esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Structure and Description
### P Fluxes and Pools in Plants  
In our model, the P cycle contained 12 pools (leaf, wood and root for plant pools; decomposable, structural, and resistant for litter pools; microbial, slow, passive P pools for soil pools and new adding labile, absorbed and strongly absorbed pools), which are based mainly on the CASA-CNP model [53; 69]. The P-C ratio for three key plant P pools (leaf, wood and root) were quantified and allowed to vary within ranges defined for each PFT by the model. All of the algorithms of pools and fluxes followed the mass balance principle. The timescale was daily for the biogeochemistry submodule. Plants were divided into leaf, wood, and root pools; the litter was divided into decomposable, structural and resistant pools; and the soil was divided into a microbial biomass pool, slow pool (protected and non-protected) and passive pool. The key parameters concerning P cycle used in fowling equations are listed in Table 1 (and the others in Table S1).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Parameter** & **Value** & **Unit** & **Explanation** & **Ref.** \\ \hline
1 & r\({}_{\text{pi}}\) & r\({}_{\text{pleaf}}\) = 0.5, \(r_{\text{pwood}}\) = 0.9, & / & Phosphorus resorption coefficient of plant (i = leaf, wood, root) & [53] \\
2 & P\({}_{\text{softmax}}\) & 644.0 & g P\(\cdot\)m\({}^{-2}\) & Maximum amount of absorbed phosphorus & [69] \\
3 & k\({}_{\text{plabor}}\) & 55.0 & g P\(\cdot\)m\({}^{-2}\) & Empirical parameter for describing the equilibrium between labile P and absorbed P & [53] \\
4 & k\({}_{\text{pup}}\) & 0.5 & g P\(\cdot\)m\({}^{-2}\) & Empirical parameter relating plant P uptake rate to labile P pool size in the soil & [53] \\
5 & k\({}_{\text{phoochem}}\) & 150.0 & g N\(\cdot\)g P\({}^{-1}\) & Empirical constant for biochemical P mineralization & [53; 69] \\
6 & k\({}_{\text{p}}\) & 0.0006 & g P\(\cdot\)g C\({}^{-1}\) & Empirical parameter for phosphorus limitation on net primary productivity & [53] \\ \hline \end{tabular}
\end{table}
Table 1: List of key parameters and their initial values.  
Equations describing changes in P pools were taken from CASA-CNP [53]. We started from  
\[\frac{\mathrm{d}\mathrm{P_{i}}}{\mathrm{dt}}=\mathrm{f_{pi}}\mathrm{F_{Pup}}- \mathrm{t_{i}}\big{(}1-\mathrm{r_{pi}}\big{)}\mathrm{P_{i}} \tag{1}\]  
where \(\mathrm{P_{i}}\) is the amount of P absorbed by plants (g P\(\cdot\)m\({}^{-2}\)) (i = leaf, wood or root here), \(\mathrm{f_{pi}}\) is the proportion of P absorbed by plants allocated to plant pools and \(\sum_{i}\mathrm{f_{pi}}=1\), \(\mathrm{F_{Pup}}\) is plant P uptake rate (g P\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)), \(\mathrm{t_{i}}\) is the turnover rate of plant pools (day\({}^{-1}\)), and \(\mathrm{r_{pi}}\) is the P reabsorption coefficient (0.5 for leaf pool, 0.9 for wood pool and 0.9 for root pool) [53].
## 3 Results
### Partial Dependence Plots
(lower value) classification. Each line represents the relationship for a single metal. The plots show: (**a**) Mean EVI (the mean EVI of the drainage area above the sample location), (**b**) Lithological SiO\({}_{2}\) (mean accumulated percentage of lithological silicon dioxide content), (**c**) Lithological sulfur (mean accumulated percentage of lithological sulfur (S) content), (**d**) Mean elevation, (**e**) Evergreen forest, (**f**) Lithological K\({}_{2}\)O (mean accumulated percentage of lithological potassium oxide content), (**g**) Mean NDVI and (**h**) Basin slope. “EVI” = Enhanced Vegetation Index, “NDVI” = Normalized Difference Vegetation Index. Lines are displayed after smoothing with a polynomial regression for display purposes. Figure 5: PDP plots for group C2. The \(x\)-axis represents a MIV, the \(y\)-axis represents the associated probability of a L.V. (lower value) classification. Each line represents the relationship for a single metal. The plots show: (**a**) Lithological K\({}_{2}\)O (mean accumulated percentage of lithological potassium oxide content), (**b**) Runoff 1999 (mean annual runoff in 1999), (**c**) Lithological SiO\({}_{2}\) (the estimated accumulated lithological silicon dioxide content), (**d**) Lithological sulfur (mean accumulated percentage of lithological sulfur (S) content), (**e**) Lithological Fe\({}_{2}\)O\({}_{3}\) (mean accumulated percentage of lithological ferric oxide content), (**f**) Compressive strength (mean accumulated lithological compressive strength), (**g**) Runoff 1996 and (**h**) Runoff 1971. Lines are displayed after smoothing with a polynomial regression for display purposes. Figure 7 displays the PDP for group C4 consisting of Runoff and Climate and their effects on Cr and Hg. Plot (a) shows the relationship between the probability of L.V. and the annual precipitation in 1999 in the basins. While the specific shape of the curves is different from each other, the general trend is the same. The probability of L.V. decreases first up to 800 mm/a precipitation and then increases again. In (b), which shows the annual runoff of 2000, the curve of Cr shows stronger fluctuations, but its linear trend is similar to that of Hg, the probability of L.V. decreases with increasing runoff. The plots (c), (e), and (f) show similar behavior while (d) shows an opposing behavior in many parts, even if the curve of Hg is much smoother. There is also opposing behavior in (g) after the first 25% of values along the \(x\)-axis. In these opposing behaviors, one of the metals reduces while the other increases. These results show that precipitation and runoff affect the amount of transported material in the stream systems. The PDP analysis shows that the metals have individual dependencies on the factors of the different F.G. Even for those metals within the same meta-group consisting of a collection of F.G., the response may be different.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 4 Electronics Design
### Flight Controller [Pixhawk 5X]  
The selection of the flight controller was based on the physical constraints of the vehicle, as well as the activities that need to be performed. There are many autopilot hardware boards that can be used to run the PX4 flight stack, such as Qualcomm Snapdragon Flight, Raspberry Pi 2/3 Navio2, and BeagleBone Blue. In addition to these solutions, there is the Pixhawk, which constitutes an independent open-hardware project that aims to provide the standard for readily available, high-quality, low-cost autopilot hardware designs; most importantly, it supports PX4. The Pixhawk Series is a prevalent set of flight controller boards that is preferred by the academic, hobby, and developer communities and provides various versions targeted towards many use cases and market segments.  
For the development of this UAV, from among the different boards that are included in the Pixhawk Series, the Pixhawk 5X, version 1.14, was used as a flight controller. Pixhawk 5X is a top-tier flight controller with triple redundancy, advanced sensors, and pre-installed PX4 Autopilot. Powered by an STMicroelectronics STM32F7 microcontroller, it offers exceptional flexibility and reliability for various autonomous vehicles, serving both academic and commercial purposes. The FMUv5X open standard [38] includes high-performance IMUs for improved stabilization and seamlessly switches to redundant sensors in case of failure. Independent power control for each sensor set, noise reduction through vibration isolation, and versatile connectivity via SPI5 and ethernet ensure precise readings and enhanced overall flight performance. Compatible with PX4 Autopilot, the Pixhawk 5X is ideal for corporate research, startups, academia, and commercial applications.
# Optimization Algorithm for 3D Smooth Path of Robotic Arm in Dynamic Obstacle Environments
## 4 Experiment Results
This section compares the performance of the trajectory planning algorithm combining rapidly exploring random tree (RRT) and artificial potential field (APF) with the D-RRT and VF-RRT algorithms. The comparison experiments are conducted in the multi-obstacle scenario, with identical obstacle tracking and dangerous node detection methods used throughout. In all experiments, the speed of the dynamic obstacles is set to 10 cm/s. The objective is to determine the efficiency of path planning by comparing the replanning time and path length of each algorithm. As shown in Table 2 below, the test results of several algorithms in the multi-obstacle scenario are compared, with the table data representing the average of 30 test experiments. Among them, the algorithm proposed in this paper slightly outperforms the other two algorithms in terms of path replanning length and also reduces the replanning time. As mentioned in the algorithm, the dynamic adjustment of the weight factors for RRT and APF in obstacle environments plays a positive role. After the simulation experiments on trajectory planning algorithms for static and dynamic obstacles mentioned above, in order to better demonstrate the effectiveness of the real-time grasping path planning for the robotic arm studied in this paper, this subsection conducts simulation experiments on the motion trajectory of the robotic arm's end effector using the URDF model. The simulation experiment environment is shown in Figure 15, where the spheres represent obstacles and the cylinders represent the robotic arm posts. The experimental results are illustrated in Figure 16, demonstrating the search points and planned trajectories in three-dimensional space. Figure 17 demonstrates the obstacle avoidance trajectories of the robotic arm's end effector in different obstacle scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **D-RRT** & **VF-RRT** & **Ours** \\ \hline Path replanning time (ms) & 5.67 & 2.19 & 2.08 \\ Path planning length & 1.56 & 1.31 & 1.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of path replanning performance of various algorithms. Figure 14: Local path replanning in multi-obstacle scenarios.
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 3 Background on Communication Protocols
### Fisheye State Routing (FSR)  
A multilayer and table-driven routing method for use in ad hoc networks, Fisheye State Routing is also known by its acronym FSR. This method utilizes the scope approach. The amount of overhead that is associated with routing in highly large networks that are prone to rapid change and dynamic behavior is something that needs to be reduced. In accordance with the methodology that underpins its scope, the connection state changes are periodically broadcast at a predetermined frequency. The entirety of the network is segmented into a variety of scopes, each of which is determined by the number of hops that are required to reach a particular node. These hops are counted from the root node backwards. Nodes that are considered to be within this distance of one another are referred to as inner nodes, while nodes that are further apart are referred to as outer nodes.  
The transmissions of the most recent connection state updates are made available to neighbors across a wide range of frequencies. Information is delivered at a lower frequency, while simultaneously increasing its frequency while connecting with neighbors that are closer to the sender. The accuracy of the connection state updates that the nodes receive as a direct result of this is much improved. It improves the precision of the path that packets are sent across when they are transferred [17].
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 2 Error Control Scheme (ECS)
### Automatic Repeat Request (ARQ)  
In automatic repeat request (ARQ) [11], the transmitter applies an error-detection code to the packet before transmission. The commonly used error-detection techniques are parity check and cyclic redundancy check (CRC). The receiver performs an error-detection operation using checksum according to the technique applied at the transmitter. If the receiver finds errors, the receiver sends Negative ACKnowledgement (NACK) to the transmitter using the feedback channel. Otherwise, the receiver sends a positive ACKnowledgement (ACK). Until the receiver receives an error-free packet, the re-transmission action is continued. Additionally, the transmitter resends the packet if it does not receive acknowledgment within a pre-specified time-frame. The main drawbacks of the ARQ scheme are the additional cost of re-transmission and delays. The re-transmission will take place when the communication link is poor and will rarely happen when a communication link is good. Thus, the efficiency of ARQ is very good during good channel conditions but will produce poor efficiency during poor channel conditions.
## 6 Evaluation
### Accuracy Evaluation of Energy Model
#### 6.3.2 Impact of Energy and Time Coefficients in the Schedule Policy Choices  
This experiment used Application 2 in four different scenarios. Each case with 500 tasks, 100 IoT devices, and one MEC server. The energy coefficients were set to 1/5, 2/5, 3/5, 4/5 and the time coefficients to 4/5, 3/5, 2/5 and 1/5.  
Table 6 lists the minimum costs identified by the system task scheduler for each case. The lowest calculated cost was the same for C2 and C3, with MEC as an offloading option. C1 case had the lowest calculated cost among all coefficient pairs. In these three cases the time coefficient had high values, and MEC was chosen because task execution got the lowest processing times. For C4, the allocation took place on the IoT device itself, with DVFS configured at 8 MHz and 4 V. Now, energy has a high-value coefficient, which made the scheduler choose the policy that provided the lowest energy cost, reducing total cost.  
To reduce energy consumption, the best option is to use 4/5 as an energy coefficient. With this configuration, the minimization of energy consumption is prioritized, saving up to 51.6% compared to the other cases. Alternatively, to reduce task completion time, coefficients from C1, C2, and C3 cases are better, with a reduction of up to 86.6% compared with the C4 case. For C1 to C3 cases, a considerable reduction in total elapsed-time was perceived. It because the task launches in the IoT devices caused the increase the time execution.
## 3 Methodology
### Speed Computation and Data Augmentation Based on Movement Feature
#### 3.3.2 Data Augmentation Based on Driving Direction  
In the BFRS modeling process, it is recognized that vehicles travel at varying speeds, which influences the detection of BFRS based on recordings and movement features. While multi-sensor recordings are captured at specific times and locations, they are inherently logged at a singular speed, omitting the diversity of speed scenarios encountered in real-world driving. To address this limitation, a simulation process that accounts for varying driving speeds is necessary to accurately model BFRS under different speed conditions. Let us assume the vehicle's average speed is \(v\), and the number of multi-sensor recordings is \(n\). To construct more realistic scenarios, it is essential to simulate speeds \(v^{\prime}\) within a range \(v^{\prime}\in[v_{min},v_{max}]\). The original recording \(s\) is then modified based on the comparison between \(v\) and \(v^{\prime}\). If \(v>v^{\prime}\), indicating slower driving conditions, the sampling period of BFRS is extended, and the recording \(s\) is stretched. Conversely, if \(v<v^{\prime}\), suggesting faster  
Figure 3: Data augmentation based on movement feature. (**a**) To be augmented by driving direction. (**b**) Augmented after driving direction. (**c**) To be augmented by driving speed. (**d**) Augmented after driving speed.  
conditions, the recording \(s\) is compressed. These stretching and compressing operations are executed using up-sampling and down-sampling methods, respectively. This augmentation is repeated \(k\) times with randomly selected speeds \(v^{\prime}\). For instance, when \(v>v^{\prime}\), a scale factor \(u\) is calculated, and only \(m\) samples \(s_{m}\{s_{1+m/2},s_{n-m/2}\}\) are chosen (as depicted in Figure 3c). To maintain the same length as the original recording s, up-sampling is performed through linear interpolation, resulting in \(s_{stretch}\) (illustrated in Figure 3d), interpolated with \(n\) timestamps to match the length of \(s\).  
\[S=\text{AugmentSpeed}\left(S,v,v^{\prime},k\right)\,v^{\prime}\in[v_{min},v_{ max}],k\in N \tag{9}\]  
In both (8) and (9), the maximum duration \(w\) for the random seed \(m\) for spatial transformation is typically set as half the average data length. For example, if the average dataset length is 170, \(w\) can be set to 80, and the operation repeated 40 times (\(k=20\)). Similarly, the randomly selected speed \(v^{\prime}\) ranges from 0 km/h to 60 km/h, with operations also repeated 40 times (\(k=40\)). Moreover, while the spectrum feature remains stable during driving direction-based data augmentation, it changes after speed-based augmentation; the frequency of BFRS increases with higher speeds and decreases with lower speeds, as denoted in (4) and (5).
# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 6 Discussion
### Comparison between the Proposed Method and Conventional Methods for Identifying Customers' Needs
#### 6.2.4 Easier to Implement than Machine Learning-Based Topic Modeling Methods  
To find the context, machine learning-based topic model methods, including latent semantic analysis (LSA) and latent Dirichlet allocation (LDA), or clustering methods, including k-means clustering, may be employed. However, these mathematical and statistical approaches require extrapolations of analysts' subjectivity to use latent dimensions or cluster centroid vectors in product development, and it is difficult to control good quality contexts via an algorithm. LSA and LDA extract latent vectors, also known as topic vectors, which are related to words. Each latent vector does not represent a single topic that humans can understand. After extracting the latent vector, the human must interpret and label it by looking at the related words and corresponding weights. Generally, this process is very subjective, and there is no guarantee that you can label with a clear topic. Therefore, deriving ideas for new products, which is this study's goal, using machine learning-based topic model methods is likely to fail. Furthermore, LSA and LDA have several hyperparameters, which should be controlled for good performance. Especially, LDA is hard to determine the number of topics beforehand [33].  
The clustering algorithm, one of the unsupervised machine learning methods, is also difficult to achieve our research purpose in the same context. By clustering documents, several cluster centroid vectors are derived, and they can be considered as the main topics. However, interpreting cluster centroid vectors is also not easy. Besides, it is difficult to produce good clustering results depending on the data.  
To prevent this problem, this study employed a method that identified the user's context from the corresponding concept space based on the extracted related keywords. Extracting users' contexts usingrelated keywords has advantages of not only convenient expert interpretation but also quantitative evaluation through the frequency of keywords that respond to the corresponding context. In addition, the proposed method is more suitable for NPD as it is more focused on the purpose of targeting the unmet needs of customers.
## 4 Description of the Application Problem and Employed Multi-Fidelity Models
### Surrogate Modeling for Space Design Exploration
#### 5.3.2 Selection of Training Points and Subsequent Obtention of HF and LF Results  
To calibrate the surrogate models corresponding to each of the five metamodeled variables, a numerical campaign of 54 simulations was produced using a Box-Behnken design (see Section 3.1.2), considering the remaining six significant parameters. Two replicates of this design were added with center points to deal with some missing data (certain combinations of operating conditions do not produce condensation, as Figure 2 shows) and also to improve the accuracy of the standard error of estimation. These replicates were obtained by including random modifications to previously discarded (non-significant) factors. Thanks to these enhancements, the percentage of missing data was reduced to 13% due to the sample size increase. The collection of previous HF and LF simulations (54 working points) was subsequently used as the training data set to obtain the surrogate models.
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 5 Computational Results
### Performance Comparison with Other Surrogate Robustness Measures  
To efficiently approximate the robustness of a schedule, a measure must perform a strong linear correlation to exact robustness. A correlation study is performed in this section. Since the robustness of the schedule cannot be calculated precisely, the correction study will be performed using a Monte Carlo simulation as an approximation. The \(R^{2}\) value is expected to predict the robustness of the schedule. A measure with a higher \(R^{2}\) value is expected to better predict schedule robustness. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{\(tc\),\(\theta\)} & \multicolumn{2}{c}{\(SRD(SR_{AC}SR_{MC})(\%)\)} & \multicolumn{2}{c}{\(QRD(QR_{AC}QR_{MC})(\%)\)} & \multicolumn{2}{c}{CPU Times(s)} \\ \cline{2-7}  & _mean_ & _std_ & _mean_ & _std_ & _TAC_ & _TMC_ & _\(\eta\)_(\%) \\ \hline
20,0.5 & 7.75 & 4.14 & 0.71 & 0.59 & 2.28 & 699.09 & 0.35 \\
20,1.0 & 5.42 & 3.28 & 0.39 & 0.35 & 2.17 & 398.73 & 0.43 \\
20,1.5 & 3.37 & 1.98 & 0.34 & 0.29 & 2.18 & 354.85 & 0.55 \\
40,0.5 & 7.69 & 3.78 & 1.39 & 0.94 & 2.37 & 526.72 & 0.40 \\
40,1.0 & 5.45 & 3.12 & 0.75 & 0.53 & 2.23 & 434.06 & 0.47 \\
40,1.5 & 4.08 & 1.97 & 0.58 & 0.53 & 2.19 & 288.29 & 0.62 \\
60,0.5 & 7.63 & 3.19 & 1.82 & 1.24 & 2.37 & 524.15 & 0.39 \\
60,1.0 & 5.15 & 3.17 & 1.16 & 0.83 & 2.31 & 321.47 & 0.59 \\
60,1.5 & 3.06 & 2.03 & 0.66 & 0.58 & 2.60 & 374.06 & 0.66 \\
80,0.5 & 7.08 & 2.93 & 2.27 & 1.42 & 2.76 & 496.42 & 0.44 \\
80,1.0 & 4.54 & 2.44 & 1.27 & 0.98 & 2.65 & 443.40 & 0.60 \\
80,1.5 & 3.67 & 1.95 & 0.87 & 0.62 & 2.65 & 295.36 & 0.74 \\ \hline Mean & 5.41 & & 1.02 & & & & 0.52 \\ \hline \end{tabular} \(\eta=T_{AC}/T_{MC}\).  
\end{table} 
Table 2: Evaluate the performance of \(M_{AC}\). Figure 7: Quartile graph of SRD, QRD for every benchmark problem. (**a**) Quartile graph of SRD at different fault levels. (**b**) Quartile graph of QRD at different fault levels. As shown in Table 3, all significance values of \(SR_{AC}\) are smaller than 0.001, whereas most significance values of \(RM1\), \(RM2\), and \(RM3\) are larger than 0.001. This indicates that \(SR_{AC}\) has a more significant linear correlation with the solution robustness. In addition, all \(R^{2}\) values of \(SR_{AC}\) are quite close to one and significantly larger than the others. This implies that \(SR_{AC}\) can more efficiently approximate the schedule stability than the other measures. Figure 8a clearly shows that \(RM2\) has the lowest linear correlation with \(SR_{\text{MC}}\), and that \(RM1\) and \(RM3\) have similar linear correction with \(SR_{\text{MC}}\). Figure 8: Comparison of \(R^{2}\) values at different fault levels. (**a**) Compare of \(R^{2}\) values in solution robustness. (**b**) Compare of \(R^{2}\) values in quality robustness.