esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Predicting Radiated Acoustic Field in Shallow Sea Based on Wave Superposition and Ray
###### Abstract  
The traditional free-space and half-space analysis method ignore the reflection of the upper and lower boundaries of shallow sea and are not suitable for analyzing shallow sea problems especially at high frequency. Hence, a method combining ray theory and wave superposition theory is proposed in this paper to predict the high-frequency radiated acoustic field in shallow water. The proposed method takes into account the effect of channel boundaries on the acoustic field and has good adaptability to complex channel environments and accuracy of the calculated acoustic field.  
sound radiation; wave superposition; ray theory +
Footnote †: journal: Applied sciences
# The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
###### Abstract  
This work aims to develop approaches to processing and interpreting spectral entropy outcomes in the context of seismic data, as well as to establish a methodological foundation for subsequent integration into practical monitoring solutions. The objective of this study is to evaluate the effectiveness of the Shannon spectral entropy method in detecting and assessing short-term seismic events through a seismogram analysis. This method has demonstrated sensitivity to variations in the spectral characteristics of the registered signals. A threshold value for the increase in spectral entropy information has been pinpointed for reliable P-wave detection. The results could be applied in real-time automated seismic monitoring systems. In addition to the conventional spectral analysis techniques, the proposed methodology may serve as the input to the neural network models used in seismological applications.  
P-wave detection; spectral entropy; Shannon information theory; earthquake early warning; seismic monitoring Article
# Crystal Sinus Lift with the Hydrodynamic Technique: Prospective Clinical Study at 3 Years Follow-Up
###### Abstract  
Aim: This study aimed to evaluate the implant survival rate, marginal bone loss (MBL), and surgical complications in single dental implants placed in the atrophic posterior maxilla using a transcrestal sinus lift with injectable graft materials. Materials and Methods: A prospective longitudinal study was conducted at IRCCS San Raffaele, Milan, Italy. Fifty-four patients with single edentulous sites and residual bone heights < 5 mm were included. A transcrestal sinus lift was performed using non-cutting drills ([PERSON]'s technique) and xenogenic bone graft in gel form (Gel40, Tecnoss, Italy). Follow-up visits were at 1 week, 3 and 6 months, and annually for 3 years. Results: The implant survival rate was 98.04%, with one implant lost. MBL values were 0.71 \(\pm\) 0.94 mm at 6 months, 1.00 \(\pm\) 0.99 mm at 1 year, 1.03 \(\pm\) 1.00 mm at 2 years, and 1.02 \(\pm\) 1.01 mm at 3 years. Our ANOVA showed a significant MBL increase from 6 months to 1 year (\(p\) = 0.015), with no significant changes thereafter. Minimal surgical complications were observed, each occurring in 1.85% of cases. Conclusion: Transcrestal sinus lifts with injectable graft materials demonstrate high implant survival, acceptable MBL, and minimal complications, making it a reliable option for posterior maxilla rehabilitation.  
bone graft; dental implants; sinus floor augmentation; bone resorption; maxillary sinus
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 6 Results
### Maximizing Efficiency  
The efficiency defined in (1) depends on three parameters: the physical bit rate R, the number of bits in a window, and the time to transmit those bits. The physical bit rate R is a value that depends on the LoRa data rate. For example, for data rate 0 (spreading factor 12), the R parameter in the AU915 band is \(250\ bit/seg\). The number of bits in a SCHC window is 630 bytes (or 5040 bits). Thus, the only variable left to determine is the transmission time for those bits. Therefore, the highest efficiency is achieved when the transmission time is minimum, and this occurs when there are no errors or no loss of SCHC messages. Therefore, by looking at Figure 5, the transmission time of an error-free SCHC window is determined by (12) considering \(T_{B2}=0\) because there are no errors in the SCHC messages.  
The transmission time depends on how many regular SCHC Fragments (regular and all-1) need to be sent to transmit the 5040 bits of SCHC window. The number of SCHC Fragments depends on the size of the MTU, and the MTU depends on the spreading factor. Table 4 shows the number of tiles and the number of SCHC Fragment messages for a SCHC window for each data rate in the AU915 frequency band. For example, in data rate 0 for AU915, only 51-byte payloads are supported. Each SCHC message has a size of 50 bytes for the payload (five tiles of 10 bytes each) and 1 byte for the header.  
To build 630 bytes of a SCHC window, we need 12 SCHC Fragment messages with its full payload (50 bytes of payload plus one byte of header), along with one SCHC Fragment message with its payload of three tiles (30 bytes of payload plus a header byte). This can  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Data Rate & 0, 1, 2, 3, 4, 5 \\ \hline LoRaWAN Frequency Band & AU915 \\ \hline SCHC Packet Size & 892 bytes \\ \hline SCHC Tile Size & 10 bytes \\ \hline  & DR5: 5223.6 [ms] \\  & DR4: 5300.8 [ms] \\ SCHC Fragment & DR3: 4661.7 [ms] \\ Processing Time (\(t_{frag\_prec}\)) & DR2: 5101.6 [ms] \\  & DR1: 5285.2 [ms] \\  & DR0: 6634.2 [ms] \\ \hline  & DR5: 5053.7 [ms] \\  & DR4: 5053.7 [ms] \\ SCHC Ack Req & DR3: 4661.7 [ms] \\ Processing Time (\(t_{ackreq\_prec}\)) & DR2: 5101.6 [ms] \\  & DR1: 5285.2 [ms] \\  & DR0: 6634.2 [ms] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used for the theoretical numerical results.  
be observed in the row with data rate 0 in Table 4. For example, consider Equation (1) to obtain the efficiency: to achieve a 100% efficiency when we transmit 5040 bits (630 bytes in a SCHC window) with a data rate 0 in the AU915 band, we need to transmit these data in 20.16 seconds (5040 bits at 250 bits per seconds of physical bit rate in spreading factor 12 or data rate 0).
## 4 Results
### Quantitative Evaluation of the SSDA Smoke Detections
#### 4.2.2 Validation of SSDA Smoke Detections Using Manually-Delineated Smoke Reference Data  
The SSDA smoke detections were validated using manually delineated smoke reference data based on true-color images, and further compared with the results of other smoke-related datasets, namely TROPOMI smoke and VIIRS ADP (Figure 10). The median smoke percentages of the SSDA smoke detections are much closer to the reference data than that of TROPOMI smoke and VIIRS ADP in all six examined regions (Figure 10a). Overall accuracy of SSDA smoke detection is the highest among these three smoke detection datasets in all regions with the exception of Indonesia (Figure 10b). Commission errors of SSDA smoke detections are 8%, 3%, 2%, 29%, 4%, and 7% in North America, South America, Africa, Indonesia, Siberia, and Australia, respectively (Figure 10b). Commission  
Figure 9: Comparison of AOD median values between smoke and non-smoke pixels of (**a**) the SSDA result with (**b**) TROPOMI smoke and (**c**) VIIRS aerosol detection product (ADP). The samples are from 109 image subsets at the global scale (see Section 2.6.3). The numbers above the boxplot are AOD median values in smoke pixels (orange), non-smoke pixels (green), and their differences (blue). The letters below the boxplot are consistent with those in Figure 1, namely, North America (**a**), South America (**c**), Africa (**d**), Indonesia (**e**), Siberia (**f**), and Australia (**g**), respectively. Region b in eastern South America is not shown because of the limited number of fire events during August.  
errors are largest in Indonesia where clouds occur frequently and always mix with smoke plumes. Correspondingly, the omission errors of smoke pixels are 17%, 51%, 48%, 35%, 24%, and 42% in the six regions, respectively (Figure 10c), which are smaller in North America and Siberia that had large and dense smoke plumes than in regions (South America, Africa, Indonesia, and Australia) characterized by small and thin smoke plumes. Relative to TROPOMI smoke, SSDA smoke detections generally produce both smaller commission errors and omission errors. Moreover, validation shows that the SSDA provides much smaller omission errors and similar, though slightly greater, commission errors relative to VIIRS ADP smoke detections in all the six regions (Figure 10b,c).
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 7 Experimental Results
### DOA Estimation and Navigating to the BLE Beacon
#### 7.2.2 Experimental Setup 2  
For the second experiment, we considered a three-room space connected by a hallway with a combined size of approximately 700 sqrt, as shown in the layout diagram in Figure 17. The BLE beacon was placed in the corner of a room, and we started the first measurement from one end of the hallway (labeled as measurement 1) using a pre-calibrated scanner device. Similar to the first experiment, the estimated DOA after the second measurement suggested moving into the right room, and three more measurements after that took us close to the BLE beacon. The measurements for this scenario can be seen in Table 2.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 3 Sustainable Smart Cities
### Definition: Sustainability  
There are a large number of definitions for Sustainability in the literature. Ref. [40] provides, e.g., a summary and overview of definitions from social and environmental perspectives. Many of the definitions are not straight forward but aim at discussing and putting things in various perspectives as to encompass all relevant aspects for a particular field. In view of ICT for smart cities we use the term in the following sense: _\"Sustainability is the capability of a system to exist in the long term based on its modularity, flexibility and intensive interaction and balanced exchange with its eco-system/environment\"_.  
Yet, the overall term of Sustainability has been structured along the Sustainable Development Goals (SDGs), which are illustrated in Figure 2. The SDGs were defined by the United Nations in 2015. Significant progress in this regard needs to be achieved by 2030 [41].  
Herein, SDG 11 is directly framed to be Sustainable Smart Cities and Communities. In addition, ICT for smart cities can directly contribute to SDG 9--Industry, Innovation and Infrastructure, SDG 8--Decent Work and Economic Growth, SDG 12--Responsible Consumption and Production, and SDG 7--Affordable and Clean Energy, and indirectly to SDG 1--No Poverty, SDG 2--Zero Hunger, SDG 3--Good Health and Well-Being, SDG6--Clean Water and Sanitation, SDG 13--Climate Action and SDG-16--Peace, Justice and Strong Institutions. An increased utilization of ICT within smart cities can positively influence a number of key areas such as mobility, (renewable) energy, health, transportation, water-management, waste-management, circular processes, public administration, or public safety and thereby contributing in parallel to job creation, economic growth, increased quality of life and poverty reduction. Hence, we believe that ICT-based smart cities are to be seen as a means for achieving the above listed SDGs [42]. Thereby, ICT tools and automations introduce additional intelligence in the city processes, which can be efficiently utilized towards achieving the SDGs and improving quality of life in cities, communities and across the globe.
## 5 Conclusions  
This paper considers the LR-based PSM system where an LR-aided ZF precoder is employed at the transmitter. It is shown that the LR-ZF-based PSM scheme without RAS selection can capture the maximum transmit diversity gain of \(N_{T}\). Furthermore, we propose an optimal LR-based RAS selection algorithm for the over-determined LR-ZF-PSM system. Additionally, the optimal LR-RAS selection of choosing \(N_{T}\) antennas among \(N_{R}\) receive antennas in the over-determined LR-ZF-PSM system with \(N_{T}\) transmit antennas and \(N_{R}\) receive antennas can achieve the overall diversity order of \(N_{T}(N_{R}-N_{T}+1)\), where \(N_{T}\) is a transmit diversity gain and \((N_{R}-N_{T}+1)\) is a receive antenna selection diversity order. The LR-ZF-PSM scheme with optimal LR-RAS selection can achieve \(N_{T}\) times larger diversity order than the conventional ZF-PSM system with optimal RAS selection. That is, the proposed LR-RAS selection algorithm yields the additional transmit antenna diversity gain compared to the conventional RAS selection and thus benefits the BER performance improvement in the LR-ZF-PSM system. If the number of antennas is large, the antenna diversity gain can also be large depending on the antenna dimension. To reduce the complexity of the optimal LR-RAS selection algorithm, a suboptimal LR-RAS selection algorithm combined with simple norm computations is presented. Even for a relatively small number of antennas, the complexity of the proposed algorithm is reduced by about more than two times compared to the optimal one. Especially under the large antenna dimension, the complexity reduction by the proposed suboptimal algorithm is huge.  
The author contributed to all presentations. All authors have read and agreed to the published version of the manuscript.  
This research and the APC was funded by Dong-A University.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This study was supported by research funds from Dong-A University.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 6: Complexity comparison of the proposed algorithm and optimal one for \(N_{R}=30\) and \(N_{iter}=3\).
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 3 Feature Extraction and Decision-Making Parameters  
### Time-Frequency Domain Features Extraction Method  
#### 3.1.1 Frequency Gap  
We assume that the communication signal occupies the frequency range between \(f_{0}\) and \(f_{N+1}\), and its bandwidth is \(B_{S}\). Additionally, the effective jamming signal shares the same frequency band with the communication signal, which is composed of \(N\) single-frequency narrowband signals. At time \(t\), the central frequencies of all the \(N\) jamming signals falling in the frequency range of \([f_{0},f_{N+1}]\) are \(f_{1}(t)\), \(f_{2}(t)\),..., \(f_{i}(t)(1\leq i\leq N)\), respectively, and their bandwidths are \(B_{i}(t)(1\leq i\leq N)\), respectively. Frequency domain representations of the jamming signals are shown in Figure 3.  
The frequency gap between any two adjacent jamming frequency points at time \(t\) is  
\[W_{i}(\text{t})=\begin{cases}f_{i+1}(t)-f_{i}(t)-(B_{i}(t)+B_{i+1}(t))/2&f_{i+1 }(t)>f_{i}(t)+(B_{i+1}(t)+B_{i}(t))/2\\ 0&f_{i+1}(t)\leq f_{i}(t)+(B_{i+1}(t)+B_{i}(t))/2\end{cases} \tag{1}\]  
where \(1\leq i\leq N\). Defining \(B_{0}(t)=B_{N+1}(t)=0\), we can obtain that  
\[W_{0}(\text{t})=\begin{cases}f_{1}(t)-f_{0}(t)-B_{1}(t)/2&f_{1}(t)>f_{0}(t)+B_ {1}(t)/2\\ 0&f_{1}(t)\leq f_{0}(t)+B_{1}(t)/2\end{cases} \tag{2}\]  
\[W_{Si}=\max[W_{i}(t)] \tag{3}\]  
If  
\[\forall i\in[0,n],W_{Si}(t)<B_{s} \tag{4}\]  
the instantaneous frequency gap does not exist.  
If  
\[\exists i\in[0,n],W_{Si}(t)\geq B_{s} \tag{5}\]  
the instantaneous frequency gap exists.  
The full-time frequency gap refers to a frequency gap that is not jammed at all times in the settings; namely, the intersection of all the instantaneous frequency gaps at different times. It can be calculated by using the above instantaneous frequency gaps. If the full-time frequency gap researches the bandwidth requirement threshold, then the system considers the full-time frequency gap as exists; otherwise, the system denies that there is a full-time frequency gap.  
Deduction:  
Figure 3: Frequency domain representation of the jamming signals.  
Full-time frequency gap exists Instantaneous frequency gap exists Instantaneous frequency gap exists Instantaneous frequency gap does not exist Full-time frequency gap does not exist
# AOA-Based Three-Dimensional Multi-Target Localization in Industrial WSNs for LOS Conditions
## 4 Multi-Target Detection and Localization
### Multi-Target Detection  
We can separate and detect each MSN's signal based on the orthogonality among the spreading sequences. The ANs perform sliding-correlations between the received signal \(\hat{y}_{i,m}(\tau)\) and the spreading sequence \(u_{k}(\tau)\) in the delay domain to extract the signal from \(\mathcal{M}_{k}\). The observed result is given as  
\[\begin{split}\hat{h}_{i,m,k}(\tau)&=\langle\hat{y}_ {i,m}(\tau),\;u_{k}(\tau)\rangle=  
is the inter-MSN interference received on the \(m\)-th antenna on \(\mathcal{A}_{i}\). Because of the correlation property of the spreading sequences, the cross-correlation result in Equation (8) generates \(L_{i,k}\) peaks at the excess delays of \(\tau_{i,k,l}\) with the observed complex amplitude of  
\[\hat{A}_{i,m,k,l}=A_{i,m,k,l}+\zeta_{i,m,k}(\tau_{i,k,l})+w^{\prime}_{i,m}(\tau_ {i,k,l}), \tag{10}\]  
for \(l=1,2,\cdots,L_{i,k}\). In this work, \(\hat{h}_{i,m,k}(\tau)\) is called the multipath spreading sequence response (MSSR).  
For the signals received on the AN antennas, \(\hat{y}_{i,m}(\tau)\), we can perform sliding-correlation using the \(K\) spreading sequences, \(u_{k}(\tau)\) for \(k=1,2,\cdots,K\). Thus, the MSSR for each MSN, \(\hat{h}_{i,m,k}\), is observed on every receiving antenna. The interference from other MSNs and also external sources is removed by the sliding correlation. Hence, the MT-SIMO scheme provides the advantages of mitigating interference and high signal-to-interference-ratio (SIR), resulting in the spread spectrum gain.
# Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation
## 3 O-Ring Dimension Measurement Algorithm Design
### Least Square Method for Circle Fitting  
After obtaining its sub-pixel edge points, it is necessary to fit the sub-pixel edge points by least squares and then obtain the center point coordinates of the circle, as well as the radius diameter and other related parameters [24].  
We know that the curve of a circle is given by Equation (22):  
\[R^{2}=(x-A)^{2}+(y-B)^{2}=x^{2}-2 Ax+A^{2}+y^{2}-2 Bx+B^{2} \tag{22}\]  
Let \(a=-2A,b=-2B,c=A^{2}+B^{2}-R^{2}\), then another expression for the equation of the circular curve can be obtained as Equation (23):  
\[x^{2}+y^{2}+ax+by+c=0 \tag{23}\]  
The sub-pixel level edge coordinates of the O-ring are obtained by the cubic spline interpolation algorithm and are recorded as the sample \((X_{i},Y_{i}),(i=1,2,3,\ldots,N)\). Let the distance of the sample to the center of the circle be \(d_{i}\). The expression is Equation (24):  
\[d_{i}^{2}=(X_{i}-A)^{2}+(Y_{i}-B)^{2} \tag{24}\]  
The difference between the square of the distance from the sampling point \((X_{i},Y_{i})\) to the fitted circle and the square of the radius of the fitted circle is Equation (25) [25].  
\[\sigma_{i}=d_{i}^{2}-R^{2}=(X_{i}-A)^{2}+(Y_{i}-B)^{2}-R^{2}=X_{i}^{2}+Y_{i}^{ 2}+aX_{i}+bY_{i}+c \tag{25}\]  
Let \(Q(a,b,c)\) be the sum of squares of \(\sigma_{i}\), Equation (26),  
\[Q(a,b,c)=\sum\sigma_{i}^{2}=\sum\left(X_{i}^{2}+Y_{i}^{2}+aX_{i}+bY_{i}+c \right)^{2} \tag{26}\]  
So, it is only necessary to find \(a,b,c\) such that the value of \(Q(a,b,c)\) is minimized. The final solution for \(a,b,c\) is [26],  
\[\begin{array}{l}a=
## 3 Results and Discussion
### SPME Fiber Selection  
Several fiber coatings are commercially available for the extraction of volatile compounds. The affinity of the fiber for an analyte depends on the principle of \"like dissolves like\", and coating fibers with different properties or thicknesses can be selected. In this study, the fiber materials PDMS/DVB and polydimethylsiloxane (PDMS) were selected according to their fiber characteristics and the literature on extraction odors [26; 27; 28].  
The selected chromatograms of volatile compounds (one of three replicates) from fresh water lily flowers extracted by using two fibers are displayed in Figure 2. As shown in Figure 2, when the extraction was performed using the PDMS/DVB fiber, the peak areas of most compounds were higher than those obtained from another fiber at equal extraction time. Hence, we selected the PDMS/DVB fiber for further study.
## 1 Introduction
Nevertheless, the completeness of configuration needs to be analyzed by employing assistant tools in the generation process of configuration, which makes the search process cumbersome. Compared with the above studies that optimize and screen configuration completely, literatures [18; 19] mainly focus on the analysis and combination for specific operating modes to generate configuration schemes, which can narrow the search range and avoid the meaningless configurations that may occur during the search process, but cannot ensure the generation of the optimal configuration. All the power-split hybrid powertrain configurations adopt two motors. In contrast, the single motor hybrid powertrain configuration has the advantages of simple structure, ease of control, and low cost. Therefore, many scholars have shifted their research focus to the single motor hybrid powertrain configuration. [PERSON] et al. studied the single motor hybrid powertrain configuration based on the connection relationship of the motor [20]. Furthermore, [PERSON] et al. improved fuel economy by optimizing the component parameters of P2 and P3 configurations [21]. On the basis of P2 configuration, [PERSON] et al. increased the system topology by introducing PG, which improved the fuel economy of the multi-mode P2 configuration [22]. At present, although some progress has been made in the research of the single motor hybrid powertrain configuration, the improvement potential for the existing single motor hybrid powertrain configuration in fuel economy is still limited [23; 24]. To further improve the comprehensive operating performance of the single motor hybrid powertrain configuration, by combining the advantages of dual PGs configuration, the single motor hybrid powertrain configuration with dual planetary gears (SMHPC-2 PG) is proposed in this paper. By applying a topology design method that characterizes the constraint relationship between power resource components and PG nodes, the dynamic models of all configuration candidates are automatically generated, and the output characteristics of configuration candidates are obtained. The optimal fuel economy and dynamic performance of configuration candidates are simulated by employing the global optimal control strategy based on dynamic programming (DP). The SMHPC-2 PG with excellent comprehensive performance is obtained by comparing the performance. The remainder of this paper is organized as follows: Section 2 depicts the basic configuration scheme of SMHPC-2 PG and the topology design method. Section 3 presents powertrain parameters and component models. Section 4 formulates the global optimal control strategy, and screens all the configuration candidates based on this control strategy. Result and comparative analysis are discussed in Section 5. Finally, the conclusions are presented in Section 6.