provided from Milos island and northern Greece (n. Greece), respectively, whereas biochar was provided from Crete island. Certain properties of the materials are presented in Table 1. Briefly, all materials had alkaline to strongly alkaline reaction and low salinity. The CECs of the two minerals were considerable but close to the lowest range value reported in literature for smectites (i.e., bentonite) and vermiculite, 80-150 and 100-200 cmolc kg\({}^{-1}\), respectively [21]. Among materials, only biochar contained N, P and B, from other plant nutrients compared with those presented in Table 1, at concentrations of 0.81 \(\pm\) 0.09%, 0.11 \(\pm\) 0.01% and 24.1 \(\pm\) 0.9 mg kg\({}^{-1}\), respectively [22]. As for the seven heavy metals, which are included in the European Council Directive 86/278/EEC [3] regarding the agronomic use of sewage sludge, Cu, Zn, Ni, Cr and Pb were detectable in all or some of the materials and Cd and Hg were not detectable in all cases [22].  
Moreover, dewatered sewage sludge was collected from the urban sewage sludge treatment plant of Thessaloniki (n. Greece) in July 2020 and some of its properties are presented in Table 1. In general, sludge had 86.2% moisture, almost neutral pH, low salinity and contained variable water-soluble and total amounts of macro- and micronutrients essential for plant growth (Table 1). Moreover, it contained 4.96 \(\pm\) 0.52% N and 18.0 \(\pm\) 1.6 mg kg\({}^{-1}\) B, and the loss on ignition (LOI) was 63.0 \(\pm\) 1.3% [22]. Cadmium and Hg were not detectable, whereas total concentrations of Cu, Zn, Ni, Cr and Pb had levels ranging far below the critical limits [22], according to the European Council Directive 86/278/EEC [3] and the 3 rd draft of working document on sludge of European Commission (2000) for Cr [23].  
The specific clay minerals, among others, as well as the biochar had been tested as materials for sewage sludge stabilization in a previous study of ours [9; 22]. The results of that study showed that the aforementioned materials added to dewatered sewage sludge at 15% decreased its microbial load considerably; the observed reduction in fecal indicators was between one and two logarithmic units. Based on these results, quantity of treated sludge with 15% addition of bentonite (B), vermiculite (V) or biochar (Bc) was prepared. Additional sludge treatments were lined sludge (with 15% Ca(OH)\({}_{2}\)) (L) and untreated (addition of no material) (D) sludge. After air-drying of all sludge treatments, the treated and untreated sludge was ground to pass a 4-mm sieve and used for the preparation of the soil treatments of the current study.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 2 Background
### Tag Size and Granularity  
The size and format of a tag decides the maximum amount of security rules which can be supported. As shown in Table 1, different types of DIFT designs have different tag structures. For hardware-based gate-level and register transfer level (RTL) DIFT designs [15; 16], logic gates and registers are extended to support the extra tag. For a gate-level-based DIFT design, an extra tag tied to a logic gate is added according to the original gate logic. The information flow in software running on such hardware logic will be tracked. The customization of security policy cannot combine the software context with the gate logic directly. As a result, false positives may be caused. For example, the benign data will be tainted following an OR tag propagation rule. Further, the security check is significantly influenced by the tag computing rules. For micro-architecture-level DIFT design [21; 22], the basic granularity of the tag depends on the data width of processor. There are two common granularities, word-level tags and byte-level tags. In a simple implementation, the tags represent two basic states of data, i.e., trusted and untrusted. In this case, it is difficult to differentiate the source of untrusted data. Moreover, this type of tag cannot be used to represent multiple-level security policies. To overcome this limitation, a simple method is to adopt more fine-grained and larger tag sizes such that more security policies can be supported. However, a larger tag size may cause a waste of tag storage, since not all data are involved in computing. Many tag bits may not be used at runtime. In addition to representing the states of data, the tag can also be used to indicate different security policies with different instruction types [21]. Different tag propagation and tag check rules are customized according to corresponding instruction types.  
For software-based DIFT designs, the tag design is more flexible than for their hardware-based counterparts. The authors of [10] designed a variable-length tag structure to support flexible and programmable security policies. The authors of [1] assigned tags to different objects in the operating system instead of data. However, a flexible tag structure introduces large performance overheads and increases the complexity of tag storage.
## 4 Command and Control
### Security Subsystem  
The Security subsystem is composed of: a Graphical UserInterface (GUI) and a smart Alarm management system.  
The GUI has been implemented as a web application connected to the message broker via web socket, so it allows final users to visualize the results on a web browser from different devices. The presentation layer is based on [37], and it allows every client to subscribe to both normal and threat commuter overall results. The GUI has been used to support the BCT evaluation of the DEXTER system.  
The Alarm management system is a commercial system of in-door location intelligence finalized to the coordination of security agents, wearing smart glasses, who react to alarms generated by the C&C. In this case, a JSON message compliant with the structure in Figure 8, and with details of the threat commuter (e.g., 2D Video re-id data and sensor results), is published by the C&C to the Message broker. The Alarm management system also incorporates a MQTT broker to handle both the internal communication of the server part of the application with the various pairs of smart glasses devices connected to the system and the communication with the INSTEAD Message broker.  
The MQTT broker of the Alarm management system filters the alarm messages published by the C&C to the INSTEAD Message broker and propagates the data to the smart glasses devices worn by the security guards. They are able to identify the person through the image supplied by the system over imposed on their field of view and may react when that person is still near the monitored corridor. A confirmation message can be published to the local MQTT broker by an interaction of a guard with the smart device, which is automatically shared with the INSTEAD system.
# Principle and Implementation of Stokes Vector Polarization Imaging Technology
## 3 Implementation of Stokes Vector Polarization Imaging Technology
### Simultaneous Polarization Imaging Mode  
Simultaneous polarization imaging can obtain four images of different polarization states of the target in one exposure, and the detection speed is fast, which is mainly used for the detection of rapidly changing targets. In addition, the polarization imaging system has no moving parts, and the system has good reliability and stability. Simultaneous polarization imaging is a parallel measurement method, which is realized through multiple measurement channels. The multi-channel polarization device disperses the polarization vectors of each row of the polarization matrix (or the polarization vectors of each column of the polarization matrix) from one physical channel to several physical channels, each of which only detects a certain polarization image.  
Figure 5: System principle diagram of imaging spectral polarizer based on acoustoptic filter type and liquid crystal phase tunable [21].  
According to the different realization methods, simultaneous polarization imaging can be divided into amplitude polarization imaging, aperture polarization imaging and focal plane polarization imaging.
## 3 Results and Discussion  
### Overcoming Pinholes  
Fabrication of the SC-O-SC devices requires the harmonious integration of superconductors and organic materials. Deposition of the organic layer on top of the SC is very straightforward, where we can benefit from the facility of spin-coating and there is no need for lattice matching. AFM measurements of MEH-PPV atop Nb depicted flat layer coatings, with an RMS roughness of \(\sim\)4 nm and thickness of 40 nm. Deposition of the top SC requires much more finesse. Sputter-deposition of NbN directly on the organic layer always resulted in Ohmic IV characteristics of the Nb/MEH-PPV/NbN junction. Cross-sectional TEM measurements of the device, Figure 2, point to two contributing factors. First, the polymer was thinned to \(\sim\)22 nm--half of its thickness prior to top electrode deposition, see Figure 2a. This is likely due to the heavy Nb atoms impinging the organic with enough momentum to mill into the layer. Moreover, in Figure 2b, the local EDS measurements of Nb across the junction show an abundance of Nb atoms in the organic layer. By gently sputtering a thin layer of Al on top of MEH-PPV prior to deposition of the top superconductor, we were able to overcome these problems. As evidenced in Figure 2c, in samples with the 5 nm Al layer, the polymer thickness after NbN deposition is nearly identical to the amount initially deposited. The EDS measurements of the Al-protected junction (Figure 2d) show that a major portion of the MEH-PPV is free of Al and Nb impurities. These findings, together with the transport properties of MEH-PPV presented in the SC-O-SC temperature dependent transport properties section below, confirm that the Al layer is efficient in protecting the organic layer and preventing pinholes.
## 2 Materials and Methods
### Characterization  
The phase composition of the starting powder and the scaffold were detected by X-ray diffraction (XRD, Bruker-AXS, Karlsruhe, Germany). It was operated at 40 kV/250 mA and scanning speed of 8\({}^{\circ}\)/min with Cu K\(\alpha\) radiation. The results were recorded from 20\({}^{\circ}\) to 70\({}^{\circ}\) (step size: 0.1\({}^{\circ}\)/s). Distribution conditions of nHap in the CaSO\({}_{4}\) matrix was obtained using scanning electron microscopy (SEM, TESCAN, Brno, Czech Republic) with energy dispersive spectroscopy (EDS). The scaffolds were fractured and placed on aluminum stubs. Then, the cross-sections were coated with a 10 nm platinum (Pt) layer using a sputter coater (JFC-1600, JEOL Co., Tokyo, Japan) due to poor conductivity of the scaffolds.  
Compression tests of the scaffolds were conducted using a mechanical testing instrument (WD-01, Zhuoji Instruments, Shanghai, China) with the 100 N loading cell at a crosshead speed of 0.5 mm/min. The compressive strength was assessed by the recorded maximum loading. The toughness measurement was evaluated using Vickers indentation technique (HXD-1000 TM, Taiming Optical Instrument Co., Shanghai, China). Indentation was carried out on the polished scaffolds surface by a diamond indenter at a loading of 4.9 N (ramp loading of 0.49 N/s and a dwell time of 15 s at maximum loading). Equation (1), proposed by [PERSON] and [PERSON] was utilized for counting the fracture toughness (\(K_{IC}\), MPa\(\cdot\)m\({}^{1/2}\)) [23]:  
\[K_{IC}=0.0824(P/c^{1.5}) \tag{1}\]  
where \(P\) and \(c\) indicate the indentation loading (N) and the radial crack length (m), respectively.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 8 Performance Evaluation
### Experiment with Fault Detection Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Accuracy**} & \multirow{2}{*}{
\begin{tabular}{c} **Detection** \\ **Time (ms)** \\ \end{tabular} } \\ \cline{3-10}  & & **Original** & **20 dB** & **15 dB** & **10 dB** & **5 dB** & **0 dB** & **Time (ms)** \\ \hline \multirow{5}{*}{Cooler} & CS+LDA & 1 & 0.961 & 0.897 & 0.784 & 0.678 & 0.557 & 3.75 \\  & PCA+XGBoost & 0.978 & 0.953 & 0.846 & 0.785 & 0.634 & 0.584 & 12.61 \\  & XFI+DNN & 0.995 & 0.962 & 0.899 & 0.865 & 0.723 & 0.652 & 13.44 \\  & CNN & 0.996 & 0.983 & 0.968 & 0.896 & 0.845 & 0.715 & 32.5 \\  & CRFS+LSTM-AE & 1 & 0.993 & 0.984 & 0.966 & 0.952 & 0.937 & 7.31 \\ \hline \multirow{5}{*}{Valve} & CS+LDA & 0.941 & 0.917 & 0.855 & 0.714 & 0.623 & 0.540 & 4.64 \\  & PCA+XGBoost & 0.966 & 0.930 & 0.822 & 0.768 & 0.659 & 0.555 & 11.74 \\  & XFI+DNN & 0.988 & 0.962 & 0.881 & 0.789 & 0.735 & 0.621 & 12.89 \\  & CNN & 1 & 0.975 & 0.933 & 0.824 & 0.765 & 0.672 & 30.15 \\  & CRFS+LSTM-AE & 0.984 & 0.971 & 0.955 & 0.924 & 0.899 & 0.831 & 9.53 \\ \hline \multirow{5}{*}{Pump} & CS+LDA & 0.955 & 0.923 & 0.873 & 0.746 & 0.635 & 0.545 & 5.13 \\  & PCA+XGBoost & 0.969 & 0.948 & 0.845 & 0.738 & 0.648 & 0.574 & 12.16 \\  & XFI+DNN & 0.972 & 0.945 & 0.898 & 0.859 & 0.745 & 0.647 & 14.19 \\  & CNN & 0.969 & 0.944 & 0.921 & 0.898 & 0.823 & 0.701 & 29.98 \\  & CRFS+LSTM-AE & 1 & 0.981 & 0.951 & 0.933 & 0.911 & 0.885 & 11.85 \\ \hline \multirow{5}{*}{Accumulator} & CS+LDA & 0.947 & 0.887 & 0.846 & 0.747 & 0.634 & 0.521 & 3.61 \\  & PCA+XGBoost & 0.941 & 0.893 & 0.867 & 0.786 & 0.646 & 0.539 & 12.33 \\ \cline{1-1}  & XFI+DNN & 0.960 & 0.896 & 0.839 & 0.798 & 0.645 & 0.597 & 15.18 \\ \cline{1-1}  & CNN & 0.982 & 0.947 & 0.886 & 0.849 & 0.734 & 0.642 & 31.21 \\ \cline{1-1}  & CRFS+LSTM-AE & 0.989 & 0.943 & 0.893 & 0.861 & 0.847 & 0.821 & 8.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison to previous methods.
# Performance Evaluation for China's Planned CO\({}_{2}\)-Ipda
## 5 Conclusions  
In this study, we analyzed the sensitivity of some critical parameters to the relative random error of China's CO\({}_{2}\)-IDPA and evaluated its performance in China and surrounding regions. We found that the satellite transit time does not have a noticeable impact on the relative random error. But the relative random error would be reduced by 0.1% (Noting: the total budget of relative random error is just 0.3%) if the current satellite height was adopted rather than a satellite height of 450 km (ASCENDS's parameter). Moreover, a high-altitude orbit would lead to obvious gaps between adjacent tracks compared with a low-altitude orbit. The current configuration of the laser, detector, and telescope can yield retrievals of adequate accuracy under normal conditions. However, the high aerosol loadings in China will certainly lead to a significant deterioration in performance. A synthetic simulation based on real measurements of environmental parameters revealed that, using the current configurations of the CO\({}_{2}\)-IPDA, accurate measurements of XCO\({}_{2}\) (random error \(<\) 0.3%) look possible for most regions, including the major forests and grasslands in China. However, our study also showed that in the most densely populated regions, the accuracy of XCO\({}_{2}\) measurements is unlikely to reach the ideal target (random error \(<\) 0.3%). A similar conclusion also holds for the coastal seas. The medium target (random error \(<\) 0.5%) can be fulfilled in all the above-mentioned areas. In contrast, passive sensors have failed to carry out successful inversions under similar conditions. However, the development of a novel inversion method for CO\({}_{2}\)-IPDA is still required to improve its performance to meet the ideal target of 0.3% in those areas where aerosol concentrations are frequently high. Finally, if the orbit height could be reduced to 450 km, the ideal target would be achieved for over 95% of regions.  
This work was supported by the National Natural Science Foundation of China (Grant No. 41127901 and No. 41601351), a China Postdoctoral Science Foundation funded project (Grant No. 2016M592389 and No. 2017T100580) and the Fundamental Research Funds for the Central Universities (Grant No. 2042016 kf0032). The authors wish to thank the Satellite Environment Center, Ministry of Environmental Protection, National Satellite Meteorological Center, China Meteorological Administration and Shanghai Institute of Optics and Fine Mechanics, the Chinese Academy of Sciences for providing the configuration of the CO2-IPDA.  
[PERSON] proposed the idea and wrote the manuscript. [PERSON] and [PERSON] corrected the manuscript and collected necessary data and parameters. [PERSON], [PERSON], and [PERSON] conducted experiments. [PERSON] funded this research and negotiated with the cooperators mentioned in the acknowledgments.  
The authors declare no conflict of interest.
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 3 Experimental Study
### Correlation Analysis of Signals  
To achieve modal analysis based on inherent modal information preferably, vibration signals, displacement signals and speed signals that are related to modal information always are selected. In this paper, thinking about modal analysis, we chose vibration signals to decompose and extract features. Figure 4 shows root mean square (RMS) of vibration signals in \(X\), \(Y\), \(Z\) directions of three groups tool in the milling process respectively.  
Figure 3: Tool conditions monitoring in high speed milling process.  
The graph shows that these has been a marked increase in the amplitude of vibration signals with time, in spite of the fluctuations. Through calculating the correlation coefficient for effective value of the signals and the tool wear, the results are shown in Table 1. It can be concluded that the characteristics of vibration signals have better relationship with the tool wear. Based on the results, it can be proposed that, as for the method to monitor the performance degradation of tool in the milling process, the vibration signals and tool degradation monitored show the consistent and high correlation. Meanwhile, vibration signals are easy to get in the processing Thus, thinking about data size and correlation coefficients, vibration signals in \(X\) direction are used as an indicator to recognize tool wear in this paper.
## 3 Results
### Linear Discriminant Analysis (LDA)

Table 6: Statistics summary of our linear discriminant analyses (LDAs) based on the major blocks of data transformations (log\({}_{10}\), cube root, none) from the 39–data instances hierarchized in Table 5.  
Table 6 also shows that, on average, the overall accuracy of all oil slicks is 67.13%. If considering the average of the \(\log_{10}\) (68.60%) and cube (68.12%) combinations, these have similar discrimination performances, though, as pointed out, the latter surpass the former with its more balanced seep-spill discrimination. The original data with no transformation had the lowest discrimination overall accuracy average: 64.68%. The second remarkable aspect observed in Table 5 is related to the hierarchy-accuracy grouping of the original not-transformed data. None of its 13 data instances are valid. They had very low (<60%) specificity (i.e., of the a priori know spills, how many the LDA identifies correctly?) and positive predictive values (i.e., of the LDA-identified seeps, how many are actually seeps?). This means that the data needs to be normalized to achieve success in discriminating the oil-slick category using our linear approach. The third noteworthy aspect observed in Table 5 concerns the choice of variables, i.e., oil-slicks' signature: size information and SAR basic qualitative-quantitative statistics--see (@) in Table 4. To this matter, we call a comparison between our current research (Table 5) and the first refined study (Table 7). Even though the SAR basic statistics we select now (_SIG.amp_: VAR and KUR) are different from the ones of the refined study (_SIG.amp_: AVG and SKW), there is not much change in the discrimination power between the two analyses. This is independent of the way the data is transformed, for instance, _SIG.amp_: \(\log_{10}\) (68.52% against 68.50%), cube (68.08% against 68.35%), and no transformation (64.25% against 63.88%), respectively, for our current research and the first refined study [31; 32]. The same holds true when we verify the size only combinations (see (@) in Table 4) between now (Table 5) and then (Table 7). In our current research we account for three size information variables (Table 4: PtoA, Compact, and Fractal), whereas in the refined study only two were used (Table 7: PtoA and Compact). Again, these discrimination outcomes are quite close, with size only \(\log_{10}\) categorizing exactly the same oil slicks in our current research (Table 5) and in the refined study (Table 7): \(\log_{10}\) (68.59% against 68.59%), cube (67.62% against 67.60%), and no transformation (63.90% against 63.85%), respectively, for our current research and the first refined study [31; 32]. We also note in Table 7 that the analyses of the size and SAR signatures together (i.e., _SIG.amp_) do not impact the outcomes much. For the log-transformed discrimination accuracy, in fact, it worsens the overall accuracy: 68.59% (size only) and 68.50% (size and SAR together). However, it does impact the
## 2 System Components
### PMSG, Three-Phase Diode Rectifier, and DC-DC Boost Converter
The relations used to determine the capacitor and inductor values of the DC-DC boost converter are defined as [48; 49]  
\[L=\frac{V_{s}D}{f\Delta I} \tag{7}\]  
\[C=\frac{I_{0}D}{f\Delta V_{c}} \tag{8}\]  
where \(\Delta I\) is the peak-to-peak inductor ripple current (\(A\)), \(\Delta V_{c}\) is the peak-to-peak capacitor ripple voltage (\(V\)), and f is the switching frequency (Hz). When determining the inductor and capacitor values, they should be equal to or greater than the calculated values.