esa-sceva/satcom-chunk-collection huggingface dataset:
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 2
#### 3.2.1 Case 2: Analysis of 10-1000 Frequency Range (Hz)  
The low-frequency band is predominantly influenced by the transformer's core characteristics, including core deformation, core movement, core looseness, and magnetostriction effects, as established in Table 7. Any deviations in this range directly correlate with structural shifts in the magnetic circuit, making it a critical region for early fault detection.  
Figure (a)a illustrates the Six Sigma control chart, displaying the deviation of the measured FRA response from the baseline. The upper control limit (UCL) and lower control limit (LCL) provide a statistical threshold for acceptable variations. Notably, the measured FRA response remains within control limits across most of the low-frequency spectrum, indicating no substantial core displacement.  
Figure 11: Case 2, 10–1000 frequency range (Hz). **(a)** Six Sigma control chart and process capability. **(b)** Six Sigma range variation and upper control limits.  
However, Figure 11b, which presents the range variation and process capability indices (\(C_{p}\) and \(C_{pk}\)), reveals marginal stability concerns. The \(C_{p}\) and \(C_{pk}\) values fluctuate between 1.0 and 1.33, signifying a potential shift toward instability (as per Table 4). Although the transformer remains within acceptable operational limits, the observed trend suggests that early-stage core movement may be developing. This necessitates periodic monitoring to ensure that the deviation does not escalate.  
Remarks: It should be noted that the transformer shows early signs of core movement or mechanical stress, requiring routine monitoring to ensure the deviations do not escalate into severe faults.
## 3 Results
### Description of the Sample  
A total of 32 implants were evaluated over 5 years. The mean age of the patients was 61.72 years (I.Q. 57.99-65.45); 14 implants were inserted in men (43.75%) and 18 in women (56.25%). Of these, 15 implants were inserted in the mandible (46.9%) and 17 in the maxilla (53.1%). Eight were placed from canine to canine (25%), 9 in pre molars (28.1%) and 15 in molars (46.9%) (Tables 1 and 2, and Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline  & & Mean & 0.5393 \\  & Q & 95\% confidence interval for mean & Lower limit & 0.1239 \\  & & 95\% confidence interval for mean & Upper limit & 0.9547 \\  & & Mean & 0.5283 \\  & S & 95\% confidence interval for mean & Lower limit & 0.0326 \\  & & & Upper limit & 1.0240 \\ \hline  & & Mean & 0.4764 \\  & Q & 95\% confidence interval for mean & Lower limit & 0.0727 \\  & & 95\% confidence interval for mean & Upper limit & 0.8801 \\  & & Mean & 0.6175 \\  & & & Lower limit & 0.0129 \\  & & 95\% confidence interval for mean & Upper limit & 1.2221 \\ \hline  & & Mean & 0.5079 \\  & Q & 95\% confidence interval for mean & Lower limit & 0.1253 \\  & & & Upper limit & 0.8904 \\  & & Mean & 0.5729 \\  & & & Lower limit & 0.0618 \\  & & & Upper limit & 1.0840 \\ \hline \end{tabular}
\end{table}
Table 1: Bone level values for the two implants at the time of loading (3 months after insertion and 5 years after insertion). BLM1, mesial bone level at 3 months; BLD1, distal bone level at 3 months; BL1, mid-bone level at 3 months (BLM1 + BLD1/2). S = Standard implant, Q = Quattro implant.  
The results for the marginal B.L. are shown in Tables 1 and 2, and Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & Mean & & 0.6871 \\  & Q & 95\% confidence interval for mean & Lower limit & 0.2225 \\ BLM2 & & & Upper limit & 1.1517 \\  & S & & Mean & 0.7042 \\  & S & 95\% confidence interval for mean & Lower limit & 0.1864 \\  & S & 95\% confidence interval for mean & Upper limit & 1.2220 \\ \hline  & & Mean & & 0.6229 \\  & Q & 95\% confidence interval for mean & Lower limit & 0.1617 \\  & & & Upper limit & 1.0841 \\  & & Mean & & 1.0217 \\  & S & 95\% confidence interval for mean & Lower limit & 0.5456 \\  & & & Upper limit & 1.4978 \\ \hline  & & Mean & & 0.6550 \\  & Q & 95\% confidence interval for mean & Lower limit & 0.2140 \\ BL2 & & & Mean & 1.0960 \\  & S & 95\% confidence interval for mean & Lower limit & 0.8629 \\  & S & 95\% confidence interval for mean & Lower limit & 0.3922 \\  & & & Upper limit & 1.3336 \\ \hline \end{tabular}
\end{table}
Table 2: Bone level values for the two implants at 5 years after loading. BLM2, mesial bone level at 5 years; BLD2, distal bone level at 5 years; BL2, mid-bone level at 5 years (BLM2 + BLD2/2). S = Standard implant, Q = Quattreo implant.  
Figure 3: Box plot of bone level measured at 3 months and 5 years, grouped according to implant type. S = Standard implant, Q = Quattreo implant. O = Outliers. * = Extreme outliers.
## 2 System Model
### LEO Satellite Mega-Constellation Network Mobility Calculation Model
#### 2.2.1 Keplerian Elements  
A satellite's orbit can be described by seven orbital elements [44] (see Figure 3a): the semimajor axis \(\alpha\), eccentricity \(e\), right ascension of the ascending node \(\Omega\), inclination \(\delta\), argument of the perigee \(\omega\), mean anomaly \(M\), and true anomaly \(\
u\).  
For ease of description, let the eccentric anomaly angle be \(E\), the average angular velocity be \(n\), and the time be \(t\). Thus, the geometric relationship among \(M\), \(v\), and \(E\) (see Figure 3b) is given as:  
\[M=nt, \tag{1}\]  
\[E-e\sin E=M=nt, \tag{2}\]  
\[v=2\tan^{-1}\sqrt{\frac{1+e}{1-e}\tan^{2}\frac{E}{2}}, \tag{3}\]  
\[r=\alpha(1-e\cos E), \tag{4}\]  
where \(r\) is the modulus of the satellite's vector radius.  
For the circular orbits commonly used in the LEO-SMC-ICN-IoT, \(e\approx 0\) and \(M=E=v\). Therefore, \(v\) is used to describe the Kepler elements of the constellation. The Keplerian elements of the Iridium constellation are listed in Table 1[41].  
\begin{table}
\begin{tabular}{c c} \hline \hline \end{tabular}
\end{table}
Table 1: Keplerian elements of the Iridium constellation.  
Figure 3: Schematic diagram: (**a**) Keplerian elements; (**b**) Geometric relationship among \(M\), \(v\), and \(E\).
## 3 Results
### Catalyst Characterization
#### 3.2.5 EPR Investigation  
EPR measurements were carried out over selected samples after catalytic tests in order to shed more insights on their catalytic behaviour. The main purpose of the EPR investigation was to analyse eventual formation of coke on the catalyst surface. In Figure 6 (line a) is shown spectrum of Au/YCeAl, because this sample demonstrated high benzene oxidation at 300 \({}^{\circ}\)C within 24 h. The products were only CO\({}_{2}\) and water, but degree of conversion was 90%. A single EPR line with g factor 2.003, which is characteristic of carbon-centered radical and coke, was recorded. The same signal was seen in the spectrum of Pd/Ce (Figure 6, line e) that showed lower activity in comparison with Au/Ce (Figure 3a). The formation of coke could be the reason for observed catalyst behaviour. In the EPR spectrum of the best performing Pd/YCeAl was registered other line related to carbon-centered radicals with g-value of 2.0023, typical for free electrons (Figure 6, line b). This sample was studied, because after 24 h in stream some products of mild oxidation were detected. According to [PERSON] et al. g-values close to that of free electron are attributed to organic radicals ([PERSON] et al., 2017). The EPR lines with g values 2.0054 and 2.0049 were registered in the spectra of Pd/YCeAl and Pd-Au/YCeAl, respectively, indicating presence of oxygen centered radicals.  
Additionally, a line at magnetic field (334-335 mT) with g-value 1.964, assigned to Ce\({}^{3+}\), was observed in the spectra of monometallic Au and Pd samples on alumina supported Y-doped ceria, pointing on the possible role of Ce\({}^{3+}\) in O\({}_{2}\) activation ([PERSON] et al., 2017). It is also worth noting the presence of several lines at lower magnetic field (320-326 mT) with g values from 2.0097 to 2.056 due to the presence of adsorbed superoxo species O\({}_{2}\)\({}^{-}\) close to the Ce\({}^{4+}\) ions in spectra of some of the most active samples, i.e., Pd/YCeAl, Au/Ce and Pd/Ce (lines b, d and e) ([PERSON] et al., 2017). These oxygen species can be absorbed onto Ce\({}^{4+}\) ions or can fill oxygen vacancies. Different EPR signals were registered due to formation of surroundings with different symmetries. A general consensus of opinion exists about  
\begin{table}
\begin{tabular}{c c} \hline
**Catalyst** & **HC (mmol-g\({}^{-1}\))** \\ \hline Au/Ce & 0.54 \\ Au/CeAl & 0.26 \\ Au/YCeAl & 0.32 \\ Pd/Ce & 0.43 \\ Pd/CeAl & 0.23 \\ Pd/YCeAl & 0.29 \\ \hline \end{tabular}
\end{table}
Table 3: Experimental H\({}_{2}\) consumption during TPR up to 300 \({}^{\circ}\)C.  
beneficial role of adsorbed oxygen species in oxidation-reduction reactions [40]. The availability of adsorbed oxygen species was related to the presence of oxygen vacancies that assisted oxygen molecules activation [41]. Reasonably, the intensity of these lines was the stronger in the spectra of Au/Ce and Pd/Ce, i.e., the samples with highest amount of ceria.
## 2 Materials and Methods  
### Chemicals, Reagents and Instruments  
We used the following chemicals and reagents: ethanol 96% (GRAM-MOL, Zagreb, Croatia), acetone (GRAM-MOL, Zagreb, Croatia), acetonitrile \(\geq\) 99.9% UHPLC gradient-grade (Fischer Scientific, Taipei City, Taiwan), formic acid 98-100% (Sigma Aldrich, Darmstadt, Germany), HPLC grade-standard amentoflavone (PhytoLab, Vestenbergsgreuth, Germany), ginkgetin (PhytoLab, Vestenbergsgreuth, Germany), isoginkgetin (PhytoLab, Vestenbergsgreuth, Germany), bilobetin (PhytoLab, Vestenbergsgreuth, Germany) and sciadopitysin (PhytoLab, Vestenbergsgreuth, Germany), gallic acid 98% (Acros Organics, China), Folin-Ciocalteu's reagent (Sigma Aldrich, Buchs, Schwitzerland), sodium carbonate (T.T.T., Sveta Nedelja, Croatia), (+)-catechin hydrate (Sigma Aldrich, St. Louis, MO, USA), aluminum chloride hexahydrate (Thermo Fischer Scientific, Kandel, Germany), hydrochloric acid 36.5% (Kemika, Zagreb, Croatia), sodium hydroxide (T.T.T., Sveta Nedelja, Croatia), sodium nitrite (Kemika, Zagreb, Croatia), sodium molybdate (VI) dihydrate (Sigma Aldrich, St. Louis, MO, USA), caffe acid \(\geq\) 98% HPLC-grade (Sigma Aldrich, St. Louis, MO, USA), 2,2-diphenyl-1-picrylhydrazyl (Sigma Aldrich, Steinheim, Germany), methanol (Kemika, Zagreb, Croatia), (\(\pm\))-6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid 97% (Sigma Aldrich, Buchs, Schwitzerland).  
For drying samples, we used a freeze dryer (LIO-5 PLT, KAMBIC, Ljubljana, Slovenia) and oven dryer (VENTI-Line 180 Prime, VWR, Gdansk, Poland). For grinding leaves, a bead mill (Bead Ruptor 12, Omni International, Kennesaw, GA, USA) was used. For weighing plant material, an analytical balance (Adam Equipment, Maidstone, UK) was utilized. For preparing extracts, an ultrasonic bath (DU-100, Argo Lab, Carpi, Italy), mechanical rotator (Bio RS-24, Biosan, Riga, Latvia), vortex (V-1 plus, Biosan, Riga, Latvia) and centrifuge (LMC-4200R, Biosan, Riga, Latvia) were employed. For spectrophotometric analyses, a UV-VIS spectrophotometer (ONDA TOUCH UV-21, China) was used. For determining bifl flavonoids, an Agilent 1260 Infinity II high-performance liquid chromatography system (Agilent, Santa Clara, CA, USA) with a diode array detector (DAD) was employed. System controlling and data analysis was performed using OpenLab CDS Workstation Software version 2.8.
# Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones
## 2 System Model
### Power Allocation  
Because of the various channel conditions, the design of modulation used in the precoders and equalizers is essential for transmission performance. The power allocation needed to assign the modulation levels considering the capacities of all channels is critical for the transmission efficiency. While interference and noise are detected, the available quantity of transmission power can be precisely assigned. Moreover, the peak-to-average power ratio (PAPR) can be lowered to avoid power degradation. In Figure 5a, the channel with the maximum quantity of capacity is the first candidate to assign power until the boundary of channel capacity, while the greedy algorithm (GA) is applied for channel selection according to the remaining power. After the first power assignment in the best channel, the GA can then process the remaining power for the channel in the second position. This procedure continues until no channel is available or no power remains to be assigned. However, GA-based power allocation may cause an extreme imbalance of channel utilization and a lack of channel diversity. To allocate power in available channels as much as possible, power allocation based on the water-filling (WF) method [18] can provide more fairness and utilization of the MIMO system. To achieve a transmission diversity with fair utilization in MIMO channels, a WF-based scheme can granularly allocate power in more channels with an equal level of total energy (including powers of signal and noise), as shown in Figure 5b. In general, the maximum quantity of transmission efficiency is presented for the best gain of the channel with the lowest noise.
## 1 Introduction  
Sea-level change, sediment supply, and accommodation are three major factors of river delta evolution [1]. As the sea level has risen since the Last Glacial Maximum (LGM), the accommodation space of the coastal area has increased. During the last 7-6 ka years, where sea level has been stable or slightly fallen in stages, the sediment discharge from the land to the ocean has increased and many river deltas have been formed [2, 3]. Therefore, deltas have become the most important depocenter of the Holocene, and many delta evolution studies have been conducted in the world. Asian deltaic sediments can provide high-resolution information about the paleoenvironmental evolution because they are suppliedwith large amounts of terrestrial sediments, which are well preserved under the present delta plain [4]. Many studies have been carried out on the effects of sea-level change on the evolution of the deltas in the Yangtze and Pearl rivers in China and those of the Red and Mekong rivers in Vietnam [3; 4; 5; 6; 7; 8; 9; 10; 11; 12].  
In the Nakdong River delta, located in southeastern Korea, the paleoenvironmental evolution related to Holocene sea-level change has been extensively studied [13; 14; 15; 16; 17; 18]. Previous studies in the Nakdong River delta have mainly focused on the early delta initiation process based on the upstream sediments [15; 19; 20]. Moreover, the depositional environment changes have been studied based on the sedimentary characteristics of a series of drill cores: the OW-1 core sediments, located on the eastern side of the middle-upper area [21]; the ND-1 core sediments, located in the southwest delta plain [17; 18]; the ND-2 core sediments [18], located in the sand bar of the downstream area of the Nakdong River delta (Figure 1). Recently, [PERSON] et al. [22] interpreted the depositional environment change of the downstream core sediments (KND-3) since the LGM using description of the sedimentary facies. [PERSON] et al. [23] discussed the depositional history of the Nakdong River delta using grain size data, dating, and seismic profiles of the present prodelta core sediments (SSDP-102).  
However, to understand the overall evolution of the Nakdong River delta, it is necessary to study the horizontal and vertical variations among the cores further. As the sea level has risen and stabilized since the LGM, the potential accommodation space of the Nakdong River mouth has changed accordingly. The change in accommodation due to the sea-level rise may have been different for each area because the basal topography of the Nakdong River delta was not flat. The core sediments of the present delta plain in the Nakdong River can provide high-resolution data for the sedimentary features, sequence, and, especially, the delta evolution in response to sea-level change.  
The aim of this study is to reveal the Holocene depositional environment change of the downstream drilling core sediments (ND-3; 46.60 m thick), and the longitudinal and cross-sectional study of the Nakdong River delta. To achieve this aim, we describe the sedimentary facies, analyze the grain size, and the age of sediments.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 3 The Proposed Scheme: RSNA
### The Description of RSNA  
The basic idea of how RSNA efficiently forwards messages is divided into three parts: delivery competency, forwarding willingness mechanism, and utility-based forwarding.
## 3 Residue Number System and Multiple Error Correction
### Chinese Remainder Theorem  
According to the Chinese Remainder Theorem (CRT), an integer \(X\) is calculated from its RNS representation \((x_{1},\ x_{2},\ \ldots,\ x_{k})\) as follows [7]:  
\[X=\left|\sum_{i=1}^{k}\frac{M_{K}}{m_{i}}\left|\frac{m_{i}}{M_{K}}\right|_{m_ {i}}x_{i}\right|_{M_{K}} \tag{2}\]  
or equivalently,  
\[X=\sum_{i=1}^{k}\frac{M_{K}}{m_{i}}\left|\frac{m_{i}}{M_{K}}\right|_{m_{i}}x_ {i}-r_{X}M_{K} \tag{3}\]  
where \(\left|\frac{m_{i}}{M_{K}}\right|_{m_{i}}\) denotes the multiplicative inversion of \(\left|\frac{M_{K}}{m_{i}}\right|_{m_{i}}\), and \(r_{X}\) is the number rank (a value showing how many times the sum in (3) exceeds the RNS range).
## 3 Methodology
### STEP 2 Determination of Reliability Block Diagram (RBD)
#### 3.2.1 RBD at Stage I (PFD Stage)  
Figure 7 shows the RBD at Stage I. It is divided into three parts as shown in Figure 6: air compression, air dryer, and nitrogen generation parts. The configuration of the air compressor part is \(3\times 50\%\). It means that three compressors are installed, and the capacity of each compressor is \(50\%\). Two compressors are in operation, and one compressor is on standby for a failure of the operating compressors. The air dryer part has \(2\times 100\%\) configuration. One air dryer is redundancy. In the nitrogen generation part, the membrane has the \(4\times 33\%\) configuration. Three membranes are operated, and the remaining membrane stands by for a failure.
###### Abstract  
The IEEE 802.15.6 standard has the potential to provide cost-effective and unobtrusive medical services to individuals with chronic health conditions. It is a low-power standard developed for wireless body area networks and enables wireless communication inside or near a human body. This standard utilizes a Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol to improve network performance under different channel access priorities. However, the CSMA/CA proposed in the IEEE 802.15.6 standard has poor throughput performance and link reliability when some of the nodes deployed on a human body are hidden from each other. We employ the RTS/CTS scheme to solve hidden node problems in IEEE 802.15.6 networks over a lossy channel. To improve performance of the RTS/CTS scheme, we adjust transmission power levels of the nodes according to transmission failures. We estimate throughput and energy consumption of the proposed model by differentiating several parameters, such as contention window size, values of bit error ratios, number of nodes in different priority classes. The performance results are obtained through analytical approximations and simulations. We observe that the proposed model significantly improves performance of the IEEE 802.15.6 CSMA/CA by resolving hidden node problems.  
IEEE 802.15.6; RTS;CTS; wireless body area network; healthcare +
Footnote †: journal: Sensors
## 3 Parameter Design Method of TBM Tunnel ASRBs
### Test Scheme Determination for ASRBs and Method for Evaluating Their Stress Release Effect
#### 3.3.2 Selection of Design Parameters  
After determining the test analysis methods, based on the stress release mechanism of ASRBs mentioned in Section 2, the layout parameters of ASRBs and the corresponding test scheme can be determined. Regarding the design parameters of ASRBs, two types of ASRB parameters need to be considered: the geometric parameters of the boreholes themselves and the distance parameters between the boreholes. The former is often determined by the relevant drilling equipment and engineering construction requirements of the ASRBs on the site, including borehole diameter and borehole depth parameters. The latter can be rationally designed based on the extent of the stress release area and the expected stress release effect, as shown in Figure 3. The ASRBs can be parameterized by selecting the parallel or divergent arrangement method to determine the distance between the ASRBs to be designed. Based on the determination of the parameters and potential rockburst seismic source areas, the initial ASRB design scheme can be determined. The parameters of location \(d\) (distance from the palm face), inclination \(\theta\), length \(l\), row spacing \(r\), interval \(h\) (in case of parallel placement), and angle \(\alpha\) (in case of divergent placement) of the initial ASRBs can be calculated using the following formula.  
\[d=d_{c}+d_{p}+\frac{l_{p}}{2}+\varepsilon_{1} \tag{1}\]  
\[\theta=\arctan\biggl{(}\frac{r_{p}}{d-d_{p}}\biggr{)} \tag{2}\]  
\[l=r_{p}\text{cos}\theta+\varepsilon_{2} \tag{3}\]  
\[r=\frac{l_{p}}{m-1} \tag{4}\]  
\[h=\frac{h_{p}}{n-1} \tag{5}\]  
\[\alpha=\frac{\alpha_{p}}{n-1} \tag{6}\]  
As shown in Figure 6, in equations 1 to 6, \(d_{c}\) is the length of the TBM cutter head, \(d_{p}\) is the distance of the potential rockburst seismic source area from the palm face, \(l_{p}\) is the length of the potential rockburst source area, \(\varepsilon_{1}\) is other distances that cause the ASRBs to be pushed back from their deployment locations, \(r_{p}\) is the distance of the potential rockburst source area from the tunnel sidewalls, \(\varepsilon_{2}\) is the redundancy length, which should be greater than half the width of the potential rockburst area, \(m\) is the row number of ASRBs, \(h_{p}\) is the height of the potential rockburst area, which should be less than the diameter of the tunnel, \(n\) is the row number of ASRBs in the vertical section, and \(\alpha_{p}\) is the distributional angle of the potential rockburst area.