esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 5 Implementation
### SPF Forces  
As previously introduced, both SPF-only and Agents strategies are supported on top of the SPF navigation algorithm. In order to determine the heading and the velocity the robots should follow, a set of attraction and repulsion forces are defined.  
Obstacle repulsion forces, just as the name implies, prevent robots from colliding with elements (other robots, obstacles, walls, etc.). The detection of obstacles can be achieved by several means such as bumper sensors, imaging, or LIDAR. In this work, we opt for LIDAR-based obstacle detection. As mentioned, obstacles can include both physical statically placed obstacles (e.g., walls or objects in the middle of the environment) and other robots. Robots can be seen as obstacles when they are physically close to each other during the deployment stage or when they are stationary. The reason robots can be stationary at other phases include the exhaustion of their batteries (thus they cannot move any more) or when they are not part of a replacement operation in the Agents strategy. The formula for obstacle repulsion forces is defined:  
Figure 3: Messages exchanged during negotiation.  
\[fr_{1}(d_{i,j})=\frac{256}{(d_{ij})^{8}} \tag{13}\]  
Not only do obstacle repulsion forces exist in SPF navigation algorithms, properly tuned robot repulsion forces aid with network expansion, whose expression is described below.  
\[fr_{2}(d_{i,j})=\frac{8\times 10^{8}}{(d_{ij})^{7}} \tag{14}\]  
In both cases, \(d_{i,j}\) represents the Euclidean distance between a robot \(i\) and an obstacle or another robot \(j\).  
With respect to attraction forces, punctual attraction forces allow robots to reach a specific point \((p)\) when an agent needs to be replaced.  
\[fa_{1}(d_{i,p})=-arctan(d_{i,p})\times 2 \tag{15}\]  
Lastly, a cohesion force to gradually contract the network is presented in Equation (16). The cohesion force depends on the parameter \(\alpha\), \(n_{fail}(t)\) is the number of depleted nodes, \(n\) is the initial number of nodes and \(n_{closeAP}(t)\) is the number of nodes within the radio coverage of the sink. This force acts by pulling robots to the origin of the coordinate frame \((z)\), as indicated by the distance \(d_{i,z}\).  
\[f_{c_{i}}(t)=-\frac{\alpha\times n_{fail}(t)}{n\times n_{closeAP}(t)}\times \arctan(d_{i,z}) \tag{16}\]  
Depending on the network phase and the chosen strategy, a set of forces commands the robots to achieve their goal (i.e., expanding forces -\(fr1\)- in the deployment or attractive forces when an agent needs to be replaced -\(fa_{1}\)-). A summary of the aforementioned forces is given in Table 1.
###### Abstract  
WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WRNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively.  
fingerprint; WiFi; pedestrian dead reckoning; particle swarm optimization; particle filter; artemisinin optimization +
Footnote †: journal: Electronics Electronics  
1
## 4 Results
### GPS-Based Precise Relative Orbit Determination (PROD)  
Due to lack of direct measures for precision assessment, the PROD results were discussed by inter-agency comparison. The difference in 3D between the NDT and CHS solutions was better than 2 mm when excluding maneuver time periods [38]. Here, we excluded time intervals for each day from midnight to 1 h after the third maneuver. Figure 4 shows the difference in 3D between the NDT and CHS solutions outside the maneuver periods. The average value of the daily RMS was 1.48 mm. The differences between the NDT and CHS solutions on DOY 305 in 2019 are displayed in Figure 5. The difference between the NDT and CHS solutions in the T direction was larger than that in the R and N directions. This may be the result of the fact that the empirical forces are modeled differently at NDT and CHS. CHS has different software packages for orbit determination from NDT, which can provide independent PROD results. The PROD results provided by CHS corresponded to those provided by NDT. By inter-agency comparison, a conclusion can be drawn that the accuracy of GPS-based PROD is millimeter-level, which would be used as a reference to analyze the BDS2-based and combined PROD results.  
Figure 4: Difference in 3D between the National University of Defense Technology (NDT) and Xi’an Research Institute of Surveying and Mapping (CHS) solutions (excluding maneuver periods).  
The GPS-based PROD was evaluated by a self-consistency test and inter-agency comparison. The difference between the dPOD results and PROD results can reflect the correctness of GNSS data processing and orbit determination strategy. Figure 6 shows the RMS of the difference between the dPOD and PROD solutions in the R, T, and N directions. The RMS of orbit differences was 1.71 mm, 2.32 mm, 2.91 mm, and 4.13 mm in the R, T, N, and 3D directions, respectively. The DD ambiguity fixed rate was 100% based on GPS-only. The method of POD was different from that of PROD. However, the difference between the dPOD and PROD solutions was only several millimeters, indicating that the accuracy of a relative orbit determined by NDT was millimeter-level.  
Figure 5: Difference between the NDT and CHS solutions on day of year (DOY) 305 in 2019 (excluding maneuver periods).  
Figure 6: Difference between the differential POD (dPOD) and precise relative orbit determination (PROD) solutions of TH-2.
# [PERSON]'s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm
## 2 Methodological Background
### The Meta-Heuristics Algorithm Derived from the Breeding Mechanism of Chinese Hybrid Rice  
The proposed meta-heuristics algorithm is derived from the breeding mechanism of Chinese hybrid rice, which uses rice genes as value in the solution space, and the fitness function is used to measure the pros and cons of the rice gene. According to the pros and cons of their genes, Chinese hybrid rice is divided into sterile lines, maintainer lines, and restorer lines. Figure 1 shows the breeding procedure of three-line Chinese hybrid rice.  
The symbols \(\times\) and \(\otimes\) denote hybridization and selfing, respectively. Sterile lines (A) refer to a special type of rice that looks similar to regular rice. Due to the abnormal development of male organs in sterile lines, sterile lines are often pollen-free or aborted, and male sterility is controlled by cytoplasmic genes. The development of female organs of male sterile lines is healthy and can accept normal pollen for fertilization. It is one of the important tools to obtain Chinese hybrid rice seeds. Maintainer lines (B) refer to rice varieties that can maintain cytoplasmic male sterility. Maintainer rice seeds have the same nuclear genotype as sterile rice seeds. However, the cytoplasmic genes of the maintainer line are fertile, and the pollen is fertile and capable of self-breeding. Since the maintainer line is the same nuclear genotype as the sterile line, it is not possible to alter sterility controlled by cytoplasm. Therefore, the progeny of the maintainer line and the sterile line are also sterile, that is, \(A\times B\to A\). The restorer line (R) is a variety that can repair cytoplasmic male sterility. It has a nuclear gene that restores cytoplasmic male sterility. When it crosses a sterile line, the seed usually has fertility and heterosis. This offspring is the hybrid rice seed (F1) we need, that is, \(A\times\mathrm{R}\rightarrow\mathrm{F1}\). Chinese hybrid rice is the progeny formed by crossing two rice cultivars, sterile line and restorer line, the yield and other important agronomic traits are better than the parent and control varieties.  
A meta-heuristic algorithm based on the breeding mechanism of Chinese hybrid rice (hereinafter referred to as HRO) simulates the \"three-line\" breeding process. There are two main breeding behaviors, that is, crossing, a sterile line is crossed with a maintainer line to update a sterile line individual, selfing, the restorer is inbred to update the next-generation restorer. The cross-breeding of sterile lines and maintainer lines is an evolutionary process, and hybrid dominance can quickly reduce the gap between populations. Therefore, individuals with relatively significant differences are crossed. The self-crossing of restorer lines enables individuals with medium fitness value to reach the current optimal solution quickly, and has the ability to jump out of the local optimal solution.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 2 Multi-Frame PCA-Based Coding Method
### Way of Matrix Construction  
An appropriate way to obtain the 16 samples from frequency domain coefficients is necessary. This paper proposes one method as follows: suppose the coefficients of one frame in frequency domain are \(a_{1}a_{2}\ldots a_{240}\). \(a_{1}\) is filled in the first column and the first row \(X_{\left[\begin{array}{cc}1&1\end{array}\right]^{\prime}}\)\(a_{2}\) is filled in the first column and the second row \(X_{\left[\begin{array}{cc}2&1\end{array}\right]^{\prime}}\), and \(a_{16}\) is filled in the first column and the 16 th row \(X_{\left[\begin{array}{cc}16&1\end{array}\right]^{\prime}}\). Then, \(a_{17}\) is filled in the first row and second column \(X_{\left[\begin{array}{cc}1&2\end{array}\right]^{\prime}}\)\(a_{18}\) is filled in the second row and second column \(X_{\left[\begin{array}{cc}2&2\end{array}\right]^{\prime}}\) and so on, until all the coefficients have been filled in the original matrix \(X_{\left[\begin{array}{cc}16&15\end{array}\right]^{\prime}}\); that is,  
Figure 4: Compression rate for different format of matrix.  
Figure 3: Scheme of PCA-based coding method. (PCA, Principle Component Analysis).  
\[X_{[\begin{array}{ccc}16&15\end{array}]}=\left[\begin{array}{ccc}a_{1},& \cdots&a_{225}\\ \vdots&\ddots&\vdots\\ a_{16},&\cdots&a_{240}\end{array}\right] \tag{3}\]  
This method has two obvious advantages, which can be find in Figure 5:  
(i) This method takes advantage of the short-time stationary characteristic of signals in the frequency domain. Therefore, the difference between different rows in the same column of the matrix constructed by this sampling method is small. In other words, the difference between the same dimensions of different samples in the matrix is small, and different dimensions have similar linear relationships, which is very conducive to dimensionality reduction.  
(ii) This method allows signal energy to gather still in the low-dimensional region of the new space. The energy of the frequency domain signal is concentrated in the low frequency region; after PCA, the advanced column of reduced-dimensional matrix still has the most signal energy. Thus, after dimensionality reduction, we can still focus on the low-dimensional region.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 3 Results and Discussion
### Impact on Specific Methane Yield and Methane Percentage
#### 3.2.2 Effect of the Inoculum
I-BGP was only significantly higher than I-LRS and I-LRD for substrate S-TGF (1.2%CH4 below average) and S-HAY (0.6%CH4 above average), but in every case lower than for I-WWP7 and I-WWP14. Inocula I-WWP7 and I-WWP14 had the highest methane percentage, as shown in Figure 3D. A statistically significant difference between I-WWP7 and I-WWP14 was only reported for S-HAY. Figure 3: Specific methane yield (**A**) and methane percentage (**C**) of various substrates digested with different inocula in the digestion system D-HBT1 (error bars indicate standard deviation, different letters indicate significant differences within substrates at \(p\leq 0.05\)); rel. specific methane yield (**B**) and rel. methane percentage (**D**) of the different inocula compared to inoculum I-LRS (boxplots show divergence of the average values of the inoculum/substrate-combination); for code of digestion systems, inocula, and substrates see Tables 2, 3 and 6, respectively. For all other substrates I-WWP7 and I-WWP14 performed similarly. Thus, in case of I-WWP7 and I-WWP14, a dependency of the methane percentage on inoculum was shown. One possible reason for this can be the larger diversity of bacteria and a higher abundance of archaea, which results in a shorter hydrolysis time with a low methane percentage at the beginning.
## 4 Results and Analysis
### Research Implications  
This study, integrating ARIMA and stepwise regression models, introduces an innovative method for forest fire prediction, significantly enhancing response accuracy for governments and environmental management agencies. Its findings are crucial in devising forest fire prevention strategies under specific climatic conditions, providing scientific support for forest management and emergency preparedness in Yunnan Province and similar regions. Furthermore, this research contributes to understanding the global impact of climate change on forest fire trends, offering significant insights for the environmental science field and natural disaster management, while also serving as a reference for policy formulation and global forest fire management, aiding in the improvement of management strategies and emergency responses in fire-prone areas.
## 3 Performance Evaluation and Discussion  
### Dataset Specifications  
We use Terrestrial Laser Scanner FARO(r) Focus3D X 330 (Lake Mary, FL, USA) to scan Nanjing metro line S3 from Tiexin bridge to Chunjiang road. The entire scene has five scans with a total length of 127 m and a total number of 200 million points. All the scans are registered into a common reference frame, i.e., coordinate system based on sphere targets. The entire preprocessing procedure relies on FARO processing and registration software SCENE (SCENE 5.5, FARO Technologies Inc., Lake Mary, FL, USA) ([[https://www.faro.com/products/product-design/faro-scene/](https://www.faro.com/products/product-design/faro-scene/)]([https://www.faro.com/products/product-design/faro-scene/](https://www.faro.com/products/product-design/faro-scene/))). The range of FARO(r) X 330 is from 0.6 m to 330 m. We acquire five scans of data where we set the distance between any adjacent scans about 23 m to maintain the point density. The point density, i.e., spacing of point clouds is highly dependent on scan resolution. In the case of the FARO(r) Focus3D X 330 instrument used herein, the scan resolution is expressed as a fraction and the user can choose among 1/1, 1/2, 1/4, 1/5, 1/8, 1/10, 1/16, 1/20 and 1/32, with higher resolution requiring higher scanning time. In our case, we set 1/4, taking 10-15 min for each scan and having 6.136 mm point density at approximately 10 m. In practice, the optimal resolution setting should make a trade-off between the required density points per square meter and the scan duration [25]. To decrease the missing data and data occlusion, we set the field of view zenith angle range from -60\({}^{\circ}\) to 90\({}^{\circ}\) and azimuth angle range from 0\({}^{\circ}\) to 360\({}^{\circ}\). The range error is defined as a systematic measurement error 10 m and 25 m, and one sigma is 0.3 mm under a constraint of a 90% reflective surface. Through an experiment of a fitting flat indoor faccade, we find that the precision of the scanner is around 1.5 mm at approximately 10 m scanning ranges.
## 7 Additional Fundamental Aspects of Chemometric Modelling: Data Preprocessing and Validation
### Data Pre-Processing
The Savitzky-Golay approach, in particular, is worth mentioning in greater detail, as it can also be exploited for signal or spectral differentiation: it fits successive subsets of adjacent variables with a low-order (usually second or third) polynomial and approximates the variable around which each subset is centered with the value resulting from the respective estimated function. Differentiation methodologies make it possible to remove additive (first derivative) and multiplicative (second derivative) effects from the registered profiles and to deconvolve, at least partially, overlapping peaks. However, they exhibit the drawback of significantly decreasing the signal-to-noise ratio. This is why they often require a preliminary smoothing operation--Savitzky-Golay differentiation, for example, couples the smoothing procedure described before to the point-to-point calculation of the derivative of the interpolating polynomial. The impact of light scattering on spectroscopic data, especially in the NIR domain, can also be regarded as a multiplicative effect and corrected by the use of second-order derivation. Alternatively, scatter correction can be achieved by techniques like Standard Normal Variate (SNV) [273], Multiplicative Scatter Correction (MSC) [274] and Extended Multiplicative Signal Correction (EMSC) [275], which enables the simultaneous removal of the effects of scattering, baseline and potential interferents (if their spectroscopic signatures are available). Finally, for the correction of a non-constant baseline, detrending [273] and penalized asymmetric least squares regression [276] represent feasible options. The former corresponds to fitting a polynomial function to all the variables constituting the spectral signal, but in those cases in which the baseline does not account for a large portion of the overall data variance, severe artifacts may be produced. The latter is underlain by a more effective weighted fitting procedure that automatically identifies the variables most likely to be affected by the baseline contribution and weights them more for the calculation of the approximating curve.
## 4 Intraday MPC Rolling Optimization
### Prediction Model  
The prediction model is tasked with forecasting the system's future output values (or states) at each sampling point across a prediction horizon, utilizing the current control input and historical state information. In the context of energy scheduling within this system, the forecasting model is primarily composed of two components: the dynamic transition model for the state variables and the prediction model for the disturbance variables.  
\[\begin{cases}x(t+1)=x(t)+u(t)\\ \omega(t)=\omega_{\text{pre}}(t)\end{cases} \tag{34}\]  
where, \(\omega_{\text{pre}}\) represents the short-term intraday forecast of \(\omega\) at time \(t\), and its value will be provided in Section 5.3.1.  
The prediction model incorporates two key parameters: the prediction horizon and the control horizon. The prediction horizon specifies the future period over which system dynamics are forecast, while the control horizon determines the duration during which control actions are optimized and applied. Due to the differing response speeds of the energy carriers--specifically, the rapid dynamics of the electrical load versus the slower transmission of thermal, cooling, gas, and hydrogen loads--a two-layer optimization control strategy is implemented. The upper layer, which manages heat, cooling, gas, and hydrogen loads, operates on a 1-h timescale with both horizons set to 2 h. The lower layer addresses the electrical load at a finer 5-min timescale, with its horizons configured to 1 h.
## 5 Classification Performed by Machine Learning Algorithms
### Traditional Machine Learning Algorithms
#### 5.4.2 Hyper-Parameters and Training  
Each of the classification algorithms contained a number of hyper-parameters that needed to be tuned prior to their use in the training procedure. For the XGBoost classification algorithm, the number of estimators \(n\) had to be selected. In the case of the SVM, with a radial kernel (the type of a kernel was chosen in the pilot test), it was necessary to tune \(C\) and \(\gamma\) values. For the Logit algorithm (with the L1 regularization chosen to benefit from its inherent feature selection property), the \(C\) value had to be adjusted.  
According to the results of the pilot test, out of the three traditional classification algorithms employed in this study, SVM was particularly sensitive to the number of features selected at its input, indicating the need for a formal feature selection procedure. To this end, the feature selection procedure was implemented, in which the importance of features was ranked according to the \(F\)-statistic derived  
Figure 6: Example learning curves of the convolutional neural network: (**a**) accuracy; (**b**) loss. A vertical blue line denotes the training termination point.  
from the ANOVA test. The number of selected features \(k\) constituted an additional hyper-parameter to be tuned. The above procedure was used only in combination with the SVM classification algorithm.  
A standard grid search technique combined with a 10-fold cross-validation procedure was followed to adjust the hyper-parameters for each of the traditional algorithms prior to their training (such procedure could not be used earlier in the case of the CNN algorithm, since it would be too time-consuming, given the computation load of the CNN algorithm). The values of the hyper-parameters considered for selection in the grid-search algorithm, along with the selected values, are presented in Appendix B (see Tables A1-A3, respectively).  
The three traditional machine learning algorithms were implemented in Python ecosystem, using scikit-learn and NumPy packages. The XGBoost algorithm was accelerated with the help of a graphical processing unit--GPU (NVIDIA RTX 2080 Ti).