esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conformity Review According to Fairway Design Standard
### Analysis of Vessel Traffic Status  
To review the adequacy of the design of the PIANC Guide for the South Sea coast fairway and the design criteria for ports and fishing ports, the current status of the maritime traffic was analyzed. Figure 4 shows the traffic flow for each target fairway. Although using data of one year for the maritime traffic analysis period is the preferred method, the traffic flow in the target sea was analyzed for 7 days, from 24 May 2020 to 30 May 2020, referring to the results of previous studies [31].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Width** & **Basic or Additional Width Range** \\ \hline Basic Maneuvering Lane \(W_{BM}\) & 1.3–1.8 B * \\ \hline Vessel Speed & 0–0.1 B \\ Wind & 0.1–1.1 B \\ Current & 0–1.6 B \\ Additional Factor & Wave & 0–1.0 B \\ Navigation Aids & 0–0.4 B \\ Bottom surface & 0–0.2 B \\ Depth & 0–0.4 B \\ \hline Two-Way Traffic & 1.0–2.0 B \\ \hline Bank Clearance & 0–1.3 B \\ \hline \hline \end{tabular}  
* B: Breadth of Target Vessel.  
\end{table}
Table 2: Basic maneuvering lane and additional channel width of PIANC.  
As a result of analyzing the traffic flow of each fairway, we found that most ships navigate along the fairway; however, small- and medium-sized ships that are not considerably restricted by water depth may deviate slightly from the fairway. However, because this does not deviate from the main traffic flow, the research was conducted focusing on the main traffic flow. Table 4 shows the detailed maritime traffic analysis data for each fairway.  
A total of 779 ships sailed through the Wando fairway over 7 days; furthermore, an average of 4.6 ships passed per hour. The largest vessel using the fairway was a 161 m long vessel, and the vessels passed in the following order: fishing vessels (13.9%), passenger vessels (13.2%), towing vessels (8.6%), cargo vessels (5.8%), and dangerous goods carriers (3.5%).  
A total of 1274 ships passed through the fairway within the Yeosu area in the traffic safety specific sea area, and approximately 7.6 ships passed per hour. The largest vessel using this fairway was 400 m long, while ships such as dangerous goods carriers (35.1%) and cargo ships (22.3) mainly passed.  
Figure 4: (**a**) Maritime traffic flow in the entrance and crossing fairways near Wando Port, (**b**) maritime traffic flow in the Yeosu area in specific traffic safety waters, (**c**) maritime traffic flow in the Masan fairway, and (**d**) maritime traffic flow in the Gadeok fairway.  
A total of 957 ships passed through the Masan fairway over 7 days, and it was analyzed that approximately 5.7 ships passed per hour. The largest vessel using the fairway was 345 m long, and towing vessels (28.7%), fishing vessels (12.4%), dangerous goods carriers (10.0%), and cargo ships (9.1%) passed in the given order.  
A total of 1002 ships passed through the Gadeok fairway over 7 days, with approximately 6.0 ships passing per hour. The largest vessel using the fairway was 400 m long, and cargo ships (30.0%), dangerous goods carriers (19.3%), and towing vessels (8.0%) passed in the given order.
## 2 Materials and Methods
### Study Selection
#### 2.2.2 Exclusion Criteria  
* Studies that did not involve comparison between the [18F]FDG-PET/CT and BMB.
* Studies that contain only BMB test, or only [18F]FDG-PET/CT exam.
* Studies carried out only for Hodgkin lymphoma patients.
* Studies that include previously diagnosed patients with NHL.
* Studies that did not assess the BMI.
* Studies that did not differentiate between the previously treated patients and HL patients from NHL-diagnosed patients.
* Studies published only as abstracts.
* Case reports, review articles, recommendations, letters, conference abstracts.
* Studies conducted on animals.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**No.**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Search Term**}} & \multicolumn{2}{c}{**Database Search Results**} \\ \cline{3-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & **PubMed** & **Scopus** \\ \hline \multirow{6}{*}{\#1} & non-Hodgkin & \multirow{2}{*}{} & \multirow{2}{*}{} & \\  & OR non-Hodgkin & & & \\  & OR PTCL OR Peripheral T-cell lymphoma & & & \\  & OR MCL OR mantle cell lymphoma & & & \\  & OR DLBCL OR diffuse large B-cell lymphoma & & & \\  & OR FL OR follicular lymphoma & & & \\  & OR PMBCL OR Primary mediastinal large B-cell lymphoma & & & \\  & OR BL OR Burkitt lymphoma & & & \\ \hline \multirow{2}{*}{\#2} & 2-fluoro-2-deoxy-D-glucose OR FDG OR Fluorodeoxyglucose & 57,777 & 91,069 \\  & OR PET/CT & & & \\ \hline \#3 & biopsy & 323,619 & 840,810 \\ \hline \#4 & Bone marrow & 229,682 & 412,081 \\ \hline \#5 & \#1 AND \#2 AND \#3 AND \#4 & [Title/Abstract] & [Title/Abstract/Keyword] \\  & & 155 & 515 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search strategy and results as on 1 November 2021.  
The articles were reviewed by applying the inclusion and exclusion criteria used for this study. Any duplicate studies were rejected, and the remaining studies were reviewed to define their eligibility for inclusion in this review.
# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
## 2 Proposed Approach
### Preprocessing  
The preprocessed fMRI dataset of the ABIDE repository [41; 42] was collected from the preprocessed connectome project (PCP) [43]. The configurable pipeline for the analysis of connectomes (CPAC) preprocessing pipeline was used, which included slice timing correction, motion correction, intensity normalization, nuisance signal removals, such as respiration, heartbeat, low-frequency scanner drifts, global mean signal regression, head motion, etc. The preprocessed data were band-pass filtered (0.01-0.1 Hz) and spatially registered to MNI152 template space. Detailed information regarding algorithms, strategies, parameters used, and software implemented can be obtained from [44].
## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Stand-Alone Applications of Satellite Remote Sensing for Infrastructure Monitoring  
An overview of the most relevant PSI applications for the monitoring of transport infrastructure is reported in this section. Four major infrastructure classes are reviewed to cover the PSI applications, i.e., roadways, bridges, airports and railways, and the most relevant have been reported. A summary of the main information from the reviewed literature sources is reported in Table 2.
## 2 Biomedical Image Segmentation
### Traditional Approaches to Biomedical Image Segmentation
#### 2.1.3 Edge-Based Methods  
Edge-based methods in traditional biomedical image segmentation focus on identifying boundaries or edges that separate different regions within an image. Edges often correspond to significant changes in pixel intensity, and the detection of these edges is crucial for outlining structures or objects of interest. Edge-based methods leverage gradient information, emphasizing the discontinuities in pixel intensities to delineate boundaries.  
These methods are particularly valuable in scenarios where regions of interest have distinct intensity gradients, and precise delineation is essential for accurate segmentation.  
[PERSON]'s edge detection algorithm is a seminal work in the field, providing a systematic approach to identifying edges by detecting local intensity gradients [24]. The Sobel operator is a classic edge detection filter that computes the gradient of the image intensity, highlighting areas of rapid intensity change [25]. Ref. [26] proposes a model of edge detection based on zero-crossings in the image intensity profile, contributing to the theoretical understanding of edge-based methods. [PERSON] and [PERSON] provide a comprehensive overview of image segmentation techniques, including edge-based methods, highlighting their applications in biomedical image analysis [27]. Ref. [28] introduces a multiscale edge representation, exploring the characterization of edges at different scales for improved edge-based segmentation. Active contour models, or \"snakes\", are edge-based deformable models used for contour delineation in biomedical image segmentation [21]. [PERSON] proposed a recursive implementation of the Canny edge detector, enhancing computational efficiency while maintaining optimality [29]. Ref. [30] showcases the application of edge-based methods for the segmentation of hip CT images, emphasizing their role in computer-aided surgery.  
A summary of the existing methods introduced in Section 2.1 is shown in Table 1.
## 5 Conclusions  
In conclusion, purified \(\beta\)-glucosidaseases (Mg-\(\beta\)gl and Hu-\(\beta\)gl) from non-_Saccharomyces_yeasts \(M\). _guilliermondii_ NM218 and \(H\). _uarum_ BF345 exhibited better enzymatic characterization and were applicable in Chardonnay wine fermentation and aging. The use of Mg-\(\beta\)gl and Hu-\(\beta\)gl during the aging process helped to improve the number and concentrations of the wine's aroma compounds. Both enzymes significantly contributed esters, terpen, C\({}_{13}\)-norisoprenoids, higher alcohols, and fatty acids to the treated wines, which enriched the aromatic complexity of the wines. Among them, \(\beta\)-glucosidaseate treatment from \(M\). _guilliermondii_ NM218 (Mg-\(\beta\)gl) imparted more sweet, floral, fruity, and banana aromas to the wine, whereas \(\beta\)-glucosidaseate treatment from \(H\). _uarum_ BF345 (Hu-\(\beta\)gl) imparted sweeter, floral, honey, pomelo, and banana aromas to the wine.  
**Supplementary Materials:** The following is available online at [[https://www.mdpi.com/article/10.3390/foods11060852/s1](https://www.mdpi.com/article/10.3390/foods11060852/s1)]([https://www.mdpi.com/article/10.3390/foods11060852/s1](https://www.mdpi.com/article/10.3390/foods11060852/s1)), Table S1: Qualitative and quantitative information of twenty-six chromographically pure standards and their calibration curves, R\({}^{2}\) values, and linear ranges.  
**Author Contributions:** Conceptualization and methodology, J.W. and P.G.; validation, P.G., S.P. and F.E.S.; formal analysis, S.P. and M.L.; investigation, P.G., J.W., M.L., S.P. and B.Z.; resources, J.W. and S.P.; data curation, P.G.; software and visualization, P.G. and F.E.S.; writing--original draft preparation, P.G.; writing--review and editing, S.P., F.E.S. and B.Z.; supervision and project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program Projects of China (2019 YED1002503) and the Key R&D Projects in Gansu Province (20 YFBNA132).  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank Ningxia Lilan Winery Co., Ltd. for providing the grape must raw material used in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Magnetic Field Intensity Measurement
### Linearity Correction
The diagram presented in Figure 13 shows the evolution of the linearity correction value (corrected via measured values), obtained by correlating the values of the current in the WPT-EC with the numerical values obtained at the output of the analog-to-digital converter using the procedure described above. The constant factor k (rel.%) ensured the numerical value correspondence at 16,000 is equivalent to 1V for the AD converter set at a full-scale rate (FSR) of 2.048 V.  
The initial idea was to scan the EMF produced by a sinusoidal current in the WPT-EC at frequencies of 100 kHz and 150 kHz. Due to the constructive limitation of the maximum generator power at a frequency around 100 kHz, the EMF strength was insufficient for traceable measurement, so a slightly higher frequency of 104 kHz, which allowed clear measurement without compensating for the inductance of the EC, was used. The graph in Figures 13 and 14 was constructed for measured values in the range 17-32,768 (15 bits). Values lower than 17 were not measured. The nonlinearity was obvious when the ratio of source values to measured values was represented graphically according to the measured values. The graphs in Figure 14 show the different ratio evolutions up to the numerical value of 500, as well as the relatively constant evolution for values measured between 500 and 20,000, which corresponded to voltages between approximately 37.5 mV and 1.25 V (considering the 15-bit ADC with a 2.048 V FSR). Different behavior was observed at 104 kHz compared to the 150 kHz frequency. Figure 5:3 The measured/corrected values ratio. Figure 5:4 Linearization graph for numerical sequences between 20 and 30,000. Using the global measurement configuration presented here, we experimentally demonstrated that successive measurements differed by a maximum of \(+/-0.2\%\) from the single measured value. Through the solution adopted in this study for averaging between 10 consecutive measurements, a maximum variation of 0.05% was finally obtained. If the supply voltage of the WPT-EC was 0 V, then the measured numerical value was around 14, a value generated by the inherent noise in the measurement process (provided by the electronic components and the EM disturbances). This revealed why, in the analysis, there were only values greater than 14, and it explained the graph shape in the area of values between 17 and 25. Even if the measurement accuracy was lower, in the area of numerical values less than 30, the linearization process described above compensated for this, practically minimizing the noise influence. Since these values were measured at the edges of the WPT workspace only, the errors did not affect the correct determination of the EMF spatial distribution.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 3 Architecture and System Models
### System Models
#### 3.2.2 Calculation Model for Communication Capacity  
Using the Friis transmission equation [39], the received power is calculated in dB notation as follows:  
\[P_{\mathrm{r}}\;[\mathrm{dBW}]=P_{\mathrm{t}}+G_{\mathrm{t}}+G_{\mathrm{r}}-L \tag{7}\]  
where \(P_{\mathrm{t}}\;[\mathrm{dBW}]\) and \(P_{\mathrm{r}}\;[\mathrm{dBW}]\) denote transmit and receive power, respectively. \(G_{\mathrm{t}}\;[\mathrm{dB}]\) and \(G_{\mathrm{r}}\;[\mathrm{dB}]\) are the gain of the transmit and receive antennas, respectively; knowing that when we specify a parameter/variable with the term [dB], the value is computed in a logarithmic scale rather than in a linear scale. Using the received power obtained here, the SINR is calculated as follows:  
\[\gamma=\frac{P_{\mathrm{r}}}{P_{\mathrm{n}}+I} \tag{8}\]  
where \(\gamma\) and \(I\;\;[\mathrm{W}]\) are the SINR and interference power, respectively, and \(P_{\mathrm{n}}\;[\mathrm{W}]\) is the thermal noise power, which is calculated as follows:  
\[P_{\mathrm{n}}=k_{\mathrm{B}}\;T\;B \tag{9}\]  
where \(k_{\mathrm{B}}\;\;[\mathrm{J}/\mathrm{K}]\) is [PERSON]'s constant, \(T\;[\mathrm{K}]\) is the absolute temperature, and \(B\;[\mathrm{Hz}]\) is the bandwidth of the system.  
The communication capacity is calculated by the following equation, based on [PERSON]'s communication capacity theorem [40], which indicates the marginal capacity.  
\[C\;[\mathrm{bps}]=B\log_{2}(1+\gamma) \tag{10}\]  
Since this paper assumes a TDMA (Time Division Multiple Access) communication method, the communication capacity per single user is obtained by dividing the communication capacity per UAV by the number of users accommodated by the UAV.
## 6 Optimization of Energy Distribution
### Gain in Energy \(P_{uav}\)  
Thus, Figure 5 shows that the sum of energy gain at some points denotes \(p_{i}(\tau_{i})\) from the trips \((\tau,2\tau...)\) of an energy renewable cycle.  
\[P_{UAV}(e_{i}(t))=P_{UAV}+\sum_{i=1}^{N}p_{i}(\tau_{i})-\sum_{i=1}^{N}U\tau_{i} \tag{8}\]  
\[P_{UAV}(g_{i}(t))=P_{UAV}+\sum_{i=1}^{N}p_{i}(\tau_{i})-\sum_{i=1}^{N}U\tau_{i} \tag{9}\]  
The first renewable cycle \(\tau\sum\limits_{i=1}^{N}p_{i}(\tau_{i})=0\) in \(g_{i}(t)\) becomes  
\[P_{UAV}(g_{i}(t))=P_{UAV}-\sum_{i=1}^{N}U\tau_{i} \tag{10}\]  
so \(P_{UAV}(e_{i}(t))\geq P_{UAV}(g_{i}(t))\). In the another renewable cycle, \(2\tau\sum\limits_{i=1}^{N}p_{i}(\tau_{i})>0\)  
\[P_{UAV}(e_{i}(t))=P_{UAV}+\sum_{i=1}^{N}p_{i}(\tau i)-\sum_{i=1}^{N}U\tau_{i} \geq P_{UAV}(g_{i}(t)) \tag{11}\]  
If \(\sum\limits_{i=1}^{N}U\tau_{i}>\sum\limits_{i=1}^{N}p_{i}(\tau_{i})\), \(P_{UAV}(e_{i}(t))<P_{UAV}(e_{i}(a_{i}))\). If \(\sum\limits_{i=1}^{N}p_{i}(\tau_{i})>\sum\limits_{i=1}^{N}U\tau_{i}\), check whether \(N_{com}<\dfrac{N}{2}\) mostly satisfies \(P_{UAV}(e_{i}(t))>P_{UAV}(e_{i}(a_{i}))\). Furthermore, \(\sum\limits_{i=1}^{N}p_{i}(\tau_{i})\) is considered \(\tau_{\text{i}vac}\). Node and UAV energy: The solution \(\varphi^{*}\)._Full_ achieves same maximum ratio of vacation time to the cycle time similar to the solution \(\varphi^{*}\)[25]. Under \(\varphi\), it is proven that \(e_{i}(a_{i}+\tau_{i})=E_{max}\). Thus, it is a feasible cycle for node energy renewal. The assumption \(\varphi^{*}\) is the optimal solution for further increasing the objective value, as shown in Figure 5.  
**Lemma 1**.: _At this condition, \(\varphi^{\beta}\) satisfies \(P_{UAV}(e_{i}(t)\) at UAV and \(e_{i}(t)\) at the nodes, both greater than \(P_{UAV}(e_{i}(a_{i}))\) and \(e_{i}(a_{i})\), respectively, at the arrival time in our proposed solution. It satisfies the condition that both energy renewable sources and energy receiving sources co-ordinate with each other to utilize the waste energy of compromised nodes, recycled to increase the overall network lifetime._  
Total energy gain at \(\tau_{\text{i}vac}\) :\(N_{com}\)\({}^{*}\)\(p_{i}(\tau_{i})\) when \((W_{i}<W_{min})\). This is the energy transferred to \(P_{UAV}\) in addition to the energy charged in the energy station at \(\tau_{\text{i}vac}\) time at cycles \((\tau,2\tau...)\) until \(N_{com}>\dfrac{N}{2}\), where the total number of nodes detected as compromised node in the network is denoted as \(N_{com}\). \(P_{UAV}\) is maximum at some points at each cycle \((\tau,2\tau...)\)when \(W_{i}<W_{min}\) nodes transfer energy at \(\tau_{i}\) is \(p_{i}(\tau_{i})\) to the sensor nodes by isolating the malicious nodes through the calculated weights denoted with \(W_{i}\).
# Evaluation of the GPS Precise Orbit and Clock Corrections from MADOCA Real-Time Products
## 7 Summary and Conclusions  
In this paper, a detailed description of QZSS and the MADOCA real-time messages was provided. The availability of MADOCA real-time products for three months was counted. The accuracy of orbit and clock of all available GPS satellites provided by MADOCA real-time products was assessed from both seven-day-short-term and one-year-long-run perspectives through comparison with IGS final products while PPP verification was also conducted from an application perspective.  
The statistic results show that the epoch availability of MADOCA real-time products over three months for GPS satellites was about 92.6% and the vast majority of available epochs have over 30 usable GPS satellites. The seven-day epoch by epoch comparison results show that the orbits errors range from 1.8 cm to 3.9 cm after the unification of coordinate reference using the 7-parameters Helmert transformation model, and the clock errors range from 0.04 ns to 0.15 ns (1.2 cm to 4.5 cm in ranges). The corresponding orbit-error-only SISRE and SISRE for all available satellites are respectively less than 5 cm and 10 cm. In the long-term daily SISRE RMS statistics, the accuracy of MADOCA real-time products shows the consistent accuracy with the short-term epoch-by-epoch results except for a few jumps. The open source software RTKLIB was used on the daily kinematic PPP positioning evaluation, and over 20 selected IGS stations positioning results show 4.9, 4.2, 11.7 cm accuracy could be reached in the east, north, up directions after the convergence, respectively, which is consistent with the QZSS CLAS service.  
Figure 12: Daily RMS values of PPP positioning results at the 20 selected global IGS stations using MADOCA real-time products.  
In conclusion, the current MADOCA real-time ephemeris products prove to meet the accuracy demands of the RTPPP solution which will better serve the vast majority of RTPPP users who can receive the augmentation signals.  
Conceptualization, S.Z. and W.L. Software, S.D. and G.W. Validation, S.Z., S.D., and W.L. Writing--original draft, S.D. Writing--review and editing, S.Z., W.L., and G.W.  
This work was sponsored by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA17010304), the National Science Foundation of China (Grant No. 41504023 and 41804033), and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUGL170821 and CUGL180831), Open Research Fund Program of State Key Laboratory of Geodesy and Earth's Dynamics (SKLGED2019-3-2-E).  
The authors would like to thank IGS and JAXA for providing MADOCA real-time products and GNSS precise products, which enable this study.  
The authors declare no conflicts of interest.
## 3 Results and Analysis
### The Impact of Sponge Iron on the Microbial Community Structure
#### 3.3.2 Analysis of the Microbial Community Structure
_Arenimonas_ is a hydrogenotrophic denitrifying bacterium that can utilize iron as an electron donor, reducing nitrate to nitrite [54]. _Arenimonas_ can perform hydrogenotrophic denitrification under certain conditions [55], with a relative abundance of 3.19% in the sludge of the BSIS system at the final-operation phase, much higher than in the conventional sludge system and the BSIS system at the initial- and stable-operation phases (1.17%, 0.19%, and 1.60%). The enrichment of _Arenimonas_ in the BSIS system may be attributed to the dissolution of iron ions from the corrosion of sponge iron and the production of cathodic H\({}_{2}\), which creates a favorable environment for its growth. _Simplicispira_, an iron-reducing bacterium with denitrification function, can use hydrogen produced by iron corrosion for denitrification of nitrate and nitrite under both aerobic and anaerobic conditions [56]. Their relative abundances in the stable conventional activated sludge system and the BSIS system sludge during the initial-, stable-, and final-operation phases are 0.24%, 0.64%, 0.16%, and 0.15%, respectively. The relative abundance of _Simplicispira_ was 0.4% higher in the initial phase of SFe addition than in the conventional activated sludge system, indicating that trace amounts of iron ions from SFe corrosion increased the activity of _Simplicispira_. _Nitrospira_ plays an important role in the nitrification process, with relative abundances of 1.10%, 0.89%, 2.37%, and 1.15%. _Acidovorax_ is a typical nitrate-dependent divalent iron-oxidizing bacterium, and the addition of SFe promotes its growth and reproduction to some extent. The concentration of iron ions has an inhibitory effect on the relative abundances of aerobic denittifiers such as _Rhodobacter_, _Thuera_, and _Cupriavidus_, while promoting the relative abundances of _Leptothrix_, _Theromonas_, and _Defluvimonas_. Additionally, the relative abundances of denittifying bacteria with different functions, such as _Haliangium_, _Runella_, _OLB12_, and _CL500-29_marine_group_, have all been enhanced to some extent, making the denitrification performance of the BSIS system superior to that of the conventional activated sludge system.