esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning
## 4 Results and Discussion  
### Model Construction  
This section presents the model construction and the design of the experiment. After rejecting six outliers, we had 237 samples remaining. During the PSO-BL mode, we first divided the 237 samples into the training data set, the validation data set and the test data set. To improve the stability of the BL model and use the data more efficiently, 10-fold cross-validation was employed here. The total data set was split into 10 parts. Eight parts was applied in the training process of the BL model, and one part was utilized as the validation data to return the fitness value of the given parameters. The remaining part was used as the test data set to evaluate the performance of the PSO-BL model. The program  
Figure 4: Process of the parameter optimization of Broad Learning (BL) with PSO.  
was run 10 times, and each sample was given the opportunity to be used in the hold-out test set. The modelling process of the PSO-BL model is shown in Figure 5.  
To improve the predictive ability of the BL system, in this study, we employed the PSO algorithm to optimize the construction of the BL network. The fitness value of the initial population is shown in Figure 6, where the swarm size is 50. It can be seen from Figure 6 that the fitness value of the initial population was mostly in the range of [0.84 0.96]. The performance was largely dependent on the value of the parameters. As mentioned above, 10-fold cross-validation was employed here. Hence, we repeated the experiment 10 times, and the global optimal fitness values of these 10 iterations are shown in Figure 7. Table 1 demonstrates the process of cross-validation and shows the performance on 10 different test sets. We also calculate the accuracy corresponding to each region (i.e., five countries). As shown in Table 2, the PSO-BL model totally misclassified seven samples and achieved the state-of-the-art performance of 97.05%. We also evaluated the performance of the proposed PSO-BL algorithm without outlier rejection, and the corresponding accuracy was 91.88%, significantly lower than that of the scheme with outlier removal.  
Figure 5: Process of the PSO-BL model construction.  
Figure 6: The fitness value of the initial swarm.  
Furthermore, we compared the proposed PSO-BL method with six traditional machine learning methods to identify the geographical origin of coal samples, including SVM, K-Nearest Neighbour (K-NN) [36], Radial Basis Function Neural Network (RBFNN) [37], the BPNN algorithm, RF and the BL model. Principal components analysis was employed to reduce the dimension and hence to enhance the computational efficiency of the SVM model. Moreover, the PSO algorithm was also used to obtain the optimal two parameters of the SVM model, namely \(c\) and \(gamma\) of the RBF kernel SVM.
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 5 Bandwidth Extension Techniques
### Equalization
A data rate of 20 Gb/s is achieved without using any inductive peaking technique with a relative small area of 0.027 mm\({}^{2}\).
## 4 Experiments and Results
### Evaluation Metrics
#### 4.3.6 Key Space Analysis  
As mentioned in Section 3, the secret keys of our scheme are:  
* The initial condition \(x_{0}\) and the control parameter \(\mu\) of the logistic map.
* The threshold \(T\) used for quantification of the generated pseudo-random sequence.
* The number of iterations \(t\) of the Arnold cat map  
The precision of the cat map initial condition \(x_{0}\) and control parameter \(\mu\) reach \(10^{-15}\). This means for these two first secret parameters, the key space reaches \(10^{30}\) (\(10^{-15}\) for \(x_{0}\) and \(10^{-15}\) for \(\mu\)). This number is almost \(2^{100}\), which is a huge number that makes the key space large enough to resist exhaustive attacks. Add to that the number of iterations of the Arnold cat map, which has a size of \(2^{7}\) and the threshold \(T\), whose precision is set to \(10^{-12}\approx 2^{40}\). This make the key space reach \(2^{147}\). We can say with confidence that brute-force attack is not an option to attack this cryptosystem.
## 3 The Proposed Solution Method Using the PA-Jaya Algorithm
### PA-Jaya for the User Fairness Issue in Cognitive OFDM Radio Network
After the subcarrier allocation procedure, we use the proposed PA-Jaya algorithm to further distribute power. Motivated by the work in Ref. [50], in order to better apply our proposed PA-Jaya algorithm, we designed a special fitness function for this issue, which is  
\[\mathbf{F}=\left\{ \begin{array}{ll}&W_{c}\sum\limits_{n=1}^{N}\sum\limits_{ k=1}^{K}\lambda_{k,n}b_{k,n}&\text{if}\quad\mathbf{I}\geqslant\epsilon\\ &\mathbf{I}-\epsilon&\text{otherwise}\end{array} \right. \tag{17}\]  
Here, \(\epsilon\) is a predefined constant representing the minimum requirement for the fairness indicator. In this function, it is obvious that for those solutions which do not meet the user fairness constraint, the algorithm selects them through the fitness function so that they can gradually move in the direction of better satisfying the user fairness requirement. For those solutions satisfying the requirement,the algorithm helps them acquire more channel capacity, eventually converging to the optimal solution. The introduction of \(\epsilon\) makes it possible to maximize the channel capacity while satisfying the minimum user fairness requirement. Moreover, the specific algorithm structure is similar to the previous one; it is not described in detail here.
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods
### Experimental Setup and Sound Recordings  
At the research center, the Fundacion para el Desarrollo Fruticola (FDF: Foundation for Fruit Development) has several laboratories where research is performed with insects that are a threat to Chilean agriculture. They have the facilities for breeding _Lobesia botrana_ in its different growth states. All the tracks of the sound emission of the _Lobesia botrana_ were recorded at the FDF. A schematic for the experimental setup used for recording the _Lobesia botrana_ flight is shown in Figure 1. A special device was constructed for the recording of the sound (see Figure 2) using a 3D printer. The device held the plastic container used at the FDF to carry the insects. The container was surrounded by acoustic absorbing foam to reduce the noise from external sources. It held a wideband microphone, which could record frequencies from 100 Hz to 125 kHz (in Figure 2, the microphone is the upper vertical tube). The wideband microphone was chosen to record the acoustic response of the flying moth without knowing beforehand where this signal would appear on the spectrum. The container also had bright LEDs to illuminate the inside for pictures and video recordings of the moths. With it, the sound events could be associated with the flight of the moths. For this experiment, the sound of the flying _Lobesia botrana_ was recorded using Audacity [39], which is free audio recorder software, running on a laptop.  
Figure 1: Schematic of the experimental setup used for the acoustic characterization of the flying moth.  
Figure 2: (**a**) Picture of the moth container with the acoustic absorbers and the wideband microphone and (**b**) a picture of the container (without microphone) where several moths are visible. A moistened piece of cotton wool was used so that the moths could drink is also shown.  
Figure 3 shows examples of the signals obtained using our acquisition setup. Figure 3a shows the signal obtained from the container without moths inside and Figure 3b shows the acoustic signal obtained from the moths. From the obtained signals, it was possible to observe that the sound of the moths was impulsive, showing peaks at different instants of time. To characterize signals with registers of the impulsive type, the Welch PSD tool is commonly used to calculate the power spectral density (PSD). As our temporal signal registers had different durations, the Welch PSD allowed our method to work better than other methods for averaging the periodograms calculated from the Fourier transform (FFT). In this particular case, the Welch PSD was implemented using Matlab R016a. By analyzing the variations that existed in different frequency ranges resulting from the moth acoustic signals (time signals), it was possible to design a filter to characterize the sound.
## 4 Simulation Analysis of Overburden Rock under Different Mining Intensity of Fracture
### Development Height of Hydraulic Fracture Zone of Overburden Rock under Different Mining Height  
The mining height is an important factor affecting the height of the hydraulic conductivity fracture zone, which directly influences the development height of the hydraulic conductivity fracture zone. Under the condition of keeping the thickness, burial depth, and slope length of loose layer unchanged, the height of the water-conducting rift zone development in overlying rock is studied by simulating different mining heights (2, 3, 4, 5, 6, 7, 8 and 9 m), as shown in Figure 10.  
As the mining height increases, the more space is given for the overburden to collapse. When the mining height is 2 m\(\sim\)3 m, the development height of the water-conducting fissure zone is 23-36 m, which is 12 times the thickness of the mining area, the direct top collapses, and the overburden around the collapsed area will be affected by tensile stress. At this time, a vertical fissure also appears; it slowly increases until there is an arch-shaped water-conducting fissure circle.  
When the mining height is 4-5 m, the development height of hydraulic fracture zone is 42-58 m, which is 11 times the thickness of the mining area, the hydraulic fracture zone has been developing upward, and the off-layer fracture and longitudinal fracture are also increasing gradually. The off-layer fracture and amount are more significant when the mining height is 4-5 m than when the mining height is 2-3 m. When the mining height is 6-7 m, compared with the mining height of 2-5 m, the scope of a direct top collapse is  
Figure 10: Height of water flowing fractured zone under different mining height.  
bigger, the overlying rock layer is affected by mining to expand the scope, and fissures start to appear in the higher rock layer. At this time, the hydraulic fissure zone has developed a height of about 90 m. When the mining height is 8-9 m, after reaching the full mining, two aspects will appear: the maximum, with the height of the hydraulic fissure zone development and the other is the amount of surface subsidence. The final development height of hydraulic fissure zone is 163 m when the mining height is 8 \(\sim\)10 m. Through the above analysis, we can find that the development height of hydraulic fissure zone increases linearly with the increase of mining height. Therefore, as an important factor affecting the development height of hydraulic fissure zone, the mining height has an important influence on the safety of coal mining.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 2 Materials and Methods
### LiDAR Data
#### 2.3.2 The Calculated Leaf Area Indices of Individual Trees  
To analyze the impact of the background, the LAI of individual trees was selected as the parameter for evaluating the relationship between SDR and CST, which was calculated using the point clouds from the LiDAR data. The point clouds of individual trees were extracted from the segmentation results using a circumcircle of the crown and divided into canopy and ground parts. Then, the porosity of the canopy was calculated on the basis of the principle that a specific ground point was the reflection point at which the laser penetrated the canopy and hit the ground. Finally, the LAI of an individual tree was calculated according to the exponential relationship between the probability of finding canopy \(P\) and LAI in the random Beer-Lambert law of discontinuous canopy proposed by [PERSON] [38]:  
\[P=cloud_{tree}/cloud_{total}, \tag{6}\]  
\[LAI=-k*\ln(1-P), \tag{7}\]  
where \(cloud_{true}\) is the point clouds of canopy, \(cloud_{total}\) is the total point cloud of the circumcircle, and k is the empirical coefficient that describes the relevance between the LAI and the porosity of the tree crown (typically, \(k=0.5\) for a conifer forest).
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### The Key Hardware Components for Physical Testbed
#### 3.1.1 Texas Instruments CC2538 Sensor Nodes  
Texas Instruments CC2538 (TI CC2538) wireless transceiver chips consist of on-chip temperature and received signal strength indicator (RSSI) sensors to collect and transmit real-world data. It is an advanced chip with IP configurability. The CC2538 EM is used in conjunction with the SmartRF 06 evaluation board, as shown in Figure 4, for programming purposes, consisting of two on-board sensors for the accelerometer and ambient light sensor. These sensing features, together with the flexibility to configure the TI boards with the same code used for the Cooja simulation motes (i.e., the virtual platform), offer advantages to a variety of use cases wherein network performance can be tested prior to testbed implementation.  
These sensor nodes can be configured with either sensing, routing, or/and gateway functionalities through the Contiki IDE. The C code for hardware configuration \"include\" the related libraries that enable these chips to be configured, such as \"finclude 'cpu.h\"\", \"#include 'dev/uart.h\"\", \"#include 'dev/adc-sensor.h\", and \"#include 'dev/sys-ctrl.h\"\". Each physical node needs to be assigned a unique ID by accessing the \"contiki-conf.h\" header file within the Contiki; for example, a coordinator node can have an ID of \"0 xDA\". A channel can also be assigned to the nodes for the purpose of regulating traffic within the shared communication medium by reading the same header file and assigning one of the available and desired channels to the nodes. As an example, channel 25 can be assigned as \"#define CC2538_RF_CONF_CHANNEL 25\".  
Figure 4: SmartRF06 evaluation board.  
From the perspective of mobility, a scenario where one of the nodes is converted into a mobile node by plugging a micro-USB charger into the chip to provide the mobility for conducting outdoor experiments can be used to investigate the influence of RSSI on the physical network. The TI CC2538 evaluation module (EM) with micro-USB is shown in Figure 5.
## 3 Flux-corrected transport method
This spacing between grid points, termed \(\ell_{\text{FCT}}\), is not to be meant as the distance between neighboring nodes, but instead an algorithmic parameter to be tuned. The velocity at each point of this grid is obtained from the nodal ones through interpolation, using the shape functions gathered in \(\mathbf{\Phi}\) (see Equation 12). A proof of the conservation of local momenta, or error estimators for the conservation laws are beyond the goals of this paper, and are left for future investigations. To deal with compliant interfaces, whose behavior is described by constitutive laws like (10), a specific treatment of phase boundaries is needed. Here we propose the following rationale: during the FCT  
Figure 3: Sketch of the shape of nodal supports, either when they are wholly included in \(\Omega\) or when they intersect the boundary of any phase. correction step, each phase is assumed to be an independent body. Therefore, if a local support intersects either the outer boundary \(\Gamma_{u}\cup\Gamma_{\tau}\) or the interface \(\Gamma\), it is re-shaped according to what sketched in \[\varphi_{D}^{j+1/2}= \eta_{D}\left(\dot{u}_{i}^{j+1}-\dot{u}_{i}^{j}\right)\]0 Since in Equations (25)-(32) the \((j-2)-\)th, \((j-1)-\)th, \((j+1)-\)th and \((j+2)-\)th terms are dropped when the relevant points (or nodes in the standard formulation) fall outside \(\Omega\), the algorithm is enhanced by adopting the same criterion if grid points fall outside the phase the \(j-\)th node belongs to. If this procedure is not followed, the FCT correction step can not feel interfaces endowed with their own constitutive laws. Forthcoming results will show that the two enhancements here proposed permit to increase much the  
\[\varphi_{D}^{j+1/2}= \eta_{D}\left(\dot{u}_{i}^{j+1}-\dot{u}_{i}^{j}\right)\]1 Longitudinal wave propagation in a two-phase rod: sketch of geometry and loading condition. \[\varphi_{D}^{j+1/2}= \eta_{D}\left(\dot{u}_{i}^{j+1}-\dot{u}_{i}^{j}\right)\]2 Wave propagating along a homogeneous rod discretized with a structured mesh: stress field at (a) \(t=0.5\cdot 10^{-2}\) s and at (b) \(t=1.8\cdot 10^{-2}\) s. Blue solid line: FE solution; black solid line: standard FE-FCT solution; orange dashed line: newly proposed FE-FCT solution. accuracy of FE results, specially close to stress wave fronts, by preventing macroscopic fluctuations to show up in the velocity and stress fields, even when unstructured space discretizations are adopted.
## 5 Results
### Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode** & **Thresh.** & **Hours** & **Precis.** & **Recall** & **Accur.** & **F2** \\ \hline \multirow{2}{*}{Multi 5\%} & \multirow{2}{*}{0.25} & First 3 & **0.556** & **0.663** & **0.460** & **0.639** \\ \cline{3-6}  & & Last 3 & 0.682 & 1.000 & 0.693 & 0.915 \\ \cline{3-6}  & \multirow{2}{*}{0.5} & First 3 & **0.512** & **0.337** & **0.387** & **0.362** \\ \cline{3-6}  & & Last 3 & 0.589 & 0.619 & 0.467 & 0.613 \\ \cline{3-6}  & \multirow{2}{*}{1} & First 3 & **0.000** & **0.377** & \\ \cline{3-6}  & & Last 3 & 0.580 & 0.386 & 0.413 & 0.413 \\ \hline \multirow{2}{*}{Multi 10\%} & \multirow{2}{*}{0.25} & First 3 & **0.495** & **0.535** & **0.377** & **0.527** \\ \cline{3-6}  & & Last 3 & 0.663 & 1.000 & 0.663 & 0.908 \\ \cline{3-6}  & \multirow{2}{*}{0.5} & First 3 & **0.370** & **0.200** & **0.297** & **0.220** \\ \cline{3-6}  & & Last 3 & 0.555 & 0.558 & 0.410 & 0.557 \\ \cline{3-6}  & \multirow{2}{*}{1} & First 3 & **0.000** & **0.383** & \\ \cline{3-6}  & & Last 3 & 0.463 & 0.251 & 0.310 & 0.277 \\ \hline \multirow{2}{*}{Multi 25\%} & \multirow{2}{*}{0.25} & First 3 & **0.675** & **0.608** & **0.493** & **0.620** \\ \cline{3-6}  & & Last 3 & 0.660 & 1.000 & 0.660 & 0.907 \\ \cline{3-6}  & \multirow{2}{*}{0.5} & First 3 & **0.530** & **0.239** & **0.280** & **0.268** \\ \cline{3-6}  & & Last 3 & 0.575 & 0.581 & 0.440 & 0.580 \\ \cline{3-6}  & \multirow{2}{*}{1} & First 3 & **1.000** & **0.018** & **0.273** & **0.022** \\ \cline{3-6}  & & Last 3 & 0.481 & 0.253 & 0.327 & 0.279 \\ \hline \multirow{2}{*}{Rand. 5\%} & \multirow{2}{*}{0.25} & First 3 & **0.605** & **0.733** & **0.590** & **0.703** \\ \cline{3-6}  & & Last 3 & 0.543 & 1.000 & 0.543 & 0.856 \\ \cline{3-6}  & \multirow{2}{*}{0.5} & First 3 & **0.610** & **0.370** & **0.523** & **0.401** \\ \cline{3-6}  & & Last 3 & 0.560 & 0.687 & 0.537 & 0.657 \\ \cline{3-6}  & \multirow{2}{*}{1} & First 3 & **1.000** & **0.006** & **0.453** & **0.008** \\ \cline{3-6}  & & Last 3 & 0.631 & 0.399 & 0.547 & 0.430 \\ \hline \multirow{2}{*}{Rand. 10\%} & \multirow{2}{*}{0.25} & First 3 & **0.540** & **0.647** & **0.497** & **0.622** \\ \cline{3-6}  & & Last 3 & 0.513 & 1.000 & 0.513 & 0.841 \\ \cline{3-6}  & \multirow{2}{*}{0.5} & First 3 & **0.600** & **0.359** & **0.510** & **0.391** \\ \cline{3-6}  & & Last 3 & 0.530 & 0.688 & 0.527 & 0.650 \\ \cline{3-6}  & \multirow{2}{*}{1} & First 3 & **0.000** & **0.443** & \\ \cline{3-6}  & & Last 3 & 0.491 & 0.344 & 0.480 & 0.366 \\ \hline \multirow{2}{*}{Rand. 25\%} & \multirow{2}{*}{0.25} & First 3 & **0.545** & **0.686** & **0.530** & **0.652** \\ \cline{3-6}  & & Last 3 & 0.557 & 1.000 & 0.557 & 0.863 \\ \cline{3-6}  & \multirow{2}{*}{0.5} & First 3 & **0.530** & **0.333** & **0.490** & **0.360** \\ \cline{3-6}  & & Last 3 & 0.560 & 0.671 & 0.523 & 0.645 \\ \hline \multirow{2}{*}{1} & First 3 & **1.000** & **0.013** & **0.477** & **0.016** \\ \cline{3-6}  & \multirow{2}{*}{1} & Last 3 & 0.600 & 0.377 & 0.513 & 0.408 \\ \hline \end{tabular}
\end{table} 
Table 1: Cont.
## 3 Measurement Equipment  
Conducting measurements of NB and UWB in the real environment required the development of heterogeneous nodes adapted to work around the human body. The proposed measurement equipment consists of three types of devices: mobile node (MN), stationary R,N and data acquisition server (DAS).  
The MN and the RN have similar hardware design. The only difference, from the electrical diagram point of view, is the existence of the RS-232 wired interface in RN. In Figure 1, a block diagram of the MN is presented.  
Each MN and RN is heterogeneous, in the sense that it has two simultaneously working, time synchronized radio interfaces:  
* NB radio module CC1120 from Texas Instruments, working in the ultra-high frequency (UHF) 868 MHz band [17],
* UWB radio module DWM1000 from DecaWave, working in the super-high frequency (SHF) 6489 MHz band [18].  
Both radio interfaces work in accordance with the IEEE 802.15.4 standard, including the operating frequencies in the Wireless Medical Telemetry Service (WMTS) and UWB bands for Europe, which is indicated as possible for use in WBANs [19].  
Figure 1: A block diagram of the MN.  
The selected parameters of the 868 MHz module should be mentioned: center frequency 868.3 MHz, 5 dBm of output power, 2-GFSK (Gaussian frequency shift keying) modulation with frequency deviation of 50 kHz, 50 kbps bitrate, 67 B packet data length with data whitening included. The antenna for the NB interface is Yageo's compact ceramic antenna ANT1204F007R0870A [20], with 1.67 dBi gain, resonance frequency equal to 870 MHz, and reflection loss \(-\)28 dB. The choice of antenna was motivated by its physical dimension and the possibility of using it in miniaturized devices.  
The selected UWB parameters for the measurements are as follows: 6489 MHz center frequency, \(-\)41.3 dBm/MHz transmitted power spectral density and 499.2 MHz of the occupied bandwidth. The ACS5200 HFAUWB ceramic omnidirectional antenna element for the UWB interface is integrated in the DW1000 module [21].  
The DAS includes a GETAC F110 industrial tablet [22], together with a dedicated DAS server application. The custom developed dedicated software is a multi-threaded application, which allows for continuous monitoring of the obtained telemetry data and the status of each network node.
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
## 3 Forward Estimation and Online Optimizing
### Lazy Theta*  
Different from Theta*, Lazy Theta* only performs LoS-Check on the current vertex that is waiting to be extended, but not every newly-generated vertex. Because many generated vertices will have no chance of being extended, there is no use to perform LoS-Check on them. Lazy Theta* delays the LoS-Check until the vertex is to be extended, which will reduce the check amount, speeding up the planning process, while continuing to find the close-to-shortest path. As Figure 2 (right) shows, there are only two times of LoS-Checks in the extension process of Lazy Theta*, and they are performed on the vertices B3 and A4 with their grandparent vertices. Activate the code in Line 5 of Algorithm 1, and the algorithm will become Lazy Theta*.
## 2 Optimization Methodology  
### Contour Tracking Control  
In this section, we describe the parameters and the concept of controlling the position domain controller in order to control contour tracking with the UR5 manipulator robot. In this paper, we use the position domain controller because gain tuning is very important in order to obtain good performances for a given controller. Contour tracking performance is mainly determined by the selected control gains of a position domain PID controller.