esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 4 Dataset Construction
### Experimental Data Acquisition Setup  
To acquire the acceleration signals used for training and testing the proposed DNN and the overall hybrid model, a P ZERO racing tire of size 325/680-18 with a hardness rating of 71 Shore A according to the ASTM D2240 measurement standard was utilized. As depicted in Figure 4, the tire was mounted on a B1008 HST model high-speed testing machine produced by Tianjin Jiurong, facilitating the collection of necessary data. During all experimental data collection operations, the camber and slip angles were set to zero, and the test cabin temperature was maintained between 22 and 25 \({}^{\circ}\)C.  
The test machine is equipped with measurement systems that can assess vertical load, rotational speed, tire internal pressure, cabin temperature, and camber angle values. It is primarily utilized to obtain and test the wear characteristics and durability periods of tires when rotated under various loads, speeds, and camber angles. Additionally, the driving characteristics of racetracks are prescribed to the machine, facilitating the testing of tires under these specific conditions. A MEAS 234M1-2000 tri-axial acceleration sensor, capable of measuring up to 2000 g, was placed inside the tire's inner layer. The sensor was connected to the external environment through cables for power supply and acceleration data transmission.  
The slip ring shown in Figure 4 was used to transfer the vertical acceleration acting on the tire, converted into an analog voltage by the accelerometer, to the data acquisition card shown in Figure 5, and subsequently to the data acquisition PC. The acceleration sensor, as shown in Figure 4, was attached by adhering it to the inner surface of the tire.  
The acceleration sensor is powered through a regulated power supply at a supply voltage of 3.3 volts. The acceleration data, representing vertical force, are then transferred to a PC via the NI USB-6346 data acquisition hardware and NI DAQ Express software 5.0. The sampling frequency used was 10 kHz.  
The tire's internal pressure was adjusted and stabilized according to pressure set values using a calibrated air pump, and the tire's rotation speed and the force acting on the tire were controlled via the high-speed machine's monitoring and control screen, shown in Figure 5. The tire's rotational movement was provided by the wheel, as shown in Figure 5, and the reference loads applied to the tire were delivered by the hydraulic-driven load applicator, also depicted in Figure 5.
## 6 Conclusions  
A computational method was developed in this study to numerically predict the effective elastic modulus of porous materials taking account of the influence of practical pores' size, shape and distribution mode, and its validity was further demonstrated with both the analytical extreme bounds of the effective modulus of a simplified porous model and the experimental data in the open literature. To be noted, the computational method developed in this work can be easily applied to predict the overall elastic modulus of engineering porous materials by selecting a proper pore model and identifying the relevant model parameters according to the experimental observations and statistic analysis of the pore characteristics. To be noted, though modeling the irregular pores of extremely complex configurations, see e.g., [15; 16], are not discussed in this study, our element-based selection method can be easily extended to generate this kind of porous microstructures by combining the level set method, which will be conducted in our future work.  
Figure 15: Normalized effective elastic moduli of porous tantalum involving different distribution modes of pores.  
It was found that the overall elastic behavior of porous materials is largely influenced by the pore shape, the pore distribution mode and the porosity. Hence, different measures can be taken to better serve the designing process of the porous materials in actual applications.  
conceptualization, H.L., S.D. and J.L.; data curation, H.L. and M.W.; formal analysis, Z.Z.; funding acquisition, J.L.; Methodology, H.L. and J.L.; project administration, J.L.; software, H.L. and Y.Y.; supervision, J.L.; validation, S.D. and M.W.; visualization, Z.Z.; writing--original draft, S.D.; writing--review and editing, H.L. and J.L.  
This work was funded by the National Key R & D Program of China (grant number 2017 YFB0703200), the National Natural Science Foundation of China (grant number 51972268, 51305362, 11672099, and 51720105005), the Young Scientific Innovation Team of Science and Technology of Sichuan (grant number 2017 TD0017), and the Fundamental Research Funds for the Central Universities (grant number 2682016 CX024).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
FEM Finite element method  
RVE Representative volume element  
CRS Completely random selection  
POS Partially overlapping selection  
IS Isolated selection
# Near-Surface Material Phases and Microstructure of Scandate Cathodes
## 2 Materials and Methods  
The scandate cathodes characterized in this paper were prepared with W precursor powder mixed with nanoscale scandia, and this powder mixture had been prepared using the liquid-solid (L-S) technique. Detailed procedures of the L-S approach can be found in an earlier paper by one of the authors [1]. The L-S process utilizes an aqueous nitrate solution to dissolve scandia, and then solid tungsten powder is added as a substrate for formation of scandia nanoparticles. The nitrate solution may etch or roughen the surfaces of tungsten particles. Multiple scandate cathodes were provided by e beam inc., where emission testing was also performed. Fabrication included the following steps: for each cathode, the W-scandia powder mixture was pressed into a pellet \(\sim\)2 mm diameter and \(\sim\)1 mm tall; then the pellet was sintered and subsequently impregnated with barium calcium aluminate (6 BaO-1 CaO-2 Al\({}_{2}\)O\({}_{3}\)); afterwards, the sample was washed with deionized water to remove residual impregnate material; finally, the cathodes were activated by heating at 1150 \({}^{\circ}\)C\({}_{\text{b}}\) for 1 h. Prior to microstructural characterization, two cathodes (designated cathode #1 and cathode #2) were emission tested in a close-spaced diode (CSD) configuration. These scandate cathodes exhibited excellent emission capabilities as indicated by knee temperatures of 820 \({}^{\circ}\)C\({}_{\text{b}}\) and 837 \({}^{\circ}\)C\({}_{\text{b}}\) (brightness temperature, with respect to W) at the end of extensive emission testing: 3024 and 9840 h, respectively.  
Morphological characterization and location-specific chemical analysis of scandate cathode surfaces was performed in a dual-beam focused ion beam and scanning electron microscope (FIB-SEM, FEI Helios NanoLab 660, Thermo Fisher Scientific, Hillsboro, OR, USA) equipped with an X-ray energy dispersive spectrometer (EDS; Oxford X-Max 80 mm\({}^{2}\) detector, Oxford Instruments, Abingdon, UK). Two lift-out lamellae were extracted from cathode #1 using the FIB and were examined in a transmission electron microscope (TEM; FEI Talos F200X (Thermo Fisher Scientific, Hillsboro, OR, USA) and JEOL JEM-F200 microscopes (JEOL, Tokyo, Japan) were used in this study). Along with scanning transmission electron microscope (STEM) imaging for structural characterization and scanning nanobeam electron diffraction for crystal structure determination, high-resolution elemental mapping was performed on the TEM cross-section lamella using the Super-X EDS system (which includes 4 confocal EDS detectors that simultaneously collect X-rays from the same sample location) in the FEI Talos F200X TEM. Serial sectioning for 3D reconstruction was performed on cathode #2 using the AutoSlice and View technique on the FIB-SEM. Back scattered electron (BSE) micrographs were collected in automated mode by the FIB-SEM, and these were subsequently used for 3D reconstruction of the W crystal shape using Avizo visualization software (Avizo 9.3).
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
## 2 FSBDG Structure of NLECSLs
### Littrow Structure of NLECSLs  
The Littrow structure of NLECSLs is shown in Figure 1. The output light of the Littrow ECSL is collimated by the lens group to obtain the horizontal parallel light, which is incident to the FSBDG external cavity for optical feedback. After FSBDG splitting, the first-order diffraction is fed back to the active region of the laser, and the light field in the active region interacts with each other, resulting in the gain difference between the longitudinal modes and the gain is larger. The longitudinal mode excitation satisfying the laser excitation condition is excited, and the mode with the small gain is lost. By changing the wavelength of the FSBDG external cavity feedback light, the laser output with different wavelengths can be obtained, so as to realize wavelength tuning [11].  
In 2016, [PERSON] et al. [12] used the SAF gain chip and FSBDG Littrow structure of ECSL and realized the maximum injection current was 195 mA, the maximum output power was 83 mW, the Lorentz linewidth was 4.2 kHz, and the Gaussian linewidth was 22 kHz.  
In 2018, [PERSON] et al. [13] used a commercially available high-power green LD as a gain device and the influence of FSBDG parameters on the performance of external cavity laser was studied. For the Littrow structure of ECSL with the first-order diffraction beam as the feedback and the zero-order diffraction beam as the coupling output, the tuning bandwidth was 11.0 nm and the output power was close to 400 mW.  
In 2019, [PERSON], et al. [14] built an ECSL with Littrow structure using a 1200 groove/mm FSBDG with 91% first-order diffraction efficiency as an external cavity. The maximum SMSR could reach 65 dB and the tunable range could reach 209.9 nm.  
In 2021, [PERSON] et al. [15] reported an ECSL based on an improved Littrow structure, by placing a piezoelectric transducer behind the 780 nm diode laser; the wavelength adjustment by rotating the FSBDG was separated from the fine adjustment of the external cavity. The free spectral range was 3.6 GHz, the SMSR reached 48 dB, and the Lorentz linewidth was 540 kHz. Table 1 shows the performances of the Littrow structure of NLECSLs. Littrows structure of NLECSLs are widely used in many applications, due to its advantages of simple structure and convenient operation. However, the direction of output beam will rotate in tuning. This shortcoming must be overcome for many applications.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 3 Target Tracking with IMM Filtering  
### System Modeling  
The dynamic state of the target is modeled as a nearly constant velocity (NCV) model; the targets' maneuvering is modeled by the uncertainty of the process noise, which is assumed to follow the Gaussian distribution. The following is the discrete state equation of target \(t\):  
\[\mathbf{x}_{t}(k)=F(\Delta)\mathbf{x}_{t}(k-1)+q(\Delta)\mathbf{v}(k-1),\ t=1, \ldots,n(k), \tag{5}\]  
where \(\mathbf{x}_{t}(k)\) is the state vector of target \(t\) at frame \(k\), which is composed of positions and velocities in \(x\) and \(y\) directions as \(\mathbf{x}_{t}(k)=\left[x_{t}(k)\ \dot{x}_{t}(k)\ y_{t}(k)\ \dot{y}_{t}(k)\right]^{T}\), \(T\) denotes matrix transpose, \(\Delta\) is the sampling time, \(\mathbf{v}(k)\) is a process noise vector composed of Gaussian white noise in \(x\) and \(y\) directions as \(\mathbf{v}(k)=\left[v_{x}(k)\ v_{y}(k)\right]^{T}\), \(n(k)\) is the number of targets at frame \(k\), and \(F(\Delta)\) and \(q(\Delta)\) are the transition and the noise gain matrix, respectively. They are defined as:  
\[F(\Delta)=\left[\begin{array}{ccc}1&\Delta&0&0\\ 0&1&0&0\\ 0&0&1&\Delta\\ 0&0&0&1\end{array}\right], \tag{6}\]  
\[q(\Delta)=\left[\begin{array}{ccc}\Delta^{2}/2&0\\ \Delta&0\\ 0&\Delta^{2}/2\\ 0&\Delta\end{array}\right]. \tag{7}\]  
The filter modes of the IMM filter is set up with different covariance matrices of \(\mathbf{v}\) as \(Q_{j}=\mathit{diag}\left(\left[\sigma_{jx}^{2}\ \ \sigma_{jy}^{2}\right]\right)\ j=1, \ldots,M\), where \(M\) is the number of filter modes. The following is the measurement equation of target \(t\):  
\[\mathbf{z}_{t}(k)=H\ \mathbf{x}_{t}(k)+\mathbf{w}(k), \tag{8}\]  
where \(\mathbf{z}_{t}(k)\) is the measurement vector of target \(t\) which is composed of positions in \(x\) and \(y\) directions as \(\mathbf{z}_{t}(k)=\left[z_{tx}(k)\ z_{ty}(k)\right]^{T}\), \(\mathbf{w}(k)\) is a measurement noise vector composed of Gaussian white noise in \(x\) and \(y\) directions as \(\mathbf{w}(k)=\left[w_{x}(k)\ w_{y}(k)\right]^{T}\). It is assumed that the covariance matrix of \(\mathbf{w}(k)\) is \(R=\mathit{diag}\left(\left[r_{x}^{2}\ r_{y}^{2}\right]\right)\), and \(H\) is the measurement matrix, defined as:  
\[H\ =\ \left[\begin{array}{ccc}1&0&0&0\\ 0&0&1&0\end{array}\right]. \tag{9}\]
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 2 Methods
### Point Cloud Data Preprocessing
#### 2.2.1 Pass-through Filter  
The point cloud filter can eliminate some noisy points in the scene to improve the efficiency of subsequent point cloud registration. The pass-through filter, as one of the most common point cloud filters, reduces noise by eliminating values in a dimension that are not within a set threshold.  
In this paper, the pass-through filter was used to eliminate the noise points on the \(Z\)-axis, and the point cloud data in this paper cover a range of \([-851,\,-715]\) on the \(Z\)-axis. After several tests, the point cloud filter threshold was set to \([-790,\,-736]\). The data points outside the range were treated as noise and removed from it, and the obtained results are shown in Figure 5.  
Figure 5: Results of point cloud pass-through filter.  
The number of points in the point cloud before noise removal was 157,551, and the number of points in the point cloud after noise removal was 153,265. After evaluation through the pass-through filter to reduce noise, the shape and position of the point cloud data do not change.
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 2 Spatiotemporal Fusion Approaches
### Starfm  
If ground cover type and system errors at pixel \((x,y)\) have not changed between the observation date \(t_{k}\) and the prediction date \(t_{p}\), where a WV scene is not available, then the temporal changes of surface reflectance from WV should be equivalent to the temporal changes in the corresponding Planet pixel between two dates. If the bias between WV and Planet stays the same for two dates (\(\varepsilon_{p}=\varepsilon_{k}\)), we have  
\[W(x,y,t_{p})=W(x,y,t_{k})+(P(x,y,t_{p})-P(x,y,t_{k})) \tag{1}\]  
where \(W,P\) denote pixels in WV and Planet images, respectively.  
To address mixed pixels in the prediction, STARFM [3; 7] introduces additional information from neighboring pixels and uses spectrally similar pixels for prediction. That is, the predicted surface reflectance for the central pixel at date \(t_{p}\) is computed with a weighting function from spectrally similar neighboring pixels within a specified search window:  
\[W(w/2,w/2,t_{p})=\sum\limits_{k=1}^{M}\sum\limits_{i=1}^{N}\alpha_{ik}\times \left(W(x,y,t_{k})+(P(x,y,t_{p})-P(x,y,t_{k}))\right) \tag{2}\]  
where \(w\) is the searching window size and \((w/2,w/2)\) is the central pixel of this moving window. The size of the searching window was determined as three coarse resolution pixels. In this paper, the size of the searching window is defined as 12 m (\(\pm 6\) m searching distance), which covers about three Planet pixels. \(N\) represents the total number of similar Planet pixels in the moving window and \(i\) is the index. \(M\) represents the total number of pairs of observed Planet and WV images. The weighting function \(\alpha_{ik}\) determines how much each neighboring pixel, \(i\), contributes to the estimated reflectance of the central pixel from image pair \(k\). Mathematically the weighting \(\alpha_{ik}\) is given by  
\[\begin{split}\alpha_{ik}=(1.0/C_{ik})/\sum\limits_{i=1}^{N}\left( 1.0/C_{ik}\right)\\ C_{ik}=\left|P(x,y,t_{k})-P(x,y,t_{p})\right|*\left|P(x,y,t_{k} )-W(x,y,t_{k})\right|*D_{i}\end{split} \tag{3}\]  
where \(D_{i}\) is the distance between candidate pixel (\(i\)) at location (\(x\),\(y\)) and the central pixel of the moving window at location (\(w/2\), \(w/2\)). It is determined by three measures based on: (1) spectral difference between the Planet and WV data at a given location; (2) temporal difference between the input Planet data (\(P(x,y,t_{k})\)) and the Planet data at the prediction date (\(P(x,y,t_{p})\)); and (3) the geographic distance between the central pixel and the candidate pixel. Closer pixels are weighted more. Pixels with smaller spectral and temporal differences also receive higher weight. Weights for all spectrally similar pixels are normalized (so the sum of weights equals one) before applying to Equation (2). These measures ensure that pure and close neighbor pixels get higher weights in the prediction.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 4 Discussion  
\(R_{mod}\) was designed to maintain several key features. First, \(R_{mod}\) can be generated for arbitrary samples in any dataset given the appropriate inputs. Second, \(R_{mod}\) is designed to maintain traceability from component error sources with reasonable physical assumptions. Third, \(R_{mod}\) is designed to be tuned to allow for calibration and validation as more data becomes available. Finally, \(R_{mod}\) is intended to be implementable in a dynamic error model without significant computational expense.
## 6 Computational Experiments
### Numerical Results  
Table 3 shows the values of both objective functions: \(F_{1}\) (Cost of used trucks per destination) and \(F_{2}\) (Energy cost consumption). The first four columns present the parameters of the generated instances. The next three columns show the results obtained after solving the mathematical model using Lexicographic Goal Programming in CPLEX (\(F_{1}\) and then \(F_{2}\)). The computational times are presented in the seventh column. CPLEX was able to determine the optimal solution for 23 instances. In the last four instances, CPLEX exceeds the time limit of 3600s without providing any feasible solution. The remaining of the columns shows the results of the two meta-heuristics (MO-VNSSA and MO-VNSTS) which provide optimal results for the first objective \(F_{1}\) and near optimal values for the second objective \(F_{2}\) within fast computational times compared to the ones of CPLEX. The optimal values are presented in bold. In order to evaluate more the performance of the two meta-heuristics, several large instances are generated. Parameters are modified for those large sized instances (Max_VNS = 1500, \(\Delta t\) = 0.002 and Max_TS = 500). The obtained results are presented in Table 4, and illustrated in Figure 4.  
As it can be seen in Figure 5, the PI-conveyors' energy consumption, which represented by objective function \(F_{2}\), increases significantly with the number of PI-containers.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Data** & **Abbreviations** & **Values** \\ \hline Number of PI-containers & \(N\) & [4; 12] \\ Number of destinations & \(D\) & [1; 3] \\ Number of docks & \(K\) & 15 \\ Lengths of PI-containers & \(L_{i}\) & [1, 2, 3, 4, 5, 10] \\ Cost of energy consumption for one PI-conveyor & \(C^{E}\) & 0.5 \\ Cost of using a truck for each destination & \(C^{T}_{d}\) & [200, 800] \\ Positions of PI-containers in the train & \(P_{i}\) & [1; 75] \\ Destination of PI-containers in the train & \(G_{di}\) & Random (Binary) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of the parameters and the input data for the small instances.  
Figure 4: Objective functions for MO-VNSSA and MO-VNSTS for large instances.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Algorithm Parameters** & **Abbreviations** & **Values** \\ \hline Number of iterations for each neighborhood & Max\_VNS & 500 \\ Number of generated neighborhoods & Nb\_N & 3 \\ Number of iterations before shaking PI-containers’ assignments in trucks & Max\_Shake & 10 \\ Simulated Annealing temperature decreasing step & \(\Delta t\) & 0.01 \\ Initial temperature in the Simulated Annealing & T\_Max & 1 \\ Tabu List Size & TL\_Size & 10 \\ Number of iterations for the Tabu Search process & Max\_TS & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of parameters in the meta-heuristics: MO-VNSSA and MO-VNSTS for small instances.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Industrial Applications
#### 2.5.1 Logistics  
The domain of logistics is an area of interest where WSNs can be applied, because many logistics systems need real time monitoring of various environmental parameters and better handling of packages. These requirements can be fulfilled by combining the logistics systems with WSNs. Some types of these applications are described below.  
The transport logistics sector requires low cost and high-quality during deliveries. In [36], the development and deployment of a WSN based system for monitoring transportation conditions, such as temperature and humidity, within a cargo container travelling via both a trans-Atlantic cargo vessel and a lorry is described. The main idea for the deployment of the monitoring system is depicted in Figure 15. It is shown that the use of a system of this kind, can increase quality by providing better supervision and lower the cost by reducing losses during transportation.  
The application of WSNs in Cold Chain Logistics (a continuous temperature-controlled supply chain) can greatly improve the monitoring and management of these chains [37]. WSNs are suitable for real time monitoring of many environmental parameters and provide accurate data collection that meets the demand of Cold Chain Logistics. The researchers used a Zigbee ad hoc network model to build the system's framework. By using fuzzy control decision, the environmental parameters are maintained in a stable range and with Maximum Similarities Multiple Characteristic Recognition (MSMCR) the safety of cold-chain food is ensured.  
Figure 14: The subcategories of the industrial applications of WSNs and the types of the sensors used in them.  
Figure 15: Depiction of the basic configuration of a WSN based system for transport logistics applications [36].  
Another application of WSNs developed for Cold Chain Logistics is studied in [38]. This system focuses only on aquatic products and their transportation. In order to develop such a system, the authors proposed a WSN integrated with Compressed Sending (CS), in order to combat heavy data traffic from the real time sensor data transmission. Also, CS provides a low complexity approximation which assists storage, transmission, processing, and meets the recourse constraints of WSNs.  
A WSN application in logistics that utilizes GPS technology is described in [39]. This system monitors in real-time the status of the goods and has embedded a terminal, which is used to locate the goods and a cloud services platform, which is used to identify the recipient.