esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2014**, _14_, 16932-16954; doi:10.3390/s140916932  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey**  
**[PERSON] 1, [PERSON] 2,* and [PERSON] 2**  
1 Lab of Ocean Remote Sensing & Information Technology, Guangdong Ocean University,  
Zhanjiang 524088, China; E-Mail: [EMAIL_ADDRESS]  
2 Department of Electrical and Computer Engineering, The University of Western Ontario, London, Ontario N6A 5B9, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +1-519-430-7134; Fax: +1-519-430-7064.  
_Received: 9 July 2014; in revised form: 29 August 2014 / Accepted: 3 September 2014 / Published: 11 September 2014_  
**Abstract:** With the rapid development of society and the economy, an increasing number of human activities have gradually destroyed the marine environment. Marine environment monitoring is a vital problem and has increasingly attracted a great deal of research and development attention. During the past decade, various marine environment monitoring systems have been developed. The traditional marine environment monitoring system using an oceanographic research vessel is expensive and time-consuming and has a low resolution both in time and space. Wireless Sensor Networks (WSNs) have recently been considered as potentially promising alternatives for monitoring marine environments since they have a number of advantages such as unmanned operation, easy deployment, real-time monitoring, and relatively low cost. This paper provides a comprehensive review of the state-of-the-art technologies in the field of marine environment monitoring using wireless sensor networks. It first describes application areas, a common architecture of WSN-based oceanographic monitoring systems, a general architecture of an oceanographic sensor node, sensing parameters and sensors, and wireless communication technologies. Then, it presents a detailed review of some related projects, systems, techniques, approaches and algorithms. It also discusses challenges and opportunities in the research, development, and deployment of wireless sensor networks for marine environment monitoring.  
keywords:wireless sensors; wireless sensor networks; marine environment monitoring; buoys; energy harvesting +
Footnote †: journal: Journal of the American Statistical Association
## 5 Conclusions  
A novel self-moving famed piezoelectric actuator was proposed, simulated, assembled, and tested in this study. The proposed framed piezoelectric actuator can directly move on the rail to construct a railed carrying system, presenting the potential application for inspection and maintenance tasks of magnetic confinement fusion. Compared to the electromagnetic driving method, the proposed actuator based on the piezoelectric driving technology holds unique characteristics of (1) directly drive without other reduction mechanisms, greatly simplifying the system configuration and enhancing the system stability, (2) no need for lubrication, eliminating the problem of lubricant volatilization in vacuum environment, (3) self-locked when power-off, achieving the high positioning accuracy of the system, (4) good electromagnetic compatibility, being able to operate in the strong magnetic field environment.  
Numerical simulation determined the optimized geometrical sizes of the proposed framed piezoelectric actuator, and the vibration characteristic measurements of its prototype confirmed the feasibility of its operating principle and verified the validation of the simulation results. Experimental investigation results indicated that (1) the optimal driving frequency of the proposed actuator prototype is 20.75 kHz, (2) its maximum mean velocity is 384.9 mm/s at the excitation of 400 V\({}_{\text{pp}}\), and (3) its maximum load weight reaches up to 900 g at the excitation voltage of 300 V\({}_{\text{pp}}\). It confirmed the validation of the railed carrier system utilizing the piezoelectric actuation method.  
Conceptualization, writing--review and editing, project administration, funding acquisition, L.W.; methodology, data curation, writing--original draft preparation, B.H., R.W. and J.J.; validation, L.W. and B.H.; software, R.W.; resources, Q.X.; All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant Nos. 51905262, and 51775263), the Natural Science Foundation of Jiangsu Province (Grant No. BK20190398), the State Key Laboratory of Mechanical System and Vibration (Grant No. MSV202011), Macao Youth Scholars Program (Grant No. AM201910), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
The authors declare no conflict of interest.  
Figure 12: Load capacity of the proposed actuator prototype.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 2 Methodology
### Measurement Generation
#### 2.3.3 Global Time Tag Determination
In principle, the time tag can be chosen at any secondary code edge, but since the CTN filter has provided a coarse time estimate \(t_{cl}\) with millisecond level accuracy, it will be used as an entry point and the time tag will be computed in the following three sub-steps. The first sub-step is to transfer the millisecond level timing accuracy from the TOW solution inside the CTN filter output to the satellite transmission time based on Equation (12):  
\[t_{tx\_rough}^{(k)}=t_{ct}-\frac{\rho_{a}^{(k)}}{c}, \tag{12}\]  
where \(\rho_{a}^{(k)}\) is the geometric range computed with the assistance data. As already mentioned, any assistance data inaccuracy will impact the signal's flight time for less than half a millisecond; thus, it can be safely used for the computation of the rough transmission time while still maintaining a millisecond level accuracy. Ideally, by adding the fractional code delay to the actual satellite transmission time should bring us to an epoch at the edge of a secondary code period. Thus, it can be expected that the term \(t_{tx\_rough}^{(k)}+\tau_{a}^{(k)}\) will also be near a secondary code edge with a millisecond-level error. Then, we can choose the satellite with the largest secondary code period, denoted by \(T_{sc}^{max}\), as the reference satellite, and set the global time tag as  
\[t_{g}=\text{round}\left(\frac{t_{tx\_rough}^{(k)}+\tau_{a}^{(k)}}{T_{sc}^{max }}\right)\cdot T_{sc}^{max}. \tag{13}\]  
Finally, the corresponding secondary code integer value \(N_{sc}^{(k)}\) for satellite \(k\) can be computed as  
\[N_{sc}^{(k)}=\text{round}\left(\frac{t_{g}-t_{tx\_rough}^{(k)}-\tau_{a}^{(k)} }{T_{sc}^{(k)}}\right). \tag{14}\]  
It must be pointed out that the global time tag and the integer number of secondary code periods come as a pair. When one value changes, the other one also need to be modified correspondingly in order not to impact the satellite transmission time accuracy. The final pseudorange measurements corresponding to the receiver time tag \(t_{g}\) can be computed by  
\[P_{final}^{(k)}=c\cdot(\tau_{a}^{(k)}+N_{sc}^{(k)}\cdot T_{sc}^{(k)}). \tag{15}\]  
Although this method should work for most scenarios, it may still be prone to errors when the TOW solutions generated in the CTN filter contain an error larger than half the period of the maximum secondary code. That can happen, for example, when the Dilution Of Precision (DOP) is really high. As an example, the secondary code period for Galileo E1C signal is 100 ms. If this signal is used in the SRTK filter, as in our experiments, the coarse time TOW solution error should not exceed 50 ms, even though it is very unlikely [25], otherwise the global time tag determination method explained above could fail to produce satellite transmission times with nanosecond-level accuracy.
## 3 Topology Optimization and Parametric Analysis
### Parametric Analysis
In the growth interval (80 mm, 100 mm, and 120 mm), the RMS of the reflector surface gradually increased with the augmentation of back reinforced rib thickness. In the reduction interval, both the height and thickness of the reinforced rib had a positive influence on the RMS of the reflector surface. Indicating that reducing the RMS could be achieved by increasing the height and thickness of the reinforced rib. However, in the turning point interval, their influence was not significant, as there was no clear trend of increase or decrease. In the growth interval, both the height and thickness of the reinforced rib negatively influenced the RMS of the reflector surface. Compared with the other interval parameters, increasing the height and thickness of the reinforced rib in this interval not  
Figure 19: RMS of the reflector surface at heights of 40, 45, and 50 mm. Figure 20: RMS of the reflector surface at a height of 65 mm. only increased the volume and weight of the reinforced rib but also increased the RMS of the reflector surface. This deviates from the original intentions of the reinforced rib design. Considering various engineering factors, including weight, the final decision was to set the height of the reinforced rib at 55 mm and the thickness at 30 mm. Applying the same temperature excitation as in Section 2.3, the deformation contour of the reflector was as illustrated in Figure 22.  
The information of the reflector nodes of the model in Figure 22 was extracted and, through Equation (9), it was calculated that the RMS of the optimized surface was 0.5561 mm. Figure 21: RMS of the reflector surface at heights of 80, 100, and 120 mm. Figure 22: Deformation contour map of the antenna with the new layout and dimensions of reinforced ribs. The optimized ratios of the reconstructed model to the model under the original reinforced rib layout and without reinforced ribs are shown in Table 3:
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 6 Discussion
Localization of objects in complex background is still a challenging task. DISAM can be used that has the ability to precisely localize the heads in complex scenes. Localization performance is primarily affected by changing the threshold value, so finding an optimum strategy for this issue is a new direction of research.
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### Abnormal KPI Screening Based on 1D-CNN-TCN Model
#### 3.3.3 Abnormal KPI Screening Based on 3-Sigma  
Since the residual sequence values of each KPI are quite different, selecting a unified threshold can have a great impact on the accuracy of results. The 3-sigma method [26] conducts a separate analysis of each KPI data point, and considers that 99.7% of the probability of a given data point's value is concentrated in the interval \([\mu-3\sigma,\mu+3\sigma]\) (\(\mu\) is the average, \(\sigma\) is standard deviation). The probability of exceeding this range is only 0.3%, which is very low. Therefore, values not in the \([\mu-3\sigma,\mu+3\sigma]\) range are anomalies.  
In this paper, the 3-sigma method is used to filter all KPIs based on the residual sequence. All kinds of abnormal KPIs in the known time period are obtained and constitute a set. Finally, the time range of KPIs selected as outliers is recorded.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion
### Model Comparison
#### 4.6.2 Parameter Settings for Benchmark Models  
All models were trained with identical dataset configurations and preprocessing techniques for a fair comparison. The hyperparameter settings used for each model are summarized in the Table 5 below:  
The HGE-ACN model was tested in real-world scenarios to evaluate its performance. The model showed high accuracy (above 87%) even under challenging conditions, demonstrating robust generalization. Inference time increased slightly in real-world scenarios but remained efficient for real-time applications. Error rates were highest in low-light and high-noise conditions, indicating sensitivity to external environmental factors. The model performed consistently across signers with different hand sizes, proving adaptability to user variability. However, the model has limitations, including sensitivity to low-light conditions, computational complexity in large-scale deployment, and a limited dataset for extreme conditions. Future improvements include integrating adaptive filtering and feature enhancement techniques, optimizing model architecture with quantization and pruning techniques, and expanding the dataset with additional real-world variations.
# Technology-Economy-Policy: Biochar in the Low-Carbon Energy Transition--A Review
## 6 Economy: Application of Biochar
Although biochar is often considered a valuable tool for carbon sequestration due to its stability and long-term ability to store carbon, it is crucial to assess the overall carbon balance by considering the energy consumption and carbon emissions associated with pyrolysis processes. While pyrolysis itself is an energy-intensive process, the energy required for its operation can come from both renewable and non-renewable sources, and this choice can significantly impact the overall carbon footprint of biochar production. In particular, the net carbon sequestration benefits of biochar can be influenced by several factors, such as the type of feedstock, pyrolysis temperature, and heating rate. High-temperature pyrolysis, while producing biochar with a higher carbon content, requires more energy and can potentially lead to higher carbon emissions, thus potentially offsetting some of the sequestration benefits. On the other hand, lower-temperature processes may be more energy-efficient, but could result in lower biochar yields. Furthermore, lignocellulosic materials such as wood generally require more energy to pyrolyze than agricultural residues, which can impact the net carbon balance [33; 42].
###### Abstract  
The spatial allocation rationality of the service facilities of residential areas, which is affected by the scope of the population and the capacity of service facilities, is meaningful for harmonious urban development. The growth of the internet, especially Internet map and location-based service (LBS) data, provides micro-scale knowledge about residential areas. The purpose is to characterize the spatial allocation rationality of the service facilities of residential areas from Internet map and LBS data. An Internet map provides exact geographical data (e.g., points of interests (POI)) and stronger route planning analysis capability through an application programming interface (API) (e.g., route planning API). Meanwhile, LBS data collected from mobile equipment afford detailed population distribution values. Firstly, we defined the category system of service facilities and calculated the available service facilities capacity of residential areas (ASFC-RA) through a scrapy algorithm integrated with the modified cumulative opportunity measure model. Secondly, we used Thiessen polygon spatial subdivision to gain the population distribution capacity of residential areas (PDC-RA) from Tencent LBS data at the representative moment. Thirdly, we measured the spatial allocation rationality of service facilities of residential areas (SARSF-RA) by combining ASFC-RA and PDC-RA. In this case, a trial strip census, consisting of serval urban residential areas from Wuxi City, Jiangsu Province, is selected as research area. Residential areas have been grouped within several ranges according to their SARSF-RA values. Different residential areas belong to different groups, even if they are spatially contiguous. Spatial locations and other investigation information coordinate with these differences. Those results show that the method that we proposed can express the micro-spatial allocation rationality of different residential areas dramatically, which provide a new data lens for various researchers and applications, such as urban residential areas planning and service facilities allocation.  
service facilities; Tencent location-based data; points of interest (POI); spatial allocation; internet map +
Footnote †: journal: Journal of the Operational Society of America
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 3 Proposed Method  
This section presents the proposed method, which adopts a two-stage processing architecture, where interference suppression is the first stage and channel combining is the second stage. The details are presented in the following subsections.
## 3 Presentation of the Proposed Model
### Sub-Modules of the Presented Model
#### 3.3.2 ANN Input Data Choosing Module  
The input data module allows the analysis of a statistical relationship between input data and order-picking times. In the case of expressive dependencies, this module determines (see Table 3) the weights, adjusting the results of product classification for each model. For each pair (input parameter and product classification method), the module checks the value of the coefficient \(p\), which, for all possible pairs, is less than 0.05 (it is a statistical linear correlation between the values in the pair at the 5% significance level). The correlation coefficient is next calculated on a scale from \(-1\) (strong inversely proportional correlation) to \(+1\) (strong directly proportional correlation).  
The graphs showing the relationships for individual pairs are shown in Figure 2. The correlation coefficients are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **COI Index** & **ABC (Weight)** & **ABC (Volume)** & \multicolumn{2}{c}{
\begin{tabular}{c} **Algorithm of** \\ **Relationships Search** \\ \end{tabular} } & **Sum** \\ \hline Number of products & 0.7384 & 0.7724 & 0.7723 & 0.6891 & 2.9722 \\ Number of product types & 0.7982 & 0.8375 & 0.8342 & 0.7433 & 3.2132 \\ Products volume (m\({}^{3}\)) & 0.6250 & 0.6735 & 0.6514 & 0.5594 & 2.5093 \\ Products weight (kg) & 0.7139 & 0.7512 & 0.7319 & 0.6548 & 2.8518 \\ Average & 0.7189 & 0.7587 & 0.7475 & 0.6617 & 2.8866 \\ \hline \hline \end{tabular} Source: own elaboration (Statgraphics Centurion 15.2.11.0).  
\end{table}
Table 3: Correlation coefficients of input parameters with the analyses results.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## Materials  
### Study Area  
The study area is located in the Caprivi Strip in the north-eastern part of Namibia, bordered by the countries Zambia, Zimbabwe, and Botswana (Figure 1). The focus is on the Chobe-Sambesi flood plain, which is formed by the Zambezi and Chobe river and is influenced by seasonal floods in March and April [33; 50]. Large areas of high grasslands dominate the landscape [51]. During flooding, the area is therefore characterized not only by TOW areas but also by extensive TFV areas. The extent of the study area is indicated by the red rectangle in Figure 1.
## 3 Results
### Water Quality
Cojimies nitrate levels (0.130 \(\pm\) 0.132 mg/L) were higher than Palma Real (0.018 \(\pm\) 0.010 mg/L) and Muisne (0.022 \(\pm\) 0.009 mg/L), while no differences between estuaries (F\({}_{1,38}\) = 1.986, \(p\) > 0.05), sampling seasons (F\({}_{1,38}\) = 1.578, \(p\) > 0.05) and type of drainage (F\({}_{1,38}\) = 0.031, \(p\) > 0.05) were observed. The total nitrogen in water exhibited differences by sectors (F\({}_{3,36}\) = 7.575, \(p\) \(<\) 0.001) which occurred between Tambillo, with the lowest nitrogen content (2.830 \(\pm\) 0.338 mg/L), and Muisine (4.567 \(\pm\) 1.853 mg/L) and Cojima (5.726 \(\pm\) 1.327 mg/L). The differences were amplified between main systems (F\({}_{1,38}\) = 12.97, \(p\) \(<\) 0.001), without differing between Drainage Types (F\({}_{1,38}\) = 2.428, \(p\) \(>\) 0.05) or seasons (F\({}_{1,38}\) = 3.431, \(p\) \(>\) 0.05). The Phosphate content showed marked differences by sector (KW = 6.55, \(p\) \(<\) 0.001) in Muisne (0.20 \(\pm\) 0.08 mg/L), exceeding the values observed at Cojima (0.12 \(\pm\) 0.04 mg/L), Palma Real (0.09 \(\pm\) 0.02 mg/L) and Tambillo (0.07 \(\pm\) 0.03 mg/L). Significant differences were observed in this parameter between the main systems (KW = 19.001, \(p\) \(<\) 0.001) and type of drainage (KW = 4.667, \(p\) \(<\) 0.05), where shrimp drainages (0.147 \(\pm\) 0.129 mg/L) exceeded the value in natural drainages (0.060 \(\pm\) 0.059 mg/L), but not observed between seasons. Total phosphorus content differed between the estuaries (KW = 14.877, \(p\) \(<\) 0.05) and drainage type (KW = 6.936, \(p\) \(<\) 0.05). The phosphorus content in RVS (0.16 \(\pm\) 0.11 mg/L) surpassed REM (0.07 \(\pm\) 0.10 mg/L), without differing between seasons. The concentration of chlorophyll \(a\) differed between the type of drainage (KW = 8.314, \(p\) \(<\) 0.01), with the values for modified drainages (31.165 \(\pm\) 13.87 ug/L) exceeding the values observed in natural drainages (17.76 \(\pm\) 9.31 ug/L). No differences by sector (KW = 3.8363, \(p\) \(>\) 0.05), larger systems (KW = 1.778, \(p\) \(>\) 0.05) or sampling periods (KW = 0.38033, \(p\) \(>\) 0.05) were found. Finally, the content for settleable solids only varied by season (KW = 9.824, \(p\) \(<\) 0.01), doubling the sedimentation during the summer (73.86 \(\pm\) 50.98 mg/L) with respect to the winter period (34 \(\pm\) 15.47 mg/L).
## 4 Discussion
### Signal Purity Outputs  
Overall, \(P_{out}\) on the LVP (Table 2) was satisfactory (\(>\)80%), especially at 25 Hz. At AN and KN, almost perfect sinusoidal waveforms were maintained (\(>\)96%), as reported previously [24; 25]. Similar outcomes have been reported that showed few minimum violations below 80% at HI, L3, and HE, especially when TS and DS postures are adopted. In the present study, \(P_{out}\) was also satisfactory at HI and L3 with few minimum violations at TS, DS, and LU. Thus, at the HI, the LVP showed less \(P_{out}\) (\(<\)80%) when adopting TS and LU at 20 and 30 Hz, whereas ER, HS, and DS were satisfactory for all the \(f_{\text{set}}\). Overall, at L3, the \(P_{out}\) was satisfactory at ER, HS, DS, and LU for all the \(f_{\text{set}}\), whereas it was not satisfactory at TS. Overall, these results showed a good feasibility of the vibration stimulus;however, practitioners should take into account the specific values in order to obtain the desired changes in the body structures.