esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Testing of Rod Cluster Control Assemblies in Pressured-Water Reactor Power Plants
## 5 Conclusions  
This paper presents an electromagnetic testing system for a 17\(\times\)17 type rod cluster control assembly (RCCA). The system has 24 sensor probes that enable the inspection of all the control rods in one RCCA. Each sensor probe is composed of 16 Hall sensors arrayed in a circumferential direction and an exciting bobbin coil. Artificial short-circumferential grooves (SCGs), sliding wears (SWs), and circumferential cracks (CCs) on real control rods have been tested to verify the effectiveness of the inspection system. The system was able to inspect the SCGs, SWs, and CCs with the depths of 10%, 30%, and 40% of cladding tube wall thickness (W.T, 0.47 mm), respectively. The shapes the defects could be classified using the width of the defects' signal. The depth of the defects could be estimated by the average amplitude of the signal. The standard deviations of depth estimation are 18%, 5.8%, and 6.0% for the CCs (PCCs + FCCs), SCGs, and SWs. The total estimation of the depth of all the defects is with a standard deviation of 12.1% W.T (0.057 mm). The SCGs and SWs have a small and similar estimation error, but the CCs have the highest error of estimation and have a small width of 0.2 mm. In future research, more improvement of power for the magnetic source should be taken into account, and it is necessary to minimize the lift-off between the sensor and specimen and the bobbin coil and specimen for obtaining a better quality of the defects' signal. More different widths and angles of the defects should be considered for a possibility of capturing the relationship with the signal. In addition to this, more advanced signal processing should be carried out for better estimation of the sizes and possibility of shape reconstruction.  
Conceptualization, J.L.; methodology, J.L., S.S. and M.L; software, M.L; validation, J.L. and M.L.; formal analysis, M.L.; investigation, J.L. and M.L.; resources, J.L. and S.S.; data curation, S.S. and M.L.; writing--original draft preparation, M.L.; writing--review and editing, M.L. and J.L.; visualization, M.L. and S.S.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. This paper was prepared the contributions of all authors. This research received no external funding. This work was supported by the Energy Technology Development of the Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy (No.20171520101610), and by The Phenikaa University Foundation for Science and Technology Development. The authors declare no conflict of interest.
## 4 Experiments  
### Experimental Equipment and Samples  
The ultrasonic testing platform used for detecting defects in flat steel is shown in Figure 11a. It comprises an electromagnetic ultrasonic chamber, an electromagnetic ultrasonic transducer, a flat steel specimen, and a laptop computer (LabVIEW). The electromagnetic ultrasound box is an integrated device that houses a signal generation module, power amplification, impedance matching, pre-amplification, filter, and data acquisition card modules. During testing, the signal generation module furnishes a 250 kHz bipolar six-period square wave pulse as the excitation signal. After power amplification, this signal propels the electromagnetic ultrasonic transducer to emit an ultrasonic guided wave. The resultant echo vibration signal is then gathered by the ultrasonic transducer, filtered, amplified, and ultimately transformed into an electrical signal. Finally, the data acquisition card transmits this electrical signal to the computer.  
Typically, transmission towers employ Q235 flat steel as their grounding electrode. Therefore, five Q235 flat steel specimens (NO. 1, 2, 3, 4, 5), each measuring 2000 mm in length, 60 mm in width, and 5.8 mm in thickness, were selected for the test. The specimens and their defective structure schematic diagram are shown in Figure 11b.  
Among them, No. 1 is a defect-free sample; No. 2 is a manually manufactured sample with three groove defects of 5 mm in length, 30 mm in width, and 1, 2, and 3 mm in depth, with a distance of 500 mm between each defect; in No. 3 the defects are widened to 60 mm from those in No. 2; No. 4 has three groove defects of 3 mm in depth, 5 mm in width, and 20, 40, and 60 mm in length; and No. 5 has groove defects of 60 \(\times\) 3 in length and depth and 5, 10, and 15 mm in width.  
Figure 11: (**a**) Ultrasonic testing test platform and (**b**) flat steel specimen sample (three views).
# Phylosymbiosis in the Rhizosphere Microbiome Extends to Nitrogen Cycle Functional Potential
## 3 Results
### Phylosynthesis and N Cycling Traits  
Considering all the traits together, the variation in the distribution of sequences supporting N cycling is weakly correlated with the phylogenetic distance between pairs of plants (R\({}_{\text{Pearson}}\) = 0.04, \(p\) = 0.049, R\({}_{\text{Spearman}}\) = 0.09, \(p\) < 0.001) (Figure 5A,B). However, as not all the pathways were evenly distributed across plants (Supplementary Figures S4 and S5), we next investigated the correlations and linear regressions between plant relatedness and the variation in the distribution of each individual pathway (Figure 5, Table 1). When considering individual pathways, the variation in sequence distribution correlated with plant phylogenetic distance, with the exception of the traits for denitrification, which were evenly distributed across samples (pSpearman = 0.18), and the two traits for the assimilatory nitrate to nitrite pathway (pSpearman = 0.12) (Figure 5A).  
Across samples, variation in the frequency of sequences for each individual pathway was strongly correlated with changes in the microbial community composition, except traits for the nitrification pathways. In addition, variation in the distribution of sequences for most pathways correlated with each other, except sequences for assimilatory nitrate to nitrite (Figure 5A).  
The distribution of sequences for N fixation was the pathway most affected by the plant phylogeny and one of the pathways least affected by the microbial community composition (Table 1, Figure 5C,D). On the other hand, the distribution of the abundant sequences for the ammonia assimilation pathway were only marginally affected by both the plant phylogeny and the microbial community composition (Table 1, Figure 5E,F).  
\begin{table}
\begin{tabular}{c c c c}  & **\# of Traits** & \
\begin{tabular}{c} **Plant Phylogeny Slope** \\ **(Std. Error)** \\ \end{tabular} &
\begin{tabular}{c} **Community Composition Slope** \\ **(Std. Error)** \\ \end{tabular} \\ Model & & lm(Pathway\(\sim\)PlantPhylo) & lm(Pathway\(\sim\)Com.Comp.) \\ All pathways & 44 & 0.042 * (0.021) & 0.241 *** (0.004) \\ Ammo. Ass. & 12 & 0.044 *** (0.012) & 0.101 *** (0.003) \\ Ass. Nitrate to Nitrite & 2 & NS & NS \\ Ass. Nitrite to Ammonia & 3 & \(-\)0.197 *** (0.033) & 0.335 *** (0.007) \\ Denitrification & 5 & NS & 0.236 *** (0.013) \\ Diss. Nitrate to Nitrite & 9 & \(-\)0.126 *** (0.031) & 0.219 *** (0.008) \\ Diss. Nitrite to Ammo. & 4 & 0.141 *** (0.052) & 0.249 *** (0.015) \\ Nitrification & 4 & \(-\)1.470 (0.299) & NS \\ N-Fixation & 5 & 0.456 *** (0.065) & 0.127 *** (0.020) \\ \end{tabular}
\end{table}
Table 1: Linear regression statistics for the relationships among variation in the plant phylogeny, change in the microbial community composition (at the genus level), and variation in the distribution of traits supporting the functional potential for N cycling. Pairwise comparisons among replicate samples were systematically excluded. Significance level: *** \(p\) < 0.001, ** \(p\) < 0.01, ** \(p\) < 0.05, NS \(>\) 0.05.
# Parallel Accelerated Fifth-Order WENO Scheme-Based Pipeline Transient Flow Solution Model
## 3 Numerical Calculation Method
### Fifth-Order WENO Scheme Flux Reconstruction  
According to Equation (7), the numerical flux can be obtained by summing the positive and negative parts, while the following template is chosen for the positive flux \(\mathbf{F}^{+}(\mathbf{U}_{i})\): \(S_{0}=\{x_{i-2},x_{i-1},x_{i}\},S_{1}=\{x_{i-1},x_{i},x_{i+1}\},S_{2}=\{x_{i },x_{i+1},x_{i+2}\}\). Figure 2 shows the location of the templates.  
From the above template, the flux reconstruction method of the fifth-order WENO scheme can be obtained as  
\[\mathbf{f}_{0}^{n}=\frac{1}{3}\mathbf{F}_{i-2}^{n}-\frac{7}{6}\mathbf{F}_{i-1}^{n}+\frac{ 11}{6}\mathbf{F}_{i}^{n} \tag{8}\]  
\[\mathbf{f}_{1}^{n}=-\frac{1}{6}\mathbf{F}_{i-2}^{n}+\frac{5}{6}\mathbf{F}_{i-1}^{n}+\frac{ 1}{3}\mathbf{F}_{i}^{n} \tag{9}\]  
\[\mathbf{f}_{2}^{n}=\frac{1}{3}\mathbf{F}_{i-2}^{n}+\frac{5}{6}\mathbf{F}_{i-1}^{n}-\frac{ 1}{6}\mathbf{F}_{i}^{n} \tag{10}\]  
The above three templates are employed for weighted reconstruction in the actual calculation, and the weights can be determined using the template smoothness function. This paper employs the template smoothness function \(IS_{k}^{\pm}\) proposed by [PERSON] and [PERSON] [15].  
\[IS_{k}^{+}=\sum_{\alpha=1}^{3}\int\limits_{x_{i-1}/2}^{x_{i+1}/2}(\Delta x)^{ 2\alpha-1}\bigg{(}\frac{d^{n}\mathbf{f}_{k}(x)}{dx^{n}}\bigg{)}^{2}dx\,\ (k=0,1,2) \tag{11}\]  
Bringing \(\mathbf{f}_{k}^{n}\) from Equations (8)-(10) into Equation (11) gives  
\[IS_{0}^{+}=\frac{13}{12}\big{(}\mathbf{F}_{i-2}^{n}-2\mathbf{F}_{i-1}^{n}+\mathbf{F}_{i}^{ n}\big{)}^{2}+\frac{1}{4}\big{(}\mathbf{F}_{i-2}^{n}-4\mathbf{F}_{i-1}^{n}+3\mathbf{F}_{i}^{ n}\big{)}^{2} \tag{12}\]  
Figure 2: Computational stencils of WENO.  
\[IS_{1}^{+}=\frac{13}{12}\big{(}\mathbf{F}_{i-1}^{n}-2\mathbf{F}_{i}^{n}+\mathbf{F}_{i+1}^{n} \big{)}^{2}+\frac{1}{4}\big{(}\mathbf{F}_{i-1}^{n}+0\mathbf{F}_{i}^{n}-\mathbf{F}_{i+1}^{n} \big{)}^{2} \tag{13}\]  
\[IS_{2}^{+}=\frac{13}{12}\big{(}\mathbf{F}_{i}^{n}-2\mathbf{F}_{i+1}^{n}+\mathbf{F}_{i+2}^{n} \big{)}^{2}+\frac{1}{4}\big{(}\mathbf{3}\mathbf{F}_{i}^{n}-4\mathbf{F}_{i+1}^{n}+\mathbf{F}_{i+2 }^{n}\big{)}^{2} \tag{14}\]  
where the weighting factor \(\omega_{k}\) is defined as  
\[\omega_{k}=\frac{\beta_{k}}{\sum\limits_{i=1}^{3}\beta_{k}} \tag{15}\]  
where \(\beta_{k}=\frac{C_{k}}{\left(\varepsilon+IS_{k}^{+}\right)^{2}}\quad,(k=0,1,2)\), and linear combination coefficients are chosen as  
\[C_{0}=0.3,C_{1}=0.6,C_{2}=0.1 \tag{16}\]  
Based on the template smoothness function, the positive vector flux expression can be derived as  
\[\mathbf{F}_{i+1/2}^{+}(\mathbf{U}_{i})=\sum\limits_{k=0}^{2}\omega_{k}IS_{k}^{+}(\mathbf{ F}_{i-2}^{n},\mathbf{F}_{i-1}^{n},\mathbf{F}_{i}^{n},\mathbf{F}_{i+1}^{n},\mathbf{F}_{i+2}^{n}) \tag{17}\]  
Similarly, the negative flux expression \(\mathbf{F}_{i+1/2}^{-}(\mathbf{U}_{i})\) can be obtained from the symmetric distribution of the grid interface \(i+1/2\), while the value of the interface flux \(\mathbf{F}_{i+1/2}^{n+1}(\mathbf{U}_{i})\) at the moment of \(n+1\) can be obtained from Equation (7).
## 3 Channel Proximity Discovery  
The first part of this section provides a brief explanation about route establishment and data forwarding in IP multicast. Then the novel system of channel proximity discovery using IP multicast is presented in detail.
## 6 Future Work, Challenges, and Limitations
### Energy Consumption  
Energy consumption has remained another concern for IoT-based positioning systems [8]. The trade-off between energy consumption and accuracy performance is a formidable challenge. For the high-accuracy positioning system, it is usually required to have more signal features for ML algorithms to reach higher NLoS classification accuracy with very high energy consumption of the systems, which could significantly reduce the battery life of IoT-based smart devices. Therefore, the technique and algorithm have to maintain a balance between positioning accuracy and energy requirements, especially crucial in mobile objects with fast movement. In such cases, ML algorithm optimization could be the approach, where the IoT-based positioning system continuously consumes a small amount of energy with remaining high-precision accuracy. The authors in [112] proposed an offline method to achieve minimum power consumption and an online solution to save energy for energy-aware video streaming on smartphones. Experimental results show that that the method can save energy, while achieving a better trade-off by implementing the online solution on Android-based smartphones.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 3 CLAMBDA-Search Model
### Error Analysis  
Once the ambiguity is fixed by (16), we can get all the ambiguity solutions and the ambiguity-fixed baseline vector. According to the relationship between the baseline and the attitude angle in (13), the differential equation of the attitude angles and the covariance propagation law, the error expression of the attitude angle can be derived. Define the posterior baseline error as follows:  
\[\sigma_{\overset{-n}{b}_{1}}=\left(\begin{array}{cc}\sigma_{e_{1}}&\sigma_{n _{1}}&\sigma_{u_{1}}\end{array}\right)\sigma_{\overset{-n}{b}_{2}}=\left( \begin{array}{cc}\sigma_{e_{2}}&\sigma_{n_{2}}&\sigma_{u_{2}}\end{array}\right) \tag{23}\]  
Conduct a differential process to \(\psi\) in (13), as follows:  
\[\begin{array}{l}d\psi=-\frac{1}{1+(\epsilon_{1}/n_{1})^{2}}\left(\frac{d \epsilon_{1}}{n_{1}}-\frac{\epsilon_{1}}{n_{1}^{2}}dn_{1}\right)=\frac{-n_{1} d\epsilon_{1}+\epsilon_{1}dn_{1}}{\epsilon_{1}^{2}+n_{1}^{2}}\\ d\psi=\frac{-\cos\psi d\epsilon_{1}+\sin\psi d\epsilon_{1}}{l_{1}\cos\theta} \end{array} \tag{24}\]  
If one ignores the covariance between the baseline components, the heading angle error is expressed as follows:  
\[\sigma_{\psi}=\frac{\sqrt{\sigma_{e}^{2}\cos^{2}\psi+\sigma_{n}^{2}sin^{2} \psi}}{l_{1}\cos\theta} \tag{25}\]  
Figure 2: Procedure of CLAMBDA-search method for attitude determination.
## 2 Designed Methodology
### Stage 1: Charging Schedule Optimization Without BESSs  
#### 2.1.1 EVCS Scenario and Constraint Definition  
EVCSs are built by assembling the same number of chargers as the number of plug-in EVs. In order to define the load profile of the station, a 15-min-step profile is used, and the study is focused on an urban fleet of electric buses. The buses have a certain daily driving schedule, where the time they are on the move and in the depot is distinguished, as shown in the example of Figure 3. **The original charging strategy of the bus fleet can be seen in Figure A1, which is the original charging schedule of a public bus fleet**.  
Figure 3: Basic illustration of the time during which buses are on the move and in the depot.  
Figure 2: EVCS EMS design methodology.  
Three constrains are defined. The first one is related to the maximum power contracted from the grid:  
\[P_{Gmax}\geq P_{EVCS}(\mathrm{t})=\sum_{n=1}^{N}P_{EVCS}(\mathrm{n},t) \tag{1}\]  
\(P_{EVCS}(t)\) is the total power required by the buses at each time step (\(P_{EVCS}(\mathrm{n},t)\) for the \(n\)th charger) and \(N\) is the number of chargers.  
Buses batteries must contain enough energy, \(E_{B}(\mathrm{n})\), in order to be able to drive the whole route over the day:  
\[E_{B}(\mathrm{n})=\sum_{t=1}^{T}P_{EVCS}(\mathrm{n},t) \tag{2}\]  
In addition, the \(n\)th EV charger power, \(P_{EVCS}(\mathrm{n},t)\), is limited to a maximum value:  
\[P_{EVCS}(\mathrm{n},t)\leq P_{Cmax} \tag{3}\]
## 3 Named Data Networking Architecture
### Data-Centric Security  
To ensure the integrity of the data, all named contents (data) packets are digitally signed by their content producer. This is also known as NDN data-centric security, as discussed by [15]. It also supports data trust and allows the consumer to check the producer's public key validity. The validation can be also carried out by a hierarchical trust model, where some namespaces can be certified by private companies or entities (third parties).  
NDN application layers can manage the access control to data through encryption and distribution of the keys. Additionally, using the signatures on control messages and network packets allows for securing the routing protocols.
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 4 Experimental Results
### Massive Deployment in Large-Scale Computers  
The second experiment verified the feasibility of the second type of massive deployment, the network connection. The image of a Linux system was deployed to a maximum of 32 machines within a server and a network switch. The experimental environment consisted of:  
* A server: a Dell T1700 machine which plays the role of server. The CPU of the Dell T1700 computer is the 3.3 GHz Intel Xeon E3-1226. The size of DRAM is 16 GB. The size of the hard drive is 1 Tera Byte (TB).
* PC clients: the Dell T1700 PCs with the same configuration as the one fulfilling the role as the server were used to act as the PC clients.
* Network switch: the Cisco Catalyst 3560G switch with 48 gigabits ports was used as the network switch.  
Figure 5: (**a**) Writing rate for two types of USB flash drives; (**b**) total time for deploying various number of USB flash drives; and, (**c**) average time for deploying various number of USB flash drives.  
The PC clients were connected to the Cisco network switch with the Category 5e network cables, as shown in the schematic Figure 6.  
In the beginning, an image was taken from a template machine that was installed with an Ubuntu Linux system. The size of the blocks that were being used on the disk was 50 GB, and the Linux system was saved as an image of 34 GB by Clonezilla live using pigz. The image was put in the local hard drive belonging to the server. The server was booted with the 'to ram' option in the Clonezilla live boot menu, and the local hard drive was mounted so that the image that was saved from the template machine can be read. After that, the server entered the lite-server mode, which relays the DHCP requests from the PC clients to the existing DHCP service in the LAN. After choosing the image, the multicast mode is then assigned. The client machines were booted from the PXE. Six tests were conducted, i.e., deploying the image to 1, 2, 4, 8, 16 and 32 clients. Figure 7 demonstrates the results of the massive deployment. Figure 7a,b illustrate the total time and the average time to massively deploy the image, respectively. Based on the illustrations, the average time to deploy a machine is mainly reduced by using the multicast mechanism in the massive deployment. All of the client machines were verified as bootable and could enter the Ubuntu Linux. Hence, the second experiment verified the feasibility of massive deployment in large-scale computers.  
Figure 6: The experiments of massive deployment in large-scale computers. 32 PC clients were connected to the PC server with 48-port Cisco network switch and the Category 5e network cables.  
Figure 7: (**a**) Total time and (**b**) average time being required for deploying various numbers of computers using the multicast mechanism.
## 5 Solving Non-Ideal Issues during the Tests
### Analysis of Sum of Integer-Order Integrator and Fractional-Order Differentiator Responses
The evaluation of simulated and measured frequency responses in the selected case (integer-order integrator and fractional-order differentiator) yields a maximal difference of magnitude of 7 dB in the tested band (10 Hz-1 MHz) and 1.1 dB in the expected (precise) operational range (50 Hz-10 kHz). The deviation 7 dB means the largest magnitude error is about 40% (at high frequencies above 500 kHz) between measured and simulated traces. However, the error is below 12% in the operational band (up to 10 kHz). Note that the relative error (%) seems to be large (tens of %) for small values of gain in units of dB, but the absolute error is acceptable. The phase difference reaches 11\({}^{\circ}\) maximally (10 Hz-1 MHz) and 4\({}^{\circ}\) (around 10% error) in the suggested operational band (50 Hz-10 kHz). The very similar behavior of all responses indicates that very similar differences are valid also for other cases (magnitude differences between 1-2 dB and phase differences up to 10-15\({}^{\circ}\) in the range between 50 Hz and 10 kHz, and higher phase differences especially for integer-order differentiator, as shown in Figure 14). The results indicate that the specific position of the fractional-order device, as well as the particular combination of two-ports influences slope (point of break in the case of two integrators or two differentiators in resulting topology), or creates a global minimum (combination of integrator and differentiator in resulting topology) and an initial or final phase value in the operational band. Electronic adjustment of transfer responses (gains \(A_{1}\), \(A_{2}\)) in both paths may be useful for special control applications where the position of global minimum (or point of break) should be impacted immediately in order to optimize effectiveness of regulation during the operation. Table 2 summarizes the results of maximally achievable magnitude slopes and phase shifts for a theoretical variation of the \(\alpha\) parameter and typical experimental results for our case \(\alpha\) = 0.5 tested in detail. Presented solutions divide the frequency characteristic to two sub-bands (low-frequency and high-frequency--below and above the point of break or global minimum). The fractional-order two-port has a capability to set the initial or final phase shift as well as the slope of magnitude in a specified sub-band arbitrarily in dependence on the \(\alpha\) value. The best accuracy with theory was obtained for solutions in Figures 7 and 9. Solutions in Figures 3 and 5 reflect the imperfections and effects of real active devices at high frequencies and the initial behavior of RC approximant at low frequencies, all due to very high processed dynamics in the observed bandwidth. \begin{table}
\begin{tabular}{c} integer-order \\ differentiator \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summarization of theoretical features and experimentally obtained results for studied case (\(\alpha=0.5\)).