esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The FQS Algorithm
### An Example
#### 3.4.1 Computing the Maximal Expected Shift Position (\(pos\)) for Pattern \(P\)  
For calculating the maximal expected shift value of the pattern \(P\) = \"GCAGTCAG\", in Line 3 of Algorithm 3, the recent occurrence position (\(PrePos\) array in Algorithm 3) for these four symbols are initialized to \"\(-1\)\". Then, Algorithm 3 calculates \(pos\) for the pattern, \(P\), by scanning from left to right.  
When the first character \(P[0]=G\) is read (\(i=0\)), \(PrePos[G]\) is the initial value of \(-1\). Line 6 sets \(ES=0+4-(0-(-1)1)=3\). This \(ES=3\) is the expected shift distance for Position \(0\) in pattern \(P\). In Line 7, \(PrePos[G]\) is set to the current Position \(0\); this indicates that character \(G\) has appeared at least once at this time. In Lines 8-10, the maximal expected shift distance is set to \(maxES=3\).  
When the second character \(P[1]=C\) is read (\(i=1\)), Line 6 sets \(ES=3+4-(1-(-1))=5\). In Line 7, \(PrePos[C]\) is set to its current position, \(1\). In Line 9, the maximal expected shift distance is set to five.  
When the third character \(P[2]=A\) is read (\(i=2\)), Line 6 will set \(ES=5+4-(2-(-1))=6\). In Line 7, \(PrePos[A]\) is set to its current position, 2. In Line 9, the maximal expected shift distance is set to six.  
When the fourth character \(P[3]=G\) is read (\(i=3\)), the \(PrePos[G]\) value has been changed to its previous appearing position; in this case, \(PrePos[G]=0\). Line 6 will set \(ES=6+4-(3-(0))=7\). In Line 7, \(PrePos[G]\) is set to its current position, 3. In Line 9, the maximal expected shift distance is set to seven.  
The remaining characters in pattern \(P\) are processed in a similar manner. The final expected shift distances for each position in pattern \(P[0,...,m-1]\) are _3, 5, 6, 7, 6, 6, 6, 6_. The maximal expected shift position is in \(P[3]=G\), which has a value of seven. Hence, we have the maximal expected shift distance in Position 3 of pattern \(P\), that is, \(pos=3\) (see Table 1).
## 4 Results and Discussion
### Finding the Optimum Number of Clusters  
Silhouette Criteria Clustering Evaluation was used to determine the best number of clusters, ranging between \(-1\) to \(+1\). A positive value implies good clustering, and the best number of clusters are associated with the highest criterion values. The results are shown in Table 2, indicating that the best number of clusters are 8 followed by 10. The wrongly assigned audiograms were removed in another two consecutive stages, then the corresponding criteria values were recalculated as shown in Table 2. The criteria values for 8 and 10 clusters are found to be close.  
The following 2 subsubsections are introduced to show and discuss the results of different stages of 8 and 10 clusters. These stages are implemented to remove the wrongly assigned audiograms with a negative Silhouette coefficient.
## 2 Materials and Methods
### CDM Application and Comparison  
To test the applicability and accuracy of the CDM, overall survival of a breast cancer cohort was calculated via the CDM and source system and compared to the real results. The Null Hypothesis (H0) was tested, which assumed that the calculated overall survival of the two systems was the same. The Alternative Hypothesis (H1), on the other hand, assumed that there were differences in overall survival between the systems. The probability of error (alpha error) was set at 5% for this test. This means that, if \(p>0.05\), H1 would be rejected and the H0 hypothesis could be accepted. The calculations of the survival analyses were performed in a dynamic R Markdown report. The DatabaseConnector package was used to extract the survival cohorts from the Source and CDM database. These cohorts were merged into one dataset using the dplyr package and then stratified analyzed using the Survminer and Survival packages.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Energy Hole Avoidance  
In a large-scale CR-WSN, CR-WS nodes forward packets in multihop fashion to deliver the collected data to a BS. The nodes closer to the BS have to receive and retransmit more packets generated by the sensors than those farther away from the BS. This leads to energy depletion in the nodes closer to the BS, leaving a hole near the BS called an energy hole. This phenomenon partitionsthe whole network, and prevents outside nodes from sending information to the BS, even though many nodes in CR-WSNs still have plenty of energy. It is difficult to solve the energy hole problem completely; however, it can be mitigated by efficient node deployment, energy harvesting, and efficient CH selection strategies.
## 4 Numerical Analysis
In addition, the CR-OFDMA has the highest channel utilization, followed by the DRA-OFDMA, and the conventional OFDMA has the lowest channel utilization, as shown in Figure 10. The performance of the CR-OFDMA is also excellent from the perspective of channel utilization. Figure 11 illustrates the impact of collision probability on fairness. As can be seen from the figure, the fairness index generally decreases as the collision probability increases. The reason is that the protocol designed in this paper is more advantageous to PUs in the case of high collision probability, which is to guarantee the QoS of PUs. This is evident when \(p_{1}\) is greater than 0.9. It is due to the fact that SUs hardly succeed in competing for subchannels when the collision probability is particularly large, but PUs can obtain many remaining subchannels, which is unfair. In particular, a slightly unfair phenomenon also emerges when \(p_{1}\) is near 0.4, because \(\theta p_{1}/(r-n\theta(1-p_{1}))\) reaches the maximum value when \(p_{1}=4\), which is the most favorable for PUs. In addition, the fairness index decreases as the number of low-priority patients increases, which is because higher numbers of patients lead to higher collision probabilities. However, CR-OFDMA is fair because the fairness index is close to one in most conditions. Figure 11: Fairness index vs. collision probability \(p_{1}\) with different numbers of low-priority patients \(n_{2}\). Figure 10: Channel utilization versus the number of low-priority patients.
## 3 Results and Discussion
### Magnetic Field
#### 3.3.4 Activation Functions  
Activation functions introduce non-linearity to the neural network, enabling it to model complex relationships and learn intricate patterns from the data. In this paper, four activation functions including Purelin, Tansig, Sattin, and Logsig have been considered for this step of analysis as the most common functions used in the literature. The equations of these activation functions are as follows [40; 41]:  
\[\text{Pure linear}\qquad\quad F(x)=x \tag{8}\]  
\[\text{Saturated linear}\quad\quad F(x)=\begin{cases}0\ for\ x<0\\ 1\ for\ x>1\end{cases} \tag{9}\]  
\[\text{Hyperbolic }\tan\text{gentsigmoid}\quad F(x)=tanh(x) \tag{10}\]  
\[\text{Log-sigmoid}\quad\quad F(x)=\frac{1}{1+exp(-x)} \tag{11}\]  
It was considered that the activation functions between the input and hidden layer, and the hidden layer and hidden layer are the same. Also, eight combinations of activation functions out of all possible conditions were chosen from [19]. Then, they were assessed to check whether they result in higher accuracy than others.  
As can be seen in Figure 7 and Table 5, for the electric field, Purelin-Logsig for CFNN and Tansig-Purelin for FFNN resulted in the lowest RMSE and highest \(R^{2}\), respectively. Also, according to Figure 8 and Table 6, Logsig-Purelin for both the CFNN and FFNN demonstrated the highest accuracy. Also, it is worth noting that the CFNN has a higher \(R^{2}\) than the FFNN, while it has lower RMSE values. This means that the CFNN gives more accurate estimates of both electric and magnetic fields in most pairs of activation functions.  
Figure 7: RMSE values of the pairs of activation functions for both FFNN and CFNN for the electric field.
## 1 Introduction
Since the serverless model can ensure that the amount of resources consumed by an IoT application is dynamically controlled and is tailored to the ongoing computing needs, this clearly provides service providers with a valuable feature for implementing IoT for short and on-demand tasks, in particular when devices are silent for most of the time, and transmit short data packets periodically or randomly. However, this scalable approach with enhanced responsiveness to load variation may cause some latency issues, which are analyzed in this paper. In summary, the main contributions of this paper are as follows:  
* We present an analysis of some key IoT use cases, discussing the suitability of implementing NFV through serverless computing functions, analyzing service requirements and matching them with serverless capabilities. * We present a specific architecture for serverless computing. It is based on the integration of Firecracker and Kata containers, and it is capable of running both classic containerized applications and new packet processing applications, with minimal overhead. It is realized by means of NetBricks, a novel NFV framework optimized for speed of execution and rapid code development. * We show the results of an experimental campaign to evaluate the overall costs and performance of the proposed serverless framework, which is based exclusively on open source software, discussing its suitability with respect to IoT service requirements. The paper is organized as follows. In Section 2, we provide some background on serverless computing and previous research that used surveys, dealing with adoption of serverless computing for IoT services. In Section 3, we analyze some IoT scenarios, highlighting different service requirements that are useful to evaluate the possibility of using the serverless approach for their implementation. Section 4 presents a NFV architecture based on these technologies. Its expected performance is analyzed in Section 5, making use of both results from the literature and lab experiments. Finally, Section 6 reports our conclusions.
## 3 Time-Gated SPAD Optimization Technology
### BER of Time-Gated SPAD
If the count value is lower than the decision threshold value, it is considered that the \"1\" signal transmission is wrong, which can be represented by \(P_{e01}\). Similarly, when the \"0\" signal is transmitted, the photon count is higher than the threshold, it is considered that the transmission is wrong, which can be represented by \(P_{e10}\). Based on this, the BER of external time-gated AQ-SPAD can be expressed as:  
\[\text{BER}=\frac{1}{2}(P_{e01}+P_{e10})=\frac{1}{2}\Bigg{(}\sum_{i=0}^{N_{th}- 1}p_{1}\big{(}T_{Bg,i},\lambda_{1}\big{)}+\sum_{j=N_{th}}^{N_{g}}p_{0}\big{(}T _{Bg,i},\lambda_{0}\big{)}\Bigg{)}. \tag{18}\]  
where \(P_{1}\) and \(P_{0}\) represent the probability density function of \"1\" signal and \"0\" signal, respectively. In actual calculations, it is necessary to perform multiple calculations to obtain average BER to reduce the effects of random vectors \(\mathbf{T_{Bg}}\) on the results.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 4 Results
### Simulation Results  
Gazebo software was used to study the behavior of a simulated UAV, equipped with our system. The working environment consisted of three-phase transmission grid, whose main characteristics are summarized in Table 3, a simulated UAV and a simulated visual camera. In addition, the pre-calibrated algorithm was programmed in C/C++ under Ubuntu 16.04 operating system to reduce the processing time. The procedure can be summarized in three main steps:  
1. First, the parameter reference separation by the acquisition an image set (30 images) at a fixed attitude in off-line mode was proposed.
2. Then, the simulated UAV automatically flew on the pre-determinate path, acquiring the visual data.
3. Finally, our approach analyzed the data and returned the estimated placement of the UAV in real time. At the same time, the results were plotted in a GUI, developed in MATLAB programming environment.  
Two experiments were developed in the simulated environment. First, the UAV attitude was maintained all the time. The corresponding results are shown in Figure 8. It is possible to determine that the estimated placement converges to the simulated configuration and the absolute error of the positioning system was less than 1%.  
Under the same conditions, the simulated UAV was maintained at constant height and flew over the simulated transmission lines, as shown in Figure 9. It is possible to see that the estimated placement converges to the simulated configuration, provided that the transmission lines are visible in the analyzed images. Otherwise, the system was able to delete the false estimations, providing better performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Transmission Line 500 kV** \\ \hline Height & 20 m \\ \(3\phi\)-Separation & 10.50 m between phases \\ Distribution & Horizontal \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transmission line parameters in GAZEBO environment.  
Figure 7: Laboratory results. Comparative analysis between pre-established path and estimated path by our approach (blue boxes represents the spatial position of the camera): (**a**) straight line path; (**b**) circular path; and (**c**) circular variable path.
## 2 Materials and Methods
### Methodology  
This section provides a detailed description of the applied methods and approaches that have been developed based on the knowledge presented in the previous section on the current state of knowledge. The key parameters and mathematical formulas that are  
Figure 4: Impulsive manoeuvre of changing the orbit plane in the apocentre—diagram. Created based on [7]. (1) Initial elliptical orbit; (2) Final elliptical orbit.  
necessary to conduct the analyses were determined based on the literature on the problems of orbital mechanics, the properties of nanosatellites, and propulsion technologies. The presented methodology takes into consideration both the theoretical assumptions related to orbital manoeuvres and the properties of satellites, as well as the limitations that result from the characteristics of nanosatellites, such as their weight, fuel consumption, or energy capacity. Special attention was paid to the analysis of the duration of the flight of a nanosatellite over the given area with the known angle of inclination of its trajectory in reference to the equatorial plane, with a varying weight of the satellite and varying fuel weight. The authors also focused on the determination of the efficiency of various orbital manoeuvres (inclination change, Hohmann transfer) and optimising the orientation of the nanosatellite in the context of maximising the observations of a specific area. Finally, the authors attempted to choose the optimal nanosatellite in terms of fuel consumption during orbital manoeuvres.  
The developed methods were based on existing formulas and models that were adapted and adjusted to the specificity of the analysed nanosatellites. The following subsections contain detailed descriptions of the equations used, the input parameters, and the analytical process that made it possible to conduct the simulation and to obtain the results that fulfil the predefined research objectives.  
The authors used a hybrid approach, combining the analytical computational methods with computer analysis in the STK (Systems Tool Kit 12.7.1, AGI (Analytical Graphics, Inc.), Exton, PA, USA) environment. At the first stage, detailed analytical calculations were conducted, with the use of the formula and mathematical models described below. The results of the analytical calculations then served as input data for further analysis conducted in the STK 12.7.1 environment. This tool enabled the authors to perform computer simulations to verify the correctness of the preceding calculations and enable a more detailed analysis of the satellite's trajectory, orbit parameters, and the efficiency of manoeuvres in a realistic environment that models the conditions in space. This approach ensured that the analysis was comprehensive and combined the accuracy and a theoretical basis for analytical calculations with a practical verification and visualisation in computer simulations.
# RISC-Based 10K+ Core Finite Difference Method Accelerator for CFD
## 3 Design and Implementation of Multi-Core Accelerator Architecture
### FAcc Working Modes
#### 3.3.1 Loading Mode  
The Loading Mode initializes each FCore by transferring instruction programs, initial conditions, and boundary conditions from off-chip storage to on-chip FCores via the FMesh network. During this phase, FCores suspend computations and inter-core data transfers. Data and instructions propagate unidirectionally from designated edge nodes to the entire chip, with no output generated. This mode enables flexible configuration of boundary and initial conditions, enhancing network programmability.  
Key operational principles are as follows.  
* Unidirectional Propagation: Data injected into FMesh flows away from the input port, ensuring each FRouter receives valid data from only one port per cycle. This eliminates overhead arbitration caused by concurrent valid data arrivals at a single FRouter, thereby significantly simplifying FRouter design.
* Local Routing Logic: As illustrated in Figure 9, upon receiving a data packet, the local FRouter's routing computation unit (rc_unit) parses and forwards it to downstream FRouters. A tag decoder determines whether the packet targets the local FCore, subsequently routing it to either the IM or GPR.  
Figure 8: The encoding method of Flit.  
* Hybrid Transmission Strategies: To minimize packet size and optimize transmission efficiency, FMesh supports broadcast and point-to-point modes. Identical instruction programs are broadcast to all FCores, while initial/boundary conditions employ hybrid transmission: broadcast for shared data (dark green FCores in Figure 10) and point-to-point for unique data (light green FCores in Figure 10).
# Functional Evaluation of a Novel Multi-Axial Alveolar Distractor--Preliminary In Vivo Animal Study
###### Abstract  
This study evaluates the biomechanical performance of a new multi-axial alveolar distractor using an animal study. The multi-axial alveolar distractor is designed with a ball and socket joint mechanism that can rotate up to 60\({}^{\circ}\) toward the buccal/lingual and mesial /distal sides intraoperatively to achieve vector control. The transport segment can be moved through activating the transport screw with 0.25 pitch, allowing 13 mm in distraction height. This distractor was fixed at the right angulus mandibular of experimental rabbits and adjusted 15\({}^{\circ}\) toward the mesial side and 25\({}^{\circ}\) toward the buccal side as Group TMB (toward mesial-buccal) (\(n\) = 3), and 15\({}^{\circ}\) toward the mesial side as Group TM (toward mesial) (\(n\) = 3). Group TC (control) was the control group. The distractors were activated 1 mm/day for 13 days. Living bone growth was observed at various periods. The total bone growth length at the angulus region and buccal side distraction thickness after distraction were calculated. The variations in bone growth geometric shape at the mandible angulus were also recorded. Fracture testing was performed to understand the variations in the mechanical strength between the distracted and intact bone specimens. The digital radiography results showed that the osteotomy areas at the mandible angulus were healed and the bone growth completed after surgery. The average bone growth length of Group TMB was 17.68 mm. This was greater than that of Group TM at 14.79 mm. The corresponding buccal side distractor thicknesses for Group TMB and TM after distraction were 5.12 \(\pm\) 0.52 mm and 3.32 \(\pm\) 0.37 mm, respectively. The tensile strengths of the bone specimens after distraction of Groups TMB, TM and TC were 172.13 N, 119.27 N and 304.24 N, respectively, and the percentage of distraction bone tensile strength to normal bone was 57% and 39% for Groups TMB and TM, respectively. This study concluded that this new multi-axial alveolar bone distractor can drive bones to grow in accordance with the direction/angle of the distraction plan. The bone growth healed gradually and presented insufficient mechanical strength.  
distractor; multi-axial; bone regeneration; ball and socket joint +
Footnote †: journal: Applied Sciences  
1 Oral & Maxillofacial Surgery, Taipei Veterans General Hospital, School of Dentistry, National Yang-Ming Chiao-Tung University, Taipei 112, Taiwan; [EMAIL_ADDRESS]  
2 Department of Biomedical Engineering, National Yang-Ming Chiao-Tung University, Taipei 112, Taiwan; [EMAIL_ADDRESS] (K.-C.C.C.); [EMAIL_ADDRESS] (Y.-S.L.); [EMAIL_ADDRESS] (Y.-S.L.); [EMAIL_ADDRESS] (Y.-C.L.)
# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 4 Discussion
This method lacks the more mechanistic scheme that our NEE model (Equation (9)) has, but the advantage is the high number of free parameters that makes the model adjustable. This method could be used to estimate NEE at an individual site that differs from other sites in its characteristics, e.g., at Mycklemossen, which is more light-dominated than the other sites in our study (Figure S3). During the summer of 2018, northwestern Europe experienced a severe drought, which increased air temperatures, lowered WTD, and thus reduced the \(\mathrm{CO_{2}}\) uptake at several northern peatlands [61]. [PERSON] et al. [61] found that the 2018 drought did not affect all peatlands equally, as local hydrological features can make a peatland less sensitive to climatic variations, as was the case at Lompolojankka. Similarly, compared to 2017 and/or 2019, the EC-derived NEE we present was lower in 2018 at Abisko-Stordalen, Degero and Siikaneva, but not at Lompolojankka. The decrease in NEE at these sites mainly originated from reduced GPP, although increased ER in 2018 was observed at Siikaneva. Lower GPP and ER were found at Degero in 2019, which was also a dry year at that site. However, the dry conditions in 2019 did not influence the spectral properties of the vegetation, as the EVI2 in 2019 is at the same level as in 2017, which causes the mismatch between modelledand measured GPP. It is also possible the 2018 drought has a legacy effect in 2019, i.e., the vegetation did not fully recover from the drought in the previous year, and therefore both GPP and ER were lowin 2019 at Degero. Mycklemossen experienced droughts both in 2017 and 2018 [61], and was also the southern-most (i.e., warmest and most light-dominated) site in the study, which may partly explain why our models with the average parameters performed poorest here. Overall, with only two or three years of data, it is difficult to draw strong conclusions about the temporal variations of the fluxes, especially if a severe drought has been experienced at a site. Despite the limitations discussed above, our findings suggest that using satellite data in regression models is a simple yet powerful tool for modelling and upscaling GPP and ER. Due to its high spatial resolution, Sentinel-2 data has great potential to upscale GPP from the flux footprint to larger areas. The main input of the ER model, MODIS LST, has a coarse spatial resolution (1 km), which makes it less feasible to study heterogeneous ecosystems like peatlands. However, in this study, the MODIS LST was chosen over the 30 m resolution thermal Landsat data because of the higher temporal resolution and because it is a readily available LST product. To improve the spatial resolution of MODIS data, an appropriate downscaling procedure from MODIS reflectance to the Landsat 30 m resolution could be used [62]. Preserving the high temporal resolution while increasing the spatial resolution would enable the mapping of the spatial variation of LST within a peatland and within the flux tower footprint and may lead to improved ER and GPP models.