esa-sceva/satcom-chunk-collection huggingface dataset:
## Time Series Prediction Method of Bank Cash Flow and Simulation Comparison  
**[PERSON], [PERSON] * and [PERSON]**  
School of Electronic and Information Engineering, University of Science and Technology Liaoning,  
Anshan 114044, Liaoning, China; E-Mails: [EMAIL_ADDRESS] (W.-H.C.);  
[EMAIL_ADDRESS] (C.-X.N.)  
* Author to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +0412-253-8355; Fax: +0412-253-8244.  
External Editors: [PERSON]  
_Received: 25 September 2014; in revised form: 5 November 2014 / Accepted: 14 November 2014 / Published: 26 November 2014_  
**Abstract:** In order to improve the accuracy of all kinds of information in the cash business and enhance the linkage between cash inventory forecasting and cash management information in the commercial bank, the first moving average prediction method, the second moving average prediction method, the first exponential smoothing prediction and the second exponential smoothing prediction methods are adopted to realize the time series prediction of bank cash flow, respectively. The prediction accuracy of the cash flow time series is improved by optimizing the algorithm parameters. The simulation experiments are carried out on the reality commercial bank's cash flow data and the predictive performance comparison results show the effectiveness of the proposed methods.  
**Keywords:** time series prediction; moving average prediction; exponential smoothing prediction
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 6 Data Preprocessing and Feature Selection  
This section examines the impact of feature selections (Section 6.1) and features scaling, i.e., the standardization technique in this manuscript (Section 6.2), for the three evaluated ML models. The experimental results presented in Section 7 were performed based on the outcomes of this section.
# AVSS: Airborne Video Surveillance System
## 5 AVSS Features  
The native implementation is insufficient as the performances of the commercial products are not powerful enough to support all required functionalities concurrently in real time. Optimizations are made through video compression/selection, network flow control, and other techniques. As video data is the main source of network traffic, we use a compression algorithm to first minimize the required bandwidth, then use weight-based algorithm to allocate the network resource to the most demanding video stream. The compression and selection are carried out based on the event detection algorithm based on an optical flow technique. The use of optical flow may seem as if it is affected by the movement of the UAV. However, as we subtract the average movement vector from the image, the UAV's movement does not affect the event count.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 4 Proposed Classification Methods
### Random Forest Classifier  
The random forest (RF) classifiers are considered to be one of the most powerful ensemble classification techniques and are used frequently in the field of data science for solving countless problems across many industrial areas [55,56]. The random forest technique was first introduced by [PERSON] [55] to integrate an ensemble of randomized decision trees in order to build a prediction model. As one of the ensemble methods, the RF method relies on aggregating the results of an ensemble of decision tree estimators. Generally, decision tree classifiers suffer from high variance, in which the rules obtained by splitting the training data into two random parts to fit two decision trees are likely to be different. To deal with this technical problem, an ensemble of randomized decision trees is used to create parallel estimators; then, by averaging the decision trees, the variance component of the obtained model can be minimized, which will eventually bring the prediction close to an ideal model. Usually, the RF classifier concentrates on sampling both variables and observations of training data to synthesize independent decision trees, and then majority voting is applied to obtain a better classifier. The RF classifier can be summarized as follows [55, 56, 57].  
* First, \(M\) samples are selected from the training set using the bootstrapping algorithm;
* Next, \(N_{Tree}\) samples are composed to form \(N_{Tree}\) training sets and used to train \(N_{Tree}\) parallel decision tree models, respectively;
* For each decision tree model, the best split is selected from the randomly selected \(M_{Tree}\) feature subset. Note that no pruning of the decision tree is requested in the splitting process, and the splitting process can only be stopped when all training samples of each tree belong to the same class at the corresponding node;
* Finally, the results obtained by these \(N_{Tree}\) decision trees are combined to form one random forest model for new data prediction. Note that the sensitivity, out of bag (OOB) error, accuracy and specificity are statistical factors used to evaluate the performance of the discrimination models of the random forest algorithm. More details about these statistical factors can be found in [55].
## 3 Experiments and Results
### Data Acquisition  
The database consists of images captured by the Landsat-8 satellite and acquired from the US Geological Survey's online server--EarthExplorer. According to the information described in [32], Landsat products (data) are divided into UTM (Universal Transverse Mecator), WGS (World Geodetic System), OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), wherein the size of the area pixels corresponds to 15 m in panchromatic products, 30 m in multispectral and 100 m in thermal. The data captured belong to Landsat 8--UTM and was grouped into scenes and divided into regions.  
Landsat 8 8s 400 scenes per day to the USGS data archive, but is capable of regularly acquiring 725 scenes per day. The size of the Landsat 8 scene is 185 km by 180 km. The spacecraft's nominal altitude is 705 km with an inclination of 98.2\({}^{\circ}\) \(\pm\) 0.15\({}^{\circ}\). For Landsat 8 data products, a cartographic accuracy of 12 m or more is required (including compensation for terrain effects). The region analyzed belongs to the territory of the Acre state in Brazil. Figure 7 shows the scenes, in red, that mark the territory. Of all the scenes present in the study area, those with low cloud coverage were acquired and 1/67, 2/66, 2/67, 3/66, 3/67, 4/66, 4/65 and 5/66 were selected.  
Figure 6: Study area.  
Figure 7: Study area with Landsat-8 scenes highlighted.  
Table 2 shows more detailed information on the number of images acquired for each scene and their respective year of capture.  
The database was generated from 26 images with 7800 \(\times\) 7600 pixels of resolution, covering the period between 2021 and 2023. These 26 images were selected due to the non-existence of cloud coverage. For the real data experiments, it was necessary to add a scene with cloud cover to the database.
## 2 Mathematical Formulation
### Objective Function Formulation  
The problem of the optimal locating and sizing of PV sources in AC networks is formulated from an economical point of view. The main idea is to minimize the investment and maintenance costs of the PV sources summed with the total energy purchasing costsin the substation bus [16]. The objective function and its components are presented in Equations (1)-(3), respectively:  
\[A_{\text{cost}}=z_{1}+z_{2}, \tag{1}\] \[z_{1}=C_{\text{KWh}}T\Bigg{(}\frac{t_{a}}{1-\left(1+t_{a}\right)^ {-N_{t}}}\Bigg{)}\left(\sum_{h\in\mathcal{H}}\sum_{i\in\mathcal{N}}p_{i,h}^{cg} \Delta h\right)\left(\sum_{t_{g}\in\mathcal{T}}\left(\frac{1+t_{e}}{1+t_{a}} \right)_{y}^{t}\right),\] (2) \[z_{2}=C_{\text{PV}}\Bigg{(}\frac{t_{a}}{1-\left(1+t_{a}\right)^ {-N_{t}}}\Bigg{)}\left(\sum_{i\in\mathcal{N}}p_{i}^{pv}\right)+C_{Q&M}T\sum_{i \in\mathcal{N}}\sum_{h\in\mathcal{H}}p_{i}^{pv}G_{h}^{pv}\Delta h, \tag{3}\]  
where \(A_{\text{cost}}\) represents the total annual operative cost of the network; \(z_{1}\) is the component of the objective function regarding the expected annualized energy purchasing costs in the substation buses; \(z_{2}\) is the component of the objective function regarding the investment costs in PV sources summed with their maintenance and operation costs. \(C_{\text{KWh}}\) is the average energy purchasing costs in the substation bus; \(T\) represents the number of days in an ordinary year (i.e., 365 days); \(t_{a}\) corresponds to the internal return rate expected for the investments made by the utility during the duration of the project; \(N_{t}\) is the total number of periods of the planning project in years; \(p_{i,h}^{cg}\) is the active power generation output at each conventional generator connected to node \(i\) during the period of time \(h\); \(\Delta h\) is the length of the period of time where the electrical variables are assumed to be constants; \(t_{e}\) is the average expected percentage of increment in the energy purchasing cost during the planning horizon. \(C_{\text{PV}}\) represents the average costs of installing a kW of PV power; \(p_{i}^{pv}\) is the size of a PV source connected to node \(i\); \(C_{Q&M}\) is the maintenance and operating costs of a PV source; and \(G_{h}^{pv}\) is the expected PV generation curve in the area of influence of the distribution network. Note that \(\mathcal{H}\), \(\mathcal{N}\), and \(\mathcal{T}\) are the sets that contain all the periods of time in a daily operation scenario, the nodes of the network; and the number of years of the planning period, respectively.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 3 Method
### Update Parent Population Parameter
#### 3.4.2 Case with More than One Nondomination Level
The update procedure aims to update the \((t)\)-th iteration of parent population of UAV's location \(I_{\mathbf{U}}^{(t)}\) and buoy transmit power \(\mathbf{p}^{(t)}\) to obtain the optimal parameters by generating offspring parameters \(I_{\mathbf{U}}^{off}\) and \(\mathbf{p}^{off}\). The overall update process is shown in Algorithm 5. Firstly, the sub-region associated with the offspring solutions \(I_{\mathbf{U}}^{off}\) and \(\mathbf{p}^{off}\) is calculated by (15). The hybrid population \(I_{\mathbf{U}}^{up}\) and \(\mathbf{p}^{up}\) are formed by combining \(I_{\mathbf{U}}^{(t)}\) with \(I_{\mathbf{U}}^{off}\) and \(\mathbf{p}^{(t)}\) with \(\mathbf{p}^{off}\), respectively. Then, the non-domination level structure is updated according to [55]. Finally, the \((t+1)\)-th iteration of parent populations \(I_{\mathbf{U}}^{(t+1)}\) and \(\mathbf{p}^{(t+1)}\) are generated.
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 3 Multi-Objective Integrated Optimization Model  
In this section, we introduce a multi-objective integrated optimization model to solve the train reschedule problem, which includes the train timetable and train set circulation plan when an emergency occurs. In Section 3.1, we start by explaining the symbols and parameters we use in the model, including sets, symbols, and decision variables. Thereafter, in Section 3.2, we introduce the three objectives of the model, including additional operation cost, total delay, and number of transfer passengers. Finally, in Section 3.3, we explain the constraints of the model, which totals 3 categories and 26 kinds.  
The following assumptions are made:  
1. In order to minimize additional operating costs, if the one train set circulation is not affected, no rescheduling will be made, and only the affected train set circulation will be rescheduled.
2. Before the interruption occurs, all trains operate according to the original timetable.
3. Only the affected trains and standby train sets can execute the subsequent circulation of the affected trains.
4. The standby train sets from different depots can serve all the lines of the railway network.
###### Abstract  
Nanowire interconnection is the basis for the construction and integration of micro-nano functional devices. But so far, it is still difficult to achieve a reliable interconnection of metal oxide nanowires. This letter proposes an approach for soldering ZnO nanowires through femtosecond laser irradiation of Ag nanoparticles solder. In this paper, the effect of femtosecond laser fluence and irradiation time on the morphology of Ag solders and the interconnection state of ZnO nanowires are studied, respectively. The I-V electrical characterization of nanowire interconnection before and after soldering is completed. The results demonstrate that ZnO nanowires achieve better interconnection. The UV light response of the ZnO-Ag-ZnO interconnection structure after soldering is investigated. The approach confirms the effectiveness of a femtosecond irradiated metal nanoparticles solder to achieve metal oxide interconnection, offering the prospect of more metal oxide nanowires interconnection and device development.  
ZnO nanowires interconnection; femtosecond laser irradiation; Ag nanoparticles solder; photoelectric property +
Footnote †: journal: Applied  
Article  
The Realization of ZnO Nanowires Interconnection through Femtosecond Laser Irradiation of Ag Nanoparticles Solder
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation
### Reflected Field Strength at Receiving Point
#### 5.2.3 Relative Permittivity of Roadway Walls  
Figure 16 shows the variation in reflected field strength with increasing distance between the relay node and the receiving point for relative dielectric constants of the roadway wall of 6, 10, and 14. Let the distance from the relay node to the reflection plane \(m_{5}\) be \(w_{3}\) = 2.5 m and the distance from the receiving point to the reflection plane \(m_{6}\) be \(w_{2}\) = 2.5 m; the heights of both the relay node and the receiving point are 2 m, and the receiving point is moved in the same way as in Figure 14.  
As can be seen from Figure 16, when the transmission frequency is 2.5 GHz, as the distance between the relay node and the receiving point increases, the reflected field strength at the receiving point gradually attenuates. Especially when passing through a corner, the attenuation of the reflected field strength at the receiving point accelerates. As the dielectric constant increases, the attenuation of the reflected field strength at the receiving point becomes greater. This is because an increase in the dielectric constant leads  
Figure 15: Reflected field strength at the receiving point as a function of the distance between the relay node and the receiving point at different frequencies.  
to an increase in the reflection coefficients in both the vertical and horizontal directions, thus reducing the reflected field strength at the receiving point [30].
## 5 Conclusions  
The use of PZT actuators to generate acoustic waves in non-piezoelectric substrates has been proposed to steer and confine cells dispersed in liquid. A test device with two screen-printed PZT actuators used to generate acoustic waves on alumina substrate has been fabricated. The proposed device has the advantage of exploiting an inexpensive substrate which does not need to be piezoelectric, thereby allowing to use of high-temperature reinforced glasses which have the property of being  
Figure 9: Aligned endothelial cells in fibrin clot at 96 h (**a**). DAPI staining showing cellular nuclei (**b**). CD31 (clone PECAM-1) staining of clot-entrapped human umbilical vein endothelial cells (HUVECs) (c). CD31 staining of clot-entrapped HUVECs and hMSCs (**d**).  
transparent. A tailored electronic circuit has been developed to generate the excitation signals for the actuators. Experimental results demonstrate that, by exciting a single actuator at its resonant frequencies, FPW modes are generated in the substrate. Accumulation of cells dispersed in liquid on lines can be obtained by selecting the proper excitation frequency. These results confirm the capability of the proposed PZT actuators to generate acoustic waves for applications in microfluidic devices as controlled positioning of cells dispersed in liquid.  
From a biological point of view, devices similar to the one proposed are required to obtain ordered structures for tissue engineering purposes. Indeed, alignment of cells is a key step in several biological processes. For example, the basic unit of a muscle, i.e., the fiber, is formed by the fusion of several myocytes whose aligned actin and myosin molecules will produce effective muscle contraction [39]. Moreover, in engineered tissues, the presence of blood vessels that allow the distribution of nutrients or the cellular waste removal is an essential requirement. Therefore, the effective realization of an engineered tissue cannot be disjointed from an adequate architecture that, as shown, can be obtained using acoustic waves.  
Design and development of the device, M.F., M.D., M.B., and V.F.; experimental activity, M.B., M.D., M.S., S.C., and M.C.; analysis of experimental data, M.F., P.L.P., and P.D.; writing--original draft preparation, M.F. and P.D.; drawings, M.F.; writing--review and editing, M.F., M.B., V.F., M.S., and S.C.; funding acquisition, M.F. V.F., and P.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project \"Ml crosystems MeRging ACoustics and fLuidics to build human engineered tissue (MIRACLE)\" funded by University of Brescia in the context of \"Health and Wealth 2015\" athenaeum grant and partially supported by Fondazione Cariplo (grant number 2014-0822).  
The authors declare no conflict of interest.
# Drive-by Bridge Damage Detection Using Continuous Wavelet Transform
## 3 Continuous Wavelet Transform and Development of the Proposed Method
These edge effects can clearly be seen in the first and last 5 m of the WCM (Figure 10). Due to these edge effects, the proposed method are limited to detecting and locating damage where these edge effects are not prominent (i.e., between 0.2\(L\) and 0.8\(L\) where \(L\) is the length of the bridge). As such, the horizontal sections of the WCM will be presented for a bridge section between \(x\) = 5 m and 20 m. This limitation is also mentioned in other studies that rely on WCM for damage detection [50; 51; 52]. The proposed damage detection and location method can be summarized as:  
1. Drive the instrumented vehicle over the bridge and record the accelerations at the front and rear axles. 2. Subtract the rear-axle accelerations from the front-axle accelerations with a time lag to compute the residual accelerations. 3. Conduct a continuous wavelet transform of the residual accelerations to obtain the wavelet coefficient map. 4. Determine the window which is sufficiently lower than the scale corresponding to the driving frequency and higher than the scales corresponding to the vehicle and the bridge frequencies. Decide on the scale where the horizontal section will be taken. Although, any scale within this window can be a viable option for the horizontal section, as they correspond to relatively low frequencies that capture static response, some engineering judgment can be required to select the final horizontal section. Plot the horizontal section of the WCM at the selected scale for a bridge segment between \(x\approx 0.2\ L\) and \(x\approx 0.8\ L\) and evaluate the variation of the wavelet coefficients over the length of the bridge. Due to the edge effects that are commonly encountered in WCM of signals, the proposed method cannot detect damage that is located between 0-0.2 \(L\) and 0.8-1 \(L\). 6. If the wavelet coefficients remain stable around zero, the bridge can be assessed to be undamaged. However, disturbances of the wavelet coefficients similar to those in Figure 9 indicate presence of damage. 7. Once damage is detected, its approximate location can be estimated as the point where the wavelet coefficients crosses the zero line between the negative and the positive peaks of the disturbance; see \[W(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{+\infty}f(t)\Psi(\frac{t-b}{a})dt=0 \tag{5}\]8
## 3 Results and Discussion  
### Characterization Synthesized CuO  
Figure 2 shows the XRD patterns of the synthesized and commercial CuO. In the figure, all synthesized materials exhibit the same peaks as commercial CuO (JCPDS # 48-1548), confirming that they had a monoclinic crystal structure of CuO. For the two strongest peaks, those of L-CuO are sharper than those observed in S-CuO, which is seen as an increase in the volumes of NaOH. The results of the FE-SEM and EDX analyses for each material are shown in Figures 3 and 4, respectively. The hydrothermal synthesis method can adjust the shape and size of particles through the control of several variables, including pH, NaOH concentration, solvent, temperature, and time. The volume of NaOH was adjusted to control the shape of CuO. As shown in Figure 3a,c, S-CuO was synthesized in sizes of about 500-700 nm. In addition, the agglomeration of small rod-like primary particles led to the formation of a sphere-like secondary phase. Figure 3b,d shows the L-CuO, which had a leaf-like shape because of increases in the volume of NaOH, and its particle size was about 800 nm. The C-CuO, shown in Figure 3c,f, had a layered structure with a particle size of 800 nm-1 \(\upmu\)m. The surface of the particle was also smooth. Figure 4 is the result of the EDX analysis of each material. As shown, all characterized materials were composed of Cu and O. Moreover, the spectra of Cu and O are shown in Figure 4d-f. There were no significant differences between synthesized materials (S-CuO and L-CuO) and the commercial material (C-CuO), meaning that the S-CuO and L-CuO materials were well synthesized.  
Figure 2: XRD patterns of nano CuO.  
Figure 3: FE-SEM images of (**a**) S-CuO, (**b**) L-CuO, and (**c**) C-CuO; the corresponding magnified images of (**d**) S-CuO, (**e**) L-CuO, and (**f**) C-CuO.
## 6 Discussion
### Performance Analysis of Binocular Depth Measurement  
In this system, a Stereolabs ZED2i binocular camera was chosen to measure the distance between the FPLIR and the power line. Since it uses passive measurement, it is less affected by light, thus improving its reliability in outdoor environments. The baseline length, image resolution and measurement distance influence the depth measurement accuracy of the binocular camera. Therefore, the further away the camera is from the power line, the lower the accuracy of the depth measurement. In addition, the diameter of the power line is small, resulting in the power line occupying only a smaller number of pixels in the image. When the distance between the FPLIR and the power line is greater than 4 m, the distance cannot be obtained by the binocular camera.  
When the distance is greater than 4 m, the depth estimation of the binocular camera fails. The distance between the FPLIR and the power lines can be roughly estimated using the relationship between pixel width and distance in Figure 21. As the FPLIR gradually approaches the power line, the depth estimation becomes more accurate. Therefore, the depth estimation obtained using the binocular camera is valid during the landing line.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Resolution** & **Backbone** & **mIoU (\%)** & **Speed (FPS)** \\ \hline STDC1-Seg70 & \(1280\times 720\) & STDC1 & 82.848 & 29.3 \\ STDC2-Seg70 & \(1280\times 720\) & STDC2 & 83.294 & 24.1 \\ STDC1-Seg90 & \(1920\times 1080\) & STDC1 & 85.521 & 17.5 \\ STDC2-Seg90 & \(1920\times 1080\) & STDC2 & 85.784 & 13.2 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of different models and resolutions on Jetson NX.