esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 2 Materials and Methods
### CYGNSS Wind Speed Data Products
#### 2.1.2 NOAA v1.2 CYGNSS Wind Speed Product  
The NOAA Center for Satellite Applications and Research developed their own CYGNSS wind speed retrieval algorithm and they have produced a data product based on it. There are several differences between their approach and the one used by this project's algorithm, which are summarized here. A more complete description is provided in [16]. L1 calibration of the NBRCS by the project algorithm uses calibration targets on the satellites together with software corrections for the flight hardware behavior [8]. The NOAA algorithm performs a \"trackwise correction\" to the NBRCS that forces the mean value of the calibrated NBRCS along a specular point track (a \(\sim\)1000 km long sequence of measurements) to equal the mean NBRCS predicted by passing ERA5 wind speed values through a CYGNSS forward model that predicts NBRCS from wind. Trackwise correction forces better agreement between satellite observations and model winds at the expense of some loss in their independence. NOAA's algorithm for wind speed retrieval from NBRCS also uses an empirical GMF derived from near-coincident matchups between NBRCS measurements and reference model winds. In order to account for the differences in NBRCS response to fully developed vs. young sea states, the NOAA matchup dataset from which its GMF is derived consists of ERA5 reference winds at lower wind speeds and HWRF winds at higher wind speeds. This avoids the complication of having multiple wind speed products, which is the case for the project algorithm approach. How well this approach handles high wind retrievals in Fully Developed Sea conditions is one of the performance assessment tests considered below. Another difference with the NOAA approach is the way it handles corrections for SWH, which is to add an additional dimension to their GMF indexed by SWH. This is in contrast to the project algorithm's approach of using a secondary correction table after the GMF conversion from L1 observables to wind speed. Differences between these two approaches with respect to sensitivity to SWH are also examined below. Finally, NOAA's product applies additional spatial averaging to the individual samples using a 25 \(\times\) 25 km averaging window. Individual samples have a spatial resolution of slightly less than 25 km but there is significant overlap between them, so this type of averaging reduces sample noise significantly while only slightly degrading the spatial resolution.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 5 Conclusions  
We implemented end-to-end group mixing in this study. The original server-side mixing method using homomorphic encryption involved mixing the audio sent from each terminal on the server using homomorphic encryption and then forwarding the mixed audio to each terminal. The method presented three issues: (1) The audio encoding had to be unified as PCM; (2) key exchange took too long when adding new members; (3) terminal devices lacked sufficient performance for homomorphic decryption. To address these issues, we designed a terminal-side mixing mechanism, shifting the mixing process originally performed on the server using homomorphic encryption to the conference-initiating terminal. The developed mechanism uses different audio encoding methods depending on the network environment, enabling operation even in low-bandwidth satellite networks. Additionally, when new members join, key exchange is only required, reducing the complexity of key exchange and improving the overall communication experience. The application of end-to-end encryption technology with terminal-side mixing in voice conferencing significantly enhances security and usability as a new solution for secure communication.  
Conceptualization, C.-H.L. and J.-C.C.; methodology, Y.-C.T. and S.-L.L.; software, J.-C.C. and C.-Y.H.; validation, J.-J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
The Northeast region of China is known as the \"Green Treasure Prove\" for its vast forest cover and abundant forest resources. This fertile land in China's high latitude region not only nurtures diverse biological populations, but also provides a steady stream of high-quality raw materials for China's and the world's timber markets. However, as the global warming trend intensifies, the cold climate characteristics of the Northeast region are becoming more and more prominent, bringing unprecedented challenges to the sustainable utilization of local forest resources.  
Traditional buckling machines play an important role in the development of forest resources, but their performance stability and work efficiency under extreme climatic conditions are greatly limited [1]. In the severe winters, traditional buckling machines face many problems such as the easy freezing of mechanical parts, the increased viscosity of lubricating oil, and difficulty in starting the equipment, which not only affects the progress of forest harvesting, but also increases the operating costs and safety hazards. Therefore, the development of a mobile buckling machine sawing mechanism that can adapt to the high-latitude cold environment, with high stability and reliability, has become an urgent need for the development of current forestry machinery technology.  
A new type of forestry machinery and equipment is the mobile buckling machine sawing mechanism, the design concept of which is aimed at solving the limitations of traditional buckling machines that are working in harsh environments [2; 3; 4]. Through the introduction of advanced material science, mechanical design, and intelligent control technology, the mobile buckling machine saw-cutting mechanism can still maintain efficient operation in low-temperature conditions, which not only improves the efficiency and safety of forest logging, but also reduces the degree of damage to the environment [5]. In addition, its strong mobility also means the equipment can flexibly adapt to different terrains and operational needs, providing a strong guarantee for the rational use of forest resources.  
In the context of environmental protection and sustainable development, the importance of the mobile buckling machine saw-cutting mechanism is self-evident. It not only helps to realize the sustainable utilization of forest resources, but also can protect the integrity and diversity of ecosystems while promoting local economic development. Therefore, this study will deeply explore the application prospects and key technologies of the mobile buckling machine sawing mechanism in the utilization of forest resources in the northeast region, with a view to providing new ideas and methods for the development of forestry machinery technology [6].
## 6 Discussion
### Path Loss and Operational Considerations  
As mentioned in various textbooks, while the reflective surface of Earth and the resultant multipath fading play a role in slightly degrading the _Source_ to GNSS receiver link budget, the most considerable attenuation is due to the line-of-sight Path Loss and radial reduction in power density. Given that the GNSS receiver has a predefined dynamic range and can deliver accurate navigational information for up to a certain \(P_{RX}\), this degradation limits the maximum distance of the _Source_ from the landing site to be considered effective. A sample case illustrating such a scenario is shown in Figure 10. The hashed red area indicates the distance of the flying platform from the landing site when the \(P_{RX}\) value drops below the GNSS critical value. This is \(\sim\)7.7 km, which can be passed in \(\sim\)460 s at a speed of 60 km/h (a WCC assumption for a typical flying platform such as a quadcopter). During this time period, a 25 Hz GNSS receiver makes \(\sim\)11,550 attempts to update its output. So, the GNSS receiver has an excellent chance to update its navigational output during its final phase of flight, and, logically, the effect of the interfering signal can be termed unsuccessful.
## 4 Classification Process
### Lib-Support Vector Machine (LibSVM)  
In this study, a support vector machine pattern recognition software package which had been developed by [PERSON] was utilized. The code was a free download from the website: ([[http://www.csie.ntu.edu.tw/~l](http://www.csie.ntu.edu.tw/~l)]([http://www.csie.ntu.edu.tw/~l](http://www.csie.ntu.edu.tw/~l))|cJlin/libsvm). This study also chose the AGC and PP values from the C and Ku Bands for the classification process. The results are shown in Table 4.  
The classification accuracy rates of the Antarctic seawater were found to be low. However, the data for the Arctic region had also provided an accuracy similar to the KNN and threshold segmentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Satellite** & **Band** & **Sea Ice** & **Sewater** \\ \hline \multirow{4}{*}{Arctic} & HY2A & C & 87.66\% & 95.39\% \\  & Ku & 85.76\% & 98.36\% \\  & HY2B & C & 92.54\% & 98.01\% \\  & Ku & 87.81\% & 97.32\% \\ \hline \multirow{4}{*}{Antarctic} & HY2A & C & 94.01\% & 14.36\% \\  & Ku & 82.52\% & 37.71\% \\ \cline{1-1}  & HY2B & C & 79.78\% & 15.57\% \\ \cline{1-1} \cline{2-5}  & HY2B & Ku & 92.94\% & 91.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average classification accuracy rates.  
Figure 17: Classification accuracy rates of the different K values: Figure 17A,B used the data in the Arctic regions; Figure 17C,D used the data in the Antarctic regions; The data of Band Ku were used in Figure 17A,C; The data of Band C were used in Figure 17B,D.
## 5 Conclusions and Future Works  
In this work, we addressed the challenge of low-complexity fractional delay-Doppler (DD) channel estimation in orthogonal time-frequency space (OTFS) systems due to inter-path interference (IPI). A variant of the delay-Doppler inter-path interference cancellation (DDIPIC) algorithm is proposed, which progressively cancels IPI as estimates are obtained, requiring only a final refinement procedure. This approach reduces the latency of the algorithm, with the time difference in latency being almost proportional to the square of the number of estimated paths. Numerical results demonstrate that the proposed algorithm achieves both lower normalized mean square error (NMSE) and bit error rate (BER) compared to other schemes with comparable detection capabilities. The high-IPI scenario is also taken into account, in which the delay and Doppler resolutions are not sufficiently fine, and IPI cancellation becomes tricky.  
In future work, the use of deep learning (DL) approaches, as in [24], can be considered to further reduce computational complexity while maintaining good performance and the analysis in presence of interference will be included.  
Conceptualization, M.M., P.S. ([PERSON]) and P.S. ([PERSON]); Methodology, M.M., H.W. and P.S. ([PERSON]); Validation, H.W. and P.S. ([PERSON]); Investigation, M.M.; Supervision, H.W. and P.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors wish to thank [PERSON] and [PERSON] for the insightful discussions, which helped them to refine the direction of this research.  
The authors declare no conflicts of interest.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 4 Our Proposed Routing Schema
### Intrachuster Communication  
Long distance communication usually consumes a great deal of energy and shortens the lifetime of the network. Therefore, it is necessary to avoid long-distance communication during the intracluster communication. In our presented schema, member nodes can calculate the cost of energy consumption of different routing paths to select an optimal relay node or transmit data directly. As [PERSON] et al. [33] discusses, the energy consumption of transmission is about 1000 times that of calculation. Therefore, it is wise for members to choose a better routing path via calculation. The energy consumption in the routing path using direct communication can be calculated as:  
\[E_{1}(S_{i},CH_{Si})=\left\{\begin{array}{ll}l\cdot E_{ele}+l\cdot\varepsilon_ {fs}\cdot d(S_{i},CH_{Si})^{2}&\text{if }d(S_{i},CH_{Si})<d_{0}\\ l\cdot E_{ele}+l\cdot\varepsilon_{mp}\cdot d(S_{i},CH_{Si})^{4}&\text{if }d(S_{i},CH_{Si}) \geq d_{0}\end{array}\right. \tag{10}\]  
where \(\text{d}(S_{i},CH_{Si})\) denotes the distance between node \(i\) and its corresponding CH. \(E_{1}(S_{i},CH_{Si})\) or \(E_{1}(S_{i},S_{j})\) represents the direct communication between the source and target nodes. If the source node is far away from its CH, a relay node \(j\) will be chosen to forward the data package. The total energy consumption for the whole routing can be calculated using:  
\[E_{2}\big{(}S_{i},S_{j},CH_{Si}\big{)} =E_{Tx}\big{(}l,d\big{(}S_{i},S_{j}\big{)}\big{)}+E_{Rx}(l)+R_{Tx} \big{(}l,d\big{(}S_{j},CH_{Si}\big{)}\big{)} \tag{11}\] \[=3l\cdot E_{ele}+\varepsilon\cdot d^{2}\big{(}S_{i},S_{j}\big{)}+ \varepsilon\cdot d^{2}\big{(}S_{j},CH_{Si}\big{)}\]  
Nodes compare \(E_{1}(S_{i},CH_{Si})\) with \(E_{2}\big{(}S_{i},S_{j},CH_{Si}\big{)}\) and choose the lower one as the routing path. The intracluster communication can be determined using:  
\[\text{E}(S_{i})=\min\big{(}E_{1}(S_{i},CH_{Si}),E_{2}\big{(}S_{i},S_{j},CH_{Si }\big{)}\big{)} \tag{12}\]
# Spatially Adaptive Regularization in Image Segmentation
## 2 Defining Local Regularization by Exploiting Spatial Information  
The regularization parameter \(\lambda\) plays an important role in the segmentation process, because it controls the tradeoff between data fidelity and regularization. In general, the smaller the parameter in (3) the smoother the image content, i.e., image details are flattened or blurred. Conversely, the larger the parameter the more the enhancement of image details and, hence, noise may be retained or amplified. Therefore, \(\lambda\) should be selected according to local spatial information. A small value of \(\lambda\) should be used in the smooth regions of the image to suppress noise, while a large value of \(\lambda\) should be considered to preserve spatial features in the nonsmooth regions. In other words, a matrix \(\Lambda=(\lambda_{i,j})\) should be associated with the image, where \(\lambda_{i,j}\) weighs pixel \((i,j)\), as follows:  
\[\underset{u,\epsilon_{1},\epsilon_{2}}{\text{minimize}} \sum_{i,j}\left(|\
abla_{x}u|_{i,j}+|\
abla_{y}u|_{i,j}\right)\,+ \,\sum_{i,j}\lambda_{i,j}\left(\left(c_{1}-\bar{u}_{i,j}\right)^{2}u_{i,j}+ \left(c_{2}-\bar{u}_{i,j}\right)^{2}(1-u_{i,j})\right),\] (5) s.t. \[0\leq u_{i,j}\leq 1,\] \[c_{1},c_{2}>0.\]  
Furthermore, in order to avoid oversegmentation or undersegmentation, it is convenient to fix a minimum and a maximum value for the entries of \(\Lambda\), so as to drive the level of regularization in a reasonable range, depending on the image to be segmented.  
We define \(\Lambda\) as a function of the image \(\bar{u}\) to be segmented:  
\[f:\bar{u}_{i,j}\rightarrow\lambda_{i,j}\in[\lambda_{min},\lambda_{max}], \tag{6}\]  
where \(0<\lambda_{min}<\lambda_{max}<\infty\). We propose three choices of \(f\), detailed in the next subsections.  
We note that problem (5) still has a unique solution for any fixed \((c_{1},c_{2})\), as a consequence of the next proposition.  
**Proposition 1**: _For any fixed \((c_{1},c_{2})\), problem (5) is a convex problem._  
Because the CEN model is convex for any fixed \((c_{1},c_{2})\), the thesis immediately follows from the fact that the parameters \(\lambda_{i,j}\) are constant with respect to \(u\).
## 3 Results
### Multivariate Analysis
The model variables in the regression model (Equation (1)) including location, loading condition, travel direction, and truck count showed impacts on emissions (e.g., higher emissions from loaded trucks on steeper terrain), but speed was the most statistically significant predictor. With Brockway Avenue (Figure 9) having a Pearson correlation coefficient value of 0.77 and Beechurst Avenue (Figure 10) having a Pearson correlation coefficient value of 0.40 and a projected RMSE of 406.75, the general multivariate model performance showed a moderate degree of explanatory and predictive power, as defined by [PERSON] (2018). The multivariate analysis of the combined variables of the number of trucks and speed of trucks result in a statistically significant correlation (\(p<0.05\)) with the particle count from specific bins (particle sizes) shown in Figures 9 and 10 at each of the two sampling sites, as seen in Table 3. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Brockway Avenue** & & & & \\ \hline
**Term** & **Estimate** & **Std Error** & **t Ratio** & **Prob\textgreater{}1 \(\|\)t\(\|\)** \\ \hline Intercept & \(-3295.57\) & \(1738.675\) & \(-1.9\) & \(0.0789\) \\ No. of Trucks & \(277.6243\) & \(129.4606\) & \(2.14\) & \(0.05\) \\ Truck Speed (miles per hour) & \(345.1936\) & \(148.376\) & \(2.33\) & \(\ast\) 0.0355 \\ \hline Beechurst Avenue & & & & \\ \hline
**Term** & **Estimate** & **Std Error** & **t Ratio** & **Prob\textgreater{}1 \(\|\)t\(\|\)** \\ \hline Intercept & \(2522.49\) & \(621.9073\) & \(4.06\) & \(\ast\)0.0023 \\ No. of Trucks & \(-102.924\) & \(64.72284\) & \(-1.59\) & \(0.1429\) \\ Truck Speed (miles per hour) & \(-83.5287\) & \(29.97084\) & \(-2.79\) & \(\ast\)0.0192 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multivariate regression analysis for number and speed of trucks at both sampling sites. (\(\ast\) means that value has a probability of \(p<0.05\)).
## 2 Digital Twin in Literature  
### History of Digital Twin  
Even though DT technology has gained massive popularity in the past couple of years, the concept is not entirely new. Its concept came into being in relation to Product Lifecycle Management (PLM) in 2002 at the University of Michigan by [PERSON] [15]. The proposed model has three components: real space, virtual space, and linking mechanism for the flow of data/information between the two; the model was then referred to as 'Mirrored Spaces Model' [16]. A similar concept in which software models mimic reality from information input from the physical world was imagined by [PERSON] in 1991 and was called 'Mirror Worlds' [17]. In 2003, [PERSON] et al. also proposed \"an agent-based architecture where each product item has a corresponding 'virtual counterpart' or agent associated with it\" as a solution to the inefficiency of transfer of production information via paper for PLM [18]. By 2006, the name of the conceptual model proposed by [PERSON] was changed from 'Mirrored Spaces Model' to 'Information Mirroring Model' [15; 19]. The model put emphasis on the linking mechanism between two spaces being bidirectional and having multiple virtual spaces for a single real space where alternate ideas or designs can be explored (Figure 1). Due to the limitations of the technologies, such as low computing power, low or no connectivity of devices with the internet, data storage and management, underdeveloped machine algorithms, etc., DT had no practical applications at the time.  
Figure 1: Mirrored Spaces Model/Information Mirroring Model as proposed by [PERSON]. (Adapted from [16; 19].)  
The name 'Digital Twin' (DT) first appears in NASA's draft version of the technological roadmap in 2010 [20]. In the NASA roadmaps, DT was also referred to as 'Virtual Digital Fleet Leader'. NASA was the first association to forge the definition of DT; it was described as \"an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin\". Even though the first mention of DT is in the 2010 roadmap, NASA had used a similar concept before for the Apollo program, where two identical space vehicles were built to mirror each other [21; 22]. Soon, the US Air Force followed the footsteps of NASA and used DT technology for the design, maintenance, and prediction of their aircraft [23; 24; 25]. The idea was to use DT to simulate physical and mechanical properties of the aircraft to forecast any fatigue or cracks in the structure, thus prolonging the remaining useful life of the aircraft. [PERSON] [23] and [PERSON] et al. [25] in their papers defined DT only for the aircraft and called it 'Airframe Digital Twin' or ADT, which was a computational model to manage the aircraft over its entire lifecycle. Besides monitoring, DT was also proposed for sustainable space exploration as well as for future generations of aerospace vehicles [26]. The timeline of the evolution of DT can be seen in Figure 2.
## 5 Analysis  
Based on the simulation findings, our proposed approach IES outperformed existing approaches in terms of performance. To observe service selection, we performed the simulation with 1000 services in a dataset. According to the results of an experiment conducted by [8], PSO failed after it reached 800 services in its dataset. When compared to WOA and PSO, ES outperforms both. Since it performed better than the others, we chose to modify the ES with K-means to address the issue in dynamic web service selection in a large-scale repository, which was one of the study's objectives.  
According to [28], numerous researchers have developed combinatory or hybrid approaches to attain the least time possible; however, previous methodologies are inadequately efficient in terms of compiling the important services in a reasonable amount of time due to the ever-increasing number of services, which leads to the growth of the search space [5; 28].  
Aside from that, while selecting the proper service for service composition from a large-scale repository without human interaction, the functional attribute must be taken into account. Nonetheless, the majority of proposed methodologies for service selection focus solely on non-functional attributes of service effect to reduce SLA violations [29; 30].  
When the scope of the search is limited, the proposed approach can select the service in the shortest amount of time, hence saving time. The lower scale was achieved by the application of the K-means clustering technique. Aside from that, when selecting a service, the suggested strategy takes into account both functional and non-functional attributes. As a result, user satisfaction is achieved, which is related to the second purpose of our research.